62699 WORLD DEVELOPMENT INDICATORS INCOME MAP The world by income Low income Honduras Grenada Hong Kong SAR, China Afghanistan India Iran, Islamic Rep. Hungary Bangladesh Indonesia Jamaica Iceland Benin Iraq Kazakhstan Ireland Burkina Faso Jordan Lebanon Isle of Man Burundi Kiribati Libya Israel Cambodia Kosovo Lithuania Italy Central African Republic Lesotho Macedonia, FYR Japan Chad Maldives Malaysia Korea, Rep. Comoros Marshall Islands Mauritius Kuwait Congo, Dem. Rep. Micronesia, Fed. Sts. Mayotte Latvia Eritrea Moldova Mexico Liechtenstein Ethiopia Mongolia Montenegro Luxembourg Gambia, The Morocco Namibia Macao SAR, China Ghana Nicaragua Palau Malta Guinea Nigeria Panama Monaco Guinea-Bissau Pakistan Peru Netherlands Haiti Papua New Guinea Romania Netherlands Antilles Kenya Paraguay Russian Federation New Caledonia Korea, Dem. Rep. Philippines Serbia New Zealand Kyrgyz Republic Samoa Seychelles Northern Mariana Islands Lao PDR São Tomé and Principe South Africa Norway Liberia Senegal St. Kitts and Nevis Oman Madagascar Sri Lanka St. Lucia Poland Malawi Sudan St. Vincent and the Portugal Mali Swaziland Grenadines Puerto Rico Mauritania Syrian Arab Republic Suriname Qatar Mozambique Thailand Turkey San Marino Myanmar Timor-Leste Uruguay Saudi Arabia Nepal Tonga Venezuela, RB Singapore Niger Tunisia Slovak Republic Rwanda Turkmenistan High income Slovenia Sierra Leone Tuvalu Andorra Spain Solomon Islands Ukraine Aruba Sweden Somalia Uzbekistan Australia Switzerland Tajikistan Vanuatu Austria Trinidad and Tobago Tanzania Vietnam Bahamas, The Turks and Caicos Islands Togo West Bank and Gaza Bahrain United Arab Emirates Uganda Yemen, Rep. Barbados United Kingdom Zambia Belgium United States Zimbabwe Upper middle income Bermuda Virgin Islands (U.S.) Albania Brunei Darussalam Lower middle income Algeria Canada Angola American Samoa Cayman Islands Armenia Antigua and Barbuda Channel Islands Belize Argentina Croatia Bhutan Azerbaijan Cyprus Bolivia Belarus Czech Republic Cameroon Bosnia and Herzegovina Denmark Cape Verde Botswana Equatorial Guinea China Brazil Estonia Congo, Rep. Bulgaria Faeroe Islands Côte d'Ivoire Chile Finland Djibouti Colombia France Ecuador Costa Rica French Polynesia Egypt, Arab Rep. Cuba Germany El Salvador Dominica Gibraltar Georgia Dominican Republic Greece Guatemala Fiji Greenland Guyana Gabon Guam Designed and edited by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London 2011 WORLD DEVELOPMENT INDICATORS Copyright 2011 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, D.C. 20433 USA All rights reserved Manufactured in the United States of America First printing April 2011 This volume is a product of the staff of the Development Data Group of the World Bank’s Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank’s Board of Execu- tive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsi- bility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth’s surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemina- tion of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credits: Front cover, Curt Carnemark/World Bank; page xxiv, Curt Carnemark/World Bank; page 30, Trevor Samson/World Bank; page 122, Curt Carnemark/World Bank; page 188, Curt Carnemark/World Bank; page 262, Ray Witlin/World Bank; page 318, Curt Carnemark/World Bank. If you have questions or comments about this product, please contact: Development Data Group The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or data.worldbank.org ISBN 978-0-8213-8709-2 ECO-AUDIT Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. The Office of the Publisher has chosen to print World Development Indicators 2011 on recycled paper with 50 percent post-consumer fiber in accordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www. greenpressinitiative.org. Saved: 91 trees 29 million Btu of total energy 8,609 pounds of net greenhouse gases 41,465 gallons of waste water 2,518 pounds of solid waste 2011 WORLD DEVELOPMENT INDICATORS PREFACE World Development Indicators 2011, the 15th edition in its current format, aims to provide relevant, high-quality, inter- nationally comparable statistics about development and the quality of people’s lives around the globe. This latest printed volume is one of a group of products; others include an online dataset, accessible at http://data.worldbank. org; the popular Little Data Book series; and DataFinder, a data query and charting application for mobile devices. Fifteen years ago, World Development Indicators was overhauled and redesigned, organizing the data to present an integrated view of development, with the goal of putting these data in the hands of policymakers, development spe- cialists, students, and the public in a way that makes the data easy to use. Although there have been small changes, the format has stood the test of time, and this edition employs the same sections as the first one: world view, people, environment, economy, states and markets, and global links. Technical innovation and the rise of connected computing devices have gradually changed the way users obtain and consume the data in the World Development Indicators database. Last year saw a more abrupt change: the decision in April 2010 to make the dataset freely available resulted in a large, immediate increase in the use of the on-line resources. Perhaps more important has been the shift in how the data are used. Software developers are now free to use the data in applications they develop—and they are doing just that. We applaud and encourage all efforts to use the World Bank’s databases in creative ways to solve the world’s most pressing development challenges. This edition of World Development Indicators focuses on the impact of the decision to make data freely available under an open license and with better online tools. To help those who wish to use and reuse the data in these new ways, the section introductions discuss key issues in measuring the economic and social phenomena described in the tables and charts and introduce new sources of data. World Development Indicators is possible only through the excellent collaboration of many partners who provide the data that form part of this collection, and we thank them all: the United Nations family, the International Monetary Fund, the World Trade Organization, the Organisation for Economic Co-operation and Development, the statistical offices of more than 200 economies, and countless others who make this unique product possible. As always, we welcome your ideas for making the data in World Development Indicators useful and relevant for improving the lives of people around the world. Shaida Badiee Director Development Economics Data Group 2011 World Development Indicators v ACKNOWLEDGMENTS This book was prepared by a team led by Soong Sup Lee under the management of Neil Fantom and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, Maja Bresslauer, David Cieslikowski, Mahyar Eshragh- Tabary, Shota Hatakeyama, Masako Hiraga, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim Levent, Johan Mistiaen, Sulekha Patel, William Prince, Premi Rathan Raj, Evis Rucaj, Eric Swanson, Jomo Tariku, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. World Development Indicators electronic products were prepared by a team led by Reza Farivari, consisting of Ramvel Chandrasekaran, Ying Chi, Jean-Pierre Djomalieu, Ramgopal Erabelly, Shelley Fu, Gytis Kanchas, Ugendran Makhachkala, Vilas Mandlekar, Nacer Megherbi, Parastoo Oloumi, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the direction of Shaida Badiee. Valuable advice was provided by Shahrokh Fardoust. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank’s four thematic networks—Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Financial and Private Sector Development—and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substan- tial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book’s content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated (CDI) provided editorial services, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott. Jomo Tariku designed the cover, Deborah Arroyo and Elaine Wilson typeset the book, and Katrina Van Duyn provided proofreading. Azita Amjadi and Alison Kwong oversaw the production process. Staff from External Affairs Office of the Publisher oversaw printing and dissemination of the book. 2011 World Development Indicators vii TABLE OF CONTENTS FRONT 2. PEOPLE Preface v Introduction 31 Acknowledgments vii Tables Partners xii Users guide xxii 2.1 Population dynamics 36 2.2 Labor force structure 40 2.3 Employment by economic activity 44 1. WORLD VIEW 2.4 Decent work and productive employment 48 2.5 Unemployment 52 2.6 Children at work 56 2.7 Poverty rates at national poverty lines 60 Introduction 1 2.8 Poverty rates at international poverty lines 63 Tables 2.9 Distribution of income or consumption 68 1.1 Size of the economy 10 2.10 Assessing vulnerability and security 72 1.2 Millennium Development Goals: eradicating poverty and 2.11 Education inputs 76 saving lives 14 2.12 Participation in education 80 1.3 Millennium Development Goals: protecting our common 2.13 Education efficiency 84 environment 18 2.14 Education completion and outcomes 88 1.4 Millennium Development Goals: overcoming obstacles 22 2.15 Education gaps by income and gender 92 1.5 Women in development 24 2.16 Health systems 94 1.6 Key indicators for other economies 28 2.17 Health information 98 2.18 Disease prevention coverage and quality 102 Text figures, tables, and boxes 2.19 Reproductive health 106 1a Use of World Bank data has risen with the launch of the 2.20 Nutrition 110 Open Data Initiative 1 2.21 Health risk factors and future challenges 114 1b Terms of use for World Bank data 2 2.22 Mortality 118 1c Access to information at the World Bank 3 1d Progress toward eradicating poverty 4 Text figures, tables, and boxes 1e Progress toward universal primary education completion 4 2a Maternal mortality ratios have declined in all developing 1f Progress toward gender parity 4 country regions since 1990 31 1g Progress toward reducing child mortality 5 2b Maternal mortality ratios have declined fastest 1h Progress toward improving maternal health 5 among low- and lower middle-income countries but remain high 31 1i HIV incidence is remaining stable or decreasing in many 2c The births of many children in Asia and Africa go unregistered 32 developing countries, but many lack data 5 2d In Nigeria, children’s births are more likely to be unregistered 1j Progress on access to an improved water source 6 in rural areas . . . 33 1k Progress on access to improved sanitation 6 2e . . . in poor households . . . 33 1l Official development assistance provided by Development 2f . . . and where the mother has a lower education level 33 Assistance Committee members 7 2g Most people live in countries with low-quality cause of death 1.2a Location of indicators for Millennium Development Goals 1–4 17 statistics 34 1.3a Location of indicators for Millennium Development Goals 5–7 21 2h More countries used surveys for mortality statistics, but civil 1.4a Location of indicators for Millennium Development Goal 8 23 registration did not expand 34 2i Estimates of infant mortality in the Philippines differ by source 35 2.6a The largest sector for child labor remains agriculture, and the majority of children work as unpaid family members 59 2.8a While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2.00 a day has increased 65 2.8b Poverty rates have begun to fall 65 2.8c Regional poverty estimates 66 2.13a There are more overage children among the poor in primary school in Zambia 87 2.17a South Asia has the highest number of unregistered births 101 viii 2011 World Development Indicators 3. ENVIRONMENT Introduction 123 3.4a At least 33 percent of assessed species are estimated to be threatened 141 Tables 3.1 Rural population and land use 126 3.5a Agriculture is still the largest user of water, accounting for some 70 percent of global withdrawals . . . 145 3.2 Agricultural inputs 130 3.5b . . . and approaching 90 percent in some developing regions 145 3.3 Agricultural output and productivity 134 3.6a Emissions of organic water pollutants vary among countries 3.4 Deforestation and biodiversity 138 from 1990 to 2007 149 3.5 Freshwater 142 3.7a A person in a high-income economy uses more than 14 times 3.6 Water pollution 146 as much energy on average as a person in a low-income economy in 3.7 Energy production and use 150 2008 153 3.8 Energy dependency and efficiency and carbon dioxide emissions 154 3.7b Fossil fuels are still the primary global energy source in 2008 153 3.9 Trends in greenhouse gas emissions 158 3.8a High-income economies depend on imported energy 157 3.10 Sources of electricity 162 3.9a The six largest contributors to methane emissions account 3.11 Urbanization 166 for about 50 percent of emissions 161 3.12 Urban housing conditions 170 3.9b The five largest contributors to nitrous oxide emissions 3.13 Traffic and congestion 174 account for about 50 percent of emissions 161 3.14 Air pollution 178 3.10a More than 50 percent of electricity in Latin America is 3.15 Government commitment 180 produced by hydropower 165 3.16 Contribution of natural resources to gross domestic product 184 3.10b Lower middle-income countries produce the majority of their Text figures, tables, and boxes power from coal 165 3a The 10 countries with the highest natural resource rents are 3.11a Urban population is increasing in developing economies, primarily oil and gas producers 124 especially in low and lower middle-income economies 169 3b Countries with negative adjusted net savings are depleting 3.11b Latin America and Caribbean has the greatest share of natural capital without replacing it and are becoming poorer 124 urban population, even greater than the high-income 3.1a What is rural? Urban? 129 economies in 2009 169 3.2a Nearly 40 percent of land globally is devoted to agriculture 133 3.12a Selected housing indicators for smaller economies 173 3.2b Rainfed agriculture plays a significant role in Sub-Saharan 3.13a Biogasoline consumption as a share of total agriculture where about 95 percent of cropland depends on consumption is highest in Brazil . . . 177 precipitation, 2008 133 3.13b . . . but the United States consumes the most biogasoline 177 3.3a The food production index has increased steadily since early 3.16a Oil dominates the contribution of natural resources in the 1960, and the index for low-income economies has been Middle East and North Africa 187 higher than the world average since early 2000 137 3.16b Upper middle-income countries have the highest contribution 3.3b Cereal yield in Sub-Saharan Africa increased between 1990 of natural resources to GDP 187 and 2009 but still is the lowest among the regions 137 2011 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 189 Introduction 263 Tables Tables 4.a Recent economic performance 192 5.1 Private sector in the economy 266 4.1 Growth of output 194 5.2 Business environment: Enterprise Surveys 270 4.2 Structure of output 198 5.3 Business environment: Doing Business indicators 274 4.3 Structure of manufacturing 202 5.4 Stock markets 278 4.4 Structure of merchandise exports 206 5.5 Financial access, stability, and efficiency 282 4.5 Structure of merchandise imports 210 5.6 Tax policies 286 4.6 Structure of service exports 214 5.7 Military expenditures and arms transfers 290 4.7 Structure of service imports 218 5.8 Fragile situations 294 4.8 Structure of demand 222 5.9 Public policies and institutions 298 4.9 Growth of consumption and investment 226 5.10 Transport services 302 4.10 Toward a broader measure of national income 230 5.11 Power and communications 306 4.11 Toward a broader measure of saving 234 5.12 The information age 310 4.12 Central government finances 238 5.13 Science and technology 314 4.13 Central government expenses 242 Text figures, tables, and boxes 4.14 Central government revenues 246 5a The average business in Latin America and the Caribbean 4.15 Monetary indicators 250 spends about 400 hours a year in preparing, filing, and 4.16 Exchange rates and prices 254 paying business taxes, 2009 264 4.17 Balance of payments current account 258 5b Firms in East Asia and the Pacific have the lowest business Text figures, tables, and boxes tax rate, 2010 264 4a Differences in GDP growth among developing country regions 189 5c Two approaches to collecting business environment data: 4b Developing countries are contributing more to global growth 189 Doing Business and Enterprise Surveys 265 4c Economies—both developing and high income—rebounded 5d People living in developing countries of East Asia and Pacific in 2010 190 have more commercial bank accounts than those in other 4d Revisions to GDP decline over time, and GDP data become developing country regions, 2009 265 more stable on average 190 4e Ghana’s revised GDP was 60 percent higher in the new base year, 2006 190 4f Revised data for Ghana show a larger share of services in GDP 190 4g Commission on the Measurement of Economic and Social Progress 191 4.3a Manufacturing continues to show strong growth in East Asia and Pacific through 2009 205 4.4a Developing economies’ share of world merchandise exports continues to expand 209 4.5a Top 10 developing economy exporters of merchandise goods in 2009 213 4.6a Top 10 developing economy exporters of commercial services in 2009 217 4.7a The mix of commercial service imports by developing economies is changing 221 4.9a GDP per capita is still lagging in some regions 229 4.10a GDP and adjusted net national income in Sub-Saharan Africa, 2000–09 233 4.12a Twenty selected economies had a central government debt to GDP ratio of 65 percent or higher 241 4.13a Interest payments are a large part of government expenses for some developing economies 245 4.14a Rich economies rely more on direct taxes 249 4.17a Top 15 economies with the largest reserves in 2009 261 x 2011 World Development Indicators 6. GLOBAL LINKS Introduction 319 Text figures, tables, and boxes Tables 6a Source of data for bilateral trade flows 320 6.1 Integration with the global economy 324 6b Trade in professional services faces the highest barriers 320 6.2 Growth of merchandise trade 328 6c Discrepancies persist in measures of FDI net flows 321 6.3 Direction and growth of merchandise trade 332 6d Source of data on FDI 322 6.4 High-income economy trade with low- and middle-income 6e At least 30 percent of remittance inflows go unrecorded by the sending economies 323 economies 335 6.5 Direction of trade of developing economies 338 6f Migrants originating from low- and middle-income economies and residing in high-income economies rose fivefold over 6.6 Primary commodity prices 341 1960–2000 323 6.7 Regional trade blocs 344 6g The ratio of central government debt to GDP has increased 6.8 Tariff barriers 348 for most economies, 2007–10 323 6.9 Trade facilitation 352 6.3a More than half of the world’s merchandise trade takes place 6.10 External debt 356 between high-income economies. But low- and middle-income 6.11 Ratios for external debt 360 economies’ participation in the global trade has increased in 6.12 Global private financial flows 364 the past 15 years 334 6.13 Net official financial flows 368 6.4a Low-income economies have a small market share in the 6.14 Financial flows from Development Assistance Committee global market of various commodities 337 members 372 6.15 Allocation of bilateral aid from Development Assistance 6.5a Developing economies are trading more with other developing economies 340 Committee members 374 6.16 Aid dependency 376 6.6a Primary commodity prices soared again in 2010 343 6.17 Distribution of net aid by Development Assistance 6.7a Global Preferential Trade Agreements Database 347 Committee members 380 6.11a Ratio of debt services to exports for middle-income economies have sharply increased in 2009 as export revenues declined 363 6.18 Movement of people across borders 384 6.16a Official development assistance from non-DAC donors, 6.19 Travel and tourism 388 2005–09 379 6.17a Beyond the DAC: The role of other providers of development assistance 383 BACK Primary data documentation 393 Statistical methods 404 Credits 406 Bibliography 408 Index of indicators 418 2011 World Development Indicators xi PARTNERS Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental organiza- tions and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank’s efforts, and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2011. Information about the World Bank is also provided. International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and infor- mation analysis center of the U.S. Department of Energy. The CDIAC’s scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere, the role of the ter- restrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases, emissions of carbon dioxide to the atmosphere, long-term climate trends, the effects of elevated carbon dioxide on vegetation, and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/. Deutsche Gesellschaft für Internationale Zusammenarbeit The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GIZ’s aim is to positively shape politi- cal, economic, ecological, and social development in partner countries, thereby improving people’s living conditions and prospects. For more information, see www.giz.de/. xii 2011 World Development Indicators Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/. Internal Displacement Monitoring Centre The Internal Displacement Monitoring Centre was established in 1998 by the Norwegian Refugee Council and is the leading international body monitoring conflict-induced internal displacement worldwide. The center contributes to improving national and international capacities to protect and assist the millions of people around the globe who have been displaced within their own country as a result of conflicts or human rights violations. For more information, see www.internal-displacement.org/. International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is respon- sible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO’s strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int/. International Energy Agency The International Energy Agency (IEA) was founded in 1973/74 with a mandate to facilitate cooperation among the IEA member countries to increase energy efficiency, promoting use of clean energy and technol- ogy, and diversify their energy sources while protecting the environment. IEA publishes annual and quarterly statistical publications covering both OECD and non-OECD countries’ statistics on oil, gas, coal, electricity and renewable sources of energy, energy supply and consumption, and energy prices and taxes. IEA also con- tributes in analysis of all aspects of sustainable development globally and provides policy recommendations. For more information, see www.iea.org/. International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people 2011 World Development Indicators xiii PARTNERS a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/. International Monetary Fund The International Monetary Fund (IMF) is an international organization of 187 member countries established to promote international monetary cooperation, a stable system of exchange rates, and the balanced expan- sion of international trade and to foster economic growth and high levels of employment. The IMF reviews national, regional, and global economic and financial developments; provides policy advice to member countries; and serves as a forum where they can discuss the national, regional, and global consequences of their policies. The IMF also makes financing temporarily available to member countries to help them address balance of payments problems. Among the IMF’s core missions are the collection and dissemination of high-quality macroeconomic and financial statistics as an essential prerequisite for formulating appropriate policies. The IMF provides technical assistance and training to member countries in areas of its core expertise, including the development of economic and financial data in accordance with international standards. For more information, see www.imf.org/. International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and communica- tion technologies. ITU’s mission is to enable the growth and sustained development of telecommunications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so-called Digital Divide by building information and communication infrastructure, promoting adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster prevention and mitigation. For more information, see www.itu.int/. National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF’s goals—discovery, learning, research infrastructure, and stewardship—provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation’s research capabil- ity through investments in advanced instrumentation and facilities, and support excellence in science and engineering research and education through a capable and responsive organization. For more information, see www.nsf.gov/. xiv 2011 World Development Indicators Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 34 member countries shar- ing a commitment to democratic government and the market economy to support sustainable economic growth, boost employment, raise living standards, maintain financial stability, assist other countries’ eco- nomic development, and contribute to growth in world trade. With active relationships with some 100 other countries, it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bodies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to sup- port sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.oecd.org/. Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an under- standing of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI’s main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/. Understanding Children’s Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labour Organization, the United Nations Children’s Fund (UNICEF), and the World Bank initiated the joint interagency research program “Understanding Children’s Work and Its Impact” in December 2000. The Understanding Children’s Work (UCW) project was located at UNICEF’s Innocenti Research Centre in Florence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW’s online data- base contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/. United Nations The United Nations currently has 192 member states. The purposes of the United Nations, as set forth in its charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promot- ing respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org/. 2011 World Development Indicators xv PARTNERS United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity-building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/. United Nations Children’s Fund The United Nations Children’s Fund (UNICEF) works with other UN bodies and with governments and non- governmental organizations to improve children’s lives in more than 190 countries through various programs in education and health. UNICEF focuses primarily on five areas: child survival and development, basic education and gender equality (including girls’ education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org/. United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/. United Nations Department of Peacekeeping Operations The United Nations Department of Peacekeeping Operations contributes to the most important function of the United Nations—maintaining international peace and security. The department helps countries torn by conflict to create the conditions for lasting peace. The first peacekeeping mission was established in 1948 and has evolved to meet the demands of different conflicts and a changing political landscape. Today’s peacekeepers undertake a wide variety of complex tasks, from helping build sustainable institutions of gov- ernance, to monitoring human rights, to assisting in security sector reform, to disarmaming, demobilizing, and reintegrating former combatants. For more information, see www.un.org/en/peacekeeping/. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations that promotes international cooperation among member states and associate members in education, science, culture, and communications. The UNESCO Institute for Statistics is the organization’s xvi 2011 World Development Indicators statistical branch, established in July 1999 to meet the growing needs of UNESCO member states and the international community for a wider range of policy-relevant, timely, and reliable statistics on these topics. For more information, see www.uis.unesco.org/. United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partner- ship in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/. United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/. United Nations Office on Drugs and Crime The United Nations Office on Drugs and Crime was established in 1977 and is a global leader in the fight against illicit drugs and international crime. The office assists member states in their struggle against illicit drugs, crime, and terrorism by helping build capacity, conducting research and analytical work, and assist- ing in the ratification and implementation of relevant international treaties and domestic legislation related to drugs, crime, and terrorism. For more information, see www.unodc.org/. The UN Refugee Agency The UN Refugee Agency (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org/. Upsalla Conflict Data Program The Upsalla Conflict Data Program has collected information on armed violence since 1946 and is one of the most accurate and well used data sources on global armed conflicts. Its definition of armed conflict is becoming a standard in how conflicts are systematically defined and studied. In addition to data collection on armed violence, its researchers conduct theoretically and empirically based analyses of the causes, escalation, spread, prevention, and resolution of armed conflict. For more information, see www.pcr.uu.se/research/UCDP/. 2011 World Development Indicators xvii PARTNERS World Bank The World Bank is a vital source of financial and technical assistance for developing countries. The World Bank is made up of two unique development institutions owned by 187 member countries—the International Bank for Reconstruction and Development (IBRD)  and the International Development Association (IDA). These institutions play different but collaborative roles to advance the vision of an inclusive and sustainable globalization. The IBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries. Together they provide low-interest loans, interest-free credits, and grants to developing countries for a wide array of purposes, including investments in education, health, public administration, infrastructure, financial and private sector development, agriculture, and environmental and natural resource management. The World Bank’s work focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty. For more information, see http://data.worldbank.org/. World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int/. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativ- ity, stimulates innovation, and contributes to economic development while safeguarding the public interest. WIPO carries out a wide variety of tasks related to the protection of IP rights. These include developing international IP laws and standards, delivering global IP protection services, encouraging the use of IP for economic development, promoting better understanding of IP, and providing a forum for debate. For more information, see www.wipo.int/. World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promot- ing and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.unwto.org/. xviii 2011 World Development Indicators World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as pos- sible. It does this by administering trade agreements, acting as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues—through technical assistance and training programs—and cooperating with other international organizations. At the heart of the system—known as the multilateral trading system—are the WTO’s agreements, negotiated and signed by a large majority of the world’s trading nations and ratified by their parliaments. For more information, see www.wto.org/. Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/. DHL DHL provides shipping and customized transportation solutions for customers in more than 220 countries and territories. It offers expertise in express, air, and ocean freight; overland transport; contract logistics solutions; and international mail services. For more information, see www.dhl.com/. International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/. International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization whose mission is to encourage and promote development and maintenance of better, safer, and more sustainable roads and road networks. Working together with its members and associates, the IRF promotes social and economic benefits that flow from well planned and environmentally sound road transport networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF works in all aspects of road policy and development worldwide with governments and financial institutions, members, and the community of road professionals. For more information, see www.irfnet.org/. 2011 World Development Indicators xix PARTNERS Netcraft Netcraft provides Internet security services such as antifraud and antiphishing services, application testing, code reviews, and automated penetration testing. Netcraft also provides research data and analysis on many aspects of the Internet and is a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, script- ing languages, and content technologies on the Internet. For more information, see http://news.netcraft.com/. PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused services in the fields of assurance, tax, human resources, transactions, performance improvement, and crisis management services to help address client and stake- holder issues. For more information, see www.pwc.com/. Standard & Poor’s Standard & Poor’s is the world’s foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P’s Global Stock Markets Factbook draws on data from S&P’s Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor’s calculates indexes to serve as benchmarks that are consistent across national boundaries. For more information, see www.standardandpoors.com/. World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world’s living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world’s living resources. For more information, see www.unep-wcmc.org/. xx 2011 World Development Indicators World Economic Forum The World Economic Forum (WEF) is an independent international organization committed to improving the state of the world by engaging leaders in partnerships to shape global, regional, and industry agendas. Economic research at the WEF—led by the Global Competitiveness Programme—focuses on identifying the impediments to growth so that strategies to achieve sustainable economic progress, reduce poverty, and increase prosperity can be developed. The WEF’s competitiveness reports range from global coverage, such as Global Competitiveness Report, to regional and topical coverage, such as Africa Competitiveness Report, The Lisbon Review, and Global Information Technology Report. For more information, see www.weforum.org/. World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides—and helps other institutions provide— objective information and practical proposals for policy and institutional change that will foster environmen- tally sound, socially equitable development. The institute’s current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/. 2011 World Development Indicators xxi USERS GUIDE Tables gap-filled estimates for missing data and by an s, for complex technical and conceptual problems that can- The tables are numbered by section and display the simple totals, where they do not), median values (m), not be resolved unequivocally. Data coverage may identifying icon of the section. Countries and econo- weighted averages (w), or simple averages (u). Gap not be complete because of special circumstances mies are listed alphabetically (except for Hong Kong filling of amounts not allocated to countries may result affecting the collection and reporting of data, such SAR, China, which appears after China). Data are in discrepancies between subgroup aggregates and as problems stemming from conflicts. shown for 155 economies with populations of more overall totals. For further discussion of aggregation For these reasons, although data are drawn from than 1 million, as well as for Taiwan, China, in selected methods, see Statistical methods. sources thought to be the most authoritative, they tables. Table 1.6 presents selected indicators for 58 should be construed only as indicating trends and other economies—small economies with populations Aggregate measures for regions characterizing major differences among economies between 30,000 and 1 million and smaller econo- The aggregate measures for regions include only rather than as offering precise quantitative mea- mies if they are members of the International Bank low- and middle-income economies including econo- sures of those differences. Discrepancies in data for Reconstruction and Development (IBRD) or, as it mies with populations of less than 1 million listed presented in different editions of World Development is commonly known, the World Bank. Data for these in table 1.6. Indicators reflect updates by countries as well as economies are included on the World Development The country composition of regions is based on the revisions to historical series and changes in meth- Indicators CD-ROM and the World Bank’s Open Data World Bank’s analytical regions and may differ from odology. Thus readers are advised not to compare website at data.worldbank.org/. common geographic usage. For regional classifica- data series between editions of World Development The term country, used interchangeably with tions, see the map on the inside back cover and the Indicators or between different World Bank publica- economy, does not imply political independence, but list on the back cover flap. For further discussion of tions. Consistent time-series data for 1960–2009 refers to any territory for which authorities report aggregation methods, see Statistical methods. are available on the World Development Indicators separate social or economic statistics. When avail- CD-ROM and at data.worldbank.org/. able, aggregate measures for income and regional Statistics Except where otherwise noted, growth rates are groups appear at the end of each table. Data are shown for economies as they were con- in real terms. (See Statistical methods for information Indicators are shown for the most recent year or stituted in 2009, and historical data are revised to on the methods used to calculate growth rates.) Data period for which data are available and, in most tables, reflect current political arrangements. Exceptions are for some economic indicators for some economies for an earlier year or period (usually 1990 or 1995 in noted throughout the tables. are presented in fiscal years rather than calendar this edition). Time-series data for all 213 economies Additional information about the data is provided years; see Primary data documentation. All dollar fig- are available on the World Development Indicators CD- in Primary data documentation. That section sum- ures are current U.S. dollars unless otherwise stated. ROM and at data.worldbank.org/. marizes national and international efforts to improve The methods used for converting national currencies Known deviations from standard definitions or basic data collection and gives country-level informa- are described in Statistical methods. breaks in comparability over time or across countries tion on primary sources, census years, fiscal years, are either footnoted in the tables or noted in About statistical methods and concepts used, and other Country notes the data. When available data are deemed to be background information. Statistical methods provides • Unless otherwise noted, data for China do not too weak to provide reliable measures of levels and technical information on some of the general calcula- include data for Hong Kong SAR, China; Macao trends or do not adequately adhere to international tions and formulas used throughout the book. SAR, China; or Taiwan, China. standards, the data are not shown. • Data for Indonesia include Timor-Leste through Data consistency, reliability, and comparability 1999 unless otherwise noted. Aggregate measures for income groups Considerable effort has been made to standardize • Montenegro declared independence from Serbia The aggregate measures for income groups include the data, but full comparability cannot be assured, and Montenegro on June 3, 2006. Where avail- 213 economies (the economies listed in the main and care must be taken in interpreting the indicators. able, data for each country are shown separately. tables plus those in table 1.6) whenever data are Many factors affect data availability, comparability, However, for the Serbia listing, some indicators available. To maintain consistency in the aggregate and reliability: statistical systems in many develop- continue to include data for Montenegro through measures over time and between tables, missing ing economies are still weak; statistical methods, 2005; these data are footnoted in the tables. data are imputed where possible. The aggregates coverage, practices, and definitions differ widely; and Moreover, data from 1999 onward for Serbia for are totals (designated by a t if the aggregates include cross-country and intertemporal comparisons involve most indicators exclude data for Kosovo, 1999 xxii 2011 World Development Indicators being the year when Kosovo became a territory more. The 17 participating member countries of the under international administration pursuant to Euro area are presented as a subgroup under high- UN Security Council Resolution 1244 (1999); any income economies. Estonia joined the Euro area on exceptions are noted. Kosovo became a World January 1, 2011. Bank member on June 29, 2009; available data are shown separately for Kosovo in the main tables. Symbols • Netherlands Antilles ceased to exist on October .. 10, 2010. Curaçao and St. Maarten became means that data are not available or that aggregates countries within the Kingdom of the Netherlands. cannot be calculated because of missing data in the Bonaire, St. Eustatius, and Saba became special years shown. municipalities of the Netherlands. 0 or 0.0 Classification of economies means zero or small enough that the number would For operational and analytical purposes the World round to zero at the displayed number of decimal Bank’s main criterion for classifying economies is places. gross national income (GNI) per capita (calculated by the World Bank Atlas method). Every economy / is classified as low income, middle income (subdi- in dates, as in 2003/04, means that the period of vided into lower middle and upper middle), or high time, usually 12 months, straddles two calendar income. For income classifications see the map on years and refers to a crop year, a survey year, or a the inside front cover and the list on the front cover fiscal year. flap. Low- and middle-income economies are some- times referred to as developing economies. The term $ is used for convenience; it is not intended to imply means current U.S. dollars unless otherwise noted. that all economies in the group are experiencing similar development or that other economies have > reached a preferred or final stage of development. means more than. Note that classification by income does not neces- sarily reflect development status. Because GNI per < capita changes over time, the country composition means less than. of income groups may change from one edition of World Development Indicators to the next. Once the Data presentation conventions classification is fixed for an edition, based on GNI • A blank means not applicable or, for an aggre- per capita in the most recent year for which data are gate, not analytically meaningful. available (2009 in this edition), all historical data • A billion is 1,000 million. presented are based on the same country grouping. • A trillion is 1,000 billion. Low-income economies are those with a GNI per • Figures in italics refer to years or periods other capita of $995 or less in 2009. Middle-income econ- than those specified or to growth rates calculated omies are those with a GNI per capita of more than for less than the full period specified. $995 but less than $12,196. Lower middle-income • Data for years that are more than three years and upper middle-income economies are separated from the range shown are footnoted. at a GNI per capita of $3,945. High-income econo- mies are those with a GNI per capita of $12,196 or The cutoff date for data is February 1, 2011. 2011 World Development Indicators xxiii WORLD VIEW Introduction 1 “Our aim is for open data, open knowledge, and open solutions.” —Robert Zoellick, Georgetown University, September 2010 W orld Development Indicators provides a comprehensive selection of national and international data that focus attention on critical development issues, facilitate research, encourage debate and analysis of policy options, and monitor prog- ress toward development goals. Organized around six themes—world view, people, environment, economy, states and markets, and global links—the book contains more than 800 indicators for 155 economies with a population of 1 million people or more, together with relevant aggregates. The online database includes more than 1,100 indicators for 213 economies, with many time series extending back to 1960. In 2010, to improve the impact of the indicators and org—has recorded well over 20 million page views. to provide a platform for others to use the data to And at the time of printing this edition of World De- solve pressing development challenges, the World velopment Indicators, it provides data to more than Development Indicators database and many other 100,000 unique visitors each week, three times as public databases maintained by the World Bank many as before (figure 1a). were made available as open data: free of charge, Making the World Development Indicators and in accessible nonproprietary formats on the World other databases free was only the first step in creat- Wide Web. This year, the first part of the introduc- ing an open data environment. Open data should tion to the World View section provides an overview mean that users can access and search public of the initiative, the impact of moving to an open datasets at no cost, combine data from different data platform, a brief survey of the global open data sources, add data and select data records to include movement, and an examination of its relevance to or exclude in derived works, change the format or development. The second part reviews progress structure of the data, and give away or sell any prod- toward the Millennium Development Goals—whose ucts they create. For the World Bank, this required target date of 2015 is now just four years away. designing new user interfaces and developing new search tools to more easily find and report the data. The World Bank Open Data Initiative It also required a new license defining the terms of The Open Data Initiative is a new strategy for reach- ing data users and a major change in the Bank’s Use of World Bank data has risen with business model for data, which had previously been the launch of the Open Data Initiative 1a a subscription-based model for licensing data ac- Weekly unique visitors to http://data.worldbank.org (thousands) cess and use, using a network of university librar- 125 April 2010 ies, development agencies, and private firms, and 100 Launch of the Open Data Initiative free access provided through the World Bank’s Public Information Centers and depository libraries. 75 Recess period for At the time of the open data announcement there 50 US and European academic teaching were around 140,000 regular users of the subscrip- institutions 25 tion database annually—a substantial number for a highly specialized data product. But providing free 0 January April July October January and easier access to the databases has had an im- 2010 2010 2010 2010 2011 mediate and lasting impact on data use. Since April Source: World Bank staff calculations from Omniture data. 2010 the new data website—http://data.worldbank. 2011 World Development Indicators 1 Terms of use for World Bank data 1b enabling citizens to access and create value through the reuse of public sector information” Why do open data need to be licensed? Because a license conveys certain rights to the (Rahemtulla 2011). licensee—in this case, the data user—while protecting the interests of the licensor. If there is no explicit license attached to a dataset, users may be uncertain of their rights. Can they The Sunlight Foundation, a U.S.-based civil republish these data? Can they include them in a new dataset along with data from other society organization, describes its goals as sources? Can they give them away or resell them? “improving access to government information Intellectual property laws differ by country. In an international environment where data are published on the World Wide Web, it may not be clear what law applies. Lacking a license, by making it available online, indeed redefining a cautious data user would assume that he or she should seek permission of the dataset ‘public’ information as meaning ‘online,’ and . . . owner or publisher, creating a real or imagined impediment to using the data. A license can creating new tools and websites to enable indi- help encourage data use by making clear exactly what is permitted, true even for free data. Use of data in the World Bank’s Data Catalog is governed by the Terms of Use of Datasets viduals and communities to better access that posted at http://data.worldbank.org. The terms follow the general model of the Creative information and put it to use. . . . We want to Commons Attribution License (http://creativecommons.org/licenses/by/3.0) and the Open catalyze greater government transparency by Data Commons Attribution License (www.opendatacommons.org/licenses/by/1.0). These licenses require users to acknowledge the original source when they publish or reuse the engaging individual citizens and communities— data, particularly important for World Development Indicators, where many datasets are technologists, policy wonks, open government obtained from sources such as specialized UN and international agencies. The terms of advocates, and ordinary citizens —demanding use impose some further limitations, still within the spirit of an open data license: users may not claim endorsement by the World Bank or use its name or logos without permission. policies that will enable all of us to hold govern- Acknowledging data sources is good practice, regardless of the terms of a license. Iden- ment accountable” (http://sunlightfoundation. tifying sources makes it possible for others to locate the same or similar data. And credit com/about/). to data producers or publisher recognizes their effort and encourages them to continue. The World Bank’s Terms of Use for Datasets provide a suggested form of attribution: Digital information and communication The World Bank: Dataset name: Data source. technologies permitting dissemination of The information for completing this form of attribution is available in the metadata sup- large amounts of data at little or no cost have plied with data downloaded from http://data.worldbank.org. strengthened the argument for providing free access to public sector information. Pollock use for data (box 1b). And it required new think- (2010) estimates the direct benefit to the U.K. ing to promote the use and reuse of data. To public of providing free access to public sec- reach out to new audiences and communities of tor information that was previously sold to be data users, the World Bank organized a global £1.6–£6 billion, 4–15 times the forgone sales “Apps for Development” competition—one of revenues of £400 million. Additional indirect the first of its kind—inviting developers to cre- benefits come from new products and services ate new applications for desktop computers using open datasets or complementary prod- or mobile devices using World Bank datasets, ucts and services and from reducing the trans- including World Development Indicators data. action costs to data users and reusers. Open data and open government initiatives Open data and open government have progressed farther in rich countries than in Advocates of greater transparency in public developing ones. This may reflect a lack of polit- agencies—the open government movement— ical will or popular demand, but it often reflects have been among the most vocal proponents a lack of technical capacity and resources to of open data. Likewise, those seeking data- make data available in accessible formats. A bases to build new applications have supported study commissioned by the Transparency and freedom of information laws and unrestricted Accountability Initiative (Hogge 2010) identified access to data created by public agencies. three drivers behind the success of the U.K. Opening public databases empowers people and U.S. data.gov initiatives: because data are essential for monitoring the • Civil society, particularly a small and moti- performance of governments and the impact of vated group of “civic hackers” responsible public policies on citizens. for developing grassroots political engage- For advocates of open data, governments ment websites. are vast repositories of statistical and nonsta- • An engaged and well resourced “middle tistical information with unrealized potential for layer” of skilled government bureaucrats. creative applications. The political, philosophi- • A top-level mandate, motivated by an out- cal, and economic impulses for open data and side force (in the United Kingdom) or a open government are often linked. One advo- refreshed political administration hungry cate of open data writes, “The term ‘Open Data’ for change (in the United States). refers to the philosophical and methodologi- Statistical offices exemplify the “middle cal approach to the democratization of data layer” of a government bureaucracy, uniquely 2 2011 World Development Indicators WORLD VIEW skilled in collecting and organizing large data- Access to information at the World Bank 1c sets. But even they may lack the motivation or Opening the World Bank’s databases is part of a broader effort to introduce greater transpar- resources to make their products freely avail- ency in the World Bank’s operations, and a new policy on information disclosure went into able to the public unless they enjoy full support effect on July 1, 2010. Besides formalizing the Open Data Initiative, the Access to Informa- from the top. tion Policy (www.worldbank.org/wbaccess) establishes the principle that the World Bank will disclose any information in its possession that is not on a specific list of exceptions. In developing countries aid donors can act In the past, only documents selected for disclosure were available to the public. The new as fourth driver by providing technical assis- policy reverses the process and presumes that most information is disclosable. Exceptions tance and funding for open data projects and by include personal information and staff records, internal deliberations and administrative matters, and information received in confidence from clients and third parties. Some docu- modeling transparency in their own practices. ments with restricted access are subject to a declassification schedule, ensuring that they The International Aid Transparency Initiative— will become available to the public in due course. A process for requesting documents has the World Bank is a founding member—aims to also been established that allows users to search for documents by country and topic in seven languages. create a global repository of information on aid flows, starting from the commitment of fund- ing from donors and continuing through its dis- the data become “local” and much more acces- bursement to recipient countries, the allocation sible and relevant to project stakeholders. The of aid money in national budgets, the procure- data are open and available directly to software ment of goods and services, and the measure- developers though an application programming ment of results. interface and through an interactive web-based To fulfill the initiative’s goal of providing a application called Mapping for Results (http:// complete accounting of aid to the citizens of maps.worldbank.org). donor and developing countries will require In keeping with the philosophy of the Open cooperation among donors and recipients. Data Initiative, the Mapping for Results appli- Terminology and coding systems must be cation uses the dataset of geo-located project standardized and agreements reached on activities and combines the data with sub- everything from the timing of reports to the national human and social development indica- mechanisms for posting and accessing the tors, such as child mortality rates, poverty inci- datasets. In many cases donor governments dence, malnutrition, and population measures. and international agencies will have to change But even more value may lie in what other their rules on access to information to provide researchers and software developers might do full transparency to their aid programs (box 1c). with the data, combining them with their own For more information on the initiative, see www. data or with data from other sources, perform- aidtransparency.net. ing their own analysis, or providing applications that help citizens and beneficiaries connect Mapping for results—making data directly with the project during implementation, not just accessible but useful through feedback or other mechanisms. The new Access to Information Policy and the Open Data Initiative provide much greater ac- Countdown to the Millennium cess to the World Bank Group’s knowledge Development Goals in 2015 resources than before. But accessible informa- There are four years to the target date for the tion is not the same as usable information. Proj- Millennium Development Goals (MDGs). The ect documents contain a wealth of data about MDGs have focused the world’s attention on planned activities—for instance, on their loca- the living conditions of billions of people who tion. But it may be difficult for many interested live in poor and developing countries and on parties, such as project beneficiaries, citizen the need to improve the quality, frequency, and groups, and civil society organizations, to ex- timeliness of the statistics used to track their tract and visualize relevant data from long texts progress. Progress toward the MDGs has been or tables. marked by slow changes in outcome indicators To help solve this problem, the World Bank, and by improvements in data availability. on a pilot basis, has started to provide geo- World Development Indicators has moni- location codes along with data and information tored global and regional trends in poverty about the projects that it supports. The objec- reduction, education, health, and the envi- tive is to improve aid effectiveness through ronment since 1997. After the UN Millennium enhanced transparency and accountability of Summit in 2000, World Development Indicators project activities. Location information makes began closely tracking the progress of countries 2011 World Development Indicators 3 Progress toward against the targets selected for the MDGs. The eradicating poverty 1d MDGs highlight important outcomes, but the Share of countries focus on this limited set of indicators should making progress toward Reached target On track reducing extreme poverty Off track Insufficient not obscure the fact that development is a com- by half (percent) Seriously off track data 100 plex process whose course is determined in part by geographic location, historical circum- stances, institutional capacity, and uncontrol- 50 lable events such as weather and natural disas- ters. Success or failure, while not arbitrary or 0 entirely accidental, still has a large component of chance. 50 This review employs the same assessment method that World Development Indicators has 100 used since 2004 to track progress of countries 2004 2011 toward the time-bound and quantified targets 140 countries 144 countries Source: World Bank staff estimates. of the MDGs. Countries are “on track” if their past progress equals or exceeds the rate of change necessary to reach an MDG target. A few countries have already reached their tar- Progress toward universal primary education completion 1e gets. They are counted as having achieved the goal, although some may slip back. Countries Share of countries making progress toward Reached target On track making less than necessary progress are “off full completion of primary Off track Insufficient education (percent) Seriously off track data track,” or “seriously off track” if their past rate 100 progress would not allow them to reach the tar- get even in another 25 years. The remaining 50 countries do not have sufficient data to evalu- ate their progress—in some cases because 0 there are no data for the benchmark period of 1990–99 and in others because more recent data are missing. But the situation is improv- 50 ing: starting from the earliest World Develop- ment Indicators progress assessments in 2004 100 2004 2011 (based on data for 1990–2002), the number 140 countries 144 countries of countries with insufficient data has fallen, Source: World Bank staff estimates. enhancing our picture of progress toward the MDGs. For more information on the work of the Progress toward World Bank and its partners to achieve the gender parity 1f MDGs, see www.worldbank.org/mdgs, which Share of countries making Reached On track includes a link to the World Bank’s MDG eAtlas. progress toward gender target Off track parity in primary and Seriously off track secondary education (percent) Insufficient data 100 Goal 1. Eradicate extreme poverty and hunger The number of people living on less than $1.25 a day fell from 1.8 billion in 1990 to 1.4 billion 50 in 2005. New global and regional estimates, to become available later in 2011, are likely to 0 show a continuation of past trends, although the financial crisis of 2008 and the recent surge 50 in food prices will have slowed progress in some countries. Because household income and ex- 100 penditure surveys are expensive and time con- 2004 2011 suming, they are not conducted frequently and 140 countries 144 countries Source: World Bank staff estimates. there are often difficulties in making reliable comparisons over time or across countries. 4 2011 World Development Indicators WORLD VIEW For 140 developing countries, figure 1d com- Progress toward pares the progress assessments in 2005 and reducing child mortality 1g in 2011, based on available data. Forty-three Share of countries making Reached On track progress toward reducing target Off track countries are on track or have reached the tar- under-five child mortality by Seriously off track two-thirds (percent) Insufficient data get of cutting the extreme poverty rate in half, 100 twice as many as in 2005. They include China, Brazil, and the Russian Federation. India, with 50 more than 400 million people living in poverty lags behind, but with faster economic growth may well reach the 2015 target. 0 Goal 2. Achieve universal primary education 50 The goal of providing universal primary educa- tion has proved surprisingly hard to achieve. 100 Completion rates measure the proportion of 2004 2011 140 countries 144 countries children enrolled in the final year of primary ed- Source: World Bank staff estimates. ucation after adjusting for repetition. In 2011, 49 countries had achieved or were on track to achieve 100 percent primary completion rates, Progress toward only three more than in 2004, and the number improving maternal health 1h of countries seriously off track has increased, Share of countries making Reached On track especially in Sub-Saharan Africa (figure 1e). progress toward providing target Off track skilled attendants at births Seriously off track There are more and better data, but the goal (percent) Insufficient data remains elusive. 100 Goal 3. Promote gender equality 50 Gender equality and empowering women foster progress toward all the Millennium Development 0 Goals. Equality of educational opportunities, measured by the ratio of girls’ to boys’ enroll- 50 ments in primary and secondary education, is a starting point. Since the 2004 assessment, the number of countries on track to reach the tar- 100 2004 2011 get has increased steadily, driven by rising en- 140 countries 144 countries rollments of girls, and the number of countries Source: World Bank staff estimates. without sufficient data to measure progress has dropped (figure 1f). HIV incidence is remaining stable Goal 4. Reduce child mortality or decreasing in many developing countries, but many lack data 1i Of 144 countries with data in February 2011, 11 had achieved a two-thirds reduction in their Change in HIV incidence rate, 2001–09 (number of developing countries) under-five child mortality rate, and another 25 100 were on track to do so (figure 1g). This is re- markable progress since 2004, but more than 75 100 countries remain off track, and only a few of them are likely to reach the MDG target by 50 2015. Measuring child mortality is the product of a successful collaboration of international 25 statisticians. By bringing together the most reliable data from multiple sources and apply- 0 ing appropriate estimation methods, consis- Incidence Stable Incidence No data increased by decreased by tent time series comparable across countries more than 25% more than 25% are available for monitoring this important in- Source: Joint United Nations Programme on HIV/AIDS. dicator. More information about data sources 2011 World Development Indicators 5 Progress on access to Indicators. While the number of countries se- an improved water source 1j riously off track has increased, the number Share of countries reducing Reached On track without adequate data has decreased, and the proportion of population target Off track without access to an improved Seriously off track number providing skilled attendants at birth has water source by half (percent) Insufficient data 100 risen 35 percent. Goal 6. Combat HIV/AIDS, 50 malaria, and other diseases When the MDGs were formulated, the HIV/AIDS 0 epidemic was spreading rapidly, engulfing many poor countries in Southern Africa. Data on the 50 extent of the epidemic were derived from sen- tinel sites and limited reporting through health 100 systems. The goal refers to halting and reversing 2004 2011 the spread of HIV/AIDS. Under the circumstanc- 140 countries 144 countries Source: World Bank staff estimates. es it was impossible to set time-bound quan- tified targets. Now the statistical record is be- ginning to improve. UNAIDS, in its 2010 Report on the Global AIDS Epidemic, estimates that the Progress on access to improved sanitation 1k annual number of new HIV infections has fallen 21 percent since its peak in 1997 (figure 1i). But Share of countries making progress toward Reached target On track reliable estimates of incidence are available for improved sanitation Off track Insufficient (percent) Seriously off track data only 60 developing countries and do not include 100 Brazil, China, and the Russian Federation. 50 Goal 7. Ensure environmental sustainability Reversing environmental losses and ensuring 0 a sustainable flow of services from the Earth’s resources have many dimensions: preserving forests, protecting plant and animal species, 50 reducing carbon emissions, and limiting and adapting to the effects of climate change. Im- 100 2004 2011 proving the built environment is also important. 140 countries 144 countries The MDGs set targets for reducing the propor- Source: World Bank staff estimates. tion of people without access to safe water and sanitation by half. The ability to measure prog- ress toward both targets has improved signifi - and estimation methods is available at www. cantly since 2004, and almost half the develop- childmortality.org. ing countries with sufficient data are on track to meet the water target (figure 1j). Progress in Goal 5. Improve maternal health providing access to sanitation has been slower: Reliable measurements of maternal mortality almost half the countries are seriously off track are difficult to obtain. Many national estimates (figure 1k). are not comparable over time or across coun- tries because of differences in methods and Goal 8. Develop a global estimation techniques. Consistently modeled partnership for development estimates that became available only recently Partnership between high-income and develop- show that 30 countries are on track to achieve ing economies, fundamental to achieving the a three-quarter reduction in their maternal mor- MDGs, rests on four pillars: reducing external tality ratio and that 94 are off track or seriously debt of developing countries, increasing their off track. Figure 1h compares the availability of access to markets in OECD countries, realizing skilled birth attendants, a critical factor for re- the benefits of new technologies and essential ducing maternal and infant deaths, using data drugs, and providing financing for development from the 2004 and 2011 World Development programs in the poorest countries. Following 6 2011 World Development Indicators WORLD VIEW the adoption of the MDGs, the International Official development assistance provided by Conference on Financing for Development in Development Assistance Committee members 1l 2002 urged developed countries “to make con- Official development 0.7% GNI or more assistance provided, 0.3% to <0.7% GNI crete efforts toward the target of 0.7 percent of by share of GNI 0.2% to <0.3% GNI (2009 $ billions) <0.2% GNI gross national income [GNI] as official develop- 150 ment assistance to developing countries.” Since then many countries have increased their official development assistance, but few 100 have reached the target of 0.7 percent (fig- ure 1l). In 2009, five countries provided more than 0.7 percent of their GNI as aid, but their 50 share of total aid was only 15 percent. The larg- est share of total aid was provided by 10 donors that gave 0.3–0.7 percent of their GNI. The larg- 0 est single donor, the United States, provided 2000 2009 0.21 percent of its GNI as official development Source: World Bank staff estimates. assistance. 2011 World Development Indicators 7 Millennium Development Goals Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of 1.1 Proportion of population below $1 purchasing power people whose income is less than $1 a day parity (PPP) a day1 1.2 Poverty gap ratio [incidence × depth of poverty] 1.3 Share of poorest quintile in national consumption Target 1.B Achieve full and productive employment and decent 1.4 Growth rate of GDP per person employed work for all, including women and young people 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 (PPP) a day 1.7 Proportion of own-account and contributing family workers in total employment Target 1.C Halve, between 1990 and 2015, the proportion of 1.8 Prevalence of underweight children under five years of age people who suffer from hunger 1.9 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and 2.1 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 2.2 Proportion of pupils starting grade 1 who reach last primary schooling grade of primary education 2.3 Literacy rate of 15- to 24-year-olds, women and men Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary 3.1 Ratios of girls to boys in primary, secondary, and tertiary education, preferably by 2005, and in all levels of education education no later than 2015 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament Goal 4 Reduce child mortality Target 4.A Reduce by two-thirds, between 1990 and 2015, the 4.1 Under-five mortality rate under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, 5.1 Maternal mortality ratio the maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel Target 5.B Achieve by 2015 universal access to reproductive 5.3 Contraceptive prevalence rate health 5.4 Adolescent birth rate 5.5 Antenatal care coverage (at least one visit and at least four visits) 5.6 Unmet need for family planning Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the 6.1 HIV prevalence among population ages 15–24 years spread of HIV/AIDS 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 years Target 6.B Achieve by 2010 universal access to treatment for 6.5 Proportion of population with advanced HIV infection with HIV/AIDS for all those who need it access to antiretroviral drugs Target 6.C Have halted by 2015 and begun to reverse the 6.6 Incidence and death rates associated with malaria incidence of malaria and other major diseases 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on 14 November 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries “to create an environment—at the national and global levels alike—which is conducive to development and the elimination of poverty.” All indicators should be disaggregated by sex and urban-rural location as far as possible. 8 2011 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development 7.1 Proportion of land area covered by forest into country policies and programs and reverse the 7.2 Carbon dioxide emissions, total, per capita and loss of environmental resources per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances Target 7.B Reduce biodiversity loss, achieving, by 2010, a 7.4 Proportion of fish stocks within safe biological limits significant reduction in the rate of loss 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction Target 7.C Halve by 2015 the proportion of people without 7.8 Proportion of population using an improved drinking water sustainable access to safe drinking water and basic source sanitation 7.9 Proportion of population using an improved sanitation facility Target 7.D Achieve by 2020 a significant improvement in the 7.10 Proportion of urban population living in slums2 lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, Some of the indicators listed below are monitored separately nondiscriminatory trading and financial system for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states. (Includes a commitment to good governance, development, and poverty reduction—both Official development assistance (ODA) nationally and internationally.) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donors’ gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic Target 8.B Address the special needs of the least developed education, primary health care, nutrition, safe water, and countries sanitation) 8.3 Proportion of bilateral official development assistance of (Includes tariff and quota-free access for the least OECD/DAC donors that is untied developed countries’ exports; enhanced program of 8.4 ODA received in landlocked developing countries as a debt relief for heavily indebted poor countries (HIPC) proportion of their gross national incomes and cancellation of official bilateral debt; and more 8.5 ODA received in small island developing states as a generous ODA for countries committed to poverty proportion of their gross national incomes reduction.) Market access Target 8.C Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity Target 8.D Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services Target 8.E In cooperation with pharmaceutical companies, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 8.F In cooperation with the private sector, make 8.14 Telephone lines per 100 population available the benefits of new technologies, 8.15 Cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population 1. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. 2. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material. 2011 World Development Indicators 9 1.1 Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 Afghanistan 30 652 46 9.1 125 310 207 25.1a 860a 201 40.8 37.1 Albania 3 29 115 12.6 114 4,000 116 27.3 8,640 106 2.5 2.1 Algeria 35 2,382 15 154.2 49 4,420 112 283.2a 8,110a 110 2.1 0.6 Angola 18 1,247 15 69.4 63 3,750 123 96.1 5,190 131 0.7 –1.9 Argentina 40 2,780 15 304.1 29 7,550 85 567.5 14,090 76 0.9 –0.1 Armenia 3 30 108 9.5 124 3,100 131 16.7 5,410 128 –14.4 –14.6 Australia 22 7,741 3 957.5 15 43,770 23 842.3 38,510 24 1.3 –0.8 Austria 8 84 101 388.5 25 46,450 17 321.3 38,410 25 –3.9 –4.2 Azerbaijan 9 87 106 42.5 76 4,840 106 79.2 9,020 101 9.3 8.0 Bangladesh 162 144 1,246 93.5 57 580 189 250.6 1,550 181 5.7 4.3 Belarus 10 208 48 53.7 68 5,560 100 123.1 12,740 88 1.4 1.6 Belgium 11 31 356 488.4 19 45,270 20 395.0 36,610 32 –2.8 –3.5 Benin 9 113 81 6.7 138 750 182 13.5 1,510 183 3.8 0.6 Bolivia 10 1,099 9 16.1 105 1,630 155 41.9 4,250 146 3.4 1.6 Bosnia and Herzegovina 4 51 74 17.7 103 4,700 107 33.0 8,770 105 –2.9 –2.7 Botswana 2 582 3 12.2 117 6,260 92 25.0 12,840 87 –3.7 –5.1 Brazil 194 8,515 23 1,564.2 8 8,070 83 1,968.0 10,160 98 –0.6 –1.5 Bulgaria 8 111 70 46.0 73 6,060 95 100.6 13,260 84 –4.9 –4.5 Burkina Faso 16 274 58 8.0 133 510 190 18.4 1,170 193 3.5 0.1 Burundi 8 28 323 1.2 186 150 213 3.3 390 211 3.5 0.6 Cambodia 15 181 84 9.7 123 650 185 27.0 1,820 176 –1.9 –3.5 Cameroon 20 475 41 23.2 93 1,190 162 42.8 2,190 169 2.0 –0.3 Canada 34 9,985 4 1,416.4 10 41,980 28 1,257.7 37,280 29 –2.5 –3.7 Central African Republic 4 623 7 2.0 177 450 195 3.3 750 207 2.4 0.5 Chad 11 1,284 9 6.7 139 600 187 13.0 1,160 194 –1.6 –4.2 Chile 17 756 23 160.7 48 9,470 75 227.7 13,420 81 –1.5 –2.5 China 1,331 9,600 143 4,856.2 3 3,650 125 9,170.1 6,890 119 9.1 8.5 Hong Kong SAR, China 7 1 6,721 221.1 37 31,570 40 311.9 44,540 18 –2.8 –3.1 Colombia 46 1,142 41 227.8 36 4,990 103 392.5 8,600 107 0.8 –0.6 Congo, Dem. Rep. 66 2,345 29 10.6 121 160 211 19.6 300 212 2.7 0.0 Congo, Rep. 4 342 11 7.7 135 2,080 147 11.2 3,040 157 7.6 5.6 Costa Rica 5 51 90 28.7 86 6,260 92 50.0a 10,930a 95 –1.5 –2.8 Côte d’Ivoire 21 322 66 22.5 95 1,070 168 34.5 1,640 179 3.6 1.2 Croatia 4 57 79 61.0 66 13,770 65 85.1 19,200 65 –5.8 –5.8 Cuba 11 110 105 62.2 65 5,550 98 .. .. 4.3 4.3 Czech Republic 10 79 136 181.6 43 17,310 57 251.1 23,940 59 –4.2 –4.8 Denmark 6 43 130 326.5 28 59,060 9 214.4 38,780 23 –4.9 –5.5 Dominican Republic 10 49 209 45.9 74 4,550 110 81.9a 8,110a 110 3.5 2.0 Ecuador 14 256 55 54.1 67 3,970 b 118 110.4 8,100 112 0.4 –0.7 Egypt, Arab Rep. 83 1,001 83 172.1 45 2,070 148 471.2 5,680 126 4.6 2.8 El Salvador 6 21 297 20.8 100 3,370 127 39.6a 6,420a 121 –3.5 –4.0 Eritrea 5 118 50 1.6 180 320 207 2.9a 580a 210 3.6 0.6 Estonia 1 45 32 18.9 102 14,060 63 25.6 19,120 66 –14.1 –14.1 Ethiopia 83 1,104 83 27.2 89 330 206 77.3 930 200 8.7 5.9 Finland 5 338 18 245.3 33 45,940 19 188.3 35,280 34 –8.0 –8.4 France 63c 549c 114 c 2,750.9 5 42,620 25 2,191.2 33,950 36 –2.6 –3.2 Gabon 1 268 6 10.9 120 7,370 86 18.4 12,450 89 –1.0 –2.7 Gambia, The 2 11 171 0.7 196 440 196 2.3 1,330 186 4.6 1.8 Georgia 4 70 61 11.1d 118 2,530 d 140 20.6d 4,700 d 137 –3.9d –4.0 d Germany 82 357 235 3,476.1 4 42,450 26 3,017.3 36,850 31 –4.7 –4.5 Ghana 24 239 105 28.4 87 1,190e 162 36.6 1,530 182 4.7 2.5 Greece 11 132 88 327.7 27 29,040 42 325.0 28,800 46 –2.0 –2.4 Guatemala 14 109 131 37.2 81 2,650 138 64.1a 4,570a 139 0.6 –1.9 Guinea 10 246 41 3.8 162 370 202 9.5 940 199 –0.3 –2.6 Guinea-Bissau 2 36 57 0.8 194 510 190 1.7 1,060 196 3.0 0.7 Haiti 10 28 364 .. ..f .. .. 2.9 1.3 Honduras 7 112 67 13.5 111 1,800 153 27.7a 3,710a 148 –1.9 –3.8 10 2011 World Development Indicators 1.1 WORLD VIEW Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 Hungary 10 93 112 130.1 51 12,980 66 191.3 19,090 67 –6.3 –6.2 India 1,155 3,287 389 1,405.7 11 1,220 160 3,786.3 3,280 154 9.1 7.7 Indonesia 230 1,905 127 471.0 20 2,050 149 855.0 3,720 147 4.5 3.4 Iran, Islamic Rep. 73 1,745 45 330.6 26 4,530 111 836.5 11,470 94 1.8 0.5 Iraq 31 438 72 69.7 62 2,210 146 105.0 3,330 151 4.2 1.6 Ireland 4 70 65 197.1 39 44,280 22 147.0 33,040 38 –7.1 –7.6 Israel 7 22 344 192.0 40 25,790 46 201.0 27,010 52 0.8 –1.0 Italy 60 301 205 2,114.5 7 35,110 35 1,919.2 31,870 41 –5.0 –5.7 Jamaica 3 11 249 12.4 116 4,590 109 19.5a 7,230a 117 –3.0 –3.5 Japan 128 378 350 4,857.2 2 38,080 32 4,265.3 33,440 37 –5.2 –5.1 Jordan 6 89 67 23.7 92 3,980 b 117 34.1 5,730 125 2.3 –0.1 Kazakhstan 16 2,725 6 110.0 55 6,920 89 164.0 10,320 97 1.2 –0.2 Kenya 40 580 70 30.3 84 760 181 62.5 1,570 180 2.6 –0.1 Korea, Dem. Rep. 24 121 199 .. ..f .. .. .. .. Korea, Rep. 49 100 503 966.6 13 19,830 54 1,328.0 27,240 51 0.2 –0.1 Kosovo 2 11 166 5.9 143 3,240 129 .. .. 4.0 3.4 Kuwait 3 18 157 117.0 50 43,930 10 143.5 53,890 6 4.4 1.9 Kyrgyz Republic 5 200 28 4.6 153 870 179 11.7 2,200 167 2.3 1.5 Lao PDR 6 237 27 5.6 146 880 178 13.9 2,200 167 6.4 4.5 Latvia 2 65 36 27.9 88 12,390 68 39.7 17,610 71 –18.0 –17.6 Lebanon 4 10 413 34.1 82 8,060 84 56.6 13,400 82 9.0 8.2 Lesotho 2 30 68 2.0 175 980 b 175 3.7 1,800 178 0.9 0.0 Liberia 4 111 41 0.7 197 160 211 1.2 290 213 4.6 0.3 Libya 6 1,760 4 77.2 61 12,020 71 105.3a 16,400a 74 2.1 0.1 Lithuania 3 65 53 38.1 80 11,410 72 57.8 17,310 72 –15.0 –14.6 Macedonia, FYR 2 26 81 9.0 128 4,400 113 22.2 10,880 96 –0.7 –0.8 Madagascar 20 587 34 8.5 131 430 200 19.5 990 197 –3.7 –6.2 Malawi 15 118 162 4.4 156 290 210 11.9 780 206 7.6 4.7 Malaysia 27 331 84 201.8 38 7,350 87 376.6 13,710 78 –1.7 –3.3 Mali 13 1,240 11 8.9 129 680 184 15.4 1,190 189 4.3 1.9 Mauritania 3 1,031 3 3.3 166 990 174 6.4 1,940 173 –1.1 –3.3 Mauritius 1 2 628 9.2 127 7,250 88 16.9 13,270 83 2.1 1.6 Mexico 107 1,964 55 962.1 14 8,960 78 1,506.3 14,020 77 –6.5 –7.5 Moldova 4 34 110 5.6g 145 1,560 g 157 10.7g 3,010 g 158 –6.5g –6.4g Mongolia 3 1,564 2 4.4 157 1,630 155 8.9 3,330 151 –1.6 –2.7 Morocco 32 447 72 89.9h 58 2,770h 136 143.1h 4,400h 143 4.9h 3.6h Mozambique 23 799 29 10.0 122 440 196 20.1 880 201 6.3 4.0 Myanmar 50 677 77 .. ..f .. .. .. .. Namibia 2 824 3 9.3 126 4,270 114 13.8 6,350 122 –0.8 –2.7 Nepal 29 147 205 13.0 113 440 196 34.7 1,180 191 4.7 2.8 Netherlands 17 42 490 801.1 16 48,460 15 657.0 39,740 22 –4.0 –4.5 New Zealand 4 268 16 124.3 53 28,810 43 120.0 27,790 48 –0.4 –1.5 Nicaragua 6 130 48 5.7 144 1,000 171 14.6a 2,540a 163 –5.6 –6.9 Niger 15 1,267 12 5.2 148 340 204 10.3 680 209 1.0 –2.9 Nigeria 155 924 170 184.7 42 1,190 162 321.0 2,070 170 5.6 3.2 Norway 5 324 16 408.5 24 84,640 3 267.5 55,420 8 –1.6 –2.8 Oman 3 310 9 49.8 69 17,890 56 68.3 24,530 54 12.8 10.4 Pakistan 170 796 220 169.8 46 1,000 171 454.7 2,680 162 3.6 1.4 Panama 3 75 46 22.7 94 6,570 91 42.1a 12,180a 91 2.4 0.8 Papua New Guinea 7 463 15 7.9 134 1,180 165 15.2a 2,260a 166 4.5 2.1 Paraguay 6 407 16 14.3 108 2,250 145 28.1 4,430 142 –3.8 –5.5 Peru 29 1,285 23 122.4 54 4,200 115 236.7 8,120 109 0.9 –0.3 Philippines 92 300 308 164.6 47 1,790 154 325.6 3,540 149 1.1 –0.7 Poland 38 313 125 467.6 21 12,260 69 697.9 18,290 69 1.7 1.6 Portugal 11 92 116 232.9 35 21,910 51 256.1 24,080 57 –2.6 –2.7 Puerto Rico 4 9 447 .. ..i .. .. .. .. Qatar 1 12 122 .. ..i .. .. 8.6 –1.3 2011 World Development Indicators 11 1.1 Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 Romania 21 238 93 178.9 44 8,330 81 312.4 14,540 75 –8.5 –8.4 Russian Federation 142 17,098 9 1,324.4 12 9,340 76 2,599.4 18,330 68 –7.9 –7.8 Rwanda 10 26 405 4.9 150 490 193 11.3 1,130 195 4.1 1.2 Saudi Arabia 25 2,000j 13 436.9 23 17,210 58 609.8 24,020 58 0.6 –1.7 Senegal 13 197 65 13.1 112 1,040 170 22.7 1,810 177 2.2 –0.4 Serbia 7 88 83 43.9 75 6,000 96 85.6 11,700 93 –3.0 –2.6 Sierra Leone 6 72 80 1.9 178 340 204 4.5 790 205 4.0 1.5 Singapore 5 1 7,125 185.7 41 37,220 33 248.3 49,780 11 –1.3 –4.2 Slovak Republic 5 49 113 87.4 60 16,130 60 119.8 22,110 63 –6.2 –6.4 Slovenia 2 20 101 48.1 72 23,520 49 54.1 26,470 53 –7.8 –8.8 Somalia 9 638 15 .. ..f .. .. .. .. South Africa 49 1,219 41 284.3 31 5,760 97 495.6 10,050 99 –1.8 –2.8 Spain 46 505 92 1,476.2 9 32,120 39 1,447.2 31,490 43 –3.6 –4.5 Sri Lanka 20 66 324 40.4 77 1,990 151 95.8 4,720 136 3.5 2.8 Sudan 42 2,506 18 51.5 70 1,220 160 84.1 1,990 171 4.5 2.2 Swaziland 1 17 69 2.9 167 2,470 143 5.7 4,790 134 1.2 –0.3 Sweden 9 450 23 454.4 22 48,840 14 353.9 38,050 28 –5.1 –6.0 Switzerland 8 41 193 505.8 18 65,430 8 364.1 47,100 14 –1.9 –3.0 Syrian Arab Republic 21 185 115 50.9 71 2,410 144 97.3 4,620 138 4.0 1.5 Tajikistan 7 143 50 4.8 151 700 183 13.5 1,950 172 3.4 1.7 Tanzania 44 947 49 21.4k 97 500k 192 57.9k 1,360k 184 6.0k 3.0k Thailand 68 513 133 254.7 32 3,760 122 517.5 7,640 115 –2.2 –2.8 Timor-Leste 1 15 76 2.7 169 2,460 141 5.2a 4,730a 133 1.9 –1.3 Togo 7 57 122 2.9 168 440 196 5.6 850 203 2.5 0.0 Trinidad and Tobago 1 5 261 22.4 96 16,700 59 33.4 a 24,970a 55 –3.0 –3.4 Tunisia 10 164 67 38.9 78 3,720 124 81.4 7,810 113 3.1 2.1 Turkey 75 784 97 652.4 17 8,720 79 1,009.8 13,500 80 –4.7 –5.8 Turkmenistan 5 488 11 17.5 104 3,420 126 35.7a 6,980a 118 8.0 6.6 Uganda 33 241 166 15.2 106 460 194 39.0 1,190 189 7.1 3.6 Ukraine 46 604 79 128.9 52 2,800 135 284.4 6,180 123 –15.1 –14.6 United Arab Emirates 5 84 55 .. ..i .. .. –0.7 –3.2 United Kingdom 62 244 256 2,558.1 6 41,370 29 2,217.4 35,860 33 –4.9 –5.6 United States 307 9,832 34 14,233.5 1 46,360 18 14,011.0 45,640 16 –2.6 –3.5 Uruguay 3 176 19 30.2 85 9,010 77 43.1 12,900 86 2.9 2.5 Uzbekistan 28 447 65 30.6 83 1,100 167 80.9a 2,910a 159 8.1 6.3 Venezuela, RB 28 912 32 286.4 30 10,090 74 346.9 12,220 90 –3.3 –4.8 Vietnam 87 331 281 87.7 59 1,000 b 171 243.6 2,790 161 5.3 4.0 West Bank and Gaza 4 6 672 .. ..l .. .. .. .. Yemen, Rep. 24 528 45 25.0 90 1,060 169 55.0 2,330 165 3.8 0.8 Zambia 13 753 17 12.5 115 960 176 16.5 1,280 187 6.4 3.8 Zimbabwe 13 391 32 4.6 154 360 203 .. .. 5.7 5.2 World 6,775 s 134,123 s 52 w 59,162.8 t 8,732 w 71,774.4 t 10,594 w –1.9 w –3.0 w Low income 846 17,838 49 431.0 509 1,032.5 1,220 4.6 2.4 Middle income 4,813 80,558 61 16,346.7 3,397 30,653.8 6,370 2.6 1.5 Lower middle income 3,811 31,898 124 8,845.9 2,321 18,229.1 4,784 7.1 5.9 Upper middle income 1,002 48,659 21 7,515.1 7,502 12,461.9 12,440 –2.6 –3.4 Low & middle income 5,659 98,396 59 16,792.6 2,968 31,684.3 5,599 2.7 1.4 East Asia & Pacific 1,944 16,302 123 6,148.6 3,163 11,712.8 6,026 7.4 6.6 Europe & Central Asia 404 23,549 18 2,745.8 6,793 5,097.0 12,609 –5.8 –6.1 Latin America & Carib. 572 20,394 28 4,011.3 7,007 5,888.7 10,286 –1.9 –3.0 Middle East & N. Africa 331 8,778 38 1,190.2 3,597 2,617.6 7,911 3.4 1.6 South Asia 1,568 5,131 329 1,735.4 1,107 4,658.7 2,972 8.1 6.5 Sub-Saharan Africa 840 24,242 36 944.2 1,125 1,722.2 2,051 1.7 –0.7 High income 1,117 35,727 33 42,417.7 37,990 40,433.9 36,213 –3.3 –3.9 Euro area 327 2,583 128 12,723.2 38,872 11,127.6 33,997 –4.1 –4.5 a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Included in the aggregates for lower middle-income economies based on earlier data. c. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. d. Excludes Abkhazia and South Ossetia. e. Included in the aggregates for low-income economies based on earlier data. f. Estimated to be low income ($995 or less). g. Excludes Transnistria. h. Includes Former Spanish Sahara. i. Estimated to be high income ($12,196 or more). j. Provisional estimate. k. Covers mainland Tanzania only. l. Estimated to be lower middle income ($996–$3,945). 12 2011 World Development Indicators 1.1 WORLD VIEW Size of the economy About the data Definitions Population, land area, income, and output are basic conventional price indexes allow comparison of real • Population is based on the de facto definition of measures of the size of an economy. They also values over time. population, which counts all residents regardless of provide a broad indication of actual and potential PPP rates are calculated by simultaneously com- legal status or citizenship—except for refugees not resources. Population, land area, income (as mea- paring the prices of similar goods and services permanently settled in the country of asylum, who sured by gross national income, GNI), and output among a large number of countries. In the most are generally considered part of the population of (as measured by gross domestic product, GDP) are recent round of price surveys conducted by the Inter- their country of origin. The values shown are midyear therefore used throughout World Development Indica- national Comparison Program (ICP), 146 countries estimates. See also table 2.1. •  Surface area is tors to normalize other indicators. and territories participated in the data collection, a country’s total area, including areas under inland Population estimates are generally based on including China for the first time, India for the first bodies of water and some coastal waterways. • Pop- extrapolations from the most recent national cen- time since 1985, and almost all African countries. ulation density is midyear population divided by land sus. For further discussion of the measurement of The PPP conversion factors presented in the table area in square kilometers. • Gross national income population and population growth, see About the data come from three sources. For 45 high- and upper (GNI) is the sum of value added by all resident pro- for table 2.1. middle-income countries conversion factors are ducers plus any product taxes (less subsidies) not The surface area of an economy includes inland provided by Eurostat and the Organisation for Eco- included in the valuation of output plus net receipts bodies of water and some coastal waterways. Sur- nomic Co-operation and Development (OECD), with of primary income (compensation of employees and face area thus differs from land area, which excludes PPP estimates for 34 European countries incorpo- property income) from abroad. Data are in current bodies of water, and from gross area, which may rating new price data collected since 2005. For the U.S. dollars converted using the World Bank Atlas include offshore territorial waters. Land area is par- remaining 2005 ICP countries the PPP estimates are method (see Statistical methods). • GNI per capita is ticularly important for understanding an economy’s extrapolated from the 2005 ICP benchmark results, GNI divided by midyear population. GNI per capita in agricultural capacity and the environmental effects which account for relative price changes between U.S. dollars is converted using the World Bank Atlas of human activity. (For measures of land area and each economy and the United States. For countries method. • Purchasing power parity (PPP) GNI is GNI data on rural population density, land use, and agri- that did not participate in the 2005 ICP round, the converted to international dollars using PPP rates. An cultural productivity, see tables 3.1–3.3.) Innova- PPP estimates are imputed using a statistical model. international dollar has the same purchasing power tions in satellite mapping and computer databases More information on the results of the 2005 ICP over GNI that a U.S. dollar has in the United States. have resulted in more precise measurements of land is available at www.worldbank.org/data/icp. • Gross domestic product (GDP) is the sum of value and water areas. All 213 economies shown in World Development added by all resident producers plus any product GNI measures total domestic and foreign value Indicators are ranked by size, including those that taxes (less subsidies) not included in the valuation added claimed by residents. GNI comprises GDP appear in table 1.6. The ranks are shown only in of output. Growth is calculated from constant price plus net receipts of primary income (compensation table 1.1. No rank is shown for economies for which GDP data in local currency. • GDP per capita is GDP of employees and property income) from nonresident numerical estimates of GNI per capita are not pub- divided by midyear population. sources. The World Bank uses GNI per capita in U.S. lished. Economies with missing data are included in dollars to classify countries for analytical purposes the ranking at their approximate level, so that the rel- and to determine borrowing eligibility. For definitions ative order of other economies remains consistent. of the income groups in World Development Indica- tors, see Users guide. For discussion of the useful- ness of national income and output as measures of productivity or welfare, see About the data for tables Data sources 4.1 and 4.2. When calculating GNI in U.S. dollars from GNI Population estimates are prepared by World Bank reported in national currencies, the World Bank fol- staff from a variety of sources (see Data sources lows the World Bank Atlas conversion method, using for table 2.1). Data on surface and land area are a three-year average of exchange rates to smooth from the Food and Agriculture Organization (see the effects of transitory fluctuations in exchange Data sources for table 3.1). GNI, GNI per capita, rates. (For further discussion of the World Bank Atlas GDP growth, and GDP per capita growth are esti- method, see Statistical methods.) mated by World Bank staff based on national Because exchange rates do not always refl ect accounts data collected by World Bank staff during differences in price levels between countries, economic missions or reported by national statis- the table also converts GNI and GNI per capita tical offices to other international organizations estimates into international dollars using purchas- such as the OECD. PPP conversion factors are ing power parity (PPP) rates. PPP rates provide estimates by Eurostat/OECD and by World Bank a standard measure allowing comparison of real staff based on data collected by the ICP. levels of expenditure between countries, just as 2011 World Development Indicators 13 1.2 Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary education mortality rate % % of total employment under age 5 % % per 1,000 1995– 2009a,b 1990 2008 1990 2004–09a 1991 2009c 1991 2009c 1990 2009 Afghanistan 9.0 .. .. .. 32.9 28 .. 54 62 250 199 Albania 8.1 .. .. .. 6.6 .. 90 96 100 51 15 Algeria 6.9 .. .. 9.2 3.7 80 91 83 .. 61 32 Angola 2.0 d .. .. .. .. 33 .. .. .. 258 161 Argentina 4.1d .. 20e .. 2.3 .. 102 .. 105 28 14 Armenia 8.8 .. .. .. 4.2 .. 98 .. 103 56 22 Australia .. 10 9 .. .. .. .. 100 97 9 5 Austria 8.6 .. 9 .. .. .. 99 95 97 9 4 Azerbaijan 8.0 .. 53 .. 8.4 95 92 100 102 98 34 Bangladesh 9.4 .. .. 61.5 41.3 41 61 75 108 148 52 Belarus 9.2 .. .. .. 1.3 94 96 .. 101 24 12 Belgium 8.5 16 10 .. .. 79 86 101 98 10 5 Benin 6.9 .. .. .. 20.2 22 62 .. .. 184 118 Bolivia 2.8 40e .. 9.7 4.5 71 99 .. 99 122 51 Bosnia and Herzegovina 6.7 .. .. .. 1.6 .. .. .. 102 23 14 Botswana .. .. .. .. .. 90 95 109 100 60 57 Brazil 3.3 29e 27 .. 2.2 93 .. .. 103 56 21 Bulgaria 5.0 .. 9 .. 1.6 90 90 99 97 18 10 Burkina Faso 7.0 .. .. 29.6 26.0 20 43 .. 86 201 166 Burundi 9.0 .. .. 30.2 .. 46 52 82 93 189 166 Cambodia 6.6 .. .. .. 28.8 .. 83 .. 90 117 88 Cameroon 5.6 .. .. 18.0 16.6 53 73 83 86 148 154 Canada 7.2 .. 10e .. .. .. .. 99 .. 8 6 Central African Republic 5.2 .. .. .. .. 28 38 61 69 175 171 Chad 6.3 94 .. .. 33.9 18 33 41 64 201 209 Chile 8.6 .. 25 .. 0.5 .. 95 100 99 22 9 China 5.7 .. .. 12.6 4.5 107 .. 86 105 46 19 Hong Kong SAR, China 5.3 6 7e .. .. 102 93 .. 102 .. .. Colombia 2.5 28e 41 8.8 5.1 73 115 108 105 35 19 Congo, Dem. Rep. 5.5 .. .. .. 28.2 48 56 70 77 199 199 Congo, Rep. 5.0 .. .. 21.1 11.8 54 74 89 .. 104 128 Costa Rica 4.2 25 20 2.5 .. 79 96 101 102 18 11 Côte d’Ivoire 5.6 .. .. .. 16.7 42 46 .. .. 152 119 Croatia 8.1 .. 22 f .. 1.0 .. 100 103 102 13 5 Cuba .. .. .. .. .. 99 98 106 99 14 6 Czech Republic 10.2 7 13 0.9 .. 92 95 98 101 12 4 Denmark 8.3 7 5 .. .. 98 101 101 102 9 4 Dominican Republic 4.4 39 42 8.4 3.4 .. 90 .. 97 62 32 Ecuador 4.2 36e 34e .. 6.2 .. 103 100 103 53 24 Egypt, Arab Rep. 9.0 28e 25 10.5 6.8 .. 95 81 .. 90 21 El Salvador 4.3 35 36 11.1 .. 65 89 101 98 62 17 Eritrea .. .. .. 36.9 .. .. 48 82 77 150 55 Estonia 6.8 2e 6e .. .. .. 100 103 101 17 6 Ethiopia 9.3 .. 52e .. 34.6 23 55 68 88 210 104 Finland 9.6 .. 9 .. .. 97 98 109 102 7 3 France 7.2 11 6 .. .. 106 .. 102 100 9 4 Gabon 6.1 48 .. .. .. 62 .. 96 .. 93 69 Gambia, The 4.8 .. .. .. 15.8 45 79 65 102 153 103 Georgia 5.3 .. 62 .. 2.3 .. 107 98 96 47 29 Germany 8.5 .. 7 .. 1.1 100 104 99 98 9 4 Ghana 5.2 .. .. 24.1 14.3 64 83 78 95 120 69 Greece 6.7 40e 27 .. .. 99 101 99 97 11 3 Guatemala 3.4 .. .. 27.8 .. .. 80 87 94 76 40 Guinea 6.4 .. .. .. 20.8 17 62 45 77 231 142 Guinea-Bissau 7.2 .. .. .. 17.4 .. .. 55 .. 240 193 Haiti 2.5 .. .. 23.7 18.9 27 .. .. .. 152 87 Honduras 2.0 49e .. 15.8 8.6 64 90 104 107 55 30 14 2011 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary education mortality rate % % of total employment under age 5 % % per 1,000 1995– 2009a,b 1990 2008 1990 2004–09a 1991 2009c 1991 2009c 1990 2009 Hungary 8.4 7e 7 2.3 .. 82 95 100 98 17 6 India 8.1 .. .. 59.5 43.5 .. 95 73 92 118 66 Indonesia 7.6 .. 63 31.0 17.5g 93 109 93 98 86 39 Iran, Islamic Rep. 6.4 .. 43 .. .. 88 101 85 97 73 31 Iraq .. .. .. 10.4 7.1 58 64 79 81 53 44 Ireland 7.4 20 12 .. .. 103 99 104 103 9 4 Israel 5.7 .. 7 .. .. .. 99 105 101 11 4 Italy 6.5 27 19 .. .. 98 104 100 99 10 4 Jamaica 5.2 42 35 4.0 2.2 94 89 103 100 33 31 Japan .. 19 11 .. .. 102 101 101 100 6 3 Jordan 7.2 .. .. 4.8 1.9 101 100 101 102 39 25 Kazakhstan 8.7 .. .. .. 4.9 .. 106 .. 99 60 29 Kenya 4.7 .. .. 20.1 16.4 .. .. .. 95 99 84 Korea, Dem. Rep. .. .. .. .. 20.6 .. .. .. .. 45 33 Korea, Rep. 7.9 .. 25 .. .. 99 99 99 97 9 5 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. 1.7 57 93 100 101 17 10 Kyrgyz Republic 8.8 .. 47 .. 2.7 .. 94 102 101 75 37 Lao PDR 7.6 .. .. 39.8 31.6 41 75 77 87 157 59 Latvia 6.8 .. 7 .. .. .. 95 101 100 16 8 Lebanon .. .. .. .. 4.2 .. 85 101 104 40 12 Lesotho 3.0 38 .. 13.8 16.6 59 70 124 107 93 84 Liberia 6.4 .. .. .. 20.4 .. 58 .. .. 247 112 Libya .. .. .. .. 5.6 .. .. .. .. 36 19 Lithuania 6.6 .. 9 .. .. .. 92 96 100 15 6 Macedonia, FYR 5.4 .. 22 .. 1.8 98 92 99 98 36 11 Madagascar 6.2 84 .. 35.5 36.8 36 79 96 97 167 58 Malawi 7.0 .. .. 24.4 15.5 31 59 82 100 218 110 Malaysia 4.5 29 22 22.1 .. 91 97 101 103 18 6 Mali 6.5 .. .. 29.0 27.9 .. 59 58 78 250 191 Mauritania 6.2 .. .. 43.3 16.7 33 64 71 103 129 117 Mauritius .. 12 17 .. .. 115 89 102 101 24 17 Mexico 3.9 26 30 13.9 3.4 88 104 97 102 45 17 Moldova 6.8 .. 32 .. 3.2 .. 93 105 101 37 17 Mongolia 7.1 .. .. 10.8 5.3 .. 93 109 103 101 29 Morocco 6.5 .. 51 8.1 9.9 48 80 70 88 89 38 Mozambique 5.2 .. .. .. .. 26 57 71 88 232 142 Myanmar .. .. .. 28.8 .. .. 99 95 100 118 71 Namibia .. .. .. 21.5 17.5 74 87 106 104 73 48 Nepal 6.1 .. .. .. 38.8 51 .. 59 .. 142 48 Netherlands 7.6 8 9 .. .. .. .. 97 98 8 4 New Zealand 6.4 13 12 .. .. .. .. 100 103 11 6 Nicaragua 3.8 .. 45 9.6 4.3 42 75 119 102 68 26 Niger 8.3 .. .. 41.0 39.9 17 40 53 75 305 160 Nigeria 5.1 .. .. 35.1 26.7 .. 79 77 85 212 138 Norway 9.6 .. 6 .. .. 100 98 102 99 9 3 Oman .. .. .. 21.4 .. 74 80 89 97 48 12 Pakistan 9.0 .. 62 39.0 .. .. 61 48 82 130 87 Panama 3.6 34 28 .. .. 86 102 99 101 31 23 Papua New Guinea 4.5 .. .. .. 18.1 46 .. 80 .. 91 68 Paraguay 3.8 23e 47 2.8 .. 68 94 98 100 42 23 Peru 3.9 36e 40e 8.8 5.4 .. 101 96 99 78 21 Philippines 5.6 .. 45e 29.8 .. 88 94 99 102 59 33 Poland 7.6 28e 19 .. .. 96 96 101 99 17 7 Portugal 5.8 25e 19 .. .. .. .. 103 100 15 4 Puerto Rico .. .. .. .. .. .. .. .. 102 .. .. Qatar 3.9 .. .. .. .. 71 108 98 120 19 11 2011 World Development Indicators 15 1.2 Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary education mortality rate % % of total employment under age 5 % % per 1,000 1995– 2009a,b 1990 2008 1990 2004–09a 1991 2009c 1991 2009c 1990 2009 Romania 8.1 27e 31 5.0 .. 96 96 99 99 32 12 Russian Federation 6.0 1 6 .. .. .. 95 105 98 27 12 Rwanda 4.2 .. .. 24.3 18.0 50 54 95 100 171 111 Saudi Arabia .. .. .. .. 5.3 .. 93 .. 91 43 21 Senegal 6.2 83 .. 19.0 14.5 39 57 69 95 151 93 Serbia 9.1 .. 23 .. 1.8 .. 96 .. 101 29 7 Sierra Leone 6.1 .. .. 25.4 21.3 .. 88 64 84 285 192 Singapore 5.0 8 10 .. .. .. .. .. .. 8 3 Slovak Republic 8.8 .. 11 .. .. 95 96 102 100 15 7 Slovenia 8.2 12e 11 .. .. 95 96 103 99 10 3 Somalia .. .. .. .. 32.8 .. .. .. 53 180 180 South Africa 3.1 .. 3 .. .. 76 93 104 99 62 62 Spain 7.0 22e 12 .. .. 104 100 104 103 9 4 Sri Lanka 6.9 .. 41e 29.3 21.6 101 97 102 .. 28 15 Sudan .. .. .. 31.8 31.7 .. 57 78 89 124 108 Swaziland 4.5 .. .. .. 6.1 61 72 .. 92 92 73 Sweden 9.1 .. 7 .. .. 96 94 102 99 7 3 Switzerland 7.6 9 10 .. .. 53 94 97 97 8 4 Syrian Arab Republic 7.7 .. .. 11.5 10.0 89 112 85 97 36 16 Tajikistan 9.3 .. .. .. 14.9 .. 98 .. 91 117 61 Tanzania 6.8 .. 88e 25.1 16.7 55 102 97 96 162 108 Thailand 3.9 70 53 16.3 7.0 .. .. 99 103 32 14 Timor-Leste 9.0 .. .. .. .. .. 80 .. .. 184 56 Togo 5.4 .. .. 21.2 22.3 35 61 59 75 150 98 Trinidad and Tobago .. 22 .. 4.7 .. 102 93 101 101 34 35 Tunisia 5.9 .. .. 8.5 3.3 74 93 86 103 50 21 Turkey 5.7 .. 35 8.7 3.5 90 93 81 93 84 20 Turkmenistan 6.0 .. .. .. .. .. .. .. .. 99 45 Uganda 5.8 .. .. 19.7 16.4 .. 72 77 99 184 128 Ukraine 9.4 .. .. .. .. 92 95 102 99 21 15 United Arab Emirates .. .. .. .. .. 103 99 104 100 17 7 United Kingdom 6.1 10 11 .. .. .. .. 102 101 10 6 United States 5.4 .. .. .. 1.3 .. 95 100 100 11 8 Uruguay 5.6 .. 25e 6.5 6.0 94 106 .. 104 24 13 Uzbekistan 7.1 .. .. .. 4.4 .. 92 .. 99 74 36 Venezuela, RB 4.9 .. 30 6.7 3.7 81 95 105 102 32 18 Vietnam 7.3 .. .. 40.7 20.2 .. .. .. .. 55 24 West Bank and Gaza .. .. 36 .. 2.2 .. 82 .. 104 43 30 Yemen, Rep. 7.2 .. .. 29.6 .. .. 61 .. .. 125 66 Zambia 3.6 65 .. 21.2 14.9 .. 87 .. 96 179 141 Zimbabwe 4.6 .. .. 8.0 14.0 97 .. 92 97 81 90 World .. w .. w .. w 21.3 w 79 w 88 w 87 w 96 w 92 w 61 w Low income .. .. .. 27.7 44 63 80 91 171 118 Middle income .. .. 31.7 20.8 83 92 85 97 85 51 Lower middle income .. .. 33.5 24.0 82 90 81 95 93 57 Upper middle income .. 26 .. .. 88 100 98 101 51 22 Low & middle income .. .. 32.5 22.4 78 87 84 96 100 66 East Asia & Pacific .. .. 18.0 8.8 101 99 89 102 55 26 Europe & Central Asia .. 19 .. .. 92 96 98 97 52 21 Latin America & Carib. .. 30 .. 3.8 84 101 99 102 52 23 Middle East & N. Africa .. 37 .. 6.8 .. 95 80 96 76 33 South Asia .. .. 57.2 42.5 62 79 69 91 125 71 Sub-Saharan Africa .. .. .. 24.7 51 64 82 88 181 130 High income .. 12 .. .. .. 98 100 99 12 7 Euro area .. 11 .. .. 101 .. .. .. 9 4 a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Covers urban areas only. e. Limited coverage. f. Data are for 2009. g. Data are for 2010. 16 2011 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives About the data Definitions Tables 1.2–1.4 present indicators for 17 of the 21 nutrients, and undernourished mothers who give • Share of poorest quintile in national consump- targets specified by the Millennium Development birth to underweight children. tion or income is the share of the poorest 20 per- Goals. Each of the eight goals includes one or more Progress toward universal primary education is cent of the population in consumption or, in some targets, and each target has several associated measured by the primary completion rate. Because cases, income. • Vulnerable employment is the sum indicators for monitoring progress toward the target. many school systems do not record school comple- of unpaid family workers and own-account workers Most of the targets are set as a value of a specific tion on a consistent basis, it is estimated from the as a percentage of total employment. • Prevalence indicator to be attained by a certain date. In some gross enrollment rate in the final grade of primary of malnutrition is the percentage of children under cases the target value is set relative to a level in education, adjusted for repetition. Offi cial enroll- age 5 whose weight for age is more than two stan- 1990. In others it is set at an absolute level. Some ments sometimes differ signifi cantly from atten- dard deviations below the median for the interna- of the targets for goals 7 and 8 have not yet been dance, and even school systems with high average tional reference population ages 0–59 months. The quantified. enrollment ratios may have poor completion rates. data are based on the new international child growth The indicators in this table relate to goals 1–4. Eliminating gender disparities in education would standards for infants and young children, called the Goal 1 has three targets between 1990 and 2015: help increase the status and capabilities of women. Child Growth Standards, released in 2006 by the to halve the proportion of people whose income is The ratio of female to male enrollments in primary World Health Organization. • Primary completion less than $1.25 a day, to achieve full and productive and secondary education provides an imperfect mea- rate is the percentage of students completing the employment and decent work for all, and to halve the sure of the relative accessibility of schooling for girls. last year of primary education. It is calculated as proportion of people who suffer from hunger. Esti- The targets for reducing under-five mortality rates the total number of students in the last grade of mates of poverty rates are in tables 2.7 and 2.8. are among the most challenging. Under-five mortal- primary education, minus the number of repeaters The indicator shown here, the share of the poorest ity rates are harmonized estimates produced by a in that grade, divided by the total number of children quintile in national consumption or income, is a dis- weighted least squares regression model and are of official graduation age. • Ratio of girls to boys tributional measure. Countries with more unequal available at regular intervals for most countries. enrollments in primary and secondary education distributions of consumption (or income) have a Most of the 60 indicators relating to the Millennium is the ratio of the female to male gross enrollment higher rate of poverty for a given average income. Development Goals can be found in World Develop- rate in primary and secondary education. • Under- Vulnerable employment measures the portion of the ment Indicators. Table 1.2a shows where to find the five mortality rate is the probability that a newborn labor force that receives the lowest wages and least indicators for the first four goals. For more informa- baby will die before reaching age five, if subject to security in employment. No single indicator captures tion about data collection methods and limitations, current age-specific mortality rates. The probability the concept of suffering from hunger. Child malnutri- see About the data for the tables listed there. For is expressed as a rate per 1,000. tion is a symptom of inadequate food supply, lack information about the indicators for goals 5–8, see of essential nutrients, illnesses that deplete these About the data for tables 1.3 and 1.4. Location of indicators for Millennium Development Goals 1–4 1.2a Goal 1. Eradicate extreme poverty and hunger Table 1.1 Proportion of population below $1.25 a day 2.8 1.2 Poverty gap ratio 2.7, 2.8 1.3 Share of poorest quintile in national consumption 1.2, 2.9 1.4 Growth rate of GDP per person employed 2.4 1.5 Employment to population ratio 2.4 1.6 Proportion of employed people living below $1 per day — 1.7 Proportion of own-account and unpaid family workers in total employment 1.2, 2.4 1.8 Prevalence of underweight in children under age five 1.2, 2.20 1.9 Proportion of population below minimum level of dietary energy consumption 2.20 Goal 2. Achieve universal primary education Data sources 2.1 Net enrollment ratio in primary education 2.12 The indicators here and throughout this book have 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.13 2.3 Literacy rate of 15- to 24-year-olds 2.14 been compiled by World Bank staff from primary Goal 3. Promote gender equality and empower women and secondary sources. Efforts have been made 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 1.2, 2.12* to harmonize the data series used to compile this 3.2 Share of women in wage employment in the nonagricultural sector 1.5, 2.3* table with those published on the United Nations 3.3 Proportion of seats held by women in national parliament 1.5 Millennium Development Goals Web site (www. Goal 4. Reduce child mortality 4.1 Under-five mortality rate 1.2, 2.22 un.org/millenniumgoals), but some differences in 4.2 Infant mortality rate 2.22 timing, sources, and definitions remain. For more 4.3 Proportion of one-year-old children immunized against measles 2.18 information see the data sources for the indica- — No data are available in the World Development Indicators database. * Table shows information on related indicators. tors listed in table 1.2a. 2011 World Development Indicators 17 1.3 Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15–49 ages 15–49 people metric tons % % of population peoplea 2008 1990 2004–09b 2009 2009 1990 2007 2008 1990 2008 2009 Afghanistan 1,400 .. 15 .. 189 0.1 0.0 0.7 .. 37 3.4 Albania 31 .. 69 .. 15 2.3 1.4 1.5 .. 98 41.2 Algeria 120 47 61 0.1 59 3.1 4.1 2.1 88 95 13.5 Angola 610 .. .. 2.0 298 0.4 1.4 1.4 25 57 3.3 Argentina 70 .. 78 0.5 28 3.5 4.6 1.9 90 90 30.4 Armenia 29 .. 53 0.1 73 1.1 1.6 0.9 .. 90 6.8 Australia 8 .. .. 0.1 6 17.2 17.7 4.7 100 100 72.0 Austria 5 .. .. 0.3 11 7.9 8.3 1.9 100 100 73.5 Azerbaijan 38 .. 51 0.1 110 6.0 3.7 0.8 .. 45 42.0 Bangladesh 340 40 53 <0.1 225 0.1 0.3 1.9 39 53 0.4 Belarus 15 .. 73 0.3 39 9.6 6.9 0.7 .. 93 45.9 Belgium 5 78 75 0.2 9 10.8 9.7 1.3 100 100 75.2 Benin 410 .. 17 1.2 93 0.1 0.5 1.5 5 12 2.2 Bolivia 180 30 61 0.2 140 0.8 1.4 0.8 19 25 11.2 Bosnia and Herzegovina 9 .. 36 .. 50 1.2 7.7 13.1 .. 95 37.7 Botswana 190 33 53 24.8 694 1.6 2.6 0.5 36 60 6.2 Brazil 58 59 81 .. 45 1.4 1.9 1.3 69 80 39.2 Bulgaria 13 .. .. 0.1 41 8.8 6.8 1.1 99 100 44.8 Burkina Faso 560 .. 17 1.2 215 0.1 0.1 1.0 6 11 1.1 Burundi 970 .. 9 3.3 348 0.1 0.0 1.5 44 46 0.8 Cambodia 290 .. 40 0.5 442 0.0 0.3 29.8 9 29 0.5 Cameroon 600 16 29 5.3 182 0.1 0.3 5.4 47 47 3.8 Canada 12 .. .. 0.2 5 16.2 16.9 1.8 100 100 77.7 Central African Republic 850 .. 19 4.7 327 0.1 0.1 0.6 11 34 0.5 Chad 1,200 .. 3 3.4 283 0.0 0.0 1.0 6 9 1.7 Chile 26 56 58 0.4 11 2.6 4.3 2.4 84 96 34.0 China 38 85 85 0.1c 96 2.2 5.0 2.4 41 55 28.8 Hong Kong SAR, China .. 86 .. .. 82 4.8 5.8 13.2 .. .. 61.4 Colombia 85 66 78 0.5 35 1.7 1.4 1.2 68 74 45.5 Congo, Dem. Rep. 670 8 21 .. 372 0.1 0.0 2.5 9 23 0.6 Congo, Rep. 580 .. 44 3.4 382 0.5 0.4 1.0 .. 30 6.7 Costa Rica 44 .. 80 0.3 10 1.0 1.8 1.9 93 95 34.5 Côte d’Ivoire 470 .. 13 3.4 399 0.5 0.3 3.9 20 23 4.6 Croatia 14 .. .. <0.1 25 3.8 5.6 1.8 .. 99 50.4 Cuba 53 .. 78 0.1 6 3.1 2.4 4.2 80 91 14.3 Czech Republic 8 78 .. <0.1 9 13.5 12.1 1.5 100 98 63.7 Denmark 5 78 .. 0.2 7 9.8 9.1 1.6 100 100 85.9 Dominican Republic 100 56 73 0.9 70 1.3 2.1 2.1 73 83 26.8 Ecuador 140 53 73 0.4 68 1.6 2.2 10.4 69 92 15.1 Egypt, Arab Rep. 82 47 60 <0.1 19 1.3 2.3 4.1 72 94 20.0 El Salvador 110 47 73 0.8 30 0.5 1.1 1.8 75 87 14.4 Eritrea 280 .. .. 0.8 99 .. 0.1 15.0 9 14 4.9 Estonia 12 .. .. 1.2 30 16.3 15.2 0.6 .. 95 72.3 Ethiopia 470 4 15 .. 359 0.1 0.1 1.3 4 12 0.5 Finland 8 77 .. 0.1 9 10.2 12.1 1.3 100 100 83.9 France 8 81 71 0.4 6 7.0 6.0 2.5 100 100 71.3 Gabon 260 .. .. 5.2 501 6.6 1.4 2.1 .. 33 6.7 Gambia, The 400 12 .. 2.0 269 0.2 0.2 2.2 .. 67 7.6 Georgia 48 .. 47 0.1 107 2.9 1.4 1.0 96 95 30.5 Germany 7 75 .. 0.1 5 12.0 9.6 2.2 100 100 79.5 Ghana 350 13 24 1.8 201 0.3 0.4 3.7 7 13 5.4 Greece 2 .. .. 0.1 5 7.2 8.8 2.1 97 98 44.1 Guatemala 110 .. 54 0.8 62 0.6 1.0 2.4 65 81 16.3 Guinea 680 .. 9 1.3 318 0.2 0.1 2.2 9 19 0.9 Guinea-Bissau 1,000 .. 10 2.5 229 0.2 0.2 2.4 .. 21 2.3 Haiti 300 10 32 1.9 238 0.1 0.2 2.3 26 17 10.0 Honduras 110 47 65 0.8 58 0.5 1.2 3.5 44 71 9.8 18 2011 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15–49 ages 15–49 people metric tons % % of population peoplea 2008 1990 2004–09b 2009 2009 1990 2007 2008 1990 2008 2009 Hungary 13 .. .. <0.1 16 6.1 5.6 1.8 100 100 61.6 India 230 43 54 0.3 168 0.8 1.4 3.3 18 31 5.3 Indonesia 240 50 57 0.2 189 0.8 1.8 3.4 33 52 8.7 Iran, Islamic Rep. 30 49 79 0.2 19 4.2 7.0 1.0 83 .. 38.3 Iraq 75 14 50 .. 64 2.8 3.3 11.0 .. 73 1.0 Ireland 3 60 89 0.2 9 8.6 10.2 1.8 99 99 68.4 Israel 7 68 .. 0.2 5 7.2 9.3 4.3 100 100 49.7 Italy 5 .. .. 0.3 6 7.5 7.7 2.2 .. .. 48.5 Jamaica 89 55 .. 1.7 7 3.3 5.2 7.7 83 83 58.6 Japan 6 58 54 <0.1 21 9.3 9.8 4.9 100 100 77.7 Jordan 59 40 59 .. 6 3.3 3.8 3.4 .. 98 29.3 Kazakhstan 45 .. 51 0.1 163 15.9 14.7 1.1 96 97 33.4 Kenya 530 27 46 6.3 305 0.2 0.3 3.9 26 31 10.0 Korea, Dem. Rep. 250 62 .. .. 345 12.1 3.0 1.3 .. .. 0.0 Korea, Rep. 18 79 80 <0.1 90 5.6 10.4 1.7 100 100 80.9 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 9 .. .. .. 35 19.2 32.3 6.3 100 100 39.4 Kyrgyz Republic 81 .. 48 0.3 159 2.4 1.2 0.8 .. 93 41.2 Lao PDR 580 .. 38 0.2 89 0.1 0.3 1.2 .. 53 4.7 Latvia 20 .. .. 0.7 45 5.1 3.4 1.4 .. 78 66.7 Lebanon 26 .. 58 0.1 15 3.1 3.2 1.2 .. .. 23.7 Lesotho 530 23 47 23.6 634 .. .. 0.6 32 29 3.7 Liberia 990 .. 11 1.5 288 0.2 0.2 3.8 11 17 0.5 Libya 64 .. .. .. 40 9.2 9.3 1.6 97 97 5.5 Lithuania 13 .. .. 0.1 71 6.0 4.5 0.9 .. .. 58.8 Macedonia, FYR 9 .. 14 .. 23 5.6 5.5 0.9 .. 89 51.8 Madagascar 440 17 40 0.2 261 0.1 0.1 6.4 8 11 1.6 Malawi 510 13 41 11.0 304 0.1 0.1 3.3 42 56 4.7 Malaysia 31 50 .. 0.5 83 3.1 7.3 6.9 84 96 57.6 Mali 830 .. 8 1.0 324 0.0 0.0 1.0 26 36 1.9 Mauritania 550 3 9 0.7 330 1.3 0.6 2.9 16 26 2.3 Mauritius 36 75 .. 1.0 22 1.4 3.1 24.3 91 91 22.7 Mexico 85 .. 73 0.3 17 4.3 4.5 3.2 66 85 26.5 Moldova 32 .. 68 0.4 178 4.8 1.3 1.3 .. 79 35.9 Mongolia 65 .. 55 <0.1 224 4.5 4.0 1.1 .. 50 13.1 Morocco 110 42 63 0.1 92 0.9 1.5 1.9 53 69 32.2 Mozambique 550 .. 16 11.5 409 0.1 0.1 2.9 11 17 2.7 Myanmar 240 17 41 0.6 404 0.1 0.3 2.7 .. 81 0.2 Namibia 180 29 55 13.1 727 0.0 1.5 2.1 25 33 5.9 Nepal 380 23 48 0.4 163 0.0 0.1 1.1 11 31 2.1 Netherlands 9 76 69 0.2 8 11.0 10.6 1.3 100 100 90.0 New Zealand 14 .. .. 0.1 8 6.9 7.7 5.1 .. .. 83.4 Nicaragua 100 .. 72 0.2 44 0.6 0.8 1.3 43 52 3.5 Niger 820 4 11 0.8 181 0.1 0.1 1.0 5 9 0.8 Nigeria 840 6 15 3.6 295 0.5 0.6 4.3 37 32 28.4 Norway 7 74 88 0.1 6 7.4 9.1 1.5 100 100 91.8 Oman 20 9 .. 0.1 13 5.6 13.7 4.2 85 .. 43.5 Pakistan 260 15 30 0.1 231 0.6 1.0 1.7 28 45 12.0 Panama 71 .. .. 0.9 48 1.3 2.2 2.9 58 69 27.8 Papua New Guinea 250 .. 32 0.9 250 0.5 0.5 3.6 47 45 1.9 Paraguay 95 48 79 0.3 47 0.5 0.7 0.5 37 70 15.8 Peru 98 59 73 0.4 113 1.0 1.5 2.8 54 68 27.7 Philippines 94 36 51 <0.1 280 0.7 0.8 6.6 58 76 6.5 Poland 6 49 .. 0.1 24 9.1 8.3 1.2 .. 90 58.8 Portugal 7 .. 67 0.6 30 4.5 5.5 2.8 92 100 48.6 Puerto Rico 18 .. .. .. 2 .. .. 3.6 .. .. 25.2 Qatar 8 .. .. 0.1 49 25.2 55.4 .. 100 100 28.3 2011 World Development Indicators 19 1.3 Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15–49 ages 15–49 people metric tons % % of population peoplea 2008 1990 2004–09b 2009 2009 1990 2007 2008 1990 2008 2009 Romania 27 .. 70 0.1 125 6.8 4.4 1.6 71 72 36.2 Russian Federation 39 34 80 1.0 106 13.9 10.8 1.3 87 87 42.1 Rwanda 540 21 36 2.9 376 0.1 0.1 1.6 23 54 4.5 Saudi Arabia 24 .. 24 .. 18 13.2 16.6 3.8 .. .. 38.6 Senegal 410 .. 12 0.9 282 0.4 0.5 2.2 38 51 7.4 Serbia 8 .. 41 0.1 21 .. .. .. .. 92 56.1 Sierra Leone 970 .. 8 1.6 644 0.1 0.2 3.2 .. 13 0.3 Singapore 9 65 .. 0.1 36 15.4 11.8 9.7 99 100 73.3 Slovak Republic 6 74 .. <0.1 9 8.6 6.8 1.1 100 100 75.0 Slovenia 18 .. .. <0.1 12 6.2 7.5 2.1 100 100 63.6 Somalia 1,200 1 15 0.7 285 0.0 0.1 3.2 .. 23 1.2 South Africa 410 57 .. 17.8 971 9.5 9.0 1.6 69 77 9.0 Spain 6 .. 66 0.4 17 5.9 8.0 3.8 100 100 61.2 Sri Lanka 39 .. 68 <0.1 66 0.2 0.6 14.0 70 91 8.7 Sudan 750 9 8 1.1 119 0.2 0.3 2.4 34 34 9.9 Swaziland 420 20 51 25.9 1,257 0.5 0.9 0.8 .. 55 7.6 Sweden 5 .. .. 0.1 6 6.0 5.4 1.4 100 100 90.3 Switzerland 10 .. .. 0.4 5 6.4 5.0 1.4 100 100 70.9 Syrian Arab Republic 46 .. 58 .. 21 2.9 3.5 2.0 83 96 18.7 Tajikistan 64 .. 37 0.2 202 3.9 1.1 0.8 .. 94 10.1 Tanzania 790 10 26 5.6 183 0.1 0.1 5.1 24 24 1.5 Thailand 48 .. 77 1.3 137 1.7 4.1 3.4 80 96 25.8 Timor-Leste 370 .. 22d .. 498 .. 0.2 .. .. 50 .. Togo 350 34 17 3.2 446 0.2 0.2 1.2 13 12 5.4 Trinidad and Tobago 55 .. 43 1.5 23 13.9 27.9 1.7 93 92 36.2 Tunisia 60 50 60 <0.1 24 1.6 2.3 2.1 74 85 33.5 Turkey 23 63 73 <0.1 29 2.7 4.0 1.4 84 90 35.3 Turkmenistan 77 .. 48 .. 67 7.2 9.2 10.7 98 98 1.6 Uganda 430 5 24 6.5 293 0.0 0.1 2.5 39 48 9.8 Ukraine 26 .. 67 1.1 101 11.7 6.8 1.1 95 95 33.3 United Arab Emirates 10 .. .. .. 4 29.3 31.0 14.1 97 97 82.2 United Kingdom 12 .. .. 0.2 12 10.0 8.8 2.8 100 100 83.2 United States 24 71 .. 0.6 4 19.5 19.3 5.7 100 100 78.1 Uruguay 27 .. 78 0.5 22 1.3 1.9 2.6 94 100 55.5 Uzbekistan 30 .. 65 0.1 128 5.3 4.3 1.0 84 100 16.9 Venezuela, RB 68 .. .. .. 33 6.2 6.0 1.1 82 .. 31.2 Vietnam 56 53 80 0.4 200 0.3 1.3 3.5 35 75 27.5 West Bank and Gaza .. .. 50 .. 19 .. 0.6 .. .. 89 8.8 Yemen, Rep. 210 10 28 .. 54 0.8 1.0 12.6 18 52 1.8 Zambia 470 15 41 13.5 433 0.3 0.2 0.7 46 49 6.3 Zimbabwe 790 43 65 14.3 742 1.5 0.8 0.9 43 44 11.4 World 260 w 57 w 61 w 0.8 w 137 w 4.3e w 4.6e w   52 w 61 w 27.1 w Low income 580 23 33 2.7 294 0.7 0.3   23 35 2.7 Middle income 200 58 66 0.6 138 2.6 3.3   45 57 20.9 Lower middle income 230 60 63 0.4 147 1.6 2.8   37 50 17.2 Upper middle income 82 52 75 1.4 101 6.1 5.3   78 84 34.6 Low & middle income 290 54 61 0.9 161 2.4 2.9   43 54 18.1 East Asia & Pacific 89 75 77 0.2 136 1.9 4.0   42 59 24.1 Europe & Central Asia 32 .. 69 0.6 89 10.7 7.2   87 89 36.4 Latin America & Carib. 86 .. 75 0.5 45 2.3 2.7   69 79 31.5 Middle East & N. Africa 88 42 62 0.1 39 2.5 3.7   73 84 21.5 South Asia 290 40 51 0.3 180 0.7 1.2   22 36 5.5 Sub-Saharan Africa 650 15 21 5.4 342 0.9 0.8   27 31 8.8 High income 15 70 .. 0.3 14 11.9 12.5 100 99 72.3 Euro area 7 .. .. 0.3 9 8.6 8.2 100 100 67.3 a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite ITU for third-party use of these data. b. Data are for the most recent year available. c. Includes Hong Kong SAR, China. d. Data are for 2010. e. Includes emissions not allocated to specific countries. 20 2011 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment About the data Definitions The Millennium Development Goals address con- between contraction of the virus and the appearance • Maternal mortality ratio is the number of women cerns common to all economies. Diseases and envi- of symptoms, or malaria, which has periods of dor- who die from pregnancy-related causes during preg- ronmental degradation do not respect national bound- mancy, can be particularly difficult. The table shows nancy and childbirth, per 100,000 live births. Data aries. Epidemic diseases, wherever they occur, pose the estimated prevalence of HIV among adults ages are from various years and adjusted to a common a threat to people everywhere. And environmental 15–49. Prevalence among older populations can be 2008 base year. The values are modeled estimates damage in one location may affect the well-being of affected by life-prolonging treatment. The incidence of (see About the data for table 2.19). • Contraceptive plants, animals, and humans far away. The indicators tuberculosis is based on case notifications and esti- prevalence rate is the percentage of women ages in the table relate to goals 5, 6, and 7 and the targets mates of cases detected in the population. 15–49 married or in union who are practicing, or of goal 8 that address access to new technologies. Carbon dioxide emissions are the primary source whose sexual partners are practicing, any form of For the other targets of goal 8, see table 1.4. of greenhouse gases, which contribute to global contraception. • HIV prevalence is the percentage The target of achieving universal access to repro- warming, threatening human and natural habitats. of people ages 15–49 who are infected with HIV. ductive health has been added to goal 5 to address In recognition of the vulnerability of animal and plant • Incidence of tuberculosis is the estimated number the importance of family planning and health ser- species, a new target of reducing biodiversity loss of new tuberculosis cases (pulmonary, smear posi- vices in improving maternal health and preventing has been added to goal 7. tive, and extrapulmonary). • Carbon dioxide emis- maternal death. Women with multiple pregnancies Access to reliable supplies of safe drinking water and sions are those stemming from the burning of fossil are more likely to die in childbirth. Access to contra- sanitary disposal of excreta are two of the most impor- ception is an important way to limit and space births. tant means of improving human health and protecting fuels and the manufacture of cement. They include Measuring disease prevalence or incidence can be the environment. Improved sanitation facilities prevent emissions produced during consumption of solid, difficult. Most developing economies lack reporting human, animal, and insect contact with excreta. liquid, and gas fuels and gas flaring (see table 3.8). systems for monitoring diseases. Estimates are often Internet use includes narrowband and broadband • Proportion of species threatened with extinction derived from survey data and report data from sentinel Internet. Narrowband is often limited to basic appli- is the total number of threatened mammal (exclud- sites, extrapolated to the general population. Tracking cations; broadband is essential to promote e-busi- ing whales and porpoises), bird, and higher native, diseases such as HIV/AIDS, which has a long latency ness, e-learning, e-government, and e-health. vascular plant species as a percentage of the total number of known species of the same categories. Location of indicators for Millennium Development Goals 5–7 1.3a •  Access to improved sanitation facilities is the percentage of the population with at least adequate Goal 5. Improve maternal health Table access to excreta disposal facilities (private or 5.1 Maternal mortality ratio 1.3, 2.19 shared, but not public) that can effectively prevent 5.2 Proportion of births attended by skilled health personnel 2.19 human, animal, and insect contact with excreta 5.3 Contraceptive prevalence rate 1.3, 2.19 (facilities do not have to include treatment to ren- 5.4 Adolescent fertility rate 2.19 5.5 Antenatal care coverage 1.5, 2.19 der sewage outflows innocuous). Improved facilities 5.6 Unmet need for family planning 2.19 range from simple but protected pit latrines to flush Goal 6. Combat HIV/AIDS, malaria, and other diseases toilets with a sewerage connection. To be effective, 6.1 HIV prevalence among pregnant women ages 15–24 1.3*, 2.21* facilities must be correctly constructed and properly 6.2 Condom use at last high-risk sex 2.21* maintained. • Internet users are people with access 6.3 Proportion of population ages 15–24 with comprehensive, correct knowledge — to the worldwide network. of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of — nonorphans ages 10–14 6.5 Proportion of population with advanced HIV infection with access to — antiretroviral drugs 6.6 Incidence and death rates associated with malaria — 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 2.18 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 2.18 6.9 Incidence, prevalence, and death rates associated with tuberculosis 1.3, 2.21 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course 2.18 Data sources Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 3.1 The indicators here and throughout this book have 7.2 Carbon dioxide emissions, total, per capita and per $1 purchasing power parity been compiled by World Bank staff from primary GDP 3.8 7.3 Consumption of ozone-depleting substances 3.9* and secondary sources. Efforts have been made 7.4 Proportion of fish stocks within safe biological limits — to harmonize the data series used to compile this 7.5 Proportion of total water resources used 3.5 table with those published on the United Nations 7.6 Proportion of terrestrial and marine areas protected — Millennium Development Goals Web site (www. 7.7 Proportion of species threatened with extinction 1.3 7.8 Proportion of population using an improved drinking water source 1.3, 2.18, 3.5 un.org/millenniumgoals), but some differences in 7.9 Proportion of population using an improved sanitation facility 1.3, 2.18, 3.11 timing, sources, and definitions remain. For more Proportion of urban population living in slums — information see the data sources for the indica- — No data are available in the World Development Indicators database. * Table shows information on related indicators. tors listed in tables 1.3a and 1.4a. 2011 World Development Indicators 21 1.4 Millennium Development Goals: overcoming obstacles Development Assistance Committee members Official development Least developed countries’ access Support to assistance (ODA) to high-income markets agriculture by donor For basic Goods Net social services a (excluding arms) Average tariff on exports of disbursements % of total sector- admitted free of tariffs least developed countries % of donor allocable ODA % of exports from least % GNI commitments developed countries Agricultural products Textiles Clothing % of GDP 2009 2009 2002 2008 2002 2008 2002 2008 2002 2008 2009b Australia 0.29 14.5 95.9 100.0 0.2 0.0 5.1 0.0 19.7 0.0 0.15 Canada 0.30 25.5 67.2 100.0 0.3 0.1 5.7 0.2 17.9 1.7 0.75 European Union 97.0 98.7 1.8 0.9 0.1 0.1 1.2 1.2 0.84 Austria 0.30 6.3 Belgium 0.55 12.7 Denmark 0.88 21.3 Finland 0.54 5.8 France 0.46 8.8 Germany 0.35 8.7 Greece 0.19 11.2 Ireland 0.54 32.1 Italy 0.16 12.9 Luxembourg 1.04 35.4 Netherlands 0.82 11.9 Portugal 0.23 3.6 Spain 0.46 24.2 Sweden 1.12 10.8 United Kingdom 0.52 21.4 Japan 0.18 18.6 33.2 99.6 4.8 1.4 2.8 2.6 0.1 0.1 1.11 Korea, Rep.c 0.10 6.7 14.6 57.7 26.1 28.5 11.4 4.0 12.5 3.7 2.44 New Zealandc 0.28 27.7 98.0 98.2 3.1 0.0 0.3 0.0 0.3 0.0 0.20 Norway 1.06 21.9 97.9 99.9 3.8 18.0 3.1 0.0 1.3 1.0 1.07 Switzerland 0.45 9.5 93.4 100.0 5.1 0.1 0.0 0.0 0.0 0.0 1.37 United States 0.21 31.7 61.7 83.8 6.3 5.8 6.6 5.7 12.5 11.3 0.87 Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC MDRI HIPC HIPC HIPC MDRI decision completion Initiative assistance decision completion Initiative assistance pointd pointd assistance pointd pointd assistance end-2009 end-2009 net present value net present value $ millions $ millions Afghanistan Jul. 2007 Jan. 2010 654 20 Haiti Nov. 2006 Jun. 2009 164 665 Benin Jul. 2000 Mar. 2003 385 754 Honduras Jul. 2000 Apr. 2005 816 1,893 Boliviae Feb. 2000 Jun. 2001 1,949 1,953 Liberia Mar. 2008 Jun. 2010 2,958 243 Burkina Fasoe,f Jul. 2000 Apr. 2002 812 764 Madagascar Dec. 2000 Oct. 2004 1,228 1,598 Burundi Aug. 2005 Jan. 2009 1,009 58 Malawif Dec. 2000 Aug. 2006 1,379 898 Cameroon Oct. 2000 Apr. 2006 1,861 646 Malie Sep. 2000 Mar. 2003 792 1,308 Central African Republic Sep. 2007 Jun. 2009 675 435 Mauritania Feb. 2000 Jun. 2002 913 558 Chad May 2001 Floating 241 .. Mozambiquee Apr. 2000 Sep. 2001 3,147 1,322 Comoros Jun. 2010 Floating 151 .. Nicaragua Dec. 2000 Jan. 2004 4,861 1,191 Congo, Dem. Rep. Jul. 2003 Jul. 2010 9,493 515 Niger f Dec. 2000 Apr. 2004 947 651 Congo, Rep. Mar. 2006 Jan. 2010 1,906 120 Rwandaf Dec. 2000 Apr. 2005 956 283 Côte d’Ivoire Mar. 2009 Floating 3,245 .. São Tomé & Principef Dec. 2000 Mar. 2007 172 34 Ethiopiaf Nov. 2001 Apr. 2004 2,735 1,862 Senegal Jun. 2000 Apr. 2004 717 1,661 Gambia, The Dec. 2000 Dec. 2007 98 232 Sierra Leone Mar. 2002 Dec. 2006 919 465 Ghana Feb. 2002 Jul. 2004 3,091 2,570 Tanzania Apr. 2000 Nov. 2001 2,977 2,517 Guinea Dec. 2000 Floating 801 .. Togo Nov. 2008 Dec. 2010 305 463 Guinea-Bissau Dec. 2000 Dec. 2010 746 77 Ugandae Feb. 2000 May 2000 1,509 2,245 Guyanae Nov. 2000 Dec. 2003 897 493 Zambia Dec. 2000 Apr. 2005 3,672 1,962 a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease control including HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services. b. Provisional data. c. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development’s Trade Analysis and Information Systems database. d. Refers to the Enhanced HIPC Initiative. e. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. f. Assistance includes topping up at completion point. 22 2011 World Development Indicators 1.4 WORLD VIEW Millennium Development Goals: overcoming obstacles About the data Definitions Achieving the Millennium Development Goals lines with “international peaks”). The averages in •  Official development assistance (ODA) net dis- requires an open, rule-based global economy in the table include ad valorem duties and equivalents. bursements are grants and loans (net of repayments of which all countries, rich and poor, participate. Many Subsidies to agricultural producers and exporters principal) that meet the DAC definition of ODA and are poor countries, lacking the resources to finance in OECD countries are another barrier to developing made to countries on the DAC list of recipients. • ODA for basic social services is aid commitments by DAC development, burdened by unsustainable debt, and economies’ exports. Agricultural subsidies in OECD donors for basic education, primary health care, nutri- unable to compete globally, need assistance from economies are estimated at $384 billion in 2009. tion, population policies and programs, reproductive rich countries. For goal 8—develop a global partner- The Debt Initiative for Heavily Indebted Poor Coun- health, and water and sanitation services. • Goods ship for development—many indicators therefore tries (HIPCs), an important step in placing debt relief admitted free of tariffs are exports of goods (excluding monitor the actions of members of the Organisa- within the framework of poverty reduction, is the first arms) from least developed countries admitted without tion for Economic Co-operation and Development’s comprehensive approach to reducing the external tariff. • Average tariff is the unweighted average of (OECD) Development Assistance Committee (DAC). debt of the world’s poorest, most heavily indebted the effectively applied rates for all products subject to Official development assistance (ODA) has risen countries. A 1999 review led to an enhancement of tariffs. • Agricultural products are plant and animal in recent years as a share of donor countries’ gross the framework. In 2005, to further reduce the debt products, including tree crops but excluding timber and national income (GNI), but the poorest economies of HIPCs and provide resources for meeting the Mil- fish products. • Textiles and clothing are natural and need additional assistance to achieve the Millen- lennium Development Goals, the Multilateral Debt synthetic fibers and fabrics and articles of clothing nium Development Goals. In 2009 total net ODA from Relief Initiative (MDRI), proposed by the Group of made from them. • Support to agriculture is the value OECD DAC members rose 0.7 percent in real terms Eight countries, was launched. of gross transfers from taxpayers and consumers aris- to $119.6 billion, representing 0.31 percent of DAC Under the MDRI four multilateral institutions—the ing from policy measures, net of associated budgetary members’ combined gross national income. International Development Association (IDA), Inter- receipts, regardless of their objectives and impacts on farm production and income or consumption of farm One important action that high-income economies national Monetary Fund (IMF), African Development products. • HIPC decision point is the date when a can take is to reduce barriers to exports from low- Fund (AfDF), and Inter-American Development Bank heavily indebted poor country with an established and middle- income economies. The European Union (IDB)—provide 100 percent debt relief on eligible track record of good performance under adjustment has begun to eliminate tariffs on exports of “every- debts due to them from countries having completed programs supported by the IMF and the World Bank thing but arms” from least developed countries, and the HIPC Initiative process. Data in the table refer commits to additional reforms and a poverty reduc- the United States offers special concessions to Sub- to status as of March 2011 and might not show tion strategy and starts receiving debt relief. • HIPC Saharan African exports. However, these programs countries that have since reached the decision or completion point is the date when a country success- still have many restrictions. completion point. Debt relief under the HIPC Initia- fully completes the key structural reforms agreed on Average tariffs in the table refl ect high-income tive has reduced future debt payments by $59 bil- at the decision point, including implementing a poverty OECD member tariff schedules for exports of coun- lion (in end-2009 net present value terms) for 36 reduction strategy. The country then receives full debt tries designated least developed countries by the countries that have reached the decision point. And relief under the HIPC Initiative without further policy United Nations. Although average tariffs have been 32 countries that have reached the completion point conditions. • HIPC Initiative assistance is the debt falling, averages may disguise high tariffs on specific have received additional assistance of $30 billion (in relief committed as of the decision point (assuming full goods (see table 6.8 for each country’s share of tariff end-2009 net present value terms) under the MDRI. participation of creditors). Topping-up assistance and assistance provided under the original HIPC Initiative Location of indicators for Millennium Development Goal 8 1.4a were committed in net present value terms as of the decision point and are converted to end-2009 terms. Goal8. Develop a global partnership for development Table • MDRI assistance is 100 percent debt relief on eli- 8.1 Net ODA as a percentage of DAC donors’ gross national income 1.4, 6.14 8.2 Proportion of ODA for basic social services 1.4 gible debt from IDA, IMF, AfDF, and IDB, delivered in full 8.3 Proportion of ODA that is untied 6.15b to countries having reached the HIPC completion point. 8.4 Proportion of ODA received in landlocked countries as a percentage of GNI — 8.5 Proportion of ODA received in small island developing states as a percentage of GNI — 8.6 Proportion of total developed country imports (by value, excluding arms) from least Data sources developed countries admitted free of duty 1.4 Data on ODA are from the OECD. Data on goods 8.7 Average tariffs imposed by developed countries on agricultural products and admitted free of tariffs and average tariffs are textiles and clothing from least developed countries 1.4, 6.8* 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 1.4 from the World Trade Organization, in collabora- 8.9 Proportion of ODA provided to help build trade capacity — tion with the United Nations Conference on Trade 8.10 Number of countries reaching HIPC decision and completion points 1.4 and Development and the International Trade Cen- 8.11 Debt relief committed under new HIPC initiative 1.4 tre. These data are available at www.mdg-trade. 8.12 Debt services as a percentage of exports of goods and services 6.11* 8.13 Proportion of population with access to affordable, essential drugs on a org. Data on subsidies to agriculture are from sustainable basis — the OECD’s Producer and Consumer Support Esti- 8.14 Telephone lines per 100 people 1.3*, 5.11 mates, OECD Database 1986–2009. Data on the 8.15 Cellular subscribers per 100 people 1.3*, 5.11 HIPC Initiative and MDRI are from the World Bank’s 8.16 Internet users per 100 people 5.12 Economic Policy and Debt Department. — No data are available in the World Development Indicators database. * Table shows information on related indicators. 2011 World Development Indicators 23 1.5 Women in development Female Life expectancy Pregnant Teenage Women in wage Unpaid Female Ratio Women in population at birth women mothers employment in family workers part-time of female parliaments receiving nonagricultural employment to male prenatal sector wages in care manufacturing % of nonagricultural Male Female years % of women wage % of male % of female % of % % of total Male Female % ages 15–19 employment employment employment % of total total seats 2009 2009 2009 2004–09a 2004–09a 2008 2008 2008 2004–09a 2004–09a 1990 2010 Afghanistan  48.2 44 44 36 .. .. .. .. .. .. 4 28 Albania 50.6 74 80 97 .. .. .. .. .. .. 29 16 Algeria 49.5 71 74 89 .. 13 .. .. .. .. 2 8 Angola 50.7 46 50 80 29 .. .. .. .. .. 15 39 Argentina 51.0 72 79 99 .. 45 0.7b 1.6 b 61b .. 6 39 Armenia 53.4 71 77 93 5 45 .. .. .. .. 36 9 Australia 50.3 79 84 .. .. 47 0.2 0.4 71b 90 6 25 Austria 51.2 77 83 .. .. 47 2.0 2.7 81 .. 12 28 Azerbaijan 51.1 68 73 77 6 44 0.0 0.0 .. .. .. 11 Bangladesh 49.4 66 68 51 33 .. .. .. .. .. 10 19 Belarus 53.5 65 76 99 .. 56 .. .. .. .. .. 35 Belgium 51.0 78 84 .. .. 47 0.4 2.2 81 86 9 39 Benin 49.5 61 63 84 21 .. .. .. .. .. 3 11 Bolivia 50.1 64 68 86 .. 38 .. .. .. .. 9 25 Bosnia and Herzegovina  51.9 73 78 99 .. 36 2.0 8.9 .. .. .. 19 Botswana 50.0 55 55 94 .. 43 .. .. .. 66 5 8 Brazil 50.8 69 76 97 .. 42 4.6 8.1 .. .. 5 9 Bulgaria 51.7 70 77 .. .. 51 0.6 1.5 54 69 21 21 Burkina Faso 50.1 52 55 85 .. .. .. .. .. .. .. 15 Burundi 51.0 49 52 92 .. .. .. .. .. .. .. 32 Cambodia 51.1 60 63 83b 8 .. .. .. .. .. .. 21 Cameroon 50.0 51 52 82 28 .. .. .. .. .. 14 14 Canada 50.5 79 84 .. .. 50 0.1 0.2 68b .. 13 22 Central African Republic 50.9 46 49 69 .. .. .. .. .. .. 4 10 Chad 50.3 48 50 39 37 .. .. .. .. .. .. 5 Chile 50.5 76 82 .. .. 36 0.9 2.8 56 .. .. 14 China 48.1c 72c 75c 91 .. .. .. .. .. .. 21 21 Hong Kong SAR, China 52.6 80 86 .. .. 49 0.1b 1.1b .. 59 .. .. Colombia 50.8 70 77 94 21 48 3.2 6.1 .. 60 5 8 Congo, Dem. Rep. 50.4 46 49 85 24 .. .. .. .. .. 5 8 Congo, Rep. 50.1 53 55 86 27 .. .. .. .. .. 14 7 Costa Rica 49.2 77 82 90 .. 42 1.3 2.8 .. 70 11 39 Côte d’Ivoire 49.1 57 59 85 .. .. .. .. .. .. 6 9 Croatia 51.8 73 80 100 b 4 45d 0.9d 3.9d 59 77 .. 24 Cuba 49.9 77 81 100 .. 43 .. .. .. .. 34 43 Czech Republic 50.9 74 80 .. .. 46 0.3 1.0 69 .. .. 22 Denmark 50.4 77 81 .. .. 49 0.3 0.5 62 87 31 38 Dominican Republic 49.8 70 76 99 21 39 2.9 3.4 .. .. 8 21 Ecuador 49.9 72 78 84 19 39 4.4b 11.1b .. .. 5 32 Egypt, Arab Rep. 49.7 69 72 74 10 19 8.6 32.6 .. 76 4 2 El Salvador 52.8 67 76 94 .. 48 8.8 9.9 .. 85 12 19 Eritrea 50.8 58 62 .. .. .. .. .. .. .. .. 22 Estonia 53.9 70 80 .. .. 52 0.0 b 0.0 b 68 .. .. 23 Ethiopia 50.3 54 57 28 17 47 7.8b 12.7b 56b .. .. 28 Finland 51.0 77 83 .. .. 51 0.6 0.4 64 84 32 40 France  51.4 78 85 .. .. 49 0.3 0.9 80 82 7 19 Gabon 50.0 60 62 .. .. .. .. .. .. .. 13 15 Gambia, The 50.4 55 58 98 .. .. .. .. .. .. 8 8 Georgia 53.0 68 75 94 10 46 .. .. 56 61 .. 7 Germany 51.0 77 83 .. .. 47 0.4 1.5 80 74 .. 33 Ghana 49.3 56 58 90 13 .. .. .. .. .. .. 8 Greece 50.4 78 83 .. .. 42 3.4 9.8 68 .. 7 17 Guatemala 51.3 67 74 .. .. 43 .. .. .. .. 7 12 Guinea 49.5 56 60 88 32 .. .. .. .. .. .. 19 Guinea-Bissau 50.5 47 50 78 .. .. .. .. .. .. 20 10 Haiti 50.6 60 63 85 14 .. .. .. .. .. .. 4 Honduras 50.0 70 75 92 22 34 .. .. .. .. 10 18 24 2011 World Development Indicators 1.5 WORLD VIEW Women in development Female Life expectancy Pregnant Teenage Women in wage Unpaid Female Ratio Women in population at birth women mothers employment in family workers part-time of female parliaments receiving nonagricultural employment to male prenatal sector wages in care manufacturing % of nonagricultural Male Female years % of women wage % of male % of female % of % % of total Male Female % ages 15–19 employment employment employment % of total total seats 2009 2009 2009 2004–09a 2004–09a 2008 2008 2008 2004–09a 2004–09a 1990 2010 Hungary 52.5 70 78 .. .. 48 0.3 0.5 65 77 21 9 India 48.3 63 66 75 16 .. .. .. .. .. 5 11 Indonesia 50.1 69 73 93 9 32 7.8 33.6 .. .. 12 18 Iran, Islamic Rep. 49.2 70 73 98 .. .. 5.4 32.7 .. .. 2 3 Iraq 49.4 65 72 84 .. 12 .. .. .. .. 11 25 Ireland 49.9 77 82 .. .. 49 0.6 0.8 77 .. 8 14 Israel 50.4 80 84 .. .. 49 0.1 0.4 73 .. 7 18 Italy 51.4 79 84 .. .. 44 1.2 2.5 78 .. 13 21 Jamaica  51.1 69 75 91 .. 48 0.5 2.2 .. .. 5 13 Japan 51.3 80 86 .. .. 42 1.1 7.3 70 60 1 11 Jordan 48.7 71 75 99 4 16 .. .. .. 61 0 6 Kazakhstan 52.4 64 74 100 7 50 .. .. .. 70 .. 18 Kenya 50.0 54 55 92 .. .. .. .. .. .. 1 10 Korea, Dem. Rep. 50.6 65 70 .. .. .. .. .. .. .. 21 16 Korea, Rep. 50.5 77 84 .. .. 42 1.2 12.7 59 57 2 15 Kosovo .. 68 72 .. .. .. .. .. .. .. .. .. Kuwait 40.5 76 80 .. .. .. .. .. .. .. .. 8 Kyrgyz Republic 50.7 62 72 97 .. 51 8.8 19.3 .. .. .. 26 Lao PDR 50.1 64 67 35 17 .. 26.4 64.2 .. .. 6 25 Latvia 53.9 68 78 .. .. 53 1.4 1.2 59 77 .. 22 Lebanon 51.0 70 74 96 .. .. .. .. .. .. 0 3 Lesotho 52.8 45 46 92 20 .. .. .. .. .. .. 24 Liberia 50.3 57 60 79 38 .. .. .. .. .. .. 13 Libya  48.3 72 77 .. .. .. .. .. .. .. .. 8 Lithuania 53.2 68 79 .. .. 53 1.0 2.0 60 71 .. 19 Macedonia, FYR 50.1 72 77 94 .. 42 7.0 14.9 47 .. .. 33 Madagascar 50.2 59 62 86 34 .. .. .. .. .. 7 8 Malawi 50.3 53 55 92 34 .. .. .. .. .. 10 21 Malaysia 49.2 72 77 79 .. 39 2.7 8.8 .. .. 5 10 Mali 50.6 48 50 70 36 .. .. .. .. .. .. 10 Mauritania  49.3 55 59 75 .. .. .. .. .. .. .. 22 Mauritius 50.4 69 76 .. .. 37 0.9 4.7 44 .. 7 19 Mexico 50.8 73 78 94 .. 39 4.9 10.0 65 70 12 26 Moldova 52.5 65 72 98 6 54 1.3 3.4 .. .. .. 24 Mongolia 50.5 64 70 100 .. 51 .. .. .. 77 25 4 Morocco 50.9 69 74 68 7 21 16.5 51.8 .. .. 0 11 Mozambique 51.4 47 49 89 .. .. .. .. .. .. 16 39 Myanmar 51.2 60 64 80 .. .. .. .. .. 89 .. .. Namibia 50.7 61 62 95 15 .. 0.9 1.1 .. .. 7 24 Nepal 50.3 66 68 44 19 .. .. .. .. .. 6 33 Netherlands 50.4 79 83 .. .. 48 0.2 0.8 75 82 21 41 New Zealand 50.6 78 82 .. .. 48 0.8 1.5 72b 82 14 34 Nicaragua 50.5 70 77 90 25 38 12.2 9.1 .. .. 15 21 Niger 49.9 51 53 46 39 36 .. .. .. .. 5 12 Nigeria 49.9 48 49 58 23 .. .. .. .. .. .. 7 Norway 50.3 79 83 .. .. 49 0.2 0.4 71 89 36 40 Oman 43.6 75 78 .. .. 22 .. .. .. .. .. 0 Pakistan 48.5 67 67 61 9 13 18.6 61.9 .. .. 10 22 Panama 49.6 73 79 .. .. 42 2.3 4.0 47 95 8 9 Papua New Guinea 49.2 59 64 79 .. .. .. .. .. .. 0 1 Paraguay 49.5 70 74 96 13 40 10.8 8.9 .. .. 6 13 Peru 49.9 71 76 94 26 38 4.7b 9.9 b .. .. 6 28 Philippines 49.6 70 74 91 10 42 9.0 b 18.0 b .. 91 9 21 Poland 51.8 72 80 .. .. 47 2.7 5.9 68 .. 14 20 Portugal 51.6 76 82 .. .. 48 0.7 1.2 68 69 8 27 Puerto Rico 52.0 75 83 .. .. 42 0.0 0.0 .. .. .. .. Qatar 24.6 75 77 .. .. 13 .. .. .. 142 .. 0 2011 World Development Indicators 25 1.5 Women in development Female Life expectancy Pregnant Teenage Women in wage Unpaid Female Ratio Women in population at birth women mothers employment in family workers part-time of female parliaments receiving nonagricultural employment to male prenatal sector wages in care manufacturing % of nonagricultural Male Female years % of women wage % of male % of female % of % % of total Male Female % ages 15–19 employment employment employment % of total total seats 2009 2009 2009 2004–09a 2004–09a 2008 2008 2008 2004–09a 2004–09a 1990 2010 Romania 51.4 70 77 94 .. 46 6.0 18.9 49 74 34 11 Russian Federation 53.8 63 75 .. .. 51 0.1 0.1 62 .. .. 14 Rwanda 51.6 49 52 96 4 .. .. .. .. .. 17 56 Saudi Arabia 44.8 73 74 .. .. 15 .. .. .. .. .. 0 Senegal 50.4 54 57 94 18 .. .. .. .. .. 13 23 Serbia 50.5 71 76 98 .. 44 3.1 11.9 .. .. .. 22 Sierra Leone 51.3 47 49 87 34 .. .. .. .. .. .. 13 Singapore 49.8 79 84 .. .. 46 0.4b 1.3b .. 65 5 23 Slovak Republic 51.5 71 79 .. .. 48 0.1 0.2 59 .. .. 15 Slovenia 51.2 76 82 .. .. 47 3.2 5.4 57 .. .. 14 Somalia 50.4 49 52 26 .. .. .. .. .. .. 4 7 South Africa 50.7 50 53 .. .. 44 0.3 0.6 .. .. 3 45 Spain 50.7 79 85 .. .. 45 0.8 1.4 79 .. 15 37 Sri Lanka 50.8 71 78 99 .. 31 4.4b 21.7b .. 93 5 5 Sudan 49.6 57 60 64 .. .. .. .. .. .. .. 26 Swaziland 51.1 47 46 85 23 .. .. .. .. .. 4 14 Sweden 50.4 79 83 .. .. 50 0.2 0.3 64 90 38 45 Switzerland  51.2 80 84 .. .. 48 1.7b 3.2b 81 77 14 29 Syrian Arab Republic 49.5 73 76 84 .. 16 .. .. .. .. 9 12 Tajikistan 50.6 64 70 80 .. 37 .. .. .. .. .. 19 Tanzania 50.1 56 57 76 26 31 9.7 13.0 .. .. .. 31 Thailand 50.8 66 72 98 .. 45 14.0 29.9 .. .. 3 13 Timor-Leste 49.1 61 63 .. .. .. .. .. .. .. .. 29 Togo 50.5 61 65 84 .. .. .. .. .. .. 5 11 Trinidad and Tobago 51.4 66 73 96 .. .. .. .. .. .. 17 29 Tunisia 49.7 73 77 96 .. .. .. .. .. .. 4 28 Turkey 49.8 70 75 95 .. 22 5.3 37.7 58 .. 1 9 Turkmenistan 50.7 61 69 99 .. .. .. .. .. .. 26 17 Uganda 49.9 53 54 94 25 .. .. .. .. .. 12 32 Ukraine 53.9 64 75 99 4 55 0.4 0.3 .. 71 .. 8 United Arab Emirates 32.7 77 79 .. .. 20 .. .. .. .. 0 23 United Kingdom 50.9 78 82 .. .. 52 0.2 0.5 76 80 6 22 United States 50.7 76 81 .. .. 48 0.1 0.1 67b .. 7 17 Uruguay 51.7 73 80 96 .. 46 0.9 b 3.0 b 59b .. 6 15 Uzbekistan 50.3 65 71 99 .. 39 .. .. .. .. .. 22 Venezuela, RB 49.8 71 77 .. .. 42 0.6 1.6 .. .. 10 19 Vietnam 50.6 73 77 91 .. .. .. .. .. .. 18 26 West Bank and Gaza 49.1 72 75 99 .. 18 6.6 31.5 .. 53 .. .. Yemen, Rep. 49.4 62 65 47 .. 6 .. .. .. .. 4 0 Zambia 50.1 46 47 94 28 .. .. .. .. .. 7 14 Zimbabwe  51.7 45 46 93 21 .. .. .. .. .. 11 15 World 49.6 w 67 w 71 w 82 w   .. w .. w .. w .. w 71 m 13 w 19 w Low income 50.1 56 59 67    ..  .. .. .. 89 .. 19 Middle income 49.3 67 71 85   .. .. .. .. 71 13 18 Lower middle income 48.8 66 70 83   .. .. .. .. 85 13 17 Upper middle income 50.9 69 75 95   43 3.3 7.2 .. 70 12 19 Low & middle income 49.4 65 69 82   .. .. .. .. 71 13 18 East Asia & Pacific 48.8 71 74 91   .. .. .. .. 91 17 19 Europe & Central Asia 52.2 66 75 ..   48 1.9 5.3 .. 71 .. 15 Latin America & Carib. 50.6 71 77 95   41 4.0 7.5 .. 70 12 24 Middle East & N. Africa 49.6 69 73 83   .. .. .. .. 53 4 9 South Asia 48.5 63 66 70   .. .. .. .. 93 6 19 Sub-Saharan Africa 50.2 51 54 71   .. .. .. .. 66 .. 20 High income 50.6 77 83 ..   46 0.6 2.4 71 71 12 23 Euro area 51.1 78 83 ..   47 0.8 1.8 78 73 12 26 a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2009. 26 2011 World Development Indicators 1.5 WORLD VIEW Women in development About the data Definitions Despite much progress in recent decades, gender in non-agricultural wage employment. The indicator • Female population is the percentage of the popu- inequalities remain pervasive in many dimensions of does not reveal any differences in the quality of the lation that is female. • Life expectancy at birth is life—worldwide. But while disparities exist through- different types of non-agricultural wage employment, the number of years a newborn infant would live if out the world, they are most prevalent in developing regarding earnings, conditions of work, or the legal prevailing patterns of mortality at the time of its birth countries. Gender inequalities in the allocation of and social protection, which they offer. The indica- were to stay the same throughout its life. • Pregnant such resources as education, health care, nutrition, tor cannot reflect whether women are able to reap women receiving prenatal care are the percentage and political voice matter because of the strong the economic benefits of such employment, either. of women attended at least once during pregnancy association with well-being, productivity, and eco- Finally it should be noted that the female employ- by skilled health personnel for reasons related to nomic growth. These patterns of inequality begin at ment of any kind tends to be underreported in all pregnancy. • Teenage mothers are the percentage of an early age, with boys routinely receiving a larger kinds of surveys. In addition, the employment share women ages 15–19 who already have children or are share of education and health spending than do girls, of the agricultural sector, for both men and women, currently pregnant. • Women in wage employment for example. is severely underreported. in nonagricultural sector are female wage employ- Because of biological differences girls are Women’s wage work is important for economic ees in the nonagricultural sector as a percentage expected to experience lower infant and child mor- growth and the well-being of families. But women of total nonagricultural wage employment. • Unpaid tality rates and to have a longer life expectancy than often face such obstacles as restricted access to family workers are those who work without pay in a boys. This biological advantage may be overshad- credit markets, capital, land, training, and educa- market-oriented establishment or activity operated owed, however, by gender inequalities in nutrition tion, time constraints due to their traditional family by a related person living in the same household. and medical interventions and by inadequate care responsibilities, and labor market bias and discrimi- • Part-time employment, female is a female share during pregnancy and delivery, so that female rates nation. These obstacles force women to limit their of total part-time workers. Part-time worker is an of illness and death sometimes exceed male rates. full participation in paid economic activities, and to employed person whose normal hours of work are These gender bias can be seen in the child mortal- be less productive and to receive lower wages. More less than those of comparable full-time workers. Defi - ity rates (table 2.22) or life expectancy by gender. women than men are found in unpaid family employ- nition of part-time varies across countries. • Ratio of Female child mortality rates that are as high as or ment and part time employment. The gender wage female to male wages in manufacturing is a ratio of higher than male child mortality rates may indicate gap in manufacturing remains an unfortunate reality women’s wage to men’s in manufacturing. • Women discrimination against girls. of almost all countries of the world, even though the in parliaments are the percentage of parliamentary Having a child during the teenage years limits girls’ gap may not be attributed entirely to discrimination. seats in a single or lower chamber held by women. opportunities for better education, jobs, and income. Women are vastly underrepresented in decision- Data sources Pregnancy is more likely to be unintended during making positions in government, although there is the teenage years, and births are more likely to be some evidence of recent improvement. Gender parity Data on female population are from the United premature and are associated with greater risks of in parliamentary representation is still far from being Nations Population Division’s World Population complications during delivery and of death. In many realized. In 2010 women accounted for 19 percent Prospects: The 2008 Revision, and data on life countries maternal mortality (tables 1.3 and 2.19) is of parliamentarians worldwide, compared with 9 per- expectancy for more than half the countries in the a leading cause of death among women of reproduc- cent in 1987. Without representation at this level, it table (most of them developing countries) are from tive age, although most of them are preventable. is difficult for women to influence policy. its World Population Prospects: The 2008 Revision, Women in wage employment in nonagricultural sec- For information on other aspects of gender, see with additional data from census reports, other tor shows the extent that women have access to paid tables 1.2 (Millennium Development Goals: eradicat- statistical publications from national statistical employment, which will affect their integration into ing poverty and saving lives), 1.3 (Millennium Devel- offices, Eurostat’s Demographic Statistics, the the monetary economy. It also indicates the degree opment Goals: protecting our common environment), Secretariat of the Pacific Community’s Statistics that labour markets are open to women in industry 2.3 (Employment by economic activity), 2.4 (Decent and Demography Programme, and the U.S. Bureau and services sectors which affects not only equal work and productive employment), 2.5 (Unemploy- of the Census International Data Base. Data on employment opportunity for women, but also eco- ment), 2.6 (Children at work), 2.10 (Assessing vulner- pregnant women receiving prenatal care are from nomic efficiency through flexibility of the labor market ability and security), 2.13 (Education efficiency), 2.14 UNICEF’s The State of the World’s Children 2010 and the economy’s capacity to adapt to changes over (Education completion and outcomes), 2.15 (Educa- based on household surveys including Demo- Data sources time. In many developing countries, non-agricultural tion gaps by income and gender), 2.19 (Reproductive graphic and Health Surveys by Macro International wage employment represents only a small portion health), 2.21 (Health risk factors and future chal- and Multiple Indicator Cluster Surveys by UNICEF. of total employment. As a result the contribution of lenges), and 2.22 (Mortality). Data on teenage mothers are from Demographic women to the national economy is underestimated and Health Surveys by Macro International. Data and therefore misrepresented. The indicator is dif- on labor force, employment and wage are from the ficult to interpret, unless additional information is International Labour Organization’s Key Indicators available on the share of women in total employ- of the Labour Market, 6th edition. Data on women in ment, which would allow an assessment to be made parliaments are from the Inter-Parliamentary Union. of whether women are under- or over-represented 2011 World Development Indicators 27 1.6 Key indicators for other economies Population Surface Population Gross national income Gross domestic Life Adult Carbon area density product expectancy literacy dioxide at birth rate emissions Purchasing Atlas method power parity thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 2009 2005–09a 2007 American Samoa 67 0.2 336 .. ..b .. .. .. .. .. .. .. Andorra 85 0.5 181 3,447 41,130 .. .. 3.6 1.6 .. .. 539 Antigua and Barbuda 88 0.4 199 1,062 12,130 1,548 c 17,670 c –8.5 –9.5 .. 99 436 Aruba 107 0.2 592 .. ..d .. .. .. .. 75 98 2,396 Bahamas, The 342 13.9 34 7,136 21,390 .. .. 2.8 1.5 74 .. 2,147 Bahrain 791 0.8 1,041 19,712 25,420 26,130 33,690 6.3 4.1 76 91 22,446 Barbados 256 0.4 595 .. ..d .. .. .. .. 77 .. 1,345 Belize 333 23.0 15 1,205 3,740 1,929c 5,990 c 0.0 –3.4 77 .. 425 Bermuda 64 0.1 1,288 .. ..d .. .. –8.1 –8.4 79 .. 513 Bhutan 697 38.4 18 1,405 2,020 3,692 5,290 7.4 5.8 67 53 579 Brunei Darussalam 400 5.8 76 .. ..d 19,706 51,200 0.6 –1.3 78 95 7,599 Cape Verde 506 4.0 125 1,520 3,010 1,783 3,530 2.8 1.4 71 84 308 Cayman Islands 55 0.3 229 .. ..d .. .. .. .. .. 99 539 Channel Islands 150 0.2 789 10,242 68,610 .. .. 5.9 5.7 79 .. .. Comoros 659 1.9 354 531 810 779 1,180 1.8 –0.6 66 74 121 Cyprus 871 9.3 94 24,400e 30,480e 24,250e 30,290e –1.0e –1.9e 80 98 8,193 Djibouti 864 23.2 37 1,106 1,280 2,140 2,480 5.0 3.2 56 .. 487 Dominica 74 0.8 98 360 4,900 623c 8,460 c –0.8 –1.3 .. .. 121 Equatorial Guinea 676 28.1 24 8,398 12,420 13,069 19,330 –5.4 –7.8 51 93 4,793 Faeroe Islands 49 1.4 35 .. ..d .. .. .. .. 80 .. 696 Fiji 849 18.3 46 3,259 3,840 f 3,850 4,530 –3.0 –3.6 69 .. 1,458 French Polynesia 269 4.0 74 .. ..d .. .. .. .. 75 .. 806 Gibraltar 31 0.0 3,105 .. ..d .. .. .. .. .. .. 407 Greenland 56 410.5 0g 1,467 26,160 .. .. –5.4 –5.0 68 .. 520 Grenada 104 0.3 306 580 5,580 802c 7,710 c –6.8 –7.1 75 .. 242 Guam 178 0.5 329 .. ..d .. .. .. .. 76 .. .. Guyana 762 215.0 4 2,026 2,660 2,491c 3,270 c 3.3 3.4 68 .. 1,506 Iceland 319 103.0 3 13,858 43,430 10,478 32,840 –6.5 –7.0 81 .. 2,338 Isle of Man 80 0.6 141 3,972 49,310 .. .. 7.5 7.4 .. .. .. About the data Definitions The table shows data for economies with populations • Population is based on the de facto definition of included in the valuation of output plus net receipts between 30,000 and 1 million and for smaller econo- population, which counts all residents regardless of of primary income (compensation of employees mies if they are members of the World Bank. Where legal status or citizenship—except for refugees not and property income) from abroad. Data are in cur- data on gross national income (GNI) per capita are permanently settled in the country of asylum, who rent U.S. dollars converted using the World Bank not available, the estimated range is given. For more are generally considered part of the population of Atlas method (see Statistical methods). • Purchasing information on the calculation of GNI and purchasing their country of origin. The values shown are midyear power parity (PPP) GNI is GNI converted to interna- power parity (PPP) conversion factors, see About the estimates. For more information, see About the data tional dollars using PPP rates. An international dollar data for table 1.1. Additional data for the economies for table 2.1. •  Surface area is a country’s total has the same purchasing power over GNI that a U.S. in the table are available on the World Development area, including areas under inland bodies of water dollar has in the United States. • GNI per capita is Indicators CD-ROM or in WDI Online. and some coastal waterways. • Population density GNI divided by midyear population. • Gross domes- is midyear population divided by land area in square tic product (GDP) is the sum of value added by all kilometers. •  Gross national income (GNI), Atlas resident producers plus any product taxes (less sub- method, is the sum of value added by all resident sidies) not included in the valuation of output. Growth producers plus any product taxes (less subsidies) not is calculated from constant price GDP data in local 28 2011 World Development Indicators 1.6 WORLD VIEW Key indicators for other economies Population Surface Population Gross national income Gross domestic Life Adult Carbon area density product expectancy literacy dioxide at birth rate emissions Purchasing Atlas method power parity thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 2009 2005–09a 2007 Kiribati 98 0.8 121 180 1,830 324 c 3,310 c –0.7 –2.2 .. .. 33 Liechtenstein 36 0.2 224 4,906 136,630 .. .. –1.2 –1.9 83 .. .. Luxembourg 498 2.6 192 38,188 76,710 29,669 59,590 –4.1 –5.8 80 .. 10,834 Macao SAR, China 538 0.0 19,213 21,275 39,550 30,874 57,390 1.3 –0.9 81 93 1,554 Maldives 309 0.3 1,031 1,229 3,970h 1,625 5,250 –3.0 –4.4 72 98 898 Malta 415 0.3 1,297 7,621 18,360 9,616 23,170 –2.1 –2.8 80 92 2,722 Marshall Islands 61 0.2 339 186 3,060 .. .. 0.0 –2.2 .. .. 99 Mayotte 197 0.4 531 .. ..b .. .. .. .. 76 .. .. Micronesia, Fed. Sts. 111 0.7 158 277 2,500 359c 3,240 c –1.5 –1.8 69 .. 62 Monaco 33 0.0 16,406 6,483 197,590 .. .. –2.6 –2.9 .. .. .. Montenegro 624 13.8 46 4,149 6,650 8,183 13,110 –5.7 –6.0 74 .. .. Netherlands Antilles 198 0.8 248 .. ..d .. .. .. .. 76 96 6,232 New Caledonia 250 18.6 14 .. ..d .. .. .. .. 77 96 2,847 Northern Mariana Islands 87 0.5 189 .. ..d .. .. .. .. .. .. .. Palau 20 0.5 44 127 6,220 .. .. –2.1 –2.7 .. .. 213 Samoa 179 2.8 63 508 2,840 763c 4,270 c –5.5 –5.5 72 99 161 San Marino 31 0.1 524 1,572 50,670 .. .. 1.9 0.4 83 .. .. Sao Tome and Principe 163 1.0 170 185 1,130 301 1,850 4.0 2.4 66 88 128 Seychelles 88 0.5 191 746 8,480 1,477c 16,790 c –7.6 –8.7 74 92 623 Solomon Islands 523 28.9 19 477 910 974 c 1,860 c –2.2 –4.5 67 .. 198 St. Kitts and Nevis 50 0.3 191 503 10,150 676c 13,640 c –8.0 –8.8 .. .. 249 St. Lucia 172 0.6 282 894 5,190 1,525c 8,860 c –3.8 –4.9 .. .. 381 St. Vincent and the Grena- 109 0.4 280 560 5,130 964 c 8,830 c –2.8 –2.8 72 .. 202 dines Suriname 520 163.8 3 2,454 4,760 3,469c 6,730 c 5.1 4.2 69 95 2,437 Tonga 104 0.8 144 339 3,260 475c 4,570 c –0.4 –0.8 72 99 176 Turks and Caicos Islands 33 1.0 35 .. ..d .. .. .. .. .. .. 158 Tuvalu .. 0.0 .. .. ..i .. .. .. .. .. .. .. Vanuatu 240 12.2 20 627 2,620 1,028 c 4,290 c 4.0 1.4 71 81 103 Virgin Islands (U.S.) 110 0.4 314 .. ..d .. .. .. .. 79 .. .. a. Data are for the most recent year available. b. Estimated to be upper middle income ($3,946–$12,195). c. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. d. Estimated to be high income ($12,196 or more). e. Data are for the area controlled by the government of the Republic of Cyprus. f. Included in the aggregates for upper middle-income economies based on earlier data. g. Less than 0.5. h. Included in the aggregates for lower middle-income economies based on earlier data. i. Estimated to be lower middle income ($996–$3,945). currency. • GDP per capita is GDP divided by midyear population. • Life expectancy at birth is the number of years a newborn infant would live if prevailing pat- terns of mortality at the time of its birth were to stay the same throughout its life. • Adult literacy rate is Data sources the percentage of adults ages 15 and older who can, with understanding, read and write a short, simple The indicators here and throughout the book statement about their everyday life. • Carbon dioxide are compiled by World Bank staff from primary emissions are those stemming from the burning of and secondary sources. More information about fossil fuels and the manufacture of cement. They the indicators and their sources can be found in include carbon dioxide produced during consumption the About the data, Definitions, and Data sources of solid, liquid, and gas fuels and gas flaring. entries that accompany each table in subsequent sections. 2011 World Development Indicators 29 Text figures, tables, and boxes PEOPLE Introduction S 2 ustainable development is about improving the quality of peoples’ lives and expanding their abilities to shape their futures. This generally calls for higher per capita incomes, but also for human capital development through improvements in health and education. Although developing countries have made large investments in human capital, good health and basic education remain elusive to many. This limits people’s ability to take advantage of employment opportunities and work their way out of poverty. The tables in this section review the achievements for which estimates are available since 2000. But countries have made in improving the welfare of their the full range of these poverty estimates can be people. They show the levels of poverty prevalent in accessed through the Bank’s Open Data Initiative countries, the distribution of income, and the preva- (data.worldbank.org), and the entire database of lence of child labour—which while it reduces house- $1.25 and $2 a day purchasing power parity poverty hold poverty, is always at the expense of children’s rate and poverty gap estimates will also be available education and future human capital. The section through PovcalNet. also looks at investments in health and education In addition, several new indicators have been and their impact on the worst aspects of nonincome added to existing tables. Data on children’s learn- poverty by reducing hunger and malnutrition, lowering ing assessment, from the Programme for Interna- mortality rates, and improving education outcomes. tional Student Assessment, have been added to This year’s national and international poverty table 2.14, and the lifetime risk of maternal death estimates were prepared by the World Bank’s Global has been added to table 2.19. The new maternal Poverty Working Group, recently established by the mortality ratio, estimated by the Inter-Agency group, Poverty Board. The results of their work are evident is now available in a consistent time series for the in tables 2.7–2.9. The baseline database, with esti- first time, and data for 1990 and the most recent mates for 231 data points (country and year com- year are presented in table 2.19. The entire time binations) covering 104 countries, was updated series can be accessed through data.worldbank.org; to include estimates for 577 data points covering regional and income group aggregates for maternal 115 countries. Because of space restrictions in the mortality ratios are in figures 2a and 2b. printed edition, this report cannot include estimates The next sections look at civil registration, high- for all countries. Thus, it includes only countries lighting the problems countries face in planning for Maternal mortality ratios have declined in Maternal mortality ratios have declined fastest among all developing country regions since 1990 2a low- and lower middle-income countries but remain high 2b Maternal mortality ratio by region Maternal mortality ratio by income group Maternal mortality ratio, modeled estimates (per 100,000 live births) Maternal mortality ratio, modeled estimates (per 100,000 live births) 1,000 1,000 Sub-Saharan Africa 750 750 South Asia Low income 500 500 Latin America and Caribbean Lower middle income Middle East and East Asia 250 North Africa and Pacific 250 Europe and Central Asia Upper middle income High income 0 0 1990 1995 2000 2005 2008 1990 1995 2000 2005 2008 Source: WHO, UNICEF, UNFPA, World Bank. Trends in Maternal Mortality: 1990–2008. Source: WHO, UNICEF, UNFPA, World Bank. Trends in Maternal Mortality: 1990–2008. 2011 World Development Indicators 31 the welfare of their people. Countries need to provide detailed characteristics of the popula- know, at a minimum, how many people are born tion recorded by censuses and civil registration and die each year. In most developing countries systems. Administrative records from health this is not easy. The discussion highlights the and education systems add further information obstacles countries must surmount in record- to manage those services and—combined with ing births and deaths and the interim measures census, survey, and vital statistics —are used they have adopted, and it indicates the way to plan for future needs. forward for countries and their development Civil registration has two functions: partners. administrative —providing legal documenta- tion that protects identities, citizenship, prop- Civil registration, the missing pillar erty, and other economic, social, and human In 2009 the births of 50 million children went rights—and statistical—providing regular, fre- unrecorded. They entered the world with no quent, and timely information on the dynamics proof of age, citizenship, or parentage. That of population growth, size, and distribution and same year 40 million people died unnoted ex- on records of births and deaths by age, sex, and cept by family or friends. There are no records cause at the national and subnational levels. of where they died, when they died, and more Vital statistics from civil registration systems importantly how they died. are essential for planning basic social services In most high-income countries these vital and infrastructure development and for under- events (births and deaths) are recorded by civil standing and monitoring health status and registration systems, which also record mar- health issues in the country. riages, adoptions, and divorces. But in many A complete civil registration system has developing countries registration systems are three strengths: it costs less than conducting a incomplete or absent. In South Asia only 1 per- census or survey, data are based on a record of cent of the population is covered by complete events rather than recall, and information can vital registration records (at least 90 percent be made available at low cost. In a well function- coverage for births and deaths), and in Sub- ing civil registration system a family member or Saharan Africa only 2 percent (UN, Population caretaker reports births and deaths at the reg- and Vital Statistics Report, 2011). Lacking effec- istration office in the local area and receives tive registration systems, countries must rely appropriate legal documentation. Medical cer- on infrequent and expensive censuses and sur- tification of death from a health care provider veys to estimate the vital statistics needed to identifies the cause of death. support the core functions of government and To be considered complete, civil registration to plan for the future. systems must collect information on at least 90 A state-of-the-art statistical system has percent of vital events. Systems in most devel- three pillars: censuses and surveys, administra- oping country regions fall well short of that stan- tive records, and civil registration, each with an dard. So today, most people in Africa and South important and complementary role. Censuses Asia are born and die without a trace in any give benchmark estimates that provide a base legal record or official statistic (figure 2c), caus- for and a check on vital statistics, and surveys ing a vicious cycle. These are the regions where most premature deaths occur and where the The births of many children in Asia and Africa go unregistered 2c need for robust information for planning is most Children under age 5 whose births are unregistered, 2007 (percent) critical. Roughly half the countries claim to have 75 complete registration of births and deaths (UN, Population and Vital Statistics Report, 2011), 50 leaving nearly 40 percent of births and 70 per- cent of deaths unregistered (WHO 2007). In many countries vital events are unre- 25 ported or only partially reported for certain areas, ages, or populations for a variety of rea- 0 CEE/CISa East Asia Latin America Middle East & South Sub-Saharan sons. People may not know their responsibility & Pacificb & Caribbean North Africa Asia Africa to register events or where to register. They may a. Central and Eastern Europe and Commonwealth of Independent States. choose not to register because of the distance b. Excludes China. Source: UNICEF Childinfo (www.childinfo.org/birth_registration_progress.html). to the registration offices or for cultural reasons. 32 2011 World Development Indicators PEOPLE Or they cannot afford the registration costs. impact of major diseases in developing coun- Data from Nigeria show that most unregistered tries can be estimated using only models or births are found among the rural poor, for whom intuition and educated guesses rather than a significant barrier may be the distance to the facts (Cooper and others 1998). Without data nearest registration facility, and among poorly on the cause of death, verbal autopsy (an inter- educated mothers (figures 2d–2f). view with caregivers or family members after Where many infants die young, parents may a death to establish probable cause of death) be reluctant to go through the formalities of can be used. In Tanzania several districts imple- registration until they have some confidence in mented sentinel demographic surveillance sys- the child’s survival or need a birth certificate tems that provided routine monitoring of vital for administrative purposes. In many cultures, events and data for cause of death derived especially in Western Africa, a child’s death from a validated set of core verbal autopsy pro- before age 2 is generally not registered. In cedures. District councils used this information Burkina Faso, for example, there are different words to express or describe death. The word In Nigeria, children’s births are more likely for infant death among the Mossi is lebame, to be unregistered in rural areas . . . 2d which translates literally to “s/he went back,” Registered births, by area, Nigeria 2007 (percent) which is different from kiime, which is used for 50 a teenager or adult who has died (private con- 40 versation). Reporting is lower for deaths than for births because people perceive death as a 30 private, sad event and because there are fewer 20 incentives associated with registering a death, 10 especially where formal inheritance is rare. Such recording lapses have consequences 0 Urban Rural for data quality. Even where there is complete Source: Multiple Indicator Cluster Survey 2007. registration, births and deaths may be recorded as need arises, rather than when they occur, reducing the timeliness and relevance of data. . . . in poor households . . . 2e Not all administrative levels have the same Registered births, by wealth quintile, Nigeria 2007 (percent) capacity to maintain registers, resulting in omis- 60 sions that may be difficult to quantify and there- fore rectify, since underregistration cannot be 40 assumed to be uniform across the population. Correct information on cause of death is critical for guiding policies and priorities for the 20 health system. Routine data from civil registra- tion in the United Kingdom helped identify the 0 Poorest Secondary Middle Fourth Richest causal association between smoking and lung cancer in the 1950s. But even when deaths Source: Multiple Indicator Cluster Survey 2007. are recorded, age or cause of death may be misreported or miscoded. Correct reporting of cause of death is particularly difficult in devel- . . . and where the mother has a lower education level 2f oping countries, where many deaths occur at Registered births, by mother’s education, Nigeria 2007 (percent) home without medical care or certification. In 60 Myanmar only 10 percent of deaths occur in the hospital (Mahar 2010). More than two-thirds of 40 people live in countries where cause of death statistics are partially reported and therefore of 20 limited use or where deaths are not reported at all (table 2g; Mahapatra and others 2007). 0 Because of the lack of reliable vital sta- Nonstandard curriculum None Primary Secondary tistics from civil registration systems, the Source: Multiple Indicator Cluster Survey 2007. long-term social, economic, and demographic 2011 World Development Indicators 33 Most people live in countries with Other obstacles relate to the need for low-quality cause of death statistics 2g human and physical infrastructure to set up Classification of countries based on the quality of cause of death statistics reported to the and maintain a civil registration system. While World Health Organization, 2007 technical assistance and development grants Quality Number of countries Percent of global population can finance fixed costs and provide initial staff High 31 13 training, countries need to finance recurring costs to run a civil registration system effi - Medium 50 15 ciently. Because many developing countries Low 26 7 have enormous economic and social develop- Limited use 17 41 ment needs, this would claim low priority. A No report 68 24 first and inexpensive step is adequate legis- Total 192 100 lation. But while most countries have legisla- Source: Mahapatra and others 2007. tion requiring registration of vital events, many have not established organizational arrange- ments to direct, coordinate, and supervise the to identify disease burdens, set priorities, and operation. allocate resources (Setel 2007). But verbal autopsy is often limited to small areas, such as Interim approaches sample vital registration and demographic sur- Because of the time and expense of building veillance systems, because it is expensive, and complete civil registration systems, many coun- accuracy depends on family members’ knowl- tries have adopted alternative approaches to edge of events leading to the death, the skill of measure and monitor vital events and related interviewers, and the competence of physicians sociodemographic information. But as depen- who do the diagnosis and coding. dence on these measures (often intended as interim) grows, national authorities have fewer Why civil registration incentives to invest in complete civil registra- fails to develop tion systems (figure 2h; Setel and others 2007). Good civil registration systems require long- These alternative approaches—notably term political commitment, a supportive legal censuses, demographic household surveys, framework, allocation of roles and responsi- sample registration systems with verbal autop- bilities among stakeholders, mobilization of sies, demographic surveillance sites, and financial and human resources, and most criti- facility -based information—effectively fill data cally, the trust of citizens (AbouZahr and others gaps with up-to-date information in many devel- 2007). Although establishing civil registration oping countries. Figure 2i illustrates the high systems takes time, there is no substitute in underreporting of deaths in the civil registration the long run. But when civil registration systems system in the Philippines, based on calcula- lack a sponsor or key stakeholder, or citizens tions by the Inter-agency Group for Child Mortal- lack incentives to participate, and when high ity Estimation, using surveys and other sources initial costs deter investments, civil registration of mortality data. fails to take root. No single blueprint for establishing and More countries used surveys for mortality maintaining civil registration systems ensures statistics, but civil registration did not expand 2h the availability of timely and sound vital statis- Collection and reporting of data for mortality by sources in 57 low-income countries, 1980–2004 (number of countries) tics. Each country faces different challenges, 50 and strategies must be tailored accordingly. Surveys 40 Some obstacles to a viable civil registration system can be removed only through long-term 30 social and economic development. These gen- 20 erally relate to geography and population dis- 10 tribution, with widely dispersed populations Civil register requiring transportation to registration cen- 0 1980–84 1985–89 1990–94 1995–99 2000–04 ters. And a largely illiterate population may be unaware of the need to comply with the law or Source: Boerma and Stansfield 2007. be unmotivated to do so. 34 2011 World Development Indicators PEOPLE These interim approaches also produce Estimates of infant mortality supplemental information that is not col- in the Philippines differ by source 2i lected through civil registration, such as socio- Infant mortality rate (per 1,000 live births) economic information, risk factors, and health 80 Estimate by Inter-Agency Group status. But these approaches are not a com- 70 for Child Mortality Estimation 60 plete or permanent solution. Censuses and 50 surveys are expensive, and developing coun- 40 tries often require international technical and 30 financial assistance. They must be repeated World Health Organization vital registration 20 regularly to yield useful data. And they must 10 be supplemented or adjusted to produce sat- 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2009 isfactory estimates. Burkina Faso, which has Note: Dotted lines are Demographic and Health Surveys, World Fertility Surveys, and Family Planning Surveys for partial coverage of civil registration (birth regis- various years. Source: Inter-agency Group for Child Mortality Estimation (www.childmortality.org). tration coverage is 60 percent), has conducted four censuses (1975, 1985, 1996, 2006), five Demographic and Health Surveys (1991, 1993, aims to ensure consistency and comparability 1998, 2003, 2010), two Multiple Indicator Clus- of statistics across countries and over time. ter Surveys (1996, 2006), and a migration and Used correctly, these principles and guide- urbanization survey (1993). lines improve data quality, as in Chile and Tan- zania (Setel and others 2007), but in reality How to build a good civil few countries have pursued or attained most registration system recommendations. Over the years, international and development The WHO’s International Classification of agencies have tried to identify the strengths Diseases and Related Health Problems has and weaknesses of national civil registration improved the comparability of cause of death systems and assess the quality of the data data. Still, there are substantial differences in they produce. In 2001 the United Nations up- interpretation and application of these codes. dated the Principles and Recommendations In 2007 only 31 of 192 WHO member countries for a Vital Statistics System, fi rst published (13 percent of the world’s population) reported in 1973, to offer best practice guidelines for reliable cause-of-death statistics to the WHO, establishing a civil registration system and most of them high-income countries (WHO producing timely, complete, and accurate sta- 2007). tistics. Regional initiatives by the United Na- tions include the 1994 African Workshop on International support Strategies for Accelerating the Improvement of The international community can continue its Civil Registration and Vital Statistics Systems. strong supportive rule by setting standards and In 2005 the World Health Organization (WHO) guidelines for collecting and validating systems established the Health Metrics Network, which and data, publicizing the importance of civil recommends an integrated approach for de- registration, and providing comprehensive and veloping health information systems, includ- integrated technical and financial assistance. ing civil registration. Some 85 countries have Since no single UN agency has a clear mandate used the network’s Framework and Standards for guidance and technical support for civil reg- for Country Health Information Systems, which istration, good coordination is key. 2011 World Development Indicators 35 Tables 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0–14 15–64 65+ Young Old people people 1990 2009 2015 1990–2009 2009–15 2009 2009 2009 2009 2009 2009 2009 Afghanistan 18.6 29.8 35.0 2.5 2.7 46 52 2 89 4 19 46 Albania 3.3 3.2 3.3 –0.2 0.5 24 67 10 35 14 6 15 Algeria 25.3 34.9 38.1 1.7 1.4 27 68 5 40 7 5 21 Angola 10.7 18.5 21.7 2.9 2.6 45 53 2 86 5 16 42 Argentina 32.5 40.3 42.4 1.1 0.9 25 64 11 39 16 8 17 Armenia 3.5 3.1 3.1 –0.7 0.2 20 68 11 30 16 9 15 Australia 17.1 21.9 23.4 1.3 1.2 19 67 14 28 20 6 14 Austria 7.7 8.4 8.4 0.4 0.1 15 68 17 22 26 9 9 Azerbaijan 7.2 8.8 9.4 1.1 1.1 24 69 7 35 10 6 17 Bangladesh 115.6 162.2 176.3 1.8 1.4 31 65 4 49 6 6 21 Belarus 10.2 9.7 9.4 –0.3 –0.4 15 72 14 21 19 14 12 Belgium 10.0 10.8 11.0 0.4 0.3 17 66 17 25 26 10 12 Benin 4.8 8.9 10.6 3.3 2.9 43 54 3 80 6 9 39 Bolivia 6.7 9.9 10.8 2.1 1.6 36 59 5 61 8 7 27 Bosnia and Herzegovina 4.3 3.8 3.7 –0.7 –0.2 15 71 14 22 20 10 9 Botswana 1.4 1.9 2.1 1.9 1.3 33 63 4 53 6 12 24 Brazil 149.6 193.7 202.4 1.4 0.7 26 67 7 39 10 6 16 Bulgaria 8.7 7.6 7.3 –0.7 –0.6 13 69 17 19 25 14 11 Burkina Faso 8.8 15.8 19.0 3.1 3.1 46 52 2 90 4 13 47 Burundi 5.7 8.3 9.4 2.0 2.1 38 59 3 65 5 14 34 Cambodia 9.7 14.8 16.4 2.2 1.7 33 63 3 53 6 8 25 Cameroon 12.2 19.5 22.2 2.5 2.1 41 56 4 74 6 14 36 Canada 27.8 33.7 35.7 1.0 0.9 17 70 14 24 20 7 11 Central African Republic 2.9 4.4 4.9 2.2 1.8 41 55 4 73 7 17 35 Chad 6.1 11.2 13.1 3.2 2.6 46 51 3 89 6 16 45 Chile 13.2 17.0 17.9 1.3 0.9 23 68 9 33 13 5 15 China 1,135.2 1,331.5 1,377.7 0.8 0.6 20a 72a 8a 28a 11a 7 12 Hong Kong SAR, China 5.7 7.0 7.3 1.1 0.8 12 75 13 16 17 6 12 Colombia 33.2 45.7 49.3 1.7 1.3 29 65 5 45 8 6 20 Congo, Dem. Rep. 37.0 66.0 77.4 3.0 2.6 47 51 3 92 5 17 44 Congo, Rep. 2.4 3.7 4.2 2.2 2.3 40 56 4 73 7 13 34 Costa Rica 3.1 4.6 4.9 2.1 1.3 26 68 6 38 9 4 16 Côte d’Ivoire 12.6 21.1 24.2 2.7 2.3 41 55 4 73 7 11 34 Croatia 4.8 4.4 4.4 –0.4 –0.2 15 68 17 22 25 12 10 Cuba 10.6 11.2 11.2 0.3 0.0 18 70 12 25 17 7 10 Czech Republic 10.4 10.5 10.6 0.1 0.2 14 71 15 20 21 10 11 Denmark 5.1 5.5 5.6 0.4 0.2 18 65 16 28 25 10 11 Dominican Republic 7.4 10.1 10.8 1.7 1.1 31 63 6 50 10 6 22 Ecuador 10.3 13.6 14.6 1.5 1.1 31 62 7 50 10 5 20 Egypt, Arab Rep. 57.8 83.0 91.7 1.9 1.7 32 63 5 51 7 6 24 El Salvador 5.3 6.2 6.4 0.8 0.6 32 61 7 53 12 7 20 Eritrea 3.2 5.1 6.0 2.5 2.8 42 56 2 74 4 8 36 Estonia 1.6 1.3 1.3 –0.8 –0.1 15 68 17 22 25 12 12 Ethiopia 48.3 82.8 96.2 2.8 2.5 44 53 3 82 6 12 38 Finland 5.0 5.3 5.4 0.4 0.3 17 67 17 25 25 9 11 Franceb 56.7 62.6 63.9 0.5 0.3 18 65 17 28 26 9 13 Gabon 0.9 1.5 1.6 2.4 1.8 36 60 4 61 7 10 27 Gambia, The 0.9 1.7 2.0 3.4 2.5 42 55 3 77 5 11 36 Georgia 5.5 4.3 4.1 –1.3 –0.7 17 69 14 24 21 12 12 Germany 79.4 81.9 80.6 0.2 –0.3 14 66 20 20 31 10 8 Ghana 15.0 23.8 26.6 2.4 1.8 38 58 4 66 6 11 32 Greece 10.2 11.3 11.4 0.6 0.2 14 68 18 21 27 10 11 Guatemala 8.9 14.0 16.2 2.4 2.4 42 54 4 78 8 6 32 Guinea 6.1 10.1 11.8 2.6 2.7 43 54 3 79 6 11 39 Guinea-Bissau 1.0 1.6 1.8 2.4 2.3 43 54 3 79 6 17 41 Haiti 7.1 10.0 10.7 1.8 1.1 36 59 4 61 7 9 27 Honduras 4.9 7.5 8.4 2.2 1.9 37 58 4 64 7 5 27 36 2011 World Development Indicators 2.1 PEOPLE Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0–14 15–64 65+ Young Old people people 1990 2009 2015 1990–2009 2009–15 2009 2009 2009 2009 2009 2009 2009 Hungary 10.4 10.0 9.9 –0.2 –0.2 15 69 16 22 24 13 10 India 849.5 1,155.3 1,246.9 1.6 1.3 31 64 5 49 8 7 22 Indonesia 177.4 230.0 247.5 1.4 1.2 27 67 6 40 9 6 18 Iran, Islamic Rep. 54.4 72.9 78.6 1.5 1.2 24 71 5 34 7 6 19 Iraq 18.9 31.5 36.3 2.7 2.4 41 56 3 74 6 6 31 Ireland 3.5 4.5 4.8 1.3 1.1 21 68 11 30 16 7 17 Israel 4.7 7.4 8.2 2.5 1.6 28 62 10 45 16 5 22 Italy 56.7 60.2 60.8 0.3 0.1 14 66 20 22 31 10 10 Jamaica 2.4 2.7 2.8 0.6 0.4 29 63 8 47 12 7 16 Japan 123.5 127.6 125.3 0.2 –0.3 13 65 22 21 34 9 9 Jordan 3.2 6.0 6.8 3.3 2.2 34 62 4 56 6 4 25 Kazakhstan 16.3 15.9 16.9 –0.2 1.0 24 69 7 34 10 9 22 Kenya 23.4 39.8 46.4 2.8 2.6 43 55 3 78 5 11 38 Korea, Dem. Rep. 20.1 23.9 24.4 0.9 0.3 22 69 10 32 14 10 14 Korea, Rep. 42.9 48.7 49.3 0.7 0.2 17 73 11 23 15 5 10 Kosovo 1.9 1.8 1.9 –0.2 0.6 .. .. .. .. .. 7 19 Kuwait 2.1 2.8 3.2 1.4 2.1 23 74 2 31 3 2 17 Kyrgyz Republic 4.4 5.3 5.7 1.0 1.3 29 65 5 45 8 7 25 Lao PDR 4.2 6.3 7.0 2.1 1.8 38 59 4 64 6 7 27 Latvia 2.7 2.3 2.2 –0.9 –0.5 14 69 17 20 25 13 10 Lebanon 3.0 4.2 4.4 1.8 0.8 25 67 7 38 11 7 16 Lesotho 1.6 2.1 2.2 1.3 0.8 39 56 5 69 8 17 29 Liberia 2.2 4.0 4.8 3.2 3.2 43 54 3 79 6 10 38 Libya 4.4 6.4 7.2 2.0 1.8 30 66 4 46 6 4 23 Lithuania 3.7 3.3 3.2 –0.5 –0.7 15 69 16 22 23 13 11 Macedonia, FYR 1.9 2.0 2.0 0.4 0.0 18 70 12 26 17 9 11 Madagascar 11.3 19.6 22.8 2.9 2.5 43 54 3 79 6 9 35 Malawi 9.5 15.3 18.0 2.5 2.7 46 51 3 91 6 12 40 Malaysia 18.1 27.5 30.0 2.2 1.5 29 66 5 45 7 5 20 Mali 8.7 13.0 15.4 2.1 2.8 44 54 2 83 4 15 42 Mauritania 2.0 3.3 3.7 2.7 2.1 39 58 3 68 5 10 33 Mauritius 1.1 1.3 1.3 1.0 0.4 23 70 7 32 10 7 12 Mexico 83.2 107.4 113.1 1.3 0.9 28 65 6 44 10 5 18 Moldova 4.4 3.6 3.5 –1.0 –0.7 17 72 11 23 15 13 12 Mongolia 2.2 2.7 2.9 1.0 1.1 26 70 4 37 6 7 19 Morocco 24.8 32.0 34.3 1.3 1.2 28 66 5 43 8 6 20 Mozambique 13.5 22.9 25.9 2.8 2.1 44 53 3 83 6 16 38 Myanmar 40.8 50.0 53.0 1.1 1.0 27 68 5 40 8 10 20 Namibia 1.4 2.2 2.4 2.2 1.7 37 60 4 62 6 8 27 Nepal 19.1 29.3 32.5 2.3 1.7 37 59 4 62 7 6 25 Netherlands 15.0 16.5 16.8 0.5 0.3 18 67 15 26 22 8 11 New Zealand 3.4 4.3 4.6 1.2 1.0 20 67 13 31 19 7 15 Nicaragua 4.1 5.7 6.3 1.7 1.4 35 60 5 58 7 5 24 Niger 7.9 15.3 19.1 3.5 3.7 50 48 2 104 4 15 53 Nigeria 97.3 154.7 178.7 2.4 2.4 43 54 3 78 6 16 39 Norway 4.2 4.8 5.1 0.7 0.8 19 66 15 29 22 9 13 Oman 1.8 2.8 3.2 2.3 1.9 31 66 3 48 5 3 22 Pakistan 108.0 169.7 193.5 2.4 2.2 37 59 4 63 7 7 30 Panama 2.4 3.5 3.8 1.9 1.5 29 64 7 46 10 5 20 Papua New Guinea 4.1 6.7 7.7 2.6 2.2 40 58 2 69 4 8 31 Paraguay 4.2 6.3 7.0 2.1 1.6 34 61 5 56 8 6 24 Peru 21.8 29.2 31.2 1.5 1.1 30 64 6 48 9 5 21 Philippines 62.4 92.0 102.7 2.0 1.8 34 62 4 55 7 5 24 Poland 38.1 38.1 38.0 0.0 –0.1 15 72 13 21 19 10 11 Portugal 9.9 10.6 10.7 0.4 0.0 15 67 18 23 26 10 9 Puerto Rico 3.5 4.0 4.0 0.6 0.3 20 66 14 31 21 8 12 Qatar 0.5 1.4 1.6 5.8 c 2.4 16 83 1 19 1 2 12 2011 World Development Indicators 37 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0–14 15–64 65+ Young Old people people 1990 2009 2015 1990–2009 2009–15 2009 2009 2009 2009 2009 2009 2009 Romania 23.2 21.5 21.0 –0.4 –0.4 15 70 15 22 21 12 10 Russian Federation 148.3 141.9 139.0 –0.2 –0.3 15 72 13 21 18 14 12 Rwanda 7.2 10.0 11.7 1.8 2.7 42 55 2 77 5 14 41 Saudi Arabia 16.3 25.4 28.6 2.3 2.0 32 65 3 50 5 4 24 Senegal 7.5 12.5 14.5 2.7 2.4 44 54 2 81 4 11 38 Serbia 7.6 7.3 7.2 –0.2 –0.3 18d 68d 14 d 26d 21d 14 10 Sierra Leone 4.1 5.7 6.6 1.8 2.3 43 55 2 79 3 15 40 Singapore 3.0 5.0 5.4 2.6 1.2 16 74 10 22 13 4 10 Slovak Republic 5.3 5.4 5.4 0.1 0.1 15 73 12 21 17 10 11 Slovenia 2.0 2.0 2.1 0.1 0.3 14 70 16 20 23 9 11 Somalia 6.6 9.1 10.7 1.7 2.7 45 52 3 86 5 16 44 South Africa 35.2 49.3 51.1 1.8 0.6 31 65 4 47 7 15 22 Spain 38.8 46.0 47.9 0.9 0.7 15 68 17 22 25 8 11 Sri Lanka 17.1 20.3 21.2 0.9 0.7 24 68 7 36 11 5 19 Sudan 27.1 42.3 47.7 2.3 2.0 39 57 4 68 6 10 31 Swaziland 0.9 1.2 1.3 1.7 1.4 39 57 3 69 6 15 30 Sweden 8.6 9.3 9.6 0.4 0.5 17 65 18 25 28 10 12 Switzerland 6.7 7.7 7.9 0.7 0.4 15 68 17 23 25 8 10 Syrian Arab Republic 12.7 21.1 24.1 2.7 2.2 35 62 3 57 5 3 27 Tajikistan 5.3 7.0 7.8 1.4 1.8 37 59 4 62 6 6 28 Tanzania 25.5 43.7 52.1 2.8 2.9 45 52 3 86 6 11 41 Thailand 56.7 67.8 69.9 0.9 0.5 22 71 8 31 11 9 14 Timor-Leste 0.7 1.1 1.4 2.2 3.3 45 52 3 86 6 8 40 Togo 3.9 6.6 7.6 2.7 2.3 40 57 4 71 6 8 32 Trinidad and Tobago 1.2 1.3 1.4 0.5 0.3 21 73 7 28 9 8 15 Tunisia 8.2 10.4 11.1 1.3 1.1 23 70 7 33 10 6 18 Turkey 56.1 74.8 79.9 1.5 1.1 27 67 6 40 9 6 18 Turkmenistan 3.7 5.1 5.5 1.7 1.2 29 66 4 45 6 8 22 Uganda 17.7 32.7 39.7 3.2 3.2 49 49 3 101 5 12 46 Ukraine 51.9 46.0 44.4 –0.6 –0.6 14 70 16 20 22 15 11 United Arab Emirates 1.9 4.6 5.2 4.7 2.0 19 80 1 24 1 2 14 United Kingdom 57.2 61.8 63.8 0.4 0.5 17 66 16 26 25 9 13 United States 249.6 307.0 323.5 1.1 0.9 20 67 13 30 19 8 14 Uruguay 3.1 3.3 3.4 0.4 0.2 23 63 14 36 22 9 15 Uzbekistan 20.5 27.8 30.2 1.6 1.4 29 66 4 44 7 5 22 Venezuela, RB 19.8 28.4 31.0 1.9 1.5 30 65 5 46 8 5 21 Vietnam 66.2 87.3 92.8 1.5 1.0 26 68 6 38 9 5 17 West Bank and Gaza 2.0 4.0 4.8 3.8 2.8 45 52 3 86 6 3 35 Yemen, Rep. 12.3 23.6 27.8 3.4 2.7 44 54 2 81 4 7 36 Zambia 7.9 12.9 15.0 2.6 2.4 46 51 3 91 6 17 42 Zimbabwe 10.5 12.5 14.0 0.9 1.9 40 56 4 71 7 15 30 World 5,278.9 s 6,775.2 s 7,241.9 s 1.3 w 1.1 w 27 w 65 w 8w 42 w 12 w 8w 20 w Low income 547.3 846.1 962.6 2.3 2.1 39 57 4 69 6 11 34 Middle income 3,751.3 4,812.5 5,131.2 1.3 1.1 27 66 6 41 10 8 19 Lower middle income 2,930.9 3,810.8 4,084.9 1.4 1.2 28 66 6 42 9 8 20 Upper middle income 820.3 1,001.7 1,046.3 1.1 0.7 25 68 8 36 11 8 17 Low & middle income 4,298.6 5,658.7 6,093.8 1.4 1.2 29 65 6 45 9 8 21 East Asia & Pacific 1,599.6 1,943.8 2,035.8 1.0 0.8 23 70 7 32 11 7 14 Europe & Central Asia 392.4 404.2 409.0 0.2 0.2 19 70 11 28 16 11 15 Latin America & Carib. 435.6 572.5 606.9 1.4 1.0 28 65 7 43 10 6 18 Middle East & N. Africa 227.4 330.9 366.1 2.0 1.7 31 64 4 48 7 6 24 South Asia 1,128.7 1,567.7 1,706.5 1.7 1.4 32 63 5 51 7 7 24 Sub-Saharan Africa 514.9 839.6 969.5 2.6 2.4 43 54 3 78 6 14 38 High income 980.4 1,116.6 1,148.0 0.7 0.5 17 67 15 26 23 8 12 Euro area 301.6 327.3 332.3 0.4 0.3 15 66 18 23 27 9 10 a. Includes Taiwan, China. b. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo. 38 2011 World Development Indicators 2.1 PEOPLE Population dynamics About the data Definitions Population estimates are usually based on national Dependency ratios capture variations in the propor- • Population is based on the de facto definition of popu- population censuses. Estimates for the years before tions of children, elderly people, and working-age peo- lation, which counts all residents regardless of legal sta- and after the census are interpolations or extrapola- ple in the population that imply the dependency burden tus or citizenship—except for refugees not permanently tions based on demographic models. Errors and under- that the working-age population bears in relation to settled in the country of asylum, who are generally con- counting occur even in high income countries; in devel- children and the elderly. But dependency ratios show sidered part of the population of their country of origin. oping countries errors may be substantial because only the age composition of a population, not economic The values shown are midyear estimates for 1990 and of limits in the transport, communications, and other dependency. Some children and elderly people are part 2009 and projections for 2015. • Average annual popu- resources required to conduct and analyze a full census. of the labor force, and many working-age people are not. lation growth is the exponential change for the period The quality and reliability of official demographic Vital rates are based on data from birth and death indicated. See Statistical methods for more information. data are also affected by public trust in the govern- registration systems, censuses, and sample surveys • Population age composition is the percentage of the ment, government commitment to full and accurate by national statistical offices and other organiza- total population that is in specific age groups. • Depen- enumeration, confidentiality and protection against tions, or on demographic analysis. Data for 2009 dency ratio is the ratio of dependents—people younger misuse of census data, and census agencies’ inde- for most high-income countries are provisional esti- than 15 or older than 64—to the working age popula- pendence from political influence. Moreover, compara- mates based on vital registers. The estimates for tion—those ages 15–64. • Crude death rate and crude bility of population indicators is limited by differences many countries are projections based on extrapo- birth rate are the number of deaths and the number of in the concepts, definitions, collection procedures, lations of levels and trends from earlier years or live births occurring during the year, per 1,000 people, and estimation methods used by national statistical interpolations of population estimates and projec- estimated at midyear. Subtracting the crude death rate agencies and other organizations that collect the data. tions from the United Nations Population Division. from the crude birth rate provides the rate of natural Of the 155 economies in the table and the 55 econo- Vital registers are the preferred source for these increase, which is equal to the population growth rate in mies in table 1.6, 180 (about 86 percent) conducted a data, but in many developing countries systems for the absence of migration. census during the 2000 census round (1995–2004). registering births and deaths are absent or incomplete As of January 2011, 119 countries have completed because of deficiencies in the coverage of events or a census for the 2010 census round (2005–14). geographic areas. Many developing countries carry out The currentness of a census and the availability of special household surveys that ask respondents about complementary data from surveys or registration recent births and deaths. Estimates derived in this systems are objective ways to judge demographic way are subject to sampling errors and recall errors. data quality. Some European countries’ registration The United Nations Statistics Division monitors the systems offer complete information on population in completeness of vital registration systems. Progress the absence of a census. See table 2.17 and Primary has been made over the past 60 years in some coun- data documentation for the most recent census or tries. But many countries still have deficiencies in civil survey year and for the completeness of registration. registration systems. For example, only 60 percent of Current population estimates for developing coun- countries and areas register at least 90 percent of tries that lack recent census data and pre- and post- births, and only 47 percent register at least 90 percent census estimates for countries with census data are of deaths. Some of the most populous developing coun- Data sources provided by the United Nations Population Division and tries—Bangladesh, Brazil, India, Indonesia, Nigeria, The World Bank’s population estimates are compiled other agencies. The cohort component method—a Pakistan—lack complete vital registration systems. and produced by its Development Data Group in con- standard method for estimating and projecting popu- International migration is the only other factor sultation with its Human Development Network, oper- lation— requires fertility, mortality, and net migration besides birth and death rates that directly deter- ational staff, and country offices. The United Nations data, often collected from sample surveys, which can mines a country’s population growth. From 1990 to Population Division’s World Population Prospects: The be small or limited in coverage. Population estimates 2005 the number of migrants in high-income coun- 2008 Revision is a source of the demographic data for are from demographic modeling and so are susceptible tries rose 40 million. About 195 million people (3 more than half the countries, most of them developing to biases and errors from shortcomings in the model percent of the world population) live outside their countries, and the source of data on age composi- and in the data. Because the five-year age group is the home country. Estimating migration is difficult. At tion and dependency ratios for all countries. Other cohort unit and five-year period data are used, interpo- any time many people are located outside their important sources are census reports and other sta- lations to obtain annual data or single age structure home country as tourists, workers, or refugees or tistical publications from national statistical offices; may not reflect actual events or age composition. for other reasons. Standards for the duration and household surveys conducted by national agencies, The growth rate of the total population conceals purpose of international moves that qualify as migra- Macro International, and the U.S. Centers for Disease age-group differences in growth rates. In many tion vary, and estimates require information on flows Control and Prevention; Eurostat’s Demographic Sta- developing countries the once rapidly growing under- into and out of countries that is difficult to collect. tistics; Secretariat of the Pacific Community, Statistics 15 population is shrinking. Previously high fertility and Demography Programme; and U.S. Bureau of the rates and declining mortality rates are now reflected Census, International Data Base. in the larger share of the working-age population. 2011 World Development Indicators 39 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2009 1990 2009 1990 2009 1990–2009 1990 2009 Afghanistan 84 85 32 33 5.9 9.6 2.5 26.2 26.6 Albania 74 70 51 49 1.4 1.4 0.2 39.9 42.5 Algeria 75 80 23 37 7.0 14.8 3.9 23.4 31.6 Angola 90 88 74 75 4.6 8.3 3.1 46.3 46.9 Argentina 78 78 43 52 13.5 19.6 1.9 36.9 41.6 Armenia 78 75 61 60 1.7 1.6 –0.2 46.3 49.6 Australia 76 72 52 58 8.5 11.5 1.6 41.3 45.4 Austria 70 68 43 53 3.5 4.3 1.0 40.9 45.5 Azerbaijan 74 67 59 60 3.1 4.2 1.5 46.8 49.5 Bangladesh 89 83 61 59 49.5 78.6 2.4 39.9 41.2 Belarus 75 67 60 55 5.3 5.0 –0.3 48.9 49.5 Belgium 61 61 36 47 3.9 4.8 1.0 39.0 44.9 Benin 89 78 57 67 1.9 3.7 3.5 41.1 46.2 Bolivia 82 82 59 62 2.8 4.5 2.6 43.1 43.8 Bosnia and Herzegovina 67 68 53 55 2.0 1.9 0.0 45.2 47.1 Botswana 82 81 64 72 0.5 1.0 3.2 45.5 47.4 Brazil 85 82 45 60 62.6 101.5 2.5 35.1 43.7 Bulgaria 63 61 55 48 4.1 3.6 –0.7 47.9 46.1 Burkina Faso 91 91 77 78 3.9 7.1 3.2 48.0 47.1 Burundi 90 88 91 91 2.8 4.6 2.6 52.5 52.6 Cambodia 84 86 78 74 4.3 7.8 3.1 52.8 48.3 Cameroon 83 81 48 54 4.4 7.7 3.0 37.5 40.1 Canada 76 73 58 63 14.7 19.1 1.4 44.1 47.0 Central African Republic 87 87 69 72 1.3 2.1 2.5 45.6 46.5 Chad 81 78 65 63 2.4 4.3 3.1 45.6 45.2 Chile 77 73 32 42 5.0 7.5 2.1 30.5 37.2 China 85 80 73 67 643.9 783.2 1.0 44.8 44.6 Hong Kong SAR, China 80 69 47 52 2.9 3.7 1.4 36.3 46.3 Colombia 78 78 29 41 11.2 19.0 2.8 28.2 35.8 Congo, Dem. Rep. 85 86 53 57 13.4 24.9 3.3 39.9 40.6 Congo, Rep. 84 83 59 63 1.0 1.6 2.6 42.1 43.6 Costa Rica 84 80 33 45 1.2 2.1 3.2 27.4 35.5 Côte d’Ivoire 88 82 43 51 4.7 8.4 3.1 30.1 36.9 Croatia 69 60 47 46 2.2 2.0 –0.4 42.7 45.8 Cuba 73 67 36 41 4.4 5.0 0.6 33.0 38.1 Czech Republic 71 68 52 49 4.9 5.2 0.3 44.4 43.2 Denmark 75 71 62 60 2.9 3.0 0.1 46.1 46.9 Dominican Republic 85 80 43 51 2.9 4.5 2.3 33.2 38.8 Ecuador 78 78 33 47 3.5 5.9 2.7 29.5 38.0 Egypt, Arab Rep. 74 75 27 22 16.8 27.4 2.6 26.6 23.0 El Salvador 83 77 41 46 1.9 2.5 1.4 35.2 41.9 Eritrea 84 83 55 63 1.2 2.2 3.2 41.4 44.5 Estonia 77 69 63 55 0.8 0.7 –1.0 49.5 49.1 Ethiopia 91 90 72 81 21.5 40.0 3.3 45.1 47.9 Finland 72 65 59 57 2.6 2.7 0.2 47.1 48.1 France 65 62 46 51 25.0 28.7 0.7 43.3 46.8 Gabon 83 81 63 70 0.4 0.7 3.1 44.2 46.7 Gambia, The 86 85 71 71 0.4 0.8 3.4 46.2 46.2 Georgia 78 74 60 55 2.8 2.3 –1.2 46.9 46.8 Germany 73 67 45 53 38.8 42.3 0.5 40.7 45.6 Ghana 73 75 70 74 6.0 11.0 3.2 48.9 49.1 Greece 67 65 36 43 4.2 5.2 1.1 36.2 40.5 Guatemala 88 88 39 48 3.1 5.5 3.0 31.0 37.9 Guinea 90 89 79 79 2.9 4.8 2.7 46.8 46.9 Guinea-Bissau 81 84 59 60 0.4 0.7 2.4 43.0 42.4 Haiti 81 83 57 58 2.8 4.5 2.5 43.0 42.3 Honduras 88 80 41 40 1.7 2.8 2.6 32.3 33.9 40 2011 World Development Indicators 2.2 PEOPLE Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2009 1990 2009 1990 2009 1990–2009 1990 2009 Hungary 65 59 46 43 4.5 4.3 –0.3 44.5 45.1 India 84 81 34 33 317.8 457.5 1.9 27.1 27.6 Indonesia 81 86 50 52 74.9 115.6 2.3 38.4 38.1 Iran, Islamic Rep. 80 73 22 32 15.5 29.2 3.3 20.1 29.8 Iraq 73 69 11 14 4.3 7.7 3.0 13.1 16.7 Ireland 71 73 35 54 1.3 2.2 2.7 33.9 43.0 Israel 64 63 42 52 1.7 3.1 3.1 40.6 46.5 Italy 66 61 35 38 23.7 25.4 0.4 36.5 40.5 Jamaica 80 74 65 56 1.1 1.2 0.5 46.6 44.9 Japan 77 72 50 48 63.9 65.8 0.2 40.7 41.6 Jordan 71 74 15 23 0.7 1.9 5.0 16.2 23.0 Kazakhstan 78 76 62 66 7.8 8.6 0.5 47.0 49.8 Kenya 90 88 75 76 9.8 18.7 3.4 46.0 46.7 Korea, Dem. Rep. 80 78 55 55 10.0 12.4 1.1 42.6 42.7 Korea, Rep. 73 72 47 50 19.2 24.7 1.3 39.7 41.9 Kosovo .. .. .. .. .. .. .. .. .. Kuwait 82 83 36 45 0.9 1.5 2.8 22.4 25.0 Kyrgyz Republic 74 79 58 55 1.8 2.5 1.7 46.1 42.3 Lao PDR 83 79 80 78 1.9 3.1 2.5 49.8 50.4 Latvia 77 70 63 54 1.4 1.2 –1.0 49.6 48.3 Lebanon 72 72 20 22 0.9 1.5 2.8 23.3 25.0 Lesotho 83 78 68 71 0.7 0.9 1.9 51.7 52.4 Liberia 78 76 65 67 0.8 1.6 3.4 46.7 47.6 Libya 75 79 15 25 1.2 2.4 3.7 14.8 22.5 Lithuania 74 62 59 50 1.9 1.6 –1.0 48.1 48.7 Macedonia, FYR 68 65 46 43 0.8 0.9 0.6 40.7 40.1 Madagascar 89 89 83 84 5.4 9.7 3.1 48.4 49.2 Malawi 80 79 76 75 3.9 6.3 2.5 50.7 49.8 Malaysia 80 79 43 44 7.0 12.0 2.8 34.5 35.4 Mali 68 67 37 38 2.5 3.8 2.2 36.1 37.3 Mauritania 82 81 53 59 0.7 1.4 3.3 39.8 42.0 Mauritius 81 75 38 41 0.4 0.6 1.3 32.1 36.1 Mexico 84 81 34 43 29.9 47.2 2.4 30.0 36.2 Moldova 74 53 61 47 2.1 1.5 –1.8 48.7 49.9 Mongolia 77 78 63 68 0.9 1.4 2.5 45.6 47.4 Morocco 81 80 25 26 7.8 12.0 2.2 23.7 25.8 Mozambique 88 87 85 85 6.3 11.0 3.0 53.2 52.0 Myanmar 89 85 71 63 20.7 27.0 1.4 45.3 44.2 Namibia 64 63 48 52 0.4 0.8 3.0 44.9 46.5 Nepal 85 80 52 63 7.5 13.3 3.0 38.0 45.4 Netherlands 70 73 43 60 6.9 9.0 1.4 38.8 45.7 New Zealand 74 76 54 62 1.7 2.4 1.7 43.0 46.1 Nicaragua 85 78 39 47 1.4 2.3 2.8 32.3 38.7 Niger 91 88 27 39 2.3 4.8 3.8 24.7 31.6 Nigeria 76 73 36 39 29.4 50.0 2.8 33.0 35.1 Norway 73 71 57 63 2.2 2.6 0.9 44.7 47.7 Oman 80 77 19 25 0.6 1.1 3.4 13.7 18.8 Pakistan 85 85 14 22 31.0 58.1 3.3 12.7 19.4 Panama 79 81 39 48 0.9 1.6 2.8 32.4 37.4 Papua New Guinea 74 74 71 72 1.8 3.0 2.8 46.9 48.9 Paraguay 87 87 47 57 1.7 3.0 3.1 34.9 39.4 Peru 75 76 49 58 8.3 13.6 2.6 39.7 43.6 Philippines 83 79 48 49 24.1 38.8 2.5 36.5 38.6 Poland 72 62 55 46 18.1 17.4 –0.2 45.4 45.0 Portugal 73 69 49 56 4.7 5.6 0.9 42.4 46.9 Puerto Rico 61 58 31 36 1.2 1.5 1.2 35.8 40.8 Qatar 94 93 40 50 0.3 1.0 6.9 13.5 11.9 2011 World Development Indicators 41 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2009 1990 2009 1990 2009 1990–2009 1990 2009 Romania 73 60 60 45 11.8 9.5 –1.1 46.3 45.0 Russian Federation 76 69 60 58 76.8 75.9 –0.1 48.6 50.1 Rwanda 89 85 87 87 3.2 5.0 2.3 52.1 52.8 Saudi Arabia 80 74 15 17 5.0 8.6 2.8 11.5 14.9 Senegal 90 89 62 65 3.0 5.4 3.0 40.8 43.3 Serbia .. .. .. .. .. .. .. .. .. Sierra Leone 68 68 66 65 1.6 2.1 1.6 50.9 51.4 Singapore 79 76 51 54 1.6 2.7 2.9 39.1 41.5 Slovak Republic 72 69 59 51 2.6 2.7 0.3 46.8 44.7 Slovenia 59 65 47 53 0.8 1.0 1.2 46.8 46.2 Somalia 84 85 58 57 2.6 3.5 1.6 41.8 40.9 South Africa 62 63 36 47 10.4 18.8 3.1 37.5 43.7 Spain 67 69 34 49 15.6 22.9 2.0 34.8 42.8 Sri Lanka 79 75 37 34 6.8 8.3 1.1 31.8 32.4 Sudan 79 74 27 31 8.0 13.5 2.7 26.0 29.5 Swaziland 81 75 45 53 0.3 0.5 2.7 41.2 43.4 Sweden 72 69 63 61 4.7 5.0 0.3 47.7 47.4 Switzerland 81 74 57 61 3.8 4.4 0.7 42.9 46.8 Syrian Arab Republic 81 80 18 21 3.3 6.9 4.0 18.3 20.9 Tajikistan 80 78 59 57 2.1 2.9 1.8 43.3 43.9 Tanzania 91 91 87 86 12.3 21.4 2.9 49.8 49.4 Thailand 87 81 75 66 32.1 38.7 1.0 47.0 46.1 Timor-Leste 82 83 58 59 0.3 0.4 1.8 40.4 40.9 Togo 87 86 56 64 1.5 3.0 3.5 40.1 43.5 Trinidad and Tobago 76 78 39 55 0.5 0.7 2.3 35.0 43.3 Tunisia 76 71 21 26 2.4 3.8 2.4 21.6 26.7 Turkey 81 70 34 24 20.7 25.6 1.1 29.7 25.7 Turkmenistan 72 74 58 62 1.4 2.4 2.9 46.1 47.1 Uganda 91 91 81 78 7.9 14.1 3.0 47.7 46.5 Ukraine 71 65 56 52 25.5 23.0 –0.5 49.2 49.0 United Arab Emirates 92 92 25 42 1.0 2.9 5.8 9.8 15.7 United Kingdom 74 70 52 55 29.0 31.8 0.5 43.2 45.7 United States 76 72 57 58 129.2 159.0 1.1 44.4 46.0 Uruguay 76 76 48 54 1.4 1.7 0.9 40.8 44.1 Uzbekistan 68 71 53 58 7.3 12.7 2.9 45.5 45.9 Venezuela, RB 81 80 36 52 7.2 13.1 3.2 30.5 39.3 Vietnam 82 76 74 68 31.1 46.6 2.1 50.7 48.6 West Bank and Gaza 66 68 11 17 0.4 1.0 4.4 13.8 19.0 Yemen, Rep. 74 74 16 20 2.6 6.2 4.5 18.0 21.1 Zambia 79 79 61 60 3.0 4.8 2.5 44.3 43.4 Zimbabwe 80 74 67 60 4.1 5.0 1.0 46.3 47.5 World 81 w 78 w 52 w 52 w 2,342.6 t 3,175.8 t 1.6 w 39.4 w 40.1 w Low income 86 84 65 66 232.9 384.5 2.6 43.8 44.6 Middle income 82 79 52 50 1,646.7 2,244.8 1.6 38.1 38.4 Lower middle income 83 80 54 50 1,317.1 1,786.5 1.6 38.2 37.7 Upper middle income 78 75 45 48 329.6 458.2 1.7 37.6 40.8 Low & middle income 83 80 53 52 1,879.5 2,629.2 1.8 38.8 39.3 East Asia & Pacific 84 80 69 64 853.5 1,090.7 1.3 44.2 43.9 Europe & Central Asia 75 69 56 50 180.3 187.2 0.2 45.8 45.5 Latin America & Carib. 82 80 40 52 169.1 269.3 2.4 33.8 40.5 Middle East & N. Africa 77 75 22 26 63.3 115.2 3.2 22.0 25.7 South Asia 85 82 35 35 418.8 625.9 2.1 27.8 29.0 Sub-Saharan Africa 82 81 57 61 194.6 341.0 3.0 42.0 43.6 High income 73 70 49 52 463.0 546.6 0.9 41.6 43.9 Euro area 69 65 42 49 135.2 158.5 0.8 39.8 44.4 42 2011 World Development Indicators 2.2 PEOPLE Labor force structure About the data Definitions The labor force is the supply of labor available for pro- information on source, reference period, or defini- • Labor force participation rate is the proportion ducing goods and services in an economy. It includes tion, consult the original source. of the population ages 15 and older that engages people who are currently employed and people who The labor force participation rates in the table are actively in the labor market, either by working or are unemployed but seeking work as well as first-time from the ILO’s Key Indicators of the Labour Market, looking for work during a reference period. • Total job-seekers. Not everyone who works is included, 6th edition, database. These harmonized estimates labor force is people ages 15 and older who engage however. Unpaid workers, family workers, and stu- use strict data selection criteria and enhanced actively in the labor market, either by working or look- dents are often omitted, and some countries do not methods to ensure comparability across countries ing for work during a reference period. It includes count members of the armed forces. Labor force size and over time, including collection and tabulation both the employed and the unemployed. • Average tends to vary during the year as seasonal workers methodologies and methods applied to such country- annual percentage growth of the labor force is cal- enter and leave. specific factors as military service requirements. culated using the exponential endpoint method (see Data on the labor force are compiled by the Inter- Estimates are based mainly on labor force surveys, Statistical methods for more information). • Female national Labour Organization (ILO) from labor force with other sources (population censuses and nation- labor force as a percentage of the labor force shows surveys, censuses, establishment censuses and ally reported estimates) used only when no survey the extent to which women are active in the labor surveys, and administrative records such as employ- data are available. force. ment exchange registers and unemployment insur- The labor force estimates in the table were calcu- ance schemes. For some countries a combination lated by applying labor force participation rates from of these sources is used. Labor force surveys are the ILO database to World Bank population estimates the most comprehensive source for internationally to create a series consistent with these population comparable labor force data. They can cover all estimates. This procedure sometimes results in noninstitutionalized civilians, all branches and sec- labor force estimates that differ slightly from those tors of the economy, and all categories of workers, in the ILO’s Yearbook of Labour Statistics and its including people holding multiple jobs. By contrast, database Key Indicators of the Labour Market. labor force data from population censuses are often Estimates of women in the labor force and employ- based on a limited number of questions on the eco- ment are generally lower than those of men and are nomic characteristics of individuals, with little scope not comparable internationally, reflecting that demo- to probe. The resulting data often differ from labor graphic, social, legal, and cultural trends and norms force survey data and vary considerably by country, determine whether women’s activities are regarded depending on the census scope and coverage. Estab- as economic. In many countries many women work lishment censuses and surveys provide data only on farms or in other family enterprises without pay, on the employed population, not unemployed work- and others work in or near their homes, mixing work ers, workers in small establishments, or workers in and family activities during the day. the informal sector (ILO, Key Indicators of the Labour Market 2001–2002). The reference period of a census or survey is another important source of differences: in some countries data refer to people’s status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In devel- oping countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Data sources Differing definitions of employment age also affect comparability. For most countries the working age is Data on labor force participation rates are from 15 and older, but in some countries children younger the ILO’s Key Indicators of the Labour Market, 6th than 15 work full- or part-time and are included in the edition, database. Labor force numbers were cal- estimates. Similarly, some countries have an upper culated by World Bank staff, applying labor force age limit. As a result, calculations may systemati- participation rates from the ILO database to popu- cally over- or underestimate actual rates. For further lation estimates. 2011 World Development Indicators 43 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b,c 1c 0 b,c 0 b,c 40 c 33c 18 c 11c 59c 66c 81c 89c Armenia .. 46 .. 46 .. 21 .. 10 .. 33 .. 45 Australia 6 4 4 2 32 31 12 9 61 64 84 89 Austria 6 6 8 6 47 37 20 12 46 57 72 82 Azerbaijan .. 40 .. 38 .. 17 .. 9 .. 44 .. 53 Bangladesh 54 42 85 68 16 15 9 13 25 43 2 19 Belarus .. 15 .. 9 .. 33 .. 24 .. 37 .. 64 Belgium 3 2 2 1 41 36 16 11 56 61 81 88 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia .. .. .. .. .. .. .. .. .. .. .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 35 .. 24 .. 19 .. 11 .. 46 .. 65 Brazil 31c 23 25c 15 27c 28 10 c 13 43c 50 65c 72 Bulgaria .. 9 .. 6 .. 42 .. 29 .. 49 .. 65 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 6c 3c 2c 2c 31c 32c 11c 11c 64 c 65c 87c 88 c Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 16 6 6 32 31 15 11 45 53 79 84 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China 1c 0 b,c 0 b,c 0 b,c 37c 21c 27c 6c 63c 78 c 73c 94 c Colombia .. 27 .. 6 .. 22 .. 16 .. 51 .. 78 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 18 5 5 27 28 25 13 41 54 69 82 Côte d’Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 13d .. 15d .. 39d .. 15d .. 48d .. 69d Cuba .. 25 .. 9 .. 22 .. 12 .. 54 .. 79 Czech Republic .. 4 .. 2 .. 51 .. 27 .. 45 .. 71 Denmark 7 4 3 1 37 32 16 12 56 64 82 86 Dominican Republic 26 21 3 2 23 26 21 14 52 53 76 84 Ecuador 10 c 11c 2c 4c 29c 28 c 17c 13c 62c 61c 81c 83c Egypt, Arab Rep. 35 28 52 43 25 26 10 6 41 46 37 51 El Salvador 48 29 15 5 23 26 23 19 29 45 63 76 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23 5 13 2 42 48 30 23 36 46 57 75 Ethiopia .. 9c,d .. 10 c,d .. 25c,d .. 20 c,d .. 76c,d .. 64 c,d Finland 11 6 6 3 38 39 15 11 51 54 78 86 France 7 4 5 2 39 34 17 11 54 61 78 86 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 51 .. 57 .. 17 .. 4 .. 33 .. 39 Germany 4 3 4 2 50 41 24 16 46 56 73 83 Ghana 66 .. 59 .. 10 .. 10 .. 23 .. 32 .. Greece 20 11 26 12 29 30 17 9 51 59 57 79 Guatemala .. 44 .. 16 .. 24 .. 21 .. 32 .. 63 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 76 .. 50 .. 9 .. 9 .. 13 .. 38 .. Honduras 53c 51c 6c 13c 18 c 20 c 25c 23c 29c 29c 69c 63c 44 2011 World Development Indicators 2.3 PEOPLE Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a Hungary 19 6 13 2 43 42 29 21 38 52 58 77 India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 54 41 57 41 15 21 13 15 31 38 31 44 Iran, Islamic Rep. .. 21 .. 33 .. 33 .. 29 .. 47 .. 38 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 19 9 3 2 33 38 18 10 48 53 78 88 Israel 5 3 2 1 38 32 15 11 57 65 83 88 Italy 8 5 9 3 41 39 23 16 52 57 68 81 Jamaica 36 26 16 8 25 27 12 5 39 47 72 87 Japan 6 4 7 4 40 35 27 17 54 59 65 77 Jordan .. .. .. .. .. .. .. .. .. .. .. .. Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 14 7 18 8 40 33 28 16 46 60 54 76 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 37 .. 35 .. 26 .. 11 .. 37 .. 54 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 10 .. 6 .. 40 .. 17 .. 49 .. 77 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 10 .. 6 .. 41 .. 19 .. 49 .. 75 Macedonia, FYR .. 19 .. 17 .. 33 .. 29 .. 48 .. 54 Madagascar .. 82 .. 83 .. 5 .. 2 .. 13 .. 16 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 18 20 10 31 32 32 23 46 51 48 67 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 15 10 13 8 36 36 48 26 48 54 39 66 Mexico 34 19 11 4 25 31 19 18 41 50 70 77 Moldova .. 36 .. 30 .. 25 .. 12 .. 39 .. 58 Mongolia .. 41 .. 35 .. 21 .. 15 .. 39 .. 50 Morocco .. 35 .. 60 .. 24 .. 15 .. 41 .. 25 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 23 52 8 21 24 8 9 34 24 40 63 Nepal 75 .. 91 .. 4 .. 1 .. 20 .. 8 .. Netherlands 5 3 2 2 33 27 10 8 60 63 81 85 New Zealand 13c 9 8c 5 31c 32 13c 10 56c 58 79c 85 Nicaragua .. 42 .. 8 .. 20 .. 18 .. 38 .. 73 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 7 4 3 1 34 33 10 8 58 63 86 90 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 45 36 69 72 20 23 15 13 35 41 16 15 Panama 35 21 3 3 20 25 11 10 45 54 85 87 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay .. 33 .. 24 .. 24 .. 9 .. 43 .. 68 Peru 1c 12c 0 b,c 6c 30 c 41c 13c 43c 69c 46c 87c 51c Philippines 53c 42d 32c 23d 17c 18d 14 c 10 d 29c 41d 55c 68d Poland .. 15c .. 14 c .. 41c .. 18 c .. 44 c .. 68 c Portugal 10 11 13 12 39 40 24 17 51 49 63 71 Puerto Rico 5 2 0b 0b 27 26 19 10 67 72 80 89 Qatar .. 4 .. 0 .. 48 .. 4 .. 48 .. 96 2011 World Development Indicators 45 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a Romania 29 27 38 30 44 38 30 24 28 35 33 46 Russian Federation .. 11 .. 7 .. 38 .. 20 .. 51 .. 73 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5d .. 0 b,d .. 23d .. 2d .. 72d .. 98d Senegal .. 34 .. 33 .. 20 .. 5 .. 33 .. 42 Serbia .. 22 .. 20 .. 37 .. 20 .. 42 .. 61 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 2 0b 1 36 26 32 18 63 72 68 82 Slovak Republic .. 6 .. 2 .. 52 .. 24 .. 43 .. 74 Slovenia .. 10 c .. 10 c .. 44 c .. 23c .. 45c .. 65c Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 5d .. 3d .. 31d .. 13d .. 57d .. 79d Spain 11 6 8 3 41 40 17 11 49 55 75 86 Sri Lanka .. 28 c .. 37c .. 26c .. 27c .. 41c .. 34 c Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5c 3c 2c 1c 40 c 33c 12c 9c 55c 64 c 86c 90 c Switzerland 5 5 4 3 39 34 15 12 57 62 81 86 Syrian Arab Republic 23 .. 54 .. 28 .. 8 .. 49 .. 38 .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania .. 71 .. 78 .. 7 .. 3 .. 22 .. 19 Thailand 59 43 62 40 17 22 13 19 24 35 25 41 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 6 6 2 34 41 14 16 51 52 80 82 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 18d 72 42d 26 21d 11 15d 41 53d 17 43d Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. 6 .. 0b .. 45 .. 6 .. 49 .. 92 United Kingdom 3 2 1 1 41 32 16 9 55 66 82 90 United States 4 2 1 1 34 30 14 9 62 68 85 90 Uruguay .. 16c .. 5c .. 29c .. 13c .. 56c .. 83c Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 13 2 2 32 30 16 12 52 56 82 86 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 11 .. 36 .. 27 .. 10 .. 61 .. 53 Yemen, Rep. 44 .. 83 .. 14 .. 2 .. 38 .. 13 .. Zambia 47 .. 56 .. 15 .. 3 .. 22 .. 18 .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w . w. .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 17 .. 12 .. 32 .. 20 .. 50 .. 68 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 18 .. 18 .. 34 .. 20 .. 48 .. 63 Latin America & Carib. .. 20 .. 9 .. 29 .. 16 .. 51 .. 75 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 5 3 38 34 19 13 55 61 76 84 Euro area 7 5 6 3 42 38 20 13 50 57 73 83 Note: Data across sectors may not sum to 100 percent because of workers not classified by sector. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. d. Data are for 2009. 46 2011 World Development Indicators 2.3 PEOPLE Employment by economic activity About the data Definitions The International Labour Organization (ILO) classi- Such broad classification may obscure fundamental • Agriculture corresponds to division 1 (ISIC revi- fies economic activity using the International Stan- shifts within countries’ industrial patterns. A slight sion 2) or tabulation categories A and B (ISIC revi- dard Industrial Classification (ISIC) of All Economic majority of countries report economic activity accord- sion 3) and includes hunting, forestry, and fishing. Activities, revision 2 (1968) and revision 3 (1990). ing to the ISIC revision 2 instead of revision 3. The • Industry corresponds to divisions 2–5 (ISIC revi- Because this classification is based on where work use of one classification or the other should not have sion 2) or tabulation categories C–F (ISIC revision is performed (industry) rather than type of work per- a significant impact on the information for the three 3) and includes mining and quarrying (including oil formed (occupation), all of an enterprise’s employees broad sectors presented in the table. production), manufacturing, construction, and public are classified under the same industry, regardless The distribution of economic wealth in the world utilities (electricity, gas, and water). • Services corre- of their trade or occupation. The categories should remains strongly correlated with employment by spond to divisions 6–9 (ISIC revision 2) or tabulation sum to 100 percent. Where they do not, the differ- economic activity. The wealthier economies are categories G–P (ISIC revision 3) and include whole- ences are due to workers who cannot be classified those with the largest share of total employment in sale and retail trade and restaurants and hotels; by economic activity. services, whereas the poorer economies are largely transport, storage, and communications; financing, Data on employment are drawn from labor force agriculture based. insurance, real estate, and business services; and surveys, household surveys, official estimates, cen- The distribution of economic activity by gender community, social, and personal services. suses and administrative records of social insurance reveals some clear patterns. Men still make up the schemes, and establishment surveys when no other majority of people employed in all three sectors, but information is available. The concept of employment the gender gap is biggest in industry. Employment in generally refers to people above a certain age who agriculture is also male-dominated, although not as worked, or who held a job, during a reference period. much as industry. Segregating one sex in a narrow Employment data include both full-time and part-time range of occupations significantly reduces economic workers. efficiency by reducing labor market flexibility and thus There are many differences in how countries define the economy’s ability to adapt to change. This seg- and measure employment status, particularly mem- regation is particularly harmful for women, who have bers of the armed forces, self-employed workers, and a much narrower range of labor market choices and unpaid family workers. Where members of the armed lower levels of pay than men. But it is also detri- forces are included, they are allocated to the service mental to men when job losses are concentrated sector, causing that sector to be somewhat over- in industries dominated by men and job growth is stated relative to the service sector in economies centered in service occupations, where women have where they are excluded. Where data are obtained better chances, as has been the recent experience from establishment surveys, data cover only employ- in many countries. ees; thus self-employed and unpaid family workers There are several explanations for the rising impor- are excluded. In such cases the employment share tance of service jobs for women. Many service jobs— of the agricultural sector is severely underreported. such as nursing and social and clerical work—are Caution should be also used where the data refer considered “feminine” because of a perceived simi- only to urban areas, which record little or no agricul- larity to women’s traditional roles. Women often do tural work. Moreover, the age group and area covered not receive the training needed to take advantage of could differ by country or change over time within a changing employment opportunities. And the greater country. For detailed information on breaks in series, availability of part-time work in service industries consult the original source. may lure more women, although it is unclear whether Countries also take different approaches to the this is a cause or an effect. treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribu- tion of employment by economic activity may not be fully comparable across countries. Data sources The ILO reports data by major divisions of the ISIC revision 2 or revision 3. In the table the reported Data on employment are from the ILO’s Key Indica- divisions or categories are aggregated into three tors of the Labour Market, 6th edition, database. broad groups: agriculture, industry, and services. 2011 World Development Indicators 47 2.4 Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2009a 1990 2008 1990 2008 1990–92 2005–08 Afghanistan 54 55 45 47 16 44 .. .. .. .. .. .. Albania 49 46 37 36 89 72 .. .. .. .. –17.5 6.1 Algeria 39 49 25 31 60 .. .. .. .. .. –4.0 –0.7 Angola 77 76 71 69 12 .. .. .. .. .. –5.0 14.6 Argentina 53 57 42 36 74 85 .. 22b .. 17b 9.0 3.7 Armenia 38 38 24 25 .. 93 .. .. .. .. –24.8 12.2 Australia 56 59 58 64 132 149 12 11 9 7 3.3 0.7 Austria 52 55 61 53 102 100 .. 9 .. 9 0.7 0.4 Azerbaijan 57 60 38 39 88 99 .. 41 .. 66 –12.6 21.4 Bangladesh 74 68 66 56 18 42 .. .. .. .. 1.9 4.0 Belarus 58 52 40 35 93 95 .. .. .. .. –4.0 8.7 Belgium 44 47 31 27 101 108 17 11 15 9 1.6 0.7 Benin 70 72 64 59 .. .. .. .. .. .. .. .. Bolivia 61 71 48 49 .. 81 32b .. 50 b .. 2.6 1.8 Bosnia and Herzegovina 42 42 17 18 .. 91 .. .. .. .. –14.8 1.6 Botswana 47 46 34 27 49 82 .. .. .. .. .. .. Brazil 56 64 54 53 .. 101 29b 30 30 b 24 –0.3 3.2 Bulgaria 45 46 27 27 98 89 .. 10 .. 8 3.1 3.0 Burkina Faso 82 82 77 74 7 20 .. .. .. .. 1.3 1.3 Burundi 85 84 74 73 5 21 .. .. .. .. .. .. Cambodia 77 75 66 68 25 40 .. .. .. .. 4.0 6.5 Cameroon 59 59 37 33 26 41 .. .. .. .. –6.7 1.0 Canada 58 61 57 61 101 .. .. 12b .. 9b 0.8 0.2 Central African Republic 73 73 59 58 12 14 .. .. .. .. .. .. Chad 67 70 51 50 6 24 .. .. .. .. .. .. Chile 51 50 34 24 97 90 .. 25 .. 24 6.6 0.2 China 75 71 71 55 41 78 .. .. .. .. 6.8 10.6 Hong Kong SAR, China 62 57 54 38 .. 82 .. 10 b .. 4b 5.3 3.0 Colombia 52 62 38 43 53 95 30 b 41 26 b 41 –0.7 4.8 Congo, Dem. Rep. 68 67 60 62 21 37 .. .. .. .. –12.9 2.9 Congo, Rep. 66 65 49 46 46 .. .. .. .. .. .. .. Costa Rica 56 57 48 43 45 96 26 20 21 20 2.4 1.9 Côte d’Ivoire 63 60 52 45 .. .. .. .. .. .. –3.6 –0.7 Croatia 50 46 27 29 83 90 .. 23c .. 20 c –7.7 2.8 Cuba 52 54 40 32 94 90 .. .. .. .. .. .. Czech Republic 58 54 48 29 91 95 .. 15 .. 9 –5.2 3.4 Denmark 59 60 65 61 109 119 7 7 6 3 2.5 –0.7 Dominican Republic 44 53 28 34 .. 77 42 49 30 30 0.7 5.4 Ecuador 52 61 39 40 55 81 33b 29 b 41b 41b –0.1 0.5 Egypt, Arab Rep. 43 43 22 23 69 .. .. 20 .. 44 2.1 4.4 El Salvador 59 54 42 39 38 64 .. 29 .. 44 .. .. Eritrea 66 66 60 54 11 32 .. .. .. .. .. .. Estonia 61 55 43 29 100 99 2b 8b 3b 4b –9.4 2.4 Ethiopia 71 81 64 74 14 34 .. 48b .. 56 b –8.4 7.4 Finland 57 55 45 44 116 110 .. 11 .. 7 1.4 1.5 France 47 48 28 29 100 113 11 7 10 5 1.4 0.6 Gabon 58 58 37 33 40 .. .. .. .. .. .. .. Gambia, The 73 72 59 55 19 51 .. .. .. .. .. .. Georgia 57 54 28 22 95 108 .. .. .. .. –25.3 10.1 Germany 54 52 58 44 98 102 .. 7 .. 6 3.7 0.9 Ghana 68 65 40 40 35 57 .. .. .. .. 2.8 3.7 Greece 44 48 31 28 94 102 .. 27 .. 27 2.4 2.4 Guatemala 55 62 50 52 23 57 .. .. .. .. 1.0 1.4 Guinea 82 81 75 73 11 37 .. .. .. .. .. .. Guinea-Bissau 66 67 57 63 5 .. .. .. .. .. .. .. Haiti 56 55 37 47 .. .. .. .. .. .. .. .. Honduras 59 56 49 43 33 65 48b .. 50 b .. .. .. 48 2011 World Development Indicators 2.4 PEOPLE Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2009a 1990 2008 1990 2008 1990–92 2005–08 Hungary 48 45 37 20 86 97 8b 8 7b 6 0.3 2.0 India 58 56 46 40 46 60 .. .. .. .. 1.0 5.9 Indonesia 63 62 46 41 46 79 .. 60 .. 68 6.2 3.8 Iran, Islamic Rep. 46 49 33 36 53 83 .. 40 .. 56 6.5 1.8 Iraq 37 37 27 23 40 51 .. .. .. .. –33.6 1.9 Ireland 44 58 38 44 100 115 25 17 9 5 2.4 0.7 Israel 45 50 25 27 92 90 .. 9 .. 5 0.0 1.3 Italy 43 44 30 25 79 101 29 21 24 15 0.6 –0.3 Jamaica 61 56 40 29 70 91 46 38 37 31 0.7 –2.2 Japan 61 54 43 40 97 101 15 10 26 12 0.7 1.2 Jordan 36 38 25 20 82 88 .. .. .. .. –5.5 2.5 Kazakhstan 63 64 46 42 98 99 .. .. .. .. –15.1 4.8 Kenya 73 73 62 59 .. 59 .. .. .. .. –3.9 2.5 Korea, Dem. Rep. 62 64 46 39 .. .. .. .. .. .. .. .. Korea, Rep. 59 58 36 28 91 97 .. 23 .. 28 5.0 3.1 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 62 65 29 30 53 90 .. .. .. .. –0.2 3.2 Kyrgyz Republic 58 58 41 40 100 84 .. 47 .. 47 –13.1 4.3 Lao PDR 80 78 74 64 21 44 .. .. .. .. .. .. Latvia 58 55 43 35 92 98 .. 8 .. 6 –19.6 2.9 Lebanon 44 46 31 29 61 82 .. .. .. .. .. .. Lesotho 48 54 40 40 24 45 .. .. .. .. .. .. Liberia 66 66 57 57 .. .. .. .. .. .. .. .. Libya 45 49 28 27 .. .. .. .. .. .. .. .. Lithuania 54 50 36 18 92 99 .. 11 .. 8 –13.9 5.2 Macedonia, FYR 37 35 17 13 76 84 .. 24 .. 20 –5.6 1.2 Madagascar 79 83 65 71 19 32 .. .. .. .. –5.9 2.2 Malawi 72 72 48 49 17 30 .. .. .. .. –1.9 5.6 Malaysia 60 61 47 45 57 69 31 23 25 21 6.0 3.1 Mali 49 47 40 35 7 38 .. .. .. .. 0.4 1.9 Mauritania 67 47 54 23 13 24 .. .. .. .. .. .. Mauritius 56 54 45 37 55 87 13 18 7 15 .. .. Mexico 57 57 50 42 54 90 29 28 15 32 1.0 1.0 Moldova 58 45 39 17 90 88 .. 35 .. 30 –22.0 6.9 Mongolia 50 52 39 35 82 92 .. .. .. .. .. .. Morocco 46 46 40 35 36 56 .. 46 .. 65 –1.7 2.8 Mozambique 80 78 67 66 7 23 .. .. .. .. –3.0 5.5 Myanmar 74 74 62 53 23 53 .. .. .. .. 2.0 5.8 Namibia 45 43 24 14 43 66 .. .. .. .. .. .. Nepal 60 62 52 46 34 .. .. .. .. .. .. .. Netherlands 51 59 55 67 120 121 7 10 10 8 0.4 1.0 New Zealand 55 63 55 56 92 119 15 14 10 10 0.5 –0.3 Nicaragua 57 58 46 48 43 68 .. 45 .. 46 .. .. Niger 59 60 50 52 7 12 .. .. .. .. –5.7 2.3 Nigeria 53 52 29 24 24 30 .. .. .. .. –2.9 3.3 Norway 58 62 49 56 103 112 .. 8 .. 3 3.9 –1.1 Oman 53 51 30 29 45 91 .. .. .. .. 0.2 3.7 Pakistan 48 52 38 44 23 33 .. 58 .. 75 6.5 2.5 Panama 50 59 33 40 62 73 44 30 19 24 .. .. Papua New Guinea 70 70 57 54 12 .. .. .. .. .. .. .. Paraguay 61 73 51 58 31 67 17b 45 31b 50 .. .. Peru 53 69 34 53 67 89 30 b 33b 46b 47b –0.8 0.2 Philippines 59 60 42 39 70 82 .. 44b .. 47b –3.3 3.9 Poland 53 48 31 27 87 100 .. 20 .. 18 2.8 1.9 Portugal 58 56 53 35 66 104 22 18 30 19 2.2 0.9 Puerto Rico 37 41 21 29 .. 84 .. .. .. .. .. .. Qatar 73 77 35 47 84 85 .. .. .. .. 0.1 13.3 2011 World Development Indicators 49 2.4 Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2009a 1990 2008 1990 2008 1990–92 2005–08 Romania 56 48 42 24 92 92 21 31 33 32 –9.3 6.5 Russian Federation 57 57 34 33 93 85 1 6 1 6 –7.9 6.4 Rwanda 87 80 79 64 18 27 .. .. .. .. .. .. Saudi Arabia 50 48 26 13 .. 97 .. .. .. .. 4.9 0.7 Senegal 67 66 60 55 15 30 77 .. 91 .. –1.0 0.9 Serbia 49d 44 d 28d 21d .. 91 .. 25 .. 20 .. .. Sierra Leone 64 65 38 42 16 35 .. .. .. .. .. .. Singapore 64 62 56 38 .. .. 10 12 6 7 1.5 –1.8 Slovak Republic 55 53 43 30 88 92 .. 14 .. 6 –0.8 6.1 Slovenia 55 54 38 32 89 97 .. 12 .. 10 –2.3 3.0 Somalia 66 67 59 58 .. 8 .. .. .. .. .. .. South Africa 39 41 19 15 69 94 .. 2 .. 3 –4.5 3.7 Spain 41 49 36 37 105 120 20 b 13 24b 10 2.4 0.7 Sri Lanka 51 55 31 36 72 .. .. 39 b .. 44b 5.5 9.3 Sudan 46 47 29 23 20 38 .. .. .. .. –1.3 7.5 Swaziland 54 50 34 26 49 53 .. .. .. .. .. .. Sweden 62 58 59 45 90 103 .. 9 .. 4 1.9 0.6 Switzerland 65 61 69 63 98 96 8 10 11 11 –0.6 1.0 Syrian Arab Republic 47 45 38 32 48 75 .. .. .. .. 6.5 0.3 Tajikistan 54 55 36 38 102 84 .. .. .. .. –20.4 6.3 Tanzania 87 78 79 70 5 27 .. 82b .. 93b –2.4 4.5 Thailand 77 72 70 46 31 76 67 51 74 56 6.8 2.7 Timor-Leste 64 67 51 58 .. 51 .. .. .. .. .. .. Togo 66 65 58 53 20 41 .. .. .. .. .. .. Trinidad and Tobago 45 61 33 46 82 89 22 .. 21 .. –3.5 5.4 Tunisia 41 41 29 22 45 92 .. .. .. .. 2.6 2.7 Turkey 53 42 48 31 48 82 .. 30 .. 49 1.0 2.6 Turkmenistan 56 58 35 34 .. .. .. .. .. .. –13.0 7.9 Uganda 82 83 73 75 10 27 .. .. .. .. –1.1 6.1 Ukraine 57 54 37 34 94 94 .. .. .. .. –7.9 5.9 United Arab Emirates 71 76 43 46 68 95 .. .. .. .. –3.9 0.7 United Kingdom 56 56 66 56 87 99 13 14 6 7 2.0 2.2 United States 59 59 56 51 92 94 .. .. .. .. 1.7 1.4 Uruguay 53 56 42 39 84 88 .. 26 b .. 24b 5.2 4.9 Uzbekistan 54 58 36 39 99 104 .. .. .. .. –7.8 5.9 Venezuela, RB 51 61 35 40 56 82 .. 28 .. 33 4.5 4.3 Vietnam 75 69 75 51 35 .. .. .. .. .. 4.6 5.6 West Bank and Gaza 30 30 19 15 .. 87 .. 34 .. 44 .. .. Yemen, Rep. 38 39 23 22 .. .. .. .. .. .. 0.9 –0.8 Zambia 57 61 40 46 21 49 56 .. 81 .. –2.5 3.9 Zimbabwe 70 65 48 50 49 .. .. .. .. .. –4.7 –7.7 World 62 w 60 w 52 w 45 w 50 w 67 w .. w .. w .. w .. w 0.7 w 3.1 w Low income 71 70 60 58 26 38 .. .. .. .. –3.2 4.4 Middle income 63 61 52 42 47 68 .. .. .. .. 1.3 6.2 Lower middle income 65 62 55 44 42 63 .. .. .. .. 3.2 7.4 Upper middle income 53 56 41 38 67 88 .. 26 .. 26 –2.3 3.6 Low & middle income 63 62 53 45 44 63 .. .. .. .. 1.1 6.1 East Asia & Pacific 73 69 67 51 41 74 .. .. .. .. 6.5 8.7 Europe & Central Asia 55 53 38 33 85 89 .. 19 .. 19 –9.1 5.8 Latin America & Carib. 55 61 46 45 57 89 .. 30 .. 30 1.8 2.6 Middle East & N. Africa 43 45 29 29 54 73 .. 33 .. 52 1.4 2.2 South Asia 59 57 48 42 37 52 .. .. .. .. 3.1 5.5 Sub-Saharan Africa 64 64 50 49 22 34 .. .. .. .. –5.3 4.1 High income 55 55 47 43 91 100 .. 13 .. 11 2.3 1.2 Euro area 48 50 41 37 .. .. .. 12 .. 9 2.4 0.7 a. Provisional data. b. Limited coverage. c. Data are for 2009. d. Includes Montenegro. 50 2011 World Development Indicators 2.4 PEOPLE Decent work and productive employment About the data Definitions Four targets were added to the UN Millennium Dec- Data on employment by status are drawn from • Employment to population ratio is the proportion laration at the 2005 World Summit High-Level Ple- labor force surveys and household surveys, supple- of a country’s population that is employed. People nary Meeting of the 60th Session of the UN General mented by offi cial estimates and censuses for a ages 15 and older are generally considered the Assembly. One was full and productive employment small group of countries. The labor force survey is working-age population. People ages 15–24 are and decent work for all, which is seen as the main the most comprehensive source for internationally generally considered the youth population. • Gross route for people to escape poverty. The four indi- comparable employment, but there are still some enrollment ratio, secondary, is the ratio of total cators for this target have an economic focus, and limitations for comparing data across countries and enrollment in secondary education, regardless of three of them are presented in the table. over time even within a country. Information from age, to the population of the age group that officially The employment to population ratio indicates how labor force surveys is not always consistent in what corresponds to secondary education. • Vulnerable efficiently an economy provides jobs for people who is included in employment. For example, informa- employment is unpaid family workers and own- want to work. A high ratio means that a large pro- tion provided by the Organisation for Economic account workers as a percentage of total employ- portion of the population is employed. But a lower Co-operation and Development relates only to civil- ment. •  Labor productiv ity is the growth rate employment to population ratio can be seen as a ian employment, which can result in an underesti- of gross domestic product (GDP) divided by the num- positive sign, especially for young people, if it is mation of “employees” and “workers not classified ber of people engaged in the production of goods caused by an increase in their education. This indi- by status,” especially in countries with large armed and services. cator has a gender bias because women who do not forces. While the categories of unpaid family work- consider their work employment or who are not per- ers and self-employed workers, which include own- ceived as working tend to be undercounted. This bias account workers, would not be affected, their relative has different effects across countries and reflects shares would be. Geographic coverage is another demographic, social, legal, and cultural trends and factor that can limit cross-country comparisons. The norms. employment by status data for many Latin Ameri- Comparability of employment ratios across coun- can countries covers urban areas only. Similarly, in tries is also affected by variations in definitions of some countries in Sub-Saharan Africa, where limited employment and population (see About the data for information is available anyway, the members of pro- table 2.3). The biggest difference results from the ducer cooperatives are usually excluded from the age range used to define labor force activity. The self-employed category. For detailed information on population base for employment ratios can also vary definitions and coverage, consult the original source. (see table 2.1). Most countries use the resident, Labor productivity is used to assess a country’s noninstitutionalized population of working age living economic ability to create and sustain decent in private households, which excludes members of employment opportunities with fair and equitable the armed forces and individuals residing in men- remuneration. Productivity increases obtained tal, penal, or other types of institutions. But some through investment, trade, technological progress, or countries include members of the armed forces in changes in work organization can increase social pro- the population base of their employment ratio while tection and reduce poverty, which in turn reduce vul- excluding them from employment data (International nerable employment and working poverty. Productiv- Labour Organization, Key Indicators of the Labour ity increases do not guarantee these improvements, Market, 6th edition). but without them—and the economic growth they The proportion of unpaid family workers and bring—improvements are highly unlikely. For compa- own-account workers in total employment is derived rability of individual sectors labor productivity is esti- from information on status in employment. Each mated according to national accounts conventions. status group faces different economic risks, and However, there are still significant limitations on the unpaid family workers and own-account workers availability of reliable data. Information on consis- are the most vulnerable—and therefore the most tent series of output in both national currencies and Data sources likely to fall into poverty. They are the least likely to purchasing power parity dollars is not easily avail- have formal work arrangements, are the least likely able, especially in developing countries, because the Data on employment to population ratio, vulner- to have social protection and safety nets to guard definition, coverage, and methodology are not always able employment, and labor productivity are from against economic shocks, and often are incapable of consistent across countries. For example, countries the ILO’s Key Indicators of the Labour Market, generating sufficient savings to offset these shocks. employ different methodologies for estimating the 6th edition, database. Data on gross enrollment A high proportion of unpaid family workers in a coun- missing values for the nonmarket service sectors ratios are from the United Nations Educational, try indicates weak development, little job growth, and and use different definitions of the informal sector. Scientific, and Cultural Organization Institute for often a large rural economy. Statistics. 2011 World Development Indicators 51 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990–92a 2006–09a 1990–92a 2006–09a 1990–92a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 12.7 .. .. .. .. .. .. .. .. .. .. Algeria 23.0 11.3 24.2 11.0 20.3 10.1 .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.7b 8.6b 6.4b 7.8b 7.0 b 9.8b .. .. .. 48.1b 36.7b 15.3b Armenia .. 28.6b .. 21.9b .. 35.0 b .. .. .. 5.2 83.0 11.9 Australia 10.8 5.6b 11.4 5.7b 10.0 5.4b 14.7b 15.0 b 14.4b 48.0 34.1 17.9 Austria 3.6 4.8 3.5 5.0 3.8 4.5 20.3 19.7 21.0 37.9b 52.1b 10.0 b Azerbaijan .. 6.1 .. 7.1 .. 4.9 .. .. .. 6.3 78.9 14.9 Bangladesh 1.9 .. 2.0 .. 1.9 .. .. .. .. .. .. .. Belarus .. .. .. .. .. .. .. .. .. 10.8 38.6 50.6 Belgium 6.7 7.9 4.8 7.7 9.5 8.1 44.2 43.5 45.0 42.1 38.2 19.7 Benin 1.5 .. 2.2 .. 0.6 .. .. .. .. .. .. .. Bolivia 5.5b 5.2b 5.5b 4.5b 5.6b 6.0 b .. .. .. .. .. .. Bosnia and Herzegovina 17.6 23.9 15.5 21.8 21.6 27.1 .. .. .. 95.7 .. 4.0 Botswana 13.8 17.6b 11.7 15.3b 17.2 19.9b .. .. .. .. .. .. Brazil 6.4b 8.3 5.4b 6.1 7.9b 11.0 .. .. .. 51.6 33.6 3.6 Bulgaria .. 6.8 .. 7.0 .. 6.6 43.3 40.7 46.4 41.8 49.7 8.6 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi 0.5 .. 0.7 .. 0.3 .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. 2.9 .. 2.5 .. 3.3 .. .. .. .. .. .. Canada 11.2b 8.3b 12.0 b 9.4b 10.2b 7.0 b 7.8b 8.1b 7.4b 27.7b 41.1b 31.2b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 4.4 9.7 3.9 9.1 5.3 10.7 .. .. .. 17.8 58.5 23.5 China 2.3b 4.3 .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China 2.0 b 5.2b 2.0 b 6.0 b 1.9b 4.3b .. .. .. 40.8b 41.4b 16.6b Colombia 9.5b 12.0 6.8b 9.3 13.0 b 15.8 .. .. .. 76.6 .. 20.6 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 4.1 4.9 3.5 4.1 5.4 6.2 .. .. .. 65.2 27.3 6.4 Côte d’Ivoire 6.7 .. .. .. .. .. .. .. .. .. .. .. Croatia 11.1 9.1 11.1 8.0 11.2 10.2 56.2 50.8 61.0 16.0 70.4 11.6 Cuba .. 1.6 .. 1.4 .. 2.0 .. .. .. 43.0 52.4 4.6 Czech Republic 2.3 6.7 2.4 5.8 2.1 7.7 31.2 29.0 33.4 26.8 68.8 4.3 Denmark 9.0 6.0 8.3 6.5 9.9 5.4 9.1 8.9 9.4 35.9 35.1 23.0 Dominican Republic 20.7 14.2 12.0 8.5 35.2 22.8 .. .. .. 35.0 44.5 16.4 Ecuador 8.9b 6.5 6.0 b 5.2 13.2b 8.4 .. .. .. 74.0 b .. 23.6b Egypt, Arab Rep. .. 9.4 .. 5.2 .. 22.9 .. .. .. .. .. .. El Salvador 7.9b 5.9 8.4b 7.5 7.2b 3.6 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.7b 13.7 3.9b 17.0 3.5b 10.8 27.4 26.8 28.4 23.1b 57.8b 16.6b Ethiopia 1.3 20.5b 1.1 12.1b 1.6 29.9b .. .. .. .. .. .. Finland 11.6 8.2 13.3 8.9 9.6 7.5 16.6 18.2 14.7 35.5 45.9 18.6 France 10.2 9.1 8.1 8.9 12.8 9.3 35.4 35.6 35.3 39.9 39.6 19.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 16.5 .. 16.8 .. 16.1 .. .. .. 5.1b 52.5b 42.3b Germany 6.6 7.7 5.3 8.1 8.4 7.3 45.5 44.4 47.0 33.1 56.3 10.6 Ghana 4.7 .. 3.7 .. 5.5 .. .. .. .. .. .. .. Greece 7.8 9.5 4.9 6.9 12.9 13.1 40.8 34.4 45.6 29.3b 48.4b 21.8b Guatemala .. 1.8 .. 1.5 .. 2.4 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 12.7 .. 11.9 .. 13.8 .. .. .. .. .. .. .. Honduras 3.2b 2.9b 3.3b 2.9b 3.0 b 2.9b .. .. .. .. .. .. 52 2011 World Development Indicators 2.5 PEOPLE Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990–92a 2006–09a 1990–92a 2006–09a 1990–92a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a Hungary 9.9 10.0 11.0 10.3 8.7 9.7 42.6 42.4 42.8 33.1b 58.7b 8.1b India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 2.8 7.9 2.7 7.5 3.0 8.5 .. .. .. 44.4 40.7 9.6 Iran, Islamic Rep. 11.1 10.5 9.5 9.1 24.4 16.8 .. .. .. .. .. .. Iraq .. 17.5 .. 16.2 .. 22.5 .. .. .. .. .. .. Ireland 15.0 11.7 14.9 14.7 15.2 8.0 29.0 32.1 21.7 39.8 37.2 18.2 Israel 11.2 7.6 9.2 7.6 13.9 7.6 28.6 32.3 25.0 12.2 12.8 72.5 Italy 9.3 7.8 6.7 6.8 13.9 9.3 44.4 42.0 46.9 46.5 40.6 11.3 Jamaica 15.4 11.4 9.4 8.5 22.2 14.8 .. .. .. 9.7 4.3 8.4 Japan 2.2 5.0 2.1 5.3 2.2 4.7 28.5 34.8 18.8 67.2 .. 32.8 Jordan .. 12.9 .. 10.3 .. 24.1 .. .. .. .. .. .. Kazakhstan .. 6.6 .. 5.6 .. 7.5 .. .. .. .. .. .. Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.5b 3.6b 2.8b 4.1b 2.1b 3.0 b 0.5 0.6 0.3 15.2 49.7 35.2 Kosovo .. 45.4 .. 40.7 .. 56.4 81.7 82.8 79.8 64.0 46.0 15.0 Kuwait .. .. .. .. .. .. .. .. .. 19.4 41.4 9.6 Kyrgyz Republic .. 8.2 .. 7.3 .. 9.4 .. .. .. 13.3 77.1 9.6 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 17.1 .. 20.4 .. 14.0 26.7 27.1 26.0 24.3b 59.9b 14.6b Lebanon .. 9.0 .. 8.6 .. 10.1 .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. 5.6 .. 6.8 .. 4.2 .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 13.7 .. 17.1 .. 10.4 23.2 21.0 26.8 14.2b 70.4b 15.4b Macedonia, FYR .. 32.2 .. 31.7 .. 33.0 81.6 82.2 80.6 .. .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 3.7 3.7 .. 3.2 .. 3.7 .. .. .. 13.3 61.6 25.1 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 3.3 7.3 3.2 4.4 3.6 12.3 .. .. .. 44.2 48.5 6.4 Mexico 3.1 5.2 2.7 5.4 4.0 4.8 1.9 1.8 2.1 50.7 24.5 22.9 Moldova .. 6.4 .. 7.8 .. 4.9 .. .. .. .. .. .. Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 16.0 b 10.0 13.0 b 9.8 25.3b 10.5 .. .. .. .. .. .. Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.0 .. 4.7 .. 8.8 .. .. .. .. .. .. .. Namibia 19.0 37.6 20.0 32.5 19.0 43.0 .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 5.6 3.4 4.0 3.4 7.8 3.5 24.8 23.7 26.1 41.3 39.7 17.0 New Zealand 10.6b 6.1b 11.4b 6.1b 9.7b 6.1b 6.3b 6.3b 6.4b 30.6 38.8 26.9 Nicaragua 14.4 5.0 11.3 4.9 19.5 5.1 .. .. .. 72.8 2.1 18.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 5.9 3.2 6.6 3.6 5.1 2.6 7.7 7.5 8.0 25.4 49.2 20.6 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 5.2 5.0 3.8 4.0 14.0 8.7 .. .. .. 14.3 11.4 26.0 Panama 14.7 5.9 10.8 4.6 22.3 7.9 .. .. .. 36.0 39.6 24.0 Papua New Guinea 7.7 .. 9.0 .. 5.9 .. .. .. .. .. .. .. Paraguay 5.0 b 5.6 6.0 b 4.4 3.7b 7.5 .. .. .. 49.9 38.0 9.9 Peru 9.4b 6.8b 7.5b 5.4b 12.5b 8.3b .. .. .. 30.0 b 31.9b 37.6b Philippines 8.6b 7.5 7.9b 7.5 9.9b 7.4 .. .. .. 13.8 45.2 41.1 Poland 13.3 8.2 12.2 7.8 14.7 8.7 25.2 23.3 27.3 16.4b 73.2b 10.4b Portugal 4.1b 9.5 3.5b 8.9 5.0 b 10.1 44.2 40.8 47.5 68.1b 15.4b 13.2b Puerto Rico 16.9 13.4 19.1 14.9 13.3 11.6 .. .. .. .. .. .. Qatar .. 0.5 .. 0.2 .. 2.6 .. .. .. .. .. .. 2011 World Development Indicators 53 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990–92a 2006–09a 1990–92a 2006–09a 1990–92a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a Romania .. 6.9 .. 7.7 .. 5.8 31.6 32.2 30.6 25.8 66.3 6.1 Russian Federation 5.2 8.2 5.2 8.4 5.2 7.9 35.7 33.3 38.4 13.7 54.2 32.1 Rwanda 0.3 .. 0.6 .. 0.2 .. .. .. .. .. .. .. Saudi Arabia .. 5.4 .. 3.5 .. 15.9 .. .. .. 7.5 48.6 43.6 Senegal .. 10.0 .. 7.9 .. 13.6 .. .. .. 40.2 6.9 2.5 Serbia .. 16.6 .. 15.3 .. 18.4 71.1 70.1 72.1 20.3 68.4 11.2 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7b 5.9 2.7b 5.4 2.6b 6.5 .. .. .. 31.0 25.6 43.2 Slovak Republic .. 12.1 .. 11.4 .. 12.9 50.9 47.8 54.4 29.2 65.3 5.3 Slovenia 7.1 5.9 8.1 5.9 6.0 5.8 30.1 28.3 32.1 25.0 b 60.4b 12.5b Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 23.8 .. 22.0 .. 25.9 14.4 .. .. 36.2 56.3 4.5 Spain 18.1 18.0 13.9 17.7 25.8 18.4 30.2 26.9 34.4 54.8b 23.6b 20.4b Sri Lanka 14.2b 7.6 .. 7.2 .. 8.1 .. .. .. 45.4b 22.0 b 32.6b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5.7 8.3 6.7 8.6 4.6 8.0 12.8 13.1 12.4 32.2b 46.0 b 17.1b Switzerland 2.8 4.1 2.3 3.7 3.5 4.5 30.0 26.4 33.6 28.8 53.2 17.9 Syrian Arab Republic 6.8 8.4 5.2 5.2 14.0 25.7 .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. 66.5 28.8 4.6 Tanzania 3.6b 4.3 2.8b 2.8 4.3b 5.8 .. .. .. .. .. .. Thailand 1.4 1.2 1.3 1.2 1.5 1.1 .. .. .. 40.5 45.5 0.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 19.6 5.3 17.0 3.5 23.9 6.2 .. .. .. .. .. .. Tunisia .. 14.2 .. .. .. .. .. .. .. .. .. .. Turkey 8.5 14.0 8.8 13.9 7.8 14.3 25.3 22.6 32.2 52.3 28.2 12.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 1.0 .. 1.3 .. 0.6 .. .. .. .. .. .. .. Ukraine .. 8.8 .. 6.6 .. 6.1 .. .. .. 8.5 52.2 39.3 United Arab Emirates .. 4.0 .. 2.0 .. 12.0 .. .. .. .. .. .. United Kingdom 9.7 7.7 11.5 8.8 7.3 6.4 24.6 26.5 21.5 37.3 47.7 14.3 United States 7.5b 9.3b 7.9b 10.3b 7.0 b 8.1b 16.3b 16.4b 16.1b 18.7 35.5 45.7 Uruguay 9.0 b 7.3 6.8b 5.3 11.8b 9.7 .. .. .. 59.1b 27.0 b 13.8b Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7.7 7.6 8.2 7.2 6.8 8.1 .. .. .. .. .. .. Vietnam .. 2.4 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 24.5 .. 17.7 .. 38.6 .. .. .. 54.3 14.2 23.5 Yemen, Rep. .. 15.0 .. 11.5 .. 40.9 .. .. .. .. .. .. Zambia 18.9 .. 16.3 .. 22.4 .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income 6.7 9.1 6.4 8.5 7.4 10.3 .. .. .. 43.4 40.9 14.3 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 2.5 4.6 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 9.2 .. 9.9 .. 8.6 .. .. .. 26.7 50.2 24.1 Latin America & Carib. 6.6 7.9 5.4 6.6 8.4 9.8 .. .. .. 50.8 34.9 12.3 Middle East & N. Africa .. 10.6 .. 8.9 .. 16.7 .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.5 8.1 7.1 8.4 8.0 7.7 24.8 25.3 23.8 33.9 43.7 25.7 Euro area 9.1 9.4 7.2 9.2 11.9 9.6 38.2 36.7 39.8 41.3 43.0 14.9 a. Data are for the most recent year available. b. Limited coverage. 54 2011 World Development Indicators 2.5 PEOPLE Unemployment About the data Definitions Unemployment and total employment are the broad- generate statistics that are more comparable inter- • Unemployment is the share of the labor force with- est indicators of economic activity as reflected by nationally. But the age group, geographic coverage, out work but available for and seeking employment. the labor market. The International Labour Organiza- and collection methods could differ by country or Definitions of labor force and unemployment may tion (ILO) defines the unemployed as members of the change over time within a country. For detailed infor- differ by country (see About the data). • Long-term economically active population who are without work mation, consult the original source. unemployment is the number of people with continu- but available for and seeking work, including people Women tend to be excluded from the unemploy- ous periods of unemployment extending for a year or who have lost their jobs or who have voluntarily left ment count for various reasons. Women suffer more longer, expressed as a percentage of the total unem- work. Some unemployment is unavoidable. At any from discrimination and from structural, social, and ployed. • Unemployment by educational attainment time some workers are temporarily unemployed— cultural barriers that impede them from seeking is the unemployed by level of educational attainment between jobs as employers look for the right workers work. Also, women are often responsible for the as a percentage of the total unemployed. The levels and workers search for better jobs. Such unemploy- care of children and the elderly and for household of educational attainment accord with the ISCED97 ment, often called frictional unemployment, results affairs. They may not be available for work during of the United Nations Educational, Scientific, and from the normal operation of labor markets. the short reference period, as they need to make Cultural Organization. Changes in unemployment over time may reflect arrangements before starting work. Furthermore, changes in the demand for and supply of labor; they women are considered to be employed when they may also refl ect changes in reporting practices. are working part-time or in temporary jobs, despite Paradoxically, low unemployment rates can disguise the instability of these jobs or their active search for substantial poverty in a country, while high unemploy- more secure employment. ment rates can occur in countries with a high level of Long-term unemployment is measured by the economic development and low rates of poverty. In length of time that an unemployed person has been countries without unemployment or welfare benefits without work and looking for a job. The data in the people eke out a living in vulnerable employment. In table are from labor force surveys. The underlying countries with well developed safety nets workers assumption is that shorter periods of joblessness can afford to wait for suitable or desirable jobs. But are of less concern, especially when the unem- high and sustained unemployment indicates serious ployed are covered by unemployment benefi ts or inefficiencies in resource allocation. similar forms of support. The length of time that a The ILO definition of unemployment notwithstand- person has been unemployed is difficult to measure, ing, reference periods, the criteria for people consid- because the ability to recall that time diminishes as ered to be seeking work, and the treatment of people the period of joblessness extends. Women’s long- temporarily laid off or seeking work for the first time term unemployment is likely to be lower in countries vary across countries. In many developing countries where women constitute a large share of the unpaid it is especially difficult to measure employment and family workforce. unemployment in agriculture. The timing of a survey, Unemployment by level of educational attainment for example, can maximize the effects of seasonal provides insights into the relation between the edu- unemployment in agriculture. And informal sector cational attainment of workers and unemployment employment is difficult to quantify where informal and may be used to draw inferences about changes activities are not tracked. in employment demand. Information on educational Data on unemployment are drawn from labor force attainment is the best available indicator of skill sample surveys and general household sample levels of the labor force. Besides the limitations to surveys, censuses, and offi cial estimates, which comparability raised for measuring unemployment, are generally based on information from different the different ways of classifying the education level sources and can be combined in many ways. Admin- may also cause inconsistency. Education level is istrative records, such as social insurance statistics supposed to be classifi ed according to Interna- and employment office statistics, are not included tional Standard Classifi cation of Education 1997 in the table because of their limitations in cover- (ISCED97). For more information on ISCED97, see age. Labor force surveys generally yield the most About the data for table 2.11. comprehensive data because they include groups not covered in other unemployment statistics, par- Data sources ticularly people seeking work for the first time. These surveys generally use a definition of unemployment Data on unemployment are from the ILO’s Key Indi- that follows the international recommendations more cators of the Labour Market, 6th edition, database. closely than that used by other sources and therefore 2011 World Development Indicators 55 2.6 Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7–14 % of children ages 7–14 % of children ages 7–14 % of children in employment in employment in employment ages 7–14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2005 25.0 18.8 22.0 6.7 93.3 .. .. .. .. 1.4 94.5 Algeria .. .. .. .. .. .. .. .. .. .. Angolab 2001 30.1 30.0 30.1 26.6 73.4 .. .. .. .. 6.2 80.1 Argentina 2004 12.9 15.7 9.8 4.8 95.2 .. .. .. 34.2 8.1 56.2 Armenia .. .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2005 5.2 5.8 4.5 6.3 93.7 91.7 0.7 7.4 4.1 3.8 92.1 Bangladesh 2006 16.2 25.7 6.4 37.8 62.2 .. .. .. - 17.0 77.8 Belarus 2005 11.7 12.1 11.2 0.0 100.0 .. .. .. .. 9.2 78.8 Belgium .. .. .. .. .. .. .. .. .. .. .. Benin 2006 74.4 72.8 76.1 36.1 63.9 .. .. .. .. .. .. Bolivia 2008 32.1 33.0 31.1 5.2 94.8 73.2 6.1 19.2 0.9 9.2 89.9 Bosnia and Herzegovina 2006 10.6 11.7 9.5 0.1 99.9 .. .. .. .. 1.6 92.1 Botswana .. .. .. .. .. .. .. .. .. .. .. Brazil 2008 5.2 6.9 3.5 4.8 95.2 54..7 7.6 34.6 5.5 24.7 69.8 c Bulgaria .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 2006 42.1 49.0 34.5 67.7 32.3 70.9 1.4 24.9 1.9 2.2 95.8 Burundi 2005 11.7 12.5 11.0 38.9 61.1 .. .. .. .. 25.9 68.6 Cambodiad 2003/04 48.9 49.6 48.1 13.8 86.2 82.3 4.2 12.9 6.0 4.1 89.4 Cameroon 2007 43.4 43.5 43.4 21.9 78.1 88.5 3.1 8.2 2.5 9.5 87.6 Canada .. .. .. .. .. .. .. .. .. .. .. Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. 2.0 56.4 Chad 2004 60.4 64.4 56.2 49.1 50.9 .. .. .. .. 1.8 77.2 Chile 2003 4.1 5.1 3.1 3.2 96.8 24.1 6.9 66.9 .. .. .. China .. .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. Colombia 2007 3.9 5.3 2.3 24.8 75.2 41.2 10.8 46.1 22.7 29.1 45.6 Congo, Dem. Rep.d 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. 6.6 76.7 Congo, Rep 2005 30.1 29.9 30.2 9.9 90.1 .. .. .. .. 4.2 84.5 Costa Ricad 2004 5.7 8.1 3.5 44.6 55.4 40.3 9.5 49.0 15.8 57.7 26.6 Côte d’Ivoire 2006 45.7 47.7 43.6 46.8 53.2 .. .. .. .. 2.4 88.0 Croatia .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republicd 2005 5.8 9.0 2.7 6.2 93.8 18.5 9.8 57.5 23.8 19.5 56.2e Ecuador 2006 14.3 16.9 11.6 21.0 79.0 69.3 6.3 22.8 3.6 15.2 81.2 Egypt, Arab Rep. 2005 7.9 11.5 4.3 21.0 79.0 .. .. .. 11.4 87.4 El Salvador 2007 7.1 10.1 3.8 24.9 75.1 50.1 13.3 35.2 2.2 23.6 74.2 Eritrea .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2005 56.0 64.3 47.1 69.4 30.6 94.6 1.5 3.7 1.7 2.4 95.8 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2005 43.5 33.9 52.3 32.1 67.9 .. .. .. .. 1.1 87.3 Georgia 2006 31.8 33.6 29.9 1.0 99.0 .. .. .. .. 4.3 77.0 Germany .. .. .. .. .. .. .. .. .. .. .. Ghana 2006 48.9 49.9 48.0 18.7 81.3 .. .. .. .. 6.1 76.2 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala 2006 18.2 24.5 11.7 28.4 71.6 63.7 9.7 24.7 2.0 18.8 79.2 Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. .. Guinea-Bissau 2006 50.5 52.8 48.1 36.4 63.6 .. .. .. .. 4.0 87.7 Haiti 2005 33.4 37.3 29.6 17.7 82.3 .. .. .. .. 1.8 79.4 Honduras 2007 8.7 13.3 4.1 45.1 54.9 61.6 10.4 25.1 3.5 23.0 73.5 56 2011 World Development Indicators 2.6 PEOPLE Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7–14 % of children ages 7–14 % of children ages 7–14 % of children in employment in employment in employment ages 7–14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Hungary .. .. .. .. .. .. .. .. .. .. .. India 2004/05 4.2 4.2 4.2 84.9 15.2 69.4 16.0 12.4 7.1 6.8 59.3 Indonesia 2000 8.9 8.8 9.1 24.9 75.1 .. .. .. .. 17.8 75.8e Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. Iraq 2006 14.7 17.9 11.3 32.4 67.6 .. .. .. .. 7.0 85.3 Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica 2005 9.8 11.3 8.3 2.5 97.5 .. .. .. .. 16.3 74.9 Japan .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 2006 3.6 4.4 2.8 1.6 98.4 .. .. .. - 4.0 75.0 Kenya 2000 37.7 40.1 35.2 14.1 85.9 .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2006 5.2 5.8 4.6 7.9 92.1 .. .. .. - 3.7 81.9 Lao PDR .. .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. .. Lesotho 2002 2.6 4.0 1.3 74.4 25.6 58.0 0.0 10.4 3.7 36.6 59.7c Liberia 2007 37.4 37.8 37.1 45.0 55.0 .. .. .. .. 1.7 79.3 Libya .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 2005 11.8 14.8 8.6 2.8 97.2 .. .. .. .. 3.9 89.5 Madagascar 2007 26.0 27.7 24.2 40.9 59.1 87.6 2.9 8.2 0.1 10.0 89.9 Malawi 2006 40.3 41.3 39.4 10.5 89.5 .. .. .. .. 6.7 75.5 Malaysia .. .. .. .. .. .. .. .. .. .. .. Mali 2006 49.5 55.0 44.1 59.5 40.5 .. .. .. .. 1.6 80.4 Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. Mexicof 2009 12.2 16.5 7.6 22.6 77.4 38.2 11.7 47.0 2.7 34.3 63.1 Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. 2.9 82.0 Mongolia 2006/07 10.1 11.4 8.6 16.4 83.6 91.3 0.3 6.3 5.1 0.1 94.7 Morocco 1998/99 13.2 13.5 12.8 93.2 6.8 60.6 8.3 10.1 2.1 10.0 81.7 Mozambiqued 1996 1.8 1.9 1.7 100.0 0.0 .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 0.4 8.0 0.1 4.5 95.0 Nepal 1999 47.2 42.2 52.4 35.6 64.4 87.0 1.4 11.1 4.2 3.3 92.4 Netherlands .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2005 10.1 16.2 3.9 30.8 69.2 70.5 9.7 19.3 1.2 13.8 85.0 c Niger 2006 47.1 49.2 45.0 66.5 33.5 .. .. .. 4.8 74.5 Nigeria .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. Pakistan .. .. .. .. .. .. .. .. .. .. .. Panama 2008 8.9 12.1 5.4 14.6 85.4 73.3 2.9 22.9 12.6 11.3 76.1c Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. Paraguayc 2005 15.3 22.6 7.7 24.2 75.7 60.8 6.2 32.1 9.3 24.8 65.8 Peru 2007 42.2 44.8 39.5 4.0 96.0 62.6 5.0 31.1 3.8 7.6 88.6 Philippines 2001 13.3 16.3 10.0 14.8 85.2 64.3 4.1 30.6 4.1 22.8 73.1 Poland .. .. .. .. .. .. .. .. .. .. .. Portugal 2001 3.6 4.6 2.6 3.6 96.4 48.5 11.2 33.3 .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 57 2.6 Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7–14 % of children ages 7–14 % of children ages 7–14 % of children in employment in employment in employment ages 7–14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Romania 2000 1.4 1.7 1.1 20.7 79.3 97.1 0.0 2.3 4.5 .. 92.9e Russian Federation .. .. .. .. .. .. .. .. .. .. .. Rwanda 2008 7.5 8.0 7.0 18.5 81.5 85.5 0.7 10.5 14.8 12.8 72.3 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. Senegal 2005 18.5 24.4 12.6 61.9 38.1 79.1 5.0 14.0 6.3 4.4 84.1 Serbia 2005 6.9 7.2 6.6 2.1 97.9 .. .. .. .. 5.2 89.4 Sierra Leone 2007 14.9 14.9 14.9 57.7 42.3 83.8 0.8 13.4 9.7 0.9 87.8 Singapore .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. Somalia 2006 43.5 45.5 41.5 53.5 46.5 .. .. .. .. 1.6 94.8 South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. 7.1 7.1 85.8 Spain .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 1999 17.0 20.4 13.4 5.4 94.6 71.2 13.1 15.0 2.9 8.3 88.0 Sudang 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. 7.3 81.3 Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. 10.4 85.9 Sweden .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2006 6.6 8.8 4.3 34.6 65.4 .. .. .. .. 21.5 68.8 Tajikistan 2005 8.9 8.7 9.1 9.0 91.0 .. .. .. .. 24.2 71.3 Tanzaniah 2005/06 31.1 35.0 27.1 28.2 71.8 85.3 0.7 14.0 56.3 0.9 42.8e Thailand 2005 15.1 15.7 14.4 4.2 95.8 .. .. .. .. 13.5 80.0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 2006 38.7 39.8 37.4 29.8 70.2 82.9 1.3 15.1 5.0 1.6 93.4 Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. 29.8 64.9 Tunisia .. .. .. .. .. .. .. .. .. .. Turkeyi 2006 2.6 3.3 1.8 38.8 61.2 57.1 14.3 27.1 2.1 34.1 63.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 2005/06 38.2 39.8 36.5 7.7 92.3 95.5 1.4 3.0 1.4 1.5 97.1 Ukraine 2005 17.3 18.0 16.6 0.1 99.9 .. .. .. .. 3.1 79.3 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. .. Uzbekistan 2005 5.1 5.3 4.9 1.0 99.0 .. .. .. .. 3.8 78.6 Venezuela, RBd 2006 5.1 6.9 3.3 19.8 80.2 32.3 7.2 55.7 31.6 33.1 35.3 Vietnam 2006 21.3 21.0 21.6 11.9 88.1 .. .. .. .. 5.9 91.2 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2006 18.3 20.7 15.9 30.9 69.1 .. .. .. .. 6.1 86.1 Zambia 2008 34.4 35.4 33.3 18.6 81.4 91.9 0.7 7.0 2.9 3.9 93.1 Zimbabwe 1999 14.3 15.3 13.3 12.0 88.0 .. .. .. 3.4 28.4 68.2 a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Refers to unpaid workers, regardless of whether they are family workers. d. Covers children ages 10–14. e. Refers to family workers, regardless of whether they are paid. f. Covers children ages 12–14. g. Northern Sudan only. h. Refers mainly to work on own shamba. i. Estimates are for children ages 6–14. 58 2011 World Development Indicators 2.6 PEOPLE Children at work About the data Definitions The data in the table refer to children’s work in the data on children in employment and in the sampling •  Survey year is the year in which the underlying sense of “economic activity”—that is, children in design underlying the surveys. Differences exist data were collected. • Children in employment are employment, a broader concept than child labor not only across different household surveys in the children involved in any economic activity for at least (see ILO 2009a for details on this distinction). same country but also across the same type of sur- one hour in the reference week of the survey. • Work In line with the definition of economic activity vey carried out in different countries, so estimates only refers to children who are employed and not adopted by the 13th International Conference of of working children are not fully comparable across attending school. • Study and work refer to children Labour Statisticians, the threshold for classifying a countries. attending school in combination with employment. person as employed is to have been engaged at least The table aggregates the distribution of children in • Employment by economic activity is the distribu- one hour in any activity during the reference period employment by the industrial categories of the Inter- tion of children in employment by the major industrial relating to the production of goods and services national Standard Industrial Classifi cation (ISIC): categories (ISIC revision 2 or revision 3). • Agricul- set by the 1993 UN System of National Accounts. agriculture, manufacturing, and services. A residual ture corresponds to division 1 (ISIC revision  2) or Children seeking work are thus excluded. Economic category—which includes mining and quarrying; categories A and B (ISIC revision  3) and includes activity covers all market production and certain non- electricity, gas, and water; construction; extraterri- agriculture and hunting, forestry and logging, and market production, including production of goods for torial organization; and other inadequately defined fishing. • Manufacturing corresponds to division 3 own use. It excludes unpaid household services (com- activities—is not presented. Both ISIC revision 2 and (ISIC revision 2) or category D (ISIC revision 3). • Ser- monly called “household chores”)—that is, the pro- revision 3 are used, depending on the country’s codi- vices correspond to divisions 6–9 (ISIC revision duction of domestic and personal services by house- fication for describing economic activity. This does 2) or categories G–P (ISIC revision  3) and include hold members for own-household consumption. not affect the definition of the groups in the table. wholesale and retail trade, hotels and restaurants, Data are from household surveys conducted by The table also aggregates the distribution of transport, financial intermediation, real estate, pub- the International Labor Organization (ILO), the United children in employment by status in employment, lic administration, education, health and social work, Nations Children’s Fund (UNICEF), the World Bank, based on the International Classification of Status in other community services, and private household and national statistical offices. The surveys yield data Employment (1993), which shows the distribution in activity. • Self-employed workers are people whose on education, employment, health, expenditure, and employment by three major categories: selfemployed remuneration depends directly on the profits derived consumption indicators related to children’s work. workers, wage workers (also known as employees), from the goods and services they produce, with or Household survey data generally include information and unpaid family workers. A residual category— without other employees, and include employers, on work type—for example, whether a child is working which includes those not classifiable by status—is own-account workers, and members of produc- for payment in cash or in kind or is involved in unpaid not presented. ers cooperatives. • Wage workers (also known as work, working for someone who is not a member of the In most countries more boys are involved in employ- employees) are people who hold explicit (written or household, or involved in any type of family work (on the ment or the gender difference is small. However, girls oral) or implicit employment contracts that provide farm or in a business). Country surveys define the ages are often more present in hidden or under-reported basic remuneration that does not depend directly on for child labor as 5–17. The data in the table have been forms of employment such as domestic service, and the revenue of the unit for which they work. • Unpaid recalculated to present statistics for children ages 7–14. in almost all societies girls bear greater responsibil- family workers are people who work without pay in a Although efforts are made to harmonize the defini- ity for household chores in their own homes, work market-oriented establishment operated by a related tion of employment and the questions on employ- that lies outside the System of National Accounts person living in the same household. ment in survey questionnaires, signifi cant differ- production boundary and is thus not considered in ences remain in the survey instruments that collect estimates of children’s employment. Data sources The largest sector for child labor remains agriculture, and the majority of children Data on children at work are estimates produced work as unpaid family members 2.6a by the Understanding Children’s Work project based on household survey data sets made avail- Child labor by sector Child labor by status in employment (% of children ages 5–17), 2004–08 (% of children ages 5–17), 2004–08 able by the ILO’s International Programme on the Elimination of Child Labour under its Statistical Not Not defined defined Monitoring Programme on Child Labour, UNICEF 6% Industry 7% Self-employment 5% 7% under its Multiple Indicator Cluster Survey pro- gram, the World Bank under its Living Standards Paid Measurement Study program, and national sta- employment Service 21% 26% Agriculture Unpaid tistical offices. Information on how the data were 60% family workers collected and some indication of their reliability 68% can be found at www.ilo.org/public/english/ standards/ipec/simpoc/, www.childinfo.org, and www.worldbank.org/lsms. Detailed country statis- Source: Accelerating Action Against Child Labour, ILO, Geneva 2010. tics can be found at www.ucw-project.org. 2011 World Development Indicators 59 2.7 Poverty rates at national poverty lines Population below national poverty linea Poverty gap at national poverty linea Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year b % % % year b % % % year b % % % Afghanistanc .. .. .. 2008d 37.5 29.0 36.0 2008d 8.3 6.2 7.9 Albaniac 2005 24.2 11.2 18.5 2008 14.6 10.1 12.4 2008 2.6 1.9 2.3 Angola .. .. .. 2000 d .. 62.3 .. .. .. .. Argentina 2008e .. 15.3 .. 2009e .. 13.2 .. .. .. .. Armeniac 2008 22.9 23.8 23.5 2009 25.5 26.9 26.5 2009 .. .. 4.9 Azerbaijanc 2001 42.5 55.7 49.6 2008 18.5 14.8 15.8 2008 .. .. 2.0 Bangladesh 2000 52.3 35.2 48.9 2005 43.8 28.4 40.0 2005 9.8 6.5 9.0 Belarus 2008 .. .. 6.1 2009 .. .. 5.4 .. .. .. Benin .. .. .. 2003d 46.0 29.0 39.0 2003d 14.0 8.0 12.0 Bhutan .. .. .. 2007d 30.9 1.7 23.2 2007d 8.1 0.4 6.1 Bolivia 2006e 76.5 50.3 59.9 2007e 77.3 50.9 60.1 .. .. .. Bosnia and Herzegovinac 2004 22.0 11.3 17.7 2007 17.8 8.2 14.0 .. .. .. Botswana 1993 40.4 24.7 32.9 2003 44.8 19.4 30.6 2003 18.4 6.5 11.7 Brazil 2008e .. .. 22.6 2009e .. .. 21.4 .. .. .. Bulgariac 1997 .. .. 36.0 2001 .. .. 12.8 2001 .. .. 4.2 Burkina Faso .. .. .. 2003d 52.4 19.2 46.4 2003d 17.6 5.1 15.3 Burundi .. .. .. 2006d 68.9 34.0 66.9 2006d 24.2 10.3 23.4 Cambodiac 2004 37.8 17.6 34.7 2007 34.5 11.8 30.1 2007 8.3 2.8 7.2 Cameroon .. .. .. 2007d 55.0 12.2 39.9 2007d 17.5 2.8 12.3 Cape Verde .. .. .. 2007d 44.3 13.2 26.6 2007d 14.3 3.3 8.1 Central African Republic .. .. .. 2008d 69.4 49.6 62.0 2008d 35.0 29.8 33.1 Chad .. .. .. 2003d 58.6 24.6 55.0 2003d 23.3 7.4 21.6 Chile 2006e 12.3 13.9 13.7 2009e 12.9 15.5 15.1 .. .. .. China 2004 e 2.8 .. .. 2005e 2.5 .. .. .. .. .. Colombia 2008e 65.2 39.8 46.0 2009e 64.3 39.6 45.5 .. .. .. Comoros .. .. .. 2004 d 48.7 34.5 44.8 2004 d 17.8 12.1 16.3 Congo, Dem. Rep. .. .. .. 2005 75.7 61.5 71.3 2005 34.9 26.2 32.2 Congo, Rep. .. .. .. 2005 57.7 .. 50.1 2005 20.6 .. 18.9 Costa Rica 2008e 22.2 19.5 20.7 2009e .. .. 21.7 .. .. .. Croatia 2002 .. .. 11.2 2004 .. .. 11.1 2004 .. .. 2.6 Côte d’Ivoirec 2002 45.8 32.3 40.2 2008 54.2 29.4 42.7 2008 20.3 9.5 15.3 Dominican Republic 2005e 60.2 49.9 53.5 2006e 57.1 45.3 49.4 .. .. .. Ecuador 2008e 59.7 22.6 35.1 2009e 57.5 25.0 36.0 .. .. .. Egypt, Arab Rep. 2005 26.8 10.1 19.6 2008 30.0 10.6 22.0 .. .. .. El Salvador 2007e,f 43.8 29.8 34.6 2008e,f 49.0 35.7 40.0 .. .. .. Ethiopia 1999 45.4 36.9 44.2 2004 39.3 35.1 38.9 2004 8.5 7.7 8.3 Fiji 2003 40.0 28.0 35.0 2009 43.3 18.6 31.0 2009 14.8 5.4 10.1 Gabon .. .. .. 2005 44.6 29.8 32.7 2005 16.0 8.5 10.0 Gambia, Thec .. .. .. 2003d 67.8 39.6 58.0 2003d 30.5 14.8 25.1 Georgiac .. .. .. 2007 29.7 18.3 23.6 2007 9.2 5.3 7.2 Ghana 1998 49.6 19.4 39.5 2006 39.2 10.8 28.5 2006 13.5 3.1 9.6 Guatemala 2000e 74.5 27.1 56.2 2006e 70.5 30.0 51.0 .. .. .. Guinea .. .. .. 2007d 63.0 30.5 53.0 2007d 22.0 7.7 17.6 Guinea-Bissau .. .. .. 2002 69.1 51.6 64.7 2002 27.8 16.9 25.0 Haiti .. .. .. 2001e 88.0 45.0 77.0 .. .. .. Honduras 2008e,f 64.1 55.0 59.6 2009e,f 64.4 52.8 58.8 .. .. .. India 1994 37.3 32.4 36.0 2005 28.3 25.7 27.5 .. .. .. Indonesia 2009 17.4 10.7 14.2 2010 16.6 9.9 13.3 2010 2.8 1.6 2.2 Iraq .. .. .. 2007 39.3 16.1 22.9 2007 9.0 2.7 4.5 Jamaica 2006e .. .. 14.3 2007e .. .. 9.9 .. .. .. Jordan 2002 18.7 12.9 14.2 2006 19.0 12.0 13.0 2006 .. .. 2.8 Kazakhstanc 2001 23.2 13.0 17.6 2002 21.7 10.2 15.4 2002 4.5 2.0 3.1 Kenya .. .. .. 2005d 49.1 33.7 45.9 2005d 17.5 11.4 16.3 Kosovoc 2005 37.2 30.3 34.8 2006 49.2 37.4 45.0 2006 14.3 11.3 13.3 Kyrgyz Republicc 2003 57.5 35.7 49.9 2005 50.8 29.8 43.1 2005 12.0 7.0 10.0 Lao PDRc 2003 .. .. 33.5 2008 31.7 17.4 27.6 .. .. .. Latviac 2002 11.6 .. 7.5 2004 12.7 .. 5.9 2004 .. .. 1.2 60 2011 World Development Indicators 2.7 PEOPLE Poverty rates at national poverty lines Population below national poverty linea Poverty gap at national poverty linea Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year b % % % year b % % % year b % % % Lesothoc 1994 68.9 36.7 66.6 2003 60.5 41.5 56.6 .. .. .. Liberiac .. .. .. 2007 67.7 55.1 63.8 2007 26.3 20.2 24.4 Macedonia, FYRc 2005 21.2 19.8 20.4 2006 21.3 17.7 19.0 2006 7.7 6.9 7.2 Madagascar 2004 77.3 53.7 72.1 2005 73.5 52.0 68.7 2005 28.9 19.3 26.8 Malawi 1998 58.1 18.5 54.1 2004 55.9 25.4 52.4 2004 19.2 7.1 17.8 Malaysiac 2007 7.1 2.0 3.6 2009 8.2 1.7 3.8 2009 1.8 0.3 0.8 Mali .. .. .. 2006d 57.6 25.5 47.4 2006d .. .. 16.7 Mauritania .. .. .. 2000 d 61.2 25.4 46.3 2000 d 24.1 6.3 17.0 Mexico 2006e 54.7 35.6 42.6 2008e 60.8 39.8 47.4 .. .. .. Moldovac 2004 .. .. 26.5 2005 .. .. 29.0 .. .. .. Mongolia .. .. .. 2008d 46.6 26.9 35.2 2008d 13.4 7.7 10.1 Montenegro 2007 12.0 5.5 8.0 2008 8.9 2.4 4.9 2008 1.4 0.6 0.9 Morocco .. .. .. 2001 25.1 7.6 15.3 .. .. .. Mozambique 2002 55.3 51.5 54.1 2008 56.9 49.6 54.7 2008 22.2 19.1 21.2 Namibia .. .. .. 2003d 49.0 17.0 38.0 2003d 16.0 6.0 13.0 Nepal 1996 43.3 21.6 41.8 2004 34.6 9.6 30.9 2004 8.5 2.2 7.5 Nicaragua 2001e 67.8 30.1 45.8 2005e 67.9 29.1 46.2 .. .. .. Niger .. .. .. 2007d 63.9 36.7 59.5 2007d 21.2 11.3 19.6 Nigeria .. .. .. 2004 d 63.8 43.1 54.7 2004 d 26.6 16.2 22.8 Pakistan 2005 28.1 14.9 23.9 2006 27.0 13.1 22.3 .. .. .. Panama 2003 62.7 20.0 36.8 2008 59.8 17.7 32.7 .. .. .. Paraguay 2008e 48.8 30.2 37.9 2009e 49.8 24.7 35.1 .. .. .. Peru 2008 59.8 23.5 36.2 2009 60.3 21.1 34.8 .. .. .. Philippines 2006 .. .. 26.4 2009 .. .. 26.5 2009 .. .. 2.7 Polandc 2001 .. .. 15.6 2002 .. .. 16.6 .. .. .. Romaniac 2005 23.5 8.1 15.1 2006 22.3 6.8 13.8 2006 5.3 1.4 3.2 Russian Federationc 2005 22.7 8.1 11.9 2006 21.2 7.4 11.1 2006 5.5 1.7 2.7 Rwanda .. .. .. 2006d 64.2 23.2 58.5 2006d 26.0 8.0 24.0 São Tomé and Príncipe .. .. .. 2001 64.9 45.0 53.8 2001 24.7 14.9 19.2 Senegalc .. .. .. 2005d 61.9 35.1 50.8 2005d 21.5 9.3 16.4 Serbiac 2006 13.9 5.2 9.0 2007 9.8 4.3 6.6 2007 2.0 0.8 1.3 Sierra Leone .. .. .. 2003d 78.5 47.0 66.4 2003d 34.6 16.3 27.5 South Africa 2000 .. .. 38.0 2005 .. .. 23.0 2005 .. .. 7.0 Sri Lanka 2002 24.7 7.9 22.7 2007 15.7 6.7 15.2 2007 3.2 1.3 3.1 Swaziland .. .. .. 2001d 75.0 49.0 69.2 2001d 37.0 20.0 32.9 Tajikistanc 2007 54.4 49.3 53.1 2009 49.2 41.8 47.2 .. .. .. Tanzania 2000 38.6 23.1 35.6 2007 37.4 21.8 33.4 2007 11.0 6.5 9.9 Thailand 2008 11.5 3.0 9.0 2009 10.4 3.0 8.1 .. .. .. Timor-Leste 2001 .. .. 39.7 2007 .. .. 49.9 .. .. .. Togo .. .. .. 2006 74.3 36.8 61.7 2006 29.3 10.3 22.9 Turkey 2008 34.6 9.4 17.1 2009 38.7 8.9 18.1 .. .. .. Uganda 2005 34.2 13.7 31.1 2009 27.2 9.1 24.5 2009 7.6 1.8 6.8 Ukrainec 2004 18.1 12.0 14.0 2005 11.3 6.3 7.9 2005 2.3 1.1 1.5 Uruguay 2007e 29.4 25.5 26.0 2008e 22.2 20.3 20.5 .. .. .. Venezuela, RB 2008e .. .. 32.6 2009e .. .. 29.0 .. .. .. Vietnam 2006 20.4 3.9 16.0 2008 18.7 3.3 14.5 2008 4.6 0.5 3.5 West Bank and Gaza 2007 .. .. 31.2 2009 .. .. 21.9 2009 .. .. 4.9 Yemen, Rep. 1998 42.5 32.3 40.1 2005 40.1 20.7 34.8 2005 10.6 4.5 8.9 Zambia 2004 77.3 29.1 58.4 2006 76.8 26.7 59.3 2006 38.8 9.4 28.5 Zimbabwe .. .. .. 2003d .. .. 72.0 .. .. .. a. Based on per capita consumption estimated from household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected; in cases for which the data collection period bridged two calender years, the year in which most of the data were collected is reported. c. World Bank estimates. d. Estimates based on survey data from earlier year(s) are available, but are not comparable with the most recent year reported here; these are available online at http://data.worldbank.org. e. Based on income per capita estimated from household survey data. f. Measured as a share of households. 2011 World Development Indicators 61 2.7 Poverty rates at national poverty lines About the data Definitions Estimates of poverty rates and gaps at national pov- As with any indicator measured from household • Survey year is the year in which the underlying erty lines are useful for comparing poverty across surveys, data quality issues can affect the precision household survey data were collected; when the data time within but not across countries. Table 2.8 shows of poverty estimates and their comparability over collection period bridged two calendar years, the year poverty indicators at international poverty lines that time. These include selective survey nonresponse, in which most of the data were collected is reported. allow for comparisons across countries. seasonality effects, differences in the number of • Population below national poverty line is the per- For countries with an active poverty monitoring pro- income or consumption items in the questionnaire, centage of the rural, urban, and national population gram, the World Bank—in collaboration with national and the time period over which respondents are living below the corresponding rural, urban, national institutions, other development agencies, and civil asked to recall their expenditures. poverty line, based on consumption estimated from society—periodically prepares poverty assessments household survey data, unless otherwise noted. and other analytical reports to assess the extent National poverty lines • Poverty gap at national poverty line is the mean and causes of poverty. These reports review levels National poverty lines are the benchmark for esti- shortfall from the rural, urban, or national poverty and changes in poverty indicators over time and mating poverty indicators that are consistent with line (counting the nonpoor as having zero shortfall) across regions within countries, assess the impact the country’s specific economic and social circum- as a percentage of the corresponding rural, urban, of growth and public policy on poverty and inequal- stances. National poverty lines reflect local percep- or national poverty line, based on consumption esti- ity, review the adequacy of monitoring and evalua- tions of the level and composition of consumption or mated from household survey data, unless otherwise tion, and contain detailed technical overviews of income needed to be nonpoor. The perceived bound- noted. This measure reflects the depth of poverty as the underlying household survey data and poverty ary between poor and nonpoor typically rises with the well as its incidence. measurement methods used. The reports are a key average income of a country and thus does not pro- source of comprehensive information on poverty indi- vide a uniform measure for comparing poverty rates cators at national poverty lines and generally feed across countries. While poverty rates at national into country-owned processes to reduce poverty, poverty lines should not be used for comparing pov- build in-country capacity, and support joint work. erty rates across countries, they are appropriate for An increasing number of countries have their guiding and monitoring the results of country-specific own national programs to monitor and disseminate national poverty reduction strategies. official poverty estimates at national poverty lines Almost all national poverty lines are anchored to along with well documented household survey data the cost of a food bundle—based on the prevailing sources and estimation methodology. Estimates national diet of the poor—that provides adequate from national poverty monitoring programs and the nutrition for good health and normal activity, plus underlying methods used are periodically reviewed by an allowance for nonfood spending. National poverty the World Bank and included in the table. lines must be adjusted for inflation between survey The complete online database of poverty estimates years to remain constant in real terms and thus allow at national poverty lines (available at http://data. for meaningful comparisons of poverty over time. worldbank.org) is regularly updated and may con- Because diets and consumption baskets change tain more recent data or revisions not incorporated over time, countries periodically recalculate the pov- in the table. It is maintained by the Global Poverty erty line based on new survey data. In such cases Working Group, a team of poverty experts from the the new poverty lines should be deflated to obtain Poverty Reduction and Equity Network, the Develop- comparable poverty estimates from earlier years. ment Research Group, and the Development Data The table reports indicators based on the two most Group, which recently updated the database to cover recent years for which survey data is available. Coun- 115 countries and more than 575 sets of poverty tries for which the most recent indicators reported Data sources estimates at national poverty lines for 1974−2010. are not comparable to those based on survey data Poverty rates at national poverty lines are com- from an earlier year are footnoted in the table. piled by the Global Poverty Working Group, based Data quality on data from World Bank’s country poverty assess- Poverty estimates at national poverty lines are com- ments and analytical reports as well as country puted from household survey data collected from Poverty Reduction Strategies and official poverty nationally representative samples of households. estimates. Further documentation of the data, These data must contain sufficiently detailed infor- measurement methods and tools, and research, mation to compute a comprehensive estimate of as well as poverty assessments and analytical total household income or consumption (including reports, are available at http://data.worldbank. consumption or income from own production), from org, www.worldbank.org/poverty, and http://econ. which it is possible to construct a correctly weighted worldbank.org. distribution of per capita consumption or income. 62 2011 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines International poverty Population below International poverty linea line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year b % % % % year b % % % % Albania 75.5 120.8 2005 <2 <0.5 7.9 1.5 2008 <2 <0.5 4.3 0.9 Algeria 48.4 c 77.5c 1988 6.6 1.8 23.8 6.6 1995 6.8 1.4 23.6 6.5 Angola 88.1 141.0   ..  ..   ..  .. 2000 d 54.3 29.9 70.2 42.4 Argentina 1.7 2.7 2006d,e 2.8 0.6 8.0 2.4 2009d,e <2 <0.5 <2 <0.5 Armenia 245.2 392.4 2003 10.6 1.9 43.5 11.3 2008 <2 <0.5 12.4 2.3 Azerbaijan 2,170.9 3,473.5 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 7.8 1.5 Bangladesh 31.9 51.0 2000 f 57.8 17.3 85.4 38.8 2005f 49.6 13.1 81.3 33.8 Belarus 949.5 1,519.2 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Belize 1.8 c 2.9c 1995 14.0 5.4 23.6 10.5 1999e 12.1 4.7 23.9 9.7 Benin 344.0 550.4   ..  ..   .. ..  2003 47.3 15.7 75.3 33.5 Bhutan 23.1 36.9   ..  ..  ..  ..  2003 26.2 7.0 49.5 18.8 Bolivia 3.2 5.1 2005e 19.6 9.7 30.4 15.5 2007e 14.0 5.8 24.7 10.9 Bosnia and Herzegovina 1.1 1.7 2004 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5 Botswana 4.2 6.8 1986 35.6 13.8 54.7 25.8 1994 31.2 11.0 49.4 22.3 Brazil 2.0 3.1 2008e 4.3 1.4 10.4 3.6 2009e 3.8 1.1 9.9 3.2 Bulgaria 0.9 1.5 2003 <2 <0.5 2.4 0.9 2007 <2 <0.5 7.3 1.5 Burkina Faso 303.0 484.8 1998 70.0 30.2 87.6 49.1 2003 56.5 20.3 81.2 39.3 Burundi 558.8 894.1 1998 86.4 47.3 95.4 64.1 2006 81.3 36.4 93.5 56.1 Cambodia 2,019.1 3,230.6 2004 40.2 11.3 68.2 28.0 2007 28.3 6.1 56.5 20.2 Cameroon 368.1 589.0 2001 32.8 10.2 57.7 23.7 2007 9.6 1.2 30.8 8.4 Cape Verde 97.7 156.3   ..  ..  ..   .. 2001 20.6 5.9 40.3 14.9 Central African Republic 384.3 614.9 1993 82.8 57.0 90.8 68.4 2003 62.4 28.3 81.9 45.3 Chad 409.5 655.1    ..  ..  .. ..  2003 61.9 25.6 83.3 43.9 Chile 484.2 774.7 2006e <2 <0.5 2.4 <0.5 2009e <2 <0.5 <2 <0.5 China 5.1g 8.2g 2002h 28.4 8.7 51.1 20.6 2005h 15.9 4.0 36.3 12.2 Colombia 1,489.7 2,383.5 2003e 15.4 6.1 26.3 10.9 2006e 16.0 5.7 27.9 11.9 Comoros 368.0 588.8  ..  ..  .. ..  2004 46.1 20.8 65.0 34.2 Congo, Dem. Rep. 395.3 632.5  ..  ..  .. ..  2006 59.2 25.3 79.6 42.4 Congo, Rep. 469.5 751.1  ..  ..  .. ..  2005 54.1 22.8 74.4 38.8 Costa Rica 348.7c 557.9c 2005e 2.4 <0.5 8.6 2.3 2009e <2 <0.5 4.8 0.9 Croatia 5.6 8.9 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Czech Republic 19.0 30.4 1993e <2 <0.5 <2 <0.5 1996e <2 <0.5 <2 <0.5 Côte d’Ivoire 407.3 651.6 2002 23.3 6.8 46.8 17.6 2008 23.8 7.5 46.0 17.9 Djibouti 134.8 215.6 1996 4.8 1.6 15.1 4.5 2002 18.8 5.3 41.2 14.6 Dominican Republic 25.5c 40.8 c 2006e 4.0 0.7 13.5 3.7 2007e 4.3 0.9 13.6 3.9 Ecuador 0.6 1.0 2007e 4.7 1.2 12.8 4.0 2009e 5.1 1.6 13.4 4.4 Egypt, Arab Rep. 2.5 4.0 2000 <2 <0.5 19.4 3.5 2005 <2 <0.5 18.5 3.5 El Salvador 6.0 c 9.6c 2005e 11.0 4.8 20.5 8.9 2008e 5.1 1.1 15.2 4.5 Estonia 11.0 17.7 2003 <2 <0.5 2.7 0.9 2004 <2 <0.5 <2 <0.5 Ethiopia 3.4 5.5 2000 55.6 16.2 86.4 37.9 2005 39.0 9.6 77.6 28.9 Gabon 554.7 887.5    ..  ..  .. ..  2005 4.8 0.9 19.6 5.0 Gambia, The 12.9 20.7 1998 66.7 34.7 82.0 50.0 2003 34.3 12.1 56.7 24.9 Georgia 1.0 1.6 2005 13.4 4.4 30.4 10.9 2008 14.7 4.6 32.6 11.8 Ghana 5,594.8 8,951.6 1998 39.1 14.4 63.3 28.5 2006 30.0 10.5 53.6 22.3 Guatemala 5.7c 9.1c 2002e 16.9 6.5 29.8 12.9 2006e 12.7 3.8 25.7 9.6 Guinea 1,849.5 2,959.1 2003 70.1 32.2 87.2 50.3 2007 43.8 15.2 70.0 31.3 Guinea-Bissau 355.3 568.6 1993 52.1 20.6 75.7 37.4 2002 48.8 16.5 77.9 34.8 Guyana 131.5c 210.3c 1993e 5.8 2.6 15.0 5.4 1998 e 7.7 3.9 16.8 6.9 Haiti 24.2c 38.7c    ..  ..  .. ..  2001e 54.9 28.2 72.2 41.8 Honduras 12.1c 19.3c 2006e 18.2 8.2 29.7 14.2 2007e 23.2 11.3 35.6 18.1 Hungary 171.9 275.0 2004 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5 India 19.5i 31.2i 1994h 49.4 14.4 81.7 35.3 2005h 41.6 10.8 75.6 30.4 Indonesia 5,241.0i 8,385.7i 2005h 21.4 4.6 53.8 17.3 2009h 18.7 3.6 50.7 15.5 Iraq 799.8 1,279.7    ..  ..  .. ..  2007 4.0 0.6 25.3 5.6 Jamaica 54.2c 86.7c 2002 <2 <0.5 8.7 1.6 2004 <2 <0.5 5.9 0.9 Jordan 0.6 1.0 2003 <2 <0.5 11.0 2.1 2006 <2 <0.5 3.5 0.6 Kazakhstan 81.2 129.9 2003 3.1 <0.5 17.2 3.9 2007 <2 <0.5 <2 <0.5 2011 World Development Indicators 63 2.8 Poverty rates at international poverty lines International poverty Population below International poverty linea line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year b % % % % year b % % % % Kenya 40.9 65.4 1997 19.6 4.6 42.7 14.7 2005 19.7 6.1 39.9 15.1 Kyrgyz Republic 16.2 26.0 2004 21.8 4.4 51.9 16.8 2007 <2 <0.5 29.4 5.5 Lao PDR 4,677.0 7,483.2 2002 44.0 12.1 76.9 31.1 2008 33.9 9.0 66.0 24.8 Latvia 0.4 0.7 2004 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Lesotho 4.3 6.9 1995 47.6 26.7 61.1 37.3 2003 43.4 20.8 62.3 33.1 Liberia 0.6 1.0  .. ..   .. ..  2007 83.7 40.8 94.8 59.5 Lithuania 2.1 3.3 2004 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Macedonia, FYR 29.5 47.2 2003 <2 <0.5 3.2 0.7 2008 <2 <0.5 4.3 0.7 Madagascar 945.5 1,512.8 2001 76.3 41.4 88.8 57.2 2005 67.8 26.5 89.6 46.9 Malawi 71.2 113.8 1998 83.1 46.0 93.5 62.3 2004 73.9 32.3 90.5 51.8 Malaysia 2.6 4.2 2004 e <2 <0.5 7.8 1.4 2009e <2 <0.5 2.3 <0.5 Maldives 12.2 19.5    .. ..   .. ..  2004 <2 <0.5 12.2 2.5 Mali 362.1 579.4 2001 61.2 25.8 82.0 43.6 2006 51.4 18.8 77.1 36.5 Mauritania 157.1 251.3 1996 23.4 7.1 48.3 17.8 2000 21.2 5.7 44.1 15.9 Mexico 9.6 15.3 2006 <2 <0.5 4.8 1.0 2008 <2 <0.5 8.6 2.0 Micronesia, Fed. Sts. 0.8 c 1.3c    .. ..   .. ..  2000 31.1 16.3 44.7 24.5 Moldova 6.0 9.7 2004 8.1 1.7 29.0 7.9 2008 <2 <0.5 12.5 2.6 Mongolia 653.1 1,045.0  ..  ..  .. ..  2002 15.5 3.6 38.9 12.3 Montenegro 0.6 1.0    .. ..   .. ..  2008 <2 <0.5 <2 <0.5 Morocco 6.9 11.0 2001 6.3 0.9 24.3 6.3 2007 2.5 0.5 14.0 3.2 Mozambique 14,532.1 23,251.4 2003 74.7 35.4 90.0 53.6 2008 60.0 25.2 81.6 42.9 Namibia 6.3 10.1    .. ..   .. ..  1993e 49.1 24.6 62.2 36.5 Nepal 33.1 52.9 1996 68.4 26.7 88.1 46.8 2004 55.1 19.7 77.6 37.8 Nicaragua 9.1c 14.6c 2001e 19.4 6.7 37.5 14.5 2005e 15.8 5.2 31.9 12.3 Niger 334.2 534.7 2005 65.9 28.1 85.6 46.7 2007 43.1 11.9 75.9 30.6 Nigeria 98.2 157.2 1996 68.5 32.1 86.4 49.7 2004 64.4 29.6 83.9 46.9 Pakistan 25.9 41.4 2005 22.6 4.4 60.3 18.7 2006 22.6 4.1 61.0 18.8 Panama 0.8c 1.2c 2006e 9.5 3.1 17.9 7.1 2009e 2.4 <0.5 9.5 2.4 Papua New Guinea 2.1c 3.4 c    .. ..   .. ..  1996 35.8 12.3 57.4 25.5 Paraguay 2,659.7 4,255.6 2007e 6.5 2.7 14.2 5.5 2008e 5.1 1.5 13.2 4.3 Peru 2.1 3.3 2006e 7.9 1.9 18.5 6.0 2009e 5.9 1.4 14.7 4.7 Philippines 30.2 48.4 2003 22.0 5.5 43.8 16.0 2006 22.6 5.5 45.0 16.4 Poland 2.7 4.3 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Romania 2.1 3.4 2005 <2 <0.5 3.4 0.9 2008 <2 <0.5 <2 0.5 Russian Federation 16.7 26.8 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Rwanda 295.9 473.5 2000 76.6 38.2 90.3 55.7 2005 76.8 40.9 89.6 57.2 São Tomé and Príncipe 7,953.9 12,726.3    .. ..   .. ..  2001 28.6 8.2 57.3 21.6 Senegal 372.8 596.5 2001 44.2 14.3 71.3 31.2 2005 33.5 10.8 60.4 24.7 Serbia 42.9 68.6    .. ..   .. ..  2008 <2 <0.5 <2 <0.5 Seychelles 5.6c 9.0 c 2000 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5 Sierra Leone 1,745.3 2,792.4 1990 62.8 44.8 75.0 54.0 2003 53.4 20.3 76.1 37.5 Slovak Republic 23.5 37.7 1992e <2 <0.5 <2 <0.5 1996e <2 <0.5 <2 <0.5 Slovenia 198.2 317.2 2002 <2 <0.5 <2 <0.5 2004 <2 <0.5 <2 <0.5 South Africa 5.7 9.1 1995 21.4 5.2 39.9 15.0 2000 26.2 8.2 42.9 18.3 Sri Lanka 50.0 80.1 2002 14.0 2.6 39.7 11.9 2007 7.0 1.0 29.1 7.4 St. Lucia 2.4 c 3.8 c    .. ..   .. ..  1995e 20.9 7.2 40.6 15.5 Suriname 2.3c 3.7c    .. ..   .. ..  1999e 15.5 5.9 27.2 11.7 Swaziland 4.7 7.5 1995 78.6 47.7 89.3 61.7 2001 62.9 29.4 81.0 45.8 Syrian Arab Republic 30.8 49.3    .. ..   .. ..  2004 <2 <0.5 16.9 3.3 Tajikistan 1.2 1.9 2003 36.3 10.3 68.8 26.7 2004 21.5 5.1 50.9 16.8 Tanzania 603.1 964.9 2000 88.5 46.8 96.6 64.4 2007 67.9 28.1 87.9 47.5 Thailand 21.8 34.9 2004 <2 <0.5 11.5 2.0 2009 12.8 2.4 26.5 8.3 Timor-Leste 0.6c 1.0 c 2001 52.9 19.1 77.5 37.1 2007 37.4 8.9 72.8 27.0 Togo 352.8 564.5    .. ..   .. ..  2006 38.7 11.4 69.3 27.9 Trinidad and Tobago 5.8 c 9.2c 1988e <2 <0.5 8.6 1.9 1992e 4.2 1.1 13.5 3.9 Tunisia 0.9 1.4 1995 6.5 1.3 20.4 5.8 2000 2.6 <0.5 12.8 3.0 64 2011 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines International poverty Population below International poverty linea line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year b % % % % year b % % % % Turkmenistan 5,961.1c 9,537.7c 1993e 63.5 25.8 85.7 44.9 1998 24.8 7.0 49.7 18.4 Uganda 930.8 1,489.2 2005 51.5 19.1 75.6 36.4 2009 37.7 12.1 64.5 27.2 Ukraine 2.1 3.4 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Uruguay 19.1 30.6 2006e <2 <0.5 4.2 0.6 2009e <2 <0.5 <2 <0.5 Uzbekistan 470.1c 752.1c 2002 42.3 12.4 75.6 30.6 2003 46.3 15.0 76.7 33.2 Venezuela, RB 1,563.9 2,502.2 2005e 10.0 4.5 19.8 8.4 2006e 3.5 1.1 10.2 3.2 Vietnam 7,399.9 11,839.8 2006 21.5 4.6 48.4 16.2 2008 13.1 2.3 38.4 10.8 Yemen, Rep. 113.8 182.1 1998 12.9 3.0 36.4 11.1 2005 17.5 4.2 46.6 14.8 Zambia 3,537.9 5,660.7 2003 64.6 27.1 85.2 45.8 2004 64.3 32.8 81.5 48.3 a. Based on nominal per capita consumption averages and distributions estimated from household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected; in cases for which the data collection period bridged two calender years, the year in which most of the data were collected is reported. c. Based on purchasing power parity (PPP) dollars imputed using regression. d. Urban areas only. e. Based on per capita income averages and distribution data estimated from household survey data. f. Adjusted by spatial consumer price index data. g. PPP conversion factor based on urban prices. h. Population-weighted average of urban and rural estimates. i. Based on benchmark national PPP estimate rescaled to account for cost-of-living differences in urban and rural areas. Regional poverty estimates and progress toward 84 percent to 16 percent, leaving 620 million fewer developing countries in 2005 was $2.00 a day. The the Millennium Development Goals people in poverty. poverty rate for all developing countries measured Global poverty measured at the $1.25 a day pov- Over the same period the poverty rate in South Asia at this line fell from nearly 70 percent in 1981 to 47 erty line has been decreasing since the 1980s. The fell from 59 percent to 40 percent (table 2.8c). In con- percent in 2005, but the number of people living on share of population living on less than $1.25 a day trast, the poverty rate fell only slightly in Sub- Saharan less than $2.00 a day has remained nearly constant fell 10 percentage points, to 42 percent, in 1990 and Africa—from less than 54 percent in 1981 to more at 2.5 billion. The largest decrease, both in number then fell nearly 17 percentage points between 1990 than 58 percent in 1999 then down to 51 percent and proportion, occurred in East Asia and Pacific, led and 2005. The number of people living in extreme in 2005. But the number of people living below the by China. Elsewhere, the number of people living on poverty fell from 1.9 billion in 1981 to 1.8 billion poverty line has nearly doubled. Only East Asia and less than $2.00 a day increased, and the number of in 1990 to about 1.4 billion in 2005 (figure 2.8a). Pacific is consistently on track to meet the Millennium people living between $1.25 and $2.00 a day nearly This substantial reduction in extreme poverty over Development Goal target of reducing 1990 poverty doubled, to 1.2 billion. the past quarter century, however, disguises large rates by half by 2015. A slight acceleration over his- Once household survey data collected after 2005 regional differences. torical growth rates could lift Latin America and the in large countries—such as China and India, as well The greatest reduction in poverty occurred in East Caribbean and South Asia to the target. However, the as some countries in Sub-Saharan Africa and the Asia and Pacific, where the poverty rate declined recent slowdown in the global economy may leave Middle East and North Africa—become available, from 78 percent in 1981 to 17 percent in 2005 and these regions and many countries short of the target. the World Bank’s Development Research Group will the number of people living on less than $1.25 a day Most of the people who have escaped extreme update regional poverty estimates at international dropped more than 750 million (figure 2.8b). Much poverty remain very poor by the standards of mid- poverty lines; see http://iresearch.worldbank.org/ of this decline was in China, where poverty fell from dle- income economies. The median poverty line for povcalnet/. While the number of people living on less than $1.25 a day has Poverty rates fallen, the number living on $1.25–$2.00 a day has increased 2.8a have begun to fall 2.8b People living in poverty (billions) Share of population living on less than $1.25 a day, by region (percent) 3.0 80 2.5 People living on more than $1.25 and less than $2.00 Sub-Saharan Africa People living on less than a day, all developing regions 60 2.0 $1.25 a day, other developing regions 1.5 40 People living on less than South Asia $1.25 a day, East Asia & Pacific 1.0 East Asia Europe & Central Asia & Pacific 20 People living on less than Middle East & North Africa 0.5 Latin America & Caribbean $1.25 a day, South Asia People living on less than $1.25 a day, Sub-Saharan Africa 0 0 1981 1984 1987 1990 1993 1996 1999 2002 2005 1981 1984 1987 1990 1993 1996 1999 2002 2005 Source: World Bank PovcalNet. Source: World Bank PovcalNet. 2011 World Development Indicators 65 2.8 Poverty rates at international poverty lines Regional poverty estimates 2.8c Region or country 1981 1984 1987 1990 1993 1996 1999 2002 2005 People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific 1,072 947 822 873 845 622 635 507 316 China 835 720 586 683 633 443 447 363 208 Europe & Central Asia 7 6 5 9 20 22 24 22 17 Latin America & Caribbean 47 59 57 50 47 53 55 57 45 Middle East & North Africa 14 12 12 10 10 11 12 10 11 South Asia 548 548 569 579 559 594 589 616 596 India 420 416 428 436 444 442 447 460 456 Sub-Saharan Africa 211 242 258 297 317 356 383 390 388 Total 1,900 1,814 1,723 1,818 1,799 1,658 1,698 1,601 1,374 Share of people living on less than 2005 PPP $1.25 a day (percent) East Asia & Pacific 77.7 65.5 54.2 54.7 50.8 36.0 35.5 27.6 16.8 China 84.0 69.4 54.0 60.2 53.7 36.4 35.6 28.4 15.9 Europe & Central Asia 1.7 1.3 1.1 2.0 4.3 4.6 5.1 4.6 3.7 Latin America & Caribbean 12.9 15.3 13.7 11.3 10.1 10.9 10.9 10.7 8.2 Middle East & North Africa 7.9 6.1 5.7 4.3 4.1 4.1 4.2 3.6 3.6 South Asia 59.4 55.6 54.2 51.7 46.9 47.1 44.1 43.8 40.3 India 59.8 55.5 53.6 51.3 49.4 46.6 44.8 43.9 41.6 Sub-Saharan Africa 53.4 55.8 54.5 57.6 56.9 58.8 58.4 55.0 50.9 Total 51.9 46.7 41.9 41.7 39.2 34.5 33.7 30.5 25.2 People living on less than 2005 PPP $2.00 a day (millions) East Asia & Pacific 1,278 1,280 1,238 1,274 1,262 1,108 1,105 954 729 China 972 963 907 961 926 792 770 655 474 Europe & Central Asia 35 28 25 32 49 56 68 57 42 Latin America & Caribbean 90 110 103 96 96 107 111 114 94 Middle East & North Africa 46 44 47 44 48 52 52 51 51 South Asia 799 836 881 926 950 1,009 1,031 1,084 1,092 India 609 635 669 702 735 757 783 813 828 Sub-Saharan Africa 294 328 351 393 423 471 509 536 556 Total 2,542 2,625 2,646 2,765 2,828 2,803 2,875 2,795 2,564 Share of people living on less than 2005 PPP $2.00 a day (percent) East Asia & Pacific 92.6 88.5 81.6 79.8 75.8 64.1 61.8 51.9 38.7 China 97.8 92.9 83.7 84.6 78.6 65.1 61.4 51.2 36.3 Europe & Central Asia 8.3 6.5 5.6 6.9 10.3 11.9 14.3 12.0 8.9 Latin America & Caribbean 24.6 28.1 24.9 21.9 20.7 22.0 21.8 21.6 17.1 Middle East & North Africa 26.7 23.1 22.7 19.7 19.8 20.2 19.0 17.6 16.9 South Asia 86.5 84.8 83.9 82.7 79.7 79.9 77.2 77.1 73.9 India 86.6 84.8 83.8 82.6 81.7 79.8 78.4 77.6 75.6 Sub-Saharan Africa 73.8 75.5 74.0 76.1 75.9 77.9 77.6 75.6 72.9 Total 69.4 67.7 64.3 63.4 61.6 58.3 57.1 53.3 47.0 Source: World Bank PovcalNet. 66 2011 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines About the data The World Bank produced its first global poverty esti- The statistics reported here are based on consump- PPP rates were designed for comparing aggregates from mates for developing countries for World Development tion data or, when unavailable, on income surveys. national accounts, not for making international poverty Report 1990: Poverty using household survey data for Analysis of some 20 countries for which income and comparisons. As a result, there is no certainty that an 22 countries (Ravallion, Datt, and van de Walle 1991). consumption expenditure data were both available from international poverty line measures the same degree Since then there has been considerable expansion in the same surveys found income to yield a higher mean of need or deprivation across countries. So-called pov- the number of countries that field household income than consumption but also higher inequality. When pov- erty PPPs, designed to compare the consumption of and expenditure surveys. The World Bank’s poverty erty measures based on consumption and income were the poorest people in the world, might provide a better monitoring database now includes more than 600 compared, the two effects roughly cancelled each other basis for comparison of poverty across countries. Work surveys representing 115 developing countries. More out: there was no significant statistical difference. on these measures is ongoing. than 1.2 million randomly sampled households were Definitions interviewed in these surveys, representing 96 percent International poverty lines of the population of developing countries. International comparisons of poverty estimates entail • International poverty line in local currency is the both conceptual and practical problems. Countries have international poverty lines of $1.25 and $2.00 a day in Data availability different definitions of poverty, and consistent compari- 2005 prices, converted to local currency using the PPP The number of data sets within two years of any given sons across countries can be difficult. Local poverty conversion factors estimated by the International Com- year rose dramatically, from 13 between 1978 and lines tend to have higher purchasing power in rich coun- parison Program. • Survey year is the year in which the 1982 to 158 between 2001 and 2006. Data cover- tries, where more generous standards are used, than underlying household survey data were collected; when age is improving in all regions, but the Middle East in poor countries. the data collection period bridged two calendar years, and North Africa and Sub-Saharan Africa continue to Poverty measures based on an international poverty the year in which most of the data were collected is lag. A complete database of estimates, maintained line attempt to hold the real value of the poverty line con- reported. • Population below $1.25 a day and popula- by a team in the World Bank’s Development Research stant across countries, as is done when making com- tion below $2 a day are the percentages of the popula- Group, is updated annually as new survey data parisons over time. Since World Development Report tion living on less than $1.25 a day and $2.00 a day at become available, and a major reassessment of prog- 1990 the World Bank has aimed to apply a common 2005 international prices based on nominal per capita ress against poverty is made about every three years. standard in measuring extreme poverty, anchored to consumption averages and distributions estimated from The most recent estimates and a complete overview what poverty means in the world’s poorest countries. household survey data, unless otherwise noted. As a of data availability by year and country are available The welfare of people living in different countries can result of revisions in PPP exchange rates, poverty rates at http://iresearch.worldbank.org/povcalnet/. be measured on a common scale by adjusting for dif- for individual countries cannot be compared with pov- ferences in the purchasing power of currencies. The erty rates reported in earlier editions. • Poverty gap Data quality commonly used $1 a day standard, measured in 1985 is the mean shortfall from the poverty line (counting Besides the frequency and timeliness of survey data, international prices and adjusted to local currency using the nonpoor as having zero shortfall), expressed as a other data quality issues arise in measuring household purchasing power parities (PPPs), was chosen for World percentage of the poverty line. This measure reflects living standards. The surveys ask detailed questions on Development Report 1990 because it was typical of the the depth of poverty as well as its incidence. sources of income and how it was spent, which must poverty lines in low-income countries at the time. be carefully recorded by trained personnel. Income is Early editions of World Development Indicators used Data sources generally more difficult to measure accurately, and PPPs from the Penn World Tables to convert values in The poverty measures are prepared by the World consumption comes closer to the notion of living stan- local currency to equivalent purchasing power measured Bank’s Development Research Group. The interna- dards. And income can vary over time even if living in U.S dollars. Later editions used 1993 consumption tional poverty lines are based on nationally repre- standards do not. But consumption data are not always PPP estimates produced by the World Bank. Interna- sentative primary household surveys conducted by available: the latest estimates reported here use con- tional poverty lines were recently revised using the new national statistical offices or by private agencies sumption for about two-thirds of countries. data on PPPs compiled in the 2005 round of the Inter- under the supervision of government or interna- However, even similar surveys may not be strictly national Comparison Program, along with data from an tional agencies and obtained from government comparable because of differences in timing or in expanded set of household income and expenditure statistical offices and World Bank Group country the quality and training of enumerators. Comparisons surveys. The new extreme poverty line is set at $1.25 departments. The World Bank Group has pre- of countries at different levels of development also a day in 2005 PPP terms, which represents the mean pared an annual review of its poverty work since pose a potential problem because of differences in of the poverty lines found in the poorest 15 countries 1993. For details on data sources and methods the relative importance of the consumption of nonmar- ranked by per capita consumption. The new poverty line used to derive the World Bank’s latest estimates, ket goods. The local market value of all consumption maintains the same standard for extreme poverty— further discussion of the results, and related in kind (including own production, particularly impor- the poverty line typical of the poorest countries in the publications, see http://iresearch.worldbank.org/ tant in underdeveloped rural economies) should be world—but updates it using the latest information on povcalnet/ and Shaohua Chen and Martin Rav- included in total consumption expenditure, but may the cost of living in developing countries. allion’s “The Developing World Is Poorer Than not be. Most survey data now include valuations for PPP exchange rates are used to estimate global pov- We Thought, but No Less Successful in the Fight consumption or income from own production, but valu- erty, because they take into account the local prices against Poverty” (2008). ation methods vary. of goods and services not traded internationally. But 2011 World Development Indicators 67 2.9 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Afghanistan 2008b 29.4 3.8 9.0 13.1 16.9 22.3 38.7 24.0 Albania 2008b 34.5 3.5 8.1 12.1 15.9 20.9 43.0 29.0 Algeria 1995b 35.3 2.8 6.9 11.5 16.3 22.8 42.4 26.9 Angolac 2000 b 58.6 0.6 2.0 5.7 10.8 19.7 61.9 44.7 Argentinac 2009d 45.8 1.5 4.1 8.9 14.3 22.2 50.5 33.6 Armenia 2008b 30.9 3.7 8.8 12.8 16.7 21.9 39.8 25.4 Australia 1994 d 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 2000 d 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0 Azerbaijan 2008b 33.7 3.4 8.0 12.1 16.2 21.7 42.1 27.4 Bangladesh 2005b 31.0 4.3 9.4 12.6 16.1 21.1 40.8 26.6 Belarus 2008b 27.2 3.8 9.2 13.8 17.8 22.9 36.4 21.9 Belgium 2000 d 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Belize 1999d 54.4 1.2 3.4 7.2 11.9 19.1 58.5 43.5 Benin 2003b 38.6 2.9 6.9 10.9 15.1 21.2 45.9 31.0 Bolivia 2007d 57.3 1.0 2.8 6.4 11.1 18.8 61.0 45.4 Bosnia and Herzegovina 2007b 36.2 2.7 6.7 11.3 16.1 22.7 43.2 27.3 Botswana 1994b 61.0 1.3 3.1 5.8 9.6 16.4 65.0 51.2 Brazil 2009d 53.9 1.2 3.3 7.2 11.9 19.5 58.1 42.5 Bulgaria 2007b 45.3 2.0 5.0 9.1 13.9 21.0 51.0 35.2 Burkina Faso 2003b 39.6 3.0 7.0 10.6 14.7 20.6 47.1 32.4 Burundi 2006b 33.3 4.1 9.0 11.9 15.4 21.0 42.8 28.0 Cambodia 2007b 44.4 3.0 6.6 9.4 13.1 19.2 51.7 37.3 Cameroon 2001b 44.6 2.4 5.6 9.3 13.7 20.5 50.9 35.5 Canada 2000 d 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Central African Republic 2003b 43.6 2.1 5.2 9.4 14.3 21.7 49.4 33.0 Chad 2003b 39.8 2.6 6.3 10.4 15.0 21.8 46.6 30.8 Chile 2009d 22.6 3.1 8.6 15.5 20.2 24.7 30.9 16.5 China 2005d 41.5 2.4 5.7 9.8 14.7 22.0 47.8 31.4 Hong Kong SAR, China 1996d 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2006d 58.5 0.9 2.5 6.0 10.7 18.7 62.1 46.2 Congo, Dem. Rep. 2006b 44.4 2.3 5.5 9.2 13.8 20.9 50.6 34.7 Congo, Rep. 2005b 47.3 2.1 5.0 8.4 13.0 20.5 53.1 37.1 Costa Rica 2009d 50.3 1.7 4.2 7.8 12.5 20.1 55.4 39.4 Côte d’Ivoire 2008b 41.5 2.2 5.6 10.1 14.9 21.8 47.6 31.8 Croatia 2008b 33.7 3.3 8.1 12.2 16.2 21.6 42.0 27.5 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996d 25.8 4.3 10.2 14.3 17.5 21.7 36.2 22.7 Denmark 1997d 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 2007d 48.4 1.7 4.4 8.4 13.1 20.5 53.6 37.8 Ecuador 2009d 49.0 1.6 4.2 8.3 13.2 20.4 53.9 38.3 Egypt, Arab Rep. 2005b 32.1 3.9 9.0 12.6 16.1 20.9 41.5 27.6 El Salvador 2007d 46.9 1.6 4.3 9.0 13.9 20.9 51.9 36.3 Eritrea .. .. .. .. .. .. .. .. Estonia 2004b 36.0 2.7 6.8 11.6 16.2 22.5 43.0 27.7 Ethiopia 2005b 29.8 4.1 9.3 13.2 16.8 21.4 39.4 25.6 Finland 2000 d 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995d 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon 2005b 41.5 2.5 6.1 10.1 14.6 21.2 47.9 32.7 Gambia, The 2003b 47.3 2.0 4.8 8.6 13.2 20.6 52.8 36.9 Georgia 2008b 41.3 2.0 5.3 10.3 15.2 22.1 47.2 31.3 Germany 2000 d 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 2006b 42.8 1.9 5.2 9.8 14.8 21.9 48.3 32.5 Greece 2000 d 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2006d 53.7 1.3 3.4 7.2 12.0 19.5 57.8 42.4 Guinea 2007b 39.4 2.7 6.4 10.5 15.1 21.9 46.2 30.3 Guinea-Bissau 2002b 35.5 2.9 7.2 11.6 16.0 22.1 43.0 28.0 Haiti 2001d 59.5 0.9 2.5 5.9 10.5 18.1 63.0 47.8 Honduras 2007d 57.7 0.6 2.0 6.0 11.3 20.0 60.8 43.8 68 2011 World Development Indicators 2.9 PEOPLE Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Hungary 2007b 31.2 3.5 8.4 12.9 16.9 22.0 39.9 25.4 India 2005b 36.8 3.6 8.1 11.3 14.9 20.4 45.3 31.1 Indonesia 2009b 36.8 3.3 7.6 11.3 15.1 21.1 44.9 29.9 Iran, Islamic Rep. 2005b 38.3 2.6 6.4 10.9 15.6 22.2 45.0 29.6 Iraq .. .. .. .. .. .. .. .. Ireland 2000 d 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2 Israel 2001d 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8 Italy 2000 d 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2004b 45.5 2.1 5.2 9.0 13.8 20.9 51.2 35.6 Japan 1993d 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2006b 37.7 3.0 7.2 11.1 15.2 21.1 45.4 30.7 Kazakhstan 2007b 30.9 3.8 8.7 12.8 16.7 22.0 39.9 25.2 Kenya 2005b 47.7 1.8 4.7 8.8 13.3 20.3 53.0 37.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998d 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kosovo .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2007b 33.4 4.1 8.8 11.8 15.5 21.2 42.8 27.9 Lao PDR 2008b 36.7 3.3 7.6 11.3 15.3 20.9 44.8 30.3 Latvia 2008b 35.7 2.7 6.8 11.7 16.3 22.4 42.9 27.6 Lebanon .. .. .. .. .. .. .. .. Lesotho 2003b 52.5 1.0 3.0 7.2 12.5 21.0 56.4 39.4 Liberia 2007b 52.6 2.4 6.4 11.4 15.7 21.6 45.0 30.1 Libya .. .. .. .. .. .. .. .. Lithuania 2008b 37.6 2.6 6.6 11.1 15.7 22.1 44.4 29.1 Macedonia, FYR 2008b 44.2 2.2 5.4 9.3 14.0 21.0 50.3 34.5 Madagascar 2005b 47.2 2.6 6.2 9.6 13.1 17.7 53.5 41.5 Malawi 2004b 39.0 2.9 7.0 10.8 14.9 20.9 46.4 31.7 Malaysia 2009d 46.2 1.8 4.5 8.7 13.7 21.6 51.5 34.7 Maldives 2004b 37.4 2.7 6.5 10.9 15.7 22.7 44.2 28.0 Mali 2006b 39.0 2.7 6.5 10.7 15.2 21.6 46.0 30.5 Mauritania 2000 b 39.0 2.5 6.2 10.5 15.4 22.3 45.7 29.6 Mauritius .. .. .. .. .. .. .. .. Mexico 2008d 51.7 1.5 3.9 7.9 12.5 19.4 56.2 41.4 Micronesia 2000 b 61.1 0.4 1.6 5.2 10.2 19.1 64.0 47.1 Moldova 2008b 38.0 2.9 6.8 10.9 15.4 21.7 45.3 29.8 Mongolia 2008b 36.5 3.0 7.1 11.2 15.6 22.1 44.0 28.4 Montenegro 2008b 30.0 3.6 8.5 13.1 17.2 22.4 38.8 24.1 Morocco 2007b 40.9 2.7 6.5 10.5 14.5 20.6 47.9 33.2 Mozambique 2008b 45.6 1.9 5.2 9.5 13.7 20.1 51.5 36.7 Myanmar .. .. .. .. .. .. .. .. Namibia 1993d 74.3 0.6 1.5 2.8 5.5 12.0 78.3 65.0 Nepal 2004b 47.3 2.7 6.1 8.9 12.5 18.4 54.2 40.4 Netherlands 1999d 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9 New Zealand 1997d 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2005d 52.3 1.4 3.8 7.7 12.3 19.4 56.9 41.8 Niger 2007b 34.0 3.7 8.3 12.0 15.8 21.1 42.8 28.5 Nigeria 2004b 42.9 2.0 5.1 9.7 14.7 21.9 48.6 32.4 Norway 2000 d 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2006b 32.7 4.0 9.0 12.4 15.8 20.7 42.1 28.3 Panama 2009d 52.3 1.3 3.6 7.4 12.2 20.1 56.8 40.6 Papua New Guinea 1996b 50.9 1.9 4.5 7.7 12.1 19.3 56.4 40.9 Paraguay 2008d 52.0 1.4 3.8 7.7 12.4 19.7 56.5 41.0 Peru 2009d 48.0 1.4 3.9 8.4 13.6 21.5 52.6 35.9 Philippines 2006b 44.0 2.4 5.6 9.1 13.7 21.2 50.4 33.9 Poland 2008b 34.2 3.2 7.6 12.0 16.3 22.0 42.2 27.2 2011 World Development Indicators 69 2.9 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Portugal 1997d 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. Qatar 2007b 41.1 1.3 3.9 .. .. .. 52.0 35.9 Romania 2008 b 31.2 3.3 8.1 12.8 17.1 22.7 39.3 24.5 Russian Federation 2008b 42.3 2.6 6.0 9.8 14.3 20.9 48.9 33.5 Rwanda 2005b 53.1 1.7 4.2 7.7 11.7 18.2 58.2 44.0 São Tomé & Príncipe 2000 b 50.8 2.2 5.2 8.5 12.2 17.7 56.4 43.6 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 2005b 39.2 2.5 6.2 10.6 15.3 22.0 45.9 30.1 Serbia 2008b 28.2 3.9 9.1 13.5 17.5 22.5 37.4 22.8 Seychelles 2007b 19.0 4.7 10.8 15.7 19.9 24.2 29.4 15.4 Sierra Leone 2003b 42.5 2.6 6.1 9.7 14.0 20.9 49.3 33.6 Singapore 1998d 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996d 25.8 3.1 8.8 14.9 18.6 22.9 34.8 20.8 Slovenia 2004b 31.2 3.4 8.2 12.8 17.0 22.6 39.4 24.6 Somalia .. .. .. .. .. .. .. .. South Africa 2000 b 57.8 1.3 3.1 5.6 9.9 18.8 62.7 44.9 Spain 2000 d 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 2007b 40.3 3.1 6.9 10.4 14.4 20.5 47.8 32.9 Sudan .. .. .. .. .. .. .. .. Swaziland 2001b 50.7 1.8 4.5 8.0 12.3 19.4 55.9 40.8 Sweden 2000 d 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 2000 d 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9 Syrian Arab Republic 2004b 35.8 3.4 7.7 11.4 15.5 21.4 43.9 28.9 Tajikistan 2007b 29.4 4.0 9.3 13.4 16.7 21.5 39.0 25.2 Tanzania 2007b 37.6 2.8 6.8 11.1 15.6 21.7 44.8 29.6 Thailand 2009b 53.6 1.6 3.9 7.0 11.4 19.2 58.6 42.6 Timor-Leste 2007b 31.9 4.0 9.0 12.5 16.1 21.2 41.3 27.0 Togo 2006b 34.4 2.0 5.4 10.3 15.2 22.0 47.1 31.3 Trinidad and Tobago 1992d 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 2000 b 40.8 2.4 5.9 10.2 14.9 21.8 47.2 31.6 Turkey 2008b 39.7 2.1 5.7 10.8 15.6 22.1 45.8 30.3 Turkmenistan 1998b 40.8 2.5 6.0 10.2 14.9 21.7 47.2 31.8 Uganda 2009b 44.3 2.4 5.8 9.6 13.8 20.0 50.7 36.1 Ukraine 2008 b 27.5 4.1 9.4 13.6 17.5 22.5 37.1 22.6 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 1999d 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000d 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguay 2009d 42.4 2.3 5.6 9.8 14.5 21.4 48.6 32.9 Uzbekistan 2003b 36.7 2.9 7.1 11.5 15.7 21.5 44.2 29.5 Venezuela, RB 2006d 43.5 1.9 4.9 9.6 14.7 21.8 49.0 33.0 Vietnam 2008b 37.6 3.2 7.3 10.9 15.1 21.3 45.4 30.2 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 2005b 37.7 2.9 7.2 11.3 15.3 21.0 45.3 30.8 Zambia 2004b 50.7 1.3 3.6 7.8 12.8 20.6 55.2 38.9 Zimbabwe 1995b 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Covers urban areas only. d. Refers to income shares by percentiles of population, ranked by per capita income. 70 2011 World Development Indicators 2.9 PEOPLE Distribution of income or consumption About the data Definitions Inequality in the distribution of income is reflected • Survey year is the year in which the underlying data in the percentage shares of income or consumption were collected. •  Gini index measures the extent accruing to portions of the population ranked by to which the distribution of income (or consump- income or consumption levels. The portions ranked tion expenditure) among individuals or households lowest by personal income receive the smallest within an economy deviates from a perfectly equal shares of total income. The Gini index provides a con- distribution. A Lorenz curve plots the cumulative venient summary measure of the degree of inequal- percentages of total income received against the ity. Data on the distribution of income or consump- cumulative number of recipients, starting with the tion come from nationally representative household poorest individual. The Gini index measures the area surveys. Where the original data from the house- between the Lorenz curve and a hypothetical line of hold survey were available, they have been used to absolute equality, expressed as a percentage of the directly calculate the income or consumption shares maximum area under the line. Thus a Gini index of by quintile. Otherwise, shares have been estimated 0 represents perfect equality, while an index of 100 from the best available grouped data. implies perfect inequality. •  Percentage share of The distribution data have been adjusted for income or consumption is the share of total income household size, providing a more consistent measure or consumption that accrues to subgroups of popula- of per capita income or consumption. No adjustment tion indicated by deciles or quintiles. has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle- income economies, see Ravallion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achiev- ing strict comparability is still impossible (see About the data for tables 2.7 and 2.8). Two sources of non-comparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or con- sumption expenditure as the living standard indi- cator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (num- ber of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. World Bank staff have made an effort to ensure Data sources that the data are as comparable as possible. Wher- ever possible, consumption has been used rather Data on distribution are compiled by the World than income. Income distribution and Gini indexes for Bank’s Development Research Group using pri- high-income economies are calculated directly from mary household survey data obtained from govern- the Luxembourg Income Study database, using an ment statistical agencies and World Bank country estimation method consistent with that applied for departments. Data for high-income economies are developing countries. from the Luxembourg Income Study database. 2011 World Development Indicators 71 2.10 Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15–24 ages 15–24 total % of labor age % of average 2006–09a 2006–09a 2006–09a Year force population Year GDP Year wage Afghanistan .. .. .. 2005 .. 2.2 2005 0.5   .. Albania .. .. .. 2007 51.1 34.7 2009 6.1   .. Algeria .. .. .. 2002 36.7 22.1 2002 3.2   .. Angola .. .. 25   .. ..   ..   .. Argentina 19b 25b 34 2008 41.9 31.3 2007 8.0 2000 43.8 Armenia 47b 69b .. 2008 39.2 23.9 2008 4.3 2007 20.3 Australia 13b 10 b .. 2005 92.6 69.6 2005 3.5   .. Austria 10 9 .. 2005 96.4 68.7 2005 12.6   .. Azerbaijan 19 10 25 2007 35.4 24.7 2007 3.8 2006 24.3 Bangladesh .. .. 13 2004 2.8 2.1 2006 0.3   .. Belarus .. .. .. 2008 93.5 66.8 2008 10.2 2002 41.6 Belgium 21 22 .. 2005 94.2 61.6 2005 9.0   .. Benin .. .. 23   .. .. 2006 1.5   .. Bolivia .. .. .. 2008 11.4 8.9 2000 4.5   .. Bosnia and Herzegovina 45 52 .. 2009 70.2 28.7 2009 9.4   .. Botswana .. .. .. 2006 9.0 7.3   ..   .. Brazil 14 23 .. 2008 53.8 41.7 2004 12.6   .. Bulgaria 18 14 .. 2008 72.7 49.6 2007 9.8 2004 42.9 Burkina Faso .. .. .. 2004 1.2 1.0   ..   .. Burundi .. .. ..   .. ..   ..   .. Cambodia .. .. ..   .. ..   ..   .. Cameroon .. .. ..   .. .. 2001 0.8   .. Canada 18b 12b .. 2007 66.9 53.6 2005 4.1   .. Central African Republic .. .. .. 2004 1.5 1.3 2004 0.8   .. Chad .. .. ..   .. ..   ..   .. Chile 21 24 .. 2008 53.8 36.2 2001 2.9 2006 53.5 China .. .. .. 2007 19.3 15.9   ..   .. Hong Kong SAR, China 15b 10 b .. 2008 .. 55.6   ..   .. Colombia 18 30 19 2008 31.3 20.0 2008 3.0   .. Congo, Dem. Rep. .. .. 21   .. ..   ..   .. Congo, Rep. .. .. ..   .. .. 2004 0.9   .. Costa Rica 10 13 .. 2004 55.3 37.6 2006 2.4   .. Côte d’Ivoire .. .. ..   .. ..   ..   .. Croatia 19 27 24 2010 82.9 52.6 2009 10.3 2005 32.4 Cuba 3 4 46   .. ..   ..   .. Czech Republic 17 17 .. 2007 84.5 67.3 2007 8.5 2005 40.7 Denmark 12 10 .. 2007 94.4 86.9 2005 5.4   .. Dominican Republic 21 45 35 2008 21.0 15.2 2000 0.8   .. Ecuador 12b 18b .. 2004 31.6 21.1 2002 2.5   .. Egypt, Arab Rep. 17 48 .. 2009 57.0 31.0 2004 4.1   .. El Salvador 13 8 .. 2008 23.9 16.2 2006 1.9   .. Eritrea .. .. ..   .. .. 2001 0.3   .. Estonia 32 21 .. 2004 95.2 68.6 2007 10.9 2007 35.4 Ethiopia 20 b 29b ..   .. .. 2006 0.3   .. Finland 22 19 .. 2005 88.7 67.2 2005 8.4   .. France 23 22 .. 2005 89.9 61.4 2005 12.4   .. Gabon .. .. ..   .. ..   ..   .. Gambia, The .. .. .. 2006 2.7 2.2   ..   .. Georgia 32 41 .. 2004 29.9 22.7 2004 3.0 2003 13.0 Germany 12 10 .. 2005 88.2 65.5 2005 11.4   .. Ghana .. .. 34 2004 9.1 7.1 2002 1.3   .. Greece 19 34 .. 2005 85.2 58.5 2005 11.5   .. Guatemala .. .. .. 2008 20.3 14.7 2005 1.0   .. Guinea .. .. .. 1993 1.5 1.8   ..   .. Guinea-Bissau .. .. .. 2004 1.9 1.5 2005 2.1   .. Haiti .. .. 44   .. ..   ..   .. Honduras .. .. 26 2008 18.7 12.6   ..   .. 72 2011 World Development Indicators 2.10 PEOPLE Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15–24 ages 15–24 total % of labor age % of average 2006–09a 2006–09a 2006–09a Year force population Year GDP Year wage Hungary 28 24 .. 2008 92.0 56.7 2008 10.5 2005 39.8 India .. .. 14 2006 10.3 6.4 2007 2.2   .. Indonesia 22 23 13 2008 11.7 8.7   ..   .. Iran, Islamic Rep. 20 34 .. 2001 35.1 20.0 2000 1.1   .. Iraq .. .. 11 2009 16.8 15.2 2009 3.9   .. Ireland 31 17 .. 2005 88.0 63.9 2005 3.4   .. Israel 16 14 ..   .. ..   ..   .. Italy 23 29 .. 2005 92.4 58.4 2005 14.0   .. Jamaica 22 33 .. 2004 17.4 12.6   ..   .. Japan 10 8 .. 2005 95.3 75.0 2005 8.7   .. Jordan 23 46 10 2006 38.4 19.9 2001 2.2   .. Kazakhstan 7 8 .. 2004 34.4 26.5 2009 3.2 2003 24.9 Kenya .. .. .. 2006 7.5 6.5 2003 1.1   .. Korea, Dem. Rep. .. .. ..   .. ..   ..   .. Korea, Rep. 12b 9b .. 2005 49.5 34.3 2005 1.6   .. Kosovo .. .. ..   .. .. 2007 2.7c   .. Kuwait .. .. ..   .. .. ..   .. Kyrgyz Republic 14 16 25 2006 42.2 28.9 2010 2.7 2003 27.5 Lao PDR .. .. ..   .. ..   ..   .. Latvia 38 28 .. 2003 92.4 66.5 2009 8.5 2005 33.1 Lebanon 22 22 .. 2003 33.1 19.9 2003 2.1   .. Lesotho .. .. .. 2005 5.7 3.6   ..   .. Liberia 6b 4b 31   .. ..   ..   .. Libya .. .. .. 2004 65.5 38.1 2001 2.1   .. Lithuania 35 22 .. 2007 99.3 68.7 2009 8.9 2005 30.9 Macedonia, FYR 53 59 8 2008 47.9 30.4 2008 9.4 2006 55.0 Madagascar .. .. ..   .. ..   ..   .. Malawi .. .. ..   .. ..   ..   .. Malaysia 10 12 .. 2008 49.0 32.5   ..   .. Mali .. .. 12   .. ..   ..   .. Mauritania .. .. ..   .. ..   ..   .. Mauritius 18 26 .. 2000 51.4 33.6   ..   .. Mexico 10 11 .. 2008 30.3 20.6 2005 1.3   .. Moldova 16 15 .. 2009 58.7 32.1 2009 9.1 2003 20.9 Mongolia .. .. 29 2005 27.9 21.3 2007 6.5d   .. Morocco 23 19 .. 2007 23.8 13.6 2003 1.9   .. Mozambique .. .. ..   .. ..   ..   .. Myanmar .. .. ..   .. ..   ..   .. Namibia .. .. 44   .. ..   ..   .. Nepal .. .. 23 2008 3.4 2.6 2006 0.2   .. Netherlands 7 6 .. 2005 90.7 70.7 2005 5.0e   .. New Zealand 16b 17b .. 2003 92.7 72.3 2005 4.4 e   .. Nicaragua 8 10 .. 2008 21.7 14.6   ..   .. Niger .. .. 19 2006 1.9 1.2 2006 0.7   .. Nigeria .. .. .. 2004 1.9 1.1   ..   .. Norway 10 8 .. 2005 93.2 75.2 2005 4.8e   .. Oman .. .. ..   .. ..   ..   .. Pakistan 7 10 10 2008 3.9 2.2 2004 0.5   .. Panama 12 21 ..   .. ..   ..   .. Papua New Guinea .. .. ..   .. ..   ..   .. Paraguay 9 17 .. 2004 11.6 9.1 2001 1.2   .. Peru 13b 16b 22 2008 19.1 13.9 2000 2.6   .. Philippines 16 19 19 2007 25.0 17.0   ..   .. Poland 20 21 .. 2005 83.8 54.7 2009 10.0 2007 47.1 Portugal 19 22 .. 2005 92.0 71.6 2005 10.2e   .. Puerto Rico 29 b 22b ..   .. ..   ..   .. Qatar 1 7 ..   .. ..   ..   .. 2011 World Development Indicators 73 2.10 Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15–24 ages 15–24 total % of labor age % of average 2006–09a 2006–09a 2006–09a Year force population Year GDP Year wage Romania 21 20 .. 2007 54.8 36.4 2009 8.3 2005 41.5 Russian Federation 18 19 .. 2007 67.0 50.0 2007 4.7 2003 29.2 Rwanda .. .. .. 2004 4.6 4.1   ..   .. Saudi Arabia 24 46 ..   .. ..   ..   .. Senegal 12 20 .. 2003 5.1 4.1 2003 1.3   .. Serbia 31 41 29 2003 45.0 35.4 2010 14.0   .. Sierra Leone .. .. .. 2004 5.5 3.8   ..   .. Singapore 10 17 .. 2008 61.7 45.3   ..   .. Slovak Republic 28 27 .. 2003 78.9 55.3 2007 9.3e 2005 44.7 Slovenia 14 13 .. 2008 87.4 63.2 2007 12.7 2005 44.3 Somalia .. .. ..   .. ..   ..   .. South Africa 45 53 .. 2007 6.5 3.7 2006 1.2   .. Spain 39 36 .. 2005 69.4 48.7 2005 8.1e 2006 58.6 Sri Lanka 17 28 .. 2006 24.1 14.9 2007 2.0   .. Sudan .. .. 19   .. ..   ..   .. Swaziland .. .. 48   .. ..   ..   .. Sweden 26 24 .. 2005 88.8 72.2 2005 7.7e   .. Switzerland 8 9 .. 2005 95.4 78.7 2005 6.8e 2000 40.0 Syrian Arab Republic 13 49 .. 2008 26.8 13.8 2004 1.3   .. Tajikistan .. .. ..   .. .. .. 2003 25.7 Tanzania 7 10 .. 2006 4.3 4.0 2006 0.9   .. Thailand 4 5 30 2008 23.0 18.6   ..   .. Timor-Leste .. .. ..   .. ..   ..   .. Togo .. .. ..   .. ..   ..   .. Trinidad and Tobago 9 13 .. 2008 76.4 54.2   ..   .. Tunisia .. .. .. 2004 48.6 25.5 2003 4.3   .. Turkey 25 25 .. 2007 60.3 31.0 2008 6.2 2007 61.3 Turkmenistan .. .. ..   .. .. ..   .. Uganda .. .. 30 2004 10.3 9.2 2003 0.3   .. Ukraine .. .. 49 2010 65.3 52.3 2010 17.8 2007 48.3 United Arab Emirates 8 22 ..   .. ..   ..   .. United Kingdom 22 16 .. 2005 93.2 71.5 2005 5.7   .. United States 20 b 15b .. 2005 92.2 71.5 2005 6.0e 2006 29.2 Uruguay 16 25 .. 2007 72.7 56.9 2007 10.0e   .. Uzbekistan .. .. .. 2005 86.1 57.5 2005 6.5 2005 40.0 Venezuela, RB 12 16 .. 2008 32.1 22.7 2001 2.7   .. Vietnam .. .. .. 2008 19.3 15.2 ..   .. West Bank and Gaza 39 47 .. 2009 18.5 8.0 2009 4.0   .. Yemen, Rep. .. .. .. 2006 10.4 5.0 ..   .. Zambia .. .. 24 2006 10.9 8.0 2008 1.0   .. Zimbabwe .. .. 38   .. .. 2002 2.3   .. World .. w .. w                 Low income .. ..   Middle income .. ..                 Lower middle income .. ..                 Upper middle income 19 23                 Low & middle income .. ..                 East Asia & Pacific .. ..                 Europe & Central Asia 17 18                 Latin America & Carib. 12 18                 Middle East & N. Africa 18 37               South Asia .. ..                 Sub-Saharan Africa .. ..                 High income 19 16                 Euro area 21 21               a. Data are for the most recent year available. b. Limited coverage. c. Includes only expenditure on social pensions. d. Includes old-age, survivors, disability, military, work accident or disease pensions. e. Includes only expenditures on old-age and survivors’ benefi ts. 74 2011 World Development Indicators 2.10 PEOPLE Assessing vulnerability and security About the data Definitions As traditionally measured, poverty is a static con- citizenship, residency, or income status. In contri- • Youth unemployment is the share of the labor force cept, and vulnerability a dynamic one. Vulnerabil- bution-related schemes, however, eligibility is usually ages 15–24 without work but available for and seek- ity reflects a household’s resilience in the face of restricted to individuals who have contributed for a ing employment. • Female-headed households are shocks and the likelihood that a shock will lead to a minimum number of years. Definitional issues—relat- the percentage of households with a female head. decline in well-being. Thus, it depends primarily on ing to the labor force, for example—may arise in •  Pension contributors are the share of the labor the household’s assets and insurance mechanisms. comparing coverage by contribution-related schemes force or working-age population (here defined as Because poor people have fewer assets and less over time and across countries (for country-specific ages 15 and older) covered by a pension scheme. diversified sources of income than do the better-off, information, see Hinz and others 2011). The share • Public expenditure on pensions is all government fluctuations in income affect them more. of the labor force covered by a pension scheme may expenditures on cash transfers to the elderly, the Enhancing security for poor people means reduc- be overstated in countries that do not try to count disabled, and survivors and the administrative costs ing their vulnerability to such risks as ill health, pro- informal sector workers as part of the labor force. of these programs. • Average pension is the aver- viding them the means to manage risk themselves, Public interventions and institutions can provide age pension payment of all pensioners of the main and strengthening market or public institutions for services directly to poor people, although whether pension schemes (including old-age, survivors, dis- managing risk. Tools include microfinance programs, these interventions and institutions work well for the ability, military, and work accident or disease pen- public provision of education and basic health care, poor is debated. State action is often ineffective, sions) divided by the average wage of all formal sec- and old age assistance (see tables 2.11 and 2.16). in part because governments can influence only a tor workers. Poor households face many risks, and vulnerability few of the many sources of well-being and in part is thus multidimensional. The indicators in the table because of difficulties in delivering goods and ser- focus on individual risks—youth unemployment, vices. The effectiveness of public provision is further female-headed households, income insecurity in constrained by the fiscal resources at governments’ old age—and the extent to which publicly provided disposal and the fact that state institutions may not services may be capable of mitigating some of these be responsive to the needs of poor people. risks. Poor people face labor market risks, often hav- The data on public pension spending cover the ing to take up precarious, low-quality jobs and to pension programs of the social insurance schemes increase their household’s labor market participa- for which contributions had previously been made. tion by sending their children to work (see tables In many cases noncontributory pensions or social 2.4 and 2.6). Income security is a prime concern assistance targeted to the elderly and disabled are for the elderly. also included. A country’s pattern of spending is cor- Youth unemployment is an important policy issue related with its demographic structure—spending for many economies. Experiencing unemployment increases as the population ages. may permanently impair a young person’s produc- tive potential and future employment opportunities. The table presents unemployment among youth ages 15–24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could dif- fer across countries. Also, since this age group is likely to include school leavers, the level of youth unemployment varies considerably over the year as a result of different school opening and closing dates. The youth unemployment rate shares similar limita- tions on comparability as the general unemployment Data sources rate. For further information, see About the data for table 2.5 and the original source. Data on youth unemployment are from the ILO’s The definition of female-headed household differs Key Indicators of the Labour Market, 6th edition, greatly across countries, making cross-country com- database. Data on female-headed households are parison difficult. In some cases it is assumed that a from Macro International Demographic and Health woman cannot be the head of any household with an Surveys. Data on pension contributors and pen- adult male, because of sex-biased stereotype. Cau- sion spending are from Hinz and others’ Interna- tion should be used in interpreting the data. tional Patterns of Pension Provision II: Facts and Pension scheme coverage may be broad or Figures of the 2000s (2011). even universal where eligibility is determined by 2011 World Development Indicators 75 2.11 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil–teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2009a 1999 2009a 1999 2009a 2009a 2009a 2009a 2009a Afghanistan .. .. .. .. .. .. .. .. .. 43 Albania .. .. .. .. .. .. .. .. .. 20 Algeria 12.0 .. .. .. .. .. 4.3 20.3 .. 23 Angola .. .. .. .. .. .. .. .. .. .. Argentina 12.9 14.7 18.2 21.9 17.7 15.6 4.9 13.5 .. 16 Armenia .. 11.0 .. 18.8 .. 6.8 3.0 15.0 .. 19 Australia 16.4 16.4 15.0 14.5 26.6 20.2 4.5 .. .. .. Austria 25.1 23.3 30.2 26.7 52.1 47.6 5.4 11.1 .. 12 Azerbaijan 6.9 .. 17.0 .. 19.1 15.6 2.8 9.1 99.9 11 Bangladesh .. 10.7 12.5 14.9 50.7 39.8 2.4 14.0 58.4 44 Belarus .. .. .. .. .. 15.0 4.5 10.6 99.9 15 Belgium 18.2 20.5 23.8 33.3 38.3 35.3 6.0 12.4 .. 11 Benin 12.1 .. 24.6 .. 212.7 .. 3.5 15.9 40.4 45 Bolivia 14.2 .. 11.7 .. 44.1 .. .. .. .. 24 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 12.4 .. 37.6 .. 251.5 8.9 22.0 97.4 25 Brazil 10.8 17.3 9.5 18.0 57.1 29.6 5.1 16.1 .. 23 Bulgaria 15.5 23.5 18.8 22.3 17.9 20.1 4.1 10.0 .. 16 Burkina Faso .. 29.0 .. 30.2 .. 307.1 4.6 21.8 86.1 49 Burundi 14.7 21.1 .. 59.4 1,051.5 520.4 8.3 23.4 91.2 51 Cambodia 5.9 .. 11.5 .. 43.6 .. 2.1 12.4 99.5 49 Cameroon .. 7.4 .. 30.7 .. 35.8 3.7 19.2 .. 46 Canada .. .. .. .. 44.0 .. 4.9 .. .. .. Central African Republic .. 4.5 .. 16.1 .. 124.1 1.3 11.7 .. 95 Chad .. 12.7 .. 24.1 .. 217.8 3.2 12.6 34.6 61 Chile 14.4 14.7 14.8 16.0 19.4 12.1 4.0 18.2 .. 25 China .. .. 11.5 .. 90.0 .. .. .. .. 18 Hong Kong SAR, China 12.4 13.8 17.7 16.7 .. 56.2 4.5 24.1 95.1 16 Colombia 15.2 15.9 16.1 15.4 37.7 27.4 4.8 14.9 100.0 29 Congo, Dem. Rep. .. .. .. .. .. .. .. .. 93.4 37 Congo, Rep. .. .. .. .. .. .. .. .. .. 64 Costa Rica 15.5 14.6 21.4 14.4 .. .. 6.3 37.7 87.6 18 Côte d’Ivoire 14.8 .. 42.8 .. 146.3 119.1 4.6 24.6 100.0 42 Croatia .. 21.8 .. 25.2 35.8 26.2 4.6 10.4 100.0 11 Cuba 27.8 44.7 41.2 51.9 86.2 58.8 13.6 17.5 100.0 9 Czech Republic 11.2 13.0 21.7 22.0 33.7 30.5 4.2 9.9 .. 18 Denmark 24.6 24.5 38.1 32.2 65.9 53.8 7.8 15.4 .. .. Dominican Republic 7.2 7.3 .. 7.4 .. .. 2.3 12.0 83.6 25 Ecuador 4.4 .. 9.6 .. .. .. .. .. 82.6 17 Egypt, Arab Rep. .. .. .. .. .. .. 3.8 11.9 .. 27 El Salvador 8.6 8.5 7.5 9.1 8.9 13.7 3.6 13.1 93.2 31 Eritrea 15.0 .. 37.3 .. 429.6 .. .. .. 92.2 38 Estonia 20.9 20.0 27.2 23.9 31.8 20.8 4.8 13.9 .. 12 Ethiopia .. 12.4 .. 8.9 .. 642.9 5.5 23.3 84.6 58 Finland 17.4 17.5 25.8 30.8 40.4 31.7 5.9 12.5 .. 14 France 17.3 17.7 28.5 26.4 29.7 34.8 5.6 10.7 .. 19 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. 34 Georgia .. 14.5 .. 15.2 .. 11.2 3.2 7.7 94.6 9 Germany .. 15.7 .. 21.8 .. .. 4.5 10.3 .. 13 Ghana .. .. .. .. .. .. .. .. 47.6 33 Greece 11.7 .. 15.5 .. 26.2 .. .. .. .. 10 Guatemala 6.7 10.5 4.3 6.2 .. 19.0 3.2 .. .. 29 Guinea .. 7.1 .. 6.3 .. 102.3 2.4 19.2 73.1 44 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. Honduras .. .. .. .. .. .. .. .. 36.4 33 76 2011 World Development Indicators 2.11 PEOPLE Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil–teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2009a 1999 2009a 1999 2009a 2009a 2009a 2009a 2009a Hungary 18.0 24.9 19.1 23.1 34.2 23.8 5.2 10.4 .. 10 India 11.9 .. 24.7 .. 95.0 .. .. .. .. .. Indonesia .. 11.0 .. 12.5 .. 16.2 2.8 17.9 .. 17 Iran, Islamic Rep. 9.1 15.1 9.9 21.0 34.8 22.2 4.7 20.9 98.4 20 Iraq .. .. .. .. .. .. .. .. .. 17 Ireland 11.0 15.7 16.8 23.2 28.6 26.2 4.9 13.8 .. 16 Israel 20.5 19.4 21.9 19.0 30.9 22.7 5.9 13.1 .. 13 Italy 24.0 22.6 27.7 25.2 27.6 22.1 4.3 9.0 .. 10 Jamaica 13.4 15.8 21.0 26.8 70.4 42.4 5.8 .. .. .. Japan 21.1 21.7 20.9 22.4 15.1 20.1 3.5 9.4 .. 18 Jordan 13.7 12.7 15.8 16.3 .. .. .. .. .. .. Kazakhstan .. .. .. .. .. 7.9 2.8 .. .. 16 Kenya 21.5 .. 14.5 .. 209.0 .. .. .. 96.8 47 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 18.4 17.0 15.7 22.2 8.4 9.0 4.2 14.8 .. 24 Kosovo .. .. .. .. .. .. 4.3 17.4 .. .. Kuwait 19.2 10.9 .. 14.9 .. .. .. .. 100.0 9 Kyrgyz Republic .. .. .. .. 24.3 17.3 5.9 19.0 65.7 24 Lao PDR 2.3 .. 4.5 .. 68.6 .. 2.3 12.2 96.9 29 Latvia 19.5 23.3 23.7 24.1 27.9 16.3 5.0 13.9 .. 11 Lebanon .. .. .. .. 13.9 10.2 1.8 7.2 .. 14 Lesotho 34.5 22.6 76.7 50.8 875.4 .. 12.4 23.7 57.6 37 Liberia .. 5.7 .. 8.4 .. .. 2.8 12.1 40.2 24 Libya .. .. .. .. 23.9 .. .. .. .. .. Lithuania .. 15.8 .. 20.1 34.2 17.1 4.7 13.4 .. 13 Macedonia, FYR .. .. .. .. .. .. .. .. .. 17 Madagascar 5.7 7.1 .. 10.5 .. 132.4 3.0 13.4 .. 48 Malawi 14.0 .. 10.0 .. 2,613.3 .. .. .. .. .. Malaysia 12.5 14.3 21.7 12.4 81.1 34.0 4.1 17.2 .. 15 Mali 14.3 13.0 56.1 32.6 241.3 117.7 4.4 22.3 50.0 50 Mauritania 11.4 .. 35.9 .. 79.0 .. .. .. 100.0 39 Mauritius 9.3 9.3 14.2 15.1 25.4 16.7 3.2 11.4 100.0 22 Mexico 11.7 13.3 14.2 13.4 47.8 37.0 4.8 .. 95.4 28 Moldova .. 42.4 .. 40.3 .. 46.1 9.6 21.0 .. 16 Mongolia .. 16.2 .. .. .. .. 5.6 14.6 100.0 30 Morocco 17.2 16.1 45.1 38.7 96.2 71.1 5.6 25.7 100.0 27 Mozambique .. .. .. .. 1,412.2 .. .. .. 71.2 61 Myanmar .. .. 6.9 .. 28.0 .. .. .. 98.9 29 Namibia 21.4 15.6 35.2 15.8 152.2 .. 6.4 22.4 95.6 30 Nepal 9.1 17.6 13.1 11.3 141.6 55.5 4.6 19.5 66.4 33 Netherlands 15.2 16.9 22.2 24.5 47.4 40.2 5.3 11.7 .. .. New Zealand 20.2 17.6 24.1 19.6 40.1 28.6 6.1 .. .. 15 Nicaragua .. .. .. .. .. .. .. .. 72.7 29 Niger .. 28.3 .. 56.6 .. 429.3 4.5 19.3 98.0 39 Nigeria .. .. .. .. .. .. .. .. .. 46 Norway 21.8 18.5 30.4 26.5 45.8 47.3 6.8 16.5 .. .. Oman 11.2 .. 21.8 .. .. .. .. .. 100.0 12 Pakistan .. .. .. .. .. .. 2.7 11.2 85.2 40 Panama 13.7 7.5 19.1 9.9 33.6 21.6 3.8 .. 91.5 24 Papua New Guinea .. .. .. .. .. .. .. .. .. .. Paraguay 13.6 10.8 18.5 16.3 58.9 26.0 4.0 11.9 .. 26 Peru 7.6 8.1 10.8 9.9 21.2 .. 2.7 20.7 .. 21 Philippines 12.8 9.0 11.0 9.1 15.4 9.6 2.8 16.9 .. 34 Poland .. 24.3 10.9 22.0 21.1 16.6 4.9 11.7 .. 10 Portugal 19.5 .. 27.5 .. 28.1 .. .. .. .. 11 Puerto Rico .. .. .. .. .. .. .. .. 6.6 12 Qatar .. 9.2 .. 9.8 .. 337.7 .. .. 48.9 11 2011 World Development Indicators 77 2.11 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil–teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2009a 1999 2009a 1999 2009a 2009a 2009a 2009a 2009a Romania .. 20.0 .. 16.6 32.6 26.2 4.3 11.8 .. 16 Russian Federation .. .. .. .. 10.9 .. .. .. .. 17 Rwanda 11.0 8.2 41.9 34.3 1,206.8 222.8 4.1 20.4 93.9 68 Saudi Arabia .. 18.4 .. 18.3 .. .. 5.6 19.3 91.5 11 Senegal 14.1 20.9 .. 25.7 .. 191.5 5.8 19.0 .. 35 Serbia .. 56.9 .. 13.6 .. 40.1 4.7 9.3 94.2 16 Sierra Leone .. 7.1 .. 18.0 .. .. 4.3 18.1 49.4 44 Singapore .. 10.5 .. 15.7 .. 27.3 3.0 11.6 94.3 19 Slovak Republic 10.2 15.6 18.4 14.7 32.9 19.5 3.6 10.5 .. 17 Slovenia 26.3 .. 25.7 .. 27.9 .. .. .. .. 17 Somalia .. .. .. .. .. .. .. .. .. 36 South Africa 14.2 15.1 20.0 17.7 .. .. 5.4 16.9 87.4 31 Spain 18.0 19.4 24.4 24.1 19.6 25.1 4.3 11.1 .. 12 Sri Lanka .. .. .. .. .. .. .. .. .. 23 Sudan .. .. .. .. .. .. .. .. 59.7 38 Swaziland 8.5 13.0 23.7 36.2 444.5 .. 7.8 21.6 94.0 32 Sweden 22.5 25.0 26.2 30.6 52.1 38.3 6.6 12.7 .. 10 Switzerland 22.7 22.5 27.3 25.2 53.8 46.7 5.2 16.1 .. .. Syrian Arab Republic 11.2 18.3 21.7 15.5 .. .. 4.9 16.7 .. 18 Tajikistan .. .. .. .. .. 21.8 3.5 18.7 88.3 23 Tanzania .. 22.1 .. 18.8 .. .. 6.8 27.5 100.0 54 Thailand 17.8 24.0 15.9 9.1 36.0 22.3 4.1 20.3 .. 16 Timor-Leste .. 27.6 .. .. .. 92.7 16.8 15.5 .. 29 Togo 8.5 13.0 30.3 19.1 .. 155.2 4.6 17.6 14.6 41 Trinidad and Tobago 11.5 9.0 12.2 9.9 148.7 .. .. .. 88.0 17 Tunisia 15.6 .. 27.1 .. 89.4 54.5 7.1 22.4 .. 17 Turkey 9.8 .. 9.6 .. 33.5 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 7.3 .. 21.2 .. 105.4 3.2 15.0 89.4 49 Ukraine .. .. .. .. 36.5 25.1 5.3 20.2 99.9 16 United Arab Emirates 8.7 4.9 11.6 6.7 41.4 15.5 1.2 23.4 100.0 16 United Kingdom 13.9 23.0 23.8 28.2 25.6 24.4 5.5 11.7 .. 18 United States 17.9 22.0 22.5 24.2 27.0 21.7 5.5 14.1 .. 14 Uruguay 7.2 .. 9.9 .. .. .. .. .. .. 15 Uzbekistan .. .. .. .. .. .. .. .. 100.0 17 Venezuela, RB .. 9.2 .. 8.2 .. .. 3.7 .. 86.3 16 Vietnam .. 19.7 .. 17.3 .. 61.7 5.3 19.8 99.6 20 West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 28 Yemen, Rep. .. .. .. .. .. .. 5.2 16.0 .. .. Zambia 7.2 .. 19.4 .. 164.6 .. 1.3 .. .. 61 Zimbabwe 12.7 .. 19.3 .. 193.0 .. .. .. .. .. World .. m .. m .. m .. m .. m .. m 4.5 m .. m .. m 24 w Low income .. .. .. .. .. .. 3.7 .. 80.4 46 Middle income .. .. .. .. .. .. 4.1 .. .. 23 Lower middle income .. .. .. .. .. .. .. .. .. 23 Upper middle income 12.0 13.8 16.4 17.0 .. .. 4.5 13.5 .. 21 Low & middle income .. .. .. .. .. .. .. .. .. 26 East Asia & Pacific .. .. .. .. 38.2 .. 3.5 15.9 .. 18 Europe & Central Asia .. .. .. .. .. .. 4.2 13.4 .. 17 Latin America & Carib. 12.7 12.2 13.7 13.4 .. .. 4.0 .. .. 24 Middle East & N. Africa .. .. .. .. .. .. 4.6 18.0 .. 23 South Asia .. .. 13.6 .. 90.8 .. 2.9 .. .. .. Sub-Saharan Africa .. .. .. .. .. .. 3.8 .. .. 45 High income 18.0 19.4 22.5 23.9 31.4 25.2 5.1 12.5 .. 15 Euro area 17.4 17.6 25.1 24.8 29.1 28.9 5.2 11.1 .. 15 a. Provisional data. 78 2011 World Development Indicators 2.11 PEOPLE Education inputs About the data Definitions Data on education are collected by the United The primary school pupil–teacher ratio reflects the • Public expenditure per student is public current Nations Educational, Scientific, and Cultural Organi- average number of pupils per teacher at the specified and capital spending on education divided by the zation (UNESCO) Institute for Statistics from official level of education. It differs from the average class number of students by level as a percentage of gross responses to its annual education survey. The data size because of the different practices countries domestic product (GDP) per capita. • Public expen- are used for monitoring, policymaking, and resource employ, such as part-time teachers, school shifts, diture on education is current and capital expendi- allocation. While international standards ensure and multigrade classes. The comparability of pupil– tures on education by local, regional, and national comparable datasets, data collection methods may teacher ratios across countries is affected by the governments, including municipalities. • Trained vary by country and within countries over time. definition of teachers and by differences in class size teachers in primary education are the percentage For most countries the data on education spend- by grade and in the number of hours taught, as well of primary school teachers who have received the ing in the table refer to public spending—total gov- as the different practices mentioned above. More- minimum organized teacher training (pre-service or ernment spending on education at all levels plus over, the underlying enrollment levels are subject to in-service) required for teaching at the specified level subsidies provided to households and other private a variety of reporting errors (for further discussion of of education in their country. • Primary school pupil– entities—and generally exclude the part of foreign enrollment data, see About the data for table 2.12). teacher ratio is the number of pupils enrolled in pri- aid for education that is not included in the govern- While the pupil–teacher ratio is often used to com- mary school divided by the number of primary school ment budget. The data may also exclude spending pare the quality of schooling across countries, it is teachers (regardless of their teaching assignment). by religious schools, which play a significant role in often weakly related to student learning and quality many developing countries. Data are gathered from of education. ministries of education and from other ministries or All education data published by the UNESCO Insti- agencies involved in education spending. tute for Statistics are mapped to the International The share of public expenditure devoted to educa- Standard Classification of Education 1997 (ISCED tion allows an assessment of the priority a govern- 1997). This classification system ensures the com- ment assigns to education relative to other public parability of education programs at the international investments, as well as a government’s commitment level. UNESCO developed the ISCED to facilitate to investing in human capital development. However, comparisons of education statistics and indicators returns on investment to education, especially pri- of different countries on the basis of uniform and mary and lower secondary education, cannot be internationally agreed definitions. First developed in understood simply by comparing current education the 1970s, the current version was formally adopted indicators with national income. It takes a long time in November 1997. before currently enrolled children can productively The reference years shown in the table reflect the contribute to the national economy (Hanushek school year for which the data are presented. In 2002). some countries the school year spans two calendar High-quality data on education finance are scarce. years (for example, from September 2009 to June Improving the quality of education finance data is a 2010); in these cases the reference year refers to priority of the UNESCO Institute for Statistics. Addi- the year in which the school year ended (2010 in the tional resources are being allocated for technical previous example). assistance to countries in need, especially those in Sub-Saharan Africa. Interagency partnerships and collaborations with national ministries in charge of education finance data are improving, and actual expenditure data are increasingly being collected. Tracking private educational spending is still a chal- lenge for all countries. The share of trained teachers in primary educa- tion reveals a country’s commitment to invest in the development of its human capital engaged in teaching, but it does not take into account differ- ences in teachers’ experiences and status, teaching methods, teaching materials, and classroom condi- Data sources tions—all factors that affect the quality of teaching and learning. Some teachers without this formal Data on education inputs are from the UNESCO training may have acquired equivalent pedagogical Institute for Statistics (www.uis.unesco.org). skills through professional experience. 2011 World Development Indicators 79 2.12 Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio rate enrollment rate, school primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2009a 2009a 2009a 2009a 1991 2009a 1999 2009a 2009a 2009a 2009a 2009a Afghanistan .. 104 44 4 28 .. .. 27 .. .. .. .. Albania 58 119 72 .. .. 85 70 .. 86 84 15 16 Algeria 23 108 .. 31 89 94 .. .. 96 94 59 82 Angola 40 128 .. .. .. .. .. .. .. .. .. .. Argentina 69 116 85 68 .. .. 76 79 .. .. .. .. Armenia 33 99 93 50 .. 84 86 87 92 94 5 3 Australia 82 106 149 77 98 97 90 88 97 98 33 22 Austria 95 100 100 55 90 .. .. .. .. .. .. .. Azerbaijan 24 95 99 19 89 85 75 93 86 85 38 37 Bangladesh 10 95 42 8 64 86 40 41 86 93 1,234 575 Belarus 102 99 95 77 .. 94 82 87 94 96 12 7 Belgium 122 103 108 63 96 98 .. .. 98 99 6 4 Benin 14 122 .. .. 51 95 18 .. 99 86 7 91 Bolivia 47 107 81 38 .. 91 68 69 92 92 58 53 Bosnia and Herzegovina 15 109 91 37 .. 87 .. .. 86 88 11 9 Botswana 17 109 82 .. 89 87 54 60 86 88 21 18 Brazil 65 120 90 38 .. 95 66 52 96 94 289 393 Bulgaria 81 101 89 51 .. 96 85 83 97 98 4 3 Burkina Faso 3 78 20 3 27 63 9 15 68 60 392 473 Burundi 10 147 21 3 50 99 .. 9 98 100 9 1 Cambodia 19 116 40 10 .. 95 15 34 90 87 99 131 Cameroon 26 114 41 9 69 92 .. .. 97 86 38 210 Canada 71 98 .. .. 98 .. 95 .. .. .. .. .. Central African Republic 5 89 14 2 53 67 .. 10 77 57 78 149 Chad 1 90 24 2 .. .. 7 .. .. .. .. .. Chile 55 106 90 55 .. 95 .. 85 96 95 35 41 China 47 113 78 25 97 .. .. .. .. .. .. .. Hong Kong SAR, China 121 104 82 57 .. 94 74 75 97 100 6 0b Colombia 51 120 95 37 71 90 56 74 93 93 155 152 Congo, Dem. Rep. 4 90 37 6 56 .. .. .. .. .. .. .. Congo, Rep. 13 120 .. 6 .. .. .. .. .. .. .. .. Costa Rica 70 110 96 .. 87 .. .. .. .. .. .. .. Côte d’Ivoire 4 74 .. 8 46 57 19 .. 62 52 609 774 Croatia 60 94 90 51 .. 91 81 .. 91 92 8 8 Cuba 105 104 90 118 94 99 73 83 100 99 2 2 Czech Republic 111 103 95 58 .. .. 81 .. .. .. .. .. Denmark 96 98 119 78 98 95 88 90 94 97 12 7 Dominican Republic 37 106 77 .. .. 87 38 61 96 89 23 70 Ecuador 131 117 81 42 .. 97 46 59 .. .. .. .. Egypt, Arab Rep. 16 100 .. 28 .. 94 71 .. 97 93 137 324 El Salvador 60 115 64 25 .. 94 47 55 95 96 23 15 Eritrea 13 48 32 2 20 36 17 27 39 34 190 202 Estonia 95 100 99 64 .. 94 84 89 96 97 1 1 Ethiopia 4 102 34 4 30 83 12 .. 86 81 929 1,255 Finland 65 97 110 94 99 96 95 96 96 96 7 7 France 110 110 113 55 100 98 94 98 99 99 18 15 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 22 86 51 5 50 69 26 42 69 74 40 33 Georgia 63 108 108 25 .. 100 76 81 96 93 6 10 Germany 109 105 102 .. 84 98 .. .. .. .. .. .. Ghana 70 105 57 9 .. 76 33 46 76 77 430 398 Greece 69 101 102 91 95 99 82 91 99 100 2 0b Guatemala 29 114 57 18 .. 95 24 40 98 95 23 55 Guinea 12 90 37 9 27 73 12 29 78 68 174 244 Guinea-Bissau .. .. .. .. .. .. 10 .. .. .. .. .. Haiti .. .. .. .. 21 .. .. .. .. .. .. .. Honduras 40 116 65 19 88 97 .. .. 96 96 22 9 80 2011 World Development Indicators 2.12 PEOPLE Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio rate enrollment rate, school primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2009a 2009a 2009a 2009a 1991 2009a 1999 2009a 2009a 2009a 2009a 2009a Hungary 87 99 97 65 .. 90 82 91 95 95 9 9 India 54 117 60 13 .. 91 .. .. 91 88 5,543 7,112 Indonesia 50 121 79 24 95 95 50 69 .. .. .. .. Iran, Islamic Rep. 40 103 83 36 97 99 .. .. .. .. .. .. Iraq 6 103 51 .. 76 88 30 43 93 82 176 415 Ireland .. 105 115 58 90 97 84 88 96 98 9 5 Israel 97 111 90 60 .. 97 86 86 97 98 13 9 Italy 100 103 101 67 .. 98 88 95 100 99 5 15 Jamaica 86 93 91 24 97 80 83 77 82 79 31 35 Japan 89 102 101 58 100 100 99 98 .. .. .. .. Jordan 36 97 88 41 .. 89 79 82 93 94 30 23 Kazakhstan 52 108 99 41 .. 89 87 89 89 90 52 42 Kenya 51 113 59 4 .. 83 33 50 83 84 532 497 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 111 105 97 98 99 99 97 95 100 98 4 31 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 76 95 90 29 47 88 89 80 94 93 6 8 Kyrgyz Republic 18 95 84 51 .. 84 .. 79 91 91 19 18 Lao PDR 22 121 44 13 59 93c 26 36 84 81 65 76 Latvia 89 98 98 69 .. .. .. .. .. .. .. .. Lebanon 77 103 82 53 .. 90 .. 75 92 90 19 21 Lesotho .. 104 45 .. 72 73 17 29 71 76 54 45 Liberia 145 91 .. .. .. .. 20 .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 72 96 99 77 .. 92 90 92 96 96 3 3 Macedonia, FYR 23 88 84 40 .. 86 79 .. 91 92 6 5 Madagascar 10 160 32 4 72 98 .. 26 99 100 16 3 Malawi .. 119 30 0 .. 91 29 25 89 94 152 85 Malaysia 71 95 69 36 .. 94 65 68 94 94 97 95 Mali 4 95 38 6 .. 73 .. 30 84 70 165 304 Mauritania .. 104 24 4 .. 76 14 16 74 79 66 51 Mauritius 98 100 87 26 93 94 67 .. 93 95 4 3 Mexico 114 114 90 27 98 98 56 72 99 100 39 23 Moldova 74 94 88 38 .. 88 79 80 91 90 8 8 Mongolia 59 110 92 53 .. 90 58 82 99 99 1 1 Morocco 57 107 56 13 56 90 30 .. 92 88 154 203 Mozambique .. 114 23 .. 42 91 3 15 93 88 149 264 Myanmar 7 116 53 11 .. .. 31 50 .. .. .. .. Namibia .. 112 66 9 82 89 39 54 88 92 22 14 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 100 107 121 61 95 99 91 88 99 99 4 9 New Zealand 94 101 119 78 100 99 90 .. 99 100 1 0b Nicaragua 56 117 68 .. 70 92 35 45 93 94 29 24 Niger 3 62 12 1 23 54 6 9 60 48 511 637 Nigeria 16 93 30 .. .. 61 .. 26 66 60 4,023 4,626 Norway 95 99 112 73 100 99 96 96 99 99 3 3 Oman 38 84 91 26 69 77 65 82 82 81 33 34 Pakistan .. 85 33 6 .. 66 .. 33 72 60 3,108 4,191 Panama 66 109 73 45 92 97 59 66 98 97 4 6 Papua New Guinea .. .. .. .. 65 .. .. .. .. .. .. .. Paraguay 109 102 67 29 94 87 46 59 88 88 52 50 Peru 72 109 89 .. 86 94 62 71 97 98 54 43 Philippines 49 110 82 29 96 92 50 61 91 93 555 407 Poland 62 97 100 69 .. 95 90 94 95 95 62 55 Portugal 81 115 104 60 98 99 82 88 99 99 2 4 Puerto Rico 154 91 84 78 .. .. .. .. .. .. .. .. Qatar 53 106 85 10 89 93 74 77 98 98 1 1 2011 World Development Indicators 81 2.12 Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio rate enrollment rate, school primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2009a 2009a 2009a 2009a 1991 2009a 1999 2009a 2009a 2009a 2009a 2009a Romania 73 100 92 66 73 90 75 73 96 97 16 14 Russian Federation 90 97 85 77 .. .. .. .. .. .. .. .. Rwanda 17 151 27 5 .. 96 .. .. 95 97 38 22 Saudi Arabia 11 99 97 37 .. 86 .. 72 88 85 205 244 Senegal 12 84 30 8 45 73 .. .. 74 76 262 232 Serbia 51 98 91 50 .. 94 .. 90 96 96 5 6 Sierra Leone 5 158 35 .. .. .. .. 25 .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 94 103 92 54 .. .. .. .. .. .. .. .. Slovenia 83 97 97 87 .. 97 90 91 98 97 1 1 Somalia .. 33 8 .. .. .. .. .. .. .. .. .. South Africa 64 101 94 .. 90 85 63 72 89 91 385 331 Spain 126 107 120 71 100 100 88 95 100 100 1 3 Sri Lanka .. 97 .. .. .. 95 .. .. 95 96 45 36 Sudan 28 74 38 .. .. .. .. .. .. .. .. .. Swaziland .. 108 53 .. 74 83 32 29 82 84 19 18 Sweden 102 95 103 71 100 95 96 99 95 94 16 17 Switzerland 102 103 96 49 84 94 84 85 99 99 3 1 Syrian Arab Republic 9 122 75 .. 91 .. 36 69 .. .. .. .. Tajikistan 9 102 84 20 .. 97 63 83 99 96 2 15 Tanzania 33 105 27 .. 51 96 5 .. 96 97 160 107 Thailand 92 91 76 45 .. 90 .. 71 91 89 281 305 Timor-Leste .. 113 51 15 .. 82 23 .. 84 82 15 17 Togo 7 115 41 5 65 94 20 .. 98 89 10 56 Trinidad and Tobago 81 104 89 .. 90 93 70 74 97 94 2 4 Tunisia .. 107 92 34 94 98 63 71 99 100 6 0b Turkey 18 99 82 38 89 95 62 74 96 94 147 214 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 12 122 27 4 .. 92 8 22 91 94 310 213 Ukraine 101 98 94 79 .. 89 91 85 89 90 89 81 United Arab Emirates 94 105 95 30 97 90 69 83 98 97 2 4 United Kingdom 81 106 99 57 97 100 95 93 100 100 5 1 United States 58 99 94 83 97 92 88 88 93 94 944 770 Uruguay 86 114 88 65 91 99 .. 70 99 99 1 2 Uzbekistan 26 92 104 10 .. 87 .. 92 91 89 101 119 Venezuela, RB 77 103 82 79 .. 92 47 71 94 94 108 96 Vietnam .. .. .. .. .. .. 59 .. .. .. .. .. West Bank and Gaza 34 79 87 46 .. 75 77 85 78 77 57 55 Yemen, Rep. .. 85 .. 10 .. 73 32 .. 80 66 395 641 Zambia .. 113 49 .. .. 91 17 46 91 94 112 78 Zimbabwe .. .. .. 3 .. .. 40 .. .. .. .. .. World 44 w 107 w 67 w 26 w .. w 88 w 52 w 59 w 91 w 89 w Low income 15 104 38 6 .. 80 .. .. 83 79 Middle income 46 109 68 24 .. 88 .. .. 92 90 Lower middle income 42 107 63 19 .. 87 .. .. 91 88 Upper middle income 63 111 88 42 .. 93 67 75 94 94 Low & middle income 40 107 63 21 .. 87 .. 55 90 88 East Asia & Pacific 44 111 74 .. 96 .. .. .. .. .. Europe & Central Asia 55 99 89 55 90 92 79 81 94 94 Latin America & Carib. 68 116 89 35 .. 94 59 73 95 95 Middle East & N. Africa 20 105 73 27 .. 89 60 64 92 89 South Asia .. 108 52 11 68 86 .. .. 92 88 Sub-Saharan Africa 17 100 34 6 .. 75 .. .. 78 75 High income 77 101 100 67 95 95 88 90 95 96 Euro area 110 .. .. .. .. .. .. .. .. .. a. Provisional data. b. Less than 0.5. c. Data are for 2010. 82 2011 World Development Indicators 2.12 PEOPLE Participation in education About the data Definitions School enrollment data are reported to the United children of primary age enrolled in preprimary edu- • Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organi- cation) are compiled from administrative data. Large ment, regardless of age, to the population of the age zation (UNESCO) Institute for Statistics by national numbers of children out of school create pressure group that officially corresponds to the level of educa- education authorities and statistical offices. Enroll- to enroll children and provide classrooms, teachers, tion shown. • Preprimary education (ISCED O) refers ment indicators help monitor whether a country is on and educational materials, a task made difficult in to programs at the initial stage of organized instruc- track to achieve the Millennium Development Goal of many countries by limited education budgets. How- tion, designed primarily to introduce very young chil- universal primary education by 2015, and whether ever, getting children into school is a high priority for dren, usually from age 3, to a school-type environment an education system has the capacity to meet the countries and crucial for achieving the Millennium and to provide a bridge between the home and school. needs of universal primary education. Development Goal of universal primary education. On completing these programs, children continue their Enrollment indicators are based on annual school In 2006 the UNESCO Institute for Statistics education at the primary level. • Primary education surveys but do not necessarily reflect actual atten- changed its convention for citing the reference year. (ISCED 1) refers to programs normally designed to dance or dropout rates during the year. Also, the For more information, see About the data for table give students a sound basic education in reading, length of primary education differs across coun- 2.11. writing, and mathematics along with an elementary tries and can influence enrollment rates and ratios, understanding of other subjects such as history, although the International Standard Classification of geography, natural science, social science, art, and Education (ISCED) tries to minimize the difference. music. Religious instruction may also be featured. It A shorter duration for primary education tends to is sometimes called elementary education. • Sec- increase the ratio; a longer one to decrease it (in ondary education refers to programs of lower (ISCED part because older children are more at risk of drop- 2) and upper (ISCED 3) secondary education. Lower ping out). secondary education continues the basic programs Over- or under-age enrollments are frequent, par- of the primary level, but the teaching is typically more ticularly when parents prefer children to start school subject focused, requiring more specialized teachers at other than the official age. Age at enrollment may for each subject area. In upper secondary educa- be inaccurately estimated or misstated, especially tion, instruction is often organized even more along in communities where registration of births is not subject lines, and teachers typically need a higher or strictly enforced. more subject-specific qualification. • Tertiary educa- Population data used to calculate population- tion refers to a wide range of programs with more based indicators are drawn from the United Nations advanced educational content. The first stage of ter- Population Division. Using a single source for popula- tiary education (ISECD 5) refers to theoretically based tion data standardizes definitions, estimations, and programs intended to provide sufficient qualifications interpolation methods, ensuring a consistent meth- to enter advanced research programs or professions odology across countries and minimizing potential with high-skill requirements and programs that are enumeration problems in national censuses. practical, technical, or occupationally specific. The Gross enrollment ratios indicate the capacity of second stage of tertiary education (ISCED 6) refers each level of the education system, but a high ratio to programs devoted to advanced study and original may reflect a substantial number of over-age children research and leading to the award of an advanced enrolled in each grade because of repetition or late research qualification. • Net enrollment rate is the entry, rather than a successful education system. ratio of total enrollment of children of official school The net enrollment rate excludes over- and under- age to the population of the age group that offi - age students and more accurately captures the sys- cially corresponds to the level of education shown. tem’s coverage and internal efficiency. Differences • Adjusted net enrollment rate, primary, is the ratio between the gross enrollment ratio and net enroll- of total enrollment of children of official school age ment rate show the incidence of over- and under-age for primary education who are enrolled in primary or enrollments. secondary education to the total primary school-age The adjusted net enrollment rate in primary educa- population. • Children out of school are the number tion captures primary-school-age children who have of primary-school-age children not enrolled in primary progressed to secondary education faster than their or secondary school. peers and who would not be counted in the tradi- Data sources tional net enrollment rate. Data on children out of school (primary-school- Data on participation in education are from age children not enrolled in primary or secondary the UNESCO Institute for Statistics, www.uis. school—dropouts, children never enrolled, and unesco.org. 2011 World Development Indicators 83 2.13 Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in first grade of survival rate primary education secondary education primary education % of grade 1 students % of relevant Reaching Reaching last grade of % of age group grade 5 primary education enrollment % Male Female Male Female Male Female Male Female Male Female 2009a 2009a 1991 2008a 1991 2008a 2008a 2008a 2009a 2009a 2008a 2008a Afghanistan 129 93 89 .. 89 .. .. .. .. .. .. .. Albania 89 82 .. .. .. .. .. .. 2 1 .. .. Algeria 101 99 82 94 79 95 91 95 13 8 90 92 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 111 111 .. 95 .. 98 93 97 7 5 93 96 Armenia 86 89 .. .. .. .. 98 97 0b 0b 100 98 Australia .. .. 98 .. 99 .. .. .. .. .. .. .. Austria 104 100 .. .. .. .. 96 99 0 0 100 99 Azerbaijan 95 94 .. .. .. .. 100 97 0b 0b 100 98 Bangladesh 101 105 .. 67 .. 66 67 66 14 13 .. .. Belarus 97 102 .. .. .. .. 99 99 0b 0b 100 100 Belgium 97 98 87 90 90 92 86 88 4 3 100 99 Benin 161 152 30 .. 31 .. .. .. 14 14 .. .. Bolivia 114 113 57 86 51 85 85 82 1 1 96 94 Bosnia and Herzegovina 89 92 .. .. .. .. .. .. 0b 0b .. .. Botswana 114 112 73 .. 81 .. .. .. 6 4 98 97 Brazil .. .. .. .. .. .. .. .. .. .. .. .. Bulgaria 107 108 .. .. .. .. 93 94 2 1 95 95 Burkina Faso 90 83 61 73c 58 78 c 61c 67c 11 11 56c 51c Burundi 152 146 66 62 61 68 56 64 32 32 48 23 Cambodia 158 157 .. 68 .. 71 60 63 10 8 80 81 Cameroon 134 117 67 76 66 79 68 69 15 14 42 45 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 110 86 52 58 39 48 51 41 24 24 45 45 Chad 131 98 43 .. 22 .. .. .. 22 24 64 65 Chile 101 98 .. 96 .. 97 .. .. 3 2 86 100 China 94 98 .. .. .. .. .. .. 0b 0b .. .. Hong Kong SAR, China 117 124 .. 100 .. 100 100 100 1 1 100 100 Colombia 118 114 53 82 59 89 82 89 2 2 100 100 Congo, Dem. Rep. 119 106 66 78 55 77 78 73 15 16 83 76 Congo, Rep. 115 112 66 75 68 79 71 71 21 19 65 62 Costa Rica 98 96 70 95 73 97 93 96 6 4 97 91 Côte d’Ivoire 77 67 68 66 61 66 62 59 19 19 47 45 Croatia 95 94 .. .. .. .. 97 99 0b 0b 100 99 Cuba 100 102 .. 96 .. 96 96 95 1 0b 99 98 Czech Republic 109 107 .. 99 .. 99 99 99 1 1 99 99 Denmark 98 99 98 100 99 99 99 99 0 0 95 98 Dominican Republic 109 90 .. .. .. .. .. .. 9 5 88 92 Ecuador 119 124 .. 80 .. 83 79 82 6 5 81 77 Egypt, Arab Rep. 98 96 .. .. .. .. .. .. 4 2 .. .. El Salvador 123 119 54 78 57 82 74 78 7 5 92 92 Eritrea 45 39 .. 74 .. 72 74 72 14 13 85 81 Estonia 102 102 .. 99 .. 98 99 98 1 0b 97 99 Ethiopia 158 141 .. 43 .. 49 35 41 6 5 84 87 Finland 100 98 96 99 97 100 99 100 1 0b 100 100 France .. .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. 47 .. 46 .. .. .. .. .. .. .. Gambia, The 91 96 59 71 53 72 68 72 6 5 83 83 Georgia 107 112 .. 96 .. 95 95 94 0b 0b 99 99 Germany 100 99 .. .. .. .. 95 96 1 1 99 99 Ghana 109 111 72 80 65 78 75 71 7 6 91 92 Greece 102 103 .. 98 .. 97 98 97 1 1 .. .. Guatemala 123 121 .. 71 .. 70 65 64 13 11 93 90 Guinea 106 96 43 72 35 64 68 57 15 16 50 40 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. 47 .. 46 .. .. .. .. .. .. .. Honduras 126 122 50 75 43 80 74 79 6 5 82 86 84 2011 World Development Indicators 2.13 PEOPLE Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in first grade of survival rate primary education secondary education primary education % of grade 1 students % of relevant Reaching Reaching last grade of % of age group grade 5 primary education enrollment % Male Female Male Female Male Female Male Female Male Female 2009a 2009a 1991 2008a 1991 2008a 2008a 2008a 2009a 2009a 2008a 2008a Hungary 103 103 .. .. .. .. 99 99 2 1 99 99 India 132 124 .. 67 .. 70 67 70 3 3 81 81 Indonesia 125 122 .. 83 .. 89 77 83 4 3 91 93 Iran, Islamic Rep. 100 100 75 94 67 94 94 95 2 2 96 97 Iraq 105 103 75 .. 70 .. .. .. 19 14 .. .. Ireland 99 101 .. 98 .. 100 .. .. 1 1 .. .. Israel 96 98 .. 100 .. 98 99 98 2 1 71 70 Italy 102 101 .. 99 .. 100 99 100 0b 0b 100 100 Jamaica 90 86 92 .. 94 .. .. .. 3 3 .. .. Japan 102 102 100 100 100 100 100 100 0 0 .. .. Jordan 99 99 93 .. 89 .. .. .. 1 1 99 98 Kazakhstan 105 106 .. .. .. .. 98 c 99c 0b 0b 100 c 100 c Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 106 104 92 98 92 99 98 99 0b 0b 100 100 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 95 93 .. 95 .. 96 95 96 1 1 99 100 Kyrgyz Republic 97 97 .. .. .. .. 96 97 0b 0b 99 100 Lao PDR 124 115 34 66 32 68 66 68 15d 13d 80 77 Latvia 104 105 .. 98 .. 94 97 94 5 2 92 97 Lebanon 100 105 .. 94 .. 96 90 93 11 7 84 89 Lesotho 106 98 53 56 77 69 38 56 23 16 68 66 Liberia 117 107 .. 64 .. 56 49 43 6 7 64 60 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 97 94 .. .. .. .. 98 98 1 1 99 99 Macedonia, FYR 92 93 .. .. .. .. 98 97 0b 0b 99 100 Madagascar 198 196 31 48 31 50 48 50 21 20 57 55 Malawi 136 144 37 51 33 50 42 42 19 18 75 74 Malaysia 89 89 86 96 87 97 96 96 .. .. 100 99 Mali 102 89 48 88 42 85 81 77 13 14 72 68 Mauritania 112 119 52 48 47 51 40 42 2 2 38 31 Mauritius 99 99 .. 96 .. 99 94 98 4 3 64 75 Mexico 122 122 81 93 82 95 90 93 4 3 94 93 Moldova 94 93 .. .. .. .. 95 96 0b 0b 99 98 Mongolia 147 142 .. 94 .. 95 94 95 0b 0b 96 99 Morocco 107 106 70 84 64 85 78 78 13 9 80 78 Mozambique 163 156 42 56c 34 51c 37c 34 c 7 7 52c 55c Myanmar 140 135 .. 70 .. 69 70 69 0b 0b 74 73 Namibia 98 99 52 90 57 93 80 85 18 14 80 83 Nepal .. .. 44 60 32 64 60 64 17 17 81 81 Netherlands 101 101 .. 99 .. 100 .. .. .. .. .. .. New Zealand .. .. 96 .. 95 .. .. .. .. .. .. .. Nicaragua 158 148 39 48 48 55 45 52 13 9 .. .. Niger 97 83 68 66c 65 62c 63c 60 c 5 5 56c 62c Nigeria 102 83 .. .. .. .. .. .. .. .. 44 44 Norway 97 99 99 99 100 100 99 99 .. .. 100 100 Oman 88 86 77 .. 78 .. .. .. 1 2 .. .. Pakistan 111 96 .. 61 .. 60 61 60 3 3 73 72 Panama 105 103 .. 88 .. 91 86 88 6 4 96 97 Papua New Guinea .. .. 55 .. 52 .. .. .. .. .. .. .. Paraguay 101 97 58 82 60 85 77 81 5 3 88 89 Peru 100 100 .. 87 .. 88 82 84 7 7 94 93 Philippines 139 130 .. 75 .. 82 71 80 3 2 100 98 Poland .. .. .. .. .. .. .. .. 2 1 .. .. Portugal 107 103 .. .. .. .. .. .. .. .. .. .. Puerto Rico 97 94 .. .. .. .. .. .. .. .. .. .. Qatar 103 108 98 92 99 99 91 97 0b 0b 100 100 2011 World Development Indicators 85 2.13 Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in first grade of survival rate primary education secondary education primary education % of grade 1 students % of relevant Reaching Reaching last grade of % of age group grade 5 primary education enrollment % Male Female Male Female Male Female Male Female Male Female 2009a 2009a 1991 2008a 1991 2008a 2008a 2008a 2009a 2009a 2008a 2008a Romania 101 99 .. .. .. .. 93 94 2 1 97 97 Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 194 189 49 46 51 51 .. .. 15 14 .. .. Saudi Arabia 102 101 80 99 76 93 98 91 4 4 93 100 Senegal 96 102 78 69 68 71 56 59 8 7 62 57 Serbia 95 94 .. .. .. .. 99 97 1 1 100 99 Sierra Leone 201 182 .. .. .. .. .. .. 10 10 .. .. Singapore .. .. .. 99 .. 99 99 99 0b 0b 86 92 Slovak Republic 100 99 .. .. .. .. 97 98 3 3 97 97 Slovenia 97 97 .. .. .. .. .. .. 1 0b .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 92 87 61 .. 67 .. .. .. 8 8 90 91 Spain 105 106 .. 99 .. 100 99 100 3 2 .. .. Sri Lanka 92 93 97 88 98 89 88 89 1 1 95 97 Sudan 86 76 .. 89 .. 100 86 98 4 4 90 98 Swaziland 105 101 58 75 64 86 70 74 21 15 .. .. Sweden 104 103 99 100 99 100 100 100 0 0 100 100 Switzerland 93 96 72 .. 72 .. .. .. 2 1 99 100 Syrian Arab Republic 117 113 87 .. 85 .. 93 94 9 7 94 96 Tajikistan 106 101 .. .. .. .. .. .. 0b 0b 98 98 Tanzania 99 100 69 79 71 83 71 77 2 2 40 32 Thailand .. .. .. .. .. .. .. .. 12 6 85 89 Timor-Leste 142 134 .. 72 .. 80 68 78 21 18 86 88 Togo 105 102 55 80 38 71 76 62 23 22 66 58 Trinidad and Tobago 102 100 98 97 99 95 93 93 7 5 86 92 Tunisia 106 107 76 96 70 96 94 95 10 6 79 86 Turkey 101 98 93 94 92 94 94 94 2 2 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 140 143 .. 57 .. 58 54 53 14 14 58 55 Ukraine 100 100 .. .. .. .. 96 98 0b 0b 100 100 United Arab Emirates 113 113 78 97 80 97 97 97 2 2 98 99 United Kingdom .. .. .. .. .. .. .. .. 0 0 .. .. United States 103 109 .. .. .. .. .. .. 0 0 .. .. Uruguay 101 111 98 93 100 96 93 96 8 5 81 93 Uzbekistan 94 91 .. .. .. .. 98 99 0b 0b 100 99 Venezuela, RB 101 98 69 92 80 96 89 95 4 3 97 97 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 77 77 .. .. .. .. 99 97 0 0 97 97 Yemen, Rep. 110 98 .. .. .. .. .. .. 6 5 .. .. Zambia 116 119 .. 71 .. 70 55 52 6 6 66 67 Zimbabwe .. .. 70 .. 72 .. .. .. .. .. .. .. World 114 w 110 w .. w .. w .. w .. w .. w .. w 5w 4w .. w .. w Low income 133 126 .. .. .. .. .. .. 11 11 .. ..  Middle income 114 110 .. .. .. .. .. .. 4 3 .. .. Lower middle income 115 110 .. .. .. .. .. .. 4 3 .. .. Upper middle income .. .. .. .. .. .. .. .. .. .. .. .. Low & middle income 115 111 .. .. .. .. .. .. 5 4 .. .. East Asia & Pacific 105 107 .. .. .. .. .. .. 1 1 .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. .. Latin America & Carib. .. .. .. .. .. .. .. .. .. .. .. .. Middle East & N. Africa 104 101 .. .. .. .. .. .. 9 5 .. .. South Asia 126 117 .. 68 .. 70 68 70 4 4 80 80 Sub-Saharan Africa 121 113 .. .. .. .. .. .. 10 10 66 65 High income 102 104 .. .. .. .. .. .. 1 1 .. .. Euro area 102 101 .. .. .. .. 98 99 2 1 .. .. a. Provisional data. b. Less than 0.5. c. Data are for 2009. d. Data are for 2010. 86 2011 World Development Indicators 2.13 PEOPLE Education efficiency About the data Definitions The United Nations Educational, Scientific, and Cul- data on repeaters by grade for the most recent of • Gross intake ratio in first grade of primary edu- tural Organization (UNESCO) Institute for Statistics those two years to reflect current patterns of grade cation is the number of new entrants in grade 1, calculates indicators of students’ progress through transition. Rates approaching 100 percent indicate regardless of age, expressed as a percentage of the school. These indicators measure an education sys- high retention and low dropout levels. population of the official school age. • Cohort sur- tem’s success in reaching students, efficiently mov- Data on repeaters are often used to indicate an vival rate is the percentage of children enrolled in ing students from one grade to the next, and trans- education system’s internal efficiency. Repeaters not the first grade of primary education who eventually mitting knowledge at a particular level of education. only increase the cost of education for the family reach grade 5 or the last grade of primary education. The gross intake ratio to the first grade of primary and the school system, but also use limited school The estimate is based on the reconstructed cohort education indicates the level of access to primary resources. Country policies on repetition and promo- method (see About the data). • Repeaters in primary education and the education system’s capacity to tion differ. In some cases the number of repeaters education are the number of students enrolled in the provide access to primary education. A low gross is controlled because of limited capacity. In other same grade as in the previous year as a percentage intake ratio in grade 1 reflects the fact that many chil- cases the number of repeaters is almost 0 because of all students enrolled in primary school. • Transi- dren do not enter primary school even though school of automatic promotion—suggesting a system that tion rate to secondary education is the number of attendance, at least through the primary level, is is highly efficient but that may not be endowing stu- new entrants to the first grade of secondary edu- mandatory in most countries. Because the gross dents with enough cognitive skills. cation (general programs only) in a given year as a intake ratio includes all new entrants regardless of The transition rate from primary to secondary percentage of the number of pupils enrolled in the age, it can exceed 100 percent in some situations, school conveys the degree of access or transition final grade of primary education in the previous year. such as immediately after fees have been abolished between the two levels. As completing primary edu- or when the number of reenrolled children is large. cation is a prerequisite for participating in lower The indicator is not calculated when new entrants secondary school, growing numbers of primary and repeaters are not correctly distinguished in completers will inevitably create pressure for more grade 1. available places at the secondary level. A low transi- The survival rate to grade 5 and to the last grade tion rate can signal such problems as an inadequate of primary education shows the percentage of stu- examination and promotion system or insufficient dents entering primary school who are expected to secondary school capacity. The quality of data on reach the specified grade. It measures an education the transition rate is affected when new entrants and system’s holding power and internal efficiency. Sur- repeaters are not correctly distinguished in the first vival rates are calculated based on the reconstructed grade of secondary school. Students who interrupt cohort method, which uses data on enrollment by their studies after completing primary school could grade for the two most recent consecutive years and also affect data quality. In 2006 the UNESCO Institute for Statistics There are more overage children changed its convention for citing the reference year. among the poor in primary For more information, see About the data for table school in Zambia 2.13a 2.11. Percent of total Overage children enrollment Underage children On-time Children 100 75 50 25 0 Poorest Middle Richest wealth wealth Data sources quintile quintile Data on education efficiency are from the UNESCO Source: World Bank, EdStats. Institute for Statistics, www.uis.unesco.org. 2011 World Development Indicators 87 2.14 Education completion and outcomes Primary completion Youth literacy Adult literacy PISA rate rate rate mathematics literacy % ages 15 % of relevant age group % ages 15–24 and older Mean Total Male Female Male Female Total score 1991 2009a 1991 2009a 1991 2009a 1990 2005–09b 1990 2005–09b 2005–09b 2009 Afghanistan 28 .. 41 .. 14 .. .. .. .. .. .. .. Albania .. 90 .. 90 .. 89 .. 99 .. 99 96 377 Algeria 80 91 86 90 73 91 86 94 62 89 73 .. Angola 33 .. .. .. .. .. .. 81 .. 66 70 .. Argentina 100 102 .. 100 .. 104 .. 99 .. 99 98 388 Armenia 105 98 .. 96 .. 100 100 100 100 100 100 .. Australia .. .. .. .. .. .. .. .. .. .. .. 514 Austria .. 99 .. 99 .. 98 .. .. .. .. .. 496 Azerbaijan 95 92 96 92 94 91 .. 100 .. 100 100 431 Bangladesh 41 61 .. 58 .. 63 .. 74 .. 77 56 .. Belarus 94 96 95 93 95 92 100 100 100 100 100 .. Belgium 79 86 76 84 82 88 .. .. .. .. .. 515 Benin 22 62 30 71 14 53 .. 65 .. 43 42 .. Bolivia 71 99 78 99 64 98 .. 99 .. 99 91 .. Bosnia and Herzegovina .. .. .. .. .. .. .. 100 .. 100 98 .. Botswana 90 95 83 93 98 97 .. 94 .. 97 84 .. Brazil 93 .. .. .. .. .. .. 97 .. 99 90 386 Bulgaria 90 90 88 91 92 89 .. 98 .. 97 98 428 Burkina Faso 20 43 25 46 15 40 .. 47 .. 33 29 .. Burundi 46 52 49 54 43 51 59 77 48 76 67 .. Cambodia 45 83 .. 83 .. 84 .. 89 .. 86 78 .. Cameroon 53 73 57 80 49 67 .. 89 .. 77 71 .. Canada .. .. .. .. .. .. .. .. .. .. .. 527 Central African Republic 28 38 37 47 20 29 63 72 35 57 55 .. Chad 18 33 29 42 7 24 26 54 9 39 34 .. Chile .. 95 .. 101 .. 88 .. 99 .. 99 99 421 China 107 .. .. .. .. .. 97 99 91 99 94 .. Hong Kong SAR, China 102 93 .. 92 .. 93 .. .. .. .. .. 555 Colombia 73 115 70 113 76 117 .. 97 .. 98 93 381 Congo, Dem. Rep. 48 56 61 66 36 46 .. 73 .. 62 67 .. Congo, Rep. 54 74 59 77 49 72 .. 87 .. 78 .. .. Costa Rica 79 96 77 95 81 97 .. 98 .. 99 96 .. Côte d’Ivoire 42 46 53 54 32 39 60 72 38 61 55 .. Croatia 85 100 .. 99 .. 100 .. 100 .. 100 99 460 Cuba 99 98 .. 98 .. 98 .. 100 .. 100 100 .. Czech Republic 92 95 91 95 93 95 .. .. .. .. .. 493 Denmark 98 101 98 100 98 101 .. .. .. .. .. 503 Dominican Republic 61 90 .. 90 .. 89 .. 95 .. 97 88 .. Ecuador 91 103 91 101 92 104 97 97 96 97 84 .. Egypt, Arab Rep. .. 95 .. 97 .. 93 71 88 54 82 66 .. El Salvador 65 89 64 88 66 91 .. 95 .. 95 84 .. Eritrea 18 48 21 52 15 43 .. 92 .. 86 67 .. Estonia .. 100 .. 100 .. 101 100 100 100 100 100 512 Ethiopia 23 55 28 57 18 53 .. 56 .. 33 30 .. Finland 97 98 98 99 97 97 .. .. .. .. .. 541 France 106 .. .. .. .. .. .. .. .. .. .. 497 Gabon 62 .. 59 .. 65 .. .. 99 .. 97 88 .. Gambia, The 45 79 56 76 34 83 .. 71 .. 60 46 .. Georgia .. 107 .. 110 .. 104 .. 100 .. 100 100 .. Germany 100 104 99 103 100 104 .. .. .. .. .. 513 Ghana 64 83 71 85 56 81 .. 81 .. 79 67 .. Greece 99 101 99 102 98 101 .. 99 .. 99 97 466 Guatemala .. 80 .. 83 .. 77 .. 89 .. 84 74 .. Guinea 17 62 24 71 9 53 .. 68 .. 54 39 .. Guinea-Bissau 5 .. 7 .. 3 .. .. 78 .. 64 52 .. Haiti 27 .. 29 .. 26 .. .. .. .. .. 49 .. Honduras 64 90 67 87 61 93 .. 93 .. 95 84 .. 88 2011 World Development Indicators 2.14 PEOPLE Education completion and outcomes Primary completion Youth literacy Adult literacy PISA rate rate rate mathematics literacy % ages 15 % of relevant age group % ages 15–24 and older Mean Total Male Female Male Female Total score 1991 2009a 1991 2009a 1991 2009a 1990 2005–09b 1990 2005–09b 2005–09b 2009 Hungary 82 95 89 97 90 94 .. 99 .. 99 99 490 India 64 95 76 95 52 94 .. 88 .. 74 63 .. Indonesia 93 109 .. 109 .. 110 97 100 95 99 92 371 Iran, Islamic Rep. 88 101 93 101 82 101 85 99 66 99 85 .. Iraq 58 64 63 73 52 54 .. 85 .. 80 78 .. Ireland 103 99 103 99 103 99 .. .. .. .. .. 487 Israel .. 99 .. 99 .. 100 .. .. .. .. .. 447 Italy 98 104 98 104 97 104 .. 100 .. 100 99 483 Jamaica 94 89 90 88 98 90 .. 92 .. 98 86 .. Japan 102 101 102 100 102 101 .. .. .. .. .. 529 Jordan 101 100 101 99 101 100 .. 99 .. 99 92 387 Kazakhstan 103 106 103 106 103 106 100 100 100 100 100 405 Kenya .. .. .. .. .. .. .. 92 .. 94 87 .. Korea, Dem. Rep. .. .. .. .. .. .. .. 100 .. 100 100 .. Korea, Rep. 99 99 99 100 100 97 .. .. .. .. .. 546 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 57 93 58 94 56 93 91 99 84 99 94 .. Kyrgyz Republic .. 94 .. 94 .. 95 .. 100 .. 100 99 331 Lao PDR 41 75 46 78 36 71 .. 89 .. 79 73 .. Latvia .. 95 .. 97 .. 93 100 100 100 100 100 482 Lebanon .. 85 .. 83 .. 87 .. 98 .. 99 90 .. Lesotho 59 70 42 60 76 81 .. 86 .. 98 90 .. Liberia .. 58 .. 63 .. 53 .. 70 .. 81 59 .. Libya .. .. .. .. .. .. .. 100 .. 100 89 .. Lithuania .. 92 .. 92 .. 92 100 100 100 100 100 477 Macedonia, FYR 98 92 .. 91 .. 93 .. 99 .. 99 97 .. Madagascar 36 79 35 79 37 79 .. 66 .. 64 64 .. Malawi 31 59 35 58 27 60 70 87 49 86 74 .. Malaysia 91 97 91 97 91 97 .. 98 .. 99 92 .. Mali 9 59 12 67 7 52 .. 47 .. 31 26 .. Mauritania 33 64 39 63 26 66 .. 71 .. 64 57 .. Mauritius 115 89 115 89 115 90 91 96 92 98 88 .. Mexico 88 104 91 104 92 105 96 99 95 98 93 419 Moldova .. 93 .. 94 .. 91 100 99 100 100 98 .. Mongolia .. 93 .. 94 .. 92 .. 95 .. 97 97 .. Morocco 48 80 57 84 39 77 .. 87 .. 72 56 .. Mozambique 26 57 32 63 21 51 .. 78 .. 64 55 .. Myanmar .. 99 .. 98 .. 100 .. 96 .. 95 92 .. Namibia 74 87 67 83 81 91 .. 91 .. 95 89 .. Nepal 51 .. 70 .. 41 .. .. 87 .. 77 59 .. Netherlands .. .. .. .. .. .. .. .. .. .. .. 526 New Zealand .. .. .. .. .. .. .. .. .. .. .. 519 Nicaragua 42 75 43 71 53 78 .. 85 .. 89 78 .. Niger 17 40 21 47 13 34 .. 52 .. 23 29 .. Nigeria .. 79 .. 84 .. 74 .. 78 .. 65 61 .. Norway 100 98 100 98 100 97 .. .. .. .. .. 498 Oman 74 80 78 80 70 79 .. 98 .. 98 87 .. Pakistan .. 61 .. 68 .. 54 .. 79 .. 61 56 .. Panama 86 102 86 102 86 101 95 97 95 96 94 360 Papua New Guinea 46 .. 51 .. 42 .. .. 65 .. 70 60 .. Paraguay 68 94 68 93 69 95 .. 99 .. 99 95 .. Peru .. 101 .. 101 .. 101 .. 98 .. 97 90 365 Philippines 88 94 85 91 86 97 96 97 97 98 95 .. Poland 96 96 .. .. .. .. .. 100 .. 100 100 495 Portugal .. .. .. .. .. .. .. 100 .. 100 95 487 Puerto Rico .. .. .. .. .. .. 92 87 94 88 90 .. Qatar 71 108 71 109 72 106 89 98 91 98 95 368 2011 World Development Indicators 89 2.14 Education completion and outcomes Primary completion Youth literacy Adult literacy PISA rate rate rate mathematics literacy % ages 15 % of relevant age group % ages 15–24 and older Mean Total Male Female Male Female Total score 1991 2009a 1991 2009a 1991 2009a 1990 2005–09b 1990 2005–09b 2005–09b 2009 Romania 96 96 96 96 96 96 .. 97 .. 98 98 427 Russian Federation 92 95 92 .. 93 .. 100 100 100 100 100 468 Rwanda 50 54 51 52 50 56 .. 77 .. 77 71 .. Saudi Arabia .. 93 .. 95 .. 90 .. 99 .. 97 86 .. Senegal 39 57 48 56 31 57 49 74 28 56 50 .. Serbia .. 96 .. 97 .. 96 .. .. .. .. .. 442 Sierra Leone .. 88 .. 101 .. 75 .. 68 .. 48 41 .. Singapore .. .. .. .. .. .. 99 100 99 100 95 562 Slovak Republic 95 96 95 96 96 96 .. .. .. .. .. 497 Slovenia 95 96 .. 97 .. 96 .. 100 .. 100 100 501 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 76 93 72 93 80 94 .. 97 .. 98 89 .. Spain 104 100 104 100 103 100 .. 100 .. 100 98 483 Sri Lanka 101 97 101 97 101 98 .. 97 .. 99 91 .. Sudan .. 57 .. 53 .. 47 .. 89 .. 83 70 .. Swaziland 61 72 57 75 64 69 83 92 84 95 87 .. Sweden 96 94 96 95 96 94 .. .. .. .. .. 494 Switzerland 53 94 53 93 54 95 .. .. .. .. .. 534 Syrian Arab Republic 89 112 94 113 84 111 .. 96 .. 93 84 .. Tajikistan .. 98 .. 97 .. 93 100 100 100 100 100 .. Tanzania 55 102 56 102 55 102 86 78 78 76 73 .. Thailand .. .. .. .. .. .. .. 98 .. 98 94 419 Timor-Leste .. 80 .. 80 .. 79 .. .. .. .. 51 .. Togo 35 61 48 71 22 52 .. 85 .. 68 57 .. Trinidad and Tobago 102 93 99 93 105 93 99 100 99 100 99 414 Tunisia 74 93 79 93 70 93 .. 98 .. 96 78 371 Turkey 90 93 93 95 86 92 97 99 88 97 91 445 Turkmenistan .. .. .. .. .. .. .. 100 .. 100 100 .. Uganda .. 72 .. 72 .. 73 .. 90 c .. 85c 73c .. Ukraine 92 95 99 98 99 99 .. 100 .. 100 100 .. United Arab Emirates 103 99 104 100 103 98 81 94 85 97 90 .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. 492 United States .. 95 .. 94 .. 97 .. .. .. .. .. 487 Uruguay 94 106 91 104 96 108 98 98 99 100 98 427 Uzbekistan 80 92 .. 93 .. 91 .. 100 .. 100 99 .. Venezuela, RB 81 95 76 94 86 96 95 98 96 99 95 .. Vietnam .. .. .. .. .. .. 94 97 93 96 93 .. West Bank and Gaza .. 82 .. 82 .. 81 .. 99 .. 99 95 .. Yemen, Rep. .. 61 .. 72 .. 49 .. 96 .. 72 62 .. Zambia .. 87 .. 92 .. 82 67 82 66 67 71 .. Zimbabwe 97 .. 99 .. 96 .. .. 98 .. 99 92 .. World 79 w 88 w 86 w 90 w 75 w 87 w 87 w 92 w 78 w 87 w 84 w   Low income 44 63 .. 66 .. 60 66 76 52 69 62   Middle income 83 92 89 93 77 91 88 94 78 88 83   Lower middle income 82 90 89 92 74 89 87 93 74 86 80   Upper middle income 88 100 89 100 88 100 94 98 92 97 92   Low & middle income 78 87 85 89 73 85 86 91 75 85 80   East Asia & Pacific 101 99 105 98 97 100 96 99 91 99 94   Europe & Central Asia 92 96 93 97 92 95 99 99 98 99 98   Latin America & Carib. 84 101 84 100 85 102 91 97 92 97 91   Middle East & N. Africa .. 95 .. 97 .. 92 84 93 67 87 74   South Asia 62 79 75 82 52 76 71 85 47 72 61   Sub-Saharan Africa 51 64 57 69 47 60 73 77 58 67 62   High income .. 98 .. 98 .. 98 99 99 99 99 98   Euro area 101 .. 100 .. 100 .. .. .. .. .. ..   a. Provisional data. b. Data are for the most recent year available. c. Data are for 2010. 90 2011 World Development Indicators 2.14 PEOPLE Education completion and outcomes About the data Definitions Many governments publish statistics that indicate Many countries estimate the number of literate • Primary completion rate is approximated by the how their education systems are working and devel- people from self-reported data. Some use educa- gross intake ratio to last grade of primary educa- oping—statistics on enrollment and such efficiency tional attainment data as a proxy but apply different tion, which is the total number of new entrants in indicators as repetition rates, pupil–teacher ratios, lengths of school attendance or levels of completion. the last grade of primary education, regardless of and cohort progression. The World Bank and the Because definitions and methodologies of data col- age, expressed as a percentage of the population United Nations Educational, Scientific, and Cultural lection differ across countries, data should be used at the entrance age to the last grade of primary. Organization (UNESCO) Institute for Statistics jointly cautiously. • Youth literacy rate is the percentage of the popula- developed the primary completion rate indicator. The reported literacy data are compiled by the tion ages 15–24 that can, with understanding, both Increasingly used as a core indicator of an educa- UNESCO Institute for Statistics based on national cen- read and write a short simple statement on their tion system’s performance, it reflects an education suses and household surveys during 1985–2009. For everyday life. • Adult literacy rate is the percentage system’s coverage and the educational attainment countries without recent literacy data, the UNESCO of the population ages 15 and older that can, with of students. The indicator is a key measure of edu- Institute for Statistics estimates literacy rates with understanding, both read and write a short simple cation outcome at the primary level and of progress the Global Age-specific Literacy Projections Model statement on their everyday life. • PISA mathemat- toward the Millennium Development Goals and the (GALP). For detailed information on sources, defini- ics literacy is the country’s mean mathematics Education for All initiative. However, a high primary tions, and methodology, consult www.uis.unesco.org. score from the Programme for International Student completion rate does not necessarily mean high lev- Literacy statistics for most countries cover the Assessment (PISA). els of student learning. population ages 15 and older, but some include The primary completion rate reflects the primary younger ages or are confined to age ranges that tend cycle as defined by the International Standard Classi- to inflate literacy rates. The youth literacy rate for fication of Education (ISCED 97), ranging from three or ages 15–24 reflects recent progress in education: it four years of primary education (in a very small num- measures the accumulated outcomes of primary edu- ber of countries) to five or six years (in most coun- cation over the previous 10 years or so by indicating tries) and seven (in a small number of countries). the proportion of people who have passed through The table shows the primary completion rate, also the primary education system and acquired basic called the gross intake ratio to last grade of primary literacy and numeracy skills. Generally, literacy also education. It is the total number of new entrants in encompasses numeracy, the ability to make simple the last grade of primary education, regardless of age, arithmetic calculations. expressed as a percentage of the population at the In many countries national assessments enable entrance age to the last grade of primary education. ministries of education to monitor progress in learn- Data limitations preclude adjusting for students who ing outcomes. Of the handful of internationally or drop out during the final year of primary education. regionally comparable assessments, one of the Thus, this rate is a proxy that should be taken as an largest is the Programme for International Student upper estimate of the actual primary completion rate. Assessment (PISA). Coordinated by the Organisation There are many reasons why the primary comple- for Economic Co-operation and Development (OECD), tion rate can exceed 100 percent. The numerator it measures the knowledge and skills of 15-year-olds, may include late entrants and overage children the age at which students in most countries are near- who have repeated one or more grades of primary ing the end of their compulsory time in school. The education as well as children who entered school assessment tests reading, mathematical, and sci- early, while the denominator is the number of chil- entific literacy in terms of general competencies— dren at the entrance age to the last grade of primary that is, how well students can apply the knowledge education. and skills they have learned at school to real-life Basic student outcomes include achievements in challenges. It does not test how well a student has reading and mathematics judged against established mastered a school’s specific curriculum. standards. The UNESCO Institute for Statistics has The table presents the mean PISA mathematical established literacy as an outcome indicator based literacy score, as demonstrated through students’ on an internationally agreed definition. The literacy ability to analyze, reason, and communicate effec- rate is the percentage of the population who can, tively while posing, solving, and interpreting math- with understanding, both read and write a short, ematical problems that involve quantitative, spatial, Data sources simple statement about their everyday life. In prac- probabilistic, or other mathematical concepts. The Data on education completion and outcomes are tice, literacy is diffi cult to measure. To estimate average score in 2009 was 496. Because the figures from the UNESCO Institute for Statistics. Data literacy using such a definition requires census or are derived from samples, the scores reflect a small on PISA mathematics literacy are from the OECD. survey measurements under controlled conditions. measure of statistical uncertainty. 2011 World Development Indicators 91 2.15 Education gaps by income and gender Survey Gross intake Gross primary Average years Primary Children year rate in grade 1 participation rate of schooling completion rate out of school % of relevant % of relevant % of relevant % of relevant age group age group Ages 15–19 age group age group Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile Male Female quintile quintile Armenia 2005 93 80 106 102 9 10 119 116 113 112 2 1 Azerbaijan 2006 92 118 100 108 9 11 94 109 103 105 20 11 Bangladesh 2006 144 147 96 105 8 13 65 97 83 86 12 6 Belize 2006 80 89 106 113 8 11 59 130 107 72 5 7 Benin 2006 67 107 61 114 6 8 31 95 67 52 57 12 Bolivia 2003 92 95 108 129 6 9 76 98 90 81 22 5 Burundi 2005 201 191 91 144 4 7 20 70 44 39 5 3 Cambodia 2005 208 151 113 134 5 8 42 121 88 85 37 13 Cameroon 2006 108 75 93 116 6 14 43 111 90 74 3 2 Colombia 2005 161 84 127 99 6 10 94 109 100 103 11 2 Côte d’Ivoire 2006 51 77 57 110 5 8 47 127 88 71 4 3 Dominican Republic 2007 130 112 113 107 7 11 69 109 88 106 12 4 Egypt, Arab Rep. 2005 107 97 95 99 9 12 84 92 92 88 12 1 Ethiopia 2005 86 124 47 112 3 6 14 90 46 33 74 30 Georgia 2006 90 104 101 103 15 14 102 102 106 104 2 1 Ghana 2006 107 121 81 117 5 8 62 88 93 86 22 12 Guatemala 2000 176 124 81 114 4 8 15 80 34 36 7 3 Guinea 2005 55 119 52 121 5 7 32 93 76 48 60 16 Guinea-Bissau 2006 135 184 94 166 4 7 34 125 80 54 12 11 Guyana 2006 74 76 105 101 10 10 109 118 91 112 2 1 Haiti 2005 177 188 87 159 4 7 31 136 73 82 69 24 Kazakhstan 2006 118 101 106 103 9 9 102 115 102 97 0 1 Kenya 2003 134 125 92 106 6 9 40 76 71 72 38 11 Kosovo 2000 104 119 95 104 9 11 82 94 98 83 1 4 Lesotho 2004 169 111 116 124 5 8 36 122 69 85 18 3 Macedonia, FYR 2005 102 190 89 97 8 10 120 119 133 78 0 0 Madagascar 2003/04 250 153 118 145 3 8 42 141 77 77 33 3 Malawi 2004 235 145 98 122 5 8 24 81 47 35 23 4 Malawi 2006 234 207 133 169 5 7 30 80 49 52 0 0 Mali 2006 41 98 46 110 5 8 36 79 55 41 67 20 Mauritania 2007 67 96 62 116 5 9 17 89 48 52 2 2 Moldova 2005 96 84 99 95 9 12 97 100 96 98 2 1 Mozambique 2003 128 143 75 143 3 6 13 100 57 43 46 7 Namibia 2006 112 104 118 109 7 10 81 109 94 90 11 2 Nepal 2001 184 141 109 139 5 8 49 96 69 62 33 6 Nicaragua 2001 149 106 85 105 4 9 34 124 78 83 40 4 Niger 2006 50 90 35 89 4 7 31 71 60 30 74 28 Nigeria 2003 78 101 70 108 7 10 48 71 70 54 52 6 Panama 2003 125 116 108 102 7 11 100 94 105 88 1 1 Peru 2004 121 90 118 96 7 11 106 99 100 97 6 1 Rwanda 2005 274 195 131 151 3 5 31 88 48 42 13 8 Serbia 2005 90 98 98 100 9 10 86 96 94 89 1 0 Somalia 2005 13 44 8 93 8 10 2 58 26 20 87 46 Swaziland 2006 147 117 117 114 6 9 69 110 85 98 17 4 Syrian Arab Republic 2006 110 149 102 107 7 8 92 93 93 92 0 0 Tanzania 2004 123 123 82 119 5 7 32 108 58 60 44 15 Togo 2006 115 148 99 128 6 7 40 82 67 56 1 1 Turkey 2003 108 111 97 97 6 7 95 85 100 81 20 5 Uganda 2006 180 144 107 124 5 8 27 68 50 42 25 7 Vietnam 2006 99 100 108 100 .. .. 99 104 96 103 3 2 Yemen, Rep. 2006 66 109 50 101 7 10 25 103 84 31 2 2 Zambia 2007 135 123 105 112 5 9 50 101 88 73 22 3 Zimbabwe 1999 106 111 144 144 7 10 36 80 51 57 22 8 92 2011 World Development Indicators 2.15 PEOPLE Education gaps by income and gender About the data Definitions The data in the table describe basic information on exclusion. To that extent the index provides only a • Survey year is the year in which the underlying school participation and educational attainment partial view of the multidimensional concepts of pov- data were collected. • Gross intake rate in grade 1 by individuals in different socioeconomic groups erty, inequality, and inequity. is the number of students in the first grade of pri- within countries. The data are from Demographic Creating one index that includes all asset indica- mary education regardless of age as a percentage and Health Surveys (DHS) conducted by Macro tors limits the types of analysis that can be per- of the population of the offi cial primary school International with the support of the U.S. Agency for formed. In particular, the use of a unified index does entrance age. These data may differ from those in International Development, Multiple Indicator Clus- not permit a disaggregated analysis to examine table 2.13. • Gross primary participation rate is ter Surveys (MICS) conducted by the United Nations which asset indicators have a more or less important the ratio of total students attending primary school Children’s Fund (UNICEF), and Living Standards association with education status. In addition, some regardless of age to the population of the age group Measurement Study conducted by the World Bank asset indicators may reflect household wealth better that offi cially corresponds to primary education. Development Economics Research Group. These in some countries than in others—or reflect differ- • Average years of schooling are the years of for- large-scale household sample surveys, conducted ent degrees of wealth in different countries. Taking mal schooling received, on average, by youths and periodically in developing countries, collect infor- such information into account and creating country adults ages 15–19. • Primary completion rate is mation on a large number of health, nutrition, and specific asset indexes with country-specific choices the total number of students regardless of age in the population measures as well as on respondents’ of asset indicators might produce a more effective last grade of primary school, minus the number of social, demographic, and economic characteristics and accurate index for each country. The asset index repeaters in that grade, divided by the total number using detailed questionnaires. The data presented used in the table does not have this flexibility. of children of official graduation age. These data dif- here draw on responses to individual and household The analysis was carried out for around 80 coun- fer from those in table 2.14 because the source is questionnaires. tries. The table only shows the estimates for the different. • Children out of school are the number Typically, those surveys collect basic information poorest and richest quintiles, gender, and latest of children in the official primary school ages who on educational attainment and enrollment levels data; the full set of estimates for all indicators, other are not attending primary or secondary education, from every household member ages 5 or 6 and older subgroups including urban and rural areas, and older expressed as a percentage of children of the official as part of the household’s socioeconomic charac- data are available in the country reports (see Data primary school ages. Children in the official primary teristics. The surveys are not intended for the col- sources). The data in the table differ from data for school age, who are attending pre-primary education, lection of detailed education data. As a result, the similar indicators in preceding tables either because are considered out-of-school. These data differ from education section of the surveys does not replace the indicator refers to a period a few years preceding those in table 2.12 because the source is different. education flows, nor are as detailed as, for instance, the survey date or because the indicator definition the health section for the case of the DHS and MICS. or methodology is different. Findings should be used Still, the education data are very useful for providing with caution because of measurement error inherent micro-level information on education that cannot be in the use of survey data. obtained from administrative data, such as informa- tion on children not attending school. Socioeconomic status as displayed in the table is based on a household’s assets, including ownership of consumer items, features of the household’s dwell- ing, and other characteristics related to wealth. Each household asset on which information was collected was assigned a weight generated through principal- component analysis which was then used to create Data sources break-points defining wealth quintiles, expressed as quintiles of individuals in the population. Data on education gaps by income and gender are The selection of the asset index for defining socio- from an analysis of Demographic and Health Sur- economic status was based on pragmatic rather than veys by Macro International, Multiple Indicators conceptual considerations: Demographic and Health Cluster surveys by UNICEF, and Living Standards Surveys do not collect consumption data but do have Measurement Study by World Bank, and these detailed information on households’ ownership of sources are analyzed by the EdStats team of the consumer goods and access to a variety of goods World Bank Human Development Network Edu- and services. Like income or consumption, the asset cation using ADePT Education. Country reports, index defines disparities primarily in economic terms. further updates, and ADePT Education software It therefore excludes other possibilities of disparities are available at www.worldbank.org/education/ among groups, such as those based on gender, edu- edstats/. cation, ethnic background, or other facets of social 2011 World Development Indicators 93 2.16 Health systems Health Health workers Hospital Outpatient expenditure beds visits Out of External per 1,000 people Total Public pocket resources Per capita Nurses and per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives people per capita 2008 2008 2008 2008 2008 2008 2004–09a 2004–09a 2004–09a 2000–09a Afghanistan 7.4b 21.5b 77.7b 17.3b 47b 57b 0.2 0.5 0.4 .. Albania 6.8 39.4 58.6 2.1 281 569 1.1 4.0 2.9 1.5 Algeria 5.4 86.1 13.2 0.0 272 437 1.2 2.0 1.7 .. Angola 3.3c 85.0 c 15.0 c 3.0 c 148 c 183c 0.1 1.4 0.8 .. Argentina 8.4 62.6 22.2 0.0 695 1,062 3.2 0.5 4.0 .. Armenia 3.8 44.5 51.8 10.4 143 224 3.7 4.9 4.1 2.8 Australia 8.5d 65.4 d 17.9d 0.0 d 4,180 d 3,365d 3.0 9.6 3.8 6.2 Austria 10.5 73.7 15.1 0.0 5,201 4,150 4.7 7.8 7.7 6.7 Azerbaijan 4.3 19.3 73.3 0.6 240 395 3.8 8.4 7.9 4.6 Bangladesh 3.3 31.4 66.2 5.8 17 44 0.3 0.3 0.4 .. Belarus 5.6 72.2 19.9 0.2 351 688 5.1 12.6 11.2 13.2 Belgium 11.1 66.8 20.5 0.0 5,243 4,096 3.0 0.3 6.6 7.0 Benin 4.1 51.7 44.7 17.7 32 61 0.1 0.8 0.5 .. Bolivia 4.4 63.1 30.1 9.1 75 187 .. .. 1.1 .. Bosnia and Herzegovina 10.3 58.2 41.8 1.3 506 937 1.4 4.7 3.0 3.3 Botswana 7.6 78.2 7.2 4.2 530 1,053 0.3 2.8 1.8 .. Brazil 8.4 44.0 31.9 0.0 721 875 1.7 6.5 2.4 .. Bulgaria 7.1 57.8 36.5 0.0 482 974 3.6 4.7 6.5 .. Burkina Faso 5.9 59.1 38.1 29.2 37 82 0.1 0.7 0.9 .. Burundi 13.0 c 40.0 c 38.1c 34.5c 19c 50 c 0.0 0.2 0.7 .. Cambodia 5.7 23.8 64.4 17.1 43 118 0.2 0.8 0.1 .. Cameroon 5.3c 22.7c 73.5c 5.5c 65c 117c 0.2 1.6 1.5 .. Canada 9.8 69.5 15.5 0.0 4,445 3,867 1.9 10.1 3.4 6.3 Central African Republic 4.3 39.3 57.7 31.5 20 32 0.1 0.4 1.2 .. Chad 6.4 50.6 47.8 5.3 49 86 0.0 0.3 0.4 .. Chile 7.5 44.0 36.5 0.0 762 1,088 1.3 .. 2.1 .. China 4.3 47.3 43.5 0.2 146 265 1.4 1.4 4.1 .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 5.9 83.9 7.9 0.1 317 517 1.4 .. 1.0 .. Congo, Dem. Rep. 7.3 54.2 39.2 18.8 13 23 0.1 0.5 0.8 .. Congo, Rep. 2.7 49.9 50.1 4.7 81 108 0.1 0.8 1.6 .. Costa Rica 9.4 66.9 29.3 0.1 618 1,059 .. .. 1.2 .. Côte d’Ivoire 5.4 16.9 75.6 5.9 61 88 0.1 0.5 0.4 .. Croatia 7.8 84.9 14.5 0.0 1,230 1,553 2.7 5.6 5.5 6.4 Cuba 12.0 95.5 4.1 0.2 672 495 6.4 8.6 5.9 .. Czech Republic 7.1 80.1 15.7 0.0 1,469 1,830 3.6 8.6 7.2 15.0 Denmark 9.9 80.1 13.6 0.0 6,133 3,814 3.4 14.5 3.6 4.1 Dominican Republic 5.7 37.1 41.8 1.6 261 465 .. .. 1.0 .. Ecuador 5.3 42.3 50.4 1.1 216 466 .. .. 1.5 .. Egypt, Arab Rep. 4.8 42.2 56.5 0.6 97 261 2.8 3.5 1.7 .. El Salvador 6.0 59.6 35.8 3.5 217 410 1.6 0.4 1.1 .. Eritrea 3.1c 44.9c 55.1c 60.8 c 10 c 18 c 0.1 0.6 1.2 .. Estonia 6.1 77.8 19.7 1.5 1,074 1,325 3.4 6.8 5.7 6.9 Ethiopia 4.3 51.9 38.5 40.7 14 37 0.0 0.2 0.2 .. Finland 8.8 70.7 18.5 0.0 4,481 3,299 2.7 15.5 6.5 4.3 France 11.2 75.9 7.4 0.0 4,966 3,851 3.5 8.9 7.1 6.9 Gabon 2.6c 43.7c 56.3c 2.3c 264 c 384 c 0.3 5.0 1.3 .. Gambia, The 5.5 48.1 25.1 38.0 27 75 0.0 0.6 1.1 .. Georgia 8.7 30.9 66.5 10.5 258 433 4.5 3.9 3.3 2.2 Germany 10.5 74.6 11.8 0.0 4,720 3,922 3.5 10.8 8.2 7.0 Ghana 7.8 50.0 39.4 14.0 55 114 0.1 1.1 0.9 .. Greece 10.1 60.9 37.0 0.0 3,110 3,010 6.0 3.7 4.8 .. Guatemala 6.5 35.7 57.4 1.8 184 308 .. .. 0.6 .. Guinea 5.5 13.6 85.9 10.1 21 58 0.1 0.0 0.3 .. Guinea-Bissau 6.0 c 26.0 c 40.7c 77.3c 17c 32c 0.0 0.6 1.0 .. Haiti 6.1 22.1 47.4 34.7 40 69 .. .. 1.3 .. Honduras 6.3 58.6 34.5 10.4 121 248 .. .. 0.8 .. 94 2011 World Development Indicators 2.16 PEOPLE Health systems Health Health workers Hospital Outpatient expenditure beds visits Out of External per 1,000 people Total Public pocket resources Per capita Nurses and per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives people per capita 2008 2008 2008 2008 2008 2008 2004–09a 2004–09a 2004–09a 2000–09a Hungary 7.2 68.9 23.9 0.0 1,119 1,506 3.1 6.3 7.0 12.9 India 4.2 32.4 50.3 1.6 45 122 0.6 1.3 0.9 .. Indonesia 2.3 54.4 32.1 1.7 51 91 0.3 2.0 .. .. Iran, Islamic Rep. 5.5 42.4 55.6 0.0 254 613 0.9 1.6 1.4 .. Iraq 3.2c,e 70.2c,e 29.8 c,e 8.2c,e 109c,e 107c,e 0.7 1.4 1.3 .. Ireland 8.7 76.9 14.4 0.0 5,253 3,796 3.2 15.7 5.2 .. Israel 7.6 58.4 30.5 0.0 2,093 2,093 3.6 6.2 5.8 7.1 Italy 8.7 76.3 20.2 0.0 3,343 2,836 4.2 6.5 3.7 6.1 Jamaica 4.8 50.4 35.2 1.5 256 364 .. .. 1.7 .. Japan 8.3 80.5 14.5 0.0 3,190 2,817 2.1 4.1 13.8 14.4 Jordan 9.4f 62.7f 30.8f 1.8f 325f 496f 2.5 4.0 1.8 .. Kazakhstan 3.9 58.5 41.0 0.2 333 444 3.8 7.8 7.6 6.7 Kenya 4.2 36.3 49.2 26.8 33 66 0.1 .. 1.4 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 6.5 53.9 35.0 0.0 1,245 1,806 2.0 5.3 12.3 .. Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 2.0 76.3 21.7 0.0 990 932 1.8 4.6 1.8 .. Kyrgyz Republic 5.7 48.4 45.0 12.6 54 123 2.3 5.7 5.1 3.6 Lao PDR 4.0 17.6 62.6 16.1 34 84 0.3 1.0 1.2 .. Latvia 6.6 60.0 38.7 0.0 979 1,206 3.0 4.8 6.4 5.5 Lebanon 8.5 48.3 40.7 4.8 604 1,009 3.5 2.2 3.5 .. Lesotho 7.6 63.3 25.3 19.3 60 119 .. .. 1.3 .. Liberia 11.9 33.0 35.0 47.0 26 46 0.0 0.3 0.7 .. Libya 3.0 c 70.3c 29.7c 0.1c 458 c 502c 1.9 6.8 3.7 .. Lithuania 6.6 68.3 26.8 1.1 931 1,318 3.7 7.3 6.8 6.6 Macedonia, FYR 6.8 68.2 31.6 1.8 328 738 2.5 4.3 4.6 6.0 Madagascar 4.4 70.2 20.2 16.1 22 46 0.2 0.3 0.3 0.5 Malawi 6.5 59.4 11.6 87.0 18 50 0.0 0.3 1.1 .. Malaysia 4.3 44.1 40.9 0.0 353 621 0.9 2.7 1.8 .. Mali 5.6 47.1 52.6 22.2 39 65 0.0 0.3 0.6 .. Mauritania 2.6c 61.4 c 38.6c 27.4 c 27c 54 c 0.1 0.7 0.4 .. Mauritius 5.5 34.8 57.8 2.0 402 681 1.1 3.7 3.3 .. Mexico 5.9 46.9 49.3 0.0 588 837 2.9 4.0 1.6 2.5 Moldova 10.7g 50.6g 48.3g 4.7g 181g 320 g 2.7 6.7 6.1 6.0 Mongolia 3.8 81.4 14.6 7.5 73 131 2.8 3.5 5.9 .. Morocco 5.3 36.3 55.0 0.2 149 231 0.6 0.9 1.1 .. Mozambique 4.7 75.2 7.0 80.8 21 39 0.0 0.3 0.8 .. Myanmar 2.0 8.8 87.1 10.7 10 23 0.5 0.8 0.6 .. Namibia 6.9 54.6 8.1 21.4 284 440 0.4 2.8 2.7 .. Nepal 6.0 37.7 45.1 11.0 24 66 0.2 0.5 5.0 .. Netherlands 9.9 75.3 5.7 0.0 5,243 4,233 3.9 0.2 4.3 5.4 New Zealand 9.7 80.2 14.0 0.0 2,917 2,655 2.4 10.9 .. 4.4 Nicaragua 9.4 54.6 41.8 10.3 105 251 .. .. 0.9 .. Niger 5.9 57.7 40.7 26.3 21 40 0.0 0.1 0.3 .. Nigeria 5.2c 36.7c 60.4 c 4.6c 73c 113c 0.4 1.6 0.5 .. Norway 8.5 78.6 15.5 0.0 8,019 5,207 4.1 14.8 3.5 .. Oman 2.1 76.4 14.4 0.0 454 593 1.9 4.1 1.9 .. Pakistan 2.6 32.3 53.7 4.8 22 62 0.8 0.6 0.6 .. Panama 7.2 69.3 25.7 0.2 493 924 .. .. 2.2 .. Papua New Guinea 3.2 80.1 8.2 20.6 39 70 0.1 0.5 .. .. Paraguay 6.0 40.1 52.8 1.6 161 281 .. .. 1.3 .. Peru 4.5 59.4 30.6 0.8 200 381 0.9 1.3 1.5 .. Philippines 3.7 34.7 53.9 1.5 68 129 1.2 6.0 0.5 .. Poland 7.0 67.4 22.4 0.0 971 1,271 2.1 5.7 6.6 6.1 Portugal 10.6 67.4 22.1 0.0 2,434 2,578 3.8 5.3 3.4 3.9 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar 2.1 79.8 14.8 0.0 1,775 1,689 2.8 7.4 1.4 .. 2011 World Development Indicators 95 2.16 Health systems Health Health workers Hospital Outpatient expenditure beds visits Out of External per 1,000 people Total Public pocket resources Per capita Nurses and per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives people per capita 2008 2008 2008 2008 2008 2008 2004–09a 2004–09a 2004–09a 2000–09a Romania 5.4 78.9 17.6 0.0 517 840 1.9 4.2 6.5 5.6 Russian Federation 4.8 64.3 29.1 0.0 568 985 4.3 8.5 9.7 9.0 Rwanda 9.4 47.8 23.2 42.6 45 102 0.0 0.5 1.6 .. Saudi Arabia 3.6 68.2 17.0 0.0 676 831 0.9 2.1 2.2 .. Senegal 5.7 55.4 35.0 11.4 62 102 0.1 0.4 0.3 .. Serbia 10.0 62.5 35.5 0.4 499 867 2.0 4.4 5.4 .. Sierra Leone 13.3 6.5 83.7 17.0 47 104 0.0 0.2 0.4 .. Singapore 3.3 34.1 62.1 0.0 1,404 1,833 1.8 5.9 3.1 .. Slovak Republic 8.0 67.1 24.9 0.0 1,395 1,849 3.0 6.6 6.6 12.5 Slovenia 8.3 68.6 12.8 0.0 2,238 2,420 2.5 8.2 4.7 6.6 Somalia .. .. .. .. .. .. 0.0 0.1 .. .. South Africa 8.2 39.7 17.9 1.2 459 843 0.8 4.1 2.8 .. Spain 9.0 69.7 20.7 0.0 3,132 2,941 3.7 5.2 3.2 9.5 Sri Lanka 4.1 43.7 48.8 1.8 83 187 0.5 1.9 3.1 .. Sudan 6.9 33.1 64.1 4.3 97 147 0.3 0.8 0.7 .. Swaziland 5.8 60.8 16.6 11.1 141 287 0.2 6.3 2.1 .. Sweden 9.4 78.1 15.6 0.0 4,858 3,622 3.6 11.6 .. 2.8 Switzerland 10.7 59.1 30.8 0.0 6,988 4,815 4.1 16.0 5.3 .. Syrian Arab Republic 3.1 38.8 61.2 0.5 71 123 1.5 1.9 1.5 .. Tajikistan 5.0 27.7 68.8 10.5 37 95 2.0 5.0 5.4 8.3 Tanzania 4.5 71.9 18.3 59.2 22 57 0.0 0.2 1.1 .. Thailand 4.1 74.3 17.5 0.3 164 328 0.3 1.5 .. .. Timor-Leste 13.8 73.4 6.8 21.8 71 126 0.1 2.2 .. .. Togo 5.9 24.5 63.5 14.1 38 70 0.1 0.3 0.9 .. Trinidad and Tobago 4.7 48.9 41.8 0.3 908 1,237 1.2 3.6 2.5 .. Tunisia 6.2 54.1 40.0 0.5 248 501 1.2 3.3 2.1 .. Turkey 6.1 73.1 17.4 0.0 623 845 1.6 1.9 2.4 3.1 Turkmenistan 2.2c 49.1c 50.9c 0.3c 82c 146c 2.4 4.5 4.1 3.7 Uganda 8.4 17.4 54.0 27.9 44 112 0.1 1.3 0.4 .. Ukraine 6.8 55.9 40.9 0.4 268 502 3.1 8.5 8.7 10.8 United Arab Emirates 2.5 67.1 21.7 0.0 1,427 868 1.9 4.1 1.9 .. United Kingdom 8.7 82.6 11.1 0.0 3,771 3,222 2.7 10.3 3.4 4.9 United States 15.2 47.8 12.7 0.0 7,164 7,164 2.7 9.8 3.1 9.0 Uruguay 7.8 63.1 12.1 0.2 725 982 3.7 5.6 2.9 .. Uzbekistan 4.9 50.5 48.5 2.4 51 134 2.6 10.8 4.8 8.7 Venezuela, RB 5.4 44.9 49.3 0.0 597 683 .. .. 1.3 .. Vietnam 7.2 38.5 55.5 1.7 76 201 1.2 1.0 2.9 .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 5.3 30.1 68.9 4.6 67 137 0.3 0.7 0.7 .. Zambia 5.9 62.0 28.3 38.4 68 80 0.1 0.7 1.9 .. Zimbabwe .. .. .. .. .. .. 0.2 0.7 3.0 .. World 9.4 w 60.5 w 17.9 w 0.2 w 857 w 901 w 1.4 w 3.0 w 2.9 w .. w Low income 5.3 41.9 47.9 24.2 25 55 0.2 0.5 .. .. Middle income 5.3 51.4 37.0 0.6 186 314 1.3 2.3 2.4 .. Lower middle income 4.3 45.5 45.0 1.1 95 188 1.0 1.7 1.9 .. Upper middle income 6.3 55.4 31.4 0.2 531 792 2.3 4.8 4.5 .. Low & middle income 5.3 51.2 37.2 1.1 163 277 1.1 2.0 2.3 .. East Asia & Pacific 4.2 48.2 42.2 0.5 125 231 1.2 1.7 4.0 .. Europe & Central Asia 5.4 65.4 28.2 0.3 448 738 3.2 6.8 7.3 7.6 Latin America & Carib. 7.2 50.3 34.3 0.2 542 733 2.2 4.8 .. .. Middle East & N. Africa 5.0 53.0 44.3 1.0 176 350 1.5 2.2 1.6 .. South Asia 4.0 32.6 51.5 2.4 40 106 0.6 1.1 0.9 .. Sub-Saharan Africa 6.1 42.9 36.5 9.3 74 132 0.2 1.0 .. .. High income 11.0 62.2 14.2 0.0 4,455 4,136 2.9 7.9 6.1 8.5 Euro area 10.0 73.7 14.2 0.0 4,132 3,458 3.8 7.5 5.8 6.8 a. Data are for the most recent year available. b. GDP includes measures of illicit activities such as opium production. Government expenditures include external assistance (external budget). c. Derived from incomplete data. d. Excludes expenditure in residential facilities for care of the aged. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine. g. Excludes Transdniestria. 96 2011 World Development Indicators 2.16 PEOPLE Health systems About the data Definitions Health systems—the combined arrangements of this reason, data for this indicator should not be • Total health expenditure is the sum of public and institutions and actions whose primary purpose compared across editions. private health expenditure. It covers the provision is to promote, restore, or maintain health (World External resources for health are disbursements of health services (preventive and curative), family Health Organization, World Health Report 2000)— to recipient countries as reported by donors, lagged planning and nutrition activities, and emergency aid are increasingly being recognized as key to com- one year to account for the delay between disburse- for health but excludes provision of water and sani- bating disease and improving the health status of ment and expenditure. Disbursement data are not tation. • Public health expenditure is recurrent and populations. The World Bank’s Healthy Develop- available before 2002, so commitments are used. capital spending from central and local governments, ment: Strategy for Health, Nutrition, and Population Except where a reliable full national health account external borrowing and grants (including donations Results emphasizes the need to strengthen health study has been done, most data are from the Organ- from international agencies and nongovernmental systems, which are weak in many countries, in order isation for Economic Co-operation and Development organizations), and social (or compulsory) health to increase the effectiveness of programs aimed at Development Assistance Committee’s Creditor insurance funds. • Out-of-pocket health expendi- reducing specific diseases and further reduce mor- Reporting System database, which compiles data ture is the percentage of total expenditure that is bidity and mortality (World Bank 2007). To evaluate from government expenditure accounts, government direct household outlays, including gratuities and health systems, the World Health Organization (WHO) records on external assistance, routine surveys of in-kind payments, for health practitioners and phar- has recommended that key components—such as external financing assistance, and special services. maceutical suppliers, therapeutic appliances, and financing, service delivery, workforce, governance, Because of the variety of sources, care should be other goods and services whose primary intent is and information—be monitored using several key taken in interpreting the data. to restore or enhance health. • External resources indicators (WHO 2008b). The data in the table are In countries where the fiscal year spans two cal- for health are funds or services in kind that are pro- a subset of the first four indicators. Monitoring endar years, expenditure data have been allocated vided by entities not part of the country in ques- health systems allows the effectiveness, efficiency, to the later year (for example, 2008 data cover fis- tion. The resources may come from international and equity of different health system models to be cal year 2007/08). Many low-income countries use organizations, other countries through bilateral compared. Health system data also help identify Demographic and Health Surveys or Multiple Indica- arrangements, or foreign nongovernmental orga- weaknesses and strengths and areas that need tor Cluster Surveys funded by donors to obtain health nizations and are part of public and private health investment, such as additional health facilities, system data. expenditure. • Health expenditure per capita is better health information systems, or better trained Data on health worker (physicians, nurses, and total health expenditure divided by population in human resources. midwives) density show the availability of medical U.S. dollars and in international dollars converted Health expenditure data are broken down into pub- personnel. The WHO estimates that at least 2.5 using 2005 purchasing power parity (PPP) rates from lic and private expenditures. In general, low-income physicians, nurses, and midwives per 1,000 people the World Bank’s International Comparison Project. economies have a higher share of private health are needed to provide adequate coverage with pri- • Physicians include generalist and specialist medi- expenditure than do middle- and high-income coun- mary care interventions associated with achieving cal practitioners. • Nurses and midwives include pro- tries, and out-of-pocket expenditure (direct payments the Millennium Development Goals (WHO, World fessional nurses and midwives, auxiliary nurses and by households to providers) makes up the largest Health Report 2006). The WHO compiles data from midwives, enrolled nurses and midwives, and other proportion of private expenditure. High out-of-pocket household and labor force surveys, censuses, and personnel, such as dental nurses and primary care expenditures may discourage people from access- administrative records. Data comparability is limited nurses. • Hospital beds are inpatient beds for both ing preventive or curative care and can impoverish by differences in definitions and training of medical acute and chronic care available in public, private, households that cannot afford needed care. Health personnel varies. In addition, human resources tend general, and specialized hospitals and rehabilita- financing data are collected through national health to be concentrated in urban areas, so that average tion centers. • Outpatient visits per capita are the accounts, which systematically, comprehensively, densities do not provide a full picture of health per- number of visits to health care facilities per capita, and consistently monitoring health system resource sonnel available to the entire population. including repeat visits. flows. To establish a national health account, coun- Availability and use of health services, shown by tries must define the boundaries of the health system hospital beds per 1,000 people and outpatient visits and classify health expenditure information along per capita, reflect both demand- and supply-side fac- several dimensions, including sources of financing, tors. In the absence of a consistent definition these Data sources providers of health services, functional use of health are crude indicators of the extent of physical, finan- Data on health expenditures are from the WHO’s expenditures, and beneficiaries of expenditures. The cial, and other barriers to health care. National Health Account database (latest updates accounting system can then provide an accurate pic- are available at www.who.int/nha/), supple- ture of resource envelopes and financial flows and mented by country data. Data on physicians, and allow analysis of the equity and efficiency of financing nurses and midwives, are from WHO’s Global Atlas to inform policy. of the Health Workforce. For the latest updates and This year’s table presents out-of-pocket expendi- metadata, see http://apps.who.int/globalatlas/. ture as a percentage of total health expenditure; pre- Data on hospital beds and outpatient visits are vious editions presented out-of-pocket expenditure from the WHO, supplemented by country data. as a percentage of private health expenditure. For 2011 World Development Indicators 97 2.17 Health information Year last national Number of Year Year Completeness health account national health of last of last completed accounts health survey census completed % Birth Infant death Total death registration reporting reporting 1995–2009 2001–11 2004–09a 2004–09a 2004–09a Afghanistan 0 2003 .. .. .. Albania 2009 3 2008/09 2001 99 28 76 Algeria 2003 3 2006 2008 99 .. 90 Angola 0 2006/07 .. .. .. Argentina 1997 1 2010 91 100 100 Armenia 2009 6 2005 2001 96 38 100 Australia 2007 13 2006 .. 100 96 Austria 2008 14 2001 .. 90 100 Azerbaijan 0 2006 2009 94 24 100 Bangladesh 2008 13 2007 2001 10 .. .. Belarus 0 2005 2009 .. 55 96 Belgium 2008 6 2001 .. 100 97 Benin 2008 4 2006 2002 60 .. .. Bolivia 2007 13 2008 2001 .. .. 30 Bosnia and Herzegovina 2009 6 2006 100 54 92 Botswana 2002 3 2000 2001 72 35 47 Brazil 2006 7 1996 2010 91 48 87 Bulgaria 2007 6 2001 .. 79 100 Burkina Faso 2008 6 2006 2006 64 29 88 Burundi 2007 1 2005 2008 60 .. .. Cambodia 0 2005 2008 66 0 100 Cameroon 1995 1 2006 2005 70 .. .. Canada 2009 15 2006 .. 100 98 Central African Republic 0 2006 2003 49 .. .. Chad 0 2004 2009 9 .. .. Chile 2008 5 2002 99 100 100 China 2007 13 2010 .. .. 99 Hong Kong SAR, China 0 2006 .. 66 91 Colombia 2003 9 2005 2006 90 52 71 Congo, Dem. Rep. 2009 7 2010 31 .. .. Congo, Rep. 2005 1 2009 2007 81 .. .. Costa Rica 2003 2 1993 2000 .. 90 98 Côte d’Ivoire 2008 2 2006 55 .. .. Croatia 0 2001 .. 75 100 Cuba 0 2006 2002 100 99 100 Czech Republic 2008 14 1993 2001 .. 84 94 Denmark 2007 13 2001 .. 97 97 Dominican Republic 2008 8 2007 2010 78 1 54 Ecuador 2008 7 2004 2010 85 58 86 Egypt, Arab Rep. 2008 3 2008 2006 99 47 97 El Salvador 2009 14 2008 2007 99 36 75 Eritrea 0 2002 .. .. .. Estonia 2008 10 2000 .. 68 94 Ethiopia 2008 4 2005 2007 7 .. 88 Finland 2008 14 2010 .. 84 98 France 2008 14 2006 .. 95 100 Gabon 0 2000 2003 .. .. .. Gambia, The 2004 3 2005/06 2003 55 .. .. Georgia 2009 9 2005 2002 92 54 83 Germany 2008 14 .. 96 99 Ghana 2002 1 2008 2010 71 95 .. Greece 0 2001 .. 78 95 Guatemala 2008 14 2002 2002 .. 62 93 Guinea 0 2005 43 .. .. Guinea-Bissau 0 2010 2009 39 .. .. Haiti 2006 1 2005/06 2003 81 .. .. Honduras 2005 3 2005/06 2001 94 100 99 98 2011 World Development Indicators 2.17 PEOPLE Health information Year last national Number of Year Year Completeness health account national health of last of last completed accounts health survey census completed % Birth Infant death Total death registration reporting reporting 1995–2009 2001–11 2004–09a 2004–09a 2004–09a Hungary 2008 14 2001 .. 84 97 India 2004 2 2005/06 2001 41 .. .. Indonesia 2008 8 2007 2010 53 .. .. Iran, Islamic Rep. 2007 4 2000 2006 .. .. 99 Iraq 0 2006 95 100 100 Ireland 2008 14 2006 .. 75 99 Israel 2006 1 2009 .. 90 99 Italy 2008 4 2001 .. 99 98 Jamaica 2000 1 2005 2001 89 76 68 Japan 2007 13 2010 .. 88 98 Jordan 2008 5 2009 2004 .. .. 76 Kazakhstan 2007 1 2006 2009 99 95 82 Kenya 2006 2 2008/09 2009 60 37 39 Korea, Dem. Rep. 0 2010 2008 .. 43 91 Korea, Rep. 2008 14 2005 .. 80 92 Kosovo 0 .. .. .. Kuwait 0 1996 2010 .. 100 100 Kyrgyz Republic 2009 5 2005/06 2009 94 78 95 Lao PDR 0 2006 2005 72 .. .. Latvia 2007 5 2000 .. 79 96 Lebanon 2005 4 2000 .. .. 72 Lesotho 0 2009/10 2006 26 .. .. Liberia 2008 1 2009 2008 4 .. .. Libya 0 2000 2006 .. .. .. Lithuania 2008 7 2001 .. 68 95 Macedonia, FYR 0 2005 2002 94 87 99 Madagascar 2007 2 2008/09 75 .. .. Malawi 2006 5 2006 2008 .. .. 75 Malaysia 2006 10 2010 .. 62 100 Mali 2004 6 2006 2009 53 .. .. Mauritania 0 2007 2000 56 .. .. Mauritius 2004 2 2000 .. 80 97 Mexico 2009 15 1995 2010 .. 89 100 Moldova 0 2005 2004 .. 62 89 Mongolia 2003 5 2005 2010 98 60 96 Morocco 2006 3 2006 2004 .. .. .. Mozambique 2006 4 2009 2007 31 .. .. Myanmar 2007 10 2000 .. 56 55 Namibia 2008 11 2006/07 2001 67 .. 100 Nepal 2005 5 2006 2001 35 .. .. Netherlands 2008 14 2001 .. 84 97 New Zealand 2008 14 2006 .. 100 98 Nicaragua 2008 14 2006/07 2005 .. 66 68 Niger 2006 4 2006 2001 32 .. .. Nigeria 2005 8 2008 2006 30 .. 1 Norway 2008 12 2001 .. 97 100 Oman 1998 1 1995 2010 .. 100 97 Pakistan 2006 1 2006/07 27 85 84 Panama 2003 1 2003 2010 .. 70 88 Papua New Guinea 2000 3 1996 2000 .. .. .. Paraguay 2008 13 2004 2002 .. 34 71 Peru 2005 11 2008 2007 93 41 70 Philippines 2007 13 2008 2010 .. 39 100 Poland 2008 14 2002 .. 95 100 Portugal 2007 8 2001 .. 85 95 Puerto Rico 0 1996 2010 .. 100 95 Qatar 0 2010 .. 95 77 2011 World Development Indicators 99 2.17 Health information Year last national Number of Year Year Completeness health account national health of last of last completed accounts health survey census completed % Birth Infant death Total death registration reporting reporting 1995–2009 2001–11 2004–09a 2004–09a 2004–09a Romania 2006 9 1999 2002 .. 76 96 Russian Federation 2007 13 1996 2010 .. 80 95 Rwanda 2006 5 2007 2002 82 .. .. Saudi Arabia 0 2007 2010 .. 94 100 Senegal 2005 2 2008/09 2002 55 .. .. Serbia 2009 7 2005/06 2002 99 38 90 Sierra Leone 2006 3 2008 2004 51 .. .. Singapore 0 2005 2010 .. 93 72 Slovak Republic 2008 12 2001 .. 93 98 Slovenia 2008 14 2002 .. 72 96 Somalia 0 2006 3 .. .. South Africa 1998 3 2003 2001 92 81 81 Spain 2008 14 2001 .. 99 100 Sri Lanka 2006 12 2006/07 2001 97 63 91 Sudan 2008 1 2006 2008 33 .. .. Swaziland 0 2006/07 2007 30 .. .. Sweden 2008 8 .. 83 99 Switzerland 2009 15 2010 .. 100 99 Syrian Arab Republic 0 2006 2004 95 .. 100 Tajikistan 2008 2 2005 2010 88 19 69 Tanzania 2006 3 2007/08 2002 22 .. .. Thailand 2007 13 2005/06 2010 99 86 65 Timor-Leste 0 2009 2010 .. .. .. Togo 2002 1 2006 2010 78 .. .. Trinidad and Tobago 2000 1 2006 2000 96 50 94 Tunisia 2005 5 2006 2004 .. .. 98 Turkey 2005 8 2003 2000 94 56 100 Turkmenistan 0 2006 96 .. .. Uganda 2006 6 2009/10 2002 21 .. .. Ukraine 2008 6 2007 2001 100 90 100 United Arab Emirates 0 2010 .. 75 100 United Kingdom 2008 12 2001 .. 100 95 United States 2009 15 2009 2010 .. 100 100 Uruguay 2008 13 2004 .. 78 100 Uzbekistan 0 2006 100 .. .. Venezuela, RB 0 2000 2001 .. 62 84 Vietnam 2007 10 2006 2009 88 72 83 West Bank and Gaza 1 2006 2007 96 31 66 Yemen, Rep. 2007 4 2006 2004 22 .. 15 Zambia 2006 11 2007 2000 14 .. .. Zimbabwe 2001 3 2005/06 2002 74 .. .. a. Data are for the most recent year available. 100 2011 World Development Indicators 2.17 PEOPLE Health information About the data Definitions According to the World Health Organization (WHO), the institutional frameworks needed to ensure data • Year last national health account completed is the health information systems are crucial for moni- quality, including independence, transparency, and latest year for which the health expenditure data are toring and evaluating health systems, which are access. Benchmarks include the availability of inde- available using the national health account approach. increasingly recognized as important for combating pendent coordination mechanisms and micro- and • Number of national health accounts completed is disease and improving health status. Health informa- meta-data (WHO 2008a). the number of national health accounts completed tion systems underpin decisionmaking through four The indicators in the table are all related to data between 1995 and 2008. • Year of last health sur- data functions: generation, compilation, analysis and generation, including the years the last national vey is the latest year the national survey that collects synthesis, and communication and use. The health health account, last health survey, and latest popu- health information was conducted. • Year of last cen- information system collects data from the health sec- lation census were completed. Frequency of data col- sus is the latest year a census was conducted in the tor and other relevant sectors; analyzes the data and lection, a benchmark of data generation, is shown last 10 years. • Completeness of birth registration is ensures their overall quality, relevance, and timeli- as the number of years for which a national health the percentage of children under age 5 whose births ness; and converts data into information for health- account was completed between 1995 and 2009. were registered at the time of the survey. The numera- related decisionmaking (WHO 2008b). National health account data may be collected tor of completeness of birth registration includes chil- Numerous indicators have been proposed to using different approaches such as Organisation for dren whose birth certificate was seen by the interviewer assess a country’s health information system. Economic Co-operation and Development (OECD) or whose mother or caretaker says the birth has been They can be grouped into two broad types: indica- System of Health Accounts, WHO National Health registered. • Completeness of infant death reporting tors related to data generation using core sources Account producers guide approach, local national is the number of infant deaths reported by national and methods (health surveys, civil registration, cen- health accounting methods, or Pan American statistical authorities to the United Nations Statistics suses, facility reporting, health system resource Health Organization/WHO satellite health accounts Division’s Demographic Yearbook divided by the number tracking) and indicators related to capacity for approach. of infant deaths estimated by the United Nations Popu- data synthesis, analysis, and validation. Indicators Indicators related to data generation include com- lation Division. • Completeness of total death report- related to data generation reflect a country’s capac- pleteness of birth registration, infant death report- ing is the number of total deaths from civil registration ity to collect relevant data at suitable intervals using ing, and total death reporting. system reported by national statistical authorities to the most appropriate data sources. Benchmarks the United Nations Statistics Division’s Demographic include periodicity, timeliness, contents, and avail- Yearbook divided by the number of total deaths esti- ability. Indicators related to capacity for synthesis, mated by the United Nations Population Division. analysis, and validation measure the dimensions of Data sources Data on year last national health account completed South Asia has the highest number of unregistered births 2.17a and number of national health accounts completed were compiled by staff in the World Health Organiza- tion’s Health Financing Department and the World Number of unregistered births, 2007 (millions) Bank’s Health, Nutrition, and Population Unit using data on the health expenditures reported by the Latin America and Caribbean 1.3 CEE/CIS 0.4 WHO and OECD and consultation with colleagues Middle East and North Africa 2.4 from countries and other international organizations. East Asia Data on year of last health survey are from Macro and Pacific, excluding China 3.5 International and the United Nations Children’s Fund (UNICEF). Data on year of last census are from Eastern and Southern Africa South Asia United Nations Statistics Division’s 2011 World 9.7 24.3 Population and Housing Census Program (http:// unstats.un.org/unsd/demographic/sources/cen- West and Central Africa sus/2010_PHC/default.htm.) Data on completeness 9.8 of birth registration are compiled by UNICEF in State of the World’s Children 2010 based mostly on house- hold surveys and ministry of health data. Data used to calculate completeness of infant death reporting Too many people, especially poor, are never counted. They are born, live, and die uncounted and and total death reporting are from the United Nations ignored. Around 50 million, or 40 percent of children born in 2007, have not been registered. Statistics Division’s Population and Vital Statistics Report and the United Nations Population Division’s Source: United Nations Children’s Fund Childinfo. World Population Prospects: The 2008 Revision. 2011 World Development Indicators 101 2.18 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis an improved improved immunization with acute diarrhea who sleeping with fever water source sanitation rate respiratory received oral under receiving facilities infection rehydration treated antimalarial taken to and continuous netsa drugs Treatment Case health feeding success detection provider rate rate % of children ages % of children % of children % of % of children % of new % of new % of % of 12–23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2008 1990 2008 2009 2009 2004–09c 2004–09c 2004–09c 2004–09c 2008 2009 Afghanistan .. 48 .. 37 76 83 .. .. .. .. 88 48 Albania .. 97 .. 98 97 98 70 63 .. .. 91 94 Algeria 94 83 88 95 88 93 53 24 .. .. 90 100 Angola 36 50 25 57 77 73 .. .. 17.7 29.3 70 75 Argentina 94 97 90 90 99 94 .. .. .. .. 44 67 Armenia .. 96 .. 90 96 93 36 59 .. .. 73 70 Australia 100 100 100 100 94 92 .. .. .. .. 80 89 Austria 100 100 100 100 83 83 .. .. .. .. 47 48 Azerbaijan 70 80 .. 45 67 73 33 31 .. .. 56 75 Bangladesh 78 80 39 53 89 94 37 68 .. .. 91 44 Belarus 100 100 .. 93 99 96 90 54 .. .. 71 140 Belgium 100 100 100 100 94 99 .. .. .. .. 76 88 Benin 56 75 5 12 72 83 36 42 20.1 54.0 89 47 Bolivia 70 86 19 25 86 85 51 .. .. .. 84 64 Bosnia and Herzegovina .. 99 .. 95 93 90 91 53 .. .. 92 91 Botswana 93 95 36 60 94 96 .. .. .. .. 65 62 Brazil 88 97 69 80 99 99 50 .. .. .. 71 86 Bulgaria 100 100 99 100 96 94 .. .. .. .. 85 86 Burkina Faso 41 76 6 11 75 82 39 42 9.6 48.0 76 14 Burundi 70 72 44 46 91 92 38 23 8.3 30.0 90 25 Cambodia 35 61 9 29 92 94 48 50 4.2 0.2 95 60 Cameroon 50 74 47 47 74 80 35 22 13.1 57.8 76   70 Canada 100 100 100 100 93 80 .. .. .. .. 78 93 Central African Republic 58 67 11 34 62 54 32 47 15.1 57.0 71 60 Chad 38 50 6 9 23 23 12 27 .. 53.0 54   26 Chile 90 96 84 96 96 97 .. .. .. .. 72 130 China 67 89 41 55 94 97 .. .. .. .. 94 75 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. 68 89 Colombia 88 92 68 74 95 92 62 39 .. .. 76 70 Congo, Dem. Rep. 45 46 9 23 76 77 42 42 5.8 29.8 87 46 Congo, Rep. .. 71 .. 30 76 91 48 39 6.1 48.0 76 69 Costa Rica 93 97 93 95 81 86 .. .. .. .. 89 93 Côte d’Ivoire 76 80 20 23 67 81 35 45 3.0 36.0 76 27 Croatia .. 99 .. 99 98 96 .. .. .. .. 58 76 Cuba 82 94 80 91 96 96 .. .. .. .. 88 120 Czech Republic 100 100 100 98 98 99 .. .. .. .. 68 70 Denmark 100 100 100 100 84 89 .. .. .. .. 41 79 Dominican Republic 88 86 73 83 79 82 70 55 .. 0.6 75 60 Ecuador 72 94 69 92 66 75 .. .. .. .. 78 51 Egypt, Arab Rep. 90 99 72 94 95 97 73 19 .. .. 89 63 El Salvador 74 87 75 87 95 91 67 .. .. .. 91 92 Eritrea 43 61 9 14 95 99 .. .. .. .. 76 58 Estonia 98 98 .. 95 95 95 .. .. .. .. 60 89 Ethiopia 17 38 4 12 75 79 19 15 33.1 9.5 84 50 Finland 100 100 100 100 98 99 .. .. .. .. 72 110 France 100 100 100 100 90 99 .. .. .. .. .. 77 Gabon .. 87 .. 33 55 45 .. .. .. .. 53 42 Gambia, The 74 92 .. 67 96 98 69 38 49.0 62.6 84 47 Georgia 81 98 96 95 83 88 74 37 .. .. 73 100 Germany 100 100 100 100 96 93 .. .. .. .. 68 91 Ghana 54 82 7 13 93 94 51 45 28.2 43.0 86 31 Greece 96 100 97 98 99 99 .. .. .. .. .. 92 Guatemala 82 94 65 81 92 92 .. .. .. .. 83 33 Guinea 52 71 9 19 51 57 42 38 4.5 43.5 78 26 Guinea-Bissau .. 61 .. 21 76 68 57 25 39.0 45.7 70 59 Haiti 47 63 26 17 59 59 31 43 .. 5.1 82   60   Honduras 72 86 44 71 99 98 56 49 .. 0.5 85 68 102 2011 World Development Indicators 2.18 PEOPLE Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis an improved improved immunization with acute diarrhea who sleeping with fever water source sanitation rate respiratory received oral under receiving facilities infection rehydration treated antimalarial taken to and continuous netsa drugs Treatment Case health feeding success detection provider rate rate % of children ages % of children % of children % of % of children % of new % of new % of % of 12–23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2008 1990 2008 2009 2009 2004–09c 2004–09c 2004–09c 2004–09c 2008 2009 Hungary 96 100 100 100 99 99 .. .. .. .. 53 82 India 72 88 18 31 71 66 69 33 .. 8.2 87 67 Indonesia 71 80 33 52 82 82 66 54 3.3 0.8 91 67 Iran, Islamic Rep. 91 .. 83 .. 99 99 .. .. .. .. 83 74 Iraq 81 79 .. 73 69 65 82 64 .. .. 88 48 Ireland 100 100 99 99 89 93 .. .. .. .. 76 89 Israel 100 100 100 100 96 93 .. .. .. .. 81 89 Italy 100 100 .. .. 91 96 .. .. .. .. .. 66 Jamaica 93 94 83 83 88 90 75 39 .. .. 64 78 Japan 100 100 100 100 94 98 .. .. .. .. 48 89 Jordan 97 96 .. 98 95 98 75 32 .. .. 84 100 Kazakhstan 96 95 96 97 99 98 71 48 .. .. 64 80 Kenya 43 59 26 31 74 75 56 .. 46.1 23.2 85 85 Korea, Dem. Rep. 100 100 .. .. 98 93 93 .. .. .. 89 93 Korea, Rep. .. 98 100 100 93 94 .. .. .. .. 84 89 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 99 99 100 100 97 98 .. .. .. .. 80 89 Kyrgyz Republic .. 90 .. 93 99 95 62 22 .. .. 84 66 Lao PDR .. 57 .. 53 59 57 32 49 40.5 8.2 93 68 Latvia 99 99 .. 78 96 95 .. .. .. .. 33 94 Lebanon 100 100 .. .. 53 74 .. .. .. .. 77 78 Lesotho 61 85 32 29 85 83 66 53 .. .. 73 93 Liberia 58 68 11 17 64 64 62 47 26.4 67.2 79 52 Libya 54 .. 97 97 98 98 .. .. .. .. 69 82 Lithuania .. .. .. .. 96 98 .. .. .. .. 82 81 Macedonia, FYR .. 100 .. 89 96 96 93 45 .. .. 89 98 Madagascar 31 41 8 11 64 78 42 47 45.8 19.7 81 44 Malawi 40 80 42 56 92 93 52 27 24.7 24.9 87 49 Malaysia 88 100 84 96 95 95 .. .. .. .. 78 76 Mali 29 56 26 36 71 74 38 38 27.1 31.7 82 16 Mauritania 30 49 16 26 59 64 45 32 2.1 20.7 68 24 Mauritius 99 99 91 91 99 99 .. .. .. .. 87 41 Mexico 85 94 66 85 95 89 .. .. .. .. 85 99 Moldova .. 90 .. 79 90 85 60 48 .. .. 62 68 Mongolia 58 76 .. 50 94 95 63 47 .. .. 87 75 Morocco 74 81 53 69 98 99 38 46 .. .. 85 93 Mozambique 36 47 11 17 77 76 65 47 22.8 36.7 84 46 Myanmar 57 71 .. 81 87 90 .. .. .. .. 85 64 Namibia 64 92 25 33 76 83 72 48 10.5 9.8 82 76 Nepal 76 88 11 31 79 82 43 37 .. 0.1 89 73 Netherlands 100 100 100 100 96 97 .. .. .. .. 85 89 New Zealand 100 100 .. .. 89 92 .. .. .. .. 73 89 Nicaragua 74 85 43 52 99 98 .. .. .. .. 89 90 Niger 35 48 5 9 73 70 47 34 42.8 33.0 81 36 Nigeria 47 58 37 32 41 42 45 25 5.5 33.2 78 19 Norway 100 100 100 100 92 92 .. .. .. .. 84 91 Oman 80 88 85 .. 97 98 .. .. .. .. 98 89 Pakistan 86 90 28 45 80 85 69 37 .. 3.3 90 63 Panama 84 93 58 69 85 84 .. .. .. .. 79 94 Papua New Guinea 41 40 47 45 58 64 63 .. .. .. 64 73 Paraguay 52 86 37 70 91 92 .. .. .. .. 81 78 Peru 75 82 54 68 91 93 72 60 .. .. 82 97 Philippines 84 91 58 76 88 87 50 60 .. 0.0 88 57 Poland 100 100 .. 90 98 99 .. .. .. .. 74 84 Portugal 96 99 92 100 95 96 .. .. .. .. 87 86 Puerto Rico .. .. .. .. .. .. .. .. .. .. 63 89 Qatar 100 100 100 100 99 99 .. .. .. .. 73 89 2011 World Development Indicators 103 2.18 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis an improved improved immunization with acute diarrhea who sleeping with fever water source sanitation rate respiratory received oral under receiving facilities infection rehydration treated antimalarial taken to and continuous netsa drugs Treatment Case health feeding success detection provider rate rate % of children ages % of children % of children % of % of children % of new % of new % of % of 12–23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2008 1990 2008 2009 2009 2004–09c 2004–09c 2004–09c 2004–09c 2008 2009 Romania .. .. 71 72 97 97 .. .. .. .. 37 79 Russian Federation 93 96 87 87 98 98 .. .. .. .. 57 84 Rwanda 68 65 23 54 92 97 28 24 55.7 5.6 87 19 Saudi Arabia 89 .. .. .. 98 98 .. .. .. .. 61 89 Senegal 61 69 38 51 79 86 47 43 29.2 9.1 84 31 Serbia .. 99 .. 92 95 95 93 71 .. .. 86 89 Sierra Leone .. 49 .. 13 71 75 46 57 25.8 30.1 86 31 Singapore 100 100 99 100 95 97 .. .. .. .. 81 89 Slovak Republic .. 100 100 100 99 99 .. .. .. .. 93 89 Slovenia 100 99 100 100 95 96 .. .. .. .. 80 80 Somalia .. 30 .. 23 24 31 13 7 11.4 7.9 81 42 South Africa 83 91 69 77 62 69 .. .. .. .. 76 74 Spain 100 100 100 100 98 96 .. .. .. .. .. 89 Sri Lanka 67 90 70 91 96 97 58 67 2.9 0.3 85 70 Sudan 65 57 34 34 82 84 90 56 27.6 54.2 81 52 Swaziland .. 69 .. 55 95 95 73 22 0.6 0.6 68 67 Sweden 100 100 100 100 97 98 .. .. .. .. 87 89 Switzerland 100 100 100 100 90 95 .. .. .. .. .. 89 Syrian Arab Republic 85 89 83 96 81 80 77 34 .. .. 86 88 Tajikistan .. 70 .. 94 89 93 64 22 1.3 1.9 82 44 Tanzania 55 54 24 24 91 85 59 53 63.8d 59.1d 88 77 Thailand 91 98 80 96 98 99 84 46 .. .. 82 69 Timor-Leste .. 69 .. 50 70 72 71 .. .. .. 85 84 Togo 49 60 13 12 84 89 23 22 38.4 47.7 79 10 Trinidad and Tobago 88 94 93 92 94 90 74 32 .. .. 67 89 Tunisia 81 94 74 85 98 99 59 62 .. .. 86 86 Turkey 85 99 84 90 97 96 .. 22 .. .. 92 77 Turkmenistan .. .. 98 98 99 96 83 25 .. .. 83 92 Uganda 43 67 39 48 68 64 73 39 9.7 61.3 70 44 Ukraine .. 98 95 95 94 90 .. .. .. .. 62 78 United Arab Emirates 100 100 97 97 92 92 .. .. .. .. 68 61 United Kingdom 100 100 100 100 86 93 .. .. .. .. 78 94 United States 99 99 100 100 92 95 .. .. .. .. 85 89 Uruguay 96 100 94 100 94 95 .. .. .. .. 83 96 Uzbekistan 90 87 84 100 95 98 68 28 .. .. 81 50 Venezuela, RB 90 .. 82 .. 83 83 .. .. .. .. 83 68 Vietnam 58 94 35 75 97 96 83 65 5.0 2.6 92 54 West Bank and Gaza .. 91 .. 89 .. .. .. .. .. .. 94 4 Yemen, Rep. .. 62 18 52 58 66 .. 48 .. .. 85 67 Zambia 49 60 46 49 85 81 68 56 41.1 43.3 88 80 Zimbabwe 78 82 43 44 76 73 25 35 17.3 23.6 74 46 World 77 w 87 w 52 w 61 w 82 w 82 w .. w .. w .. w .. w 86 w 62 w Low income 55 64 23 35 78 80   45 39 .. 30.6 86 50   Middle income 74 88 45 57 82 81 .. .. .. .. .. .. Lower middle income 70 86 37 50 79 79 .. .. .. .. 89 63 Upper middle income 89 95 78 84 93 93 .. .. .. .. 72 79 Low & middle income 72 84 43 54 81 81 .. .. .. .. .. .. East Asia & Pacific 69 88 42 59 91 93 .. .. .. .. 92 70 Europe & Central Asia 91 95 87 89 96 95 .. .. .. .. 67 78 Latin America & Carib. 85 93 69 79 93 92 .. .. .. .. 77 73 Middle East & N. Africa 87 87 73 84 87 88 .. .. .. .. 86 78 South Asia 74 87 22 36 75 72 67 37 .. 7.2 88 64 Sub-Saharan Africa 49 60 27 31 68 70 .. 33 20.2 34.4 79 48 High income 99 100 100 99 93 95 .. .. .. .. 69 87 Euro area 100 100 100 100 94 96 .. .. .. .. .. .. a. For malaria prevention only. b. Refers to children who were immunized before 12 months or in some cases at any time before the survey (12–23 months). c. Data are for the most recent year available. d. Data are for 2010. 104 2011 World Development Indicators 2.18 PEOPLE Disease prevention coverage and quality About the data Definitions People’s health is influenced by the environment the use of oral rehydration therapy have changed over •  Access to an improved water source refers to in which they live. Lack of clean water and basic time based on scientific progress, so it is difficult people with access to at least 20 liters of water sanitation is the main reason diseases transmitted to accurately compare use rates across countries. a person a day from an improved source, such as by feces are so common in developing countries. Until the current recommended method for home piped water into a dwelling, public tap, tubewell, Access to drinking water from an improved source management of diarrhea is adopted and applied in protected dug well, and rainwater collection, within and access to improved sanitation do not ensure all countries, the data should be used with caution. 1 kilometer of the dwelling. • Access to improved safety or adequacy, as these characteristics are Also, the prevalence of diarrhea may vary by season. sanitation facilities refers to people with at least not tested at the time of the surveys. But improved Since country surveys are administered at different adequate access to excreta disposal facilities that drinking water technologies and improved sanitation times, data comparability is further affected. can effectively prevent human, animal, and insect facilities are more likely than those characterized Malaria is endemic to the poorest countries in the contact with excreta. Improved facilities range from as unimproved to provide safe drinking water and to world, mainly in tropical and subtropical regions of protected pit latrines to flush toilets. • Child immu- prevent contact with human excreta. The data are Africa, Asia, and the Americas. Insecticide-treated nization rate refers to children ages 12–23 months derived by the Joint Monitoring Programme (JMP) nets, properly used and maintained, are one of the who, before 12 months or at any time before the of the World Health Organization (WHO) and United most important malaria-preventive strategies to limit survey, had received one dose of measles vaccine Nations Children’s Fund (UNICEF) based on national human-mosquito contact. and three doses of diphtheria, pertussis (whooping censuses and nationally representative household Prompt and effective treatment of malaria is a criti- cough), and tetanus (DTP3) vaccine. • Children with surveys. The coverage rates for water and sanita- cal element of malaria control. It is vital that suffer- acute respiratory infection (ARI) taken to health tion are based on information from service users ers, especially children under age 5, start treatment provider are children under age 5 with ARI in the on the facilities their households actually use rather within 24 hours of the onset of symptoms, to pre- two weeks before the survey who were taken to an than on information from service providers, which vent progression—often rapid—to severe malaria appropriate health provider. • Children with diarrhea may include nonfunctioning systems. While the esti- and death. who received oral rehydration and continuous feed- mates are based on use, the JMP reports use as Data on the success rate of tuberculosis treatment ing are children under age 5 with diarrhea in the two access, because access is the term used in the Mil- are provided for countries that have submitted data weeks before the survey who received either oral lennium Development Goal target for drinking water to the WHO. The treatment success rate for tuber- rehydration therapy or increased fluids, with con- and sanitation. culosis provides a useful indicator of the quality of tinuous feeding. • Children sleeping under treated Governments in developing countries usually health services. A low rate suggests that infectious nets are children under age 5 who slept under an finance immunization against measles and diphthe- patients may not be receiving adequate treatment. insecticide-treated net to prevent malaria the night ria, pertussis (whooping cough), and tetanus (DTP) An important complement to the tuberculosis treat- before the survey. • Children with fever receiving as part of the basic public health package. In many ment success rate is the case detection rate, which antimalarial drugs are children under age 5 who were developing countries lack of precise information on indicates whether there is adequate coverage by the ill with fever in the two weeks before the survey and the size of the cohort of one-year-old children makes recommended case detection and treatment strat- received any appropriate (locally defined) antimalarial immunization coverage diffi cult to estimate from egy. Uncertainty bounds for the case detection rate, drugs. • Tuberculosis treatment success rate is new program statistics. The data shown here are based not shown in the table, are available at http://data. registered infectious tuberculosis cases that were on an assessment of national immunization cover- worldbank.org or the original source. cured or that completed a full course of treatment as age rates by the WHO and UNICEF. The assessment Editions before 2010 included the tuberculosis a percentage of smear-positive cases registered for considered both administrative data from service detection rates by DOTS, the internationally rec- treatment outcome evaluation. • Tuberculosis case providers and household survey data on children’s ommended strategy for tuberculosis control. This detection rate is newly identified tuberculosis cases immunization histories. Based on the data available, year’s edition, like last year’s, shows the tuberculo- (including relapses) as a percentage of estimated consideration of potential biases, and contributions sis detection rate for all detection methods, so data incident cases (case detection, all forms). of local experts, the most likely true level of immuni- on the case detection rate cannot be compared with Data sources zation coverage was determined for each year. Acute data in previous editions. Data on access to water and sanitation are from respiratory infection continues to be a leading cause For indicators that are from household surveys, the the WHO and UNICEF’s Progress on Sanitation and of death among young children, killing about 2 million year in the table refers to the survey year. For more Drinking Water (2010). Data on immunization are children under age 5 in developing countries each information, consult the original sources. from WHO and UNICEF estimates (www.who.int/ year. Data are drawn mostly from household health immunization_monitoring). Data on children with surveys in which mothers report on number of epi- ARI, with diarrhea, sleeping under treated nets, and sodes and treatment for acute respiratory infection. receiving antimalarial drugs are from UNICEF’s State Since 1990 diarrhea-related deaths among children of the World’s Children 2010, Childinfo, and Demo- have declined tremendously. Most diarrhea-related graphic and Health Surveys by Macro International. deaths are due to dehydration, and many of these Data on tuberculosis are from the WHO’s Global Tuber- deaths can be prevented with the use of oral rehydra- culosis Control: A Short Update to the 2010 Report. tion salts at home. However, recommendations for 2011 World Development Indicators 105 2.19 Reproductive health Total fertility Adolescent Unmet Contraceptive Pregnant Births attended Maternal Lifetime risk rate fertility need for prevalence women by skilled mortality of maternal rate contraception rate receiving health staff ratio death prenatal care any method births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Probability woman ages 15–19 15–49 15–49 % % of total estimates Modeled estimates 1 woman in: 1990 2009 2009 2004–09a 2004–09a 2004–09a 1990 2004–09a 2004–09a 1990 2008 2008 Afghanistan 8.0 6.5 117 .. 15 36 .. 24 .. 1,700 1,400 11 Albania 2.9 1.9 14 .. 69 97 .. 99 21 48 31 1,700 Algeria 4.7 2.3 7 11 61 89 77 95 .. 250 120 340 Angola 7.2 5.6 121 .. .. 80 .. 47 .. 1,000 610 29 Argentina 3.0 2.2 56 .. 78 99 96 95 40 72 70 600 Armenia 2.5 1.7 35 13 53 93 .. 100 27 51 29 1,900 Australia 1.9 1.9 14 .. .. .. 100 .. .. 10 8 7,400 Austria 1.5 1.4 12 .. .. .. .. .. .. 10 5 14,300 Azerbaijan 2.7 2.3 33 23 51 77 .. 88 26 64 38 1,200 Bangladesh 4.4 2.3 68 17 53 51 .. 24 348 870 340 110 Belarus 1.9 1.5 20 .. 73 99 .. 100 3 37 15 5,100 Belgium 1.6 1.9 7 .. 75 .. .. .. .. 7 5 10,900 Benin 6.7 5.4 108 30 17 84 .. 74 397 790 410 43 Bolivia 4.9 3.4 76 .. 61 86 43 71 310 510 180 150 Bosnia and Herzegovina 1.7 1.2 15 23 36 99 97 100 3 18 9 9,300 Botswana 4.7 2.8 50 .. 53 94 77 95 198 83 190 180 Brazil 2.8 1.8 74 .. 81 97 72 97 75 120 58 860 Bulgaria 1.8 1.6 40 .. .. .. .. 100 6 24 13 5,800 Burkina Faso 6.8 5.8 125 31 17 85 .. 54 307 770 560 28 Burundi 6.6 4.5 18 .. 9 92 .. 34 615 1,200 970 25 Cambodia 5.8 2.9 37 25 51b 83b .. 71b 461 690 290 110 Cameroon 5.9 4.5 122 3 29 82 58 63 669 680 600 35 Canada 1.8 1.6 12 .. .. .. .. 100 .. 6 12 5,600 Central African Republic 5.8 4.7 96 .. 19 69 .. 44 543 880 850 27 Chad 6.7 6.1 155 21 3 39 .. 14 1,099 1,300 1,200 14 Chile 2.6 1.9 59 .. 58 .. .. 100 18 56 26 2,000 China 2.3 1.8 10 .. 85 91 50 99 34 110 38 1,500 Hong Kong SAR, China 1.3 1.0 6 .. .. .. .. 100 .. .. .. .. Colombia 3.1 2.4 72 6 78 94 82 96 76 140 85 460 Congo, Dem. Rep. 7.1 5.9 191 24 21 85 .. 74 549 900 670 24 Congo, Rep. 5.4 4.3 106 16 44 86 .. 83 781 460 580 39 Costa Rica 3.2 1.9 67 .. 80 90 98 99 27 35 44 1,100 Côte d’Ivoire 6.3 4.5 122 29 13 85 .. 57 543 690 470 44 Croatia 1.6 1.5 14 .. .. 100 b 100 100 b 13b 8 14 5,200 Cuba 1.8 1.5 46 8 78 100 .. 100 47 63 53 1,400 Czech Republic 1.9 1.5 10 .. .. .. .. 100 6 15 8 8,500 Denmark 1.7 1.8 6 .. .. .. .. .. .. 7 5 10,900 Dominican Republic 3.5 2.6 107 11 73 99 93 98 159 220 100 320 Ecuador 3.7 2.5 82 .. 73 84 .. 98 60 230 140 270 Egypt, Arab Rep. 4.6 2.8 37 9 60 74 37 79 55 220 82 380 El Salvador 4.0 2.3 81 .. 73 94 52 96 59 200 110 350 Eritrea 6.2 4.5 62 .. .. .. .. .. .. 930 280 72 Estonia 2.0 1.6 20 .. .. .. .. 100 7 48 12 5,300 Ethiopia 7.1 5.2 94 34 15 28 .. 6 673 990 470 40 Finland 1.8 1.9 11 .. .. .. .. .. .. 7 8 7,600 France 1.8 2.0 6 .. 71 .. .. .. .. 13 8 6,600 Gabon 5.2 3.2 85 .. .. .. .. .. .. 260 260 110 Gambia, The 6.1 5.0 87 .. .. 98 44 57 .. 750 400 49 Georgia 2.2 1.6 44 .. 47 94 .. 98 14 58 48 1,300 Germany 1.5 1.4 7 .. .. .. .. 100 .. 13 7 11,100 Ghana 5.6 3.9 61 35 24 90 40 57 451 630 350 66 Greece 1.4 1.5 8 .. .. .. .. .. .. 6 2 31,800 Guatemala 5.6 4.0 104 .. 54 .. .. 51 133 140 110 210 Guinea 6.7 5.3 147 21 9 88 31 46 980 1,200 680 26 Guinea-Bissau 5.9 5.7 125 25 10 78 .. 39 405 1,200 1,000 18 Haiti 5.4 3.4 45 38 32 85 23 26 630 670 300 93 Honduras 5.1 3.2 90 17 65 92 45 67 .. 210 110 240 106 2011 World Development Indicators 2.19 PEOPLE Reproductive health Total fertility Adolescent Unmet Contraceptive Pregnant Births attended Maternal Lifetime risk rate fertility need for prevalence women by skilled mortality of maternal rate contraception rate receiving health staff ratio death prenatal care any method births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Probability woman ages 15–19 15–49 15–49 % % of total estimates Modeled estimates 1 woman in: 1990 2009 2009 2004–09a 2004–09a 2004–09a 1990 2004–09a 2004–09a 1990 2008 2008 Hungary 1.8 1.3 19 .. .. .. .. 100 17 23 13 5,500 India 4.0 2.7 64 13 54 75 .. 53 254 570 230 140 Indonesia 3.1 2.1 37 9 57 93 32 75 228 620 240 190 Iran, Islamic Rep. 4.8 1.8 17 .. 79 98 .. 97 25 150 30 1,500 Iraq 6.0 3.9 80 .. 50 84 54 80 84 93 75 300 Ireland 2.1 2.1 15 .. 89 .. .. .. .. 6 3 17,800 Israel 2.8 3.0 14 .. .. .. .. .. .. 12 7 5,100 Italy 1.3 1.4 5 .. .. .. .. .. .. 10 5 15,200 Jamaica 2.9 2.4 75 .. .. 91 79 95 .. 66 89 450 Japan 1.5 1.4 5 .. 54 .. 100 100 .. 12 6 12,200 Jordan 5.5 3.4 24 11 59 99 87 99 19 110 59 510 Kazakhstan 2.7 2.6 29 .. 51 100 .. 100 37 78 45 950 Kenya 6.0 4.9 101 .. 46 92 50 44 488 380 530 38 Korea, Dem. Rep. 2.4 1.9 0 .. .. .. .. .. 77 270 250 230 Korea, Rep. 1.6 1.3 6 .. 80 .. 98 .. .. 18 18 4,700 Kosovo 3.9 2.3 .. .. .. .. .. .. .. .. .. .. Kuwait 3.5 2.2 13 .. .. .. .. .. .. 10 9 4,500 Kyrgyz Republic 3.7 2.8 32 1 48 97 .. 98 55 77 81 450 Lao PDR 6.0 3.4 34 .. 38 35 .. 20 405 1,200 580 49 Latvia 2.0 1.3 14 .. .. .. .. 100 8 57 20 3,600 Lebanon 3.1 1.8 16 .. 58 96 .. 98 .. 52 26 2,000 Lesotho 4.9 3.3 69 31 47 92 .. 62 762 370 530 62 Liberia 6.5 5.8 136 36 11 79 .. 46 994 1,100 990 20 Libya 4.8 2.6 3 .. .. .. .. .. .. 100 64 540 Lithuania 2.0 1.5 20 .. .. .. .. 100 9 34 13 5,800 Macedonia, FYR 2.1 1.4 21 34 14 94 .. 100 4 16 9 7,300 Madagascar 6.3 4.6 127 24 40 86 57 44 498 710 440 45 Malawi 7.0 5.5 127 28 41 92 55 54 807 910 510 36 Malaysia 3.7 2.5 12 .. .. 79 .. 99 29 56 31 1,200 Mali 6.7 6.5 155 31 8 70 .. 49 464 1,200 830 22 Mauritania 5.9 4.4 82 25 9 75 40 61 686 780 550 41 Mauritius 2.3 1.5 41 .. .. .. 91 99 .. 72 36 1,600 Mexico 3.4 2.1 63 .. 73 94 .. 93 63 93 85 500 Moldova 2.4 1.5 33 7 68 98 .. 100 38 62 32 2,000 Mongolia 4.2 2.0 15 14 55 100 .. 99 81 130 65 730 Morocco 4.0 2.3 19 10 63 68 31 63 132 270 110 360 Mozambique 6.2 5.0 139 .. 16 89 .. 55 .. 1,000 550 37 Myanmar 3.4 2.3 18 .. 41 80 .. 64 316 420 240 180 Namibia 5.2 3.3 67 7 55 95 68 81 449 180 180 160 Nepal 5.2 2.8 91 25 48 44 7 19 281 870 380 80 Netherlands 1.6 1.8 4 .. 69 .. .. .. .. 10 9 7,100 New Zealand 2.2 2.1 21 .. .. .. .. .. .. 18 14 3,800 Nicaragua 4.8 2.7 111 8 72 90 .. 74 77 190 100 300 Niger 7.9 7.1 152 16 11 46 15 33 648 1,400 820 16 Nigeria 6.6 5.6 118 .. 15 58 33 39 545 1,100 840 23 Norway 1.9 2.0 8 .. 88 .. 100 .. .. 9 7 7,600 Oman 6.6 3.0 10 .. .. .. .. 99 17 49 20 1,600 Pakistan 6.1 3.9 42 25 30 61 19 39 276 490 260 93 Panama 3.0 2.5 80 .. .. .. .. 92 60 86 71 520 Papua New Guinea 4.8 4.0 50 .. 32 79 .. 53 733 340 250 94 Paraguay 4.5 3.0 69 .. 79 96 66 82 118 130 95 310 Peru 3.8 2.5 52 8 73 94 80 83 .. 250 98 370 Philippines 4.3 3.0 43 22 51 91 .. 62 162 180 94 320 Poland 2.0 1.4 13 .. .. .. .. 100 5 17 6 13,300 Portugal 1.4 1.3 15 .. 67 .. 98 .. .. 15 7 9,800 Puerto Rico 2.2 1.7 50 .. .. .. .. 100 .. 29 18 3,000 Qatar 4.4 2.4 15 .. .. .. .. .. .. 15 8 4,400 2011 World Development Indicators 107 2.19 Reproductive health Total fertility Adolescent Unmet Contraceptive Pregnant Births attended Maternal Lifetime risk rate fertility need for prevalence women by skilled mortality of maternal rate contraception rate receiving health staff ratio death prenatal care any method births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Probability woman ages 15–19 15–49 15–49 % % of total estimates Modeled estimates 1 woman in: 1990 2009 2009 2004–09a 2004–09a 2004–09a 1990 2004–09a 2004–09a 1990 2008 2008 Romania 1.8 1.4 29 .. 70 94 .. 99 14 170 27 2,700 Russian Federation 1.9 1.6 24 .. 80 .. .. 100 32b 74 39 1,900 Rwanda 6.8 5.3 35 38 36 96 26 52 750 1,100 540 35 Saudi Arabia 5.8 3.0 25 .. 24 .. .. 96 14 41 24 1,300 Senegal 6.7 4.7 97 32 12 94 .. 52 401 750 410 46 Serbia 1.8 1.4 21 29 41 98 .. 99 6 13 8 7,500 Sierra Leone 5.5 5.2 124 .. 8 87 .. 42 857 1,300 970 21 Singapore 1.9 1.2 4 .. .. .. .. 100 .. 6 9 10,000 Slovak Republic 2.1 1.4 20 .. .. .. .. 100 4 15 6 13,300 Slovenia 1.5 1.5 5 .. .. .. 100 100 26 11 18 4,100 Somalia 6.6 6.4 69 26 15 26 .. 33 1,044 1,100 1,200 14 South Africa 3.7 2.5 56 .. .. .. .. .. .. 230 410 100 Spain 1.3 1.4 12 .. 66 .. .. .. .. 7 6 11,400 Sri Lanka 2.5 2.3 29 .. 68 99 .. 99 39 91 39 1,100 Sudan 6.0 4.1 53 6 8 64 69 49 1,107 830 750 32 Swaziland 5.7 3.5 78 24 51 85 .. 69 589 260 420 75 Sweden 2.1 1.9 7 .. .. .. .. .. .. 7 5 11,400 Switzerland 1.6 1.5 5 .. .. .. .. 100 .. 8 10 7,600 Syrian Arab Republic 5.5 3.1 55 11 58 84 .. 93 .. 120 46 610 Tajikistan 5.2 3.4 27 24 37 80 .. 88 38 120 64 430 Tanzania 6.2 5.5 128 22 26 76 53 43 578 880 790 23 Thailand 2.1 1.8 36 .. 77 98 .. 97 12 50 48 1,200 Timor-Leste 5.3 6.4 52 .. 22b .. .. .. .. 650 370 44 Togo 6.3 4.2 62 41 17 84 31 62 .. 650 350 67 Trinidad and Tobago 2.4 1.6 34 27 43 96 .. 98 .. 86 55 1,100 Tunisia 3.5 2.1 7 .. 60 96 69 95 .. 130 60 860 Turkey 3.1 2.1 36 18 73 95 .. 95 29 68 23 1,900 Turkmenistan 4.3 2.4 18 .. 48 99 .. 100 15 91 77 500 Uganda 7.1 6.3 142 41 24 94 38 42 435 670 430 35 Ukraine 1.8 1.5 27 10 67 99 .. 99 16 49 26 3,000 United Arab Emirates 4.4 1.9 15 .. .. .. .. .. .. 28 10 4,200 United Kingdom 1.8 2.0 22 .. .. .. .. .. .. 10 12 4,700 United States 2.1 2.1 33 .. .. .. 99 .. 13 12 24 2,100 Uruguay 2.5 2.0 60 .. 78 96 .. 99 34 39 27 1,700 Uzbekistan 4.1 2.7 13 8 65 99 .. 100 21 53 30 1,400 Venezuela, RB 3.4 2.5 89 .. .. .. .. .. 61 84 68 540 Vietnam 3.7 2.0 16 .. 80 91 .. 88 75 170 56 850 West Bank and Gaza 6.4 4.9 73 .. 50 99 .. 99 .. .. .. .. Yemen, Rep. 8.1 5.1 64 24 28 47 16 36 .. 540 210 91 Zambia 6.5 5.7 133 27 41 94 51 47 591 390 470 38 Zimbabwe 5.2 3.4 61 13 65 93 70 60 555 390 790 42 World 3.3 w 2.5 w 50 w .. w 61 w 82 w 50 w 65 w 400 w 260 w 140 w Low income 5.6 4.2 97 25 33 67 ..  41    850  580  39 Middle income 3.3 2.4 46 .. 66 85 46  71    350 200 190 Lower middle income 3.4 2.5 45 .. 63 83 41 66 400 230 160 Upper middle income 3.0 2.0 49 .. 75 95 .. 96 120 82 570 Low & middle income 3.6 2.7 54 .. 61 82 46 64 440 290 120 East Asia & Pacific 2.6 1.9 17 .. 77 91 48 89 200 89 580 Europe & Central Asia 2.3 1.8 27 .. 69 .. .. 97 69 32 1,700 Latin America & Carib. 3.2 2.2 71 .. 75 95 72 89 140 86 480 Middle East & N. Africa 4.9 2.7 34 .. 62 83 47 80 210 88 380 South Asia 4.3 2.8 63 15 51 70 32 47 610 290 110 Sub-Saharan Africa 6.3 5.1 112 24 21 71 .. 44 870 650 31 High income 1.8 1.7 18 .. .. .. .. .. 15 15 3,900 Euro area 1.5 1.6 8 .. .. .. .. .. 11 7 10,100 a. Data are for the most recent year available. b. Data are for 2010. 108 2011 World Development Indicators 2.19 PEOPLE Reproductive health About the data Reproductive health is a state of physical and mental estimates of maternal mortality that it produces per- using contraception. • Contraceptive prevalence rate well-being in relation to the reproductive system and its tain to 12 years or so before the survey, making them is the percentage of women married or in union ages functions and processes. Means of achieving reproduc- unsuitable for monitoring recent changes or observ- 15–49 who are practicing, or whose sexual partners tive health include education and services during preg- ing the impact of interventions. In addition, measure- are practicing, any form of contraception. • Pregnant nancy and childbirth, safe and effective contraception, ment of maternal mortality is subject to many types of women receiving prenatal care are the percentage of and prevention and treatment of sexually transmitted errors. Even in high-income countries with vital regis- women attended at least once during pregnancy by diseases. Complications of pregnancy and childbirth tration systems, misclassification of maternal deaths skilled health personnel for reasons related to preg- are the leading cause of death and disability among has been found to lead to serious underestimation. nancy. • Births attended by skilled health staff are the women of reproductive age in developing countries. The national estimates of maternal mortality ratios percentage of deliveries attended by personnel trained Total and adolescent fertility rates are based on data in the table are based on national surveys, vital regis- to give the necessary care to women during pregnancy, on registered live births from vital registration systems tration records, and surveillance data or are derived labor, and postpartum; to conduct deliveries on their or, in the absence of such systems, from censuses from community and hospital records. The modeled own; and to care for newborns. • Maternal mortality or sample surveys. The estimated rates are generally estimates are based on an exercise by the World ratio is the number of women who die from pregnancy- considered reliable measures of fertility in the recent Health Organization (WHO), United Nations Children’s related causes during pregnancy and childbirth per past. Where no empirical information on age-specific Fund (UNICEF), United Nations Population Fund 100,000 live births. • Lifetime risk of maternal death fertility rates is available, a model is used to estimate (UNFPA), and World Bank. This year’s estimates of refers to the probability that a 15-year-old girl will the share of births to adolescents. For countries with- maternal mortality include country-level time-series eventually die from a maternal cause if throughout her out vital registration systems fertility rates are gener- data for the first time. For countries with complete lifetime she experiences the risks of maternal death ally based on extrapolations from trends observed in vital registration systems with good attribution of and the overall level of fertility and mortality that are censuses or surveys from earlier years. cause of death, the data are used to directly estimate observed for a given population. Data are presented More couples in developing countries want to limit maternal mortality. For countries without complete as 1 in the number of women who are likely to die from or postpone childbearing but are not using effective registration data but with other types of data and for a maternal cause. contraception. These couples have an unmet need for countries with no empirical national data, maternal contraception. Common reasons are lack of knowledge mortality is estimated with a multilevel regression Data sources about contraceptive methods and concerns about pos- model using available national-level maternal mortal- sible side effects. This indicator excludes women not ity data and socioeconomic information, including Data on total fertility are compiled from the United exposed to the risk of unintended pregnancy because fertility, birth attendants, and GDP. The methodol- Nations Population Division’s World Population of menopause, infertility, or postpartum anovulation. ogy of this year’s interagency estimates differs from Prospects: The 2008 Revision, census reports Contraceptive prevalence reflects all methods— previous years’, so the data should not be compared and other statistical publications from national ineffective traditional methods as well as highly effec- with data in previous editions. For further information statistical offices, household surveys conducted tive modern methods. Contraceptive prevalence rates on methodology, see the original source. by national agencies, Macro International, and the are obtained mainly from household surveys, includ- Neither set of ratios can be assumed to provide an U.S. Centers for Disease Control and Prevention, ing Demographic and Health Surveys, Multiple Indicator exact estimate of maternal mortality for any of the Eurostat’s Demographic Statistics, and the U.S. Cluster Surveys, and contraceptive prevalence surveys countries in the table. Bureau of the Census International Data Base. (see Primary data documentation for the most recent In countries with a high risk of maternal death, Data on adolescent fertility are from World Popu- survey and year). Unmarried women are often excluded many girls die before reaching reproductive age. Life- lation Prospects: The 2008 Revision, with annual from such surveys, which may bias the estimates. time risk of maternal mortality refers to the prob- data linearly interpolated by the Development Good prenatal and postnatal care improves mater- ability that a 15-year-old girl will eventually die from Data Group. Data on women with unmet need for nal health and reduces maternal and infant mortality. a maternal cause. contraception and contraceptive prevalence are Indicators on use of antenatal care services, however, For the indicators that are from household surveys, from household surveys, including Demographic provide no information on the content or quality of the the year in the table refers to the survey year. For and Health Surveys by Macro International and services. Data on antenatal care are obtained mostly more information, consult the original sources. Multiple Indicator Cluster Surveys by UNICEF. from household surveys, which ask women who have Data on pregnant women receiving prenatal had a live birth whether and from whom they received Definitions care, births attended by skilled health staff, and antenatal care. The share of births attended by skilled national estimates of maternal mortality ratios are health staff is an indicator of a health system’s ability • Total fertility rate is the number of children that would from UNICEF’s State of the World’s Children 2011 to provide adequate care for pregnant women. be born to a woman if she were to live to the end of and Childinfo and Demographic and Health Sur- Maternal mortality ratios are generally of unknown her childbearing years and bear children in accordance veys by Macro International. Modeled estimates reliability, as are many other cause-specific mortality with current age-specific fertility rates. • Adolescent of maternal mortality ratios and lifetime risk of indicators. Household surveys such as Demographic fertility rate is the number of births per 1,000 women maternal death are from WHO, UNICEF, UNFPA and Health Surveys attempt to measure maternal mor- ages 15–19. • Unmet need for contraception is the and the World Bank’s Trends in Maternal Mortal- tality by asking respondents about survivorship of sis- percentage of fertile, married women of reproductive ity: 1990–2008 (2010). ters. The main disadvantage of this method is that the age who do not want to become pregnant and are not 2011 World Development Indicators 109 2.20 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6–59 months under age 5 women 1990–92 2005–09 2004–09a 2004–09a 2004–09a 2004–09a 2004–09a 2004–09a 2009 2004–09 a 2004–09a Afghanistan .. .. 32.9 59.3 4.6 .. 83 28 95 38 61 Albania 10 <5 6.6 27.0 25.2 7 39 76 .. 31 34 Algeria <5 <5 3.7 15.9 12.9 6 7 61 .. 43 43 Angola 67 41 .. .. .. .. .. 45 28 .. 57 Argentina <5 <5 2.3 8.2 9.9 7 .. .. .. 17 31 Armenia 45 22 4.2 18.2 11.7 7 33 97 .. 37 .. Australia <5 <5 .. .. .. .. .. .. .. 8 12 Austria <5 <5 .. .. .. .. .. .. .. 11 15 Azerbaijan 27 <5 8.4 26.8 13.9 10 12 54 79b .. .. Bangladesh 38 27 41.3 43.2 1.1 22 43 84 91 58 39 Belarus <5 <5 1.3 4.5 9.7 4 9 55 .. 27 26 Belgium <5 <5 .. .. .. .. .. .. .. 9 13 Benin 20 12 20.2 44.7 11.4 15 43 67 56 78 75 Bolivia 29 27 4.5 27.2 8.7 6 60 89 45 52 37 Bosnia and Herzegovina 8 <5 1.6 11.8 25.6 5 18 62 .. 27 35 Botswana 19 25 .. .. .. 13 20 .. 89 .. 21 Brazil 11 6 2.2 7.1 7.3 8 40 96 .. 55 29 Bulgaria <5 10 1.6 8.8 13.6 9 .. 100 .. 27 30 Burkina Faso 14 9 26.0 35.1 7.7 16 16 34 100 .. .. Burundi 44 62 .. .. .. 11 45 98 90 56 47 Cambodia 38 22 28.8 39.5 2.0 9 66 73 98 62 57 Cameroon 33 21 16.6 36.4 9.6 11 21 49 .. 68 51 Canada <5 <5 .. .. .. .. .. .. .. 8 12 Central African Republic 44 40 .. .. .. 13 23 62 87 .. .. Chad 60 37 33.9 44.8 4.4 22 2 56 71 71 60 Chile 7 <5 0.5 2.0 9.5 6 85 .. .. 24 28 China 18 c 10 c 4.5 11.7 5.9 3 28 96 .. .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. Colombia 15 10 5.1 16.2 4.2 6 47 .. .. 28 31 Congo, Dem. Rep. 26 69 28.2 45.8 6.8 10 36 79 89 71 67 Congo, Rep. 42 15 11.8 31.2 8.5 13 19 82 8 66 55 Costa Rica <5 <5 .. .. .. 7 15 .. .. .. .. Côte d’Ivoire 15 14 16.7 40.1 9.0 17 4 84 88 69 55 Croatia 18 <5 1.0 0.6 8.1 5 98 88 .. 23 28 Cuba 6 <5 .. .. .. 5 26 88 .. 27 39 Czech Republic <5 <5 .. .. .. .. .. .. .. 18 22 Denmark <5 <5 .. .. .. .. .. .. .. 9 12 Dominican Republic 28 24 3.4 10.1 8.3 11 9 19 .. 35 40 Ecuador 23 15 6.2 29.0 5.1 10 40 .. .. 38 38 Egypt, Arab Rep. <5 <5 6.8 30.7 20.5 13 53 79 68b 49 34 El Salvador 13 9 .. .. .. .. 31 .. 20 .. .. Eritrea 67 64 .. .. .. .. .. .. 44 70 55 Estonia 10 <5 .. .. .. .. .. .. .. 23 23 Ethiopia 69 41 34.6 50.7 5.1 20 49 20 84 75 63 Finland <5 <5 .. .. .. .. .. .. .. 11 15 France <5 <5 .. .. .. .. .. .. .. 8 11 Gabon 6 <5 .. .. .. .. .. .. 0 44 46 Gambia, The 14 19 15.8 27.6 2.7 20 41 7 28 .. .. Georgia 58 <5 2.3 14.7 21.0 5 11 100 .. 41 42 Germany <5 <5 1.1 1.3 3.5 .. .. .. .. 8 12 Ghana 27 5 14.3 28.6 5.9 13 63 32 90 .. .. Greece <5 <5 .. .. .. .. .. .. .. 12 19 Guatemala 15 21 .. .. .. .. 50 76 43 .. .. Guinea 20 17 20.8 40.0 5.1 12 48 41 94 76 .. Guinea-Bissau 22 22 17.4 47.7 17.0 24 16 1 80 75 58 Haiti 63 57 18.9 29.7 3.9 25 41 3 .. .. 50 Honduras 19 12 8.6 29.9 5.8 10 30 .. .. .. 21 110 2011 World Development Indicators 2.20 PEOPLE Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6–59 months under age 5 women 1990–92 2005–09 2004–09a 2004–09a 2004–09a 2004–09a 2004–09a 2004–09a 2009 2004–09 a 2004–09a Hungary <5 <5 .. .. .. .. .. .. .. 19 21 India 20 21 43.5 47.9 1.9 28 46 51 66 74 50 Indonesia 16 13 17.5d 35.6d 11.2 11d 15d 62d 84 44 44 Iran, Islamic Rep. <5 <5 .. .. .. 7 23 99 .. 35 .. Iraq .. .. 7.1 27.5 15.0 15 25 28 .. 56 38 Ireland <5 <5 .. .. .. .. .. .. .. 10 15 Israel <5 <5 .. .. .. .. .. .. .. 12 17 Italy <5 <5 .. .. .. .. .. .. .. 11 15 Jamaica 11 5 2.2 3.7 7.5 14 15 .. .. .. .. Japan <5 <5 .. .. .. .. .. .. .. 11 15 Jordan <5 5 1.9 8.3 6.6 13 22 .. .. .. .. Kazakhstan <5 <5 4.9 17.5 14.8 6 17 92 .. .. 26 Kenya 33 31 16.4 35.2 5.0 8 32 98 51 .. .. Korea, Dem. Rep. 21 33 20.6 43.1 .. .. 65 40 99 .. .. Korea, Rep. <5 <5 .. .. .. .. .. .. .. .. 23 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 20 5 1.7 3.8 9.0 .. .. .. .. .. 31 Kyrgyz Republic 17 10 2.7 18.1 10.7 5 32 76 99 .. 34 Lao PDR 31 23 31.6 47.6 1.3 11 26 84 88 .. 56 Latvia <5 <5 .. .. .. .. .. .. .. 27 25 Lebanon <5 <5 4.2 16.5 16.7 .. .. 92 .. .. 32 Lesotho 15 14 16.6 45.2 6.8 13 54 91 85 49 25 Liberia 30 33 20.4 39.4 4.2 14 29 .. 92 .. .. Libya <5 <5 5.6 21.0 22.4 .. .. .. .. 34 34 Lithuania <5 <5 .. .. .. .. .. .. .. 24 24 Macedonia, FYR 11 <5 1.8 11.5 16.2 6 16 94 .. .. 32 Madagascar 21 25 36.8 49.2 6.2 16 51 53 95 68 50 Malawi 43 28 15.5 53.2 11.3 13 57 50 95 73 47 Malaysia <5 <5 .. .. .. 11 .. .. .. 32 38 Mali 27 12 27.9 38.5 4.7 19 38 79 100 .. .. Mauritania 12 7 16.7 24.2 2.3 34 35 23 89 68 53 Mauritius 7 5 .. .. .. .. .. .. .. .. .. Mexico <5 <5 3.4 15.5 7.6 8 .. .. .. 24 21 Moldova 5 6 3.2 11.3 9.1 6 46 60 .. 41 36 Mongolia 28 26 5.3 27.5 14.2 5 57 83 95 21 37 Morocco 6 <5 9.9 23.1 13.3 .. 31 21 .. .. .. Mozambique 59 38 .. .. .. 15 37 25 97 .. 52 Myanmar 47 16 .. .. .. .. .. 93 95 63 50 Namibia 32 19 17.5 29.6 4.6 16 24 .. .. 41 31 Nepal 21 16 38.8 49.3 0.6 21 53 .. 95 48 42 Netherlands <5 <5 .. .. .. .. .. .. .. 9 13 New Zealand <5 <5 .. .. .. .. .. .. .. 11 18 Nicaragua 50 19 4.3 18.8 5.2 8 31 .. 6 17 .. Niger 37 20 39.9 54.8 3.5 27 10 46 95 81 61 Nigeria 16 6 26.7 41.0 10.5 12 13 .. 78 .. .. Norway <5 <5 .. .. .. .. .. .. .. 6 9 Oman .. .. .. .. .. 9 .. .. .. 42 .. Pakistan 25 26 .. .. .. 32 37 .. 91 .. .. Panama 18 15 .. .. .. .. .. .. .. .. .. Papua New Guinea .. .. 18.1 43.9 3.4 10 56 92 12 60 55 Paraguay 16 11 .. .. .. 9 22 94 .. 30 39 Peru 27 15 5.4 29.8 9.1 8 70 91 .. 50 43 Philippines 24 15 .. .. .. 21 34 45 91 21 43 Poland <5 <5 .. .. .. .. .. .. .. 23 25 Portugal <5 <5 .. .. .. .. .. .. .. 13 17 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 29 2011 World Development Indicators 111 2.20 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6–59 months under age 5 women 1990–92 2005–09 2004–09a 2004–09a 2004–09a 2004–09a 2004–09a 2004–09a 2009 2004–09 a 2004–09a Romania <5 <5 .. .. .. 8 16 74 .. 40 30 Russian Federation <5 <5 .. .. .. 6 .. .. .. 27 21 Rwanda 44 34 18.0 51.7 6.7 6 88 88 94 56 .. Saudi Arabia <5 <5 5.3 9.3 6.1 .. .. .. .. 33 32 Senegal 22 17 14.5 20.1 2.4 19 34 41 97 70 58 Serbia <5e 8e 1.8 8.1 19.3 6 15 32 .. .. .. Sierra Leone 45 35 21.3 37.4 10.1 14 11 58 99 83 60 Singapore .. .. .. .. .. .. .. .. .. 19 24 Slovak Republic <5 <5 .. .. .. .. .. .. .. 23 25 Slovenia <5 <5 .. .. .. .. .. .. .. 14 19 Somalia .. .. 32.8 42.1 4.7 11 9 1 62 .. .. South Africa <5 <5 .. .. .. .. .. .. 39 .. 22 Spain <5 <5 .. .. .. .. .. .. .. 13 18 Sri Lanka 28 19 21.6 19.2 0.8 17 76 92 .. .. .. Sudan 39 22 31.7 37.9 5.3 .. 34 11 84 85 58 Swaziland 12 18 6.1 29.5 11.4 9 33 80 27 47 24 Sweden <5 <5 .. .. .. .. .. .. .. 9 13 Switzerland <5 <5 .. .. .. .. .. .. .. 6 .. Syrian Arab Republic <5 <5 10.0 28.6 18.7 9 29 .. .. 41 39 Tajikistan 34 30 14.9 33.1 6.7 10 25 62 87 .. 45 Tanzania 28 34 16.7 44.4 4.9 10 50 d 43 94 72 58 Thailand 26 16 7.0 15.7 8.0 9 5 47 .. .. .. Timor-Leste 39 31 .. .. .. .. 52d 60 45 .. .. Togo 43 30 22.3 27.8 4.7 12 48 25 100 52 50 Trinidad and Tobago 11 11 .. .. .. 19 13 28 .. 30 30 Tunisia <5 <5 3.3 9.0 8.8 5 6 .. .. .. .. Turkey <5 <5 3.5 15.6 9.1 11 42 69 .. 33 40 Turkmenistan 9 6 .. .. .. 4 11 87 .. .. 30 Uganda 19 21 16.4 38.7 4.9 14 60 96 64 73 64 Ukraine <5 <5 .. .. .. 4 18 18 .. .. 27 United Arab Emirates <5 <5 .. .. .. .. .. .. .. 28 28 United Kingdom <5 <5 .. .. .. .. .. .. .. .. 15 United States <5 <5 1.3 3.9 8.0 .. .. .. .. .. 6 Uruguay 5 <5 6.0 13.9 9.4 8 57 .. .. 19 27 Uzbekistan 5 11 4.4 19.6 12.8 5 26 53 65 .. .. Venezuela, RB 10 8 3.7 15.6 6.1 8 .. .. .. 33 40 Vietnam 31 11 20.2 30.5 3.0 5 17 93 99b .. .. West Bank and Gaza 10 18 2.2 11.8 11.4 7 27 86 .. .. .. Yemen, Rep. 30 31 .. .. .. .. .. .. 47b 68 58 Zambia 35 43 14.9 45.8 8.4 11 61 .. 91 .. .. Zimbabwe 40 30 14.0 35.8 9.1 11 26 91 77 58 47 World 17 w 14 w 21.3 w 31.7 w 6.1 w 15 w 37 w 71 w .. w .. w .. w Low income 38 31 27.7 44.0 4.9 15 44 62 86 66 56 Middle income 17 13 20.8 30.0 6.3 15 35 73 .. .. .. Lower middle income 19 15 24.0 33.1 5.9 17 34 71 .. .. .. Upper middle income 8 6 .. .. .. 8 .. .. .. 36 31 Low & middle income 19 16 22.4 33.3 6.0 15 37 71 .. .. .. East Asia & Pacific 20 11 8.8 19.0 6.6 6 29 87 .. .. .. Europe & Central Asia 7 6 .. .. .. 7 .. .. .. 30 31 Latin America & Carib. 13 9 3.8 14.1 7.2 8 44 89 .. 38 33 Middle East & N. Africa 7 7 6.8 25.0 16.6 10 31 69 .. 48 .. South Asia 23 22 42.5 47.5 1.9 27 46 55 73 71 49 Sub-Saharan Africa 31 26 24.7 42.0 7.0 14 33 52 81 .. .. High income 5 5 .. .. .. .. .. .. .. .. 13 Euro area 5 5 .. .. .. .. .. .. .. 10 14 a. Data are for the most recent year available. b. Country’s vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Includes Hong Kong SAR, China; Macao SAR, China; and Taiwan, China. d. Data are for 2010. e. Includes Montenegro. 112 2011 World Development Indicators 2.20 PEOPLE Nutrition About the data Definitions Data on undernourishment are from the Food and emerging evidence that low-birthweight babies are • Prevalence of undernourishment is the percent- Agriculture Organization (FAO) of the United Nations more prone to noncommunicable diseases such as age of the population whose dietary energy consump- and measure food deprivation based on average diabetes and cardiovascular diseases. Estimates of tion is continuously below a minimum requirement food available for human consumption per person, low-birthweight infants are drawn mostly from hos- for maintaining a healthy life and carrying out light the level of inequality in access to food, and the pital records and household surveys. Many births physical activity with an acceptable minimum weight minimum calories required for an average person. in developing countries take place at home and are for height. • Prevalence of child malnutrition is the From a policy and program standpoint, however, seldom recorded. A hospital birth may indicate higher percentage of children under age 5 whose weight for this measure has its limits. First, food insecurity income and therefore better nutrition, or it could indi- age (underweight) or height for age (stunting) is more exists even where food availability is not a problem cate a higher risk birth. The data should therefore be than two standard deviations below the median for because of inadequate access of poor households used with caution. the international reference population ages 0–59 to food. Second, food insecurity is an individual Improved breastfeeding can save an estimated 1.3 months. Height is measured by recumbent length or household phenomenon, and the average food million children a year. Breast milk alone contains for children up to two years old and by stature while available to each person, even corrected for possible all the nutrients, antibodies, hormones, and antioxi- standing for older children. Data are based on the effects of low income, is not a good predictor of food dants an infant needs to thrive. It protects babies WHO child growth standards released in 2006. • Prevalence of over weight children is the percent- insecurity among the population. And third, nutrition from diarrhea and acute respiratory infections, stimu- age of children under age 5 whose weight for height is security is determined not only by food security but lates their immune systems and response to vacci- more than two standard deviations above the median also by the quality of care of mothers and children nation, and may confer cognitive benefits. The data for the international reference population of the corre- and the quality of the household’s health environ- on breastfeeding are derived from national surveys. sponding age as established by the WHO child growth ment (Smith and Haddad 2000). Iodine defi ciency is the single most important standards released in 2006. •  Low-birthweight Estimates of child malnutrition, based on preva- cause of preventable mental retardation, and it babies are the percentage of newborns weighing lence of underweight and stunting, are from national contributes significantly to the risk of stillbirth and less than 2.5 kilograms within the first hours of life, survey data. The proportion of underweight children miscarriage. Widely used and inexpensive, iodized before significant postnatal weight loss has occurred. is the most common malnutrition indicator. Being salt is the best source of iodine, and a global cam- • Exclusive breastfeeding is the percentage of chil- even mildly underweight increases the risk of death paign to iodize edible salt is significantly reducing dren less than six months old who were fed breast and inhibits cognitive development in children. And the risks. The data on iodized salt are derived from milk alone (no other liquids) in the past 24 hours. it perpetuates the problem across generations, as household surveys. • Consumption of iodized salt is the percentage of malnourished women are more likely to have low- Vitamin A is essential for immune system function- households that use edible salt fortified with iodine. birthweight babies. Stunting, or being below median ing. Vitamin A deficiency, a leading cause of blind- • Vitamin A supplementation is the percentage of height for age, is often used as a proxy for multi- ness, also causes a greater risk of dying from a range children ages 6–59 months who received at least two faceted deprivation and as an indicator of long-term of childhood ailments such as measles, malaria, doses of vitamin A in the previous year. • Prevalence changes in malnutrition. Estimates of overweight and diarrhea. Giving vitamin A to new breastfeed- of anemia, children under age 5, is the percentage of children are also from national survey data. Over- ing mothers helps protect their children during the children under age 5 whose hemoglobin level is less weight children have become a growing concern in first months of life. Food fortification with vitamin A than 110 grams per liter at sea level. • Prevalence developing countries. Research shows an associa- is being introduced in many developing countries. of anemia, pregnant women, is the percentage of tion between childhood obesity and a high preva- Data on anemia are compiled by the WHO based pregnant women whose hemoglobin level is less than lence of diabetes, respiratory disease, high blood mainly on nationally representative surveys, which 110 grams per liter at sea level. pressure, and psychosocial and orthopedic disorders measured hemoglobin in the blood. WHO’s hemoglo- (de Onis and Blössner 2000). bin thresholds were then used to determine anemia Data sources New international growth reference standards for status based on age, sex, and physiological status. infants and young children were released in 2006 Children under age 5 and pregnant women have the Data on undernourishment are from www.fao. by the World Health Organization (WHO) to monitor highest risk for anemia. Data should be used with org/faostat/foodsecurity/index_en.htm. Data children’s nutritional status. Differences in growth caution because surveys differ in quality, coverage, on malnutrition and overweight children are from to age 5 are influenced more by nutrition, feeding age group interviewed, and treatment of missing val- the WHO’s Global Database on Child Growth and practices, environment, and healthcare than by ues across countries and over time. Malnutrition (www.who.int/nutgrowthdb). Data on genetics or ethnicity. The previously reported data For indicators from household surveys, the year in low-birthweight babies, breastfeeding, iodized salt were based on the U.S. National Center for Health the table refers to the survey year. For more informa- consumption, and vitamin A supplementation are Statistics–WHO growth reference. Because of the tion, consult the original sources. from the United Nations Children’s Fund’s State of change in standards, the data in this edition should the World’s Children 2011 and Childinfo. Data on not be compared with data in editions prior to 2008. anemia are from the WHO’s Worldwide Prevalence Low birthweight, which is associated with maternal of Anemia 1993–2005 (2008c) and Integrated malnutrition, raises the risk of infant mortality and WHO Nutrition Global Databases. stunts growth in infancy and childhood. There is also 2011 World Development Indicators 113 2.21 Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIVa Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15–24 ages 15–24 Male Female people ages 20–79 ages 15–49 with HIV Male Female Male Female 2006 2006 2009 2010 1990 2009 2009 2009 2009 2004–09b 2004–09b Afghanistan .. .. 189 8.6 .. .. .. .. .. .. .. Albania 43 4 15 4.5 .. .. .. .. .. .. .. Algeria 26 0 59 8.5 <0.1 0.1 30 0.1 <0.1  .. .. Angola .. .. 298 3.5 0.5 2.0 60 0.6 1.6 .. .. Argentina 34 24 28 5.7 0.3 0.5 32 0.3 0.2 .. .. Armenia 61 3 73 7.8 <0.1 0.1 <43 <0.1 <0.1  68 5 Australia 22 19 6 5.7 0.1 0.1 31 0.1 0.1 .. .. Austria 47 41 11 8.9 <0.1 0.3 29 0.3 0.2 .. .. Azerbaijan .. .. 110 7.5 <0.1 0.1 60 <0.1 0.1 25 1 Bangladesh 43 1 225 6.6 <0.1 <0.1 30 <0.1 <0.1  .. .. Belarus 64 22 39 7.6 <0.1 0.3 50 <0.1 0.1 .. .. Belgium 30 24 9 5.3 <0.1 0.2 31 <0.1 <0.1  .. .. Benin 13 1 93 4.6 0.2 1.2 58 0.3 0.7 39 10 Bolivia 34 26 140 6.0 0.1 0.2  32 0.1 0.1 .. .. Bosnia and Herzegovina 49 35 50 7.1 .. .. .. .. .. .. .. Botswana .. .. 694 5.4 3.5 24.8 57 5.2 11.8 .. .. Brazil 19 12 45 6.4 .. .. .. .. .. .. .. Bulgaria 49 38 41 6.5 <0.1 0.1 29 <0.1 <0.1  .. .. Burkina Faso 13 1 215 3.8 3.9 1.2 60 0.5 0.8 .. .. Burundi .. .. 348 1.8 3.9 3.3 60 1.0 2.1 .. .. Cambodia 55c 20 c 442 5.2 0.5 0.5 63 0.1 0.1 31 3 Cameroon 9 1 182 3.9 0.6 5.3 58 1.6 3.9 52 24 Canada 21 18 5 9.2 0.1 0.2 21 0.1 0.1 .. .. Central African Republic .. .. 327 4.5 3.1 4.7 61 1.0 2.2 .. .. Chad 12 1 283 3.7 1.1 3.4 59 1.0 2.5 18 2 Chile 42 31 11 5.7 <0.1 0.4 31 0.2 0.1 .. .. China 59 4 96 4.2 .. 0.1d .. .. .. .. .. Hong Kong SAR, China .. .. 82 8.5 .. .. .. .. .. .. .. Colombia .. .. 35 5.2 0.2 0.5 33 0.2 0.1 .. 24 Congo, Dem. Rep. 10 1 372 3.2 .. .. .. .. .. 16 26 Congo, Rep. 9 0 382 5.1 5.2 3.4 59 1.2 2.6 36 16 Costa Rica 26 7 10 9.3 <0.1 0.3  29 0.2 0.1 .. .. Côte d’Ivoire 11 1 399 4.7 2.4 3.4 58 0.7 1.5 .. .. Croatia 34 e 27e 25 6.9 <0.1 <0.1 <33 <0.1 <0.1  .. .. Cuba 36 28 6 9.5 <0.1 0.1 31 0.1 0.1 .. .. Czech Republic 35 27 9 6.4 <0.1 <0.1 <42 <0.1 <0.1  .. .. Denmark 35 30 7 5.6 <0.1 0.2 27 0.1 0.1 .. .. Dominican Republic 15 11 70 11.2 0.4 0.9 59 0.3 0.7 58 19 Ecuador 23 5 68 5.9 0.3 0.4 31 0.2 0.2 .. .. Egypt, Arab Rep. 24 1 19 11.4 <0.1 <0.1 23 <0.1 <0.1  .. .. El Salvador .. .. 30 9.0 0.1 0.8 34 0.4 0.3 .. .. Eritrea 15 1 99 2.5 0.3 0.8 60 0.2 0.4 .. .. Estonia 48 25 30 7.6 <0.1 1.2 31 0.3 0.2 .. .. Ethiopia 8 1 359 2.5 .. .. .. .. .. 18 2 Finland 33 23 9 5.7 <0.1 0.1 <36 0.1 <0.1  .. .. France 36 27 6 6.7 0.3 0.4 32 0.2 0.1 .. .. Gabon .. .. 501 5.0 0.9 5.2 58 1.4 3.5 .. .. Gambia, The 17 1 269 4.3 0.1 2.0 58 0.9 2.4 .. .. Georgia 57 6 107 7.5 <0.1 0.1 43 <0.1 <0.1 .. .. Germany 37 26 5 8.9 0.1 0.1 18 0.1 <0.1 .. .. Ghana 7 1 201 4.3 0.3 1.8 59 0.5 1.3 .. .. Greece 63 39 5 6.0 0.1 0.1 31 0.1 0.1 .. .. Guatemala 24 4 62 8.6 0.1 0.8 33 0.5 0.3 .. .. Guinea .. .. 318 4.3 1.1 1.3 59 0.4 0.9 35 10 Guinea-Bissau .. .. 229 3.9 0.3 2.5 60 0.8 2.0 .. .. Haiti .. .. 238 7.2 1.3 1.9 60 0.6 1.3 42 37 Honduras .. .. 58 9.1 1.1 0.8 32 0.3 0.2 .. 7 114 2011 World Development Indicators 2.21 PEOPLE Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIVa Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15–24 ages 15–24 Male Female people ages 20–79 ages 15–49 with HIV Male Female Male Female 2006 2006 2009 2010 1990 2009 2009 2009 2009 2004–09b 2004–09b Hungary 45 35 16 6.4 0.1 <0.1 <33 <0.1 <0.1 .. .. India 28 1 168 7.8 0.1 0.3  39 0.1 0.1 15 6 Indonesia 66f 5f 189 4.8 <0.1 0.2 30 0.1 <0.1 .. .. Iran, Islamic Rep. 24 2 19 8.0 <0.1 0.2 29 <0.1 <0.1 .. .. Iraq 29 3 64 10.2 .. .. .. .. .. .. .. Ireland 34 28 9 5.2 <0.1 0.2 29 0.1 0.1 .. .. Israel 31 18 5 6.5 <0.1 0.2 29 0.1 <0.1 .. .. Italy 34 19 6 5.9 0.3 0.3 33 <0.1 <0.1 .. .. Jamaica 18 8 7 10.6 2.1 1.7 33 1.0 0.7 74 66 Japan 42 13 21 5.0 <0.1 <0.1 34 <0.1 <0.1 .. .. Jordan 59 10 6 10.1 .. .. .. .. .. .. .. Kazakhstan 43 9 163 5.8 <0.1 0.1 60 0.1 0.2 .. .. Kenya 23 1 305 3.5 3.9 6.3 59 1.8 4.1 64 40 Korea, Dem. Rep. 58 .. 345 5.3 .. .. .. .. .. .. .. Korea, Rep. 53 6 90 7.9 <0.1 <0.1 31 <0.1 <0.1 .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 36 4 35 14.6 .. .. .. .. .. .. .. Kyrgyz Republic 46 2 159 5.2 <0.1 0.3 29 0.1 0.1 .. .. Lao PDR 60 13 89 5.6 <0.1 0.2 42 0.1 0.2 .. .. Latvia 53 24 45 7.6 <0.1 0.7 30 0.2 0.1 .. .. Lebanon 31 7 15 7.8 <0.1 0.1 31 0.1 <0.1 .. .. Lesotho .. .. 634 3.9 0.8 23.6 62 5.4 14.2 44 26 Liberia 10 .. 288 4.7 0.3 1.5 61 0.3 0.7 19 9 Libya .. .. 40 9.0 .. .. .. .. .. .. .. Lithuania 50 22 71 7.6 <0.1 0.1 <33 <0.1 <0.1 .. .. Macedonia, FYR .. .. 23 6.9 .. .. .. .. .. .. .. Madagascar .. .. 261 3.2 0.2 0.2 31 0.1 0.1 6 3 Malawi 17 2 304 2.3 7.2 11.0 59 3.1 6.8 32 9 Malaysia 49 2 83 11.6 0.1 0.5  11 0.1 <0.1 .. .. Mali 13 1 324 4.2 0.4 1.0 62 0.2 0.5 29 4 Mauritania 24 1 330 4.8 0.2 0.7 31 0.4 0.3 .. .. Mauritius 34 1 22 16.2 <0.1 1.0 29 0.3 0.2 .. .. Mexico 36 12 17 10.8 0.4 0.3 27 0.2 0.1 .. .. Moldova 45 5 178 7.6 <0.1 0.4 42 0.1 0.1 55 22 Mongolia 46 6 224 1.6 <0.1 <0.1 <29 <0.1 <0.1 .. .. Morocco 27 0 92 8.3 <0.1 0.1 32 0.1 0.1 .. .. Mozambique 19 1 409 4.0 1.2 11.5 61 3.1 8.6 .. .. Myanmar 40 13 404 3.2 0.2 0.6 35 0.3 0.3 .. .. Namibia 22 8 727 4.4 1.6 13.1 59 2.3 5.8 78 55 Nepal 30 28 163 3.9 0.2 0.4 33 0.2 0.1 24 8 Netherlands 33 28 8 5.3 0.1 0.2 30 0.1 <0.1  .. .. New Zealand 22 20 8 5.2 0.1 0.1 <37 <0.1 <0.1  .. .. Nicaragua .. .. 44 10.0 <0.1 0.2 31 0.1 0.1 .. .. Niger .. .. 181 3.9 0.1 0.8 53 0.2 0.5 14 1 Nigeria 8 0 295 4.7 1.3 3.6 59 1.2 2.9 50 36 Norway 30 30 6 3.6 <0.1 0.1 30 <0.1 <0.1  .. .. Oman 20 0 13 13.4 <0.1 0.1 <33 <0.1 <0.1  .. .. Pakistan 30 3 231 9.1 <0.1 0.1 29 0.1 <0.1  .. .. Panama .. .. 48 9.6 0.2 0.9 31 0.4 0.3 .. .. Papua New Guinea .. .. 250 3.0 <0.1 0.9 58 0.3 0.8 .. .. Paraguay 33 14 47 4.9 <0.1 0.3 31 0.2 0.1 .. .. Peru .. .. 113 6.2 0.4 0.4 25 0.2 0.1 .. .. Philippines 50 11 280 7.7 <0.1 <0.1 30 <0.1 <0.1  .. .. Poland 30 38 24 7.6 <0.1 0.1 31 <0.1 <0.1  .. .. Portugal 34 15 30 9.7 0.1 0.6 31 0.3 0.2 .. .. Puerto Rico .. .. 2 10.6 .. .. .. .. .. .. .. Qatar .. .. 49 15.4 <0.1 0.1 <50 <0.1 <0.1  .. .. 2011 World Development Indicators 115 2.21 Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIVa Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15–24 ages 15–24 Male Female people ages 20–79 ages 15–49 with HIV Male Female Male Female 2006 2006 2009 2010 1990 2009 2009 2009 2009 2004–09b 2004–09b Romania 46 24 125 6.9 <0.1 0.1 30 0.1 <0.1  .. .. Russian Federation 60 g 22g 106 7.6 <0.1 1.0 49 0.2 0.3 .. .. Rwanda .. .. 376 1.6 5.2 2.9 61 1.3 1.9 19 5 Saudi Arabia 22 3 18 16.8 .. .. .. .. .. .. .. Senegal 13 1 282 4.7 0.2 0.9 59 0.3 0.7 48 5 Serbia 40 27 21 6.9 0.1 0.1 24 0.1 0.1 .. .. Sierra Leone .. .. 644 4.4 <0.1 1.6 60 0.6 1.5 20 9 Singapore 34 5 36 10.2 <0.1 0.1 30 <0.1 <0.1  .. .. Slovak Republic 41 20 9 6.4 <0.1 <0.1 <17 <0.1 <0.1  .. .. Slovenia 32 21 12 7.7 <0.1 <0.1 <29 <0.1 <0.1  .. .. Somalia .. .. 285 3.0 0.1 0.7 47 0.4 0.6 .. .. South Africa 27 8 971 4.5 0.7 17.8 62 4.5 13.6 .. .. Spain 37 27 17 6.6 0.4 0.4 24 0.2 0.1 .. .. Sri Lanka 27 0 66 10.9 <0.1 <0.1 <32 <0.1 <0.1  .. .. Sudan 25 2 119 4.2 0.1 1.1 58 0.5 1.3 .. .. Swaziland 21 2 1,257 4.2 2.3 25.9 58 6.5 15.6 66 44 Sweden 17 23 6 5.2 0.1 0.1 31 <0.1 <0.1  .. .. Switzerland 32 23 5 8.9 0.2 0.4 32 0.2 0.1 .. .. Syrian Arab Republic 40 .. 21 10.8 .. .. .. .. .. .. .. Tajikistan .. .. 202 5.0 <0.1 0.2 30 <0.1 <0.1  .. .. Tanzania 20 2 183 3.2 4.8 5.6 59 1.7 3.9 36 13 Thailand 40 2 137 7.1 1.0 1.3 40 .. .. .. .. Timor-Leste .. .. 498 3.5 .. .. .. .. .. .. .. Togo .. .. 446 4.3 0.6 3.2 59 0.9 2.2 .. .. Trinidad and Tobago .. .. 23 11.7 0.2 1.5 33 1.0 0.7 .. .. Tunisia 53 6 24 9.3 <0.1 <0.1 <37 <0.1 <0.1  .. .. Turkey 48e 15e 29 8.0 <0.1 <0.1 30 <0.1 <0.1  .. .. Turkmenistan .. .. 67 5.3 .. .. .. .. .. .. .. Uganda 17 2 293 2.2 10.2 6.5 58 2.3 4.8 36 13 Ukraine 65 24 101 7.6 0.1 1.1 49 0.2 0.3 64 43 United Arab Emirates 24 2 4 18.7 .. .. .. .. .. .. .. United Kingdom 26 24 12 3.6 0.1 0.2 31 0.2 0.1 .. .. United States 25 19 4 10.3 0.5 0.6 25 0.3 0.2 .. .. Uruguay 39 29 22 5.7 0.1 0.5 32 0.3 0.2 .. .. Uzbekistan 23 3 128 5.2 <0.1 0.1 29 <0.1 <0.1  .. .. Venezuela, RB 32 27 33 6.5 .. .. .. .. .. .. .. Vietnam 41 2 200 3.5 <0.1 0.4  30 0.1 0.1 16 8 West Bank and Gaza .. .. 19 8.6 .. .. .. .. .. .. .. Yemen, Rep. 28 6 54 3.0 .. .. .. .. .. .. .. Zambia 17 2 433 4.0 12.7 13.5 57 4.2 8.9 39 17 Zimbabwe 28 2 742 4.1 10.1 14.3 60 3.3 6.9 52 9 World 39 w 8w 137 w 6.4 w 0.3 0.8 w 37 w 0.4 w 0.7 w .. w .. w Low income 28 4 294 4.4  2.0 2.7 46 0.9 2.0 ..  .. Middle income 42 6 138 6.3 0.2 0.6 .. .. .. .. .. Lower middle income 43 3 147 6.0 0.2 0.4 .. .. .. .. .. Upper middle income 38 16 101 7.5 0.3 1.4 36 0.5 1.2 .. .. Low & middle income 40 6 161 6.1 0.3 0.9 39 .. .. .. .. East Asia & Pacific 56 4 136 4.6 0.1 0.2 .. 0.1 0.1 .. .. Europe & Central Asia 58 22 89 7.3 0.1 0.6 42 0.1 0.2 .. .. Latin America & Carib. 27 15 45 7.4 0.4 0.5 .. 0.2 0.2 .. .. Middle East & N. Africa 28 2 39 9.1 0.1 0.1 28 0.1 0.1 .. .. South Asia 30 2 180 7.8 0.1 0.3  36 0.1  0.1  15 6 Sub-Saharan Africa 14 2 342 3.8 2.4 5.4 58 1.5 3.8 36 19 High income 33 21 14 7.9 0.2 0.3 28 0.2 0.1 .. .. Euro area 37 25 9 7.1 0.2 0.3 27 0.1 0.1 .. .. a. See plausible bounds in the database and original source. b. Data are for the most recent year available. c. Data are for 2010. d. Includes Hong Kong SAR, China. e. Data are for 2008. f. Data are for 2007. g. Data are for 2009. 116 2011 World Development Indicators 2.21 PEOPLE Health risk factors and future challenges About the data Definitions The limited availability of data on health status is a many developing countries most new infections • Prevalence of smoking is the adjusted and age- major constraint in assessing the health situation in occur in young adults, with young women especially standardized prevalence estimate of smoking among developing countries. Surveillance data are lacking vulnerable. adults. The age range varies but in most countries is for many major public health concerns. Estimates Data on HIV are from the Joint United Nations 18 and older or 15 and older. • Incidence of tuber- of prevalence and incidence are available for some Programme on HIV/AIDS (UNAIDS) Global Report: culosis is the number of new and relapse cases of diseases but are often unreliable and incomplete. UNAIDS Report Global AIDS Epidemic 2010. Changes tuberculosis (all types) per 100,000 people. • Preva- National health authorities differ widely in capacity in procedures and assumptions for estimating the lence of diabetes refers to the percentage of people and willingness to collect or report information. To data and better coordination with countries have ages 20–79 who have type 1 or type 2 diabetes. compensate for this and improve reliability and inter- resulted in improved estimates of HIV and AIDS. For • Prevalence of HIV is the percentage of people who national comparability, the World Health Organiza- example, improved software was used to model the are infected with HIV. Total and youth rates are per- tion (WHO) prepares estimates in accordance with course of HIV epidemics and their impacts, making centages of the relevant age group. Female rate is as epidemiological models and statistical standards. full use of information on HIV prevalence trends from a percentage of the total population living with HIV. Smoking is the most common form of tobacco use surveillance data as well as survey data. The soft- • Condom use is the percentage of the population and the prevalence of smoking is therefore a good ware explicitly includes the effect of antiretroviral ages 15–24 who used a condom at last intercourse measure of the tobacco epidemic (Corrao and others therapy (ART) when calculating HIV incidence and in the last 12 months. 2000). Tobacco use causes heart and other vascular models reducted infectivity among people receiv- diseases and cancers of the lung and other organs. ing ART, which is having an increasing impact on Given the long delay between starting to smoke and HIV prevalence, with HIV-positive people living lon- the onset of disease, the health impact of smoking ger lives. The software also allows for changes in in developing countries will increase rapidly only in urbanization over time—important because preva- the next few decades. Because the data present a lence is higher in urban areas and because many one-time estimate, with no information on intensity countries have seen rapid urbanization over the past or duration of smoking, and because the definition of two decades. adult varies, the data should be used with caution. The estimates include plausible bounds, not shown Tuberculosis is one of the main causes of adult in the table, which reflect the certainty associated deaths from a single infectious agent in develop- with each of the estimates. The bounds are avail- ing countries. In developed countries tuberculosis able at http://data.worldbank.org or from the original has reemerged largely as a result of cases among source. immigrants. Since tuberculosis incidence cannot Data on condom use are from household surveys be directly measured, estimates are obtained by and refer to condom use at last intercourse. How- eliciting expert opinion or are derived from mea- ever, condoms are not as effective at preventing the surements of prevalence or mortality. These esti- transmission of HIV unless used consistently. Some mates include uncertainty intervals, which are not surveys have asked directly about consistent use, shown in the table, which are available at http:// but the question is subject to recall and other biases. data.worldbank.org or from the original source. Caution should be used in interpreting the data. Diabetes, an important cause of ill health and a For indicators from household surveys, the year in risk factor for other diseases in developed countries, the table refers to the survey year. For more informa- is spreading rapidly in developing countries. Highest tion, consult the original sources. among the elderly, prevalence rates are rising among younger and productive populations in developing Data sources countries. Economic development has led to the Data on smoking are from the WHO’s Report on spread of Western lifestyles and diet to develop- the Global Tobacco Epidemic 2009: Implementing ing countries, resulting in a substantial increase in Smoke-Free Environments. Data on tuberculosis diabetes. Without effective prevention and control are from the WHO’s Global Tuberculosis Control programs, diabetes will likely continue to increase. Report 2010. Data on diabetes are from the Inter- Data are estimated based on sample surveys. national Diabetes Federation’s Diabetes Atlas, Adult HIV prevalence rates reflect the rate of HIV 3rd edition. Data on prevalence of HIV are from infection in each country’s population. Low national UNAIDS’s Global Report: UNAIDS Report on the prevalence rates can be misleading, however. They Global AIDS Epidemic 2010. Data on condom use often disguise epidemics that are initially concen- are from Demographic and Health Surveys by trated in certain localities or population groups and Macro International. threaten to spill over into the wider population. In 2011 World Development Indicators 117 2.22 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2009 1990 2009 1990 2009 2004–09a,b 2004–09a,b 2005–09a 2005–09a 2009 2009 Afghanistan 41 44 167 134 250 199 .. .. 435 409 34 36 Albania 72 77 41 14 51 15 3 1 98 51 82 90 Algeria 67 73 51 29 61 32 .. .. 118 98 78 82 Angola 42 48 153 98 258 161 .. .. 406 350 37 44 Argentina 72 76 25 13 28 14 .. .. 163 75 75 87 Armenia 68 74 48 20 56 22 8 3 162 79 73 85 Australia 77 82 8 4 9 5 .. .. 82 47 88 93 Austria 76 80 8 3 9 4 .. .. 99 50 85 93 Azerbaijan 65 70 78 30 98 34 9 5 178 108 69 79 Bangladesh 54 67 102 41 148 52 16 20 206 172 66 71 Belarus 71 70 20 11 24 12 .. .. 330 115 54 83 Belgium 76 81 9 4 10 5 .. .. 108 62 85 92 Benin 54 62 111 75 184 118 64 65 207 170 62 67 Bolivia 59 66 84 40 122 51 18 20 232 172 64 72 Bosnia and Herzegovina 67 75 21 13 23 14 .. .. 132 61 79 89 Botswana 64 55 46 43 60 57 .. .. 487 505 42 43 Brazil 66 73 46 17 56 21 .. .. 226 118 67 81 Bulgaria 72 73 14 8 18 10 .. .. 213 91 72 87 Burkina Faso 47 53 110 91 201 166 .. .. 331 277 46 52 Burundi 46 51 114 101 189 166 65 65 382 346 42 47 Cambodia 55 62 85 68 117 88 20 20 288 218 56 64 Cameroon 55 51 91 95 148 154 73 72 401 398 43 45 Canada 77 81 7 5 8 6 .. .. 92 55 87 92 Central African Republic 49 47 115 112 175 171 74 82 452 426 36 41 Chad 51 49 120 124 201 209 96 101 358 317 42 47 Chile 74 79 18 7 22 9 .. .. 129 64 81 90 China 68 c 73c 37 17 46 19 .. .. 147 88 76 83 Hong Kong SAR, China 77 83 .. .. .. .. .. .. 198 92 72 84 Colombia 68 73 28 16 35 19 4 3 397 348 38 44 Congo, Dem. Rep. 48 48 126 126 199 199 70 64 373 350 46 50 Congo, Rep. 59 54 67 81 104 128 49 43 111 59 82 90 Costa Rica 76 79 16 10 18 11 .. .. 305 271 53 59 Côte d’Ivoire 58 58 105 83 152 119 .. .. 144 57 77 90 Croatia 72 76 11 5 13 5 1 1 108 68 83 89 Cuba 75 79 10 4 14 6 .. .. 143 65 79 90 Czech Republic 71 77 10 3 12 4 .. .. 107 67 83 89 Denmark 75 79 8 3 9 4 .. .. 206 134 70 79 Dominican Republic 68 73 48 27 62 32 6 4 164 86 76 86 Ecuador 69 75 41 20 53 24 5 5 161 105 72 80 Egypt, Arab Rep. 63 70 66 18 90 21 5 5 285 121 63 81 El Salvador 66 71 48 15 62 17 .. .. 374 281 46 58 Eritrea 48 60 92 39 150 55 .. .. 283 92 64 87 Estonia 69 75 13 4 17 6 .. .. 334 293 49 54 Ethiopia 47 56 124 67 210 104 56 56 129 57 84 93 Finland 75 80 6 3 7 3 .. .. 121 55 85 93 Franced   77 81 7 3 9 4 .. .. 317 276 56 61 Gabon 61 61 68 52 93 69 .. .. 324 264 48 55 Gambia, The 51 56 104 78 153 103 46 39 195 77 70 84 Georgia 70 72 41 26 47 29 5 4 102 54 85 92 Germany 75 80 7 4 9 4 .. .. 323 286 51 56 Ghana 57 57 76 47 120 69 38 28 92 37 86 94 Greece 77 80 9 3 11 3 .. .. 232 127 68 80 Guatemala 62 71 57 33 76 40 .. .. 252 195 55 63 Guinea 48 58 137 88 231 142 89 86 398 347 39 45 Guinea-Bissau 44 48 142 115 240 193 110 88 284 223 57 64 Haiti 55 61 105 64 152 87 33 36 170 119 73 80 Honduras 66 72 43 25 55 30 8 9 75 33 88 94 118 2011 World Development Indicators 2.22 PEOPLE Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2009 1990 2009 1990 2009 2004–09a,b 2004–09a,b 2005–09a 2005–09a 2009 2009 Hungary 69 74 15 5 17 6 .. .. 250 104 68 86 India 58 64 84 50 118 66 9 12 256 170 59 68 Indonesia 62 71 56 30 86 39 13 12 162 113 72 81 Iran, Islamic Rep. 65 72 55 26 73 31 .. .. 142 96 75 82 Iraq 65 68 42 35 53 44 6 7 211 105 66 81 Ireland 75 80 8 4 9 4 .. .. 88 56 87 92 Israel 77 82 10 3 11 4 .. .. 86 48 87 93 Italy 77 81 8 3 10 4 .. .. 82 43 86 94 Jamaica 71 72 28 26 33 31 5 6 221 116 70 81 Japan 79 83 5 2 6 3 .. .. 86 43 88 95 Jordan 67 73 32 22 39 25 3 7 159 109 74 82 Kazakhstan 68 68 51 26 60 29 5 4 400 151 47 76 Kenya 60 55 64 55 99 84 27 25 392 403 47 48 Korea, Dem. Rep. 70 67 23 26 45 33 .. .. 169 117 67 77 Korea, Rep. 71 80 8 5 9 5 .. .. 105 41 83 93 Kosovo 68 70 .. .. .. .. .. .. .. .. .. .. Kuwait 75 78 14 8 17 10 .. .. 84 51 85 90 Kyrgyz Republic 68 67 63 32 75 37 8 4 257 122 61 78 Lao PDR 54 65 108 46 157 59 .. .. 222 180 63 70 Latvia 69 73 12 7 16 8 .. .. 311 114 64 86 Lebanon 69 72 33 11 40 12 .. .. 150 98 74 83 Lesotho 59 45 74 61 93 84 22 19 666 633 25 29 Liberia 49 59 165 80 247 112 62 64 251 206 56 63 Libya 68 75 32 17 36 19 .. .. 144 89 75 84 Lithuania 71 73 12 5 15 6 .. .. 346 116 60 86 Macedonia, FYR 71 74 32 10 36 11 2 1 132 79 77 85 Madagascar 51 61 102 41 167 58 30 31 266 216 57 63 Malawi 49 54 129 69 218 110 52 54 434 395 44 49 Malaysia 70 75 16 6 18 6 .. .. 147 84 76 85 Mali 43 49 139 101 250 191 117 114 386 355 39 42 Mauritania 56 57 81 74 129 117 53 44 304 236 50 59 Mauritius 69 73 21 15 24 17 .. .. 230 114 67 81 Mexico 71 75 36 15 45 17 .. .. 137 76 79 87 Moldova 67 69 30 15 37 17 7 4 279 125 60 78 Mongolia 61 67 73 24 101 29 11 10 284 180 58 71 Morocco 64 72 69 33 89 38 9 11 144 94 74 83 Mozambique 43 48 155 96 232 142 .. .. 489 469 36 40 Myanmar 59 62 84 54 118 71 .. .. 250 188 58 66 Namibia 62 62 49 34 73 48 24 19 346 334 55 59 Nepal 54 67 99 39 142 48 21 18 196 171 67 71 Netherlands 77 81 7 4 8 4 .. .. 81 59 87 92 New Zealand 75 80 9 5 11 6 .. .. 87 58 87 91 Nicaragua 64 73 52 22 68 26 .. .. 201 113 71 81 Niger 42 52 144 76 305 160 138 135 344 295 44 49 Nigeria 45 48 126 86 212 138 91 93 404 380 40 42 Norway 77 81 7 3 9 3 .. .. 82 50 88 92 Oman 70 76 37 9 48 12 .. .. 96 71 83 87 Pakistan 61 67 101 71 130 87 14 22 162 131 68 72 Panama 72 76 25 16 31 23 .. .. 136 72 79 87 Papua New Guinea 55 61 67 52 91 68 .. .. 344 251 50 61 Paraguay 68 72 34 19 42 23 .. .. 170 123 73 80 Peru 66 73 62 19 78 21 13 4 162 100 74 83 Philippines 65 72 41 26 59 33 10 9 153 99 74 83 Poland 71 76 15 6 17 7 .. .. 209 80 73 89 Portugal 74 79 12 3 15 4 .. .. 124 53 83 92 Puerto Rico 75 79 .. .. .. .. .. .. 130 52 80 91 Qatar 70 76 17 10 19 11 .. .. 109 100 82 83 2011 World Development Indicators 119 2.22 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2009 1990 2009 1990 2009 2004–09a,b 2004–09a,b 2005–09a 2005–09a 2009 2009 Romania 70 73 25 10 32 12 .. .. 192 82 70 86 Russian Federation 69 69 23 11 27 12 .. .. 396 147 47 78 Rwanda 33 51 103 70 171 111 69 55 397 351 40 47 Saudi Arabia 68 73 35 18 43 21 3 4 137 88 76 85 Senegal 52 56 73 51 151 93 43 39 325 266 48 55 Serbia 71   74 25 6 29 7 4 3 153e 82e 75 86 Sierra Leone 40 48 166 123 285 192 67 61 498 464 30 34 Singapore 74 81 6 2 8 3 .. .. 80 41 86 93 Slovak Republic 71 75 13 6 15 7 .. .. 195 73 72 88 Slovenia 73 79 9 2 10 3 .. .. 149 57 81 92 Somalia 45 50 109 109 180 180 53 54 368 315 42 47 South Africa 61 52 48 43 62 62 .. .. 575 517 32 41 Spain 77 82 8 4 9 4 .. .. 106 44 86 94 Sri Lanka 70 74 23 13 28 15 .. .. 192 76 71 86 Sudan 53 58 78 69 124 108 38 30 302 257 53 59 Swaziland 60 46 67 52 92 73 32 30 605 638 30 29 Sweden 78 81 6 2 7 3 .. .. 78 48 88 93 Switzerland 77 82 7 4 8 4 .. .. 78 46 88 93 Syrian Arab Republic 68 74 30 14 36 16 5 3 120 81 79 86 Tajikistan 63 67 91 52 117 61 18 13 208 137 64 74 Tanzania 51 56 99 68 162 108 56 52 369 355 49 52 Thailand 69 69 27 12 32 14 .. .. 291 170 63 77 Timor-Leste 46 62 138 48 184 56 .. .. 259 224 58 63 Togo 58 63 89 64 150 98 55 43 238 197 61 68 Trinidad and Tobago 69 70 30 31 34 35 5 8 236 139 63 78 Tunisia 70 74 40 18 50 21 .. .. 122 70 78 87 Turkey 65 72 69 19 84 20 6 6 149 83 74 84 Turkmenistan 63 65 81 42 99 45 .. .. 298 151 55 73 Uganda 48 53 111 79 184 128 75 62 401 399 44 47 Ukraine 70 69 18 13 21 15 4 1 385 142 53 80 United Arab Emirates 73 78 15 7 17 7 .. .. 76 63 86 89 United Kingdom 76 80 8 5 10 6 .. .. 100 61 86 91 United States 75 79 9 7 11 8 .. .. 141 81 84 89 Uruguay 73 76 21 11 24 13 .. .. 139 63 77 89 Uzbekistan 67 68 61 32 74 36 11 7 237 135 62 75 Venezuela, RB 71 74 27 15 32 18 .. .. 175 91 74 84 Vietnam 65 75 39 20 55 24 5 4 134 88 78 85 West Bank and Gaza 68 74 35 25 43 30 3 3 125 90 78 84 Yemen, Rep. 54 63 88 51 125 66 10 11 247 198 60 67 Zambia 51 46 108 86 179 141 66 55 528 518 33 35 Zimbabwe 61 45 54 56 81 90 21 21 687 664 24 27 World 65 w 69 w 64 w 43 w 92 w 61 w .. w .. w 213 w 151 w 68 w 77 w Low income 52 57 108 76 171 118 52 49 312 275 52 58 Middle income 64 69 61 38 85 51 .. .. 201 134 67 77 Lower middle income 63 68 66 43 93 57 .. .. 201 136 67 75 Upper middle income 68 72 41 19 51 22 .. .. 201 122 67 81 Low & middle income 63 67 70 47 100 66 .. .. 216 153 65 74 East Asia & Pacific 67 72 41 21 55 26 .. .. 158 99 74 82 Europe & Central Asia 68 70 43 19 52 21 .. .. 286 123 59 80 Latin America & Carib. 68 74 42 19 52 23 .. .. 190 103 72 83 Middle East & N. Africa 64 71 57 27 76 33 .. .. 155 104 73 81 South Asia 58 64 89 55 125 71 11 15 242 169 61 69 Sub-Saharan Africa 50 53 109 81 181 130 68 65 390 358 44 48 High income 75 80 10 6 12 7 .. .. 120 63 84 91 Euro area 76 81 8 3 9 4 .. .. 107 52 85 93 a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Taiwan, China. d. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. e. Includes Kosovo. 120 2011 World Development Indicators 2.22 PEOPLE Mortality About the data Definitions Mortality rates for different age groups (infants, chil- weighted least squares method to fit a regression • Life expectancy at birth is the number of years dren, and adults) and overall mortality indicators (life line to the relationship between mortality rates and a newborn infant would live if prevailing patterns of expectancy at birth or survival to a given age) are their reference dates and then extrapolate the trend mortality at the time of its birth were to stay the important indicators of health status in a country. to the present. (For further discussion of childhood same throughout its life. • Infant mortality rate is Because data on the incidence and prevalence of mortality estimates, see UNICEF, WHO, World Bank, the number of infants dying before reaching one year diseases are frequently unavailable, mortality rates and United Nations Population Division 2010; for a of age, per 1,000 live births in a given year. • Under- are often used to identify vulnerable populations. graphic presentation and detailed background data, five mortality rate is the probability per 1,000 that a And they are among the indicators most frequently see www.childmortality.org.) newborn baby will die before reaching age 5, if sub- used to compare socioeconomic development Infant and child mortality rates are higher for boys ject to current age-specific mortality rates. • Child across countries. than for girls in countries in which parental gender mortality rate is the probability per 1,000 of dying The main sources of mortality data are vital reg- preferences are insignificant. Child mortality cap- between ages 1 and 5—that is, the probability of a istration systems and direct or indirect estimates tures the effect of gender discrimination better than 1-year-old dying before reaching age 5—if subject to based on sample surveys or censuses. A “complete” infant mortality does, as malnutrition and medical current age-specific mortality rates. • Adult mortal- vital registration system—covering at least 90 per- interventions are more important in this age group. ity rate is the probability per 1,000 of dying between cent of vital events in the population—is the best Where female child mortality is higher, as in some the ages of 15 and 60—that is, the probability of a source of age-specific mortality data. Where reliable countries in South Asia, girls probably have unequal 15-year-old dying before reaching age 60—if subject age-specific mortality data are available, life expec- access to resources. Child mortality rates in the to current age-specific mortality rates between those tancy at birth is directly estimated from the life table table are not compatible with infant mortality and ages. •  Survival to age 65 refers to the percent- constructed from age-specific mortality data. under-five mortality rates because of differences in age of a hypothetical cohort of newborn infants that But complete vital registration systems are fairly methodology and reference year. Child mortality data would survive to age 65, if subject to current age- uncommon in developing countries. Thus estimates were estimated directly from surveys and cover the specific mortality rates. must be obtained from sample surveys or derived 10 years preceding the survey. In addition to esti- by applying indirect estimation techniques to reg- mates from Demographic Health Surveys, estimates Data sources istration, census, or survey data (see table  2.17 derived from Multiple Indicator Cluster Surveys have and Primary data documentation). Survey data are been added to the table; they cover the 5 years pre- Data on infant and under-five mortality are from subject to recall error, and surveys estimating infant ceding the survey. Levels and Trends in Child Mortality, Report 2010 deaths require large samples because households Rates for adult mortality and survival to age 65 by the Inter-agency Group for Child Mortality Esti- in which a birth has occurred during a given year come from life tables. Adult mortality rates increased mation, covered in About the data, based mainly cannot ordinarily be preselected for sampling. Indi- notably in a dozen countries in Sub-Saharan Africa on household surveys, censuses, and vital regis- rect estimates rely on model life tables that may be between 1995–2000 and 2000–05 and in several tration data, supplemented by the World Bank’s inappropriate for the population concerned. Because countries in Europe and Central Asia during the first Human Development Network estimates based life expectancy at birth is estimated using infant mor- half of the 1990s. In Sub-Saharan Africa the increase on vital registration and sample registration data. tality data and model life tables for many develop- stems from AIDS-related mortality and affects both Data on child mortality are from Demographic and ing countries, similar reliability issues arise for this sexes, though women are more affected. In Europe Health Surveys by Macro International and World indicator. Extrapolations based on outdated surveys and Central Asia the causes are more diverse (high Bank calculations based on infant and under-five may not be reliable for monitoring changes in health prevalence of smoking, high-fat diet, excessive alco- mortality from Multiple Indicator Cluster Surveys status or for comparative analytical work. hol use, stressful conditions related to the economic by UNICEF. Data on survival to age 65 and most Estimates of infant and under-five mortality tend to transition) and affect men more. data on adult mortality are linear interpolations of vary by source and method for a given time and place. The percentage of a hypothetical cohort surviv- five-year data from World Population Prospects: The Years for available estimates also vary by country, ing to age 65 reflects both child and adult mortality 2008 Revision. Remaining data on adult mortality making comparison across countries and over time rates. Like life expectancy, it is a synthetic mea- are from the Human Mortality Database by the Uni- difficult. To make infant and under-five mortality esti- sure based on current age-specific mortality rates. versity of California, Berkeley, and the Max Planck mates comparable and to ensure consistency across It shows that even in countries where mortality is Institute for Demographic Research (www.mortal- estimates by different agencies, the Inter-agency high, a certain share of the current birth cohort will ity.org). Data on life expectancy at birth are World Group for Child Mortality Estimation, comprising the live well beyond the life expectancy at birth, while in Bank calculations based on male and female data United Nations Children’s Fund (UNICEF), the United low-mortality countries close to 90 percent will reach from World Population Prospects: The 2008 Revi- Nations Population Division, the World Health Organi- at least age 65. sion (for more than half of countries, most of them zation (WHO), the World Bank, and other universities Annual data series from the United Nations are developing countries), census reports and other and research institutes, developed and adopted a interpolated based on five-year estimates and thus statistical publications from national statistical statistical method that uses all available informa- may not reflect actual events. offices, Eurostat’s Demographic Statistics, and the tion to reconcile differences. The method uses the U.S. Bureau of the Census International Data Base. 2011 World Development Indicators 121 Text figures, tables, and boxes ENVIRONMENT Introduction 3 Environmental sustainability T he United Nations Conference on the Human Environment, held in Stockholm in 1972, drew worldwide attention to the growing impact of human activity on the envi- ronment and to the need for sustainable management of environmental resources. Twenty years later the United Nations Conference on Environment and Development in Rio de Janeiro adopted a comprehensive plan of action for a sustainable future. That plan later became part of the Millennium Declaration, with some of the more important targets included in Millennium Development Goal 7: ensuring environmental sustainability. Understanding climate change is a central issue Environmental indicators for environmental sustainability and for develop- Monitoring progress toward the environment targets ment policy. Public policy should help people cope of the Millennium Development Goals and measur- with new or worsened risks, facilitate investments ing the complexity of environmental phenomena in clean energy technologies, and adapt land and require new measurement frameworks and new water management to better protect a threatened data. This year’s Environment section of World De- natural environment while feeding an expanding and velopment Indicators includes a new table on natural more prosperous population. resource rents that measures human dependence The World Bank Group plays a key role in financ- on environmental assets. And in recognition of the ing climate change adaptation and mitigation. Since mainstreaming of green accounting, the data on 1999 it has led in forming carbon markets, which adjusted net savings — gross savings adjusted for are now directing funds toward clean low-carbon capital depreciation, resource depletion, pollution development. At the UN Climate Change Conference damage, and human capital investment— have been in Copenhagen in 2009, it launched the Carbon moved to the Economy section (table 4.11), joining Partnership Facility, the latest addition in a family a new table showing corresponding adjustments to of carbon funds and facilities. The facility assists national income (table 4.10). Together these tables developing countries in pursuing low-carbon growth provide a clearer picture of the impact of the envi- and in accelerating reductions of greenhouse gas ronment on the long-term sustainability of economic emissions; it uses carbon finance innovatively to growth. leverage capital for both public and private invest- Other indicators in this section describe land ment in clean technologies. At the UN Framework use, agriculture and food production, forests and Convention on Climate Change conference in Can- biodiversity, water resources, energy use and effi - cun in 2010, the World Bank joined global leaders ciency, urbanization, environmental impacts, govern- and policymakers in the Roadmap for Action: Agri- ment commitments, and threatened species. Where culture, Food Security, and Climate Change, which possible, the indicators come from international outlines concrete actions linking agricultural invest- sources to facilitate cross- country comparison. ments and policies with the transition to climate- Important to keep in mind is that country coverage smart growth. It highlights a “triple-win” approach: may be uneven, ecosystems span national bound- increasing farm productivity and incomes, making aries, and natural resource use may differ locally, agriculture more resilient to climate change, and regionally, and globally. For example, greenhouse making agriculture part of the solution to climate gas emissions and climate change may be mea- change by sequestering more carbon in the soil and sured globally, but their effects are also manifested biomass. locally, shaping people’s lives and opportunities. 2011 World Development Indicators 123 Measuring dependence on and minerals, account for 30–50 percent of GNI environmental assets (figure 3a) — almost 70 percent in Iraq. Rents Accounting for the contribution of natural re- from nonrenewable resources —fossil fuels and sources to economic output is important in minerals — as well as rents from overharvesting building an analytical framework for sustain- of forests indicate the liquidation of a country’s able development. The extraction or harvest- capital stock. When countries use such rents ing of natural resources can produce sub- to support current consumption rather than to stantial rents — revenues above the cost of invest in new capital to replace what is being extracting them—which are calculated as the used up, they are, in effect, borrowing against difference between the price of a commodity their future. and the average cost of producing it. This is For resource-rich countries—where resource done by estimating the world price of units rents are at least 5 percent of GNI—transforming of specifi c commodities and subtracting es- nonrenewable natural capital into other forms of timates of the average unit costs of extrac- wealth is a major development challenge. Figure tion or harvesting. These unit rents are then 3b plots adjusted net savings— net national sav- multiplied by the physical quantities countries ings plus education expenditure, minus energy extract or harvest to determine the rents for depletion, mineral depletion, net forest deple- each commodity, as a share of gross national tion, and carbon dioxide and particulate emis- income (GNI). sions damage — against energy and mineral Table 3.16 presents data on rents from oil, rents for resource-rich countries. Countries with gas, coal, and other mineral production and negative adjusted net savings, such as Angola from forests as a share of GNI. In some coun- and Republic of Congo, are depleting natural tries those rents, especially from fossil fuels capital without replacing it and becoming poorer over time. Countries with positive adjusted net The 10 countries with the highest natural savings, such as Botswana and China, are add- resource rents are primarily oil and gas producers 3a ing to wealth and well-being and reducing natural Natural resource rents, 2009 (percent of GNI) Forest Minerals Coal Natural gas Oil resource depletion by investing in other types 75 of capital. (See About the data for tables 4.10 and 4.11.) 50 Mainstreaming environmental 25 and wealth accounting in country statistical systems There has been considerable effort over the 0 Iraq Congo, Rep. Libya Saudi Arabia Gabon Azerbaijan Turkmenistan Oman Angola Chad past 20 years to develop statistical methods Source: Table 3.16. for environmental accounting (a broad frame- work that includes natural capital accounting) under the aegis of the United Nations Statisti- Countries with negative adjusted net savings are depleting cal Commission. The commission established natural capital without replacing it and are becoming poorer 3b the London Group on Environmental Account- Adjusted net savings in resource-rich countries, 2008 (percent of GNI) ing and later a high-level body, the UN Com- 50 mittee of Experts on Environmental and Eco- China Botswana nomic Accounting, to develop methodological 25 guidelines. In 2003 the United Nations and 0 other international organizations produced the Handbook of National Accounting: Integrated –25 Environmental and Economic Accounting (UN Angola Congo, Rep. and others 2003). It is currently under revision –50 0 25 50 75 and will become part of the statistical stan- Energy and mineral rents (percent of GNI) dard, like the System of National Accounts, Note: The underlying data were produced as part of a long-term World Bank project on measuring sustainable which establishes methodology for national development. Estimates of natural resource rents are used in calculating comprehensive wealth and adjusted accounts. net savings, which are now in tables 4.10 and 4.11. For further discussion of wealth accounting, see The Chang- ing Wealth of Nations (World Bank 2011). Other institutions and individual scholars Source: World Development Indicators data files. have also done work on wealth accounting over 124 2011 World Development Indicators ENVIRONMENT the past 20 years. Official statistical offices in and poverty reduction, especially in resource- more than 30 countries have institutionalized rich countries. wealth accounting, and 16 of them regularly Stiglitz, Sen, and Fitoussi (2009) offer compile at least one type of natural resource further support for the comprehensive wealth asset account. The majority of countries focus approach to sustainable development. They pro- on mineral and energy assets, but some, nota- pose ways to modify and extend conventional bly Australia and Norway, construct more com- national accounts to provide a more accurate prehensive accounts for natural capital. and useful guide for policy. An important part National statistical offices, the academic of the proposed changes, to better reflect the community, and nongovernmental organizations sustainability of economies, is comprehensive have produced empirical work on natural capi- wealth. They recommend compiling accounts tal accounting nationally, regionally, and locally. for all assets (natural, human-made, and human Together, these studies have deepened our capital) and changes in those assets, which knowledge of wealth accounting, leading to bet- correspond to the components of adjusted net ter understanding of the prospects for growth savings. 2011 World Development Indicators 125 Tables 3.1 Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2009 1990–2009 2009 1990 2010 1990 2008 1990 2008 1990 2008 Afghanistan 82 76 2.1 652.2 2.1 2.1 0.2 0.2 12.1 11.9 42.6 26.9 Albania 64 53 –1.2 27.4 28.8 28.3 4.6 3.2 21.1 22.3 17.6 19.4 Algeria 48 34 –0.1 2,381.7 0.7 0.6 0.2 0.4 3.0 3.1 28.0 21.8 Angola 63 42 0.8 1,246.7 48.9 46.9 0.4 0.2 2.3 2.7 27.2 18.9 Argentina 13 8 –1.6 2,736.7 12.7 10.7 0.4 0.4 9.6 11.7 81.2 80.2 Armenia 33 36 –0.2 28.5 12.2 9.2 2.1 1.9 14.9 15.8 1.5 14.6 Australia 15 11 –0.1 7,682.3 20.1 19.4 0.0 0.0 6.2 5.7 280.7 205.4 Austria 34 33 0.2 82.5 45.8 47.1 1.0 0.8 17.3 16.7 18.5 16.5 Azerbaijan 46 48 1.3 82.6 11.2 11.3 3.7 2.8 20.5 22.5 0.8 21.4 Bangladesh 80 72 1.2 130.2 11.5 11.1 2.5 6.1 70.0 60.7 7.9 4.9 Belarus 34 26 –1.7 202.9 38.4 42.5 0.9 0.6 30.0 27.2 0.5 57.0 Belgium 4 3 –1.3 30.3 22.4 22.4 0.5a 0.8 23.3a 27.9 0.2 7.9 Benin 66 58 2.7 110.6 52.1 41.2 0.9 2.7 14.6 23.1 33.7 29.4 Bolivia 44 34 0.6 1,083.3 58.0 52.8 0.1 0.2 1.9 3.3 31.5 37.1 Bosnia and Herzegovina 61 52 –1.5 51.2 43.2 42.7 2.9 1.8 16.6 19.7 3.5 26.7 Botswana 58 40 –0.1 566.7 24.2 20.0 0.0 0.0 0.7 0.4 31.1 13.0 Brazil 25 14 –1.7 8,459.4 68.0 61.4 0.8 0.9 6.0 7.2 33.9 31.8 Bulgaria 34 29 –1.6 108.6 30.1 36.2 2.7 1.7 34.9 28.2 44.2 40.2 Burkina Faso 86 80 2.7 273.6 25.0 20.6 0.2 0.2 12.9 23.0 39.9 41.4 Burundi 94 89 1.7 25.7 11.3 6.7 14.0 15.2 36.2 35.0 16.4 11.1 Cambodia 87 78 1.6 176.5 73.3 57.2 0.6 0.9 20.9 22.1 38.1 26.8 Cameroon 59 42 0.7 472.7 51.4 42.1 2.6 2.5 12.6 12.6 48.6 31.2 Canada 23 20 0.1 9,093.5 34.1 34.1 0.7 0.8 5.0 5.0 163.7 135.4 Central African Republic 63 61 2.0 623.0 37.2 36.3 0.1 0.1 3.1 3.1 65.6 44.5 Chad 79 73 2.8 1,259.2 10.4 9.2 0.0 0.0 2.6 3.4 53.6 39.4 Chile 17 11 –0.7 743.5 20.5 21.8 0.3 0.6 3.8 1.7 21.2 7.5 China 73 56 –0.5 9,327.5 16.8 22.2 0.8 1.5 13.3 11.6 10.9 8.2 Hong Kong SAR, China 1 0 .. 1.0 .. .. .. .. .. .. .. .. Colombia 32 25 0.5 1,109.5 56.3 54.5 1.5 1.5 3.0 1.6 10.0 4.1 Congo, Dem. Rep. 72 65 2.5 2,267.1 70.7 68.0 0.5 0.3 2.9 3.0 18.0 10.4 Congo, Rep. 46 38 1.2 341.5 66.5 65.6 0.1 0.2 1.4 1.4 19.6 13.6 Costa Rica 49 36 0.5 51.1 50.2 51.0 4.9 5.9 5.1 3.9 8.4 4.4 Côte d’Ivoire 60 51 1.8 318.0 32.1 32.7 11.0 13.4 7.6 8.8 19.3 13.6 Croatia 46 43 –0.8 56.0 33.1 34.3 2.0 1.5 21.7 15.4 2.4 19.4 Cuba 27 24 –0.2 106.4 19.2 27.0 4.2 3.8 31.6 33.5 32.0 31.9 Czech Republic 25 27 0.4 77.3 34.0 34.4 3.1 3.1 41.1 39.2 32.1 29.0 Denmark 15 13 –0.4 42.4 10.5 12.8 0.2 0.2 60.4 56.6 49.8 43.7 Dominican Republic 45 30 –0.4 48.3 40.8 40.8 9.3 10.3 18.6 16.6 12.2 8.0 Ecuador 45 34 0.0 248.4 49.9 39.7 4.8 5.1 5.8 5.0 15.6 9.2 Egypt, Arab Rep. 57 57 2.0 995.5 0.0 0.1 0.4 0.8 2.3 2.8 4.0 3.4 El Salvador 51 39 –0.6 20.7 18.2 13.9 12.5 11.1 26.5 33.1 10.3 11.2 Eritrea 84 79 2.1 101.0 16.0 15.2 0.0 0.0 4.9 6.6 0.1 13.6 Estonia 29 31 –0.5 42.4 49.3 52.3 0.3 0.2 26.3 14.1 3.5 44.6 Ethiopia 87 83 2.5 1,000.0 15.1 12.3 0.5 0.9 10.0 13.6 1.4 16.9 Finland 39 36 0.1 303.9 71.9 72.9 0.0 0.0 7.4 7.4 45.5 42.4 France 26 22 –0.2 547.7 26.5 29.1 2.2 2.0 32.9 33.3 31.7 29.3 Gabon 31 15 –1.5 257.7 85.4 85.4 0.6 0.6 1.1 1.3 31.8 22.4 Gambia, The 62 43 1.5 10.0 44.2 48.0 0.5 0.5 18.2 39.0 20.3 23.5 Georgia 45 47 –1.0 69.5 40.0 39.5 4.8 1.7 11.4 6.7 1.0 10.9 Germany 27 26 0.0 348.6 30.8 31.8 1.3 0.6 34.3 34.2 15.1 14.5 Ghana 64 49 1.1 227.5 32.7 21.7 6.6 12.5 11.9 19.3 18.0 18.8 Greece 41 39 0.2 128.9 25.6 30.3 8.3 8.7 22.5 16.3 28.5 18.7 Guatemala 59 51 1.6 107.2 44.3 34.1 4.5 8.8 12.1 12.4 14.6 9.7 Guinea 72 65 2.1 245.7 29.6 26.6 2.0 2.8 3.3 9.8 13.1 24.4 Guinea-Bissau 72 70 2.3 28.1 78.8 71.9 4.2 8.9 8.9 10.7 24.5 19.0 Haiti 72 52 0.1 27.6 4.2 3.7 11.6 10.9 28.3 36.3 11.0 10.1 Honduras 60 52 1.5 111.9 72.7 46.4 3.2 3.7 13.1 9.1 29.8 13.9 126 2011 World Development Indicators 3.1 ENVIRONMENT Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2009 1990–2009 2009 1990 2010 1990 2008 1990 2008 1990 2008 Hungary 34 32 –0.5 89.6 20.0 22.6 2.6 2.2 56.2 51.0 48.7 45.6 India 75 70 1.3 2,973.2 21.5 23.0 2.2 3.8 54.8 53.2 19.2 13.9 Indonesia 69 47 –0.6 1,811.6 65.4 52.1 6.5 8.3 11.2 12.1 11.4 9.7 Iran, Islamic Rep. 44 31 –0.3 1,628.6 6.8 6.8 0.8 1.1 9.3 10.5 27.9 23.7 Iraq 30 34 3.2 437.4 1.8 1.9 0.7 0.6 13.3 11.9 30.7 16.9 Ireland 43 38 0.7 68.9 6.7 10.7 0.0 0.0 15.1 16.0 29.7 24.9 Israel 10 8 1.7 21.6 6.1 7.1 4.1 3.6 15.9 13.9 7.4 4.1 Italy 33 32 0.1 294.1 25.8 31.1 10.1 9.0 30.6 24.2 15.9 11.9 Jamaica 51 47 0.2 10.8 31.9 31.1 9.2 10.2 11.0 11.5 5.0 4.7 Japan 37 33 –0.4 364.5 68.4 68.5 1.3 0.9 13.1 11.8 3.9 3.4 Jordan 28 22 2.1 88.2 1.1 1.1 0.8 0.9 2.0 1.7 5.6 2.6 Kazakhstan 44 42 –0.4 2,699.7 1.3 1.2 0.1 0.0 13.0 8.4 0.3 144.8 Kenya 82 78 2.5 569.1 6.5 6.1 0.8 0.9 8.8 9.3 21.3 13.7 Korea, Dem. Rep. 42 37 0.3 120.4 68.1 47.1 1.5 1.7 19.0 22.4 11.4 11.3 Korea, Rep. 26 18 –1.2 96.9 64.5 64.2 1.6 1.9 19.8 16.0 4.6 3.2 Kosovo .. .. .. 10.9b .. .. .. .. .. 27.6 .. 16.8 Kuwait 2 2 0.3 17.8 0.2 0.3 0.1 0.2 0.2 0.6 0.2 0.4 Kyrgyz Republic 62 64 1.1 191.8 4.4 5.0 0.4 0.4 6.9 6.7 1.2 24.2 Lao PDR 85 68 1.0 230.8 75.0 68.2 0.3 0.4 3.5 5.4 19.0 20.1 Latvia 31 32 –0.7 62.2 51.1 53.9 0.4 0.1 27.2 18.8 2.0 51.6 Lebanon 17 13 0.4 10.2 12.8 13.4 11.9 13.9 17.9 14.1 6.2 3.4 Lesotho 86 74 0.5 30.4 1.3 1.4 0.1 0.1 10.4 11.7 19.8 17.3 Liberia 55 39 1.4 96.3 51.2 44.9 1.6 2.3 3.6 4.2 16.2 10.5 Libya 24 22 1.6 1,759.5 0.1 0.1 0.2 0.2 1.0 1.0 41.4 27.8 Lithuania 32 33 –0.5 62.7 31.0 34.5 0.7 0.4 46.0 29.7 1.5 55.4 Macedonia, FYR 42 33 –1.0 25.2 35.9 39.6 2.2 1.4 23.8 17.1 3.0 21.2 Madagascar 76 70 2.5 581.5 23.5 21.6 1.0 1.0 4.7 5.1 24.1 15.4 Malawi 88 81 2.0 94.1 41.4 34.4 1.4 1.3 23.9 37.2 23.8 23.6 Malaysia 50 29 –0.7 328.6 68.1 62.3 16.0 17.6 5.2 5.5 9.4 6.7 Mali 77 67 1.5 1,220.2 11.5 10.2 0.1 0.1 1.7 4.0 23.7 38.2 Mauritania 60 59 2.5 1,030.7 0.4 0.2 0.0 0.0 0.4 0.4 20.1 12.4 Mauritius 56 58 1.1 2.0 19.2 17.2 3.0 2.0 49.3 42.9 9.5 6.9 Mexico 29 23 0.1 1,944.0 36.2 33.3 1.0 1.4 12.5 12.8 29.2 23.3 Moldova 53 59 –0.5 32.9 9.7 11.7 12.8 9.2 52.8 55.4 39.7 50.1 Mongolia 43 43 0.9 1,553.6 8.1 7.0 0.0 0.0 0.9 0.5 61.8 32.2 Morocco 52 44 0.5 446.3 11.3 11.5 1.6 2.1 19.5 18.0 35.1 25.5 Mozambique 79 62 1.5 786.4 55.2 49.6 0.3 0.3 4.4 5.7 25.5 20.1 Myanmar 75 67 0.4 653.5 60.0 48.6 0.8 1.7 14.6 16.2 23.4 21.4 Namibia 72 63 1.5 823.3 10.6 8.9 0.0 0.0 0.8 1.0 46.6 37.6 Nepal 91 82 1.7 143.4 33.7 25.4 0.5 0.8 16.0 16.4 12.0 8.2 Netherlands 31 18 –2.5 33.8 10.2 10.8 0.9 1.0 26.0 31.6 5.9 6.5 New Zealand 15 13 0.5 263.3 29.3 31.4 0.2 0.3 10.0 1.7 76.7 10.6 Nicaragua 48 43 1.2 120.3 37.5 25.9 1.6 1.9 10.8 15.8 31.4 33.5 Niger 85 83 3.4 1,266.7 1.5 1.0 0.0 0.0 8.7 11.4 139.6 98.6 Nigeria 65 51 1.2 910.8 18.9 9.9 2.8 3.3 32.4 41.2 30.3 24.8 Norway 28 23 –0.5 305.5 30.0 32.9 0.0 0.0 2.8 2.8 20.3 17.7 Oman 34 28 1.3 309.5 0.0 0.0 0.1 0.1 0.1 0.2 1.9 2.0 Pakistan 69 63 1.9 770.9 3.3 2.2 0.6 1.1 26.6 26.4 19.0 12.2 Panama 46 26 –1.1 74.3 51.0 43.7 2.1 2.0 6.7 7.4 20.7 16.1 Papua New Guinea 85 88 2.7 452.9 69.6 63.4 1.2 1.4 0.4 0.6 4.6 4.1 Paraguay 51 39 0.7 397.3 53.3 44.3 0.2 0.3 5.3 10.6 49.7 67.3 Peru 31 29 1.1 1,280.0 54.8 53.1 0.3 0.6 2.7 2.9 16.1 12.7 Philippines 51 34 –0.1 298.2 22.0 25.7 14.8 16.8 18.4 17.8 8.8 5.9 Poland 39 39 0.0 304.2 29.2 30.7 1.1 1.3 47.3 41.3 37.7 33.0 Portugal 52 40 –1.0 91.5 36.4 37.8 8.5 6.4 25.6 11.5 23.7 9.9 Puerto Rico 28 1 –15.0 8.9 32.4 62.2 5.6 4.2 7.3 6.8 1.8 1.5 Qatar 8 4 2.7 11.6 0.0 0.0 0.1 0.3 0.9 1.1 2.1 1.0 2011 World Development Indicators 127 3.1 Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2009 1990–2009 2009 1990 2010 1990 2008 1990 2008 1990 2008 Romania 47 46 –0.5 229.9 27.8 28.6 2.6 1.6 41.2 37.9 40.7 40.5 Russian Federation 27 27 –0.1 16,376.9 49.4 49.4 0.1 0.1 8.1 7.4 0.0 85.7 Rwanda 95 81 1.0 24.7 12.9 17.6 12.4 11.3 35.7 52.3 12.3 13.3 Saudi Arabia 23 18 0.8 2,000.0 c 0.5 0.5 0.0 0.1 1.7 1.7 20.9 13.9 Senegal 61 57 2.4 192.5 48.6 44.0 0.2 0.3 16.1 18.2 41.0 28.7 Serbia 50 48 –0.4 88.4 26.2 30.7 .. 3.4 .. 37.4 4.5 44.9 Sierra Leone 67 62 1.3 71.6 43.5 38.1 1.9 1.9 6.8 25.1 11.9 32.3 Singapore 0 0 .. 0.7 3.0 2.9 1.5 0.3 1.5 0.7 0.0 0.0 Slovak Republic 44 43 0.1 48.1 40.0 40.2 1.0 0.5 32.5 28.7 31.0 25.6 Slovenia 50 52 0.3 20.1 59.0 62.2 1.8 1.3 9.9 9.0 1.8 9.0 Somalia 70 63 1.1 627.3 13.2 10.8 0.0 0.0 1.6 1.6 15.5 11.2 South Africa 48 39 0.7 1,214.5 6.8 4.7 0.7 0.8 11.1 11.9 38.2 29.7 Spain 25 23 0.5 499.1 27.7 36.4 9.7 9.6 30.7 25.0 39.5 27.4 Sri Lanka 83 85 1.0 62.7 37.5 29.7 15.9 15.1 14.4 19.9 5.3 6.2 Sudan 73 56 0.9 2,376.0 32.1 29.4 0.0 0.1 5.4 8.7 47.2 50.1 Swaziland 77 75 1.5 17.2 27.4 32.7 0.7 0.8 10.5 10.3 20.8 15.2 Sweden 17 15 –0.1 410.3 66.5 68.7 0.0 0.0 6.9 6.4 33.2 28.5 Switzerland 27 27 0.7 40.0 28.8 31.0 0.6 0.6 10.3 10.2 6.1 5.3 Syrian Arab Republic 51 45 2.0 183.6 2.0 2.7 4.0 5.3 26.6 25.6 38.4 22.8 Tajikistan 68 74 1.8 140.0 2.9 2.9 0.9 1.0 6.1 5.3 1.0 10.8 Tanzania 81 74 2.4 885.8 46.8 37.7 1.1 1.5 10.2 10.8 35.4 22.6 Thailand 71 66 0.6 510.9 38.3 37.1 6.1 7.1 34.2 29.8 30.9 22.6 Timor-Leste 79 72 1.8 14.9 65.0 49.9 3.9 4.4 7.4 10.8 14.9 14.6 Togo 70 57 1.7 54.4 12.6 5.3 1.7 3.1 38.6 45.2 53.5 38.1 Trinidad and Tobago 92 86 0.2 5.1 47.0 44.1 6.8 4.3 7.0 4.9 3.0 1.9 Tunisia 42 33 0.0 155.4 4.1 6.5 12.5 14.2 18.7 18.2 35.7 27.5 Turkey 41 31 0.0 769.6 12.6 14.7 3.9 3.8 32.0 28.0 43.9 29.2 Turkmenistan 55 51 1.4 469.9 8.8 8.8 0.1 0.1 2.9 3.9 36.7 36.7 Uganda 89 87 3.1 197.1 24.1 15.2 9.4 11.4 25.4 28.7 28.2 17.8 Ukraine 33 32 –0.8 579.3 16.0 16.8 1.9 1.6 57.6 56.1 0.1 70.2 United Arab Emirates 21 22 5.0 83.6 2.9 3.8 0.2 2.4 0.4 0.8 1.9 1.4 United Kingdom 11 10 –0.2 241.9 10.8 11.9 0.3 0.2 27.4 24.8 11.6 9.8 United States 25 18 –0.6 9,147.4 32.4 33.2 0.2 0.3 20.3 18.6 74.4 56.0 Uruguay 11 8 –1.6 175.0 5.3 10.0 0.3 0.2 7.2 9.4 40.6 49.2 Uzbekistan 60 63 1.9 425.4 7.2 7.7 0.9 0.8 10.5 10.1 21.8 15.7 Venezuela, RB 16 6 –2.9 882.1 59.0 52.5 0.9 0.7 3.2 3.1 14.3 9.7 Vietnam 80 72 0.9 310.1 28.8 44.5 3.2 10.0 16.4 20.3 8.1 7.3 West Bank and Gaza 32 28 3.0 6.0 1.5 1.5 19.1 19.5 18.1 16.8 .. 2.8 Yemen, Rep. 79 69 2.7 528.0 1.0 1.0 0.2 0.6 2.9 2.4 12.4 5.6 Zambia 61 64 2.9 743.4 71.0 66.5 0.0 0.0 3.1 3.2 29.0 18.7 Zimbabwe 71 62 0.2 386.9 57.3 40.4 0.3 0.3 7.5 9.6 27.6 29.9 World 57 w 50 w 0.6 w 129,561.8 s 32.1 w 31.1 w 1.1 w 1.1 w 9.0 w 10.7 w 22.2 w 20.7 w Low income 78 71 1.8 17,303.9 31.9 28.2 0.7 0.9 6.4 8.6 20.1 17.9 Middle income 62 52 0.4 78,352.9 33.9 32.8 1.4 1.4 8.4 11.0 17.7 18.2 Lower middle income 70 59 0.5 30,841.8 26.2 25.9 1.8 2.4 14.9 16.0 15.8 13.1 Upper middle income 33 25 –0.3 47,511.0 38.8 37.2 1.0 0.7 4.2 7.8 24.5 37.4 Low & middle income 64 55 0.7 95,656.7 33.5 31.9 1.2 1.3 8.1 10.6 18.0 18.2 East Asia & Pacific 71 55 –0.3 15,853.7 29.0 29.6 2.2 3.1 12.1 11.3 12.0 9.3 Europe & Central Asia 37 36 0.0 22,686.7 38.4 38.6 0.4 0.4 2.1 10.4 12.1 58.8 Latin America & Carib. 29 21 –0.3 20,116.2 51.6 47.0 0.9 1.0 6.6 7.4 30.3 26.4 Middle East & N. Africa 48 42 1.3 8,643.6 2.4 2.4 0.8 1.0 5.9 6.0 22.4 16.0 South Asia 75 70 1.4 4,771.2 16.6 17.1 1.8 2.9 42.6 41.5 18.0 12.8 Sub-Saharan Africa 72 63 1.9 23,585.4 31.3 28.0 0.8 1.0 6.2 8.5 28.4 24.4 High income 27 23 –0.3 33,905.1 28.1 28.9 0.7 0.7 11.8 10.9 41.5 34.0 Euro area 29 27 –0.1 2,552.0 33.6 37.3 4.8 4.2 26.7 24.4 22.1 18.9 a. Includes Luxembourg. b. Data are from national sources. c. Provisional estimate. 128 2011 World Development Indicators 3.1 ENVIRONMENT Rural population and land use About the data Definitions With more than 3 billion people, including 70 percent Satellite images show land use that differs from • Rural population is calculated as the difference of the world’s poor people, living in rural areas, ade- that of ground-based measures in area under cultiva- between the total population and the urban popula- quate indicators to monitor progress in rural areas tion and type of land use. Moreover, land use data tion (see Definitions for tables 2.1 and 3.11). • Land are essential. However, few indicators are disaggre- in some countries (India is an example) are based area is a country’s total area, excluding area under gated between rural and urban areas (for some that on reporting systems designed for collecting tax rev- inland water bodies and national claims to the con- are, see tables 2.7, 3.5, and 3.11). The table shows enue. With land taxes no longer a major source of tinental shelf and to exclusive economic zones. In indicators of rural population and land use. Rural government revenue, the quality and coverage of land most cases the definition of inland water bodies population is approximated as the midyear nonurban use data have declined. Data on forest area may be includes major rivers and lakes. (See table 1.1 for population. While a practical means of identifying the particularly unreliable because of irregular surveys the total surface area of countries.) Variations from rural population, it is not precise (see box 3.1a for and differences in definitions (see About the data year to year may be due to updated or revised data further discussion). for table 3.4). The forest area statistics released by rather than to change in area. • Land use is a coun- The data in the table show that land use patterns FAO between 1948 and 1963 were based mostly on try’s total area, excluding area under inland water are changing. They also indicate major differences data from country questionnaires. Remote sensing, bodies and national claims to the continental shelf in resource endowments and uses among countries. statistical modeling, and expert analysis of country and to exclusive economic zones. In most cases defi - True comparability of the data is limited, however, surveys have been applied since 1980 to improve nitions of inland water bodies includes major rivers by variations in definitions, statistical methods, and the forest coverage estimates. FAO’s Global Forest and lakes. (See table 1.1 for the total surface area of quality of data. Countries use different definitions of Resources Assessment 2010 covers 233 countries countries.) Variations from year to year may be due rural and urban population and land use. The Food and is the most comprehensive assessment of for- to updated or revised data rather than to change in and Agriculture Organization of the United Nations ests, forestry, and the benefits of forest resources area. • Forest area is land under natural or planted (FAO), the primary compiler of the data, occasion- in both scope and number of countries and people stands of trees of at least 5 meters in situ, whether ally adjusts its definitions of land use categories involved. It examines status and trends for about 90 productive or not, and excludes tree stands in agricul- and revises earlier data. Because the data reflect variables on the extent, condition, uses, and values tural production systems (for example, in fruit planta- changes in reporting procedures as well as actual of forests and other wooded land. tions and agroforestry systems) and trees in urban changes in land use, apparent trends should be inter- parks and gardens. • Permanent cropland is land preted cautiously. cultivated with crops that occupy the land for long periods and need not be replanted after each har- What is rural? Urban? 3.1a vest, such as cocoa, coffee, and rubber. Land under The rural population identified in table 3.1 is approximated as the difference between total population flowering shrubs, fruit trees, nut trees, and vines and urban population, calculated using the urban share reported by the United Nations Population is included, but land under trees grown for wood or Division. There is no universal standard for distinguishing rural from urban areas, and any urban-rural timber is not. • Arable land is land defined by the FAO dichotomy is an oversimplification (see About the data for table 3.11). The two distinct images—isolated as under temporary crops (double-cropped areas are farm, thriving metropolis—represent poles on a continuum. Life changes along a variety of dimensions, counted once), temporary meadows for mowing or moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through pasture, land under market or kitchen gardens, and small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, land temporarily fallow. Land abandoned as a result eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and of shifting cultivation is excluded. nonfarm employment increase, and with them population density and income. Because rurality has many dimensions, for policy purposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.11 is inad- equate. A 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development Data sources interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the Data on urban population shares used to estimate unit cost of delivering most social services and many types of infrastructure is high. Where large urban rural population are from the United Nations Popu- areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and lation Division’s World Urbanization Prospects: The it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population 2009 Revision, and data on total population are density and remoteness together define a set of rural areas that face special development challenges. World Bank estimates. Data on land area, perma- Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors’ estimates of nent cropland, and arable land are from the FAO’s the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their electronic files. The FAO gathers these data from estimates range from 13 percent of the population, based on a population density of less than 20 people national agencies through annual questionnaires per square kilometer, to 64 percent, based on a population density of more than 500 people per square and by analyzing the results of national agricultural kilometer. Taking remoteness into account, the estimated rural population would be 13–52 percent. The censuses. Data on forest area are from the FAO’s estimate for Latin America and the Caribbean in table 3.1 is 21 percent. Global Forest Resources Assessment 2010. 2011 World Development Indicators 129 3.2 Agricultural inputs Agricultural Average Land under Fertilizer Agricultural Agricultural landa annual cereal production consumption employment machinery precipitation kilograms % of per hectare % of % thousand fertilizer of arable % of total Tractors per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990 2008 2008 2008 1990 2009 2008 2008 1990 2008 1990 2008 Afghanistan 58 58 5.8 327 2,253.0 3,188.0 146.9 3.2 .. .. 0.2 1.2 Albania 41 43 10.0 1,485 321.0 146.2 .. 38.4 .. 58.0 212.4 121.9 Algeria 16 17 2.1 89 2,366.0 3,176.3 226.4 6.8 .. .. 129.1 139.6 Angola 46 46 .. 1,010 775.1 1,752.1 .. 8.3 5.1 .. .. .. Argentina 47 49 .. 591 9,015.0 8,031.6 305.9 38.8 0.4 0.8 100.2 .. Armenia 41 61 8.9 562 162.8 169.3 .. 18.1 .. 46.2 345.5 327.8 Australia 60 54 0.4 534 13,428.8 19,805.6 161.3 33.9 5.6 3.4 .. .. Austria 42 38 1.4 1,110 948.4 838.0 .. 109.6 7.9 5.6 2,373.7 2,390.3 Azerbaijan 53 58 30.0 447 627.0 1,113.8 .. 20.9 30.9 38.7 194.8 116.1 Bangladesh 77 71 62.0 2,666 11,140.6 12,032.5 141.1 164.5 64.9 48.1 2.4 3.9 Belarus 46 44 0.7 618 2,603.0 2,418.0 22.0 237.4 .. .. 206.9 89.8 Belgium 44b 45 1.7 847 368.2b 345.0 .. .. 3.1 1.8 1,523.3b 1,127.1 Benin 21 31 .. 1,039 643.9 976.1 .. 0.0 .. .. 1.0 .. Bolivia 33 34 .. 1,146 582.5 897.0 .. 5.5 1.2 .. 24.8 .. Bosnia and Herzegovina 43 42 .. 1,028 304.1 295.8 .. 11.9 .. .. 235.3 .. Botswana 46 46 0.0 416 205.1 85.7 .. .. .. 29.9 140.5 134.8 Brazil 29 31 .. 1,782 18,512.4 20,220.4 313.5 165.7 22.8 19.3 143.8 129.2 Bulgaria 56 48 1.4 608 2,055.3 1,829.2 68.4 81.8 18.5 7.5 135.8 173.5 Burkina Faso 35 45 .. 748 2,528.9 4,178.6 .. 3.9 .. .. 2.4 .. Burundi 83 85 .. 1,274 217.5 222.0 .. 2.2 .. .. 1.8 .. Cambodia 25 31 .. 1,904 1,900.0 2,888.0 .. 22.7 .. .. 3.3 11.8 Cameroon 19 19 .. 1,604 657.6 1,223.3 .. 8.6 .. .. 0.9 .. Canada 7 7 .. 537 21,547.9 14,863.2 25.1 56.9 4.1 2.5 164.8 162.5 Central African Republic 8 8 .. 1,343 110.5 264.3 .. .. .. .. .. .. Chad 38 39 .. 322 1,075.4 2,486.7 .. .. 83.0 .. .. .. Chile 21 21 5.6 1,522 823.5 567.5 102.8 588.8 19.3 12.3 127.6 425.9 China 57 56 10.2 .. 93,555.2 88,592.8 99.2 468.0 53.4 .. 66.6 277.1 Hong Kong SAR, China .. .. .. .. .. .. .. .. 0.9 0.2 .. .. Colombia 41 38 .. 2,612 1,742.8 1,186.0 278.7 492.4 1.4 18.4 96.8 .. Congo, Dem. Rep. 10 10 .. 1,543 1,863.6 1,977.3 .. 1.0 .. .. .. .. Congo, Rep. 31 31 .. 1,646 9.6 27.5 .. 1.1 .. .. .. .. Costa Rica 45 35 .. 2,926 92.6 74.4 .. 707.5 25.9 13.2 .. .. Côte d’Ivoire 60 64 .. 1,348 1,400.0 853.5 .. 18.9 .. .. 19.9 .. Croatia 43 23 0.7 1,113 592.7 562.7 70.7 387.6 .. 12.8 35.2 .. Cuba 63 62 .. 1,335 230.5 419.9 554.4 39.7 24.9 18.7 226.2 203.2 Czech Republic 55 55 0.2 677 1,613.6 1,544.4 117.1 135.1 7.7 3.3 264.6 276.4 Denmark 66 63 9.5 703 1,570.3 1,497.7 .. 128.3 5.5 2.7 634.7 486.3 Dominican Republic 53 52 .. 1,410 122.2 158.2 .. .. 20.3 14.5 25.9 .. Ecuador 28 30 10.2 2,087 802.2 819.1 .. 214.1 7.5 8.3 54.2 .. Egypt, Arab Rep. 3 4 .. 51 2,283.4 3,129.8 68.5 723.6 39.0 31.2 249.6 372.1 El Salvador 68 75 2.1 1,724 425.4 363.0 .. 118.4 10.2 18.9 .. .. Eritrea 73 75 .. 384 329.3 492.3 .. 0.0 .. .. 5.0 .. Estonia 32 19 .. 626 453.7 316.4 65.4 100.3 21.0 3.7 455.3 604.7 Ethiopia 31 35 0.5 848 4,040.3 8,748.0 .. 7.7 .. 8.6 .. .. Finland 8 8 2.8 536 1,212.6 1,133.1 77.4 134.2 8.8 4.5 916.5 784.7 France 56 53 5.4 867 9,060.4 9,388.2 153.8 146.1 5.6 3.0 800.0 635.3 Gabon 20 20 .. 1,831 14.4 20.5 .. 14.1 41.6 .. .. .. Gambia, The 64 66 .. 836 90.0 295.2 .. 2.6 64.7 .. .. .. Georgia 46 36 4.0 1,026 248.5 193.8 13.0 37.1 .. 53.4 295.6 594.0 Germany 52 49 .. 700 6,944.9 6,908.4 55.7 160.4 4.1 2.2 1,309.4 646.0 Ghana 55 69 .. 1,187 853.0 1,570.7 .. 6.4 62.0 .. 7.1 4.5 Greece 72 36 27.4 652 1,470.4 1,174.8 472.4 143.8 23.9 11.4 744.2 1,196.9 Guatemala 40 39 .. 1,996 718.5 855.9 .. 92.0 12.9 33.2 .. .. Guinea 49 56 .. 1,651 729.6 1,863.0 .. 1.5 .. .. 45.0 .. Guinea-Bissau 51 58 .. 1,577 109.3 152.6 .. .. .. .. 0.8 .. Haiti 58 65 .. 1,440 351.5 437.0 .. .. 65.6 .. 2.6 .. Honduras 30 28 .. 1,976 465.1 382.6 .. 107.7 50.1 39.2 30.9 .. 130 2011 World Development Indicators 3.2 ENVIRONMENT Agricultural inputs Agricultural Average Land under Fertilizer Agricultural Agricultural landa annual cereal production consumption employment machinery precipitation kilograms % of per hectare % of % thousand fertilizer of arable % of total Tractors per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990 2008 2008 2008 1990 2009 2008 2008 1990 2008 1990 2008 Hungary 72 64 1.4 589 2,778.6 2,883.6 227.2 94.3 18.2 4.5 97.7 261.9 India 61 60 .. 1,083 102,536.5 99,880.0 190.6 153.5 .. .. 60.7 .. Indonesia 25 27 16.3 2,702 13,660.5 17,044.2 117.6 189.1 55.9 41.2 2.2 .. Iran, Islamic Rep. 38 30 19.0 228 9,468.1 9,095.5 148.1 90.9 .. 22.8 141.5 182.8 Iraq 23 22 .. 216 3,256.3 2,141.4 132.8 43.8 .. .. 65.8 .. Ireland 82 61 .. 1,118 298.9 293.5 .. 480.3 15.1 5.6 1,623.4 1,476.4 Israel 27 23 .. 435 113.8 79.1 2.6 252.6 4.1 1.6 798.8 705.3 Italy 57 46 19.2 832 4,413.4 3,453.8 264.9 156.0 8.8 3.8 1,586.5 .. Jamaica 44 43 .. 2,051 2.1 1.5 .. 51.3 27.3 18.2 .. .. Japan 16 13 35.1 1,668 2,471.5 1,936.2 135.7 278.2 7.2 4.2 4,492.9 4,382.4 Jordan 12 11 9.5 111 105.8 48.3 3.1 337.4 6.6 .. 340.4 366.8 Kazakhstan 82 77 .. 250 22,152.4 16,575.0 38.1 3.1 .. .. 62.0 17.7 Kenya 47 48 0.1 630 1,785.5 2,329.0 .. 33.3 .. .. 20.0 .. Korea, Dem. Rep. 21 25 .. 1,054 1,605.0 1,265.5 .. .. .. .. .. .. Korea, Rep. 22 19 51.6 1,274 1,441.0 1,018.5 134.0 479.5 17.9 7.4 211.0 1,632.5 Kosovo .. 52 .. .. .. .. .. .. .. .. .. .. Kuwait 8 8 .. 121 0.5 1.4 3.2 1,250.9 1.3 .. 220.0 95.6 Kyrgyz Republic 53 56 9.3 533 578.0 612.4 .. 19.0 32.7 36.3 189.4 191.1 Lao PDR 7 10 .. 1,834 687.0 1,048.9 .. .. .. .. .. .. Latvia 41 29 0.0 641 696.7 540.9 .. 124.3 .. 7.7 363.7 501.4 Lebanon 59 67 19.9 661 41.2 67.9 8.6 56.2 .. .. 174.9 .. Lesotho 76 78 .. 788 233.5 179.2 .. .. .. .. 57.7 .. Liberia 26 27 .. 2,391 175.0 190.0 .. .. .. .. .. .. Libya 9 9 .. 56 404.1 342.9 17.2 27.3 .. .. 184.3 .. Lithuania 54 43 .. 656 1,134.0 1,103.5 13.5 79.1 .. 7.7 256.0 631.6 Macedonia, FYR 51 42 2.7 619 235.2 178.9 .. 56.2 .. 18.2 730.3 1,243.8 Madagascar 62 70 2.2 1,513 1,326.9 1,476.5 .. 4.3 .. 82.0 4.9 .. Malawi 45 58 .. 1,181 1,425.3 1,780.1 3,197.3 1.7 .. .. .. .. Malaysia 22 24 .. 2,875 700.7 678.6 242.6 929.9 26.0 14.8 152.9 .. Mali 26 32 .. 282 2,438.7 3,988.4 .. 9.0 .. .. 10.2 2.7 Mauritania 38 38 .. 92 118.9 242.9 .. .. .. .. 8.4 9.8 Mauritius 56 48 21.4 2,041 0.6 0.1 .. 210.1 16.7 9.1 .. .. Mexico 53 53 5.2 752 10,543.1 10,182.4 319.7 44.7 22.6 13.5 123.5 97.2 Moldova 78 76 9.1 450 675.6 881.6 .. 12.5 33.8 32.8 310.1 197.5 Mongolia 81 75 .. 241 654.1 252.4 .. 8.2 39.5 40.6 80.3 38.0 Morocco 68 67 4.4 346 5,603.3 5,316.7 40.0 53.8 3.9 43.3 45.0 .. Mozambique 61 62 .. 1,032 1,549.5 1,892.0 .. 0.0 .. .. .. .. Myanmar 16 18 24.8 2,091 5,221.4 8,912.0 1,515.4 3.3 69.7 .. 13.6 10.9 Namibia 47 47 .. 285 214.2 307.2 .. 0.3 48.2 .. .. .. Nepal 29 29 27.7 1,500 3,045.2 3,418.0 .. 7.7 81.2 .. 21.9 122.9 Netherlands 59 57 10.6 778 195.3 220.8 17.9 269.1 4.5 2.7 2,073.1 1,301.5 New Zealand 61 43 .. 1,732 172.5 162.7 320.3 1,721.0 10.6 7.2 .. .. Nicaragua 33 43 .. 2,391 320.0 438.2 .. 32.3 39.3 29.1 20.0 .. Niger 26 34 .. 151 6,882.3 9,929.1 .. 0.4 .. .. 0.2 .. Nigeria 79 86 .. 1,150 15,400.0 18,899.0 1,929.1 13.3 .. .. 4.7 6.6 Norway 3 3 5.4 1,414 356.4 305.9 24.4 219.0 6.4 2.8 1,779.1 1,539.1 Oman 3 6 .. 125 2.4 5.0 2.4 395.0 9.3 .. 41.1 .. Pakistan 34 34 73.0 494 11,864.1 13,689.0 115.6 163.3 51.1 43.6 129.7 204.8 Panama 29 30 .. 2,692 184.6 144.3 .. 35.3 29.1 14.7 102.0 .. Papua New Guinea 2 2 .. 3,142 1.7 3.3 .. 78.6 .. .. 59.4 .. Paraguay 43 51 .. 1,130 393.7 1,344.8 .. 66.8 1.9 29.5 71.6 61.5 Peru 17 17 .. 1,738 683.7 1,286.1 .. 81.6 1.2 9.3 36.3 .. Philippines 37 40 .. 2,348 7,138.5 7,216.3 973.8 131.2 45.2 36.1 65.2 .. Poland 62 53 0.5 600 8,530.9 8,582.8 100.0 190.4 25.2 14.7 823.6 1,246.0 Portugal 43 38 12.0 854 760.0 305.9 190.1 236.5 17.9 11.5 563.1 1,397.7 Puerto Rico 49 21 8.5 2,054 0.5 0.3 .. .. 3.6 1.1 438.5 525.0 Romania 64 59 1.9 637 5,704.1 5,265.5 42.1 45.6 29.1 28.7 140.6 200.4 2011 World Development Indicators 131 3.2 Agricultural inputs Agricultural Average Land under Fertilizer Agricultural Agricultural landa annual cereal production consumption employment machinery precipitation kilograms % of per hectare % of % thousand fertilizer of arable % of total Tractors per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990 2008 2008 2008 1990 2009 2008 2008 1990 2008 1990 2008 Russian Federation 14 13 2.0 460 59,541.3 41,715.7 11.9 15.9 13.9 9.0 97.8 30.0 Rwanda 76 82 .. 1,212 254.1 368.2 .. 8.3 90.1 .. 1.0 .. Qatar 5 6 .. 74 1.1 2.0 0.3 276.9 .. 3.0 84.0 56.2 Saudi Arabia .. .. .. 59 974.6 466.4 14.5 75.2 .. 4.7 19.2 .. Senegal 46 48 0.7 686 1,229.0 1,647.2 53.7 2.4 .. 33.7 1.6 .. Serbia .. 57 0.5 .. .. 1,919.0 252.9 115.2 .. 20.8 .. 17.7 Sierra Leone 39 58 .. 2,526 468.6 1,111.4 .. .. .. .. 4.1 .. Singapore 3 1 .. 2,497 .. 1,918.9 .. .. 0.4 1.1 .. .. Slovak Republic 51 40 1.3 824 836.6 768.7 81.1 130.0 .. 4.0 197.5 154.7 Slovenia 28 25 0.8 1,162 112.5 101.9 .. 283.6 10.7 10.2 .. .. Somalia 70 70 .. 282 732.5 470.2 .. .. .. .. 16.0 12.0 South Africa 80 82 .. 495 6,162.9 3,318.7 262.4 49.7 .. 8.8 107.9 .. Spain 61 56 11.9 636 7,551.4 6,043.3 84.8 106.5 11.5 4.3 483.1 824.4 Sri Lanka 37 42 .. 1,712 869.8 1,017.9 2,961.4 284.3 47.8 31.3 .. .. Sudan 52 58 1.3 416 3,734.6 9,453.8 .. 3.6 .. .. 7.2 12.4 Swaziland 72 71 .. 788 85.7 48.7 .. .. .. .. 228.9 87.1 Sweden 8 8 .. 624 1,285.2 1,032.1 414.7 142.1 3.4 2.2 601.9 592.4 Switzerland 40 39 .. 1,537 211.9 153.0 .. 226.3 4.2 3.9 2,783.1 2,597.2 Syrian Arab Republic 73 76 9.8 252 4,134.4 2,774.1 184.5 88.0 26.5 .. 128.1 233.9 Tajikistan 32 34 15.0 691 266.5 409.5 452.5 0.0 44.7 .. 415.4 216.1 Tanzania 38 39 .. 1,071 2,629.3 5,087.0 .. 6.0 .. 74.6 8.2 .. Thailand 42 38 .. 1,622 10,536.9 12,282.7 1,462.8 130.9 63.3 41.7 33.0 .. Timor-Leste 21 25 .. .. 82.6 103.4 .. .. .. .. 8.5 .. Togo 59 67 .. 1,168 648.0 826.7 .. 4.9 .. .. 0.5 0.5 Trinidad and Tobago 15 11 .. 2,200 5.9 2.1 17.8 2,337.2 12.3 4.3 .. .. Tunisia 56 64 4.0 207 1,445.7 876.6 8.7 32.1 25.8 .. 82.4 142.6 Turkey 52 51 13.3 593 13,640.1 11,955.9 242.3 88.7 46.9 26.2 279.8 488.5 Turkmenistan 69 69 .. 161 331.3 970.3 .. .. .. .. 464.7 .. Uganda 61 66 .. 1,180 1,055.0 1,826.0 .. 3.4 .. .. .. .. Ukraine 72 71 5.3 565 12,542.3 15,114.8 40.7 32.8 19.8 16.7 153.3 103.3 United Arab Emirates 3 7 .. 78 1.3 0.0 7.6 336.3 .. 4.9 51.4 .. United Kingdom 75 73 .. 1,220 3,657.3 3,173.0 133.5 208.2 2.1 1.4 760.6 .. United States 47 45 .. 715 65,700.0 58,001.4 96.2 103.1 2.9 1.4 238.4 257.6 Uruguay 85 85 1.2 1,265 514.6 1,049.4 1,492.7 118.3 0.0 11.0 260.4 222.4 Uzbekistan 65 63 .. 206 1,225.3 1,607.5 .. .. .. .. .. .. Venezuela, RB 25 24 .. 1,875 753.9 1,237.1 74.5 232.9 13.4 8.7 .. .. Vietnam 21 32 .. 1,821 6,474.6 8,528.5 218.6 286.6 .. .. 47.0 .. West Bank and Gaza 62 61 4.6 402 0.0 35.0 .. .. .. 15.6 442.2 767.9 Yemen, Rep. 45 45 3.3 167 844.9 672.8 .. 14.3 52.6 .. 39.0 .. Zambia 27 30 .. 1,020 895.2 1,062.5 .. 50.1 49.8 .. 27.2 .. Zimbabwe 34 41 .. 657 1,576.1 2,236.8 164.7 27.9 .. .. 60.1 .. World 37 w 38 w ..   708,090.3 s 708,451.8 s 94.9 w 119.4 w .. w .. w 186.4 w .. w Low income 35 37 ..   63,834.3 92,275.5 221.9 16.7 .. .. 15.7 .. Middle income 37 38 ..   482,334.4 468,186.3 103.8 139.6 .. .. 91.7 .. Lower middle income 49 50 ..   307,764.8 321,963.9 115.0 191.8 .. .. 66.0 .. Upper middle income 30 30 ..   174,569.6 146,222.5 76.9 70.7 20.8 14.9 125.1 .. Low & middle income 37 38 ..   546,168.7 560,461.8 104.8 123.1 .. .. 85.0 .. East Asia & Pacific 49 48 ..   142,232.3 148,824.2 108.5 .. 53.6 .. 53.2 .. Europe & Central Asia 28 28 ..   127,839.3 104,480.3 26.9 33.3 22.9 16.3 132.3 111.0 Latin America & Carib. 34 35 ..   47,401.7 50,290.2 279.7 111.8 18.7 15.8 120.9 .. Middle East & N. Africa 24 23 ..   29,953.1 27,642.2 57.6 95.3 .. .. 114.6 190.2 South Asia 55 55 ..   131,803.8 133,310.2 176.9 148.0 .. .. 62.2 119.9 Sub-Saharan Africa 42 45 ..   66,938.5 95,914.7 578.1 11.6 .. .. 20.2 .. High income 39 37 ..   161,921.6 147,990.0 73.6 109.3 6.5 3.5 474.9 .. Euro area 51 44 ..   34,697.5 31,367.7 95.1 150.5 7.2 3.8 977.3 811.1 a. Includes permanent pastures, arable land, and land under permanent crops. b. Includes Luxembourg. 132 2011 World Development Indicators 3.2 ENVIRONMENT Agricultural inputs About the data Agriculture is still a major sector in many economies, land as share of total agricultural land area and data (July–June). Previous editions of World Development and agricultural activities provide developing coun- on average precipitation to illustrate how countries Indicators reported data on a crop year basis, but tries with food and revenue. But agricultural activi- obtain water for agricultural use. this edition uses the calendar year, as adopted by ties also can degrade natural resources. Poor farming The data here and in table 3.3 are collected by the FAO. Caution should thus be used when compar- practices can cause soil erosion and loss of soil fertil- the Food and Agriculture Organization of the United ing data over time. ity. Efforts to increase productivity by using chemical Nations (FAO) through annual questionnaires. The Definitions fertilizers, pesticides, and intensive irrigation have FAO tries to impose standard definitions and report- environmental costs and health impacts. Excessive ing methods, but complete consistency across • Agricultural land is permanent pastures, arable, use of chemical fertilizers can alter the chemistry of countries and over time is not possible. Thus, data and land under permanent crops. Permanent pasture soil. Pesticide poisoning is common in developing on agricultural land in different climates may not is land used for five or more years for forage, includ- countries. And salinization of irrigated land dimin- be comparable. For example, permanent pastures ing natural and cultivated crops. Arable land includes ishes soil fertility. Thus, inappropriate use of inputs are quite different in nature and intensity in African land defined by the FAO as land under temporary for agricultural production has far-reaching effects. countries and dry Middle Eastern countries. Data crops (double-cropped areas are counted once), The table provides indicators of major inputs to on agricul-tural employment, in particular, should temporary meadows for mowing or for pasture, land agricultural production: land, fertilizer, labor, and be used with caution. In many countries much agri- under market or kitchen gardens, and land tempo- machinery. There is no single correct mix of inputs: cultural employment is informal and unrecorded, rarily fallow. Land abandoned as a result of shift- appropriate levels and application rates vary by coun- including substantial work performed by women ing cultivation is excluded. Land under permanent try and over time and depend on the type of crops, the and children. To address some of these concerns, crops is land cultivated with crops that occupy the climate and soils, and the production process used. this indicator is heavily footnoted in the database in land for long periods and need not be replanted after The agriculture sector is the most water-intensive sources, definition, and coverage. each harvest, such as cocoa, coffee, and rubber. sector, and water delivery in agriculture is increas- Fertilizer consumption measures the quantity of Land under flowering shrubs, fruit trees, nut trees, ingly important. The table shows irrigated agricultural plant nutrients. Consumption is calculated as pro- and vines is included, but land under trees grown duction plus imports minus exports. Because some for wood or timber is not. • Irrigated land refers to Nearly 40 percent of land globally chemical compounds used for fertilizers have other areas purposely provided with water, including land is devoted to agriculture 3.2a industrial applications, the consumption data may irrigated by controlled flooding. •  Average annual overstate the quantity available for crops. Fertilizer precipitation is the long-term average in depth Total land area in 2008: 130 million sq. km consumption as a share of production shows the (over space and time) of annual precipitation in the agriculture sector’s vulnerability to import and energy country. Precipitation is defined as any kind of water price fluctuation. The FAO recently revised the time that falls from clouds as a liquid or a solid. • Land Permanent Other pastures series for fertilizer consumption and irrigation for under cereal production refers to harvested areas, 31% 26% 2002 onward, but recent data are not available for although some countries report only sown or culti- all countries. FAO collects fertilizer statistics for pro- vated area. • Fertilizer consumption is the quantity Arable land 11% duction, imports, exports, and consumption through of plant nutrients applied to arable land. Fertilizer Forests 31% the new FAO fertilizer resources questionnaire. In products cover nitrogen, potash, and phosphate Permanent the previous release, the data were based on total fertilizers (including ground rock phosphate). Tradi- crops 1% consumption of fertilizers, but the data in the recent tional nutrients—animal and plant manures—are Note: Agricultural land includes permanent pastures, release are based on the nutrients in fertilizers. not included. •  Fertilizer production is fertilizer arable land, and land under permanent crops. Some countries compile fertilizer data on a calendar consumption, exports, and nonfertilizer use of fertil- Source: Tables 3.1 and 3.2. year basis, while others do so on a crop year basis izer products minus fertilizer imports. • Agricultural employment is employment in agriculture, forestry, Rainfed agriculture plays a significant role in Sub-Saharan agriculture hunting, and fishing (see table 2.3). • Agricultural where about 95 percent of cropland depends on precipitation, 2008 3.2b machinery refers to wheel and crawler tractors (excluding garden tractors) in use in agriculture at Sierra Leone Liberia the end of the calendar year specified or during the Mauritius first quarter of the following year. Gabon Guinea Congo, Rep. Cameroon Guinea-Bissau Congo, Dem. Rep. Madagascar Data sources 0 500 1,000 1,500 2,000 2,500 3,000 Data on agricultural inputs are from electronic files Average annual precipitation (millimeters per year) that the FAO makes available to the World Bank and from the FAO web site (www.fao.org). Source: Table 3.2. 2011 World Development Indicators 133 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999–2001 = 100 1999–2001 = 100 1999–2001 = 100 per hectare 2000 $ 1990 2009 1990 2009 1990 2009 1990 2009 1990 2009 Afghanistan 96.0 176.0 77.0 127.0 63.0 89.0 1,201 1,983 .. .. Albania 107.0 140.0 82.0 115.0 65.0 92.0 2,794 4,315 764 .. Algeria 66.0 196.0 69.0 163.0 80.0 121.0 688 1,654 1,703 2,184 Angola 56.0 250.0 61.0 198.0 72.0 92.0 321 588 200 313 Argentina 64.0 101.0 72.0 106.0 90.0 108.0 2,232 3,167 6,702 9,987 Armenia 106.0 195.0 108.0 191.0 108.0 177.0 1,843 2,230 1,607 5,049 Australia 58.0 88.0 68.0 95.0 82.0 95.0 1,716 1,764 20,150 29,257 Austria 97.0 108.0 89.0 97.0 90.0 94.0 5,577 6,136 13,413 24,715 Azerbaijan 136.0 152.0 107.0 151.0 102.0 152.0 2,113 2,607 1,000 1,342 Bangladesh 74.0 130.0 72.0 132.0 70.0 137.0 2,491 3,890 251 435 Belarus 110.0 161.0 135.0 157.0 146.0 150.0 2,741 3,372 2,042 5,184 Belgium 72.0a 113.0 85.0a 96.0 88.0a 91.0 5,755a 9,632 .. 42,035 Benin 53.0 110.0 58.0 116.0 88.0 135.0 848 1,330 422 .. Bolivia 62.0 126.0 72.0 133.0 80.0 139.0 1,361 2,089 681 733 Bosnia and Herzegovina 107.0 125.0 119.0 138.0 119.0 167.0 3,553 4,539 .. 14,299 Botswana 102.0 120.0 109.0 113.0 110.0 112.0 265 465 770 597 Brazil 74.0 149.0 65.0 148.0 58.0 142.0 1,755 3,526 1,625 3,760 Bulgaria 160.0 103.0 151.0 76.0 165.0 63.0 3,954 3,413 3,983 10,227 Burkina Faso 62.0 144.0 62.0 136.0 65.0 132.0 600 1,035 113 .. Burundi 109.0 108.0 109.0 110.0 131.0 118.0 1,349 1,313 116 .. Cambodia 66.0 202.0 64.0 184.0 59.0 109.0 1,362 2,947 .. 411 Cameroon 69.0 117.0 72.0 120.0 83.0 105.0 1,241 1,574 419 730 Canada 90.0 119.0 84.0 119.0 76.0 105.0 2,636 3,301 28,898 44,752 Central African Republic 75.0 110.0 68.0 123.0 64.0 132.0 807 948 321 .. Chad 59.0 118.0 64.0 125.0 82.0 120.0 559 812 168 .. Chile 74.0 113.0 71.0 120.0 66.0 134.0 3,620 5,472 3,453 6,618 China 68.0 130.0 59.0 133.0 45.0 133.0 4,323 5,460 263 525 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 91.0 120.0 81.0 128.0 79.0 140.0 2,475 4,017 3,122 2,861 Congo, Dem. Rep. 122.0 97.0 119.0 98.0 102.0 96.0 800 772 209 168 Congo, Rep. 81.0 116.0 79.0 123.0 73.0 157.0 624 776 .. .. Costa Rica 68.0 118.0 68.0 126.0 77.0 126.0 3,097 3,770 2,984 5,232 Côte d’Ivoire 71.0 109.0 73.0 120.0 90.0 132.0 887 1,724 653 926 Croatia 80.0 96.0 100.0 104.0 126.0 129.0 3,975 6,117 5,546 15,137 Cuba 119.0 82.0 127.0 83.0 152.0 109.0 2,342 2,069 4,117 3,647 Czech Republic .. 98.0 .. 99.0 .. 90.0 .. 5,074 .. 5,687 Denmark 113.0 113.0 100.0 107.0 86.0 104.0 6,118 6,810 14,588 45,905 Dominican Republic 118.0 111.0 101.0 131.0 74.0 151.0 3,996 4,246 1,925 4,579 Ecuador 75.0 114.0 67.0 126.0 61.0 140.0 1,724 2,974 2,105 1,766 Egypt, Arab Rep. 66.0 136.0 64.0 139.0 62.0 134.0 5,703 7,635 1,737 3,024 El Salvador 95.0 102.0 85.0 116.0 74.0 131.0 1,939 2,727 1,742 2,778 Eritrea .. 154.0 .. 126.0 .. 101.0 .. 938 .. 66 Estonia 121.0 127.0 180.0 134.0 192.0 118.0 1,304 2,761 3,288 3,207 Ethiopia .. 153.0 .. 151.0 .. 140.0 .. 1,652 .. 215 Finland 116.0 115.0 113.0 104.0 111.0 100.0 3,543 3,760 17,163 43,813 France 96.0 101.0 97.0 98.0 95.0 94.0 6,083 7,460 21,423 58,070 Gabon 81.0 104.0 91.0 103.0 84.0 100.0 1,643 1,663 1,216 1,869 Gambia, The 55.0 114.0 60.0 117.0 95.0 132.0 1,004 1,053 272 275 Georgia 122.0 61.0 99.0 66.0 78.0 67.0 1,998 1,917 2,359 1,872 Germany 86.0 102.0 102.0 103.0 115.0 105.0 5,411 7,201 13,669 31,659 Ghana 43.0 156.0 46.0 155.0 89.0 127.0 989 1,660 .. .. Greece 75.0 76.0 84.0 83.0 105.0 99.0 3,036 4,103 6,707 10,779 Guatemala 73.0 138.0 72.0 141.0 81.0 118.0 1,998 1,624 2,243 2,783 Guinea 71.0 133.0 72.0 133.0 56.0 167.0 1,455 1,711 156 225 Guinea-Bissau 72.0 120.0 73.0 122.0 78.0 128.0 1,531 1,422 .. .. Haiti 111.0 110.0 101.0 112.0 65.0 111.0 1,027 961 .. .. Honduras 100.0 153.0 90.0 145.0 66.0 133.0 1,468 1,752 1,180 1,958 134 2011 World Development Indicators 3.3 ENVIRONMENT Agricultural output and productivity Crop Food Livestock Cereal Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999–2001 = 100 1999–2001 = 100 1999–2001 = 100 per hectare 2000 $ 1990 2009 1990 2009 1990 2009 1990 2009 1990 2009 Hungary 119.0 102.0 123.0 100.0 140.0 85.0 4,521 4,713 4,232 10,948 India 78.0 116.0 75.0 119.0 70.0 133.0 1,891 2,471 362 468 Indonesia 80.0 145.0 80.0 146.0 81.0 157.0 3,800 4,813 512 734 Iran, Islamic Rep. 71.0 117.0 69.0 124.0 64.0 142.0 1,445 2,291 1,906 3,061 Iraq 105.0 83.0 111.0 92.0 140.0 128.0 1,061 1,222 .. .. Ireland 91.0 84.0 94.0 90.0 93.0 93.0 6,577 6,798 .. 13,573 Israel 112.0 108.0 90.0 122.0 69.0 130.0 3,484 3,250 .. .. Italy 88.0 91.0 90.0 95.0 94.0 103.0 3,945 5,035 10,410 29,498 Jamaica 83.0 95.0 75.0 100.0 64.0 110.0 1,116 1,253 2,224 2,716 Japan 115.0 89.0 109.0 95.0 106.0 101.0 5,846 5,920 20,934 52,062 Jordan 102.0 157.0 81.0 156.0 52.0 139.0 1,220 1,044 2,077 3,030 Kazakhstan 163.0 150.0 163.0 145.0 178.0 141.0 1,338 1,254 1,781 2,033 Kenya 79.0 107.0 83.0 126.0 89.0 147.0 1,562 1,204 400 334 Korea, Dem. Rep. 111.0 107.0 104.0 112.0 124.0 133.0 3,926 3,698 .. .. Korea, Rep. 88.0 96.0 79.0 101.0 62.0 106.0 5,853 7,073 5,338 19,105 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 57.0 122.0 53.0 114.0 63.0 102.0 3,653 2,679 .. .. Kyrgyz Republic 68.0 108.0 78.0 103.0 110.0 105.0 2,772 3,034 684 1,041 Lao PDR 65.0 157.0 60.0 148.0 57.0 126.0 2,268 3,808 387 516 Latvia 129.0 147.0 222.0 138.0 274.0 126.0 1,641 3,075 1,896 3,636 Lebanon 99.0 96.0 92.0 111.0 64.0 149.0 1,878 2,828 .. 41,037 Lesotho 101.0 72.0 91.0 72.0 86.0 78.0 1,036 421 260 207 Liberia 71.0 115.0 88.0 131.0 91.0 127.0 1,029 1,553 .. .. Libya 78.0 102.0 77.0 109.0 77.0 116.0 674 623 .. .. Lithuania 79.0 138.0 157.0 138.0 185.0 116.0 1,938 3,450 .. 5,369 Macedonia, FYR 108.0 112.0 108.0 115.0 101.0 115.0 2,652 3,387 2,413 5,811 Madagascar 93.0 115.0 91.0 114.0 99.0 111.0 1,945 2,291 214 192 Malawi 54.0 141.0 47.0 129.0 78.0 153.0 992 1,599 89 162 Malaysia 74.0 141.0 67.0 144.0 71.0 133.0 2,740 3,750 3,850 6,529 Mali 68.0 162.0 79.0 183.0 94.0 153.0 726 1,588 406 523 Mauritania 60.0 116.0 86.0 116.0 91.0 115.0 870 873 653 408 Mauritius 108.0 95.0 95.0 106.0 57.0 138.0 4,193 7,895 3,446 5,556 Mexico 81.0 111.0 74.0 117.0 68.0 123.0 2,424 3,111 2,275 3,364 Moldova 135.0 100.0 159.0 105.0 197.0 98.0 2,928 2,417 1,349 1,531 Mongolia 293.0 270.0 101.0 110.0 94.0 101.0 1,098 1,552 1,241 1,888 Morocco 100.0 142.0 93.0 140.0 81.0 128.0 1,120 1,003 1,806 3,306 Mozambique 70.0 130.0 68.0 102.0 47.0 89.0 474 846 132 220 Myanmar 60.0 151.0 61.0 162.0 51.0 248.0 2,762 3,585 .. .. Namibia 77.0 140.0 98.0 101.0 101.0 90.0 457 465 1,267 1,638 Nepal 75.0 135.0 76.0 130.0 78.0 120.0 1,920 2,374 247 238 Netherlands 92.0 100.0 102.0 94.0 101.0 101.0 6,959 9,032 23,593 45,969 New Zealand 78.0 109.0 74.0 115.0 77.0 114.0 5,034 6,922 19,782 25,446 Nicaragua 70.0 117.0 63.0 135.0 59.0 149.0 1,524 1,872 .. 2,495 Niger 64.0 210.0 61.0 186.0 56.0 153.0 310 489 235 .. Nigeria 60.0 134.0 60.0 135.0 70.0 121.0 1,148 1,598 .. .. Norway 143.0 90.0 112.0 95.0 101.0 95.0 4,399 3,094 17,454 40,666 Oman 59.0 95.0 57.0 102.0 65.0 122.0 2,160 3,358 1,037 .. Pakistan 77.0 125.0 67.0 132.0 64.0 135.0 1,766 2,803 739 903 Panama 116.0 114.0 91.0 116.0 67.0 116.0 1,867 2,735 2,303 4,185 Papua New Guinea 76.0 112.0 77.0 119.0 79.0 127.0 2,395 3,727 563 672 Paraguay 93.0 122.0 76.0 136.0 81.0 134.0 1,979 2,358 1,657 1,338 Peru 51.0 145.0 55.0 153.0 65.0 158.0 2,601 3,910 907 1,545 Philippines 87.0 131.0 78.0 131.0 57.0 134.0 2,065 3,229 911 1,204 Poland 124.0 99.0 119.0 111.0 123.0 110.0 3,284 3,475 1,605 2,776 Portugal 110.0 86.0 101.0 95.0 82.0 103.0 1,878 3,455 4,495 6,764 Puerto Rico 153.0 105.0 125.0 94.0 119.0 91.0 1,080 1,897 .. .. Qatar 51.0 121.0 64.0 77.0 77.0 46.0 2,897 3,820 .. .. 2011 World Development Indicators 135 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999–2001 = 100 1999–2001 = 100 1999–2001 = 100 per hectare 2000 $ 1990 2009 1990 2009 1990 2009 1990 2009 1990 2009 Romania 98.0 100.0 105.0 107.0 124.0 115.0 3,011 2,825 2,351 8,993 Russian Federation 126.0 136.0 129.0 130.0 147.0 118.0 1,743 2,279 1,917 3,031 Rwanda 97.0 132.0 94.0 134.0 79.0 158.0 1,043 1,097 172 .. Saudi Arabia 118.0 124.0 103.0 124.0 64.0 135.0 4,245 5,212 7,863 20,431 Senegal 72.0 130.0 73.0 134.0 80.0 144.0 795 1,135 252 245 Serbia 98.0 b .. 109.0 b .. 103.0 b .. 2,926 b 4,626 .. .. Sierra Leone 127.0 204.0 121.0 201.0 105.0 144.0 1,202 989 .. .. Singapore 223.0 440.0 335.0 132.0 481.0 105.0 .. .. .. 49,867 Slovak Republic .. 102.0 .. 97.0 .. 80.0 .. 4,335 .. 9,728 Slovenia 82.0 94.0 76.0 97.0 76.0 97.0 3,279 5,266 13,217 67,838 Somalia 144.0 96.0 101.0 104.0 95.0 105.0 793 417 .. .. South Africa 86.0 111.0 87.0 122.0 93.0 130.0 1,877 4,395 2,290 3,641 Spain 91.0 97.0 88.0 97.0 77.0 103.0 2,485 2,957 8,947 21,831 Sri Lanka 90.0 115.0 91.0 120.0 88.0 123.0 2,965 3,722 678 926 Sudan 49.0 112.0 51.0 119.0 58.0 123.0 456 587 501 922 Swaziland 112.0 101.0 106.0 115.0 108.0 140.0 1,278 560 1,025 1,176 Sweden 126.0 103.0 110.0 100.0 100.0 93.0 4,964 5,086 23,307 51,057 Switzerland 112.0 103.0 104.0 104.0 103.0 106.0 5,984 6,579 20,451 26,726 Syrian Arab Republic 66.0 115.0 71.0 131.0 74.0 143.0 750 1,707 2,613 4,717 Tajikistan 123.0 148.0 134.0 162.0 196.0 168.0 1,020 2,250 370 542 Tanzania 91.0 154.0 86.0 134.0 75.0 104.0 1,506 1,224 220 283 Thailand 76.0 129.0 77.0 126.0 74.0 111.0 2,009 2,954 446 708 Timor-Leste 88.0 105.0 94.0 111.0 89.0 114.0 1,608 1,276 .. .. Togo 71.0 109.0 74.0 132.0 85.0 137.0 747 1,136 351 .. Trinidad and Tobago 120.0 89.0 90.0 125.0 72.0 149.0 2,826 2,659 1,825 1,502 Tunisia 93.0 119.0 81.0 115.0 57.0 110.0 1,143 1,401 2,736 3,602 Turkey 87.0 112.0 88.0 119.0 91.0 125.0 2,214 2,808 2,175 3,491 Turkmenistan 98.0 128.0 60.0 137.0 64.0 129.0 2,210 2,974 1,272 2,930 Uganda 77.0 109.0 78.0 112.0 79.0 120.0 1,498 1,539 177 203 Ukraine 131.0 155.0 147.0 123.0 170.0 101.0 2,834 3,004 1,232 2,461 United Arab Emirates 16.0 52.0 19.0 45.0 54.0 125.0 2,216 2,000 9,042 .. United Kingdom 101.0 96.0 105.0 98.0 105.0 99.0 6,171 7,008 21,400 26,370 United States 86.0 112.0 82.0 115.0 81.0 108.0 4,755 7,238 18,523 49,512 Uruguay 66.0 187.0 74.0 144.0 83.0 127.0 2,182 4,047 6,166 9,064 Uzbekistan 107.0 145.0 93.0 155.0 99.0 137.0 1,777 4,578 1,427 2,584 Venezuela, RB 77.0 116.0 73.0 122.0 73.0 128.0 2,486 3,826 4,443 7,941 Vietnam 57.0 137.0 59.0 138.0 50.0 157.0 3,073 5,075 225 356 West Bank and Gaza .. 104.0 .. 102.0 .. 93.0 .. 1,684 .. .. Yemen, Rep. 75.0 130.0 71.0 144.0 63.0 165.0 908 1,003 428 .. Zambia 81.0 170.0 87.0 135.0 83.0 106.0 1,352 2,068 212 216 Zimbabwe 77.0 55.0 87.0 82.0 79.0 107.0 1,625 313 270 141 World 81.0c w 122.2 w 80.0c w 123.0 w 82.0c w 120.3 w 2,755c w 3,514 w 803 w 998 w Low income 76.2 134.1 76.5 134.4 78.3 131.0 1,561 1,966 236 278 Middle income 73.6 128.2 68.8 130.2 62.5 131.5 2,563 3,210 489 777 Lower middle income 71.9 128.6 66.5 130.5 56.5 132.6 2,696 3,446 360 604 Upper middle income 79.2 126.9 75.6 129.3 75.1 129.4 2,103 2,690 2,270 3,683 Low & middle income 73.8 128.7 69.4 130.5 63.3 131.4 2,429 3,005 460 704 East Asia & Pacific 69.9 133.1 62.7 135.1 48.8 135.2 3,795 4,843 313 570 Europe & Central Asia 111.3 129.3 117.7 126.2 136.8 119.1 2,596 2,471 2,188 3,182 Latin America & Carib. 75.8 128.1 71.2 131.2 69.7 132.6 2,089 3,282 2,227 3,436 Middle East & N. Africa 74.7 127.3 72.8 131.6 69.3 134.7 1,471 2,352 1,760 2,896 South Asia 78.0 119.3 74.5 122.7 69.2 132.9 1,926 2,628 372 495 Sub-Saharan Africa 71.1 128.7 72.9 130.0 80.1 125.1 1,033 1,302 304 318 High income 90.7 103.9 90.4 106.3 90.7 104.1 4,138 5,439 14,116 25,066 Euro area 90.9 96.9 95.6 97.7 98.2 100.0 4,490 5,822 11,982 26,730 a. Includes Luxembourg. b. Includes Montenegro. c. FAO estimate. 136 2011 World Development Indicators 3.3 ENVIRONMENT Agricultural output and productivity About the data Definitions The agricultural production indexes in the table are production. These prices, expressed in international • Crop production index is agricultural production prepared by the Food and Agriculture Organization of dollars (equivalent in purchasing power to the U.S. for each period relative to the average over the base the United Nations (FAO). The FAO obtains data from dollar), are derived using a Geary-Khamis formula period 1999–2001. It includes all crops except fod- official and semiofficial reports of crop yields, area applied to agricultural outputs (see Inter-Secretariat der crops. The regional and income group aggregates under production, and livestock numbers. If data are Working Group on National Accounts 1993, sections for the FAO’s production indexes are calculated from unavailable, the FAO makes estimates. The indexes 16.93–96). This method assigns a single price to the underlying values in international dollars, normal- are calculated using the Laspeyres formula: produc- each commodity so that, for example, one metric ton ized to the average over the base period 1999–2001. tion quantities of each commodity are weighted by of wheat has the same price regardless of where it • Food production index covers food crops that are average international commodity prices in the base was produced. The use of international prices elimi- considered edible and that contain nutrients. Cof- period and summed for each year. Because the FAO’s nates fluctuations in the value of output due to transi- fee and tea are excluded because, although edible, indexes are based on the concept of agriculture as a tory movements of nominal exchange rates unrelated they have no nutritive value. • Livestock production single enterprise, estimates of the amounts retained to the purchasing power of the domestic currency. index includes meat and milk from all sources, dairy for seed and feed are subtracted from the produc- Data on cereal yield may be affected by a variety products such as cheese, and eggs, honey, raw silk, tion data to avoid double counting. The aggregates of reporting and timing differences. Millet and sor- wool, and hides and skins. • Cereal yield, measured represent production available for any use except as ghum, which are grown as feed for livestock and poul- in kilograms per hectare of harvested land, includes seed and feed and presented as “net”. The FAO’s try in Europe and North America, are used as food wheat, rice, maize, barley, oats, rye, millet, sorghum, indexes may differ from those from other sources in Africa, Asia, and countries of the former Soviet buckwheat, and mixed grains. Production data on because of differences in coverage, weights, con- Union. So some cereal crops are excluded from the cereals refer to crops harvested for dry grain only. cepts, time periods, calculation methods, and use data for some countries and included elsewhere, Cereal crops harvested for hay or harvested green for of international prices. depending on their use. food, feed, or silage, and those used for grazing, are To facilitate cross-country comparisons, the excluded. The FAO allocates production data to the FAO uses international commodity prices to value calendar year in which the bulk of the harvest took place. But most of a crop harvested near the end of The food production index has increased steadily since early 1960, and the index for a year will be used in the following year. • Agricul- low-income economies has been higher than the world average since early 2000 3.3a tural productivity is the ratio of agricultural value added, measured in 2000 U.S. dollars, to the num- Index (1999–2001=100) ber of workers in agriculture. Agricultural productivity 160 is measured by value added per unit of input. (For Middle income World further discussion of the calculation of value added 120 in national accounts, see About the data for tables 90 4.1 and 4.2.) Agricultural value added includes that Low income High income from forestry and fishing. Thus interpretations of land 60 productivity should be made with caution. 30 0 1990 1995 2000 2005 2008 Source: Table 3.3. Cereal yield in Sub-Saharan Africa increased between 1990 and 2009 but still is the lowest among the regions 3.3b 6 Kilograms per hectare (thousands) 1990 2009 5 4 Data sources 3 Data on agricultural production indexes, cereal 2 yield, and agricultural employment are from elec- tronic files that the FAO makes available to the 1 World Bank. The files may contain more recent 0 information than published versions. Data on agri- East Asia & Europe & Latin America Middle East & South Asia Sub-Saharan Central Asia & Caribbean North Africa Africa cultural value added are from the World Bank’s Source: Table 3.3. national accounts files. 2011 World Development Indicators 137 3.4 Deforestation and biodiversity Forest Average annual Threatened GEF benefits Terrestrial Marine area deforestationa species index for protected areas protected areas biodiversity 0–100 (no % of total land area % of territorial waters biodiversity thousand Higher to maximum sq. km % Mammals Birds Fish plantsb biodiversity) 1990 2010 1990–2000 2000–10 2010 2010 2010 2010 2008 1990 2009 1990 2009 Afghanistan 14 14 0.00 0.00 11 13 5 2 3.4 0.4 0.4 .. .. Albania 8 8 0.26 –0.09 3 6 38 0 0.2 4.3 9.8 0.1 1.5 Algeria 17 15 0.54 0.57 14 11 33 15 2.9 6.3 6.3 0.2 0.3 Angola 610 585 0.21 0.21 15 21 37 33 8.3 12.4 12.4 0.1 0.1 Argentina 348 294 0.88 0.80 37 50 36 44 17.7 4.6 5.4 0.8 1.1 Armenia 3 3 1.31 1.48 9 10 3 1 0.2 6.9 8.0 .. .. Australia 1,545 1,493 –0.03 0.37 55 52 100 67 87.7 7.4 10.5 10.9 28.3 Austria 38 39 –0.16 –0.13 3 8 11 4 0.3 20.1 22.9 .. .. Azerbaijan 9 9 0.00 0.00 7 15 10 0 0.8 6.2 7.1 .. .. Bangladesh 15 14 0.18 0.18 34 29 19 16 1.4 1.5 1.6 0.4 0.8 Belarus 78 86 –0.62 –0.42 4 4 2 0 0.0 6.5 7.3 .. .. Belgium 7 7 0.15 –0.16 3 2 10 1 0.0 0.6 0.9 0.0 0.0 Benin 58 46 1.29 1.03 11 5 27 14 0.2 23.8 23.8 0.0 0.0 Bolivia 628 572 0.44 0.49 20 33 0 72 12.5 8.5 18.2 .. .. Bosnia and Herzegovina 22 22 0.11 0.00 4 6 31 1 0.4 0.5 0.6 0.7 0.7 Botswana 137 114 0.90 0.99 7 9 2 0 1.4 30.3 30.9 .. .. Brazil 5,748 5,195 0.51 0.49 80 123 80 387 100.0 10.8 28.0 11.4 20.1 Bulgaria 33 39 –0.14 –1.53 7 12 18 0 0.8 1.9 9.1 0.1 3.0 Burkina Faso 68 56 0.91 1.00 9 6 4 3 0.3 13.3 13.9 .. .. Burundi 3 2 3.71 1.40 10 10 17 2 0.3 3.8 4.8 .. .. Cambodia 129 101 1.14 1.33 37 24 28 30 3.5 0.0 24.0 0.0 0.9 Cameroon 243 199 0.94 1.04 39 16 110 378 12.5 7.0 9.2 0.4 0.4 Canada 3,101 3,101 0.00 0.00 12 15 32 2 21.5 6.0 8.0 0.8 1.2 Central African Republic 232 226 0.13 0.13 8 7 3 17 1.5 14.4 14.7 .. .. Chad 131 115 0.62 0.66 13 9 1 2 2.2 9.4 9.4 .. .. Chile 153 162 –0.37 –0.25 20 34 19 41 15.3 16.0 16.5 3.4 3.7 China 1,571 2,069 –1.20 –1.57 74 85 97 453 66.6 13.5 16.6 0.4 1.4 Hong Kong SAR, China .. .. .. .. 2 17 11 6 .. 41.1 41.8 0.0 0.0 Colombia 625 605 0.16 0.17 51 91 50 227 51.5 20.3 20.4 3.7 5.9 Congo, Dem. Rep. 1,604 1,541 0.20 0.20 30 34 81 83 19.9 10.0 10.0 3.7 4.3 Congo, Rep. 227 224 0.08 0.06 11 3 45 37 3.6 5.4 9.4 0.0 2.1 Costa Rica 26 26 0.76 –0.92 9 19 46 116 9.7 18.7 20.9 12.1 12.3 Côte d’Ivoire 102 104 –0.10 –0.07 24 14 43 106 3.4 22.6 22.6 0.1 0.1 Croatia 19 19 –0.19 –0.18 7 10 56 3 0.6 7.1 7.3 1.2 1.2 Cuba 21 29 –1.70 –1.66 14 17 30 166 12.5 4.3 6.2 1.3 2.7 Czech Republic 26 27 –0.03 –0.08 2 6 2 4 0.1 13.7 15.1 .. .. Denmark 4 5 –0.89 –1.13 2 2 14 3 0.2 4.8 5.0 3.7 3.8 Dominican Republic 20 20 0.00 0.00 6 14 17 30 6.0 22.1 22.1 30.4 30.4 Ecuador 138 99 1.53 1.81 43 71 49 1,837 29.3 21.6 25.1 0.1 13.0 Egypt, Arab Rep. 0 1 –2.98 –1.72 17 10 36 2 2.9 1.9 5.9 4.4 9.3 El Salvador 4 3 1.26 1.45 5 5 12 27 0.9 0.6 0.8 3.2 3.2 Eritrea 16 15 0.28 0.28 10 10 18 3 0.8 4.9 5.0 0.0 0.0 Estonia 21 22 –0.71 0.12 1 3 4 0 0.1 19.6 20.0 26.1 26.1 Ethiopia 151 123 0.97 1.08 32 23 14 26 8.4 17.7 18.4 .. .. Finland 219 222 –0.26 0.14 1 4 5 1 0.2 4.2 9.1 3.5 5.0 France 145 160 –0.55 –0.38 9 7 40 15 5.3 10.1 15.1 1.1 3.4 Gabon 220 220 0.00 0.00 14 5 59 120 3.0 4.2 14.9 0.2 7.1 Gambia, The 4 5 –0.42 –0.40 10 6 21 4 0.1 1.5 1.5 0.1 0.1 Georgia 28 27 0.04 0.09 10 10 9 0 0.6 2.8 3.7 0.2 0.4 Germany 107 111 –0.31 0.00 6 6 21 12 0.6 31.8 40.5 35.7 36.3 Ghana 74 49 1.99 2.08 16 9 42 118 1.9 13.9 14.0 0.0 0.0 Greece 33 39 –0.88 –0.81 10 11 73 13 2.8 5.7 13.8 0.6 2.5 Guatemala 47 37 1.20 1.39 16 10 20 82 8.0 26.0 30.6 0.3 12.5 Guinea 73 65 0.51 0.53 22 13 63 22 2.3 6.8 6.8 0.0 0.0 Guinea-Bissau 22 20 0.44 0.47 12 3 30 4 0.6 7.6 16.1 2.7 45.8 Haiti 1 1 0.62 0.76 5 13 17 29 5.2 0.3 0.3 0.0 0.0 Honduras 81 52 2.38 2.06 7 9 22 113 7.2 13.6 18.2 0.0 1.9 138 2011 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity Forest Average annual Threatened GEF benefits Terrestrial Marine area deforestationa species index for protected areas protected areas biodiversity 0–100 (no % of total land area % of territorial waters biodiversity thousand Higher to maximum sq. km % Mammals Birds Fish plantsb biodiversity) 1990 2010 1990–2000 2000–10 2010 2010 2010 2010 2008 1990 2009 1990 2009 Hungary 18 20 –0.57 –0.62 2 9 8 1 0.2 4.6 5.1 .. .. India 639 684 –0.22 –0.46 94 78 122 255 39.9 5.0 5.3 1.5 1.7 Indonesia 1,185 944 1.75 0.51 183 119 138 393 81.0 10.0 14.1 0.5 1.9 Iran, Islamic Rep. 111 111 0.00 0.00 16 21 29 1 7.3 5.2 7.1 1.3 1.9 Iraq 8 8 –0.17 –0.09 13 18 11 0 1.6 0.1 0.1 0.0 0.0 Ireland 5 7 –3.16 –1.53 5 1 18 1 0.6 0.6 1.0 0.1 0.1 Israel 1 2 –1.49 –0.07 15 13 35 0 0.8 17.2 18.7 1.0 1.0 Italy 76 91 –0.98 –0.90 7 8 42 27 3.8 5.0 9.9 0.5 16.7 Jamaica 3 3 0.12 0.12 5 10 17 209 4.4 10.2 18.9 0.2 4.2 Japan 250 250 0.03 –0.04 28 40 59 15 36.0 13.2 16.3 2.0 5.6 Jordan 1 1 0.00 0.00 13 10 13 1 0.4 8.4 9.4 0.0 20.8 Kazakhstan 34 33 0.17 0.17 16 21 14 16 5.1 2.4 2.5 .. .. Kenya 37 35 0.35 0.33 28 30 66 129 8.8 11.5 11.6 5.1 10.4 Korea, Dem. Rep. 82 57 1.67 2.00 9 22 12 6 0.7 3.9 4.0 0.1 0.1 Korea, Rep. 64 62 0.13 0.11 9 30 17 3 1.7 2.2 2.4 5.0 5.3 Kosovo .. 5c .. .. .. .. .. .. .. .. .. .. .. Kuwait 0 0 –5.24 –1.84 6 9 11 0 0.1 1.6 1.6 0.0 0.0 Kyrgyz Republic 8 10 –0.26 –1.07 6 12 3 14 1.1 6.4 6.9 .. .. Lao PDR 173 158 0.46 0.48 45 22 23 22 5.0 0.8 16.3 .. .. Latvia 32 34 –0.21 –0.34 1 3 5 0 0.0 6.4 17.8 4.6 6.6 Lebanon 1 1 0.00 –0.45 10 7 21 1 0.2 0.5 0.5 0.0 0.1 Lesotho 0 0 –0.49 –0.47 2 7 1 4 0.3 0.5 0.5 .. .. Liberia 49 43 0.63 0.67 19 11 52 47 2.6 18.1 18.1 0.0 0.0 Libya 2 2 0.00 0.00 12 4 21 2 1.6 0.1 0.1 0.0 0.0 Lithuania 19 22 –0.38 –0.67 3 4 5 0 0.0 1.4 4.5 0.8 2.7 Macedonia, FYR 9 10 –0.49 –0.41 5 10 14 0 0.2 4.2 4.8 .. .. Madagascar 137 126 0.42 0.44 63 35 83 280 29.2 2.1 2.9 0.0 0.1 Malawi 39 32 0.88 0.97 7 14 101 14 3.5 15.0 15.0 .. .. Malaysia 224 205 0.36 0.54 70 45 60 692 13.9 16.9 17.9 1.1 1.6 Mali 141 125 0.58 0.61 12 7 3 6 1.5 2.3 2.4 .. .. Mauritania 4 2 2.66 2.66 15 9 30 0 1.3 0.5 0.5 32.1 32.1 Mauritius 0 0 0.00 1.08 6 11 12 88 3.3 1.7 4.5 0.3 0.3 Mexico 703 648 0.52 0.30 99 55 150 255 68.7 2.4 11.1 1.9 16.7 Moldova 3 4 –0.16 –1.77 4 9 9 0 0.0 0.9 1.4 .. .. Mongolia 125 109 0.67 0.72 11 21 1 0 4.2 4.1 13.4 .. .. Morocco 50 51 0.06 –0.22 18 10 45 31 3.5 1.2 1.5 0.7 1.2 Mozambique 434 390 0.52 0.54 12 23 52 52 7.2 14.8 15.8 1.8 3.3 Myanmar 392 318 1.17 0.93 45 41 33 42 10.0 3.1 6.3 0.3 0.3 Namibia 88 73 0.87 0.96 12 24 25 26 5.2 14.4 14.5 0.5 0.5 Nepal 48 36 2.09 0.70 31 33 8 7 2.1 7.7 17.0 .. .. Netherlands 3 4 –0.43 –0.14 4 2 12 0 0.2 11.0 12.4 13.5 21.2 New Zealand 77 83 –0.69 0.00 9 70 21 21 20.2 25.0 25.8 0.4 7.1 Nicaragua 45 31 1.67 2.01 6 11 26 43 3.3 15.4 36.7 0.7 20.1 Niger 19 12 3.74 0.98 12 6 4 2 0.9 6.8 6.8 .. .. Nigeria 172 90 2.68 3.67 27 13 56 172 6.0 11.6 12.8 0.2 0.2 Norway 91 101 –0.19 –0.79 7 2 18 2 1.3 4.7 14.4 1.0 2.3 Oman 0 0 0.00 0.00 9 10 24 6 3.7 0.0 10.7 0.0 1.3 Pakistan 25 17 1.76 2.24 23 26 33 2 4.9 10.3 10.3 1.8 1.8 Panama 38 33 1.18 0.36 15 17 36 202 10.9 17.2 18.7 3.1 4.0 Papua New Guinea 315 287 0.45 0.48 39 37 41 143 25.4 1.9 3.1 0.3 0.3 Paraguay 212 176 0.88 0.96 8 27 0 10 2.8 2.9 5.4 .. .. Peru 702 680 0.14 0.18 54 96 19 274 33.4 4.7 13.6 2.8 2.8 Philippines 66 77 –0.80 –0.74 39 72 65 222 32.3 8.7 10.9 0.2 1.5 Poland 89 93 –0.20 –0.30 5 6 6 4 0.5 15.3 21.8 3.8 4.5 Portugal 33 35 –0.28 –0.10 11 9 47 21 5.5 5.9 5.9 1.8 1.8 Puerto Rico 3 6 –4.92 –1.75 3 8 15 53 4.0 10.1 10.1 1.5 1.6 Qatar 0 0 0.00 0.00 2 5 11 0 0.1 0.0 0.7 0.0 0.3 2011 World Development Indicators 139 3.4 Deforestation and biodiversity Forest Average annual Threatened GEF benefits Terrestrial Marine area deforestationa species index for protected areas protected areas biodiversity 0–100 (no % of total land area % of territorial waters biodiversity thousand Higher to maximum sq. km % Mammals Birds Fish plantsb biodiversity) 1990 2010 1990–2000 2000–10 2010 2010 2010 2010 2008 1990 2009 1990 2009 Romania 64 66 0.01 –0.32 7 12 18 1 0.7 2.8 7.1 1.5 33.2 Russian Federation 8,090 8,091 0.00 0.00 32 18 35 8 34.1 8.2 9.0 3.1 9.1 Rwanda 3 4 –0.79 –2.37 20 12 9 4 0.9 9.9 10.0 .. .. Saudi Arabia 10 10 0.00 0.00 9 14 22 3 3.2 7.6 31.3 0.6 3.4 Senegal 93 85 0.49 0.49 16 9 41 9 1.0 24.1 24.1 5.8 12.4 Serbia 23 27 –0.62 –0.98 6 11 11 1 0.2 3.0 6.0 .. .. Sierra Leone 31 27 0.65 0.69 17 10 45 48 1.3 5.0 5.0 0.0 0.0 Singapore 0 0 0.00 0.00 11 17 25 57 0.1 5.0 5.4 0.0 1.6 Slovak Republic 19 19 0.01 –0.06 3 7 5 2 0.1 19.5 23.5 .. .. Slovenia 12 13 –0.37 –0.16 4 4 26 0 0.2 7.5 12.1 0.0 0.6 Somalia 83 67 0.97 1.07 15 11 26 21 6.1 0.6 0.6 0.0 0.0 South Africa 82 57 1.67 2.00 24 39 81 97 20.7 6.5 6.9 0.7 6.5 Spain 138 182 –2.09 –0.68 16 15 62 55 6.8 7.7 8.6 0.6 3.4 Sri Lanka 24 19 1.20 1.12 30 14 41 283 7.9 19.6 20.8 0.1 1.1 Sudan 764 699 0.80 0.08 15 14 17 18 5.1 4.7 4.9 0.0 0.0 Swaziland 5 6 –0.93 –0.84 5 9 4 11 0.1 3.0 3.0 .. .. Sweden 273 282 –0.04 –0.29 1 3 11 3 0.3 7.1 11.3 3.7 5.3 Switzerland 12 12 –0.37 –0.38 2 2 9 3 0.2 14.5 22.8 .. .. Syrian Arab Republic 4 5 –1.51 –1.29 16 13 33 3 0.9 0.3 0.6 0.0 0.6 Tajikistan 4 4 –0.05 0.00 8 9 5 14 0.7 1.9 4.1 .. .. Tanzania 415 334 1.02 1.13 35 42 172 298 14.8 26.5 27.7 3.7 10.0 Thailand 195 190 0.28 0.02 57 45 72 91 8.0 14.2 19.6 4.0 4.3 Timor-Leste 10 7 1.22 1.40 4 7 5 0 0.6 0.0 6.0 0.0 6.7 Togo 7 3 3.37 5.13 11 3 24 10 0.3 11.3 11.3 0.0 0.0 Trinidad and Tobago 2 2 0.29 0.35 2 2 19 1 2.2 30.5 31.2 0.2 2.8 Tunisia 6 10 –2.67 –1.86 13 7 31 7 0.5 1.3 1.3 1.1 1.2 Turkey 97 113 –0.47 –1.11 17 15 67 5 6.2 1.7 1.9 2.4 2.4 Turkmenistan 41 41 0.00 0.00 9 15 11 3 1.8 3.0 3.0 .. .. Uganda 48 30 2.03 2.55 22 19 61 41 2.8 7.3 9.7 .. .. Ukraine 93 97 –0.25 –0.20 11 12 21 1 0.5 1.8 3.5 4.1 4.9 United Arab Emirates 2 3 –2.38 –0.22 7 10 13 0 0.2 0.3 5.6 0.3 2.6 United Kingdom 26 29 –0.68 –0.31 5 2 41 14 3.5 21.8 24.4 4.7 5.2 United States 2,963 3,040 –0.13 –0.13 37 74 177 245 94.2 14.8 14.8 18.3 24.7 Uruguay 9 17 –4.38 –2.13 11 23 35 1 1.2 0.3 0.3 0.2 0.2 Uzbekistan 30 33 –0.54 –0.20 10 15 7 15 1.1 2.1 2.3 .. .. Venezuela, RB 520 463 0.57 0.60 32 27 34 70 25.3 39.3 53.7 7.0 15.3 Vietnam 94 138 –2.28 –1.64 54 40 46 146 12.1 4.4 6.2 0.3 2.1 West Bank and Gaza 0 0 0.00 0.00 3 8 0 0 .. .. .. .. .. Yemen, Rep. 5 5 0.00 0.00 9 14 21 159 3.2 0.0 0.5 0.0 1.9 Zambia 528 495 0.32 0.33 9 14 20 9 3.8 36.0 36.0 .. .. Zimbabwe 222 156 1.58 1.88 9 13 3 16 1.9 18.0 28.0 .. .. World 41,582 s 40,204 s 0.20 w 0.13 w 1,131 s 1,240 s 1,851 s 8,724 s 9.1 w 12.5 w 4.8 w 9.2 w Low income 5,524 4,881 0.63 0.61 10.0 11.2 .. .. Middle income 26,552 25,660 0.24 0.10 8.6 12.4 2.9 6.6 Lower middle income 8,103 7,996 0.26 –0.13 8.8 11.5 0.8 2.0 Upper middle income 18,449 17,664 0.23 0.20 8.4 13.0 4.1 9.4 Low & middle income 32,076 30,541 0.31 0.18 8.9 12.2 3.2 6.6 East Asia & Pacific 4,602 4,698 0.17 –0.38 10.8 14.9 0.5 1.5 Europe & Central Asia 8,703 8,750 –0.02 –0.03 6.6 7.4 3.1 8.8 Latin America & Carib. 10,389 9,460 0.48 0.45 10.5 20.8 6.7 13.1 Middle East & N. Africa 207 211 –0.08 –0.13 3.1 4.0 0.9 2.0 South Asia 795 817 0.01 –0.27 5.5 6.1 1.5 1.7 Sub-Saharan Africa 7,379 6,605 0.58 0.52 11.0 11.7 3.2 4.7 High income 9,506 9,663 –0.13 –0.03 9.9 13.4 8.7 15.1 Euro area 838 930 –0.73 –0.31 11.1 15.4 6.5 10.1 a. Negative values indicate an increase in forest area. b. Flowering plants. c. National sources. 140 2011 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity About the data Definitions As threats to biodiversity mount, the international com- ecoregions, and threatened ecoregions. To combine • Forest area is land spanning more than 0.5 hectares munity is increasingly focusing on conserving diversity. these dimensions into one measure, the indicator with trees higher than 5 meters and a canopy cover Deforestation is a major cause of loss of biodiversity, uses dimensional weights that reflect the consensus of more than 10 percent or with trees able to reach and habitat conservation is vital for stemming this of conservation scientists at the GEF, IUCN, WWF Inter- these thresholds in situ. It does not include land that loss. Conservation efforts have focused on protecting national, and other nongovernmental organizations. is predominantly under agricultural or urban land use. areas of high biodiversity. The Food and Agriculture The World Conservation Monitoring Centre (WCMC) • Average annual deforestation is the permanent con- Organization of the United Nations (FAO) Global Forest compiles data on protected areas, numbers of certain version of natural forest area to other uses, including Resources Assessment 2010 provides detailed informa- species, and numbers of those species under threat agriculture, ranching, settlements, and infrastruc- tion on forest cover in 2010 and adjusted estimates from various sources. Because of differences in defini- ture. Deforested areas do not include areas logged of forest cover in 1990 and 2000. The current survey tions, reporting practices, and reporting periods, cross- but intended for regeneration or areas degraded by uses a uniform definition of forest. Because of space country comparability is limited. Nationally protected fuelwood gathering, acid precipitation, or forest fires. limitations, the table does not break down forest cover areas are defined using the six IUCN management cat- • Threatened species are the number of species clas- between natural forest and plantation, a breakdown egories for areas of at least 1,000 hectares: scientific sified by the IUCN as endangered, vulnerable, rare, the FAO provides for developing countries. Thus the reserves and strict nature reserves with limited public indeterminate, out of danger, or insufficiently known. deforestation data in the table may underestimate the access; national parks of national or international sig- Mammals exclude whales and porpoises. Birds are rate at which natural forest is disappearing in some nificance and not materially affected by human activ- listed for the country where their breeding or wintering countries. ity; natural monuments and natural landscapes with ranges are located. Plants are native vascular plant The number of threatened species is an important unique aspects; managed nature reserves and wildlife species. • GEF benefits index for biodiversity is a measure of the immediate need for conservation in sanctuaries; protected landscapes (which may include composite index of relative biodiversity potential based an area. Global analyses of the status of threatened cultural landscapes); and areas managed mainly for the on the species represented in each country and their species have been carried out for few groups of organ- sustainable use of natural systems to ensure long-term threat status and diversity of habitat types. The index isms. Only for mammals, birds, and amphibians has the protection and maintenance of biological diversity. The has been normalized from 0 (no biodiversity potential) status of virtually all known species been assessed. data in the table cover these six categories as well as to 100 (maximum biodiversity potential). • Nationally Threatened species are defined using the International terrestrial protected areas that are not assigned to a protected areas are totally or partially protected areas Union for Conservation of Nature’s (IUCN) classifica- category by the IUCN. Designating an area as protected of at least 1,000 hectares that are designated as sci- tion: endangered (in danger of extinction and unlikely does not mean that protection is in force. And for small entific reserves with limited public access, national to survive if causal factors continue operating) and vul- countries that only have protected areas smaller than parks, natural monuments, nature reserves or wildlife nerable (likely to move into the endangered category in 1,000 hectares, the size limit in the definition leads to sanctuaries, and protected landscapes. Terrestrial the near future if causal factors continue operating). an underestimate of protected areas. protected areas exclude marine areas, unclassified The Global Environment Facility’s (GEF) benefits Due to variations in consistency and methods of areas, littoral (intertidal) areas, and sites protected index for biodiversity is a comprehensive indicator collection, data quality is highly variable across coun- under local or provincial law. Marine protected areas of national biodiversity status and is used to guide tries. Some countries update their information more are areas of intertidal or subtidal terrain—and overly- its biodiversity priorities. For each country the biodi- frequently than others, some have more accurate data ing water and associated flora and fauna and histori- versity indicator incorporates the best available and on extent of coverage, and many underreport the num- cal and cultural features—that have been reserved to comparable information in four relevant dimensions: ber or extent of protected areas. protect part or the entire enclosed environment. represented species, threatened species, represented At least 33 percent of assessed species are estimated to be threatened 3.4a Data sources Data on forest area are from the FAO’s Global Forest Species (thousands) Resources Assessment 2010 and the FAO’s data web 80 site. Data on species are from the electronic files of the United Nations Environment Programme and WCMC, the 2010 IUCN Red List of Threatened Spe- 60 cies, and Froese and Pauly’s (2008) FishBase data- Assessed base. The GEF benefits index for biodiversity is from 40 Kiran Dev Pandey, Piet Buys, Ken Chomitz, and David Wheeler’s, “Biodiversity Conservation Indicators: New Threatened 20 Tools for Priority Setting at the Global Environment Facility” (2006). Data on protected areas are from the 0 United Nations Environment Programme and WCMC, 2000 2002 2004 2006 2008 2010 based on data from national authorities and national Source: International Union for Conservation of Nature. legislation and international agreements. 2011 World Development Indicators 141 3.5 Freshwater Internal renewable Annual freshwater Water Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of rural % of urban billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007b 2007b 2007b 2007b 2007b 2007b 2008 2008 Afghanistan 55 1,946 23.3 42.3 98 0 2 .. 39 78 Albania 27 8,588 1.7 6.4 62 11 27 3 98 96 Algeria 11 332 6.1 54.0 65 13 22 12 79 85 Angola 148 8,431 0.4 0.2 60 17 23 61 38 60 Argentina 276 6,989 29.2 10.6 74 9 17 13 80 98 Armenia 9 2,952 3.0 32.5 66 4 30 1 93 98 Australia 492 23,348 23.9 4.9 75 10 15 22 100 100 Austria 55 6,626 2.1 3.8 1 64 35 105 100 100 Azerbaijan 8 946 12.2 150.5 76 19 4 1 71 88 Bangladesh 105 666 79.4 75.6 96 1 3 1 78 85 Belarus 37 3,834 2.8 7.5 30 47 23 8 99 100 Belgium 12 1,129 0.0 .. .. .. .. .. 100 100 Benin 10 1,227 0.1 1.3 45 23 32 23 69 84 Bolivia 304 31,868 1.4 0.5 81 7 13 7 67 96 Bosnia and Herzegovina 36 9,395 0.0 .. .. .. .. .. 98 100 Botswana 2 1,268 0.2 8.1 41 18 41 41 90 99 Brazil 5,418 28,498 59.3 1.1 62 18 20 14 84 99 Bulgaria 21 2,742 10.5 50.0 19 78 3 2 100 100 Burkina Faso 13 849 0.8 6.4 86 1 13 5 72 95 Burundi 10 1,283 0.3 2.9 77 6 17 3 71 83 Cambodia 121 8,417 4.1 3.4 98 0 1 2 56 81 Cameroon 273 14,630 1.0 0.4 74 8 18 13 51 92 Canada 2,850 86,426 46.0 1.6 12 69 20 19 99 100 Central African Republic 141 33,119 0.0 0.0 4 16 80 39 51 92 Chad 15 1,412 0.2 1.5 83 0 17 13 44 67 Chile 884 53,137 12.6 1.4 64 25 11 8 75 99 China 2,813 2,134 554.1 22.4 65 23 12 4 82 98 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 2,112 47,611 10.7 0.5 46 4 50 13 73 99 Congo, Dem. Rep. 900 14,395 0.4 0.0 31 17 53 16 28 80 Congo, Rep. 222 62,516 0.0 0.0 9 22 70 89 34 95 Costa Rica 112 25,209 2.7 2.4 53 17 29 9 91 100 Côte d’Ivoire 77 3,819 0.9 1.2 65 12 24 11 68 93 Croatia 38 8,499 0.0 .. .. .. .. .. 97 100 Cuba 38 3,402 8.2 21.5 69 12 19 6 89 96 Czech Republic 13 1,272 2.6 19.6 2 57 41 30 100 100 Denmark 6 1,099 1.3 21.2 43 25 32 141 100 100 Dominican Republic 21 2,139 3.4 16.1 66 2 32 10 84 87 Ecuador 432 32,379 17.0 3.9 82 5 12 1 88 97 Egypt, Arab Rep. 2 22 68.3 3,794.4 86 6 8 2 98 100 El Salvador 18 2,907 1.3 7.2 59 16 25 13 76 94 Eritrea 3 586 0.6 20.8 95 0 5 1 57 74 Estonia 13 9,475 0.2 1.2 5 38 57 64 97 99 Ethiopia 122 1,551 5.6 4.6 94 0 6 2 26 98 Finland 107 20,232 2.5 2.3 3 84 14 62 100 100 France 200 3,229 31.8 22.4 12 69 18 38 100 100 Gabon 164 115,340 0.1 0.1 42 8 50 49 41 95 Gambia, The 3 1,857 0.0 1.0 65 12 23 19 86 96 Georgia 58 13,339 1.6 2.8 65 13 22 3 96 100 Germany 107 1,301 47.1 44.0 20 68 12 44 100 100 Ghana 30 1,325 1.0 3.2 66 10 24 7 74 90 Greece 58 5,182 7.8 13.4 80 3 16 22 99 100 Guatemala 109 8,177 2.0 1.8 80 13 6 12 90 98 Guinea 226 23,505 1.5 0.7 90 2 8 3 61 89 Guinea-Bissau 16 10,383 0.2 1.1 82 5 13 1 51 83 Haiti 13 1,338 1.0 7.6 94 1 5 4 55 71 Honduras 96 13,372 0.9 0.9 80 12 8 12 77 95 142 2011 World Development Indicators 3.5 ENVIRONMENT Freshwater Internal renewable Annual freshwater Water Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of rural % of urban billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007b 2007b 2007b 2007b 2007b 2007b 2008 2008 Hungary 6 597 7.6 127.3 32 59 9 8 100 100 India 1,276 1,134 40.4 51.2 91 2 7 1 84 96 Indonesia 2,019 8,987 82.8 2.9 82 7 12 1.2 71 89 Iran, Islamic Rep. 129 1,809 93.3 72.6 92 1 7 2 .. 98 Iraq 35 1,175 66.0 187.5 79 15 7 0 55 91 Ireland 49 11,246 .. .. .. .. .. 125 100 100 Israel 1 104 2.0 260.5 58 6 36 79 100 100 Italy 183 3,074 44.4 24.3 45 37 18 27 100 100 Jamaica 9 3,514 0.4 4.4 49 17 34 25 89 98 Japan 430 3,365 88.4 20.6 62 18 20 59 100 100 Jordan 1 120 0.9 138.0 65 4 31 14 91 98 Kazakhstan 75 4,871 35.0 46.4 82 17 2 1 90 99 Kenya 21 548 2.7 13.2 79 4 17 6 52 83 Korea, Dem. Rep. 67 2,824 9.0 13.5 55 25 20 .. 100 100 Korea, Rep. 65 1,338 18.6 28.7 48 16 36 40 88 100 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 0 0 0.5 .. 54 2 44 67 99 99 Kyrgyz Republic 46 8,873 10.1 21.7 94 3 3 0 85 99 Lao PDR 190 31,256 3.0 1.6 90 6 4 1 51 72 Latvia 17 7,355 0.3 1.8 13 33 53 48 96 100 Lebanon 5 1,153 1.3 27.3 60 11 29 17 100 100 Lesotho 5 2,574 0.1 1.0 20 40 40 18 81 97 Liberia 200 55,138 0.1 0.1 55 18 27 5 51 79 Libya 1 97 4.3 721.0 83 3 14 11 .. .. Lithuania 16 4,610 0.3 1.7 7 15 78 73 .. .. Macedonia, FYR 5 2,647 0.0 .. .. .. .. .. 99 100 Madagascar 337 18,114 15.0 4.4 96 2 3 0 29 71 Malawi 16 1,118 1.0 6.3 80 5 15 2 77 95 Malaysia 580 21,841 9.0 1.6 62 21 17 15 99 100 Mali 60 4,835 6.5 10.9 90 1 9 1 44 81 Mauritania 0c 127 1.7 425.0 88 3 9 1 47 52 Mauritius 3 2,182 0.7 26.4 68 3 30 8 99 100 Mexico 409 3,885 78.2 19.1 77 5 17 9 87 96 Moldova 1 273 2.3 231.0 33 58 10 1 85 96 Mongolia 35 13,326 0.4 1.3 52 27 20 4 49 97 Morocco 29 929 12.6 43.4 87 3 10 4 60 98 Mozambique 100 4,586 0.6 0.6 87 2 11 12 29 77 Myanmar 1003 20,415 33.2 3.8 89 1 10 .. 69 75 Namibia 6 2,949 0.3 4.9 71 5 24 19 88 99 Nepal 198 7,007 10.2 5.1 96 1 3 1 87 93 Netherlands 11 671 7.9 72.2 34 60 6 55 100 100 New Zealand 327 77,336 2.1 0.6 42 9 48 31 100 100 Nicaragua 190 33,912 1.3 0.7 83 2 15 4 68 98 Niger 4 248 2.2 62.3 95 0 4 1 39 96 Nigeria 221 1,496 8.0 3.6 69 10 21 9 42 75 Norway 382 81,119 2.2 0.6 11 67 23 90 100 100 Oman 1 514 1.3 94.4 88 1 10 20 77 92 Pakistan 55 338 169.4 308.0 96 2 2 1 87 95 Panama 147 44,094 0.8 0.6 28 5 67 21 83 97 Papua New Guinea 801 124,716 0.1 0.0 1 42 56 59 33 87 Paraguay 94 15,343 0.5 0.5 71 8 20 18 66 99 Peru 1,616 56,685 20.1 1.2 82 10 8 4 61 90 Philippines 479 5,399 28.5 6.0 74 9 17 4 87 93 Poland 54 1,406 16.2 30.2 8 79 13 14 100 100 Portugal 38 3,582 11.3 29.6 78 12 10 11 100 99 Puerto Rico 7 1,801 0.0 .. .. .. .. .. .. .. Qatar 0 45 0.4 870.6 59 2 39 90 100 100 2011 World Development Indicators 143 3.5 Freshwater Internal renewable Annual freshwater Water Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of rural % of urban billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007b 2007b 2007b 2007b 2007b 2007b 2008 2008 Romania 42 1,963 23.2 54.8 57 34 9 2 .. .. Russian Federation 4,313 30,350 76.7 1.8 18 63 19 5 89 98 Rwanda 10 1,005 0.2 1.6 68 8 24 19 62 77 Saudi Arabia 2 99 23.7 986.1 88 3 9 10 .. 97 Senegal 26 2,169 2.2 8.6 93 3 4 3 52 92 Serbia 44 c 5,419c 0.0 c .. .. .. .. .. 98 99 Sierra Leone 160 29,518 0.4 0.2 92 3 5 4 26 86 Singapore 1 131 0.0 .. .. .. .. .. .. 100 Slovak Republic 13 2,334 0.0 .. .. .. .. .. 100 100 Slovenia 19 9,251 0.0 .. .. .. .. .. 99 100 Somalia 6 687 3.3 55.0 99 0 0 .. 9 67 South Africa 45 928 12.5 27.9 63 6 31 14 78 99 Spain 111 2,478 35.6 32.0 68 19 13 21 100 100 Sri Lanka 50 2,499 12.6 25.2 95 2 2 2 88 98 Sudan 30 742 37.3 124.4 97 1 3 1 52 64 Swaziland 3 2,293 1.0 39.5 97 1 2 2 61 92 Sweden 171 18,692 3.0 1.7 9 54 37 103 100 100 Switzerland 40 5,350 2.6 6.4 2 74 24 111 100 100 Syrian Arab Republic 7 349 16.7 238.4 88 4 9 2 84 94 Tajikistan 66 9,855 12.0 18.0 92 5 4 0 61 94 Tanzania 84 2,035 5.2 6.2 89 0 10 3 45 80 Thailand 210 3,135 87.1 41.5 95 2 2 2 98 99 Togo 12 1,825 0.2 1.5 45 2 53 9 41 87 Trinidad and Tobago 4 2,891 0.3 8.1 6 26 68 46 93 98 Tunisia 4 410 2.6 62.9 82 4 14 10 84 99 Turkey 227 3,109 40.1 17.7 74 11 15 9 96 100 Turkmenistan 1 273 24.7 1,812.5 98 1 2 0 72 97 Uganda 39 1,273 0.0 .. 40 16 43 .. 64 91 Ukraine 53 1,142 37.5 70.7 52 35 12 1 97 98 United Arab Emirates 0 34 4.0 2,665.3 83 2 15 28 100 100 United Kingdom 145 2,378 9.5 6.6 3 75 22 185 100 100 United States 2,818 9,344 477.8 17.1 40 46 14 24 94 100 Uruguay 59 17,750 3.2 5.3 96 1 3 8 100 100 Uzbekistan 16 608 58.3 357.0 93 2 5 0 81 98 Venezuela, RB 722 26,287 8.4 1.2 47 7 46 19 75 94 Vietnam 367 4,304 71.4 19.5 68 24 8 1 92 99 West Bank and Gaza 1 212 0.4 .. 45 7 48 .. 91 91 Yemen, Rep. 2 94 3.4 161.9 90 2 8 4 57 72 Zambia 80 6,513 1.7 2.2 76 7 17 3 46 87 Zimbabwe 12 985 4.2 34.3 79 7 14 1 72 99 World 43,464 s 6,616 w 3,850.0 s 9.0 w 70 w 20 w 10 w 10 w 78 w 96 w Low income 4,418 5,452 240.9 5.6 93 2 5 1 56 85 Middle income 29,421 6,271 2,672.1 9.1 78 14 9 3 81 95 Lower middle income 11,728 3,155 2,103.9 17.9 81 12 7 2 81 94 Upper middle income 17,694 18,142 568.2 3.2 65 19 15 7 86 98 Low & middle income 33,839 6,150 2,913.0 8.6 79 13 8 3 76 94 East Asia & Pacific 9,454 4,940 959.0 10.2 74 20 7 3 81 96 Europe & Central Asia 5,059 12,911 351.9 7.0 63 27 10 3 89 98 Latin America & Carib. 13,425 24,001 264.9 2.0 71 10 19 10 80 97 Middle East & N. Africa 225 714 275.6 122.3 86 6 8 2 80 95 South Asia 1,819 1,194 941.1 51.7 90 4 6 1 83 95 Sub-Saharan Africa 3,858 4,826 120.5 3.2 87 3 10 4 47 82 High income 9,624 9,017 937.0 10.5 42 43 15 32 98 100 Euro area 955 2,932 200.2 22.0 38 48 15 34 100 100 a. Excludes river flows from other countries because of data unreliability. b. Data are for the most recent year available (see Primary data documentation). c. Includes Kosovo and Montenegro. 144 2011 World Development Indicators 3.5 ENVIRONMENT Freshwater About the data Definitions The data on freshwater resources are based on to variations in collection and estimation methods. •  Internal renewable freshwater resources are estimates of runoff into rivers and recharge of In addition, inflows and outflows are estimated at the average annual flows of rivers and groundwater groundwater. These estimates are based on differ- different times and at different levels of quality and from rainfall in the country. Natural incoming flows ent sources and refer to different years, so cross- precision, requiring caution in interpreting the data, originating outside a country’s borders are excluded. country comparisons should be made with caution. particularly for water-short countries, notably in the Overlapping water resources between surface run- Because the data are collected intermittently, they Middle East and North Africa. off and groundwater recharge are also deducted. may hide significant variations in total renewable Water productivity is an indication only of the •  Renewable internal freshwater resources per water resources from year to year. The data also effi ciency by which each country uses its water capita are calculated using the World Bank’s popu- fail to distinguish between seasonal and geographic resources. Given the different economic structure lation estimates (see table 2.1). • Annual freshwater variations in water availability within countries. Data of each country, these indicators should be used withdrawals are total water withdrawals, not count- for small countries and countries in arid and semiarid carefully, taking into account the countries’ sectoral ing evaporation losses from storage basins. With- zones are less reliable than those for larger countries activities and natural resource endowments. drawals also include water from desalination plants and countries with greater rainfall. The data on access to an improved water source in countries where they are a significant source. With- Caution should also be used in comparing data measure the percentage of the population with ready drawals can exceed 100 percent of total renewable on annual freshwater withdrawals, which are subject access to water for domestic purposes. The data resources where extraction from nonrenewable aqui- are based on surveys and estimates provided by fers or desalination plants is considerable or where Agriculture is still the largest user of governments to the Joint Monitoring Programme of water reuse is significant. Withdrawals for agriculture water, accounting for some 70 percent of global withdrawals . . . 3.5a the World Health Organization (WHO) and the United and industry are total withdrawals for irrigation and Nations Children’s Fund (UNICEF). The coverage livestock production and for direct industrial use Percent Domestic Agriculture Industry rates are based on information from service users (including for cooling thermoelectric plants). With- 100 on actual household use rather than on information drawals for domestic uses include drinking water, from service providers, which may include nonfunc- municipal use or supply, and use for public services, 80 tioning systems. Access to drinking water from an commercial establishments, and homes. •  Water improved source does not ensure that the water productivity is calculated as GDP in constant prices 60 is safe or adequate, as these characteristics are divided by annual total water withdrawal. • Access not tested at the time of survey. While information to an improved water source is the percentage of the 40 on access to an improved water source is widely population with reasonable access to an adequate used, it is extremely subjective, and such terms as amount of water from an improved source, such as 20 safe, improved, adequate, and reasonable may have piped water into a dwelling, plot, or yard; public tap different meaning in different countries despite offi - or standpipe; tubewell or borehole; protected dug 0 Low Lower Upper High World cial WHO definitions (see Definitions). Even in high- well or spring; and rainwater collection. Unimproved income middle middle income income countries treated water may not always be sources include unprotected dug wells or springs, income income Source: Table 3.5. safe to drink. Access to an improved water source is carts with small tank or drum, bottled water, and equated with connection to a supply system; it does tanker trucks. Reasonable access is defined as the . . . and approaching 90 percent not take into account variations in the quality and availability of at least 20 liters a person a day from in some developing regions 3.5b cost (broadly defined) of the service. a source within 1 kilometer of the dwelling. Percent Domestic Industry Agriculture 100 80 60 Data sources 40 Data on freshwater resources and withdrawals are from the Food and Agriculture Organization 20 of the United Nations AQUASTAT data. The GDP estimates used to calculate water productivity 0 are from the World Bank national accounts data- East Europe Latin Middle South Sub- Asia & America East & Asia Saharan base. Data on access to water are from WHO and & Central & North Africa Pacific Asia Caribbean Africa UNICEF’s Progress on sanitation and drinking water Source: Table 3.5. (2010). 2011 World Development Indicators 145 3.6 Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2007a 1990 2007a 2007a 2007a 2007a 2007a 2007a 2007a 2007a 2007a Afghanistan .. 0.2 .. 0.21 .. 19.7 27.9 14.1 11.7 23.3 .. 3.1 Albania 2.4 3.6 0.25 0.25 .. .. .. 39.8 .. 60.2 .. 11.9 Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 181.4 155.5 0.21 0.23 3.8 8.4 15.8 30.5 3.5 14.3 2.1 21.6 Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria 90.5 84.4 0.15 0.14 5.7 7.1 9.3 12.2 5.8 4.3 6.0 49.5 Azerbaijan 41.3 20.0 0.15 0.18 8.8 3.0 18.5 19.6 8.4 11.7 1.5 28.6 Bangladesh 250.8 303.0 0.15 0.14 0.7 2.3 3.0 7.6 2.6 79.3 0.5 4.2 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 107.8 95.9 0.17 0.17 6.4 7.9 18.6 16.4 3.1 5.5 2.2 40.0 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 11.3 11.5 0.24 0.25 0.9 9.8 13.1 35.4 7.7 18.4 5.3 9.5 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 2.5 3.2 0.30 0.23 .. 2.4 .. 43.8 0.6 3.9 .. 50.0 Brazil .. .. .. .. .. .. .. .. .. .. .. .. Bulgaria 124.3 102.1 0.17 0.17 3.7 4.3 8.0 17.7 4.8 26.8 3.0 31.7 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia 3.8 .. 0.17 .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 300.9 306.6 0.17 0.16 4.3 8.9 10.9 14.0 2.8 7.3 6.5 45.3 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 92.5 .. 0.25 7.6 6.3 13.7 35.1 3.6 9.1 6.9 17.7 China .. 9,428.9 .. 0.13 7.2 3.9 13.0 7.4 6.3 20.6 1.7 39.9 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 87.0 .. 0.20 2.3 8.9 17.3 21.3 5.3 24.1 0.9 19.9 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica .. .. .. .. .. .. .. .. .. .. .. .. Côte d’Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia 48.5 42.9 0.17 0.17 3.1 7.2 9.5 17.6 5.9 14.5 4.9 37.2 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 177.1 146.5 0.14 0.13 5.4 4.8 10.9 10.9 6.4 7.4 4.4 49.8 Denmark 84.5 61.0 0.18 0.16 1.4 11.5 13.1 16.4 4.8 1.5 4.0 47.3 Dominican Republic 88.6 .. 0.18 .. .. .. .. .. .. .. .. .. Ecuador 28.6 44.7 0.24 0.28 1.8 7.8 12.8 46.4 4.4 12.3 2.2 12.3 Egypt, Arab Rep. 206.5 .. 0.19 .. .. .. .. .. .. .. .. .. El Salvador .. .. .. .. .. .. .. .. .. .. .. .. Eritrea 2.4 2.5 0.19 0.20 0.2 4.4 9.5 27.3 9.6 29.0 0.1 20.3 Estonia 21.7 16.0 0.15 0.14 0.4 7.3 7.1 14.6 5.5 8.0 16.4 40.8 Ethiopia 18.5 32.2 0.23 0.24 1.4 6.0 10.9 34.7 8.3 27.9 1.5 9.3 Finland 72.5 55.3 0.19 0.14 1.0 15.4 8.7 9.0 4.4 2.8 7.3 51.4 France 326.5 569.4 0.11 0.16 3.2 7.4 15.0 16.6 3.8 4.8 2.4 46.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 0.8 .. 0.27 .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 806.6 936.2 0.13 0.14 3.8 7.1 12.4 11.4 3.4 2.4 1.9 57.6 Ghana .. 16.0 .. 0.17 3.0 3.8 15.9 18.6 4.1 10.2 33.3 11.2 Greece 50.9 60.8 0.19 0.20 3.9 9.0 10.1 23.9 7.0 14.4 2.8 28.9 Guatemala .. .. .. .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 5.2 .. 0.20 .. .. .. .. .. .. .. .. .. Honduras .. .. .. .. .. .. .. .. .. .. .. .. 146 2011 World Development Indicators 3.6 ENVIRONMENT Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2007a 1990 2007a 2007a 2007a 2007a 2007a 2007a 2007a 2007a 2007a Hungary 122.1 110.6 0.18 0.15 2.7 6.4 10.6 15.2 3.7 9.1 3.3 49.0 India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 721.8 883.0 0.18 0.19 1.4 4.1 12.0 23.1 4.0 29.2 6.3 19.9 Iran, Islamic Rep. 131.6 160.8 0.16 0.15 7.1 2.8 12.8 16.1 13.8 11.2 0.7 35.5 Iraq 7.7 7.7 0.27 0.27 13.1 25.6 29.9 16.9 5.4 9.1 .. .. Ireland 36.1 28.4 0.19 0.16 1.3 10.2 17.6 14.8 5.9 0.8 3.8 45.5 Israel 54.6 52.7 0.16 0.16 1.6 8.9 13.4 16.4 2.9 7.9 1.2 47.6 Italy 378.3 479.2 0.13 0.13 3.5 5.2 10.3 9.3 5.4 13.6 2.9 49.6 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 1,455.0 1,126.9 0.14 0.15 3.3 7.0 11.2 15.0 3.6 5.3 2.0 52.5 Jordan 15.0 29.1 0.18 0.18 2.3 6.1 13.7 20.8 11.5 18.6 2.3 24.5 Kazakhstan 123.5 97.4 0.23 0.24 33.3 2.3 8.9 18.7 9.3 3.9 0.6 23.0 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 366.9 319.6 0.12 0.11 4.2 5.4 12.1 6.3 3.0 9.3 0.9 58.9 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 28.9 12.2 0.14 0.20 9.8 6.3 8.5 24.2 17.5 9.8 1.6 22.4 Lao PDR 4.3 4.3 0.14 0.14 1.8 2.2 3.8 9.2 7.5 49.2 21.4 4.9 Latvia 39.8 28.4 0.12 0.18 2.7 7.7 5.8 21.1 4.4 11.8 19.1 27.3 Lebanon 14.7 14.7 0.19 0.19 0.5 7.5 6.0 25.5 12.9 16.7 4.5 26.3 Lesotho .. 5.3 .. 0.13 0.9 0.5 0.3 2.6 0.8 93.5 .. 1.4 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 54.0 42.2 0.15 0.17 0.9 5.7 8.3 20.5 4.7 17.6 11.4 30.8 Macedonia, FYR 27.0 20.3 0.20 0.18 5.8 4.7 6.3 15.1 3.2 44.7 2.9 17.3 Madagascar .. 92.8 .. 0.14 0.3 1.6 12.4 7.6 2.8 58.9 6.3 10.0 Malawi 37.2 32.7 0.40 0.39 .. 1.4 3.7 82.1 0.6 7.5 1.1 3.6 Malaysia .. 208.3 .. 0.12 2.8 4.9 16.5 9.1 3.8 6.6 7.8 48.5 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 16.8 15.4 0.16 0.17 0.4 3.6 5.9 14.7 .. 63.9 0.7 10.9 Mexico 425.0 .. 0.18 .. .. .. .. .. .. .. .. .. Moldova 29.2 18.8 0.44 0.45 .. 3.8 .. 95.2 .. .. .. 0.9 Mongolia .. 8.8 .. 0.22 3.7 5.1 3.3 27.2 9.5 41.6 5.4 4.1 Morocco .. 74.0 .. 0.16 1.0 2.9 7.9 16.3 6.5 43.5 2.0 19.9 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 26.4 26.8 0.14 0.16 1.6 3.9 7.2 19.2 29.9 29.4 2.0 6.8 Netherlands 142.3 128.2 0.20 0.19 3.1 13.4 14.1 18.2 4.0 2.1 2.6 42.5 New Zealand 46.7 61.6 0.24 0.23 2.0 12.2 8.6 31.1 3.1 5.8 8.0 29.3 Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 51.8 46.9 0.20 0.18 4.9 12.1 7.5 19.1 4.3 2.0 6.0 44.2 Oman 3.8 7.6 0.15 0.16 4.0 4.6 17.8 20.4 20.5 2.4 4.0 26.3 Pakistan .. 153.7 .. 0.17 2.2 1.9 9.1 15.1 4.3 55.6 0.4 11.2 Panama 10.3 13.7 0.30 0.32 0.9 11.6 6.9 55.2 4.0 4.7 1.6 15.0 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 15.3 10.8 0.20 0.28 3.1 9.3 16.7 42.6 5.9 11.0 4.5 6.9 Peru .. .. .. .. .. .. .. .. .. .. .. .. Philippines 169.0 144.6 0.17 0.15 2.6 4.2 9.5 14.4 2.7 21.6 2.1 42.9 Poland 446.7 359.7 0.16 0.16 3.3 5.1 11.3 18.1 5.5 10.3 4.9 41.5 Portugal 140.6 87.7 0.14 0.17 0.2 8.1 3.4 19.8 5.2 16.3 8.5 38.5 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. 6.4 .. 0.12 3.7 6.7 10.5 6.5 18.1 20.7 12.5 21.3 2011 World Development Indicators 147 3.6 Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2007a 1990 2007a 2007a 2007a 2007a 2007a 2007a 2007a 2007a 2007a Romania 411.2 222.1 0.12 0.15 4.5 3.5 7.1 13.9 4.0 25.0 5.3 36.8 Russian Federation 1,521.4 1,381.7 0.16 0.17 8.4 4.9 11.6 17.9 8.3 6.3 4.2 38.4 Rwanda 8.1 8.1 0.37 0.37 .. .. 9.0 77.1 4.3 1.9 2.9 4.8 Saudi Arabia .. 106.6 .. 0.18 3.2 6.9 11.6 20.0 10.7 14.4 3.3 30.0 Senegal 6.1 6.6 0.30 0.29 4.9 6.3 23.8 44.6 3.9 10.5 0.8 5.3 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 33.1 38.3 0.09 0.09 0.5 5.5 11.9 5.3 1.3 2.3 0.5 72.7 Slovak Republic 72.8 47.9 0.13 0.14 7.9 5.4 9.1 10.7 6.0 5.0 4.2 51.7 Slovenia 28.1 28.8 0.13 0.13 4.6 6.1 12.2 7.7 4.1 10.8 4.9 49.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 260.5 229.6 0.17 0.17 9.9 6.6 10.6 15.7 5.2 10.4 4.2 37.4 Spain 348.0 378.8 0.16 0.15 3.1 8.0 10.8 15.3 7.9 8.4 3.8 42.7 Sri Lanka .. 266.1 .. 0.19 2.6 4.3 9.0 22.4 6.3 43.6 2.5 9.3 Sudan .. 38.6 .. 0.29 0.6 1.9 7.0 57.5 14.2 8.0 1.7 9.1 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 116.8 96.9 0.15 0.14 5.3 11.9 9.9 8.6 2.6 1.2 5.6 54.9 Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 59.7 80.4 0.16 0.16 1.6 1.9 7.3 19.9 11.3 32.0 5.2 20.9 Tajikistan 29.1 12.8 0.17 0.24 28.2 2.7 2.0 18.0 8.9 38.4 0.3 1.8 Tanzania .. 30.3 .. 0.34 2.6 4.8 8.6 61.2 1.9 12.7 2.9 5.3 Thailand 369.4 581.4 0.15 0.15 1.9 4.2 12.4 16.4 4.7 20.5 2.8 37.2 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 7.0 7.6 0.23 0.29 4.8 18.2 21.3 39.3 8.0 7.7 8.5 5.0 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 175.8 346.4 0.18 0.15 3.8 3.8 8.6 12.4 6.6 32.2 1.7 30.9 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 3.3 2.1 0.29 0.23 .. 7.8 7.3 34.8 13.3 17.2 2.3 19.6 Ukraine .. 498.2 .. 0.19 13.9 4.3 11.2 19.7 6.8 5.6 2.1 36.5 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 599.9 521.7 0.16 0.17 2.7 12.5 13.5 14.9 3.6 4.3 2.5 46.1 United States 2,307.0 1,850.8 0.14 0.14 3.5 8.1 13.1 12.0 3.9 4.3 4.1 51.1 Uruguay .. .. .. .. .. .. .. .. .. .. .. .. Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB .. .. .. .. .. .. .. .. .. .. .. .. Vietnam 141.0 544.8 0.16 0.14 1.4 3.5 6.8 12.7 6.4 40.2 3.3 25.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 12.6 46.5 0.24 0.21 .. 2.1 7.4 35.9 14.6 15.5 5.1 19.4 Zambia .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe 29.3 .. 0.20 .. .. .. .. .. .. .. .. .. a. Data are derived using the United Nations Industrial Development Organization’s (UNIDO) industry database four-digit International Standard Classification (ISIC). Data in italics are for the most recent year available and are derived using UNIDO’s industry database at the three-digit ISIC. 148 2011 World Development Indicators 3.6 ENVIRONMENT Water pollution About the data Definitions Emissions of organic pollutants from industrial emissions of organic water pollutants. Such data are • Emissions of organic water pollutants are mea- activities are a major cause of degradation of water fairly reliable because sampling techniques for mea- sured as biochemical oxygen demand, or the amount quality. Water quality and pollution levels are gener- suring water pollution are more widely understood of oxygen that bacteria in water will consume in ally measured as concentration or load—the rate of and much less expensive than those for air pollution. breaking down waste, a standard water treatment occurrence of a substance in an aqueous solution. Hettige, Mani, and Wheeler (1998) used plant- and test for the presence of organic pollutants. Emis- Polluting substances include organic matter, metals, sector-level information on emissions and employ- sions per worker are total emissions divided by the minerals, sediment, bacteria, and toxic chemicals. ment from 13 national environmental protection number of industrial workers. • Industry shares of The table focuses on organic water pollution result- agencies and sector-level information on output emissions of organic water pollutants are emissions ing from industrial activities. Because water pollu- and employment from the United Nations Industrial from manufacturing activities as defined by two-digit tion tends to be sensitive to local conditions, the Development Organization (UNIDO). Their economet- divisions of the International Standard Industrial national-level data in the table may not reflect the ric analysis found that the ratio of BOD to employ- Classification revision 3. quality of water in specific locations. ment in each industrial sector is about the same The data in the table come from an international across countries. This finding allowed the authors to study of industrial emissions that may have been estimate BOD loads across countries and over time. the first to include data from developing countries The estimated BOD intensities per unit of employ- (Hettige, Mani, and Wheeler 1998). These data were ment were multiplied by sectoral employment num- updated through 2007 by the World Bank’s Develop- bers from UNIDO’s industry database for 1980–98. ment Research Group. Unlike estimates from earlier These estimates of sectoral emissions were then studies based on engineering or economic models, used to calculate kilograms of emissions of organic these estimates are based on actual measurements water pollutants per day for each country and year. of plant-level water pollution. The focus is on organic The data in the table were derived by updating these water pollution caused by organic waste, measured in estimates through 2007. terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water’s dissolved oxygen content. Wastewater treat- ment, by contrast, reduces BOD. Data on water pollution are more readily available than are other emissions data because most indus- trial pollution control programs start by regulating Emissions of organic water pollutants vary among countries from 1990 to 2007 3.6a Kilograms per day (millions) 1990–98 2000–07 3.0 2.0 1.5 Data sources 1.0 Data on water pollutants are from Hettige, Mani, and Wheeler, “Industrial Pollution in Economic 0.5 Development: Kuznets Revisited” (1998). The data were updated through 2007 by the World 0.0 United Russian Japan Germany Indonesia Thailand France Vietnam United Bank’s Development Research Group using the States Federation Kingdom same methodology as the initial study. Data on Note: Data are for the most recent year available during the period specified. industrial sectoral employment are from UNIDO’s Source: Table 3.6. industry database. 2011 World Development Indicators 149 3.7 Energy production and use Energy Energy Alternative and production use nuclear energy production % of total Total Total million million average Per capita Combustible metric tons of metric tons of annual kilograms of renewables % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel and waste energy use 1990 2008 1990 2008 1990–2008 1990 2008 1990 2008 1990 2008 1990 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. .. Albania 2.4 1.2 2.7 2.1 2.0 809 664 76.5 63.7 13.6 10.3 9.2 15.9 Algeria 100.1 162.0 22.2 37.1 2.8 878 1,078 99.9 99.8 0.1 0.1 0.1 0.1 Angola 28.7 105.8 5.9 11.0 3.6 552 609 25.5 33.5 73.5 63.5 1.1 3.0 Argentina 48.4 82.9 46.1 76.4 2.5 1,418 1,915 88.7 89.8 3.7 3.7 7.5 5.9 Armenia 0.1 0.8 7.7 3.0 –2.8 2,171 974 97.2 73.4 0.1 0.0 1.7 26.6 Australia 157.5 302.1 86.2 130.1 2.3 5,053 6,071 93.9 94.6 4.6 4.2 1.5 1.2 Austria 8.1 11.0 24.8 33.2 1.9 3,214 3,988 79.2 71.6 10.0 16.3 11.0 10.8 Azerbaijan 21.3 58.6 25.8 13.4 –2.7 3,609 1,540 100.0 98.9 0.0 0.0 0.2 1.4 Bangladesh 10.8 23.4 12.7 27.9 4.6 110 175 45.5 68.4 53.9 31.1 0.6 0.5 Belarus 3.3 4.0 45.5 28.1 -1.8 4,470 2,907 95.6 92.1 0.4 5.5 0.0 0.0 Belgium 13.1 14.5 48.3 58.6 1.0 4,844 5,471 76.0 73.8 1.6 4.0 23.1 20.4 Benin 1.8 1.8 1.7 3.0 3.2 346 347 4.8 37.1 94.2 61.0 0.0 0.0 Bolivia 4.9 16.8 2.8 5.7 3.2 416 587 69.1 82.1 27.2 14.4 3.6 3.4 Bosnia and Herzegovina 4.6 4.3 7.0 6.0 2.6 1,627 1,588 93.9 92.8 2.3 3.1 3.8 6.5 Botswana 0.9 1.0 1.3 2.1 2.5 933 1,102 66.1 67.2 33.4 22.3 0.1 0.0 Brazil 104.2 228.1 140.2 248.5 3.1 938 1,295 51.2 52.6 34.1 31.6 13.1 14.3 Bulgaria 9.6 10.2 28.6 19.8 –1.2 3,277 2,595 84.3 76.2 0.6 3.8 13.9 22.3 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 3.6 .. 5.2 3.5 .. 358 .. 29.7 .. 69.6 .. 0.1 Cameroon 11.0 10.1 5.0 7.1 2.2 407 372 18.7 23.9 76.7 71.0 4.6 5.1 Canada 273.8 407.4 208.7 266.8 1.6 7,509 8,008 74.5 74.9 4.0 4.5 21.5 21.6 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. .. Chile 7.4 9.0 13.8 31.4 4.8 1,049 1,871 75.1 77.6 19.3 15.5 5.5 6.6 China 886.3 1,993.3 863.0 2,116.4 4.9 760 1,598 75.5 86.9 23.2 9.6 1.3 3.5 Hong Kong SAR, China 0.0 0.1 8.7 14.1 2.5 1,534 2,026 100.0 94.9 0.6 0.4 0.0 0.0 Colombia 48.2 93.6 24.2 30.8 0.7 730 684 67.4 72.7 22.8 14.7 9.8 13.0 Congo, Dem. Rep. 12.0 22.7 11.8 22.3 3.9 319 346 11.2 4.0 84.7 93.4 4.1 2.9 Congo, Rep. 8.7 13.2 0.8 1.4 3.0 326 378 35.0 43.5 59.5 51.3 5.3 2.3 Costa Rica 1.0 2.7 2.0 4.9 5.0 658 1,084 48.3 45.6 36.6 17.3 14.4 37.3 Côte d’Ivoire 3.4 11.4 4.3 10.3 5.1 343 499 23.3 25.0 73.5 74.0 2.6 1.6 Croatia 5.1 3.9 9.0 9.1 1.4 1,884 2,047 86.5 85.1 3.5 3.6 3.6 5.1 Cuba 6.6 5.1 16.5 12.1 –1.1 1,558 1,076 64.3 89.9 35.6 10.0 0.1 0.1 Czech Republic 40.1 32.8 48.8 44.6 0.2 4,705 4,282 93.2 81.2 0.0 4.9 6.9 16.0 Denmark 10.1 26.6 17.3 19.0 0.2 3,374 3,460 89.6 80.4 6.6 15.6 0.3 3.3 Dominican Republic 1.0 1.7 4.1 8.2 3.8 556 820 74.8 79.2 24.4 18.9 0.7 1.8 Ecuador 16.5 28.5 6.0 10.3 3.9 583 767 79.1 83.9 13.8 6.3 7.2 9.4 Egypt, Arab Rep. 54.9 87.5 31.8 70.7 4.8 551 867 94.0 96.1 3.3 2.1 2.7 1.9 El Salvador 1.7 3.0 2.5 4.9 3.7 463 796 31.4 38.4 48.2 31.2 20.3 30.3 Eritrea 0.7 0.5 0.9 0.7 –2.1 276 138 19.3 19.8 80.7 80.0 0.0 0.0 Estonia 5.1 4.2 9.6 5.4 –1.8 6,101 4,026 100.0 88.3 2.0 11.7 0.0 0.2 Ethiopia 14.1 29.6 14.9 31.7 3.5 308 393 5.5 6.7 93.9 92.4 0.6 0.9 Finland 12.1 16.6 28.4 35.3 1.7 5,692 6,635 55.5 48.0 16.1 21.8 20.9 21.2 France 111.9 136.6 223.9 266.5 1.0 3,946 4,279 58.1 51.0 4.9 5.2 38.7 45.3 Gabon 14.6 13.5 1.2 2.1 2.6 1,275 1,431 32.0 43.8 62.9 52.5 5.2 3.7 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. .. Georgia 1.8 1.1 12.1 3.0 -6.8 2,217 694 88.6 66.6 3.8 12.7 5.4 21.1 Germany 186.2 134.1 351.4 335.3 -0.1 4,424 4,083 86.8 80.1 1.4 7.0 11.8 13.3 Ghana 4.4 6.9 5.3 9.5 3.4 353 405 18.2 27.8 73.7 66.8 9.3 5.6 Greece 9.2 9.9 21.4 30.4 2.4 2,110 2,707 94.6 92.8 4.2 3.4 1.0 2.2 Guatemala 3.4 5.4 4.4 8.1 3.8 498 590 28.1 42.9 68.5 53.3 3.4 4.0 Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1.3 2.0 1.6 2.8 3.6 219 281 19.7 28.3 77.8 71.2 2.5 0.6 Honduras 1.7 2.1 2.4 4.6 3.7 486 632 30.0 54.1 62.9 41.7 8.2 4.3 150 2011 World Development Indicators 3.7 ENVIRONMENT Energy production and use Energy Energy Alternative and production use nuclear energy production % of total Total Total million million average Per capita Combustible metric tons of metric tons of annual kilograms of renewables % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel and waste energy use 1990 2008 1990 2008 1990–2008 1990 2008 1990 2008 1990 2008 1990 2008 Hungary 14.6 10.5 28.7 26.5 0.0 2,762 2,636 81.5 77.8 2.3 5.8 12.8 15.2 India 291.8 468.3 318.9 621.0 3.6 375 545 55.7 71.1 41.9 26.3 2.4 2.4 Indonesia 172.2 347.0 103.9 198.7 3.5 586 874 54.3 65.6 43.3 26.7 2.4 7.7 Iran, Islamic Rep. 179.8 326.9 68.3 202.1 6.1 1,256 2,808 98.2 99.4 1.0 0.5 0.8 0.2 Iraq 104.9 117.7 18.1 34.0 3.8 957 1,107 98.6 99.4 0.1 0.1 1.2 0.1 Ireland 3.5 1.5 10.0 15.0 2.8 2,849 3,385 84.6 90.2 1.1 1.8 0.6 2.0 Israel 0.4 3.3 11.5 22.0 3.5 2,462 3,011 97.2 96.6 0.0 0.0 3.1 4.8 Italy 25.3 26.9 146.6 176.0 1.4 2,584 2,942 93.4 89.9 0.6 3.0 3.9 5.1 Jamaica 0.5 0.5 2.8 4.4 2.7 1,167 1,633 82.6 88.5 17.1 11.1 0.3 0.4 Japan 75.2 88.7 439.3 495.8 0.8 3,556 3,883 84.5 83.0 1.1 1.4 14.4 15.6 Jordan 0.2 0.3 3.3 7.1 4.3 1,028 1,215 98.2 98.0 0.1 0.1 1.8 1.6 Kazakhstan 90.5 148.2 72.7 70.9 –0.8 4,450 4,525 96.9 98.8 0.2 0.2 0.9 0.9 Kenya 9.0 15.1 10.9 18.0 2.8 467 465 17.5 16.2 77.9 76.9 4.5 7.0 Korea, Dem. Rep. 28.9 20.8 33.2 20.3 –2.1 1,649 851 93.1 88.9 2.9 5.1 4.0 6.0 Korea, Rep. 22.6 44.7 93.1 226.9 4.8 2,171 4,669 83.8 81.2 0.8 1.3 15.4 17.5 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 50.4 152.8 7.8 26.3 7.2 3,681 9,637 99.9 100.0 0.1 0.0 0.0 0.0 Kyrgyz Republic 2.5 1.2 7.5 2.9 -3.8 1,693 542 93.5 69.2 0.1 0.1 11.5 32.3 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. .. Latvia 1.1 1.8 7.9 4.5 -2.3 2,941 1,979 81.8 64.3 8.4 24.8 4.9 6.1 Lebanon 0.1 0.2 2.2 5.2 3.5 755 1,250 93.5 95.3 4.6 2.7 1.9 1.0 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. .. Libya 73.2 103.7 11.3 18.2 2.2 2,596 2,895 98.9 99.1 1.1 0.9 0.0 0.0 Lithuania 4.9 3.9 16.1 9.2 –2.1 4,357 2,733 75.8 60.8 1.8 8.8 28.2 29.1 Macedonia, FYR 1.3 1.7 2.5 3.1 0.9 1,298 1,520 98.0 84.2 0.0 5.6 1.7 2.6 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 48.8 93.1 22.0 72.7 6.1 1,215 2,693 88.8 95.1 9.7 4.1 1.6 0.9 Mali .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. .. Mexico 193.4 233.6 121.3 180.6 2.1 1,457 1,698 88.1 88.8 6.1 4.6 5.9 6.7 Moldova 0.1 0.1 9.9 3.2 –5.2 2,261 867 100.0 89.1 0.4 2.5 0.2 0.2 Mongolia 2.7 3.9 3.4 3.2 –0.9 1,541 1,193 97.0 96.2 2.5 3.3 0.0 0.0 Morocco 0.8 0.6 6.9 15.0 4.0 280 474 93.8 93.7 4.6 3.2 1.5 0.7 Mozambique 5.6 11.5 5.9 9.3 2.8 437 416 5.5 7.3 93.9 81.9 0.4 14.0 Myanmar 10.7 23.1 10.7 15.7 2.4 261 316 14.4 31.0 84.7 66.8 1.0 2.2 Namibia 0.2 0.3 0.7 1.8 5.2 446 823 62.0 71.6 16.0 11.2 17.5 7.0 Nepal 5.5 8.7 5.8 9.8 3.1 303 340 5.1 10.9 93.7 86.4 1.3 2.7 Netherlands 60.5 66.5 65.7 79.7 1.0 4,392 4,845 96.0 92.5 1.4 3.9 1.4 1.9 New Zealand 11.4 14.9 12.7 16.9 1.5 3,682 3,967 67.3 66.7 4.3 6.1 28.1 27.0 Nicaragua 1.5 2.2 2.1 3.5 3.1 506 621 28.3 38.5 53.9 52.3 17.5 9.2 Niger .. .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 150.5 226.8 70.6 111.2 2.5 725 735 19.3 18.3 80.2 81.2 0.5 0.4 Norway 119.1 219.7 21.0 29.7 1.6 4,952 6,222 51.9 58.6 4.9 4.6 49.6 40.7 Oman 38.3 63.5 3.9 16.4 6.6 2,105 5,903 100.0 100.0 0.0 0.0 0.0 0.0 Pakistan 34.3 63.3 43.0 82.8 3.7 398 499 52.8 61.8 43.7 34.8 3.6 3.4 Panama 0.6 0.7 1.5 2.9 3.4 618 853 58.4 75.7 28.3 12.3 12.7 11.8 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 4.6 7.4 3.1 4.4 1.5 723 699 21.3 28.2 72.5 53.7 76.0 109.4 Peru 10.6 12.3 9.7 14.7 2.3 447 510 63.3 76.1 27.5 12.8 9.2 11.2 Philippines 15.7 23.3 27.5 41.1 2.2 440 455 45.8 56.9 35.2 18.6 19.0 24.5 Poland 103.9 71.4 103.1 97.9 -0.5 2,705 2,567 97.8 93.8 2.2 6.0 0.1 0.3 Portugal 3.4 4.4 16.7 24.2 2.6 1,691 2,274 80.4 78.3 14.8 13.0 4.8 5.4 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. .. Qatar 26.6 124.8 6.9 24.1 7.1 14,732 18,830 99.9 100.0 0.1 0.0 0.0 0.0 2011 World Development Indicators 151 3.7 Energy production and use Energy Energy Alternative and production use nuclear energy production % of total Total Total million million average Per capita Combustible metric tons of metric tons of annual kilograms of renewables % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel and waste energy use 1990 2008 1990 2008 1990–2008 1990 2008 1990 2008 1990 2008 1990 2008 Romania 40.8 28.8 62.3 39.4 –1.9 2,683 1,830 96.1 79.4 1.0 10.3 1.6 11.2 Russian Federation 1,293.1 1,253.9 879.2 686.8 –1.1 5,929 4,838 93.4 90.9 1.4 0.9 5.2 8.4 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 370.6 579.0 59.0 161.6 4.8 3,631 6,514 100.0 100.0 0.0 0.0 0.0 0.0 Senegal 1.0 1.2 1.7 2.9 3.5 224 234 43.2 57.3 56.8 41.7 0.0 0.7 Serbia 13.4 a 9.9 19.3a 16.0 0.2 2,550a 2,181 90.6a 89.5 6.0a 5.0 4.2a 5.4 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. .. Singapore 0.0 0.0 11.5 18.5 1.8 3,760 3,828 100.0 100.0 0.0 0.0 0.0 0.0 Slovak Republic 5.3 6.4 21.3 18.3 –0.1 4,037 3,385 81.6 70.0 0.8 3.7 15.5 26.0 Slovenia 3.1 3.7 5.7 7.7 2.0 2,858 3,827 71.3 69.4 4.7 6.7 25.6 25.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. .. South Africa 114.5 163.0 90.9 134.5 2.2 2,581 2,756 86.1 87.2 11.5 10.4 2.5 2.6 Spain 34.6 30.4 90.1 138.8 3.0 2,320 3,047 77.4 81.7 4.5 4.2 18.1 14.6 Sri Lanka 4.2 5.1 5.5 8.9 3.3 322 443 24.1 43.4 71.0 52.6 4.9 4.0 Sudan 8.8 34.9 10.6 15.4 2.6 392 372 17.5 31.2 81.8 68.0 0.8 0.8 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. .. Sweden 29.7 33.2 47.2 49.6 0.4 5,514 5,379 37.3 33.1 11.7 20.0 50.9 45.9 Switzerland 10.0 12.7 24.0 26.7 0.6 3,581 3,491 59.3 52.7 4.8 8.1 36.7 39.6 Syrian Arab Republic 22.3 23.5 11.4 19.7 2.8 895 957 97.9 98.7 0.0 0.0 2.1 1.3 Tajikistan 2.0 1.5 5.3 2.5 –3.1 1,001 365 71.3 42.3 0.0 0.0 26.7 54.7 Tanzania 9.1 17.5 9.7 19.0 4.1 382 446 6.9 10.6 91.7 88.2 1.4 1.2 Thailand 26.5 63.9 42.0 107.2 5.1 742 1,591 63.9 80.6 34.9 18.7 1.0 0.6 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. .. Togo 1.1 2.1 1.3 2.6 4.3 322 397 15.0 14.3 82.8 83.1 0.6 0.3 Trinidad and Tobago 12.6 40.0 6.0 19.4 7.6 4,899 14,557 99.2 99.9 0.8 0.1 0.0 0.0 Tunisia 5.7 7.5 4.9 9.2 3.7 607 889 87.0 86.3 12.9 13.6 0.1 0.1 Turkey 25.8 29.0 52.8 98.5 3.6 941 1,333 81.8 90.6 13.7 4.9 4.6 4.6 Turkmenistan 74.9 68.6 19.6 18.8 1.5 5,352 3,730 100.0 100.0 0.0 0.0 0.3 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 135.8 81.3 251.8 136.1 –3.0 4,852 2,943 91.8 81.8 0.1 0.7 8.2 17.9 United Arab Emirates 110.2 180.5 19.9 58.4 5.4 10,645 13,030 100.0 100.0 0.0 0.0 0.0 0.0 United Kingdom 208.0 166.7 205.9 208.5 0.1 3,597 3,395 90.7 90.2 0.3 2.2 8.5 7.1 United States 1,652.5 1,706.1 1,915.0 2,283.7 1.1 7,672 7,503 86.4 85.0 3.3 3.7 10.3 11.2 Uruguay 1.1 1.4 2.3 4.2 1.8 725 1,254 58.7 64.9 24.3 23.9 26.8 9.3 Uzbekistan 38.6 62.0 46.4 50.5 0.6 2,261 1,849 99.2 98.1 0.0 0.0 1.2 1.9 Venezuela, RB 148.9 180.7 43.6 64.1 1.6 2,206 2,295 91.5 87.6 1.2 0.8 7.3 11.7 Vietnam 24.7 71.4 24.3 59.4 5.2 367 689 20.4 54.0 77.7 41.8 1.9 3.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9.4 15.3 2.5 7.5 6.2 204 326 97.0 99.0 3.1 1.0 0.0 0.0 Zambia 4.9 6.8 5.4 7.4 1.7 683 583 15.6 7.5 74.3 81.0 12.7 11.3 Zimbabwe 8.6 8.5 9.3 9.5 –0.1 889 763 44.8 26.1 50.9 65.3 4.0 3.9 World 8,840.1 t 12,357.7 t 8,569.9 t 11,899.4 t 1.9 w 1,669 w 1,835 w 81.0 w 81.1 w 10.1 w 9.8 w 8.7 w 9.1 w Low income 172.8 264.0 200.3 279.9 2.1 380 357 39.8 29.2 56.0 66.2 4.4 4.4 Middle income 4,796.0 7,284.5 3,864.0 6,002.2 2.5 1,029 1,261 78.9 81.5 16.9 13.3 4.1 5.2 Lower middle income 2,168.7 4,001.4 1,993.4 3,842.7 3.6 679 1,019 70.1 79.0 27.1 16.9 2.9 4.2 Upper middle income 2,627.2 3,284.5 1,871.1 2,161.8 1.0 2,283 2,177 88.2 86.0 6.1 6.8 5.4 7.0 Low & middle income 4,966.5 7,544.8 4,049.8 6,266.2 2.5 966 1,157 77.4 79.7 18.4 15.2 4.1 5.2 East Asia & Pacific 1,226.1 2,658.9 1,139.4 2,655.4 4.6 716 1,380 71.5 83.7 26.6 12.4 1.9 4.0 Europe & Central Asia 1,769.6 1,772.9 1,577.0 1,215.0 –1.1 4,038 3,030 93.0 89.7 1.5 1.7 5.3 8.7 Latin America & Carib. 609.0 922.0 454.0 729.2 2.5 1,044 1,290 71.2 72.4 19.7 16.8 9.2 10.8 Middle East & N. Africa 558.6 856.3 185.5 431.3 4.7 814 1,329 97.2 98.3 1.7 1.1 1.1 0.6 South Asia 349.5 573.6 389.2 756.8 3.6 348 495 53.8 68.9 43.6 28.5 2.5 2.5 Sub-Saharan Africa 475.6 810.3 310.5 497.4 2.6 676 678 41.2 39.8 56.6 57.7 2.3 2.5 High income 3,892.9 4,843.0 4,544.3 5,672.5 1.4 4,649 5,131 84.2 82.6 2.8 3.9 12.8 13.3 Euro area 476.5 463.1 1,059.7 1,226.5 1.0 3,527 3,763 79.8 75.0 3.2 5.9 16.7 18.6 a. Includes Kosovo and Montenegro. 152 2011 World Development Indicators 3.7 ENVIRONMENT Energy production and use About the data Definitions In developing economies growth in energy use is Data sources). All forms of energy—primary energy •  Energy production refers to forms of primary closely related to growth in the modern sectors— and primary electricity—are converted into oil equiva- energy—petroleum (crude oil, natural gas liquids, industry, motorized transport, and urban areas— lents. A notional thermal efficiency of 33 percent is and oil from nonconventional sources), natural gas, but energy use also reflects climatic, geographic, assumed for converting nuclear electricity into oil solid fuels (coal, lignite, and other derived fuels), and economic factors (such as the relative price equivalents and 100 percent efficiency for converting and combustible renewables and waste—and pri- of energy). Energy use has been growing rapidly in hydroelectric power. mary electricity, all converted into oil equivalents low- and middle-income economies, but high-income The IEA makes these estimates in consultation (see About the data). • Energy use refers to the use economies still use almost five times as much energy with national statistical offices, oil companies, elec- of primary energy before transformation to other on a per capita basis. tric utilities, and national energy experts. The IEA end-use fuels, which is equal to indigenous produc- Energy data are compiled by the International occasionally revises its time series to reflect politi- tion plus imports and stock changes, minus exports Energy Agency (IEA). IEA data for economies that cal changes, and energy statistics undergo contin- and fuels supplied to ships and aircraft engaged in are not members of the Organisation for Economic ual changes in coverage or methodology as more international transport (see About the data). • Fos- Co-operation and Development (OECD) are based detailed energy accounts become available. Breaks sil fuel comprises coal, oil, petroleum, and natural on national energy data adjusted to conform to in series are therefore unavoidable. gas products. • Combustible renewables and waste annual questionnaires completed by OECD member comprise solid biomass, liquid biomass, biogas, governments. industrial waste, and municipal waste. • Alternative Total energy use refers to the use of primary energy and nuclear energy production is noncarbohydrate before transformation to other end-use fuels (such energy that does not produce carbon dioxide when as electricity and refined petroleum products). It generated. It includes hydropower and nuclear, geo- includes energy from combustible renewables and thermal, and solar power, among others. waste—solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences (see A person in a high-income economy uses Fossil fuels are still the more than 14 times as much energy primary global energy on average as a person in a low- source in 2008 3.7b income economy in 2008 3.7a Energy use per capita Fossil Combustible Alternative and fuel renewables nuclear energy (thousands of kilograms Percent and waste of oil equivalent) 1990 2008 100 6 5 80 4 60 3 40 Data sources 2 Data on energy production and use are from IEA 20 1 electronic files and are published in IEA’s annual publications, Energy Statistics and Balances of 0 0 High Upper Middle Lower Low World High Upper Lower Low World Non-OECD Countries, Energy Statistics of OECD income middle income middle income income middle middle income income income income income Countries, and Energy Balances of OECD Countries. Source: Table 3.7. Source: Table 3.7. 2011 World Development Indicators 153 3.8 Energy dependency and efficiency and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2008 1990 2008 1990 2007 1990 2007 1990 2007 1990 2007 Afghanistan .. .. .. .. 2.7 0.7 .. .. 0.1 0.0 .. 0.0 Albania 8 45 4.8 11.0 7.5 4.2 2.8 2.0 2.3 1.4 0.6 0.2 Algeria –351 –337 7.1 6.8 78.8 140.0 3.6 3.8 3.1 4.1 0.5 0.6 Angola –387 –865 5.8 8.8 4.4 24.7 0.8 2.3 0.4 1.4 0.1 0.3 Argentina –5 –9 5.3 6.9 112.5 183.6 2.4 2.5 3.5 4.6 0.5 0.4 Armenia 98 73 1.4 5.8 3.7 5.1 0.5 1.8 1.2 1.6 0.4 0.3 Australia –83 –132 4.7 5.7 292.9 373.7 3.4 3.0 17.2 17.7 0.7 0.5 Austria 67 67 8.0 9.1 60.9 68.7 2.5 2.1 7.9 8.3 0.3 0.2 Azerbaijan 17 –338 1.3 5.3 44.1 31.7 1.9 2.7 7.0 3.7 1.5 0.5 Bangladesh 16 16 6.2 7.1 15.5 43.7 1.2 1.7 0.1 0.3 0.2 0.2 Belarus 93 86 1.5 4.0 98.5 66.7 2.6 2.4 10.9 6.9 1.7 0.7 Belgium 73 75 5.2 6.1 107.5 103.0 2.2 1.8 10.8 9.7 0.4 0.3 Benin –7 39 3.2 3.9 0.7 3.9 0.4 1.3 0.1 0.5 0.1 0.3 Bolivia –77 –195 7.0 6.7 5.5 13.2 2.0 2.4 0.8 1.4 0.3 0.4 Bosnia and Herzegovina 34 28 .. 4.7 4.7 29.0 1.0 5.2 1.6 7.7 .. 1.1 Botswana 28 53 7.6 11.6 2.2 5.0 1.7 2.5 1.6 2.6 0.2 0.2 Brazil 26 8 7.7 7.4 208.7 368.0 1.5 1.6 1.4 1.9 0.2 0.2 Bulgaria 66 48 2.3 4.6 76.6 51.7 2.7 2.6 8.8 6.8 1.2 0.6 Burkina Faso .. .. .. .. 0.6 1.7 .. .. 0.1 0.1 0.1 0.1 Burundi .. .. .. .. 0.3 0.2 .. .. 0.1 0.0 0.1 0.1 Cambodia .. 30 .. 5.0 0.5 4.4 .. 0.9 0.0 0.3 .. 0.2 Cameroon –120 –42 5.1 5.4 1.7 6.2 0.3 0.8 0.1 0.3 0.1 0.2 Canada –31 -53 3.6 4.5 449.7 556.9 2.2 2.1 16.2 16.9 0.6 0.5 Central African Republic .. .. .. .. 0.2 0.3 .. .. 0.1 0.1 0.1 0.1 Chad .. .. .. .. 0.1 0.4 .. .. 0.0 0.0 0.0 0.0 Chile 46 71 6.3 7.2 34.9 71.6 2.5 2.3 2.6 4.3 0.4 0.3 China –3 6 1.4 3.6 2,458.7 6,533.0 2.8 3.3 2.2 5.0 2.0 0.9 Hong Kong SAR, China 100 100 15.5 20.0 27.6 39.9 3.1 2.9 4.8 5.8 0.2 0.1 Colombia –99 –204 8.4 12.0 57.3 63.4 2.4 2.2 1.7 1.4 0.3 0.2 Congo, Dem. Rep. –2 –2 1.9 0.8 4.1 2.4 0.3 0.1 0.1 0.0 0.2 0.1 Congo, Rep. –997 -868 10.7 9.6 1.2 1.6 1.5 1.3 0.5 0.4 0.1 0.1 Costa Rica 49 45 9.5 9.6 3.0 8.1 1.5 1.7 1.0 1.8 0.2 0.2 Côte d’Ivoire 22 –11 5.5 3.1 5.8 6.4 1.3 0.6 0.5 0.3 0.2 0.2 Croatia 43 57 7.1 8.5 25.0 24.8 2.8 2.7 5.2 5.6 0.4 0.3 Cuba 60 58 .. .. 33.3 27.0 2.0 2.7 3.1 2.4 .. .. Czech Republic 18 26 3.5 5.4 162.6 124.9 3.3 2.7 15.7 12.1 1.0 0.5 Denmark 42 –40 7.5 9.9 50.4 50.0 2.9 2.5 9.8 9.1 0.4 0.3 Dominican Republic 75 79 6.7 9.2 9.6 20.7 2.3 2.6 1.3 2.1 0.3 0.3 Ecuador –175 –176 9.4 9.9 16.8 30.0 2.8 2.5 1.6 2.2 0.3 0.3 Egypt, Arab Rep. –72 –24 5.8 5.8 75.9 184.5 2.4 2.7 1.3 2.3 0.4 0.5 El Salvador 31 38 8.0 7.9 2.6 6.7 1.1 1.4 0.5 1.1 0.1 0.2 Eritrea 19 20 1.9 3.8 .. 0.6 .. 0.8 .. 0.1 .. 0.2 Estonia 47 22 1.7 4.7 28.2 20.5 2.9 3.6 18.0 15.2 1.8 0.8 Ethiopia 5 7 1.8 2.0 3.0 6.5 0.2 0.3 0.1 0.1 0.1 0.1 Finland 57 53 4.1 5.1 50.9 64.1 1.8 1.8 10.2 12.1 0.4 0.4 France 50 49 6.3 7.4 398.7 371.5 1.8 1.4 7.0 6.0 0.3 0.2 Gabon –1,139 –552 11.8 9.4 6.1 2.0 5.2 1.1 6.6 1.4 0.4 0.1 Gambia, The .. .. .. .. 0.2 0.4 .. .. 0.2 0.2 0.2 0.2 Georgia 85 64 2.4 6.6 15.3 6.0 1.4 1.8 3.2 1.4 0.6 0.3 Germany 47 60 5.8 8.3 960.2 787.3 2.8 2.4 12.0 9.6 0.4 0.3 Ghana 17 27 2.5 3.4 3.9 9.8 0.7 1.0 0.3 0.4 0.3 0.3 Greece 57 68 8.3 10.0 72.7 98.0 3.4 3.0 7.2 8.8 0.4 0.3 Guatemala 24 33 6.7 7.4 5.1 12.9 1.1 1.6 0.6 1.0 0.2 0.2 Guinea .. .. .. .. 1.1 1.4 .. .. 0.2 0.1 0.2 0.2 Guinea-Bissau .. .. .. .. 0.3 0.3 .. .. 0.2 0.2 0.2 0.2 Haiti 20 28 6.4 3.7 1.0 2.4 0.6 0.9 0.1 0.2 0.1 0.2 Honduras 29 55 5.5 5.7 2.6 8.8 1.1 1.9 0.5 1.2 0.2 0.3 154 2011 World Development Indicators 3.8 ENVIRONMENT Energy dependency and efficiency and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2008 1990 2008 1990 2007 1990 2007 1990 2007 1990 2007 Hungary 49 60 4.4 6.8 63.4 56.4 2.2 2.1 6.1 5.6 0.5 0.3 India 8 25 3.3 5.1 690.0 1,611.0 2.2 2.7 0.8 1.4 0.7 0.5 Indonesia –66 –75 3.6 4.2 149.4 396.8 1.5 2.1 0.8 1.8 0.4 0.5 Iran, Islamic Rep. –163 –62 5.0 3.7 227.0 495.6 3.3 2.7 4.2 7.0 0.7 0.7 Iraq –480 –246 .. 2.9 52.5 100.0 2.9 3.0 2.8 3.3 .. 1.1 Ireland 65 90 6.2 11.6 30.3 44.3 3.0 2.9 8.6 10.2 0.5 0.2 Israel 96 85 7.3 8.5 33.5 66.7 2.9 3.0 7.2 9.3 0.4 0.4 Italy 83 85 9.2 9.6 424.7 456.1 2.9 2.6 7.5 7.7 0.3 0.3 Jamaica 83 88 5.1 4.4 8.0 14.0 2.9 2.8 3.3 5.2 0.6 0.7 Japan 83 82 7.3 8.1 1,152.3 1,253.5 2.6 2.4 9.3 9.8 0.4 0.3 Jordan 95 96 3.2 4.2 10.4 21.4 3.2 3.0 3.3 3.8 1.0 0.8 Kazakhstan –24 –109 1.6 2.3 261.1 227.2 4.0 3.4 18.0 14.7 2.5 1.4 Kenya 18 16 3.0 3.1 5.8 11.2 0.5 0.6 0.2 0.3 0.2 0.2 Korea, Dem. Rep. 13 -3 .. .. 244.6 70.7 7.4 3.8 12.1 3.0 .. .. Korea, Rep. 76 80 5.2 5.5 241.5 502.9 2.6 2.3 5.6 10.4 0.5 0.4 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait –544 –481 2.8 4.8 40.7 86.1 5.2 3.4 19.2 32.3 0.6 0.7 Kyrgyz Republic 67 58 1.5 3.8 11.0 6.1 1.6 2.1 2.8 1.2 1.1 0.6 Lao PDR .. .. .. .. 0.2 1.5 .. .. 0.1 0.3 0.1 0.1 Latvia 86 60 3.4 7.9 13.3 7.8 1.9 1.7 5.6 3.4 0.6 0.2 Lebanon 94 96 7.5 8.8 9.1 13.3 4.0 3.3 3.1 3.2 0.5 0.3 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. 0.5 0.7 .. .. 0.2 0.2 0.5 0.5 Libya –546 –469 .. 5.2 40.3 57.3 3.6 3.2 9.2 9.3 .. 0.6 Lithuania 69 58 2.9 6.4 22.1 15.3 1.5 1.7 6.8 4.5 0.5 0.3 Macedonia, FYR 49 45 6.4 5.8 10.8 11.3 6.4 3.7 8.3 5.5 1.0 0.7 Madagascar .. .. .. .. 1.0 2.2 .. .. 0.1 0.1 0.1 0.1 Malawi .. .. .. .. 0.6 1.1 .. .. 0.1 0.1 0.1 0.1 Malaysia –122 –28 5.5 4.9 56.5 194.3 2.5 2.7 3.1 7.3 0.5 0.6 Mali .. .. .. .. 0.4 0.6 .. .. 0.0 0.0 0.1 0.0 Mauritania .. .. .. .. 2.7 1.9 .. .. 1.3 0.6 0.9 0.3 Mauritius .. .. .. .. 1.5 3.9 .. .. 1.4 3.1 0.2 0.3 Mexico –60 -29 6.9 7.9 357.2 471.1 2.9 2.6 4.3 4.5 0.4 0.3 Moldova 99 97 1.7 3.1 21.0 4.7 2.4 1.4 5.4 1.3 1.4 0.5 Mongolia 20 –23 1.4 2.8 10.0 10.6 2.9 3.4 4.5 4.0 2.0 1.3 Morocco 89 96 9.7 8.4 23.5 46.4 3.4 3.2 0.9 1.5 0.4 0.4 Mozambique 5 –23 0.9 1.9 1.0 2.6 0.2 0.3 0.1 0.1 0.2 0.2 Myanmar 0 –47 .. .. 4.3 13.2 0.4 0.8 0.1 0.3 .. .. Namibia 67 82 9.4 7.3 0.0 3.0 0.0 1.9 0.0 1.5 0.0 0.2 Nepal 5 11 2.3 3.0 0.6 3.4 0.1 0.4 0.0 0.1 0.0 0.1 Netherlands 8 16 6.0 7.9 164.0 173.1 2.5 2.2 11.0 10.6 0.4 0.3 New Zealand 10 12 5.1 6.3 23.9 32.6 1.8 1.9 6.9 7.7 0.4 0.3 Nicaragua 29 39 3.7 4.1 2.6 4.6 1.3 1.3 0.6 0.8 0.3 0.3 Niger .. .. .. .. 1.0 0.9 .. .. 0.1 0.1 0.2 0.1 Nigeria –113 –104 2.0 2.6 45.3 95.2 0.6 0.9 0.5 0.6 0.3 0.3 Norway –467 –640 6.5 7.9 31.3 42.7 1.5 1.6 7.4 9.1 0.2 0.2 Oman –888 –286 7.1 4.0 10.3 37.3 2.4 2.4 5.6 13.7 0.4 0.6 Pakistan 20 24 4.2 4.7 68.5 156.3 1.6 1.9 0.6 1.0 0.4 0.4 Panama 59 76 9.8 13.8 3.1 7.2 2.1 2.6 1.3 2.2 0.2 0.2 Papua New Guinea .. .. .. .. 2.1 3.4 .. .. 0.5 0.5 0.3 0.3 Paraguay –49 –69 5.5 6.2 2.3 4.1 0.7 1.0 0.5 0.7 0.1 0.2 Peru –9 16 10.0 15.4 21.1 43.0 2.2 3.1 1.0 1.5 0.2 0.2 Philippines 43 43 5.4 7.1 44.5 70.9 1.6 1.8 0.7 0.8 0.3 0.3 Poland –1 27 3.0 6.4 347.6 317.1 3.4 3.3 9.1 8.3 1.1 0.5 Portugal 80 82 9.6 9.7 44.3 58.1 2.6 2.3 4.5 5.5 0.3 0.2 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar –286 –418 .. 4.5 11.8 63.0 1.7 2.8 25.2 55.4 .. 0.7 2011 World Development Indicators 155 3.8 Energy dependency and efficiency and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2008 1990 2008 1990 2007 1990 2007 1990 2007 1990 2007 Romania 34 27 2.9 6.4 158.7 94.1 2.5 2.4 6.8 4.4 0.9 0.4 Russian Federation –47 –83 2.1 3.1 2,073.5 1,536.1 2.7 2.3 15.8 10.8 1.2 0.8 Rwanda .. .. .. .. 0.7 0.7 .. .. 0.1 0.1 0.1 0.1 Saudi Arabia –528 –258 5.3 3.3 214.9 402.1 3.6 2.7 13.2 16.6 0.7 0.8 Senegal 43 57 6.3 7.1 3.2 5.5 1.9 2.0 0.4 0.5 0.3 0.3 Serbia 31 38 4.6 4.7 45.3b 53.5b 1.5b .. 6.4b 6.3b .. .. Sierra Leone .. .. .. .. 0.4 1.3 .. .. 0.1 0.2 0.1 0.3 Singapore 100 100 6.2 12.5 46.9 54.1 4.1 2.0 15.4 11.8 0.7 0.2 Slovak Republic 75 65 3.1 6.1 44.3 37.0 2.6 2.1 10.4 6.8 0.8 0.4 Slovenia 46 53 5.7 7.1 12.3 15.1 3.2 2.1 9.1 7.5 0.6 0.3 Somalia .. .. .. .. 0.0 0.6 .. .. 0.0 0.1 .. .. South Africa –26 –21 3.1 3.5 333.2 433.2 3.7 3.2 9.5 9.0 1.2 1.0 Spain 62 78 8.5 9.3 227.4 359.0 2.5 2.5 5.9 8.0 0.3 0.3 Sri Lanka 24 43 6.3 9.5 3.8 12.3 0.7 1.3 0.2 0.6 0.1 0.2 Sudan 17 –127 2.5 5.3 5.6 11.5 0.5 0.8 0.2 0.3 0.2 0.2 Swaziland .. .. .. .. 0.4 1.1 .. .. 0.5 0.9 0.1 0.2 Sweden 37 33 4.5 6.4 51.7 49.2 1.1 1.0 6.0 5.4 0.2 0.2 Switzerland 59 52 9.3 10.9 42.9 38.0 1.8 1.5 6.4 5.0 0.2 0.1 Syrian Arab Republic –96 –19 3.3 4.4 37.4 69.8 3.3 3.6 2.9 3.5 1.0 0.8 Tajikistan 62 40 3.1 4.8 21.3 7.2 4.3 1.9 4.5 1.1 1.5 0.6 Tanzania 7 8 2.2 2.6 2.4 6.0 0.2 0.3 0.1 0.1 0.1 0.1 Thailand 37 40 5.3 4.7 95.8 277.3 2.3 2.7 1.7 4.1 0.4 0.6 Timor-Leste .. .. .. .. .. 0.2 .. .. .. 0.2 .. 0.3 Togo 17 17 2.7 1.9 0.8 1.3 0.6 0.5 0.2 0.2 0.2 0.3 Trinidad and Tobago –111 –106 2.2 1.7 16.9 37.0 2.8 2.4 13.9 27.9 1.3 1.2 Tunisia –16 18 6.6 8.3 13.3 23.8 2.7 2.7 1.6 2.3 0.4 0.3 Turkey 51 71 8.3 8.9 150.7 288.4 2.9 2.9 2.7 4.0 0.3 0.3 Turkmenistan –281 –265 0.7 1.7 28.0 45.8 1.6 2.5 8.6 9.2 2.3 1.6 Uganda .. .. .. .. 0.8 3.2 .. .. 0.0 0.1 0.1 0.1 Ukraine 46 40 1.7 2.3 611.0 317.3 2.7 2.3 13.3 6.8 1.6 1.0 United Arab Emirates –454 –209 4.8 4.2 54.8 135.4 2.8 2.6 29.3 31.0 0.6 0.6 United Kingdom –1 20 6.6 10.0 569.8 539.2 2.8 2.6 10.0 8.8 0.4 0.3 United States 14 25 4.2 5.8 4,861.0 5,832.2 2.5 2.5 19.5 19.3 0.6 0.4 Uruguay 49 67 10.1 9.3 4.0 6.2 1.8 2.0 1.3 1.9 0.2 0.2 Uzbekistan 17 –23 0.9 1.3 113.9 116.0 2.8 2.4 6.3 4.3 3.1 1.9 Venezuela, RB –242 –182 4.3 5.1 122.1 165.4 2.8 2.6 6.2 6.0 0.6 0.5 Vietnam -2 –20 2.5 3.7 21.4 111.3 0.9 2.0 0.3 1.3 0.4 0.5 West Bank and Gaza .. .. .. .. .. 2.3 .. .. .. 0.6 .. .. Yemen, Rep. –273 –104 8.7 6.8 10.1 22.0 3.3 3.0 0.8 1.0 0.5 0.4 Zambia 9 8 1.8 2.1 2.4 2.7 0.5 0.4 0.3 0.2 0.2 0.2 Zimbabwe 8 10 .. .. 15.5 9.6 1.7 1.0 1.5 0.8 .. .. World –3c w –4c w 4.2 w 5.5 w 22,529.9d t 30,649.4d t 2.6d w 2.5d w 4.3d w 4.6d w 0.6d w 0.5d w Low income 14 6 2.6 3.2 357.6 228.2 2.2 1.0 0.7 0.3 0.8 0.3 Middle income –24 –21 3.0 4.4 9,758.0 15,574.9 2.6 2.7 2.6 3.3 0.8 0.6 Lower middle income –9 –4 2.4 4.0 4,772.5 10,391.5 2.4 2.9 1.6 2.8 1.0 0.7 Upper middle income –40 –52 3.7 5.2 4,984.4 5,175.3 2.7 2.5 6.1 5.3 0.7 0.5 Low & middle income –23 –20 3.0 4.4 10,115.2 15,802.5 2.5 2.7 2.4 2.9 0.8 0.6 East Asia & Pacific –8 0 2.0 3.8 3,091.2 7,693.8 2.7 3.1 1.9 4.0 1.4 0.8 Europe & Central Asia –12 –46 2.2 3.6 4,214.9 2,897.1 2.7 2.4 10.7 7.2 1.2 0.7 Latin America & Carib. –34 –26 6.9 7.7 1,017.3 1,538.1 2.2 2.2 2.3 2.7 0.3 0.3 Middle East & N. Africa –201 –99 5.7 4.7 578.7 1,177.0 3.1 2.9 2.5 3.7 0.6 0.6 South Asia 10 24 3.5 5.2 781.4 1,828.9 2.0 2.5 0.7 1.2 0.6 0.5 Sub-Saharan Africa –54 –63 2.8 3.2 465.1 679.5 1.7 1.6 0.9 0.8 0.6 0.4 High income 15 15 5.3 6.6 11,669.7 13,761.0 2.6 2.4 11.9 12.5 0.5 0.4 Euro area 55 62 6.6 8.2 2,595.7 2,656.8 2.4 2.2 8.6 8.2 0.4 0.3 a. Negative values indicate that a country is a net exporter. b. Includes Kosovo and Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includes emissions not allocated to specific countries. 156 2011 World Development Indicators 3.8 ENVIRONMENT Energy dependency and efficiency and carbon dioxide emissions About the data Definitions Because commercial energy is widely traded, its pro- elemental carbon, were converted to actual carbon • Net energy imports are estimated as energy use duction and use need to be distinguished. Net energy dioxide mass by multiplying them by 3.664 (the ratio less production, both measured in oil equivalents. imports show the extent to which an economy’s use of the mass of carbon to that of carbon dioxide). • GDP per unit of energy use is the ratio of gross exceeds its production. High-income economies are Although estimates of global carbon dioxide emis- domestic product (GDP) per kilogram of oil equiva- net energy importers; middle-income economies are sions are probably accurate within 10 percent (as cal- lent of energy use, with GDP converted to 2005 their main suppliers. culated from global average fuel chemistry and use), international dollars using purchasing power parity The ratio of gross domestic product (GDP) to energy country estimates may have larger error bounds. (PPP) rates. An international dollar has the same use indicates energy efficiency. To produce compa- Trends estimated from a consistent time series tend purchasing power over GDP that a U.S. dollar has rable and consistent estimates of real GDP across to be more accurate than individual values. Each year in the United States. Energy use refers to the use economies relative to physical inputs to GDP—that the CDIAC recalculates the entire time series since of primary energy before transformation to other is, units of energy use—GDP is converted to 2005 1949, incorporating recent findings and corrections. end-use fuel, which is equal to indigenous produc- international dollars using purchasing power parity Estimates exclude fuels supplied to ships and aircraft tion plus imports and stock changes minus exports (PPP) rates. Differences in this ratio over time and in international transport because of the difficulty of and fuel supplied to ships and aircraft engaged in across economies reflect structural changes in an apportioning the fuels among benefiting countries. international transport (see About the data for table economy, changes in sectoral energy efficiency, and The ratio of carbon dioxide per unit of energy shows 3.7). • Carbon dioxide emissions are emissions from differences in fuel mixes. carbon intensity, which is the amount of carbon diox- the burning of fossil fuels and the manufacture of Carbon dioxide emissions, largely by-products of ide emitted as a result of using one unit of energy in cement and include carbon dioxide produced during energy production and use (see table 3.7), account the process of production. The proportion of carbon consumption of solid, liquid, and gas fuels and gas for the largest share of greenhouse gases, which are dioxide per unit of GDP indicates how clean produc- flaring. associated with global warming. Anthropogenic car- tion processes are. bon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combus- tion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. The U.S. Department of Energy’s Carbon Diox- ide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fos- sil fuel consumption (from the United Nations Sta- tistics Division’s World Energy Data Set) and world cement manufacturing (from the U.S. Bureau of Mines’s Cement Manufacturing Data Set). Carbon dioxide emissions, often calculated and reported as High-income economies depend on imported energy 3.8a Net energy imports (% of energy use) 1990 2008 Low income Lower middle income Upper middle income Data sources High income Data on energy use are from the electronic files Euro area of the International Energy Agency. Data on car- bon dioxide emissions are from the CDIAC, Envi- –60 –40 –20 0 20 40 60 80 ronmental Sciences Division, Oak Ridge National Note: Negative values indicate that the income group is a net energy exporter. Source: Table 3.8. Laboratory, Tennessee, United States. 2011 World Development Indicators 157 3.9 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons of carbon % of total of carbon % of total of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb Energy and dioxide % changeb equivalent processes Agricultural equivalent industry Agricultural equivalent 1990– 1990– 1990– 1990– 1990– 2007 2007 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan –7.8 –73.3 .. .. .. .. .. .. .. .. .. .. Albania 2.5 –43.3 2,407 –5.1 20.0 70.8 1,036 –18.7 7.1 78.4 62 .. Algeria 3.7 77.6 54,219 33.1 83.2 8.2 4,898 27.5 22.6 58.6 489 50.0 Angola 10.1 459.0 45,409 –8.3 15.6 27.9 38,881 –6.7 0.4 38.4 20 .. Argentina 2.4 63.1 101,821 –8.3 18.9 70.6 49,821 29.6 3.9 89.2 785 –65.8 Armenia 1.2 21.8 2,962 2.5 50.8 36.7 580 –27.6 1.2 81.6 335 .. Australia 1.4 27.6 126,488 9.7 29.7 55.1 62,966 –0.1 10.3 78.2 6,505 33.5 Austria 1.1 12.7 8,515 –15.0 21.7 48.6 4,448 –13.5 31.0 52.5 2,329 46.2 Azerbaijan –2.2 –36.2 36,607 110.7 82.0 13.6 2,633 0.4 8.3 77.5 89 –49.5 Bangladesh 6.5 181.7 92,414 6.5 10.0 70.5 21,386 42.1 7.5 83.1 0 .. Belarus –2.6 –39.9 11,498 –32.8 7.6 70.9 11,680 –28.3 23.1 72.9 467 .. Belgium –0.3 –4.2 10,063 –21.8 11.6 56.7 6,571 –27.6 38.1 44.3 2,106 583.8 Benin 9.1 442.1 4,080 –15.8 15.6 47.8 2,902 –21.5 4.0 61.5 0 .. Bolivia 4.0 139.6 30,350 30.9 25.6 34.1 15,092 3.2 0.7 36.5 0 .. Bosnia and Herzegovina 14.1 315.2 2,741 –53.5 46.7 42.4 1,196 –40.8 24.7 57.8 571 –7.4 Botswana 4.1 130.2 4,501 –22.6 8.6 84.1 3,081 –44.1 1.4 92.0 0 .. Brazil 3.3 76.3 492,160 56.4 7.6 61.1 235,987 52.6 3.4 67.0 11,816 40.5 Bulgaria –2.1 –32.5 10,867 –24.8 13.0 18.9 4,227 –55.2 36.0 48.1 383 .. Burkina Faso 5.9 188.8 .. .. .. .. .. .. .. .. .. .. Burundi –4.7 –41.0 .. .. .. .. .. .. .. .. .. .. Cambodia 16.0 884.6 20,215 35.0 4.9 76.1 5,794 46.9 3.5 66.1 0 .. Cameroon 4.2 254.9 18,518 37.1 39.1 42.4 9,127 –13.3 2.6 75.9 419 –55.0 Canada 1.5 23.8 89,338 30.8 32.2 29.3 40,171 –5.5 23.7 58.9 21,943 69.7 Central African Republic 1.0 27.8 .. .. .. .. .. .. .. .. .. .. Chad 10.5 162.5 .. .. .. .. .. .. .. .. .. .. Chile 4.6 105.4 18,149 49.8 24.4 39.4 8,135 57.5 16.6 73.4 13 –29.5 China 5.2 165.7 1,333,098 28.5 45.8 38.8 467,213 48.5 12.9 74.3 141,394 1,073.0 Hong Kong SAR, China 2.0 44.5 2,820 84.0 26.7 0.0 422 –1.0 38.5 0.0 119 –68.6 Colombia –0.3 10.6 58,108 13.5 19.9 68.0 21,288 5.2 4.4 86.1 83 98.3 Congo, Dem. Rep. –3.7 –40.2 56,445 –41.6 10.2 23.1 54,643 –37.3 2.2 31.3 0 .. Congo, Rep. –0.8 33.6 5,584 –10.4 32.2 31.9 3,566 –17.2 1.0 51.8 5 .. Costa Rica 5.1 174.7 2,580 –31.3 9.5 67.2 1,334 –26.2 4.5 85.4 62 .. Côte d’Ivoire 1.5 10.1 10,997 –2.2 16.9 17.4 7,364 –1.6 2.7 29.3 0 .. Croatia 1.8 –0.8 3,864 –60.5 57.0 33.3 2,851 –24.5 36.6 52.4 59 –93.4 Cuba –1.3 –18.9 9,455 –21.0 11.2 62.4 6,356 –31.8 15.1 78.7 129 .. Czech Republic –1.1 –23.2 11,497 –40.3 49.4 33.6 8,878 –10.2 53.0 36.9 1,121 .. Denmark –1.0 –0.8 7,935 –0.5 16.4 65.2 6,290 –21.5 18.0 73.4 1,422 458.3 Dominican Republic 4.7 116.9 6,081 3.8 7.8 63.7 2,255 11.0 7.8 76.8 0 .. Ecuador 2.9 78.1 17,125 31.2 31.2 57.8 4,571 42.3 3.8 84.9 63 .. Egypt, Arab Rep. 5.2 143.2 46,996 68.8 50.7 31.7 18,996 60.7 8.3 80.0 3,181 54.5 El Salvador 4.7 155.9 3,131 18.0 12.4 53.1 1,377 7.7 8.2 76.2 77 .. Eritrea 7.4 .. 2,467 30.9 11.2 73.2 1,189 15.6 3.8 90.9 0 .. Estonia –2.1 –27.5 2,108 –36.8 42.3 30.5 932 –50.7 21.5 60.5 40 1,790.5 Ethiopia 4.6 115.7 52,243 32.8 14.3 72.5 30,510 19.4 5.2 88.8 10 .. Finland 1.3 26.0 9,742 –2.8 7.4 20.7 7,124 –4.1 42.8 41.7 826 724.4 France –0.3 –6.8 77,252 –0.3 44.3 47.7 49,058 –30.6 24.2 66.8 15,539 57.1 Gabon –6.2 –66.6 8,218 1.4 90.4 1.1 482 57.9 10.0 23.3 9 .. Gambia, The 4.2 107.7 .. .. .. .. .. .. .. .. .. .. Georgia –6.2 –65.1 4,410 –12.4 36.1 50.8 2,019 –26.9 35.5 56.9 12 .. Germany –1.1 –18.0 67,582 –44.8 32.1 43.8 56,560 –23.9 38.2 52.2 31,543 8.1 Ghana 4.9 149.5 8,990 24.2 23.3 39.5 4,899 –5.5 9.3 70.5 15 –97.5 Greece 2.2 34.9 7,289 2.1 26.3 50.0 5,977 –17.1 22.1 58.2 1,842 –20.9 Guatemala 6.0 154.2 8,306 74.7 12.4 48.8 5,376 121.2 5.5 56.8 481 .. Guinea 1.5 31.6 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau –0.4 13.0 .. .. .. .. .. .. .. .. .. .. Haiti 7.6 141.3 4,006 34.9 12.1 56.2 1,438 59.6 6.2 84.2 0 .. Honduras 7.4 240.7 5,191 31.5 7.2 78.4 2,865 26.1 3.8 85.9 0 .. 158 2011 World Development Indicators 3.9 ENVIRONMENT Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons of carbon % of total of carbon % of total of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb Energy and dioxide % changeb equivalent processes Agricultural equivalent industry Agricultural equivalent 1990– 1990– 1990– 1990– 1990– 2007 2007 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Hungary –0.6 –11.0 7,767 –22.9 29.1 33.6 6,961 –31.2 30.9 60.1 1,552 121.1 India 4.8 133.5 583,978 10.5 15.9 64.4 212,927 33.3 12.8 73.4 8,433 –11.9 Indonesia 4.6 165.5 208,944 18.4 25.5 46.4 123,275 43.5 3.7 71.5 1,027 –40.6 Iran, Islamic Rep. 4.7 118.3 114,585 32.5 70.6 18.2 26,644 41.1 11.4 75.3 2,569 –2.9 Iraq 3.7 90.5 15,937 –45.8 58.4 18.6 3,440 –9.9 9.7 63.3 86 –66.0 Ireland 2.5 46.1 15,331 14.3 11.9 76.7 7,486 –8.3 4.5 90.5 1,151 3,062.9 Israel 3.6 99.0 3,517 83.8 18.4 31.2 1,793 41.6 15.3 53.0 1,981 88.7 Italy 0.6 7.4 40,790 –13.4 14.7 39.8 28,620 –5.4 39.1 43.7 13,968 211.1 Jamaica 2.4 75.3 1,302 14.4 11.4 50.3 599 29.5 12.1 59.0 51 .. Japan 0.4 8.8 42,771 –36.5 8.1 71.2 29,785 –17.0 41.6 27.9 53,786 81.1 Jordan 4.3 106.2 1,796 111.5 25.0 21.8 667 39.6 8.2 55.4 112 .. Kazakhstan –2.1 –22.9 47,119 –27.3 66.2 25.3 17,594 –46.2 12.8 62.5 339 .. Kenya 4.6 92.9 22,130 23.3 16.9 65.5 10,542 14.3 5.0 88.8 0 .. Korea, Dem. Rep. –9.2 –71.1 18,195 –15.0 58.6 23.5 3,422 –60.6 13.2 62.3 2,794 .. Korea, Rep. 4.0 108.2 32,069 2.4 19.9 38.6 13,548 34.7 41.3 35.9 10,221 66.0 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 9.0 111.4 14,380 119.4 93.4 1.1 650 157.1 27.7 16.9 931 253.9 Kyrgyz Republic –4.1 –51.2 3,591 –38.1 6.8 72.3 1,510 –57.7 11.2 72.6 24 .. Lao PDR 14.0 554.7 .. .. .. .. .. .. .. .. .. .. Latvia –4.4 –47.9 3,108 –42.1 53.6 27.7 1,253 –58.7 11.6 77.4 890 .. Lebanon 3.0 46.8 1,003 46.6 9.7 25.5 672 79.1 12.6 58.8 0 .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia 5.3 39.4 .. .. .. .. .. .. .. .. .. .. Libya 2.2 42.2 14,682 –34.7 86.3 5.7 1,285 9.2 11.2 51.9 280 –0.7 Lithuania –3.1 –38.9 5,516 –34.1 32.0 33.8 2,451 –45.7 5.0 86.0 656 .. Macedonia, FYR –0.4 –29.3 1,403 –36.5 32.1 46.6 599 –33.9 15.9 63.9 120 .. Madagascar 5.1 128.3 .. .. .. .. .. .. .. .. .. .. Malawi 3.8 72.5 .. .. .. .. .. .. .. .. .. .. Malaysia 6.4 243.6 46,501 64.7 69.3 12.4 15,087 13.5 6.7 64.9 994 66.3 Mali 1.9 37.4 .. .. .. .. .. .. .. .. .. .. Mauritania –4.6 –26.8 .. .. .. .. .. .. .. .. .. .. Mauritius 6.2 165.7 .. .. .. .. .. .. .. .. .. .. Mexico 1.6 31.9 128,209 26.3 40.2 42.3 42,514 8.9 10.6 75.2 4,555 53.1 Moldova –10.5 –80.1 3,372 –17.5 45.2 29.4 849 –51.0 5.5 73.5 8 .. Mongolia –0.7 5.4 6,067 –25.9 2.5 92.1 3,489 –30.0 2.2 93.2 0 .. Morocco 3.8 97.1 10,573 15.8 8.0 51.7 5,814 12.2 3.0 82.6 0 .. Mozambique 5.4 159.7 12,843 18.2 22.7 44.2 9,501 –12.7 3.4 71.4 282 .. Myanmar 6.9 208.5 77,211 –7.4 12.6 69.0 30,932 –23.9 2.6 42.9 0 .. Namibia 42.7 .. 5,057 47.2 0.3 94.9 3,797 47.1 1.1 94.3 0 .. Nepal 8.1 439.9 22,142 9.7 5.9 82.9 4,516 26.0 13.0 76.8 0 .. Netherlands 0.0 5.6 21,259 –30.4 23.4 43.4 14,596 –10.7 52.5 39.5 3,750 –40.9 New Zealand 2.2 36.5 27,635 3.6 3.6 90.2 12,930 23.5 3.5 94.2 973 3.4 Nicaragua 4.4 73.6 6,018 26.3 6.6 74.8 3,340 10.1 3.3 91.7 0 .. Niger –1.2 –4.6 .. .. .. .. .. .. .. .. .. .. Nigeria 5.9 110.0 130,317 10.9 68.9 19.8 21,565 12.6 9.1 77.3 669 176.6 Norway 2.9 36.5 16,870 47.2 74.6 12.6 4,737 –3.1 46.5 39.0 5,202 –39.4 Oman 8.2 260.5 17,849 194.9 94.1 3.0 561 82.6 16.0 68.0 175 .. Pakistan 4.8 128.1 137,401 50.7 23.7 63.5 26,838 46.0 14.5 74.2 819 –18.8 Panama 4.4 131.2 3,219 16.5 4.0 79.2 1,204 18.1 4.9 83.7 0 .. Papua New Guinea 5.3 57.2 .. .. .. .. .. .. .. .. .. .. Paraguay 3.0 82.7 15,388 2.0 3.9 84.1 9,067 0.6 1.7 82.6 0 .. Peru 3.6 103.1 17,187 22.7 13.5 61.3 7,560 35.4 2.9 81.9 330 .. Philippines 3.1 59.2 51,889 28.6 9.3 63.7 12,950 34.0 9.1 73.1 365 125.6 Poland –1.0 –8.8 70,023 –36.6 62.0 21.9 30,198 4.7 33.5 57.7 2,451 360.6 Portugal 2.0 31.2 12,173 22.4 13.8 35.4 5,958 24.3 22.0 43.8 783 606.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 5.9 435.5 15,706 387.2 96.5 0.4 200 105.1 33.9 25.0 0 .. 2011 World Development Indicators 159 3.9 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons of carbon % of total of carbon % of total of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb Energy and dioxide % changeb equivalent processes Agricultural equivalent industry Agricultural equivalent 1990– 1990– 1990– 1990– 1990– 2007 2007 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania –2.9 –40.7 24,331 –35.1 42.7 36.0 11,537 –44.0 32.4 56.2 746 –62.8 Russian Federation –2.2 –34.3 562,801 –18.3 79.3 9.1 76,121 –48.7 27.8 44.3 59,673 130.6 Rwanda 0.6 4.8 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 2.6 87.1 48,152 67.4 83.6 4.0 6,501 17.5 14.0 46.1 2,193 –10.6 Senegal 3.0 72.1 7,129 35.1 9.9 68.3 4,083 37.2 2.7 88.5 0 .. Serbia 0.6c –20.1c 7,782 –58.7 41.5 43.7 4,581 –8.8 24.2 63.6 4,493 353.7 Sierra Leone 7.4 237.7 .. .. .. .. .. .. .. .. .. .. Singapore 0.3 15.4 2,237 136.6 60.1 1.3 1,068 162.8 77.6 2.8 2,532 396.0 Slovak Republic –1.6 –32.8 3,911 –39.7 18.2 39.0 3,354 –37.1 52.0 37.7 395 478.0 Slovenia 0.6 –17.3 3,498 0.6 30.7 32.1 1,156 –12.2 13.2 70.4 473 –38.5 Somalia 38.1 .. .. .. .. .. .. .. .. .. .. .. South Africa 1.3 30.0 63,785 24.6 45.4 31.4 24,048 12.9 12.6 59.8 2,552 71.1 Spain 2.9 57.9 36,338 11.9 10.4 56.8 26,529 6.5 18.7 62.6 9,080 47.7 Sri Lanka 7.5 226.3 10,210 –11.2 5.3 65.2 2,056 18.0 12.1 65.1 0 .. Sudan 6.4 107.3 67,441 55.5 7.1 85.2 49,472 34.9 1.3 92.6 0 .. Swaziland 10.0 150.0 .. .. .. .. .. .. .. .. .. .. Sweden –0.5 –4.8 11,311 1.3 9.9 28.1 5,865 –13.1 26.8 60.2 2,078 133.8 Switzerland –0.3 –11.6 4,748 –17.1 19.8 67.6 2,415 –15.5 20.8 59.3 2,109 97.4 Syrian Arab Republic 3.6 86.6 12,458 –10.8 53.8 28.1 5,509 33.4 9.0 78.1 0 .. Tajikistan –6.9 –69.9 3,898 –9.3 12.8 68.6 1,378 0.2 1.4 86.9 383 –86.3 Tanzania 4.9 154.7 32,024 24.0 12.6 63.2 21,647 0.8 2.5 78.8 0 .. Thailand 5.7 189.6 83,257 5.7 16.9 66.0 22,304 15.1 21.7 65.5 1,104 –22.8 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 3.6 70.1 2,889 5.0 23.5 39.8 1,738 –21.3 5.6 67.5 0 .. Trinidad and Tobago 4.1 118.4 10,070 32.0 83.9 0.7 230 12.4 11.5 60.3 0 .. Tunisia 3.3 79.9 8,160 106.2 55.6 25.5 2,366 18.0 21.4 66.4 0 .. Turkey 3.4 91.4 64,251 46.4 16.0 33.6 32,781 12.8 22.7 66.4 5,066 96.9 Turkmenistan 2.8 44.7 27,984 –5.0 75.2 21.6 4,276 93.8 16.4 78.1 73 .. Uganda 8.3 291.9 .. .. .. .. .. .. .. .. .. .. Ukraine –4.4 –54.0 70,360 –42.2 62.1 23.3 26,097 –51.4 42.8 45.6 693 209.3 United Arab Emirates 5.5 147.3 23,283 58.0 93.1 2.6 1,169 78.7 18.3 43.6 1,075 27.4 United Kingdom –0.5 –5.4 65,788 –44.1 24.8 38.2 30,565 –44.7 24.8 60.0 10,403 96.7 United States 1.2 20.0 548,074 –14.4 41.0 34.8 317,153 1.8 30.6 56.4 239,517 158.7 Uruguay 2.0 55.7 19,589 24.1 1.5 94.3 7,017 16.1 1.4 96.9 59 .. Uzbekistan 0.1 –9.7 39,602 24.0 57.3 33.7 10,003 9.4 6.2 84.2 608 .. Venezuela, RB 2.9 35.5 61,183 5.9 47.4 40.0 14,935 23.4 5.0 75.2 2,468 –24.0 Vietnam 11.7 420.3 82,978 40.1 22.7 63.9 23,030 98.3 6.1 83.0 0 .. West Bank and Gaza 19.7 .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 4.5 117.4 6,677 73.5 17.0 54.9 3,250 57.4 11.2 72.5 0 .. Zambia –0.2 10.0 19,294 –28.4 6.7 59.3 25,068 –29.7 2.6 71.7 0 .. Zimbabwe –3.3 –37.9 9,539 –5.7 11.4 73.3 6,114 –16.1 3.7 85.2 0 .. World 1.8 w 36.0 w 7,135,973 s 6.2 w 37.3 w 42.6 w 2,852,592 s 5.8 w 15.4 w 66.2 w 724,183 s 122.4 w Low income –4.1 –36.2 464,616 –4.0 13.6 60.7 239,126 –16.7 4.1 63.6 .. .. Middle income 2.6 59.6 5,128,922 13.9 39.0 42.6 1,799,128 18.3 10.7 70.8 259,893 208.5 Lower middle income 4.3 117.7 3,120,011 18.6 35.1 46.9 1,153,692 30.5 10.8 72.3 159,984 439.4 Upper middle income 0.3 3.8 2,008,911 7.3 45.0 35.8 645,436 1.5 10.6 68.1 99,909 83.1 Low & middle income 2.4 56.2 5,593,538 12.2 36.9 44.1 2,038,253 12.8 10.0 70.0 263,401 200.5 East Asia & Pacific 4.7 148.9 1,928,355 24.5 39.2 43.6 707,496 38.0 10.5 72.2 .. .. Europe & Central Asia –2.0 –31.3 933,500 –17.2 67.5 17.4 213,150 –38.8 25.4 56.6 74,802 112.0 Latin America & Carib. 2.4 51.2 1,008,557 30.3 17.3 58.6 442,132 32.9 4.6 72.4 20,972 23.5 Middle East & N. Africa 4.2 103.4 287,084 20.0 64.7 20.7 73,539 36.8 10.7 74.5 6,717 20.7 South Asia 4.8 134.1 846,145 14.6 16.1 65.4 267,722 34.9 12.6 74.3 9,253 –12.5 Sub-Saharan Africa 2.2 46.1 589,897 5.4 30.5 44.0 334,216 –7.6 3.7 66.1 .. .. High income 1.0 17.9 1,542,435 –10.8 38.9 37.0 814,339 –8.4 29.1 56.9 460,781 93.7 Euro area 0.2 2.4 315,597 –18.0 25.9 46.9 218,258 –17.8 31.7 55.4 84,190 37.2 a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emission since 1990, which is the baseline for Kyoto Protocal requirements. c. Includes Kosovo and Montenegro. 160 2011 World Development Indicators 3.9 ENVIRONMENT Trends in greenhouse gas emissions About the data Definitions Greenhouse gases—which include carbon dioxide, compared. A kilogram of methane is 21 times as •  Carbon dioxide emissions are emissions from methane, nitrous oxide, hydrofluorocarbons, per- effective at trapping heat in the earth’s atmosphere the burning of fossil fuels and the manufacture of fluorocarbons, and sulfur hexafluoride—contribute as a kilogram of carbon dioxide within 100 years. cement and include carbon dioxide produced during to climate change. Nitrous oxide emissions are mainly from fossil fuel consumption of solid, liquid, and gas fuels and gas Carbon dioxide emissions, largely a byproduct of combustion, fertilizers, rainforest fires, and animal flaring. •  Methane emissions are emissions from energy production and use (see table 3.7), account waste. Nitrous oxide is a powerful greenhouse gas, human activities such as agriculture and from indus- for the largest share of greenhouse gases. Anthro- with an estimated atmospheric lifetime of 114 years, trial methane production. • Methane emissions from pogenic carbon dioxide emissions result primarily compared with 12 years for methane. The per kilo- energy processes are emissions from the produc- from fossil fuel combustion and cement manufactur- gram global warming potential of nitrous oxide is tion, handling, transmission, and combustion of fos- ing. Burning oil releases more carbon dioxide than nearly 310 times that of carbon dioxide within 100 sil fuels and biofuels. • Agricultural methane emis- burning natural gas, and burning coal releases even years. sions are emissions from animals, animal waste, rice more for the same level of energy use. Cement manu- Other greenhouse gases covered under the Kyoto production, agricultural waste burning (nonenergy, facturing releases about half a metric ton of carbon Protocol are hydrofluorocarbons, perfluorocarbons, on-site), and savannah burning. •  Nitrous oxide dioxide for each metric ton of cement produced. and sulfur hexafluoride. Although emissions of these emissions are emissions from agricultural biomass Methane emissions result largely from agricultural artificial gases are small, they are more powerful burning, industrial activities, and livestock manage- activities, industrial production landfills and waste- greenhouse gases than carbon dioxide, with much ment. • Nitrous oxide emissions from energy pro- water treatment, and other sources such as tropi- higher atmospheric lifetimes and high global warm- cesses are emissions produced by the combustion cal forest and other vegetation fires. The emissions ing potential. of fossil fuels and biofuels. •  Agricultural nitrous are usually expressed in carbon dioxide equivalents For a discussion of carbon dioxide sources and oxide emissions are emissions produced through using the global warming potential, which allows the methodology behind emissions calculation, see fertilizer use (synthetic and animal manure), ani- the effective contributions of different gases to be About the data for table 3.8. mal waste management, agricultural waste burning (nonenergy, on-site), and savannah burning. • Other The six largest contributors to methane emissions greenhouse gas emissions include hydrofluorocar- account for about 50 percent of emissions 3.9a bons, perfluorocarbons, and sulfur hexafluoride, which are to be curbed under the Kyoto Protocol. Methane emissions, 2005 (million metric tons of carbon dioxide equivalent) Hydrofluorocarbons, used as a replacement for chlo- 1,400 rofluorocarbons, are used mainly in refrigeration and 1,050 semiconductor manufacturing. Perfl uorocarbons, also used as a replacement for chlorofluorocarbons 700 in manufacturing semiconductors, are a byproduct of aluminum smelting and uranium enrichment. Sulfur 350 hexafluoride is used largely to insulate high-voltage electric power equipment. 0 China India Russian United States Brazil Indonesia Federation Source: Table 3.9. The five largest contributors to nitrous oxide emissions account for about 50 percent of emissions 3.9b Nitrous oxide emissions, 2005 (million metric tons of carbon dioxide equivalent) 500 375 Data sources Data on carbon dioxide emissions are from the 250 Carbon Dioxide Information Analysis Center, Envi- ronmental Sciences Division, Oak Ridge National 125 Laboratory, Tennessee, United States. Data on methane, nitrous oxide, and other greenhouse 0 China United States Brazil India Indonesia gases emissions are compiled by the International Source: Table 3.9. Energy Agency. 2011 World Development Indicators 161 3.10 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Natural Gas Oil Hydropower Nuclear power 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.2 3.8 0.0 0.0 0.0 0.0 10.9 0.0 89.1 100.0 0.0 0.0 Algeria 16.1 40.2 0.0 0.0 93.7 97.3 5.4 2.0 0.8 0.7 0.0 0.0 Angola 0.8 4.0 0.0 0.0 0.0 0.0 13.8 3.7 86.2 96.3 0.0 0.0 Argentina 50.7 121.4 1.3 2.3 39.2 53.6 9.8 11.7 35.2 24.9 14.3 6.0 Armenia 10.4 5.8 0.0 0.0 16.4 26.2 68.6 0.0 15.0 31.1 0.0 42.6 Australia 154.3 257.1 78.7 76.9 9.3 15.0 2.3 1.1 9.2 4.6 0.0 0.0 Austria 49.3 64.4 14.2 10.7 15.7 17.4 3.8 1.9 63.9 59.0 0.0 0.0 Azerbaijan 23.2 23.9 0.0 0.0 0.0 84.1 97.0 6.6 3.0 9.3 0.0 0.0 Bangladesh 7.7 35.0 0.0 1.8 84.3 89.0 4.3 5.0 11.4 4.2 0.0 0.0 Belarus 39.5 35.0 0.0 0.0 58.1 96.9 41.8 2.7 0.1 0.1 0.0 0.0 Belgium 70.3 83.6 28.2 8.7 7.7 29.5 1.9 0.5 0.4 0.5 60.8 54.5 Benin 0.0 0.1 0.0 0.0 0.0 0.0 100.0 99.3 0.0 0.7 0.0 0.0 Bolivia 2.1 6.2 0.0 0.0 37.6 46.5 5.3 14.0 55.3 36.6 0.0 0.0 Bosnia and Herzegovina 14.6 13.3 71.8 64.4 0.0 0.0 7.3 1.3 20.9 34.3 0.0 0.0 Botswana 0.9 0.6 88.1 100.0 0.0 0.0 11.9 0.0 0.0 0.0 0.0 0.0 Brazil 222.8 463.4 2.1 2.7 0.3 6.3 2.2 3.8 92.8 79.8 1.0 3.0 Bulgaria 42.1 44.6 50.3 52.1 7.6 5.3 2.9 0.6 4.5 6.3 34.8 35.4 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 1.5 .. 0.0 .. 0.0 .. 96.5 .. 3.1 .. 0.0 Cameroon 2.7 5.6 0.0 0.0 0.0 7.7 1.5 15.9 98.5 76.2 0.0 0.0 Canada 482.0 651.2 17.1 17.2 2.0 6.2 3.4 1.5 61.6 58.7 15.1 14.4 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 18.4 59.7 38.3 23.6 2.1 3.7 9.2 26.9 48.5 40.5 0.0 0.0 China 621.2 3,456.9 71.3 79.1 0.4 0.9 7.9 0.7 20.4 16.9 0.0 2.0 Hong Kong SAR, China 28.9 38.0 98.3 68.2 0.0 31.5 1.7 0.3 0.0 0.0 0.0 0.0 Colombia 36.4 56.0 10.1 5.4 12.4 10.3 1.0 0.3 75.6 82.8 0.0 0.0 Congo, Dem. Rep. 5.7 7.5 0.0 0.0 0.0 0.4 0.4 0.2 99.6 99.4 0.0 0.0 Congo, Rep. 0.5 0.5 0.0 0.0 0.0 18.7 0.6 0.0 99.4 81.3 0.0 0.0 Costa Rica 3.5 9.5 0.0 0.0 0.0 0.0 2.5 7.1 97.5 78.0 0.0 0.0 Côte d’Ivoire 2.0 5.8 0.0 0.0 0.0 65.1 33.3 0.2 66.7 32.7 0.0 0.0 Croatia 9.2 12.2 6.8 20.4 20.2 20.1 31.6 16.2 41.3 42.7 0.0 0.0 Cuba 15.0 17.7 0.0 0.0 0.2 0.0 91.4 97.0 0.8 0.8 0.0 0.0 Czech Republic 62.3 83.2 76.4 59.9 0.6 1.2 0.9 0.2 1.9 2.4 20.2 31.9 Denmark 26.0 36.4 90.7 48.0 2.7 19.0 3.4 3.1 0.1 0.1 0.0 0.0 Dominican Republic 3.7 15.4 1.2 13.8 0.0 12.9 88.6 61.8 9.4 11.2 0.0 0.0 Ecuador 6.3 18.6 0.0 0.0 0.0 7.3 21.5 29.8 78.5 60.7 0.0 0.0 Egypt, Arab Rep. 42.3 131.0 0.0 0.0 39.6 68.4 36.9 19.7 23.5 11.2 0.0 0.0 El Salvador 2.2 6.0 0.0 0.0 0.0 0.0 6.9 38.6 73.5 34.2 0.0 0.0 Eritrea 0.1 0.3 0.0 0.0 0.0 0.0 100.0 99.3 0.0 0.0 0.0 0.0 Estonia 17.4 10.6 85.8 91.0 5.5 4.0 8.3 0.3 0.0 0.3 0.0 0.0 Ethiopia 1.2 3.8 0.0 0.0 0.0 0.0 11.6 12.4 88.4 87.3 0.0 0.0 Finland 54.4 77.4 18.5 11.8 8.6 14.5 3.1 0.5 20.0 22.1 35.3 29.6 France 417.2 570.3 8.5 4.8 0.7 3.8 2.1 1.0 12.9 11.2 75.3 77.1 Gabon 1.0 2.0 0.0 0.0 16.4 24.7 11.2 31.2 72.1 43.8 0.0 0.0 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 13.7 8.4 0.0 0.0 15.6 15.2 29.2 0.0 55.2 84.8 0.0 0.0 Germany 547.7 631.2 58.7 46.0 7.4 13.9 1.9 1.5 3.2 3.3 27.8 23.5 Ghana 5.7 8.4 0.0 0.0 0.0 0.0 0.0 25.9 100.0 74.1 0.0 0.0 Greece 34.8 62.9 72.4 53.0 0.3 21.9 22.3 15.9 5.1 5.3 0.0 0.0 Guatemala 2.3 8.7 0.0 13.0 0.0 0.0 9.0 26.6 76.0 42.6 0.0 0.0 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.6 0.5 0.0 0.0 0.0 0.0 20.6 62.8 76.5 37.2 0.0 0.0 Honduras 2.3 6.5 0.0 0.0 0.0 0.0 1.7 61.9 98.3 35.0 0.0 0.0 162 2011 World Development Indicators 3.10 ENVIRONMENT Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Natural Gas Oil Hydropower Nuclear power 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 Hungary 28.4 40.0 30.5 18.0 15.7 37.9 4.8 0.9 0.6 0.5 48.3 37.0 India 289.4 830.1 66.2 68.6 3.4 9.9 3.5 4.1 24.8 13.8 2.1 1.8 Indonesia 33.3 149.4 31.5 41.1 2.3 16.9 42.7 28.8 20.2 7.7 0.0 0.0 Iran, Islamic Rep. 59.1 214.5 0.0 0.2 52.5 80.8 37.3 16.6 10.3 2.3 0.0 0.0 Iraq 24.0 36.8 0.0 0.0 0.0 0.0 89.2 98.5 10.8 1.5 0.0 0.0 Ireland 14.2 29.4 41.6 17.8 27.7 54.7 10.0 5.9 4.9 3.3 0.0 0.0 Israel 20.9 56.4 50.1 62.7 0.0 26.2 49.9 10.6 0.0 0.0 0.0 0.0 Italy 213.1 313.5 16.8 15.5 18.6 55.1 48.2 10.0 14.8 13.3 0.0 0.0 Jamaica 2.5 7.8 0.0 0.0 0.0 0.0 92.4 96.0 3.6 2.0 0.0 0.0 Japan 835.5 1,075.0 14.0 26.8 20.0 26.3 18.5 9.7 10.7 7.1 24.2 24.0 Jordan 3.6 13.8 0.0 0.0 11.9 80.6 87.8 18.9 0.3 0.4 0.0 0.0 Kazakhstan 87.4 80.3 71.1 70.3 10.5 10.7 10.0 9.7 8.4 9.3 0.0 0.0 Kenya 3.2 7.1 0.0 0.0 0.0 0.0 7.1 38.4 76.6 40.4 0.0 0.0 Korea, Dem. Rep. 27.7 23.2 40.1 36.0 0.0 0.0 3.6 3.4 56.3 60.6 0.0 0.0 Korea, Rep. 105.4 443.9 16.8 43.2 9.1 18.3 17.9 3.5 6.0 0.7 50.2 34.0 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 18.5 51.7 0.0 0.0 45.7 30.4 54.3 69.6 0.0 0.0 0.0 0.0 Kyrgyz Republic 15.7 11.9 13.1 3.5 23.5 6.1 0.0 0.0 63.5 90.4 0.0 0.0 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 6.6 5.3 0.0 0.0 26.1 39.0 5.4 0.0 67.6 58.9 0.0 0.0 Lebanon 1.5 10.6 0.0 0.0 0.0 0.0 66.7 96.5 33.3 3.5 0.0 0.0 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 10.2 28.7 0.0 0.0 0.0 41.0 100.0 59.0 0.0 0.0 0.0 0.0 Lithuania 28.4 13.3 0.0 0.0 23.8 15.2 14.6 4.2 1.5 3.0 60.0 74.2 Macedonia, FYR 5.8 6.3 89.7 83.8 0.0 0.0 1.8 2.9 8.5 13.3 0.0 0.0 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23.0 97.4 12.3 26.9 20.4 63.6 50.0 1.9 17.3 7.7 0.0 0.0 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 115.8 258.9 6.7 8.3 12.5 50.6 53.6 19.0 20.3 15.1 2.5 3.8 Moldova 16.2 3.6 30.8 0.0 42.3 95.6 25.4 0.4 1.6 2.3 0.0 0.0 Mongolia 3.5 4.1 92.4 96.1 0.0 0.0 7.6 3.9 0.0 0.0 0.0 0.0 Morocco 9.6 20.8 23.0 56.2 0.0 13.8 64.4 24.2 12.7 4.5 0.0 0.0 Mozambique 0.5 15.1 13.9 0.0 0.0 0.1 23.6 0.0 62.6 99.9 0.0 0.0 Myanmar 2.5 6.6 1.6 0.0 39.3 35.7 10.9 3.5 48.1 60.8 0.0 0.0 Namibia 1.4 2.1 1.5 31.1 0.0 0.0 3.3 1.4 95.2 67.5 0.0 0.0 Nepal 0.9 3.1 0.0 0.0 0.0 0.0 0.1 0.4 99.9 99.6 0.0 0.0 Netherlands 71.9 107.6 38.3 24.9 50.9 58.9 4.3 1.9 0.1 0.1 4.9 3.9 New Zealand 32.3 43.8 2.1 11.0 17.7 24.3 0.0 0.3 71.9 51.0 0.0 0.0 Nicaragua 1.4 3.4 0.0 0.0 0.0 0.0 39.8 64.5 28.8 15.9 0.0 0.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 13.5 21.1 0.1 0.0 53.7 58.2 13.7 14.7 32.6 27.1 0.0 0.0 Norway 121.6 141.7 0.1 0.1 0.0 0.3 0.0 0.0 99.6 98.5 0.0 0.0 Oman 4.5 15.7 0.0 0.0 81.6 82.0 18.4 18.0 0.0 0.0 0.0 0.0 Pakistan 37.7 91.6 0.1 0.1 33.6 32.4 20.6 35.4 44.9 30.3 0.8 1.8 Panama 2.7 6.4 0.0 0.0 0.0 0.0 14.7 37.9 83.2 61.8 0.0 0.0 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 27.2 55.5 0.0 0.0 0.0 0.0 0.0 0.0 99.9 100.0 0.0 0.0 Peru 13.8 32.4 0.0 2.7 1.7 28.0 21.5 9.0 75.8 58.7 0.0 0.0 Philippines 27.4 60.8 7.0 25.9 0.0 32.2 45.3 8.0 22.1 16.2 0.0 0.0 Poland 134.4 155.6 97.5 92.2 0.1 2.0 1.2 1.5 1.1 1.4 0.0 0.0 Portugal 28.4 45.5 32.1 24.6 0.0 33.4 33.1 9.1 32.3 15.0 0.0 0.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 4.8 21.6 0.0 0.0 100.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 2011 World Development Indicators 163 3.10 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Natural Gas Oil Hydropower Nuclear power 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 Romania 64.3 65.0 28.8 39.8 35.1 15.3 18.4 1.1 17.7 26.5 0.0 17.3 Russian Federation 1,082.2 1,038.4 14.3 18.9 47.3 47.6 11.5 1.5 15.3 15.9 10.9 15.7 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 69.2 204.2 0.0 0.0 48.1 43.1 51.9 56.9 0.0 0.0 0.0 0.0 Senegal 0.9 2.4 0.0 0.0 2.3 1.7 93.0 85.8 0.0 9.5 0.0 0.0 Serbia 40.9 36.8 69.1 72.4 3.2 1.1 4.6 0.5 23.1 26.0 0.0 0.0 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 15.7 41.7 0.0 0.0 0.0 80.3 100.0 19.7 0.0 0.0 0.0 0.0 Slovak Republic 25.5 28.8 31.9 17.9 7.1 5.6 6.4 2.4 7.4 14.0 47.2 58.1 Slovenia 12.4 16.4 31.3 32.5 0.0 2.9 7.9 0.1 23.7 24.5 37.1 38.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 165.4 255.5 94.3 94.2 0.0 0.0 0.0 0.1 0.6 0.5 5.1 5.1 Spain 151.2 311.1 40.1 16.1 1.0 39.1 5.7 5.8 16.8 7.6 35.9 19.0 Sri Lanka 3.2 9.2 0.0 0.0 0.0 0.0 0.2 55.1 99.8 44.7 0.0 0.0 Sudan 1.5 4.5 0.0 0.0 0.0 0.0 36.8 67.6 63.2 32.4 0.0 0.0 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 146.0 149.9 1.1 1.1 0.3 0.4 0.9 0.6 49.7 46.1 46.7 42.6 Switzerland 55.0 67.1 0.1 0.0 0.6 1.1 0.7 0.2 54.2 53.7 43.0 41.3 Syrian Arab Republic 11.6 41.0 0.0 0.0 20.5 31.3 56.0 61.7 23.5 7.0 0.0 0.0 Tajikistan 18.1 16.1 0.0 0.0 9.1 1.9 0.0 0.0 90.9 98.1 0.0 0.0 Tanzania 1.6 4.4 0.0 2.7 0.0 36.2 4.9 0.9 95.1 60.1 0.0 0.0 Thailand 44.2 147.4 25.0 21.4 40.2 69.4 23.5 1.1 11.3 4.8 0.0 0.0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 0.2 0.1 0.0 0.0 0.0 0.0 39.9 24.4 60.1 74.0 0.0 0.0 Trinidad and Tobago 3.6 7.9 0.0 0.0 99.0 99.6 0.1 0.2 0.0 0.0 0.0 0.0 Tunisia 5.8 15.3 0.0 0.0 63.7 88.7 35.5 10.8 0.8 0.2 0.0 0.0 Turkey 57.5 198.4 35.1 29.1 17.7 49.7 6.9 3.8 40.2 16.8 0.0 0.0 Turkmenistan 14.6 15.0 0.0 0.0 95.2 100.0 0.0 0.0 4.8 0.0 0.0 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 298.6 192.5 38.2 35.6 16.7 11.4 16.1 0.4 3.5 5.9 25.5 46.7 United Arab Emirates 17.1 86.3 0.0 0.0 96.3 98.3 3.7 1.7 0.0 0.0 0.0 0.0 United Kingdom 317.8 385.3 65.0 32.9 1.6 45.9 10.9 1.6 1.6 1.3 20.7 13.6 United States 3,202.8 4,343.8 53.1 49.1 11.9 21.0 4.1 1.3 8.5 5.9 19.1 19.3 Uruguay 7.4 8.8 0.0 0.0 0.0 0.0 5.1 39.1 94.2 51.4 0.0 0.0 Uzbekistan 56.3 49.4 7.4 4.1 76.4 70.0 4.4 2.9 11.8 23.0 0.0 0.0 Venezuela, RB 59.3 119.3 0.0 0.0 26.2 14.7 11.5 12.5 62.3 72.8 0.0 0.0 Vietnam 8.7 73.0 23.1 20.8 0.1 41.5 15.0 2.1 61.8 35.6 0.0 0.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.7 6.5 0.0 0.0 0.0 0.0 100.0 100.0 0.0 0.0 0.0 0.0 Zambia 8.0 9.7 0.5 0.0 0.0 0.0 0.3 0.3 99.2 99.7 0.0 0.0 Zimbabwe 9.4 8.0 53.3 46.3 0.0 0.0 0.0 0.3 46.7 53.4 0.0 0.0 World 11,839.5 t 20,201.4 t 37.3 w 40.8 w 14.6 w 21.3 w 10.2 w 5.1 w 18.0 w 15.8 w 17.0 w 13.5 w Low income 138.9 206.7 13.2 6.4 9.2 17.5 1.7 5.0 53.7 48.9 0.0 0.0 Middle income 3,984.6 8,948.5 32.4 47.4 22.3 19.8 14.6 6.0 23.1 20.4 6.4 4.7 Lower middle income 1,654.0 5,548.8 47.7 63.3 11.7 9.9 14.2 5.0 20.3 16.8 5.0 3.2 Upper middle income 2,330.2 3,402.8 21.6 21.3 29.8 35.8 14.9 7.6 25.1 26.4 7.3 7.2 Low & middle income 4,122.5 9,174.7 31.8 46.4 21.8 19.7 14.1 6.0 24.1 21.0 6.1 4.6 East Asia & Pacific 796.3 4,044.1 61.0 71.6 3.4 6.7 12.6 2.0 21.4 16.4 0.0 1.7 Europe & Central Asia 1,935.8 1,864.6 23.1 25.3 36.7 40.2 13.6 2.0 14.5 16.4 11.7 15.7 Latin America & Carib. 598.1 1,285.3 4.0 4.5 9.4 20.7 17.8 13.5 64.4 55.3 2.1 2.4 Middle East & N. Africa 187.9 566.8 1.2 2.1 36.9 62.5 48.3 29.4 12.4 4.4 0.0 0.0 South Asia 341.7 977.2 56.1 58.3 8.5 14.6 5.3 7.5 27.4 15.4 1.9 1.7 Sub-Saharan Africa 260.2 424.1 62.2 58.0 2.8 4.4 1.9 3.8 15.9 17.2 3.2 3.1 High income 7,736.5 11,079.9 40.2 36.1 10.7 22.5 8.1 4.4 14.7 11.3 22.7 20.8 Euro area 1,694.1 2,352.2 33.7 22.4 8.6 24.0 9.6 3.9 11.1 9.5 35.6 31.6 a. Shares may not sum to 100 percent because some sources of generated electricity (such as wind, solar, and geothermal) are not shown. 164 2011 World Development Indicators 3.10 ENVIRONMENT Sources of electricity About the data Definitions Use of energy is important in improving people’s as more detailed energy accounts have become • Electricity production is measured at the termi- standard of living. But electricity generation also available. Breaks in series are therefore unavoidable. nals of all alternator sets in a station. In addition to can damage the environment. Whether such damage hydropower, coal, oil, gas, and nuclear power gen- occurs depends largely on how electricity is gener- eration, it covers generation by geothermal, solar, ated. For example, burning coal releases twice as wind, and tide and wave energy as well as that from much carbon dioxide—a major contributor to global combustible renewables and waste. Production warming—as does burning an equivalent amount includes the output of electric plants designed to of natural gas (see About the data for table 3.8). produce electricity only, as well as that of combined Nuclear energy does not generate carbon dioxide heat and power plants. • Sources of electricity are emissions, but it produces other dangerous waste the inputs used to generate electricity: coal, gas, oil, products. The table provides information on electric- hydropower, and nuclear power. • Coal is all coal and ity production by source. brown coal, both primary (including hard coal and The International Energy Agency (IEA) compiles lignite-brown coal) and derived fuels (including pat- data on energy inputs used to generate electricity. ent fuel, coke oven coke, gas coke, coke oven gas, IEA data for countries that are not members of the and blast furnace gas). Peat is also included in this Organisation for Economic Co-operation and Devel- category. • Gas is natural gas but not natural gas opment (OECD) are based on national energy data liquids. • Oil is crude oil and petroleum products. adjusted to conform to annual questionnaires com- • Hydropower is electricity produced by hydroelectric pleted by OECD member governments. In addition, power plants. • Nuclear power is electricity produced estimates are sometimes made to complete major by nuclear power plants. aggregates from which key data are missing, and adjustments are made to compensate for differ- ences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. It occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statis- tics for other countries have undergone continuous changes in coverage or methodology in recent years More than 50 percent of electricity Lower middle-income countries in Latin America is produced produce the majority of their power by hydropower 3.10a from coal 3.10b Percent Other Nuclear power Oil Percent Other Nuclear power Oil Hydropower Natural gas Coal Hydropower Natural gas Coal 100 100 80 80 60 60 40 40 20 20 Data sources Data on electricity production are from the IEA’s 0 0 East Asia Europe Latin America Middle South Low Lower Upper Low & High electronic files and its annual publications Energy & Pacific & Central & East Asia income middle middle middle income Asia Caribbean & North income income income Statistics and Balances of Non-OECD Countries, Africa Energy Statistics of OECD Countries, and Energy Source: Table 3.10. Source: Table 3.10. Balances of OECD Countries. 2011 World Development Indicators 165 3.11 Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2008 1990 2008 Afghanistan 3 7 18 24 4.0 7 12 38 49 .. 60 .. 30 Albania 1 1 36 47 1.2 .. .. 21 29 .. 98 .. 98 Algeria 13 23 52 66 2.9 7 8 14 12 99 98 77 88 Angola 4 11 37 58 5.2 15 24 40 42 58 86 6 18 Argentina 28 37 87 92 1.4 39 39 37 35 93 91 73 77 Armenia 2 2 68 64 -1.0 33 36 49 56 95 95 .. 80 Australia 15 19 85 89 1.5 60 59 25 23 100 100 100 100 Austria 5 6 66 67 0.6 20 20 30 30 100 100 100 100 Azerbaijan 4 5 54 52 0.9 24 22 45 43 .. 51 .. 39 Bangladesh 23 45 20 28 3.5 8 13 29 32 59 56 34 52 Belarus 7 7 66 74 0.3 16 19 24 26 .. 91 .. 97 Belgium 10 11 96 97 0.5 17 18 17 18 100 100 100 100 Benin 2 4 35 42 4.3 .. .. 30 22 14 24 1 4 Bolivia 4 7 56 66 3.0 25 33 29 25 29 34 6 9 Bosnia and Herzegovina 2 2 39 48 0.4 .. .. 24 22 .. 99 .. 92 Botswana 1 1 42 60 3.8 .. .. 22 17 58 74 20 39 Brazil 112 167 75 86 2.1 35 40 13 12 81 87 35 37 Bulgaria 6 5 66 71 -0.3 14 16 21 22 100 100 98 100 Burkina Faso 1 3 14 20 5.0 6 11 44 56 28 33 2 6 Burundi 0 1 6 11 4.8 .. .. 66 51 41 49 44 46 Cambodia 1 3 13 22 5.2 6 10 50 46 38 67 5 18 Cameroon 5 11 41 58 4.3 14 19 19 18 65 56 35 35 Canada 21 27 77 81 1.3 40 44 18 20 100 100 99 99 Central African Republic 1 2 37 39 2.4 .. .. 43 41 21 43 5 28 Chad 1 3 21 27 4.6 .. .. 38 27 20 23 2 4 Chile 11 15 83 89 1.7 35 35 42 39 91 98 48 83 China 311 586 27 44 3.3 9 17 3 3 48 58 38 52 Hong Kong SAR, China 6 7 100 100 1.1 100 100 100 99 .. .. .. .. Colombia 23 34 68 75 2.2 31 37 21 24 80 81 43 55 Congo, Dem. Rep. 10 23 28 35 4.2 13 17 35 37 23 23 4 23 Congo, Rep. 1 2 54 62 2.8 29 35 53 57 .. 31 .. 29 Costa Rica 2 3 51 64 3.3 24 31 47 48 94 95 91 96 Côte d’Ivoire 5 10 40 49 3.9 17 19 42 38 38 36 8 11 Croatia 3 3 54 58 -0.1 .. .. 27 27 .. 99 .. 98 Cuba 8 8 73 76 0.5 20 19 27 25 86 94 64 81 Czech Republic 8 8 75 74 0.0 12 11 16 15 100 99 98 97 Denmark 4 5 85 87 0.5 20 21 24 24 100 100 100 100 Dominican Republic 4 7 55 70 2.9 21 21 37 30 83 87 61 74 Ecuador 6 9 55 66 2.5 26 33 28 29 86 96 48 84 Egypt, Arab Rep. 25 35 44 43 1.8 21 18 36 31 91 97 57 92 El Salvador 3 4 49 61 1.9 18 25 37 41 88 89 62 83 Eritrea 0 1 16 21 4.0 .. .. 72 60 58 52 0 4 Estonia 1 1 71 69 -1.0 .. .. 43 43 .. 96 .. 94 Ethiopia 6 14 13 17 4.5 4 3 29 20 21 29 1 8 Finland 3 3 61 64 0.5 17 21 28 33 100 100 100 100 France 42 49 74 78 0.8 23 23 22 21 100 100 100 100 Gabon 1 1 69 86 3.6 .. .. 62 49 .. 33 .. 30 Gambia, The 0 1 38 57 5.5 .. .. 66 45 .. 68 .. 65 Georgia 3 2 55 53 -1.5 22 26 41 50 97 96 95 93 Germany 58 60 73 74 0.2 8 8 6 6 100 100 100 100 Ghana 5 12 36 51 4.2 13 17 22 19 11 18 4 7 Greece 6 7 59 61 0.8 30 29 51 47 100 99 92 97 Guatemala 4 7 41 49 3.3 9 8 22 16 84 89 51 73 Guinea 2 4 28 35 3.8 15 16 52 45 18 34 6 11 Guinea-Bissau 0 0 28 30 2.7 .. .. 53 63 .. 49 .. 9 Haiti 2 5 29 48 4.6 16 26 56 55 44 24 19 10 Honduras 2 4 40 48 3.2 12 13 29 28 68 80 28 62 166 2011 World Development Indicators 3.11 ENVIRONMENT Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2008 1990 2008 Hungary 7 7 66 68 0.0 19 17 29 25 100 100 100 100 India 217 345 26 30 2.4 10 13 4 6 49 54 7 21 Indonesia 54 121 31 53 4.2 10 9 15 8 58 67 22 36 Iran, Islamic Rep. 31 50 56 69 2.6 24 24 21 14 86 .. 78 .. Iraq 13 21 70 67 2.4 26 23 31 27 .. 76 .. 66 Ireland 2 3 57 62 1.7 26 24 46 39 100 100 98 98 Israel 4 7 90 92 2.5 56 57 48 47 100 100 100 100 Italy 38 41 67 68 0.4 19 17 9 8 .. .. .. .. Jamaica 1 1 49 54 1.1 .. .. 49 40 82 82 83 84 Japan 78 85 63 67 0.5 46 49 42 43 100 100 100 100 Jordan 2 5 72 78 3.9 27 18 37 23 98 98 .. 97 Kazakhstan 9 9 56 58 0.0 7 9 12 15 96 97 97 98 Kenya 4 9 18 22 3.8 6 8 32 39 24 27 27 32 Korea, Dem. Rep. 12 15 58 63 1.3 13 12 21 19 .. .. .. .. Korea, Rep. 32 40 74 82 1.2 51 48 33 25 100 100 100 100 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 2 3 98 98 1.5 65 80 67 81 100 100 100 100 Kyrgyz Republic 2 2 38 36 0.8 .. .. 38 44 94 94 .. 93 Lao PDR 1 2 15 32 6.0 .. .. 70 39 .. 86 .. 38 Latvia 2 2 69 68 -1.0 .. .. 49 46 .. 82 .. 71 Lebanon 2 4 83 87 2.1 43 45 52 52 100 100 .. .. Lesotho 0 1 14 26 4.6 .. .. 50 41 29 40 32 25 Liberia 1 2 45 61 4.7 .. 29 106 37 21 25 3 4 Libya 3 5 76 78 2.2 20 17 26 22 97 97 96 96 Lithuania 2 2 68 67 -0.6 .. .. 23 24 .. .. .. .. Macedonia, FYR 1 1 58 67 1.2 .. .. 40 35 .. 92 .. 82 Madagascar 3 6 24 30 4.2 8 9 36 31 14 15 6 10 Malawi 1 3 12 19 5.2 .. .. 24 28 50 51 41 57 Malaysia 9 20 50 71 4.1 8 9 12 8 88 96 81 95 Mali 2 4 23 33 3.9 9 13 37 38 36 45 23 32 Mauritania 1 1 40 41 2.8 .. .. 53 52 29 50 8 9 Mauritius 0 1 44 43 0.8 .. .. 30 28 93 93 90 90 Mexico 59 83 71 78 1.8 34 36 26 23 80 90 30 68 Moldova 2 1 47 41 -1.6 .. .. 32 43 .. 85 .. 74 Mongolia 1 2 57 57 1.0 .. .. 45 62 .. 64 .. 32 Morocco 12 18 48 56 2.1 18 19 22 18 81 83 27 52 Mozambique 3 9 21 38 5.8 6 7 27 18 36 38 4 4 Myanmar 10 17 25 33 2.6 9 11 29 26 .. 86 .. 79 Namibia 0 1 28 37 3.8 .. .. 35 42 66 60 9 17 Nepal 2 5 9 18 5.9 .. .. 23 19 41 51 8 27 Netherlands 10 14 69 82 1.5 13 12 9 8 100 100 100 100 New Zealand 3 4 85 87 1.3 25 32 30 36 .. .. 88 .. Nicaragua 2 3 52 57 2.2 18 23 34 29 59 63 26 37 Niger 1 3 15 17 3.9 5 7 35 40 19 34 2 4 Nigeria 34 76 35 49 4.2 12 15 14 13 39 36 36 28 Norway 3 4 72 78 1.1 .. .. 22 23 100 100 100 100 Oman 1 2 66 72 2.7 .. .. 27 31 97 97 61 .. Pakistan 33 62 31 37 3.3 16 18 22 21 73 72 8 29 Panama 1 3 54 74 3.6 35 39 65 53 73 75 40 51 Papua New Guinea 1 1 15 13 1.6 .. .. 32 37 78 71 42 41 Paraguay 2 4 49 61 3.3 26 31 53 51 61 90 15 40 Peru 15 21 69 72 1.7 27 30 39 42 71 81 16 36 Philippines 30 60 49 66 3.6 14 14 26 19 70 80 46 69 Poland 23 23 61 61 0.0 4 4 7 7 96 96 .. 80 Portugal 5 6 48 60 1.6 37 39 54 44 97 100 87 100 Puerto Rico 3 4 72 99 2.3 44 69 60 70 .. .. .. .. Qatar 0 1 92 96 6.0 .. .. 54 32 100 100 100 100 2011 World Development Indicators 167 3.11 Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2008 1990 2008 Romania 12 12 53 54 -0.3 9 9 17 17 88 88 52 54 Russian Federation 109 103 73 73 -0.3 17 18 8 10 93 93 70 70 Rwanda 0 2 5 19 8.3 .. .. 57 49 35 50 22 55 Saudi Arabia 12 21 77 82 2.7 34 41 19 23 100 100 .. .. Senegal 3 5 39 43 3.1 19 22 48 52 62 69 22 38 Serbia 4 4 50 52 0.0 15 15 30 29 .. 96 .. 88 Sierra Leone 1 2 33 38 2.5 .. .. 39 40 .. 24 .. 6 Singapore 3 5 100 100 2.6 99 95 99 95 99 100 .. .. Slovak Republic 3 3 57 57 0.1 .. .. .. .. 100 100 100 99 Slovenia 1 1 50 48 -0.1 .. .. 27 26 100 100 100 100 Somalia 2 3 30 37 2.9 16 15 53 40 .. 52 .. 6 South Africa 18 30 52 61 2.6 28 34 10 12 80 84 58 65 Spain 29 36 75 77 1.0 22 23 15 16 100 100 100 100 Sri Lanka 3 3 17 15 0.2 .. .. 21 22 85 88 67 92 Sudan 7 19 27 44 5.0 9 12 33 27 63 55 23 18 Swaziland 0 0 23 25 2.2 .. .. 22 25 .. 61 .. 53 Sweden 7 8 83 85 0.5 12 14 15 16 100 100 100 100 Switzerland 5 6 73 74 0.8 15 15 20 20 100 100 100 100 Syrian Arab Republic 6 12 49 55 3.2 30 32 25 26 94 96 72 95 Tajikistan 2 2 32 26 0.5 .. .. 35 38 93 95 .. 94 Tanzania 5 11 19 26 4.5 5 7 27 28 27 32 23 21 Thailand 17 23 29 34 1.7 10 10 35 30 93 95 74 96 Timor-Leste 0 0 21 28 3.8 .. .. 79 53 .. 76 .. 40 Togo 1 3 30 43 4.6 16 24 52 56 25 24 8 3 Trinidad and Tobago 0 0 9 14 3.0 .. .. 44 32 93 92 93 92 Tunisia 5 7 58 67 2.1 .. .. 14 11 95 96 44 64 Turkey 33 52 59 69 2.3 23 28 20 20 96 97 66 75 Turkmenistan 2 3 45 49 2.2 .. .. 25 25 99 99 97 97 Uganda 2 4 11 13 4.1 4 5 38 36 35 38 40 49 Ukraine 35 31 67 68 -0.5 12 14 7 9 97 97 91 90 United Arab Emirates 1 4 79 78 4.7 25 33 32 42 98 98 95 95 United Kingdom 51 56 89 90 0.5 26 26 15 15 100 100 100 100 United States 188 252 75 82 1.5 42 45 9 8 100 100 99 99 Uruguay 3 3 89 92 0.6 50 49 56 53 95 100 83 99 Uzbekistan 8 10 40 37 1.1 10 8 26 22 95 100 76 100 Venezuela, RB 17 27 84 94 2.5 34 32 17 11 89 .. 45 .. Vietnam 13 25 20 28 3.2 9 12 25 24 61 94 29 67 West Bank and Gaza 1 3 68 72 4.1 .. .. .. .. .. 91 .. 84 Yemen, Rep. 3 7 21 31 5.5 5 9 25 30 64 94 6 33 Zambia 3 5 39 36 2.0 10 11 24 31 62 59 36 43 Zimbabwe 3 5 29 38 2.3 10 13 35 34 58 56 37 37 World 2,257 s 3,398 s 43 w 50 w 2.2 w 17 w 20 w 17 w 16 w 77 w 76 w 35 w 45 w Low income 121 243 22 29 3.7 8 11 33 32 39 44 19 32 Middle income 1,437 2,309 38 48 2.5 14 18 14 13 69 71 31 43 Lower middle income 883 1,559 30 41 3.0 11 15 11 11 58 63 28 41 Upper middle income 554 750 68 75 1.6 25 29 19 19 87 90 58 67 Low & middle income 1,558 2,552 36 45 2.6 13 17 16 15 67 69 29 41 East Asia & Pacific 461 875 29 45 3.4 .. .. 9 7 54 64 37 54 Europe & Central Asia 246 258 63 64 0.2 16 18 14 16 94 94 75 80 Latin America & Carib. 308 452 71 79 2.0 32 35 24 22 81 86 38 54 Middle East & N. Africa 117 191 52 58 2.6 20 20 26 22 90 92 57 76 South Asia 281 467 25 30 2.7 10 13 9 12 53 57 11 27 Sub-Saharan Africa 145 310 28 37 4.0 11 14 27 26 43 43 21 24 High income 699 845 73 77 1.0 .. .. 20 19 100 100 99 98 Euro area 213 241 71 73 0.6 18 18 16 16 100 100 99 100 168 2011 World Development Indicators 3.11 ENVIRONMENT Urbanization About the data Definitions There is no consistent and universally accepted populous nations were to change their definition of • Urban population is the midyear population of standard for distinguishing urban from rural areas, in urban centers. According to China’s State Statis- areas defined as urban in each country and reported part because of the wide variety of situations across tical Bureau, by the end of 1996 urban residents to the United Nations (see About the data). • Popula- countries (see About the data for table 3.1). Most accounted for about 43 percent of China’s popula- tion in urban agglomerations of more than 1 million countries use an urban classification related to the tion, more than double the 20 percent considered is the percentage of a country’s population living in size or characteristics of settlements. Some define urban in 1994. In addition to the continuous migra- metropolitan areas that in 2005 had a population of urban areas based on the presence of certain infra- tion of people from rural to urban areas, one of the more than 1 million. • Population in largest city is structure and services. And other countries designate main reasons for this shift was the rapid growth in the percentage of a country’s urban population living urban areas based on administrative arrangements. the hundreds of towns reclassified as cities in recent in that country’s largest metropolitan area. • Access The population of a city or metropolitan area years. to improved sanitation facilities is the percentage depends on the boundaries chosen. For example, in Because the estimates in the table are based on of the urban or rural population with access to at 1990 Beijing, China, contained 2.3 million people in national definitions of what constitutes a city or met- least adequate excreta disposal facilities (private or 87 square kilometers of “inner city” and 5.4 million ropolitan area, cross-country comparisons should be shared but not public) that can effectively prevent in 158 square kilometers of “core city.” The popula- made with caution. To estimate urban populations, human, animal, and insect contact with excreta. tion of “inner city and inner suburban districts” was UN ratios of urban to total population were applied Improved facilities range from simple but protected 6.3 million and that of “inner city, inner and outer to the World Bank’s estimates of total population pit latrines to flush toilets with a sewerage connec- suburban districts, and inner and outer counties” (see table 2.1). tion. To be effective, facilities must be correctly con- was 10.8 million. (Most countries use the last defini- The table shows access to improved sanitation structed and properly maintained. tion.) For further discussion of urban-rural issues see facilities for both urban and rural populations to box 3.1a in About the data for table 3.1. allow comparison of access. Definitions of access Estimates of the world’s urban population would and urban areas vary, however, so comparisons change significantly if China, India, and a few other between countries can be misleading. Urban population is increasing in developing economies, especially in low and lower middle-income economies 3.11a Urban population (millions) 1990 2009 1,600 1,200 800 400 0 Low income Lower middle income Upper middle income High income Source: Table 3.11. Latin America and Caribbean has the greatest share of urban population, even greater than the high-income economies in 2009 3.11b Percent Urban Rural 100 Data sources 75 Data on urban population and the population in urban agglomerations and in the largest city are 50 from the United Nations Population Division’s World Urbanization Prospects: The 2009 Revi- 25 sion. Data on total population are World Bank estimates. Data on access to sanitation are from 0 the World Health Organization and United Nations East Asia Europe & Latin America Middle East & South Sub-Saharan High & Pacific Central Asia & Caribbean North Africa Asia Africa income Children’s Fund’s Progress on sanitation and drink- Source: Tables 3.1 and 3.11. ing water (2010). 2011 World Development Indicators 169 3.12 Urban housing conditions Census Household Overcrowding Durable dwelling Home Multiunit Vacancy year size units ownership dwellings rate Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2001 4.2 3.9 .. .. .. .. 65b 30 b .. .. 12 13 Algeria 1998 4.9 .. .. .. .. .. 67 .. .. .. 19 .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2001 3.6 .. 19 .. 97 .. .. .. 4 .. 16b .. Armenia 2001 4.1 4.0 4 6 93 93 95 90 1 1 .. .. Australia 2001 3.8 .. 1 .. .. .. .. .. .. .. .. .. Austria 2001 2.4 .. 2 .. .. .. 48 .. .. .. .. .. Azerbaijan 1999 4.7 4.4 .. .. .. .. 74 62 4 5 .. .. Bangladesh 2001 4.8 4.8 .. .. 21b 42b 88b 61b .. .. .. .. Belarus 1999 .. .. .. .. .. .. .. .. .. .. .. .. Belgium 2001 2.6 .. 0b .. .. .. 67 .. 32b .. .. .. Benin 1992 5.9 .. .. .. 26 .. 59 .. .. .. .. .. Bolivia 2001 4.2 4.3 40 .. 43 58 70 59 3b 5b 6 4 Bosnia and Herzegovina  .. .. .. .. .. .. .. .. .. .. .. .. Botswana 2001 4.2 3.9 27 47 88 90 b 61 47 1 .. .. .. Brazil 2000 3.8 3.7 .. .. .. .. 74 75 .. .. .. .. Bulgaria 2001 2.7 2.7 .. .. 79 89 98 98 .. .. 23 17 Burkina Faso 1996 6.2 5.8 30 53 .. .. .. .. .. .. .. .. Burundi 1990 4.7 .. .. .. .. .. .. .. .. .. .. .. Cambodia 2005 5.0 4.9 35 32 79 88 58 57 27 32 .. .. Cameroon 1987 5.2 5.1 67 77 77 .. 73 48 27 42 .. .. Canada 2001 2.6 .. .. .. .. .. 64 .. 32 .. 8 .. Central African Republic  2003 5.2 5.8 32 36b 78 92 85 74 .. .. .. .. Chad 1993 5.1 5.1 .. .. .. .. .. .. .. .. .. .. Chile 2002 3.4 3.5 .. .. 91 92 66 65 13 15 11 10 China 2000 3.4 3.2 .. .. 82 .. 88 74 .. .. 1 .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 1993 4.8 .. 27b .. 83b .. 68b .. 13 .. 10 b .. Congo, Dem. Rep. 1984 5.4 .. 55 .. .. .. .. .. .. .. .. .. Congo, Rep. 1984 10.5 .. .. .. .. .. 76 .. .. .. .. .. Costa Rica 2000 4.0 .. 22 .. 88 .. 72 .. 2 3 9 6 Côte d’Ivoire 1998 5.4 .. .. .. .. .. .. .. .. .. .. .. Croatia 2001 3.0 .. .. .. .. .. .. .. .. .. 12 .. Cuba 2002 3.1 .. 5 .. .. .. .. .. .. .. .. .. Czech Republic 2001 2.4 .. .. .. .. .. 52 .. 49 .. 12 .. Denmark 2001 2.2 .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2002 3.9 .. .. .. 97 .. .. .. 8 .. 11 .. Ecuador 2001 3.5 3.7 30 .. 81 88 68 b 58b 9 14 12 7 Egypt, Arab Rep. 1996 4.7 .. .. .. .. .. .. .. 75 .. .. .. El Salvador 1992 .. .. 63 .. 67 83 70 68 3 6 11 11 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 2000 2.4 2.3 3 .. .. .. .. .. 72 .. 13 .. Ethiopia 1994 4.8 4.7 .. .. .. 23 .. 54 .. .. .. .. Finland 2000 2.2 .. .. .. .. .. 64 .. 44 .. .. .. France 1999 2.5 .. .. .. .. .. 55 .. .. .. 7 .. Gabon 2003 5.2 .. .. .. .. .. .. .. .. .. .. .. Gambia 1993 8.9 .. .. .. 18 .. 68 .. .. .. .. .. Georgia 2002 3.5 3.5 .. .. .. .. .. .. .. .. .. .. Germany 2001 2.3 .. .. .. .. .. 43 .. .. .. 7 .. Ghana 2000 5.1 5.1 .. .. 45 .. 57 .. 53 .. 5 .. Greece 2001 3.0 .. 1 .. .. .. .. .. .. .. .. .. Guatemala 2002 4.4 4.7 .. .. 67 80 81 74 2 4 13 11 Guinea 1996 6.7 .. 63 .. .. .. 76 .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1982 4.2 .. 26 .. .. .. 92 68 .. .. 9 19 Honduras 2001 4.4 .. .. .. 69 85 .. .. .. .. 14 .. 170 2011 World Development Indicators 3.12 ENVIRONMENT Urban housing conditions Census Household Overcrowding Durable dwelling Home Multiunit Vacancy year size units ownership dwellings rate Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Hungary 2001 2.6 .. 2 .. .. .. .. .. .. .. 4 .. India 2001 5.3 5.3 77 71 83 81 87 67 .. .. 6 9 Indonesia 2000 4.0 .. .. .. .. .. .. .. .. .. .. .. Iran, Islamic Rep. 1996 4.8 4.6 33b 26b 72 76 73 67 .. .. .. .. Iraq 1997 7.7 7.2 .. .. 88 96 70 66 4 5 13 15 Ireland 2002 3.0 .. .. .. .. .. .. .. 8b .. .. .. Israel 1995 3.5 .. .. .. .. .. .. .. .. .. .. .. Italy 2001 2.8 .. .. .. .. .. .. .. .. .. 21 .. Jamaica 2001 3.5 .. .. .. 98b .. 58b .. 2b .. .. .. Japan 2000 2.7 .. .. .. .. .. 61 .. 37 .. .. .. Jordan 2004 5.3 5.1 35 34 .. .. 64 60 72 80 .. .. Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya 1999 4.6 3.4 .. .. 35 72 72 25 .. .. 39 17 Korea, Dem. Rep. 2000 3.8 .. 23 .. .. .. 50 .. 15 .. .. .. Korea, Rep. 1993 4.4 .. .. .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 1995 6.4 .. .. .. .. .. .. .. 9b .. 11 .. Kyrgyz Republic 1999 4.4 3.6 .. .. .. .. .. .. .. .. .. .. Laos 1995 6.1 6.1 .. .. 49 77 96 86 .. .. .. .. Latvia 2000 3.0 2.6 4 .. 88 .. 58 .. 74 .. 0 .. Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho 2001 5.0 .. 10 b .. .. .. 84 .. 0 .. .. .. Liberia 1974 4.8 .. 31 .. 20 .. 1 .. .. .. .. .. Libya 6.4 .. .. .. .. .. .. .. .. .. 7 .. Lithuania 2001 2.6 .. 7 .. .. .. .. .. .. .. .. .. Macedonia, FYR 2002 3.6 3.6 8b .. 95b 95b 48b .. .. .. 7b 3b Madagascar 1993 4.9 4.8 64 57 .. .. 81 59 .. .. .. .. Malawi 1998 4.4 4.4 30 .. 48 84 86 47 .. .. .. .. Malaysia 2000 4.5 4.4 .. .. .. .. .. .. 10 b 16b .. .. Mali 1998 5.6 .. .. .. .. .. .. .. .. .. .. .. Mauritania 1988 .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 2000 3.9 3.8 6 7 91 94 87 81 .. .. 7 6 Mexico 2005 4.0 3.9 24 20 .. .. .. .. .. .. 3 2 Moldova 2003 .. .. .. .. .. .. .. .. .. .. .. .. Mongolia 2000 4.4 4.5 .. .. .. .. .. .. 48 56 .. .. Morocco 1982 5.9 5.3 .. .. .. .. .. .. .. .. .. .. Mozambique 1997 4.4 4.9 37 28 7 20 92 83 1 1 0 .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2001 5.3 .. .. .. .. .. .. .. .. .. .. .. Nepal 2001 5.4 4.9 .. .. .. .. 88 .. .. .. 0 .. Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand 2001 2.8 .. 1b .. .. .. 65 .. 17 .. 10 .. Nicaragua 1995 5.3 .. .. .. 79 87 84 86 0 0 8 .. Niger 2001 6.4 6.0 .. .. .. .. 77 40 .. .. .. .. Nigeria 1991 5.0 4.7 .. .. .. .. .. .. .. .. .. .. Norway 1980 2.7 .. 1 .. .. .. 67 .. 38 .. .. .. Oman 2003 7.1 .. .. .. .. .. .. .. .. .. .. .. Pakistan 1998 6.8 6.8 .. .. 58 86 81 .. .. .. .. .. Panama 2000 4.1 .. 28b .. 88 98b 80 66b 10 b 10 b 14 .. Papua New Guinea 1990 4.5b 6.5 .. .. .. .. .. 44 .. 8 .. .. Paraguay 2002 4.6 4.5 38b ..b 95b 98b 79 75 1b 2b 6b 6b Peru 2007 3.9 3.9 35 31 .. .. .. .. .. .. .. .. Philippines 2000 4.9 .. .. .. .. .. 71 .. 12       Poland 1988 3.2 .. .. .. .. .. .. .. .. .. 1 .. Portugal 2001 2.8 .. .. .. .. .. 76 .. 86 .. .. .. Puerto Rico 2005 2.8 . 1 .. .. .. 75 .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 171 3.12 Urban housing conditions Census Household Overcrowding Durable dwelling Home Multiunit Vacancy year size units ownership dwellings rate Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Romania 2002 2.9 2.8 20 20 .. .. 84 72 .. .. .. .. Russian Federation 2002 2.8 2.7 7 5 .. .. .. .. 73 86 .. .. Rwanda 2002 4.4 3.7 43 36 13 31 79 41 36 60 .. .. Saudi Arabia 2004 5.5 .. .. .. 92b .. 43 .. .. .. .. .. Senegal 2002 9.2 8.0 72 68 .. .. 74 54 .. .. .. .. Serbia 2001 2.9 2.2 .. .. .. .. .. .. .. .. .. .. Sierra Leone 1985 6.8 .. .. .. 34 .. 68 .. .. .. .. .. Singapore 2000 4.4 .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia 2002 2.8 2.7 14 17 .. .. 91 87 33 56 .. .. Somalia 1975 .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2007 3.0 2.8 16 15 .. .. 43 40 .. .. .. .. Spain 2001 2.9 .. 1 .. .. .. 82 .. .. .. .. .. Sri Lanka 2001 3.8 .. .. .. 93b 92b 70 b 58b 1 14b 13 1b Sudan 1993 5.8 6.0 .. .. .. .. 86b 58b 0b 1b .. .. Swaziland 1997 5.4 3.7 .. .. .. .. .. .. .. .. .. .. Sweden 1990 2.0 .. .. .. .. .. .. .. 54 .. 1 .. Switzerland 2000 2.2 .. 1 .. .. .. 34 .. 77 .. .. .. Syrian Arab Republic 1981 6.3 6.0 .. .. .. .. .. .. .. .. .. .. Tajikistan 2000 .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 2002 4.9 4.5 33b 7b .. .. 82b 43b .. .. .. .. Thailand 2000 3.8 .. .. .. 93 93 81 62 3 .. 3 .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 2000 3.7 .. 9b .. 98b .. 74b .. 17b .. .. .. Tunisia 1994 8.0 .. .. .. 99 .. 71 89b 6 10 b 15 12b Turkey 1990 5.0 .. .. .. .. .. 70 .. .. .. .. .. Turkmenistan ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  Uganda 2002 4.7 3.9 .. .. 19 61 76 28 37 71 .. .. Ukraine 2003 .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 2001 .. 2.4 .. .. .. .. .. 69 .. 19 .. .. United States 2005 2.5 .. 0 .. .. .. 74 .. 26 .. .. .. Uruguay 1996 3.3 3.4 22b .. .. .. 57b 57b .. .. 13b 13b Uzbekistan ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  Venezuela. RB 2001 4.4 .. .. .. .. .. 78 .. 14 .. 16 .. Vietnam 1999 4.6 4.5 .. .. 77 89 95 86 .. .. .. .. West Bank and Gaza 1997 7.1  ..  ..  ..  ..  ..  78  ..  45  ..  ..  ..  Yemen 1994 6.7 6.8 54b 6b .. .. 88b 68b 3b 11b .. .. Zambia 2000 5.3  5.9  ..  ..  ..  ..  94  30  ..  ..  ..  ..  Zimbabwe 1992 4.8 4.2 .. .. .. .. 94 30 6 .. .. .. a. More than two people per room. b. Data are from a previous census. 172 2011 World Development Indicators 3.12 ENVIRONMENT Urban housing conditions About the data Definitions Urbanization can yield important social benefi ts, There is a strong demand for quantitative indi- • Census year is the year in which the underlying improving access to public services and the job mar- cators that can measure housing conditions on a data were collected. • Household size is the aver- ket. It also leads to significant demands for services. regular basis to monitor progress. However, data age number of people within a household, calcu- Inadequate living quarters and demand for housing deficiencies and lack of rigorous quantitative analy- lated by dividing total population by the number and shelter are major concerns for policymakers. sis hamper informed decisionmaking on desirable of households in the country and in urban areas. The unmet demand for affordable housing, along policies to improve housing conditions. The data • Overcrowding refers to the number of households with urban poverty, has led to the emergence of in the table are from housing and population cen- living in dwellings with two or more people per room slums in many poor countries. Improving the shel- suses, collected using similar definitions. The table as a percentage of total households in the country ter situation requires a better understanding of the will incorporate household survey data in future edi- and in urban areas. • Durable dwelling units are mechanisms governing housing markets and the pro- tions. The table focuses attention on urban areas, the number of housing units in structures made of cesses governing housing availability. That requires where housing conditions are typically most severe. durable building materials (concrete, stone, cement, good data and adequate policy-oriented analysis so Not all the compiled indicators are presented in the brick, asbestos, zinc, and stucco) expected to main- that housing policy can be formulated in a global table because of space limitations. tain their stability for 20 years or longer under local comparative perspective and drawn from lessons conditions with normal maintenance and repair, tak- learned in other countries. Housing policies and ing into account location and environmental hazards outcomes affect such broad socioeconomic condi- such as floods, mudslides, and earthquakes, as a tions as the infant mortality rate, performance in percentage of total dwellings. • Home ownership school, household saving, productivity levels, capital refers to the number of privately owned dwellings as formation, and government budget deficits. A good a percentage of total dwellings. When the number understanding of housing conditions thus requires of private dwellings is not available from the census an extensive set of indicators within a reasonable data, the share of households that own their housing framework. unit is used. Privately owned and owner-occupied units are included, depending on the definition used Selected housing indicators for smaller economies 3.12a in the census data. State- and community-owned units and rented, squatted, and rent-free units are Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate excluded. • Multiunit dwellings are the number units of multiunit dwellings, such as apartments, flats, Households living in Buildings Privately condominiums, barracks, boardinghouses, orphan- overcrowded with durable owned Unoccupied ages, retirement houses, hostels, hotels, and col- number of dwellingsa structure dwellings dwellings people % of total % of total % of total % of total % of total lective dwellings, as a percentage of total dwellings. Antigua and Barbuda 2001 3.0 .. 99b 65b 3b 22 • Vacancy rate is the percentage of completed dwell- Bahamas 1990 3.8 12 99 55 13 14 ing units that are currently unoccupied. It includes Bahrain 2001 5.9 .. 94b 51 28 6 all vacant units, whether on the market or not (such Barbados 1990 3.5 3 100 76 9 9 Belize 2000 4.6 .. 93 63 4 .. as second homes). Cape Verde 1990 5.1 28 78 72 2 .. Cayman Islands 1999 3.1 .. 100 53 38 19 Equatorial Guinea 1993 7.5 14 56b 75 14 .. Fiji 1996 5.4 .. 60 65 7 .. Guam 2000 4.0 2b 93 48 29 19 Isle of Man 2001 2.4 0 .. 68 16 .. Maldives 2000 6.6 .. 93 .. 1 15 Marshall Islands 1999 7.8 .. 95 72 12 8 Netherlands Antilles 2001 2.9 24b 99 60 16 12 New Caledonia 1989 4.1 .. 77 53 9 13 Northern Mariana Islands 1995 4.9 9b 99 33 27 17 Palau 2000 5.7 8 76 79 11 3 Seychelles 1997 4.2 15b 97 78 .. 0 Solomon Islands 1999 6.3 51 23 85 1 .. St. Vincent & Grenadines 1991 3.9 .. 98 71 7 .. Turks and Caicos 1990 3.3 4 96 66 11 .. Virgin Islands (UK) 1991 3.0 2 99 40 46 .. Data sources Western Samoa 1991 7.3 .. 42 90 47 30 Data on urban housing conditions are from a. More than two people per room. b. Data are from a previous census. Source: National population and housing censuses. national population and housing censuses. 2011 World Development Indicators 173 3.13 Traffic and congestion Motor Passenger Road Road sector Fuel Particulate matter vehicles cars density energy consumption price concentration km. of road per Urban-population- per per per 100 sq. kilograms of oil equivalent per capita $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total Diesel Gasoline Super grade micrograms per people of road people land area consumption Total fuel fuel gasoline Diesel cubic meter 2008 2008 2008 2008 2008 2008 2008 2008 2010 2010 1990 2008 Afghanistan 27 19 19 6 .. .. .. .. 1.15 1.00 68 37 Albania 114 20 84 63 32 213 184 24 1.46 1.40 92 46 Algeria 112 35 72 5 16 173 92 63 0.32 0.19 113 69 Angola 40 .. 8 .. 11 65 31 30 0.65 0.43 111 55 Argentina 314 .. .. 8 18 346 179 102 0.96 1.05 104 68 Armenia 105 42 96 26 10 100 0 64 1.08 0.99 481 69 Australia 687 18 551 11 18 1,091 327 645 1.27 1.23 22 14 Austria 562 42 514 132 22 877 605 199 1.63 1.55 39 29 Azerbaijan 89 13 72 61 12 188 72 108 0.75 0.56 132 33 Bangladesh 2 2 1 166 6 11 5 2 1.09 0.63 237 134 Belarus 282 .. 240 46 6 161 89 51 1.08 0.86 23 7 Belgium 543 38 479 503 15 827 659 134 1.87 1.62 30 21 Benin 21 .. 17 17 23 79 27 47 1.04 1.21 78 45 Bolivia 68 7 18 6 25 149 72 48 0.70 0.54 113 74 Bosnia and Herzegovina 135 23 119 43 15 242 151 84 1.42 1.42 36 19 Botswana 113 7 56 4 31 340 136 186 0.93 0.97 93 69 Brazil 198 18 158 21 23 298 148 73 1.58 1.14 39 21 Bulgaria 353 67 310 36 13 335 196 78 1.51 1.58 108 51 Burkina Faso 11 2 7 34 .. .. .. .. 1.44 1.28 144 64 Burundi 6 .. 2 44 .. .. .. .. 1.43 1.42 68 31 Cambodia 20 6 18 21 7 26 14 11 1.15 0.98 88 41 Cameroon .. .. 11 11 10 36 17 18 1.20 1.10 122 47 Canada 605 14 399 14 17 1,324 336 889 1.21 1.08 25 15 Central African Republic 0 0 0 .. .. .. .. .. 1.71 1.69 60 34 Chad 6 2 .. 3 .. .. .. .. 1.32 1.31 209 81 Chile 172 36 109 .. 18 345 191 137 1.38 1.02 92 62 China 37 13 27 39 5 85 36 45 1.11 1.04 115 66 Hong Kong SAR, China 73 248 55 187 10 204 149 47 1.92 1.32 .. .. Colombia 58 16 41 14 25 171 81 70 1.41 0.95 38 20 Congo, Dem. Rep. 5 .. .. 7 1 3 0 3 1.28 1.27 71 40 Congo, Rep. 26 .. 15 5 26 98 67 27 1.27 0.84 129 68 Costa Rica 163 19 126 74 30 320 159 144 1.14 0.97 43 32 Côte d’Ivoire 20 5 16 25 4 21 14 6 1.68 1.30 87 32 Croatia 388 59 346 52 21 432 249 153 1.59 1.49 45 27 Cuba 38 7 21 .. 3 29 21 5 1.72 1.24 42 23 Czech Republic 513 41 424 166 13 553 330 189 1.75 1.69 67 18 Denmark 477 36 377 170 23 779 442 312 2.00 1.79 29 16 Dominican Republic 123 .. 62 .. 18 144 48 89 1.23 1.03 43 16 Ecuador 63 19 38 15 38 289 123 151 0.53 0.28 36 20 Egypt, Arab Rep. 43 33 31 10 17 145 79 54 0.48 0.32 212 97 El Salvador 84 .. 41 .. 16 131 58 67 0.92 0.89 44 28 Eritrea 11 .. 6 .. 5 7 6 1 2.54 1.07 141 71 Estonia 477 11 412 128 13 542 286 239 1.54 1.57 44 13 Ethiopia 3 4 1 4 4 16 13 2 0.91 0.78 108 59 Finland 534 36 461 23 11 740 419 284 1.94 1.60 22 15 France 598 39 495 173 16 666 483 129 1.98 1.72 18 13 Gabon .. .. .. 3 10 143 106 31 1.14 0.90 9 7 Gambia, The 7 3 5 33 .. .. .. .. 0.79 0.75 136 62 Georgia 116 16 95 29 19 135 45 81 1.13 1.13 204 49 Germany 554 71 502 180 15 609 309 243 1.90 1.68 27 16 Ghana 33 13 21 24 12 49 23 23 0.82 0.83 38 24 Greece 560 54 443 88 21 581 192 359 2.05 1.78 64 32 Guatemala 117 .. .. .. 23 134 64 63 0.95 0.85 69 60 Guinea .. .. .. 18 .. .. .. .. 0.95 0.95 103 53 Guinea-Bissau 33 15 27 12 .. .. .. .. .. .. 114 47 Haiti .. .. .. .. 9 25 0 23 1.16 0.89 68 35 Honduras 97 .. 69 .. 21 135 73 55 1.04 0.92 44 42 174 2011 World Development Indicators 3.13 ENVIRONMENT Traffic and congestion Motor Passenger Road Road sector Fuel Particulate matter vehicles cars density energy consumption price concentration km. of road per Urban-population- per per per 100 sq. kilograms of oil equivalent per capita $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total Diesel Gasoline Super grade micrograms per people of road people land area consumption Total fuel fuel gasoline Diesel cubic meter 2008 2008 2008 2008 2008 2008 2008 2008 2010 2010 1990 2008 Hungary 384 20 304 212 16 435 254 149 1.67 1.61 33 16 India 15 4 10 129 7 36 22 10 1.15 0.82 111 59 Indonesia 77 40 43 23 12 103 31 67 0.79 0.51 133 72 Iran, Islamic Rep. 128 53 113 10 19 522 223 249 0.10 0.02 86 55 Iraq .. .. .. .. 30 330 185 129 0.78 0.56 164 138 Ireland 534 24 451 137 29 996 570 385 1.78 1.69 23 13 Israel 313 126 260 82 16 481 155 300 1.85 1.87 66 28 Italy 673 83 596 162 21 626 389 181 1.87 1.69 41 23 Jamaica 188 24 138 202 12 204 0 190 0.98 0.98 55 37 Japan 593 63 319 318 14 541 175 331 1.60 1.37 42 27 Jordan 146 110 102 9 22 264 105 150 1.04 0.73 107 33 Kazakhstan 197 33 164 3 6 277 25 238 0.71 0.51 42 15 Kenya 21 10 15 11 6 26 16 9 1.33 1.27 64 30 Korea, Dem. Rep. .. .. .. 21 2 17 9 7 1.51 1.40 180 59 Korea, Rep. 346 161 257 105 12 559 278 152 1.52 1.35 51 31 Kosovo .. .. .. .. .. .. .. .. 1.63 1.60 .. .. Kuwait 507 233 282 32 14 1,343 401 868 0.23 0.21 77 95 Kyrgyz Republic 59 9 44 17 17 94 0 89 0.85 0.79 76 26 Lao PDR 21 10 2 15 .. .. .. .. 1.26 0.97 87 39 Latvia 474 15 412 108 24 481 293 163 1.48 1.49 38 13 Lebanon .. .. .. 67 29 360 3 334 1.13 0.77 64 36 Lesotho .. .. .. .. .. .. .. .. 0.97 1.07 123 46 Liberia 3 .. 2 .. .. .. .. .. 0.98 0.96 68 31 Libya 291 .. 225 .. 19 542 325 192 0.17 0.13 101 76 Lithuania 546 23 498 124 18 486 275 122 1.59 1.42 52 17 Macedonia, FYR 144 21 129 54 13 194 106 58 1.52 1.27 45 20 Madagascar 27 10 8 .. .. .. .. .. 1.52 1.26 91 33 Malawi 9 .. 4 13 .. .. .. .. 1.71 1.54 93 35 Malaysia 334 83 298 30 19 523 193 310 0.59 0.56 35 20 Mali 9 .. 7 2 .. .. .. .. 1.42 1.25 259 112 Mauritania .. .. .. 1 .. .. .. .. 1.16 0.99 145 69 Mauritius 159 99 123 99 .. .. .. .. 1.55 1.23 21 18 Mexico 264 77 181 19 28 472 128 312 0.81 0.72 67 33 Moldova 139 39 101 38 10 85 53 26 1.21 1.08 109 36 Mongolia 72 4 48 3 13 157 8 139 1.11 1.04 190 111 Morocco 71 38 53 13 24 112 93 15 1.23 0.88 38 27 Mozambique 13 10 9 4 4 18 13 4 1.11 0.86 112 26 Myanmar 7 13 5 4 7 22 11 8 0.80 0.80 113 46 Namibia 109 4 52 .. 33 274 77 170 1.06 1.09 73 48 Nepal 5 .. 3 12 3 11 7 2 1.18 0.91 67 32 Netherlands 515 62 449 328 15 708 396 252 2.13 1.71 45 31 New Zealand 733 33 616 35 25 1,004 423 529 1.47 0.97 14 12 Nicaragua 57 16 17 16 13 79 44 32 1.09 0.99 44 23 Niger 5 4 4 1 .. .. .. .. 1.07 1.16 199 96 Nigeria 31 .. 31 21 8 58 8 46 0.44 0.77 195 46 Norway 575 29 461 29 12 733 437 271 2.12 2.01 21 16 Oman 225 12 174 17 11 665 56 567 0.31 0.38 136 94 Pakistan 11 8 9 33 13 63 40 9 0.86 0.92 220 109 Panama 120 30 131 18 17 148 0 138 0.85 0.77 58 34 Papua New Guinea 9 .. 6 .. .. .. .. .. 0.94 0.90 35 18 Paraguay 82 .. 39 .. 26 181 140 31 1.28 1.01 106 67 Peru 55 15 35 8 29 148 106 29 1.41 1.10 96 51 Philippines 33 14 11 67 17 76 43 28 1.05 0.84 56 19 Poland 495 49 422 123 15 391 216 105 1.57 1.50 60 35 Portugal 509 70 495 90 25 579 409 140 1.85 1.58 49 21 Puerto Rico 642 .. 614 287 .. .. .. .. 0.65 0.78 23 21 Qatar 724 .. 335 67 12 2,245 1,388 756 0.19 0.19 71 35 2011 World Development Indicators 175 3.13 Traffic and congestion Motor Passenger Road Road sector Fuel Particulate matter vehicles cars density energy consumption price concentration km. of road per Urban-population- per per per 100 sq. kilograms of oil equivalent per capita $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total Diesel Gasoline Super grade micrograms per people of road people land area consumption Total fuel fuel gasoline Diesel cubic meter 2008 2008 2008 2008 2008 2008 2008 2008 2010 2010 1990 2008 Romania 219 24 187 83 12 216 136 67 1.46 1.46 36 12 Russian Federation 245 35 206 6 7 318 80 222 0.84 0.72 41 16 Rwanda 4 3 2 53 .. .. .. .. 1.63 1.62 60 27 Saudi Arabia .. 20 415 11 20 1,279 568 646 0.16 0.07 157 104 Senegal 23 19 17 8 24 57 45 10 1.57 1.34 92 81 Serbia 227 42 202 45 12 251 182 63 1.50 1.48 33a 14 a Sierra Leone 5 2 3 .. .. .. .. .. 0.94 0.94 87 38 Singapore 150 218 114 475 13 494 305 167 1.42 1.04 107 31 Slovak Republic 319 35 272 89 11 379 230 115 1.70 1.53 46 13 Slovenia 565 29 520 192 26 985 628 316 1.67 1.62 38 29 Somalia .. .. .. .. .. .. .. .. 1.12 1.15 94 31 South Africa 159 .. 108 .. 11 293 121 161 1.19 1.14 33 22 Spain 606 41 486 132 23 703 539 135 1.56 1.47 41 28 Sri Lanka 61 13 19 148 19 86 56 25 1.19 0.66 94 74 Sudan 28 .. 20 .. 14 54 36 15 0.62 0.43 282 159 Swaziland 89 25 46 21 .. .. .. .. 1.07 1.10 55 35 Sweden 521 8 464 128 16 844 410 365 1.87 1.82 15 11 Switzerland 567 61 522 173 22 754 283 441 1.66 1.77 35 22 Syrian Arab Republic 62 20 27 35 20 189 118 62 0.96 0.45 145 69 Tajikistan 38 .. 29 .. 4 15 0 12 1.02 0.91 112 43 Tanzania 73 3 4 9 6 26 19 6 1.22 1.19 56 22 Thailand .. .. 54 35 16 262 153 73 1.41 0.95 77 55 Timor-Leste .. .. .. .. .. .. .. .. 1.40 0.90 .. .. Togo 2 .. 2 21 11 45 15 27 1.18 1.17 57 29 Trinidad and Tobago 351 .. .. .. 4 587 229 327 0.36 0.24 135 105 Tunisia 114 61 76 12 17 151 101 41 0.94 0.82 71 26 Turkey 138 24 92 54 14 181 114 31 2.52 2.03 76 37 Turkmenistan 106 22 80 .. 5 191 0 182 0.22 0.20 259 65 Uganda 7 3 3 29 .. .. .. .. 1.42 1.11 33 12 Ukraine 152 41 138 28 6 177 55 114 1.01 0.92 71 18 United Arab Emirates 313 .. 293 5 14 1,884 964 829 0.47 0.71 281 87 United Kingdom 526 77 462 172 19 641 335 271 1.92 1.98 24 13 United States 809 38 451 68 23 1,703 399 1,148 0.76 0.84 30 19 Uruguay 176 .. 151 44 21 259 163 81 1.49 1.44 236 160 Uzbekistan .. .. .. .. 3 63 8 49 0.92 0.83 145 40 Venezuela, RB 147 .. 107 .. 24 553 81 416 0.02 0.01 21 9 Vietnam 13 7 13 48 13 90 50 38 0.88 0.77 123 53 West Bank and Gaza 39 29 30 85 .. .. .. .. 1.71 1.54 .. .. Yemen, Rep. 35 .. .. 14 27 90 18 62 0.35 0.23 137 67 Zambia 18 .. 11 .. 2 10 0 10 1.66 1.52 124 39 Zimbabwe 106 .. 91 25 4 27 15 11 1.29 1.15 55 40 World .. w .. w 118 w 28 w 14 w 261 w 103 w 135 w 1.21 m 1.07 m 80 w 46 w Low income .. .. .. .. 5 19 10 7 1.18 1.11 128 60 Middle income 42 .. 36 25 10 129 56 61 1.08 0.96 96 53 Lower middle income 23 8 15 50 8 78 37 36 1.05 0.89 121 63 Upper middle income .. .. 129 .. 15 320 127 156 1.14 1.03 57 31 Low & middle income .. .. 35 22 10 116 51 55 1.11 0.98 98 54 East Asia & Pacific 47 16 33 36 7 97 42 50 1.08 0.93 112 61 Europe & Central Asia 185 30 152 8 8 228 82 128 1.17 1.11 58 24 Latin America & Carib. 169 .. 118 18 23 302 121 136 1.04 0.98 58 32 Middle East & N. Africa 88 .. 66 12 19 259 128 111 0.94 0.56 124 71 South Asia 16 4 10 129 7 36 23 9 1.12 0.83 133 72 Sub-Saharan Africa 34 .. 25 .. 8 57 24 31 1.22 1.15 119 49 High income 622 38 432 43 19 964 356 526 1.63 1.54 38 24 Euro area 592 .. 418 140 18 665 422 194 1.78 1.62 33 20 a. Includes Montenegro. 176 2011 World Development Indicators 3.13 ENVIRONMENT Traffic and congestion About the data Definitions Traffic congestion in urban areas constrains eco- associations. If they lack data or do not respond, • Motor vehicles include cars, buses, and freight nomic productivity, damages people’s health, and other agencies are contacted, including road direc- vehicles but not two-wheelers. Population fi gures degrades the quality of life. In recent years own- torates, ministries of transport or public works, and refer to the midyear population in the year for ership of passenger cars has increased, and the central statistical offices. As a result, data quality which data are available. Roads refer to motor- expansion of economic activity has led to more is uneven. Coverage of each indicator may differ ways, highways, main or national roads, and sec- goods and services being transported by road over across countries because of different definitions. ondary or regional roads. A motorway is a road greater distances (see table 5.10). These devel- Comparability is also limited when time series data designed and built for motor traffi c that sepa- opments have increased demand for roads and are reported. The IRF is taking steps to improve the rates the traffi c fl owing in opposite directions. vehicles, adding to urban congestion, air pollution, quality of the data in its World Road Statistics 2010. • Passenger cars are road motor vehicles, other than health hazards, and traffic accidents and injuries. Because this effort covers 2003–08 only, time two-wheelers, intended for the carriage of passen- The data on motor vehicles, passenger cars, and series data may not be comparable. Another rea- gers and designed to seat no more than nine people road density in the table are compiled by the Interna- son is coverage. Road density is a rough indicator of (including the driver). • Road density is the ratio of tional Road Federation (IRF) through questionnaires accessibility and does not capture road width, type, the length of the country’s total road network to the sent to national organizations. The IRF uses a hier- or condition. Thus comparisons over time and across country’s land area. The road network includes all archy of sources to gather as much information as countries should be made with caution. roads in the country—motorways, highways, main possible. Primary sources are national road Road sector energy consumption includes energy or national roads, secondary or regional roads, and from petroleum products, natural gas, renewable and other urban and rural roads. • Road sector energy Biogasoline consumption as a share combustible waste, and electricity. Biodiesel and bio- consumption is the total energy used in the road of total consumption is highest in Brazil . . . 3.13a gasoline, forms of renewable energy, are biodegrad- sector, including energy from petroleum products, able and emit less sulfur and carbon monoxide than natural gas, combustible and renewable waste, Biogasoline consumption (percent of total consumption) 2000 2008 petroleum-derived ones. They can be produced from and electricity. • Total energy consumption is the 40 vegetable oils, such as soybean, corn, palm, peanut, country’s total energy consumption from all sources or sunflower oil, and can be used directly only in a (see table 3.7). • Gasoline is light hydrocarbon oil modified internal combustion engine. Data are pro- use in internal combustion engines such as motor 30 vided by the International Energy Agency. vehicles, excluding aircraft. • Diesel is heavy oils Data on fuel prices are compiled by the German used as a fuel for internal combustion in diesel 20 Agency for International Cooperation (GIZ), from its engines. • Fuel price is the pump price of super global network, and other sources, including the grade gasoline and of diesel fuel, converted from the Allgemeiner Deutscher Automobile Club (for Europe) local currency to U.S. dollars (see About the data). 10 and the Latin American Energy Organization for Latin • Particulate matter concentration is fi ne sus- America. Local prices are converted to U.S. dollars pended particulates of less than 10 microns in diam- 0 using the exchange rate in the Financial Times inter- eter (PM10) that are capable of penetrating deep Brazil France Canada Germany China United States national monetary table on the survey date. When into the respiratory tract and causing severe health Source: International Energy Agency. multiple exchange rates exist, the market, parallel, damage. Data are urban-population-weighted PM10 or black market rate is used. Prices were compiled levels in residential areas of cities with more than . . . but the United States consumes in mid-November 2010, based on the crude oil price 100,000 residents. The estimates represent the the most biogasoline 3.13b of $81 per barrel Brent. average annual exposure level of the average urban Considerable uncertainty surrounds estimates of resident to outdoor particulate matter. particulate matter concentrations, and caution should be used in interpreting them. They allow for cross- Data sources country comparisons of the relative risk of particulate matter pollution facing urban residents. Major sources Data on vehicles and road density are from the of urban outdoor particulate matter pollution are IRF’s electronic files and its annual World Road traffic and industrial emissions, but nonanthropogenic Statistics, except where noted. Data on road sector sources such as dust storms may also be a substan- energy consumption are from the IRF and the Inter- tial contributor for some cities. Country technology national Energy Agency. Data on fuel prices are and pollution controls are important determinants of from the GIZ’s electronic files. Data on particulate particulate matter. Data on particulate matter for matter concentrations are from Pandey and oth- selected cities are in table 3.14. ers’ “Ambient Particulate Matter Concentrations in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model Source: International Energy Agency. of Ambient Particulates (GMAPS)” (2006b). 2011 World Development Indicators 177 3.14 Air pollution City City Particulate Sulfur Nitrogen About the data population matter dioxide dioxide concentration Urban- Indoor and outdoor air pollution places a major burden population- on world health. More than half the world’s people weighted PM10 rely on dung, wood, crop waste, or coal to meet basic micrograms per micrograms per micrograms per thousands cubic meter cubic meter cubic meter energy needs. Cooking and heating with these fuels on 2009 1990 2008 2001 a 2001 a open fires or stoves without chimneys lead to indoor air Argentina Buenos Aires 12,988 159 104 .. .. pollution, which is responsible for 1.6 million deaths a Córdoba 1,479 78 51 .. 97 year—one every 20 seconds. In many urban areas air Australia Melbourne 3,813 17 11 .. 30 pollution exposure is the main environmental threat to Perth 1,578 16 11 5 19 health. Long-term exposure to high levels of soot and Sydney 4,395 27 18 28 81 small particles contributes to a range of health effects, Austria Vienna 1,693 45 34 14 42 including respiratory diseases, lung cancer, and heart Belgium Brussels 1,892 33 23 20 48 Brazil Rio de Janeiro 11,836 49 26 129 .. disease. Particulate pollution, alone or with sulfur diox- São Paulo 19,960 57 30 43 83 ide, creates an enormous burden of ill health. Bulgaria Sofia 1,192 118 55 39 122 Sulfur dioxide and nitrogen dioxide emissions lead Canada Montréal 3,750 24 15 10 42 to deposition of acid rain and other acidic compounds Toronto 5,377 29 17 17 43 over long distances, which can lead to the leaching Vancouver 2,197 17 10 14 37 of trace minerals and nutrients critical to trees and Chile Santiago 5,883 103 69 29 81 plants. Sulfur dioxide emissions can damage human China Anshan 1,632 132 75 115 88 Beijing 12,214 141 80 90 122 health, particularly that of the young and old. Nitrogen Changchun 3,504 117 66 21 64 dioxide is emitted by bacteria, motor vehicles, indus- Chengdu 4,869 136 77 77 74 trial activities, nitrogen fertilizers, fuel and biomass Chongqing 9,348 194 110 340 70 combustion, and aerobic decomposition of organic Dalian 3,252 79 45 61 100 matter in soils and oceans. Guangzhou 8,735 99 56 57 136 Where coal is the primary fuel for power plants Guiyang 2,125 111 63 424 53 Harbin 4,224 121 69 23 30 without effective dust controls, steel mills, industrial Jinan 3,186 148 84 132 45 boilers, and domestic heating, high levels of urban Kunming 3,062 111 63 19 33 air pollution are common—especially particulates and Lanzhou 2,243 145 82 102 104 sulfur dioxide. Elsewhere the worst emissions are from Liupanshui 1,221 94 53 102 .. petroleum product combustion. Nanchang 2,648 124 70 69 29 Sulfur dioxide and nitrogen dioxide concentration Shanghai 16,344 115 65 53 73 data are based on average observed concentrations Shenyang 5,074 160 90 99 73 Shenzhen 8,847 89 50 .. .. at urban monitoring sites, which not all cities have. Tianjin 7,759 198 112 82 50 The data on particulate matter are estimated aver- Wuhan 7,582 125 71 40 43 age annual concentrations in residential areas away Zhengzhou 2,914 154 87 63 95 from air pollution “hotspots,” such as industrial Zibo 2,396 117 66 198 43 districts and transport corridors. The data are from Foshan 4,876 107 61 .. .. the World Bank’s Development Research Group and Chengdu 4,869 136 77 .. .. Environment Department estimates of annual ambi- Xi’an 4,704 221 125 .. .. Colombia Bogotá 8,262 51 27 .. .. ent concentrations of particulate matter in cities with Croatia Zagreb 779 b 48 28 31 .. populations exceeding 100,000 (Pandey and others Cuba Havana 2,140 47 26 1 5 2006b). A country’s technology and pollution controls Czech Republic Prague 1,162 68 19 14 33 are important determinants of particulate matter Denmark Copenhagen 1,174 30 17 7 54 concentrations. Ecuador Guayaquil 2,634 33 18 15 .. Pollutant concentrations are sensitive to local condi- Quito 1,801 44 24 22 .. Egypt, Arab Rep. Cairo 10,902 272 124 69 .. tions, and even monitoring sites in the same city may Finland Helsinki 1,107 24 17 4 35 register different levels. Thus these data should be France Paris 10,410 14 10 14 57 considered only a general indication of air quality, and Germany Berlin 3,438 30 18 18 26 comparisons should be made with caution. Current Frankfurt 680 b 27 16 11 45 World Health Organization (WHO) air quality guidelines Munich 1,334 27 16 8 53 are annual mean concentrations of 20 micrograms Ghana Accra 2,269 37 24 .. .. per cubic meter for particulate matter less than 10 Greece Athens 3,252 69 34 34 64 Hungary Budapest 1,705 35 16 39 51 microns in diameter and 40 micrograms for nitrogen Iceland Reykjavik 319b 23 14 5 42 dioxide and daily mean concentrations of 20 micro- India Ahmadabad 5,606 127 68 30 21 grams per cubic meter for sulfur dioxide. 178 2011 World Development Indicators 3.14 ENVIRONMENT Air pollution City City Particulate Sulfur Nitrogen Definitions population matter dioxide dioxide concentration Urban- • City population is the number of residents of population- the city or metropolitan area as defined by national weighted PM10 micrograms per micrograms per micrograms per authorities and reported to the United Nations. thousands cubic meter cubic meter cubic meter •  Particulate matter concentration is fi ne sus- 2009 1990 2008 2001 a 2001 a pended particulates of less than 10 microns in Bangalore 7,079 69 37 .. .. diameter (PM10) that are capable of penetrating Chennai 7,416 57 30 15 17 deep into the respiratory tract and causing severe Delhi 21,720 229 122 24 41 health damage. Data are urban-population-weighted Hyderabad 6,627 62 33 12 17 Kanpur 3,298 166 89 15 14 PM10 levels in residential areas of cities with more Kolkata 15,294 195 104 49 34 than 100,000 residents. The estimates represent Lucknow 2,815 167 89 26 25 the average annual exposure level of the average Mumbai 19,695 96 51 33 39 urban resident to outdoor particulate matter. • Sulfur Nagpur 2,556 85 45 6 13 dioxide is an air pollutant produced when fossil fuels Pune 4,898 71 38 .. .. Indonesia Jakarta 9,121 138 74 .. .. containing sulfur are burned. • Nitrogen dioxide is Iran, Islamic Rep. Tehran 7,190 86 55 209 .. a poisonous, pungent gas formed when nitric oxide Ireland Dublin 1,084 24 13 20 .. combines with hydrocarbons and sunlight, producing Italy Milan 2,962 46 26 31 248 a photochemical reaction. These conditions occur in Rome 3,357 44 25 .. .. both natural and anthropogenic activities. Turin 1,662 66 38 .. .. Japan Osaka-Kobe 11,325 48 31 19 63 Tokyo 36,507 54 35 18 68 Yokohama 3,654b 42 27 100 13 Kenya Nairobi 3,375 67 32 .. .. Korea, Rep Pusan 3,439 52 31 60 51 Seoul 9,778 55 33 44 60 Taegu 2,458 59 36 81 62 Malaysia Kuala Lumpur 1,493 36 20 24 .. Mexico Mexico City 19,319 89 43 74 130 Netherlands Amsterdam 1,044 45 31 10 58 New Zealand Auckland 1,360 13 11 3 20 Norway Oslo 875 27 20 8 43 Philippines Manila 11,449 78 26 33 .. Poland Katowice 309b 62 36 83 79 Lódz 742b 61 36 21 43 Warsaw 1,710 67 39 16 32 Portugal Lisbon 2,808 44 19 8 52 Romania Bucharest 1,933 40 14 10 71 Russian Federation Moscow 10,523 42 16 109 .. Omsk 1,128 44 17 20 34 Singapore Singapore 4,737 107 31 20 30 Slovak Republic Bratislava 500 b 44 13 21 27 South Africa Cape Town 3,353 20 13 21 72 Data sources Durban 2,837 40 27 31 .. Johannesburg 3,607 42 28 19 31 Data on city population are from the United Spain Barcelona 5,029 43 29 11 43 Nations Population Division’s World Urbanization Madrid 5,762 37 25 24 66 Prospects: The 2009 Revision. Data on particulate Sweden Stockholm 1,279 14 10 3 20 matter concentrations are from Kiran D. Pandey, Switzerland Zurich 1,143 33 21 11 39 David Wheeler, Bart Ostro, Uwe Deichman, Kirk Thailand Bangkok 6,902 88 63 11 23 Turkey Ankara 3,846 74 36 55 46 Hamilton, and Kathrine Bolt’s “Ambient Particulate Istanbul 10,378 87 42 120 .. Matter Concentration in Residential and Pollution Ukraine Kiev 2,779 91 22 14 51 Hotspot Areas of World Cities: New Estimates United Kingdom Birmingham 2,296 22 11 9 45 Based on the Global Model of Ambient Particu- London 8,615 27 17 25 77 lates (GMAPS)” (2006). Data on sulfur dioxide Manchester 2,247 24 12 26 49 United States Chicago 9,134 33 21 14 57 and nitrogen dioxide concentrations are from the Los Angeles 12,675 45 29 9 74 WHO’s Healthy Cities Air Management Information New York-Newark 19,300 28 18 26 79 System and the World Resources Institute. Venezuela, RB Caracas 3,051 32 14 33 57 a. Data are for the most recent year available. b. Data are from national sources. 2011 World Development Indicators 179 3.15 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocolb CITES CCD Convention Afghanistan 2002 2004 d 2004 d 2002 1985d 1996d Albania 1993 1995 1999d 1999d 2003d 1994 d 2005 2003d 2000 d 2004 Algeria 2001 1994 1992d 1992d 1996 1995 2005 1983d 1996 2006 Angola 2000 2000 d 2000 d 1994 1998 2007 1997 2006d Argentina 1992 1994 1990 1990 1995 1994 2005 1981 1997 2005 Armenia 1994 1999d 1999d 2002d 1993e 2005 1997 2003 Australia 1992 1994 1994 1987d 1989 1994 1993 2008 1976 2000 2004 Austria 1994 1987 1989 1995 1994 2005 1982d 1997d 2002 Azerbaijan 1998 1995 1996d 1996d 2000 f 2005 1998d 1998d 2004 d Bangladesh 1991 1990 1994 1990 d 1990 d 2001 1994 2005 1981 1996 2007 Belarus 2000 1986e 1988e 2006d 1993 2005 1995d 2001d 2004 d Belgium 1996 1988 1988 1998 1996 2005 1983 1997d 2006 Benin 1993 1994 1993d 1993d 1997 1994 2005 1984 d 1996 2004 Bolivia 1994 1988 1995 1994 d 1994 d 1995 1994 2005 1979 1996 2003 Bosnia and Herzegovina 2000 1992f 1992f 1994f 2002d 2007 2002 2002d 2010 Botswana 1990 1991 1994 1991d 1991d 1994 1995 2005 1977d 1996 2002d Brazil 1988 1994 1990 d 1990 d 1994 1994 2005 1975 1997 2004 Bulgaria 1994 1995 1990 d 1990 d 1996 1996 2005 1991d 2001d 2004 Burkina Faso 1993 1994 1989 1989 2005 1993 2005 1989d 1996 2004 Burundi 1994 1989 1997 1997d 1997d 1997 2005 1988d 1997 2005 Cambodia 1999 1996 2001d 2001d 1995d 2005 1997 1997 2006 Cameroon 1989 1995 1989d 1989d 1994 1994 2005 1981d 1997 2009 Canada 1990 1994 1994 1986 1988 2003 1992 2005 1975 1996 2001 Central African Republic 1995 1993d 1993d 1995 2008 1980 d 1996 2008 Chad 1990 1994 1989d 1994 1994 2009 1989d 1996 2004 Chile 1993 1995 1990 1990 1997 1994 2005 1975 1998 2005 China 1994 1994 1994 1989d 1991d 1996 1993 2005 1981d 1997 2004 Hong Kong SAR, China Colombia 1998 1988 1995 1990 d 1993d 1994 2005 1981 1999 2008 Congo, Dem. Rep. 1990 1995 1994 d 1994 d 1995 1996 2005 1976d 2004 2005d Congo, Rep. 1990 1997 1994 d 1994 d 2008 1994 2007 1983d 1997 2007 Costa Rica 1990 1992 1994 1991d 1991d 1994 1994 2005 1975 1998 2007 Côte d’Ivoire 1994 1991 1995 1993d 1993d 1994 1994 2007 1994 d 1997 2004 Croatia 2001 2000 1996 1991e 1991e 1994f 1996 2007 2000 d 2001e 2007 Cuba 1994 1992d 1992d 1994 1994 2005 1990 d 1997 2007 Czech Republic 1994 1994 1993e 1993e 1996 1993g 2005 1993f 2000 d 2002 Denmark 1994 1994 1988 1988 2004 1993 2005 1977 1996d 2003 Dominican Republic 1995 1999 1993d 1993d   1996 2005 1986d 1997d 2007 Ecuador 1993 1995 1994 1990 d 1990 d 1993 2005 1975 1996 2004 Egypt, Arab Rep. 1992 1988 1995 1988 1988 1994 1994 2005 1978 1996 2003 El Salvador 1994 1988 1996 1992 1992 1994 2005 1987d 1997d 2008 Eritrea 1995 1995 2005d 2005d 1996d 2005 1994 d 1996 2005d Estonia 1998 1994 1996d 1996d 2005d 1994 2005 1992d 2008d Ethiopia 1994 1991 1994 1994 d 1994 d 1994 2005 1989d 1997 2003  Finland 1995 1994 1986 1988 1996 1994 e 2005 1976d 1996e 2002e France 1990 1994 1987g 1988g 1996 1994 2005 1978 1997 2004g Gabon 1990 1998 1994 d 1994 d 1998 1997 2007 1989d 1996d 2007 Gambia, The 1992 1989 1994 1990 d 1990 d 1994 1994 2005 1977d 1996 2006 Georgia 1998 1994 1996d 1996d 1996d 1994 d 2005 1996d 1999 2006 Germany 1994 1988 1988 1994 d 1993 2005 1976 1996 2002 Ghana 1992 1988 1995 1989d 1989 1994 1994 2005 1975 1997 2003 Greece 1994 1988 1988 1995 1994 2005 1992d 1997 2006 Guatemala 1994 1988 1996 1987d 1989d 1997 1995 2005 1979 1998d 2008 Guinea 1994 1988 1994 1992d 1992d 1994 1993 2005 1981d 1997 2007 Guinea-Bissau 1993 1991 1996 2002d 2002d 1994 1995 2005 1990 d 1996 2008 Haiti 1999 1996 2000d 2000 d 1996 1996 2005 1996 Honduras 1993 1996 1993d 1993d 1994 1995 2005 1985d 1997 2005 180 2011 World Development Indicators 3.15 ENVIRONMENT Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocolb CITES CCD Convention Hungary 1995 1994 1988d 1989d 2002 1994 2005 1985d 1999d 2008 India 1993 1994 1994 1991d 1992d 1995 1994 2005 1976 1997 2006 Indonesia 1993 1993 1994 1992d 1992 1994 1994 2005 1978d 1998 Iran, Islamic Rep. 1996 1990 d 1990 d 1996 2005 1976 1997 2006 Iraq 1994 2009 2010 Ireland 1994 1988d 1988 1996 1996 2005 2002 1997 2010 Israel 1996 1992d 1992 1995 2005 1979 1996 Italy 1994 1988 1988 1995 1994 2005 1979 1997 Jamaica 1994 1995 1993d 1993d 1994 1995 2005 1997d 1998d 2007 Japan 1994 1988d 1988 1996 1993e 2005 1980 1998e 2002d Jordan 1991 1994 1989d 1989d 1995d 1993 2005 1978d 1997 2004  Kazakhstan 1995 1998d 1998d 1994 2009 2000 d 1997 2007 Kenya 1994 1992 1994 1988d 1988 1994 1994 2005 1978 1997 2004 Korea, Dem. Rep. 1995 1995d 1995d 1994g 2005 2004 d 2002d Korea, Rep. 1994 1992 1992 1996 1994 2005 1993d 1999 2007 Kosovo Kuwait 1995 1992d 1992d 1994 2002 2005 2002 1997 2006 Kyrgyz Republic 1995 2000 2000 d 2000 d 1996g 2005 1997d 2006 Lao PDR 1995 1995 1998d 1998d 1998 1996g 2005 2004 d 1996e 2006 Latvia 1995 1995d 1995d 2004 d 1995 2005 1997d 2003d 2004 Lebanon 1995 1993d 1993d 1995 1994 2007 1996 2003  Lesotho 1989 1995 1994 d 1994 d 2007 1995 2005 2003 1996 2002 Liberia 2003 1996d 1996d 2008 2000 2005 2005d 1998d 2002d Libya 1999 1990 d 1990 d 2001 2006 2003d 1996 2005d Lithuania 1995 1995d 1995d 2003d 1996 2005 2001d 2003d 2006 Macedonia, FYR 1998 1994f 1994f 1994f 1997d 2005 2000d 2002d 2004 Madagascar 1988 1991 1999 1996d 1996d 2001 1996 2005 1975 1997 2005 Malawi 1994 1994 1991d 1991d 1994 2005 1982d 1996 2009 Malaysia 1991 1988 1994 1989d 1989d 1996 1994 2005 1977d 1997 Mali 1989 1995 1994 d 1994 d 1994 1995 2005 1994 d 1996 2003 Mauritania 1988 1994 1994 d 1994 d 1996 1996 2005 1998d 1996 2005 Mauritius 1990 1994 1992d 1992d 1994 1992 2005 1975 1996 2004 Mexico 1988 1994 1987 1988 1994 1993 2005 1991d 1996 2003 Moldova 2002 1995 1996d 1996d 2007 1995 2005 2001d 1999d 2004 Mongolia 1995 1994 1996d 1996d 1996 1993 2005 1996d 1996 2004 Morocco 1988 1996 1995 1995 2007 1995 2005 1975 1997 2004 Mozambique 1994 1995 1994 d 1994 d 1997 1995 2005 1981d 1997 2005 Myanmar 1989 1995 1993d 1993d 1996 1995 2005 1997d 1997d 2004 d Namibia 1992 1995 1993d 1993d 1994 1997 2005 1990 d 1997 2005d Nepal 1993 1994 1994 d 1994 d 1998 1993 2005 1975d 1997 2007 Netherlands 1994 1994 1988d 1988e 1996 1994 e 2005 1984 1996e 2002e New Zealand 1994 1994 1987 1988 1996 1993 2005 1989d 2000 d 2004 Nicaragua 1994 1996 1993d 1993d 2000 1995 2005 1977d 1998 2005 Niger 1991 1995 1992d 1992d 1995 2005 1975 1996 2006 Nigeria 1990 1992 1994 1988d 1988d 1994 1994 2005 1974 1997 2004 Norway 1994 1994 1986 1988 1996 1993 2005 1976 1996 2002 Oman 1995 1999d 1999d 1994 1995 2005 1996 2005 Pakistan 1994 1991 1994 1992d 1992d 1997 1994 2005 1976d 1997 2008 Panama 1990 1995 1989d 1989 1996 1995 2005 1978 1996 2003 Papua New Guinea 1992 1993 1994 1992d 1992d 1997 1993 2005 1975d 2001d 2003 Paraguay 1994 1992d 1992d 1994 1994 2005 1976 1997 2004 Peru 1988 1994 1989 1993d 1993 2005 1975 1996 2005 Philippines 1989 1989 1994 1991d 1991 1994 1993 2005 1981 2000 2004 Poland 1993 1991 1994 1990 d 1990 d 1998 1996 2005 1989 2002 2008 Portugal 1995 1994 1988d 1988 1997 1993 2005 1980 1996 2004 d Puerto Rico Qatar 2005 1999 2004 d 2011 World Development Indicators 181 3.15 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocolb CITES CCD Convention Romania 1995 1994 1993d 1993d 1996 1994 2005 1994 d 1998 2004 Russian Federation 1999 1994 1995 1986e 1988e 1997 1995 2005 1992 2003 Rwanda 1991 1998 2001d 2001d 1996 2005 1980 d 1999 2002d Saudi Arabia 1995 1993d 1993d 1996 2001g 2005 1996d 1997 Senegal 1984 1991 1995 1993d 1993 1994 1994 2005 1977d 1996 2003 Serbia 2008 2008 2009  Sierra Leone 1994 1995 2001d 2001d 1994 1994g 2007 1994 d 1997 2003d Singapore 1993 1995 1997 1989d 1989d 1994 1995 2006 1986d 1999 2005 Slovak Republic 1994 1993f 1993f 1996 1994g 2005 1993 2002 2002 Slovenia 1994 1996 1992f 1992f 1995f 1996 2005 2000d 2001 2004 Somalia 2001d 2001d 1994 2011 1985d 2002 2010 d South Africa 1993 1997 1990 d 1990 d 1997 1995 2005 1975 1997 2002 Spain 1994 1988d 1988 1997 1995 2005 1986d 1996 2004 Sri Lanka 1994 1991 1994 1989d 1989d 1994 1994 2005 1979d 1999 2005 Sudan 1994 1993d 1993d 1994 1995 2005 1982 1996 2006 Swaziland 1997 1992d 1992d 1994 2006 1997d 1997 2006 Sweden 1994 1986 1988 1996 1993 2005 1974 1996 2002 Switzerland 1994 1987 1988 1994 2005 1974 1996 2003 Syrian Arab Republic 1999 1996 1989d 1989d 1996 2006 2003d 1997 2005 Tajikistan 1998 1996d 1998d 1997g 2009 1997 2007 Tanzania 1994 1988 1996 1993d 1993d 1994 1996 2005 1979 1997 2004 Thailand 1995 1989d 1989 2004 2005 1983 2001 2005 Togo 1991 1995 1991d 1991 1994 1995e 2005 1978 1996 2004 Trinidad and Tobago 1994 1989d 1989d 1994 1996 2005 1984 d 2000 2002d Tunisia 1994 1988 1994 1989d 1989d 1994 1993 2005 1974 1996 2004 Turkey 1998 2004 1991d 1991d 1997 2009  1996d 1998 2009 Turkmenistan 1995 1993d 1993d 1996g 2005 1996 Uganda 1994 1988 1994 1988d 1988 1994 1993 2005 1991d 1997 2004 d Ukraine 1999 1997 1986e 1988e 1999 1995 2005 1999d 2002 United Arab Emirates 1996 1989d 1989d 2000 2005 1990 d 1999 2002 United Kingdom 1995 1994 1994 1987 1988 1997d 1994 2005 1976 1997 2005 United States 1995 1995 1994 1986 1988 1974 2001 Uruguay 1994 1989d 1991d 1994 1993 2005 1975 1999 2004 Uzbekistan 1994 1993d 1993d 1995g 2005 1997d 1996 Venezuela 1995 1988d 1989 1994 2005 1977 1998 2005 Vietnam 1993 1995 1994 d 1994 d 2006d 1994 2005 1994 d 1998 2002 West Bank and Gaza Yemen, Rep. 1996 1992 1996 1996d 1996d 1994 1996 2005 1997d 1997 2004 Zambia 1994 1994 1990 d 1990 d 1994 1993 2006 1980 d 1996 2006 Zimbabwe 1987 1994 1992d 1992d 1994 1994 2009 1981d 1997 a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention became effective November 16, 1994. d. Accession. e. Acceptance. f. Succession. g. Approval. 182 2011 World Development Indicators 3.15 ENVIRONMENT Government commitment About the data Definitions National environmental strategies and participation Environment and Development (the Earth Summit) in •  Environmental strategies or action plans pro- in international treaties on environmental issues pro- Rio de Janeiro, which produced Agenda 21—an array vide a comprehensive analysis of conservation and vide some evidence of government commitment to of actions to address environmental challenges: resource management issues that integrate envi- sound environmental management. But the signing • The Framework Convention on Climate Change ronmental concerns with development. They include of these treaties does not always imply ratification, aims to stabilize atmospheric concentrations of national conservation strategies, environmental nor does it guarantee that governments will comply greenhouse gases at levels that will prevent human action plans, environmental management strategies, with treaty obligations. activities from interfering dangerously with the and sustainable development strategies. The date In many countries efforts to halt environmental global climate. is the year a country adopted a strategy or action degradation have failed, primarily because govern- • The Vienna Convention for the Protection of the plan. •  Biodiversity assessments, strategies, or ments have neglected to make this issue a priority, a Ozone Layer aims to protect human health and the action plans include biodiversity profiles (see About reflection of competing claims on scarce resources. environment by promoting research on the effects the data). •  Participation in treaties covers nine To address this problem, many countries are prepar- of changes in the ozone layer and on alternative international treaties (see About the data). •  Cli- ing national environmental strategies—some focus- substances (such as substitutes for chlorofluoro- mate change refers to the Framework Convention ing narrowly on environmental issues, and others carbon) and technologies, monitoring the ozone on Climate Change (signed in 1992). • Ozone layer integrating environmental, economic, and social layer, and taking measures to control the activities refers to the Vienna Convention for the Protection concerns. Among such initiatives are conservation that produce adverse effects. of the Ozone Layer (signed in 1985). • CFC control strategies and environmental action plans. Some • The Montreal Protocol for Chlorofl uorocarbon refers to the Protocol on Substances That Deplete countries have also prepared country environmen- Control requires that countries help protect the the Ozone Layer (the Montreal Protocol for Chloro- tal profiles and biodiversity strategies and profiles. earth from excessive ultraviolet radiation by cut- fluorocarbon Control) (signed in 1987). •  Law of National conservation strategies—promoted by ting chlorofluorocarbon consumption by 20 percent the Sea refers to the United Nations Convention on the World Conservation Union (IUCN)—provide a over their 1986 level by 1994 and by 50 percent the Law of the Sea (signed in 1982). • Biological comprehensive, cross-sectoral analysis of conser- over their 1986 level by 1999, with allowances for diversity refers to the Convention on Biological Diver- vation and resource management issues to help inte- increases in consumption by developing countries. sity (signed at the Earth Summit in 1992). • Kyoto grate environmental concerns with the development • The United Nations Convention on the Law of the Protocol refers to the protocol on climate change process. Such strategies discuss current and future Sea, which became effective in November 1994, adopted at the third conference of the parties to the needs, institutional capabilities, prevailing technical establishes a comprehensive legal regime for seas United Nations Framework Convention on Climate conditions, and the status of natural resources in and oceans, establishes rules for environmental Change in December 1997. • CITES is the Conven- a country. standards and enforcement provisions, and devel- tion on International Trade in Endangered Species of National environmental action plans, supported by ops international rules and national legislation to Wild Fauna and Flora, an agreement among govern- the World Bank and other development agencies, prevent and control marine pollution. ments to ensure that the survival of wild animals describe a country’s main environmental concerns, • The Convention on Biological Diversity promotes and plants is not threatened by uncontrolled exploita- identify the principal causes of environmental prob- conservation of biodiversity through scientifi c tion. Adopted in 1973, it entered into force in 1975. lems, and formulate policies and actions to deal with and technological cooperation among countries, • CCD is the United Nations Convention to Combat them. These plans are a continuing process in which access to financial and genetic resources, and Desertification, an international convention address- governments develop comprehensive environmental transfer of ecologically sound technologies. ing the problems of land degradation in the world’s policies, recommend specific actions, and outline But 10 years after the Earth Summit in Rio de drylands. Adopted in 1994, it entered into force in the investment strategies, legislation, and institu- Janeiro the World Summit on Sustainable Develop- 1996. • Stockholm Convention is an international tional arrangements required to implement them. ment in Johannesburg recognized that many of the legally binding instrument to protect human health Biodiversity profiles—prepared by the World Con- proposed actions had yet to materialize. To help and the environment from persistent organic pollut- servation Monitoring Centre and the IUCN—provide developing countries comply with their obligations ants. Adopted in 2001, it entered into force in 2004. basic background on species diversity, protected under these agreements, the Global Environment areas, major ecosystems and habitat types, and Facility (GEF) was created to focus on global improve- Data sources legislative and administrative support. In an effort ment in biodiversity, climate change, international to establish a scientific baseline for measuring prog- waters, and ozone layer depletion. The UNEP, United Data on environmental strategies and participa- ress in biodiversity conservation, the United Nations Nations Development Programme, and World Bank tion in international environmental treaties are Environment Programme (UNEP) coordinates global manage the GEF according to the policies of its gov- from the Secretariat of the United Nations Frame- biodiversity assessments. erning body of country representatives. The World work Convention on Climate Change, the Ozone To address global issues, many governments have Bank is responsible for the GEF Trust Fund and chairs Secretariat of the UNEP, the World Resources also signed international treaties and agreements the GEF. Institute, the UNEP, the Center for International launched in the wake of the 1972 United Nations Earth Science Information Network, and the Conference on the Human Environment in Stock- United Nations Treaty Series. holm and the 1992 United Nations Conference on 2011 World Development Indicators 183 3.16 Contribution of natural resources to gross domestic product Total natural Oil Natural gas Coal rents, Mineral Forest resources rents rents rents hard and soft rents rents % of % of % of % of % of % of GDP GDP GDP GDP GDP GDP 2009 2009 2009 2009 2009 2009 Afghanistan 4.0 0.0 0.0 0.0 .. 4.0 Albania 1.8 1.7 0.0 0.0 0.0 0.1 Algeria 25.2 15.1 9.7 0.0 0.2 0.1 Angola 39.0 38.6 0.1 0.0 0.0 0.2 Argentina 6.0 3.5 1.9 0.0 0.5 0.1 Armenia 0.8 0.0 0.0 0.0 0.8 0.0 Australia 6.7 0.9 0.8 1.2 4.9 0.1 Austria 0.3 0.1 0.1 0.0 0.0 0.1 Azerbaijan 44.5 39.6 4.9 0.0 0.0 0.0 Bangladesh 3.9 0.0 3.2 0.0 0.0 0.6 Belarus 1.7 1.2 0.1 0.0 0.0 0.5 Belgium 0.0 0.0 0.0 0.0 0.0 0.0 Benin 1.9 0.0 0.0 0.0 0.0 1.9 Bolivia 17.5 4.5 10.3 0.0 2.2 0.4 Bosnia and Herzegovina 2.0 0.0 0.0 1.2 1.5 0.5 Botswana 3.5 0.0 0.0 0.4 3.4 0.2 Brazil 5.0 2.1 0.1 0.0 2.4 0.4 Bulgaria 1.2 0.0 0.0 0.6 1.0 0.2 Burkina Faso 3.7 0.0 0.0 0.0 0.0 3.7 Burundi 11.3 0.0 0.0 0.0 1.2 10.1 Cambodia 1.5 0.0 0.0 0.0 0.0 1.5 Cameroon 9.4 6.8 0.3 0.0 0.1 2.2 Canada 3.7 2.1 0.6 0.1 0.6 0.4 Central African Republic 7.3 0.0 0.0 0.0 0.0 7.3 Chad 36.4 33.7 0.0 0.0 0.0 2.7 Chile 15.6 0.0 0.1 0.0 14.8 0.6 China 2.0 1.4 0.2 2.7 0.3 0.2 Hong Kong SAR, China 0.0 0.0 0.0 0.0 0.0 0.0 Colombia 6.3 5.2 0.5 1.0 0.5 0.1 Congo, Dem. Rep. 28.0 3.9 0.0 0.0 11.6 12.5 Congo, Rep. 56.8 52.8 0.0 0.0 0.0 3.9 Costa Rica 0.4 0.0 0.0 0.0 0.1 0.4 Côte d’Ivoire 5.9 3.6 1.0 0.0 0.0 1.3 Croatia 1.1 0.4 0.5 0.0 0.0 0.2 Cuba .. .. .. .. .. .. Czech Republic 0.3 0.0 0.0 0.3 0.0 0.2 Denmark 1.8 1.4 0.4 0.0 0.0 0.0 Dominican Republic 0.8 0.0 0.0 0.0 0.8 0.0 Ecuador 15.7 15.3 0.1 0.0 0.0 0.2 Egypt, Arab Rep. 10.7 5.3 5.1 0.0 0.2 0.1 El Salvador 0.5 0.0 0.0 0.0 0.0 0.5 Eritrea 1.4 0.0 0.0 0.0 0.0 1.4 Estonia 0.7 0.0 0.0 1.1 0.0 0.7 Ethiopia 5.0 0.0 0.0 0.0 0.2 4.8 Finland 0.6 0.0 0.0 0.0 0.1 0.5 France 0.1 0.0 0.0 0.0 0.0 0.0 Gabon 45.0 39.9 0.3 0.0 0.1 4.7 Gambia, The 3.2 0.0 0.0 0.0 0.0 3.2 Georgia 0.3 0.2 0.0 0.0 0.0 0.1 Germany 0.1 0.0 0.1 0.1 .. 0.0 Ghana 8.6 0.0 0.0 0.0 6.5 2.1 Greece 0.1 0.0 0.0 0.2 0.1 0.0 Guatemala 1.6 0.7 0.0 0.0 0.0 0.9 Guinea 10.5 0.0 0.0 0.0 5.3 5.3 Guinea-Bissau 3.5 0.0 0.0 0.0 0.0 3.5 Haiti 0.7 0.0 0.0 0.0 0.0 0.7 Honduras 1.8 0.0 0.0 0.0 0.6 1.2 184 2011 World Development Indicators 3.16 ENVIRONMENT Contribution of natural resources to gross domestic product Total natural Oil Natural gas Coal rents, Mineral Forest resources rents rents rents hard and soft rents rents % of % of % of % of % of % of GDP GDP GDP GDP GDP GDP 2009 2009 2009 2009 2009 2009 Hungary 0.5 0.2 0.2 0.1 0.0 0.1 India 4.0 0.8 0.5 2.2 1.7 1.0 Indonesia 5.9 2.4 1.3 2.5 1.6 0.5 Iran, Islamic Rep. 28.4 21.4 6.6 0.0 0.3 0.0 Iraq 68.6 68.1 0.5 0.0 0.0 0.0 Ireland 0.1 0.0 0.0 0.0 0.0 0.0 Israel 0.3 0.0 0.2 0.0 0.1 0.0 Italy 0.1 0.1 0.0 0.0 0.0 0.0 Jamaica 1.2 0.0 0.0 0.0 1.1 0.2 Japan 0.0 0.0 0.0 0.0 .. 0.0 Jordan 1.7 0.0 0.1 0.0 1.6 0.0 Kazakhstan 27.3 20.9 4.7 4.3 1.7 0.0 Kenya 1.4 0.0 0.0 0.0 0.0 1.4 Korea, Dem. Rep. .. .. .. .. .. .. Korea, Rep. 0.0 0.0 0.0 0.0 .. 0.0 Kosovo 0.0 0.0 0.0 .. .. .. Kuwait .. .. .. .. .. .. Kyrgyz Republic 0.5 0.5 0.0 0.3 0.0 0.0 Lao PDR 1.9 0.0 0.0 0.0 0.0 1.9 Latvia 1.1 0.0 0.0 0.0 0.0 1.1 Lebanon 0.0 0.0 0.0 0.0 0.0 0.0 Lesotho 1.8 0.0 0.0 0.0 0.0 1.8 Liberia 15.6 0.0 0.0 0.0 0.7 14.9 Libya 48.4 44.7 3.7 0.0 0.0 0.0 Lithuania 1.4 0.1 0.0 0.0 0.0 1.3 Macedonia, FYR 0.1 0.0 0.0 0.0 0.0 0.1 Madagascar 2.0 0.0 0.0 0.0 0.1 1.9 Malawi 2.5 0.0 0.0 0.0 0.0 2.5 Malaysia 12.3 6.1 5.7 0.0 0.0 0.5 Mali 1.3 0.0 0.0 0.0 0.0 1.3 Mauritania 30.1 0.0 0.0 0.0 29.4 0.6 Mauritius 0.0 0.0 0.0 0.0 0.0 0.0 Mexico 6.8 5.5 0.7 0.1 0.4 0.1 Moldova 0.2 0.1 0.0 0.0 0.0 0.1 Mongolia 12.7 1.4 0.0 3.9 11.0 0.3 Morocco 2.3 0.0 0.0 0.0 2.2 0.1 Mozambique 8.5 0.0 5.1 0.0 0.0 3.4 Myanmar .. .. .. .. .. .. Namibia 0.5 0.0 0.0 0.0 0.5 0.0 Nepal 5.6 0.0 0.0 0.0 0.0 5.6 Netherlands 1.1 0.1 1.1 0.0 0.0 0.0 New Zealand 2.3 0.7 0.5 0.1 0.4 0.6 Nicaragua 2.9 0.0 0.0 0.0 1.0 1.8 Niger 1.7 0.0 0.0 0.0 .. 1.7 Nigeria 23.3 20.3 1.8 0.0 0.0 1.2 Norway 13.2 9.5 3.6 0.0 0.0 0.1 Oman 40.1 32.3 7.7 0.0 0.0 0.0 Pakistan 4.4 0.7 2.7 0.1 0.0 1.0 Panama 0.1 0.0 0.0 0.0 0.0 0.1 Papua New Guinea 32.7 0.0 0.0 0.0 29.7 3.0 Paraguay 1.7 0.0 0.0 0.0 0.0 1.7 Peru 8.2 0.9 0.4 0.0 6.8 0.1 Philippines 1.7 0.0 0.4 0.1 1.1 0.2 Poland 0.8 0.1 0.1 0.9 0.4 0.2 Portugal 0.1 0.0 0.0 0.0 .. 0.1 Puerto Rico .. .. .. .. .. .. Qatar 28.6 14.0 14.6 0.0 0.0 .. 2011 World Development Indicators 185 3.16 Contribution of natural resources to gross domestic product Total natural Oil Natural gas Coal rents, Mineral Forest resources rents rents rents hard and soft rents rents % of % of % of % of % of % of GDP GDP GDP GDP GDP GDP 2009 2009 2009 2009 2009 2009 Romania 2.0 0.9 0.8 0.2 .. 0.2 Russian Federation 20.7 13.4 5.8 1.0 1.1 0.3 Rwanda 3.3 0.0 0.0 0.0 0.0 3.3 Saudi Arabia 47.2 43.8 3.4 0.0 0.0 0.0 Senegal 1.8 0.0 0.0 0.0 0.4 1.3 Serbia and Montenegro .. .. .. .. .. .. Sierra Leone 4.5 0.0 0.0 0.0 0.6 3.8 Singapore 0.0 0.0 0.0 0.0 0.0 0.0 Slovak Republic 0.3 0.0 0.0 0.0 0.0 0.3 Slovenia 0.1 0.0 0.0 0.1 0.0 0.1 Somalia .. .. .. .. .. .. South Africa 4.7 0.1 0.1 4.2 3.3 1.2 Spain 0.0 0.0 0.0 0.0 .. 0.0 Sri Lanka 0.8 0.0 0.0 0.0 0.0 0.8 Sudan 16.9 16.0 0.0 0.0 0.1 0.8 Swaziland 2.3 0.0 0.0 0.0 .. 2.3 Sweden 0.8 0.0 0.0 0.0 0.4 0.5 Switzerland 0.0 0.0 0.0 0.0 0.0 0.0 Syrian Arab Republic 14.4 12.4 1.8 0.0 0.2 0.0 Tajikistan 0.2 0.1 0.1 0.1 0.0 0.0 Tanzania 6.3 0.0 0.4 0.0 3.5 2.4 Thailand 3.6 1.6 1.7 0.1 0.0 0.3 Timor-Leste 0.4 0.0 0.0 0.0 0.0 0.4 Togo 4.5 0.0 0.0 0.0 2.1 2.4 Trinidad and Tobago 35.2 10.3 24.9 0.0 0.0 0.0 Tunisia 4.9 3.8 0.9 0.0 .. 0.2 Turkey 0.3 0.1 0.0 0.1 0.1 0.1 Turkmenistan 41.0 17.1 24.0 0.0 0.0 .. Uganda 5.2 0.0 0.0 0.0 0.0 5.2 Ukraine 3.6 0.9 2.4 2.4 0.0 0.3 United Arab Emirates 20.9 17.6 3.3 0.0 0.0 .. United Kingdom 1.4 1.0 0.4 0.0 0.0 0.0 United States 0.9 0.5 0.2 0.2 0.1 0.1 Uruguay 0.7 0.0 0.0 0.0 0.0 0.7 Uzbekistan 28.2 2.8 25.4 0.1 0.0 0.0 Venezuela, RB 15.6 13.8 1.2 0.0 0.5 0.0 Vietnam 8.1 6.0 1.3 2.2 0.1 0.7 West Bank and Gaza .. .. .. .. .. .. Yemen, Rep. 19.7 19.4 0.3 0.0 0.0 0.0 Zambia 18.4 0.0 0.0 0.1 16.4 2.0 Zimbabwe 5.2 0.0 0.0 3.2 1.9 3.3 World 3.7 w 1.9 w 0.6 w 0.5 w 0.5 w 0.2 w Low income 6.3 0.7 0.9 0.1 2.0 2.6 Middle income 8.7 4.7 1.4 1.4 1.0 0.3 Lower middle income 6.8 2.9 0.8 2.1 0.6 0.4 Upper middle income 11.2 6.8 2.1 0.5 1.5 0.3 Low & middle income 8.7 4.6 1.3 1.3 1.0 0.4 East Asia & Pacific 5.3 1.6 0.6 2.4 0.4 0.2 Europe & Central Asia 13.7 8.2 3.7 0.9 0.7 0.2 Latin America & Carib. 7.0 4.1 0.5 0.1 2.0 0.3 Middle East & N. Africa 22.7 17.6 4.6 0.0 0.4 0.1 South Asia 5.8 0.8 0.8 1.8 1.4 1.1 Sub-Saharan Africa 14.2 8.8 0.5 1.3 1.8 1.7 High income 1.6 0.9 0.3 0.1 0.2 0.1 Euro area 0.2 0.0 0.1 0.0 .. 0.0 Note: Components may not sum to 100 percent because of rounding. 186 2011 World Development Indicators 3.16 ENVIRONMENT Contribution of natural resources to gross domestic product About the data Accounting for the contribution of natural resources the price of a commodity and the average cost of savings measures the net additions or subtractions to economic output is important in building an analyt- producing it. This is done by estimating the world from a country’s stock of tangible and intangible ical framework for sustainable development. In some price of units of specific commodities and subtract- capital. This table is now included in the Economy countries earnings from natural resources, especially ing estimates of average unit costs of extraction section as table 4.11 along with the closely related from fossil fuels and minerals, account for a sizable or harvesting costs (including a normal return on table 4.10 “Toward a broader measure of income.” share of GDP, and much of these come in the form of capital). These unit rents are then multiplied by the economic rents—revenues above the cost of extract- physical quantities countries extract or harvest to Definitions ing them. Natural resources give rise to economic determine the rents for each commodity as a share rents because they are not produced. For produced of gross national income. • Oil rents are the difference between the value of goods and services competitive forces expand sup- This definition of economic rent differs from that crude oil production at world prices and total costs ply until economic profits are driven to zero, but natu- used in the System of National Accounts, where of production. • Natural gas rents are the differ- ral resources in fixed supply often command returns rents are a form of property income, consisting of ence between the value of natural gas production well in excess of their cost of production. Rents from payments to landowners by a tenant for the use of at world prices and total costs of production. • Coal nonrenewable resources—fossil fuels and miner- the land or payments to the owners of subsoil assets rents are the difference between the value of both als—as well as rents from overharvesting of forests by institutional units permitting them to extract sub- hard and soft coal production at world prices and indicate the liquidation of a country’s capital stock. soil deposits. their total costs of production. • Mineral rents are When countries use such rents to support current The Environment section of previous editions of the the difference between the value of production for consumption rather than to invest in new capital to World Development Indicators included a table “Toward a stock of minerals at world prices and their total replace what is being used up, they are, in effect, a broader measure of savings,” which showed the costs of production. Minerals included in the calcu- borrowing against their future. derivation of adjusted net savings taking into account lation are tin, gold, lead, zinc, iron, copper, nickel, The estimates of natural resources rents shown in consumption of fixed and natural capital and pollution silver, bauxite, and phosphate. • Forest rents are the table are calculated as the difference between damage and additions to human capital. Adjusted net roundwood harvest times the product of average prices and a region-specific rental rate (based on a Oil dominates the contribution of natural resources number of reviews, World Bank 2011). • Total natu- in the Middle East and North Africa 3.16a ral resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, Natural resources rents (percent of GDP) Oil Natural gas Coal Mineral Forest and forest rents. 25 20 15 10 5 0 East Asia Europe & Latin America Middle East South Asia Sub-Saharan & Pacific Central Asia & Carib. & N. Africa Africa Source: Table 3.16. Upper middle-income countries have the highest contribution of natural resources to GDP 3.16b Natural resources rents (percent of GDP) Oil Natural gas Coal Mineral Forest 9.6 7.2 4.8 Data sources 2.4 Data on contributions of natural resources to GDP are estimates based on sources and meth- 0.0 Low income Lower middle Upper middle High World ods described in The Changing Wealth of Nations: income income income Measuring Sustainable Development in the New Source: Table 3.16. Millennium (World Bank 2011a). 2011 World Development Indicators 187 Text figures, tables, and boxes ECONOMY Introduction R 4 ecently revised data now confirm that in 2009 the world economy experienced the steepest global recession since the Great Depression. World gross domes- tic product (GDP) contracted 1.9 percent in 2009, with high-income economies contracting 3.3 percent and developing economies expanding just 2.7 percent, down from 8.6 percent in 2008. Among developing country regions, Europe and Central Asia fared the worst, contracting 5.8 percent (figure 4a). Contrast that with East Asia and Pacific, which grew at 7.4 percent, and South Asia, at 7 percent. The global economy rebounded in 2010, with domestic demand in developing countries accounting for 46 percent of global growth. Developing economies’ contribution to global growth has been rising since 2000 and was more stable than that of high-income economies during the recent recession (figure 4b). Preliminary estimates, often revised, indicate that the world economy grew 3.9 percent—2.8 percent in high-income economies and 7 percent in developing economies (figure 4c). Revisions to GDP Differences in GDP growth Revisions to GDP usually occur one to two months among developing country regions 4a after the initial release, as additional data sources GDP growth (percent) 2008 2009 2010a become available. For example, the U.S. Bureau of 10 Economic Analysis releases three versions of quar- 5 terly GDP estimates—advance (about a month after the quarter ends), preliminary (two months after), 0 and final (three months after). Other countries follow a similar process, although the reporting lag varies. –5 And some countries compile GDP only annually not –10 quarterly. The differences between GDP estimates East Asia Europe & Latin America Middle East & South Sub-Saharan & Pacific Central Asia & Caribbean North Africa Asia Africa decline with each revision, and GDP data become more stable on average (figure 4d). a. Data are preliminary estimates. More significant revisions to GDP involve new Source: World Development Indicators data files. methodologies and new or improved data sources and data collection practices. Countries with Developing countries are advanced statistical capacity comprehensively contributing more to global growth 4b revise GDP estimates every five years. These revi- Contribution to GDP growth (percent) High-income economies Developing economies World GDP sions take into account the latest recommendations 6 of the Intersecretariat Working Group on National Accounts. They may also incorporate a change in the 4 base year used for the constant price data (rebas- 2 ing). Rebasing adjusts the weights used to compute aggregate measures by selecting a new set of rela- 0 tive component prices in the newly chosen base –2 year. Comprehensive revisions of GDP estimates are –4 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010a usually higher as improved data sources increase the coverage of the economy and new weights for grow- a. Data are preliminary estimates. Source: World Development Indicators data files. ing industries more accurately reflect contributions 2011 World Development Indicators 189 Economies—both developing and high income—rebounded in 2010 4c to the economy. This has been the case for GDP growth (percent) several countries that recently undertook such 10 revisions to their national accounts statistics. Developing economies In November 2010 the Ghana Statistical Service revised Ghana’s national accounts 5 World series, increasing GDP 60  percent in 2006, the new base year (figure 4e). Of the increase, 0 11  percentage points are in agriculture, 6 in industry, and 44 in services (figure 4f). Other High-income economies countries have made similar revisions to their –5 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010a national accounts, incorporating improved a. Data are preliminary estimates. methodology and data sources. Namibia Source: World Development Indicators data files. revised its national accounts in 2008, result- ing in 10–30 percent higher GDP estimates for 2000–07. Malawi revised its national accounts Revisions to GDP decline over time, and GDP data become more stable on average 4d in 2007, raising GDP 37  percent. São Tomé Average difference in GDP (percent) and Príncipe revised its national accounts in 3 2006, resulting in 47.5 percent higher GDP in the new base year 2001. For more informa- 2 tion on countries that have recently revised their national accounts data, see Primary data 1 documentation. Many countries do not incorporate new 0 sources of data into national accounts data 2000 2001 2002 2003 2004 2005 2006 2007 2008 compilation until they change the base year, Note: Average differences in current price GDP between World Development Indicators 2010 and 2011. which is the base or pricing period for constant Source: World Development Indicators data files. price calculations. Such revisions can be sub- stantial because of the long lag between rebas- Ghana’s revised GDP was 60 percent higher in the new base year, 2006 4e ing exercises. The adjustments arising from GDP ($ billions) World Development Indicators 2010 World Development Indicators 2011 rebasing can be reduced by incorporating new 30 data sources in a timely manner and ensuring that the accounts are rebased at least every 20 five years. Data users should be aware that rebasing creates a break in the time series. New data 10 sources and methodologies are usually imple- mented only for recent years, creating a jump 0 in GDP between the last year of the old data 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 and the first year of the new. For constant price Source: World Development Indicators data files. GDP these breaks can be eliminated by linking the old series to the new using historical growth Revised data for Ghana show a larger share of services in GDP 4f rates. But for nominal GDP data the break in the time series cannot be avoided unless the sta- 2008 value added by industry (percent of GDP) World Development Indicators 2010 World Development Indicators 2011 tistics office revises historical series backward 50 at a detailed level. 40 Broader measures of 30 income and savings 20 Two tables have been added to the Economy 10 section this year. Table 4.10 contains new measures of adjusted net national income, and 0 Agriculture Industry Services table 4.11 contains measures of adjusted net Source: World Development Indicators data files. savings, previously included in the Environment section. Both tables follow recommendations of 190 2011 World Development Indicators ECONOMY the recently published The Changing Wealth of Commission on the Measurement of Economic and Social Progress 4g Nations (World Bank 2011a). Gross domestic product (GDP), the most quoted measure of economic activity, is often Adjusted net savings measures the change used as a measure of welfare. But as the Commission on the Measurement of Economic in a country’s national wealth. It begins with and Social Progress points out, GDP has many shortcomings as the sole measure of gross national savings and then adjusts for con- well-being. The commission’s report identified problems with the GDP measure itself and recommended including additional measures of the objective and subjective dimensions of sumption of fixed capital, depletion of natural well-being and measures of the sustainability of current consumption levels. The commis- resources, changes in human capital, and dam- sion endorsed the adjusted net savings approach as the “relevant economic counterpart ages from carbon dioxide and particulate emis- of the notion of sustainability” (Stiglitz, Sen, and Fitoussi 2009, p. 108). But it pointed out that the adjustment for environmental degradation has so far been limited mostly to sions. If adjusted net savings is negative, capi- carbon dioxide emissions. The report also notes the difficulties of pricing natural resources tal stocks are declining and future well-being and environmental degradation. Other recommendations for improving GDP measurement is reduced. The report argues that the key to include accounting more accurately for improvements in the quality of goods and services produced and the value of government services (usually based on inputs rather than on increasing living standards is building national actual outputs produced). wealth through investment and national savings to finance the investment. The table on adjusted net national income this new presentation. A majority of these coun- presents growth rates of GDP, gross national tries transmit the data on standardized report income (GNI), and adjusted net national income. forms for the country’s monetary aggregates GNI is more useful than GDP for measuring the and for the assets and liabilities of the central economic resources available to residents of an bank, other depository corporations, and other economy because it takes into account inflows financial corporations. This new presentation of income (profits, wages, and rents) from out- better classifies financial institution assets and side the economy, net of outflows to other econ- liabilities by financial instrument, sector of the omies (box 4g). Adjusted net national income domestic economy, and residency. For many goes one step further by subtracting from GNI countries the new presentation provides broad- a charge for the consumption of fixed capital er institutional coverage of other depository cor- (or depreciation) and the depletion of natu- porations and monetary aggregates. ral resources. For some countries, adjusted net In the new presentation, the IMF has national income growth rates tell a story quite adopted broad money as the flagship concept. different from that of the more widely used GDP Broad money consists of currency in circulation growth rates. outside depository corporations, transferable deposits, and other liquid components. Table Changes to monetary indicators 4.15 has replaced money and quasi money The monetary indicators in table 4.15 have with broad money. Claims on the private sec- been revised to reflect the International Mone- tor have been replaced with other claims on the tary Fund’s (IMF) new presentation of monetary domestic economy, consisting of the private data for countries reporting in compliance with sector plus state and local governments, pub- the Monetary and Financial Statistics Manual lic nonfinancial corporations, and other finan- (IMF 2000) and Monetary and Financial Statis- cial corporations. Claims on governments and tics Compilation Guide (IMF 2008). More than other public entities have been replaced with 120 countries report their monetary data under net claims on the central government. 2011 World Development Indicators 191 Tables 4.a Recent economic performance Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross international product and services and services balance reserves months average annual average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2009 2010a 2009 2010a 2009 2010a 2009 2010a 2009 2010a 2010a 2010a Albania 2.5 3.0 5.9 12.7 –12.0 5.2 2.3 2.0 –15.6 –12.2 2,496 4.5 Algeria 2.1 2.4 –3.0 3.0 16.7 12.5 –9.4 8.6 –10.0 4.6 166,989 42.0 Angola 0.7 3.0 2.4 10.0 6.6 8.5 –5.8 36.1 –10.0 –5.1 .. .. Argentinab 0.9 8.0 –6.4 12.8 –19.0 23.1 10.0 9.4 2.8 1.8 52,208 10.2 Armenia –14.4 4.0 –32.8 8.5 –21.0 4.2 1.4 7.5 –15.7 –12.7 1,859 6.7 Australia 1.3 2.8 2.9 15.0 –9.0 28.7 4.9 5.7 –4.2 –2.2 42,268 1.8 Austria –3.9 1.5 –16.1 8.2 –14.4 6.8 0.8 0.6 2.9 3.9 22,339 1.4 Azerbaijan 9.3 3.7 2.8 11.0 –5.3 3.5 –16.8 –2.7 23.7 27.2 6,409 7.0 Bangladesh 5.7 5.8 0.0 –9.0 –2.6 –12.5 6.5 10.7 3.7 2.4 11,175 6.4 Belarus 1.4 7.0 –8.2 6.0 –8.6 3.4 3.9 6.4 –13.0 –14.0 5,025 2.0 Belgium –2.8 2.1 –11.4 9.7 –11.1 8.2 1.1 –2.8 0.7 0.6 26,779 0.9 Bolivia 3.4 4.1 –10.8 11.4 –10.2 12.3 –2.4 6.5 4.7 8.0 .. .. Botswana –3.7 7.8 –28.0 12.0 –9.3 8.9 –5.7 6.0 –4.4 –2.1 .. .. Brazil –0.6 7.6 –10.2 26.0 –11.5 35.1 5.7 5.3 –1.5 –2.7 288,575 13.9 Bulgaria –4.9 0.0 –10.3 11.0 –21.5 3.0 4.1 –0.6 –9.8 –2.4 17,223 7.6 Cambodia –1.9 4.9 –6.3 8.0 –4.9 12.6 5.1 3.9 –8.8 –8.6 3,787 6.0 Cameroon 2.0 3.0 –4.8 17.0 –5.2 12.0 –3.4 3.4 –5.1 –2.7 .. .. Canada –2.5 3.0 –14.2 15.5 –13.9 14.6 –2.1 2.7 –2.9 –2.0 57,151 1.4 Chile –1.5 5.5 –5.6 8.5 –14.3 25.5 4.2 6.6 2.6 0.6 27,827 5.4 China 9.1 10.0 –10.3 33.0 4.1 35.0 –0.6 1.7 6.0 5.5 2,711,162 21.6 Hong Kong SAR, China –2.8 6.0 –10.1 22.1 –8.8 22.5 0.2 0.3 8.3 22.0 266,055 6.7 Colombia 0.8 4.3 –2.8 17.4 –7.9 21.4 4.9 4.8 –2.1 –2.7 28,076 6.6 Congo, Dem. Rep. 2.7 5.2 5.4 9.3 –11.9 10.8 30.2 21.7 –13.7 –17.2 1,768 7.3 Costa Rica –1.5 3.6 0.6 6.2 –12.4 13.1 8.9 7.9 –1.8 –3.2 4,630 4.1 Côte d’Ivoire 3.6 3.0 9.3 4.4 11.0 5.0 1.3 1.3 7.2 4.1 3,502 4.8 Croatia –5.8 –0.8 –16.2 2.5 –20.7 1.5 3.3 1.6 –5.3 –4.4 14,133 7.1 Czech Republic –4.2 1.7 –10.2 9.4 –10.2 10.4 2.7 1.6 –1.1 –2.7 42,328 3.9 Denmark –4.9 2.1 –9.7 5.1 –12.5 1.0 0.4 5.3 3.6 5.6 75,077 6.7 Dominican Republic 3.5 4.4 –7.4 8.1 –9.8 11.8 3.0 7.1 –4.6 –5.9 3,501 2.7 Ecuador 0.4 2.3 –6.4 –2.0 –8.0 5.0 4.3 4.4 –0.5 –0.8 2,622 1.1 Egypt, Arab Rep. 4.6 5.1 –14.5 11.8 –17.9 12.0 10.8 11.2 –1.8 –4.1 36,517 5.7 El Salvador –3.5 1.3 –16.4 9.4 –23.3 15.2 –1.0 3.6 –1.8 –3.1 2,897 3.9 Estonia –14.1 1.0 –11.2 5.4 –26.8 4.0 –0.6 –1.1 4.7 4.0 2,567 2.3 Ethiopia 8.7 9.0 6.9 11.7 16.4 4.4 24.4 9.9 –7.7 –8.5 .. .. Finland –8.0 3.0 –20.5 6.8 –18.1 3.5 0.9 –0.4 2.9 3.7 9,547 1.4 France –2.6 1.6 –12.2 6.6 –10.7 5.2 0.5 1.4 –2.0 –2.1 165,852 2.9 Gabon –1.0 5.1 –4.9 7.0 –2.8 4.8 –19.0 9.1 .. 12.7 .. .. Gambia, The 4.6 5.0 2.5 5.2 3.8 3.1 2.4 4.8 8.6 5.2 .. .. Georgia –3.9 5.5 –8.4 11.0 –6.4 9.0 –2.0 7.4 –11.3 –12.1 2,264 5.0 Germany –4.7 3.5 –14.3 10.7 –9.4 9.1 1.4 1.6 5.0 5.9 215,978 2.0 Ghana 4.7 6.6 12.6 8.9 –14.1 10.5 16.7 10.6 –4.6 –3.6 .. .. Greece –2.0 –4.0 –6.2 0.5 –18.6 –12.1 1.3 4.8 –10.9 –8.5 6,352 0.9 Guatemala 0.6 2.2 –6.2 9.9 –9.4 14.3 2.4 6.4 0.0 –2.5 5,949 5.1 Haiti 2.9 –8.5 9.9 –7.1 5.8 5.9 3.5 12.6 –3.6 –13.6 1,282 5.3 Honduras –1.9 2.4 –12.6 4.5 –26.0 10.4 4.4 10.5 –3.1 –4.7 .. .. Hungary –6.3 0.3 –9.1 6.8 –15.4 5.4 4.6 2.7 –0.5 –0.4 44,988 5.5 India 9.1 9.5 –6.7 8.1 –7.3 6.8 7.5 11.5 –1.9 –3.8 300,480 9.7 Indonesia 4.5 5.9 –9.7 24.7 –15.0 32.5 8.4 6.2 2.0 2.6 92,815 7.1 Iran, Islamic Rep. 1.8 1.5 8.5 –3.0 7.8 16.5 0.6 15.0 3.4 6.1 .. .. Ireland –7.1 –0.6 –4.2 1.7 –9.7 2.1 –3.2 0.6 –2.9 –3.6 2,114 0.2 Israel 0.8 3.8 –11.9 17.8 –17.7 17.5 5.2 4.6 3.9 4.9 70,914 12.0 Italy –5.0 1.1 –19.1 8.0 –14.5 9.4 2.1 1.6 –3.1 –3.6 158,478 3.5 Jamaica –3.0 0.6 –10.8 5.7 –11.4 9.3 6.5 16.7 –9.3 –7.9 2,330 4.0 Japan –5.2 4.4 –24.2 28.7 –16.7 15.6 –0.9 –1.0 2.8 3.8 1,096,069 17.6 Jordan 2.3 4.0 –2.7 5.2 –7.8 6.5 8.1 8.5 –5.0 –4.6 13,388 9.5 192 2011 World Development Indicators 4.a ECONOMY Recent economic performance Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross international product and services and services balance reserves months average annual average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2009 2010a 2009 2010a 2009 2010a 2009 2010a 2009 2010a 2010a 2010a Kazakhstan 1.2 5.5 –6.2 13.0 –15.9 6.0 4.7 6.9 –3.7 3.8 28,281 8.4 Kenya 2.6 5.0 –7.0 12.0 –0.2 14.5 6.7 4.8 –5.7 –5.7 4,327 4.1 Korea, Rep. 0.2 6.2 –0.8 28.0 –8.2 28.0 3.4 –0.5 5.1 3.7 292,143 7.0 Kuwait –4.0 1.9 –11.1 –2.0 –17.0 22.0 –14.7 13.9 25.6 25.1 24,805 8.2 Latvia –18.0 –2.2 –13.9 4.0 –34.2 1.6 –0.7 –4.9 8.7 1.4 7,604 7.9 Lebanon 9.0 8.0 5.3 20.0 6.5 18.5 5.8 4.5 –21.9 –23.6 44,476 27.3 Lithuania –15.0 0.4 –14.3 6.5 –29.4 4.2 –2.1 0.0 4.4 2.6 6,836 4.1 Malaysia –1.7 7.4 –10.4 28.0 –12.3 30.0 –6.7 1.5 16.5 14.7 106,501 6.9 Mauritius 2.1 4.2 –4.8 –4.0 –4.6 3.9 1.5 2.1 –7.9 –9.4 2,619 5.7 Mexico –6.5 5.2 –14.8 15.5 –18.2 19.4 4.3 4.9 –0.7 –1.0 120,583 4.7 Morocco 4.9 3.5 –13.1 18.4 –6.0 7.6 1.8 2.2 –5.4 –3.2 23,585 7.7 Namibia –0.8 4.2 –14.0 5.3 5.3 8.3 6.5 4.3 1.3 –1.6 .. .. Nepal 4.7 3.3 38.4 6.4 20.2 6.8 12.1 15.1 –0.1 –3.0 .. .. Netherlands –4.0 1.7 –7.9 11.7 –8.5 12.7 –0.3 2.9 4.6 5.6 46,147 1.0 New Zealand –0.4 2.2 0.4 10.5 –14.8 17.5 1.7 4.4 –2.9 –2.5 15,787 4.8 Nigeria 5.6 7.6 1.1 5.9 7.3 8.2 –0.6 17.0 12.5 10.7 .. .. Norway –1.6 –0.2 –3.9 4.6 –11.4 8.1 –4.0 7.9 13.1 11.8 50,036 5.3 Oman 3.6 4.8 –0.4 8.0 –13.0 18.0 –26.0 21.2 –0.6 8.5 13,025 7.3 Pakistan 3.6 4.4 –3.3 14.1 –15.2 11.2 20.0 13.4 –2.2 –3.1 17,256 5.7 Panama 2.4 5.7 –0.9 5.3 –5.6 13.1 4.1 2.4 –0.2 –6.1 .. .. Paraguay –3.8 8.5 –12.8 30.1 –13.2 30.3 –0.1 4.8 0.6 –1.8 3,962 5.0 Peru 0.9 8.0 –2.5 –4.1 –11.9 15.3 3.0 3.2 0.2 –1.7 44,215 17.1 Philippines 1.1 6.8 –13.4 23.0 –1.9 23.8 2.6 5.6 5.3 5.3 62,324 12.1 Poland 1.7 3.5 –9.1 6.4 –14.3 7.5 3.7 2.4 –2.2 –3.1 93,472 6.3 Portugal –2.6 1.4 –11.7 7.4 –10.8 3.6 0.1 1.0 –10.3 –10.6 20,937 2.9 Romania –8.5 –1.9 –11.8 12.0 –24.6 8.5 6.5 5.5 –4.5 –6.3 48,048 8.0 Russian Federation –7.9 3.8 –4.7 5.2 –30.4 17.5 2.5 8.0 4.0 5.1 479,222 19.2 Saudi Arabia 0.6 3.7 –2.8 1.5 –8.8 7.5 –21.6 16.7 6.1 7.8 452,391 32.9 Senegal 2.2 4.0 –8.8 6.8 –17.1 4.0 –0.5 0.7 –13.6 –14.3 1,911 3.9 Singapore –1.3 17.5 –10.1 29.7 –11.7 26.7 –1.8 –2.2 17.9 22.6 .. .. Slovak Republic –6.2 3.7 8.8 6.9 8.4 6.0 0.0 2.9 –3.2 –0.1 2,156 0.3 Slovenia –7.8 1.5 –19.3 1.4 –7.9 –4.1 1.9 –0.2 –1.5 –2.2 1,108 0.5 South Africa –1.8 2.7 –19.5 6.5 –17.4 12.7 7.3 5.6 –4.0 –4.1 43,820 5.8 Spain –3.6 –0.4 –11.6 7.8 –17.8 7.0 0.2 0.1 –5.5 –6.0 31,872 1.0 Sri Lanka 3.5 7.1 –12.3 2.0 –9.1 11.5 5.7 8.2 –0.5 –3.6 7,240 6.7 Sudan 4.5 5.9 –4.4 7.2 –7.3 7.2 –0.8 13.0 –7.1 –1.9 .. .. Sweden –5.1 5.2 –13.3 12.2 –13.2 15.0 2.0 0.9 7.7 6.7 48,246 2.9 Switzerland –1.9 2.7 –8.7 6.7 –5.4 8.3 0.3 0.9 7.9 7.7 269,396 14.5 Syrian Arab Republic 4.0 5.0 5.6 –2.0 6.4 4.5 –7.6 10.2 –4.5 –3.9 .. .. Tanzania 6.0 7.0 15.5 5.3 14.1 6.2 7.4 8.7 –8.5 –8.3 .. .. Thailand –2.2 7.5 –12.7 21.0 –21.8 32.0 2.0 –1.9 8.3 6.0 172,028 10.4 Trinidad and Tobago –3.0 2.2 –3.8 3.0 –4.1 4.2 –15.7 4.6 21.8 25.7 .. .. Tunisia 3.1 3.8 –1.6 13.0 6.7 16.1 2.9 3.8 –3.1 –4.8 .. .. Turkey –4.7 8.1 –5.3 6.5 –14.3 16.0 5.2 7.1 –2.3 –5.9 85,959 5.3 Uganda 7.1 6.3 16.2 3.4 25.2 10.5 16.5 6.1 –2.8 –3.6 .. .. Ukraine –15.1 4.3 –25.6 9.5 –38.6 5.5 13.4 9.2 –1.5 –2.2 34,571 7.2 United Kingdom –4.9 1.7 –10.1 7.0 –12.3 9.4 1.4 3.0 –1.7 –2.9 82,365 1.4 United States –2.6 2.8 –9.5 15.0 –13.8 18.8 0.9 0.6 –2.7 –3.3 488,928 2.3 Uruguay 2.9 7.9 2.5 15.6 –8.6 19.2 5.9 7.1 0.7 –0.6 7,744 9.7 Venezuela, RB –3.3 –2.3 –12.9 3.2 –19.6 –3.0 8.4 38.5 2.6 5.9 27,700 5.9 Vietnam 5.3 6.7 11.1 25.0 6.7 32.5 6.0 12.5 –7.0 –15.5 .. .. Yemen, Rep. 3.8 8.0 –16.3 43.6 –4.7 14.2 –4.1 13.8 –9.7 –0.6 5,986 10.0 Zambia 6.4 6.4 21.5 20.0 15.6 12.3 12.7 –5.8 –3.2 –4.5 2,094 5.6 Zimbabwe 5.7 5.7 5.2 10.5 36.0 6.2 25.3 4.2 –1.8 –1.3 .. .. a. Data are preliminary estimates based on World Bank staff estimates and National Sources. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and believe that GDP volume growth has been significantly higher than official reports indicate since the last quarter of 2008. Source: World Development Indicators data files, the World Bank’s Global Economic Prospects 2011, and the International Monetary Fund’s International Financial Statistics. 2011 World Development Indicators 193 4.1 Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Afghanistan .. 10.5 .. 4.9 .. 14.5 .. 8.7 .. 13.5 Albania 3.8 5.4 4.3 1.4 –0.5 4.4 .. .. 6.9 8.3 Algeria 1.9 4.0 3.6 4.6 1.8 3.3 –2.1 2.6 1.8 5.3 Angolaa 1.6 13.1 –1.4 14.0 4.4 13.4 –0.3 20.2 –2.2 12.1 Argentina 4.3 5.4b 3.5 2.5 3.8 6.1 2.7 5.8 4.5 4.7 Armenia –1.9 10.5 0.5 6.6 –7.8 11.3 –4.3 4.6 6.4 12.1 Australia 3.7 3.3 3.1 0.0 2.7 2.6 1.8 1.3 4.2 3.7 Austria 2.4 2.0 –0.1 1.3 2.5 2.3 2.5 2.9 2.5 2.1 Azerbaijan –6.3 17.9 –1.7 5.3 –2.1 23.1 –15.7 10.8 –2.7 10.6 Bangladesh 4.8 5.9 2.9 3.3 7.3 7.8 7.2 7.9 4.5 6.1 Belarus –1.6 8.4 –4.0 5.2 –1.8 12.3 –0.7 10.8 –0.4 5.9 Belgium 2.2 1.7 2.7 –1.0 1.8 0.7 .. .. 2.0 2.0 Benina 4.8 4.0 5.8 4.6 4.1 3.8 5.8 2.7 4.2 3.2 Bolivia 4.0 4.1 2.9 3.1 4.1 5.3 3.8 4.5 4.3 3.1 Bosnia and Herzegovina .. 5.0 .. 4.9 .. 6.8 .. 7.6 .. 4.4 Botswana 5.0 4.4 –0.5 1.2 3.7 2.5 4.7 4.8 9.1 5.6 Brazil 2.7 3.6 3.6 3.7 2.4 2.8 2.0 2.6 3.8 3.8 Bulgaria –1.1 5.4 –3.9 –2.5 –19.5 5.9 .. 6.2 .. 6.1 Burkina Faso 5.5 5.4 5.9 6.2 5.9 7.3 5.9 6.3 3.9 5.5 Burundi –2.9 3.0 –1.9 –1.5 –4.3 –6.2 .. .. –2.8 10.4 Cambodia 7.0 9.0 3.7 5.7 14.3 12.0 18.6 11.3 7.1 9.5 Cameroon 1.7 3.3 5.4 3.4 –0.9 –0.4 1.4 .. 0.2 6.2 Canada 3.1 2.1 1.1 1.4 3.2 0.1 4.5 –1.6 3.1 3.0 Central African Republic 2.0 0.8 3.8 0.3 0.7 –0.4 –0.2 –0.1 0.2 –2.5 Chad 2.2 10.2 4.9 .. 0.6 .. .. .. 0.8 .. Chile 6.6 4.1 2.2 5.2 5.6 2.7 4.4 3.2 6.9 4.6 Chinaa 10.6 10.9 4.1 4.4 13.7 11.8 12.9 11.4 11.0 11.6 Hong Kong SAR, China 3.6 4.7 .. –3.3 .. –2.6 .. .. .. 5.3 Colombia 2.8 4.5 –2.7 2.5 1.4 4.4 –2.5 4.0 4.1 4.7 Congo, Dem. Rep. –4.9 5.2 1.4 1.7 –8.0 8.7 –8.7 6.3 –13.0 11.2 Congo, Rep.a 1.0 4.0 .. .. .. .. .. .. .. .. Costa Rica 5.3 5.1 4.1 3.5 6.2 5.1 6.8 4.7 4.7 5.6 Côte d’Ivoirea 3.2 0.8 3.5 1.4 6.3 –0.2 5.5 –1.7 2.0 1.0 Croatia 0.5 3.9 –5.5 2.0 –2.2 4.6 –3.5 3.7 2.2 4.0 Cuba –0.7 6.7 –3.3 –0.9 –1.0 2.3 0.8 –1.5 –0.7 8.3 Czech Republic 1.1 4.1 0.0 0.1 0.2 5.7 4.3 7.0 1.2 4.3 Denmark 2.7 1.2 4.6 –1.8 2.5 –0.5 2.2 0.4 2.7 1.5 Dominican Republica 6.3 5.5 1.9 3.2 7.1 2.4 7.0 2.7 5.9 7.1 Ecuador 1.9 5.0 –1.7 3.7 2.6 4.2 1.5 5.3 2.4 3.6 Egypt, Arab Rep. 4.4 4.9 3.1 3.3 5.1 5.3 6.3 4.7 4.1 5.4 El Salvador 4.8 2.6 1.2 3.6 5.1 1.7 5.2 2.1 4.0 3.2 Eritrea 5.7 0.2 1.5 2.7 15.0 0.6 10.6 –6.0 5.7 0.5 Estonia 0.4 5.9 –6.2 –2.9 –2.4 8.6 7.3 8.9 3.2 7.1 Ethiopia 3.8 8.5 2.6 7.0 4.1 9.3 3.9 7.2 5.2 10.2 Finland 2.7 2.5 –0.3 2.4 3.8 3.6 6.4 4.1 2.6 1.6 France 1.9 1.5 2.0 0.3 1.1 0.5 .. 0.1 2.2 1.9 Gabona 2.3 2.1 2.0 1.4 1.6 0.9 3.0 3.1 3.1 3.2 Gambia, The 3.0 5.2 3.3 3.0 1.0 7.4 0.9 .. 3.7 6.1 Georgia –7.1 7.4 –11.0 0.6 –8.1 10.0 .. 10.9 –0.3 8.9 Germany 1.8 1.0 0.1 –0.3 –0.1 0.3 0.1 0.8 2.9 1.5 Ghana 4.3 5.8 .. .. .. .. .. .. .. .. Greece 2.2 3.6 0.5 –1.4 1.0 1.4 .. 1.7 2.6 4.7 Guatemala 4.2 3.7 2.8 2.9 4.3 2.8 2.8 2.8 4.7 4.4 Guinea 4.4 3.0 4.3 6.7 4.9 4.4 4.0 3.1 3.6 –2.7 Guinea-Bissau 1.2 1.0 .. .. .. .. .. .. .. .. Haiti 0.5 0.7 .. .. .. .. .. .. .. .. Honduras 3.2 4.9 2.2 3.3 3.6 4.1 4.0 4.6 3.8 6.2 194 2011 World Development Indicators 4.1 ECONOMY Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Hungary 1.5 2.9 –1.9 5.3 3.5 3.5 7.7 5.0 1.3 3.4 India 5.9 7.9 3.2 2.9 6.1 8.6 6.7 8.7 7.7 9.5 Indonesiaa 4.2 5.3 2.0 3.4 5.2 4.1 6.7 4.7 4.0 6.2 Iran, Islamic Rep. 3.1 5.4 3.2 5.9 2.6 6.9 5.1 9.9 3.8 5.3 Iraq .. –0.3 .. .. .. .. .. .. .. .. Ireland 7.4 3.9 0.0 –4.6 11.6 4.0 .. .. 8.7 4.4 Israela 5.5 3.6 .. .. .. .. .. .. .. .. Italy 1.5 0.5 2.1 –0.2 1.0 –0.5 1.6 –1.1 1.6 1.0 Jamaica 1.6 1.5 –0.6 –0.7 –0.8 0.2 –1.8 –1.5 3.8 1.9 Japan 1.0 1.1 –1.3 –0.3 –0.3 1.7 0.5 2.8 1.8 1.5 Jordan 5.0 6.9 –3.0 8.3 5.2 8.4 5.6 9.6 5.0 6.1 Kazakhstan –4.1 8.8 –8.0 4.6 –8.6 9.6 .. 6.6 1.1 8.6 Kenya 2.2 4.4 1.9 2.2 1.2 4.8 1.3 4.3 3.2 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 5.8 4.2 1.6 2.0 6.0 5.4 7.3 6.3 5.6 3.7 Kosovo .. 4.8 .. .. .. .. .. .. .. .. Kuwait a 4.9 8.4 1.0 .. 0.3 .. –0.1 .. 3.5 .. Kyrgyz Republic –4.1 4.6 1.5 1.8 –10.3 0.8 –7.5 –1.2 –5.2 7.9 Lao PDR 6.4 6.9 4.8 3.3 11.1 11.9 11.7 –1.9 6.6 7.6 Latvia –1.5 6.2 –5.2 2.7 –8.3 5.2 –7.3 3.1 2.7 7.0 Lebanon 5.3 4.6 2.9 1.4 –0.2 4.4 1.9 2.2 1.5 4.3 Lesotho 4.0 3.1 2.8 –2.4 5.5 3.6 7.9 5.7 4.5 3.7 Liberia 4.1 0.0 .. .. .. .. .. .. .. .. Libya .. 5.4 .. .. .. .. .. .. .. .. Lithuania –2.5 6.3 –0.4 1.7 3.3 9.6 6.6 9.0 5.8 7.4 Macedonia, FYR –0.8 3.1 0.2 2.2 –2.3 3.5 –5.3 2.9 0.5 3.0 Madagascar 2.0 3.6 1.8 2.4 2.4 4.2 2.0 5.1 2.3 3.6 Malawi 3.7 4.8 8.6 2.4 2.0 5.5 0.5 5.0 1.6 6.5 Malaysiaa 7.0 5.1 0.3 3.5 8.6 3.5 9.5 4.3 8.2 6.4 Mali 4.1 5.3 2.6 4.8 6.4 4.5 –1.4 5.1 3.0 6.5 Mauritania 2.9 4.7 –0.2 0.9 3.4 5.0 5.8 –1.4 4.9 5.5 Mauritius 5.2 3.7 0.0 –0.8 5.4 1.7 5.3 0.4 6.3 5.7 Mexico 3.1 2.2 1.5 2.0 3.8 1.3 4.3 1.1 2.9 2.6 Moldova –9.6 5.6 –11.2 –0.6 –13.6 –1.7 –7.1 1.3 0.7 10.5 Mongolia 1.0 7.4 2.5 5.9 –2.5 6.5 –9.7 7.1 0.7 8.7 Morocco 2.4 5.0 –0.4 5.8 3.2 4.1 2.6 3.1 3.1 5.0 Mozambique 6.1 7.9 5.2 8.2 12.3 9.1 10.2 7.9 5.0 7.0 Myanmar a .. .. .. .. .. .. .. .. .. .. Namibia 4.0 5.3 3.8 0.5 2.4 6.2 7.4 5.6 4.2 5.5 Nepal 4.9 3.7 2.5 3.1 7.1 2.8 8.9 1.0 6.2 4.1 Netherlands 3.2 1.7 1.8 1.5 1.7 0.9 2.6 1.2 3.6 2.1 New Zealand 3.2 2.5 2.9 1.8 2.5 1.9 .. .. 3.6 3.4 Nicaragua 3.7 3.3 4.7 2.7 5.5 3.7 5.3 4.8 5.0 3.7 Niger a 2.4 4.3 3.0 .. 2.0 .. 2.6 .. 1.9 .. Nigeria 2.5 6.6 .. .. .. .. .. .. .. .. Norway 3.9 2.1 2.6 2.4 3.8 –0.3 1.5 2.6 3.8 3.0 Omana 4.5 4.5 5.0 .. 3.9 .. 6.0 .. 5.0 .. Pakistan 3.8 5.2 4.4 3.5 4.1 6.8 3.8 8.7 4.4 5.9 Panama 4.7 6.9 3.1 3.5 6.0 5.7 2.7 1.5 4.5 7.4 Papua New Guinea 3.8 3.4 4.5 2.2 5.4 4.1 4.6 3.8 –0.6 3.8 Paraguaya 2.2 3.4 3.3 2.3 0.6 1.8 1.4 1.2 2.5 4.3 Peru 4.7 6.0 5.5 4.1 5.4 6.5 3.8 6.2 4.0 6.0 Philippinesa 3.3 4.9 1.7 3.6 3.5 4.0 3.0 3.9 4.0 6.1 Poland 4.7 4.4 0.5 0.8 7.1 5.8 9.9 8.5 5.1 3.7 Portugal 2.9 0.8 –0.6 –0.3 3.1 –0.8 2.7 –0.6 2.5 1.6 Puerto Ricoa 4.2 .. .. .. .. .. .. .. .. .. Qatar .. 14.2 .. .. .. .. .. .. .. .. 2011 World Development Indicators 195 4.1 Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Romania –0.6 5.6 –1.9 7.3 –1.2 6.0 .. .. 0.9 5.2 Russian Federation –4.7 6.0 –4.9 2.1 –7.1 4.6 .. .. –1.7 7.0 Rwandaa –0.2 7.6 2.5 .. –3.8 .. –5.8 .. –0.9 .. Saudi Arabiaa 2.1 3.8 1.6 1.4 2.2 3.6 5.6 5.9 2.2 4.2 Senegal 3.0 4.3 2.4 2.0 3.8 3.3 3.1 1.4 3.0 6.3 Serbia –4.2 5.0 .. .. .. .. .. .. .. .. Sierra Leone –5.0 9.5 .. .. .. .. .. .. .. .. Singapore 7.6 6.5 .. 2.3 7.8 5.4 .. .. 7.8 6.2 Slovak Republic 2.2 5.8 0.2 5.0 3.7 10.5 9.3 10.7 5.4 2.4 Slovenia 2.7 3.8 0.4 –0.7 1.6 4.1 1.8 3.7 3.3 4.0 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.1 4.1 1.0 1.5 1.0 2.9 1.6 3.1 3.0 4.1 Spain 2.7 2.8 3.1 –0.2 2.3 1.3 5.2 –0.2 2.7 3.5 Sri Lankaa 5.3 5.5 1.8 2.8 6.9 5.5 8.1 4.4 5.7 6.2 Sudan 5.5 7.3 7.4 2.4 8.5 10.2 7.5 4.4 1.9 10.1 Swaziland 3.4 2.6 0.9 1.3 3.2 1.7 2.8 1.8 3.9 3.9 Sweden 2.3 2.4 –0.8 3.5 4.6 2.8 8.9 3.3 1.8 2.2 Switzerland 1.0 1.9 –0.9 0.3 0.3 2.1 1.0 2.5 1.2 1.8 Syrian Arab Republic 5.1 4.4 6.0 3.8 9.2 2.4 .. 14.5 1.5 7.7 Tajikistan –10.4 8.2 –6.8 7.7 –11.4 9.2 –12.6 8.6 –10.8 8.3 Tanzaniac 3.0 7.1 3.2 4.4 3.1 9.5 2.8 8.7 2.6 7.8 Thailanda 4.2 4.6 1.0 2.3 5.7 5.6 6.9 6.6 3.7 4.2 Timor-Lestea .. 2.4 .. .. .. .. .. .. .. .. Togoa 3.5 2.5 4.0 2.8 1.8 8.1 1.8 7.5 3.9 –0.7 Trinidad and Tobago 3.2 7.4 2.7 –7.2 3.2 10.2 4.9 9.5 3.2 5.3 Tunisiaa 4.7 4.9 2.3 2.6 4.6 3.6 5.5 3.6 5.3 5.9 Turkey 3.9 4.9 1.3 1.5 4.7 5.4 4.7 5.3 4.0 5.3 Turkmenistan –4.9 13.9 –4.7 14.3 –2.7 30.3 .. .. –5.8 16.0 Uganda 7.2 7.8 3.9 2.3 12.0 9.5 13.9 6.7 8.3 8.5 Ukraine –9.3 5.6 –5.6 3.1 –12.6 4.6 –11.2 7.8 –8.1 5.8 United Arab Emirates 4.8 7.0 13.2 3.6 3.0 6.0 11.9 8.1 7.2 9.5 United Kingdom 2.8 2.0 –1.3 0.6 1.3 –0.6 .. .. 3.5 2.9 United States 3.6 2.0 3.8 2.1 3.8 0.9 .. 2.4 3.6 2.3 Uruguay 3.3 3.4 2.6 2.9 1.1 4.0 –0.1 6.2 1.5 3.4 Uzbekistan –0.2 6.9 0.5 6.5 –3.4 4.7 0.7 2.3 0.4 8.5 Venezuela, RB 1.6 4.9 1.2 3.6 1.2 3.3 4.5 3.6 –0.1 5.9 Vietnama 7.9 7.6 4.3 3.8 11.9 9.6 11.2 11.3 7.5 7.5 West Bank and Gaza 7.3 –0.9 .. .. .. .. .. .. .. .. Yemen, Rep.a 6.0 3.9 5.6 .. 8.2 .. 5.7 .. 5.0 .. Zambia 0.5 5.4 4.2 1.2 –4.2 9.2 0.8 5.0 2.5 5.6 Zimbabwe 2.3 –7.5 4.3 –10.8 0.4 –5.8 0.4 –6.6 3.0 –4.8 World 2.9 w 2.9 w 1.9 w 2.5 w 2.4 w 2.8 w .. w 4.0 w 3.2 w 2.9 w Low income 3.1 5.4 2.9 3.6 3.4 7.4 3.7 6.4 2.9 5.9 Middle income 3.9 6.4 2.4 3.6 4.5 7.2 6.2 7.6 4.3 6.6 Lower middle income 6.5 8.5 3.1 3.8 8.7 9.6 9.2 9.8 6.8 9.3 Upper middle income 2.1 4.4 0.9 3.0 1.3 3.9 3.3 3.6 3.0 4.5 Low & middle income 3.9 6.4 2.4 3.6 4.5 7.2 6.2 7.6 4.3 6.6 East Asia & Pacific 8.5 9.4 3.4 4.1 11.0 10.2 10.9 10.2 8.6 10.0 Europe & Central Asia –1.8 5.9 –2.1 3.0 –4.3 6.2 .. .. 0.3 6.3 Latin America & Carib. 3.2 3.8 2.0 3.0 3.0 3.2 2.9 2.9 3.5 3.9 Middle East & N. Africa 3.8 4.7 2.9 4.4 4.2 3.6 4.3 6.0 3.3 5.5 South Asia 5.5 7.3 3.3 3.0 6.0 8.2 6.4 8.5 6.9 8.7 Sub-Saharan Africa 2.5 5.1 3.2 3.2 1.9 4.9 2.2 3.4 2.6 4.8 High income 2.7 2.0 1.2 0.9 1.9 1.1 .. 2.9 3.0 2.2 Euro area 2.1 1.5 1.5 0.0 1.1 0.7 2.4 0.5 2.5 1.9 a. Components are at producer prices. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and believe that GDP volume growth has been significantly higher than official reports indicate since the last quarter of 2008. c. Covers mainland Tanzania only. 196 2011 World Development Indicators 4.1 ECONOMY Growth of output About the data Definitions An economy’s growth is measured by the change in Rebasing national accounts • Gross domestic product (GDP) at purchaser prices the volume of its output or in the real incomes of When countries rebase their national accounts, they is the sum of gross value added by all resident pro- its residents. The 1993 United Nations System of update the weights assigned to various components ducers in the economy plus any product taxes (less National Accounts (1993 SNA) offers three plausible to better reflect current patterns of production or subsidies) not included in the valuation of output. It indicators for calculating growth: the volume of gross uses of output. The new base year should represent is calculated without deducting for depreciation of domestic product (GDP), real gross domestic income, normal operation of the economy—it should be a fabricated capital assets or for depletion and degra- and real gross national income. The volume of GDP year without major shocks or distortions. Some dation of natural resources. Value added is the net is the sum of value added, measured at constant developing countries have not rebased their national output of an industry after adding up all outputs and prices, by households, government, and industries accounts for many years. Using an old base year subtracting intermediate inputs. The industrial origin operating in the economy. can be misleading because implicit price and vol- of value added is determined by the International Each industry’s contribution to growth in the econ- ume weights become progressively less relevant Standard Industrial Classifi cation (ISIC) revision omy’s output is measured by growth in the industry’s and useful. 3. •  Agriculture is the sum of gross output less value added. In principle, value added in constant To obtain comparable series of constant price data, the value of intermediate input used in production prices can be estimated by measuring the quantity the World Bank rescales GDP and value added by for industries classified in ISIC divisions 1–5 and of goods and services produced in a period, valu- industrial origin to a common reference year. This includes forestry and fishing. • Industry is the sum ing them at an agreed set of base year prices, and year’s World Development Indicators continues to of gross output less the value of intermediate input subtracting the cost of intermediate inputs, also in use 2000 as the reference year. Because rescaling used in production for industries classified in ISIC constant prices. This double-deflation method, rec- changes the implicit weights used in forming regional divisions 10–45, which cover mining, manufactur- ommended by the 1993 SNA and its predecessors, and income group aggregates, aggregate growth ing (also reported separately), construction, electric- requires detailed information on the structure of rates in this year’s edition are not comparable with ity, water, and gas. • Manufacturing is the sum of prices of inputs and outputs. those from earlier editions with different base years. gross output less the value of intermediate input In many industries, however, value added is Rescaling may result in a discrepancy between used in production for industries classified in ISIC extrapolated from the base year using single volume the rescaled GDP and the sum of the rescaled com- divisions 15–37. • Services correspond to ISIC divi- indexes of outputs or, less commonly, inputs. Par- ponents. Because allocating the discrepancy would sions 50–99. This sector is derived as a residual ticularly in the services industries, including most of cause distortions in the growth rates, the discrep- (from GDP less agriculture and industry) and may not government, value added in constant prices is often ancy is left unallocated. As a result, the weighted properly reflect the sum of services output, including imputed from labor inputs, such as real wages or average of the growth rates of the components gen- banking and financial services. For some countries number of employees. In the absence of well defined erally will not equal the GDP growth rate. it includes product taxes (minus subsidies) and may measures of output, measuring the growth of ser- also include statistical discrepancies. vices remains difficult. Computing growth rates Moreover, technical progress can lead to improve- Growth rates of GDP and its components are calcu- ments in production processes and in the quality of lated using the least squares method and constant goods and services that, if not properly accounted price data in the local currency. Constant price U.S. for, can distort measures of value added and thus dollar series are used to calculate regional and of growth. When inputs are used to estimate output, income group growth rates. Local currency series are as for nonmarket services, unmeasured technical converted to constant U.S. dollars using an exchange progress leads to underestimates of the volume of rate in the common reference year. The growth rates output. Similarly, unmeasured improvements in qual- in the table are average annual compound growth ity lead to underestimates of the value of output and rates. Methods of computing growth are described Data sources value added. The result can be underestimates of in Statistical methods. growth and productivity improvement and overesti- Data on national accounts for most developing mates of inflation. Changes in the System of National Accounts countries are collected from national statistical Informal economic activities pose a particular mea- World Development Indicators adopted the termi- organizations and central banks by visiting and surement problem, especially in developing coun- nology of the 1993 SNA in 2001. Although many resident World Bank missions. Data for high tries, where much economic activity is unrecorded. countries continue to compile their national accounts income economies are from Organisation for A complete picture of the economy requires estimat- according to the SNA version 3 (referred to as the Economic Co-operation and Development (OECD) ing household outputs produced for home use, sales 1968 SNA), more and more are adopting the 1993 data files. The United Nations Statistics Division in informal markets, barter exchanges, and illicit or SNA. Some low-income countries still use concepts publishes detailed national accounts for UN mem- deliberately unreported activities. The consistency from the even older 1953 SNA guidelines, including ber countries in National Accounts Statistics: Main and completeness of such estimates depend on the valuations such as factor cost, in describing major Aggregates and Detailed Tables and publishes skill and methods of the compiling statisticians. economic aggregates. Countries that use the 1993 updates in the Monthly Bulletin of Statistics. SNA are identified in Primary data documentation. 2011 World Development Indicators 197 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan .. 14,483 .. 33 .. 22 .. 13 .. 45 Albania 2,424 12,015 56 21 23 20 14 20 22 60 Algeria 41,764 140,577 11 12 50 55 12 6 39 34 Angolaa 5,040 75,493 7 10 66 59 4 6 26 31 Argentina 258,032 307,155 6 8 28 32 18 21 66 61 Armenia 1,468 8,714 42 21 32 35 25 16 26 45 Australia 371,091 924,843 3 3 29 29 15 10 68 68 Austria 238,314 381,084 3 2 31 29 20 19 67 69 Azerbaijan 3,052 43,019 27 8 34 60 13 4 39 32 Bangladesh 37,940 89,360 26 19 25 29 15 18 49 53 Belarus 13,973 49,037 17 10 37 42 31 30 46 48 Belgium 284,142 471,161 2 1 28 22 20 14 70 78 Benina 2,009 6,656 34 .. 15 .. 9 .. 51 .. Bolivia 6,715 17,340 17 14 33 36 19 14 50 50 Bosnia and Herzegovina 1,867 17,042 21 8 26 28 11 13 54 64 Botswana 4,774 11,823 4 3 51 40 5 4 45 57 Brazil 768,951 1,594,490 6 6 28 25 19 16 67 69 Bulgaria 13,069 48,722 16 6 28 30 26 15 56 64 Burkina Faso 2,380 8,141 35 .. 21 .. 15 .. 43 .. Burundi 1,000 1,325 48 .. 19 .. 9 .. 33 .. Cambodia 3,441 10,447 50 35 15 23 10 15 36 42 Cameroon 8,733 22,186 24 19 31 31 22 17 45 50 Canada 590,517 1,336,068 3 .. 31 .. 18 .. 66 .. Central African Republic 1,122 2,006 46 56 21 15 10 .. 33 30 Chad 1,446 6,839 36 14 14 49 11 7 51 38 Chile 71,349 163,669 9 3 35 42 18 13 55 55 Chinaa 728,007 4,985,461 20 10 47 46 34 34 33 43 Hong Kong SAR, China 144,230 210,568 .. .. 15 8 8 2 85 92 Colombia 92,507 234,045 15 7 32 34 16 14 53 58 Congo, Dem. Rep. 5,643 10,575 57 43 17 24 9 5 26 33 Congo, Rep.a 2,116 9,580 10 5 45 71 8 4 45 24 Costa Rica 11,722 29,240 14 7 30 27 22 19 57 66 Côte d’Ivoirea 11,000 23,304 25 24 21 25 15 18 55 50 Croatia 22,046 63,034 7 7 32 27 23 16 61 66 Cuba 30,428 62,705 9 5 23 20 15 10 68 75 Czech Republic 55,257 190,274 5 2 38 37 24 23 57 61 Denmark 181,984 309,596 3 1 25 22 17 13 71 77 Dominican Republica 16,358 46,788 10 6 36 32 26 24 54 61 Ecuador 20,206 57,249 .. 6 .. 23 .. 10 .. 71 Egypt, Arab Rep. 60,159 188,413 17 14 32 37 17 16 51 49 El Salvador 9,500 21,101 14 12 30 27 23 21 56 60 Eritrea 578 1,873 21 14 17 22 9 6 62 63 Estonia 4,353 19,084 6 3 33 29 21 17 61 68 Ethiopia 7,606 28,526 57 51 10 11 5 4 33 39 Finland 130,700 237,989 4 3 33 28 25 18 62 69 France 1,569,983 2,649,390 3 2 25 19 .. 11 72 79 Gabona 4,959 11,062 8 5 52 54 5 4 40 41 Gambia, The 382 733 30 27 13 15 6 5 57 57 Georgia 2,694 10,744 52 10 16 21 11 12 32 69 Germany 2,522,792 3,330,032 1 1 32 26 23 19 67 73 Ghana 6,457 26,169 43 32 27 19 10 7 31 49 Greece 131,718 329,924 9 3 21 18 .. 10 70 79 Guatemala 14,657 37,322 24 12 20 28 14 20 56 59 Guinea 3,694 4,103 19 17 29 53 4 5 52 30 Guinea-Bissau 254 837 55 55 12 13 8 10 33 32 Haiti 2,695 6,479 .. .. .. .. .. .. .. .. Honduras 3,911 14,318 22 12 31 27 18 19 48 60 198 2011 World Development Indicators 4.2 ECONOMY Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 44,656 128,964 7 4 32 29 24 22 61 66 India 356,299 1,377,265 26 18 28 27 18 15 46 55 Indonesiaa 202,132 540,274 17 16 42 49 24 27 41 35 Iran, Islamic Rep. 90,829 331,015 18 10 34 44 12 11 47 45 Iraq 10,114 65,837 9 .. 75 .. 1 .. 16 .. Ireland 67,061 227,193 7 1 38 31 30 24 55 68 Israela 96,065 195,392 .. .. .. .. .. .. .. .. Italy 1,126,041 2,112,780 3 2 30 25 22 16 66 73 Jamaica 5,813 12,070 9 6 37 22 16 9 54 72 Japan 5,264,380 5,068,996 2 1 34 28 23 20 64 71 Jordan 6,727 25,092 4 3 29 32 15 20 67 65 Kazakhstan 20,374 115,306 13 6 31 40 15 11 56 53 Kenya 9,046 29,376 31 23 16 15 10 9 53 62 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 517,118 832,512 6 3 42 37 28 28 52 61 Kosovo .. 5,387 .. 12 .. 20 .. 17 .. 68 Kuwait a 27,192 148,024 0 .. 55 .. 4 .. 45 .. Kyrgyz Republic 1,661 4,578 44 29 20 19 9 13 37 51 Lao PDR 1,764 5,939 56 35 19 28 14 9 25 37 Latvia 5,236 26,195 9 3 30 20 21 10 61 77 Lebanon 11,719 34,528 8 5 25 17 14 9 68 78 Lesotho 814 1,579 19 8 43 34 17 17 38 58 Liberia 135 876 82 61 5 17 3 13 13 22 Libya 25,541 62,360 .. 2 .. 78 .. 4 .. 20 Lithuania 7,905 37,206 11 4 31 31 19 18 58 64 Macedonia, FYR 4,449 9,221 13 11 30 36 23 23 57 52 Madagascar 3,160 8,590 27 29 9 16 8 14 64 55 Malawi 1,397 4,727 30 31 20 16 16 10 50 53 Malaysiaa 88,832 193,093 13 10 41 44 26 25 46 46 Mali 2,466 8,996 50 37 19 24 8 3 32 .. Mauritania 1,415 3,024 37 21 25 35 8 4 37 45 Mauritius 4,040 8,589 10 4 32 29 23 19 58 67 Mexico 286,698 874,810 6 4 28 35 21 17 66 61 Moldova 1,753 5,405 33 10 32 13 26 13 35 77 Mongolia 1,227 4,202 41 24 29 33 12 5 30 44 Morocco 32,986 91,375 15 16 34 29 19 16 51 55 Mozambique 2,247 9,790 35 31 15 24 8 14 51 45 Myanmar a .. .. 60 .. 10 .. 7 .. 30 .. Namibia 3,503 9,265 12 9 28 33 13 15 60 58 Nepal 4,401 12,531 42 34 23 16 10 7 35 50 Netherlands 418,969 792,128 3 2 27 24 17 13 69 74 New Zealand 62,795 126,679 7 .. 27 .. 18 .. 66 .. Nicaragua 3,191 6,140 23 19 27 30 19 20 49 51 Niger a 1,881 5,383 40 .. 17 .. 6 .. 43 .. Nigeria 28,109 173,004 .. 33 .. 41 .. .. .. 27 Norway 148,920 381,766 3 1 34 40 13 10 63 59 Omana 13,803 46,114 3 .. 46 .. 5 .. 51 .. Pakistan 60,636 161,990 26 22 24 24 16 17 50 54 Panama 7,906 24,711 8 6 18 17 9 6 74 77 Papua New Guinea 4,636 7,893 35 36 34 45 8 6 31 20 Paraguaya 8,066 14,236 21 19 23 21 16 13 56 59 Peru 53,674 130,325 9 7 31 34 17 14 60 59 Philippinesa 74,120 161,196 22 15 32 30 23 20 46 55 Poland 139,062 430,076 8 4 35 30 21 16 57 66 Portugal 116,419 232,874 6 2 28 23 19 13 66 75 Puerto Ricoa 42,647 .. 1 .. 44 .. 42 .. 55 .. Qatar 8,138 98,313 .. .. .. .. .. .. .. .. 2011 World Development Indicators 199 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 35,477 161,110 21 7 43 26 29 22 36 67 Russian Federation 395,528 1,231,893 7 5 37 33 .. 15 56 62 Rwandaa 1,293 5,216 44 34 16 15 10 6 40 51 Saudi Arabiaa 142,458 375,766 6 3 49 51 10 10 45 46 Senegal 4,879 12,822 21 17 24 22 17 13 55 62 Serbia 21,381 42,984 .. 13 .. 28 .. .. .. 59 Sierra Leone 871 1,942 43 51 39 22 9 .. 18 27 Singapore 84,291 182,232 .. .. 35 26 27 19 65 74 Slovak Republic 25,240 87,642 6 3 38 35 27 19 56 63 Slovenia 20,814 48,477 4 2 35 34 26 22 60 64 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 151,113 285,366 4 3 35 31 21 15 61 66 Spain 596,751 1,460,250 5 3 29 26 18 13 66 71 Sri Lankaa 13,030 41,979 23 13 27 30 16 18 50 58 Sudan 13,830 54,681 39 30 11 26 5 7 51 44 Swaziland 1,699 3,001 12 7 45 49 39 44 43 43 Sweden 253,680 406,072 3 2 30 25 22 16 66 73 Switzerland 315,940 491,924 2 1 30 27 20 19 68 72 Syrian Arab Republic 11,397 52,177 32 21 20 34 15 13 48 45 Tajikistan 1,232 4,978 38 22 39 24 28 11 22 54 Tanzaniab 5,255 21,368 47 29 15 24 7 10 38 47 Thailanda 168,019 263,772 10 12 41 43 30 34 50 45 Timor-Lestea .. 558 .. .. .. .. .. .. .. .. Togoa 1,309 2,855 38 .. 22 .. 10 .. 40 .. Trinidad and Tobago 5,329 21,204 2 0 47 52 9 6 51 47 Tunisiaa 18,031 39,561 11 8 29 30 19 17 59 62 Turkey 169,486 614,603 16 9 33 26 23 17 50 65 Turkmenistan 2,482 19,947 17 12 63 54 40 47 20 34 Uganda 5,756 16,043 49 25 14 26 7 8 36 50 Ukraine 48,214 113,545 15 8 43 29 35 18 42 62 United Arab Emirates 42,807 230,252 3 2 52 61 10 12 45 38 United Kingdom 1,157,119 2,174,530 2 1 31 21 21 11 67 78 United States 7,359,300 14,119,000 2 1 26 21 19 13 72 77 Uruguay 19,298 31,511 9 10 29 26 20 16 62 64 Uzbekistan 13,350 32,104 32 20 28 33 12 13 40 47 Venezuela, RB 74,889 326,133 6 .. 41 .. 15 .. 53 .. Vietnama 20,736 97,180 27 21 29 40 15 20 44 39 West Bank and Gaza 3,220 .. .. .. .. .. .. .. .. .. Yemen, Rep.a 4,236 26,365 20 .. 32 .. 14 .. 48 .. Zambia 3,478 12,805 18 22 36 34 11 10 46 44 Zimbabwe 7,111 5,625 15 18 29 29 22 17 56 53 World 29,692,820 t 58,259,785 t 4w 3w 30 w 27 w 21 w 17 w 65 w 70 w Low income 153,755 432,171 37 26 20 24 11 12 43 50 Middle income 4,811,047 16,213,154 14 10 35 35 23 21 51 55 Lower middle income 1,992,261 8,887,269 21 13 39 39 26 26 40 48 Upper middle income 2,818,895 7,318,398 8 6 32 31 19 17 60 62 Low & middle income 4,965,895 16,657,552 15 10 34 35 22 21 51 55 East Asia & Pacific 1,312,902 6,353,790 19 11 44 45 31 32 36 43 Europe & Central Asia 763,913 2,591,705 14 8 35 30 22 17 51 62 Latin America & Carib. 1,770,557 4,017,912 7 6 29 31 19 17 64 63 Middle East & N. Africa 315,651 1,062,419 16 11 34 43 15 12 50 46 South Asia 476,175 1,700,339 26 18 27 27 17 15 46 55 Sub-Saharan Africa 327,608 945,923 18 13 29 30 16 13 53 57 High income 24,722,778 41,607,730 2 1 30 25 20 16 68 74 Euro area 7,286,803 12,465,331 3 2 29 24 21 15 68 74 a. Components are at producer prices. b. Covers mainland Tanzania only. 200 2011 World Development Indicators 4.2 ECONOMY Structure of output About the data Definitions An economy’s gross domestic product (GDP) rep- Ideally, industrial output should be measured • Gross domestic product (GDP) at purchaser prices resents the sum of value added by all its produc- through regular censuses and surveys of fi rms. is the sum of gross value added by all resident pro- ers. Value added is the value of the gross output of But in most developing countries such surveys are ducers in the economy plus any product taxes (less producers less the value of intermediate goods and infrequent, so earlier survey results must be extrapo- subsidies) not included in the valuation of output. services consumed in production, before accounting lated using an appropriate indicator. The choice of It is calculated without deducting for depreciation for consumption of fixed capital in production. The sampling unit, which may be the enterprise (where of fabricated assets or for depletion and degrada- United Nations System of National Accounts calls responses may be based on financial records) or tion of natural resources. Value added is the net for value added to be valued at either basic prices the establishment (where production units may be output of an industry after adding up all outputs and (excluding net taxes on products) or producer prices recorded separately), also affects the quality of subtracting intermediate inputs. The industrial origin (including net taxes on products paid by producers the data. Moreover, much industrial production is of value added is determined by the International but excluding sales or value added taxes). Both valu- organized in unincorporated or owner-operated ven- Standard Industrial Classifi cation (ISIC) revision ations exclude transport charges that are invoiced tures that are not captured by surveys aimed at the 3. •  Agriculture is the sum of gross output less separately by producers. Total GDP shown in the formal sector. Even in large industries, where regu- the value of intermediate input used in production table and elsewhere in this volume is measured at lar surveys are more likely, evasion of excise and for industries classified in ISIC divisions 1–5 and purchaser prices. Value added by industry is normally other taxes and nondisclosure of income lower the includes forestry and fishing. • Industry is the sum measured at basic prices. When value added is mea- estimates of value added. Such problems become of gross output less the value of intermediate input sured at producer prices, this is noted in Primary data more acute as countries move from state control of used in production for industries classified in ISIC documentation and footnoted in the table. industry to private enterprise, because new firms and divisions 10–45, which cover mining, manufactur- While GDP estimates based on the production growing numbers of established firms fail to report. ing (also reported separately), construction, electric- approach are generally more reliable than estimates In accordance with the System of National Accounts, ity, water, and gas. • Manufacturing is the sum of compiled from the income or expenditure side, dif- output should include all such unreported activity gross output less the value of intermediate input ferent countries use different definitions, methods, as well as the value of illegal activities and other used in production for industries classified in ISIC and reporting standards. World Bank staff review the unrecorded, informal, or small-scale operations. divisions 15–37. • Services correspond to ISIC divi- quality of national accounts data and sometimes Data on these activities need to be collected using sions 50–99. This sector is derived as a residual make adjustments to improve consistency with techniques other than conventional surveys of firms. (from GDP less agriculture and industry) and may not international guidelines. Nevertheless, significant In industries dominated by large organizations properly reflect the sum of services output, including discrepancies remain between international stan- and enterprises, such as public utilities, data on banking and financial services. For some countries dards and actual practice. Many statistical offices, output, employment, and wages are usually read- it includes product taxes (minus subsidies) and may especially those in developing countries, face severe ily available and reasonably reliable. But in the also include statistical discrepancies. limitations in the resources, time, training, and bud- services industry the many self-employed workers gets required to produce reliable and comprehensive and one-person businesses are sometimes difficult series of national accounts statistics. to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Data problems in measuring output Compounding these problems are the many forms Among the difficulties faced by compilers of national of economic activity that go unrecorded, including accounts is the extent of unreported economic activ- the work that women and children do for little or no ity in the informal or secondary economy. In develop- pay. For further discussion of the problems of using ing countries a large share of agricultural output is national accounts data, see Srinivasan (1994) and either not exchanged (because it is consumed within Heston (1994). Data sources the household) or not exchanged for money. Agricultural production often must be estimated Dollar conversion Data on national accounts for most developing indirectly, using a combination of methods involv- To produce national accounts aggregates that are countries are collected from national statistical ing estimates of inputs, yields, and area under cul- measured in the same standard monetary units, organizations and central banks by visiting and tivation. This approach sometimes leads to crude the value of output must be converted to a single resident World Bank missions. Data for high approximations that can differ from the true values common currency. The World Bank conventionally income economies are from Organisation for over time and across crops for reasons other than uses the U.S. dollar and applies the average official Economic Co-operation and Development (OECD) climate conditions or farming techniques. Similarly, exchange rate reported by the International Monetary data files. The United Nations Statistics Division agricultural inputs that cannot easily be allocated to Fund for the year shown. An alternative conversion publishes detailed national accounts for UN mem- specific outputs are frequently “netted out” using factor is applied if the official exchange rate is judged ber countries in National Accounts Statistics: Main equally crude and ad hoc approximations. For further to diverge by an exceptionally large margin from the Aggregates and Detailed Tables and publishes discussion of the measurement of agricultural pro- rate effectively applied to transactions in foreign cur- updates in the Monthly Bulletin of Statistics. duction, see About the data for table 3.3. rencies and traded products. 2011 World Development Indicators 201 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1998 2009 1998 2007 1998 2007 1998 2007 1998 2007 1998 2007 Afghanistan .. 1,632 .. .. .. .. .. .. .. .. .. .. Albania 268 1,995 20 17 27 22 3 3 5 17 46 41 Algeria 4,372 7,315 33 .. 8 .. .. .. 11 .. 48 .. Angola 407 4,586 .. .. .. .. .. .. .. .. .. .. Argentina 53,326 60,116 26 .. 8 .. 13 .. 15 .. 38 .. Armenia 377 1,213 .. .. .. .. .. .. .. .. .. .. Australia 51,505 95,726 .. 19 .. 3 .. 14 .. 7 .. 58 Austria 37,828 64,124 10 9 5 2 24 28 7 7 54 54 Azerbaijan 370 1,927 .. 18 .. 1 .. 9 .. 5 .. 66 Bangladesh 6,887 15,472 24 .. 40 .. 3 .. 11 .. 21 .. Belarus 4,487 12,638 .. .. .. .. .. .. .. .. .. .. Belgium 45,588 59,032 13 12 6 4 19 19 18 23 44 43 Benin 200 .. .. .. .. .. .. .. .. .. .. .. Bolivia 1,189 2,014 35 .. 5 .. 0 .. 5 .. 55 .. Bosnia and Herzegovina 497 1,816 .. .. .. .. .. .. .. .. .. .. Botswana 253 475 23 22 8 5 15 .. 5 .. 69 73 Brazil 117,276 216,924 20 18 7 6 20 21 13 11 40 44 Bulgaria 2,180 6,424 22 16 13 12 18 14 9 7 39 50 Burkina Faso 387 .. .. .. .. .. .. .. .. .. .. .. Burundi 64 .. .. .. .. .. .. .. .. .. .. .. Cambodia 436 1,403 7 .. 87 .. 0 .. 0 .. 7 .. Cameroon 1,843 3,328 35 .. 9 .. 1 .. 6 .. 49 .. Canada 104,352 172,050 14 .. 4 .. 29 .. 9 .. 44 .. Central African Republic 91 .. .. .. .. .. .. .. .. .. .. .. Chad 188 381 .. .. .. .. .. .. .. .. .. .. Chile 13,540 19,665 32 14 4 2 3 2 10 14 52 69 China 324,603 1,691,153 16 12 12 10 15 24 11 11 46 43 Hong Kong SAR, China 8,868 4,971 12 14 22 12 15 13 3 5 49 55 Colombia 13,770 30,690 32 27 10 9 5 6 17 13 36 45 Congo, Dem. Rep. 370 582 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 136 429 .. .. .. .. .. .. .. .. .. .. Costa Rica 2,972 5,034 46 44 8 5 3 3 11 10 32 39 Côte d’Ivoire 2,499 4,187 42 .. 10 .. 2 .. 12 .. 34 .. Croatia 4,163 8,789 .. .. .. .. .. .. .. .. .. .. Cuba 3,103 4,955 .. .. .. .. .. .. .. .. .. .. Czech Republic 14,416 39,662 13 9 6 3 23 29 6 6 52 53 Denmark 24,894 34,971 19 17 3 2 22 19 10 13 46 50 Dominican Republic 5,136 10,577 .. .. .. .. .. .. .. .. .. .. Ecuador 2,912 5,316 22 30 3 4 2 3 3 5 69 58 Egypt, Arab Rep. 14,403 28,712 16 .. 16 .. 12 .. 21 .. 35 .. El Salvador 2,569 4,319 29 .. 28 .. 2 .. 16 .. 25 .. Eritrea 64 102 49 44 12 19 1 1 6 5 31 31 Estonia 870 2,393 17 12 15 4 10 10 4 4 53 69 Ethiopia 373 1,071 55 41 13 9 1 5 7 5 24 40 Finland 29,158 37,557 8 6 2 2 30 32 6 6 54 54 France 209,123 253,608 13 14 5 3 26 24 12 13 44 45 Gabon 252 479 .. .. .. .. .. .. .. .. .. .. Gambia, The 22 32 .. .. .. .. .. .. .. .. .. .. Georgia 307 1,073 37 34 1 2 12 6 7 8 43 50 Germany 449,216 567,902 8 8 3 2 35 36 10 10 44 45 Ghana 672 1,759 .. .. .. .. .. .. .. .. .. .. Greece 12,338 29,718 24 22 12 8 11 10 10 6 43 54 Guatemala 2,631 6,937 .. .. .. .. .. .. .. .. .. .. Guinea 132 201 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 19 44 .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras 826 2,470 42 .. 22 .. 1 .. 5 .. 30 .. 202 2011 World Development Indicators 4.3 ECONOMY Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1998 2009 1998 2007 1998 2007 1998 2007 1998 2007 1998 2007 Hungary 9,959 28,619 15 11 7 3 27 31 11 10 40 44 India 59,562 190,333 13 9 12 9 15 19 24 16 37 47 Indonesia 23,857 142,532 21 26 18 13 14 18 13 11 33 32 Iran, Islamic Rep. 13,607 29,832 13 10 8 4 16 24 13 13 49 50 Iraq 91 .. 31 .. 15 .. 2 .. 23 .. 29 .. Ireland 26,279 48,709 17 18 2 0 16 16 38 33 28 33 Israel .. .. 12 10 6 3 24 22 12 20 46 44 Italy 236,315 306,459 9 9 13 10 23 23 8 7 47 51 Jamaica 914 973 48 .. 7 .. .. .. 19 .. 27 .. Japan 868,624 970,204 11 11 4 2 33 37 10 11 42 39 Jordan 1,047 4,416 27 23 6 10 4 3 21 17 42 48 Kazakhstan 2,659 12,536 .. .. .. .. .. .. .. .. .. .. Kenya 1,540 2,801 46 30 8 4 4 2 8 4 34 62 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 85,569 208,142 9 6 9 5 35 46 11 8 36 35 Kosovo .. 773 .. .. .. .. .. .. .. .. .. .. Kuwait 1,037 .. 8 .. 5 .. 2 .. 3 .. 83 .. Kyrgyz Republic 233 570 .. .. .. .. .. .. .. .. .. .. Lao PDR 216 478 46 .. 22 .. 8 .. 3 .. 22 .. Latvia 965 2,278 26 20 11 7 8 10 3 4 52 60 Lebanon 2,144 2,645 26 .. 10 .. 3 .. 6 .. 55 .. Lesotho 140 243 .. .. .. .. .. .. .. .. .. .. Liberia 17 105 .. .. .. .. .. .. .. .. .. .. Libya .. 3,879 .. .. .. .. .. .. .. .. .. .. Lithuania 1,807 7,562 27 23 18 9 12 10 3 9 40 48 Macedonia, FYR 645 1,816 31 18 21 17 9 4 8 6 31 55 Madagascar 399 1,115 31 0 33 30 .. 1 6 2 30 67 Malawi 216 447 44 .. 8 .. 5 .. 16 .. 28 .. Malaysia 20,774 49,213 10 9 4 2 8 30 11 15 67 44 Mali 101 195 .. .. .. .. .. .. .. .. .. .. Mauritania 100 115 .. .. .. .. .. .. .. .. .. .. Mauritius 877 1,483 22 31 51 31 1 1 4 .. 26 37 Mexico 82,015 144,431 24 25 4 3 23 18 15 19 32 35 Moldova 238 568 .. 39 .. 15 .. 4 .. .. .. 42 Mongolia 46 176 53 38 33 17 0 1 2 3 12 41 Morocco 6,136 12,909 34 30 18 13 4 5 15 16 28 35 Mozambique 422 1,219 .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 369 1,247 .. .. .. .. .. .. .. .. .. .. Nepal 436 807 35 .. 34 .. 0 .. 6 .. 26 .. Netherlands 58,120 89,029 18 18 2 2 15 19 13 14 51 47 New Zealand 8,495 17,968 30 27 .. 2 .. 13 .. .. 70 58 Nicaragua 538 1,086 .. .. .. .. .. .. .. .. .. .. Niger 128 .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. 30 .. 11 .. 7 .. 26 .. 26 .. Norway 16,863 32,575 16 20 2 1 23 25 8 9 51 45 Oman 654 .. 17 8 7 0 2 1 7 12 67 79 Pakistan 9,131 26,290 23 22 26 29 5 8 16 14 30 26 Panama 1,135 1,490 52 .. 7 .. .. .. 7 .. 34 .. Papua New Guinea 351 464 .. .. .. .. .. .. .. .. .. .. Paraguay 1,239 1,850 .. .. .. .. .. .. .. .. .. .. Peru 8,080 16,897 26 30 10 12 4 2 10 12 51 44 Philippines 14,254 32,889 35 24 7 6 21 25 10 8 28 38 Poland 30,022 61,948 25 16 7 4 16 20 7 8 45 52 Portugal 19,959 26,690 12 14 20 12 15 11 6 6 46 63 Puerto Rico 22,994 .. 10 9 4 1 5 9 62 62 20 20 Qatar .. .. 4 1 8 2 0 0 21 17 67 80 2011 World Development Indicators 203 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1998 2009 1998 2007 1998 2007 1998 2007 1998 2007 1998 2007 Romania 9,601 31,753 29 15 11 12 14 17 5 5 40 51 Russian Federation .. 161,878 22 15 3 2 18 10 9 8 48 65 Rwanda 223 335 75 .. 2 .. .. .. 6 .. 17 .. Saudi Arabia 15,492 39,128 .. 19 .. 5 .. 6 .. 27 .. 43 Senegal 723 1,490 44 .. 3 .. 0 .. 29 .. 24 .. Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 26 .. .. .. .. .. .. .. .. .. .. .. Singapore 18,839 33,499 4 2 1 1 52 45 13 32 30 20 Slovak Republic 6,036 15,375 12 7 7 3 21 27 9 4 52 59 Slovenia 4,860 10,566 10 7 10 6 17 20 11 14 51 53 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 23,678 39,014 18 17 6 3 14 14 11 7 51 58 Spain 103,971 172,433 15 15 7 4 20 17 10 8 47 55 Sri Lanka 2,343 7,618 39 29 30 29 4 0 7 14 21 27 Sudan 957 3,515 .. .. .. .. .. .. .. .. .. .. Swaziland 519 1,114 .. .. .. .. .. .. .. .. .. .. Sweden 48,915 56,948 8 7 1 1 37 34 9 13 46 46 Switzerland 51,047 88,054 10 .. 3 .. 15 .. .. .. 71 .. Syrian Arab Republic 1,286 6,686 .. .. .. .. .. .. .. .. .. .. Tajikistan 255 479 .. .. .. .. .. .. .. .. .. .. Tanzaniab 919 1,844 45 62 0 8 2 1 7 2 46 29 Thailand 34,534 89,881 25 16 12 9 27 35 4 6 32 34 Timor-Leste 9 .. .. .. .. .. .. .. .. .. .. .. Togo 110 .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 552 1,334 30 11 1 1 1 0 26 39 42 49 Tunisia 3,660 6,527 17 .. 36 .. 3 .. 11 .. 33 .. Turkey 64,408 92,715 15 12 18 19 14 20 8 7 45 42 Turkmenistan 452 9,158 .. .. .. .. .. .. .. .. .. .. Uganda 545 1,190 65 .. 5 .. .. .. 10 .. 20 .. Ukraine 10,578 17,992 .. .. .. .. .. .. .. .. .. .. United Arab Emirates 6,532 24,643 .. .. .. .. .. .. .. .. .. .. United Kingdom 251,809 217,594 13 16 5 3 26 23 10 11 46 47 United States 1,440,500 1,779,474 13 14 4 2 30 25 12 15 41 44 Uruguay 3,598 4,377 36 42 9 7 3 4 8 8 44 39 Uzbekistan 1,346 3,979 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17,380 .. 22 .. 2 .. 9 .. 34 .. 41 .. Vietnam 4,666 18,099 30 .. 22 .. 11 .. 7 .. 30 .. West Bank and Gaza .. .. 15 27 23 13 2 1 5 4 55 55 Yemen, Rep. 638 .. 45 60 5 9 0 0 2 4 48 27 Zambia 372 1,192 .. .. .. .. .. .. .. .. .. .. Zimbabwe 923 826 .. .. .. .. .. .. .. .. .. .. World 5,516,751 t 9,102,310 t .. .. .. .. .. .. .. .. .. .. Low income 20,369 44,786 .. .. .. .. .. .. .. .. .. .. Middle income 1,085,340 3,432,566 .. .. .. .. .. .. .. .. .. .. Lower middle income 535,090 2,342,311 .. .. .. .. .. .. .. .. .. .. Upper middle income 563,006 1,036,562 .. .. .. .. .. .. .. .. .. .. Low & middle income 1,105,587 3,479,229 .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 425,997 2,036,104 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. .. Latin America & Carib. 334,974 570,166 .. .. .. .. .. .. .. .. .. .. Middle East & N. Africa 49,450 117,926 .. .. .. .. .. .. .. .. .. .. South Asia 78,797 241,774 .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa 43,316 83,017 .. .. .. .. .. .. .. .. .. .. High income 4,411,013 5,603,504 .. .. .. .. .. .. .. .. .. .. Euro area 1,250,663 1,686,936 .. .. .. .. .. .. .. .. .. .. a. Includes unallocated data. b. Covers mainland Tanzania only. 204 2011 World Development Indicators 4.3 ECONOMY Structure of manufacturing About the data Definitions The data on the distribution of manufacturing value revision 3. Concordances matching ISIC categories • Manufacturing value added is the sum of gross added by industry are provided by the United Nations to national classifi cation systems and to related output less the value of intermediate inputs used Industrial Development Organization (UNIDO). UNIDO systems such as the Standard International Trade in production for industries classified in ISIC major obtains the data from a variety of national and inter- Classification are available. division D. • Food, beverages, and tobacco cor- national sources, including the United Nations Sta- In establishing classifi cations systems compil- respond to ISIC divisions 15 and 16. • Textiles tistics Division, the World Bank, the Organisation for ers must define both the types of activities to be and clothing correspond to ISIC divisions 17–19. Economic Co-operation and Development, and the described and the units whose activities are to • Machinery and transport equipment correspond to International Monetary Fund. To improve comparabil- be reported. There are many possibilities, and the ISIC divisions 29, 30, 32, 34, and 35. • Chemicals ity over time and across countries, UNIDO supple- choices affect how the statistics can be interpreted correspond to ISIC division 24. • Other manufactur- ments these data with information from industrial and how useful they are in analyzing economic ing is calculated as a residual. It covers wood and censuses, statistics from national and international behavior. The ISIC emphasizes commonalities in the related products (ISIC division 20), paper and related organizations, unpublished data that it collects in the production process and is explicitly not intended to products (ISIC divisions 21 and 22), petroleum and field, and estimates by the UNIDO Secretariat. Nev- measure outputs (for which there is a newly devel- related products (ISIC division 23), basic metals and ertheless, coverage may be incomplete, particularly oped Central Product Classification). Nevertheless, mineral products (ISIC division 27), fabricated metal for the informal sector. When direct information on the ISIC views an activity as defined by “a process products and professional goods (ISIC division 28), inputs and outputs is not available, estimates may resulting in a homogeneous set of products” (United and other industries (ISIC divisions 25, 26, 31, 33, be used, which may result in errors in industry totals. Nations 1990 [ISIC, series M, no. 4, rev. 3], p. 9). 36, and 37). Moreover, countries use different reference periods Firms typically use multiple processes to produce (calendar or fiscal year) and valuation methods (basic a product. For example, an automobile manufac- or producer prices) to estimate value added. (See turer engages in forging, welding, and painting as About the data for table 4.2.) well as advertising, accounting, and other service The data on manufacturing value added in U.S. dol- activities. Collecting data at such a detailed level lars are from the World Bank’s national accounts files is not practical, nor is it useful to record produc- and may differ from those UNIDO uses to calculate tion data at the highest level of a large, multiplant, shares of value added by industry, in part because multiproduct firm. The ISIC has therefore adopted as of differences in exchange rates. Thus value added the definition of an establishment “an enterprise or in a particular industry estimated by applying the part of an enterprise which independently engages in shares to total manufacturing value added will not one, or predominantly one, kind of economic activity match those from UNIDO sources. Classification of at or from one location . . . for which data are avail- manufacturing industries in the table accords with able . . .” (United Nations 1990, p. 25). By design, the United Nations International Standard Industrial this definition matches the reporting unit required Classifi cation (ISIC) revision  3. Editions of World for the production accounts of the United Nations Development Indicators prior to 2008 used revision System of National Accounts. The ISIC system is 2, first published in 1948. Revision 3 was completed described in the United Nations’ International Stan- in 1989, and many countries now use it. But revi- dard Industrial Classification of All Economic Activi- sion 2 is still widely used for compiling cross-country ties, Third Revision (1990). The discussion of the ISIC data. UNIDO has converted these data to accord with draws on Ryten (1998). Manufacturing continues to show strong growth in East Asia and Pacific through 2009 4.3a Value added in manufacturing (index, 1990 = 100) 600 East Asia & Pacific 500 400 South Asia 300 Latin America & Caribbean Middle East & North Africa Data sources 200 Data on manufacturing value added are from 100 the World Bank’s National Accounts files. Data Sub-Saharan Africa 0 used to calculate shares of industry value added 1990 1995 2000 2005 2009 are provided to the World Bank in electronic files Manufacturing continues to be the dominant sector in East Asia and Pacific, growing an average of about by UNIDO. The most recent published source is 10.5 percent a year between 1990 and 2009. UNIDO’s International Yearbook of Industrial Sta- Source: World Development Indicators data files. tistics 2010. 2011 World Development Indicators 205 4.4 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan 166 560 .. 55 .. 8 .. .. .. 0 .. 18 Albania 202 1,088 11 6 9 3 3 12 12 10 65 70 Algeria 10,258 45,194 1 0 0 0 95 98 1 0 4 2 Angola 3,642 40,080 .. .. .. .. .. .. .. .. .. .. Argentina 20,967 55,668 50 50 4 1 10 10 2 4 34 33 Armenia 271 698 11 20 5 1 1 0 26 47 54 33 Australia 53,111 154,234 22 14 8 2 19 32 18 27 30 19 Austria 57,738 137,672 4 7 3 2 1 3 3 3 88 81 Azerbaijan 635 21,097 4 4 8 0 66 93 1 0 20 3 Bangladesh 3,501 15,084 10 7 3 3 0 2 0 0 85 88 Belarus 4,803 21,283 .. 11 .. 2 .. 37 .. 1 .. 48 Belgium 178,265a 369,854 10a 10 1a 1 3a 7 4a 3 77a 77 Benin 420 1,000 14 .. 75 .. 5 .. 0 .. 6 .. Bolivia 1,100 4,848 21 20 10 1 15 40 35 33 19 6 Bosnia and Herzegovina 152 3,929 .. 8 .. 6 .. 13 .. 9 .. 61 Botswana 2,142 3,458 .. 5 .. 0 .. 0 .. 16 .. 78 Brazil 46,506 152,995 29 34 5 4 1 9 10 12 54 39 Bulgaria 5,355 16,455 18 17 3 1 7 13 10 15 60 53 Burkina Faso 276 850 25 27 69 60 0 0 0 1 6 12 Burundi 105 64 91 67 4 5 0 2 1 5 3 21 Cambodia 855 4,200 .. 1 .. 1 .. 0 .. 3 .. 96 Cameroon 1,651 3,000 27 .. 28 .. 29 .. 8 .. 8 .. Canada 192,197 316,713 8 11 9 4 9 25 7 7 63 50 Central African Republic 171 120 4 .. 20 .. 1 .. 30 .. 45 .. Chad 243 2,800 .. .. .. .. .. .. .. .. .. .. Chile 16,024 53,735 24 21 12 5 0 1 48 58 13 11 China† 148,780 1,201,534 8 3 2 0 4 2 2 1 84 94 Hong Kong SAR, Chinab 173,871 329,422 3 7 0 2 0 4 1 6 94 79 Colombia 10,056 32,853 31 16 5 4 28 51 1 2 35 28 Congo, Dem. Rep. 1,563 3,100 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 1,172 5,600 1 .. 8 .. 88 .. 0 .. 3 .. Costa Rica 3,453 8,788 63 25 5 2 1 1 1 1 25 47 Côte d’Ivoire 3,806 8,900 63 48 20 6 10 30 0 0 7 15 Croatia 4,517 10,474 11 13 5 4 9 13 2 4 74 66 Cuba 1,600 3,109 .. .. .. .. .. .. .. .. .. .. Czech Republic 21,335 113,437 6 5 4 1 4 4 3 2 82 87 Denmark 50,906 93,344 24 19 3 2 3 8 1 1 60 65 Dominican Republic 3,780 5,463 19 25 0 1 0 0 0 3 78 70 Ecuador 4,307 13,799 53 36 3 4 36 50 0 0 8 9 Egypt, Arab Rep. 3,450 23,062 10 11 6 2 37 44 6 6 40 37 El Salvador 1,652 3,797 57 23 1 1 0 3 3 1 39 72 Eritrea 86 15 .. .. .. .. .. .. .. .. .. .. Estonia 1,840 9,031 16 10 10 4 6 16 3 2 65 62 Ethiopia 422 1,596 73 77 13 12 3 0 0 1 11 9 Finland 40,490 62,798 2 2 8 4 2 7 3 4 83 77 France 301,162 484,725 14 12 1 1 2 4 3 2 79 79 Gabon 2,713 5,100 0 .. 13 .. 83 .. 2 .. 2 .. Gambia, The 16 15 60 53 1 1 0 0 1 7 36 39 Georgia 151 1,135 29 18 3 2 19 3 8 22 41 55 Germany 523,461 1,126,383 5 6 1 1 1 2 3 2 87 82 Ghana 1,724 5,500 58 63 15 9 5 2 9 6 13 19 Greece 11,054 20,093 30 25 4 3 7 9 7 7 50 54 Guatemala 2,155 7,214 65 44 4 3 2 4 0 5 28 43 Guinea 702 1,010 8 2 1 5 0 2 67 59 24 32 Guinea-Bissau 24 115 89 .. 11 .. 0 .. 0 .. 0 .. Haiti 110 576 37 .. 0 .. 0 .. 0 .. 62 .. Honduras 1,769 5,196 87 54 3 1 0 4 0 4 9 35 †Data for Taiwan, China 113,047 203,675 3 1 2 1 1 6 1 2 93 89 206 2011 World Development Indicators 4.4 ECONOMY Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 12,865 83,778 21 8 2 1 3 2 5 1 68 82 India 30,630 162,613 19 8 1 1 2 13 3 6 74 67 Indonesia 45,417 119,481 11 17 7 5 25 28 6 9 51 41 Iran, Islamic Rep. 18,360 78,113 4 .. 1 .. 86 .. 1 .. 9 .. Iraq 496 39,500 .. 0 .. 0 .. 99 .. 0 .. 0 Ireland 44,705 114,587 19 9 1 0 0 1 1 1 72 86 Israel 19,046 47,935 5 3 2 1 0 0 1 1 89 94 Italy 233,766 405,777 7 8 1 1 1 4 1 2 89 83 Jamaica 1,427 1,316 22 27 0 0 1 17 6 8 71 47 Japan 443,116 580,719 0 1 1 1 1 2 1 3 95 88 Jordan 1,769 6,366 25 17 2 0 0 1 24 9 49 73 Kazakhstan 5,250 43,196 10 4 3 0 25 71 24 11 38 14 Kenya 1,878 4,421 56 44 7 13 6 4 3 2 28 37 Korea, Dem. Rep. 959 1,550 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 125,058 363,534 2 1 1 1 2 6 1 2 93 90 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 12,785 50,328 0 0 0 0 95 93 0 0 5 6 Kyrgyz Republic 409 1,439 23 24 13 4 11 6 13 3 40 34 Lao PDR 311 940 .. .. .. .. .. .. .. .. .. .. Latvia 1,305 7,688 14 17 23 10 2 5 1 3 58 61 Lebanon 816 4,187 20 16 2 1 0 0 8 8 70 72 Lesotho 160 750 .. .. .. .. .. .. .. .. .. .. Liberia 820 150 .. .. .. .. .. .. .. .. .. .. Libya 8,975 35,600 0 .. 0 .. 95 .. 0 .. 5 .. Lithuania 2,705 16,452 18 19 8 2 11 21 5 1 58 55 Macedonia, FYR 1,204 2,692 18 18 5 1 0 1 18 3 58 51 Madagascar 507 1,140 69 29 6 5 1 5 7 3 14 57 Malawi 405 920 90 87 2 4 0 0 0 1 7 9 Malaysia 73,914 157,433 10 11 6 2 7 15 1 2 75 70 Mali 441 2,100 23 28 75 42 0 6 0 1 2 22 Mauritania 488 1,370 57 12 0 0 1 22 42 60 0 0 Mauritius 1,538 1,942 29 32 1 1 0 0 0 1 70 65 Mexico 79,542 229,637 8 7 1 0 10 14 3 3 78 76 Moldova 745 1,288 72 74 2 1 1 0 3 2 23 23 Mongolia 473 1,903 2 2 28 12 0 10 60 70 10 6 Morocco 6,881 13,863 31 22 3 2 2 2 12 9 51 65 Mozambique 168 2,147 66 23 16 3 2 17 2 4 13 12 Myanmar 860 6,710 .. .. .. .. .. .. .. .. .. .. Namibia 1,409 3,553 .. 23 .. 0 .. 0 .. 31 .. 45 Nepal 345 813 8 25 1 3 0 0 0 5 84 67 Netherlands 203,171 498,330 20 15 4 3 7 8 3 2 63 56 New Zealand 13,645 24,932 45 56 19 10 2 5 5 3 29 23 Nicaragua 466 1,391 75 87 3 1 1 1 1 1 21 10 Niger 288 900 17 18 1 4 0 2 80 69 1 7 Nigeria 12,342 52,500 2 5 2 1 96 90 0 0 1 4 Norway 41,992 120,880 8 6 2 0 47 65 9 5 27 20 Oman 6,068 27,651 5 3 0 0 79 79 2 4 14 10 Pakistan 8,029 17,680 12 17 4 2 1 4 0 1 83 76 Panama 625 948 75 84 0 1 3 1 1 4 20 10 Papua New Guinea 2,654 4,328 13 .. 20 .. 38 .. 25 .. 4 .. Paraguay 919 3,167 44 85 36 4 0 0 0 1 19 11 Peru 5,575 26,885 31 23 3 1 5 10 46 49 15 16 Philippines 17,502 38,436 13 8 1 1 2 2 4 4 42 86 Poland 22,895 134,466 10 11 3 1 8 3 7 4 71 80 Portugal 22,783 43,358 7 11 5 2 3 5 2 3 83 72 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 3,651 40,500 0 0 0 0 82 94 0 0 17 5 2011 World Development Indicators 207 4.4 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 7,910 40,633 7 7 3 2 8 6 3 4 78 79 Russian Federation 81,095 303,388 2 3 3 2 43 67 10 6 26 17 Rwanda 54 193 57 42 16 2 0 0 12 32 14 19 Saudi Arabia 50,040 192,296 1 1 0 0 88 88 1 0 10 8 Senegal 993 2,180 9 30 7 1 22 24 12 3 48 41 Serbia .. 8,345 28 19 4 2 2 3 15 10 49 66 Sierra Leone 42 231 .. .. .. .. .. .. .. .. .. .. Singaporeb 118,268 269,832 4 2 1 0 7 15 2 1 84 74 Slovak Republic 8,580 55,980 6 5 4 1 4 5 4 2 82 87 Slovenia 8,316 26,369 4 4 2 2 1 4 3 3 90 87 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 27,853c 62,603 8c 10 4c 2 9c 11 8c 29 44 c 47 Spain 97,849 218,511 15 16 2 1 2 4 2 3 78 73 Sri Lanka 3,798 7,345 21 26 4 3 0 0 1 1 73 67 Sudan 555 7,834 44 6 47 1 0 92 0 0 6 0 Swaziland 866 1,500 .. 21 .. 7 .. 1 .. 1 .. 70 Sweden 80,440 131,243 2 5 6 4 2 6 3 4 79 76 Switzerland 81,641 172,850 3 4 1 0 0 3 3 3 94 90 Syrian Arab Republic 3,563 10,400 12 22 7 1 63 39 1 4 17 33 Tajikistan 750 1,009 .. .. .. .. .. .. .. .. .. .. Tanzania 682 3,096 65 35 23 10 0 1 0 25 10 25 Thailand 56,439 152,498 19 15 5 4 1 5 1 1 73 75 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 378 800 19 16 42 9 0 0 32 13 7 62 Trinidad and Tobago 2,455 9,126 8 3 0 0 48 79 0 2 43 15 Tunisia 5,475 14,445 10 9 1 0 8 14 2 1 79 75 Turkey 21,637 102,129 20 11 1 0 1 4 3 3 74 80 Turkmenistan 1,880 6,595 1 .. 13 .. 77 .. 1 .. 8 .. Uganda 460 2,478 90 63 5 6 0 1 1 2 4 27 Ukraine 13,128 39,703 19 24 1 1 4 5 7 6 68 63 United Arab Emirates 28,364 175,000 8 1 0 0 9 65 55 1 28 4 United Kingdom 237,953 352,491 8 7 1 1 6 11 3 3 81 72 United States 584,743 1,056,043 11 10 4 2 2 6 3 4 77 67 Uruguay 2,106 5,386 44 64 15 8 1 1 1 0 39 26 Uzbekistan 3,430 10,735 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 18,457 57,595 3 0 0 0 77 96 6 1 14 3 Vietnam 5,449 57,096 30 20 3 3 18 20 0 1 44 55 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,945 5,594 3 6 1 0 95 92 1 0 1 2 Zambia 1,040 4,312 3 8 1 1 3 1 87 81 7 8 Zimbabwe 2,118 2,269 43 19 7 23 1 1 12 22 37 34 World 5,172,552 t 12,492,190 t 9w 8w 3w 2w 7w 12 w 3w 4w 76 w 70 w Low income 24,093 76,170 31 25 10 8 2 3 11 14 44 50 Middle income 894,340 3,720,635 14 11 3 2 12 22 5 5 63 59 Lower middle income 400,844 2,099,993 14 9 3 2 8 14 3 3 69 71 Upper middle income 493,582 1,619,211 15 12 4 2 15 29 6 7 58 48 Low & middle income 918,419 3,796,791 15 11 3 2 11 22 5 5 63 59 East Asia & Pacific 354,784 1,747,540 11 8 4 2 6 8 2 2 74 80 Europe & Central Asia 154,880 650,244 8 8 3 2 29 45 9 5 42 37 Latin America & Carib. 223,980 677,205 20 18 3 2 15 20 7 8 55 51 Middle East & N. Africa 62,002 276,399 6 .. 1 .. 73 .. 3 .. 17 .. South Asia 46,657 204,760 17 11 2 1 1 11 3 5 76 68 Sub-Saharan Africa 76,554 242,566 18 14 7 3 36 37 8 15 28 31 High income 4,253,742 8,697,557 8 8 2 1 6 9 3 3 78 73 Euro area 1,744,036 3,597,614 11 10 2 1 2 4 3 2 80 77 Note: Components may not sum to 100 percent because of unclassified trade. Exports of gold are excluded. a. Includes Luxembourg. b. Includes re-exports. c. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 208 2011 World Development Indicators 4.4 ECONOMY Structure of merchandise exports About the data Definitions Data on merchandise trade are from customs b and c are classified as re-exports. Because of dif- • Merchandise exports are the f.o.b. value of goods reports of goods moving into or out of an economy ferences in reporting practices, data on exports may provided to the rest of the world. • Food corresponds or from reports of financial transactions related to not be fully comparable across economies. to the commodities in SITC sections 0 (food and live merchandise trade recorded in the balance of pay- The data on total exports of goods (merchandise) animals), 1 (beverages and tobacco), and 4 (animal ments. Because of differences in timing and defi - are from the World Trade Organization (WTO), which and vegetable oils and fats) and SITC division 22 nitions, trade flow estimates from customs reports obtains data from national statistical offices and the (oil seeds, oil nuts, and oil kernels). • Agricultural and balance of payments may differ. Several inter- IMF’s International Financial Statistics, supplemented raw materials correspond to SITC section 2 (crude national agencies process trade data, each correct- by the Comtrade database and publications or data- materials except fuels), excluding divisions 22, 27 ing unreported or misreported data, leading to other bases of regional organizations, specialized agen- (crude fertilizers and minerals excluding coal, petro- differences. cies, economic groups, and private sources (such as leum, and precious stones), and 28 (metalliferous The most detailed source of data on international Eurostat, the Food and Agriculture Organization, and ores and scrap). • Fuels correspond to SITC section trade in goods is the United Nations Statistics Divi- country reports of the Economist Intelligence Unit). 3 (mineral fuels). • Ores and metals correspond to sion’s Commodity Trade (Comtrade) database. The Country websites and email contact have improved the commodities in SITC divisions 27, 28, and 68 International Monetary Fund (IMF) also collects collection of up-to-date statistics, reducing the pro- (nonferrous metals). • Manufactures correspond to customs-based data on trade in goods. Exports are portion of estimates. The WTO database now covers the commodities in SITC sections 5 (chemicals), 6 recorded as the cost of the goods delivered to the most major traders in Africa, Asia, and Latin America, (basic manufactures), 7 (machinery and transport frontier of the exporting country for shipment—the which together with high-income countries account equipment), and 8 (miscellaneous manufactured free on board (f.o.b.) value. Many countries report for nearly 95 percent of world trade. Reliability of goods), excluding division 68. trade data in U.S. dollars. When countries report in data for countries in Europe and Central Asia has local currency, the United Nations Statistics Division also improved. applies the average official exchange rate to the U.S. Export shares by major commodity group are from dollar for the period shown. Comtrade. The values of total exports reported Countries may report trade according to the gen- here have not been fully reconciled with the esti- eral or special system of trade. Under the general mates from the national accounts or the balance system exports comprise outward-moving goods that of payments. are (a) goods wholly or partly produced in the country; The classification of commodity groups is based (b) foreign goods, neither transformed nor declared on the Standard International Trade Classification for domestic consumption in the country, that move (SITC) revision 3. Previous editions contained data outward from customs storage; and (c) goods previ- based on the SITC revision 1. Data for earlier years in ously included as imports for domestic consumption previous editions may differ because of this change but subsequently exported without transformation. in methodology. Concordance tables are available Under the special system exports comprise cat- to convert data reported in one system to another. egories a and c. In some compilations categories Developing economies’ share of world merchandise exports continues to expand 4.4a 1995 2009 ($5.2 trillion) ($12.5 trillion) Data sources Data on merchandise exports are from the WTO. High income Data on shares of exports by major commodity 82% High group are from Comtrade. The WTO publishes data income East Asia & 70% on world trade in its Annual Report. The IMF pub- Pacific 7% East Asia & Pacific 14% lishes estimates of total exports of goods in its Europe & Central Asia 3% Latin America & Caribbean 4% International Financial Statistics and Direction of Middle East & N. Africa 1% South Asia 1% Europe & Central Asia 5% Trade Statistics, as does the United Nations Sta- Sub-Saharan Africa 2% Latin America & Caribbean 5% Middle East & N. Africa 2% tistics Division in its Monthly Bulletin of Statistics. South Asia 2% And the United Nations Conference on Trade and Sub-Saharan Africa 2% Development publishes data on the structure of Developing economies’ share of world merchandise exports increased 12 percentage points from exports in its Handbook of Statistics. Tariff line 1995 to 2009. East Asia and the Pacifi c was the biggest gainer, capturing an additional 7 per- centage points. All other developing country regions also increased their share in world trade. records of exports are compiled in the United Source: World Development Indicators data files and World Trade Organization. Nations Statistics Division’s Comtrade database. 2011 World Development Indicators 209 4.5 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan 387 3,970 .. 18 .. 0 .. 24 .. 0 .. 17 Albania 714 4,548 34 17 1 1 2 12 1 2 61 68 Algeria 10,100 39,294 29 16 3 1 1 1 2 1 65 80 Angola 1,468 17,000 .. .. .. .. .. .. .. .. .. .. Argentina 20,122 38,780 5 4 2 1 4 6 2 2 86 86 Armenia 674 3,304 31 19 0 1 27 16 0 4 39 59 Australia 61,283 165,471 5 6 2 1 5 13 1 1 86 76 Austria 66,237 143,382 6 8 3 2 4 11 4 4 82 75 Azerbaijan 668 6,514 39 16 1 1 4 1 2 1 53 79 Bangladesh 6,694 21,833 17 22 3 8 8 11 2 3 69 54 Belarus 5,564 28,563 .. 8 .. 1 .. 40 .. 3 .. 45 Belgium 164,934 a 351,945 11a 9 2a 1 6a 12 5a 3 71a 73 Benin 746 2,040 27 .. 3 .. 9 .. 1 .. 59 .. Bolivia 1,424 4,410 10 9 2 1 5 11 3 1 82 78 Bosnia and Herzegovina 1,082 8,773 .. 19 .. 1 .. 15 .. 2 .. 62 Botswana 1,911 4,728 .. 13 .. 1 .. 13 .. 2 .. 70 Brazil 54,137 133,669 11 5 3 1 12 15 3 3 71 76 Bulgaria 5,660 23,330 8 10 3 1 34 20 4 7 48 59 Burkina Faso 455 2,083 21 16 2 1 14 24 1 1 62 59 Burundi 234 402 21 13 2 1 11 2 1 1 64 81 Cambodia 1,187 6,200 .. 7 .. 1 .. 8 .. 2 .. 82 Cameroon 1,199 4,250 17 .. 3 .. 3 .. 2 .. 76 .. Canada 168,426 329,904 6 8 2 1 4 10 3 2 83 77 Central African Republic 175 300 16 .. 10 .. 9 .. 2 .. 64 .. Chad 365 1,950 24 .. 1 .. 18 .. 1 .. 56 .. Chile 15,900 42,427 7 7 2 1 9 21 2 2 79 59 China† 132,084 1,005,688 7 5 5 3 4 13 4 14 79 64 Hong Kong SAR, China 196,072 352,241 5 4 2 1 2 3 2 2 88 89 Colombia 13,853 32,898 9 10 3 1 3 4 2 2 78 82 Congo, Dem. Rep. 871 3,600 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 670 2,900 21 .. 1 .. 20 .. 1 .. 58 .. Costa Rica 4,036 11,395 10 7 1 1 9 9 2 1 78 60 Côte d’Ivoire 2,931 6,050 21 23 1 1 19 25 1 1 57 49 Croatia 7,352 21,203 12 10 2 1 12 17 3 2 67 70 Cuba 2,825 9,623 .. .. .. .. .. .. .. .. .. .. Czech Republic 25,085 105,179 7 6 3 1 8 9 4 3 77 78 Denmark 45,939 82,947 12 13 3 2 3 6 2 2 73 74 Dominican Republic 5,170 12,283 .. 14 .. 1 .. 21 .. 1 .. 63 Ecuador 4,152 15,093 8 9 3 1 6 12 2 1 82 76 Egypt, Arab Rep. 11,760 44,946 28 17 7 3 1 11 3 8 61 60 El Salvador 3,329 7,255 15 19 2 2 9 15 2 1 72 63 Eritrea 454 540 .. .. .. .. .. .. .. .. .. .. Estonia 2,546 10,122 14 12 3 2 11 19 1 1 71 60 Ethiopia 1,145 7,963 14 11 2 1 11 16 1 1 72 72 Finland 29,470 60,753 6 7 4 2 9 15 6 5 74 64 France 289,391 559,817 11 9 3 1 7 13 4 2 76 74 Gabon 882 2,200 19 .. 1 .. 4 .. 1 .. 75 .. Gambia, The 182 304 36 34 1 1 14 16 0 1 46 48 Georgia 392 4,378 36 15 0 1 39 18 0 2 24 64 Germany 463,872 938,295 10 8 3 1 6 11 4 3 73 67 Ghana 1,906 8,140 8 15 1 1 6 14 0 1 77 69 Greece 25,898 59,858 16 13 2 1 7 15 3 2 71 69 Guatemala 3,292 11,531 12 14 2 1 12 19 1 1 73 64 Guinea 819 1,400 31 13 1 0 19 33 1 0 47 53 Guinea-Bissau 133 230 44 .. 0 .. 16 .. 0 .. 40 .. Haiti 653 2,050 .. .. .. .. .. .. .. .. .. .. Honduras 1,879 7,788 13 19 1 1 12 19 1 1 74 60 †Data for Taiwan, China 103,558 174,371 6 5 4 1 7 21 6 7 75 65 210 2011 World Development Indicators 4.5 ECONOMY Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 15,465 78,175 6 5 3 1 12 8 4 2 75 72 India 34,707 249,590 4 4 4 2 24 34 7 6 54 52 Indonesia 40,630 91,749 9 9 6 3 8 20 4 3 73 65 Iran, Islamic Rep. 13,882 50,375 21 .. 2 .. 2 .. 3 .. 71 .. Iraq 665 37,000 .. .. .. .. .. .. .. .. .. .. Ireland 32,340 62,507 8 12 1 1 3 10 2 1 76 68 Israel 29,578 49,278 7 8 2 1 6 17 2 2 82 72 Italy 205,990 412,721 12 10 6 2 7 18 5 3 68 65 Jamaica 2,818 5,064 14 18 2 1 13 28 1 0 68 51 Japan 335,882 551,960 16 10 6 1 16 28 7 6 54 52 Jordan 3,697 14,075 21 17 2 1 13 18 3 2 61 60 Kazakhstan 3,807 28,409 10 9 2 1 25 10 5 1 59 80 Kenya 2,991 10,207 10 15 2 1 15 21 2 2 71 60 Korea, Dem. Rep. 1,380 2,080 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 135,119 323,085 6 5 6 2 14 28 6 7 68 58 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 7,790 17,920 16 15 1 1 1 1 2 3 81 81 Kyrgyz Republic 522 3,037 18 17 3 1 36 4 3 1 40 50 Lao PDR 589 1,260 .. .. .. .. .. .. .. .. .. .. Latvia 1,815 9,765 10 17 2 1 21 16 1 2 66 56 Lebanon 7,278 16,574 21 15 2 1 9 21 2 2 66 61 Lesotho 1,107 1,950 .. .. .. .. .. .. .. .. .. .. Liberia 510 552 .. .. .. .. .. .. .. .. .. .. Libya 5,392 10,150 23 .. 1 .. 0 .. 1 .. 75 .. Lithuania 3,650 18,234 13 14 4 2 19 28 4 2 58 53 Macedonia, FYR 1,719 5,043 17 13 3 1 12 5 3 1 64 62 Madagascar 628 3,250 16 11 2 1 14 10 1 0 65 78 Malawi 475 1,700 14 13 1 1 11 10 1 1 73 74 Malaysia 77,691 123,832 5 8 1 2 2 8 3 4 86 76 Mali 772 2,644 20 12 1 0 16 21 1 1 62 65 Mauritania 431 1,430 24 28 1 1 22 35 0 0 53 36 Mauritius 1,976 3,728 17 22 3 2 7 16 1 1 72 59 Mexico 74,427 241,515 6 7 2 1 2 7 2 2 80 80 Moldova 840 3,278 8 15 3 1 46 22 2 1 42 61 Mongolia 415 2,131 14 12 1 0 19 27 1 1 65 60 Morocco 10,023 32,892 20 11 6 2 14 21 4 2 56 63 Mozambique 704 3,764 22 15 3 1 10 15 1 0 62 55 Myanmar 1,348 4,316 .. .. .. .. .. .. .. .. .. .. Namibia 1,616 5,120 .. 14 .. 1 .. 14 .. 1 .. 70 Nepal 1,333 4,392 12 15 3 2 12 17 3 3 46 62 Netherlands 185,232 445,496 14 11 2 1 8 13 3 2 72 58 New Zealand 13,957 25,545 7 11 1 1 5 15 3 1 83 72 Nicaragua 975 3,477 18 18 1 1 18 22 1 0 63 59 Niger 374 1,500 32 25 1 5 13 17 3 2 51 52 Nigeria 8,222 39,000 18 12 1 1 1 1 2 2 77 84 Norway 32,968 69,292 7 8 3 1 3 5 6 5 81 79 Oman 4,379 18,020 20 11 1 1 2 5 2 3 70 77 Pakistan 11,515 31,710 18 11 6 4 16 28 3 4 57 52 Panama 2,510 7,801 11 12 1 0 14 17 1 1 73 70 Papua New Guinea 1,452 3,200 .. .. .. .. .. .. .. .. .. .. Paraguay 3,144 6,940 19 8 0 1 7 15 1 1 74 76 Peru 7,584 21,706 14 11 2 1 9 14 1 1 75 72 Philippines 28,341 45,878 8 12 2 1 9 17 3 4 58 67 Poland 29,050 146,626 10 8 3 2 9 9 3 3 74 74 Portugal 32,610 69,844 14 13 4 1 8 13 2 2 72 62 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 3,398 23,000 9 6 1 0 1 1 2 3 87 90 2011 World Development Indicators 211 4.5 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 10,278 54,247 8 9 2 1 21 9 4 2 63 75 Russian Federation 60,945 191,803 18 17 1 1 3 2 2 2 45 76 Rwanda 236 1,227 19 12 3 1 12 8 3 2 64 76 Saudi Arabia 28,091 95,567 17 11 1 0 0 0 4 3 76 36 Senegal 1,412 4,713 25 24 2 2 30 23 1 1 42 50 Serbia .. 15,582 14 6 4 2 14 17 7 6 60 69 Sierra Leone 133 520 .. .. .. .. .. .. .. .. .. .. Singapore 124,507 245,785 5 3 1 0 8 24 2 2 83 67 Slovak Republic 8,770 55,301 9 7 3 1 13 12 6 2 70 78 Slovenia 9,492 26,464 8 9 5 3 7 11 4 4 74 72 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 30,546b 73,172 7b 7 2b 1 8b 21 2b 1 78b 64 Spain 113,537 287,567 14 11 3 1 8 16 4 3 71 68 Sri Lanka 5,306 10,207 16 16 2 1 6 19 1 1 75 62 Sudan 1,218 9,691 24 15 2 1 14 4 0 1 59 78 Swaziland 1,008 1,600 .. 21 .. 1 .. 14 .. 1 .. 63 Sweden 65,036 119,839 7 10 2 1 6 12 4 3 80 70 Switzerland 80,152 155,706 6 6 2 1 3 7 3 4 85 81 Syrian Arab Republic 4,709 16,300 17 14 3 3 1 31 1 4 76 47 Tajikistan 810 2,569 .. .. .. .. .. .. .. .. .. .. Tanzania 1,675 6,347 10 9 1 1 1 23 4 1 84 66 Thailand 70,786 133,801 4 6 4 2 7 19 3 4 81 69 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 594 1,500 18 15 2 1 30 27 1 2 49 55 Trinidad and Tobago 1,714 6,955 16 10 1 1 1 33 6 3 76 53 Tunisia 7,902 19,096 13 9 4 2 7 11 3 3 73 75 Turkey 35,709 140,921 7 4 6 2 13 14 6 7 68 64 Turkmenistan 1,365 6,750 24 .. 0 .. 3 .. 2 .. 71 .. Uganda 1,056 4,310 16 13 3 1 2 19 2 1 78 66 Ukraine 15,484 45,436 8 11 2 1 48 32 3 3 38 52 United Arab Emirates 23,778 140,000 15 7 0 0 4 1 6 5 75 73 United Kingdom 267,250 481,707 10 11 2 1 4 10 3 3 80 69 United States 770,852 1,605,296 5 5 2 1 8 17 3 2 79 70 Uruguay 2,867 6,907 10 10 4 2 10 24 1 1 74 62 Uzbekistan 2,750 9,023 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 12,649 40,597 14 16 4 1 1 1 4 1 77 79 Vietnam 8,155 69,949 5 7 2 3 10 16 2 4 76 70 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,582 8,500 29 28 2 1 8 21 1 1 59 50 Zambia 700 3,793 10 6 2 1 13 14 2 13 72 65 Zimbabwe 2,660 2,900 6 22 2 0 9 13 2 5 78 58 World 5,228,194 t 12,595,548 t 9w 8w 3w 1w 7w 15 w 4w 3w 75 w 69 w Low income 36,735 127,386 16 16 3 3 12 16 2 2 66 60 Middle income 947,153 3,519,888 8 8 4 2 7 14 3 5 75 69 Lower middle income 434,758 2,038,080 9 7 5 2 8 18 4 8 72 64 Upper middle income 512,441 1,475,992 8 9 3 1 6 11 3 3 77 74 Low & middle income 983,905 3,647,212 8 8 4 2 7 14 3 5 75 69 East Asia & Pacific 366,062 1,493,538 6 7 4 3 5 14 4 9 78 68 Europe & Central Asia 163,415 626,665 12 10 3 2 15 14 4 4 57 66 Latin America & Carib. 240,278 668,496 8 8 2 1 5 10 2 2 78 77 Middle East & N. Africa 77,167 289,612 22 .. 4 .. 6 .. 3 .. 66 .. South Asia 60,322 323,199 8 7 4 2 21 31 6 5 56 53 Sub-Saharan Africa 78,377 253,161 12 11 2 1 10 17 2 2 73 66 High income 4,244,063 8,955,148 9 8 3 1 7 15 4 3 75 69 Euro area 1,647,277 3,519,840 11 10 3 1 7 13 4 3 73 68 Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 212 2011 World Development Indicators 4.5 ECONOMY Structure of merchandise imports About the data Definitions Data on imports of goods are derived from the and free trade zones. Goods transported through a • Merchandise imports are the c.i.f. value of goods same sources as data on exports. In principle, world country en route to another are excluded. purchased from the rest of the world valued in U.S. exports and imports should be identical. Similarly, The data on total imports of goods (merchandise) dollars. • Food corresponds to the commodities in exports from an economy should equal the sum of in the table come from the World Trade Organization SITC sections 0 (food and live animals), 1 (beverages imports by the rest of the world from that economy. (WTO). For further discussion of the WTO’s sources and tobacco), and 4 (animal and vegetable oils and But differences in timing and definitions result in dis- and methodology, see About the data for table 4.4. fats) and SITC division 22 (oil seeds, oil nuts, and oil crepancies in reported values at all levels. For further The import shares by major commodity group are kernels). • Agricultural raw materials correspond to discussion of indicators of merchandise trade, see from the United Nations Statistics Division’s Com- SITC section 2 (crude materials except fuels), exclud- About the data for tables 4.4 and 6.2. modity Trade (Comtrade) database. The values of ing divisions 22, 27 (crude fertilizers and minerals The value of imports is generally recorded as the total imports reported here have not been fully recon- excluding coal, petroleum, and precious stones), cost of the goods when purchased by the importer ciled with the estimates of imports of goods and ser- and 28 (metalliferous ores and scrap). • Fuels cor- plus the cost of transport and insurance to the fron- vices from the national accounts (shown in table 4.8) respond to SITC section 3 (mineral fuels). •  Ores tier of the importing country—the cost, insurance, or those from the balance of payments (table 4.17). and metals correspond to the commodities in SITC and freight (c.i.f.) value, corresponding to the landed The classification of commodity groups is based divisions 27, 28, and 68 (nonferrous metals). • Man- cost at the point of entry of foreign goods into the on the Standard International Trade Classification ufactures correspond to the commodities in SITC country. A few countries, including Australia, Canada, (SITC) revision 3. Previous editions contained data sections 5 (chemicals), 6 (basic manufactures), 7 and the United States, collect import data on a free based on the SITC revision 1. Data for earlier years in (machinery and transport equipment), and 8 (miscel- on board (f.o.b.) basis and adjust them for freight and previous editions may differ because of this change laneous manufactured goods), excluding division 68. insurance costs. Many countries report trade data in in methodology. Concordance tables are available U.S. dollars. When countries report in local currency, to convert data reported in one system to another. the United Nations Statistics Division applies the average official exchange rate to the U.S. dollar for the period shown. Countries may report trade according to the general or special system of trade. Under the general system imports include goods imported for domestic con- sumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses Top 10 developing economy exporters of merchandise goods in 2009 4.5a Merchandise exports ($ billions) 1995 2009 1,500 Data sources 1,200 Data on merchandise imports are from the WTO. Data on shares of imports by major commodity 900 group are from Comtrade. The WTO publishes data on world trade in its Annual Report. The Interna- 600 tional Monetary Fund publishes estimates of total imports of goods in its International Financial Sta- tistics and Direction of Trade Statistics, as does the 300 United Nations Statistics Division in its Monthly 0 Bulletin of Statistics. And the United Nations Con- China Russian Mexico India Malaysia Brazil Thailand Indonesia Turkey Iran, ference on Trade and Development publishes data Federation Islamic Rep. on the structure of imports in its Handbook of Sta- China continues to dominate merchandise exports among developing economies. Even when developed tistics. Tariff line records of imports are compiled economies are included, China ranks as the second leading merchandise exporter. in the United Nations Statistics Division’s Com- Source: World Development Indicators data files and World Trade Organization. trade database. 2011 World Development Indicators 213 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 94 2,348 19 11 69 78 1 0 10 11 Algeria .. .. .. .. .. .. .. .. .. .. Angola 113 623 32 5 1 86 9 0 59 9 Argentina 3,676 10,758 27 15 60 37 0 0 12 48 Armenia 27 580 53 19 5 58 7 3 41 21 Australia 16,076 44,513 29 18 51 56 5 3 15 22 Austria 31,692 54,080 12 22 42 35 4 4 42 38 Azerbaijan 166 1,670 46 40 42 21 0 0 12 39 Bangladesh 469 935 15 15 5 7 0 6 80 72 Belarus 466 3,453 65 66 5 11 0 0 30 23 Belgium 35,466a 79,815a ..a 27a ..a 12a ..a 5a ..a 55a Benin 159 328 26 4 53 72 7 2 14 22 Bolivia 174 498 45 13 32 56 10 14 14 17 Bosnia and Herzegovina 457 1,396 4 20 54 49 3 1 39 30 Botswana 236 842 16 10 68 54 8 4 7 33 Brazil 6,005 26,245 43 15 16 20 17 7 24 57 Bulgaria 1,431 6,889 35 21 33 55 0 3 32 22 Burkina Faso 38 109 17 19 48 57 0 2 35 21 Burundi 4 2 46 22 32 62 0 14 21 2 Cambodia 103 1,592 31 12 52 74 0 0 18 13 Cameroon 242 1,158 48 41 15 19 7 2 30 38 Canada 25,425 57,476 21 15 31 24 11 11 37 50 Central African Republic 0 .. 34 .. 34 .. 20 .. 12 .. Chad 23 .. 5 .. 50 .. 2 .. 44 .. Chile 3,249 8,401 37 56 28 19 7 4 28 22 China 18,430 128,600 18 18 47 31 10 2 24 49 Hong Kong SAR, China 33,790 86,306 33 31 17 17 9 13 41 39 Colombia 1,641 4,109 34 28 40 49 6 1 19 23 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 61 303 52 4 22 18 0 31 25 47 Costa Rica 957 3,694 14 8 71 49 0 0 15 43 Côte d’Ivoire 426 816 29 29 21 14 12 0 38 57 Croatia 2,223 11,889 32 9 61 76 1 1 6 15 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 6,638 20,278 22 27 43 32 1 1 34 40 Denmark 15,171 55,346 45 .. 24 .. .. .. 31 .. Dominican Republic 1,894 4,864 2 9 83 83 0 1 15 7 Ecuador 687 1,130 47 31 37 59 0 0 16 10 Egypt, Arab Rep. 8,262 21,302 39 31 32 50 1 1 28 17 El Salvador 342 806 28 34 25 40 8 4 39 23 Eritrea 49 .. 70 .. 3 .. 1 .. 27 .. Estonia 868 4,368 43 37 41 25 0 2 16 36 Ethiopia 310 1,676 77 59 5 20 2 1 16 20 Finland 7,334 27,536 28 10 22 10 2 2 48 78 France 83,108 142,487 25 23 33 35 5 2 37 41 Gabon 191 .. 46 .. 9 .. 3 .. 41 .. Gambia, The 38 104 22 19 73 60 0 0 5 21 Georgia 188 1,225 48 51 25 39 0 2 27 8 Germany 73,576 226,638 27 23 25 15 5 7 44 54 Ghana 139 1,722 59 19 8 56 3 1 30 24 Greece 9,528 37,690 4 50 43 39 0 2 52 9 Guatemala 628 1,818 9 14 34 65 4 2 54 20 Guinea 17 67 75 22 5 4 1 9 18 65 Guinea-Bissau 2 44 18 0 14 87 0 1 82 12 Haiti 98 327 5 .. 92 96 1 0 2 4 Honduras 221 933 26 5 36 66 2 2 36 28 214 2011 World Development Indicators 4.6 ECONOMY Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 5,086 18,419 8 19 58 31 3 1 31 49 India 6,763 90,193 28 12 38 12 3 5 31 70 Indonesia 5,342 13,238 1 18 98 48 0 2 2 32 Iran, Islamic Rep. 533 .. 26 .. 13 .. 9 .. 53 .. Iraq .. 1,721 .. 22 .. 0 .. 0 .. 78 Ireland 4,799 92,964 22 4 46 5 0 20 32 70 Israel 7,906 21,961 25 14 38 17 0 0 36 68 Italy 61,173 101,237 18 13 47 40 7 9 29 38 Jamaica 1,568 2,616 16 13 68 74 1 2 15 11 Japan 63,966 125,918 35 25 5 8 1 4 59 62 Jordan 1,689 4,192 25 19 39 69 0 0 36 12 Kazakhstan 535 3,813 66 57 23 25 0 4 12 14 Kenya 1,183 2,198 59 48 36 31 1 1 3 20 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 22,133 57,304 42 51 23 16 0 5 34 28 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 1,124 10,425 84 30 11 2 6 1 0 66 Kyrgyz Republic 39 850 40 16 12 54 0 2 48 28 Lao PDR 68 368 23 8 76 73 1 3 1 16 Latvia 718 3,812 92 51 3 19 2 7 3 23 Lebanon .. 16,869 .. 2 .. 40 .. 2 .. 56 Lesotho 30 63 7 1 91 64 1 1 1 34 Liberia .. 142 .. 10 .. 87 .. .. .. 3 Libya 20 385 63 68 12 13 .. 16 25 3 Lithuania 482 3,769 60 56 16 30 1 1 23 14 Macedonia, FYR 151 845 32 30 14 26 4 2 51 43 Madagascar 219 .. 30 .. 26 .. 2 .. 42 .. Malawi 24 .. 28 .. 72 .. 0 .. 0 .. Malaysia 11,438 28,727 22 15 35 55 0 2 44 28 Mali 68 442 32 7 37 62 5 1 25 30 Mauritania 19 .. 9 .. 58 .. 0 .. 33 .. Mauritius 773 2,225 26 15 56 50 0 4 19 30 Mexico 9,585 15,420 12 10 64 73 7 10 17 6 Moldova 143 647 30 39 40 26 12 1 19 34 Mongolia 47 412 32 33 44 57 5 1 19 9 Morocco 2,020 11,892 20 18 64 56 1 2 14 25 Mozambique 242 544 25 28 .. 36 .. 1 75 35 Myanmar 353 256 6 51 43 18 0 .. 51 31 Namibia 301 505 .. 23 92 72 1 1 6 4 Nepal 592 548 9 7 30 68 0 0 61 25 Netherlands 44,646 90,853 40 27 15 14 1 2 44 57 New Zealand 4,401 7,760 35 19 53 59 0 1 13 21 Nicaragua 94 429 18 10 52 81 2 1 27 8 Niger 12 126 3 9 58 62 0 7 39 21 Nigeria 608 1,769 16 62 3 34 1 1 80 3 Norway 13,458 38,537 63 41 17 11 4 5 16 44 Oman 13 1,792 100 32 81 39 0 1 0 28 Pakistan 1,432 2,463 58 44 8 11 1 6 33 39 Panama 1,298 5,463 60 56 24 27 6 7 10 9 Papua New Guinea 321 162 11 9 8 1 1 7 80 84 Paraguay 566 1,288 13 13 24 16 5 2 57 69 Peru 1,042 3,517 32 21 41 58 7 9 19 12 Philippines 9,323 10,101 3 11 12 23 1 1 84 65 Poland 10,637 28,856 29 30 22 31 8 2 41 37 Portugal 8,161 22,539 19 26 59 43 5 2 18 30 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 215 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 1,476 9,737 32 30 40 13 5 2 23 56 Russian Federation 10,567 41,068 36 30 41 23 1 4 23 44 Rwanda 11 249 61 22 22 70 0 1 18 8 Saudi Arabia 3,475 9,335 .. 20 .. 64 .. 13 .. 3 Senegal 364 1,177 15 12 46 46 1 1 38 40 Serbia .. 3,478 .. 21 .. 25 .. 1 .. 53 Sierra Leone 71 53 14 35 80 48 0 1 6 15 Singapore 27,234 90,690 30 34 28 10 15 12 27 44 Slovak Republic 2,378 6,259 26 30 26 37 5 6 43 26 Slovenia 2,016 5,999 25 25 54 42 1 2 21 31 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 4,414 11,656 24 12 48 65 10 8 18 15 Spain 40,019 122,101 16 15 63 44 4 5 17 36 Sri Lanka 800 1,874 42 46 28 19 3 4 27 31 Sudan 82 392 1 4 10 76 4 15 86 24 Swaziland 150 191 18 4 32 21 0 11 50 64 Sweden 15,336 59,073 32 16 23 17 2 4 43 62 Switzerland 25,179 72,309 15 8 38 19 28 30 20 43 Syrian Arab Republic 1,632 3,770 15 5 77 84 0 4 8 7 Tajikistan .. 142 .. 50 .. 2 .. 5 .. 44 Tanzania 566 1,795 0 19 89 65 0 1 11 16 Thailand 14,652 29,677 17 19 55 53 1 1 28 27 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 64 253 34 43 20 16 2 5 44 36 Trinidad and Tobago 331 918 59 24 23 43 9 25 9 8 Tunisia 2,401 5,241 25 26 64 53 2 2 10 18 Turkey 14,475 32,758 12 23 34 65 2 3 52 9 Turkmenistan 79 .. 80 .. 9 .. 1 .. 10 .. Uganda 104 854 18 4 75 78 0 4 7 14 Ukraine 2,846 13,324 76 47 7 27 3 3 15 23 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 77,549 236,254 21 13 26 13 18 28 35 46 United States 198,501 475,979 23 13 38 25 4 15 35 47 Uruguay 1,309 2,132 31 16 47 62 1 4 21 18 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 1,529 1,805 38 39 56 44 0 0 6 18 Vietnam 2,243 5,656 .. .. .. .. .. .. .. .. West Bank and Gaza 265 407 0 4 96 66 0 0 4 30 Yemen, Rep. 141 1,085 22 4 35 83 0 0 43 13 Zambia 112 241 64 48 26 41 0 2 10 9 Zimbabwe 353 .. 26 .. 51 .. 0 .. 23 .. World 1,228,960 t 3,417,725 t 27 w 21 w 33 w 26 w 5w 8w 36 w 45 w Low income 6,429 21,036 28 20 28 37 1 3 44 41 Middle income 174,925 641,508 25 21 45 42 5 4 27 33 Lower middle income 87,678 377,784 21 21 46 36 5 2 30 42 Upper middle income 87,180 264,293 27 22 43 47 5 5 25 25 Low & middle income 180,841 660,929 25 21 44 42 5 4 28 33 East Asia & Pacific 62,745 220,270 17 17 49 40 5 1 31 41 Europe & Central Asia 35,079 131,431 38 33 34 29 1 3 27 34 Latin America & Carib. 38,013 98,855 24 18 51 54 7 7 18 21 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. South Asia 10,333 97,113 32 20 30 13 2 5 36 62 Sub-Saharan Africa 12,144 35,613 26 28 31 53 6 4 40 16 High income 1,047,874 2,755,581 27 21 30 22 6 9 38 48 Euro area 425,302 1,087,280 25 21 33 24 4 6 37 49 a. Includes Luxembourg. 216 2011 World Development Indicators 4.6 ECONOMY Structure of service exports About the data Definitions Balance of payments statistics, the main source of affiliates. Another important dimension of service •  Commercial service exports are total service information on international trade in services, have trade not captured by conventional balance of pay- exports minus exports of government services not many weaknesses. Disaggregation of important ments statistics is establishment trade—sales in included elsewhere. • Transport covers all transport components may be limited and varies considerably the host country by foreign affiliates. By contrast, services (sea, air, land, internal waterway, space, across countries. There are inconsistencies in the cross-border intrafirm transactions in merchandise and pipeline) performed by residents of one economy methods used to report items. And the recording of may be reported as exports or imports in the balance for those of another and involving the carriage of major flows as net items is common (for example, of payments. passengers, movement of goods (freight), rental of insurance transactions are often recorded as premi- The data on exports of services in the table and on carriers with crew, and related support and auxiliary ums less claims). These factors contribute to a down- imports of services in table 4.7, unlike those in edi- services. Excluded are freight insurance, which is ward bias in the value of the service trade reported tions before 2000, include only commercial services included in insurance services; goods procured in in the balance of payments. and exclude the category “government services not ports by nonresident carriers and repairs of trans- Efforts are being made to improve the coverage, included elsewhere.” The data are compiled by the port equipment, which are included in goods; repairs quality, and consistency of these data. Eurostat and IMF based on returns from national sources. Data on of harbors, railway facilities, and airfield facilities, the Organisation for Economic Co-operation and total trade in goods and services from the IMF’s Bal- which are included in construction services; and Development, for example, are working together ance of Payments database are shown in table 4.17. rental of carriers without crew, which is included to improve the collection of statistics on trade in International transactions in services are defined in other services. •  Travel covers goods and ser- services in member countries. In addition, the Inter- by the IMF’s Balance of Payments Manual (1993) as vices acquired from an economy by travelers in that national Monetary Fund (IMF) has implemented the economic output of intangible commodities that economy for their own use during visits of less than the new classifi cation of trade in services intro- may be produced, transferred, and consumed at the one year for business or personal purposes. • Insur- duced in the fifth edition of its Balance of Payments same time. Definitions may vary among reporting ance and financial services cover freight insurance Manual (1993). economies. Travel services include the goods and on goods exported and other direct insurance such Still, difficulties in capturing all the dimensions of services consumed by travelers, such as meals, as life insurance; financial intermediation services international trade in services mean that the record lodging, and transport (within the economy visited), such as commissions, foreign exchange transac- is likely to remain incomplete. Cross-border intrafirm including car rental. tions, and brokerage services; and auxiliary services service transactions, which are usually not captured such as financial market operational and regulatory in the balance of payments, have increased in recent services. •  Computer, information, communica- years. An example is transnational corporations’ use tions, and other commercial services cover such of mainframe computers around the clock for data activities as international telecommunications and processing, exploiting time zone differences between postal and courier services; computer data; news- their home country and the host countries of their related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, Top 10 developing economy exporters of commercial services in 2009 4.6a and technical services; and personal, cultural, and Commercial service exports ($ billions) 1995 2009 recreational services. 150 120 90 60 30 0 China India Russian Turkey Thailand Malaysia Brazil Egypt, Mexico Lebanona Federation Arab Rep. Data sources The top 10 developing country exporters of commercial services accounted for almost 68 percent of Data on exports of commercial services are from developing country commercial service exports and 13 percent of world commercial service exports. the IMF, which publishes balance of payments a. Data are unavailable for 1995. data in its International Financial Statistics and Source: International Monetary Fund balance of payments data files. Balance of Payments Statistics Yearbook. 2011 World Development Indicators 217 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 98 2,215 61 15 7 72 22 5 10 9 Algeria .. .. .. .. .. .. .. .. .. .. Angola 1,665 18,210 18 23 5 1 3 4 75 72 Argentina 6,992 11,445 30 23 47 39 7 5 16 33 Armenia 52 839 83 46 6 39 10 7 1 8 Australia 16,979 47,613 37 31 30 39 7 3 26 27 Austria 27,552 36,894 12 29 40 29 6 4 43 38 Azerbaijan 297 3,297 31 24 49 11 1 3 19 62 Bangladesh 1,192 3,202 65 83 20 8 6 2 10 8 Belarus 276 2,031 36 40 32 29 4 4 29 28 Belgium 33,134 a 73,008 24 a 25 28a 25 10a 4 38a 47 Benin 235 500 59 62 15 13 10 5 16 20 Bolivia 321 993 66 38 15 29 9 13 10 19 Bosnia and Herzegovina 262 625 51 32 31 38 10 4 8 25 Botswana 440 1,040 43 40 33 22 8 4 16 34 Brazil 13,161 44,074 44 18 26 25 10 8 21 49 Bulgaria 1,278 5,037 42 22 15 35 0 8 43 35 Burkina Faso 116 564 56 59 20 11 5 17 20 13 Burundi 62 160 49 53 41 39 6 3 4 6 Cambodia 181 939 46 58 5 11 4 5 45 27 Cameroon 485 2,081 35 33 22 17 7 4 36 45 Canada 32,985 77,579 24 22 31 31 11 12 34 35 Central African Republic 114 .. 44 .. 38 .. 8 .. 10 .. Chad 174 .. 55 .. 15 .. 2 .. 29 .. Chile 3,524 9,351 54 52 20 17 4 10 22 20 China 24,635 158,107 39 29 15 28 17 8 29 35 Hong Kong SAR, China 24,962 44,379 22 34 54 34 6 8 18 24 Colombia 2,813 6,860 42 34 31 26 12 8 15 32 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 690 3,523 19 15 8 5 7 5 67 75 Costa Rica 895 1,407 41 36 36 26 5 9 18 29 Côte d’Ivoire 1,235 2,324 50 58 15 15 11 0 23 27 Croatia 1,373 3,812 28 18 31 27 3 5 38 51 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 4,860 18,887 16 21 34 22 5 3 45 54 Denmark 13,945 50,912 45 .. 31 .. .. .. 24 .. Dominican Republic 957 1,733 61 58 18 20 10 9 11 13 Ecuador 1,141 2,556 42 54 21 21 6 6 31 18 Egypt, Arab Rep. 4,511 12,765 35 45 28 20 5 11 32 24 El Salvador 488 1,231 55 57 15 15 11 15 19 13 Eritrea 45 .. 2 .. 7 .. 0 .. 93 .. Estonia 420 2,496 53 33 22 24 5 2 21 41 Ethiopia 337 2,190 63 67 8 6 7 4 22 22 Finland 9,418 25,687 23 19 24 17 5 2 48 62 France 64,523 126,425 33 26 25 31 6 3 36 41 Gabon 832 .. 18 .. 17 .. 9 .. 57 .. Gambia, The 47 83 60 46 30 11 6 8 4 36 Georgia 249 910 27 54 63 20 8 14 2 12 Germany 128,865 253,467 18 21 47 32 2 4 33 43 Ghana 331 2,166 61 41 6 27 6 4 26 28 Greece 4,003 19,525 30 51 33 17 5 8 33 24 Guatemala 672 2,058 41 46 21 35 9 10 29 9 Guinea 252 288 58 37 8 5 7 9 26 50 Guinea-Bissau 27 85 53 38 14 54 5 5 28 4 Haiti 236 736 78 72 15 9 2 1 6 19 Honduras 326 1,077 60 42 18 27 2 6 20 25 218 2011 World Development Indicators 4.7 ECONOMY Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 3,765 16,407 13 17 40 22 5 3 43 57 India 10,062 80,274 57 44 10 12 6 10 28 35 Indonesia 13,230 27,625 37 44 16 19 3 5 43 32 Iran, Islamic Rep. 2,192 .. 43 .. 11 .. 10 .. 36 .. Iraq .. 7,565 .. 53 .. 10 .. 27 .. 10 Ireland 11,252 104,551 16 2 18 8 1 14 65 76 Israel 8,131 16,865 45 32 26 17 3 2 26 48 Italy 54,613 114,581 24 20 27 24 10 5 39 50 Jamaica 1,073 1,824 46 43 14 12 9 11 31 34 Japan 121,547 146,965 30 28 30 17 2 6 38 50 Jordan 1,385 3,657 52 53 31 29 6 8 11 10 Kazakhstan 776 9,881 38 19 36 11 0 6 25 64 Kenya 900 1,634 46 51 21 14 10 8 22 26 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 25,394 74,978 38 31 25 18 2 2 36 49 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 3,826 11,297 39 31 59 66 2 1 0 2 Kyrgyz Republic 193 858 27 48 3 31 4 2 65 19 Lao PDR 119 114 43 12 25 72 4 –4 28 20 Latvia 225 2,260 68 26 11 35 7 5 14 34 Lebanon .. 14,301 .. 15 .. 28 .. 2 .. 55 Lesotho 58 91 75 79 23 15 0 0 2 6 Liberia .. 141 .. 60 .. 20 .. 2 .. 17 Libya 510 4,323 60 48 15 37 .. 14 25 2 Lithuania 457 2,883 64 38 23 41 1 2 12 19 Macedonia, FYR 300 789 50 39 9 13 21 4 21 45 Madagascar 277 .. 56 .. 21 .. 4 .. 20 .. Malawi 151 .. 67 .. 26 .. 0 .. 7 .. Malaysia 14,821 27,257 38 34 16 24 0 4 47 38 Mali 412 1,022 60 63 12 14 1 5 27 18 Mauritania 197 .. 62 .. 12 .. 1 .. 25 .. Mauritius 630 1,586 40 32 25 22 5 5 30 40 Mexico 9,021 21,402 38 13 35 33 12 52 14 2 Moldova 193 678 52 38 29 36 9 3 10 24 Mongolia 87 545 70 37 22 39 0 3 8 21 Morocco 1,350 5,302 48 44 22 21 4 5 26 30 Mozambique 350 1,004 33 35 .. 21 2 2 65 42 Myanmar 233 547 11 46 8 7 1 .. 81 47 Namibia 538 602 37 37 17 18 9 4 37 41 Nepal 305 771 36 28 45 56 3 4 16 12 Netherlands 43,618 84,625 29 21 27 25 3 3 41 51 New Zealand 4,571 7,825 41 29 28 33 5 4 26 34 Nicaragua 207 517 39 48 19 28 3 11 38 13 Niger 120 599 74 67 11 11 3 4 12 18 Nigeria 4,398 16,127 22 38 21 25 3 3 54 34 Norway 13,052 36,504 38 26 32 34 6 4 24 37 Oman 985 5,555 42 38 5 16 5 10 49 36 Pakistan 2,431 5,844 67 54 18 12 4 4 10 30 Panama 1,049 2,118 71 58 12 16 9 15 9 11 Papua New Guinea 642 1,915 25 23 9 2 3 12 63 63 Paraguay 676 511 66 61 20 25 12 11 1 3 Peru 1,781 4,619 51 37 17 24 10 11 22 28 Philippines 6,906 8,344 30 44 6 29 2 4 63 23 Poland 7,008 23,789 25 22 6 31 14 6 55 41 Portugal 6,339 14,186 27 30 33 27 9 4 31 40 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 219 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 1,801 10,154 34 28 39 15 5 7 22 51 Russian Federation 20,205 59,241 16 16 57 35 0 4 26 45 Rwanda 58 503 73 63 17 14 0 1 10 22 Saudi Arabia 8,670 45,540 25 25 .. 41 3 6 72 28 Senegal 405 1,384 57 55 18 13 7 11 18 21 Serbia .. 3,406 .. 27 .. 28 .. 4 .. 40 Sierra Leone 79 107 17 57 63 12 4 9 16 22 Singapore 21,111 82,189 44 32 22 19 10 6 24 42 Slovak Republic 1,800 7,933 17 22 18 26 5 14 60 37 Slovenia 1,429 4,330 31 20 40 31 2 4 27 45 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 5,756 14,390 40 41 32 29 14 4 14 26 Spain 22,354 86,988 31 20 20 19 7 8 41 52 Sri Lanka 1,169 2,487 58 62 16 17 5 6 21 15 Sudan 150 2,684 27 51 29 32 0 1 44 68 Swaziland 206 539 16 33 21 13 4 6 59 48 Sweden 17,112 44,373 28 16 32 27 1 1 38 56 Switzerland 14,899 38,867 35 19 50 27 1 8 14 46 Syrian Arab Republic 1,358 3,127 57 58 37 26 6 9 6 7 Tajikistan .. 289 .. 49 .. 2 .. 10 .. 38 Tanzania 729 1,685 30 36 49 45 3 4 18 15 Thailand 18,629 37,541 42 45 23 12 5 5 30 38 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 148 358 71 71 12 5 4 10 12 14 Trinidad and Tobago 223 271 42 47 31 28 8 3 19 22 Tunisia 1,245 2,812 45 53 20 15 6 10 28 23 Turkey 4,654 15,607 30 42 20 27 8 13 42 19 Turkmenistan 403 .. 40 .. 18 .. 7 .. 35 .. Uganda 563 1,408 38 61 14 13 4 10 43 16 Ukraine 1,334 11,070 34 32 16 30 7 13 43 25 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 62,524 160,036 27 18 40 32 4 7 29 44 United States 129,227 334,311 32 20 36 24 6 21 26 35 Uruguay 814 1,072 46 42 29 31 5 6 20 21 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 4,654 9,223 31 44 37 17 3 6 30 33 Vietnam 2,304 7,044 .. .. .. .. .. .. .. .. West Bank and Gaza 349 786 28 8 46 68 3 1 25 23 Yemen, Rep. 604 2,038 36 46 12 11 7 9 45 35 Zambia 282 674 79 57 9 6 0 11 12 26 Zimbabwe 645 .. 56 .. 19 .. 3 .. 23 .. World 1,221,691 t 3,144,723 t 31 w 25 w 31 w 25 w 6w 10 w 32 w 40 w Low income 9,833 29,059 51 58 18 18 5 4 27 19 Middle income 218,955 749,008 39 32 24 26 9 14 28 28 Lower middle income 109,579 443,081 42 38 16 22 10 7 32 32 Upper middle income 109,232 304,268 38 27 30 28 8 20 25 25 Low & middle income 228,417 777,282 40 33 23 25 9 14 28 28 East Asia & Pacific 82,593 272,307 38 35 16 24 10 6 37 35 Europe & Central Asia 35,575 139,286 30 30 33 29 5 8 33 33 Latin America & Carib. 52,313 127,915 41 24 31 29 10 30 17 17 Middle East & N. Africa 19,571 62,588 45 47 21 19 .. 11 28 23 South Asia 15,377 93,734 59 51 13 13 5 8 23 29 Sub-Saharan Africa 24,587 88,519 40 42 24 23 9 4 28 31 High income 992,976 2,368,417 29 22 33 25 5 9 33 43 Euro area 422,763 995,810 25 23 32 27 5 4 38 46 a. Includes Luxembourg. 220 2011 World Development Indicators 4.7 ECONOMY Structure of service imports About the data Definitions Trade in services differs from trade in goods because •  Commercial service imports are total service services are produced and consumed at the same imports minus imports of government services not time. Thus services to a traveler may be consumed included elsewhere. • Transport covers all transport in the producing country (for example, use of a hotel services (sea, air, land, internal waterway, space, room) but are classified as imports of the traveler’s and pipeline) performed by residents of one economy country. In other cases services may be supplied for those of another and involving the carriage of from a remote location; for example, insurance passengers, movement of goods (freight), rental of services may be supplied from one location and carriers with crew, and related support and auxiliary consumed in another. For further discussion of the services. Excluded are freight insurance, which is problems of measuring trade in services, see About included in insurance services; goods procured in the data for table 4.6. ports by nonresident carriers and repairs of trans- The data on imports of services in the table and on port equipment, which are included in goods; repairs exports of services in table 4.6, unlike those in edi- of harbors, railway facilities, and airfield facilities, tions before 2000, include only commercial services which are included in construction services; and and exclude the category “government services not rental of carriers without crew, which is included included elsewhere.” The data are compiled by the in other services. •  Travel covers goods and ser- International Monetary Fund (IMF) based on returns vices acquired from an economy by travelers in that from national sources. economy for their own use during visits of less than International transactions in services are defined one year for business or personal purposes. • Insur- by the IMF’s Balance of Payments Manual (1993) as ance and financial services cover freight insurance the economic output of intangible commodities that on goods imported and other direct insurance such may be produced, transferred, and consumed at the as life insurance; financial intermediation services same time. Definitions may vary among reporting such as commissions, foreign exchange transac- economies. tions, and brokerage services; and auxiliary services Travel services include the goods and services such as financial market operational and regulatory consumed by travelers, such as meals, lodging, and services. •  Computer, information, communica- transport (within the economy visited), including car tions, and other commercial services cover such rental. activities as international telecommunications, and postal and courier services; computer data; news- related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and The mix of commercial service imports by developing economies is changing 4.7a recreational services. 1995 2009 ($228 billion) ($777 billion) Other 28% Transport 40% Other 28% Transport Travel 33% Insurance and financial 9% 23% Insurance and financial 14% Travel 25% Data sources Between 1995 and 2009 developing economies’ commercial service imports more than tripled. Insur- Data on imports of commercial services are from ance and financial services and travel services are displacing transport as the most important services the IMF, which publishes balance of payments imported. data in its International Financial Statistics and Source: International Monetary Fund balance of payments data files. Balance of Payments Statistics Yearbook. 2011 World Development Indicators 221 4.8 Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan .. 88 .. 9 .. 25 .. 16 .. 48 .. .. Albania 87 87 14 10 21 29 12 29 35 54 20 17 Algeria 55 41 17 14 31 41 26 40 29 36 .. .. Angola 34 .. 40 .. 35 15 82 52 68 46 78 10 Argentina 69 59 13 15 18 21 10 21 10 16 16 23 Armenia 109 82 11 11 18 31 24 12 62 36 –9 20 Australia 60 57 18 17 24 28 18 20 20 22 18 21 Austria 56 54 20 20 25 21 35 51 36 46 22 24 Azerbaijan 77 37 13 14 24 22 28 52 42 25 13 45 Bangladesh 83 77 5 5 19 24 11 19 17 27 22 39 Belarus 59 56 21 17 25 38 50 51 54 62 21 25 Belgium 54 52 21 25 21 20 65 73 62 70 29 22 Benin 82 .. 11 .. 20 25 20 14 33 28 11 11 Bolivia 76 66 14 15 15 17 23 36 27 33 11 23 Bosnia and Herzegovina .. 80 .. 23 20 22 20 33 71 58 .. 13 Botswana 34 63 29 24 25 24 51 34 38 45 36 16 Brazil 62 62 21 22 18 17 7 11 9 11 16 15 Bulgaria 66 66 17 16 16 26 52 48 50 56 15 16 Burkina Faso 63 .. 25 .. 24 .. 14 .. 27 .. 29 .. Burundi 89 .. 19 .. 6 .. 13 .. 27 .. 6 .. Cambodia 95 74 6 8 15 21 31 60 47 63 6 19 Cameroon 72 72 9 9 13 18 24 27 18 31 14 20 Canada 57 59 21 22 19 21 37 29 34 30 18 18 Central African Republic 79 93 15 4 14 11 20 14 28 22 11 .. Chad 91 79 7 16 13 34 22 42 34 70 12 .. Chile 61 60 10 13 26 19 29 38 27 30 25 22 China 43 35 14 13 42 48 20 27 19 22 42 54 Hong Kong SAR, China 62 62 8 9 34 23 143 194 148 187 .. 31 Colombia 65 64 15 16 26 23 15 16 21 18 19 18 Congo, Dem. Rep. 81 74 5 8 9 30 28 10 24 22 .. .. Congo, Rep. 49 42 13 12 37 25 65 72 64 51 –2 18 Costa Rica 71 62 14 17 18 20 38 43 40 42 15 20 Côte d’Ivoire 66 72 11 9 16 11 42 42 34 34 12 15 Croatia 67 57 26 20 16 27 33 36 42 39 11 22 Cuba 71 54 24 33 7 11 13 20 16 18 .. .. Czech Republic 51 51 21 22 33 22 51 70 55 64 29 20 Denmark 51 49 25 30 20 17 38 48 33 44 22 22 Dominican Republic 81 85 5 8 18 15 36 22 39 30 16 10 Ecuador 68 69 13 10 22 32 26 37 28 48 17 24 Egypt, Arab Rep. 74 76 11 11 20 19 23 25 28 32 22 17 El Salvador 87 92 9 10 20 13 22 22 38 38 18 11 Eritrea 94 86 44 31 23 11 22 4 83 20 19 .. Estonia 54 53 26 22 28 19 68 71 76 65 24 24 Ethiopia 80 88 8 8 18 22 10 11 16 29 21 16 Finland 52 54 23 25 18 18 37 37 29 35 22 20 France 57 58 24 25 19 19 23 23 22 25 19 16 Gabon 41 41 12 12 23 28 59 52 36 33 33 .. Gambia, The 90 78 14 16 20 26 49 30 73 50 8 19 Georgia 102 83 11 24 4 12 26 30 42 49 1 0 Germany 58 59 20 20 22 16 24 41 23 36 20 21 Ghana 76 82 12 10 20 20 24 31 33 41 18 16 Greece 76 75 15 19 18 16 17 19 27 29 18 3 Guatemala 86 86 6 10 15 13 19 23 25 33 11 12 Guinea 74 75 8 8 21 22 21 41 25 45 21 8 Guinea-Bissau 95 83 6 14 22 23 12 26 35 47 10 .. Haiti 86 .. 7 .. 26 27 9 14 29 44 .. .. Honduras 64 80 9 19 32 20 44 42 48 61 27 16 222 2011 World Development Indicators 4.8 ECONOMY Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 68 67 11 9 21 22 46 81 46 80 17 15 India 64 56 11 12 27 36 11 20 12 24 27 35 Indonesia 62 57 8 10 32 31 26 24 28 21 28 23 Iran, Islamic Rep. 46 45 16 11 29 33 22 32 13 22 37 .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 54 52 16 19 18 14 76 89 65 74 23 9 Israel 56 57 28 24 25 16 29 35 37 32 13 20 Italy 58 60 18 22 20 19 26 24 22 24 22 16 Jamaica 70 81 11 16 29 21 51 35 61 53 25 13 Japan 55 60 15 20 28 20 9 13 8 12 30 24 Jordan 65 83 24 24 33 15 52 43 73 65 29 10 Kazakhstan 71 50 14 12 20 30 39 42 44 34 15 28 Kenya 70 76 15 16 22 21 33 25 39 38 23 15 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 52 54 11 16 38 26 29 50 30 46 36 30 Kosovo .. .. .. 18 .. 28 .. 14 .. 54 .. .. Kuwait 43 28 32 13 15 19 52 66 42 26 38 59 Kyrgyz Republic 75 86 20 23 18 22 29 50 42 81 8 14 Lao PDR .. 66 .. 8 .. 37 23 33 37 44 .. 25 Latvia 63 61 24 21 14 19 43 42 45 43 14 29 Lebanon 103 79 12 16 36 30 11 22 62 47 .. 13 Lesotho 93 79 35 50 76 31 24 51 128 112 39 28 Liberia .. 202 .. 19 .. 20 9 31 72 173 .. –2 Libya 59 23 22 9 12 28 29 67 22 27 .. 67 Lithuania 68 65 21 19 21 27 47 60 58 72 12 15 Macedonia, FYR 70 81 19 18 21 24 33 44 43 67 13 18 Madagascar 90 80 7 11 11 33 24 28 32 52 2 .. Malawi 79 62 21 21 17 25 30 30 48 38 8 .. Malaysia 48 50 12 14 44 14 94 96 98 75 34 31 Mali 83 77 10 10 23 22 21 26 36 36 15 19 Mauritania 77 72 11 21 20 25 37 50 45 68 14 .. Mauritius 63 75 14 15 26 21 59 48 61 59 25 17 Mexico 67 67 10 12 20 22 30 28 28 29 19 22 Moldova 57 87 27 22 25 27 49 37 58 73 18 19 Mongolia 56 55 13 1 32 50 48 56 49 63 35 42 Morocco 68 57 17 18 21 36 27 29 34 39 17 31 Mozambique 90 84 8 13 27 21 16 25 41 44 9 9 Myanmar .. .. .. .. 14 .. 1 .. 2 .. .. .. Namibia 54 62 30 24 22 27 49 47 56 60 32 27 Nepal 75 81 9 11 25 30 25 16 35 37 21 38 Netherlands 49 46 24 29 21 18 59 69 54 62 27 22 New Zealand 59 60 17 20 23 18 29 28 28 27 18 16 Nicaragua 83 91 11 12 22 23 19 35 35 61 –1 10 Niger 86 .. 14 .. 7 .. 17 .. 24 .. –1 .. Nigeria .. .. .. .. .. .. 44 36 42 27 .. .. Norway 50 43 22 22 22 20 38 42 32 27 26 32 Oman 51 34 25 15 15 30 44 59 36 38 10 39 Pakistan 72 80 12 8 19 19 17 13 19 20 21 22 Panama 52 49 15 10 30 25 101 77 98 61 30 35 Papua New Guinea 44 69 17 11 22 20 61 58 44 57 35 20 Paraguay 76 78 10 12 26 16 59 47 71 52 18 12 Peru 71 64 10 10 25 22 13 24 18 20 16 23 Philippines 74 74 11 11 22 15 36 32 44 31 19 40 Poland 60 61 20 19 19 20 23 39 21 39 20 19 Portugal 65 67 17 21 24 20 27 28 34 36 24 10 Puerto Rico .. .. .. .. .. .. 72 .. 97 .. .. .. Qatar 32 21 32 25 35 39 44 47 43 31 .. .. 2011 World Development Indicators 223 4.8 Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 68 61 14 15 24 31 28 33 33 40 19 29 Russian Federation 52 54 19 20 25 19 29 28 26 20 28 23 Rwanda 97 81 10 15 13 22 5 12 26 29 20 15 Saudi Arabia 47 38 24 25 20 26 38 54 28 43 20 32 Senegal 80 83 13 9 14 28 31 24 37 44 8 16 Serbia 73 74 23 19 12 24 17 27 24 44 .. 17 Sierra Leone 88 84 14 14 6 15 19 16 26 29 –3 8 Singapore 41 43 8 10 34 29 .. 221 .. 203 53 45 Slovak Republic 52 47 22 20 24 38 58 99 56 104 27 29 Slovenia 60 55 19 20 24 23 50 59 52 57 23 22 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 63 60 18 21 18 19 23 27 22 28 17 15 Spain 60 56 18 21 22 24 22 23 22 26 22 20 Sri Lanka 73 64 11 18 26 25 36 21 46 28 20 24 Sudan 85 67 5 14 14 25 5 15 10 21 3 12 Swaziland 82 73 15 27 16 17 60 60 74 76 16 2 Sweden 49 49 27 28 17 17 40 49 33 42 20 24 Switzerland 60 58 12 11 23 20 36 52 31 41 30 32 Syrian Arab Republic 66 72 13 14 27 16 31 34 38 36 27 14 Tajikistan 62 93 16 28 29 22 66 13 72 56 .. 12 Tanzaniaa 86 62 12 20 20 30 24 23 42 35 7 21 Thailand 55 54 10 13 42 22 42 68 49 58 34 30 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 77 .. 12 9 16 .. 32 42 37 62 17 .. Trinidad and Tobago 53 49 12 10 21 12 54 68 39 39 27 31 Tunisia 63 63 16 13 25 27 45 52 49 55 20 23 Turkey 68 72 11 15 25 15 20 23 24 24 22 13 Turkmenistan 44 49 12 10 49 11 84 76 84 46 50 .. Uganda 85 76 11 11 12 24 12 23 21 35 13 18 Ukraine 55 65 21 19 27 17 47 46 50 48 23 16 United Arab Emirates 48 46 16 10 30 20 69 87 63 64 .. .. United Kingdom 63 65 20 23 17 14 28 28 28 30 15 12 United States 68 71 15 17 18 14 11 11 12 14 16 10 Uruguay 73 68 12 13 15 18 19 26 19 26 14 17 Uzbekistan 51 56 22 18 27 26 28 36 28 36 .. .. Venezuela, RB 69 64 7 13 18 25 27 18 22 20 21 22 Vietnam 74 66 8 6 27 38 33 68 42 79 20 29 West Bank and Gaza 98 .. 18 .. 35 .. 16 .. 68 .. 12 .. Yemen, Rep. 71 .. 14 .. 22 .. 51 .. 58 .. 26 .. Zambia 72 61 15 13 16 22 36 36 40 32 9 19 Zimbabwe 65 113 18 14 20 2 38 36 41 65 18 .. World 61 w 62 w 17 w 19 w 22 w 19 w 21 w 24 w 21 w 24 w 22 w 19 w Low income 81 78 9 10 18 24 18 23 26 36 17 24 Middle income 60 56 14 15 27 28 23 27 24 26 26 29 Lower middle income 55 50 12 13 34 37 23 29 24 28 33 40 Upper middle income 63 62 15 16 22 20 23 25 23 24 20 19 Low & middle income 60 57 14 15 27 28 23 27 24 26 26 29 East Asia & Pacific 48 42 13 13 40 40 27 35 28 30 38 47 Europe & Central Asia 61 62 16 17 25 19 29 30 31 29 23 19 Latin America & Carib. 66 64 15 16 20 20 18 21 19 21 18 19 Middle East & N. Africa 63 55 15 13 25 28 26 38 29 33 .. .. South Asia 67 61 10 11 25 33 12 19 15 24 25 34 Sub-Saharan Africa 69 67 16 18 18 21 28 30 30 34 16 15 High income 61 63 17 20 21 17 21 24 20 24 21 16 Euro area 57 58 20 22 21 19 29 36 28 35 21 19 a. Covers mainland Tanzania only. 224 2011 World Development Indicators 4.8 ECONOMY Structure of demand About the data Definitions Gross domestic product (GDP) from the expenditure 1993 SNA guidelines are capital outlays on defense • Household final consumption expenditure is the side is made up of household final consumption establishments that may be used by the general pub- market value of all goods and services, including expenditure, general government final consumption lic, such as schools, airfields, and hospitals, and durable products (such as cars and computers), expenditure, gross capital formation (private and intangibles such as computer software and mineral purchased by households. It excludes purchases public investment in fixed assets, changes in inven- exploration outlays. Data on capital formation may of dwellings but includes imputed rent for owner- tories, and net acquisitions of valuables), and net be estimated from direct surveys of enterprises and occupied dwellings. It also includes government fees exports (exports minus imports) of goods and ser- administrative records or based on the commodity for permits and licenses. Expenditures of nonprofit vices. Such expenditures are recorded in purchaser flow method using data from production, trade, and institutions serving households are included, even prices and include net taxes on products. construction activities. The quality of data on govern- when reported separately. Household consumption Because policymakers have tended to focus on ment fixed capital formation depends on the quality expenditure may include any statistical discrepancy fostering the growth of output, and because data on of government accounting systems (which tend to in the use of resources relative to the supply of production are easier to collect than data on spend- be weak in developing countries). Measures of fixed resources. •  General government fi nal consump- ing, many countries generate their primary estimate capital formation by households and corporations— tion expenditure is all government current expendi- of GDP using the production approach. Moreover, particularly capital outlays by small, unincorporated tures for purchases of goods and services (including many countries do not estimate all the components enterprises—are usually unreliable. compensation of employees). It also includes most of national expenditures but instead derive some Estimates of changes in inventories are rarely expenditures on national defense and security but of the main aggregates indirectly using GDP (based complete but usually include the most important excludes military expenditures with potentially wider on the production approach) as the control total. activities or commodities. In some countries these public use that are part of government capital forma- Household final consumption expenditure (private estimates are derived as a composite residual along tion. • Gross capital formation is outlays on addi- consumption in the 1968 United Nations System of with household fi nal consumption expenditure. tions to fixed assets of the economy, net changes in National Accounts, or SNA) is often estimated as According to national accounts conventions, adjust- inventories, and net acquisitions of valuables. Fixed a residual, by subtracting all other known expendi- ments should be made for appreciation of the value assets include land improvements (fences, ditches, tures from GDP. The resulting aggregate may incor- of inventory holdings due to price changes, but this drains); plant, machinery, and equipment purchases; porate fairly large discrepancies. When household is not always done. In highly inflationary economies and construction (roads, railways, schools, buildings, consumption is calculated separately, many of the this element can be substantial. and so on). Inventories are goods held to meet tem- estimates are based on household surveys, which Data on exports and imports are compiled from porary or unexpected fluctuations in production or tend to be one-year studies with limited coverage. customs reports and balance of payments data. sales, and “work in progress.” • Exports and imports Thus the estimates quickly become outdated and Although the data from the payments side provide of goods and services are the value of all goods and must be supplemented by estimates using price- and reasonably reliable records of cross-border transac- other market services provided to or received from quantity-based statistical procedures. Complicating tions, they may not adhere strictly to the appropriate the rest of the world. They include the value of mer- the issue, in many developing countries the distinc- definitions of valuation and timing used in the bal- chandise, freight, insurance, transport, travel, royal- tion between cash outlays for personal business ance of payments or correspond to the change-of- ties, license fees, and other services (communica- and those for household use may be blurred. World ownership criterion. This issue has assumed greater tion, construction, financial, information, business, Development Indicators includes in household con- significance with the increasing globalization of inter- personal, government services, and so on). They sumption the expenditures of nonprofit institutions national business. Neither customs nor balance of exclude compensation of employees and investment serving households. payments data usually capture the illegal transac- income (factor services in the 1968 SNA) and trans- General government final consumption expenditure tions that occur in many countries. Goods carried fer payments. •  Gross savings are gross national (general government consumption in the 1968 SNA) by travelers across borders in legal but unreported income less total consumption, plus net transfers. includes expenditures on goods and services for shuttle trade may further distort trade statistics. individual consumption as well as those on services Gross savings represent the difference between for collective consumption. Defense expenditures, disposable income and consumption and replace including those on capital outlays (with certain excep- gross domestic savings, a concept used by the World tions), are treated as current spending. Bank and included in World Development Indicators Data sources Gross capital formation (gross domestic invest- editions before 2006. The change was made to con- ment in the 1968 SNA) consists of outlays on form to SNA concepts and definitions. For further Data on national accounts indicators for most additions to the economy’s fixed assets plus net discussion of the problems in compiling national developing countries are collected from national changes in the level of inventories. It is generally accounts, see Srinivasan (1994), Heston (1994), statistical organizations and central banks by vis- obtained from industry reports of acquisitions and and Ruggles (1994). For an analysis of the reliability iting and resident World Bank missions. Data for distinguishes only the broad categories of capital of foreign trade and national income statistics, see high-income economies are from Organisation for formation. The 1993 SNA recognizes a third cat- Morgenstern (1963). Economic Co-operation and Development (OECD) egory of capital formation: net acquisitions of valu- data files. ables. Included in gross capital formation under the 2011 World Development Indicators 225 4.9 Growth of consumption and investment Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 1.3 5.3 2.2 4.9 14.5 7.9 25.8 6.1 18.9 9.8 15.7 13.7 Algeria –0.1 3.6 –1.9 2.1 3.6 9.0 –0.6 8.8 3.2 2.3 –1.0 7.8 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2.8 4.7 1.5 3.7 2.2 3.6 7.4 11.1 8.7 6.3 15.6 9.2 Armenia –0.5 8.8 1.1 8.7 –1.5 10.9 –1.9 18.3 –18.4 5.0 –12.7 8.6 Australia 3.2 3.9 2.0 2.4 2.9 3.2 5.1 7.6 7.7 2.2 7.6 9.2 Austria 1.7 1.4 1.4 0.9 2.7 1.6 2.3 1.2 5.8 4.7 4.8 3.9 Azerbaijan 2.0 14.0 1.0 12.9 –4.8 23.0 41.6 19.3 5.7 23.0 14.1 19.7 Bangladesh 2.6 4.5 0.6 2.8 4.7 8.8 9.2 7.8 13.1 11.5 9.7 8.8 Belarus –0.5 11.2 –0.3 11.7 –1.9 0.0 –7.5 18.8 –4.8 5.7 –8.7 10.9 Belgium 1.8 1.2 1.6 0.6 1.6 1.6 2.4 3.0 5.3 2.8 5.0 2.9 Benin 2.6 2.3 –0.7 –1.1 4.4 8.3 12.2 7.7 1.8 2.7 2.1 1.8 Bolivia 3.6 3.4 1.4 1.5 3.6 3.5 8.5 3.9 4.5 7.7 6.0 5.6 Bosnia and Herzegovina .. .. .. .. .. .. .. 5.3 .. 9.0 .. 2.6 Botswana 3.9 6.7 1.4 5.2 6.9 4.9 5.3 3.0 4.9 2.8 4.9 4.8 Brazil 3.7 3.6 2.2 2.4 1.0 3.2 4.2 4.0 5.9 7.1 11.6 7.5 Bulgaria –2.6 6.3 –2.0 6.9 –8.0 2.0 –5.3 13.5 4.3 7.9 2.9 10.5 Burkina Faso 5.7 4.5 2.8 1.1 2.9 8.7 3.1 9.0 4.4 10.9 1.9 7.2 Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia 6.0 8.2 3.4 6.4 7.2 11.4 10.3 14.2 21.7 15.2 14.8 14.8 Cameroon 3.1 4.5 0.5 2.1 0.7 2.8 0.4 4.4 3.2 –0.4 5.1 3.8 Canada 2.6 3.4 1.6 2.3 0.3 2.7 4.6 4.7 8.7 –0.4 7.1 3.3 Central African Republic .. –0.9 .. –2.7 .. –1.3 .. –0.1 .. –3.6 .. –3.9 Chad 1.5 2.7 –1.7 –0.8 –8.3 2.7 4.0 –2.4 2.3 33.6 –1.8 –3.7 Chile 7.3 5.5 5.6 4.4 3.7 4.8 9.3 7.7 9.4 5.6 11.7 10.5 China 8.9 7.7 7.7 7.1 9.6 8.8 10.8 13.9 15.5 20.2 16.7 16.9 Hong Kong SAR, China 3.8 3.7 2.0 3.1 3.7 1.6 4.8 2.2 7.8 9.7 8.4 7.8 Colombia 2.4 4.0 0.6 2.4 10.9 4.0 2.1 9.8 5.0 5.7 9.3 9.7 Congo, Dem. Rep. –1.1 .. –3.8 .. –20.4 .. 2.6 .. –0.5 6.5 –2.4 16.3 Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 5.1 4.2 2.5 2.4 2.0 2.7 5.1 5.8 10.9 6.9 9.2 5.4 Côte d’Ivoire 4.1 .. 0.9 .. 0.8 3.1 8.1 2.5 1.9 2.4 8.2 3.9 Croatia 2.3 3.5 3.0 3.5 1.7 2.9 7.2 9.2 6.3 3.8 4.9 5.7 Cuba 4.0 5.0 3.5 4.9 –2.9 7.6 0.7 8.8 –9.0 12.2 –2.9 10.1 Czech Republic 3.0 3.6 3.0 3.3 –0.9 2.2 4.6 2.9 8.7 10.5 12.0 9.1 Denmark 2.2 2.1 1.8 1.7 2.4 1.8 5.7 1.3 5.0 3.4 6.0 5.3 Dominican Republic 6.1 6.7 4.2 5.1 7.0 4.9 11.7 1.7 8.3 1.1 9.9 2.4 Ecuador 2.1 5.4 0.3 4.3 –1.5 4.2 –0.6 7.8 5.3 6.1 2.8 8.7 Egypt, Arab Rep. 3.7 4.4 1.7 2.5 4.4 2.7 5.8 7.3 3.5 16.8 3.0 14.4 El Salvador 5.3 3.3 4.1 2.9 2.8 1.5 7.1 0.7 13.4 2.9 11.6 3.3 Eritrea –5.0 1.6 –6.6 –2.2 22.6 1.2 19.1 –1.0 –2.5 –6.3 7.5 –3.7 Estonia 0.6 6.8 2.1 7.1 5.7 2.2 0.5 14.6 11.0 6.7 12.0 7.4 Ethiopia 3.6 10.7 0.4 7.9 9.0 0.7 6.5 11.3 7.1 10.1 5.8 16.5 Finland 1.8 3.1 1.4 2.7 0.9 1.6 3.2 2.0 10.3 4.5 6.7 5.1 France 1.6 2.1 1.2 1.4 1.4 1.7 1.8 1.8 6.9 1.4 5.7 3.3 Gabon –0.3 4.5 –3.1 2.5 3.7 2.1 3.0 5.6 2.1 –2.0 0.1 3.8 Gambia, The 3.6 .. –0.2 .. –2.2 .. 1.9 .. 0.1 1.1 0.1 1.3 Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 1.9 0.3 1.6 0.4 1.9 0.9 1.1 –0.1 6.0 5.9 5.8 4.7 Ghana .. .. .. .. .. .. .. .. .. .. .. .. Greece 2.2 3.8 1.4 3.4 2.1 3.1 4.1 1.9 7.6 2.9 7.4 2.7 Guatemala 4.2 3.8 1.8 1.3 5.1 3.0 6.1 0.5 6.1 2.1 9.2 2.1 Guinea 5.2 4.1 2.0 2.1 –0.5 0.3 0.1 –0.5 0.3 2.3 –1.1 0.5 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. 9.0 1.5 10.1 4.4 19.4 2.1 Honduras 3.0 5.2 0.6 3.1 2.0 6.6 6.9 3.9 1.6 5.1 3.8 5.4 226 2011 World Development Indicators 4.9 ECONOMY Growth of consumption and investment Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Hungary –0.1 3.8 0.1 4.1 0.9 1.3 9.6 1.3 9.9 11.2 11.4 10.0 India 4.8 6.9 2.9 5.4 6.6 5.7 6.9 13.4 12.3 16.0 14.4 16.5 Indonesia 6.6 4.3 5.0 2.9 0.1 8.2 –0.6 5.9 5.9 7.8 5.7 8.6 Iran, Islamic Rep. 3.2 7.4 1.6 5.8 1.6 3.6 –0.1 8.3 1.2 5.0 –6.8 13.2 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 5.6 3.7 4.7 1.8 4.1 4.3 9.9 1.5 15.7 4.2 14.5 3.9 Israel 5.0 3.4 2.5 1.5 2.7 1.4 2.0 2.3 10.9 5.9 7.6 3.8 Italy 1.6 0.6 1.5 –0.1 –0.2 1.6 1.6 0.3 5.9 0.4 4.4 1.2 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 1.4 1.0 1.1 0.9 2.9 1.6 –0.8 –0.9 4.3 5.5 4.3 2.5 Jordan 4.9 7.5 1.1 5.0 4.7 6.7 0.3 6.7 2.6 5.7 1.5 6.9 Kazakhstan –7.5 9.3 –6.4 8.5 –7.1 7.8 –19.0 17.2 –1.9 5.9 –12.7 5.6 Kenya 3.6 4.0 0.6 1.3 6.9 2.3 6.1 9.0 1.0 6.6 9.4 8.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 4.9 3.0 3.9 2.6 4.7 4.9 3.4 3.1 16.0 10.6 10.0 8.3 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 4.5 .. 0.6 .. –2.4 .. 1.0 .. –1.6 .. 0.8 .. Kyrgyz Republic –4.8 9.9 –5.8 9.0 –7.2 4.2 –1.1 3.8 –1.6 5.1 –8.2 16.0 Lao PDR .. –7.8 .. –9.4 .. 9.7 .. 15.2 .. –7.6 .. –7.2 Latvia –3.9 8.0 –2.7 8.6 1.8 2.1 –3.7 16.4 4.3 7.1 7.6 8.0 Lebanon –0.2 .. –1.9 .. 10.9 .. –5.8 6.3 18.6 10.2 –1.1 6.3 Lesotho 1.8 9.5 0.1 8.4 8.1 6.4 0.2 –0.5 10.3 10.0 2.7 12.2 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 5.3 9.7 6.1 10.3 1.9 4.3 11.1 13.6 4.9 11.2 7.5 14.0 Macedonia, FYR 2.2 4.8 1.7 4.6 –0.4 0.0 3.6 4.7 4.2 2.4 7.5 4.0 Madagascar 2.2 2.2 –0.8 –0.7 0.0 5.5 3.3 14.1 3.8 6.7 4.1 9.3 Malawi 5.4 .. 3.2 .. –4.4 .. –8.4 .. 4.0 .. –1.1 .. Malaysia 5.3 7.5 2.6 5.6 4.8 7.9 5.3 2.1 12.0 5.3 10.3 6.1 Mali 3.0 0.9 1.0 –1.5 3.2 .. 0.4 6.2 9.9 6.3 3.5 3.9 Mauritania .. 7.4 .. 4.5 .. 3.1 .. 23.8 –1.3 –2.1 0.6 14.1 Mauritius 5.1 5.6 3.9 4.7 3.6 3.8 4.8 5.3 5.6 2.0 5.1 2.3 Mexico 3.9 3.2 2.2 2.1 1.8 0.8 4.7 0.4 14.6 4.3 12.3 4.7 Moldova 9.9 7.9 10.0 8.2 –12.4 5.9 –15.5 9.8 0.7 9.1 5.6 11.1 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 1.8 4.7 0.3 3.5 3.9 3.8 2.5 8.9 5.9 6.4 5.1 8.3 Mozambique 5.8 6.2 2.6 3.6 3.2 –4.6 8.6 5.9 13.1 16.0 7.6 6.2 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 4.8 5.7 2.3 3.7 3.3 4.5 7.3 9.4 3.8 6.0 5.4 9.5 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 3.1 0.6 2.5 0.3 2.0 3.2 4.4 1.1 7.3 4.1 7.6 3.8 New Zealand 3.2 3.4 2.0 2.0 2.4 4.1 6.1 3.7 5.2 2.2 6.2 4.5 Nicaragua 6.1 3.7 3.9 2.3 –1.5 2.7 11.3 2.1 9.3 8.3 12.2 5.1 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 3.5 3.7 3.0 2.9 2.7 2.4 6.0 5.0 5.5 0.7 5.8 5.0 Oman 5.4 .. 2.6 .. 2.4 .. 4.0 .. 6.2 .. 5.9 .. Pakistan 4.9 4.6 2.3 2.2 0.7 8.3 1.8 6.3 1.7 7.1 2.5 7.3 Panama 6.4 7.2 4.2 5.4 1.7 3.6 10.4 10.2 –0.4 7.8 1.2 6.9 Papua New Guinea 2.5 .. –0.2 .. 2.5 .. 1.9 .. 5.1 .. 3.4 .. Paraguay 2.6 3.0 0.3 1.1 2.5 3.3 0.7 3.0 3.1 7.0 2.9 6.0 Peru 4.0 5.2 2.2 3.9 5.2 5.2 7.4 10.5 8.5 7.8 9.0 9.5 Philippines 3.7 5.1 1.5 3.1 3.8 3.1 4.1 1.3 7.8 5.2 7.8 2.9 Poland 5.2 3.7 5.1 3.7 3.7 4.2 10.6 5.9 11.3 9.0 16.7 8.0 Portugal 3.0 1.5 2.7 1.1 2.9 1.5 5.9 –1.8 5.7 3.3 7.6 2.7 Puerto Rico .. .. .. .. .. .. .. .. 1.6 .. 4.5 .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 227 4.9 Growth of consumption and investment Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Romania 1.3 6.2 1.7 6.7 0.8 4.3 –5.1 11.5 8.1 9.6 6.0 13.5 Russian Federation –0.9 9.9 –0.7 10.3 –2.2 2.1 –19.1 9.0 0.8 7.1 –6.1 15.9 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5.3 .. 2.9 .. 7.6 .. 11.4 .. 6.9 .. 16.9 Senegal 2.6 5.3 –0.2 2.5 0.9 –0.6 3.5 9.6 4.1 4.0 2.0 7.8 Serbia .. 3.3 .. 3.6 .. 4.5 .. 18.3 .. 10.5 .. 10.7 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 6.0 5.3 5.8 5.2 1.8 3.3 7.7 7.8 9.6 11.0 12.4 9.6 Slovenia 3.9 3.2 4.0 3.0 2.2 3.2 10.4 7.5 1.7 9.1 5.2 8.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2.9 4.6 0.6 3.4 0.3 5.0 4.7 9.1 5.8 2.7 7.1 8.1 Spain 2.4 2.8 2.0 1.2 2.7 5.1 3.2 3.3 10.5 2.9 9.4 4.7 Sri Lanka .. .. .. .. 10.5 .. 6.9 .. 7.5 .. 8.6 .. Sudan 3.7 5.9 1.1 3.7 5.5 8.4 22.0 11.2 11.6 14.3 8.4 12.0 Swaziland 7.3 2.0 4.9 1.0 7.1 6.0 –4.7 –0.3 6.4 5.2 6.2 4.7 Sweden 1.5 2.2 1.1 1.7 0.7 0.9 2.0 3.3 8.6 4.6 6.4 4.2 Switzerland 1.1 1.4 0.5 0.6 0.5 1.2 0.7 0.4 4.1 4.7 4.3 3.6 Syrian Arab Republic 3.0 7.5 0.3 4.6 2.0 8.4 3.3 –0.4 12.0 6.5 4.4 11.3 Tajikistan –11.8 6.1 –13.1 4.8 –15.7 1.6 –17.6 7.3 –5.3 9.5 –6.0 10.6 Tanzaniaa 5.1 6.2 2.0 3.3 –8.8 13.5 –1.1 12.8 11.7 11.6 4.7 15.9 Thailand 3.7 4.0 2.7 3.1 5.1 5.3 –4.0 4.8 9.5 5.8 4.5 5.7 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 5.0 0.5 2.0 –2.1 0.0 1.3 –0.1 5.9 1.2 6.0 1.1 3.1 Trinidad and Tobago 0.7 13.3 0.1 12.9 0.3 4.3 12.5 4.2 6.9 5.8 9.9 9.5 Tunisia 4.3 5.3 2.6 4.3 4.1 4.4 3.6 2.9 5.1 4.1 3.8 3.6 Turkey 3.8 5.3 2.1 3.9 4.6 4.0 4.7 6.9 11.1 6.4 10.8 8.8 Turkmenistan .. .. .. .. .. .. .. 1.6 –2.4 22.4 7.2 15.2 Uganda 6.7 1.9 3.3 –1.3 7.7 4.0 9.2 12.0 15.4 19.5 9.7 11.2 Ukraine –6.9 12.1 –6.4 12.9 –4.1 2.6 –18.5 5.1 –3.6 1.1 –6.6 5.2 United Arab Emirates 7.1 .. 1.2 .. 6.8 .. 5.5 .. 5.5 .. 6.4 .. United Kingdom 3.0 2.1 2.8 1.5 1.0 2.2 4.7 1.6 6.5 3.1 6.8 3.4 United States 3.7 2.4 2.5 1.4 0.7 2.1 7.6 0.0 7.3 4.5 9.8 3.3 Uruguay 5.0 2.9 4.3 2.8 2.3 1.3 6.1 6.6 6.0 7.8 9.9 6.4 Uzbekistan .. .. .. .. .. .. –2.5 4.7 2.5 4.9 –0.4 4.2 Venezuela, RB 0.6 8.7 –1.5 6.8 3.7 7.0 11.0 11.2 1.0 –2.0 8.2 13.8 Vietnam 5.4 7.8 3.9 6.4 3.2 7.7 19.8 12.3 19.2 11.4 19.5 13.6 West Bank and Gaza 5.3 –1.5 1.1 –4.9 12.7 1.3 9.2 –3.0 8.7 –3.1 7.5 –2.3 Yemen, Rep. 3.2 .. –0.7 .. 1.7 .. 11.4 .. 16.6 .. 8.3 .. Zambia 2.4 0.1 –0.5 –2.1 –8.1 24.9 3.9 6.6 6.7 21.9 15.5 15.6 Zimbabwe .. .. .. .. .. .. .. .. 3.9 –10.7 3.1 –5.8 World 3.0 w 2.7 w 1.6 w 1.5 w 1.7 w 2.6 w 3.3 w 3.1 w 7.0 w 5.9 w 7.0 w 5.7 w Low income 2.9 4.5 0.5 2.2 –1.3 6.8 5.5 8.7 5.5 9.7 5.3 9.4 Middle income 4.1 5.7 2.6 4.5 3.3 5.4 2.6 9.9 7.5 10.4 6.4 10.7 Lower middle income 5.7 6.6 4.1 5.3 6.6 7.4 6.3 12.2 9.3 14.7 8.3 12.9 Upper middle income 3.0 5.0 1.8 4.1 1.4 3.7 –0.5 6.3 6.3 5.5 5.1 8.5 Low & middle income 4.0 5.7 2.4 4.3 3.3 5.5 2.7 9.9 7.4 10.4 6.4 10.7 East Asia & Pacific 7.4 6.9 6.1 6.0 8.0 8.4 7.8 12.4 11.8 14.5 10.9 12.6 Europe & Central Asia 0.5 7.6 0.4 7.5 –0.8 3.3 –11.2 9.1 1.8 7.2 –2.3 11.9 Latin America & Carib. 3.6 4.1 2.0 2.9 1.9 3.3 5.4 5.0 8.1 5.0 10.4 6.7 Middle East & N. Africa 2.8 5.3 0.6 3.3 3.5 3.6 1.2 7.4 4.0 7.7 0.0 9.9 South Asia 4.6 6.4 2.6 4.8 5.9 6.1 6.5 12.4 10.0 14.6 11.2 14.8 Sub-Saharan Africa 3.3 4.9 0.6 2.4 0.3 5.1 4.6 8.5 .. .. 5.7 8.8 High income 2.8 2.0 2.1 1.3 1.5 2.1 3.4 0.8 6.9 4.6 7.2 4.3 Euro area 2.0 1.4 1.6 0.8 1.5 1.9 2.2 1.2 6.8 3.8 6.3 3.8 a. Covers mainland Tanzania only. 228 2011 World Development Indicators 4.9 ECONOMY Growth of consumption and investment About the data Definitions Measures of growth in consumption and capital for- the change in government employment. Neither • Household final consumption expenditure is the mation are subject to two kinds of inaccuracy. The technique captures improvements in productivity market value of all goods and services, including first stems from the difficulty of measuring expendi- or changes in the quality of government services. durable products (such as cars and computers), tures at current price levels, as described in About Deflators for household consumption are usually cal- purchased by households. It excludes purchases the data for table 4.8. The second arises in deflat- culated on the basis of the consumer price index. of dwellings but includes imputed rent for owner- ing current price data to measure volume growth, Many countries estimate household consumption occupied dwellings. It also includes government fees where results depend on the relevance and reliabil- as a residual that includes statistical discrepancies for permits and licenses. Expenditures of nonprofit ity of the price indexes and weights used. Measur- associated with the estimation of other expenditure institutions serving households are included, even ing price changes is more difficult for investment items, including changes in inventories; thus these when reported separately. Household consumption goods than for consumption goods because of the estimates lack detailed breakdowns of household expenditure may include any statistical discrepancy one-time nature of many investments and because consumption expenditures. in the use of resources relative to the supply of the rate of technological progress in capital goods resources. • Household final consumption expen- makes capturing change in quality diffi cult. (An diture per capita is household final consumption example is computers—prices have fallen as qual- expenditure divided by midyear population. • Gen- ity has improved.) Several countries estimate capital eral government final consumption expenditure is formation from the supply side, identifying capital all government current expenditures for goods and goods entering an economy directly from detailed services (including compensation of employees). It production and international trade statistics. This also includes most expenditures on national defense means that the price indexes used in deflating pro- and security but excludes military expenditures with duction and international trade, reflecting delivered potentially wider public use that are part of govern- or offered prices, will determine the deflator for capi- ment capital formation. • Gross capital formation is tal formation expenditures on the demand side. outlays on additions to fixed assets of the economy, Growth rates of household fi nal consumption net changes in inventories, and net acquisitions expenditure, household final consumption expen- of valuables. Fixed assets include land improve- diture per capita, general government fi nal con- ments (fences, ditches, drains); plant, machinery, sumption expenditure, gross capital formation, and and equipment purchases; and construction (roads, exports and imports of goods and services are esti- railways, schools, buildings, and so on). Inventories mated using constant price data. (Consumption, cap- are goods held to meet temporary or unexpected ital formation, and exports and imports of goods and fluctuations in production or sales, and “work in prog- services as shares of GDP are shown in table 4.8.) ress.” • Exports and imports of goods and services To obtain government consumption in constant are the value of all goods and other market services prices, countries may defl ate current values by provided to or received from the rest of the world. applying a wage (price) index or extrapolate from They include the value of merchandise, freight, insur- ance, transport, travel, royalties, license fees, and GDP per capita is still lagging in some regions 4.9a other services (communication, construction, finan- cial, information, business, personal, government GDP per capita (2000 $) services, and so on). They exclude compensation of 5,000 Latin America & Caribbean employees and investment income (factor services in 4,000 the 1968 System of National Accounts) and transfer payments. 3,000 Europe & Central Asia 2,000 Middle East & North Africa Data sources East Asia & Pacific 1,000 South Asia Data on national accounts indicators for most Sub-Saharan Africa developing countries are collected from national 0 1990 1995 2000 2005 2009 statistical organizations and central banks by vis- iting and resident World Bank missions. Data for Although GDP per capita has more than tripled in East Asia and Pacific between 1990 and 2009, high-income economies are from Organisation for it is still less than GDP per capita in Latin America and Carribean and in Europe and Central Asia. Economic Co-operation and Development (OECD) data files. Source: World Development Indicators data files. 2011 World Development Indicators 229 4.10 Toward a broader measure of national income Gross domestic Gross national Adjustments Adjusted net Gross Gross Adjusted product income national income domestic national net national product income income Consumption of Natural resource fi xed capital depletion $ billions $ billions % of GNI % of GNI $ billions % growth % growth % growth 2009 2009 2009 2009 2009 2000–2009 2000–2009 2000–2009 Afghanistan 14.5 10.6 7.7 3.4 9.5 .. .. .. Albania 12.0 11.9 10.5 1.3 10.5 5.4 5.8 7.3 Algeria 140.6 139.6 10.5 16.9 101.2 4.0 3.7 4.9 Angola 75.5 67.5 11.7 29.1 40.0 13.1 .. .. Argentina 307.2 297.7 11.8 4.9 248.2 5.4 5.1 5.4 Armenia 8.7 8.9 9.7 0.5 8.0 10.5 10.5 11.4 Australia 924.8 900.7 14.4 5.1 725.2 3.3 3.6 3.3 Austria 381.1 377.1 14.3 0.1 322.8 2.0 1.9 2.0 Azerbaijan 43.0 40.3 11.5 32.7 22.5 17.9 19.4 19.8 Bangladesh 89.4 97.5 6.8 2.6 88.3 5.9 5.3 5.9 Belarus 49.0 47.9 11.1 0.9 42.2 8.4 8.6 10.6 Belgium 471.2 475.0 14.0 0.0 408.7 1.7 1.9 1.3 Benin 6.7 6.6 7.9 1.2 6.0 4.0 3.9 3.6 Bolivia 17.3 16.7 9.5 11.2 13.2 4.1 4.3 3.0 Bosnia and Herzegovina 17.0 17.4 10.4 .. .. 5.0 5.9 .. Botswana 11.8 11.3 11.5 2.8 9.7 4.4 4.2 3.0 Brazil 1,594.5 1,562.4 11.8 3.1 1,330.0 3.6 3.4 3.6 Bulgaria 48.7 46.6 11.7 1.1 40.7 5.4 6.1 4.9 Burkina Faso 8.1 8.0 7.4 1.6 7.3 5.4 6.0 5.5 Burundi 1.3 1.3 5.5 10.6 1.1 3.0 .. .. Cambodia 9.9 9.4 8.1 0.2 8.6 9.0 9.3 10.2 Cameroon 22.2 22.1 8.6 4.8 19.1 3.3 2.7 4.4 Canada 1,336.1 1,317.3 14.2 2.3 1,100.4 2.1 1.9 2.9 Central African Republic 2.0 2.0 7.2 0.0 1.8 0.8 –0.9 –1.2 Chad 6.8 6.1 9.9 25.2 4.1 10.2 20.2 –5.5 Chile 163.7 153.4 12.6 10.0 118.8 4.1 5.0 4.9 China 4,985.5 5,028.8 10.2 3.1 4,355.8 10.9 10.6 9.9 Hong Kong SAR, China 210.6 216.9 13.6 0.0 188.5 4.7 4.4 4.2 Colombia 234.0 224.5 11.3 6.2 185.3 4.5 4.7 4.3 Congo, Dem. Rep. 10.6 9.8 5.9 10.7 8.2 5.2 5.5 7.5 Congo, Rep. 9.6 6.9 13.6 50.6 2.5 4.0 .. .. Costa Rica 29.2 28.8 11.3 0.2 25.5 5.1 4.6 4.0 Côte d’Ivoire 23.3 22.4 8.8 3.1 19.7 0.8 0.6 0.6 Croatia 63.0 60.5 12.9 0.8 52.2 3.9 4.0 5.2 Cuba 62.7 61.8 .. 3.3 52.8 6.7 6.6 6.7 Czech Republic 190.3 178.1 13.6 0.3 153.3 4.1 4.6 4.6 Denmark 309.6 318.3 16.5 1.5 261.1 1.2 0.7 2.0 Dominican Republic 46.8 45.0 11.1 0.5 39.8 5.5 5.4 5.1 Ecuador 57.2 56.1 10.7 9.9 44.5 5.0 4.5 5.2 Egypt, Arab Rep. 188.4 188.6 9.6 7.3 156.9 4.9 5.0 2.8 El Salvador 21.1 20.4 10.5 0.5 18.2 2.6 2.7 2.3 Eritrea 1.9 1.9 6.8 0.8 1.7 0.2 1.4 4.0 Estonia 19.1 18.5 12.8 0.7 16.0 5.9 6.1 6.7 Ethiopia 28.5 28.5 6.7 4.5 25.3 8.5 8.4 10.3 Finland 238.0 238.1 17.0 0.1 197.6 2.5 2.4 1.7 France 2,649.4 2,671.2 14.2 0.0 2,292.1 1.5 1.5 1.4 Gabon 11.1 9.5 13.2 29.2 5.5 2.1 2.6 3.3 Gambia, The 0.7 0.7 7.5 1.0 0.6 5.2 5.4 3.6 Georgia 10.7 10.6 8.8 0.1 9.6 7.4 .. .. Germany 3,330.0 3,377.0 13.8 0.1 2,908.2 1.0 0.6 1.4 Ghana 26.2 25.9 8.6 6.9 19.7 5.8 .. .. Greece 329.9 320.8 13.9 0.2 275.8 3.6 4.1 3.0 Guatemala 37.3 36.1 10.1 1.2 32.0 3.7 3.8 3.2 Guinea 4.1 3.7 7.7 6.6 3.2 3.0 4.1 0.9 Guinea-Bissau 0.8 0.8 7.4 0.0 0.8 1.0 .. .. Haiti 6.5 .. .. .. .. 0.7 .. .. Honduras 14.3 13.8 9.6 0.4 12.4 4.9 4.8 3.0 230 2011 World Development Indicators 4.10 ECONOMY Toward a broader measure of national income Gross domestic Gross national Adjustments Adjusted net Gross Gross Adjusted product income national income domestic national net national product income income Consumption of Natural resource fi xed capital depletion $ billions $ billions % of GNI % of GNI $ billions % growth % growth % growth 2009 2009 2009 2009 2009 2000–2009 2000–2009 2000–2009 Hungary 129.0 121.2 13.0 0.2 105.2 2.9 4.0 3.1 India 1,377.3 1,369.3 8.6 4.2 1,194.1 7.9 7.8 7.5 Indonesia 540.3 478.4 10.9 6.5 395.3 5.3 5.1 3.0 Iran, Islamic Rep. 331.0 328.6 10.7 17.9 234.7 5.4 6.2 6.7 Iraq 65.8 61.8 10.1 45.7 27.4 –0.3 .. .. Ireland 227.2 184.4 17.7 0.1 151.8 3.9 3.8 2.4 Israel 195.4 190.8 13.6 0.2 164.4 3.6 2.6 4.4 Italy 2,112.8 2,076.3 14.0 0.1 1,784.1 0.5 0.6 0.4 Jamaica 12.1 11.4 11.2 0.7 10.1 1.5 .. .. Japan 5,069.0 5,228.3 13.5 0.0 4,521.8 1.1 0.8 1.5 Jordan 25.1 25.7 10.3 1.1 22.8 6.9 6.7 6.9 Kazakhstan 115.3 103.4 12.7 22.0 67.6 8.8 9.9 9.1 Kenya 29.4 29.3 7.4 1.2 26.8 4.4 4.2 5.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 832.5 836.9 13.3 0.0 725.3 4.2 4.1 3.3 Kosovo 5.4 5.6 .. .. .. 4.8 .. .. Kuwait 148.0 158.1 5.2 37.0 91.3 8.4 .. .. Kyrgyz Republic 4.6 4.4 8.4 0.5 4.0 4.6 4.3 4.8 Lao PDR 5.9 5.8 8.4 0.0 5.3 6.9 6.6 1.0 Latvia 26.2 28.1 11.3 0.3 24.8 6.2 6.0 8.2 Lebanon 34.5 35.4 11.2 0.0 31.4 4.6 3.9 4.7 Lesotho 1.6 1.9 6.4 1.4 1.8 3.1 0.8 8.9 Liberia 0.9 0.6 8.2 11.0 0.5 0.0 .. .. Libya 62.4 62.0 11.9 30.5 35.8 5.4 .. .. Lithuania 37.2 37.2 12.0 0.2 32.7 6.3 8.1 9.3 Macedonia, FYR 9.2 9.0 10.9 0.1 8.0 3.1 3.2 2.9 Madagascar 8.6 8.5 7.3 0.2 7.9 3.6 3.5 2.3 Malawi 4.7 4.7 7.4 0.9 4.3 4.8 .. .. Malaysia 193.1 188.9 11.6 7.9 152.3 5.1 4.5 7.2 Mali 9.0 9.0 7.7 0.0 8.3 5.3 5.9 5.7 Mauritania 3.0 3.0 8.1 18.8 2.2 4.7 3.2 5.1 Mauritius 8.6 8.9 10.9 0.0 7.9 3.7 3.3 2.2 Mexico 874.8 860.2 11.7 5.4 713.2 2.2 2.1 1.5 Moldova 5.4 5.7 8.5 0.2 5.2 5.6 5.0 6.1 Mongolia 4.2 4.0 9.5 11.1 3.1 7.4 .. .. Morocco 91.4 89.5 10.1 1.4 79.2 5.0 4.8 4.4 Mozambique 9.8 9.7 7.1 3.8 8.6 7.9 7.7 6.3 Myanmar .. .. .. .. .. .. .. .. Namibia 9.3 9.2 10.6 0.3 8.2 5.3 5.7 6.1 Nepal 12.5 12.8 6.8 4.2 11.4 3.7 .. .. Netherlands 792.1 773.9 14.6 0.8 654.9 1.7 2.1 1.3 New Zealand 126.7 121.4 14.1 0.9 103.3 2.5 2.6 2.8 Nicaragua 6.1 5.9 8.8 0.8 5.3 3.3 3.0 2.5 Niger 5.4 5.3 2.9 1.2 5.1 4.3 .. .. Nigeria 173.0 162.9 9.0 15.0 123.8 6.6 .. .. Norway 381.8 376.4 15.2 10.6 279.2 2.1 2.2 3.7 Oman 46.1 58.1 13.5 37.8 28.3 4.5 .. .. Pakistan 162.0 166.4 8.0 3.1 147.8 5.2 4.8 4.7 Panama 24.7 23.1 12.1 0.0 20.3 6.9 7.2 6.7 Papua New Guinea 7.9 7.8 8.6 19.9 5.6 3.4 .. .. Paraguay 14.2 14.0 9.7 0.0 12.6 3.4 3.7 3.3 Peru 130.3 122.6 11.3 5.9 101.6 6.0 6.7 5.2 Philippines 161.2 161.1 8.0 1.0 168.2 4.9 4.1 4.3 Poland 430.1 416.1 12.4 1.0 360.4 4.4 4.7 4.2 Portugal 232.9 225.1 17.2 0.1 186.1 0.8 1.0 0.5 Puerto Rico .. .. .. .. .. .. .. .. Qatar 98.3 .. .. .. .. 14.2 .. .. 2011 World Development Indicators 231 4.10 Toward a broader measure of national income Gross domestic Gross national Adjustments Adjusted net Gross Gross Adjusted product income national income domestic national net national product income income Consumption of Natural resource fi xed capital depletion $ billions $ billions % of GNI % of GNI $ billions % growth % growth % growth 2009 2009 2009 2009 2009 2000–2009 2000–2009 2000–2009 Romania 161.1 164.1 11.2 1.3 143.5 5.6 5.4 7.2 Russian Federation 1,231.9 1,192.4 12.0 14.5 876.2 6.0 6.1 8.4 Rwanda 5.2 5.2 7.4 2.4 4.7 7.6 .. .. Saudi Arabia 375.8 384.4 12.6 28.9 226.8 3.8 3.4 6.2 Senegal 12.8 12.8 8.4 0.3 11.7 4.3 4.1 .. Serbia 43.0 42.3 .. .. .. 5.0 5.3 .. Sierra Leone 1.9 1.9 6.8 2.1 1.7 9.5 .. .. Singapore 182.2 179.2 14.1 0.0 154.0 6.5 .. .. Slovak Republic 87.6 84.7 13.0 0.3 73.4 5.8 6.1 5.6 Slovenia 48.5 47.3 13.5 0.2 40.8 3.8 4.7 4.4 Somalia .. .. .. .. .. .. .. .. South Africa 285.4 279.0 14.1 5.4 224.6 4.1 4.1 3.8 Spain 1,460.3 1,430.2 13.9 0.0 1,231.5 2.8 2.9 2.7 Sri Lanka 42.0 41.5 9.5 0.5 37.3 5.5 .. .. Sudan 54.7 49.3 9.7 11.1 39.0 7.3 7.5 5.7 Swaziland 3.0 2.9 10.2 0.1 2.6 2.6 3.2 1.7 Sweden 406.1 413.4 13.3 0.2 357.4 2.4 2.0 2.5 Switzerland 491.9 512.3 14.1 0.0 440.2 1.9 2.6 1.5 Syrian Arab Republic 52.2 50.9 9.9 10.2 40.7 4.4 4.0 6.3 Tajikistan 5.0 4.9 7.9 0.2 4.5 8.2 7.8 5.5 Tanzaniaa 21.4 21.4 7.3 2.5 19.3 7.1 6.9 6.4 Thailand 263.8 252.0 10.9 3.2 216.6 4.6 4.8 4.4 Timor-Leste 0.6 2.9 1.2 .. .. 2.4 .. .. Togo 2.9 2.8 7.0 3.6 2.5 2.5 2.3 3.0 Trinidad and Tobago 21.2 20.7 12.9 28.2 12.2 7.4 8.3 5.7 Tunisia 39.6 37.3 11.0 4.6 31.5 4.9 5.0 3.7 Turkey 614.6 606.9 11.7 0.2 534.7 4.9 4.8 4.0 Turkmenistan 19.9 19.2 10.8 .. .. 13.9 14.0 .. Uganda 16.0 15.7 7.4 4.7 13.8 7.8 7.8 7.5 Ukraine 113.5 111.1 9.9 3.8 95.9 5.6 5.6 8.1 United Arab Emirates 230.3 .. .. .. .. 7.0 .. .. United Kingdom 2,174.5 2,218.1 13.5 1.2 1,892.3 2.0 1.8 2.1 United States 14,119.0 14,011.0 14.3 0.7 11,909.0 2.0 2.2 1.4 Uruguay 31.5 30.8 12.0 0.4 27.0 3.4 3.7 2.8 Uzbekistan 32.1 32.5 8.4 17.8 24.0 6.9 5.0 –6.4 Venezuela, RB 326.1 323.5 12.2 9.8 252.4 4.9 4.6 8.6 Vietnam 90.1 85.2 8.8 7.2 71.5 7.6 8.0 7.0 West Bank and Gaza .. .. .. .. .. –0.9 0.2 .. Yemen, Rep. 26.4 24.9 9.0 13.2 19.4 3.9 .. .. Zambia 12.8 11.4 9.3 11.5 9.1 5.4 7.4 5.3 Zimbabwe 5.6 5.2 .. 3.5 4.6 –7.5 –7.2 –9.0 World 58,252.1 w 57,867.2 w 13.1 w 2.4 w 48,996.8 w 2.9 w 2.8 w 2.6 w Low income 431.5 433.8 7.2 3.8 383.4 5.4 5.6 5.6 Middle income 16,206.0 16,112.0 10.7 5.8 13,495.7 6.4 6.4 6.2 Lower middle income 8,880.2 8,952.6 9.9 4.5 7,727.3 8.5 8.4 7.8 Upper middle income 7,318.4 7,173.2 11.8 7.5 5,782.6 4.4 4.3 4.7 Low & middle income 16,649.8 16,558.2 10.7 5.8 13,887.1 6.4 6.3 6.2 East Asia & Pacific 6,346.0 6,307.5 10.3 3.6 5,456.9 9.4 9.2 8.6 Europe & Central Asia 2,591.7 2,521.8 11.7 9.2 1,977.5 5.9 5.9 6.8 Latin America & Carib. 4,017.9 3,921.9 11.7 4.8 3,277.5 3.8 3.7 3.8 Middle East & N. Africa 1,062.4 1,192.9 10.4 14.8 945.9 4.7 4.9 5.0 South Asia 1,700.3 1,702.0 8.4 3.9 1,492.3 7.3 7.3 7.0 Sub-Saharan Africa 945.9 904.2 10.6 9.3 722.6 5.1 4.5 4.2 High income 41,607.7 41,369.3 14.1 1.0 35,134.3 2.0 1.9 1.7 Euro area 12,465.3 12,368.9 14.2 0.1 10,599.8 1.5 1.4 1.4 a. Covers mainland Tanzania only. 232 2011 World Development Indicators 4.10 ECONOMY Toward a broader measure of national income About the data Definitions An economy’s growth is typically measured by the control of institutional units. The calculation of • Gross domestic product is the sum of value change in the volume of its output, as shown in table adjusted net national income, which accounts for added by all resident producers plus any product 4.1. However the widely tracked gross domestic prod- net forest, energy, and mineral depletion, thus taxes (less subsidies) not included in the valu- uct (GDP) may not always be the most relevant sum- remains within the SNA boundaries. This point is ation of output. • Gross national income is GDP mary of aggregated economic performance for all critical because it allows for comparisons across plus net receipts of primary income (compensation economies, such as when production occurs at the GDP, GNI, and adjusted net national income; such of employees and property income) from abroad. expense of consuming capital stock. For countries comparisons reveal the impact of natural resource • Consumption of fixed capital is the replacement with significant exhaustible natural resources and depletion, which is otherwise ignored by the popular value of capital used up in production. • Natural important foreign-investor presence, adjusted net economic indicators. resource depletion is the sum of net forest deple- national income complements GDP in assessing eco- Adjusted net national income is particularly useful tion, energy depletion, and mineral depletion. Net for- nomic progress (Hamilton and Ley 2010). in monitoring low-income, resource-rich economies, est depletion is unit resource rents times the excess The table presents three measures of economic like many countries in Sub-Saharan Africa, because of roundwood harvest over natural growth. Energy progress: GDP, gross national income (GNI), and such economies often see large natural resources depletion is the ratio of the value of the stock of adjusted net national income. GDP accounts for depletion as well as substantial exports of resource energy resources to the remaining reserve lifetime all domestic production, regardless of whether the rents to foreign mining companies. For recent years (capped at 25 years). It covers coal, crude oil, and income accrues to domestic or foreign institutions. adjusted net national income gives a picture of eco- natural gas. Mineral depletion is the ratio of the value GNI accounts for the operation of foreign inves- nomic growth that is strikingly different from the one of the stock of mineral resources to the remaining tors, who may be repatriating some of the income provided by GDP. reserve lifetime (capped at 25 years). It covers tin, produced domestically. GNI comprises GDP plus The key to increasing future consumption and gold, lead, zinc, iron, copper, nickel, silver, bauxite, net receipts of primary income from nonresident thus the standard of living lies in increasing national and phosphate. • Adjusted net national income is sources. Adjusted net national income goes a step wealth—including not only the traditional measures GNI minus consumption of fixed capital and natural further by subtracting from GNI a charge for the con- of capital (such as produced and human capital), resources depletion. sumption of fixed capital (a calculation that yields but also natural capital. Natural capital comprises net national income) and for the depletion of natural such assets as land, forests, and subsoil resources. resources. The deduction for the depletion of natural All three types of capital are key to sustaining eco- resources, which covers net forest depletion, energy nomic growth. By accounting for the consumption depletion, and mineral depletion, reflects the decline of fixed and natural capital depletion, adjusted net in asset values associated with the extraction and national income better measures the income avail- harvest of natural resources. For more discussion able for consumption or for investment to increase of the estimates and methodology of produced capi- a country’s future consumption. For a measure of tal consumption and natural capital depletion, see how comprehensive wealth is changing over time, About the data in table 4.11. see table 4.11. The United Nations System of National Accounts Methods of computing growth are described in Sta- (SNA) includes nonproduced natural assets (such tistical methods. For a detailed note on methodology, as land, mineral resources, and forests) within the see data.worldbank.org/. asset boundary when they are under the effective GDP and adjusted net national income in Sub-Saharan Africa, 2000–09 (2000 $ billions) 4.10a Data sources (2000 $ billions) GNI and GDP are estimated by World Bank staff based on national accounts data collected by 550 GDP World Bank staff during economic missions or 500 reported by national statistical offices to other international organizations such as the OECD. 450 Data on consumption of fi xed capital are from Adjusted net the United Nations Statistics Division’s National national income 400 Accounts Statistics: Main Aggregates and Detailed Tables, extrapolated to 2009. Data on energy, min- 350 eral, and forest depletion are estimates based on sources and methods in World Bank’s The 300 2000 2002 2004 2006 2008 2009 Changing Wealth of Nations: Measuring Sustain- able Development in the New Millennium (2011a). Source: World Development Indicators data files. 2011 World Development Indicators 233 4.11 Toward a broader measure of saving Gross Consumption Education Net forest Energy Mineral Carbon dioxide Local pollution Adjusted net savings of fixed expenditure depletion depletion depletion damage damage savings capital % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI 2009 2009 2009 2009 2009 2009 2009 2009 2009 Afghanistan .. 7.7 .. 3.4 0.0 0.0 0.1 0.7 .. Albania 17.6 10.5 2.8 0.0 1.3 0.0 0.3 0.2 8.2 Algeria .. 10.5 4.5 0.1 16.7 0.1 0.8 0.2 .. Angola 10.9 11.7 2.3 0.0 29.1 0.0 0.3 1.2 –29.2 Argentina 23.9 11.8 4.9 0.0 4.5 0.3 0.5 1.1 10.6 Armenia 19.5 9.7 2.2 0.0 0.0 0.5 0.5 1.6 9.6 Australia 22.4 14.4 4.5 0.0 1.9 3.1 0.3 0.0 1.7 Austria 24.4 14.3 5.2 0.0 0.1 0.0 0.1 0.1 15.0 Azerbaijan 48.0 11.5 2.9 0.0 32.7 0.0 1.0 0.3 5.4 Bangladesh 35.3 6.8 2.0 0.6 2.1 0.0 0.4 0.4 27.1 Belarus 25.7 11.1 4.4 0.0 0.9 0.0 1.3 0.0 16.9 Belgium 21.7 14.0 5.8 0.0 0.0 0.0 0.2 0.1 13.2 Benin 10.6 7.9 3.3 1.2 0.0 0.0 0.4 0.3 4.1 Bolivia 23.8 9.5 4.7 0.0 9.7 1.5 0.6 1.0 6.2 Bosnia and Herzegovina 12.9 10.4 .. .. 0.7 0.9 1.3 0.1 .. Botswana 17.1 11.5 7.4 0.0 0.3 2.5 0.3 0.2 9.6 Brazil 15.0 11.8 4.8 0.0 1.6 1.5 0.2 0.1 4.6 Bulgaria 16.7 11.7 3.8 0.0 0.4 0.7 0.9 0.8 6.1 Burkina Faso .. 7.4 4.3 1.6 0.0 0.0 0.1 0.6 .. Burundi .. 5.5 7.1 9.8 0.0 0.8 0.1 0.1 .. Cambodia 20.3 8.1 1.6 0.2 0.0 0.0 0.4 0.3 13.0 Cameroon 20.4 8.6 3.1 0.0 4.7 0.1 0.2 0.4 6.8 Canada 18.0 14.2 4.7 0.0 1.9 0.4 0.3 0.0 5.8 Central African Republic .. 7.2 1.3 0.0 0.0 0.0 0.1 0.2 .. Chad .. 9.9 2.3 0.0 25.2 0.0 0.0 1.0 .. Chile 23.0 12.6 3.6 0.0 0.1 9.9 0.4 0.5 3.2 China 53.2 10.2 1.8 0.0 2.9 0.2 1.1 0.8 39.7 Hong Kong SAR, China 30.3 13.6 3.0 0.0 0.0 0.0 0.1 .. .. Colombia 19.2 11.3 4.0 0.0 5.9 0.3 0.2 0.1 5.4 Congo, Dem. Rep. .. 5.9 0.9 0.0 2.9 7.9 0.2 0.5 .. Congo, Rep. 26.2 13.6 2.5 0.0 50.6 0.0 0.2 0.7 –44.7 Costa Rica 20.8 11.3 6.2 0.1 0.0 0.1 0.2 0.1 15.2 Côte d’Ivoire 15.4 8.8 4.3 0.0 3.1 0.0 0.2 0.3 7.3 Croatia 22.6 12.9 3.9 0.2 0.7 0.0 0.3 0.2 12.3 Cuba .. .. 13.6 0.0 2.4 1.0 0.3 0.1 .. Czech Republic 21.8 13.6 4.0 0.0 0.3 0.0 0.6 0.0 11.3 Denmark 21.4 16.5 7.4 0.0 1.5 0.0 0.1 0.0 10.7 Dominican Republic 10.5 11.1 1.9 0.0 0.0 0.5 0.4 0.0 0.4 Ecuador 24.1 10.7 1.4 0.0 9.8 0.0 0.4 0.0 4.4 Egypt, Arab Rep. 16.7 9.6 4.4 0.1 7.0 0.1 0.8 0.5 3.1 El Salvador 11.7 10.5 3.3 0.5 0.0 0.0 0.3 0.1 3.7 Eritrea .. 6.8 1.6 0.8 0.0 0.0 0.2 0.3 .. Estonia 24.2 12.8 4.4 0.0 0.7 0.0 0.8 0.0 14.4 Ethiopia 16.2 6.7 3.7 4.4 0.0 0.1 0.2 0.2 8.3 Finland 19.8 17.0 5.5 0.0 0.0 0.1 0.2 0.0 8.1 France 16.3 14.2 5.0 0.0 0.0 0.0 0.1 0.0 7.0 Gabon .. 13.2 3.1 0.0 29.1 0.1 0.2 0.0 .. Gambia, The 20.0 7.5 2.1 1.0 0.0 0.0 0.4 0.4 12.9 Georgia 0.2 8.8 2.8 0.0 0.1 0.0 0.4 0.7 –7.1 Germany 21.2 13.8 4.3 0.0 0.1 0.0 0.2 0.0 11.4 Ghana 15.7 8.6 4.7 2.1 0.0 4.8 0.3 0.0 –4.7 Greece 3.4 13.9 3.3 0.0 0.1 0.0 0.2 0.3 –7.9 Guatemala 12.9 10.1 2.9 0.8 0.4 0.0 0.3 0.1 4.0 Guinea 8.5 7.7 2.3 2.9 0.0 3.7 0.3 0.5 –4.2 Guinea-Bissau .. 7.4 2.3 0.0 0.0 0.0 0.3 0.6 .. Haiti .. .. 1.5 .. .. .. .. 0.4 .. Honduras 16.6 9.6 3.5 0.0 0.0 0.4 0.5 0.2 9.5 234 2011 World Development Indicators 4.11 ECONOMY Toward a broader measure of saving Gross Consumption Education Net forest Energy Mineral Carbon dioxide Local pollution Adjusted net savings of fixed expenditure depletion depletion depletion damage damage savings capital % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI 2009 2009 2009 2009 2009 2009 2009 2009 2009 Hungary 16.0 13.0 5.3 0.0 0.2 0.0 0.4 0.0 4.5 India 35.2 8.6 3.1 0.9 2.2 1.1 0.9 0.5 24.1 Indonesia 26.2 10.9 3.3 0.0 5.3 1.2 0.6 0.5 11.0 Iran, Islamic Rep. .. 10.7 4.0 0.0 17.7 0.2 1.1 0.5 .. Iraq .. 10.1 .. 0.0 45.7 0.0 1.3 2.6 .. Ireland 11.5 17.7 5.2 0.0 0.0 0.0 0.2 0.0 –1.1 Israel 20.8 13.6 5.7 0.0 0.1 0.1 0.3 0.1 12.2 Italy 16.3 14.0 4.1 0.0 0.1 0.0 0.2 0.1 6.1 Jamaica 13.5 11.2 6.2 0.0 0.0 0.7 0.8 0.2 6.9 Japan 22.9 13.5 3.2 0.0 0.0 0.0 0.2 0.2 12.1 Jordan 9.6 10.3 5.6 0.0 0.1 1.0 0.7 0.2 3.0 Kazakhstan 30.8 12.7 4.4 0.0 20.8 1.2 1.6 0.1 –1.2 Kenya 15.4 7.4 6.6 1.2 0.0 0.0 0.3 0.1 13.1 Korea, Dem. Rep. .. .. .. .. .. .. .. 0.8 .. Korea, Rep. 30.1 13.3 3.9 0.0 0.0 0.0 0.5 0.3 20.0 Kosovo .. .. .. .. .. .. 0.0 .. .. Kuwait 55.5 5.2 3.2 0.0 37.0 0.0 0.4 0.3 15.7 Kyrgyz Republic 14.4 8.4 5.2 0.0 0.5 0.0 1.1 0.2 9.4 Lao PDR 25.7 8.4 1.1 0.0 0.0 0.0 0.2 0.4 17.8 Latvia 26.6 11.3 5.6 0.3 0.0 0.0 0.2 0.0 20.4 Lebanon 12.9 11.2 1.6 0.0 0.0 0.0 0.4 0.2 2.7 Lesotho 22.9 6.4 9.4 1.4 0.0 0.0 0.0 0.1 24.4 Liberia –2.7 8.2 3.1 10.4 0.0 0.7 1.0 0.3 –18.3 Libya 66.8 11.9 .. 0.0 30.4 0.0 0.8 1.0 .. Lithuania 15.1 12.0 4.4 0.1 0.1 0.0 0.3 0.1 6.0 Macedonia, FYR 18.8 10.9 4.9 0.1 0.0 0.0 1.0 0.1 11.6 Madagascar .. 7.3 2.6 0.2 0.0 0.0 0.2 0.1 .. Malawi .. 7.4 3.5 0.9 0.0 0.0 0.2 0.1 .. Malaysia 31.7 11.6 4.0 0.0 7.9 0.0 0.8 0.0 15.4 Mali 18.6 7.7 3.3 0.0 0.0 0.0 0.1 1.1 13.5 Mauritania .. 8.1 3.1 0.5 0.0 18.3 0.5 0.4 .. Mauritius 16.2 10.9 3.1 0.0 0.0 0.0 0.3 0.0 8.0 Mexico 22.1 11.7 4.8 0.0 5.1 0.3 0.4 0.2 9.1 Moldova 17.8 8.5 8.4 0.1 0.1 0.0 0.7 0.6 16.2 Mongolia 44.7 9.5 4.6 0.0 3.8 7.3 2.1 1.6 24.9 Morocco 31.8 10.1 5.2 0.0 0.0 1.4 0.4 0.1 25.0 Mozambique 9.2 7.1 4.0 0.5 3.2 0.0 0.2 0.1 2.0 Myanmar .. .. 0.8 .. .. .. .. 0.4 .. Namibia 26.8 10.6 6.4 0.0 0.0 0.3 0.2 0.0 21.9 Nepal 36.8 6.8 3.5 4.2 0.0 0.0 0.2 0.0 29.1 Netherlands 22.7 14.6 4.7 0.0 0.8 0.0 0.2 0.2 11.6 New Zealand 16.6 14.1 6.6 0.0 0.6 0.3 0.2 0.0 8.0 Nicaragua 10.6 8.8 3.0 0.1 0.0 0.7 0.6 0.0 3.4 Niger .. 2.9 3.6 1.2 0.0 0.0 0.1 1.1 .. Nigeria .. 9.0 0.9 0.3 14.7 0.0 0.5 0.5 .. Norway 32.6 15.2 6.2 0.0 10.6 0.0 0.1 0.0 12.8 Oman 40.3 13.5 3.7 0.0 37.8 0.0 0.5 0.0 –7.9 Pakistan 21.5 8.0 1.9 1.0 2.2 0.0 0.7 0.8 10.7 Panama 37.4 12.1 3.5 0.0 0.0 0.0 0.3 0.1 28.4 Papua New Guinea 19.7 8.6 .. 0.0 0.0 19.9 0.5 0.0 .. Paraguay 12.3 9.7 3.6 0.0 0.0 0.0 0.2 0.8 5.2 Peru 24.0 11.3 2.4 0.0 0.7 5.2 0.3 0.4 8.6 Philippines 35.0 8.0 2.5 0.1 0.3 0.7 0.3 0.1 28.0 Poland 19.2 12.4 4.8 0.1 0.7 0.2 0.6 0.2 9.7 Portugal 10.4 17.2 5.3 0.0 0.0 0.1 0.2 0.0 –1.8 Puerto Rico .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. 0.1 .. 2011 World Development Indicators 235 4.11 Toward a broader measure of saving Gross Consumption Education Net forest Energy Mineral Carbon dioxide Local pollution Adjusted net savings of fixed expenditure depletion depletion depletion damage damage savings capital % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI % of GNI 2009 2009 2009 2009 2009 2009 2009 2009 2009 Romania 28.4 11.2 3.4 0.0 1.4 0.0 0.5 0.0 18.8 Russian Federation 23.4 12.0 3.5 0.0 13.8 0.7 1.1 0.1 –0.8 Rwanda 15.2 7.4 3.6 2.4 0.0 0.0 0.1 0.1 8.8 Saudi Arabia 31.5 12.6 7.2 0.0 28.9 0.0 0.8 0.7 –3.9 Senegal 16.1 8.4 5.4 0.0 0.0 0.3 0.3 0.5 7.8 Serbia 17.5 .. 4.7 .. 0.4 0.0 0.0 .. .. Sierra Leone 8.0 6.8 3.4 1.7 0.0 0.4 0.5 0.8 1.2 Singapore 45.2 14.1 2.8 0.0 0.0 0.0 0.3 0.4 33.0 Slovak Republic 29.9 13.0 3.6 0.3 0.0 0.0 0.4 0.0 19.8 Slovenia 22.7 13.5 4.9 0.1 0.1 0.0 0.3 0.1 13.6 Somalia .. .. .. .. .. .. .. 0.4 .. South Africa 15.8 14.1 5.4 0.3 2.8 2.2 1.2 0.1 0.4 Spain 19.9 13.9 4.0 0.0 0.0 0.0 0.2 0.2 9.7 Sri Lanka 24.3 9.5 2.6 0.5 0.0 0.0 0.2 0.2 16.4 Sudan 13.5 9.7 0.9 0.0 11.1 0.0 0.2 0.5 –7.1 Swaziland 2.5 10.2 7.2 0.1 0.0 0.0 0.3 0.0 –0.9 Sweden 23.6 13.3 6.1 0.0 0.0 0.2 0.1 0.0 16.0 Switzerland 31.0 14.1 4.8 0.0 0.0 0.0 0.1 0.1 21.6 Syrian Arab Republic 13.9 9.9 2.6 0.0 10.0 0.1 1.1 0.7 –14.1 Tajikistan 12.4 7.9 3.2 0.0 0.2 0.0 1.1 0.3 6.2 Tanzaniaa 21.1 7.3 2.4 0.0 0.2 2.3 0.2 0.1 13.5 Thailand 31.0 10.9 4.6 0.2 3.0 0.0 0.9 0.2 20.5 Timor-Leste .. 1.2 1.6 .. 0.0 0.0 0.0 .. .. Togo .. 7.0 4.5 2.3 0.0 1.3 0.4 0.1 .. Trinidad and Tobago 34.3 12.9 4.0 0.0 28.2 0.0 1.4 0.2 –32.4 Tunisia 24.1 11.0 6.7 0.1 3.5 1.0 0.5 0.1 14.6 Turkey 13.0 11.7 2.6 0.0 0.2 0.0 0.3 0.6 2.9 Turkmenistan .. 10.8 .. .. 30.4 0.0 2.1 0.9 .. Uganda 17.9 7.4 3.0 4.7 0.0 0.0 0.1 0.0 8.6 Ukraine 15.9 9.9 5.9 0.0 3.8 0.0 2.4 0.1 5.6 United Arab Emirates .. .. .. .. .. .. .. 0.5 .. United Kingdom 11.9 13.5 5.1 0.0 1.2 0.0 0.2 0.0 2.2 United States 9.8 14.3 4.8 0.0 0.7 0.1 0.3 0.1 –0.8 Uruguay 17.5 12.0 2.3 0.4 0.0 0.0 0.2 1.1 6.1 Uzbekistan .. 8.4 9.4 0.0 17.8 0.0 3.2 0.3 .. Venezuela, RB 21.8 12.2 3.6 0.0 9.5 0.3 0.4 0.0 2.9 Vietnam 31.2 8.8 2.8 0.2 7.0 0.0 1.1 0.3 16.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. Yemen, Rep. .. 9.0 4.2 0.0 13.2 0.0 0.7 .. .. Zambia 21.3 9.3 1.3 0.0 0.0 11.5 0.2 0.2 1.4 Zimbabwe .. .. 6.9 0.0 2.2 1.3 1.1 0.2 .. World 21.1 w 13.1 w 4.2 w 0.0 w 2.0 w 0.3 w 0.4 w 0.2 w 6.4 w Low income 23.9 7.2 3.2 1.4 1.2 1.3 0.3 0.3 .. Middle income 33.2 10.7 3.2 0.1 5.1 0.7 0.8 0.5 14.5 Lower middle income 43.3 9.9 2.4 0.2 4.0 0.4 1.0 0.7 26.2 Upper middle income 20.0 11.8 4.1 0.0 6.4 1.0 0.6 0.2 3.9 Low & middle income 33.0 10.7 3.2 0.1 5.0 0.7 0.8 0.5 14.6 East Asia & Pacific 48.7 10.3 2.1 0.0 3.3 0.3 1.0 0.7 33.1 Europe & Central Asia 21.1 11.7 3.6 0.0 8.7 0.4 0.9 0.2 1.4 Latin America & Carib. 18.9 11.7 4.4 0.0 3.5 1.3 0.3 0.3 6.8 Middle East & N. Africa .. 10.4 4.3 0.0 14.5 0.3 0.9 0.6 .. South Asia 33.6 8.4 2.9 0.9 2.1 0.9 0.9 0.5 21.6 Sub-Saharan Africa 15.4 10.6 3.6 0.6 7.5 1.2 0.6 0.3 –1.8 High income 16.5 14.1 4.6 0.0 0.9 0.1 0.2 0.1 5.2 Euro area 18.6 14.2 4.5 0.0 0.1 0.0 0.2 0.1 8.7 a. Covers mainland Tanzania only. 236 2011 World Development Indicators 4.11 ECONOMY Toward a broader measure of saving About the data Definitions Adjusted net savings measures the change in of production. Natural resources give rise to rents • Gross savings is the difference between gross value of a specified set of assets, excluding capital because they are not produced; in contrast, for pro- national income and public and private consump- gains. If a country’s net savings are positive and duced goods and services competitive forces will tion, plus net current transfers. • Consumption of the accounting includes a sufficiently broad range expand supply until economic profits are driven to fi xed capital is the replacement value of capital of assets, economic theory suggests that the pres- zero. For each type of resource and each country, unit used up in production. • Education expenditure ent value of social welfare is increasing. Conversely, resource rents are derived by taking the difference is public current operating expenditures in educa- persistently negative adjusted net savings indicate between world prices (to reflect the social oppor- tion, including wages and salaries and excluding that an economy is on an unsustainable path. tunity cost of resource extraction) and the average capital investments in buildings and equipment. The table shows the extent to which today’s rents unit extraction or harvest costs (including a “normal” • Net forest depletion is unit resource rents times from natural resources and changes in human capital return on capital). Unit rents are then multiplied by the excess of roundwood harvest over natural are balanced by net savings—that is, this genera- the physical quantity extracted or harvested to arrive growth. • Energy depletion is the ratio of the value tion’s bequest to future generations. at total rent. To estimate the value of the resource, of the stock of energy resources to the remaining Adjusted net savings is derived from standard rents are assumed to be constant over the life of the reserve lifetime (capped at 25 years). It covers coal, national accounting measures of gross savings resource (the El Serafy approach), and the present crude oil, and natural gas. • Mineral depletion is the by making four adjustments. First, estimates of value of the rent flow is calculated using a 4 percent ratio of the value of the stock of mineral resources to fixed capital consumption of produced assets are social discount rate. For details on the estimation of the remaining reserve lifetime (capped at 25 years). deducted to obtain net savings. Second, current natural wealth see World Bank (2011a). It covers tin, gold, lead, zinc, iron, copper, nickel, public expenditures on education are added to net A positive net depletion figure for forest resources silver, bauxite, and phosphate. • Carbon dioxide savings (in standard national accounting these implies that the harvest rate exceeds the rate of damage is estimated at $20 per ton of carbon (the expenditures are treated as consumption). Third, natural growth; this is not the same as deforesta- unit damage in 1995 U.S. dollars) times tons of estimates of the depletion of a variety of natural tion, which represents a change in land use (see carbon emitted. • Particulate emissions damage resources are deducted to reflect the decline in asset Definitions for table 3.4). In principle, there should is the willingness to pay to avoid illness and death values associated with their extraction and harvest. be an addition to savings in countries where growth attributable to particulate emissions. • Adjusted net And fourth, deductions are made for damages from exceeds harvest, but empirical estimates suggest savings is net savings plus education expenditure carbon dioxide and particulate emissions. that most of this net growth is in forested areas that minus energy depletion, mineral depletion, net for- The exercise treats public education expenditures cannot currently be exploited economically. Because est depletion, and carbon dioxide and particulate as an addition to savings. However, because of the the depletion estimates reflect only timber values, emissions damage. wide variability in the effectiveness of public edu- they ignore all the external and nontimber benefits cation expenditures, these figures cannot be con- associated with standing forests. strued as the value of investments in human capital. Pollution damage from emissions of carbon dioxide Data sources A current expenditure of $1 on education does not is calculated as the marginal social cost per unit mul- necessarily yield $1 of human capital. The calcula- tiplied by the increase in the stock of carbon dioxide. Data on gross savings are from World Bank tion should also consider private education expen- The unit damage figure represents the present value national accounts data files (see table 4.8). diture, but data are not available for a large number of global damage to economic assets and to human Data on consumption of fi xed capital are from of countries. welfare over the time the unit of pollution remains the United Nations Statistics Division’s National While extensive, the accounting of natural in the atmosphere. Accounts Statistics: Main Aggregates and Detailed resources depletion and pollution costs still has Pollution damage from particulate emissions is Tables, extrapolated to 2009. Data on educa- some gaps. Key estimates missing on the resource estimated by valuing the human health effects from tion expenditure are from the United Nations side include the value of fossil water extracted from exposure to particulate matter pollution in urban Educational, Scientific, and Cultural Organization aquifers, net depletion of fish stocks, and depletion areas. The estimates are calculated as willingness to Institute for Statistics online database; missing and degradation of soils. Important pollutants affect- pay to avoid illness and death, from cardiopulmonary data are estimated by World Bank staff. Data on ing human health and economic assets are excluded disease and lung cancer in adults and acute respira- energy, mineral, and forest depletion are esti- because no internationally comparable data are tory infections in children, that are attributable to mates based on sources and methods in World widely available on damage from ground-level ozone particulate emissions. Bank (2011a). Data on carbon dioxide damage or sulfur oxides. Adjusted net savings aims to be as comprehensive are from Fankhauser’s Valuing Climate Change: Estimates of resource depletion are based on the a measure as possible to provide a better under- The Economics of the Greenhouse (1995). Data “change in real wealth” method described in Hamil- standing of the rate of country wealth creation or on particulate emissions damage are from Pandey ton and Ruta (2008), which estimates depletion as depletion. To do so, it treats education as investment and others’ “The Human Cost of Air Pollution: New the ratio between the total value of the resource and accounts for pollution damages to assets and Estimates for Developing Countries” (2006). The and the remaining reserve lifetime. The total value human welfare, which goes outside the boundaries conceptual underpinnings of the savings measure of the resource is the present value of current and of the United Nations System of National Accounts. appear in Hamilton and Clemens’ “Genuine Sav- future rents from resource extractions. An economic For a detailed note on methodology, see data. ings Rates in Developing Countries” (1999). rent represents an excess return to a given factor worldbank.org/. 2011 World Development Indicators 237 4.12 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Total Interest % of GDP debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 2009 2009 Afghanistanb .. 9.1 .. 38.0 .. 0.2 .. 0.1 .. 0.8 .. 0.0 Albaniab 21.2 .. 25.6 .. –8.9 .. 7.4 .. 2.1 .. .. .. Algeria .. 36.6 .. 25.0 .. –4.4 .. 5.9 .. 0.0 .. 1.0 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. .. .. .. .. .. .. .. .. .. .. .. Armeniab .. 22.1 .. 23.7 .. –7.5 .. 1.3 .. 12.3 .. 2.3 Australia .. 24.6 .. 26.6 .. –2.4 .. .. .. .. 24.1 3.7 Austria 36.6 36.6 42.5 39.6 –5.5 –2.6 .. .. .. .. 70.7 7.0 Azerbaijanb .. 27.3 .. 15.5 .. 0.4 .. 0.0 .. 0.2 .. 0.3 Bangladeshb .. 11.1 .. 11.3 .. –1.7 .. 3.1 .. 0.4 .. 21.7 Belarusb 30.0 35.4 28.7 33.0 –2.7 0.2 2.2 –2.5 0.4 8.4 18.1 2.1 Belgium 41.5 40.3 45.7 45.3 –3.9 –5.1 .. 1.0 –0.5 6.5 92.4 8.5 Beninb .. 17.6 .. 15.0 .. –4.5 .. 2.2 .. 2.1 .. 2.5 Bolivia .. 23.3 .. 21.8 .. 1.2 .. –0.2 .. –0.1 .. 8.0 Bosnia and Herzegovina .. 38.6 .. 41.2 .. –4.3 .. 3.7 .. 3.2 .. 1.2 Botswanab 40.5 .. 30.3 .. 4.9 .. 0.2 .. –0.4 .. .. .. Brazilb 26.9 23.1 32.9 25.6 –2.7 –3.5 .. 8.3 .. –0.1 61.0 20.7 Bulgariab 35.6 32.3 39.5 31.6 –5.1 –0.1 7.5 –0.4 –0.8 0.5 .. 2.2 Burkina Faso .. 14.0 .. 13.0 .. –4.8 .. 4.5 .. 2.9 .. 2.2 Burundib 19.3 .. 23.6 .. –4.7 .. 3.1 .. 4.0 .. .. .. Cambodia .. 11.0 .. 11.0 .. –2.3 .. –2.0 .. 2.3 .. 1.3 Cameroonb 11.8 .. 10.6 .. 0.2 .. –0.3 .. 0.3 .. .. .. Canadab 19.8 17.4 23.8 19.2 –4.0 –1.9 .. .. .. .. 53.2 10.1 Central African Republicb .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 20.1 .. 22.6 .. –4.5 .. 0.8 .. –0.4 .. 2.8 Chinab 5.4 11.1 .. .. .. .. 1.6 0.4 .. 0.0 .. .. Hong Kong SAR, China .. 19.7 .. 18.9 .. 0.6 .. 1.0 .. –0.1 30.5 0.3 Colombia .. 17.0 .. 19.5 .. –4.0 .. 5.8 .. 0.9 59.3 18.9 Congo, Dem. Rep.b 5.3 .. 8.2 .. 0.0 .. 0.0 .. 0.2 .. .. .. Congo, Rep.b 23.6 .. 29.8 .. –8.2 .. .. .. .. .. .. .. Costa Rica .. 24.7 .. 26.0 .. –3.4 .. .. .. .. .. 8.8 Côte d’Ivoire .. 18.7 .. 17.6 .. 0.9 .. .. .. .. .. 7.1 Croatiab 36.8 34.1 36.2 36.2 –1.1 –3.0 –2.3 3.0 0.7 2.2 .. 4.8 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republicb 33.2 29.1 32.6 37.3 –0.9 –6.1 –0.5 2.9 –0.4 1.9 31.9 4.1 Denmark 37.6 40.0 41.5 42.4 –3.7 –2.1 .. .. .. .. 41.0 4.9 Dominican Republic .. 16.4 .. 16.2 .. –3.8 .. 2.4 .. 1.5 .. 9.7 Ecuador b 30.9 .. 26.3 .. 0.1 .. .. .. .. .. .. .. Egypt, Arab Rep.b 34.8 27.0 28.1 30.2 3.4 –6.6 .. 9.9 .. –0.2 79.5 15.2 El Salvador .. 17.5 .. 21.6 .. –5.0 .. 2.0 .. 5.9 48.5 12.3 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 36.2 37.1 32.8 36.8 1.6 –1.3 .. .. .. .. 9.1 0.6 Ethiopiab 12.2 .. 12.0 .. –3.1 .. 1.8 .. 2.6 .. .. .. Finland 40.4 39.0 49.7 35.0 –7.5 4.6 8.9 –0.2 0.2 –0.6 36.2 3.2 France 43.3 40.5 47.6 47.6 –4.1 –7.3 .. .. .. .. 82.8 5.4 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Theb .. .. .. .. .. .. .. .. .. .. .. .. Georgiab 12.2 25.2 15.4 31.0 –4.3 –7.8 2.2 1.3 2.4 3.7 34.7 3.4 Germany 29.9 29.4 38.6 31.7 –8.3 –2.2 .. 3.1 .. –0.2 47.2 5.5 Ghanab 17.0 15.3 .. 17.9 .. –5.6 .. 2.8 .. 2.6 .. 15.2 Greece 35.3 36.2 44.3 50.7 –9.1 –15.2 .. .. .. .. 138.5 14.3 Guatemalab 8.4 11.0 7.6 12.6 –0.5 –3.2 .. 1.4 0.4 1.4 23.3 12.6 Guineab 11.2 .. 12.1 .. –4.3 .. –0.1 .. 4.5 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras .. 20.8 .. 24.1 .. –4.5 .. 5.0 .. 1.0 .. 2.9 238 2011 World Development Indicators 4.12 ECONOMY Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Total Interest % of GDP debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 2009 2009 Hungary 43.0 40.5 53.2 45.3 –9.1 –4.0 17.0 –1.9 0.2 5.8 81.7 10.6 Indiab 12.3 11.9 14.4 16.2 –2.2 –4.9 5.1 5.6 0.0 0.2 53.0 28.5 Indonesiab 15.6 15.4 9.5 15.7 1.7 –1.7 .. 0.9 –0.4 0.4 28.3 10.9 Iran, Islamic Rep.b 24.2 31.9 15.8 24.7 1.1 0.6 .. 1.4 0.1 0.0 .. 0.6 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 35.5 30.4 37.5 43.4 –2.2 –13.9 .. .. .. .. 69.2 6.9 Israel .. 34.6 .. 40.6 .. –4.3 .. .. .. .. .. 9.7 Italy 40.4 38.5 48.0 44.0 –7.5 –4.9 .. .. .. .. 118.9 11.1 Jamaica .. 27.0 .. 41.5 .. –15.9 .. 7.4 .. 4.7 115.8 64.5 Japan .. .. .. .. .. .. .. .. .. .. 157.7 .. Jordanb 28.2 23.5 26.1 28.6 0.9 –8.5 –2.5 7.6 6.1 1.2 57.9 8.7 Kazakhstanb 14.0 9.2 18.7 16.9 –1.8 –2.0 0.8 2.8 2.8 0.5 9.5 2.5 Kenyab 21.6 20.5 25.8 21.7 –5.1 –5.5 3.9 3.0 –1.3 0.1 .. 10.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.b 17.8 23.1 14.3 21.9 2.4 0.0 –0.3 5.4 –0.1 –0.1 .. 4.7 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwaitb 36.8 47.1 44.0 21.9 –9.9 20.0 .. .. .. .. .. 0.0 Kyrgyz Republicb 16.7 19.2 25.6 19.3 –10.8 –1.4 .. 0.5 .. 7.7 .. 3.3 Lao PDR .. 13.9 .. 11.3 .. –1.6 .. –0.3 .. 2.1 .. 3.2 Latviab 25.8 24.9 28.3 34.8 –2.7 –6.4 2.4 –2.7 1.5 15.1 41.8 3.8 Lebanon .. 22.5 .. 29.5 .. –8.3 .. 11.8 .. 0.3 .. 48.7 Lesothob 57.1 66.4 39.4 52.1 5.8 5.8 0.0 –0.4 7.2 1.6 .. 1.3 Liberiab .. 0.4 .. 0.3 .. 0.0 .. 0.0 .. 0.0 .. 2.1 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 28.3 .. 38.8 .. –9.0 .. 1.9 .. 9.1 33.3 4.0 Macedonia, FYRb .. 34.0 .. 31.3 .. –0.8 .. –0.6 .. 0.2 .. 1.9 Madagascar .. 14.1 .. 11.7 .. –1.9 .. 0.6 .. 3.0 .. 3.9 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiab 23.3 23.3 18.7 22.7 1.5 –6.4 .. 6.5 .. 0.9 53.3 9.0 Mali .. 17.1 .. 14.6 .. –2.1 .. –4.4 .. 2.6 .. 1.7 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. 23.5 .. 21.6 .. 0.6 .. 3.1 .. 1.3 38.9 12.0 Mexicob 15.3 .. 15.0 .. –0.6 .. .. .. 5.5 .. .. .. Moldovab 28.4 33.1 38.4 38.3 –6.3 –5.7 3.0 2.7 2.7 3.3 24.4 4.0 Mongoliab 19.0 29.2 13.8 28.8 2.9 –4.5 1.6 8.6 1.3 5.2 64.8 1.6 Moroccob .. 33.1 .. 27.9 .. 1.0 .. 0.1 .. 1.7 46.9 3.1 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar b 6.4 .. .. .. .. .. .. .. .. .. .. .. Namibiab 31.7 29.2 35.7 24.1 –5.0 2.0 .. –0.8 .. –0.1 .. 6.3 Nepalb 10.5 14.5 .. .. .. .. 0.6 3.2 2.5 0.0 43.7 4.9 Netherlands 41.5 41.0 50.8 45.6 –9.2 –4.8 .. .. .. .. 58.3 4.6 New Zealand .. 36.1 .. 32.1 .. 3.1 .. .. .. .. 37.9 3.4 Nicaraguab 12.8 19.1 14.2 20.9 0.6 –2.3 .. .. 3.4 .. .. 6.4 Niger .. 13.6 .. 11.8 .. –0.9 .. –1.9 .. 2.4 .. 1.8 Nigeriab .. 9.7 .. 7.2 .. –1.7 .. 0.1 .. .. 3.0 6.6 Norway .. 47.2 .. 35.9 .. 10.7 .. 6.3 .. –15.3 36.3 2.1 Omanb 27.8 .. 32.4 .. –8.9 .. –0.1 .. 0.0 .. .. .. Pakistanb 17.2 14.0 19.1 16.8 –5.3 –4.8 .. .. .. .. .. 41.7 Panamab 26.1 .. 22.0 .. 1.5 .. .. .. .. .. .. .. Papua New Guineab 22.7 .. 24.5 .. –0.5 .. 1.5 .. –0.7 .. .. .. Paraguay b 17.2 19.0 14.5 17.1 0.2 0.1 0.0 1.3 –0.8 0.1 .. 3.1 Perub 17.4 17.2 17.4 17.1 –1.3 –1.5 .. 0.2 3.9 1.1 23.6 7.2 Philippinesb 17.7 14.6 15.9 18.6 –0.8 –3.9 –0.5 1.2 –0.7 2.0 .. 25.7 Poland .. 30.1 .. 35.8 .. –6.1 .. 1.6 .. 3.6 48.1 8.1 Portugal 33.2 34.7 37.1 43.2 –5.1 –8.7 –1.2 3.4 4.2 5.9 84.4 7.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar b .. 47.2 .. 19.3 .. 15.2 .. .. .. .. .. 2.1 2011 World Development Indicators 239 4.12 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Total Interest % of GDP debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 2009 2009 Romania .. 30.9 .. 33.8 .. –4.6 .. 2.4 .. 0.9 .. 2.0 Russian Federation .. 35.4 .. 30.9 .. 5.3 .. 0.8 .. –0.2 8.6 1.3 Rwandab 10.6 .. 15.0 .. –5.6 .. 2.9 .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegalb 15.2 .. .. .. .. .. .. .. .. .. .. .. Serbiab .. 36.3 .. 37.7 .. –2.6 .. 2.8 .. 1.2 .. 2.0 Sierra Leoneb 9.4 11.6 .. 22.5 .. –3.1 0.3 .. .. .. .. 8.3 Singaporeb 26.7 18.2 12.4 15.2 19.8 1.7 10.3 13.7 0.0 .. 113.3 0.1 Slovak Republic .. 28.5 .. 37.6 .. –7.3 .. 2.9 .. 3.0 38.1 4.7 Sloveniab 35.8 37.5 34.3 42.7 –0.1 –5.5 –0.4 12.4 0.3 –1.2 .. 2.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 28.2 .. 33.0 .. –4.9 .. 7.0 .. 1.0 .. 8.4 Spain 32.0 22.4 37.1 30.7 –5.8 –8.6 .. 6.4 .. 4.8 46.5 6.1 Sri Lankab 20.4 14.9 26.0 19.2 –7.6 –6.6 5.2 6.9 3.2 –0.1 85.0 31.0 Sudanb 7.2 .. 6.8 .. –0.4 .. 0.3 .. .. .. .. .. Swazilandb .. .. .. .. .. .. .. .. .. .. .. .. Sweden 38.6 34.7 .. .. .. .. .. .. .. .. 44.0 .. Switzerlandb 22.6 18.4 25.7 17.0 –0.6 1.3 –0.5 2.0 .. .. 28.9 3.5 Syrian Arab Republicb 22.9 .. .. .. .. .. .. .. .. .. .. .. Tajikistanb 9.3 .. 11.4 .. –3.3 .. 0.1 .. 2.3 .. .. .. Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 18.6 .. 19.6 .. –3.0 .. 5.3 .. 0.0 28.6 5.8 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. 18.8 .. 17.4 .. –0.6 .. 2.7 .. –0.5 .. 4.0 Trinidad and Tobagob 27.2 36.1 25.3 28.4 –0.1 2.3 2.8 –0.6 2.6 0.5 14.1 5.0 Tunisiab 30.0 31.4 28.4 29.9 –2.4 –1.7 0.9 0.3 2.9 0.0 47.1 7.0 Turkey b .. 21.8 .. 27.3 .. –5.5 .. 6.1 .. 0.6 51.4 24.1 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandab 10.6 12.4 .. 13.7 .. –0.9 .. 1.5 .. 1.8 32.7 7.7 Ukraineb .. 34.5 .. 40.6 .. –5.6 .. 6.7 .. 4.9 .. 3.1 United Arab Emiratesb 10.1 .. 11.0 .. 0.5 .. .. .. .. .. .. .. United Kingdom 35.2 35.9 40.4 46.4 –5.5 –10.9 .. .. .. .. 73.2 5.3 United States .. 15.9 .. 26.3 .. –10.4 .. 6.5 .. 4.7 67.1 11.4 Uruguay b 27.6 29.4 27.1 29.3 –1.2 –1.5 7.9 3.8 1.1 2.4 49.5 9.3 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBb 16.9 .. 18.5 .. –2.3 .. 1.1 .. 0.1 .. .. .. Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. 1.1 Yemen, Rep.b 17.3 .. 19.1 .. –3.9 .. .. .. .. .. .. .. Zambiab 20.0 17.6 21.4 22.9 –3.1 –0.8 28.0 .. 16.2 .. .. 7.2 Zimbabweb 26.7 .. 32.1 .. –5.4 .. –1.4 .. 1.6 .. .. .. World .. w 24.3 w .. w 31.1 w .. w –7.1 w .. m .. m .. m .. m .. m 5.4 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income 14.6 20.0 .. .. .. .. .. 0.9 .. 0.2 .. 7.0 Lower middle income 10.7 14.7 .. .. .. .. .. 0.5 .. .. .. 6.0 Upper middle income .. .. .. .. .. .. .. 3.7 .. 0.9 .. 7.2 Low & middle income 14.6 19.8 .. .. .. .. .. 0.6 .. .. .. 4.5 East Asia & Pacific 7.2 13.4 .. .. .. .. .. 1.2 .. 2.0 .. 5.8 Europe & Central Asia .. 29.3 .. 30.1 .. –0.4 .. 1.9 .. 3.3 .. 2.5 Latin America & Carib. 21.2 .. 23.3 .. –1.4 .. .. 0.1 .. –0.2 .. 9.1 Middle East & N. Africa .. 30.6 .. 27.3 .. –3.0 .. 6.7 .. 0.1 .. 7.0 South Asia 13.1 12.1 15.3 16.0 –2.7 –4.6 3.8 3.2 1.1 0.3 56.5 21.7 Sub-Saharan Africa .. 24.5 .. 24.2 .. –1.0 .. .. .. .. .. .. High income .. 24.7 .. 32.2 .. –7.7 .. .. .. .. 55.7 5.3 Euro area 34.9 34.5 42.3 39.8 –7.4 –5.2 .. 0.8 .. 0.4 69.9 6.1 a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework. 240 2011 World Development Indicators 4.12 ECONOMY Central government finances About the data Definitions Tables 4.12–4.14 present an overview of the size and borrowing for temporary periods can also be used. • Revenue is cash receipts from taxes, social con- role of central governments relative to national econo- Government excludes public corporations and quasi tributions, and other revenues such as fines, fees, mies. The tables are based on the concepts and recom- corporations (such as the central bank). rent, and income from property or sales. Grants, usu- mendations of the second edition of the International Units of government at many levels meet this defini- ally considered revenue, are excluded. • Expense is Monetary Fund’s (IMF) Government Finance Statistics tion, from local administrative units to the national cash payments for government operating activities in Manual 2001. Before 2005 World Development Indica- government, but inadequate statistical coverage pre- providing goods and services. It includes compensa- tors reported data derived on the basis of the 1986 cludes presenting subnational data. Although data for tion of employees, interest and subsidies, grants, manual’s cash-based method. The 2001 manual, general government under the 2001 manual are avail- social benefi ts, and other expenses such as rent harmonized with the 1993 United Nations System of able for a few countries, only data for the central gov- and dividends. • Cash surplus or deficit is revenue National Accounts, recommends an accrual account- ernment are shown to minimize disparities. Still, differ- (including grants) minus expense, minus net acquisi- ing method, focusing on all economic events affecting ent accounting concepts of central government make tion of nonfinancial assets. In editions before 2005 assets, liabilities, revenues, and expenses, not only cross-country comparisons potentially misleading. nonfinancial assets were included under revenue those represented by cash transactions. It takes all Central government can refer to consolidated or bud- and expenditure in gross terms. This cash surplus stocks into account, so that stock data at the end of an getary accounting. For most countries central govern- or deficit is close to the earlier overall budget balance accounting period equal stock data at the beginning of ment finance data have been consolidated into one (still missing is lending minus repayments, which are the period plus flows over the period. The 1986 manual account, but for others only budgetary central gov- included as a financing item under net acquisition considered only the debt stock data. Further, the new ernment accounts are available. Countries reporting of financial assets). • Net incurrence of liabilities manual no longer distinguishes between current and budgetary data are noted in Primary data documenta- is domestic financing (obtained from residents) and capital revenue or expenditures, and it introduces the tion. Because budgetary accounts may not include foreign financing (obtained from nonresidents), or concepts of nonfinancial and financial assets. Most all central government units (such as social security the means by which a government provides financial countries still follow the 1986 manual, however. The funds), they usually provide an incomplete picture. resources to cover a budget deficit or allocates finan- IMF has reclassified historical Government Finance Sta- Data on government revenue and expense are col- cial resources arising from a budget surplus. The net tistics Yearbook data to conform to the 2001 manual’s lected by the IMF through questionnaires to member incurrence of liabilities should be offset by the net format. Because of reporting differences, the reclassi- countries and by the Organisation for Economic Co- acquisition of financial assets (a third financing item). fied data understate both revenue and expense. operation and Development. Despite IMF efforts to The difference between the cash surplus or deficit The 2001 manual describes government’s eco- standardize data collection, statistics are often incom- and the three financing items is the net change in nomic functions as the provision of goods and ser- plete, untimely, and not comparable across countries. the stock of cash. • Total debt is the entire stock of vices on a nonmarket basis for collective or individual Government finance statistics are reported in local direct government fixed-term contractual obligations consumption, and the redistribution of income and currency. The indicators here are shown as percent- to others outstanding on a particular date. It includes wealth through transfer payments. Government ages of GDP. Many countries report government domestic and foreign liabilities such as currency and activities are financed mainly by taxation and other finance data by fiscal year; see Primary data docu- money deposits, securities other than shares, and income transfers, though other financing such as mentation for information on fiscal year end by country. loans. It is the gross amount of government liabili- ties reduced by the amount of equity and financial Twenty selected economies had a central government debt derivatives held by the government. Because debt to GDP ratio of 65 percent or higher 4.12a is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Central government debt, 2009 (percent of GDP) 160 • Interest payments are interest payments on gov- ernment debt—including long-term bonds, long-term loans, and other debt instruments—to domestic and 120 foreign residents. 80 Data sources Data on central government finances are from the 40 IMF’s Government Finance Statistics database. Each country’s accounts are reported using the system of common definitions and classifications 0 in the IMF’s Government Finance Statistics Manual n ce Ja ly ng a e d us Ba vis Be s m ka l Hu e y ta Ki p. m ria d es ga ar o pa c or an c ite lan Ita iu do e t, Mal ee ai an an ad at pr st Ne r tu ng ite b R ap lg Ja el m Un Ire ng St . K Cy Au Gr Fr iL rb Ic Po 2001. See these sources for complete and author- & a Sr d Ar Si s d itt yp itative explanations of concepts, definitions, and Un Eg St Note: Data are for the most recent year for 2005–2009. data sources. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. 2011 World Development Indicators 241 4.13 Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistana .. 72 .. 23 .. 0 .. 4 .. 0 Albaniaa 18 .. 14 .. 9 .. 59 .. 0 .. Algeria .. 11 .. 34 .. 1 .. 45 .. 8 Angola .. .. .. .. .. .. .. .. .. .. Argentina .. .. .. .. .. .. .. .. .. .. Armeniaa .. 13 .. 25 .. 2 .. 37 .. 23 Australia .. 10 .. 10 .. 3 .. 73 .. 6 Austria 5 6 14 14 9 7 68 71 6 5 Azerbaijana .. 9 .. 12 .. 1 .. 18 .. 61 Bangladesha .. 12 .. 19 .. 22 .. 35 .. 12 Belarusa 39 12 5 11 1 2 55 70 0 6 Belgium 3 3 7 7 18 8 71 53 2 0 Benina .. 18 .. 47 .. 3 .. 30 .. 2 Bolivia .. 14 .. 22 .. 10 .. 47 .. 7 Bosnia and Herzegovina .. 23 .. 28 .. 1 .. 44 .. 4 Botswanaa 32 .. 30 .. 2 .. 36 .. 2 .. Brazila 5 13 8 19 45 19 45 49 1 0 Bulgariaa 18 9 7 19 37 2 38 64 2 6 Burkina Faso .. 19 .. 46 .. 3 .. 11 .. 21 Burundia 20 .. 30 .. 6 .. 14 .. 10 .. Cambodia .. 32 .. 43 .. 2 .. 21 .. 2 Cameroona 17 .. 40 .. 26 .. 14 .. .. .. Canadaa 8 8 10 12 24 9 57 69 3 3 Central African Republica .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile .. 10 .. 20 .. 2 .. 51 .. 19 Chinaa .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China .. 26 .. 22 .. 0 .. 17 .. 38 Colombia .. 6 .. 16 .. 17 .. 47 .. 15 Congo, Dem. Rep.a 37 .. 58 .. 1 .. 2 .. .. .. Congo, Rep.a 7 .. 35 .. 47 .. 10 .. .. .. Costa Rica .. 11 .. 46 .. 8 .. 21 .. 14 Côte d’Ivoire .. 29 .. 38 .. 9 .. 16 .. 7 Croatiaa 35 8 27 26 3 5 32 56 3 5 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republica 7 6 9 8 3 3 75 72 5 11 Denmark 8 9 12 13 14 5 59 17 10 2 Dominican Republic .. 15 .. 31 .. 10 .. 39 .. 5 Ecuador a 6 .. 49 .. 26 .. .. .. .. .. Egypt, Arab Rep.a 18 8 22 25 26 14 6 45 .. 9 El Salvador .. 15 .. 36 .. 10 .. 22 .. 18 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia 21 13 23 21 1 1 39 48 4 4 Ethiopiaa 35 .. 40 .. 15 .. 18 .. 0 .. Finland 8 10 9 10 8 4 68 71 11 8 France 8 6 23 21 6 5 59 54 6 2 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, Thea .. .. .. .. .. .. .. .. .. .. Georgiaa 52 19 11 17 10 3 26 49 .. 12 Germany 4 5 5 5 6 5 67 81 20 4 Ghanaa .. 16 .. 40 .. 16 .. 28 .. 12 Greece 10 12 21 24 25 10 38 50 8 7 Guatemalaa 15 15 50 29 12 11 18 33 6 12 Guineaa 17 .. 34 .. 28 .. 9 .. 1 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. Honduras .. 17 .. 54 .. 3 .. 7 .. 19 242 2011 World Development Indicators 4.13 ECONOMY Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 8 10 10 13 17 10 56 63 13 8 Indiaa 14 11 10 10 27 21 33 51 0 7 Indonesiaa 22 9 20 14 17 11 40 54 2 12 Iran, Islamic Rep.a 21 11 56 40 0 1 .. 34 .. 14 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 5 10 15 23 14 5 33 40 1 1 Israel .. 27 .. 25 .. 9 .. 32 .. 9 Italy 4 4 14 15 24 10 54 66 6 6 Jamaica .. 6 .. 14 .. 43 .. 6 .. 31 Japan .. .. .. .. .. .. .. .. .. .. Jordana 7 11 67 50 11 8 12 30 4 2 Kazakhstana .. 19 .. 8 3 2 58 69 .. 2 Kenyaa 15 20 28 37 46 10 .. 31 2 1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 16 11 15 10 3 5 63 57 3 17 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait a 34 10 33 16 5 0 21 58 7 15 Kyrgyz Republica 32 30 36 29 5 4 27 34 .. 3 Lao PDR .. 27 .. 49 .. 5 .. 10 .. 10 Latviaa 20 8 20 15 3 3 56 70 0 4 Lebanon .. 3 .. 21 .. 38 .. 36 .. 2 Lesothoa 32 42 45 35 5 2 8 14 3 6 Liberiaa .. 37 .. 36 .. 2 .. 24 .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. 10 .. 16 .. 3 .. 68 .. 6 Macedonia, FYRa .. 28 .. 17 .. 2 .. 49 .. 4 Madagascar .. 15 .. 40 .. 7 .. 25 .. 14 Malawi .. .. .. .. .. .. .. .. .. .. Malaysiaa 14 17 34 28 14 9 36 46 1 0 Mali .. 31 .. 34 .. 2 .. 15 .. 17 Mauritania .. .. .. .. .. .. .. .. .. .. Mauritius .. 12 .. 34 .. 14 .. 31 .. 10 Mexicoa 9 .. 19 .. 19 .. .. .. .. .. Moldovaa 10 19 8 15 11 4 71 56 1 6 Mongoliaa 30 20 12 33 2 2 56 45 0 1 Moroccoa .. 9 .. 48 .. 4 .. 27 .. 13 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar a .. .. .. .. .. .. .. .. .. .. Namibiaa 28 20 53 45 1 8 .. 13 4 14 Nepala .. .. .. .. .. .. .. .. .. .. Netherlands 5 8 8 7 9 4 77 79 3 4 New Zealand .. 30 .. 25 .. 4 .. 38 .. 7 Nicaraguaa 14 13 25 39 17 7 29 36 14 5 Niger .. 30 .. 30 .. 3 .. 9 .. 28 Nigeriaa .. 15 .. 24 .. 9 .. 53 .. .. Norway .. 11 .. 16 .. 3 .. 67 .. 5 Omana 55 .. 30 .. 7 .. 8 .. 0 .. Pakistana .. 22 .. 4 28 35 2 21 .. 18 Panamaa 16 .. 45 .. 8 .. 30 .. 1 .. Papua New Guineaa 19 .. 36 .. 20 .. 26 .. 1 .. Paraguaya 12 9 51 50 5 4 31 29 0 9 Perua 20 20 19 18 19 7 33 47 8 7 Philippinesa 15 28 34 30 33 20 15 20 .. 2 Poland .. 5 .. 12 .. 7 .. 71 .. 7 Portugal 9 7 30 24 15 6 43 51 7 1 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar a .. 25 .. 32 .. 5 .. 21 .. 16 2011 World Development Indicators 243 4.13 Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania .. 13 .. 19 .. 2 .. 60 .. 8 Russian Federation .. 12 .. 16 .. 1 .. 68 .. 10 Rwandaa 52 .. 36 .. 12 .. 5 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegala .. .. .. .. .. .. .. .. .. .. Serbiaa .. 13 .. 26 .. 2 .. 58 .. 1 Sierra Leonea .. 24 .. 28 .. 7 .. 23 .. 18 Singaporea 38 36 39 27 8 0 15 0 .. .. Slovak Republic .. 7 .. 12 .. 4 .. 68 .. 14 Sloveniaa 19 13 21 20 3 3 55 62 3 3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. 13 .. 13 .. 7 .. 63 .. 4 Spain 5 4 14 8 11 5 42 80 2 5 Sri Lankaa 23 14 20 28 22 25 24 23 10 10 Sudana 44 .. 38 .. 8 .. 10 .. .. .. Swazilanda .. .. .. .. .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. Switzerlanda 24 6 6 6 4 4 66 83 0 3 Syrian Arab Republica .. .. .. .. .. .. .. .. .. .. Tajikistana 47 .. 8 .. 12 .. 33 .. .. .. Tanzania .. .. .. .. .. .. .. .. .. .. Thailand .. 31 .. 36 .. 5 .. 28 .. 3 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo .. 24 .. 40 .. 5 .. 18 .. 13 Trinidad and Tobagoa 20 14 36 21 20 6 24 38 1 21 Tunisiaa 7 7 37 36 13 7 36 38 7 13 Turkeya .. 10 .. 23 .. 20 .. 44 .. 5 Turkmenistan .. .. .. .. .. .. .. .. .. .. Ugandaa .. 31 .. 14 .. 9 .. 45 .. 1 Ukrainea .. 12 .. 13 .. 3 .. 70 .. 2 United Arab Emiratesa 48 .. 33 .. .. .. .. .. .. .. United Kingdom 14 18 15 14 9 4 57 53 8 12 United States .. 15 .. 12 .. 7 .. 62 .. 6 Uruguaya 13 12 17 25 6 9 64 47 0 7 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 6 .. 22 .. 27 .. 61 .. 2 .. Vietnam .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 12 .. 67 .. 1 .. 18 .. 1 Yemen, Rep.a 8 .. 67 .. 16 .. 8 .. 0 .. Zambiaa 32 32 35 30 16 7 19 24 0 7 Zimbabwea 16 .. 34 .. 31 .. 19 .. .. .. World .. m 12 m .. m 21 m .. m 5m .. m 46 m .. m 6m Low income .. .. .. .. .. .. .. .. .. .. Middle income .. 12 .. 25 .. 7 .. 45 .. 7 Lower middle income .. 15 .. 31 .. 7 .. 36 .. 8 Upper middle income .. 12 .. 20 .. 7 .. 47 .. 6 Low & middle income .. 15 .. 27 .. 6 .. 37 .. 7 East Asia & Pacific .. 27 .. 33 .. 5 .. 28 .. 2 Europe & Central Asia .. 13 .. 17 .. 2 .. 58 .. 6 Latin America & Carib. .. 13 .. 27 .. 9 .. 35 .. 13 Middle East & N. Africa .. 9 .. 36 .. 7 .. 36 .. 9 South Asia .. 17 .. 14 27 21 24 28 .. 10 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. High income 10 9 15 14 9 5 56 62 4 5 Euro area 5 7 14 15 10 5 55 62 5 4 Note: Components may not sum to 100 percent because of rounding or missing data. a. Data were reported on a cash basis and have been adjusted to the accrual framework. 244 2011 World Development Indicators 4.13 ECONOMY Central government expenses About the data Definitions The term expense has replaced expenditure in the to households are shown as subsidies and other • Goods and services are all government payments table since the 2005 edition of World Development transfers, and other expenses. The economic clas- in exchange for goods and services used for the Indicators in accordance with use in the International sification can be problematic. For example, subsidies production of market and nonmarket goods and ser- Monetary Fund’s (IMF) Government Finance Statis- to public corporations or banks may be disguised vices. Own-account capital formation is excluded. tics Manual 2001. Government expenses include all as capital financing or hidden in special contractual • Compensation of employees is all payments in nonrepayable payments, whether current or capital, pricing for goods and services. For further discussion cash, as well as in kind (such as food and hous- requited or unrequited. The concept of total central of government finance statistics, see About the data ing), to employees in return for services rendered, government expense as presented in the IMF’s Gov- for tables 4.12 and 4.14. and government contributions to social insurance ernment Finance Statistics Yearbook is comparable to schemes such as social security and pensions that the concept used in the 1993 United Nations System provide benefits to employees. • Interest payments of National Accounts. are payments made to nonresidents, to residents, Expenses can be measured either by function and to other general government units for the use of (health, defense, education) or by economic type borrowed money. (Repayment of principal is shown (interest payments, wages and salaries, purchases as a financing item, and commission charges are of goods and services). Functional data are often shown as purchases of services.) • Subsidies and incomplete, and coverage varies by country because other transfers include all unrequited, nonrepayable functional responsibilities stretch across levels of transfers on current account to private and public government for which no data are available. Defense enterprises; grants to foreign governments, inter- expenses, usually the central government’s respon- national organizations, and other government units; sibility, are shown in table 5.7. For more information and social security, social assistance benefits, and on education expenses, see table 2.11; for more on employer social benefits in cash and in kind. • Other health expenses, see table 2.16. expense is spending on dividends, rent, and other The classification of expenses by economic type in miscellaneous expenses, including provision for con- the table shows whether the government produces sumption of fixed capital. goods and services and distributes them, purchases the goods and services from a third party and dis- tributes them, or transfers cash to households to make the purchases directly. When the government produces and provides goods and services, the cost is reflected in compensation of employees, use of goods and services, and consumption of fixed capi- tal. Purchases from a third party and cash transfers Interest payments are a large part of government expenses for some developing economies 4.13a Central government interest payments as a share of total expense, 2009 (percent) 50 40 30 20 Data sources 10 Data on central government expenses are from the 0 IMF’s Government Finance Statistics database. ca vis n an ka sh a es il a a s y p. s ke az lle iu no di bi an ai Ne Re an de in st rit In m m Br r Each country’s accounts are reported using the he ba Gh Tu pp ki iL la lo Ja au & ab yc Pa Le ng ili Co Sr s M Ar Se Ph itt Ba system of common definitions and classifications t, .K yp St Eg in the IMF’s Government Finance Statistics Manual Interest payments accounted for more than 14 percent of total expenses in 2009 for 15 countries. 2001. See these sources for complete and author- itative explanations of concepts, definitions, and Source: International Monetary Fund, Government Finance Statistics data files. data sources. 2011 World Development Indicators 245 4.14 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and International taxes contributions other revenue capital gains services trade % of % of % of % of % of % of revenue revenue revenue revenue revenue revenue 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistana .. 4 .. 3 .. 5 .. 0 .. 0 .. 88 Albaniaa 8 .. 39 .. 14 .. 1 .. 15 .. 22 .. Algeria .. 60 .. 28 .. 4 .. 1 .. .. .. 6 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. .. .. .. .. .. .. .. .. .. .. .. Armeniaa .. 20 .. 41 .. 3 .. 8 .. 14 .. 14 Australia .. 65 .. 23 .. 2 .. 0 .. .. .. 10 Austria 21 23 22 23 0 0 5 5 43 42 9 7 Azerbaijana .. 33 .. 23 .. 4 .. 1 .. .. .. 39 Bangladesha .. 19 .. 29 .. 24 .. 3 .. .. .. 24 Belarusa 16 6 33 29 6 16 11 3 31 33 3 13 Belgium 36 34 23 24 .. .. 2 0 36 37 3 3 Benina .. 17 .. 39 .. 18 .. 6 .. 2 .. 18 Bolivia .. 10 .. 43 .. 3 .. 9 .. 7 .. 28 Bosnia and Herzegovina .. 5 .. 43 .. 0 .. 2 .. 39 .. 11 Botswanaa 21 .. 4 .. 15 .. 0 .. .. .. 59 .. Brazila 14 30 24 33 2 2 4 2 31 26 26 6 Bulgariaa 17 16 28 45 8 1 3 0 21 23 23 16 Burkina Faso .. 14 .. 37 .. 12 .. 2 .. .. .. 36 Burundia 14 .. 30 .. 20 .. 1 .. 5 .. 30 .. Cambodia .. 11 .. 36 .. 16 .. 0 .. .. .. 37 Cameroona 17 .. 25 .. 28 .. 3 .. 2 .. 25 .. Canadaa 48 55 18 15 3 1 .. .. 21 24 10 8 Central African Republica .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 28 .. 46 .. 1 .. 2 .. 7 .. 16 Chinaa 9 26 61 55 7 5 0 3 .. .. 22 12 Hong Kong SAR, China .. 44 .. 9 .. 0 .. 13 .. 0 .. 34 Colombia .. 26 .. 32 .. 5 .. 5 .. 6 .. 25 Congo, Dem. Rep.a 21 .. 12 .. 21 .. 5 .. 1 .. 41 .. Congo, Rep.a 6 .. 21 .. 18 .. 1 .. .. .. 54 .. Costa Rica .. 17 .. 32 .. 4 .. 3 .. 34 .. 10 Côte d’Ivoire .. 15 .. 20 .. 33 .. 8 .. 6 .. 18 Croatiaa 11 10 42 43 9 2 1 2 33 35 4 9 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republica 15 15 32 27 4 0 1 1 40 45 8 12 Denmark 37 45 40 36 .. .. 8 5 4 3 11 .. Dominican Republic .. 22 .. 54 .. 10 .. 5 .. 2 .. 8 Ecuador a 50 .. 26 .. 11 .. 1 .. .. .. 12 .. Egypt, Arab Rep.a 17 28 13 22 10 5 10 2 10 .. 41 43 El Salvador .. 27 .. 39 .. 5 .. 0 .. 12 .. 17 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 18 8 35 39 0 .. 0 .. 34 36 .. .. Ethiopiaa 19 .. 13 .. 27 .. 3 .. 1 .. 36 .. Finland 16 20 31 32 0 .. 1 2 34 31 17 15 France 17 22 25 23 0 0 3 4 47 45 8 6 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Thea .. .. .. .. .. .. .. .. .. .. .. .. Georgiaa 7 32 48 51 10 1 .. 1 13 17 22 15 Germany 16 16 20 24 .. .. 0 .. 58 55 6 4 Ghanaa 15 23 31 29 24 16 .. .. .. .. 9 32 Greece 17 21 32 29 0 0 3 3 31 36 16 12 Guatemalaa 19 29 46 56 23 7 3 2 2 3 6 4 Guineaa 8 .. 4 .. 62 .. 2 .. 1 .. 23 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras .. 20 .. 39 .. 3 .. 1 .. 13 .. 23 246 2011 World Development Indicators 4.14 ECONOMY Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and International taxes contributions other revenue capital gains services trade % of % of % of % of % of % of revenue revenue revenue revenue revenue revenue 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 16 23 28 32 10 0 1 1 35 32 9 12 Indiaa 23 47 28 23 24 13 0 0 0 0 25 18 Indonesiaa 52 37 32 31 5 2 1 4 .. .. 10 26 Iran, Islamic Rep.a 12 19 5 3 9 6 1 1 6 19 66 52 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 37 33 .. .. 0 0 2 2 17 22 .. .. Israel .. 26 .. 31 .. 1 .. 5 .. 17 .. 19 Italy 32 32 21 20 .. .. 5 7 35 36 6 5 Jamaica .. 25 .. 37 .. 7 .. 10 .. 3 .. 18 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordana 10 17 23 38 22 6 9 3 .. 0 36 36 Kazakhstana 11 24 28 20 3 6 5 0 48 .. 6 51 Kenyaa 35 40 40 41 14 10 1 1 0 .. 10 8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 31 28 32 26 7 4 10 9 8 16 12 17 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait a 1 1 0 .. 2 1 0 0 .. .. 97 98 Kyrgyz Republica 26 12 56 42 5 9 1 .. .. .. 11 37 Lao PDR .. 21 .. 46 .. 9 .. 1 .. .. .. 22 Latviaa 7 8 41 35 3 0 0 0 35 31 13 26 Lebanon .. 15 .. 44 .. 6 .. 10 .. 1 .. 23 Lesothoa 15 17 12 12 49 57 1 3 .. .. 24 11 Liberiaa .. 28 .. 15 .. 39 .. 1 .. .. .. 18 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 10 .. 36 .. .. .. 0 .. 42 .. 13 Macedonia, FYRa .. 13 .. 40 .. 5 .. 0 .. 29 .. 13 Madagascar .. 12 .. 15 .. 31 .. 6 .. 4 .. 32 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiaa 38 46 27 16 12 2 6 3 .. .. 17 33 Mali .. 19 .. 29 .. 10 .. 10 .. .. .. 31 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. 23 .. 46 .. 2 .. 7 .. 4 .. 17 Mexicoa 27 .. 54 .. 4 .. 2 .. 14 .. 16 .. Moldovaa 6 1 38 46 5 4 1 0 38 33 2 16 Mongoliaa 31 21 18 30 9 6 0 0 15 17 27 26 Moroccoa .. 28 .. 31 .. 6 .. 5 .. 12 .. 17 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar a 20 .. 26 .. 12 .. .. .. .. .. 42 .. Namibiaa 27 28 32 19 28 44 2 1 .. 0 11 7 Nepala 10 14 33 35 26 16 4 5 .. .. 27 29 Netherlands 26 26 24 27 .. .. 2 2 40 35 8 10 New Zealand .. 57 .. 26 .. 3 .. 0 .. 0 .. 15 Nicaraguaa 9 29 52 49 7 4 0 0 .. .. 31 18 Niger .. 12 .. 18 .. 26 .. 3 .. .. .. 41 Nigeriaa .. 1 .. 2 .. .. .. .. .. .. .. 97 Norway .. 28 .. 24 .. 0 .. 1 .. 21 .. 26 Omana 21 .. 1 .. 3 .. 2 .. .. .. 74 .. Pakistana 18 25 27 32 24 8 7 0 .. .. 24 35 Panamaa 20 .. 17 .. 11 .. 3 .. 16 .. 34 .. Papua New Guineaa 40 .. 8 .. 27 .. 2 .. 0 .. 23 .. Paraguaya 15 16 36 43 18 7 4 1 6 7 22 26 Perua 15 30 46 39 10 2 8 6 10 10 11 13 Philippinesa 33 39 26 29 29 20 4 .. .. .. 8 13 Poland .. 14 .. 37 .. 0 .. 1 .. 37 .. 10 Portugal 23 23 33 31 0 0 2 2 30 33 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar a .. 40 .. .. .. 2 .. .. .. .. .. 58 2011 World Development Indicators 247 4.14 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods International taxes contributions other revenue capital gains and services trade % of % of % of % of % of % of revenue revenue revenue revenue revenue revenue 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania .. 22 .. 35 .. 0 .. 0 .. 33 .. 10 Russian Federation .. 1 .. 16 .. 18 .. 0 .. 17 .. 48 Rwandaa 11 .. 25 .. 23 .. 3 .. 2 .. 36 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegala 17 .. 19 .. 36 .. 2 .. .. .. 26 .. Serbiaa .. 9 .. 43 .. 5 .. 0 .. 35 .. 7 Sierra Leonea 15 17 34 25 39 14 0 .. .. .. 12 44 Singaporea 26 36 20 26 1 0 15 14 .. .. 38 24 Slovak Republic .. 9 .. 33 .. 0 .. 0 .. 43 .. 15 Sloveniaa 13 13 33 33 9 0 0 0 42 41 3 12 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 53 .. 32 .. 3 .. 2 .. 2 .. 8 Spain 28 24 21 13 0 .. 0 0 40 58 .. 4 Sri Lankaa 12 18 49 45 17 14 4 8 1 1 18 14 Sudana 17 .. 41 .. 27 .. 1 .. .. .. 14 .. Swazilanda .. .. .. .. .. .. .. .. .. .. .. .. Sweden 13 11 33 37 .. .. 4 13 32 25 .. .. Switzerlanda 11 24 21 26 1 6 2 3 49 36 17 5 Syrian Arab Republica 23 .. 37 .. 13 .. 8 .. 0 .. 19 .. Tajikistana 6 .. 63 .. 12 .. 0 .. 13 .. 5 .. Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 38 .. 38 .. 5 .. 1 .. 5 .. 14 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. 17 .. 34 .. 18 .. 3 .. .. .. 28 Trinidad and Tobagoa 50 63 26 13 6 4 1 8 2 4 15 9 Tunisiaa 16 27 20 31 28 6 4 4 15 19 17 12 Turkeya .. 26 .. 51 .. 1 .. 5 .. .. .. 16 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandaa 10 22 45 47 7 10 2 0 .. .. 37 22 Ukrainea .. 10 .. 34 .. 2 .. 0 .. 37 .. 17 United Arab Emiratesa .. .. 15 .. .. .. .. .. 1 .. 84 .. United Kingdom 37 36 32 28 .. .. 6 7 20 23 5 6 United States .. 47 .. 3 .. 1 .. 1 .. 43 .. 6 Uruguaya 10 18 32 41 4 3 10 2 31 30 8 6 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 38 .. 33 .. 9 .. 0 .. 4 .. 19 .. Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 2 .. 21 .. 11 .. 0 .. 0 .. 66 Yemen, Rep.a 17 .. 10 .. 18 .. 3 .. .. .. 51 .. Zambiaa 27 33 22 36 36 8 0 0 0 .. 15 23 Zimbabwea 36 .. 22 .. 17 .. 3 .. 2 .. 19 .. World .. m 23 m .. m 32 m .. m 5m .. m 2m .. m .. m .. m 17 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. 25 .. 36 .. 5 .. 2 .. .. .. 17 Lower middle income 17 26 27 33 16 6 2 1 .. .. 23 17 Upper middle income .. 23 .. 36 .. 4 .. 2 .. 22 .. 16 Low & middle income .. 21 .. 36 .. 7 .. 2 .. .. .. 18 East Asia & Pacific 32 37 26 31 10 6 2 1 .. .. 23 26 Europe & Central Asia .. 10 .. 42 .. 4 .. 0 .. 29 .. 16 Latin America & Carib. .. 27 .. 39 .. 4 .. 2 .. 10 .. 17 Middle East & N. Africa 16 27 16 31 16 6 6 3 .. 6 38 23 South Asia 15 19 31 29 24 13 4 0 .. 0 25 29 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 21 24 28 27 .. 0 2 2 34 36 10 12 Euro area 22 23 24 27 0 0 2 2 36 37 7 8 Note: Components may not sum to 100 percent because of missing data or adjustment to tax revenue. a. Data were reported on a cash basis and have been adjusted to the accrual framework. 248 2011 World Development Indicators 4.14 ECONOMY Central government revenues About the data Definitions The International Monetary Fund (IMF) classifi es Direct taxes tend to be progressive, whereas indirect • Taxes on income, profits, and capital gains are government revenues as taxes, grants, and property taxes are proportional. levied on the actual or presumptive net income income. Taxes are classified by the base on which Social security taxes do not reflect compulsory pay- of individuals, on the profi ts of corporations and the tax is levied, grants by the source, and property ments made by employers to provident funds or other enterprises, and on capital gains, whether real- income by type (for example, interest, dividends, agencies with a like purpose. Similarly, expenditures ized or not, on land, securities, and other assets. Intra-governmental payments are eliminated in con- or rent). The most important source of revenue is from such funds are not reflected in government solidation. • Taxes on goods and services include taxes. Grants are unrequited, nonrepayable, non- expenses (see table 4.13). For further discussion of general sales and turnover or value added taxes, compulsory receipts from other government units taxes and tax policies, see About the data for table selective excises on goods, selective taxes on ser- and foreign governments or from international orga- 5.6. For further discussion of government revenues vices, taxes on the use of goods or property, taxes nizations. Transactions are generally recorded on an and expenditures, see About the data for tables 4.12 on extraction and production of minerals, and prof- accrual basis. and 4.13. its of fiscal monopolies. • Taxes on international The IMF’s Government Finance Statistics Manual trade include import duties, export duties, profi ts 2001 describes taxes as compulsory, unrequited of export or import monopolies, exchange profi ts, payments made to governments by individuals, busi- and exchange taxes. • Other taxes include employer nesses, or institutions. Taxes are classified in six payroll or labor taxes, taxes on property, and taxes major groups by the base on which the tax is levied: not allocable to other categories, such as penalties income, profits, and capital gains; payroll and work- for late payment or nonpayment of taxes. • Social force; property; goods and services; international contributions include social security contributions by trade and transactions; and other. However, the dis- employees, employers, and self-employed individu- tinctions are not always clear. Taxes levied on the als, and other contributions whose source cannot be determined. They also include actual or imputed income and profits of individuals and corporations contributions to social insurance schemes operated are classified as direct taxes, and taxes and duties by governments. • Grants and other revenue include levied on goods and services are classified as indi- grants from other foreign governments, international rect taxes. This distinction may be a useful simplifica- organizations, and other government units; interest; tion, but it has no particular analytical significance dividends; rent; requited, nonrepayable receipts except with respect to the capacity to fix tax rates. for public purposes (such as fines, administrative fees, and entrepreneurial income from government Rich economies rely more on direct taxes 4.14a ownership of property); and voluntary, unrequited, nonrepayable receipts other than grants. Taxes on income and capital gains as a share of central government revenue, 2009 (percent) 70 60 50 40 30 Data sources 20 Data on central government revenues are from the 10 IMF’s Government Finance Statistics database. Each country’s accounts are reported using the 0 system of common definitions and classifications 100 1,000 10,000 100,000 in the IMF’s Government Finance Statistics Manual GNI per capita ($, log scale) 2001. The IMF receives additional information Low income Middle income High income from the Organisation for Economic Co-operation High-income economies tend to tax income and property, whereas low-income economies tend to rely and Development on the tax revenues of some of on indirect taxes on international trade and goods and services. But there are exceptions in all groups. its members. See the IMF sources for complete and authoritative explanations of concepts, defini- Note: Data are for the most recent year for 2005–09. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions, and data sources. 2011 World Development Indicators 249 4.15 Monetary indicators Broad money Claims on Claims on Interest rate domestic economy central government Annual growth Annual growth % annual % growth % of broad money % of broad money Deposit Lending Real 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Afghanistan .. 33.0 .. 8.0 .. –9.5 .. .. .. 15.0 .. 36.1 Albania 12.0 6.8 0.9 5.4 4.8 2.4 8.3 6.8 22.1 12.7 17.0 10.1 Algeria 14.1 1.6 8.4 7.7 –11.6 0.2 7.5 1.8 10.0 8.0 –11.7 19.2 Angolaa 303.7 62.6 35.8 33.3 –413.7 48.1 39.6 7.6 103.2 15.7 –60.8 22.8 Argentinaa 1.5 17.0 –2.9 5.1 –0.8 18.9 8.3 11.6 11.1 15.7 9.9 5.2 Armenia 38.6 16.4 0.3 15.0 –5.7 –11.8 18.1 8.7 31.6 18.8 33.4 17.1 Australiaa 3.7 0.5 13.3 8.7 –1.8 –2.7 4.2 2.8 9.3 6.0 6.5 1.0 Austriab .. .. .. .. .. .. 2.2 .. 5.6 .. 5.2 .. Azerbaijan 73.4 –0.3 –23.9 13.2 15.4 4.3 12.9 12.2 19.7 20.0 6.4 44.2 Bangladesh 19.3 20.3 10.7 13.3 5.6 1.3 8.6 8.2 15.5 14.6 13.4 7.6 Belarus 219.3 25.9 59.9 64.6 22.2 –40.9 37.6 10.7 67.7 11.7 –41.2 7.5 Belgiumb .. .. .. .. .. .. 3.6 .. 8.0 9.2 5.9 7.1 Benina 26.0 8.0 8.5 6.7 0.9 7.5 3.5 3.5 .. .. .. .. Bolivia 1.6 11.8 –1.3 6.0 3.1 –3.0 11.0 3.4 34.6 12.4 27.9 15.1 Bosnia and Herzegovinaa 11.3 –0.1 10.3 –3.8 –0.4 –0.1 14.7 3.6 30.5 7.9 1.3 7.9 Botswana 1.4 –1.3 10.3 5.3 –56.2 18.7 9.4 7.5 15.5 13.8 15.4 20.6 Brazil 19.7 15.8 8.3 6.7 13.5 1.2 17.2 9.3 56.8 44.7 47.7 36.8 Bulgaria 30.8 4.2 6.5 4.2 8.5 2.5 3.1 6.2 11.3 11.3 4.4 7.0 Burkina Fasoa 6.2 22.3 8.3 1.0 5.3 2.7 3.5 3.5 .. .. .. .. Burundi 15.5 14.5 15.0 8.3 –22.6 13.0 .. .. 15.8 14.1 2.3 0.4 Cambodia 26.9 35.6 5.4 6.3 –6.9 5.7 6.8 1.7 .. .. .. .. Cameroona 19.1 6.3 7.4 4.5 –12.3 0.9 5.0 3.3 22.0 15.0 18.6 12.7 Canada 6.6 15.1 3.6 23.3 2.4 4.7 3.5 0.1 7.3 2.4 3.0 4.6 Central African Republica 2.4 13.3 2.9 2.8 6.8 –0.3 5.0 3.3 22.0 15.0 18.3 12.2 Chada 19.4 1.1 0.4 5.7 15.1 72.5 5.0 3.3 22.0 15.0 15.9 9.4 Chile 9.1 1.3 4.1 –0.6 4.0 0.6 9.2 2.0 14.8 7.3 9.8 2.9 Chinaa 12.3 28.4 9.5 22.7 0.0 0.6 2.3 2.3 5.9 5.3 3.7 6.0 Hong Kong SAR, Chinaa 9.3 5.2 1.7 3.6 0.4 8.8 4.8 0.0 9.5 5.0 13.6 4.8 Colombia 3.6 8.1 8.9 2.7 6.0 7.2 12.1 6.1 18.8 13.0 –10.3 7.7 Congo, Dem. Rep.a 40.0 50.4 3.8 19.2 –34.0 –14.5 .. 15.9 .. 65.4 .. 27.0 Congo, Rep.a 58.5 5.0 –23.0 5.2 –11.7 12.0 5.0 3.3 22.0 15.0 –17.0 14.4 Costa Rica 24.0 8.0 14.1 4.9 –0.2 2.8 13.4 7.0 24.9 19.7 16.7 9.9 Côte d’Ivoirea –1.9 17.2 2.9 6.0 –7.6 7.4 3.5 3.5 .. .. .. .. Croatia 29.1 –0.6 21.3 –0.6 2.0 0.2 3.7 3.2 12.1 11.6 7.1 8.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 16.0 0.2 –11.0 0.7 2.6 3.9 3.4 1.3 7.2 6.0 5.6 3.2 Denmark –12.1 7.0 26.1 –4.4 3.0 6.3 3.2 .. 8.1 .. 4.9 .. Dominican Republic 16.8 13.4 13.2 5.3 2.8 8.0 17.7 7.8 26.8 18.1 18.6 14.7 Ecuador 47.0 10.1 –10.8 5.5 –28.1 8.8 8.8 4.8 17.1 12.1 26.0 6.3 Egypt, Arab Rep. 11.6 9.5 4.1 0.5 7.7 10.5 9.5 6.5 13.2 12.0 7.9 1.0 El Salvador 1.6 2.1 2.6 –4.1 2.3 –1.3 9.3 .. 14.0 .. 10.5 .. Eritrea 17.3 15.7 3.7 0.2 25.7 11.9 .. .. .. .. .. .. Estonia 25.7 –0.1 .. –9.0 –3.2 –3.6 3.8 4.8 7.4 9.4 2.4 10.0 Ethiopiaa 13.1 23.4 3.0 17.7 19.8 2.5 6.0 4.7 10.9 8.0 3.8 –17.2 Finlandb .. .. .. .. .. .. 1.6 .. 5.6 .. 2.9 .. Franceb .. .. .. .. .. .. 2.6 1.9 6.7 .. 5.2 .. Gabona 18.3 2.1 6.2 –2.6 –42.2 4.0 5.0 3.3 22.0 15.0 –4.8 9.3 Gambia, Thea 34.8 19.4 4.2 5.4 2.7 5.2 12.5 15.5 24.0 27.0 19.6 24.1 Georgia 39.2 8.2 18.7 –18.1 19.8 11.0 10.2 10.3 32.8 25.5 26.8 28.1 Germany b .. .. .. .. .. .. 3.4 .. 9.6 .. 10.4 .. Ghana 54.2 39.2 7.5 30.4 32.9 22.1 28.6 17.1 .. .. .. .. Greeceb .. .. .. .. .. .. 6.1 .. 12.3 .. 8.6 .. Guatemala 21.4 11.3 4.2 –2.4 10.2 6.8 10.2 5.6 20.9 13.8 13.2 11.2 Guineaa 12.9 .. 2.3 .. 7.9 .. 7.5 .. 19.4 .. 7.4 .. Guinea-Bissaua 60.8 6.9 5.5 3.9 16.2 –13.3 3.5 3.5 .. .. .. .. Haiti 20.3 10.3 12.3 6.2 13.8 –12.4 12.1 1.1 19.1 17.3 7.3 13.3 Honduras 15.4 0.6 7.9 7.1 –2.6 4.8 15.9 10.8 26.8 19.4 –3.1 14.4 250 2011 World Development Indicators 4.15 ECONOMY Monetary indicators Broad money Claims on Claims on Interest rate domestic economy central government Annual growth Annual growth % annual % growth % of broad money % of broad money Deposit Lending Real 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Hungary 12.6 3.3 14.5 –4.2 –2.0 0.1 9.5 5.8 12.6 11.0 0.9 6.1 Indiaa 15.2 18.0 9.9 7.8 4.7 9.4 .. .. 12.3 12.2 8.5 4.3 Indonesia 16.6 13.0 7.2 6.9 17.2 2.5 12.5 9.3 18.5 14.5 –1.7 5.6 Iran, Islamic Rep.a 22.4 27.7 15.8 10.2 –7.9 2.0 11.7 13.1 .. 12.0 .. 11.3 Iraq .. 26.7 .. 2.0 .. 33.6 .. 7.8 .. 15.6 .. 61.5 Irelandb .. .. .. .. .. .. 0.1 .. 4.8 .. –1.1 .. Israela 8.0 6.1 10.7 –0.5 –4.8 1.1 8.6 1.1 12.9 3.7 11.1 –1.4 Italy b .. .. .. .. .. .. 1.8 .. 7.0 4.8 5.0 2.6 Jamaica –7.0 5.4 9.1 2.6 –2.3 9.4 11.6 7.0 23.3 16.4 11.5 9.3 Japan 1.3 2.1 –5.4 –2.9 2.6 4.4 0.1 0.4 2.1 1.7 3.9 2.7 Jordana 7.6 24.3 3.2 0.8 –1.2 2.5 7.0 4.9 11.8 9.2 12.2 1.1 Kazakhstan 45.0 19.5 32.2 14.1 –3.2 –4.7 .. .. .. .. .. .. Kenya 4.9 16.5 4.7 11.5 –2.1 8.2 8.1 6.0 22.3 14.8 15.3 7.6 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 25.4 12.2 21.9 3.9 –1.4 2.2 7.9 3.5 8.5 5.6 3.4 2.2 Kosovo –12.2 11.2 12.1 7.6 –37.7 1.8 .. 4.0 .. 14.1 .. 18.1 Kuwait 6.3 13.4 8.5 7.1 –7.4 –1.0 5.9 2.8 8.9 6.2 –9.7 2.5 Kyrgyz Republica 11.7 33.2 3.5 29.2 7.8 –8.8 18.4 3.9 51.9 23.0 19.5 20.5 Lao PDRa 46.0 18.3 22.4 19.4 –17.6 –3.4 12.0 4.7 32.0 24.0 5.5 14.4 Latvia 27.0 –2.7 31.2 –25.9 7.8 –9.6 4.4 8.0 11.9 16.2 7.4 17.1 Lebanona 9.8 19.6 2.9 4.8 10.5 4.5 11.2 7.3 18.2 9.6 20.7 3.5 Lesotho 1.4 17.7 6.6 7.2 14.9 –0.5 4.9 4.9 17.1 13.0 14.4 9.2 Liberiaa 18.3 43.4 –10.0 17.1 197.0 47.7 6.2 4.1 20.5 14.2 22.1 6.3 Libyaa 3.1 17.4 0.2 2.0 –10.4 1.8 3.0 2.5 7.0 6.0 –9.6 57.8 Lithuania 16.5 0.6 14.4 –12.9 0.5 –4.1 3.9 4.8 12.1 8.4 11.1 10.8 Macedonia, FYR 22.2 5.5 2.7 3.1 –15.9 1.3 11.2 7.0 18.9 10.1 9.9 7.1 Madagascara 17.2 11.3 7.9 3.7 0.1 8.9 15.0 11.5 26.5 45.0 18.0 33.8 Malawia 45.5 24.6 16.5 19.3 7.7 21.2 33.3 3.5 53.1 25.3 17.3 15.6 Malaysia 10.0 7.7 5.5 5.5 2.1 3.3 3.4 2.1 7.7 5.1 –1.1 12.6 Malia 12.2 14.6 –1.5 7.0 –5.0 –13.3 3.5 3.5 .. .. .. .. Mauritaniaa 16.1 .. 41.1 .. –64.3 .. 9.4 8.0 25.6 23.5 23.9 15.1 Mauritius 9.2 8.1 5.8 0.8 –4.7 1.1 9.6 8.4 20.8 19.3 18.3 17.5 Mexico –4.5 11.5 10.1 8.1 3.5 4.1 8.3 2.0 16.9 7.1 4.3 2.7 Moldova 41.7 3.2 24.4 –4.0 –5.7 4.0 24.9 14.9 33.8 20.5 5.1 18.1 Mongolia 17.6 26.9 29.6 1.2 –7.1 –6.4 16.8 13.3 37.0 21.7 8.6 21.3 Morocco 8.4 5.8 3.6 9.0 3.6 –1.1 5.2 3.8 13.3 .. 14.0 .. Mozambique 38.3 32.6 11.9 32.7 6.9 0.2 9.7 9.5 19.0 15.7 6.3 12.0 Myanmar a 42.5 30.6 13.9 5.2 25.0 29.9 9.8 12.0 15.3 17.0 12.5 .. Namibia 13.2 5.9 19.4 11.3 –4.0 –4.1 7.4 6.2 15.3 11.1 –9.0 4.4 Nepal 18.8 29.4 –4.6 26.4 2.6 –1.6 6.0 2.5 9.5 8.0 4.8 –3.6 Netherlandsb .. .. .. .. .. .. 2.9 2.6 4.8 2.0 0.6 2.3 New Zealanda 1.5 –0.6 8.0 1.3 –0.9 2.7 6.4 4.0 9.3 10.4 5.9 8.6 Nicaragua 9.4 14.3 7.0 –7.4 10.0 7.0 10.8 6.0 18.1 14.0 8.8 –2.0 Niger a 12.4 18.7 14.8 12.1 –14.1 28.9 3.5 3.5 .. .. .. .. Nigeria 48.1 14.4 5.8 17.5 –43.0 12.7 11.7 13.3 21.3 18.4 –12.2 19.1 Norwaya 8.7 .. 18.0 .. –4.8 .. 6.7 2.3 8.9 4.3 –5.8 8.7 Oman 6.0 4.7 1.1 7.3 9.5 1.4 7.6 4.1 10.1 7.4 –8.3 –16.0 Pakistan 12.1 14.8 2.0 8.0 2.6 7.4 .. 8.7 .. 14.5 .. –4.6 Panama 9.3 10.3 –8.4 2.5 0.2 –0.6 7.1 3.5 10.5 8.2 11.9 4.0 Papua New Guinea 5.0 21.9 1.2 8.4 –4.6 10.1 8.5 2.3 17.5 10.1 3.9 14.2 Paraguay 2.8 22.2 1.7 14.8 4.7 –3.5 15.7 1.5 26.8 28.3 13.1 28.4 Perua –0.4 2.6 –2.7 0.8 2.3 0.3 9.8 2.8 30.0 21.0 25.4 17.5 Philippines 8.1 10.0 2.2 5.4 1.5 2.5 8.3 2.7 10.9 8.6 4.3 5.9 Poland 11.6 8.1 .. 7.9 –5.8 1.7 14.2 2.2 20.0 5.5 12.0 3.9 Portugalb .. .. .. .. .. .. 2.4 .. 5.2 .. 1.8 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 10.7 16.9 –1.7 2.9 –23.1 26.7 0.0 4.2 .. 7.0 .. 31.0 2011 World Development Indicators 251 4.15 Monetary indicators Broad money Claims on Claims on Interest rate domestic economy central government Annual growth Annual growth % annual % growth % of broad money % of broad money Deposit Lending Real 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Romania 40.8 9.0 20.0 1.9 –1.1 10.7 33.1 12.0 53.9 17.3 6.7 10.1 Russian Federation 57.9 16.4 33.2 2.1 –18.1 14.0 6.5 8.6 24.4 15.3 –9.6 12.5 Rwandaa 15.6 .. 10.3 .. –11.4 .. 10.1 6.7 17.0 16.5 20.6 3.3 Saudi Arabiaa 4.5 10.8 3.3 0.0 –3.5 8.9 .. .. .. .. .. .. Senegala 10.7 11.4 19.1 2.6 –3.9 4.3 3.5 3.5 .. .. .. .. Serbia 160.8 21.3 –71.0 18.1 22.5 4.9 78.7 11.8 6.3 11.8 –40.1 1.6 Sierra Leonea 12.1 27.5 1.6 14.2 54.6 4.0 9.2 9.7 26.3 24.5 19.0 12.0 Singaporea –2.0 11.3 5.1 1.6 –1.6 8.9 1.7 0.3 5.8 5.4 2.0 7.4 Slovak Republicb .. .. .. .. .. .. 8.5 3.7 14.9 5.8 5.0 2.8 Sloveniab .. .. .. .. .. .. 10.0 1.4 15.8 5.9 9.9 4.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 7.2 1.8 –11.8 0.1 0.2 5.5 9.2 8.5 14.5 11.7 5.2 4.1 Spainb .. .. .. .. .. .. 3.0 .. 5.2 .. 1.7 .. Sri Lankaa 12.9 18.7 9.1 –4.6 12.5 4.4 9.2 10.6 16.2 15.7 8.3 9.5 Sudan 36.9 23.7 16.9 13.6 33.9 13.0 .. .. .. .. .. .. Swaziland –6.6 26.8 16.9 12.5 1.7 17.4 6.5 5.4 14.0 11.4 13.8 5.6 Sweden 1.9 2.5 8.5 3.8 2.4 1.6 2.2 .. 5.8 .. 4.3 .. Switzerlanda –16.9 7.6 –1.2 5.1 2.1 0.6 3.0 0.1 4.3 2.8 3.1 2.5 Syrian Arab Republic 19.0 8.6 –4.1 8.6 –6.1 1.4 4.0 6.4 9.0 10.0 –0.6 19.0 Tajikistana 63.3 –3.6 8.2 145.1 36.6 –9.8 1.3 5.8 25.6 22.9 2.4 8.5 Tanzania 14.8 17.7 12.2 5.8 0.7 6.2 7.4 8.0 21.6 15.0 13.0 7.1 Thailand 4.9 6.8 6.2 3.6 0.5 0.9 3.3 1.0 7.8 6.0 6.4 3.9 Timor-Leste 41.1 39.3 45.7 0.6 –36.8 12.1 0.8 0.8 16.7 11.2 11.4 1.1 Togoa 15.2 16.0 0.5 9.7 –0.5 6.3 3.5 3.5 .. .. .. .. Trinidad and Tobagoa 11.7 30.6 8.8 –3.1 –13.2 25.3 8.2 3.4 16.5 11.9 3.2 32.8 Tunisiaa 14.1 12.5 23.7 9.7 5.6 1.4 .. .. .. .. .. .. Turkey 40.7 12.7 16.2 9.4 26.8 12.4 47.2 17.6 .. .. .. .. Turkmenistana 83.3 .. 10.8 .. –53.4 .. .. .. .. .. .. .. Uganda 18.1 17.5 8.2 10.1 29.4 0.4 9.8 9.8 22.9 21.0 10.6 3.8 Ukraine 44.5 –5.5 30.9 –3.4 –1.7 9.4 13.7 13.8 41.5 20.9 15.0 6.6 United Arab Emiratesa 15.3 9.8 8.7 1.4 –9.6 13.3 6.2 .. 9.7 .. –9.9 .. United Kingdoma 11.1 0.0 17.4 –2.6 –2.4 7.9 4.5 .. 6.0 0.6 4.7 –0.7 United States 8.1 –0.6 5.0 –1.3 0.5 4.5 .. .. 9.2 3.3 6.9 2.3 Uruguay 9.5 –2.6 45.1 –10.3 –1.8 3.0 18.3 4.4 46.1 15.3 41.1 8.9 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 33.7 26.1 14.3 18.6 –6.4 –1.9 16.3 16.4 25.2 19.9 –3.3 10.6 Vietnama 35.4 26.2 29.6 35.0 –2.4 7.0 3.7 12.7 10.6 10.1 6.9 3.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.a 25.3 12.8 3.6 –1.2 –45.6 26.2 14.0 10.7 19.5 18.0 –4.9 23.1 Zambia 73.8 7.7 –11.4 –3.4 162.0 16.2 20.2 7.1 38.8 22.1 6.7 8.3 Zimbabwea 45.7 111.3 27.2 56.4 29.5 –28.7 50.2 121.5 68.2 579.0 67.8 .. a. For these countries data reported under Claims on domestic economy include claims on private sector only. b. As members of the European Monetary Union, these countries share a single currency, the euro. 252 2011 World Development Indicators 4.15 ECONOMY Monetary indicators About the data Definitions Money and the financial accounts that record the reporting period. The valuation of financial deriva- • Broad money (IFS line 35L..ZK) is the sum of supply of money lie at the heart of a country’s tives and the net liabilities of the banking system currency outside banks; demand deposits other financial system. There are several commonly used can also be difficult. The quality of commercial bank than those of the central government; the time, defi nitions of the money supply. The narrowest, reporting also may be adversely affected by delays in savings, and foreign currency deposits of resident M1, encompasses currency held by the public and reports from bank branches, especially in countries sectors other than the central government; bank demand deposits with banks. M2 includes M1 plus where branch accounts are not computerized. Thus and traveler’s checks; and other securities such time and savings deposits with banks that require the data in the balance sheets of commercial banks as certifi cates of deposit and commercial paper. prior notice for withdrawal. M3 includes M2 as well may be based on preliminary estimates subject to Change in broad money is measured as the differ- as various money market instruments, such as cer- constant revision. This problem is likely to be even ence in end-of-year totals relative to the preceding tificates of deposit issued by banks, bank deposits more serious for nonbank financial intermediaries. year. For countries reporting under the old presen- denominated in foreign currency, and deposits with Many interest rates coexist in an economy, reflect- tation of monetary statistics and for all countries fi nancial institutions other than banks. However ing competitive conditions, the terms governing prior to 2001, data are based on money plus quasi defined, money is a liability of the banking system, loans and deposits, and differences in the position money. • Claims on domestic economy (IFS line distinguished from other bank liabilities by the spe- and status of creditors and debtors. In some econo- 32S..ZK) include gross credit from the fi nancial cial role it plays as a medium of exchange, a unit of mies interest rates are set by regulation or adminis- system to households, nonprofi t institutions serv- account, and a store of value. trative fiat. In economies with imperfect markets, or ing households, nonfinancial corporations, state The banking system’s assets include its net for- where reported nominal rates are not indicative of and local governments, and social security funds. eign assets and net domestic credit. Net domestic effective rates, it may be difficult to obtain data on For countries where claims on domestic economy credit includes credit extended to the private sector interest rates that reflect actual market transactions. are not available, data are claims on private sec- and general government and credit extended to the Deposit and lending rates are collected by the Inter- tor (IFS line 32D..ZK or 32D..ZF) • Claims on cen- nonfinancial public sector in the form of investments national Monetary Fund (IMF) as representative inter- tral government (IFS line 32AN..ZK) include loans in short- and long-term government securities and est rates offered by banks to resident customers. to central government institutions net of deposits. loans to state enterprises; liabilities to the public The terms and conditions attached to these rates • Deposit interest rate is the rate paid by commer- and private sectors in the form of deposits with the differ by country, however, limiting their comparabil- cial or similar banks for demand, time, or savings banking system are netted out. Net domestic credit ity. Real interest rates are calculated by adjusting deposits. • Lending interest rate is the rate charged also includes credit to banking and nonbank financial nominal rates by an estimate of the inflation rate in by banks on loans to prime customers. • Real inter- institutions. the economy. A negative real interest rate indicates est rate is the lending interest rate adjusted for infla- Domestic credit is the main vehicle through which a loss in the purchasing power of the principal. The tion as measured by the GDP deflator. changes in the money supply are regulated, with cen- real interest rates in the table are calculated as (i – tral bank lending to the government often playing the P) / (1 + P), where i is the nominal lending interest most important role. The central bank can regulate rate and P is the inflation rate (as measured by the lending to the private sector in several ways—for GDP deflator). example, by adjusting the cost of the refinancing In 2009 the IMF began publishing a new presenta- facilities it provides to banks, by changing market tion of monetary statistics for countries that report interest rates through open market operations, or by data in accordance with the IMF’s Monetary and Data sources controlling the availability of credit through changes Financial Statistics Manual 2000. The presentation in the reserve requirements imposed on banks and for countries that report data in accordance with the Data on monetary and financial statistics are ceilings on the credit provided by banks to the pri- IMF’s International Financial Statistics (IFS) remains published by the IMF in its monthly International vate sector. the same. Financial Statistics and annual International Finan- Monetary accounts are derived from the balance cial Statistics Yearbook. The IMF collects data on sheets of financial institutions—the central bank, the financial systems of its member countries. The commercial banks, and nonbank financial interme- World Bank receives data from the IMF in elec- diaries. Although these balance sheets are usually tronic files that may contain more recent revisions Data sources reliable, they are subject to errors of classification, than the published sources. The discussion of valuation, and timing and to differences in account- monetary indicators draws from an IMF publication ing practices. For example, whether interest income by Marcello Caiola, A Manual for Country Econo- is recorded on an accrual or a cash basis can make mists (1995). Also see the IMF’s Monetary and a substantial difference, as can the treatment of non- Financial Statistics Manual (2000) for guidelines performing assets. Valuation errors typically arise for the presentation of monetary and financial sta- for foreign exchange transactions, particularly in tistics. Data on real interest rates are derived from countries with flexible exchange rates or in countries World Bank data on the GDP deflator. that have undergone currency devaluation during the 2011 World Development Indicators 253 4.16 Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2009 2010a 1995 2009 2009 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Afghanistan 50.23 45.21 .. 18.1 0.4 .. .. 9.0 .. 9.5 .. .. Albania 94.98 104.95 24.4 41.5 0.4 .. 37.7 3.5 27.8 2.8 .. 4.5 Algeria 72.65 74.25 15.3 35.8 0.5 102.1 18.5 8.6 17.3 3.0 .. 4.0 Angola 79.33 92.35 0.0 55.7 0.7 .. 739.4 41.1 711.0 41.1 .. .. Argentina 3.71 3.96 1.0 2.0 0.5 .. 5.2 12.9 b 8.9 10.0 0.1 15.7 Armenia 363.28 360.50 116.6 194.5 0.5 124.4 212.5 4.5 70.5 4.0 .. 1.3 Australia 1.28 1.01 1.3 1.5 1.1 100.8 1.4 4.1 2.1 3.0 1.1 3.6 Austriac 0.72 0.76 0.9 0.8 1.2 101.5 1.6 1.7 2.2 2.0 0.3 2.4 Azerbaijan 0.80 0.80 0.2 0.4 0.5 .. 203.0 9.9 179.7 8.3 .. .. Bangladesh 69.04 70.63 19.2 26.8 0.4 .. 4.1 5.2 5.5 6.8 .. .. Belarus 2,789.49 3,010.98 3.4 1,085.6 0.4 .. 355.1 23.1 271.3 18.7 267.8 22.5 Belgiumc 0.72 0.76 0.9 0.9 1.2 104.3 1.8 2.1 1.9 2.1 1.2 2.9 Benin 472.19 496.24 187.4 233.3 0.5 .. 8.7 3.4 8.7 3.2 .. .. Bolivia 7.02 7.02 1.7 2.8 0.4 127.6 8.6 6.9 8.7 5.3 .. .. Bosnia and Herzegovina 1.41 1.48 0.6 0.7 0.5 .. 4.1 3.9 .. .. .. .. Botswana 7.16 6.58 1.4 3.2 0.5 .. 9.7 9.0 10.4 8.9 .. .. Brazil 2.00 1.70 0.7 1.6 0.8 .. 211.8 8.3 199.5 6.9 204.9 10.0 Bulgaria 1.41 1.48 0.0 0.7 0.5 126.0 102.1 6.0 117.5 6.4 85.7 6.2 Burkina Faso 472.19 496.24 189.5 205.5 0.4 .. 3.7 2.5 5.5 3.1 .. .. Burundi 1,230.18 1,230.91 126.6 500.6 0.4 109.4 13.4 10.4 16.1 9.2 .. .. Cambodia 4,139.33 4,096.00 1,142.3 1,526.8 0.4 .. 4.4 5.0 6.3 6.0 .. .. Cameroon 472.19 496.24 241.1 243.3 0.5 108.0 6.3 2.1 6.5 2.5 .. .. Canada 1.14 1.01 1.2 1.2 1.1 96.8 1.5 2.6 1.7 2.1 2.7 1.4 Central African Republic 472.19 496.24 271.9 282.8 0.6 115.8 4.5 2.7 5.3 3.2 6.0 4.4 Chad 472.19 496.24 163.1 221.6 0.5 .. 7.1 5.6 6.9 2.7 .. .. Chile 560.86 474.78 264.1 377.1 0.7 100.3 7.9 6.3 .. .. 7.0 6.5 China 6.83 6.65 3.4 3.8 0.6 119.8 7.9 4.3 8.6 2.3 .. .. Hong Kong, SAR China 7.75 7.77 7.9 5.4 0.7 .. 4.5 –1.3 5.9 0.3 0.6 –0.2 Colombia 2,166.79 1,925.90 417.8 1,233.7 0.6 113.1 22.6 6.1 20.2 5.8 16.4 4.9 Congo, Dem. Rep. 809.79 907.62 0.0 414.3 0.5 597.2 964.9 27.2 930.2 26.9 .. .. Congo, Rep. 472.19 496.24 149.2 289.8 0.6 .. 9.0 7.4 9.3 3.4 .. .. Costa Rica 573.29 512.34 103.0 329.5 0.6 108.4 15.9 10.2 15.6 11.2 14.1 13.0 Côte d’Ivoire 472.19 496.24 261.8 306.9 0.7 105.7 9.2 3.5 7.2 3.0 .. .. Croatia 5.28 5.59 3.1 3.8 0.7 108.6 90.0 3.9 86.3 2.9 69.8 3.0 Cuba .. .. .. .. .. .. 6.4 3.3 .. .. .. .. Czech Republic 19.06 19.03 11.1 13.5 0.7 120.4 12.8 2.2 7.8 2.5 8.2 2.3 Denmark 5.36 5.64 8.5 8.0 1.5 105.6 1.6 2.3 2.1 2.0 1.1 2.4 Dominican Republic 36.03 37.41 7.3 19.7 0.6 96.2 9.8 13.7 8.7 14.6 .. .. Ecuador .. .. 0.4 0.5 0.5 98.8 4.4 9.1 37.1 6.6 .. 7.9 Egypt, Arab Rep. 5.54 5.74 1.2 2.2 0.4 .. 8.7 8.3 8.8 8.0 6.1 9.6 El Salvador 8.75 8.75 0.4 0.5 0.5 .. 6.2 3.6 8.5 3.9 .. 4.7 Eritrea 15.38 15.38 1.9 9.8 0.6 .. 7.9 18.6 .. .. .. .. Estonia 11.26 11.82 4.8 8.1 0.7 .. 53.7 5.3 21.6 4.4 8.1 3.4 Ethiopia 11.78 .. 2.1 4.3 0.4 .. 6.5 10.8 5.5 12.3 .. .. Finlandc 0.72 0.76 1.0 0.9 1.3 103.8 1.9 1.1 1.5 1.5 0.9 2.1 Francec 0.72 0.76 1.0 0.9 1.2 101.8 1.3 2.1 1.6 1.8 .. 1.8 Gabon 472.19 496.24 187.9 245.7 0.5 105.3 7.0 5.0 4.6 2.1 .. .. Gambia, The 26.64 28.12 3.9 8.1 0.3 104.4 4.2 9.8 4.0 7.6 .. .. Georgia 1.67 1.76 0.4 0.9 0.5 124.3 356.7 7.0 24.7 7.0 .. 6.7 Germanyc 0.72 0.76 1.0 0.8 1.1 102.3 1.7 1.1 2.1 1.7 0.4 2.5 Ghana 1.41 1.49 0.1 1.0 0.7 91.9 26.7 27.2 28.4 16.2 .. .. Greecec 0.72 0.76 0.6 0.7 1.0 106.9 9.2 3.1 9.0 3.2 3.6 4.3 Guatemala 8.16 7.98 2.9 4.6 0.6 .. 10.4 5.4 10.1 7.3 .. .. Guinea .. .. 747.4 2,066.8 0.4 .. 5.5 16.1 .. .. .. .. Guinea-Bissau 472.19 496.24 58.6 229.0 0.5 .. 32.5 11.8 34.0 2.4 .. .. Haiti 41.20 39.90 5.8 23.1 0.6 .. 18.1 15.3 21.9 16.5 .. .. Honduras 18.90 18.90 3.0 9.4 0.5 .. 19.9 6.4 18.8 7.9 .. .. 254 2011 World Development Indicators 4.16 ECONOMY Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2009 2010a 1995 2009 2009 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Hungary 202.34 209.67 61.7 128.2 0.6 103.8 19.6 4.9 20.3 5.5 16.8 3.5 India 48.41 45.16 10.8 17.2 0.4 .. 8.1 5.6 9.1 5.3 7.4 5.1 Indonesia 10,389.94 8,948.00 1,031.3 5,813.6 0.6 .. 15.8 11.1 13.7 9.1 15.4 11.2 Iran, Islamic Rep. 9,864.30 10,364.64 567.2 3,875.0 0.4 142.1 27.7 16.4 26.0 15.4 28.4 10.8 Iraq 1,170.00 1,170.00 252.5 689.4 0.6 .. .. 11.6 .. .. .. .. Irelandc 0.72 0.76 0.8 0.9 1.3 107.4 3.6 2.1 2.3 3.2 1.6 –0.1 Israel 3.93 3.60 2.8 3.7 1.0 110.2 11.0 1.3 9.7 1.8 8.1 4.5 Italyc 0.72 0.76 0.8 0.8 1.1 103.2 3.8 2.6 3.7 2.3 2.9 2.7 Jamaica 87.89 85.67 14.6 52.0 0.6 .. 24.8 11.2 23.5 11.7 .. .. Japan 93.57 83.43 175.0 114.7 1.2 101.4 0.0 –1.1 0.8 –0.1 –1.0 0.7 Jordan 0.71 0.71 0.4 0.5 0.8 .. 3.2 6.1 3.5 4.4 .. 9.1 Kazakhstan 147.50 147.41 17.5 93.0 0.6 .. 204.7 14.9 67.8 8.6 16.3 13.3 Kenya 77.35 80.57 15.8 36.3 0.5 .. 16.6 6.0 15.6 11.3 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 1,276.93 1,146.23 709.6 804.7 0.6 .. 5.9 2.2 5.1 3.1 3.7 2.5 Kosovo 0.72 0.76 .. .. .. .. .. 0.8 .. 1.5 .. .. Kuwait 0.29 0.28 0.2 0.3 0.9 .. 1.5 9.8 2.0 3.4 1.4 2.5 Kyrgyz Republic 42.90 47.00 3.5 16.2 0.4 .. 110.6 8.3 23.3 6.9 35.6 10.2 Lao PDR 8,516.05 8,245.42 327.6 3,548.2 0.4 .. 27.2 8.9 28.3 8.3 .. .. Latvia 0.51 0.53 0.2 0.4 0.7 .. 48.0 8.8 29.2 6.5 12.0 7.3 Lebanon 1,507.50 1,507.50 774.7 942.9 0.6 .. 19.0 2.6 .. .. .. .. Lesotho 8.47 6.84 2.1 4.5 0.5 93.2 9.7 8.1 5.9 7.8 .. .. Liberia 68.29 71.85 0.6 38.2 0.6 .. 51.8 10.3 .. .. .. .. Libya 1.25 1.23 .. 0.7 0.6 .. .. 17.9 5.6 0.4 .. .. Lithuania 2.48 2.61 1.2 1.6 0.6 .. 75.0 4.1 32.6 3.1 24.8 4.8 Macedonia, FYR 44.10 46.55 18.0 17.8 0.4 104.2 79.3 3.8 10.6 2.4 8.5 2.5 Madagascar 1,956.21 2,117.83 287.5 852.8 0.4 .. 19.1 11.2 18.7 10.7 .. .. Malawi 141.17 150.80 4.2 55.1 0.4 107.5 33.6 17.0 33.8 12.2 .. .. Malaysia 3.52 3.13 1.4 1.8 0.5 103.3 4.1 4.0 3.6 2.4 3.4 4.8 Mali 472.19 496.24 226.7 275.4 0.6 .. 7.0 4.5 5.2 2.5 .. .. Mauritania 262.37 .. 62.4 125.0 0.5 .. 8.7 10.8 6.1 7.3 .. .. Mauritius 31.96 30.54 10.5 16.8 0.5 .. 6.3 6.0 6.9 6.3 .. .. Mexico 13.51 12.40 2.9 7.7 0.6 .. 19.0 7.8 19.5 4.5 18.4 6.1 Moldova 11.11 12.15 1.2 5.9 0.5 135.3 119.6 11.0 21.4 10.8 .. .. Mongolia 1,437.80 1,256.47 158.6 643.7 0.5 .. 57.8 14.6 35.7 8.7 .. .. Morocco 8.06 8.43 4.9 5.0 0.6 102.4 4.0 2.0 3.9 2.0 2.9 .. Mozambique 27.52 35.64 4.0 13.0 0.5 .. 34.1 8.0 31.8 10.9 .. .. Myanmar 5.52 5.42 .. .. .. .. 25.3 .. 25.9 22.4 .. .. Namibia 8.47 6.84 2.2 5.6 0.7 .. 11.1 7.1 .. 5.9 .. .. Nepal 77.55 72.38 15.4 28.4 0.4 .. 8.0 6.6 8.7 6.2 .. .. Netherlandsc 0.72 0.76 0.9 0.9 1.2 102.7 2.1 2.1 2.4 1.9 1.3 2.7 New Zealand 1.60 1.29 1.5 1.5 1.0 86.4 1.7 3.1 1.8 2.7 1.5 3.3 Nicaragua 20.34 21.84 3.5 8.2 0.4 107.8 42.4 7.7 .. 8.8 .. .. Niger 472.19 496.24 203.1 241.0 0.5 .. 6.0 3.1 6.1 2.8 .. .. Nigeria 148.90 148.57 15.5 75.6 0.5 109.4 29.5 15.3 32.5 12.5 .. .. Norway 6.29 5.98 9.2 8.9 1.4 97.8 2.7 4.6 2.2 1.8 1.6 7.9 Oman 0.38 0.38 0.2 0.3 0.9 .. 0.1 9.8 .. 2.9 .. .. Pakistan 81.71 85.77 10.1 28.8 0.4 98.6 11.1 8.5 9.7 8.0 10.4 8.9 Panama 1.00 1.00 0.5 0.6 0.6 .. 3.6 2.4 1.1 2.5 1.0 3.8 Papua New Guinea 2.76 2.64 0.7 1.4 0.5 116.1 7.6 6.5 9.3 5.9 .. .. Paraguay 4,965.39 4,667.57 948.9 2,462.5 0.5 135.7 11.5 10.2 13.1 8.4 .. 10.3 Peru 3.01 2.82 1.2 1.6 0.5 .. 26.7 3.5 27.3 2.4 23.7 2.8 Philippines 47.68 43.95 14.1 23.6 0.5 121.3 8.4 5.1 7.7 5.5 6.3 7.0 Poland 3.12 3.02 1.2 1.9 0.6 98.5 24.7 2.7 25.3 2.5 19.8 2.7 Portugalc 0.72 0.76 0.7 0.6 0.9 102.1 5.2 2.6 4.5 2.7 .. 2.6 Puerto Rico .. .. .. .. .. .. 3.0 .. .. .. .. .. Qatar 3.64 3.64 .. 2.8 0.8 .. .. 10.6 2.8 7.2 .. .. 2011 World Development Indicators 255 4.16 Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2009 2010a 1995 2009 2009 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 Romania 3.05 3.24 0.1 1.6 0.5 102.2 98.0 15.9 100.5 11.5 93.8 15.3 Russian Federation 31.74 30.85 1.7 14.6 0.5 115.3 161.5 15.8 99.1 12.5 99.8 15.7 Rwanda 568.28 594.45 126.3 261.0 0.5 .. 14.3 10.5 16.2 8.9 .. .. Saudi Arabia 3.75 3.75 1.8 2.4 0.6 103.8 1.6 7.6 1.0 2.2 1.3 2.5 Senegal 472.19 496.24 251.9 265.2 0.6 .. 6.0 2.8 5.4 2.2 .. .. Serbia 67.58 80.39 2.9 33.4 0.5 .. .. 16.5 50.2 15.4 .. .. Sierra Leone 3,385.65 .. 379.5 1,399.7 0.4 104.5 31.9 9.5 .. .. .. .. Singapore 1.45 1.31 1.3 1.1 0.7 107.9 1.3 1.2 1.7 1.5 –1.0 2.8 Slovak Republicc 0.72 0.76 0.4 0.5 0.7 137.2 11.1 3.4 8.4 4.8 9.5 4.7 Sloveniac 0.72 0.76 0.4 0.6 0.9 .. 29.3 4.0 12.0 4.2 9.1 3.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 8.47 6.84 2.3 4.8 0.6 87.8 9.9 7.2 8.7 5.7 7.7 6.7 Spainc 0.72 0.76 0.7 0.7 1.0 106.2 3.9 3.7 3.8 3.1 2.4 3.2 Sri Lanka 114.94 111.11 18.2 49.8 0.4 .. 9.1 10.7 9.9 11.1 8.1 12.4 Sudan 2.30 .. 0.3 1.4 0.6 .. 65.5 10.0 72.0 8.6 .. .. Swaziland 8.47 6.84 2.2 4.2 0.5 .. 10.5 7.9 9.5 7.3 .. .. Sweden 7.65 6.85 9.4 8.9 1.2 89.5 2.2 1.7 1.9 1.5 2.5 2.9 Switzerland 1.09 0.97 2.0 1.5 1.4 101.6 1.1 1.2 1.6 1.0 –0.4 1.1 Syrian Arab Republic 11.23 11.23 12.8 24.4 0.5 .. 7.9 8.0 6.4 6.2 4.7 3.2 Tajikistan 4.14 4.40 0.0 1.5 0.4 .. 235.0 20.9 .. 12.7 .. .. Tanzania 1,320.31 1,462.88 159.4 487.3 0.4 .. 23.0 7.3 20.9 6.5 .. .. Thailand 34.29 30.12 15.1 16.7 0.5 .. 4.2 3.2 4.9 2.9 3.8 5.5 Timor-Leste .. .. .. 0.6 0.6 .. .. 4.5 .. 5.1 .. .. Togo 472.19 496.24 238.5 239.5 0.5 104.8 7.0 1.4 8.5 2.8 .. .. Trinidad and Tobago 6.32 6.37 2.8 3.9 0.6 123.7 5.4 6.5 5.7 6.5 2.8 3.8 Tunisia 1.35 1.45 0.5 0.6 0.5 94.2 4.4 3.2 4.4 3.3 3.6 4.5 Turkey 1.55 1.52 0.0 0.9 0.6 .. 81.7 15.3 79.9 16.9 75.2 16.9 Turkmenistan .. .. 0.0 1.5 0.5 .. 408.2 13.0 .. .. .. .. Uganda 2,030.31 .. 500.3 767.5 0.4 103.2 11.6 5.6 8.3 6.7 .. .. Ukraine 7.79 7.96 0.3 3.2 0.4 96.8 271.0 16.4 155.7 10.9 161.6 14.6 United Arab Emirates 3.67 3.67 1.7 3.2 0.9 .. 2.2 10.2 .. .. .. .. United Kingdom 0.64 0.64 0.6 0.6 1.0 80.8 2.8 2.6 2.9 2.9 2.4 1.8 United States 1.00 1.00 1.0 1.0 1.0 95.2 2.0 2.6 2.7 2.7 1.2 4.2 Uruguay 22.57 19.99 5.5 16.1 0.7 120.9 32.6 8.4 33.9 9.1 27.2 13.6 Uzbekistan .. .. 11.2 602.5 0.4 .. 245.8 24.7 .. .. .. .. Venezuela, RB 2.15 2.59 0.1 2.0 0.9 191.2 45.3 25.0 49.0 21.2 44.1 26.0 Vietnam 17,065.08 18,932.00 3,168.8 6,434.3 0.4 .. 15.2 8.3 4.1 7.8 .. .. West Bank and Gaza .. .. .. .. .. .. 5.7 3.4 .. .. .. .. Yemen, Rep. 202.85 214.40 22.1 91.8 0.5 .. 22.4 13.0 26.3 11.4 .. .. Zambia 5,046.11 4,735.74 404.0 3,492.7 0.7 119.5 52.1 16.4 57.0 15.9 101.4 .. Zimbabwe .. .. .. .. .. .. –3.9 4.1 29.0 497.7 25.9 .. Note: The differences in the growth rates of the GDP deflator and the consumer and wholesale price indexes are due mainly to differences in data availability for each of the indexes during the period. a. Average for December or latest monthly data available. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and that GDP volume growth has been significantly lower than official reports indicate since the last quarter of 2008. c. As members of the euro area, these countries share a single currency, the euro. 256 2011 World Development Indicators 4.16 ECONOMY Exchange rates and prices About the data Definitions In a market-based economy, household, producer, cost indicator of relative normalized unit labor costs • Official exchange rate is the exchange rate deter- and government choices about resource allocation in manufacturing. For selected other countries the mined by national authorities or the rate determined are influenced by relative prices, including the real nominal effective exchange rate index is based on in the legally sanctioned exchange market. It is cal- exchange rate, real wages, real interest rates, and manufactured goods and primary products trade with culated as an annual average based on monthly aver- other prices in the economy. Relative prices also partner or competitor countries. For these countries ages (local currency units relative to the U.S. dollar). largely reflect these agents’ choices. Thus relative the real effective exchange rate index is the nomi- • Purchasing power parity (PPP) conversion factor prices convey vital information about the interaction nal index adjusted for relative changes in consumer is the number of units of a country’s currency required of economic agents in an economy and with the rest prices; an increase represents an appreciation of to buy the same amount of goods and services in the of the world. the local currency. Because of conceptual and data domestic market that a U.S. dollar would buy in the The exchange rate is the price of one currency limitations, changes in real effective exchange rates United States. • Ratio of PPP conversion factor to in terms of another. Offi cial exchange rates and should be interpreted with caution. market exchange rate is the result obtained by divid- exchange rate arrangements are established by Inflation is measured by the rate of increase in a ing the PPP conversion factor by the market exchange governments. Other exchange rates recognized by price index, but actual price change can be nega- rate. • Real effective exchange rate is the nominal governments include market rates, which are deter- tive. The index used depends on the prices being effective exchange rate (a measure of the value of a mined largely by legal market forces, and for coun- examined. The GDP deflator reflects price changes currency against a weighted average of several for- tries with multiple exchange arrangements, principal for total GDP. The most general measure of the over- eign currencies) divided by a price deflator or index rates, secondary rates, and tertiary rates. all price level, it accounts for changes in government of costs. • GDP implicit deflator measures the aver- Official or market exchange rates are often used consumption, capital formation (including inventory age annual rate of price change in the economy as a to convert economic statistics in local currencies to appreciation), international trade, and the main com- whole for the periods shown. • Consumer price index a common currency in order to make comparisons ponent, household final consumption expenditure. reflects changes in the cost to the average consumer across countries. Since market rates reflect at best The GDP deflator is usually derived implicitly as the of acquiring a basket of goods and services that may the relative prices of tradable goods, the volume of ratio of current to constant price GDP—or a Paasche be fixed or may change at specified intervals, such goods and services that a U.S. dollar buys in the index. It is defective as a general measure of inflation as yearly. The Laspeyres formula is generally used. United States may not correspond to what a U.S. for policy use because of long lags in deriving esti- • Wholesale price index refers to a mix of agricul- dollar converted to another country’s currency at mates and because it is often an annual measure. tural and industrial goods at various stages of pro- the official exchange rate would buy in that country, Consumer price indexes are produced more fre- duction and distribution, including import duties. The particularly when nontradable goods and services quently and so are more current. They are also con- Laspeyres formula is generally used. account for a significant share of a country’s output. structed explicitly, based on surveys of the cost of An alternative exchange rate—the purchasing power a defined basket of consumer goods and services. parity (PPP) conversion factor—is preferred because Nevertheless, consumer price indexes should be it reflects differences in price levels for both tradable interpreted with caution. The definition of a house- and nontradable goods and services and therefore hold, the basket of goods, and the geographic (urban provides a more meaningful comparison of real out- or rural) and income group coverage of consumer put. See table 1.1 for further discussion. price surveys can vary widely by country. In addi- The ratio of the PPP conversion factor to the official tion, weights are derived from household expendi- exchange rate—the national price level or compara- ture surveys, which, for budgetary reasons, tend to tive price level—measures differences in the price be conducted infrequently in developing countries, level at the gross domestic product (GDP) level. The impairing comparability over time. Although useful for price level index tends to be lower in poorer coun- measuring consumer price inflation within a country, tries and to rise with income. The real effective consumer price indexes are of less value in compar- exchange rate is a nominal effective exchange rate ing countries. index adjusted for relative movements in national Wholesale price indexes are based on the prices price or cost indicators of the home country, selected at the first commercial transaction of commodities countries, and the euro area. A nominal effective that are important in a country’s output or consump- Data sources exchange rate index is the ratio (expressed on the tion. Prices are farm-gate for agricultural commodi- base 2000 = 100) of an index of a currency’s period- ties and ex-factory for industrial goods. Preference Data on offi cial and real effective exchange rates average exchange rate to a weighted geometric aver- is given to indexes with the broadest coverage of and consumer and wholesale price indexes are age of exchange rates for currencies of selected the economy. The least squares method is used to from the International Monetary Fund’s Interna- countries and the euro area. For most high-income calculate growth rates of the GDP implicit deflator, tional Financial Statistics. PPP conversion fac- countries weights are derived from industrial coun- consumer price index, and wholesale price index. tors and GDP deflators are from the World Bank’s try trade in manufactured goods. Data are compiled data files. from the nominal effective exchange rate index and a 2011 World Development Indicators 257 4.17 Balance of payments current account Goods and Net Net current Current account Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 304 3,458 836 6,495 44 –145 477 1,307 –12 –1,875 265 2,369 Algeria .. .. .. .. .. .. .. .. .. .. 4,164 155,112 Angola 3,836 41,451 3,519 41,829 –767 –6,823 156 –370 –295 –7,572 213 13,664 Argentina 24,987 66,563 26,066 48,951 –4,636 –9,013 597 34 –5,118 8,632 15,979 48,007 Armenia 300 1,338 726 3,688 40 166 168 814 –218 –1,369 111 2,004 Australia 69,710 234,298 74,841 242,311 –14,036 –39,399 –109 –374 –19,277 –47,786 14,952 41,742 Austria 89,906 189,999 92,055 175,559 –1,597 –1,148 –1,702 –2,296 –5,448 10,995 23,369 17,904 Azerbaijan 785 22,847 1,290 9,872 –6 –3,519 111 722 –401 10,178 121 5,364 Bangladesh 4,431 17,011 7,589 23,165 68 –1,376 2,265 10,875 –824 3,345 2,376 10,342 Belarus 5,269 24,843 5,752 30,360 –51 –1,114 76 242 –458 –6,389 377 5,640 Belgium 190,686b 334,175b 178,798b 328,387b ..b 6,641b 7,822b –8,907b ..b 3,522b 24,120 b 23,862b Benin 614 1,630 895 2,400 –8 –11 121 245 –167 –536 198 1,230 Bolivia 1,234 5,433 1,574 5,159 –207 –674 244 1,213 –303 813 1,005 8,575 Bosnia and Herzegovina .. 5,480 .. 9,464 .. 535 .. 2,275 .. –1,175 80 3,245 Botswana 2,421 4,179 2,050 5,131 –32 –452 –39 878 300 –526 4,695 8,704 Brazil 52,641 180,723 63,293 174,679 –11,105 –33,684 3,621 3,338 –18,136 –24,302 51,477 238,539 Bulgaria 6,776 23,270 6,502 27,196 –432 –2,116 132 1,291 –26 –4,751 1,635 18,522 Burkina Faso 272 744 483 2,858 –29 –4 255 409 15 –1,709 347 1,296 Burundi 129 116 259 520 –13 –17 153 257 10 –164 216 323 Cambodia 969 5,927 1,375 6,898 –57 –468 277 574 –186 –866 192 3,286 Cameroon 2,040 5,313 1,608 6,540 –412 –303 69 393 90 –1,137 15 3,676 Canada 219,501 383,759 200,991 407,655 –22,721 –12,591 –117 –1,892 –4,328 –38,380 16,369 54,356 Central African Republic 179 .. 244 .. –23 .. 63 .. –25 .. 238 211 Chad 190 .. 411 .. –7 .. 191 .. –38 .. 147 617 Chile 19,358 62,242 18,301 49,335 –2,714 –10,306 307 1,616 –1,350 4,217 14,860 25,292 China† 147,240 1,333,346 135,282 1,113,234 –11,774 43,282 1,435 33,748 1,618 297,142 80,288 2,452,899 Hong Kong SAR, China .. 408,142 .. 393,077 .. 5,530 .. –3,177 .. 17,418 55,424 255,841 Colombia 12,294 38,222 16,012 38,404 –1,596 –9,432 799 4,614 –4,516 –5,001 8,452 24,987 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 157 1,615 Congo, Rep. 1,374 6,127 1,346 6,386 –695 –1,885 42 –38 –625 –2,181 64 3,806 Costa Rica 4,451 12,566 4,717 12,286 –226 –1,176 134 359 –358 –537 1,060 4,068 Côte d’Ivoire 4,337 11,478 3,806 8,803 –787 –890 –237 –115 –492 1,670 529 3,267 Croatia 6,972 22,626 9,152 24,900 –53 –2,491 802 1,450 –1,431 –3,314 1,896 14,895 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 28,202 132,920 30,044 122,069 –104 –12,194 572 –805 –1,374 –2,147 14,613 41,608 Denmark 65,655 147,276 57,860 134,738 –4,549 3,933 –1,391 –5,248 1,855 11,222 11,652 76,618 Dominican Republic 5,731 10,465 6,137 14,160 –769 –1,769 992 3,305 –183 –2,159 373 2,905 Ecuador 5,196 15,574 5,708 16,876 –930 –1,463 442 2,497 –1,000 –268 1,788 3,792 Egypt, Arab Rep. 13,260 44,609 17,140 53,842 –405 –2,076 4,031 7,960 –254 –3,349 17,122 34,897 El Salvador 2,040 4,696 3,623 7,966 –67 –664 1,389 3,561 –262 –373 940 3,122 Eritrea 135 .. 498 .. 8 .. 324 .. –31 .. 40 58 Estonia 2,573 13,539 2,860 12,435 3 –529 126 318 –158 893 583 3,981 Ethiopia 768 3,433 1,446 9,046 –19 –37 736 3,459 39 –2,191 815 1,781 Finland 47,973 90,571 37,705 83,807 –4,440 2,394 –597 –2,344 5,231 6,814 10,657 11,429 France 362,717 617,335 333,746 663,242 –8,964 31,844 –9,167 –37,796 10,840 –51,857 58,510 131,786 Gabon 2,945 .. 1,723 .. –665 .. –42 .. 515 .. 153 1,993 Gambia, The 175 278 230 343 –5 –8 52 135 –8 63 106 224 Georgia 575 3,207 1,413 5,266 127 –118 197 967 –514 –1,210 199 2,110 Germany 600,347 1,376,861 586,662 1,212,133 –2,814 47,352 –38,768 –46,610 –27,897 165,471 121,816 179,040 Ghana 1,582 7,809 2,120 10,789 –129 –296 523 2,078 –144 –1,198 804 .. Greece 15,523 59,150 24,711 84,204 –1,684 –12,516 8,008 1,657 –2,864 –35,913 16,119 5,486 Guatemala 2,823 9,220 3,728 12,726 –159 –1,111 491 4,626 –572 8 783 5,205 Guinea 700 1,122 1,011 1,391 –85 –168 179 34 –216 –403 87 .. Guinea-Bissau 30 172 89 284 –21 –15 46 98 –35 –29 20 169 Haiti 192 933 802 2,813 –31 13 553 1,635 –87 –232 199 790 Honduras 1,635 6,028 1,852 8,641 –226 –487 243 2,652 –201 –449 270 2,492 †Data for Taiwan, China 128,369 235,091 124,171 202,629 4,188 12,512 –2,912 .. 5,474 42,911 95,559 363,010 258 2011 World Development Indicators 4.17 ECONOMY Balance of payments current account Goods and Net Net current Current account Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 19,765 100,098 19,916 93,412 –1,701 –7,890 203 505 –1,650 –699 12,017 44,181 India 38,013 258,822 48,225 328,036 –3,734 –6,514 8,382 49,102 –5,563 –26,626 22,865 284,683 Indonesia 52,923 133,255 54,461 112,233 –5,874 –15,140 981 4,861 –6,431 10,743 14,908 66,119 Iran, Islamic Rep. 18,953 .. 15,113 .. –478 .. –4 .. 3,358 .. .. .. Iraq .. 65,695 .. 37,731 .. 2,106 .. –2,936 .. 27,133 8,347 46,461 Ireland 49,439 199,942 42,169 166,569 –7,325 –38,752 1,776 –1,109 1,721 –6,488 8,770 2,151 Israel 27,478 67,877 35,287 63,129 –2,654 –4,558 5,673 7,402 –4,790 7,592 8,123 60,611 Italy 295,618 509,797 250,319 520,563 –15,644 –38,480 –4,579 –16,952 25,076 –66,199 60,690 131,497 Jamaica 3,394 4,038 3,729 6,356 –371 –668 607 1,860 –99 –1,126 681 2,076 Japan 493,991 673,615 419,556 650,364 44,285 131,339 –7,676 –12,397 111,044 142,194 192,620 1,048,991 Jordan 3,479 10,915 4,903 16,300 –279 612 1,444 3,523 –259 –1,251 2,279 12,135 Kazakhstan 5,975 48,258 6,102 38,877 –146 –12,729 59 –900 –213 –4,248 1,660 23,183 Kenya 3,526 7,414 5,922 11,314 –219 –58 1,037 2,297 –1,578 –1,661 384 3,850 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 147,761 432,097 155,104 393,172 –1,303 4,554 –19 –811 –8,665 42,668 32,804 270,437 Kosovo .. .. .. .. .. .. .. .. .. .. .. 830 Kuwait 14,215 61,692 12,615 30,679 4,881 7,726 –1,465 –10,133 5,016 28,605 4,543 23,028 Kyrgyz Republic 448 2,560 726 3,680 –35 –190 79 1,208 –235 –102 134 1,584 Lao PDR 408 1,444 748 1,581 –6 –47 110 193 –237 9 99 1,010 Latvia 2,088 11,231 2,193 11,486 19 1,655 71 883 –16 2,284 602 6,902 Lebanon .. 21,600 .. 30,215 .. –767 .. 1,827 .. –7,555 8,100 39,132 Lesotho 199 789 1,046 1,792 314 424 210 547 –323 –32 457 .. Liberia .. 454 .. 1,704 .. –128 .. 1,101 .. –277 28 372 Libya 7,513 37,440 5,755 27,065 133 578 –220 –1,572 1,672 9,381 7,415 103,754 Lithuania 3,191 20,309 3,902 20,605 –13 318 109 1,625 –614 1,646 829 6,657 Macedonia, FYR 1,302 3,548 1,773 5,665 –30 –128 213 1,599 –288 –646 275 2,288 Madagascar 749 .. 987 .. –167 .. 129 .. –276 .. 109 1,135 Malawi 470 .. 660 .. –44 .. 157 .. –78 .. 115 163 Malaysia 83,369 186,424 86,851 144,873 –4,144 –4,170 –1,017 –5,580 –8,644 31,801 24,699 96,704 Mali 529 2,551 991 3,760 –41 –313 219 455 –284 –1,066 323 1,604 Mauritania 504 .. 510 .. –48 .. 76 .. 22 .. 90 238 Mauritius 2,349 4,181 2,454 5,106 –19 27 101 224 –22 –675 887 2,316 Mexico 89,321 245,206 82,168 257,976 –12,689 –14,925 3,960 21,468 –1,576 –6,228 17,046 99,889 Moldova 884 2,000 1,006 3,989 –18 303 56 1,221 –85 –465 257 1,480 Mongolia 508 2,300 521 2,632 –25 –195 77 186 39 –342 158 1,327 Morocco 9,044 26,381 11,243 37,307 –1,318 –1,495 2,330 7,451 –1,186 –4,971 3,874 23,568 Mozambique 411 2,464 1,055 4,305 –140 –95 339 764 –445 –1,171 195 2,181 Myanmar 1,307 .. 2,020 .. –110 .. 562 .. –261 .. 651 .. Namibia 1,734 4,057 2,100 5,128 139 –70 403 1,261 176 120 221 2,051 Nepal 1,029 1,493 1,624 5,086 9 158 230 3,426 –356 –10 646 .. Netherlands 241,517 518,122 216,558 459,194 7,247 –12,001 –6,434 –10,345 25,773 36,581 47,162 39,284 New Zealand 17,883 33,210 17,248 31,953 –3,955 –5,148 255 267 –3,065 –3,624 4,410 15,594 Nicaragua 662 2,857 1,150 4,482 –372 –235 138 1,018 –722 –841 142 1,573 Niger 321 1,043 457 1,951 –47 26 31 230 –152 –651 95 656 Nigeria 12,342 61,545 12,841 47,843 –2,878 –10,020 799 17,977 –2,578 21,659 1,709 45,510 Norway 56,058 160,687 46,848 104,496 –1,919 –1,660 –2,059 –4,408 5,233 50,122 22,976 48,859 Oman 6,078 29,443 5,035 21,607 –374 –2,810 –1,469 –5,313 –801 –287 1,943 12,204 Pakistan 10,214 22,220 14,185 35,008 –1,939 –3,619 2,562 12,824 –3,349 –3,583 2,528 13,606 Panama 7,610 16,652 7,768 15,446 –466 –1,460 153 210 –471 –44 781 3,028 Papua New Guinea 2,992 4,579 1,905 4,802 –488 –625 75 176 674 –672 267 2,629 Paraguay 4,802 7,253 5,200 7,374 110 –312 195 519 –92 86 1,106 3,862 Peru 6,622 30,538 9,597 25,777 –2,482 –7,371 832 2,856 –4,625 247 8,653 33,225 Philippines 26,795 47,611 33,317 54,950 3,662 –69 880 15,960 –1,980 8,552 7,781 44,206 Poland 35,716 171,071 33,825 170,631 –1,995 –16,575 958 6,537 854 –9,598 14,957 79,522 Portugal 32,260 67,268 39,545 83,259 21 –10,952 7,132 2,992 –132 –23,952 22,063 15,829 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 848 18,804 2011 World Development Indicators 259 4.17 Balance of payments current account Goods and Net Net current Current account Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 9,404 50,491 11,306 60,470 –241 –2,968 369 5,649 –1,774 –7,298 2,624 44,383 Russian Federation 92,987 344,934 82,809 253,233 –3,372 –39,474 157 –2,862 6,963 49,365 18,024 439,342 Rwanda 75 534 374 1,479 7 –37 350 604 57 –379 99 743 Saudi Arabia 53,450 201,964 44,874 160,639 2,800 8,613 –16,694 –27,172 –5,318 22,765 10,399 420,984 Senegal 1,506 3,500 1,821 7,020 –124 –48 195 1,685 –244 –1,884 272 2,123 Serbia .. 11,858 .. 18,486 .. –710 .. 4,925 .. –2,412 .. 15,228 Sierra Leone 128 323 260 628 –30 –36 43 148 –118 –193 35 405 Singapore 159,488 364,332 144,904 325,605 541 –3,061 –894 –3,037 14,230 32,628 68,816 187,803 Slovak Republic 10,969 61,792 10,658 61,806 –14 –1,837 93 –959 390 –2,810 3,863 1,804 Slovenia 10,377 28,542 10,749 27,980 201 –1,081 95 –202 –75 –720 1,821 1,078 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 34,402 78,563 33,375 80,816 –2,875 –6,389 –645 –2,684 –2,493 –11,327 4,464 39,603 Spain 133,910 346,893 135,000 374,259 –5,402 –42,120 4,525 –10,889 –1,967 –80,375 40,531 28,051 Sri Lanka 4,617 8,977 5,982 11,708 –137 –488 732 3,005 –770 –215 2,112 5,354 Sudan 681 8,226 1,238 11,212 –3 –2,402 60 1,480 –500 –3,908 163 1,094 Swaziland 1,020 1,860 1,274 2,344 81 –123 144 192 –30 –414 298 959 Sweden 95,525 194,516 81,142 165,275 –6,473 7,303 –2,970 –5,083 4,940 31,460 25,870 47,255 Switzerland 123,320 280,162 108,916 243,800 10,708 14,922 –4,409 –12,312 20,703 38,972 68,620 134,566 Syrian Arab Republic 5,757 19,374 5,541 19,309 –560 –1,149 607 1,150 263 66 448 18,300 Tajikistan .. 1,218 .. 3,062 .. –71 .. 1,735 .. –180 39 .. Tanzania 1,265 5,219 2,139 7,543 –110 –175 395 683 –590 –1,816 270 3,470 Thailand 70,292 180,653 82,246 155,777 –2,114 –7,499 487 4,484 –13,582 21,861 36,939 138,419 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. 250 Togo 465 1,136 671 1,666 –34 –15 118 324 –122 –222 130 703 Trinidad and Tobago 2,799 19,622 2,110 9,948 –390 –1,202 –4 47 294 8,519 379 9,245 Tunisia 7,979 19,917 8,811 21,091 –716 –2,011 774 1,951 –774 –1,234 1,689 11,294 Turkey 36,581 142,865 40,113 151,453 –3,204 –8,121 4,398 2,299 –2,338 –14,410 13,891 74,933 Turkmenistan 1,774 .. 1,796 .. 17 .. 5 .. 0 .. 1,168 .. Uganda 664 3,954 1,490 5,210 –96 –329 639 1,133 –281 –451 459 2,994 Ukraine 17,090 54,253 18,280 56,206 –434 –2,440 472 2,661 –1,152 –1,732 1,069 26,501 United Arab Emirates .. .. .. .. .. .. .. .. .. .. 7,778 36,104 United Kingdom 322,114 595,914 327,000 650,834 3,393 40,655 –11,943 –22,786 –13,436 –37,050 49,144 66,550 United States 794,397 1,570,797 890,784 1,945,705 20,899 121,418 –38,073 –124,944 –113,561 –378,435 175,996 404,099 Uruguay 3,507 8,557 3,568 7,794 –227 –689 76 140 –213 215 1,813 8,038 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 20,753 59,600 16,905 48,064 –1,943 –2,652 109 –323 2,014 8,561 10,715 34,318 Vietnam 9,498 62,752 12,334 72,446 –384 –3,028 1,200 6,448 –2,020 –6,274 1,324 16,447 West Bank and Gaza 764 1,168 2,789 4,962 607 911 435 3,418 –984 535 .. .. Yemen, Rep. 2,160 7,092 2,471 10,001 –561 –1,171 1,056 1,515 184 –2,565 638 6,990 Zambia 1,222 4,560 1,338 4,119 –249 –1,363 182 516 –182 –406 223 1,892 Zimbabwe 2,344 .. 2,515 .. –294 .. 40 .. –425 .. 888 .. World 6,395,661 t 15,641,184 t 6,248,111 t 15,144,783 t .. .. .. .. .. .. .. .. Low income 29,028 104,191 46,738 149,627 .. .. .. .. .. .. .. .. Middle income 1,087,422 4,483,392 1,137,135 4,125,043 .. .. .. .. .. .. .. .. Lower middle income 492,428 2,563,013 532,363 2,390,741 .. .. .. .. .. .. .. .. Upper middle income 594,996 1,906,819 604,453 1,722,447 .. .. .. .. .. .. .. .. Low & middle income 1,115,105 4,583,161 1,182,581 4,271,461 .. .. .. .. .. .. .. .. East Asia & Pacific 397,583 1,969,911 413,806 1,684,481 .. .. .. .. .. .. .. .. Europe & Central Asia 193,610 795,858 205,686 759,347 .. .. .. .. .. .. .. .. Latin America & Carib. 273,265 796,196 288,584 781,728 .. .. .. .. .. .. .. .. Middle East & N. Africa .. .. 106,423 334,137 .. .. .. .. .. .. .. .. South Asia 58,893 310,779 78,652 407,949 .. .. .. .. .. .. .. .. Sub-Saharan Africa 89,266 296,829 99,774 327,513 .. .. .. .. .. .. .. .. High income 5,304,481 11,224,885 5,072,079 11,020,075 .. .. .. .. .. .. .. .. Euro area 2,100,300 4,450,297 1,977,018 4,275,187 .. .. .. .. .. .. .. .. a. International reserves including gold valued at London gold price. b. Includes Luxembourg. 260 2011 World Development Indicators 4.17 ECONOMY Balance of payments current account About the data Definitions The balance of payments records an economy’s system, external debt records, information provided • Exports and imports of goods and services are all transactions with the rest of the world. Balance of by enterprises, surveys to estimate service transac- transactions between residents of an economy and payments accounts are divided into two groups: tions, and foreign exchange records. Differences in the rest of the world involving a change in ownership the current account, which records transactions in collection methods—such as in timing, definitions of general merchandise, goods sent for processing goods, services, income, and current transfers, and of residence and ownership, and the exchange rate and repairs, nonmonetary gold, and services. • Net the capital and financial account, which records capi- used to value transactions—contribute to net errors income is receipts and payments of employee com- tal transfers, acquisition or disposal of nonproduced, and omissions. In addition, smuggling and other ille- pensation for nonresident workers, and investment nonfinancial assets, and transactions in financial gal or quasi-legal transactions may be unrecorded or income (receipts and payments on direct investment, assets and liabilities. The table presents data from misrecorded. For further discussion of issues relat- portfolio investment, and other investments and the current account plus gross international reserves. ing to the recording of data on trade in goods and receipts on reserve assets). Income derived from The balance of payments is a double-entry services, see About the data for tables 4.4–4.7. the use of intangible assets is recorded under busi- accounting system that shows all flows of goods and The concepts and definitions underlying the data in ness services. • Net current transfers are recorded services into and out of an economy; all transfers the table are based on the fifth edition of the Inter- in the balance of payments whenever an economy that are the counterpart of real resources or financial national Monetary Fund’s (IMF) Balance of Payments provides or receives goods, services, income, or claims provided to or by the rest of the world without Manual (1993). That edition redefined as capital financial items without a quid pro quo. All transfers a quid pro quo, such as donations and grants; and transfers some transactions previously included in the not considered to be capital are current. • Current all changes in residents’ claims on and liabilities to current account, such as debt forgiveness, migrants’ account balance is the sum of net exports of goods nonresidents that arise from economic transactions. capital transfers, and foreign aid to acquire capital and services, net income, and net current transfers. All transactions are recorded twice—once as a credit goods. Thus the current account balance now reflects • Total reserves are holdings of monetary gold, spe- and once as a debit. In principle the net balance more accurately net current transfer receipts in addi- cial drawing rights, reserves of IMF members held by should be zero, but in practice the accounts often do tion to transactions in goods, services (previously the IMF, and holdings of foreign exchange under the not balance, requiring inclusion of a balancing item, nonfactor services), and income (previously factor control of monetary authorities. The gold component net errors and omissions. income). Many countries maintain their data collection of these reserves is valued at year-end (December Discrepancies may arise in the balance of pay- systems according to the fourth edition of the Balance 31) London prices ($386.75 an ounce in 1995 and ments because there is no single source for balance of Payments Manual (1977). Where necessary, the IMF $1,087.50 an ounce in 2009). of payments data and therefore no way to ensure converts such reported data to conform to the fifth that the data are fully consistent. Sources include edition (see Primary data documentation). Values are customs data, monetary accounts of the banking in U.S. dollars converted at market exchange rates. Top 15 economies with the largest reserves in 2009 4.17a Total reserves ($ billions) Share of world Annual Months of total (%) change (%) imports 2008 2009 2009 2008–09 2009 China 1,966 2,453 26.1 24.8 25.0 Japan 1,031 1,049 11.2 1.8 18.1 Russian Federation 426 439 4.7 3.0 16.1 Saudi Arabia 451 421 4.5 –6.7 29.4 United States 294 404 4.3 37.4 2.0 Data sources Taiwan, China 304 363 3.9 19.6 20.7 India 257 285 3.0 10.6 9.8 Data on the balance of payments are published in the IMF’s Balance of Payments Statistics Yearbook Korea, Rep. 202 270 2.9 34.2 8.0 and International Financial Statistics. The World Hong Kong SAR, China 183 256 2.7 40.2 6.3 Data sources Bank exchanges data with the IMF through elec- Brazil 194 239 2.5 23.1 13.2 tronic files that in most cases are more timely and Singapore 174 188 2.0 7.8 5.9 cover a longer period than the published sources. Germany 139 179 1.9 29.1 1.5 More information about the design and compila- Algeria 148 155 1.7 4.7 .. tion of the balance of payments can be found in Thailand 111 138 1.5 24.7 9.8 the IMF’s Balance of Payments Manual, fifth edition Switzerland 74 135 1.4 81.6 5.0 (1993), Balance of Payments Textbook (1996), and Source: International Monetary Fund, International Financial Statistics data files. Balance of Payments Compilation Guide (1995). 2011 World Development Indicators 261 STATES AND MARKETS Introduction N 5 ew firm creation recently declined sharply in most countries, according to the 2010 World Bank Group Entrepreneurial Snapshots. The economic and financial crisis that began in 2008 increased unemployment in many countries, and the fight against poverty could be hampered as spending for human and productive capital is strained. Governments around the world face fiscal deficits and pressure to improve public spending and accelerate business reforms. Partnership between the private sec- tor, which employs people and makes investments, and a capable public sector, which creates a stable regulatory environment, is a key ingredient to successful development. This section includes a range of indicators show- regulation reforms, making it easier to start and ing how effective and accountable government, operate businesses, strengthening property rights, together with a vibrant private sector, produces and improving commercial dispute resolution and employment opportunities and services that em- bankruptcy procedures. Using data from the Enter- power poor people. Its 13 tables cover cross-cut- prise Snapshots and Doing Business to analyze ting themes: private sector development, public whether some reforms are more important than sector policies, infrastructure, information, com- others, Klapper and Love (2010a) find that small munications, telecommunications, and science reforms that reduce costs, time, or number of proce- and technology. New data show that business dures to register a business by less than 40 percent reforms are making it easier to do business and do not have a significant impact on new firm regis- create new firms and that more-inclusive financial tration. This suggests that “token” reforms do not systems are removing barriers to economic growth boost private sector activity and that countries with and development. weak business environments require larger reforms to increase new firm registration. They find that two Businesses are created faster in reforms occurring simultaneously tend to have more a good business environment impact than two reforms occuring sequentially over The World Bank Group Entrepreneurship Snapshots a longer period. (www.enterprisesurveys.org), which cover 112 coun- tries, show that new businesses are created faster Forty countries made it easier to in countries with good governance, low corporate pay taxes between 2009 and 2010 taxes, minimal red tape, and a strong legal and reg- The World Bank’s Doing Business project collects ulatory environment. Countries with well developed information for 183 countries on tax payments, time financial markets also have higher new firm creation spent paying taxes, and the total tax rate borne by a than countries with less developed financial mar- standard firm. In cooperation with Pricewaterhouse- kets. The downside is that countries with well de- Coopers, the project collects information on busi- veloped financial markets also had steeper declines ness tax systems around the world, allowing govern- in new firm creation during the recent financial and ments to benchmark their tax system with others to economic crisis, probably due to the credit crunch. identify good practices, and researchers to analyze High-income countries created more new limited li- the impact of higher corporate tax rates on business ability firms—more than 4 per 1,000 working-age start-ups and investments. people, compared with only about 0.3 in low-income Over June 2009–May 2010, 40 countries made countries. Data on business entry and density are tax compliance easier, reducing costs for firms and in table 5.1. encouraging job creation. Higher tax compliance The Doing Business database (www.doing costs are associated with larger informal sectors business.org) shows that between June 2009 and and more corruption, ultimately limiting employ- May 2010, 117 countries adopted 216 business ment, investment, and growth. Keeping rules simple 2011 World Development Indicators 263 and clear improves compliance and reduces disallowances in others can increase the effec- tax evasion. And better compliance keeps the tive rate. system working and supports government pro- In this year’s edition table 5.6 on tax poli- grams and services. cies includes the total business tax rate as In the past six years more than 60 percent a percent of commercial profi t, with details of the countries covered by the Doing Business on corporate taxes, labor taxes paid by the project made paying taxes easier or lowered employer, social contributions, and other the tax burden for local enterprises. Countries taxes. The total tax rate is a comprehensive that make paying taxes easy for domestic firms measure of the cost of all the taxes a busi- usually offer electronic systems for tax filing ness bears. It differs from the statutory tax and payment, have one tax per tax base, and rate, which merely provides the factor to be use a filing system based on self-assessment. applied to the tax base. In computing the total In high-income countries the average business tax rate, tax payable is divided by commercial spends about 180 hours a year preparing, fil- profit. The total tax rate is lowest in East Asia ing, and paying taxes; in Latin America and and Pacifi c and is highest in Sub-Saharan the Caribbean, more than 400 hours a year Africa (figure 5b). Note that these tax rates are (figure 5a). “de jure” tax rates based on case studies of Previous editions of World Development a “standardized business” as defined by the Indicators included data on the highest mar- Doing Business project. ginal corporate tax rate (the statutory rate of corporate income tax). It is not a comprehen- Benchmarking the quality of sive indicator of the amount of tax a company the business environment— pays, however, because it is only one of the Doing Business and Enterprise many taxes businesses pay. Generous tax Surveys are complementary allowances in some countries signifi cantly The World Bank’s Enterprise Surveys are based reduce the corporate income tax paid, while on firm-level surveys of a representative sam- ple of the nonagricultural private sector in a The average business in Latin America and the Caribbean spends about country. The surveys cover a broad range of 400 hours a year in preparing, filing, and paying business taxes, 2009 5a business environment topics including corrup- Time to prepare, file, and pay taxes (hours a year) tion, infrastructure, crime, competition, per- 500 formance measures, and access to finance. 400 Data from Enterprise Surveys are presented in table 5.2. 300 The Doing Business project uses indicator 200 sets and rankings to measure business regula- 100 tions and quantify the ease of doing business across countries. The indicators cover common 0 East Asia Europe & Latin America Middle East & South Sub-Saharan High income transactions such as starting a business or reg- & Pacific Central Asia & Caribbean North Africa Asia Africa Source: Doing Business 2011. istering property based on standardized case studies. Data are collected through surveys of local experts on business transactions and Firms in East Asia and the Pacific have reflect the country’s laws and regulations. Data the lowest business tax rate, 2010 5b on Doing Business indicators are in tables 5.3 Total tax rate (% of commercial profits) and 5.6. 80 Box 5c compares the data sources, cover- age, and information collected by Enterprise 60 Surveys and the Doing Business project. 40 About half the world’s households 20 do not have deposit accounts in formal financial institutions 0 East Asia Europe & Latin America Middle East & South Sub-Saharan High income Financial exclusion is a barrier to economic & Pacific Central Asia & Caribbean North Africa Asia Africa Source: Doing Business 2011. development. Evidence from household sur- veys indicates that access to basic financial 264 2011 World Development Indicators STATES AND MARKETS services such as savings, payments, and credit Two approaches to collecting business environment data: can make an important difference in poor peo- Doing Business and Enterprise Surveys 5c ple’s lives. For firms, lack of access to finance Topic Enterprise Surveys Doing Business is often the main obstacle to growth. In an in- Global 125 countries 183 countries creasingly digitized and globalized world many coverage countries are promoting access to financial Data Collects firm-level data; face-to- Collects information through surveys services—from establishing a credit facility for source face interview with owner or top administered by local experts (law- manager. Businesses surveyed yers, accountants, and architects). indigenous farmers in rural areas to introducing include manufacturing, retail, The information is confirmed through construction, transport, commu- the underlying laws and regulations broad consumer protection legislation. nications, and other services Although financial inclusion mandates, from Number of 150–360 observations in smaller Underlying laws and regula- consumer protection to rural finance promotion, observations countries; 1,200–1,800 inter- tions in addition to an average views in larger countries of 39 surveys per country are on the agenda of many financial regulators, Geographical Main cities or regions of Main (most populous) busi- insufficient authority and resources to provide coverage within economic activity ness city and subnational broad financial access limit implementation a country studies in other cities capacity in many developing countries. Never- Information Objective data on the business Time and cost to complete com- gathered environment as experienced by mon business transactions based theless, more than 70 percent of financial regu- firms, performance measures, firm on standardized case studies; characteristics, and perceptions underlying laws and regulations lators in developing countries have programs regarding obstacles to growth to protect consumers, and almost 60 percent Business characteristics; approxi- Standardized business; 10 promote financial literacy. mately 20 Investment climate topics  business regulation topics Five new financial indicators from Financial Examples Hard data: number of days to Hard data: laws and regula- of data obtain a construction permit. tions, number of procedures, and Access 2010 (www.cgap.org/financialindicators) Soft data: opinion on whether costs to build a warehouse. are included in table 5.5 this year: commer- access to land is an obstacle Soft data: experts’ estimates faced by the establishment  on the number of days re- cial bank deposits, commercial bank loans, quired for each procedure commercial bank branches, automated teller Inference from Stratified random sampling design Standardized case studies that relate the data of the surveys, which ensures to a common business situation, machines (ATMs), and point-of-sale terminals. that data are representative which makes comparisons and Although many nonbank institutions (coop- of the universe of formal firms benchmarks valid across countries (with five or more employees) eratives, specialized state financial institutions, Measures what happens to existing Expectations of a standardized firm and microfinance institutions) provide financial firms—their actual experiences with following official legal requirements investment climate issues such and costs. For instance, “paying services, the most complete information avail- as payment of taxes. Also surveys taxes” measures the number of able to central banks and financial regulators is obstacles to business growth payments, time to file, and tax rates on commercial banks, which account for 85 per- Measures what happens in practice Assumes that firms comply with all in the normal course of business; formal regulations and minimize cent of deposits and 96 percent of accounts. for instance, whether a firm pays information gathering time and a bribe when obtaining an im- that all regulations are enforced. Although financial inclusion, measured as peo- port license and the actual time Measures what would happen if the ple with commercial bank accounts, is high in it takes to obtain the license firm complied with all regulatory requirements in a lawful manner. some developing country regions such as East Can be used to identify potential Can be used to identify areas for Asia and Pacific, it remains low in Sub-Saharan areas of reform in the business reform based on bottlenecks or environment as well as assess the weaknesses in specific areas of Africa (figure 5d). impact of reforms on businesses. private sector regulation and learn Access to deposit and credit services var- from practices in other countries. ies by region. Access is greater in countries Source: Summary of www.enterprisesurveys.org/Methodology/Compare.aspx. with higher incomes, better infrastructure, and a well functioning legal environment. People without access to bank accounts and credit People living in developing countries of East Asia and Pacific have more from regulated institutions have to rely on commercial bank accounts than those in other developing country regions, 2009 5d informal nonregulated financial services, often Deposit accounts in commercial banks (median per 1,000 adults) more costly and less reliable. Low- and middle- 2,000 income countries lag behind high-income coun- 1,500 tries in the number of bank branches, ATMs, and point-of-sale terminals, but the number of 1,000 Developing country median ATMs exceeds the number of bank branches in low-income countries. And new technology, 500 including the expansion of electronic pay- 0 ments through mobile and Internet banking, East Asia Europe & Latin America Middle East & South Sub-Saharan High income & Pacific Central Asia & Caribbean North Africa Asia Africa offer hope for bringing financial services to the Source: Financial Access 2010, CGAP and World Bank. unbanked. 2011 World Development Indicators 265 Tables 5.1 Private sector in the economy Investment commitments in infrastructure Domestic Businesses projects with private participationa credit to registered private sector $ millions Water and Entry Telecommunications Energy Transport sanitation % of GDP New density 2000–05 2006–09 2000–05 2006–09 2000–05 2006–09 2000–05 2006–09 2009 2009 2009 Afghanistan 466.1 1,040.4 1.6 .. .. .. .. .. 9.1 .. .. Albania 569.2 670.0 790.6 664.0 308.0 .. 8.0 0.0 37.0 2,045 0.84 Algeria 3,422.5 1,925.0 962.0 2,320.0 120.9 269.0 510.0 1,572.0 16.2 10,544 0.44 Angola 278.7 1,129.0 45.0 9.4 .. 53.0 .. .. 21.2 .. .. Argentina 5,836.8 5,033.6 3,826.9 3,479.0 203.6 1,402.6 791.6 .. 13.5 11,924 0.46 Armenia 317.1 488.8 74.0 127.0 63.0 715.0 0.0 0.0 23.1 2,698 1.28 Australia .. .. .. .. .. .. .. .. 127.8 89,960 6.38 Austria .. .. .. .. .. .. .. .. 126.9 3,228 0.58 Azerbaijan 355.6 1,283.5 375.2 .. .. .. 0.0 .. 19.6 5,314 0.93 Bangladesh 1,294.3 3,729.8 501.5 243.5 0.0 0.0 .. .. 41.5 .. .. Belarus 735.4 2,219.2 .. 1,875.0 .. 4.0 .. .. 37.3 5,508 0.80 Belgium .. .. .. .. .. .. .. .. 97.9 29,548 4.28 Benin 116.9 399.7 590.0 .. .. .. .. .. 22.2 .. .. Bolivia 520.5 284.7 884.4 137.3 16.6 .. .. .. 37.0 2,504 0.43 Bosnia and Herzegovina 0.0 1,086.6 .. 800.0 .. .. .. .. 57.3 1,896 0.58 Botswana 104.0 183.9 .. .. .. .. .. .. 25.5 .. .. Brazil 41,053.8 31,121.4 26,171.6 46,690.5 3,398.4 22,086.9 1,234.4 1,365.4 54.0 315,645 2.38 Bulgaria 2,179.1 1,866.5 3,253.5 2,246.7 2.1 536.2 152.0 .. 75.6 35,545 7.20 Burkina Faso 41.9 680.6 .. .. .. .. .. .. 17.5 610 0.08 Burundi 53.6 0.0 .. .. .. .. .. .. 21.7 .. .. Cambodia 136.1 436.9 82.1 695.8 125.3 40.1 .. .. 24.5 2,003 0.22 Cameroon 394.4 701.4 91.8 440.0 0.0 .. .. 0.0 11.3 .. .. Canada .. .. .. .. .. .. .. .. 128.6 174,000 7.56 Central African Republic 0.0 20.8 .. .. .. .. .. .. 7.0 .. .. Chad 11.0 246.4 0.0 .. .. .. .. .. 5.2 .. .. Chile 3,561.6 4,167.6 1,590.5 2,397.7 4,821.2 1,311.1 1,495.2 3.1 97.5 23,541 2.12 China 8,548.0 0.0 10,970.9 7,170.5 15,350.1 15,795.0 3,505.2 3,992.2 127.3 .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. 158.0 101,023 19.19 Colombia 1,570.9 5,294.7 351.6 944.6 1,005.4 2,344.4 314.3 305.0 29.9 31,132 1.07 Congo, Dem. Rep. 473.4 880.0 .. .. .. .. .. .. 7.5 .. .. Congo, Rep. 61.8 330.7 .. .. .. 735.0 0.0 .. 4.8 .. .. Costa Rica .. .. 80.0 190.0 465.2 373.0 .. .. 49.4 26,765 8.78 Côte d’Ivoire 134.9 885.4 0.0 0.0 176.4 .. .. 0.0 17.1 .. .. Croatia 1,205.7 3,035.0 7.1 85.0 451.0 492.0 298.7 .. 66.3 7,800 2.57 Cuba 60.0 0.0 116.0 60.0 0.0 .. 600.0 .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. 55.3 21,717 3.00 Denmark .. .. .. .. .. .. .. .. 231.6 16,519 4.57 Dominican Republic 393.0 220.1 1,306.6 0.0 898.9 879.9 .. .. 21.3 12,881 2.13 Ecuador 357.8 1,764.7 302.0 129.0 685.0 766.0 510.0 .. 25.3 .. .. Egypt, Arab Rep. 3,471.9 8,864.0 678.0 469.0 821.5 1,370.0 .. .. 36.2 6,291 0.13 El Salvador 1,110.6 901.9 85.0 0.0 .. .. .. .. 41.3 4,400 1.19 Eritrea 40.0 0.0 .. .. .. .. .. .. 16.6 .. .. Estonia .. .. .. .. .. .. .. .. 110.2 7,199 8.10 Ethiopia .. .. .. 4.0 .. .. .. .. 17.8 1,327 0.03 Finland .. .. .. .. .. .. .. .. 94.4 11,820 3.37 France .. .. .. .. .. .. .. .. 110.3 128,906 3.08 Gabon 26.6 278.8 0.0 0.0 177.4 3.9 .. .. 10.1 3,490 4.27 Gambia, The 6.6 35.0 .. 0.0 .. .. .. .. 18.9 .. .. Georgia 173.8 612.2 40.0 634.2 .. 573.0 .. 435.0 31.2 7,226 2.32 Germany .. .. .. .. .. .. .. .. 112.3 64,840 1.19 Ghana 156.5 2,916.0 590.0 100.0 10.0 .. 0.0 .. 15.9 9,606 0.72 Greece .. .. .. .. .. .. .. .. 91.7 8,426 1.18 Guatemala 560.1 1,511.4 110.0 263.8 .. .. .. 6.7 25.4 5,133 0.68 Guinea 50.6 242.2 .. .. .. 159.0 .. .. .. .. .. Guinea-Bissau 21.9 96.4 .. .. .. .. .. .. 5.6 .. .. Haiti 18.0 306.0 5.5 .. .. .. .. 0.0 14.5 .. .. Honduras 135.0 930.5 358.8 .. 120.0 .. 207.9 .. 52.6 .. .. 266 2011 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy Investment commitments in infrastructure Domestic Businesses projects with private participationa credit to registered private sector $ millions Water and Entry Telecommunications Energy Transport sanitation % of GDP New density 2000–05 2006–09 2000–05 2006–09 2000–05 2006–09 2000–05 2006–09 2009 2009 2009 Hungary 5,172.8 1,523.3 851.6 1,707.0 3,297.5 1,588.0 0.0 0.0 71.3 42,951 6.26 India 20,030.5 33,682.4 8,369.2 50,754.4 4,172.2 23,012.8 112.9 241.7 46.8 84,800 0.12 Indonesia 6,557.2 9,748.1 1,860.5 3,779.3 159.2 1,731.5 44.8 20.2 27.6 28,998 0.18 Iran, Islamic Rep. 695.0 1,506.0 650.0 .. .. .. .. .. 36.7 .. .. Iraq 984.0 4,521.0 .. 590.0 .. .. .. .. 6.4 .. .. Ireland .. .. .. .. .. .. .. .. 230.3 13,188 4.67 Israel .. .. .. .. .. .. .. .. 84.5 19,758 4.46 Italy .. .. .. .. .. .. .. .. 110.8 68,508 1.78 Jamaica 700.3 301.6 201.0 78.0 565.0 .. .. .. 28.5 2,003 1.16 Japan .. .. .. .. .. .. .. .. 171.0 105,698 1.28 Jordan 1,589.0 648.6 .. 989.0 0.0 1,380.0 169.0 951.0 71.7 2,737 0.74 Kazakhstan 1,153.7 3,170.2 300.0 0.0 231.0 31.0 .. .. 50.3 27,978 2.59 Kenya 1,434.0 2,973.8 .. 332.7 .. 404.0 .. .. 31.5 17,896 0.85 Korea, Dem. Rep. .. 400.0 .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. 107.6 60,039 1.72 Kosovo .. .. .. .. .. .. .. .. 36.1 141 0.12 Kuwait .. .. .. .. .. .. .. .. 63.3 .. .. Kyrgyz Republic 11.5 115.9 .. .. .. .. 0.0 .. 15.1 4,412 1.26 Lao PDR 87.7 135.0 1,250.0 1,425.0 0.0 .. .. .. 9.5 .. .. Latvia 700.0 468.1 158.1 184.0 .. 135.0 .. .. 107.8 7,175 4.62 Lebanon 138.1 0.0 .. .. 153.0 .. 0.0 .. 73.9 .. .. Lesotho 88.4 30.6 0.0 .. .. .. .. .. 13.5 .. .. Liberia 70.3 73.8 .. .. .. .. .. .. 16.1 .. .. Libya .. .. .. .. .. .. .. .. 10.9 .. .. Lithuania 993.0 490.2 514.3 417.6 .. .. .. .. 70.9 5,399 2.18 Macedonia, FYR 706.6 489.6 .. 655.0 .. 295.0 .. .. 44.3 8,074 5.63 Madagascar 12.6 304.8 0.0 .. 61.0 17.5 .. .. 11.5 724 0.07 Malawi 36.3 197.7 0.0 .. .. .. .. .. 14.2 619 0.08 Malaysia 3,777.0 1,700.0 6,637.6 384.5 4,263.0 1,379.0 6,502.2 0.0 117.1 41,638 2.55 Mali 82.6 583.0 365.9 .. 55.4 .. .. .. 17.4 .. .. Mauritania 92.1 133.1 .. .. .. .. .. .. .. .. .. Mauritius 413.0 102.1 0.0 .. .. .. .. 0.0 85.1 6,626 7.33 Mexico 18,758.0 12,622.6 6,749.3 1,483.0 2,970.4 11,434.1 523.7 303.8 23.3 44,084 0.61 Moldova 46.1 392.3 227.2 68.0 0.0 60.0 .. .. 36.2 4,180 1.32 Mongolia 22.1 0.0 .. .. .. .. .. .. 43.9 .. .. Morocco 6,139.5 2,549.6 1,049.0 .. 200.0 200.0 .. .. 64.4 26,166 1.28 Mozambique 123.0 156.2 1,205.8 .. 334.6 0.0 .. .. 25.1 .. .. Myanmar .. .. .. 556.1 .. .. .. .. .. .. .. Namibia 35.0 8.5 1.0 .. .. .. 0.0 .. 46.8 .. .. Nepal 109.3 26.0 15.1 .. .. .. .. .. 59.4 .. .. Netherlands .. .. .. .. .. .. .. .. 215.3 35,100 3.10 New Zealand .. .. .. .. .. .. .. .. 147.0 47,897 17.08 Nicaragua 218.5 380.1 126.3 95.0 104.0 .. .. .. 34.4 .. .. Niger 85.5 251.7 .. .. .. .. 3.4 .. 12.2 24 0.00 Nigeria 6,949.7 11,348.1 1,920.0 280.0 2,355.4 644.1 .. .. 37.6 65,089 0.79 Norway .. .. .. .. .. .. .. .. .. 13,805 4.49 Oman .. .. .. .. .. .. .. .. 49.0 3,165 1.67 Pakistan 6,594.9 8,706.5 375.4 4,058.2 112.8 923.7 .. .. 23.5 2,759 0.03 Panama 211.4 1,224.0 449.3 576.7 51.4 0.0 .. .. 85.7 548 0.26 Papua New Guinea .. 150.0 .. .. .. .. .. .. 32.1 .. .. Paraguay 199.0 591.4 .. .. .. .. .. .. 29.1 .. .. Peru 2,241.4 2,485.0 2,498.9 1,142.9 522.5 3,157.6 152.0 .. 24.1 51,151 2.65 Philippines 4,616.4 4,177.0 3,428.4 9,463.3 943.5 678.9 0.0 530.5 30.3 11,435 0.19 Poland 16,800.1 7,750.0 2,620.5 2,475.4 1,672.0 3,642.3 64.3 0.8 52.9 14,434 0.52 Portugal .. .. .. .. .. .. .. .. 187.8 27,759 3.92 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. 51.5 .. .. 2011 World Development Indicators 267 5.1 Private sector in the economy Investment commitments in infrastructure Domestic Businesses projects with private participationa credit to registered private sector $ millions Water and Entry Telecommunications Energy Transport sanitation % of GDP New density 2000–05 2006–09 2000–05 2006–09 2000–05 2006–09 2000–05 2006–09 2009 2009 2009 Romania 3,906.9 4,188.9 1,240.8 6,288.7 .. 116.8 116.0 41.0 47.1 56,698 3.66 Russian Federation 22,049.4 24,525.8 1,726.0 27,214.2 109.4 191.0 904.7 1,241.7 45.3 261,633 2.61 Rwanda 72.3 351.0 1.6 .. .. .. .. .. .. 3,028 0.51 Saudi Arabia .. .. .. .. .. .. .. .. 52.1 .. .. Senegal 593.1 1,333.0 93.3 .. 55.4 398.0 0.0 0.0 24.7 1,636 0.22 Serbia 563.5 3,297.4 .. .. .. .. 0.0 .. 42.2 9,715 1.94 Sierra Leone 48.8 111.2 .. 1.2 .. .. .. .. 9.3 .. .. Singapore .. .. .. .. .. .. .. .. 103.2 26,416 7.40 Slovak Republic .. .. .. .. .. .. .. .. 44.7 15,825 4.04 Slovenia .. .. .. .. .. .. .. .. 94.0 5,836 4.16 Somalia 13.4 0.0 .. .. .. .. .. .. .. .. .. South Africa 10,519.5 7,714.0 1,251.3 9.9 504.7 3,483.0 31.3 0.0 147.1 24,700 0.77 Spain .. .. .. .. .. .. .. .. 211.5 79,757 2.92 Sri Lanka 766.1 1,444.5 270.8 .. .. .. .. .. 24.8 4,223 0.29 Sudan 747.7 1,748.3 .. .. .. 30.0 .. 120.7 12.3 .. .. Swaziland 27.7 48.3 .. .. .. .. .. .. 25.0 .. .. Sweden .. .. .. .. .. .. .. .. 139.3 24,228 4.09 Switzerland .. .. .. .. .. .. .. .. 174.8 25,250 4.88 Syrian Arab Republic 583.0 307.7 .. .. .. 82.0 .. .. 20.3 .. .. Tajikistan 8.5 125.0 16.0 .. .. .. .. .. 29.0 2,171 0.48 Tanzania 515.3 1,484.5 348.0 28.4 27.7 134.0 8.5 .. 15.3 .. .. Thailand 5,602.7 3,106.0 4,693.3 2,341.0 939.0 .. 522.7 18.8 116.3 27,520 0.59 Timor-Leste 0.0 0.0 .. .. .. .. .. .. 18.6 .. .. Togo 0.0 44.0 657.7 190.0 .. .. .. .. 21.9 125 0.04 Trinidad and Tobago .. .. .. .. .. .. .. .. 31.5 .. .. Tunisia 751.0 2,805.0 30.0 .. .. 840.0 .. .. 68.4 9,079 1.23 Turkey 12,788.6 12,068.7 6,754.8 8,862.7 3,118.6 4,138.5 .. .. 36.5 44,472 0.87 Turkmenistan 20.0 158.1 .. .. .. .. .. .. .. .. .. Uganda 387.6 1,463.0 113.9 1,000.6 .. 404.0 0.0 .. 13.1 11,152 0.72 Ukraine 3,162.9 4,508.8 160.0 54.0 .. 130.0 100.0 102.0 73.3 19,300 0.60 United Arab Emirates .. .. .. .. .. .. .. .. 93.0 .. .. United Kingdom .. .. .. .. .. .. .. .. 213.5 330,100 8.05 United States .. .. .. .. .. .. .. .. 202.9 .. .. Uruguay 114.2 158.5 330.0 .. 251.1 .. 368.0 .. 20.6 4,664 2.08 Uzbekistan 285.6 942.1 .. .. .. 25.0 0.0 .. .. 14,428 0.78 Venezuela, RB 3,337.0 2,619.8 39.5 .. 34.0 .. 15.0 .. 21.7 .. .. Vietnam 430.0 1,593.7 2,360.6 297.0 20.0 965.0 266.0 .. 112.7 .. .. West Bank and Gaza 279.8 47.0 150.0 .. .. .. .. .. .. .. .. Yemen, Rep. 376.8 392.2 .. 15.8 .. 220.0 .. .. 7.4 .. .. Zambia 208.3 624.0 3.0 .. 15.6 .. 0.0 .. 12.0 5,509 0.88 Zimbabwe 72.0 343.0 .. .. .. .. .. .. .. .. .. World .. s .. s .. s .. s .. s .. s .. s .. s 138.2 w     Low income 6,362.3 20,932.3 .. .. .. .. .. .. 26.4     Middle income 227,575.0 248,323.5 107,077.9 191,687.3 50,686.8 105,160.9 16,175.1 6,654.9 72.8     Lower middle income 84,109.2 27,585.0 38,840.9 82,564.1 26,511.7 51,724.0 3,704.9 5,271.6 92.9     Upper middle income 143,465.8 134,452.2 49,324.0 109,123.2 5,696.9 41,208.8 407.0 .. 47.8     Low & middle income 233,937.3 269,255.8 87,324.8 196,264.6 5,403.2 86,781.7 .. .. 72.0     East Asia & Pacific 29,862.2 4,662.0 31,290.4 26,112.4 21,800.1 20,589.5 10,840.9 4,561.7 117.1     Europe & Central Asia 50,274.6 62,911.8 5,316.0 47,981.4 .. .. .. .. 45.0     Latin America & Carib. 81,401.1 72,021.9 45,682.0 57,940.1 16,150.3 43,755.5 2,516.1 .. 40.8     Middle East & N. Africa 13,435.4 23,566.1 .. .. .. .. .. .. 34.5     South Asia 29,314.5 48,647.1 9,533.6 55,257.1 4,285.0 23,936.5 112.9 241.7 43.5     Sub-Saharan Africa 24,654.4 40,481.6 .. .. .. .. .. .. 65.1     High income .. .. .. .. .. .. .. .. 165.1     Euro area .. .. .. .. .. .. .. .. 133.0     a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached financial closure in 1990–2009. 268 2011 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy About the data Definitions Private sector development and investment—tapping involving local and small-scale operators—may be •  Investment commitments in infrastructure private sector initiative and investment for socially omitted because they are not publicly reported. projects with private participation refers to infra- useful purposes—are critical for poverty reduction. The database is a joint product of the World Bank’s structure projects in telecommunications, energy In parallel with public sector efforts, private invest- Finance, Economics, and Urban Development (electricity and natural gas transmission and dis- ment, especially in competitive markets, has tre- Department and the Public-Private Infrastructure tribution), transport, and water and sanitation that mendous potential to contribute to growth. Private Advisory Facility. Geographic and income aggregates have reached financial closure and directly or indi- markets are the engine of productivity growth, creat- are calculated by the World Bank’s Development rectly serve the public. Incinerators, movable assets, ing productive jobs and higher incomes. And with gov- Data Group. For more information, see http://ppi. standalone solid waste projects, and small projects ernment playing a complementary role of regulation, worldbank.org/. such as windmills are excluded. Included are opera- funding, and service provision, private initiative and Credit is an important link in money transmission; tion and management contracts, concessions (oper- investment can help provide the basic services and it finances production, consumption, and capital for- ation and management contracts with major capital conditions that empower poor people—by improving mation, which in turn affect economic activity. The expenditure), greenfield projects (new facilities built health, education, and infrastructure. data on domestic credit to the private sector are and operated by a private entity or a public-private Investment in infrastructure projects with private taken from the banking survey of the International joint venture), and divestitures. Investment commit- participation has made important contributions to Monetary Fund’s (IMF) International Financial Statistics ments are the sum of investments in physical assets easing fiscal constraints, improving the efficiency or, when unavailable, from its monetary survey. The and payments to the government. Investments in of infrastructure services, and extending delivery monetary survey includes monetary authorities (the physical assets are resources the project company to poor people. Developing countries have been in central bank), deposit money banks, and other bank- commits to invest during the contract period in new the forefront, pioneering better approaches to infra- ing institutions, such as finance companies, develop- facilities or in expansion and modernization of exist- structure services and reaping the benefits of greater ment banks, and savings and loan institutions. Credit ing facilities. Payments to the government are the competition and customer focus. to the private sector may sometimes include credit resources the project company spends on acquir- The data on investment in infrastructure projects to state-owned or partially state-owned enterprises. ing government assets such as state-owned enter- with private participation refer to all investment (pub- Entrepreneurship is essential to the dynamism of prises, rights to provide services in a specific area, or lic and private) in projects in which a private com- the modern market economy, and a greater entry rate use of specific radio spectrums. • Domestic credit pany assumes operating risk during the operating of new businesses can foster competition and eco- to private sector is financial resources provided period or development and operating risk during the nomic growth. The table includes data on business to the private sector—such as through loans, pur- contract period. Investment refers to commitments registrations from the 2008 World Bank Group Entre- chases of nonequity securities, and trade credits and not disbursements. Foreign state-owned companies preneurship Survey, which includes entrepreneurial other accounts receivable—that establish a claim for are considered private entities for the purposes of activity in more than 100 countries for 2000–08. repayment. For some countries these claims include this measure. Survey data are used to analyze firm creation, its credit to public enterprises. • New businesses regis- Investments are classified into two types: invest- relationship to economic growth and poverty reduc- tered are the number of limited liability corporations ments in physical assets—the resources a com- tion, and the impact of regulatory and institutional registered in the calendar year. • Entry density is the pany commits to invest in expanding and modern- reforms. The 2008 survey improves on earlier sur- number of newly registered limited liability corpora- izing facilities—and payments to the government to veys’ methodology and country coverage for better tions per 1,000 people ages 15–64. acquire state-owned enterprises or rights to provide cross-country comparability. Data on total and newly services in a specific area or to use part of the radio registered businesses were collected directly from spectrum. national registrars of companies. For cross-country The data are from the World Bank’s Private Par- comparability, only limited liability corporations ticipation in Infrastructure (PPI) Project database, that operate in the formal sector are included. For which tracks infrastructure projects with private par- additional information on sources, methodology, ticipation in developing countries. It provides infor- calculation of entrepreneurship rates, and data limi- Data sources mation on more than 4,600 infrastructure projects tations see http://econ.worldbank.org/research/ in 137 developing economies from 1984 to 2009. entrepreneurship. Data on investment commitments in infra- The database contains more than 30 fields per proj- structure projects with private participation are ect record, including country, financial closure year, from the World Bank’s PPI Project database infrastructure services provided, type of private par- (http://ppi.worldbank.org). Data on domestic ticipation, investment, technology, capacity, project credit are from the IMF’s International Financial location, contract duration, private sponsors, bidding Statistics. Data on business registration are from process, and development bank support. Data on the the World Bank’s Entrepreneurship Survey and projects are compiled from publicly available infor- database (http://econ.worldbank.org/research/ mation. The database aims to be as comprehensive entrepreneurship). as possible, but some projects—particularly those 2011 World Development Indicators 269 5.2 Business environment: Enterprise Surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Firms Inter- Average Time Losses due formally nationally time to dealing with Time required Informal to theft, registered Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, when female banks to Value lost due quality exports offering number operating to public vandalism, operations participation finance to electrical certification through formal % of of times license officials and arson started in ownership investment outages ownership customs training a management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Afghanistan 2008 6.8 1.2 13.8 41.5 1.5 88.0 2.8 1.4 6.5 8.5 14.6 14.6 Albania 2007 18.7 3.9 21.2 57.7 0.5 89.4 10.8 12.4 13.7 24.6 1.9 19.9 Algeria 2007 25.1 2.3 19.3 64.7 0.9 98.3 15.0 8.9 4.0 5.0 14.1 17.3 Angola 2006 7.1 3.3 24.1 46.8 0.4 .. 23.4 2.1 3.7 5.1 16.5 19.4 Argentina 2006 13.8 2.2 78.3 18.7 1.5 93.8 30.3 6.9 1.6 26.9 5.5 52.2 Armenia 2009 10.3 2.1 20.0 11.6 0.6 96.2 31.8 31.9 1.8 26.9 3.3 30.4 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2009 3.0 2.1 15.8 32.0 0.3 85.1 10.8 19.0 1.8 18.2 1.9 10.5 Bangladesh 2007 3.2 1.3 6.0 85.1 0.1 .. 16.1 24.7 10.6 7.8 8.4 16.2 Belarus 2008 13.6 1.1 38.2 13.5 0.4 98.5 52.9 35.8 0.8 13.9 2.6 44.4 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 2009 20.7 1.2 64.3 54.5 1.9 87.9 43.9 4.2 7.5 7.3 9.6 32.4 Bolivia 2006 13.5 1.7 26.0 32.4 0.9 90.5 41.1 22.2 4.4 13.8 15.3 53.9 Bosnia and Herzegovina 2009 11.2 1.0 21.4 8.1 0.2 98.6 32.8 59.7 1.9 30.1 1.4 66.5 Botswana 2006 5.0 0.9 13.7 27.6 1.3 .. 40.9 11.3 1.4 12.7 1.4 37.7 Brazil 2009 18.7 1.2 83.5 9.7 1.7 95.8 59.3 48.4 3.0 25.7 15.9 52.9 Bulgaria 2009 10.6 2.2 20.8 8.5 0.5 98.5 33.9 34.7 1.6 19.9 4.2 30.7 Burkina Faso 2009 22.2 1.5 35.8 8.5 0.3 77.7 19.2 25.6 5.8 14.4 7.4 24.8 Burundi 2006 5.7 1.8 27.3 56.5 1.1 .. 34.8 12.3 10.7 7.1 .. 22.1 Cambodia 2007 5.6 1.0 .. 61.2 0.0 87.5 .. 11.3 2.4 2.8 1.5 48.4 Cameroon 2009 7.0 4.4 30.0 50.8 1.7 82.1 15.7 31.4 4.9 20.4 15.1 25.5 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad 2009 20.8 3.4 24.3 41.8 2.5 77.1 40.1 4.2 3.3 43.3 11.9 43.4 Chile 2006 9.0 3.0 67.7 8.2 0.6 97.8 27.8 29.1 1.8 22.0 5.8 46.9 China 2003 18.3 14.4 11.6 72.6 0.1 .. .. 28.8 1.3 35.9 6.6 84.8 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2006 14.3 0.6 28.2 8.2 0.7 85.6 43.0 30.6 2.3 5.9 7.0 39.5 Congo, Dem. Rep. 2010 29.4 8.0 40.0 65.7 1.8 61.9 38.9 6.7 22.7 8.5 18.0 24.1 Congo, Rep. 2009 6.0 2.7 .. 49.2 3.3 84.3 31.8 7.7 16.4 19.6 .. 37.5 Costa Rica 2005 9.6 0.5 .. 33.8 0.4 .. 65.3 14.9 1.9 10.5 3.5 46.4 Côte d’Ivoire 2009 1.8 3.6 14.5 30.6 3.4 56.4 61.9 13.9 5.0 4.3 16.6 19.1 Croatia 2007 10.9 0.7 26.5 14.5 0.2 98.1 33.5 60.0 0.8 16.5 1.3 28.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 2009 10.4 1.5 19.9 8.7 0.4 98.0 25.0 33.4 0.6 43.5 5.7 70.7 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2005 8.8 0.5 .. 26.3 0.7 .. .. 12.5 15.2 9.6 11.4 53.3 Ecuador 2006 17.3 0.6 19.9 21.5 0.9 91.1 32.7 24.0 2.7 18.2 7.0 61.6 Egypt, Arab Rep. 2008 8.8 3.4 90.6 15.2 3.0 14.3 34.0 5.6 3.4 21.1 6.2 21.7 El Salvador 2006 9.2 2.7 35.4 34.3 2.6 79.5 39.6 17.3 2.9 11.0 2.5 49.6 Eritrea 2009 0.5 0.2 .. 0.0 0.0 100.0 4.2 11.9 0.2 15.1 9.6 26.1 Estonia 2009 5.5 0.4 8.3 1.6 0.9 97.4 36.3 41.5 0.5 21.2 1.8 69.3 Ethiopia 2006 3.8 1.1 11.4 12.4 1.4 .. 30.9 11.0 0.9 4.2 4.3 38.2 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 2009 2.8 15.2 12.1 26.1 0.4 63.7 33.1 6.3 1.7 18.6 3.8 30.9 Gambia, The 2006 7.3 2.5 8.4 52.4 2.7 .. 21.3 7.6 11.8 22.2 5.0 25.6 Georgia 2008 2.1 0.6 11.8 4.1 0.7 99.6 40.8 38.2 1.4 16.0 3.8 14.5 Germany 2005 1.2 1.3 .. .. 0.5 .. 20.3 45.0 .. .. 4.7 35.4 Ghana 2007 4.0 4.1 6.4 38.8 0.9 66.4 44.0 16.0 6.0 6.8 7.8 33.0 Greece 2005 1.8 1.7 .. 21.6 0.0 .. 24.4 25.9 .. 11.7 5.5 20.0 Guatemala 2006 9.2 2.1 75.4 15.7 1.5 91.3 28.4 12.8 4.5 8.0 4.5 28.1 Guinea 2006 2.7 2.8 13.0 84.8 2.0 .. 25.4 0.9 14.0 5.2 4.3 21.1 Guinea-Bissau 2006 2.9 3.4 30.4 62.7 1.1 .. 19.9 0.7 5.3 8.4 5.6 12.4 Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras 2006 4.6 1.5 31.6 16.7 2.2 89.4 39.9 8.5 3.8 16.5 6.0 33.3 270 2011 World Development Indicators 5.2 STATES AND MARKETS Business environment: Enterprise Surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Firms Inter- Average Time Losses due formally nationally time to dealing with Time required Informal to theft, registered Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, when female banks to Value lost due quality exports offering number operating to public vandalism, operations participation finance to electrical certification through formal % of of times license officials and arson started in ownership investment outages ownership customs training a management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Hungary 2009 13.5 0.8 35.6 4.0 0.1 100.0 42.4 48.7 0.9 39.4 4.3 14.8 India 2006 6.7 2.6 .. 47.5 0.1 .. 9.1 46.7 6.6 22.5 15.1 15.9 Indonesia 2009 1.9 0.2 21.1 14.6 0.3 29.1 42.8 11.7 2.4 2.9 2.4 4.7 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 2005 2.3 1.3 .. 8.3 0.3 .. 41.6 37.4 1.5 17.2 2.6 73.2 Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 2005 6.3 1.8 .. 17.7 1.1 .. 32.2 37.0 11.8 16.4 4.3 53.5 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 2006 6.7 1.7 6.4 18.1 0.1 .. 13.1 8.6 1.7 15.5 3.8 23.9 Kazakhstan 2009 4.7 2.6 30.8 23.3 1.0 97.4 34.4 31.0 3.7 10.8 8.5 40.9 Kenya 2007 5.1 6.7 23.4 79.2 3.9 .. 37.1 22.9 6.4 9.8 5.6 40.7 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2005 0.1 2.2 .. 14.1 0.0 .. 19.1 39.9 .. 17.6 7.2 39.5 Kosovo 2009 9.8 4.5 18.8 2.2 0.3 89.2 10.9 25.3 17.1 7.9 1.7 24.6 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2009 4.9 2.1 18.0 37.5 0.3 95.9 60.4 17.9 10.5 16.2 15.8 29.7 Lao PDR 2009 1.6 4.4 13.6 39.8 0.3 93.5 39.4 0.0 4.3 7.2 7.5 11.1 Latvia 2009 9.7 1.5 11.5 11.3 0.3 98.5 46.3 37.3 1.1 18.2 1.9 43.4 Lebanon 2009 8.9 2.2 81.0 23.0 0.0 97.6 33.5 23.8 9.4 17.9 7.6 52.4 Lesotho 2009 5.6 1.8 16.4 14.0 2.9 86.8 18.4 32.7 6.7 24.7 5.4 42.5 Liberia 2009 7.5 6.5 16.0 55.2 2.8 73.8 53.0 10.1 2.9 2.4 .. 17.0 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 2009 9.3 0.8 65.5 8.5 0.4 97.1 38.7 47.4 0.7 15.6 2.4 46.0 Macedonia, FYR 2009 14.5 3.0 33.8 11.5 0.7 99.2 36.4 47.0 5.9 21.5 2.5 19.0 Madagascar 2009 17.1 0.9 41.3 19.2 1.2 97.5 50.0 12.2 7.7 8.7 14.2 27.0 Malawi 2009 3.5 2.7 15.0 10.8 5.7 78.6 23.9 20.6 17.0 17.9 4.9 48.4 Malaysia 2007 7.8 2.6 22.4 .. 1.0 53.0 13.1 48.6 3.0 54.1 2.7 50.1 Mali 2007 2.4 1.6 41.0 28.9 0.6 85.4 18.4 7.0 1.8 8.6 4.8 22.5 Mauritania 2006 5.8 1.8 10.7 82.1 0.6 .. 17.3 3.2 1.6 5.9 3.9 25.5 Mauritius 2009 9.4 0.5 19.1 1.6 1.4 84.2 16.9 37.5 2.2 11.1 10.3 25.6 Mexico 2006 20.5 0.6 11.2 22.6 0.7 94.1 24.8 2.6 2.4 20.3 5.2 24.6 Moldova 2009 7.0 1.9 13.9 25.4 0.4 97.9 53.1 30.8 2.0 9.1 2.4 33.1 Mongolia 2009 12.1 2.0 43.5 30.4 0.6 90.1 52.0 26.5 0.8 16.7 18.6 61.2 Morocco 2007 11.4 0.9 3.4 13.4 0.0 86.0 13.1 12.3 1.3 17.3 1.8 24.7 Mozambique 2007 3.3 1.9 35.2 14.8 1.8 85.9 24.4 10.5 2.4 18.7 10.1 22.1 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2006 2.9 0.3 9.6 11.4 1.3 .. 33.4 8.1 0.7 17.6 1.4 44.5 Nepal 2009 6.5 1.3 14.5 15.2 0.9 94.0 27.4 17.5 27.0 3.1 5.6 8.8 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2006 9.3 1.3 19.7 17.2 0.9 85.4 41.4 13.0 8.7 18.7 5.0 28.9 Niger 2009 21.1 1.6 39.7 35.2 0.9 90.5 17.6 9.3 1.9 4.6 2.6 32.1 Nigeria 2007 6.1 3.0 12.1 40.9 4.1 .. 20.0 2.7 8.9 8.5 7.5 25.7 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 2003 .. 4.4 11.8 33.2 .. .. .. 31.0 4.2 10.8 3.4 20.9 Pakistan 2007 2.2 1.6 16.4 27.2 0.5 .. 6.7 9.7 9.9 9.6 4.8 6.7 Panama 2006 10.3 1.4 41.2 25.4 0.5 98.0 37.1 19.2 2.4 14.7 5.7 43.9 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 2006 7.9 0.7 37.8 84.8 0.9 94.0 44.8 8.2 2.5 7.1 5.5 46.9 Peru 2006 13.5 1.4 81.1 11.3 0.4 99.2 32.8 30.9 3.2 14.6 5.4 57.7 Philippines 2009 9.1 1.6 10.6 18.6 1.1 97.5 69.4 22.0 3.4 15.7 8.1 31.1 Poland 2009 12.8 0.6 14.6 5.0 0.5 99.3 47.9 40.7 1.9 17.3 6.0 60.9 Portugal 2005 1.1 1.6 .. 14.5 0.2 .. 50.8 24.4 .. 12.7 7.2 31.9 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 271 5.2 Business environment: Enterprise Surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Firms Inter- Average Time Losses due formally nationally time to dealing with Time required Informal to theft, registered Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, when female banks to Value lost due quality exports offering number operating to public vandalism, operations participation finance to electrical certification through formal % of of times license officials and arson started in ownership investment outages ownership customs training a management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Romania 2009 9.2 2.3 23.7 9.8 0.3 98.7 47.9 37.3 2.2 26.1 2.0 24.9 Russian Federation 2009 19.9 1.6 57.4 29.4 0.8 94.7 33.1 30.6 1.2 11.7 4.6 52.2 Rwanda 2006 5.9 3.3 6.5 20.0 1.3 .. 41.0 15.9 8.7 10.8 6.7 27.6 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2007 2.9 1.3 21.4 18.1 0.5 78.9 26.3 19.8 5.0 6.1 7.4 16.3 Serbia 2009 12.2 1.4 28.0 18.0 0.6 95.0 28.8 42.8 1.3 21.8 1.6 36.5 Sierra Leone 2009 7.4 1.9 12.6 18.8 0.8 89.2 7.9 6.9 6.6 13.8 .. 18.6 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 2009 6.7 0.9 32.1 9.1 0.7 100.0 29.6 33.5 0.3 28.6 2.4 33.1 Slovenia 2009 7.3 0.3 56.1 5.4 0.4 99.9 42.2 52.2 0.5 28.0 2.2 47.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2007 6.0 0.8 36.2 15.1 1.0 91.0 22.6 34.8 1.6 26.4 4.5 36.8 Spain 2005 0.8 1.5 .. 4.4 0.2 .. 34.1 32.6 3.0 21.3 4.9 51.3 Sri Lanka 2004 3.5 4.9 49.5 16.3 0.5 .. .. 26.2 .. .. 7.6 32.6 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 2006 4.4 1.4 24.0 40.6 1.3 .. 28.6 7.7 2.5 22.1 2.1 51.0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2009 12.2 2.3 169.2 83.8 0.8 .. 14.4 20.7 8.6 7.4 5.1 38.3 Tajikistan 2008 11.7 1.4 22.6 40.5 0.3 92.7 34.4 21.4 15.1 16.7 20.4 21.1 Tanzania 2006 4.0 2.7 15.9 49.5 1.2 .. 30.9 6.8 9.6 14.7 5.7 36.5 Thailand 2006 0.4 1.0 32.1 .. 0.1 .. .. 74.4 1.5 39.0 1.3 75.3 Timor-Leste 2009 4.1 0.9 16.6 19.4 1.5 91.8 42.9 1.6 7.6 2.2 .. 49.7 Togo 2009 2.7 1.2 56.4 16.7 2.4 75.8 31.8 16.9 10.5 6.6 6.7 31.0 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 2008 27.1 1.3 36.0 17.7 0.4 94.1 40.7 51.9 2.8 30.0 5.2 28.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2006 5.2 2.4 9.3 51.7 1.0 .. 34.7 7.7 10.2 15.5 3.2 35.0 Ukraine 2008 11.3 2.1 31.0 22.9 0.6 95.8 47.1 32.1 4.4 13.0 3.4 24.8 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 2006 7.0 0.7 133.8 7.3 0.7 97.8 41.6 6.9 0.9 6.8 2.5 24.6 Uzbekistan 2008 11.1 0.7 9.1 56.2 0.7 100.0 39.8 8.2 5.4 1.3 5.1 9.6 Venezuela, RB 2006 33.6 2.9 41.6 .. 1.4 97.3 .. 35.7 4.4 12.5 14.1 42.3 Vietnam 2009 4.9 1.1 15.9 52.1 0.3 87.5 59.2 21.5 3.7 16.7 4.5 43.6 West Bank and Gaza 2006 5.7 1.7 21.3 13.3 1.2 .. 18.0 4.2 4.6 18.2 6.0 26.5 Yemen, Rep. 2010 11.8 7.3 6.5 68.2 0.6 81.7 6.4 4.2 13.2 4.4 6.2 12.9 Zambia 2007 4.6 1.9 48.3 14.3 1.0 96.2 37.2 10.2 3.7 17.2 2.3 26.0 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. Note: Enterprise surveys are updated several times a year; see www.enterprisesurveys.org for the most recent updates. a. For survey data collected in 2006 and 2007, data refer to the manufacturing module only. 272 2011 World Development Indicators 5.2 STATES AND MARKETS Business environment: Enterprise Surveys About the data Definitions The World Bank Group’s Enterprise Survey gath- The reliability and availability of infrastructure ben- • Survey year is the year in which the underlying data ers firm-level data on the business environment efit households and support development. Firms with were collected. • Time dealing with officials is the to assess constraints to private sector growth and access to modern and efficient infrastructure—tele- average percentage of senior management’s time enterprise performance. Standardized surveys are communications, electricity, and transport—can be that is spent in a typical week dealing with require- conducted all over the world, and data are available more productive. Firm-level innovation and use of ments imposed by government regulations. • Aver- on more than 120,000 firms in 125 countries. The modern technology may help firms compete. age number of times meeting with tax officials is survey covers 11 dimensions of the business envi- Delays in clearing customs can be costly, deterring the average number of visits or required meetings ronment, including regulation, corruption, crime, firms from engaging in trade or making them uncom- with tax officials. • Time required to obtain operat- informality, finance, infrastructure, trade. For some petitive globally. Ill-considered labor regulations dis- ing license is the average wait to obtain an operating countries, firm-level panel data are available, making courage firms from creating jobs, and while employed license from the day applied for to the day granted. it possible to track changes in the business environ- workers may benefit, unemployed, low-skilled, and •  Informal payments to public offi cials are the ment over time. informally employed workers will not. A trained labor percentage of firms that answered positively to the Firms evaluating investment options, governments force enables firms to thrive, compete, innovate, and question “Was a gift or informal payment expected interested in improving business conditions, and adopt new technology. or requested during a meeting with tax officials?” economists seeking to explain economic perfor- The data in the table are from Enterprise Surveys •  Losses due to theft, robbery, vandalism, and mance have all grappled with defining and measur- implemented by the World Bank’s Financial and Pri- arson are the estimated losses from those causes ing the business environment. The firm-level data vate Sector Development Enterprise Analysis Unit. All that occurred on establishments’ premises as a from Enterprise Surveys provide a useful tool for economies in East Asia and Pacific, Europe and Cen- percentage of annual sales. • Firms formally regis- benchmarking economies across a large number of tral Asia, Latin America and the Caribbean, Middle tered when operations started are the percentage indicators measured at the firm level. East and North Africa, and Sub-Saharan Africa (for of firms formally registered when they started opera- Most countries can improve regulation and taxa- 2009) and Afghanistan, Bangladesh, and India draw tions in the country. Firms not formally registered (the tion without compromising broader social interests. a sample of registered nonagricultural businesses, residual) are in the informal sector of the economy. Excessive regulation may harm business perfor- excluding those in the financial and public sectors. • Firms with female participation in ownership are mance and growth. For example, time spent with Samples for other economies are drawn only from the the percentage of firms with a woman among the own- tax officials is a burden firms may face in paying manufacturing sector and are footnoted in the table. ers. • Firms using banks to finance investment are taxes. The business environment suffers when gov- Typical Enterprise Survey sample sizes range from the percentage of firms that invested in fixed assets ernments increase uncertainty and risks or impose 150 to 1,800, depending on the size of the economy. during the last fiscal year that used banks to finance unnecessary costs and unsound regulation and taxa- In each country samples are selected by stratified fixed assets. • Value lost due to electrical outages tion. Time to obtain licenses and permits and the random sampling, unless otherwise noted. Stratified is losses that resulted from power outages as a per- associated red tape constrain firm operations. random sampling allows indicators to be computed centage of annual sales. • Internationally recognized In some countries doing business requires informal by sector, firm size, and region and increases the quality certification ownership is the percentage of payments to “get things done” in customs, taxes, precision of economywide indicators compared with firms that have an internationally recognized quality licenses, regulations, services, and the like. Such alternative simple random sampling. Stratification certification, such as International Organization for corruption harms the business environment by dis- by sector of activity divides the economy into manu- Standardization 9000, 9001, 9002, or 14000 or torting policymaking, undermining government cred- facturing and retail and other services sectors. For Hazard Analysis and Critical Control Points. • Aver- ibility, and diverting public resources. Crime, theft, medium-size and large economies the manufacturing age time to clear direct exports through customs and disorder also impose costs on businesses and sector is further stratified by industry. Firm size is is the average number of days to clear direct exports society. stratified into small (5–19 employees), medium-size through customs. • Firms offering formal training In many developing countries informal businesses (20–99 employees), and large (more than 99 employ- are the percentage of firms offering formal training operate without formal registration. These firms have ees). Geographic stratification divides the national programs for their permanent, full-time employees. less access to financial and public services and can economy into the main centers of economic activity. engage in fewer types of contracts and investments, constraining growth. Equal opportunities for men and women contribute to development. Female participation in firm ownership is a measure of women’s integration as decision makers. Financial markets connect firms to lenders and Data sources investors, allowing firms to grow their businesses: creditworthy firms can obtain credit from financial Data on the business environment are from the intermediaries at competitive prices. But too often World Bank Group’s Enterprise Surveys website market imperfections and government-induced distor- (www.enterprisesurveys.org). tions limit access to credit and thus restrain growth. 2011 World Development Indicators 273 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Enforcing Protecting Closing a business property construction permits contracts investors business Time Disclosure Cost Number of required index 0–10 Time to Time % of Time procedures to build a Time (least resolve Number of required per capita Number of required to build a warehouse Number of required to most insolvency procedures days income procedures days warehouse days procedures days disclosure) years June June June June June June June June June June June 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan 4 7 26.7 9 250 13 340 47 1,642 1 .. Albania 5 5 16.8 6 42 24 331 39 390 8 .. Algeria 14 24 12.9 11 47 22 240 46 630 6 2.5 Angola 8 68 163.0 7 184 12 328 46 1,011 5 6.2 Argentina 14 26 14.2 6 52 28 338 36 590 6 2.8 Armenia 6 15 3.1 3 7 20 137 49 285 5 1.9 Australia 2 2 0.7 5 5 16 221 28 395 8 1.0 Austria 8 28 5.2 3 21 14 194 25 397 3 1.1 Azerbaijan 6 8 3.1 4 11 31 207 39 237 7 2.7 Bangladesh 7 19 33.3 8 245 14 231 41 1,442 6 4.0 Belarus 5 5 1.6 3 15 16 151 28 225 5 5.8 Belgium 3 4 5.4 8 79 14 169 26 505 8 0.9 Benin 7 31 152.6 4 120 15 320 42 825 6 4.0 Bolivia 15 50 100.8 7 92 17 249 40 591 1 1.8 Bosnia and Herzegovina 12 55 17.7 7 33 16 255 37 595 3 3.3 Botswana 10 61 2.2 5 16 24 167 29 625 7 1.7 Brazil 15 120 7.3 14 42 18 411 45 616 6 4.0 Bulgaria 4 18 1.6 8 15 24 139 39 564 10 3.3 Burkina Faso 4 14 49.8 4 59 15 122 37 446 6 4.0 Burundi 11 32 129.3 5 94 25 212 44 832 4 .. Cambodia 9 85 128.3 7 56 23 709 44 401 5 .. Cameroon 6 19 51.2 5 93 14 213 43 800 6 3.2 Canada 1 5 0.4 6 17 14 75 36 570 8 0.8 Central African Republic 8 22 228.4 5 75 21 239 43 660 6 4.8 Chad 13 75 226.9 6 44 14 164 41 743 6 .. Chile 8 22 6.8 6 31 18 155 36 480 8 4.5 China 14 38 4.5 4 29 37 336 34 406 10 1.7 Hong Kong SAR, China 3 6 2.0 5 36 7 67 24 280 10 1.1 Colombia 9 14 14.7 7 20 10 50 34 1,346 8 3.0 Congo, Dem. Rep. 10 84 735.1 6 54 14 128 43 625 3 5.2 Congo, Rep. 10 160 111.4 6 55 17 169 44 560 6 3.3 Costa Rica 12 60 10.5 6 21 23 191 40 852 2 3.5 Côte d’Ivoire 10 40 133.0 6 62 21 592 33 770 6 2.2 Croatia 6 7 8.6 5 104 13 315 38 561 1 3.1 Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic 9 20 9.3 4 43 36 150 27 611 2 3.2 Denmark 4 6 0.0 3 42 6 69 35 410 7 1.1 Dominican Republic 8 19 19.2 7 60 17 214 34 460 5 3.5 Ecuador 13 56 32.6 9 16 19 155 39 588 1 5.3 Egypt, Arab Rep. 6 7 6.3 7 72 25 218 41 1,010 8 4.2 El Salvador 8 17 45.0 5 31 34 155 30 786 5 4.0 Eritrea 13 84 69.2 11 78 .. .. 39 405 4 .. Estonia 5 7 1.9 3 18 14 134 36 425 8 3.0 Ethiopia 5 9 14.1 10 41 12 128 37 620 4 3.0 Finland 3 14 1.1 3 14 18 66 32 375 6 0.9 France 5 7 0.9 8 59 13 137 29 331 10 1.9 Gabon 9 58 21.9 7 39 16 210 38 1,070 6 5.0 Gambia, The 8 27 199.6 5 66 17 146 32 434 2 3.0 Georgia 3 3 5.0 1 2 10 98 36 285 8 3.3 Germany 9 15 4.8 5 40 12 100 30 394 5 1.2 Ghana 7 12 20.3 5 34 18 220 36 487 7 1.9 Greece 15 19 20.7 11 22 15 169 39 819 1 2.0 Guatemala 12 37 49.1 4 23 22 178 31 1,459 3 3.0 Guinea 13 41 146.6 6 104 32 255 50 276 6 3.8 Guinea-Bissau 17 216 183.3 9 211 15 167 40 1,140 6 .. Haiti 13 105 212.0 5 405 11 1,179 35 508 2 5.7 Honduras 13 14 47.2 7 23 17 106 45 900 0 3.8 274 2011 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators Starting a Registering Dealing with Enforcing Protecting Closing a business property construction permits contracts investors business Time Disclosure Cost Number of required index 0–10 Time to Time % of Time procedures to build a Time (least resolve Number of required per capita Number of required to build a warehouse Number of required to most insolvency procedures days income procedures days warehouse days procedures days disclosure) years June June June June June June June June June June June 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Hungary 4 4 8.2 4 17 31 189 35 395 2 2.0 India 12 29 56.5 5 44 37 195 46 1,420 7 7.0 Indonesia 9 47 22.3 6 22 14 160 40 570 10 5.5 Iran, Islamic Rep. 6 8 4.0 9 36 17 322 39 505 5 4.5 Iraq 11 77 107.8 5 51 14 215 51 520 4 .. Ireland 4 13 0.4 5 38 11 192 20 515 10 0.4 Israel 5 34 4.3 7 144 20 235 35 890 7 4.0 Italy 6 6 18.5 8 27 14 257 41 1,210 7 1.8 Jamaica 6 8 5.2 6 37 10 156 35 655 4 1.1 Japan 8 23 7.5 6 14 15 187 30 360 7 0.6 Jordan 8 13 44.6 7 21 19 87 38 689 5 4.3 Kazakhstan 6 19 1.0 4 40 34 219 38 390 8 1.5 Kenya 11 33 38.3 8 64 11 120 40 465 3 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 8 14 14.7 7 11 13 34 35 230 7 1.5 Kosovo 10 58 28.7 8 33 21 320 53 420 3 2.0 Kuwait 13 35 1.3 8 55 25 104 50 566 7 4.2 Kyrgyz Republic 2 10 3.7 4 5 13 143 39 260 8 4.0 Lao PDR 7 100 11.3 9 135 24 172 42 443 2 .. Latvia 5 16 1.5 6 42 24 186 27 309 5 3.0 Lebanon 5 9 75.0 8 25 21 218 37 721 9 4.0 Lesotho 7 40 26.0 6 101 15 601 41 785 2 2.6 Liberia 5 20 54.6 10 50 24 77 41 1,280 4 3.0 Libya .. .. .. .. .. .. .. .. .. .. .. Lithuania 6 22 2.8 3 3 17 162 30 275 5 1.5 Macedonia, FYR 3 3 2.5 5 58 21 146 37 370 9 2.9 Madagascar 2 7 12.9 7 74 16 178 38 871 5 .. Malawi 10 39 108.4 6 49 21 268 42 312 4 2.6 Malaysia 9 17 17.5 5 56 25 261 30 585 10 2.3 Mali 6 8 79.7 5 29 15 168 36 620 6 3.6 Mauritania 9 19 33.6 4 49 25 201 46 370 5 8.0 Mauritius 5 6 3.8 4 26 18 107 36 645 6 1.7 Mexico 6 9 12.3 5 74 11 105 38 415 8 1.8 Moldova 8 10 10.9 5 5 30 292 31 365 7 2.8 Mongolia 7 13 3.2 5 11 21 215 32 314 5 4.0 Morocco 6 12 15.8 8 47 19 163 40 615 7 1.8 Mozambique 9 13 13.9 8 42 17 381 30 730 5 5.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 10 66 18.5 9 23 12 139 33 270 5 1.5 Nepal 7 31 46.6 3 5 15 424 39 735 6 5.0 Netherlands 6 8 5.7 5 7 18 230 26 514 4 1.1 New Zealand 1 1 0.4 2 2 7 65 30 216 10 1.3 Nicaragua 6 39 117.9 8 124 17 219 35 540 4 2.2 Niger 9 17 118.6 4 35 17 265 39 545 6 5.0 Nigeria 8 31 78.9 13 82 18 350 40 457 5 2.0 Norway 5 7 1.8 1 3 14 252 33 280 7 0.9 Oman 5 12 3.3 2 16 15 186 51 598 8 4.0 Pakistan 10 21 10.7 6 50 12 223 47 976 6 2.8 Panama 6 9 10.3 8 32 20 116 31 686 1 2.5 Papua New Guinea 6 51 17.7 4 72 24 217 42 591 5 3.0 Paraguay 7 35 55.1 6 46 13 179 38 591 6 3.9 Peru 6 27 13.6 4 7 19 188 41 428 8 3.1 Philippines 15 38 29.7 8 33 26 169 37 842 2 5.7 Poland 6 32 17.5 6 152 32 311 38 830 7 3.0 Portugal 6 6 6.5 1 1 19 272 31 547 6 2.0 Puerto Rico 7 7 0.7 8 194 22 209 39 620 7 3.8 Qatar 8 12 9.7 10 16 19 76 43 570 5 2.8 2011 World Development Indicators 275 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Enforcing Protecting Closing a business property construction permits contracts investors business Time Disclosure Cost Number of required index 0–10 Time to Time % of Time procedures to build a Time (least resolve Number of required per capita Number of required to build a warehouse Number of required to most insolvency procedures days income procedures days warehouse days procedures days disclosure) years June June June June June June June June June June June 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Romania 6 10 2.6 8 48 17 228 31 512 9 3.3 Russian Federation 9 30 3.6 6 43 53 540 37 281 6 3.8 Rwanda 2 3 8.8 4 55 14 195 24 230 7 .. Saudi Arabia 4 5 7.0 2 2 12 89 43 635 9 1.5 Senegal 4 8 63.1 6 122 16 210 44 780 6 3.0 Serbia 7 13 7.9 6 91 20 279 36 635 7 2.7 Sierra Leone 6 12 110.7 7 86 25 252 40 515 6 2.6 Singapore 3 3 0.7 3 5 11 25 21 150 10 0.8 Slovak Republic 6 16 1.9 3 17 13 287 31 565 3 4.0 Slovenia 2 6 0.0 6 113 14 199 32 1,290 3 2.0 Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 6 22 6.0 6 24 17 174 30 600 8 2.0 Spain 10 47 15.1 4 18 11 233 39 515 5 1.0 Sri Lanka 4 35 5.4 8 83 22 214 40 1,318 4 1.7 Sudan 10 36 33.6 6 9 19 271 53 810 0 .. Swaziland 12 56 33.0 9 44 14 116 40 972 2 2.0 Sweden 3 15 0.6 1 7 8 116 30 508 8 2.0 Switzerland 6 20 2.1 4 16 14 154 31 417 0 3.0 Syrian Arab Republic 7 13 38.1 4 19 26 128 55 872 7 4.1 Tajikistan 8 27 36.9 6 37 30 228 34 430 8 1.7 Tanzania 12 29 30.9 9 73 22 328 38 462 3 3.0 Thailand 7 32 5.6 2 2 11 156 36 479 10 2.7 Timor-Leste 10 83 18.4 .. .. 22 208 51 1,285 3 .. Togo 7 75 178.1 5 295 15 277 41 588 6 3.0 Trinidad and Tobago 9 43 0.8 8 162 20 261 42 1,340 4 .. Tunisia 10 11 5.0 4 39 20 97 39 565 5 1.3 Turkey 6 6 17.2 6 6 25 188 35 420 9 3.3 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 18 25 94.4 13 77 18 171 38 490 2 2.2 Ukraine 10 27 6.1 10 117 22 374 30 345 5 2.9 United Arab Emirates 8 15 6.4 1 2 17 64 49 537 4 5.1 United Kingdom 6 13 0.7 2 8 11 95 28 399 10 1.0 United States 6 6 1.4 4 12 19 40 32 300 7 1.5 Uruguay 11 65 42.1 8 66 30 234 41 720 3 2.1 Uzbekistan 7 15 11.9 12 78 28 274 42 195 4 4.0 Venezuela, RB 17 141 30.2 8 47 11 395 29 510 3 4.0 Vietnam 9 44 12.1 4 57 13 194 34 295 6 5.0 West Bank and Gaza 11 49 93.7 7 47 21 199 44 540 6 .. Yemen, Rep. 6 12 82.1 6 19 15 107 36 520 6 3.0 Zambia 6 18 27.9 5 40 17 254 35 471 3 2.7 Zimbabwe 9 90 182.8 5 31 17 1,012 38 410 8 3.3 World 8u 34 u 40.7 u 6u 58 u 18 u 207 u 38 u 605 u 5u 2.9 u Low income 8 41 107.9 7 94 18 275 39 613 5 3.7 Middle income 8 39 31.7 6 54 19 201 39 638 5 3.1 Lower middle income 8 35 44.4 6 65 18 197 40 679 5 3.3 Upper middle income 8 43 16.1 6 41 19 206 38 588 6 2.9 Low & middle income 8 39 52.6 6 65 19 221 39 631 5 3.3 East Asia & Pacific 8 40 31.5 5 99 19 181 37 564 5 3.1 Europe & Central Asia 6 18 8.9 6 36 23 235 38 382 7 2.9 Latin America & Carib. 9 60 39.6 7 62 16 220 39 698 4 3.2 Middle East & N. Africa 8 23 54.6 7 39 20 181 42 701 6 3.5 South Asia 7 25 24.5 6 100 18 241 44 1,053 4 4.5 Sub-Saharan Africa 9 43 95.2 7 69 18 240 39 641 5 3.4 High income 6 18 7.3 5 38 17 169 35 532 6 2.1 Euro area 6 14 6.7 5 35 14 227 31 602 5 1.6 Note: Regional aggregates are for developing countries only. 276 2011 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators About the data Definitions The economic health of a country is measured not The Doing Business project encompasses two • Number of procedures for starting a business is the only in macroeconomic terms but also by other types of data: data from readings of laws and regu- number of procedures required to start a business, factors that shape daily economic activity such as lations and data on time and motion indicators that including interactions to obtain necessary permits and laws, regulations, and institutional arrangements. measure efficiency in achieving a regulatory goal. licenses and to complete all inscriptions, verifications, The Doing Business indicators measure business Within the time and motion indicators cost estimates and notifications to start operations for businesses regulation, gauge regulatory outcomes, and measure are recorded from official fee schedules where appli- with specific characteristics of ownership, size, and the extent of legal protection of property, the flex- cable. The data from surveys are subjected to numer- type of production. • Time required for starting a ibility of employment regulation, and the tax burden ous tests for robustness, which lead to revision or business is the number of calendar days to complete on businesses. expansion of the information collected. the procedures for legally operating a business using The table presents a subset of Doing Business The Doing Business methodology has limitations the fastest procedure, independent of cost. • Cost indicators covering 6 of the 10 sets of indicators: that should be considered when interpreting the for starting a business is normalized as a percentage starting a business, registering property, dealing with data. First, the data collected refer to businesses of gross national income (GNI) per capita. It includes construction permits, enforcing contracts, protecting in the economy’s largest city and may not represent all official fees and fees for legal or professional ser- investors, and closing a business. Table 5.5 includes regulations in other locations of the economy. To vices if such services are required by law. • Number of Doing Business measures of getting credit, and table address this limitation, subnational indicators are procedures for registering property is the number of 5.6 presents data on paying taxes. being collected for selected economies. These sub- procedures required for a business to legally transfer The fundamental premise of the Doing Business national studies point to significant differences in property. • Time required for registering property is project is that economic activity requires good rules the speed of reform and the ease of doing business the number of calendar days for a business to legally and regulations that are efficient, accessible to all across cities in the same economy. Second, the data transfer property. • Number of procedures for deal- who need to use them, and simple to implement. often focus on a specific business form—generally ing with licenses to build a warehouse is the number Thus some Doing Business indicators give a higher a limited liability company of a specified size—and of interactions of a company’s employees or manag- score for more regulation, such as stricter disclosure may not represent regulation for other types of busi- ers with external parties, including government staff, requirements in related-party transactions, and oth- nesses such as sole proprietorships. Third, transac- public inspectors, notaries, land registry and cadastre ers give a higher score for simplified regulations, tions described in a standardized business case refer staff, and technical experts apart from architects and such as a one-stop shop for completing business to a specific set of issues and may not represent the engineers. • Time required for dealing with construc- startup formalities. full set of issues a business encounters. Fourth, the tion permits to build a warehouse is the number of In constructing the indicators, it is assumed that time measures involve an element of judgment by the calendar days to complete the required procedures entrepreneurs know about all regulations and comply expert respondents. When sources indicate different for building a warehouse using the fastest procedure, with them; in practice, entrepreneurs may not be estimates, the Doing Business time indicators repre- independent of cost. • Number of procedures for aware of all required procedures or may avoid legally sent the median values of several responses given enforcing contracts is the number of independent required procedures altogether. But where regula- under the assumptions of the standardized case. actions, mandated by law or court regulation, that tion is particularly onerous, levels of informality are Fifth, the methodology assumes that a business has demand interaction between the parties to a con- higher, which comes at a cost: firms in the informal full information on what is required and does not tract or between them and the judge or court officer. sector usually grow more slowly, have less access waste time when completing procedures. • Time required for enforcing contracts is the number to credit, and employ fewer workers—and those of calendar days from the time of the filing of a law- workers remain outside the protections of labor law. suit in court to the final determination and payment. The indicators in the table can help policymakers • Extent of disclosure index measures the degree understand the business environment in a country to which investors are protected through disclosure and—along with information from other sources such of ownership and financial information. Higher values as the World Bank’s Enterprise Surveys—provide indicate more disclosure. • Time to resolve insolvency insights into potential areas of reform. is the number of years from time of filing for insolvency Doing Business data are collected with a stan- in court until resolution of distressed assets and pay- dardized survey that uses a simple business case ment of creditors. to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location, and nature of its oper- ation. Surveys in 183 countries are administered Data sources through more than 8,200 local experts, including lawyers, business consultants, accountants, freight Data on the business environment are from forwarders, government officials, and other profes- the World Bank’s Doing Business project sionals who routinely administer or advise on legal (www.doingbusiness.org). and regulatory requirements. 2011 World Development Indicators 277 5.4 Stock markets Market Market Turnover Listed domestic S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2010 2000 2009 2000 2009 2000 2010 2000 2010 2009 2010 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 166,068 63,910 58.4 15.9 2.1 0.9 4.8 4.6 127 101 97.8a 55.3a Armenia 2 28 0.1 1.6 0.0 0.0 11.9 0.2 105 2 .. .. Australia 372,794 1,454,547 89.4 136.1 54.3 82.4 56.5 90.1 1,330 1,913 72.4 12.5 Austria 29,935 67,683 15.7 14.1 4.9 6.7 29.8 79.4 97 72 57.0 10.9 Azerbaijan 3 .. 0.1 .. .. .. .. .. 2 .. .. .. Bangladesh 1,186 47,000 2.5 7.9 1.6 16.3 74.8 54.4 221 302 38.6a 37.6a Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 182,481 269,342 78.5 55.5 16.4 27.1 20.7 42.0 174 161 54.5 0.5 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 1,742 3,388 20.7 16.1 0.8 0.1 5.7 0.4 26 38 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 978 4,076 17.4 33.8 0.8 0.9 4.7 3.5 16 21 24.3a –6.8a Brazil 226,152 1,545,566 35.1 73.2 15.7 40.7 44.6 66.4 459 373 125.1 6.5 Bulgaria 617 7,276 4.8 14.6 0.4 0.8 8.7 2.8 503 390 17.2a –15.2a Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 841,385 2,160,229 116.1 125.8 87.6 92.8 77.3 71.1 1,418 3,805 57.5 22.0 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 60,401 341,584 80.3 128.0 8.1 23.0 9.5 19.7 258 227 84.0 47.2 China 580,991 4,762,837 48.5 100.4 60.2 179.6 158.3 164.4 1,086 2,063 66.3 6.9 Hong Kong SAR, China 623,398 2,711,334 368.6 1,088.3 223.4 707.4 61.3 63.9 779 1,396 67.1 21.3 Colombia 9,560 208,502 9.5 57.0 0.4 5.5 3.8 13.4 126 84 75.7a 44.1a Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2,924 1,445 18.3 5.0 0.7 0.1 4.0 2.8 21 9 .. .. Côte d’Ivoire 1,185 7,099 11.4 26.4 0.3 0.6 2.5 2.0 41 38 –10.7a 19.3a Croatia 2,742 24,912 12.8 40.7 0.9 2.3 7.1 4.1 64 221 31.1a –0.4 a Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 11,002 43,056 19.4 27.7 11.6 10.8 57.7 29.4 131 16 23.0 0.2 Denmark 107,666 231,746 67.3 60.4 57.2 47.9 86.0 69.1 225 196 40.6 25.1 Dominican Republic .. .. .. .. .. .. .. .. .. .. .. .. Ecuador 704 5,263 4.4 7.4 0.1 2.4 2.0 3.8 30 40 –13.1a 9.7a Egypt, Arab Rep. 28,741 82,495 28.8 47.7 11.1 28.0 36.1 43.0 1,076 211 35.6 11.5 El Salvador 2,041 4,227 15.5 21.0 0.2 .. 1.2 .. 40 61 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 1,846 2,260 32.5 13.9 5.7 2.0 18.0 13.1 23 15 32.9a 56.0a Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 293,635 118,160 241.2 38.2 169.8 38.3 64.3 97.4 154 123 17.5 10.7 France 1,446,634 1,926,488 108.9 74.4 81.6 51.6 74.1 42.5 808 901 25.6b –9.9b Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 24 1,060 0.8 6.8 0.1 0.0 11.3 0.3 269 143 .. .. Germany 1,270,243 1,429,707 66.8 39.0 56.3 38.7 79.1 103.0 1,022 571 25.8 c 7.4 c Ghana 502 3,531 10.1 9.6 0.2 0.2 1.4 3.4 22 35 –42.7a 94.1a Greece 110,839 72,639 88.3 16.6 75.7 15.7 60.4 67.7 329 287 22.1 –43.8 Guatemala 172 .. 0.9 .. 0.1 .. 6.4 .. 7 .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras 458 .. 8.8 .. .. .. .. .. 94 .. .. .. 278 2011 World Development Indicators 5.4 STATES AND MARKETS Stock markets Market Market Turnover Listed domestic S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2010 2000 2009 2000 2009 2000 2010 2000 2010 2009 2010 Hungary 12,021 27,708 25.1 21.9 25.4 20.1 85.8 94.5 60 48 73.0 –10.8 India 148,064 1,615,860 32.2 85.6 110.8 79.1 306.5 75.6 5,937 4,987 94.1 18.7 Indonesia 26,834 360,388 16.3 33.0 8.7 21.3 31.5 48.1 290 420 130.1 37.9 Iran, Islamic Rep. 7,350 86,616 7.3 19.1 1.1 5.2 7.4 22.9 304 341 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 81,882 33,722 84.8 13.2 14.9 8.1 19.2 52.9 76 50 44.7 –7.7 Israel 64,081 218,055 51.4 93.2 18.8 45.2 36.6 66.7 654 596 56.8 7.4 Italy 768,364 318,140 70.0 15.0 70.9 21.8 104.0 169.7 291 291 23.1 –17.4 Jamaica 3,582 6,626 39.8 51.4 0.8 1.0 2.5 3.3 46 39 –15.8 a 22.4 a Japan 3,157,222 4,099,591 67.6 66.6 57.7 82.7 69.9 114.5 2,561 3,553 16.4 d 9.6d Jordan 4,943 30,864 58.4 127.0 4.9 54.4 7.7 30.1 163 277 –13.9a –8.6a Kazakhstan 1,342 60,742 7.3 50.0 0.5 3.5 4.9 3.9 23 60 1.5a –1.0a Kenya 1,283 14,461 10.1 36.6 0.4 1.7 3.5 8.6 57 53 0.6a 33.8a Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 171,587 1,089,217 32.2 100.5 200.2 190.0 376.6 168.9 1,308 1,781 67.2 25.3 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 20,772 119,621 55.1 72.4 11.2 82.9 21.3 38.8 77 215 –10.4 a 29.1a Kyrgyz Republic 4 79 0.3 1.6 1.7 1.5 580.6 11.9 80 11 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 563 1,252 7.2 7.0 2.9 0.1 47.8 1.8 64 33 2.2a 39.4 a Lebanon 1,583 12,586 9.2 37.3 0.7 3.0 6.7 14.7 12 10 43.4 a –8.7a Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 1,588 5,661 13.9 12.0 1.8 0.8 14.8 5.8 54 39 36.7a 44.0a Macedonia, FYR 7 2,647 0.2 10.0 3.3 0.7 1,612.9 2.0 1 34 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. 1,363 .. 29.3 .. 0.4 .. 1.5 .. 14 .. .. Malaysia 116,935 410,534 124.7 132.6 62.4 37.8 44.6 27.1 795 957 46.7 35.1 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 1,331 6,506 29.0 55.2 1.6 3.8 5.1 6.4 40 86 44.2a 8.2a Mexico 125,204 454,345 21.5 38.9 7.8 8.8 32.5 27.3 179 130 55.8 26.6 Moldova 38 .. 3.2 .. 1.9 0.2 80.2 .. 34 .. .. .. Mongolia 37 1,093 3.4 10.2 0.7 0.4 23.2 6.4 410 336 .. .. Morocco 10,899 69,153 29.4 68.8 3.0 32.2 8.9 16.3 53 73 –1.7 13.1 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 311 1,176 8.0 9.1 0.6 0.2 4.4 1.8 13 7 22.6a 24.2a Nepal 790 4,843 14.4 43.8 0.6 1.8 5.4 1.9 110 190 .. .. Netherlands 640,456 661,204 166.3 68.5 175.9 76.3 101.4 98.4 234 113 41.7 1.2 New Zealand 18,866 36,295 36.7 52.9 21.0 29.4 45.9 20.8 142 102 40.4 5.2 Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 4,237 50,883 9.2 19.3 0.6 2.6 7.3 12.5 195 215 –35.4 a 20.3a Norway 65,034 250,922 38.6 59.5 35.7 64.9 93.4 90.8 191 195 91.4 13.7 Oman 3,463 20,267 17.4 37.5 2.8 12.6 14.2 18.2 131 120 22.0a 12.2a Pakistan 6,581 38,169 8.9 20.5 44.6 14.5 486.8 36.2 762 644 56.7a 15.3a Panama 2,794 10,917 24.0 32.6 1.3 0.2 4.7 2.0 29 34 15.4a 12.8a Papua New Guinea 1,520 9,742 49.3 116.1 0.0 0.2 0.1 .. 7 10 .. .. Paraguay 224 42 3.5 0.3 0.1 0.1 3.5 .. 56 50 .. .. Peru 10,562 99,831 19.8 53.5 2.9 2.4 12.7 4.7 230 199 79.3 51.3 Philippines 25,957 157,321 34.2 49.7 10.8 10.7 24.1 22.6 228 251 71.5 56.7 Poland 31,279 190,235 18.3 31.5 8.5 13.0 48.1 47.6 225 569 41.9 11.3 Portugal 60,681 81,996 51.9 42.4 46.5 19.7 85.5 34.6 109 47 35.0 –16.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 5,152 123,592 29.0 89.4 1.3 25.9 4.5 17.3 22 43 5.1a 27.7a 2011 World Development Indicators 279 5.4 Stock markets Market Market Turnover Listed domestic S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2010 2000 2009 2000 2009 2000 2010 2000 2010 2009 2010 Romania 1,069 32,385 2.9 18.8 0.6 1.2 24.3 5.4 5,555 1,383 26.1a –6.6a Russian Federation 38,922 1,004,525 15.0 69.9 7.8 55.4 36.6 85.7 249 345 106.6 21.7 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 67,171 353,414 35.6 84.8 9.2 89.7 27.1 60.5 75 146 28.5e 9.0e Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia 734 9,690 4.9 26.8 0.1 1.3 .. 2.2 6 7 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 152,827 370,091 164.8 170.5 98.7 138.4 52.1 82.9 418 461 76.7 18.4 Slovak Republic 1,217 4,150 4.2 5.3 3.1 0.2 78.7 3.9 493 90 –23.1a 5.4 a Slovenia 2,547 9,428 12.8 24.3 2.3 2.1 19.7 2.6 38 71 16.1a –20.3a Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 204,952 1,012,538 154.2 247.0 58.3 120.0 33.2 39.6 616 360 53.7 32.1 Spain 504,219 1,171,615 86.8 88.8 169.8 109.5 210.7 76.0 1,019 3,310 29.0 –24.5 Sri Lanka 1,074 19,924 6.6 19.4 0.9 2.1 10.8 23.6 239 241 118.0a 84.6a Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 73 .. 4.9 6.9 0.0 .. 0.3 .. 6 5 .. .. Sweden 328,339 581,174 132.8 106.5 157.7 96.1 111.2 86.8 292 331 66.0 32.6 Switzerland 792,316 1,229,357 317.0 217.7 243.7 161.7 82.0 75.6 252 246 24.5 11.0 Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 233 1,264 2.3 5.4 0.4 0.1 19.4 .. 4 11 .. .. Thailand 29,489 277,732 24.0 52.4 19.0 51.2 52.9 104.8 381 541 72.8 52.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 4,330 12,158 53.1 52.6 1.7 1.1 3.1 1.2 27 37 –10.2a 0.8a Tunisia 2,828 10,682 14.5 23.1 3.2 3.2 22.6 17.2 44 54 40.6a 11.7a Turkey 69,659 306,662 26.1 36.7 67.2 39.6 196.5 158.4 315 337 99.6 21.4 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 35 .. 0.6 .. 0.0 .. 1.7 .. 2 8 .. .. Ukraine 1,881 39,457 6.0 14.8 0.9 0.5 19.2 7.5 139 183 31.1a 53.8a United Arab Emirates 5,727 104,669 8.1 47.6 0.2 28.5 1.8 25.6 54 101 24.6a –6.8a United Kingdom 2,576,992 3,107,038 174.4 128.6 124.2 156.5 66.6 101.9 1,904 2,056 35.2f 5.2f United States 15,104,037 17,138,978 152.6 106.8 321.9 331.0 200.8 189.1 7,524 4,279 23.5g 12.8g Uruguay 161 157 0.7 0.4 0.0 0.0 0.9 .. 16 6 .. .. Uzbekistan 32 .. 0.2 .. 0.1 0.0 25.7 .. 5 .. .. .. Venezuela, RB 8,128 3,991 6.9 2.7 0.6 0.0 8.8 0.8 85 55 .. .. Vietnam .. 20,385 .. 21.8 .. 6.8 .. 141.4 .. 164 46.9a 0.5a West Bank and Gaza 765 2,450 18.6 .. 4.6 .. 23.4 18.7 24 41 .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 236 2,817 7.3 17.4 0.2 0.8 3.1 .. 9 19 16.7a 17.4 a Zimbabwe 2,432 11,476 36.8 161.4 4.2 16.1 11.3 .. 69 76 –83.8 .. World 32,187,124 s 56,172,634 s 101.7 w 85.2 w 151.4 w 142.5 w 140.2 w 122.0 w 47,751 s 47,071 s Low income .. 86,835 .. 37.7 .. 7.9 18.3 32.5 .. 719 Middle income 1,941,548 13,277,006 36.5 73.2 34.5 82.7 93.8 101.1 21,522 16,778 Lower middle income 879,123 7,570,880 36.2 82.2 54.6 124.3 162.2 132.4 11,444 11,088 Upper middle income 1,062,425 5,706,126 36.8 62.1 17.5 31.5 44.2 55.7 10,078 5,690 Low & middle income 1,948,214 13,363,841 36.1 72.6 34.0 81.9 93.5 100.8 22,094 17,497 East Asia & Pacific 780,487 6,001,435 47.1 91.0 49.8 149.0 116.2 146.0 3,190 4,758 Europe & Central Asia 115,145 1,473,816 17.5 50.8 30.1 38.3 131.0 91.2 7,199 2,963 Latin America & Carib. 620,023 2,750,758 31.7 52.9 8.4 20.9 27.1 46.1 1,672 1,457 Middle East & N. Africa 57,110 294,845 19.9 38.0 5.1 16.2 21.4 27.7 1,676 1,007 South Asia 157,695 1,725,795 26.1 73.3 90.2 67.0 308.8 73.5 7,269 6,364 Sub-Saharan Africa 217,754 1,117,191 89.8 154.1 32.3 48.1 31.7 37.1 1,088 948 High income 30,238,910 42,808,793 115.2 89.9 175.5 165.3 143.0 128.5 25,657 29,574 Euro area 5,435,393 6,276,893 86.8 49.3 80.2 45.6 90.1 75.0 5,051 6,278 a. Refers to the S&P Frontier BMI index. b. Refers to the CAC 40 index. c. Refers to the DAX index. d. Refers to the Nikkei 225 index. e. Refers to Saudi Arabia country index. f. Refers to the FTSE 100. g. Refers to the S&P 500 index. 280 2011 World Development Indicators 5.4 STATES AND MARKETS Stock markets About the data Definitions The development of an economy’s financial markets countries. Market capitalization shows the overall •  Market capitalization (also known as market is closely related to its overall development. Well size of the stock market in U.S. dollars and as a value) is the share price times the number of shares functioning financial systems provide good and eas- percentage of GDP. The number of listed domestic outstanding. •  Market liquidity is the total value ily accessible information. That lowers transaction companies is another measure of market size. Mar- of shares traded during the period divided by gross costs, which in turn improves resource allocation and ket size is positively correlated with the ability to domestic product (GDP). This indicator complements boosts economic growth. Both banking systems and mobilize capital and diversify risk. the market capitalization ratio by showing whether stock markets enhance growth, the main factor in Market liquidity, the ability to easily buy and sell market size is matched by trading. • Turnover ratio poverty reduction. At low levels of economic develop- securities, is measured by dividing the total value is the total value of shares traded during the period ment commercial banks tend to dominate the finan- of shares traded by GDP. The turnover ratio—the divided by the average market capitalization for the cial system, while at higher levels domestic stock value of shares traded as a percentage of market period. Average market capitalization is calculated as markets tend to become more active and efficient capitalization—is also a measure of liquidity as well the average of the end-of-period values for the cur- relative to domestic banks. as of transaction costs. (High turnover indicates low rent period and the previous period. • Listed domes- Open economies with sound macroeconomic poli- transaction costs.) The turnover ratio complements tic companies are the domestically incorporated cies, good legal systems, and shareholder protection the ratio of value traded to GDP, because the turn- companies listed on the country’s stock exchanges attract capital and therefore have larger financial mar- over ratio is related to the size of the market and the at the end of the year. This indicator does not include kets. Recent research on stock market development value traded ratio to the size of the economy. A small, investment companies, mutual funds, or other col- shows that modern communications technology and liquid market will have a high turnover ratio but a low lective investment vehicles. •  S&P/Global Equity increased financial integration have resulted in more value of shares traded ratio. Liquidity is an impor- Indices measure the U.S. dollar price change in the cross-border capital flows, a stronger presence of tant attribute of stock markets because, in theory, stock markets. financial firms around the world, and the migration of liquid markets improve the allocation of capital and stock exchange activities to international exchanges. enhance prospects for long-term economic growth. Many firms in emerging markets now cross-list on inter- A more comprehensive measure of liquidity would national exchanges, which provides them with lower include trading costs and the time and uncertainty cost capital and more liquidity-traded shares. However, in finding a counterpart in settling trades. this also means that exchanges in emerging markets Standard & Poor’s Index Services, the source for may not have enough financial activity to sustain them, all the data in the table, provides regular updates on putting pressure on them to rethink their operations. 21 emerging stock markets and 36 frontier markets. The indicators in the table are from Standard & Standard & Poor’s maintains a series of indexes for Poor’s Emerging Markets Data Base. They include investors interested in investing in stock markets in measures of size (market capitalization, number of developing countries. The S&P/IFCI index, Standard listed domestic companies) and liquidity (value of & Poor’s leading emerging markets index, is designed shares traded as a percentage of gross domestic to be sufficiently investable to support index tracking product, value of shares traded as a percentage of portfolios in emerging market stocks that are legally market capitalization). The comparability of such indi- and practically open to foreign portfolio investment. cators across countries may be limited by concep- The S&P/Frontier BMI measures the performance of tual and statistical weaknesses, such as inaccurate 36 smaller and less liquid markets. The individual reporting and differences in accounting standards. country indexes include all publicly listed equities The percentage change in stock market prices in U.S. representing an aggregate of at least 80 percent or dollars for developing economies is from Standard more of market capitalization in each market. These & Poor’s Global Equity Indices (S&P IFCI) and Stan- indexes are widely used benchmarks for international dard & Poor’s Frontier Broad Market Index (BMI). The portfolio management. See www.standardandpoors. percentage change for France, Germany, Japan, the com for further information on the indexes. Data sources United Kingdom, and the United States is from local Because markets included in Standard & Poor’s stock market prices. The indicator is an important emerging markets category vary widely in level of Data on stock markets are from Standard & Poor’s measure of overall performance. Regulatory and development, it is best to look at the entire category Global Stock Markets Factbook 2010, which draws institutional factors that can affect investor confi - to identify the most significant market trends. And it on the Emerging Markets Data Base, supple- dence, such as entry and exit restrictions, the exis- is useful to remember that stock market trends may mented by other data from Standard & Poor’s. tence of a securities and exchange commission, and be distorted by currency conversions, especially when The firm collects data through an annual survey the quality of laws to protect investors, may influence a currency has registered a significant devaluation. of the world’s stock exchanges, supplemented by the functioning of stock markets but are not included About the data is based on Demirgüç-Kunt and information provided by its network of correspon- in the table. Levine (1996), Beck and Levine (2001), and Claes- dents and by Reuters. Data on GDP are from the Stock market size can be measured in various sens, Klingebiel, and Schmukler (2002). World Bank’s national accounts data files. ways, and each may produce a different ranking of 2011 World Development Indicators 281 5.5 Financial access, stability, and efficiency Getting Financial access Bank Ratio of Domestic Interest Risk premium credit and outreach capital bank non- credit rate spread on lending to asset performing provided Strength Deposit Loan accounts accounts ratio loans to total by banking of legal  Depth of Prime lending at at Commercial Automated Point- gross loans sector rights credit commercial commercial bank teller of-sale Lending rate minus index information banks banks branches machines terminals rate minus treasury 0–10 index per per per per per deposit rate bill rate (weak to 0–6 1,000 1,000 100,000 100,000 100,000 percentage percentage strong) (low to high) adults adults adults adults adults % % % of GDP points points June 2010 June 2010 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Afghanistan 6 0 .. 4 1.1 0.18 .. .. .. 1.5 .. .. Albania 9 4 451 102 21.4 26.87 123 8.7 10.5 68.5 5.9 6.4 Algeria 3 2 683 .. 5.3 4.13 8 .. .. -8.9 6.3 7.3 Angola 4 3 .. .. 5.5 7.82 25 .. .. 29.2 8.1 .. Argentina 4 6 875 503 13.3 33.04 .. 13.3 3.0 28.0 4.1 .. Armenia 6 5 572 192 15.7 22.22 94 21.0 4.8 19.9 10.1 9.3 Australia 9 5 .. .. 31.8 159.30 3,939 5.0 1.2 143.6 3.2 2.9 Austria 7 6 2,442 .. .. 118.37 4,890 7.0 2.3 141.1 .. .. Azerbaijan 6 5 702 .. 8.6 23.05 112 .. .. 23.1 7.8 16.7 Bangladesh 7 2 319 42 5.2 .. .. 6.5 11.2 60.4 6.4 .. Belarus 3 5 .. .. 44.9 29.71 165 16.6 4.2 34.6 1.0 .. Belgium 7 4 3,725 .. 50.0 85.96 1,086 4.5 2.7 119.3 .. 5.6 Benin 3 1 .. .. .. .. .. .. .. 19.1 .. .. Bolivia 1 6 274 72 6.3 15.11 33 8.7 3.5 49.5 8.9 9.5 Bosnia and Herzegovina 5 5 380 344 25.0 27.14 502 15.2 5.9 58.3 4.3 .. Botswana 7 4 481 80 6.9 29.26 .. .. .. -1.0 6.3 .. Brazil 3 5 .. 390 12.2 110.19 1,471 9.5 4.2 97.5 35.4 34.9 Bulgaria 8 6 1,987 456 88.1 78.22 683 10.8 6.4 69.4 5.2 6.2 Burkina Faso 3 1 .. .. .. .. .. .. .. 15.2 .. .. Burundi 2 1 21 1 1.7 0.04 0 .. .. 36.5 .. .. Cambodia 8 0 76 25 3.7 .. 36 .. .. 19.0 .. .. Cameroon 3 2 .. .. .. .. .. .. .. 6.9 10.8 .. Canada 6 6 .. .. 23.7 202.78 2,202 5.7 1.3 178.1 2.3 2.0 Central African Republic 3 2 .. .. .. .. .. .. .. 17.2 10.8 .. Chad 3 1 .. .. .. .. .. .. .. 8.3 10.8 .. Chile 4 5 746 629 15.0 55.56 450 7.4 3.0 98.8 5.2 .. China 6 4 .. .. .. .. .. 5.6 1.6 145.2 3.1 .. Hong Kong SAR, China 10 5 .. .. 24.4 .. .. 12.7 1.1 166.8 5.0 4.9 Colombia 5 5 1,151 .. 13.7 26.31 .. 13.6 4.1 37.2 6.9 .. Congo, Dem. Rep. 3 0 6 .. 0.3 .. .. .. .. 7.6 49.5 .. Congo, Rep. 3 2 .. .. .. .. .. .. .. -15.9 10.8 .. Costa Rica 5 5 .. .. .. 53.35 0 13.9 2.0 54.3 12.8 .. Côte d’Ivoire 3 1 .. .. .. .. .. .. .. 22.8 .. .. Croatia 6 4 .. .. 33.2 88.62 2,121 13.9 7.8 76.9 8.4 .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 6 5 1,680 .. 22.4 38.40 651 6.1 4.6 62.4 4.7 4.7 Denmark 9 4 .. .. 46.7 70.42 2,023 5.7 0.3 223.0 .. .. Dominican Republic 3 6 .. 310 10.0 27.21 .. 9.1 4.0 40.6 10.3 .. Ecuador 3 5 494 .. 1.6 26.01 .. 7.7 2.9 18.9 7.1 .. Egypt, Arab Rep. 3 6 .. .. .. .. .. 6.4 13.4 75.4 5.5 2.1 El Salvador 5 6 737 .. 8.2 22.86 250 13.2 3.6 44.5 .. .. Eritrea 2 0 .. .. .. .. .. .. .. 112.1 .. .. Estonia 7 5 2,752 1,022 22.2 89.09 1,417 8.5 5.2 106.2 4.6 .. Ethiopia 4 2 82 1 1.2 .. .. .. .. 37.1 3.3 7.3 Finland 7 5 .. .. 18.5 38.74 66 6.4 0.7 98.7 .. .. France 7 4 .. .. 23.0 102.55 2,153 4.5 3.6 128.4 .. .. Gabon 3 2 .. .. .. .. .. 16.2 9.8 7.5 10.8 .. Gambia, The 5 0 269 44 5.5 1.48 5 .. .. 38.7 11.5 .. Georgia 7 6 661 349 18.6 28.77 169 18.3 6.3 33.2 15.2 19.5 Germany 7 6 .. .. 16.3 79.74 799 4.8 3.3 131.8 .. .. Ghana 8 3 270 .. 4.4 4.16 4 17.0 16.2 27.9 .. .. Greece 3 5 3,219 1,297 38.8 76.06 3,827 6.1 7.7 112.7 .. .. Guatemala 8 6 1,050 374 33.1 22.18 486 10.5 2.7 37.7 8.3 .. Guinea 3 0 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 3 1 .. .. .. .. .. .. .. 4.9 .. .. Haiti 3 2 330 11 .. 0.58 .. .. .. 25.8 16.2 .. Honduras 6 6 744 .. 1.5 21.89 .. .. .. 54.1 8.6 .. 282 2011 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency Getting Financial access Bank Ratio of Domestic Interest Risk premium credit and outreach capital bank non- credit rate spread on lending to asset performing provided Strength Deposit Loan accounts accounts ratio loans to total by banking of legal  Depth of Prime lending at at Commercial Automated Point- gross loans sector rights credit commercial commercial bank teller of-sale Lending rate minus index information banks banks branches machines terminals rate minus treasury 0–10 index per per per per per deposit rate bill rate (weak to 0–6 1,000 1,000 100,000 100,000 100,000 percentage percentage strong) (low to high) adults adults adults adults adults % % % of GDP points points June 2010 June 2010 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Hungary 7 5 1,571 .. 17.1 54.24 585 8.5 6.7 79.9 5.2 2.6 India 8 4 680 124 9.3 3.55 .. 6.4 2.3 69.4 .. .. Indonesia 3 4 484 181 6.7 13.44 120 10.3 3.3 36.9 5.2 .. Iran, Islamic Rep. 4 4 .. .. 28.8 23.97 1,353 .. .. 37.2 -1.1 .. Iraq 3 0 .. .. .. .. .. .. .. -16.3 7.8 1.8 Ireland 8 5 .. .. 34.1 .. .. 5.6 9.0 219.8 .. .. Israel 9 5 2,254 1,055 19.8 47.38 .. 6.0 1.5 78.1 2.6 2.3 Italy 3 5 763 597 53.0 93.93 2,386 8.0 7.0 141.6 .. 3.8 Jamaica 8 0 1,172 215 7.2 21.89 674 .. .. 59.8 9.5 -3.5 Japan 7 6 .. .. 12.5 .. .. 4.7 1.7 320.5 1.3 1.6 Jordan 4 2 814 160 16.2 .. .. 11.0 6.7 99.3 4.3 .. Kazakhstan 4 5 .. .. 21.6 52.83 173 -9.3 21.2 54.6 .. .. Kenya 10 4 296 70 4.0 6.67 .. 12.7 7.9 44.8 8.8 7.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 7 6 .. .. 12.6 .. .. 10.9 1.2 112.4 2.2 .. Kosovo 8 4 .. .. .. .. .. .. 4.4 14.3 10.1 .. Kuwait 4 4 .. .. 15.1 50.05 904 12.1 9.7 65.1 3.3 5.2 Kyrgyz Republic 10 3 115 25 6.3 .. .. .. .. 14.0 19.2 12.5 Lao PDR 4 0 .. .. 1.7 3.06 .. .. .. 10.5 19.3 11.5 Latvia 9 5 1,219 687 12.0 .. .. 7.4 16.4 93.2 8.2 5.8 Lebanon 3 5 1,310 .. 29.1 38.55 1,293 7.0 6.0 165.0 2.3 4.7 Lesotho 6 0 199 18 1.9 7.13 .. 7.9 4.0 -15.5 8.2 5.2 Liberia 4 1 .. .. .. .. .. .. .. 149.5 10.1 .. Libya .. .. .. .. .. .. .. .. .. -65.9 3.5 .. Lithuania 5 6 2,142 381 28.8 51.69 1,413 7.9 19.3 69.3 3.6 -0.1 Macedonia, FYR 7 4 1,302 962 22.1 45.98 1,297 11.4 8.9 44.0 3.0 .. Madagascar 2 0 34 21 1.0 0.96 2 .. .. 11.6 33.5 37.4 Malawi 7 0 124 17 1.8 1.48 2 .. .. 32.0 21.8 15.1 Malaysia 10 6 2,227 973 11.6 43.25 941 9.0 3.7 137.4 3.0 3.0 Mali 3 1 .. .. .. .. .. .. .. 10.7 .. .. Mauritania 3 1 37 .. 3.8 0.74 .. .. .. .. 15.5 13.1 Mauritius 5 3 2,110 417 19.4 37.71 647 .. .. 109.7 10.8 .. Mexico 5 6 1,014 .. 14.0 40.15 .. 9.7 3.1 44.1 5.1 1.6 Moldova 8 0 .. .. 9.7 .. .. 16.0 16.3 41.6 5.6 9.2 Mongolia 6 3 1,935 272 56.7 18.18 448 .. .. 32.2 8.4 15.0 Morocco 3 5 277 .. 11.6 16.65 46 7.6 5.5 100.5 .. .. Mozambique 2 4 112 20 2.9 4.32 34 7.7 1.8 22.8 6.2 5.1 Myanmar .. .. .. .. .. .. .. .. .. .. 5.0 .. Namibia 8 5 466 356 7.3 27.31 217 7.9 2.7 43.5 4.9 2.9 Nepal 6 2 165 38 3.2 1.13 .. .. .. 69.6 5.5 1.7 Netherlands 6 5 1,772 .. 26.1 63.78 2,286 4.3 .. 224.4 -0.6 .. New Zealand 10 5 .. .. 31.7 72.34 3,916 .. .. 154.2 6.3 7.6 Nicaragua 3 5 198 185 6.8 .. .. .. .. 67.5 8.0 .. Niger 3 1 .. .. .. .. .. .. .. 12.2 .. .. Nigeria 8 0 .. .. .. .. .. 18.4 6.6 35.9 5.1 14.6 Norway 7 4 .. .. 35.0 59.73 2,827 6.0 1.5 .. 2.0 .. Oman 4 2 .. .. 22.1 .. .. 13.5 3.5 41.9 3.3 .. Pakistan 6 4 226 47 7.5 3.39 47 10.1 12.2 48.4 5.9 2.0 Panama 6 6 757 435 18.9 36.94 427 11.7 1.4 81.6 4.8 .. Papua New Guinea 5 3 .. .. 2.8 .. .. .. .. 39.1 7.8 3.0 Paraguay 3 6 80 89 6.2 .. .. 8.7 1.6 25.5 26.8 .. Peru 7 6 716 367 7.5 17.67 40 9.9 2.7 18.1 18.2 .. Philippines 3 3 517 .. 10.5 13.33 .. 11.1 4.1 49.4 5.8 5.3 Poland 9 4 1,527 .. 32.6 42.16 253 9.0 7.6 61.5 .. .. Portugal 3 5 .. .. 55.9 189.60 2,548 6.5 3.2 196.1 .. .. Puerto Rico 7 5 1,026 .. 16.6 43.33 1,398 .. .. .. .. .. Qatar 3 2 .. .. .. .. .. .. .. 75.7 2.8 .. 2011 World Development Indicators 283 5.5 Financial access, stability, and efficiency Getting Financial access Bank Ratio of Domestic Interest Risk premium credit and outreach capital bank non- credit rate spread on lending to asset performing provided Strength Deposit Loan accounts accounts ratio loans to total by banking of legal  Depth of Prime lending at at Commercial Automated Point- gross loans sector rights credit commercial commercial bank teller of-sale Lending rate minus index information banks banks branches machines terminals rate minus treasury 0–10 index per per per per per deposit rate bill rate (weak to 0–6 1,000 1,000 100,000 100,000 100,000 percentage percentage strong) (low to high) adults adults adults adults adults % % % of GDP points points June 2010 June 2010 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Romania 8 5 .. 431 27.6 50.63 460 7.6 15.3 52.7 5.3 6.4 Russian Federation 3 5 .. .. 2.9 65.60 275 15.7 9.7 33.8 6.7 .. Rwanda 8 4 202 2 3.1 0.38 1 13.0 13.1 .. 9.8 8.9 Saudi Arabia 5 6 .. .. .. .. .. 11.9 3.3 0.6 .. .. Senegal 3 1 .. .. .. .. .. 9.3 18.7 26.6 .. .. Serbia 8 5 .. .. 44.9 41.31 959 21.0 15.5 44.8 0.0 1.4 Sierra Leone 6 0 .. .. .. .. .. 18.9 16.5 10.7 14.8 9.0 Singapore 10 4 2,305 899 11.0 50.64 1,887 10.5 2.3 91.2 5.1 5.0 Slovak Republic 9 4 .. .. 25.7 47.76 611 9.6 5.3 53.8 4.3 .. Slovenia 5 2 1,394 .. 15.7 99.47 1,925 8.3 2.3 94.5 4.5 4.8 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 9 6 788 297 8.0 54.85 .. 6.7 5.9 183.5 3.2 3.9 Spain 6 5 741 310 40.5 157.10 3,523 6.8 5.1 228.4 .. .. Sri Lanka 4 5 1,652 487 9.1 10.46 .. .. .. 39.6 5.1 2.7 Sudan 5 0 .. .. .. .. .. .. .. 20.0 .. .. Swaziland 6 5 270 98 2.9 15.96 52 16.9 8.1 9.1 6.0 3.4 Sweden 5 4 .. .. 22.8 36.94 .. 5.0 2.0 143.8 .. .. Switzerland 8 5 .. .. .. 93.70 2,004 5.5 0.4 191.0 2.7 2.8 Syrian Arab Republic 1 2 157 23 2.2 0.95 .. .. .. 45.1 3.7 .. Tajikistan 3 0 .. .. 3.9 2.97 2 .. .. 27.5 17.1 .. Tanzania 8 0 .. .. 1.8 2.63 11 .. .. 18.1 7.1 7.9 Thailand 4 5 1,498 276 10.9 65.48 .. 9.8 5.3 136.9 4.9 4.7 Timor-Leste 1 0 .. .. .. .. .. .. .. -18.4 10.3 .. Togo 3 1 .. .. .. .. .. .. .. 30.2 .. .. Trinidad and Tobago 8 4 .. .. .. .. .. .. .. 26.5 8.5 9.2 Tunisia 3 5 672 176 13.6 14.26 172 .. 13.2 75.2 .. .. Turkey 4 5 1,851 315 17.3 40.99 3,046 13.3 5.6 63.0 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 7 4 154 21 1.9 2.24 3 13.4 4.2 11.2 11.2 13.9 Ukraine 9 3 3,755 .. 3.3 70.09 293 13.1 40.2 88.5 7.1 .. United Arab Emirates 4 5 .. .. .. .. .. 16.0 4.8 114.5 .. .. United Kingdom 9 6 .. .. .. 127.07 2,177 5.4 3.5 228.9 .. 0.1 United States 8 6 1,761 .. 35.4 169.23 2,156 11.0 5.4 230.5 .. 3.1 Uruguay 5 6 507 439 13.9 30.57 275 8.9 1.0 27.9 10.9 3.4 Uzbekistan 2 3 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 2 0 518 484 18.5 27.99 .. 9.4 3.0 20.5 3.5 .. Vietnam 8 5 .. .. 3.3 .. .. .. .. 123.0 3.1 2.0 West Bank and Gaza 0 3 .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2 2 106 6 1.8 2.44 17 .. .. 19.3 7.3 4.5 Zambia 9 5 293 19 3.5 4.54 11 .. .. 18.5 15.0 6.7 Zimbabwe 6 0 139 .. 2.8 .. .. .. .. .. 457.5 330.2 World 5.5 u 3.0 u 9.4 m 4.2 m 169.0 w 6.2 m Low income 4.9 1.3 .. .. 35.1 11.5 Middle income 5.1 3.1 10.1 4.8 89.4 6.3 Lower middle income 4.6 2.6 10.0 5.1 110.3 7.3 Upper middle income 5.7 3.6 9.7 4.2 63.3 5.5 Low & middle income 5.0 2.6 .. 5.3 88.4 6.8 East Asia & Pacific 5.8 1.9 .. .. 134.2 7.1 Europe & Central Asia 6.3 4.1 13.3 9.3 47.1 5.7 Latin America & Carib. 5.2 3.4 9.6 3.0 67.1 7.7 Middle East & N. Africa 2.5 3.1 .. .. 40.9 4.3 South Asia 5.4 2.1 6.4 10.5 65.6 5.9 Sub-Saharan Africa 4.6 1.6 .. .. 78.5 8.5 High income 6.7 4.3 6.8 3.4 201.8 .. Euro area 6.3 4.1 6.5 3.6 152.0 .. 284 2011 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency About the data Access to finance can expand opportunities for all with all nonfinancial and financial assets. Data are from consumer loans, business loans, trade loans, student higher levels of access and use of banking services internally consistent financial statements. loans, emergency loans, agricultural loans, and the associated with lower financing obstacles for people The ratio of bank nonperforming loans to total gross like. • Commercial banks branches are retail locations and businesses. A stable financial system that pro- loans, a measure of bank health and efficiency, helps offering a wide array of face-to-face and automated motes efficient savings and investment is also crucial identify problems with asset quality in the loan portfo- financial services. • Automated teller machines are for a thriving democracy and market economy. lio. A high ratio may signal deterioration of the credit computerized telecommunications devices that pro- There are several aspects of access to financial ser- portfolio. International guidelines recommend that vide clients of a financial institution with access to vices: availability, cost, and quality of services. The loans be classified as nonperforming when payments financial transactions in a public place. • Point-of-sale development and growth of credit markets depend on of principal and interest are 90 days or more past terminals are the equipment used to manage the sell- access to timely, reliable, and accurate data on bor- due or when future payments are not expected to be ing process by a salesperson-accessible interface in rowers’ credit experiences. Access to credit can be received in full. Domestic credit provided by the bank- the location where a transaction takes place. • Bank improved by making it easy to create and enforce col- ing sector as a share of GDP is a measure of bank- capital to asset ratio is the ratio of bank capital and lateral agreements and increasing information about ing sector depth and financial sector development in reserves to total assets. Capital and reserves include potential borrowers’ creditworthiness. Lenders look at terms of size. In a few countries governments may hold funds contributed by owners, retained earnings, gen- a borrower’s credit history and collateral. Where credit international reserves as deposits in the banking sys- eral and special reserves, provisions, and valuation registries and effective collateral laws are absent— tem rather than in the central bank. Since the claims adjustments. • Ratio of bank nonperforming loans to as in many developing countries—banks make fewer on the central government are a net item (claims on total gross loans is the value of nonperforming loans loans. Indicators that cover getting credit include the the central government minus central government divided by the total value of the loan portfolio (including strength of legal rights index and the depth of credit deposits), this net figure may be negative, resulting nonperforming loans before the deduction of loan loss information index. in a negative figure of domestic credit provided by the provisions). The amount recorded as nonperforming The “unbanked” have to resort to informal ser- banking sector. should be the gross value of the loan as recorded vices to manage their money—saving under the The interest rate spread—the margin between on the balance sheet, not just the amount overdue. mattress, borrowing from family and friends, or the cost of mobilizing liabilities and the earnings on • Domestic credit provided by banking sector is all money lenders—that are usually less reliable and assets—is a measure of financial sector efficiency in credit to various sectors on a gross basis, except to more costly than formal banking institutions. The intermediation. A narrow interest rate spread means the central government, which is net. The banking table presents data on fi nancial access cover- low transaction costs, which reduces the cost of funds sector includes monetary authorities, deposit money ing deposits and loans, and outreach indicators for investment, crucial to economic growth. banks, and other banking institutions for which data such as the number of branches, automatic teller The risk premium on lending is the spread between are available. • Interest rate spread is the interest rate machines, and point-of-sale terminals. the lending rate to the private sector and the “risk- charged by banks on loans to prime customers minus Data on financial access cover 142 coun- free” government rate. Spreads are expressed as the interest rate paid by commercial or similar banks tries and present indicators on savings, credit, annual averages. A small spread indicates that the for demand, time, or savings deposits. • Risk premium and payment services in banks and regulated market considers its best corporate customers to be on lending is the interest rate charged by banks on nonbank fi nancial institutions. Data were col- low risk. A negative rate indicates that the market loans to prime private sector customers minus the lected for commercial banks and regulated considers its best corporate clients to be lower risk “risk-free” treasury bill interest rate at which short- nonbank financial institutions such as cooperatives, than the government. term government securities are issued or traded in credit unions, specialized state financial institutions, the market. Definitions and microfinance institutions. The size and mobility of international capital flows • Strength of legal rights index measures the degree make it increasingly important to monitor the strength to which collateral and bankruptcy laws protect the of financial systems. Robust financial systems can rights of borrowers and lenders and thus facilitate increase economic activity and welfare, but instability lending. Higher values indicate that the laws are bet- in the financial system can disrupt financial activity and ter designed to expand access to credit. • Depth of impose widespread costs on the economy. The ratio credit information index measures rules affecting Data sources of bank capital to assets, a measure of bank solvency the scope, accessibility, and quality of information Data on getting credit are from the World Bank’s and resiliency, shows the extent to which banks can available through public or private credit registries. Doing Business project (www.doingbusiness.org). deal with unexpected losses. Capital includes tier 1 Higher values indicate the availability of more credit Data on financial access and outreach are from capital (paid-up shares and common stock), a com- information. • Deposit accounts are accounts at com- the Consultative Group to Assist the Poor and the mon feature in all countries’ banking systems, and mercial banks that allow money to be deposited and World Bank Group’s Financial Access 2010. Data total regulatory capital, which includes several types of withdrawn by the account holder. The major types of on bank capital and nonperforming loans are from subordinated debt instruments that need not be repaid deposits are checking accounts, savings accounts, the IMF’s Global Financial Stability Report. Data if the funds are required to maintain minimum capital and time deposits. • Loan accounts at commer- on credit and interest rates are from the IMF’s levels (tier 2 and tier 3 capital). Total assets include cial banks include loans from banks to individuals, International Financial Statistics. businesses, and others, including home mortgages, 2011 World Development Indicators 285 5.6 Tax policies Tax revenue collected Taxes payable by central government by businesses Labor tax and Time to prepare, Profi t tax contributions Other taxes Total tax rate Number file, and pay taxes % of commercial % of commercial % of commercial % of commercial % of GDP of payments hours profi ts profi ts profi ts profi ts 2000 2009 June 2010 June 2010 June 2010 June 2010 June 2010 June 2010 Afghanistan .. 7.3 8 275 0.0 0.0 36.4 36.4 Albania 16.1 .. 44 360 8.5 27.3 4.9 40.6 Algeria .. 34.3a 34 451 6.6 29.7 35.7 72.0 Angola .. .. 31 282 24.6 9.0 19.5 53.2 Argentina 9.8a .. 9 453 2.8 29.4 76.0 108.2 Armenia .. 16.4 50 581 16.6 23.0 1.1 40.7 Australia 23.0a 22.1a 11 109 25.9 20.7 1.3 47.9 Austria 19.9a 18.7a 22 170 15.7 34.6 5.1 55.5 Azerbaijan .. 16.7 18 306 13.8 24.8 2.2 40.9 Bangladesh 7.6 8.6 21 302 25.7 0.0 9.2 35.0 Belarus 16.6 19.4 82 798 22.0 39.3 19.2 80.4 Belgium 27.4 a 24.0a 11 156 4.8 50.4 1.8 57.0 Benin 15.5a 16.1a 55 270 14.8 27.3 23.9 66.0 Bolivia 13.2a 17.0a 42 1,080 0.0 15.5 64.6 80.0 Bosnia and Herzegovina .. 19.6a 51 422 5.3 12.6 5.0 23.0 Botswana .. .. 19 152 15.9 0.0 3.6 19.5 Brazil 14.0 15.6 10 2,600 21.4 40.9 6.6 69.0 Bulgaria 17.9 20.9 17 616 4.6 20.4 3.9 29.0 Burkina Faso 10.5a 12.9a 46 270 16.1 22.6 6.2 44.9 Burundi 13.6 .. 32 211 19.4 7.8 126.2 153.4 Cambodia 8.2a 9.6a 39 173 18.9 0.1 3.5 22.5 Cameroon 11.2 .. 44 654 29.9 18.3 0.9 49.1 Canada 15.3a 11.8a 8 131 9.8 12.6 6.9 29.2 Central African Republic .. .. 54 504 176.8 8.1 18.9 203.8 Chad .. .. 54 732 31.3 28.4 5.7 65.4 Chile 16.7a 15.3a 9 316 18.0 3.8 3.2 25.0 China 6.8 10.3 7 398 6.0 49.6 7.9 63.5 Hong Kong SAR, China 9.1a 13.0a 3 80 18.7 5.3 0.1 24.1 Colombia 11.0a 11.9a 20 208 17.7 33.9 27.1 78.7 Congo, Dem. Rep. 3.5 .. 32 336 58.9 7.9 272.8 339.7 Congo, Rep. 5.9 .. 61 606 0.0 32.9 32.6 65.5 Costa Rica .. 13.9a 42 272 18.9 29.5 6.6 55.0 Côte d’Ivoire .. 16.4 a 64 270 8.8 20.1 15.5 44.4 Croatia 22.4 19.1 17 196 11.4 19.4 1.6 32.5 Cuba .. .. .. .. .. .. .. .. Czech Republic 15.4 13.5 12 557 7.4 38.4 3.0 48.8 Denmark 30.8a 34.5a 9 135 21.9 3.6 3.7 29.2 Dominican Republic .. 14.9a 9 324 20.5 18.3 1.8 40.7 Ecuador .. .. 8 654 18.4 13.7 3.2 35.3 Egypt, Arab Rep. 13.4 15.7 29 433 13.2 25.8 3.6 42.6 El Salvador 10.7a 12.5a 53 320 17.0 17.2 0.8 35.0 Eritrea .. .. 18 216 8.8 0.0 75.8 84.5 Estonia 15.8a 17.6a 7 81 8.0 39.2 2.4 49.6 Ethiopia 8.1 .. 19 198 26.8 0.0 4.3 31.1 Finland 24.7a 21.3a 8 243 15.9 27.7 1.0 44.6 France 23.2a 19.6a 7 132 8.2 51.7 5.9 65.8 Gabon .. .. 26 488 18.4 22.7 2.3 43.5 Gambia, The .. .. 50 376 41.4 12.9 238.0 292.3 Georgia 7.7 23.2 18 387 13.3 0.0 2.0 15.3 Germany 11.9a 12.0a 16 215 23.0 22.0 3.3 48.2 Ghana 17.2 12.5 33 224 18.1 14.1 0.5 32.7 Greece 23.3a 19.1a 10 224 13.9 31.7 1.6 47.2 Guatemala 10.1 10.4 24 344 25.9 14.3 0.7 40.9 Guinea 11.1 .. 56 416 19.4 24.5 10.8 54.6 Guinea-Bissau .. .. 46 208 14.9 24.8 6.1 45.9 Haiti .. .. 42 160 23.3 12.4 4.3 40.1 Honduras .. 14.4 a 47 224 26.7 10.7 10.9 48.3 286 2011 World Development Indicators 5.6 STATES AND MARKETS Tax policies Tax revenue collected Taxes payable by central government by businesses Labor tax and Time to prepare, Profi t tax contributions Other taxes Total tax rate Number file, and pay taxes % of commercial % of commercial % of commercial % of commercial % of GDP of payments hours profi ts profi ts profi ts profi ts 2000 2009 June 2010 June 2010 June 2010 June 2010 June 2010 June 2010 Hungary 21.9a 23.5a 14 277 16.7 34.4 2.2 53.3 India 9.0 9.8 56 258 24.0 18.2 21.1 63.3 Indonesia 11.6 11.4 51 266 26.6 10.6 0.1 37.3 Iran, Islamic Rep. 6.3 9.3 20 344 17.8 25.9 0.4 44.1 Iraq .. .. 13 312 14.9 13.5 0.0 28.4 Ireland 26.0a 20.8a 9 76 11.9 11.6 3.0 26.5 Israel 28.7a 23.0a 33 235 23.8 5.3 2.6 31.7 Italy 23.2a 23.0a 15 285 22.8 43.4 2.4 68.6 Jamaica .. 21.9a 72 414 28.6 13.0 8.5 50.1 Japan .. 9.2a 14 355 27.9 14.7 6.0 48.6 Jordan 19.0 16.2 26 101 15.2 12.4 3.6 31.2 Kazakhstan 10.2 8.1 9 271 16.3 11.5 1.9 29.6 Kenya 16.8 19.6 41 393 33.1 6.8 9.9 49.7 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 15.4 15.5 14 250 15.3 12.9 1.6 29.8 Kosovo .. 21.1 33 163 10.2 5.6 0.6 16.5 Kuwait 1.3 0.9 15 118 4.7 10.7 0.0 15.5 Kyrgyz Republic 11.7 15.4 48 202 8.9 21.5 26.7 57.2 Lao PDR .. 12.5 34 362 25.2 5.6 2.9 33.7 Latvia 14.2 12.6 7 293 6.5 27.2 4.8 38.5 Lebanon 11.9a 17.3a 19 180 6.1 24.1 0.0 30.2 Lesotho 37.4 60.0 21 324 16.4 0.0 3.2 19.6 Liberia .. 0.3 32 158 0.0 5.4 38.3 43.7 Libya .. .. .. .. .. .. .. .. Lithuania 14.6a 13.8a 11 175 0.0 35.1 3.6 38.7 Macedonia, FYR .. 19.7 40 119 6.3 0.6 3.8 10.6 Madagascar 11.3a 13.0a 23 201 15.8 20.3 1.6 37.7 Malawi .. .. 19 157 23.3 1.1 0.7 25.1 Malaysia 13.7 15.7 12 145 16.7 15.6 1.4 33.7 Mali 13.2a 14.7a 59 270 12.9 32.6 6.7 52.2 Mauritania .. .. 38 696 44.2 17.6 6.6 68.4 Mauritius .. 19.2a 7 161 11.8 5.0 7.3 24.1 Mexico 11.7 .. 6 404 23.1 26.1 1.3 50.5 Moldova 14.7 17.8 48 228 0.0 30.2 0.7 30.9 Mongolia 14.5 18.0 43 192 9.5 12.4 1.0 23.0 Morocco 19.9a 23.8a 28 358 18.1 22.2 1.4 41.7 Mozambique .. .. 37 230 27.7 4.5 2.1 34.3 Myanmar 3.0 .. .. .. .. .. .. .. Namibia 27.5 27.3 37 375 4.0 1.0 4.6 9.6 Nepal 8.7 12.2 34 338 16.2 11.3 10.7 38.2 Netherlands 22.3a 22.7a 9 134 20.9 17.9 1.7 40.5 New Zealand 29.2a 30.8a 8 192 30.4 3.0 0.9 34.3 Nicaragua 13.8 17.8 64 222 24.8 19.2 19.2 63.2 Niger .. 11.5a 41 270 20.1 19.6 6.8 46.5 Nigeria .. 0.3 35 938 21.8 9.7 0.7 32.2 Norway 27.4 a 25.4 a 4 87 24.4 15.9 1.3 41.6 Oman 7.2 .. 14 62 9.7 11.8 0.1 21.6 Pakistan 10.1 9.3 47 560 14.3 15.0 2.3 31.6 Panama 10.2 .. 62 482 17.0 22.6 10.5 50.1 Papua New Guinea 19.0 .. 33 194 22.0 11.7 8.6 42.3 Paraguay 10.9 13.0 35 311 9.6 18.6 6.7 35.0 Peru 12.2 13.4 9 380 26.0 11.0 3.2 40.2 Philippines 13.7 12.8 47 195 21.3 10.3 14.2 45.8 Poland 16.0a 16.4 a 29 325 17.7 22.1 2.5 42.3 Portugal 20.6a 19.7a 8 298 14.9 26.8 1.6 43.3 Puerto Rico .. .. 16 218 26.3 14.4 27.0 67.7 Qatar .. 19.8 3 36 0.0 11.3 0.0 11.3 2011 World Development Indicators 287 5.6 Tax policies Tax revenue collected Taxes payable by central government by businesses Labor tax and Time to prepare, Profi t tax contributions Other taxes Total tax rate Number file, and pay taxes % of commercial % of commercial % of commercial % of commercial % of GDP of payments hours profi ts profi ts profi ts profi ts 2000 2009 June 2010 June 2010 June 2010 June 2010 June 2010 June 2010 Romania 11.7a 17.9a 113 222 10.4 32.3 2.2 44.9 Russian Federation 13.6a 12.9a 11 320 9.0 31.8 5.7 46.5 Rwanda .. .. 26 148 21.2 5.7 4.4 31.3 Saudi Arabia .. .. 14 79 2.1 12.4 0.0 14.5 Senegal 16.1 .. 59 666 14.8 24.1 7.0 46.0 Serbia .. 21.0 66 279 11.6 20.2 2.2 34.0 Sierra Leone 10.2 10.8 29 357 0.0 11.3 224.3 235.6 Singapore 15.4 13.8 5 84 7.4 14.9 3.1 25.4 Slovak Republic .. 12.4 a 31 257 7.0 39.6 2.1 48.7 Slovenia 20.6 18.3 22 260 14.8 18.2 2.4 35.4 Somalia .. .. .. .. .. .. .. .. South Africa 24.0a 25.4 a 9 200 24.4 2.5 3.7 30.5 Spain 16.2a 8.5a 8 197 20.9 35.0 0.7 56.5 Sri Lanka 14.5 13.3 62 256 27.4 16.9 20.3 64.7 Sudan 6.4 .. 42 180 13.8 19.2 3.1 36.1 Swaziland 24.9 .. 33 104 28.1 4.0 4.7 36.8 Sweden 23.6a 21.5a 2 122 16.4 36.6 1.6 54.6 Switzerland 11.1 10.9 19 63 8.9 17.5 3.6 30.1 Syrian Arab Republic .. .. 20 336 23.2 19.3 0.5 42.9 Tajikistan 7.7 .. 54 224 17.7 28.5 39.9 86.0 Tanzania .. .. 48 172 19.9 18.0 7.3 45.2 Thailand .. 15.1a 23 264 28.9 5.7 2.8 37.4 Timor-Leste .. .. 6 276 0.0 0.0 0.2 0.2 Togo .. 17.0a 53 270 8.8 28.3 13.7 50.8 Trinidad and Tobago 22.1 31.6 40 210 21.6 5.8 5.8 33.1 Tunisia 21.3 21.9 8 144 15.0 25.2 22.5 62.8 Turkey .. 18.9a 15 223 17.0 23.1 4.4 44.5 Turkmenistan .. .. .. .. .. .. .. .. Uganda 10.4 12.0 32 161 23.3 11.3 1.1 35.7 Ukraine 14.1 16.4 135 657 10.4 43.3 1.8 55.5 United Arab Emirates 1.7 .. 14 12 0.0 14.1 0.0 14.1 United Kingdom 28.4 a 26.0a 8 110 23.1 10.8 3.3 37.3 United States 12.5a 8.2a 11 187 27.6 10.0 9.2 46.8 Uruguay 14.7 18.8 53 336 23.6 15.6 2.9 42.0 Uzbekistan .. .. 44 205 1.6 27.1 66.9 95.6 Venezuela, RB 13.3 .. 70 864 10.0 18.0 24.6 52.6 Vietnam .. .. 32 941 12.5 20.3 0.3 33.1 West Bank and Gaza .. .. 27 154 16.2 0.0 0.6 16.8 Yemen, Rep. 9.4 .. 44 248 35.1 11.3 1.4 47.8 Zambia 18.6 17.1 37 132 1.7 10.4 4.0 16.1 Zimbabwe .. .. 49 242 24.0 6.2 10.1 40.3 World 15.5 w 14.2 w 30 u 282 u 17.9 u 16.3 u 13.7 u 47.8 u Low income 10.4 11.6 38 271 24.8 12.6 39.2 76.5 Middle income 10.9 14.1 34 337 17.1 15.8 8.6 41.5 Lower middle income 8.2 11.3 36 326 16.3 14.3 9.6 40.2 Upper middle income .. 15.8 32 351 18.0 17.5 7.5 43.0 Low & middle income 10.9 14.0 35 319 19.2 14.9 17.0 51.1 East Asia & Pacific 7.7 11.1 27 233 18.4 10.3 7.8 36.5 Europe & Central Asia .. 15.0 47 340 10.0 22.7 9.6 42.2 Latin America & Carib. 13.0 .. 34 408 21.4 15.3 11.2 47.9 Middle East & N. Africa 12.0 17.5 25 263 16.6 18.9 6.1 41.6 South Asia 9.3 9.7 31 283 17.8 7.8 14.2 39.9 Sub-Saharan Africa .. 17.9 37 311 23.3 13.2 31.7 68.2 High income 16.4 14.2 15 179 14.3 20.1 4.2 38.6 Euro area 19.1 17.1 15 190 13.9 29.2 2.4 45.5 Note: Regional aggregates for Taxes payable by businesses are for developing countries only. a. Data were reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fund’s Government Finance Statistics Manual 2001. 288 2011 World Development Indicators 5.6 STATES AND MARKETS Tax policies About the data Definitions Taxes are the main source of revenue for most To make the data comparable across countries, • Tax revenue collected by central government governments. The sources of tax revenue and their several assumptions are made about businesses. is compulsory transfers to the central government relative contributions are determined by government The main assumptions are that they are limited liabil- for public purposes. Certain compulsory transfers policy choices about where and how to impose taxes ity companies, they operate in the country’s most such as fines, penalties, and most social security and by changes in the structure of the economy. Tax populous city, they are domestically owned, they per- contributions are excluded. Refunds and corrections policy may refl ect concerns about distributional form general industrial or commercial activities, and of erroneously collected tax revenue are treated as effects, economic efficiency (including corrections they have certain levels of start-up capital, employ- negative revenue. The analytic framework of the for externalities), and the practical problems of ees, and turnover. For details about the assump- International Monetary Fund’s (IMF) Government administering a tax system. There is no ideal level tions, see the World Bank’s Doing Business 2011. Finance Statistics Manual 2001 (GFSM 2001) is of taxation. But taxes influence incentives and thus The Doing Business methodology on business based on accrual accounting and balance sheets. the behavior of economic actors and the economy’s taxes is consistent with the Total Tax Contribution For countries still reporting government finance data competitiveness. framework developed by PricewaterhouseCoopers, on a cash basis, the IMF adjusts reported data to the The level of taxation is typically measured by tax which measures the taxes that are borne by compa- GFSM 2001 accrual framework. These countries are revenue as a share of gross domestic product (GDP). nies and affect their income statements. However, footnoted in the table. • Number of tax payments Comparing levels of taxation across countries pro- PricewaterhouseCoopers bases its calculation on by businesses is the total number of taxes paid by vides a quick overview of the fiscal obligations and data from the largest companies in the economy, businesses during one year. When electronic filing is incentives facing the private sector. The table shows while Doing Business focuses on a standardized available, the tax is counted as paid once a year even only central government data, which may significantly medium-sized company. if payments are more frequent. • Time to prepare, understate the total tax burden, particularly in coun- file, and pay taxes is the time, in hours per year, it tries where provincial and municipal governments are takes to prepare, file, and pay (or withhold) three large or have considerable tax authority. major types of taxes: the corporate income tax, the Low ratios of tax revenue to GDP may reflect weak value-added or sales tax, and labor taxes, includ- administration and large-scale tax avoidance or eva- ing payroll taxes and social security contributions. sion. Low ratios may also reflect a sizable parallel • Profit tax is the amount of taxes on profits paid economy with unrecorded and undisclosed incomes. by the business. • Labor tax and contributions is Tax revenue ratios tend to rise with income, with the amount of taxes and mandatory contributions on higher income countries relying on taxes to finance labor paid by the business. • Other taxes includes a much broader range of social services and social the amounts paid for property taxes, turnover taxes, security than lower income countries are able to. and other small taxes such as municipal fees and The total tax rate payable by businesses provides vehicle and fuel taxes. • Total tax rate measures a comprehensive measure of the cost of all the taxes the amount of taxes and mandatory contributions a business bears. It differs from the statutory tax payable by the business in the second year of opera- rate, which is the factor applied to the tax base. In tion, expressed as a share of commercial profi ts. computing business tax rates, actual tax payable is Doing Business 2011 reports the total tax rate for divided by commercial profit. The indicators cover- fiscal 2009. Taxes withheld (such as sales or value ing taxes payable by businesses measure all taxes added tax or personal income tax) but not paid by and contributions that are government mandated the company are excluded. For further details on the (at any level—federal, state, or local), apply to stan- method used for assessing the total tax payable, see dardized businesses, and have an impact in their the World Bank’s Doing Business 2011. income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general gov- ernment) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value-added taxes. The indicators account for government-mandated contributions paid Data sources by the employer to a requited private pension fund Data on central government tax revenue are from or workers insurance fund but exclude value-added print and electronic editions of the IMF’s Govern- taxes because they do not affect the accounting prof- ment Finance Statistics Yearbook. Data on taxes its of the business—that is, they are not reflected in payable by businesses are from Doing Business the income statement. 2011 (www.doingbusiness.org). 2011 World Development Indicators 289 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers % of central Trend indicator values government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Afghanistan .. 1.8 .. 4.6 400 256 5.4 2.7 .. .. 33 344 Albania 1.2 2.1 5.4 .. 68 15 5.2 1.0 .. .. .. 25 Algeria 3.4 3.8 .. 15.0 305 334 2.7 2.3 .. .. 418 942 Angola 6.4 4.2 .. .. 118 117 1.9 1.4 2 .. 200 11 Argentina 1.1 0.8 5.5 .. 102 104 0.6 0.5 2 .. 209 11 Armenia 3.6 4.0 .. 17.1 42 56 2.9 3.4 .. .. 2 1 Australia 1.9 1.9 7.8 7.3 52 57 0.5 0.5 43 51 364 757 Austria 1.0 0.9 2.5 2.3 41 26 1.0 0.6 21 33 25 330 Azerbaijan 2.3 3.5 .. 22.9 87 82 2.5 2.0 .. .. 3 49 Bangladesh 1.4 1.1 14.9 10.0 137 221 0.2 0.3 .. .. 205 12 Belarus 1.3 1.8 5.3 5.5 91 183 1.9 3.7 295 292 41 .. Belgium 1.4 1.1 3.2 2.5 39 39 0.9 0.8 24 217 39 84 Benin 0.6 1.0 4.7 6.8 7 7 0.3 0.2 .. .. 6 2 Bolivia 1.9 1.6 7.6 7.9 70 83 2.0 1.8 .. .. 19 5 Bosnia and Herzegovina 3.6 1.5 .. 3.8 76 11 4.1 0.5 4 .. 25 .. Botswana 3.3 3.1 .. .. 10 11 1.3 1.1 .. .. 52 10 Brazil 1.8 1.6 8.1 6.4 673 713 0.8 0.7 26 49 124 210 Bulgaria 2.7 2.3 8.6 7.2 114 65 3.2 1.8 2 7 7 153 Burkina Faso 1.2 1.3 9.8 10.4 11 11 0.2 0.2 .. .. .. 1 Burundi 6.0 3.8 30.3 .. 46 51 1.4 1.1 .. .. 1 .. Cambodia 2.2 1.2 16.8 13.9 360 191 6.1 2.4 1 .. .. 4 Cameroon 1.3 1.5 12.4 .. 22 23 0.4 0.3 .. .. 1 1 Canada 1.1 1.4 6.0 7.5 69 66 0.4 0.3 110 177 550 80 Central African Republic 1.0 1.8 .. .. 5 3 0.3 0.2 .. .. .. .. Chad 1.9 6.4 .. .. 35 35 1.1 0.8 .. .. 15 23 Chile 3.7 3.1 17.7 13.6 117 104 1.9 1.4 1 133 179 231 China 1.8a 2.0a 19.8a 16.1a 3,910 2,945 0.5 0.4 272 870 2,015 595 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2.8 4.1 15.6 20.9 247 442 1.6 2.3 .. .. 62 250 Congo, Dem. Rep. 1.0 1.1 11.4 .. 93 159 0.5 0.6 .. .. 74 .. Congo, Rep. 1.4 1.2 5.9 .. 15 12 1.2 0.8 .. .. 0 0 Costa Rica .. .. .. .. 15 10 1.0 0.5 .. 0 .. .. Côte d’Ivoire .. 1.6 .. 8.8 15 19 0.2 0.2 .. .. 33 .. Croatia 3.1 1.8 7.8 5.0 101 22 5.1 1.1 2 .. 70 3 Cuba .. 3.2 .. .. 85 76 1.8 1.5 .. .. .. .. Czech Republic 2.0 1.5 6.1 4.1 63 27 1.2 0.5 78 19 16 5 Denmark 1.5 1.4 4.3 3.3 22 19 0.8 0.6 20 12 64 47 Dominican Republic 0.7 0.6 .. 3.8 40 40 1.1 0.9 .. .. 13 6 Ecuador 1.7 3.3 .. .. 58 59 1.2 1.0 .. .. 12 46 Egypt, Arab Rep. 3.2 2.1 12.3 7.1 679 866 3.1 3.2 .. .. 788 217 El Salvador 0.9 0.6 4.3 3.0 29 33 1.3 1.3 .. .. 16 4 Eritrea 36.4 .. .. .. 200 202 14.5 9.4 0 .. 17 4 Estonia 1.4 2.3 4.7 6.2 8 5 1.2 0.8 .. .. 27 56 Ethiopia 7.6 1.3 29.7 .. 353 138 1.2 0.3 .. .. 124 .. Finland 1.3 1.5 3.7 3.8 35 25 1.3 0.9 9 40 516 70 France 2.5 2.4 5.7 5.1 389 342 1.5 1.2 1,055 1,851 106 149 Gabon 1.8 1.1 .. .. 7 7 1.2 0.9 .. .. .. 21 Gambia, The 0.8 0.7 .. .. 1 1 0.1 0.1 .. .. .. .. Georgia 0.6 5.6 5.3 18.1 33 32 1.4 1.4 54 .. 6 81 Germany 1.5 1.4 4.7 4.3 221 251 0.5 0.6 1,603 2,473 135 137 Ghana 1.0 0.4 3.3 2.4 8 16 0.1 0.1 .. .. 1 13 Greece 4.3 4.0 9.8 7.9 163 143 3.3 2.8 2 .. 710 1,269 Guatemala 0.8 0.4 7.5 3.5 53 34 1.3 0.6 .. .. 1 0 Guinea 1.5 .. 11.8 .. 19 19 0.5 0.4 .. .. 19 0 Guinea-Bissau 4.4 .. .. .. 9 6 1.7 1.0 .. .. .. .. Haiti .. .. .. .. 5 0 0.1 0.0 .. .. .. 1 Honduras 0.5 0.8 .. 3.2 14 20 0.6 0.7 .. .. .. 0 290 2011 World Development Indicators 5.7 STATES AND MARKETS Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers % of central Trend indicator values government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Hungary 1.7 1.3 4.1 2.9 58 42 1.4 1.0 34 6 14 2 India 3.1 2.7 19.5 16.6 2,372 2,626 0.6 0.6 16 22 911 2,116 Indonesia 1.0 0.9 5.8 5.6 492 582 0.5 0.5 16 .. 171 452 Iran, Islamic Rep. 3.8 2.7 22.5 12.2 753 563 3.4 1.9 0 5 415 91 Iraq .. 6.3 .. .. 479 659 8.0 8.6 .. .. .. 365 Ireland 0.7 0.6 2.6 1.5 12 10 0.7 0.5 .. 4 0 1 Israel 7.8 6.9 17.6 17.0 181 185 7.2 6.0 354 760 357 158 Italy 2.0 1.7 5.2 3.9 503 327 2.2 1.3 189 588 37 112 Jamaica 0.5 0.6 .. 1.6 3 3 0.3 0.2 .. .. 5 2 Japan 1.0 1.0 .. .. 249 260 0.4 0.4 .. .. 431 391 Jordan 6.2 5.5 23.1 19.3 149 111 10.4 5.7 .. 44 130 195 Kazakhstan 0.8 1.2 5.7 6.9 99 81 1.3 0.9 19 .. 147 49 Kenya 1.3 1.9 7.8 8.7 27 29 0.2 0.2 .. .. 9 35 Korea, Dem. Rep. .. .. .. .. 1,244 1,379 11.2 11.2 13 .. 18 5 Korea, Rep. 2.6 2.9 15.6 13.2 688 660 3.0 2.7 8 163 1,262 1,172 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 7.1 3.2 24.9 7.5 20 23 1.8 1.5 99 .. 238 17 Kyrgyz Republic 2.9 3.6 18.0 21.4 14 20 0.7 0.8 .. 16 .. .. Lao PDR 0.8 0.4 .. 3.6 129 129 5.2 4.2 .. .. 7 7 Latvia 0.9 2.6 3.2 7.5 9 6 0.8 0.5 .. .. 3 0 Lebanon 5.4 4.1 17.7 14.0 77 79 6.5 5.4 45 .. 4 47 Lesotho 4.1 2.8 7.8 3.1 2 2 0.2 0.2 .. .. 6 .. Liberia .. 0.8 .. .. 15 2 1.3 0.1 .. .. 8 .. Libya 3.2 1.2 .. .. 77 76 4.2 3.2 11 12 145 11 Lithuania 1.7 1.7 6.5 4.4 17 25 1.0 1.6 3 .. 5 26 Macedonia, FYR 1.9 2.1 .. 5.8 24 8 2.8 0.9 .. .. 11 .. Madagascar 1.2 1.1 11.5 9.3 29 22 0.4 0.2 .. .. .. .. Malawi 0.7 1.2 .. .. 6 5 0.1 0.1 1 .. .. .. Malaysia 1.6 2.0 9.9 8.9 116 134 1.2 1.1 8 .. 30 1,494 Mali 2.4 2.0 20.7 13.4 15 12 0.5 0.3 .. .. 7 7 Mauritania 3.5 3.8 .. .. 21 21 2.0 1.5 .. .. 31 .. Mauritius 0.2 0.2 .. .. 2 2 0.3 0.4 .. .. .. .. Mexico 0.6 0.5 3.7 .. 208 332 0.5 0.7 .. .. 227 57 Moldova 0.4 0.5 1.4 1.2 13 8 0.7 0.5 6 11 .. .. Mongolia 2.2 1.4 9.5 5.8 16 17 1.4 1.2 .. .. .. 12 Morocco 2.3 3.3 12.0 12.0 241 246 2.4 2.1 .. .. 123 49 Mozambique 1.3 0.9 .. .. 6 11 0.1 0.1 .. .. 0 .. Myanmar 2.3 .. .. .. 429 513 1.7 1.9 .. .. 3 3 Namibia 2.4 3.3 8.3 10.7 9 15 1.5 1.9 .. .. 18 10 Nepal 1.0 1.6 .. .. 90 158 0.9 1.2 .. .. 11 .. Netherlands 1.6 1.5 4.0 3.4 57 43 0.7 0.5 280 608 141 243 New Zealand 1.2 1.1 3.5 3.1 9 10 0.5 0.4 1 .. 45 48 Nicaragua 0.8 0.7 4.7 3.2 16 12 0.9 0.5 .. .. .. .. Niger 1.1 .. .. .. 11 11 0.3 0.2 .. .. .. 0 Nigeria 0.8 0.9 .. 10.8 107 162 0.3 0.3 .. .. 38 73 Norway 1.7 1.5 5.3 4.1 27 26 1.1 1.0 3 17 263 576 Oman 10.6 8.7 40.4 .. 48 47 5.4 4.3 .. .. 120 93 Pakistan 4.0 3.0 23.4 18.0 900 921 2.2 1.6 3 .. 158 1,146 Panama 1.0 .. 4.6 .. 12 12 0.9 0.8 .. .. 0 .. Papua New Guinea 0.9 0.5 2.9 .. 4 3 0.2 0.1 .. .. .. .. Paraguay 1.1 0.9 6.4 5.2 35 25 1.5 0.8 .. .. 6 .. Peru 2.0 1.2 10.9 6.7 193 192 1.7 1.4 10 .. 24 33 Philippines 1.1 0.8 6.2 4.6 149 166 0.5 0.4 .. 4 9 4 Poland 1.8 2.0 5.4 5.7 239 121 1.4 0.7 45 93 159 94 Portugal 1.9 2.0 5.1 4.6 91 91 1.7 1.6 .. 40 2 431 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 4.7 2.2 .. 13.7 12 12 3.6 1.2 9 .. 11 285 2011 World Development Indicators 291 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers % of central Trend indicator values government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Romania 2.5 1.4 8.9 4.4 283 152 2.4 1.6 3 3 23 56 Russian Federation 3.7 4.3 19.3 14.0 1,427 1,495 2.0 2.0 3,985 4,469 .. 1 Rwanda 3.5 1.4 .. .. 76 35 2.0 0.7 .. .. 14 6 Saudi Arabia 10.6 11.0 .. .. 217 249 3.4 2.9 .. .. 80 626 Senegal 1.3 1.6 10.4 .. 15 19 0.4 0.3 .. .. .. 3 Serbia 5.5 2.2 .. 5.9 136 29 .. .. .. .. .. .. Sierra Leone 3.7 2.3 12.8 11.2 4 11 0.2 0.5 .. .. 13 .. Singapore 4.7 4.3 28.7 27.9 169 148 8.2 5.5 10 124 622 1,729 Slovak Republic 1.7 1.5 .. 4.0 41 17 1.6 0.6 92 8 2 1 Slovenia 1.1 1.8 2.9 4.1 14 12 1.4 1.2 .. .. 1 6 Somalia .. .. .. .. 50 2 1.7 0.1 .. .. 1 .. South Africa 1.6 1.4 5.6 4.4 72 77 0.5 0.4 18 154 16 139 Spain 1.2 1.3 3.9 4.1 242 222 1.3 1.0 46 925 332 430 Sri Lanka 5.0 3.5 21.9 18.5 204 223 2.6 2.7 .. .. 274 64 Sudan 4.7 .. 53.0 .. 120 127 1.1 0.9 .. .. 107 39 Swaziland 1.6 2.1 7.3 .. 3 .. 0.8 .. .. .. 1 .. Sweden 2.0 1.3 .. .. 88 22 2.0 0.4 306 353 210 46 Switzerland 1.1 0.8 4.2 4.7 28 26 0.7 0.6 176 270 14 31 Syrian Arab Republic 5.3 4.2 .. .. 425 403 8.6 5.8 .. .. 19 175 Tajikistan 1.2 .. 13.4 .. 7 16 0.4 0.6 .. .. .. 7 Tanzania 1.3 1.0 .. .. 35 28 0.2 0.1 .. .. .. 0 Thailand 1.4 1.8 .. 9.1 417 420 1.2 1.1 .. .. 90 34 Timor-Leste .. 11.8 .. .. .. 1 .. 0.3 .. .. .. .. Togo .. 2.0 .. 13.0 8 9 0.4 0.3 .. .. .. .. Trinidad and Tobago .. .. .. .. 8 4 1.3 0.6 .. .. 10 6 Tunisia 1.7 1.4 6.2 4.6 47 48 1.5 1.2 .. .. 11 8 Turkey 3.7 2.8 .. 10.1 828 613 3.6 2.4 15 36 1,170 675 Turkmenistan 2.9 .. .. .. 15 22 0.8 0.9 .. .. .. 47 Uganda 2.5 2.2 16.0 15.9 51 47 0.5 0.3 .. .. 6 1 Ukraine 3.6 2.9 13.5 7.0 420 215 1.8 0.9 288 214 .. .. United Arab Emirates 9.4 5.6 .. .. 66 51 3.5 1.8 .. 3 243 604 United Kingdom 2.4 2.7 6.6 5.8 213 178 0.7 0.6 1,484 1,024 829 288 United States 3.0 4.7 15.6 17.8 1,455 1,564 1.0 1.0 7,220 6,795 301 831 Uruguay 1.3 1.6 5.0 5.3 25 25 1.6 1.5 1 .. 4 37 Uzbekistan 1.2 .. .. .. 79 87 0.9 0.7 .. 90 6 .. Venezuela, RB 1.5 1.3 7.1 .. 79 115 0.8 0.9 .. 17 108 172 Vietnam .. 2.2 .. .. 524 495 1.4 1.1 .. .. 5 44 West Bank and Gaza .. .. .. .. .. 56 .. 5.9 .. .. .. 14 Yemen, Rep. 5.0 4.4 23.9 .. 136 138 3.2 2.2 .. .. 158 45 Zambia 1.8 1.7 10.3 5.7 23 17 0.6 0.3 .. .. 27 3 Zimbabwe 5.2 2.8 .. .. 62 51 1.2 1.0 3 .. 2 .. World 2.3 w 2.6 w 10.2 w 10.0 w 29,353 s 27,924 s 1.1 w 0.9 w .. s .. s 18,088 s 22,223 s Low income 2.2 1.5 .. .. 4,040 3,845 1.3 1.0 .. .. 572 329 Middle income 2.1 2.2 15.0 12.2 18,924 18,350 1.0 0.8 .. .. 8,353 10,467 Lower middle income 2.2 2.1 18.0 14.3 12,446 12,108 0.8 0.7 983 1,251 5,109 5,682 Upper middle income 2.0 2.2 .. 9.8 6,478 6,242 1.6 1.4 .. .. 3,244 4,785 Low & middle income 2.1 2.1 15.0 12.2 22,965 22,195 1.0 0.8 .. .. 8,925 10,889 East Asia & Pacific 1.7 1.9 18.7 14.6 7,794 6,978 0.8 0.6 389 870 2,339 2,644 Europe & Central Asia 3.4 3.3 .. 12.0 3,871 3,227 2.1 1.7 4,667 4,830 .. 1,162 Latin America & Carib. 1.4 1.5 7.2 .. 2,084 2,439 0.9 0.9 .. .. 970 1,058 Middle East & N. Africa 3.5 3.5 12.7 12.3 3,379 3,591 3.8 3.1 .. .. 2,056 2,065 South Asia 3.1 2.6 19.9 16.5 4,114 4,404 0.8 0.7 19 22 1,548 3,606 Sub-Saharan Africa 2.0 1.7 .. .. 1,724 1,554 0.7 0.5 .. .. 647 354 High income 2.3 2.8 10.1 9.9 6,388 5,729 1.2 1.0 13,136 16,637 9,163 11,334 Euro area 1.8 1.7 4.8 4.2 1,869 1,569 1.3 1.0 3,319 6,779 2,075 3,322 Note: For some countries data are partial or uncertain or based on rough estimates. See SIPRI (2010). a. Estimates differ from statistics of the government of China, which has published the following estimates: military expenditure as 1.2 percent of GDP in 2000 and 1.4 percent in 2008 and 7.6 percent of national government expenditure in 2000 and 6.7 percent in 2008 (see National Bureau of Statistics of China, www.stats.gov.cn). 292 2011 World Development Indicators 5.7 STATES AND MARKETS Military expenditures and arms transfers About the data Definitions Although national defense is an important function of always strictly comparable across countries. How- • Military expenditures are SIPRI data derived from government and security from external threats that ever, SIPRI puts a high priority on ensuring that the data the NATO definition, which includes all current and contributes to economic development, high levels of series for each country is comparable over time. More capital expenditures on the armed forces, including military expenditures for defense or civil conflicts bur- information on SIPRI’s military expenditure project can peacekeeping forces; defense ministries and other gov- den the economy and may impede growth. Data on be found at www.sipri.org/contents/milap/. ernment agencies engaged in defense projects; para- military expenditures as a share of gross domestic Data on armed forces refer to military personnel on military forces, if judged to be trained and equipped product (GDP) are a rough indicator of the portion of active duty, including paramilitary forces. Because for military operations; and military space activities. national resources used for military activities and of data exclude personnel not on active duty, they Such expenditures include military and civil person- the burden on the national economy. As an “input” underestimate the share of the labor force working nel, including retirement pensions and social services measure military expenditures are not directly related for the defense establishment. Governments rarely for military personnel; operation and maintenance; to the “output” of military activities, capabilities, or report the size of their armed forces, so such data procurement; military research and development; security. Comparisons of military spending between typically come from intelligence sources. and military aid (in the military expenditures of the countries should take into account the many fac- SIPRI’s Arms Transfers Programme collects data donor country). Excluded are civil defense and current tors that influence perceptions of vulnerability and on arms transfers from open sources. Since publicly expenditures for previous military activities, such as risk, including historical and cultural traditions, the available information is inadequate for tracking all for veterans benefits, demobilization, and weapons length of borders that need defending, the quality of weapons and other military equipment, SIPRI covers conversion and destruction. This definition cannot be relations with neighbors, and the role of the armed only what it terms major conventional weapons. Data applied for all countries, however, since that would forces in the body politic. cover the supply of weapons through sales, aid, gifts, require more detailed information than is available Data on military spending reported by governments and manufacturing licenses; therefore the term arms about military budgets and off-budget military expen- are not compiled using standard definitions. They transfers rather than arms trade is used. SIPRI data ditures (for example, whether military budgets cover are often incomplete and unreliable. Even in coun- also cover weapons supplied to or from rebel forces civil defense, reserves and auxiliary forces, police and tries where the parliament vigilantly reviews bud- in an armed conflict as well as arms deliveries for paramilitary forces, and military pensions). • Armed gets and spending, military expenditures and arms which neither the supplier nor the recipient can be forces personnel are active duty military personnel, transfers rarely receive close scrutiny or full, public identified with acceptable certainty; these data are including paramilitary forces if the training, organiza- disclosure (see Ball 1984 and Happe and Wakeman- available in SIPRI’s database. tion, equipment, and control suggest they may be used Linn 1994). Therefore, the Stockholm International SIPRI’s estimates of arms transfers are designed to support or replace regular military forces. Reserve Peace Research Institute (SIPRI) has adopted a defi - as a trend-measuring device in which similar weap- forces, which are not fully staffed or operational in nition of military expenditure derived from the North ons have similar values, reflecting both the quantity peace time, are not included. The data also exclude Atlantic Treaty Organization (NATO) definition (see and quality of weapons transferred. SIPRI cautions civilians in the defense establishment and so are not Definitions). The data on military expenditures as a that the estimated values do not reflect financial consistent with the data on military expenditures on share of GDP and as a share of central government value (payments for weapons transferred) because personnel. • Arms transfers cover the supply of military expenditure are estimated by SIPRI. Central govern- reliable data on the value of the transfer are not avail- weapons through sales, aid, gifts, and manufacturing ment expenditures are from the International Mon- able, and even when values are known, the transfer licenses. Weapons must be transferred voluntarily by etary Fund (IMF). Therefore the data in the table may usually includes more than the actual conventional the supplier, have a military purpose, and be destined differ from comparable data published by national weapons, such as spares, support systems, and for the armed forces, paramilitary forces, or intelligence governments. training, and details of the financial arrangements agencies of another country. The trends shown in the SIPRI’s primary source of military expenditure data (such as credit and loan conditions and discounts) table are based on actual deliveries only. Data cover is official data provided by national governments. are usually not known. major conventional weapons such as aircraft, armored These data are derived from national budget docu- Given these measurement issues, SIPRI’s method vehicles, artillery, radar systems and other sensors, ments, defense white papers, and other public docu- of estimating the transfer of military resources missiles, and ships designed for military use, as well as ments from official government agencies, including includes an evaluation of the technical parameters some major components such as turrets for armored governments’ responses to questionnaires sent by of the weapons. Weapons for which a price is not vehicles and engines. Excluded are transfers of other SIPRI, the United Nations, or the Organization for known are compared with the same weapons for military equipment such as most small arms and light Security and Co-operation in Europe. Secondary which actual acquisition prices are available (core weapons, trucks, small artillery, ammunition, support sources include international statistics, such as weapons) or for the closest match. These weapons equipment, technology transfers, and other services. those of NATO and the IMF’s Government Finance are assigned a value in an index that reflects their Data sources Statistics Yearbook. Other secondary sources include military resource value in relation to the core weap- country reports of the Economist Intelligence Unit, ons. These matches are based on such characteris- Data on military expenditures are from SIPRI’s Year- country reports by IMF staff, and specialist journals tics as size, performance, and type of electronics, book 2010: Armaments, Disarmament, and Interna- and newspapers. and adjustments are made for secondhand weap- tional Security. Data on armed forces personnel are In the many cases where SIPRI cannot make inde- ons. More information on SIPRI’s Arms Transfers from the International Institute for Strategic Stud- pendent estimates, it uses the national data pro- Programme is available at www.sipri.org/research/ ies’ The Military Balance 2011. Data on arms trans- vided. Because of the differences in definitions and armaments/transfers. fers are from SIPRI’s Arms Transfers Programme the difficulty in verifying the accuracy and complete- (www.sipri.org/research/armaments/transfers). ness of data, data on military expenditures are not 2011 World Development Indicators 293 5.8 Fragile situations International Peacebuilding and Battle- Intentional Military Business environment Development peacekeeping related homicides expenditures Association deaths Resource per 100,000 people Troops, police, Losses Allocation Law due to theft, Firms formally and military enforcement Index robbery, registered when observers Public and criminal Operation 1–6 health justice vandalism, operations namea number (low to high) number sources sources % of GDP and arson started December December Survey 2009 2010 2010 2000–08b 2004 2004–08c 2009 year % of sales % of firms Afghanistan 2.8 UNAMA 16 26,589 3.4 .. 1.8 2008 1.5 88.0 Angola 2.8   .. 3,534 38.6 5.0 4.2 2006 0.4 .. Bosnia and Herzegovina 3.7   .. 0 1.9 1.9 1.5 2009 0.2 98.6 Burundi 3.1 BINUB 4 4,937 37.4 .. 3.8 2006 1.1 .. Central African Republic 2.6 MINURCATe 3 350 29.8 .. 1.8   .. .. Chad 2.5 MINURCAT .. 4,328 19.2 .. 6.4 2009 2.5 77.1 Comoros 2.5   .. 0 11.9 .. ..   .. .. Congo, Dem. Rep. 2.7 MONUC 19,105 75,118 35.0 .. 1.1 2010 1.8 61.9 Congo, Rep. 2.8   .. 116 19.9 .. 1.2 2009 3.3 84.3 Côte d’Ivoire 2.8 UNOCI 9,071 1,265 50.8 0.4 1.6 2009 3.4 56.4 Eritrea 2.2   .. 57 16.1 .. .. 2009 0.0 100.0 Georgia 4.4   .. 648 3.7 7.6 5.6 2008 0.7 99.6 Guinea 2.8   .. 1,174 16.9 0.4 .. 2006 2.0 .. Guinea-Bissau 2.6   .. 0 17.6 .. .. 2006 1.1 .. Haiti 2.9 MINUSTAH 11,984 244 21.8 .. 0.0   .. .. Iraq .. UNAMI 235 124,002 7.3 .. 6.3   .. .. Kiribati 3.1   .. 0 6.6 .. ..   .. .. Kosovo 3.4 UNMIK 16 0 .. .. .. 2009 0.3 89.2 Liberia 2.8 UNMIL 9,392 2,487 17.4 .. 0.8 2009 2.8 73.8 Myanmar ..   .. 2,833 15.6 .. ..   .. .. Nepal 3.3 UNMIN 72 11,520 13.6 2.2 1.6 2009 0.9 94.0 São Tomé and Príncipe 2.9   .. 0 5.3 .. ..   .. .. Sierra Leone 3.2   .. 212 37.2 2.6 2.3 2009 0.8 89.2 Solomon Islands 2.8 RAMSI 580 0 1.5 .. ..   .. .. Somalia ..   .. 3,983 3.2 .. ..   .. .. Sudan 2.5 UNMISg 10,416 12,363 27.2 .. ..   .. .. Tajikistan 3.2   .. 0 1.9h 2.3 .. 2008 0.3 92.7 Timor-Leste 2.9 UNMIT 1,517 0 12.5 .. 11.8 2009 1.5 91.8 Togo 2.8   .. 0 14.3 .. 2.0 2009 2.4 75.8 West Bank and Gaza ..   .. 0 .. 3.9 .. 2006 1.2 .. Western Saharaj .. MINURSO 242 .. .. .. .. .. .. Yemen, Rep. 3.2   .. 0 2.5 4.0 4.4 2010 0.6 81.7 Zimbabwe 1.9   .. 0 34.3 8.7 2.8   .. .. Fragile situations       275,761 s 21.1 w .. 3.2 w       Low income       146,844 17.6 .. 1.4       Note: The countries with fragile situations in the table are primarily International Development Association–eligible countries and nonmember or inactive countries and territories with a 3.2 or lower harmonized average of the World Bank's Country Policy and Institutional Assessment rating and the corresponding rating by a regional development bank, or that have had a UN or regional peacebuilding and political mission (for example, by the African Union, European Union, or Organization of American States) or peacekeeping mission (for example, by the African Union, European Union, North Atlantic Treaty Organization, or Organization of American States) during the last three years. This definition is pursuant to an agreement between the World Bank and other multilateral development banks at the start of the International Development Association 15 round in 2007. The list of countries and territories with fragile situations is an interim one, and the World Bank will continue to improve and refine its understanding of fragility. a. UNAMA is United Nations Assistance Mission in Afghanistan, BINUB is Bureau Intégré des Nations Unies au Burundi (United Nations Integrated Office in Burundi), MINURCAT is United Nations Mission in the Central African Republic and Chad, MONUC is United Nations Organization Mission in DR Congo, UNOCI is United Nations Operation in Côte d'Ivoire, MINUSTAH is United Nations Stabilization Mission in Haiti, UNAMI is United Nations Assistance Mission for Iraq, UNMIK is Interim Administration Mission in Kosovo, UNMIL is United Nations Mission in Liberia, UNMIN is United Nations Mission in Nepal, RAMSI is Regional Assistance Mission to Solomon Islands, UNMIS is United Nations Missions in Sudan, UNMIT is United Nations Integrated Mission in Timor-Leste, and MINURSO is United Nations Mission for the Referendum in Western Sahara. b. Total over the period. c. Data are for the most recent year available. d. Average over the period. e. Includes peacekeepers in Chad. The mission ended in 2010. f. The Internal Displacement Monitoring Centre's (IDMC) high estimate; the low estimate is 50,000. g. Does not include 22,444 troops, police, and military observers from the African Union–UN Hybrid Operation in Darfur. h. Data are for 2005. i. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East, who are not included in data from the UN High Commissioner for Refugees. j. The designation Western Sahara is used instead of Former Spanish Sahara (the designation used on the maps on the front and back cover flaps) because it is the designation used by the UN operation established there by Security Council resolution 690/1991. Neither designation expresses any World Bank view on the status of the territory so-identified. k. IDMC's high estimate; the low estimate is 570,000. 294 2011 World Development Indicators 5.8 STATES AND MARKETS Fragile situations Children in Refugees Internally Access Access Maternal mortality Under-five Depth of Primary employment displaced to an to ratio mortality hunger gross persons improved improved rate enrollment water sanitation ratio source facilities per 100,000 live births kilocalories % of By country By country % of % of National Modeled per person % of relevant children population population age group of origin of asylum number estimates estimates per 1,000 per day Survey ages year 7–14 2009 2009 2009 2008 2008 2004–09c 2008 2009 2005–07d 2009 Afghanistan   .. 2,887,123 37 297,000 48 37 .. 1,400 199 .. 104 Angola 2001 30.1 141,021 14,734 20,000 50 57 .. 610 161 320 128 Bosnia and Herzegovina 2006 10.6 70,018 7,132 114,000 99 95 3 9 14 140 109 Burundi 2005 11.7 94,239 24,967 100,000 72 46 615 970 166 380 147 Central African Republic 2000 67.0 159,554 27,047 162,000 67 34 543 850 171 300 89 Chad 2004 60.4 55,014 338,495 168,000 50 9 1,099 1,200 209 310 90 Comoros   .. 268 .. .. 95 36 .. 340 104 300 119 Congo, Dem. Rep. 2000 39.8 455,852 185,809 1,900,000 46 23 549 670 199 410 90 Congo, Rep. 2005 30.1 20,544 111,411 7,800 71 30 781 580 128 230 120 Côte d’Ivoire 2006 45.7 23,153 24,604 621,000 80 23 543 470 119 230 74 Eritrea   .. 209,168 4,751 10,000 61 14 .. 280 55 350 48 Georgia 2006 31.8 15,020 870 230,000 98 95 14 48 29 150 108 Guinea 1994 48.3 10,920 15,325 .. 71 19 980 680 142 260 90 Guinea-Bissau 2006 50.5 1,109 7,898 .. 61 21 405 1,000 193 250 120 Haiti 2005 33.4 24,116 3 .. 63 17 630 300 87 430 .. Iraq 2006 14.7 1,785,212 35,218 2,764,000 79 73 84 75 44 .. 103 Kiribati   .. 33 .. .. 61 31 .. .. 46 180 116 Kosovo   .. .. .. 19,700 .. .. .. .. .. .. .. Liberia 2007 37.4 71,599 6,952 .. 68 17 994 990 112 340 91 Myanmar   .. 406,669 .. 470,000 71 81 316 240 71 230 116 Nepal 1999 47.2 5,108 108,461 70,000 f 88 31 281 380 48 220 .. São Tomé and Príncipe   .. 33 .. .. 89 26 148 .. 78 160 131 Sierra Leone 2007 14.9 15,417 9,051 .. 49 13 857 970 192 340 158 Solomon Islands   .. 66 .. .. 69 29 .. 100 36 180 107 Somalia 2006 43.5 678,309 1,815 1,500,000 30 23 1,044 1,200 180 .. 33 Sudan 2000 19.1 368,195 186,292 4,900,000 57 34 1,107 750 108 240 74 Tajikistan 2005 8.9 562 2,679 .. 70 94 38 64 61 240 102 Timor-Leste   .. 7 1 400 69 50 .. 370 56 260 113 Togo 2006 38.7 18,378 8,531 1,500 60 12 .. 350 98 280 115 West Bank and Gaza   .. 95,201 1,885,188i .. 91 89 .. .. 30 190 79 Western Saharaj   .. .. .. 160,000 .. .. .. .. .. .. .. Yemen, Rep. 2006 18.3 1,934 170,854 175,000 62 52 .. 210 66 270 85 Zimbabwe 1999 14.3 22,449 3,995 1,000,000k 82 44 555 790 90 300 .. Fragile situations   7,636,291 s 3,182,120 s 14,047,900 s 64 w 43 w .. 640 w 132 w 290 w 94 w Low income   5,427,548 1,893,823 .. 64 35 .. 580 118 285 104 About the data The table focuses on countries with fragile situations According to the Geneva Declaration on Armed Vio- have to build their own institutions tailored to their and highlights the links among weak institutions, lence and Development, more than 740,000 people own needs. Peacekeeping operations in post-conflict poor development outcomes, fragility, and risk of die each year because of the violence associated with situations have been effective in reducing the risks conflict. These countries and territories often have armed conflict and large- and small-scale criminality. of reversion to conflict. weak institutions that are ill-equipped to handle eco- Recovery and rebuilding can take years, and the chal- The countries with fragile situations in the table nomic shocks, natural disasters, and illegal trade lenges are numerous: infrastructure to be rebuilt, are primarily International Development Association– or to resist conflict, which increasingly spills across persistently high crime, widespread health problems, eligible countries and nonmember or inactive coun- borders. Organized violence, including violent crime, education systems in disrepair, and landmines to be tries or territories of the World Bank with a 3.2 or interrupts economic and social development through cleared. Most countries emerging from conflict lack lower harmonized average of the World Bank’s Country lost human and social capital, disrupted services, the capacity to rebuild the economy. Thus, capacity Policy and Institutional Assessment rating and the cor- displaced populations and reduced confidence for building is one of the first tasks for restoring growth responding rating by a regional development bank or future investment. As a result, countries with fragile and is linked to building peace and creating the con- that have had a UN or regional peacebuilding mission situations achieve lower development outcomes and ditions that lead to sustained poverty reduction. The (for example, by the African Union, European Union, make slower progress toward the Millennium Develop- World Bank and other international development agen- or Organization of American States) or peacekeeping ment Goals. cies can help, but countries with fragile situations mission (for example, by the African Union, European 2011 World Development Indicators 295 5.8 Fragile situations About the data (continued) Union, North Atlantic Treaty Organization (NATO), or fewer types of contracts and investments, constrain- • Troops, police, and military observers in peace- Organization of American States) during the last three ing growth. The table presents data on the loss of building and peacekeeping refer to people active in years. Peacebuilding and peacekeeping involve many sales due to theft, robbery, vandalism, and arson and peacebuilding and peacekeeping as part of an official elements—military, police, and civilian—working on the percentage of firms operating informally. For operation. Peacekeepers deploy to war-torn regions together to lay the foundations for sustainable peace. further information on enterprise surveys, see About where no one else is willing or able to go to prevent The list of countries and territories with fragile situa- the data for table 5.2. conflict from returning or escalating. • Battle-related tions is an interim one, and the World Bank will continue As the table shows, the human toll of armed vio- deaths are deaths of members of warring parties in to improve and refine its understanding of fragility. lence across various contexts is severe. Additionally, battle-related confl icts. Typically, battle-related An armed conflict is a contested incompatibility in countries with fragile situations weak institutional deaths occur in warfare involving the armed forces that concerns a government or territory where the capacity often results in poor performance and fail- of the warring parties (battlefield fighting, guerrilla use of armed force between two parties (one of them ure to meet expectations of effective service deliv- activities, and all kinds of bombardments of military the government) results in at least 25 battle-related ery. Failure to deliver water, health, and education units, cities, and villages). The targets are usually deaths in a calendar year. There were 35 active services can weaken struggling governments. The the military and its installations or state institutions armed conflicts in 26 locations in 2009. Separate table includes several indicators related to living con- and state representatives, but there is often sub- measures are presented for intentional homicides— ditions in fragile situations: children in employment, stantial collateral damage of civilians killed in cross- unlawful deaths purposefully inflicted on a person refugees, internally displaced persons, access to fire, indiscriminate bombings, and other military by another person—which exclude deaths arising water and sanitation, maternal and under-five mortal- activities. All deaths—civilian as well as military— from armed conflict. One measure draws from inter- ity, depth of hunger, and primary school enrollment. incurred in such situations are counted as bat- national public health data sources, while the other For more detailed information on these indicators, tlerelated deaths. • Intentional homicides are esti- draws from estimates by the United Nations Office on see About the data for table 2.6 (children in employ- mates of unlawful homicides purposely inflicted as Drugs and Crime, which obtains data from national ment), table 6.18 (refugees), table 2.18 (access to a result of domestic disputes, interpersonal violence, and international law enforcement and criminal jus- improved water and sanitation), table 2.19 (maternal violent conflicts over land resources, intergang vio- tice sources. Data from these two sources measure mortality), table 2.22 (under-five mortality), and table lence over turf or control, and predatory violence and different phenomena and are therefore unlikely to 2.12 (primary school enrollment). killing by armed groups. Intentional homicide does provide identical numbers. not include all intentional killing; the difference is Data on military expenditures reported by govern- usually in the organization of the killing. Individuals Definitions ments are not compiled using standard definitions or small groups usually commit homicide, whereas and are often incomplete and unreliable. Even in • International Development Association Resource killing in armed conflict is usually committed by fairly countries where the parliament vigilantly reviews Allocation Index is from the Country Policy and Insti- cohesive groups of up to several hundred members budgets and spending, military expenditures and tutional Assessment rating, which is the average and is thus usually excluded. Data are from interna- arms transfers rarely receive close scrutiny or full score of four clusters of indicators designed to mea- tional public health organizations such as the World public disclosure. Data are from the Stockholm sure macroeconomic, governance, social, and struc- Health Organization (WHO) and the Pan American International Peace Research Institute (SIPRI), which tural dimensions of development: economic manage- Health Organization and from the United Nations uses NATO’s pre-2004 definition of military expen- ment, structural policies, policies for social inclusion Survey of Crime Trends and Operations of Criminal diture (see Definitions). Therefore, the data in the and equity, and public sector management and insti- Justice Systems (CTS), which draws from national table may differ from comparable data published by tutions (see table 5.9). Countries are rated on a and international law enforcement and criminal jus- national governments. For a more detailed discus- scale of 1 (low) to 6 (high). • Peacebuilding and tice sources. • Military expenditures are SIPRI data sion of military expenditures, see About the data for peacekeeping refer to operations that engage in derived from NATO's pre-2004 definition, which table 5.7. peacebuilding (reducing the risk of lapsing or relaps- includes all current and capital expenditures on the Along with public sector efforts, private sector ing into conflict by strengthening national capacities armed forces, including peacekeeping forces; development and investment, especially in competi- for conflict management and laying the foundation defense ministries and other government agencies tive markets, has tremendous potential to contribute for sustainable peace and development) or peace- engaged in defense projects; paramilitary forces, if to growth and poverty reduction. The World Bank’s keeping (providing essential security to preserve the judged to be trained and equipped for military opera- Enterprise Surveys review the business environment, peace where fighting has been halted and to assist tions; and military space activities. Such expendi- assessing constraints to private sector growth and in implementing agreements achieved by the peace- tures include military and civil personnel, including enterprise performance. In some countries doing makers). UN peacekeeping operations are authorized retirement pensions and social services for military business requires informal payments to “get things by the UN Secretary-General and planned, managed, personnel; operation and maintenance; procure- done” in customs, taxes, licenses, regulations, ser- directed, and supported by the United Nations ment; military research and development; and mili- vices, and the like. Crime, theft, and disorder also Department of Peacekeeping Operations and the tary aid (in the military expenditures of the donor impose costs on businesses and society. And in Department of Field Support. The UN Charter gives country). Excluded are civil defense and current many developing countries informal businesses oper- the Security Council primary responsibility for main- expenditures for previous military activities, such as ate without licenses. These firms have less access taining international peace and security, including for veterans benefits, demobilization, and weapons to financial and public services and can engage in the establishment of a UN peacekeeping operation. conversion and destruction. This definition cannot 296 2011 World Development Indicators 5.8 STATES AND MARKETS Fragile situations be applied to all countries, however, since the neces- disposal facilities that can effectively prevent on intentional homicides are from the UN Office sary detailed information is missing in some cases human, animal, and insect contact with excreta. on Drugs and Crime’s International Homicide Sta- for military budgets and off-budget military expendi- Improved facilities range from protected pit latrines tistics database. Data on military expenditures are tures (for example, whether military budgets cover to flush toilets. • Maternal mortality ratio is the from SIPRI’s Yearbook 2010: Armaments, Disar- civil defense, reserves and auxiliary forces, police number of women who die from pregnancy-related mament, and International Security and database and paramilitary forces, and military pensions). causes during pregnancy and childbirth per 100,000 (www.sipri.org/databases/milex). Data on the • Survey year is the year in which the underlying live births. National estimates are based on national business environment are from the World Bank’s data were collected. • Losses due to theft, robbery, surveys, vital registration records, and surveillance Enterprise Surveys (www.enterprisesurveys. vandalism, and arson are the estimated losses from data or are derived from community and hospital org). Data on children in employment are esti- those causes that occurred on business establish- records. Modeled estimates are based on an exer- mates produced by the Understanding Children’s ment premises calculated as a percentage of annual cise by the WHO, United Nations Children’s Fund Work project based on household survey data sales. • Firms formally registered when operations (UNICEF), United Nations Population Fund (UNFPA), sets made available by the International Labour started are the percentage of firms formally regis- and the World Bank. See About the data for table Organization’s International Programme on the tered when they started operations in the country. 2.19 for further details. • Under-five mortality rate Elimination of Child Labour under its Statistical • Children in employment are children involved in is the probability per 1,000 that a newborn baby will Monitoring Programme on Child Labour, UNICEF any economic activity for at least one hour in the die before reaching age 5, if subject to current age- under its Multiple Indicator Cluster Survey pro- reference week of the survey. • Refugees are people specific mortality rates. • Depth of hunger, or the gram, the World Bank under its Living Standards who are recognized as refugees under the 1951 Con- intensity of food deprivation, indicates how much Measurement Study program, and national sta- vention Relating to the Status of Refugees or its people who are food-deprived fall short of minimum tistical offices (see table 2.6). Data on refugees 1967 Protocol, the 1969 Organization of African food needs in terms of dietary energy. It is measured are from the UNHCR’s Statistical Yearbook 2009, Unity Convention Governing the Specific Aspects of by comparing the average amount of dietary energy complemented by statistics on Palestinian refu- Refugee Problems in Africa, people recognized as that undernourished people get from the foods they gees under the mandate of the United Nations refugees in accordance with the UN Refugee Agency eat with the minimum amount of dietary energy they Relief and Works Agency for Palestine Refugees (UNHCR) statute, people granted refugee-like human- need to maintain body weight and undertake light in the Near East as published on its website (www. itarian status, and people provided temporary protec- activity. Depth of hunger is low when it is less than unrwa.org). Data on internally displaced persons tion. Asylum seekers—people who have applied for 200 kilocalories per person per day and high when are from the Internal Displacement Monitoring asylum or refugee status and who have not yet it is above 300. • Primary gross enrollment ratio is Centre. Data on access to water and sanitation received a decision, or who are registered as asylum the ratio of total enrollment, regardless of age, to the are from the WHO and UNICEF’s Progress on Sani- seekers—are excluded. Palestinian refugees are population of the age group that offi cially corre- tation and Drinking Water (2010). National esti- people (and their descendants) whose residence was sponds to the primary level of education. Primary mates of maternal mortality are from UNICEF’s The Palestine between June 1946 and May 1948 and education provides children with basic reading, writ- State of the World’s Children 2009 and Childinfo who lost their homes and means of livelihood as a ing, and mathematics skills along with an elementary and Demographic and Health Surveys by Macro result of the 1948 Arab-Israeli conflict. • Country of understanding of such subjects as history, geogra- International. Modeled estimates for maternal origin refers to the nationality or country of citizen- phy, natural science, social science, art, and music. mortality are from WHO, UNICEF, UNFPA, and ship of a claimant. • Country of asylum is the country the World Bank’s Trends in Maternal Mortality in where an asylum claim was filed and granted. • Inter- 1990–2008 (2010). Data on under-fi ve mortal- nally displaced persons are people or groups of ity estimates by the Inter-agency Group for Child Data sources people who have been forced or obliged to flee or to Mortality Estimation (which comprises UNICEF, leave their homes or places of habitual residence, in Data on the International Development Asso- WHO, the World Bank, United Nations Population particular as a result of armed conflict, or to avoid ciation Resource Allocation Index are from Division, and other universities and research insti- the effects of armed conflict, situations of general- the World Bank Group’s International Develop- tutes) and are based mainly on household surveys, ized violence, violations of human rights, or natural ment Association database (www.worldbank. censuses, and vital registration data, supple- or human-made disasters and who have not crossed org/ida). Data on peacebuilding and peace- mented by the World Bank’s Human Development an international border. • Access to an improved keeping operations are from the UN Depart- Network estimates based on vital registration and water source refers to people with reasonable ment of Peacekeeping Operations. Data on sample registration data (see table 2.22). Data on access to water from an improved source, such as battle-related deaths are primarily from the depth of hunger are from the Food and Agriculture piped water into a dwelling, public tap, tubewell, pro- Peace Research Institute Oslo/Uppsala Conflict Organization’sFood Security Statistics (www.fao. tected dug well, and rainwater collection. Reasonable Data Program (UCDP) Armed Conflict Dataset (v.4- org/economic/ess/food-security-statistics/en/). access is the availability of at least 20 liters a person 2010) 1946-2009 (www.pcr.uu.se/research/ucdp/ Data on primary gross enrollment are from the a day from a source within 1 kilometer of the dwell- datasets), supplemented with data from the UCDP United Nations Educational, Scientific, and Cul- ing. • Access to improved sanitation facilities refers Battle-Related Deaths Dataset (v.5-2010). Data tural Organization’s Institute for Statistics. to people with at least adequate access to excreta 2011 World Development Indicators 297 5.9 Public policies and institutions International Economic management Structural policies Development 1–6 (low to high) 1–6 (low to high) Association Resource Allocation Index 1–6 Business (low to high) Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2009 2009 2009 2009 2009 2009 2009 2009 2009 Afghanistan 2.8 3.5 3.0 3.5 3.3 3.0 2.5 2.5 2.7 Angola 2.8 3.0 3.0 3.0 3.0 4.0 2.5 2.0 2.8 Armenia 4.2 5.0 5.0 5.0 5.0 4.5 4.0 4.0 4.2 Azerbaijan 3.8 4.0 4.5 5.0 4.5 4.0 3.5 4.0 3.8 Bangladesh 3.5 4.0 4.0 4.0 4.0 3.5 3.5 3.5 3.5 Benin 3.5 4.0 3.5 3.5 3.7 4.0 3.5 3.5 3.7 Bhutan 3.9 4.5 4.5 4.5 4.5 3.0 3.0 3.5 3.2 Bolivia 3.8 4.0 4.0 4.5 4.2 5.0 4.0 2.5 3.8 Bosnia and Herzegovina 3.7 4.0 3.5 4.0 3.8 4.0 4.0 4.0 4.0 Burkina Faso 3.8 4.5 4.5 4.0 4.3 4.0 3.0 3.5 3.5 Burundi 3.1 3.5 3.5 3.0 3.3 4.0 2.5 2.5 3.0 Cambodia 3.3 4.5 3.5 3.5 3.8 4.0 2.5 3.5 3.3 Cameroon 3.2 4.0 4.0 3.0 3.7 3.5 3.0 3.0 3.2 Cape Verde 4.2 4.5 4.5 4.5 4.5 4.0 4.0 3.5 3.8 Central African Republic 2.6 3.5 3.0 2.5 3.0 3.5 2.5 2.0 2.7 Chad 2.5 2.5 2.5 2.5 2.5 3.0 3.0 2.5 2.8 Comoros 2.5 3.0 2.0 2.0 2.3 3.0 2.5 2.5 2.7 Congo, Dem. Rep. 2.7 3.5 3.5 2.5 3.2 3.5 2.0 2.0 2.5 Congo, Rep. 2.8 3.5 3.0 2.5 3.0 3.5 3.0 2.5 3.0 Côte d’Ivoire 2.8 3.5 2.5 2.5 2.8 4.0 3.0 3.0 3.3 Djibouti 3.2 3.5 3.0 2.5 3.0 4.0 3.5 3.5 3.7 Dominica 3.8 4.0 4.5 3.0 3.8 4.0 3.5 4.5 4.0 Eritrea 2.2 2.0 2.0 1.5 1.8 1.5 1.0 2.0 1.5 Ethiopia 3.4 3.5 4.0 3.5 3.7 3.0 3.0 3.5 3.2 Gambia, The 3.3 4.0 3.5 3.0 3.5 3.5 3.0 3.5 3.3 Georgia 4.4 4.5 4.5 5.0 4.7 6.0 3.5 5.5 5.0 Ghana 3.8 3.5 3.5 4.0 3.7 4.0 4.0 4.0 4.0 Grenada 3.7 3.5 2.5 3.0 3.0 4.5 4.0 4.0 4.2 Guinea 2.8 2.5 2.5 2.0 2.3 4.0 3.0 3.0 3.3 Guinea-Bissau 2.6 2.5 2.5 1.5 2.2 4.0 3.0 2.5 3.2 Guyana 3.4 3.5 3.0 4.0 3.5 4.0 3.5 3.0 3.5 Haiti 2.9 4.0 3.5 2.5 3.3 4.0 3.0 2.5 3.2 Honduras 3.5 3.0 3.5 4.0 3.5 4.5 3.0 3.5 3.7 India 3.8 4.5 3.5 4.0 4.0 3.5 4.0 3.5 3.7 Kenya 3.7 4.5 4.0 4.0 4.2 4.0 4.0 4.0 4.0 Kiribati 3.1 2.5 3.0 5.0 3.5 3.0 3.0 3.0 3.0 Kosovo 3.4 3.5 3.0 3.5 3.3 5.0 3.5 3.5 4.0 Kyrgyz Republic 3.7 4.5 4.0 4.0 4.2 5.0 3.0 3.5 3.8 About the data The International Development Association (IDA) is the assessments have been carried out annually since terms. The IRAI is a key element in the country per- part of the World Bank Group that helps the poorest the mid-1970s by World Bank staff. Over time the cri- formance rating. countries reduce poverty by providing concessional loans teria have been revised from a largely macroeconomic The CPIA exercise is intended to capture the quality and grants for programs aimed at boosting economic focus to include governance aspects and a broader of a country’s policies and institutional arrangements, growth and improving living conditions. IDA funding helps coverage of social and structural dimensions. Country focusing on key elements that are within the country’s these countries deal with the complex challenges they performance is assessed against a set of 16 criteria control, rather than on outcomes (such as economic face in meeting the Millennium Development Goals. grouped into four clusters: economic management, growth rates) that are influenced by events beyond The World Bank’s IDA Resource Allocation Index structural policies, policies for social inclusion and the country’s control. More specifically, the CPIA (IRAI), presented in the table, is based on the results equity, and public sector management and institu- measures the extent to which a country’s policy and of the annual Country Policy and Institutional Assess- tions. IDA resources are allocated to a country on per institutional framework supports sustainable growth ment (CPIA) exercise, which covers the IDA-eligible capita terms based on its IDA country performance and poverty reduction and, consequently, the effective countries. The table does not include Myanmar and rating and, to a limited extent, based on its per capita use of development assistance. Somalia because they were not rated in the 2009 gross national income. This ensures that good per- All criteria within each cluster receive equal weight, exercise even though they are IDA eligible. Country formers receive a higher IDA allocation in per capita and each cluster has a 25 percent weight in the overall 298 2011 World Development Indicators 5.9 STATES AND MARKETS Public policies and institutions International Economic management Structural policies Development 1–6 (low to high) 1–6 (low to high) Association Resource Allocation Index 1–6 Business (low to high) Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2009 2009 2009 2009 2009 2009 2009 2009 2009 Lao PDR 3.2 4.0 4.0 3.0 3.7 3.5 2.0 3.0 2.8 Lesotho 3.5 4.0 4.0 4.0 4.0 3.5 3.5 3.0 3.3 Liberia 2.8 3.5 3.5 2.5 3.2 3.0 2.5 3.0 2.8 Madagascar 3.5 4.0 3.0 4.0 3.7 4.0 3.0 3.5 3.5 Malawi 3.4 3.0 3.5 3.0 3.2 4.0 3.0 3.5 3.5 Maldives 3.4 2.5 2.0 3.0 2.5 4.0 3.0 4.0 3.7 Mali 3.7 4.5 4.0 4.5 4.3 4.0 3.0 3.5 3.5 Mauritania 3.2 3.5 2.5 3.5 3.2 4.0 2.5 3.5 3.3 Moldova 3.7 3.5 3.5 4.0 3.7 4.5 3.5 3.5 3.8 Mongolia 3.4 3.5 3.0 3.0 3.2 4.5 2.0 3.5 3.3 Mozambique 3.7 4.5 4.5 4.5 4.5 4.5 3.5 3.0 3.7 Nepal 3.3 3.5 3.5 3.0 3.3 3.5 3.0 3.0 3.2 Nicaragua 3.7 4.0 4.0 4.5 4.2 4.5 3.0 3.5 3.7 Niger 3.3 4.0 3.5 4.0 3.8 4.0 3.0 3.0 3.3 Nigeria 3.5 4.0 4.5 4.5 4.3 3.5 3.5 3.5 3.5 Pakistan 3.2 3.0 3.0 3.5 3.2 3.5 3.5 4.0 3.7 Papua New Guinea 3.3 4.0 3.5 4.5 4.0 4.5 3.0 3.0 3.5 Rwanda 3.8 4.0 4.0 3.5 3.8 4.0 3.5 4.0 3.8 Samoa 4.1 4.0 4.0 5.0 4.3 5.0 4.0 3.5 4.2 São Tomé and Príncipe 2.9 3.0 3.0 2.5 2.8 4.0 2.5 2.5 3.0 Senegal 3.7 4.0 4.0 4.0 4.0 4.0 3.5 4.0 3.8 Sierra Leone 3.2 4.0 3.5 3.5 3.7 3.5 3.0 3.0 3.2 Solomon Islands 2.8 3.5 2.5 3.0 3.0 3.0 3.0 2.5 2.8 Sri Lanka 3.5 3.0 3.0 3.5 3.2 3.5 3.5 4.0 3.7 St. Lucia 3.8 4.0 3.5 3.5 3.7 4.0 3.5 4.5 4.0 St. Vincent & Grenadines 3.8 4.0 3.5 3.5 3.7 4.0 3.5 4.5 4.0 Sudan 2.5 3.5 3.0 1.5 2.7 2.5 2.5 3.0 2.7 Tajikistan 3.2 3.5 3.5 3.5 3.5 4.0 2.5 3.0 3.2 Tanzania 3.8 4.5 4.5 4.0 4.3 4.0 4.0 3.5 3.8 Timor-Leste 2.9 3.0 3.5 3.5 3.3 4.5 2.5 1.5 2.8 Togo 2.8 3.0 3.0 2.5 2.8 4.0 2.5 3.0 3.2 Tonga 3.5 3.0 3.0 3.0 3.0 5.0 3.5 3.0 3.8 Uganda 3.9 4.5 4.5 4.5 4.5 4.0 3.5 4.0 3.8 Uzbekistan 3.3 4.0 4.0 4.0 4.0 2.5 3.0 3.0 2.8 Vanuatu 3.4 4.0 3.5 4.5 4.0 3.5 3.0 3.5 3.3 Vietnam 3.8 4.5 4.5 4.0 4.3 3.5 3.0 3.5 3.3 Yemen, Rep. 3.2 3.5 2.5 3.5 3.2 4.5 2.0 3.5 3.3 Zambia 3.4 4.0 3.0 3.5 3.5 4.0 3.5 3.0 3.5 Zimbabwe 1.9 2.0 2.0 1.0 1.7 3.0 1.5 2.0 2.2 score, which is obtained by averaging the average criteria are designed in a developmentally neutral man- two key phases. In the benchmarking phase a small scores of the four clusters. For each of the 16 criteria ner. Accordingly, higher scores can be attained by a representative sample of countries drawn from all countries are rated on a scale of 1 (low) to 6 (high). country that, given its stage of development, has a regions is rated. Country teams prepare proposals The scores depend on the level of performance in policy and institutional framework that more strongly that are reviewed first at the regional level and then a given year assessed against the criteria, rather fosters growth and poverty reduction. in a Bankwide review process. A similar process is than on changes in performance compared with the The country teams that prepare the ratings are very followed to assess the performance of the remaining previous year. All 16 CPIA criteria contain a detailed familiar with the country, and their assessments are countries, using the benchmark countries’ scores as description of each rating level. In assessing country based on country diagnostic studies prepared by the guideposts. The final ratings are determined following performance, World Bank staff evaluate the country’s World Bank or other development organizations and a Bankwide review. The overall numerical IRAI score performance on each of the criteria and assign a rat- on their own professional judgment. An early con- and the separate criteria scores were first publicly ing. The ratings reflect a variety of indicators, observa- sultation is conducted with country authorities to disclosed in June 2006. tions, and judgments based on country knowledge and make sure that the assessments are informed by See IDA’s website at www.worldbank.org/ida for on relevant publicly available indicators. In interpreting up-to-date information. To ensure that scores are more information. the assessment scores, it should be noted that the consistent across countries, the process involves 2011 World Development Indicators 299 5.9 Public policies and institutions Policies for social inclusion and equity Public sector management and institutions 1–6 (low to high) 1–6 (low to high) Quality of budgetary Transparency, Equity Policies and Property and accountability, of public Building Social institutions for rights and financial Efficiency Quality and corruption Gender resource human protection environmental rule-based manage- of revenue of public in the public equality use resources and labor sustainability Average governance ment mobilization administration sector Average 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Afghanistan 2.0 3.0 3.0 2.5 2.5 2.6 1.5 3.5 3.0 2.0 2.0 2.4 Angola 3.5 2.5 2.5 3.0 3.0 2.9 2.0 2.5 2.5 2.5 2.5 2.4 Armenia 4.5 4.5 4.0 4.5 3.0 4.1 3.5 4.5 3.5 4.0 3.0 3.7 Azerbaijan 4.0 4.0 4.0 4.0 3.0 3.8 3.0 4.0 3.5 3.0 2.5 3.2 Bangladesh 4.0 3.5 4.0 3.5 3.0 3.6 3.0 3.0 3.0 3.0 3.0 3.0 Benin 3.5 3.0 3.5 3.0 3.5 3.3 3.0 3.5 3.5 3.0 3.5 3.3 Bhutan 4.0 4.0 4.0 3.5 4.5 4.0 3.5 3.5 4.0 4.0 4.5 3.9 Bolivia 4.0 4.0 4.0 3.5 3.5 3.8 2.5 3.5 4.0 3.0 3.5 3.3 Bosnia and Herzegovina 4.5 3.5 3.5 3.5 3.5 3.7 3.0 3.5 4.0 3.0 3.0 3.3 Burkina Faso 3.5 4.0 3.5 3.5 3.5 3.6 3.5 4.5 3.5 3.5 3.5 3.7 Burundi 4.0 3.5 3.0 3.0 3.0 3.3 2.5 3.0 3.0 2.5 2.0 2.6 Cambodia 4.0 3.0 3.5 3.0 3.0 3.3 2.5 3.5 3.0 2.5 2.0 2.7 Cameroon 3.0 3.0 3.5 3.0 3.0 3.1 2.5 3.0 3.5 3.0 2.5 2.9 Cape Verde 4.5 4.5 4.5 4.5 3.5 4.3 4.0 4.0 3.5 4.0 4.5 4.0 Central African Republic 2.5 2.5 2.5 2.0 3.0 2.5 2.0 2.5 2.5 2.5 2.5 2.4 Chad 2.5 2.5 2.5 2.5 2.0 2.4 2.0 2.0 2.5 2.5 2.0 2.2 Comoros 3.0 2.5 3.0 2.5 2.0 2.6 2.5 2.0 2.5 2.5 2.5 2.4 Congo, Dem. Rep. 2.5 3.0 3.0 3.0 2.5 2.8 2.0 2.5 2.5 2.0 2.0 2.2 Congo, Rep. 3.0 2.5 3.0 2.5 2.5 2.7 2.5 2.5 3.0 2.5 2.5 2.6 Côte d'Ivoire 2.5 2.0 2.5 2.5 2.5 2.4 2.0 2.5 4.0 2.0 2.5 2.6 Djibouti 3.0 3.0 3.5 3.0 3.5 3.2 2.5 3.0 3.5 2.5 2.5 2.8 Dominica 3.5 3.5 4.0 3.5 3.5 3.6 4.0 3.5 4.0 3.5 4.0 3.8 Eritrea 3.5 2.5 3.5 2.5 2.0 2.8 2.5 2.5 3.5 3.0 2.0 2.7 Ethiopia 3.0 4.5 4.0 3.5 3.0 3.6 3.0 3.5 3.5 3.5 2.5 3.2 Gambia, The 3.5 3.5 3.5 2.5 3.5 3.3 3.0 3.0 3.5 3.0 2.0 2.9 Georgia 4.5 4.5 4.5 4.5 3.0 4.2 3.5 4.0 4.5 4.0 3.0 3.8 Ghana 4.0 4.0 4.5 3.5 3.5 3.9 3.5 3.5 4.5 3.5 4.0 3.8 Grenada 4.5 3.5 4.0 3.5 4.0 3.9 3.5 4.0 3.5 3.5 4.0 3.7 Guinea 3.5 3.0 3.0 3.0 2.5 3.0 2.0 3.0 3.0 3.0 2.0 2.6 Guinea-Bissau 2.5 3.0 2.0 2.5 2.5 2.5 2.5 2.5 3.0 2.5 2.5 2.6 Guyana 4.0 3.5 4.0 3.0 3.0 3.5 3.0 3.5 3.5 2.5 3.0 3.1 Haiti 3.0 3.0 2.5 2.5 2.5 2.7 2.0 3.0 2.5 2.5 2.5 2.5 Honduras 4.0 4.0 3.5 3.5 3.5 3.7 3.0 4.0 4.0 2.5 3.0 3.3 India 3.5 4.0 4.0 3.5 3.5 3.7 3.5 4.0 4.0 3.5 3.5 3.7 Kenya 3.0 3.5 4.0 3.5 3.5 3.5 2.5 3.5 4.0 3.5 3.0 3.3 Kiribati 2.5 3.5 2.5 3.0 3.0 2.9 3.5 3.0 3.0 3.0 3.0 3.1 Kosovo 3.5 3.5 2.5 3.5 3.0 3.2 3.0 4.0 3.5 2.5 3.0 3.2 Kyrgyz Republic 4.5 3.5 3.5 3.5 3.0 3.6 2.5 3.5 3.5 3.0 2.5 3.0 Definitions • International Development Association Resource long-term debt sustainability. • Structural policies protection under law. • Equity of public resource use Allocation Index is obtained by calculating the aver- cluster: Trade assesses how the policy framework assesses the extent to which the pattern of public age score for each cluster and then by averaging fosters trade in goods. • Financial sector assesses expenditures and revenue collection affects the poor those scores. For each of 16 criteria countries are the structure of the financial sector and the poli- and is consistent with national poverty reduction rated on a scale of 1 (low) to 6 (high) • Economic cies and regulations that affect it. • Business regu- priorities. •  Building human resources assesses management cluster: Macro economic manage- latory environment assesses the extent to which the national policies and public and private sec- ment assesses the monetary, exchange rate, and the legal, regulatory, and policy environments help tor service delivery that affect the access to and aggregate demand policy framework. • Fiscal policy or hinder private businesses in investing, creating quality of health and education services, including assesses the short- and medium-term sustainability jobs, and becoming more productive. • Policies for prevention and treatment of HIV/AIDS, tuberculosis, of fiscal policy (taking into account monetary and social inclusion and equity cluster: Gender equal- and malaria. • Social protection and labor assess exchange rate policy and the sustainability of the ity assesses the extent to which the country has government policies in social protection and labor public debt) and its impact on growth. • Debt policy installed institutions and programs to enforce laws market regulations that reduce the risk of becoming assesses whether the debt management strategy is and policies that promote equal access for men poor, assist those who are poor to better manage conducive to minimizing budgetary risks and ensuring and women in education, health, the economy, and further risks, and ensure a minimal level of welfare 300 2011 World Development Indicators 5.9 STATES AND MARKETS Public policies and institutions Policies for social inclusion and equity Public sector management and institutions 1–6 (low to high) 1–6 (low to high) Quality of budgetary Transparency, Equity Policies and Property and accountability, of public Building Social institutions for rights and financial Efficiency Quality and corruption Gender resource human protection environmental rule-based manage- of revenue of public in the public equality use resources and labor sustainability Average governance ment mobilization administration sector Average 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Lao PDR 3.5 4.0 3.0 2.5 4.0 3.4 3.0 3.5 3.0 3.0 2.0 2.9 Lesotho 4.0 3.0 3.5 3.0 3.0 3.3 3.5 3.0 4.0 3.0 3.5 3.4 Liberia 2.5 3.0 2.5 2.5 2.0 2.5 2.5 2.5 3.5 2.5 3.0 2.8 Madagascar 3.5 4.0 3.5 3.5 3.5 3.6 3.5 3.0 4.0 3.5 2.5 3.3 Malawi 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.0 4.0 3.5 3.0 3.4 Maldives 4.0 4.0 3.5 3.5 4.0 3.8 4.0 3.0 4.0 3.5 3.0 3.5 Mali 3.5 3.5 3.5 3.5 3.0 3.4 3.5 3.5 3.5 3.0 3.5 3.4 Mauritania 4.0 3.5 3.5 3.0 3.0 3.4 3.0 3.0 3.5 3.0 2.5 3.0 Moldova 5.0 3.5 4.0 3.5 3.5 3.9 3.5 4.0 3.5 3.0 3.0 3.4 Mongolia 3.5 3.5 4.0 3.5 3.0 3.5 3.0 4.0 3.5 3.5 3.0 3.4 Mozambique 3.5 3.5 3.5 3.0 3.0 3.3 3.0 4.0 4.0 3.0 3.0 3.4 Nepal 4.0 4.0 4.0 3.0 3.5 3.7 2.5 3.0 3.5 3.0 3.0 3.0 Nicaragua 3.5 3.5 3.5 3.5 3.5 3.5 3.0 4.0 4.0 3.0 3.0 3.4 Niger 2.5 3.5 3.5 3.0 3.0 3.1 3.0 3.5 3.5 3.0 2.5 3.1 Nigeria 3.0 3.5 3.0 3.5 3.0 3.2 2.5 3.0 3.0 3.0 3.0 2.9 Pakistan 2.0 3.5 3.0 3.0 3.0 2.9 2.5 3.5 3.0 3.5 2.5 3.0 Papua New Guinea 2.5 3.5 2.5 3.0 2.0 2.7 2.0 3.0 3.5 2.5 3.0 2.8 Rwanda 3.5 4.5 4.5 3.5 3.5 3.9 3.0 4.0 3.5 3.5 3.5 3.5 Samoa 3.5 4.5 4.0 3.5 4.0 3.9 4.0 3.5 4.5 4.0 4.0 4.0 São Tomé and Príncipe 3.0 3.0 3.0 2.5 2.5 2.8 2.5 3.0 3.5 3.0 3.5 3.1 Senegal 3.5 3.5 3.5 3.0 3.5 3.4 3.5 3.0 4.0 3.5 3.0 3.4 Sierra Leone 3.0 3.0 3.5 3.5 2.5 3.1 2.5 3.5 2.5 3.0 3.0 2.9 Solomon Islands 3.0 2.5 3.0 2.5 2.0 2.6 3.0 2.5 2.5 2.0 3.0 2.6 Sri Lanka 4.0 3.5 4.5 3.5 3.5 3.8 3.5 4.0 3.5 3.0 3.0 3.4 St. Lucia 3.5 4.0 4.0 3.5 3.5 3.7 4.0 3.5 4.5 3.5 4.5 4.0 St. Vincent & Grenadines 4.0 3.5 4.0 3.5 3.5 3.7 4.0 3.5 4.0 3.5 4.0 3.8 Sudan 2.0 2.5 2.5 2.5 2.0 2.3 2.0 2.0 3.0 2.5 1.5 2.2 Tajikistan 4.0 3.5 3.0 3.5 3.0 3.4 2.5 3.0 3.0 3.0 2.0 2.7 Tanzania 3.5 4.0 4.0 3.5 3.5 3.7 3.5 3.5 4.0 3.5 3.0 3.5 Timor-Leste 3.5 3.0 2.5 2.5 2.5 2.8 2.0 3.0 3.0 2.5 3.0 2.7 Togo 3.0 2.0 3.0 3.0 2.5 2.7 2.5 2.5 3.0 2.0 2.0 2.4 Tonga 3.0 4.0 4.0 3.0 3.0 3.4 3.5 3.5 4.0 3.5 3.5 3.6 Uganda 3.5 4.0 4.0 3.5 4.0 3.8 3.5 4.0 3.5 3.0 2.5 3.3 Uzbekistan 4.0 3.5 4.0 3.5 3.5 3.7 2.5 3.5 3.5 3.0 1.5 2.8 Vanuatu 3.5 3.5 2.5 2.0 3.0 2.9 3.5 3.5 3.5 3.0 3.0 3.3 Vietnam 4.5 4.5 4.0 3.5 3.5 4.0 3.5 4.0 4.0 3.5 3.0 3.6 Yemen, Rep. 2.0 3.5 3.0 3.5 3.5 3.1 2.5 3.5 3.0 3.0 3.0 3.0 Zambia 3.5 3.5 4.0 3.0 3.5 3.5 3.0 3.5 3.5 3.0 3.0 3.2 Zimbabwe 2.5 1.5 1.0 1.0 2.0 1.6 1.5 2.0 3.5 1.5 1.5 2.0 to all people. •  Policies and institutions for envi- and timely and accurate accounting and fiscal report- extent to which public employees within the executive ronmental sustainability assess the extent to which ing, including timely and audited public accounts. are required to account for administrative decisions, environmental policies foster the protection and sus- • Efficiency of revenue mobilization assesses the use of resources, and results obtained. The three tainable use of natural resources and the manage- overall pat tern of revenue mobilization—not only main dimensions assessed are the accountability ment of pollution. • Public sector management and the de facto tax structure, but also revenue from of the executive to oversight institutions and of pub- institutions cluster: Property rights and rule-based all sources as actually collected. • Quality of public lic employees for their performance, access of civil governance assess the extent to which private eco- administration assesses the extent to which civilian society to information on public affairs, and state nomic activity is facilitated by an effective legal sys- central government staff is structured to design and capture by narrow vested interests. tem and rule-based governance structure in which implement government policy and deliver services property and contract rights are reliably respected effectively. • Transparency, accountability, and cor- Data sources and enforced. • Quality of budgetary and financial ruption in the public sector assess the extent to Data on public policies and institutions are from management assesses the extent to which there is which the executive can be held accountable for its the World Bank Group’s CPIA database available a comprehensive and credible budget linked to policy use of funds and for the results of its actions by the at www.worldbank.org/ida. priorities, effective financial management systems, elector ate, the legislature, and the judiciary and the 2011 World Development Indicators 301 5.10 Transport services Roads Railways Ports Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000–08a 2000–08a 2000–08a 2000–08a 2000–09a 2000–09a 2000–09a 2009 2009 2009 2009 Afghanistan 42,150 29.3 .. .. .. .. .. .. .. .. .. Albania 18,000 39.0 197 2,200 423 32 46 .. 5 231 0 Algeria 111,261 73.5 .. .. 4,723 1,141 1,184 .. 53 4,371 4 Angola 51,429 10.4 166,045 4,709 .. .. .. .. 3 275 64 Argentina 231,374 30.0 .. .. 25,023 6,979 12,025 1,555 75 5,695 112 Armenia 7,704 90.5 2,742 179 845 27 354 .. 8 653 6 Australia 818,356 .. 302,369 189,847 9,674 1,546 62,083 6,197 403 50,027 2,769 Austria 110,778 100.0 69,000 26,411 5,784 10,210 20,202 .. 139 8,521 342 Azerbaijan 52,942 50.6 14,041 9,947 2,079 1,025 7,592 .. 10 840 7 Bangladesh 239,226 9.5 .. .. 2,835 5,609 870 1,182 16 1,409 0 Belarus 94,797 88.6 8,184 22,767 5,510 7,401 42,742 .. 6 333 1 Belgium 153,595 78.2 132,404 46,891 3,578 10,493 6,542 9,701 250 4,859 1,427 Benin 19,000 9.5 .. .. 758 .. 36 .. .. .. .. Bolivia 62,479 7.0 .. .. 2,866 313 1,060 .. 19 1,537 7 Bosnia and Herzegovina 21,846 52.3 .. 300 1,016 61 988 .. 1 80 0 Botswana 25,798 32.6 .. .. 888 94 674 .. 6 234 0 Brazil 1,751,868 5.5 .. .. 29,817 .. 267,700 6,246 752 67,946 1,782 Bulgaria 40,231 98.4 13,688 11,843 4,150 2,144 3,152 .. 11 798 2 Burkina Faso 92,495 4.2 .. .. 622 .. .. .. 1 79 0 Burundi 12,322 10.4 .. .. .. .. .. .. .. .. .. Cambodia 38,257 6.3 201 .. 650 45 92 .. 3 184 1 Cameroon 51,346 8.4 .. .. 977 377 978 .. 10 466 23 Canada 1,409,000 39.9 493,814 129,600 58,345 2,901 258,280 4,175 1,198 52,584 1,347 Central African Republic 24,307 .. .. .. .. .. .. .. .. .. .. Chad 40,000 0.8 .. .. .. .. .. .. .. .. .. Chile 79,814 20.2 .. .. 5,352 840 4,032 2,814 97 8,097 1,179 China 3,730,164 53.5 1,247,611 3,286,819 65,491 787,890 2,523,917 105,977 2,140 229,062 11,976 Hong Kong SAR, China 2,040 100.0 .. .. .. .. .. 21,040 150 23,973 13,293 Colombia 164,183 .. 157 39,726 1,672 .. 11,884 2,042 196 12,115 2,420 Congo, Dem. Rep. 153,497 1.8 .. .. 3,641 35 182 .. .. .. .. Congo, Rep. 17,000 7.1 .. .. 795 211 234 .. .. .. .. Costa Rica 38,049 25.3 27 1 .. .. .. 876 33 933 9 Côte d’Ivoire 81,996 7.9 .. .. 639 10 675 .. .. .. .. Croatia 29,248 86.9 4,093 11,042 2,723 1,835 2,641 .. 25 1,679 2 Cuba .. 49.0 6,551 2,222 5,076 1,285 1,351 .. 11 780 27 Czech Republic 130,573 100.0 88,468 50,877 9,539 6,462 11,249 .. 78 5,048 22 Denmark 73,257 100.0 70,173 10,717 2,131 7,312 2,030 .. 86 6,773 14 Dominican Republic 12,600 49.4 .. .. .. .. .. 1,263 .. .. .. Ecuador 43,670 14.8 11,819 1,193 .. .. .. 1,001 46 2,897 3 Egypt, Arab Rep. 104,918 86.9 12,793 .. 5,195 40,837 3,840 6,250 56 6,216 180 El Salvador 10,029 19.8 .. .. .. .. .. .. 19 1,997 15 Eritrea 4,010 21.8 .. .. .. .. .. .. .. .. .. Estonia 58,034 28.8 3,190 7,641 929 274 5,780 .. 9 396 1 Ethiopia 44,359 13.7 219,113 2,456 .. .. .. .. 44 2,914 424 Finland 78,860 65.5 71,800 28,500 5,919 3,876 8,872 1,064 105 7,423 484 France 951,200 100.0 769,000 313,000 33,778 87,667 26,482 4,491 772 58,318 6,625 Gabon 9,170 10.2 .. .. 810 95 2,485 .. 5 525 62 Gambia, The 3,742 19.3 16 .. .. .. .. .. .. .. .. Georgia 20,329 94.1 5,269 586 1,566 626 5,417 .. 5 294 2 Germany 644,288 100.0 949,306 472,700 33,706 76,772 93,946 12,765 1,081 103,397 10,188 Ghana 57,614 14.9 .. .. 953 85 181 .. .. .. .. Greece 116,711 91.8 .. 18,360 1,552 1,413 538 935 113 8,795 31 Guatemala 14,095 34.5 .. .. .. .. .. 906 .. .. .. Guinea 44,348 9.8 .. .. .. .. .. .. .. .. .. Guinea-Bissau 3,455 27.9 .. .. .. .. .. .. .. .. .. Haiti 4,160 24.3 .. .. .. .. .. .. .. .. .. Honduras 13,600 20.4 .. .. .. .. .. .. .. .. .. 302 2011 World Development Indicators 5.10 STATES AND MARKETS Transport services Roads Railways Ports Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000–08a 2000–08a 2000–08a 2000–08a 2000–09a 2000–09a 2000–09a 2009 2009 2009 2009 Hungary 197,534 37.7 20,449 35,743 7,793 5,708 447 .. 46 2,953 10 India 4,236,429 49.3 .. .. 63,273 838,032 551,448 7,889 602 54,446 1,235 Indonesia 437,759 59.1 .. .. 3,370 14,344 4,390 6,394 330 27,421 277 Iran, Islamic Rep. 174,301 73.3 .. .. 7,555 15,312 20,540 2,206 134 13,053 96 Iraq 45,550 84.3 .. .. 2,025 54 121 .. .. .. .. Ireland 96,424 100.0 .. 15,900 1,919 1,683 79 817 528 77,747 121 Israel 18,096 100.0 .. .. 1,005 1,968 1,055 2,033 48 4,605 985 Italy 487,700 100.0 97,560 192,700 16,959 45,590 13,569 9,532 383 33,195 400 Jamaica 22,210 73.3 .. .. .. .. .. 1,690 17 1,380 10 Japan 1,200,858 79.6 947,562 327,632 20,036 253,555 22,100 16,286 642 86,897 10,486 Jordan 7,816 100.0 .. .. 294 .. 353 .. 32 2,324 163 Kazakhstan 93,612 89.9 106,878 63,481 14,205 14,860 197,302 .. 19 1,193 15 Kenya 63,265 14.1 .. 22 1,917 226 1,399 .. 34 2,949 272 Korea, Dem. Rep. 25,554 2.8 .. .. .. .. .. .. 2 101 2 Korea, Rep. 104,237 78.5 97,854 12,545 3,378 31,298 9,273 16,054 256 34,169 15,163 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 5,749 85.0 .. .. .. .. .. .. 18 2,597 281 Kyrgyz Republic 34,000 91.1 6,468 903 417 106 745 .. 5 309 2 Lao PDR 34,994 13.5 2,113 287 .. .. .. .. 10 303 2 Latvia 69,684 100.0 17,966 12,344 1,885 75 18,693 .. 27 1,302 18 Lebanon 6,970 .. .. .. .. .. .. 995 14 1,308 94 Lesotho 5,940 18.3 .. .. .. .. .. .. .. .. .. Liberia 10,600 6.2 .. .. .. .. .. .. .. .. .. Libya 83,200 57.2 .. .. .. .. .. .. 10 1,147 0 Lithuania 81,030 28.6 42,739 20,419 1,767 357 11,888 .. 12 617 7 Macedonia, FYR 13,922 56.5 1,239 3,978 699 154 497 .. 1 87 0 Madagascar 49,827 11.6 .. .. 854 10 12 .. 10 500 14 Malawi 15,451 45.0 .. .. 797 44 33 .. 4 157 1 Malaysia 98,722 82.8 .. .. 1,665 1,527 1,384 15,843 182 23,766 2,853 Mali 18,912 19.0 .. .. 733 196 189 .. .. .. .. Mauritania 11,066 26.8 .. .. 728 47 7,566 .. 1 142 0 Mauritius 2,028 98.0 .. .. .. .. .. .. 11 1,093 153 Mexico 366,096 35.3 463,865 227,290 26,704 449 71,136 2,869 222 15,728 714 Moldova 12,778 85.8 1,640 1,577 1,157 423 1,017 .. 5 402 1 Mongolia 49,250 3.5 1,215 782 1,814 1,009 7,852 .. 5 257 3 Morocco 58,256 67.8 .. 794 2,110 4,190 4,111 1,222 62 4,931 63 Mozambique 30,331 20.8 .. .. 3,116 114 695 .. 11 490 6 Myanmar 27,000 11.9 .. .. .. 4,163 885 .. 28 1,527 3 Namibia 66,467 12.8 47 591 .. .. .. .. 5 455 0 Nepal 17,782 55.9 .. .. .. .. .. .. 7 484 6 Netherlands 136,135 90.0 .. 77,100 2,886 15,400 4,331 10,066 292 29,109 4,520 New Zealand 93,911 65.9 .. .. .. .. 4,078 2,955 217 12,104 799 Nicaragua 20,333 12.0 123 .. .. .. .. .. .. .. .. Niger 18,948 20.7 .. .. .. .. .. .. .. .. .. Nigeria 193,200 15.0 .. .. 3,528 174 77 .. 17 1,365 8 Norway 93,247 80.5 63,362 17,564 4,114 2,877 2,092 .. 110 8,786 14 Oman 53,430 43.5 .. .. .. .. .. 3,768 26 2,361 39 Pakistan 260,420 65.4 263,788 129,249 7,791 24,731 6,187 2,058 51 5,303 304 Panama 13,727 38.1 .. .. .. .. .. 4,597 66 6,348 0 Papua New Guinea 19,600 3.5 .. .. .. .. .. .. 21 847 19 Paraguay 29,500 50.8 .. .. .. .. .. .. 10 428 0 Peru 102,887 13.9 .. .. 2,020 78 900 1,335 66 5,843 257 Philippines 200,037 9.9 .. .. 479 83 1 4,116 87 10,481 227 Poland 383,313 68.2 26,791 174,223 19,764 16,454 29,940 859 83 4,279 55 Portugal 82,900 86.0 .. 46,406 2,842 3,766 872 1,042 124 9,904 314 Puerto Rico 25,645 95.0 .. 10 .. .. .. 1,674 .. .. .. Qatar 7,790 90.0 .. .. .. .. .. .. 77 10,211 2,276 2011 World Development Indicators 303 5.10 Transport services Roads Railways Ports Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000–08a 2000–08a 2000–08a 2000–08a 2000–09a 2000–09a 2000–09a 2009 2009 2009 2009 Romania 198,817 30.2 20,194 56,377 10,776 5,975 8,902 1,381 58 3,268 4 Russian Federation 963,000 80.1 78,000 206,000 85,194 153,500 1,865,305 2,178 475 34,403 2,306 Rwanda 14,008 19.0 .. .. .. .. .. .. .. .. .. Saudi Arabia 221,372 21.5 .. .. 1,020 337 1,748 4,431 157 17,508 1,838 Senegal 14,805 29.3 .. .. 906 129 384 .. 0 573 0 Serbia 40,130 47.7 4,719 1,112 4,058 683 3,013 .. 17 927 2 Sierra Leone 11,300 8.0 .. .. .. .. .. .. 0 22 8 Singapore 3,325 100.0 5,964 .. .. .. .. 25,866 84 18,427 7,391 Slovak Republic 43,848 87.0 32,214 22,114 3,623 2,247 6,465 .. 32 3,441 0 Slovenia 38,872 100.0 815 16,261 1,228 840 2,668 .. 25 953 3 Somalia 22,100 11.8 .. .. .. .. .. .. .. .. .. South Africa 362,099 17.3 .. 434 22,051 13,865 113,342 3,726 151 12,504 676 Spain 667,064 99.0 397,117 132,868 15,043 22,959 7,348 10,193 548 49,289 1,080 Sri Lanka 97,286 81.0 21,067 .. 1,463 4,767 135 3,464 17 2,418 279 Sudan 11,900 36.3 .. .. 4,508 34 766 .. 7 607 42 Swaziland 3,594 30.0 .. .. 300 .. .. .. .. .. .. Sweden 574,741 23.6 108,100 42,400 9,946 7,038 11,500 1,251 62 5,824 16 Switzerland 71,355 100.0 93,675 16,226 3,544 17,417 12,460 .. 168 14,701 1,058 Syrian Arab Republic 64,983 91.0 589 .. 1,801 1,120 2,370 685 19 1,343 11 Tajikistan 27,767 .. 150 14,572 616 45 1,282 .. 10 765 6 Tanzania 87,524 7.4 .. .. 2,600 b 475b 728b .. 21 684 1 Thailand 180,053 98.5 .. .. 4,429 8,037 3,161 5,898 124 19,619 2,133 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 11,652 21.0 .. .. .. .. .. .. .. .. .. Trinidad and Tobago 8,320 51.1 .. .. .. .. .. .. 14 1,014 70 Tunisia 19,371 75.2 .. 16,611 1,991 1,493 2,073 .. 24 2,279 14 Turkey 426,951 .. 206,098 181,935 8,686 5,374 9,681 4,522 272 31,339 856 Turkmenistan 24,000 81.2 .. .. 3,095 1,685 11,547 .. 15 1,706 9 Uganda 70,746 23.0 .. .. 259 .. 218 .. 0 64 27 Ukraine 169,502 97.8 60,671 36,866 21,678 48,327 196,188 1,112 59 3,428 63 United Arab Emirates 4,080 100.0 .. .. .. .. .. 14,425 171 31,762 8,960 United Kingdom 419,634 100.0 736,000 173,077 16,173 51,467 12,512 5,987 1,004 102,465 6,615 United States 6,506,221 67.4 7,980,611 1,889,923 226,205 9,476 2,431,181c 34,300 9,182d 679,423d 61,684 d Uruguay 77,732 .. 2,032 .. 2,993 15 284 .. 9 564 4 Uzbekistan 81,600 87.3 56,674 21,038 4,230 2,832 24,238 .. 23 1,850 76 Venezuela, RB 96,155 33.6 .. .. 336 .. 81 1,168 124 5,121 2 Vietnam 160,089 47.6 49,372 24,647 2,347 4,129 3,807 4,751 84 11,074 312 West Bank and Gaza 5,147 100.0 .. .. .. .. .. .. .. .. .. Yemen, Rep. 71,300 8.7 .. .. .. .. .. .. 15 1,050 26 Zambia 66,781 22.0 .. .. 1,273 183 .. .. 4 62 .. Zimbabwe 97,267 19.0 .. .. 2,583 .. 1,580 .. 6 261 7 World   49.0 m .. m ..m .. s 2,264 m 5,321 m 443,740 s 26,379 s 2,270,901 s 202,136 s Low income   12.0 .. .. .. .. .. .. 228 13,439 783 Middle income   35.4 .. .. .. 1,343 4,072 206,537 7,169 664,804 31,329 Lower middle income   36.3 .. .. .. 1,917 4,049 150,612 3,954 398,922 17,548 Upper middle income   36.8 .. .. .. 1,083 5,812 55,926 3,215 265,882 13,781 Low & middle income   24.3 .. .. .. .. 3,910 207,719 7,398 678,243 32,112 East Asia & Pacific   11.4 .. .. .. 4,248 3,483 142,980 3,093 326,294 17,878 Europe & Central Asia   .. 27,816 21,038 171,322 1,025 7,592 11,018 1,018 83,523 3,365 Latin America & Carib.   22.0 .. .. .. .. .. 28,362 1,794 138,460 6,576 Middle East & N. Africa   81.0 .. .. .. 1,493 2,222 .. 419 38,022 653 South Asia   51.8 .. .. .. 24,731 3,529 14,593 700 64,196 1,825 Sub-Saharan Africa   12.1 .. .. .. .. .. .. 373 27,749 1,815 High income   93.4 .. 29,505 .. 7,038 8,872 236,021 18,981 1,592,658 170,024 Euro area   100.0 69,000 45,032 130,021 10,210 6,542 62,931 4,488 399,964 33,950 a. Data are for the latest year available in the period shown. b. Includes Tazara railway. c. Refers to class 1 railways only. d. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers. 304 2011 World Development Indicators 5.10 STATES AND MARKETS Transport services About the data Definitions Transport infrastructure—highways, railways, ports But when traffic is merely transshipment, much of • Total road network covers motorways, highways, and waterways, and airports and air traffic control the economic benefit goes to the terminal operator main or national roads, secondary or regional roads, systems—and the services that flow from it are cru- and ancillary services for ships and containers rather and all other roads in a country. • Paved roads are cial to the activities of households, producers, and than to the country more broadly. In transshipment roads surfaced with crushed stone (macadam) and governments. Because performance indicators vary centers empty containers may account for as much hydrocarbon binder or bituminized agents, with con- widely by transport mode and focus (whether physical as 40 percent of traffic. crete, or with cobblestones. • Passengers carried infrastructure or the services flowing from that infra- The air transport data represent the total (interna- by road are the number of passengers transported structure), highly specialized and carefully specified tional and domestic) scheduled traffic carried by the by road times kilometers traveled. • Goods hauled indicators are required. The table provides selected air carriers registered in a country. Countries submit by road are the volume of goods transported by road indicators of the size, extent, and productivity of air transport data to ICAO on the basis of standard vehicles, measured in millions of metric tons times roads, railways, and air transport systems and of the instructions and definitions issued by ICAO. In many kilometers traveled. • Rail lines are the length of rail- volume of traffic in these modes as well as in ports. cases, however, the data include estimates by ICAO way route available for train service, irrespective of Data for transport sectors are not always inter- for nonreporting carriers. Where possible, these esti- the number of parallel tracks. • Passengers carried nationally comparable. Unlike for demographic sta- mates are based on previous submissions supple- by railway are the number of passengers transported tistics, national income accounts, and international mented by information published by the air carriers, by rail times kilometers traveled. •  Goods hauled trade data, the collection of infrastructure data has such as flight schedules. by railway are the volume of goods transported by not been “internationalized.” But data on roads are The data cover the air traffic carried on scheduled railway, measured in metric tons times kilometers collected by the International Road Federation (IRF) services, but changes in air transport regulations traveled. • Port container traffic measures the flow and data on air transport by the International Civil in Europe have made it more diffi cult to classify of containers from land to sea transport modes and Aviation Organization (ICAO). traffic as scheduled or nonscheduled. Thus recent vice versa in twenty-foot-equivalent units (TEUs), a National road associations are the primary source increases shown for some European countries may standard-size container. Data cover coastal shipping of IRF data. In countries where a national road asso- be due to changes in the classification of air traffic as well as international journeys. Transshipment traf- ciation is lacking or does not respond, other agencies rather than actual growth. For countries with few air fic is counted as two lifts at the intermediate port are contacted, such as road directorates, ministries carriers or only one, the addition or discontinuation (once to off-load and again as an outbound lift) and of transport or public works, or central statistical of a home-based air carrier may cause significant includes empty units. •  Registered carrier depar- offices. As a result, definitions and data collection changes in air traffic. tures worldwide are domestic takeoffs and takeoffs methods and quality differ, and the compiled data abroad of air carriers registered in the country. • Pas- are of uneven quality. Moreover, the quality of trans- sengers carried by air include both domestic and port service (reliability, transit time, and condition of international passengers of air carriers registered goods delivered) is rarely measured, though it may be in the country. • Air freight is the volume of freight, as important as quantity in assessing an economy’s express, and diplomatic bags carried on each flight transport system. stage (operation of an aircraft from takeoff to its next Unlike the road sector, where numerous qualified landing), measured in metric tons times kilometers motor vehicle operators can operate anywhere on traveled. the road network, railways are a restricted transport system with vehicles confined to a fixed guideway. Considering the cost and service characteristics, railways generally are best suited to carry—and can effectively compete for—bulk commodities and con- Data sources tainerized freight for distances of 500–5,000 kilo- Data on roads are from the IRF’s World Road meters, and passengers for distances of 50–1,000 Statistics, supplemented by World Bank staff kilometers. Below these limits road transport estimates. Data on railways are from a database tends to be more competitive, while above these maintained by the World Bank’s Transport, Water, limits air transport for passengers and freight and and Information and Communication Technologies sea transport for freight tend to be more competi- Department, Transport Division, based on data tive. The railways indicators in the table focus on from the International Union of Railways. Data on scale and output measures: total route-kilometers, port container traffic are from Containerisation passenger-kilometers, and goods (freight) hauled in International’s Containerisation International Year- ton-kilometers. book. Data on air transport are from the ICAO’s Measures of port container traffi c, much of it Civil Aviation Statistics of the World and ICAO staff commodities of medium to high value added, give estimates. some indication of economic growth in a country. 2011 World Development Indicators 305 5.11 Power and communications Electric power Telephonesa Access and use Quality Affordability and efficiency Population Mobile Inter national voice traffic Transmission covered by $ per month cellular and minutes per person and per 100 people mobile Mobile Telecom- fi xed-line Consumption distribution Mobile cellular Residential cellular munications subscribers per capita losses Fixed cellular Fixed network fi xed-line prepaid revenue per kWh % of output lines subscriptions lines Total % tariff tariff % of GDP employee 2008 2008 2009 2009 2008 2008 2008 2009 2009 2008 2008 Afghanistan .. .. 0 40 1 7 75 .. .. 0.0 58 Albania 1,372 50 12 132 127 263 99 6.0 13.4 6.0 871 Algeria 957 18 7 94 15 34 82 4.2 6.3 2.5 285 Angola 189 15 2 44 .. .. 40 16.6 11.0 .. .. Argentina 2,789 13 24 129 42 .. 94 3.9 13.7 3.1 1,929 Armenia 1,578 15 20 85 .. .. 88 4.1 5.8 4.5 .. Australia 11,217 7 41 111 .. .. 99 26.0 34.9 3.4 346 Austria 8,218 5 39 141 .. .. 99 27.3 6.8 1.7 843 Azerbaijan 2,317 13 16 88 .. 77 99 2.5 4.4 2.4 484 Bangladesh 208 5 1 31 6 .. 90 1.6 1.3 .. .. Belarus 3,427 11 41 100 .. .. 99 1.0 3.4 2.1 .. Belgium 8,523 5 39 115 .. .. 100 33.6 20.8 2.8 732 Benin 76 .. 1 56 12 309 80 10.0 14.8 1.0 1,652 Bolivia 561 13 8 72 80 .. 46 23.5 7.3 6.8 376 Bosnia and Herzegovina 2,467 17 27 86 109 .. 99 8.8 9.4 5.5 567 Botswana 1,503 52 7 96 115 .. 99 18.0 8.1 2.9 1,018 Brazil 2,232 17 21 90 .. .. 91 13.4 34.6 4.5 358 Bulgaria 4,594 10 29 140 27 105 100 13.8 17.6 5.1 565 Burkina Faso .. .. 1 21 11 .. 61 11.5 14.4 4.0 .. Burundi .. .. 0 10 .. .. 80 .. .. 3.1 492 Cambodia 113 13 0 38 .. .. 87 7.8 5.0 .. 1,712 Cameroon 263 10 2 38 4 .. 58 14.1 14.0 3.1 1,050 Canada 17,061 8 54 68 .. .. 98 18.3 17.7 2.5 .. Central African Republic .. .. 0 4 .. .. 19 10.1 12.9 .. 293 Chad .. .. 0 24 .. .. 24 .. .. .. .. Chile 3,319 9 21 97 35 43 100 23.6 10.2 .. 592 China 2,455 6 24 56 9 .. 97 2.3 3.7 2.5b 1,310 Hong Kong SAR, China 5,866 13 60 174 1,435 1,435 100 7.1 0.8 3.6 980 Colombia 974 19 16 92 142 .. 83 5.7 9.5 3.7 .. Congo, Dem. Rep. 95 11 0 15 .. 6 50 .. .. 7.4 3,628 Congo, Rep. 150 77 1 59 .. .. 53 .. .. .. .. Costa Rica 1,866 10 33 43 120 132 69 4.1 2.3 1.8 497 Côte d’Ivoire 186 24 1 63 .. .. 59 21.7 11.5 5.5 3,274 Croatia 3,878 14 42 136 229 302 100 19.2 18.4 4.6 892 Cuba 1,327 16 10 4 .. .. 77 13.2 22.7 .. .. Czech Republic 6,464 6 20 136 136 197 100 29.3 17.7 3.8 812 Denmark 6,460 6 37 134 210 357 .. 24.5 6.5 2.4 543 Dominican Republic 1,377 11 10 86 .. .. .. 12.3 8.5 .. .. Ecuador 1,137 20 15 100 3 .. 84 1.3 9.4 4.1 513 Egypt, Arab Rep. 1,425 11 12 67 27 44 95 3.0 4.1 3.7 855 El Salvador 953 2 18 123 578 510 95 11.5 7.1 4.8 2,275 Eritrea .. .. 1 3 17 29 80 .. .. 3.0 117 Estonia 6,348 11 37 203 .. .. 100 13.2 12.3 4.5 742 Ethiopia 42 9 1 5 2 5 10 0.9 2.4 1.3 233 Finland 16,350 4 27 144 .. .. 100 18.5 13.4 2.3 708 France 7,931 6 57 95 242 301 99 29.3 35.2 2.0 695 Gabon 1,158 18 2 93 .. .. 79 .. .. 2.0 .. Gambia, The .. .. 3 84 .. .. 85 2.4 6.3 .. 466 Georgia 1,678 13 15 67 44 268 98 3.5 7.6 6.9 355 Germany 7,149 5 59 128 .. .. 99 32.7 9.5 2.5 787 Ghana 268 22 1 63 6 61 73 3.8 4.3 .. 1,780 Greece 5,723 8 53 118 .. .. 100 25.4 23.6 3.7 813 Guatemala 543 14 10 123 .. 206 76 7.8 7.3 .. .. Guinea .. .. 0 56 .. .. 80 3.0 3.1 .. .. Guinea-Bissau .. .. 0 35 .. .. 65 .. .. .. .. Haiti 23 53 1 36 .. .. .. .. .. .. .. Honduras 708 21 11 103 39 224 90 .. .. 7.2 391 306 2011 World Development Indicators 5.11 STATES AND MARKETS Power and communications Electric power Telephonesa Access and use Quality Affordability and efficiency Population Mobile Inter national voice traffic Transmission covered by $ per month cellular and minutes per person and per 100 people mobile Mobile Telecom- fi xed-line Consumption distribution Mobile cellular Residential cellular munications subscribers per capita losses Fixed cellular Fixed network fi xed-line prepaid revenue per kWh % of output lines subscriptions lines Total % tariff tariff % of GDP employee 2008 2008 2009 2009 2008 2008 2008 2009 2009 2008 2008 Hungary 3,989 10 31 118 120 159 99 24.0 15.4 3.8 1,127 India 566 23 3 45 .. .. 61 3.1 1.4 1.9 .. Indonesia 591 10 15 69 .. .. 90 5.6 2.8 .. .. Iran, Islamic Rep. 2,423 18 35 72 .. .. 95 0.2 3.6 .. 913 Iraq 1,164 7 4 63 0 .. 72 .. .. .. 1,098 Ireland 6,301 8 47 109 .. .. 99 43.8 20.9 2.5 .. Israel 7,054 2 44 121 413 .. 100 17.0 13.8 4.0 .. Italy 5,661 7 35 150 .. .. 100 28.2 18.4 2.9 1,657 Jamaica 2,552 12 11 110 39 224 95 9.6 5.6 1.4 .. Japan 8,071 5 35 90 .. .. 100 22.8 44.3 3.1 12 Jordan 2,087 14 8 101 67 258 99 9.4 5.7 6.3 1,132b Kazakhstan 4,689 9 24 94 47 52 94 1.9 8.8 2.9 253 Kenya 155 15 2 49 3 6 83 10.1 7.5 6.4 2,354 Korea, Dem. Rep. 820 16 5 0 .. .. 0 .. .. .. .. Korea, Rep. 8,853 4 40 98 33 64 94 5.2 12.2 4.7 657 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 16,747 12 20 107 .. .. 100 8.6 7.8 .. .. Kyrgyz Republic 1,449 31 9 84 .. .. 24 1.3 2.9 4.8 311 Lao PDR .. .. 2 51 .. .. .. 3.8 3.5 .. 748 Latvia 3,087 15 29 99 .. .. 99 11.2 7.3 4.0 697 Lebanon 2,267 16 18 36 .. 190 100 10.3 15.8 .. .. Lesotho .. .. 2 32 .. .. 55 12.8 12.9 .. .. Liberia .. .. 0 21 .. .. .. .. .. 8.2 .. Libya 3,909 14 17 78 65 .. 71 .. .. .. 1,717 Lithuania 3,557 8 22 149 57 132 100 14.3 8.6 2.8 402 Macedonia, FYR 3,723 23 22 95 159 256 100 13.4 13.4 6.3 1,065 Madagascar .. .. 1 31 1 8 23 12.2 10.5 3.9 2,427 Malawi .. .. 1 16 .. .. 93 3.3 10.8 3.6 .. Malaysia 3,490 3 16 111 .. .. 92 4.8 4.9 .. .. Mali .. .. 1 29 2 13 22 9.4 10.0 4.3 2,059 Mauritania .. .. 2 66 4 57 62 11.9 9.9 6.9 2,842 Mauritius .. .. 30 85 100 215 99 5.6 4.5 3.6 .. Mexico 2,020 17 18 78 174 .. 100 17.3 8.6 2.7 838 Moldova 1,287 53 32 77 155 457 98 2.9 8.2 10.1 294 Mongolia 1,473 11 7 84 5 .. 82b 0.7 3.6 6.7b 341b Morocco 736 11 11 79 21 87 98 23.5 22.2 5.1 .. Mozambique 461 9 0 26 .. .. 44 13.1 8.0 1.2 .. Myanmar 97 27 2 1 .. 3 10 0.9 12.8 .. 90 Namibia 1,797 18 7 56 .. .. 95 13.0 12.8 .. .. Nepal 89 19 3 26 .. .. 60 b 3.0 1.2 1.0 565 Netherlands 7,226 4 44 128 .. .. 98 27.8 29.7 0.7 .. New Zealand 9,492 7 43 109 310 .. 97 33.1 27.9 2.9 605 Nicaragua 457 24 4 56 39 .. .. 4.7 14.0 .. .. Niger .. .. 0 17 .. .. 45 12.9 15.3 .. .. Nigeria 126 9 1 47 1 26 83 5.7 10.4 3.4 .. Norway 24,867 7 39 111 .. .. .. 29.4 8.7 1.2 .. Oman 4,894 13 11 140 30 431 96 12.8 6.2 2.5 967 Pakistan 436 21 2 61 .. .. 90 2.9 1.0 2.7 50 Panama 1,646 14 16 164 61 118 83 12.0 5.0 3.2 380 Papua New Guinea .. .. 1 13 .. .. .. 4.0 17.8 .. .. Paraguay 1,002 5 6 88 35 .. .. 6.6 5.3 4.8 799 Peru 1,032 8 10 85 .. 113 95 14.3 8.9 3.1 624 Philippines 588 13 4 81 .. .. 99 15.9 6.2 .. .. Poland 3,732 8 25 117 .. 32 99 17.4 9.6 3.9 396 Portugal 4,822 9 38 143 .. .. 99 27.5 9.2 4.5 1,534 Puerto Rico .. .. 22 68 .. .. .. .. .. .. .. Qatar 15,682 7 20 175 .. .. 100 9.1 8.6 1.7 597 2011 World Development Indicators 307 5.11 Power and communications Electric power Telephonesa Access and use Quality Affordability and efficiency Population Mobile Inter national voice traffic Transmission covered by $ per month cellular and minutes per person and per 100 people mobile Mobile Telecom- fi xed-line Consumption distribution Mobile cellular Residential cellular munications subscribers per capita losses Fixed cellular Fixed network fi xed-line prepaid revenue per kWh % of output lines subscriptions lines Total % tariff tariff % of GDP employee 2008 2008 2009 2009 2008 2008 2008 2009 2009 2008 2008 Romania 2,488 11 25 118 41 124 98 19.3 10.6 3.4 564 Russian Federation 6,435 11 32 162 .. .. 95 5.4 5.8 2.6 .. Rwanda .. .. 0 24 11 8 92 8.1 6.6 3.0 1,952 Saudi Arabia 7,527 9 16 177 .. .. 98 9.2 7.4 2.7 1,618 Senegal 158 20 2 55 27 101 85 24.0 8.3 9.8 1,859 Serbia 4,284 16 42 135 136b 203b 94b 3.9 5.2 4.9b 883b Sierra Leone .. .. 1 20 .. .. 70 .. .. .. .. Singapore 8,185 5 37 133 1,531 .. 100 7.7 3.9 2.6 .. Slovak Republic 5,268 3 19 101 123 228 100 22.7 24.9 3.3 665 Slovenia 6,920 5 51 103 96 220 100 19.6 15.8 3.3 644 Somalia .. .. 1 7 .. .. .. .. .. .. .. South Africa 4,759 9 9 94 .. .. 100 21.6 12.6 7.3 .. Spain 6,315 5 44 111 .. .. 99 28.5 31.6 4.1 855 Sri Lanka 409 11 17 69 34 .. 95 4.7 0.9 .. 919 Sudan 96 12 1 36 6 13 66 3.9 3.4 3.2 2,168 Swaziland .. .. 4 55 .. 41 91 4.9 12.8 4.5 1,118 Sweden 14,869 7 55 123 .. .. 98 26.2 14.8 2.7 894 Switzerland 8,307 6 60 120 .. .. 100 31.5 33.7 3.3 601 Syrian Arab Republic 1,521 24 18 46 78 .. 96 1.3 7.6 3.0 409 Tajikistan 2,072 18 4 70 .. .. .. 0.9 2.9 .. .. Tanzania 84 19 0 40 0 1 65 12.2 10.2 .. .. Thailand 2,079 6 10 123 .. .. 38 8.3 2.4 4.0 1,957 Timor-Leste .. .. .. .. .. .. .. .. .. 7.9 .. Togo 99 .. 3 33 6 28 85 12.8 12.4 7.4 1,059 Trinidad and Tobago 5,789 2 24 147 .. 443 100 19.5 6.5 2.6 .. Tunisia 1,298 12 12 93 79 .. 100 b 2.8 7.2 4.3 1,004 Turkey 2,308 14 22 84 39 60 100 13.8 23.9 2.3 2,145 Turkmenistan 2,273 14 9 29 .. .. 14 .. .. .. .. Uganda .. .. 1 29 7 .. 100 9.9 7.9 .. .. Ukraine 3,534 12 28 120 0 .. 100 2.8 4.3 5.7 .. United Arab Emirates 16,891 12 34 232 .. .. 100 4.1 4.1 3.1 924 United Kingdom 6,061 7 54 130 .. .. 100 24.1 16.5 4.3 .. United States 13,654 6 50 97 .. 216 100 12.8 15.3 2.8 416 Uruguay 2,393 20 29 114 .. 125 100 12.5 12.7 3.2 692 Uzbekistan 1,646 9 7 59 .. .. 93 1.1 1.1 2.5 739 Venezuela, RB 3,074 28 24 99 .. 79 90 9.0 28.6 3.5 1,500 Vietnam 799 10 35 101 .. .. 70 2.1 3.2 .. .. West Bank and Gaza .. .. 9 30 .. .. 95 .. .. .. 880 Yemen, Rep. 220 23 5 16 .. .. 68 0.7 4.8 .. .. Zambia 602 23 1 34 .. .. 50 24.6 12.7 2.6 .. Zimbabwe 1,022 6 3 24 22 19 75 .. .. .. 711 World 2,875 w 8w 18 w 69 w .. w .. w 80 w 10.1 m 8.7 m 3.1 w 755 m Low income 231 15 1 27 .. .. 53 8.8 8.0 .. .. Middle income 1,670 11 15 67 .. .. 80 5.7 7.6 3.2 665 Lower middle income 1,318 10 13 58 .. .. 77 4.7 7.1 3.0 .. Upper middle income 3,001 13 22 101 .. .. 94 10.0 8.8 3.3 576 Low & middle income 1,505 11 13 61 .. .. 76 6.6 7.9 3.2 624 East Asia & Pacific 1,972 6 20 62 9 .. 93 4.0 3.7 2.6 .. Europe & Central Asia 4,052 12 25 119 .. .. 91 3.9 7.6 2.8 462 Latin America & Carib. 1,907 16 18 90 .. .. 92 10.6 8.8 3.8 586 Middle East & N. Africa 1,494 15 16 67 27 .. 93 3.0 6.3 .. 880 South Asia 503 22 3 45 .. .. 61 3.0 1.2 2.0 565 Sub-Saharan Africa 531 11 1 37 .. .. 56 11.5 10.4 .. .. High income 9,518 6 45 111 .. .. 99 22.8 14.8 3.0 765 Euro area 6,970 5 48 123 .. .. 99 27.6 19.6 2.6 765 a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Report database. Please cite ITU for third-party use of these data. b. Data are for 2009. 308 2011 World Development Indicators 5.11 STATES AND MARKETS Power and communications About the data Definitions The quality of an economy’s infrastructure, includ- Access to telephone services rose on an unprece- • Electric power consumption per capita measures ing power and communications, is an important ele- dented scale over the past 15 years. This growth was the production of power plants and combined heat ment in investment decisions for both domestic and driven primarily by wireless technologies and liberal- and power plants less transmission, distribution, foreign investors. Government effort alone is not ization of telecommunications markets, which have and transformation losses and own use by heat and enough to meet the need for investments in modern enabled faster and less costly network rollout. In power plants divided by midyear population. • Elec- infrastructure; public-private partnerships, especially 2002 the number of mobile phones in the world sur- tric power transmission and distribution losses are those involving local providers and financiers, are passed the number of fixed telephones. The Interna- losses in transmission between sources of supply critical for lowering costs and delivering value for tional Telecommunication Union (ITU) estimates that and points of distribution and in distribution to con- money. In telecommunications, competition in the there were 5 billion mobile subscriptions globally in sumers, including pilferage. • Fixed telephone lines marketplace, along with sound regulation, is lower- 2010. No technology has ever spread faster around are telephone lines connecting a subscriber to the ing costs, improving quality, and easing access to the world. Mobile communications have a particu- telephone exchange equipment. •  Mobile cellular services around the globe. larly important impact in rural areas. The mobility, telephone subscriptions are subscriptions to a pub- An economy’s production and consumption of elec- ease of use, flexible deployment, and relatively low lic mobile telephone service using cellular technol- tricity are basic indicators of its size and level of and declining rollout costs of wireless technologies ogy, which provide access to the public switched development. Although a few countries export elec- enable them to reach rural populations with low lev- telephone network. Post-paid and prepaid subscrip- tric power, most production is for domestic consump- els of income and literacy. The next billion mobile tions are included. •  International voice traffic is tion. Expanding the supply of electricity to meet the subscribers will consist mainly of the rural poor. the sum of international incoming and outgoing tele- growing demand of increasingly urbanized and indus- Access is the key to delivering telecommunications phone traffic (in minutes) divided by total population. trialized economies without incurring unacceptable services to people. If the service is not affordable to • Population covered by mobile cellular network is social, economic, and environmental costs is one most people, then goals of universal usage will not the percentage of people that live in areas served by of the great challenges facing developing countries. be met. Two indicators of telecommunications afford- a mobile cellular signal regardless of whether they Data on electric power production and consump- ability are presented in the table: fixed-line telephone use it. • Residential fixed-line tariff is the monthly tion are collected from national energy agencies by service tariff and prepaid mobile cellular service tar- subscription charge plus the cost of 30 three-minute the International Energy Agency (IEA) and adjusted iff. Telecommunications efficiency is measured by local calls (15 peak and 15 off-peak). • Mobile cel- by the IEA to meet international definitions (for data total telecommunications revenue divided by GDP lular prepaid tariff is based on the Organisation for on electricity production, see table 3.10). Electricity and by mobile cellular and fixed-line telephone sub- Economic Co-operation and Development’s low-user consumption is equivalent to production less power scribers per employee. definition, which includes the cost of monthly mobile plants’ own use and transmission, distribution, and Operators have traditionally been the main source use for 25 outgoing calls per month spread over the transformation losses less exports plus imports. It of telecommunications data, so information on sub- same mobile network, other mobile networks, and includes consumption by auxiliary stations, losses scribers has been widely available for most coun- mobile to fixed-line calls and during peak, off-peak, in transformers that are considered integral parts tries. This gives a general idea of access, but a and weekend times as well as 30 text messages of those stations, and electricity produced by pump- more precise measure is the penetration rate—the per month. •  Telecommunications revenue is the ing installations. Where data are available, it covers share of households with access to telecommunica- revenue from the provision of telecommunications electricity generated by primary sources of energy— tions. During the past few years more information services such as fixed-line, mobile, and data divided coal, oil, gas, nuclear, hydro, geothermal, wind, tide on information and communication technology use by GDP. • Mobile cellular and fixed-line subscribers and wave, and combustible renewables. Neither pro- has become available from household and business per employee are telephone subscribers (fixed-line duction nor consumption data capture the reliability surveys. Also important are data on actual use of plus mobile) divided by the total number of telecom- of supplies, including breakdowns, load factors, and telecommunications equipment. Ideally, statistics munications employees. frequency of outages. on telecommunications (and other information and Over the past decade new financing and technol- communications technologies) should be compiled ogy, along with privatization and liberalization, have for all three measures: subscription and possession, spurred dramatic growth in telecommunications access, and use. The quality of data varies among Data sources in many countries. With the rapid development of reporting countries as a result of differences in regu- mobile telephony and the global expansion of the lations covering data provision and availability. Data on electricity consumption and losses are Internet, information and communication technolo- from the IEA’s Energy Statistics and Balances of gies are increasingly recognized as essential tools of Non-OECD Countries 2010, the IEA’s Energy Sta- development, contributing to global integration and tistics of OECD Countries 2010, and the United enhancing public sector effectiveness, efficiency, Nations Statistics Division’s Energy Statistics and transparency. The table presents telecommuni- Yearbook. Data on telecommunications are from cations indicators covering access and use, quality, the ITU’s World Telecommunication Development and affordability and efficiency. Report database and TeleGeography. 2011 World Development Indicators 309 5.12 The information age Daily Households Personal computers and the Internet Information and communications newspapers with technology trade televisiona Access and use Quality Affordability Application Fixed International broadband Internet Fixed Secure Goods Services Internet bandwidtha broadband Internet Exports Imports Exports per 100 people a subscribers bits per Internet servers % of total % of total % of total per 1,000 Personal Internet per 100 second per access tariff a per million goods goods service people % computersa usersa people capita $ per month people exports imports exports 2000–05b 2008 2008 2009 2009 2009 2009 December 2010 2009 2009 2009 Afghanistan .. .. 0.4 3.4 0.00 550 .. 1 .. 0.4 .. Albania 24 .. 4.6 41.2 2.85 1,902 22 9 1.1 5.4 5.7 Algeria .. .. .. 13.5 2.34 .. 15 1 0.0 4.9 .. Angola 2 36 0.6 3.3 0.11 17 157 3 .. .. 5.4 Argentina 36 .. .. 30.4 8.80 2,320 31 26 0.4 11.2 12.2 Armenia 8 97 .. 6.8 0.19 .. 31 17 1.5 5.0 16.1 Australia 155 .. .. 72.0 24.69 5,457 26 1,761 1.4 11.4 4.9 Austria 311 97 .. 73.5 22.45 20,323 36 857 5.5 7.0 6.5 Azerbaijan 16 99 8.0 42.0 1.14 1,399 49 5 0.0 8.5 4.7 Bangladesh .. 30 2.3 0.4 0.03 4 50 0 0.6 5.7 11.5 Belarus 81 95 .. 45.9 11.30 2,277 7 9 0.7 2.4 9.0 Belgium 165 99 .. 75.2 29.05 24,945 29 490 2.8 4.3 9.8 Benin 0 25 0.7 2.2 0.02 35 118 0 .. .. 0.7 Bolivia .. 69 .. 11.2 2.86 225 35 8 0.0 4.6 12.6 Bosnia and Herzegovina .. 97 6.4 37.7 7.76 1,195 19 16 0.6 3.7 9.2 Botswana 41 .. 6.2 6.2 0.77 220 62 9 0.4 5.5 3.3 Brazil 36 97 .. 39.2 7.51 2,108 28 41 1.8 11.4 2.0 Bulgaria 79 98 11.0 44.8 12.91 37,657 15 73 3.6 6.4 5.6 Burkina Faso .. 18 0.6 1.1 0.04 15 91 0 0.0 2.0 11.6 Burundi .. .. 0.9 0.8 0.00 2 .. 0 1.9 10.9 0.0 Cambodia .. .. 0.4 0.5 0.20 19 89 2 0.1 4.0 6.5 Cameroon .. 32 .. 3.8 0.00 23 89 1 .. .. 6.6 Canada 175 99 94.3 77.7 29.55 16,193 25 1,237 4.4 9.6 11.2 Central African Republic .. .. .. 0.5 0.00 .. 1,329 0 .. .. .. Chad .. .. .. 1.7 0.00 1 .. .. .. .. .. Chile 51 100 .. 34.0 9.81 4,076 48 53 0.2 6.8 2.8 China 74 .. 5.7 28.8 7.78 651 18 2 29.5 24.0 6.0 Hong Kong SAR, China 222 99 69.3 61.4 29.42 560,989 13 455 44.6 43.6 1.7 Colombia 23 88 11.2 45.5 4.64 2,940 35 14 0.3 9.9 7.4 Congo, Dem. Rep. .. 14 .. 0.6 0.00 1 .. 0 .. .. .. Congo, Rep. .. .. .. 6.7 0.00 0 .. 1 .. .. .. Costa Rica 65 96 .. 34.5 6.01 4,333 6 108 18.7 17.9 21.9 Côte d’Ivoire .. .. .. 4.6 0.05 40 44 1 0.4 4.5 0.0 Croatia .. 97 .. 50.4 15.45 15,892 21 168 5.1 6.3 3.6 Cuba 65 88 5.6 14.3 0.02 27 1,630 0 .. .. .. Czech Republic 183 .. .. 63.7 19.26 7,075 43 318 15.6 16.7 8.9 Denmark 353 98 54.9 85.9 37.46 34,506 29 1,866 4.8 8.9 .. Dominican Republic 39 77 .. 26.8 3.93 1,387 26 15 3.6 5.4 4.1 Ecuador 99 83 13.0 15.1 1.77 484 40 15 0.2 7.5 4.9 Egypt, Arab Rep. .. 97 3.9 20.0 1.30 1,172 8 2 1.8 4.4 4.7 El Salvador 38 83 .. 14.4 2.42 243 20 13 2.9 5.5 16.9 Eritrea .. .. 1.0 4.9 0.00 6 .. .. .. .. .. Estonia 191 98 25.5 72.3 25.25 12,680 28 434 5.8 6.5 8.6 Ethiopia 5 .. 0.7 0.5 0.00 3 487 0 0.7 9.5 5.3 Finland 431 93 .. 83.9 29.33 17,221 39 1,246 12.6 11.3 25.4 France 164 97 65.2 71.3 30.98 29,356 36 306 5.6 7.8 4.3 Gabon .. .. 3.4 6.7 0.20 141 .. 8 .. .. .. Gambia, The .. .. 3.5 7.6 0.02 38 307 3 0.4 4.0 17.8 Georgia 4 .. 5.5 30.5 3.52 752 42 13 0.4 7.8 2.6 Germany 267 95 65.6 79.5 30.53 25,654 43 874 6.8 9.3 8.4 Ghana .. 43 1.1 5.4 0.11 97 44 2 0.1 7.3 0.0 Greece .. 100 9.4 44.1 16.99 4,537 24 124 3.0 5.9 2.2 Guatemala .. 69 .. 16.3 0.78 186 34 10 0.7 6.3 14.1 Guinea .. .. .. 0.9 0.00 0 503 0 0.0 5.8 21.6 Guinea-Bissau .. .. .. 2.3 0.00 1 .. 1 .. .. 0.2 Haiti .. 25 5.1 10.0 0.00 16 .. 1 .. .. 2.5 Honduras .. 68 2.5 9.8 0.00 241 .. 8 0.2 6.6 26.8 310 2011 World Development Indicators 5.12 STATES AND MARKETS The information age Daily Households Personal computers and the Internet Information and communications newspapers with technology trade televisiona Access and use Quality Affordability Application Fixed International broadband Internet Fixed Secure Goods Services Internet bandwidtha broadband Internet Exports Imports Exports per 100 people a subscribers bits per Internet servers % of total % of total % of total per 1,000 Personal Internet per 100 second per access tariff a per million goods goods service people % computersa usersa people capita $ per month people exports imports exports 2000–05b 2008 2008 2009 2009 2009 2009 December 2010 2009 2009 2009 Hungary 217 99 25.6 61.6 18.76 5,987 30 166 24.6 18.8 8.8 India 71 55 3.3 5.3 0.67 32 5 2 3.8 8.8 53.1 Indonesia .. 69 2.0 8.7 0.74 110 21 2 5.7 9.7 8.4 Iran, Islamic Rep. .. .. 10.6 38.3 0.55 151 30 1 .. .. .. Iraq .. .. .. 1.0 0.00 3 .. 0 .. .. 0.6 Ireland 182 98 58.2 68.4 21.94 15,261 36 1,005 11.5 14.0 37.1 Israel .. 90 .. 49.7 24.86 2,003 7 399 19.2 11.0 36.1 Italy 137 94 .. 48.5 19.59 12,989 29 154 3.0 6.7 2.4 Jamaica .. .. .. 58.6 4.16 741 22 39 0.8 3.9 7.1 Japan 551 99 .. 77.7 24.86 5,770 37 650 14.7 12.0 1.2 Jordan .. 97 7.6 29.3 3.42 1,811 30 20 3.1 5.4 .. Kazakhstan .. .. .. 33.4 8.61 1,342 17 5 0.1 4.3 3.0 Kenya .. .. .. 10.0 0.02 477 40 3 1.3 6.2 14.5 Korea, Dem. Rep. .. .. .. 0.0 0.00 0 .. 0 .. .. .. Korea, Rep. .. .. 57.6 80.9 33.54 6,065 25 1,167 22.6 14.6 1.5 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. 39.4 1.61 871 19 133 0.4 7.2 60.9 Kyrgyz Republic 1 99 .. 41.2 0.10 112 48 1 0.3 2.6 1.2 Lao PDR 3 .. .. 4.7 0.13 142 194 1 .. .. 8.5 Latvia 154 99 32.7 66.7 11.48 3,537 25 173 6.1 6.2 5.9 Lebanon 54 .. 10.2 23.7 5.26 223 23 28 3.0 3.5 2.9 Lesotho .. .. .. 3.7 0.02 5 50 0 .. .. .. Liberia .. 9 .. 0.5 .. .. .. 1 .. .. .. Libya .. .. .. 5.5 0.16 50 .. 1 .. .. 2.6 Lithuania 108 98 24.2 58.8 18.98 14,300 15 176 2.9 4.3 3.8 Macedonia, FYR 89 99 36.8 51.8 10.59 17 14 24 0.5 5.5 14.3 Madagascar .. .. .. 1.6 0.02 12 102 0 1.6 3.9 .. Malawi .. 9 .. 4.7 0.02 5 493 0 0.3 5.3 .. Malaysia 109 97 23.1 57.6 6.09 5,097 19 42 38.1 32.0 7.0 Mali .. 22 0.8 1.9 0.07 51 55 1 0.2 3.6 23.2 Mauritania .. .. 4.5 2.3 0.27 76 58 2 .. 1.6 .. Mauritius 77 96 17.6 22.7 7.25 364 17 87 0.6 4.2 3.7 Mexico 93 93 14.4 26.5 9.24 312 16 22 22.9 20.9 1.3 Moldova .. .. 11.4 35.9 5.19 6,660 13 13 7.5 5.1 20.2 Mongolia 20 88 24.6 13.1 0.91 2,920 8 11 0.1 5.1 3.0 Morocco 12 .. 5.7 32.2 1.49 1,600 17 3 4.6 6.0 7.5 Mozambique 3 .. .. 2.7 0.05 56 80 1 0.4 3.9 5.8 Myanmar .. .. 0.9 0.2 0.03 20 28 0 .. .. .. Namibia 28 37 23.9 5.9 0.02 27 47 14 0.6 4.9 2.4 Nepal .. 33 .. 2.1 0.26 5 22 2 0.2 5.6 .. Netherlands 307 98 91.2 90.0 35.70 78,156 36 2,276 12.6 13.5 11.3 New Zealand 182 99 52.6 83.4 22.73 4,544 29 1,489 1.8 9.4 4.8 Nicaragua .. 67 .. 3.5 0.82 144 34 8 0.4 4.4 7.2 Niger 0 10 .. 0.8 0.01 11 266 0 0.7 3.6 11.6 Nigeria .. 39 .. 28.4 0.05 5 105 1 0.0 7.2 1.6 Norway 516 95 62.9 91.8 37.19 26,904 51 1,653 2.4 8.8 8.6 Oman .. 88 16.9 43.5 1.44 1,365 31 27 1.5 3.2 .. Pakistan 50 58 .. 12.0 0.37 43 15 1 0.3 3.7 12.0 Panama 65 83 6.3 27.8 5.82 15,964 17 127 0.0 7.3 4.8 Papua New Guinea 9 .. .. 1.9 0.00 2 142 3 .. .. 1.2 Paraguay .. 85 .. 15.8 2.22 662 22 7 0.2 21.6 1.4 Peru .. 73 .. 27.7 2.79 2,646 36 14 0.1 8.3 3.5 Philippines 79 71 7.2 6.5 1.87 113 22 7 54.2 34.5 16.2 Poland 114 98 16.9 58.8 13.54 2,748 14 211 7.5 8.9 5.3 Portugal .. 99 18.2 48.6 17.54 4,790 29 174 4.6 6.6 4.5 Puerto Rico .. .. .. 25.2 10.78 1,764 .. 84 .. .. .. Qatar .. .. 15.7 28.3 9.22 2,044 55 99 0.0 8.2 .. 2011 World Development Indicators 311 5.12 The information age Daily Households Personal computers and the Internet Information and communications newspapers with technology trade televisiona Access and use Quality Affordability Application Fixed International broadband Internet Fixed Secure Goods Services Internet bandwidtha broadband Internet Exports Imports Exports per 100 people a subscribers bits per Internet servers % of total % of total % of total per 1,000 Personal Internet per 100 second per access tariff a per million goods goods service people % computersa usersa people capita $ per month people exports imports exports 2000–05b 2008 2008 2009 2009 2009 2009 December 2010 2009 2009 2009 Romania 70 97 19.2 36.2 13.05 18,271 7 40 8.4 9.3 18.9 Russian Federation 92 .. 13.3 42.1 9.09 573 13 20 0.6 8.4 6.3 Rwanda .. 3 0.3 4.5 0.08 35 88 1 1.4 12.3 0.1 Saudi Arabia .. .. 69.3 38.6 5.66 1,731 27 18 0.3 4.6 .. Senegal 9 46 .. 7.4 0.47 372 40 1 0.4 4.5 15.6 Serbia .. .. 25.8 56.1 8.07 12,660 14 20 2.2 5.4 8.0 Sierra Leone .. 10 .. 0.3 0.00 .. .. 0 .. .. 2.2 Singapore 361 .. 74.3 73.3 22.52 22,783 17 523 35.4 28.2 2.9 Slovak Republic 126 99 58.1 75.0 14.36 7,567 29 128 17.5 14.7 8.0 Slovenia 173 99 42.5 63.6 22.79 6,720 22 301 3.8 5.6 7.2 Somalia .. .. .. 1.2 0.00 .. .. 0 .. .. .. South Africa 30 69 .. 9.0 0.98 70 27 63 2.0 9.8 3.9 Spain 144 100 39.3 61.2 21.05 11,008 29 233 3.0 8.4 6.6 Sri Lanka 26 76 .. 8.7 0.84 190 4 4 1.0 3.6 17.2 Sudan .. .. 10.7 9.9 0.11 322 23 0 0.0 4.7 1.2 Swaziland 24 35 3.7 7.6 0.13 35 858 10 0.1 3.6 11.3 Sweden 481 94 88.1 90.3 40.85 49,828 35 1,266 10.0 11.5 14.8 Switzerland 420 92 96.2 70.9 33.91 29,413 33 1,876 3.3 6.6 .. Syrian Arab Republic .. .. 9.0 18.7 0.16 261 31 0 0.2 1.4 4.4 Tajikistan .. .. .. 10.1 0.05 37 364 0 .. .. 19.6 Tanzania 2 9 .. 1.5 0.02 2 64 0 0.6 6.9 2.7 Thailand .. 92 .. 25.8 1.47 818 19 13 19.8 18.1 .. Timor-Leste .. .. .. .. .. .. .. 1 .. .. .. Togo 2 .. .. 5.4 0.04 23 186 2 0.1 4.2 18.6 Trinidad and Tobago 149 .. 13.2 36.2 7.84 7,916 13 72 0.2 4.0 .. Tunisia 23 .. 9.7 33.5 3.57 2,699 12 14 6.0 7.5 4.9 Turkey .. 98 6.1 35.3 8.54 4,323 18 95 2.3 5.9 1.9 Turkmenistan 9 .. .. 1.6 0.05 48 .. 0 .. .. .. Uganda .. 7 1.7 9.8 0.02 36 194 1 4.9 9.3 6.1 Ukraine 131 97 4.5 33.3 4.15 206 7 13 1.3 2.6 5.6 United Arab Emirates .. 94 33.1 82.2 15.01 13,233 41 243 2.0 5.3 .. United Kingdom 290 99 80.2 83.2 29.68 39,664 24 1,396 8.6 10.5 7.9 United States 193 .. 80.5 78.1 27.78 11,279 20 1,443 13.0 15.1 4.6 Uruguay .. 91 .. 55.5 7.33 903 18 45 0.1 7.0 9.5 Uzbekistan .. .. 3.1 16.9 0.32 46 199 0 .. .. .. Venezuela, RB 93 95 .. 31.2 6.56 628 31 7 0.1 9.3 7.4 Vietnam .. .. 9.6 27.5 3.04 581 15 3 3.8 7.1 .. West Bank and Gaza 10 95 .. 8.8 5.76 313 .. 4 .. .. 5.4 Yemen, Rep. 4 .. 2.8 1.8 0.00 28 220 0 0.1 2.5 8.5 Zambia 5 24 .. 6.3 0.06 8 51 1 0.1 3.7 8.0 Zimbabwe .. 31 7.6 11.4 0.14 17 .. 1 0.6 4.8 .. World 105 w .. m 15.3 w 27.1 w 7.30 w 3,526 w 30 m 156 w 13.0 w 13.9 w 9.1 w Low income .. .. .. 2.7 0.04 7 90 1 0.6 .. 6.5 Middle income 68 .. 5.5 20.9 4.07 348 22 9 16.3 16.6 13.3 Lower middle income 71 .. 4.5 17.2 3.37 151 30 3 21.3 18.4 19.9 Upper middle income .. 93 .. 34.6 6.69 1,120 19 32 12.2 15.1 5.4 Low & middle income 59 .. 5.1 18.1 3.53 299 31 8 16.2 16.4 13.1 East Asia & Pacific 74 .. 5.6 24.1 5.81 742 21 3 28.9 24.4 6.8 Europe & Central Asia .. .. 9.8 36.4 7.66 1,087 17 33 1.5 6.6 6.1 Latin America & Carib. 64 85 .. 31.5 6.62 1,408 30 27 11.6 15.2 5.5 Middle East & N. Africa .. .. 5.7 21.5 1.25 323 23 2 .. .. .. South Asia 68 55 3.3 5.5 0.55 31 15 2 3.0 7.4 49.9 Sub-Saharan Africa .. .. .. 8.8 0.13 31 88 5 1.0 7.8 4.5 High income 255 98 65.4 72.3 25.78 19,521 29 906 12.2 13.3 8.1 Euro area 201 98 56.0 67.3 25.90 32,455 29 545 6.6 8.6 9.8 a. Data are from the International Telecommunicaton Union’s (ITU) World Telecommunication Development Report database. Please cite the ITU for third party use of these data. b. Data are for the most recent year available. 312 2011 World Development Indicators 5.12 STATES AND MARKETS The information age About the data Definitions The digital and information revolution has changed counts reported by Internet service providers by a • Daily newspapers are newspapers issued at least the way the world learns, communicates, does busi- multiplier. This method may undercount actual four times a week that report mainly on events in the ness, and treats illnesses. New information and users, particularly in developing countries, where 24-hour period before going to press. The indicator is communications technologies (ICT) offer vast oppor- many commercial subscribers rent out computers average circulation (or copies printed) per 1,000 peo- tunities for progress in all walks of life in all coun- connected to the Internet or prepaid cards are used ple. • Households with television are the percentage tries—opportunities for economic growth, improved to access the Internet. of households with a television set. • Personal com- health, better service delivery, learning through dis- Broadband refers to technologies that provide puters are self-contained computers designed for use tance education, and social and cultural advances. Internet speeds of at least 256 kilobits a second by a single individual, including laptops and notebooks Comparable statistics on access, use, quality, of upstream and downstream capacity and includes and excluding terminals connected to mainframe and and affordability of ICT are needed to formulate digital subscriber lines, cable modems, satellite minicomputers intended primarily for shared use and growth-enabling policies for the sector and to moni- broadband Internet, fiber-to-home Internet access, devices such as smart phones and personal digital tor and evaluate the sector’s impact on develop- ethernet local access networks, and wireless area assistants. • Internet users are people with access ment. Although basic access data are available for networks. Bandwidth refers to the range of frequen- to the worldwide network. • Fixed broadband Internet many countries, in most developing countries little cies available for signals. The higher the bandwidth, subscribers are the number of broadband subscrib- is known about who uses ICT; what they are used for the more information that can be transmitted at ers with a digital subscriber line, cable modem, or (school, work, business, research, government); and one time. Reporting countries may have different other high-speed technology. • International Internet how they affect people and businesses. The global definitions of broadband, so data are not strictly bandwidth is the contracted capacity of international Partnership on Measuring ICT for Development is comparable. connections between countries for transmitting helping to set standards, harmonize information and The number of secure Internet servers, from the Internet traffic. • Fixed broadband Internet access communications technology statistics, and build sta- Netcraft Secure Server Survey, indicates how many tariff is the lowest sampled cost per 100 kilobits a tistical capacity in developing countries. For more companies conduct encrypted transactions over the second per month and are calculated from low- and information see www.itu.int/ITU-D/ict/partnership/. Internet. The survey examines the use of encrypted high-speed monthly service charges. Monthly charges Data on daily newspapers in circulation are from transactions through extensive automated explora- do not include installation fees or modem rentals. United Nations Educational, Scientific, and Cultural tion, tallying the number of Web sites using a secure • Secure Internet servers are servers using encryp- Organization (UNESCO) Institute for Statistics surveys socket layer (SSL). The country of origin of more than tion technology in Internet transactions. • Information on circulation, online newspapers, journalists, com- a third of the 1.5  million distinct valid third-party and communication technology goods exports and munity newspapers, and news agencies. certificates is unknown. Some countries, such as the imports include telecommunications, audio and video, Estimates of households with television are derived Republic of Korea, use application layers to estab- computer and related equipment; electronic compo- from household surveys. Some countries report only lish the encryption channel, which is SSL equivalent; nents; and other information and communication the number of households with a color television set, these data are reported in the table. technology goods. Software is excluded. • Informa- and so the true number may be higher than reported. Information and communication technology goods tion and communication technology service exports Estimates of personal computers are from an exports and imports are defi ned by the Working include computer and communications services (tele- annual International Telecommunication Union (ITU) Party on Indicators for the Information Society and communications and postal and courier services) and questionnaire sent to member states, supplemented are reported in the Organisation for Economic Co- information services (computer data and news-related by other sources. Many governments lack the capac- operation and Development’s Guide to Measuring the service transactions). ity to survey all places where personal computers Information Society (2005). Information and commu- are used (homes, schools, businesses, government nication technology service exports data are based Data sources offices, libraries, Internet cafes) so most estimates on the International Monetary Fund’s (IMF) Balance of are derived from the number of personal computers Payments Statistics Yearbook classification. Data on newspapers are compiled by the UNESCO sold each year. Annual shipment data can also be Institute for Statistics. Data on televisions, per- multiplied by an estimated average useful lifespan sonal computers, Internet users, Internet broad- before replacement to approximate the number of band users and cost, and Internet bandwidth are personal computers. There is no precise method from the ITU’s World Telecommunication Develop- for determining replacement rates, but in general ment Report database and TeleGeography. Data personal computers are replaced every three to five on secure Internet servers are from Netcraft (www. years. netcraft.com/) and official government sources. Data on Internet users and related indicators Data on information and communication tech- (broadband and bandwidth) are based on nation- nology goods trade are from the United Nations ally reported data to the ITU. Some countries derive Statistics Division’s Commodity Trade (Comtrade) these data from surveys, but since survey questions database. Data on information and communication and definitions differ, the estimates may not be technology service exports are from the IMF’s Bal- strictly comparable. Countries without surveys gen- ance of Payments Statistics database. erally derive their estimates by multiplying subscriber 2011 World Development Indicators 313 5.13 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda,c journal articles % of manu- per million per million factured $ millions Non- people people % of GDP $ millions exports Receipts Payments Residents residents Total 2000–08 d 2000–08 d 2007 2000–08 d 2009 2009 2009 2009 2009 2009 2009 Afghanistan .. .. 4 .. .. .. .. .. .. .. .. Albania .. .. 12 .. 10 1 6 14 .. .. 3,072 Algeria 170 35 481 0.07 4 1 .. .. 84 765 2,144 Angola .. .. 3 .. .. .. 0 0 .. .. .. Argentina 980 196 3,362 0.51 1,548 9 106 1,331 .. .. 73,717 Armenia .. .. 175 0.21 7 4 0 0 116 11 4,398 Australia 4,224 .. 17,831 2.06 3,550 13 703 3,026 2,821 23,525 8,611 Austria 4,123 1,960 4,825 2.66 12,097 11 752 1,280 2,263 292 11,699 Azerbaijan .. .. 97 0.17 6 1 2 19 222 5 3,221 Bangladesh .. .. 235 .. 97 1 0 11 29 270 8,232 Belarus .. .. 412 0.96 315 3 9 73 1,510 220 5,403 Belgium 3,435 1,407 7,071 1.92 29,676 10 2,376 2,144 669 148 25,566e Benin .. .. 43 .. 0 0 0 3 .. .. .. Bolivia 120 .. 51 0.28 15 5 3 19 .. .. 6,081 Bosnia and Herzegovina 197 71 54 0.03 76 3 12 6 59 12 3,786 Botswana .. .. 62 0.50 24 1 1 12 .. .. 712 Brazil 694 .. 11,885 1.10 8,316 14 434 2,512 4,023 17,802 119,841 Bulgaria 1,499 476 801 0.49 714 8 9 117 242 24 7,904 Burkina Faso .. .. 43 0.11 0 1 0 0 .. .. .. Burundi .. .. 3 .. 2 12 0 0 .. .. .. Cambodia 17 13 26 0.05 4 0 0 8 .. .. 2,866 Cameroon .. .. 154 .. 3 3 0 0 .. .. .. Canada 4,260 1,690 27,800 1.84 25,080 18 3,221 7,716 5,067 32,410 40,956 Central African Republic .. .. 4 .. .. .. .. .. .. .. .. Chad .. .. 3 .. .. .. .. .. .. .. .. Chile 833 302 1,740 0.68 266 4 59 461 531 3,421 33,026 China 1,071 .. 56,806 1.44 348,295 31 429 11,065 229,096 85,477 808,546 Hong Kong SAR, China 2,650 459 .. 0.81 1,849 31 380 1,610 149 11,708 24,754 Colombia 126 .. 489 0.16 466 5 48 258 121 1,860 23,952 Congo, Dem. Rep. .. .. 7 0.48 .. .. .. .. .. .. .. Congo, Rep. 34 37 21 .. .. .. .. .. .. .. .. Costa Rica 122 .. 100 0.32 1,682 41 1 65 .. .. 11,754 Côte d’Ivoire 66 .. 37 .. 187 12 0 0 .. .. .. Croatia 1,514 605 1,102 0.90 756 11 32 213 250 68 5,990 Cuba .. .. 244 0.49 248 35 .. .. 69 189 1,450 Czech Republic 2,886 1,466 3,689 1.47 15,200 16 96 726 789 92 11,047 Denmark 5,670 2,166 5,236 2.72 10,743 18 .. .. 1,518 131 8,329 Dominican Republic .. .. 8 .. 177 5 0 53 .. .. 5,208 Ecuador 69 20 66 0.15 51 4 0 47 .. 794 12,605 Egypt, Arab Rep. 617 378 1,934 0.23 95 1 0 285 490 1,452 2,828 El Salvador 49 .. 5 0.09 136 5 0 26 .. .. .. Eritrea .. .. 8 .. .. .. .. .. .. .. .. Estonia 2,966 617 502 1.29 656 10 24 46 76 20 3,230 Ethiopia 21 12 149 0.17 7 4 2 3 12 25 719 Finland 7,707 .. 4,989 3.46 8,599 18 1,738 1,282 1,806 127 5,564 France 3,496 1,880 30,740 2.02 83,827 23 9,397 5,274 14,295 1,809 84,213 Gabon .. .. 16 .. 71 32 .. .. .. .. .. Gambia, The .. .. 17 .. 0 1 0 0 .. .. 327 Georgia .. .. 129 0.18 21 3 7 9 250 218 4,382 Germany 3,532 1,301 44,408 2.54 142,449 16 13,785 14,104 47,859 11,724 74,676 Ghana .. .. 109 .. 6 1 0 0 .. .. 677 Greece 1,873 764 4,980 0.57 1,212 11 48 654 698 22 2,458 Guatemala 29 37 22 0.06 141 5 13 86 7 322 11,003 Guinea .. .. 4 .. 0 0 0 0 .. .. .. Guinea-Bissau .. .. 10 .. .. .. .. 0 .. .. 6 Haiti .. .. 4 .. .. .. 3 0 .. .. .. Honduras .. .. 6 0.04 7 1 0 18 .. .. 7,403 314 2011 World Development Indicators 5.13 STATES AND MARKETS Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda,c journal articles % of manu- per million per million factured $ millions Non- people people % of GDP $ millions exports Receipts Payments Residents residents Total 2000–08 d 2000–08 d 2007 2000–08 d 2009 2009 2009 2009 2009 2009 2009 Hungary 1,733 512 2,452 0.96 17,444 26 862 1,369 757 30 6,671 India 137 94 18,194 0.80 10,143 9 193 1,860 5,314 23,626 130,172 Indonesia 205 .. 198 0.05 5,940 13 38 1,530 282 4,324 52,649 Iran, Islamic Rep. 706 .. 4,366 0.67 375 6 .. .. 5,970 557 3,013 Iraq .. .. 73 .. 0 0 1,312 396 .. .. .. Ireland 3,090 684 2,487 1.42 24,738 25 1,697 34,873 908 53 4,091 Israel .. .. 6,623 4.86 10,268 23 761 897 1,387 5,387 10,742 Italy 1,616 .. 26,544 1.18 25,988 8 1,115 1,899 8,814 903 40,702 Jamaica .. .. 49 0.06 4 1 9 45 21 132 1,708 Japan 5,573 589 52,896 3.44 99,210 20 21,698 16,835 295,315 53,281 110,622 Jordan .. .. 344 0.34 49 1 0 0 59 507 9,145 Kazakhstan .. .. 106 0.22 1,802 30 0 64 11 162 3,500 Kenya .. .. 262 .. 78 5 19 21 38 33 1,430 Korea, Dem. Rep. .. .. 10 .. .. .. .. .. 7,956 55 1,351 Korea, Rep. 4,627 720 18,467 3.21 103,400 32 3,185 7,049 127,316 36,207 134,211 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 166 33 242 0.09 6 0 0 0 .. .. .. Kyrgyz Republic .. .. 16 0.23 11 5 4 12 135 3 2,580 Lao PDR 16 .. 12 0.04 .. .. 0 0 .. .. .. Latvia 1,935 543 147 0.61 363 8 7 26 114 37 3,566 Lebanon .. .. 238 .. 138 7 0 1 .. .. .. Lesotho 10 11 3 0.06 .. .. 18 .. .. .. 634 Liberia .. .. 0 .. .. .. .. .. .. .. 489 Libya .. .. 30 .. .. .. 0 0 .. .. .. Lithuania 2,547 553 456 0.80 931 10 0 29 91 16 4,465 Macedonia, FYR 521 75 58 0.21 42 3 6 20 34 406 3,788 Madagascar 50 15 48 0.14 10 2 .. .. 1 43 1,605 Malawi .. .. 63 .. 3 3 .. .. .. .. 804 Malaysia 372 44 808 0.64 51,560 47 266 1,133 818 4,485 24,070 Mali 42 13 19 .. 3 3 0 2 .. .. .. Mauritania .. .. 3 .. .. .. .. .. .. .. .. Mauritius .. .. 18 0.37 13 1 0 5 2 22 24 Mexico 353 186 4,223 0.37 37,354 22 656 0 822 13,459 75,250 Moldova 726 117 70 0.55 10 5 4 11 134 5 5,046 Mongolia .. .. 21 0.23 7 8 0 1 103 110 1,399 Morocco 647 48 378 0.64 646 7 2 49 177 834 3,774 Mozambique 16 35 24 0.53 24 10 0 4 18 22 870 Myanmar 18 137 13 0.16 .. .. .. .. .. .. .. Namibia .. .. 14 .. 21 1 0 6 .. .. 858 Nepal 59 137 72 .. 2 0 .. .. .. .. 1,132 Netherlands 3,089 1,764 14,210 1.63 58,450 24 5,473 4,073 2,575 279 .. New Zealand 4,365 894 3,173 1.21 504 10 159 529 1,555 4,803 16,190 Nicaragua .. .. 11 0.05 7 6 0 0 .. .. 5,975 Niger 8 10 22 .. 2 8 0 0 .. .. .. Nigeria .. .. 427 .. 46 3 0 208 .. .. .. Norway 5,468 .. 4,079 1.62 4,694 20 637 553 1,140 4,280 13,607 Oman .. .. 129 .. 7 0 .. .. .. .. 2,103 Pakistan 152 64 741 0.67 227 2 6 90 170 1,375 14,872 Panama 144 106 78 0.21 0 0 0 25 .. 371 8,553 Papua New Guinea .. .. 21 .. .. .. .. .. 1 45 612 Paraguay 71 .. 12 0.09 38 11 295 2 .. .. .. Peru .. .. 153 0.15 87 3 2 147 37 657 24,825 Philippines 81 10 195 0.12 21,531 66 2 421 216 3,095 14,912 Poland 1,623 191 7,136 0.61 7,172 5 103 1,542 2,899 241 17,877 Portugal 3,799 403 3,424 1.51 1,288 4 148 507 381 24 2,681 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. 48 .. 0 0 .. .. .. .. .. 2011 World Development Indicators 315 5.13 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda,c journal articles % of manu- per million per million factured $ millions Non- people people % of GDP $ millions exports Receipts Payments Residents residents Total 2000–08 d 2000–08 d 2007 2000–08 d 2009 2009 2009 2009 2009 2009 2009 Romania 908 216 1,252 0.59 3,230 10 193 339 1,054 37 12,977 Russian Federation 3,191 493 13,953 1.03 4,576 9 494 4,107 25,598 12,966 49,189 Rwanda .. .. 12 .. 11 31 0 1 .. .. 238 Saudi Arabia .. .. 589 0.05 40 0 .. .. 128 642 .. Senegal 276 .. 68 0.09 104 14 0 9 .. .. .. Serbia 1,196 299 1,057 0.35 .. .. 63 144 319 40 7,237 Sierra Leone .. .. 3 .. .. .. 1 1 .. .. 750 Singapore 6,088 529 3,792 2.52 97,207 49 1,340 11,686 750 7,986 15,332 Slovak Republic 2,331 392 971 0.47 3,171 5 92 155 176 63 5,534 Slovenia 3,490 1,696 1,280 1.66 1,264 7 36 290 373 12 4,073 Somalia .. .. 0 .. .. .. .. .. .. .. .. South Africa 393 123 2,805 0.93 1,418 6 48 1,658 .. 10,753 26,621 Spain 2,944 1,143 20,981 1.34 10,841 5 1,041 3,449 3,596 207 46,711 Sri Lanka 93 65 125 0.17 44 1 0 0 201 264 5,916 Sudan .. .. 36 0.29 11 34 0 0 3 13 743 Swaziland .. .. 4 .. 0 0 0 116 .. .. 680 Sweden 5,239 1,871 9,914 3.75 17,059 17 4,709 1,832 2,549 306 12,706 Switzerland 3,436 2,317 9,190 2.90 38,556 25 .. .. 1,684 394 28,945 Syrian Arab Republic .. .. 80 .. 83 2 0 30 124 133 2,432 Tajikistan .. .. 22 0.06 .. .. 1 0 11 1 2,496 Tanzania .. .. 123 .. 24 4 0 0 .. .. 556 Thailand 311 160 1,728 0.25 28,655 26 145 2,250 802 5,939 36,087 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 34 17 12 .. 0 0 0 5 .. .. .. Trinidad and Tobago .. .. 67 0.06 3 0 .. .. .. 551 .. Tunisia 1,588 43 757 1.02 663 6 25 14 .. .. .. Turkey 680 102 8,638 0.72 1,463 2 .. 648 2,555 177 71,466 Turkmenistan .. .. 2 .. .. .. .. .. .. .. 2,337 Uganda .. .. 164 0.39 5 1 3 3 6 1 .. Ukraine 1,458 325 1,847 0.85 1,519 3 112 644 2,434 2,380 8,568 United Arab Emirates .. .. 214 .. 29 3 .. .. .. .. .. United Kingdom 4,269 893 47,121 1.88 57,178 23 12,928 9,498 15,985 6,480 33,542 United States 4,663 .. 209,695 2.82 141,519 23 89,791 25,230 224,912 231,194 266,845 Uruguay 346 .. 215 0.64 73 5 0 17 33 706 11,501 Uzbekistan .. .. 166 .. .. .. .. .. 238 174 4,541 Venezuela, RB 187 .. 497 .. 66 4 0 352 .. .. .. Vietnam 115 .. 283 0.19 1,685 5 .. .. .. .. 4,187 West Bank and Gaza .. .. .. .. .. .. 0 1 .. .. .. Yemen, Rep. .. .. 18 .. 0 0 33 –5 11 24 4,518 Zambia .. .. 36 0.03 6 2 0 0 .. .. 795 Zimbabwe .. .. 80 .. 7 1 .. .. .. .. .. World 1,281 w .. w 758,132 s 2.07 w 1,858,138 s 20 w 181,636 s 188,861 s 994,324 s 634,131 s 2,884,372 s Low income .. .. 1,690 .. .. 3 34 67 .. .. .. Middle income 596 .. 144,072 0.98 576,048 20 3,767 32,422 179,049 198,050 1,559,267 Lower middle income 479 .. 85,227 1.19 414,058 25 1,336 18,747 134,475 131,207 955,629 Upper middle income 1,112 .. 58,845 0.79 117,380 14 2,431 13,675 36,842 55,416 603,638 Low & middle income 579 .. 145,762 0.98 540,234 20 3,800 32,489 185,505 198,493 1,575,589 East Asia & Pacific 1,071 .. 60,164 1.44 .. 32 881 16,411 237,052 85,532 890,552 Europe & Central Asia 2,064 351 29,335 0.88 16,275 9 923 6,257 33,042 16,049 214,396 Latin America & Carib. 487 .. 23,240 0.68 50,434 13 1,629 5,477 5,287 41,517 313,022 Middle East & N. Africa .. .. 8,700 .. 1,571 2 60 343 .. .. 14,191 South Asia 129 87 19,375 0.79 .. 8 208 1,962 5,580 25,831 151,906 Sub-Saharan Africa .. .. 4,946 .. 3,260 6 99 2,039 .. .. .. High income 3,945 .. 612,370 2.29 1,116,596 19 177,835 156,372 764,583 406,316 1,104,532 Euro area 2,977 1,376 167,647 1.68 392,305 16 38,296 70,574 84,182 15,710 313,484 a. Original information was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. b. Excludes applications filed under the auspices of the African Intellectual Property Organization (448 by nonresidents), European Patent Office (134,580 by nonresidents), and the Eurasian Patent Organization (2,801 by nonresidents). c. Excludes applications filed under the auspices of the Office for Harmonization in the Internal Market (88,086). d. Data are for the most recent year available. e. Includes Luxembourg and the Netherlands. 316 2011 World Development Indicators 5.13 STATES AND MARKETS Science and technology About the data Definitions The United Nations Educational, Scientifi c, and for filing patent applications. An applicant files an • Researchers in R&D are professionals engaged Cultural Organization (UNESCO) Institute for Statis- international application for which the 142 eligible in conceiving of or creating new knowledge, prod- tics collects data on researchers, technicians, and countries in 2009 are automatically designated. ucts, processes, methods, and systems and in expenditure on R&D through surveys and from other The application is searched and published, and, managing the projects concerned. Postgraduate international sources. R&D covers basic research, optionally, a supplementary international search or doctoral students (ISCED97 level 6) engaged in R&D applied research, and experimental development. preliminary examination can be conducted. In the are considered researchers. • Technicians in R&D Data on researchers and technicians are calculated national or regional phase the applicant requests and equivalent staff are people whose main tasks as full-time equivalents. national processing of the application and initiates require technical knowledge and experience in engi- Scientific and technical article counts are from jour- the national search and granting procedure in the neering, physical and life sciences (technicians), nals classified by the Institute for Scientific Informa- countries where protection is sought. International and social sciences and humanities (equivalent tion’s Science Citation Index (SCI) and Social Sci- applications under the treaty provide for a national staff). They engage in R&D by performing scientific ences Citation Index (SSCI). Counts are based on patent grant only—there is no international patent. and technical tasks involving the application of fractional assignments; articles with authors from The national filing represents the applicant’s seeking concepts and operational methods, normally under different countries are allocated proportionately to of patent protection for a given territory, whereas researcher supervision. • Scientific and technical each country (see Definitions for fields covered). international filings, while representing a legal right, journal articles are published articles in physics, The SCI and SSCI databases cover the core set of do not accurately reflect where patent protection is biology, chemistry, mathematics, clinical medicine, scientific journals but may exclude some of local sought. Resident filings are those from residents of biomedical research, engineering and technology, importance and may reflect some bias toward Eng- the country concerned. Nonresident filings are from and earth and space sciences. • Expenditures for lish-language journals. applicants abroad. For regional offices such as the R&D are current and capital expenditures on creative R&D expenditures include all expenditures for R&D European Patent Office, applications from residents work undertaken to increase the stock of knowledge, performed within a country, including capital costs of any member state of the regional patent conven- including on humanity, culture, and society, and the and current costs (wages and associated costs of tion are considered nonresident filings. Some offices use of knowledge to devise new applications. • High- researchers, technicians, and supporting staff and (notably the U.S. Patent and Trademark Office) use technology exports are products with high R&D inten- other current costs, including noncapital purchases the residence of the inventor rather than the appli- sity, such as in aerospace, computers, pharmaceuti- of materials, supplies, and R&D equipment such as cant to classify filings. For further information on cals, scientific instruments, and electrical machinery. utilities, reference materials, subscriptions to librar- the PCT, see the PCT Yearly Review at http://www. • Royalty and license fees are payments and receipts ies and scientific societies, and lab materials). wipo.int/export/sites/www/ipstats/en/statistics/ between residents and nonresidents for authorized The method for determining high-technology pct/pdf/901e_2009.pdf. use of intangible, nonproduced, nonfinancial assets exports was developed by the Organisation for Eco- A trademark is a distinctive sign identifying goods and proprietary rights (such as patents, copyrights, nomic Co-operation and Development in collabora- or services as produced or provided by a specific trademarks, and industrial processes) and for tion with Eurostat. It takes a “product approach” (as person or enterprise. A trademark protects the owner the use, through licensing, of produced originals distinguished from a “sectoral approach”) based on of the mark by ensuring the exclusive right to use it of prototypes (such as films and manuscripts). R&D intensity (expenditure divided by total sales) to identify goods or services or to authorize another • Patent applications filed are patent applications for groups of products from Germany, Italy, Japan, to use it. Period of protection varies, but a trade- filed at a national or regional office; an international the Netherlands, Sweden, and the United States. mark can be renewed indefinitely for an additional patent application (or PCT filing) is in the interna- Because industrial sectors specializing in a few high- fee. Detailed components of trademark filings, avail- tional phase of the PCT. • Trademark applications technology products may also produce low-technol- able on the World Development Indicators CD-ROM filed are applications to register a trademark with a ogy products, the product approach is more appro- and WDI Online, include applications filed by direct national or regional IP office. priate for international trade. The method takes only residents (domestic applicants filing directly at a R&D intensity into account, but other characteristics given national or regional intellectual property [IP] Data sources of high technology are also important, such as know- office); direct nonresident (applicants from abroad Data on R&D are provided by the UNESCO Institute how, scientific personnel, and technology embodied filing directly at a given national or regional IP office); for Statistics. Data on scientific and technical journal in patents. Considering these characteristics would aggregate direct (applicants not identified as direct articles are from the U.S. National Science Board’s yield a different list (see Hatzichronoglou 1997). resident or direct nonresident by the national or Science and Engineering Indicators 2010. Data on A patent is an exclusive right granted for a specified regional office); and Madrid (designations received high-technology exports are from the United Nations period (generally 20 years) for a new way of doing by the national or regional IP office based on inter- Statistics Division’s Commodity Trade (Comtrade) something or a new technical solution to a problem— national applications filed via the World Intellectual database. Data on royalty and license fees are an invention. The invention must be of practical use Property Organization (WIPO)–administered Madrid from the International Monetary Fund’s Balance of and display a characteristic unknown in the existing System). Data are based on information supplied to Payments Statistics Yearbook. Data on patents and body of knowledge in its field. WIPO by IP offices in annual surveys supplemented trademarks are from the World Intellectual Property Most countries have systems to protect patent- by data in national IP office reports. Data may be Organization’s World Intellectual Property Indicators able inventions. The international Patent Coop- missing for some offices or periods. (2010) and www.wipo.int/econ_stat. eration Treaty (PCT) provides a two-phase system 2011 World Development Indicators 317 Text figures, tables, and boxes GLOBAL LINKS Introduction T 6 he past three years show dramatically how events in one part of the world can affect people in the rest of the world, though sometimes with a lag. The financial crisis that struck high-income economies in 2008 reached low- and middle-income economies in 2009. World exports of goods and services fell 20 percent, from $19.6 trillion in 2008 to $15.6 trillion in 2009, more in high-income economies and some- what less in low- and middle-income economies. Developing economies’ share of world exports increased by 1 percentage point over 2008, continuing a rising trend from 19 percent in 2000 to 27 percent in 2009. Imports of goods and services by high-income economies fell 22 percent, from $14.0 trillion in 2008 to $10.9 trillion in 2009; imports by low and middle income economies fell 19 percent. The financial crisis also reduced the external financ- than 400 indicators for monitoring exchanges ing available to developing economies from private between economies on an annual basis, and the sources, which dropped to $521 billion in 2009 from topics covered have expanded each year. Many oth- the record high of $932 billion in 2007. Net inflows ers are not included in the database because of of foreign direct investment dropped to $359 billion their structure or limited country coverage, but they in 2009 from a high of $597 billion in 2008. In con- are necessary for understanding global links. Most trast, net inflows of portfolio equity investments rose high-income economies and some low- and middle- to $108 billion following net outflows of $53 billion income economies now produce economic statistics in 2008. Bond issuances, which dropped from $88 on a quarterly or monthly basis. This introduction billion in 2007 to $24 billion in 2008, recovered in highlights some of these data. 2009 to reach $51 billion. But commercial and trade- related lending, which declined from $195 billion Data sources for bilateral trade flows in 2007 to $172 billion in 2008, dried up in 2009, World Development Indicators publishes data on dropping to $1.7 billion. Total debt flows from private merchandise trade values by commodity groups creditors fell 70 percent in 2009, to $59 billion. But (tables  4.4 and 4.5), values of trade in services net flows from official creditors reached $171 billion (tables 4.6 and 4.7), intra- and extra-regional trade in 2009, a 50 percent increase over 2008, driven (table 6.5), merchandise trade indices (table 6.2), by such multilateral institutions as the International tariff rates (table 6.8), and indicators for measur- Monetary Fund (IMF) and the World Bank. ing trade facilitation (table 6.9). Demand is rising Global food prices soared again in 2010 and for more detailed data, such as trade flows by part- 2011, with some commodities exceeding their ner economies and by commodities and sectors. record high in 2008. The World Bank food price Table 6a summarizes the main sources of data on index (table 6.6) averaged 311 in February 2011, bilateral trade flows. Some of these databases are exceeding the June 2008 record of 293. Food price accessible through the World Integrated Trade Solu- inflation has accelerated in several low- and middle- tions platform (http://wits.worldbank.org). income economies, where consumers often spend more than half their income on food. During the 12 Barriers to trade in services months ending in August 2010, food prices rose Trade in services makes up 22 percent of world 13.2 percent a year in Indonesia, 10.4  percent a trade, up from 20 percent in 2000. In developing year in India, and 9.6 percent a year in Bangladesh. economies the nominal value of trade in services The financial crisis has also demonstrated the grew 16 percent a year over 2000–09, doubling need for more data and more frequently updated the rate of growth over 1990–2000 and surpassing data to monitor global transactions. The World that of high-income economies, which grew at 11 Development Indicators database contains more percent a year over 2000–09. Despite this growing 2011 World Development Indicators 319 Source of data for bilateral trade flows 6a Name of publication Compiling organization and database Country coverage Data coverage Periodicity Links International Direction of Trade Most developing and Merchandise trade Quarterly and annual http://www2. Monetary Fund Statistics database developed economies data, no breakdowns imfstatistics.org/DOT/ of sectors and partners. Available This is a link to through subscription a 5-day trial United Nations UNCTADstat Most developing and Merchandise trade by Annual http://unctadstat. Conference on Trade Merchandise developed economies partner economies and unctad.org/ and Development Trade Matrix by product groups United Nations Commodity Trade Most developing and Merchandise trade by Annual http://wits.worldbank. Statistics Division Statistics (Comtrade) developed economies partner economies org/wits/ and by commodity classifications Organisation Monthly Statistics of OECD member Total merchandise Monthly http://stats.oecd.org/ for Economic International Trade economies trade by partners Co-operation and Extract databases Development (OECD) International Trade by OECD member Merchandise trade Annual are available under Commodity Statistics economies plus EU by partners and “International Trade by products and balance of Payments” theme Trade in services OECD member Trade in services Annual economies plus EU and by partners and by Full databases are a few more economies service category subscription based Eurostat External Trade 27 EU members Merchandise trade Monthly, quarterly, http://epp.eurostat. database by partners and and annual ec.europa.eu/portal/ by products page/portal/external_ trade/data/database importance, little is known about policies af- low- and middle-income and high-income econ- fecting services trade, a major impediment to omies (figure 6b). In both high-income and low- the analysis of trade policy and trade flows. and middle-income economies, professional To address this gap, the World Bank has services (including the movement of individu- built the Services Policy Restrictiveness Data- als) face the highest trade barriers, followed base, with information on 102 countries for five by transportation services. High-income econo- major service sectors disaggregated by subsec- mies exhibit more open financial, telecommuni- tors and relevant modes of supply in each sub- cations, and retail distribution sectors than do sector. So far, the information focuses mainly low- and middle-income economies (Borchert, on discriminatory policy measures affecting Gootiiz, and Mattoo forthcoming). foreign service providers. The full database will be released in the second quarter of 2011 at Foreign direct investment http://econ.worldbank.org/programs/trade/ Countries are increasingly compiling more data services. on foreign direct investment (FDI) transactions Restrictiveness is assessed by the newly and stocks. Despite recent improvements, how- created Services Trade Restrictiveness Index ever, deficiencies in coverage remain. For exam- score. The index reveals patterns of restric- ple, if recording of FDI transactions were com- tiveness by major service sector and across plete and comparable, the total outflows of FDI from investing economies would equal the total Trade in professional services faces the highest barriers 6b inflows recorded by the recipient economies. Services trade restrictiveness index, But in 2009 the divergence between outflows 0 (fully open) to 100 (entirely closed) High-income economies Low- and middle-income economies and inflows of FDI at the global level was about 50 $82 billion (7 percent of global outflows; figure 40 6c). The discrepancies arise from differences in 30 reporting practices. For example, some coun- tries include reinvested earnings in their outflow 20 statistics while others do not include them in 10 their inflow statistics. Furthermore, corporate 0 accounting practices and valuation methods Financial Professional Retail Telecommunications Transportation services services services services services may differ by reporters. Note: Aggregate values are the simple average of individual country scores. Data are for 102 countries. Discrepancies exist among FDI statistics Source: World Bank Services Policy Restrictiveness Database. published by various international agencies, 320 2011 World Development Indicators GLOBAL LINKS even when the agencies adopt common meth- Discrepancies persist in odological standards. Such discrepancies may measures of FDI net flows 6c reflect differences in comparability and timing Global FDI net flows ($ trillions) Inflows Outflows 3.0 of FDI data reported by different countries, discrepancies in sector coverage, and lags in 2.5 reporting revisions. Recognizing these issues, 2.0 the IMF is leading a worldwide statistical data 1.5 collection effort to improve the quality of FDI 1.0 data (the Coordinated Direct Investment Sur- 0.5 vey; http://cdis.imf.org). Preliminary results 0.0 were released in December 2010. 2004 2005 2006 2007 2008 2009 Data on FDI are published in table 6.12. Source: World Development Indicators data files. These data cover FDI net inflows received by the reporting economy from foreign residents, and FDI net outflows by the reporting economy data depends on how well the destination coun- residents. Breakdowns of FDI transactions and tries survey migrants within their borders. Sys- investment positions by sector and partner, tematic recording of migrants is difficult, espe- increasingly sought by users, are not published cially for countries with weak statistical capacity in World Development Indicators but are avail- and for those affected by civil disorder and natu- able from other sources. Table 6d summarizes ral disasters. Moreover, ensuring the compara- the availability of FDI statistics for some of the bility of migration data is a long-standing chal- main data compilers. lenge, in part because destination countries classify migrants using various criteria. Many Bilateral remittance flows countries compile migration data based on im- World Development Indicators publishes data on migrants’ nationality, while others collect data total workers’ remittances and compensation of based on the immigrants’ place of birth. employees received and sent by the reporting World Development Indicators publishes economies (table 6.18). Data coverage and qual- aggregate data on international migrant stocks ity have been improving, but inconsistencies and and net migration estimated by the United lack of reporting remain. For example, if all econo- Nations Population Division based on popula- mies reported completely and consistently, the tion censuses supplemented by border statis- sum of remittances flows recorded by receiving tics, administrative records, surveys, and refu- economies would equal the sum of remittance gee registrations (tables 6.1 and 6.18). flows recorded by sending economies. But as of Efforts to produce complete data on bilateral 2009 there was a discrepancy of $127 billion (30 migration have been rare. A 2008 database on percent of total inflows; figure 6e). Large amounts immigrants in OECD countries contains data of remittance flows are sent through private and on bilateral migrant stock for OECD members informal channels that are not officially recorded. (http://stats.oecd.org/). The dataset includes No comprehensive dataset is available on sociodemographic information such as age, the bilateral flow of remittances. Bilateral remit- gender, education, and occupation. A series tance flows estimated through approximation of studies have published data on OECD immi- and allocation methods using the proportions grants by educational attainment (Docquier and of migrant stocks in destination and sending Marfouk 2006), gender and educational attain- countries or the incomes of destination and ment (Docquier, Lowell, and Marfouk 2009), sending countries are available at www.world- and age of entry and educational attainment bank.org/prospects/migrationandremittances (Beine, Docquier, and Marfouk 2006). Global (Ratha and Shaw 2007). The data shed light on bilateral databases have been constructed for patterns of remittance flows, but the estimates the 2000 census round (Parsons and others are sensitive to the assumptions and allocation 2007) and for bilateral migration and remit- method chosen. tance flows (Ratha and Shaw 2007). The United Nations Population Division in Bilateral migration stocks cooperation with the World Bank, the United Because migration data come mostly from des- Nations Statistics Division, and the Universities tination countries, the quality of global migration of Nottingham and Sussex created the Global 2011 World Development Indicators 321 Source of data on FDI 6d Name of publication Compiling organization and database Country coverage Data coverage Periodicity Links International Monetary Balance of Payments Most developing and Aggregate FDI flows Quarterly and annual http://www2. Fund (IMF) Statistics Yearbook developed economies and stock by reporting imfstatistics.org/BOP and database economy. By-partner, by- sector breakdowns are This is a link to not available. Available 5-day trial through subscription United Nations World Investment Most developing and Aggregate FDI Annual http://unctadstat. Conference on Trade Report and Foreign developed economies flows and stock by unctad.org and Development Direct Investment reporting economy (UNCTAD) database Transnational Transnational Detailed data Annual www.unctad.org/ Corporations Corporations Worldwide on transactions Templates/Page.asp Statistics database of transnational ?intItemID=3159& corporations and mergers lang=1 and acquisitions, by partner and by sector; available through data extract service Organisation for International Direct 32 OECD member FDI stock (annual) Quarterly and annual http://stats.oecd.org/ Economic Co- Investment database economies and flows (annual and operation and quarterly) by partner Extract databases Development (OECD) economies and by are available under sectors. Full dataset is Globalisation theme available to subscribers Eurostat European Union Foreign 27 EU members Aggregate and bilateral Annual http://epp.eurostat. Direct Investment FDI flows and stock, by ec.europa.eu/ Yearbook and database partner and by sector portal/page/portal/ balance_of_payments/ data/database Association of Foreign Direct 10 ASEAN member Bilateral FDI inflows Annual www.aseansec. Southeast Asian Investment Statistics economies and outflows org/18144.htm Nations Centre d’Etudes Foreign Direct 96 countries of the Harmonized bilateral Annual for 2004 only www.cepii.fr/ Prospectives et Investment database GTAP 6.2 database flows and stocks of anglaisgraph/ d’Informations for stocks and 70 FDI for 26 sectors. bdd/fdi.htm Internationales countries for flows Data are gap filled using gravity-based regressions and raw data from IMF, UNCTAD, OECD, and Eurostat. Financial Times FDI database All countries with Greenfield FDI projects Daily www.fdimarkets.com greenfield FDI projects; since 2003; subscription FDI Intelligence based. Methodology differs significantly from balance of payments and international investment position standards. The data are based on press reports. Dealogic M&A Analytics Mergers and Information for mergers Monthly www.dealogic.com acquisitions activity and acquisitions activity, worldwide covering an including information on array of transactions target and acquiror, deal value, and financials. Migration database (www.unmigration.org) in (forthcoming at www.data.worldbank.org/data 2008. It contains all publicly available data -catalog). Construction of such a matrix entails from more than 230 destination countries and formidable challenges, including selecting the territories over the last five decades on interna- most relevant sources, allocating migrants who tional migrants, classified by age, gender, place “originated” in aggregate geographic regions and of birth, and country of citizenship. However, it migrants of unknown origins to specific coun- still does not include all raw data points needed tries, and accounting for varying survey dates for a global migration matrix. and definitions. Of all cell-level values in the final These raw data were assembled to construct matrix, about 12–14 percent are from raw census a global bilateral migration matrix using empiri- data, 40–60 percent are based primarily on raw cal methods to fill holes in the data (Özden and data scaled to United Nations Population Divi- others forthcoming). The resulting database cov- sion estimates of migrant stocks or augmented ers 226 origin and 226 destination countries by the disaggregation of aggregate categories, 322 2011 World Development Indicators GLOBAL LINKS and the remaining 26–48 percent are estimated At least 30 percent of remittance inflows through interpolation and extrapolation. go unrecorded by the sending economies 6e This new dataset reveals that the total stock Global remittance flows ($ billions) Inflows Outflows of migrants increased from 92 million in 1960 500 to 165 million in 2000. The number of migrants 400 from high-income economies remained stable, 300 while the number from low- and middle-income economies rose from 14 million in 1960 to 60 200 million in 2000 (figure 6f). The increase was 100 driven largely by an increase in migrants resid- 0 ing in the United States (up 24 million) and 2004 2005 2006 2007 2008 2009 Western Europe (up 22 million). Note: Incudes workers’ remittances and compensation of employees. Source: World Development Indicators data files. Public sector debt World Development Indicators publishes data debt among developing economies averaged on public and publicly guaranteed external 67 percent of total government debt (excluding debt (tables 6.10, 6.11, and 6.13). But these Brazil and China, with upwards of 96 percent). data present only a portion of total public sec- Financing needed to support fiscal deficits tor debt, much of which is held by domestic led to a significant increase in the ratio of sover- creditors. Domestic debt data are important for eign debt to GDP. Among developing economies, economic policymaking because of the implica- central government debt for 2009 averaged 46 tions for local financial markets. To fill the gap, percent of GDP, up from 42 percent in 2009. Bra- the World Bank and the IMF launched an online zil, which undertook aggressive countercyclical Quarterly Public Sector Debt database in 2009 spending and tax cuts to stimulate the economy, (http://data.worldbank.org/data-catalog). The had the highest share of gross debt in gross database provides data on clearly defined tiers domestic product (about 70 percent; figure 6g). of debt for central, state, and local government in developing or emerging market economies as Migrants originating from low- and middle-income economies and well as on extrabudgetary agencies and funds. residing in high-income economies rose fivefold over 1960–2000 6f It also includes debt data by instruments, valu- International migrant stock by origin and destination, millions ation methods, maturity types, and creditors. 80 The level and composition of public sector 60 Low- and middle-income debt are affected by many external and domes- to low- and middle-income tic economic factors. The recent global financial Low- and middle-income to high-income 40 crisis limited the private sector’s ability to bor- High-income to high-income row. The public sector, usually more creditworthy, 20 High-income to low- and middle-income increased external borrowing to stimulate slug- 0 gish domestic economies. Most external financ- 1960 1970 1980 1990 2000 ing for developing economies in 2009 was pro- Source: Özden and others forthcoming. vided by official multilateral institutions such as the IMF and the World Bank. After the Asian finan- cial crises in the late 1990s many governments The ratio of central government debt to GDP switched from external to domestic borrowing to has increased for most economies, 2007–10 6g reduce their exposure to exchange rate fluctua- Central government debt (percent of GDP) tions, dramatically increasing the size of domes- 80 Brazil tic debt in emerging market economies. Today, Pakistan 60 Mauritius domestic debt represents about 78 percent of Kenya Turkey the total general government debt in developing 40 economies with data. Comparison with earlier Mexico period is not possible due to lack of data. 20 Honduras Emerging market economies have also Lithuania 0 issued local currency–denominated debt to Q3 2007 Q3 2008 Q3 2009 Q3 2010a correct currency and maturity mismatches. In a. Derived using 2009 GDP because 2010 GDP was not available. Source: World Bank Quarterly Public Sector Debt database September 2010 the estimated local currency 2011 World Development Indicators 323 Tables 6.1 Integration with the global economy Trade International finance Movement of people Communication % of GDP Financing Emigration of through Workers’ people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2009 2009 2009 2009 2009 2009 2005–10 2010 2000 2008 2009 Afghanistan 31.3 .. 0.0 1.3 .. .. 1,000 0.3 22.6 7 550 Albania 46.9 38.6 0.0 8.1 0.3 11.0 –75 2.8 17.5 263 1,902 Algeria 60.1 .. 0.0 2.0 .. 1.5b –140 0.7 9.5 34 .. Angola 75.6 26.2 2.2 2.9 0.0 0.1 80 0.3 3.7 .. 17 Argentina 30.7 7.4 0.2 1.3 0.2 0.2 30 3.6 2.8 .. 2,320 Armenia 45.9 16.6 0.0 8.9 0.6 8.8 –75 10.5 8.9 .. .. Australia 34.6 9.0 .. 2.4 3.7 0.4b 500 21.1 2.7 .. 5,457 Austria 73.8 24.1 .. 2.3 1.4 0.9 160 15.6 13.5 .. 20,323 Azerbaijan 64.2 11.9 0.1 1.1 0.8 3.0 –50 3.0 1.8 77 1,399 Bangladesh 41.3 6.0 0.2 0.8 0.0 11.8 –570 0.7 4.4 .. 4 Belarus 101.6 11.3 0.5 3.8 0.2 0.7 0 11.3 3.2 .. 2,277 Belgium 153.2 33.0 .. –8.2 –16.7 2.2 200 9.0 5.5 .. 24,945 Benin 45.7 12.8 0.0 1.4 –0.1 3.6b 50 2.5 8.7 309 35 Bolivia 53.4 8.8 0.0 2.4 0.0 6.2 –100 1.5 5.8 .. 225 Bosnia and Herzegovina 74.5 11.9 0.0 1.4 –0.1 12.2 –10 0.7 20.3 .. 1,195 Botswana 69.2 15.9 0.0 2.1 0.0 0.7 15 5.8 5.1 .. 220 Brazil 18.0 4.7 3.9 1.6 –0.6 0.3 –229 0.4 2.0 .. 2,108 Bulgaria 81.7 24.5 0.0 9.4 –0.3 3.2 –50 1.4 9.6 105 37,657 Burkina Faso 36.0 8.7 0.0 2.1 0.6 1.2b –65 6.4 2.6 .. 15 Burundi 35.2 17.1 0.0 0.0 0.0 2.1 323 0.7 9.3 .. 2 Cambodia 105.3 26.8 0.0 5.4 0.2 3.4 –5 2.2 21.5 .. 19 Cameroon 32.7 15.2 0.6 1.5 1.8 0.7 –19 1.0 17.3 .. 23 Canada 48.4 10.3 .. 1.5 3.0 .. 1,050 21.1 4.7 .. 16,193 Central African Republic 20.9 .. 0.0 2.1 .. .. 5 1.8 7.3 .. .. Chad 69.5 .. 0.0 6.8 .. .. –75 3.4 9.1 .. 1 Chile 58.8 11.1 3.2 7.8 4.9 0.0 30 1.9 6.0 43 4,076 China 44.3 5.8 1.0 1.6 0.9 1.0 b –1,731c 0.1c 3.8 .. 651 Hong Kong SAR, China 323.7 62.1 .. 24.9 30.4 0.2 113 38.9 29.6 1,435 560,989 Colombia 28.1 4.8 3.4 3.1 1.3 1.8 –120 0.2 10.4 .. 2,940 Congo, Dem. Rep. 63.4 .. 0.0 9.0 .. .. –100 0.7 14.9 6 1 Congo, Rep. 88.7 46.1 0.0 21.7 .. 0.1b –50 3.8 28.2 .. 0 Costa Rica 69.0 17.5 0.0 4.6 0.0 1.8 30 10.5 7.1 132 4,333 Côte d’Ivoire 64.2 14.8 0.0 1.6 0.0 0.8 –145 11.2 6.2 .. 40 Croatia 50.3 25.0 .. 4.7 2.1 2.3 10 15.8 24.6 302 15,892 Cuba 30.9 .. 0.0 .. .. .. –194 0.1 28.8 .. 27 Czech Republic 114.9 20.7 .. 1.4 0.7 0.6 226 4.3 8.5 197 7,075 Denmark 56.9 34.3 .. 0.9 2.1 0.3 30 8.7 7.8 357 34,506 Dominican Republic 37.9 14.6 0.0 4.4 0.0 7.4 –140 4.2 22.4 .. 1,387 Ecuador 50.5 6.7 0.0 0.6 0.0 4.4 –350 2.9 9.5 .. 484 Egypt, Arab Rep. 36.1 18.8 1.4 3.6 0.3 3.8 –340 0.3 4.7 44 1,172 El Salvador 52.4 9.9 0.0 2.0 –0.6 16.5 –280 0.7 31.7 510 243 Eritrea 29.6 .. 0.0 0.0 .. .. 55 0.3 35.2 29 6 Estonia 100.4 36.4 .. 9.2 8.2 1.7 0 13.6 9.9 .. 12,680 Ethiopia 33.5 14.4 0.0 0.8 0.0 0.9 –300 0.6 9.8 5 3 Finland 51.9 22.4 .. 0.0 1.6 0.4 55 4.2 7.2 .. 17,221 France 39.4 10.2 .. 2.3 5.6 0.6 500 10.6 3.5 301 29,356 Gabon 66.0 .. 0.4 0.3 .. 0.1b 5 18.9 14.6 .. 141 Gambia, The 43.5 25.5 0.0 5.4 0.0 10.9 15 16.6 67.8 .. 38 Georgia 51.3 21.3 0.0 6.1 0.0 6.6 –250 4.0 2.8 268 752 Germany 62.0 14.6 .. 1.2 1.8 0.3 550 13.2 5.8 .. 25,654 Ghana 52.1 18.0 4.7 6.4 0.0 0.4 –51 7.6 44.7 61 97 Greece 24.2 17.5 .. 0.7 0.6 0.6 150 10.0 12.2 .. 4,537 Guatemala 50.2 10.7 0.0 1.6 0.1 10.8 –200 0.4 23.9 206 186 Guinea 58.7 9.8 0.0 1.2 0.0 1.6 –300 3.8 4.7 .. 0 Guinea-Bissau 41.2 15.2 0.0 1.7 –0.1 5.6 –12 1.2 27.7 .. 1 Haiti 40.5 17.9 0.0 0.6 0.0 21.2 –140 0.4 83.4 .. 16 Honduras 90.7 14.1 0.2 3.5 0.0 17.6 –100 0.3 24.8 224 241 324 2011 World Development Indicators 6.1 GLOBAL LINKS Integration with the global economy Trade International finance Movement of people Communication % of GDP Financing Emigration of through Workers’ people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2009 2009 2009 2009 2009 2009 2005–10 2010 2000 2008 2009 Hungary 125.6 27.3 .. 2.2 2.1 1.7 75 3.7 12.8 159 5,987 India 29.9 12.5 1.6 2.5 1.1 3.6 –1,000 0.5 4.3 .. 32 Indonesia 39.1 7.7 2.3 0.9 0.5 1.3 –730 0.1 2.9 .. 110 Iran, Islamic Rep. 38.8 .. 0.0 0.9 .. 0.3b –500 2.9 14.3 .. 151 Iraq 116.2 11.5 0.0 1.6 0.0 0.1 –577 0.3 10.9 .. 3 Ireland 77.9 86.9 .. 11.1 10.6 0.3 200 20.2 33.7 .. 15,261 Israel 49.8 20.0 .. 2.0 0.6 0.6 85 38.8 7.8 .. 2,003 Italy 38.7 10.4 .. 1.4 2.1 0.1 1,650 7.4 9.7 .. 12,989 Jamaica 52.9 37.5 9.0 4.5 0.5 15.8 –100 1.1 84.7 224 741 Japan 22.3 5.5 .. 0.2 1.5 0.0 150 1.7 1.2 .. 5,770 Jordan 81.5 33.3 0.0 9.5 0.3 14.3 250 48.8 7.4 258 1,811 Kazakhstan 62.1 12.4 2.1 11.8 2.7 0.1 –100 19.1 1.2 52 1,342 Kenya 49.8 16.1 0.2 0.5 0.2 5.7b –189 2.0 38.5 6 477 Korea, Dem. Rep. .. .. .. .. .. .. 0 0.2 .. .. 0 Korea, Rep. 82.5 16.1 .. 0.2 1.3 0.3 –30 1.1 7.5 64 6,065 Kosovo .. .. 0.0 7.5 .. .. .. .. .. .. .. Kuwait 75.9 18.0 .. 0.0 6.1 .. 120 73.3 7.1 .. 871 Kyrgyz Republic 97.8 37.7 0.0 4.1 0.0 21.7b –75 4.2 0.9 .. 112 Lao PDR 37.0 8.6 0.0 5.4 0.0 0.6 –75 0.3 37.2 .. 142 Latvia 66.6 23.4 0.0 0.4 –0.2 2.3 –10 14.9 8.5 .. 3,537 Lebanon 60.1 90.4 2.7 13.9 3.3 21.9 –13 17.8 43.9 190 223 Lesotho 171.0 12.5 0.0 4.0 0.0 26.2 –36 0.3 4.1 .. 5 Liberia 80.1 162.0 0.0 24.9 0.0 6.2b 248 2.3 44.3 .. .. Libya 73.4 8.7 0.0 2.7 1.9 0.0 b 20 10.4 4.3 .. 50 Lithuania 93.2 18.4 6.4 0.6 0.5 3.1 –100 3.9 8.4 132 14,300 Macedonia, FYR 83.9 18.3 2.6 2.7 0.1 4.1 –10 6.3 29.4 256 17 Madagascar 51.1 .. 0.0 6.3 .. 0.1b –5 0.2 7.7 8 12 Malawi 55.4 .. 0.0 1.3 .. 0.0 b –20 1.8 20.9 .. 5 Malaysia 145.7 29.1 5.8 0.7 4.2 0.6 130 8.4 10.5 .. 5,097 Mali 52.7 17.0 0.0 1.2 0.0 4.5b –202 1.2 14.8 13 51 Mauritania 92.6 .. 0.0 –1.3 .. 0.1b 10 2.9 8.6 57 76 Mauritius 66.0 44.8 0.0 3.0 0.4 2.5b 0 3.3 56.0 215 364 Mexico 53.9 4.5 3.1 1.7 0.9 2.5 –2,430 0.7 15.5 .. 312 Moldova 84.5 25.6 0.0 2.4 0.1 22.4 –172 11.4 4.1 457 6,660 Mongolia 96.0 23.1 0.1 14.8 1.3 4.8 –10 0.4 7.4 .. 2,920 Morocco 51.2 21.1 0.0 2.2 0.5 6.9 –425 0.2 18.6 87 1,600 Mozambique 60.4 17.1 0.6 9.0 0.0 1.1 –20 1.9 22.6 .. 56 Myanmar .. .. .. .. .. .. –500 0.2 3.9 3 20 Namibia 93.6 12.2 0.0 5.3 0.0 0.1 –1 6.3 3.4 .. 27 Nepal 41.5 11.5 0.0 0.3 .. 23.8 –100 3.2 4.0 .. 5 Netherlands 119.2 22.6 .. 4.2 3.5 0.5 100 10.5 9.6 .. 78,156 New Zealand 39.8 12.5 .. –1.0 –0.5 0.5 50 22.0 21.8 .. 4,544 Nicaragua 79.3 16.7 0.0 7.1 0.0 12.5 –200 0.7 30.2 .. 144 Niger 44.6 13.7 0.0 13.7 0.5 1.7 –28 1.3 5.5 .. 11 Nigeria 52.9 11.4 0.7 3.3 0.1 5.5b –300 0.7 10.5 26 5 Norway 49.8 19.8 .. 3.0 7.1 0.2 135 9.9 6.2 .. 26,904 Oman 99.0 15.9 .. 4.8 0.9 0.1 20 28.4 0.4 431 1,365 Pakistan 30.5 6.4 0.2 1.5 0.0 5.4 –1,416 2.4 12.7 .. 43 Panama 35.4 31.2 8.8 7.2 0.0 0.7 11 3.4 16.7 118 15,964 Papua New Guinea 95.4 26.9 58.3 5.4 0.1 0.2 0 0.4 27.8 .. 2 Paraguay 71.0 13.9 0.0 1.4 0.1 4.3 –40 2.5 3.8 .. 662 Peru 37.3 6.5 2.6 3.7 0.3 1.8 –625 0.1 5.8 113 2,646 Philippines 52.3 11.6 4.5 1.2 0.2 12.3 –900 0.5 13.6 .. 113 Poland 65.4 12.4 3.8 3.2 1.2 1.9 –120 2.2 14.3 32 2,748 Portugal 48.6 15.9 .. 1.2 0.5 1.5 200 8.6 19.0 .. 4,790 Puerto Rico .. .. .. .. .. .. –21 8.1 .. .. 1,764 Qatar 64.6 .. .. .. .. .. 562 86.5 2.1 .. 2,044 2011 World Development Indicators 325 6.1 Integration with the global economy Trade International finance Movement of people Communication % of GDP Financing Emigration of through Workers’ people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2009 2009 2009 2009 2009 2009 2005–10 2010 2000 2008 2009 Romania 58.9 12.4 0.1 3.9 0.1 3.1 –200 0.6 11.3 124 18,271 Russian Federation 40.2 8.4 2.4 3.0 3.6 0.4 250 8.7 1.4 .. 573 Rwanda 27.2 16.5 0.0 2.3 0.0 1.8 15 4.5 31.7 8 35 Saudi Arabia 76.6 22.1 .. 2.8 0.6 0.1 150 28.0 0.9 .. 1,731 Senegal 53.8 20.6 2.8 1.6 1.0 10.6 –100 1.6 17.2 101 372 Serbia 55.7 16.2 0.0 4.5 0.1 12.6b,d 0 7.2 .. 203 12,660 Sierra Leone 38.7 8.7 0.0 3.8 0.0 2.4 60 1.8 49.2 .. .. Singapore 282.9 95.1 .. 9.2 3.3 .. 500 38.3 14.5 .. 22,783 Slovak Republic 127.0 16.3 .. 0.0 0.5 1.9 20 2.4 14.3 228 7,567 Slovenia 109.0 21.6 .. –1.2 0.3 0.6 22 7.9 11.0 220 6,720 Somalia .. .. .. .. .. .. –250 0.2 34.5 .. .. South Africa 47.6 9.4 2.7 1.9 0.5 0.3 700 3.7 7.4 .. 70 Spain 34.7 14.4 .. 0.4 0.5 0.7 1,750 13.8 4.2 .. 11,008 Sri Lanka 41.8 10.5 1.3 1.0 0.0 8.0 –300 1.7 28.2 .. 190 Sudan 32.1 5.6 0.0 4.9 0.0 5.5b 135 1.7 6.8 13 322 Swaziland 103.3 25.4 0.0 2.2 0.2 3.1 –6 3.4 5.4 41 35 Sweden 61.8 25.6 .. 2.8 7.9 0.2 150 13.9 4.5 .. 49,828 Switzerland 66.8 23.0 .. 5.6 6.8 0.5 100 22.6 9.6 .. 29,413 Syrian Arab Republic 51.2 13.3 0.1 2.7 0.0 2.6b 800 10.2 6.2 .. 261 Tajikistan 71.9 9.5 0.0 0.3 0.0 35.1 –200 4.0 0.6 .. 37 Tanzania 44.2 16.7 0.0 1.9 0.0 0.1 –300 1.5 12.1 1 2 Thailand 108.5 25.7 0.3 1.9 1.6 0.6 300 1.7 2.2 .. 818 Timor-Leste .. .. 0.0 .. .. .. 10 1.2 16.5 .. .. Togo 80.6 22.2 19.9 1.8 –0.5 10.7b –5 2.7 16.5 28 23 Trinidad and Tobago 75.8 4.9 .. 3.3 2.7 0.5b –20 2.6 78.9 443 7,916 Tunisia 84.8 21.4 0.1 4.0 0.2 5.0 –20 0.3 12.6 .. 2,699 Turkey 39.5 8.2 1.7 1.4 0.3 0.2 –44 1.9 5.8 60 4,323 Turkmenistan 66.9 .. 0.0 6.8 .. .. –25 4.0 0.4 .. 48 Uganda 42.3 14.9 0.0 3.8 0.0 4.7 –135 1.9 36.0 .. 36 Ukraine 75.0 22.3 0.9 4.2 0.1 4.5 –80 11.5 4.3 .. 206 United Arab Emirates 136.8 .. .. .. .. .. 343 70.0 0.7 .. 13,233 United Kingdom 38.4 18.6 .. 3.4 2.0 0.3 948 10.4 17.1 .. 39,664 United States 18.8 6.1 .. 1.0 1.9 0.0 5,052 13.8 0.5 216 11,279 Uruguay 39.0 10.5 1.6 4.0 0.0 0.3 –50 2.4 9.0 125 903 Uzbekistan 61.5 .. 0.0 2.3 .. .. –400 4.2 0.8 .. 46 Venezuela, RB 30.1 3.6 1.5 –1.0 0.6 0.0 40 3.5 3.8 79 628 Vietnam 141.0 14.1 1.5 8.4 0.8 7.4b –200 0.1 27.0 .. 581 West Bank and Gaza .. .. .. .. .. .. –10 46.3 12.0 .. 313 Yemen, Rep. 53.5 12.8 0.0 0.5 0.0 4.4 –135 2.1 6.0 .. 28 Zambia 63.3 7.4 0.5 5.5 0.0 0.3 –85 1.8 16.4 .. 8 Zimbabwe 91.9 .. 0.0 1.1 .. .. –700 2.9 13.1 19 17 World 42.8 w 11.2 w .. w 1.8 w 2.1 w 0.8 w ..e s 3.1 w 5.4 w .. 3,526 w Low income 48.9 13.3 0.6 2.7 0.0 6.6 –2,737 1.5 13.1 .. 7 Middle income 44.7 8.9 1.8 2.2 0.9 1.8 –13,203 1.4 6.8 .. 348 Lower middle income 46.7 9.2 1.2 2.0 0.8 2.4 –9,231 0.9 6.6 .. 151 Upper middle income 42.3 8.5 2.5 2.4 1.1 1.1 –3,972 3.3 7.0 .. 1,120 Low & middle income 44.8 9.0 1.8 2.2 0.9 1.9 –15,941 1.4 7.1 .. 299 East Asia & Pacific 51.5 7.9 1.4 1.6 1.0 1.4 –3,781 0.3 7.0 .. 742 Europe & Central Asia 48.3 10.5 1.8 3.3 2.0 1.4 –1,671 6.8 3.4 .. 1,087 Latin America & Carib. 33.6 5.9 2.9 1.9 0.3 1.4 –5,214 1.1 10.6 .. 1,408 Middle East & N. Africa 53.5 .. 0.3 2.6 .. 3.2 –1,089 3.6 10.5 .. 323 South Asia 31.0 11.5 1.3 2.3 0.9 4.5 –2,376 0.8 5.3 .. 31 Sub-Saharan Africa 52.7 13.4 1.4 3.1 0.2 2.5 –1,810 2.1 12.6 .. 31 High income 42.0 12.5 .. 2.0 2.8 0.3 15,894 12.0 4.1 .. 19,521 Euro area 57.1 16.9 .. 3.0 3.8 0.5 5,607 11.0 7.1 .. 32,455 a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. World Bank estimate. c. Includes Taiwan, China. d. Includes Montenegro. e. World totals computed by the United Nations sum to zero, but because the aggregates shown here refer to World Bank definitions, regional and income group totals do not equal zero. 326 2011 World Development Indicators 6.1 GLOBAL LINKS Integration with the global economy About the data Definitions Globalization—the integration of the world econ- statistics agencies (see About the data for table • Trade in merchandise is the sum of merchandise omy— has been a persistent theme of the past 25 6.12). FDI data are recorded on a directional basis, exports and imports. • Trade in services is the sum years. Growth of cross-border economic activity has as an inward flow to the economy of the direct invest- of services exports and imports. • Financing through changed countries’ economic structure and political ment enterprise, and as an outward flow from the international capital markets is the sum of the abso- and social organization. Not all effects of globaliza- economy of the direct investor. Net flows refer to lute values of new bond issuance, syndicated bank tion can be measured directly. But the scope and new investments during the reporting period netted lending, and new equity placements. • Foreign direct pace of change can be monitored along four key against disinvestments. investment net inflows and outflows are net inflows dimensions: trade in goods and services, financial The data on workers’ remittances and compensa- and outflows of FDI (equity capital, reinvestment of flows, movement of people, and communication. tion of employees are the sum of three items defined earnings, and other short- and long-term capital). Trade data are based on gross flows that capture in the IMF’s Balance of Payments Manual, 5th edi- • Workers’ remittances and compensation of employ- the two-way flow of goods and services. In conven- tion: workers’ remittances, compensation of employ- ees received are current transfers by migrant work- tional balance of payments accounting, exports ees, and migrants’ transfers. The distinction among ers and wages and salaries of nonresident workers. are recorded as a credit and imports as a debit. these three items is not always consistent in the • Net migration is the number of immigrants minus The data on merchandise trade are from the World data reported by countries to the IMF. In some cases the number of emigrants, including citizens and nonciti- Trade Organization (WTO), which obtains data from countries compile data on the basis of the citizenship zens, for the five-year period. • International migrant national statistical offi ces and the International of migrant workers rather than their residency status. stock is the number of people born in a country other Monetary Fund’s (IMF) International Financial Sta- Some countries also report remittances entirely as than that in which they live, including refugees. • Emi- tistics, supplemented by the Comtrade database and worker’s remittances or compensation of employees. gration of people with tertiary education to OECD publications or databases of regional organizations, Following the fifth edition of the Balance of Payments countries is adults ages 25 and older, residing in an specialized agencies, economic groups, and private Manual in 1993, migrants’ transfers are considered OECD country other than that in which they were born, sources. Because of differences in timing and defi - a capital transaction, but previous editions regarded with at least one year of tertiary education. • Interna- nitions, trade flow estimates from customs reports them as current transfers. For these reasons the tional voice traffic is the sum of international incoming and balance of payments may differ. See tables 4.4 figures presented in the table take all three items and outgoing telephone traffic (in minutes) divided by and 4.5 for data on the main trade components of into account. See About the data for table 6.18 for total population. • International Internet bandwidth merchandise trade and tables 4.6 and 4.7 for the more information. is the contracted capacity of international connections same data on services trade. Migration has increased in importance, accounting between countries for transmitting Internet traffic. Financing through international capital markets for a substantial part of global integration. Data on includes gross bond issuance, bank lending, and new net migration are estimated by the United Nations Data sources equity placement as reported by Dealogic, a com- Population Division, based on data on immigrant pany specializing in the investment banking industry. stock and on fertility and mortality assumptions, tak- Data on merchandise trade are from the WTO’s In financial accounting inward investment is a credit ing into account the migration history of a country or Annual Report. Data on trade in services are from the and outward investment a debit. Gross flow is a bet- area, the migration policy of a country, and the influx International Monetary Fund’s (IMF) Balance of Pay- ter measure of integration than net flow because of refugees in recent periods. The estimates of the ments database. Data on international capital market gross flow shows the total value of financial trans- international migrant stock are derived from data on financing are based on data from Dealogic. Data on actions over a period, while net flow is the sum of people who reside in one country but were born in FDI are based on balance of payments data from the credits and debits and represents a balance in which another, mainly from population censuses (see About IMF, supplemented by staff estimates using data from many transactions are canceled out. Components of the data and Definitions for table 6.18). the United Nations Conference on Trade and Develop- financing through international capital markets are One negative effect of migration is “brain drain”— ment and official national sources. Data on workers’ reported in U.S. dollars by market sources. emigration of highly educated people. The table remittances are World Bank staff estimates based Foreign direct investment (FDI) includes equity shows data on emigration of people with tertiary on IMF balance of payments data. Data on net migra- investment, reinvested earnings, and short- and education, drawn from Docquier, Lowell, and Mar- tion are from the United Nations Population Division’s long-term loans between parent firms and foreign fouk (2009), who analyzed skilled migration using World Population Prospects: The 2008 Revision. Data affiliates. Distinguished from other kinds of interna- data from censuses and registers of Organisation on international migrant stock are from the United tional investment, FDI establishes a lasting interest for Economic Development and Co-operation (OECD) Nations Population Division’s Trends in Total Migrant in or effective management control over an enter- countries and provide data disaggregated by gender Stock: The 2008 Revision. Data on emigration of prise in another country. FDI may be understated for 1990 and 2000. people with tertiary education are from Docquier, Low- in developing countries because some fail to report Well developed communications infrastructure ell, and Marfouk’s “A Gendered Assessment of Highly reinvested earnings and because the definition of attracts investments and allows investors to capi- Skilled Emigration” (2009). Data on international voice long-term loans differs by country. However, data talize on benefits of the digital age. See About the traffic and international Internet bandwidth are from quality and coverage are improving as a result of data for tables 5.11 and 5.12 for more information. the International Telecommunication Union’s World continuous efforts by international and national Telecomunication Development Report database. 2011 World Development Indicators 327 6.2 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1995 2009 Afghanistana .. 15.2 .. 3.9 –0.2 24.0 20.6 9.4 .. 107.6 Albania .. .. .. .. .. .. .. .. .. .. Algeria 2.8 –0.1 –0.8 12.7 2.0 16.3 –1.3 18.8 57.9 161.0 Angola 6.2 12.9 7.1 20.1 6.2 30.7 7.8 24.6 80.8 170.8 Argentina 8.4 5.9 17.7 11.3 10.1 12.2 17.0 14.4 91.6 126.0 Armenia .. .. .. .. .. .. .. .. .. .. Australiaa 7.3 7.6 9.2 7.5 5.7 20.0 8.7 12.3 99.4 163.0 Austriaa 6.2 4.9 5.6 4.2 .. .. .. .. .. .. Azerbaijan .. .. .. .. .. .. .. .. .. .. Bangladesh 12.9 11.0 5.9 4.6 15.8 12.5 10.4 13.0 111.8 64.5 Belarusb .. 6.0 .. 10.1 .. 18.4 .. 19.4 .. 121.0 Belgiuma 6.0 2.9 5.7 3.5 4.8 10.9 5.3 11.4 104.3 103.1 Benin 1.0 5.9 8.2 6.8 3.3 14.0 9.7 16.1 106.6 83.1 Bolivia 2.8 9.9 9.1 7.8 4.3 21.9 9.7 13.3 89.4 136.9 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 4.8 2.9 4.0 5.1 4.8 7.4 4.2 11.5 89.3 79.1 Brazil 5.1 7.6 16.7 7.2 5.9 16.2 12.6 14.3 110.4 107.8 Bulgaria .. .. .. .. .. .. .. .. .. .. Burkina Faso 13.2 11.7 3.6 7.3 12.9 17.1 3.6 15.6 131.0 78.6 Burundi 8.6 –4.2 4.0 10.4 –4.3 6.2 –6.9 15.9 163.6 137.9 Cambodia .. 12.8 .. 9.4 26.9 15.2 25.2 15.1 .. 85.0 Cameroon 0.3 –1.8 5.0 3.7 –3.6 11.2 2.1 13.1 90.4 121.6 Canadaa 9.1 –0.7 9.0 3.3 9.4 5.6 8.9 7.3 103.2 114.8 Central African Republic 20.0 –3.7 4.3 5.7 3.5 –0.5 0.2 12.7 193.0 78.5 Chada –0.9 31.7 2.0 6.5 –3.5 49.6 0.5 12.2 92.6 136.0 Chile 11.1 5.1 10.7 11.3 9.4 18.3 10.3 15.3 135.6 166.7 China† 13.8 21.9 12.8 15.4 14.5 23.7 13.0 21.6 101.9 79.7 Hong Kong SAR, China 8.4 7.1 8.9 6.9 8.3 7.9 8.8 8.2 99.1 97.6 Colombia 4.5 6.6 8.5 11.4 7.3 14.9 9.7 15.8 86.8 114.4 Congo, Dem. Rep. –1.8 8.3 4.6 14.6 –7.2 18.8 –0.5 21.5 79.8 112.0 Congo, Rep. 6.6 1.2 4.9 17.7 7.5 16.9 8.7 24.4 52.0 147.5 Costa Rica 14.0 7.6 14.9 7.6 17.0 7.5 13.9 9.9 104.6 87.2 Côte d’Ivoire 5.0 0.9 –0.3 6.6 6.1 12.4 3.0 15.7 122.0 140.4 Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. 1.5 .. 8.2 –1.7 11.9 2.5 13.7 .. 111.1 Czech Republic .. .. .. .. .. .. .. .. .. .. Denmarka 5.4 2.7 5.8 3.7 4.1 9.8 4.9 10.7 102.1 102.9 Dominican Republic 3.9 –0.7 11.6 2.7 4.2 2.3 12.0 6.4 98.2 96.8 Ecuador 6.3 8.2 5.9 12.3 6.8 17.3 7.8 17.8 80.6 109.7 Egypt, Arab Rep. –0.2 10.2 1.8 8.8 0.7 24.6 4.7 17.2 116.3 128.1 El Salvador 2.9 2.4 7.6 4.2 9.0 4.7 10.9 7.3 121.1 99.1 Eritrea –28.3 –8.7 –3.2 –5.2 –31.0 –5.1 –0.2 1.7 101.7 73.3 Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia 10.5 7.8 7.3 17.5 10.7 17.8 7.3 25.1 151.0 121.1 Finlanda .. .. .. .. .. .. .. .. 110.6 83.1 Francea 8.3 4.9 6.6 6.4 4.9 10.5 3.7 12.2 106.4 99.8 Gabon 5.2 –1.2 2.5 7.2 0.8 13.8 2.2 12.4 125.4 155.3 Gambia, The –11.6 –3.0 0.1 2.2 –12.3 2.1 0.2 9.8 100.0 85.5 Georgia .. .. .. .. .. .. .. .. .. .. Germanya 6.5 5.6 4.9 5.1 3.7 11.6 2.9 10.9 107.5 105.9 Ghana 7.7 4.8 8.6 10.0 9.0 16.6 8.3 16.5 106.7 178.4 Greecea 8.9 .. 9.3 .. 8.2 .. 8.2 .. 89.6 90.8 Guatemala 8.5 8.8 10.0 5.8 10.1 13.1 10.4 11.5 117.9 91.4 Guinea 5.0 –7.4 –1.4 3.4 0.6 6.6 –2.6 10.5 89.6 143.3 Guinea-Bissaua 12.2 3.6 –16.0 7.2 18.6 9.1 –15.7 17.5 102.7 66.0 Haiti 12.6 5.9 13.3 2.3 12.2 8.6 14.4 9.8 113.2 70.6 Honduras 2.5 3.5 12.7 4.3 5.3 5.8 12.8 9.3 96.3 81.9 †Data for Taiwan, China 3.1 7.6 4.8 2.3 7.2 8.0 8.5 7.8 89.9 69.2 328 2011 World Development Indicators 6.2 GLOBAL LINKS Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1995 2009 Hungarya 10.1 10.7 11.6 8.0 10.1 16.2 11.8 14.3 104.3 95.6 India 6.9 12.3 9.0 18.4 5.3 20.3 7.9 25.3 108.0 99.4 Indonesia 9.1 8.7 2.9 5.7 8.1 10.9 2.7 15.6 90.4 63.2 Iran, Islamic Rep. .. 2.4 .. 10.8 1.2 18.0 –4.8 18.4 .. 132.4 Iraqa .. 1.0 .. 6.8 118.9 17.2 70.3 13.7 .. 140.8 Irelanda 15.2 1.9 11.3 0.9 13.8 5.4 10.9 5.4 103.9 96.6 Israela 9.7 3.7 8.9 1.8 10.0 8.7 8.2 7.1 92.1 102.7 Italya 4.8 0.3 4.2 0.6 4.6 9.3 3.2 10.2 96.6 103.3 Jamaica 2.2 0.3 .. 1.2 2.2 6.0 6.9 9.2 .. 77.1 Japana 2.6 2.4 5.3 1.7 2.1 3.2 5.2 8.0 114.9 74.4 Jordan 4.7 4.2 3.8 6.8 6.6 16.3 5.1 17.2 115.6 120.4 Kazakhstana .. .. .. .. .. .. .. .. .. .. Kenya 3.9 5.1 7.4 8.6 6.3 12.6 6.0 17.7 103.9 94.7 Korea, Dem. Rep.a .. 4.3 .. –3.1 –8.5 10.9 1.0 6.3 .. 83.9 Korea, Rep. 15.8 12.4 10.0 7.2 10.1 12.7 7.1 13.0 138.5 68.6 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait .. 4.6 .. 9.8 16.5 20.5 5.5 14.4 .. 156.1 Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. Lao PDR .. 9.9 .. 7.7 15.4 18.3 12.7 14.5 .. 103.9 Latviaa 7.2 .. .. .. 11.8 .. .. .. .. .. Lebanon .. 12.9 .. 4.0 4.6 21.7 8.7 11.9 .. 109.1 Lesotho 13.3 14.7 3.1 7.7 12.4 15.0 2.0 12.4 100.0 78.3 Liberiaa .. –6.3 .. 5.4 –14.5 –0.4 2.6 9.9 .. 111.4 Libya .. 4.5 0.0 16.6 –2.6 21.2 –1.4 23.9 .. 140.4 Lithuania .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar 4.1 2.8 4.5 10.6 8.5 6.0 6.4 17.6 79.6 75.5 Malawi 2.7 5.7 –2.4 8.0 0.9 10.9 –0.6 15.4 105.7 94.2 Malaysia 13.6 5.8 10.6 5.2 12.2 9.6 9.5 8.7 108.6 99.7 Mali 10.3 2.0 6.4 8.7 6.3 15.4 4.7 17.1 109.6 165.4 Mauritania 1.9 10.4 4.2 11.9 –1.9 24.0 –1.6 18.7 102.2 150.9 Mauritius 2.7 3.2 3.4 6.6 2.2 3.1 3.3 9.5 88.5 81.3 Mexico 15.5 2.7 13.2 3.5 16.1 7.0 14.2 6.8 92.5 104.0 Moldova .. .. .. .. .. .. .. .. .. .. Mongolia .. 4.5 .. 12.0 0.7 21.3 0.5 20.6 .. 170.2 Morocco 7.5 0.1 7.2 8.6 7.2 10.3 5.5 16.3 89.1 137.4 Mozambique 15.2 12.5 1.0 8.7 10.2 22.4 1.1 16.3 151.1 98.2 Myanmar 15.5 6.7 13.8 –1.0 14.4 17.2 22.6 6.5 214.3 117.1 Namibia 2.4 7.3 7.7 11.0 0.9 14.9 3.9 15.5 82.6 113.5 Nepal .. –1.5 .. 2.9 11.0 4.0 9.3 12.4 .. 80.7 Netherlandsa 8.0 4.6 8.4 4.5 5.7 11.5 5.5 11.1 97.6 102.5 New Zealanda 4.7 3.0 6.0 5.8 4.3 9.6 5.9 10.5 99.0 111.0 Nicaragua 10.4 9.1 9.3 5.7 10.3 12.2 11.6 11.0 128.9 83.9 Niger 3.1 –2.6 –2.1 10.0 0.0 15.9 0.8 18.6 121.4 185.2 Nigeria 3.3 3.2 2.5 14.6 2.9 19.7 3.1 21.7 55.6 145.3 Norwaya 6.6 0.2 7.8 5.7 5.7 12.8 4.4 12.5 60.3 128.6 Oman 4.0 –1.3 .. 11.8 5.7 14.7 6.1 18.1 .. 150.1 Pakistan 2.5 7.0 2.4 8.0 4.3 10.0 3.1 18.3 119.2 63.4 Panama 6.0 1.5 7.8 9.7 9.4 3.4 8.7 13.6 100.0 92.4 Papua New Guinea –7.7 –3.5 .. 7.0 3.7 13.8 –0.8 15.8 .. 164.1 Paraguay –0.2 14.5 5.4 15.5 1.7 19.0 6.7 19.4 118.3 104.9 Peru 9.4 8.1 10.6 9.7 8.9 21.6 12.7 17.3 123.4 129.1 Philippines 16.0 2.6 11.3 0.6 18.8 3.0 12.5 5.6 80.2 72.0 Polanda 9.8 11.8 19.0 9.4 9.5 21.8 17.0 18.4 102.4 107.1 Portugala 0.3 –2.0 0.5 –1.4 –3.0 4.0 –2.5 4.5 104.7 107.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. 4.8 .. 25.6 10.1 21.4 7.4 30.9 .. 173.1 2011 World Development Indicators 329 6.2 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1995 2009 Romania .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. Rwanda –8.0 3.6 0.8 15.2 –4.0 17.8 –1.7 22.7 110.1 155.3 Saudi Arabia 2.9 0.5 .. 11.4 3.1 17.6 0.8 17.0 .. 175.6 Senegal 10.6 0.8 4.9 7.1 4.0 9.4 3.6 16.5 156.3 99.2 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leonea .. 28.7 .. 3.1 .. 35.2 .. 14.3 .. 64.6 Singapore 11.7 10.9 8.3 8.0 9.9 12.6 7.8 12.0 104.4 82.6 Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. Somaliaa .. 0.4 .. 5.4 2.3 8.3 4.5 13.0 .. 101.3 South Africa 4.5 0.5 7.6 6.6 2.5 12.8 5.9 15.7 106.0 135.0 Spaina 11.4 3.1 9.3 4.7 8.6 10.6 6.2 11.8 104.3 107.2 Sri Lanka 7.4 3.1 8.0 1.7 11.3 6.3 8.9 9.1 99.0 78.5 Sudan 12.6 8.3 8.4 17.9 14.0 24.7 9.8 23.5 100.0 152.5 Swaziland 4.0 2.5 3.1 4.3 5.9 8.9 5.0 10.2 100.0 112.8 Swedena 8.9 3.5 6.4 4.4 7.4 9.7 5.4 12.1 110.2 89.6 Switzerlanda 3.7 3.7 4.2 2.5 4.4 5.7 3.6 4.4 96.4 106.6 Syrian Arab Republic 2.2 0.4 .. 12.5 0.9 14.1 3.6 20.5 .. 148.3 Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania 6.0 6.4 –2.0 11.8 6.4 17.0 0.1 20.5 98.0 121.1 Thailand 9.6 7.4 2.6 7.8 10.5 12.6 5.0 13.0 116.0 97.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 9.1 3.2 6.0 –1.7 6.6 9.9 5.5 14.8 99.1 28.6 Trinidad and Tobago .. 2.8 .. 2.8 6.8 17.9 12.1 12.1 .. 131.0 Tunisia 5.7 7.7 4.3 5.0 6.0 13.4 5.2 11.7 95.8 94.3 Turkey 10.7 11.5 11.1 9.8 9.1 19.3 10.3 18.4 105.7 95.0 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 17.8 15.7 22.4 8.8 15.6 25.9 21.0 16.0 197.2 120.4 Ukraine .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. 7.8 .. 15.9 6.6 20.9 10.7 21.6 .. 134.7 United Kingdoma 6.3 1.1 6.5 3.2 6.2 6.2 6.5 7.9 100.1 104.0 United Statesa 6.6 4.0 9.1 2.9 7.2 6.6 9.5 6.5 103.3 99.0 Uruguay 6.1 8.1 10.5 5.9 5.2 14.3 10.1 13.3 116.2 98.5 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 5.2 –1.9 4.8 12.3 5.4 13.9 5.2 15.7 63.4 187.1 Vietnam .. 11.8 .. 12.8 22.7 19.7 22.7 21.2 .. 97.4 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. –4.7 4.4 9.5 20.6 9.9 0.6 18.4 .. 126.6 Zambia 6.1 8.9 2.9 15.1 –4.6 25.7 1.3 21.8 189.7 155.9 Zimbabwe 8.8 –5.1 8.0 –2.1 3.4 2.8 1.9 7.4 96.8 90.9 a. Data are from the International Monetary Fund’s International Financial Statistics database. b. Data are from national sources. 330 2011 World Development Indicators 6.2 GLOBAL LINKS Growth of merchandise trade About the data Definitions Data on international trade in goods are available from national and international sources such as the •  Export and import volumes are indexes of the from each country’s balance of payments and IMF’s International Financial Statistics database, quantity of goods traded. They are derived from customs records. While the balance of payments the United Nations Economic Commission for Latin UNCTAD’s volume index series and are the ratio of focuses on the financial transactions that accom- America and the Caribbean, the U.S. Bureau of Labor the export or import value indexes to the correspond- pany trade, customs data record the direction of Statistics, Japan Customs and Bank of Japan, and ing unit value indexes. Unit value indexes are based trade and the physical quantities and value of goods UNCTAD’s Commodity Price Statistics. The IMF also on data reported by countries that demonstrate entering or leaving the customs area. Customs data compiles data on trade prices and volumes in its consistency under UNCTAD quality controls, supple- may differ from data recorded in the balance of pay- International Financial Statistics (IFS) database. mented by UNCTAD’s estimates using the previous ments because of differences in valuation and time Unless otherwise noted, the growth rates and year’s trade values at the Standard International of recording. The 1993 United Nations System of terms of trade in the table were calculated from Trade Classifi cation three-digit level as weights. National Accounts and the fifth edition of the Inter- index numbers compiled by UNCTAD. The growth To improve data coverage, especially for the latest national Monetary Fund’s (IMF) Balance of Payments rates and terms of trade for selected economies periods, UNCTAD constructs a set of average prices Manual (1993) attempted to reconcile definitions and were calculated from index numbers compiled in indexes at the three-digit product classification of the reporting standards for international trade statistics, the IMF’s International Financial Statistics. In some Standard International Trade Classification revision 3 but differences in sources, timing, and national prac- cases price and volume indexes from different using UNCTAD’s Commodity Price Statistics, interna- tices limit comparability. Real growth rates derived sources vary significantly as a result of differences tional and national sources, and UNCTAD secretariat from trade volume indexes and terms of trade based in estimation procedures. Because the IMF does not estimates and calculates unit value indexes at the on unit price indexes may therefore differ from those publish trade value indexes, for selected economies country level using the current year’s trade values as derived from national accounts aggregates. the trade value indexes were derived from the vol- weights. For economies for which UNCTAD does not Trade in goods, or merchandise trade, includes all ume and price indexes. All indexes are rescaled to publish data, the export and import volume indexes goods that add to or subtract from an economy’s a 2000 base year. (lines 72 and 73) in the IMF’s International Financial material resources. Trade data are collected on the The terms of trade measures the relative prices of Statistics are used to calculate the average annual basis of a country’s customs area, which in most a country’s exports and imports. There are several growth rates. • Export and import values are the cur- cases is the same as its geographic area. Goods ways to calculate it. The most common is the net rent value of exports (free on board, f.o.b.) or imports provided as part of foreign aid are included, but barter (or commodity) terms of trade index, or the (cost, insurance, and freight, c.i.f.), converted to U.S. goods destined for extraterritorial agencies (such ratio of the export price index to the import price dollars and expressed as a percentage of the aver- as embassies) are not. index. When a country’s net barter terms of trade age for the base period (2000). UNCTAD’s export or Collecting and tabulating trade statistics are dif- index increases, its exports become more valuable import value indexes are reported for most econo- ficult. Some developing countries lack the capacity or its imports cheaper. mies. For selected economies for which UNCTAD to report timely data, especially landlocked coun- does not publish data, the value indexes are derived tries and countries whose territorial boundaries are from export or import volume indexes (lines 72 and porous. Their trade has to be estimated from the data 73) and corresponding unit value indexes of exports reported by their partners. (For further discussion of or imports (lines 74 and 75) in the IMF’s International the use of partner country reports, see About the Financial Statistics. • Net barter terms of trade index data for table 6.3.) Countries that belong to common is calculated as the percentage ratio of the export customs unions may need to collect data through unit value indexes to the import unit value indexes, direct inquiry of companies. Economic or political measured relative to the base year 2000. concerns may lead some national authorities to sup- press or misrepresent data on certain trade flows, such as oil, military equipment, or the exports of a dominant producer. In other cases reported trade data may be distorted by deliberate under- or overin- voicing to affect capital transfers or avoid taxes. And in some regions smuggling and black market trading result in unreported trade flows. By international agreement customs data are Data sources reported to the United Nations Statistics Division, which maintains the Commodity Trade (Comtrade) Data on trade indexes are from UNCTAD’s annual and Monthly Bulletin of Statistics databases. The Handbook of Statistics for most economies and United Nations Conference on Trade and Develop- from the IMF’s International Financial Statistics for ment (UNCTAD) compiles international trade sta- selected economies. tistics, including price, value, and volume indexes, 2011 World Development Indicators 331 6.3 Direction and growth of merchandise trade Direction of trade High-income importers % of world trade, 2009 European United Other high- Source of exports Union Japan States income Total High-income economies 28.5 2.3 6.5 13.1 50.4 European Union 21.9 0.4 2.3 4.6 29.2 Japan 0.6 .. 0.8 1.5 2.8 United States 1.8 0.4 .. 3.1 5.3 Other high-income economies 4.2 1.5 3.4 3.8 13.0 Low- and middle-income economies 6.5 1.6 5.4 6.2 19.7 East Asia & Pacific 2.3 1.3 2.3 4.2 10.2 China 1.8 0.8 1.8 2.9 7.3 Europe & Central Asia 1.9 0.1 0.1 0.6 2.7 Russian Federation 0.9 0.1 0.1 0.3 1.3 Latin America & Caribbean 0.7 0.1 2.2 0.5 3.5 Brazil 0.3 0.0 0.1 0.2 0.6 Middle East & N. Africa 0.8 0.1 0.2 0.2 1.3 Algeria 0.2 0.0 0.1 0.0 0.3 South Asia 0.4 0.0 0.2 0.5 1.1 India 0.3 0.0 0.1 0.4 0.9 Sub-Saharan Africa 0.4 0.1 0.3 0.2 1.0 South Africa 0.1 0.0 0.0 0.1 0.3 World 35.0 4.0 11.8 19.3 70.1 Low- and middle-income importers % of world trade, 2009 Europe Latin Middle East Asia & Central America East & South Sub-Saharan Source of exports & Pacific Asia & Caribbean N. Africa Asia Africa Total High-income economies 8.2 2.6 3.3 1.4 1.5 1.0 18.2 European Union 1.2 2.0 0.7 0.9 0.4 0.6 5.7 Japan 1.4 0.0 0.2 0.0 0.1 0.1 1.8 United States 0.8 0.1 1.8 0.1 0.2 0.1 3.2 Other high-income economies 4.8 0.4 0.5 0.4 0.9 0.3 7.5 Low- and middle-income economies 2.7 1.6 1.7 0.9 1.1 0.8 9.2 East Asia & Pacific 1.7 0.5 0.5 0.3 0.5 0.3 3.9 China 0.6 0.4 0.4 0.2 0.3 0.3 2.4 Europe & Central Asia 0.2 1.0 0.0 0.3 0.1 0.0 1.6 Russian Federation 0.2 0.3 0.0 0.1 0.0 0.0 0.6 Latin America & Caribbean 0.4 0.1 1.0 0.1 0.1 0.1 1.8 Brazil 0.2 0.0 0.3 0.0 0.0 0.0 0.6 Middle East & N. Africa 0.2 0.1 0.0 0.2 0.2 0.0 0.7 Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.1 South Asia 0.2 0.0 0.0 0.1 0.1 0.1 0.5 India 0.2 0.0 0.0 0.1 0.1 0.1 0.4 Sub-Saharan Africa 0.1 0.0 0.1 0.0 0.1 0.2 0.7 South Africa 0.1 0.0 0.0 0.0 0.0 0.1 0.2 World 11.7 4.3 5.2 2.3 2.5 1.8 27.5 332 2011 World Development Indicators 6.3 GLOBAL LINKS Direction and growth of merchandise trade Nominal growth of trade High-income importers average annual % growth, 1999–2009 European United Other high- Source of exports Union Japan States income Total High-income economies 9.1 6.6 3.8 9.1 8.1 European Union 9.3 4.5 5.8 10.8 9.1 Japan 2.8 .. –0.6 6.9 3.4 United States 5.4 0.1 .. 5.5 5.0 Other high-income economies 11.3 9.9 3.9 11.8 8.6 Low- and middle-income economies 16.7 11.3 11.0 17.4 14.6 East Asia & Pacific 18.9 10.7 15.2 17.3 16.1 China 26.4 13.3 21.8 22.7 21.9 Europe & Central Asia 19.8 17.1 7.8 18.5 18.6 Russian Federation 20.3 16.9 4.6 17.5 18.3 Latin America & Caribbean 12.5 11.5 7.1 15.7 9.0 Brazil 12.6 10.5 7.3 19.8 12.0 Middle East & N. Africa 13.3 14.0 19.9 17.1 14.9 Algeria 13.8 22.9 26.4 25.1 17.5 South Asia 14.5 6.7 8.6 20.5 14.6 India 16.5 8.1 10.8 22.9 17.2 Sub-Saharan Africa 11.7 19.5 18.2 14.3 14.6 South Africaa 10.4 22.0 14.8 14.3 13.3 World 10.2 8.3 6.4 11.1 9.6 Low- and middle-income importers average annual % growth, 1999–2009 Europe Latin Middle East Asia & Central America East & South Sub-Saharan Source of exports & Pacific Asia & Caribbean N. Africa Asia Africa Total High-income economies 15.4 19.4 8.0 13.6 19.6 13.0 14.2 European Union 15.8 19.0 8.8 11.8 15.9 12.0 14.7 Japan 12.6 27.1 8.9 11.9 12.1 10.8 12.4 United States 12.0 13.5 6.5 12.1 20.3 13.1 8.8 Other high-income economies 17.0 22.7 12.6 19.2 22.5 15.6 17.5 Low- and middle-income economies 22.6 22.9 17.6 23.2 25.2 21.7 22.1 East Asia & Pacific 21.4 37.9 26.6 25.5 27.2 26.9 25.2 China 27.4 40.9 31.3 30.5 35.3 31.7 32.2 Europe & Central Asia 18.7 20.1 20.4 22.9 23.2 22.1 20.7 Russian Federation 19.0 19.3 21.0 21.5 19.8 14.6 19.6 Latin America & Caribbean 30.9 20.3 14.5 17.4 25.1 24.1 17.9 Brazil 32.2 21.9 16.6 20.8 20.3 26.8 20.6 Middle East & N. Africa 25.0 17.8 15.2 24.9 34.1 25.0 24.7 Algeria 42.4 12.5 8.4 21.6 60.7 10.1 17.2 South Asia 26.1 15.9 21.8 23.7 19.9 24.6 22.7 India 27.9 14.2 24.2 26.8 20.1 25.8 24.2 Sub-Saharan Africa 20.5 24.7 21.8 13.8 15.8 15.7 21.0 South Africaa 28.0 18.7 12.0 20.4 20.7 12.9 17.0 World 17.3 20.8 10.7 16.4 21.2 16.0 16.3 a. Data for 1999 are based on imports from South Africa reported by other economies because data on exports for South Africa were not available. 2011 World Development Indicators 333 6.3 Direction and growth of merchandise trade About the data Definitions The table provides estimates of the flow of trade in Most countries report their trade data in national • Merchandise trade includes all trade in goods; goods between groups of economies. The data are currencies, which are converted into U.S. dollars trade in services is excluded. • High-income econo- from the International Monetary Fund’s (IMF) Direc- using the IMF’s published period average exchange mies are those classified as such by the World Bank tion of Trade database. All high-income economies rate (series rf or rh, monthly averages of the mar- (see front cover flap). • European Union is defined and major developing economies report trade on ket or official rates) for the reporting country or, if as all high-income EU members: Austria, Belgium, a timely basis, covering about 85 percent of trade unavailable, monthly average rates in New York. Cyprus, Czech Republic, Denmark, Estonia, Finland, for recent years. Trade by less timely reporters and Because imports are reported at cost, insurance, France, Germany, Greece, Hungary, Ireland, Italy, Lux- by countries that do not report is estimated using and freight (c.i.f.) valuations, and exports at free on embourg, Malta, the Netherlands, Portugal, Slovak reports of trading partner countries. Because the board (f.o.b.) valuations, the IMF adjusts country Republic, Slovenia, Spain, Sweden, and the United largest exporting and importing countries are reli- reports of import values by dividing them by 1.10 Kingdom. • Other high-income economies include able reporters, a large portion of the missing trade to estimate equivalent export values. The accuracy all high-income economies (both Organisation for flows can be estimated from partner reports. Part- of this approximation depends on the set of part- Economic Co-operation and Development members ner country data may introduce discrepancies due to ners and the items traded. Other factors affecting and others) except the high-income European Union, smuggling, confidentiality, different exchange rates, the accuracy of trade data include lags in reporting, Japan, and the United States. • Low- and middle- overreporting of transit trade, inclusion or exclusion recording differences across countries, and whether income regional groupings are based on World of freight rates, and different points of valuation and the country reports trade according to the general or Bank classifications (see back cover flap for regional times of recording. special system of trade. (For further discussion of groupings) and may differ from those used by other In addition, estimates of trade within the Euro- the measurement of exports and imports, see About organizations. pean Union (EU) have been significantly affected by the data for tables 4.4 and 4.5.) changes in reporting methods following the creation The regional trade flows in the table are calculated of a customs union. The current system for collect- from current price values. The growth rates are in ing data on trade between EU members—Intrastat, nominal terms; that is, they include the effects of introduced in 1993—has less exhaustive coverage changes in both volumes and prices. than the previous customs–based system and has resulted in some problems of asymmetry (estimated imports are about 5 percent less than exports). Despite these issues, only a small portion of world trade is estimated to be omitted from the IMF’s Direction of Trade Statistics Yearbook and Direction of Trade database. More than half of the world’s merchandise trade takes place between high-income economies. But low- and middle-income economies’ participation in the global trade has increased in the past 15 years 6.3a 1996 2009 Low- and middle-income to Unspecified 3.0% Low- and middle-income to Unspecified 2.5% low- and middle-income 4.5% low- and middle-income 9.2% Low- and middle-income to high-income 14.1% Low- and middle-income High-income to to high-income High-income High-income to to high-income low- and middle- high-income 19.7% income 50.4% 62.4% 16.0% High-income to low- and middle- Data sources income 18.2% Data on the direction and growth of merchandise trade were calculated using the IMF’s Direction of Trade database. Regional and income group Trade among low- and middle-income economies accounted for about 9.2 percent of the world’s mer- chandise trade in 2009, compared with 4.5 percent in 1996. The share of trade from low- and middle- classifications are according to the World Bank income economies to high-income economies increased 9.8 percentage points between 1996 and 2009. classification of economies as of July 1, 2010, Source: World Bank staff calculations based on data from the International Monetary Fund’s Direction of Trade database. and are as shown on the cover flaps of this report. 334 2011 World Development Indicators 6.4 GLOBAL LINKS High-income economy trade with low- and middle-income economies Exports to low-income economies High-income economies European Union Japan United States 1999 2009 1999 2009 1999 2009 1999 2009 Total ($ billions) 32.0 86.9 15.7 40.2 3.5 6.1 3.4 12.0 % of total exports Food 12.5 10.5 14.2 9.8 0.4 0.3 25.2 17.2 Cereals 4.0 4.1 3.2 3.0 0.2 0.2 17.4 12.6 Agricultural raw materials 2.5 2.1 1.8 1.5 1.2 2.3 4.8 4.6 Ores and nonferrous metals 1.0 1.4 0.9 1.3 0.6 0.7 0.6 1.5 Fuels 4.9 11.5 3.1 15.6 0.3 0.3 1.8 5.9 Crude petroleum 0.1 0.4 0.1 0.0 0.0 0.0 0.0 0.0 Petroleum products 4.4 10.7 2.7 15.3 0.3 0.1 1.2 5.1 Manufactured goods 77.1 67.3 78.4 68.0 96.4 94.4 62.0 58.7 Chemical products 12.3 11.0 15.2 12.0 3.4 3.1 10.6 7.3 Iron and steel 2.6 2.9 2.3 2.2 6.9 8.1 0.8 1.2 Machinery and transport equipment 44.2 42.0 43.6 40.4 74.2 74.5 37.9 41.9 Furniture 0.4 0.3 0.6 0.4 0.1 0.1 0.3 0.2 Textiles 5.9 2.2 2.5 1.7 3.0 1.2 5.2 1.0 Footwear 0.2 0.1 0.2 0.2 0.0 0.0 0.2 0.2 Other 11.6 9.0 14.0 11.1 8.8 7.4 6.8 6.9 Miscellaneous goods 2.0 7.2 1.5 3.8 1.2 2.1 5.5 12.2 Imports from low-income economies Total ($ billions) 40.2 100.4 20.1 47.7 2.1 2.9 11.8 34.5 % of total imports Food 23.0 15.1 31.9 22.0 37.1 23.7 7.5 4.3 Cereals 0.7 0.7 0.3 0.3 0.0 0.0 0.1 0.1 Agricultural raw materials 5.5 2.4 6.8 3.6 9.9 2.7 1.1 0.6 Ores and nonferrous metals 5.1 5.1 5.6 5.0 17.1 23.3 2.1 0.6 Fuels 23.3 39.5 13.3 29.6 8.9 23.0 41.4 64.3 Crude petroleum 21.2 34.1 12.5 23.5 7.6 5.3 36.3 61.2 Petroleum products 1.7 1.6 0.5 0.2 0.1 5.0 4.6 2.8 Manufactured goods 41.5 33.2 41.2 38.9 24.0 26.4 47.6 29.5 Chemical products 0.6 0.8 0.9 1.2 0.3 0.6 0.1 0.2 Iron and steel 0.5 0.1 0.4 0.1 2.3 0.8 0.4 0.0 Machinery and transport equipment 1.9 1.6 2.4 2.9 1.6 0.8 0.3 0.1 Furniture 0.2 0.2 0.2 0.1 0.1 0.1 0.2 0.3 Textiles 30.1 27.4 25.5 31.0 15.0 14.3 42.3 28.0 Footwear 0.4 0.7 0.5 0.9 1.7 7.6 0.0 0.1 Other 7.9 2.4 11.3 2.7 2.9 2.1 4.4 1.0 Miscellaneous goods 1.7 4.8 1.2 0.9 3.0 1.0 0.4 0.7 Simple applied tariff rates on imports from low-income economies (%)a Average 4.3 2.7 1.3 0.8 3.0 1.3 5.2 3.5 Food 6.8 3.0 3.1 0.7 9.1 2.6 3.8 2.3 Cereals 16.9 5.8 24.0 0.1 2.6 5.3 2.7 0.4 Agricultural raw materials 3.3 1.6 0.1 0.1 0.7 0.1 0.4 0.2 Ores and nonferrous metals 1.2 1.1 0.2 0.2 1.3 0.0 0.1 0.1 Fuels 3.1 1.2 0.2 0.0 2.0 0.5 0.4 0.2 Crude petroleum 1.3 0.5 0.0 0.0 1.2 0.0 0.3 0.0 Petroleum products 4.5 1.6 0.4 0.0 6.3 1.1 0.9 0.4 Manufactured goods 4.1 2.8 1.1 0.9 2.1 1.3 5.9 4.0 Chemical products 2.7 2.1 1.2 0.3 1.1 0.2 0.3 0.1 Iron and steel 4.2 2.4 0.8 0.2 0.2 0.0 0.4 0.0 Machinery and transport equipment 1.7 1.3 0.4 0.2 0.1 0.0 0.2 0.1 Furniture 3.2 2.2 0.1 0.1 0.0 0.0 0.6 0.9 Textiles 7.5 4.5 2.5 2.4 3.6 1.6 10.7 7.3 Footwear 7.2 4.4 2.9 1.7 6.4 4.4 12.5 8.7 Other 2.1 1.6 0.6 0.2 0.7 1.1 1.0 0.7 Miscellaneous goods 0.7 0.8 0.2 0.2 0.0 0.0 0.4 0.0 2011 World Development Indicators 335 6.4 High-income economy trade with low- and middle-income economies Exports to middle-income economies High-income economies European Union Japan United States 1999 2009 1999 2009 1999 2009 1999 2009 Total ($ billions) 646.4 1845.4 224.9 700.5 89.0 222.8 184.4 346.7 % of total exports Food 6.7 6.5 8.1 6.1 0.4 0.4 8.2 12.8 Cereals 1.7 1.4 1.5 1.1 0.1 0.0 3.0 2.8 Agricultural raw materials 1.8 1.9 1.3 1.5 1.0 1.1 2.1 3.6 Ores and nonferrous metals 2.0 4.5 1.5 2.7 1.9 3.8 1.5 3.9 Fuels 3.1 6.2 1.7 3.2 0.5 1.6 2.1 7.1 Crude petroleum 0.5 0.5 0.2 0.1 0.0 0.0 0.0 0.0 Petroleum products 1.9 4.4 1.3 2.7 0.4 1.4 1.5 5.4 Manufactured goods 83.8 75.2 85.7 82.5 93.3 88.2 81.6 63.4 Chemical products 11.7 14.0 13.7 14.6 8.2 9.9 10.5 14.4 Iron and steel 2.5 3.4 2.4 3.3 5.9 6.8 1.0 1.5 Machinery and transport equipment 48.8 41.8 46.3 45.4 63.9 58.0 50.0 33.2 Furniture 0.5 0.4 0.9 0.7 0.1 0.3 0.7 0.3 Textiles 6.2 2.6 5.4 3.3 3.6 1.7 5.7 1.9 Footwear 0.1 0.2 0.3 0.4 0.0 0.0 0.1 0.0 Other 14.1 13.3 16.7 14.8 11.6 11.6 13.7 12.1 Miscellaneous goods 2.6 5.7 1.6 4.0 2.9 4.8 4.4 9.3 Imports from middle-income economies Total ($ billions) 1,010.3 2,816.6 285.1 998.3 106.5 243.4 364.4 796.0 % of total imports Food 10.0 7.6 14.2 9.4 15.6 9.2 6.5 5.9 Cereals 0.4 0.5 0.3 0.4 0.4 0.3 0.2 0.3 Agricultural raw materials 2.3 1.1 3.4 1.3 4.3 2.0 1.2 0.7 Ores and nonferrous metals 4.8 3.7 6.3 3.3 8.8 8.7 2.7 1.8 Fuels 13.3 19.8 18.8 25.6 13.8 16.6 11.3 19.1 Crude petroleum 9.0 12.7 13.0 16.2 6.6 7.0 8.9 15.8 Petroleum products 1.9 3.6 2.5 3.8 1.1 1.8 2.1 2.9 Manufactured goods 67.8 64.0 56.4 56.9 56.2 62.0 75.5 69.6 Chemical products 2.9 3.6 3.7 3.7 2.7 4.1 2.0 2.9 Iron and steel 1.9 1.7 2.1 1.8 1.0 1.0 1.6 1.0 Machinery and transport equipment 29.2 31.8 18.6 24.6 21.6 27.8 36.9 36.2 Furniture 1.7 1.8 1.4 1.6 1.5 1.7 2.4 2.7 Textiles 13.9 9.5 15.3 11.0 14.9 12.0 12.6 9.4 Footwear 2.6 1.7 2.1 2.0 1.7 1.4 3.2 2.1 Other 15.7 14.2 13.3 12.3 12.7 14.0 16.8 15.3 Miscellaneous goods 1.8 3.8 0.9 3.4 1.3 1.6 2.8 2.8 Simple applied tariff rates on imports from middle-income economies (%)a Average 5.6 3.2 3.9 1.1 2.9 2.2 3.4 2.5 Food 10.3 4.3 9.7 2.9 13.5 6.9 3.6 2.9 Cereals 15.2 6.7 22.1 0.7 10.0 10.5 2.3 1.1 Agricultural raw materials 2.5 1.9 1.0 0.4 0.9 0.5 0.5 0.4 Ores and nonferrous metals 1.9 1.3 1.4 0.5 0.1 0.0 0.3 0.4 Fuels 2.8 1.5 0.9 0.1 1.3 0.2 0.6 1.3 Crude petroleum 1.5 0.4 0.0 0.0 1.2 0.0 0.5 0.0 Petroleum products 5.5 2.1 2.9 0.1 4.2 0.6 1.7 3.0 Manufactured goods 5.2 3.1 3.4 1.0 1.6 1.8 3.6 2.5 Chemical products 3.6 2.0 3.3 0.6 0.6 0.3 1.2 1.1 Iron and steel 3.6 1.6 2.3 0.1 0.1 0.2 2.0 0.3 Machinery and transport equipment 3.0 1.9 1.6 0.2 0.0 0.0 0.4 0.5 Furniture 5.0 3.2 0.7 0.0 0.0 0.1 0.3 0.4 Textiles 9.7 6.0 7.6 3.3 4.2 4.9 10.3 6.8 Footwear 11.6 6.4 8.6 3.4 19.7 16.9 13.3 8.0 Other 3.8 2.3 2.3 0.3 0.4 0.7 0.9 0.8 Miscellaneous goods 1.7 0.9 1.2 0.5 0.0 0.0 0.5 0.3 a. Includes ad valorem equivalents of specific rates. 336 2011 World Development Indicators 6.4 GLOBAL LINKS High-income economy trade with low- and middle-income economies About the data Definitions Developing economies are becoming increasingly trade between developing economies has grown The product groups in the table are defined in accor- important in the global trading system. Since the substantially over the past decade, a result of their dance with SITC revision 2: food (0, 1, 22, and 4) and early 1990s trade between high-income economies increasing share of world output and liberalization of cereals (04); agricultural raw materials (2 excluding and low- and middle-income economies has grown trade, among other influences. 22, 27, and 28); ores and nonferrous metals (27, 28, faster than trade among high-income economies. Yet trade barriers remain high. The table includes and 68); fuels (3), crude petroleum (crude petroleum The increased trade benefi ts consumers and pro- information about tariff rates by selected product oils and oils obtained from bituminous minerals; ducers. But as was apparent at the World Trade Orga- groups. Applied tariff rates are the tariffs in effect 333), and petroleum products (noncrude petroleum nization’s (WTO) Ministerial Conferences in Doha, for partners in preferential trade agreements such and preparations; 334); manufactured goods (5–8 Qatar, in October 2001; Cancun, Mexico, in Septem- as the North American Free Trade Agreement. When excluding 68), chemical products (5), iron and steel ber 2003; and Hong Kong SAR, China, in December these rates are unavailable, most favored nation (67), machinery and transport equipment (7), furni- 2005, achieving a more pro-development outcome rates are used. The difference between most favored ture (82), textiles (65 and 84), footwear (85), and from trade remains a challenge. Doing so will require nation and applied rates can be substantial. Simple other manufactured goods (6 and 8 excluding 65, strengthening international consultation. After the averages of applied rates are shown because they 67, 68, 82, 84, and 85); and miscellaneous goods Doha meetings negotiations were launched on ser- are generally a better indicator of tariff protection (9). • Exports are all merchandise exports by high- vices, agriculture, manufactures, WTO rules, the than weighted average rates are. income economies to low-income and middle-income environment, dispute settlement, intellectual prop- The data on trade flows are from the United Nations economies as recorded in the United Nations Sta- erty rights protection, and disciplines on regional Statistics Division’s Commodity Trade (Comtrade) tistics Division’s Comtrade database. Exports are integration. At the most recent negotiations in Hong database. Partner country reports by high-income recorded free on board (f.o.b.). •  Imports are all Kong SAR, China, trade ministers agreed to eliminate economies were used for both exports and imports. merchandise imports by high-income economies subsidies of agricultural exports by 2013; to abolish Because of differences in sources of data, timing, from low–income and middle-income economies as cotton export subsidies and grant unlimited export and treatment of missing data, the numbers in the recorded in the United Nations Statistics Division’s access to selected cotton-growing countries in Sub- table may not be fully comparable with those used Commodity Trade (Comtrade) database. Imports Saharan Africa; to cut more domestic farm supports to calculate the direction of trade statistics in tables include insurance and freight charges (c.i.f.). • High-, in the European Union, Japan, and the United States; 6.3 and 6.5 or the aggregate flows in tables 4.4, 4.5, middle-, and low-income economies are those and to offer more aid to developing countries to help and 6.2. Tariff data are from United Nations Confer- classified as such by the World Bank as of July 1, them compete in global trade. ence on Trade and Development (UNCTAD)’s Trade 2010 (see front cover flap). •  European Union is Trade flows between high-income and low- and Analysis and Information System (TRAINS) database. defined as all high-income EU members: Austria, Bel- middle-income economies reflect the changing mix of Tariff line data were matched to Standard Interna- gium, Cyprus, Czech Republic, Denmark, Estonia, exports to and imports from developing economies. tional Trade Classification (SITC) revision 2 codes to Finland, France, Germany, Greece, Hungary, Ireland, While food and primary commodities have continued define commodity groups. For further discussion of Italy, Luxembourg, Malta, the Netherlands, Portugal, to fall as a share of high-income economies’ imports, merchandise trade statistics, see About the data for Slovak Republic, Slovenia, Spain, Sweden, and the manufactures as a share of goods imports from both tables 4.4, 4.5, 6.2, 6.3, and 6.5, and for informa- United Kingdom. low- and middle-income economies have grown. And tion about tariff barriers, see table 6.8. Low-income economies have a small market share in the global market of various commodities 6.4a Exports from upper middle-income economies Share of world exports (percent) Exports from lower middle-income economies Exports from low-income economies 70 60 50 40 30 20 10 0 Data sources 1990 2009 1990 2009 1990 2009 1990 2009 1990 2009 1990 2009 Agricultural Manufactured Textiles Fuels Clothing Footwear Data on trade values are from United Nations products goods Statistics Division’s Comtrade database. Data Low-income economies specialize in labor-intensive sectors, but their share in the global on tariffs are from UNCTAD’s TRAINS database market of labor intensive products is very small. Lower middle-income economies provided and are calculated by World Bank staff using the most of the textiles, clothing, and footwear traded globally in 2009. High-income econo- mies accounted for the majority of trade in agricultural products and manufactured goods. World Integrated Trade Solution system, available Source: World Bank staff estimates, based on data from United Nations Statistics Division’s Comtrade database. at http://wits.worldbank.org. 2011 World Development Indicators 337 6.5 Direction of trade of developing economies Exports Imports % of total merchandise exports % of total merchandise imports To developing economies To high-income From developing economies From high-income Within region Outside region economies Within region Outside region economies 1999 2009 1999 2009 1999 2009 1999 2009 1999 2009 1999 2009 East Asia & Pacific 8.1 w 11.9 w 7.2 w 15.5 w 83.9 w 73.7 w 11.0 w 15.9 w 8.9 w 18.0 w 80.5 w 64.1 w Cambodia 7.4 3.6 0.3 1.4 60.5 96.5 38.0 54.6 1.8 2.7 60.0 44.0 China 4.2 6.6 8.6 17.7 87.2 77.9 6.8 9.1 8.0 17.1 82.8 65.4 Fiji 8.9 19.6 0.1 1.5 80.6 51.2 8.0 18.7 2.2 3.4 89.1 76.0 Indonesia 11.1 22.4 8.1 14.1 80.8 64.0 14.6 26.4 7.9 10.0 76.8 63.4 Korea, Dem. Rep. 7.5 50.8 42.0 35.7 50.5 13.7 34.8 43.2 25.4 50.3 39.8 6.5 Lao PDR .. .. 0.6 0.2 32.1 18.0 81.8 83.5 1.0 1.7 16.0 13.5 Malaysia 9.9 23.8 6.6 10.9 83.5 65.5 12.7 28.1 3.1 6.6 82.8 64.7 Mongolia .. .. 14.5 4.1 28.4 34.3 19.1 27.0 37.1 41.1 53.8 32.0 Myanmar 20.1 57.6 13.4 22.1 53.2 14.3 49.2 66.7 1.7 4.8 49.0 28.4 Papua New Guinea 8.0 10.1 0.1 1.3 63.3 50.4 11.6 24.3 1.7 1.2 85.8 73.3 Philippines 8.7 16.2 1.8 2.5 88.2 79.7 12.4 26.2 4.2 5.1 82.2 68.7 Thailand 13.2 27.0 6.0 11.9 79.2 60.7 14.7 27.0 7.3 7.1 76.0 64.3 Vietnam 20.7 20.2 6.2 6.7 72.0 69.1 17.8 37.2 4.9 7.8 77.0 53.5 Europe & Central Asia 22.5 w 19.9 w 11.3 w 14.2 w 64.0 w 55.7 w 27.9 w 26.1 w 12.9 w 14.7 w 61.1 w 54.6 w Albania 3.3 5.1 0.1 6.9 96.6 87.7 12.3 16.8 1.1 8.3 86.5 72.9 Armenia 24.8 35.7 10.5 7.0 56.6 56.3 32.4 43.1 13.6 19.2 50.4 37.7 Azerbaijan 31.0 12.4 5.3 12.6 62.3 77.3 46.3 46.2 7.6 14.7 45.7 39.5 Belarus 65.6 46.5 11.3 10.6 22.8 42.9 66.5 65.5 3.4 6.3 30.1 26.8 Bosnia and Herzegovina 5.6 5.9 .. .. 91.7 92.4 4.0 9.9 .. .. 95.8 89.2 Bulgaria 25.8 27.4 5.7 6.9 66.5 64.7 28.4 33.1 7.1 7.8 64.0 59.1 Georgia 58.3 66.3 5.8 4.4 35.6 29.4 55.7 53.8 3.4 9.1 40.9 37.8 Kazakhstan 29.0 26.1 14.6 18.1 49.5 43.2 46.9 41.7 6.6 27.7 46.4 30.7 Kyrgyz Republic 42.7 80.0 .. .. 47.8 9.5 46.6 22.6 10.1 71.5 41.7 6.0 Lithuania 19.5 24.5 1.3 3.6 79.1 72.8 25.3 33.9 4.0 4.4 69.1 62.4 Macedonia, FYR 29.0 31.5 1.6 1.8 68.8 55.2 31.2 33.3 4.3 11.2 64.5 55.6 Moldova 66.4 62.3 1.7 1.9 32.0 34.9 59.6 51.5 1.6 11.0 38.9 37.6 Romania 11.7 16.0 8.1 6.6 79.7 77.9 12.9 15.5 5.4 8.7 80.3 77.1 Russian Federation 20.0 14.5 11.5 12.2 67.2 55.9 29.7 12.8 13.8 23.4 56.1 62.2 Serbia .. 32.3 .. 1.9 .. 57.4 .. 20.6 .. 4.7 .. 64.6 Tajikistan 46.1 36.7 .. .. 50.7 15.1 78.9 62.2 .. .. 18.6 15.8 Turkey 8.7 13.3 11.6 24.1 74.9 59.1 11.2 21.0 12.2 23.1 72.9 55.6 Turkmenistan 52.1 45.2 .. .. 25.2 36.3 51.9 42.8 .. .. 35.0 35.4 Ukraine 38.5 42.9 21.8 26.0 39.6 29.8 60.0 48.1 5.3 12.0 34.5 40.3 Uzbekistan 51.4 69.3 .. .. 40.2 13.1 33.9 41.9 .. .. 63.4 36.9 Latin America & Carib. 14.4 w 18.8 w 4.0 w 13.9 w 77.6 w 66.0 w 14.3 w 19.2 w 3.5 w 12.8 w 78.0 w 62.1 w Argentina 45.1 42.3 15.3 23.6 39.5 32.6 30.4 40.1 9.4 17.5 58.4 38.1 Bolivia 38.4 64.4 0.6 3.7 59.4 31.5 41.8 67.2 2.3 4.6 55.7 28.0 Brazil 23.0 22.4 10.9 28.2 64.3 48.0 19.0 17.1 9.9 26.8 71.0 56.1 Chile 20.3 16.3 5.3 28.7 67.9 51.4 28.1 29.5 8.6 14.9 49.9 45.0 Colombia 24.1 29.3 1.1 6.7 73.4 63.1 25.9 25.9 4.4 15.8 68.7 55.0 Costa Rica 12.4 26.2 0.7 12.1 26.9 61.3 20.1 22.5 3.3 9.6 41.1 63.5 Cuba 8.8 24.9 36.0 31.6 55.3 43.5 17.0 43.5 17.5 23.2 65.5 33.4 Dominican Republic 2.8 17.4 0.5 2.6 96.5 71.1 17.5 25.1 1.7 7.6 80.7 63.6 Ecuador 21.6 42.3 5.5 6.4 72.2 50.7 32.7 39.9 4.4 11.7 61.9 47.3 El Salvador 60.9 44.0 1.5 1.2 37.1 58.3 40.0 41.4 2.1 6.2 56.3 61.4 Guatemala 22.2 37.7 1.0 2.2 74.2 56.7 29.3 34.6 3.9 8.9 65.7 55.0 Haiti 2.5 9.7 0.8 2.1 96.9 87.9 15.2 34.8 3.9 11.4 80.5 53.7 Honduras 7.7 29.0 0.1 3.0 82.6 68.0 15.1 44.4 2.8 7.8 72.2 47.8 Jamaica 2.9 5.0 7.1 3.9 89.5 89.9 11.7 24.5 3.7 7.3 81.4 66.2 Mexico 3.2 6.3 0.3 1.9 96.1 94.5 2.3 4.3 3.4 18.5 93.7 80.7 Nicaragua 31.9 47.7 0.1 0.6 62.4 50.9 48.6 53.5 0.4 12.1 45.4 35.5 Panama 23.6 45.9 0.8 10.7 73.6 43.0 24.4 9.2 1.2 15.7 60.7 68.5 Paraguay 62.0 69.6 0.6 14.4 30.3 14.1 54.9 48.5 3.2 32.7 41.7 18.4 Peru 16.2 15.5 8.6 17.9 75.1 81.2 30.4 33.5 3.3 23.2 66.2 48.1 Uruguay 53.0 44.1 9.2 22.2 36.8 33.5 47.8 52.6 8.9 20.6 42.8 28.3 Venezuela, RB 15.2 12.0 .. 10.5 63.2 56.0 18.2 38.4 0.2 10.9 69.4 47.3 338 2011 World Development Indicators 6.5 GLOBAL LINKS Direction of trade of developing economies Exports Imports % of total merchandise exports % of total merchandise imports To developing economies To high-income From developing economies From high-income Within region Outside region economies Within region Outside region economies 1999 2009 1999 2009 1999 2009 1999 2009 1999 2009 1999 2009 Middle East & N. Africa 3.2 w 8.0 w 13.0 w 25.8 w 78.2 w 61.1 w 3.5 w 7.4 w 12.3 w 22.1 w 72.7 w 59.9 w Algeria 1.8 3.0 15.1 15.6 83.1 81.4 1.5 3.2 17.2 32.9 81.4 64.5 Egypt, Arab Rep. 7.2 21.1 11.9 22.8 65.6 52.9 1.1 2.9 21.7 34.7 69.3 60.9 Iran, Islamic Rep. .. 2.1 13.7 42.1 73.4 39.8 .. 0.6 22.3 38.8 65.6 59.5 Iraq 4.1 2.4 5.9 29.0 90.0 68.6 12.8 22.9 37.1 42.5 50.1 34.7 Jordan 20.8 31.2 35.4 22.2 40.8 44.6 15.3 10.7 17.1 28.2 65.4 60.9 Lebanon 17.6 40.5 12.2 10.5 69.4 48.3 6.0 14.2 18.5 26.1 73.9 58.6 Libya 4.1 3.7 8.5 14.1 87.4 82.1 11.4 11.9 9.9 27.9 78.6 60.1 Morocco 2.4 3.3 10.4 22.3 80.7 73.1 1.7 6.2 9.0 18.3 79.0 75.8 Syrian Arab Republic 8.5 52.5 11.9 5.7 76.2 41.8 4.8 17.4 26.8 36.2 45.9 46.5 Tunisia 5.7 11.8 5.9 7.5 84.1 77.9 4.4 8.6 7.8 16.0 85.9 74.8 Yemen, Rep. 0.9 2.7 62.0 73.5 35.8 22.8 4.0 3.9 20.7 43.9 72.8 51.3 South Asia 4.3 w 5.4 w 14.7 w 25.1 w 78.8 w 67.4 w 3.8 w 3.6 w 10.7 w 15.5 w 66.8 w 58.1 w Afghanistan 46.6 48.3 17.6 19.3 35.8 32.4 24.8 30.1 35.2 24.5 40.0 45.5 Bangladesh 1.9 2.6 4.4 6.8 78.7 76.8 13.5 14.3 16.4 36.1 52.5 43.5 India 4.2 4.5 17.5 27.4 78.2 65.3 0.9 0.6 29.6 39.7 69.5 59.4 Nepal 29.6 64.6 .. .. 60.2 29.6 14.0 52.9 .. .. 45.1 15.8 Pakistan 4.5 12.4 12.0 23.8 81.2 61.9 2.3 4.2 23.3 31.8 72.8 63.1 Sri Lanka 3.1 5.7 10.4 16.0 82.4 115.8 10.1 19.9 14.3 35.5 61.4 61.2 Sub-Saharan Africa 13.3 w 13.7 w 13.8 w 27.9 w 66.4 w 57.9 w 12.0 w 11.8 w 12.7 w 22.7 w 70.3 w 52.3 w Angola 1.0 3.9 8.9 47.1 90.1 49.0 11.5 5.1 12.7 32.7 75.8 62.7 Benin 5.9 30.8 68.7 54.7 25.1 14.5 24.3 7.2 16.9 58.4 58.5 34.5 Burkina Faso 10.2 15.5 31.6 37.9 55.8 43.1 39.5 37.2 5.1 13.8 51.1 44.5 Burundi 2.1 8.8 .. 12.5 72.3 66.7 23.8 24.2 8.1 18.9 57.8 46.9 Cameroon 6.9 12.8 8.4 18.1 84.1 68.4 20.0 18.6 10.7 26.5 68.1 55.1 Central African Republic 1.4 9.2 14.6 32.8 84.0 58.0 18.0 14.9 7.9 14.3 57.9 43.8 Chad 5.5 0.4 .. .. 81.3 96.0 31.6 19.1 .. .. 62.4 55.9 Comoros .. .. .. .. 93.4 66.5 .. .. .. .. 51.4 46.4 Congo, Dem. Rep. 1.1 21.0 0.6 46.9 98.0 31.9 50.6 48.1 5.6 15.2 41.5 36.7 Congo, Rep. 1.6 1.3 8.1 33.2 88.0 65.3 12.9 4.7 9.0 30.0 64.3 63.9 Cote d’Ivoire 24.8 27.6 16.5 7.7 58.7 84.4 16.8 26.8 14.3 26.3 63.0 53.3 Ethiopia 1.7 5.1 18.4 24.9 70.5 55.1 2.3 2.7 19.1 35.1 70.5 33.8 Gabon 0.9 3.2 10.1 24.9 83.3 56.0 5.7 9.7 2.6 16.8 90.9 72.3 Gambia, The 18.1 5.9 4.7 61.7 77.2 32.4 8.4 16.3 24.2 53.4 67.4 30.4 Ghana 7.8 10.3 12.1 24.6 74.0 53.2 23.9 24.5 15.0 33.0 60.4 41.8 Guinea 4.7 2.6 1.1 24.1 90.0 51.8 11.1 5.8 16.3 17.3 72.5 33.6 Guinea-Bissau 1.6 26.8 .. .. 16.8 2.4 15.4 18.7 .. .. 44.1 36.6 Kenya 30.7 34.3 16.2 14.8 51.9 42.9 9.5 12.2 15.2 33.6 74.6 53.4 Liberia 2.4 18.3 9.3 8.9 88.3 72.9 4.6 0.9 1.8 19.0 93.5 80.1 Madagascar 5.8 4.8 8.5 11.0 74.6 76.2 8.7 8.4 22.1 46.7 59.5 37.2 Malawi 19.4 19.7 9.0 32.6 71.2 47.3 67.5 56.4 6.3 16.1 25.1 27.7 Mali 5.7 9.2 32.3 50.9 60.1 28.3 23.7 27.2 5.8 10.9 38.6 32.9 Mauritania 11.3 14.1 9.0 46.9 78.6 37.8 6.3 5.4 17.2 35.6 68.3 49.8 Mauritius 6.7 14.2 0.9 3.5 92.4 82.4 13.9 11.4 24.1 46.0 62.0 42.6 Mozambique 45.1 16.5 13.2 8.3 40.6 63.2 29.9 36.6 7.1 18.8 20.2 32.1 Niger 39.1 26.9 0.3 0.5 60.6 72.8 31.0 17.6 21.1 31.7 46.2 51.0 Nigeria 10.6 10.9 27.6 24.8 61.2 63.1 3.8 4.7 24.6 27.2 71.3 52.0 Rwanda 4.8 56.5 14.7 19.8 43.3 23.0 27.8 42.0 6.2 12.4 47.3 44.7 Senegal 25.5 44.3 18.8 11.7 49.1 37.9 12.3 15.9 19.6 32.8 66.5 74.0 Sierra Leone .. 9.3 .. 11.0 66.9 75.7 11.4 24.5 11.9 32.9 72.0 38.2 Somalia 0.7 4.2 30.9 21.4 68.4 74.4 12.9 8.5 61.1 65.0 15.0 14.1 South Africa 16.3 18.7 8.5 21.8 60.1 60.3 3.7 7.0 14.4 35.3 81.7 58.0 Sudan 10.4 1.6 24.2 77.0 65.2 21.3 4.0 6.5 37.1 48.7 58.8 41.3 Tanzania 16.7 18.1 24.1 28.4 57.3 44.6 18.8 16.2 23.4 39.5 57.7 40.7 Togo 24.0 58.8 30.5 32.5 41.1 8.1 21.4 16.0 9.7 31.6 65.2 50.5 Uganda 3.1 46.8 5.0 7.3 92.0 43.2 49.4 25.2 10.3 23.1 39.7 51.8 Zambia 35.6 22.8 12.2 16.0 32.0 63.8 55.8 60.2 4.5 9.7 35.3 30.2 Zimbabwe 29.1 49.1 14.5 20.6 54.8 30.4 46.0 73.3 6.6 8.7 39.1 14.0 Note: Bilateral trade data are not available for Timor-Leste, Kosovo, West Bank and Gaza, Botswana, Eritrea, Lesotho, Namibia, and Swaziland. Components may not sum to 100 percent because of trade with unspecified partners or with economies not covered by World Bank classification. 2011 World Development Indicators 339 6.5 Direction of trade of developing economies About the data Developing economies are an increasingly important those in Sub-Saharan Africa—are not well recorded, affinity. The direction of trade is also influenced by part of the global trading system. Their share of world and the value of trade among developing economies preferential trade agreements that a country has trade rose from 15 percent in 1990 to 30 percent may be understated. The table does not include some made with other economies. Though formal agree- in 2009. And trade between high-income economies developing economies because data on their bilateral ments on trade liberalization do not automatically and low- and middle-income economies has grown trade flows are not available. Data on the direction increase trade, they nevertheless affect the direction faster than trade between high-income economies. of trade between selected high-income economies of trade between the participating economies. Table This increased trade benefits both producers and are presented and discussed in tables 6.3 and 6.4. 6.7 illustrates the size of existing regional trade blocs consumers in developing and high-income economies. At the regional level most exports from developing that have formal preferential trade agreements. The table shows trade in goods between develop- economies are to high-income economies, but the Although global integration has increased, develop- ing economies in the same region and other regions share of intraregional trade is increasing. Geographic ing economies still face trade barriers when accessing and between developing economies and high-income patterns of trade vary widely by country and commod- other markets (see table 6.8). economies. Data on exports and imports are from ity. Larger shares of exports from oil- and resource- Definitions the International Monetary Fund’s (IMF) Direction of rich economies are to high-income economies. Trade database and should be broadly consistent with The relative importance of intraregional trade is • Exports to developing economies within region data from other sources, such as the United Nations higher for both landlocked countries and small coun- are the sum of merchandise exports from the report- Statistics Division’s Commodity Trade (Comtrade) tries with close trade links to the largest regional ing economy to other developing economies in the database. All high-income economies and major devel- economy. For most developing economies—especially same World Bank region as a percentage of total oping economies report trade to the IMF on a timely smaller ones—there is a “geographic bias” favoring merchandise exports by the economy. • Exports to basis, covering about 85 percent of trade for recent intraregional trade. Despite the broad trend toward developing economies outside region are the sum years. Trade by less timely reporters and by countries globalization and the reduction of trade barriers, of merchandise exports from the reporting econ- that do not report is estimated using reports of trading the relative share of intraregional trade increased omy to other developing economies in other World partner countries. Therefore, data on trade between for most economies between 1999 and 2009. This Bank regions as a percentage of total merchandise developing and high-income economies shown in the is due partly to trade-related advantages, such as exports by the economy. • Exports to high-income table should be generally complete. But trade flows proximity, lower transport costs, increased knowledge economies are the sum of merchandise exports from between many developing economies—particularly from repeated interaction, and cultural and historical the reporting economy to high-income economies as a percentage of total merchandise exports by the Developing economies are trading more with other developing economies 6.5a economy. • Imports from developing economies within region are the sum of merchandise imports by Low-income economies the reporting economy from other developing econo- Share of merchandise exports (percent) 100 mies in the same World Bank region as a percent- Exports to high-income economies age of total merchandise imports by the economy. 75 • Imports from developing economies outside region are the sum of merchandise imports by the 50 reporting economy from other developing economies Exports to developing economies within region 25 in other World Bank regions as a percentage of total merchandise imports by the economy. • Imports Exports to developing economies outside region 0 from high-income economies are the sum of mer- 1990 1995 2000 2005 2009 chandise imports by the reporting economy from Middle-income economies high-income economies as a percentage of total 100 merchandise imports by the economy. Exports to high-income economies 75 50 Data sources 25 Data on merchandise trade flows are published in Exports to developing economies within region the IMF’s Direction of Trade Statistics Yearbook and Exports to developing economies outside region 0 Direction of Trade Statistics Quarterly; the data in 1990 1995 2000 2005 2009 the table were calculated using the IMF’s Direction Share of merchandise exports to high-income economies have been declining for both low- and middle-income of Trade database. Regional and income group economies. On the other hand, their exports to other developing economies have increased, especially classifications are according to the World Bank exports to developing economies within the same region. classification of economies as of July 1, 2010, Source: World Bank staff calculations based on data from International Monetary Fund’s Direction of Trade database. and are as shown on the cover flaps of this report. 340 2011 World Development Indicators 6.6 GLOBAL LINKS Primary commodity prices 1970 1980 1990 1995 2000 2004 2005 2006 2007 2008 2009 2010 World Bank commodity price index  (2000= 100) Energy 19 153 79 53 100 123 171 197 209 274 179 225 Nonenergy commodities 183 177 115 117 100 121 135 172 192 218 178 224 Agriculture 188 195 113 122 100 118 121 134 154 184 165 192 Beverages 230 273 117 136 100 109 125 130 145 168 184 210 Food 201 199 116 117 100 123 121 131 158 198 171 186 Fats and oils 237 196 105 126 100 134 120 123 178 222 181 203 Grains 204 199 121 124 100 115 115 134 161 225 179 179 Other food 151 205 124 101 100 117 129 140 127 142 152 170 Raw materials 136 143 105 125 100 109 119 143 149 157 141 197 Timber 97 92 88 105 100 90 100 113 117 120 116 119 Other raw materials 179 198 124 146 100 129 140 177 185 196 168 282 Fertilizers 82 177 98 110 100 125 148 151 205 453 245 232 Metals and minerals 185 141 122 106 100 126 162 251 268 261 197 288 Base metals 200 145 124 112 100 127 152 253 272 230 174 247 Steel productsa .. 134 131 118 100 153 170 162 155 231 190 190   Commodity prices (2000 prices) Energy Coal, Australian ($/mt) .. 49 39 33 26 48 43 44 56 102 60 82 Natural gas, Europe ($/mmBtu) .. 5.21 2.48 2.26 3.86 3.88 5.74 7.57 7.30 10.72 7.27 6.87 Natural gas, U.S. ($/mmBtu) 0.57 1.91 1.65 1.43 4.31 5.35 8.09 6.01 5.96 7.09 3.30 3.64 Natural gas, liquefied, Japan ($mmBtu) .. 7.02 3.54 2.86 4.71 4.66 5.44 6.32 6.56 10.04 7.46 9.00 Petroleum, avg., spot ($/bbl) 4 45 22 14 28 34 48 57 61 78 52 66   Beverages (cents/kg) Cocoa 233 321 123 119 91 141 140 142 167 206 241 260 Coffee, Arabica 397 427 192 277 192 161 230 225 232 247 265 358 Coffee, robusta 316 400 115 230 91 72 101 133 163 186 137 144 Tea, avg., 3 auctions 289 205 200 124 188 153 150 168 174 194 227 239 Tea, Colombo auctions 217 137 182 118 179 162 167 171 215 223 262 273 Tea, Kolkata auctions 343 253 273 145 181 156 147 157 164 180 210 233 Tea, Mombasa auctions 307 224 144 108 203 141 134 175 142 177 210 212   Food Fats and oils ($/mt) Coconut oil 1,376 831 327 556 450 600 560 542 784 979 606 932 Copraa 779 558 224 364 305 409 376 360 518 653 401 622 Groundnut oil 1,312 1,059 937 823 714 1,054 963 867 1,154 1,705 988 1,164 Palm oil 901 719 282 521 310 428 383 427 666 759 570 747 Palmkernell oila .. .. .. .. 444 588 569 519 758 904 585 982 Soybeans 405 365 240 215 212 278 249 240 328 418 365 373 Soybean meal 355 324 195 164 189 219 195 187 263 340 340 314 Soybean oil 992 737 435 519 338 559 495 535 752 1,007 709 833 Grains ($/mt) Barley .. 96 78 86 77 90 86 104 147 160 107 131 Maize 202 154 106 103 89 102 90 109 140 178 138 154 Rice, Thailand, 5% 438 506 263 266 202 216 260 272 279 520 463 405 Rice, Thailand, 25% a .. .. 254 247 173 205 241 248 262 425 382 366 Rice, Thailand, A1a .. .. 152 218 143 186 198 196 232 386 273 318 Sorghuma 179 159 101 99 88 100 87 110 139 166 126 137 Wheat, Canadaa 218 235 152 172 147 169 179 194 256 364 251 259 Wheat, U.S., hard red winter 190 213 132 147 114 142 138 172 218 261 187 185 Wheat, U.S., soft red winter a 197 208 125 139 99 131 123 142 204 217 155 190 2011 World Development Indicators 341 6.6 Primary commodity prices 1970 1980 1990 1995 2000 2004 2005 2006 2007 2008 2009 2010 Commodity prices (continued) (2000 prices) Food (continued) Other food Bananas, U.S. ($/mt) 573 467 526 369 424 476 547 605 577 675 707 720 Beef (cents/kg) 452 340 249 158 193 228 238 228 222 251 220 278 Chicken meat (cents/kg) .. 85 96 92 119 138 135 124 134 136 143 143 Fishmeal ($/mt)a 682 621 401 411 413 589 664 1,040 1,005 906 1,027 1,399 Oranges ($/mt) 582 482 516 441 363 780 794 741 817 886 759 857 Shrimp. Mexico (cents/kg) .. 1,420 1,039 1,253 1,515 928 939 915 862 855 789 1,033 Sugar, EU domestic (cents/kg) 39 60 57 57 56 61 60 58 58 56 44 37 Sugar, U.S. domestic  (cents/kg) 57 82 50 42 43 41 43 44 39 37 46 66 Sugar, world (cents/kg) 29 78 27 24 18 14 20 29 19 23 33 39   Agricultural raw materials Cotton A index (cents/kg) 219 252 177 177 130 124 110 113 119 126 115 189 Logs, Cameroon ($/cu. m)a 149 310 334 282 275 301 304 285 325 421 352 355 Logs, Malaysia ($/cu. m) 149 241 172 212 190 179 184 214 229 234 240 231 Rubber, Singapore (cents/kg) 141 176 84 131 67 116 135 186 193 207 160 303 Rubber, TSR 20 (cents/kg)a .. .. .. .. 63 110 126 174 184 202 150 280 Plywood (cents/sheet)a 357 338 345 485 448 422 462 532 547 516 471 472 Sawnwood, Malaysia ($/cu. m) 608 489 518 614 595 528 599 670 688 711 673 703 Tobacco ($/mt)a 3,727 2,806 3,297 2,194 2,976 2,488 2,533 2,653 2,830 2,871 3,541 3,570 Woodpulp ($/mt)a 615 661 792 708 664 582 577 624 655 656 513 719 Fertilizers ($/mt) Diammonium phosphate 187 274 167 180 154 201 224 233 369 774 270 415 Phosphate rock 38 58 39 29 44 37 38 40 61 276 102 102 Potassium chloride 109 143 95 98 123 113 144 156 171 456 526 275 Triple superphosphate 147 222 128 124 138 169 183 180 289 703 215 317 Urea 63 237 116 155 101 159 199 199 264 394 208 239   Metals and minerals Aluminum ($/mt) 1,926 1,795 1,593 1,499 1,549 1,558 1,724 2,297 2,252 2,058 1,390 1,802 Copper ($/mt) 4,904 2,690 2,586 2,437 1,813 2,602 3,340 6,007 6,076 5,564 4,300 6,248 Gold ($/toz)a 125 750 373 319 279 372 404 540 595 697 812 1,016 Iron ore (cents/dmtu) 34 35 32 24 29 34 59 69 72 112 84 134 Iron ore, spot, cfr China ($/dmtu) .. .. .. .. .. .. .. .. 108 125 69 126 Lead (cents/kg) 105 112 79 52 45 80 89 115 220 167 144 178 Nickel ($/mt) 9,860 8,037 8,614 6,830 8,638 12,551 13,387 21,675 31,778 16,888 12,237 18,084 Silver (cents/toz)a 614 2,544 475 431 500 607 666 1,034 1,145 1,200 1,227 1,675 Tin (cents/kg) 1,273 2,068 591 516 544 773 670 785 1,241 1,481 1,133 1,692 Zinc (cents/kg) 102 94 147 86 113 95 125 293 277 150 138 179   MUV G-5 index 29 81 103 120 100 110 110 112 117 125 120 121 Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal unit, mt = metric ton, toz = troy ounce. a. Series not included in the nonenergy index. 342 2011 World Development Indicators 6.6 GLOBAL LINKS Primary commodity prices About the data Definitions Primary commodities—raw or partially processed commodity price index contains 41 price series for • Energy price index is the composite price index for materials that will be transformed into fi nished 34 nonenergy commodities. coal, petroleum, and natural gas, weighted by exports goods—are often developing countries’ most impor- Separate indexes are compiled for energy and steel of each commodity from low- and middle-income tant exports, and commodity revenues can affect liv- products, which are not included in the nonenergy countries. • Nonenergy commodity price index cov- ing standards. Price data are collected from various commodity price index. ers the 34 nonenergy primary commodities that sources, including international commodity study The MUV index is a composite index of prices make up the agriculture, fertilizer, and metals and groups, government agencies, industry trade jour- for manufactured exports from the five major (G-5) minerals indexes. • Agriculture includes beverages, nals, and Bloomberg and Datastream. Prices are industrial economies (France, Germany, Japan, the food, and agricultural raw materials. •  Beverages compiled in U.S. dollars or converted to U.S. dollars United Kingdom, and the United States) to low- and include cocoa, coffee, and tea. •  Food includes when quoted in local currencies. middle-income economies, valued in U.S. dollars. fats and oils, grains, and other food items. Fats The table is based on frequently updated price The index covers products in groups 5–8 of SITC and oils include coconut oil, groundnut oil, palm oil, reports. Prices are those received by exporters when revision 1. For the MUV G-5 index, unit value indexes soybeans, soybean oil, and soybean meal. Grains available, or the prices paid by importers or trade in local currency for each country are converted to include barley, maize, rice, and wheat. Other food unit values. Annual price series are generally simple U.S. dollars using market exchange rates and are items include bananas, beef, chicken meat, oranges, averages based on higher frequency data. The con- combined using weights determined by each coun- shrimp, and sugar. •  Agricultural raw materials stant price series in the table are deflated by the try’s export share in the base year (1995). The export include timber and other raw materials. Timber manufactures unit value (MUV) index for the Group shares were 8.2 percent for France, 17.4 percent includes tropical hard logs and sawnwood. Other of Five (G-5) countries (see below). for Germany, 35.6 percent for Japan, 6.6 percent raw materials include cotton, natural rubber, and Commodity price indexes are calculated as for the United Kingdom, and 32.2 percent for the tobacco. • Fertilizers include phosphate, phosphate Laspeyres index numbers; the fixed weights are the United States. rock, potassium, and nitrogenous products. • Met- 2002–04 average export values for low- and middle- als and minerals include base metals and iron ore. income economies (based on 2001 gross national • Base metals include aluminum, copper, lead, income) rebased to 2000. Data for exports are from nickel, tin, and zinc. •  Steel products price index the United Nations Statistics Division’s Commod- is the composite price index for eight steel prod- ity Trade Statistics (Comtrade) database Standard ucts based on quotations free on board (f.o.b.) International Trade Classification (SITC) revision 3, Japan excluding shipments to the United States the Food and Agriculture Organization’s FAOSTAT for all years and to China prior to 2001, weighted database, the International Energy Agency data- by product shares of apparent combined consump- base, BP’s Statistical Review of World Energy, the tion (volume of deliveries) for Germany, Japan, and World Bureau of Metal Statistics, and World Bank the United States. •  Commodity prices—for defi - staff estimates. nitions and sources, see “Commodity price data” Each index in the table represents a fixed basket of (also known as the “Pink Sheet”) at the World Bank primary commodity exports over time. The nonenergy Prospects for Development website (www.worldbank. org/prospects, click on Products). • MUV G-5 index is the manufactures unit value index for G-5 country exports to low- and middle-income economies. Primary commodity prices soared again in 2010 6.6a World Bank commodity price index, current prices (2000 = 100) 500 Energy 400 Raw materials 300 Food 200 Data sources 100 Data on commodity prices and the MUV G-5 2005 2006 2007 2008 2009 2010 2011 index are compiled by the World Bank’s Develop- The food commodity price index started rising again in the beginning of 2009, and by the end of February ment Prospects Group. Monthly updates of com- 2011 exceeded the record high in June 2008. The price index for raw materials reached new highs, and modity prices are available at www.worldbank. the energy price index also rose throughout 2009 and 2010. org/prospects and http://data.worldbank.org/ Source: World Bank commodity price data. data-catalog. 2011 World Development Indicators 343 6.7 Regional trade blocs Merchandise exports within bloc Year of entry into Type force of the of most $ millions Year of most recent recent creation agreement agreementa 1990 1995 2000 2005 2007 2008 2009 High-income and low- and middle-income economies APECb 1989 None 901,560 1,688,708 2,261,791 3,318,699 4,192,784 4,606,339 3,738,989 EEA 1994 1994 EIA 1,079,711 1,463,232 1,714,018 3,037,759 4,025,418 4,446,686 3,392,597 EFTA 1960 2002 EIA 782 925 831 1,252 2,196 2,910 2,006 European Union 1957 1958 EIA, CU 1,032,397 1,404,255 1,641,609 2,905,551 3,846,547 4,233,112 3,237,024 NAFTA 1994 1994 FTA 226,273 394,472 676,141 824,359 951,258 1,013,245 768,820 SPARTECA 1981 1981 PTA 5,299 9,135 8,579 15,201 18,617 20,263 17,079 Trans-Pacific SEP 2006 2006 EIA, FTA 1,110 2,614 1,438 2,345 3,290 4,262 3,548 East Asia and Pacific and South Asia APTA 1975 1976 PTA 2,429 21,728 37,895 127,340 193,951 233,617 204,745 ASEAN 1967 1992 FTA 27,365 79,544 98,060 165,458 216,727 251,285 198,915 MSG 1993 1994 PTA 5 18 22 51 78 89 78 PICTA 2001 2003 FTA 4 4 8 22 34 38 34 SAARC 1985 2006 FTA 945 2,081 2,894 8,619 12,747 13,177 11,095 Europe, Central Asia, and Middle East Agadir Agreement 2004 NNA 156 226 294 635 1,046 1,913 2,075 CEFTA 1992 1994 FTA .. 619 1,187 2,847 6,160 7,543 5,083 CEZ 2003 2004 FTA .. 10,154 13,283 23,469 43,003 47,731 19,094 CIS 1991 1994 FTA .. 31,277 28,422 58,113 98,050 123,052 60,389 EAEC 1997 2000 CU .. 10,919 13,936 24,818 45,714 51,186 21,872 ECO 1985 2003 PTA 1,243 4,746 4,518 12,579 22,064 26,739 18,412 GCC 1981 2003c CU 6,906 6,832 8,029 15,408 24,372 31,514 21,849 PAFTA (GAFTA) 1997 1998 FTA 13,204 12,948 16,188 41,659 61,100 82,267 61,881 UMA 1989 1994 c NNA 958 1,109 1,041 1,885 2,695 4,570 3,422 Latin America and the Caribbean Andean Community 1969 1988 CU 544 1,788 2,046 4,572 5,926 7,029 5,785 CACM 1961 1961 CU 667 1,594 2,655 4,311 5,637 6,475 5,287 CARICOM 1973 1997 EIA 456 877 1,078 2,235 3,112 3,808 2,716 LAIA (ALADI) 1980 1981 PTA 13,350 35,986 44,253 71,711 110,006 143,283 98,510 MERCOSUR 1991 2005 EIA 4,127 14,199 17,829 21,128 32,421 46,657 32,689 OECS 1981 1981c NNA 29 39 38 68 104 118 104 Sub-Saharan Africa CEMAC 1994 1999 CU 139 120 96 201 305 355 300 CEPGL 1976 NNA 7 8 10 20 29 73 64 COMESA 1994 1994 FTA 1,146 1,367 1,443 2,695 4,021 6,676 6,114 EAC 1996 2000 CU 335 628 689 1,075 1,385 1,797 1,572 ECCAS 1983 2004 c NNA 160 157 182 255 385 449 378 ECOWAS 1975 1993 PTA 1,532 1,875 2,715 5,497 6,717 9,355 7,312 Indian Ocean Commission 1984 2005c NNA 63 113 106 162 214 217 183 SADC 1992 2000 FTA 1,655 3,615 4,427 7,799 12,051 16,011 11,697 UEMOA 1994 2000 CU 621 560 741 1,390 1,735 2,281 1,927 Note: Regional bloc memberships are as follows: Agadir Agreement, the Arab Republic of Egypt, Jordan, Morocco, and Tunisia; Andean Community, Bolivia, Colombia, Ecuador, and Peru; Arab Maghreb Union (UMA), Algeria, Libyan Arab Republic, Mauritania, Morocco, and Tunisia; Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong SAR, China, Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; Asia-Pacific Trade Agreement (APTA; formerly Bangkok Agreement), Bangladesh, China, India, the Republic of Korea, the Lao People’s Democratic Republic, and Sri Lanka; Association of South East Asian Nations (ASEAN), Brunei Darussalam, Cambodia, Indonesia, the Lao People’s Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Central European Free Trade Area (CEFTA), Albania, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Moldova, Montenegro, and Serbia; Common Economic Zone (CEZ), Belarus, Kazakhstan, and the Russian Federation; Common Market for Eastern and Southern Africa (COMESA), Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Libyan Arab Republic, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia, and Zimbabwe; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan; East African Community (EAC), Burundi, Kenya, Rwanda, Tanzania, and Uganda; Economic and Monetary Community of Central Africa (CEMAC; formerly Central African Customs and Economic Union [UDEAC]), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Príncipe; Economic Community of the Great Lakes Countries (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and 344 2011 World Development Indicators 6.7 GLOBAL LINKS Regional trade blocs Merchandise exports within bloc Year of entry into Type force of the of most % of total bloc exports Year of most recent recent creation agreement agreementa 1990 1995 2000 2005 2007 2008 2009 High-income and low- and middle-income economies APECb 1989 None 68.3 71.7 73.0 70.8 67.3 65.2 66.3 EEA 1994 1994 EIA 68.8 67.9 69.0 73.0 73.3 72.8 71.9 EFTA 1960 2002 EIA 0.8 0.7 0.6 0.5 0.7 0.8 0.7 European Union 1957 1958 EIA, CU 67.3 66.5 67.7 71.6 71.9 71.4 70.4 NAFTA 1994 1994 FTA 41.4 46.2 55.7 55.7 51.3 49.5 48.0 SPARTECA 1981 1981 PTA 10.5 12.9 10.7 11.4 10.5 8.9 9.1 Trans-Pacific SEP 2006 2006 EIA, FTA 1.5 1.7 0.8 0.8 0.8 1.0 1.0 East Asia and Pacific and South Asia APTA 1975 1976 PTA 1.6 6.8 8.0 11.0 11.0 11.4 11.6 ASEAN 1967 1992 FTA 18.9 24.4 23.0 25.3 25.2 25.5 24.5 MSG 1993 1994 PTA 0.3 0.4 0.6 0.8 0.8 0.8 0.8 PICTA 2001 2003 FTA 0.3 0.1 0.3 0.4 0.4 0.4 0.4 SAARC 1985 2006 FTA 3.5 4.5 4.6 6.6 6.6 5.9 5.4 Europe, Central Asia, and Middle East Agadir Agreement 2004 NNA 1.3 1.4 1.4 1.8 2.0 2.7 3.8 CEFTA 1992 1994 FTA .. 9.0 14.5 16.3 21.2 22.4 20.2 CEZ 2003 2004 FTA .. 11.6 11.0 8.4 10.4 8.8 5.6 CIS 1991 1994 FTA .. 28.4 19.8 17.7 20.1 18.0 14.8 EAEC 1997 2000 CU .. 12.3 11.5 8.9 10.9 9.3 6.3 ECO 1985 2003 PTA 3.2 7.9 5.6 6.9 8.0 6.8 7.2 GCC 1981 2003c CU 8.0 6.8 4.9 4.4 5.0 4.5 5.1 PAFTA (GAFTA) 1997 1998 FTA 10.2 9.8 7.2 9.2 9.4 8.9 10.6 UMA 1989 1994 c NNA 2.9 3.8 2.2 1.9 2.0 2.5 3.1 Latin America and the Caribbean Andean Community 1969 1988 CU 4.0 8.6 7.7 9.0 7.8 7.5 7.5 CACM 1961 1961 CU 15.3 21.8 19.6 23.2 23.5 24.8 22.3 CARICOM 1973 1997 EIA 8.0 12.0 14.4 12.1 13.1 12.9 13.7 LAIA (ALADI) 1980 1981 PTA 11.6 17.3 13.2 13.6 15.3 16.5 15.5 MERCOSUR 1991 2005 EIA 8.9 20.3 20.0 12.9 14.7 14.7 15.2 OECS 1981 1981c NNA 8.1 12.6 10.0 11.5 12.1 12.0 13.0 Sub-Saharan Africa CEMAC 1994 1999 CU 2.3 2.1 1.0 0.9 1.1 0.8 1.2 CEPGL 1976 NNA 0.5 0.5 0.8 1.2 1.4 1.9 2.2 COMESA 1994 1994 FTA 4.7 6.1 4.6 4.6 4.5 5.3 7.2 EAC 1996 2000 CU 17.7 19.5 22.6 18.0 17.8 19.2 18.9 ECCAS 1983 2004 c NNA 1.4 1.5 1.0 0.6 0.6 0.4 0.6 ECOWAS 1975 1993 PTA 8.0 9.0 7.6 9.3 7.8 8.5 9.9 Indian Ocean Commission 1984 2005c NNA 3.9 5.9 4.4 4.9 5.8 5.7 5.8 SADC 1992 2000 FTA 6.6 10.2 9.5 9.3 10.2 10.3 11.0 UEMOA 1994 2000 CU 13.0 10.3 13.1 13.4 14.9 15.9 13.2 Uzbekistan; Eurasian Economic Community (EAEC), Belarus, Kazakhstan, Kyrgyz Republic, the Russian Federation, Tajikistan, and Uzbekistan; European Economic Area (EEA), European Union plus Iceland, Liechtenstein, and Norway; European Free Trade Association (EFTA), Iceland, Liechtenstein, Norway, and Switzerland; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Bolivarian Republic of Venezuela; Melanesian Spearhead Group (MSG), Fiji, Papua New Guinea, Solomon Islands, and Vanuatu; North American Free Trade Agreement (NAFTA), Canada, Mexico, and the United States; Organization of Eastern Caribbean States (OECS), Anguilla, Antigua and Barbuda, British Virgin Islands, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Pacific Island Countries Trade Agreement (PICTA), Cook Islands, Kiribati, Nauru, Niue, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu; Pan-Arab Free Trade Area (PAFTA; also known as Greater Arab Trade Area [GAFTA]), Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, the United Arab Emirates, and Yemen; South Asian Association for Regional Cooperation (SAARC), Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; South Pacific Regional Trade and Economic Cooperation Agreement (SPARTECA), Australia, Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Zealand, Niue, Papua New Guinea, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Western Samoa; Southern African Development Community (SADC), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Southern Common Market (MERCOSUR), Argentina, Brazil, Paraguay, Uruguay, and Bolivarian Republic of Venezuela; Trans-Pacific Strategic Economic Partnership (Trans-Pacific SEP), Brunei Darussalam, Chile, New Zealand, and Singapore; West African Economic and Monetary Union (WAEMU or UEMOA), Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo. 2011 World Development Indicators 345 6.7 Regional trade blocs Merchandise exports by bloc Year of entry into Type force of the of most % of world exports Year of most recent recent creation agreement agreementa 1990 1995 2000 2005 2007 2008 2009 High-income and low- and middle-income economies APECb 1989 None 39.0 46.4 48.5 45.1 44.8 44.1 45.7 EEA 1994 1994 EIA 46.4 42.4 38.9 40.1 39.5 38.1 38.3 EFTA 1960 2002 EIA 2.9 2.4 2.2 2.3 2.3 2.3 2.4 European Union 1957 1958 EIA, CU 45.3 41.5 38.0 39.1 38.5 37.0 37.3 NAFTA 1994 1994 FTA 16.2 16.8 19.0 14.3 13.4 12.8 13.0 SPARTECA 1981 1981 PTA 1.5 1.4 1.3 1.3 1.3 1.4 1.5 Trans-Pacific SEP 2006 2006 EIA, FTA 2.2 3.0 2.7 2.9 2.9 2.8 2.9 East Asia and Pacific and South Asia APTA 1975 1976 PTA 4.5 6.3 7.5 11.2 12.7 12.8 14.3 ASEAN 1967 1992 FTA 4.3 6.4 6.7 6.3 6.2 6.1 6.6 MSG 1993 1994 PTA 0.1 0.1 0.1 0.1 0.1 0.1 0.1 PICTA 2001 2003 FTA 0.0 0.1 0.0 0.1 0.1 0.1 0.1 SAARC 1985 2006 FTA 0.8 0.9 1.0 1.3 1.4 1.4 1.7 Europe, Central Asia, and Middle East Agadir Agreement 2004 NNA 0.3 0.3 0.3 0.3 0.4 0.4 0.4 CEFTA 1992 1994 FTA .. 0.1 0.1 0.2 0.2 0.2 0.2 CEZ 2003 2004 FTA .. 1.7 1.9 2.7 3.0 3.4 2.8 CIS 1991 1994 FTA .. 2.2 2.2 3.2 3.5 4.3 3.3 EAEC 1997 2000 CU .. 1.7 1.9 2.7 3.0 3.4 2.8 ECO 1985 2003 PTA 1.1 1.2 1.3 1.8 2.0 2.5 2.1 GCC 1981 2003c CU 2.6 2.0 2.6 3.3 3.5 4.3 3.5 PAFTA (GAFTA) 1997 1998 FTA 3.8 2.6 3.5 4.4 4.7 5.8 4.7 UMA 1989 1994 c NNA 1.0 0.6 0.8 0.9 1.0 1.1 0.9 Latin America and the Caribbean Andean Community 1969 1988 CU 0.4 0.4 0.4 0.5 0.5 0.6 0.6 CACM 1961 1961 CU 0.1 0.1 0.2 0.2 0.2 0.2 0.2 CARICOM 1973 1997 EIA 0.2 0.1 0.1 0.2 0.2 0.2 0.2 LAIA (ALADI) 1980 1981 PTA 3.4 4.1 5.3 5.1 5.2 5.4 5.2 MERCOSUR 1991 2005 EIA 1.4 1.4 1.4 1.6 1.6 2.0 1.7 OECS 1981 1981c NNA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Sub-Saharan Africa CEMAC 1994 1999 CU 0.2 0.1 0.1 0.2 0.2 0.3 0.2 CEPGL 1976 NNA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 COMESA 1994 1994 FTA 0.7 0.4 0.5 0.6 0.6 0.8 0.7 EAC 1996 2000 CU 0.1 0.1 0.0 0.1 0.1 0.1 0.1 ECCAS 1983 2004 c NNA 0.3 0.2 0.3 0.4 0.5 0.7 0.5 ECOWAS 1975 1993 PTA 0.6 0.4 0.6 0.6 0.6 0.7 0.6 Indian Ocean Commission 1984 2005c NNA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SADC 1992 2000 FTA 0.7 0.7 0.7 0.8 0.9 1.0 0.9 UEMOA 1994 2000 CU 0.1 0.1 0.1 0.1 0.1 0.1 0.1 a. CU is customs union; EIA is economic integration agreement; FTA is free trade agreement; PTA is preferential trade agreement; and NNA is not notified agreement, which refers to preferential trade arrangements established among member countries that are not notified to the World Trade Organization (these agreements may be functionally equivalent to any of the other agreements). b. No preferential trade agreement. c. Years of the most recent agreement are collected from the official website of the trade bloc. 346 2011 World Development Indicators 6.7 GLOBAL LINKS Regional trade blocs About the data Trade blocs are groups of countries that have estab- preferential arrangements, it is included because of one trade bloc, so shares of world exports exceed lished preferential arrangements governing trade the volume of trade between its members. 100 percent. Exports include all commodity trade, between members. Although in some cases the pref- The data on country exports are from the Interna- which may include items not specified in trade bloc erences—such as lower tariff duties or exemptions tional Monetary Fund’s (IMF) Direction of Trade data- agreements. Differences from previously published from quantitative restrictions—may be no greater than base and should be broadly consistent with those estimates may be due to changes in membership or those available to other trading partners, such arrange- from sources such as the United Nations Statistics revisions in underlying data. ments are intended to encourage exports by bloc mem- Division’s Commodity Trade (Comtrade) database. All Definitions bers to one another—sometimes called intratrade. high-income economies and major developing econo- Most countries are members of a regional trade mies report trade to the IMF on a timely basis, cover- • Merchandise exports within bloc are the sum of bloc, and more than a third of the world’s trade takes ing about 85 percent of trade for recent years. Trade merchandise exports by members of a trade bloc to place within such arrangements. While trade blocs by less timely reporters and by countries that do not other members of the bloc. They are shown both in vary in structure, they all have the same objective: report is estimated using reports of trading partner U.S. dollars and as a percentage of total merchan- to reduce trade barriers between member countries. countries. Therefore, data on trade between develop- dise exports by the bloc. • Merchandise exports by But effective integration requires more than reduc- ing and high-income economies shown in the table bloc as a share of world exports are the bloc’s total ing tariffs and quotas. Economic gains from compe- should be generally complete. But trade flows between merchandise exports (within the bloc and to the rest tition and scale may not be achieved unless other many developing countries—particularly those in Sub- of the world) as a share of total merchandise exports barriers that divide markets and impede the free flow Saharan Africa—are not well recorded, and the value of by all economies in the world. • Type of most recent of goods, services, and investments are lifted. For trade among developing countries may be understated. agreement includes customs union, under which example, many regional trade blocs retain contingent Membership in the trade blocs shown is based members substantially eliminate all tariff and nontariff protections on intrabloc trade, including antidumping, on the most recent information available (see Data barriers among themselves and establish a common countervailing duties, and “emergency protection” to sources). Other types of preferential trade agreements external tariff for nonmembers; economic integration address balance of payments problems or protect an may have entered into force earlier than those shown agreement, which liberalizes trade in services among industry from import surges. Other barriers include in the table and may still be effective. Unless other- members and covers a substantial number of sec- differing product standards, discrimination in public wise indicated in the footnotes, information on the type tors, affects a sufficient volume of trade, includes procurement, and cumbersome border formalities. of agreement and date of enforcement are based on substantial modes of supply, and is nondiscriminatory Membership in a regional trade bloc may reduce the World Trade Organization’s (WTO) list of regional (in the sense that similarly situated service suppliers the frictional costs of trade, increase the credibility trade agreements. Information on trade agreements are treated the same); free trade agreement, under of reform initiatives, and strengthen security among not notified to the WTO was collected from the Global which members substantially eliminate all tariff and partners. But making it work effectively is challenging. Preferential Trade Agreements database (box 6.7a) nontariff barriers but set tariffs on imports from non- All economic sectors may be affected, and some may and from official websites of the trade blocs. members; preferential trade agreement, which is an expand while others contract, so it is important to Although bloc exports have been calculated back agreement notified to the WTO that is not a free trade weigh the potential costs and benefits of membership. to 1990 on the basis of current membership, several agreement, a customs union, or an economic integra- The table shows the value of merchandise intra- blocs came into existence after that and membership tion agreement; and not notified agreement, which is trade (service exports are excluded) for important may have changed over time. For this reason, and a preferential trade arrangement established among regional trade blocs and the size of intratrade rela- because systems of preferences also change over member countries that is not notified to the World tive to each bloc’s exports of goods and the share time, intratrade in earlier years may not have been Trade Organization (the agreement may be functionally of the bloc’s exports in world exports. Although the affected by the same preferences as in recent years. equivalent to any of the other agreements). Asia Pacific Economic Cooperation (APEC) has no In addition, some countries belong to more than Global Preferential Trade Agreements Database 6.7a Data sources The Global Preferential Trade Agreement Database (GPTAD) provides information on preferential trade Data on merchandise trade flows are published in agreements around the world, including those not notified to the World Trade Organization (WTO). It is the IMF’s Direction of Trade Statistics Yearbook and designed to help trade policymakers, scholars, and business operators better understand and navigate the Direction of Trade Statistics Quarterly; the data in world of preferential trade agreements. The GPTAD is updated regularly and currently comprises more than the table were calculated using the IMF’s Direction 330 preferential trade agreements in their original language, which have been indexed by WTO criteria and of Trade database. Data on trade bloc membership can be downloaded as PDFs. Users can search by provision or keyword, compare provisions across multiple are from the World Bank Policy Research Report agreements, and sort agreements by membership, date of signature, in-force status, and other key criteria. Trade Blocs (2000), UNCTAD’s Trade and Develop- The database was developed jointly by the World Bank and the Center for International Business at the ment Report 2007, WTO’s Regional Trade Agree- Tuck School of Business at Dartmouth College. It is supported by the Multidonor Trust Fund for Trade and ments Information System, and the World Bank Development with financing from the governments of Finland, Norway, Sweden, and the United Kingdom. and the Center for International Business at the The GPTAD is integrated with the World Integrated Trade Solution database and is part of the World Bank’s Tuck School of Business at Dartmouth College’s Open Data initiative (http://wits.worldbank.org/gptad/). Global Preferential Trade Agreements Database. 2011 World Development Indicators 347 6.8 Tariff barriers All Primary Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Afghanistan 2008 .. .. 6.2 6.4 4.4 0.0 7.0 6.7 6.1 6.3 Albania 2009 100.0 7.1 5.7 5.1 0.0 0.0 6.8 5.4 5.5 4.9 Algeria 2009 .. .. 14.2 8.6 53.2 0.0 14.5 7.8 14.0 8.8 Angola 2009 100.0 59.2 7.4 7.4 23.4 0.0 11.6 13.9 6.7 5.9 Antigua and Barbuda 2009 97.9 58.7 13.8 14.6 49.4 0.0 17.2 14.8 13.0 14.5 Argentina 2010 100.0 31.9 11.4 6.2 24.3 0.0 7.5 1.6 11.8 7.0 Armenia 2008 100.0 8.5 3.7 2.3 0.0 0.3 5.6 2.2 3.5 2.4 Australia 2010 97.0 10.0 2.9 1.9 0.0 0.0 1.3 0.4 3.1 2.5 Azerbaijan 2009 .. .. 8.3 3.9 46.5 0.0 9.5 3.8 8.0 3.9 Bahamas, The 2006 .. .. 28.5 23.9 77.4 0.0 24.4 15.1 29.4 29.7 Bahrain 2009 73.6 34.8 4.3 3.6 0.2 0.0 6.7 6.9 4.0 3.1 Bangladesh 2008 15.9 169.9 13.9 13.0 38.0 0.0 16.3 8.8 13.5 14.0 Barbados 2007 97.8 78.1 15.1 14.8 44.9 0.6 26.3 21.9 13.4 12.2 Belarus 2009 .. .. 8.0 2.3 27.2 0.0 6.8 0.6 8.2 4.3 Belize 2009 97.9 58.4 11.2 5.9 30.1 0.0 17.2 4.0 10.1 9.3 Benin 2010 39.5 28.7 13.3 15.4 50.2 0.0 15.5 12.4 12.9 17.0 Bermuda 2009 .. .. 18.1 27.8 66.7 0.0 10.0 16.1 19.5 28.8 Bhutan 2007 .. .. 18.2 17.8 50.7 0.0 43.5 44.9 15.6 16.0 Bolivia 2010 100.0 40.0 9.6 5.4 11.9 0.0 8.4 5.8 9.6 5.2 Bosnia and Herzegovina 2009 .. .. 3.7 2.0 5.7 0.0 1.6 1.3 3.9 2.5 Botswana 2010 96.1 19.0 8.8 5.2 20.2 0.0 6.1 0.5 9.0 6.6 Brazil 2010 100.0 31.4 13.4 7.6 26.4 0.0 8.1 1.5 13.9 9.6 Brunei Darussalam 2010 95.3 24.1 3.8 4.1 20.8 0.0 0.2 0.1 4.4 5.0 Burkina Faso 2010 39.4 42.5 12.4 8.8 44.5 0.0 11.4 8.1 12.5 9.2 Burundi 2010 22.3 67.8 9.8 5.5 29.8 0.0 15.4 9.4 9.1 4.5 Cambodia 2008 100.0 19.1 12.4 9.9 19.7 0.0 13.8 11.8 12.1 9.6 Cameroon 2009 13.7 79.9 18.4 15.0 52.5 0.0 20.5 12.9 18.1 16.0 Canada 2010 99.7 5.2 3.3 1.0 7.2 0.0 2.1 0.3 3.5 1.3 Cape Verde 2010 100.0 15.8 14.7 11.6 44.3 0.0 16.2 12.2 14.3 10.9 Central African Republic 2007 62.5 36.0 17.5 13.6 47.4 0.0 18.9 13.8 17.3 13.3 Chad 2009 13.9 79.9 17.6 14.7 47.4 0.0 22.5 17.2 16.7 13.8 Chile 2010 100.0 25.1 4.9 4.0 0.0 0.0 4.4 2.7 4.9 4.8 China† 2009 100.0 10.0 8.2 4.2 13.4 0.0 8.1 1.7 8.1 5.5 Hong Kong SAR, China 2010 45.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Macao SAR, China 2010 28.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Colombia 2010 100.0 43.1 11.2 8.9 19.8 0.0 10.9 8.8 11.2 8.8 Comoros 2008 .. .. 7.8 7.8 42.8 0.0 4.2 3.8 8.7 10.3 Congo, Dem. Rep. 2009 100.0 96.2 12.9 11.0 42.5 0.0 14.2 10.8 12.6 11.1 Congo, Rep. 2007 16.5 27.4 18.6 14.7 52.6 0.0 21.9 18.6 18.1 14.1 Costa Rica 2009 100.0 43.2 4.8 2.4 0.7 0.0 6.3 3.3 4.6 2.0 Côte d’Ivoire 2010 33.8 11.2 13.1 7.3 47.9 0.0 15.1 5.4 12.8 9.3 Croatia 2010 100.0 6.0 2.4 1.2 4.1 0.0 4.5 1.9 2.1 0.9 Cuba 2010 31.7 21.4 10.5 8.7 11.6 0.0 11.1 6.2 10.4 9.8 Djibouti 2009 100.0 41.2 20.6 15.2 69.4 0.0 15.9 8.7 21.4 18.6 Dominica 2007 94.7 58.7 11.9 7.9 43.3 0.0 19.2 5.7 10.5 9.3 Dominican Republic 2008 100.0 34.9 9.0 4.9 28.8 0.0 11.6 4.5 8.6 5.2 Ecuador 2010 100.0 21.7 9.3 6.0 20.2 0.0 9.0 4.3 9.3 6.7 Egypt, Arab Rep. 2009 99.3 37.3 12.6 8.0 18.3 0.0 37.5 6.2 9.3 9.1 El Salvador 2010 100.0 36.9 5.1 5.5 1.9 0.0 8.4 7.4 4.7 4.2 Equatorial Guinea 2007 .. .. 18.3 15.6 52.3 0.0 21.5 21.4 17.7 14.3 Eritrea 2006 .. .. 9.6 5.4 22.4 0.0 9.2 3.5 9.5 7.1 Ethiopia 2009 .. .. 18.1 9.7 55.4 0.0 19.2 5.6 17.9 12.8 European Union 2010 100.0 4.2 1.8 1.4 1.1 0.0 2.4 0.6 1.6 1.9 Fiji 2009 51.4 40.1 11.9 10.1 20.9 0.0 13.7 7.7 11.6 12.8 French Polynesia 2009 .. .. 6.8 4.2 28.1 0.0 4.1 2.7 7.3 5.2 Gabon 2009 100.0 21.4 18.7 14.5 53.1 0.0 21.2 15.1 18.3 14.3 Gambia, The 2009 13.7 101.8 18.7 14.8 91.2 0.0 16.9 12.8 19.1 16.9 Georgia 2009 100.0 7.2 0.5 0.4 0.0 0.0 4.0 1.0 0.1 0.0 Ghana 2009 14.4 92.5 13.0 8.6 40.5 0.0 16.6 8.9 12.4 8.5 †Data for Taiwan, China 2010 100.0 6.0 5.3 2.5 6.0 0.0 8.4 2.0 4.7 2.7 348 2011 World Development Indicators 6.8 GLOBAL LINKS Tariff barriers All Primary Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Grenada 2008 100.0 56.8 10.6 8.8 43.3 0.0 14.1 9.9 10.0 8.4 Guatemala 2009 100.0 42.3 4.4 2.7 18.1 0.0 4.9 2.1 4.3 3.1 Guinea 2009 38.6 20.3 13.5 11.9 56.1 0.0 15.6 13.9 13.2 10.2 Guinea-Bissau 2010 97.6 48.6 13.3 9.9 51.8 0.0 14.6 10.0 12.9 9.7 Guyana 2008 100.0 56.8 10.7 6.8 41.3 0.0 17.7 5.9 9.7 7.3 Haiti 2009 89.8 17.6 3.0 5.1 5.1 0.0 5.8 4.1 2.5 5.9 Honduras 2009 100.0 32.5 6.4 6.5 0.5 0.0 9.9 8.1 5.9 5.4 Iceland 2009 95.0 13.5 1.9 0.9 5.7 0.0 2.4 1.1 1.8 0.8 India 2009 74.5 50.2 10.2 7.9 6.6 0.0 20.0 7.3 8.7 8.0 Indonesia 2009 96.6 37.5 5.2 3.1 11.4 0.0 5.6 2.0 5.2 3.5 Iran, Islamic Rep. 2008 .. .. 24.8 19.6 56.5 0.0 21.7 12.5 24.8 21.1 Iraq .. .. .. .. .. .. .. .. .. .. Israel 2009 75.2 22.0 5.5 3.2 1.1 0.0 5.5 2.2 5.4 3.6 Jamaica 2007 100.0 49.7 9.2 9.0 36.1 0.0 16.1 8.6 8.3 9.3 Japan 2010 99.7 3.0 2.6 1.6 8.6 0.0 5.1 1.6 2.1 1.6 Jordan 2009 100.0 16.3 9.7 5.2 29.5 0.0 14.2 3.9 8.9 5.9 Kazakhstan 2008 .. .. 4.3 2.7 8.8 11.5 7.3 1.3 4.0 3.1 Kenya 2010 15.2 95.3 12.1 9.2 36.6 0.0 16.0 12.6 11.7 6.6 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2010 95.1 16.1 10.3 8.7 7.0 0.0 26.3 12.7 7.3 5.0 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 2009 99.9 100.0 4.1 4.2 0.0 0.0 3.2 3.1 4.2 4.4 Kyrgyz Republic 2009 99.9 7.5 3.6 8.4 0.9 0.0 4.4 1.3 3.5 9.4 Lao PDR 2008 .. .. 9.3 13.2 20.4 0.0 16.0 14.2 8.4 12.6 Lebanon 2007 .. .. 5.6 4.8 11.6 0.0 8.2 5.0 5.2 5.0 Lesotho 2010 100.0 78.9 9.5 10.5 21.6 0.0 9.2 1.6 9.5 10.9 Liberia .. .. .. .. .. .. .. .. .. .. Libya 2006 .. .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Macedonia, FYR 2009 100.0 6.9 4.3 3.2 14.5 0.0 7.6 6.0 3.9 2.4 Madagascar 2008 30.5 27.3 12.1 8.3 41.1 0.0 13.9 4.2 11.9 10.4 Malawi 2009 32.0 75.9 13.0 7.0 47.5 0.0 14.8 8.6 12.7 6.5 Malaysia 2009 83.9 14.6 5.3 3.1 16.3 0.0 2.4 2.1 5.8 3.6 Maldives 2009 97.0 37.2 21.7 20.6 88.1 0.0 17.5 18.4 22.8 22.6 Mali 2010 40.5 28.9 12.8 8.4 47.9 0.0 12.8 7.9 12.8 8.7 Mauritania 2007 39.4 19.6 12.6 10.1 49.0 0.0 11.1 9.2 12.8 11.0 Mauritius 2009 17.7 98.3 2.0 1.0 10.4 0.0 1.2 0.3 2.1 1.6 Mayotte 2009 .. .. 5.3 1.8 2.6 0.0 3.8 1.3 5.5 2.1 Mexico 2010 100.0 35.1 7.8 6.1 6.4 0.0 10.7 11.5 7.4 4.6 Moldova 2008 99.9 6.7 4.2 3.0 7.7 3.4 6.6 3.6 3.8 2.7 Mongolia 2009 100.0 17.5 4.9 5.1 0.1 0.0 5.2 5.4 4.9 4.9 Montenegro 2009 .. .. 2.2 3.2 5.4 0.0 6.2 5.2 1.6 2.4 Morocco 2009 100.0 41.3 9.1 7.1 23.6 0.0 18.0 8.9 8.2 5.7 Mozambique 2009 14.0 97.4 7.7 4.5 25.4 0.0 8.2 4.4 7.5 4.3 Myanmar 2008 17.6 83.8 4.0 3.2 4.1 0.0 5.1 2.7 3.9 3.4 Namibia 2010 96.1 19.4 6.3 1.8 16.7 0.0 4.1 2.1 6.7 1.6 Nepal 2009 99.4 26.2 12.8 14.3 50.4 0.0 15.6 11.0 12.5 16.5 New Zealand 2010 100.0 10.0 2.5 1.6 0.0 0.0 1.4 0.4 2.6 2.1 Nicaragua 2009 100.0 41.7 4.4 2.6 17.1 0.0 5.9 3.0 4.2 2.2 Niger 2010 96.6 44.9 13.0 9.1 48.9 0.0 14.0 10.7 12.8 7.6 Nigeria 2010 19.5 119.4 10.9 10.6 34.9 0.0 11.8 9.1 10.7 10.8 Norway 2009 100.0 3.0 0.4 0.3 0.5 0.0 1.8 1.0 0.3 0.2 Oman 2009 100.0 13.9 3.6 3.2 0.2 0.0 4.4 3.3 3.5 3.2 Pakistan 2009 98.6 60.0 14.8 9.5 45.3 0.0 14.2 6.4 14.7 12.1 Palau 2006 .. .. 2.6 2.2 0.5 0.0 0.5 0.6 3.1 3.2 Panama 2009 99.9 23.5 7.6 7.6 2.8 0.0 11.5 8.4 7.1 7.2 Papua New Guinea 2008 100.0 31.5 4.8 2.6 24.4 0.7 15.2 3.3 3.4 2.2 Paraguay 2010 100.0 33.5 8.1 3.7 18.3 0.0 5.8 0.8 8.2 4.8 Peru 2010 100.0 30.1 4.8 2.5 10.0 0.0 3.8 1.3 4.9 3.0 Philippines 2010 67.2 25.8 5.3 4.8 5.4 0.0 6.8 5.1 5.0 4.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 349 6.8 Tariff barriers All Primary Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Qatar 2009 100.0 16.0 4.2 3.8 0.2 0.0 5.0 4.0 4.1 3.8 Russian Federation 2009 .. .. 8.1 5.9 24.6 0.0 7.7 4.4 8.2 6.2 Rwanda 2010 100.0 89.3 9.9 6.0 31.4 0.0 11.5 6.4 9.7 5.9 Saudi Arabia 2009 100.0 10.8 4.0 3.9 0.0 0.0 3.3 2.8 4.1 4.2 Senegal 2010 100.0 30.0 13.4 8.9 50.5 0.0 14.1 7.7 13.2 10.2 Serbia 2005a .. .. 8.1 6.0 17.8 0.0 10.9 4.5 7.8 6.8 Seychelles 2007 .. .. 6.5 28.3 12.8 0.0 14.0 50.5 4.8 6.4 Sierra Leone 2004 100.0 47.4 .. .. .. .. .. .. .. .. Singapore 2010 69.6 7.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Solomon Islands 2008 100.0 78.7 9.9 17.3 2.6 0.8 14.8 23.3 9.2 8.8 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2010 96.1 19.4 7.6 4.4 17.9 0.0 5.4 1.9 7.8 5.6 Sri Lanka 2009 38.1 30.1 10.1 6.4 42.7 0.0 15.3 8.4 9.4 5.2 St. Kitts and Nevis 2009 97.9 75.9 14.3 13.7 43.1 0.0 16.5 13.5 13.7 13.7 St. Lucia 2007 99.6 61.9 9.6 9.0 39.9 0.0 12.7 4.9 9.1 12.2 St. Vincent & Grenadines 2007 99.7 62.5 11.3 8.4 44.4 0.2 15.1 7.8 10.5 8.6 Sudan 2009 .. .. 13.4 7.9 25.4 0.0 15.9 7.7 13.0 7.9 Suriname 2010 27.6 18.1 11.6 11.9 36.2 0.0 18.3 15.0 10.4 10.4 Swaziland 2010 96.1 19.4 10.9 10.2 26.2 0.0 9.7 1.3 11.1 15.9 Switzerland 2010 99.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Syrian Arab Republic 2010 .. .. 6.7 6.1 27.6 0.0 6.5 6.1 6.5 5.7 Tajikistan 2006 .. .. 4.9 3.8 0.1 0.7 5.4 2.1 4.9 5.3 Tanzania 2010 13.8 120.0 12.9 8.2 39.9 0.0 17.5 8.7 12.4 8.0 Thailand 2009 74.7 26.1 10.8 4.9 19.3 0.0 14.0 2.7 10.2 5.9 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 2010 14.3 80.0 12.8 14.2 47.3 0.0 14.4 12.4 12.6 14.9 Tonga 2009 100.0 17.6 10.8 7.3 64.7 0.0 12.1 5.5 10.5 9.0 Trinidad and Tobago 2008 100.0 55.8 8.7 10.0 43.6 0.4 16.6 3.1 7.6 17.2 Tunisia 2008 58.3 58.0 21.9 16.0 57.8 0.0 26.8 12.0 21.2 17.9 Turkey 2009 50.3 29.2 2.4 2.3 4.6 0.0 13.8 4.3 1.2 1.4 Turkmenistan 2002 .. .. 5.4 2.9 14.8 2.8 14.7 12.6 3.8 1.1 Uganda 2010 16.1 73.5 12.1 8.2 37.5 0.0 15.7 8.8 11.6 7.9 Ukraine 2010 100.0 5.8 4.5 2.8 1.1 0.0 5.9 2.5 4.3 3.0 United Arab Emirates 2009 100.0 14.8 4.3 3.7 0.2 0.0 4.5 2.7 4.2 4.2 United States 2010 100.0 3.7 2.9 1.8 3.4 0.0 2.6 1.2 3.0 2.0 Uruguay 2010 100.0 31.6 9.6 3.6 29.3 0.0 5.6 1.1 9.9 5.2 Uzbekistan 2009 .. .. 11.8 6.9 20.1 0.0 12.6 3.9 11.7 7.3 Vanuatu 2009 .. .. 16.8 15.0 65.0 0.0 19.5 16.9 16.1 14.2 Venezuela, RB 2010 100.0 36.5 13.1 10.6 21.9 0.0 12.2 10.0 13.1 10.7 Vietnam 2008 100.0 11.5 8.0 5.2 19.8 0.0 10.7 4.1 7.4 5.7 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2009 .. .. 5.5 4.2 1.4 0.0 7.1 3.8 5.2 4.6 Zambia 2009 17.1 106.9 10.8 3.8 51.2 0.0 9.2 3.1 10.9 4.1 Zimbabwe 2007b 22.2 91.4 16.7 17.3 38.8 0.0 17.4 20.4 16.1 14.7 World 77.8 w 27.3 w 6.2 w 2.5 w 10.8 w 0.0 w 6.6 w 2.4 w 6.1 w 2.5 w Low income 42.2 57.7 12.1 10.0 40.6 0.0 14.4 9.4 11.8 10.2 Middle income 86.6 30.3 8.9 6.3 16.0 0.0 8.6 5.4 8.9 6.5 Lower middle income 84.7 31.8 8.4 5.8 15.4 0.0 9.7 4.8 8.2 6.2 Upper middle income 88.3 29.0 9.2 6.4 16.3 0.0 7.9 5.6 9.3 6.6 Low & middle income 73.9 35.5 9.5 6.4 18.5 0.0 9.8 5.7 9.4 6.6 East Asia & Pacific 67.2 25.8 5.3 4.8 5.4 0.0 6.8 5.1 5.0 4.6 Europe & Central Asia 100.0 5.8 4.5 2.8 1.1 0.0 5.9 2.5 4.3 3.0 Latin America & Carib. 90.0 32.5 9.2 6.6 15.7 0.0 8.6 6.2 9.2 6.7 Middle East & North Africa 99.9 30.4 6.7 6.1 27.6 0.0 6.5 6.1 6.5 5.7 South Asia 81.5 41.6 13.0 8.2 37.4 0.0 17.1 7.3 12.3 8.4 Sub-Saharan Africa 61.7 41.8 11.1 7.5 33.6 0.0 12.1 5.9 10.9 8.1 High income 87.9 7.9 2.7 1.8 3.5 0.0 4.2 1.9 2.5 1.8 OECD 99.0 10.7 3.6 2.2 4.0 0.0 5.3 2.3 3.3 2.1 Non-OECD 73.1 9.1 1.8 0.6 3.2 0.0 2.5 0.7 1.6 0.6 a. Includes Montenegro. b. Rates are most favored nation rates. 350 2011 World Development Indicators 6.8 GLOBAL LINKS Tariff barriers About the data Definitions Poor people in developing countries work primarily trade and reduce the weights applied to these tariffs. •  Binding coverage is the percentage of product in agriculture and labor–intensive manufactures, Bound rates result from trade negotiations incorpo- lines with an agreed bound rate. •  Simple mean sectors that confront the greatest trade barriers. rated into a country’s schedule of concessions and bound rate is the unweighted average of all the lines Removing barriers to merchandise trade could are thus enforceable. in the tariff schedule in which bound rates have been increase growth in these countries—even more if Some countries set fairly uniform tariff rates set. • Simple mean tariff is the unweighted average trade in services. across all imports. Others are selective, setting high of effectively applied rates or most favored nation In general, tariffs in high-income countries on tariffs to protect favored domestic industries. The rates for all products subject to tariffs calculated imports from developing countries, though low, are share of tariff lines with international peaks provides for all traded goods. • Weighted mean tariff is the twice those collected from other high-income coun- an indication of how selectively tariffs are applied. average of effectively applied rates or most favored tries. But protection is also an issue for developing The effective rate of protection—the degree to which nation rates weighted by the product import shares countries, which maintain high tariffs on agricultural the value added in an industry is protected—may corresponding to each partner country. • Share of commodities, labor-intensive manufactures, and exceed the nominal rate if the tariff system system- tariff lines with international peaks is the share other products and services. atically differentiates among imports of raw materi- of lines in the tariff schedule with tariff rates that Countries use a combination of tariff and nontariff als, intermediate products, and finished goods. exceed 15 percent. • Share of tariff lines with spe- measures to regulate imports. The most common The share of tariff lines with specific rates shows cific rates is the share of lines in the tariff schedule form of tariff is an ad valorem duty, based on the the extent to which countries use tariffs based on that are set on a per unit basis or that combine ad value of the import, but tariffs may also be levied physical quantities or other, non–ad valorem mea- valorem and per unit rates. • Primary products are on a specific, or per unit, basis or may combine ad sures. Some countries such as Switzerland apply commodities classified in SITC revision 2 sections valorem and specific rates. Tariffs may be used to mainly specific duties. To the extent possible, these 0–4 plus division 68 (nonferrous metals). • Manu- raise fiscal revenues or to protect domestic indus- specifi c rates have been converted to their ad factured products are commodities classified in tries from foreign competition—or both. Nontariff valorem equivalent rates and have been included in SITC revision 2 sections 5–8 excluding division 68. barriers, which limit the quantity of imports of a par- the calculation of simple and weighted tariffs. ticular good, include quotas, prohibitions, licensing Data are classified using the Harmonized System schemes, export restraint arrangements, and health at the six- or eight-digit level. Tariff data are from and quarantine measures. Because of the difficulty the United Nations Conference on Trade and Devel- of combining nontariff barriers into an aggregate indi- opment’s (UNCTAD) Trade Analysis and Information cator, they are not included in the table. System (TRAINS) database and the World Trade Unless specified as most favored nation rates, the Organization’s (WTO) Integrated Data Base (IDB) tariff rates used in calculating the indicators in the and Consolidated Tariff Schedules (CTS) database. table are effectively applied rates. Effectively applied Tariff line data were matched to Standard Interna- rates are those in effect for partners in preferen- tional Trade Classification (SITC) revision 2 codes to tial trade arrangements such as the North Ameri- define commodity groups and import weights. Import can Free Trade Agreement. The difference between weights were calculated using the United Nations most favored nation and applied rates can be sub- Statistics Division’s Commodity Trade (Comtrade) stantial. Because more countries now report their database. The table shows tariff rates for three com- free trade agreements, suspensions of tariffs, and modity groups: all products, primary products, and other special preferences, this year’s World Develop- manufactured products. Effectively applied rates at ment Indicators includes effectively applied rates for the six- and eight-digit product level are averaged for most countries. All estimates are calculated using products in each commodity group. When an effec- the most recent information, which is not necessarily tively applied rate is not available, the most favored revised every year. As a result, data for the same year nation rate is used instead. may differ from data in last year’s edition. Data are shown only for the last year for which com- Data sources Three measures of average tariffs are shown: sim- plete data are available and for all economies with ple bound rates and the simple and the weighted populations of 1 million or more and for economies All indicators in the table were calculated by World tariffs. Bound rates are based on all products in a with populations of less than 1 million when avail- Bank staff using the World Integrated Trade Solu- country’s tariff schedule, while the most favored able. EU member countries apply a common tariff tion system, available at http://wits.worldbank. nation or applied rates are calculated using all traded schedule that is listed under European Union and org. Data on tariffs were provided by UNCTAD’s items. Weighted mean tariffs are weighted by the are thus not listed separately. TRAINS database and the WTO’s IDB and CTS value of the country’s trade with each trading part- database. Data on global imports are from the ner. Simple averages are often a better indicator of United Nations Statistics Division’s Comtrade tariff protection than weighted averages, which are database. biased downward because higher tariffs discourage 2011 World Development Indicators 351 6.9 Trade facilitation Logistics Burden of Lead time Documents Liner Quality of Freight Performance customs Shipping port costs to the Index procedures Connectivity infrastructure United Index States days number 1 kilogram DHL nondocument 1–5 1–7 0–100 1–7 air packagea (worst to best) (worst to best) To export To import To export To import (low to high) (worst to best) $ 2009 2009–10 b 2009 2009 June 2010 June 2010 2010 2009–10 b 2011 Afghanistan 2.24 .. 2.0 4.0 12 11 .. .. 143.10 Albania 2.46 4.0 1.7 2.0 7 9 4.3 3.5 155.85 Algeria 2.36 3.2 4.6 7.1 8 9 31.4 3.2 157.10 Angola 2.25 2.8 6.0 8.0 11 8 10.7 2.1 157.10 Argentina 3.10 2.7 3.7 3.8 9 7 27.6 3.8 90.75 Armenia 2.52 2.6 .. .. 3 6 .. 2.9c 143.10 Australia 3.84 5.0 2.6 2.8 6 5 28.1 4.9 98.00 Austria 3.76 5.3 2.0 3.7 4 5 .. 4.8c 129.45 Azerbaijan 2.64 3.5 7.0 3.0 9 14 .. 4.2c 155.85 Bangladesh 2.74 3.4 1.4 1.4 6 8 7.5 3.4 98.00 Belarus 2.53 .. .. .. 8 8 .. .. 155.85 Belgium 3.94 4.6 1.7 1.6 4 5 84.0 6.4 112.50 Benin 2.79 4.2 3.0 7.0 7 7 11.5 4.0 157.10 Bolivia 2.51 2.7 15.0 28.3 8 7 .. 2.9c 90.75 Bosnia and Herzegovina 2.66 3.6 2.0 2.0 5 7 .. 1.6 155.85 Botswana 2.32 4.7 .. .. 6 9 .. 3.8 c 157.10 Brazil 3.20 3.3 2.8 3.9 8 7 31.7 2.9 90.75 Bulgaria 2.83 3.5 2.0 3.9 5 7 5.5 3.8 155.85 Burkina Faso 2.23 4.4 4.0 14.0 10 10 .. 3.9 c 157.10 Burundi 2.29 3.0 .. .. 9 10 .. 3.0 c 157.10 Cambodia 2.37 3.5 1.3 4.0 10 10 4.5 3.9 95.70 Cameroon 2.55 3.8 3.4 8.9 11 12 11.3 3.3 157.10 Canada 3.87 4.9 2.8 3.7 3 4 42.4 5.7 72.20 Central African Republic .. .. .. .. 9 17 .. .. 157.10 Chad 2.49 2.7 74.0 35.0 6 10 .. 2.6c 157.10 Chile 3.09 5.7 3.5 3.0 6 7 22.1 5.5 90.75 China 3.49 4.5 2.8 2.6 7 5 143.6 4.3 84.55 Hong Kong SAR, China 3.88 6.5 1.7 1.6 4 4 113.6 6.8 90.45 Colombia 2.77 4.1 7.0 7.0 6 8 26.1 3.5 90.75 Congo, Dem. Rep. 2.68 .. 2.0 3.0 8 9 5.2 .. 157.10 Congo, Rep. 2.48 .. .. .. 11 10 10.5 .. 157.10 Costa Rica 2.91 4.0 2.0 2.0 6 7 12.8 2.7 90.75 Côte d’Ivoire 2.53 3.8 1.0 1.0 10 9 17.5 5.0 157.10 Croatia 2.77 4.1 1.0 1.0 7 8 9.0 4.0 155.85 Cuba 2.07 .. .. .. .. .. 6.6 .. 75.05 Czech Republic 3.51 4.6 2.5 3.5 4 7 0.4 4.6c 155.85 Denmark 3.85 5.6 1.0 1.0 4 3 26.8 6.1 129.45 Dominican Republic 2.82 4.7 2.2 3.5 6 7 22.2 4.3 75.05 Ecuador 2.77 3.5 2.1 3.4 9 7 18.7 3.7 90.75 Egypt, Arab Rep. 2.61 4.5 1.3 3.1 6 6 47.5 4.2 143.10 El Salvador 2.67 4.2 2.0 2.0 8 8 9.6 4.1 90.75 Eritrea 1.70 .. 3.0 3.0 9 13 0.0 .. 157.10 Estonia 3.16 5.3 4.0 4.0 3 4 5.7 5.6 155.85 Ethiopia 2.41 3.6 5.0 6.0 8 8 .. 4.4 c 157.10 Finland 3.89 5.7 1.6 1.8 4 5 8.4 6.4 129.45 France 3.84 4.9 3.2 4.5 2 2 74.9 5.9 112.50 Gabon 2.41 .. 4.3 13.0 7 8 8.5 .. 157.10 Gambia, The 2.49 5.4 4.6 3.5 6 8 5.4 5.1 157.10 Georgia 2.61 4.7 .. .. 4 4 4.0 4.0 155.85 Germany 4.11 5.1 3.6 2.4 4 5 90.9 6.4 112.50 Ghana 2.47 3.8 2.9 6.8 6 7 17.3 4.5 157.10 Greece 2.96 4.1 3.0 3.5 5 6 34.3 4.0 129.45 Guatemala 2.63 4.2 2.6 3.4 10 10 13.3 4.5 90.75 Guinea 2.60 .. 3.5 3.9 7 9 6.3 .. 157.10 Guinea-Bissau 2.10 .. .. .. 6 6 3.5 .. 157.10 Haiti 2.59 .. 4.2 5.3 8 10 7.6 .. 75.05 Honduras 2.78 4.2 2.4 3.2 6 10 9.1 5.3 90.75 352 2011 World Development Indicators 6.9 GLOBAL LINKS Trade facilitation Logistics Burden of Lead time Documents Liner Quality of Freight Performance customs Shipping port costs to the Index procedures Connectivity infrastructure United Index States days number 1 kilogram DHL nondocument 1–5 1–7 0–100 1–7 air packagea (worst to best) (worst to best) To export To import To export To import (low to high) (worst to best) $ 2009 2009–10 b 2009 2009 June 2010 June 2010 2010 2009–10 b 2011 Hungary 2.99 4.3 3.5 5.0 5 7 .. 4.0 c 155.85 India 3.12 4.0 2.3 5.3 8 9 41.4 3.9 98.00 Indonesia 2.76 3.9 2.1 5.4 5 6 25.6 3.6 98.00 Iran, Islamic Rep. 2.57 3.5 2.6 28.3 7 8 30.7 3.9 143.10 Iraq 2.11 .. .. .. 10 10 4.2 .. 143.10 Ireland 3.89 5.2 1.0 1.0 4 4 7.6 4.4 112.00 Israel 3.41 4.3 2.0 2.0 5 4 33.2 4.6 143.10 Italy 3.64 4.2 2.6 3.0 4 4 59.6 3.9 112.50 Jamaica 2.53 3.8 10.0 10.0 6 6 33.1 5.3 75.05 Japan 3.97 4.6 1.0 1.0 4 5 67.4 5.2 120.80 Jordan 2.74 4.5 3.2 4.6 7 7 17.8 4.4 143.10 Kazakhstan 2.83 3.5 2.8 11.5 10 12 .. 3.3c 155.85 Kenya 2.59 3.3 3.0 5.9 8 7 13.1 3.8 157.10 Korea, Dem. Rep. .. .. .. .. .. .. .. .. 95.70 Korea, Rep. 3.64 4.5 1.6 2.0 3 3 82.6 5.5 98.00 Kosovo .. .. .. .. 8 8 .. .. .. Kuwait 3.28 4.1 2.0 3.0 8 10 8.3 4.4 143.10 Kyrgyz Republic 2.62 3.0 2.0 .. 7 7 .. 1.4 c 155.85 Lao PDR 2.46 .. .. .. 9 10 .. .. 95.70 Latvia 3.25 4.1 1.3 1.6 5 6 6.0 4.7 155.85 Lebanon 3.34 3.5 3.4 2.2 5 7 30.3 4.5 143.10 Lesotho 2.30 3.8 .. .. 6 8 .. 3.1c 157.10 Liberia 2.38 .. 4.0 5.0 10 9 5.9 .. 157.10 Libya 2.33 3.5 3.2 10.0 .. .. 5.4 3.2 157.10 Lithuania 3.13 4.8 2.0 2.3 6 6 9.5 4.7 155.85 Macedonia, FYR 2.77 4.3 .. .. 6 6 .. 3.7c 155.85 Madagascar 2.66 3.9 .. .. 4 9 7.4 3.4 157.10 Malawi 2.42 3.9 4.2 3.7 11 10 .. 3.6c 157.10 Malaysia 3.44 4.8 2.6 2.8 7 7 88.1 5.6 98.00 Mali 2.27 4.1 5.0 4.0 7 10 .. 3.7c 157.10 Mauritania 2.63 4.5 2.0 3.0 11 11 5.6 3.6 157.10 Mauritius 2.72 4.6 3.0 2.4 5 6 16.7 4.5 157.10 Mexico 3.05 3.9 2.1 2.5 5 4 36.3 3.7 58.80 Moldova 2.57 3.4 .. .. 6 7 .. 2.9 155.85 Mongolia 2.25 3.3 14.0 12.0 8 8 .. 3.3c 95.70 Morocco 2.38 4.3 2.0 3.2 7 10 49.4 4.4 157.10 Mozambique 2.29 3.7 .. .. 7 10 8.2 3.5 157.10 Myanmar 2.33 .. 4.6 8.4 .. .. 3.7 .. 95.70 Namibia 2.02 4.2 3.0 3.0 11 9 14.4 5.6 157.10 Nepal 2.20 3.4 1.8 6.3 9 10 .. 2.9c 95.70 Netherlands 4.07 5.2 1.8 1.9 4 5 90.0 6.6 112.50 New Zealand 3.65 5.8 1.3 1.6 7 5 18.4 5.4 98.00 Nicaragua 2.54 3.6 3.2 3.2 5 5 8.7 2.9 90.75 Niger 2.54 .. .. .. 8 10 .. .. 157.10 Nigeria 2.59 3.1 2.5 4.1 10 9 18.3 3.0 157.10 Norway 3.93 5.2 1.0 2.0 4 4 7.9 5.7 129.45 Oman 2.84 5.2 .. .. 9 9 48.5 5.3 143.10 Pakistan 2.53 3.6 2.3 1.6 9 8 29.5 4.0 143.10 Panama 3.02 4.4 1.4 1.4 3 4 41.1 6.0 90.75 Papua New Guinea 2.41 .. .. .. 7 9 6.4 .. 95.70 Paraguay 2.75 3.8 1.0 4.0 8 10 0.0 3.4 c 90.75 Peru 2.80 4.5 2.0 3.8 6 8 21.8 3.3 90.75 Philippines 3.14 3.0 1.8 5.0 8 8 15.2 2.8 98.00 Poland 3.44 4.3 3.0 3.6 5 5 26.2 3.3 155.85 Portugal 3.34 4.9 2.5 5.0 4 5 38.1 4.9 129.45 Puerto Rico .. 4.7 .. .. 7 10 .. 5.4 .. Qatar 2.95 4.9 3.8 2.3 5 7 7.7 5.4 143.10 2011 World Development Indicators 353 6.9 Trade facilitation Logistics Burden of Lead time Documents Liner Quality of Freight Performance customs Shipping port costs to the Index procedures Connectivity infrastructure United Index States days number 1 kilogram DHL nondocument 1–5 1–7 0–100 1–7 air packagea (worst to best) (worst to best) To export To import To export To import (low to high) (worst to best) $ 2009 2009–10 b 2009 2009 June 2010 June 2010 2010 2009–10 b 2011 Romania 2.84 3.9 2.0 2.0 5 6 15.5 3.0 155.85 Russian Federation 2.61 2.9 4.0 2.9 8 13 20.9 3.7 155.85 Rwanda 2.04 4.8 .. .. 8 8 .. 2.8 157.10 Saudi Arabia 3.22 4.9 2.3 6.3 5 5 50.4 5.2 143.10 Senegal 2.86 4.7 1.4 2.7 6 5 13.0 4.7 157.10 Serbia 2.69d 3.6 2.0 d 3.0 d 6 6 3.0 d 2.8 155.85 Sierra Leone 1.97 .. 2.0 32.0 7 7 5.8 .. 157.10 Singapore 4.09 6.3 2.2 1.8 4 4 103.8 6.8 90.45 Slovak Republic 3.24 4.4 3.0 5.0 6 8 .. 4.0 c 155.85 Slovenia 2.87 5.2 1.0 2.0 6 8 20.6 5.3 155.85 Somalia 1.34 .. .. .. .. .. 4.2 .. 157.10 South Africa 3.46 4.4 2.3 3.3 8 9 32.5 4.7 157.10 Spain 3.63 4.6 4.0 7.1 6 7 74.3 5.6 129.45 Sri Lanka 2.29 4.2 1.3 2.5 8 6 40.2 4.9 98.00 Sudan 2.21 .. 39.0 5.0 6 6 10.1 .. 157.10 Swaziland .. 3.5 .. .. 9 10 .. 4.2 157.10 Sweden 4.08 5.8 1.0 2.6 3 3 30.6 6.2 129.45 Switzerland 3.97 5.1 2.6 2.6 4 5 2.6 5.2c 129.45 Syrian Arab Republic 2.74 2.8 2.5 3.2 8 9 15.2 3.1 143.10 Tajikistan 2.35 3.6 7.0 .. 10 9 .. 1.9c 155.85 Tanzania 2.60 3.4 3.2 7.1 5 7 10.6 3.0 157.10 Thailand 3.29 4.1 1.6 2.6 4 3 43.8 5.0 98.00 Timor-Leste 1.71 3.6 .. .. 6 7 .. 2.5 95.70 Togo 2.60 .. .. .. 6 8 14.2 .. 157.10 Trinidad and Tobago .. 3.1 .. .. 5 6 15.8 4.3 75.05 Tunisia 2.84 4.7 1.7 7.0 4 7 6.5 5.0 157.10 Turkey 3.22 3.8 2.2 3.8 7 8 36.1 4.1 143.10 Turkmenistan 2.49 .. 3.0 .. .. .. .. .. 155.85 Uganda 2.82 4.1 5.5 14.0 6 8 .. 3.5c 157.10 Ukraine 2.57 3.0 1.7 7.0 6 8 21.1 3.6 155.85 United Arab Emirates 3.63 5.8 2.5 2.0 4 5 63.4 6.2 143.10 United Kingdom 3.95 4.8 3.3 1.9 4 4 87.5 5.5 112.50 United States 3.86 4.5 2.8 4.0 4 5 83.8 5.5 .. Uruguay 2.75 4.0 3.0 3.0 10 10 24.5 5.2 90.75 Uzbekistan 2.79 .. 1.4 2.0 7 9 .. .. 155.85 Venezuela, RB 2.68 2.2 9.4 12.1 8 9 18.6 2.4 90.75 Vietnam 2.96 3.6 1.4 1.7 6 8 31.4 3.6 98.00 West Bank and Gaza .. .. .. .. 6 6 .. .. .. Yemen, Rep. 2.58 .. 3.1 3.6 6 9 12.5 .. 143.10 Zambia 2.28 4.2 9.2 4.0 6 8 .. 3.6c 157.10 Zimbabwe 2.29 3.6 25.0 18.0 7 9 .. 4.4 c 157.10 World 2.87 e u 4.2 u 3.8 e u 4.6 e u 7u 7u .. 4.3 u .. Low income 2.38 3.8 6.8 7.2 8 9 .. 3.5 .. Middle income 2.69 3.8 3.8 5.0 7 8 .. 3.8 .. Lower middle income 2.62 3.7 4.4 5.1 7 8 .. 3.8 .. Upper middle income 2.75 3.9 3.1 4.9 7 8 .. 3.9 .. Low & middle income 2.59 3.8 4.6 5.6 7 8 .. 3.7 .. East Asia & Pacific 2.73 3.8 3.6 4.9 7 7 .. 3.8 .. Europe & Central Asia 2.68 3.7 2.9 3.1 7 8 .. 3.3 .. Latin America & Carib. 2.74 3.8 3.9 5.5 7 7 .. 3.9 .. Middle East & N. Africa 2.60 3.8 2.7 7.2 7 8 .. 4.0 .. South Asia 2.49 3.7 1.9 3.3 9 9 .. 3.8 .. Sub-Saharan Africa 2.42 3.9 8.1 7.0 8 9 .. 3.8 .. High income 3.54 4.9 2.1 2.7 5 5 .. 5.3 .. Euro area 3.57 4.9 2.2 2.9 4 5 .. 5.3 .. a. Transportation charges only; excludes fuel, assessorial/surcharges, duties and taxes. b. Average of the 2009 and 2010 survey ratings. c. Landlocked country. d. Includes Montenegro. e. Aggregates are computed according to the World Bank classification of economies as of July 1, 2010 and may differ from data published in the original source. 354 2011 World Development Indicators 6.9 GLOBAL LINKS Trade facilitation About the data Definitions Broadly defi ned, trade facilitation encompasses include the value of time to import or export and the •  Logistics Performance Index refl ects percep- customs efficiency and other physical and regulatory risk of delay or loss of shipments. Long lead times tions of a country’s logistics based on efficiency of environments where trade takes place, harmoniza- and burdensome regulatory procedures may lower customs clearance process, quality of trade- and tion of standards and conformance to international competitiveness. Data on lead time are from the LPI transport-related infrastructure, ease of arranging regulations, and the logistics of moving goods and survey. Respondents provided separate values for competitively priced shipments, quality of logistics associated documentation through countries and the best case (10 percent of shipments) and the services, ability to track and trace consignments, and ports. Though collection of trade facilitation data median case (50 percent of shipments). The data frequency with which shipments reach the consignee has improved over the last decade, data that allow are exponentiated averages of the logarithm of sin- within the scheduled time. The index ranges from 1 meaningful evaluation, especially for developing gle value responses and of midpoint values of range to 5, with a higher score representing better perfor- economies, are lacking. Data on trade facilitation responses for the median case. mance. • Burden of customs procedure measures are drawn from research by private and international Data on the number of documents needed to export business executives’ perceptions of their country’s agencies. Most data are perception-based evalua- or import are from the World Bank’s Doing Business efficiency of customs procedures. Values range from tions by business executives and professionals. surveys, which compile procedural requirements for 1 to 7, with a higher rating indicating greater effi - Because of different backgrounds, values, and per- exporting and importing a standardized cargo of goods ciency. • Lead time to export is the median time (the sonalities, those surveyed may evaluate the same by ocean transport from local freight forwarders, ship- value for 50 percent of shipments) from shipment situation quite differently. Caution should thus be ping lines, customs brokers, port officials, and banks. point to port of loading. • Lead time to import is the used when interpreting perception-based indicators. To make the data comparable across economies, sev- median time (the value for 50 percent of shipments) Nevertheless, they convey much needed information eral assumptions about the business and the traded from port of discharge to arrival at the consignee. on trade facilitation. goods are used (see www.doingbusiness.org). • Documents to export and documents to import are The table presents data from Logistics Performance Access to global shipping and air freight networks all documents required per shipment by government Surveys conducted by the World Bank in partnership and the quality and accessibility of ports and roads ministries, customs authorities, port and container with academic and international institutions and affect logistics performance. The table shows two terminals, health and technical control agencies, private companies and individuals engaged in inter- indicators related to trade and transport service infra- and banks to export or import goods. Documents national logistics. The Logistics Performance Index structure: the Liner Shipping Connectivity Index and renewed annually and not requiring renewal per ship- assesses logistics performance across six aspects the quality of port infrastructure rating. The Liner Ship- ment are excluded. • Liner Shipping Connectivity of the logistics environment (see Definitions), based ping Connectivity Index captures how well countries Index indicates how well countries are connected to on more than 5,000 country assessments by nearly are connected to global shipping networks. It is com- global shipping networks based on the status of their 1,000 international freight forwarders. Respondents puted by the United Nations Conference on Trade and maritime transport sector. The highest value in 2004 evaluate eight markets on six core dimensions on Development (UNCTAD) based on five components of is 100. • Quality of port infrastructure measures a scale from 1 (worst) to 5 (best). The markets are the maritime transport sector: number of ships, their business executives’ perceptions of their country’s chosen based on the most important export and container-carrying capacity, maximum vessel size, port facilities. Values range from 1 to 7, with a higher import markets of the respondent’s country, random number of services, and number of companies that rating indicating better development of port infra- selection, and, for landlocked countries, neighboring deploy container ships in a country’s ports. For each structure. • Freight costs to the United States is countries that connect them with international mar- component a country’s value is divided by the maxi- the DHL international U.S. inbound worldwide priority kets. Scores for the six areas are averaged across all mum value of each component in 2004, the five com- respondents and aggregated to a single score. Details ponents are averaged for each country, and the aver- express rate for a 1 kilogram nondocument air pack- of the survey methodology and index construction age is divided by the maximum average for 2004 and age. Fuel, assessorial/surcharges, duties, and taxes methodology are in Arvis and others (2010). multiplied by 100. The index generates a value of 100 are excluded. Data on the burden of customs procedures are for the country with the highest average index in 2004. Data sources from the World Economic Forum’s Executive Opinion The quality of port infrastructure measures busi- Survey. The 2010 round included more than 15,000 ness executives’ perception of their country’s port Data on the Logistics Performance Index and lead respondents from 139 countries. Sampling follows facilities. Values range from 1 (port infrastructure time to export and import are from Arvis and others’ a dual stratification based on company size and the considered extremely underdeveloped) to 7 (port Connecting to Compete: Trade Logistics in the Global sector of activity. Data are collected online or through infrastructure considered efficient by international Economy 2010. Data on the burden of customs in-person interviews. Responses are aggregated using standards). Respondents in landlocked countries procedure and quality of port infrastructure ratings sector-weighted averaging. The data for the latest were asked: “How accessible are port facilities (1 = are from the World Economic Forum’s Global Com- year are combined with the data for the previous year extremely inaccessible; 7 = extremely accessible.)” petitiveness Report 2010–2011. Data on number of to create a two-year moving average. Respondents The costs of transport services are a crucial deter- documents to export and import are from the World evaluated the efficiency of customs procedures in minant of export competitiveness. The proxy indica- Bank’s Doing Business project (www.doingbusiness. their country. The lowest value (1) rates the customs tor in the table is the shipping rates to the United org). Data on the Liner Shipping Connectivity Index are procedure as extremely inefficient, and the highest States of an international freight moving business. from UNCTAD’s Review of Maritime Transport 2010. score (7) as extremely efficient. Freight costs to the United States are based on DHL’s The direct costs of cross-border trade include “DHL Express Standard Rate Guideline 2011” (2011). freight, customs, and storage fees. Indirect costs 2011 World Development Indicators 355 6.10 External debt Total external Long-term Short-term Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan .. 2,328 .. 2,203 .. 471 .. 0 .. 20 .. 106 Albania 456 4,719 330 2,829 109 874 0 983 62 835 65 71 Algeria 33,053 5,345 31,314 2,871 2,049 10 0 982 261 1,492 1,478 0 Angola 11,500 16,715 9,543 13,722 81 385 0 0 1,958 2,634 0 359 Argentina 98,465 120,183 54,913 72,923 4,913 5,305 16,066 27,723 21,355 19,537 6,131 0 Armenia 371 4,935 298 2,376 96 1,214 0 1,461 2 512 70 587 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 321 4,865 206 3,403 30 939 0 590 14 810 101 62 Bangladesh 15,726 23,820 14,905 21,206 5,692 10,746 0 0 199 1,939 622 675 Belarus 1,694 17,158 1,301 4,758 116 256 0 1,504 110 8,024 283 2,871 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 1,398 1,073 1,267 990 498 309 0 0 47 45 84 39 Bolivia 5,272 5,745 4,459 2,545 865 316 239 2,647 307 554 268 0 Bosnia and Herzegovina .. 9,583 .. 3,569 472 1,520 .. 4,051 .. 1,677 48 286 Botswana 717 1,617 707 1,388 108 5 0 0 10 229 0 0 Brazil 160,469 276,932 98,260 87,317 6,038 10,065 30,830 149,826 31,238 39,789 142 0 Bulgaria 10,379 40,582 8,808 4,772 444 1,509 342 17,232 512 18,578 717 0 Burkina Faso 1,271 1,835 1,140 1,725 608 721 0 0 56 0 75 110 Burundi 1,162 518 1,099 420 591 147 0 0 15 7 48 91 Cambodia 2,284 4,364 2,110 4,099 65 566 0 0 102 265 72 0 Cameroon 10,950 2,941 9,620 2,128 1,082 303 288 615 991 23 51 175 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 946 396 854 250 414 9 0 0 57 67 35 78 Chad 843 1,743 777 1,711 379 896 0 0 17 4 49 29 Chile 22,038 71,646 7,178 9,282 1,383 216 11,429 44,888 3,431 17,476 0 0 China 118,090 428,442 94,674 93,125 14,248 22,226 1,090 94,808 22,325 240,509 0 0 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 25,044 52,223 13,946 35,364 2,559 6,571 5,553 12,749 5,545 4,110 0 0 Congo, Dem. Rep. 13,239 12,183 9,636 10,788 1,413 2,497 0 0 3,118 596 485 800 Congo, Rep. 5,887 5,041 4,867 4,785 279 298 0 0 1,002 213 19 43 Costa Rica 3,766 8,070 3,097 3,190 303 58 214 2,538 430 2,341 24 0 Côte d’Ivoire 18,899 11,701 11,902 10,979 2,386 1,823 2,660 271 3,910 99 427 352 Croatia .. .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 4,447 11,003 3,653 7,714 300 756 19 843 616 1,679 160 767 Ecuador 13,877 12,930 11,951 6,910 1,108 542 440 4,600 1,312 1,419 173 0 Egypt, Arab Rep. 33,475 33,257 30,687 30,622 2,356 3,250 313 74 2,372 2,561 103 0 El Salvador 2,509 11,384 1,979 6,131 327 578 5 3,139 525 2,114 0 0 Eritrea 37 1,019 37 1,013 24 477 0 0 0 6 0 0 Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 10,322 5,025 9,788 4,812 1,470 1,422 0 0 460 45 73 168 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 4,361 2,130 3,977 2,022 110 18 0 0 287 108 97 0 Gambia, The 426 520 385 449 162 64 0 0 15 42 26 29 Georgia 1,240 4,231 1,039 2,596 84 1,253 0 518 85 330 116 786 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 5,495 5,720 4,200 4,126 2,434 1,581 27 0 620 1,323 648 271 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 3,282 13,801 2,328 4,931 158 1,112 142 7,644 811 1,226 0 0 Guinea 3,248 2,926 2,991 2,827 847 1,269 0 0 164 40 94 59 Guinea-Bissau 895 1,111 794 950 210 304 0 0 95 151 6 10 Haiti 821 1,244 766 1,078 389 39 0 0 27 0 29 166 Honduras 4,851 3,675 4,247 2,446 828 502 123 880 382 317 99 32 356 2011 World Development Indicators 6.10 GLOBAL LINKS External debt Total external Long-term Short-term Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 95,174 237,692 81,091 76,531 27,348 34,028 6,618 118,211 5,049 42,950 2,416 0 Indonesia 124,413 157,517 65,323 86,020 13,259 10,111 33,123 52,834 25,966 18,662 0 0 Iran, Islamic Rep. 21,565 13,435 15,116 7,524 316 836 0 0 6,449 5,911 0 0 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 4,581 10,959 3,721 6,664 595 398 128 3,241 492 1,054 240 0 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 7,661 6,615 6,624 5,445 806 1,109 0 0 785 1,158 251 12 Kazakhstan 3,750 109,873 2,834 2,487 295 547 103 98,710 381 8,676 432 0 Kenya 7,309 8,005 5,857 6,543 2,412 3,156 445 0 634 1,011 374 451 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kosovo .. 359 .. 359 .. 359 .. 0 .. 0 .. 0 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 609 2,900 472 2,320 141 656 0 332 13 81 124 167 Lao PDR 2,155 5,539 2,091 2,923 285 680 0 2,601 0 0 64 16 Latvia .. .. .. .. .. .. .. .. .. .. .. .. Lebanon 2,974 24,864 1,559 20,979 113 318 50 670 1,365 3,096 0 119 Lesotho 684 705 642 681 207 313 0 0 4 0 38 24 Liberia 2,466 1,660 1,153 677 269 69 0 0 978 92 336 891 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 769 31,717 430 9,059 62 23 29 16,708 49 5,949 262 0 Macedonia, FYR 1,277 5,589 788 1,874 181 653 289 1,816 143 1,900 57 0 Madagascar 4,302 2,213 3,687 1,846 1,121 1,105 0 4 542 262 73 101 Malawi 2,238 1,093 2,078 899 1,306 213 0 0 44 67 116 127 Malaysia 34,343 66,390 16,023 21,364 1,059 39 11,046 21,332 7,274 23,695 0 0 Mali 2,958 2,667 2,739 2,592 863 698 0 0 72 32 147 44 Mauritania 2,396 2,029 2,127 1,851 347 282 0 0 169 163 100 16 Mauritius 1,416 742 1,148 661 157 212 267 81 1 0 0 0 Mexico 165,379 192,008 93,902 99,374 13,823 10,143 18,348 69,299 37,300 23,335 15,828 0 Moldova 695 3,457 450 783 152 443 9 1,203 6 1,318 230 154 Mongolia 531 2,212 472 1,817 59 392 0 141 12 72 47 182 Morocco 23,771 23,752 23,190 19,219 3,999 2,557 331 2,354 198 2,179 52 0 Mozambique 7,458 4,168 5,209 3,354 890 1,356 1,769 0 279 643 202 171 Myanmar 5,771 8,186 5,378 6,320 777 777 0 0 393 1,866 0 0 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 2,410 3,683 2,339 3,563 1,023 1,483 0 0 23 44 48 76 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 10,396 4,420 8,572 2,461 341 418 0 1,093 1,785 716 39 150 Niger 1,604 991 1,347 909 598 266 133 7 72 18 52 57 Nigeria 34,092 7,846 28,140 4,157 3,489 2,852 301 175 5,651 3,514 0 0 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 30,169 53,710 23,727 41,484 6,403 11,844 1,593 3,265 3,235 1,466 1,613 7,495 Panama 6,098 12,418 3,781 11,282 175 435 0 1,136 2,207 0 111 0 Papua New Guinea 2,506 1,555 1,668 1,037 407 231 711 397 78 121 50 0 Paraguay 2,574 4,323 1,453 2,308 189 296 338 1,263 784 752 0 0 Peru 30,833 29,593 18,931 20,791 1,729 2,846 1,288 4,073 9,659 4,730 955 0 Philippines 39,379 62,911 28,525 41,738 5,185 2,669 4,847 17,171 5,279 4,002 728 0 Poland .. .. .. .. .. .. .. .. .. .. .. .. Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 357 6.10 External debt Total external Long-term Short-term Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 6,832 117,511 3,957 17,904 844 2,995 534 69,031 1,303 21,032 1,038 9,544 Russian Federation 121,401 381,339 101,582 99,990 1,524 3,211 0 250,725 10,201 30,624 9,617 0 Rwanda 1,029 747 971 725 512 254 0 0 32 6 26 15 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 3,916 3,503 3,266 2,961 1,160 921 44 357 260 18 347 167 Serbia 10,785a 33,402 6,788a 8,725 1,252a 2,459 1,773a 19,076 2,139a 4,000 84 a 1,601 Sierra Leone 1,220 444 1,028 371 234 124 0 0 27 0 165 73 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 2,678 2,973 1,961 1,987 432 448 0 0 551 810 166 176 South Africa 25,358 42,101 9,837 15,063 0 21 4,935 13,764 9,673 13,274 913 0 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 8,395 17,208 7,175 13,647 1,512 2,487 90 967 535 1,873 595 721 Sudan 17,603 20,139 9,779 12,998 1,279 1,306 496 0 6,368 6,739 960 403 Swaziland 249 418 238 391 25 10 0 0 11 27 0 0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 21,897 5,236 16,955 4,480 471 16 0 0 4,942 756 0 0 Tajikistan 634 2,514 590 1,603 0 373 0 855 43 15 0 41 Tanzania 7,365 7,325 6,204 4,637 2,269 2,598 0 1,016 964 1,342 197 329 Thailand 100,039 58,755 16,826 11,185 1,906 133 39,117 19,689 44,095 27,881 0 0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 1,476 1,640 1,286 1,502 541 586 0 0 85 47 105 91 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 10,818 21,709 9,022 14,837 1,766 1,405 193 2,070 1,310 4,801 293 0 Turkey 73,781 251,372 50,317 84,875 5,069 9,816 7,079 118,814 15,701 39,725 685 7,958 Turkmenistan 402 576 385 463 1 13 0 38 17 75 0 0 Uganda 3,609 2,490 3,089 2,245 1,792 1,379 0 0 103 235 417 9 Ukraine 8,429 93,153 6,581 10,449 491 3,294 84 51,857 223 19,873 1,542 10,974 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 5,318 12,159 3,833 10,955 513 1,099 127 80 1,336 1,124 21 0 Uzbekistan 1,799 4,109 1,415 3,238 157 368 15 727 212 144 157 0 Venezuela, RB 35,744 54,503 28,428 35,184 1,639 0 2,013 3,310 3,063 16,009 2,239 0 Vietnam 25,428 28,674 21,778 23,403 231 6,270 0 0 3,272 5,186 377 84 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,251 6,356 5,562 5,861 827 2,187 0 0 689 442 0 53 Zambia 6,958 3,049 5,291 1,210 1,434 407 13 1,020 415 474 1,239 345 Zimbabwe 4,989 5,015 3,462 3,742 896 985 381 89 685 1,068 461 116 World .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s Low income 130,267 135,593 109,551 110,863 33,428 39,578 2,818 5,946 11,139 12,833 6,760 5,951 Middle income 1,729,983 3,409,521 1,151,625 1,296,127 144,453 185,309 205,673 1,346,264 319,724 720,903 52,961 46,227 Lower middle income 841,940 1,417,085 578,607 597,241 97,821 123,481 94,497 394,555 156,647 402,423 12,188 22,866 Upper middle income 888,043 1,992,436 573,018 698,886 46,632 61,827 111,176 951,710 163,077 318,479 40,772 23,361 Low & middle income 1,860,250 3,545,114 1,261,176 1,406,990 177,881 224,887 208,491 1,352,210 330,863 733,736 59,721 52,179 East Asia & Pacific 455,544 825,602 255,399 293,956 37,604 44,253 89,982 208,994 108,826 322,361 1,337 291 Europe & Central Asia 246,178 1,126,252 189,044 269,524 11,522 33,110 10,256 656,239 31,250 165,385 15,628 35,103 Latin America & Carib. 608,666 912,980 371,875 432,115 38,485 41,907 87,303 340,984 122,856 138,637 26,632 1,243 Middle East & N. Africa 161,737 141,321 140,298 112,569 12,751 11,847 887 6,150 18,375 22,402 2,177 200 South Asia 152,282 339,983 129,636 159,965 42,036 61,257 8,301 122,442 9,051 48,495 5,293 9,081 Sub-Saharan Africa 235,842 198,976 174,924 138,861 35,483 32,512 11,760 17,399 40,504 36,456 8,654 6,261 High income .. .. .. .. .. .. .. .. .. .. .. .. Euro area .. .. .. .. .. .. .. .. .. .. .. .. a. Includes Montenegro. 358 2011 World Development Indicators 6.10 GLOBAL LINKS External debt About the data Definitions External indebtedness affects a country’s credit- Variations in reporting rescheduled debt also affect • Total external debt is debt owed to nonresident worthiness and investor perceptions. Data on exter- cross-country comparability. For example, reschedul- creditors and repayable in foreign currencies, goods, nal debt are gathered through the World Bank’s ing of official Paris Club creditors may be subject to or services by public and private entities in the coun- Debtor Reporting System. Indebtedness is calculated lags between completion of the general rescheduling try. It is the sum of long-term external debt, short- using loan-by-loan reports submitted by countries on agreement and completion of the specific bilateral term debt, and use of IMF credit. Debt repayable long-term public and publicly guaranteed borrowing agreements that define the terms of the rescheduled in domestic currency is excluded. • Long-term debt and information on short-term debt collected by the debt. Other areas of inconsistency include country is debt that has an original or extended maturity of countries or from creditors through the reporting sys- treatment of arrears and of nonresident national more than one year. It has three components: pub- tems of the Bank for International Settlements (BIS). deposits denominated in foreign currency. lic, publicly guaranteed, and private nonguaranteed These data are supplemented by information from Aggregate data on long-term private nonguaran- debt. • Public and publicly guaranteed debt com- major multilateral banks and official lending agen- teed debt are reported annually. DRS countries rec- prises the long-term external obligations of public cies in major creditor countries and by estimates by ognize the importance of monitoring borrowing by debtors, including the national government and politi- World Bank and International Monetary Fund (IMF) their private sector, particularly when it accounts for cal subdivisions (or an agency of either) and autono- staff. The table includes data on long-term private a significant share of total external debt, but many mous public bodies, and the external obligations of nonguaranteed debt reported to the World Bank or find doing so difficult. Detailed data are available private debtors that are guaranteed for repayment estimated by its staff. only from countries with registration requirements by a public entity. • IBRD loans and IDA credits are Data coverage, quality, and timeliness vary by coun- for private nonguaranteed debt, most commonly in extended by the World Bank. The International Bank try. Coverage varies for debt instruments and borrow- connection with exchange controls. Where formal for Reconstruction and Development (IBRD) lends ers. The widening spectrum of debt instruments and registration of private nonguaranteed debt is not at market rates. The International Development investors alongside the expansion of private nonguar- mandatory, compilers must rely on balance of pay- Association (IDA) provides credits at concessional anteed borrowing makes comprehensive coverage ments data and financial surveys. The data on private rates. • Private nonguaranteed debt consists of the of external debt more complex. Reporting countries nonguaranteed debt in the table are as reported or long-term external obligations of private debtors that differ in their capacity to monitor debt, especially estimated for countries where this type of external are not guaranteed for repayment by a public entity. private nonguaranteed debt. Even data on public debt is known to be significant. Estimates are based • Short-term debt is debt owed to nonresidents hav- and publicly guaranteed debt are affected by cover- on national data on quarterly external debt statistics. ing an original maturity of one year or less and inter- age and reporting accuracy—because of monitoring The DRS encourages debtor countries to volun- est in arrears on long-term debt and on the use of capacity and sometimes because of unwillingness to tarily provide information on their short-term external IMF credit. • Use of IMF credit denotes members’ provide information. A key part often underreported obligations. By its nature, short-term external debt drawings on the IMF other than those drawn against is military debt. Currently, 128 developing countries is diffi cult to monitor: loan-by-loan registration is the country’s reserve tranche position and includes report to the Debtor Reporting System (DRS). Nonre- normally impractical, and monitoring systems typi- purchases and drawings under the Extended Credit porting countries might have outstanding debt with cally rely on information requested periodically by Facility, Standby Credit Facility, Rapid Credit Facility, the World Bank, other international financial institu- the central bank from the banking sector. The World Stand-By Arrangements, Flexible Credit Line, and the tions, and private creditors. Bank regards the debtor country as the authorita- Extended Fund Facility. Debt data, normally reported in the currency of tive source of information on its short-term debt. repayment, are converted into U.S. dollars to pro- Where such information is not available from the duce summary tables. Stock fi gures (amount of debtor country, data from creditor sources may be debt outstanding) are converted using end-of-period used as an indication of the magnitude of a coun- exchange rates, as published in the IMF’s Interna- try’s short-term external debt. These data are derived tional Financial Statistics (line ae). Flow figures are from BIS data on international bank lending based converted at annual average exchange rates (line on time remaining to original maturity. The data are rf). Projected debt service is converted using end- reported based on residual maturity, but an estimate of-period exchange rates. Debt repayable in multiple of short-term external liabilities by original maturity currencies, goods, or services and debt with a provi- can be derived by deducting from claims due in one Data sources sion for maintenance of the value of the currency of year those that, 12 months earlier, had a maturity repayment are shown at book value. of between one and two years. However, not all com- Data on external debt are mainly from reports to Because flow data are converted at annual aver- mercial banks report to the BIS in a way that allows the World Bank through its Debtor Reporting Sys- age exchange rates and stock data at end-of-period the full maturity distribution to be determined, and tem from member countries that have received exchange rates, year-to-year changes in debt out- the BIS data include liabilities only to banks within IBRD loans or IDA credits, with additional infor- standing and disbursed are sometimes not equal to the BIS reporting area. The results should thus be mation from the files of the World Bank, the IMF, net flows (disbursements less principal repayments); interpreted with caution. the African Development Bank and African Devel- similarly, changes in debt outstanding, including Data related to the operations of the IMF are pro- opment Fund, the Asian Development Bank and undisbursed debt, differ from commitments less vided by the IMF Treasurer’s Department. They are Asian Development Fund, and the Inter-American repayments. Discrepancies are particularly notable converted from special drawing rights into U.S. dol- Development Bank. Summary tables of the exter- when exchange rates have moved sharply during lars using end-of-period exchange rates for stocks nal debt of developing countries are published the year. Cancellations and reschedulings of other and average-over-the-period exchange rates for flows. annually in the World Bank’s Global Development liabilities into long-term public debt also contribute The IMF’s loan instruments have changed over time to Finance, Global Development Finance CD-ROM, to the differences. address the specific circumstances of its members. and Global Development Finance database. 2011 World Development Indicators 359 6.11 Ratios for external debt Total Total debt Multilateral Short-term Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNIa and incomea 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 2009 2009 Afghanistan .. .. .. 0.4 .. 73.4 .. 0.9 .. .. 5 25 Albania 18.5 40.3 2.8 6.9 11.4 43.1 13.7 17.7 23.5 35.2 31 96 Algeria 83.5 3.8 .. .. 17.7 0.4 0.8 27.9 6.3 1.0 3 5 Angola 311.9 28.2 12.0 8.4 0.6 0.3 17.0 15.8 919.7 19.3 24 21 Argentina 38.9 40.1 30.2 17.3 21.6 44.7 21.7 16.3 133.6 40.7 41 156 Armenia 25.3 55.3 3.2 20.9 69.8 55.6 0.6 10.4 1.9 25.5 36 148 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 10.6 12.1 1.3 1.7 21.8 28.4 4.4 16.6 11.6 15.1 10 14 Bangladesh 40.2 24.0 16.1 5.6 28.0 70.2 1.3 8.1 8.4 18.8 17 90 Belarus 12.2 35.6 3.4 5.0 55.4 3.1 6.5 46.8 29.2 142.3 30 51 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 71.2 16.1 7.5 .. 54.6 74.5 3.4 4.2 23.7 3.6 12b 62b Bolivia 81.2 34.5 29.5 14.4 75.5 84.0 5.8 9.6 30.5 6.5 16b 34b Bosnia and Herzegovina .. 54.6 .. 10.5 .. 72.7 .. 17.5 .. 51.7 45 106 Botswana 15.1 14.1 3.1 1.2 76.0 59.6 1.4 14.2 0.2 2.6 8 16 Brazil 21.2 17.9 38.5 23.4 18.5 28.3 19.5 14.4 60.7 16.7 17 125 Bulgaria 81.9 90.4 16.5 21.3 10.5 55.1 4.9 45.8 31.3 100.3 85 132 Burkina Faso 53.6 22.9 .. .. 76.7 65.0 4.4 0.0 16.1 0.0 17b 154b Burundi 117.6 38.9 27.6 .. 70.6 95.8 1.3 1.4 6.9 2.3 13b 143b Cambodia 67.6 45.0 0.7 0.8 11.9 75.1 4.5 6.1 53.1 8.1 38 60 Cameroon 133.4 13.6 21.0 7.4 61.0 37.3 9.0 0.8 6,444.5 0.6 4b 12b Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 85.9 20.0 .. .. 100.0 65.0 6.0 17.0 24.0 31.9 12b 75b Chad 58.5 28.6 .. 2.8 86.1 85.3 2.0 0.2 11.6 0.6 22b 41b Chile 32.1 46.7 24.5 22.6 76.2 3.7 15.6 24.4 23.1 69.1 43 84 China 16.5 8.7 9.9 2.9 7.6 27.2 18.9 56.1 27.8 9.8 9 25 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 27.5 23.6 33.5 22.4 32.7 36.6 22.1 7.9 65.6 16.4 20 111 Congo, Dem. Rep. 271.4 121.4 .. .. .. 35.5 23.6 4.9 1,980.9 36.9 24b 71b Congo, Rep. 479.3 83.8 13.5 .. 21.1 18.4 17.0 4.2 1,575.1 5.6 20 b 18b Costa Rica 32.8 28.1 14.2 9.6 50.6 25.4 11.4 29.0 40.5 57.5 27 50 Côte d’Ivoire 188.7 53.0 23.1 9.5 59.3 96.0 20.7 0.8 739.1 3.0 46b 88b Croatia .. .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 28.5 24.6 7.0 12.1 39.8 25.2 13.8 15.3 165.3 57.8 22 73 Ecuador 72.0 23.3 26.6 40.8 32.0 12.9 9.5 11.0 73.4 37.4 23 59 Egypt, Arab Rep. 55.8 17.6 16.0 6.5 26.3 30.2 7.1 7.7 13.9 7.3 16 53 El Salvador 26.4 54.3 13.4 25.2 55.1 67.9 20.9 18.6 55.9 67.7 49 162 Eritrea 6.3 .. 0.1 .. 100.0 56.1 0.0 0.6 0.0 .. 34b 811b Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 136.8 17.6 18.5 3.1 41.9 45.8 4.5 0.9 56.5 2.5 12b 89b Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 101.6 22.3 15.3 8.1 17.9 16.9 6.6 5.1 187.8 5.4 19 18 Gambia, The 113.0 75.3 15.5 .. 49.1 51.4 3.5 8.1 14.0 18.9 30 b 81b Georgia 48.2 40.0 .. 7.3 0.4 47.4 6.9 7.8 43.0 15.7 28 80 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 86.9 37.3 24.2 2.9 48.4 18.7 11.3 23.1 77.1 .. 27b 60 b Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 22.6 38.8 12.5 18.4 47.5 74.9 24.7 8.9 103.6 23.6 33 126 Guinea 90.0 48.3 24.9 .. 30.5 62.8 5.0 1.4 188.9 .. 44b 152b Guinea-Bissau 379.4 253.2 52.4 .. 86.3 100.0 10.6 13.6 469.2 89.5 203b 647b Haiti .. .. 51.0 4.6 92.2 81.0 3.2 0.0 13.4 0.0 15b 113b Honduras 132.9 25.9 34.7 6.8 55.9 44.0 7.9 8.6 141.7 .. 13b 25b 360 2011 World Development Indicators 6.11 GLOBAL LINKS Ratios for external debt Total Total debt Multilateral Short-term Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNIa and incomea 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 2009 2009 Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 27.0 18.2 34.4 5.9 24.2 31.9 5.3 18.1 22.1 15.1 17 71 Indonesia 63.4 30.2 30.3 18.4 28.4 25.3 20.9 11.8 174.2 28.2 30 99 Iran, Islamic Rep. 23.9 4.1 29.7 .. 1.3 4.2 29.9 44.0 .. .. 4 .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 82.3 77.8 18.8 33.9 40.6 17.6 10.7 9.6 72.2 50.8 82 178 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 118.8 28.3 16.7 4.8 33.5 50.7 10.2 17.5 34.4 9.5 27 46 Kazakhstan 18.5 113.0 3.9 80.2 7.8 45.7 10.2 7.9 23.0 37.4 96 157 Kenya 83.8 26.5 25.3 5.0 32.5 40.7 8.7 12.6 164.9 26.3 19 72 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kosovo .. 6.4 .. 20.8 .. 100.0 .. 0.0 .. 0.0 4 25 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 37.5 65.8 13.3 14.0 59.0 78.1 2.1 2.8 9.7 5.1 36b 62b Lao PDR 122.6 95.5 6.1 .. 37.4 79.7 0.0 0.0 0.0 0.0 78 233 Latvia .. .. .. .. .. .. .. .. .. .. .. .. Lebanon 24.4 70.7 .. 18.0 13.5 5.7 45.9 12.5 16.9 7.9 80 105 Lesotho 55.8 33.2 6.1 3.0 60.3 81.7 0.6 0.0 0.9 .. 19 27 Liberia .. 257.5 .. .. .. 30.3 39.6 5.5 3,481.0 .. 316b 347b Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 9.8 85.3 1.3 31.0 31.8 8.9 6.4 18.8 6.0 89.4 72 120 Macedonia, FYR 29.0 62.2 .. 14.8 99.9 63.9 11.2 34.0 51.9 83.0 59 100 Madagascar 143.3 .. 7.7 2.3 74.3 61.9 12.6 11.8 497.1 23.0 17b 59b Malawi 165.8 24.7 24.9 .. 51.4 33.4 1.9 6.1 37.8 41.0 16b 65b Malaysia 40.6 35.8 7.0 5.2 15.5 1.9 21.2 35.7 29.5 24.5 31 27 Mali 122.3 29.6 16.1 .. 45.5 57.5 2.4 1.2 22.2 2.0 14b 51b Mauritania 175.3 66.6 23.1 .. 49.6 58.1 7.1 8.0 187.9 68.4 83b 153b Mauritius 35.2 8.4 8.7 2.7 34.5 35.6 0.1 0.0 0.1 0.0 7 11 Mexico 60.5 22.3 28.1 16.0 19.5 9.3 22.6 12.2 218.8 23.4 18 61 Moldova 40.3 59.7 7.9 14.9 79.1 43.1 0.9 38.1 2.3 89.0 55 109 Mongolia 44.2 55.8 10.2 4.8 2.8 29.9 2.2 3.3 7.4 5.4 35 57 Morocco 75.1 26.4 40.4 12.5 30.3 49.8 0.8 9.2 5.1 9.2 23 65 Mozambique 360.6 43.0 34.5 1.6 17.4 71.4 3.7 15.4 142.8 .. 18b 53b Myanmar .. .. 18.9 .. 15.0 8.4 6.8 22.8 60.4 .. .. .. Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 54.7 28.7 7.9 10.4 54.2 77.6 0.9 1.2 3.5 .. 23 154 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 368.6 76.2 43.1 17.2 30.3 49.8 17.2 16.2 1,256.8 45.5 36b 68b Niger 87.6 18.8 17.1 4.5 95.5 92.0 4.5 1.9 75.6 2.8 13b 67b Nigeria 131.7 5.1 14.7 0.8 45.4 61.0 16.6 44.8 330.7 7.7 4 8 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 49.4 31.3 30.9 15.0 43.2 50.5 10.7 2.7 128.0 10.8 24 157 Panama 80.9 52.5 3.4 5.5 52.7 22.5 36.2 0.0 282.4 0.0 54 66 Papua New Guinea 57.3 19.9 20.8 11.7 31.7 58.0 3.1 7.8 29.1 4.6 18 21 Paraguay 31.5 29.5 5.8 6.1 48.0 52.6 30.4 17.4 70.8 19.5 26 48 Peru 60.3 24.8 17.3 11.8 49.9 33.2 31.3 16.0 111.6 14.2 23 78 Philippines 51.7 39.2 16.3 18.5 29.2 13.5 13.4 6.4 67.8 9.1 35 90 Poland .. .. .. .. .. .. .. .. .. .. .. .. Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 361 6.11 Ratios for external debt Total Total debt Multilateral Short-term Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNIa and incomea 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 2009 2009 Romania 19.4 71.6 10.5 31.4 21.3 44.1 19.1 17.9 49.7 47.4 53 166 Russian Federation 31.0 31.9 6.3 17.7 9.7 4.7 8.4 8.0 56.6 7.0 26 74 Rwanda 79.2 14.9 20.5 4.7 99.0 70.4 3.1 0.8 32.3 0.9 8b 64b Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 82.9 27.1 17.8 .. 62.2 59.1 6.6 0.5 95.6 0.8 20 b 73b Serbia .. 79.7 .. 37.1 100.0 c 51.9 19.8 c 12.0 .. 26.3 71 223 Sierra Leone 149.0 23.4 63.6 2.2 8.4 62.9 2.2 0.0 77.8 0.0 20 b 104b Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. 20.6 27.2 .. .. .. .. South Africa 17.1 15.1 9.5 9.3 0.0 2.5 38.1 31.5 216.7 33.5 15 44 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 65.3 41.5 9.3 15.6 14.0 20.0 6.4 10.9 25.3 35.0 35 136 Sudan 136.3 40.5 10.1 5.8 100.0 22.3 36.2 33.5 3,898.2 615.9 73b 352b Swaziland 14.0 15.4 1.5 2.1 64.0 82.8 4.5 6.5 3.7 2.8 13 16 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 188.9 10.3 4.5 .. 55.3 30.2 22.6 14.4 1,102.7 4.1 9 24 Tajikistan 53.6 51.2 .. 38.4 .. 39.8 6.8 0.6 .. .. 39 114 Tanzania 143.5 34.0 17.4 3.5 66.7 69.6 13.1 18.3 356.6 38.7 13b 57b Thailand 60.5 23.3 11.6 6.8 20.9 4.8 44.1 47.5 119.4 20.1 22 28 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 116.7 57.5 6.2 .. 75.5 98.3 5.8 2.9 65.1 6.7 50 b 136b Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 63.0 58.2 18.3 10.1 45.2 41.7 12.1 22.1 77.6 42.5 54 80 Turkey 44.4 41.2 30.1 41.6 20.7 13.4 21.3 15.8 113.0 53.0 35 144 Turkmenistan 16.1 3.0 .. .. 1.9 2.2 4.3 13.0 1.5 .. 3 4 Uganda 63.3 16.2 19.8 2.0 69.7 66.0 2.8 9.4 22.4 7.9 8b 34b Ukraine 17.8 83.8 6.6 36.2 13.6 16.9 2.6 21.3 20.9 75.0 62 123 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 28.0 34.5 22.1 21.0 27.3 22.3 25.1 9.2 73.7 14.0 37 121 Uzbekistan 13.5 12.5 .. .. 1.9 21.6 11.8 3.5 .. .. 12 29 Venezuela, RB 49.0 16.7 22.9 6.4 11.6 13.4 8.6 29.4 28.6 46.6 19 66 Vietnam 124.0 32.3 .. 1.8 2.9 18.3 12.9 18.1 247.2 31.5 27 34 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 169.9 25.5 4.6 .. 78.3 58.7 11.0 7.0 107.9 6.3 17 47 Zambia 215.1 26.8 .. 3.8 50.6 48.6 6.0 15.6 186.2 25.1 10 b 24b Zimbabwe 73.5 .. .. .. 33.6 0.0 13.7 21.3 77.2 .. .. 335 World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. .. Low income 88.4 30.9 .. 3.9 40.2 57.1 8.6 9.5 96.0 16.2 .. .. Middle income 36.8 21.8 18.0 11.6 22.5 20.2 18.5 21.1 72.7 14.9 .. .. Lower middle income 40.4 15.6 17.2 6.2 25.5 27.9 18.6 28.4 70.4 12.2 .. .. Upper middle income 33.9 30.3 18.6 19.5 20.0 15.4 18.4 16.0 74.8 21.0 .. .. Low & middle income 38.8 22.1 18.0 11.3 23.0 21.0 17.8 20.7 73.3 15.0 .. .. East Asia & Pacific 35.5 13.2 12.7 4.8 18.2 18.4 23.9 39.0 64.9 11.3 .. .. Europe & Central Asia 32.7 44.7 10.9 26.9 16.6 13.3 12.7 14.7 67.6 24.4 .. .. Latin America & Carib. 35.8 23.7 27.3 17.9 26.2 23.3 20.2 15.2 88.6 25.0 .. .. Middle East & N. Africa 59.2 15.4 21.1 .. 19.7 23.3 11.4 15.9 31.1 .. .. .. South Asia 32.2 20.7 29.7 6.8 27.4 38.4 5.9 14.3 29.5 15.4 .. .. Sub-Saharan Africa 76.1 22.9 16.2 5.9 35.0 25.1 17.2 18.3 193.5 21.3 .. .. High income .. .. .. .. .. .. .. .. .. .. .. .. Euro area .. .. .. .. .. .. .. .. .. .. .. .. a. The numerator refers to 2009, whereas the denominator is a three-year average of 2007–09 data. b. Data are from debt sustainability analyses for low-income countries. Present value estimates for these countries are for public and publicly guaranteed debt only. c. Includes Montenegro. 362 2011 World Development Indicators 6.11 GLOBAL LINKS Ratios for external debt About the data Definitions A country’s external debt burden, both debt outstand- value of external debt provides a measure of future • Total external debt is debt owed to nonresidents ing and debt service, affects its creditworthiness debt service obligations. and comprises public, publicly guaranteed, and pri- and vulnerability. The table shows total external debt The present value of external debt is calculated by vate nonguaranteed long-term debt, short-term debt, relative to a country’s size—gross national income discounting the debt service (interest plus amortiza- and use of IMF credit. It is presented as a share of (GNI). Total debt service is contrasted with countries’ tion) due on long-term external debt over the life of GNI. •  Total debt service is the sum of principal ability to obtain foreign exchange through exports of existing loans. Short-term debt is included at face repayments and interest actually paid in foreign cur- goods, services, income, and workers’ remittances. value. The data on debt are in U.S. dollars converted rency, goods, or services on long-term debt; inter- Multilateral debt service (shown as a share of the at official exchange rates (see About the data for est paid on short-term debt; and repayments (repur- country’s total public and publicly guaranteed debt table 6.10). The discount rate on long-term debt chases and charges) to the IMF. • Exports of goods, service) are obligations to international financial depends on the currency of repayment and is based services, and income are the total value of exports institutions, such as the World Bank, the Interna- on commercial interest reference rates established of goods and services, receipts of compensation of tional Monetary Fund (IMF), and regional develop- by the Organisation for Economic Co-operation and nonresident workers, and investment income from ment banks. Multilateral debt service takes priority Development. Loans from the International Bank abroad. • Multilateral debt service is the repayment over private and bilateral debt service, and borrowers for Reconstruction and Development (IBRD), cred- of principal and interest to the World Bank, regional must stay current with multilateral debts to remain its from the International Development Association development banks, and other multilateral and inter- creditworthy. While bilateral and private creditors (IDA), and obligations to the IMF are discounted using governmental agencies. • Short-term debt includes often write off debts, international financial institu- a special drawing rights reference rate. When the all debt having an original maturity of one year or less tion bylaws prohibit granting debt relief or canceling discount rate is greater than the loan interest rate, and interest in arrears on long-term debt. •  Total debts directly. However, the recent decrease in multi- the present value is less than the nominal sum of reserves comprise holdings of monetary gold, spe- lateral debt service ratios for some countries reflects future debt service obligations. cial drawing rights, reserves of IMF members held debt relief from special programs, such as the Heav- Debt ratios are used to assess the sustainability of by the IMF, and holdings of foreign exchange under ily Indebted Poor Countries (HIPC) Debt Initiative and a country’s debt service obligations, but no absolute the control of monetary authorities. • Present value the Multilateral Debt Relief Initiative (MDRI) (see rules determine what values are too high. Empirical of debt is the sum of short-term external debt plus table 1.4.) Other countries have accelerated repay- analysis of developing countries’ experience and the discounted sum of total debt service payments ment of debt outstanding. Indebted countries may debt service performance shows that debt service due on public, publicly guaranteed, and private non- also apply to the Paris and London Clubs to renegoti- difficulties become increasingly likely when the pres- guaranteed long-term external debt over the life of ate obligations to public and private creditors. ent value of debt reaches 200 percent of exports. existing loans. Because short-term debt poses an immediate Still, what constitutes a sustainable debt burden var- burden and is particularly important for monitoring ies by country. Countries with fast-growing econo- vulnerability, it is compared with the total debt and mies and exports are likely to be able to sustain foreign exchange reserves that are instrumental in higher debt levels. providing coverage for such obligations. The present Data sources Ratio of debt services to exports for middle-income economies have sharply increased in 2009 as export revenues declined 6.11a Data on external debt are mainly from reports to the World Bank through its Debtor Reporting Sys- Total debt service (% of exports of goods, services, and income) 30 tem from member countries that have received IBRD loans or IDA credits, with additional infor- mation from the files of the World Bank, the IMF, 20 the African Development Bank and African Devel- opment Fund, the Asian Development Bank and Middle-income economies Asian Development Fund, and the Inter-American 10 Development Bank. Data on GNI, exports of goods Low-income economies and services, and total reserves are from the World Bank’s national accounts files and the IMF’s 0 Balance of Payments and International Financial 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Statistics databases. Summary tables of the exter- Due to global financial crisis, export revenues in 2009 declined by 20 percent for middle-income economies, and nal debt of developing countries are published by 8 percent for low-income economies. Reduction in export revenues caused sharp raise in the ratio of debt annually in the World Bank’s Global Development service to exports, which has been declining since 2000 thanks to debt reduction efforts and export growth. Finance, Global Development Finance CD-ROM, Source: Global Development Finance data files. and Global Development Finance database. 2011 World Development Indicators 363 6.12 Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan 0 185 .. .. .. 0 .. 0 Albania 70 978 0 4 0 0 0 451 Algeria .. 2,847 .. .. –278 0 788 –607 Angola 472 2,205 0 0 0 0 123 156 Argentina 5,609 3,902 1,552 –212 3,705 –1,114 754 –1,849 Armenia 25 777 .. 1 0 0 0 42 Australia 12,026 22,572 2,585 .. .. .. .. .. Austria 1,901 8,714 1,262 498 .. .. .. .. Azerbaijan 330 473 .. 0 0 0 0 400 Bangladesh 2 674 –15 –154 0 0 –20 –13 Belarus 15 1,884 .. 1 0 0 103 –31 Belgium 10,689a –38,860 6,505a –3,242 .. .. .. .. Benin 13 93 0 .. 0 0 0 0 Bolivia 393 423 0 0 0 –10 41 –156 Bosnia and Herzegovina .. 235 .. .. .. 0 .. –40 Botswana 70 252 6 18 0 0 -6 –1 Brazil 4,859 25,949 2,775 37,071 2,636 19,111 8,283 4,731 Bulgaria 90 4,595 0 8 –6 –372 –93 304 Burkina Faso 10 171 .. .. 0 0 0 –3 Burundi 2 0 0 .. 0 0 –1 0 Cambodia 151 530 .. 0 0 0 13 0 Cameroon 7 340 0 0 0 0 –65 –12 Canada 9,319 19,898 –3,077 23,349 .. .. .. .. Central African Republic 6 42 .. .. 0 0 0 0 Chad 33 462 .. .. 0 0 0 0 Chile 2,957 12,702 –249 316 489 1,900 1,773 2,572 China 35,849 78,193 0 28,161 317 –39 4,696 –12,050 Hong Kong SAR, China .. 52,395 .. 9,492 .. .. .. .. Colombia 968 7,207 165 67 1,008 6,768 1,250 –1,018 Congo, Dem. Rep. –22 951 0 .. 0 0 0 –61 Congo, Rep. 125 2,083 0 .. 0 0 –53 –1 Costa Rica 337 1,347 0 0 –4 –225 –20 538 Côte d’Ivoire 211 381 1 –9 0 0 14 –143 Croatia 108 2,951 4 23 .. .. .. .. Cuba .. .. .. .. .. .. .. .. Czech Republic 2,568 2,666 1,236 -311 .. .. .. .. Denmark 4,139 2,905 .. 8,152 .. .. .. .. Dominican Republic 414 2,067 .. 0 0 –125 –31 –213 Ecuador 452 316 13 2 0 –2,987 59 –997 Egypt, Arab Rep. 598 6,712 0 393 0 0 –311 –33 El Salvador 38 431 0 0 0 0 –31 175 Eritrea .. 0 .. .. 0 0 0 0 Estonia 201 1,751 10 –131 .. .. .. .. Ethiopia 14 221 .. 0 0 0 –48 1,019 Finland 1,044 60 2,027 –273 .. .. .. .. France 23,736 59,989 6,823 68,285 .. .. .. .. Gabon –315 33 .. .. 0 –44 –75 74 Gambia, The 8 39 .. 0 0 0 0 0 Georgia .. 658 .. 13 0 0 0 135 Germany 11,985 39,153 –1,513 11,806 .. .. .. .. Ghana 107 1,685 0 0 0 0 38 224 Greece 1,053 2,419 0 764 .. .. .. .. Guatemala 75 600 .. 0 44 –50 –34 -574 Guinea 1 50 .. 0 0 0 –15 4 Guinea-Bissau 0 14 .. .. 0 0 0 0 Haiti 7 38 .. 0 0 0 0 0 Honduras 50 500 0 0 –13 50 38 222 364 2011 World Development Indicators 6.12 GLOBAL LINKS Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 4,804 2,783 –62 954 .. .. .. .. India 2,144 34,577 1,590 21,112 285 1,822 955 8,343 Indonesia 4,346 4,877 1,493 787 2,248 5,112 60 5,872 Iran, Islamic Rep. 17 3,016 0 .. 0 0 –37 –1,417 Iraq 2 1,070 .. .. .. .. .. .. Ireland 1,447 25,233 0 29,184 .. .. .. .. Israel 1,350 3,894 991 2,122 .. .. .. .. Italy 4,842 28,976 5,358 20,915 .. .. .. .. Jamaica 147 541 0 0 13 740 15 –62 Japan 39 11,834 50,597 12,432 .. .. .. .. Jordan 13 2,382 0 -30 0 -2 –201 –3 Kazakhstan 964 13,619 .. 46 0 -2,108 240 6,554 Kenya 42 141 5 3 0 0 –163 24 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1,776 1,506 4,219 25,661 .. .. .. .. Kosovo .. 406 .. 0 .. 0 .. 0 Kuwait 7 145 0 0 .. .. .. .. Kyrgyz Republic 96 189 .. 1 0 0 0 29 Lao PDR 95 319 0 0 0 0 0 387 Latvia 180 94 0 –8 .. .. .. .. Lebanon .. 4,804 .. 929 350 789 333 –41 Lesotho 275 63 .. .. 0 0 12 –1 Liberia 5 218 .. 0 0 0 0 –32 Libya –88 1,711 .. 0 .. .. .. .. Lithuania 73 230 6 –2 0 2,488 55 –1,971 Macedonia, FYR 9 248 .. –14 0 244 0 244 Madagascar 10 543 .. .. 0 0 –4 0 Malawi 6 60 .. .. 0 0 –23 0 Malaysia 4,178 1,387 0 –449 2,440 143 1,231 –1,592 Mali 111 109 .. .. 0 0 0 1 Mauritania 7 –38 0 .. 0 0 0 –1 Mauritius 19 257 22 –33 150 0 126 29 Mexico 9,526 14,462 519 4,169 3,758 7,499 1,401 –9,314 Moldova 26 128 –1 2 0 –6 24 –18 Mongolia 10 624 0 4 0 0 –14 46 Morocco 92 1,970 20 –4 0 0 158 –61 Mozambique 45 881 0 0 0 0 24 20 Myanmar 280 323 .. .. 0 0 36 0 Namibia 153 490 46 4 .. .. .. .. Nepal .. 38 0 .. 0 0 –5 –1 Netherlands 12,206 33,287 –743 19,256 .. .. .. .. New Zealand 3,316 –1,259 .. 967 .. .. .. .. Nicaragua 89 434 0 0 0 0 –81 –75 Niger 7 739 .. .. 0 0 –24 –7 Nigeria 1,079 5,787 0 522 0 0 –448 –55 Norway 2,393 11,271 636 2,470 .. .. .. .. Oman 46 2,210 0 326 .. .. .. .. Pakistan 723 2,387 10 –37 0 –500 317 26 Panama 223 1,773 0 0 0 1,323 –12 70 Papua New Guinea 455 423 .. .. –32 0 –311 25 Paraguay 103 205 0 0 0 0 –16 425 Peru 2,557 4,760 171 47 0 2,828 43 –258 Philippines 1,478 1,948 0 –1,096 1,110 3,527 –215 –783 Poland 3,659 13,796 219 1,579 .. .. .. .. Portugal 685 2,808 –179 1,616 .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. 2011 World Development Indicators 365 6.12 Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2009 1995 2009 1995 2009 1995 2009 Romania 419 6,310 0 7 0 32 413 7,022 Russian Federation 2,065 36,751 47 3,369 –810 –1,968 444 7,328 Rwanda 2 119 0 0 0 0 0 0 Saudi Arabia –1,875 10,499 0 .. .. .. .. .. Senegal 32 208 4 .. 0 200 –25 157 Serbia 45b 1,921 .. 23 0 0 0 104 Sierra Leone 7 74 0 6 0 0 –28 0 Singapore 11,535 16,809 –159 2,058 .. .. .. .. Slovak Republic 236 –31 –16 182 .. .. .. .. Slovenia 150 –579 .. 31 .. .. .. .. Somalia 1 108 .. .. 0 0 0 0 South Africa 1,248 5,354 2,914 9,364 731 1,750 748 2,291 Spain 8,086 6,451 4,216 9,378 .. .. .. .. Sri Lanka 56 404 .. –382 0 400 103 238 Sudan 12 2,682 0 0 0 0 0 0 Swaziland 52 66 1 –7 0 0 0 0 Sweden 14,939 11,538 1,853 1,400 .. .. .. .. Switzerland 4,158 27,588 5,851 9,241 .. .. .. .. Syrian Arab Republic 100 1,434 0 .. 0 0 –1 0 Tajikistan 10 16 .. 0 0 0 0 –54 Tanzania 120 415 0 3 0 0 18 84 Thailand 2,068 4,976 2,253 1,334 2,123 –341 3,702 –1,134 Timor-Leste .. .. .. .. .. .. .. .. Togo 26 50 0 .. 0 0 0 0 Trinidad and Tobago 299 709 17 .. .. .. .. .. Tunisia 264 1,595 12 –89 588 –313 –96 30 Turkey 885 8,403 195 2,827 627 1,152 174 –12,036 Turkmenistan 233 1,355 .. .. 0 0 20 –24 Uganda 121 604 0 122 0 0 –9 0 Ukraine 267 4,816 .. 105 –200 –1,115 –19 –1,605 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 21,731 72,924 8,070 78,845 .. .. .. .. United States 57,800 134,710 16,523 160,534 .. .. .. .. Uruguay 157 1,262 0 -12 144 –420 39 –19 Uzbekistan –24 750 .. .. 0 0 201 –118 Venezuela, RB 985 –3,105 270 121 –468 4,992 -216 –322 Vietnam 1,780 7,600 .. 128 0 –20 356 –1 West Bank and Gaza 123 .. 0 .. .. .. .. .. Yemen, Rep. –218 129 .. 0 0 0 –2 –1 Zambia 97 699 .. –13 0 0 –37 –36 Zimbabwe 118 60 .. .. –30 0 140 0 World 340,573 s 1,163,874 s 127,074 s 744,295 s .. s .. s .. s .. s Low income 1,540 10,950 –10 –33 –30 0 –107 1,601 Middle income 93,318 348,451 13,835 108,577 20,954 51,121 26,661 88 Lower middle income 54,045 177,583 5,397 50,913 6,470 8,555 8,991 –2,246 Upper middle income 39,273 170,868 8,438 57,663 14,484 42,566 17,670 2,335 Low & middle income 94,858 359,401 13,824 108,544 20,924 51,121 26,554 1,689 East Asia & Pacific 50,797 101,428 3,746 28,868 8,206 8,383 9,554 –9,217 Europe & Central Asia 5,599 86,067 248 6,386 –389 –1,653 1,563 6,921 Latin America & Carib. 30,212 76,629 5,216 41,570 11,311 40,290 13,240 –6,172 Middle East & N. Africa 907 27,766 32 1,200 660 473 632 –2,132 South Asia 2,931 38,414 1,585 20,539 285 1,722 1,350 8,575 Sub-Saharan Africa 4,411 29,096 2,998 9,981 851 1,906 214 3,715 High income 245,715 804,473 113,249 635,751 .. .. .. .. Euro area 89,322 371,020 23,747 296,975 .. .. .. .. a. Includes Luxembourg. b. Includes Montenegro. 366 2011 World Development Indicators 6.12 GLOBAL LINKS Global private financial flows About the data Definitions Private financial flows—equity and debt—account for International Development Association. The reports • Foreign direct investment is net inflows of invest- the bulk of development finance. Equity flows com- are cross-checked with data from market sources ment to acquire a lasting interest in or management prise foreign direct investment (FDI) and portfolio that include transactions data. Information on private control over an enterprise operating in an economy equity. Debt flows are financing raised through bond nonguaranteed bonds and bank lending is collected other than that of the investor. It is the sum of equity issuance, bank lending, and supplier credits. Data from market sources when data are not reported by capital, reinvested earnings, other long-term capi- on equity flows are based on balance of payments countries to the Debtor Reporting System. tal, and short-term capital, as shown in the balance data reported by the International Monetary Fund Data on equity flows are shown for all countries of payments. Net inflows refer to new investments (IMF). FDI data are supplemented by staff estimates for which data are available. Debt flows are shown made during the reporting period netted against dis- using data from the United Nations Conference on only for 128 developing countries that report to the investments. • Portfolio equity includes net inflows Trade and Development and official national sources. Debtor Reporting System; nonreporting countries from equity securities other than those recorded The internationally accepted definition of FDI (from may also receive debt flows. as direct investment and including shares, stocks, the fifth edition of the IMF’s Balance of Payments The volume of global private fi nancial fl ows depository receipts, and direct purchases of shares Manual [1993]), includes three components: equity reported by the World Bank generally differs from in local stock markets by foreign investors • Bonds investment, reinvested earnings, and short- and that reported by other sources because of differ- are securities issued with a fixed rate of interest for a long-term loans between parent firms and foreign ences in sources, classification of economies, and period of more than one year. They include net flows affiliates. Distinguished from other kinds of interna- method used to adjust and disaggregate reported through cross–border public and publicly guaranteed tional investment, FDI is made to establish a lasting information. In addition, particularly for debt financ- and private nonguaranteed bond issues. • Commer- interest in or effective management control over an ing, differences may also reflect how some install- cial bank and other lending includes net commercial enterprise in another country. A lasting interest in ments of the transactions and certain offshore issu- bank lending (public and publicly guaranteed and pri- investment enterprise typically involves establish- ances are treated. vate nonguaranteed) and other private credits. ing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield invest- ment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisi- tion, where the investor acquires an existing enter- prise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. FDI data do not give a complete picture of inter- national investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as Data sources intrafirm flows of goods and services. For a detailed discussion of the data issues, see the World Bank’s Data on equity and debt flows are compiled from a World Debt Tables 1993–94 (vol. 1, chap. 3). variety of public and private sources, including the Statistics on bonds, bank lending, and supplier World Bank’s Debtor Reporting System, the IMF’s credits are produced by aggregating transactions of International Financial Statistics and Balance of public and publicly guaranteed debt and private non- Payments databases, and Dealogic. These data guaranteed debt. Data on public and publicly guar- are also published annually in the World Bank’s anteed debt are reported through the Debtor Report- Global Development Finance, Global Develop- ing System by World Bank member economies that ment Finance CD-ROM, and Global Development have received loans from the International Bank for Finance database. Reconstruction and Development or credits from the 2011 World Development Indicators 367 6.13 Net official financial flows Total International financial institutions United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b,c IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Afghanistan 1.0 194.1 26.7 0.0 17.4 0.0 73.9 0.0 7.4 39.5 0.0 1.0 28.2 Albania 26.3 130.8 25.5 6.9 –12.1 1.9 0.0 21.1 83.0 1.0 0.0 0.4 3.1 Algeria –84.8 7.2 0.0 –0.5 0.0 0.0 0.0 0.0 –0.8 1.0 0.0 0.9 6.6 Angola 786.6 386.4 13.5 0.0 0.0 353.3 1.6 –0.4 0.8 8.5 0.0 0.8 8.3 Argentina 282.5 1,437.1 0.0 235.6 0.0 0.0 0.0 914.8 282.3 0.8 0.0 1.0 2.6 Armenia 610.9 758.9 128.5 48.6 –23.4 465.7 119.1 1.3 11.4 0.8 0.0 1.6 5.3 Australia Austria Azerbaijan –17.5 304.9 36.1 121.6 –15.6 –2.9 15.1 93.8 47.1 1.0 0.0 0.6 8.1 Bangladesh –146.1 1,004.7 62.8 0.0 –23.4 0.0 149.8 701.9 38.7 22.2 0.0 0.8 51.9 Belarus 975.7 3,040.3 0.0 213.5 0.0 2,825.2 0.0 –2.1 0.0 0.7 0.0 0.5 2.5 Belgium Benin 25.3 134.2 51.4 0.0 15.7 0.0 25.5 0.0 22.9 4.9 0.0 0.8 13.0 Bolivia 61.9 168.3 32.3 0.0 0.0 0.0 95.7 –36.1 67.9 1.5 0.0 0.6 6.4 Bosnia and Herzegovina 33.8 483.8 18.0 –24.7 0.0 281.7 0.0 129.5 69.4 0.8 0.0 0.8 8.3 Botswana –5.1 982.5 –0.5 0.0 0.0 0.0 –3.6 971.7 8.3 1.2 0.0 0.4 5.0 Brazil 2,998.3 441.6 0.0 –597.9 0.0 0.0 0.0 1,018.9 12.1 1.1 0.0 1.5 5.9 Bulgaria –5.2 259.3 0.0 285.0 0.0 0.0 0.0 –13.6 –12.1 .. .. .. .. Burkina Faso 13.6 270.6 89.7 0.0 54.2 0.0 78.3 0.0 4.1 17.7 0.0 1.1 25.5 Burundi 0.0 63.5 8.6 0.0 13.4 0.0 4.2 0.0 2.6 9.9 0.0 0.6 24.2 Cambodia 116.0 96.5 16.4 0.0 0.0 0.0 47.6 0.0 4.5 7.3 0.0 0.8 19.9 Cameroon –38.9 225.1 46.3 –5.4 147.3 0.0 23.6 –21.9 12.1 6.8 0.0 1.0 15.3 Canada Central African Republic –3.4 30.1 2.1 0.0 20.3 0.0 –1.8 0.0 –2.0 4.5 0.0 0.5 6.5 Chad –1.9 25.3 –14.6 0.0 –12.7 0.0 0.4 0.0 9.4 13.4 0.0 0.5 28.9 Chile –20.8 58.6 –0.7 14.9 0.0 0.0 0.0 40.7 0.0 0.8 0.0 0.9 2.0 China –339.4 1,098.7 –329.8 298.5 0.0 0.0 0.0 1,069.1 17.7 10.5 0.0 2.2 30.5 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. .. Colombia –113.6 1,633.2 –0.7 1,115.6 0.0 0.0 –2.6 534.9 –23.6 1.3 0.0 0.8 7.5 Congo, Dem. Rep. –168.9 264.6 78.1 0.0 131.7 0.0 14.3 –43.1 –13.1 55.4 0.0 1.3 40.0 Congo, Rep. –62.6 2.8 0.8 0.0 3.7 0.0 –0.4 –8.5 –3.6 1.3 0.0 0.2 9.3 Costa Rica 74.8 143.9 –0.2 16.7 0.0 0.0 –9.2 3.8 128.7 0.8 0.0 0.7 2.6 Côte d’Ivoire –15.4 –289.9 –27.3 –73.3 282.9 –125.4 –4.2 –369.4 –4.3 8.4 0.0 1.1 21.6 Croatia .. .. 0.0 39.8 .. .. .. .. .. 0.3 0.0 0.6 2.9 Cuba .. .. .. .. .. .. .. .. .. 1.0 0.0 1.4 3.4 Czech Republic .. .. 0.0 0.0 .. .. .. .. .. .. .. .. .. Denmark Dominican Republic 203.2 977.0 –0.7 298.6 0.0 261.2 –21.3 373.5 62.3 0.8 0.0 0.8 1.8 Ecuador –175.1 61.4 –1.1 –80.7 0.0 0.0 –26.4 125.6 38.5 0.8 0.0 0.8 3.9 Egypt, Arab Rep. –907.4 858.9 –50.5 595.4 0.0 0.0 –5.0 145.0 160.8 3.5 0.0 1.4 8.3 El Salvador –38.8 402.1 –0.8 169.3 0.0 0.0 –22.9 233.3 17.6 1.3 0.0 0.7 3.6 Eritrea 41.6 23.0 0.8 0.0 0.0 0.0 3.4 0.0 0.6 2.7 0.0 1.1 14.4 Estonia .. .. 0.0 –6.7 .. .. .. .. .. .. .. .. .. Ethiopia 335.1 977.1 549.2 0.0 165.0 0.0 163.0 –6.7 21.6 35.9 0.0 1.1 48.0 Finland France Gabon –99.4 20.1 0.0 –2.3 0.0 0.0 –0.2 33.3 –16.0 0.7 0.0 0.4 4.2 Gambia, The 2.9 46.5 2.0 0.0 15.8 0.0 7.4 0.0 12.9 1.4 0.0 0.3 6.7 Georgia 23.8 655.4 155.2 100.0 –27.7 340.6 111.4 –5.9 –27.6 0.8 0.0 0.8 7.8 Germany Ghana 99.2 476.8 239.7 0.0 104.3 0.0 99.6 –2.0 2.2 8.2 0.0 0.9 23.9 Greece .. .. 0.0 0.0 .. .. .. .. .. .. .. .. .. Guatemala –10.8 554.1 0.0 306.3 0.0 0.0 –6.9 255.5 –5.4 0.8 0.0 0.6 3.2 Guinea 3.9 –29.3 –27.2 0.0 –12.8 0.0 2.9 –5.6 –12.3 7.6 0.0 0.5 17.6 Guinea-Bissau 0.0 9.6 0.0 0.0 –1.6 2.7 –1.2 0.0 0.0 2.7 0.0 0.2 6.8 Haiti 109.1 159.2 –11.0 0.0 57.4 0.0 75.6 0.0 11.8 2.4 0.0 0.8 22.2 Honduras 12.7 87.4 49.4 0.0 0.0 0.0 32.7 –19.8 16.2 0.7 0.0 1.1 7.1 368 2011 World Development Indicators 6.13 GLOBAL LINKS Net official financial flows Total International financial institutions United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b,c IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Hungary .. –23.1 0.0 –23.1 .. .. .. .. .. .. .. .. .. India –152.3 2,079.7 455.3 671.5 0.0 0.0 0.0 857.9 12.0 42.0 0.0 0.3 40.7 Indonesia –1,099.1 1,131.2 212.8 908.6 0.0 0.0 88.6 –99.4 0.0 6.3 0.0 1.1 13.2 Iran, Islamic Rep. –247.4 81.8 0.0 74.7 0.0 0.0 0.0 0.0 0.0 1.7 0.0 0.6 4.8 Iraq .. .. .. .. .. .. .. .. .. 2.0 0.0 0.4 7.8 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica –61.3 185.4 0.0 71.1 0.0 0.0 –4.6 81.6 34.7 1.1 0.0 0.3 1.2 Japan .. Jordan –65.1 548.3 –2.6 240.0 0.0 –15.9 0.0 0.0 190.1 0.8 133.5 0.9 1.5 Kazakhstan –13.3 604.2 0.0 83.8 0.0 0.0 –0.2 532.6 –16.1 1.0 0.0 0.3 2.8 Kenya 59.8 385.0 82.9 0.0 191.2 0.0 54.0 –5.0 11.1 11.8 0.0 1.9 37.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. 5.5 0.0 1.4 7.6 Korea, Rep. Kosovo 0.0 –199.4 0.0 –207.7 0.0 0.0 0.0 0.0 0.0 1.5 0.0 0.0 6.8 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 332.0 17.8 –4.1 0.0 –0.3 0.0 12.2 –4.6 4.7 1.4 0.0 1.3 7.2 Lao PDR 114.9 44.0 –9.6 0.0 –5.6 0.0 8.1 0.5 33.9 2.7 0.0 0.7 13.3 Latvia .. .. 0.0 273.2 .. .. .. .. .. .. .. .. .. Lebanon –95.9 106.3 0.0 –49.8 0.0 0.0 0.0 0.0 29.1 0.8 123.0 1.0 2.2 Lesotho 12.8 3.9 5.9 –0.7 –5.9 0.0 –4.6 0.0 0.4 1.4 0.0 0.6 6.8 Liberia 0.0 37.6 –3.3 0.0 17.6 0.0 –1.0 0.0 0.0 5.7 0.0 0.4 18.2 Libya .. .. .. .. .. .. .. .. .. 0.0 0.0 0.4 2.5 Lithuania –2.3 1,000.6 0.0 –3.1 0.0 0.0 0.0 –8.0 1,011.7 .. .. .. .. Macedonia, FYR 6.4 20.5 –7.0 33.3 0.0 0.0 0.0 –4.9 –5.4 0.9 0.0 1.0 2.6 Madagascar 34.8 92.5 30.4 0.0 0.0 0.0 27.1 0.0 –0.9 12.7 0.0 1.3 21.9 Malawi 12.2 84.8 24.2 0.0 0.0 0.0 18.3 –2.0 7.9 9.3 0.0 1.0 26.1 Malaysia –912.1 –89.6 0.0 –46.7 0.0 0.0 0.0 –40.3 –7.2 0.7 0.0 0.6 3.3 Mali 84.3 383.8 159.2 0.0 3.1 0.0 58.8 0.0 132.1 14.7 0.0 0.7 15.2 Mauritania 33.3 204.6 37.9 0.0 0.0 0.0 24.8 –8.0 133.0 2.1 0.0 0.7 14.1 Mauritius –24.8 107.1 –0.6 101.0 0.0 0.0 –0.2 13.6 –10.0 0.0 0.0 0.6 2.7 Mexico 466.6 6,463.4 0.0 4,213.3 0.0 0.0 0.0 2,247.5 0.0 1.0 0.0 1.0 0.6 Moldova –22.8 11.9 18.0 –17.6 –8.6 –6.4 0.0 –3.5 19.2 0.9 0.0 1.5 8.4 Mongolia 57.2 262.9 51.0 0.0 –6.5 165.5 43.3 0.0 1.6 0.8 0.0 1.2 6.0 Morocco 606.8 1,301.8 –1.4 2.7 0.0 0.0 –1.1 545.2 751.1 1.5 0.0 0.9 2.9 Mozambique 193.6 484.7 197.1 0.0 153.3 0.0 68.8 0.0 20.6 16.3 0.0 0.8 27.8 Myanmar –7.9 34.7 0.0 0.0 0.0 0.0 0.0 0.0 –0.8 17.0 0.0 1.1 17.4 Namibia .. .. .. .. .. .. .. .. .. 1.1 0.0 0.7 5.3 Nepal –10.7 35.6 –33.5 0.0 –2.2 0.0 14.9 0.0 16.1 7.4 0.0 1.1 31.8 Netherlands New Zealand Nicaragua –11.3 265.8 66.7 0.0 36.7 0.0 106.6 25.8 20.7 1.3 0.0 1.4 6.6 Niger 6.3 110.5 15.8 0.0 5.1 0.0 19.5 0.0 35.2 18.2 0.0 0.7 16.0 Nigeria –72.1 386.2 475.6 –96.1 0.0 0.0 15.4 –91.3 4.3 48.8 0.0 1.0 28.5 Norway .. Oman .. .. 0.0 0.0 .. .. .. .. .. 0.0 0.0 0.1 0.4 Pakistan 887.5 4,639.2 988.8 –163.3 –223.3 3,307.1 223.6 419.4 18.1 19.8 0.0 1.9 47.1 Panama 15.2 292.0 0.0 164.3 0.0 0.0 –6.1 131.6 2.0 0.7 0.0 0.5 –1.0 Papua New Guinea –20.1 6.5 10.5 –9.0 0.0 0.0 –9.2 9.5 –3.9 1.5 0.0 0.0 7.1 Paraguay –12.9 60.3 –1.5 68.1 0.0 0.0 –15.7 –2.1 7.7 0.8 0.0 0.5 2.5 Peru –961.9 1,689.9 0.0 134.4 0.0 0.0 –3.5 1,380.8 171.0 0.9 0.0 0.8 5.5 Philippines –425.8 1,067.9 –7.0 –32.7 0.0 0.0 –38.9 1,116.4 13.0 3.1 0.0 0.8 13.2 Poland .. .. 0.0 2,658.3 .. .. .. .. .. .. .. .. .. Portugal Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 369 6.13 Net official financial flows Total International financial institutions United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b,c IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Romania –14.4 12,394.0 0.0 441.6 0.0 9,390.6 0.0 –26.0 2,587.8 .. .. .. .. Russian Federation –296.3 –764.1 0.0 –634.9 0.0 0.0 0.0 –130.5 1.3 .. .. .. .. Rwanda 12.0 115.1 10.5 0.0 3.6 0.0 21.7 0.0 46.4 9.6 0.0 0.8 22.5 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. .. Senegal 127.2 324.2 134.5 0.0 99.8 0.0 38.5 –12.8 43.1 6.3 0.0 1.3 13.5 Serbia 477.1 1,916.3 16.6 55.7 0.0 1,575.1 0.0 109.1 151.2 0.6 0.0 1.0 7.0 Sierra Leone –1.5 79.5 15.1 0.0 18.8 0.0 16.8 0.0 2.9 8.4 0.0 1.1 16.4 Singapore .. .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. 0.0 –43.4 .. .. .. .. .. .. .. .. .. Slovenia .. .. 0.0 –6.1 .. .. .. .. .. .. .. .. .. Somalia 0.0 38.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 28.9 South Africa 0.0 –25.2 0.0 –5.5 0.0 0.0 0.0 –32.1 0.0 4.0 0.0 0.5 7.9 Spain Sri Lanka 341.6 827.9 90.8 0.0 –11.8 552.6 60.4 88.5 21.9 3.4 0.0 1.2 20.9 Sudan 551.3 99.2 0.0 0.0 0.0 –10.6 0.0 –2.7 57.6 13.8 0.0 0.8 40.3 Swaziland 9.0 1.9 –0.3 –6.6 0.0 0.0 –1.4 –5.0 9.0 0.9 0.0 0.6 4.7 Sweden Switzerland Syrian Arab Republic –324.9 181.8 –1.5 0.0 0.0 0.0 0.0 0.0 108.3 0.8 60.1 1.3 12.8 Tajikistan 88.0 125.3 4.9 0.0 25.1 0.0 62.7 1.8 16.2 3.4 0.0 0.8 10.4 Tanzania 4.8 1,256.8 607.6 0.0 306.8 0.0 222.9 –1.0 41.2 21.4 0.0 1.1 56.8 Thailand –334.6 –46.6 –3.4 9.3 0.0 0.0 –46.2 –4.4 –11.3 0.9 0.0 1.2 7.3 Timor-Leste .. .. .. .. .. .. .. .. .. 1.1 0.0 0.5 6.0 Togo 22.2 17.4 –21.8 0.0 41.3 0.0 –1.9 0.0 –13.5 4.5 0.0 0.5 8.3 Trinidad and Tobago .. .. 0.0 –7.7 .. .. .. .. .. 0.0 0.0 0.1 0.7 Tunisia 40.3 443.7 –2.1 31.7 0.0 0.0 0.0 149.0 260.6 0.9 0.0 0.8 2.8 Turkey 405.0 1,984.6 –5.9 1,619.0 0.0 –706.5 0.0 0.0 1,067.5 1.3 0.0 0.6 8.6 Turkmenistan –87.2 –0.2 0.0 –1.3 0.0 0.0 0.0 0.0 –1.5 0.9 0.0 0.0 1.7 Uganda 9.8 508.9 363.3 0.0 0.0 0.0 73.5 –0.9 –1.0 22.1 0.0 1.1 50.8 Ukraine –154.6 6,992.3 0.0 274.5 0.0 6,081.6 0.0 549.7 78.3 0.8 0.0 1.8 5.6 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay –21.2 704.6 0.0 364.7 0.0 0.0 –2.1 318.0 20.2 0.8 0.0 0.6 2.4 Uzbekistan 100.9 157.1 27.6 –27.3 0.0 0.0 18.7 78.0 48.2 3.5 0.0 0.4 8.0 Venezuela, RB 151.3 443.9 0.0 0.0 0.0 0.0 0.0 143.6 292.0 1.4 0.0 0.5 6.4 Vietnam 922.2 2,218.4 1,158.6 0.0 –38.3 0.0 392.4 647.3 26.2 3.7 0.0 1.5 27.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. 4.9 455.3 0.1 8.5 Yemen, Rep. 66.4 121.1 58.8 0.0 –41.0 –2.8 0.0 0.0 68.8 9.2 0.0 0.9 27.2 Zambia –5.0 312.7 32.5 0.0 243.6 0.0 32.4 –5.4 –15.5 9.0 0.0 1.6 14.5 Zimbabwe 12.9 25.3 0.0 0.0 –0.1 0.0 0.0 0.0 0.0 6.6 0.0 0.5 18.3 World .. s .. s .. s .. s .. s .. s .. .. s .. s 1,086.2 s 771.8 s 645.3 s 2,561.1 s Low income 1,421.6 8,153.0 2,579.9 0.0 1,552.6 1.0 1,469.8 619.9 579.9 456.0 0.0 33.1 860.8 Middle income 4,206.7 65,736.3 3,871.5 11,287.2 198.1 24,750.4 1,257.5 14,527.3 8,079.3 268.3 771.8 111.8 613.1 Lower middle income 114.0 30,161.2 3,782.9 2,998.2 193.2 11,114.1 1,293.0 7,063.5 2,251.4 236.9 648.8 39.3 539.9 Upper middle income 4,092.7 35,590.5 88.5 8,289.0 4.9 13,636.3 –35.6 7,463.8 5,827.9 31.5 123.0 25.0 136.2 Low & middle income 5,628.3 75,594.8 6,451.3 11,287.1 1,750.7 24,751.5 2,727.3 15,147.1 8,659.3 1,085.1 771.8 644.3 2,319.3 East Asia & Pacific –1,882.0 5,945.1 1,099.7 1,126.6 –41.5 165.5 482.7 2,699.7 70.8 66.8 0.0 98.3 176.5 Europe & Central Asia 2,478.3 29,917.9 417.2 2,357.9 –62.6 20,246.6 338.9 1,315.9 5,162.0 21.7 0.0 13.7 106.6 Latin America & Carib. 2,975.6 16,487.2 136.2 6,489.2 126.7 269.6 251.2 7,784.3 1,174.5 28.4 0.0 67.8 159.3 Middle East & N. Africa –997.0 4,246.7 6.4 894.2 –42.9 –18.7 10.5 839.2 1,584.6 29.2 771.8 71.9 100.5 South Asia 1,006.3 8,845.7 1,614.0 508.2 –240.2 3,861.3 545.5 2,075.8 110.4 137.5 0.0 6.4 226.8 Sub-Saharan Africa 2,047.0 8,987.7 3,177.8 –88.9 2,011.1 227.1 1,098.5 432.3 557.0 454.7 0.0 156.0 962.1 High income .. .. .. .. .. .. .. .. .. 1.1 0.0 1.0 6.2 Euro area .. .. .. .. .. .. .. .. .. .. .. .. .. a. Aggregates include amounts for economies that do not report to the World Bank’s Debtor Reporting System and may differ from aggregates published in Global Development Finance 2011. b. Aggregates include amounts for economies not specified elsewhere. c. World and income group aggregates include flows not allocated by country or region. 370 2011 World Development Indicators 6.13 GLOBAL LINKS Net official financial flows About the data Definitions The table shows concessional and nonconcessional and the Rapid Credit Facility. Eligibility is based prin- • Total net official financial flows are disbursements financial flows from offi cial bilateral sources, the cipally on a country’s per capita income and eligibility of public or publicly guaranteed loans and credits, major international financial institutions, and UN under IDA. Nonconcessional lending from the IMF less repayments of principal. • IDA is the Interna- agencies. The international fi nancial institutions is provided mainly through Stand-by Arrangements, tional Development Association, the concessional fund nonconcessional lending operations primarily the Flexible Credit Line, and the Extended Fund Facil- arm of the World Bank Group. • IBRD is the Inter- by selling low-interest, highly rated bonds backed ity. The IMF’s loan instruments have changed over national Bank for Reconstruction and Development, by prudent lending and financial policies and the time to address the specific circumstances of its the founding and largest member of the World Bank strong financial support of their members. Funds members. Group. • IMF is the International Monetary Fund, which are then on-lent to developing countries at slightly Regional development banks also maintain conces- provides concessional lending through its Extended higher interest rates with 15- to 20-year maturities. sional windows. Their loans are recorded in the table Credit Facility, Standby Credit Facility, and Rapid Credit Lending terms vary with market conditions and insti- according to each institution’s classification and not Facility and nonconcessional lending through credit tutional policies. according to the DAC definition. to members, mainly for balance of payments needs. Concessional flows from international financial Data for flows from international financial institu- • Regional development banks are the African Devel- institutions are credits provided through conces- tions are available for 128 countries that report to opment Bank, which serves Africa, including North sional lending facilities. Subsidies from donors or the World Bank’s Debtor Reporting System. World Africa; the Asian Development Bank, which serves other resources reduce the cost of these loans. Bank flows for nonreporting countries were collected South and Central Asia and East Asia and Pacific; Grants are not included in net flows. The Organisa- from its operational records. Nonreporting countries the European Bank for Reconstruction and Develop- tion for Economic Co-operation and Development’s may have net flows from other international financial ment, which serves Europe and Central Asia; and (OECD) Development Assistance Committee (DAC) institutions. the Inter-American Development Bank, which serves defines concessional flows from bilateral donors as Official flows from the United Nations are mainly the Americas. • Concessional financial flows are dis- flows with a grant element of at least 25 percent, eval- concessional fl ows classifi ed as offi cial develop- bursements through concessional lending facilities. uated assuming a 10 percent nominal discount rate. ment assistance but may include nonconcessional • Nonconcessional financial flows are all disburse- World Bank concessional lending is done by the flows classified as other official flows in OECD DAC ments that are not concessional. • Other institutions, International Development Association (IDA) based databases. a residual category, include such institutions as the on gross national income (GNI) per capita and per- Caribbean Development Fund, Council of Europe, formance standards assessed by World Bank staff. European Development Fund, Islamic Development The cutoff for IDA eligibility, set at the beginning of Bank, and Nordic Development Fund. • United Nations the World Bank’s fiscal year, has been $1,165 since includes the United Nations Children’s Fund (UNICEF), July 1, 2010, measured in 2009 U.S. dollars using United Nations Relief and Works Agency for Palestine the Atlas method (see Users Guide). In exceptional Refugees in the Near East (UNRWA), United Nations circumstances IDA extends temporary eligibility to Regular Programme for Technical Assistance (UNTA), countries above the cutoff that are undertaking and other UN agencies, such as the International major adjustments but are not creditworthy for Inter- Atomic Energy Agency, International Fund for Agricul- national Bank for Reconstruction and Development tural Development, Joint United Nations Programme (IBRD) lending. Exceptions are also made for small on HIV/AIDS, United Nations Development Programme, island economies. The IBRD lends to creditworthy United Nations Economic Commission for Europe, countries at a variable base rate of six-month LIBOR United Nations Population Fund, United Nations Refu- plus a spread, either variable or fixed, for the life of gee Agency, World Food Programme, and World Health the loan. The lending rate is reset every six months Organization. and applies to the interest period beginning on that Data sources date. Although some outstanding IBRD loans have a low enough interest rate to be classified as conces- Data on net financial flows from international finan- sional under the DAC definition, all IBRD loans in the cial institutions are from the World Bank’s Debtor table are classified as nonconcessional. Lending by Reporting System and published in the World the International Finance Corporation, Multilateral Bank’s Global Development Finance: External Debt Investment Guarantee Agency, and the International of Developing Countries and electronically in Global Centre for the Settlement of Investment Disputes Development Finance database. Data on official is excluded. flows from UN agencies are from the OECD DAC The International Monetary Fund (IMF) makes con- annual Development Co-operation Report and are cessional funds available through its Extended Credit available electronically on the OECD DAC Interna- Facility (which replaced the Poverty Reduction and tional Development Statistics CD-ROM and at www. Growth Facility in 2010), the Standby Credit Facility, oecd.org/dac/stats/idsonline. 2011 World Development Indicators 371 6.14 Financial flows from Development Assistance Committee members Net disbursements Total net Official Other Private Net flowsa development assistancea official flowsa grants by flowsa NGOsa Contributions Foreign Bilateral Multilateral Private Bilateral Bilateral to multilateral direct portfolio portfolio export Total grants loans institutions Total investment investment investment credits 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 $ millions Australia 3,188 2,762 2,224 88 450 426 0 0 0 0 0 0 Austria 3,273 1,142 513 –6 635 –44 2,035 2,551 46 0 –562 140 Belgium 3,224 2,610 1,594 –9 1,025 90 147 3 0 0 144 377 Canada 7,340 4,000 3,182 –41 859 –1,138 3,140 6,604 –37 0 –3,427 1,338 Denmark 3,757 2,810 1,914 –8 904 233 599 599 0 0 0 116 Finland 3,185 1,290 765 26 499 137 1,741 791 950 0 0 17 France 38,418 12,600 5,814 1,205 5,581 294 25,524 16,300 9,434 0 –210 0 Germany 26,003 12,079 6,747 350 4,983 187 12,367 9,726 58 1,242 1,341 1,369 Greece 850 607 297 0 310 0 241 241 0 0 0 2 Ireland 4,188 1,006 693 0 313 0 3,000 0 3,000 0 0 182 Italy 5,569 3,297 871 4 2,423 –72 2,181 129 1,590 0 463 162 Japan 49,405 9,469 5,327 674 3,467 8,216 31,187 19,440 10,981 1,987 –1,220 533 Korea, Rep 6,442 816 366 214 235 452 5,018 5,018 0 0 0 156 Luxembourg 428 415 266 0 149 0 0 0 0 0 0 13 Netherlands 6,045 6,426 4,914 –116 1,628 0 –923 540 –2,853 989 401 542 New Zealand 387 309 226 0 83 8 24 24 0 0 0 46 Norway 4,089 4,086 3,125 43 918 4 0 0 0 0 0 0 Portugal 1,209 513 225 52 236 0 692 –2 –63 0 757 4 Spain 12,809 6,584 4,098 375 2,111 0 6,225 6,294 0 0 –70 0 Sweden 7,164 4,548 2,919 90 1,539 68 2,473 885 0 0 1,588 74 Switzerland 9,106 2,310 1,734 16 559 0 6,438 5,570 0 1,462 –593 357 United Kingdom 68,936 11,491 6,994 663 3,834 –13 57,129 55,947 –2,143 0 3,326 329 United States 115,276 28,831 25,992 –819 3,658 988 69,168 28,275 27,223 13,160 510 16,288 Total 380,290 120,000 80,800 2,802 36,398 9,836 228,407 158,934 48,185 18,839 2,449 22,047 Official development assistance Commitmentsb Gross Net disbursementsb disbursements % of general Per capita government $ millions $ millions $ millionsb $b % of GNIa disbursementsa 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Australia 2,251 2,963 1,939 2,912 1,939 2,912 101 136 0.27 0.29 0.71 0.87 Austria 1,026 1,252 792 1,188 787 1,174 97 141 0.23 0.30 0.44 0.57 Belgium 1,558 3,068 1,558 2,750 1,517 2,670 148 250 0.36 0.55 0.72 1.02 Canada 3,412 4,925 3,023 4,372 2,981 4,328 97 130 0.25 0.30 0.59 0.68 Denmark 2,994 2,938 3,193 2,960 3,159 2,923 592 530 1.06 0.88 1.94 1.54 Finland 618 1,639 661 1,323 649 1,323 125 248 0.31 0.54 0.63 0.97 France 8,699 14,928 9,276 15,933 7,616 12,920 129 207 0.30 0.47 0.60 0.85 Germany 9,825 16,924 9,973 13,693 8,641 12,397 105 151 0.27 0.35 0.59 0.76 Greece 451 618 451 618 451 618 41 55 0.20 0.19 0.39 0.36 Ireland 450 1,083 450 1,083 450 1,083 119 250 0.29 0.54 0.77 0.93 Italy 3,115 3,918 3,082 3,514 2,653 3,334 46 56 0.13 0.16 0.27 0.30 Japan 16,257 16,429 15,485 14,848 12,833 8,545 101 67 0.28 0.18 0.74 0.45 Korea, Rep 399 2,206 282 949 260 910 6 19 0.04 0.10 0.18 0.31 Luxembourg 257 435 257 435 257 435 583 889 0.70 1.04 1.61 1.86 Netherlands 6,580 6,490 6,171 6,841 5,995 6,676 376 405 0.84 0.82 1.84 1.57 New Zealand 229 358 216 333 216 333 56 78 0.25 0.28 0.56 0.61 Norway 2,481 5,902 2,800 4,650 2,787 4,650 621 969 0.76 1.06 1.77 2.32 Portugal 822 633 822 565 535 528 52 51 0.26 0.23 0.56 0.45 Spain 2,940 6,724 2,940 7,213 2,531 6,800 63 147 0.22 0.46 0.53 0.98 Sweden 2,287 5,230 2,861 5,090 2,861 5,085 323 549 0.80 1.12 1.32 2.03 Switzerland 1,536 2,753 1,513 2,286 1,509 2,276 210 296 0.34 0.45 1.01 1.39 United Kingdom 6,723 17,757 6,723 13,400 6,649 13,162 113 216 0.32 0.52 0.83 1.03 United States 15,431 33,018 13,293 29,286 12,182 28,469 44 94 0.10 0.21 0.30 0.48 DAC Countries, Total 90,339 152,192 87,757 136,242 79,456 123,551 89 131 0.22 0.31 0.56 0.69 Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2008 prices and exchange rates. 372 2011 World Development Indicators 6.14 GLOBAL LINKS Financial flows from Development Assistance Committee members About the data The flows of official and private financial resources to accommodate changes in respect of Kosovo and discount rate). • Contributions to multilateral insti- from the members of the Development Assistance the Former Yugoslav Republic of Macedonia. In the tutions are concessional funding received by multi- Committee (DAC) of the Organisation for Economic past DAC distinguished aid going to Part I and Part lateral institutions from DAC members as grants Co-operation and Development (OECD) to developing II countries. Part I countries, the recipients of ODA, or capital subscriptions. •  Other offi cial fl ows economies are compiled by DAC, based principally on comprised many of the countries classified by the are transactions by the official sector whose main reporting by DAC members using standard question- World Bank as low- and middle-income economies. objective is other than development or whose grant naires issued by the DAC Secretariat. Part II countries, whose assistance was designated element is less than 25 percent. • Private flows are The table shows data reported by DAC member official aid, included the more advanced countries flows at market terms financed from private sector economies and does not include aid provided by the of Central and Eastern Europe, countries of the for- resources in donor countries. They include changes European Union Institutions—a multilateral member mer Soviet Union, and certain advanced developing in holdings of private long-term assets by reporting of DAC. countries and territories. This distinction has been country residents. •  Foreign direct investment is DAC exists to help its members coordinate their dropped with the 2005 aid flows. investment by residents of DAC member countries development assistance and to encourage the Flows are transfers of resources, either in cash or to acquire a lasting management interest (at least expansion and improve the effectiveness of the in the form of commodities or services measured on 10 percent of voting stock) in an enterprise operating aggregate resources flowing to recipient economies. a cash basis. Short-term capital transactions (with in the recipient country. The data reflect changes in In this capacity DAC monitors the flow of all financial one year or less maturity) are not counted. Repay- the net worth of subsidiaries in recipient countries resources, but its main concern is official develop- ments of the principal (but not interest) of ODA loans whose parent company is in the DAC source coun- ment assistance (ODA). Grants or loans to countries are recorded as negative flows. Proceeds from offi - try. • Bilateral portfolio investment is bank lending and territories on the DAC list of aid recipients have cial equity investments in a developing country are and the purchase of bonds, shares, and real estate to meet three criteria to be counted as ODA. They reported as ODA, while proceeds from their later sale by residents of DAC member countries in recipi- are provided by official agencies, including state and are recorded as negative flows. ent countries. •  Multilateral portfolio investment local governments, or by their executive agencies. The table is based on donor country reports and is transactions of private banks and nonbanks in They promote economic development and welfare as does not provide a complete picture of the resources DAC member countries in the securities issued by the main objective. And they are provided on conces- received by developing economies for two reasons. multilateral institutions. • Private export credits are sional financial terms (loans must have a grant ele- First, flows from DAC members are only part of the loans extended to recipient countries by the private ment of at least 25 percent, calculated at a discount aggregate resource flows to these economies. Sec- sector in DAC member countries to promote trade; rate of 10 percent). The DAC Statistical Reporting ond, the data that record contributions to multilateral they may be supported by an official guarantee. • Net Directives provide the most detailed explanation of institutions measure the flow of resources made grants by nongovernmental organizations (NGOs) this definition and all ODA-related rules. available to those institutions by DAC members, not are private grants by NGOs, net of subsidies from This definition excludes nonconcessional fl ows the flow of resources from those institutions to devel- the official sector. • Commitments are obligations, from official creditors, which are classified as “other oping economies. expressed in writing and backed by funds, under- official flows,” and aid for military and anti-terrorism Aid as a share of gross national income (GNI), aid taken by an official donor to provide specified assis- purposes. Transfer payments to private individuals, per capita, and ODA as a share of the general gov- tance to a recipient country or multilateral organiza- such as pensions, reparations, and insurance pay- ernment disbursements of the donor are calculated tion. •  Gross disbursements are the international outs, are in general not counted. In addition to finan- by the OECD. The denominators used in calculating transfer of financial resources, goods, and services, cial flows, ODA includes technical cooperation, most these ratios may differ from corresponding values valued at the cost to the donor. expenditures for peacekeeping under UN mandates elsewhere in this book because of differences in tim- and assistance to refugees, contributions to multi- ing or definitions. lateral institutions such as the United Nations and Definitions its specialized agencies, and concessional funding to multilateral development banks. •  Net disbursements are gross disbursements of The DAC list of aid recipients shows all countries grants and loans minus repayments of principal Data sources and territories eligible to receive ODA. These con- on earlier loans. •  Total net flows are ODA flows, sist of all low- and middle-income countries, except other official flows, private flows, and net grants by Data on financial flows are compiled by OECD DAC members of the Group of Eight or the European Union nongovernmental organizations. • Official develop- and published in its annual statistical report, Geo- (including countries with a firm date for EU acces- ment assistance refers to flows that meet the DAC graphical Distribution of Financial Flows to Devel- sion). The DAC revises the list every three years. definition of ODA and are made to countries and ter- oping Countries, and its annual Development Countries that have exceeded the high-income ritories on the DAC list of aid recipients. • Bilateral Co-operation Report. Data are available electroni- threshold for three consecutive years at the time grants are transfers of money or in kind for which no cally on the OECD DAC International Development of the review are removed. In line with this review repayment is required. • Bilateral loans are loans Statistics CD-ROM and at www.oecd.org/dac/ process, the DAC last revised the list in September extended by governments or official agencies with a stats/idsonline. 2008. A further update took place in August 2009 grant element of at least 25 percent (at a 10 percent 2011 World Development Indicators 373 6.15 Allocation of bilateral aid from Development Assistance Committee members 6.15a Aid by purpose Net disbursements Share of bilateral ODA net disbursements % Development projects, programs, and other Technical Debt-related Humanitarian Administrative $ millionsa resource provisions cooperationb aid assistance costs 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Australia 758 2,312 27.8 32.7 55.1 49.2 1.1 0.1 9.7 13.3 6.2 4.7 Austria 273 507 28.7 23.6 41.8 49.7 20.4 11.6 2.7 7.2 6.4 7.9 Belgium 477 1,585 33.6 40.9 46.9 39.2 6.6 6.6 5.4 7.4 7.5 6.0 Canada 1,160 3,141 39.6 15.4 43.0 64.2 1.1 1.5 5.0 10.3 11.4 8.6 Denmark 1,024 1,905 65.8 71.9 25.3 11.0 1.0 1.9 0.0 6.8 8.0 8.5 Finland 217 791 40.8 31.1 41.4 45.6 0.0 0.0 10.5 13.1 7.2 10.1 France 2,829 7,019 25.4 10.9 50.6 42.7 17.0 39.5 0.4 0.6 6.7 6.3 Germany 2,687 7,097 16.8 24.6 63.8 65.0 6.6 1.2 4.1 5.2 8.7 4.1 Greece 99 297 69.6 14.8 23.8 72.2 0.0 0.0 6.4 5.1 0.2 7.9 Ireland 154 693 79.1 75.9 0.4 3.6 0.0 0.0 15.5 14.1 5.1 6.5 Italy 377 875 10.2 49.4 8.1 10.9 57.5 19.9 18.3 13.0 5.9 6.8 Japan 9,768 6,001 60.4 59.7 24.9 38.4 4.2 –14.5 0.9 4.4 9.5 12.1 Korea, Rep. 131 581 77.8 66.8 15.7 25.5 0.0 0.0 0.4 2.9 6.1 4.8 Luxembourg 99 266 84.4 74.6 3.2 3.6 0.8 0.0 10.4 14.4 1.2 7.3 Netherlands 2,243 4,798 41.1 71.3 33.7 14.5 6.8 0.9 9.1 6.3 9.4 6.9 New Zealand 85 226 39.7 54.3 48.1 28.3 0.0 0.0 3.4 6.9 8.8 10.6 Norway 934 3,168 57.9 56.2 23.0 27.9 1.0 0.5 11.3 8.6 6.9 6.8 Portugal 179 277 30.4 49.6 50.4 53.4 14.6 –10.0 1.9 0.4 2.7 6.6 Spain 720 4,473 69.3 60.1 17.9 23.5 2.3 2.2 3.7 9.9 6.8 4.2 Sweden 1,242 3,009 60.9 64.1 13.6 15.9 3.1 0.7 14.6 12.0 7.7 7.3 Switzerland 627 1,751 58.6 42.3 19.4 30.1 0.9 9.3 20.2 9.1 0.9 9.3 United Kingdom 2,710 7,657 47.7 75.9 25.5 8.9 5.7 0.6 12.7 9.5 8.4 5.2 United States 7,405 25,174 14.6 70.5 64.4 6.0 1.7 0.7 9.6 17.4 9.7 5.4 Total 36,195 83,602 40.6 54.6 39.3 25.2 5.3 3.5 6.1 10.3 8.6 6.3 a. At current exchange rates and prices. b. Includes aid for promoting development awareness and aid provided to refugees in the donor economy. About the data Aid can be used in many ways. The sector to which provide debt relief on liabilities that recipient coun- human resources from donors or action directed to aid goes, the form it takes, and the procurement tries have difficulty servicing. Thus, this type of aid human resources (such as training or advice). Also restrictions attached to it are important influences may not provide a full value of new resource flows included are aid for promoting development aware- on aid effectiveness. The data on allocation of offi - for development, in particular for heavily indebted ness and aid provided to refugees in the donor econ- cial development assistance (ODA) in the table are poor countries. Humanitarian assistance provides omy. Assistance specifically to facilitate a capital based principally on reporting by members of the relief following sudden disasters and supports food project is not included. • Debt-related aid groups Organisation for Economic Co-operation and Devel- programs in emergency situations. This type of aid all actions relating to debt, including forgiveness, opment (OECD) Development Assistance Committee does not generally contribute to financing long-term swaps, buybacks, rescheduling, and refinancing. (DAC). For more detailed explanation of ODA, see development. • Humanitarian assistance is emergency and dis- About the data for table 6.14. tress relief (including aid to refugees and assistance Definitions The form in which an ODA contribution reaches for disaster preparedness). • Administrative costs the benefiting sector or the economy is important. A • Net disbursements are gross disbursements of are the total current budget outlays of institutions distinction is made between resource provision and grants and loans minus repayments of principal on responsible for the formulation and implementation technical cooperation. Resource provision involves earlier loans • Development projects, programs, and of donor’s aid programs and other administrative mainly cash or in-kind transfers and financing of other resource provisions are aid provided as cash costs incurred by donors in aid delivery. capital projects, with the deliverables being finan- transfers, aid in kind, development food aid, and the cial support and the provision of commodities and financing of capital projects, intended to increase Data sources supplies. Technical cooperation includes grants to or improve the recipient’s stock of physical capital nationals of aid-recipient countries receiving educa- and to support recipient’s development plans and Data on aid flows are published by OECD DAC in tion or training at home or abroad, and payments other activities with finance and commodity supply. its annual statistical report, Geographical Distri- to consultants, advisers, and similar personnel and • Technical cooperation is the provision of resources bution of Financial Flows to Developing Countries, to teachers and administrators serving in recipient whose main aim is to augment the stock of human and its annual Development Co-operation Report. countries. Technical cooperation is spent mostly in intellectual capital, such as the level of knowledge, Data are available electronically on the OECD DAC the donor economy. skills, and technical know-how in the recipient coun- International Development Statistics CD-ROM and Two other types of aid are presented because they try (including the cost of associated equipment). at www.oecd.org/dac/stats/idsonline. serve distinctive purposes. Debt-related aid aims to Contributions take the form mainly of the supply of 374 2011 World Development Indicators 6.15 GLOBAL LINKS Allocation of bilateral aid from Development Assistance Committee members 6.15b Aid by sector Total Social infrastructure and services Economic infrastructure, Multi- Untied sector- services, and production sector sector or aida allocable Water Government Transport cross- aid supply and and civil and com- cutting Share of bilateral Total Education Health Population sanitation society Total munication Agriculture ODA commitments (%) 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Australia 77.7 48.7 11.9 7.5 2.2 1.9 22.6 11.8 5.3 4.6 17.2 90.8 Austria 67.5 45.8 23.8 5.9 0.4 4.3 9.1 15.2 1.8 2.0 6.6 55.2 Belgium 73.2 39.5 13.1 9.2 1.6 3.2 9.7 28.0 4.7 7.7 5.7 95.5 Canada 72.7 52.4 15.4 15.2 2.5 2.0 16.2 12.2 0.4 6.8 8.1 98.3 Denmark 68.8 42.2 5.1 7.6 2.8 8.5 16.3 18.0 3.4 5.8 8.5 96.6 Finland 72.9 32.6 6.7 3.3 0.5 4.3 14.1 28.1 6.2 7.8 12.2 90.3 France 68.8 36.3 19.2 1.5 0.3 8.8 1.6 16.3 6.8 5.3 16.2 89.2 Germany 87.6 49.6 19.1 3.6 1.9 8.7 14.7 27.8 2.6 3.7 10.1 97.1 Greece 74.5 62.8 32.4 4.6 2.6 1.0 16.0 6.1 2.4 1.3 5.6 49.8b Ireland 70.8 58.0 13.0 13.5 4.0 2.5 16.4 9.5 0.1 8.0 3.3 100.0b Italy 64.1 35.6 10.3 9.4 0.9 4.9 6.5 23.9 3.9 16.7 4.6 56.2 Japan 74.5 28.9 5.3 2.0 0.4 18.9 1.2 41.3 26.7 4.9 4.2 94.7 Korea, Rep. 96.2 27.6 9.7 10.4 0.2 4.7 1.7 64.5 52.1 3.5 4.0 48.3 Luxembourg 68.1 47.2 12.5 12.7 4.6 8.4 5.1 10.7 0.2 5.0 10.2 100.0 b Netherlands 71.1 26.8 4.2 2.7 2.1 3.7 10.7 12.7 1.0 3.3 31.6 80.8 New Zealand 61.9 45.5 21.0 5.5 2.3 1.1 14.4 14.6 7.2 3.7 1.9 90.1 Norway 64.0 40.6 8.6 6.2 2.0 1.3 19.9 14.3 0.2 6.8 9.2 100.0 Portugal 70.3 56.9 24.1 2.9 0.1 0.1 22.3 10.2 8.3 1.5 3.2 27.9 Spain 70.8 44.6 7.1 6.2 4.3 12.7 9.9 20.4 2.5 3.8 5.8 76.6 Sweden 54.3 33.4 3.1 3.5 1.9 2.5 19.8 12.3 1.2 2.5 8.6 99.9 Switzerland 42.9 21.6 3.1 3.1 0.2 2.4 11.8 11.5 0.8 3.5 9.7 99.2 United Kingdom 72.4 41.7 8.9 7.8 5.0 1.4 14.7 20.1 2.6 1.7 10.6 100.0 United States 73.2 53.5 4.0 3.7 19.0 1.6 18.6 15.1 4.5 5.0 4.6 69.8 Total 72.8 42.7 8.8 4.6 6.7 6.2 12.5 21.3 7.4 4.7 8.8 84.5 a. Excludes technical cooperation and administrative costs. b. Gross disbursements. About the data Definitions The Development Assistance Committee (DAC) • Bilateral official development assistance (ODA) administrative apparatus and planning and activities records the sector classifi cation of aid using a commitments are firm obligations, expressed in writ- promoting good governance and civil society. • Eco- three-level hierarchy. The top level is grouped by ing and backed by the necessary funds, undertaken nomic infrastructure, services, and production sec- themes, such as social infrastructure and services; by official bilateral donors to provide specified assis- tor group assistance for networks, utilities, services economic infrastructure, services, and production; tance to a recipient country or a multilateral organi- that facilitate economic activity, and contributions and multisector or cross-cutting areas. The second zation. Bilateral commitments are recorded in the to all directly productive sectors. •  Transport and level is more specifi c. Education and health and full amount of expected transfer, irrespective of the communication refer to road, rail, water, and air transport and storage are examples. The third level time required for completing disbursements. • Total transport; post and telecommunications; and televi- comprises subsectors such as basic education and sector-allocable aid is the sum of aid that can be sion and print media. • Agriculture refers to sector basic health. Some contributions are reported as assigned to specific sectors or multisector activi- policy, development, and inputs; crop and livestock non-sector-allocable aid. ties. •  Social infrastructure and services refer to production; and agricultural credit, cooperatives, and Reporting on the sectoral destination and the efforts to develop the human resources potential and research. • Multisector or cross-cutting refers to form of aid by donors may not be complete. Also, improve the living conditions of aid recipients. • Edu- support for projects that straddle several sectors. measures of aid allocation may differ from the per- cation refers to general teaching and instruction at •  Untied aid is ODA not subject to restrictions by spectives of donors and recipients because of dif- all levels, as well as construction to improve or adapt donors on procurement sources. ference in classification, available information, and educational establishments. Training in a particular Data sources recording time. field is reported for the sector concerned. • Health The proportion of untied aid is reported because refers to assistance to hospitals, clinics, other medi- Data on aid flows are published annually by the tying arrangements may prevent recipients from cal and dental services, public health administra- Organisation for Economic Co-operation and obtaining the best value for their money. Tying tion, and medical insurance programs. • Population Development (OECD) DAC in Geographical Distri- requires recipients to purchase goods and services refers to all activities related to family planning and bution of Financial Flows to Developing Countries from the donor country or from a specified group of research into population problems. • Water supply and Development Co-operation Report. Data are countries. Such arrangements prevent a recipient and sanitation refer to assistance for water supply available electronically on the OECD DAC Interna- from misappropriating or mismanaging aid receipts, and use, sanitation, and water resources develop- tional Development Statistics CD-ROM and at www. but they may also be motivated by a desire to benefit ment (including rivers). • Government and civil soci- oecd.org/dac/stats/idsonline. donor country suppliers. ety refer to assistance to strengthen government 2011 World Development Indicators 375 6.16 Aid dependency Net official Aid dependency development assistance (ODA) ratios Net ODA as Net ODA as Net ODA as Total Per capita Net ODA as % of gross capital % of imports of goods, % of central government $ millions $ % of GNI formation services, and income expense 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Afghanistan 136 6,070 6 204 .. .. .. .. .. .. .. 112.5 Albania 317 358 103 113 8.4 3.0 34.8 10.3 21.0 5.1 .. .. Algeria 200 319 7 9 0.4 0.2 1.5 0.6 .. .. .. 0.9 Angola 302 239 21 13 4.1 0.4 22.0 2.1 4.1 0.5 .. .. Argentina 52 128 1 3 0.0 0.0 0.1 0.2 0.1 0.2 .. .. Armenia 216 528 70 171 11.0 5.9 60.6 19.3 21.2 12.5 .. 25.6 Australia Austria Azerbaijan 139 232 17 26 2.8 0.6 12.8 2.5 5.8 1.7 .. .. Bangladesh 1,172 1,227 8 8 2.4 1.3 10.8 5.6 11.7 5.0 .. 12.2 Belarus .. 98 .. 10 .. 0.2 .. 0.5 .. 0.3 .. 0.6 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 243 683 37 76 10.9 10.3 57.0 41.1 32.7 .. .. 68.2 Bolivia 482 726 58 74 5.9 4.4 31.6 24.7 19.7 12.0 .. .. Bosnia and Herzegovina 737 415 199 110 12.1 2.4 65.1 11.0 17.4 4.3 .. 5.9 Botswana 31 280 18 143 0.6 2.5 1.7 9.8 1.0 4.7 .. .. Brazil 231 338 1 2 0.0 0.0 0.2 0.1 0.2 0.2 0.2 0.1 Bulgaria .. .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 180 1,084 15 69 6.9 13.5 41.1 .. 26.0 .. .. 102.5 Burundi 93 549 14 66 12.9 41.2 213.8 .. 56.5 102.0 .. .. Cambodia 396 722 31 49 10.9 7.7 60.3 34.3 16.1 9.7 .. 62.9 Cameroon 377 649 24 33 4.0 2.9 22.4 .. 12.7 9.4 .. .. Canada Central African Republic 75 237 20 54 8.0 11.9 82.4 111.2 .. .. .. .. Chad 130 561 15 50 9.5 9.2 40.4 24.2 .. .. .. .. Chile 49 80 3 5 0.1 0.1 0.3 0.3 0.2 0.1 0.3 0.2 China 1,712 1,132 1 1 0.1 0.0 0.4 0.0 0.6 0.1 .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 186 1,060 5 23 0.2 0.5 1.2 2.0 1.0 2.2 .. 2.3 Congo, Dem. Rep. 177 2,354 3 36 4.5 23.9 119.1 74.6 .. .. 15.2 .. Congo, Rep. 32 283 11 77 1.4 4.1 4.4 12.0 1.6 .. 5.0 .. Costa Rica 10 109 2 24 0.1 0.4 0.4 1.9 0.1 0.8 .. 1.4 Côte d’Ivoire 351 2,366 20 112 3.6 10.6 31.2 90.4 7.9 23.9 .. 57.6 Croatia 66 169 15 38 0.3 0.3 1.6 1.0 0.6 0.6 0.8 0.7 Cuba 44 116 4 10 0.1 .. 1.2 .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark Dominican Republic 56 120 6 12 0.2 0.3 1.0 1.7 0.5 0.7 .. .. Ecuador 146 209 12 15 1.0 0.4 4.6 1.1 2.3 1.1 .. .. Egypt, Arab Rep. 1,327 925 19 11 1.3 0.5 6.8 2.5 5.6 1.6 .. 1.6 El Salvador 180 277 30 45 1.4 1.4 8.1 10.0 3.0 3.1 .. 53.2 Eritrea 176 145 48 29 27.7 7.8 116.6 .. 34.4 .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 686 3,820 10 46 8.4 13.4 41.4 59.7 41.0 42.0 .. .. Finland France Gabon 12 78 9 53 0.3 0.8 1.1 2.5 0.5 .. .. .. Gambia, The 50 128 38 75 12.4 18.5 67.8 67.3 .. 35.3 .. .. Georgia 169 908 36 213 5.3 8.6 20.8 69.7 13.6 15.6 47.9 27.3 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 598 1,583 31 66 12.4 6.1 50.0 30.9 17.2 14.1 .. 33.8 Greece Guatemala 263 376 23 27 1.4 1.0 7.6 7.7 4.4 2.7 12.5 8.0 Guinea 153 215 18 21 5.0 5.8 24.9 24.2 15.7 13.6 .. .. Guinea-Bissau 81 146 62 90 39.9 17.6 333.0 .. .. .. .. .. Haiti 208 1,120 24 112 .. .. 20.7 63.1 15.1 39.5 .. .. Honduras 448 457 72 61 6.4 3.3 22.3 16.3 8.9 5.0 .. 13.3 376 2011 World Development Indicators 6.16 GLOBAL LINKS Aid dependency Net official Aid dependency development assistance (ODA) ratios Net ODA as Net ODA as Net ODA as Total Per capita Net ODA as % of gross capital % of imports of goods, % of central government $ millions $ % of GNI formation services, and income expense 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 1,373 2,393 1 2 0.3 0.2 1.2 0.5 1.7 0.7 1.9 1.1 Indonesia 1,651 1,049 8 5 1.1 0.2 4.5 0.6 2.5 0.8 .. 1.2 Iran, Islamic Rep. 130 93 2 1 0.1 0.0 0.4 .. 0.7 .. 0.2 0.1 Iraq 100 2,791 4 89 .. 4.5 .. .. .. .. .. .. Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica 9 150 3 55 0.1 1.3 .. 5.8 0.2 2.1 .. 3.0 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 552 761 115 128 6.5 3.0 29.2 20.5 8.7 4.5 24.1 10.6 Kazakhstan 189 298 13 19 1.1 0.3 5.6 0.8 1.8 0.6 7.5 1.5 Kenya 509 1,778 16 45 4.1 6.1 23.0 29.0 12.9 15.4 23.9 27.9 Korea, Dem. Rep. 73 67 3 3 .. .. .. .. .. .. .. .. Korea, Rep. Kosovo 1 788 1 437 .. 14.0 .. 52.7 .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 215 315 44 59 16.7 7.1 78.3 31.2 28.5 8.1 99.2 35.7 Lao PDR 281 420 52 66 16.9 7.2 57.2 .. 44.0 25.2 .. 62.5 Latvia .. .. .. .. .. .. .. .. .. .. .. .. Lebanon 199 641 53 152 1.1 1.8 5.7 6.2 .. 1.9 3.8 6.3 Lesotho 37 123 19 60 3.8 6.4 11.1 24.8 4.4 6.7 .. .. Liberia 67 505 24 128 17.4 78.3 .. .. .. 27.3 .. .. Libya .. 39 .. 6 .. 0.1 .. .. .. 0.1 .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 250 193 124 95 7.1 2.2 31.3 8.6 10.5 3.2 .. .. Madagascar 320 445 21 23 8.4 5.2 54.9 15.9 20.2 .. 77.8 .. Malawi 446 772 38 51 26.1 16.6 188.6 65.6 65.6 .. .. .. Malaysia 45 144 2 5 0.1 0.1 0.2 0.5 0.0 0.1 0.3 0.3 Mali 288 985 27 76 12.0 11.0 48.4 .. 27.5 .. 102.4 74.9 Mauritania 221 287 85 87 20.2 9.4 105.5 37.7 .. .. .. .. Mauritius 20 156 17 122 0.4 1.8 1.7 8.5 0.7 2.8 .. 8.4 Mexico –58 185 –1 2 0.0 0.0 0.0 0.1 0.0 0.1 –0.1 .. Moldova 123 245 30 68 9.4 4.3 39.7 16.7 11.2 5.7 32.9 11.8 Mongolia 217 372 91 139 20.0 9.4 68.6 17.6 27.4 13.1 85.2 30.7 Morocco 419 912 15 28 1.2 1.0 4.4 2.8 3.1 2.3 .. 3.6 Mozambique 906 2,013 50 88 22.6 20.8 68.9 98.2 51.4 44.1 .. .. Myanmar 106 357 2 7 .. .. .. .. 4.0 .. .. .. Namibia 152 326 84 150 3.9 3.6 22.8 13.0 8.2 5.9 13.7 .. Nepal 386 855 16 29 7.0 6.7 28.9 23.0 21.1 16.6 .. .. Netherlands New Zealand Nicaragua 560 774 110 135 15.0 13.1 47.2 53.7 23.5 16.4 86.4 60.2 Niger 208 470 19 31 11.7 8.9 101.4 .. 43.0 .. .. .. Nigeria 174 1,659 1 11 0.4 1.0 .. .. 1.1 2.8 .. .. Norway Oman 45 212 19 75 0.2 .. 1.9 .. 0.6 0.8 0.9 .. Pakistan 700 2,781 5 16 1.0 1.7 5.5 9.1 4.8 7.1 5.7 10.6 Panama 15 66 5 19 0.1 0.3 0.5 1.1 0.1 0.4 0.6 .. Papua New Guinea 275 414 51 61 8.3 5.3 35.7 26.3 13.7 7.6 26.2 .. Paraguay 82 148 15 23 1.1 1.1 6.1 6.7 2.3 1.8 6.6 6.1 Peru 397 442 15 15 0.8 0.4 3.7 1.5 3.4 1.3 4.2 2.0 Philippines 572 310 7 3 0.8 0.2 3.6 1.3 1.1 0.5 4.3 1.0 Poland .. .. .. .. .. .. .. .. .. .. .. .. Portugal Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 377 6.16 Aid dependency Net official Aid dependency development assistance (ODA) ratios Net ODA as Net ODA as Net ODA as Total Per capita Net ODA as % of gross capital % of imports of goods, % of central government $ millions $ % of GNI formation services, and income expense 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 2000 2009 Romania .. .. .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 321 934 40 93 18.7 18.0 101.2 82.3 71.2 61.0 .. .. Saudi Arabia 22 .. 1 .. 0.0 .. 0.1 .. 0.0 .. .. .. Senegal 429 1,018 43 81 9.3 8.0 44.7 28.4 22.3 .. 71.9 .. Serbia 1,134 a 608 151a 83 18.6a 1.4 212.2a 5.9 .. 3.1 .. 3.8 Sierra Leone 181 437 43 77 29.3 23.0 413.2 148.8 68.8 64.8 98.8 101.9 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia 61 .. 31 .. 0.3 .. 1.1 .. 0.5 .. 187.8 .. Somalia 101 662 14 72 .. .. .. .. .. .. .. .. South Africa 486 1,075 11 22 0.4 0.4 2.3 1.9 1.3 1.2 1.3 1.1 Spain Sri Lanka 275 704 15 35 1.7 1.7 6.0 6.8 3.2 5.7 7.3 .. Sudan 220 2,289 6 54 1.9 4.6 9.7 16.6 8.5 16.8 .. .. Swaziland 13 58 12 49 0.9 2.0 5.1 11.4 0.9 2.1 3.9 .. Sweden Switzerland Syrian Arab Republic 158 245 10 12 0.9 0.5 4.7 2.9 2.4 .. .. .. Tajikistan 124 409 20 59 15.0 8.3 152.5 37.9 .. 13.0 160.3 .. Tanzania 1,063 2,934 31 67 10.6 13.7 62.0 46.1 47.6 37.2 .. .. Thailand 697 –77 11 –1 0.6 0.0 2.5 –0.1 0.9 0.0 .. –0.1 Timor-Leste 231 217 284 191 71.6 .. 285.9 .. .. .. .. .. Togo 70 499 13 75 5.4 17.5 29.4 .. 10.5 .. .. 100.6 Trinidad and Tobago –2 7 –1 5 0.0 0.0 –0.1 .. 0.0 .. .. .. Tunisia 222 474 23 45 1.2 1.3 4.2 4.5 2.1 2.0 4.1 4.0 Turkey 327 1,362 5 18 0.1 0.2 0.6 1.5 0.5 0.8 .. 0.8 Turkmenistan 31 40 7 8 1.2 0.2 3.1 1.8 .. .. .. .. Uganda 853 1,786 35 55 14.0 11.4 70.7 46.8 54.2 32.0 96.5 86.9 Ukraine .. 668 .. 15 .. 0.6 .. 3.4 .. 1.1 .. 1.4 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay 17 51 5 15 0.1 0.2 0.5 0.9 0.3 0.6 0.3 0.5 Uzbekistan 186 190 8 7 1.4 0.6 8.3 2.3 .. .. .. .. Venezuela, RB 76 67 3 2 0.1 0.0 0.3 0.1 0.3 0.1 0.3 .. Vietnam 1,681 3,744 22 43 5.5 4.4 18.2 10.9 9.3 4.9 .. .. West Bank and Gaza 637 3,026 212 748 13.3 .. 47.4 .. 19.2 .. .. .. Yemen, Rep. 263 500 14 21 3.0 2.0 14.3 .. 6.2 4.4 .. .. Zambia 795 1,269 76 98 25.8 11.1 140.8 44.7 53.1 23.1 .. .. Zimbabwe 176 737 14 59 2.8 14.1 19.6 581.9 .. .. .. .. World 49,527 s 127,527 s 8w 19 w 0.2 w 0.2 w 0.7 w 1.0 w 0.5 w 0.7 w .. .. Low income 12,349 39,834 18 47 7.0 9.2 35.3 38.7 22.8 24.9 .. .. Middle income 25,127 50,840 6 11 0.5 0.3 1.8 1.0 1.5 1.1 .. .. Lower middle income 18,635 39,070 5 10 0.7 0.4 2.5 1.1 2.4 1.5 .. .. Upper middle income 5,777 10,762 6 11 0.2 0.2 0.9 0.7 0.6 0.5 .. .. Low & middle income 49,234 127,093 10 22 0.9 0.8 3.5 2.5 2.8 2.7 .. .. East Asia & Pacific 8,563 10,278 5 5 0.5 0.2 1.6 0.4 1.4 0.6 .. .. Europe & Central Asia 4,462 8,101 11 20 0.6 0.3 3.2 1.6 1.8 0.9 .. .. Latin America & Carib. 4,847 9,104 9 16 0.2 0.2 1.2 1.2 0.9 1.0 .. .. Middle East & N. Africa 4,472 13,589 16 41 1.0 1.1 4.0 .. 3.3 3.9 .. .. South Asia 4,114 14,332 3 9 0.7 0.8 2.9 2.5 3.5 3.3 .. .. Sub-Saharan Africa 13,067 44,510 19 53 4.0 4.9 23.0 25.0 10.9 12.0 .. .. High income 294 433 0 0 0.0 0.0 0.0 0.0 0.0 0.0 .. .. Euro area .. .. .. .. .. .. .. .. .. .. .. .. Note: Regional aggregates include data for economies not listed in the table. World and income group totals include aid not allocated by country or region—including administrative costs, research on development issues, and aid to nongovernmental organizations. Thus regional and income group totals do not sum to the world total. a. Includes Montenegro. 378 2011 World Development Indicators 6.16 GLOBAL LINKS Aid dependency About the data The table shows data for official development assis- conclusions. For foreign policy reasons some coun- The nominal values used here may overstate the tance (ODA; see About the data for table 6.14) for tries have traditionally received large amounts of real value of aid to recipients. Changes in interna- aid-receiving countries. The data cover loans and aid. Thus aid dependency ratios may reveal as much tional prices and exchange rates can reduce the pur- grants from Development Assistance Committee about a donor’s interests as about a recipient’s chasing power of aid. Tying aid, still prevalent though (DAC) member countries, multilateral organizations, needs. Ratios are generally much higher in Sub-Saha- declining in importance, also tends to reduce its pur- and non-DAC donors. They do not reflect aid given by ran Africa than in other regions, and they increased chasing power (see About the data for table 6.15). recipient countries to other developing countries. As in the 1980s. High ratios are due only in part to aid The aggregates refer to World Bank classifications a result, some countries that are net donors (such as flows. Many African countries saw severe erosion of economies and therefore may differ from those Saudi Arabia) are shown in the table as aid recipients in their terms of trade in the 1980s, which, along of the Organisation for Economic Co-operation and (see table 6.16a). with weak policies, contributed to falling incomes, Development (OECD). The table does not distinguish types of aid (pro- imports, and investment. Thus the increase in aid Definitions gram, project, or food aid; emergency assistance; dependency ratios reflects events affecting both the postconflict peacekeeping assistance; or technical numerator (aid) and the denominator (GNI). • Net official development assistance is flows (net cooperation), which may have different effects on the Because the table relies on information from of repayment of principal) that meet the Development economy. Expenditures on technical cooperation do donors, it is not necessarily consistent with infor- Assistance Committee (DAC) definition of ODA and not always directly benefit the economy to the extent mation recorded by recipients in the balance of pay- are made to countries and territories on the DAC list that they defray costs incurred outside the country ments, which often excludes all or some technical of aid recipients. See About the data for table 6.14. on salaries and benefi ts of technical experts and assistance—particularly payments to expatriates • Net official development assistance per capita is overhead costs of firms supplying technical services. made directly by the donor. Similarly, grant com- net ODA divided by midyear population. • Aid depen- Ratios of aid to gross national income (GNI), gross modity aid may not always be recorded in trade dency ratios are calculated using values in U.S. dol- capital formation, imports, and government spending data or in the balance of payments. Moreover, DAC lars converted at official exchange rates. Imports of provide measures of recipient country dependency statistics exclude aid for military and antiterrorism goods, services, and income refer to international on aid. But care must be taken in drawing policy purposes. transactions involving a change in ownership of general merchandise, goods sent for processing Official development assistance from non-DAC donors, 2005–09 6.16a and repairs, nonmonetary gold, services, receipts of employee compensation for nonresident workers, Net disbursements ($ millions) and investment income. For definitions of GNI, gross 2005 2006 2007 2008 2009 capital formation, and central government expense, OECD members (non-DAC) see Definitions for tables 1.1, 4.8, and 4.10. Czech Republic 135 161 179 249 215 Hungary 100 149 103 107 117 Iceland 27 41 48 48 34 Israela 95 90 111 138 124 Poland 205 297 363 372 375 Slovak Republic 56 55 67 92 75 Slovenia 35 44 54 68 71 Turkey 601 714 602 780 707 Arab countries Kuwait 218 158 110 283 221 Data sources Saudi Arabia 1,026 2,025 1,551 4,979 3,134 Data on financial flows are compiled by OECD DAC United Arab Emirates 141 219 429 88 834 and published in its annual statistical report, Geo- Other donors graphical Distribution of Financial Flows to Devel- Taiwan, China 483 513 514 435 411 oping Countries, and in its annual Development Thailand .. 74 67 178 40 Co- operation Report. Data are available electroni- Othersb 51 77 134 275 313 cally on the OECD DAC International Development Total 3,175 4,617 4,333 8,094 6,672 Statistics CD-ROM and at www.oecd.org/dac/ Note: The table does not reflect aid provided by several major emerging non–Organisation for Economic Co-operation and stats/idsonline. Data on population, GNI, gross Development (OECD) donors because information on their aid has not been disclosed. a. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of capital formation, imports of goods and services, such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem, and Israeli settlements in the West Bank under the terms of international law. The figures include $49.2 million in 2005, $45.5 million in 2006, $42.9 and central government expense used in comput- million in 2007, $43.6 million in 2008, and $35.4 million in 2009 for first-year sustenance expenses for people arriving from developing countries (many of which are experiencing civil war or severe unrest) or people who have left their country ing the ratios are from World Bank and Interna- for humanitarian or political reasons. b. Includes Cyprus, Estonia, Latvia, Liechstenstein, Lithuania, Malta, and Romania. tional Monetary Fund databases. Source: Organisation for Economic Co-operation and Development. 2011 World Development Indicators 379 6.17 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United EU United DAC donors $ millions States Institutions Kingdom Germany France Japan Netherlands Spain Norway Canada $ millions 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Afghanistan 5,319.2 2,979.9 395.4 324.4 337.3 49.8 170.5 147.9 98.9 115.9 232.6 466.5 Albania 314.9 33.0 69.3 2.2 58.8 4.2 -2.0 8.2 14.3 1.0 0.1 125.7 Algeria 282.9 8.1 82.8 3.6 13.1 94.5 1.9 0.0 54.4 0.9 2.8 20.9 Angola 170.4 41.5 38.9 4.4 8.4 4.2 6.8 -3.3 20.3 17.8 0.9 30.4 Argentina 100.2 2.6 21.3 1.0 22.7 12.3 9.0 0.2 24.1 0.1 2.1 4.8 Armenia 273.8 78.5 38.8 1.0 31.0 5.7 98.7 3.0 0.4 3.1 0.7 13.0 Australia Austria Azerbaijan 136.5 40.4 12.5 1.4 42.7 27.9 -2.0 0.0 0.7 4.0 0.3 8.7 Bangladesh 849.5 63.8 131.9 250.1 67.3 -3.6 14.1 70.4 6.0 14.6 52.5 182.5 Belarus 72.3 12.2 11.1 0.6 21.7 4.5 0.6 0.0 0.8 2.6 0.0 18.3 Belgium Benin 472.3 58.9 146.6 0.0 43.1 50.4 25.8 42.0 3.5 0.0 7.0 94.9 Bolivia 562.8 101.6 77.8 0.5 45.7 10.0 31.8 45.6 97.6 6.4 24.3 121.6 Bosnia and Herzegovina 349.0 31.1 72.6 9.6 27.6 4.7 5.0 21.8 36.9 15.9 4.1 120.0 Botswana 255.7 214.4 32.3 0.9 2.1 1.0 -2.6 0.0 0.1 1.8 1.3 4.4 Brazil 328.0 8.1 18.8 13.1 196.1 47.1 -93.2 0.6 64.9 29.5 10.8 32.2 Bulgaria .. .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 618.3 51.1 165.4 0.2 47.5 77.4 49.8 66.0 10.2 0.5 23.5 126.6 Burundi 391.9 47.6 131.1 14.4 27.9 12.9 20.4 18.3 5.7 25.1 6.1 82.5 Cambodia 516.8 68.6 43.1 32.3 37.9 29.8 127.5 0.1 29.1 3.2 10.9 134.4 Cameroon 326.9 31.4 59.2 2.3 91.0 90.6 8.1 0.1 4.0 0.4 7.1 32.7 Canada Central African Republic 153.3 30.5 54.7 2.4 6.6 25.9 6.1 2.8 4.3 0.6 3.8 15.6 Chad 474.5 169.6 119.0 5.6 27.9 41.0 14.0 8.4 13.2 2.2 12.1 61.6 Chile 70.5 1.8 10.8 0.6 11.5 9.6 7.9 0.2 9.6 13.3 2.0 3.2 China 1,199.8 52.9 42.9 116.0 340.9 364.4 142.0 5.3 45.8 21.7 11.1 57.1 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 1,044.4 652.3 45.9 7.8 45.2 22.5 -6.7 32.5 148.6 11.6 25.3 59.4 Congo, Dem. Rep. 1,332.0 238.7 232.8 225.5 79.4 30.3 65.7 43.4 42.7 28.1 44.9 300.7 Congo, Rep. 252.3 9.3 26.2 0.0 25.8 93.2 0.4 0.0 44.4 0.1 7.6 45.4 Costa Rica 105.5 -0.6 6.8 2.6 15.0 4.7 58.3 3.8 9.3 0.7 2.1 2.8 CÔ te d'Ivoire 1,794.4 230.7 71.9 0.2 15.1 1,200.6 10.4 36.5 50.8 1.6 43.7 133.1 Croatia 160.7 3.7 129.9 1.9 12.6 4.0 -0.7 0.2 0.7 3.6 0.1 4.6 Cuba 103.5 20.0 16.9 1.0 2.5 2.7 3.6 0.1 37.7 0.8 7.7 10.7 Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark Dominican Republic 118.3 14.1 66.1 0.1 –2.2 3.4 0.2 0.0 29.2 0.3 2.6 4.4 Ecuador 209.9 52.1 62.6 -0.2 24.7 1.2 -11.8 1.6 48.7 1.6 3.2 26.0 Egypt, Arab Rep. 784.7 185.1 204.7 35.6 138.8 111.6 -18.8 17.8 20.6 0.7 17.0 71.7 El Salvador 284.6 82.1 24.9 0.0 18.1 2.4 -3.8 0.4 125.7 0.5 3.2 31.1 Eritrea 86.3 3.6 42.9 6.5 1.4 0.5 8.8 3.7 1.8 9.6 0.6 7.0 Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2,019.0 726.0 202.5 342.9 79.8 38.3 97.8 85.9 94.0 37.8 87.2 226.9 Finland France Gabon 61.8 1.2 9.2 0.0 -3.3 54.0 0.1 0.0 0.4 0.0 1.0 –0.8 Gambia, The 37.1 5.0 15.2 3.7 0.3 0.3 11.4 0.7 3.0 0.1 1.2 –3.9 Georgia 603.6 279.1 167.7 7.3 67.0 14.0 12.3 5.1 0.9 11.0 0.8 38.5 Germany Ghana 987.2 150.5 166.9 153.9 61.2 49.7 64.8 98.3 24.1 2.5 99.8 115.5 Greece Guatemala 369.3 83.9 28.0 0.7 16.1 2.9 26.0 28.4 113.4 7.7 7.1 55.2 Guinea 212.2 34.9 41.2 0.9 19.5 82.1 18.2 0.0 5.0 0.0 5.2 5.4 Guinea-Bissau 110.7 1.1 60.1 0.1 0.4 6.1 9.4 0.0 13.1 0.0 1.2 19.2 Haiti 806.8 319.6 102.7 8.0 16.9 49.0 24.8 0.2 144.9 4.3 119.7 16.8 Honduras 344.5 128.8 39.8 0.1 15.9 1.4 41.7 0.8 58.4 1.4 24.1 32.2 380 2011 World Development Indicators 6.17 GLOBAL LINKS Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United EU United DAC donors $ millions States Institutions Kingdom Germany France Japan Netherlands Spain Norway Canada $ millions 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 1,567.6 48.1 98.9 521.1 263.4 –29.0 517.0 7.2 25.3 16.1 11.5 87.8 Indonesia 446.0 121.3 113.1 68.8 -34.8 187.1 -512.8 81.1 3.4 12.9 20.0 385.9 Iran, Islamic Rep. 67.7 0.7 1.9 0.7 46.1 14.6 -17.4 4.5 5.2 0.8 3.3 7.3 Iraq 2,686.0 2,346.3 57.3 48.6 38.2 9.3 28.1 7.3 2.4 11.6 12.1 124.9 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica 112.3 –2.1 105.9 8.3 –6.9 –0.8 –5.3 –4.3 1.2 0.1 5.9 10.2 Japan Jordan 571.7 394.6 85.4 1.5 39.8 58.9 –57.4 0.6 10.2 0.8 11.0 26.3 Kazakhstan 185.5 97.3 13.3 7.0 17.5 2.9 37.1 0.6 -0.4 3.1 0.1 7.0 Kenya 1,308.3 590.2 84.3 131.2 85.7 44.8 33.7 25.4 50.7 15.5 31.7 215.1 Korea, Dem. Rep. 49.8 13.5 3.4 0.1 2.7 0.3 0.0 1.2 2.0 4.8 3.6 18.2 Korea, Rep. Kosovo 744.6 207.4 315.9 11.8 32.6 1.0 0.2 0.5 0.9 21.2 0.0 153.3 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 168.4 52.5 28.7 8.9 24.0 0.9 17.8 0.1 1.3 3.4 0.1 30.7 Lao PDR 285.9 7.4 25.9 0.3 27.4 19.1 92.4 0.0 1.7 3.2 1.8 106.8 Latvia .. .. .. .. .. .. .. .. .. .. .. .. Lebanon 463.3 136.9 74.3 5.4 31.6 102.5 3.5 0.7 24.2 9.8 13.9 60.7 Lesotho 86.8 24.7 16.1 8.2 5.4 -1.5 2.6 0.0 9.8 1.0 1.0 19.7 Liberia 400.3 96.9 59.5 33.4 28.1 0.3 14.7 0.0 5.8 15.4 2.2 144.1 Libya 34.4 5.7 2.2 1.9 3.6 19.1 0.1 0.0 0.0 0.0 0.1 1.8 Lithuania .. .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 186.5 29.9 53.2 2.0 18.8 3.0 24.2 18.3 1.8 7.0 0.0 28.6 Madagascar 297.2 76.6 55.6 1.3 17.8 97.5 19.0 0.3 4.1 8.4 2.1 14.5 Malawi 519.3 111.4 84.1 111.7 30.2 0.3 35.8 0.9 9.8 63.6 19.5 52.0 Malaysia 133.0 16.3 0.1 4.2 11.0 –0.1 91.8 0.1 0.1 0.7 0.1 8.8 Mali 676.4 111.3 101.7 0.1 46.9 74.7 35.5 77.3 24.3 12.6 83.5 108.5 Mauritania 157.9 10.2 35.7 0.8 11.6 35.0 9.6 0.0 44.7 0.7 1.3 8.3 Mauritius 156.8 0.1 93.2 20.8 0.5 43.2 –2.1 0.0 0.0 0.4 0.3 0.4 Mexico 164.8 129.4 6.1 11.6 40.8 13.1 –30.7 –0.3 –14.5 0.0 4.4 4.8 Moldova 202.4 32.2 106.2 3.2 9.0 7.0 3.1 2.1 0.4 3.7 0.0 35.5 Mongolia 212.6 34.9 5.4 0.7 25.4 2.1 74.7 9.6 –1.3 1.3 2.7 57.1 Morocco 987.1 31.6 282.4 4.8 81.7 238.1 97.9 1.7 190.7 0.0 8.4 49.9 Mozambique 1,492.3 255.6 204.7 54.9 113.8 14.7 60.7 99.3 68.8 80.4 75.2 464.4 Myanmar 310.8 35.2 76.8 53.1 9.7 2.1 48.3 5.8 1.1 18.9 2.5 57.4 Namibia 279.1 90.3 32.6 0.7 36.7 50.1 39.8 1.9 12.0 –6.7 0.7 21.0 Nepal 548.8 73.5 44.0 103.2 59.6 –3.4 45.3 3.1 49.6 45.3 5.5 123.2 Netherlands New Zealand Nicaragua 519.0 89.3 46.1 7.1 28.8 1.1 17.4 31.0 142.4 17.5 13.6 124.8 Niger 319.8 37.1 64.4 6.2 22.0 57.4 35.1 0.1 22.2 1.6 9.8 63.9 Nigeria 769.4 354.0 81.9 188.9 26.7 9.1 28.9 4.5 7.0 9.2 17.5 41.8 Norway Oman 8.4 5.3 0.0 0.6 0.7 0.7 0.7 0.3 0.0 0.0 0.0 0.1 Pakistan 1,428.3 613.0 97.6 217.5 107.5 8.8 131.4 38.9 13.7 46.6 41.9 111.4 Panama 60.8 16.7 2.2 0.1 1.7 0.1 33.5 0.0 6.3 0.0 0.8 -0.5 Papua New Guinea 354.5 2.8 32.4 1.0 2.5 0.1 –4.2 0.0 0.9 1.7 0.2 317.1 Paraguay 152.9 26.5 31.5 0.0 6.2 0.6 37.3 0.0 38.9 0.9 2.2 8.6 Peru 412.5 104.4 73.8 1.1 79.8 9.0 –36.8 0.3 100.2 –7.3 17.9 70.1 Philippines 294.8 89.5 50.4 4.4 40.1 –7.3 –8.4 2.2 -31.4 1.8 17.0 136.3 Poland .. .. .. .. .. .. .. .. .. .. .. .. Portugal Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 381 6.17 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United EU United DAC donors $ millions States Institutions Kingdom Germany France Japan Netherlands Spain Norway Canada $ millions 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Romania .. .. .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 624.3 145.9 104.5 89.9 44.0 3.5 21.3 54.2 25.0 3.6 13.7 118.8 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 648.8 67.7 134.5 6.5 22.2 140.9 46.7 45.7 59.3 0.5 54.5 70.5 Serbia 565.4 46.5 292.9 7.7 114.5 12.7 3.7 2.6 4.0 19.9 4.8 56.0 Sierra Leone 305.3 17.0 108.9 80.3 15.8 0.3 37.4 1.5 3.4 3.1 8.9 28.8 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 607.5 194.9 108.0 43.8 20.9 4.7 22.6 14.8 52.8 33.3 25.7 85.9 South Africa 1,014.6 523.7 153.3 67.3 86.9 –15.6 4.7 48.9 5.3 36.1 13.0 90.9 Spain Sri Lanka 433.2 32.3 59.2 18.2 –5.6 12.7 91.6 2.7 18.6 35.3 25.0 143.2 Sudan 2,136.8 954.6 225.8 292.4 47.2 10.4 111.0 97.3 26.0 92.1 105.0 174.9 Swaziland 33.7 15.6 15.1 –3.8 –0.2 0.2 1.2 0.0 1.2 3.2 0.9 0.3 Sweden Switzerland Syrian Arab Republic 116.0 18.6 54.8 1.1 37.8 25.7 –54.5 0.1 6.3 0.0 0.9 25.3 Tajikistan 177.6 40.5 37.3 4.5 26.1 4.7 26.2 0.3 6.3 3.1 2.5 26.0 Tanzania 1,547.2 283.7 138.4 216.7 87.1 7.9 120.5 62.6 25.1 116.4 94.0 394.9 Thailand –71.2 23.6 21.3 9.9 1.9 –11.7 –150.3 3.6 4.5 0.7 2.8 22.5 Timor-Leste 193.3 29.1 10.3 0.1 5.6 0.1 11.9 0.0 10.8 8.5 2.0 114.8 Togo 408.2 3.8 46.4 10.4 24.0 40.5 34.1 0.9 3.8 0.1 2.5 241.7 Trinidad and Tobago 6.0 0.5 1.6 0.4 0.2 1.1 0.1 0.0 0.1 0.0 1.8 0.2 Tunisia 457.6 –5.3 108.1 3.8 30.8 170.0 14.4 –0.8 124.1 0.0 2.1 10.3 Turkey 1,345.1 –6.5 787.0 2.2 6.7 154.6 210.8 –0.3 135.3 0.2 –2.3 57.5 Turkmenistan 17.4 10.8 4.0 0.3 1.9 0.2 –1.2 0.0 0.0 0.6 0.0 0.8 Uganda 1,141.3 366.9 128.0 117.4 60.1 14.6 54.1 45.0 5.9 67.3 16.9 265.3 Ukraine 574.0 103.0 177.0 2.4 121.6 19.5 61.9 0.0 3.8 3.1 18.0 63.7 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay 44.2 1.0 11.8 0.0 –0.3 1.4 2.4 0.0 12.2 0.2 1.3 14.1 Uzbekistan 83.6 9.9 6.1 1.8 32.1 2.9 20.4 0.0 0.7 0.3 0.0 9.4 Venezuela, RB 50.2 11.7 3.4 2.2 8.7 7.1 2.1 0.1 12.9 0.0 0.5 1.5 Vietnam 2,127.8 78.1 51.9 93.8 112.5 142.9 1,191.4 45.4 32.7 15.9 35.3 327.9 West Bank and Gaza 2,275.9 844.3 538.3 94.9 98.7 79.2 76.7 46.2 99.4 100.1 41.2 256.9 Yemen, Rep. 276.0 26.2 23.6 35.9 82.9 5.9 37.2 30.9 3.9 0.7 2.5 26.3 Zambia 852.9 231.9 152.4 73.5 55.5 7.4 36.6 64.8 11.8 62.7 13.0 143.4 Zimbabwe 700.1 249.7 79.7 109.9 34.7 4.6 12.4 22.3 8.2 28.9 28.3 121.4 World 96,623.9 s 25,173.7 s 13,021.4 s 7,657.0 s 7,096.7 s 7,019.4 s 6,001.2 s 4,798.0 s 4,473.1 s 3,168.2 s 3,141.0 s 15,074.3 s Low income 27,536.0 7,955.5 3,842.7 2,622.5 1,702.3 993.6 1,553.4 1,067.9 927.6 836.8 1,152.7 4,881.1 Middle income 39,141.0 10,578.1 6,543.1 2,083.3 3,199.0 4,510.2 2,729.5 899.5 2,240.8 655.9 775.7 4,925.8 Lower middle income 28,607.7 8,235.8 3,859.8 1,852.0 2,103.0 3,157.9 2,329.6 609.1 1,461.4 484.1 610.8 3,904.0 Upper middle income 9,649.3 2,276.5 2,327.5 225.9 965.2 1,254.3 398.9 255.7 738.7 150.8 144.1 911.8 Low & middle income 96,408.4 25,163.7 12,879.0 7,653.4 7,083.1 7,010.9 6,001.0 4,797.4 4,450.5 3,164.6 3,135.4 15,069.4 East Asia & Pacific 7,305.6 823.5 526.8 389.5 604.5 899.2 1,228.5 157.0 100.4 97.1 146.1 2,333.0 Europe & Central Asia 6,418.5 1,340.6 2,240.2 74.4 714.3 273.7 522.3 66.2 209.7 110.5 29.4 837.4 Latin America & Carib. 7,714.7 2,030.9 1,117.6 158.6 917.4 231.7 142.5 262.0 1,501.4 138.1 452.6 761.9 Middle East & N. Africa 9,508.6 4,082.5 1,623.7 247.5 703.5 1,000.9 142.1 108.9 589.7 137.1 125.4 747.3 South Asia 10,370.5 3,906.6 845.4 1,438.0 844.6 35.4 1,013.7 274.8 212.4 280.3 372.5 1,146.7 Sub-Saharan Africa 30,845.3 7,436.4 4,816.7 2,708.4 1,781.1 3,396.9 1,374.3 1,197.6 1,127.2 902.4 1,308.7 4,795.8 High income 215.5 9.9 142.4 3.6 13.5 8.5 0.3 0.5 22.6 3.6 5.6 4.9 Euro area .. .. .. .. .. .. .. .. .. .. .. .. Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. 382 2011 World Development Indicators 6.17 GLOBAL LINKS Distribution of net aid by Development Assistance Committee members About the data Definitions The table shows net bilateral aid to low- and middle- on behalf of DAC members are classified as bilateral • Net aid refers to net bilateral official development income economies from members of the Develop- aid (since it is the donor country that effectively con- assistance that meets the DAC definition of official ment Assistance Committee (DAC) of the Organisation trols the use of the funds) and are included in the development assistance and is made to countries for Economic Co-operation and Development (OECD). data reported in this table. and territories on the DAC list of aid recipients. See DAC has 24 members, of which 23 are economies The data include aid to some countries and terri- About the data for table 6.14. • Other DAC donors and 1 is a multilateral institution (the European Union tories not shown in the table and aid to unspecified are Australia, Austria, Belgium, Denmark, Finland, Institutions). Previous editions of the table included economies recorded only at the regional or global Greece, Ireland, Italy, the Republic of Korea, Lux- only DAC member economies; this year’s edition level. Aid to countries and territories not shown in embourg, New Zealand, Portugal, Sweden, and includes data for the European Union Institutions. the table has been assigned to regional totals based Switzerland. The table is based on donor country reports of on the World Bank’s regional classification system. bilateral programs, which may differ from reports by r Aid to unspecified economies is included in regional ecipient countries. Recipients may lack access to totals and, when possible, income group totals. Aid information on such aid expenditures as develop- not allocated by country or region—including admin- ment-oriented research, stipends and tuition costs istrative costs, research on development, and aid to for aid-financed students in donor countries, and nongovernmental organizations—is included in the payment of experts hired by donor countries. More- world total. Thus regional and income group totals over, a full accounting would include donor country do not sum to the world total. contributions to multilateral institutions, the flow Some of the aid recipients shown in table are also of resources from multilateral institutions to recipi- aid donors. Development cooperation activities by ent countries, and flows from countries that are not non-DAC members have increased in recent years members of DAC. and in some cases surpass those of individual DAC Data in this table exclude DAC members’ multilat- members. Some non-DAC donors report their devel- eral aid (contributions to the regular budgets of the opment cooperation activities to DAC on a voluntary multilateral institutions). These are included in data basis. Many others do not yet report their aid flows reported in table 6.14. Projects executed by multi- to DAC. See table 6.16a for a summary of ODA from lateral institutions or nongovernmental organizations non-DAC countries. Beyond the DAC: The role of other providers of development assistance 6.17a Development assistance flows from non-DAC donor countries ($ millions) Country Estimate Year Source 20 countries reporting to DAC (see table 6.16a) 8,094 2008 OECD/DAC Statistics DAC Development Co-operation Report, Brazil 437 2007 estimates by Brazilian officials Fiscal Yearbook, Ministry of Finance, China. China 1,800–3,000 2008 Upper estimate: Brautigam 2009 Annual Reports, Ministry of Foreign Affairs, India 610 2008/09 India Russian Federation statement at DAC Senior Russian Federation 200 2008 Level Meeting, April 2010 Estimates of Public Expenditures 2009, Foreign Affairs, National Treasury of South South Africa 109 2008/09 Africa Many countries that are not members of the OECD DAC have provided development assistance for decades. The past 10 years have seen their numbers rise fast, and in some cases their levels of development assistance now surpass those of Data sources individual DAC members. DAC estimates total net development assistance flows from non-DAC donors at $12–$14 billion in 2008, or 9–10 percent of global official development assistance (ODA) flows (assuming that the flows were consistent Data on financial flows are compiled by DAC and with the definition of ODA). Estimating overall aid volumes from non-DAC donors is challenging. Twenty countries, mostly published in its annual statistical report, Geographi- emerging donors and Arab donors, voluntarily report aid volumes to DAC annually (see table 6.16a). Many others, including most major providers of aid from developing countries to developing countries (such as Brazil, China, India, cal Distribution of Financial Flows to Aid Recipients, and South Africa), do not. Estimates of aid volumes of countries that do not report to DAC must be treated with caution. and its annual Development Cooperation Report. Official figures often omit important cooperation activities, such as contributions to international organizations focused on development, leading to underestimates, and they often include expenditures that would not qualify as ODA, such as Data are available electronically on the OECD DAC security-related or culturally motivated spending, or insufficiently concessional loans, leading to overestimates. International Development Statistics CD-ROM and Source: Smith, Fordelone, and Zimmermann 2010 and OECD 2010. at www.oecd.org/dac/stats/idsonline. 2011 World Development Indicators 383 6.18 Movement of people across borders Net migration International Refugees Workers’ remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990–95 2005–10 1995 2010 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan 3,266 1,000 70 91 2,679.1 2,887.1 19.6 0.0 .. .. .. .. Albania –423 –75 71 89 5.8 15.7 4.7 0.1 427 1,317 .. 10 Algeria –50 –140 299 242 1.5 8.2 192.5 94.1 1,120a 2,059a .. .. Angola 143 80 38 65 246.7 141.0 10.9 14.7 5 82 210 716 Argentina 120 30 1,588 1,449 0.3 0.6 10.3 3.2 64 658 195 702 Armenia –500 –75 682 324 201.4 18.0 219.0 3.6 65 769 17 145 Australia 371 500 3,854 4,711 0.0 0.0 62.1 22.5 1,651 4,089a 700 3,000a Austria 234 160 989 1,310 0.0 0.0 34.4 38.9 1,012 3,286 346 3,377 Azerbaijan –116 –50 525 264 200.5 16.9 233.7 1.6 3 1,274 9 652 Bangladesh –500 –570 1,006 1,085 57.0 10.4 51.1 228.6 1,202 10,523 1 8 Belarus 0 0 1,185 1,090 0.1 5.5 29.0 0.6 29 358 12 112 Belgium 85 200 916 975 0.0 0.1 31.7 15.5 4,937 10,437 3,252 4,136 Benin 105 50 146 232 0.1 0.4 23.8 7.2 100 243a 26 88 Bolivia –100 –100 70 146 .. .. 0.7 0.7 7 1,069 9 103 Bosnia and Herzegovina –1,025 –10 73 28 769.8 70.0 40.0 7.1 .. 2,081 .. 61 Botswana 14 15 39 115 0.0 0.0 0.3 3.0 59 88 200 102 Brazil –184 –229 731 688 0.1 1.0 2.1 4.2 3,315 4,234 347 1,003 Bulgaria –349 –50 47 107 4.2 2.7 1.3 5.4 42 1,558 34 101 Burkina Faso –128 –65 464 1,043 0.1 1.0 29.8 0.5 78a 99a 50a 100 Burundi –250 323 295 61 350.6 94.2 173.0 25.0 .. 28 5 1 Cambodia 150 –5 116 336 61.2 17.0 0.0 0.1 12 338 52 215 Cameroon –5 –19 246 197 2.0 14.8 45.8 100.0 11 148 22 94 Canada 643 1,050 5,047 7,202 0.0 0.1 152.1 169.4 .. .. .. .. Central African Republic 37 5 67 80 0.2 159.6 33.9 27.0 0 .. 27 .. Chad –10 –75 78 388 59.7 55.0 0.1 338.5 1 .. 15 .. Chile 90 30 136 320 14.3 1.3 0.3 1.5 .. 4 13a 6 China –829b –1,731b 437b 686b 124.7c 200.6c 288.3 301.0 878a 48,729a 86a 4,444 Hong Kong SAR, China 300 113 2,431 2,742 0.2 0.0 1.5 0.1 .. 348 .. 413 Colombia –250 –120 109 110 1.9 389.8 0.2 0.2 815 4,180 150 92 Congo, Dem. Rep. 1,208 –100 1,919 445 89.7 455.9 1,433.8 185.8 .. .. .. .. Congo, Rep. –14 –50 131 143 0.2 20.5 19.4 111.4 4 14 a 27 102 Costa Rica 62 30 228 489 0.2 0.3 24.2 19.1 123 513 36 239 Côte d’Ivoire 375 –145 1,985 2,407 0.2 23.2 297.9 24.6 151 185 457 756 Croatia 153 10 721 700 245.6 76.5 198.6 1.2 544 1,476 16 99 Cuba –120 –194 25 15 24.9 7.5 1.8 0.5 .. .. .. .. Czech Republic 8 226 454 453 2.0 1.1 2.7 2.3 191 1,201 101 2,562 Denmark 58 30 297 484 0.0 0.0 64.8 20.4 523 894 209 3,413 Dominican Republic –129 –140 322 434 0.0 0.2 1.0 .. 839 3,467 7 29 Ecuador –50 –350 88 394 0.2 1.0 0.2 116.6 386 2,502 4 81 Egypt, Arab Rep. –498 –340 174 245 0.9 7.0 5.4 94.4 3,226 7,150 223 255 El Salvador –249 –280 28 40 23.5 5.1 0.2 0.0 1,064 a 3,482 1a 21 Eritrea –359 55 12 16 286.7 209.2 1.1 4.8 .. .. .. .. Estonia –108 0 309 182 0.4 0.2 .. 0.0 1 325 3 81 Ethiopia 768 –300 795 548 101.0 62.9 393.5 121.9 27 262 0 27 Finland 43 55 103 226 0.0 0.0 10.2 7.4 74 859 54 454 France 239 500 6,085 6,685 0.0 0.1 155.2 196.4 4,640 15,551 4,935 5,224 Gabon 20 5 164 284 0.0 0.1 0.8 8.8 4 10a 99 186 Gambia, The 45 15 148 290 0.2 2.0 6.6 10.1 19a 80 .. 8 Georgia –544 –250 250 167 0.3 15.0 0.1 0.9 284 714 12 32 Germany 2,649 550 8,992 10,758 0.4 0.2 1,267.9 593.8 4,523 10,879 11,348 15,924 Ghana 40 –51 1,038 1,852 13.6 14.9 83.2 13.7 17 114 5 6 Greece 470 150 549 1,133 0.2 0.1 4.4 1.7 3,286 2,020 300 1,843 Guatemala –360 –200 46 59 42.9 5.8 1.5 0.1 358 4,019 8 22 Guinea 350 –300 814 395 0.4 10.9 672.3 15.3 1 64 10 45 Guinea-Bissau 20 –12 32 19 0.8 1.1 15.4 7.9 2a 47 3 17a Haiti –133 –140 22 35 13.9 24.1 .. 0.0 .. 1,376 .. 135 Honduras –120 –100 31 24 1.2 1.2 0.1 0.0 124 2,520 8 12 384 2011 World Development Indicators 6.18 GLOBAL LINKS Movement of people across borders Net migration International Refugees Workers’ remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990–95 2005–10 1995 2010 1995 2009 1995 2009 1995 2009 1995 2009 Hungary 104 75 293 368 2.3 1.5 11.4 6.0 152 2,130 146 1,223 India –960 –1,000 7,022 5,436 5.0 19.5 227.5 185.3 6,223 49,468 419 2,893 Indonesia –725 –730 219 123 9.8 18.2 0.0 0.8 651 6,793 .. 2,702 Iran, Islamic Rep. –1,164 –500 3,016 2,129 112.4 72.8 2,072.0 1,070.5 1,600a 1,045a .. .. Iraq –154 –577 134 83 718.7 1,785.2 116.7 35.2 .. 71 .. 31a Ireland –1 200 264 899 0.0 0.0 0.4 9.6 347 576 173 1,988 Israel 484 85 1,919 2,940 0.9 1.3 .. 17.7 701 1,267 1,407 3,283 Italy 294 1,650 1,723 4,463 0.1 0.0 74.3 55.0 2,364 2,683 1,824 12,986 Jamaica –113 –100 22 30 0.0 0.9 0.0 0.0 653 1,912 74 314 Japan 474 150 1,363 2,176 0.0 0.2 5.4 2.3 1,151 1,776 1,820 4,069 Jordan 509 250 1,608 2,973 0.5 2.1 1,288.9d 2,434.5d 1,441 3,597 107 502 Kazakhstan –1,509 –100 3,295 3,079 0.1 3.7 15.6 4.3 116 124 503 3,138 Kenya 222 –189 528 818 9.3 9.6 234.7 358.9 298 a 1,686a 9 61 Korea, Dem. Rep. 0 0 35 37 0.0 0.9 .. .. .. .. .. .. Korea, Rep. –627 –30 584 535 0.0 0.6 0.0 0.3 1,080 2,522 635 3,120 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait –598 120 1,090 2,098 0.8 0.9 3.3 0.2 .. .. 1,354 9,912 Kyrgyz Republic –273 –75 482 223 0.0 2.6 13.4 0.4 1 992a 41 188 Lao PDR –30 –75 23 19 58.2 8.4 .. .. 22 38 9 22 Latvia –134 –10 527 335 0.2 0.8 .. 0.0 41 591 1 46 Lebanon 230 –13 656 758 13.5 16.3 348.0d 476.1d 1,225a 7,558 .. 5,749 Lesotho –84 –36 6 6 0.0 0.0 0.1 .. 411 414 75 35 Liberia –523 248 199 96 744.6 71.6 120.1 7.0 .. 54 a .. 1 Libya 10 20 506 682 0.6 2.2 4.0 9.0 .. 14 a 222 1,361 Lithuania –99 –100 272 129 0.1 0.5 0.0 0.8 1 1,169 1 620 Macedonia, FYR –27 –10 115 130 12.9 7.9 9.0 1.5 68 381 1 26 Madagascar –7 –5 44 38 0.1 0.3 0.1 .. 14 10a 11 21 Malawi –920 –20 325 276 0.0 0.1 1.0 5.4 1 1a 1 0a Malaysia 287 130 1,193 2,358 0.1 0.5 5.3 66.1 116 1,131 1,329 6,529 Mali –260 –202 174 163 77.2 2.9 17.9 13.5 112 405a 42 105 Mauritania –15 10 118 99 84.3 39.1 34.4 26.8 5 2a 14 .. Mauritius –7 0 18 43 0.0 0.0 .. .. 132a 211a 1 12 Mexico –1,364 –2,430 458 726 0.4 6.4 38.7 1.2 4,368 21,953 .. .. Moldova –121 –172 473 408 0.5 5.9 .. 0.1 1 1,211 1 104 Mongolia –173 –10 7 10 0.0 1.5 .. 0.0 .. 200 .. 83 Morocco –450 –425 55 49 0.3 2.3 0.1 0.8 1,970 6,270 20 61 Mozambique 650 –20 246 450 125.6 0.1 0.1 3.5 59 111 21 63 Myanmar –126 –500 114 89 152.3 406.7 .. .. 81 137a .. 32a Namibia –13 –1 118 139 0.0 0.9 1.7 7.2 16 14 11 16 Nepal –101 –100 625 946 0.0 5.1 124.8 108.5 57 2,986 9 12 Netherlands 191 100 1,387 1,753 0.1 0.0 80.0 76.0 1,359 3,691 2,802 14,212 New Zealand 143 50 594 962 .. 0.0 3.8 3.3 1,652 628 427 977 Nicaragua –114 –200 27 40 23.9 1.5 0.6 0.1 75 768 .. .. Niger –3 –28 171 202 10.3 0.8 27.6 0.3 8 89 29 22a Nigeria –96 –300 582 1,128 1.9 15.6 8.1 9.1 804 a 9,585a 5 66 Norway 42 135 237 485 0.0 0.0 47.6 37.8 239 631 603 4,174 Oman 23 20 582 826 0.0 0.1 .. 0.0 39 39 1,537 5,313 Pakistan –2,611 –1,416 4,077 4,234 5.3 35.1 1,202.5 1,740.7 1,712 8,717 4 8 Panama 8 11 73 121 0.2 0.1 0.9 16.9 112 175 20 229 Papua New Guinea 0 0 31 25 2.0 0.1 9.6 9.7 16 12 16 323 Paraguay –30 –40 183 161 0.1 0.1 0.1 0.1 287 609 .. .. Peru –300 –625 51 38 5.9 6.3 0.6 1.1 599 2,378 34 85 Philippines –900 –900 210 435 0.5 1.0 0.8 0.1 5,360 19,766 151 58 Poland –77 –120 964 827 19.7 2.1 0.6 15.3 724 8,126 262 1,330 Portugal 0 200 528 919 0.0 0.0 0.2 0.4 3,953 3,585 527 1,460 Puerto Rico –4 –21 339 324 0.0 .. .. .. .. .. .. .. Qatar 14 562 406 1,305 0.0 0.1 .. 0.0 .. .. .. .. 2011 World Development Indicators 385 6.18 Movement of people across borders Net migration International Refugees Workers’ remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990–95 2005–10 1995 2010 1995 2009 1995 2009 1995 2009 1995 2009 Romania –529 –200 135 133 17.0 4.4 0.2 1.1 9 4,929 2 310 Russian Federation 2,220 250 11,707 12,270 207.0 109.5 246.7 4.9 2,502 5,359 3,938 18,548 Rwanda –1,681 15 337 465 1,819.4 129.1 7.8 54.0 21 93 1 71 Saudi Arabia –500 150 4,611 7,289 0.3 0.6 13.2 0.6 .. 217 16,594 25,969 Senegal –100 –100 291 210 17.6 16.3 66.8 22.2 146 1,365 76 144 a Serbia 451 0 874 525 86.1e 195.6 650.7e 86.4 1,295 5,406a .. 91 Sierra Leone –450 60 101 107 379.5 15.4 4.7 9.1 24 47 0 3 Singapore 250 500 992 1,967 0.0 0.1 0.1 0.0 .. .. .. .. Slovak Republic –3 20 114 131 0.0 0.3 2.3 0.4 26 1,671 3 134 Slovenia 38 22 200 164 12.9 0.0 22.3 0.3 272 279 31 191 Somalia –893 –250 19 23 638.7 678.3 0.6 1.8 .. .. .. .. South Africa 900 700 1,098 1,863 0.5 0.4 101.4 48.0 105 902 629 1,158 Spain 324 1,750 1,041 6,378 0.0 0.0 5.9 4.0 3,237 9,904 868 12,646 Sri Lanka –256 –300 426 340 107.6 145.7 0.0 0.3 809 3,363 16 435 Sudan –168 135 1,111 753 445.3 368.2 674.1 186.3 346 2,993a 1 2a Swaziland –38 –6 35 40 0.0 0.0 0.7 0.8 83 93 4 11 Sweden 151 150 906 1,306 0.0 0.0 199.2 81.4 288 652 336 787 Switzerland 227 100 1,471 1,763 0.0 0.0 82.9 46.2 1,473 2,524 10,114 19,562 Syrian Arab Republic –70 800 817 2,206 8.0 17.9 373.5d 1,526.6d 339 1,332a 15 212a Tajikistan –296 –200 305 284 59.0 0.6 0.6 2.7 .. 1,748 .. 124 Tanzania 591 –300 1,134 659 0.1 1.2 829.7 118.7 1 23 1 81 Thailand –39 300 549 1,157 0.2 0.5 106.6 105.3 1,695 1,637 .. .. Timor-Leste 0 10 10 14 .. 0.0 .. 0.0 .. .. .. .. Togo –122 –5 169 185 93.2 18.4 10.9 8.5 15 307a 5 58a Trinidad and Tobago –24 –20 46 34 0.0 0.2 .. 0.0 32 99a 14 .. Tunisia –43 –20 38 34 0.3 2.3 0.2 0.1 680 1,964 36 13 Turkey –70 –44 1,212 1,411 44.9 146.4 12.8 10.4 3,327 970 .. 141 Turkmenistan 50 –25 260 208 0.0 0.7 23.3 0.1 4 .. 7 .. Uganda 120 –135 661 647 24.2 7.6 229.4 127.3 .. 750 .. 463 Ukraine 100 –80 6,172 5,258 1.7 24.5 5.2 7.3 6 5,073 1 25 United Arab Emirates 340 343 1,716 3,293 0.0 0.4 0.4 0.3 .. .. .. .. United Kingdom 167 948 4,191 6,452 0.1 0.2 90.9 269.4 2,469 7,252 2,581 3,400 United States 6,565 5,052 28,522 42,813 0.2 2.4 623.3 275.5 2,179 2,947 22,181 48,308 Uruguay –20 –50 93 80 0.3 0.2 0.1 0.2 .. 101 .. 6 Uzbekistan –340 –400 1,474 1,176 0.1 6.7 2.6 0.6 .. .. .. .. Venezuela, RB 40 40 1,019 1,007 0.5 6.2 1.6 201.3 2 131 203 581 Vietnam –840 –200 39 69 543.5 339.3 34.4 2.4 .. 6,626a .. .. West Bank and Gaza 1 –10 1,201 1,924 72.8 95.2 1,201.0d 1,885.2d 582 1,261a 19 9a Yemen, Rep. 650 –135 378 518 0.4 1.9 53.5 170.9 1,081 1,160 61 337 Zambia –11 –85 271 233 0.0 0.2 130.0 56.8 .. 41 59 66 Zimbabwe –192 –700 433 372 0.0 22.4 0.5 4.0 44 .. 7 .. World ..f s ..f s 165,674g s 213,450g s 18,068.7s,d h 15,163.2s,d h 18,068.7d s 15,163.2d s 101,254 s 416,158 s 100,950 s 289,122 s Low income 287 –2,737 13,555 13,368 7,990.4 5,427.5 4,727.2 1,893.8 2,189 22,706 357 2,047 Middle income –13,401 –13,203 63,453 67,824 4,260.8 4,558.6 10,086.9 11,285.2 53,012 284,357 10,230 57,377 Lower middle income –9,961 –9,231 31,848 34,166 2,733.4 3,451.1 6,322.0 9,104.7 31,182 206,323 2,147 15,095 Upper middle income –3,441 –3,972 31,605 33,657 1,527.3 1,107.4 3,764.8 2,180.5 21,830 78,033 8,084 42,283 Low & middle income –13,114 –15,941 77,009 81,192 12,251.1 9,986.1 14,814.0 13,179.1 55,202 307,063 10,587 59,425 East Asia & Pacific –3,285 –3,781 3,048 5,434 952.9 996.7 447.0 485.5 8,925 85,788 1,703 14,459 Europe & Central Asia –3,386 –1,671 29,607 27,346 1,611.6 655.6 1,221.3 163.8 6,482 35,433 4,507 24,427 Latin America & Carib. –3,388 –5,214 5,454 6,569 155.5 462.0 93.9 367.4 13,322 56,590 1,138 3,788 Middle East & N. Africa –1,044 –1,089 8,985 11,957 948.0 2,014.0 5,683.0 7,809.4 13,275 33,442 704 8,536 South Asia –1,262 –2,376 13,257 12,175 2,958.7 3,192.1 1,625.5 2,263.4 10,005 75,061 476 3,471 Sub-Saharan Africa –749 –1,810 16,659 17,710 5,624.4 2,665.8 5,743.4 2,089.5 3,193 20,749 2,060 4,743 High income 13,097 15,894 88,665 132,259 287.1 90.8 3,254.7 1,984.1 46,052 109,095 90,363 229,697 Euro area 4,604 5,607 23,080 36,135 13.9 1.0 1,690.4 1,011.4 30,827 67,529 28,741 85,677 a. World Bank estimate. b. Includes Taiwan, China. c. Includes Tibetans, who are listed separately by the UN Refugee Agency (UNHCR). d. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), who are not included in data from the UNHCR. e. Includes Montenegro. f. World totals computed by the United Nations sum to zero, but because the aggregates refer to World Bank definitions, regional and income group totals do not. g. World totals are computed by the World Bank and include only economies covered by World Development Indicators, so data may differ from what is published by the United Nations Population Division. h. Includes refugees without specified country of origin and Palestinian refugees under the mandate of the UNRWA, so regional and income group totals do not sum to the world total. 386 2011 World Development Indicators 6.18 GLOBAL LINKS Movement of people across borders About the data Definitions Movement of people, most often through migration, is granted refugee or refugee-like status or temporary • Net migration is the net total of migrants during a significant part of global integration. Migrants con- protection. Asylum seekers and internally displaced the period. It is the total number of immigrants less tribute to the economies of both their host country people—who are often confused with refugees—are the total number of emigrants, including both citi- and their country of origin. Yet reliable statistics on not included. Unlike refugees, internally displaced zens and noncitizens. Data are five-year estimates. migration are difficult to collect and are often incom- people remain under the protection of their own gov- • International migrant stock is the number of people plete, making international comparisons a challenge. ernment, even if their reason for fleeing was similar born in a country other than that in which they live. The United Nations Population Division provides to that of refugees. It includes refugees. • Refugees are people who are data on net migration and migrant stock. Net migra- Registrations, together with other sources—includ- recognized as refugees under the 1951 Convention tion is the total number of immigrants minus the ing estimates and surveys—are the main sources Relating to the Status of Refugees or its 1967 Proto- total number of emigrants. However, data on emi- of refugee data. But there are difficulties in collect- col, the 1969 Organization of African Unity Convention grant stock are not collected because it is difficult ing accurate statistics. Although refugees are often Governing the Specific Aspects of Refugee Problems for countries to gather information on people who are registered individually, the accuracy of registrations in Africa, people recognized as refugees in accordance not within their borders. To derive estimates of net varies greatly. Many refugees may not be aware of with the UNHCR statute, people granted refugee-like migration, the migration history of a country or area, the need to register or may choose not to do so. And humanitarian status, and people provided temporary the migration policy of a country, and the influx of administrative records tend to overestimate the num- protection. Asylum seekers—people who have applied refugees in recent periods are taken into account. ber of refugees because it is easier to register than to for asylum or refugee status and who have not yet The data to calculate these official estimates come de-register. The UN Refugee Agency (UNHCR) collects received a decision or who are registered as asylum from a variety of sources, including border statistics, and maintains data on refugees, except for Palestin- seekers—are excluded. Palestinian refugees are administrative records, surveys, and censuses. When ian refugees residing in areas under the mandate people (and their descendants) whose residence was no official estimates can be made because of insuf- of the United Nations Relief and Works Agency for Palestine between June 1946 and May 1948 and who ficient data, net migration is derived through the bal- Palestine Refugees in the Near East (UNRWA). The lost their homes and means of livelihood as a result ance equation, which is the difference between overall UNRWA provides services to Palestinian refugees who of the 1948 Arab-Israeli conflict. • Country of origin population growth and the natural increase during the live in certain areas and who register with the agency. refers to the nationality or country of citizenship of a 1990–2000 intercensal period. Registration is voluntary, and estimates by the UNRWA claimant. • Country of asylum is the country where an The data used to estimate the international migrant are not an accurate count of the Palestinian refugee asylum claim was filed and granted. • Workers’ remit- stock at a particular time are obtained mainly from population. The table shows estimates of refugees tances and compensation of employees received and population censuses. The estimates are derived from collected by the UNHCR, complemented by estimates paid comprise current transfers by migrant workers the data on foreign-born population—people who have of Palestinian refugees under the UNRWA mandate. and wages and salaries earned by nonresident work- residence in one country but were born in another Thus, the aggregates differ from those published by ers. Remittances are classified as current private country. When data on the foreign-born population the UNHCR. transfers from migrant workers resident in the host are not available, data on foreign population— that Workers’ remittances and compensation of employ- country for more than a year, irrespective of their is, people who are citizens of a country other than the ees are World Bank staff estimates based on data immigration status, to recipients in their country of country in which they reside—are used as estimates. from the International Monetary Fund’s (IMF) Bal- origin. Migrants’ transfers are defined as the net worth After the breakup of the Soviet Union in 1991 people ance of Payments Statistics Yearbook. The IMF data of migrants who are expected to remain in the host living in one of the newly independent countries who are supplemented by World Bank staff estimates for country for more than one year that is transferred to were born in another were classified as international missing data for countries where workers’ remittances another country at the time of migration. Compensa- migrants. Estimates of migrant stock in the newly are important. The data reported here are the sum of tion of employees is the income of migrants who have independent states from 1990 on are based on the three items defined in the fifth edition of the IMF’s lived in the host country for less than a year. 1989 census of the Soviet Union. Balance of Payments Manual: workers’ remittances, For countries with information on the international compensation of employees, and migrants’ transfers. Data sources migrant stock for at least two points in time, inter- The distinction among these three items is not polation or extrapolation was used to estimate the always consistent in the data reported by countries to Data on net migration are from the United Nations international migrant stock on July 1 of the reference the IMF. In some cases countries compile data on the Population Division’s World Population Prospects: years. For countries with only one observation, esti- basis of the citizenship of migrant workers rather than The 2008 Revision. Data on migration stock are mates for the reference years were derived using rates their residency status. Some countries also report from the United Nations Population Division’s of change in the migrant stock in the years preceding remittances entirely as workers’ remittances or com- Trends in Total Migrant Stock: The 2008 Revision. or following the single observation available. A model pensation of employees. Following the fifth edition of Data on refugees are from the UNHCR’s Statisti- was used to estimate migrants for countries that had the Balance of Payments Manual in 1993, migrants’ cal Yearbook 2009, complemented by statistics no data. transfers are considered a capital transaction, but on Palestinian refugees under the mandate of The table shows data on refugees because they previous editions regarded them as current transfers. the UNRWA as published on its website. Data on are an important part of migrant stock. Refugee fig- For these reasons the figures presented in the table remittances are World Bank staff estimates based ures shown here refer to people who have crossed an take all three items into account. on IMF balance of payments data. international border to find sanctuary and have been 2011 World Development Indicators 387 6.19 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 304 a,b 1,856a,b 12 3,404 70 2,012 23.2 58.2 19 1,692 2.3 26.0 Algeria 520a,c 1,912a,c 1,090 1,677 32d 330 d .. .. 186d 470 d .. .. Angola 9 366 3 .. 27 554 0.7 1.3 113 270 3.2 0.6 Argentina 2,289 4,329 3,815 4,975 2,550 4,478 10.2 6.7 4,013 5,759 15.4 11.8 Armenia 12 575 .. 526 14 374 4.7 27.9 12 379 1.7 10.3 Australia 3,726a 5,584 a 2,519 6,285 11,915 27,864 17.1 11.9 7,260 21,459 9.7 10.0 Austria 17,173e 21,355e 3,713 10,121 14,529 21,239 16.2 11.2 11,686 12,771 12.7 7.3 Azerbaijan .. 1,409 432 2,162 87 516 11.1 2.3 165 456 12.8 4.6 Bangladesh 156 267 830 2,254 25f 76f 0.6 0.4 234f 651 3.1 2.8 Belarus 161 95 626 316 28 562 0.5 2.3 101 702 1.8 2.3 Belgium 5,560e 6,815e 5,645 11,123 4,548f 11,144 2.4 3.3 8,115f 19,673 4.5 6.0 Benin 138 190 .. .. 85f 236 13.8 14.5 48 102 5.4 4.3 Bolivia 284 671 249 628 92 306 7.5 5.6 72 388 4.6 7.5 Bosnia and Herzegovina 115e 311e .. .. 257 761 22.9 13.9 97 284 2.4 3.0 Botswana 521 1,553 .. .. 176 454f 7.3 10.9 153 231f 7.5 4.5 Brazil 1,991 4,802 2,600 4,952 1,085 5,635 2.1 3.1 3,982 12,897 6.3 7.4 Bulgaria 3,466 5,739 3,524 4,993 662 4,273 9.8 18.4 312 1,955 4.8 7.2 Burkina Faso 124g 269g .. .. .. 82 .. 11.0 .. 110 .. 3.8 Burundi 34 c 201c 36 .. 2 2 1.9 1.5 25f 71 9.7 13.7 Cambodia .. 2,046 31 340 71 1,312 7.3 22.1 22 162 1.6 2.3 Cameroon 100 g 185g .. .. 75 222 3.7 4.2 140 549 8.7 8.4 Canada 16,932 15,737 18,206 27,037 9,176 15,555 4.2 4.1 12,658 30,232 6.3 7.4 Central African Republic 26b 52b .. 11 4d 6 .. .. 43d 61 .. .. Chad 19g 25g .. .. 43d .. .. .. 38d .. .. .. Chile 1,540 2,750 1,070 2,895 1,186 2,270 6.1 3.6 934 1,956 5.1 4.0 China 20,034 50,875 4,520 47,656 8,730 f 42,632 5.9 3.2 3,688f 47,108 2.7 4.2 Hong Kong SAR, China 7,137 16,926 .. 81,958 9,604 d,f 20,884 d 3.5 5.1 10,497d,f 15,960 d,f 6.5 4.1 Colombia 1,399a 2,147a 1,057 2,122 887 2,671 7.2 7.0 1,162 2,302 7.3 6.0 Congo, Dem. Rep. 35b 53b 50 .. .. .. .. .. .. .. .. .. Congo, Rep. 37g 85g .. .. 15 54 1.1 0.9 69 168 5.1 2.6 Costa Rica 785 1,923 273 579 763 1,985 17.1 15.8 336 463 7.1 3.8 Côte d’Ivoire 188 .. .. .. 103 113f 2.4 1.0 312 345f 8.2 3.9 Croatia 1,485e 9,335e .. 2,497 1,349 f 9,224 19.3 40.8 422f 1,034 4.6 4.2 Cuba 742b 2,405b 72 206 1,100 d 2,106 .. .. .. .. .. .. Czech Republic 3,381e 6,032e .. 6,618 2,880 f 7,396 10.2 5.6 1,635f 4,157 5.4 3.4 Denmark 2,124 e 4,503e 5,035 6,347 3,691f 6,686f 5.6 3.6 4,288f 9,678f 7.4 5.5 Dominican Republic 1,776b,c 3,992b,c 168 415 1,571f 4,051f 27.4 38.7 267 514f 4.4 3.6 Ecuador 440a,h 968a,h 271 814 315 674 6.1 4.3 331 806 5.8 4.8 Egypt, Arab Rep. 2,871 11,914 2,683 4,531 2,954 11,757 22.3 26.4 1,371 2,941 8.0 5.5 El Salvador 235 1,091 348 1,012 152 549 7.5 11.7 99 253 2.7 3.2 Eritrea 315a,c 79a,c .. .. 58d 26 43.1 .. .. .. .. .. Estonia 530 1,900 1,764 752 452 1,444 17.6 10.7 121 697 4.2 5.6 Ethiopia 103b 330 c 120 .. 177 1,119 23.1 32.6 30 139 f 2.1 1.5 Finland 2,644 3,423 5,147 5,832 2,383 4,141 5.0 4.6 2,853 5,205 7.6 6.2 France 60,033 76,800 18,686 23,347 31,295 58,480 8.6 9.5 20,699 45,938 6.2 6.9 Gabon 125b 358 203 .. 94 .. 3.2 .. 182 .. 10.6 .. Gambia, The 45 142 .. 307 28f 64 16.0 23.0 16 9 7.0 2.6 Georgia 85a 1,500a 228 1,980 75 531 13.1 16.6 171 311 12.1 5.9 Germany 14,847e 24,220e 55,800 72,300 24,052 47,505 4.0 3.5 66,527 92,738 11.3 7.7 Ghana 286c 803c .. .. 30 1,049 1.9 13.4 74 848 3.5 7.9 Greece 10,130 14,915 .. .. 4,182 14,796 26.9 25.0 1,495 3,401 6.0 4.0 Guatemala 563a 1,777a 333 1,326 216 820 f 7.7 8.9 167 680 4.5 5.3 Guinea 12b 30 b .. .. 1 5 0.1 0.4 29 28 2.9 2.0 Guinea-Bissau .. 30 .. .. 3 38 5.5 22.2 6 46 6.5 16.2 Haiti 145 304 .. .. 90 f 315 46.8 33.8 35f 443 4.4 15.7 Honduras 271 870 149 395 85 611 5.2 10.1 99 355 5.3 4.1 388 2011 World Development Indicators 6.19 GLOBAL LINKS Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Hungary .. 9,058 13,083 16,906 2,938 6,740 14.9 6.7 1,501 4,117 7.5 4.4 India 2,124h 5,109h 3,056 11,067 2,582f 11,509 6.8 4.4 996f 11,507 2.1 3.5 Indonesia 4,324 6,324 .. 5,053 5,229 f 6,773 9.9 5.1 2,172f 9,579 4.0 8.5 Iran, Islamic Rep. 489 2,034 1,000 .. 205 2,196 1.1 .. 247 9,482 1.6 .. Iraq 61a .. .. .. 18f 555 .. 1.4 117f 705 .. 3.3 Ireland 4,818 7,189 2,547 7,047 2,698 8,187 5.5 4.1 2,034f 8,887 4.8 5.3 Israel 2,215h 2,321h 2,259 4,007 3,491 4,332 12.7 6.4 2,626 3,869 7.4 6.1 Italy 31,052 43,239 18,173 29,060 30,426 41,872 10.3 8.2 17,219 34,329 6.9 6.6 Jamaica 1,147b,c 1,831b,c .. .. 1,199 2,070 35.3 51.3 173 259 4.6 4.1 Japan 3,345a,h 6,790a,h 15,298 15,446 4,894 12,537 1.0 1.9 46,966 34,788 11.2 5.3 Jordan 1,075h 3,789c 1,128 2,368 973 3,468 28.0 31.8 719 1,202 14.7 7.4 Kazakhstan .. 3,118 523 5,243 155 1,184 2.6 2.5 296 1,320 4.9 3.4 Kenya 918 1,392 .. .. 785 1,095 22.3 14.8 230 234f 3.9 2.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 3,753a,c 7,818a,c 3,819 9,494 6,670 12,927 4.5 3.0 6,947 14,648 4.5 3.7 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 72g 297g 878 2,649 307 553 2.2 0.9 2,514 8,244 19.9 26.9 Kyrgyz Republic 36 2,435 42 1,521 5f 506 1.1 19.8 7f 391 1.0 10.6 Lao PDR 60 1,239 .. .. 52 271f 12.8 18.8 34 91f 4.5 5.8 Latvia 539 1,323 1,812 3,268 37 1,013 1.8 9.0 62 906 2.8 7.9 Lebanon 450 1,844 .. .. 710 7,157 .. 33.1 .. 4,928 .. 16.3 Lesotho 87 320 .. .. 29 40 f 14.6 5.1 17 22 1.6 1.2 Liberia .. .. .. .. .. 123f .. 27.1 .. 51 .. 3.0 Libya .. 34 484 .. 4 159 0.1 0.4 493 1,683 8.6 6.2 Lithuania 650 1,341 1,925 1,288 102 1,183 3.2 5.8 107 1,140 2.7 5.5 Macedonia, FYR 147e 259e .. .. 19 f 232 2.7 6.5 27f 150 1.7 2.6 Madagascar 75b 163b 39 .. 106 518 14.2 .. 79 123f 8.0 .. Malawi 192 755 .. .. 22 48 4.7 .. 53 84 8.0 .. Malaysia 7,469 23,646 20,642 .. 5,044 17,231 6.1 9.2 2,722 7,196 3.1 5.0 Mali 42b,g 160 b,g .. .. 26 286 4.9 11.2 74 228 7.5 6.1 Mauritania .. .. .. .. 11f .. 2.2 .. 30 .. 5.9 .. Mauritius 422 871 107 196 616 1,390 26.2 33.2 184 384 7.5 7.5 Mexico 20,241c 21,454 c 8,450 13,942 6,847 12,309 7.7 5.0 3,587 8,628 4.4 3.3 Moldova 32 7 71 93 71 235 8.0 11.7 73 307 7.3 7.7 Mongolia 108 411 .. .. 33 253f 6.5 11.0 22 242 4.2 9.2 Morocco 2,602c 8,341c 1,317 2,293 1,469 7,978 16.2 30.2 356 1,712 3.2 4.6 Mozambique .. 2,224 .. .. 49 217 10.2 8.8 68 249 6.6 5.8 Myanmar 117 243 .. .. 169 59 12.9 1.2 18f 40 0.9 1.4 Namibia 272 931 .. .. 278f 469 16.0 11.6 90 f 109 4.3 2.1 Nepal 363 510 100 589 232 397 22.5 26.6 167 511 10.3 10.0 Netherlands 6,574 e 9,921e 12,313 18,408 10,611 17,876 4.4 3.5 13,151 21,076 6.1 4.6 New Zealand 1,475 2,422 920 1,917 2,318f 4,396f 13.0 13.2 1,259 f 2,559 f 7.3 8.0 Nicaragua 281 932 255 858 51 346f 7.7 12.1 56 224 4.9 5.0 Niger 35 73 10 .. 7f 86 2.2 8.2 26 98 5.7 5.0 Nigeria 656 1,313 .. .. 47 791 0.4 1.3 938 5,308 7.3 11.1 Norway 2,880 4,288 590 3,395 2,730 4,444 4.9 2.8 4,481 12,366f 9.6 11.8 Oman 279g 1,273g .. .. 193 1,108 2.5 3.8 349 f 1,277 6.3 5.9 Pakistan 378 823 .. .. 582 903 5.7 4.1 654 1,098 4.6 3.1 Panama 345 1,200 185 336 372 2,279 4.9 13.7 181 503 2.3 3.3 Papua New Guinea 42 114 51 .. 25f 1 0.8 0.0 58f 48 3.0 1.0 Paraguay 438h 439h 427 280 162 247 3.4 3.4 173 288 3.3 3.9 Peru 479 2,140 508 1,958 521 2,471 7.9 8.1 428 1,379 4.5 5.3 Philippines 1,760 c 3,017c 1,615 3,066 1,141 2,837 4.3 6.0 551 2,989 1.7 5.4 Poland 19,215 11,890 36,387 50,243 6,927 9,853 19.4 5.8 5,865 7,842 17.3 4.6 Portugal 9,511h 12,321c .. 20,989 5,646 12,329 17.5 18.3 2,539 4,604 6.4 5.5 Puerto Rico 3,131b 3,551b 1,237 1,319 1,828d 3,473d .. .. 1,155d 1,613d .. .. Qatar 309g 1,405g .. .. .. 874d .. .. .. 3,751d .. .. 2011 World Development Indicators 389 6.19 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 1995 2009 Romania 5,445a 7,575a 5,737 11,723 689 1,669 7.3 3.3 749 1,769 6.6 2.9 Russian Federation 10,290a 23,676a 21,329 36,538 4,312f 12,300 4.6 3.6 11,599 f 23,529 14.0 9.3 Rwanda .. 699 .. .. 4 218f 5.4 40.8 13 115 3.5 7.8 Saudi Arabia 3,325 10,897 .. 6,032 .. 6,678d .. 3.3 .. 20,964 d .. 13.1 Senegal .. 875 .. .. 168 637 11.2 18.2 154 276 8.5 3.9 Serbia .. 645 .. .. .. 986 .. 8.3 .. 1,076 .. 5.8 Sierra Leone 38b 36 b 6 73 57f 25f 44.4 7.7 51 16 19.4 2.5 Singapore 6,070 7,489 2,867 6,961 7,611f 9,200 f 4.8 2.5 4,663f 15,808f 3.2 4.9 Slovak Republic 903e 1,298e 218 19,917 630 2,539 5.7 4.1 338 2,249 3.2 3.6 Slovenia 732e 1,824 e .. 2,586 1,128 2,733 10.9 9.6 606 1,533 5.6 5.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 4,488 9,934 2,520 4,424 2,654 8,683 7.7 11.1 2,414 6,420 7.2 7.9 Spain 34,920 52,231 3,648 12,844 27,369 58,586 20.4 16.9 5,826 21,482 4.3 5.7 Sri Lanka 403h 448h 504 963 367 754 7.9 8.4 279 735 4.7 6.3 Sudan 29 420 195 .. 8f 299 f 1.2 3.6 43f 868f 3.5 7.7 Swaziland 300i 908g .. 1,245 54 40 5.3 2.2 45 98 3.5 4.2 Sweden 2,310e 4,678 e 10,127 11,699 4,390 12,114 4.6 6.2 6,816 13,432 8.4 8.1 Switzerland 6,946g 8,294g 11,148 11,147 11,354 16,335 9.2 5.8 9,478 12,552 8.7 5.1 Syrian Arab Republic 815e 6,092e 1,746 5,215 1,258f 5,152 21.9 16.4 498f 910 f 9.0 4.7 Tajikistan .. .. .. .. .. 20 .. 1.6 .. 6f .. 0.2 Tanzania 285 714 157 .. 502f 1,192 39.7 22.8 360 f 806 16.8 10.7 Thailand 6,952c 14,150 1,820 4,535 9,257 19,421 13.2 10.8 4,791 5,659 5.8 3.6 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 53g 150 g .. .. 13f 44 2.8 3.9 40 68 6.0 4.1 Trinidad and Tobago 260 b 413b 261 .. 232 557 8.3 2.8 91 102 4.3 1.0 Tunisia 4,120h 6,901h 1,778 2,623 1,838 3,526 23.0 17.7 294 492 3.3 2.3 Turkey 7,083 25,506 3,981 10,493 4,957f 24,556 13.6 17.2 911f 4,627 2.3 3.1 Turkmenistan 218 8 21 38 13 .. 0.7 .. 74 .. 4.1 .. Uganda 160 817 148 337 78f 683 11.7 17.3 80 f 336 5.4 6.4 Ukraine 3,716 20,798 6,552 15,334 191f 4,349 1.1 8.0 210 f 3,751 1.1 6.7 United Arab Emirates 2,315c,i .. .. .. 632 7,162d .. .. .. 13,288d .. .. United Kingdom 21,719 28,199 41,345 58,614 27,577 38,545 8.6 6.5 30,749 61,130 9.4 9.4 United States 43,490 54,884 51,285 61,419 93,700 147,554 11.8 9.4 60,924 105,202 6.8 5.4 Uruguay 2,022 2,056 562 826 725 1,408 20.7 16.5 332 436 9.3 5.6 Uzbekistan 92 1,069 246 1,150 15 64d .. .. .. .. .. .. Venezuela, RB 700 615 534 1,651 995 853 4.8 1.4 1,852 2,234 11.0 4.6 Vietnam 1,351a 3,747a .. .. .. 3,050 d .. 4.9 .. 1,100 .. 1.5 West Bank and Gaza 220 g 396g .. .. 255f 269 f 33.4 23.0 162f 544f 5.8 11.0 Yemen, Rep. 61g 434g .. .. 50 f 496f 2.3 7.0 76f 277 3.1 2.8 Zambia 163 710 .. .. 29 98 2.4 2.1 83 83 6.2 2.0 Zimbabwe 1,416a 1,956a 256 593 145 294d .. .. 106d .. .. .. World 537,385 t 894,012 t 555,382 t 961,575 t 487,033 t 1,022,301 t 7.6 w 6.4 w 458,869 t 923,915 t 7.4 w 5.9 w Low income 6,379 18,801 .. .. 3,253 11,845 12.2 12.9 2,591 7,641 5.1 5.0 Middle income 139,405 329,738 129,489 327,671 82,794 270,868 7.7 6.2 60,850 214,809 5.4 5.1 Lower middle income 58,101 160,100 35,678 134,442 40,251 135,230 8.5 5.3 20,926 110,735 4.0 4.5 Upper middle income 82,221 171,751 86,296 .. 42,566 135,451 7.1 7.3 39,917 104,850 6.7 5.9 Low & middle income 147,674 352,280 141,222 363,391 85,892 281,994 7.8 6.3 63,336 222,402 5.4 5.1 East Asia & Pacific 43,654 107,674 33,153 .. 31,197 94,687 7.8 4.8 14,770 75,780 3.5 4.4 Europe & Central Asia 33,946 106,987 47,292 106,450 12,014 58,244 6.3 7.3 16,380 48,211 8.1 6.2 Latin America & Carib. 39,151 60,093 21,841 41,194 21,838 49,773 7.6 6.0 18,774 41,573 6.5 5.3 Middle East & N. Africa 13,555 44,880 13,407 25,352 9,771 43,050 13.0 20.5 4,844 19,825 5.7 6.7 South Asia 3,819 7,949 5,151 17,100 4,016 14,339 6.8 4.6 2,393 14,787 3.0 3.6 Sub-Saharan Africa 12,978 31,497 .. .. 6,928 22,170 7.8 7.5 6,810 25,420 6.8 6.4 High income 384,359 535,465 374,257 564,431 401,084 740,277 7.6 6.4 394,726 703,266 7.9 6.3 Euro area 203,060 280,972 141,785 235,326 164,475 310,544 7.8 6.9 155,113 280,349 7.8 6.5 Note: Aggregates are based on World Bank country classifications and differ from those of the World Tourism Organization. Regional and income group totals include countries not shown in the table for which data are available. a. Arrivals of nonresident visitors at national borders. b. Excludes nationals residing abroad. c. Includes nationals residing abroad. d. Data are from national sources. e. Arrivals in all types of accommodation establishments. f. Refers to expenditure of travel-related items only; excludes passenger transport items. g. Arrivals in hotels and similar establishments. h. Arrivals in hotels only. i. Arrivals by air only. 390 2011 World Development Indicators 6.19 GLOBAL LINKS Travel and tourism About the data Definitions Tourism is defined as the activities of people trav- For some countries number of arrivals is limited to • International inbound tourists (overnight visitors) eling to and staying in places outside their usual arrivals by air and for others to arrivals staying in are the number of tourists who travel to a country environment for no more than one year for leisure, hotels. Some countries include arrivals of nationals other than that in which they usually reside, and out- business, and other purposes not related to an activ- residing abroad while others do not. Caution should side their usual environment, for a period not exceed- ity remunerated from within the place visited. The thus be used in comparing arrivals across countries. ing 12 months and whose main purpose in visiting social and economic phenomenon of tourism has The World Tourism Organization is improving its is other than an activity remunerated from within the grown substantially over the past quarter century. coverage of tourism expenditure data, using balance country visited. When data on number of tourists are Statistical information on tourism is based mainly of payments data from the International Monetary not available, the number of visitors, which includes on data on arrivals and overnight stays along with Fund (IMF) supplemented by data from individual tourists, same–day visitors, cruise passengers, and balance of payments information. These data do not countries. These data, shown in the table, include crew members, is shown instead. • International out- completely capture the economic phenomenon of travel and passenger transport items as defined in bound tourists are the number of departures that tourism or provide the information needed for effec- the IMF’s (1993) Balance of Payments Manual. When people make from their country of usual residence tive public policies and efficient business operations. the IMF does not report data on passenger transport to any other country for any purpose other than an Data are needed on the scale and significance of items, expenditure data for travel items are shown. activity remunerated in the country visited. • Inbound tourism. Information on the role of tourism in national Tourism expenditure does not include all types of tourism expenditure is expenditures by international economies is particularly defi cient. Although the payments that visitors might make. It excludes pay- inbound visitors, including payments to national carri- World Tourism Organization reports progress in har- ments not for consumption of goods and services, ers for international transport. These receipts include monizing definitions and measurement, differences such as taxes and duties that are not part of the any other prepayment made for goods or services in national practices still prevent full comparability. purchase prices of the products acquired by the visi- received in the destination country. They may include The usual environment of an individual is a key tor; purchase of financial and nonfinancial assets receipts from same–day visitors, except when these concept in tourism statistics and is defined as the including land and real estate; purchase of goods for are important enough to justify separate classifica- geographical area within which an individual con- resale; and donations to charities or other individu- tion. For some countries they do not include receipts ducts regular life routines. This concept excludes as als. The timing of tourism expenditure is also impor- for passenger transport items. Their share in exports visitors travelers who commute regularly between tant because transportation and accommodation are is calculated as a ratio to exports of goods and ser- their place of usual residence and place of work or often booked and paid for before being consumed. vices (all transactions between residents of a coun- study or who frequently visit places within their cur- Payment might also happen after consumption of try and the rest of the world involving a change of rent life routine—for instance, homes of friends or such services, such as when a visitor pays off a ownership from residents to nonresidents of general relatives; shopping centers, and religious, health- credit card or a special loan drawn for travel pur- merchandise, goods sent for processing and repairs, care, or other facilities a substantial distance away poses. Tourism expenditure should be reported for nonmonetary gold, and services). • Outbound tour- or in a different administrative area that are regularly the period when the services are actually consumed ism expenditure is expenditures of international out- and frequently visited. and goods are actually acquired, regardless of when bound visitors in other countries, including payments Tourism can be either domestic or international. payment was made. Finally, the valuation of tour- to foreign carriers for international transport. These The table shows data relevant to international tour- ism expenditure depends on the form of acquisition expenditures may include those by residents travel- ism, where the traveler’s country of residence dif- of the goods and services concerned. In a market ing abroad as same–day visitors, except when these fers from the visiting country. International tourism transaction expenditure should be valued using the are important enough to justify separate classifica- consists of inbound and outbound tourism. The data purchaser price—value paid by the visitor. This price tion. For some countries they do not include expen- are from the World Tourism Organization, a United should include all taxes and voluntary and compul- ditures for passenger transport items. Their share in Nations agency. The data on inbound and outbound sory tips prevalent in the accommodation and food imports is calculated as a ratio to imports of goods tourists refer to the number of arrivals and depar- services sectors. Discounts and rebates of sales and services (all transactions between residents of a tures, not to the number of people traveling. Thus a tax or value added tax to nonresidents should be country and the rest of the world involving a change of person who makes several trips to a country during taken into account, even if refunded at the border. ownership from nonresidents to residents of general a given period is counted each time as a new arrival. However, following these recommendations for tour- merchandise, goods sent for processing and repairs, Unless otherwise indicated in the footnotes, the data ism statistics may not be easy for countries. Tourism nonmonetary gold, and services). on inbound tourism show the arrivals of nonresident expenditures reported in the table may not be fully Data sources tourists (overnight visitors) at national borders. comparable, so caution should be used when making When data on international tourists are unavailable cross-country comparisons. Data on visitors and tourism expenditure are or incomplete, the table shows the arrivals of inter- The aggregates are calculated using the World from the World Tourism Organization’s Yearbook national visitors, which include tourists, same-day Bank’s weighted aggregation methodology (see Sta- of Tourism Statistics and Compendium of Tourism visitors, cruise passengers, and crew members. tistical methods) and differ from the World Tourism Statistics 2011. Data in the table are updated Sources and collection methods for arrivals differ Organization’s aggregates. from electronic files provided by the World Tour- across countries. In some cases data are from bor- ism Organization. Data on exports and imports der statistics (police, immigration, and the like) and are from the IMF’s Balance of Payments Statistics supplemented by border surveys. In other cases data Yearbook and data files. are from tourism accommodation establishments. 2011 World Development Indicators 391 Text figures, tables, and boxes PRIMARY DATA DOCUMENTATION As a major user of socioeconomic data, the World Bank recognizes the impor- tance of data documentation to inform users of differences in the methods and conventions used by primary data collectors—usually national statistical agen- cies, central banks, and customs services—and by international organizations, which compile the statistics that appear in the World Development Indicators database. These differences may give rise to significant discrepancies over time both within countries and across them. Delays in reporting data and the use of old surveys as the base for current estimates may further compromise the qual- ity of data reported here. The tables in this section provide information on sources, methods, and reporting standards of the principal demographic, economic, and environ- mental indicators in World Development Indicators. Additional documentation is available from the World Bank’s Bulletin Board on Statistical Capacity at http://data.worldbank.org/. The demand for good-quality statistical data is increasing. Timely and reli- able statistics are key to the broad development strategy often referred to as “managing for results.” Monitoring and reporting on publicly agreed indicators are central to implementing poverty reduction strategies and lie at the heart of the Millennium Development Goals and the Results Measurement System adopted for the 14th replenishment of the International Development Association. A global action plan to improve national and international statistics was agreed on during the Second Roundtable on Managing for Development Results in February 2004 in Marrakech, Morocco. The plan, now referred to as the Mar- rakech Action Plan for Statistics, or MAPS, has been widely endorsed and forms the overarching framework for statistical capacity building. The third roundtable conference, held in February 2007 in Hanoi, Vietnam, reaffirmed MAPS as the guiding strategy for improving the capacity of the national and international sta- tistical systems. See www.mfdr.org/RT3 for reports from the conference. 2011 World Development Indicators 393 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Afghanistan Afghan afghani 2002/03 VAB Actual G C G Albania Albanian lek a 1996 b VAB 2005 BPM5 Actual S C G Algeria Algerian dinar 1980 VAB BPM4 Actual S B G American Samoa U.S. dollar Andorra Euro G Angola Angolan kwanza 1997 VAP 1991–96 2005 BPM5 Actual G Antigua and Barbuda East Caribbean dollar 1990 VAB BPM5 G G Argentina Argentine peso 1993 b VAB 1971–84 2005 BPM5 Actual S C S Armenia Armenian dram a 1996 b VAB 1990–95 2005 BPM5 Actual G C S Aruba Aruban florin 1995 BPM5 G Australia Australian dollar a 2007 b VAB 2005 BPM5 G C S Austria Euro 2000 b VAB 2005 BPM5 S C S Azerbaijan New Azeri manat a 2003 b VAB 1992–95 2005 BPM5 Actual G B G Bahamas, The Bahamian dollar 2006 b VAB BPM5 G B G Bahrain Bahraini dinar 1985 VAP 2005 BPM5 G B G Bangladesh Bangladeshi taka 1995/96 b VAB 2005 BPM5 Actual G C G Barbados Barbados dollar 1974 VAB BPM5 G B G Belarus Belarusian rubel a 2000 b VAB 1990–95 2005 BPM5 Actual G C S Belgium Euro 2000 b VAB 2005 BPM5 S C S Belize Belize dollar 2000 b VAB BPM5 Actual G B G Benin CFA franc 1985 VAP 1992 2005 BPM5 Actual G B G Bermuda Bermuda dollar 1996 VAB BPM5 Bhutan Bhutanese ngultrum 2000 b VAB 2005 Actual G C G Bolivia Bolivian Boliviano 1990 b VAB 1960–85 2005 BPM5 Actual S C G Bosnia and Herzegovina Bosnia and Herzegovina a 1996 b VAB 2005 BPM5 Actual S C convertible mark Botswana Botswana pula 1993/94 b VAB 2005 BPM5 Actual S B G Brazil Brazilian real 2000 b VAB 2005 BPM5 Actual S C S Brunei Darussalam Brunei dollar 2000 VAP 2005 S G Bulgaria Bulgarian lev a 2002 b VAB 1978–89, 2005 BPM5 Actual S C S 1991–92 Burkina Faso CFA franc 1999 VAB 1992–93 2005 BPM4 Estimate G B G Burundi Burundi franc 1980 VAB 2005 BPM5 Actual G C Cambodia Cambodian riel 2000 VAB 2005 BPM5 Actual G C G Cameroon CFA franc 2000 b VAB 2005 BPM5 Actual S B G Canada Canadian dollar 2000 b VAB 2005 BPM5 G C S Cape Verde Cape Verde escudo 1980 VAP 2005 BPM5 Actual S C G Cayman Islands Cayman Islands dollar Central African Republic CFA franc 2000 VAB 2005 BPM4 Preliminary S B G Chad CFA franc 1995 b VAB 2005 BPM4 Actual G Channel Islands Pound sterling 2003, 2007 2007 b VAB Chile Chilean peso 2003 b VAB 2005 BPM5 Actual S C S China Chinese yuan 2000 b VAP 1978–93 2005 BPM5 Preliminary G B G Hong Kong SAR, China Hong Kong dollar 2008 b VAB 2005 BPM5 G C S Macao SAR, China Macao pataca 2002 VAB 2005 BPM5 G C G Colombia Colombian peso 2005 b VAB 1992–94 2005 BPM5 Actual G B S Comoros Comorian franc 1990 VAP 2005 Preliminary G Congo, Dem. Rep. Congolese franc 1987 b VAB 1999–2001 2005 BPM4 Estimate C G Congo, Rep. CFA franc 1978 VAP 1993 2005 BPM5 Preliminary C G Costa Rica Costa Rican colon 1991 b VAB BPM5 Actual S C S Côte d’Ivoire CFA franc 1996 VAP 2005 BPM5 Actual S C G Croatia Croatian kuna a 2000 b VAB 2005 BPM5 S C S Cuba Cuban peso 1990 VAB S Cyprus Euro a 2000 VAB 2005 BPM5 G C S Czech Republic Czech koruna 2000 1995 b VAB 2005 BPM5 S C S Denmark Danish krone 2000 b VAB 2005 BPM5 G C S Djibouti Djibouti franc 1990 VAB 2005 Actual 394 2011 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Afghanistan 1979 MICS, 2003 IHS, 2008 2009 2009 2000 Albania 2001 DHS, 2008/09 LSMS, 2008 Yes 1998 2009 2008 2000 Algeria 2008 MICS, 2006 IHS, 1995 2001 2009 2008 2000 American Samoa 2010 Yes 2009 Andorra c Yes 2006 Angola 1970 MICS, 2001; MIS, 2006/07 IHS, 2000 1964–65 2009 1991 2000 Antigua and Barbuda 2001 Yes 2009 2007 1990 Argentina 2010 IHS, 2009 Yes 2002 2009 2009 2000 Armenia 2001 DHS, 2005 IHS, 2009 Yes 2009 2009 2000 Aruba 2010 Yes 2009 Australia 2006 ES/BS, 1994 Yes 2001 2008 2009 2000 Austria 2001 IS, 2000 Yes 1999–2000 2009 2009 2000 Azerbaijan 2009 DHS, 2006 ES/BS, 2008 Yes 2008 2009 2005 Bahamas, The 2010 2006 2009 Bahrain 2010 Yes 1995 2007 2003 Bangladesh 2001 DHS, 2007 IHS, 2005 2005 2009 2007 2000 Barbados 2010 Yes 2005 2009 2000 Belarus 2009 MICS, 2005 ES/BS, 2009 Yes 1994 2009 2009 2000 Belgium 2001 IHS, 2000 Yes 1999–2000d 2009 2009 Belize 2010 MICS, 2006 ES/BS, 1999 2008 2008 2000 Benin 2002 DHS, 2006 CWIQ, 2003 1992 2005 2006 2001 Bermuda 2010 Yes 2009 Bhutan 2005 IHS, 2003 2000 2009 2009 2000 Bolivia 2001 DHS, 2008 IHS, 2007 1984–88 2009 2009 2000 Bosnia and Herzegovina 1991 MICS, 2006 LSMS, 2007 Yes 2009 2010 Botswana 2001 MICS, 2000 ES/BS, 2003 1993 2009 2009 2000 Brazil 2010 DHS, 1996 LFS, 2008 1996 2009 2010 2000 Brunei Darussalam 2001 Yes 2006 2006 Bulgaria 2001 ES/BS, 2007 Yes 2009 2009 2000 Burkina Faso 2006 MICS, 2006 CWIQ, 2003 1993 2006 2009 2000 Burundi 2008 MICS, 2005 CWIQ, 2007 2005 2009 2000 Cambodia 2008 DHS, 2005 IHS, 2007 2009 2008 2000 Cameroon 2005 MICS, 2006 PS, 2007 1984 2007 2006 2000 Canada 2006 LFS, 2000 Yes 1996/2001 2007 2009 2000 Cape Verde 2010 DHS, 2005 ES/BS, 2007 Yes 2004 2009 2009 Cayman Islands 2010 Yes Central African Republic 2003 MICS, 2006 PS, 2008 1985 2006 2005 2000 Chad 2009 DHS, 2004 PS, 2002/03 2008 1995 2000 Channel Islands 2001 Chile 2002 IHS, 2009 Yes 1997 2009 2009 2000 China 2010 NSS, 2007 IHS, 2005 1997 2009 2009 2000 Hong Kong SAR, China 2006 Yes 2008 2009 Macao SAR, China 2006 Yes 2007 2009 Colombia 2006 DHS, 2005 IHS, 2009 2001 2009 2009 2000 Comoros 2003 MICS, 2000 IHS, 2004 2009 2007 Congo, Dem. Rep. 1984 MICS, 2010 1-2-3, 2005/06 1990 2009 1986 2000 Congo, Rep. 2007 DHS, 2005; AIS, 2009 CWIQ/PS, 2005 1985–86 2009 2005 2002 Costa Rica 2000 RHS, 1993 LFS, 2009 Yes 1973 2009 2009 2000 Côte d’Ivoire 1998 MICS, 2006 IHS, 2008 2001 2009 2009 Croatia 2001 ES/BS, 2008 Yes 2003 2009 2009 Cuba 2002 MICS, 2006 Yes 2008 2006 2000 Cyprus 2001 Yes 2008 2009 2000 Czech Republic 2001 RHS, 1993 IS, 1996 Yes 2000 2009 2009 2000 Denmark 2001 ITR, 1997 Yes 1999–2000 2009 2009 2000 Djibouti 2009 MICS, 2006 PS, 2002 2007 2009 2000 2011 World Development Indicators 395 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Dominica East Caribbean dollar 1990 b VAB BPM5 Actual S G Dominican Republic Dominican peso 1991 VAB BPM5 Actual G C G Ecuador U.S. dollar 2000 b VAB 2005 BPM5 Actual S B S Egypt, Arab Rep. Egyptian pound 1991/92 VAB 2005 BPM5 Actual G C S El Salvador U.S. dollar 1990 VAB BPM5 Actual G C S Equatorial Guinea CFA franc 2000 VAB 1965–84 2005 Eritrea Eritrean nakfa 1992 VAB BPM4 Actual Estonia Estonian kroon 2000 b VAB 1987–95 2005 BPM5 G C S Ethiopia Ethiopian birr 1999/2000 b VAB 2005 BPM5 Actual G B G Faeroe Islands Danish krone VAB BPM5 G Fiji Fijian dollar 2005 VAB 2005 BPM5 Actual G B G Finland Euro 2000 b VAB 2005 BPM5 S C S France Euro a 2000 b VAB 2005 BPM5 S C S French Polynesia CFP franc S Gabon CFA franc 1991 VAP 1993 2005 BPM5 Preliminary S G Gambia, The Gambian dalasi 1987 VAB 2005 BPM5 Estimate G C G Georgia Georgian lari a 1996 b VAB 1990–95 2005 BPM5 Actual G C S Germany Euro 2000 b VAB 2005 BPM5 S C S Ghana New Ghanaian cedi 2006 VAB 1973–87 2005 BPM5 Actual G B G Gibraltar Gibraltar pound Greece Euro a 2000 VAB 2005 BPM5 S C S Greenland Danish krone G Grenada East Caribbean dollar 1990 VAB BPM5 Actual S B G Guam U.S. dollar Guatemala Guatemalan quetzal 2001 b VAB BPM5 Actual G B G Guinea Guinean franc 1996 VAB 2005 BPM5 Estimate S B G Guinea-Bissau CFA franc 2005 VAB 2005 BPM5 Estimate G Guyana Guyana dollar 2006 VAB BPM5 Actual S Haiti Haitian gourde 1986/87 VAB 1991 BPM5 Actual G Honduras Honduran lempira 2000 b VAB 1988–89 BPM5 Actual S C G Hungary Hungarian forint a 2000 b VAB 2005 BPM5 S C S Iceland Iceland krona 2000 VAB 2005 BPM5 S C S India Indian rupee 2004/05 b VAB 2005 BPM5 Actual G C S Indonesia Indonesian rupiah 2000 VAP 2005 BPM5 Actual G B S Iran, Islamic Rep. Iranian rial 1997/98 VAB 1980–2002 2005 BPM4 Actual S C Iraq Iraqi dinar 1997 VAB 1997, 2004 2005 BPM5 G Ireland Euro 2000 b VAB 2005 BPM5 G C S Isle of Man Pound sterling 2005 2003 Israel Israeli new shekel 2005 b VAP 2005 BPM5 S C S Italy Euro 2000 b VAB 2005 BPM5 S C S Jamaica Jamaican dollar 2003 VAB BPM5 Actual G C G Japan Japanese yen 2000 VAB 2005 BPM5 G C S Jordan Jordanian dinar 1994 VAB 2005 BPM5 Actual S B S Kazakhstan Kazakh tenge a 2000 b VAB 1987–95 2005 BPM5 Actual G C S Kenya Kenyan shilling 2001 b VAB 2005 BPM5 Actual G B G Kiribati Australian dollar 2006 VAB S G Korea, Dem. Rep. Democratic People’s BPM4 Republic of Korean won Korea, Rep. Korean won 2000 b VAB 2005 BPM5 G C S Kosovo Euro Actual Kuwait Kuwaiti dinar 1995 VAP 2005 BPM5 S B G Kyrgyz Republic Kyrgyz som a 1995 b VAB 1990–95 2005 BPM5 Actual G B S Lao PDR Lao kip 1990 VAB 2005 BPM5 Preliminary B Latvia Latvian lats 2000 b VAB 1987–95 2005 BPM5 S C S Lebanon Lebanese pound 1997 VAB 2005 BPM5 Actual S B G Lesotho Lesotho loti 1995 b VAB 2005 BPM5 Actual G C G 396 2011 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Dominica 2001 Yes 2009 2008 Dominican Republic 2010 DHS, 2007 IHS, 2007 1971 2009 2009 2000 Ecuador 2010 RHS, 2004 LFS, 2009 1999–2000 2009 2009 2000 Egypt, Arab Rep. 2006 DHS, 2008 ES/BS, 2004/05 Yes 1999–2000 2009 2008 2000 El Salvador 2007 RHS, 2008 IHS, 2008 Yes 1970–71 2009 2009 2000 Equatorial Guinea 2002 2009 2000 Eritrea 1984 DHS, 2002 2009 2003 2004 Estonia 2000 ES/BS, 2004 Yes 2001 2009 2009 2000 Ethiopia 2007 DHS, 2005 ES/BS, 2005 2001–02 2009 2009 2002 Faeroe Islands c Yes 2009 Fiji 2007 ES/BS, 2009 Yes 2009 2009 2000 Finland 2010 IS, 2000 Yes 1999–2000 2009 2009 2000 France 2006e ES/BS, 1994/95 Yes 1999–2000 2009 2009 2000 French Polynesia 2007 Yes 2010 Gabon 2003 DHS, 2000 CWIQ/IHS, 2005 1974–75 2009 2006 2000 Gambia, The 2003 MICS, 2005/06 IHS, 2003 2001–02 2009 2009 2000 Georgia 2002 MICS, 2005; RHS, 2005 IHS, 2008 Yes 2004 2009 2008 2005 Germany c IHS, 2000 Yes 1999–2000 2009 2009 2000 Ghana 2010 DHS, 2008 LSMS, 2006 1984 2009 2008 2000 Gibraltar 2001 Yes Greece 2001 IHS, 2000 Yes 1999–2000 2009 2009 2000 Greenland 2010 Yes 2007 Grenada 2001 Yes 2009 2009 Guam 2010 Yes Guatemala 2002 RHS, 2002 LSMS, 2006 Yes 2003 2009 2009 2000 Guinea 1996 DHS, 2005 CWIQ, 2007 2000–01 2009 2008 2000 Guinea-Bissau 2009 MICS, 2010 CWIQ, 2002 1988 2002 2005 2000 Guyana 2002 MICS, 2006 IHS, 1998 2009 2009 2000 Haiti 2003 DHS, 2005/06 IHS, 2001 1971 1997 2000 Honduras 2001 DHS, 2005/06 IHS, 2007 1993 2009 2009 2000 Hungary 2001 ES/BS, 2007 Yes 2000 2008 2009 2000 Iceland c Yes 2008 2009 2000 India 2001 DHS, 2005/06 IHS, 2004/05 1995–96/ 2009 2009 2000 2000–01 Indonesia 2010 DHS, 2007 IHS, 2007 2003 2009 2009 2000 Iran, Islamic Rep. 2006 DHS, 2000 ES/BS, 2005 Yes 2003 2007 2006 2004 Iraq 1997 MICS, 2006 IHS, 2007 1981 2002 2008 2000 Ireland 2006 IHS, 2000 Yes 2000 2009 2009 2000 Isle of Man 2006 Yes Israel 2009 ES/BS, 2001 Yes 1981 2009 2004 Italy 2001 ES/BS, 2000 Yes 2000 2009 2009 2000 Jamaica 2001 MICS, 2005 LSMS, 2007 Yes 1996 2009 2009 2000 Japan 2010 IS, 1993 Yes 2000 2008 2009 2000 Jordan 2004 DHS, 2009 ES/BS, 2006 1997 2009 2009 2005 Kazakhstan 2009 MICS, 2006 ES/BS, 2007 Yes 2009 2009 2000 Kenya 2009 SPA, 2004; DHS, 2008/09 IHS, 2005-06 1977–79 2009 2009 2003 Kiribati 2005 2009 2009 Korea, Dem. Rep. 2009 MICS, 2010 2000 Korea, Rep. 2005 ES/BS, 1998 Yes 2000 2009 2009 2000 Kosovo 1981 IHS, 2006 2009 Kuwait 2010 FHS, 1996 Yes 1970 2003 2009 2002 Kyrgyz Republic 2009 MICS, 2005/06 ES/BS, 2007 Yes 2002 2008 2009 2000 Lao PDR 2005 MICS, 2006 ES/BS, 2008 1998–99 2008 1975 2000 Latvia 2000 IHS, 2008 Yes 2001 2009 2009 2000 Lebanon 1970 MICS, 2000 Yes 1998–99 2009 2009 2005 Lesotho 2006 DHS, 2009/10 ES/BS, 2002/03 1999–2000 2009 2004 2000 2011 World Development Indicators 397 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Liberia Liberian dollar 1992 VAP 2005 BPM5 Estimate B G Libya Libyan dinar 1999 VAB 1986 BPM5 G Liechtenstein Swiss franc VAB S Lithuania Lithuanian litas 2000 b VAB 1990–95 2005 BPM5 Actual S C S Luxembourg Euro 2000 VAB 2005 BPM5 S C S Macedonia, FYR Macedonian denar 1997 1995 b VAB 2005 BPM5 Actual S G Madagascar Malagasy ariary 1984 VAB 2005 BPM5 Actual G C G Malawi Malawi kwacha 1994 VAB 2005 BPM5 Actual G G Malaysia Malaysian ringgit 2000 VAP 2005 BPM5 Estimate G B S Maldives Maldivian rufiyaa 1995 VAB 2005 BPM5 Actual G C Mali CFA franc 1987 VAB 2005 BPM4 Preliminary G B G Malta Euro 2005 VAB 2005 BPM5 G C S Marshall Islands U.S. dollar 1991 VAB Mauritania Mauritanian ouguiya 1998 VAB 2005 BPM4 Actual G G Mauritius Mauritian rupee 2006 VAB 2005 BPM5 Preliminary G C G Mayotte Euro G Mexico Mexican peso 2003 b VAB 2005 BPM5 Actual G C S Micronesia, Fed. Sts. U.S. dollar 1998 VAB Moldova Moldovan leu a 1996 b VAB 1990–95 2005 BPM5 Actual G C S Monaco Euro S Mongolia Mongolian tugrik 2005 b VAB 2005 BPM5 Actual G C G Montenegro Euro 2000 b VAB 2005 BPM5 Actual Morocco Moroccan dirham 1998 VAB 2005 BPM5 Actual S C S Mozambique New Mozambican 2003 VAB 1992–95 2005 BPM5 Actual G G metical Myanmar Myanmar kyat 1985/86 VAP BPM5 Estimate C Namibia Namibian dollar 2004/05 b VAB 2005 BPM5 G B G Nepal Nepalese rupee 2000/01 VAB 2005 BPM5 Actual C G Netherlands Antilles Netherlands BPM5 S Antilles guilder Netherlands Euro a 2000 b VAB 2005 BPM5 S C S New Caledonia CFP franc S New Zealand New Zealand dollar 2000/01 VAB 2005 BPM5 G C Nicaragua Nicaraguan gold cordoba 1994 b VAB 1965–95 BPM5 Actual S B G Niger CFA franc 1987 VAP 1993 2005 BPM4 Actual G B G Nigeria Nigerian naira 2002 VAB 1971–98 2005 BPM5 Actual G B G Northern Mariana Islands U.S. dollar Norway Norwegian krone a 2000 b VAB 2005 BPM5 G C S Oman Rial Omani 1988 VAP 2005 BPM5 G B G Pakistan Pakistani rupee 1999/2000 b VAB 2005 BPM5 Actual G B G Palau U.S. dollar 1995 VAB Panama Panamanian balboa 1996 b VAB BPM5 Actual S C G Papua New Guinea Papua New Guinea kina 1998 VAB 1989 BPM5 Actual G B Paraguay Paraguayan guarani 1994 VAP 2005 BPM5 Actual G B G Peru Peruvian new sol 1994 VAB 1985–90 2005 BPM5 Actual S C S Philippines Philippine peso 1985 VAP 2005 BPM5 Actual G B S Poland Polish zloty a 2002 b VAB 2005 BPM5 S C S Portugal Euro 2000 b VAB 2005 BPM5 S C S Puerto Rico U.S. dollar 1954 VAP G Qatar Qatari riyal 2001 VAP 2005 S B G Romania New Romanian leu a 2005 b VAB 1987–89, 2005 BPM5 Actual S C S 1992 Russian Federation Russian ruble 2000 b VAB 1987–95 2005 BPM5 Preliminary G C S Rwanda Rwandan franc 1995 VAP 1994 2005 BPM5 Estimate G C G Samoa Samoan tala 2002 VAB BPM5 Actual G San Marino Euro 1995 2000 b VAB C G 398 2011 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Liberia 2008 DHS, 2007; MIS, 2009 CWIQ, 2007 2008 1985 2000 Libya 2006 MICS, 2000 2001 2008 2004 2000 Liechtenstein 2010 Yes Lithuania 2001 ES/BS, 2008 Yes 2003 2008 2009 2000 Luxembourg 2001 Yes 1999–2000d 2009 2009 Macedonia, FYR 2002 MICS, 2005 ES/BS, 2008 Yes 1994 2009 2009 Madagascar 1993 DHS, 2008/09 PS, 2005 2004 2009 2009 2000 Malawi 2008 MICS, 2006 LSMS, 2004/05 1993 2009 2010 2000 Malaysia 2010 ES/BS, 2009 Yes 2009 2009 2000 Maldives 2006 DHS, 2009 IHS, 2004 Yes 2009 2008 Mali 2009 DHS, 2006 IHS, 2006 1984 2007 2008 2000 Malta 2005 Yes 2001 2009 2009 2000 Marshall Islands 1999 1999 Mauritania 2000 MICS, 2007 IHS, 2000 1984–85 2009 2008 2000 Mauritius 2000 Yes 2009 2009 2003 Mayotte 2007 Yes 2009 Mexico 2010 ENPF, 1995 LFS, 2008 1991 2009 2009 2000 Micronesia, Fed. Sts. 2000 IHS, 2000 Moldova 2004 DHS, 2005 ES/BS, 2008 Yes 2009 2009 2000 Monaco 2008 Yes Mongolia 2010 MICS, 2005 LSMS, 2007/08 Yes 2009 2007 2000 Montenegro 2003 MICS, 2005/06 ES/BS, 2008 Yes 2009 Morocco 2004 MICS, 2006 ES/BS, 2007 1996 2009 2009 2000 Mozambique 2007 DHS, 2003; AIS, 2009 ES/BS, 2008 1999–2000 2009 2009 2000 Myanmar 1983 MICS, 2000 2003 2001 2000 Namibia 2001 DHS, 2006/07 ES/BS, 1993/94 1996–97 2009 2008 2000 Nepal 2001 DHS, 2006 LSMS, 2003/04 2002 2009 2009 2000 Netherlands Antilles 2001 Yes 2008 2000 Netherlands 2001 IHS, 1999 Yes 1999–2000d 2009 2009 New Caledonia 2009 Yes 1997 2008 New Zealand 2006 IS, 1997 Yes 2002 2009 2010 2000 Nicaragua 2005 RHS, 2006/07 LSMS, 2005 2001 2009 2009 2000 Niger 2001 DHS, 2006 CWIQ/PS, 2005 1980 2003 2008 2000 Nigeria 2006 DHS, 2008 IHS, 2003/04 1960 2006 2009 2000 Northern Mariana Islands 2010 Norway 2001 IS, 2000 Yes 1999 2009 2010 2000 Oman 2010 FHS, 1995 1978–79 2004 2009 2003 Pakistan 1998 DHS, 2006/07 IHS, 2006 2000 2009 2009 2000 Palau 2010 Yes 2007 Panama 2010 LSMS, 2003 LFS, 2009 2001 2009 2009 2000 Papua New Guinea 2000 DHS, 1996 IHS, 1996 2009 2004 2000 Paraguay 2002 RHS, 2004 IHS, 2008 1991 2009 2009 2000 Peru 2007 DHS, 2007/08 IHS, 2009 1994 2009 2009 2000 Philippines 2010 DHS, 2008 ES/BS, 2009 Yes 2002 2009 2009 2000 Poland 2002 ES/BS, 2008 Yes 1996/2002 2009 2009 2000 Portugal 2001 IS, 1997 Yes 1999 2009 2009 2000 Puerto Rico 2010 RHS, 1995/96 Yes 1997/2002 2001 Qatar 2010 Yes 2000–01 2008 2005 Romania 2002 RHS, 1999 LFS, 2008 Yes 2002 2009 2009 2000 Russian Federation 2010 RHS, 1996 IHS, 2008 Yes 1994–95 2009 2009 2000 Rwanda 2002 DHS, 2007/08 IHS, 2005 1984 2009 2009 2000 Samoa 2006 DHS, 2009 1999 2009 2009 San Marino 2010 Yes 2011 World Development Indicators 399 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept São Tomé & Príncipe São Tomé & 2001 VAP 2005 Preliminary S G Príncipe dobra Saudi Arabia Saudi Arabian riyal 1999 VAP 2005 BPM5 G G Senegal CFA franc 1999 1987 b VAB 2005 BPM5 Actual G B G Serbia Serbian dinar a 2002 b VAB 2005 BPM5 Actual G C G Seychelles Seychelles rupee 1986 VAP BPM5 Actual G C G Sierra Leone Sierra Leonean leone 1990 b VAB 2005 BPM5 Preliminary B G Singapore Singapore dollar 2000 b VAB 2005 BPM5 G C S Slovak Republic Euro 2000 1995 b VAB 2005 BPM5 S C S Slovenia Euro a 2000 b VAB 2005 BPM5 S C S Solomon Islands Solomon Islands dollar 1990 VAB BPM5 Actual S Somalia Somali shilling 1985 VAB 1977–90 Estimate South Africa South African rand 2005 b VAB 2005 BPM5 Preliminary G C S Spain Euro 2000 b VAB 2005 BPM5 S C S Sri Lanka Sri Lankan rupee 2002 VAP 2005 BPM5 Actual G B G St. Kitts and Nevis East Caribbean dollar 1990 b VAB BPM5 Preliminary S C G St. Lucia East Caribbean dollar 1990 VAB BPM5 Actual G G St. Vincent & Grenadines East Caribbean dollar 1990 VAB BPM5 Actual S B G Sudan Sudanese pound 1981/82f 1996 VAB 2005 BPM5 Actual G B G Suriname Suriname dollar 1990 b VAB BPM5 G G Swaziland Swaziland lilangeni 2000 VAB 2005 BPM5 Actual G B G Sweden Swedish krona a 2000 VAB 2005 BPM5 S C S Switzerland Swiss franc 2000 VAB 2005 BPM5 S C S Syrian Arab Republic Syrian pound 2000 VAB 1970–2008 2005 BPM5 Actual S C G Tajikistan Tajik somoni a 2000 b VAB 1990–95 2005 BPM4 Actual C G Tanzania Tanzanian shilling a 2001 VAB 2005 BPM5 Actual G G Thailand Thai baht 1988 VAP 2005 BPM5 Actual S C S Timor-Leste U.S. dollar 2000 VAP G Togo CFA franc 1978 VAP 2005 BPM5 Actual S B G Tonga Tongan pa’anga 2000/01 VAB BPM5 Actual G G Trinidad and Tobago Trinidad and 2000 b VAB BPM5 S C G Tobago dollar Tunisia Tunisian dinar 1990 VAP 2005 BPM5 Actual G C S Turkey New Turkish lira 1998 VAB 2005 BPM5 Actual S B S Turkmenistan New Turkmen manat a 2007 b VAB 1987–95, BPM4 Estimate 1997–2007 Turks and Caicos Islands U.S. dollar G Tuvalu Australian dollar Uganda Ugandan shilling 2001/02 VAB 2005 BPM5 Actual G B G Ukraine Ukrainian hryvnia a 2003 b VAB 1987–95 2005 BPM5 Actual G C S United Arab Emirates U.A.E. dirham 1995 VAB BPM4 S B G United Kingdom Pound sterling 2000 b VAB 2005 BPM5 G C S United States U.S. dollar a 2000 VAB 2005 BPM5 G C S Uruguay Uruguayan peso 2005 VAB 2005 BPM5 Actual S C S Uzbekistan Uzbek sum a 1997 b VAB 1990–95 BPM4 Actual Vanuatu Vanuatu vatu 2006 VAP BPM5 Actual G C G Venezuela, R.B. Venezuelan bolivar fuerte 1997 VAB 2005 BPM5 Actual G C G Vietnam Vietnamese dong 1994 b VAP 1991 2005 BPM4 Preliminary S G Virgin Islands (U.S.) U.S. dollar 1982 G West Bank and Gaza Israeli new shekel 1997 VAB BPM5 S B G Yemen, Rep. Yemeni rial 1990 VAP 1990–96 2005 BPM5 Actual S B G Zambia Zambian kwacha 1994 VAB 1990–92 2005 BPM5 Preliminary S B G Zimbabwe U.S. dollar 2009 VAB 1991, 1998 2005 BPM4 Actual G C G 400 2011 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data São Tomé & Príncipe 2001 DHS, 2008/09 PS, 2000/01 2005 2009 Saudi Arabia 2010 Demographic survey, 2007 1999 2009 2009 2006 Senegal 2002 DHS, 2005; MIS, 2008/09 PS, 2005 1998–99 2009 2009 2002 Serbia 2002 MICS, 2005/06 IHS, 2008 Yes 2008 Seychelles 2010 IHS, 2007 Yes 1998 2009 2008 2003 Sierra Leone 2004 DHS, 2008 IHS, 2003 1984–85 2003 2002 2000 Singapore 2010 General household, 2005 Yes 2009 2009 Slovak Republic 2001 IS, 1996 Yes 2001 2009 2009 Slovenia 2002 ES/BS, 2004 Yes 2000 2008 2009 Solomon Islands 2009 2009 2007 Somalia 1987 MICS, 2006 1990 1982 2003 South Africa 2001 DHS, 2003 ES/BS, 2005 2000 2009 2009 2000 Spain 2001 IHS, 2000 Yes 1999 2009 2009 2000 Sri Lanka 2001 DHS, 2006/07 ES/BS, 2007 Yes 2002 2009 2009 2000 St. Kitts and Nevis 2001 Yes 2009 2008 St. Lucia 2010 IHS, 1995 Yes 2009 2008 St. Vincent & Grenadines 2001 Yes 2009 2009 Sudan 2008 MICS/PAPFAM, 2006 2009 2009 2000 Suriname 2004 MICS, 2006 ES/BS, 1999 Yes 2008 2008 2000 Swaziland 2007 DHS, 2006/07 ES/BS, 2000/01 2003 2009 2007 2000 Sweden c IS, 2000 Yes 1999–2000 2009 2009 2000 Switzerland 2010 ES/BS, 2000 Yes 2000 2009 2009 2000 Syrian Arab Republic 2004 MICS, 2006 ES/BS, 2004 1981 2009 2008 2003 Tajikistan 2010 MICS, 2005 LSMS, 2004 1994 2009 2000 2000 Tanzania 2002 DHS, 2004/05; ES/BS, 2007 2002–03 2009 2009 2002 AIS, 2007/08 Thailand 2010 MICS, 2005/06 IHS, 2009 2003 2009 2009 2000 Timor-Leste 2010 DHS, 2009 LSMS, 2007 2000 2005 Togo 2010 MICS, 2006 CWIQ, 2006 1996 2005 2007 2002 Tonga 2006 Yes 2001 2009 2007 Trinidad and Tobago 2000 MICS, 2006 IHS, 1992 Yes 2004 2009 2009 2000 Tunisia 2004 MICS, 2006 IHS, 2000   2004 2009 2009 2000 Turkey 2000 DHS, 2003 LFS, 2008 2001 2009 2009 2003 Turkmenistan 1995 MICS, 2006 LSMS, 1998 Yes 2009 2000 2000 Turks and Caicos Islands 2001 Yes 2009 Tuvalu 2002 2008 Uganda 2002 DHS, 2006; MIS, 2009/10 PS, 2005 1991 2009 2008 Ukraine 2001 DHS, 2007 ES/BS, 2008 Yes 2009 2009 2000 United Arab Emirates 2010 1998 2007 2009 2005 United Kingdom 2001 IS, 1999 Yes 1999–2000d 2009 2009 2000 United States 2010 CPS (monthly) LFS, 2000 Yes 1997/2002 2009 2009 2000 Uruguay 2004 IHS, 2009 Yes 2000 2009 2009 2000 Uzbekistan 1989 MICS, 2006 ES/BS, 2003 Yes 2009 2000 Vanuatu 2009 MICS, 2007 2008 2007 Venezuela, R.B. 2001 MICS, 2000 IHS, 2009 Yes 1997 2005 2009 Vietnam 2009 MICS, 2006 IHS, 2008 Yes 2001 2009 2008 2000 Virgin Islands (U.S.) 2010 Yes West Bank and Gaza 2007 PAPFAM, 2006 IHS, 2009 1971 2008 Yemen, Rep. 2004 MICS, 2006 ES/BS, 2005 2002 2003 2009 2000 Zambia 2000 DHS, 2007 IHS, 2004/05 1990 2009 2009 2000 Zimbabwe 2002 DHS, 2005/06 IHS, 2003 1960 2009 2009 2002 Note: For explanation of the abbreviations used in the table see notes following the table. a. Original chained constant price data are rescaled. b. Country uses the 1993 System of National Accounts methodology. c. Register based. d. Conducted annually. e. Rolling census. f. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year. 2011 World Development Indicators 401 Primary data documentation notes • Base year is the base or pricing period used for estimates. • System of trade refers to the United age and sex, as well as the detailed definition of count- constant price calculations in the country’s national Nations general trade system (G) or special trade sys- ing, coverage, and completeness. Countries that hold accounts. Price indexes derived from national accounts tem (S). Under the general trade system goods entering register-based censuses produce similar census aggregates, such as the implicit deflator for gross directly for domestic consumption and goods entered tables every 5 or 10 years. Germany’s 2001 census is domestic product (GDP), express the price level relative into customs storage are recorded as imports at a register-based test census using a sample of 1.2 to base year prices. • Reference year is the year in arrival. Under the special trade system goods are percent of the population. A rare case, France has been which the local currency, constant price series of a recorded as imports when declared for domestic con- conducting a rolling census every year since 2004; the country is valued. The reference year is usually the sumption whether at time of entry or on withdrawal 1999 general population census was the last to cover same as the base year used to report the constant from customs storage. Exports under the general sys- the entire population simultaneously (www.insee.fr/ price series. However, when the constant price data tem comprise outward-moving goods: (a) national en/recensement/page_accueil_rp.htm). • Latest are chain linked, the base year is changed annually, so goods wholly or partly produced in the country; (b) demographic, education, or health household survey the data are rescaled to a specific reference year to foreign goods, neither transformed nor declared for indicates the household surveys used to compile the provide a consistent time series. When the country has domestic consumption in the country, that move out- demographic, education, and health data in section 2. not rescaled following a change in base year, World ward from customs storage; and (c) nationalized goods AIS is HIV/AIDS Indicator Survey, CPS is Current Popu- Bank staff rescale the data to maintain a longer histori- that have been declared for domestic consumption and lation Survey, DGHS is Demographic and General cal series. To allow for cross-country comparison and move outward without being transformed. Under the Health Survey, DHS is Demographic and Health Survey, data aggregation, constant price data reported in World special system of trade, exports are categories a and ENPF is National Family Planning Survey (Encuesta Development Indicators are rescaled to a common ref- c. In some compilations categories b and c are classi- Nacional de Planificacion Familiar), FHS is Family erence year (2000) and currency (U.S. dollars). • Sys- fied as re-exports. Direct transit trade—goods entering Health Survey, LSMS is Living Standards Measurement tem of National Accounts identifies countries that use or leaving for transport only—is excluded from both Survey, MICS is Multiple Indicator Cluster Survey, MIS the 1993 System of National Accounts (1993 SNA), import and export statistics. See About the data for is Malaria Indicator Survey, NSS is National Sample the terminology applied in World Development Indica- tables 4.4, 4.5, and 6.2 for further discussion. • Gov- Survey on Population Change, PAPFAM is Pan Arab tors since 2001, to compile national accounts. ernment finance accounting concept is the account- Project for Family Health, RHS is Reproductive Health Although more countries are adopting the 1993 SNA, ing basis for reporting central government financial Survey, and SPA is Service Provision Assessments. many still follow the 1968 SNA, and some low-income data. For most countries government finance data have Detailed information for AIS, DHS, MIS, and SPA are countries use concepts from the 1953 SNA. • SNA been consolidated (C) into one set of accounts captur- available at www.measuredhs.com/aboutsurveys; for price valuation shows whether value added in the ing all central government fiscal activities. Budgetary MICS at www.childinfo.org; and for RHS at www.cdc. national accounts is reported at basic prices (VAB) or central government accounts (B) exclude some central gov/reproductivehealth/surveys. • Source of most producer prices (VAP). Producer prices include taxes government units. See About the data for tables 4.12, recent income and expenditure data shows household paid by producers and thus tend to overstate the actual 4.13, and 4.14 for further details. • IMF data dissemi- surveys that collect income and expenditure data. value added in production. However, VAB can be higher nation standard shows the countries that subscribe to Names and detailed information on household surveys than VAP in countries with high agricultural subsidies. the IMF’s Special Data Dissemination Standard (SDDS) can be found on the website of the International House- See About the data for tables 4.1 and 4.2 for further or General Data Dissemination System (GDDS). S hold Survey Network (www.surveynetwork.org). Core discussion of national accounts valuation. • Alterna- refers to countries that subscribe to the SDDS and Welfare Indicator Questionnaire Surveys (CWIQ), devel- tive conversion factor identifies the countries and have posted data on the Dissemination Standards Bul- oped by the World Bank, measure changes in key social years for which a World Bank–estimated conversion letin Board at http://dsbb.imf.org. G refers to countries indicators for different population groups—specifically factor has been used in place of the official exchange that subscribe to the GDDS. The SDDS was estab- indicators of access, utilization, and satisfaction with rate (line rf in the International Monetary Fund’s [IMF] lished for member countries that have or might seek core social and economic services. Expenditure sur- International Financial Statistics). See Statistical meth- access to international capital markets to guide them vey/budget surveys (ES/BS) collect detailed informa- ods for further discussion of alternative conversion in providing their economic and financial data to the tion on household consumption as well as on general factors. • Purchasing power parity (PPP) survey year public. The GDDS helps countries disseminate com- demographic, social, and economic characteristics. is the latest available survey year for the International prehensive, timely, accessible, and reliable economic, Integrated household surveys (IHS) collect detailed Comparison Program’s estimates of PPPs. See About financial, and sociodemographic statistics. IMF mem- information on a wide variety of topics, including the data for table 1.1 for a more detailed description ber countries elect to participate in either the SDDS or health, education, economic activities, housing, and of PPPs. • Balance of Payments Manual in use refers the GDDS. Both standards enhance the availability of utilities. Income surveys (IS) collect information on the to the classification system used to compile and report timely and comprehensive data and therefore contrib- income and wealth of households as well as various data on balance of payments items in table 4.17. BPM4 ute to the pursuit of sound macroeconomic policies. social and economic characteristics. Labor force sur- refers to the 4th edition of the IMF’s Balance of Pay- The SDDS is also expected to improve the functioning veys (LFS) collect information on employment, unem- ments Manual (1977), and BPM5 to the 5th edition of financial markets. • Latest population census ployment, hours of work, income, and wages. Living (1993). • External debt shows debt reporting status shows the most recent year in which a census was Standards Measurement Studies (LSMS), developed for 2009 data. Actual indicates that data are as conducted and in which at least preliminary results by the World Bank, provide a comprehensive picture of reported, preliminary that data are based on reported have been released. The preliminary results from the household welfare and the factors that affect it; they or collected information but include an element of staff very recent censuses could be reflected in timely revi- typically incorporate data collection at the individual, estimation, and estimate that data are World Bank staff sions if basic data are available, such as population by household, and community levels. Priority surveys (PS) 402 2010 World Development Indicators Primary data documentation notes are a light monitoring survey, designed by the World Economies with exceptional reporting periods Nations Statistics Division. The new base year is Bank, for collecting data from a large number of house- Reporting period 1990, and the SNA price valuation has been changed Fiscal for national holds cost-effectively and quickly. Income tax registers Economy year end accounts data to basic prices. • Fiji. The new base year is 2005. (ITR) provide information on a population’s income and Data are revised from 2005 onward based on official Afghanistan Mar. 20 FY allowance, such as gross income, taxable income, and government data. • Ghana. The Ghana Statistical Australia Jun. 30 FY taxes by socioeconomic group. 1-2-3 surveys (1-2-3) Service revised Ghana’s national accounts series Bangladesh Jun. 30 FY are implemented in three phases and collect socio- from 1993 to 2006. New GDP data are about 60 Botswana Jun. 30 FY demographic and employment data, data on the infor- percent higher than previously reported and incor- Canada Mar. 31 CY mal sector, and information on living conditions and porate improved data sources and methodology. Egypt, Arab Rep. Jun. 30 FY household consumption. • Vital registration complete • Guinea-Bissau. National accounts data for 2003– Ethiopia Jul. 7 FY identifies countries which report to have at least 90 09 are revised. The new data have broader coverage Gambia, The Jun. 30 CY percent complete registries of vital (birth and death) of all sectors of the economy, and the new base Haiti Sep. 30 FY statistics to the United Nations Statistics Division and year is 2005. GDP in current prices average 89 per- India Mar. 31 FY reported in Population and Vital Statistics Reports. cent higher than previous estimates. • Guyana. The Indonesia Mar. 31 CY Countries with complete vital statistics registries may Bureau of Statistics has introduced a new series of Iran, Islamic Rep. Mar. 20 FY have more accurate and more timely demographic indi- GDP rebased to year 2006. Current price GDP aver- Japan Mar. 31 CY cators than other countries. • Latest agricultural cen- age 63 percent higher than previous estimates. • Kenya Jun. 30 CY sus shows the most recent year in which an agricultural India. The base year has been changed from 1999 Kuwait Jun. 30 CY census was conducted and reported to the Food and to 2004. Data are revised from 2004 onward with Lesotho Mar. 31 CY Agriculture Organization of the United Nations. • Lat- official government data. GDP at current prices Malawi Mar. 31 CY est industrial data show the most recent year for which average 4 percent higher than previous estimates. Myanmar Mar. 31 FY manufacturing value added data at the three-digit level • Kazakhstan. National accounts data have been Namibia Mar. 31 CY of the International Standard Industrial Classification revised by the National Statistical Office. The new Nepal Jul. 14 FY (ISIC, revision 2 or 3) are available in the United base year is 2000. • Kiribati. The base year has New Zealand Mar. 31 FY Nations Industrial Development Organization data- been changed from 2005 to 2006. Data are revised Pakistan Jun. 30 FY base. • Latest trade data show the most recent year from 2000 onward with official government data. Puerto Rico Jun. 30 FY for which structure of merchandise trade data from the • Namibia. The Central Bureau of Statistics has Sierra Leone Jun. 30 CY United Nations Statistics Division’s Commodity Trade revised national accounts data for 2000–07. An Singapore Mar. 31 CY (Comtrade) database are available. • Latest water expanded data survey has resulted in a substantial withdrawal data show the most recent year for which South Africa Mar. 31 CY upward adjustment to estimates of output, particularly data on freshwater withdrawals have been compiled Swaziland Mar. 31 CY in mining, services, and manufacturing. The constant from a variety of sources. See About the data for table Sweden Jun. 30 CY price series were rebased from 1995 to 2004 prices. 3.5 for more information. Thailand Sep. 30 CY GDP in current prices average 14 percent higher than Uganda Jun. 30 FY previous estimates. • South Africa. The base year Exceptional reporting periods United States Sep. 30 CY has been changed from 2000 to 2005. Data are In most economies the fiscal year is concurrent with Zimbabwe Jun. 30 CY revised from 2000 onward with official government the calendar year. Exceptions are shown in the table Revisions to national accounts data data. • Tonga. Data are revised from 1995 onward at right. The ending date reported here is for the fis- National accounts data are revised by national sta- with official government data. GDP in current prices cal year of the central government. Fiscal years for tistical offices when methodologies change or data average 20 percent higher than previous estimates. other levels of government and reporting years for sources improve. National accounts data in World • Vanuatu. The base year has been changed from statistical surveys may differ. Development Indicators are also revised when data 1983 to 2006. Data are revised from 1998 onward The reporting period for national accounts data sources change. The following notes, while not com- with official government data. GDP in current prices is designated as either calendar year basis (CY) or prehensive, provide information on revisions from average 11 percent higher than previous estimates. fiscal year basis (FY). Most economies report their previous data. • Bulgaria. The National Statistical national accounts and balance of payments data Office has revised national accounts data from 1995 Changes to national currencies using calendar years, but some use fiscal years. In onward. GDP in current prices are about 4 percent • Malta. On January 1, 2008, the euro replaced World Development Indicators fiscal year data are higher than previous estimates. • Colombia. The the Maltese liri as Malta’s currency. • Zimbabwe. assigned to the calendar year that contains the larger base year has been changed from 2000 to 2005, As of January 2009, multiple hard currencies, such share of the fiscal year. If a country’s fiscal year ends and data from 2000 onward are new. GDP in cur- as rand, pound sterling, euro and U.S. dollar are in before June 30, data are shown in the first year of rent prices average 2.8 percent higher than previous use. Data are reported in U.S. dollars, the most- the fiscal period; if the fiscal year ends on or after estimates. • Croatia. The Statistical Bureau revised used currency. June 30, data are shown in the second year of the national accounts for 1995–2007. The new base period. Balance of payments data are reported in year is 2000. • Cuba. National accounts data for World Development Indicators by calendar year. 1970–2008 are revised with data from the United 2011 World Development Indicators 403 STATISTICAL METHODS This section describes some of the statistical procedures used in preparing World • Aggregates of ratios are denoted by a w when calculated as weighted averages Development Indicators. It covers the methods employed for calculating regional of the ratios (using the value of the denominator or, in some cases, another and income group aggregates and for calculating growth rates, and it describes the indicator as a weight) and denoted by a u when calculated as unweighted World Bank Atlas method for deriving the conversion factor used to estimate gross averages. The aggregate ratios are based on available data, including data national income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures for economies not shown in the main tables. Missing values are assumed and calculations are described in the About the data sections following each table. to have the same average value as the available data. No aggregate is cal- culated if missing data account for more than a third of the value of weights Aggregation rules in the benchmark year. In a few cases the aggregate ratio may be computed Aggregates based on the World Bank’s regional and income classifications of as the ratio of group totals after imputing values for missing data according economies appear at the end of most tables. The countries included in these to the above rules for computing totals. classifications are shown on the flaps on the front and back covers of the book. • Aggregate growth rates are denoted by a w when calculated as a weighted Most tables also include the aggregate euro area. This aggregate includes the average of growth rates. In a few cases growth rates may be computed from member states of the Economic and Monetary Union (EMU) of the European Union time series of group totals. Growth rates are not calculated if more than half that have adopted the euro as their currency: Austria, Belgium, Cyprus, Finland, the observations in a period are missing. For further discussion of methods France, Germany, Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Por- of computing growth rates see below. tugal, Slovak Republic, Slovenia, and Spain. Other classifications, such as the • Aggregates denoted by an m are medians of the values shown in the table. European Union and regional trade blocs, are documented in About the data for No value is shown if more than half the observations for countries with a the tables in which they appear. population of more than 1 million are missing. Because of missing data, aggregates for groups of economies should be Exceptions to the rules occur throughout the book. Depending on the judg- treated as approximations of unknown totals or average values. Regional and ment of World Bank analysts, the aggregates may be based on as little as 50 income group aggregates are based on the largest available set of data, includ- percent of the available data. In other cases, where missing or excluded values ing values for the 155 economies shown in the main tables, other economies are judged to be small or irrelevant, aggregates are based only on the data shown in table 1.6, and Taiwan, China. The aggregation rules are intended to shown in the tables. yield estimates for a consistent set of economies from one period to the next and for all indicators. Small differences between sums of subgroup aggregates and Growth rates overall totals and averages may occur because of the approximations used. In Growth rates are calculated as annual averages and represented as percentages. addition, compilation errors and data reporting practices may cause discrepan- Except where noted, growth rates of values are computed from constant price cies in theoretically identical aggregates such as world exports and world imports. series. Three principal methods are used to calculate growth rates: least squares, Five methods of aggregation are used in World Development Indicators: exponential endpoint, and geometric endpoint. Rates of change from one period • For group and world totals denoted in the tables by a t, missing data are to the next are calculated as proportional changes from the earlier period. imputed based on the relationship of the sum of available data to the total in the year of the previous estimate. The imputation process works forward Least squares growth rate. Least squares growth rates are used wherever and backward from 2000. Missing values in 2000 are imputed using one of there is a sufficiently long time series to permit a reliable calculation. No growth several proxy variables for which complete data are available in that year. The rate is calculated if more than half the observations in a period are missing. imputed value is calculated so that it (or its proxy) bears the same relation- The least squares growth rate, r, is estimated by fitting a linear regression trend ship to the total of available data. Imputed values are usually not calculated line to the logarithmic annual values of the variable in the relevant period. The if missing data account for more than a third of the total in the benchmark regression equation takes the form year. The variables used as proxies are GNI in U.S. dollars, total population, exports and imports of goods and services in U.S. dollars, and value added ln Xt = a + bt in agriculture, industry, manufacturing, and services in U.S. dollars. • Aggregates marked by an s are sums of available data. Missing values are which is the logarithmic transformation of the compound growth equation, not imputed. Sums are not computed if more than a third of the observations Xt = Xo (1 + r ) t. in the series or a proxy for the series are missing in a given year. 404 2011 World Development Indicators In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are States, and the euro area. A country’s infl ation rate is measured by the change parameters to be estimated. If b* is the least-squares estimate of b, then the in its GDP defl ator. average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by The inflation rate for Japan, the United Kingdom, the United States, and the 100 for expression as a percentage. The calculated growth rate is an average euro area, representing international inflation, is measured by the change in the rate that is representative of the available observations over the entire period. “SDR deflator.” (Special drawing rights, or SDRs, are the International Monetary It does not necessarily match the actual growth rate between any two periods. Fund’s unit of account.) The SDR deflator is calculated as a weighted average of these countries’ GDP deflators in SDR terms, the weights being the amount of Exponential growth rate. The growth rate between two points in time for cer- each country’s currency in one SDR unit. Weights vary over time because both tain demographic indicators, notably labor force and population, is calculated the composition of the SDR and the relative exchange rates for each currency from the equation change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conver- r = ln(pn/p 0)/n sion factor is then applied to a country’s GNI. The resulting GNI in U.S. dollars is divided by the midyear population to derive GNI per capita. where pn and p 0 are the last and first observations in the period, n is the number When official exchange rates are deemed to be unreliable or unrepresenta- of years in the period, and ln is the natural logarithm operator. This growth rate is tive of the effective exchange rate during a period, an alternative estimate of the based on a model of continuous, exponential growth between two points in time. exchange rate is used in the Atlas formula (see below). It does not take into account the intermediate values of the series. Nor does it The following formulas describe the calculation of the Atlas conversion fac- correspond to the annual rate of change measured at a one-year interval, which tor for year t: is given by (pn – pn–1)/pn–1. Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at and the calculation of GNI per capita in U.S. dollars for year t: intervals, in which case the compound growth model is appropriate. The average growth rate over n periods is calculated as Yt$ = (Yt/Nt)/et* r = exp[ln(pn/p 0)/n] – 1. where et* is the Atlas conversion factor (national currency to the U.S. dollar) for year t, et is the average annual exchange rate (national currency to the U.S. dollar) Like the exponential growth rate, it does not take into account intermediate for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar values of the series. terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current GNI (local currency) for year t, and Nt is the midyear population for year t. World Bank Atlas method In calculating GNI and GNI per capita in U.S. dollars for certain operational Alternative conversion factors purposes, the World Bank uses the Atlas conversion factor. The purpose of the The World Bank systematically assesses the appropriateness of official exchange Atlas conversion factor is to reduce the impact of exchange rate fluctuations in rates as conversion factors. An alternative conversion factor is used when the the cross-country comparison of national incomes. official exchange rate is judged to diverge by an exceptionally large margin from the The Atlas conversion factor for any year is the average of a country’s rate effectively applied to domestic transactions of foreign currencies and traded exchange rate (or alternative conversion factor) for that year and its exchange products. This applies to only a small number of countries, as shown in Primary data rates for the two preceding years, adjusted for the difference between the rate documentation. Alternative conversion factors are used in the Atlas methodology of infl ation in the country and that in Japan, the United Kingdom, the United and elsewhere in World Development Indicators as single-year conversion factors. 2011 World Development Indicators 405 CREDITS 1. World view resources to the book, for which the team is very grateful. Other contributors were Section 1 was prepared by a team led by Eric Swanson. Eric Swanson Brian Blankespoor, Lopamudra Chakraborti, Susmita Dasgupta, Olivier Dupriez, wrote the introduction with input from Uranbileg Batjargal and Neil Fan- Kirk Hamilton, Esther Grace Lee, Craig Meisner, Kiran Pandey, Giovanni Ruta, tom. Bala Bhaskar Naidu Kalimili coordinated tables 1.1 and 1.6. Shota and Akiko Saesaka. Hatakeyama, Mehdi Akhlaghi, Buyant Khaltarkhuu, and Masako Hiraga pre- pared tables 1.2, 1.3, and 1.5. Uranbileg Batjargal prepared table 1.4, with 4. Economy input from Azita Amjadi. Signe Zeikate of the World Bank’s Economic Policy Section 4 was prepared by Bala Bhaskar Naidu Kalimili, Mahyar Eshragh-Tabary, and Debt Department provided the estimates of debt relief for the Heavily and Soong Sup Lee in close collaboration with the Sustainable Development and Indebted Poor Countries Debt Initiative and Multilateral Debt Relief Initiative. Economic Data Team of the World Bank’s Development Data Group. Soong Sup Lee wrote the introduction with valuable suggestions from Eric Swanson and the 2. People IMF’s Financial Institutions Division, Statistics Department. Contributions to the Section 2 was prepared by Masako Hiraga and Shota Hatakeyama, in partner- section were provided by Azita Amjadi, Lopamudra Chakraborti, Kirk Hamilton, ship with the World Bank’s Human Developmebnt Network and the Development Barbro Hexeberg, Esther Grace Lee, Giovanni Ruta, and from Justin Thyme Matz Research Group in the Development Economics Vice Presidency. The introduc- and Yutong Li of the IMF’s Statistical Information Management Division, Statistics tion was written by Sulekha Patel and Masako Hiraga, with valuable inputs and Department. The national accounts data for low- and middle-income economies comments from Eric Swanson. The poverty estimates at national poverty lines were gathered by the World Bank’s regional staff through the annual Unified Sur- were compliled by the Global Poverty Working Group: a team of poverty experts vey. Maja Bresslauer, Mahyar Eshragh-Tabary, Bala Bhaskar Naidu Kalimili, and from the Poverty Reduction and Equality Network, the Development Research Buyant Khaltarkhuu worked on updating, estimating, and validating the databases Group, and the Development Data Group. The poverty estimates at international for national accounts. The team is grateful to Eurostat, the International Monetary poverty lines were prepared by Shaohua Chen and Prem Sangraula of the World Fund, Organisation for Economic Co-operation and Development, United Nations Bank’s Development Research Group. The data on children at work were prepared Industrial Development Organization, and World Trade Organization for access by Lorenzo Guarcello and Furio Rosati from the Understanding Children’s Work to their databases. project. Other contributions were provided by Emi Suzuki (population, health, and nutrition); Montserrat Pallares-Miralles and Carolina Romero Robayo (vulnerability 5. States and markets and security); Sara Elder of the International Labour Organization (labor force); Section 5 was prepared by David Cieslikowski and Buyant Khaltarkhuu, in partner- Amelie Gagnon, Said Ould Voffal, and Weixin Lu of the United Nations Educa- ship with the World Bank’s Financial and Private Sector Development Network, tional, Scientific, and Cultural Organization Institute for Statistics (education and Poverty Reduction and Economic Management Network, Sustainable Develop- literacy); the World Health Organization’s Chandika Indikadahena (health expen- ment Network, the International Finance Corporation, and external partners. diture), Charu Garg (national health account), Monika Bloessner and Mercedes David Cieslikowski wrote the introduction to the section with input from Eric de Onis (malnutrition and overweight), Neeru Gupta and Teena Kunjument (health Swanson. Other contributors include Ada Karina Izaguirre (privatization and workers), Jessica Ho (hospital beds), Rifat Hossain (water and sanitation), and infrastructure projects); Leora Klapper and Inessa Love (business registration); Hazim Timimi (tuberculosis); Delice Gan of the International Diabetes Federation Federica Saliola and Joshua Wimpey (Enterprise Surveys); Sylvia Solf and Carolin (diabetes); and Nyein Nyein Lwin of the United Nations Children’s Fund (health). Geginat (Doing Business); Alka Banerjee and Michael Orzano (Standard & Poor’s Eric Swanson provided valuable comments and suggestions on the introduction global stock market indexes); Oya Pinar Ardic Alper (financial access); Satish and at all stages of production. Mannan (public policies and institutions); Henry Boyd and James Hackett of the International Institute for Strategic Studies (military personnel); Sam Perlo- 3. Environment Freeman and Siemon Wezeman of the Stockholm International Peace Research Section 3 was prepared by Mehdi Akhlaghi in partnership with the World Bank’s Institute (military expenditures and arms transfers); Kacem Iaych of the Interna- Sustainable Development Network. The introdcution was prepared by Soong Sup tional Road Federation, Narjess Teyssier and Zubair Anwar of the International Lee and Neil Fantom. The guidance of Glenn-Marie Lange is gratefully acknowl- Civil Aviation Organization, and Hélène Stephan (transport); Jane Degerlund of edged. Carola Fabi and Edward Gillin of the Food and Agriculture Organization Containerisation International (ports); Vanessa Grey, Esperanza Magpantay, and of the United Nations; Ricardo Quercioli and Karen Treanton of the International Susan Teltscher of the International Telecommunication Union; Georges Boade of Energy Agency; Laura Battlebury of the World Conservation Monitoring Centre; the United Nations Educational, Scientific, and Cultural Organization Institute for and Gerhard Metchies and Armin Wagner of German International Cooperation Statistics (research and development, researchers, and technicians); and Ryan (GIZ). The World Bank’s Environment Department devoted substantial staff Lamb of the World Intellectual Property Organization (patents and trademarks). 406 2011 World Development Indicators 6. Global links Client services Section 6 was prepared by Uranbileg Batjargal in partnership with the Finan- The Development Data Group’s Client Services and Communications Team (Azita cial Data Team of the World Bank’s Development Data Group, Development Amjadi, Buyant Erdene Khaltarkhuu, Alison Kwong, Beatriz Prieto-Oramas, Jomo Research Group (trade), Development Prospects Group (commodity prices and Tariku, and Vera Wen) contributed to the design and planning and helped coordi- remittances), International Trade Department (trade facilitation), and external nate work with the Office of the Publisher. partners. Uranbileg Batjargal wrote the introduction, with substantial input from Ingo Borchert (Services Policy Restrictiveness Database), Caglar Ozden (bilateral Administrative assistance, office technology, and systems support migration matrix), and Evis Rucaj (public sector debt). Eric Swanson provided Awatif Abuzeid, Elysee Kiti, Premi Ratham Raj and Estela Zamora provided admin- valuable comments. Substantial input for the data and tables came from Azita istrative assistance. Jean-Pierre Djomalieu, Gytis Kanchas, and Nacer Megherbi Amjadi (trade and tariffs) and Yasue Sakuramoto (external debt and financial provided information technology support. Ramvel Chandrasekaran, Ugendran data). Other contributors include Frederic Docquier (emigration rates); Flavine Machakkalai, Atsushi Shimo, and Malarvizhi Veerappan provided systems support Creppy and Yumiko Mochizuki of the United Nations Conference on Trade and on the Development Data Platform application. Development (trade); Betty Dow (commodity prices); Thierry Geiger of the World Economic Forum (trade facilitation); Jeff Reynolds and Joseph Siegel of DHL Publishing and dissemination (freight costs); Yasmin Ahmad and Elena Bernaldo of the Organisation for Eco- The Office of the Publisher, under the direction of Carlos Rossel, provided valu- nomic Co-operation and Development (aid); Hiroko Maeda and Ibrahim Levent able assistance throughout the production process. Denise Bergeron, Nazim (external debt); Henrik Pilgaard of the United Nations Refugee Agency (refugees); Aziz Gokdemir, Stephen McGroarty, and Nora Ridolfi coordinated printing and Costanza Giovannelli and Bela Hovy of the United Nations Population Division supervised marketing and distribution. Merrell Tuck-Primdahl of the Develop- (migration); Sanket Mohapatra and Ani Rudra Silwal (remittances); and Teresa ment Economics Vice President’s Office managed the communications strategy. Ciller of the World Tourism Organization (tourism). Ramgopal Erabelly, Shelley Lai Fu, and William Prince provided valuable technical assistance. World Development Indicators CD-ROM Software preparation and testing was managed by Vilas Mandlekar with the assis- Other parts of the book tance of Ramgopal Erabelly, Buyant Erdene Khaltarkhuu, Parastoo Oloumi, and Jeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of the William Prince. Systems development was undertaken by the Data and Informa- maps on the inside covers. William Prince prepared Users guide. Eric Swanson tion Systems Team led by Reza Farivari. William Prince coordinated user interface wrote Statistical methods. Maja Bresslauer, Buyant Khaltarkhuu, and William design and overall production and provided quality assurance, with assistance Prince prepared Primary data documentation. Alison Kwong prepared Partners from Jomo Tariku. Photo credits belong to the World Bank photo library. and Index of indicators. Open Data and Online Access Database management Coordination of the Open Data website (data.worldbank.org/) was provided by William Prince coordinated management of the World Development Indicators Neil Fantom and Nicole Frost. Design, programming, and testing were carried database. Operation of the database management system was made possible out by Reza Farivari and his team: Azita Amjadi, Ramvel Chandrasekaran, Shelley by Ramgopal Erabelly, Shelley Fu, and Shahin Outadi in the Data and Information Fu, Buyant Erdene Khaltarkhuu, Ugendran Machakkalai, Shanmugam Natarajan, Systems Team under the leadership of Reza Farivari. Atsushi Shimo, Lakshmikanthan Subramanian, Jomo Tariku, Malarvizhi Veerap- pan, and Vera Wen. William Prince coordinated production and provided quality Design, production, and editing assurance. Support from the Corporate Communications Unit in External Affairs Azita Amjadi, Alison Kwong, and Jomo Tariku coordinated all stages of production. was provided by a team including Livia Barton, George Gongadze and Jeffrey Jomo Tariku prepared the cover. Deborah Arroyo, Jomo Tariku, and Elaine Wilson Mccoy. The multilingual web team was led by Valerie Hufbauer. typeset the book. Communications Development Incorporated provided overall design direction and editing, led by Meta de Coquereaumont, Bruce Ross-Larson, Client feedback and Christopher Trott. Katrina Van Duyn proofread of the book. Staff from External The team is grateful to the many people who have taken the time to provide Affairs Office of the Publisher oversaw printing and dissemination of the book. feedback and suggestions, which have helped improve this year’s edition. 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Geneva. 416 2011 World Development Indicators 2011 World Development Indicators 417 INDEX OF INDICATORS References are to table numbers. annual growth 4.1 A Agriculture Aid as share of GDP 4.2 agricultural raw materials by recipient commodity prices 6.6 aid dependency ratios 6.16 exports per capita 6.16 as share of total exports 4.4 total 6.16 from high-income economies as share of total exports 6.4 net flows imports from bilateral sources 6.13 as share of total imports 4.5 from international financial institutions 6.13 by high-income economies as share of total imports 6.4 from multilateral sources 6.13 tariff rates applied by high-income countries 6.4 from UN agencies 6.13 cereal official development assistance by DAC members area under production 3.2 administrative costs, as share of net bilateral ODA exports from high-income economies as share of total exports 6.4 disbursements 6.15a imports, by high-income economies as share of total imports 6.4 bilateral aid 6.15a, 6.15b, 6.17 tariff rates applied by high-income countries 6.4 by purpose 6.15a yield 3.3 by sector 6.15b employment, as share of total 3.2 commitments 6.14, 6.15b fertilizer debt-related aid, as share of net bilateral ODA disbursements 6.15a commodity prices 6.6 development projects, programs, and other resource provisions, consumption, per hectare of arable land 3.2 as share of net bilateral ODA disbursements 6.15a food disbursements net commodity prices 6.6 gross disbursements 6.14 exports 4.4 humanitarian assistance, as share of net bilateral ODA exports from high-income economies as share of total exports 6.4 disbursements 6.15a imports 4.5 net disbursements imports by high-income economies as share of total imports 6.4 as share of general government disbursements 6.14 tariff rates applied by high-income countries 6.4 as share of donor GNI 1.4, 6.14 freshwater withdrawals for, as share of total 3.5 from major donors, by recipient 6.17 land per capita of donor country 6.14 agricultural, as share of land area 3.2 total 6.14, 6.15a arable, as share of land area 3.1 gross 6.14 arable, per 100 people 3.1 tfor basic social services, as share of sector-allocable area under cereal production 3.2 bilateral ODA commitments 1.4 Irrigated 3.2 humanitarian assistance, as share of bilateral ODA permanent cropland, as share of land area 3.1 net disbursements 6.15a machinery technical cooperation, as share of bilateral ODA tractors per 100 square kilometers of arable land 3.2 net disbursements 6.15a production indexes total sector allocable, as share of bilateral ODA crop 3.3 commitments 6.15b food 3.3 untied aid 6.15b livestock 3.3 AIDS—see HIV, prevalence value added 418 2011 World Development Indicators Air pollution—see Pollution Bonds—see Debt flows; Private financial flows Air transport Brain drain—see Emigration of people with tertiary education to OECD air freight 5.10 countries passengers carried 5.10 registered carrier departures worldwide 5.10 Breastfeeding, exclusive 2.20 Asylum seekers—see Migration; Refugees Broad Money 4.15 B Balance of payments Business environment businesses registered entry density 5.1 current account balance 4.17 new 5.1 as share of GDP 4.a total 5.1 exports and imports of goods and services 4.17 closing a business net current transfers 4.17 time to resolve insolvency 5.3 net income 4.17 corruption total reserves 4.a, 4.17 informal payments to public officials 5.2 See also Exports; Imports; Investment; Private financial flows; Trade crime Base metal losses due to theft, robbery, vandalism, and arson 5.2, 5.8 commodity prices and price index 6.6 dealing with construction permits to build a warehouse number of procedures 5.3 Battle-related deaths 5.8 time required 5.3 enforcing contracts Beverages number of procedures 5.3 commodity prices 6.6 time required 5.3 finance Biodiversity—see Biological diversity firms using banks to finance investment 5.2 gender Biological diversity female participation in ownership 5.2 assessment, date prepared, by country 3.15 informality GEF benefits index 3.4 firms formally registered when operations started 5.2, 5.8 threatened species 3.4 infrastructure birds 3.4 value lost due to electrical outages 5.2 fish 3.4 innovation higher plants 3.4 internationally recognized quality certification ownership 5.2 mammals 3.4 permits and licenses treaty 3.15 time required to obtain operating license 5.2 protecting investors Birth rate, crude 2.1 disclosure, index 5.3 See also Fertility rate registering property number of procedures 5.3 Births attended by skilled health staff 2.19 time to register 5.3 regulation and tax Birthweight, low 2.20 average number of times firms spend meeting with tax officials 5.2 time dealing with officials 5.2 2011 World Development Indicators 419 INDEX OF INDICATORS starting a business fixed capital 4.10, 4.11 cost to start a business 5.3 government, general final expenditure number of start-up procedures 5.3 annual growth 4.9 time to start a business 5.3 as share of GDP 4.8 trade household final expenditure average time to clear direct exports 5.2 multilateral, as share of public and publicly workforce guaranteed debt service 6.11 firms offering formal training 5.2 average annual growth 4.9 per capita 4.9 C Carbon dioxide as share of GDP See also Purchasing power parity (PPP) 4.8 damage, as share of GNI 4.11 Contraceptives emissions condom use, male and female 2.21 per unit of GDP 3.8 prevalence rate 1.3, 2.19 per capita 1.3, 3.8 unmet need for 2.19 total 1.6, 3.8 intensity 3.8 Contract enforcement number of procedures 5.3 Children at work time required for 5.3 by economic activity 2.6 male and female 2.6 Corruption, informal payments to public officials 5.2 study and work 2.6 status in employment 2.6 Country Policy and Institutional Assessment (CPIA)—see Economic total 2.6, 5.8 management; Social inclusion and equity policies; Public sector management work only 2.6 and institutions; Structural policies Cities—see Urban environment; Credit getting credit Closing a business—see Business environment depth of credit information index 5.5 strength of legal rights index 5.5 Commercial bank and other lending 6.12 provided by banking sector 5.5 See also Debt flows; Private financial flows to private sector 5.1 Commodity prices and price indexes 6.6 Crime intentional homicide rate 5.8 Communications—see Internet; Newspapers, daily; Telephones; Television, losses due to 5.2 households with Current account balance 4.17 Compensation See also Balance of payments of central government employees 4.13 See also Remittances Customs average time to clear 5.2 Computers (personal) per 100 people 5.12 burden of procedures 6.9 Consumption distribution—see Income distribution 420 2011 World Development Indicators D E Economic management (Country Policy and Institutional Assessment) DAC (Development Assistance Committee)—see Aid debt policy 5.9 economic management cluster average 5.9 Death rate, crude 2.1 fiscal policy 5.9 See also Mortality rate macroeconomic management 5.9 Debt, external Education as share of GNI 6.11 children out of school debt service male and female 2.12 multilateral, as share of public and publicly guaranteed debt poorest and richest wealth quintile 2.15 service 6.11 cohort survival rate total, as share of exports of goods and services and income 6.11 to grade 5, male and female 2.13 IMF credit, use of 6.10 to last grade of primary education, male and female 2.13 long-term completion rate, primary private nonguaranteed 6.10 male and female 2.14, 2.15 public and publicly guaranteed poorest and richest wealth quintiles 2.15 IBRD loans and IDA credits 6.10 total 1.2, 2.14 total 6.10 enrollment ratio present value girls to boys enrollment in primary and secondary education 1.2 as share of GNI 6.11 gross as share of exports of goods and services and income 6.11 by level 2.12 short-term primary 5.8 as share of total debt 6.11 secondary 2.4 as share of total reserves 6.11 net total 6.10 by level 2.12 total 6.10 primary, adjusted 2.12 intake ratio, gross Debt flows first grade of primary education 2.13 bonds 6.12 grade 1 2.15 commercial banks and other lending 6.12 primary participation rate, gross 2.15 See also Private financial flows public expenditure on as share of GDP 2.11 Deforestation, average annual 3.4 as share of GNI 4.11 as share of total government expenditure 2.11 Demand—see Comsumption; Imports; Exports; Savings per student, as share of GDP per capita, by level 2.11 pupil–teacher ratio, primary 2.11 Density—see Population, density repeaters, primary, male and female 2.13 teachers, primary, trained 2.11 Dependency ratio—See Population, age dependency ratio transition to secondary school, male and female 2.13 unemployment by level of educational attainment 2.5 Development assistance—see Aid years of schooling, average 2.15 Disease—see Health risks Electricity consumption 5.11 Distribution of income or consumption—see Income distribution production 2011 World Development Indicators 421 INDEX OF INDICATORS total 3.10 combustible renewables and waste, as share of total 3.7 sources 3.10 fossil fuel consumption, as share of total 3.7 transmission and distribution losses 5.11 GDP per unit of energy use 3.8 value lost due to outages 5.2 per capita 3.7 total 3.7 See also Electricity; Fuels Emissions carbon dioxide Enforcing contracts—see Business environment average annual growth 3.9 intensity 3.8 Enrollment—see Education per capita 1.3, 3.8 per unit of GDP 3.8 Entry regulations for business—see Business environment total 1.6, 3.8 methane Environmental strategy or action plans, year adopted 3.15 agricultural, as share of total 3.9 from energy processes, as share of total 3.9 Equity flows total 3.9 foreign direct investment 6.12 nitrous oxide portfolio equity 6.12 agricultural, as share of total 3.9 See also Private financial flows energy and industry, as share of total 3.9 total 3.9 European Commission other greenhouse gases 3.9 distribution of net aid from 6.17 Employment Exchange rates children in employment 2.6, 5.8 official, local currency units to U.S. dollar 4.16 in agriculture purchasing power parity conversion factor 4.16 as share of total employment 3.2 ratio of PPP conversion factor to official exchange rate 4.16 female 1.5, 2.3 real effective 4.16 male 2.3 See also Purchasing power parity (PPP) in industry, male and female 2.3 in services, male and female 2.3 Export credits to population ratio 2.4 private, from DAC members 6.14 vulnerable 1.2, 2.4 See also Labor force; Unemployment Exports arms 5.7 Endangered species—see Biological diversity; Plants, higher documents required for 6.9 goods and services Energy as share of GDP 4.8 commodity price index 6.6 average annual growth 4.a, 4.9 consumption, road sector 3.13 total 4.17 depletion, as share of GNI 4.11 high-technology emissions—see Pollution share of manufactured exports 5.13 imports, net 3.8 total 5.13 production 3.7 information and communications technology 5.12 use lead time 6.9 alternative and nuclear energy 3.7 merchandise average annual growth 3.7 annual growth 6.2, 6.3 422 2011 World Development Indicators from high-income countries, by product 6.4 other official flows 6.14 from developing countries, by recipient 6.5 private 6.14 by regional trade blocs 6.7 total 6.14 direction of trade 6.3 See also Aid structure 4.4 total 4.4 Financing through international capital markets 6.1 value, average annual growth 6.2 See also Private financial flows volume, average annual growth 6.2 services Food—see Agriculture, production indexes; Commodity prices and price structure 4.6 indexes total 4.6 travel 4.6, 6.19 Foreign direct investment, net—see Investment; Private financial flows See also Trade Forest F Female-headed households 2.10 area as share of total land area total 3.1 3.4 deforestation, average annual 3.4 Female participation in ownership 5.2 net depletion, as share of GNI 4.11 Fertility rate Fuels adolescent 2.19 consumption crude birth rate 2.1 road sector 3.13 desired 2.19 exports total 2.18, 2.21 as share of total merchandise exports 4.5 crude petroleum, from high-income economies, Finance, firms using banks to finance investment 5.2 as share of total exports 6.4 from high-income economies, as share of total exports 6.4 Financial access, stability, and efficiency petroleum products, from high-income economies, automated teller machines 5.5 as share of total exports 6.4 bank capital to asset ratio 5.5 imports bank nonperforming loans, ratio to total gross loans 5.5 as share of total imports 4.4 commercial bank branches 5.5 crude petroleum, by high-income economies, as share of total deposit accounts at commercial banks 5.5 imports 6.4 loan accounts at commercial banks 5.5 by high-income economies, as share of total imports 6.4 point-of-sale terminals 5.5 petroleum products, by high-income economies, as share of total imports 6.4 Financial flows, net prices 3.13 from DAC members 6.14 tariff rates applied by high-income countries 6.4 official from bilateral sources from international financial institutions from multilateral sources 6.13 6.13 6.13 G GEF benefits index for biodiversity 3.4 from UN agencies 6.13 total 6.13 Gender differences official development assistance 6.14 in children in employment 2.6, 5.8 net grants by NGOs 6.14 in condom use 2.21 2011 World Development Indicators 423 INDEX OF INDICATORS in education 1.2, 2.12, 2.13, 2.14 Gross domestic product (GDP) in employment 2.3 annual growth 1.1, 1.6, 4.1, 4.10 by economic activity 2.3 contribution of natural resources 3.16 unemployment implicit deflator—see Prices total 2.5 per capita, annual growth 1.1, 1.6 youth 2.10 total 4.2, 4.10 nonagricultural wage employment 1.5 unpaid family workers 1.5 Gross enrollment—see Education vulnerable employment 2.4 in HIV prevalence 2.21 Gross national income (GNI) in labor force participation 2.2 adjusted net national income in life expectancy at birth 1.5 annual growth 4.10 in literacy total 4.10 adult 2.14 annual growth 4.10 youth 2.14 per capita in mortality PPP dollars 1.1, 1.6 adult 2.22 rank 1.1 child 2.22 U.S. dollars 1.1, 1.6 in ownership of firms 5.2 rank in parliaments 1.5 PPP dollars 1.1 in smoking 2.21 U.S. dollars 1.1 in survival to age 65 2.22 total PPP dollars 1.1, 1.6 Gini index 2.9 U.S. dollars 1.1, 1.6, 4.10 Government, central cash surplus or deficit debt 4.12 H Health care as share of GDP 4.12 children sleeping under treated nets 2.18 interest, as share of revenue 4.12 children with acute respiratory infection taken to health provider 2.18 expense children with diarrhea who received oral rehydration and as share of GDP 4.12 continued feeding 2.18 by economic type 4.13 children with fever receiving antimalarial drugs 2.18 net incurrence of liabilities, as share of GDP, domestic and foreign 4.12 HIV, prevalence 1.3 revenue hospital beds per 1,000 people 2.16 as share of GDP 4.12 immunization rate, child 2.18 grants and other 4.14 nurses and midwives per 1,000 people 2.16 social contributions 4.14 outpatient visits per capita 2.16 taxes physicians per 1,000 people 2.16 as share of GDP 5.6 reproductive by source, as share of revenue 4.14 anemia, prevalence of, pregnant women 2.20 births attended by skilled health staff 2.19 Greenhouse gases—see Emissions contraceptive prevalence rate 1.3, 2.19 fertility rate Gross capital formation adolescent 2.19 annual growth 4.9 total 2.19 as share of GDP 4.8 low-birthweight babies 2.20 424 2011 World Development Indicators maternal mortality ratio 1.3, 2.19, 5.8 prevalence lifetime risk of maternal death 2.19 female 2.21 pregnant women receiving prenatal care 1.5, 2.19 population ages 15–24, male and female 2.21 unmet need for contraception 2.19 total 1.3, 2.21 tuberculosis prevention incidence 1.3, 2.20 condom use, male and female 2.21 treatment success rate 2.18 Homicide rate, intentional 5.8 Health expenditure as share of GDP 2.16 Hospital beds—see Health care external resources 2.16 out of pocket 2.16 Housing conditions, national and urban per capita 2.16 durable dwelling units 3.12 public 2.16 home ownership 3.12 household size 3.12 Health information multiunit dwellings 3.12 census, year last completed 2.17 overcrowding 3.12 completeness of vital registration vacancy rate 3.12 birth registration 2.17 infant death 2.17 Hunger, depth 5.8 total death 2.17 health survey, year last completed national health account number completed 2.17 2.17 I year last completed 2.17 Immunization rate, child DPT, share of children ages 12–23 months 2.18 Health risks measles, share of children ages 12–23 months 2.18 anemia, prevalence of children under age 5 2.20 Imports pregnant women 2.20 arms 5.7 child malnutrition, prevalence 1.2, 2.20 documents required for 6.9 condom use, male and female 2.21 energy, net, as share of total energy use 3.8 diabetes, prevalence 2.21 goods and services HIV, prevalence 1.3, 2.21 as share of GDP 4.8 low-birthweight babies 2.20 average annual growth 4.9 overweight children, prevalence 2.20 total 4.17 smoking, prevalence, male and female 2.21 information and communications technology goods 5.12 tuberculosis, incidence 1.3, 2.21 lead time 6.9 undernourishment, prevalence 2.20 merchandise annual growth 6.3 Heavily indebted poor countries (HIPCs) by high-income countries, by product 6.4 assistance 1.4 by developing countries, by partner 6.5 completion point 1.4 structure 4.5 decision point 1.4 tariffs 6.4, 6.8 Multilateral Debt Relief Initiative (MDRI) assistance 1.4 total 4.5 value, average annual growth 6.2 HIV volume, average annual growth 6.2 2011 World Development Indicators 425 INDEX OF INDICATORS services net financial flows from 6.13 structure 4.7 use of IMF credit 6.10 total 4.7 travel 4.7, 6.18 Internet See also Trade broadband subscribers 5.12 fixed broadband access tariff 5.12 Income distribution international Internet bandwidth 5.12, 6.1 Gini index 2.9 secure servers 5.12 percentage of 1.2, 2.9 users 5.12 Industry Investment annual growth 4.1 foreign direct, net inflows as share of GDP 4.2 as share of GDP 6.1 employment, male and female 2.3 from DAC members 6.14 freshwater withdrawals for output 3.5 total 6.12 foreign direct, net outflows Inflation—see Prices as share of GDP 6.1 infrastructure, private participation in Informal economy, firms formally registered when operations started 5.2 energy 5.1 telecommunications 5.1 Information and communications technology trade 5.11 transport 5.1 water and sanitation 5.1 Innovation, internationally recognized certification ownership 5.2 See also Gross capital formation; Private financial flows Integration, global economic, indicators 6.1 Iodized salt, consumption of 2.20 Interest payments—see Government, central, debt Interest rates L Labor force deposit 4.15 annual growth 2.2 lending 4.15 armed forces 5.7 real 4.15 children at work 2.6 risk premium on lending 5.5 female 2.2 spread 5.5 nonagricultural 1.5 part-time 1.5 Internally displaced persons 5.8 participation of population ages 15 and older, male and female 2.2 total 2.2 International Bank for Reconstruction and Development (IBRD) See also Employment; Migration; Unemployment IBRD loans and IDA credits 6.10 net financial flows from 6.13 Land area arable—see Agriculture, land; Land use International Development Association (IDA) See also Protected areas; Surface area IBRD loans and IDA credits 6.10 net concessional flows from 6.13 Land use Resource Allocation Index 5.8, 5.9 arable land, as share of total land 3.1 per 100 people 3.1 International Monetary Fund (IMF) area under cereal production 3.2 426 2011 World Development Indicators by type 3.1 clothing 1.4 forest area, as share of total land 3.1 textiles 1.4 irrigated land 3.2 permanent cropland, as share of total land 3.1 Merchandise total area 3.1 exports agricultural raw materials 4.4 Life expectancy at birth from regional trade blocs 6.7 male and female 1.5 from developing countries, by partner 6.5 total 1.6, 2.22 cereals 6.4 chemical products 6.4 Literacy crude petroleum 6.4 adult food 4.4, 6.4 male and female 2.14 footwear 6.4 total 1.6 fuels 4.4 mathematics, PISA mean score 2.14 furniture 6.4 youth, male and female 2.14 information and communications technology goods 5.12 information and communications technology services 5.12 Logistics Performance Index 6.9 iron and steel 6.4 machinery and transport equipment 6.4 M Malnutrition, in children under age 5 1.2, 2.20 manufactures ores and metals ores and nonferrous materials 4.4 4.4 6.4 petroleum products 6.4 Malaria structure 4.4 children sleeping under treated bednets 2.18 textiles 6.4 children with fever receiving antimalarial drugs 2.18 total 4.4 to low-income economies from high-income economies, by product 6.4 Management time dealing with officials 5.2 to middle-income economies from high-income economies, by product 6.4 Manufacturing value, average annual growth 6.2 annual growth 4.1 volume, average annual growth 6.2 as share of GDP 4 .2 within regional trade blocs 6.7 value added imports chemicals 4.3 agricultural raw materials 4.5 food, beverages, and tobacco 4.3 by developing countries, by partner 6.5 machinery and transport equipment 4.3 cereals 6.4 other 4.3 chemicals 6.4 structure 4.3 crude petroleum 6.4 textiles and clothing 4.3 food 4.5 total 4.3 footwear 6.4 See also Merchandise to low-income economies by high-income economies, by product 6.4 to middle-income economies by high-income economies, by product 6.4 Market access to high-income countries fuels 4.5 goods admitted free of tariffs 1.4 furniture 6.4 support to agriculture 1.4 information and communications technology goods 5.12 tariffs on exports from least developed countries iron and steel 6.4 agricultural products 1.4 machinery and transport equipment 6.4 2011 World Development Indicators 427 INDEX OF INDICATORS manufactures 4.5 average tariff imposed by developed countries on exports of ores and metals 4.5 least developed countries 1.4 ores and nonferrous materials 6.4 births attended by skilled health staff 2.19 petroleum products 6.4 carbon dioxide emissions per capita 1.3, 3.8 textiles 6.4 children sleeping under treated bednets 2.18 total 4.5 contraceptive prevalence rate 1.3, 2.19 value, average annual growth 6.2 employment to population ratio 2.4 volume, average annual growth 6.2 enrollment ratio, net, primary 2.12 trade female to male enrollments, primary and secondary 1.2 as share of GDP 6.1 fertility rate, adolescent 2.19 by developing countries, by partner 6.5 goods admitted free of tariffs from least developed countries 1.4 direction 6.3 heavily indebted poor countries (HIPCs) growth 6.3 assistance 1.4 regional trade blocs 6.7 completion point 1.4 decision point 1.4 Metals and minerals Multilateral Debt Relief Initiative (MDRI) assistance commodity prices and price index 6.6 nominal debt service relief committed 1.4 immunization rate, child Methane emissions DPT 2.18 agricultural as share of total 3.9 measles 2.18 industrial as share of total 3.9 income or consumption, national share of poorest quintile 1.2, 2.9 total 3.9 infant mortality rate 2.22 Internet users per 100 people 1.3, 5.12 Migration labor productivity, GDP per person employed 2.4 emigration of people with tertiary education to OECD countries 6.1 literacy rate of 15- to 24-year-olds 2.14 international migrant stock malnutrition, prevalence 1.2, 2.20 as share of total population 6.1 malaria total 6.18 children under age 5 sleeping under insecticide treated bednets 2.18 net 6.1, 6.18 children under age 5 with fever who are treated with See also Refugees; Remittances appropriate antimalarial drugs 2.18 maternal mortality ratio 1.3, 2.19, 5.8 Military national parliament seats held by women 1.5 armed forces personnel Mobile cellular subscriptions per 100 people 5.11 as share of labor force 5.7 official development assistance total 5.7 for basic social services as share of total sector allocable arms transfers ODA commitments 1.4 exports 5.7 net disbursements, as share of donor GNI 1.4, 6.14 imports 5.7 untied commitments 6.15b military expenditure poverty gap 2.7, 2.8 as share of central government expenditure 5.7 pregnant women receiving prenatal care 1.5, 2.19 as share of GDP 5.7, 5.8 share of cohort reaching last grade of primary education 2.13 support to agriculture 1.4 Millennium Development Goals, indicators for telephone lines, fixed, per 100 people 5.11 access to improved sanitation facilities 1.3, 2.18, 3.11, 5.8 tuberculosis access to improved water source 2.18, 3.5, 5.8 case detection rate 2.18 incidence 1.3, 2.21 428 2011 World Development Indicators treatment success rate 2.18 total 3.9 under-five mortality rate 1.2, 2.22, 5.8 undernourishment, prevalence 2.20 Nutrition unmet need for contraception 2.19 anemia, prevalence of vulnerable employment 1.2, 2.4 children ages under 5 2.20 women in wage employment in the nonagricultural sector 1.5 pregnant women 2.20 breastfeeding, exclusive 2.20 Minerals depletion, as share of GNI 4.11 iodized salt consumption 2.20 malnutrition, child 1.2, 2.20 Monetary indicators overweight children, prevalence 2.20 broad money 4.15 undernourishment, prevalence 2.20 claims central government 4.15 vitamin A supplementation 2.20 other claims on domestic economy 4.15 Mortality rate adult, male and female 2.22 O child, male and female 2.22 Official development assistance—see Aid children under age 5 1.2, 2.22, 5.8 crude death rate 2.1 Official flows—see Aid; Financial flows, net infant 2.22 life expectancy at birth maternal lifetime risk of maternal death 2.22 1.3, 2.19, 5.8 2.19 P Passenger cars per 1,000 people 3.13 survival to age 65 2.22 Particulate matter Motor vehicles selected cities 3.14 passenger cars 3.13 urban-population-weighted PM10 3.13 per 1,000 people 3.13 per kilometer of road 3.13 Patent applications filed 5.13 road density 3.13 See also Roads; Traffic Peacebuilding and peacekeeping operations operation name 5.8 MUV G-5 index 6.6 troops, police, and military observers 5.8 N Pension average, as share of per capita income contributors 2.10 Natural resource depletion, as share of GNI 4.10 as share of labor force 2.10 as share of working age population 2.10 Net enrollment—see Education public expenditure on, as share of GDP 2.10 Newspapers, daily 5.12 Permits and licenses, time required to obtain operating license 5.2 Nitrous oxide emissions Physicians—see Health care agricultural as share of total 3.9 industrial as share of total 3.9 Plants, higher 2011 World Development Indicators 429 INDEX OF INDICATORS threatened species 3.4 in selected cities 3.14 in urban agglomerations 3.11 Pollution total 3.11 carbon dioxide See also Migration damage, as share of GNI 4.11 emissions Portfolio—see Equity flows; Private financial flows average annual growth 3.9 intensity 3.8 Ports per unit of GDP 3.8 container traffic in 5.9 per capita 1.3, 3.8 quality of infrastructure 6.9 total 1.6, 3.8 local damage 4.11 Poverty methane emissions international poverty line agricultural, as share of total 3.9 local currency 2.8 from energy processes, as share of total 3.9 population living below total 3.9 $1.25 a day 2.8 nitrogen dioxide, selected cities 3.14 $2 a day 2.8 nitrous oxide emissions national poverty line agricultural, as share of total 3.9 population living below, national, rural, and urban 2.7 energy and industry, as share of total 3.9 poverty gap, national, rural, and urban 2.7 total 3.9 organic water pollutants, emissions Power—see Electricity, production by industry 3.6 per day 3.6 Prenatal care, pregnant women receiving 1.5, 2.19 per worker 3.6 particulate matter concentration Prices selected cities 3.14 commodity prices and price indexes 6.6 total 3.13 consumer, annual growth 4.16 sulfur dioxide, selected cities 3.14 fuel 3.8 GDP implicit deflator, annual growth 4.16 Population net barter terms of trade 6.2 age dependency ratio, young and old 2.1 wholesale, annual growth 4.16 average annual growth 2.1 by age group, as share of total Primary education—see Education 0–14 2.11 5–64 2.1 Private financial flows 65 and older 2.1 debt flows density 1.1, 1.6 bonds 6.12 female, as share of total 1.5 commercial bank and other lending 6.12 rural equity flows annual growth 3.1 foreign direct investment, net inflows 6.12 as share of total 3.1 portfolio equity 6.12 total 1.1, 1.6, 2.1 financing through international capital markets, as share of GDP 6.1 urban from DAC members 6.14 as share of total 3.11 See also Investment average annual growth 3.11 in largest city 3.11 Productivity 430 2011 World Development Indicators agricultural 3.3 Regulation and tax administration labor 2.4 management time dealing with officials 5.2 water 3.5 meeting with tax officials, number of times 5.2 Protected areas Relative prices (PPP)—see Purchasing power parity (PPP) marine as share of total surface area 3.4 Remittances total 3.4 workers’ remittances and compensation of employees terrestrial as share of GDP 6.1 as share of total surface area 3.4 paid 6.18 total 3.4 received 6.18 Protecting investors disclosure index 5.3 Research and development expenditures 5.13 Public sector management and institutions (Country Policy and Institutional researchers 5.13 Assessment) technicians 5.13 efficiency of revenue mobilization 5.9 property rights and rule-based governance 5.9 Reserves, gross international—see Balance of payments public sector management and institutions cluster average 5.9 quality of budgetary and financial management 5.9 Roads quality of public administration 5.9 goods hauled by 5.10 transparency, accountability, and corruption in the public sector 5.9 passengers carried 5.10 paved, as share of total 5.10 Purchasing power parity (PPP) sectoral energy consumption 3.13 conversion factor 4.16 total network 5.10 gross national income 1.1, 1.6 Royalty and license fees R Railways payments receipts 5.13 5.13 goods hauled by 5.10 Rural environment lines, total 5.10 access to improved sanitation facilities 3.11 passengers carried 5.10 access to an improved water source 3.5 population Refugees annual growth 3.1 by country of asylum 5.8, 6.18 as share of total 3.1 by country of origin 5.8, 6.18 internally displaced persons Regional development banks, net financial flows from 5.8 6.13 S S&P/Global Equity Indices 5.4 Regional trade agreements—see Trade blocs, regional Sanitation, access to improved facilities, population with Registering property total 1.3, 2.18, 5.8 number of procedures 5.3 urban and rural 3.11 time to register 5.3 Savings 2011 World Development Indicators 431 INDEX OF INDICATORS adjusted net 4.11 Stock markets gross, as share of GDP 4.8 listed domestic companies 5.4 gross, as share of GNI 4.11 market capitalization as share of GDP 5.4 Schooling—see Education total 5.4 market liquidity 5.4 Science and technology S&P/Global Equity Indices 5.4 scientific and technical journal articles 5.13 turnover ratio 5.4 See also Research and development Steel products, commodity prices and price index 6.6 Secondary education—see Education Structural policies (Country Policy and Institutional Assessment) Services business regulating environment 5.9 employment, male and female 2.3 financial sector 5.9 exports structural policies cluster average 5.9 computer, information and communications, and other commercial trade 5.9 services 4.6 insurance and financial services 4.6 Sulfur dioxide emissions—see Pollution structure 4.6 total commercial 4.6 Surface area 1.1, 1.6 transport 4.6 See also Land use travel 4.6 imports Survival to age 65, male and female 2.22 computer, information and communications, and other commercial services 4.7 Suspended particulate matter—see Pollution insurance and financial services 4.7 structure total commercial transport 4.7 4.7 4.7 T Tariffs travel 4.7 all products trade, as share of GDP 6.1 binding coverage 6.8 value added simple mean bound rate 6.8 annual growth 4.1 simple mean tariff 6.8 as share of GDP 4.2 weighted mean tariff 6.8 applied rates on imports from low- and middle-income economies 6.4 Smoking, prevalence, male and female 2.21 manufactured products simple mean tariff 6.8 Social inclusion and equity policies (Country Policy and Institutional Assessment) weighted mean tariff 6.8 building human resources 5.9 on exports of least developed countries 1.4 equity of public resource use 5.9 primary products gender equity 5.9 simple mean tariff 6.8 policy and institutions for environmental sustainability 5.9 weighted mean tariff 6.8 social inclusion and equity cluster average 5.9 share of tariff lines with international peaks 6.8 social protection and labor 5.9 share of tariff lines with specific rates 6.9 Starting a business—see Business environment Taxes and tax policies 432 2011 World Development Indicators business taxes tourists average number of times firms meet with tax officials 5.2 inbound 6.19 labor tax, as share of commercial profits 5.6 outbound 6.19 number of payments 5.6 other taxes, as share of commercial profits 5.6 Trade profit tax, as share of commercial profits 5.6 arms 5.7 time to prepare, file, and pay 5.6 barriers 6.8 total tax rate, as share of commercial profits 5.6 facilitation goods and services taxes, domestic 4.14 burden of customs procedures 6.9 income, profit, and capital gains taxes 4.14 documents international trade taxes 4.14 to export 6.9 other taxes 4.14 to import 6.9 social contributions 4.14 freight costs to the United States 6.9 tax revenue collected by central government, as share of GDP 5.6 lead time to export 6.9 Technology—see Computers; Exports, high-technology; Internet; Research and to import 6.9 development; Science and technology liner shipping connectivity index 6.9 logistics performance index 6.9 Telephones quality of port infrastructure 6.9 fixed line information and communications technology 5.12 per 100 people 5.11 merchandise residential tariff 5.11 as share of GDP 6.1 international voice traffic 5.11, 6.1 direction of, by developing countries 6.5 mobile cellular direction of, by region 6.3 per 100 people 1.3, 5.11 high-income economy with low- and middle-income economies, population covered 5.11 by product 6.4 prepaid tariff 5.11 nominal growth, by region 6.3 mobile cellular and fixed-line subscribers per employee 5.11 regional trading blocs 6.7 total revenue 5.11 structure 4.4, 4.5 total 4.4, 4.5 Television, households with 5.12 services as share of GDP 6.1 Terms of trade index, net barter 6.2 structure 4.6, 4.7 total 4.6, 4.7 Tertiary education—see Education See also Balance of payments; Exports; Imports; Manufacturing; Merchandise; Terms of trade; Trade blocs Threatened species—see Animal species; Biological diversity; Plants, higher Trade blocs, regional Tourism, international exports within bloc 6.7 tourism expenditure total exports, by bloc 6.7 inbound type of agreement 6.7 as share of exports 6.19 year of creation 6.7 total 6.19 year of entry into force of the most recent agreement 6.7 outbound as share of imports 6.19 Trademark applications filed 5.13 total 6.19 Trade policies—see Tariffs 2011 World Development Indicators 433 INDEX OF INDICATORS Traffic—see Fuels; Motor vehicles; Roads sulfur dioxide 3.14 housing conditions Transport—see Air transport; Ports; Railways; Roads durable dwelling units 3.12 home ownership 3.12 Travel—see Tourism, international household size 3.12 multiunit dwellings 3.12 Treaties, participation in overcrowding 3.12 biological diversity 3.15 vacancy rate 3.12 CFC control 3.15 population climate change 3.15 as share of total 3.11 Convention on International Trade on Endangered Species (CITES) 3.15 average annual growth 3.11 Convention to Combat Desertification (CCD) 3.15 in largest city 3.11 Kyoto Protocol 3.15 in selected cities 3.14 Law of the Sea 3.15 in urban agglomerations 3.11 Ozone layer 3.15 total 3.11 Stockholm Convention on Persistent Organic Pollutants 3.15 See also Pollution; Population; Sanitation; Water Tuberculosis case detection rate incidence 2.18 1.3, 2.21 V Value added treatment rate 2.18 as share of GDP in agriculture 4.2 U UN agencies, net official financial flows from 6.13 in industry in manufacturing in services 4.2 4.2 4.1, 4.2 per worker Undernourishment, prevalence of 2.20 in agriculture 3.3 Unemployment incidence of long-term, total, male, and female by level of educational attainment, primary, secondary, tertiary 2.5 2.5 W Water total, male, and female 2.5 access to improved source of, population with youth, male, and female 1.3, 2.10 total 2.18, 5.8 urban and rural 3.5 UNICEF, net official financial flows from 6.13 freshwater annual withdrawals UNTA, net official financial flows from 6.13 as share of internal resources 3.5 for agriculture 3.5 UNRWA for domestic use 3.5 net official financial flows from 6.13 for industry 3.5 total 3.5 Urban environment internal renewable resources access to improved sanitation facilities 3.11 flows 3.5 access to an improved water source 3.5 per capita 3.5 emissions, selected cities pollution—see Pollution, organic water pollutants nitrogen dioxide 3.14 particulate matter 3.14 434 2011 World Development Indicators productivity 3.5 Women in development female-headed households 2.10 female population, as share of total 1.5 life expectancy at birth 1.5 pregnant women receiving prenatal care 1.5, 2.19 teenage mothers 1.5 unpaid family workers 1.5 vulnerable employment 2.4 women in nonagricultural sector 1.5 women in parliaments 1.5 Workforce, firms offering formal training 5.2 World Bank, net financial flows from 6.13 See also International Bank for Reconstruction and Development; International Development Association 2011 World Development Indicators 435 REGION MAP The world by region East Asia and Pacific Colombia Cape Verde Japan American Samoa Costa Rica Central African Republic Korea, Rep. Cambodia Cuba Chad Luxembourg * China Dominica Comoros Netherlands * Fiji Dominican Republic Congo, Dem. Rep. New Zealand Indonesia Ecuador Congo, Rep. Norway Kiribati El Salvador Côte d'Ivoire Poland Korea, Dem. Rep. Grenada Eritrea Portugal * Lao PDR Guatemala Ethiopia Slovak Republic * Malaysia Guyana Gabon Slovenia * Marshall Islands Haiti Gambia, The Spain * Micronesia, Fed. Sts. Honduras Ghana Sweden Mongolia Jamaica Guinea Switzerland Myanmar Mexico Guinea-Bissau United Kingdom Palau Nicaragua Kenya United States Papua New Guinea Panama Lesotho Philippines Paraguay Liberia Other high income Samoa Peru Madagascar Andorra Solomon Islands St. Kitts and Nevis Malawi Aruba Thailand St. Lucia Mali Bahamas, The Timor-Leste St. Vincent and the Mauritania Bahrain Tonga Grenadines Mauritius Barbados Tuvalu Suriname Mayotte Bermuda Vanuatu Uruguay Mozambique Brunei Darussalam Vietnam Venezuela, RB Namibia Cayman Islands Niger Channel Islands Europe and Middle East and Nigeria Croatia Central Asia North Africa Rwanda Cyprus * Albania Algeria São Tomé and Principe Equatorial Guinea Armenia Djibouti Senegal Faeroe Islands Azerbaijan Egypt, Arab Rep. Seychelles French Polynesia Belarus Iran, Islamic Rep. Sierra Leone Gibraltar Bosnia and Herzegovina Iraq Somalia Greenland Bulgaria Jordan South Africa Guam Georgia Lebanon Sudan Hong Kong SAR, China Kazakhstan Libya Swaziland Isle of Man Kosovo Morocco Tanzania Kuwait Kyrgyz Republic Syrian Arab Republic Togo Latvia Lithuania Tunisia Uganda Liechtenstein Macedonia, FYR West Bank and Gaza Zambia Macao SAR, China Moldova Yemen, Rep. Zimbabwe Malta * Montenegro Monaco Romania South Asia High-income OECD Netherlands Antilles Russian Federation Afghanistan Australia New Caledonia Serbia Bangladesh Austria * Northern Mariana Islands Tajikistan Bhutan Belgium * Oman Turkey India Canada Puerto Rico Turkmenistan Maldives Czech Republic Qatar Ukraine Nepal Denmark San Marino Uzbekistan Pakistan Estonia * Saudi Arabia Sri Lanka Finland * Singapore Latin America and France * Taiwan, China the Caribbean Sub-Saharan Africa Germany * Turks and Caicos Islands Antigua and Barbuda Angola Greece * Trinidad and Tobago Argentina Benin Hungary United Arab Emirates Belize Botswana Iceland Virgin Islands (U.S.) Bolivia Burkina Faso Ireland * Brazil Burundi Israel * Member of the Euro area Chile Cameroon Italy * The World Bank 1818 H Street N.W. ISBN 978-0-8213-8709-2 Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: data.worldbank.org SKU 18709 Email: data@worldbank.org The World Development Indicators • Includes more than 800 indicators for 155 economies • Provides definitions, sources, and other information about the data • Organizes the data into six thematic areas WORLD VIEW PEOPLE ENVIRONMENT Living standards Natural resources and development Gender, health, and and environmental progress employment changes ECONOMY STATES & MARKETS GLOBAL LINKS New opportunities Elements of a good Evidence on for growth investment climate globalization Saved: 91 trees 29 million Btu of total energy 8,609 pounds of net greenhouse gases 41,465 gallons of waste water 2,518 pounds of solid waste