68172 WORLD DEVELOPMENT INDICATORS 12 INCOME MAP The world by income Low income Kosovo Jordan Greenland Afghanistan Lao PDR Kazakhstan Guam Bangladesh Lesotho Latvia Hong Kong SAR, China Benin Marshall Islands Lebanon Hungary Burkina Faso Mauritania Libya Iceland Burundi Micronesia, Fed. Sts. Lithuania Ireland Cambodia Moldova Macedonia, FYR Isle of Man Central African Republic Mongolia Malaysia Israel Chad Morocco Maldives Italy Comoros Nicaragua Mauritius Japan Congo, Dem. Rep. Nigeria Mayotte Korea, Rep. Eritrea Pakistan Mexico Kuwait Ethiopia Papua New Guinea Montenegro Liechtenstein Gambia, The Paraguay Namibia Luxembourg Guinea Philippines Palau Macao SAR, China Guinea-Bissau Samoa Panama Malta Haiti São Tomé and Príncipe Peru Monaco Kenya Senegal Romania Netherlands Korea, Dem. Rep. Solomon Islands Russian Federation New Caledonia Kyrgyz Republic Sri Lanka Serbia New Zealand Liberia South Sudan Seychelles Northern Mariana Islands Madagascar Sudan South Africa Norway Malawi Swaziland St. Kitts and Nevis Oman Mali Syrian Arab Republic St. Lucia Poland Mozambique Timor-Leste St. Vincent & Grenadines Portugal Myanmar Tonga Suriname Puerto Rico Nepal Turkmenistan Thailand Qatar Niger Tuvalu Tunisia San Marino Rwanda Ukraine Turkey Saudi Arabia Sierra Leone Uzbekistan Uruguay Singapore Somalia Vanuatu Venezuela, RB Sint Maarten Tajikistan Vietnam Slovak Republic Tanzania West Bank and Gaza High income Slovenia Togo Yemen, Rep. Andorra Spain Uganda Zambia Aruba St. Martin Zimbabwe Australia Sweden Upper middle income Austria Switzerland Lower middle income Albania Bahamas, The Trinidad and Tobago Angola Algeria Bahrain Turks and Caicos Islands Armenia American Samoa Barbados United Arab Emirates Belize Antigua and Barbuda Belgium United Kingdom Bhutan Argentina Bermuda United States Bolivia Azerbaijan Brunei Darussalam Virgin Islands (U.S.) Cameroon Belarus Canada Cape Verde Bosnia and Herzegovina Cayman Islands Congo, Rep. Botswana Channel Islands Côte d'Ivoire Brazil Croatia Djibouti Bulgaria Curaçao Egypt, Arab Rep. Chile Cyprus El Salvador China Czech Republic Fiji Colombia Denmark Georgia Costa Rica Equatorial Guinea Ghana Cuba Estonia Guatemala Dominica Faeroe Islands Guyana Dominican Republic Finland Honduras Ecuador France India Gabon French Polynesia Indonesia Grenada Germany Iraq Iran, Islamic Rep. Gibraltar Kiribati Jamaica Greece The world by income Low ($1,005 or less) Classi�ed according to Lower middle ($1,006–$3,975) World Bank estimates of 2010 GNI per capita Upper middle ($3,976–$12,275) High ($12,276 or more) No data Greenland (Den) Iceland Faeroe Norway Islands (Den) Sweden Finland Russian Federation The Netherlands Estonia Isle of Man (UK) Canada Denmark Russian Latvia Fed. Lithuania United Ireland Kingdom Germany Poland Belarus Channel Islands (UK) Belgium Ukraine Luxembourg Moldova Kazakhstan Mongolia Liechtenstein France Italy Romania Switzerland Bulgaria Georgia Uzbekistan Kyrgyz Andorra Armenia Azer- Rep. Dem.People’s United States Spain baijan Turkmenistan Rep.of Korea Portugal Turkey Tajikistan Monaco Greece Japan Cyprus Syrian Rep.of Gibraltar (UK) Arab Islamic Rep. Korea Bermuda Malta Lebanon China Tunisia Rep. of Iran Afghanistan (UK) Israel Iraq Morocco Kuwait West Bank and Gaza Jordan Algeria Bahrain Pakistan Bhutan Libya Arab Rep. Qatar Nepal The Bahamas Former of Egypt Spanish Saudi Sahara Arabia Bangladesh Cayman Is.(UK) United Arab Turks and Caicos Is. (UK) Emirates India Mexico Cuba Myanmar Mauritania Oman Lao Haiti Cape Verde P.D.R. Mali N. Mariana Islands (US) Belize Jamaica Niger Chad Eritrea Rep. of Yemen Thailand Guatemala Honduras Senegal Sudan The Gambia Vietnam Guam (US) El Salvador Nicaragua Burkina Cambodia Guinea-Bissau Faso Djibouti Philippines Guinea Federated States of Micronesia Costa Rica Benin Marshall Islands Panama Nigeria Central Ethiopia Sri R.B. de Guyana Sierra Leone Côte Ghana Lanka Venezuela d’Ivoire African South Suriname Republic Sudan Brunei Darussalam Liberia Palau French Guiana (Fr) Cameroon Malaysia Colombia Togo Somalia Equatorial Guinea Maldives Uganda São Tomé and Príncipe Kenya Nauru Kiribati Congo Singapore Ecuador Gabon Rwanda Kiribati Dem.Rep.of Burundi Seychelles Congo Solomon Tanzania Papua New Guinea Islands Comoros Indonesia Tuvalu Peru Brazil Timor-Leste Samoa French Polynesia (Fr) Angola Malawi Zambia Mayotte American (Fr) Vanuatu Fiji Samoa (US) Bolivia Mozambique Fiji Zimbabwe Madagascar Tonga Mauritius Namibia Botswana New Paraguay Réunion (Fr) Caledonia Australia (Fr) Swaziland Dominican St. Martin (Fr) Germany South Republic Puerto Poland Lesotho Rico (US) St. Maarten (Neth) Africa Czech Republic Ukraine Uruguay Slovak Republic Antigua and Barbuda Chile U.S. Virgin Argentina Islands (US) Guadeloupe (Fr) Austria St. Kitts Hungary New and Nevis Zealand Dominica Slovenia Romania Croatia Martinique (Fr) Bosnia and St. Lucia Herzegovina Serbia Aruba (Neth) St. Vincent and San Curaçao (Neth) the Grenadines Barbados Marino Kosovo Bulgaria Grenada Italy Montenegro FYR Macedonia Trinidad Vatican Albania and Tobago City Greece R.B. de Venezuela Antarctica IBRD 39125 MARCH 2012 Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London 2012 WORLD DEVELOPMENT INDICATORS Copyright 2012 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 2012 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: World Bank photo library, except page 282, David Cieslikowski/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 978-0-8213-8985-0 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 2012 on recycled paper with 50 percent postconsumer 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: 64 trees 26 million Btu of total energy 6,503 pounds of net greenhouse gases 29,321 gallons of waste water 1,859 pounds of solid waste 2012 WORLD DEVELOPMENT INDICATORS PREFACE World Development Indicators 2012 is a compilation of relevant, high-quality, and internationally comparable statistics about development and the quality of people’s lives. Organized around six themes—world view, people, the environment, the economy, states and markets, and global links—it aims to put data into the hands of policy makers, development specialists, students, and the public. We encourage and applaud the use of the data presented here to help reduce poverty and to solve the world’s most pressing development challenges. The full dataset used to produce World Development Indicators contains more than 1,000 indicators for 216 econo- mies, with many time series extending back to 1960. Highly visual, interactive, and multilingual presentations of the data are available at the popular website http://data.worldbank.org and through the DataFinder application for mobile devices. And, as a major part of the World Bank’s Open Data Initiative, the data are freely available for use and reuse under an open license. A companion printed volume, The Little Data Book 2012, presents a selection of indicators for each economy, and the biennial Statistics for Small States presents data for less-populated developing countries. This 16th edition of World Development Indicators relies heavily on statistics produced by national authorities and agencies. Since the first edition in 1997, there has been a substantial increase in the availability and quality of the data, thanks to improvements in statistical capacity in many countries. More remains to be done: the capacity to use statistical data remains weak; demand is growing for greater disaggregation of indicators (for instance by sex, age, or geography); and data in some key areas, such as agriculture, are often missing or outdated. A new global statistical action plan (www.paris21.org/busan-action-plan), endorsed in November 2011 at the highest political levels at the Fourth High Level Forum on Aid Effectiveness in Busan, Republic of Korea, provides an important framework to address remaining challenges, to integrate statistics into decision making, to promote open access to data and improve their use, and to increase resources for statistical systems. World Development Indicators is possible only through the excellent collaboration of many partners who provide the data for this collection, and I would like to thank them all: the United Nations family, the International Monetary Fund, the International Telecommunication Union, the Organisation for Economic Co-operation and Development, the sta- tistical offices of more than 200 economies, and countless others whose support and advice have made this unique product possible. As always, we welcome your ideas for making the data in World Development Indicators useful and relevant for improv- ing the lives of people around the world. Shaida Badiee Director Development Economics Data Group 2012 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, Azita Amjadi, Maja Bresslauer, David Cieslikowski, Liu Cui, Mahyar Eshragh-Tabary, Shota Hatakeyama, Masako Hiraga, Wendy Ven-dee Huang, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim  Levent, Hiroko  Maeda, Johan Mistiaen, Maurice Nsabimana, Sulekha Patel, Beatriz Prieto-Oramas, William Prince, Premi Rathan Raj, Evis Rucaj, Emi Suzuki, Eric Swanson, Jomo Tariku, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. World Develop- ment Indicators electronic products were prepared by a team led by Reza Farivari and comprising Ramvel Chandrasek- aran, Ying  Chi, Jean-Pierre  Djomalieu, Ramgopal Erabelly, Federico Escaler, Shelley Fu, Gytis Kanchas, Ugendran Makhachkala, Vilas Mandlekar, Nacer  Megherbi, Shanmugam Natarajan, Parastoo Oloumi, Atsushi Shimo, Maryna Taran, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the direction of Shaida Badiee. Valuable advice was provided by Zia M. Qureshi and David Rosenblatt. 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—Financial and Private Sector Development, Human Development, Poverty Reduction and Economic Management, and Sustainable Development—and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial 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 provided overall design direction, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott and assisted by Rob Elson. Elaine Wilson created the cover and graphics and typeset the book. Joseph Caponio provided production assistance. Peter Grundy, of Peter Grundy Art & Design, designed the report. Staff from External Affairs oversaw printing and dissemination of the book. 2012 World Development Indicators vii TABLE OF CONTENTS FRONT Preface v Acknowledgments vii Partners xii Users guide xxii 1. WORLD VIEW Introduction 1 1t Preventing childhood diseases 9 Tables 1u For some, better than expected improvements 9 1v Maternal mortality rates have been falling but large regional 1.1 Size of the economy 20 differences persist 10 1.2 Millennium Development Goals: eradicating poverty and saving lives 24 1w The 12 countries with highest lifetime risk of maternal death 10 1x Progress in reducing maternal mortality 11 1.3 Millennium Development Goals: protecting our common environment 28 1y Planning for motherhood 11 1z Fewer young women giving birth 11 1.4 Millennium Development Goals: overcoming obstacles 32 1aa Help for mothers 11 1.5 Women in development 34 1bb Bringing HIV/AIDS under control 12 1.6 Key indicators for other economies 38 1cc Millions of people still afflicted with HIV/AIDS 12 Text �gures, tables, and boxes 1dd Progress toward reversing the HIV epidemic 13 1a Poverty rates fell sharply in the new millennium 2 1ee Turning the tide of tuberculosis 13 1b Fewer people living in extreme poverty 2 1ff Protecting children from malaria 13 1c Progress toward poverty reduction 3 1gg Carbon dioxide emissions continue to rise 14 1d Progress toward reducing undernourishment 3 1hh Forest losses and gains 14 1e More and less income equality 3 1ii Progress toward improved sanitation 15 1f Many children remain malnourished 3 1jj Progress toward improved water sources 15 1g The last step toward education for all 4 1kk Many still lack access to sanitation 15 1h 64 million children out of school 4 1ll Water demand strains supplies 15 1i Progress toward education for all 5 1mm Most donors have maintained their aid levels 16 1j The missing enrollments 5 1nn But their domestic subsidies to agricultural are greater 16 1k How much schooling 5 1oo Developing countries have easier access to Organisation 1l Increasing participation by girls at all levels of education 6 for Economic Co-operation and Development markets 17 1m Progress toward gender equality in education 7 1pp Cellular phones are connecting developing countries 17 1n Women have become a larger part of the workforce 7 1qq Debt service burdens have been falling 17 1o More women decisionmakers 7 1rr A more connected world 17 1p A slim lead for girls 7 1.2a Location of indicators for Millennium Development Goals 1–4 27 1q Still far to go in reducing under-five mortality 8 1.3a Location of indicators for Millennium Development Goals 5–7 31 1r Most deaths occur in the first year of life 8 1.4a Location of indicators for Millennium Development Goal 8 33 1s Progress toward reducing child mortality 9 viii 2012 World Development Indicators 2. PEOPLE 3. ENVIRONMENT Introduction 41 Introduction 137 Tables Tables 2.1 Population dynamics 42 3.1 Rural population and land use 138 2.2 Labor force structure 46 3.2 Agricultural inputs 142 2.3 Employment by economic activity 50 3.3 Agricultural output and productivity 146 2.4 Decent work and productive employment 54 3.4 Deforestation and biodiversity 150 2.5 Unemployment 58 3.5 Freshwater 154 2.6 Children at work 62 3.6 Water pollution 158 2.7 Poverty rates at national poverty lines 66 3.7 Energy production and use 162 2.8 Poverty rates at international poverty lines 72 3.8 Electricity production, sources, and access 166 2.9 Distribution of income or consumption 74 3.9 Energy dependency and efficiency and carbon dioxide 2.10 Assessing vulnerability and security 78 emissions 170 2.11 Education inputs 82 3.10 Trends in greenhouse gas emissions 174 2.12 Participation in education 86 3.11 Carbon dioxide emissions by sector 178 2.13 Education efficiency 90 3.12 Climate variability, exposure to impact, and resilience 182 2.14 Education completion and outcomes 94 3.13 Urbanization 186 2.15 Education gaps by income and gender 98 3.14 Urban housing conditions 190 2.16 Health systems 100 3.15 Traffic and congestion 194 2.17 Health information 104 3.16 Air pollution 198 2.18 Disease prevention coverage and quality 108 3.17 Government commitment 200 2.19 Reproductive health 112 3.18 Contribution of natural resources to gross domestic product 204 2.20 Nutrition and growth 116 Text �gures, tables, and boxes 2.21 Nutrition intake and supplements 120 3.1a What is rural? Urban? 141 2.22 Health risk factors and future challenges 124 2.23 Mortality 128 2.24 Health gaps by income 132 Text �gures, tables, and boxes 2.8a While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2 a day has increased 71 2.8b Poverty rates are falling in all developing regions 71 2.8c Regional poverty estimates 72 2012 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 209 Introduction 283 Tables Tables 4.a Recent economic performance 210 5.1 Private sector in the economy 284 4.1 Growth of output 214 5.2 Business environment: enterprise surveys 288 4.2 Structure of output 218 5.3 Business environment: Doing Business indicators 292 4.3 Structure of manufacturing 222 5.4 Stock markets 296 4.4 Structure of merchandise exports 226 5.5 Financial access, stability, and efficiency 300 4.5 Structure of merchandise imports 230 5.6 Tax policies 304 4.6 Structure of service exports 234 5.7 Military expenditures and arms transfers 308 4.7 Structure of service imports 238 5.8 Fragile situations 312 4.8 Structure of demand 242 5.9 Public policies and institutions 316 4.9 Growth of consumption and investment 246 5.10 Transport services 320 4.10 Toward a broader measure of national income 250 5.11 Power and communications 324 4.11 Toward a broader measure of savings 254 5.12 The information society 328 4.12 Central government finances 258 5.13 Science and technology 332 4.13 Central government expenses 262 4.14 Central government revenues 266 4.15 Monetary indicators 270 4.16 Exchange rates and prices 274 4.17 Balance of payments current account 278 x 2012 World Development Indicators 6. GLOBAL LINKS BACK Introduction 337 Primary data documentation 391 Statistical methods 402 Tables Credits 404 6.1 Growth of merchandise trade 338 Bibliography 406 6.2 Direction and growth of merchandise trade 342 Index of indicators 414 6.3 High-income economy trade with low- and middle-income economies 344 6.4 Direction of trade of developing economies 346 6.5 Primary commodity prices 349 6.6 Regional trade blocs 352 6.7 Tariff barriers 354 6.8 Trade facilitation 358 6.9 External debt 362 6.10 Global private financial flows 366 6.11 Net official financial flows 370 6.12 Aid dependency 374 6.13 Distribution of net aid by Development Assistance Committee members 378 6.14 Movement of people across borders 382 6.15 Travel and tourism 386 Text �gures, tables, and boxes 6.6a Global Preferential Trade Agreement Database 353 6.13a Official development assistance from non-DAC donors, 2006–10 381 2012 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, 2012. 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.ornl.gov. Centre for Research on the Epidemiology of Disasters Since 1988 the World Health Organization Collaborating Centre for Research on the Epidemiology of Disas- ters has maintained the Emergency Events Database, which was created with support from the Belgian government. The main objective of the database is to serve the purposes of humanitarian action at the national and international levels. It aims to rationalize decisionmaking for disaster preparedness and provide an objective base for vulnerability assessment and priority setting. The database contains essential core data—compiled from various sources, including UN agencies, nongovernmental organizations, insurance companies, research institutes, and press agencies—on the occurrence and effects of more than 18,000 mass disasters since 1900. For more information, see www.emdat.be. xii 2012 World Development Indicators 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. 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 con- tributes 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 Founded in 1974, the International Energy Agency’s (IEA) mandate is to facilitate cooperation among member countries in order to increase energy efficiency, promote use of clean energy and technology, and diversify energy sources while protecting the environment. The IEA publishes annual and quarterly statistical pub- lications covering both Organisation for Economic Co-operation and Development (OECD) and non-OECD countries’ data on oil, gas, coal, electricity, and renewable sources of energy; energy supply and consump- tion; and energy prices and taxes. The IEA also analyzes all aspects of sustainable development globally and provides policy recommendations. For more information, see www.iea.org. 2012 World Development Indicators xiii PARTNERS 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 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 2012 World Development Indicators The Of�ce of U.S. Foreign Disaster Assistance On November 3, 1961, U.S. President John F. Kennedy established the U.S. Agency for International Develop- ment (USAID), the first U.S. foreign assistance organization whose primary emphasis was long-range economic and social development assistance to foreign countries. The Office of U.S. Foreign Disaster Assistance is the office within USAID responsible for providing nonfood humanitarian assistance in response to international crises and disasters. The USAID administrator is designated as the president’s special coordinator for inter- national disaster assistance, which the Office of U.S. Foreign Disaster Assistance assists in coordinating. For more information see www.globalcorps.com/ofda.html and www.usaid.gov/our_work/humanitarian_ assistance/disaster_assistance. 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 understand- ing of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI’s main publication, SIPRI Yearbook, is an authoritative 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. 2012 World Development Indicators xv PARTNERS United Nations The United Nations currently has 193 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. 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 governance, to monitoring human rights, to assisting in security sector reform, to disarming, demobilizing, and reintegrating former combatants. For more information, see www.un.org/en/peacekeeping. xvi 2012 World Development Indicators United Nations Educational, Scienti�c, 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 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 International Strategy for Disaster Reduction Created in December 1999 as the successor to the International Decade for Natural Disaster Reduction, the man- date of the secretariat of the United Nations International Strategy for Disaster Reduction is to serve as the focal point in the UN system for coordination of disaster reduction and to ensure synergies among disaster relief activities. For more information, see www.unisdr.org. United Nations Of�ce 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 assisting 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. United Nations Of�ce of the High Commissioner for Refugees The United Nations Office of the High Commissioner for Refugees (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. 2012 World Development Indicators xvii PARTNERS 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. 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 attain- ment 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 promoting 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 2012 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 Center for International Earth Science Information Network The Center for International Earth Science Information Network, a center within the Earth Institute at Colum- bia University, works at the intersection of the social, natural, and information sciences and specializes in online data and information management, spatial data integration and training, and interdisciplinary research related to human interactions in the environment. For more information, see www.ciesin.org. 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. 2012 World Development Indicators xix PARTNERS 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. 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. PwC PwC provides industry-focused services in the fields of assurance, tax, human resources, transactions, performance improvement, and crisis management services to help address client and stakeholder 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 2012 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. 2012 World Development Indicators xxi USERS GUIDE Tables are totals (designated by a t if the aggregates include cross-country and intertemporal comparisons involve The tables are numbered by section and display gap-filled estimates for missing data and by an s, complex technical and conceptual problems that can- the identifying icon of the section. Countries and for simple totals, where they do not), median values not be resolved unequivocally. Data coverage may economies are listed alphabetically (except for Hong (m), weighted averages (w), or simple averages (u). not be complete because of special circumstances Kong SAR, China, which appears after China). Data Gap filling of amounts not allocated to countries may affecting the collection and reporting of data, such are shown for 158 economies with a population of result in discrepancies between subgroup aggregates as problems stemming from conflicts. more than 1 million, as well as for Taiwan, China, in and overall totals. For further discussion of aggrega- For these reasons, although data are drawn from selected tables. Table 1.6 presents selected indi- tion methods, see Statistical methods. the sources thought to be most authoritative, they cators for 58 other economies—small economies should be construed only as indicating trends and with a population between 30,000 and 1 million and Aggregate measures for regions characterizing major differences among economies smaller economies if they are members of the Inter- The aggregate measures for regions cover only low- rather than as offering precise quantitative mea- national Bank for Reconstruction and Development and middle-income economies, including econo- sures of those differences. Discrepancies in data or, as it is commonly known, the World Bank. Data for mies with populations of less than 1 million listed presented in different editions of World Development these economies are included on the World Develop- in table 1.6. Indicators reflect updates by countries as well as revi- ment Indicators CD-ROM and the World Bank’s Open The country composition of regions is based on sions to historical series and changes in methodol- Data website (http://data.worldbank.org). The term the World Bank’s analytical regions and may differ ogy. Thus readers are advised not to compare data country, used interchangeably with economy, does from common geographic usage. For regional clas- series across editions of World Development Indica- not imply political independence but refers to any sifications, see the map on the inside back cover and tors or across World Bank publications. Consistent territory for which authorities report separate social the list on the back cover flap. For further discussion time series data for 1960–2010 are available on or economic statistics. When available, aggregate of aggregation methods, see Statistical methods. the World Development Indicators CD-ROM and the measures for income and regional groups appear at World Bank’s Open Data website (http://data.world- the end of each table. Statistics bank.org). Indicators are shown for the most recent year Data are shown for economies as they were con- Except where otherwise noted, growth rates are or period for which data are available and, in most stituted in 2010, and historical data are revised to in real terms. (See Statistical methods for information tables, for an earlier year or period (usually 1990 or reflect current political arrangements. Exceptions are on the methods used to calculate growth rates.) Data 2000 in this edition). Time series data for all 216 noted throughout the tables. for some economic indicators for some economies economies are available on the World Development Additional information about the data is provided are presented in fiscal years rather than calendar Indicators CD-ROM and the World Bank’s Open Data in Primary data documentation, which summarizes years; see Primary data documentation. The methods website (http://data.worldbank.org). national and international efforts to improve basic used for converting national currencies are described Known deviations from standard definitions or data collection and gives country-level information in Statistical methods. breaks in comparability over time or across countries 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 Ser- The aggregate measures for income groups include the data, but full comparability cannot be assured, bia and Montenegro on June 3, 2006. When 216 economies (the economies listed in the main and care must be taken in interpreting the indicators. available, data for each country are shown tables plus those in table 1.6) whenever data are Many factors affect data availability, comparability, separately. However, some indicators for Serbia 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 for most indicators from 1999 xxii 2012 World Development Indicators onward for Serbia exclude data for Kosovo, which Low-income economies are those with a GNI per • Data for years that are more than three years in 1999 became a territory under international capita of $1,005 or less in 2010. Middle-income from the range shown are footnoted. administration pursuant to UN Security Council economies are those with a GNI per capita of $1,006– Resolution  1244  (1999); any exceptions are $12,275. Lower middle-income and upper middle- The cutoff date for data is February 1, 2012. noted. Kosovo became a World Bank member income economies are separated at a GNI per capita on June 29, 2009, and its data are shown in the of $3,976. High-income economies are those with a tables when available. GNI per capita of $12,276 or more. The 17 participat- • Netherlands Antilles, for which data were listed in ing member countries of the euro area are presented previous editions, ceased to exist on October 10, as a subgroup under high-income economies. 2010. Data for Curaçao and Sint Maarten, which became countries within the Kingdom of the Symbols Netherlands, are now listed separately. Data for .. Bonaire, Saba, and St. Eustatius, which became means that data are not available or that aggregates special municipalities of the Netherlands, are cannot be calculated because of missing data in the included in data for the Netherlands. years shown. • South Sudan declared its independence on July  9, 2011. When available, data are shown 0 or 0.0 separately for South Sudan; data for Sudan means zero or small enough that the number would include South Sudan unless otherwise noted. round to zero at the displayed number of decimal places. Classi�cation of economies For operational and analytical purposes the World / Bank’s main criterion for classifying economies is in dates, as in 2009/10, means that the period of gross national income (GNI) per capita (calculated time, usually 12 months, straddles two calendar by the World Bank Atlas method). Every economy years and refers to a crop year, a survey year, or a is classified as low income, middle income (subdi- fiscal year. vided into lower middle and upper middle), or high income. For income classifications see the map on $ the inside front cover and the list on the front cover means current U.S. dollars unless otherwise noted. 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 more than. that all economies in the group are experiencing similar development or that other economies have < reached a preferred or final stage of development. means less than. Note that classification by income does not neces- sarily reflect development status. Because GNI per Data presentation conventions capita changes over time, the country composition • A blank means not applicable or, for an aggre- of income groups may change from one edition of gate, not analytically meaningful. World Development Indicators to the next. Once the • A billion is 1,000 million. classification is fixed for an edition, based on GNI • A trillion is 1,000 billion. per capita in the most recent year for which data are • Figures in italics refer to years or periods other available (2010 in this edition), all historical data than those specified or to growth rates calculated presented are based on the same country grouping. for less than the full period specified. 2012 World Development Indicators xxiii WORLD VIEW W 1 e now have data to monitor the first 10 capacity and information systems, including years of the Millennium Development those for managing aid� (OECD 2008a). More Goals. Results are starting to appear, recently, the 2009 Dakar Declaration on the and we have a better view of where we will be Development of Statistics reaffirmed that “con- in 2015. We will not achieve all the targets certed and co-ordinated actions are required we set for ourselves, but progress measured to make more effective use of statistical data against 1990 benchmarks accelerated in the to support poverty reduction policies and pro- last decade, lifting millions of people out of pov- grams and to strengthen and sustain the capac- erty, enrolling millions of children in school, and ity of statistical systems especially in develop- sharply reducing loss of life from preventable ing countries� (PARIS21 2009a). causes. We know this because we have access Much progress has been made. When the to greatly improved statistics. current round of censuses concludes in 2014, The need for reliable and timely statistics 98 percent of the world’s population will have was recognized long before the Millennium been counted. Since donors began reporting Development Goals were proposed in 2000, support for statistical capacity development in but the widespread attention given to their 2008, financial commitments to statistics have quantitative targets has increased demand for increased 60 percent to $1.6 billion over 2008– regular and uniform reporting of key indicators. 10. More than 55 developing countries have The International Development Goals proposed improved the data collection, management, by the Organisation for Economic Co-operation and dissemination of household surveys. The and Development in 1996 (OECD  DAC 1996) United Nations Inter-Agency and Expert Group included 21 indicators under seven headings on the Millennium Development Goal Indicators that anticipated the Millennium Development has conducted a series of regional workshops Goals. The World Bank’s 1992 Poverty Reduc- aimed at improving the MDG monitoring and tion Handbook (World Bank 1992) noted the has reported annually on progress. The quality need for an overall strategy for country statisti- of statistics as measured by the World Bank’s cal capacity and institution building. statistical capacity indicator has improved from Faced with large gaps in the international its benchmark level of 54 in 1999 to 67 in database, the Partnership in Statistics for 2011. The availability of data for monitoring the Development in the 21st Century (PARIS21) Millennium Development Goals has improved was established in 1999 to coordinate efforts to commensurately: in 2003 only 4 countries increase developing countries’ statistical capac- had two data points for at least 16 of 22 prin- ity. In 2004 the Second Roundtable on Man- cipal Millennium Development Goals indica- aging for Development Results endorsed the tors; by 2009 this had risen to 118 countries Marrakech Action Plan for Statistics, creating (PARIS21 2009b). an international agenda for support to statistics Any assessment of the Millennium Develop- in developing countries. Subsequently the Accra ment Goals must acknowledge that amid all the Agenda for Action made broad commitments signs of progress, there are gaps. Shortcom- on behalf of donors and developing countries ings. Disappointments. Some targets will not be to strengthen national statistical systems; pro- reached in this decade or the next. Likewise the vide more data disaggregated by sex, region, statistical record is still incomplete, Continuing and economic status; and “invest in strength- progress will require renewed commitment and ening developing countries’ national statistical careful monitoring. 2012 World Development Indicators 1 Eradicate extreme poverty and hunger Goal 1 P overty and hunger remain, but fewer people live in extreme poverty. The proportion of people living on less than $1.25 a day fell from 43.1 percent in 1990 to 22.2 per- cent in 2008. While the food, fuel, and financial crises over the past four years have worsened the situation of vulnerable populations and slowed poverty reduction in some countries, global poverty rates have continued to fall. Between 2005 and 2008 both the poverty rate and the number of people living in extreme poverty fell in all six developing country regions, the first time that has happened. Preliminary estimates for 2010 show that the extreme poverty rate fell further, reaching the global target of the Millennium Development Goals of halving world poverty five years early. Further progress is possible and likely before the 2015 target date of the Millennium Development Goals, if developing countries maintain the robust growth rates achieved over much of the past decade. But even then, hundreds of millions of people will remain mired in poverty, especially in Sub-Saharan Africa and South Asia and wherever poor health and lack of education deprive people of productive employment; environmental resources have been depleted or spoiled; and corruption, conflict, and misgovernance waste public resources and discourage private investment. Poverty rates fell sharply in the new millennium 1a Fewer people living in extreme poverty 1b Poverty rate at $1.25 a day (percent) People living on $1.25 a day or less (billions) 75 2.0 Europe & Central Asia Middle East & North Africa Sub-Saharan Africa 1.5 Latin America & Caribbean 50 South Asia East Asia & Pacific 1.0 25 Sub-Saharan Africa East Asia & Pacific 0.5 Latin America & Caribbean Middle East & North Africa South Asia 0 Europe & Central Asia 0 1990 1993 1996 1999 2002 2005 2008 1990 1993 1996 1999 2002 2005 2008 Source: World Bank staff estimates. Source: World Bank staff estimates. The most rapid decline in poverty occurred in East Asia and the In 2008, 1.28 billion people lived on less than $1.25 a day. Pacific, where extreme poverty in China fell from 60 percent in Since 1990 the number of people living in extreme poverty has 1990 to 13 percent. In developing countries outside China, the fallen in all regions except Sub-Saharan Africa, where the rate poverty rate fell from 37 percent to 25 percent. Poverty remains of population growth exceeded the rate of poverty reduction, widespread in Sub-Saharan Africa and South Asia, but progress increasing the number of extremely poor people from 290 mil- has been substantial. In South Asia the poverty rate fell from lion in 1990 to 356 million in 2008. The largest number of 54 percent in 1990 to 36 percent in 2008. And over 2005– poor people remain in South Asia, where 571 million people 2008 the poverty rate in Sub-Saharan Africa fell 4.8 percentage live on less than $1.25 a day, down from a peak of 641 million points to less than 50 percent, the largest drop in Sub-Saharan in 2002. Africa since international poverty rates have been computed. 2 2012 World Development Indicators WORLD VIEW Progress toward poverty reduction 1c More and less income equality 1e Share of countries making progress Reached target On track Off track Gini coefficient Low income Lower middle income toward poverty reduction (percent) Seriously off track Insufficient data (most recent value, 2000–09) Upper middle income No change 100 70 Less equal 60 50 50 0 40 50 30 More equal 100 20 East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia & Carib. & N. Africa Asia Africa 20 30 40 50 60 70 Gini coefficient (most recent value, 1990–99) Source: World Bank staff calculations. Source: World Development Indicators database. Individual country progress is assessed by comparing the rate Is income inequality improving or getting worse? At any level of of poverty reduction with the average rate required to achieve a income per person, the less equal the distribution of income 50 percent reduction in 25 years. Countries that have already the greater the poverty rate. The Gini coefficient is a common reached the target are listed as “achieved.� Those matching the measure of inequality. A higher value indicates greater inequal- required rate are listed as “on track.� Countries that will take ity. Poor countries often have less equal distributions of income longer but could reach the target by 2040, based on past per- than do rich countries, but there are significant regional differ- formance, are listed as “off track.� And those that would need ences as well. Data for 81 countries with values measured be- still longer or where poverty rates have increased are listed as fore and after 2000 show Gini coefficients fell in 44, including “seriously off track.� many low-income economies. Progress toward reducing undernourishment 1d Many children remain malnourished 1f Share of countries making Malnutrition prevalence, weight for age Low income Lower middle income progress toward reducing Reached target On track Off track (percent of children under age 5) Upper middle income undernourishment (percent) Seriously off track Insufficient data 50 100 40 50 30 0 20 50 10 100 0 East Asia Europe & Latin America Middle East South Sub-Saharan 1990 2010 & Pacific Central Asia & Carib. & N. Africa Asia Africa Source: World Bank staff calculations. Source: World Health Organization and World Development Indicators database. Undernourishment measures the availability of food to meet Malnutrition rates have dropped substantially since 1990, but people’s basic energy needs. The Millennium Development more than 100 million children under age 5 remain malnour- Goals call for halving the proportion of undernourished people, ished. Only 40 of 90 countries with adequate data to monitor but few countries will reach that target by 2015. Rising agri- trends are on track to reach the Millennium Development Goal cultural production has kept ahead of population growth, but target of halving the number of people who suffer from hunger. increasing food prices and the diversion of food crops to fuel Malnutrition in children often begins at birth, when poorly nour- production have reversed the declining rate of undernourish- ished mothers give birth to underweight babies. Malnourished ment since 2004–06. The Food and Agriculture Organization children develop more slowly, enter school later, and perform estimated that there were 739 million people without adequate less well. Programs to encourage breastfeeding and improve daily food intake in 2008. the diets of mothers and children can help. 2012 World Development Indicators 3 Achieve universal primary education Goal 2 T he commitment to provide primary education to every child is the oldest of the Millennium Development Goals, having been set at the first Education for All confer- ence in Jomtien, Thailand, more than 20 years ago. Achieving this goal has often seemed tantalizingly near, but only Latin America and the Caribbean has reached the goal, although East Asia and Pacific and Europe and Central Asia are close. Progress among the poorest countries, slow in the 1990s, has accelerated since 2000, particularly in South Asia and Sub-Saharan Africa, but full enrollment remains elusive. Many children start school but drop out before completing the primary stage, discouraged by cost, distance, physical danger, and failure to progress. Even as coun- tries approach the target of Millennium Development Goal 2, the education demands of modern economies expand. In the 21st century primary education will be of value only as a stepping stone toward secondary and higher education. The last step toward education for all 1g 64 million children out of school 1h Primary school completion rate (percent) Number of children not attending primary school (millions) 1999 2009 110 50 East Asia & Latin America & Caribbean, East Asia & Pacific, target Pacific, actual actual 100 40 Europe & Central Asia, target Europe & Central Asia, actual 90 30 Middle East & Latin America & North Africa, target Middle East & 80 Caribbean, target North Africa, actual 20 South Asia, actual 70 South Asia, target 10 60 Sub-Saharan Africa, target Sub-Saharan Africa, actual 0 50 East Asia Europe & Latin America Middle East South Sub-Saharan 1991 1995 2000 2005 2010 2015 & Pacific Central Asia & Carib. & N. Africa Asia Africa Source: United Nations Educational, Scientific and Cultural Organization Institute of Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database. Statistics. In 2009, 87 percent of children in developing countries complet- Many children enroll in primary school but attend intermittently ed primary school. In most regions school enrollment picked up or drop out entirely. This is particularly the case for girls whose after the Millennium Development Goals were promulgated in work is needed at home. In rural areas the work of children of 2000, when the completion rate was 80 percent. Sub-Saharan both sexes may be needed during planting and harvest. Other Africa and South Asia, which started out farthest behind, have obstacles, including the lack of suitable facilities, absence of made substantial progress but will still fall short of the goal. The teachers, and school fees, discourage parents from sending Middle East and North Africa has stalled at completion rates their children to school. The problem is worst in South Asia and of around 90 percent, while Europe and Central Asia and East Sub-Saharan Africa, where more than 48 million children of pri- Asia and Pacific are within striking distance but have made little mary school age are not in school. progress in the last five years. 4 2012 World Development Indicators WORLD VIEW Progress toward education for all 1i How much schooling 1k Share of countries making Madagascar, 2008 progress toward universal Reached target On track Off track Share of people ages 15–19 completing each year of schooling, primary education(percent) Seriously off track Insufficient data by year and wealth quintile (percent) 100 100 50 75 Second quintile Richest quintile 0 50 Fourth quintile Third Poorest quintile quintile 50 25 100 0 East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia & Carib. & N. Africa Asia Africa Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Source: World Bank staff calculations. Bangladesh, 2007 Share of people ages 15–19 completing each year of schooling, by sex and location (percent) Sixty developing countries, half the countries for which ad- 100 equate data are available, have achieved or are on track to achieve the Millennium Development Goal target of a full course 75 of primary schooling for all children. Twelve more will miss the Urban, girls 2015 deadline. At their current rate of progress they will achieve Urban, boys Rural, girls full enrollment sometime after 2015. That leaves at least 48 50 countries seriously off track, making little or no progress, 30 of Rural, boys them in Sub-Saharan Africa. 25 0 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 The missing enrollments 1j Ratio of girls’ to boys’ primary school enrollment (percent) Cambodia, 2005 Share of people ages 15–19 completing each year of schooling, by parents’ education level (percent) 100 Boys underenrolled 100 Girls underenrolled 75 Secondary Some higher 90 50 Incomplete secondary 80 Primary 25 Incomplete primary 70 No education 30 50 70 90 100 0 Net primary enrollment rate (percent) Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database. Source: Demographic and Health Surveys. A major obstacle to achieving universal primary education is the Many factors affect how long students stay in school. Children shortfall in girls’ enrollments. Almost all school systems with from poor families are less likely to attend or stay in school. In low enrollment rates show underenrollment of girls in primary most countries girls and children from rural areas are also less school. In only a few places are boys’ enrollment rates lower likely to attend, but Bangladesh has used targeted incentives to than girls’. Starting at such a disadvantage, most girls will never raise girls’ attendance rates. In Cambodia and everywhere else catch up. Achieving the Millennium Development Goal target to parents with lower levels of education are less likely to keep enroll and keep girls in school is essential. their children in school. Achieving the Millennium Development Goal target will require breaking the cycle of lack of education– poverty– low enrollment. 2012 World Development Indicators 5 Promote gender equity and empower women Goal 3 W omen are making progress along the three dimensions of gender equality and women’s empowerment that the Millennium Development Goals monitor: educa- tion, employment, and participation in public decisionmaking. These are impor- tant, but there are others. Efforts are under way to improve monitoring of women’s access to financial services, entrepreneurship, and migration and remittances as well as of violence against women. Time-use surveys, for example, can illuminate differences in the roles of women and men within the household and the workplace. Disaggregating other statistical indicators by sex can also reveal patterns of disadvantage or, occasion- ally, advantage for women. Whatever the case, women make important contributions to economic and social development. Expanding opportunities for them in the public and private sectors is a core development strategy. And good statistics are essential for developing policies that effectively promote gender equity and increase the welfare and productivity of women. Increasing participation by girls at all levels of education 1l Ratio of girls’ to boys’ enrollment rate, 2009 (percent) 150 125 100 75 50 25 0 Primary Secondary Tertiary Primary Secondary Tertiary Primary Secondary Tertiary Primary Secondary Tertiary Primarya Secondary Tertiarya Primary Secondary Tertiary East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa South Asia Sub-Saharan Africa a. Data are for 2008. Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database. Girls have made substantial gains in primary and secondary school. In some countries the situation changes at the second- school enrollment. In many countries girls’ enrollment rates out- ary level. Girls who complete primary school may be more likely number boys’, particularly in secondary school. And more girls to stay in school, while boys drop out. In Europe and Central are staying in school. In 1991 only 73 percent of girls in devel- Asia and Latin American and the Caribbean the differences in oping countries finished primary school; by 2010 the comple- higher education enrollment are substantial. This is an unsatis- tion rate stood at 86 percent. But this comparison obscures the factory path to equity. Rapid growth and poverty reduction truly underlying problem of underenrollment. Girls are still less likely require education for all. to enroll in primary school or to stay through the end of primary 6 2012 World Development Indicators WORLD VIEW Progress toward gender equality in education 1m More women decisionmakers 1o Share of countries making progress Proportion of seats held by women in national parliaments (percent) toward gender equality in primary Reached target On track Off track 25 and secondary education (percent) Seriously off track Insufficient data Latin America & Caribbean 100 High income East Asia & South Asia 20 Pacific 50 15 Sub-Saharan Africa Europe & Central Asia 0 10 Middle East & North Africa 50 5 100 0 East Asia Europe & Latin America Middle East South Sub-Saharan 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 & Pacific Central Asia & Carib. & N. Africa Asia Africa Source: World Bank staff calculations. Source: Inter-Parliamentary Union and World Development Indicators database. Substantial progress has been made toward increasing the pro- The proportion of parliamentary seats held by women has portion of girls enrolled in primary and secondary education. By increased everywhere. In Latin America and the Caribbean the end of the 2009/10 school year, 96 countries had achieved women now hold 24 percent of all parliamentary seats. The equality in enrollment rates and 7 more were on track to do so most impressive gains have been made in South Asia, where by 2015. That leaves only 27 countries off track or seriously off the number of seats held by women tripled between 1999 and track, mostly low- and lower middle-income economies in the Mid- 2000. In Sub-Saharan Africa Rwanda leads the way, making his- dle East and North Africa, South Asia, and Sub-Saharan Africa. tory in 2008 when it elected a parliament composed 56 percent Fourteen countries lacked adequate data to assess progress. of women. The Middle East and North Africa lags far behind. Women have become a larger part of the workforce 1n A slim lead for girls 1p Share of women employed in the nonagricultural sector Underweight children under age 5, (percent of total nonagricultural employment) 1990 Most recent year most recent year available, 2005–11 (percent) Female Male 50 50 40 40 30 30 20 20 10 10 0 0 e a sh r l ia ia n R ti pa ge East Asia Europe & Latin America Middle East South st di da ou PD op al de In Ne Ni -Le m Su ib hi & Pacific Central Asia & Carib. & N. Africa Asia la o So Dj or Et La ng m Ba Ti Note: Insufficient data are available for Sub-Saharan Africa. Source: International Labour Organization and World Development Indicators database. Source: World Health Organization and World Bank staff calculations. Women’s share in paid employment in the nonagricultural Girls are less likely to attend school, have secure jobs, or hold sector has risen marginally but remains less than 20 percent public office. But by most measures, they have an advantage in the Middle East and North Africa and South Asia. In many in one area: malnutrition. Out of 99 countries with data for countries the majority of women who work hold insecure jobs 2005–11, 19 had a larger proportion of underweight girls than outside the formal sector. Overall labor force participation of underweight boys; 74 had a larger proportion of underweight rates of women follow a similar pattern, but they are highest in boys than of underweight girls, and 6 had no difference. The Sub-Saharan Africa, where 60 percent of women ages 15 and chart shows the 10 countries with the highest proportion of older are in the labor force, although many are employed as underweight children during the period. unpaid family workers. 2012 World Development Indicators 7 Reduce child mortality Goal 4 I n 1990, 12 million children died before their fifth birthday. By 1999 there were fewer than 10 million child deaths, and the number has continued to fall to just over 7.5 million in 2010. That is good news, but the ambitious Millennium Development Goal target of a two-thirds reduction in the under-five mortality rate will be met by no more than 40 countries. Only Latin America and the Caribbean and upper middle-income economies as a whole will, on average, reach the target. Most children die from causes that are readily preventable or curable with existing interventions, such as acute respiratory infections, diarrhea, measles, and malaria. And most die in the first year of life. Rapid improvements prior to 1990 in a few countries gave hope that mortality rates for infants and children could be cut further in the follow- ing 25 years, but progress slowed almost everywhere after 1990, leaving most countries far behind the target before the goals were announced. Was the target too challenging? Perhaps. But the more important question is whether it encouraged countries to use their resources wisely to achieve the fastest possible progress. Still far to go in reducing under-�ve mortality 1q Most deaths occur in the �rst year of life 1r Under-five mortality rate (per 1,000 live births) Deaths, 2010 (millions) Children ages 1–5 Infants 200 4 Sub-Saharan Africa 150 3 South Asia 100 2 Middle East & North Africa East Asia & Pacific Europe & Central Asia 1 50 Latin America & Caribbean High income 0 0 East Asia Europe & Latin America Middle East South Sub-Saharan 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 & Pacific Central Asia & Carib. & N. Africa Asia Africa Source: World Health Organization and World Development Indicators database. Source: World Health Organization and World Development Indicators database. Mortality rates have been falling everywhere. In developing Almost 70 percent of deaths of children under age 5 occur in countries the mortality rate fell from an average of 98 per 1,000 the first year of life, and half in the first month. Therefore, reduc- live births in 1990 to 63 in 2010. But rates remain much higher ing child mortality requires addressing the causes of neonatal in many countries, especially in Sub-Saharan Africa and parts of and infant deaths: inadequate care at birth and afterward, mal- South Asia. In Sub-Saharan Africa one child in eight dies before nutrition, poor sanitation, and exposure to acute and chronic his or her fifth birthday. The odds are somewhat better in South disease. Improvements in infant and child mortality are, in turn, Asia, where 1 child in 15 dies. But even in these regions there the largest contributors to increased life expectancy in most are countries exhibiting rapid progress. countries. 8 2012 World Development Indicators WORLD VIEW Progress toward reducing child mortality 1s For some, better than expected improvements 1u Share of countries making progress Reached target On track Off track Reduction since 1990 2010, actual 2010, target 2010, predicted toward reducing child mortality (percent) Seriously off track Insufficient data Seychelles 100 Mauritius Botswana Cape Verde South Africa 50 Namibia Zimbabwe Lesotho Gabon 0 São Tomé and Príncipe Swaziland Kenya 50 Congo, Rep. Ghana Mauritania Sudan 100 Comoros Cameroon East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia & Carib. & N. Africa Asia Africa Senegal Eritrea Togo Source: World Bank staff calculations. Côte d’Ivoire Tanzania Madagascar Rwanda A concerted effort by academic researchers and international Central African Republic Gambia, The statistical agencies has greatly improved measurement of in- Uganda Benin fant and child mortality. Therefore, few countries lack estimates Somalia Congo, Dem. Rep. of child mortality rates, although many are derived from statisti- Zambia cal models. Ten countries have already achieved a two-thirds Burundi Ethiopia reduction in under-five mortality rates since 1990, and 26 are Equatorial Guinea Burkina Faso on track to do so by 2015. But that leaves 105 countries, with Chad Guinea-Bissau half of developing countries’ population, off track or seriously Nigeria off track. Mozambique Malawi Liberia Guinea Angola Preventing childhood diseases 1t Mali Sierra Leone Children ages 12–23 months immunized against measles (percent) Niger 100 High income 0 100 200 300 400 Under-five mortality rate (deaths per 1,000 live births) Upper middle income 75 Source: World Bank staff calculations. Lower middle income Low income In 1990 the under-five mortality rate in Niger stood at 311 per 50 1,000 live births, the worst in the world. In the same year, Sey- chelles, with an under-five mortality rate of 16, was the best in 25 Sub-Saharan Africa. How have they fared since? In the 20 years from the Millennium Development Goals baseline, Niger’s mortal- ity rate fell by 168, the greatest in the region, while Seychelles’s 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 fell by 3. In proportional terms Niger experienced a 54 percent reduction—second greatest in the region—and Seychelles a Source: World Health Organization and World Development Indicators database. 16 percent reduction. Both are short of the Millennium Develop- ment Goal target, but Niger, having started in last place, has pro- Illnesses that could be prevented by early childhood vaccina- gressed faster. Has this been the general rule? On average, coun- tions still account for many child deaths. Despite years of vac- tries in Sub-Saharan Africa that started in worse positions have cination campaigns, many children in low- and lower middle-in- done better. But experience has been mixed: conflict- affected come economies remain unprotected. Measles is one example. countries such as the Democratic Republic of the Congo and Other recommended immunizations include diphtheria, pertus- Somalia have made almost no progress, while similarly situated sis, tetanus, and the BCG immunization for tuberculosis. To be countries such as Uganda and Zambia have done much better. successful, vaccination campaigns must reach all children and Two countries, Madagascar and Malawi, are on track to achieve be sustained over time. the Millennium Development Goal target. Several others, includ- ing Eritrea, Niger, and Tanzania, are close. Only one country, Zim- babwe, moved backward from 1990 to 2010. 2012 World Development Indicators 9 Reduce maternal mortality Goal 5 A n estimated 358,000 maternal deaths occurred worldwide in 2008, a 34 percent decrease since 1990. The Millennium Development Goals call for reducing the mater- nal mortality ratio by 75 percent between 1990 and 2015, but few countries and no developing country region on average will achieve this target. What makes maternal mortality such a compelling problem is that it strikes young women experiencing a natural life event. They die because they are poor. Malnourished. Weakened by disease. They die because they lack access to trained health care workers and modern medical facilities. And because women in poor countries have more children, their lifetime risk of maternal death may be more than 200 times greater than for women in Western Europe and North America. Reducing maternal mortality requires a comprehensive approach to women’s repro- ductive health, starting with family planning and access to contraception. Many health problems among pregnant women are preventable or treatable through visits with trained health workers before childbirth. Good nutrition, vaccinations, and treatment of infections can improve outcomes for mother and child. Skilled attendants at time of delivery and access to hospital treatments are essential for dealing with life-threatening emergencies such as severe bleeding and hypertensive disorders. Maternal mortality rates have been falling, The 12 countries with highest lifetime risk of maternal death 1w but large regional differences persist 1v Lifetime risk of maternal death, 2008 (percent) Maternal mortality ratio, modeled 1990 1995 2000 10 estimate (per 100,000 live births) 2005 2008 1,000 8 750 6 4 500 2 250 0 i . ria ia i e ria u r ia ad n nd al ep ge on sa ta an al M Ch ge be ru Ni .R is m is Le nz Ni 0 an Li Bu -B So m Ta ra ea gh De er in Af East Asia Europe & Latin America Middle East South Sub-Saharan Si o, Gu ng & Pacific Central Asia & Carib. & N. Africa Asia Africa Co Source: World Health Organization and World Development Indicators database. Source: World Health Organization and World Development Indicators database. About half of all maternal deaths occur in Sub-Saharan Africa In high-fertility countries women are repeatedly exposed to the and a third in South Asia, but mothers in other regions face risk of maternal mortality. In Afghanistan in 2008, where the substantial risks as well. The maternal mortality ratio may be lifetime risk of maternal death was over 9 percent, one woman many times higher in fragile and conflict-afflicted states than in 11 is expected to die in childbirth; in Burundi one woman in in countries with strong institutions and well organized health 25 is at risk. In high-income economies the lifetime risk is less systems. than 0.03 percent, or less than 1 woman in 3,500. 10 2012 World Development Indicators WORLD VIEW Progress in reducing maternal mortality 1x Fewer young women giving birth 1z Share of countries making Adolescent fertility rate (births per 1,000 women ages 15–19) progress toward reducing Reached target On track Off track maternal mortality (percent) Seriously off track Insufficient data 150 100 Sub-Saharan Africa South Asia 50 100 Latin America & Caribbean 0 50 Middle East & North Africa 50 Europe & Central Asia East Asia & Pacific 0 100 East Asia Europe & Latin America Middle East South Sub-Saharan 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 & Pacific Central Asia & Carib. & N. Africa Asia Africa Source: World Bank staff calculations. Source: World Health Organization and World Development Indicators database. Progress in reducing maternal mortality ratios has been slow, The adolescent fertility rate is highest in Sub- Saharan Africa far slower than imagined by the Millennium Development Goal and is declining slowly. Women who give birth at an early age target of a 75 percent reduction from 1990 levels. Only four are likely to bear more children and are at greater risk of death countries have achieved this target, and five more are on track. or serious complications from pregnancy. In many developing Accurately measuring maternal mortality is difficult and requires countries the number of women ages 15–19 is still increas- specialized surveys and good reporting of vital events. Recent ing. Preventing unintended pregnancies and delaying childbirth efforts by statisticians have improved estimates, but for many among young women increase the chances of their attending countries the need for improved monitoring of maternal health school and eventually obtaining paid employment. will continue long past 2015. Planning for motherhood 1y Help for mothers 1aa Contraceptive prevalence, 2010 (percent of women ages 15–49) Births attended by skilled health staff, 2010 (percent) 80 100 60 75 40 50 20 25 0 0 East Asia Europe & Latin America Middle East South Sub-Saharan East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia & Carib. & N. Africa Asia Africa & Pacific Central Asia & Carib. & N. Africa Asia Africa Source: World Health Organization and World Development Indicators database. Source: World Health Organization and World Development Indicators database. Contraceptive use has increased in most developing countries In South Asia and Sub- Saharan Africa less than half of all births for which data are available. In almost all regions more than half are attended by doctors, nurses, or trained midwives. Having of women who are married or in union use some method of birth skilled health workers present for deliveries is key to reducing control. More than 200 million women want to delay or cease maternal mortality. In many places women have only untrained childbearing, and a substantial proportion say that their last caregivers or family members to attend them during childbirth. birth was unwanted or mistimed. Worldwide an estimated 120 Skilled health workers are trained to give necessary care be- million women have an unmet need for family planning. fore, during, and after delivery; they can conduct deliveries on their own, summon additional help in emergencies, and provide care for newborns. 2012 World Development Indicators 11 Combat HIV/AIDS, malaria, and other diseases Goal 6 E pidemic diseases exact a huge toll in human suffering and lost opportunities for development. Poverty, armed conflict, and natural disasters contribute to the spread of disease and are made worse by it. In Africa the spread of HIV/AIDS has reversed decades of improvement in life expectancy and left millions of children orphaned. It is draining the supply of teachers and eroding the quality of education. There are 300–500 million cases of malaria each year, leading to more than 1 mil- lion deaths. Malaria is a disease of poverty. Nearly all the cases occur in Sub-Saharan Africa, where the most lethal form of the malaria parasite is abundant. Most deaths from malaria are among children under age 5, but the disease can be debilitating in adults as well. Tuberculosis kills some 2 million people a year, most of them ages 15–45. The dis- ease, once controlled by antibiotics, is spreading again because of the emergence of drug-resistant strains. People living with HIV/AIDS, which reduces resistance to tuber- culosis, are particularly vulnerable, as are refugees, displaced persons, and prisoners living in close quarters and unsanitary conditions. Well managed medical intervention using appropriate drug therapy is the key to stopping the spread of tuberculosis. Bringing HIV/AIDS under control 1bb Millions of people still afflicted with HIV/AIDS 1cc Prevalence of HIV (percent of population ages 15–49) Population living with HIV, 2009 (millions) 6 7.5 5 Sub-Saharan Africa 4 5.0 3 2 2.5 1 Latin America & Caribbean 0 High income Other developing regions n a i n a da e e ia a a ria a aw ny bw oo in tio bi qu di ric an an ge Ch m In al Ke Af er bi ra ba nz Ni Za Ug M am 0.0 m de h m Ta ut Ca Fe Zi oz So M n 1990 1992 1994 1996 1998 2000 2002 2004 2006 2009 ia ss Ru Source: World Health Organization, Joint United Nations Programme on HIV/AIDS, and Source: World Health Organization, Joint United Nations Programme on HIV/AIDS, and World Development Indicators database. World Development Indicators database. Sub-Saharan Africa remains the center of the HIV/AIDS epidem- In 2009, 31–33 million people were living with HIV/AIDS, and ic, but the proportion of adults living with AIDS has begun to approximately 1.5 million of them were under age 15. Anoth- fall even as the survival rate of those with access to antiretro- er 16.9 million children, 14.8 million of them in Sub-Saharan viral drugs has increased. In Africa 58 percent of adults with Africa, have lost one or both parents to AIDS. By the end of HIV/AIDS are women. Among young people ages 15–24, the 2009, 5.25 million people were receiving antiretroviral drugs, or prevalence rate among women is more than twice that among 36 percent of the population for which the World Health Organi- men. Latin America and the Caribbean, where 0.5 percent of zation recommends treatment. adults are infected, has the next highest prevalence rate. 12 2012 World Development Indicators WORLD VIEW Progress toward reversing the HIV epidemic 1dd Protecting children from malaria 1ff Share of countries making First observation (2000 or earlier) Most recent observation (2006 or later) progress against HIV/AIDS Halted and reversed Halted or reversed (percent) Stable low prevalence Not improving No data Swaziland 100 Mauritania Côte d’Ivoire 50 Guinea Congo, Rep. Comoros 0 Burkina Faso Chad 50 Somalia Cameroon Central African Republic 100 East Asia Europe & Latin America Middle East South Sub-Saharan Zimbabwe & Pacific Central Asia & Carib. & N. Africa Asia Africa Angola Source: World Bank staff calculations. Benin Mozambique Sudan The Millennium Development Goals call for halting and then re- Sierra Leone versing the spread of HIV/AIDS by 2015. The progress assess- Liberia ment shown here is based on prevalence rates for adults ages Ghana 15–49. Countries that have a declining prevalence rate since Nigeria 2005 are assessed to have halted the epidemic; those that have Senegal a prevalence rate less than their earliest measured rate have Uganda reversed the epidemic. Countries that have a prevalence rate of Ethiopia less than 0.2 percent are considered stable. Countries that have Namibia a prevalence rate greater than 0.2 percent and that have neither Guinea-Bissau Congo, Dem. Rep. halted nor reversed the epidemic are shown as not improving. Burundi Madagascar Turning the tide of tuberculosis 1ee Kenya Eritrea Tuberculosis incidence and prevalence rates (per 100,000 people) 500 Gambia, The Low income, prevalence Zambia Lower middle income, prevalence Gabon 400 São Tomé and Príncipe Malawi 300 Low income, incidence Togo Lower middle income, incidence Tanzania 200 Upper middle income, Niger prevalence Rwanda 100 Mali Upper middle income, incidence 0 25 50 75 0 Use of insecticide-treated nets (percent of population under age 5) 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Source: World Health Organization and World Development Indicators database. Source: World Health Organization and World Development Indicators database. Malaria is endemic in most tropical and subtropical regions, but Tuberculosis is one of the main causes of adult deaths from 90 percent of malaria deaths occur in Sub-Saharan Africa. Those a single infectious agent in developing countries. The data most severely affected are children under age 5. Even those who shown here illustrate the association of tuberculosis with pov- survive malaria do not escape unharmed. Repeated episodes of erty. The incidence rate is three times higher in low-income fever and anemia take a toll on mental and physical development. economies than in upper middle-income economies. The num- Insecticide-treated nets have proved to be an effective preventa- ber of new tuberculosis cases peaked in 2004, and prevalence tive, and their use has grown rapidly. Between 2008 and 2010, 290 rates are also declining, but the targets of halving the 1990 million nets were distributed in Sub-Saharan Africa. But coverage prevalence and death rates by 2015 are unlikely to be met. remains uneven. In some countries with large numbers of reported cases, use of nets for children remains at less than 20 percent. 2012 World Development Indicators 13 Ensure environmental sustainability Goal 7 S ustainable development can be ensured only by protecting the environment and using its resources wisely. Poor people, often dependent on natural resources for their livelihood, are the most affected by environmental degradation and natural disasters (fires, storms, earthquakes), whose effects are worsened by environmental mismanagement. Poor people also suffer from shortcomings in the built environment: whether in urban or rural areas, they are more likely to live in substandard housing, lack basic services, and be exposed to unhealthy living conditions. The seventh goal of the Millennium Development Goals is the most wide ranging but perhaps the least well specified. Among its 10 indicators, only 3 are associated with quantified and timebound targets. For others we can only monitor trends. Most countries have adopted principles of sustainable development and agreed to international accords on protecting the environment. But the failure to reach a compre- hensive agreement on limiting greenhouse gas emissions leaves billions of people and future generations vulnerable to the impacts of climate change. Growing populations put more pressure on marginal lands and expose more people to hazardous conditions that will be exacerbated by global warming. Carbon dioxide emissions continue to rise 1gg Forest losses and gains 1hh Carbon dioxide emissions (millions of metric tons) Average annual change in forest area, 2000–10 (thousands of square kilometers) 30 25 High income 20 0 Upper middle income –25 10 Lower middle income Low income –50 0 East Asia Europe & Latin Middle South Sub-Saharan High 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 & Pacific Central Asia America East & Asia Africa income & Carib. N. Africa Source: Carbon Dioxide Information Analysis Center and World Development Indicators database. Source: Food and Agriculture Organization and World Development Indicators database. Annual emissions of carbon dioxide reached 32 million metric Loss of forests threatens the livelihood of poor people, destroys tons in 2008 and are still rising. High-income economies re- habitat that harbors biodiversity, and eliminates an important main the largest emitters, but the rapidly growing upper middle- carbon sink that helps moderate climate change. Net losses income economies are not far behind. Measured by emissions since 1990 have been substantial, especially in Latin Ameri- per capita, however, emissions by high-income economies are can and the Caribbean and Sub-Saharan Africa, and only par- more than three times higher than average emissions by low- tially compensated for by net gains in Asia and high-income and middle-income economies. economies. 14 2012 World Development Indicators WORLD VIEW Progress toward improved sanitation 1ii Many still lack access to sanitation 1kk Share of countries making progress Share of population with access to improved sanitation, 2008 (percent) Rural Urban toward universal access to improved Reached target On track Off track sanitation (percent) Seriously off track Insufficient data 100 100 75 50 50 0 25 50 0 100 East Asia Europe & Latin America Middle East South Sub-Saharan East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia & Carib. & N. Africa Asia Africa & Pacific Central Asia & Carib. & N. Africa Asia Africa Source: World Bank staff calculations. Source: World Health Organization and World Development Indicators database. The Millennium Development Goals call for cutting the propor- In 1990, 63 percent of the people living in low- and middle- tion of the population without access to improved sanitation income economies lacked access to a flush toilet or other form and water sources in half by 2015. By 2010, 2.7 billion people of improved sanitation. By 2010 the access rate had improved still lacked access to improved sanitation, and more than 1 bil- 19 percentage points to 44 percent. The situation is worse in lion people practiced open defecation, posing enormous health rural areas, where 57 percent of the population lack access to risks. At the present pace only 37 countries are likely to reach improved sanitation. The large urban–rural disparity, especially the target—an increase of two since 2008. East Asia and Pa- in Sub-Saharan Africa and South Asia, is the principal reason cific and Middle East and North Africa are the only developing the sanitation target of the Millennium Development Goals will regions on track to reach the target by 2015. not be reached. Progress toward improved water sources 1jj Water demand strains supplies 1ll Share of countries making progress Annual freshwater withdrawals, 2009 (percent of internal resources) toward universal access to an Reached target On track Off track improved water source (percent) Seriously off track Insufficient data 75 100 50 50 0 25 50 0 100 East Asia Europe & Latin Middle South Sub-Saharan High East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia America East & Asia Africa income & Pacific Central Asia & Carib. & N. Africa Asia Africa & Carib. N. Africa Source: World Bank staff calculations. Source: Food and Agriculture Organization and World Development Indicators database. In 1990 more than 1 billion people lacked access to drinking Worldwide more than 70 percent of freshwater withdrawals go water from a convenient, protected source, but the situation is to agricultural and 20 percent to industrial uses. Only 10 per- improving. The proportion of people in developing countries with cent go to households, many of which are underserved. The access to an improved water source increased from 71 percent potential impacts of global warming and increased demand in 1990 to 86 percent in 2008, reaching the Millennium Devel- for water will require more efficient use of available resources. opment Goal target of halving the proportion of people without As pressure grows on internal water resources, conflicts over access to an improved water source. Seventy-three countries shared, external resources may also increase. have reached or are on track to reach the target. At this rate, only Middle East and North Africa and Sub-Saharan Africa will fall short. 2012 World Development Indicators 15 Develop a global partnership for development Goal 8 T he eighth and final goal distinguishes the Millennium Development Goals from previ- ous sets of resolutions and targeted programs. It recognizes the multidimensional nature of development and the need for wealthy countries and developing countries to work together to create an environment in which rapid, sustainable development is possible. Following the Millennium Summit, world leaders meeting at Monterrey, Mexico, in 2002 agreed to provide financing for development through a coherent process that recognized the need for domestic as well as international resources. Subsequent high- level meetings expanded on these commitments. Along with increased aid flows and debt relief for the poorest, highly indebted countries, goal 8 recognizes the need to reduce barriers to trade and to share the benefits of new medical and communication technologies. Goal 8 also reminds us that development challenges differ for large and small countries and for those that are landlocked or isolated by large expanses of ocean. Building and sustaining a partnership is an ongoing process that does not stop at a spe- cific date or when a target is reached. However it is measured, a strong commitment to partnership should be the continuing legacy of the Millennium Development Goals. Most donors have maintained their aid levels 1mm But their domestic subsidies to agricultural are greater 1nn Official development assistance (percent of GNI) Agricultural support (percent of GDP) 1.25 9 Norway Korea, Rep. 1.00 6 0.75 United Kingdom 0.50 Norway Germany 3 Switzerland Japan 0.25 Canada United States Japan United States All DAC donors 0.00 0 All DAC donors (ODA as share of GNI) 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Source: Organisation for Economic Co-operation and Development. Source: Organisation for Economic Co-operation and Development. The financial crisis that began in 2008 and fiscal austerity in Organisation for Economic Co-operation and Development many high-income economies have threatened to undermine (OECD) members (which include some upper middle-income commitments to increase official development assistance. So economies such as Chile and Mexico) spend more on support far leading donors have maintained their level of support. Total to domestic agricultural producers than on official development disbursements by members of the Organisation for Economic assistance. In 2010 the OECD producer support estimate was Co-operation and Development’s Development Assistance $227 billion, down about 10 percent from the previous three Committee reached $130 billion in 2010, a real increase of 4.3 years. percent over 2008. 16 2012 World Development Indicators WORLD VIEW Developing countries have easier access to OECD markets 1oo Debt service burdens have been falling 1qq Share of total imports from least developed countries Total debt service (percent of exports of goods, services, and income) admitted free of duty, excluding arms (percent) 50 100 European Union Latin America & Caribbean United States 40 75 South Asia 30 Europe & Japan Central Asia 50 Middle East 20 & North Africa East Asia & Pacific 25 10 Sub-Saharan Africa 0 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1996 1998 2000 2002 2004 2006 2009 Source: World Trade Organization, International Trade Center, and United Nations Source: World Development Indicators database. Conference on Trade and Development. Many rich countries have pledged to admit the exports of the Growing economies, better debt management, and debt relief least developed countries duty free. However, arcane rules of for the poorest countries have allowed developing countries to origin and phytosanitary standards keep many countries from substantially reduce their debt burdens. Despite the financial qualifying for duty-free access. And uncertainty over market ac- crisis and a 2.3 percent contraction in the global economy in cess may inhibit development of export industries. Compared 2009, debt service ratios continued to fall in most developing with the European Union, the large U.S. market retains many country regions. Only in Europe and Central Asia has the ratio of barriers to the exports of the poorest countries. debt service to exports risen since 2008, although rising export earnings have helped moderate the trend. Cellular phones are connecting developing countries 1pp A more connected world 1rr Fixed telephones and mobile cellular subscriptions (per 100 people) Internet users (per 100 people) 125 100 High income, mobile subscriptions 100 High income Upper middle 75 income, mobile subscriptions 75 Lower middle income, mobile 50 subscriptions High income, fixed telephones 50 Europe & Central Asia Low income, Latin America & Caribbean Upper middle income, mobile fixed telephones subscriptions 25 East Asia & Pacific 25 Lower middle income, Middle East & North Africa fixed telephones Sub-Saharan Africa South Asia 0 0 Low income, fixed telephones 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Source: International Telecommunication Union and World Development Indicators Source: International Telecommunication Union and World Development Indicators database. database. Telecommunications is an essential tool for development, and In 2000 Internet use was spreading rapidly in high-income econ- new technologies are creating new opportunities everywhere. omies but was barely under way in developing country regions. The growth of fixed-telephone systems has peaked in high- Now developing countries are beginning to catch up. By 2010 income economies and will never reach the same level of use in there were an average of 34 internet users per 100 people in developing countries, where mobile cellular subscriptions con- upper middle-income economies. Like telephones, Internet use tinue to grow rapidly. In high-income economies, with more than is strongly correlated with income. In low-income economies one subscription per person, the pace of growth appears to be there were only 5.4 users per 100 people in 2010. But growth slowing. Gradually the world is becoming more connected. is picking up. 2012 World Development Indicators 17 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 daya 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 November 14, 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. 18 2012 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 slumsb 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 a. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. b. 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. 2012 World Development Indicators 19 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 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10 Afghanistan 34 652 53 14.3 109 410 204 36.5a 1,060a 199 8.2 5.2 Albania 3 29 117 12.7 117 3,960 b 124 27.3 8,520 114 3.5 3.1 Algeria 35 2,382 15 155.7 49 4,390 119 287.2a 8,100a 117 3.3 1.8 Angola 19 1,247 15 75.2 62 3,940 125 103.1 5,410 135 2.3 3.0 Argentina 40 2,780 15 348.4 26 8,620 85 629.3 15,570 78 9.2 8.2 Armenia 3 30 109 9.9 127 3,200 135 17.5 5,660 133 2.1 1.9 Australia 22 7,741 3 1,030.3 13 46,200 20 823.0 36,910 32 2.3 0.7 Austria 8 84 102 394.6 25 47,030 18 333.9 39,790 24 2.3 2.0 Azerbaijan 9 87 110 48.3 74 5,330 109 83.9 9,270 108 5.0 3.8 Bahrain 1 1 1,661 19.7 99 18,730 62 26.0 24,710 55 6.3 –6.5 Bangladesh 149 144 1,142 104.7 57 700 187 269.7 1,810 182 6.1 4.9 Belarus 9 208 47 56.5 67 5,950 104 129.0 13,590 88 7.6 7.8 Belgium 11 31 360 499.5 20 45,840 21 417.3 38,290 28 2.3 1.3 Benin 9 113 80 7.0 139 780 184 14.0 1,590 186 3.0 0.1 Bolivia 10 1,099 9 17.9 104 1,810 158 46.0 4,640 142 4.1 2.5 Bosnia and Herzegovina 4 51 74 17.9 105 4,770 112 33.5 8,910 113 0.8 1.0 Botswana 2 582 4 13.6 113 6,790 95 27.5 13,700 86 7.2 5.9 Brazil 195 8,515 23 1,830.4 8 9,390 82 2,144.9 11,000 98 7.5 6.6 Bulgaria 8 111 69 47.3 75 6,280 101 101.2 13,440 89 0.2 0.9 Burkina Faso 16 274 60 9.0 131 550 193 20.7 1,250 190 9.2 6.0 Burundi 8 28 326 1.4 184 170 215 3.4 400 213 3.9 1.3 Cambodia 14 181 80 10.6 121 750 185 29.4 2,080 174 6.0 4.8 Cameroon 20 475 41 23.2 95 1,180 168 44.4 2,270 170 2.6 1.0 Canada 34 9,985 4 1,475.9 10 43,250 23 1,309.5 38,370 27 3.2 2.0 Central African Republic 4 623 7 2.1 177 470 199 3.5 790 209 3.3 1.4 Chad 11 1,284 9 6.9 140 620 189 13.7 1,220 193 4.3 1.6 Chile 17 756 23 173.2 47 10,120 78 250.5 14,640 80 5.2 4.2 China 1,338 9,600 143 5,720.8 2 4,270 121 10,221.7 7,640 120 10.4 9.8 Hong Kong SAR, China 7 1 6,783 231.7 37 32,780 36 335.6 47,480 15 7.0 6.0 Colombia 46 1,142 42 255.3 34 5,510 108 419.6 9,060 109 4.3 2.9 Congo, Dem. Rep. 66 2,345 29 12.0 119 180 214 21.4 320 215 7.2 4.3 Congo, Rep. 4 342 12 8.7 135 2,150 152 13.0 3,220 160 8.8 6.0 Costa Rica 5 51 91 31.7 87 6,810 94 52.5a 11,270a 96 4.2 2.7 Côte d’Ivoire 20 322 62 23.0 96 1,160 171 35.8 1,810 182 3.0 1.0 Croatia 4 57 79 61.4 66 13,890 67 83.4 18,890 73 –1.2 –0.9 Cuba 11 110 106 62.2 64 5,520 107 .. ..   4.3 4.3 Cyprus 1 9 119 23.7c 93 29,430 c 40 24.4 c 30,300 c 42 1.0 c 0.6c Czech Republic 11 79 136 188.3 43 17,890 59 241.0 22,910 63 2.3 2.0 Denmark 6 43 131 329.5 29 59,400 10 228.0 41,100 23 1.3 0.9 Dominican Republic 10 49 205 49.9 72 5,030 110 89.6a 9,030a 111 7.8 6.3 Ecuador 14 256 58 55.7 69 3,850 b 126 114.0 7,880 118 3.6 2.1 Egypt, Arab Rep. 81 1,001 81 196.2 41 2,420 148 491.3 6,060 129 5.1 3.3 El Salvador 6 21 299 21.0 97 3,380 131 40.6a 6,550a 126 1.4 0.9 Eritrea 5 118 52 1.8 180 340 209 2.8a 540a 212 2.2 –0.8 Estonia 1 45 32 19.4 101 14,460 66 26.5 19,810 68 3.1 3.1 Ethiopia 83 1,104 83 32.4 85 390 207 86.1 1,040 200 10.1 7.8 Finland 5 338 18 255.2 35 47,570 16 198.9 37,070 31 3.7 3.2 France 65 549 118 2,749.8 5 42,370 26 2,254.9 34,750 34 1.5 0.9 Gabon 2 268 6 11.7 120 7,740 89 19.8 13,180 90 5.7 3.8 Gambia, The 2 11 173 0.8 198 450 201 2.2 1,300 189 5.0 2.1 Georgia 4d 70 78d 12.0 d 118 2,690 d 145 22.2d 4,990 d 139 6.4 d 5.4 d Germany 82 357 235 3,522.0 4 43,070 25 3,115.4 38,100 29 3.7 3.8 Ghana 24 239 107 30.1 88 1,230 166 40.5 1,660 185 6.6 5.2 Greece 11 132 88 305.0 31 26,950 44 312.7 27,630 51 –3.5 –3.8 Guatemala 14 109 134 39.4 79 2,740 142 66.8a 4,650a 141 2.8 0.2 Guinea 10 246 41 4.0 160 400 206 10.2 1,020 202 1.9 –0.3 Guinea-Bissau 2 36 54 0.9 194 590 191 1.8 1,180 196 3.5 1.4 Haiti 10 28 363 6.6 141 670 188 11.6a 1,180a 195 –5.1 –6.3 20 2012 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 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10 Honduras 8 112 68 14.2 110 1,870 155 28.6a 3,770a 151 2.8 0.7 Hungary 10 93 110 128.6 53 12,860 69 195.5 19,550 69 1.3 1.5 India 1,225 3,287 412 1,553.9 9 1,270 164 4,159.7 3,400 157 8.8 7.3 Indonesia 240 1,905 132 599.2 18 2,500 147 1,008.2 4,200 148 6.1 5.0 Iran, Islamic Rep. 74 1,745 45 330.4 27 4,520 116 840.0 11,490 95 1.8 0.6 Iraq 32 435 74 74.9 63 2,340 149 108.1 3,370 158 0.8 –2.1 Ireland 4 70 65 187.1 44 41,820 29 150.1 33,540 37 –0.4 –0.8 Israel 8 22 352 207.2 39 27,180 43 210.8 27,660 50 4.7 2.8 Italy 60 301 206 2,159.3 7 35,700 33 1,923.7 31,810 39 1.5 1.1 Jamaica 3 11 249 13.0 115 4,800 111 19.7a 7,310a 122 –0.6 –0.8 Japan 127 378 350 5,334.4 3 41,850 28 4,411.7 34,610 35 4.0 4.1 Jordan 6 89 68 26.3 90 4,340 120 35.1 5,800 130 3.1 0.9 Kazakhstan 16 2,725 6 123.8 56 7,580 90 175.7 10,770 103 7.3 5.8 Kenya 41 580 71 31.8 86 790 183 68.1 1,680 184 5.3 2.8 Korea, Dem. Rep. 24 121 202 ..   ..e   .. ..   .. .. Korea, Rep. 49 100 503 972.3 15 19,890 55 1,422.7 29,110 43 6.2 5.9 Kosovo 2 11 167 6.0 147 3,290 132 .. ..   4.0 3.4 Kuwait 3 18 154 ..   ..f   .. ..   .. .. Kyrgyz Republic 5 200 28 4.5 157 830 181 11.3 2,070 175 –1.4 –2.5 Lao PDR 6 237 27 6.5 144 1,050 176 15.3 2,460 168 9.4 7.9 Latvia 2 65 36 26.1 91 11,640 74 36.7 16,380 76 –0.3 0.4 Lebanon 4 10 413 37.5 82 8,880 84 59.5 14,090 84 7.0 6.2 Lesotho 2 30 72 2.3 174 1,040 178 4.3 1,960 177 3.3 2.6 Liberia 4 111 41 0.8 197 200 213 1.4 340 214 5.5 1.3 Libya 6 1,760 4 77.1 61 12,320 72 105.7a 16,880a 75 2.1 0.3 Lithuania 3 65 52 37.8 81 11,510 76 59.4 18,060 74 1.3 2.9 Macedonia, FYR 2 26 82 9.4 128 4,570 115 22.5 10,920 100 1.8 1.6 Madagascar 21 587 36 8.8 134 430 203 19.9 960 204 1.6 –1.3 Malawi 15 118 158 4.9 156 330 211 12.7 850 207 7.1 3.8 Malaysia 28 331 86 220.4 38 7,760 87 403.9 14,220 83 7.2 5.5 Mali 15 1,240 13 9.2 130 600 190 15.8 1,030 201 4.5 1.4 Mauritania 3 1,031 3 3.6 162 1,030 179 6.6 1,910 180 5.0 2.7 Mauritius 1 2 631 9.9 126 7,750 88 17.9 13,960 85 4.0 3.7 Mexico 113 1,964 58 1,008.0 14 8,890 83 1,627.0 14,340 81 5.4 4.1 Moldova 4g 34 124g 6.5g 145 1,810 g 158 12.0 g 3,360 g 159 6.9g 7.1g Mongolia 3 1,564 2 5.2 153 1,870 155 10.1 3,670 152 6.4 4.7 Morocco 32 447 72 92.6h 59 2,850h 140 149.3h 4,600h 143 3.7h 2.6h Mozambique 23 799 30 10.3 123 440 202 21.7 930 205 7.2 4.8 Myanmar 48 677 73 ..   ..e   93.5a 1,950a 178 10.4 9.6 Namibia 2 824 3 10.3 124 4,510 118 14.7 6,420 127 4.8 2.9 Nepal 30 147 209 14.5 108 490 197 36.2 1,210 194 4.6 2.7 Netherlands 17 42 493 814.8 16 49,030 14 694.7 41,810 22 1.7 1.2 New Zealand 4 268 17 124.2 54 28,770 42 121.3 28,100 46 –0.5 –1.6 Nicaragua 6 130 48 6.4 146 1,110 173 16.1a 2,790a 164 7.6 6.1 Niger 16 1,267 12 5.7 149 370 208 11.2 720 210 8.8 5.0 Nigeria 158 924 174 186.4 45 1,180 168 344.2 2,170 172 7.9 5.2 Norway 5 324 16 427.1 24 87,350 4 286.3 58,570 6 0.7 –0.6 Oman 3 310 9 49.5 71 18,260 58 68.3 25,190 56 1.1 –1.7 Pakistan 174 796 225 182.8 46 1,050 176 484.4 2,790 164 4.1 2.3 Panama 4 75 47 24.5 92 6,970 92 44.9a 12,770a 91 4.8 3.2 Papua New Guinea 7 463 15 8.9 133 1,300 161 16.6a 2,420a 169 8.0 5.6 Paraguay 6 407 16 17.6 106 2,720 144 32.8 5,080 137 15.0 13.0 Peru 29 1,285 23 136.7 52 4,700 114 259.6 8,930 112 8.8 7.6 Philippines 93 300 313 192.2 42 2,060 153 370.7 3,980 149 7.6 5.8 Poland 38 313 126 474.9 21 12,440 71 731.5 19,160 71 3.9 3.9 Portugal 11 92 116 232.7 36 21,870 48 261.6 24,590 59 1.4 1.3 Puerto Rico 4 9 448 61.7 65 15,500 63 .. ..   –2.1 –2.3 Qatar 2 12 152 ..   ..f   .. ..   8.6 –5.1 2012 World Development Indicators 21 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 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10 Romania 21 238 93 168.2 48 7,850 86 306.4 14,290 82 0.9 1.1 Russian Federation 142 17,098 9 1,403.9 12 9,900 79 2,726.8 19,240 70 4.0 4.1 Rwanda 11 26 431 5.5 151 520 195 12.3 1,150 197 7.5 4.3 Saudi Arabia 27 2,150i 13 434.1 23 16,190 61 609.8 22,750 64 3.8 1.3 Senegal 12 197 65 13.5 114 1,090 174 23.8 1,910 180 4.2 1.4 Serbia 7 88 83 41.0 78 5,630 106 80.8 11,090 97 1.0 1.4 Sierra Leone 6 72 82 2.0 178 340 209 4.9 830 208 4.9 2.7 Singapore 5 1 7,253 203.4 40 40,070 30 283.3 55,790 7 14.5 12.5 Slovak Republic 5 49 113 91.5 60 16,840 60 124.8 22,980 62 4.2 4.0 Slovenia 2 20 102 49.0 73 23,900 47 54.4 26,530 54 1.4 0.9 Somalia 9 638 15 ..   ..e   .. ..   .. .. South Africa 50 1,219 41 304.6 32 6,090 102 517.9 10,360 105 2.8 1.5 South Sudan .. .. .. ..   ..j   .. ..   .. .. Spain 46 505 92 1,462.9 11 31,750 39 1,465.2 31,800 40 –0.1 –0.5 Sri Lanka 21 66 333 46.7 76 2,240 150 104.6 5,010 138 8.0 7.0 Sudan 44 2,506 18 55.3 70 1,270 164 88.6 2,030 176 4.5 1.9 Swaziland 1 17 61 3.1 168 2,950 138 5.7 5,430 134 1.1 0.8 Sweden 9 450 23 469.8 22 50,100 13 372.6 39,730 25 5.6 4.7 Switzerland 8 41 196 559.7 19 71,520 7 391.0 49,960 14 2.7 1.6 Syrian Arab Republic 20 185 111 56.3 68 2,750 141 104.6 5,120 136 3.2 1.1 Tajikistan 7 143 49 5.5 152 800 182 14.7 2,140 173 3.8 2.4 Tanzania 45 947 51 23.4k 94 530k 194 62.6k 1,430k 187 7.0k 3.9k Thailand 69 513 135 286.6 33 4,150 123 565.8 8,190 115 7.8 7.2 Timor-Leste 1 15 76 2.5 171 2,220 151 4.0a 3,600a 153 7.4 5.1 Togo 6 57 111 3.0 169 490 197 5.4 890 206 3.4 1.2 Trinidad and Tobago 1 5 261 20.6 98 15,380 64 32.3a 24,050a 60 0.1 –0.2 Tunisia 11 164 68 43.9 77 4,160 122 95.6 9,060 109 3.7 2.6 Turkey 73 784 95 719.9 17 9,890 80 1,129.9 15,530 79 9.0 7.6 Turkmenistan 5 488 11 19.1 103 3,790 128 37.8a 7,490a 121 9.2 7.9 Uganda 33 242 167 16.6 107 500 196 41.8 1,250 190 5.2 1.9 Ukraine 46 604 79 137.8 51 3,000 136 303.8 6,620 125 4.2 4.6 United Arab Emirates 8 84 90 290.9 30 41,930 27 351.0 50,580 11 1.4 –6.3 United Kingdom 62 244 257 2,377.2 6 38,200 31 2,230.6 35,840 33 2.1 1.4 United States 309 9,832 34 14,645.6 1 47,340 17 14,635.6 47,310 16 3.0 2.1 Uruguay 3 176 19 34.3 84 10,230 77 45.7 13,620 87 8.5 8.1 Uzbekistan 28 447 66 36.1 83 1,280 162 87.7a 3,110a 161 8.5 6.7 Venezuela, RB 29 912 33 334.1 28 11,590 75 350.2 12,150 93 –1.5 –3.0 Vietnam 87 331 280 101.1 58 1,160 171 267.0 3,070 162 6.8 5.7 West Bank and Gaza 4 6 690 .. ..j   .. ..   .. .. Yemen, Rep. 24 528 46 28.1 89 1,170 170 60.1 2,500 166 8.0 4.8 Zambia 13 753 17 13.8 111 1,070 175 17.8 1,380 188 7.6 5.9 Zimbabwe 13 391 32 5.8 148 460 200 .. ..   9.0 8.2 World 6,895 s 134,222 s 53 w 62,525.2 t   9,069 w   76,295.6 t 11,066 w   4.2 w 3.0 w Low income 796 15,551 53 420.2   528   1,040.5 1,307   5.9 3.7 Middle income 4,971 82,896 62 18,508.7   3,723   33,538.4 6,747   7.6 6.4 Lower middle income 2,519 23,568 111 4,077.7   1,619   9,147.8 3,632   6.9 5.3 Upper middle income 2,452 59,328 42 14,429.0   5,884   24,447.4 9,970   7.8 7.1 Low & middle income 5,767 98,447 60 18,948.9   3,286   34,577.9 5,996   7.6 6.2 East Asia & Pacific 1,962 16,302 124 7,249.4   3,696   13,058.0 6,657   9.7 8.9 Europe & Central Asia 405 23,603 18 2,946.7   7,272   5,428.2 13,396   5.7 5.3 Latin America & Carib. 583 20,394 29 4,505.0   7,733   6,365.1 10,926   6.2 5.0 Middle East & N. Africa 331 8,775 38 1,283.5   3,874   2,627.3 8,068   4.3 2.5 South Asia 1,633 5,131 342 1,920.1   1,176   5,101.4 3,124   8.1 6.6 Sub-Saharan Africa 853 24,243 36 1,003.6   1,176   1,833.4 2,148   4.8 2.3 High income 1,127 35,774 33 43,682.7   38,745   42,072.5 37,317   3.1 2.5 Euro area 332 2,628 130 12,794.4   38,565   11,399.6 34,360   2.0 1.6 a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Included in the aggregates for upper middle-income economies based on earlier data. c. Refers to the area controlled by the government of the Republic of Cyprus. d. Excludes Abkhazia and South Ossetia. e. Estimated to be low income ($1,005 or less). f. Estimated to be high income ($12,276 or more). g. Excludes Transnistria. h. Includes Former Spanish Sahara. i. Provisional estimate. j. Estimated to be lower middle income ($1,006–$3,975). k. Covers mainland Tanzania only. 22 2012 World Development Indicators 1.1 WORLD VIEW Size of the economy About the data De�nitions 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 compar- legal status or citizenship—except for refugees not resources. Population, land area, income (as mea- ing the prices of similar goods and services among a permanently settled in the country of asylum, who sured by gross national income, GNI), and output large number of countries. In the most recent round are generally considered part of the population of (as measured by gross domestic product, GDP) are of price surveys conducted by the International Com- their country of origin. The values shown are midyear therefore used throughout World Development Indica- parison Program (ICP), 146 countries and territories estimates. See also table 2.1. •  Surface area is tors to normalize other indicators. participated in the data collection, including China a country’s total area, including areas under inland Population estimates are generally based on for the first time, India for the first time since 1985, bodies of water and some coastal waterways. • Pop- extrapolations from the most recent national cen- and almost all African countries. The PPP conver- ulation density is midyear population divided by land sus. For further discussion of the measurement of sion factors presented in the table come from three area in square kilometers. • Gross national income population and population growth, see About the data sources. For 47 high- and upper middle-income (GNI) is the sum of value added by all resident pro- for table 2.1. countries conversion factors are provided by Euro- ducers plus any product taxes (less subsidies) not The surface area of an economy includes inland stat and the Organisation for Economic Co-operation included in the valuation of output plus net receipts bodies of water and some coastal waterways. Sur- and Development (OECD), with PPP estimates for of primary income (compensation of employees and face area thus differs from land area, which excludes 35 European countries incorporating new price data property income) from abroad. Data are in current bodies of water, and from gross area, which may collected since 2005. For the remaining 2005 ICP U.S. dollars converted using the World Bank Atlas include offshore territorial waters. Land area is par- countries the PPP estimates are extrapolated from method (see Statistical methods). • Gross national ticularly important for understanding an economy’s the 2005 ICP benchmark results, which account for income per capita is GNI divided by midyear popula- agricultural capacity and the environmental effects relative price changes between each economy and tion. GNI per capita in U.S. dollars is converted using of human activity. (For measures of land area and the United States. For countries that did not partici- the World Bank Atlas method. • Purchasing power data on rural population density, land use, and agri- pate in the 2005 ICP round, the PPP estimates are parity (PPP) gross national income is GNI converted cultural productivity, see tables 3.1–3.3.) Innova- imputed using a statistical model. More information to international dollars using PPP rates. An interna- tions in satellite mapping and computer databases on the results of the 2005 ICP is available at www. tional dollar has the same purchasing power over GNI have resulted in more precise measurements of land worldbank.org/data/icp. that a U.S. dollar has in the United States. • Gross and water areas. All 216 economies shown in World Development domestic product (GDP) is the sum of value added GNI measures total domestic and foreign value Indicators are ranked by size, including those that by all resident producers plus any product taxes (less added claimed by residents. GNI comprises GDP appear in table 1.6. The ranks are shown only in subsidies) not included in the valuation of output. plus net receipts of primary income (compensation table 1.1. No rank is shown for economies for which Growth is calculated from constant price GDP data in of employees and property income) from nonresident numerical estimates of GNI per capita are not pub- local currency. • Gross domestic product per capita sources. The World Bank uses GNI per capita in U.S. lished. Economies with missing data are included in is GDP divided by midyear population. 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 2012 World Development Indicators 23 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 live births 2000–11 a,b 1990 2007–10a 1990 2005–10a 1991 2010 1991 2010c 1990 2010 Afghanistan 9.4 .. .. .. .. 28 .. 54 64 209 149 Albania 8.1 .. .. .. 6.3 .. 86 96 98 41 18 Algeria .. .. .. 9.2 3.7 80 96 83 98 68 36 Angola 2.0 d .. .. .. 15.6 33 47 .. 79 243 161 Argentina 4.4 d 26e 20e .. 2.3 100 106 107 104 27 14 Armenia 8.8 .. 38 .. 4.2 105 101 .. 102 55 20 Australia .. 10 9 .. .. .. .. 100 98 9 5 Austria 8.6 .. 9 .. .. .. 99 95 97 9 4 Azerbaijan 8.0 .. 55 .. 8.4 95 90 100 99 93 46 Bahrain .. .. 2 6.3 .. 97 .. 101 .. 17 10 Bangladesh 8.9 .. .. 61.5 41.3 41 65 75 107 143 48 Belarus 9.2 .. .. .. 1.3 94 103 .. 101 17 6 Belgium 8.5 17 10 .. .. 79 90 101 98 10 4 Benin 7.0 .. .. .. 20.2 22 63 .. .. 178 115 Bolivia 2.1 40e 57 9.7 4.5 71 99 .. 99 121 54 Bosnia and Herzegovina 6.7 .. .. .. 1.6 .. 70 .. 102 19 8 Botswana .. .. .. .. 11.2 90 94 109 100 59 48 Brazil 2.9 29e 25 .. 2.2 93 .. .. 102 59 19 Bulgaria 8.5 .. 9 .. .. 90 95 99 97 22 13 Burkina Faso 6.7 .. .. 29.6 26.0 20 45 .. 89 f 205 176 Burundi 9.0 .. .. 30.2 35.2 46 56 82 94 183 142 Cambodia 7.5 .. 83 .. 28.8 45 87 .. 94 121 51 Cameroon 6.7 .. .. 18 16.6 53 79 83 85 137 136 Canada 7.2 .. .. .. .. .. .. 99 99 8 6 Central African Republic 3.4 .. .. .. .. 28 41 61 69 165 159 Chad 6.3 94 .. .. .. 18 33 41 65 207 173 Chile 4.3 .. 26 .. 0.5 .. 96 100 99 19 9 China 5.0 .. .. 12.6 3.4 107 .. 86 103 48 18 Hong Kong SAR, China .. 6 7 .. .. 102 96 102 102 .. .. Colombia 3.0 28 49 8.8 3.4 73 114 108 104 37 22 Congo, Dem. Rep. 5.5 .. .. .. 28.2 48 59 70 79 181 170 Congo, Rep. 5.0 .. .. 21.1 11.8 54 71 89 .. 116 93 Costa Rica 3.9 25 20 2.5 1.1 79 96 101 102 17 10 Côte d’Ivoire 5.6 .. .. .. 29.4 42 59 f .. .. 151 123 Croatia 8.1 .. 18 .. 1.0 85 95 103 102 13 6 Cuba .. .. .. .. .. 99 98 106 98 13 6 Cyprus .. .. 14 .. .. 90 103 100 100 11 4 Czech Republic .. 7 14 0.9 .. 92 101 98 101 14 4 Denmark .. 7 5 .. .. 98 97 101 101 9 4 Dominican Republic 4.7 39 42 8.4 3.4 61 92 .. 97 62 27 Ecuador 4.3 36e 43 .. .. 91 106 100 103 52 20 Egypt, Arab Rep. 9.2 28 27 10.5 6.8 .. 98 81 .. 94 22 El Salvador 3.7 35 38 11.1 6.6 65 96 101 98 62 16 Eritrea .. .. .. 36.9 .. 18 40 82 80 141 61 Estonia 6.8 2 5 .. .. .. 98 103 101 21 5 Ethiopia 9.3 92 .. .. 34.6 23 72 68 89 184 106 Finland 9.6 .. 9 .. .. 97 98 109 102 7 3 France .. 11 7 .. .. 106 .. 102 100 9 4 Gabon 6.2 48 .. .. .. 62 .. 96 .. 93 74 Gambia, The 4.8 .. .. .. 15.8 45 71 65 99 165 98 Georgia 5.3 .. 63 .. 1.1 .. 116 98 97 47 22 Germany 8.5 5 7 .. 1.1 100 100 99 96 9 4 Ghana 5.2 .. .. 24.1 14.3 64 94f 78 96f 122 74 Greece 6.7 42 28 .. .. 99 101 99 97 13 4 Guatemala 3.1 .. .. 27.8 13.0 .. 84 87 95 78 32 Guinea 6.4 .. .. .. 20.8 17 64 45 77 229 130 Guinea-Bissau 7.3 .. .. .. 17.2 5 68 55 .. 210 150 Haiti 2.4 .. .. 23.7 18.9 27 .. .. .. 151 165 24 2012 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 live births 2000–11 a,b 1990 2007–10a 1990 2005–10a 1991 2010 1991 2010c 1990 2010 Honduras 2.0 49e 50 15.8 8.6 64 99 104 107 58 24 Hungary 8.4 7 7 2.3 .. 82 98 100 99 19 6 India 8.6 .. .. 59.5 43.5 64 96 73 92 115 63 Indonesia 8.3 .. 64 31.0 17.5 93 105 93 101 85 35 Iran, Islamic Rep. 6.4 .. 42 .. .. 88 104 85 96 65 26 Iraq 8.7 .. .. 10.4 7.1 58 65 79 81 46 39 Ireland 7.4 20 12 .. .. 103 .. 104 103 9 4 Israel 5.7 .. 7 .. .. .. 103 105 101 12 5 Italy 6.5 27 19 .. .. 98 103 100 99 10 4 Jamaica 5.4 42 37 4.0 1.9 94 73 103 99 38 24 Japan .. 19 10 .. .. 102 102 101 100 6 3 Jordan 7.7 .. 10 4.8 1.9 101 101 101 102 38 22 Kazakhstan 9.1 .. 32 .. 4.9 103 116f .. 98f 57 33 Kenya 4.8 .. .. 20.1 16.4 .. .. 92 95 99 85 Korea, Dem. Rep. .. .. .. .. 18.8 .. .. .. .. 45 33 Korea, Rep. .. .. 24 .. .. 99 101 99 99 8 5 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. 1.7 57 112 100 105 15 11 Kyrgyz Republic 6.8 .. .. .. 2.7 .. 97 102 99 72 38 Lao PDR 7.6 .. .. 39.8 31.6 41 79 77 87 145 54 Latvia 6.6 .. 8 .. .. .. 92 101 98 21 10 Lebanon .. .. 28 .. .. .. 87 101 104 38 22 Lesotho 3.0 .. .. 13.8 13.5 59 70 124 106 89 85 Liberia 6.4 .. 79 .. 20.4 .. 62 .. .. 227 103 Libya .. .. .. .. 5.6 .. .. .. .. 45 17 Lithuania 6.6 .. 9 .. .. .. 96 96 99 17 7 Macedonia, FYR 5.1 .. 23 .. 1.8 98 92 99 99 39 12 Madagascar 5.4 84 .. 35.5 .. 36 72 96 97 159 62 Malawi 7.0 84 .. 24.4 13.8 31 67 82 101 222 92 Malaysia 4.5 29 22 22.1 12.9 91 .. 101 .. 18 6 Mali 8.0 .. 83 29.0 27.9 9 55f 58 83f 255 178 Mauritania 6.0 .. .. 43.3 15.9 33 75 71 101 124 111 Mauritius .. 12 16 .. .. 115 96 102 100 24 15 Mexico 4.4 26 30 13.9 3.4 88 104 97 102 49 17 Moldova 7.8 .. 29 .. 3.2 .. 93 105 101 37 19 Mongolia 7.1 .. 58 10.8 5.3 .. 108 109 103 107 32 Morocco 6.5 .. 51 8.1 .. 48 85 70 89 86 36 Mozambique 5.2 .. .. .. 18.3 26 61 71 89 219 135 Myanmar .. .. .. 28.8 .. .. 104 95 102 112 66 Namibia 3.2 .. .. 21.5 17.5 74 84 106 104 73 40 Nepal 8.3 .. .. .. 38.8 51 .. 59 .. 141 50 Netherlands .. 9 11 .. .. .. .. 97 99 8 4 New Zealand .. 13 11 .. .. .. .. 100 103 11 6 Nicaragua 6.2 43e 45 9.6 5.7 42 81 119 102 68 27 Niger 8.1 .. .. 41.0 39.9 17 46f 53 78 311 143 Nigeria 4.4 .. .. 35.1 26.7 .. 74 77 90 213 143 Norway 9.6 .. 6 .. .. 100 100 102 99 9 3 Oman .. .. .. 21.4 8.6 74 101 89 98 47 9 Pakistan 9.6 .. 63 39.0 .. .. 67 48 80 124 87 Panama 3.3 34 31 .. .. 86 97 99 101 33 20 Papua New Guinea .. .. .. .. 18.1 46 .. 80 .. 90 61 Paraguay 3.3 23e 45 2.8 3.4 68 94 98 100 50 25 Peru 3.9 36e 40e 8.8 4.5 .. 102 96 99 78 19 Philippines 6.0 .. 44 29.8 20.7 88 92 99 101 59 29 Poland 7.7 28 19 .. .. 96 95 101 99 17 6 Portugal .. 24 18 .. .. .. .. 103 100 15 4 Puerto Rico .. .. .. .. .. .. .. .. 104 .. .. Qatar 3.9 .. 0 .. .. 71 100 98 109 21 8 2012 World Development Indicators 25 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 live births 2000–11 a,b 1990 2007–10a 1990 2005–10a 1991 2010 1991 2010c 1990 2010 Romania 8.3 27 33 5.0 .. 96 91 99 99 37 14 Russian Federation 6.5 1 6 .. .. 92 98 105 98 27 12 Rwanda 5.2 .. .. 24.3 18.0 50 70 95 102 163 91 Saudi Arabia .. .. .. .. 5.3 .. 93 .. 97 45 18 Senegal 6.2 83 .. 19.0 14.5 39 59 69 100 139 75 Serbia 8.9 .. 28 .. 1.8 .. 96 .. 101 29 7 Sierra Leone 6.1 .. .. 25.4 21.3 .. 74f 64 .. 276 174 Singapore .. 8 10 .. .. .. .. .. .. 8 3 Slovak Republic 10.1 .. 12 .. .. 95 98 102 101 18 8 Slovenia 8.2 12 14 .. .. 95 95 103 99 10 3 Somalia .. .. .. .. 32.8 .. .. .. 53 180 180 South Africa 2.7 .. 10 .. 8.7 76 .. 104 99 60 57 South Sudan .. .. .. .. .. .. .. .. .. .. .. Spain 7.0 23 11 .. .. 104 102 104 102 11 5 Sri Lanka 6.9 43e 40e 29.3 21.6 101 101 102 .. 32 17 Sudan 6.8 .. .. 31.8 31.7 .. 58 78 90 125 103 Swaziland 4.1 .. .. .. 7.3 61 77 .. 94 96 78 Sweden 9.1 .. 7 .. .. 96 94 102 99 7 3 Switzerland 7.6 9 9 .. .. 53 95 97 98 8 5 Syrian Arab Republic 7.7 .. 33 11.5 10.1 89 104 85 99 38 16 Tajikistan 8.3 .. .. .. 15.0 .. 104 .. 90 116 63 Tanzania 6.8 91e .. 25.1 16.2 55 90 97 96 155 92 Thailand 6.7 70 53 16.3 7.0 .. .. 99 103 32 13 Timor-Leste 9.0 .. .. .. 45.3 .. 65 .. 98 169 81 Togo 7.6 .. .. 21.2 20.5 35 74 59 75 147 103 Trinidad and Tobago .. 22 .. 4.7 .. 102 91 101 101 37 27 Tunisia 5.9 .. .. 8.5 3.3 74 91 86 101 49 16 Turkey 5.7 .. 34 8.7 .. 90 99 81 95 80 18 Turkmenistan .. .. .. .. .. .. .. .. .. 98 56 Uganda 5.8 .. .. 19.7 16.4 .. 57 77 99 175 99 Ukraine 9.7 .. .. .. .. 92 98 102 99 21 13 United Arab Emirates .. .. 1 .. .. 103 .. 104 .. 22 7 United Kingdom .. 10 11 .. .. .. .. 102 101 9 5 United States 5.4 .. .. .. .. .. 104 100 100 11 8 Uruguay 4.9 .. 23e 6.5 .. 94 106 107 104 23 11 Uzbekistan 7.1 .. .. .. 4.4 80 93f .. 98f 77 52 Venezuela, RB 4.3 .. 31 6.7 3.7 81 94 105 102 33 18 Vietnam 7.4 .. .. 40.7 20.2 .. .. .. 102 51 23 West Bank and Gaza 7.4 .. 28 .. 2.2 .. 95 .. 104 45 22 Yemen, Rep. 7.2 .. .. 29.6 .. .. 63 .. 75 128 77 Zambia 3.6 65 .. 21.2 14.9 .. 103 87 96 183 111 Zimbabwe .. .. .. 8.0 14.0 97 .. 92 .. 78 80 World .. .. w .. w .. w .. w 79 w 88 w 87 w 96 w 90 w 58 w Low income .. .. .. 40.2 23.0 44 65 80 91 165 108 Middle income .. .. .. .. .. 83 92 85 97 85 51 Lower middle income .. .. 73 38.1 24.6 68 88 81 93 113 69 Upper middle income .. .. .. 12.5 3.0 97 98 98 103 49 20 Low & middle income .. .. .. 28.7 17.9 78 87 84 96 98 63 East Asia & Pacific .. .. .. 20.4 5.8 101 97 89 103 56 24 Europe & Central Asia .. .. 19 8.5 1.9 92 95 98 97 51 23 Latin America & Carib. .. 29 31 7.5 3.0 84 102 99 102 54 23 Middle East & N. Africa .. .. 36 11.8 7.9 .. 88 80 93 74 34 South Asia .. .. 81 52.2 32.9 62 86 69 92 120 67 Sub-Saharan Africa .. .. .. 29.0 22.0 51 67 82 89 175 121 High income .. .. .. .. .. .. 97 100 100 12 6 Euro area .. 15 11 .. .. 101 101 .. 99 10 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 2011. 26 2012 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives About the data De�nitions 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 is distributions of consumption (or income) have a Most of the 60 indicators relating to the Millennium the ratio of the female to male gross enrollment rate higher rate of poverty for a given average income. Development Goals can be found in World Develop- in primary and secondary education. • Under-�ve Vulnerable employment measures the portion of the ment Indicators. Table 1.2a shows where to find the mortality rate is the probability of a child born in labor force that receives the lowest wages and least indicators for the first four goals. For more informa- a specific year dying before reaching age 5, if sub- security in employment. No single indicator captures tion about data collection methods and limitations, ject to the age-specific mortality rate of that year. the concept of suffering from hunger. Child malnutri- see About the data for the tables listed there. For The probability is derived from life tables and is tion is a symptom of inadequate food supply, lack information about the indicators for goals 5–8, see expressed as a rate per 1,000 live births. 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 2.8 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 5 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, 2.23, 5.8 un.org/millenniumgoals), but some differences in 4.2 Infant mortality rate 2.23 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. 2012 World Development Indicators 27 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 Nationally mortality ratio Contraceptive HIV protected Modeled prevalence prevalence Incidence Carbon dioxide terrestrial and estimate rate % of of tuberculosis emissions marine areas Access to improved per 100,000 % of married women population per 100,000 per capita % of total sanitation facilities Internet usersa live births ages 15–49 ages 15–49 people metric tons land area % of population per 100 people 2008 1990 2005–10b 2009 2010 1990 2008 2010 1990 2010 2010 Afghanistan 1,400 .. 23 .. 189 0.1 0.0 0.4 .. 37 3.7 Albania 31 .. 69 .. 14 2.3 1.3 8.4 76 94 45.0 Algeria 120 51 61 0.1 90 3.1 3.2 6.2 88 95 12.5 Angola 610 .. .. 2.0 304 0.4 1.4 12.1 29 58 10.0 Argentina 70 .. 78 0.5 27 3.4 4.8 5.3 90 .. 36.0 Armenia 29 56 55 0.1 73 .. 1.8 8.0 .. 90 44.0 Australia 8 .. .. 0.1 6 16.8 18.6 12.5 100 100 75.9 Austria 5 .. .. 0.3 5 7.9 8.1 22.9 100 100 72.7 Azerbaijan 38 .. 51 0.1 110 .. 5.4 7.1 .. 82 46.7 Bahrain 19 54 .. .. 23 24.1 21.4 0.7 .. .. 55.0 Bangladesh 340 40 53 <0.1 225 0.1 0.3 1.6 39 56 3.7 Belarus 15 .. 73 0.3 70 .. 6.5 7.2 93 93 32.1 Belgium 5 78 .. 0.2 9 10.9 9.8 13.2 100 100 73.7 Benin 410 .. 17 1.2 94 0.1 0.5 23.3 5 13 3.1 Bolivia 180 30 61 0.2 135 0.8 1.3 18.5 18 27 20.0 Bosnia and Herzegovina 9 .. 36 .. 50 .. 8.3 0.6 .. 95 52.0 Botswana 190 33 53 24.8 503 1.6 2.5 30.9 38 62 6.0 Brazil 58 59 81 .. 43 1.4 2.1 26.0 68 79 40.7 Bulgaria 13 .. .. 0.1 40 8.9 6.6 8.9 99 100 46.0 Burkina Faso 560 .. 17 1.2 55 0.1 0.1 14.2 8 17 1.4 Burundi 970 .. 22 3.3 129 0.1 0.0 4.8 44 46 2.1 Cambodia 290 .. 51 0.5 437 0.0 0.3 23.4 9 31 1.3 Cameroon 600 16 29 5.3 177 0.1 0.3 9.0 48 49 4.0 Canada 12 .. .. 0.2 5 16.2 16.3 6.2 100 100 81.3 Central African Republic 850 .. 19 4.7 319 0.1 0.1 17.7 11 34 2.3 Chad 1,200 .. 5 3.4 276 0.0 0.0 9.4 8 13 1.7 Chile 26 56 58 0.4 19 2.6 4.4 13.3 84 96 45.0 China 38 85 85 0.1c 78 2.2 5.3 16.0 24 64 34.4 Hong Kong SAR, China .. 86 80 .. 80 4.8 5.5 41.8 .. .. 71.8 Colombia 85 66 79 0.5 34 1.7 1.5 20.5 67 77 36.5 Congo, Dem. Rep. 670 8 17 .. 327 0.1 0.0 10.0 9 24 0.7 Congo, Rep. 580 .. 44 3.4 372 0.5 0.5 9.7 .. 18 5.0 Costa Rica 44 .. 80 0.3 13 1.0 1.8 17.6 93 95 36.5 Côte d’Ivoire 470 .. 13 3.4 139 0.5 0.4 21.8 20 24 2.6 Croatia 14 .. .. <0.1 21 .. 5.3 9.5 99 99 60.1 Cuba 53 .. 78 0.1 9 3.2 2.8 5.3 80 91 15.9 Cyprus 10 .. .. .. 4 6.1 7.9 4.5 100 100 53.0 Czech Republic 8 78 .. <0.1 7 .. 11.2 15.1 100 98 68.6 Denmark 5 78 .. 0.2 6 9.8 8.4 4.1 100 100 88.8 Dominican Republic 100 56 73 0.9 67 1.3 2.2 24.1 73 83 39.5 Ecuador 140 53 .. 0.4 65 1.6 1.9 38.0 69 92 29.0 Egypt, Arab Rep. 82 48 60 <0.1 18 1.3 2.7 6.1 72 95 26.7 El Salvador 110 47 73 0.8 28 0.5 1.0 1.4 75 87 15.9 Eritrea 280 .. .. 0.8 100 .. 0.1 3.8 9 .. 5.4 Estonia 12 .. .. 1.2 25 .. 13.6 22.6 95 95 74.2 Ethiopia 470 5 15 .. 261 0.1 0.1 18.4 3 21 0.7 Finland 8 77 .. 0.1 7 10.2 10.6 8.5 100 100 86.9 France 8 81 71 0.4 9 6.9 5.9 17.1 100 100 77.5 Gabon 260 .. .. 5.2 553 5.2 1.7 14.6 .. 33 7.2 Gambia, The 400 12 .. 2.0 273 0.2 0.3 1.3 .. 68 9.2 Georgia 48 .. 53 0.1 107 .. 1.2 3.4 96 95 26.3 Germany 7 70 .. 0.1 5 .. 9.6 42.3 100 100 82.5 Ghana 350 17 24 1.8 86 0.3 0.4 14.0 7 14 9.5 Greece 2 .. .. 0.1 5 7.2 8.7 9.9 97 98 44.6 Guatemala 110 .. 54 0.8 62 0.6 0.9 29.5 62 78 10.5 Guinea 680 .. 9 1.3 334 0.2 0.1 6.4 10 18 1.0 Guinea-Bissau 1,000 .. 14 2.5 233 0.2 0.2 26.9 .. 20 2.5 Haiti 300 10 32 1.9 230 0.1 0.3 0.1 26 17 8.4 28 2012 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 Nationally mortality ratio Contraceptive HIV protected Modeled prevalence prevalence Incidence Carbon dioxide terrestrial and estimate rate % of of tuberculosis emissions marine areas Access to improved per 100,000 % of married women population per 100,000 per capita % of total sanitation facilities Internet usersa live births ages 15–49 ages 15–49 people metric tons land area % of population per 100 people 2008 1990 2005–10b 2009 2010 1990 2008 2010 1990 2010 2010 Honduras 110 47 65 0.8 51 0.5 1.2 13.9 50 77 11.1 Hungary 13 .. .. <0.1 15 6.1 5.4 5.1 100 100 65.2 India 230 45 54 0.3 185 0.8 1.5 4.8 18 34 7.5 Indonesia 240 50 56 0.2 189 0.8 1.7 6.4 32 54 9.9 Iran, Islamic Rep. 30 49 79 0.2 17 4.1 7.4 6.9 79 100 13.0 Iraq 75 14 50 .. 64 2.9 3.4 0.1 .. 73 2.5 Ireland 3 .. 65 0.2 8 8.7 9.9 1.2 99 99 69.8 Israel 7 68 .. 0.2 5 7.2 5.2 15.1 100 100 65.4 Italy 5 .. .. 0.3 5 7.5 7.4 15.9 .. .. 53.7 Jamaica 89 55 72 1.7 7 3.3 4.5 7.3 80 80 26.5 Japan 6 58 54 <0.1 21 8.9 9.5 10.9 100 100 77.6 Jordan 59 40 59 .. 5 3.3 3.7 1.9 97 98 38.9 Kazakhstan 45 .. 51 0.1 151 .. 15.1 2.5 96 97 33.4 Kenya 530 27 46 6.3 298 0.2 0.3 11.7 25 32 25.9 Korea, Dem. Rep. 250 62 .. .. 345 .. 3.2 3.9 .. 80 0.0 Korea, Rep. 18 79 80 <0.1 97 5.7 10.5 3.0 100 100 82.5 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 9 .. .. .. 41 21.8 30.1 1.1 100 100 38.3 Kyrgyz Republic 81 .. 48 0.3 159 .. 1.2 6.9 .. 93 19.6 Lao PDR 580 .. 38 0.2 90 0.1 0.3 16.6 .. 63 7.0 Latvia 20 .. .. 0.7 39 .. 3.3 16.4 .. .. 71.5 Lebanon 26 .. .. 0.1 17 3.1 4.1 0.4 .. .. 31.0 Lesotho 530 23 47 23.6 633 .. .. 0.5 .. 26 3.9 Liberia 990 .. 11 1.5 293 0.2 0.2 1.6 .. 18 7.0 Libya 64 .. .. .. 40 9.3 9.5 0.1 97 97 14.0 Lithuania 13 .. .. 0.1 69 .. 4.5 14.4 .. .. 62.8 Macedonia, FYR 9 .. 14 .. 21 .. 5.8 4.9 .. 88 51.9 Madagascar 440 17 40 0.2 266 0.1 0.1 2.5 9 15 1.7 Malawi 510 13 41 11.0 219 0.1 0.1 15.0 39 51 2.3 Malaysia 31 50 .. 0.5 82 3.1 7.6 13.7 84 96 56.3 Mali 830 .. 8 1.0 68 0.0 0.0 2.4 15 22 2.7 Mauritania 550 4 9 0.7 337 1.3 0.6 1.1 16 26 3.0 Mauritius 36 75 .. 1.0 22 1.4 3.1 0.7 89 89 28.7 Mexico 85 63 73 0.3 16 3.9 4.3 11.9 64 85 31.1 Moldova 32 .. 68 0.4 182 .. 1.3 1.4 .. 85 40.1 Mongolia 65 .. 55 <0.1 224 4.6 4.1 13.4 .. 51 12.9 Morocco 110 42 .. 0.1 91 1.0 1.5 1.5 53 70 49.0 Mozambique 550 .. 16 11.5 544 0.1 0.1 14.8 11 18 4.2 Myanmar 240 17 41 0.6 384 0.1 0.3 5.2 .. 76 0.2 Namibia 180 41 55 13.1 603 0.0 1.8 14.7 24 32 6.5 Nepal 380 24 48 0.4 163 0.0 0.1 17.0 10 31 7.9 Netherlands 9 76 69 0.2 7 11.0 10.6 15.2 100 100 90.7 New Zealand 14 .. .. 0.1 8 7.0 7.8 20.0 .. .. 83.0 Nicaragua 100 .. 72 0.2 42 0.6 0.8 36.8 43 52 10.0 Niger 820 4 18 0.8 185 0.1 0.1 7.1 5 9 0.8 Nigeria 840 6 15 3.6 133 0.5 0.6 12.6 37 31 28.4 Norway 7 74 88 0.1 6 7.4 10.5 10.9 100 100 93.3 Oman 20 9 24 0.1 13 5.5 17.3 9.3 82 99 62.0 Pakistan 260 15 27 0.1 231 0.6 1.0 9.8 27 48 16.8 Panama 71 .. 52 0.9 48 1.3 2.0 11.5 58 69 42.7 Papua New Guinea 250 .. 32 0.9 303 0.5 0.3 1.4 47 45 1.3 Paraguay 95 48 79 0.3 46 0.5 0.7 5.4 37 71 19.8 Peru 98 59 74 0.4 106 1.0 1.4 13.1 54 71 34.3 Philippines 94 36 51 <0.1 275 0.7 0.9 5.0 57 74 25.0 Poland 6 73 .. 0.1 23 9.6 8.3 21.8 .. 90 62.5 Portugal 7 .. 67 0.6 29 4.4 5.3 6.1 92 100 51.3 Puerto Rico 18 .. .. .. 2 .. .. 4.4 .. .. 42.7 Qatar 8 .. .. <0.1 38 24.9 49.1 1.4 100 100 81.6 2012 World Development Indicators 29 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 Nationally mortality ratio Contraceptive HIV protected Modeled prevalence prevalence Incidence Carbon dioxide terrestrial and estimate rate % of of tuberculosis emissions marine areas Access to improved per 100,000 % of married women population per 100,000 per capita % of total sanitation facilities Internet usersa live births ages 15–49 ages 15–49 people metric tons land area % of population per 100 people 2008 1990 2005–10b 2009 2010 1990 2008 2010 1990 2010 2010 Romania 27 .. .. 0.1 116 6.8 4.4 7.8 71 .. 40.0 Russian Federation 39 .. 80 1.0 106 .. 12.0 9.2 74 70 43.4 Rwanda 540 21 52 2.9 106 0.1 0.1 10.0 36 55 13.0 Saudi Arabia 24 .. 24 .. 18 13.3 16.6 29.9 .. .. 41.0 Senegal 410 .. 12 0.9 288 0.4 0.4 23.5 38 52 16.0 Serbia 8 .. 41 0.1 18 .. 6.8 6.0 .. 92 43.1 Sierra Leone 970 3 8 1.6 682 0.1 0.2 4.3 11 13 0.3 Singapore 9 65 .. 0.1 35 15.4 6.7 3.4 99 100 71.1 Slovak Republic 6 74 .. <0.1 8 .. 6.9 23.2 100 100 79.9 Slovenia 18 .. .. <0.1 11 .. 8.5 13.1 100 100 69.3 Somalia 1,200 .. 15 0.7 286 0.0 0.1 0.5 .. 23 1.2 South Africa 410 57 .. 17.8 981 9.5 8.9 6.9 71 79 12.3 South Sudan .. .. .. .. .. .. .. .. .. .. .. Spain 6 .. 66 0.4 16 5.9 7.2 7.6 100 100 65.8 Sri Lanka 39 .. 68 <0.1 66 0.2 0.6 15.0 70 92 12.0 Sudan 750 9 8 1.1 119 0.2 0.3 4.2 27 26 10.2 Swaziland 420 20 49 25.9 1,287 0.5 1.1 3.0 48 57 9.0 Sweden 5 .. .. 0.1 7 6.0 5.3 10.0 100 100 90.0 Switzerland 10 .. .. 0.4 8 6.4 5.3 24.9 100 100 82.2 Syrian Arab Republic 46 .. 54 .. 20 3.0 3.6 0.6 85 95 20.7 Tajikistan 64 .. 37 0.2 206 .. 0.5 4.1 .. 94 11.5 Tanzania 790 10 34 5.6 177 0.1 0.2 26.9 7 10 11.0 Thailand 48 66 80 1.3 137 1.7 4.2 17.3 84 96 21.2 Timor-Leste 370 25 22 .. 498 .. 0.2 6.4 .. 47 0.2 Togo 350 34 15 3.2 455 0.2 0.2 11.0 13 13 5.4 Trinidad and Tobago 55 .. 43 1.5 19 14.0 37.4 9.6 93 92 48.5 Tunisia 60 50 60 <0.1 25 1.6 2.4 1.3 74 .. 36.6 Turkey 23 63 73 <0.1 28 2.8 4.0 1.9 84 90 39.8 Turkmenistan 77 .. 48 .. 66 .. 9.7 3.0 98 98 2.2 Uganda 430 5 24 6.5 209 0.0 0.1 10.3 27 34 12.5 Ukraine 26 .. 67 1.1 101 .. 7.0 3.6 .. 94 44.6 United Arab Emirates 10 .. .. .. 3 28.8 25.0 4.7 97 98 78.0 United Kingdom 12 70 84 0.2 13 10.0 8.5 18.1 100 100 84.7 United States 24 71 79 0.6 4 19.5 18.0 13.7 100 100 74.2 Uruguay 27 .. 78 0.5 21 1.3 2.5 0.3 94 100 47.9 Uzbekistan 30 .. 65 0.1 128 .. 4.6 2.3 84 100 19.4 Venezuela, RB 68 .. .. .. 33 6.2 6.1 50.2 82 .. 35.9 Vietnam 56 53 80 0.4 199 0.3 1.5 4.6 37 76 27.9 West Bank and Gaza .. .. 50 .. 5 .. 0.5 0.6 .. 92 36.4 Yemen, Rep. 210 10 28 .. 49 0.8 1.0 0.7 24 53 12.3 Zambia 470 15 41 13.5 462 0.3 0.2 36.0 46 48 10.1 Zimbabwe 790 43 59d 14.3 633 1.5 0.7 28.0 41 40 11.5 World 260 w 58 w 62 w 0.8 w 128 w 4.2e w 4.8e w 11.9 w 47 w 62 w 30.2 w Low income 590 22 34 2.6 264 .. 0.3 10.6 21 37 5.6 Middle income 210 59 65 0.7 132 2.4 3.4 11.9 39 59 23.8 Lower middle income 300 40 50 0.7 174 1.1 1.5 8.5 29 47 13.5 Upper middle income 60 75 81 0.7 89 3.6 5.3 13.2 46 73 34.1 Low & middle income 290 56 61 0.9 150 2.2 3.0 11.6 37 56 21.5 East Asia & Pacific 89 75 78 0.2 123 1.8 4.3 13.3 30 66 29.8 Europe & Central Asia 34 .. 69 0.6 90 9.8 7.8 7.7 80 84 39.3 Latin America & Carib. 86 58 75 0.5 43 2.3 2.8 19.8 68 79 34.0 Middle East & N. Africa 88 42 62 0.1 42 2.6 3.8 4.0 73 88 20.9 South Asia 290 42 51 0.3 192 0.7 1.2 5.6 22 38 8.1 Sub-Saharan Africa 650 15 22 5.5 271 0.9 0.8 11.6 26 31 11.3 High income 15 70 .. 0.3 14 11.8 11.9 12.7 100 100 73.4 Euro area 7 .. .. 0.3 .. 8.9 8.0 16.5 100 100 71.2 a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators 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 2011. e. Includes emissions not allocated to specific countries. 30 2012 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment About the data De�nitions The Millennium Development Goals address con- of symptoms, or malaria, which has periods of dor- • Maternal mortality ratio is the number of women cerns common to all economies. Diseases and envi- mancy, can be particularly difficult. The table shows who die from pregnancy-related causes during preg- ronmental degradation do not respect national bound- the estimated prevalence of HIV among adults ages nancy and childbirth, per 100,000 live births. Data aries. Epidemic diseases, wherever they occur, pose 15–49. Prevalence among older populations can be are from various years and adjusted to a common a threat to people everywhere. And environmental affected by life-prolonging treatment. The incidence of 2008 base year. The values are modeled estimates damage in one location may affect the well-being of tuberculosis is based on case notifications and esti- (see About the data for table 2.19). • Contraceptive plants, animals, and humans far away. The indicators mates of cases detected in the population. prevalence rate is the percentage of women ages in the table relate to goals 5, 6, and 7 and the targets Carbon dioxide emissions are the primary source of 15–49 married or in union who are practicing, or of goal 8 that address access to new technologies. greenhouse gases, which contribute to global warming, whose sexual partners are practicing, any form of For the other targets of goal 8, see table 1.4. threatening human and natural habitats. In recognition contraception. • HIV prevalence is the percentage The target of achieving universal access to repro- of the vulnerability of animal and plant species, a new of people ages 15–49 who are infected with HIV. ductive health has been added to goal 5 to address target of reducing biodiversity loss has been added • Incidence of tuberculosis is the estimated number the importance of family planning and health ser- to goal 7. Increasing the proportion of terrestrial and of new tuberculosis cases (pulmonary, smear posi- vices in improving maternal health and preventing marine areas protected helps defend vulnerable plant tive, and extrapulmonary). • Carbon dioxide emis- maternal death. Women with multiple pregnancies and animal species and safeguard biodiversity. sions are those stemming from the burning of fossil are more likely to die in childbirth. Access to contra- Access to reliable supplies of safe drinking water and fuels and the manufacture of cement. They include ception is an important way to limit and space births. sanitary disposal of excreta are two of the most impor- emissions produced during consumption of solid, Measuring disease prevalence or incidence can be tant means of improving human health and protecting liquid, and gas fuels and gas flaring (see table 3.9). difficult. Most developing economies lack reporting the environment. Improved sanitation facilities prevent • Nationally protected terrestrial and marine areas systems for monitoring diseases. Estimates are often human, animal, and insect contact with excreta. are terrestrial and marine protected areas as a per- derived from survey data and report data from sentinel Internet use includes narrowband and broadband centage of total territorial area, where all nationally sites, extrapolated to the general population. Tracking Internet. Narrowband is often limited to basic appli- designated protected areas with known location and diseases such as HIV/AIDS, which has a long latency cations; broadband is essential to promote e-busi- extent are included. All overlaps between different between contraction of the virus and the appearance ness, e-learning, e-government, and e-health. designations and categories, buffered points, and polygons are removed, and all the undated protected Location of indicators for Millennium Development Goals 5–7 1.3a areas are dated. • Access to improved sanitation facilities is the percentage of the population with at Goal 5. Improve maternal health Table least adequate access to excreta disposal facilities 5.1 Maternal mortality ratio 1.3, 2.19 (private or shared, but not public) that can effec- 5.2 Proportion of births attended by skilled health personnel 2.19 tively prevent human, animal, and insect contact with 5.3 Contraceptive prevalence rate 1.3, 2.19 excreta (facilities do not have to include treatment 5.4 Adolescent fertility rate 2.19 to render sewage outfl ows innocuous). Improved 5.5 Antenatal care coverage 1.5, 2.19 facilities range from simple but protected pit latrines 5.6 Unmet need for family planning 2.19 Goal 6. Combat HIV/AIDS, malaria, and other diseases to flush toilets with a sewerage connection. To be 6.1 HIV prevalence among pregnant women ages 15–24 1.3*, 2.22* effective, facilities must be correctly constructed 6.2 Condom use at last high-risk sex — and properly maintained. • Internet users are people 6.3 Proportion of population ages 15–24 with comprehensive, correct — with access to the worldwide network. knowledge 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 2.22 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, 2.22 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment, short course 2.18 Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 3.1 Data sources 7.2 Carbon dioxide emissions, total, per capita, and per $1 purchasing power parity GDP 3.9 The indicators here and throughout this book have 7.3 Consumption of ozone-depleting substances 3.10* been compiled by World Bank staff from primary 7.4 Proportion of fish stocks within safe biological limits — and secondary sources. Efforts have been made 7.5 Proportion of total water resources used 3.5 to harmonize the data series used to compile this 7.6 Proportion of terrestrial and marine areas protected 1.3 table with those published on the United Nations 7.7 Proportion of species threatened with extinction — Millennium Development Goals Web site (www. 7.8 Proportion of population using an improved drinking water source 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, 3.13 timing, sources, and definitions remain. For more 7.10 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. 2012 World Development Indicators 31 1.4 Millennium Development Goals: overcoming obstacles Development Assistance Committee members Net of�cial development Least developed countries’ access Support to assistance (ODA) to high-income markets agriculture by donor For basic Goods social servicesa (excluding arms) Average tariff on exports of % of % of total admitted free of tariffs least developed countries donor sector-allocable % of exports from least % GNI ODA developed countries Agricultural products Textiles Clothing % of GDP 2010 2010 2003 2009 2003 2009 2003 2009 2003 2009 2010 Australia 0.32 14.6 99.9 100.0 0.2 0.0 0.1 0.0 1.2 0.0 0.12 Canada 0.33 18.1 97.5 100.0 0.2 0.1 0.2 0.2 1.7 1.7 0.67 European Union     96.6 98.4 1.8 0.5 0.1 0.1 1.2 1.2 0.72 Austria 0.32 3.1 Belgium 0.64 11.7 Denmark 0.90 10.4 Finland 0.55 8.4 France 0.50 8.6 Germany 0.38 5.9 Greece 0.17 6.6 Ireland 0.53 22.9 Italy 0.15 12.5 Luxembourg 1.09 34.5 Netherlands 0.81 7.6 Portugal 0.29 6.6 Spain 0.43 13.6 Sweden 0.97 12.4 United Kingdom 0.56 12.7 Japan 0.20 2.5 44.8 99.4 4.7 1.2 2.7 2.6 0.1 0.1 1.09 Korea, Rep.b 0.12 4.3 25.1c 48.2 27.6c 50.2 10.9c 6.4 11.3c 6.2 2.01 New Zealandb 0.26 16.6 97.7 98.8 4.0 0.0 0.3 0.0 0.4 0.0 0.23 Norway 1.10 11.1 98.6 100.0 4.7 0.2 0.0 0.0 1.2 1.0 0.99 Switzerland 0.41 11.0 96.3 100.0 5.4 0.0 0.0 0.0 0.0 0.0 1.11 United States 0.21 31.0 67.2 75.8 6.3 5.7 6.4 5.7 12.3 11.3 0.91 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-2010 end-2010 net present value net present value $ millions $ millions Afghanistan Jul. 2007 Jan. 2010 653 20 Haiti Nov. 2006 Jun. 2009 163 674 Benin Jul. 2000 Mar. 2003 384 756 Honduras Jul. 2000 Apr. 2005 814 1,884 Boliviae Feb. 2000 Jun. 2001 1,948 1,956 Liberia Mar. 2008 Jun. 2010 2,957 241 Burkina Fasoe,f Jul. 2000 Apr. 2002 810 765 Madagascar Dec. 2000 Oct. 2004 1,224 1,584 Burundi Aug. 2005 Jan. 2009 1,008 91 Malawif Dec. 2000 Aug. 2006 1,375 914 Cameroon Oct. 2000 Apr. 2006 1,856 885 Malie Sep. 2000 Mar. 2003 789 1,313 Central African Republic Sep. 2007 Jun. 2009 674 231 Mauritania Feb. 2000 Jun. 2002 911 563 Chad May 2001 Floating 240 669 Mozambiquee Apr. 2000 Sep. 2001 3,140 1,318 Comoros Jun. 2010 Floating 150 45 Nicaragua Dec. 2000 Jan. 2004 4,847 1,178 Congo, Dem. Rep. Jul. 2003 Jul. 2010 9,474 528 Niger f Dec. 2000 Apr. 2004 944 646 Congo, Rep. Mar. 2006 Jan. 2010 1,903 130 Rwandaf Dec. 2000 Apr. 2005 953 286 Côte d’Ivoire Mar. 2009 Floating 3,243 1,095 São Tomé & Príncipef Dec. 2000 Mar. 2007 171 38 Ethiopiaf Nov. 2001 Apr. 2004 2,728 1,865 Senegal Jun. 2000 Apr. 2004 715 1,696 Gambia, The Dec. 2000 Dec. 2007 98 244 Sierra Leone Mar. 2002 Dec. 2006 917 423 Ghana Feb. 2002 Jul. 2004 3,083 2,549 Tanzania Apr. 2000 Nov. 2001 2,969 2,503 Guinea Dec. 2000 Floating 799 862 Togo Nov. 2008 Dec. 2010 305 466 Guinea-Bissau Dec. 2000 Dec. 2010 744 79 Ugandae Feb. 2000 May 2000 1,505 2,263 Guyanae Nov. 2000 Dec. 2003 894 488 Zambia Dec. 2000 Apr. 2005 3,662 1,871 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. 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. c. Data are for 2004. 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. 32 2012 World Development Indicators 1.4 WORLD VIEW Millennium Development Goals: overcoming obstacles About the data De�nitions Achieving the Millennium Development Goals lines with “international peaks�). The averages in • Net of�cial development assistance (ODA) is grants requires an open, rule-based global economy in the table include ad valorem duties and equivalents. and loans (net of repayments of principal) that meet which all countries, rich and poor, participate. Many Subsidies to agricultural producers and exporters the DAC definition of ODA and are made to countries poor countries, lacking the resources to finance in OECD countries are another barrier to developing on the DAC list of recipients. • ODA for basic social development, burdened by unsustainable debt, and economies’ exports. Agricultural subsidies in OECD services is aid commitments by DAC donors for basic unable to compete globally, need assistance from economies are estimated at $366 billion in 2010. education, primary health care, nutrition, population rich countries. For goal 8—develop a global partner- The Debt Initiative for Heavily Indebted Poor Coun- policies and programs, reproductive health, and water ship for development—many indicators therefore tries (HIPCs), an important step in placing debt relief and sanitation services. • Goods admitted free of monitor the actions of members of the Organisa- within the framework of poverty reduction, is the first tariffs are exports of goods (excluding arms) from tion for Economic Co-operation and Development’s comprehensive approach to reducing the external least developed countries admitted without tariff. (OECD) Development Assistance Committee (DAC). debt of the world’s poorest, most heavily indebted •  Average tariff is the unweighted average of the Official development assistance (ODA) has risen countries. A 1999 review led to an enhancement of effectively applied rates for all products subject to in recent years as a share of donor countries’ gross the framework. In 2005, to further reduce the debt tariffs. • Agricultural products are plant and animal national income (GNI), but the poorest economies of HIPCs and provide resources for meeting the Mil- products, including tree crops but excluding timber need additional assistance to achieve the Millennium lennium Development Goals, the Multilateral Debt and fish products. • Textiles and clothing are natu- Development Goals. In 2010 net ODA from OECD Relief Initiative (MDRI), proposed by the Group of ral and synthetic fibers and fabrics and articles of DAC members rose 3.2  percent in real terms, to Eight countries, was launched. clothing made from them. • Support to agriculture is $131.1 billion or 0.33 percent of OECD DAC mem- Under the MDRI four multilateral institutions—the gross transfers from taxpayers and consumers arising bers’ combined GNI. International Development Association (IDA), Inter- from policy measures, net of associated budgetary One important action that high-income economies national Monetary Fund (IMF), African Development receipts, regardless of their objectives and impacts on can take is to reduce barriers to exports from low- Fund (AfDF), and Inter-American Development Bank farm production and income or consumption of farm and middle- income economies. The European Union (IDB)—provide 100 percent debt relief on eligible products. • HIPC decision point is when a heavily has begun to eliminate tariffs on imports of “every- debts due to them from countries having completed indebted poor country with an established track record thing but arms� from least developed countries, the HIPC Initiative process. Data in the table refer of good performance under adjustment programs sup- and the United States offers special concessions to to status as of September 2011 and might not show ported by the IMF and the World Bank commits to addi- imports from Sub-Saharan Africa. However, these countries that have since reached the decision or tional reforms and a poverty reduction strategy and programs still have many restrictions. completion point. Debt relief under the HIPC Initiative starts receiving debt relief. • HIPC completion point is Average tariffs in the table refl ect high-income has reduced future debt payments by $59 billion (in when a country successfully completes the key struc- OECD member tariff schedules for exports of coun- end-2010 net present value terms) for 36 countries tural reforms agreed on at the decision point, includ- tries designated least developed countries by the that have reached the decision point. And 32 coun- ing implementing a poverty reduction strategy. The United Nations. Although average tariffs have been tries that have reached the completion point have country then receives full debt relief under the HIPC falling, averages may disguise high tariffs on specific received additional assistance of $33 billion (in end- Initiative without further policy conditions. • HIPC Ini- goods (see table 6.7 for each country’s share of tariff 2010 net present value terms) under the MDRI. tiative assistance is the debt relief committed as of the decision point (assuming full participation of credi- Location of indicators for Millennium Development Goal 8 1.4a tors). Topping-up assistance and assistance provided under the original HIPC Initiative were committed in Goal8. Develop a global partnership for development Table net present value terms as of the decision point and 8.1 Net ODA as a percentage of DAC donors’ gross national income 1.4 are converted to end-2010 terms. • MDRI assistance 8.2 Proportion of ODA for basic social services 1.4 8.3 Proportion of ODA that is untied — is 100 percent debt relief on eligible debt from IDA, 8.4 Proportion of ODA received in landlocked countries as a percentage of GNI — IMF, AfDF, and IDB, delivered in full to countries having 8.5 Proportion of ODA received in small island developing states as a percentage of GNI — reached the HIPC completion point. 8.6 Proportion of total developed country imports (by value, excluding arms) from least developed countries admitted free of duty 1.4 Data sources 8.7 Average tariffs imposed by developed countries on agricultural products and textiles and clothing from least developed countries 1.4, 6.7* Data on ODA are from the OECD. Data on goods 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 1.4 admitted free of tariffs and average tariffs are from 8.9 Proportion of ODA provided to help build trade capacity — 8.10 Number of countries reaching HIPC decision and completion points 1.4 the World Trade Organization in collaboration with 8.11 Debt relief committed under new HIPC initiative 1.4 the United Nations Conference on Trade and Devel- 8.12 Debt services as a percentage of exports of goods and services 6.9* opment and the International Trade Centre (www. 8.13 Proportion of population with access to affordable, essential drugs on a mdg-trade.org). Data on support to agriculture are sustainable basis — from the OECD’s Producer and Consumer Support 8.14 Fixed telephone lines per 100 people 5.11 8.15 Mobile cellular subscribers per 100 people 5.11 Estimates, OECD Database 1986–2010. Data on 8.16 Internet users per 100 people 1.3, 5.12 the HIPC Initiative and MDRI are from the World — No data are available in the World Development Indicators database. * Table shows information on related indicators. Bank’s Economic Policy and Debt Department. 2012 World Development Indicators 33 1.5 Women in development Female Life Pregnant Teenage Women in wage Unpaid family Female Female Women in population expectancy women mothers employment in workers part-time legislators, parliaments at birth receiving nonagricultural employment senior prenatal sector of�cials, and care managers % of % of women nonagricultural Male Female years ages wage % of male % of female % of total Male Female % 15–19 employment employment employment % of total % of total % of total seats 2010 2010 2010 2005–10a 2005–10a 2009 2010 2010 2005–10a 2005–10a 1990 2011 Afghanistan 48.3 48 48 36 .. 18 .. .. .. .. 4 28 Albania 49.9 74 80 97 3 .. .. .. .. .. 29 16 Algeria 49.5 71 74 89 .. 15 .. .. .. 5 2 8 Angola 50.5 49 52 80 29 .. .. .. .. .. 15 39 Argentina 51.1 72 79 99 .. .. 0.4b 1.2b 65b 23b 6 39 Armenia 53.5 71 77 99 5 40 7.6 17.1 56 22 36 9 Australia 50.2 80 84 98 .. 47 0.2 0.3 70 b 37 6 25 Austria 51.2 78 83 .. .. 48 1.9 2.4 80 29 12 28 Azerbaijan 50.5 68 74 77 6 43 .. .. .. 7 .. 16 Bahrain 37.6 74 76 100 .. 10 0.5 0.8 .. 12 .. 3 Bangladesh 49.4 68 69 53 33 .. .. .. .. 10 10 19 Belarus 53.5 65 77 99 .. 56 .. .. .. .. .. 32 Belgium 51.0 77 83 .. .. 47 0.3 1.8 80 34 9 39 Benin 50.7 54 57 84 21 .. .. .. .. .. 3 8 Bolivia 50.1 64 69 86 18 38 .. .. 56 29 9 25 Bosnia and Herzegovina 51.9 73 78 99 .. 36 2.6 10.4 .. .. .. 17 Botswana 49.6 54 52 94 .. 45 .. .. 55 30 5 8 Brazil 50.8 70 77 98 .. 42 3.4 6.3 68 36 5 9 Bulgaria 51.7 70 77 .. .. 51 0.7 1.5 52 34 21 21 Burkina Faso 50.4 54 56 85 .. 27 .. .. .. 31 .. 15 Burundi 50.9 49 51 99 .. .. .. .. .. .. .. 32 Cambodia 51.1 61 64 89 8 .. 25.2 60.8 .. 21 .. 21 Cameroon 50.1 50 52 82 .. .. .. .. .. .. 14 14 Canada 50.4 79 83 100 .. 51 0.1 0.2 67b 36b 13 25 Central African Republic 50.7 46 49 69 .. .. .. .. .. .. 4 13 Chad 50.3 48 51 53 .. .. .. .. .. .. .. 13 Chile 50.6 76 82 .. .. 37 1.1 2.5 58 24 .. 14 China 48.1c 72c 75c 92 .. .. .. .. .. 17 21 21 Hong Kong SAR, China 52.6 80 86 .. .. 50 0.1 1.0 .. 29 .. .. Colombia 50.8 70 77 97 20 48 3.7 7.1 60 38 5 13 Congo, Dem. Rep. 50.3 47 50 86 24 .. .. .. .. .. 5 10 Congo, Rep. 49.9 56 58 86 27 .. .. .. .. .. 14 7 Costa Rica 49.2 77 82 90 .. 41 0.9 2.6 64 30 11 39 Côte d’Ivoire 49.0 54 56 85 .. .. .. .. .. .. 6 9 Croatia 51.9 74 80 100 4 46 1.1 4.6 60 27 .. 24 Cuba 49.7 77 81 100 .. 43 .. .. .. 29 34 43 Cyprus 49.0 77 82 99 .. 48 1.3 2.9 64 13 2 11 Czech Republic 51.0 74 81 .. .. 46 0.3 1.0 69 28 .. 22 Denmark 50.4 77 81 .. .. 50 0.1 0.5 63 22 31 38 Dominican Republic 49.8 70 76 99 21 39 .. .. 50 31 8 21 Ecuador 49.9 73 78 .. .. .. 7.4 19.9 54 28b 5 32 Egypt, Arab Rep. 49.8 71 75 74 10 18 .. .. .. 11 4 2 El Salvador 52.5 67 77 94 23 48 7.1 7.2 55 25 12 19 Eritrea 50.7 59 63 .. .. .. .. .. .. .. .. 22 Estonia 53.9 71 81 .. .. 54 0.2 0.2 67 37 .. 20 Ethiopia 50.2 57 60 28 17 .. .. .. .. 16b .. 28 Finland 50.9 77 83 .. .. 52 0.6 0.5 62 30 32 43 France 51.3 78 85 .. .. 49 0.3 0.9 78 39 7 19 Gabon 49.8 61 63 .. .. .. .. .. .. .. 13 15 Gambia, The 50.6 57 59 98 .. .. .. .. .. .. 8 8 Georgia 52.9 70 77 98 10 48 19.6 37.9 .. 34 .. 7 Germany 51.0 78 83 .. .. 48 0.3 0.9 80 30 .. 33 Ghana 49.1 63 65 90 13 .. .. .. .. .. .. 8 Greece 50.5 78 83 .. .. 43 3.3 9.2 66 30 7 17 Guatemala 51.3 67 74 93 22 .. .. .. .. .. 7 12 Guinea 49.5 52 55 88 32 29 .. .. .. .. .. .. Guinea-Bissau 50.4 46 49 93 .. .. .. .. .. .. 20 10 Haiti 50.4 61 63 85 14 .. .. .. .. .. .. 11 34 2012 World Development Indicators 1.5 WORLD VIEW Women in development Female Life Pregnant Teenage Women in wage Unpaid family Female Female Women in population expectancy women mothers employment in workers part-time legislators, parliaments at birth receiving nonagricultural employment senior prenatal sector of�cials, and care managers % of % of women nonagricultural Male Female years ages wage % of male % of female % of total Male Female % 15–19 employment employment employment % of total % of total % of total seats 2010 2010 2010 2005–10a 2005–10a 2009 2010 2010 2005–10a 2005–10a 1990 2011 Honduras 50.0 71 75 92 22 42 .. .. .. 41 10 18 Hungary 52.5 71 78 .. .. 49 0.2 0.4 66 36 21 9 India 48.3 64 67 75 16 .. .. .. .. .. 5 11 Indonesia 50.1 67 71 95 9 32 8.1 32.4 .. 22 12 18 Iran, Islamic Rep. 49.3 71 75 98 .. .. 4.8 29.7 .. 13 2 3 Iraq 49.8 65 72 84 .. 12 .. .. .. .. 11 25 Ireland 50.0 78 83 .. .. 52 0.7 0.7 76 33 8 15 Israel 50.7 80 83 .. .. 50 0.1 0.3 74 32 7 19 Italy 51.1 79 84 .. .. 44 1.2 2.3 77 33 13 21 Jamaica 50.8 70 76 99 14 48 0.4 2.1 .. .. 5 13 Japan 51.3 80 86 .. .. 42 1.1 6.9 70 10 1 11 Jordan 48.6 72 75 99 5 16 0.5 0.5 .. .. 0 11 Kazakhstan 52.0 64 73 100 7 50 0.6 0.9 .. 38 .. 18 Kenya 50.1 55 58 92 18 .. .. .. .. .. 1 10 Korea, Dem. Rep. 50.9 65 72 100 .. .. .. .. .. .. 21 15 Korea, Rep. 50.1 77 84 .. .. 42 1.2 12.5 60 10 2 16 Kosovo .. 68 72 .. .. .. .. .. .. .. .. .. Kuwait 40.3 74 76 100 .. .. .. .. .. 14 .. 8 Kyrgyz Republic 50.7 65 73 97 .. 51 .. .. .. 35 .. 23 Lao PDR 50.1 66 68 71 17 .. .. .. .. .. 6 25 Latvia 54.0 69 78 .. .. 55 1.3 1.4 61 41 .. 20 Lebanon 51.2 70 75 .. .. .. .. .. .. 8 0 3 Lesotho 50.9 48 47 92 .. .. .. .. .. 52 .. 24 Liberia 49.8 55 57 79 38 .. 12.5 19.7 .. .. .. 13 Libya 49.3 72 77 93 .. .. .. .. .. .. .. 8 Lithuania 53.5 68 79 .. .. 53 1.5 1.9 61 41 .. 19 Macedonia, FYR 49.9 73 77 94 .. 42 6.3 16.6 49 28 .. 31 Madagascar 50.2 65 68 86 32 .. .. .. .. 22 7 13 Malawi 50.0 53 54 92 .. .. .. .. .. .. 10 21 Malaysia 49.3 72 76 79 .. 39 2.6 8.1 .. 24 5 10 Mali 50.0 50 52 70 36 .. .. .. .. .. .. 10 Mauritania 49.7 57 60 75 .. .. .. .. .. .. .. 22 Mauritius 50.6 69 77 .. .. 37 1.0 4.9 .. 23 7 19 Mexico 50.7 74 79 96 .. 39 4.8 9.7 54 31 12 26 Moldova 52.6 65 73 98 6 54 1.3 3.5 .. 38 .. 19 Mongolia 50.6 64 72 100 .. 51 10.3 35.1 .. 47 25 4 Morocco 51.0 70 74 .. .. 21 15.0 48.6 .. 13 0 11 Mozambique 51.3 49 51 92 .. .. .. .. .. .. 16 39 Myanmar 50.7 63 66 80 .. .. .. .. .. .. .. 4 Namibia 50.3 62 63 95 15 .. .. .. .. 36 7 24 Nepal 50.4 68 69 44 19 .. .. .. .. 14 6 33 Netherlands 50.4 79 83 .. .. 48 0.3 1.1 75 29 21 39 New Zealand 50.9 79 83 .. .. 51 0.7 1.3 72b 40 b 14 34 Nicaragua 50.5 71 77 90 25 38 .. .. 51 41 15 21 Niger 49.7 54 55 46 39 36 .. .. .. .. 5 13 Nigeria 49.4 51 52 58 23 .. .. .. .. .. .. 4 Norway 50.0 79 83 .. .. 50 0.2 0.3 70 34 36 40 Oman 41.3 71 76 99 .. 22 .. .. .. 9 .. 0 Pakistan 49.2 64 66 61 9 13 19.7 65.0 .. 3 10 22 Panama 49.6 73 79 96 .. 42 3.0 6.8 48 48 8 9 Papua New Guinea 49.0 60 65 79 .. .. .. .. .. .. 0 1 Paraguay 49.5 70 74 96 12 40 10.4 11.0 55 34 6 13 Peru 49.9 71 76 95 26 38 4.5b 8.7b 61b 19b 6 22 Philippines 49.8 65 72 91 10 42 9.0 17.4 .. 55 9 22 Poland 51.8 72 81 .. .. 48 2.5 5.5 68 36 14 20 Portugal 51.6 76 82 .. .. 49 0.7 1.2 66 32 8 27 Puerto Rico 51.9 75 83 .. .. 42 0.0 0.0 .. 43 .. .. Qatar 24.3 78 78 100 .. 10 .. .. .. 7 .. 0 2012 World Development Indicators 35 1.5 Women in development Female Life Pregnant Teenage Women in wage Unpaid family Female Female Women in population expectancy women mothers employment in workers part-time legislators, parliaments at birth receiving nonagricultural employment senior prenatal sector of�cials, and care managers % of % of women nonagricultural Male Female years ages wage % of male % of female % of total Male Female % 15–19 employment employment employment % of total % of total % of total seats 2010 2010 2010 2005–10a 2005–10a 2009 2010 2010 2005–10a 2005–10a 1990 2011 Romania 51.5 70 77 .. .. 46 6.9 20.0 47 32 34 11 Russian Federation 53.7 63 75 .. .. 53 0.1 0.1 62 37 .. 14 Rwanda 50.9 54 56 98 6 .. .. .. .. 0 17 56 Saudi Arabia 44.6 73 75 97 .. 15 .. .. .. 8 .. 0 Senegal 50.4 58 60 94 18 .. .. .. .. .. 13 23 Serbia 50.5 71 77 98 .. 44 4.1 15.6 .. 36 .. 22 Sierra Leone 51.2 47 48 87 34 .. .. .. .. .. .. 13 Singapore 49.6 79 84 .. .. 45 0.3 1.0 .. 31 5 22 Slovak Republic 51.4 72 79 .. .. 48 0.1 0.2 59 35 .. 16 Slovenia 51.1 76 83 .. .. 48 3.8 6.2 59 35 .. 14 Somalia 50.4 49 53 26 .. .. .. .. .. .. 4 7 South Africa 50.5 51 53 97 .. 45 0.4 1.4 .. 30 3 45 South Sudan .. 62 61 .. .. .. .. .. .. .. .. .. Spain 50.6 79 85 .. .. 47 0.7 1.0 78 34 15 37 Sri Lanka 50.6 72 78 99 .. 31 4.5b 22.3b .. 24b 5 6 Sudan 49.6 59 63 64 .. .. .. .. .. .. .. 26 Swaziland 50.8 49 48 97 23 .. .. .. .. .. 4 14 Sweden 50.2 80 84 .. .. 50 0.3 0.2 63 31 38 45 Switzerland 50.9 80 85 .. .. 48 1.6 2.4 80 33 14 29 Syrian Arab Republic 49.4 74 77 88 .. 15 3.3 12.4 27 10 9 12 Tajikistan 50.8 64 71 80 .. .. .. .. .. .. .. 19 Tanzania 50.1 57 58 88 23 .. .. .. .. 16 .. 36 Thailand 50.9 71 77 99 .. 46 13.6 28.5 .. 24 3 13 Timor-Leste 49.0 61 63 84 7 .. .. .. .. .. .. 29 Togo 50.5 55 58 87 .. .. .. .. .. .. 5 11 Trinidad and Tobago 51.5 66 73 96 .. .. .. .. .. 43 17 29 Tunisia 50.0 73 77 96 .. .. .. .. .. .. 4 28 Turkey 50.1 71 76 95 .. 24 5.1 35.2 58 10 1 14 Turkmenistan 50.8 61 69 99 .. .. .. .. .. .. 26 17 Uganda 50.0 53 54 94 25 .. .. .. .. 33 12 35 Ukraine 54.0 65 76 99 4 55 0.4 0.3 .. 39 .. 8 United Arab Emirates 30.5 76 78 100 .. 20 0.0 0.0 .. 10 0 23 United Kingdom 50.8 79 82 .. .. 47 0.2 0.4 75 36 6 22 United States 50.7 76 81 .. .. 48 0.1 0.1 67 43 7 17 Uruguay 51.7 73 80 96 .. 46 2.1b 0.8b 64b 40 6 15 Uzbekistan 50.3 65 71 99 .. 39 .. .. .. .. .. 22 Venezuela, RB 49.8 71 77 .. .. 42 0.5 1.2 64 27 10 17 Vietnam 50.6 73 77 91 .. .. .. .. .. 22 18 24 West Bank and Gaza 49.2 71 74 99 .. 18 5.5 19.8 .. 10 .. .. Yemen, Rep. 49.7 64 67 47 .. 6 .. .. .. 4 4 0 Zambia 49.9 48 49 94 28 .. .. .. .. 19 7 14 Zimbabwe 50.7 51 49 90d 21 .. .. .. .. .. 11 15 World 49.6 w 68 w 72 w 84 w .. w .. w .. w .. w .. w   13 w 19 w Low income 50.1 58 60 69 .. .. .. .. ..   .. 20 Middle income 49.3 67 71 86 .. .. .. .. ..   13 18 Lower middle income 49.2 64 67 78 .. .. .. .. ..   11 15 Upper middle income 49.4 71 75 94 .. .. .. .. ..   14 19 Low & middle income 49.4 66 70 83 .. .. .. .. ..   13 18 East Asia & Pacific 48.8 70 74 92 .. .. .. .. ..   17 18 Europe & Central Asia 52.3 66 75 .. .. 48 1.9 5.3 ..   .. 16 Latin America & Carib. 50.6 71 77 97 .. 41 3.5 6.8 62   12 23 Middle East & N. Africa 49.7 70 74 85 .. .. .. .. ..   4 9 South Asia 48.6 64 67 71 16 .. .. .. ..   6 20 Sub-Saharan Africa 50.0 53 55 74 .. .. .. .. ..   .. 20 High income 50.5 77 83 .. .. 47 0.6 2.2 71   12 23 Euro area 51.0 78 84 .. .. 47 0.7 1.5 77   12 26 a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2011. 36 2012 World Development Indicators 1.5 WORLD VIEW Women in development About the data Despite much progress in recent decades, gender terms of earnings, work conditions, or legal and social personnel for reasons related to pregnancy. • Teen- inequalities remain pervasive in many dimensions of protection. The indicator also does not reflect whether age mothers are women ages 15–19 who already life—worldwide. But while disparities exist through- women reap the economic benefits of such employ- have children or are currently pregnant. •  Women out the world, they are most prevalent in developing ment. Finally, female employment and the employ- in wage employment in nonagricultural sector are countries. Gender inequalities in the allocation of ment share of the agricultural sector for both men female wage employees in the nonagricultural sec- such resources as education, health care, nutrition, and women tend to be underreported. tor as a percentage of total nonagricultural wage and political voice matter because of the strong asso- Women’s wage work is important for economic employment. •  Unpaid family workers are those ciation with well-being, productivity, and economic growth and the well-being of families. But women who work without pay in a market-oriented estab- growth. These patterns of inequality begin at an early often face such obstacles as restricted access to lishment or activity operated by a related person age, with boys routinely receiving a larger share of edu- credit markets, capital, land, and training and edu- living in the same household. There is no official cation and health spending than do girls, for example. cation; time constraints due to traditional family International Labour Organization definition of full- Because of biological differences girls are expected responsibilities; and labor market bias and discrimi- time work, so the definition of part-time workers dif- to experience lower infant and child mortality rates nation. These obstacles force women to limit their fers across countries and thus comparisons should and to have a longer life expectancy than boys. This full participation in paid economic activities, to be be made with caution. • Female part-time employ- biological advantage may be overshadowed, however, less productive, and to receive lower wages. More ment is the percentage of part-time workers who by gender inequalities in nutrition and medical inter- women than men are in unpaid family employment are female. Part-time workers are employed people ventions and by inadequate care during pregnancy and part-time employment. And men and women whose normal hours of work are less than those of and delivery, so that female rates of illness and have different occupational distributions. There is no comparable full-time workers. The definition of part- death sometimes exceed male rates. Gender bias official International Labour Organization definition time varies across countries. • Female legislators, can be seen in child mortality rates (table 2.23) and of full-time work, so the definition of part-time work- senior of�cials, and managers are the percentage life expectancy at birth. Female child mortality rates ers differs across countries, and thus comparisons of legilsators, senior officials, and managers (Inter- that are as high as or higher than male child mortality should be made with caution. national Standard Classification of Occupations–88 rates may indicate discrimination against girls. The female share of high-skilled occupations such category 1) who are female. • Women in parliaments Having a child during the teenage years limits girls’ as legislators, senior officials, and managers indi- are parliamentary seats in a single or lower chamber opportunities for better education, jobs, and income. cates gender segregation of employment. Women held by women. Pregnancy is more likely to be unintended during the are vastly underrepresented in decisionmaking Data sources teenage years, and births are more likely to be pre- positions in government, although there is some mature and are associated with greater risks of com- evidence of recent improvement. Gender parity in Data on female population are from the United plications during delivery and of death. In many coun- parliamentary representation is still far from being Nations Population Division’s World Population Pros- tries maternal mortality (tables 1.3 and 2.19) is a realized. Without representation at this level, it is pects: The 2010 Revision, and data on life expec- leading cause of death among women of reproductive difficult for women to influence policy. tancy for more than half the countries in the table age, although most of those deaths are preventable. For information on other aspects of gender, see (most of them developing countries) are from its Data on women in wage employment in the non- tables 1.2 (Millennium Development Goals: eradicat- World Population Prospects: The 2010 Revision, with agricultural sector show the extent to which women ing poverty and saving lives), 1.3 (Millennium Devel- additional data from census reports, other statisti- have access to paid employment—which affects their opment Goals: protecting our common environment), cal publications from national statistical offices, integration into the monetary economy —and indicate 2.3 (Employment by economic activity), 2.4 (Decent Eurostat’s Demographic Statistics, the Secretariat the degree to which labor markets are open to women work and productive employment), 2.5 (Unemploy- of the Pacific Community’s Statistics and Demog- in industry and services—which affects not only equal ment), 2.6 (Children at work), 2.10 (Assessing vulner- raphy Programme, and the U.S. Bureau of the Cen- employment opportunity for women, but also eco- ability and security), 2.13 (Education efficiency), 2.14 sus International Data Base. Data on pregnant nomic efficiency through flexibility of the labor market (Education completion and outcomes), 2.15 (Educa- women receiving prenatal care are from the United and the economy’s capacity to adapt to changes over tion gaps by income and gender), 2.19 (Reproductive Nations Children’s Fund’s (UNICEF) The State of the time. In many developing countries nonagricultural health), 2.22  (Health risk factors and future chal- World’s Children 2012 based on household surveys, wage employment accounts for only a small portion lenges), and 2.23 (Mortality). including MEASURE DHS Demographic and Health of total employment. As a result, the contribution of Surveys by ICF International and Multiple Indica- De�nitions women to the national economy is underestimated tor Cluster Surveys by UNICEF. Data on teenage and therefore misrepresented. The indicator is dif- • Female population is the percentage of the popu- mothers are from MEASURE DHS Demographic and ficult to interpret without additional information on the lation that is female. • Life expectancy at birth is Health Surveys by ICF International. Data on labor share of women in total employment, which allows the number of years a newborn infant would live if force, employment, and occupation are from the an assessment to be made of whether women are prevailing patterns of mortality at the time of its birth International Labour Organization’s Key Indicators under-  or overrepresented in nonagricultural wage were to stay the same throughout its life. • Pregnant of the Labour Market, 7th edition. Data on women in employment. The indicator does not reveal differences women receiving prenatal care are women attended parliaments are from the Inter-Parliamentary Union. in the quality of nonagricultural wage employment in at least once during pregnancy by skilled health 2012 World Development Indicators 37 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 2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10 2010 2005–10a 2008 American Samoa 68 0.2 342 .. ..b .. .. .. .. .. .. .. Andorra 85 0.5 181 3,447 41,750 .. .. 3.6 2.1 .. .. 539 Antigua and Barbuda 88 0.4 200 1,169 13,280 c 1,795d 20,400 d –5.2 –5.4 .. 99 447 Aruba 108 0.2 600 .. ..e .. .. .. .. 75 98 2,288 Bahamas, The 343 13.9 34 6,973 20,610 8,392d 24,800d 0.9 –0.5 75 .. 2,156 Barbados 274 0.4 637 3,454 12,660 5,183 19,000 –5.3 –5.5 77 .. 1,353 Belize 345 23.0 15 1,313 3,810 2,139d 6,200 d 2.9 –0.6 76 .. 425 Bermuda 65 0.1 1,292 .. ..e .. .. –8.1 –8.4 79 .. 389 Bhutan 726 38.4 19 1,361 1,870 3,622 4,990 7.4 5.6 67 53 733 Brunei Darussalam 399 5.8 76 12,461 31,800 19,661 50,180 –1.8 –3.6 78 95 10,594 Cape Verde 496 4.0 123 1,620 3,270 1,893 3,820 5.4 4.5 74 85 308 Cayman Islands 56 0.3 234 .. ..e .. .. .. .. .. 99 557 Channel Islands 153 0.2 807 .. ..e .. .. .. .. 80 .. .. Comoros 735 1.9 395 550 750 802 1,090 2.1 –0.6 61 74 125 Curaçao 143 0.4 321 .. ..e .. .. .. .. .. .. .. Djibouti 889 23.2 38 1,105 1,270 2,149 2,460 5.0 3.0 58 .. 524 Dominica 68 0.8 91 458 6,740 812d 11,940 d 0.1 –0.1 .. .. 128 Equatorial Guinea 700 28.1 25 10,182 14,550 16,635 23,760 0.9 –1.8 51 93 4,815 Faeroe Islands 49 1.4 35 .. ..e .. .. .. .. 80 .. 708 Fiji 860 18.3 47 3,123 3,630 3,880 4,510 0.3 –0.6 69 .. 1,254 French Polynesia 271 4.0 74 .. ..e .. .. .. .. 75 .. 891 Gibraltar 29 0.0 2,924 .. ..e .. .. .. .. .. .. 422 Greenland 57 410.5 0f 1,466 26,020 .. .. –5.4 –5.4 68 .. 576 Grenada 104 0.3 306 724 6,960 1,033d 9,930 d –0.8 –0.7 76 .. 246 Guam 179 0.5 331 .. ..e .. .. .. .. 76 .. .. Guyana 755 215.0 4 2,164 2,870 2,606d 3,450 d 3.6 3.3 70 .. 1,525 Iceland 318 103.0 3 10,381 32,640 8,991 28,270 –4.0 –3.8 81 .. 2,230 Isle of Man 83 0.6 145 .. ..e .. .. .. .. .. .. .. Kiribati 100 0.8 123 200 2,000 352d 3,520 d 1.8 –0.2 .. .. 29 About the data De�nitions The table shows data for economies with a popula- • Population is based on the de facto definition of included in the valuation of output plus net receipts tion between 30,000 and 1 million and for smaller population, which counts all residents regardless of of primary income (compensation of employees and economies if they are members of the World Bank. legal status or citizenship—except for refugees not property income) from abroad. Data are in current Where data on gross national income (GNI) per capita permanently settled in the country of asylum, who U.S. dollars converted using the World Bank Atlas are not available, the estimated range is given. For are generally considered part of the population of method (see Statistical methods). •  Purchasing more information on the calculation of GNI and pur- their country of origin. The values shown are midyear power parity (PPP) GNI is GNI converted to interna- chasing power parity (PPP) conversion factors, see estimates. For more information, see About the data tional dollars using PPP rates. An international dollar About the data for table 1.1. Additional data for the for table 2.1. •  Surface area is a country’s total has the same purchasing power over GNI that a U.S. economies in the table are available on the World area, including areas under inland bodies of water dollar has in the United States. • GNI per capita is Development Indicators CD-ROM or at http://data. and some coastal waterways. • Population density GNI divided by midyear population. • Gross domes- worldbank.org. 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 38 2012 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 2010 2010 2010 2010 2010 2010 2010 2009–10 2009–10 2010 2005–10a 2008 Liechtenstein 36 0.2 225 4,903 137,070 .. .. –1.2 –1.9 .. .. .. Luxembourg 507 2.6 196 39,030 76,980 31,050 61,240 2.7 0.8 80 .. 10,502 Macao SAR, China 544 0.0 19,429 18,527 34,880 24,020 45,220 26.4 23.4 81 93 1,335 Maldives 316 0.3 1,053 1,818 5,750 2,563 8,110 9.9 8.4 77 98 920 Malta 416 0.3 1,300 7,958 19,130 10,258 24,660 3.1 2.6 81 92 2,560 Marshall Islands 54 0.2 300 197 3,640 .. .. 5.2 4.0 .. .. 99 Mayotte 204 0.4 551 .. ..b .. .. .. .. 78 .. .. Micronesia, Fed. Sts. 111 0.7 159 304 2,740 388d 3,490 d 3.1 2.8 69 .. 62 Monaco 35 0.0 17,704 6,479 183,150 .. .. –2.6 –2.7 .. .. .. Montenegro 632 13.8 47 4,260 6,740 8,073 12,770 2.5 2.2 74 .. 1,951 New Caledonia 247 18.6 14 .. ..e .. .. .. .. 76 96 3,150 Northern Mariana Islands 61 0.5 132 .. ..e .. .. .. .. .. .. .. Palau 20 0.5 45 134 6,560 225d 11,000 d 2.0 1.4 .. .. 213 Samoa 184 2.8 65 549 2,980 782d 4,250 d 1.7 0.8 72 99 161 San Marino 32 0.1 526 1,572 50,400 .. .. 1.9 1.3 83 .. .. São Tomé and Príncipe 165 1.0 172 199 1,200 318 1,930 4.5 2.9 64 89 128 Seychelles 87 0.5 189 845 9,710 1,835d 21,090 d 6.2 6.6 73 92 682 Sint Maarten 38 0.0 1,113 .. ..e .. .. .. .. .. .. .. Solomon Islands 538 28.9 19 552 1,030 1,192d 2,220 d 7.0 4.2 67 .. 198 St. Kitts and Nevis 52 0.3 200 615 11,830 831d 15,970 d –5.0 –5.5 .. .. 249 St. Lucia 174 0.6 285 1,142 6,560 1,830d 10,520 d 3.1 2.1 74 .. 396 St. Martin 30 0.1 556 .. ..e .. .. .. .. .. .. .. St. Vincent & Grenadines 109 0.4 279 688 6,320 1,184 d 10,870 d –1.3 –1.0 72 .. 202 Suriname 525 163.8 3 3,077 5,920 3,991d 7,680d 3.1 2.1 70 95 2,439 Tonga 104 0.8 144 342 3,290 477d 4,580 d –0.5 –0.9 72 99 176 Turks and Caicos Islands 38 1.0 40 .. ..e .. .. .. .. .. .. 158 Tuvalu 10 0.0 328 47 4,760 g .. .. –1.9 –2.1 .. .. .. Vanuatu 240 12.2 20 633 2,640 1,035d 4,310 d 3.0 0.4 71 82 92 Virgin Islands (U.S.) 110 0.4 314 .. ..e .. .. .. .. 79 .. .. a. Data are for the most recent year available. b. Estimated to be upper middle income ($3,976–$12,275). c. Included in the aggregates for upper middle-income economies based on earlier data. d. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. e. Estimated to be high income ($12,276 or more). f. Less than 0.5. g. Included in the aggregates for lower middle-income economies based on earlier data. 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, De�nitions, and Data sources of solid, liquid, and gas fuels and gas flaring. entries that accompany each table in subsequent sections. 2012 World Development Indicators 39 PEOPLE T 2 he People section documents demographic to 1.18 billion in 2008. This bunching up just trends, labor force structure, poverty inci- above the extreme poverty line indicates the dence, income distribution, education vulnerabilities faced by a great many people in inputs and outcomes, and health services and the world. status. Together these indicators provide a Looking at the trend from 2005 to 2008, multidimensional portrait of human develop- both the absolute number and the proportion ment and social welfare. For 2012 a new table of people living in extreme poverty declined in has been added, and the contents of others every developing country region. This across- have changed. Table 2.20 now shows data by the-board reduction over a three-year moni- sex for three measures of the prevalence of toring cycle is the first since the World Bank child malnutrition — underweight, stunting, and began monitoring extreme poverty. Since 2008 wasting— and for the proportion of overweight food, fuel, and financial crises have had sharp children. Data on antiretroviral therapy cover- negative impacts on vulnerable populations and age and cause of death have been added to slowed poverty reduction in some countries, but table 2.22. And table 2.23 now includes neo- global poverty kept falling. In fact, a preliminary natal mortality rates computed with the same survey-based estimate for 2010 — based on a method ology used to estimate infant and much smaller sample than the global update — under-five mortality rates. Also new is table indicates that the global poverty rate at $1.25 2.24, which presents selected health indica- a day fell to less than half its 1990 value. If tors by income quintile. These subnational data these results are confirmed by follow-up stud- highlight substantial within-country disparities ies, the first target of the Millennium Develop- between the poor and rich in mortality and fer- ment Goals — cutting the extreme poverty rate tility rates and in child and reproductive health to half its 1990 level — may have been achieved outcomes. on a global level before the 2015 target date. Table 2.8 provides more recent data on pov- The World Bank’s database of international erty at international poverty lines for more coun- poverty measures now includes income or con- tries, including global and regional poverty esti- sumption data collected by national statistical mates released by the World Bank in February offices drawn from 850 household surveys and 2012. In 2008, 1.29 billion people, or 22 per- interviews with 1.23 million randomly sampled cent of developing countries’ population, lived households in nearly 130 countries. The most below the extreme poverty line of $1.25 a day, recent year for which a reliable global estimate down from 1.94 billion people, or 52 percent of can be calculated is 2008, because for many developing countries’ population, in 1981. In low-income countries more recent data are 1990, the benchmark year for the Millennium either not available or not comparable with Development Goals, the extreme poverty rate previous estimates. The availability, frequency, was 43.1 percent. and quality of poverty monitoring data remain Progress has been slower at the $2 a day low, especially in small states and in countries poverty line, around which many people in lower and territories with fragile situations. The need middle-income economies live. The number of to improve household survey programs for pov- people living below $2 a day fell from 2.59 bil- erty monitoring in these countries is urgent. But lion in 1981 to 2.47 billion in 2008 — a decrease institutional, political, and financial obstacles of only 120 million — and the number of peo- continue to hamper data collection, analysis, ple living on $1.25–$2 a day almost doubled and public access. 2012 World Development Indicators 41 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 2000 2010 2020 2000–10 2010–20 2010 2010 2010 2010 2010 2010 2010 Afghanistan 26.0 34.4 44.8 2.8 2.6 46 51 2 91 4 16 44 Albania 3.1 3.2 3.3 0.4 0.2 23 68 10 34 14 6 13 Algeria 30.5 35.5 40.1 1.5 1.2 27 68 5 40 7 5 20 Angola 13.9 19.1 24.8 3.1 2.6 47 51 2 91 5 14 42 Argentina 36.9 40.4 43.7 0.9 0.8 25 65 11 39 16 8 17 Armenia 3.1 3.1 3.1 0.1 0.1 20 69 11 29 16 9 15 Australia 19.2 22.3 25.2 1.5 1.2 19 68 13 28 20 6 13 Austria 8.0 8.4 8.5 0.5 0.1 15 68 18 22 26 9 9 Azerbaijan 8.0 9.1 10.1 1.2 1.1 21 73 7 29 9 6 19 Bahrain 0.6 1.3 1.5 6.8 1.9 20 78 2 26 3 3 20 Bangladesh 129.6 148.7 167.1 1.4 1.2 31 64 5 49 7 6 20 Belarus 10.0 9.5 9.2 –0.5 –0.4 15 71 14 21 19 15 11 Belgium 10.3 10.9 11.1 0.6 0.2 17 66 17 26 27 10 12 Benin 6.5 8.9 11.5 3.1 2.6 44 53 3 82 6 12 40 Bolivia 8.3 9.9 11.6 1.8 1.5 36 59 5 61 8 7 26 Bosnia and Herzegovina 3.7 3.8 3.6 0.2 –0.4 15 71 14 21 20 10 9 Botswana 1.8 2.0 2.2 1.3 0.9 33 63 4 51 6 13 24 Brazil 174.4 194.9 209.6 1.1 0.7 25 68 7 38 10 6 15 Bulgaria 8.2 7.5 7.0 –0.8 –0.7 14 69 18 20 25 15 10 Burkina Faso 12.3 16.5 22.1 2.9 2.9 45 52 2 86 4 12 43 Burundi 6.4 8.4 10.1 2.7 1.8 38 59 3 64 5 14 34 Cambodia 12.4 14.1 15.9 1.3 1.2 32 64 4 50 6 8 22 Cameroon 15.7 19.6 24.1 2.2 2.1 41 56 4 73 6 14 36 Canada 30.8 34.1 37.1 1.0 0.8 16 69 14 24 20 8 11 Central African Republic 3.7 4.4 5.3 1.7 1.9 40 56 4 73 7 16 35 Chad 8.2 11.2 14.4 3.1 2.5 45 52 3 88 6 16 45 Chile 15.4 17.1 18.4 1.0 0.8 22 69 9 32 13 6 14 China 1,262.6 1,338.3 1,381.6 0.6 0.3 19 72 8 27 11 7 12 Hong Kong SAR, China 6.7 7.1 7.8 0.6 0.9 12 76 13 15 17 6 13 Colombia 39.8 46.3 52.1 1.5 1.2 29 66 6 44 9 5 20 Congo, Dem. Rep. 49.6 66.0 85.0 2.8 2.5 46 51 3 91 5 16 43 Congo, Rep. 3.1 4.0 5.0 2.5 2.1 41 56 4 73 7 11 35 Costa Rica 3.9 4.7 5.3 1.7 1.2 25 69 7 36 10 4 16 Côte d’Ivoire 16.6 19.7 24.5 1.7 2.2 41 55 4 74 7 12 34 Croatia 4.4 4.4 4.3 0.0 –0.3 15 68 17 22 25 12 10 Cuba 11.1 11.3 11.1 0.1 –0.1 17 70 12 25 18 7 10 Cyprus 0.9 1.1 1.2 1.6 1.0 18 71 12 25 16 7 12 Czech Republic 10.3 10.5 10.7 0.2 0.2 14 71 15 20 21 10 11 Denmark 5.3 5.5 5.7 0.4 0.3 18 66 16 27 25 10 11 Dominican Republic 8.6 9.9 11.1 1.4 1.1 31 63 6 49 10 6 22 Ecuador 12.3 14.5 16.3 1.6 1.2 30 63 6 48 10 5 21 Egypt, Arab Rep. 67.6 81.1 94.8 1.8 1.6 32 63 5 50 8 5 23 El Salvador 5.9 6.2 6.6 0.4 0.6 32 61 7 52 11 7 20 Eritrea 3.7 5.3 6.8 3.6 2.6 42 56 2 74 4 8 36 Estonia 1.4 1.3 1.3 –0.2 –0.1 15 67 17 23 25 12 12 Ethiopia 65.6 83.0 100.9 2.3 2.0 41 55 3 75 6 10 31 Finland 5.2 5.4 5.5 0.4 0.2 17 66 17 25 26 10 11 France 60.8 64.9 67.6 0.7 0.4 18 65 17 28 26 8 13 Gabon 1.2 1.5 1.8 2.0 1.9 35 60 4 59 7 9 27 Gambia, The 1.3 1.7 2.2 2.9 2.6 44 54 2 82 4 9 38 Georgia 4.4 a 4.5a 4.2a 0.1a –0.7a 17 69 14 24 21 11 12 Germany 82.2 81.8 79.8 –0.1 –0.2 13 66 20 20 31 11 8 Ghana 19.2 24.4 30.3 2.4 2.2 39 58 4 67 7 8 32 Greece 10.9 11.3 11.5 0.4 0.1 15 67 19 22 28 9 10 Guatemala 11.2 14.4 18.3 2.5 2.4 41 54 4 77 8 5 32 Guinea 8.3 10.0 12.8 1.8 2.4 43 54 3 80 6 13 39 Guinea-Bissau 1.2 1.5 1.9 2.0 2.1 41 55 3 75 6 17 38 Haiti 8.6 10.0 11.3 1.4 1.2 36 60 4 60 7 9 27 42 2012 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 2000 2010 2020 2000–10 2010–20 2010 2010 2010 2010 2010 2010 2010 Honduras 6.2 7.6 9.2 2.0 1.9 37 59 4 62 7 5 27 Hungary 10.2 10.0 9.8 –0.2 –0.2 15 69 17 21 24 13 9 India 1,053.9 1,224.6 1,385.2 1.5 1.2 31 64 5 47 8 8 22 Indonesia 213.4 239.9 262.1 1.2 0.9 27 67 6 40 8 7 18 Iran, Islamic Rep. 65.3 74.0 80.9 1.2 0.9 23 72 5 32 7 5 17 Iraq 24.3 32.0 42.3 2.8 2.8 43 54 3 81 6 6 35 Ireland 3.8 4.5 5.0 1.6 1.0 21 67 12 32 17 6 17 Israel 6.3 7.6 8.9 1.9 1.5 27 62 10 44 17 5 22 Italy 56.9 60.5 60.8 0.6 0.1 14 66 20 21 31 10 9 Jamaica 2.6 2.7 2.8 0.4 0.3 29 63 8 46 12 7 16 Japan 126.9 127.5 123.6 0.0 –0.3 13 64 23 21 35 10 9 Jordan 4.8 6.0 7.2 2.3 1.8 38 59 4 64 7 4 25 Kazakhstan 14.9 16.3 18.0 0.9 1.0 24 69 7 36 10 9 22 Kenya 31.3 40.5 52.5 2.6 2.6 42 55 3 77 5 11 38 Korea, Dem. Rep. 22.9 24.3 25.3 0.6 0.4 23 68 10 34 14 10 14 Korea, Rep. 47.0 48.9 50.2 0.4 0.3 16 72 11 23 15 5 9 Kosovo 1.7 1.8 1.9 0.7 0.7 .. .. .. .. .. 7 19 Kuwait 1.9 2.7 3.4 3.4 2.2 27 71 3 38 4 3 18 Kyrgyz Republic 4.9 5.4 6.1 1.1 1.2 30 66 4 46 7 7 27 Lao PDR 5.3 6.2 7.0 1.5 1.3 35 62 4 56 6 6 23 Latvia 2.4 2.2 2.1 –0.6 –0.4 14 68 18 20 26 13 9 Lebanon 3.7 4.2 4.5 1.2 0.6 25 68 7 36 11 7 15 Lesotho 2.0 2.2 2.4 1.0 1.0 37 58 4 64 7 16 28 Liberia 2.8 4.0 5.2 3.4 2.6 43 54 3 81 5 11 39 Libya 5.2 6.4 7.0 1.9 1.0 30 65 4 47 7 4 23 Lithuania 3.5 3.3 3.1 –0.6 –0.5 15 69 16 22 23 13 11 Macedonia, FYR 2.0 2.1 2.1 0.3 0.0 18 71 12 25 17 9 11 Madagascar 15.4 20.7 27.3 3.0 2.8 43 54 3 80 6 6 35 Malawi 11.2 14.9 20.7 2.8 3.3 46 51 3 90 6 13 44 Malaysia 23.4 28.4 33.0 1.9 1.5 30 65 5 47 7 5 20 Mali 11.3 15.4 20.5 3.1 2.9 47 51 2 93 4 15 46 Mauritania 2.6 3.5 4.3 2.7 2.2 40 57 3 69 5 10 34 Mauritius 1.2 1.3 1.3 0.8 0.4 22 71 7 31 10 7 12 Mexico 100.0 113.4 125.7 1.3 1.0 29 65 6 45 10 5 20 Moldova 3.6b 3.6b 3.3b –0.2b –0.6b 17 72 11 23 15 13 12 Mongolia 2.4 2.8 3.2 1.3 1.4 28 68 4 40 6 6 23 Morocco 28.8 32.0 35.0 1.0 0.9 28 66 5 42 8 6 20 Mozambique 18.2 23.4 29.1 2.5 2.2 44 53 3 84 6 15 38 Myanmar 45.0 48.0 51.7 0.6 0.7 26 69 5 37 7 9 17 Namibia 1.9 2.3 2.7 1.9 1.6 36 60 4 61 6 8 26 Nepal 24.4 30.0 35.1 2.1 1.6 36 60 4 61 7 6 24 Netherlands 15.9 16.6 17.0 0.4 0.2 18 67 15 26 23 8 11 New Zealand 3.9 4.4 4.8 1.2 1.0 20 67 13 31 20 7 15 Nicaragua 5.1 5.8 6.6 1.3 1.3 34 61 5 57 8 5 24 Niger 10.9 15.5 22.0 3.5 3.5 49 49 2 100 4 13 49 Nigeria 123.7 158.4 203.7 2.5 2.5 43 54 3 80 6 14 40 Norway 4.5 4.9 5.2 0.8 0.7 19 67 15 28 22 9 13 Oman 2.3 2.8 3.3 2.1 1.8 27 70 3 39 4 4 18 Pakistan 144.5 173.6 205.2 1.8 1.7 35 60 4 59 7 7 27 Panama 3.0 3.5 4.0 1.7 1.4 29 64 7 45 10 5 20 Papua New Guinea 5.4 6.9 8.5 2.4 2.1 39 58 3 67 5 8 30 Paraguay 5.3 6.5 7.6 1.9 1.6 34 61 5 55 8 5 24 Peru 25.9 29.1 32.3 1.2 1.1 30 64 6 47 10 5 20 Philippines 77.3 93.3 109.6 1.9 1.6 35 61 4 58 6 6 25 Poland 38.5 38.2 38.1 –0.1 0.0 15 72 14 21 19 10 11 Portugal 10.2 10.6 10.5 0.4 –0.1 15 67 18 23 27 10 10 Puerto Rico 3.8 4.0 4.0 0.4 –0.1 21 66 13 32 19 8 13 Qatar 0.6 1.8 2.3 10.9c 2.6 13 85 1 16 1 2 13 2012 World Development Indicators 43 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 2000 2010 2020 2000–10 2010–20 2010 2010 2010 2010 2010 2010 2010 Romania 22.4 21.4 20.9 –0.5 –0.3 15 70 15 22 21 12 10 Russian Federation 146.3 141.8 139.3 –0.3 –0.2 15 72 13 21 18 14 13 Rwanda 8.1 10.6 14.0 2.7 2.8 43 55 3 78 5 12 41 Saudi Arabia 20.0 27.4 33.1 3.1 1.9 30 67 3 46 4 4 22 Senegal 9.5 12.4 16.0 2.7 2.5 44 54 2 81 4 9 37 Serbia 7.5 7.3 7.2 –0.3 –0.2 18d 68d 14 d 26d 21d 14 9 Sierra Leone 4.1 5.9 7.2 3.5 2.0 43 55 2 78 3 16 39 Singapore 4.0 5.1 5.6 2.3 0.9 17 74 9 24 12 4 9 Slovak Republic 5.4 5.4 5.5 0.1 0.1 15 73 12 21 17 10 11 Slovenia 2.0 2.0 2.1 0.3 0.1 14 70 16 20 24 9 11 Somalia 7.4 9.3 12.2 2.3 2.7 45 52 3 86 5 15 44 South Africa 44.0 50.0 52.4 1.3 0.5 30 65 5 46 7 15 21 South Sudan .. .. .. .. .. .. .. .. .. .. 21 30 Spain 40.3 46.1 48.4 1.3 0.5 15 68 17 22 25 8 11 Sri Lanka 18.7 20.9 22.3 1.1 0.7 25 67 8 37 12 7 18 Sudan 34.2e 43.6e 54.9e 2.4 e 2.3e 40e 56e 4e 71e 6e 9e 33e Swaziland 1.0 1.1 1.2 0.4 1.2 38 58 3 66 6 14 29 Sweden 8.9 9.4 9.9 0.6 0.5 17 65 18 25 28 10 12 Switzerland 7.2 7.8 8.0 0.9 0.3 15 68 17 22 25 8 10 Syrian Arab Republic 16.0 20.4 24.3 2.5 1.7 37 59 4 62 7 4 23 Tajikistan 6.2 6.9 7.9 1.1 1.4 37 60 3 62 6 6 28 Tanzania 34.0 44.8 61.0 2.8 3.1 45 52 3 86 6 10 41 Thailand 63.2 69.1 71.9 0.9 0.4 21 71 9 29 13 7 12 Timor-Leste 0.8 1.1 1.5 3.0 2.8 46 51 3 91 6 8 38 Togo 4.8 6.0 7.3 2.3 2.0 40 57 3 70 6 11 32 Trinidad and Tobago 1.3 1.3 1.4 0.4 0.2 21 72 7 28 10 8 15 Tunisia 9.6 10.5 11.6 1.0 0.9 23 70 7 34 10 6 18 Turkey 63.6 72.8 80.7 1.3 1.0 26 68 6 39 9 5 18 Turkmenistan 4.5 5.0 5.7 1.1 1.2 29 67 4 44 6 8 22 Uganda 24.2 33.4 45.3 3.2 3.0 48 49 3 99 5 12 45 Ukraine 49.2 45.9 43.3 –0.7 –0.6 14 70 15 20 22 15 11 United Arab Emirates 3.0 7.5 9.2 9.1 2.0 17 83 0 21 1 1 13 United Kingdom 58.9 62.2 65.7 0.6 0.5 17 66 17 26 25 9 13 United States 282.2 309.3 334.9 0.9 0.8 20 67 13 30 20 8 14 Uruguay 3.3 3.4 3.5 0.2 0.3 23 64 14 35 22 9 14 Uzbekistan 24.7 28.2 31.6 1.4 1.1 29 66 4 44 7 5 23 Venezuela, RB 24.3 28.8 33.1 1.7 1.4 29 65 6 45 9 5 21 Vietnam 77.6 86.9 95.2 1.1 0.9 24 70 6 34 9 5 17 West Bank and Gaza 3.0 4.2 5.5 3.2 2.7 42 55 3 78 5 4 33 Yemen, Rep. 17.7 24.1 32.2 3.1 2.9 44 53 3 83 5 6 38 Zambia 10.2 12.9 17.7 2.4 3.1 46 51 3 92 6 16 46 Zimbabwe 12.5 12.6 15.5 0.0 2.1 39 57 4 68 7 13 29 World 6,117.8 s 6,894.6 s 7,635.2 s 1.2 w 1.0 w 27 w 66 w 8w 41 w 12 w 8w 20 w Low income 643.7 796.3 979.1 2.1 2.1 39 57 4 69 6 11 33 Middle income 4,424.5 4,970.8 5,478.1 1.2 1.0 27 67 6 40 10 8 19 Lower middle income 2,146.7 2,518.7 2,901.8 1.6 1.4 32 63 5 51 8 8 24 Upper middle income 2,277.9 2,452.1 2,576.2 0.7 0.5 22 70 8 31 11 7 14 Low & middle income 5,068.2 5,767.2 6,457.2 1.3 1.1 29 65 6 44 9 8 21 East Asia & Pacific 1,813.8 1,961.6 2,068.9 0.8 0.5 22 71 7 31 10 7 14 Europe & Central Asia 398.5 405.2 414.4 0.2 0.2 19 70 11 28 15 11 15 Latin America & Carib. 514.3 582.6 642.4 1.2 1.0 28 65 7 43 10 6 19 Middle East & N. Africa 277.4 331.3 386.5 1.8 1.6 31 64 5 48 7 5 23 South Asia 1,398.0 1,633.1 1,860.8 1.6 1.3 32 64 5 50 8 8 23 Sub-Saharan Africa 666.3 853.4 1,084.2 2.5 2.4 42 54 3 78 6 13 37 High income 1,049.6 1,127.4 1,178.1 0.7 0.4 17 67 16 26 23 8 12 Euro area 315.0 331.8 336.7 0.5 0.1 15 66 18 23 28 9 10 a. Excludes Abkhazia and South Ossetia. b. Excludes Transnistria. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo. e. Includes South Sudan. 44 2012 World Development Indicators 2.1 PEOPLE Population dynamics About the data De�nitions Population estimates are usually based on national and declining mortality rates are now reflected in the • Population is based on the de facto definition of population censuses. Estimates for the years before larger share of the working-age population. population, which counts all residents regardless of and after the census are interpolations or extrapo- Dependency ratios capture variations in the pro- legal status or citizenship—except for refugees not lations based on demographic models. Errors and portions of children, elderly people, and working-age permanently settled in the country of asylum, who undercounting occur even in high-income countries; people in the population that imply the dependency are generally considered part of the population of in developing countries errors may be substantial burden that the working-age population bears in their country of origin. The values shown are mid- because of limits in the transport, communications, relation to children and the elderly. But dependency year estimates for 2000 and 2010 and projections and other resources required to conduct and analyze ratios show only the age composition of a popula- for 2020. • Average annual population growth is a full census. tion, not economic dependency. Some children and the exponential change for the period indicated. See The quality and reliability of official demographic elderly people are part of the labor force, and many Statistical methods for more information. • Popula- data are also affected by public trust in the govern- working-age people are not. tion age composition is the percentage of the total ment, government commitment to full and accurate Vital rates are based on data from birth and death population that is in specific age groups. • Depen- enumeration, confidentiality and protection against registration systems, censuses, and sample surveys dency ratio is the ratio of dependents—people misuse of census data, and census agencies’ inde- by national statistical offices and other organiza- younger than 15 or older than 64—to the working- pendence from political influence. Moreover, compara- tions, or on demographic analysis. Data for 2010 age population —those ages 15–64. • Crude death bility of population indicators is limited by differences for some high-income countries are provisional esti- rate and crude birth rate are the number of deaths in the concepts, definitions, collection procedures, mates based on vital registers. The estimates for and the number of live births occurring during the and estimation methods used by national statistical many countries are projections based on extrapola- year, per 1,000 people, estimated at midyear. Sub- agencies and other organizations that collect the data. tions of levels and trends from earlier years or inter- tracting the crude death rate from the crude birth Of the 158 economies in the table and the 58 polations of population estimates and projections rate provides the rate of natural increase, which is economies in table 1.6, 180 (about 86 percent) from the United Nations Population Division. equal to the population growth rate in the absence conducted a census during the 2000 census round Vital registers are the preferred source for these of migration. (1995–2004). As of January 2012, 141 countries data, but in many developing countries systems for have completed a census for the 2010 census round registering births and deaths are absent or incomplete (2005–14). The currentness of a census and the avail- because of deficiencies in the coverage of events or ability of complementary data from surveys or registra- geographic areas. Many developing countries carry out tion systems are objective ways to judge demographic special household surveys that ask respondents about data quality. Some European countries’ registration recent births and deaths. Estimates derived in this systems offer complete information on population in way are subject to sampling errors and recall errors. the absence of a census. See table 2.17 and Primary The United Nations Statistics Division monitors the data documentation for the most recent census or completeness of vital registration systems. Some Data sources survey year and for the completeness of registration. countries have made progress over the last 60 years, Current population estimates for developing coun- but others still have deficiencies in civil registration The World Bank’s population estimates are com- tries that lack recent census data and pre- and systems. For example, only 57 percent of countries piled and produced by its Development Data post-census estimates for countries with census and areas register at least 90 percent of births, and Group in consultation with its Human Develop- data are provided by the United Nations Population only 53 percent register at least 90 percent of deaths. ment Network, operational staff, and country Division and other agencies. The cohort component Some of the most populous developing countries— offices. The United Nations Population Division’s method—a standard method for estimating and Bangladesh, Brazil, China, India, Indonesia, Nigeria, World Population Prospects: The 2010 Revision is projecting population —requires fertility, mortality, Pakistan —lack complete vital registration systems. a source of the demographic data for more than and net migration data, often collected from sample International migration is the only other factor half the countries, most of them developing coun- surveys, which can be small or limited in coverage. besides birth and death rates that directly deter- tries, and the source of data on age composition Population estimates are from demographic model- mines a country’s population growth. From 1990 to and dependency ratios for all countries. Other ing and so are susceptible to biases and errors from 2005 the number of migrants in high-income coun- important sources are census reports and other shortcomings in the model and in the data. Because tries rose 40 million. About 195 million people (3 statistical publications from national statistical the five-year age group is the cohort unit and five-year percent of the world population) live outside their offices; household surveys by national agencies, period data are used, interpolations to obtain annual home country. Estimating migration is difficult. At ICF International (for MEASURE DHS), and the data or single age structure may not reflect actual any time many people are located outside their home U.S. Centers for Disease Control and Prevention; events or age composition. country as tourists, workers, or refugees or for other Eurostat’s Demographic Statistics; Secretariat of The growth rate of the total population conceals reasons. Standards for the duration and purpose of the Pacific Community, Statistics and Demogra- age-group differences in growth rates. In many devel- international moves that qualify as migration vary, phy Programme; and U.S. Bureau of the Census, oping countries the once rapidly growing under-15 and estimates require information on flows into and International Data Base. population is shrinking. Previously high fertility rates out of countries that is difficult to collect. 2012 World Development Indicators 45 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 2000 2010 2000 2010 2000 2010 2000–10 2000 2010 Afghanistan 81 80 13 16 6.5 9.1 3.3 13.3 15.2 Albania 73 71 51 50 1.3 1.5 1.2 41.7 41.6 Algeria 76 72 12 15 8.8 11.2 2.4 13.7 16.9 Angola 75 77 67 63 5.2 7.1 3.1 48.4 45.9 Argentina 74 75 43 47 15.4 18.4 1.8 38.1 40.2 Armenia 73 70 58 49 1.5 1.4 –0.2 48.8 46.5 Australia 72 73 55 59 9.6 11.8 2.1 43.8 45.3 Austria 69 68 48 54 3.9 4.3 1.1 43.5 45.9 Azerbaijan 71 68 57 61 3.5 4.6 2.6 47.0 49.1 Bahrain 86 87 35 39 0.3 0.7 8.6 21.4 19.3 Bangladesh 86 84 54 57 57.3 72.3 2.3 37.5 39.9 Belarus 65 62 53 50 4.7 4.5 –0.6 48.9 49.0 Belgium 61 61 44 48 4.4 4.9 1.0 42.9 45.3 Benin 81 78 64 67 2.6 3.6 3.5 46.5 47.5 Bolivia 82 81 60 64 3.5 4.6 2.6 43.2 44.8 Bosnia and Herzegovina 58 59 33 35 1.3 1.5 1.1 39.1 40.0 Botswana 80 82 70 72 0.8 1.0 2.4 46.9 46.3 Brazil 82 81 55 59 83.7 101.6 1.9 41.2 43.7 Bulgaria 57 60 48 49 3.6 3.5 –0.3 47.1 46.8 Burkina Faso 91 91 77 78 5.5 7.5 3.2 48.2 47.6 Burundi 84 82 86 84 2.9 4.3 3.9 53.2 52.1 Cambodia 83 87 77 79 5.8 8.0 3.2 51.2 49.9 Cameroon 77 77 62 64 6.2 8.2 2.8 45.1 45.7 Canada 72 72 59 62 16.3 19.0 1.6 45.8 47.1 Central African Republic 86 85 71 73 1.7 2.1 2.1 46.4 47.1 Chad 80 80 65 65 3.2 4.4 3.2 45.5 45.3 Chile 75 74 35 47 6.1 8.0 2.8 33.1 39.6 China 83 80 71 68 724.5 799.8 1.0 45.0 44.6 Hong Kong SAR, China 73 68 49 51 3.3 3.7 1.0 41.9 46.0 Colombia 82 80 49 55 17.3 22.1 2.5 38.7 42.5 Congo, Dem. Rep. 73 72 71 70 18.5 25.3 3.1 50.1 49.9 Congo, Rep. 71 73 65 68 1.3 1.7 3.0 48.1 48.6 Costa Rica 81 79 37 46 1.6 2.2 3.2 30.8 36.2 Côte d’Ivoire 82 81 49 52 6.4 7.8 2.0 35.0 37.4 Croatia 63 60 45 46 2.0 2.0 0.0 44.1 45.9 Cuba 70 70 38 43 4.7 5.3 1.1 35.0 38.0 Cyprus 72 71 50 57 0.4 0.6 2.7 40.8 43.5 Czech Republic 70 68 52 49 5.2 5.3 0.2 44.4 43.3 Denmark 72 69 60 60 2.9 2.9 0.3 46.6 47.2 Dominican Republic 80 79 46 51 3.5 4.4 2.2 36.6 39.4 Ecuador 84 83 50 54 5.4 6.9 2.4 37.4 39.7 Egypt, Arab Rep. 73 74 20 24 20.1 27.1 3.0 21.5 24.2 El Salvador 79 79 45 47 2.2 2.6 1.6 39.6 41.4 Eritrea 90 90 75 80 1.7 2.6 4.5 47.3 48.6 Estonia 67 68 52 57 0.7 0.7 0.6 48.7 50.4 Ethiopia 91 90 73 78 29.0 40.8 3.4 45.2 47.2 Finland 67 65 57 56 2.6 2.7 0.3 47.5 47.8 France 63 62 48 51 27.2 29.9 0.9 45.5 47.1 Gabon 67 65 55 56 0.4 0.6 2.8 45.5 46.3 Gambia, The 83 83 71 72 0.5 0.8 3.2 47.0 47.9 Georgia 74 74 55 56 2.2a 2.4 a 0.8a 46.2 47.0 Germany 68 67 49 53 40.3 42.2 0.4 43.7 45.6 Ghana 77 72 73 67 8.4 10.4 2.1 48.1 47.6 Greece 65 65 40 45 4.9 5.3 0.8 39.1 41.5 Guatemala 86 88 42 49 4.0 5.7 3.6 34.6 38.1 Guinea 78 78 63 65 3.3 4.1 2.2 44.6 45.2 Guinea-Bissau 79 78 63 68 0.5 0.6 2.6 45.5 47.3 Haiti 69 71 57 60 3.2 4.2 2.5 46.5 47.0 46 2012 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 2000 2010 2000 2010 2000 2010 2000–10 2000 2010 Honduras 88 83 44 42 2.4 3.0 2.4 34.1 34.1 Hungary 58 58 41 44 4.2 4.3 0.3 44.7 46.0 India 83 81 34 29 409.4 472.6 1.4 27.8 25.3 Indonesia 85 84 50 51 99.6 118.0 1.7 37.7 38.2 Iran, Islamic Rep. 73 72 14 16 18.5 25.3 3.1 16.0 17.9 Iraq 69 69 13 14 5.5 7.5 3.1 16.0 17.5 Ireland 71 68 47 52 1.8 2.1 1.9 40.6 43.7 Israel 61 62 48 53 2.5 3.2 2.6 45.7 47.1 Italy 61 60 35 38 23.3 25.1 0.8 38.7 40.3 Jamaica 78 72 59 56 1.2 1.2 0.3 44.3 45.1 Japan 76 72 49 50 67.6 66.7 –0.1 40.7 42.3 Jordan 68 65 13 15 1.2 1.6 2.5 14.3 18.0 Kazakhstan 76 77 65 66 7.5 8.8 1.5 49.2 49.4 Kenya 73 72 63 61 11.9 15.5 2.7 46.8 46.5 Korea, Dem. Rep. 88 84 74 72 13.6 14.6 0.7 47.5 47.7 Korea, Rep. 73 72 49 49 22.7 24.6 0.8 40.5 41.3 Kosovo .. .. .. .. .. .. .. .. .. Kuwait 82 82 44 43 1.0 1.4 3.5 25.1 23.9 Kyrgyz Republic 74 78 56 55 2.1 2.5 2.0 44.6 42.7 Lao PDR 81 79 79 77 2.5 3.2 2.5 49.9 49.8 Latvia 65 66 49 55 1.1 1.2 0.6 48.1 50.1 Lebanon 71 71 19 23 1.1 1.5 2.4 22.9 25.5 Lesotho 80 73 68 59 0.8 0.9 0.5 49.1 46.0 Liberia 62 64 58 58 1.0 1.4 3.6 49.0 47.7 Libya 73 77 27 30 1.8 2.4 2.8 26.1 28.0 Lithuania 67 63 55 54 1.7 1.6 –0.3 49.5 50.3 Macedonia, FYR 66 69 41 43 0.8 0.9 1.3 38.8 38.6 Madagascar 90 89 84 84 7.3 10.1 3.3 48.7 48.9 Malawi 81 81 77 85 4.8 6.7 3.3 49.7 51.5 Malaysia 82 77 45 44 9.9 12.0 1.9 34.7 35.8 Mali 66 70 37 37 3.1 4.3 3.4 37.5 35.5 Mauritania 78 79 23 28 0.8 1.1 3.8 23.3 26.5 Mauritius 81 76 41 44 0.5 0.6 1.2 34.5 37.7 Mexico 83 81 39 44 40.3 49.6 2.1 32.9 36.5 Moldova 64 45 55 38 1.6b 1.2b –3.0 b 49.7 49.2 Mongolia 66 65 56 54 0.9 1.2 2.2 46.8 46.4 Morocco 79 75 29 26 10.2 11.4 1.1 27.9 27.1 Mozambique 83 83 88 86 8.7 11.1 2.4 55.0 53.6 Myanmar 81 82 74 75 24.2 28.0 1.5 48.3 48.9 Namibia 65 70 49 58 0.6 0.9 3.8 44.4 46.3 Nepal 90 88 82 80 12.4 16.0 2.6 48.9 49.2 Netherlands 73 72 53 58 8.2 8.9 0.8 43.1 45.6 New Zealand 73 74 57 62 1.9 2.4 2.0 45.2 46.7 Nicaragua 83 80 38 46 1.8 2.4 2.8 32.5 37.9 Niger 88 90 38 40 3.5 5.1 3.7 30.9 31.2 Nigeria 67 63 45 48 39.2 50.3 2.5 40.1 42.8 Norway 72 70 60 62 2.4 2.6 1.0 46.5 46.9 Oman 78 80 23 28 0.8 1.2 4.4 17.1 17.9 Pakistan 84 83 16 22 43.0 59.7 3.3 15.3 20.7 Panama 82 83 45 49 1.3 1.6 2.5 35.4 37.3 Papua New Guinea 74 74 71 71 2.3 3.0 2.7 48.5 48.3 Paraguay 87 86 51 57 2.3 3.1 3.0 36.6 39.7 Peru 83 85 58 67 12.0 15.5 2.6 41.2 44.6 Philippines 82 79 49 50 30.9 38.7 2.2 37.5 38.8 Poland 64 64 49 48 17.4 18.2 0.5 45.8 45.1 Portugal 70 68 53 56 5.2 5.6 0.7 45.3 47.4 Puerto Rico 60 54 35 35 1.4 1.4 0.3 39.5 42.3 Qatar 92 95 39 52 0.3 1.3 13.7 15.6 12.4 2012 World Development Indicators 47 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 2000 2010 2000 2010 2000 2010 2000–10 2000 2010 Romania 71 65 58 48 11.8 10.2 –1.5 46.6 44.7 Russian Federation 69 71 55 56 73.3 75.5 0.3 48.6 48.9 Rwanda 85 85 86 86 3.8 5.2 3.2 52.3 51.8 Saudi Arabia 74 74 16 17 6.0 9.6 4.7 14.7 14.8 Senegal 89 88 64 66 3.9 5.4 3.1 42.9 43.9 Serbia .. 67 .. 51 .. 3.5c .. .. 43.9 Sierra Leone 63 69 67 66 1.6 2.3 3.7 53.2 50.7 Singapore 78 77 53 57 2.1 2.8 3.0 40.6 42.3 Slovak Republic 68 68 53 51 2.6 2.7 0.5 45.6 44.6 Slovenia 64 65 51 53 1.0 1.0 0.8 46.3 46.3 Somalia 78 77 37 38 2.3 2.9 2.2 32.8 33.6 South Africa 61 60 44 44 15.2 18.2 1.8 43.1 42.8 South Sudan .. .. .. .. .. .. .. .. .. Spain 66 67 41 52 18.2 23.2 2.4 39.5 44.3 Sri Lanka 77 76 37 35 7.8 8.6 0.9 33.1 32.2 Sudan 76 77 29 31 10.3 14.0 3.0 27.9 28.7 Swaziland 72 71 43 44 0.3 0.4 2.2 40.3 39.7 Sweden 68 68 58 59 4.6 5.0 0.9 47.1 47.0 Switzerland 78 75 58 61 4.0 4.5 1.2 44.3 45.8 Syrian Arab Republic 80 72 20 13 4.8 5.5 1.2 20.2 15.2 Tajikistan 75 75 58 57 2.4 2.8 1.8 44.1 45.2 Tanzania 91 90 87 88 16.7 22.1 2.8 49.7 49.8 Thailand 81 80 65 64 34.8 39.4 1.2 46.1 45.7 Timor-Leste 75 74 38 38 0.2 0.3 3.6 32.6 33.3 Togo 82 81 76 80 2.2 2.9 3.1 49.0 50.5 Trinidad and Tobago 77 78 47 55 0.6 0.7 1.8 39.9 43.2 Tunisia 72 70 24 25 3.2 3.8 1.8 24.9 26.9 Turkey 74 71 27 28 21.9 26.5 1.9 26.9 28.7 Turkmenistan 74 76 48 46 1.7 2.2 2.2 40.6 39.3 Uganda 83 80 81 76 10.1 13.4 2.8 50.0 49.3 Ukraine 65 66 52 53 23.4 23.2 –0.1 49.1 49.3 United Arab Emirates 92 92 34 44 1.7 4.9 10.5 12.0 14.8 United Kingdom 70 69 54 56 29.5 31.8 0.8 45.2 45.9 United States 74 70 59 58 147.1 157.5 0.7 45.8 46.1 Uruguay 76 77 52 55 1.6 1.7 0.8 43.2 44.5 Uzbekistan 72 74 48 48 9.2 12.1 2.8 40.6 39.8 Venezuela, RB 82 80 48 52 10.5 13.4 2.4 37.2 39.3 Vietnam 83 81 74 73 41.3 51.1 2.1 49.1 48.5 West Bank and Gaza 67 66 11 15 0.6 1.0 4.6 13.4 17.8 Yemen, Rep. 71 72 22 25 4.2 6.5 4.3 24.0 25.8 Zambia 85 86 75 73 4.5 5.5 2.1 47.2 46.1 Zimbabwe 82 90 69 83 5.5 6.6 1.9 46.4 49.3 World 79 w 77 w 52 w 51 w 2,770.2 t 3,223.0 t 1.5 w 39.9 w 39.9 w Low income 83 83 66 68 279.3 363.5 2.6 44.9 45.6 Middle income 80 79 51 49 1,987.5 2,308.6 1.5 38.4 38.0 Lower middle income 81 79 39 37 828.5 994.1 1.8 32.1 31.5 Upper middle income 80 78 60 59 1,159.1 1,314.4 1.3 42.8 43.0 Low & middle income 81 79 52 51 2,266.8 2,672.1 1.6 39.2 39.1 East Asia & Pacific 83 81 68 65 991.2 1,118.0 1.2 44.3 44.1 Europe & Central Asia 69 70 50 50 176.8 191.8 0.8 45.0 44.8 Latin America & Carib. 81 80 48 53 224.6 278.2 2.1 38.3 41.2 Middle East & N. Africa 74 72 18 20 80.3 104.9 2.7 20.0 21.5 South Asia 83 81 35 32 536.8 638.8 1.7 28.2 27.1 Sub-Saharan Africa 77 76 61 63 257.2 340.4 2.8 44.9 45.6 High income 71 69 51 52 503.4 550.9 0.9 43.2 43.9 Euro area 65 65 45 50 144.9 159.6 1.0 42.6 44.8 a. Excludes Abkhazia and South Ossetia. b. Excludes Transnistria. c. Includes Kosovo. 48 2012 World Development Indicators 2.2 PEOPLE Labor force structure About the data De�nitions The labor force is the supply of labor available for pro- The labor force participation rates in the table are •  Labor force participation rate is the proportion ducing goods and services in an economy. It includes from the ILO’s Key Indicators of the Labour Market, of the population ages 15 and older that engages people who are currently employed and people who 7th edition, database. These harmonized estimates actively in the labor market, by either working or are unemployed but seeking work as well as first-time use strict data selection criteria and enhanced meth- looking for work during a reference period. • Total job-seekers. Not everyone who works is included, ods to ensure comparability across countries and labor force is people ages 15 and older who engage however. Unpaid workers, family workers, and stu- over time to avoid the inconsistencies mentioned actively in the labor market, either by working or look- dents are often omitted, and some countries do not above. Estimates are based mainly on labor force ing for work during a reference period. It includes count members of the armed forces. Labor force size surveys, with other sources (population censuses both the employed and the unemployed. • Average tends to vary during the year as seasonal workers and nationally reported estimates) used only when annual percentage growth of the labor force is cal- enter and leave. no survey data are available. culated using the exponential endpoint method (see Data on the labor force are compiled by the Inter- The labor force estimates in the table were calcu- Statistical methods for more information). • Female national Labour Organization (ILO) from labor force lated by applying labor force participation rates from labor force as a percentage of the labor force shows surveys, censuses, establishment censuses and the ILO database to World Bank population estimates the share of women active in the total labor force. surveys, and administrative records such as employ- to create a series consistent with these population ment exchange registers and unemployment insur- estimates. This procedure sometimes results in ance schemes. For some countries a combination labor force estimates that differ slightly from those of these sources is used. Labor force surveys are in the ILO’s Yearbook of Labour Statistics and its data- the most comprehensive source for internationally base Key Indicators of the Labour Market. comparable labor force data. They can cover all non- Estimates of women in the labor force and employ- institutionalized civilians, all branches and sectors of ment are generally lower than those of men and are the economy, and all categories of workers, including not comparable internationally, reflecting that demo- people holding multiple jobs. By contrast, labor force graphic, social, legal, and cultural trends and norms data from population censuses are often based on a determine whether women’s activities are regarded limited number of questions on the economic char- as economic. In many countries many women work acteristics of individuals, with little scope to probe. on farms or in other family enterprises without pay, The resulting data often differ from labor force survey and others work in or near their homes, mixing work data and vary considerably by country, depending on and family activities during the day. the census scope and coverage. Establishment cen- suses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in 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. Differing definitions of employment age also affect Data sources comparability. For most countries the working age is 15 and older, but in some countries children younger Data on labor force participation rates are from than 15 work full- or part-time and are included in the the ILO’s Key Indicators of the Labour Market, 7th estimates. Similarly, some countries have an upper edition, database. Labor force numbers were cal- age limit. As a result, calculations may systemati- culated by World Bank staff, applying labor force cally over- or underestimate actual rates. For further participation rates from the ILO database to popu- information on source, reference period, or defini- lation estimates. tion, consult the original source. 2012 World Development Indicators 49 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 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0b 2b 0b 0b 40 b 33b 18b 10 b 59b 65b 81b 89b Armenia .. 39 .. 49 .. 25 .. 8 .. 35 .. 43 Australia 6 4 4 2 32 32 12 9 61 64 84 88 Austria 6 5 8 5 47 37 20 12 46 58 72 84 Azerbaijan .. 37 .. 40 .. 19 .. 7 .. 44 .. 53 Bahrain 3 .. 0 .. 33 .. 7 .. 64 .. 92 .. Bangladesh 54 .. 85 .. 16 .. 9 .. 25 .. 2 .. Belarus .. 15 .. 9 .. 33 .. 24 .. 37 .. 64 Belgium 3 2 3 1 41 34 16 10 56 64 82 89 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 3b 34 1b 38 42b 28 17b 9 55b 38 83b 53 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. .. .. .. .. .. .. .. .. .. .. .. Brazil 31b 21 25b 12 27b 29 10 b 13 43b 50 65b 75 Bulgaria .. 8 .. 5 .. 41 .. 25 .. 51 .. 70 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 69 .. 75 .. 8 .. 9 .. 23 .. 16 Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 6b 3b 2b 1b 31b 32b 11b 10 b 64b 65b 87b 89b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 15 6 6 32 31 15 11 45 54 79 83 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China 1 0 0 0 37 19 27 4 63 80 73 96 Colombia 2b 26 1b 5 35b 23 25b 16 63b 51 74b 79 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 17 5 4 27 27 25 13 41 51 69 82 Côte d’Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 14 .. 16 .. 38 .. 15 .. 48 .. 69 Cuba .. 25 .. 9 .. 22 .. 12 .. 53 .. 80 Cyprus 11 5 13 3 31 30 23 9 56 65 63 88 Czech Republic .. 4 .. 2 .. 49 .. 23 .. 47 .. 75 Denmark 7 4 3 1 37 29 16 9 56 67 81 90 Dominican Republic 26 21 3 2 23 26 21 14 52 48 76 83 Ecuador 10 b 33 2b 22 29b 24 17b 11 62b 43 81b 67 Egypt, Arab Rep. 35 28 52 46 25 27 10 6 41 44 37 49 El Salvador 48b 33 15b 5 23b 22 23b 18 29b 45 63b 77 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23 6 13 3 42 43 30 18 36 50 57 78 Ethiopia .. 9 .. 10 .. 25 .. 20 .. 76 .. 64 Finland 11 6 6 3 38 36 15 10 51 58 78 87 France 7 4 5 2 39 33 17 10 54 63 78 88 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 51 .. 57 .. 17 .. 4 .. 33 .. 40 Germany 4 2 4 1 50 40 24 14 46 58 73 84 Ghana 66 .. 59 .. 10 .. 10 .. 23 .. 32 .. Greece 20 12 26 13 29 28 17 8 51 60 57 79 Guatemala 19b .. 3b .. 36b .. 27b .. 45b .. 70 b .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 76 .. 50 .. 9 .. 9 .. 13 .. 38 .. 50 2012 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 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a Honduras 53b 48 6b 10 18b 22 25b 22 29b 30 69b 68 Hungary 19 6 13 2 43 40 29 20 38 53 58 78 India .. 46b .. 65b .. 24b .. 18b .. 30 b .. 17b Indonesia 54 39 57 38 15 22 13 15 31 40 31 47 Iran, Islamic Rep. .. 19 .. 31 .. 33 .. 27 .. 47 .. 42 Iraq .. 17 .. 51 .. 22 .. 4 .. 61 .. 46 Ireland 17 8 3 1 35 29 18 9 49 63 79 90 Israel 5 3 2 1 38 30 15 10 57 67 83 89 Italy 8 5 9 3 41 39 23 14 52 57 68 83 Jamaica 36 28 16 10 25 24 12 7 39 48 72 83 Japan 6 4 7 4 40 33 27 15 54 62 65 80 Jordan .. 2 .. 1 .. 21 .. 9 .. 77 .. 90 Kazakhstan .. 31 .. 29 .. 26 .. 12 .. 43 .. 59 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 14 6 18 7 40 20 28 13 46 73 54 81 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 12 .. 6 .. 34 .. 14 .. 53 .. 80 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. 50 .. 48 .. 14 .. 5 .. 37 .. 47 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 12 .. 7 .. 33 .. 16 .. 55 .. 77 Macedonia, FYR .. 20 .. 20 .. 33 .. 28 .. 47 .. 52 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 17 20 9 31 32 32 23 46 51 48 68 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 15 10 13 8 36 32 48 22 48 58 39 70 Mexico 34 19 11 4 25 30 19 18 41 51 70 78 Moldova .. 34 .. 28 .. 26 .. 14 .. 41 .. 58 Mongolia .. 41b .. 39b .. 19b .. 11b .. 40 b .. 50 b Morocco 4b 34 3b 59 33b 24 46b 15 63b 42 51b 25 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 23 52 8 21 24 8 9 34 53 40 83 Nepal 75 .. 91 .. 4 .. 1 .. 20 .. 8 .. Netherlands 5 4 2 2 33 24 10 6 60 61 81 84 New Zealand 13b 9 8b 4 31b 31 13b 10 56b 61 79b 86 Nicaragua .. 42 .. 8 .. 21 .. 19 .. 37 .. 72 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 7 4 3 1 34 31 10 7 58 65 86 92 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 45 37 69 75 20 22 15 12 35 41 16 13 Panama 36 24 3 7 19 24 11 10 45 52 86 82 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 3b 31 0b 19 33b 25 19b 10 64b 44 80 b 71 Peru 1b 1b 0b 1b 30 b 32b 13b 14b 69b 67b 87b 86b Philippines 53 42 32 24 17 18 14 10 29 40 55 66 Poland .. 13 .. 13 .. 42 .. 16 .. 45 .. 71 Portugal 10 11 13 11 39 38 24 16 51 51 63 73 Puerto Rico 5 2 0 1 27 25 19 10 67 73 80 90 Qatar .. 3 .. 0 .. 58 .. 5 .. 39 .. 95 2012 World Development Indicators 51 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 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a Romania 29 29 38 31 44 36 30 20 28 35 33 49 Russian Federation .. 11 .. 7 .. 38 .. 19 .. 51 .. 74 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5 .. 0 .. 23 .. 2 .. 72 .. 98 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia .. 25 .. 23 .. 32 .. 16 .. 43 .. 61 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 2 0 1 36 26 32 17 63 73 68 83 Slovak Republic .. 4 .. 2 .. 50 .. 21 .. 46 .. 77 Slovenia .. 9 .. 9 .. 43 .. 21 .. 48 .. 71 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 6 .. 4 .. 35 .. 13 .. 59 .. 83 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 11 6 8 3 41 34 17 10 49 60 75 88 Sri Lanka .. 30 b .. 37b .. 25b .. 25b .. 27b .. 27b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5 3 2 1 40 31 12 8 55 66 86 91 Switzerland 5 4 4 2 39 30 15 10 57 61 81 82 Syrian Arab Republic 23 14 54 24 28 36 8 9 49 51 38 67 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 78b .. 90 b .. 7b .. 1b .. 15b .. 8b .. Thailand 60 44 62 39 18 21 13 18 22 35 25 43 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 5 6 2 34 44 14 15 51 51 80 82 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 18 72 39 26 30 11 16 41 52 17 45 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. 5 .. 0 .. 28 .. 7 .. 66 .. 93 United Kingdom 3 2 1 1 41 29 16 7 55 68 82 91 United States 4 2 1 1 34 25 14 7 62 72 85 92 Uruguay 7b 16 1b 5 36b 29 21b 13 57b 56 78b 83 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 13 2 2 32 31 16 11 52 57 82 87 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 10 .. 28 .. 29 .. 11 .. 61 .. 61 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 .. .. .. .. .. .. .. .. .. .. .. .. Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 16 .. 15 .. 35 .. 18 .. 49 .. 66 Latin America & Carib. 21 19 15 8 30 29 14 14 49 52 71 78 Middle East & N. Africa .. 23 .. 43 .. 29 .. 14 .. 48 .. 43 South Asia .. 46 .. 65 .. 24 .. 18 .. 30 .. 17 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 5 3 38 31 19 11 56 64 76 86 Euro area 7 4 6 3 42 36 20 12 50 59 73 85 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. Limited coverage. 52 2012 World Development Indicators 2.3 PEOPLE Employment by economic activity About the data De�nitions The International Labour Organization (ILO) classifies services. Such broad classification may obscure fun- • Agriculture corresponds to division 1 (ISIC revi- economic activity using the International Standard damental shifts within countries’ industrial patterns. sion  2), tabulation categories A and B (ISIC revi- Industrial Classification (ISIC) of All Economic Activi- A slight majority of countries report economic activity sion 3), or tabulation category A (ISIC revision 4) and ties, revision 2 (1968), revision 3 (1990), and revi- according to the ISIC revision 2 instead of revision 3 includes hunting, forestry, and fishing. • Industry cor- sion 4 (2008). Because this classification is based or revision  4. The use of one classification or the responds to divisions 2–5 (ISIC revision 2), tabula- on where work is performed (industry) rather than other should not have a significant impact on the tion categories C–F (ISIC revision 3), or tabulation type of work performed (occupation), all of an enter- information for the three broad sectors presented categories B–F (ISIC revision 4) and includes mining prise’s employees are classified under the same in the table. and quarrying (including oil production), manufac- industry, regardless of their trade or occupation. The The distribution of economic wealth in the world turing, construction, and public utilities (electricity, categories should sum to 100 percent. Where they remains strongly correlated with employment by gas, and water). • Services correspond to divisions do not, the differences are due to workers who are economic activity. The wealthier economies are 6–9 (ISIC revision  2), tabulation categories G–P not classified by economic activity. those with the largest share of total employment in (ISIC revision 3), or tabulation categories G–U (ISIC Data on employment are drawn from labor force services, whereas the poorer economies are largely revision  4) and include wholesale and retail trade surveys, household surveys, official estimates, cen- agriculture based. and restaurants and hotels; transport, storage, and suses and administrative records of social insurance The distribution of economic activity by gender communications; financing, insurance, real estate, schemes, and establishment surveys when no other reveals some clear patterns. Men still make up the and business services; and community, social, and information is available. The concept of employment majority of people employed in all three sectors, but personal services. generally refers to people above a certain age who the gender gap is biggest in industry. Employment in worked, or who held a job, during a reference period. agriculture is also male-dominated, although not as Employment data include both full-time and part-time much as industry. Segregating one sex in a narrow workers. range of occupations significantly reduces economic There are many differences in how countries define efficiency by reducing labor market flexibility and thus and measure employment status, particularly mem- the economy’s ability to adapt to change. This seg- bers of the armed forces, self-employed workers, and regation is particularly harmful for women, who have unpaid family workers. Where members of the armed a much narrower range of labor market choices and forces are included, they are allocated to the service lower levels of pay than men. But it is also detri- sector, causing that sector to be somewhat over- mental to men when job losses are concentrated stated relative to the service sector in economies in industries dominated by men and job growth is where they are excluded. Where data are obtained centered in service occupations, where women have from establishment surveys, data cover only employ- better chances, as has been the recent experience ees; thus self-employed and unpaid family workers in many countries. are excluded. In such cases the employment share There are several explanations for the rising impor- of the agricultural sector is severely underreported. tance of service jobs for women. Many service jobs— Caution should be also used where the data refer such as nursing and social and clerical work—are only to urban areas, which record little or no agricul- considered “feminine� because of a perceived simi- tural work. Moreover, the age group and area covered larity to women’s traditional roles. Women often do could differ by country or change over time within a not receive the training needed to take advantage of country. For detailed information, consult the original changing employment opportunities. And the greater source. availability of part-time work in service industries Countries also take different approaches to the may lure more women, although it is unclear whether treatment of unemployed people. In most countries this is a cause or an effect. 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, revision 3, or revision 4. In the table the Data on employment are from the ILO’s Key Indica- reported divisions or categories are aggregated tors of the Labour Market, 7th edition, database. into three broad groups: agriculture, industry, and 2012 World Development Indicators 53 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 2010 1991 2010 1991 2010a 1990 2007–10b 1990 2007–10b 1990–92 2008–10 Afghanistan 46 45 32 31 16 46 .. .. .. .. .. .. Albania 52 52 40 36 89 89 .. .. .. .. –16.6 6.6 Algeria 34 39 22 22 60 95 .. .. .. .. –4.6 0.8 Angola 66 64 47 46 12 31 .. .. .. .. –5.6 0.8 Argentina 55 56 48 34 74 89 25c 22c 27c 17c 9.0 0.2 Armenia 46 41 26 18 .. 92 .. 36 .. 40 –24.4 –6.5 Australia 57 62 58 61 132 129 12 11 9 7 3.0 0.7 Austria 54 58 61 54 102 100 .. 9 .. 9 1.3 –0.9 Azerbaijan 56 60 37 31 88 .. .. 47 .. 62 –12.6 5.5 Bahrain 61 65 32 32 100 .. .. 2 .. 1 0.8 1.9 Bangladesh 73 68 64 53 18 49 .. .. .. .. 2.3 3.4 Belarus 59 50 39 31 93 .. .. .. .. .. –4.0 3.2 Belgium 46 50 31 25 101 111 18 11 17 8 1.6 –0.4 Benin 72 72 66 57 .. .. .. .. .. .. .. .. Bolivia 63 69 48 49 .. 80 32c 49 50 c 67 2.6 1.3 Bosnia and Herzegovina 39 35 21 17 .. 90 .. .. .. .. –14.7 –1.9 Botswana 56 63 36 41 49 .. .. .. .. .. .. .. Brazil 60 65 54 53 .. 101 29c 27 30 c 22 –0.3 1.8 Bulgaria 47 49 30 24 98 88 .. 10 .. 8 3.1 1.5 Burkina Faso 82 81 76 73 7 21 .. .. .. .. 1.6 0.3 Burundi 83 77 70 56 5 25 .. .. .. .. .. .. Cambodia 80 81 71 70 25 46 .. 79 .. 86 4.0 –1.2 Cameroon 64 68 42 43 26 42 .. .. .. .. –6.6 –0.3 Canada 59 61 57 55 101 101 .. .. .. .. 0.8 0.2 Central African Republic 72 73 56 54 12 13 .. .. .. .. .. .. Chad 67 67 49 49 6 26 .. .. .. .. .. .. Chile 50 55 33 31 97 88 .. 26 .. 25 6.6 0.4 China 75 71 72 57 41 81 .. .. .. .. 6.8 8.8 Hong Kong SAR, China 63 57 54 31 .. 83 .. 10 .. 5 5.4 1.7 Colombia 46 59 35 35 53 96 30 c 48 26 c 49 –0.7 0.6 Congo, Dem. Rep. 66 66 40 39 21 38 .. .. .. .. –13.0 0.4 Congo, Rep. 62 66 40 39 46 .. .. .. .. .. .. .. Costa Rica 55 60 48 42 45 100 26 19 21 20 2.4 –0.7 Côte d’Ivoire 63 64 50 48 .. .. .. .. .. .. –4.1 0.7 Croatia 51 46 27 25 83 95 .. 17 .. 19 –7.7 –0.6 Cuba 53 56 41 40 94 89 .. .. .. .. .. .. Cyprus 57 60 41 34 72 98 .. 16 .. 12 –0.9 0.2 Czech Republic 60 54 49 25 91 90 .. 17 .. 10 –5.2 0.2 Denmark 62 60 65 58 109 117 8 7 6 4 2.5 0.8 Dominican Republic 52 56 37 37 .. 76 42 49 30 30 0.9 2.0 Ecuador 57 64 45 44 55 80 33c 37 41c 51 –0.1 –0.9 Egypt, Arab Rep. 43 44 22 25 69 .. .. 22 .. 49 –1.3 2.6 El Salvador 57 57 46 42 38 65 .. 33 .. 46 .. .. Eritrea 75 78 69 67 11 32 .. .. .. .. .. .. Estonia 67 51 50 27 100 104 2 6 3 4 –8.0 1.9 Ethiopia 76 80 70 71 14 36 .. .. .. .. –7.9 5.8 Finland 60 55 49 40 116 108 .. 12 .. 7 1.7 –1.3 France 50 51 33 31 100 113 11 8 11 6 1.4 0.1 Gabon 52 50 20 15 40 .. .. .. .. .. .. .. Gambia, The 71 72 58 56 19 54 .. .. .. .. .. .. Georgia 55 54 23 20 95 86 .. 62 .. 65 –25.4 0.3 Germany 56 55 58 47 98 103 5 8 6 6 3.7 –0.9 Ghana 68 67 43 36 35 58d .. .. .. .. 0.7 1.9 Greece 46 48 31 21 94 .. 40 29 46 27 2.1 –1.6 Guatemala 62 65 54 58 23 59 .. .. .. .. 1.0 –2.5 Guinea 69 70 51 52 11 38 .. .. .. .. .. .. Guinea-Bissau 64 68 44 48 5 .. .. .. .. .. .. .. Haiti 58 60 32 29 .. .. .. .. .. .. .. .. 54 2012 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 2010 1991 2010 1991 2010a 1990 2007–10b 1990 2007–10b 1990–92 2008–10 Honduras 57 60 49 47 33 73 48 c 49 50 c 52 .. .. Hungary 49 45 38 18 86 98 8 8 7 5 0.3 –1.3 India 58 54 46 34 46 60 .. .. .. .. 1.0 5.6 Indonesia 63 63 46 40 46 77 .. 62 .. 67 6.2 3.2 Iran, Islamic Rep. 40 40 28 24 53 84 .. 40 .. 52 5.7 –0.9 Iraq 32 34 20 17 40 .. .. .. .. .. –32.8 0.3 Ireland 45 52 38 31 100 117 25 18 10 5 2.6 2.1 Israel 46 54 24 27 92 91 .. 9 .. 5 1.6 0.3 Italy 45 44 33 20 79 99 29 21 24 15 0.6 –1.0 Jamaica 63 56 43 25 70 93 46 41 37 31 0.7 –3.1 Japan 63 57 43 39 97 102 15 10 26 11 0.5 0.0 Jordan 32 36 17 20 82 91 .. 11 .. 3 –5.0 0.1 Kazakhstan 64 67 45 44 98 97 .. 30 .. 34 –15.1 –0.9 Kenya 67 60 45 33 .. 60 .. .. .. .. –3.9 0.6 Korea, Dem. Rep. 79 74 73 57 .. .. .. .. .. .. .. .. Korea, Rep. 59 58 36 24 91 97 .. 23 .. 27 5.1 2.7 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 58 66 26 31 53 101 .. .. .. .. –0.2 –4.4 Kyrgyz Republic 60 61 41 40 100 84 .. .. .. .. –13.1 –2.0 Lao PDR 80 77 72 62 21 45 .. .. .. .. .. .. Latvia 59 49 43 27 92 95 .. 8 .. 7 –19.6 –0.2 Lebanon 39 42 26 23 61 81 .. 32 .. 16 .. .. Lesotho 49 47 38 28 24 46 .. .. .. .. .. .. Liberia 57 59 34 33 .. .. .. 69 .. 89 .. .. Libya 44 49 25 29 .. .. .. .. .. .. .. .. Lithuania 56 48 35 20 92 98 .. 10 .. 8 –13.9 –0.9 Macedonia, FYR 38 38 18 15 76 83 .. 24 .. 22 –6.9 –2.0 Madagascar 83 84 71 71 19 31 .. .. .. .. –5.8 –6.1 Malawi 72 77 47 51 17 32 .. .. .. .. –2.1 3.7 Malaysia 60 59 46 35 57 68 31 23 25 20 6.0 0.3 Mali 47 48 35 36 7 38 .. 77 .. 89 1.3 1.9 Mauritania 32 36 15 16 13 24 .. .. .. .. .. .. Mauritius 53 55 41 31 55 89 13 17 7 14 .. .. Mexico 57 58 50 43 54 87 29 27 15 32 1.0 –1.2 Moldova 59 38 38 18 90 88 .. 34 .. 28 –23.1 –2.2 Mongolia 51 57 35 32 82 93 .. 60 .. 54 .. .. Morocco 46 45 39 30 36 .. .. 47 .. 65 –1.6 2.7 Mozambique 77 78 64 57 7 25 .. .. .. .. –3.6 4.2 Myanmar 73 76 51 53 23 54 .. .. .. .. 2.0 .. Namibia 45 40 24 11 43 .. .. .. .. .. .. .. Nepal 84 82 79 73 34 .. .. .. .. .. .. .. Netherlands 53 62 53 63 120 120 7 13 12 10 0.4 –0.3 New Zealand 57 63 54 50 92 119 15 15 10 9 0.6 1.1 Nicaragua 55 60 47 46 43 69 .. 45 .. 45 .. .. Niger 54 61 47 53 7 13 .. .. .. .. –5.9 –2.5 Nigeria 53 51 29 32 24 44 .. .. .. .. –2.8 4.6 Norway 59 64 49 52 103 110 .. 8 .. 3 3.9 –0.3 Oman 52 55 28 32 45 100 .. .. .. .. 0.6 1.4 Pakistan 47 51 38 41 23 34 .. 59 .. 78 6.4 –0.1 Panama 49 62 34 42 62 74 44 33 19 28 .. .. Papua New Guinea 70 71 57 54 12 .. .. .. .. .. .. .. Paraguay 68 69 64 57 31 67 17c 42 31c 48 .. .. Peru 53 71 35 55 67 92 30 c 34 c 46c 47c –0.8 2.4 Philippines 60 60 42 39 70 85 .. 42 .. 46 –3.3 1.4 Poland 54 51 32 27 87 97 .. 20 .. 17 1.1 1.9 Portugal 59 55 53 29 66 107 22 18 28 17 2.1 1.3 Puerto Rico 38 37 20 18 .. 82 .. .. .. .. .. .. Qatar 79 86 51 66 84 94 .. 0 .. 0 –0.3 11.1 2012 World Development Indicators 55 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 2010 1991 2010 1991 2010a 1990 2007–10b 1990 2007–10b 1990–92 2008–10 Romania 57 52 49 24 92 95 21 33 33 33 –9.3 –3.2 Russian Federation 59 58 41 36 93 89 1 6 1 5 –7.9 –1.4 Rwanda 88 85 80 73 18 32 .. .. .. .. .. .. Saudi Arabia 51 47 25 12 .. 101 .. .. .. .. 4.8 –0.7 Senegal 68 69 59 57 15 37 77 .. 91 .. –1.1 0.0 Serbia 48e 46e 28e 19e .. 91 .. 30 .. 29 .. .. Sierra Leone 64 65 39 42 16 .. .. .. .. .. .. .. Singapore 64 63 56 34 .. .. 10 12 6 7 –2.0 5.0 Slovak Republic 59 51 46 21 88 89 .. 17 .. 7 –0.8 1.6 Slovenia 50 55 28 34 89 97 .. 15 .. 12 –2.3 –1.6 Somalia 52 53 42 39 .. .. .. .. .. .. .. .. South Africa 37 39 15 13 69 94 .. 8 .. 12 –5.3 –0.4 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 42 47 35 25 105 119 21 13 25 9 1.8 2.5 Sri Lanka 49 52 28 30 72 .. .. 38 c .. 44 c 5.5 4.8 Sudan 47 49 32 27 20 39 .. .. .. .. 1.4 1.7 Swaziland 44 44 27 26 49 58 .. .. .. .. .. .. Sweden 64 58 58 38 90 100 .. 9 .. 5 1.9 –0.1 Switzerland 67 65 69 61 98 95 8 9 11 9 –0.6 –1.1 Syrian Arab Republic 46 39 38 24 48 72 .. 34 .. 25 6.3 1.5 Tajikistan 58 58 38 38 102 87 .. .. .. .. –20.4 5.8 Tanzania 79 79 70 69 5 27 86 c .. 96 c .. –2.3 3.3 Thailand 77 71 70 46 31 77 67 50 74 55 6.8 0.7 Timor-Leste 58 54 46 41 .. 56 .. .. .. .. .. .. Togo 70 75 56 57 20 .. .. .. .. .. .. .. Trinidad and Tobago 45 63 33 48 82 90 22 .. 21 .. –3.5 0.6 Tunisia 41 41 29 23 45 90 .. .. .. .. 2.5 1.7 Turkey 53 44 48 32 48 78 .. 28 .. 48 1.0 –1.5 Turkmenistan 53 54 34 35 .. .. .. .. .. .. –13.0 4.9 Uganda 79 75 61 55 10 28 .. .. .. .. –0.8 3.1 Ukraine 58 54 36 34 94 96 .. .. .. .. –7.9 –4.3 United Arab Emirates 71 76 44 43 68 .. .. 1 .. 0 –3.6 –5.2 United Kingdom 58 57 63 48 87 102 13 15 6 8 6.1 –0.9 United States 61 58 54 42 92 96 .. .. .. .. 1.7 2.3 Uruguay 55 61 45 44 84 90 .. 22c .. 24 c 5.2 3.9 Uzbekistan 52 54 33 35 99 105 .. .. .. .. –7.7 5.4 Venezuela, RB 55 61 38 40 56 83 .. 30 .. 32 4.5 –3.9 Vietnam 78 75 73 58 35 77 .. .. .. .. 4.6 3.4 West Bank and Gaza 31 31 20 16 .. 86 .. 26 .. 31 .. .. Yemen, Rep. 39 42 24 27 .. 44 .. .. .. .. 0.9 2.2 Zambia 65 67 47 51 21 .. 56 .. 81 .. –2.7 3.6 Zimbabwe 69 83 48 73 49 .. .. .. .. .. –4.3 5.4 World 62 w 60 w 52 w 43 w 50 w 68 w .. w .. w .. w .. w 0.9 w 2.0 w Low income 72 71 58 55 26 39 .. .. .. .. –3.4 –2.3 Middle income 63 60 52 41 47 69 .. .. .. .. 1.2 4.2 Lower middle income 58 55 43 36 42 58 .. 70 .. 77 0.4 3.7 Upper middle income 67 65 60 48 67 83 .. .. .. .. 1.5 4.4 Low & middle income 64 61 53 43 44 64 .. .. .. .. 1.0 4.3 East Asia & Pacific 73 70 67 53 41 76 .. .. .. .. 6.4 7.9 Europe & Central Asia 57 54 41 34 85 89 .. 18 .. 19 –9.3 –0.7 Latin America & Carib. 57 62 47 45 57 90 30 31 29 31 1.3 0.1 Middle East & N. Africa 40 41 27 24 54 72 .. 33 .. 48 1.0 1.1 South Asia 59 55 47 37 37 55 .. 78 .. 86 3.2 4.8 Sub-Saharan Africa 63 64 46 46 22 36 .. .. .. .. –4.8 1.5 High income 57 55 46 38 91 100 .. .. .. .. 2.8 0.7 Euro area 50 51 42 34 .. 107 16 13 14 9 2.3 –0.1 a. Provisional data. b. Data are for the most recent year available. c. Limited coverage. d. Data are for 2011. e. Includes Montenegro. 56 2012 World Development Indicators 2.4 PEOPLE Decent work and productive employment About the data De�nitions Four targets were added to the UN Millennium Dec- supplemented by official estimates and censuses • Employment to population ratio is the proportion of laration at the 2005 World Summit High-Level Ple- for a small group of countries. The labor force survey a country’s population that is employed. People ages nary Meeting of the 60th Session of the UN General is the most comprehensive source for internationally 15 and older are generally considered the working- Assembly. One was full and productive employment comparable employment, but there are still some age population. People ages 15–24 are generally and decent work for all, which is seen as the main limitations for comparing data across countries and considered the youth population. • Gross enrollment route for people to escape poverty. The four indi- over time even within a country. Information from ratio, secondary, is the ratio of total enrollment in cators for this target have an economic focus, and labor force surveys is not always consistent in what secondary education, regardless of age, to the popu- three of them are presented in the table. is included in employment. For example, informa- lation of the age group that officially corresponds to The employment to population ratio indicates tion provided by the Organisation for Economic Co- secondary education. • Vulnerable employment is how efficiently an economy provides jobs for people operation and Development relates only to civilian unpaid family workers and own-account workers as a who want to work. A high ratio means that a large employment, which can result in an underestima- percentage of total employment. • Labor productiv- propor tion of the population is employed. But a lower tion of “employees� and “workers not classified by ity is the growth rate of gross domestic product employment to population ratio can be seen as a pos- status,� espe cially in countries with large armed (GDP) divided by the number of people engaged in itive sign, especially for young people, if it is caused forces. While the categories of unpaid family work- the production of goods and services. by an increase in their education. This indicator has ers and self-employed workers, which include own- a gender bias because women who do not consider account workers, would not be affected, their relative their work employment or who are not perceived as shares would be. Geographic coverage is another working tend to be undercounted. This bias has dif- factor that can limit cross-country comparisons. The ferent effects across countries and reflects demo- employment by status data for many Latin Ameri- graphic, social, legal, and cultural trends and norms. can countries covers urban areas only. Similarly, in Comparability of employment ratios across coun- some countries in Sub-Saharan Africa, where limited tries is also affected by variations in definitions of information is available anyway, the members of pro- employment and population (see About the data for ducer cooperatives are usually excluded from the table 2.3). The biggest difference results from the self-employed category. For detailed information on age range used to define labor force activity. The definitions and coverage, consult the original source. population base for employment ratios can also vary Labor productivity is used to assess a country’s (see table 2.1). Most countries use the resident, economic ability to create and sustain decent noninstitutionalized population of working age living employment opportunities with fair and equitable in private households, which excludes members of remuneration. Productivity increases obtained the armed forces and individuals residing in men- through investment, trade, technological progress, or tal, penal, or other types of institutions. But some changes in work organization can increase social pro- countries include members of the armed forces in tection and reduce poverty, which in turn reduce vul- the population base of their employment ratio while nerable employment and working poverty. Productiv- excluding them from employment data (International ity increases do not guarantee these improvements, Labour Organization, Key Indicators of the Labour but without them—and the economic growth they Market, 7th edition). bring—improvements are highly unlikely. For compa- The proportion of unpaid family workers and own- rability of individual sectors labor productivity is esti- account workers in total employment is derived from mated according to national accounts conventions. information on status in employment. Each status However, there are still significant limitations on the group faces different economic risks, and unpaid availability of reliable data. Information on consis- family workers and own-account workers are the tent series of output in both national currencies and most vulnerable—and therefore the most likely to purchasing power parity dollars is not easily avail- fall into poverty. They are the least likely to have for- able, especially in developing countries, because the Data sources mal work arrangements, are the least likely to have definition, coverage, and methodology are not always social protection and safety nets to guard against consistent across countries. For example, countries Data on employment to population ratio, vulner- economic shocks, and often are incapable of gen- employ different methodologies for estimating the able employment, and labor productivity are from erating sufficient savings to offset these shocks. A missing values for the nonmarket service sectors the International Labour Organization’s Key Indi- high proportion of unpaid family workers in a country and use different definitions of the informal sector. cators of the Labour Market, 7th edition, data- indicates weak development, little job growth, and base. Data on gross enrollment ratios are from often a large rural economy. the United Nations Educational, Scientific, and Data on employment by status are drawn from Cultural Organization Institute for Statistics. labor force surveys and household surveys, 2012 World Development Indicators 57 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 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 13.8 .. 12.2 .. 15.9 .. .. .. .. .. .. Algeria 23.0 11.4 24.2 10.0 20.3 20.0 .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.7b 8.6b 6.4b 7.8b 7.0 b 9.8b .. .. .. 48.1 36.7 15.3 Armenia .. 28.6 .. 21.9 .. 35.0 58.2 52.2 63.8 11.1 68.3 20.6 Australia 10.8 5.2b 11.4 5.1b 10.0 5.4b 18.5b 20.3b 16.4b 47.4 33.6 19.0 Austria 3.6 4.4 3.5 4.6 3.8 4.2 25.2 27.8 22.0 37.5 54.5 8.0 Azerbaijan .. 6.0 .. 5.2 .. 6.9 .. .. .. 9.9 75.8 14.3 Bahrain 6.3 .. 5.2 .. 11.8 .. .. .. .. 22.7 38.8 31.7 Bangladesh 1.9 5.0 2.0 4.2 1.9 7.4 .. .. .. .. .. .. Belarus .. .. .. .. .. .. .. .. .. 10.8 38.6 50.6 Belgium 6.7 8.3 4.8 8.1 9.5 8.5 48.8 49.6 47.8 33.9 40.5 19.0 Benin 1.5 .. 2.2 .. 0.6 .. .. .. .. .. .. .. Bolivia 5.5b 5.2 5.5b 4.5 5.6b 6.0 .. .. .. .. .. .. Bosnia and Herzegovina 17.6 27.2 15.5 25.6 21.6 30.0 .. .. .. 94.9 .. 4.8 Botswana 13.8 .. 11.7 .. 17.2 .. .. .. .. .. .. .. Brazil 6.4b 8.3 5.4b 6.1 7.9b 11.0 .. .. .. 49.3 35.7 4.1 Bulgaria .. 10.2 .. 10.9 .. 9.5 46.4 46.3 46.5 42.5 47.4 10.1 Burkina Faso .. 3.3 .. .. .. .. .. .. .. .. .. .. Burundi 0.5 .. 0.7 .. 0.3 .. .. .. .. .. .. .. Cambodia .. 1.7 .. 1.5 .. 1.8 .. .. .. .. .. .. Cameroon .. 2.9 .. 2.5 .. 3.3 .. .. .. .. .. .. Canada 11.2b 8.0 b 12.0 b 8.7b 10.2b 7.2b 12.0 b 12.7b 11.0 b 26.3b 41.0 b 32.7b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 4.4 8.1 3.9 7.2 5.3 9.6 .. .. .. 17.8 58.5 23.5 China 2.3b 4.3 .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China 2.0 5.2 2.0 6.0 1.9 4.3 .. .. .. 38.0 43.8 17.1 Colombia 9.5b 11.6 6.8 b 9.1 13.0 b 15.0 .. .. .. 21.0 53.7 23.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 4.1 7.8 3.5 6.6 5.4 9.9 .. .. .. 66.9 23.4 7.9 Côte d’Ivoire 6.7 .. .. .. .. .. .. .. .. .. .. .. Croatia 11.1 11.8 11.1 11.4 11.2 12.2 44.4 41.4 47.7 16.0 70.4 11.6 Cuba .. 1.6 .. 1.4 .. 2.0 .. .. .. 46.6 48.5 3.6 Cyprus 2.1 6.2 2.0 6.0 2.2 6.4 20.4 21.0 19.8 26.9 b 42.1b 29.7b Czech Republic 2.3 7.3 2.4 6.4 2.1 8.5 43.3 43.3 43.3 29.6 64.8 5.7 Denmark 9.0 7.4 8.3 8.2 9.9 6.6 19.1 20.6 16.9 35.5 37.2 20.8 Dominican Republic 20.7 14.3 12.0 9.8 35.2 21.4 .. .. .. 32.0 42.2 19.5 Ecuador 8.9b 6.5 6.0 b 5.2 13.2b 8.4 .. .. .. .. .. .. Egypt, Arab Rep. 9.0 9.4 6.4 5.2 17.0 22.9 .. .. .. .. .. .. El Salvador 7.9b 7.3 8.4b 9.0 7.2b 4.9 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.7 16.9 3.9 19.5 3.5 14.3 27.4 26.8 28.4 25.2 58.1 18.0 Ethiopia 1.3 20.5 1.1 12.1 1.6 29.9 .. .. .. .. .. .. Finland 11.6 8.4 13.3 9.0 9.6 7.7 23.6 27.0 19.3 36.6 45.3 17.4 France 10.2 9.3 8.1 9.0 12.8 9.7 40.1 41.5 38.7 39.5 41.7 18.3 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 16.5 .. 16.8 .. 16.1 .. .. .. 4.6 56.1 39.2 Germany 6.6 7.1 5.3 7.5 8.4 6.6 47.4 48.1 46.3 32.3 56.5 11.0 Ghana 4.7 .. 3.7 .. 5.5 .. .. .. .. .. .. .. Greece 7.8 12.5 4.9 9.9 12.9 16.2 45.0 38.8 50.3 27.7 48.9 22.4 Guatemala .. .. .. .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 12.7 .. 11.9 .. 13.8 .. .. .. .. .. .. .. 58 2012 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 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a Honduras 3.2b 2.9 3.3b 2.9 3.0 b 2.9 .. .. .. .. .. .. Hungary 9.9 11.2 11.0 11.6 8.7 10.7 50.6 51.2 49.9 33.8 58.4 7.8 India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 2.8 7.1 2.7 6.1 3.0 8.7 .. .. .. 43.4 40.6 10.2 Iran, Islamic Rep. 11.1 10.5 9.5 9.1 24.4 16.8 .. .. .. 40.4 31.0 25.5 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 15.0 13.5 14.9 16.7 15.3 9.5 49.0 53.9 38.2 38.3 39.0 18.5 Israel 11.2 6.6 9.2 6.8 13.9 6.5 22.4 25.7 18.5 22.1 48.0 28.1 Italy 9.3 8.4 6.7 7.6 13.9 9.7 48.5 47.2 49.9 47.2 40.3 11.2 Jamaica 15.4 11.4 9.4 8.5 22.2 14.8 .. .. .. 12.0 4.5 3.9 Japan 2.2 5.0 2.1 5.4 2.2 4.5 37.6 44.8 25.2 66.8 .. 33.2 Jordan .. 12.9 .. 10.3 .. 24.1 .. .. .. .. .. .. Kazakhstan .. 6.6 .. 5.6 .. 7.5 .. .. .. 45.1 39.7 15.2 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.5 3.7 2.8 4.0 2.1 3.3 0.3 0.5 0.3 15.3 63.7 21.1 Kosovo .. 45.4 .. 40.7 .. 56.4 81.7 82.8 79.8 64.0 46.0 15.0 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 8.6 .. 7.3 .. 9.4 .. .. .. .. .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 18.7 .. 21.7 .. 15.7 45.0 48.2 40.6 23.3 62.2 14.5 Lebanon .. 9.0 .. 8.6 .. 10.1 .. .. .. 45.5 19.7 29.7 Lesotho .. 25.3 .. 23.0 .. 28.0 .. .. .. 57.2 33.5 0.4 Liberia .. 3.7 .. 3.4 .. 4.1 .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 17.8 .. 21.2 .. 14.4 41.4 42.3 40.2 15.0 67.7 17.3 Macedonia, FYR .. 32.0 .. 31.9 .. 32.2 83.1 83.5 82.4 .. .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 3.7 3.7 3.4 3.6 4.2 3.8 .. .. .. 10.4 60.9 24.9 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 3.3 7.7 3.2 4.6 3.6 12.8 .. .. .. 43.5 29.6 7.9 Mexico 3.1 5.3 2.7 5.3 4.0 5.3 2.4 2.7 2.0 51.5 25.2 21.0 Moldova .. 6.4 .. 7.8 .. 4.9 .. .. .. .. .. .. Mongolia .. .. .. .. .. .. .. .. .. 28.2 49.0 22.1 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 .. 2.7 .. 3.1 .. 2.4 .. .. .. .. .. .. Netherlands 5.6 4.5 4.0 4.4 7.8 4.5 27.6 27.7 27.4 42.0 36.6 18.7 New Zealand 10.6b 6.5b 11.4b 6.2b 9.7b 6.8b 9.0 b 8.9b 9.0 b 30.6 39.2 25.7 Nicaragua 14.4 5.0 11.3 4.9 19.5 5.1 .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 5.9 3.6 6.6 4.1 5.1 3.0 9.5 10.6 7.7 29.9 49.3 17.9 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 5.2 5.0 3.8 4.0 14.0 8.7 .. .. .. 14.7 10.1 28.0 Panama 14.7 6.5 10.8 5.3 22.3 8.5 .. .. .. 35.8 39.8 23.8 Papua New Guinea 7.7 .. 9.0 .. 5.9 .. .. .. .. .. .. .. Paraguay 5.0 b 5.6 6.0 b 4.4 3.7b 7.5 .. .. .. 53.5 31.4 13.4 Peru 9.4b 6.3b 7.5b 4.4b 12.5b 8.8b .. .. .. 31.5b 30.5b 37.3b Philippines 8.6 7.4 7.9 7.6 9.9 6.9 .. .. .. 13.1 45.2 41.2 Poland 13.3 9.6 12.2 9.3 14.7 10.0 25.5 25.3 25.8 15.9 71.8 12.1 Portugal 4.1b 10.8 3.5b 9.8 5.0 b 11.9 52.3 51.7 52.8 67.3 15.9 13.5 Puerto Rico 16.9 13.4 19.1 14.9 13.3 11.6 .. .. .. .. .. .. Qatar .. 0.5 .. 0.2 .. 2.6 38.5 35.3 40.3 19.0 52.7 24.0 2012 World Development Indicators 59 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 2007–10a 1990–92a 2007–10a 1990–92a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a 2007–10a Romania .. 7.3 .. 7.9 .. 6.5 34.9 36.9 32.0 28.2 62.7 6.7 Russian Federation 5.2 7.5 5.2 8.0 5.2 6.9 35.2 32.7 38.0 13.1 52.8 34.1 Rwanda 0.3 .. 0.6 .. 0.2 .. .. .. .. .. .. .. Saudi Arabia .. 5.4 .. 3.5 .. 15.9 .. .. .. 7.5 48.6 43.6 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia .. 19.2 .. 18.4 .. 20.2 71.1 70.1 72.1 20.3 68.4 11.2 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7 5.9 2.7 5.4 2.6 6.5 .. .. .. 27.2 22.7 50.1 Slovak Republic .. 14.4 .. 14.2 .. 14.6 59.3 58.3 60.5 27.8 66.2 5.9 Slovenia 7.1 7.2 8.1 7.4 6.0 7.0 43.3 45.0 41.2 23.3 58.1 16.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 23.8 .. 22.0 .. 25.9 14.4 .. .. 15.4 80.7 0.8 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 18.1 20.1 13.9 19.7 25.8 20.5 45.1 44.6 45.6 58.4 22.6 17.7 Sri Lanka 14.2b 4.9 .. 3.5 .. 7.7 .. .. .. 45.4b 22.8b 31.8b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5.7 8.4 6.7 8.5 4.6 8.2 16.6 18.1 14.8 32.5 44.9 16.4 Switzerland 2.8 4.2 2.3 3.8 3.5 4.8 34.3 28.3 39.8 27.9 53.7 17.7 Syrian Arab Republic 6.8 8.4 5.2 5.7 14.0 22.5 .. .. .. 46.1 28.0 4.9 Tajikistan .. .. .. .. .. .. .. .. .. 66.5 28.8 4.6 Tanzania 3.6b .. 2.8b .. 4.3b .. .. .. .. .. .. .. Thailand 1.4 1.2 1.3 1.2 1.5 1.1 .. .. .. 41.5 49.3 0.2 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 19.6 5.3 17.0 3.5 23.9 6.2 .. .. .. 27.9 65.9 5.2 Tunisia .. 14.2 .. .. .. .. .. .. .. .. .. .. Turkey 8.5 11.9 8.8 11.4 7.8 13.0 28.6 24.7 37.0 52.5 26.0 13.9 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 1.0 4.2 1.3 3.1 0.6 5.1 .. .. .. .. .. .. Ukraine .. 8.8 .. 6.6 .. 6.1 .. .. .. 7.4 52.9 39.7 United Arab Emirates .. 4.0 .. 2.0 .. 12.0 .. .. .. 19.7 42.6 33.2 United Kingdom 9.7 7.8 11.5 8.6 7.3 6.7 32.6 37.2 26.0 37.1 46.5 14.3 United States 7.5b 9.6b 7.9b 10.5b 7.0 b 8.6b 29.0 b 29.9b 27.7b 17.9 35.5 46.5 Uruguay 9.0 b 7.3 6.8b 5.3 11.8b 9.7 .. .. .. 59.1 27.0 13.8 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 .. .. .. 53.8 14.3 24.5 Yemen, Rep. .. 14.6 .. 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 3.5 5.8 .. .. .. .. .. .. .. .. .. .. Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 2.5 4.7 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 9.5 .. 10.3 .. 8.8 .. .. .. 26.1 47.8 25.6 Latin America & Carib. 6.6 7.8 5.4 6.4 8.3 9.8 .. .. .. 42.3 38.1 13.0 Middle East & N. Africa 12.7 10.6 10.8 8.8 21.5 18.4 .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.5 8.5 7.1 8.7 8.0 8.1 32.6 34.2 31.3 33.9 42.2 26.8 Euro area 9.1 10.0 7.2 9.8 11.9 10.2 44.0 44.2 43.3 42.9 41.3 14.8 a. Data are for the most recent year available. b. Limited coverage. 60 2012 World Development Indicators 2.5 PEOPLE Unemployment About the data De�nitions 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, 7th edition, database. closely than that used by other sources and therefore 2012 World Development Indicators 61 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 Bahrain .. .. .. .. .. .. .. .. .. .. .. 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 .. .. .. .. .. .. .. .. .. .. .. Cyprus .. .. .. .. .. .. .. .. .. .. .. 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 62 2012 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 Honduras 2007 8.7 13.3 4.1 45.1 54.9 61.6 10.4 25.1 3.5 23.0 73.5 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 .. .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 63 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 South Sudan .. .. .. .. .. .. .. .. .. .. .. 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 Tanzania 2005–06 31.1 35.0 27.1 28.2 71.8 85.3 0.7 14.0 56.3h 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. Covers northern Sudan only. h. Covers mainly workers working on their own shamba. i. Covers children ages 6–14. 64 2012 World Development Indicators 2.6 PEOPLE Children at work About the data De�nitions The data in the table refer to children’s work in the defined activities —is not presented. ISIC revision 2 •  Survey year is the year in which the underlying sense of “economic activity�—that is, children in and revision 3 are both used, depending on the coun- data were collected. • Children in employment are employment, a broader concept than child labor (see try’s codification for describing economic activity. children involved in any economic activity for at least ILO 2009a for details on this distinction). This does not affect the definition of the groups in one hour in the reference week of the survey. • Work In line with the defi nition of economic activity the table. only refers to children who are employed and not adopted by the 13th International Conference of The table also aggregates the distribution of chil- attending school. • Study and work refer to children Labour Statisticians, the threshold set by the 1993 dren in employment by three major categories of attending school in combination with employment. UN System of National Accounts for classifying a status in employment, based on the International • Employment by economic activity is the distribu- person as employed is to have been engaged at Classification of Status in Employment (1993): self- tion of children in employment by the major industrial least one hour in any activity relating to the pro- employed workers, wage workers (also known as categories (ISIC revision 2 or revision 3). • Agricul- duction of goods and services during the reference employees), and unpaid family workers. A residual ture corresponds to division 1 (ISIC revision  2) or period. Children seeking work are thus excluded. category—which includes those not classifiable by categories A and B (ISIC revision  3) and includes Economic activity covers all market production and status—is not presented. agriculture and hunting, forestry and logging, and certain nonmarket production, including production In most countries more boys are involved in employ- fishing. • Manufacturing corresponds to division 3 of goods for own use. It excludes unpaid household ment, or the gender difference is small. However, girls (ISIC revision 2) or category D (ISIC revision 3). • Ser- services (commonly called “household chores�)— are often more present in hidden or underreported vices correspond to divisions 6–9 (ISIC revision that is, the production of domestic and personal forms of employment such as domestic service, and 2) or categories G–P (ISIC revision  3) and include services by household members for a household’s in almost all societies girls bear greater responsibil- wholesale and retail trade, hotels and restaurants, own consumption. ity for household chores in their own homes, work transport, financial intermediation, real estate, pub- Data are from household surveys by the Interna- that lies outside the System of National Accounts lic administration, education, health and social work, tional Labor Organization (ILO), the United Nations production boundary and is thus not considered in other community services, and private household Children’s Fund (UNICEF), the World Bank, and estimates of children’s employment. activity. • Self-employed workers are people whose national statistical offices. The surveys yield data remuneration depends directly on the profits derived on education, employment, health, expenditure, and from the goods and services they produce, with or consumption indicators related to children’s work. without other employees, and include employers, Household survey data generally include informa- own-account workers, and members of produc- tion on work type—for example, whether a child is ers cooperatives. • Wage workers (also known as working for payment in cash or in kind or is involved employees) are people who hold explicit (written or in unpaid work, working for someone who is not a oral) or implicit employment contracts that provide member of the household, or involved in any type of basic remuneration that does not depend directly on family work (on the farm or in a business). Country the revenue of the unit for which they work. • Unpaid surveys define the ages for child labor as 5–17. The family workers are people who work without pay in a data in the table have been recalculated to present market-oriented establishment operated by a related statistics for children ages 7–14. person living in the same household. Although efforts are made to harmonize the defini- Data sources tion of employment and the questions on employ- ment in survey questionnaires, signifi cant differ- Data on children at work are estimates produced ences remain in the survey instruments that collect by the Understanding Children’s Work project data on children in employment and in the sampling based on household survey data sets made avail- design underlying the surveys. Differences exist able by the ILO’s International Programme on the not only across different household surveys in the Elimination of Child Labour under its Statistical same country but also across the same type of sur- Monitoring Programme on Child Labour, UNICEF vey carried out in different countries, so estimates under its Multiple Indicator Cluster Survey pro- of working children are not fully comparable across gram, the World Bank under its Living Standards countries. Measurement Study program, and national sta- The table aggregates the distribution of children tistical offices. Information on how the data were in employment by the industrial categories of the collected and some indication of their reliability International Standard Industrial Classifi cation can be found at www.ilo.org/public/english/ (ISIC): agriculture, manufacturing, and services. standards/ipec/simpoc/, www.childinfo.org, and A residual category—which includes mining and www.worldbank.org/lsms. Detailed country statis- quarrying; electricity, gas, and water; construction; tics can be found at www.ucw-project.org. extraterritorial organization; and other inadequately 2012 World Development Indicators 65 2.7 Poverty rates at national poverty lines Population below national poverty line Poverty gap at national poverty line Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year a % % % year a % % % year a % % % Afghanistanb .. .. .. 2008 c 37.5 29.0 36.0 2008 c 8.3 6.2 7.9 Albaniab 2005 24.2 11.2 18.5 2008 14.6 10.1 12.4 2008 2.6 1.9 2.3 Angola .. .. .. 2000 c .. 62.3 .. .. .. .. Argentina 2009d .. 13.2 .. 2010 d .. 9.9 .. .. .. .. Armeniab 2009 34.9 33.7 34.1 2010 36.0 36.0 35.8 .. .. .. Azerbaijanb 2001 42.5 55.7 49.6 2008 18.5 14.8 15.8 .. .. .. Bangladesh 2005 43.8 28.4 40.0 2010 35.2 21.3 31.5 2010 7.4 4.3 6.5 Belarus 2008 .. .. 6.1 2009 .. .. 5.4 .. .. .. Benin .. .. .. 2003c 46.0 29.0 39.0 2003c 14.0 8.0 12.0 Bhutan .. .. .. 2007c 30.9 1.7 23.2 2007c 8.1 0.4 6.1 Bolivia 2006d 76.5 50.3 59.9 2007d 77.3 50.9 60.1 .. .. .. Bosnia and Herzegovinab 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 2008d .. .. 22.6 2009d .. .. 21.4 .. .. .. Bulgariab 2001 .. .. 12.8 2007 .. .. 10.6 2007 .. .. 3.0 Burkina Faso 2003 65.5 22.1 51.0 2009 52.6 27.9 46.7 2009 17.4 7.8 15.1 Burundi .. .. .. 2006c 68.9 34.0 66.9 2006c 24.2 10.3 23.4 Cambodiab 2004 37.8 17.6 34.7 2007 34.5 11.8 30.1 2007 8.3 2.8 7.2 Cameroon .. .. .. 2007c 55.0 12.2 39.9 2007c 17.5 2.8 12.3 Cape Verde .. .. .. 2007c 44.3 13.2 26.6 2007c 14.3 3.3 8.1 Central African Republic .. .. .. 2008 c 69.4 49.6 62.0 2008 c 35.0 29.8 33.1 Chad .. .. .. 2003c 58.6 24.6 55.0 2003c 23.3 7.4 21.6 Chile 2006d 12.3 13.9 13.7 2009d 12.9 15.5 15.1 .. .. .. China 2004 d 2.8 .. .. 2005d 2.5 .. .. .. .. .. Colombia 2009d 54.3 35.8 40.2 2010 d 50.3 33.0 37.2 .. .. .. Comoros .. .. .. 2004 c 48.7 34.5 44.8 2004 c 17.8 12.1 16.3 Congo, Dem. Rep. .. .. .. 2006 75.7 61.5 71.3 2006 34.9 26.2 32.2 Congo, Rep. .. .. .. 2005 57.7 .. 50.1 2005 20.6 .. 18.9 Costa Rica 2009d 23.0 20.7 21.7 2010 d .. .. 24.2 .. .. .. Côte d’Ivoireb 2002 45.8 32.3 40.2 2008 54.2 29.4 42.7 2008 20.3 9.5 15.3 Croatiab 2002 .. .. 11.2 2004 .. .. 11.1 2004 .. .. 2.6 Dominican Republic 2009d 47.0 28.6 34.6 2010 d .. .. 34.4 .. .. .. Ecuador 2009d 57.5 25.0 36.0 2010 d 53.0 22.5 32.8 .. .. .. Egypt, Arab Rep. 2005 26.8 10.1 19.6 2008 30.0 10.6 22.0 .. .. .. El Salvador 2008d,e 49.0 35.7 39.9 2009d,e 46.5 33.3 37.8 .. .. .. Ethiopia 2000 45.4 36.9 44.2 2005 39.3 35.1 38.9 2005 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, Theb .. .. .. 2010 c 73.9 32.7 48.4 .. .. .. Georgiab 2008 27.8 17.5 22.7 2009 30.7 18.4 24.7 .. .. .. Ghana 1998 49.6 19.4 39.5 2006 39.2 10.8 28.5 2006 13.5 3.1 9.6 Guatemala 2000 74.5 27.1 56.2 2006 70.5 30.0 51.0 .. .. .. Guinea .. .. .. 2007c 63.0 30.5 53.0 2007c 22.0 7.7 17.6 Guinea-Bissau .. .. .. 2002 69.1 51.6 64.7 2002 27.8 16.9 25.0 Haiti .. .. .. 2001d 88.0 45.0 77.0 .. .. .. Honduras 2009d,e 64.4 52.8 58.8 2010 d,e 65.4 54.3 60.0 .. .. .. India 2005 41.8 25.7 37.2 2010 33.8 20.9 29.8 2010 6.8 4.5 6.2 Indonesia 2010 16.6 9.9 13.3 2011 15.7 9.2 12.5 2011 2.6 1.5 2.1 Iraq .. .. .. 2007 39.3 16.1 22.9 2007 9.0 2.7 4.5 Jamaica 2006d .. .. 14.3 2007d .. .. 9.9 .. .. .. Jordan 2006 19.0 12.0 13.0 2006 .. .. 2.8 Kazakhstanb 2008 .. .. 12.1 2009 .. .. 8.2 2009 .. .. 1.3 Kenya .. .. .. 2005c 49.1 33.7 45.9 2005c 17.5 11.4 16.3 Kosovob 2005 49.2 37.4 45.1 2009 35.3 33.1 34.5 2009 .. .. 9.6 Kyrgyz Republicb 2009 .. .. 31.7 2010 .. .. 33.7 .. .. .. Lao PDRb 2002 .. .. 33.5 2008 31.7 17.4 27.6 .. .. .. Latviab 2002 11.6 .. 7.5 2004 12.7 .. 5.9 .. .. .. 66 2012 World Development Indicators 2.7 PEOPLE Poverty rates at national poverty lines Population below national poverty line Poverty gap at national poverty line Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year a % % % year a % % % year a % % % Lesothob 1994 68.9 36.7 66.6 2003 60.5 41.5 56.6 .. .. .. Liberiab .. .. .. 2007 67.7 55.1 63.8 2007 26.3 20.2 24.4 Macedonia, FYRb 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 .. .. .. 2004 55.9 25.4 52.4 2004 8.6 2.8 8.0 Malaysiab 2007 7.1 2.0 3.6 2009 8.4 1.7 3.8 2009 1.8 0.3 0.8 Mali 2006 57.0 18.5 47.5 2010 50.6 18.8 43.6 2010 15.6 4.7 13.2 Mauritania 2004 59.0 28.9 46.7 2008 59.4 20.8 42.0 2008 22.3 4.9 14.5 Mexico 2008d 60.3 40.1 47.7 2010 d 60.8 45.5 51.3 .. .. .. Moldovab 2009 36.3 12.6 26.3 2010 30.3 10.4 21.9 2010 6.5 1.8 4.5 Mongolia .. .. .. 2008 c 46.6 26.9 35.2 2008 c 13.4 7.7 10.1 Montenegro 2009 14.8 2.6 6.8 2010 11.3 4.0 6.6 2010 1.7 0.7 1.1 Morocco 2001 25.1 7.6 15.3 2007 14.5 4.8 9.0 .. .. .. Mozambique 2003 55.3 51.5 54.1 2008 56.9 49.6 54.7 2008 22.2 19.1 21.2 Namibia 1994 69.0 31.0 58.0 2004 49.0 17.0 38.0 2004 16.0 6.0 13.0 Nepal .. .. .. 2010 27.4 15.5 25.2 2010 c 6.0 3.2 5.4 Nicaragua 2001 67.8 30.1 45.8 2005 67.9 29.1 46.2 .. .. .. Niger .. .. .. 2007c 63.9 36.7 59.5 2007c 21.2 11.3 19.6 Nigeria .. .. .. 2004 c 63.8 43.1 54.7 2004 c 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 2009d 49.8 24.7 35.1 2010 d 48.9 24.7 34.7 .. .. .. Peru 2009 60.3 21.1 34.8 2010 54.2 19.1 31.3 .. .. .. Philippines 2006 .. .. 26.4 2009 .. .. 26.5 2009 .. .. 7.2 Polandb 2007 .. .. 14.6 2008 .. .. 10.6 .. .. .. Romaniab 2005 23.5 8.1 15.1 2006 22.3 6.8 13.8 2006 5.3 1.4 3.2 Russian Federation 2005 22.7 8.1 11.9 2006 21.2 7.4 11.1 2006 5.5 1.7 2.7 Rwanda 2006 64.2 23.2 58.5 2011 48.7 22.1 44.9 2011 .. .. 14.8 São Tomé and Príncipe .. .. .. 2009c .. .. 66.2 2009c .. .. 24.8 Senegalb .. .. .. 2005c 61.9 35.1 50.8 2005c 21.5 9.3 16.4 Serbiab 2009 9.6 4.9 6.9 2010 13.6 5.7 9.2 .. .. .. Sierra Leone .. .. .. 2003c 78.5 47.0 66.4 2003c 34.6 16.3 27.5 South Africa 2000 .. .. 38.0 2006 .. .. 23.0 2006 .. .. 7.0 South Sudan .. .. .. 2009 55.4 24.2 50.6 2009 26.5 8.8 23.7 Sri Lanka 2007 15.7 6.7 15.2 2010 9.4 5.3 8.9 2010 1.8 1.2 1.7 Sudan .. .. .. 2009 57.6 26.5 46.5 2009 21.3 7.1 16.2 Swaziland .. .. .. 2001c 75.0 49.0 69.2 2001c 37.0 20.0 32.9 Tajikistanb 2007 55.0 49.4 53.5 2009 .. .. 46.7 .. .. .. Tanzania 2000 38.6 23.7 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 Ukraineb 2007 8.1 2.9 4.6 2008 4.7 2.0 2.9 2008 0.7 0.3 0.4 Uruguay 2009d 9.6 21.4 20.9 2010 d 6.2 18.7 18.6 .. .. .. Venezuela, RB 2008d .. .. 32.6 2009d .. .. 28.5 .. .. .. 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 .. .. .. 2003c .. .. 72.0 .. .. .. Note: Poverty rates are based on per capita consumption estimated from household survey data, unless otherwise noted. a. Refers to the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected. b. World Bank estimates. c. Estimates based on survey data from earlier years are available but are not comparable with the most recent year reported here; these are available at http://data.worldbank.org and http://povertydata.worldbank.org. d. Based on income per capita estimated from household survey data. e. Measured as share of households. 2012 World Development Indicators 67 2.7 Poverty rates at national poverty lines About the data De�nitions Estimates of poverty rates and gaps at national pov- consumption or income from own production), from •  Survey year is the year in which the underlying erty lines are useful for comparing poverty across which it is possible to construct a correctly weighted household survey data were collected or, when the time within but not across countries. Table 2.8 shows distribution of per capita consumption or income. data collection period bridged two calendar years, poverty indicators at international poverty lines that As with any indicator measured from household the year in which most of the data were collected. allow for comparisons across countries. surveys, data quality can affect the precision of • Population below national poverty line is the per- For countries with an active poverty monitoring pro- poverty estimates and their comparability over time. centage of the rural, urban, or national population liv- gram, the World Bank—in collaboration with national These include selective survey nonresponse, sea- ing below the corresponding rural, urban, or national institutions, other development agencies, and civil sonality effects, differences in the number of income poverty line, based on consumption estimated from society—periodically prepares poverty assessments or consumption items in the questionnaire, and the household survey data, unless otherwise noted. and other analytical reports to assess the extent time period over which respondents are asked to • Poverty gap at national poverty line is the mean and causes of poverty. These reports review levels recall their expenditures. shortfall from the rural, urban, or national poverty and changes in poverty indicators over time and line (counting the nonpoor as having zero shortfall) across regions within countries, assess the impact National poverty lines as a percentage of the corresponding rural, urban, of growth and public policy on poverty and inequal- National poverty lines are the benchmark for esti- or national poverty line, based on consumption esti- ity, review the adequacy of monitoring and evalua- mating poverty indicators that are consistent with mated from household survey data, unless otherwise tion, and contain detailed technical overviews of the country’s specific economic and social circum- noted. This measure reflects the depth of poverty as the underlying household survey data and poverty stances. National poverty lines reflect local percep- well as its incidence. measurement methods used. The reports are a key tions of the level and composition of consumption or source of comprehensive information on poverty indi- income needed to be nonpoor. The perceived bound- cators at national poverty lines and generally feed ary between poor and nonpoor typically rises with the into country-owned processes to reduce poverty, average income of a country and thus does not pro- build in-country capacity, and support joint work. vide a uniform measure for comparing poverty rates An increasing number of countries have their across countries. While poverty rates at national own national programs to monitor and disseminate poverty lines should not be used for comparing pov- official poverty estimates at national poverty lines erty rates across countries, they are appropriate for along with well documented household survey data guiding and monitoring the results of country-specific sources and estimation methodology. Estimates national poverty reduction strategies. from national poverty monitoring programs and the Almost all national poverty lines are anchored to underlying methods used are periodically reviewed by the cost of a food bundle—based on the prevailing the World Bank and included in the table. national diet of the poor—that provides adequate The complete online database of poverty estimates nutrition for good health and normal activity, plus at national poverty lines (http://data. worldbank.org/ an allowance for nonfood spending. National pov- topic/poverty) is regularly updated and may contain erty lines must be adjusted for inflation between more recent data or revisions not incorporated in survey years to remain constant in real terms and the table. In addition, the poverty and equity data thus allow for meaningful comparisons of poverty portal (http://povertydata.worldbank.org/poverty/ over time. Because diets and consumption baskets home/) provides access to both the database and change over time, countries periodically recalculate user-friendly dashboards with graphs and interactive the poverty line based on new survey data. In such Data sources maps that visualize trends in key poverty and inequal- cases the new poverty lines should be deflated to ity indicators for different regions and countries. The obtain comparable poverty estimates from earlier Data on poverty rates at national poverty lines are database is maintained by the Global Poverty Work- years. The table reports indicators based on the two compiled by the Global Poverty Working Group, ing Group, a team of poverty experts from the Pov- most recent years for which survey data are avail- based on data from World Bank’s country poverty erty Reduction and Equity Network, the Development able. Countries for which the most recent indica- assessments and analytical reports as well as Research Group, and the Development Data Group. tors reported are not comparable to those based country Poverty Reduction Strategies and official on survey data from an earlier year are footnoted poverty estimates. Further documentation of Data quality in the table. the data, measurement methods and tools, and Poverty estimates at national poverty lines are com- research, as well as poverty assessments and puted from household survey data collected from analytical reports, are available at http://data. nationally representative samples of households. worldbank.org/topic/poverty, www.worldbank.org/ These data must contain sufficiently detailed infor- poverty, and http://povertydata.worldbank.org/ mation to compute a comprehensive estimate of poverty/home/. total household income or consumption (including 68 2012 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines International poverty 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 7.6 1.2 24.6 6.7 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 2009d,e 2.0 1.2 3.4 1.7 2010d,e <2 0.7 <2 0.9 Armenia 245.2 392.4 2007 3.5 0.7 20.5 4.5 2008 <2 <0.5 12.4 2.3 Azerbaijan 2,170.9 3,473.5 2001 6.3 1.1 27.1 6.8 2008 <2 <0.5 2.8 0.6 Bangladesh 31.9 51.0 2005 50.5 14.2 80.3 34.3 2010 43.3 11.2 76.5 30.4 Belarus 949.5 1,519.2 2007 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Belize 1.8 c 2.9c 1998f 11.3 4.7 26.3 10.0 1999 f 12.2 5.5 22.0 9.9 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 2007 10.2 1.8 29.8 8.5 Bolivia 3.2 5.1 2007e 13.1 6.6 24.7 10.9 2008e 15.6 8.6 24.9 13.1 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 2008f 6.0 3.4 11.3 5.3 2009 f 6.1 3.6 10.8 5.4 Bulgaria 0.9 1.5 2003 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5 Burkina Faso 303.0 484.8 2003 56.5 20.3 81.2 39.3 2009 44.6 14.7 72.6 31.7 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 2007 32.2 7.7 60.1 22.6 2008 22.8 4.9 53.3 17.4 Cameroon 368.1 589.0 2001 10.8 2.3 32.5 9.5 2007 9.6 1.2 30.4 8.2 Cape Verde 97.7 156.3   ..  .. .. .. 2002 21.0 6.1 40.9 15.2 Central African Republic 384.3 614.9 2003 62.4 28.3 81.9 45.3 2008 62.8 31.3 80.1 46.8 Chad 409.5 655.1   ..  .. .. .. 2003 61.9 25.6 83.3 43.9 Chile 484.2 774.7 2006f <2 0.5 3.2 1.1 2009 f <2 0.7 2.7 1.2 China 5.1g 8.2g 2005h 16.3 4.0 36.9 12.5 2008h 13.1 3.2 29.8 10.1 Colombia 1,489.7 2,383.5 2009 f 9.7 4.7 18.5 8.2 2010 f 8.2 3.8 15.8 6.8 Comoros 368.0 588.8   ..  .. .. .. 2004 46.1 20.8 65.0 34.2 Congo, Dem. Rep. 395.3 632.5   ..  .. .. .. 2006 87.7 52.8 95.2 67.6 Congo, Rep. 469.5 751.1   ..  .. .. .. 2005 54.1 22.8 74.4 38.8 Costa Rica 348.7c 557.9c 2008f 2.4 1.5 5.0 2.3 2009 f 3.1 1.8 6.0 2.7 Croatia 5.6 8.9 2004 <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 46.3 17.8 Djibouti 134.8 215.6   ..  .. .. .. 2002 18.8 5.3 41.2 14.6 Dominican Republic 25.5c 40.8 c 2009 f 3.0 0.7 10.0 2.7 2010 f 2.2 0.5 9.9 2.4 Ecuador 0.6 1.0 2009 f 6.4 2.9 13.5 5.5 2010 f 4.6 2.1 10.6 4.1 Egypt, Arab Rep. 2.5 4.0 2005 2.0 <0.5 18.5 3.5 2008 <2 <0.5 15.4 2.8 El Salvador 6.0 c 9.6c 2008f 5.4 1.9 14.0 4.8 2009 f 9.0 4.4 16.9 7.6 Estonia 11.0 17.7 2003 <2 <0.5 2.6 <0.5 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 Fiji 1.9 3.1 2003 29.2 11.3 48.7 21.8 2009 5.9 1.1 22.9 6.0 Gabon 554.7 887.5   ..  .. .. .. 2005 4.8 0.9 19.6 5.0 Gambia, The 12.9 20.7 1998 65.6 33.8 81.2 49.1 2003 33.6 11.7 55.9 24.4 Georgia 1.0 1.6 2007 15.2 4.1 34.9 11.8 2008 15.3 4.6 32.2 11.7 Ghana 5,594.8 8,951.6 1998 39.1 14.4 63.3 28.5 2006 28.6 9.9 51.8 21.3 Guatemala 5.7c 9.1c 2004f 24.4 13.2 39.2 20.2 2006f 13.5 4.7 26.3 10.5 Guinea 1,849.5 2,959.1 2003 56.3 21.3 80.8 39.7 2007 43.3 15.0 69.6 31.0 Guinea-Bissau 355.3 568.6 1993 52.1 20.6 75.7 37.4 2002 48.9 16.6 78.0 34.9 Guyana 131.5c 210.3c 1993e 6.9 1.5 17.1 5.4 1998e 8.7 2.8 18.0 6.7 Haiti 24.2c 38.7c   ..  .. .. .. 2001e 61.7 32.3 77.5 46.7 Honduras 12.1c 19.3c 2008f 21.4 11.8 32.6 17.5 2009 f 17.9 9.4 29.8 14.9 Hungary 171.9 275.0 2004 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5 India 19.5i 31.2i 2005h 41.6 10.5 75.6 29.5 2010h 32.7 7.5 68.7 24.5 Indonesia 5,241.0i 8,385.7i 2009h 20.4 4.1 52.7 16.5 2010h 18.1 3.3 46.1 14.3 Iran, Islamic Rep. 3,393.5 5,429.6 1998 <2 <0.5 8.3 1.8 2005 <2 <0.5 8.0 1.8 Iraq 799.8 1,279.7   ..  .. .. .. 2007 2.8 <0.5 21.4 4.4 Jamaica 54.2c 86.7c 2002 <2 <0.5 8.5 1.5 2004 <2 <0.5 5.4 0.8 2012 World Development Indicators 69 2.8 Poverty rates at international poverty lines International poverty 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 % % % % Jordan 0.6 1.0 2008 <2 <0.5 2.1 <0.5 2010 <2 <0.5 <2 <0.5 Kazakhstan 81.2 129.9 2008 <2 <0.5 <2 <0.5 2009 <2 <0.5 <2 <0.5 Kenya 40.9 65.4 1997 19.6 4.6 42.7 14.7 2005 43.4 16.9 67.2 31.8 Kyrgyz Republic 16.2 26.0 2008 6.4 1.5 20.7 5.9 2009 6.2 1.4 21.7 6.0 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 2007 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Lesotho 4.3 6.9 1994 46.2 25.6 59.7 36.1 2003 43.4 20.8 62.3 33.1 Liberia 0.6 1.0   ..  .. .. .. 2007 83.8 40.9 94.9 59.6 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 2008 <2 <0.5 4.3 0.7 2009 <2 <0.5 5.9 0.9 Madagascar 945.5 1,512.8 2005 67.8 26.5 89.6 46.9 2010 81.3 43.3 92.6 60.1 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 2007e <2 <0.5 2.9 <0.5 2009e <2 <0.5 2.3 <0.5 Mali 362.1 579.4 2006 51.4 18.8 77.1 36.5 2010 50.4 16.4 78.7 35.2 Mauritania 157.1 251.3 2004 25.4 7.0 52.6 19.2 2008 23.4 6.8 47.7 17.7 Mexico 9.6 15.3 2006 <2 <0.5 4.9 1.0 2008 <2 <0.5 5.2 1.3 Micronesia, Fed. Sts. 0.8 c 1.3c   ..  .. .. .. 2000 31.2 16.3 44.7 24.5 Moldova 6.0 9.7 2009 <2 <0.5 7.1 1.2 2010 <2 <0.5 4.4 0.7 Montenegro 0.6 1.0 2007 <2 <0.5 <2 <0.5 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 59.6 25.1 81.8 42.9 Namibia 6.3 10.1 1993 49.1 24.6 62.2 36.5 2004 e 31.9 9.5 51.1 21.8 Nepal 33.1 52.9 2003 53.1 18.4 77.3 36.6 2010 24.8 5.6 57.3 19.0 Nicaragua 9.1c 14.6c 2001e 14.4 3.7 34.4 11.5 2005e 11.9 2.4 31.7 9.6 Niger 334.2 534.7 2005 50.2 18.3 75.3 35.6 2008 43.6 12.4 75.2 30.8 Nigeria 98.2 157.2 2004 63.1 28.7 83.1 45.9 2010 68.0 33.7 84.5 50.2 Pakistan 25.9 41.4 2006 22.6 4.1 61.0 18.8 2008 21.0 3.5 60.2 17.9 Panama 0.8c 1.2c 2009 f 5.9 1.8 14.6 4.9 2010 f 6.6 2.1 13.8 5.1 Papua New Guinea 2.1c 3.4 c   ..  .. .. .. 1996 35.8 12.3 57.4 25.5 Paraguay 2,659.7 4,255.6 2009 f 7.6 3.2 14.2 6.0 2010 f 7.2 3.0 13.2 5.7 Peru 2.1 3.3 2009 f 5.5 1.6 14.0 4.6 2010 f 4.9 1.3 12.7 4.1 Philippines 30.2 48.4 2006 22.6 5.5 45.0 16.4 2009 18.4 3.7 41.5 13.8 Poland 2.7 4.3 2008 <2 <0.5 <2 <0.5 2009 <2 <0.5 <2 <0.5 Romania 2.1 3.4 2008 <2 <0.5 2.0 0.6 2009 <2 <0.5 <2 0.5 Russian Federation 16.7 26.8 2008 <2 <0.5 <2 <0.5 2009 <2 <0.5 <2 <0.5 Rwanda 295.9 473.5 2006 72.1 34.8 87.4 52.2 2011 63.2 26.6 82.4 44.6 São Tomé and Príncipe 7,953.9 12,726.3   ..  .. .. .. 2001 28.2 7.9 54.2 20.6 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 2009 <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 2008e <2 <0.5 <2 <0.5 2009e <2 <0.5 <2 <0.5 Slovenia 198.2 317.2 2003 <2 <0.5 <2 <0.5 2004 <2 <0.5 <2 <0.5 South Africa 5.7 9.1 2006 17.4 3.3 35.7 12.3 2009 13.8 2.3 31.3 10.2 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 Sudan 154.4 247.0   ..  .. .. .. 2009 19.8 5.5 44.1 15.4 Suriname 2.3c 3.7c   ..  .. .. .. 1999e 15.5 5.9 27.2 11.7 Swaziland 4.7 7.5 2001 62.9 29.4 81.0 45.8 2010 40.6 16.0 60.4 29.3 Syrian Arab Republic 30.8 49.3 ..  .. .. .. 2004 <2 <0.5 16.9 3.3 Tajikistan 1.2 1.9 2007 14.7 4.4 37.0 12.2 2009 6.6 1.2 27.7 7.0 Tanzania 603.1 964.9 2000 84.6 41.6 95.3 60.3 2007 67.9 28.1 87.9 47.5 Thailand 21.8 34.9 2008j <2 <0.5 5.0 0.8 2009j <2 <0.5 4.6 0.8 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 70 2012 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines International poverty 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 % % % % Tunisia 0.9 1.4 2000 2.6 0.5 12.8 3.0 2005 <2 <0.5 8.1 1.8 Turkey 1.3 2.0 2007 <2 <0.5 4.5 1.2 2008 <2 <0.5 4.2 0.7 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 2006 51.5 19.1 75.6 36.4 2009 38.0 12.2 64.7 27.4 Ukraine 2.1 3.4 2008 <2 <0.5 <2 <0.5 2009 <2 <0.5 <2 <0.5 Uruguay 19.1 30.6 2009 f <2 <0.5 <2 <0.5 2010 f <2 <0.5 <2 <0.5 Venezuela, RB 1,563.9 2,502.2 2005f 13.4 8.2 21.9 11.6 2006f 6.6 3.7 12.9 5.9 Vietnam 7,399.9 11,839.8 2006 21.4 5.3 48.1 16.3 2008 16.9 3.8 43.4 13.5 West Bank and Gaza 2.7c 4.3c 2007 <2 <0.5 2.5 0.5 2009 <2 <0.5 <2 <0.5 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 2004 64.3 32.8 81.5 48.3 2006 68.5 37.0 82.6 51.8 a. Based on nominal per capita consumption averages and distributions estimated parametrically from grouped household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected. c. Based on purchasing power parity (PPP) dollars imputed using regression. d. Covers urban areas only. e. Based on per capita income averages and distributions estimated parametrically from grouped household survey data. f. Estimated nonparametrically from nominal income per capita distributions based on unit-record household survey 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. j. Estimated nonparametrically from nominal consumption per capita distributions based on unit-record household survey data. Regional poverty estimates and progress toward declined from 77 percent in 1981 to 14 percent in Most of the people who have escaped extreme the Millennium Development Goals 2008 and the number of people living on less than poverty remain very poor by the standards of middle- Global poverty measured at the $1.25 a day pov- $1.25 a day dropped more than 800 million (fi gure income countries. The median poverty line for devel- erty line has been decreasing since the 1980s. The 2.8b). Much of this decline was in China, where the oping countries in 2005 was $2 a day. The poverty share of population living on less than $1.25 a day poverty rate fell from 84 percent to 13 percent, rate for all developing countries measured at this line fell almost 10 percentage points, to 43 percent, leaving about 660 million fewer people poor. Over fell from nearly 70 percent in 1981 to 43 percent in in 1990 and then fell about 20 percentage points the same period the poverty rate in South Asia 2008, but the number of people living on less than between 1990 and 2008. The number of people liv- fell from 61 percent to 36 percent (table 2.8c). $2 a day has remained nearly constant at around ing in extreme poverty fell from 1.9 billion in 1990 In contrast, the poverty rate fell only slightly in 2.5 billion. The largest decrease, in both number and to about 1.3 billion in 2008 (figure 2.8a). This sub- Sub-Saharan Africa—from less than 52 percent in proportion, occurred in East Asia and Pacific, led by stantial reduction in extreme poverty over the past 1981 to more than 59 percent in 1993 then down China. By contrast in Sub-Saharan Africa and South quarter century, however, disguises large regional to 47.5 percent in 2008. But the number of people Asia, particularly India, the number of people living differences. living below the poverty line has nearly doubled on less than $2 a day increased. And globally the The greatest reduction in poverty occurred over this period and started declining slightly only number of people living on $1.25–$2 a day nearly in East Asia and Pacifi c, where the poverty rate from 2005 onward. doubled, to 1.2 billion (see figure 2.8a). While the number of people living on less than $1.25 a day has Poverty rates are falling fallen, the number living on $1.25–$2 a day has increased 2.8a in all developing regions 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 People living on less than $1.25–$2 a day, 60 Sub-Saharan Africa $1.25 a day, other developing regions all developing regions 2.0 1.5 40 People living on less than South Asia $1.25 a day, East Asia & Paci�c Europe & Latin America & 1.0 Central Asia Middle East & East Asia Caribbean 20 North Africa & Pacific 0.5 People living on less than $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 2008 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 Source: PovcalNet, World Bank. Source: PovcalNet, World Bank. 2012 World Development Indicators 71 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 2008 People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific 1,097 970 848 926 871 640 656 523 332 284 China 835 720 586 683 633 443 446 363 212 173 Europe & Central Asia 8 7 7 9 14 18 18 11 6 2 Latin America & Caribbean 43 53 49 53 53 54 60 63 48 37 Middle East & North Africa 16 15 15 13 12 12 14 12 10 9 South Asia 568 574 593 617 632 631 619 640 598 571 India 429 427 443 448 462 463 473 484 466 445 Sub-Saharan Africa 205 239 257 290 330 349 376 390 395 386 Total 1,938 1,858 1,768 1,909 1,910 1,704 1,743 1,639 1,389 1,289 Share of people living on less than 2005 PPP $1.25 a day (percent) East Asia & Pacific 77.2 65.0 54.1 56.2 50.7 35.9 35.6 27.6 17.1 14.3 China 84.0 69.4 54.0 60.2 53.7 36.4 35.6 28.4 16.3 13.1 Europe & Central Asia 1.9 1.6 1.5 1.9 2.9 3.9 3.8 2.3 1.3 0.5 Latin America & Caribbean 11.9 13.6 12.0 12.2 11.4 11.1 11.9 11.9 8.7 6.5 Middle East & North Africa 9.6 8.0 7.1 5.8 4.8 4.8 5.0 4.2 3.5 2.7 South Asia 61.1 57.4 55.3 53.8 51.7 48.6 45.1 44.3 39.4 36.0 India 59.8 55.7 54.1 51.3 49.7 47.2 45.6 44.5 40.8 37.4 Sub-Saharan Africa 51.5 55.2 54.4 56.5 59.4 58.1 57.9 55.7 52.3 47.5 Total 52.2 47.1 42.3 43.1 41.0 34.8 34.1 30.8 25.1 22.4 People living on less than 2005 PPP $2 a day (millions) East Asia & Pacific 1,313 1,316 1,279 1,334 1,301 1,140 1,138 984 758 659 China 972 963 907 961 926 792 770 655 482 395 Europe & Central Asia 36 30 29 32 43 53 57 37 22 10 Latin America & Caribbean 87 104 92 98 100 102 111 118 92 71 Middle East & North Africa 52 51 54 53 53 57 60 57 53 44 South Asia 811 855 906 959 1,010 1,047 1,069 1,120 1,113 1,125 India 621 651 689 722 760 788 818 848 857 862 Sub-Saharan Africa 288 324 350 389 434 466 503 533 559 562 Total 2,585 2,680 2,710 2,864 2,941 2,865 2,937 2,848 2,596 2,471 Share of people living on less than 2005 PPP $2 a day (percent) East Asia & Pacific 92.4 88.3 81.6 81.0 75.8 64.0 61.7 51.9 39.0 33.2 China 97.8 92.9 83.7 84.6 78.6 65.1 61.4 51.2 36.9 29.8 Europe & Central Asia 8.3 6.7 6.3 6.9 9.2 11.2 12.1 7.9 4.6 2.2 Latin America & Caribbean 23.8 26.8 22.4 22.4 21.7 21.0 22.0 22.2 16.7 12.4 Middle East & North Africa 30.1 27.1 26.1 23.5 22.1 22.2 22.0 19.7 17.4 13.9 South Asia 87.2 85.6 84.5 83.6 82.7 80.7 77.8 77.4 73.4 70.9 India 86.6 84.9 84.1 82.6 81.9 80.2 78.9 77.9 75.0 72.4 Sub-Saharan Africa 72.2 74.7 74.3 76.0 78.1 77.5 77.4 76.1 74.1 69.2 Total 69.6 68.0 64.8 64.6 63.1 58.6 57.4 53.5 46.9 43.0 Source: World Bank PovcalNet. 72 2012 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines About the data The World Bank produced its first global poverty esti- Income is generally more difficult to measure accu- 1993 consumption PPP estimates produced by the mates for developing countries for World Development rately, and consumption comes closer to the notion World Bank. International poverty lines were recently Report 1990: Poverty (World Bank 1990) using house- of living standards. And income can vary over time revised using the new data on PPPs compiled in hold survey data for 22 countries (Ravallion, Datt, and even if living standards do not. But consumption data the 2005 round of the International Comparison van de Walle 1991). Since then there has been con- are not always available: the latest estimates reported Program, along with data from an expanded set of siderable expansion in the number of countries that here use consumption data for about two-thirds of household income and expenditure surveys. The new field household income and expenditure surveys. The countries. extreme poverty line is set at $1.25 a day in 2005 World Bank’s Development Research Group maintains However, even similar surveys may not be strictly PPP terms, which represents the mean of the poverty a database that is updated annually as new survey comparable because of differences in timing or in the lines found in the poorest 15 countries ranked by per data become available (and thus may contain more quality and training of enumerators. Comparisons capita consumption. The new poverty line maintains recent data or revisions that are not incorporated of countries at different levels of development also the same standard for extreme poverty—the poverty into the table) and conducts a major reassessment pose a potential problem because of differences line typical of the poorest countries in the world—but of progress against poverty about every three years. in the relative importance of the consumption of updates it using the latest information on the cost of PovcalNet (http://iresearch.worldbank.org/Povcal- nonmarket goods. The local market value of all con- living in developing countries. Net/) is an interactive computational tool that allows sumption in kind (including own production, particu- De�nitions users to replicate these internationally comparable larly important in underdeveloped rural economies) $1.25 and $2 a day global, regional, and country-level should be included in total consumption expenditure •  International poverty line in local currency is poverty estimates and to compute poverty measures but may not be. Most survey data now include valu- the international poverty lines of $1.25 and $2.00 for custom country groupings and for different pov- ations for consumption or income from own produc- a day in 2005 prices, converted to local currency erty lines. The Poverty and Equity Data portal (http:// tion, but valuation methods vary. using the PPP conversion factors estimated by the povertydata.worldbank.org/poverty/home/) provides The statistics reported here are based on con- International Comparison Program. •  Survey year access to the database and user-friendly dashboards sumption data or, when unavailable, on income is the year in which the underlying data were col- with graphs and interactive maps that visualize trends surveys. Analysis of some 20 countries for which lected or, when the data collection period bridged two in key poverty and inequality indicators for different income and consumption expenditure data were calendar years, the year in which most of the data regions and countries. The country dashboards dis- both available from the same surveys found income were collected. • Population below $1.25 a day and play trends in poverty measures based on the national to yield a higher mean than consumption but also population below $2 a day are the percentages of poverty lines (see table 2.7) alongside the internation- higher inequality. When poverty measures based on the population living on less than $1.25 a day and ally comparable estimates in the table, produced from consumption and income were compared, the two $2 a day at 2005 international prices. As a result of and consistent with PovcalNet. effects roughly cancelled each other out: there was revisions in PPP exchange rates, poverty rates for no significant statistical difference. individual countries cannot be compared with poverty Data availability rates reported in earlier editions. • Poverty gap is The World Bank’s internationally comparable pov- International poverty lines the mean shortfall from the poverty line (counting erty monitoring database now draws on income or International comparisons of poverty estimates the nonpoor as having zero shortfall), expressed as a detailed consumption data collected from interviews entail both conceptual and practical problems. Coun- percentage of the poverty line. This measure reflects with 1.23 million randomly sampled households tries have different definitions of poverty, and consis- the depth of poverty as well as its incidence. through more than 850 household surveys collected tent comparisons across countries can be difficult. by national statistical offices in nearly 130 coun- Local poverty lines tend to have higher purchasing Data sources tries. Despite progress in the last decade, the chal- power in rich countries, where more generous stan- The poverty measures are prepared by the World lenges of measuring poverty remain. The timeliness, dards are used, than in poor countries. Bank’s Development Research Group. The inter- frequency, quality, and comparability of household Poverty measures based on international poverty national poverty lines are based on nationally rep- surveys need to increase substantially, particularly in lines attempt to hold the real value of the poverty line resentative primary household surveys by national the poorest countries. The availability and quality of constant across countries, as is done when making statistical offices or by private agencies under the poverty monitoring data remains low in small states, comparisons over time. Since World Development supervision of government or international agen- countries with fragile situations, and low-income Report 1990 the World Bank has aimed to apply a cies and obtained from government statistical countries and even some middle-income countries. common standard in measuring extreme poverty, offi ces and World Bank Group country depart- The low frequency and lack of comparability of the anchored to what poverty means in the world’s poor- ments. Detailed information on the methodology data available in some countries create uncertainty est countries. The welfare of people living in different adopted by the Socio-Economic Database for Latin over the magnitude of poverty reduction. The need to countries can be measured on a common scale by America and the Caribbean to process the income improve household survey programs for monitoring adjusting for differences in the purchasing power of data used for countries in this region is available poverty is clearly urgent. But institutional, political, currencies. The commonly used $1 a day standard, at http://sedlac.econo.unlp.edu.ar/eng/method- and financial obstacles continue to limit data collec- measured in 1985 international prices and adjusted ology.php. The World Bank Group has prepared tion, analysis, and public access. to local currency using purchasing power parities an annual review of its poverty work since 1993. (PPPs), was chosen for World Development Report For details on data sources and methods used in Data quality 1990 because it was typical of the poverty lines in deriving the World Bank’s latest estimates, see Besides the frequency and timeliness of survey data, low-income countries at the time. http://iresearch.worldbank.org/povcalnet. For other data quality issues arise in measuring household Early editions of World Development Indicators further discussion of the results, see Ravallion, living standards. The surveys ask detailed questions used PPPs from the Penn World Tables to convert Chen, and Sangraula (2009) and Chen and Raval- on sources of income and how it was spent, which values in local currency to equivalent purchasing lion (2011). must be carefully recorded by trained personnel. power measured in U.S dollars. Later editions used 2012 World Development Indicators 73 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 27.8 4.1 9.4 13.6 17.4 22.1 37.5 23.2 Albania 2008b 34.5 3.5 8.1 12.1 15.9 20.9 43.0 29.0 Algeria 1995b 35.3 2.9 7.0 11.6 16.2 22.6 42.6 26.9 Angolac 2000 b 58.6 0.6 2.0 5.7 10.8 19.7 61.9 44.7 Argentinac 2010 d 44.5 1.5 4.4 9.3 14.8 22.2 49.4 32.3 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 2010 b 32.1 4.0 8.9 12.4 16.1 21.3 41.4 27.0 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 53.1 0.9 3.3 8.6 11.2 19.4 57.5 42.2 Benin 2003b 38.6 3.0 7.0 10.8 15.0 21.0 46.1 31.2 Bhutan 2007b 38.1 2.8 6.6 10.8 15.4 22.0 45.2 29.4 Bolivia 2007d 56.3 0.5 2.1 6.8 11.9 19.9 59.3 43.3 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 54.7 0.8 2.9 7.1 12.4 19.0 58.6 42.9 Bulgaria 2007b 28.2 3.3 8.5 13.7 17.9 23.1 36.7 22.2 Burkina Faso 2009b 39.8 2.9 6.7 10.6 14.8 20.9 47.0 32.2 Burundi 2006b 33.3 4.1 9.0 11.9 15.4 21.0 42.8 28.0 Cambodia 2008b 37.9 3.3 7.5 11.0 14.9 20.6 45.9 31.4 Cameroon 2007b 38.9 2.9 6.7 10.5 14.9 21.7 46.2 30.4 Canada 2000 d 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Cape Verde 2002b 50.5 1.9 4.5 7.9 12.3 19.4 55.9 40.6 Central African Republic 2008b 56.3 1.2 3.4 6.9 11.1 18.0 60.6 46.1 Chad 2003b 39.8 2.6 6.3 10.4 15.0 21.8 46.6 30.8 Chile 2009d 52.1 1.5 4.3 7.9 11.7 18.4 57.7 42.8 China 2005d 42.5 1.8 5.0 9.9 15.0 22.2 47.9 32.0 Hong Kong SAR, China 1996d 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2010 d 55.9 0.9 3.0 6.8 11.2 18.8 60.2 44.4 Comoros 2004b 64.3 0.9 2.6 5.4 8.9 15.1 68.0 55.2 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 50.7 1.2 3.9 8.0 12.4 19.9 55.9 39.5 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 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 Djibouti 2002b 40.0 2.4 6.0 10.5 15.2 21.8 46.5 30.9 Dominican Republic 2010 d 47.2 1.8 4.7 8.6 13.2 20.8 52.8 36.4 Ecuador 2010 d 49.3 1.4 4.3 8.2 13.0 20.7 53.8 38.3 Egypt, Arab Rep. 2008b 30.8 4.0 9.2 13.0 16.4 21.0 40.3 26.6 El Salvador 2009d 48.3 1.0 3.7 8.8 13.7 20.7 53.1 37.0 Estonia 2004b 36.0 2.7 6.8 11.6 16.2 22.2 43.2 28.0 Ethiopia 2005b 29.8 4.1 9.3 13.2 16.8 21.3 39.4 25.6 Finland 2000 d 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 Fiji 2009b 42.8 2.6 6.2 9.9 14.1 20.3 49.6 34.9 France 1995d 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon 2005b 41.5 2.6 6.2 10.1 14.5 21.0 48.2 33.0 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 2.0 5.2 9.8 14.7 21.6 48.6 32.8 Greece 2000 d 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2006d 55.9 1.1 3.1 6.9 11.4 18.5 60.3 44.9 74 2012 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% Guinea 2007b 39.4 2.7 6.4 10.5 15.1 21.9 46.2 30.3 Guinea-Bissau 2002b 35.5 3.1 7.3 11.6 16.0 21.9 43.2 28.1 Guyana 1998 d 44.5 1.3 4.5 9.8 14.7 21.6 49.5 34.0 Haiti 2001d 59.2 0.7 2.4 6.3 10.4 17.6 63.4 47.7 Honduras 2009d 57.0 0.4 2.0 6.1 11.4 20.5 59.9 42.4 Hungary 2007b 31.2 3.5 8.4 12.9 16.9 22.0 39.9 25.4 India 2005b 33.4 3.8 8.6 12.2 15.8 21.0 42.4 28.3 Indonesia 2005b 34.0 3.7 8.3 12.0 15.8 21.0 42.8 28.5 Iran, Islamic Rep. 2005b 38.3 2.6 6.4 10.9 15.5 22.0 45.2 29.6 Iraq 2007b 30.9 3.8 8.7 12.8 16.7 22.0 39.9 25.2 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 2.3 5.4 9.0 13.5 20.6 51.6 35.9 Japan 1993d 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2010 b 35.4 3.4 7.7 11.6 15.7 21.5 43.6 28.7 Kazakhstan 2009b 29.0 4.0 9.1 13.2 17.1 22.3 38.4 23.8 Kenya 2005b 47.7 2.0 4.8 8.7 13.2 20.1 53.2 38.0 Korea, Rep. 1998d 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kyrgyz Republic 2009b 36.2 2.8 6.8 11.4 16.0 22.4 43.4 27.8 Lao PDR 2008b 36.7 3.3 7.6 11.3 15.3 20.9 44.8 30.3 Latvia 2008b 36.6 2.6 6.6 11.4 16.1 22.3 43.6 28.1 Lesotho 2003b 52.5 1.0 3.0 7.2 12.5 21.0 56.4 39.4 Liberia 2007b 38.2 2.4 6.4 11.4 15.7 21.6 45.0 30.1 Lithuania 2008b 37.6 2.6 6.6 11.1 15.7 22.1 44.4 29.1 Macedonia, FYR 2009b 43.2 2.0 5.1 9.5 14.5 22.0 48.9 32.4 Madagascar 2010 b 44.1 2.2 5.4 9.5 14.1 20.9 50.1 34.7 Malawi 2004b 39.0 3.0 7.0 10.8 14.9 20.8 46.5 31.9 Malaysia 2009d 46.2 1.8 4.5 8.7 13.7 21.6 51.5 34.7 Mali 2010 b 33.0 3.5 8.0 12.0 16.3 22.4 41.3 25.8 Mauritania 2008b 40.5 2.4 6.0 10.4 15.1 21.5 47.0 31.6 Mexico 2010 d 47.7 1.4 4.4 8.9 13.3 20.4 53.0 37.2 Micronesia, Fed. Sts.c 2000 b 61.1 0.4 1.6 5.2 10.2 19.1 64.0 47.1 Moldova 2010 b 33.0 3.3 7.8 12.2 16.5 22.3 41.2 26.0 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 45.7 1.9 5.2 9.5 13.7 20.1 51.5 36.7 Namibia 2004 d 63.9 1.4 3.2 5.0 8.2 15.0 68.6 54.8 Nepal 2010 b 32.8 3.6 8.3 12.2 16.2 21.9 41.5 26.5 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 2005b 40.5 2.6 6.2 10.2 14.8 21.5 47.2 31.5 Niger 2008b 34.6 3.6 8.1 11.8 15.8 21.3 43.1 28.5 Nigeria 2010 b 48.8 1.8 4.4 8.3 13.0 20.3 54.0 38.2 Norway 2000 d 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Pakistan 2008b 30.0 4.4 9.6 12.9 16.4 21.1 40.0 26.1 Panama 2010d 51.9 1.1 3.3 7.8 12.5 20.1 56.4 40.1 Papua New Guinea 1996b 50.9 1.9 4.5 7.7 12.1 19.3 56.4 40.9 Paraguay 2010 d 52.4 1.0 3.3 7.8 12.8 19.8 56.4 41.1 Peru 2010 d 48.1 1.4 3.9 8.3 13.6 21.5 52.6 36.1 Philippines 2009b 43.0 2.6 6.0 9.4 13.9 21.0 49.7 33.6 Poland 2009 b 34.1 3.3 7.7 12.0 16.2 22.0 42.1 27.1 Portugal 1997d 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Qatar 2007b 41.1 1.3 3.9 .. .. .. 52.0 35.9 Romania 2009 b 30.0 3.4 8.3 13.1 17.4 22.9 38.3 23.5 Russian Federation 2009b 40.1 2.8 6.5 10.4 14.8 21.3 47.1 31.7 2012 World Development Indicators 75 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% Rwanda 2011b 50.8 2.1 5.2 8.3 11.9 17.8 56.8 43.2 São Tomé and Príncipe 2001b 50.8 2.2 5.2 8.5 12.2 17.7 56.4 43.6 Senegal 2005b 39.2 2.5 6.2 10.6 15.3 22.0 45.9 30.1 Serbia 2009b 27.8 3.7 8.9 13.7 17.8 22.8 36.9 22.2 Seychelles 2007b 65.8 1.6 3.7 5.7 8.3 12.7 69.6 60.2 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 2009d 26.0 4.4 10.1 14.1 17.5 22.0 36.2 22.4 Slovenia 2004b 31.2 3.4 8.2 12.8 17.0 22.6 39.4 24.6 South Africa 2009b 63.1 1.2 2.7 4.6 8.2 16.3 68.2 51.7 South Sudan 2009b 45.5 .. .. .. .. .. .. .. 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 St. Lucia 1995d 42.6 2.0 5.2 9.9 14.8 21.8 48.3 32.5 Sudan 2009b 35.3 2.7 6.8 11.7 16.4 22.7 42.4 26.7 Suriname 1999d 52.9 1.1 3.2 7.2 12.3 20.4 56.9 40.6 Swaziland 2010 b 51.5 1.7 4.1 7.4 12.0 20.0 56.6 40.1 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 2009b 30.8 3.5 8.3 12.8 17.0 22.6 39.4 24.3 Tanzania 2007b 37.6 2.8 6.8 11.1 15.6 21.7 44.8 29.6 Thailand 2009b 40.0 2.8 6.7 10.3 14.5 21.4 47.2 31.5 Timor-Leste 2007b 31.9 4.0 9.0 12.5 16.1 21.2 41.3 27.0 Togo 2006b 34.4 3.3 7.6 11.7 16.1 22.2 42.4 27.1 Trinidad and Tobago 1992d 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 2005b 41.4 2.4 5.9 10.1 14.7 21.3 47.9 32.5 Turkey 2008b 39.0 2.1 5.7 10.9 15.9 22.4 45.1 29.4 Turkmenistan 1998b 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7 Uganda 2009b 44.3 2.4 5.8 9.6 13.8 20.0 50.7 36.1 Ukraine 2009 b 26.4 4.2 9.7 14.0 17.7 22.4 36.3 22.0 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 2010 d 45.3 1.9 4.9 9.0 13.7 21.5 50.9 34.4 Uzbekistan 2003b 36.7 2.9 7.1 11.5 15.7 21.5 44.2 29.5 Venezuela, RB 2006d 44.8 1.2 4.3 9.5 14.6 22.2 49.4 33.2 Vietnam 2008b 35.6 3.2 7.4 11.5 15.8 21.8 43.4 28.2 West Bank and Gaza 2009b 35.5 3.2 7.4 11.5 15.8 21.8 43.4 28.2 Yemen, Rep. 2005b 37.7 2.9 7.2 11.3 15.3 21.0 45.3 30.8 Zambia 2006b 54.6 1.5 3.6 6.7 11.2 19.2 59.4 43.1 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. 76 2012 World Development Indicators 2.9 PEOPLE Distribution of income or consumption About the data De�nitions Inequality in the distribution of income is reflected • Survey year is the year in which the underlying in the percentage shares of income or consumption data were collected. •  Gini index measures the accruing to portions of the population ranked by extent to which the distribution of income (or con- income or consumption levels. The portions ranked sumption expenditure) among individuals or house- lowest by personal income receive the smallest holds within an economy deviates from a perfectly shares of total income. The Gini index provides a con- equal distribution. A Lorenz curve plots the cumula- venient summary measure of the degree of inequal- tive 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 hold survey were available, they have been used to of absolute equality, expressed as a percentage of directly calculate the income or consumption shares the maximum area under the line. Thus a Gini index by quintile. Otherwise, shares have been estimated of 0 represents perfect equality, while an index of from the best available grouped data. 100 implies perfect inequality. • Percentage share The distribution data have been adjusted for of income or consumption is the share of total household size, providing a more consistent measure income or consumption that accrues to subgroups of of per capita income or consumption. No adjustment population 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 noncomparability 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 Data sources often among surveys. Consumption is usually a much better welfare indicator, particularly in developing Data on distribution are compiled by the World countries. Second, households differ in size (num- Bank’s Development Research Group using ber of members) and in the extent of income sharing PovcalNet (http://iresearch.worldbank.org/ among members. And individuals differ in age and PovcalNet) based on household survey data consumption needs. Differences among countries in obtained from government statistical agencies these respects may bias comparisons of distribution. and World Bank country departments. Detailed World Bank staff have made an effort to ensure information on the methodology adopted by the that the data are as comparable as possible. Wher- Socio-Economic Database for Latin America and ever possible, consumption has been used rather the Caribbean to process the income data used than income. Income distribution and Gini indexes for for countries in this region is available at http:// high-income economies are calculated directly from sedlac.econo.unlp.edu.ar/eng/methodology.php. the Luxembourg Income Study database, using an Data for high-income economies are computed estimation method consistent with that applied for based on data from the Luxembourg Income Study developing countries. database. 2012 World Development Indicators 77 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 2007–10a 2007–10a 2007–10a Year force population Year GDP Year wage Afghanistan .. .. .. 2006 3.7 2.3 2005 0.5 .. Albania 26 28 16 2007 37.9 25.7 2009 6.1 .. Algeria .. .. .. 2002 36.7 22.0 2002 3.2 .. Angola .. .. 25 .. .. .. .. Argentina 19b 25b 34 2008 42.3 33.1 2010 7.4 2000 43.8 Armenia 37 55 36 2008 32.1 24.6 2006 3.2 2007 20.3 Australia 12b 11b .. 2005 90.7 69.7 2007 3.4 .. Austria 9 9 .. 2005 93.7 68.5 2007 12.3 .. Azerbaijan 13 16 25 2007 35.4 24.7 2007 3.8 2006 24.3 Bahrain .. .. .. 2007 20.2 13.4 2004 0.9 .. Bangladesh .. .. 13 2004 2.5 1.9 2006 0.3 .. Belarus .. .. 54 2008 93.5 66.8 2008 10.2 2002 41.6 Belgium 22 22 .. 2005 91.4 61.5 2007 8.9 .. Benin .. .. 23 2005 5.5 4.2 2006 1.5 .. Bolivia .. .. 23 2008 12.2 9.4 2009 1.5 .. Bosnia and Herzegovina 45 52 .. 2009 24.5 29.0 2009 9.4 .. Botswana .. .. .. 2006 9.0 7.2 2009 1.3 .. Brazil 14 23 .. 2008 55.2 45.7 2010 6.1 .. Bulgaria 24 22 .. 2008 78.7 54.7 2008 8.5 2004 42.9 Burkina Faso .. .. .. 2004 1.2 1.1 2004 0.7 .. Burundi .. .. .. 2006 3.5 3.3 2006 0.7 .. Cambodia 4 3 27 .. .. 2005 0.6 .. Cameroon .. .. .. 2006 16.2 11.5 2005 0.4 .. Canada 17b 12b .. 2007 66.9 53.6 2007 4.2 .. Central African Republic .. .. .. 2004 1.5 1.3 2004 0.8 .. Chad .. .. .. 2005 2.7 2.0 .. .. Chile 17 22 .. 2008 59.6 39.5 2009 5.0 2006 53.5 China .. .. .. 2007 26.9 27.7 2006 2.5 .. Hong Kong SAR, China 15 10 .. 2008 78.9 55.4 2006 1.6 .. Colombia 18 30 34 2008 31.5 20.2 2010 3.5 .. Congo, Dem. Rep. .. .. 21 2008 14.2 10.5 .. .. Congo, Rep. .. .. 23 2008 9.7 7.5 .. .. Costa Rica 10 13 .. 2004 55.5 40.2 2009 2.8 .. Côte d’Ivoire .. .. .. 2004 12.8 9.1 2006 0.7 .. Croatia 30 34 24 2010 82.9 50.0 2009 10.3 2005 32.4 Cuba 3 4 46 .. .. .. Cyprus 16 17 .. .. .. .. .. Czech Republic 18 19 .. 2007 95.4 67.5 2007 8.5 2005 40.7 Denmark 16 12 .. 2007 92.9 86.7 2007 5.6 .. Dominican Republic 21 45 35 2008 25.6 19.1 2009 0.7 .. Ecuador 12b 18b .. 2004 26.4 18.0 2010 1.8 .. Egypt, Arab Rep. 17 48 13 2009 55.1 27.9 2004 4.1 .. El Salvador 13 8 .. 2008 22.9 15.7 2010 1.7 .. Eritrea .. .. .. .. .. 2001 0.3 .. Estonia 35 30 .. 2004 94.5 68.7 2007 10.9 2007 35.4 Ethiopia .. .. 23 .. .. 2006 0.3 .. Finland 22 19 .. 2005 89.7 67.4 2007 8.3 .. France 22 23 .. 2005 87.3 61.4 2007 12.5 .. Gabon .. .. .. .. .. .. .. Gambia, The .. .. .. 2006 2.7 2.2 2003 0.1 .. Georgia 32 41 .. 2004 29.2 22.6 2004 3.0 2003 13.0 Germany 10 9 .. 2005 86.9 65.6 2007 10.7 .. Ghana .. .. 34 2004 8.1 6.4 2002 1.3 .. Greece 27 41 .. 2005 86.0 58.3 2010 13.5 .. Guatemala .. .. .. 2008 20.3 14.7 2009 1.2 .. Guinea .. .. 17 1993 12.1 10.8 .. .. Guinea-Bissau .. .. .. 2004 2.0 1.5 2005 2.1 .. Haiti .. .. 44 .. .. .. .. 78 2012 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 2007–10a 2007–10a 2007–10a Year force population Year GDP Year wage Honduras .. .. 26 2008 17.3 11.1 2010 0.0 .. Hungary 28 25 .. 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 7.1 8.0 2010 1.0 .. Iran, Islamic Rep. 20 34 .. 2001 34.2 18.9 2000 1.1 .. Iraq .. .. 11 2009 35.6 19.0 2009 3.9 .. Ireland 34 21 .. 2005 88.9 64.1 2007 3.6 .. Israel 15 13 .. 2008 .. 89.1 2007 4.8 .. Italy 27 29 .. 2005 90.1 57.1 2007 14.1 .. Jamaica 23 33 41 2004 17.2 12.7 2004 0.7 .. Japan 10 8 .. 2005 95.4 75.0 2007 8.8 .. Jordan 23 46 11 2006 38.4 19.9 2005 2.0 .. Kazakhstan 7 8 .. 2004 62.5 48.4 2009 3.2 2003 24.9 Kenya .. .. 34 2006 7.5 6.4 2003 1.1 .. Korea, Dem. Rep. .. .. .. .. .. .. .. Korea, Rep. 11 9 .. 2005 49.5 34.3 2005 1.6 .. Kosovo .. .. .. .. .. 2009 2.7c .. Kuwait .. .. .. .. .. .. .. Kyrgyz Republic .. .. 25 2006 40.4 29.6 2010 2.7 2003 27.5 Lao PDR .. .. .. 2004 .. 6.0 2005 0.2 .. Latvia 35 34 .. 2003 91.7 70.6 2009 8.5 2005 33.1 Lebanon 22 22 .. 2003 34.5 17.3 2003 2.1 .. Lesotho 29 42 .. 2005 4.4 3.5 .. .. Liberia 4 8 30 .. .. .. .. Libya .. .. .. 2004 68.5 37.5 2001 2.1 .. Lithuania 38 31 .. 2007 82.9 56.9 2010 8.6 2005 30.9 Macedonia, FYR 35 38 8 2008 52.3 33.2 2008 9.4 2006 55.0 Madagascar .. .. 22 2009 5.3 4.9 .. .. Malawi .. .. .. .. .. .. .. Malaysia 10 12 .. 2008 49.0 32.5 2004 0.3 .. Mali .. .. 12 2010 7.3 4.4 2010 1.6 .. Mauritania .. .. .. 2000 13.1 9.4 2003 0.6 .. Mauritius 19 29 .. 2000 53.4 34.5 2007 2.9 .. Mexico 9 10 .. 2008 27.4 19.0 2007 1.4 .. Moldova 16 15 34 2009 56.7 32.1 2009 9.1 2003 20.9 Mongolia .. .. 29 2005 33.5 25.6 2009 4.9 .. Morocco 23 19 .. 2007 23.8 13.5 2003 1.9 .. Mozambique .. .. .. 2006 1.9 1.7 2006 0.3 .. Myanmar .. .. .. .. .. .. .. Namibia .. .. 44 2008 9.6 5.8 2004 1.3 .. Nepal .. .. 23 2008 3.4 2.6 2006 0.2 .. Netherlands 9 9 .. 2005 90.7 70.7 2007 4.7 .. New Zealand 17b 17b .. .. .. 2007 4.3 .. Nicaragua .. .. .. 2008 21.7 14.6 .. .. Niger .. .. 19 2006 1.9 1.3 2006 0.7 .. Nigeria .. .. 19 2004 8.1 4.8 2004 0.9 .. Norway 11 8 .. 2005 93.2 75.2 2007 4.7 .. Oman .. .. .. .. .. .. .. Pakistan 7 11 10 2008 3.9 2.2 2004 0.5 .. Panama 12 21 .. .. .. .. .. Papua New Guinea .. .. .. 2009 4.4 3.3 2005 0.2 .. Paraguay 9 17 .. 2004 12.4 9.4 2001 1.2 .. Peru 13b 16b 22 2008 21.7 15.6 2010 2.5 .. Philippines 17 20 17 2007 25.0 17.0 2003 1.5 .. Poland 22 25 .. 2005 81.4 52.7 2009 10.0 2007 47.1 Portugal 21 24 .. 2005 92.0 71.6 2007 10.8 .. Puerto Rico 29 22 .. .. .. .. .. Qatar 1 8 .. .. .. .. .. 2012 World Development Indicators 79 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 2007–10a 2007–10a 2007–10a Year force population Year GDP Year wage Romania 22 22 .. 2007 67.9 45.0 2009 8.3 2005 41.5 Russian Federation 17 18 .. 2007 66.8 49.9 2010 8.3 2003 29.2 Rwanda .. .. 31 2004 4.6 4.1 2005 0.7 .. Saudi Arabia 24 46 .. .. .. .. .. Senegal .. .. 20 2003 5.1 4.1 2006 1.4 .. Serbia 31 41 29 2003 45.0 40.2 2010 14.0 .. Sierra Leone .. .. 22 2004 5.5 3.8 .. .. Singapore 10 17 .. 2008 62.1 44.6 .. .. Slovak Republic 35 32 .. 2003 78.9 55.3 2007 9.3 2005 44.7 Slovenia 15 14 .. 2008 87.4 63.8 2007 12.7 2005 44.3 Somalia .. .. .. .. .. .. .. South Africa 45 53 .. 2007 6.3 3.7 2010 2.2 .. South Sudan .. .. .. .. .. .. .. Spain 43 40 .. 2005 69.4 48.7 2007 8.0 2006 58.6 Sri Lanka 17 28 .. 2006 24.1 14.9 2007 2.0 .. Sudan .. .. 19 2005 5.2 2.9 .. .. Swaziland .. .. 48 2009 15.4 10.3 .. .. Sweden 27 24 .. 2005 88.8 72.2 2007 7.2 .. Switzerland 7 8 .. 2005 95.4 78.7 2007 6.4 2000 40.0 Syrian Arab Republic 15 40 .. 2008 26.8 14.3 2004 1.3 .. Tajikistan .. .. .. .. .. .. 2003 25.7 Tanzania .. .. 24 2006 4.3 4.0 2006 0.9 .. Thailand 4 5 30 2008 22.8 18.3 2006 0.8 .. Timor-Leste .. .. 12 .. .. .. .. Togo .. .. .. 2003 7.3 5.7 2003 0.8 .. Trinidad and Tobago 9 13 .. 2008 71.1 51.2 2010 2.8 .. Tunisia .. .. .. 2004 48.6 25.5 2003 4.3 .. Turkey 21 23 .. 2007 58.6 30.5 2008 6.2 2007 61.3 Turkmenistan .. .. .. .. .. .. .. Uganda .. .. 30 2004 10.3 9.2 2003 0.3 .. Ukraine .. .. 49 2010 65.3 44.7 2010 17.8 2007 48.3 United Arab Emirates 8 22 .. .. .. .. .. United Kingdom 21 17 .. 2005 93.2 71.5 2007 5.4 .. United States 21b 16b .. 2005 92.2 71.4 2007 6.0 2006 29.2 Uruguay 16 25 .. 2007 78.5 61.2 2010 8.8 .. Uzbekistan .. .. 18 2005 .. 86.3 2005 6.5 2005 40.0 Venezuela, RB 12 16 .. 2008 33.9 24.2 2010 5.0 .. Vietnam .. .. .. 2008 19.3 15.2 2004 2.5 .. West Bank and Gaza 39 47 .. 2009 14.0 6.5 2009 4.0 .. Yemen, Rep. .. .. .. 2006 10.4 5.0 2004 1.5 .. Zambia .. .. 24 2006 10.9 8.0 2008 1.4 .. Zimbabwe .. .. 38 .. .. 2002 2.3 .. World .. w .. w Low income .. .. Middle income .. .. Lower middle income .. .. Upper middle income .. .. Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia 17 18 Latin America & Carib. 12 18 Middle East & N. Africa 19 36 South Asia .. .. Sub-Saharan Africa .. High income 19 17 Euro area 22 22 a. Data are for the most recent year available. b. Limited coverage. c. Includes expenditure on old-age and survivors benefi ts only. 80 2012 World Development Indicators 2.10 PEOPLE Assessing vulnerability and security About the data De�nitions As traditionally measured, poverty is a static con- citizenship, residency, or income status. In contri- •  Youth unemployment is the share of the labor cept, and vulnerability a dynamic one. Vulnerabil- bution-related schemes, however, eligibility is usually force ages 15–24 without work but available for and ity reflects a household’s resilience in the face of restricted to individuals who have contributed for a seeking employment. • Female-headed households shocks and the likelihood that a shock will lead to a minimum number of years. Definitional issues—relat- are households with a female head. • Pension con- decline in well-being. Thus, it depends primarily on ing to the labor force, for example—may arise in tributors are members of the labor force or working- the household’s assets and insurance mechanisms. comparing coverage by contribution-related schemes age population (here defined as ages 15 and older) Because poor people have fewer assets and less over time and across countries (for country-specific covered by a pension scheme. • Public expenditure diversified sources of income than do the better-off, information, see Hinz and others 2011). The share on pensions is all government expenditures on cash fluctuations in income affect them more. of the labor force covered by a pension scheme may transfers to the elderly, the disabled, and survivors Enhancing security for poor people means reduc- be overstated in countries that do not try to count and the administrative costs of these programs. ing their vulnerability to such risks as ill health, pro- informal sector workers as part of the labor force. • Average pension is the average pension payment viding them the means to manage risk themselves, Public interventions and institutions can provide of all pensioners of the main pension schemes and strengthening market or public institutions for services directly to poor people, although whether (including old-age, survivors, disability, military, and managing risk. Tools include microfinance programs, these interventions and institutions work well for the work accident or disease pensions) divided by the public provision of education and basic health care, poor is debated. State action is often ineffective, average wage of all formal sector workers. and old age assistance (see tables 2.11 and 2.16). in part because governments can influence only a 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 rate. For further information, see About the data for Data sources table 2.5 and the original source. The definition of female-headed household differs Data on youth unemployment are from the ILO’s greatly across countries, making cross-country com- Key Indicators of the Labour Market, 7th edition, parison difficult. In some cases it is assumed that a database. Data on female-headed households woman cannot be the head of any household with an are from MEASURE DHS Demographic and Health adult male, because of sex-biased stereotype. Cau- Surveys by ICF International. Data on pension con- tion should be used in interpreting the data. tributors and public expenditure on pensions are Pension scheme coverage may be broad or from Hinz and others (2011). even universal where eligibility is determined by 2012 World Development Indicators 81 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 2010a 1999 2010a 1999 2010a 2010a 2010a 2010a 2010a Afghanistan .. .. .. .. .. .. .. .. .. 44 Albania .. .. .. .. .. .. .. .. .. 20 Algeria 12.0 .. .. .. .. .. 4.3 20.3 .. 23 Angola .. .. .. .. .. .. .. .. .. 46 Argentina 12.9 16.8 18.2 27.1 17.7 19.1 6.0 14.0 .. 16 Armenia .. 16.5 .. 17.8 .. 7.5 3.2 13.0 .. .. Australia 16.4 20.2 15.0 18.8 26.6 20.7 5.1 12.9 .. .. Austria 24.9 24.1 30.0 27.4 51.8 43.5 5.5 11.2 .. 11 Azerbaijan 6.9 .. 17.0 .. 19.1 22.3 3.2 10.9 100.0 11 Bahrain .. .. .. .. .. .. 2.9 11.7 .. .. Bangladesh .. 8.8 11.5 12.0 46.6 27.7 2.2 14.1 58.4 43 Belarus .. .. .. .. .. 14.7 4.5 8.9 99.8 15 Belgium 18.2 22.4 23.7 36.5 38.2 36.6 6.4 12.9 .. 11 Benin 11.8 13.0 24.0 .. 208.2 .. 4.5 18.2 42.6 46 Bolivia 14.2 .. 11.7 .. 44.1 .. .. .. .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. .. .. .. .. .. 7.8 16.2 .. .. Brazil 10.8 18.5 9.5 19.5 57.2 27.6 5.4 .. .. 23 Bulgaria 15.2 24.4 18.4 24.2 17.6 24.8 4.4 12.3 .. 17 Burkina Faso .. .. .. .. .. .. .. .. 85.7b 48b Burundi 14.4 19.0 67.8 64.1 1,036.1 477.4 9.2 25.1 91.2 51 Cambodia 5.8 6.8 11.5 6.8 42.5 .. 2.6 .. 99.1 48 Cameroon .. 6.6 .. 28.0 .. 28.0 3.5 17.9 57.1 46 Canada .. .. .. .. 44.0 .. 4.8 .. .. .. Central African Republic .. 4.4 .. 14.5 .. 96.0 1.2 12.0 .. 84 Chad .. 9.6 .. 19.1 .. 279.1 2.8 10.1 45.3 56 Chile 14.4 17.4 14.8 17.7 19.4 13.7 4.5 .. .. 23 China .. .. 11.5 .. 90.0 .. .. .. .. 17 Hong Kong SAR, China 12.4 15.1 17.7 18.0 .. 26.8 3.6 20.2 95.6 15 Colombia 15.2 15.7 16.1 15.3 37.7 29.4 4.8 14.9 100.0 28 Congo, Dem. Rep. .. .. .. .. .. .. .. .. 91.7 37 Congo, Rep. .. 11.1 .. .. .. 134.2 6.2 .. 86.8 49 Costa Rica 15.5 14.6 21.4 14.4 .. .. 6.3 23.1 89.5 18 Côte d’Ivoire 14.3 .. 41.3 .. 141.0 .. 4.6 24.6 100.0 b 49b Croatia .. 21.8 .. 25.2 35.8 29.2 4.3 .. .. 15 Cuba 25.0 44.2 37.1 51.9 77.6 61.1 13.4 18.3 100.0 9 Cyprus 17.1 28.7 28.2 38.3 47.9 57.0 7.4 17.4 .. 14 Czech Republic 11.2 13.6 21.7 22.8 33.7 25.7 4.1 9.5 .. 19 Denmark 24.6 24.9 38.1 31.5 65.9 52.1 7.7 15.0 .. .. Dominican Republic .. 7.5 .. 6.7 .. .. .. .. 84.9 26 Ecuador 4.4 .. 9.6 .. .. .. .. .. 82.6 17 Egypt, Arab Rep. .. .. .. .. .. .. 3.8 11.9 .. 27 El Salvador 8.6 8.8 7.5 9.4 8.9 11.7 3.2 .. 92.7 31 Eritrea 15.0 .. 37.4 .. 430.8 .. .. .. 93.8 38 Estonia 20.9 25.9 27.2 29.6 31.8 22.1 5.7 14.2 .. 12 Ethiopia .. 18.2 .. 9.8 .. 31.0 4.7 25.4 39.4 54 Finland 17.4 18.5 25.9 32.2 40.5 32.5 6.1 12.4 .. 14 France 17.9 18.0 29.5 27.8 30.7 37.0 5.6 10.6 .. 19 Gabon .. .. .. .. .. .. .. .. .. 25b Gambia, The .. 24.6 .. 16.2 .. 94.4 5.0 22.8 .. 37 Georgia .. 14.8 .. 15.5 .. 11.4 3.2 7.7 94.6 8 Germany .. 15.6 .. 21.8 .. .. 4.6 10.4 .. 13 Ghana 17.6 11.4 40.4 27.2 .. 171.7 5.5 24.4 50.6b 31b Greece 11.7 .. 15.6 .. 26.5 .. .. .. .. .. Guatemala 6.7 10.4 4.3 6.2 .. .. 3.2 .. .. 28 Guinea .. 7.0 .. 6.1 .. 100.0 2.4 19.2 65.2 42 Guinea-Bissau .. .. .. .. .. .. .. .. 38.9 52 Haiti .. .. .. .. .. .. .. .. .. .. 82 2012 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 2010a 1999 2010a 1999 2010a 2010a 2010a 2010a 2010a Honduras .. 18.7 .. 279.7 .. .. .. .. 36.4 33 Hungary 17.9 21.9 19.0 23.0 34.1 24.8 5.1 10.4 .. 10 India 11.9 .. 24.7 .. 95.0 .. .. .. .. .. Indonesia .. 11.4 .. 12.9 .. 16.8 4.6 26.0 .. 16 Iran, Islamic Rep. 9.3 15.2 10.1 21.1 35.5 19.2 4.7 19.8 98.4 20 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 11.0 18.6 16.8 27.5 28.6 32.7 5.7 13.4 .. 16 Israel 20.5 19.5 21.9 20.4 30.9 21.3 5.9 13.7 .. 13 Italy 24.0 24.5 27.7 26.7 27.6 25.0 4.6 9.4 .. .. Jamaica 13.4 19.9 21.0 22.6 70.4 50.2 6.1 11.5 .. 21 Japan 21.1 21.5 20.9 22.3 15.1 20.9 3.8 9.4 .. 18 Jordan 13.7 11.9 15.8 14.3 .. .. .. .. .. .. Kazakhstan .. .. .. .. .. 10.1 3.1 .. .. 16b Kenya 21.4 .. 14.4 .. 207.9 .. 6.7 17.2 96.8 47 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 18.4 19.4 15.7 23.2 8.4 10.1 4.8 15.8 .. 22 Kosovo .. .. .. .. .. .. 4.3 17.4 .. .. Kuwait 17.0 10.0 .. 13.7 .. .. .. .. 100.0 8 Kyrgyz Republic .. .. .. .. 24.3 17.6 6.0 18.6 68.4 24 Lao PDR 2.2 .. 4.4 .. 67.7 .. 3.3 13.2 96.9 30 Latvia 19.5 31.4 23.7 32.3 27.9 14.2 5.6 14.7 .. 12 Lebanon .. .. .. .. 13.8 10.0 1.8 7.2 .. 14 Lesotho 37.0 24.8 82.4 55.7 939.7 .. 13.1 23.7 63.4 34 Liberia .. .. .. .. .. .. 2.8 12.1 40.2 24 Libya .. .. .. .. 23.4 .. .. .. .. .. Lithuania .. 18.1 .. 22.6 34.2 17.0 4.9 13.1 .. 13 Macedonia, FYR .. .. .. .. .. .. .. .. .. 16 Madagascar 5.7 7.8 .. 11.5 .. 144.8 3.2 13.4 90.4 40 Malawi 13.4 6.8 9.6 23.3 2,503.9 1,937.6 5.7b 14.7b 95.9 79 Malaysia 12.6 14.6 21.9 20.2 81.6 60.7 5.8 18.9 .. 13 Mali 15.3 15.0 59.7 37.5 256.8 135.3 4.5 22.0 50.0 48b Mauritania 11.6 13.4 36.4 31.2 80.1 193.9 4.3 15.2 100.0 37 Mauritius 9.3 9.0 14.2 14.3 25.4 16.1 3.1 11.4 100.0 21 Mexico 11.9 13.7 14.5 13.6 48.8 38.9 4.9 .. 95.6 28 Moldova .. 41.4 .. 39.4 .. 44.8 9.1 22.3 .. 16 Mongolia .. 14.6 .. .. .. 6.0 5.4 14.6 97.6 30 Morocco 17.4 16.9 45.5 .. 97.1 83.3 5.4 25.7 100.0 b 26b Mozambique .. .. .. .. 1,408.9 .. .. .. 75.9 58 Myanmar .. .. 6.6 .. 27.0 .. .. .. 99.9 28 Namibia 22.2 17.8 36.3 16.4 157.3 .. 8.1 22.4 95.6 30 Nepal 9.1 17.8 13.1 11.3 141.3 39.3 4.7 20.2 80.7b 30 b Netherlands 15.2 17.2 22.2 25.0 47.4 41.5 5.5 11.9 .. .. New Zealand 20.0 21.9 23.7 23.6 39.5 31.4 7.2 16.1 .. 14 Nicaragua .. .. .. .. .. .. .. .. 74.9 30 Niger .. 21.1 .. 41.9 .. 438.8 3.8 16.9 96.4b 39b Nigeria .. .. .. .. .. .. .. .. 66.1 36 Norway 21.8 18.3 30.4 25.6 45.8 46.8 6.5 16.1 .. .. Oman 10.7 12.8 20.9 14.6 .. 41.6 4.4 .. 100.0 12 Pakistan .. .. .. .. .. .. 2.4 9.9 84.2 40 Panama 13.7 7.5 19.1 9.9 33.7 21.6 3.8 .. 91.6 23 Papua New Guinea .. .. .. .. .. .. .. .. .. .. Paraguay 13.6 .. 18.4 .. 58.9 .. .. .. .. .. Peru 7.6 7.8 10.8 8.9 21.1 .. 2.6 16.4 .. 20 Philippines 12.0 9.0 10.2 9.1 14.4 .. 2.7 16.9 .. 31 Poland .. 25.3 10.9 22.9 21.1 18.4 5.1 11.8 .. 10 Portugal 18.8 19.9 26.6 31.6 27.1 26.7 4.9 11.0 .. 11 Puerto Rico .. .. .. .. .. .. .. .. 6.6 12 Qatar .. 10.3 .. 11.0 .. 337.7 2.4 8.2 42.9 12 2012 World Development Indicators 83 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 2010a 1999 2010a 1999 2010a 2010a 2010a 2010a 2010a Romania .. .. .. .. 32.6 .. .. .. .. 16 Russian Federation .. .. .. .. 10.9 14.2 4.1 .. .. 18 Rwanda 11.2 8.2 42.7 37.1 1,228.1 186.8 4.7b 16.9b 91.5 65 Saudi Arabia .. .. .. .. .. .. 5.6 19.3 .. 11 Senegal 13.6 16.4 .. 28.0 .. 186.9 5.6 24.0 47.9 34 Serbia .. 61.6 .. 14.4 .. 43.3 5.0 9.5 94.2 16 Sierra Leone .. .. .. .. .. .. 4.3 18.1 48.0 b 31b Singapore .. 11.5 .. 17.5 .. 28.7 3.3 10.3 94.3 17 Slovak Republic 10.2 15.6 18.4 15.1 32.8 18.3 3.6 10.3 .. 16 Slovenia 26.1 .. 25.6 .. 27.8 21.2 5.2 11.8 .. 17 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 14.2 17.6 20.0 19.8 .. .. 6.0 19.2 87.4 31 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 18.0 20.3 24.4 25.8 19.6 27.3 4.6 11.2 .. 13 Sri Lanka .. 7.4 .. .. .. .. 2.1 8.1 .. 24 Sudan .. .. .. .. .. .. .. .. 59.7 38 Swaziland 8.3 17.7 23.2 37.1 434.9 .. 7.4 16.0 73.1 32 Sweden 22.3 26.2 26.1 30.9 51.7 41.3 6.8 12.9 .. 9 Switzerland 22.7 20.5 27.3 31.1 53.8 43.8 5.4 16.7 .. .. Syrian Arab Republic 10.8 16.8 21.0 14.2 .. .. .. .. .. 18 Tajikistan .. .. .. .. .. 17.5 4.0 14.7 92.9 25 Tanzania .. 21.5 .. 18.8 .. 873.3 6.2 18.3 94.5 51 Thailand 18.1 24.4 16.2 15.4 36.5 17.6 3.8 22.3 .. 16 Timor-Leste .. .. .. .. .. 83.9 14.0 11.7 .. 30 Togo 7.7 10.8 27.7 .. .. .. 4.5 17.6 76.7 41 Trinidad and Tobago 11.5 14.9 12.2 16.1 148.3 .. .. .. 88.0 18 Tunisia 14.2 17.3 24.6 24.3 81.1 46.1 6.3 22.7 .. 17 Turkey 9.8 .. 9.6 .. 35.3 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 7.2 .. 20.5 .. 104.3 3.2 15.0 89.4 49 Ukraine .. .. .. .. 36.5 .. .. .. 99.9 16 United Arab Emirates 5.4 6.2 7.2 8.6 26.2 19.9 1.0 23.4 100.0 17 United Kingdom 13.9 23.3 23.8 28.9 25.6 22.2 5.4 11.1 .. 18 United States 17.7 22.5 22.3 24.8 26.8 21.2 5.5 13.8 .. 14 Uruguay 7.2 .. 9.9 .. .. .. .. .. .. 14 Uzbekistan .. .. .. .. .. .. .. .. 100.0 b 16b Venezuela, RB .. .. .. .. .. .. .. .. 88.4 12 Vietnam .. 19.4 .. 17.0 .. 60.6 5.3 19.8 98.3 20 West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 28 Yemen, Rep. .. .. .. .. .. .. 5.2 16.0 .. 31 Zambia 7.0 .. 19.4 .. 160.4 .. 1.3 .. .. 58 Zimbabwe 12.7 .. 19.3 .. .. 75.4 2.5 8.3 .. .. World .. m 16.4 m .. m 22.7 m .. m .. m 4.6 m 15.6 m .. w 24 w Low income .. .. .. .. .. .. 3.8 18.7 82.9 45 Middle income .. .. .. .. .. .. 4.4 .. .. .. Lower middle income .. .. .. .. .. .. 4.0 .. .. .. Upper middle income 13.8 15.9 .. 15.4 .. .. 4.8 .. .. 19 Low & middle income .. .. .. .. .. .. 4.1 .. .. 26 East Asia & Pacific .. .. .. .. 37.8 .. 3.8 17.2 .. 17 Europe & Central Asia .. .. .. .. .. 17.3 4.4 14.0 .. 17 Latin America & Carib. 12.6 12.4 12.9 13.6 .. .. 4.4 .. .. 24 Middle East & N. Africa .. .. .. .. .. .. 4.8 20.0 .. 24 South Asia .. 8.8 13.1 .. 95.0 .. 2.5 12.6 .. .. Sub-Saharan Africa .. .. .. .. .. .. 5.0 18.9 .. 46 High income 17.9 19.7 22.3 25.1 31.8 27.3 5.1 12.7 .. 15 Euro area 17.9 19.9 25.9 27.5 30.7 32.5 5.5 11.5 .. 14 a. Provisional data. b. Data are for 2011. 84 2012 World Development Indicators 2.11 PEOPLE Education inputs About the data De�nitions Data on education are collected by the United class size because of the different practices coun- • Public expenditure per student is public current Nations Educational, Scientific, and Cultural Organi- tries employ, such as part-time teachers, school and capital spending on education divided by the zation (UNESCO) Institute for Statistics from official shifts, and multigrade classes. The comparability number of students by level as a percentage of gross responses to its annual education survey. The data of pupil–teacher ratios across countries is further domestic product (GDP) per capita. • Public expen- are used for monitoring, policymaking, and resource affected by the definition of teachers and by differ- diture on education is current and capital expendi- allocation. While international standards ensure ences in class size by grade and in the number of tures on education by local (including municipalities), comparable datasets, data collection methods may hours taught, as well as the different practices men- regional, and national governments as a percentage vary by country and within countries over time. tioned above. Moreover, the underlying enrollment of GDP and as a percentage of total government For most countries the data on education spending levels are subject to a variety of reporting errors (for expenditure. • Trained teachers in primary educa- in the table refer to public spending—government further discussion of enrollment data, see About the tion are the percentage of primary school teachers spending on education at all levels plus subsidies data for table 2.12).While the pupil–teacher ratio who have received the minimum organized teacher provided to households and other private entities— is often used to compare the quality of schooling training (pre-service or in-service) required for teach- and generally exclude foreign aid for education that is across countries, it is often weakly related to student ing at the specified level of education in their country. not included in the government budget. The data may learning and quality of education. • Primary school pupil–teacher ratio is the number also exclude spending by religious schools, which All education data published by the UNESCO of pupils enrolled in primary school divided by the play a significant role in many developing countries. Institute for Statistics are mapped to the Interna- number of primary school teachers (regardless of Data are gathered from ministries of education and tional Standard Classifi cation of Education 1997 their teaching assignment). from other ministries or agencies involved in educa- (ISCED97). This classification system ensures the tion spending. comparability of education programs at the interna- The share of public expenditure devoted to educa- tional level. UNESCO developed the ISCED to facili- tion allows an assessment of the priority a govern- tate comparisons of education statistics and indica- ment assigns to education relative to other public tors of different countries on the basis of uniform and investments, as well as a government’s commitment internationally agreed definitions. First developed in to investing in human capital development. However, the 1970s, the current version was formally adopted returns on investment to education, especially pri- in November 1997. mary and lower secondary education, cannot be The reference years in the table reflect the school understood simply by comparing current education year for which the data are presented. In some coun- indicators with national income. It takes a long time tries the school year spans two calendar years (for before currently enrolled children can productively example, from September 2009 to June 2010); in contribute to the national economy (Hanushek 2002). these cases the reference year refers to the year in High-quality data on education finance are scarce. which the school year ended (2010 in the example). Improving their quality is a priority of the UNESCO Institute for Statistics. Additional resources are being allocated for technical assistance to coun- tries in need, especially 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 educa- tion spending is still a challenge for all countries. The share of trained teachers in primary education reveals a country’s commitment to investing in the development of its human capital engaged in teach- ing, but it does not take into account differences in teachers’ experiences and status, teaching meth- ods, teaching materials, and classroom conditions— all factors that affect the quality of teaching and learning. Some teachers without formal training may Data sources have acquired equivalent pedagogical skills through professional experience. Data on education inputs are from the UNESCO The pupil–teacher ratio reflects the average num- Institute for Statistics. ber of pupils per teacher. It differs from the average 2012 World Development Indicators 85 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 2010a 2010a 2010a 2010a 1991 2010a 1999 2010a 2010a 2010a 2010a 2010a Afghanistan .. 97 46 3 28 .. .. .. .. .. .. .. Albania 56 87 89 .. .. 80 68 .. 79 79 23 20 Algeria 77 110 95 31 89 96 .. .. 98 96 28 53 Angola 104 124 31 4 .. 86 .. 12 93 78 119 373 Argentina 74 118 89 71 .. .. 74 82 .. .. .. .. Armenia 31 103 92 52 .. .. 86 86 78 81 13 10 Australia 81 104 129 76 98 97 90 85 97 98 31 23 Austria 96 100 100 60 90 .. .. .. .. .. .. .. Azerbaijan 25 94 .. 19 89 84 75 .. 85 84 40 38 Bahrain .. .. .. .. 99 .. 86 .. .. .. .. .. Bangladesh 13 103 49 11 64 92 44 46 91 100 718 1 Belarus 99 100 .. 83 .. 92 82 87 95 97 9 5 Belgium 118 105 111 67 96 99 .. .. 99 99 4 3 Benin 18 126 .. .. 51 94 18 .. .. .. .. .. Bolivia 45 105 80 .. .. .. 68 68 .. .. .. .. Bosnia and Herzegovina 17 88 90 36 .. 85 .. .. 86 88 14 11 Botswana .. .. .. .. 89 .. 53 .. .. .. .. .. Brazil 65 127 101 36 .. 94 66 82 96 94 290 396 Bulgaria 79 103 88 53 .. 98 85 83 99 100 1 0b Burkina Faso 3c 79c 23c 3 27 63c 8 18 c 65 58 452 531 Burundi 9 156 25 3 50 .. .. 16 98 100 10 1 Cambodia 13 127 46 8 .. 96 14 .. 96 95 32 40 Cameroon 28 120 42 11 69 92 .. .. 100 88 6 171 Canada 71 99 101 .. 98 .. 95 .. .. .. .. .. Central African Republic 4 93 13 3 53 71 .. 11 81 61 63 135 Chad 2 90 26 2 .. .. 7 .. .. .. .. .. Chile 56 106 88 59 .. 94 .. 83 94 94 46 48 China 54 111 81 26 97 .. .. .. .. .. .. .. Hong Kong SAR, China .. 102 83 60 .. 94 70 75 96 98 7 4 Colombia 49 115 96 39 71 88 56 74 92 91 187 188 Congo, Dem. Rep. 3 94 38 6 56 .. .. .. .. .. .. .. Congo, Rep. 13 115 .. 6 .. 91 .. .. 92 89 24 32 Costa Rica 71 110 100 .. 87 .. .. .. .. .. .. .. Côte d’Ivoire 4c 88 c .. .. 46 61 19 .. 67 56 497 664 Croatia 58 93 95 49 .. 87 81 92 93 93 7 6 Cuba 100 103 89 95 94 99 75 86 100 100 0b 1 Cyprus 81 105 98 52 87 99 88 96 99 99 0b 0b Czech Republic 106 106 90 61 .. .. 81 84 .. .. .. .. Denmark 96 99 117 74 98 96 88 89 95 97 11 6 Dominican Republic 38 108 76 .. .. 90 39 62 96 90 28 57 Ecuador 126 114 80 40 .. 97 46 .. .. .. .. .. Egypt, Arab Rep. 24 106 .. 30 .. 96 77 .. 100 96 15 184 El Salvador 64 114 65 23 .. 94 44 58 94 95 26 21 Eritrea 14 45 32 2 20 33 18 29 37 33 203 215 Estonia 96 99 104 63 .. 94 84 92 96 96 1 1 Ethiopia 5 102 36 5 30 81 12 .. 85 80 1,023 1,367 Finland 66 99 108 92 99 97 95 94 98 98 5 4 France 110 111 113 55 100 99 93 98 .. .. .. .. Gabon 42c 182c .. .. .. .. .. .. .. .. .. .. Gambia, The 30 83 54 4 50 66 .. .. 68 71 44 41 Georgia 58 109 86 28 .. 100 76 79 .. .. .. .. Germany 114 102 103 .. 84 97 .. .. .. .. .. .. Ghana 69 107c 58 c 9 .. 84 c 34 49c 84 c 85c 298 c 270 c Greece .. .. .. .. 95 .. 82 .. .. .. .. .. Guatemala 71 116 59 .. .. 97 24 50 100 98 5 27 Guinea 14 94 38 9 27 77 12 29 83 70 131 224 Guinea-Bissau 7 123 .. .. .. 74 9 .. 77 73 26 30 Haiti .. .. .. .. 21 .. .. .. .. .. .. .. 86 2012 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 2010a 2010a 2010a 2010a 1991 2010a 1999 2010a 2010a 2010a 2010a 2010a Honduras 44 116 73 19 88 96 .. .. 96 98 23 8 Hungary 85 102 98 62 .. 92 83 91 98 98 4 3 India 54 118 60 16 .. 92 .. .. .. .. .. .. Indonesia 43 118 77 23 95 96 47 67 .. .. .. .. Iran, Islamic Rep. 43 108 84 43 97 .. .. .. .. .. .. .. Iraq .. .. .. .. 76 .. 30 .. .. .. .. .. Ireland .. 108 117 61 90 95 91 98 97 99 6 3 Israel 106 113 91 62 .. 97 87 88 97 97 13 10 Italy 97 103 99 66 .. 98 85 93 100 99 5 11 Jamaica 113 89 93 29 97 82 83 84 84 82 28 32 Japan 90 103 102 59 100 100 99 99 .. .. .. .. Jordan 36 97 91 42 .. 90 77 84 93 95 30 21 Kazakhstan 66c 111c 100 c 41c .. 88 c 88 90 c 99c 100 c 3c 1c Kenya 52 113 60 4 .. 83 33 50 84 85 523 486 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 121 104 97 104 99 99 96 96 100 99 0b 14 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 82 106 101 .. 47 92 98 89 97 100 3 0b Kyrgyz Republic 19 100 84 49 .. 87 .. 79 95 95 9 9 Lao PDR 16 121 45 13 59 89 26 37 91 87 35 47 Latvia 84 101 95 60 .. 95 .. 84 94 95 3 3 Lebanon 81 105 81 54 .. 92 .. 75 94 93 15 15 Lesotho 33 103 46 .. 72 73 17 30 72 75 53 46 Liberia .. 96 .. .. .. .. 18 .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 75 97 98 77 .. 93 90 91 97 97 2 2 Macedonia, FYR 25 89 83 40 .. 87 .. .. 93 95 5 3 Madagascar 9 149 31 4 72 .. .. 24 .. .. .. .. Malawi .. 135 32 1 .. 97 30 28 .. .. .. .. Malaysia 67 .. 68 40 .. .. 66 68 .. .. .. .. Mali 3c 82c 39c 6 .. 63c .. 31c 71 61 377 481 Mauritania .. 102 24 4 .. 74 14 .. 73 76 72 61 Mauritius 96 99 89 25 93 93 67 .. 92 94 5 3 Mexico 103 115 87 27 98 98 56 70 99 100 39 11 Moldova 76 94 88 38 .. 88 78 79 90 90 8 7 Mongolia 77 100 93 53 .. 95 58 83 98 98 2 3 Morocco 63c 114 c .. 13 56 96c 30 .. 97c 96c 58 c 75c Mozambique .. 115 25 .. 42 92 3 16 95 89 124 243 Myanmar 10 126 54 .. .. .. 32 51 .. .. .. .. Namibia .. 107 .. 9 82 85 39 .. 84 89 31 21 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 96 108 120 63 95 100 91 87 .. .. .. .. New Zealand 93 101 119 83 100 99 90 95 99 100 1 1 Nicaragua 55 118 69 .. 70 92 35 46 93 95 27 21 Niger 6c 71c 13 1 23 62c 6 10 64 52 478 607 Nigeria 14 83 44 .. .. .. .. .. .. .. .. .. Norway 98 99 110 74 100 99 95 95 99 99 3 2 Oman 45 105 100 24 69 94 61 90 98 98 3 2 Pakistan .. 95 34 5 .. 74 .. 34 81 67 1,884 3,241 Panama 67 108 74 45 92 98 59 69 99 98 2 4 Papua New Guinea .. 60 .. .. 65 .. .. .. .. .. .. .. Paraguay 35 100 67 37 94 85 45 60 86 86 62 60 Peru 78 109 92 .. 86 95 63 78 97 97 54 43 Philippines 51 107 82 29 96 89 50 61 88 90 799 662 Poland 66 97 97 71 .. 96 91 91 96 96 47 47 Portugal 82 114 107 62 98 99 80 .. 99 100 3 2 Puerto Rico 96 93 82 86 .. 86 .. .. 83 88 28 18 Qatar 55 103 94 10 89 92 74 83 96 97 2 1 2012 World Development Indicators 87 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 2010a 2010a 2010a 2010a 1991 2010a 1999 2010a 2010a 2010a 2010a 2010a Romania 77 96 95 64 73 87 77 82 92 93 35 32 Russian Federation 90 99 89 76 .. 93 .. .. 95 96 128 93 Rwanda 10 143 32 5 .. 99 .. .. 89 92 84 60 Saudi Arabia 11 106 101 37 .. 90 .. 81 90 89 154 164 Senegal 13 87 37 8 45 75 .. .. 76 80 238 191 Serbia 53 96 91 49 .. 93 .. 90 95 94 8 8 Sierra Leone 7c 125c .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 91 102 89 54 .. .. .. .. .. .. .. .. Slovenia 86 98 97 87 .. 97 90 92 97 97 2 2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 65 102 94 .. 90 85 62 .. 89 91 372 326 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 126 107 119 73 100 100 88 94 100 100 2 1 Sri Lanka 42 99 .. 15 .. 94 .. .. 94 94 56 51 Sudan 27 73 39 .. .. .. .. .. .. .. .. .. Swaziland 23 116 58 .. 74 86 32 33 86 85 15 15 Sweden 95 100 100 71 100 99 96 96 100 99 1 3 Switzerland 102 102 95 51 84 94 84 83 99 99 4 2 Syrian Arab Republic 10 118 72 .. 91 93 39 67 100 98 2 16 Tajikistan 9 102 87 20 .. 97 63 85 99 96 2 13 Tanzania 33 102 27 2 51 98 5 .. 91 93 359 290 Thailand 100 c 91 79c 48 c .. 90 .. 74 c 90 89 304 307 Timor-Leste .. 117 56 17 .. 85 .. 37 86 86 14 14 Togo 9 140 .. .. 65 92 21 .. .. .. .. .. Trinidad and Tobago .. 105 90 .. 90 94 69 .. 97 94 2 4 Tunisia .. 109 90 34 94 98 64 .. .. .. .. .. Turkey 22 102 78 46 89 97 56 74 98 97 59 103 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 14 121 28 4 .. 91 13 .. 90 92 357 266 Ukraine 97 99 96 79 .. 91 91 86 91 91 73 64 United Arab Emirates .. .. .. .. 97 .. 75 .. .. .. .. .. United Kingdom 81 106 102 59 97 100 94 96 100 100 3 6 United States 69 102 96 95 97 95 87 89 96 98 498 247 Uruguay 89 113 90 63 91 99 .. 70 100 99 0b 1 Uzbekistan 26c 95c 106c 9c .. 90 c .. 92 94 c 91c 62c 86c Venezuela, RB 73 103 83 78 .. 93 48 72 95 95 91 80 Vietnam 82 106 77 22 .. 98 58 .. .. .. .. .. West Bank and Gaza 39 91 86 50 .. 87 75 84 90 88 23 25 Yemen, Rep. 1 87 44 .. .. 78 31 .. 86 70 290 568 Zambia .. 115 .. .. .. 91 16 .. 91 94 108 76 Zimbabwe .. .. .. 6 .. .. 40 .. .. .. .. .. World 50 w 107 w 68 w 27 w .. w 88 w 51 w 60 w 91 w 89 w Low income 12 104 39 7 .. 80 24 32 82 80 Middle income 52 109 69 24 .. 89 50 60 92 90 Lower middle income 50 107 58 16 .. 85 41 50 90 86 Upper middle income 51 111 83 33 .. 94 63 74 95 96 Low & middle income 46 108 64 21 .. 87 47 56 90 88 East Asia & Pacific 48 111 76 25 96 .. 56 .. .. .. Europe & Central Asia 55 98 89 55 90 92 78 81 94 93 Latin America & Carib. 71 117 90 37 .. 94 59 73 95 95 Middle East & N. Africa .. 102 72 27 .. 90 58 64 94 89 South Asia 54 110 55 11 68 86 .. .. 93 89 Sub-Saharan Africa 17 100 36 6 .. 75 19 27 78 74 High income 78 101 100 70 95 95 88 90 95 96 Euro area 107 105 107 60 .. 98 86 92 .. .. a. Provisional data. b. Less than 0.5. c. Data are for 2011. 88 2012 World Development Indicators 2.12 PEOPLE Participation in education About the data De�nitions 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 age 3, to a school-type environment and an education system has the capacity to meet the countries and crucial for achieving the Millennium to provide a bridge between the home and school. On needs of universal primary education. Development Goal of universal primary education. completing these programs, children continue their Enrollment indicators are based on annual school The reference years in the table reflect the school education at the primary level. • Primary education surveys, but do not necessarily reflect actual atten- year for which the data are presented. In some coun- (ISCED 1) refers to programs normally designed to dance or dropout rates during the year. Also, the tries the school year spans two calendar years (for give students a sound basic education in reading, length of primary education differs across coun- example, from September 2009 to June 2010); in writing, and mathematics along with an elementary tries and can influence enrollment rates and ratios, these cases the reference year refers to the year in understanding of other subjects such as history, although the International Standard Classification of which the school year ended (2010 in the example). 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 Overage or underage 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 education be inaccurately estimated or misstated, especially instruction is often organized even more along sub- in communities where registration of births is not ject lines, and teachers typically need a higher or strictly enforced. more subject-specific qualification. • Tertiary edu- Population data used to calculate population- cation 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 (ISCED 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 overage children research and leading to an advanced research quali- enrolled in each grade because of repetition or late fication. • Net enrollment rate is the ratio of total entry rather than a successful education system. enrollment of children of official school age to the The net enrollment rate excludes overage and under- population of the age group that officially corresponds age students and more accurately captures the sys- to the level of education shown. • Adjusted net enroll- tem’s coverage and internal efficiency. Differences ment rate, primary, is the ratio of total enrollment of between the gross enrollment ratio and the net children of official school age for primary education enrollment rate show the incidence of overage and who are enrolled in primary or secondary education to underage enrollments. the total primary school–age population. • Children The adjusted net enrollment rate in primary educa- out of school are the number of primary school–age tion captures primary school–age children who have children not enrolled in primary or secondary school. progressed to secondary education faster than their Data sources peers have and who are not counted in the traditional net enrollment rate. Data on gross enrollment ratios, net enrollment Data on children out of school (primary school– rates, and out of school children are from the age children not enrolled in primary or secondary UNESCO Institute for Statistics. school—dropouts, children never enrolled, and 2012 World Development Indicators 89 2.13 Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in �rst grade of survival rate primary education secondary education primary education % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2010a 2010a 1991 2009a 1991 2009a 2009a 2009a 2010a 2010a 2009a 2009a Afghanistan 124 91 89 .. 89 .. .. .. .. .. .. .. Albania 87 87 .. 95 .. 95 95 95 1 1 97 97 Algeria 107 105 82 93 79 97 93 97 9 6 90 92 Angola 182 148 .. 53 .. 37 37 27 10 12 26 45 Argentina 114 115 .. 96 .. 95 .. .. 6 4 96 97 Armenia 91 93 .. .. .. .. .. .. 0 0 100 98 Australia .. .. 98 .. 99 .. .. .. .. .. .. .. Austria 103 101 .. .. .. .. 96 99 0 0 100 100 Azerbaijan 91 88 .. .. .. .. 100 97 0 0 98 99 Bahrain .. .. 88 98 87 98 98 98 2 2 98 99 Bangladesh 110 115 .. 62 .. 71 67 66 13 12 .. .. Belarus 96 96 .. .. .. .. 96 100 0 0 98 100 Belgium 94 95 87 96 90 97 86 88 3 3 99 97 Benin 159 147 30 62 31 59 .. .. 13 13 .. .. Bolivia 113 111 57 86 51 85 85 82 1 1 89 91 Bosnia and Herzegovina 99 100 .. 73 .. 73 73 73 0 0 .. .. Botswana .. .. 73 .. 81 .. .. .. .. .. .. .. Brazil .. .. .. .. .. .. .. .. .. .. .. .. Bulgaria 100 99 .. .. .. .. 94 94 2 1 95 96 Burkina Faso 91b 86b 61 73 58 78 71 72 10 10 43c 62c Burundi 164 158 66 59 61 66 56 64 34 34 41 31 Cambodia 143 144 .. 60 .. 65 52 57 10 8 80 81 Cameroon 144 123 67 76 66 77 67 65 14 13 42 45 Canada 99 98 .. .. .. .. .. .. 0 0 .. .. Central African Republic 118 96 52 61 39 50 53 39 21 21 43 45 Chad 134 104 43 32 22 32 24 23 23 26 77 66 Chile 97 96 .. .. .. .. .. .. 1 1 84 98 China 95 99 .. .. .. .. .. .. 0 0 .. .. Hong Kong SAR, China 114 119 .. 100 .. 100 .. .. 1 1 100 100 Colombia 113 107 53 84 59 85 .. .. 2 2 97 96 Congo, Dem. Rep. 117 105 66 62 55 58 78 73 14 14 83 76 Congo, Rep. 109 108 66 75 68 79 71 71 20 18 69 67 Costa Rica 98 98 70 90 73 92 88 90 7 5 93 89 Côte d’Ivoire 88 b 78b 68 66 61 66 .. .. 19 19 47 45 Croatia 92 92 .. .. .. .. 97 99 0 0 100 99 Cuba 98 98 .. 97 .. 97 96 95 1 0 98 99 Cyprus 107 106 96 94 97 97 .. .. 0 0 100 100 Czech Republic 107 108 .. 99 .. 100 99 100 1 1 99 99 Denmark 100 100 98 100 99 100 99 100 0 0 99 100 Dominican Republic 113 101 .. .. .. .. .. .. 9 5 82 90 Ecuador .. .. .. .. .. .. .. .. .. .. .. .. Egypt, Arab Rep. 105 103 .. .. .. .. .. .. 4 2 .. .. El Salvador 117 110 54 89 57 90 86 87 7 5 95 94 Eritrea 44 40 .. 71 .. 67 71 67 16 13 82 82 Estonia 100 100 .. 99 .. 99 98 99 1 0 99 98 Ethiopia 145 129 .. 50 .. 51 47 48 4 4 91 87 Finland 99 99 96 100 97 100 100 99 1 0 100 100 France .. .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. 47 .. 46 .. .. .. .. .. .. .. Gambia, The 88 88 59 67 53 63 .. .. 6 5 80 82 Georgia 100 102 .. 94 .. 99 94 99 0 0 100 100 Germany 100 99 .. .. .. .. 98 99 1 1 99 99 Ghana 109b 111b 72 80 65 77 .. .. 2b 3b 91 92 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 131 131 .. 71 .. 70 65 64 12 10 93 90 Guinea 112 96 43 74 35 62 74 56 16 18 62 51 Guinea-Bissau 169 164 .. .. .. .. .. .. 14 14 .. .. Haiti .. .. 47 .. 46 .. .. .. .. .. .. .. 90 2012 World Development Indicators 2.13 PEOPLE Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in �rst grade of survival rate primary education secondary education primary education % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2010a 2010a 1991 2009a 1991 2009a 2009a 2009a 2010a 2010a 2009a 2009a Honduras 125 120 50 75 43 80 74 79 1 1 .. .. Hungary 103 102 .. .. .. .. 98 98 2 2 97 98 India 129 125 .. .. .. .. .. .. .. .. 81 81 Indonesia 121 118 .. 83 .. 89 .. .. 4 3 91 93 Iran, Islamic Rep. 107 108 75 94 67 94 94 94 2 2 96 97 Iraq .. .. 75 .. 70 .. .. .. .. .. .. .. Ireland 106 107 .. 99 .. 100 .. .. 1 1 .. .. Israel 98 101 .. 100 .. 98 100 98 2 1 70 69 Italy 101 100 .. 99 .. 100 99 100 0 0 100 100 Jamaica 81 78 92 96 94 96 94 96 3 2 92 91 Japan 103 103 100 100 100 100 100 100 0 0 .. .. Jordan 98 98 93 .. 89 .. .. .. 1 1 99 99 Kazakhstan 112b 111b .. .. .. .. .. .. 0b 0b 100 c 100 c Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 100 98 92 99 92 99 99 99 0 0 100 100 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 101 103 .. 96 .. 96 96 96 1 1 99 99 Kyrgyz Republic 106 104 .. .. .. .. 98 97 0 0 100 99 Lao PDR 136 126 34 66 32 68 .. .. 18 16 80 77 Latvia 100 102 .. 96 .. 96 95 95 3 2 94 98 Lebanon 108 106 .. 90 .. 91 .. .. 9 7 84 91 Lesotho 103 94 53 76 77 85 .. .. 23 17 75 73 Liberia 120 112 .. 64 .. 56 49 43 6 7 64 60 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 93 94 .. .. .. .. 98 99 1 0 99 99 Macedonia, FYR 97 97 .. .. .. .. .. .. 0 0 98 98 Madagascar 184 184 31 34 31 35 .. .. 21 19 65 63 Malawi 150 159 37 60 33 62 63 57 19 19 78 76 Malaysia .. .. 86 98 87 98 97 98 .. .. 100 99 Mali 82b 76b 48 89c 42 87c 81 77 13b 13b 74 72 Mauritania 104 107 52 74 47 75 71 70 3 4 38 31 Mauritius 95 99 .. 99 .. 97 94 98 4 3 65 76 Mexico 117 117 81 95 82 97 93 95 4 3 95 94 Moldova 98 97 .. .. .. .. 95 96 0 0 99 98 Mongolia 145 138 .. 93 .. 95 .. .. 0 0 96 98 Morocco 110 b 109b 70 94 64 94 78 78 13 9 84 c 80 c Mozambique 168 159 42 56 34 51 37 34 8 7 52 55 Myanmar 152 151 .. 72 .. 77 .. .. 0 0 77 77 Namibia 93 95 52 90 57 93 .. .. 18 14 80 83 Nepal .. .. 44 60 32 64 .. .. 12b 12b 81 81 Netherlands 100 99 .. 99 .. 100 .. .. .. .. .. .. New Zealand .. .. 96 .. 95 .. .. .. .. .. .. .. Nicaragua 146 138 39 48 48 55 .. .. 9 7 .. .. Niger 100 b 90 b 68 74 c 65 69c 63 60 4b 4b 63c 59c Nigeria 93 83 .. 84 .. 90 77 83 .. .. .. .. Norway 97 98 99 100 100 99 100 99 .. .. 100 100 Oman 108 103 77 .. 78 .. .. .. 1 2 .. .. Pakistan 129 108 .. 64 .. 59 64 59 5 4 73 74 Panama 103 101 .. 95 .. 94 94 94 7 4 98 96 Papua New Guinea .. .. 55 .. 52 .. .. .. .. .. .. .. Paraguay 101 98 58 81 60 84 76 80 6 4 89 89 Peru 103 102 .. .. .. .. 88 88 7 6 96 93 Philippines 129 121 .. 75 .. 82 72 80 3 2 99 97 Poland 99 99 .. 98 .. 98 97 98 1 1 99 98 Portugal 103 103 .. .. .. .. .. .. .. .. .. .. Puerto Rico 97 94 .. .. .. .. .. .. .. .. .. .. Qatar 107 107 98 92 99 99 91 97 0 0 99 99 2012 World Development Indicators 91 2.13 Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in �rst grade of survival rate primary education secondary education primary education % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2010a 2010a 1991 2009a 1991 2009a 2009a 2009a 2010a 2010a 2009a 2009a Romania 97 95 .. .. .. .. 95 96 2 1 98 97 Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 186 182 49 45 51 50 .. .. 14 14 73 72 Saudi Arabia 104 106 80 97 76 91 97 90 3 3 91 97 Senegal 100 106 78 73 68 75 58 61 6 6 71 66 Serbia 92 92 .. .. .. .. 98 99 1 0 97 99 Sierra Leone 133b 121b .. .. .. .. .. .. 15b 16b .. .. Singapore .. .. .. 99 .. 99 99 99 0 0 86 92 Slovak Republic 95 95 .. .. .. .. 98 98 3 3 97 97 Slovenia 99 98 .. 100 .. 99 100 99 1 0 99 98 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 94 88 61 .. 67 .. .. .. .. .. .. .. South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 102 102 .. 99 .. 100 99 100 3 2 92 95 Sri Lanka 94 95 97 .. 98 .. .. .. 1 1 95 97 Sudan 83 75 .. 89 .. 100 .. .. 4 4 96 92 Swaziland 123 113 58 95 64 97 81 87 17 13 90 92 Sweden 104 103 99 99 99 99 99 99 0 0 100 100 Switzerland 92 94 72 .. 72 .. .. .. 2 1 .. .. Syrian Arab Republic 116 117 87 .. 85 .. 94 95 9 6 94 95 Tajikistan 102 98 .. .. .. .. 99 99 0 0 99 98 Tanzania 96 97 69 87 71 93 76 87 3 2 45 37 Thailand .. .. .. .. .. .. .. .. .. .. .. .. Timor-Leste 141 141 .. 68 .. 74 63 70 19 15 84 87 Togo 157 150 55 78 38 77 .. .. 22 22 73 67 Trinidad and Tobago 104 101 98 90 99 94 87 92 7 5 87 89 Tunisia 106 106 76 95 70 97 .. .. 8 5 81 87 Turkey 99 97 93 91 92 93 .. .. 2 2 97 97 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 153 157 .. 56 .. 58 .. .. 11 11 60 58 Ukraine 103 104 .. .. .. .. .. .. 0 0 100 100 United Arab Emirates .. .. 78 .. 80 .. .. .. 2 2 94 99 United Kingdom .. .. .. .. .. .. .. .. 0 0 .. .. United States 101 98 .. 98 .. 89 .. .. 0 0 .. .. Uruguay 106 106 98 94 100 97 94 97 7 4 75 87 Uzbekistan 97b 94b .. .. .. .. 98 c 98 c 0b 0b 100 c 98 c Venezuela, RB 100 98 69 93 80 95 90 94 5 3 96 97 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 91 91 .. .. .. .. .. .. 0 0 96 98 Yemen, Rep. 109 96 .. .. .. .. .. .. 7 6 .. .. Zambia 114 117 .. 71 .. 70 55 52 6 6 65 68 Zimbabwe .. .. 70 .. 72 .. .. .. .. .. .. .. World 116 w 113 w .. w .. w .. w .. w .. w .. w .. w .. w Low income 130 123 .. 62 .. 63 11 11 .. .. Middle income 114 111 .. .. .. .. .. .. .. .. Lower middle income 122 116 .. .. .. .. .. .. 82 83 Upper middle income 99 101 .. .. .. .. 2 1 .. .. Low & middle income 117 114 .. .. .. .. .. .. .. .. East Asia & Pacific 99 102 .. .. .. .. 1 1 .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. Latin America & Carib. .. .. .. .. .. .. .. .. .. .. Middle East & N. Africa 105 104 .. .. .. .. 7 4 .. .. South Asia 127 121 .. .. .. .. .. .. 80 80 Sub-Saharan Africa 122 114 .. 67 .. 68 .. .. .. .. High income 101 103 .. .. .. .. 0 0 .. .. Euro area 101 100 .. .. .. .. 1 1 .. .. a. Provisional data. b. Data are for 2011. c. Data are for 2010. 92 2012 World Development Indicators 2.13 PEOPLE Education efficiency About the data De�nitions The United Nations Educational, Scientific, and Cul- low transition rate can signal such problems as an • Gross intake ratio in �rst grade of primary educa- tural Organization (UNESCO) Institute for Statistics inadequate examination and promotion system or tion is the number of new entrants in grade 1 regard- calculates indicators of students’ progress through insufficient secondary education capacity. The qual- less of age as a percentage of the population of school. These indicators measure an education sys- ity of data on the transition rate is affected when the official primary school entrance age. • Cohort tem’s success in reaching students and efficiently new entrants and repeaters are not correctly distin- survival rate is the percentage of children enrolled moving them from one grade to the next. guished in the first grade of secondary education. in the first grade of primary education who eventually The gross intake ratio in the first grade of primary Students who interrupt their studies after complet- reach grade 5 or the last grade of primary education. education indicates the level of access to primary ing primary education could also affect data quality. The estimate is based on the reconstructed cohort education and the education system’s capacity to The reference years in the table reflect the school method (see About the data). • Repeaters in primary provide access to primary education. A low gross year for which the data are presented. In some coun- education are the number of students enrolled in the intake ratio in the first grade of primary education tries the school year spans two calendar years (for same grade as in the previous year as a percentage reflects the fact that many children do not enter pri- example, from September 2009 to June 2010); in of all students enrolled in primary education. • Tran- mary education even though school attendance, at these cases the reference year refers to the year in sition rate to secondary education is the number least through the primary level, is mandatory in most which the school year ended (2010 in the example). of new entrants to the first grade of secondary edu- countries. Because the gross intake ratio includes cation (general programs only) in a given year as a all new entrants regardless of age, it can exceed percentage of the number of students enrolled in the 100 percent in some situations, such as immediately final grade of primary education in the previous year. after fees have been abolished or when the number of reenrolled children is large. The indicator is not calculated when new entrants and repeaters are not correctly distinguished in the first grade of primary education. The cohort survival rate to grade 5 and to the last grade of primary education shows the percentage of students entering primary school who are expected to reach the specified grade. It measures an educa- tion system’s holding power and internal efficiency. Cohort survival rates are calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years to reflect current patterns of grade transition. Rates approaching 100 percent indicate high retention and low dropout levels. Data on repeaters are often used to indicate an education system’s internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources. Country policies on repetition and promo- tion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion—suggesting a system that is highly efficient but that may not be endowing stu- dents with enough cognitive skills. The transition rate from primary to secondary education conveys the degree of access or tran- sition between the two levels. As completing pri- Data sources mary education is a prerequisite for participating in lower secondary education, growing numbers of Data on education efficiency are from the UNESCO primary completers will inevitably create pressure Institute for Statistics. for more available places at the secondary level. A 2012 World Development Indicators 93 2.14 Education completion and outcomes Primary completion Youth literacy Adult Students rate rate literacy at lowest rate pro�ciency on PISA mathematics % of relevant age group % ages 15–24 % ages 15 and older % Total Male Female Male Female 1991 2010a 1991 2010a 1991 2010a 1985–94b 2005–10b 1985–94b 2005–10b 2005–10b 2009 Afghanistan 28 .. 41 .. 14 .. .. .. .. .. .. .. Albania .. 86 .. 86 .. 86 .. 99 .. 99 96 40 Algeria 80 96 86 96 73 96 86 94 62 89 73 .. Angola 33 47 .. 53 .. 40 .. 81 .. 66 70 .. Argentina 100 106 .. 104 .. 108 98 99 99 99 98 37 Armenia 105 101 .. 100 .. 103 100 100 100 100 100 .. Australia .. .. .. .. .. .. .. .. .. .. .. 5 Austria .. 99 .. 99 .. 100 .. .. .. .. .. 8 Azerbaijan 95 90 96 90 94 89 .. 100 .. 100 100 11 Bahrain 97 .. 96 .. 98 .. 97 100 97 100 91 .. Bangladesh 41 65 .. 62 .. 69 52 74 38 77 56 .. Belarus 94 103 95 95 95 95 100 100 100 100 100 .. Belgium 79 90 76 89 82 92 .. .. .. .. .. 8 Benin 22 63 30 74 14 53 55 65 27 43 42 .. Bolivia 71 99 78 100 64 99 96 99 92 99 91 .. Bosnia and Herzegovina .. 70 .. 69 .. 71 .. 100 .. 100 98 .. Botswana 90 94 83 92 98 96 86 94 92 97 84 .. Brazil 93 .. .. .. .. .. .. 97 .. 99 90 38 Bulgaria 90 95 88 95 92 96 .. 98 .. 97 98 24 Burkina Faso 20 45 25 48 15 42 27 47 14 33 29 .. Burundi 46 56 49 57 43 55 59 77 48 76 67 .. Cambodia 45 87 .. 87 .. 87 .. 89 .. 86 78 .. Cameroon 53 79 57 85 49 72 .. 89 .. 77 71 .. Canada .. .. .. .. .. .. .. .. .. .. .. 3 Central African Republic 28 41 37 52 20 30 63 72 35 57 55 .. Chad 18 33 29 41 7 24 26 54 9 39 34 .. Chile .. 96 .. 102 .. 89 98 99 99 99 99 22 China 107 .. .. .. .. .. 97 99 91 99 94 .. Hong Kong SAR, China 102 96 .. 95 .. 96 .. .. .. .. .. 3 Colombia 73 114 70 113 76 115 89 97 92 98 93 39 Congo, Dem. Rep. 48 59 61 67 36 50 .. 73 .. 62 67 .. Congo, Rep. 54 71 59 73 49 69 .. 87 .. 78 .. .. Costa Rica 79 96 77 95 81 97 .. 98 .. 99 96 .. Côte d’Ivoire 42 59 c 53 65c 32 52c 60 72 38 61 55 .. Croatia 85 95 .. 95 .. 95 100 100 100 100 99 12 Cuba 99 98 .. 98 .. 99 .. 100 .. 100 100 .. Cyprus 90 103 89 103 90 103 100 100 100 100 98 .. Czech Republic 92 101 91 101 93 101 .. .. .. .. .. 7 Denmark 98 97 98 97 98 98 .. .. .. .. .. 5 Dominican Republic 61 92 .. 93 .. 91 .. 95 .. 97 88 .. Ecuador 91 106 91 105 92 106 97 97 96 97 84 .. Egypt, Arab Rep. .. 98 .. 100 .. 97 71 88 54 82 66 .. El Salvador 65 96 64 96 66 96 85 95 85 95 84 .. Eritrea 18 40 21 43 15 36 .. 92 .. 86 67 .. Estonia .. 98 .. 97 .. 98 100 100 100 100 100 3 Ethiopia 23 72 28 75 18 69 39 56 28 33 30 .. Finland 97 98 98 98 97 97 .. .. .. .. .. 2 France 106 .. .. .. .. .. .. .. .. .. .. 9 Gabon 62 .. 59 .. 65 .. 94 99 92 97 88 .. Gambia, The 45 71 56 69 34 72 .. 71 .. 60 46 .. Georgia .. 116 .. 116 .. 116 .. 100 .. 100 100 .. Germany 100 100 99 100 100 100 .. .. .. .. .. 6 Ghana 64 94 c 71 97c 56 91c .. 81 .. 79 67 .. Greece 99 101 99 101 98 100 99 99 99 99 97 11 Guatemala .. 84 .. 87 .. 81 82 89 71 84 74 .. Guinea 17 64 24 75 9 53 .. 68 .. 54 39 .. Guinea-Bissau 5 68 7 75 3 60 .. 78 .. 64 52 .. Haiti 27 .. 29 .. 26 .. .. .. .. .. 49 .. 94 2012 World Development Indicators 2.14 PEOPLE Education completion and outcomes Primary completion Youth literacy Adult Students rate rate literacy at lowest rate pro�ciency on PISA mathematics % of relevant age group % ages 15–24 % ages 15 and older % Total Male Female Male Female 1991 2010a 1991 2010a 1991 2010a 1985–94b 2005–10b 1985–94b 2005–10b 2005–10b 2009 Honduras 64 99 67 96 61 102 .. 93 .. 95 84 .. Hungary 82 98 89 98 90 97 99 99 99 99 99 8 India 64 96 76 96 52 95 74 88 49 74 63 .. Indonesia 93 105 .. 104 .. 105 97 100 95 99 92 44 Iran, Islamic Rep. 88 104 93 104 82 104 92 99 81 99 85 .. Iraq 58 65 63 74 52 55 .. 85 .. 80 78 .. Ireland 103 .. 103 .. 103 .. .. .. .. .. .. 7 Israel .. 103 .. 102 .. 105 .. .. .. .. .. 21 Italy 98 103 98 103 97 103 .. 100 .. 100 99 9 Jamaica 94 73 90 74 98 73 .. 92 .. 98 86 .. Japan 102 102 102 103 102 102 .. .. .. .. .. 4 Jordan 101 101 101 101 101 101 .. 99 .. 99 92 35 Kazakhstan 103 116c 103 116c 103 116c 100 100 100 100 100 30 Kenya .. .. .. .. .. .. .. 92 .. 94 87 .. Korea, Dem. Rep. .. .. .. .. .. .. .. 100 .. 100 100 .. Korea, Rep. 99 101 99 100 100 101 .. .. .. .. .. 2 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 57 112 58 110 56 114 91 99 84 99 94 .. Kyrgyz Republic .. 97 .. 96 .. 97 .. 100 .. 100 99 65 Lao PDR 41 79 46 83 36 75 .. 89 .. 79 73 .. Latvia .. 92 .. 94 .. 90 100 100 100 100 100 6 Lebanon .. 87 .. 85 .. 89 .. 98 .. 99 90 .. Lesotho 59 70 42 60 76 79 .. 86 .. 98 90 .. Liberia .. 62 .. 67 .. 57 66 70 54 81 59 .. Libya .. .. .. .. .. .. 99 100 96 100 89 .. Lithuania .. 96 .. 97 .. 94 100 100 100 100 100 9 Macedonia, FYR 98 92 .. 92 .. 93 99 99 99 99 97 .. Madagascar 36 72 35 72 37 73 .. 66 .. 64 64 .. Malawi 31 67 35 65 27 68 70 87 49 86 74 .. Malaysia 91 .. 91 .. 91 .. 96 98 95 99 92 .. Mali 9 55c 12 61c 7 50 c .. 47 .. 31 26 .. Mauritania 33 75 39 74 26 76 .. 71 .. 64 57 .. Mauritius 115 96 115 96 115 96 91 96 92 98 88 .. Mexico 88 104 91 104 92 104 96 99 95 98 93 22 Moldova .. 93 .. 94 .. 91 100 99 100 100 98 .. Mongolia .. 108 .. 107 .. 109 .. 95 .. 97 97 .. Morocco 48 85 57 87 39 82 71 87 46 72 56 .. Mozambique 26 61 32 66 21 55 .. 78 .. 64 55 .. Myanmar .. 104 .. 101 .. 106 .. 96 .. 95 92 .. Namibia 74 84 67 80 81 88 86 91 90 95 89 .. Nepal 51 .. 70 .. 41 .. 68 87 33 77 59 .. Netherlands .. .. .. .. .. .. .. .. .. .. .. 3 New Zealand .. .. .. .. .. .. .. .. .. .. .. 5 Nicaragua 42 81 43 78 53 84 .. 85 .. 89 78 .. Niger 17 46c 21 52c 13 40 c .. 52 .. 23 29 .. Nigeria .. 74 .. 79 .. 70 81 78 62 65 61 .. Norway 100 100 100 101 100 100 .. .. .. .. .. 6 Oman 74 101 78 102 70 100 .. 98 .. 98 87 .. Pakistan .. 67 .. 75 .. 59 .. 79 .. 61 56 .. Panama 86 97 86 97 86 97 95 97 95 96 94 51 Papua New Guinea 46 .. 51 .. 42 .. .. 65 .. 70 60 .. Paraguay 68 94 68 92 69 95 96 99 95 99 95 .. Peru .. 102 .. 102 .. 102 97 98 94 97 90 48 Philippines 88 92 85 89 86 94 96 97 97 98 95 .. Poland 96 95 .. 95 .. 95 100 100 100 100 100 6 Portugal .. .. .. .. .. .. 99 100 99 100 95 8 Puerto Rico .. .. .. .. .. .. 92 87 94 88 90 .. Qatar 71 100 71 99 72 100 89 98 91 98 95 51 2012 World Development Indicators 95 2.14 Education completion and outcomes Primary completion Youth literacy Adult Students rate rate literacy at lowest rate pro�ciency on PISA mathematics % of relevant age group % ages 15–24 % ages 15 and older % Total Male Female Male Female 1991 2010a 1991 2010a 1991 2010a 1985–94b 2005–10b 1985–94b 2005–10b 2005–10b 2009 Romania 96 91 96 91 96 91 99 97 99 98 98 20 Russian Federation 92 98 92 .. 93 .. 100 100 100 100 100 10 Rwanda 50 70 51 65 50 74 75 77 75 77 71 .. Saudi Arabia .. 93 .. 94 .. 92 94 99 81 97 86 .. Senegal 39 59 48 58 31 61 49 74 28 56 50 .. Serbia .. 96 .. 96 .. 97 .. .. .. .. .. 18 Sierra Leone .. 74 c .. 78 c .. 71c .. 68 .. 48 41 .. Singapore .. .. .. .. .. .. 99 100 99 100 95 3 Slovak Republic 95 98 95 98 96 99 .. .. .. .. .. 7 Slovenia 95 95 .. 95 .. 95 100 100 100 100 100 7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 76 .. 72 .. 80 .. .. 97 .. 98 89 .. South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 104 102 104 102 103 102 100 100 100 100 98 9 Sri Lanka 101 101 101 101 101 101 .. 97 .. 99 91 .. Sudan .. 58 .. 61 .. 55 .. .. .. .. .. .. Swaziland 61 77 57 76 64 78 83 92 84 95 87 .. Sweden 96 94 96 93 96 94 .. .. .. .. .. 8 Switzerland 53 95 53 94 54 97 .. .. .. .. .. 4 Syrian Arab Republic 89 104 94 104 84 103 .. 96 .. 93 84 .. Tajikistan .. 104 .. 106 .. 102 100 100 100 100 100 .. Tanzania 55 90 56 88 55 92 86 78 78 76 73 .. Thailand .. .. .. .. .. .. .. 98 .. 98 94 22 Timor-Leste .. 65 .. 64 .. 67 .. .. .. .. 51 .. Togo 35 74 48 84 22 64 .. 85 .. 68 57 .. Trinidad and Tobago 102 91 99 91 105 91 99 100 99 100 99 30 Tunisia 74 91 79 90 70 92 .. 98 .. 96 78 43 Turkey 90 99 93 100 86 98 97 99 88 97 91 18 Turkmenistan .. .. .. .. .. .. .. 100 .. 100 100 .. Uganda .. 57 .. 58 .. 56 77 90 63 85 73 .. Ukraine 92 98 99 97 99 98 .. 100 .. 100 100 .. United Arab Emirates 103 .. 104 .. 103 .. 81 94 85 97 90 .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. 6 United States .. 104 .. 103 .. 104 .. .. .. .. .. 8 Uruguay 94 106 91 105 96 106 98 98 99 100 98 23 Uzbekistan 80 93c .. 94 c .. 92c .. 100 .. 100 99 .. Venezuela, RB 81 94 76 93 86 95 95 98 96 99 95 .. Vietnam .. .. .. .. .. .. 94 97 93 96 93 .. West Bank and Gaza .. 95 .. 97 .. 93 .. 99 .. 99 95 .. Yemen, Rep. .. 63 .. 73 .. 53 83 96 35 72 62 .. Zambia .. 103 .. 98 .. 108 67 82 66 67 71 .. Zimbabwe 97 .. 99 .. 96 .. 97 98 94 99 92 .. World 79 w 88 w 86 w 90 w 75 w 87 w 88 w 92 w 79 w 87 w 84 w Low income 44 65 .. 68 .. 63 66 75 52 68 61 Middle income 83 92 89 93 77 91 89 94 79 88 83 Lower middle income 68 88 76 90 60 86 87 89 75 79 71 Upper middle income 97 98 101 96 94 99 94 99 93 99 93 Low & middle income 78 87 85 89 73 86 86 91 75 85 80 East Asia & Pacific 101 97 105 96 97 98 97 99 92 99 94 Europe & Central Asia 92 95 93 96 92 94 99 99 98 99 98 Latin America & Carib. 84 102 84 101 85 102 91 97 92 97 91 Middle East & N. Africa .. 88 .. 91 .. 85 84 93 67 87 74 South Asia 62 86 75 87 52 84 71 85 47 72 61 Sub-Saharan Africa 51 67 57 71 47 63 73 77 58 67 62 High income .. 97 .. 98 .. 97 99 99 99 99 98 Euro area 101 101 100 100 100 101 .. .. .. .. .. a. Provisional data. b. Data are for the most recent year available. c. Data are for 2011. 96 2012 World Development Indicators 2.14 PEOPLE Education completion and outcomes About the data De�nitions Many governments publish statistics that indicate how different lengths of school attendance or levels of • Primary completion rate, or the gross intake ratio their education systems are working and developing— completion. Because definitions and methodolo- to last grade of primary education, is the number of statistics on enrollment and such efficiency indicators gies of data collection differ across countries, data new entrants in the last grade of primary education, as repetition rates, pupil–teacher ratios, and cohort should be used cautiously. regardless of age, divided by the population at the progression. The World Bank and the United Nations The reported literacy data are compiled by the entrance age for the last grade of primary education. Educational, Scientific, and Cultural Organization UNESCO Institute for Statistics based on national • Youth literacy rate is the percentage of the popula- (UNESCO) Institute for Statistics jointly developed the censuses and household surveys during 1985– tion ages 15–24 that can, with understanding, both primary completion rate indicator. Increasingly used 2010. For countries without recent literacy data, the read and write a short simple statement about their as a core indicator of an education system’s perfor- UNESCO Institute for Statistics estimates literacy everyday life. • Adult literacy rate is the percentage mance, it reflects an education system’s coverage rates with the Global Age-Specific Literacy Projection of the population ages 15 and older that can, with and the educational attainment of students. The indi- Model. For detailed information on sources, defini- understanding, both read and write a short simple cator is a key measure of education outcome at the tions, and methodology, see www.uis.unesco.org. statement about their everyday life. • Students at primary level and of progress toward the Millennium Literacy statistics for most countries cover the pop- lowest pro�ciency on PISA mathematics is the per- Development Goals and the Education for All initia- ulation ages 15 and older, but some include younger centage of students whose mathematics score are tive. However, a high primary completion rate does ages or are confined to age ranges that tend to inflate below 357.77 (level 1) on the PISA. not necessarily mean high levels of student learning. literacy rates. The youth literacy rate for ages 15–24 The primary completion rate reflects the primary reflects recent progress in education. It measures cycle as defined by the International Standard Clas- the accumulated outcomes of primary education over sification of Education (ISCED97), ranging from three the previous 10 years or so by indicating the pro- or four years of primary education (in a very small portion of the population who have passed through number of countries) to fi ve or six years (in most the primary education system and acquired basic countries) and seven (in a small number of countries). literacy and numeracy skills. Generally, literacy also The primary completion rate is also called the gross encompasses numeracy, the ability to make simple intake ratio to last grade of primary education. It is the arithmetic calculations. number of new entrants in the last grade of primary In many countries national assessments enable education, regardless of age, divided by the population ministries of education to monitor progress in learn- at the entrance age for the last grade of primary educa- ing outcomes. Of the handful of internationally or tion. Data limitations preclude adjusting for students regionally comparable assessments, one of the who drop out during the final year of primary education. largest is the Programme for International Student Thus this rate is a proxy that should be taken as an Assessment (PISA). Coordinated by the Organisation upper estimate of the actual primary completion rate. for Economic Co-operation and Development (OECD), There are many reasons why the primary comple- it measures the knowledge and skills of 15-year-olds, tion rate can exceed 100 percent. The numerator may the age at which students in most countries are near- include late entrants and overage children who have ing the end of their compulsory time in school. The repeated one or more grades of primary education as assessment tests reading, mathematical, and sci- well as children who entered school early, while the entific literacy in terms of general competencies— denominator is the number of children at the entrance that is, how well students can apply the knowledge age for the last grade of primary 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 percentage of students established literacy as an outcome indicator based at the lowest level of proficiency on the PISA math- on an internationally agreed definition. ematics scale. Student achievement is benchmarked The literacy rate is the percentage of the popu- in terms of levels of proficiency, ranging from level lation who can, with understanding, both read and 1 (lowest) to level 6 (highest), as demonstrated write a short, simple statement about their everyday through ability to analyze, reason, and communi- Data sources life. In practice, literacy is difficult to measure. To cate effectively while posing, solving, and interpret- estimate literacy using such a definition requires ing mathematical problems that involve quantita- Data on primary completion rates and literacy census or survey measurements under controlled tive, spatial, probabilistic, or other mathematical rates are from the UNESCO Institute for Statis- conditions. Many countries estimate the number of concepts. The average score is 496. Because the tics. Data on PISA mathematics results are from literate people from self-reported data. Some use figures are derived from samples, the data reflect a the OECD. educational attainment data as a proxy but apply small measure of statistical uncertainty. 2012 World Development Indicators 97 2.15 Education gaps by income and gender Survey Gross intake rate Gross primary Average years Primary Children year in �rst grade of participation rate of schooling completion rate out of school primary education % 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 0a 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 0a 0a Madagascar 2003– 04 250 153 118 145 3 8 42 141 77 77 33 3 Malawi 2006 234 207 133 169 5 7 30 80 49 52 0a 0a 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 0a 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 0a 0a 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 13 18 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 a. Less than 0.5. 98 2012 World Development Indicators 2.15 PEOPLE Education gaps by income and gender About the data De�nitions The data in the table describe basic information on facets of social exclusion. To that extent the index • Survey year is the year in which the underlying data school participation and educational attainment provides only a partial view of the multidimensional were collected. •  Gross intake rate in �rst grade by individuals in different socioeconomic groups concepts of poverty, inequality, and inequity. of primary education is the number of students in within countries. The data are from Demographic Creating one index that includes all asset indica- grade 1 regardless of age as a percentage of the and Health Surveys by Macro International with the tors limits the types of analysis that can be per- population of the official primary school entrance support of the U.S. Agency for International Devel- formed. In particular, the use of a unified index does age. These data may differ from those in table 2.13. opment, Multiple Indicator Cluster Surveys by the not permit a disaggregated analysis to examine •  Gross primary participation rate is the ratio of United Nations Children’s Fund (UNICEF), and Liv- which asset indicators have a more or less important total students attending primary school regardless ing Standards Measurement Studies by the World association with education status. In addition, some of age to the population of the age group that offi - Bank’s Development Economics Research Group. asset indicators may reflect household wealth better cially corresponds to primary education. • Average These large-scale household sample surveys, con- in some countries than in others—or reflect differ- years of schooling are the years of formal school- ducted periodically in developing countries, collect ent degrees of wealth in different countries. Taking ing received, on average, by youths and adults ages information on a large number of health, nutrition, such information into account and creating country- 15–19. •  Primary completion rate is the number and population measures as well as on respondents’ specific asset indexes with country-specific choices of students, regardless of age, in the last grade of social, demographic, and economic characteristics of asset indicators might produce a more effective primary school minus the number of repeaters in that using detailed questionnaires. The data presented and accurate index for each country. The asset index grade, divided by the number of students of official here draw on responses to individual and household used in the table does not have this flexibility. graduation age. These data differ from those in table questionnaires. The analysis was carried out for about 80  coun- 2.14 because the source is different. • Children out Typically, the surveys collect basic information on tries. The table shows the most recent estimates of school are children of official primary school age educational attainment and enrollment levels from for the poorest and richest quintiles by gender only; who are not attending primary or secondary educa- every household member ages 5 and older as part of the full set of estimates for all indicators, other tion. Children of official primary school age who are household socioeconomic characteristics. The sur- subgroups, including by urban and rural location, attending preprimary education are considered out veys are not intended for the collection of detailed and older data are available in the country reports of school. These data differ from those in table 2.12 education data; thus the education section of the (see Data sources). The data in the table differ from because the source is different. surveys is not as detailed as, for instance, the health data for similar indicators in preceding tables either section of the Demographic and Health Survey or because the indicator refers to a period a few years the Multiple Indicator Cluster Survey, and the data preceding the survey date or because the indica- obtained from them do not replace other data on tor definition or methodology is different. Findings education flows. Still, the education data provide should be used with caution because of measure- micro-level information on education that cannot be ment error inherent 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 household assets, including ownership of consumer items, features of the household 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 used to create break- points defining wealth quintiles, expressed as quin- Data sources tiles of individuals in the population. The selection of the asset index for defining socio- Data on education gaps by income and gender economic status was based on pragmatic rather are from an analysis using the ADePT Educa- than conceptual considerations: Demographic and tion software tool (http://go.worldbank.org/ Health Surveys do not collect consumption data but X385KNDXM0) of MEASURE  DHS Demographic do have detailed information on household owner- and Health Surveys by ICF International, Mul- ship of consumer goods and access to a variety of tiple Indicator Cluster Surveys by UNICEF, and goods and services. Like income or consumption, Living Standards Measurement Studies by the the asset index defines disparities primarily in eco- World Bank. Country reports and further updates nomic terms. It therefore excludes other possibilities are available at www.worldbank.org/education/ of disparities among groups, such as those based edstats/. on gender, education, ethnic background, or other 2012 World Development Indicators 99 2.16 Health systems Health Health workers Hospital expenditure beds External per 1,000 people Total Public Out of pocket resources Per capita Nurses and Community per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives health workers people 2010 2010 2010 2010 2010 2010 2005–10a 2005–10a 2005–10a 2005–10a Afghanistan 7.6b 11.7b 83.0 b 32.0 b 38b 44b 0.2 0.5 .. 0.4 Albania 6.5 39.0 60.8 1.8 241 577 1.2 3.9 .. 2.8 Algeria 4.2 77.9 20.9 0.0 178 330 1.2 1.9 .. .. Angola 2.9c 82.5c 17.5c 2.9c 123c 168 c .. .. .. 0.8 Argentina 8.1 54.6 29.9 0.1 742 1,287 3.2 .. .. 4.5 Armenia 4.4 40.6 55.1 14.3 133 239 3.8 4.8 .. 3.7 Australia 8.7d 68.0 d 20.5d 0.0 d 4,775d 3,441d 3.0 9.6 0.0 3.8 Austria 11.0 77.5 14.6 0.0 4,958 4,388 4.9 7.9 .. 7.7 Azerbaijan 5.9 20.3 69.5 0.8 332 579 3.8 8.3 .. 7.5 Bahrain 5.0 73.3 14.5 0.0 864 1,083 1.4 3.7 .. 1.8 Bangladesh 3.5 33.6 64.1 8.0 23 57 0.3 0.3 0.3 0.3 Belarus 5.6 77.7 19.8 0.5 320 786 5.2 13.1 .. 11.1 Belgium 10.7 74.7 20.2 0.0 4,618 4,025 3.0 0.5 .. 6.5 Benin 4.1 49.5 46.8 35.9 31 65 0.1 0.8 .. 0.5 Bolivia 4.8 62.8 28.7 5.3 97 233 .. .. .. 1.1 Bosnia and Herzegovina 11.1 61.4 38.6 1.8 499 972 1.6 5.0 .. 3.4 Botswana 8.3 72.5 8.1 18.3 615 1,145 0.3 2.8 0.5 1.8 Brazil 9.0 47.0 30.6 0.0 990 1,028 1.8 6.4 .. 2.4 Bulgaria 6.9 54.5 44.2 0.0 435 947 3.7 4.7 .. 6.6 Burkina Faso 6.7 51.0 36.2 22.9 40 93 0.1 0.7 0.1 0.4 Burundi 11.6c 38.2c 37.9c 45.8 c 21c 47c .. .. .. 1.9e Cambodia 5.6 37.2 40.4 23.9 45 121 0.2 0.9 .. 0.8 Cameroon 5.1c 29.6c 66.5c 13.2c 61c 122c .. .. .. 1.3 Canada 11.3 70.5 14.7 0.0 5,222 4,404 2.0 10.4 .. 3.2 Central African Republic 4.0 35.4 61.4 13.4 18 31 .. .. .. 1.0e Chad 4.5 25.0 72.5 7.9 31 62 .. .. .. 0.4 Chile 8.0 48.2 33.3 0.0 947 1,199 1.0 0.1 .. 2.1 China 5.1 53.6 36.6 0.1 221 379 1.4 1.4 0.8 4.2 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 7.6 72.7 19.5 0.0 472 713 0.1 0.6 .. 1.0 Congo, Dem. Rep. 7.9 42.5 35.9 32.7 16 27 .. .. .. 0.8 Congo, Rep. 2.5 46.7 53.3 4.1 72 104 0.1 0.8 .. 1.6 Costa Rica 10.9 68.1 27.8 0.6 811 1,242 .. .. .. 1.2 Côte d’Ivoire 5.3 21.6 77.5 9.8 60 98 0.1 0.5 .. 0.4 Croatia 7.8 84.9 14.5 0.0 1,067 1,514 2.6 5.3 .. 5.4 Cuba 10.6 91.5 8.5 0.0 607 431 6.7 9.1 .. 5.9 Cyprus 6.0 41.5 48.8 0.0 1,705 1,842 2.6 4.3 .. 3.8 Czech Republic 7.9 83.7 14.7 0.0 1,480 2,051 3.7 8.7 .. 7.1 Denmark 11.4 85.1 13.1 0.0 6,422 4,537 3.4 16.1 .. 3.5 Dominican Republic 6.2 43.4 37.2 0.7 323 578 .. .. .. 1.6 Ecuador 8.1 37.2 49.0 0.4 328 653 1.7 2.0 .. 1.5 Egypt, Arab Rep. 4.7 37.4 61.2 0.6 123 289 2.8 3.5 .. 1.7 El Salvador 6.9 61.7 33.9 1.9 237 450 1.6 0.4 .. 1.0 Eritrea 2.7c 48.2c 51.8 c 38.0 c 12c 16c .. .. .. 0.7e Estonia 6.0 78.7 19.6 60.7 853 1,226 3.3 6.6 .. 5.4 Ethiopia 4.9 53.5 37.2 39.4 16 51 0.0 0.2 0.3 0.2 Finland 9.0 75.1 18.8 0.0 3,984 3,281 2.9 24.0 .. 6.2 France 11.9 77.8 7.3 0.0 4,691 4,021 3.4 0.3 .. 6.9 Gabon 3.5c 52.9c 47.1c 2.4 c 302c 522c .. .. .. 6.3 Gambia, The 5.7 50.8 23.8 41.2 26 80 0.0 0.6 0.1 1.1e Georgia 10.1 23.6 68.3 2.8 272 522 4.8 3.2 .. 3.1 Germany 11.6 77.1 13.0 0.0 4,668 4,332 3.6 11.1 .. 8.2 Ghana 5.2 59.5 26.9 16.9 67 85 0.1 1.0 0.2 0.9e Greece 10.2 59.4 38.4 0.0 2,729 2,853 6.2 0.2 .. 4.8 Guatemala 6.9 35.8 53.9 1.7 196 325 .. .. .. 0.6 Guinea 4.9 11.3 88.1 10.8 23 56 0.1 0.0 .. 0.3 Guinea-Bissau 8.5c 10.0 c 66.4 c 23.3c 47c 100 c 0.0 0.6 .. 1.0 Haiti 6.9 21.4 40.2 38.3 46 76 .. .. .. 1.3 100 2012 World Development Indicators 2.16 PEOPLE Health systems Health Health workers Hospital expenditure beds External per 1,000 people Total Public Out of pocket resources Per capita Nurses and Community per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives health workers people 2010 2010 2010 2010 2010 2010 2005–10a 2005–10a 2005–10a 2005–10a Honduras 6.8 65.2 31.1 6.3 137 263 .. .. .. 0.8 Hungary 7.3 69.4 24.0 0.0 942 1,469 3.0 6.4 .. 7.1 India 4.1 29.2 61.2 1.2 54 132 0.6 1.0 0.0 0.9 Indonesia 2.6 49.1 38.3 1.3 77 112 0.3 2.0 .. 0.6 Iran, Islamic Rep. 5.6 40.1 57.8 0.0 317 836 0.9 1.4 .. 1.7 Iraq 8.4 c,f 81.2c,f 18.8 c,f 0.8 c,f 247c,f 340 c,f 0.7 1.4 .. 1.3 Ireland 9.2 69.2 15.2 0.0 4,242 3,704 3.2 15.7 .. 4.9 Israel 7.6 60.3 29.2 0.0 2,183 2,186 3.7 5.2 .. 3.5 Italy 9.5 77.6 19.6 0.0 3,248 3,022 3.5 0.3 .. 3.6 Jamaica 4.8 53.5 33.0 2.1 247 372 .. .. .. 1.9 Japan 9.5 82.5 14.3 0.0 4,065 3,204 2.1 4.1 .. 13.7 Jordan 8.0 67.7 25.1 3.7 357 448 2.5 4.0 .. 1.8 Kazakhstan 4.3 59.4 40.1 0.6 393 541 4.1 8.3 .. 7.6 Kenya 4.8 44.3 42.7 36.1 37 78 .. .. .. 1.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 6.9 59.0 31.4 0.0 1,439 2,023 2.0 5.3 .. 10.3 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 2.6 80.4 17.8 0.0 1,223 1,133 1.8 4.6 .. 2.0 Kyrgyz Republic 6.2 56.2 37.8 12.8 53 140 2.3 5.7 .. 5.1 Lao PDR 4.5 33.3 51.2 15.1 46 97 0.3 1.0 .. 0.7 Latvia 6.7 61.1 37.8 0.0 718 1,093 3.0 4.8 .. 6.4 Lebanon 7.0 39.2 44.7 4.7 651 980 3.5 2.2 .. 3.5 Lesotho 11.1 76.2 16.4 19.5 109 170 .. .. .. 1.3 Liberia 11.8 32.5 35.2 55.1 29 49 0.0 0.3 .. 0.8 Libya 3.9c 68.8 c 31.2c 0.6c 484 c 713c 1.9 6.8 .. 3.7 Lithuania 7.0 73.5 25.8 1.1 781 1,299 3.6 7.2 .. 6.8 Macedonia, FYR 7.1 63.8 35.9 0.8 317 791 2.6 0.6 .. 4.5 Madagascar 3.8 60.3 27.1 9.0 16 36 0.2 .. .. 0.2 Malawi 6.6 60.2 11.1 63.8 26 65 0.0 0.3 0.7 1.3e Malaysia 4.4 55.5 34.2 0.0 368 641 0.9 2.7 .. 1.8 Mali 5.0 46.6 53.2 27.4 32 56 0.0 0.3 .. 0.1 Mauritania 4.4 c 53.1c 44.3c 10.1c 43c 79c 0.1 0.7 .. 0.4 Mauritius 6.0 41.7 51.7 2.0 449 803 .. .. .. 3.4 e Mexico 6.3 48.9 47.1 0.0 604 959 2.0 .. .. 1.6 Moldova 11.7g 45.8g 44.9g 9.6g 190 g 360 g 2.7 6.6 .. 6.2 Mongolia 5.4 55.1 41.4 3.9 120 218 2.8 3.5 0.0 5.8 Morocco 5.2 38.0 53.6 0.4 148 246 0.6 0.9 .. 1.1 Mozambique 5.2 71.7 13.7 24.2 21 49 0.0 0.3 .. 0.8 Myanmar 2.0 12.2 81.1 8.7 17 34 0.5 0.8 0.1 0.6 Namibia 6.8 58.4 7.4 19.0 361 436 0.4 2.8 .. 2.7 Nepal 5.5 33.2 48.3 11.3 30 66 .. .. .. 5.0 Netherlands 11.9 79.2 5.2 0.0 5,593 5,038 2.9 0.2 .. 4.7 New Zealand 10.1 83.2 10.5 0.0 3,279 3,020 2.7 10.9 .. .. Nicaragua 9.1 53.3 43.3 14.6 103 253 .. .. .. 0.8 Niger 5.2 50.9 41.3 29.4 18 37 0.0 0.1 .. 0.3 Nigeria 5.1c 37.9c 59.2c 9.2c 63c 121c 0.4 1.6 0.1 .. Norway 9.5 83.9 15.3 0.0 8,091 5,426 4.2 31.9 .. 3.3 Oman 2.8 80.1 12.3 0.0 574 598 1.9 4.1 .. 1.8 Pakistan 2.2 38.5 50.5 4.8 22 59 0.8 0.6 0.1 0.6 Panama 8.1 75.1 19.9 0.1 616 1,123 .. .. .. 2.2 Papua New Guinea 3.6 71.5 15.9 24.0 49 88 0.1 0.5 0.6 .. Paraguay 5.9 36.4 57.1 2.4 163 302 .. .. .. 1.3 Peru 5.1 54.0 39.5 1.7 269 481 0.9 1.3 .. 1.5 Philippines 3.6 35.3 54.0 1.3 77 142 .. .. .. 0.5 Poland 7.5 72.6 22.1 0.1 917 1,476 2.2 5.8 .. 6.7 Portugal 11.0 68.1 24.8 0.0 2,367 2,818 3.9 5.3 .. 3.3 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar 1.8 77.5 16.0 0.0 1,489 1,622 2.8 7.4 .. 1.2 2012 World Development Indicators 101 2.16 Health systems Health Health workers Hospital expenditure beds External per 1,000 people Total Public Out of pocket resources Per capita Nurses and Community per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives health workers people 2010 2010 2010 2010 2010 2010 2005–10a 2005–10a 2005–10a 2005–10a Romania 5.6 78.1 21.5 0.0 428 811 2.3 5.9 .. 6.6 Russian Federation 5.1 62.1 31.4 0.0 525 998 4.3 8.5 .. 9.7 Rwanda 10.5 50.1 22.2 47.0 56 121 0.0 0.4 .. 1.6 Saudi Arabia 4.3 62.9 18.6 0.0 680 968 0.9 2.1 .. 2.2 Senegal 5.7 55.5 35.0 18.5 59 109 0.1 0.4 .. 0.3 Serbia 10.4 61.9 36.4 0.8 546 1,169 2.1 4.5 .. 5.4 Sierra Leone 13.1 11.3 79.4 20.6 43 107 0.0 0.2 0.0 0.4 Singapore 4.0 36.3 54.0 0.0 1,733 2,273 1.8 5.9 .. 3.1 Slovak Republic 8.8 65.9 30.5 0.0 1,413 2,060 3.0 0.3 .. 6.5 Slovenia 9.4 73.7 12.6 0.0 2,154 2,552 2.5 8.4 .. 4.6 Somalia .. .. .. .. .. .. 0.0 0.1 .. .. South Africa 8.9 44.1 16.6 2.2 649 935 .. .. .. 2.8 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 9.5 72.8 20.7 0.0 2,883 3,027 4.0 5.1 .. 3.2 Sri Lanka 2.9 44.7 44.9 3.0 70 148 0.5 1.9 .. .. Sudan 6.3 29.8 67.2 3.3 84 141 0.3 0.8 .. 0.7 Swaziland 6.6 63.7 15.4 17.2 203 333 .. .. .. 2.1e Sweden 9.6 81.1 17.0 0.0 4,710 3,757 3.8 11.9 .. 2.8 Switzerland 11.5 59.0 30.9 0.0 7,812 5,394 4.1 16.5 .. 5.2 Syrian Arab Republic 3.4 46.0 54.0 0.7 97 174 1.5 1.9 .. 1.5 Tajikistan 6.0 26.7 66.5 6.1 49 128 2.1 5.3 .. 5.2 Tanzania 6.0 67.3 13.6 48.8 31 83 0.0 0.2 .. 0.7 Thailand 3.9 75.0 13.9 0.3 179 330 0.3 .. .. 2.1 Timor-Leste 9.1 55.8 11.3 33.7 57 84 .. .. .. 5.9 Togo 7.7 44.2 46.9 15.2 41 77 0.1 0.3 .. 0.9 Trinidad and Tobago 5.7 59.9 32.8 0.1 861 1,449 1.2 3.6 .. 2.6 Tunisia 6.2 54.3 39.8 0.3 238 483 1.2 3.3 .. 2.1 Turkey 6.7 75.2 16.0 0.0 678 1,029 1.5 0.6 .. 2.5 Turkmenistan 2.5c 59.4 c 40.6c 0.3c 106c 199c 2.4 4.4 .. 4.0 Uganda 9.0 21.7 49.8 25.9 47 124 0.1 1.3 .. 0.5 Ukraine 7.7 56.6 40.5 0.4 234 519 3.2 8.6 .. 8.7 United Arab Emirates 3.7 74.4 18.8 0.0 1,450 1,544 1.9 4.1 .. 1.9 United Kingdom 9.6 83.9 10.0 0.0 3,503 3,480 2.7 10.1 .. 3.3 United States 17.9 53.1 11.8 0.0 8,362 8,362 2.4 9.8 .. 3.0 Uruguay 8.4 67.1 13.0 0.0 998 1,188 3.7 5.5 .. 1.2 Uzbekistan 5.8 47.5 42.7 0.9 82 184 2.6 11.1 .. 4.6 Venezuela, RB 4.9 34.9 59.0 0.0 663 589 .. .. .. 1.1 Vietnam 6.8 37.8 57.6 3.4 83 215 1.2 1.0 .. 3.1 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 5.2 24.2 74.8 4.3 63 122 0.3 .. .. 0.7 Zambia 5.9 60.3 26.5 39.2 73 90 0.1 0.7 .. 2.0 Zimbabwe .. .. .. .. .. .. .. .. .. 3.0 World 10.5 w 62.8 w 17.7 w 0.2 w 949 w 1,023 w 1.4 w 2.8 w .. w 2.9 w Low income 5.4 38.7 48.1 25.9 27 61 0.2 0.5 .. .. Middle income 5.7 52.0 36.4 0.6 225 369 1.2 2.0 .. 2.4 Lower middle income 4.2 40.2 52.3 2.8 71 149 0.8 1.5 .. 1.4 Upper middle income 6.1 54.3 33.4 0.2 382 594 1.7 2.6 .. 3.7 Low & middle income 5.7 51.8 36.6 1.1 199 329 1.1 1.9 .. 2.2 East Asia & Pacific 4.7 53.4 36.7 0.4 183 316 1.2 1.5 0.8 3.9 Europe & Central Asia 5.8 65.0 29.0 0.3 439 797 3.2 6.7 .. 7.4 Latin America & Carib. 7.8 50.2 34.3 0.2 670 854 1.8 .. .. 1.9 Middle East & N. Africa 4.7 50.1 47.0 0.7 203 425 1.4 2.3 .. 1.6 South Asia 3.8 30.0 60.5 2.3 47 115 0.6 0.9 0.1 0.9 Sub-Saharan Africa 6.5 45.1 31.8 10.5 84 143 0.2 0.8 .. .. High income 12.7 65.1 13.7 0.0 4,879 4,660 2.8 7.1 .. 5.7 Euro area 10.8 76.2 14.3 0.1 3,969 3,685 3.6 4.7 .. 5.8 a. Data are for the most recent year available. b. Nonprofi t institutions (such as nongovernmental organizations) serving households are accounted for in external resources, which are recorded under government expenditure. GDP includes both licit and illicit activity (for example, opium production). Public expenditures include external assistance. c. Derived from incomplete data. d. Excludes expenditure on residential facilities for care of the aged. e. Data are for 2011. f. Excludes northern Iraq. g. Excludes Transnistria. 102 2012 World Development Indicators 2.16 PEOPLE Health systems About the data De�nitions Health systems—the combined arrangements of expenditure. Thus data for this indicator from 2011 • Total health expenditure is the sum of public and institutions and actions whose primary purpose onward should not be compared with data from edi- private health expenditure. It covers the provision of is to promote, restore, or maintain health (World tions before 2011. health services (preventive and curative), family plan- Health Organization, World Health Report 2000)—are External resources for health are disbursements to ning and nutrition activities, and emergency aid for increasingly being recognized as key to combating recipient countries as reported by donors, lagged one health but excludes provision of water and sanitation. disease and improving the health status of popu- year to account for the delay between disbursement • Public health expenditure is recurrent and capital lations. The World Bank’s (2007a) Healthy Develop- and expenditure. Except where a reliable full national spending from central and local governments, exter- ment: Strategy for Health, Nutrition, and Population health account study has been done, most data are nal borrowing and grants (including donations from Results emphasizes the need to strengthen health from the Organisation for Economic Co-operation and international agencies and nongovernmental organi- systems, which are weak in many countries, in order Development Development Assistance Committee’s zations), and social (or compulsory) health insurance to increase the effectiveness of programs aimed at Creditor Reporting System database, which compiles funds. • Out-of-pocket health expenditure is direct reducing specific diseases and further reduce mor- data from government expenditure accounts, govern- household outlays, including gratuities and in-kind bidity and mortality. To evaluate health systems, ment records on external assistance, routine sur- payments, for health practitioners and pharmaceuti- the World Health Organization (WHO) has recom- veys of external financing assistance, and special cal suppliers, therapeutic appliances, and other goods mended that key components—such as financing, services. Because of the variety of sources, caution and services whose primary intent is to restore or service delivery, workforce, governance, and infor- should be used in interpreting the data. enhance health. • External resources for health are mation—be monitored using several key indicators In countries where the fiscal year spans two cal- funds or services in kind provided by entities that are (WHO 2008b). The data in the table are a subset of endar years, expenditure data have been allocated not part of the country. The resources may come from the first four indicators. Monitoring health systems to the later year (for example, 2009 data cover fis- international organizations, other countries through allows the effectiveness, efficiency, and equity of cal year 2008/09). Many low-income countries use bilateral arrangements, or foreign nongovernmen- different health system models to be compared. Demographic and Health Surveys or Multiple Indica- tal organizations and are part of public and private Health system data also help identify weaknesses tor Cluster Surveys funded by donors to obtain health health expenditure. • Health expenditure per capita and strengths and areas that need investment, such system data. is total health expenditure divided by population in as additional health facilities, better health informa- Data on health worker (physicians, nurses and U.S. dollars and in international dollars converted tion systems, or better trained human resources. midwives, and community health workers) density using 2005 purchasing power parity (PPP) rates from Health expenditure data are broken down into show the availability of medical personnel. The WHO the World Bank’s International Comparison Project. public and private expenditures. In general, low- estimates that at least 2.5 physicians, nurses, and • Physicians include generalist and specialist medi- income economies have a higher share of private midwives per 1,000 people are needed to provide cal practitioners.• Nurses and midwives include pro- health expenditure than do middle- and high-income adequate coverage with primary care interventions fessional nurses and midwives, auxiliary nurses and countries, and out-of-pocket expenditure (direct pay- associated with achieving the Millennium Develop- midwives, enrolled nurses and midwives, and other ments by households to providers) makes up the ment Goals (WHO, World Health Report 2006). The personnel, such as dental nurses and primary care largest proportion of private expenditures. High WHO compiles data from household and labor force nurses. • Community health workers include tradi- out-of-pocket expenditures may discourage people surveys, censuses, and administrative records. Data tional medicine practitioners, faith healers, assistant from accessing preventive or curative care and can comparability is limited by differences in definitions or community health education workers, community impoverish households that cannot afford needed and training of medical personnel varies. In addition, health officers, family health workers, lady health visi- care. Health financing data are collected through human resources tend to be concentrated in urban tors, health extension package workers, community national health accounts, which systematically, areas, so that average densities do not provide a full midwives, and traditional birth attendants. • Hospital comprehensively, and consistently monitoring health picture of health personnel available to the entire beds are inpatient beds for both acute and chronic system resource flows. To establish a national health population. care available in public, private, general, and special- account, countries must define the boundaries of the Availability and use of health services, such ized hospitals and rehabilitation centers. health system and classify health expenditure infor- as hospital beds per 1,000 people, refl ect both Data sources mation along several dimensions, including sources demand- and supply-side factors. In the absence of of financing, providers of health services, functional a consistent definition this is a crude indicator of Data on health expenditure are from the WHO’s use of health expenditures, and benefi ciaries of the extent of physical, financial, and other barriers National Global Health Expenditure database (see expenditures. The accounting system can then pro- to health care. http://apps.who.int/nha/database for the most vide an accurate picture of resource envelopes and recent updates), supplemented by country data. financial flows and allow analysis of the equity and Data on physicians, nurses and midwives, and com- efficiency of financing to inform policy. munity health workers are from the WHO’s Global This year’s table, like last year’s, presents out-of- Atlas of the Health Workforce database (http:// pocket expenditure as a percentage of total health apps.who.int/globalatlas). Data on hospital beds expenditure; editions before 2011 presented out-of- are from the WHO, supplemented by country data. pocket expenditure as a percentage of private health 2012 World Development Indicators 103 2.17 Health information Year last national Number of Year of last Year of last Completeness health account national health health survey census completed accounts completed % Birth Infant death Total death registration reporting reporting 1995–2010 2001–11 2005–10a 2005–10a 2005–10a Afghanistan 2008 1 2010 .. .. .. Albania 2009 2 2008/09 2001 99 30 85 Algeria 2003 3 2006 2008 99 .. 91 Angola 0 2006/07 .. .. .. Argentina 1997 1 2010 .. 100 99 Armenia 2010 7 2005 2001 96 43 100 Australia 2008 14 2006 .. 92 96 Austria 2009 15 2011 .. 100 100 Azerbaijan 0 2006 2009 94 26 79 Bahrain 2000 1 2010 84 79 Bangladesh 2008 13 2007 2011 10 .. .. Belarus 2010 1 2005 2009 .. 62 100 Belgium 2009 7 2011 .. 96 99 Benin 2008 4 2006 2002 60 .. .. Bolivia 2008 14 2008 2001 .. .. 31 Bosnia and Herzegovina 2010 7 2006 100 48 93 Botswana 2002 3 2000 2011 72 32 43 Brazil 2009 10 1996 2010 91 50 88 Bulgaria 2009 9 2011 .. 98 97 Burkina Faso 2009 7 2006 2006 64 31 90 Burundi 2007 1 2005 2008 60 .. .. Cambodia 0 2010 2008 66 .. .. Cameroon 1995 1 2006 2005 70 .. .. Canada 2010 16 2011 .. 99 98 Central African Republic 0 2006 2003 49 .. .. Chad 0 2004 2009 .. .. .. Chile 2010 8 2002 99 100 100 China 2009 15 2010 .. .. 97 Hong Kong SAR, China 0 2006 .. 66 94 Colombia 2009 12 2010 2006 97 55 77 Congo, Dem. Rep. 2009 7 2010 28 .. .. Congo, Rep. 2005 1 2009 2007 81 .. .. Costa Rica 2003 2 1993 2011 .. 96 98 Côte d’Ivoire 2008 2 2006 55 .. .. Croatia 2010 2 2011 .. 94 99 Cuba 0 2006 2002 100 100 100 Cyprus 2008 6 2001 56 72 Czech Republic 2009 15 1993 2011 .. 89 100 Denmark 2009 15 2011 .. 86 97 Dominican Republic 2008 8 2007 2010 78 1 54 Ecuador 2008 9 2004 2010 90 54 84 Egypt, Arab Rep. 2008 3 2008 2006 99 56 100 El Salvador 2010 16 2008 2007 99 35 79 Eritrea 0 2002 .. .. .. Estonia 2010 12 .. 76 95 Ethiopia 2008 4 2005 2007 7 .. 100 Finland 2010 16 2010 .. 84 99 France 2010 16 2006 .. 100 98 Gabon 0 2000 2003 .. .. .. Gambia, The 2004 3 2005/06 2003 55 .. .. Georgia 2010 10 2005 2002 92 49 97 Germany 2009 15 2011 .. 97 100 Ghana 2002 1 2008 2010 71 100 .. Greece 0 2011 .. 69 91 Guatemala 2008 14 2002 2002 .. 63 92 Guinea 0 2005 43 .. .. Guinea-Bissau 0 2010 2009 24 .. .. 104 2012 World Development Indicators 2.17 PEOPLE Health information Year last national Number of Year of last Year of last Completeness health account national health health survey census completed accounts completed % Birth Infant death Total death registration reporting reporting 1995–2010 2001–11 2005–10a 2005–10a 2005–10a Haiti 2006 1 2005/06 2003 81 .. .. Honduras 2005 3 2005/06 2001 94 100 100 Hungary 2009 15 2001 .. 89 98 India 2004 2 2005/06 2011 41 .. .. Indonesia 2008 8 2007 2010 53 .. .. Iran, Islamic Rep. 2007 4 2000 2006 .. .. 100 Iraq 2010 3 2006 95 100 100 Ireland 2009 16 2011 .. 86 98 Israel 2007 2 2009 .. 100 96 Italy 2009 4 2012 .. 99 98 Jamaica 2000 1 2005 2011 89 73 88 Japan 2008 14 2010 .. 92 100 Jordan 2009 6 2009 2004 .. .. 82 Kazakhstan 2010 2 2006 2009 99 83 88 Kenya 2010 3 2010 2009 60 43 42 Korea, Dem. Rep. 0 2010 2009 100 73 93 Korea, Rep. 2010 16 2010 .. 82 98 Kosovo 0 .. .. .. Kuwait 0 1996 2010 .. 100 73 Kyrgyz Republic 2010 6 2005/06 2009 94 76 91 Lao PDR 0 2006 2005 72 .. .. Latvia 2008 5 2011 .. 65 96 Lebanon 2008 11 2000 .. .. 87 Lesotho 0 2009/10 2006 45 .. .. Liberia 2008 1 2009 2008 4 .. .. Libya 0 2000 2006 .. .. .. Lithuania 2009 8 2011 .. 71 93 Macedonia, FYR 0 2005 2010 94 61 100 Madagascar 2007 2 2008/09 80 .. .. Malawi 2006 5 2010 2008 .. .. 71 Malaysia 2009 13 2010 .. 80 99 Mali 2004 6 2010 2009 81 .. .. Mauritania 2008 1 2007 2000 56 .. .. Mauritius 2004 2 2011 .. 90 100 Mexico 2010 16 1995 2010 .. 82 100 Moldova 2010 2 2005 2004 .. 73 91 Mongolia 2003 5 2010 2010 98 60 98 Morocco 2006 3 2006 2004 .. .. .. Mozambique 2006 4 2009 2007 31 .. .. Myanmar 2007 10 2000 72 58 100 Namibia 2008 11 2009 2001 67 .. 100 Nepal 2010 11 2010 2001 35 .. .. Netherlands 2010 16 2011 .. 89 99 New Zealand 2009 15 2006 .. 97 96 Nicaragua 2008 14 2006/07 2005 .. 66 68 Niger 2009 6 2006 2001 32 .. .. Nigeria 2005 8 2008 2006 30 .. 3 Norway 2009 13 2001 .. 97 99 Oman 1998 1 1995 2010 .. 100 70 Pakistan 2006 1 2010 27 88 80 Panama 1997 1 2003 2010 .. 68 90 Papua New Guinea 2000 3 1996 .. .. .. Paraguay 2008 14 2004 2002 .. 34 71 Peru 2005 11 2008 2007 93 45 59 Philippines 2007 13 2008 2010 .. 38 84 Poland 2009 15 2011 .. 89 97 Portugal 2008 9 2011 .. 57 99 2012 World Development Indicators 105 2.17 Health information Year last national Number of Year of last Year of last Completeness health account national health health survey census completed accounts completed % Birth Infant death Total death registration reporting reporting 1995–2010 2001–11 2005–10a 2005–10a 2005–10a Puerto Rico 0 1996 2010 .. 100 100 Qatar 2010 2 2010 .. 77 78 Romania 2009 12 1999 2011 .. 73 100 Russian Federation 2008 13 1996 2010 .. 76 100 Rwanda 2006 5 2007/08 2002 82 .. 100 Saudi Arabia 2008 1 2007 2010 .. 100 98 Senegal 2005 2 2008/09 2002 55 100 100 Serbia 2010 8 2005/06 2011 99 37 89 Sierra Leone 2006 3 2008 2004 51 13 .. Singapore 0 2005 2010 .. 100 74 Slovak Republic 2009 13 2011 .. 100 100 Slovenia 2010 16 2011 .. 79 97 Somalia 0 2006 3 .. .. South Africa 1998 3 2003 2001 92 78 82 South Sudan 0 2008 Spain 2009 15 2001 .. 84 94 Sri Lanka 2008 14 2006/07 2001 97 67 94 Sudan 2010 2 2010 2008 33 .. .. Swaziland 0 2010 2010 30 .. 100 Sweden 2009 9 .. 100 98 Switzerland 2010 16 2010 .. 100 100 Syrian Arab Republic 0 2006 2004 95 .. 100 Tajikistan 2010 4 2005 2010 88 24 72 Tanzania 2006 3 2010 2002 16 .. .. Thailand 2007 13 2005/06 2010 99 53 79 Timor-Leste 0 2009/10 2010 55 .. .. Togo 2002 1 2010 2010 78 .. .. Trinidad and Tobago 2000 1 2006 2011 96 49 94 Tunisia 2005 5 2006 2004 .. .. 99 Turkey 2008 11 2003 94 60 100 Turkmenistan 0 2006 96 .. .. Uganda 2007 6 2009/10 2002 21 .. .. Ukraine 2008 6 2007 2001 100 75 93 United Arab Emirates 0 2010 .. 96 91 United Kingdom 2009 13 2011 .. 100 96 United States 2009 15 2009 2010 .. 100 98 Uruguay 2008 13 2004 .. 77 100 Uzbekistan 2010 1 2006 100 .. .. Venezuela, RB 0 2000 2001 .. 62 87 Vietnam 2007 10 2006 2009 88 71 86 West Bank and Gaza 2005 1 2006 2007 96 28 73 Yemen, Rep. 2007 4 2006 2004 22 .. 16 Zambia 2006 12 2007 2010 14 .. 73 Zimbabwe 2001 3 2005/06 2002 38 .. .. a. Data are for the most recent year available. 106 2012 World Development Indicators 2.17 PEOPLE Health information About the data De�nitions According to the World Health Organization (WHO), •  Year last national health account completed health information systems are crucial for moni- is the latest year for which the health expenditure toring and evaluating health systems, which are data are available using the national health account increasingly recognized as important for combating approach. •  Number of national health accounts disease and improving health status. Health informa- completed is the number of national health accounts tion systems underpin decisionmaking through four completed during the specified years. • Year of last data functions: generation, compilation, analysis and health survey is the latest year the national survey synthesis, and communication and use. The health that collects health information was conducted. information system collects data from the health sec- • Year of last census is the latest year a census was tor and other relevant sectors; analyzes the data and conducted in the last 10 years. • Completeness of ensures their overall quality, relevance, and timeli- birth registration is the percentage of children under ness; and converts data into information for health- age 5 whose births were registered at the time of the related decisionmaking (WHO 2008b). survey. The numerator of completeness of birth regis- Numerous indicators have been proposed to tration includes children whose birth certificate was assess a country’s health information system. seen by the interviewer or whose mother or caretaker They can be grouped into two broad types: indica- says the birth has been registered. • Completeness tors related to data generation using core sources of infant death reporting is the number of infant and methods (health surveys, civil registration, cen- deaths reported by national statistical authorities suses, facility reporting, health system resource to the United Nations Statistics Division’s Demo- tracking) and indicators related to capacity for data graphic Yearbook divided by the number of infant synthesis, analysis, and validation. Indicators related deaths estimated by the United Nations Population to data generation reflect a country’s capacity to col- Division. • Completeness of total death reporting is lect relevant data at suitable intervals using the most the number of total deaths from civil registration sys- appropriate data sources. Benchmarks include peri- tems reported by national statistical authorities to odicity, timeliness, contents, and availability. Indi- the United Nations Statistics Division’s Demographic cators related to capacity for synthesis, analysis, Yearbook divided by the number of total deaths esti- and validation measure the dimensions of the insti- mated by the United Nations Population Division. tutional frameworks needed to ensure data quality, including independence, transparency, and access. Data sources Benchmarks include the availability of independent coordination mechanisms and micro- and meta-data Data on year last national health account completed (WHO 2008a). and number of national health accounts completed The indicators in the table are all related to data were compiled by the World Bank’s Health, Nutri- generation, including the years the last national tion, and Population Unit using information on the health account, last health survey, and latest health expenditures provided by the WHO National population census were completed. Frequency of Health Accounts staff and the OECD. Data on year data collection, a benchmark of data generation, is of last health survey are from ICF International and shown as the number of years for which a national the United Nations Children’s Fund (UNICEF). Data health account was completed during the specified on year of last census are from United Nations Sta- years. National health account data may be collected tistics Division’s 2011 World Population and Hous- using different approaches such as Organisation for ing Census Program (http://unstats.un.org/unsd/ Economic Co-operation and Development (OECD) demographic/sources/census/2010_PHC/default. System of Health Accounts, WHO National Health htm). Data on completeness of birth registration Account producers guide approach, local national are compiled by UNICEF in State of the World’s health accounting methods, or Pan American Children 2012 based mostly on household surveys Health Organization/WHO satellite health accounts and ministry of health data. Data used to calculate approach. completeness of infant death reporting and total Indicators related to data generation include com- death reporting are from the United Nations Statis- pleteness of birth registration, infant death report- tics Division’s Population and Vital Statistics Report ing, and total death reporting. and the United Nations Population Division’s World Population Prospects: The 2010 Revision. 2012 World Development Indicators 107 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 (ARI) 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 2010 1990 2010 2010 2010 2005–10c 2005–10c 2005–10c 2005–10c 2009 2010 Afghanistan .. 50 .. 37 62 66 .. .. .. .. 86 47 Albania 97 95 76 94 99 99 70 63 .. .. 89 97 Algeria 94 83 88 95 95 95 53 24 .. .. 91 70 Angola 42 51 29 58 93 91 .. .. 17.7 29.3 72 77 Argentina 94 97 90 90 99 94 .. .. .. .. 46 66 Armenia .. 98 .. 90 97 94 57 59 .. .. 73 62 Australia 100 100 100 100 94 92 .. .. .. .. 80 84 Austria 100 100 100 100 76 83 .. .. .. .. 66 84 Azerbaijan 70 80 .. 82 67 72 33 31 .. .. 62 63 Bahrain .. .. .. .. 99 99 .. .. .. .. 98 84 Bangladesh 77 81 39 56 94 95 37 68 .. .. 92 46 Belarus 100 100 93 93 99 98 90 54 .. .. 64 74 Belgium 100 100 100 100 94 99 .. .. .. .. 76 87 Benin 57 75 5 13 69 83 36 42 20.1 54.0 90 45 Bolivia 70 88 18 27 79 80 51 29 .. .. 86 62 Bosnia and Herzegovina 97 99 .. 95 93 90 91 53 .. .. 99 71 Botswana 93 96 38 62 94 96 .. .. .. .. 79 70 Brazil 89 98 68 79 99 98 50 .. .. .. 72 88 Bulgaria 100 100 99 100 97 94 .. .. .. .. 85 79 Burkina Faso 43 79 8 17 94 95 39 42 9.6 48.0 76 53 Burundi 70 72 44 46 92 96 38 23 45.2 17.2 90 70 Cambodia 31 64 9 31 93 92 64 50 4.2 0.2 95 65 Cameroon 49 77 48 49 79 84 35 22 13.1 57.8 78 69 Canada 100 100 100 100 93 80 .. .. .. .. 75 83 Central African Republic 58 67 11 34 62 54 32 47 15.1 57.0 53 47 Chad 39 51 8 13 46 59 26 23 9.8 35.7 76 31 Chile 90 96 84 96 93 92 .. .. .. .. 72 75 China 67 91 24 64 99 99 .. .. .. .. 95 87 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. 70 87 Colombia 89 92 67 77 88 88 64 52 .. .. 77 72 Congo, Dem. Rep. 45 45 9 24 68 63 40 37 35.7 39.1 88 53 Congo, Rep. .. 71 .. 18 76 90 48 39 6.1 48.0 78 68 Costa Rica 93 97 93 95 83 88 .. .. .. .. 54 78 Côte d’Ivoire 76 80 20 24 70 85 35 45 3.0 36.0 79 83 Croatia 99 99 99 99 95 96 .. .. .. .. 63 73 Cuba 82 94 80 91 99 96 .. .. .. .. 90 79 Cyprus 100 100 100 100 87 99 .. .. .. .. 29 68 Czech Republic 100 100 100 98 98 99 .. .. .. .. 67 88 Denmark 100 100 100 100 85 90 .. .. .. .. 53 93 Dominican Republic 88 86 73 83 79 88 70 55 .. 0.6 85 59 Ecuador 72 94 69 92 98 99 .. .. .. .. 75 51 Egypt, Arab Rep. 93 99 72 95 96 97 73 19 .. .. 88 64 El Salvador 74 88 75 87 92 92 67 .. .. .. 89 96 Eritrea 43 61 9 14 99 99 .. .. 48.9 13.1 85 55 Estonia 98 98 95 95 95 94 .. .. .. .. 59 85 Ethiopia 14 44 3 21 81 86 19 15 33.1 9.5 84 72 Finland 100 100 100 100 98 99 .. .. .. .. 68 87 France 100 100 100 100 90 99 .. .. .. .. .. 47 Gabon .. 87 .. 33 55 45 .. .. 55.1 .. 55 42 Gambia, The 74 89 .. 68 97 98 69 38 49.0 63.0 89 44 Georgia 81 98 96 95 94 91 74 37 .. .. 75 100 Germany 100 100 100 100 96 93 .. .. .. .. 77 89 Ghana 53 86 7 14 93 94 51 45 28.2 43.0 87 70 Greece 96 100 97 98 99 99 .. .. .. .. .. 68 Guatemala 81 92 62 78 93 94 .. .. .. .. 83 37 Guinea 51 74 10 18 51 57 42 38 4.5 73.9 79 33 Guinea-Bissau 36 64 .. 20 61 76 52 53 35.5 51.2 67 62 Haiti 59 69 26 17 59 59 31 43 .. 5.1 79 62 108 2012 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 (ARI) 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 2010 1990 2010 2010 2010 2005–10c 2005–10c 2005–10c 2005–10c 2009 2010 Honduras 76 87 50 77 99 98 56 49 .. 0.5 86 74 Hungary 96 100 100 100 99 99 .. .. .. .. 57 100 India 69 92 18 34 74 72 69 33 .. 8.2 88 59 Indonesia 70 82 32 54 89 83 66 54 3.3 0.8 91 66 Iran, Islamic Rep. 90 96 79 100 99 99 .. .. .. .. 83 81 Iraq 81 79 .. 73 73 65 82 64 .. .. 90 48 Ireland 100 100 99 99 90 94 .. .. .. .. 67 88 Israel 100 100 100 100 98 96 .. .. .. .. 86 93 Italy 100 100 .. .. 90 96 .. .. .. .. .. 57 Jamaica 93 93 80 80 88 99 75 39 .. .. 70 72 Japan 100 100 100 100 94 98 .. .. .. .. 52 84 Jordan 97 97 97 98 98 98 75 32 .. .. 75 100 Kazakhstan 96 95 96 97 99 99 71 48 .. .. 62 82 Kenya 44 59 25 32 86 83 56 43 46.7 23.2 86 82 Korea, Dem. Rep. 100 98 .. 80 99 93 80 67 .. .. 89 100 Korea, Rep. .. 98 100 100 98 94 .. .. .. .. 83 90 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 99 99 100 100 98 98 .. .. .. .. 85 86 Kyrgyz Republic .. 90 .. 93 99 96 62 22 .. .. 82 66 Lao PDR .. 67 .. 63 64 74 32 49 40.5 8.2 93 72 Latvia 99 99 .. 78 93 89 .. .. .. .. 75 100 Lebanon 100 100 .. .. 53 74 .. .. .. .. 82 71 Lesotho 80 78 .. 26 85 83 66 48 .. .. 70 85 Liberia .. 73 .. 18 64 64 62 47 26.4 67.2 83 56 Libya 54 .. 97 97 98 98 .. .. .. .. 69 84 Lithuania .. .. .. .. 96 95 .. .. .. .. 73 76 Macedonia, FYR 100 100 .. 88 98 95 93 45 .. .. 90 89 Madagascar 29 46 9 15 67 74 42 49 45.8 19.7 82 44 Malawi 41 83 39 51 93 93 52 27 56.5 30.9 88 65 Malaysia 88 100 84 96 96 94 .. .. .. .. 78 80 Mali 28 64 15 22 63 76 38 38 70.2 31.7 78 51 Mauritania 30 50 16 26 67 64 45 32 .. 20.7 63 21 Mauritius 99 99 89 89 99 99 .. .. .. .. 88 44 Mexico 85 96 64 85 95 95 .. .. .. .. 86 110 Moldova .. 96 .. 85 97 90 60 48 .. .. 54 63 Mongolia 54 82 .. 51 97 96 63 47 .. .. 88 72 Morocco 73 83 53 70 98 99 .. .. .. .. 84 97 Mozambique 36 47 11 18 70 74 65 47 22.8 36.7 85 34 Myanmar 56 83 .. 76 88 90 .. .. .. .. 85 71 Namibia 64 93 24 32 75 83 72 48 34.0 20.3 85 82 Nepal 76 89 10 31 86 82 43 37 .. 0.1 90 72 Netherlands 100 100 100 100 96 97 .. .. .. .. 80 85 New Zealand 100 100 .. .. 91 93 .. .. .. .. 76 90 Nicaragua 74 85 43 52 99 98 .. .. .. .. 85 100 Niger 35 49 5 9 71 70 47 34 63.7 33.0 79 35 Nigeria 47 58 37 31 71 69 45 25 29.1 49.1 83 40 Norway 100 100 100 100 93 93 .. .. .. .. 82 93 Oman 80 89 82 99 97 99 .. .. .. .. 98 85 Pakistan 85 92 27 48 86 88 69 37 .. 3.3 91 65 Panama 84 93 58 69 95 94 .. .. .. .. 80 89 Papua New Guinea 41 40 47 45 55 56 63 .. .. .. 72 70 Paraguay 52 86 37 71 94 90 .. .. .. .. 80 77 Peru 75 85 54 71 94 93 68 60 .. .. 81 100 Philippines 85 92 57 74 88 87 50 60 .. 0.0 89 65 Poland .. 100 .. 90 98 99 .. .. .. .. 67 80 Portugal 96 99 92 100 96 98 .. .. .. .. 84 79 Puerto Rico .. .. .. .. .. .. .. .. .. .. 81 96 Qatar 100 100 100 100 99 97 .. .. .. .. 80 87 2012 World Development Indicators 109 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 (ARI) 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 2010 1990 2010 2010 2010 2005–10c 2005–10c 2005–10c 2005–10c 2009 2010 Romania 75 .. 71 72 95 97 .. .. .. .. 85 74 Russian Federation 93 97 74 70 98 97 .. .. .. .. 55 78 Rwanda 66 65 36 55 82 80 28 24 69.8 10.8 85 60 Saudi Arabia 89 .. .. .. 98 98 .. .. .. .. 65 88 Senegal 61 72 38 52 60 70 47 43 29.2 9.1 85 31 Serbia 99 99 .. 92 95 91 93 71 .. .. 86 130 Sierra Leone 38 55 11 13 82 90 46 57 25.8 30.1 79 32 Singapore 100 100 99 100 95 97 .. .. .. .. 82 87 Slovak Republic 100 100 100 100 98 99 .. .. .. .. 82 88 Slovenia 100 99 100 100 95 96 .. .. .. .. 87 79 Somalia .. 29 .. 23 46 45 13 7 11.4 7.9 85 38 South Africa 83 91 71 79 65 63 .. .. .. .. 73 72 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 100 100 100 100 95 97 .. .. .. .. .. 87 Sri Lanka 67 91 70 92 99 99 58 67 2.9 0.3 86 69 Sudan 65 58 27 26 90 90 90 56 25.3 35.8 80 50 Swaziland 39 71 48 57 94 89 73 22 0.6 0.6 69 66 Sweden 100 100 100 100 96 98 .. .. .. .. 85 87 Switzerland 100 100 100 100 90 96 .. .. .. .. .. 55 Syrian Arab Republic 86 90 85 95 82 80 77 34 .. .. 88 90 Tajikistan .. 64 .. 94 94 93 64 22 1.3 1.9 81 44 Tanzania 55 53 7 10 92 91 71 50 63.6 59.1 88 77 Thailand 86 96 84 96 98 99 84 46 .. .. 86 70 Timor-Leste .. 69 .. 47 66 72 71 63 41.0 6.0 85 87 Togo 49 61 13 13 84 92 23 24 56.9 33.8 81 10 Trinidad and Tobago 88 94 93 92 92 90 74 32 .. .. 69 87 Tunisia 81 94 74 85 97 98 59 62 .. .. 83 91 Turkey 85 100 84 90 97 96 .. 22 .. .. 91 77 Turkmenistan .. .. 98 98 99 96 83 25 .. .. 84 96 Uganda 43 72 27 34 55 60 73 39 32.8 59.6 67 61 Ukraine .. 98 .. 94 94 90 .. .. .. .. 60 73 United Arab Emirates 100 100 97 98 94 94 .. .. .. .. 73 57 United Kingdom 100 100 100 100 93 96 .. .. .. .. 82 91 United States 99 99 100 100 92 95 .. .. .. .. 60 88 Uruguay 96 100 94 100 95 95 .. .. .. .. 80 97 Uzbekistan 90 87 84 100 98 99 68 28 .. .. 81 48 Venezuela, RB 90 .. 82 .. 79 78 .. .. .. .. 84 66 Vietnam 57 95 37 76 98 93 83 65 5.0 2.6 92 54 West Bank and Gaza .. 85 .. 92 .. .. .. .. .. .. 82 16 Yemen, Rep. 67 55 24 53 73 87 .. 48 .. .. 88 76 Zambia 49 61 46 48 91 82 .. 68 56 49.9 90 90 Zimbabwe 79 80 41 40 84 83 43 35 17.3 23.6 78 56 World 76 w 88 w 47 w 62 w 85 w 85 w .. w .. w .. w .. w 86 w 65 w Low income 54 65 21 37 78 80 .. 39 .. 31.9 86 58 Middle income 73 90 39 59 86 85 .. .. .. .. 87 67 Lower middle income 70 87 29 47 80 79 67 37 .. 13.8 87 61 Upper middle income 76 93 46 73 96 96 .. .. .. .. 86 81 Low & middle income 71 86 37 56 84 84 .. .. .. .. 87 65 East Asia & Pacific 68 90 30 66 95 94 .. .. .. .. 92 76 Europe & Central Asia 90 96 80 84 96 95 .. .. .. .. 65 73 Latin America & Carib. 86 94 68 79 93 93 .. .. .. .. 77 80 Middle East & N. Africa 86 89 73 88 88 89 .. .. .. .. 87 73 South Asia 71 90 22 38 77 76 67 37 .. 7.2 88 58 Sub-Saharan Africa 49 61 26 31 75 77 .. 35 34.0 37.8 79 60 High income 99 100 100 100 93 95 .. .. .. .. 68 85 Euro area 100 100 100 100 93 96 .. .. .. .. .. .. a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or in some cases at any time before the survey (12–23 months). c. Data are for the most recent year available. 110 2012 World Development Indicators 2.18 PEOPLE Disease prevention coverage and quality About the data De�nitions People’s health is influenced by the environment progress, so it is diffi cult to accurately compare • Access to an improved water source refers to peo- in which they live. Lack of clean water and basic use rates across countries. Until the current recom- ple with access to at least 20 liters of water a person sanitation is the main reason diseases transmitted mended method for home management of diarrhea is a day from an improved source, such as piped water by feces are so common in developing countries. adopted and applied in all countries, the data should into a dwelling, public tap, tubewell, protected dug Access to drinking water from an improved source be used with caution. Also, the prevalence of diar- well, and rainwater collection, within 1 kilometer of and access to improved sanitation do not ensure rhea may vary by season. Since country surveys are the dwelling. • Access to improved sanitation facili- safety or adequacy, as these characteristics are administered at different times, data comparability ties refers to people with at least adequate access not tested at the time of the surveys. But improved is further affected. to excreta disposal facilities that can effectively pre- drinking water technologies and improved sanitation Malaria is endemic to the poorest countries in the vent human, animal, and insect contact with excreta. facilities are more likely than those characterized world, mainly in tropical and subtropical regions of Improved facilities range from protected pit latrines as unimproved to provide safe drinking water and Africa, Asia, and the Americas. Insecticide-treated to flush toilets. • Child immunization rate refers to to prevent contact with human excreta. The data nets, properly used and maintained, are one of the children ages 12–23 months who, before 12 months are derived by the Joint Monitoring Programme of most important malaria-preventive strategies to limit or at any time before the survey, had received one the World Health Organization (WHO) and United human-mosquito contact. dose of measles vaccine and three doses of diphthe- Nations Children’s Fund (UNICEF) based on national Prompt and effective treatment of malaria is a criti- ria, pertussis (whooping cough), and tetanus (DTP3) censuses and nationally representative household cal element of malaria control. It is vital that suffer- vaccine. • Children with acute respiratory infection surveys. The coverage rates for water and sanitation ers, especially children under age 5, start treatment (ARI) taken to health provider are children under age are based on information from service users on the within 24 hours of the onset of symptoms, to pre- 5 with ARI in the two weeks before the survey who facilities their households actually use rather than vent progression—often rapid—to severe malaria were taken to an appropriate health provider. • Chil- on information from service providers, which may and death. Data on malaria are from national- level dren with diarrhea who received oral rehydration and include nonfunctioning systems. While the estimates surveys, including Multiple Indicator Cluster Surveys, continuous feeding are children under age 5 with diar- are based on use, the Joint Monitoring Programme Demographic and Health Surveys, and Malaria Indi- rhea in the two weeks before the survey who received reports use as access, because access is the term cator Surveys. either oral rehydration therapy or increased fluids, used in the Millennium Development Goal target for Data on the success rate of tuberculosis treatment with continuous feeding. • Children sleeping under drinking water and sanitation. are provided for countries that have submitted data treated nets are children under age 5 who slept under Governments in developing countries usually to the WHO. The treatment success rate for tuber- an insecticide-treated net to prevent malaria the night finance immunization against measles and diphthe- culosis provides a useful indicator of the quality of before the survey. • Children with fever receiving ria, pertussis (whooping cough), and tetanus (DTP) health services. A low rate suggests that infectious antimalarial drugs are children under age 5 who were as part of the basic public health package. In many patients may not be receiving adequate treatment. ill with fever in the two weeks before the survey and developing countries lack of precise information on An important complement to the tuberculosis treat- received any appropriate (locally defined) antimalarial the size of the cohort of one-year-old children makes ment success rate is the case detection rate, which drugs. • Tuberculosis treatment success rate is new immunization coverage diffi cult to estimate from indicates whether there is adequate coverage by the registered infectious tuberculosis cases that were program statistics. The data shown here are based recommended case detection and treatment strat- cured or that completed a full course of treatment as on an assessment of national immunization cover- egy. Uncertainty bounds for the case detection rate, a percentage of smear-positive cases registered for age rates by the WHO and UNICEF. The assessment not shown in the table, are available at http://data. treatment outcome evaluation. • Tuberculosis case considered both administrative data from service worldbank.org and from the original source. detection rate is newly identified tuberculosis cases providers and household survey data on children’s The table shows the tuberculosis detection rate for (including relapses) as a percentage of estimated immunization histories. Based on the data available, all detection methods. Editions before 2010 included incident cases (case detection, all forms). consideration of potential biases, and contributions the tuberculosis detection rates by DOTS, the inter- Data sources of local experts, the most likely true level of immuni- nationally recommended strategy for tuberculosis zation coverage was determined for each year. control. Thus data on the case detection rate from Data on access to water and sanitation are from Acute respiratory infection continues to be a lead- 2010 onward cannot be compared with data in previ- the WHO and UNICEF’s Progress on Drinking Water ing cause of death among young children, killing ous editions. and Sanitation (2012). Data on immunization are nearly 1.5 million children under age 5 globally each For indicators that are from household surveys, the from WHO and UNICEF estimates (www.who.int/ year. Data are drawn mostly from household health year in the table refers to the survey year. For more immunization_monitoring). Data on children with ARI, surveys in which mothers report on number of epi- information, consult the original sources. with diarrhea, sleeping under treated nets, and receiv- sodes and treatment for acute respiratory infection. ing antimalarial drugs are from UNICEF’s State of the Most diarrhea- related deaths are due to dehydra- World’s Children 2012, Childinfo, and MEASURE DHS tion, and many of these deaths can be prevented with Demographic and Health Surveys by ICF International. the use of oral rehydration salts at home. However, Data on tuberculosis are from the WHO’s Global Tuber- recommendations for the use of oral rehydration culosis Control: A Short Update to the 2011 Report. therapy have changed over time based on scientific 2012 World Development Indicators 111 2.19 Reproductive health Total fertility Adolescent Unmet Contraceptive Pregnant Births attended Maternal Lifetime rate fertility rate need for prevalence women by skilled mortality risk of contraception rate receiving health staff ratio maternal prenatal mortality care Any method births per % of married % of married per 100,000 live births Probability births per 1,000 women women ages women ages National Modeled 1 woman woman ages 15–19 15–49 15–49 % % of total estimates estimates in: 1990 2010 2010 2005–10a 2005–10a 2005–10a 1990 2005–10a 2005–10 a 1990 2008 2008 Afghanistan 8.0 6.3 107 .. 23 36 .. 24 .. 1,700 1,400 11 Albania 3.2 1.5 16 13 69 97 93 99 21 48 31 1,700 Algeria 4.7 2.3 7 11 61 89 77 95 .. 250 120 340 Angola 7.2 5.4 157 .. .. 80 .. 47 .. 1,000 610 29 Argentina 3.0 2.2 55 .. 78 99 96 98 55 72 70 600 Armenia 2.5 1.7 34 13 55 99 100 100 27 51 29 1,900 Australia 1.9 1.9 14 .. .. 98 100 .. .. 10 8 7,400 Austria 1.5 1.4 11 .. .. .. .. .. .. 10 5 14,300 Azerbaijan 2.7 2.3 32 23 51 77 97 88 24 64 38 1,200 Bahrain 3.7 2.5 15 .. .. 100 .. 97 .. 25 19 2,200 Bangladesh 4.5 2.2 73 17 53 53 .. 27 190 870 340 110 Belarus 1.9 1.4 21 .. 73 99 100 100 1 37 15 5,100 Belgium 1.6 1.8 12 .. .. .. .. .. .. 7 5 10,900 Benin 6.7 5.3 103 30 17 84 .. 74 400 790 410 43 Bolivia 4.9 3.3 76 20 61 86 43 71 310 510 180 150 Bosnia and Herzegovina 1.7 1.1 15 23 36 99 97 100 3 18 9 9,300 Botswana 4.7 2.8 47 .. 53 94 78 95 200 83 190 180 Brazil 2.8 1.8 76 .. 81 98 70 97 75 120 58 860 Bulgaria 1.8 1.5 39 .. .. .. 99 100 5 24 13 5,800 Burkina Faso 6.8 5.9 120 31 17 85 .. 54 310 770 560 28 Burundi 6.5 4.3 20 .. 22 99 .. 60 620 1,200 970 25 Cambodia 5.7 2.6 36 17 51 89 .. 71 206 690 290 110 Cameroon 5.9 4.5 120 3 29 82 64 63 .. 680 600 35 Canada 1.8 1.7 12 .. .. 100 .. 100 .. 6 12 5,600 Central African Republic 5.8 4.6 102 .. 19 69 .. 44 540 880 850 27 Chad 6.7 6.0 149 .. 5 53 .. 23 .. 1,300 1,200 14 Chile 2.6 1.9 57 .. 58 .. .. 100 17 56 26 2,000 China 2.3 1.6 9 .. 85 92 94 99 32 110 38 1,500 Hong Kong SAR, China 1.3 1.1 4 .. 80 .. .. 100 .. .. .. .. Colombia 3.1 2.1 71 7 79 97 94 98 76 140 85 460 Congo, Dem. Rep. 7.1 5.8 183 24 17 88 .. 79 550 900 670 24 Congo, Rep. 5.4 4.5 115 16 44 86 .. 83 780 460 580 39 Costa Rica 3.2 1.8 63 .. 80 90 98 99 21 35 44 1,100 Côte d’Ivoire 6.3 4.4 115 29 13 85 .. 57 540 690 470 44 Croatia 1.6 1.5 13 .. .. 100 100 100 13 8 14 5,200 Cuba 1.8 1.5 44 8 78 100 .. 100 43 63 53 1,400 Cyprus 2.4 1.5 6 .. .. 99 .. .. .. 17 10 6,600 Czech Republic 1.9 1.5 10 .. .. .. 100 100 2 15 8 8,500 Denmark 1.7 1.9 5 .. .. .. .. .. .. 7 5 10,900 Dominican Republic 3.5 2.6 106 11 73 99 92 98 160 220 100 320 Ecuador 3.7 2.5 81 .. .. .. .. .. 61 230 140 270 Egypt, Arab Rep. 4.4 2.7 43 9 60 74 37 79 55 220 82 380 El Salvador 4.0 2.3 79 .. 73 94 90 96 59 200 110 350 Eritrea 6.2 4.5 59 .. .. .. .. .. .. 930 280 72 Estonia 2.0 1.6 19 .. .. .. 99 100 7 48 12 5,300 Ethiopia 7.1 4.2 58 34 15 28 .. 6 670 990 470 40 Finland 1.8 1.9 9 .. .. .. .. .. .. 7 8 7,600 France 1.8 2.0 6 .. 71 .. .. .. .. 13 8 6,600 Gabon 5.2 3.3 85 .. .. .. .. .. .. 260 260 110 Gambia, The 6.1 4.9 71 .. .. 98 44 57 .. 750 400 49 Georgia 2.2 1.6 42 .. 53 98 97 100 52 58 48 1,300 Germany 1.5 1.4 7 .. .. .. .. 100 .. 13 7 11,100 Ghana 5.6 4.2 66 35 24 90 40 57 450 630 350 66 Greece 1.4 1.4 10 .. .. .. .. .. .. 6 2 31,800 Guatemala 5.6 4.0 104 .. 54 93 .. 51 130 140 110 210 Guinea 6.7 5.2 143 21 9 88 31 46 980 1,200 680 26 Guinea-Bissau 6.6 5.1 102 25 14 93 .. 44 410 1,200 1,000 18 Haiti 5.4 3.3 43 38 32 85 23 26 630 670 300 93 112 2012 World Development Indicators 2.19 PEOPLE Reproductive health Total fertility Adolescent Unmet Contraceptive Pregnant Births attended Maternal Lifetime rate fertility rate need for prevalence women by skilled mortality risk of contraception rate receiving health staff ratio maternal prenatal mortality care Any method births per % of married % of married per 100,000 live births Probability births per 1,000 women women ages women ages National Modeled 1 woman woman ages 15–19 15–49 15–49 % % of total estimates estimates in: 1990 2010 2010 2005–10a 2005–10a 2005–10a 1990 2005–10a 2005–10 a 1990 2008 2008 Honduras 5.1 3.1 89 17 65 92 47 67 .. 210 110 240 Hungary 1.8 1.3 15 .. .. .. 99 100 19 23 13 5,500 India 3.9 2.6 79 13 54 75 .. 53 250 570 230 140 Indonesia 3.1 2.1 43 15 56 95 41 82 230 620 240 190 Iran, Islamic Rep. 4.8 1.7 27 .. 79 98 .. 97 25 150 30 1,500 Iraq 6.0 4.7 91 .. 50 84 54 80 84 93 75 300 Ireland 2.1 2.1 12 .. 65 .. .. .. .. 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 2.3 73 .. 72 99 92 98 .. 66 89 450 Japan 1.5 1.4 6 .. 54 .. 100 .. .. 12 6 12,200 Jordan 5.8 3.8 25 11 59 99 87 99 19 110 59 510 Kazakhstan 2.7 2.6 27 .. 51 100 99 100 37 78 45 950 Kenya 6.0 4.7 99 26 46 92 50 44 488 380 530 38 Korea, Dem. Rep. 2.4 2.0 1 .. .. 100 .. 100 77 270 250 230 Korea, Rep. 1.6 1.2 4 .. 80 .. .. .. .. 18 18 4,700 Kosovo 3.9 2.3 .. .. .. .. .. .. .. .. .. .. Kuwait 2.6 2.3 14 .. .. 100 .. 100 .. 10 9 4,500 Kyrgyz Republic 3.7 2.9 33 1 48 97 99 99 64 77 81 450 Lao PDR 6.2 2.7 34 .. 38 71 .. 37 410 1,200 580 49 Latvia 2.0 1.2 15 .. .. .. 100 100 32 57 20 3,600 Lebanon 3.1 1.8 16 .. .. .. .. .. .. 52 26 2,000 Lesotho 4.9 3.2 66 .. 47 92 .. 62 1,200 370 530 62 Liberia 6.5 5.2 131 36 11 79 .. 46 990 1,100 990 20 Libya 4.8 2.6 3 .. .. 93 .. 100 .. 100 64 540 Lithuania 2.0 1.6 18 .. .. .. 100 100 9 34 13 5,800 Macedonia, FYR 2.1 1.4 19 34 14 99 89 100 4 16 9 7,300 Madagascar 6.3 4.7 127 19 40 86 57 44 500 710 440 45 Malawi 6.8 6.0 111 .. 41 92 55 54 810 910 510 36 Malaysia 3.5 2.6 12 .. .. 79 93 99 29 56 31 1,200 Mali 7.1 6.3 176 31 8 70 .. 49 460 1,200 830 22 Mauritania 5.9 4.5 75 25 9 75 40 61 690 780 550 41 Mauritius 2.3 1.5 33 .. .. .. 91 99 .. 72 36 1,600 Mexico 3.4 2.3 68 .. 73 96 84 95 54 93 85 500 Moldova 2.4 1.5 31 7 68 98 100 100 45 62 32 2,000 Mongolia 4.1 2.5 20 14 55 100 .. 100 47 130 65 730 Morocco 4.0 2.3 13 .. .. .. 31 .. 130 270 110 360 Mozambique 6.2 4.9 134 .. 16 92 .. 55 500 1,000 550 37 Myanmar 3.4 2.0 14 .. 41 80 46 64 320 420 240 180 Namibia 5.2 3.2 62 21 55 95 68 81 450 180 180 160 Nepal 5.2 2.7 93 25 48 44 7 19 280 870 380 80 Netherlands 1.6 1.8 5 .. 69 .. .. .. .. 10 9 7,100 New Zealand 2.2 2.2 24 .. .. .. .. .. .. 18 14 3,800 Nicaragua 4.8 2.6 108 8 72 90 .. 74 67 190 100 300 Niger 7.8 7.1 199 16 18 46 15 18 650 1,400 820 16 Nigeria 6.4 5.5 114 20 15 58 31 39 550 1,100 840 23 Norway 1.9 2.0 8 .. 88 .. 100 .. .. 9 7 7,600 Oman 7.2 2.3 9 .. 24 99 .. 99 17 49 20 1,600 Pakistan 6.0 3.4 30 25 27 61 19 39 250 490 260 93 Panama 3.0 2.5 79 .. 52 96 86 89 60 86 71 520 Papua New Guinea 4.8 4.0 64 .. 32 79 .. 53 730 340 250 94 Paraguay 4.5 3.0 69 .. 79 96 66 82 130 130 95 310 Peru 3.8 2.5 51 8 74 95 53 84 93 250 98 370 Philippines 4.3 3.1 50 22 51 91 .. 62 160 180 94 320 Poland 2.0 1.4 13 .. .. .. 100 100 2 17 6 13,300 Portugal 1.4 1.3 14 .. 67 .. 98 .. .. 15 7 9,800 Puerto Rico 2.2 1.8 52 .. .. .. .. .. .. 29 18 3,000 Qatar 4.2 2.3 16 .. .. 100 .. 100 .. 15 8 4,400 2012 World Development Indicators 113 2.19 Reproductive health Total fertility Adolescent Unmet Contraceptive Pregnant Births attended Maternal Lifetime rate fertility rate need for prevalence women by skilled mortality risk of contraception rate receiving health staff ratio maternal prenatal mortality care Any method births per % of married % of married per 100,000 live births Probability births per 1,000 women women ages women ages National Modeled 1 woman woman ages 15–19 15–49 15–49 % % of total estimates estimates in: 1990 2010 2010 2005–10a 2005–10a 2005–10a 1990 2005–10a 2005–10 a 1990 2008 2008 Romania 1.8 1.4 30 .. .. .. 100 99 21 170 27 2,700 Russian Federation 1.9 1.5 26 .. 80 .. 99 100 17 74 39 1,900 Rwanda 7.0 5.4 37 38 52 98 26 69 .. 1,100 540 35 Saudi Arabia 5.8 2.8 18 .. 24 97 .. 97 14 41 24 1,300 Senegal 6.6 4.8 96 32 12 87 .. 52 400 750 410 46 Serbia 1.8 1.4 20 29 41 98 .. 99 9 13 8 7,500 Sierra Leone 5.7 5.0 120 28 8 87 .. 42 860 1,300 970 21 Singapore 1.9 1.2 6 .. .. .. .. .. .. 6 9 10,000 Slovak Republic 2.1 1.4 18 .. .. .. 100 100 10 15 6 13,300 Slovenia 1.5 1.6 5 .. .. .. 100 100 10 11 18 4,100 Somalia 6.6 6.3 69 26 15 26 .. 33 1,000 1,100 1,200 14 South Africa 3.7 2.5 54 .. .. 97 .. .. 400 230 410 100 South Sudan .. 3.9 .. .. .. 40 .. 19 .. .. .. .. Spain 1.3 1.4 12 .. 66 .. .. .. .. 7 6 11,400 Sri Lanka 2.5 2.3 23 .. 68 99 .. 99 39 91 39 1,100 Sudan 6.0 4.4 57 6 8 56 69 49 1,100 830 750 32 Swaziland 5.7 3.4 74 24 49 97 .. 82 589 260 420 75 Sweden 2.1 2.0 6 .. .. .. .. .. .. 7 5 11,400 Switzerland 1.6 1.5 4 .. .. .. .. 100 .. 8 10 7,600 Syrian Arab Republic 5.3 2.9 39 11 54 88 .. 96 .. 120 46 610 Tajikistan 5.2 3.3 27 24 37 80 90 83 86 120 64 430 Tanzania 6.2 5.5 129 25 34 88 44 49 450 880 790 23 Thailand 2.1 1.6 40 .. 80 99 .. 99 12 50 48 1,200 Timor-Leste 5.3 5.6 58 31 22 84 .. 29 560 650 370 44 Togo 6.3 4.1 59 41 15 87 31 60 .. 650 350 67 Trinidad and Tobago 2.4 1.6 33 27 43 96 .. 98 .. 86 55 1,100 Tunisia 3.6 2.0 5 .. 60 96 69 95 .. 130 60 860 Turkey 3.0 2.1 34 18 73 95 .. 95 29 68 23 1,900 Turkmenistan 4.3 2.4 18 .. 48 99 .. 100 12 91 77 500 Uganda 7.1 6.1 136 41 24 94 38 42 440 670 430 35 Ukraine 1.8 1.4 28 10 67 99 100 99 16 49 26 3,000 United Arab Emirates 4.4 1.7 25 .. .. 100 .. 100 0 28 10 4,200 United Kingdom 1.8 1.9 30 .. 84 .. .. .. .. 10 12 4,700 United States 2.1 2.1 33 .. 79 .. 99 .. 13 12 24 2,100 Uruguay 2.5 2.0 60 .. 78 96 .. 100 34 39 27 1,700 Uzbekistan 4.1 2.5 13 8 65 99 .. 100 21 53 30 1,400 Venezuela, RB 3.4 2.5 88 .. .. .. .. .. 57 84 68 540 Vietnam 3.6 1.8 24 .. 80 91 .. 88 69 170 56 850 West Bank and Gaza 6.5 4.5 50 .. 50 99 .. 99 .. .. .. .. Yemen, Rep. 8.7 5.2 71 24 28 47 16 36 .. 540 210 91 Zambia 6.5 6.3 142 27 41 94 51 47 590 390 470 38 Zimbabwe 5.2 3.3 58 13 59b 93b 70 60 b 730 390 790 42 World 3.2 w 2.5 w 53 w .. w 62 w 84 w 62 w 66 w 400 w 260 w 140 w Low income 5.7 4.1 94 25 34 69 .. 44 860 590 39 Middle income 3.3 2.3 51 .. 65 86 60 71 350 210 190 Lower middle income 4.2 2.9 68 14 50 78 38 57 540 300 100 Upper middle income 2.6 1.8 29 .. 81 94 89 98 110 60 880 Low & middle income 3.6 2.6 58 .. 61 83 58 65 440 290 120 East Asia & Pacific 2.6 1.8 19 .. 78 92 82 91 200 89 580 Europe & Central Asia 2.3 1.8 27 .. 69 .. 93 98 69 34 1,700 Latin America & Carib. 3.2 2.2 72 .. 75 97 74 90 140 86 480 Middle East & N. Africa 4.9 2.7 37 .. 62 85 47 81 210 88 380 South Asia 4.2 2.7 73 15 51 71 32 48 610 290 110 Sub-Saharan Africa 6.2 4.9 108 25 22 74 .. 46 870 650 31 High income 1.8 1.8 18 .. .. .. .. .. 15 15 3,900 Euro area 1.5 1.6 8 .. .. .. .. .. 11 7 10,100 a. Data are for most recent year available. b. Data are for 2011. 114 2012 World Development Indicators 2.19 PEOPLE Reproductive health About the data De�nitions Reproductive health is a state of physical and men- and Health Surveys attempt to measure maternal • Total fertility rate is the number of children that would tal well-being in relation to the reproductive system mortality by asking respondents about survivorship be born to a woman if she were to live to the end of her and its functions and processes. Means of achieving of sisters. The main disadvantage of this method childbearing years and bear children in accordance with reproductive health include education and services is that the estimates of maternal mortality that it the age-specific fertility rate of the specified year. • Ado- during pregnancy and childbirth, safe and effec- produces pertain to 12 years or so before the sur- lescent fertility rate is the number of births per 1,000 tive contraception, and prevention and treatment vey, making them unsuitable for monitoring recent women ages 15–19. • Unmet need for contraception is of sexually transmitted diseases. Complications of changes or observing the impact of interventions. the percentage of fertile, married women of reproductive pregnancy and childbirth are the leading cause of In addition, measurement of maternal mortality is age who do not want to become pregnant and are not death and disability among women of reproductive subject to many types of errors. Even in high-income using contraception. • Contraceptive prevalence rate age in developing countries. countries with reliable vital registration systems, is the percentage of women married or in union ages Total and adolescent fertility rates are based on misclassification of maternal deaths has been found 15–49 who are practicing, or whose sexual partners data on registered live births from vital registration to lead to serious underestimation. are practicing, any form of contraception. • Pregnant systems or, in the absence of such systems, from The national estimates of maternal mortality women receiving prenatal care are women attended at censuses or sample surveys. The estimated rates ratios in the table are based on national surveys, least once during pregnancy by skilled health person- are generally considered reliable measures of fertility vital registration records, and surveillance data or nel for pregnancy- related reasons. • Births attended by in the recent past. Where no empirical information are derived from community and hospital records. skilled health staff are live births attended by person- on age- specific fertility rates is available, a model is The modeled estimates are based on an exercise by nel trained to give women the necessary care during used to estimate the share of births to adolescents. the World Health Organization (WHO), United Nations pregnancy, labor, and postpartum; to conduct deliveries For countries without vital registration systems fertil- Children’s Fund (UNICEF), United Nations Population on their own; and to care for newborns. • Maternal mor- ity rates are generally based on extrapolations from Fund (UNFPA), and World Bank and include country- tality ratio is the number of women who die from preg- trends observed in censuses or surveys from earlier level time series data. For countries with complete nancy-related causes while pregnant or within 42 days years. vital registration systems with good attribution of of pregnancy termination per 100,000 live births. • Life- More couples in developing countries want to limit cause of death, the data are used directly to esti- time risk of maternal death is the probability (1 in the or postpone childbearing but are not using effec- mate maternal mortality. For countries without com- number of women likely to die due to a maternal cause) tive contraception. These couples have an unmet plete registration data but with other types of data that a 15-year-old girl will eventually die due to a mater- need for contraception. Common reasons are lack and for countries with no data, maternal mortality is nal cause, if throughout her lifetime she experiences of knowledge about contraceptive methods and estimated with a multilevel regression model using the maternal death risk and overall fertility and mortality concerns about possible side effects. This indica- available national maternal mortality data and socio- rates of the specified year for a given population. tor excludes women not exposed to the risk of unin- economic information, including fertility, birth atten- Data sources tended pregnancy because of menopause, infertility, dants, and GDP. The methodology differs from that Data on total fertility are from the United Nations or postpartum anovulation. used for previous estimates, so data should not be Population Division’s World Population Prospects: The Contraceptive prevalence reflects all methods— compared across editions. For further information 2010 Revision; census reports and other statistical ineffective traditional methods as well as highly on methodology, see the original source. Neither set publications from national statistical offices; house- effective modern methods. Contraceptive prevalence of ratios can be assumed to provide an exact esti- hold surveys by national agencies, ICF International rates are obtained mainly from household surveys, mate of maternal mortality for any of the countries (for MEASURE DHS), and the U.S. Centers for Dis- including Demographic and Health Surveys, Multiple in the table. ease Control and Prevention; Eurostat’s Demographic Indicator Cluster Surveys, and contraceptive preva- In countries with a high risk of maternal death, Statistics; and the U.S. Bureau of the Census Inter- lence surveys (see Primary data documentation for many girls die before reaching reproductive age. Life- national Data Base. Data on adolescent fertility are the most recent survey year). Unmarried women are time risk of maternal mortality refers to the prob- from World Population Prospects: The 2010 Revision, often excluded from such surveys, which may bias ability that a 15-year-old girl will eventually die due with annual data linearly interpolated by the World the estimates. to a maternal cause. Bank’s Development Data Group. Data on unmet need Good prenatal and postnatal care improves mater- For the indicators that are from household surveys, for contraception and contraceptive prevalence are nal health and reduces maternal and infant mortality. the year in the table refers to the survey year. For from household surveys, including MEASURE  DHS However, indicators on use of antenatal care ser- more information, consult the original sources. Demographic and Health Surveys by ICF International vices provide no information on the content or quality and Multiple Indicator Cluster Surveys by UNICEF. of the services. Data on antenatal care are obtained Data on pregnant women receiving prenatal care, mostly from household surveys, which ask women births attended by skilled health staff, and national who have had a live birth whether and from whom estimates of maternal mortality are from UNICEF’s they received antenatal care. State of the World’s Children 2012 and Childinfo and The share of births attended by skilled health staff MEASURE DHS Demographic and Health Surveys by is an indicator of a health system’s ability to provide ICF International. Modeled estimates of maternal mor- adequate care for pregnant women. tality and lifetime risk of maternal mortality are from Maternal mortality ratios are generally of unknown WHO, UNICEF, UNFPA, and the World Bank’s Trends in reliability, as are many other cause-specific mortality Maternal Mortality: 1990–2008 (2010). indicators. Household surveys such as Demographic 2012 World Development Indicators 115 2.20 Nutrition and growth Prevalence of Prevalence of child malnutrition Prevalence of undernourishment overweight children % of children under age 5 Underweight Stunting Wasting % of children under age 5 % of population Male Female Male Female Male Female Male Female 1990–92 2006–08 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a Afghanistan .. .. .. .. .. .. .. .. .. .. Albania <5 <5 6.6 6.0 22.8 23.4 11.5 7.3 23.3 23.4 Algeria <5 <5 3.7 3.7 16.7 15.0 3.9 4.1 13.4 12.4 Angola 67 41 16.6 14.6 32.4 26.1 8.2 8.1 .. .. Argentina <5 <5 2.4 2.2 8.2 8.1 1.1 1.4 10.2 9.5 Armenia 45 21 3.4 5.2 18.8 17.4 5.8 5.1 13.9 9.1 Australia <5 <5 .. .. .. .. .. .. .. .. Austria <5 <5 .. .. .. .. .. .. .. .. Azerbaijan 27 <5 8.7 8.0 28.5 24.9 7.8 5.7 14.9 12.7 Bahrain .. .. .. .. .. .. .. .. .. .. Bangladesh 38 26 40.2 42.4 43.8 42.6 18.4 16.5 1.2 1.0 Belarus <5 <5 1.5 1.0 4.7 4.2 2.8 1.6 11.3 8.1 Belgium <5 <5 .. .. .. .. .. .. .. .. Benin 20 12 22.7 17.6 47.9 41.6 9.0 7.8 11.6 11.3 Bolivia 29 27 4.9 4.0 28.1 26.2 2.0 0.8 9.2 8.1 Bosnia and Herzegovina <5 <5 2.2 1.0 12.8 10.7 3.8 4.3 27.4 23.9 Botswana 19 25 12.1 10.2 34.0 28.7 7.5 6.8 11.3 11.1 Brazil 11 6 2.2 2.1 8.3 5.8 1.8 1.4 6.9 7.7 Bulgaria <5 <5 .. .. .. .. .. .. .. .. Burkina Faso 14 8 27.1 24.7 37.9 32.0 11.9 10.7 7.9 7.6 Burundi 44 62 .. .. .. .. .. .. .. .. Cambodia 38 25 28.8b 29.1b 42.3b 39.4b 11.2b 10.5b 1.9b 1.9b Cameroon 33 22 18.9 14.3 39.1 33.7 8.2 6.4 9.8 9.5 Canada <5 <5 .. .. .. .. .. .. .. .. Central African Republic 44 40 .. .. .. .. .. .. .. .. Chad 60 39 .. .. .. .. .. .. .. .. Chile 7 <5 0.6 0.5 2.2 1.8 0.3 0.2 9.8 9.1 China 18 c 10 c 3.5 3.3 9.9 8.9 2.4 2.1 7.5 5.6 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 15 9 3.5 3.3 13.7 11.6 0.9 0.9 5.4 4.2 Congo, Dem. Rep. .. .. 30.4 26.1 48.5 43.3 15.5 12.5 6.5 7.2 Congo, Rep. 42 13 12.9 10.6 33.2 29.0 8.4 7.7 8.4 8.6 Costa Rica <5 <5 0.6 1.8 4.8 6.6 0.6 1.5 8.3 7.9 Côte d’Ivoire 15 14 30.3 28.4 40.1 37.8 15.3 12.4 5.0 4.9 Croatia <5 <5 .. .. .. .. .. .. .. .. Cuba 6 <5 .. .. .. .. .. .. .. .. Cyprus <5 <5 .. .. .. .. .. .. .. .. Czech Republic <5 <5 .. .. .. .. .. .. .. .. Denmark <5 <5 .. .. .. .. .. .. .. .. Dominican Republic 28 24 3.2 3.7 11.2 8.9 2.5 2.1 9.0 7.5 Ecuador 23 15 .. .. .. .. .. .. .. .. Egypt, Arab Rep. <5 <5 8.1 5.4 33.0 28.4 8.8 7.1 19.8 21.2 El Salvador 13 9 6.5 6.7 21.3 19.8 2.4 0.7 6.3 5.0 Eritrea 67 65 .. .. .. .. .. .. .. .. Estonia <5 <5 .. .. .. .. .. .. .. .. Ethiopia 69 41 36.5 32.8 51.8 49.6 13.7 10.8 5.7 4.5 Finland <5 <5 .. .. .. .. .. .. .. .. France <5 <5 .. .. .. .. .. .. .. .. Gabon 6 <5 .. .. .. .. .. .. .. .. Gambia, The 14 19 16.7 15.0 28.6 26.6 8.1 6.6 2.9 2.5 Georgia 58 6 1.3 1.0 12.3 10.2 1.8 1.5 21.3 18.3 Germany <5 <5 0.9 1.3 1.5 1.2 1.2 0.8 3.6 3.3 Ghana 28 5 15.7 12.9 30.5 26.7 9.7 7.7 5.8 5.9 Greece <5 <5 .. .. .. .. .. .. .. .. Guatemala 15 22 13.9 12.1 48.7 47.3 1.1 1.1 5.3 4.6 Guinea 20 16 21.9 19.7 41.5 38.5 8.9 7.8 5.2 5.0 Guinea-Bissau 22 22 16.6 17.8 29.7 26.3 5.3 5.8 17.6 16.5 Haiti 63 57 20.4 17.4 33.2 26.5 10.2 10.4 4.4 3.5 116 2012 World Development Indicators 2.20 PEOPLE Nutrition and growth Prevalence of Prevalence of child malnutrition Prevalence of undernourishment overweight children % of children under age 5 Underweight Stunting Wasting % of children under age 5 % of population Male Female Male Female Male Female Male Female 1990–92 2006–08 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a Honduras 19 12 8.9 8.3 31.5 28.3 1.6 1.1 6.3 5.2 Hungary <5 <5 .. .. .. .. .. .. .. .. India 20 19 43.1 43.9 47.9 48.0 20.7 19.3 2.2 1.7 Indonesia 16 13 20.7 18.6 41.3 38.8 15.7 13.8 11.3 11.2 Iran, Islamic Rep. <5 <5 .. .. .. .. .. .. .. .. Iraq .. .. 7.7 6.6 28.7 26.2 6.2 5.4 15.6 14.3 Ireland <5 <5 .. .. .. .. .. .. .. .. Israel <5 <5 .. .. .. .. .. .. .. .. Italy <5 <5 .. .. .. .. .. .. .. .. Jamaica 11 5 1.9 2.6 3.0 4.4 1.9 2.2 .. .. Japan <5 <5 .. .. .. .. .. .. .. .. Jordan <5 <5 1.6 2.1 7.9 8.7 1.6 1.6 7.9 5.2 Kazakhstan <5 <5 5.4 4.3 17.9 16.9 4.5 2.8 15.1 14.5 Kenya 33 33 17.3 15.5 37.3 33.1 8.2 5.8 4.7 5.3 Korea, Dem. Rep. 21 35 18.8 18.8 32.4 32.4 5.0 5.3 0.0 0.0 Korea, Rep. <5 <5 .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 20 5 2.0 1.5 4.2 3.4 1.5 2.1 10.0 8.0 Kyrgyz Republic 17 11 2.9 2.5 18.7 17.5 3.5 3.2 12.7 8.6 Lao PDR 31 22 32.5 30.6 48.3 46.8 7.8 6.8 1.5 1.0 Latvia <5 <5 .. .. .. .. .. .. .. .. Lebanon <5 <5 .. .. .. .. .. .. .. .. Lesotho 15 14 16.0 11.1 43.1 35.0 4.2 3.5 7.7 6.9 Liberia 30 32 21.9 18.7 41.9 36.7 7.9 7.8 4.9 3.5 Libya <5 <5 6.3 4.8 22.2 19.6 6.8 6.1 23.2 21.6 Lithuania <5 <5 .. .. .. .. .. .. .. .. Macedonia, FYR <5 <5 1.7 1.9 13.5 9.2 2.4 4.5 16.6 15.8 Madagascar 21 25 .. .. 51.6 46.7 .. .. .. .. Malawi 43 27 15.2 12.6 51.8 44.1 4.4 3.8 10.3 8.2 Malaysia <5 <5 13.2 12.7 17.2 17.2 .. .. .. .. Mali 27 12 29.7 26.0 40.7 36.2 16.2 14.3 4.9 4.6 Mauritania 12 8 17.8 13.9 25.8 20.0 9.4 6.8 1.3 0.6 Mauritius 7 5 .. .. .. .. .. .. .. .. Mexico <5 <5 4.3 2.6 16.0 15.0 2.5 1.5 8.4 6.7 Moldova <5 <5 3.0 3.4 11.0 11.5 6.0 5.6 8.9 9.3 Mongolia 28 27 5.3 5.3 29.2 25.6 2.6 2.8 15.6 12.6 Morocco 6 <5 .. .. .. .. .. .. .. .. Mozambique 59 38 20.6 16.0 46.8 40.7 4.9 3.5 4.1 3.2 Myanmar .. .. .. .. .. .. .. .. .. .. Namibia 32 18 18.5 16.5 32.0 27.1 7.3 7.8 4.9 4.4 Nepal 21 17 37.7 39.8 49.1 49.6 13.0 12.4 0.6 0.6 Netherlands <5 <5 .. .. .. .. .. .. .. .. New Zealand <5 <5 .. .. .. .. .. .. .. .. Nicaragua 50 19 5.6 5.9 24.0 21.9 1.5 1.4 6.7 5.6 Niger 37 16 42.1 37.5 56.8 52.6 13.8 10.9 3.6 3.5 Nigeria 16 6 28.6 24.8 43.1 38.8 14.8 14.0 10.3 10.7 Norway <5 <5 .. .. .. .. .. .. .. .. Oman .. .. 8.9 8.3 11.3 8.5 8.1 6.0 1.5 2.0 Pakistan 25 25 .. .. .. .. .. .. .. .. Panama 18 15 .. .. .. .. .. .. .. .. Papua New Guinea .. .. 21.0 14.6 47.4 39.6 4.8 4.0 4.2 2.5 Paraguay 16 10 .. .. .. .. .. .. .. .. Peru 27 16 4.5 4.5 30.5 25.9 0.9 0.8 9.8 9.8 Philippines 24 13 20.9 20.6 33.5 31.1 7.4 6.4 3.6 2.9 Poland <5 <5 .. .. .. .. .. .. .. .. Portugal <5 <5 .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 117 2.20 Nutrition and growth Prevalence of Prevalence of child malnutrition Prevalence of undernourishment overweight children % of children under age 5 Underweight Stunting Wasting % of children under age 5 % of population Male Female Male Female Male Female Male Female 1990–92 2006–08 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a 2005–10a Romania <5 <5 .. .. .. .. .. 1.8 .. .. Russian Federation <5 <5 .. .. .. .. .. .. .. .. Rwanda 44 32 18.9 17.2 53.1 50.3 5.0 4.7 7.2 6.3 Saudi Arabia <5 <5 6.1 4.5 10.8 7.8 12.7 10.8 6.3 6.0 Senegal 22 19 14.4 14.6 21.3 18.8 8.8 8.5 3.0 1.8 Serbia <5d <5d 2.2 1.3 8.2 8.0 4.8 4.2 20.4 18.2 Sierra Leone 45 35 24.2 18.5 39.5 35.4 10.4 10.6 10.3 9.9 Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic <5 <5 .. .. .. .. .. .. .. .. Slovenia <5 <5 .. .. .. .. .. .. .. .. Somalia .. .. 34.2 31.3 42.7 41.3 14.4 11.9 4.9 4.5 South Africa <5 <5 .. .. .. .. .. .. .. .. South Sudan .. .. .. .. .. .. .. .. .. .. Spain <5 <5 .. .. .. .. .. .. .. .. Sri Lanka 28 20 21.6 21.6 19.8 18.7 12.1 11.5 0.7 1.0 Sudan 39 22 33.4 30.0 39.8 35.8 21.9 20.1 5.1 5.5 Swaziland 12 19 6.3 5.9 33.0 26.1 3.5 2.4 11.8 10.9 Sweden <5 <5 .. .. .. .. .. .. .. .. Switzerland <5 <5 .. .. .. .. .. .. .. .. Syrian Arab Republic <5 <5 11.5 8.7 28.4 26.5 12.5 10.5 17.8 18.1 Tajikistan 34 26 15.9 14.0 41.1 37.2 6.8 6.7 7.2 6.2 Tanzania 29 34 17.8 14.6 45.9 39.2 5.8 4.0 6.0 5.0 Thailand 26 16 6.9 7.1 16.5 15.0 4.6 4.8 8.8 7.2 Timor-Leste 39 31 46.8 43.7 59.8 55.6 20.4 17.4 6.0 5.7 Togo 43 30 20.5 20.5 28.4 25.2 6.1 5.9 4.2 5.1 Trinidad and Tobago 11 11 .. .. .. .. .. .. .. .. Tunisia <5 <5 3.7 2.9 9.9 8.0 3.6 3.3 8.5 9.2 Turkey <5 <5 .. .. .. .. .. .. .. .. Turkmenistan 9 7 .. .. .. .. .. .. .. .. Uganda 19 22 18.2 14.6 41.2 36.1 7.7 4.9 5.0 4.7 Ukraine <5 <5 .. .. .. .. .. .. .. .. United Arab Emirates <5 <5 .. .. .. .. .. .. .. .. United Kingdom <5 <5 .. .. .. .. .. .. .. .. United States <5 <5 .. .. .. .. .. .. .. .. Uruguay 5 <5 .. .. .. .. .. .. .. .. Uzbekistan 5 11 4.6 4.3 19.5 19.7 5.3 3.7 13.1 12.5 Venezuela, RB 10 7 .. .. .. .. .. .. .. .. Vietnam 31 11 20.5 19.9 31.9 29.0 10.2 9.1 3.4 2.5 West Bank and Gaza 10 21 2.2 2.3 12.3 11.2 1.7 1.8 13.4 9.4 Yemen, Rep. 30 30 .. .. .. .. .. .. .. .. Zambia 35 44 16.9 13.0 48.8 42.9 6.0 5.2 8.6 8.2 Zimbabwe 40 30 15.1 12.9 38.6 33.1 7.6 6.9 9.5 8.7 World 16 w 13 w Low income 38 29 Middle income 17 12 Lower middle income 20 17 Upper middle income 14 9 Low & middle income 19 14 East Asia & Pacific 19 11 Europe & Central Asia 7 6 Latin America & Carib. 12 9 Middle East & N. Africa 7 7 South Asia 22 20 Sub-Saharan Africa 32 22 High income <5 <5 Euro area <5 <5 a. Data are for the most recent year available. b. Data are for 2011. c. Includes Hong Kong SAR, China; Macao SAR, China; and Taiwan, China. d. Includes Montenegro. 118 2012 World Development Indicators 2.20 PEOPLE Nutrition and growth About the data De�nitions Good nutrition is the cornerstone for survival, health childhood obesity and a high prevalence of diabe- • Prevalence of undernourishment is the percentage and development. Well nourished children perform tes, respiratory disease, high blood pressure, and of the population whose dietary energy consump- better in school, grow into healthy adults, and in psychosocial and orthopedic disorders (de Onis and tion is continuously below a minimum requirement turn give their children a better start in life. Well Blössner  2003). Childhood obesity is associated for maintaining a healthy life and carrying out light nourished women face fewer risks during pregnancy with a higher chance of obesity, premature death, physical activity with an acceptable minimum weight and childbirth, and their children set off on firmer and disability in adulthood. In addition to increased for height. • Prevalence of child malnutrition is the developmental paths, both physically and mentally future risks, obese children experience breathing percent age of children under age 5 whose weight (United Nations Children’s Fund [UNICEF], www. difficulties and increased risk of fractures, hyper- for age (underweight) or height for age (stunting) childinfo.org). tension, early markers of cardiovascular disease, is more than two standard deviations below the Data on undernourishment are from the Food and insulin resistance, and psychological effects. Chil- median for the international reference population Agriculture Organization (FAO) of the United Nations dren in low- and middle-income countries are more ages 0–59 months. Height is measured by recum- and measure food deprivation based on average vulnerable to inadequate nutrition before birth and bent length for children up to two years old and by food available for human consumption per person, in infancy and early childhood. Many of these chil- stature while standing for older children. Data are the level of inequality in access to food, and the dren are exposed to high-fat, high-sugar, high-salt, based on the WHO child growth standards released minimum calories required for an average person. calorie-dense, micronutrient-poor foods, which tend in 2006. • Prevalence of over weight children is the From a policy and program standpoint, however, to be lower in cost than more nutritious foods. These percentage of children under age 5 whose weight for this measure has its limits. First, food insecurity dietary patterns, in conjunction with low levels of height is more than two standard deviations above exists even where food availability is not a problem physical activity, result in sharp increases in child- the median for the international reference population because of inadequate access of poor households hood obesity, while undernutrition continues (World of the corresponding age as established by the WHO to food. Second, food insecurity is an individual Health Organization [WHO]). child growth standards released in 2006. or household phenomenon, and the average food New international growth reference standards for available to each person, even corrected for possible infants and young children were released in 2006 effects of low income, is not a good predictor of food by the WHO to monitor children’s nutritional status. insecurity among the population. And third, nutrition Differences in growth to age 5 are influenced more security is determined not only by food security but by nutrition, feeding practices, environment, and also by the quality of care of mothers and children healthcare than by genetics or ethnicity. The previ- and the quality of the household’s health environ- ously reported data were based on the U.S. National ment (Smith and Haddad 2000). Center for Health Statistics–WHO growth reference. Undernourished children have lower resistance to Because of the change in standards, the data in this infection and are more likely to die from common edition should not be compared with data in editions childhood ailments such as diarrheal diseases and prior to 2008. respiratory infections. Frequent illness saps the For indicators from household surveys, the year in nutritional status of those who survive, locking them the table refers to the survey year. For more informa- into a vicious cycle of recurring sickness and falter- tion, consult the original sources. ing growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of under- weight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birthweight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted depri- Data sources vation and as an indicator of long-term changes in malnutrition. Data on undernourishment are from the FAO’s Estimates of overweight children are also from The State of Food Insecurity in the World. Data on national survey data. Once considered only a high- malnutrition and overweight children are from the income economy problem, overweight children have WHO’s Global Database on Child Growth and Mal- become a growing concern in developing coun- nutrition (www.who.int/nutgrowthdb). tries. Research shows an association between 2012 World Development Indicators 119 2.21 Nutrition intake and supplements Low-birthweight Exclusive Consumption of Vitamin A Prevalence babies breastfeeding iodized supplementation of anemia salt % % of children under % of % of children % of births 6 months households 6–59 months Children under age 5 Pregnant women 2005–10a 2005–10a 2005–10a 2010 2005–10a 2005–10a Afghanistan .. 83 .. 96 .. 61 Albania 7 39 76 .. 31 34 Algeria 6 7 61 .. 43 43 Angola .. .. 45 28 .. 57 Argentina 7 .. .. .. 17 31 Armenia 7 35 97 .. 37 .. Australia .. .. .. .. 8 12 Austria .. .. .. .. 11 15 Azerbaijan 10 12 54 89b .. .. Bahrain .. .. .. .. 25 28 Bangladesh 22 43 84 100 .. .. Belarus 4 9 94 .. 27 26 Belgium .. .. .. .. 9 13 Benin 15 43 67 100 78 75 Bolivia 6 60 89 24 .. .. Bosnia and Herzegovina 5 18 62 .. 27 35 Botswana 13 20 .. 91 .. 21 Brazil 8 40 96 .. 55 29 Bulgaria 9 .. 100 .. 27 30 Burkina Faso 16 16 34 100 .. .. Burundi 11 69 98 73 56 47 Cambodia 8 74 83 95 55 44 Cameroon 11 21 49 89 .. .. Canada .. .. .. .. 8 12 Central African Republic 13 23 62 0 .. .. Chad .. 3 .. 68 71 60 Chile 6 85 .. .. 24 28 China 3 28 97 .. .. .. Hong Kong SAR, China .. .. .. .. .. .. Colombia 6 43 .. .. 28 31 Congo, Dem. Rep. 10 37 59 83 71 67 Congo, Rep. 13 19 82 84 66 55 Costa Rica 7 15 .. .. .. .. Côte d’Ivoire 17 4 .. 100 69 55 Croatia 5 98 88 .. 23 28 Cuba 5 26 88 .. 27 39 Cyprus .. .. .. .. 19 25 Czech Republic .. .. .. .. 18 22 Denmark .. .. .. .. 9 12 Dominican Republic 11 9 19 .. 35 40 Ecuador 8 .. .. .. 38 38 Egypt, Arab Rep. 13 53 79 68b 49 34 El Salvador .. 31 .. .. .. .. Eritrea .. .. .. 44 70 55 Estonia .. .. .. .. 23 23 Ethiopia 20 49 20 84 54 31 Finland .. .. .. .. 11 15 France .. .. .. .. 8 11 Gabon .. .. .. 0 44 46 Gambia, The 11 36 21 100 .. .. Georgia 5 11 100 .. 41 42 Germany .. .. .. .. 8 12 Ghana 13 63 32 93 .. .. Greece .. .. .. .. 12 19 Guatemala 11 50 76 36 .. .. Guinea 12 48 41 97 76 .. Guinea-Bissau 11 38 12 100 75 58 Haiti 25 41 3 21 .. 50 120 2012 World Development Indicators 2.21 PEOPLE Nutrition intake and supplements Low-birthweight Exclusive Consumption of Vitamin A Prevalence babies breastfeeding iodized supplementation of anemia salt % % of children under % of % of children % of births 6 months households 6–59 months Children under age 5 Pregnant women 2005–10a 2005–10a 2005–10a 2010 2005–10a 2005–10a Honduras 10 30 .. .. .. 21 Hungary .. .. .. .. 19 21 India 28 46 51 34 74 50 Indonesia 11 15 62 80 44 44 Iran, Islamic Rep. 7 23 99 .. 35 .. Iraq 15 25 28 .. 56 38 Ireland .. .. .. .. 10 15 Israel .. .. .. .. 12 17 Italy .. .. .. .. 11 15 Jamaica 14 15 .. .. .. .. Japan .. .. .. .. 11 15 Jordan 13 22 .. .. .. .. Kazakhstan 6 17 92 .. .. 26 Kenya 8 32 98 62 .. .. Korea, Dem. Rep. 6 .. 25 99 .. .. Korea, Rep. .. .. .. .. .. 23 Kosovo .. .. .. .. .. .. Kuwait .. .. .. .. .. 31 Kyrgyz Republic 5 32 76 97 .. 34 Lao PDR 11 26 84 83 .. 56 Latvia .. .. .. .. 27 25 Lebanon .. .. .. .. .. 32 Lesotho .. 54 84 .. 49 25 Liberia 14 34 .. 97 .. .. Libya .. .. .. .. 34 34 Lithuania .. .. .. .. 24 24 Macedonia, FYR 6 16 94 .. .. 32 Madagascar 16 51 53 95 .. .. Malawi 13 72 50 96 73 47 Malaysia 11 .. 18 .. 32 .. Mali 19 38 79 99 .. .. Mauritania 34 46 23 97 68 53 Mauritius .. .. .. .. .. .. Mexico 7 .. .. .. 24 21 Moldova 6 46 60 .. 41 36 Mongolia 5 57 83 61 .. 37 Morocco .. .. 21 .. .. .. Mozambique 16 37 25 100 .. 52 Myanmar 9 24 93 94 63 50 Namibia 16 24 .. 13 41 31 Nepal 21 53 .. 91 48 42 Netherlands .. .. .. .. 9 13 New Zealand .. .. .. .. 11 18 Nicaragua 9 31 .. 7 20 .. Niger 27 27 32 98 84 61 Nigeria 12 13 .. 91 .. .. Norway .. .. .. .. 6 9 Oman 12 .. .. .. .. .. Pakistan 32 37 .. 87 .. .. Panama .. .. .. .. .. .. Papua New Guinea 10 56 92 14 60 55 Paraguay 6 24 94 .. 30 39 Peru 8 68 .. .. .. .. Philippines 21 34 81 91 21 43 Poland .. .. .. .. 23 25 Portugal .. .. .. .. 13 17 Puerto Rico .. .. .. .. .. .. Qatar .. .. .. .. .. 29 2012 World Development Indicators 121 2.21 Nutrition intake and supplements Low-birthweight Exclusive Consumption of Vitamin A Prevalence babies breastfeeding iodized supplementation of anemia salt % % of children under % of % of children % of births 6 months households 6–59 months Children under age 5 Pregnant women 2005–10a 2005–10a 2005–10a 2010 2005–10a 2005–10a Romania .. .. .. .. 40 30 Russian Federation 6 .. .. .. 27 21 Rwanda 6 85 88 92 56 .. Saudi Arabia .. .. .. .. 33 32 Senegal 19 34 41 97 83 58 Serbia 6 15 32 .. .. .. Sierra Leone 14 11 58 100 83 60 Singapore .. .. .. .. 19 24 Slovak Republic .. .. .. .. 23 25 Slovenia .. .. .. .. 14 19 Somalia 11 9 1 62 .. .. South Africa .. .. .. 39 .. 22 South Sudan .. .. .. .. .. .. Spain .. .. .. .. 13 18 Sri Lanka 17 76 92 85 .. .. Sudan .. 34 11 82 85 58 Swaziland 9 44 52 38 47 24 Sweden .. .. .. .. 9 13 Switzerland .. .. .. .. 6 .. Syrian Arab Republic 10 43 79 33b 41 39 Tajikistan 10 25 62 95 .. 45 Tanzania 10 50 59 99 72 58 Thailand 7 15 47 .. .. .. Timor-Leste .. 52 60 48 .. .. Togo 11 63 32 100 52 50 Trinidad and Tobago 19 13 28 .. 30 30 Tunisia 5 6 .. .. .. .. Turkey 11 42 69 .. 33 40 Turkmenistan 4 11 87 .. .. 30 Uganda 14 60 96 64 73 64 Ukraine 4 18 18 .. .. 27 United Arab Emirates 6 .. .. .. 28 28 United Kingdom .. .. .. .. .. 15 United States .. .. .. .. .. 6 Uruguay 9 57 .. .. 19 27 Uzbekistan 5 26 53 94 .. .. Venezuela, RB 8 .. .. .. 33 40 Vietnam 5 17 93 95b .. .. West Bank and Gaza 7 27 86 .. .. .. Yemen, Rep. .. .. .. .. 68 58 Zambia 11 61 .. 92 .. .. Zimbabwe 11 32c 91 49 58 47 World 15 w 37 w 70 w .. w .. w .. w Low income 15 44 62 88 .. .. Middle income 15 35 71 .. .. .. Lower middle income 21 37 54 56 65 48 Upper middle income 5 30 91 .. .. .. Low & middle income 15 37 70 .. .. .. East Asia & Pacific 6 26 86 .. .. .. Europe & Central Asia 7 .. .. .. 30 30 Latin America & Carib. 8 .. .. .. 36 .. Middle East & N. Africa 11 34 69 .. 48 .. South Asia 27 47 55 50 74 50 Sub-Saharan Africa 13 35 50 86 .. .. High income .. .. .. .. .. 13 Euro area .. .. .. .. 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. Data are for 2011. 122 2012 World Development Indicators 2.21 PEOPLE Nutrition intake and supplements About the data De�nitions Low birthweight, which is associated with maternal newborns in low- and middle-income countries •  Low-birthweight babies are newborns weigh- malnutrition, raises the risk of infant mortality and remain unprotected from the lifelong consequences ing less than 2.5 kilograms within the first hours stunts growth in infancy and childhood. There is also of brain damage associated with iodine deficiency of life, before significant postnatal weight loss has emerging evidence that low-birthweight babies are disorders, which affect a child’s ability to learn and occurred. • Exclusive breastfeeding is the percent- more prone to noncommunicable diseases such as to earn a living as an adult, thereby preventing chil- age of children less than six months old who were diabetes and cardiovascular diseases. Low birth- dren, communities, and countries from fulfilling their fed breast milk alone (no other liquids) in the past 24 weight can arise as a result of a baby being born potential (UNICEF, www.childinfo.org). Widely used hours. • Consumption of iodized salt is the percent- too soon or too small for gestational age. Babies and inexpensive, iodized salt is the best source of age of households that use edible salt fortified with born prematurely who are also small for their ges- iodine, and a global campaign to iodize edible salt iodine. • Vitamin A supplementation is the percent- tational age have the worst prognosis. In low- and is significantly reducing the risks. The data on con- age of children ages 6–59 months old who received middle-income countries low birthweight stems sumption of iodized salt are derived from household at least two doses of vitamin A in the previous year. primarily from poor maternal health and nutrition. surveys. • Prevalence of anemia, children under age 5, is the Three factors have the most impact: poor maternal Vitamin A is essential for immune system function- percentage of children under age 5 whose hemoglo- nutritional status before conception, mother’s short ing. Vitamin A deficiency, a leading cause of blind- bin level is less than 110 grams per liter at sea level. stature (due mostly to undernutrition and infections ness, also causes a greater risk of dying from a range •  Prevalence of anemia, pregnant women, is the during childhood), and poor nutrition during preg- of childhood ailments such as measles, malaria, and percentage of pregnant women whose hemoglobin nancy (United Nations Children’s Fund [UNICEF], diarrhea. In low- and middle-income countries, where level is less than 110 grams per liter at sea level. www.childinfo.org). Estimates of low-birthweight vitamin A is consumed largely in fruits and vegeta- infants are drawn mostly from hospital records and bles, daily per capita intake is often insufficient to household surveys. Many births in developing coun- meet dietary requirements. Providing young children tries take place at home and are seldom recorded. A with two high-dose vitamin A capsules a year is a hospital birth may indicate higher income and there- safe, cost-effective, efficient strategy for eliminat- fore better nutrition, or it could indicate a higher risk ing vitamin A deficiency and improving child survival. birth. Caution should therefore be used in interpret- Giving vitamin A to new breastfeeding mothers helps ing the data. protect their children during the first months of life. For optimal infant and young child feeding, moth- Food fortification with vitamin A is being introduced ers initiate breastfeeding within one hour of birth, in many developing countries. breastfeed exclusively for the first six months, and Anemia is a condition in which the number of red continue to breastfeed for two years or more while blood cells or their oxygen-carrying capacity is insuf- providing nutritionally adequate, safe, and age- ficient to meet physiologic needs, which vary by age, appropriate solid, semisolid, and soft foods (UNICEF, sex, altitude, smoking status, and pregnancy sta- www.childinfo.org). Optimal breastfeeding can save tus. In its severe form it is associated with fatigue, an estimated 1.4 million children a year. Breast milk weakness, dizziness, and drowsiness (World Health alone contains all the nutrients, antibodies, hor- Organization [WHO], www.who.int/topics/anaemia/). mones, and antioxidants an infant needs to thrive. Children under age 5 and pregnant women have the It protects babies from diarrhea and acute respira- highest risk for anemia. Data on anemia are com- tory infections, stimulates their immune systems and piled by the WHO based mainly on nationally repre- response to vaccination, and may confer cognitive sentative surveys between 1993 and 2005, which benefits. The data on breastfeeding are derived from measured hemoglobin in the blood. WHO’s hemoglo- household surveys. bin thresholds were then used to determine anemia Iodine defi ciency is the single most important status based on age, sex, and physiological status. cause of preventable mental retardation, it con- Data should be used with caution because surveys Data sources tributes significantly to the risk of stillbirth and mis- differ in quality, coverage, age group interviewed, carriage, and it increases infant mortality. A diet low and treatment of missing values across countries Data on low-birthweight babies, breastfeeding, in iodine is the main cause of iodine deficiency. It and over time. consumption of iodized salt, and vitamin A supple- usually occurs among populations living in areas For indicators from household surveys, the year in mentation are from the United Nations Children’s where the soil has been depleted of iodine. If soil the table refers to the survey year. For more informa- Fund’s The State of the World’s Children 2012 and is deficient in iodine, so are the plants grown in it, tion, consult the original sources. Childinfo. Data on anemia are from the WHO’s including the grains and vegetables that people and Worldwide Prevalence of Anemia 1993 –2005 animals consume. There are almost no countries in (2008c) and Integrated WHO Nutrition Global the world where iodine deficiency has not been a Databases. public health problem. Every year about 40 million 2012 World Development Indicators 123 2.22 Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIVa Antiretroviral Cause of death of smoking tuberculosis of diabetes therapy coverage % of population Communicable diseases and Female Youth maternal, per % of Total % of total % of population % of population prenatal, Non- % of adults 100,000 population % of population population ages 15–24 with advanced and nutrition communicable Male Female people ages 20–79 ages 15–49 with HIV Male Female HIV infection conditions diseases Injuries 2009 2009 2010 2011 1990 2009 2009 2009 2009 2005–10 b 2005–10b 2005–10b 2005–10b Afghanistan .. .. 189 7.8 .. .. .. .. .. .. 63 29 8 Albania 60 19 14 2.9 .. .. .. .. .. .. 5 89 5 Algeria .. .. 90 7.0 <0.1 0.1 30 0.1 <0.1 25 30 63 8 Angola .. .. 304 3.0 0.5 2.0 60 0.6 1.6 24 69 25 7 Argentina 32 22 27 5.7 0.3 0.5 32 0.3 0.2 70 14 80 6 Armenia 51 2 73 8.7 <0.1 0.1 <43 <0.1 <0.1 24 6 90 4 Australia 22 19 6 6.8 0.1 0.1 31 0.1 0.1 .. 4 90 6 Austria 47 45 5 6.8 <0.1 0.3 29 0.3 0.2 .. 3 91 6 Azerbaijan 41 .. 110 2.8 <0.1 0.1 60 <0.1 0.1 21 11 85 4 Bahrain 34 8 23 19.9 .. .. .. .. .. .. 10 79 11 Bangladesh 46 2 225 10.7 <0.1 <0.1 30 <0.1 <0.1 23 38 52 10 Belarus 49 9 70 8.2 <0.1 0.3 50 <0.1 0.1 29 2 87 11 Belgium 30 22 9 4.9 <0.1 0.2 31 <0.1 <0.1 .. 8 86 6 Benin 15 1 94 2.0 0.2 1.2 58 0.3 0.7 53 60 33 6 Bolivia 42 18 135 6.8 0.1 0.2 32 0.1 0.1 19 35 57 8 Bosnia and Herzegovina 47 36 50 7.7 .. .. .. .. .. .. 2 95 4 Botswana .. .. 503 11.1 3.5 24.8 57 5.2 11.8 83 60 31 9 Brazil 22 13 43 10.4 .. .. .. .. .. 60 14 74 12 Bulgaria 48 27 40 6.9 <0.1 0.1 29 <0.1 <0.1 23 3 94 4 Burkina Faso 18 8 55 3.0 3.9 1.2 60 0.5 0.8 46 73 21 7 Burundi .. .. 129 2.8 3.9 3.3 60 1.0 2.1 19 67 26 7 Cambodia 49c 5c 437 2.9 0.5 0.5 63 0.1 0.1 94 47 46 7 Cameroon 14 2 177 6.2 0.6 5.3 58 1.6 3.9 28 63 31 6 Canada 24 17 5 8.7 0.1 0.2 21 0.1 0.1 .. 5 89 6 Central African Republic .. .. 319 3.2 3.1 4.7 61 1.0 2.2 19 65 27 7 Chad 22 3 276 3.9 1.1 3.4 59 1.0 2.5 36 73 21 5 Chile 38 33 19 9.8 <0.1 0.4 31 0.2 0.1 63 9 83 8 China 51 2 78 9.0 .. 0.1d .. .. .. .. 7 83 10 Hong Kong SAR, China .. .. 80 7.8 .. .. .. .. .. .. .. .. .. Colombia .. .. 34 10.0 0.2 0.5 33 0.2 0.1 17 13 66 21 Congo, Dem. Rep. 10 2 327 3.2 .. .. .. .. .. .. 72 21 7 Congo, Rep. 10 1 372 5.6 5.2 3.4 59 1.2 2.6 23 58 33 9 Costa Rica 24 8 13 9.9 <0.1 0.3 29 0.2 0.1 68 7 81 13 Côte d’Ivoire 17 4 139 5.0 2.4 3.4 58 0.7 1.5 28 58 33 9 Croatia 36 30 21 5.3 <0.1 <0.1 <33 <0.1 <0.1 80 3 92 6 Cuba .. .. 9 9.8 <0.1 0.1 31 0.1 0.1 <95 8 84 8 Cyprus .. .. 4 9.5 .. .. .. .. .. .. 4 90 6 Czech Republic 43 31 7 5.5 <0.1 <0.1 <42 <0.1 <0.1 .. 4 90 6 Denmark 30 28 6 5.7 <0.1 0.2 27 0.1 0.1 .. 6 90 5 Dominican Republic 17 13 67 8.3 0.4 0.9 59 0.3 0.7 47 22 68 10 Ecuador .. .. 65 6.8 0.3 0.4 31 0.2 0.2 30 20 65 15 Egypt, Arab Rep. 40 1 18 16.9 <0.1 <0.1 23 <0.1 <0.1 11 12 82 6 El Salvador .. .. 28 9.7 0.1 0.8 34 0.4 0.3 53 17 67 16 Eritrea 10 2 100 3.6 0.3 0.8 60 0.2 0.4 37 49 40 12 Estonia 46 23 25 7.2 <0.1 1.2 31 0.3 0.2 .. 2 90 8 Ethiopia 8 1 261 3.4 .. .. .. .. .. .. 57 34 9 Finland 28 22 7 6.0 <0.1 0.1 <36 0.1 <0.1 .. 2 89 9 France 36 27 9 5.6 0.3 0.4 32 0.2 0.1 .. 6 87 7 Gabon 19 3 553 10.6 0.9 5.2 58 1.4 3.5 47 52 41 7 Gambia, The 31 3 273 2.0 0.1 2.0 58 0.9 2.4 18 60 34 6 Georgia 57 6 107 2.8 <0.1 0.1 43 <0.1 <0.1 65 5 91 4 Germany 33 25 5 5.5 0.1 0.1 18 0.1 <0.1 .. 5 92 4 Ghana 11 3 86 5.1 0.3 1.8 59 0.5 1.3 24 53 39 8 Greece 63 41 5 5.3 0.1 0.1 31 0.1 0.1 .. 6 91 4 Guatemala 22 4 62 9.5 0.1 0.8 33 0.5 0.3 44 35 47 18 Guinea 25 2 334 4.4 1.1 1.3 59 0.4 0.9 40 60 32 7 Guinea-Bissau .. .. 233 3.1 0.3 2.5 60 0.8 2.0 30 67 28 6 Haiti .. .. 230 6.8 1.3 1.9 60 0.6 1.3 43 54 41 5 124 2012 World Development Indicators 2.22 PEOPLE Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIVa Antiretroviral Cause of death of smoking tuberculosis of diabetes therapy coverage % of population Communicable diseases and Female Youth maternal, per % of Total % of total % of population % of population prenatal, Non- % of adults 100,000 population % of population population ages 15–24 with advanced and nutrition communicable Male Female people ages 20–79 ages 15–49 with HIV Male Female HIV infection conditions diseases Injuries 2009 2009 2010 2011 1990 2009 2009 2009 2009 2005–10 b 2005–10b 2005–10b 2005–10b Honduras .. 3 51 6.8 1.1 0.8 32 0.3 0.2 33 23 69 8 Hungary 43 33 15 6.2 0.1 <0.1 <33 <0.1 <0.1 27 1 93 6 India 26 4 185 9.2 0.1 0.3 39 0.1 0.1 .. 37 53 10 Indonesia 61 5 189 5.2 <0.1 0.2 30 0.1 <0.1 21 28 64 9 Iran, Islamic Rep. 26 2 17 11.3 <0.1 0.2 29 <0.1 <0.1 4 13 72 14 Iraq 31 4 64 9.3 .. .. .. .. .. .. 24 44 31 Ireland .. .. 8 5.4 <0.1 0.2 29 0.1 0.1 .. 7 87 6 Israel 29 13 5 7.6 <0.1 0.2 29 0.1 <0.1 .. 8 87 5 Italy 33 19 5 5.3 0.3 0.3 33 <0.1 <0.1 .. 3 92 4 Jamaica .. .. 7 16.0 2.1 1.7 33 1.0 0.7 46 21 68 11 Japan 42 12 21 7.9 <0.1 <0.1 34 <0.1 <0.1 .. 14 80 6 Jordan 47 6 5 12.4 .. .. .. .. .. .. 15 74 11 Kazakhstan 40 9 151 7.9 <0.1 0.1 60 0.1 0.2 27 8 78 14 Kenya 26 1 298 5.2 3.9 6.3 59 1.8 4.1 48 63 28 9 Korea, Dem. Rep. .. .. 345 8.6 .. .. .. .. .. 0 29 65 6 Korea, Rep. 49 7 97 7.7 <0.1 <0.1 31 <0.1 <0.1 .. 6 82 12 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 35 4 41 21.1 .. .. .. .. .. .. 11 76 13 Kyrgyz Republic 45 2 159 6.5 <0.1 0.3 29 0.1 0.1 12 14 77 9 Lao PDR 51 4 90 3.3 <0.1 0.2 42 0.1 0.2 67 41 48 10 Latvia 50 22 39 8.1 <0.1 0.7 30 0.2 0.1 12 3 90 8 Lebanon 46 31 17 20.2 <0.1 0.1 31 0.1 <0.1 18 7 84 9 Lesotho .. .. 633 3.5 0.8 23.6 62 5.4 14.2 48 63 29 7 Liberia 14 .. 293 3.4 0.3 1.5 61 0.3 0.7 14 68 28 4 Libya 47 1 40 14.2 .. .. .. .. .. .. 12 78 11 Lithuania 50 22 69 8.0 <0.1 0.1 <33 <0.1 <0.1 27 3 86 11 Macedonia, FYR .. .. 21 7.9 .. .. .. .. .. .. 2 95 3 Madagascar .. .. 266 4.8 0.2 0.2 31 0.1 0.1 2 52 42 6 Malawi 26 4 219 5.7 7.2 11.0 59 3.1 6.8 46 63 28 9 Malaysia 50 2 82 12.3 0.1 0.5 11 0.1 <0.1 23 24 67 9 Mali 28 2 68 2.0 0.4 1.0 62 0.2 0.5 50 75 20 5 Mauritania 29 4 337 4.4 0.2 0.7 31 0.4 0.3 25 60 32 8 Mauritius 31 2 22 15.1 <0.1 1.0 29 0.3 0.2 22 7 87 6 Mexico 24 8 16 15.9 0.4 0.3 27 0.2 0.1 54 12 78 10 Moldova 43 5 182 2.8 <0.1 0.4 42 0.1 0.1 17 5 87 8 Mongolia 48 6 224 7.2 <0.1 <0.1 <29 <0.1 <0.1 8 14 72 13 Morocco 33 2 91 7.0 <0.1 0.1 32 0.1 0.1 27 19 75 6 Mozambique 18 2 544 3.1 1.2 11.5 61 3.1 8.6 30 64 28 8 Myanmar 40 8 384 7.2 0.2 0.6 35 0.3 0.3 18 33 40 27 Namibia 30 9 603 8.0 1.6 13.1 59 2.3 5.8 76 51 38 12 Nepal 36 29 163 3.7 0.2 0.4 33 0.2 0.1 11 43 50 7 Netherlands 31 26 7 5.4 0.1 0.2 30 0.1 <0.1 .. 7 89 4 New Zealand 27 24 8 8.8 0.1 0.1 <37 <0.1 <0.1 .. 3 91 6 Nicaragua .. .. 42 11.2 <0.1 0.2 31 0.1 0.1 40 20 69 11 Niger 9 1 185 4.1 0.1 0.8 53 0.2 0.5 22 81 16 3 Nigeria 10 3 133 4.9 1.3 3.6 59 1.2 2.9 21 68 27 5 Norway 31 28 6 4.8 <0.1 0.1 30 <0.1 <0.1 .. 7 87 6 Oman 12 1 13 10.8 <0.1 0.1 <33 <0.1 <0.1 <95 6 83 11 Pakistan 34 6 231 8.0 <0.1 0.1 29 0.1 <0.1 4 46 46 8 Panama 17 4 48 9.8 0.2 0.9 31 0.4 0.3 37 19 69 12 Papua New Guinea 58 31 303 7.7 <0.1 0.9 58 0.3 0.8 52 47 44 9 Paraguay 30 14 46 6.7 <0.1 0.3 31 0.2 0.1 37 20 69 12 Peru .. 9 106 6.1 0.4 0.4 25 0.2 0.1 37 30 60 10 Philippines 47 10 275 10.0 <0.1 <0.1 30 <0.1 <0.1 37 31 61 8 Poland 36 25 23 9.2 <0.1 0.1 31 <0.1 <0.1 22 4 89 7 Portugal 32 16 29 9.8 0.1 0.6 31 0.3 0.2 .. 9 86 4 Puerto Rico .. .. 2 13.3 .. .. .. .. .. .. .. .. .. Qatar .. .. 38 20.2 <0.1 <0.1 <50 <0.1 <0.1 .. 8 69 23 2012 World Development Indicators 125 2.22 Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIVa Antiretroviral Cause of death of smoking tuberculosis of diabetes therapy coverage % of population Communicable diseases and Female Youth maternal, per % of Total % of total % of population % of population prenatal, Non- % of adults 100,000 population % of population population ages 15–24 with advanced and nutrition communicable Male Female people ages 20–79 ages 15–49 with HIV Male Female HIV infection conditions diseases Injuries 2009 2009 2010 2011 1990 2009 2009 2009 2009 2005–10 b 2005–10b 2005–10b 2005–10b Romania 46 24 116 7.9 <0.1 0.1 30 0.1 <0.1 81 4 91 5 Russian Federation 59 24 106 10.0 <0.1 1.0 49 0.2 0.3 .. 5 82 12 Rwanda .. .. 106 3.2 5.2 2.9 61 1.3 1.9 88 63 29 8 Saudi Arabia 24 1 18 20.0 .. .. .. .. .. .. 13 71 15 Senegal 16 1 288 3.3 0.2 0.9 59 0.3 0.7 51 65 30 5 Serbia 38 27 18 7.9 0.1 0.1 24 0.1 0.1 38 2 95 4 Sierra Leone 39 8 682 3.2 <0.1 1.6 60 0.6 1.5 18 77 18 5 Singapore 35 6 35 9.8 <0.1 0.1 30 <0.1 <0.1 .. 16 79 5 Slovak Republic 39 19 8 5.9 <0.1 <0.1 <17 <0.1 <0.1 62 5 90 6 Slovenia 30 22 11 7.8 <0.1 <0.1 <29 <0.1 <0.1 .. 4 87 8 Somalia .. .. 286 4.3 0.1 0.7 47 0.4 0.6 6 62 27 11 South Africa 24 8 981 7.1 0.7 17.8 62 4.5 13.6 37 67 29 5 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. .. Spain 36 27 16 6.5 0.4 0.4 24 0.2 0.1 .. 5 91 4 Sri Lanka 27 1 66 7.6 <0.1 <0.1 <32 <0.1 <0.1 20 9 65 26 Sudan 24 2 119 8.7 0.1 1.1 58 0.5 1.3 5 43 44 13 Swaziland 16 2 1,287 3.1 2.3 25.9 58 6.5 15.6 59 62 28 11 Sweden .. .. 7 4.4 0.1 0.1 31 <0.1 <0.1 .. 5 90 5 Switzerland 31 21 8 6.0 0.2 0.4 32 0.2 0.1 .. 4 90 6 Syrian Arab Republic 42 .. 20 10.2 .. .. .. .. .. .. 13 77 10 Tajikistan .. .. 206 6.5 <0.1 0.2 30 <0.1 <0.1 11 37 59 4 Tanzania 21 3 177 2.8 4.8 5.6 59 1.7 3.9 30 66 27 8 Thailand 45 3 137 7.7 1.0 1.3 40 .. .. 61 17 71 12 Timor-Leste .. .. 498 7.6 .. .. .. .. .. .. 60 34 5 Togo .. .. 455 3.3 0.6 3.2 59 0.9 2.2 29 61 34 5 Trinidad and Tobago 27 11 19 13.1 0.2 1.5 33 1.0 0.7 .. 12 78 10 Tunisia 58 5 25 9.7 <0.1 <0.1 <37 <0.1 <0.1 53 22 72 7 Turkey 47 15 28 8.1 <0.1 <0.1 30 <0.1 <0.1 62 9 85 6 Turkmenistan .. .. 66 2.8 .. .. .. .. .. .. 19 73 8 Uganda 16 3 209 2.9 10.2 6.5 58 2.3 4.8 39 64 25 10 Ukraine 50 13 101 2.9 0.1 1.1 49 0.2 0.3 10 6 86 8 United Arab Emirates 19 2 3 19.2 .. .. .. .. .. .. 13 67 21 United Kingdom 25 23 13 5.4 0.1 0.2 31 0.2 0.1 .. 8 88 4 United States 33 25 4 9.6 0.5 0.6 25 0.3 0.2 .. 6 87 7 Uruguay 31 22 21 5.9 0.1 0.5 32 0.3 0.2 49 8 87 6 Uzbekistan 22 3 128 6.7 <0.1 0.1 29 <0.1 <0.1 .. 15 79 6 Venezuela, RB .. .. 33 10.5 .. .. .. .. .. .. 13 66 21 Vietnam 48 2 199 3.2 <0.1 0.4 30 0.1 0.1 34 16 75 9 West Bank and Gaza .. .. 5 9.4 .. .. .. .. .. .. .. .. .. Yemen, Rep. 35 11 49 9.9 .. .. .. .. .. .. 44 45 11 Zambia 24 4 462 4.8 12.7 13.5 57 4.2 8.9 64 64 27 9 Zimbabwe 30 4 633 9.9 10.1 14.3 60 3.3 6.9 34 75 21 4 World 37 w 8w 128 w 8.3 w 0.3 w 0.8 w 37 w 0.4 w 0.7 w 27 w 63 w 9w Low income 28 4 264 5.9 1.8 2.6 46 0.9 1.9 58 33 9 Middle income 39 6 132 8.7 0.2 0.7 .. .. .. 26 65 10 Lower middle income 32 4 174 8.0 0.3 0.7 38 0.2 0.5 38 53 9 Upper middle income 46 7 89 9.4 0.2 0.7 .. .. .. 11 79 10 Low & middle income 38 5 150 8.4 0.3 0.9 39 .. .. 31 59 10 East Asia & Pacific 52 3 123 8.3 0.1 0.2 .. .. .. 13 76 10 Europe & Central Asia 50 18 90 7.7 0.1 0.6 42 0.1 0.2 6 84 9 Latin America & Carib. 25 13 43 10.5 0.4 0.5 .. .. .. 16 72 12 Middle East & N. Africa 35 3 42 11.6 0.1 0.1 28 0.1 0.1 19 69 12 South Asia 29 4 192 9.0 0.1 0.3 37 0.1 0.1 39 51 10 Sub-Saharan Africa 16 3 271 4.6 2.4 5.5 58 1.5 3.8 65 28 7 High income 34 21 14 7.9 0.2 0.3 28 0.2 0.1 7 87 6 Euro area 35 25 .. 5.8 0.2 0.3 27 0.1 0.1 5 90 5 a. See http://data.worldbank.org or the original source for uncertainty bands. b. Data are for the most recent year available. c. Data are for 2010. d. Includes Hong Kong SAR, China. 126 2012 World Development Indicators 2.22 PEOPLE Health risk factors and future challenges About the data De�nitions The limited availability of data on health status is a Data on HIV are from the Joint United Nations Pro- • Prevalence of smoking is the percentage of the major constraint in assessing the health situation in gramme on HIV/AIDS’s (UNAIDS) Global Report: UNAIDS population ages 15 and older who smoke any tobacco developing countries. Surveillance data are lacking Report on the Global AIDS Epidemic 2010. Changes in products. It includes daily and nondaily smoking. for many major public health concerns. Estimates procedures and assumptions for estimating the data Estimates are adjusted and age- standardized preva- of prevalence and incidence are available for some and better coordination with countries have resulted lence • Incidence of tuberculosis is the number of diseases but are often unreliable and incomplete. in improved estimates of HIV and AIDS. For example, new and relapse cases of tuberculosis (all types) National health authorities differ widely in capacity improved software was used to model the course of per 100,000 people. •  Prevalence of diabetes is and willingness to collect or report information. To HIV epidemics and their impacts, making full use of the percentage of people ages 20–79 who have compensate for this and improve reliability and inter- information on HIV prevalence trends from surveillance type 1 or type 2 diabetes. •  Prevalence of HIV is national comparability, the World Health Organiza- data as well as survey data. The software explicitly the percentage of people who are infected with tion (WHO) prepares estimates in accordance with includes the effect of antiretroviral therapy when cal- HIV. Total and youth rates are percentages of the epidemiological models and statistical standards. culating HIV incidence and models reduced infectivity relevant age group. Female rate is as a percentage Smoking is the most common form of tobacco use among people receiving antiretroviral therapy, which is of the total population living with HIV. • Antiretro- and the prevalence of smoking is therefore a good having a larger impact on HIV prevalence and allowing viral therapy coverage is the percentage of adults measure of the tobacco epidemic (Corrao and others HIV-positive people to live longer. The software also and children with advanced HIV infection currently 2000). Tobacco use causes heart and other vascular allows for changes in urbanization over time—impor- receiving antiretroviral therapy according to nation- diseases and cancers of the lung and other organs. tant because prevalence is higher in urban areas and ally approved treatment protocols (or WHO/UNAIDS Given the long delay between starting to smoke and because many countries have seen rapid urbanization standards) among the estimated number of people the onset of disease, the health impact of smoking over the past two decades. The estimates include plau- with advanced HIV infection. • Cause of death is the will increase rapidly only in the next few decades. sible bounds, not shown in the table, which reflect the share of all deaths due to the specified underlying The data presented in the table are age- standardized certainty associated with each of the estimates. The cause. •  Communicable diseases and maternal, rates for adults ages 15 and older from the WHO. bounds are available at http://data.worldbank.org and perinatal, and nutrition conditions are infectious Tuberculosis is one of the main causes of adult from the original source. and parasitic diseases, respiratory infections, and deaths from a single infectious agent in develop- Standard antiretroviral therapy consists of the use nutritional defi ciencies such as underweight and ing countries. In developed countries tuberculosis of at least three antiretroviral drugs to maximally sup- stunting. • Noncommunicable diseases are cancer, has reemerged largely as a result of cases among press HIV and stop the progression of HIV disease. Anti- diabetes mellitus, cardiovascular diseases, digestive immigrants. Since tuberculosis incidence cannot be retroviral therapy has led to huge reductions in death diseases, skin diseases, musculoskeletal diseases, directly measured, estimates are obtained by elicit- and suffering of people with advanced HIV infection. and congenital anomalies. • Injuries include uninten- ing expert opinion or are derived from measurements Data are collected through three international moni- tional and intentional injuries. of prevalence or mortality. These estimates include toring and reporting processes: country responses to uncertainty intervals, which are not shown in the the WHO; research by the Interagency Task Team on table but are available at http://data.worldbank.org Prevention of HIV Infection in Women, Mothers and and from the original source. their Children; and country report to UNAIDS through Diabetes, an important cause of ill health and a the United Nations General Assembly Special Session risk factor for other diseases in developed countries, Declaration of Commitment on HIV/AIDS. is spreading rapidly in developing countries. Highest Data on cause of death are compiled by the WHO, among the elderly, prevalence rates are rising among based mainly on data from national vital registry sys- Data sources younger and productive populations in developing tems, as well as sample registration systems, popu- countries. Economic development has led to the lation laboratories, and epidemiological analysis of Data on smoking are from the WHO’s Report on spread of Western lifestyles and diet to develop- specific conditions. Data are classified based on the the Global Tobacco Epidemic 2011. Data on tuber- ing countries, resulting in a substantial increase in International Statistical Classification of Diseases and culosis are from the WHO’s Global Tuberculosis diabetes. Without effective prevention and control Related Health Problems, 10th revision. Data have Control Report 2011. Data on diabetes are from programs, diabetes will likely continue to increase. been carefully analyzed to take into account incomplete the International Diabetes Federation’s Diabetes Data are estimated based on sample surveys. coverage of vital registration and the likely differences Atlas, 5th edition. Data on HIV are from UNAIDS’s Adult HIV prevalence rates reflect the rate of HIV in cause of death patterns that would be expected in Global Report: UNAIDS Report on the Global AIDS infection in each country’s population. Low national undercovered and often poorer subpopulations. Special Epidemic 2010. Data on antiretroviral therapy prevalence rates can be misleading, however. They attention has also been paid to misattribution or mis- coverage are from the WHO. Data on cause of often disguise epidemics that are initially concen- coding of causes of death in cardiovascular diseases, death are from the WHO’s Health Statistics and trated in certain localities or population groups and cancer, injuries, and general ill-defined categories. For Health Information Systems database (www. threaten to spill over into the wider population. In further information, consult the original source. who.int/healthinfo/global_burden_disease/ many developing countries most new infections For indicators from household surveys, the year in estimates_country). occur in young adults, with young women especially the table refers to the survey year. For more informa- vulnerable. tion, consult the original sources. 2012 World Development Indicators 127 2.23 Mortality Life expectancy Neonatal Infant mortality Under-�ve Child Adult mortality at birth mortality rate rate mortality rate mortality rate rate per 1,000 per 1,000 years per 1,000 live births per 1,000 live births per 1,000 live births Male Female Male Female 1990 2010 1990 2010 1990 2010 1990 2010 2005–10a,b 2005–10a,b 2006–10a 2006–10a Afghanistan 42 48 53 45 140 103 209 149 .. .. 409 377 Albania 72 77 17 9 36 16 41 18 3 1 95 46 Algeria 67 73 29 18 55 31 68 36 .. .. 126 102 Angola 41 51 51 41 144 98 243 161 .. .. 386 337 Argentina 71 76 15 7 24 12 27 14 .. .. 160 74 Armenia 68 74 26 11 46 18 55 20 8 3 162 79 Australia 77 82 5 3 8 4 9 5 .. .. 82 47 Austria 76 80 4 2 8 4 9 4 .. .. 99 50 Azerbaijan 65 71 31 19 74 39 93 46 9 5 181 74 Bahrain 72 75 6 4 15 9 17 10 .. .. 95 73 Bangladesh 59 69 55 27 99 38 143 48 16 20 163 137 Belarus 71 70 7 3 14 4 17 6 .. .. 334 112 Belgium 76 80 4 2 9 4 10 4 .. .. 107 61 Benin 49 56 40 32 107 73 178 115 64 65 332 276 Bolivia 59 66 39 23 84 42 121 54 18 20 225 167 Bosnia and Herzegovina 67 75 12 5 17 8 19 8 .. .. 134 69 Botswana 64 53 22 19 46 36 59 48 .. .. 535 579 Brazil 66 73 28 12 50 17 59 19 .. .. 218 114 Bulgaria 72 74 11 7 18 11 22 13 .. .. 205 86 Burkina Faso 48 55 41 38 103 93 205 176 .. .. 300 249 Burundi 46 50 49 42 110 88 183 142 65 65 418 381 Cambodia 55 63 38 22 87 43 121 51 20 20 262 222 Cameroon 53 51 34 34 85 84 137 136 .. .. 410 378 Canada 77 81 4 4 7 5 8 6 .. .. 92 55 Central African Republic 49 48 43 42 110 106 165 159 74 82 469 436 Chad 51 49 45 41 113 99 207 173 .. .. 372 316 Chile 74 79 9 5 16 8 19 9 .. .. 123 57 China 69c 73c 24 11 38 16 48 18 .. .. 138 88 Hong Kong SAR, China 77 83 .. .. .. .. .. .. .. .. 74 37 Colombia 68 73 20 12 30 18 37 22 4 3 194 89 Congo, Dem. Rep. 47 48 48 46 117 112 181 170 70 64 407 354 Congo, Rep. 56 57 33 29 74 61 116 93 49 43 335 301 Costa Rica 76 79 10 6 15 9 17 10 .. .. 110 58 Côte d’Ivoire 53 55 46 41 105 86 151 123 .. .. 377 349 Croatia 72 76 8 3 11 5 13 6 1 1 140 57 Cuba 74 79 7 3 11 5 13 6 .. .. 108 68 Cyprus 77 79 5 2 10 3 11 4 .. .. 77 38 Czech Republic 71 77 9 2 12 3 14 4 .. .. 138 63 Denmark 75 79 4 2 7 3 9 4 .. .. 107 65 Dominican Republic 68 73 29 15 48 22 62 27 6 4 200 131 Ecuador 69 75 20 10 41 18 52 20 .. .. 161 84 Egypt, Arab Rep. 62 73 28 9 68 19 94 22 5 5 140 85 El Salvador 66 72 18 6 48 14 62 16 .. .. 281 119 Eritrea 48 61 31 18 87 42 141 61 .. .. 345 261 Estonia 69 75 13 3 17 4 21 5 .. .. 234 77 Ethiopia 47 59 48 35 111 68 184 106 56 56 304 259 Finland 75 80 4 2 6 2 7 3 .. .. 123 56 Franced 77 81 3 2 7 3 9 4 .. .. 118 55 Gabon 61 62 31 26 68 54 93 74 .. .. 288 263 Gambia, The 53 58 42 31 78 57 165 98 46 39 299 244 Georgia 70 73 27 15 40 20 47 22 5 4 177 67 Germany 75 80 4 2 7 3 9 4 .. .. 101 54 Ghana 57 64 38 28 77 50 122 74 38 28 255 225 Greece 77 80 9 2 11 3 13 4 .. .. 101 46 Guatemala 62 71 28 15 56 25 78 32 .. .. 226 122 Guinea 44 54 51 38 135 81 229 130 89 86 352 303 Guinea-Bissau 43 48 48 40 125 92 210 150 110 88 410 358 Haiti 55 62 38 27 104 70 151 165 33 36 264 236 128 2012 World Development Indicators 2.23 PEOPLE Mortality Life expectancy Neonatal Infant mortality Under-�ve Child Adult mortality at birth mortality rate rate mortality rate mortality rate rate per 1,000 per 1,000 years per 1,000 live births per 1,000 live births per 1,000 live births Male Female Male Female 1990 2010 1990 2010 1990 2010 1990 2010 2005–10a,b 2005–10a,b 2006–10a 2006–10a Honduras 66 73 23 12 45 20 58 24 8 9 164 115 Hungary 69 74 12 4 17 5 19 6 .. .. 229 99 India 58 65 47 32 81 48 115 63 9 12 253 168 Indonesia 62 69 31 17 56 27 85 35 13 12 205 169 Iran, Islamic Rep. 62 73 28 14 50 22 65 26 .. .. 163 80 Iraq 68 68 23 20 37 31 46 39 6 7 281 125 Ireland 75 80 5 2 8 3 9 4 .. .. 97 57 Israel 77 82 6 2 10 4 12 5 .. .. 79 45 Italy 77 82 6 2 8 3 10 4 .. .. 78 41 Jamaica 71 73 13 9 31 20 38 24 5 6 189 117 Japan 79 83 3 1 5 2 6 3 .. .. 85 42 Jordan 70 73 20 13 32 18 38 22 3 7 143 99 Kazakhstan 68 68 26 17 48 29 57 33 5 4 366 147 Kenya 59 56 31 28 64 55 99 85 27 25 380 358 Korea, Dem. Rep. 70 69 22 18 23 26 45 33 .. .. 194 124 Korea, Rep. 71 81 3 2 6 4 8 5 .. .. 90 41 Kosovo 68 70 .. .. .. .. .. .. .. .. .. .. Kuwait 73 75 9 6 13 10 15 11 .. .. 102 62 Kyrgyz Republic 68 69 30 19 59 33 72 38 8 4 304 132 Lao PDR 54 67 39 21 100 42 145 54 .. .. 207 167 Latvia 69 73 12 5 16 8 21 10 .. .. 247 94 Lebanon 69 72 18 12 31 19 38 22 .. .. 150 101 Lesotho 59 47 36 35 72 65 89 85 .. .. 578 613 Liberia 42 56 53 34 151 74 227 103 62 64 349 314 Libya 68 75 22 10 33 13 45 17 .. .. 137 85 Lithuania 71 73 10 3 14 5 17 7 .. .. 275 95 Macedonia, FYR 71 75 17 8 34 10 39 12 2 1 126 78 Madagascar 51 66 40 22 97 43 159 62 30 31 215 169 Malawi 47 53 44 27 131 58 222 92 52 54 409 411 Malaysia 70 74 9 3 15 5 18 6 .. .. 147 75 Mali 44 51 57 48 131 99 255 178 117 114 361 297 Mauritania 56 58 42 39 80 75 124 111 53 44 290 220 Mauritius 69 73 16 9 21 13 24 15 .. .. 206 102 Mexico 71 77 17 7 38 14 49 17 .. .. 133 73 Moldova 67 69 15 9 30 16 37 19 7 4 302 146 Mongolia 61 68 27 12 76 26 107 32 11 10 294 141 Morocco 64 72 36 19 67 30 86 36 .. .. 144 91 Mozambique 43 50 51 39 146 92 219 135 .. .. 482 444 Myanmar 57 65 44 32 79 50 112 66 .. .. 235 187 Namibia 61 62 25 17 49 29 73 40 24 19 345 343 Nepal 54 68 54 28 97 41 141 50 21 18 186 160 Netherlands 77 81 5 3 7 4 8 4 .. .. 75 56 New Zealand 75 81 4 3 9 5 11 6 .. .. 87 58 Nicaragua 64 74 25 12 52 23 68 27 .. .. 197 111 Niger 41 54 48 32 132 73 311 143 138 135 313 271 Nigeria 46 51 49 40 126 88 213 143 91 93 393 365 Norway 77 81 4 2 7 3 9 3 .. .. 82 50 Oman 71 73 22 5 36 8 47 9 .. .. 138 76 Pakistan 61 65 51 41 96 70 124 87 14 22 189 158 Panama 72 76 14 9 26 17 33 20 .. .. 133 70 Papua New Guinea 56 62 30 23 65 47 90 61 .. .. 315 239 Paraguay 68 72 24 14 40 21 50 25 .. .. 168 121 Peru 66 74 27 9 55 15 78 19 13 4 158 97 Philippines 65 68 23 14 42 23 59 29 10 9 262 145 Poland 71 76 11 4 15 5 17 6 .. .. 198 76 Portugal 74 79 7 2 11 3 15 4 .. .. 122 53 Puerto Rico 74 79 .. .. .. .. .. .. .. .. 133 51 Qatar 74 78 10 4 17 7 21 8 .. .. 69 59 2012 World Development Indicators 129 2.23 Mortality Life expectancy Neonatal Infant mortality Under-�ve Child Adult mortality at birth mortality rate rate mortality rate mortality rate rate per 1,000 per 1,000 years per 1,000 live births per 1,000 live births per 1,000 live births Male Female Male Female 1990 2010 1990 2010 1990 2010 1990 2010 2005–10a,b 2005–10a,b 2006–10a 2006–10a Romania 70 73 15 8 29 11 37 14 .. .. 185 76 Russian Federation 69 69 12 6 22 9 27 12 .. .. 372 139 Rwanda 33 55 41 29 99 59 163 91 69 55 348 315 Saudi Arabia 69 74 20 10 36 15 45 18 3 4 126 96 Senegal 53 59 40 27 70 50 139 75 43 39 291 239 Serbia 71 74 16 4 25 6 29 7 4 3 150e 82e Sierra Leone 39 47 57 45 162 114 276 174 67 61 464 444 Singapore 76 82 4 1 6 2 8 3 .. .. 77 45 Slovak Republic 71 75 12 4 15 7 18 8 .. .. 184 74 Slovenia 73 79 5 2 9 2 10 3 .. .. 124 54 Somalia 45 51 52 52 108 108 180 180 53 54 368 312 South Africa 62 52 18 18 47 41 60 57 .. .. 567 560 South Sudan .. 62 .. .. .. .. .. .. .. .. .. .. Spain 77 82 6 3 9 4 11 5 .. .. 94 43 Sri Lanka 70 75 18 10 26 14 32 17 .. .. 186 78 Sudan 53 61 39 35 78 66 125 103 38 30 265 211 Swaziland 59 48 24 21 70 55 96 78 32 30 562 580 Sweden 78 81 3 2 6 2 7 3 .. .. 69 41 Switzerland 77 82 4 3 7 4 8 5 .. .. 76 42 Syrian Arab Republic 71 76 18 9 31 14 38 16 5 3 110 72 Tajikistan 63 67 37 25 91 52 116 63 18 13 224 128 Tanzania 51 57 40 26 95 60 155 92 .. .. 362 343 Thailand 72 74 17 8 26 11 32 13 .. .. 205 101 Timor-Leste 46 62 48 24 127 56 169 81 .. .. 259 223 Togo 53 57 40 32 87 66 147 103 55 43 340 297 Trinidad and Tobago 69 70 23 18 32 24 37 27 5 8 233 136 Tunisia 70 75 23 9 39 14 49 16 .. .. 123 69 Turkey 63 74 33 10 66 14 80 18 6 6 136 77 Turkmenistan 63 65 33 23 78 47 98 56 .. .. 304 159 Uganda 47 54 36 26 106 63 175 99 75 62 400 385 Ukraine 70 70 9 6 18 11 21 13 4 1 385 142 United Arab Emirates 72 77 12 4 18 6 22 7 .. .. 90 68 United Kingdom 76 80 5 3 8 5 9 5 .. .. 95 58 United States 75 78 6 4 9 7 11 8 .. .. 139 80 Uruguay 72 76 11 6 20 9 23 11 .. .. 133 60 Uzbekistan 67 68 30 23 63 44 77 52 11 7 243 139 Venezuela, RB 71 74 17 10 28 16 33 18 .. .. 171 89 Vietnam 65 75 23 12 37 19 51 23 5 4 132 89 West Bank and Gaza 68 73 .. .. 36 20 45 22 3 3 142 105 Yemen, Rep. 56 65 43 32 90 57 128 77 10 11 231 186 Zambia 47 48 40 30 109 69 183 111 66 55 491 493 Zimbabwe 61 50 27 27 52 51 78 80 21 21 543 594 World 65 w 70 w 32 w 23 w 62 w 41 w 90 w 58 w .. w .. w 210 w 150 w Low income 53 59 46 33 103 70 165 108 53 51 297 260 Middle income 64 69 33 22 61 38 85 51 .. .. 202 136 Lower middle income 59 65 41 29 78 50 113 69 21 22 244 175 Upper middle income 69 73 23 11 39 17 49 20 .. .. 161 100 Low & middle income 63 68 35 25 68 45 98 63 .. .. 213 152 East Asia & Pacific 68 72 25 13 42 20 56 24 .. .. 157 105 Europe & Central Asia 68 71 21 11 42 19 51 23 .. .. 273 116 Latin America & Carib. 68 74 23 11 43 18 54 23 .. .. 181 98 Middle East & N. Africa 64 72 29 16 56 27 74 34 .. .. 160 95 South Asia 59 65 48 33 86 52 120 67 11 15 239 166 Sub-Saharan Africa 50 54 43 35 105 76 175 121 68 65 379 346 High income 75 80 6 3 10 5 12 6 .. .. 117 62 Euro area 76 81 5 2 8 3 10 4 .. .. 107 53 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. 130 2012 World Development Indicators 2.23 PEOPLE Mortality About the data De�nitions Mortality rates for different age groups (infants, chil- weighted polynomial regression to obtain a best esti- • Life expectancy at birth is the number of years dren, and adults) and overall mortality indicators (life mate trend line by fitting a set of local regressions a newborn infant would live if prevailing patterns of expectancy at birth or survival to a given age) are of mortality rates against their reference dates. (For mortality at the time of its birth were to stay the important indicators of health status in a country. further discussion of childhood mortality estimates, same throughout its life. • Neonatal mortality rate Because data on the incidence and prevalence of see UN Inter-agency Group for Child Mortality Estima- is the number of neonatal infants dying before reach- diseases are frequently unavailable, mortality rates tion 2011; for a graphic presentation and detailed ing 28 days of age, per 1,000 live births. • Infant are often used to identify vulnerable populations. background data, see www.childmortality.org). mortality rate is the number of infants dying before And they are among the indicators most frequently Neonatal, infant, and child mortality rates are reaching one year of age, per 1,000 live births. used to compare socioeconomic development higher for boys than for girls in countries in which •  Under-�ve mortality rate is the probability of a across countries. parental gender preferences are insignificant. Under- child born in a specific year dying before reaching age The main sources of mortality data are vital reg- five and child mortality rates capture the effect of 5, if subject to the age-specific mortality rate of that istration systems and direct or indirect estimates gender discrimination better than neonatal and year. The probability is derived from life tables and based on sample surveys or censuses. A “complete� infant mortality rates do, as malnutrition and medical is expressed as a rate per 1,000 live births. • Child vital registration system—covering at least 90 per- interventions are more important in this age group. mortality rate is the probability per 1,000 of dying cent of vital events in the population—is the best Where female child mortality is higher, as in some between ages 1 and 5—that is, the probability of a source of age-specific mortality data. Where reliable countries in South Asia, girls probably have unequal 1-year-old dying before reaching age 5—if subject to age-specific mortality data are available, life expec- access to resources. Child mortality rates in the current age-specific mortality rates. • Adult mortal- tancy at birth is directly estimated from the life table table are not compatible with neonatal, infant, and ity rate is the probability per 1,000 of dying between constructed from age-specific mortality data. under-five mortality rates because of differences in the ages of 15 and 60—that is, the probability of a But complete vital registration systems are fairly methodology and reference year. Child mortality data 15-year-old dying before reaching age  60—if sub- uncommon in developing countries. Thus estimates were estimated directly from surveys and cover the ject to current age-specific mortality rates between must be obtained from sample surveys or derived 10 years preceding the survey. In addition to esti- those ages. by applying indirect estimation techniques to reg- mates from Demographic Health Surveys, estimates 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- subject to recall error, and surveys estimating infant ceding the survey. Data sources deaths require large samples because households Rates for adult mortality come from life tables. in which a birth has occurred during a given year Adult mortality rates increased notably in a dozen Data on life expectancy at birth are World Bank cannot ordinarily be preselected for sampling. Indi- countries in Sub-Saharan Africa in the early 2000s calculations based on male and female data from rect estimates rely on model life tables that may be and in several countries in Europe and Central Asia World Population Prospects: The 2010 Revision (for inappropriate for the population concerned. Because in the first half of the 1990s. In Sub-Saharan Africa more than half of countries, most of them develop- life expectancy at birth is estimated using infant mor- the increase stems from AIDS-related mortality ing countries), census reports and other statistical tality data and model life tables for many develop- and affects both sexes, though women are more publications from national statistical offices, Euro- ing countries, similar reliability issues arise for this affected. In Europe and Central Asia the causes are stat’s Demographic Statistics, and the U.S. Bureau indicator. Extrapolations based on outdated surveys more diverse (high prevalence of smoking, high-fat of the Census International Data Base. Data on may not be reliable for monitoring changes in health diet, excessive alcohol use, stressful conditions neonatal, infant, and under-five mortality are from status or for comparative analytical work. related to the economic transition) and affect men the UN Inter-agency Group for Child Mortality Esti- Estimates of neonatal, infant, and under-five mor- more. mation’s Levels and Trends in Child Mortality: Report tality tend to vary by source and method for a given Annual data series from the United Nations are 2011 and are based mainly on household surveys, time and place. Years for available estimates also interpolated based on five-year estimates and thus censuses, and vital registration data. Data on child vary by country, making comparison across countries may not reflect actual events. mortality are from MEASURE DHS Demographic and over time difficult. To make neonatal, infant, and and Health Surveys by ICF International and World under-fi ve mortality estimates comparable and to Bank calculations based on infant and under-five ensure consistency across estimates by different mortality from Multiple Indicator Cluster Surveys agencies, the United Nations Inter-agency Group by UNICEF. Most data on adult mortality are linear for Child Mortality Estimation, which comprises the interpolations of five-year data from World Popula- United Nations Children’s Fund (UNICEF), the United tion Prospects: The 2010 Revision. Remaining data Nations Population Division, the World Health Organi- on adult mortality are from the Human Mortality zation (WHO), the World Bank, and other universities Database by the University of California, Berke- and research institutes, developed and adopted a ley, and the Max Planck Institute for Demographic statistical method that uses all available information Research (www.mortality.org). to reconcile differences. The method uses a locally 2012 World Development Indicators 131 Text figures, tables, and boxes 2.24 Health gaps by income Demography Survey Infant mortality rate Under-�ve mortality rate Total fertility rate Teenage mothers year per 1,000 live births per 1,000 live births births per woman % of women ages 15–19 Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile Algeria .. .. .. .. .. .. .. .. Armenia 2005 42 14 52 23 1.8 1.5 5 0 Azerbaijan 2006 52 37 63 41 2.3 1.6 6 3 Bangladesh 2007 66 36 86 43 3.2 2.2 42 20 Bolivia 2008 89 26 116 31 6.2 1.9 31 8 Bosnia and Herzegovina .. .. .. .. .. .. .. .. Burkina Faso 2006 97 78 196 111 6.6 3.6 26 12 Cambodia 2005 101 34 127 43 4.9 2.4 11 5 Cameroon 2006 101 51 189 88 6.5 3.2 36 14 Colombia 2010 22 12 29 13 3.2 1.4 29 7 Congo, Dem. Rep. 2007 113 58 184 97 7.4 4.2 29 12 Congo, Rep. 2005 92 56 135 85 6.7 2.9 35 13 Côte d’Ivoire 2006 147 59 229 83 7.4 2.9 52 12 Dominican Republic 2007 43 26 53 28 3.8 1.7 37 8 Egypt, Arab Rep. 2008 42 17 49 19 3.4 2.7 12 5 Ethiopia 2005 80 60 130 92 6.6 3.2 24 8 Gambia, The 2006 106 58 158 72 .. .. .. .. Ghana 2008 59 47 103 60 6.5 2.3 18 4 Guinea 2005 127 68 217 113 6.5 4.2 39 20 Guinea-Bissau .. .. .. .. .. .. .. .. Haiti 2006 78 45 125 55 6.6 2.0 22 7 Honduras 2006 37 19 50 20 5.6 2.1 31 10 India 2006 82 34 118 39 3.9 1.8 25 5 Indonesia 2007 56 26 77 32 3.0 2.7 6 10 Jordan 2009 32 29 36 32 4.9 2.7 5 3 Kazakhstan 2006 68 42 82 45 3.4 1.2 8 5 Kenya 2009 66 57 98 69 7.0 2.9 24 16 Kyrgyz Republic .. .. .. .. .. .. .. .. Lao PDR .. .. .. .. .. .. .. .. Liberia 2009 121 95 176 137 8.0 3.2 53 20 Madagascar 2009 61 37 106 48 6.8 2.7 51 14 Malawi 2006 72 62 123 99 7.1 4.1 43 20 Mali 2006 124 80 233 124 7.6 4.9 37 23 Mauritania 2007 89 57 144 87 5.4 3.6 14 11 Moldova 2005 20 16 29 17 2.1 1.4 8 1 Namibia 2007 60 24 92 30 5.1 2.4 22 5 Nepal 2006 71 40 98 47 4.7 1.9 18 14 Niger 2006 91 67 206 157 7.9 6.4 40 24 Nigeria 2008 100 58 219 87 7.1 4.0 46 5 Pakistan 2007 94 53 121 60 5.8 3.0 16 4 Philippines 2008 40 15 59 17 5.2 1.9 19 4 Rwanda 2008 99 45 161 84 5.8 4.4 5 4 Senegal 2005 77 43 143 56 6.7 3.3 34 8 Serbia .. .. .. .. .. .. .. .. Sierra Leone 2008 148 93 211 145 6.3 3.2 49 16 Somalia .. .. .. .. .. .. .. .. Swaziland 2007 84 84 118 101 5.5 2.6 33 15 Syrian Arab Republic 2006 18 16 22 20 .. .. .. .. Tanzania 2010 61 63 103 84 7.0 3.2 28 13 Thailand .. .. .. .. .. .. .. .. Timor-Leste 2009/10 62 38 87 52 7.3 4.2 9 3 Togo 2006 92 43 150 62 7.3 2.9 36 7 Ukraine 2007 19 9 23 9 1.7 1.0 8 1 Uzbekistan 2006 59 36 72 42 .. .. .. .. Yemen, Rep. 2006 94 36 118 118 .. .. .. .. Zambia 2007 69 74 124 124 8.4 3.4 37 14 Zimbabwe 2006 48 45 72 72 5.5 2.3 32 7 132 2012 World Development Indicators 2.24 PEOPLE Health gaps by income Child health Survey Diarrhea Acute respiratory Prevalence of Child year infection (ARI) child malnutrition immunization Treatment (underweight) Treatment Children with ARI taken Prevalence % of children Prevalence to health provider Old standards All vaccinations % of children under age 5 % of children % of children % of children % of children under age 5 with diarrhea under age 5 under age 5 with ARI under age 5 ages 12–23 months Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile Algeria 2006 10 8 .. .. 7 5 38 68 5 3 81 95 Armenia 2005 20 13 65 80 11 11 30 42 5 1 66 68 Azerbaijan 2006 13 10 66 55 4 2 15 11 17 4 7 32 Bangladesh 2007 10 8 79 88 6 3 45 73 55 31 80 88 Bolivia 2008 30 20 58 75 24 17 40 70 11 2 78 81 Bosnia and Herzegovina 2006 4 5 .. .. 5 3 97 96 2 4 66 71 Burkina Faso 2006 19 16 57 73 6 2 45 73 44 24 64 81 Cambodia 2005 22 14 66 40 12 3 41 58 43 23 56 76 Cameroon 2006 30 10 46 73 10 6 20 50 35 6 42 72 Colombia 2010 16 7 67 82 7 4 54 67 12 3 64 67 Congo, Dem. Rep. 2007 17 14 65 55 9 6 32 48 33 19 20 50 Congo, Rep. 2005 15 12 54 54 .. .. .. .. 20 5 29 73 Côte d’Ivoire 2006 20 13 55 77 8 3 21 71 26 10 58 92 Dominican Republic 2007 16 13 64 63 9 4 63 65 7 2 48 76 Egypt, Arab Rep. 2008 10 7 39 32 10 8 70 82 9 7 89 94 Ethiopia 2005 18 14 24 55 12 11 19 33 43 29 14 36 Gambia, The 2006 21 15 .. .. 6 5 68 68 26 14 83 74 Ghana 2008 25 10 58 69 6 3 45 87 23 11 75 84 Guinea 2005 18 17 47 73 10 8 30 59 28 21 29 45 Guinea-Bissau 2006 13 14 .. .. 4 7 32 82 21 10 43 64 Haiti 2006 25 18 48 67 10 5 21 37 27 7 34 56 Honduras 2006 19 11 64 68 14 6 46 74 21 2 77 68 India 2006 9 8 25 49 6 4 61 80 61 25 24 71 Indonesia 2007 18 10 60 57 8 5 48 74 .. .. 39 75 Jordan 2009 18 14 56 64 5 3 66 78 4 0 82 89 Kazakhstan 2006 1 2 46 71 1 2 37 53 5 1 98 96 Kenya 2009 20 13 80 79 11 5 57 63 31 12 61 70 Kyrgyz Republic 2006 3 4 .. .. .. .. .. .. 3 3 .. .. Lao PDR 2006 17 9 .. .. 6 3 28 12 44 18 18 45 Liberia 2007 19 19 63 83 8 9 59 89 26 17 23 56 Madagascar 2009 8 10 52 67 3 3 33 68 47 28 41 82 Malawi 2006 26 20 65 76 9 8 51 65 25 16 66 77 Mali 2006 13 8 41 68 10 11 9 50 37 22 49 56 Mauritania 2007 25 19 29 29 7 7 33 64 40 13 39 25 Moldova 2005 7 12 50 70 5 10 42 68 .. .. 82 59 Namibia 2007 13 11 57 74 7 1 65 94 27 9 59 82 Nepal 2006 13 12 27 59 6 5 36 54 54 24 68 94 Niger 2006 22 18 50 66 10 11 19 59 48 30 20 48 Nigeria 2008 14 5 25 61 4 1 32 66 40 13 5 53 Pakistan 2007 23 20 52 59 15 13 67 92 .. .. 26 64 Philippines 2008 10 7 68 82 7 3 42 64 .. .. 64 87 Rwanda 2008 16 13 32 49 16 14 16 43 30 10 82 83 Senegal 2005 24 23 51 52 10 15 35 61 25 7 59 65 Serbia 2006 7 5 .. .. 3 2 89 .. 4 2 50 54 Sierra Leone 2008 13 9 74 86 7 4 39 46 27 15 39 40 Somalia 2006 26 14 .. .. 18 12 5 28 .. .. 5 22 Swaziland 2007 23 9 89 85 10 8 66 75 9 3 82 79 Syrian Arab Republic 2006 9 6 .. .. 4 6 72 86 13 8 51 77 Tanzania 2010 15 16 56 68 4 5 18 62 25 12 69 85 Thailand 2006 10 6 .. .. 7 3 85 78 15 4 92 86 Timor-Leste 2009/10 13 17 81 76 2 2 53 80 .. .. 43 45 Togo 2006 15 9 .. .. 7 8 17 28 37 15 39 63 Ukraine .. .. .. .. .. .. .. .. .. .. .. .. Uzbekistan 2006 .. .. .. .. .. .. .. .. 6 3 90 80 Yemen, Rep. 2006 35 27 .. .. .. .. .. .. .. .. 18 73 Zambia 2007 14 16 71 79 5 6 78 56 21 14 71 78 Zimbabwe 2006 15 15 65 79 7 2 9 51 18 6 43 64 2012 World Development Indicators 133 2.24 Health gaps by income Reproductive and women’s health Survey Knowledge of Contraceptive Pregnant women Births attended by Problem accessing year contraception prevalence receiving prenatal care skilled health staffa health care rate Any method Any method % of married women % of married women ages 15–49 ages 15–49 % % % Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile Algeria 2006 .. .. 56 65 76 98 88 98 .. .. Armenia 2005 97 100 51 60 86 99 98 100 92 67 Azerbaijan 2006 96 99 55 57 56 98 76 100 94 65 Bangladesh 2007 100 100 55 60 32 85 7 57 26 17 Bolivia 2008 90 100 46 71 79 97 39 99 95 78 Bosnia and Herzegovina 2006 .. .. 35 35 98 100 99 100 .. .. Burkina Faso 2006 85 99 9 41 79 98 56 65 88 65 Cambodia 2005 98 100 31 54 58 91 22 92 93 72 Cameroon 2006 72 99 11 44 48 98 19 96 92 69 Colombia 2010 100 100 76 80 92 99 86 99 .. .. Congo, Dem. Rep. 2007 79 98 14 39 78 95 62 98 95 70 Congo, Rep. 2005 97 100 40 46 75 98 69 99 .. .. Côte d’Ivoire 2006 75 99 9 24 69 97 29 95 .. .. Dominican Republic 2007 99 100 68 73 87 98 90 98 75 43 Egypt, Arab Rep. 2008 100 100 55 65 57 93 58 97 87 45 Ethiopia 2005 76 96 4 37 .. .. .. .. 98 82 Gambia, The 2006 .. .. 6 13 98 98 28 89 .. .. Ghana 2008 93 100 14 31 93 100 23 96 78 48 Guinea 2005 87 97 5 17 68 98 15 87 93 71 Guinea-Bissau 2006 .. .. 6 23 76 89 19 79 .. .. Haiti 2006 100 100 20 38 72 97 8 71 98 75 Honduras 2006 100 100 53 73 88 99 38 98 94 69 India 2006 99 100 42 68 58 97 21 90 67 15 Indonesia 2007 96 100 53 64 83 99 47 96 62 25 Jordan 2009 100 100 54 65 97 100 99 100 81 62 Kazakhstan 2006 99 100 42 59 100 100 100 100 .. .. Kenya 2009 86 99 20 55 84 97 23 82 39 27 Kyrgyz Republic 2006 .. .. 50 51 94 99 93 100 .. .. Lao PDR 2006 .. .. .. .. 16 88 3 81 .. .. Liberia 2007 72 99 4 20 68 96 30 82 91 45 Madagascar 2009 87 100 20 57 73 97 22 90 75 37 Malawi 2006 98 100 38 46 90 95 43 77 90 53 Mali 2006 70 93 4 19 20 80 9 76 74 47 Mauritania 2007 53 93 2 19 53 94 21 95 .. .. Moldova 2005 99 100 67 70 97 98 99 100 85 56 Namibia 2007 97 100 32 71 90 97 61 98 89 33 Nepal 2006 100 100 33 61 50 92 9 64 91 63 Niger 2006 68 89 11 21 37 82 5 60 87 59 Nigeria 2008 41 96 3 35 24 94 9 86 86 49 Pakistan 2007 92 99 16 43 38 92 18 79 .. .. Philippines 2008 96 100 41 50 91 99 98 99 88 50 Rwanda 2008 99 99 28 50 95 97 49 76 89 65 Senegal 2005 89 99 4 25 86 99 21 91 88 54 Serbia 2006 .. .. 33 49 96 100 98 100 .. .. Sierra Leone 2008 65 89 4 20 84 97 28 72 96 69 Somalia 2006 .. .. 12 19 8 51 11 77 .. .. Swaziland 2007 100 100 37 62 96 99 51 93 70 22 Syrian Arab Republic 2006 .. .. 42 68 68 94 78 99 .. .. Tanzania 2010 97 100 23 51 82 95 31 88 56 16 Thailand 2006 .. .. 74 69 96 100 93 100 .. .. Timor-Leste 2009/10 66 93 15 34 75 96 12 71 94 70 Togo 2006 94 99 12 19 69 100 30 97 .. .. Ukraine 2007 99 100 62 71 98 99 99 100 .. .. Uzbekistan 2006 .. .. 66 63 98 99 100 100 .. .. Yemen, Rep. 2006 .. .. 15 44 32 79 17 74 .. .. Zambia 2007 98 100 41 54 90 98 26 92 77 42 Zimbabwe 2006 99 100 48 72 92 98 44 97 89 46 134 2012 World Development Indicators 2.24 PEOPLE Health gaps by income About the data De�nitions Health survey data at the national level do not reveal • Survey year is the year in which the underlying data survey who have received at least one antenatal within-country inequalities associated with socioeco- were collected. The reference year of the data may be care during pregnancy before the most recent birth nomic status. The data in the table describe the health preceding the survey year. • Infant mortality rate is from any skilled personnel. •  Births attended by and demographic status as well as use of health ser- the number of infants dying before reaching one year skilled health staff are live births in the one, two, vices by individuals in different socioeconomic groups of age, per 1,000 live births. • Under-�ve mortality or three years preceding the survey attended by any within countries. The data are from MEASURE DHS rate is the probability that a child born in a specific skilled personnel. • Problem accessing health care Demographic and Health Surveys by ICF International year will die before reaching age 5, if subject to the is the percentage of women who report they have and Multiple Indicator Cluster Surveys by the United age-specific mortality rate of that year. The probabil- a big problem accessing health care when they are Nations Children’s Fund. ity is derived from life tables and expressed as a sick due to inadequate knowledge of where to go Obtaining reliable data on a household’s or individ- rate per 1,000 live births. • Total fertility rate is the for treatment, need to get permission or money for ual’s socioeconomic status is challenging, and meth- number of children that would be born to a woman if treatment, distance to health facility, need to take ods have evolved over time. Earlier measurements she were to live to the end of her childbearing years transport, desire not to go alone, or concern that a relied on indicators such as household income and and bear children in accordance with age-specific fer- female provider may not be available. consumption, which are prone to bias and are time tility rates of a reference period. • Teenage mothers and labor intensive when included in survey question- are women ages 15−19 who are mothers or pregnant naires. The wealth index, developed by MEASURE DHS with their first child. •  Diarrhea prevalence is the with partial funding from the World Bank, is calculated percentage of children under age 5 who had diarrhea using easy-to-collect data on a household’s ownership in the two weeks preceding the survey. • Diarrhea of selected assets, such as televisions and bicycles; treatment is the percentage of children under age 5 materials used for housing construction; and types of with diarrhea in the two weeks preceding the survey water access and sanitation facilities. A single asset who received oral rehydration salts, recommended index is developed on the basis of data from the entire homemade fluids (rehydration salts or recommended country sample and used. Generated with a statistical home solution), or increased fluids. • Acute respira- procedure known as principal components analysis, tory infection (ARI) prevalence is the percentage the wealth index places individual households on a of children under age 5 who were ill with a cough continuous scale of relative wealth. Demographic and accompanied by rapid breathing in the two weeks Health Surveys and Multiple Indicator Cluster Surveys preceding the survey. • Children with ARI taken to separate all interviewed households into five wealth health provider are children under age 5 with ARI in quintiles to compare the influence of wealth on various the two weeks preceding the survey who were taken population, health and nutrition indicators. The wealth to a health facility. • Prevalence of child malnutri- index is presented in the final reports of these surveys. tion (underweight) is the percentage of children Data disaggregated by wealth quintile provide under age 5 whose weight for age is more than two insights into health differentials by socioeconomic standard deviations below the median for the inter- status and allow problems particular to the poor, national reference population. Data are based on the such as unequal access to health care to be identi- old standards of the U.S. Centers for Disease Con- fied. If the poor have a greater disease burden than trol National Center for Health Statistics and World the rich, programs should focus on reaching the Health Organization international reference popula- poor. But this is rare. Health services too often fail tion. • Child immunization (all vaccinations) is the poor people in access, quality, and affordability. In percentage of children ages 12−23 months who have low-income countries the poor are particularly disad- received vaccines for Bacillus Calmette-Guérin and vantaged in using health care and experience worse measles; three doses each of diphtheria, pertussis, health outcomes than the rich. The table shows the and tetanus; and polio vaccine (excluding polio 0) estimates for the poorest and richest quintiles only; by the time of the survey, according to the vaccina- the full set of estimates for up to 70 indicators is tion card or the mother’s report. •  Knowledge of available at http://data.worldbank.org and http:// contraception is the percentage of currently married Data sources data.worldbank.org/data-catalog/health-nutrition women who know at least one contraceptive method. -population-statistics. The estimates in the table are • Contraceptive prevalence rate is the percentage Data on health gaps by income are from MEASURE based on household survey data, which may refer to of women ages 15-49 married or in union who are DHS Demographic and Health Surveys, by ICF a period preceding the survey date or use a definition practicing, or whose sexual partners are practicing, International, downloaded through STATcompiler, or methodology different from the estimates in the any form of contraception. • Pregnant women receiv- and from Multiple Indicator Cluster Surveys by other tables. Thus the estimates may differ, and cau- ing prenatal care are women with one or more live UNICEF through their final reports. tion should be exercised in using the data. births in the one, two, or three years preceding the 2012 World Development Indicators 135 ENVIRONMENT T 3 he indicators in the Environment section impacts. Understanding climate change is thus measure the use of resources and the way key for development policy. Because uncertainty human activities affect the natural and built increases with climate change, better climate environment. They include measures of envi- information is critical for wise development ronmental goods (forests, water, cultivatable decisions. land) and of degradation (pollution, deforesta- This year’s Environment section includes two tion, loss of habitat, and loss of biodiversity). new tables on information related to climate Sustainable development and poverty reduction change. Table 3.11 presents data on carbon require efficient use of environmental resources. dioxide emissions by economic sector, which These indicators show that growing populations shows how differences in industrial structure and expanding economies have placed greater and production technologies affect the pro- demands on land, water, forests, minerals, duction of carbon dioxide and how these pat- and energy resources. But new technologies, terns have changed. Table 3.12 presents data increasing productivity, and better policies can on climate variability, exposure to impact, and ensure that future development is environmen- resilience. tally and socially sustainable. Other indicators in this section describe land Nowhere are these risks and opportunities use, agriculture and food production, forests more intertwined than in the global effort to and biodiversity, water resources, energy use mitigate the effects of climate change. At the and efficiency, natural resource rents, urbaniza- December 2011 United Nations Conference tion, environmental impacts, government com- on Climate Change in Durban, South Africa, all mitments, and threatened species. Table 3.8 194 participating countries adopted the Durban adds newly available data on the share of the Platform for Enhanced Action, which sets the population with access to electricity. direction of climate negotiations. The platform Where possible, the indicators come from calls for parties to negotiate a legal agreement international sources and are standardized to on climate change no later than 2015 that would facilitate comparison across countries. But eco- apply to all countries and be effective by 2020. systems span national boundaries, and access Negotiators also launched a Green Climate Fund to natural resources may vary within countries. that will eventually channel billions of dollars a For example, water may be abundant in some year to developing countries for adaptation to parts of a country but scarce in others, and and mitigation of climate change. countries often share water resources. Land As documented in World Development Report productivity and optimal land use may be loca- 2010: Development and Climate Change (World tion specific, but widely separated regions can Bank 2009j), climate change is already eroding have common characteristics. Greenhouse gas development gains and causing disruptions to emissions and climate change are measured social and economic systems in some countries. globally, but their effects are experienced Continued rise in temperature, accompanied by locally, shaping people’s lives and opportuni- changes in precipitation patterns, is projected ties. Measuring environmental phenomena for this century, and more frequent, severe, and and their effects at the subnational, national, prolonged climate-related events such as floods and supranational levels and incorporating and droughts are also projected—posing risks these values in national income accounts and for agriculture, food production, and water sup- other statistical frameworks remain major chal- plies. Poor countries and the poorest people lenges for economists, environmentalists, and in all countries are the most vulnerable to the statisticians. 2012 World Development Indicators 137 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 2010 1990–2010 2010 1990 2010 1990 2009 1990 2009 1990 2009 Afghanistan 82 75 2.3 652.2 2.1 2.1 0.2 0.2 12.1 11.9 41.6 23.3 Albania 64 52 –0.9 27.4 28.8 28.3 4.6 3.2 21.1 22.3 17.6 19.2 Algeria 48 34 –0.4 2,381.7 0.7 0.6 0.2 0.4 3.0 3.1 28.0 21.5 Angola 63 42 0.7 1,246.7 48.9 46.9 0.4 0.2 2.3 3.2 28.1 21.6 Argentina 13 8 –1.7 2,736.7 12.7 10.7 0.4 0.4 9.6 11.3 80.9 77.4 Armenia 33 36 0.5 28.5 12.2 9.2 2.1 1.9 14.9 16.0 12.3 14.8 Australia 15 11 0.1 7,682.3 20.1 19.4 0.0 0.0 6.2 6.1 280.7 214.8 Austria 34 32 –0.4 82.5 45.8 47.1 1.0 0.8 17.3 16.6 18.6 16.4 Azerbaijan 46 48 0.8 82.6 11.2 11.3 3.7 2.7 20.5 22.7 23.1 20.9 Bahrain 12 11 7.2 0.8 0.3 1.3 2.9 2.6 2.9 1.3 0.4 0.1 Bangladesh 80 72 0.5 130.2 11.5 11.1 2.5 7.5 72.6 58.1 9.0 5.2 Belarus 34 26 –1.8 202.9 38.4 42.5 0.9 0.6 30.0 27.3 59.6 58.3 Belgium 4 3 0.0 30.3 22.4 22.4 0.5a 0.7 23.3a 27.7 0.2 7.8 Benin 66 58 2.2 110.6 52.1 41.2 0.9 2.7 14.6 22.1 33.8 28.5 Bolivia 44 34 0.2 1,083.3 58.0 52.8 0.1 0.2 1.9 3.4 31.5 38.2 Bosnia and Herzegovina 61 51 –1.3 51.2 43.3 42.7 2.9 2.0 16.7 19.5 21.6 26.5 Botswana 58 39 –0.7 566.7 24.2 20.0 0.0 0.0 0.7 0.4 30.4 12.6 Brazil 25 14 –2.5 8,459.4 68.0 61.4 0.8 0.9 6.0 7.2 33.9 31.7 Bulgaria 34 28 –1.6 108.6 30.1 36.2 2.7 1.6 34.9 28.9 44.2 41.4 Burkina Faso 86 80 2.5 273.6 25.0 20.6 0.2 0.2 12.9 21.6 37.8 36.9 Burundi 94 89 2.2 25.7 11.3 6.7 14.0 13.6 36.2 35.0 16.6 11.0 Cambodia 87 77 0.3 176.5 73.3 57.2 0.6 0.9 20.9 22.1 38.8 27.9 Cameroon 59 42 0.2 472.7 51.4 42.1 2.6 3.0 12.6 12.6 48.8 31.1 Canada 23 19 0.6 9,093.5 34.1 34.1 0.7 0.8 5.0 5.0 163.7 133.7 Central African Republic 63 61 1.6 623.0 37.2 36.3 0.1 0.1 3.1 3.1 65.4 45.2 Chad 79 72 2.0 1,259.2 10.4 9.2 0.0 0.0 2.6 3.4 54.5 39.3 Chile 17 11 –1.6 743.5 20.5 21.8 0.3 0.6 3.8 1.7 21.3 7.5 China 73 55 –1.1 9,327.5 16.8 22.2 0.8 1.5 13.3 11.8 10.9 8.3 Hong Kong SAR, China 1 .. .. 1.0 .. .. .. .. .. .. .. .. Colombia 32 25 0.2 1,109.5 56.3 54.5 1.5 1.4 3.0 1.6 10.0 3.9 Congo, Dem. Rep. 72 65 1.8 2,267.1 70.7 68.0 0.5 0.3 2.9 3.0 18.3 10.4 Congo, Rep. 46 38 1.5 341.5 66.5 65.6 0.1 0.2 1.4 1.5 20.1 12.7 Costa Rica 49 36 0.0 51.1 50.2 51.0 4.9 5.9 5.1 3.9 8.5 4.4 Côte d’Ivoire 60 50 0.7 318.0 32.1 32.7 11.0 13.5 7.6 8.8 19.4 14.5 Croatia 46 42 –0.7 56.0 33.1 34.3 2.0 1.6 21.7 15.5 27.1 19.6 Cuba 27 24 –0.1 106.4 19.2 27.0 4.2 3.5 31.6 34.3 32.1 32.4 Cyprus 33 30 0.5 9.2 17.4 18.7 5.5 3.6 11.5 9.4 13.8 8.0 Czech Republic 25 27 0.4 77.3 34.0 34.4 3.1 1.0 41.1 41.2 32.1 30.3 Denmark 15 13 –1.6 42.4 10.5 12.8 0.2 0.1 60.4 57.3 49.8 44.0 Dominican Republic 45 30 –1.2 48.3 40.8 40.8 9.3 9.7 18.6 16.6 12.5 8.2 Ecuador 45 33 –0.6 248.4 49.9 39.7 4.8 5.4 5.8 4.8 15.6 8.4 Egypt, Arab Rep. 57 57 1.7 995.5 0.0 0.1 0.4 0.8 2.3 2.9 4.0 3.6 El Salvador 51 39 –0.2 20.7 18.2 13.9 12.5 11.1 26.5 32.7 10.3 11.0 Eritrea 84 78 2.4 101.0 16.0 15.2 0.0 0.0 4.9 6.8 0.1 13.5 Estonia 29 31 –0.1 42.4 49.3 52.3 0.3 0.2 26.3 14.1 72.7 44.5 Ethiopia 87 82 1.8 1,000.0 13.7 12.3 0.5 1.0 10.0 13.9 1.4 17.2 Finland 39 36 –0.4 303.9 71.9 72.9 0.0 0.0 7.4 7.4 45.5 42.3 France 26 22 –0.5 547.7 26.5 29.1 2.2 1.9 32.9 33.5 30.9 28.4 Gabon 31 14 –1.5 257.7 85.4 85.4 0.6 0.6 1.1 1.3 31.8 22.0 Gambia, The 62 42 0.8 10.0 44.2 48.0 0.5 0.5 18.2 40.0 18.8 23.8 Georgia 45 47 0.8 69.5 40.0 39.5 4.8 1.7 11.4 6.4 16.3 10.2 Germany 27 26 –0.5 348.6 30.8 31.8 1.3 0.6 34.3 34.3 15.1 14.6 Ghana 64 49 0.8 227.5 32.7 21.7 6.6 12.3 11.9 19.3 18.3 18.5 Greece 41 39 –0.2 128.9 25.6 30.3 8.3 8.9 22.5 19.8 28.5 22.6 Guatemala 59 51 1.6 107.2 44.3 34.1 4.5 8.8 12.1 14.0 14.6 10.7 Guinea 72 65 1.5 245.7 29.6 26.6 2.0 2.8 11.6 11.6 49.5 29.2 Guinea-Bissau 72 70 2.0 28.1 78.8 71.9 4.2 8.9 8.9 10.7 24.6 20.2 Haiti 72 50 –1.4 27.6 4.2 3.7 11.6 10.9 28.3 38.1 11.0 10.6 138 2012 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 2010 1990–2010 2010 1990 2010 1990 2009 1990 2009 1990 2009 Honduras 60 51 1.1 111.9 72.7 46.4 3.2 3.7 13.1 9.1 29.9 13.7 Hungary 34 32 –1.4 89.6 20.0 22.6 2.6 2.1 56.2 50.6 48.7 45.8 India 75 70 0.9 2,973.2 21.5 23.0 2.2 3.9 54.8 53.1 18.6 13.1 Indonesia 69 46 –1.4 1,811.6 65.4 52.1 6.5 10.5 11.2 13.0 11.0 9.9 Iran, Islamic Rep. 44 31 –0.6 1,628.6 6.8 6.8 0.8 1.1 9.3 10.6 27.7 23.5 Iraq 30 34 3.3 437.4 1.8 1.9 0.7 0.6 13.3 10.4 31.9 14.5 Ireland 43 38 –0.2 68.9 6.7 10.7 0.0 0.0 15.1 15.8 29.6 24.4 Israel 10 8 1.6 21.6 6.1 7.1 4.1 3.6 15.9 14.0 7.4 4.1 Italy 33 32 0.0 294.1 25.8 31.1 10.1 8.9 30.6 23.4 15.9 11.4 Jamaica 51 46 –0.2 10.8 31.9 31.1 9.2 9.2 11.0 11.1 5.0 4.5 Japan 37 33 –0.6 364.5 68.4 68.5 1.3 0.9 13.1 11.8 3.9 3.4 Jordan 28 22 2.0 88.2 1.1 1.1 0.8 0.9 2.0 2.3 5.7 3.4 Kazakhstan 44 42 1.8 2,699.7 1.3 1.2 0.1 0.0 13.0 8.7 213.2 145.4 Kenya 82 78 2.2 569.1 6.5 6.1 0.8 1.1 8.8 9.5 21.3 13.7 Korea, Dem. Rep. 42 37 –0.5 120.4 68.1 47.1 1.5 1.7 19.0 22.0 11.4 10.9 Korea, Rep. 26 18 –0.9 96.9 64.5 64.2 1.6 2.1 19.8 16.4 4.6 3.3 Kosovo .. .. .. 10.9b .. .. .. .. .. 27.6 .. 16.8 Kuwait 2 2 2.1 17.8 0.2 0.3 0.1 0.2 0.2 0.6 0.2 0.4 Kyrgyz Republic 62 63 0.6 191.8 4.4 5.0 0.4 0.4 6.9 6.7 29.0 24.0 Lao PDR 85 67 –0.3 230.8 75.0 68.2 0.3 0.5 3.5 5.9 19.1 22.3 Latvia 31 32 –0.7 62.2 51.0 53.9 0.4 0.1 27.1 18.8 64.6 51.8 Lebanon 17 13 –0.2 10.2 12.8 13.4 11.9 14.0 17.9 14.2 6.2 3.5 Lesotho 86 73 0.0 30.4 1.3 1.4 0.1 0.1 10.4 11.0 19.3 15.6 Liberia 55 39 2.3 96.3 51.2 44.9 1.6 2.2 3.6 4.2 16.5 10.4 Libya 24 22 0.7 1,759.5 0.1 0.1 0.2 0.2 1.0 1.0 41.6 27.9 Lithuania 32 33 –0.9 62.7 31.0 34.5 0.7 0.4 46.0 32.8 78.0 61.5 Macedonia, FYR 42 32 –1.4 25.2 35.9 39.6 2.2 1.4 23.8 16.7 31.3 20.4 Madagascar 76 70 2.4 581.5 23.5 21.6 1.0 1.0 4.7 5.1 24.1 14.7 Malawi 88 80 2.5 94.1 41.3 34.4 1.4 1.3 23.9 38.2 24.0 24.9 Malaysia 50 28 –1.7 328.6 68.1 62.3 16.0 17.6 5.2 5.5 9.3 6.4 Mali 77 67 2.2 1,220.2 11.5 10.2 0.1 0.1 1.7 5.2 23.7 42.7 Mauritania 60 59 2.1 1,030.7 0.4 0.2 0.0 0.0 0.4 0.4 20.0 11.6 Mauritius 56 57 0.4 2.0 19.2 17.2 3.0 2.0 49.3 42.9 9.4 6.8 Mexico 29 22 –0.1 1,944.0 36.2 33.3 1.0 1.4 12.5 12.9 28.8 22.4 Moldova 53 59 0.4 32.9 9.7 11.7 14.2 9.2 52.8 55.2 46.8 51.0 Mongolia 43 43 1.2 1,553.6 8.1 7.0 0.0 0.0 0.9 0.6 62.5 35.4 Morocco 52 43 0.2 446.3 11.3 11.5 1.6 2.2 19.5 18.0 35.1 25.5 Mozambique 79 62 1.0 786.4 55.2 49.6 0.3 0.3 4.4 6.4 25.5 22.1 Myanmar 75 66 –0.2 653.5 60.0 48.6 0.8 1.7 14.6 16.9 24.4 23.2 Namibia 72 62 0.9 823.3 10.6 8.9 0.0 0.0 0.8 1.0 46.7 35.7 Nepal 91 82 1.2 143.4 33.7 25.4 0.5 0.8 16.0 16.7 12.0 8.2 Netherlands 31 17 –2.6 33.8 10.2 10.8 0.9 1.0 26.0 31.2 5.9 6.4 New Zealand 15 13 0.3 263.3 29.3 31.4 0.2 0.3 10.0 1.8 76.7 10.9 Nicaragua 48 43 0.7 120.3 37.5 25.9 1.6 1.9 10.8 15.8 31.6 33.3 Niger 85 83 3.4 1,266.7 1.5 1.0 0.0 0.0 8.7 11.8 141.7 99.8 Nigeria 65 50 1.1 910.8 18.9 9.9 2.8 3.3 32.4 37.3 30.3 22.0 Norway 28 22 0.9 305.5 30.0 32.9 0.0 0.0 2.8 2.7 20.3 17.3 Oman 34 28 2.4 309.5 0.0 0.0 0.1 0.1 0.1 0.3 1.9 3.7 Pakistan 69 63 1.1 770.9 3.3 2.2 0.6 1.1 26.6 26.5 18.3 12.0 Panama 46 25 –1.6 74.3 51.0 43.7 2.1 2.0 6.7 7.4 20.7 15.8 Papua New Guinea 85 88 2.3 452.9 69.6 63.4 1.2 1.5 0.4 0.6 4.6 3.9 Paraguay 51 39 0.2 397.3 53.3 44.3 0.2 0.3 5.3 9.6 49.7 59.9 Peru 31 28 0.7 1,280.0 54.8 53.1 0.3 0.6 2.7 2.9 16.1 12.7 Philippines 51 34 –0.5 298.2 22.0 25.7 14.8 16.9 18.4 18.1 8.9 5.9 Poland 39 39 0.2 304.2 29.2 30.7 1.1 1.3 47.3 41.2 37.8 32.9 Portugal 52 39 –1.5 91.5 36.4 37.8 8.5 8.5 25.6 12.3 23.5 10.6 Puerto Rico 28 1 –17.9 8.9 32.4 62.2 5.6 4.5 7.3 6.8 1.8 1.5 Qatar 8 4 7.7 11.6 0.0 0.0 0.1 0.3 0.9 1.0 2.1 0.8 2012 World Development Indicators 139 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 2010 1990–2010 2010 1990 2010 1990 2009 1990 2009 1990 2009 Romania 47 45 –0.6 229.9 27.8 28.6 2.6 1.6 41.2 38.2 40.7 40.9 Russian Federation 27 27 0.0 16,376.9 49.4 49.4 0.1 0.1 8.1 7.4 88.8 85.8 Rwanda 95 81 2.6 24.7 12.9 17.6 12.4 11.3 35.7 52.7 12.4 12.6 Saudi Arabia 23 16 –5.5 2,000.0 c 0.5 0.5 0.0 0.1 1.6 1.5 21.0 11.9 Senegal 61 57 2.2 192.5 48.6 44.0 0.2 0.3 16.1 20.0 42.7 31.8 Serbia 50 48 –0.8 88.4 26.4 30.7 26.2 3.4 .. 37.7 .. 45.1 Sierra Leone 67 62 1.7 71.6 43.5 38.1 1.9 1.8 6.8 15.1 12.2 18.9 Singapore – – .. 0.7 3.0 2.9 1.5 0.0 1.5 0.0 0.0 0.0 Slovak Republic 44 43 0.0 48.1 40.0 40.2 1.0 0.5 32.5 28.7 31.0 25.5 Slovenia 50 52 1.2 20.1 59.0 62.2 1.8 1.3 9.9 8.7 10.0 8.6 Somalia 70 63 1.6 627.3 13.2 10.8 0.0 0.0 1.6 1.6 15.5 11.0 South Africa 48 38 0.1 1,214.5 6.8 4.7 0.7 0.8 11.1 11.8 38.2 29.1 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 25 23 –0.2 499.1 27.7 36.4 9.7 9.5 30.7 25.1 39.5 27.2 Sri Lanka 83 85 0.9 62.7 37.5 29.7 15.9 15.5 14.4 19.1 5.2 5.8 Sudan 73 55 0.9 2,376.0 32.1 29.4 0.0 0.1 5.4 8.5 48.3 47.5 Swaziland 77 75 1.1 17.2 27.4 32.7 0.7 0.9 10.5 10.2 20.9 15.0 Sweden 17 15 0.3 410.3 66.5 68.7 0.0 0.0 6.9 6.4 33.2 28.3 Switzerland 27 26 0.8 40.0 28.8 31.0 0.6 0.6 10.3 10.2 6.1 5.3 Syrian Arab Republic 51 45 1.3 183.6 2.0 2.7 4.0 5.4 26.6 25.4 39.6 23.3 Tajikistan 68 74 1.4 140.0 2.9 2.9 0.9 1.0 6.1 5.3 15.6 10.9 Tanzania 81 74 2.4 885.8 46.8 37.7 1.1 1.7 10.2 11.3 35.3 23.0 Thailand 71 66 0.1 510.9 38.3 37.1 6.1 7.2 34.2 29.9 30.7 22.3 Timor-Leste 79 72 1.6 14.9 65.0 49.9 3.9 4.0 7.4 11.1 14.8 15.0 Togo 70 57 0.9 54.4 12.6 5.3 1.7 3.3 38.6 40.4 57.3 37.3 Trinidad and Tobago 92 86 0.0 5.1 47.0 44.1 6.8 4.3 7.0 4.9 3.0 1.9 Tunisia 42 33 –0.2 155.4 4.1 6.5 12.5 14.3 18.7 17.4 35.7 25.9 Turkey 41 30 –0.2 769.6 12.6 14.7 3.9 3.8 32.0 27.7 45.5 29.7 Turkmenistan 55 51 0.4 469.9 8.8 8.8 0.1 0.1 2.9 3.9 34.8 37.2 Uganda 89 87 3.0 197.1 23.8 15.2 9.3 11.3 25.0 33.0 28.3 20.4 Ukraine 33 32 –0.6 579.3 16.0 16.8 1.9 1.6 57.6 56.1 64.0 70.5 United Arab Emirates 21 22 7.7 83.6 2.9 3.8 0.2 2.4 0.4 0.8 1.9 0.9 United Kingdom 11 10 –0.1 241.9 10.8 11.9 0.3 0.2 27.4 25.0 11.6 9.8 United States 25 18 –1.0 9,147.4 32.4 33.2 0.2 0.3 20.3 17.8 74.4 53.1 Uruguay 11 8 –1.0 175.0 5.3 10.0 0.3 0.2 7.2 10.7 40.5 56.2 Uzbekistan 60 63 1.3 425.4 7.2 7.7 0.9 0.8 10.5 10.1 20.9 15.5 Venezuela, RB 16 6 –3.9 882.1 59.0 52.5 0.9 0.7 3.2 3.1 14.3 9.7 Vietnam 80 71 0.4 310.1 28.8 44.5 3.2 10.8 16.4 20.3 8.1 7.3 West Bank and Gaza 32 28 2.3 6.0 1.5 1.5 19.1 19.4 18.1 16.6 5.5 2.5 Yemen, Rep. 79 68 2.2 528.0 1.0 1.0 0.2 0.5 2.9 2.2 12.8 5.0 Zambia 61 64 1.4 743.4 71.0 66.5 0.0 0.0 3.9 4.5 36.8 26.3 Zimbabwe 71 62 0.0 386.9 57.3 40.4 0.3 0.3 7.5 10.8 27.6 33.5 World 57 w 49 w 0.3 w 129,561.0 s 32.0 w 31.1 w 1.1 w 1.2 w 10.9 w 10.7 w 23.7 w 20.4 w Low income 79 72 1.5 15,043.5 31.2 27.6 0.7 0.9 7.7 9.6 23.8 18.5 Middle income 62 51 0.1 80,675.5 33.9 32.8 1.4 1.4 11.1 10.9 18.8 17.8 Lower middle income 68 60 0.8 22,789.9 30.7 27.9 1.9 2.7 14.8 16.4 18.7 15.1 Upper middle income 56 43 –0.9 57,885.6 35.2 34.6 1.1 0.9 9.2 8.7 18.9 20.6 Low & middle income 64 54 0.3 95,718.9 33.5 31.9 1.2 1.3 10.5 10.7 19.3 17.9 East Asia & Pacific 71 54 –0.9 15,853.7 29.0 29.6 2.2 3.4 12.1 11.5 12.0 9.4 Europe & Central Asia 37 36 0.1 22,748.9 38.4 38.6 0.4 0.4 11.6 10.5 68.5 59.2 Latin America & Carib. 29 21 –0.6 20,116.2 51.6 47.0 0.9 1.0 6.6 7.4 30.3 25.9 Middle East & N. Africa 48 42 1.1 8,643.6 2.4 2.4 0.8 1.0 5.9 5.9 22.5 15.6 South Asia 75 70 1.0 4,771.2 16.6 17.1 1.8 3.1 42.7 41.4 17.8 12.3 Sub-Saharan Africa 72 63 1.7 23,585.4 31.1 28.0 0.8 1.0 6.6 8.5 32.1 24.2 High income 27 22 –0.5 33,842.1 27.9 28.8 0.7 0.7 11.7 10.8 42.5 33.2 Euro area 29 26 –0.4 2,552.0 33.8 37.3 4.8 4.2 27.1 24.4 22.9 18.8 a. Includes Luxembourg. b. Data are from national sources. c. Provisional estimate. 140 2012 World Development Indicators 3.1 ENVIRONMENT Rural population and land use About the data De�nitions 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 De�nitions for tables 2.1 and 3.13). • 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 3.13). 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). Forest area statistics released by the rather than to change in area. •  Land use can be The data in the table show that land use patterns FAO between 1948 and 1963 were based mostly on broken into several categories, three of which are are changing. They also indicate major differences data from country questionnaires. Remote sensing, presented in the table (not shown are land used as in resource endowments and uses among countries. statistical monitoring, and expert analysis of country permanent pasture and land under urban develop- True comparability of the data is limited, however, surveys have been applied since 1980 to improve ment). • Forest area is land under natural or planted by variations in definitions, statistical methods, and forest coverage estimates. The FAO’s Global Forest stands of trees of at least 5 meters in height in situ, quality of data. Countries use different definitions of Resources Assessment 2010 covers 230 countries whether productive or not, and excludes tree stands rural and urban population and land use. The Food and is the most comprehensive assessment of for- in agricultural production systems (for example, in and Agriculture Organization of the United Nations ests, forestry, and the benefits of forest resources fruit plantations and agroforestry systems) and trees (FAO), the primary compiler of the data, occasion- in both scope and number of countries and people in urban parks and gardens. • Permanent cropland ally adjusts its definitions of land use categories involved. It examines status and trends for about 90 is land cultivated with crops that occupy the land for and revises earlier data. Because the data reflect variables on the extent, condition, uses, and values long periods and need not be replanted after each changes in reporting procedures as well as actual of forests and other wooded land. harvest, such as cocoa, coffee, and rubber. Land changes in land use, apparent trends should be inter- under flowering shrubs, fruit trees, nut trees, and preted cautiously. vines is included, but land under trees grown for wood or timber is not. • Arable land is land defined by the What is rural? Urban? 3.1a FAO as under temporary crops (double-cropped areas The rural population identified in table 3.1 is approximated as the difference between total population and are counted once), temporary meadows for mowing urban population, calculated using the urban share reported by the United Nations Population Division. or pasture, land under market or kitchen gardens, There is no universal standard for distinguishing rural from urban areas, and any urban-rural dichotomy is and land temporarily fallow. Land abandoned as a an oversimplification (see About the data for table 3.13). The two distinct images—isolated farm, thriving result of shifting cultivation is excluded. metropolis—represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and 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 3.13 is inadequate. 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 Data sources that these criteria are important gradients along which economic behavior and appropriate development 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. 2012 World Development Indicators 141 3.2 Agricultural inputs Agricultural Average Land under Fertilizer Agricultural Agricultural landa annual cereal production consumption employment machinery precipitation kilograms % of per hectare Tractors % of % thousand fertilizer of arable % of total per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990–92 2007–09 2007–09 2007–09 1990–92 2008–10 2007–09 2007–09 1990–92 2007–09 1990 2009 Afghanistan 58 58 4.8 327 2,250.0 3,122.0 146.8 3.2 .. .. 0.2 0.1 Albania 41 44 16.8 1,485 189.5 145.7 .. 45.5 .. 44.1 212.4 121.9 Algeria 16 17 2.1 89 3,530.5 2,988.8 487.9 7.8 .. .. 129.1 139.6 Angola 46 47 .. 1,010 1,012.0 1,670.0 .. 1.1 5.1 .. .. .. Argentina 47 51 .. 591 8,372.5 9,358.1 153.3 25.4 0.4 1.2 100.2 86.9 Armenia 41 62 8.9 562 162.8 156.7 .. 29.3 .. 44.2 345.5 291.6 Australia 61 53 0.4 534 13,319.8 19,476.4 151.7 29.0 5.3 3.3 .. .. Austria 42 38 1.4 1,110 837.7 994.8 .. 83.1 7.1 5.3 2,373.6 2,390.3 Azerbaijan 53 58 29.5 447 627.0 954.6 .. 13.6 34.7 38.6 194.8 148.2 Bahrain 13 9 .. 83 .. .. 5.9 .. 2.4 .. 45.0 75.0 Bangladesh 73 70 .. 2,666 10,860.8 12,349.1 231.5 281.7 66.4 .. 2.3 1.2 Belarus 46 44 0.5 618 2,603.0 2,380.3 46.3 281.1 22.3 .. 206.9 86.8 Belgium 44b 45 0.4 847 386.2b 329.5 .. .. 2.9 1.5 1,523.3b 1,127.0 Benin 21 30 .. 1,039 667.3 1,109.3 .. .. .. .. 1.0 .. Bolivia 33 34 .. 1,146 680.2 931.6 .. 6.0 2.1 36.1 24.8 20.0 Bosnia and Herzegovina 43 42 .. 1,028 304.1 286.2 .. 24.5 .. .. 235.3 .. Botswana 46 46 .. 416 77.9 111.3 .. .. .. .. 140.5 134.8 Brazil 29 31 .. 1,782 20,564.1 18,674.3 245.0 124.9 28.3 17.0 143.8 129.2 Bulgaria 56 46 1.4 608 2,231.6 1,917.5 115.1 167.4 21.2 7.1 135.8 172.3 Burkina Faso 35 44 .. 748 2,844.0 4,290.2 .. 9.1 .. .. 2.4 .. Burundi 83 84 .. 1,274 220.0 232.7 .. 0.9 .. .. 1.8 .. Cambodia 26 31 .. 1,904 1,733.4 3,106.8 .. 7.1 .. 72.2 3.2 5.9 Cameroon 19 20 .. 1,604 950.3 1,639.9 .. 7.4 .. .. 0.9 .. Canada 7 7 .. 537 20,176.4 13,013.6 22.2 46.8 4.1 2.4 164.8 162.5 Central African Republic 8 8 .. 1,343 95.4 163.1 .. .. .. .. .. .. Chad 38 39 .. 322 1,337.6 2,467.1 .. .. .. .. .. .. Chile 21 21 5.6 1,522 694.3 523.1 94.7 595.8 18.0 11.2 127.6 425.9 China 57 56 .. 645 92,582.2 90,131.6 96.8 488.4 58.5 39.6 66.6 81.8 Hong Kong SAR, China .. .. .. .. .. .. .. .. 0.7 0.2 .. .. Colombia 41 38 .. 2,612 1,440.7 1,047.7 227.0 496.8 1.4 19.7 96.8 .. Congo, Dem. Rep. 10 10 .. 1,543 1,921.1 1,980.4 .. 0.5 .. .. .. .. Congo, Rep. 31 31 .. 1,646 9.9 31.8 .. 1.1 .. .. .. .. Costa Rica 42 35 1.5 2,926 76.3 76.0 .. 826.6 24.1 12.3 .. .. Côte d’Ivoire 60 64 .. 1,348 1,451.0 851.0 .. 15.9 .. .. 19.9 32.1 Croatia 43 23 0.4 1,113 592.7 550.0 48.7 246.8 .. 13.9 35.2 49.4 Cuba 63 63 .. 1,335 252.3 401.6 445.3 14.2 25.3 18.6 226.2 203.2 Cyprus 17 14 20.8 498 65.1 35.7 .. 181.9 12.0 3.9 1,377.4 1,460.1 Czech Republic .. 55 0.3 677 .. 1,467.2 112.4 123.3 .. 3.1 252.0 262.3 Denmark 65 62 9.6 703 1,611.9 1,489.4 .. 103.2 5.1 2.5 634.7 486.3 Dominican Republic 54 51 .. 1,410 153.1 225.4 .. 27.2 18.7 14.5 25.9 21.5 Ecuador 29 30 12.6 2,087 918.4 875.7 .. 187.3 6.6 28.7 54.2 90.7 Egypt, Arab Rep. 3 4 .. 51 2,477.1 2,967.1 48.5 502.8 38.4 31.6 249.6 390.6 El Salvador 70 75 2.1 1,724 486.3 354.3 .. 107.4 35.8 20.9 .. .. Eritrea .. 75 .. 384 .. 461.7 .. 3.5 .. .. 5.0 8.3 Estonia 32 22 .. 626 453.6 274.1 378.1 69.5 18.1 4.0 455.3 604.7 Ethiopia .. 35 0.5 848 .. 9,340.5 .. 7.9 .. .. .. .. Finland 8 8 .. 536 914.5 954.2 75.8 108.0 8.9 4.6 916.5 784.0 France 55 53 5.1 867 9,346.0 9,258.8 437.6 148.3 5.9 2.9 800.0 635.3 Gabon 20 20 .. 1,831 16.1 22.9 .. 6.1 .. .. .. .. Gambia, The 61 67 .. 836 78.3 322.7 .. 6.8 .. .. 2.4 .. Georgia 46 36 4.0 1,026 248.5 173.2 14.3 43.0 .. 53.4 295.6 216.9 Germany 49 48 .. 700 6,514.4 6,612.9 60.8 181.4 3.7 1.7 1,309.4 838.3 Ghana 56 68 .. 1,187 1,203.5 1,602.1 .. 11.9 62.0 .. 7.1 4.5 Greece 71 64 16.9 652 1,396.1 916.8 333.5 83.7 21.9 11.9 744.2 1,004.7 Guatemala 40 41 .. 1,996 822.7 923.3 .. 106.8 13.6 .. .. .. Guinea 57 58 .. 1,651 819.8 2,028.7 .. 0.6 .. .. 12.7 25.1 Guinea-Bissau 51 58 .. 1,577 117.2 152.5 .. .. .. .. 0.8 .. Haiti 57 67 .. 1,440 462.9 446.2 .. .. 65.6 .. 2.6 .. 142 2012 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 Tractors % of % thousand fertilizer of arable % of total per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990–92 2007–09 2007–09 2007–09 1990–92 2008–10 2007–09 2007–09 1990–92 2007–09 1990 2009 Honduras 30 29 .. 1,976 516.8 538.5 .. 62.3 38.2 34.6 30.9 48.7 Hungary 68 64 1.8 589 2,746.0 2,585.7 154.0 80.0 11.3 4.6 97.7 261.8 India 61 61 35.1 1,083 99,499.5 92,610.0 166.3 167.8 .. .. 60.7 128.5 Indonesia 23 30 .. 2,702 14,732.7 17,387.5 110.9 181.4 54.9 39.7 2.2 2.0 Iran, Islamic Rep. 39 30 19.0 228 9,787.4 9,440.3 125.4 69.7 .. 21.2 141.5 152.8 Iraq 23 20 .. 216 3,919.5 2,555.5 118.6 37.2 .. 23.4 65.8 92.2 Ireland 64 61 .. 1,118 295.3 276.4 .. 477.3 11.6 5.0 1,623.4 1,476.4 Israel 27 24 28.4 435 104.9 79.1 2.5 189.5 3.5 1.7 798.8 695.3 Italy 54 47 18.8 832 4,225.4 3,494.3 186.5 135.5 8.0 3.7 1,586.5 2,117.1 Jamaica 44 41 .. 2,051 2.9 2.0 .. 58.2 27.3 20.2 .. .. Japan 15 13 35.2 1,668 2,430.9 1,941.8 114.3 235.1 6.4 3.9 4,492.9 4,532.1 Jordan 13 12 9.2 111 121.1 44.5 32.3 2,444.2 .. 3.0 340.8 301.7 Kazakhstan 82 77 .. 250 22,152.4 15,068.4 34.1 2.4 .. 29.4 62.0 24.2 Kenya 48 48 0.0 630 1,804.8 2,542.4 .. 32.4 .. .. 20.0 25.2 Korea, Dem. Rep. 21 24 .. 1,054 1,517.6 1,268.2 .. .. .. .. 295.4 .. Korea, Rep. 22 19 51.6 1,274 1,293.2 970.4 87.8 388.8 15.8 7.0 211.0 1,115.4 Kosovo .. 52 .. .. .. .. .. .. .. .. .. .. Kuwait 8 8 .. 121 0.3 1.1 0.2 54.5 .. .. 220.0 89.0 Kyrgyz Republic 53 55 9.4 533 578.0 579.8 .. 21.0 38.2 34.0 189.4 188.1 Lao PDR 7 10 .. 1,834 597.9 1,090.6 .. .. .. .. 8.5 .. Latvia 41 29 0.0 641 696.7 531.2 .. 64.9 .. 8.7 363.7 501.4 Lebanon 59 67 19.9 661 42.1 64.9 3.1 19.9 .. .. 174.9 .. Lesotho 77 77 .. 788 153.1 190.5 .. .. .. .. 57.7 .. Liberia 26 27 .. 2,391 120.0 251.2 .. .. .. 47.6 .. .. Libya 9 9 .. 56 287.7 329.0 28.2 40.3 .. .. 184.3 218.9 Lithuania 54 43 .. 656 1,134.0 1,037.6 8.5 45.4 .. 9.2 256.0 631.8 Macedonia, FYR 51 40 7.3 619 235.2 161.8 .. 56.9 .. 19.7 730.2 1,243.8 Madagascar 63 70 2.2 1,513 1,340.2 1,729.0 .. 2.6 .. .. 4.9 1.9 Malawi 45 59 0.5 1,181 1,430.2 1,831.2 53,107.2 26.6 .. .. .. .. Malaysia 23 24 .. 2,875 693.8 680.1 126.3 769.8 21.8 13.5 152.9 .. Mali 26 34 .. 282 2,451.6 3,975.1 .. 3.2 .. .. 10.2 2.2 Mauritania 38 38 .. 92 123.6 291.5 .. .. .. .. 8.4 9.8 Mauritius 56 48 21.4 2,041 0.5 0.1 .. 209.4 14.3 9.0 .. .. Mexico 54 53 5.5 752 9,928.3 9,980.0 328.9 51.7 26.8 13.5 123.5 97.7 Moldova 78 75 9.2 450 675.6 884.7 .. 9.4 40.0 31.1 310.1 197.6 Mongolia 80 75 .. 241 592.6 259.1 .. 7.9 .. 40.0 80.3 40.0 Morocco 69 67 4.6 346 5,019.6 5,059.7 12.5 20.8 3.6 40.9 45.0 .. Mozambique 61 63 .. 1,032 1,376.9 2,490.7 .. 2.9 .. .. .. .. Myanmar 16 19 24.8 2,091 5,577.5 8,732.4 185.8 5.4 69.1 .. 13.6 10.7 Namibia 47 47 .. 285 195.2 309.9 .. 1.6 48.2 16.3 .. .. Nepal 29 30 27.7 1,500 2,840.0 3,383.2 .. 1.4 81.2 .. 21.9 111.7 Netherlands 59 57 10.6 778 180.9 210.4 16.3 240.9 3.7 2.5 2,073.1 1,301.5 New Zealand 61 44 .. 1,732 135.3 135.5 238.3 1,231.7 10.8 6.6 315.1 .. Nicaragua 34 43 .. 2,391 295.1 464.9 .. 22.2 38.9 29.5 20.0 .. Niger 28 35 .. 151 7,554.6 10,629.4 .. 0.1 .. .. 0.2 .. Nigeria 80 82 .. 1,150 16,836.0 13,808.9 .. 2.1 .. .. 4.7 6.6 Norway 3 3 .. 1,414 359.2 300.6 29.3 191.3 5.5 2.7 1,779.1 1,539.1 Oman 3 6 4.0 125 2.8 3.1 2.5 236.4 .. .. 41.1 58.1 Pakistan 34 34 73.9 494 11,758.2 13,288.7 150.7 217.2 48.3 44.7 129.7 153.4 Panama 29 30 .. 2,692 183.6 154.9 .. 46.8 26.3 17.9 102.0 147.2 Papua New Guinea 2 3 .. 3,142 2.1 3.4 .. 119.8 .. .. 59.4 .. Paraguay 44 53 .. 1,130 501.0 1,439.3 .. 66.4 1.9 26.5 71.6 69.1 Peru 17 17 .. 1,738 609.0 1,248.7 98,094.6 104.5 0.8 0.8 36.3 .. Philippines 37 40 .. 2,348 6,719.2 6,853.3 1,408.8 140.5 45.4 35.2 65.2 116.8 Poland 62 53 0.4 600 8,321.1 8,423.6 101.4 144.6 25.0 13.3 823.6 1,257.9 Portugal 42 40 11.4 854 751.9 324.7 91.8 159.1 11.5 11.2 563.1 1,397.7 Puerto Rico 46 21 8.5 2,054 0.4 0.2 .. .. 3.4 1.5 438.5 525.0 Qatar 6 6 .. 74 1.2 2.1 2.8 3,191.7 .. 2.3 84.0 63.1 2012 World Development Indicators 143 3.2 Agricultural inputs Agricultural Average Land under Fertilizer Agricultural Agricultural landa annual cereal production consumption employment machinery precipitation kilograms % of per hectare Tractors % of % thousand fertilizer of arable % of total per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990–92 2007–09 2007–09 2007–09 1990–92 2008–10 2007–09 2007–09 1990–92 2007–09 1990 2009 Romania 64 59 2.2 637 5,773.9 5,016.9 69.0 48.5 33.0 29.1 140.6 201.2 Russian Federation 14 13 2.0 460 59,541.3 32,331.0 13.0 15.6 15.4 9.7 97.8 27.1 Rwanda 74 81 .. 1,212 249.5 386.3 .. 1.1 .. .. 1.0 0.5 Saudi Arabia 58 81 .. 59 1,121.9 317.4 8.8 43.8 .. 4.1 19.2 .. Senegal 46 49 .. 686 1,087.2 1,477.5 601.1 4.9 .. .. 1.6 2.1 Serbia .. 58 0.6 .. .. 1,873.4 294.0 133.8 .. 24.0 .. 17.7 Sierra Leone 39 48 .. 2,526 432.9 649.1 .. .. .. .. 4.1 .. Singapore 1 0 .. 2,497 .. .. .. .. 0.3 1.1 .. .. Slovak Republic .. 40 1.0 824 .. 701.3 59.6 95.5 .. 3.6 197.5 154.6 Slovenia 28 23 0.9 1,162 112.5 95.7 .. 241.9 .. 9.1 4,000.0 5,895.2 Somalia 70 70 .. 282 401.6 596.3 .. .. .. .. 15.9 12.0 South Africa 81 82 .. 495 5,352.0 3,540.2 168.3 49.2 .. 5.1 107.9 43.0 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 61 55 11.9 636 7,402.0 5,984.2 106.1 96.9 9.7 4.2 483.1 831.2 Sri Lanka 37 42 .. 1,712 802.6 1,124.7 2,970.1 257.9 43.6 32.6 163.1 .. Sudan 52 58 1.0 416 8,258.8 7,886.4 .. 7.9 .. .. 7.2 13.8 Swaziland 71 71 .. 788 61.5 56.2 .. .. .. .. 228.9 87.1 Sweden 8 8 .. 624 1,119.2 959.1 264.4 69.4 3.3 2.2 601.9 592.4 Switzerland 40 38 .. 1,537 201.2 151.8 .. 190.4 4.3 3.1 2,783.1 2,602.9 Syrian Arab Republic 74 76 8.9 252 3,712.6 2,620.6 153.2 65.4 28.2 15.2 128.1 215.0 Tajikistan 32 34 14.8 691 273.5 443.5 493.4 47.2 46.8 .. 415.4 310.2 Tanzania 38 40 .. 1,071 3,253.6 5,018.9 .. 8.7 84.2 .. 8.2 23.3 Thailand 42 39 .. 1,622 10,586.6 12,339.5 1,397.2 125.1 60.8 41.5 33.0 280.5 Timor-Leste 22 25 .. 1,500 77.8 106.8 .. .. .. .. 8.5 .. Togo 59 62 .. 1,168 573.2 880.8 .. 3.3 .. .. 0.5 0.6 Trinidad and Tobago 16 11 .. 2,200 7.1 2.2 0.7 85.2 11.5 3.8 1,238.9 1,972.7 Tunisia 60 63 4.1 207 1,469.9 651.8 10.3 42.3 .. .. 82.4 142.6 Turkey 52 51 13.4 593 13,731.1 12,005.1 208.5 96.5 44.7 22.9 279.8 395.3 Turkmenistan 69 69 .. 161 331.3 983.6 .. .. .. .. 464.7 .. Uganda 60 70 .. 1,180 1,139.0 1,842.5 .. 2.1 .. 65.6 .. .. Ukraine 72 71 5.3 565 12,542.3 14,184.5 44.7 29.7 20.8 15.8 153.3 102.7 United Arab Emirates 4 7 .. 78 1.4 0.0 30.8 1,033.0 .. 4.2 51.4 63.3 United Kingdom 75 72 .. 1,220 3,488.6 3,008.4 183.2 239.2 2.2 1.1 760.6 .. United States 46 44 .. 715 65,850.8 57,488.4 103.6 109.3 2.9 1.5 238.4 271.3 Uruguay 85 85 1.2 1,265 569.8 785.8 2,741.0 131.1 4.5 11.0 260.3 219.5 Uzbekistan 65 63 .. 206 1,225.3 1,642.1 77.5 193.3 .. .. .. .. Venezuela, RB 24 24 .. 1,875 763.1 1,256.3 80.7 200.2 11.8 8.5 .. .. Vietnam 22 33 .. 1,821 6,953.4 8,641.7 403.5 402.3 .. .. 47.0 262.5 West Bank and Gaza 61 61 4.6 402 0.0 32.5 .. .. .. 13.4 442.2 737.3 Yemen, Rep. 44 44 .. 167 730.0 927.3 .. 12.0 52.6 .. 39.0 41.0 Zambia 28 31 .. 1,020 797.8 1,216.2 .. 27.3 49.8 .. 21.9 .. Zimbabwe 35 42 .. 657 1,168.5 1,901.8 185.2 28.0 .. .. 60.1 .. World 38 w 38 w .. .. 699,721.0 s 681,889.9 s 94.7 w 122.1 w .. w .. w 202.4 w .. w Low income 35 38 .. .. 57,506.1 90,727.2 236.5 25.9 .. .. 8.4 .. Middle income 38 38 .. .. 486,828.8 448,296.9 101.7 142.2 .. .. 84.1 109.4 Lower middle income 45 47 .. .. 208,267.4 209,263.7 135.8 118.9 .. .. 53.0 .. Upper middle income 35 34 .. .. 278,561.3 239,033.1 89.6 159.4 48.5 31.3 111.9 114.5 Low & middle income 37 38 .. .. 544,334.9 539,024.0 102.9 128.6 .. .. 75.8 .. East Asia & Pacific 48 49 .. .. 142,379.2 150,606.8 105.4 .. 57.8 39.6 53.2 .. Europe & Central Asia 29 28 .. .. 125,257.0 92,762.3 35.7 38.8 23.2 16.3 135.9 115.6 Latin America & Carib. 34 36 .. .. 48,465.9 49,996.0 212.8 92.2 18.9 14.3 120.9 .. Middle East & N. Africa 24 23 .. .. 31,097.6 27,682.0 46.8 79.5 .. 27.2 114.6 155.8 South Asia 55 55 .. .. 128,100.5 125,939.2 168.5 176.0 .. .. 62.1 120.7 Sub-Saharan Africa 42 45 .. .. 69,034.7 92,037.8 192.9 10.5 .. .. 19.8 .. High income 38 37 .. .. 155,386.1 142,865.9 74.3 104.3 6.2 3.3 472.1 .. Euro area 49 46 .. .. 32,498.0 30,496.6 98.7 143.4 6.7 3.6 986.1 841.4 a. Includes permanent pastures, arable land, and land under permanent crops. b. Includes Luxembourg. 144 2012 World Development Indicators 3.2 ENVIRONMENT Agricultural inputs About the data De�nitions Agriculture is still a major sector in many economies, energy price fluctuation. The FAO recently revised •  Agricultural land is permanent pastures, arable and agricultural activities provide developing coun- the time series for fertilizer consumption and irriga- land, and land under permanent crops. Permanent tries with food and revenue. But agricultural activities tion for 2002 onward, but recent data are not avail- pasture is land used for five or more years for for- also can degrade natural resources. Poor farming able for all countries. FAO collects fertilizer statistics age, including natural and cultivated crops. Arable practices can cause soil erosion and loss of soil for production, imports, exports, and consumption land includes land defi ned by the FAO as land fertility. Efforts to increase productivity by using through the new FAO fertilizer resources question- under temporary crops (double-cropped areas are chemical fertilizers, pesticides, and intensive irriga- naire. In the previous release, the data were based counted once), temporary meadows for mowing or tion have environmental costs and health impacts. on total consumption of fertilizers, but the data in for pasture, land under market or kitchen gardens, Excessive use of chemical fertilizers can alter the the recent release are based on the nutrients in fer- and land temporarily fallow. Land abandoned as a chemistry of soil. Pesticide poisoning is common in tilizers. Some countries compile fertilizer data on a result of shifting cultivation is excluded. Land under developing countries. And salinization of irrigated calendar year basis, while others do so on a crop permanent crops is land cultivated with crops that land diminishes soil fertility. Thus, inappropriate use year basis (July–June). Previous editions of World occupy the land for long periods and need not be of inputs for agricultural production has far-reaching Development Indicators reported data on a crop year replanted after each harvest, such as cocoa, cof- effects. basis, but this edition uses the calendar year, as fee, and rubber. Land under flowering shrubs, fruit The table provides indicators of major inputs to adopted by the FAO. Caution should thus be used trees, nut trees, and vines is included, but land under agricultural production: land, fertilizer, labor, and when comparing data over time. trees grown for wood or timber is not. •  Irrigated machinery. There is no single correct mix of inputs: land refers to areas purposely provided with water, appropriate levels and application rates vary by coun- including land irrigated by controlled flooding. • Aver- try and over time and depend on the type of crops, age annual precipitation is the long-term average in the climate and soils, and the production process depth (over space and time) of annual precipitation used. in the country. Precipitation is defined as any kind The agriculture sector is the most water-intensive of water that falls from clouds as a liquid or a solid. sector, and water delivery in agriculture is increas- • Land under cereal production refers to harvested ingly important. The table shows irrigated agricultural areas, although some countries report only sown land as share of total agricultural land area and data or cultivated area. • Fertilizer consumption is the on average precipitation to illustrate how countries quantity of plant nutrients applied to arable land. obtain water for agricultural use. Fertilizer products cover nitrogen, potash, and phos- The data here and in table 3.3 are collected by phate fertilizers (including ground rock phosphate). the Food and Agriculture Organization of the United Traditional nutrients—animal and plant manures— Nations (FAO) through annual questionnaires. The are not included. • Fertilizer production is fertilizer FAO tries to impose standard definitions and report- consumption, exports, and nonfertilizer use of fertil- ing methods, but complete consistency across izer products minus fertilizer imports. • Agricultural countries and over time is not possible. Thus, data employment is employment in agriculture, forestry, on agricultural land in different climates may not be hunting, and fishing (see table 2.3). • Agricultural comparable. For example, permanent pastures are machinery refers to wheel and crawler tractors quite different in nature and intensity in African coun- (excluding garden tractors) in use in agriculture at tries and dry Middle Eastern countries. Data on agri- the end of the calendar year specified or during the cultural employment, in particular, should be used first quarter of the following year. with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, defini- tion, and coverage. Fertilizer consumption measures the quantity of Data sources plant nutrients. Consumption is calculated as pro- duction plus imports minus exports. Because some Data on agricultural inputs are from the FAO’s chemical compounds used for fertilizers have other electronic files. Data on agricultural employment industrial applications, the consumption data may are from the International Labour Organization’s overstate the quantity available for crops. Fertil- Key Indicators of the Labour Market, 7th edition, izer consumption as a share of production shows database. the agriculture sector’s vulnerability to import and 2012 World Development Indicators 145 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 2004–06 = 100 2004–06 = 100 2004–06 = 100 per hectare 2000 $ 1990 2009 1990 2009 1990 2009 1990 2010 1990 2010 Afghanistan 67.0 125.0 68.0 114.0 70.0 101.0 1,201 1,908 .. .. Albania 85.0 121.0 69.0 110.0 58.0 99.0 2,794 4,762 764 .. Algeria 41.0 121.0 50.0 117.0 69.0 109.0 688 1,568 1,702 2,254 Angola 33.0 153.0 42.0 144.0 74.0 110.0 321 617 206 340 Argentina 53.0 83.0 63.0 95.0 84.0 113.0 2,232 4,937 6,683 12,957 Armenia 67.0 125.0 74.0 131.0 84.0 134.0 1,843 2,074 1,607 4,723 Australia 67.0 101.0 74.0 103.0 85.0 101.0 1,716 1,721 19,447 35,208 Austria 89.0 103.0 93.0 101.0 95.0 99.0 5,577 5,358 13,413 25,771 Azerbaijan 95.0 110.0 82.0 115.0 79.0 118.0 2,113 2,019 1,001 1,241 Bahrain 61.0 116.0 90.0 117.0 137.0 117.0 .. .. .. .. Bangladesh 66.0 122.0 63.0 121.0 56.0 113.0 2,491 4,144 275 507 Belarus 77.0 111.0 110.0 120.0 127.0 129.0 2,741 2,827 2,042 5,700 Belgium .. 100.0 .. 97.0 .. 98.0 5,755a 9,231 .. .. Benin 45.0 117.0 48.0 122.0 70.0 100.0 848 1,402 427 .. Bolivia 53.0 106.0 61.0 113.0 68.0 119.0 1,361 2,333 683 716 Bosnia and Herzegovina 75.0 99.0 87.0 110.0 92.0 128.0 3,553 3,858 .. 15,028 Botswana 83.0 122.0 95.0 116.0 98.0 114.0 266 544 761 447 Brazil 59.0 117.0 51.0 116.0 44.0 113.0 1,755 4,055 1,625 4,118 Bulgaria 149.0 98.0 179.0 103.0 248.0 96.0 3,954 3,665 3,983 10,923 Burkina Faso 44.0 95.0 50.0 105.0 53.0 109.0 600 1,054 107 .. Burundi 98.0 61.0 98.0 65.0 103.0 136.0 1,349 1,346 118 .. Cambodia 46.0 147.0 46.0 137.0 53.0 92.0 1,362 3,108 .. 434 Cameroon 58.0 113.0 61.0 114.0 79.0 103.0 1,259 1,711 420 .. Canada 82.0 109.0 73.0 107.0 68.0 95.0 2,636 3,490 28,898 44,619 Central African Republic 76.0 113.0 61.0 111.0 54.0 110.0 810 1,465 321 .. Chad 51.0 105.0 56.0 112.0 74.0 110.0 559 775 170 .. Chile 60.0 101.0 58.0 101.0 55.0 101.0 3,620 6,822 3,457 6,267 China 56.0 116.0 48.0 115.0 39.0 112.0 4,325 5,521 258 545 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 80.0 108.0 70.0 112.0 68.0 117.0 2,475 3,895 3,123 2,874 Congo, Dem. Rep. 125.0 98.0 120.0 99.0 99.0 102.0 800 771 213 173 Congo, Rep. 66.0 112.0 63.0 116.0 53.0 126.0 624 785 .. .. Costa Rica 62.0 109.0 59.0 113.0 70.0 116.0 3,097 3,730 2,993 5,596 Côte d’Ivoire 68.0 103.0 66.0 108.0 80.0 111.0 887 1,717 658 1,056 Croatia 93.0 110.0 105.0 114.0 106.0 106.0 3,975 5,486 5,552 16,423 Cuba 136.0 88.0 160.0 98.0 185.0 136.0 2,342 1,940 4,117 3,618 Cyprus 136.0 73.0 98.0 87.0 69.0 95.0 1,886 1,595 5,808 7,927 Czech Republic .. 99.0 .. 98.0 .. 95.0 .. 4,691 .. 6,415 Denmark 115.0 112.0 94.0 107.0 81.0 101.0 6,118 5,889 14,588 53,407 Dominican Republic 98.0 107.0 78.0 116.0 58.0 126.0 3,996 4,234 1,974 5,083 Ecuador 66.0 114.0 56.0 115.0 48.0 114.0 1,724 3,117 2,109 2,040 Egypt, Arab Rep. 56.0 116.0 54.0 118.0 53.0 114.0 5,703 6,541 1,767 3,265 El Salvador 92.0 112.0 75.0 109.0 62.0 107.0 1,939 2,806 1,742 2,752 Eritrea .. 84.0 .. 99.0 .. 111.0 .. 536 .. 66 Estonia 129.0 126.0 157.0 115.0 175.0 109.0 1,304 2,444 3,288 3,156 Ethiopia .. 126.0 .. 122.0 .. 113.0 .. 1,674 .. 226 Finland 110.0 109.0 108.0 101.0 107.0 97.0 3,543 3,136 17,163 47,514 France 96.0 100.0 99.0 98.0 98.0 96.0 6,083 7,093 21,454 57,973 Gabon 78.0 118.0 91.0 115.0 86.0 102.0 1,750 1,782 1,216 1,972 Gambia, The 56.0 119.0 60.0 119.0 76.0 106.0 1,004 1,127 253 282 Georgia 138.0 73.0 105.0 72.0 78.0 68.0 1,998 1,271 2,359 1,817 Germany 93.0 106.0 107.0 105.0 114.0 103.0 5,411 6,716 13,730 32,866 Ghana 34.0 119.0 37.0 119.0 78.0 120.0 989 1,814 .. .. Greece 80.0 81.0 86.0 86.0 99.0 95.0 3,036 4,908 .. .. Guatemala 57.0 125.0 57.0 122.0 70.0 103.0 1,998 2,299 2,241 2,803 Guinea 59.0 105.0 59.0 107.0 43.0 120.0 1,455 1,409 166 242 Guinea-Bissau 60.0 94.0 62.0 98.0 70.0 115.0 1,531 1,555 .. .. Haiti 107.0 109.0 96.0 106.0 59.0 102.0 1,027 980 .. .. 146 2012 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 2004–06 = 100 2004–06 = 100 2004–06 = 100 per hectare 2000 $ 1990 2009 1990 2009 1990 2009 1990 2010 1990 2010 Honduras 70.0 105.0 65.0 103.0 51.0 105.0 1,468 1,094 1,184 2,041 Hungary 100.0 89.0 118.0 94.0 161.0 96.0 4,521 4,759 4,177 8,522 India 73.0 113.0 71.0 114.0 62.0 115.0 1,891 2,537 357 489 Indonesia 61.0 121.0 62.0 120.0 62.0 114.0 3,800 4,876 493 730 Iran, Islamic Rep. 55.0 99.0 54.0 104.0 51.0 115.0 1,445 2,359 1,988 .. Iraq 102.0 87.0 118.0 92.0 153.0 100.0 1,061 1,687 .. .. Ireland 91.0 82.0 97.0 91.0 96.0 93.0 6,577 7,409 .. 13,931 Israel 95.0 97.0 72.0 104.0 56.0 109.0 3,486 3,015 .. .. Italy 84.0 91.0 89.0 97.0 96.0 106.0 3,945 5,436 10,435 31,254 Jamaica 98.0 105.0 81.0 106.0 61.0 106.0 1,116 1,172 2,224 2,758 Japan 123.0 94.0 114.0 98.0 107.0 101.0 5,846 5,852 19,563 48,794 Jordan 66.0 106.0 57.0 110.0 44.0 115.0 1,220 1,963 1,976 3,401 Kazakhstan 144.0 131.0 142.0 126.0 147.0 115.0 1,338 804 1,781 1,782 Kenya 72.0 105.0 68.0 110.0 67.0 114.0 1,562 1,613 400 337 Korea, Dem. Rep. 99.0 101.0 92.0 100.0 96.0 100.0 3,916 3,582 .. .. Korea, Rep. 91.0 106.0 79.0 108.0 62.0 110.0 5,853 6,196 5,338 19,807 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 39.0 106.0 48.0 110.0 54.0 112.0 3,653 3,415 .. .. Kyrgyz Republic 64.0 106.0 76.0 101.0 107.0 102.0 2,772 2,604 685 996 Lao PDR 52.0 129.0 50.0 125.0 49.0 118.0 2,268 3,751 388 532 Latvia 106.0 114.0 194.0 118.0 253.0 114.0 1,641 2,667 1,896 3,837 Lebanon 96.0 101.0 83.0 104.0 54.0 112.0 1,878 2,740 .. 41,013 Lesotho 127.0 81.0 106.0 100.0 91.0 112.0 1,037 909 255 202 Liberia 62.0 97.0 77.0 121.0 79.0 124.0 1,029 1,179 .. .. Libya 77.0 103.0 75.0 104.0 73.0 106.0 674 662 .. .. Lithuania 81.0 136.0 133.0 110.0 154.0 95.0 1,938 2,667 .. 5,996 Macedonia, FYR 102.0 110.0 102.0 114.0 97.0 114.0 2,652 3,329 2,413 5,946 Madagascar 79.0 110.0 80.0 112.0 99.0 113.0 1,945 2,987 213 187 Malawi 50.0 131.0 47.0 127.0 64.0 151.0 992 2,206 89 169 Malaysia 59.0 109.0 53.0 114.0 58.0 109.0 2,740 3,800 3,826 6,680 Mali 54.0 134.0 61.0 146.0 75.0 135.0 726 1,615 406 .. Mauritania 63.0 124.0 76.0 107.0 79.0 104.0 870 946 651 404 Mauritius 107.0 95.0 89.0 98.0 45.0 109.0 4,191 10,000 3,446 6,401 Mexico 74.0 99.0 65.0 101.0 59.0 105.0 2,424 3,499 2,250 3,302 Moldova 121.0 90.0 139.0 91.0 196.0 91.0 2,928 2,696 1,349 1,610 Mongolia 230.0 216.0 134.0 146.0 132.0 142.0 1,098 1,370 1,477 1,524 Morocco 68.0 116.0 68.0 117.0 71.0 116.0 1,120 1,548 1,807 3,315 Mozambique 60.0 101.0 63.0 98.0 47.0 95.0 477 1,006 132 234 Myanmar 42.0 122.0 41.0 126.0 25.0 138.0 2,762 3,989 .. .. Namibia 56.0 100.0 82.0 90.0 89.0 86.0 457 373 1,267 881 Nepal 62.0 112.0 64.0 110.0 70.0 107.0 1,920 2,295 248 238 Netherlands 91.0 106.0 103.0 108.0 106.0 109.0 6,959 8,574 23,743 47,805 New Zealand 74.0 105.0 64.0 98.0 67.0 97.0 5,034 7,387 19,767 26,556 Nicaragua 59.0 111.0 51.0 115.0 45.0 119.0 1,524 2,086 .. 2,779 Niger 49.0 138.0 46.0 132.0 42.0 122.0 310 479 239 .. Nigeria 47.0 88.0 49.0 92.0 61.0 112.0 1,148 1,413 .. .. Norway 135.0 84.0 110.0 103.0 103.0 106.0 4,399 3,810 17,454 46,480 Oman 63.0 112.0 52.0 97.0 40.0 77.0 2,160 18,987 1,029 .. Pakistan 67.0 108.0 60.0 115.0 56.0 117.0 1,766 2,592 769 947 Panama 123.0 88.0 91.0 102.0 64.0 114.0 1,867 2,131 2,303 4,109 Papua New Guinea 72.0 117.0 69.0 108.0 66.0 91.0 2,603 3,839 559 683 Paraguay 70.0 92.0 64.0 110.0 72.0 117.0 1,979 3,457 1,660 2,710 Peru 46.0 117.0 49.0 123.0 55.0 128.0 2,603 3,899 911 1,607 Philippines 72.0 111.0 67.0 112.0 49.0 114.0 2,065 3,232 854 1,119 Poland 133.0 109.0 110.0 104.0 114.0 102.0 3,284 3,220 1,619 2,994 Portugal 110.0 91.0 98.0 98.0 84.0 106.0 1,878 3,463 4,516 7,019 Puerto Rico 138.0 100.0 133.0 103.0 132.0 104.0 1,080 1,878 .. .. Qatar 56.0 138.0 66.0 168.0 73.0 192.0 2,897 4,795 .. .. 2012 World Development Indicators 147 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 2004–06 = 100 2004–06 = 100 2004–06 = 100 per hectare 2000 $ 1990 2009 1990 2009 1990 2009 1990 2010 1990 2010 Romania 82.0 85.0 94.0 93.0 116.0 99.0 3,011 3,331 2,351 9,700 Russian Federation 100.0 107.0 117.0 106.0 143.0 97.0 1,743 1,844 1,915 2,731 Rwanda 76.0 122.0 72.0 121.0 59.0 122.0 1,043 1,930 174 .. Saudi Arabia 83.0 95.0 70.0 102.0 56.0 111.0 4,245 5,621 7,863 20,233 Senegal 78.0 143.0 76.0 136.0 71.0 113.0 795 1,196 262 256 Serbia 78.0 b 106.0 99.0 b 99.0 109.0 b 98.0 2,926b 4,959 .. 2,057 Sierra Leone 69.0 101.0 68.0 104.0 81.0 116.0 1,202 1,554 .. .. Singapore 90.0 149.0 492.0 98.0 535.0 93.0 .. .. 21,392 29,145 Slovak Republic .. 93.0 .. 92.0 .. 86.0 .. 3,754 .. 9,924 Slovenia 74.0 87.0 76.0 93.0 75.0 93.0 3,279 5,973 13,217 76,633 Somalia 122.0 100.0 92.0 100.0 88.0 100.0 793 432 .. .. South Africa 83.0 108.0 78.0 109.0 79.0 110.0 1,877 4,162 2,287 3,662 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 89.0 105.0 83.0 104.0 72.0 98.0 2,485 3,231 8,938 22,035 Sri Lanka 87.0 111.0 85.0 112.0 75.0 107.0 2,965 3,974 672 966 Sudan 50.0 106.0 47.0 103.0 47.0 99.0 456 452 512 929 Swaziland 99.0 97.0 90.0 101.0 85.0 110.0 1,284 1,226 1,025 1,217 Sweden 130.0 106.0 109.0 101.0 103.0 97.0 4,964 4,518 23,307 51,585 Switzerland 118.0 106.0 104.0 103.0 102.0 104.0 5,984 6,084 20,556 27,066 Syrian Arab Republic 51.0 93.0 51.0 96.0 54.0 99.0 750 1,789 2,698 .. Tajikistan 83.0 115.0 99.0 118.0 132.0 117.0 994 2,721 370 577 Tanzania 61.0 110.0 62.0 106.0 61.0 101.0 1,507 1,332 219 292 Thailand 64.0 111.0 69.0 112.0 73.0 112.0 2,009 2,938 443 706 Timor-Leste 81.0 136.0 95.0 129.0 104.0 109.0 1,608 2,451 .. .. Togo 67.0 104.0 64.0 114.0 68.0 123.0 747 1,187 376 .. Trinidad and Tobago 141.0 113.0 84.0 107.0 50.0 104.0 3,326 2,727 1,825 1,024 Tunisia 74.0 101.0 69.0 101.0 58.0 107.0 1,145 1,702 2,383 3,050 Turkey 78.0 103.0 79.0 108.0 83.0 112.0 2,214 2,727 2,254 3,770 Turkmenistan 73.0 92.0 51.0 113.0 49.0 126.0 2,210 3,289 1,275 .. Uganda 72.0 106.0 71.0 105.0 64.0 100.0 1,498 1,608 189 200 Ukraine 98.0 118.0 128.0 106.0 166.0 95.0 2,834 2,727 1,231 2,500 United Arab Emirates 28.0 100.0 29.0 113.0 43.0 143.0 2,216 2,667 .. .. United Kingdom 104.0 102.0 107.0 101.0 107.0 99.0 6,171 6,957 19,880 25,681 United States 79.0 105.0 77.0 109.0 78.0 105.0 4,755 6,988 18,703 51,120 Uruguay 48.0 134.0 60.0 109.0 70.0 100.0 2,183 4,251 5,648 8,682 Uzbekistan 83.0 118.0 76.0 123.0 82.0 114.0 1,777 4,516 1,427 2,782 Venezuela, RB 76.0 108.0 71.0 106.0 71.0 110.0 2,486 4,038 4,458 7,667 Vietnam 45.0 110.0 47.0 114.0 38.0 133.0 3,073 5,161 222 367 West Bank and Gaza .. 109.0 .. 101.0 .. 85.0 .. 1,163 .. .. Yemen, Rep. 61.0 121.0 56.0 126.0 50.0 134.0 908 1,092 621 749 Zambia 61.0 129.0 74.0 115.0 77.0 96.0 1,352 2,547 213 214 Zimbabwe 118.0 91.0 94.0 92.0 75.0 105.0 1,625 752 270 160 World 70.0c w 122.2 w 70.0c w 123.0 w 74.0c w 120.3 w 2,756c w 3,568 w 781 w 992 w Low income 78.4 132.2 78.3 133.2 78.0 131.7 1,565 2,075 240 288 Middle income 73.4 128.4 68.7 130.3 62.5 131.4 2,556 3,312 481 786 Lower middle income 74.8 127.2 72.8 128.2 69.4 131.8 1,928 2,718 472 677 Upper middle income 72.6 129.1 66.5 131.4 59.6 131.2 3,177 3,831 487 871 Low & middle income 73.8 128.7 69.4 130.5 63.3 131.4 2,429 3,103 455 728 East Asia & Pacific 69.9 133.1 62.7 135.1 48.8 135.2 3,796 4,925 305 585 Europe & Central Asia 111.4 129.4 118.2 126.3 137.7 119.2 2,596 2,239 2,245 3,204 Latin America & Carib. 75.8 128.1 71.2 131.2 69.7 132.6 2,089 3,919 2,221 3,663 Middle East & N. Africa 74.7 127.3 72.8 131.6 69.3 134.7 1,471 2,379 1,796 .. South Asia 78.0 119.3 74.5 122.7 69.2 132.9 1,926 2,691 373 521 Sub-Saharan Africa 71.1 128.7 72.9 130.0 80.1 125.1 1,033 1,335 307 322 High income 90.7 103.9 90.4 106.3 90.7 104.0 4,138 5,319 14,129 24,483 Euro area 90.9 96.9 95.6 97.7 98.2 100.0 4,490 5,685 12,590 25,752 a. Includes Luxembourg. b. Includes Montenegro. c. Food and Agriculture Organization estimate. 148 2012 World Development Indicators 3.3 ENVIRONMENT Agricultural output and productivity About the data De�nitions The agricultural production indexes in the table are • Crop production index is agricultural production prepared by the Food and Agriculture Organization of for the period specified relative to the base period the United Nations (FAO). The FAO obtains data from 2004–06. It includes all crops except fodder crops. official and semiofficial reports of crop yields, area The regional and income group aggregates for the under production, and livestock numbers. If data are FAO’s production indexes are calculated from the unavailable, the FAO makes estimates. The indexes underlying values in international dollars, normal- are calculated using the Laspeyres formula: produc- ized to the base period 2004–06. • Food produc- tion quantities of each commodity are weighted by tion index covers food crops that are considered average international commodity prices in the base edible and that contain nutrients. Coffee and tea period and summed for each year. Because the FAO’s are excluded because, although edible, they have indexes are based on the concept of agriculture as a no nutritive value. • Livestock production index single enterprise, estimates of the amounts retained includes meat and milk from all sources, dairy prod- for seed and feed are subtracted from the production ucts such as cheese, and eggs, honey, raw silk, wool, data to avoid double counting. The aggregates are and hides and skins. • Cereal yield, measured in net production available for any use except as seed kilograms per hectare of harvested land, includes and feed. The FAO’s indexes may differ from those wheat, rice, maize, barley, oats, rye, millet, sorghum, from other sources because of differences in cov- buckwheat, and mixed grains. Production data on erage, weights, concepts, time periods, calculation cereals refer to crops harvested for dry grain only. methods, and use of international prices. Cereal crops harvested for hay or harvested green for To facilitate cross-country comparisons, the FAO food, feed, or silage, and those used for grazing, are uses international commodity prices to value pro- excluded. The FAO allocates production data to the duction. These prices, expressed in international calendar year in which the bulk of the harvest took dollars (equivalent in purchasing power to the U.S. place. But most of a crop harvested near the end of dollar), are derived using a Geary-Khamis formula a year will be used in the following year. • Agricul- applied to agricultural outputs (see Inter- Secretariat tural productivity is the ratio of agricultural value Working Group on National Accounts 1993, sections added, measured in 2005 U.S. dollars, to the num- 16.93–96). This method assigns a single price to ber of workers in agriculture. Agricultural productivity each commodity so that, for example, one metric ton is measured by value added per unit of input. (For of wheat has the same price regardless of where it further discussion of the calculation of value added was produced. The use of international prices elimi- in national accounts, see About the data for tables nates fluctuations in the value of output due to transi- 4.1 and 4.2.) Agricultural value added includes that tory movements of nominal exchange rates unrelated from forestry and fishing. Thus interpretations of land to the purchasing power of the domestic currency. productivity should be made with caution. Data on cereal yield may be affected by a variety of reporting and timing differences. Millet and sor- ghum, which are grown as feed for livestock and poul- try in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use. Data sources Data on agricultural production indexes, cereal yield, and agricultural employment are from the FAO’s electronic files, available on the FAO website (www.fao.org). Data on agricultural value added are from the World Bank’s national accounts files. 2012 World Development Indicators 149 3.4 Deforestation and biodiversity Forest Average annual Threatened GEF bene�ts Nationally area deforestationa species index for protected areas biodiversity 0–100 (no biodiversity thousand Higher to maximum Terrestrial Marine sq. km % Mammals Birds Fish plantsb biodiversity) % of land area % of territorial waters 1990 2010 1990–2000 2000–10 2011 2011 2011 2011 2008 1990 2010 1990 2010 Afghanistan 14 14 0.00 0.00 11 14 5 2 3.4 0.4 0.4 .. .. Albania 8 8 0.26 –0.10 3 5 39 0 0.2 3.4 9.8 0.2 1.6 Algeria 17 15 0.54 0.57 14 9 36 12 2.9 6.3 6.3 0.2 0.3 Angola 610 585 0.21 0.21 15 23 39 34 8.3 12.4 12.4 0.1 0.1 Argentina 348 294 0.88 0.81 38 49 37 35 17.7 4.6 5.5 0.8 1.1 Armenia 3 3 1.31 1.48 9 12 3 1 0.2 6.9 8.0 .. .. Australia 1,545 1,493 –0.03 0.37 55 52 103 27 87.7 7.5 10.6 10.9 28.3 Austria 38 39 –0.16 –0.13 3 7 11 9 0.3 20.1 22.9 .. .. Azerbaijan 9 9 0.00 0.00 7 14 10 0 0.8 6.2 7.1 .. .. Bahrain 0c 0c –5.56 –3.55 3 3 8 0 0.0 1.3 1.3 0.0 0.7 Bangladesh 15 14 0.18 0.18 34 30 18 15 1.4 1.7 1.8 0.4 0.8 Belarus 78 86 –0.62 –0.43 4 4 2 0 0.0 6.5 7.2 .. .. Belgium 7 7 0.15 –0.16 3 2 11 0 0.0 3.2 13.8 0.0 0.0 Benin 58 46 1.29 1.04 11 6 27 13 0.2 23.8 23.8 0.0 0.0 Bolivia 628 572 0.44 0.50 20 34 0 72 12.5 8.8 18.5 .. .. Bosnia and Herzegovina 22 22 0.11 0.00 4 5 31 0 0.4 0.5 0.6 0.7 0.7 Botswana 137 114 0.90 0.99 7 10 2 0 1.4 30.3 30.9 .. .. Brazil 5,748 5,195 0.51 0.50 81 122 84 389 100.0 9.0 26.3 8.2 16.5 Bulgaria 33 39 –0.14 –1.53 7 11 19 5 0.8 2.0 9.2 0.2 3.2 Burkina Faso 68 56 0.91 1.01 9 7 4 3 0.3 13.7 14.2 .. .. Burundi 3 2 3.71 1.40 11 11 17 2 0.3 3.8 4.8 .. .. Cambodia 129 101 1.14 1.34 37 24 42 29 3.5 0.0 25.8 0.0 0.4 Cameroon 243 199 0.94 1.05 38 20 112 378 12.5 7.0 9.2 0.4 0.4 Canada 3,101 3,101 0.00 0.00 12 15 35 1 21.5 4.7 7.5 0.6 1.2 Central African Republic 232 226 0.13 0.13 8 9 3 17 1.5 17.5 17.7 .. .. 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 20 34 15.3 16.0 16.6 3.5 3.7 China 1,571 2,069 –1.20 –1.57 75 86 113 374 66.6 13.5 16.6 0.4 1.3 Hong Kong SAR, China .. .. .. .. 2 20 13 6 .. 41.1 41.8 .. .. Colombia 625 605 0.16 0.17 52 94 54 215 51.5 19.3 20.9 0.9 15.5 Congo, Dem. Rep. 1,604 1,541 0.20 0.20 30 35 83 80 19.9 10.0 10.0 3.8 4.4 Congo, Rep. 227 224 0.08 0.07 11 2 46 37 3.6 5.4 9.4 0.0 32.8 Costa Rica 26 26 0.76 –0.93 9 19 50 112 9.7 18.7 20.9 11.9 12.2 Côte d’Ivoire 102 104 –0.10 –0.15 23 15 45 106 3.4 22.6 22.6 0.1 0.1 Croatia 19 19 –0.19 –0.19 7 10 60 5 0.6 7.8 13.0 1.3 3.4 Cuba 21 29 –1.70 –1.66 14 17 34 155 12.5 4.3 6.4 1.3 4.4 Cyprus 2 2 –0.63 –0.09 5 4 19 16 0.5 7.1 10.5 0.3 0.6 Czech Republic 26 27 –0.03 –0.08 2 5 2 8 0.1 13.6 15.1 .. .. Denmark 4 5 –0.89 –1.14 2 2 15 1 0.2 4.2 4.9 3.0 3.2 Dominican Republic 20 20 0.00 0.00 6 14 21 27 6.0 22.2 22.2 30.4 30.4 Ecuador 138 99 1.53 1.81 43 73 50 1,714 29.3 21.6 25.1 0.2 75.4 Egypt, Arab Rep. 0c 1 –2.98 –1.73 17 9 39 2 2.9 1.9 5.9 4.4 9.3 El Salvador 4 3 1.26 1.45 5 5 14 24 0.9 0.4 0.8 3.1 3.1 Eritrea 16 15 0.28 0.28 10 12 18 4 0.8 4.9 5.0 0.0 0.0 Estonia 21 22 –0.71 0.12 1 3 5 0 0.1 17.7 20.4 25.3 26.5 Ethiopia 151 123 0.97 1.08 33 24 14 24 8.4 17.7 18.4 .. .. Finland 219 222 –0.26 0.14 1 4 6 1 0.2 4.2 9.0 3.5 5.0 France 145 160 –0.55 –0.39 9 6 44 27 5.3 10.2 16.5 0.3 21.3 Gabon 220 220 0.00 0.00 14 4 61 120 3.0 4.6 15.1 0.2 7.3 Gambia, The 4 5 –0.42 –0.41 10 8 23 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 5 23 13 0.6 31.9 42.4 35.7 40.3 Ghana 74 49 1.99 2.08 16 13 44 117 1.9 14.6 14.7 0.0 0.0 Greece 33 39 –0.88 –0.81 10 10 75 52 2.8 5.7 16.2 0.5 2.6 Guatemala 47 37 1.20 1.40 16 10 25 72 8.0 25.9 30.6 0.3 12.5 Guinea 73 65 0.51 0.54 22 13 65 22 2.3 6.8 6.8 0.0 0.0 Guinea-Bissau 22 20 0.44 0.48 12 5 32 4 0.6 7.6 16.1 2.7 45.8 Haiti 1 1 0.62 0.76 5 13 20 26 5.2 0.3 0.3 0.0 0.0 150 2012 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity Forest Average annual Threatened GEF bene�ts Nationally area deforestationa species index for protected areas biodiversity 0–100 (no biodiversity thousand Higher to maximum Terrestrial Marine sq. km % Mammals Birds Fish plantsb biodiversity) % of land area % of territorial waters 1990 2010 1990–2000 2000–10 2011 2011 2011 2011 2008 1990 2010 1990 2010 Honduras 81 52 2.38 2.06 7 9 27 107 7.2 13.6 18.2 0.0 1.9 Hungary 18 20 –0.57 –0.62 2 8 9 8 0.2 4.6 5.1 .. .. India 639 684 –0.22 –0.46 94 78 212 291 39.9 4.7 5.0 1.6 1.7 Indonesia 1,185 944 1.75 0.51 184 119 140 385 81.0 10.0 14.1 0.5 2.0 Iran, Islamic Rep. 111 111 0.00 0.00 16 20 29 1 7.3 5.2 7.1 1.0 1.7 Iraq 8 8 –0.17 –0.09 13 16 11 0 1.6 0.1 0.1 0.0 0.0 Ireland 5 7 –3.16 –1.53 5 1 20 1 0.6 0.6 1.8 0.1 0.2 Israel 1 2 –1.49 –0.07 15 13 36 0 0.8 16.3 17.8 0.4 0.4 Italy 76 91 –0.98 –0.90 7 7 47 61 3.8 5.0 15.1 0.5 17.4 Jamaica 3 3 0.11 0.11 5 10 21 206 4.4 10.2 18.9 0.2 4.2 Japan 250 250 0.03 –0.05 28 39 64 6 36.0 13.4 16.5 2.0 5.5 Jordan 1 1 0.00 0.00 13 10 13 1 0.4 0.7 1.9 0.0 30.0 Kazakhstan 34 33 0.17 0.17 16 20 14 16 5.1 2.4 2.5 .. .. Kenya 37 35 0.35 0.33 28 31 68 126 8.8 11.6 11.8 5.2 10.5 Korea, Dem. Rep. 82 57 1.67 2.00 9 24 13 5 0.7 4.3 5.9 0.1 0.1 Korea, Rep. 64 62 0.13 0.11 9 29 19 3 1.7 2.2 2.4 3.5 3.9 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 0c 0c –3.46 –2.57 6 8 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 0.49 45 23 46 17 5.0 1.5 16.6 .. .. Latvia 32 34 –0.21 –0.34 1 4 6 0 0.0 6.5 18.0 4.6 6.7 Lebanon 1 1 0.00 –0.45 10 8 22 1 0.2 0.5 0.5 0.0 0.1 Lesotho 0c 0c –0.49 –0.47 2 7 1 4 0.3 0.5 0.5 .. .. Liberia 49 43 0.63 0.67 18 11 53 47 2.6 1.6 1.8 0.0 0.0 Libya 2 2 0.00 0.00 12 3 24 2 1.6 0.1 0.1 0.0 0.1 Lithuania 19 22 –0.38 –0.68 3 4 6 0 0.0 2.0 14.5 0.8 10.7 Macedonia, FYR 9 10 –0.49 –0.41 5 9 13 0 0.2 4.2 4.9 .. .. Madagascar 137 126 0.42 0.45 65 35 85 273 29.2 2.2 3.1 0.0 0.1 Malawi 39 32 0.88 0.97 7 15 101 12 3.5 15.0 15.0 .. .. Malaysia 224 205 0.36 0.54 70 45 64 674 13.9 17.1 18.1 1.5 2.0 Mali 141 125 0.58 0.61 12 9 3 7 1.5 2.3 2.4 .. .. Mauritania 4 2 2.66 2.66 15 11 32 0 1.3 0.5 0.5 32.1 32.1 Mauritius 0c 0c 0.03 1.00 6 11 13 88 3.3 1.7 4.5 0.3 0.3 Mexico 703 648 0.52 0.30 100 56 152 191 68.7 2.2 11.1 1.1 16.7 Moldova 3 4 –0.16 –1.77 4 8 8 2 0.0 0.9 1.4 .. .. Mongolia 125 109 0.67 0.73 11 20 1 0 4.2 4.1 13.4 .. .. Morocco 50 51 0.06 –0.23 18 10 47 28 3.5 1.2 1.5 0.8 1.3 Mozambique 434 390 0.52 0.54 12 24 55 40 7.2 14.8 15.8 1.8 3.3 Myanmar 392 318 1.17 0.93 45 43 39 37 10.0 3.1 6.3 0.3 0.3 Namibia 88 73 0.87 0.97 12 25 27 25 5.2 14.4 14.9 0.5 8.2 Nepal 48 36 2.09 0.70 31 31 7 2 2.1 7.7 17.0 .. .. Netherlands 3 4 –0.43 –0.14 4 2 13 0 0.2 11.2 12.4 12.8 22.1 New Zealand 77 83 –0.69 –0.01 9 70 23 19 20.2 25.4 26.2 0.4 10.8 Nicaragua 45 31 1.67 2.01 6 12 30 40 3.3 15.4 36.7 0.6 37.2 Niger 19 12 3.74 0.98 12 7 4 2 0.9 7.1 7.1 .. .. Nigeria 172 90 2.68 3.67 26 14 59 171 6.0 11.6 12.8 0.2 0.2 Norway 91 101 –0.19 –0.80 7 2 19 2 1.3 7.0 14.6 1.2 2.4 Oman 0c 0c 0.00 0.00 9 11 26 6 3.7 0.0 10.7 0.0 1.3 Pakistan 25 17 1.76 2.24 23 27 34 2 4.9 10.1 10.1 1.8 1.8 Panama 38 33 1.18 0.36 15 17 41 192 10.9 17.2 18.7 3.1 4.0 Papua New Guinea 315 287 0.45 0.48 39 37 42 142 25.4 1.9 3.1 0.3 0.3 Paraguay 212 176 0.88 0.97 8 27 0 9 2.8 2.9 5.4 .. .. Peru 702 680 0.14 0.18 54 98 20 268 33.4 4.7 13.6 2.8 2.8 Philippines 66 77 –0.80 –0.75 38 74 71 210 32.3 8.7 10.9 0.5 2.5 Poland 89 93 –0.20 –0.31 5 6 7 8 0.5 15.3 22.4 3.4 4.1 Portugal 33 35 –0.28 –0.11 11 8 53 68 5.5 5.8 8.3 2.1 3.1 Puerto Rico 3 6 –4.92 –1.76 3 8 19 51 4.0 10.0 10.1 1.5 1.6 Qatar 0c 0c 0.00 0.00 3 4 11 0 0.1 1.7 2.5 0.0 0.3 2012 World Development Indicators 151 3.4 Deforestation and biodiversity Forest Average annual Threatened GEF bene�ts Nationally area deforestationa species index for protected areas biodiversity 0–100 (no biodiversity thousand Higher to maximum Terrestrial Marine sq. km % Mammals Birds Fish plantsb biodiversity) % of land area % of territorial waters 1990 2010 1990–2000 2000–10 2011 2011 2011 2011 2008 1990 2010 1990 2010 Romania 64 66 0.01 –0.32 7 11 19 4 0.7 2.9 7.1 1.6 33.3 Russian Federation 8,090 8,091 0.00 0.00 32 49 35 8 34.1 5.0 9.1 2.2 10.8 Rwanda 3 4 –0.79 –2.38 20 12 9 4 0.9 9.9 10.0 .. .. Saudi Arabia 10 10 0.00 0.00 9 15 23 3 3.2 7.6 31.3 0.6 3.4 Senegal 93 85 0.49 0.49 16 10 45 9 1.0 24.1 24.1 5.8 12.4 Serbia 23 27 –0.62 –0.99 6 9 11 2 0.2 5.3 6.0 .. .. Sierra Leone 31 27 0.65 0.69 17 10 47 48 1.3 4.9 4.9 0.0 0.0 Singapore 0c 0c 0.00 0.00 11 15 25 57 0.1 5.0 5.4 0.0 1.4 Slovak Republic 19 19 0.01 –0.06 3 6 5 5 0.1 19.3 23.2 .. .. Slovenia 12 13 –0.37 –0.16 4 2 29 7 0.2 7.6 13.2 0.0 0.7 Somalia 83 67 0.97 1.07 15 12 27 21 6.1 0.6 0.6 0.0 0.0 South Africa 82 57 0.00 0.00 24 40 87 65 20.7 6.5 6.9 0.7 6.5 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. .. Spain 138 182 –2.09 –0.68 16 9 71 205 6.8 7.7 8.6 0.6 3.5 Sri Lanka 24 19 1.20 1.12 29 15 44 282 7.9 20.3 21.5 0.1 1.1 Sudan 764 699 0.80 0.08 15 15 19 16 5.1 4.2 4.2 0.0 0.0 Swaziland 5 6 –0.93 –0.84 5 11 4 3 0.1 3.0 3.0 .. .. Sweden 273 282 –0.04 –0.30 1 2 12 4 0.3 6.0 10.9 3.9 5.3 Switzerland 12 12 –0.37 –0.38 2 1 9 2 0.2 14.5 24.9 .. .. Syrian Arab Republic 4 5 –1.51 –1.29 16 14 34 2 0.9 0.3 0.6 0.0 0.6 Tajikistan 4 4 –0.05 0.00 8 12 5 13 0.7 1.9 4.1 .. .. Tanzania 415 334 1.02 1.13 35 42 174 290 14.8 26.6 27.5 3.7 10.0 Thailand 195 190 0.28 0.02 57 46 97 86 8.0 14.7 20.1 4.0 4.4 Timor-Leste 10 7 1.22 1.40 4 7 5 0 0.6 .. 6.1 0.0 6.7 Togo 7 3 3.37 5.13 11 5 24 9 0.3 11.3 11.3 0.0 0.0 Trinidad and Tobago 2 2 0.30 0.32 2 2 24 1 2.2 30.5 31.2 0.2 2.8 Tunisia 6 10 –2.67 –1.86 13 6 35 6 0.5 1.3 1.3 1.1 1.2 Turkey 97 113 –0.47 –1.11 17 14 70 5 6.2 1.7 1.9 2.4 2.4 Turkmenistan 41 41 0.00 0.00 9 16 11 3 1.8 3.0 3.0 .. .. Uganda 48 30 2.03 2.56 22 21 61 36 2.8 7.9 10.3 .. .. Ukraine 93 97 –0.25 –0.21 11 11 21 16 0.5 1.8 3.5 4.1 4.9 United Arab Emirates 2 3 –2.38 –0.24 7 9 13 0 0.2 0.3 5.6 0.3 2.6 United Kingdom 26 29 –0.68 –0.31 5 2 43 11 3.5 22.0 26.4 4.9 5.7 United States 2,963 3,040 –0.13 –0.13 37 76 183 219 94.2 12.4 12.4 21.0 28.6 Uruguay 9 17 –4.38 –2.14 11 24 36 0 1.2 0.3 0.3 0.2 0.3 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 37 68 25.3 40.1 53.8 7.0 15.3 Vietnam 94 138 –2.28 –1.65 54 43 68 119 12.1 4.5 6.2 0.3 1.7 West Bank and Gaza 0c 0c 0.00 –0.10 3 9 0 0 .. 0.6 0.6 .. .. Yemen, Rep. 5 5 0.00 0.00 9 15 23 158 3.2 0.0 0.5 0.0 1.8 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 14 3 14 1.9 18.0 28.0 .. .. World 41,582 s 40,204 s 0.18 w 0.11 w 3,105 s 3,401 s 6,213 s 10,987 s .. 8.4 w 12.3 w 4.6 w 10.0 w Low income 4,721 4,155 0.63 0.61 .. .. .. .. .. 9.6 10.7 .. .. Middle income 27,387 26,420 0.22 0.08 .. .. .. .. .. 7.9 12.3 2.6 7.5 Lower middle income 7,003 6,364 0.59 0.30 .. .. .. .. .. 7.3 9.4 2.7 4.4 Upper middle income 20,384 20,056 0.10 0.01 .. .. .. .. .. 8.2 13.4 2.6 8.5 Low & middle income 32,108 30,575 0.28 0.15 .. .. .. .. .. 8.2 12.0 2.6 7.2 East Asia & Pacific 4,602 4,698 0.08 –0.44 .. .. .. .. .. 10.8 15.0 0.5 1.4 Europe & Central Asia 8,735 8,784 –0.02 –0.04 .. .. .. .. .. 4.3 7.5 2.2 10.4 Latin America & Carib. 10,389 9,460 0.47 0.45 .. .. .. .. .. 9.7 20.2 5.0 13.1 Middle East & N. Africa 207 211 –0.10 –0.15 .. .. .. .. .. 3.3 3.9 0.8 2.0 South Asia 795 817 –0.01 –0.29 .. .. .. .. .. 5.3 5.9 1.5 1.7 Sub-Saharan Africa 7,379 6,605 0.55 0.48 .. .. .. .. .. 11.1 11.7 3.3 5.8 High income 9,474 9,629 –0.14 –0.04 .. .. .. .. .. 8.9 12.9 9.3 16.5 Euro area 859 952 –0.76 –0.31 .. .. .. .. .. 11.3 17.0 6.7 15.1 a. Negative values indicate an increase in forest area. b. Flowering plants only. c. Less than 0.5. 152 2012 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity About the data De�nitions As threats to biodiversity mount, the international nature reserves with limited public access; national • Forest area is land spanning more than 0.5 hect- community is increasingly focusing on conserving parks of national or international significance and not are with trees higher than 5  meters and a canopy diversity. Deforestation is a major cause of loss materially affected by human activity; natural monu- cover of more than 10  percent or with trees able of biodiversity, and habitat conservation is vital ments and natural landscapes with unique aspects; to reach these thresholds in situ. It excludes land for stemming this loss. Conservation efforts have managed nature reserves and wildlife sanctuaries; that is predominantly under agricultural or urban focused on protecting areas of high biodiversity. protected landscapes (which may include cultural land use. •  Average annual deforestation is the The Food and Agriculture Organization of the United landscapes); and areas managed mainly for the sus- permanent conversion of natural forest area to other Nations (FAO) Global Forest Resources Assessment tainable use of natural systems to ensure long-term uses, including agriculture, ranching, settlements, 2010 provides detailed information on forest cover in protection and maintenance of biological diversity. and infrastructure. Deforested areas exclude areas 2010 and adjusted estimates of forest cover in 1990 The data in the table cover these six categories logged but intended for regeneration and areas and 2000. The current survey uses a uniform defini- as well as terrestrial protected areas that are not degraded by fuelwood gathering, acid precipitation, tion of forest. Because of space limitations, the table assigned to a category by the IUCN. Designating an or forest fires. •  Threatened species are species does not break down forest cover between natural area as protected does not mean that protection classified by the IUCN as endangered, vulnerable, forest and plantation, a breakdown the FAO provides is in force. And for small countries that have only rare, indeterminate, out of danger, or insufficiently for developing countries. Thus the deforestation data protected areas smaller than 1,000 hectares, the known. Mammals exclude whales and porpoises. in the table may underestimate the rate at which size limit in the definition leads to an underestimate Birds are listed for the country where their breeding natural forest is disappearing in some countries. of protected areas. or wintering ranges are located. Fish are cold-blooded The number of threatened species is an important Due to variations in consistency and methods of aquatic vertebrates of the superclass Pisces. Higher measure of the immediate need for conservation in collection, data quality is highly variable across coun- plants are native vascular plant species. • GEF ben- an area. Global analyses of the status of threatened tries. Some countries update their information more e�ts index for biodiversity is a composite index of species have been carried out for few groups of frequently than others, some have more accurate relative biodiversity potential based on the species organisms. Only for mammals, birds, and amphib- data on extent of coverage, and many underreport in each country and their threat status and diversity ians has the status of virtually all known species the number or extent of protected areas. of habitat types. • Nationally protected areas are been assessed. Threatened species are defined totally or partially protected areas of at least 1,000 using the International Union for Conservation of hectares that are designated as scientific reserves Nature’s (IUCN) classification: endangered (in danger with limited public access, national parks, natural of extinction and unlikely to survive if causal factors monuments, nature reserves or wildlife sanctuar- continue operating) and vulnerable (likely to move ies, and protected landscapes. Terrestrial protected into the endangered category in the near future if areas exclude marine areas, unclassified areas, litto- causal factors continue operating). ral (intertidal) areas, and sites protected under local The Global Environment Facility’s (GEF) benefi ts or provincial law. Marine protected areas are areas of index for biodiversity is a comprehensive indicator intertidal or subtidal terrain—and overlying water and of national biodiversity status and is used to guide associated flora and fauna and historical and cultural its biodiversity priorities. For each country the bio- features—that have been reserved to protect part of diversity indicator incorporates the best available or the entire enclosed environment. and comparable information in four relevant dimen- sions: represented species, threatened species, rep- resented ecoregions, and threatened ecoregions. To combine these dimensions into one measure, the Data sources indicator uses dimensional weights that reflect the consensus of conservation scientists at the GEF, Data on forest area are from the FAO’s Global IUCN, WWF International, and other nongovernmen- Forest Resources Assessment 2010 and website. tal organizations. Data on threatened species are from the elec- The World Conservation Monitoring Centre (WCMC) tronic files of the United Nations Environment Pro- compiles data on protected areas, numbers of cer- gramme (UNEP) and WCMC and the 2011 IUCN tain species, and numbers of those species under Red List of Threatened Species. The GEF benefits threat from various sources. Because of differences index for biodiversity is from Pandey and others in definitions, reporting practices, and reporting peri- (2006a). Data on nationally protected areas are ods, cross-country comparability is limited. from the UNEP and WCMC, based on data from Nationally protected areas are defined using the national authorities, national legislation, and inter- six IUCN management categories for areas of at national agreements. least 1,000 hectares: scientific reserves and strict 2012 World Development Indicators 153 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 urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2009 2009 2009b 2009b 2009b 2009b 2009b 2009b 2010 2010 Afghanistan 55 1,645 23.1 35.6 99 1 1 .. 42 78 Albania 27 8,425 1.8 4.4 58 12 30 3 94 96 Algeria 11 322 6.2 52.7 64 14 23 12 79 85 Angola 148 7,976 0.6 0.4 33 29 38 40 38 60 Argentina 276 6,889 32.6 4.0 66 12 22 12 80 98 Armenia 7 2,223 2.8 36.4 66 4 30 1 97 99 Australia 492 22,413 22.6 4.6 74 11 16 24 100 100 Austria 55 6,575 3.7 4.7 3 79 18 60 100 100 Azerbaijan 8 907 12.2 35.2 76 19 4 2 71 88 Bahrain 0c 3 0.4 219.8 45 6 50 35 .. 100 Bangladesh 105 714 35.9 2.9 88 2 10 2 80 85 Belarus 37 3,913 4.3 7.5 19 54 27 6 99 100 Belgium 12 1,111 6.2 34.0 1 88 12 42 100 100 Benin 10 1,197 0.1 0.5 45 23 32 25 68 84 Bolivia 304 31,054 2.0 0.3 57 15 28 6 71 96 Bosnia and Herzegovina 36 9,422 0.3 0.9 .. .. .. 24 98 100 Botswana 2 1,211 0.2 1.6 41 18 41 40 92 99 Brazil 5,418 28,037 58.1 0.7 55 17 28 15 85 100 Bulgaria 21 2,769 6.1 28.7 16 68 16 3 100 100 Burkina Faso 13 782 1.0 7.9 70 2 28 4 73 95 Burundi 10 1,231 0.3 2.3 77 6 17 3 71 83 Cambodia 121 8,628 2.2 0.5 94 2 4 3 58 87 Cameroon 273 14,237 1.0 0.3 76 7 17 14 52 95 Canada 2,850 84,495 46.0 1.6 12 69 20 18 99 100 Central African Republic 141 32,653 0.1 0.0 1 16 82 15 51 92 Chad 15 1,371 0.4 0.9 52 24 24 8 44 70 Chile 884 52,136 11.3 1.2 70 20 9 9 75 99 China 2,813 2,113 554.1 19.5 65 23 12 5 85 98 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 2,112 46,261 12.7 0.6 39 4 57 11 72 99 Congo, Dem. Rep. 900 14,018 0.6 0.0 c 18 20 63 10 27 79 Congo, Rep. 222 56,324 0.0 0.0 c 9 22 70 101 32 95 Costa Rica 112 24,484 2.7 2.4 53 17 29 9 91 100 Côte d’Ivoire 77 3,971 1.4 1.7 43 19 38 8 68 91 Croatia 38 8,512 0.6 0.6 2 14 85 45 97 100 Cuba 38 3,385 7.6 19.8 75 10 15 4 89 96 Cyprus 1 715 0.2 19.3 86 3 10 66 100 100 Czech Republic 13 1,254 1.7 13.3 2 57 42 45 100 100 Denmark 6 1,086 0.7 10.8 36 5 58 253 100 100 Dominican Republic 21 2,144 3.5 16.6 64 2 34 11 84 87 Ecuador 432 30,291 15.3 3.6 92 3 6 2 89 96 Egypt, Arab Rep. 2 23 68.3 119.0 86 6 8 2 99 100 El Salvador 18 2,881 1.4 5.5 55 17 28 11 76 94 Eritrea 3 549 0.6 9.2 95 0 5 1 57 74 Estonia 13 9,483 1.8 14.0 0c 97 3 5 97 99 Ethiopia 122 1,503 5.6 4.6 94 0c 6 3 34 97 Finland 107 20,042 1.6 1.5 3 72 25 86 100 100 France 200 3,099 31.6 15.0 12 69 18 46 100 100 Gabon 164 110,997 0.1 0.1 38 9 53 46 41 95 Gambia, The 3 1,784 0.1 0.9 28 24 48 8 85 92 Georgia 58 13,179 1.6 2.6 65 13 22 3 96 100 Germany 107 1,306 32.3 21.0 0c 84 16 62 100 100 Ghana 30 1,272 1.0 1.8 66 10 24 8 80 91 Greece 58 5,141 9.5 12.7 89 2 9 17 99 100 Guatemala 109 7,781 2.9 2.6 55 30 15 9 87 98 Guinea 226 23,153 1.6 0.7 84 3 13 2 65 90 Guinea-Bissau 16 10,781 0.2 0.6 82 5 13 1 53 91 Haiti 13 1,319 1.2 8.6 78 4 19 3 51 85 154 2012 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 urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2009 2009 2009b 2009b 2009b 2009b 2009b 2009b 2010 2010 Honduras 96 12,877 1.2 1.2 58 25 17 9 79 95 Hungary 6 599 5.6 5.4 6 82 12 .. 100 100 India 1,446 1,197 761.0 39.8 90 2 7 1 90 97 Indonesia 2,019 8,504 113.3 5.6 82 7 12 2 74 92 Iran, Islamic Rep. 129 1,757 93.3 67.7 92 1 7 2 92 97 Iraq 35 1,132 66.0 87.3 79 15 7 0c 56 91 Ireland 49 10,989 .. .. .. .. .. .. 100 100 Israel 1 100 2.0 101.9 58 6 36 83 100 100 Italy 183 3,032 45.4 23.7 44 36 20 25 100 100 Jamaica 9 3,489 0.6 6.2 34 22 44 17 88 98 Japan 430 3,371 90.0 20.9 63 18 19 54 100 100 Jordan 1 115 0.9 99.4 65 4 31 16 92 98 Kazakhstan 75 4,686 33.1 28.9 87 12 1 1 90 99 Kenya 21 525 2.7 8.9 79 4 17 7 52 82 Korea, Dem. Rep. 67 2,764 8.7 11.2 76 13 10 .. 97 99 Korea, Rep. 65 1,330 25.5 36.5 62 12 26 30 88 100 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 0c 0c 0.9 .. 54 2 44 43 99 99 Kyrgyz Republic 49 9,199 10.1 43.7 94 3 3 0c 85 99 Lao PDR 190 31,151 4.3 1.3 93 4 3 1 62 77 Latvia 17 7,424 0.4 1.2 12 50 39 27 96 100 Lebanon 5 1,144 1.3 28.1 60 11 29 20 100 100 Lesotho 5 2,433 0.1 1.7 20 40 40 20 73 91 Liberia 200 52,139 0.2 0.1 34 27 40 3 60 88 Libya 1 96 4.3 718.0 83 3 14 11 .. .. Lithuania 16 4,659 2.4 9.6 3 90 7 7 .. 98 Macedonia, FYR 5 2,625 1.0 16.1 12 67 21 4 99 100 Madagascar 337 16,746 14.7 4.4 97 1 2 0c 34 74 Malawi 16 1,118 1.0 5.6 84 4 12 3 80 95 Malaysia 580 20,752 13.2 2.3 34 36 30 10 99 100 Mali 60 4,024 6.5 6.5 90 1 9 1 51 87 Mauritania 0c 118 1.6 14.0 94 2 5 1 48 52 Mauritius 3 2,158 0.7 26.4 68 3 30 9 99 100 Mexico 409 3,651 79.8 17.5 77 9 14 8 91 97 Moldova 1 280 1.9 16.4 40 52 9 1 93 99 Mongolia 35 12,833 0.5 1.4 44 32 24 4 53 100 Morocco 29 917 12.6 43.4 87 3 10 5 61 98 Mozambique 100 4,388 0.7 0.3 74 3 23 11 29 77 Myanmar 1,003 21,071 33.2 2.8 89 1 10 .. 78 93 Namibia 6 2,747 0.3 1.7 71 5 24 19 90 99 Nepal 198 6,734 9.8 4.7 98 0c 2 1 88 93 Netherlands 11 665 10.6 11.7 1 87 12 41 100 100 New Zealand 327 75,768 4.8 1.5 74 4 21 13 100 100 Nicaragua 190 33,221 1.3 0.7 84 2 14 4 68 98 Niger 4 234 2.4 7.0 88 1 11 1 39 100 Nigeria 221 1,431 10.3 3.6 53 15 31 8 43 74 Norway 382 79,110 2.9 0.8 29 43 28 66 100 100 Oman 1 516 1.3 86.6 88 1 10 23 78 93 Pakistan 55 323 183.5 79.5 94 1 5 1 89 96 Panama 147 42,578 0.5 0.3 51 3 46 44 83 97 Papua New Guinea 801 119,492 0.4 0.0 0c 43 57 12 33 87 Paraguay 94 14,822 0.5 0.1 71 8 20 19 66 99 Peru 1,616 56,179 19.3 1.0 85 8 7 4 65 91 Philippines 479 5,223 81.6 17.0 82 10 8 1 92 93 Poland 54 1,405 12.0 19.4 10 60 31 20 100 100 Portugal 38 3,574 8.5 12.3 73 19 8 15 100 99 Puerto Rico 7 1,790 1.0 14.0 7 2 91 65 .. .. Qatar 0c 35 0.4 455.2 59 2 39 122 100 100 2012 World Development Indicators 155 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 urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2009 2009 2009b 2009b 2009b 2009b 2009b 2009b 2010 2010 Romania 42 1,969 6.9 3.2 17 61 22 8 .. 99 Russian Federation 4,313 30,405 66.2 1.5 20 60 20 6 92 99 Rwanda 10 921 0.2 1.6 68 8 24 22 63 76 Saudi Arabia 2 90 23.7 943.3 88 3 9 11 .. 97 Senegal 26 2,131 2.2 5.7 93 3 4 3 56 93 Serbia .. .. 4.1 .. 2 82 17 2 98 99 Sierra Leone 160 27,878 0.5 0.3 71 10 19 3 35 87 Singapore 1 120 .. .. .. .. .. .. .. 100 Slovak Republic 13 2,325 0.7 1.4 3 50 47 64 100 100 Slovenia 19 9,153 0.9 3.0 0c 82 18 27 99 100 Somalia 6 658 3.3 22.4 99 0c 0c .. 7 66 South Africa 45 908 12.5 25.0 63 6 31 15 79 99 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 111 2,422 32.5 29.0 61 22 18 22 100 100 Sri Lanka 53 2,555 13.0 24.5 87 6 6 2 90 99 Sudan 30 706 37.1 57.6 97 1 2 1 52 67 Swaziland 3 2,260 1.0 23.1 97 1 2 2 65 91 Sweden 171 18,390 2.6 1.5 4 59 37 110 100 100 Switzerland 40 5,217 2.6 4.9 2 58 41 110 100 100 Syrian Arab Republic 7 356 16.8 99.8 88 4 9 2 86 93 Tajikistan 66 9,774 12.0 74.8 92 5 4 0c 54 92 Tanzania 84 1,930 5.2 5.4 89 0 10 4 44 79 Thailand 225 3,268 57.3 13.1 90 5 5 3 95 97 Timor-Leste 8 7,469 1.2 14.3 91 0c 8 0c 60 91 Togo 12 1,949 0.2 1.2 45 2 53 10 40 89 Trinidad and Tobago 4 2,874 0.2 6.0 9 25 66 61 93 98 Tunisia 4 402 2.9 61.7 76 4 13 11 84 99 Turkey 227 3,160 40.1 18.8 74 11 15 9 99 100 Turkmenistan 1 273 24.9 100.8 97 1 2 0c 72 97 Uganda 39 1,205 0.3 0.5 36 18 46 36 68 95 Ukraine 53 1,153 38.5 27.6 51 36 12 1 98 98 United Arab Emirates 0c 22 4.0 2,032.0 83 2 15 39 100 100 United Kingdom 145 2,346 13.0 8.8 10 33 57 132 100 100 United States 2,818 9,186 478.4 15.6 40 46 14 24 94 100 Uruguay 59 17,639 3.7 2.6 87 2 11 8 100 100 Uzbekistan 16 588 59.6 118.3 91 3 6 0c 81 98 Venezuela, RB 722 25,451 9.1 0.7 44 8 49 18 75 94 Vietnam 359 4,178 82.0 9.3 95 4 1 1 93 99 West Bank and Gaza 1 201 0.4 49.9 45 7 48 9 81 86 Yemen, Rep. 2 90 3.6 168.6 91 2 7 4 47 72 Zambia 80 6,303 1.7 1.7 76 7 17 3 46 87 Zimbabwe 12 983 4.2 21.0 79 7 14 1 69 98 World 42,383 s 6,258 w 3,908.3 s 7.3 w 70 w 18 w 12 w 10 w 81 w 96 w Low income 4,197 5,381 188.6 4.3 90 2 8 2 57 86 Middle income 29,152 5,944 2,791.0 7.0 79 11 10 3 84 96 Lower middle income 7,995 3,227 1,607.4 12.6 88 5 7 1 83 93 Upper middle income 21,157 8,718 1,183.6 5.0 66 20 14 6 86 98 Low & middle income 33,348 5,867 2,979.6 6.7 79 11 10 3 80 95 East Asia & Pacific 8,773 4,506 952.0 9.9 73 16 10 4 84 97 Europe & Central Asia 5,076 12,887 330.4 5.0 63 26 11 3 91 99 Latin America & Carib. 13,425 23,323 269.5 1.6 68 11 21 10 81 98 Middle East & N. Africa 226 695 276.5 70.0 86 6 8 2 81 94 South Asia 1,990 1,236 1,026.6 33.4 91 2 7 1 88 95 Sub-Saharan Africa 3,858 4,634 124.6 2.2 84 4 12 4 49 83 High income 9,034 8,305 928.7 9.4 41 42 17 31 98 100 Euro area 977 2,952 185.6 16.4 32 52 17 36 100 100 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. Less than 0.5. 156 2012 World Development Indicators 3.5 ENVIRONMENT Freshwater About the data De�nitions The data on freshwater resources are based on •  Internal renewable freshwater resources are estimates of runoff into rivers and recharge of the average annual flows of rivers and groundwater groundwater. These estimates are based on differ- from rainfall in the country. Natural incoming flows ent sources and refer to different years, so cross- originating outside a country’s borders are excluded. country comparisons should be made with caution. Overlapping water resources between surface run- Because the data are collected intermittently, they off and groundwater recharge are also deducted. may hide significant variations in total renewable •  Internal renewable freshwater resources per water resources from year to year. The data also capita are calculated using the World Bank’s popu- fail to distinguish between seasonal and geographic lation estimates (see table 2.1). • Annual freshwater variations in water availability within countries. Data withdrawals are total water withdrawals, not count- for small countries and countries in arid and semiarid ing evaporation losses from storage basins. With- zones are less reliable than those for larger countries drawals also include water from desalination plants and countries with greater rainfall. in countries where they are a significant source. With- Caution should also be used in comparing data drawals can exceed 100 percent of total renewable on annual freshwater withdrawals, which are subject resources where extraction from nonrenewable aqui- to variations in collection and estimation methods. fers or desalination plants is considerable or where In addition, inflows and outflows are estimated at water reuse is significant. Withdrawals for agriculture different times and at different levels of quality and and industry are total withdrawals for irrigation and precision, requiring caution in interpreting the data, livestock production and for direct industrial use particularly for water-short countries, notably in the (including for cooling thermoelectric plants). With- Middle East and North Africa. drawals for domestic uses include drinking water, Water productivity is an indication only of the municipal use or supply, and use for public services, effi ciency by which each country uses its water commercial establishments, and homes. •  Water resources. Given the different economic structure productivity is calculated as GDP in constant prices of each country, these indicators should be used divided by annual total water withdrawal. • Access carefully, taking into account the countries’ sectoral to an improved water source is the percentage of the activities and natural resource endowments. population with reasonable access to an adequate The data on access to an improved water source amount of water from an improved source, such as measure the percentage of the population with ready piped water into a dwelling, plot, or yard; public tap access to water for domestic purposes. The data or standpipe; tubewell or borehole; protected dug are based on surveys and estimates provided by well or spring; and rainwater collection. Unimproved governments to the Joint Monitoring Programme of sources include unprotected dug wells or springs, the World Health Organization (WHO) and the United carts with small tank or drum, bottled water, and Nations Children’s Fund (UNICEF). The coverage tanker trucks. Reasonable access is defined as the rates are based on information from service users availability of at least 20 liters a person a day from on actual household use rather than on informa- a source within 1 kilometer of the dwelling. tion from service providers, which may include non- functioning systems. Access to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as Data sources safe, improved, adequate, and reasonable may have different meaning in different countries despite offi - Data on freshwater resources and withdrawals cial WHO definitions (see De�nitions). Even in high- are from the Food and Agriculture Organization’s income countries treated water may not always be AQUASTAT database. The GDP estimates used to safe to drink. Access to an improved water source is calculate water productivity are from the World equated with connection to a supply system; it does Bank national accounts database. Data on access not take into account variations in the quality and to water are from WHO and UNICEF’s Progress on cost (broadly defined) of the service. Drinking Water and Sanitation (2012). 2012 World Development Indicators 157 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 Bahrain .. .. .. .. .. .. .. .. .. .. .. .. 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 .. .. .. .. .. .. .. .. .. .. .. .. Cyprus 7.1 8.0 0.21 0.23 0.3 8.9 9.3 36.3 9.9 5.1 8.0 22.2 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 .. .. .. .. .. .. .. .. .. .. .. .. 158 2012 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 Haiti 5.2 .. 0.20 .. .. .. .. .. .. .. .. .. Honduras .. .. .. .. .. .. .. .. .. .. .. .. 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 .. 0.21 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 2012 World Development Indicators 159 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 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. 6.4 .. 0.12 3.7 6.7 10.5 6.5 18.1 20.7 12.5 21.3 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 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. 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 Industrial 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. 160 2012 World Development Indicators 3.6 ENVIRONMENT Water pollution About the data De�nitions Emissions of organic pollutants from industrial water pollutants per day for each country and year. • Emissions of organic water pollutants are mea- activities are a major cause of degradation of water The data in the table were derived by updating these sured as biochemical oxygen demand, or the amount quality. Water quality and pollution levels are gener- estimates through 2007. of oxygen that bacteria in water will consume in ally measured as concentration or load—the rate of breaking down waste, a standard water treatment occurrence of a substance in an aqueous solution. test for the presence of organic pollutants. Emis- Polluting substances include organic matter, metals, sions per worker are total emissions divided by the minerals, sediment, bacteria, and toxic chemicals. number of industrial workers. • Industry shares of The table focuses on organic water pollution result- emissions of organic water pollutants are emissions ing from industrial activities. Because water pollu- from manufacturing activities as defined by two-digit tion tends to be sensitive to local conditions, the divisions of the International Standard Industrial national-level data in the table may not reflect the Classification revision 3. quality of water in specific locations. The data in the table come from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank’s Develop- ment Research Group. Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in 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. Such data are fairly reliable because sampling techniques for mea- suring water pollution are more widely understood and much less expensive than those for air pollution. Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employ- ment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their economet- ric analysis found that the ratio of BOD to employ- Data sources ment in each industrial sector is about the same across countries. This finding allowed the authors to Data on water pollutants are from Hettige, Mani, estimate BOD loads across countries and over time. and Wheeler (1998). The data were updated The estimated BOD intensities per unit of employ- through 2007 by the World Bank Development ment were multiplied by sectoral employment num- Research Group using the same methodology bers from UNIDO’s industry database for 1980–98. as the initial study. Data on industrial sectoral These estimates of sectoral emissions were then employment are from UNIDO’s industry database. used to calculate kilograms of emissions of organic 2012 World Development Indicators 161 3.7 Energy production and use Energy Energy Alternative and production use nuclear energy Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2009 1990 2009 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. .. Albania 2.4 1.3 2.7 1.7 1.6 809 538 76.5 54.1 13.6 12.4 9.2 26.4 Algeria 100.1 152.3 22.2 39.8 3.0 877 1,138 99.9 99.8 0.1 0.1 0.1 0.1 Angola 28.7 101.0 5.9 11.9 3.8 569 641 25.5 37.6 73.5 60.1 1.1 2.3 Argentina 48.4 80.8 46.1 74.2 2.5 1,411 1,853 88.7 89.4 3.7 3.1 7.5 6.8 Armenia 0.1 0.8 7.7 2.6 –2.3 2,171 843 97.2 68.4 0.0 0.0 1.7 31.7 Australia 157.5 310.7 86.2 131.1 2.3 5,053 5,971 93.9 94.4 4.6 4.4 1.5 1.2 Austria 8.1 11.4 24.8 31.7 1.7 3,228 3,784 79.2 70.2 10.0 17.6 11.0 12.0 Azerbaijan 20.6 64.6 26.2 12.0 –2.8 3,665 1,338 100.0 98.2 0.0 0.0 0.2 1.7 Bahrain 13.4 17.5 4.4 9.5 4.4 8,826 8,096 100.0 99.8 0.0 0.0 0.0 0.0 Bangladesh 10.8 24.8 12.7 29.6 4.6 121 201 45.5 69.8 53.9 29.8 0.6 0.5 Belarus 3.3 4.0 45.5 26.8 –1.7 4,470 2,815 95.6 90.3 0.4 6.0 0.0 0.0 Belgium 13.1 15.3 48.3 57.2 0.9 4,844 5,300 76.0 73.6 1.6 4.9 23.1 21.8 Benin 1.8 2.0 1.7 3.5 3.7 348 404 4.8 40.4 94.1 57.4 0.0 0.0 Bolivia 4.9 14.2 2.6 6.2 3.9 392 638 67.2 79.1 28.9 17.7 3.9 3.2 Bosnia and Herzegovina 4.6 4.5 7.0 6.0 2.9 1,629 1,580 93.9 92.2 2.3 3.1 3.7 9.0 Botswana 0.9 0.9 1.3 2.0 2.7 912 1,034 66.0 64.3 33.4 23.6 0.0 0.0 Brazil 104.2 230.3 140.2 240.2 3.0 937 1,243 51.2 51.3 34.1 31.6 13.1 15.6 Bulgaria 9.6 9.8 28.6 17.5 –1.3 3,277 2,305 84.3 73.1 0.6 4.3 13.9 24.9 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 3.7 .. 5.2 3.3 .. 371 .. 27.8 .. 70.7 .. 0.1 Cameroon 11.0 8.8 5.0 6.9 1.8 409 361 18.7 30.9 76.7 64.1 4.6 5.0 Canada 273.7 389.8 208.6 254.1 1.4 7,505 7,534 74.6 74.9 3.9 4.5 21.5 21.7 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. .. Chile 7.5 9.3 13.6 28.8 4.3 1,028 1,698 74.6 74.5 19.7 17.5 5.7 7.6 China 886.3 2,084.9 863.0 2,257.1 5.1 760 1,695 75.5 87.4 23.2 9.0 1.3 3.7 Hong Kong SAR, China 0.0 0.1 8.7 14.9 2.5 1,518 2,133 100.0 95.1 0.6 0.4 0.0 0.0 Colombia 48.2 99.1 24.2 31.8 0.8 730 697 67.4 75.2 22.8 14.0 9.8 11.1 Congo, Dem. Rep. 12.0 23.3 11.8 22.9 3.9 324 357 11.2 3.7 84.7 93.7 4.1 2.9 Congo, Rep. 8.7 15.3 0.8 1.4 3.2 334 356 35.1 44.1 59.5 51.1 5.3 2.0 Costa Rica 1.0 2.7 2.0 4.9 5.0 660 1,067 48.4 44.7 36.6 15.8 14.4 39.5 Côte d’Ivoire 3.4 11.9 4.3 10.4 5.0 345 535 23.3 23.5 73.5 75.2 2.6 1.8 Croatia 5.1 4.1 9.0 8.7 1.3 1,884 1,965 86.5 83.4 3.5 4.2 3.6 6.8 Cuba 9.4 5.6 19.3 11.5 –1.8 1,824 1,022 55.7 84.1 44.3 15.8 0.0 0.1 Cyprus 0.0 0.1 1.4 2.5 2.9 1,775 2,298 99.5 95.7 0.5 1.8 0.0 2.5 Czech Republic 40.9 31.2 49.6 42.0 0.1 4,796 4,004 91.7 79.6 1.6 5.6 6.8 17.5 Denmark 10.1 23.9 17.4 18.6 0.1 3,377 3,369 89.6 80.4 6.6 16.2 0.3 3.3 Dominican Republic 1.0 1.9 4.1 8.1 3.6 570 826 74.9 76.6 24.4 21.8 0.7 1.6 Ecuador 16.5 27.3 6.0 11.4 3.9 584 796 79.1 86.7 13.8 5.4 7.2 7.0 Egypt, Arab Rep. 54.9 88.2 31.8 72.0 4.8 560 903 94.0 96.3 3.3 2.1 2.7 1.7 El Salvador 1.7 3.2 2.5 5.1 3.8 463 828 31.4 37.8 48.2 33.8 20.3 28.2 Eritrea 0.7 0.6 0.9 0.7 –2.0 276 142 19.4 22.6 80.6 77.4 0.0 0.0 Estonia 5.4 4.2 9.9 4.7 –2.0 6,316 3,543 100.0 83.4 1.9 14.7 0.0 0.4 Ethiopia 14.1 30.4 14.9 32.7 3.8 308 402 5.5 7.1 93.9 92.0 0.6 1.0 Finland 12.1 16.6 28.4 33.2 1.5 5,692 6,213 55.5 48.8 16.1 20.9 20.9 21.8 France 111.9 129.5 223.9 256.2 0.9 3,848 3,970 58.1 51.0 4.9 5.9 38.7 43.9 Gabon 14.6 13.6 1.2 1.8 2.7 1,272 1,214 32.0 34.0 62.9 61.8 5.1 4.2 Gambia, The .. .. 0.1 0.1 .. 64 84 0.0 0.0 .. .. .. .. Georgia 2.1 1.3 12.4 3.2 –6.2 2,587 723 88.9 68.0 3.7 12.0 5.2 21.4 Germany 186.2 127.1 351.4 318.5 –0.2 4,424 3,889 86.8 79.5 1.4 7.8 11.8 13.0 Ghana 4.4 7.0 5.3 9.2 3.2 358 388 18.2 24.3 73.7 69.8 9.3 6.4 Greece 9.2 10.1 21.4 29.4 2.2 2,111 2,609 94.6 92.4 4.2 3.4 1.0 3.0 Guatemala 3.4 6.1 4.4 9.8 4.0 497 701 28.1 46.1 68.5 52.1 3.4 1.8 Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. 0.1 0.1 .. 73 67 0.0 0.0 .. .. .. .. Haiti 1.3 1.9 1.6 2.6 3.4 219 263 19.7 28.1 77.7 71.2 2.5 0.7 162 2012 World Development Indicators 3.7 ENVIRONMENT Energy production and use Energy Energy Alternative and production use nuclear energy Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2009 1990 2009 Honduras 1.7 2.2 2.4 4.4 3.6 487 592 30.0 50.3 63.0 44.3 8.2 5.5 Hungary 14.6 11.0 28.7 24.9 0.0 2,762 2,480 81.5 74.2 2.3 7.1 12.8 16.8 India 291.8 502.5 316.7 675.8 3.8 362 560 55.4 73.0 42.1 24.5 2.5 2.3 Indonesia 169.1 351.8 101.3 202.0 3.5 550 851 54.7 65.6 42.9 26.0 2.4 8.4 Iran, Islamic Rep. 179.3 349.8 67.9 215.9 6.4 1,237 2,951 98.9 99.7 0.3 0.2 0.8 0.3 Iraq 104.9 119.6 18.1 32.2 3.6 994 1,035 98.6 97.6 0.1 0.1 1.2 0.9 Ireland 3.5 1.5 10.0 14.3 2.6 2,842 3,216 84.6 88.7 1.1 2.2 0.6 2.3 Israel 0.4 3.3 11.5 21.5 3.4 2,463 2,878 97.2 96.5 0.0 0.1 3.1 4.8 Italy 25.3 27.0 146.6 164.6 1.2 2,584 2,735 93.4 87.5 0.6 4.3 3.9 5.9 Jamaica 0.5 0.5 2.8 3.3 2.1 1,167 1,208 82.6 83.7 17.1 15.9 0.3 0.4 Japan 75.2 93.8 439.3 472.0 0.6 3,556 3,700 84.5 81.0 1.1 1.4 14.4 17.6 Jordan 0.2 0.3 3.3 7.5 4.3 1,028 1,260 98.1 98.0 0.1 0.1 1.8 1.7 Kazakhstan 90.5 145.8 72.7 65.8 –0.7 4,450 4,091 96.9 99.0 0.2 0.2 0.9 0.9 Kenya 9.0 15.6 10.9 18.7 2.8 467 474 17.5 16.8 77.9 76.0 4.5 7.2 Korea, Dem. Rep. 28.9 20.3 33.2 19.3 –2.0 1,649 795 93.1 89.0 2.9 5.4 4.0 5.6 Korea, Rep. 22.6 44.3 93.1 229.2 4.6 2,171 4,701 83.8 81.7 0.8 1.3 15.4 17.0 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 50.4 130.2 9.1 30.2 8.1 4,364 11,402 99.9 100.0 0.1 0.0 0.0 0.0 Kyrgyz Republic 2.5 1.2 7.5 3.0 –3.4 1,693 566 93.5 72.5 0.1 0.1 11.5 28.3 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. .. Latvia 1.1 2.1 7.9 4.2 –2.1 2,949 1,871 81.8 59.4 8.4 30.0 4.9 7.1 Lebanon 0.1 0.2 2.0 6.6 4.2 663 1,580 92.5 95.9 5.3 1.8 2.2 0.8 Lesotho .. .. .. 0.0 .. .. 9 .. 0.0 .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. .. Libya 73.2 87.1 11.3 20.4 2.3 2,614 3,258 98.9 99.2 1.1 0.8 0.0 0.0 Lithuania 4.9 4.2 16.1 8.4 –1.9 4,357 2,512 75.8 55.7 1.8 9.8 28.2 34.9 Macedonia, FYR 1.3 1.6 2.5 2.8 0.8 1,298 1,352 98.0 84.2 0.0 7.0 1.7 4.3 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 48.8 89.7 22.0 66.8 5.9 1,208 2,391 88.8 94.7 9.7 4.5 1.6 0.9 Mali .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. 0.5 1.2 .. 453 947 0.0 0.0 .. .. .. .. Mexico 194.7 220.0 122.5 174.6 2.0 1,453 1,559 87.2 88.9 7.0 4.8 5.9 6.3 Moldova 0.1 0.1 9.9 2.4 –5.2 2,669 687 100.0 91.3 0.4 3.3 0.2 0.2 Mongolia 2.7 7.7 3.4 3.2 –0.5 1,558 1,194 96.9 96.4 2.5 3.2 0.0 0.0 Morocco 0.8 0.8 6.9 15.1 4.0 280 477 93.8 92.5 4.6 3.2 1.5 1.7 Mozambique 5.6 11.9 5.9 9.8 2.9 437 427 5.5 7.7 93.9 81.8 0.4 14.9 Myanmar 10.7 22.4 10.7 15.1 2.3 271 316 14.4 27.7 84.7 69.9 1.0 2.4 Namibia 0.2 0.3 0.7 1.7 5.4 445 764 62.0 70.5 15.9 12.0 17.4 7.2 Nepal 5.5 8.8 5.8 10.0 3.0 303 338 5.1 11.1 93.7 85.8 1.3 2.7 Netherlands 60.5 63.0 65.7 78.2 0.9 4,393 4,729 95.9 93.1 1.5 4.4 1.4 2.0 New Zealand 11.5 15.2 12.8 17.4 1.5 3,720 4,032 67.0 63.7 5.7 6.3 27.1 29.8 Nicaragua 1.5 1.7 2.1 3.1 2.6 508 540 28.3 44.7 53.9 45.9 17.5 9.4 Niger .. .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 150.5 228.7 70.6 108.3 2.4 724 701 19.3 14.7 80.2 84.9 0.5 0.4 Norway 119.1 213.6 21.0 28.2 1.6 4,952 5,849 51.9 58.8 4.9 5.1 49.6 38.7 Oman 38.3 67.2 4.2 15.1 6.4 2,258 5,554 100.0 100.0 0.0 0.0 0.0 0.0 Pakistan 34.2 64.9 42.8 85.5 3.7 383 502 52.6 61.8 43.8 34.5 3.6 3.7 Panama 0.6 0.7 1.5 3.1 3.3 603 896 57.5 78.6 28.9 10.7 13.1 10.8 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 4.6 7.4 3.1 4.8 1.7 724 749 21.3 28.5 72.5 53.7 76.0 99.5 Peru 10.6 15.1 9.7 15.8 2.5 449 550 63.3 73.5 27.5 15.4 9.2 11.1 Philippines 17.2 23.5 28.9 38.8 1.7 469 424 43.4 57.0 38.5 17.9 18.1 25.0 Poland 103.9 67.5 103.1 94.0 –0.5 2,705 2,464 97.8 92.8 2.2 7.1 0.1 0.3 Portugal 3.4 4.9 16.7 24.1 2.4 1,677 2,266 80.4 78.0 14.8 13.6 4.8 6.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. .. Qatar 26.6 139.9 6.2 23.8 7.5 13,098 14,911 100.0 100.0 0.0 0.0 0.0 0.0 2012 World Development Indicators 163 3.7 Energy production and use Energy Energy Alternative and production use nuclear energy Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2009 1990 2009 1990–2009 1990 2009 1990 2009 1990 2009 1990 2009 Romania 40.8 28.3 62.3 34.4 –1.9 2,683 1,602 96.1 76.3 1.0 11.4 1.6 12.9 Russian Federation 1,293.1 1,181.6 879.2 646.9 –1.0 5,929 4,561 93.4 90.2 1.4 1.0 5.2 9.0 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 370.2 528.4 59.8 157.9 4.9 3,703 5,888 100.0 100.0 0.0 0.0 0.0 0.0 Senegal 1.0 1.3 1.7 2.9 3.4 233 243 43.3 57.8 56.7 41.1 0.0 0.7 Serbia 13.4 a 9.4 19.3a 14.4 0.4 2,550a 1,974 90.6a 92.4 6.0a 2.0 4.2a 6.4 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. .. Singapore 0.0 0.0 11.5 18.5 0.9 3,760 3,704 100.0 99.8 0.0 0.2 0.0 0.0 Slovak Republic 5.3 5.9 21.3 16.7 –0.2 4,025 3,086 81.6 69.5 0.8 5.2 15.5 24.6 Slovenia 3.1 3.5 5.7 7.0 1.8 2,858 3,417 71.3 69.3 4.7 7.2 25.5 27.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. .. South Africa 114.5 160.6 93.9 144.0 2.3 2,667 2,921 86.6 87.8 11.1 9.8 2.4 2.4 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. .. Spain 34.6 29.7 90.1 126.5 2.7 2,319 2,756 77.4 79.9 4.5 4.9 18.1 15.8 Sri Lanka 4.2 5.1 5.5 9.3 3.2 318 449 24.1 45.3 71.0 51.1 4.9 3.6 Sudan 8.8 35.2 10.6 15.8 2.4 401 372 17.4 30.2 81.8 68.0 0.8 1.8 Swaziland .. .. 0.3 0.4 .. 358 373 0.0 0.0 .. .. .. .. Sweden 29.7 30.3 47.2 45.4 0.2 5,515 4,883 37.3 32.0 11.7 22.9 50.9 42.9 Switzerland 10.3 12.8 24.3 27.0 0.6 3,621 3,480 58.6 53.3 5.9 8.2 36.3 39.2 Syrian Arab Republic 22.3 23.6 10.5 22.5 4.8 849 1,123 97.7 99.3 0.0 0.0 2.2 0.7 Tajikistan 2.0 1.5 5.3 2.3 –2.9 1,001 342 71.3 41.2 0.0 0.0 26.7 58.6 Tanzania 9.1 18.0 9.7 19.6 4.1 382 451 6.9 11.1 91.7 87.7 1.4 1.2 Thailand 26.6 61.7 41.9 103.3 4.9 735 1,504 63.8 79.4 35.0 19.9 1.0 0.6 Timor-Leste .. .. .. 0.1 .. .. 58 .. 0.0 .. .. .. .. Togo 1.1 2.2 1.3 2.6 4.2 345 445 15.1 14.3 82.8 83.1 0.6 0.3 Trinidad and Tobago 12.6 44.0 6.0 20.3 8.0 4,912 15,158 99.2 99.9 0.8 0.1 0.0 0.0 Tunisia 5.7 7.8 4.9 9.2 3.6 607 881 87.0 85.7 12.9 14.1 0.1 0.2 Turkey 25.8 30.3 52.8 97.7 3.6 975 1,359 81.8 89.9 13.7 4.8 4.6 5.4 Turkmenistan 74.9 40.9 19.6 19.6 2.4 5,352 3,933 100.0 100.0 0.0 0.0 0.3 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 135.8 76.9 251.8 115.5 –3.0 4,852 2,507 91.8 80.0 0.1 0.8 8.2 19.7 United Arab Emirates 110.2 168.8 20.4 59.6 5.4 11,258 8,588 100.0 100.0 0.0 0.0 0.0 0.0 United Kingdom 208.0 158.9 205.9 196.8 0.0 3,597 3,183 90.7 87.3 0.3 2.7 8.5 9.8 United States 1,652.5 1,686.4 1,915.0 2,162.9 1.0 7,672 7,051 86.4 84.1 3.3 3.9 10.3 11.9 Uruguay 1.1 1.5 2.3 4.1 2.1 724 1,224 58.7 60.3 24.2 26.0 26.8 11.1 Uzbekistan 38.6 60.7 46.4 48.8 0.5 2,261 1,758 99.2 98.4 0.0 0.0 1.2 1.6 Venezuela, RB 148.8 203.5 43.5 66.9 2.0 2,205 2,357 91.5 87.7 1.2 0.8 7.3 11.6 Vietnam 24.7 76.6 24.3 64.0 5.3 368 745 20.3 56.2 77.8 39.3 1.9 4.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9.4 15.2 2.5 7.6 6.0 210 324 96.9 98.7 3.1 1.3 0.0 0.0 Zambia 4.9 7.2 5.4 7.9 2.0 687 617 15.6 7.6 74.3 80.9 12.7 11.2 Zimbabwe 8.5 8.5 9.3 9.5 –0.2 888 763 44.8 25.7 50.8 65.6 4.0 3.8 World 8,815.9 t 12,241.3 t 8,574.0 t 11,787.1 t 1.9 w 1,661 w 1,788 w 80.9 w 80.7 w 10.2 w 10.0 w 8.7 w 9.2 w Low income 160.7 250.6 186.6 263.3 2.2 386 365 41.5 29.9 54.7 65.9 3.9 4.1 Middle income 4,810.5 7,351.0 3,886.6 6,158.2 2.6 1,019 1,254 78.6 81.5 17.2 13.2 4.1 5.3 Lower middle income 1,247.9 1,944.1 1,085.5 1,656.0 2.4 607 665 65.1 68.0 30.7 26.9 4.4 5.3 Upper middle income 3,564.8 5,410.7 2,803.9 4,505.9 2.7 1,375 1,848 83.7 86.3 12.0 8.3 4.0 5.3 Low & middle income 4,969.1 7,597.3 4,059.7 6,407.1 2.6 963 1,162 77.3 79.8 18.5 15.0 4.1 5.3 East Asia & Pacific 1,224.5 2,758.4 1,138.1 2,790.9 4.8 712 1,436 71.4 84.1 26.6 11.8 1.9 4.1 Europe & Central Asia 1,770.3 1,673.3 1,585.6 1,137.6 –1.0 4,067 2,831 93.0 88.9 1.5 1.9 5.3 9.3 Latin America & Carib. 613.1 937.5 457.6 716.5 2.5 1,048 1,245 70.3 71.8 20.4 17.0 9.1 11.1 Middle East & N. Africa 558.2 856.0 183.8 454.5 5.0 812 1,399 97.4 98.3 1.4 0.9 1.1 0.6 South Asia 349.4 611.2 386.8 817.1 3.8 340 514 53.5 70.7 43.9 26.8 2.5 2.4 Sub-Saharan Africa 475.6 810.5 313.9 511.2 2.6 681 689 41.7 40.1 55.9 57.3 2.2 2.5 High income 3,878.6 4,694.3 4,538.5 5,420.3 1.2 4,649 4,856 84.2 81.9 2.8 4.2 12.8 13.8 Euro area 481.9 450.2 1,069.7 1,169.7 0.9 3,509 3,536 80.0 74.2 3.1 6.7 16.5 18.6 a. Includes Kosovo and Montenegro. 164 2012 World Development Indicators 3.7 ENVIRONMENT Energy production and use About the data De�nitions In developing economies growth in energy use is •  Energy production refers to forms of primary closely related to growth in the modern sectors— energy—petroleum (crude oil, natural gas liquids, industry, motorized transport, and urban areas— and oil from nonconventional sources), natural gas, but energy use also reflects climatic, geographic, solid fuels (coal, lignite, and other derived fuels), and economic factors (such as the relative price and combustible renewables and waste—and pri- of energy). Energy use has been growing rapidly in mary electricity, all converted into oil equivalents low- and middle-income economies, but high-income (see About the data). • Energy use refers to the use economies still use almost five times as much energy of primary energy before transformation to other on a per capita basis. end-use fuels, which is equal to indigenous produc- Energy data are compiled by the International tion plus imports and stock changes, minus exports Energy Agency (IEA). IEA data for economies that and fuels supplied to ships and aircraft engaged in are not members of the Organisation for Economic international transport (see About the data). • Fos- Co-operation and Development (OECD) are based sil fuel comprises coal, oil, petroleum, and natural on national energy data adjusted to conform to 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 Data sources). All forms of energy—primary energy and primary electricity—are converted into oil equiva- lents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power. The IEA makes these estimates in consultation with national statistical offices, oil companies, elec- tric utilities, and national energy experts. The IEA occasionally revises its time series to reflect politi- cal changes, and energy statistics undergo contin- ual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable. Data sources Data on energy production and use are from IEA electronic files and are published in IEA’s annual publications Energy Statistics of Non-OECD Coun- tries, Energy Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. 2012 World Development Indicators 165 3.8 Electricity production, sources, and access Electricity Sources of Access to production electricitya electricity % of total billion kilowatt Renewable hours Coal Natural gas Oil Hydropower sourcesb Nuclear power % of population 2009 2009 2009 2009 2009 2009 2009 2009 Afghanistan .. .. .. .. .. .. .. 15.6 Albania 5.3 0.0 0.0 0.6 99.4 0.0 0.0 .. Algeria 42.8 0.0 97.2 2.0 0.8 0.0 0.0 99.3 Angola 4.2 0.0 0.0 23.9 76.1 0.0 0.0 26.2 Argentina 121.9 2.3 51.3 10.5 27.8 1.4 6.7 97.2 Armenia 5.7 0.0 20.3 0.0 35.6 0.1 44.0 .. Australia 260.9 77.9 13.7 1.0 4.7 2.6 0.0 .. Austria 65.6 7.7 18.8 1.7 61.4 9.6 0.0 .. Azerbaijan 18.9 0.0 85.1 2.6 12.2 0.0 0.0 .. Bahrain 12.1 0.0 100.0 0.0 0.0 0.0 0.0 99.4 Bangladesh 37.9 1.7 89.4 4.8 4.1 0.0 0.0 41.0 Belarus 30.4 0.0 81.7 17.6 0.1 0.2 0.0 .. Belgium 89.8 6.8 32.6 0.3 0.4 5.7 52.6 .. Benin 0.1 0.0 0.0 100.0 0.0 0.0 0.0 24.8 Bolivia 6.1 0.0 59.7 1.7 37.5 1.0 0.0 77.5 Bosnia and Herzegovina 15.7 59.9 0.2 0.1 39.8 0.0 0.0 .. Botswana 0.4 100.0 0.0 0.0 0.0 0.0 0.0 45.4 Brazil 466.5 2.1 2.9 3.1 83.8 5.2 2.8 98.3 Bulgaria 42.4 49.8 4.6 0.8 8.2 0.6 36.0 .. Burkina Faso .. .. .. .. .. .. .. 14.6 Burundi .. .. .. .. .. .. .. .. Cambodia 1.2 0.0 0.0 95.6 3.9 0.5 0.0 24.0 Cameroon 5.7 0.0 7.2 22.7 70.0 0.2 0.0 48.7 Canada 603.1 15.2 6.2 1.4 60.3 1.9 15.0 .. Central African Republic .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. Chile 60.7 24.5 6.5 20.0 41.7 7.2 0.0 98.5 China 3,695.9 78.8 1.4 0.4 16.7 0.8 1.9 99.4 Hong Kong SAR, China 38.7 70.8 28.9 0.4 0.0 0.0 0.0 .. Colombia 57.3 7.3 19.3 0.6 71.7 1.2 0.0 93.6 Congo, Dem. Rep. 7.8 0.0 0.4 0.1 99.6 0.0 0.0 11.1 Congo, Rep. 0.5 0.0 36.0 0.0 64.0 0.0 0.0 37.1 Costa Rica 9.3 0.0 0.0 4.9 77.8 17.4 0.0 99.3 Côte d’Ivoire 5.9 0.0 61.9 0.1 35.9 2.1 0.0 47.3 Croatia 12.7 13.1 17.4 15.9 53.0 0.6 0.0 .. Cuba 17.7 0.0 13.4 82.8 0.9 2.9 0.0 97.0 Cyprus 5.2 0.0 0.0 99.2 0.0 0.1 0.0 .. Czech Republic 81.7 59.6 1.2 0.2 3.0 2.7 33.3 .. Denmark 36.4 48.6 18.5 3.2 0.1 27.6 0.0 .. Dominican Republic 15.0 12.9 13.5 63.6 9.8 0.2 0.0 95.9 Ecuador 17.2 0.0 6.9 37.5 53.5 2.1 0.0 92.2 Egypt, Arab Rep. 139.0 0.0 68.7 21.3 9.3 0.8 0.0 99.6 El Salvador 5.8 0.0 0.0 43.7 26.0 30.3 0.0 86.4 Eritrea 0.3 0.0 0.0 99.3 0.0 0.7 0.0 32.0 Estonia 8.8 91.4 1.2 0.5 0.4 5.8 0.0 .. Ethiopia 4.1 0.0 0.0 12.4 87.3 0.4 0.0 17.0 Finland 72.1 16.0 13.6 0.7 17.6 12.5 32.6 .. France 537.4 5.3 3.9 1.1 10.6 2.4 76.2 .. Gabon 1.7 0.0 26.4 20.0 53.2 0.4 0.0 36.7 Gambia, The .. .. .. .. .. .. .. .. Georgia 8.6 0.0 12.9 0.5 86.6 0.0 0.0 .. Germany 586.4 43.8 13.5 1.6 3.2 12.8 23.0 .. Ghana 9.0 0.0 0.0 23.2 76.8 0.0 0.0 60.5 Greece 61.1 56.0 18.0 12.6 8.8 4.6 0.0 .. Guatemala 9.0 8.2 0.0 34.5 23.3 34.0 0.0 80.5 Guinea .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. Haiti 0.7 0.0 0.0 71.3 28.7 0.0 0.0 38.5 166 2012 World Development Indicators 3.8 ENVIRONMENT Electricity production, sources, and access Electricity Sources of Access to production electricitya electricity % of total billion kilowatt Renewable hours Coal Natural gas Oil Hydropower sourcesb Nuclear power % of population 2009 2009 2009 2009 2009 2009 2009 2009 Honduras 6.6 0.0 0.0 54.9 42.5 2.6 0.0 70.3 Hungary 35.9 17.9 29.0 1.8 0.6 7.4 43.0 .. India 899.4 68.6 12.4 2.9 11.9 2.2 2.1 66.3 Indonesia 155.5 41.8 22.1 22.8 7.3 6.0 0.0 64.5 Iran, Islamic Rep. 203.2 0.2 70.4 25.8 3.6 0.1 0.0 98.4 Iraq 46.1 0.0 0.0 93.0 7.0 0.0 0.0 86.0 Ireland 27.9 14.4 58.4 3.3 3.2 11.2 0.0 .. Israel 55.0 62.5 32.8 4.0 0.0 0.1 0.0 99.7 Italy 288.3 15.1 51.1 9.0 17.0 7.0 0.0 .. Jamaica 5.5 0.0 0.0 96.4 2.0 1.6 0.0 92.0 Japan 1,041.0 26.8 27.4 7.2 7.2 2.5 26.9 .. Jordan 14.3 0.0 88.1 11.4 0.4 0.1 0.0 99.9 Kazakhstan 78.7 74.9 13.1 3.2 8.7 0.0 0.0 .. Kenya 6.9 0.0 0.0 44.1 31.6 24.4 0.0 16.1 Korea, Dem. Rep. 21.1 38.1 0.0 2.8 59.1 0.0 0.0 26.0 Korea, Rep. 451.7 46.2 15.6 4.4 0.6 0.4 32.7 .. Kosovo .. .. .. .. .. .. .. .. Kuwait 53.2 0.0 28.8 71.2 0.0 0.0 0.0 100.0 Kyrgyz Republic 11.1 2.8 8.0 0.0 89.3 0.0 0.0 .. Lao PDR .. .. .. .. .. .. .. 55.0 Latvia 5.6 0.0 36.0 0.1 62.1 1.8 0.0 .. Lebanon 13.8 0.0 1.4 94.1 4.5 0.0 0.0 99.9 Lesotho .. .. .. .. .. .. .. 16.0 Liberia .. .. .. .. .. .. .. .. Libya 30.4 0.0 41.0 59.0 0.0 0.0 0.0 99.8 Lithuania 14.6 0.0 14.3 5.0 2.9 1.8 74.1 .. Macedonia, FYR 6.8 77.7 0.0 3.7 18.6 0.0 0.0 .. Madagascar .. .. .. .. .. .. .. 19.0 Malawi .. .. .. .. .. .. .. 9.0 Malaysia 105.1 30.9 60.7 2.0 6.3 0.0 0.0 99.4 Mali .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. 99.4 Mexico 261.0 11.3 53.1 17.5 10.2 3.9 4.0 .. Moldova 3.6 0.0 95.0 1.3 1.5 0.0 0.0 .. Mongolia 4.2 96.4 0.0 3.6 0.0 0.0 0.0 67.0 Morocco 21.4 52.4 13.3 20.3 12.1 1.8 0.0 97.0 Mozambique 17.0 0.0 0.1 0.0 99.9 0.0 0.0 11.7 Myanmar 5.9 0.0 19.6 8.9 71.5 0.0 0.0 13.0 Namibia 1.7 17.5 0.0 0.5 82.0 0.0 0.0 34.0 Nepal 3.1 0.0 0.0 0.4 99.6 0.0 0.0 43.6 Netherlands 113.5 23.4 60.5 1.3 0.1 9.5 3.7 .. New Zealand 43.5 7.6 20.6 0.0 55.7 15.9 0.0 .. Nicaragua 3.5 0.0 0.0 69.1 8.6 22.3 0.0 72.1 Niger .. .. .. .. .. .. .. .. Nigeria 19.8 0.0 64.3 12.8 22.9 0.0 0.0 50.6 Norway 132.0 0.1 3.2 0.0 95.7 0.9 0.0 .. Oman 17.8 0.0 82.0 18.0 0.0 0.0 0.0 98.0 Pakistan 95.4 0.1 29.4 38.0 29.4 0.0 3.0 62.4 Panama 6.9 0.0 0.0 43.6 56.1 0.2 0.0 88.1 Papua New Guinea .. .. .. .. .. .. .. .. Paraguay 55.0 0.0 0.0 0.0 100.0 0.0 0.0 96.7 Peru 35.4 2.5 34.3 4.1 57.6 1.4 0.0 85.7 Philippines 61.9 26.6 32.1 8.7 15.8 16.8 0.0 89.7 Poland 151.1 89.1 3.2 1.8 1.6 4.2 0.0 .. Portugal 49.5 26.1 29.7 6.6 16.7 20.2 0.0 .. Puerto Rico .. .. .. .. .. .. .. .. Qatar 24.8 0.0 100.0 0.0 0.0 0.0 0.0 98.7 2012 World Development Indicators 167 3.8 Electricity production, sources, and access Electricity Sources of Access to production electricitya electricity % of total billion kilowatt Renewable hours Coal Natural gas Oil Hydropower sourcesb Nuclear power % of population 2009 2009 2009 2009 2009 2009 2009 2009 Romania 57.7 37.7 13.2 1.8 26.9 0.0 20.4 .. Russian Federation 990.0 16.5 47.4 1.6 17.6 0.1 16.5 .. Rwanda .. .. .. .. .. .. .. .. Saudi Arabia 217.1 0.0 44.8 55.2 0.0 0.0 0.0 99.0 Senegal 2.9 0.0 1.7 86.4 8.4 1.9 0.0 42.0 Serbia 37.4 71.9 0.5 0.3 27.4 0.0 0.0 .. Sierra Leone .. .. .. .. .. .. .. .. Singapore 41.8 0.0 81.0 18.8 0.0 0.1 0.0 100.0 Slovak Republic 25.9 16.5 7.6 2.4 16.9 2.1 54.3 .. Slovenia 16.4 31.3 3.6 0.2 28.7 1.2 35.0 .. Somalia .. .. .. .. .. .. .. .. South Africa 246.8 94.1 0.0 0.0 0.6 0.1 5.2 75.0 South Sudan .. .. .. .. .. .. .. .. Spain 291.0 12.8 36.9 6.5 9.0 16.2 18.1 .. Sri Lanka 9.9 0.0 0.0 60.3 39.5 0.2 0.0 76.6 Sudan 6.8 0.0 0.0 52.2 47.8 0.0 0.0 35.9 Swaziland .. .. .. .. .. .. .. .. Sweden 136.6 0.7 1.1 0.5 48.2 10.2 38.2 .. Switzerland 66.7 0.0 1.0 0.2 53.6 2.0 41.5 .. Syrian Arab Republic 43.3 0.0 45.7 50.0 4.3 0.0 0.0 92.7 Tajikistan 16.1 0.0 2.0 0.0 98.0 0.0 0.0 .. Tanzania 4.6 2.7 36.2 0.9 60.2 0.0 0.0 13.9 Thailand 148.4 19.9 70.7 0.5 4.8 4.0 0.0 99.3 Timor-Leste .. .. .. .. .. .. .. 22.0 Togo 0.1 0.0 0.0 24.6 73.8 1.6 0.0 20.0 Trinidad and Tobago 7.7 0.0 99.4 0.3 0.0 0.2 0.0 99.0 Tunisia 15.7 0.0 89.7 9.2 0.5 0.6 0.0 99.5 Turkey 194.8 28.6 49.3 2.5 18.5 1.1 0.0 .. Turkmenistan 16.0 0.0 100.0 0.0 0.0 0.0 0.0 .. Uganda .. .. .. .. .. .. .. 9.0 Ukraine 173.5 36.6 8.1 0.5 6.8 0.0 48.0 .. United Arab Emirates 90.6 0.0 98.2 1.8 0.0 0.0 0.0 100.0 United Kingdom 372.0 28.5 44.5 1.2 1.4 5.4 18.6 .. United States 4,165.4 45.4 22.8 1.2 6.6 3.7 19.9 .. Uruguay 8.9 0.0 0.2 31.1 59.4 9.3 0.0 98.3 Uzbekistan 49.9 4.1 75.1 2.1 18.7 0.0 0.0 .. Venezuela, RB 123.4 0.0 14.7 12.5 72.8 0.0 0.0 99.0 Vietnam 83.2 18.0 43.4 2.5 36.0 0.0 0.0 97.6 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 6.7 0.0 0.0 100.0 0.0 0.0 0.0 39.6 Zambia 10.3 0.0 0.0 0.3 99.7 0.0 0.0 18.8 Zimbabwe 7.9 46.4 0.0 0.3 53.3 0.0 0.0 41.5 World 20,079.3 w 40.4 w 21.4 w 4.8 w 16.1 w 3.0 13.4 w 74.1 w Low income 185.6 6.9 20.4 4.7 45.7 0.9 0.0 23.0 Middle income 9,255.1 47.9 19.2 5.7 20.6 1.4 4.6 81.6 Lower middle income 2,007.9 39.6 22.0 12.1 16.9 2.4 5.3 67.3 Upper middle income 7,245.8 50.2 18.5 3.9 21.6 1.2 4.4 98.0 Low & middle income 9,461.8 47.0 19.2 5.7 21.0 1.4 4.5 73.7 East Asia & Pacific 4,307.5 71.6 7.2 1.5 16.2 1.3 1.6 90.8 Europe & Central Asia 1,785.3 24.0 39.5 1.9 18.0 0.2 16.1 .. Latin America & Carib. 1,296.7 5.0 20.7 12.1 55.5 3.9 2.4 93.4 Middle East & N. Africa 584.2 2.0 58.5 32.9 4.9 0.3 0.0 92.9 South Asia 1,054.5 58.5 16.4 6.6 13.6 1.9 2.0 62.1 Sub-Saharan Africa 419.3 56.5 4.6 4.1 18.2 0.5 3.1 32.5 High income 10,673.5 34.3 23.2 4.1 11.6 4.5 21.3 .. Euro area 2,244.3 21.6 23.3 3.7 10.2 9.1 30.8 .. a. Shares may not sum to 100 percent because some sources of generated electricity are not shown. b. Excludes hydropower. 168 2012 World Development Indicators 3.8 ENVIRONMENT Electricity production, sources, and access About the data De�nitions Use of energy is important in improving people’s • Electricity production is measured at the termi- standard of living. But electricity generation also 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 of includes the output of electric plants designed to natural gas (see About the data for table 3.9). Nuclear produce electricity only, as well as that of combined energy does not generate carbon dioxide emissions, heat and power plants. • Sources of electricity are but it produces other dangerous waste products. The the inputs used to generate electricity: coal, gas, table provides information on electricity production oil, hydropower, and nuclear power. • Coal is all coal by source. and brown coal, both primary (including hard coal The International Energy Agency (IEA) compiles and lignite-brown coal) and derived fuels (including data on energy inputs used to generate electricity. patent fuel, coke oven coke, gas coke, coke oven IEA data for countries that are not members of the gas, and blast furnace gas). Peat is also included in Organisation for Economic Co-operation and Devel- this category. • Natural gas is natural gas but not opment (OECD) are based on national energy data natural gas liquids. • Oil is crude oil and petroleum adjusted to conform to annual questionnaires com- products. •  Hydropower is electricity produced by pleted by OECD member governments. In addition, hydroelectric power plants. •  Renewable sources estimates are sometimes made to complete major are geothermal, solar photovoltaic, solar thermal, aggregates from which key data are missing, and tide, wind, industrial waste, municipal waste, primary adjustments are made to compensate for differ- solid biofuels, biogases, biogasoline, biodiesels, ences in definitions. The IEA makes these estimates other liquid biofuels, nonspecified primary biofuels in consultation with national statistical offices, oil and waste, and charcoal. • Nuclear power is electric- companies, electric utilities, and national energy ity produced by nuclear power plants. • Access to experts. It occasionally revises its time series to electricity is the percentage of the population with reflect political changes. For example, the IEA has access to electricity. 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 as more detailed energy accounts have become available. Breaks in series are therefore unavoidable. Data on access to electricity are collected by the IEA from industry, national surveys, and international sources. Data sources Data on electricity production and sources are from the IEA’s electronic files and its annual pub- lications Energy Statistics of Non-OECD Countries, Energy Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. Data on access to electricity are from the IEA’s World Energy Outlook (2011). 2012 World Development Indicators 169 3.9 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 2009 1990 2009 1990 2008 1990 2008 1990 2008 1990 2008 Afghanistan .. .. .. .. 2.7 0.8 .. .. 0.1 0.0 .. 0.0 Albania 8 27 4.8 13.8 7.5 4.2 2.8 2.0 2.3 1.3 0.6 0.2 Algeria –351 –283 7.1 6.5 78.9 111.3 3.6 3.0 3.1 3.2 0.5 0.4 Angola –387 –749 5.8 8.4 4.4 24.4 0.8 2.1 0.4 1.4 0.1 0.2 Argentina –5 –9 5.3 7.2 112.6 192.4 2.4 2.5 3.4 4.8 0.5 0.4 Armenia 98 68 1.4 5.7 3.7 5.5 0.9 1.9 1.1 1.8 0.7 0.3 Australia –83 –137 4.7 5.7 287.3 399.2 3.3 3.1 16.8 18.6 0.7 0.5 Austria 67 64 7.9 9.2 61.0 67.7 2.5 2.0 7.9 8.1 0.3 0.2 Azerbaijan 21 –439 1.3 6.4 44.2 47.1 2.1 3.5 6.0 5.4 1.7 0.7 Bahrain –209 –85 2.0 2.7 11.9 22.5 2.7 2.4 24.1 21.4 1.3 0.9 Bangladesh 16 16 6.2 7.0 15.5 46.5 1.2 1.7 0.1 0.3 0.2 0.2 Belarus 93 85 1.4 4.1 87.5 62.8 2.4 2.2 8.6 6.5 1.5 0.6 Belgium 73 73 5.2 6.1 108.5 104.9 2.2 1.8 10.9 9.8 0.4 0.3 Benin –7 43 3.2 3.5 0.7 4.1 0.4 1.2 0.1 0.5 0.1 0.3 Bolivia –89 –128 7.8 6.7 5.5 12.8 2.1 2.1 0.8 1.3 0.3 0.3 Bosnia and Herzegovina 34 25 .. 4.6 3.9 31.3 0.9 5.2 1.0 8.3 .. 1.1 Botswana 28 54 7.6 11.4 2.2 4.8 1.7 2.2 1.6 2.5 0.2 0.2 Brazil 26 4 7.7 7.6 208.9 393.2 1.5 1.6 1.4 2.1 0.2 0.2 Bulgaria 66 44 2.3 4.9 77.7 50.5 2.7 2.6 8.9 6.6 1.2 0.6 Burkina Faso .. .. .. .. 0.6 1.9 .. .. 0.1 0.1 0.1 0.1 Burundi .. .. .. .. 0.3 0.2 .. .. 0.1 0.0 0.1 0.1 Cambodia .. 29 .. 5.1 0.5 4.6 .. 0.9 0.0 0.3 .. 0.2 Cameroon –120 –28 5.1 5.6 1.7 5.3 0.3 0.8 0.1 0.3 0.1 0.1 Canada –31 –53 3.6 4.6 450.1 544.1 2.2 2.0 16.2 16.3 0.6 0.5 Central African Republic .. .. .. .. 0.2 0.3 .. .. 0.1 0.1 0.1 0.1 Chad .. .. .. .. 0.1 0.5 .. .. 0.0 0.0 0.0 0.0 Chile 45 68 6.4 7.7 34.9 73.1 2.6 2.5 2.6 4.4 0.4 0.3 China –3 8 1.4 3.7 2,460.7 7,031.9 2.9 3.3 2.2 5.3 2.0 0.9 Hong Kong SAR, China 99 100 15.6 18.4 27.7 38.6 3.2 2.7 4.8 5.5 0.2 0.1 Colombia –99 –211 8.4 11.8 57.3 67.7 2.4 2.2 1.7 1.5 0.3 0.2 Congo, Dem. Rep. –2 –2 1.9 0.8 4.1 2.8 0.3 0.1 0.1 0.0 0.2 0.2 Congo, Rep. –997 –989 10.7 10.1 1.2 1.9 1.5 1.5 0.5 0.5 0.1 0.1 Costa Rica 49 45 9.5 9.5 3.0 8.0 1.5 1.6 1.0 1.8 0.2 0.2 Côte d’Ivoire 22 –15 5.5 3.2 5.8 7.0 1.3 0.7 0.5 0.4 0.2 0.2 Croatia 43 53 7.1 8.3 16.4 23.3 2.4 2.6 3.7 5.3 0.4 0.3 Cuba 51 52 .. .. 33.3 31.4 1.7 3.0 3.2 2.8 .. .. Cyprus 100 97 7.7 8.2 4.7 8.6 3.4 3.3 6.1 7.9 0.4 0.4 Czech Republic 17 26 3.4 5.5 139.5 117.0 3.2 2.6 13.5 11.2 0.9 0.5 Denmark 42 –29 7.5 9.5 50.4 46.0 2.9 2.4 9.8 8.4 0.4 0.2 Dominican Republic 75 77 6.7 9.5 9.6 21.6 2.3 2.7 1.3 2.2 0.3 0.3 Ecuador –175 –141 9.3 8.9 16.8 26.8 2.8 2.4 1.6 1.9 0.3 0.3 Egypt, Arab Rep. –72 –22 5.8 5.9 75.9 210.3 2.4 3.0 1.3 2.7 0.4 0.5 El Salvador 31 38 7.9 7.2 2.6 6.1 1.1 1.2 0.5 1.0 0.1 0.2 Eritrea 19 23 1.8 3.5 .. 0.4 .. 0.6 .. 0.1 .. 0.2 Estonia 45 12 1.6 4.5 23.0 18.3 3.5 3.4 15.0 13.6 2.0 0.7 Ethiopia 5 7 1.8 2.2 3.0 7.1 0.2 0.2 0.1 0.1 0.1 0.1 Finland 57 50 4.1 4.9 50.9 56.5 1.8 1.6 10.2 10.6 0.4 0.3 France 50 49 6.3 7.4 399.0 377.0 1.8 1.4 6.9 5.9 0.3 0.2 Gabon –1,138 –657 11.8 10.7 4.8 2.5 4.1 1.2 5.2 1.7 0.3 0.1 Gambia, The .. .. 17.6 14.0 0.2 0.4 3.1 3.0 0.2 0.3 0.2 0.2 Georgia 83 61 2.4 6.0 15.3 5.2 1.8 1.7 3.1 1.2 1.2 0.3 Germany 47 60 5.9 8.3 961.0 786.7 2.8 2.4 12.0 9.6 0.4 0.3 Ghana 17 24 2.5 3.6 3.9 8.6 0.7 0.9 0.3 0.4 0.3 0.3 Greece 57 66 8.2 9.6 72.7 97.8 3.4 3.2 7.2 8.7 0.4 0.3 Guatemala 24 39 6.7 6.1 5.1 11.9 1.1 1.5 0.6 0.9 0.2 0.2 Guinea .. .. .. .. 1.1 1.4 .. .. 0.2 0.1 0.2 0.1 Guinea-Bissau .. .. 16.5 15.4 0.3 0.3 3.4 3.0 0.2 0.2 0.2 0.2 Haiti 20 28 6.4 4.0 1.0 2.4 0.6 0.9 0.1 0.3 0.1 0.2 170 2012 World Development Indicators 3.9 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 2009 1990 2009 1990 2008 1990 2008 1990 2008 1990 2008 Honduras 29 50 5.5 5.9 2.6 8.7 1.1 1.9 0.5 1.2 0.2 0.3 Hungary 49 56 4.7 6.7 63.0 54.6 2.2 2.1 6.1 5.4 0.5 0.3 India 8 26 3.3 5.1 690.6 1,742.7 2.2 2.8 0.8 1.5 0.7 0.5 Indonesia –67 –74 3.7 4.3 149.6 406.0 1.5 2.1 0.8 1.7 0.4 0.5 Iran, Islamic Rep. –164 –62 5.0 3.5 227.2 538.4 3.3 2.6 4.1 7.4 0.7 0.7 Iraq –480 –272 .. 3.2 52.6 102.9 2.9 3.0 2.9 3.4 .. 1.1 Ireland 65 89 6.4 11.3 30.4 43.6 3.0 2.9 8.7 9.9 0.5 0.3 Israel 96 85 7.3 8.8 33.5 37.7 2.9 1.7 7.2 5.2 0.4 0.2 Italy 83 84 9.2 9.8 424.2 445.1 2.9 2.5 7.5 7.4 0.3 0.3 Jamaica 83 84 5.1 5.7 8.0 12.2 2.9 2.9 3.3 4.5 0.6 0.6 Japan 83 80 7.3 7.9 1,094.7 1,208.2 2.5 2.4 8.9 9.5 0.3 0.3 Jordan 95 96 3.2 4.1 10.4 21.4 3.2 3.0 3.3 3.7 1.0 0.7 Kazakhstan –24 –121 1.6 2.5 261.3 237.0 3.3 3.4 15.9 15.1 2.7 1.4 Kenya 18 17 3.0 3.0 5.8 10.4 0.5 0.6 0.2 0.3 0.2 0.2 Korea, Dem. Rep. 13 –5 .. .. .. 78.4 .. 3.9 .. 3.2 .. .. Korea, Rep. 76 81 5.2 5.4 243.8 509.2 2.6 2.2 5.7 10.5 0.5 0.4 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait –453 –332 2.5 4.6 45.4 76.7 5.0 2.8 21.8 30.1 0.6 0.6 Kyrgyz Republic 67 61 1.5 3.7 10.9 6.2 2.2 2.3 2.4 1.2 1.2 0.6 Lao PDR .. .. .. .. 0.2 1.5 .. .. 0.1 0.3 0.1 0.1 Latvia 86 50 3.4 6.9 14.1 7.6 2.3 1.7 5.4 3.3 0.9 0.2 Lebanon 93 97 8.7 7.5 9.1 17.1 4.7 3.2 3.1 4.1 0.5 0.4 Lesotho .. .. .. 142.1 .. .. .. .. .. .. .. .. Liberia .. .. .. .. 0.5 0.6 .. .. 0.2 0.2 0.5 0.4 Libya –546 –327 .. 4.7 40.3 58.3 3.6 3.0 9.3 9.5 .. 0.6 Lithuania 69 50 2.9 6.0 22.2 15.1 2.0 1.6 6.0 4.5 0.6 0.3 Macedonia, FYR 49 42 6.6 6.7 10.8 11.8 4.0 3.9 5.6 5.8 0.8 0.6 Madagascar .. .. .. .. 1.0 1.9 .. .. 0.1 0.1 0.1 0.1 Malawi .. .. .. .. 0.6 1.2 .. .. 0.1 0.1 0.1 0.1 Malaysia –122 –34 5.5 5.2 56.6 208.3 2.6 2.9 3.1 7.6 0.5 0.6 Mali .. .. .. .. 0.4 0.6 .. .. 0.0 0.0 0.1 0.0 Mauritania .. .. .. .. 2.7 2.0 .. .. 1.3 0.6 0.8 0.3 Mauritius .. .. 13.5 11.6 1.5 4.0 3.0 3.3 1.4 3.1 0.2 0.3 Mexico –59 –26 6.9 7.7 325.6 475.8 2.7 2.6 3.9 4.3 0.4 0.3 Moldova 99 96 1.7 3.8 21.0 4.8 3.1 1.5 5.7 1.3 2.1 0.5 Mongolia 20 –138 1.6 2.9 10.0 10.9 2.9 3.5 4.6 4.1 1.9 1.1 Morocco 89 95 9.7 8.8 23.5 47.9 3.4 3.2 1.0 1.5 0.4 0.4 Mozambique 5 –22 0.9 1.9 1.0 2.3 0.2 0.2 0.1 0.1 0.2 0.1 Myanmar 0 –48 1.3 5.0 4.3 12.8 0.4 0.8 0.1 0.3 0.3 0.2 Namibia 67 81 9.4 7.4 0.0 4.0 0.0 2.2 0.0 1.8 0.0 0.3 Nepal 5 11 2.3 3.1 0.6 3.5 0.1 0.4 0.0 0.1 0.0 0.1 Netherlands 8 19 6.0 7.7 164.1 173.7 2.5 2.2 11.0 10.6 0.4 0.3 New Zealand 11 13 5.0 6.1 24.0 33.1 1.9 1.9 7.0 7.8 0.4 0.3 Nicaragua 29 45 3.7 4.6 2.6 4.3 1.3 1.4 0.6 0.8 0.3 0.3 Niger .. .. .. .. 0.8 0.9 .. .. 0.1 0.1 0.2 0.1 Nigeria –113 –111 2.0 2.9 45.4 95.8 0.6 0.9 0.5 0.6 0.3 0.3 Norway –467 –656 6.5 8.1 31.3 50.0 1.5 1.7 7.4 10.5 0.2 0.2 Oman –808 –346 6.5 4.4 10.4 45.7 2.5 2.8 5.5 17.3 0.4 0.7 Pakistan 20 24 4.2 4.7 68.6 163.2 1.6 2.0 0.6 1.0 0.4 0.4 Panama 58 79 10.1 13.2 3.1 6.9 2.2 2.5 1.3 2.0 0.2 0.2 Papua New Guinea .. .. .. .. 2.1 2.1 .. .. 0.5 0.3 0.3 0.2 Paraguay –49 –56 5.5 5.5 2.3 4.1 0.7 0.9 0.5 0.7 0.1 0.2 Peru –9 4 10.0 14.4 21.2 40.5 2.2 2.7 1.0 1.4 0.2 0.2 Philippines 40 40 5.4 7.9 44.5 83.2 1.5 2.1 0.7 0.9 0.3 0.3 Poland –1 28 3.0 6.8 366.8 316.1 3.6 3.2 9.6 8.3 1.2 0.5 Portugal 80 80 9.6 9.4 43.7 56.3 2.6 2.3 4.4 5.3 0.3 0.2 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar –328 –487 .. 4.9 11.8 68.5 1.9 3.0 24.9 49.1 .. 0.6 2012 World Development Indicators 171 3.9 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 2009 1990 2009 1990 2008 1990 2008 1990 2008 1990 2008 Romania 34 18 2.9 6.7 158.9 94.7 2.6 2.4 6.8 4.4 0.9 0.4 Russian Federation –47 –83 2.1 3.0 2,220.7 1,708.7 2.8 2.5 14.9 12.0 1.5 0.8 Rwanda .. .. .. .. 0.7 0.7 .. .. 0.1 0.1 0.1 0.1 Saudi Arabia –519 –235 5.2 3.4 215.1 433.6 3.6 2.8 13.3 16.6 0.7 0.8 Senegal 43 57 6.3 7.1 3.2 5.0 1.9 1.7 0.4 0.4 0.3 0.2 Serbia 31 35 4.6 4.8 45.3b 49.9 3.0 b 3.0 5.9 b 6.8 0.8b 0.7 Sierra Leone .. .. .. .. 0.4 1.3 .. .. 0.1 0.2 0.1 0.3 Singapore 100 100 6.7 12.5 46.9 32.3 4.1 1.9 15.4 6.7 0.6 0.1 Slovak Republic 75 65 3.2 6.3 45.6 37.6 2.5 2.1 8.6 6.9 0.9 0.3 Slovenia 46 49 5.8 7.3 13.0 17.2 2.5 2.2 6.5 8.5 0.5 0.3 Somalia .. .. .. .. 0.0 0.6 .. .. 0.0 0.1 .. .. South Africa –22 –12 3.0 3.2 333.5 435.9 3.6 2.9 9.5 8.9 1.2 0.9 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 62 77 8.5 9.8 227.6 329.3 2.5 2.4 5.9 7.2 0.3 0.3 Sri Lanka 24 45 6.3 9.5 3.8 11.8 0.7 1.3 0.2 0.6 0.1 0.1 Sudan 17 –123 2.6 5.3 5.6 14.1 0.5 1.0 0.2 0.3 0.2 0.2 Swaziland .. .. 10.9 12.6 0.4 1.1 1.4 2.5 0.5 1.1 0.1 0.2 Sweden 37 33 4.5 6.6 51.7 49.0 1.1 1.0 6.0 5.3 0.2 0.2 Switzerland 58 53 9.2 10.6 43.0 40.4 1.8 1.5 6.4 5.3 0.2 0.1 Syrian Arab Republic –113 –5 3.5 4.2 37.5 71.6 3.6 2.8 3.0 3.6 1.0 0.8 Tajikistan 62 35 3.0 5.5 7.2 3.1 1.7 1.3 1.3 0.5 0.7 0.3 Tanzania 7 8 2.2 2.7 2.4 6.5 0.2 0.3 0.1 0.2 0.1 0.1 Thailand 37 40 5.4 4.8 95.8 285.7 2.3 2.7 1.7 4.2 0.4 0.6 Timor-Leste .. .. .. 11.8 .. 0.2 .. 3.0 .. 0.2 .. 0.2 Togo 17 17 2.7 2.0 0.8 1.4 0.6 0.6 0.2 0.2 0.2 0.3 Trinidad and Tobago –111 –117 2.2 1.5 17.0 49.8 2.8 2.6 14.0 37.4 1.3 1.6 Tunisia –16 15 7.4 9.5 13.3 25.0 2.7 2.7 1.6 2.4 0.4 0.3 Turkey 51 69 8.3 8.6 150.8 284.0 2.9 2.9 2.8 4.0 0.3 0.3 Turkmenistan –281 –109 0.7 1.7 28.1 47.8 2.5 2.1 7.2 9.7 2.3 1.5 Uganda .. .. .. .. 0.8 3.7 .. .. 0.0 0.1 0.1 0.1 Ukraine 46 33 1.7 2.3 641.7 323.5 2.9 2.4 12.3 7.0 1.9 1.0 United Arab Emirates –441 –183 6.5 5.3 52.0 155.1 2.6 2.7 28.8 25.0 0.4 0.5 United Kingdom –1 19 6.2 10.1 570.2 522.9 2.8 2.5 10.0 8.5 0.4 0.3 United States 14 22 4.2 5.9 4,879.4 5,461.0 2.5 2.4 19.5 18.0 0.6 0.4 Uruguay 49 63 10.1 9.6 4.0 8.3 1.8 2.0 1.3 2.5 0.2 0.2 Uzbekistan 17 –24 0.9 1.5 114.0 124.9 2.5 2.5 5.3 4.6 3.1 1.9 Venezuela, RB –242 –204 4.3 4.8 122.2 169.5 2.8 2.6 6.2 6.1 0.6 0.5 Vietnam –2 –20 2.5 3.7 21.4 127.4 0.9 2.2 0.3 1.5 0.4 0.6 West Bank and Gaza .. .. .. .. .. 2.1 .. .. .. 0.5 .. .. Yemen, Rep. –273 –101 8.6 7.0 9.6 23.4 3.8 3.2 0.8 1.0 0.4 0.5 Zambia 9 8 1.8 2.1 2.4 1.9 0.5 0.2 0.3 0.2 0.2 0.1 Zimbabwe 8 10 .. .. 15.5 9.1 1.7 1.0 1.5 0.7 .. .. World –3c w –4c w 4.2 w 5.5 w 22,310.9d w 32,082.6d w 2.5d w 2.5d w 4.2d w 4.8d w 0.6d w 0.5d w Low income 14 5 2.6 3.4 .. 219.1 0.6 1.0 .. 0.3 .. 0.3 Middle income –24 –19 3.0 4.5 9,256.8 16,638.0 2.5 2.8 2.4 3.4 0.8 0.6 Lower middle income –15 –17 3.0 4.6 2,001.1 3,743.9 1.8 2.4 1.1 1.5 0.6 0.5 Upper middle income –27 –20 3.0 4.4 7,255.3 12,894.1 2.8 2.9 3.6 5.3 0.9 0.7 Low & middle income –22 –19 3.0 4.4 9,398.8 16,856.9 2.4 2.7 2.2 3.0 0.8 0.6 East Asia & Pacific –8 1 2.0 3.9 2,895.4 8,259.3 2.6 3.1 1.8 4.3 1.3 0.8 Europe & Central Asia –12 –47 2.2 3.6 3,893.0 3,130.1 3.0 2.6 9.8 7.8 1.3 0.7 Latin America & Carib. –34 –31 6.9 7.7 986.2 1,584.1 2.2 2.2 2.3 2.8 0.3 0.3 Middle East & N. Africa –204 –88 5.7 4.7 579.0 1,230.3 3.2 2.8 2.6 3.8 0.6 0.6 South Asia 10 25 3.6 5.2 782.0 1,970.2 2.0 2.6 0.7 1.2 0.6 0.5 Sub-Saharan Africa –52 –59 2.8 3.2 464.1 684.6 1.7 1.5 0.9 0.8 0.6 0.4 High income 15 13 5.3 6.7 11,572.3 13,284.7 2.5 2.4 11.8 11.9 0.5 0.4 Euro area 55 62 6.6 8.3 2,718.9 2,633.3 2.5 2.1 8.9 8.0 0.4 0.3 a. A negative value indicates 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. 172 2012 World Development Indicators 3.9 ENVIRONMENT Energy dependency and efficiency and carbon dioxide emissions About the data De�nitions Because commercial energy is widely traded, its pro- individual values. Each year the CDIAC recalculates • Net energy imports are estimated as energy use duction and use need to be distinguished. Net energy the entire time series since 1949, incorporating less production, both measured in oil equivalents. imports show the extent to which an economy’s use recent findings and corrections. Estimates exclude • GDP per unit of energy use is the ratio of gross exceeds its production. High-income economies are fuels supplied to ships and aircraft in international domestic product (GDP) per kilogram of oil equiv- net energy importers; middle-income economies are transport because of the difficulty of apportioning alent of energy use, with GDP converted to 2005 their main suppliers. the fuels among benefiting countries. international dollars using purchasing power parity The ratio of gross domestic product (GDP) to energy (PPP) rates. An international dollar has the same use indicates energy efficiency. To produce compa- purchasing power over GDP that a U.S. dollar has rable and consistent estimates of real GDP across in the United States. Energy use refers to the use economies relative to physical inputs to GDP—that of primary energy before transformation to other is, units of energy use—GDP is converted to 2005 end-use fuel, which is equal to indigenous produc- international dollars using purchasing power parity tion plus imports and stock changes minus exports (PPP) rates. Differences in this ratio over time and and fuel supplied to ships and aircraft engaged in across economies reflect structural changes in an international transport (see About the data for table economy, changes in sectoral energy efficiency, and 3.7). • Carbon dioxide emissions are emissions from differences in fuel mixes. the burning of fossil fuels and the manufacture of Carbon dioxide emissions, largely by-products of cement and include carbon dioxide produced dur- energy production and use (see table 3.7), account ing consumption of solid, liquid, and gas fuels and for the largest share of greenhouse gases, which are gas flaring. associated with global warming. Anthropogenic car- 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. Carbon intensity is the ratio of carbon dioxide per unit of energy, or the amount of carbon dioxide emitted as a result of using one unit of energy in production. Kilograms per 2005 PPP dollars of GDP shows the share of carbon dioxide per unit of GDP, a measure of how clean production processes are. The U.S. Department of Energy’s Carbon Diox- ide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division’s World Energy Data Set) and world cement manufacturing (from the U.S. Bureau of Mines Cement Manufacturing Data Set). Carbon dioxide emissions are often calculated and reported as elemental carbon. The values in the table were con- verted to actual carbon dioxide mass by multiplying Data sources them by 3.667 (the ratio of the mass of carbon to that of carbon dioxide). Although estimates of global Data on energy use are from the electronic files carbon dioxide emissions are probably accurate of the International Energy Agency. Data on car- within 10  percent (as calculated from global aver- bon dioxide emissions are from the CDIAC, Envi- age fuel chemistry and use), country estimates may ronmental Sciences Division, Oak Ridge National have larger error bounds. Trends estimated from a Laboratory, Tennessee, United States. consistent time series tend to be more accurate than 2012 World Development Indicators 173 3.10 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand % of total thousand average metric tons metric tons metric tons of carbon % of total of carbon From of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb energy dioxide % changeb equivalent processes Agricultural equivalent processes Agricultural equivalent 1990– 1990– 1990– 1990– 1990– 2008 2008 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan –6.9 –69.6 .. .. .. .. .. .. .. .. .. .. Albania 2.5 –44.2 2,407 –5.1 20.0 70.8 1,036 –18.7 7.1 78.4 62 .. Algeria 1.5 41.1 54,219 33.1 83.2 8.2 4,898 27.5 22.6 58.6 489 50.0 Angola 10.4 450.2 45,409 –8.3 15.6 27.9 38,881 –6.7 0.4 38.4 20 .. Argentina 2.6 70.8 101,821 –8.3 18.9 70.6 49,821 29.6 3.9 89.2 785 –65.8 Armenia 3.0 50.7 2,962 2.5 50.8 36.7 580 –27.6 1.2 81.6 335 .. Australia 1.7 38.9 126,488 9.7 29.7 55.1 62,966 –0.1 10.3 78.2 6,505 33.5 Austria 1.1 11.1 8,515 –15.0 21.7 48.6 4,448 –13.5 31.0 52.5 2,329 46.2 Azerbaijan 0.0 6.7 36,607 110.7 82.0 13.6 2,633 0.4 8.3 77.5 89 –49.4 Bahrain 3.0 89.1 2,766 54.9 88.7 0.6 82 70.8 39.7 16.0 279 –89.0 Bangladesh 6.7 199.5 92,414 6.5 10.0 70.5 21,386 42.1 7.5 83.1 0 .. Belarus –1.3 –28.2 11,498 –32.8 7.6 70.9 11,680 –28.3 23.1 72.9 467 .. Belgium –0.4 –3.3 10,063 –21.8 11.6 56.7 6,571 –27.6 38.1 44.3 2,106 583.8 Benin 9.3 468.7 4,080 –15.8 15.6 47.8 2,902 –21.5 4.0 61.5 0 .. Bolivia 3.6 133.2 30,350 30.9 25.6 34.1 15,092 3.2 0.7 36.5 0 .. Bosnia and Herzegovina 16.6 694.1 2,741 –53.5 46.7 42.4 1,196 –40.8 24.7 57.8 571 –7.5 Botswana 3.7 122.2 4,501 –22.6 8.6 84.1 3,081 –44.1 1.4 92.0 0 .. Brazil 3.3 88.2 492,160 56.4 7.6 61.1 235,987 52.6 3.4 67.0 11,816 40.5 Bulgaria –1.9 –35.0 10,867 –24.8 13.0 18.9 4,227 –55.2 36.0 48.1 383 .. Burkina Faso 6.1 216.3 .. .. .. .. .. .. .. .. .. .. Burundi –4.5 –41.0 .. .. .. .. .. .. .. .. .. .. Cambodia 15.3 920.4 20,215 35.0 4.9 76.1 5,794 46.9 3.5 66.1 0 .. Cameroon 4.1 205.1 18,518 37.1 39.1 42.4 9,127 –13.3 2.6 75.9 419 –55.0 Canada 1.4 20.9 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 31.5 .. .. .. .. .. .. .. .. .. .. Chad 11.0 237.4 .. .. .. .. .. .. .. .. .. .. Chile 4.3 109.5 18,149 49.8 24.4 39.4 8,135 57.5 16.6 73.4 13 –31.6 China 5.7 185.8 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 1.9 39.5 2,820 84.1 26.7 .. 422 –0.9 38.5 0.0 119 –68.6 Colombia 0.0 18.1 58,108 13.5 19.9 68.0 21,288 5.2 4.4 86.1 83 97.6 Congo, Dem. Rep. –3.1 –30.8 56,445 –41.6 10.2 23.1 54,643 –37.3 2.2 31.3 0 .. Congo, Rep. –0.1 63.0 5,584 –10.4 32.2 31.9 3,566 –17.2 1.0 51.8 5 .. Costa Rica 5.0 171.2 2,580 –31.2 9.5 67.2 1,334 –26.2 4.5 85.4 62 .. Côte d’Ivoire 1.4 21.0 10,997 –2.2 16.9 17.4 7,364 –1.6 2.7 29.3 0 .. Croatia 2.6 41.7 3,864 –60.5 57.0 33.3 2,851 –24.5 36.6 52.4 59 –93.4 Cuba –0.8 –5.8 9,455 –21.0 11.2 62.4 6,356 –31.8 15.1 78.7 129 .. Cyprus 3.1 83.8 616 40.0 1.8 44.0 292 19.2 13.0 65.5 190 .. Czech Republic –0.7 –16.1 11,497 –40.3 49.4 33.6 8,878 –10.2 53.0 36.9 1,121 .. Denmark –1.0 –8.7 7,935 –0.5 16.4 65.2 6,290 –21.5 18.0 73.4 1,422 457.6 Dominican Republic 4.5 125.9 6,081 3.8 7.8 63.7 2,255 11.0 7.8 76.8 0 .. Ecuador 3.1 59.3 17,125 31.2 31.2 57.7 4,571 42.2 3.8 84.9 63 .. Egypt, Arab Rep. 5.9 176.9 46,996 68.8 50.7 31.7 18,996 60.7 8.3 80.0 3,181 54.5 El Salvador 4.4 133.5 3,131 18.0 12.4 53.1 1,377 7.7 8.2 76.2 77 .. Eritrea 5.4 .. 2,467 30.9 11.2 73.2 1,189 15.7 3.8 90.9 0 .. Estonia –0.6 –20.6 2,108 –36.8 42.3 30.5 932 –50.7 21.5 60.5 40 1,900.0 Ethiopia 4.7 135.5 52,243 32.8 14.3 72.5 30,510 19.4 5.2 88.8 10 .. Finland 1.1 10.9 9,742 –2.8 7.4 20.7 7,124 –4.1 42.8 41.7 826 726.0 France –0.3 –5.5 77,252 –0.3 44.3 47.7 49,058 –30.6 24.2 66.8 15,539 57.1 Gabon –3.6 –49.0 8,218 1.4 90.4 1.1 482 58.0 10.0 23.3 9 .. Gambia, The 4.4 115.4 .. .. .. .. .. .. .. .. .. .. Georgia –2.6 –66.1 4,410 –12.4 36.1 50.8 2,019 –26.8 35.5 56.9 12 .. Germany –1.1 –18.1 67,582 –44.8 32.1 43.8 56,560 –23.9 38.2 52.2 31,543 8.1 Ghana 4.7 118.6 8,990 24.2 23.3 39.5 4,899 –5.6 9.3 70.5 15 –97.5 Greece 2.0 34.5 7,289 2.0 26.3 50.0 5,977 –17.1 22.1 58.2 1,842 –20.8 Guatemala 5.6 134.2 8,306 74.7 12.4 48.8 5,376 121.1 5.5 56.8 481 .. Guinea 1.5 31.9 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau –0.2 11.6 .. .. .. .. .. .. .. .. .. .. Haiti 7.6 145.0 4,006 34.8 12.1 56.2 1,438 59.6 6.2 84.2 0 .. 174 2012 World Development Indicators 3.10 ENVIRONMENT Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand % of total thousand average metric tons metric tons metric tons of carbon % of total of carbon From of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb energy dioxide % changeb equivalent processes Agricultural equivalent processes Agricultural equivalent 1990– 1990– 1990– 1990– 1990– 2008 2008 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Honduras 7.4 234.5 5,191 31.5 7.2 78.4 2,865 26.0 3.8 85.9 0 .. Hungary –0.6 –13.3 7,767 –22.9 29.1 33.6 6,961 –31.2 30.9 60.1 1,552 121.1 India 4.8 152.4 583,978 10.5 15.9 64.4 212,927 33.3 12.8 73.4 8,433 –11.9 Indonesia 4.5 171.5 208,944 18.4 25.5 46.4 123,275 43.5 3.7 71.5 1,027 –40.5 Iran, Islamic Rep. 5.0 137.0 114,585 32.5 70.6 18.2 26,644 41.1 11.4 75.3 2,569 –2.9 Iraq 3.7 95.9 15,937 –45.8 58.4 18.6 3,440 –9.9 9.7 63.3 86 –66.0 Ireland 2.4 43.4 15,331 14.3 11.9 76.7 7,486 –8.3 4.4 90.5 1,151 3,097.2 Israel 1.6 12.3 3,517 83.8 18.4 31.2 1,793 41.5 15.3 53.0 1,981 88.8 Italy 0.6 4.9 40,790 –13.4 14.7 39.8 28,620 –5.4 39.1 43.7 13,968 211.2 Jamaica 2.4 53.2 1,302 14.4 11.4 50.3 599 29.4 12.1 59.0 51 .. Japan 0.7 10.4 42,771 –36.5 8.1 71.2 29,785 –17.0 41.6 27.9 53,786 81.1 Jordan 4.2 105.5 1,796 111.5 25.0 21.8 667 39.5 8.2 55.4 112 .. Kazakhstan 0.4 –9.3 47,119 –27.3 66.2 25.3 17,594 –46.2 12.8 62.5 339 .. Kenya 3.0 78.5 22,130 23.3 16.9 65.5 10,542 14.3 5.0 88.8 0 .. Korea, Dem. Rep. 1.2 .. 18,195 –15.0 58.6 23.5 3,422 –60.6 13.2 62.3 2,794 .. Korea, Rep. 3.7 108.8 32,069 2.4 19.9 38.6 13,548 34.7 41.3 35.9 10,221 66.0 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 7.4 69.0 14,380 119.4 93.4 1.1 650 156.9 27.7 16.9 931 254.0 Kyrgyz Republic –1.7 –42.8 3,591 –38.2 6.8 72.3 1,510 –57.7 11.2 72.6 24 .. Lao PDR 13.1 553.1 .. .. .. .. .. .. .. .. .. .. Latvia –3.3 –46.0 3,108 –42.1 53.6 27.7 1,253 –58.7 11.6 77.4 890 .. Lebanon 3.0 87.9 1,003 46.6 9.7 25.5 672 79.2 12.6 58.8 0 .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia 5.0 25.8 .. .. .. .. .. .. .. .. .. .. Libya 1.8 44.7 14,682 –34.7 86.3 5.7 1,285 9.3 11.2 51.9 280 –0.7 Lithuania –1.8 –31.7 5,516 –34.1 32.0 33.8 2,451 –45.7 5.0 86.0 656 .. Macedonia, FYR 0.4 9.6 1,403 –36.5 32.1 46.6 599 –33.9 15.9 63.9 120 .. Madagascar 3.4 93.7 .. .. .. .. .. .. .. .. .. .. Malawi 3.1 100.6 .. .. .. .. .. .. .. .. .. .. Malaysia 6.3 268.0 46,501 64.7 69.3 12.4 15,087 13.5 6.7 64.9 994 66.2 Mali 1.9 40.9 .. .. .. .. .. .. .. .. .. .. Mauritania –3.9 –25.0 .. .. .. .. .. .. .. .. .. .. Mauritius 6.1 170.2 .. .. .. .. .. .. .. .. .. .. Mexico 1.9 46.1 128,209 26.3 40.2 42.3 42,514 8.9 10.6 75.2 4,555 53.1 Moldova –8.5 –77.2 3,372 –17.5 45.2 29.4 849 –51.0 5.5 73.5 8 .. Mongolia –0.2 8.5 6,067 –25.9 2.5 92.1 3,489 –30.0 2.2 93.2 0 .. Morocco 3.7 103.5 10,573 15.8 8.0 51.7 5,814 12.2 3.0 82.6 0 .. Mozambique 5.4 131.1 12,843 18.2 22.7 44.2 9,501 –12.7 3.4 71.4 282 .. Myanmar 6.6 198.8 77,211 –7.4 12.6 69.0 30,932 –23.9 2.6 42.9 0 .. Namibia 39.5 .. 5,057 47.2 0.3 94.9 3,797 47.2 1.1 94.3 0 .. Nepal 7.6 458.4 22,142 9.7 5.9 82.9 4,516 26.0 13.0 76.8 0 .. Netherlands 0.0 5.9 21,259 –30.4 23.4 43.4 14,596 –10.7 52.5 39.5 3,750 –40.9 New Zealand 2.0 37.8 27,635 3.6 3.6 90.2 12,930 23.5 3.5 94.2 973 3.4 Nicaragua 4.1 63.8 6,018 26.3 6.6 74.8 3,340 10.1 3.3 91.7 0 .. Niger –0.2 2.2 .. .. .. .. .. .. .. .. .. .. Nigeria 5.4 111.0 130,317 10.9 68.9 19.8 21,565 12.6 9.1 77.3 669 176.4 Norway 2.7 59.6 16,870 47.2 74.6 12.6 4,737 –3.1 46.5 39.0 5,202 –39.4 Oman 8.5 341.9 17,849 194.9 94.1 3.0 561 82.7 16.0 68.0 175 .. Pakistan 4.9 138.0 137,401 50.7 23.7 63.5 26,838 46.0 14.5 74.2 819 –18.8 Panama 4.0 120.5 3,219 16.5 4.0 79.2 1,204 18.0 4.9 83.7 0 .. Papua New Guinea 3.9 –1.5 .. .. .. .. .. .. .. .. .. .. Paraguay 2.7 82.0 15,388 2.0 3.9 84.1 9,067 0.6 1.7 82.6 0 .. Peru 3.7 91.5 17,187 22.7 13.5 61.3 7,560 35.4 2.9 81.9 330 .. Philippines 3.2 86.7 51,889 28.6 9.3 63.7 12,950 34.0 9.1 73.1 365 125.3 Poland –1.1 –13.8 70,023 –36.6 62.0 21.9 30,198 4.7 33.5 57.7 2,451 360.7 Portugal 1.8 28.8 12,173 22.4 13.8 35.4 5,958 24.3 22.0 43.8 783 605.4 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 6.8 481.6 15,706 387.2 96.5 0.4 200 104.1 33.9 25.0 0 .. 2012 World Development Indicators 175 3.10 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand % of total thousand average metric tons metric tons metric tons of carbon % of total of carbon From of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb energy dioxide % changeb equivalent processes Agricultural equivalent processes Agricultural equivalent 1990– 1990– 1990– 1990– 1990– 2008 2008 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania –2.4 –40.4 24,331 –35.1 42.7 36.0 11,537 –44.0 32.4 56.2 746 –62.8 Russian Federation –1.0 –23.1 562,801 –18.3 79.3 9.1 76,121 –48.7 27.8 44.3 59,673 130.6 Rwanda 0.6 3.2 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 2.8 101.6 48,152 67.4 83.6 4.0 6,501 17.5 14.0 46.1 2,193 –10.6 Senegal 3.0 56.3 7,129 35.1 9.9 68.3 4,083 37.2 2.7 88.5 0 .. Serbia .. .. 7,782 –58.7 41.5 43.7 4,581 –8.8 24.2 63.6 4,493 353.8 Sierra Leone 7.3 243.4 .. .. .. .. .. .. .. .. .. .. Singapore –2.1 –31.2 2,237 136.7 60.1 1.3 1,068 163.1 77.6 2.8 2,532 396.5 Slovak Republic –0.9 –17.7 3,911 –39.7 18.2 39.0 3,354 –37.1 52.0 37.7 395 480.9 Slovenia 1.6 32.3 3,498 0.6 30.7 32.1 1,156 –12.2 13.2 70.4 473 –38.5 Somalia 35.9 3,447.0 .. .. .. .. .. .. .. .. .. .. South Africa 1.4 30.7 63,785 24.6 45.4 31.4 24,048 12.9 12.6 59.8 2,552 71.2 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 2.7 44.7 36,338 11.9 10.4 56.8 26,529 6.5 18.7 62.6 9,080 47.7 Sri Lanka 7.1 211.8 10,210 –11.2 5.3 65.2 2,056 18.0 12.1 65.1 0 .. Sudan 7.1 152.8 67,441 55.5 7.1 85.2 49,472 34.9 1.3 92.6 0 .. Swaziland 9.2 156.9 .. .. .. .. .. .. .. .. .. .. Sweden –0.6 –5.1 11,311 1.3 9.9 28.1 5,865 –13.1 26.8 60.2 2,078 133.7 Switzerland –0.3 –6.0 4,748 –17.1 19.8 67.6 2,415 –15.5 20.8 59.3 2,109 97.3 Syrian Arab Republic 3.2 91.2 12,458 –10.8 53.8 28.1 5,509 33.4 9.0 78.1 0 .. Tajikistan –2.8 –56.4 3,898 –9.3 12.8 68.6 1,378 0.2 1.4 86.9 383 –86.4 Tanzania 5.3 172.5 32,024 24.0 12.6 63.2 21,647 0.8 2.5 78.8 0 .. Thailand 5.6 198.2 83,257 5.7 16.9 66.0 22,304 15.1 21.7 65.5 1,104 –22.8 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 3.5 83.4 2,889 5.0 23.5 39.8 1,738 –21.3 5.6 67.5 0 .. Trinidad and Tobago 4.7 193.5 10,070 32.0 83.9 0.7 230 12.2 11.5 60.3 0 .. Tunisia 3.3 88.5 8,160 106.2 55.6 25.5 2,366 17.9 21.4 66.4 0 .. Turkey 3.4 88.3 64,251 46.4 16.0 33.6 32,781 12.8 22.7 66.4 5,066 96.9 Turkmenistan 3.3 70.4 27,984 –5.0 75.2 21.6 4,276 93.7 16.4 78.1 73 .. Uganda 8.7 358.3 .. .. .. .. .. .. .. .. .. .. Ukraine –3.1 –49.6 70,360 –42.2 62.1 23.3 26,097 –51.3 42.8 45.6 693 209.4 United Arab Emirates 6.1 198.2 23,283 58.0 93.1 2.6 1,169 78.7 18.3 43.6 1,075 27.5 United Kingdom –0.6 –8.3 65,788 –44.1 24.8 38.2 30,565 –44.7 24.8 60.0 10,403 96.7 United States 0.7 11.9 548,074 –14.4 41.0 34.8 317,153 1.8 30.6 56.4 239,517 158.7 Uruguay 2.3 108.5 19,589 24.1 1.5 94.3 7,017 16.1 1.4 96.9 59 .. Uzbekistan 0.6 9.6 39,602 24.0 57.3 33.7 10,003 9.4 6.2 84.2 608 .. Venezuela, RB 2.6 38.8 61,183 5.9 47.4 40.0 14,935 23.4 5.0 75.2 2,468 –24.0 Vietnam 11.3 495.0 82,978 40.1 22.7 63.9 23,030 98.3 6.1 83.0 0 .. West Bank and Gaza 17.3 .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 4.7 143.7 6,677 73.5 17.0 54.9 3,250 57.4 11.2 72.5 0 .. Zambia –1.3 –22.8 19,294 –28.4 6.7 59.3 25,068 –29.7 2.6 71.7 0 .. Zimbabwe –3.4 –41.5 9,539 –5.7 11.4 73.3 6,114 –16.1 3.7 85.2 0 .. World 2.1 w 43.8 w 7,135,850 s 6.2 w 37.3 w 42.6 w 2,852,537 s 5.8 w 15.4 w 66.2 w 724,183 s 122.4 w Low income 3.0 .. 436,333 –3.0 13.7 61.2 209,159 –15.0 4.1 62.4 .. .. Middle income 3.0 79.7 5,160,313 13.6 38.8 42.6 1,830,347 17.0 10.6 70.8 260,798 207.4 Lower middle income 3.1 87.1 1,704,859 11.4 27.2 52.4 686,537 17.2 8.8 71.1 17,327 3.3 Upper middle income 3.0 77.7 3,455,454 14.7 44.6 37.8 1,143,810 16.9 11.7 70.6 243,471 257.7 Low & middle income 3.0 79.4 5,596,645 12.1 36.9 44.1 2,039,506 12.7 10.0 70.0 264,291 201.6 East Asia & Pacific 5.6 185.3 1,928,355 24.5 39.2 43.6 707,496 38.0 10.5 72.2 .. .. Europe & Central Asia –0.8 –19.6 936,608 –17.3 67.5 17.5 214,402 –38.9 25.3 56.8 75,692 114.6 Latin America & Carib. 2.5 60.6 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 4.2 112.5 287,084 20.0 64.7 20.7 73,539 36.8 10.7 74.5 6,717 20.7 South Asia 4.9 151.9 846,145 14.6 16.1 65.4 267,722 34.9 12.6 74.3 9,253 –12.5 Sub-Saharan Africa 2.2 47.5 589,897 5.4 30.5 44.0 334,216 –7.6 3.7 66.1 .. .. High income 0.8 14.8 1,539,204 –10.8 38.9 37.1 813,031 –8.2 29.1 56.8 459,891 93.3 Euro area 0.1 –3.1 317,704 –18.2 26.0 46.8 219,189 –18.1 31.7 55.4 84,230 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 emissions since 1990, which is the baseline for Kyoto Protocol requirements. 176 2012 World Development Indicators 3.10 ENVIRONMENT Trends in greenhouse gas emissions About the data De�nitions Greenhouse gases—which include carbon dioxide, • Carbon dioxide emissions are those from the burn- methane, nitrous oxide, hydrofluorocarbons, per- ing of fossil fuels and the manufacture of cement and fluorocarbons, and sulfur hexafluoride—contribute include carbon dioxide produced during consumption to climate change. of solid, liquid, and gas fuels and gas flaring. • Meth- Carbon dioxide emissions, largely a by-product of ane emissions are those from human activities such energy production and use (see table 3.7), account as agriculture and from industrial methane produc- for the largest share of greenhouse gases. Anthro- tion. • Methane emissions from energy processes pogenic carbon dioxide emissions result primarily are those from the production, handling, transmis- from fossil fuel combustion and cement manufactur- sion, and combustion of fossil fuels and biofuels. ing. Burning oil releases more carbon dioxide than •  Agricultural methane emissions are those from burning natural gas, and burning coal releases even animals, animal waste, rice production, agricultural more for the same level of energy use. Cement manu- waste burning (nonenergy, on-site), and savannah facturing releases about half a metric ton of carbon burning. • Nitrous oxide emissions are those from dioxide for each metric ton of cement produced. agricultural biomass burning, industrial activities, Methane emissions result largely from agricultural and livestock management. •  Nitrous oxide emis- activities, industrial production landfills and waste- sions from energy processes are those produced water treatment, and other sources such as tropi- by the combustion of fossil fuels and biofuels. cal forest and other vegetation fires. The emissions •  Agricultural nitrous oxide emissions are those are usually expressed in carbon dioxide equivalents produced through fertilizer use (synthetic and ani- using the global warming potential, which allows the mal manure), animal waste management, agricultural effective contributions of different gases to be com- waste burning (nonenergy, on-site), and savannah pared. A kilogram of methane is 21 times as effec- burning. •  Other greenhouse gas emissions are tive at trapping heat in the earth’s atmosphere as a those of hydrofluorocarbons (used as a replacement kilogram of carbon dioxide within 100 years. for chlorofluorocarbons, mainly in refrigeration and Nitrous oxide emissions are mainly from fossil fuel semiconductor manufacturing), perfluorocarbons (a combustion, fertilizers, rainforest fires, and animal by-product of aluminum smelting and uranium enrich- waste. Nitrous oxide is a powerful greenhouse gas, ment, used as a replacement for chlorofluorocarbons with an estimated atmospheric lifetime of 114 years, in semiconductor manufacturing), and sulfur hexaflu- compared with 12 years for methane. The per kilo- oride (used to insulate high-voltage electricity power gram global warming potential of nitrous oxide is equipment), all of which are to be curbed under the nearly 310 times that of carbon dioxide within 100 Kyoto Protocol. years. Other greenhouse gases covered under the Kyoto Protocol are hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Although emissions of these artificial gases are small, they are more powerful greenhouse gases than carbon dioxide, with much higher atmospheric lifetimes and high global warm- ing potential. For a discussion of carbon dioxide sources and the methodology behind emissions calculation, see About the data for table 3.9. Data sources Data on carbon dioxide emissions are from the Carbon Dioxide Information Analysis Center, Envi- ronmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data on methane, nitrous oxide, and other greenhouse gas emissions are compiled by the International Energy Agency. 2012 World Development Indicators 177 3.11 Carbon dioxide emissions by sector Carbon dioxide emissions % of total fuel combustion Residential buildings Electricity and Manufacturing industries and commercial and heat production and construction public services Transport Other sectors 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 12.0 5.2 44.2 16.1 4.8 9.8 11.2 59.6 27.8 9.1 Algeria 42.9 39.2 14.1 13.6 20.3 24.9 22.6 22.4 0.0 0.0 Angola 12.5 3.7 46.1 24.1 15.7 21.7 25.2 50.3 0.7 0.3 Argentina 34.0 34.9 16.2 22.2 17.2 12.9 28.1 24.6 4.5 5.3 Armenia 29.6 19.6 22.3 37.6 30.9 0.0 14.3 15.8 2.9 26.8 Australia 54.1 62.9 17.7 12.6 3.3 3.0 23.6 20.1 1.3 1.5 Austria 35.0 34.1 17.9 18.1 20.7 14.7 24.2 31.9 2.2 1.2 Azerbaijan 44.3 50.3 24.4 7.4 7.2 22.1 5.4 17.9 18.7 2.4 Bahrain 56.2 54.2 34.3 31.1 1.0 0.9 8.5 13.8 0.0 0.0 Bangladesh 32.7 43.7 33.3 24.5 16.2 11.9 12.2 14.2 5.7 5.7 Belarus 47.4 52.9 28.1 19.9 10.3 12.4 8.1 10.2 6.2 4.6 Belgium 28.9 25.4 28.4 24.7 22.6 23.5 18.5 24.4 1.5 1.9 Benin 12.0 3.1 12.0 5.2 12.0 30.9 64.0 61.2 0.0 0.0 Bolivia 29.0 34.8 11.6 17.2 10.5 10.6 39.3 36.0 9.5 1.5 Bosnia and Herzegovina 40.6 69.5 26.4 6.6 1.2 0.6 9.2 14.2 22.6 9.0 Botswana 55.3 25.0 18.1 26.8 3.4 3.1 22.2 43.6 1.4 1.3 Brazil 14.2 19.0 29.8 29.7 8.4 5.4 41.9 41.0 5.7 4.9 Bulgaria 63.4 64.3 17.9 15.1 3.9 2.7 8.5 16.9 6.2 1.0 Burkina Faso .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. Cambodia .. 36.7 .. 3.7 .. 26.5 .. 25.0 .. 7.8 Cameroon 1.1 34.3 8.2 7.0 25.5 8.4 65.2 50.6 0.0 0.0 Canada 32.9 33.5 19.8 17.8 16.8 16.4 28.6 29.4 1.9 2.9 Central African Republic .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile 33.0 39.0 24.9 19.1 9.2 6.2 31.7 35.1 1.2 0.6 China 32.2 51.9 40.9 33.3 16.5 6.2 5.3 7.0 5.1 1.6 Hong Kong SAR, China 72.4 68.1 9.5 15.7 4.5 6.0 13.6 10.2 0.0 0.0 Colombia 24.3 18.7 27.4 31.7 8.1 8.5 37.0 38.5 3.2 2.7 Congo, Dem. Rep. 3.4 1.1 29.4 35.3 10.8 10.6 18.9 22.6 37.8 30.7 Congo, Rep. 0.0 3.4 8.6 4.1 10.0 5.4 78.6 87.2 0.0 0.0 Costa Rica 7.3 10.2 26.8 17.3 4.2 3.6 60.2 65.5 1.5 3.5 Côte d’Ivoire 22.8 43.9 16.3 9.3 11.4 12.1 44.9 24.3 4.9 10.5 Croatia 37.3 33.1 28.2 21.2 10.5 12.6 18.2 29.5 5.9 3.7 Cuba 45.2 53.3 18.4 28.3 9.1 10.0 16.0 3.0 11.2 5.4 Cyprus 45.3 51.0 20.1 15.1 4.7 4.5 29.9 26.0 0.0 3.6 Czech Republic 42.7 56.9 30.0 17.7 19.0 8.8 4.6 15.3 3.6 1.3 Denmark 51.7 50.1 10.9 10.0 12.5 7.7 20.3 28.3 4.6 3.9 Dominican Republic 41.2 50.0 10.3 6.3 12.1 13.9 35.5 29.1 1.0 0.7 Ecuador 15.2 20.7 16.9 17.5 14.5 11.4 51.7 49.2 1.8 1.2 Egypt, Arab Rep. 32.5 43.0 35.8 23.4 11.9 8.1 19.8 21.9 0.0 3.6 El Salvador 8.3 26.6 25.9 23.5 7.4 8.9 57.9 40.9 0.0 0.0 Eritrea .. 42.2 .. 4.4 .. 28.9 .. 24.4 .. 0.0 Estonia 73.2 75.8 12.4 8.2 4.1 2.3 6.6 12.9 3.8 0.9 Ethiopia 13.6 6.6 27.1 24.5 5.4 12.4 41.6 56.7 12.2 0.0 Finland 36.2 47.7 26.7 21.5 11.9 4.8 21.3 22.4 3.8 3.5 France 18.1 18.9 22.7 19.2 24.1 22.8 31.9 33.9 3.2 5.3 Gabon 34.4 28.5 15.6 38.9 13.3 9.7 35.6 21.1 2.2 1.7 Gambia, The .. .. .. .. .. .. .. .. .. .. Georgia 46.3 19.5 22.4 15.5 16.5 18.5 13.1 37.6 1.7 8.9 Germany 42.4 45.2 18.9 14.7 20.4 20.5 16.7 18.5 1.7 1.1 Ghana 3.0 26.2 17.7 15.3 15.9 6.0 59.0 49.8 4.4 2.7 Greece 52.3 53.4 14.8 9.8 7.3 10.5 21.5 23.6 4.1 2.6 Guatemala 7.3 27.6 22.7 15.5 14.2 5.7 52.4 51.3 3.0 0.1 Guinea .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 25.5 9.8 21.3 22.6 8.5 10.3 44.7 57.3 0.0 0.0 178 2012 World Development Indicators 3.11 ENVIRONMENT Carbon dioxide emissions by sector Carbon dioxide emissions % of total fuel combustion Residential buildings Electricity and Manufacturing industries and commercial and heat production and construction public services Transport Other sectors 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 Honduras 0.9 34.4 35.2 19.9 15.5 3.3 48.4 37.7 0.0 4.9 Hungary 34.8 37.6 23.2 13.2 25.2 22.7 12.5 24.2 4.4 2.2 India 44.5 59.9 28.6 19.6 9.8 6.2 13.8 9.2 3.3 5.1 Indonesia 37.3 37.7 23.7 34.0 13.7 6.3 22.7 19.7 2.7 2.3 Iran, Islamic Rep. 22.7 29.0 26.8 22.4 22.3 24.2 21.5 21.8 6.6 2.5 Iraq 28.3 35.6 27.3 24.0 9.2 9.3 35.1 31.1 0.0 0.0 Ireland 36.1 33.7 15.8 11.5 29.5 22.3 16.5 30.6 2.2 1.8 Israel 57.6 66.6 12.9 2.8 4.2 4.4 19.6 16.4 5.7 9.9 Italy 35.9 38.3 21.1 15.8 16.7 16.8 24.0 27.2 2.2 2.0 Jamaica 27.8 51.1 8.3 3.3 4.0 3.3 15.0 21.6 44.9 20.5 Japan 38.4 44.6 27.0 21.5 12.9 13.3 19.7 19.7 1.9 0.9 Jordan 38.6 48.0 14.2 14.1 11.3 10.9 28.9 24.8 7.1 2.2 Kazakhstan 47.9 47.4 34.9 22.3 0.2 0.7 6.0 7.0 11.0 22.6 Kenya 8.0 32.4 25.8 15.8 10.9 9.6 48.3 37.2 6.9 5.1 Korea, Dem. Rep. 13.9 16.2 67.4 62.9 0.5 0.1 4.1 1.7 14.2 19.1 Korea, Rep. 27.4 52.4 23.4 19.1 26.9 10.0 18.8 16.8 3.4 1.7 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 62.7 68.2 21.7 15.6 0.7 0.7 15.0 15.5 0.0 0.0 Kyrgyz Republic 17.3 23.1 36.9 28.5 0.0 0.0 13.3 24.2 32.6 24.3 Lao PDR .. .. .. .. .. .. .. .. .. .. Latvia 52.3 25.8 13.6 13.8 13.4 11.4 16.6 44.9 4.1 4.2 Lebanon 43.3 49.2 5.0 11.5 22.7 10.8 28.8 28.5 0.0 0.0 Lesotho .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. Libya 59.7 62.1 13.2 9.4 4.8 5.6 22.4 22.8 0.0 0.0 Lithuania 41.3 35.3 21.6 21.2 16.1 6.9 16.5 35.0 4.5 1.5 Macedonia, FYR 64.8 68.1 18.8 14.2 4.9 4.0 8.9 13.3 2.6 0.6 Madagascar .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. Malaysia 35.7 49.6 30.7 24.2 4.4 2.5 29.2 23.3 0.0 0.4 Mali .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. Mexico 34.9 40.2 23.9 14.9 7.8 5.8 31.5 37.1 1.9 2.0 Moldova 44.8 47.7 6.7 9.8 2.9 23.5 7.9 14.7 37.6 4.4 Mongolia 51.3 62.6 22.1 13.2 6.4 7.3 12.1 13.6 8.1 3.2 Morocco 40.0 37.1 25.7 17.0 7.3 9.5 18.7 25.7 8.2 10.7 Mozambique 10.2 1.0 13.0 20.7 7.4 7.8 55.6 69.9 13.9 1.0 Myanmar 39.9 20.8 28.1 25.4 0.3 2.6 31.7 28.1 0.0 23.1 Namibia .. 22.6 .. 6.6 .. 0.3 .. 48.6 .. 21.9 Nepal 0.0 0.3 22.7 33.9 28.4 31.5 38.6 27.9 11.4 6.6 Netherlands 38.2 38.2 21.9 21.2 18.5 17.9 16.6 19.7 4.8 3.0 New Zealand 21.6 32.8 28.8 18.4 6.0 4.2 39.1 41.7 4.4 2.8 Nicaragua 30.6 40.1 18.0 14.7 7.1 6.5 40.4 35.3 3.8 3.1 Niger .. .. .. .. .. .. .. .. .. .. Nigeria 28.5 36.5 17.3 10.1 14.3 4.8 39.9 48.6 0.0 0.0 Norway 26.9 33.4 24.5 21.3 8.7 3.2 35.1 37.3 4.9 4.8 Oman 54.8 56.7 21.8 23.1 1.9 1.3 16.8 15.3 4.7 3.6 Pakistan 27.0 32.3 34.0 32.4 14.4 11.4 23.1 23.5 1.6 0.3 Panama 22.8 27.0 21.1 18.8 9.3 5.8 46.7 47.6 0.0 0.8 Papua New Guinea .. .. .. .. .. .. .. .. .. .. Paraguay 1.6 0.0 7.9 3.0 6.8 4.9 83.8 92.1 0.0 0.0 Peru 20.7 26.8 22.4 26.6 18.1 6.7 35.5 37.8 3.4 2.2 Philippines 35.5 44.0 21.0 18.0 6.7 5.7 34.9 31.3 2.0 1.1 Poland 65.1 55.8 13.7 12.6 12.9 13.3 6.0 14.8 2.3 3.4 Portugal 41.6 39.9 24.8 16.1 5.5 6.8 24.6 35.7 3.5 1.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar 53.3 52.1 35.9 31.5 0.6 0.4 10.1 16.0 0.0 0.0 2012 World Development Indicators 179 3.11 Carbon dioxide emissions by sector Carbon dioxide emissions % of total fuel combustion Residential buildings Electricity and Manufacturing industries and commercial and heat production and construction public services Transport Other sectors 1990 2008 1990 2008 1990 2008 1990 2008 1990 2008 Romania 47.3 50.4 35.8 22.3 5.2 9.1 6.9 16.6 4.8 1.6 Russian Federation 55.9 59.5 13.2 14.4 13.1 9.7 13.6 15.3 4.2 1.2 Rwanda .. .. .. .. .. .. .. .. .. .. Saudi Arabia 50.3 51.4 17.8 22.9 1.6 1.0 30.4 24.7 0.0 0.0 Senegal 36.8 28.4 12.4 17.4 6.5 7.3 35.8 46.2 8.5 0.8 Serbia 64.6 63.3 16.6 16.9 3.3 4.9 7.2 13.1 8.3 1.8 Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore 78.7 71.4 6.7 11.9 0.6 0.5 14.0 16.2 0.0 0.0 Slovak Republic 30.3 36.9 32.6 25.7 26.7 16.9 7.1 19.5 3.2 1.0 Slovenia 43.0 37.4 20.7 14.5 15.4 10.9 20.8 35.5 0.2 1.7 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 56.1 64.5 26.9 13.5 4.4 7.3 11.5 13.6 1.2 1.1 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 37.4 37.7 22.1 17.4 7.9 8.1 30.6 34.3 2.1 2.5 Sri Lanka 4.5 33.1 13.1 10.6 2.7 3.4 66.0 48.2 13.9 4.6 Sudan 9.1 27.0 16.9 10.0 2.5 5.7 71.3 55.6 0.2 1.5 Swaziland .. .. .. .. .. .. .. .. .. .. Sweden 18.4 22.8 24.2 21.0 17.3 3.7 37.5 50.7 2.6 1.7 Switzerland 4.1 7.0 14.3 14.9 44.3 37.1 35.4 39.5 2.0 1.5 Syrian Arab Republic 25.2 49.9 20.6 20.9 5.0 2.9 30.6 22.2 18.6 4.2 Tajikistan 14.0 17.5 0.0 0.0 0.0 0.0 6.6 9.2 79.4 73.3 Tanzania 17.5 18.5 22.2 13.3 19.9 10.4 40.4 56.8 0.0 1.2 Thailand 36.5 41.1 18.8 29.2 3.1 2.8 34.5 22.3 7.1 4.6 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 12.3 1.8 3.5 7.3 10.5 12.7 73.7 78.2 0.0 0.0 Trinidad and Tobago 41.6 35.9 45.6 56.6 1.2 1.4 11.7 6.1 0.0 0.0 Tunisia 33.0 39.5 27.6 17.7 13.4 14.6 20.4 23.1 5.5 5.0 Turkey 30.2 42.7 26.6 14.6 16.8 19.3 21.9 17.1 4.6 6.3 Turkmenistan 31.6 43.4 0.0 0.0 0.0 0.0 5.2 5.9 63.3 50.8 Uganda .. .. .. .. .. .. .. .. .. .. Ukraine 51.4 45.2 29.0 29.4 7.8 13.5 7.9 10.5 3.8 1.4 United Arab Emirates 26.4 50.8 51.4 29.8 0.6 2.3 21.6 17.1 0.0 0.0 United Kingdom 44.2 44.5 15.2 11.5 17.2 18.5 20.8 24.4 2.5 1.0 United States 43.9 47.7 14.4 11.3 11.1 9.9 29.2 30.2 1.3 0.8 Uruguay 13.9 39.8 19.5 11.4 15.5 7.1 40.0 34.3 11.2 7.5 Uzbekistan 39.1 33.3 4.9 19.0 0.0 36.6 4.7 7.8 51.3 3.3 Venezuela, RB 40.1 36.3 28.0 28.0 5.0 4.4 26.9 31.0 0.0 0.2 Vietnam 27.7 29.3 32.6 35.1 11.2 9.5 24.3 24.5 4.0 1.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 24.6 34.3 3.1 11.2 10.6 9.3 61.7 27.1 0.0 18.0 Zambia 6.2 5.0 51.5 49.1 8.5 2.5 29.6 32.1 4.2 11.9 Zimbabwe 42.8 56.8 29.4 16.1 5.9 4.0 12.9 12.5 9.0 10.6 World 41.9 w 47.6 w 22.3 w 21.0 w 12.7 w 9.6 w 19.5 w 19.6 w 3.7 w 2.2 w Low income 18.1 25.5 50.4 36.7 2.8 6.9 10.2 17.5 18.4 13.5 Middle income 42.5 49.1 26.5 26.0 11.7 8.0 13.7 14.1 5.6 2.7 Lower middle income 42.6 48.1 25.6 23.0 8.9 8.6 14.8 16.0 8.0 4.4 Upper middle income 42.4 49.4 26.7 26.8 12.6 7.9 13.3 13.6 4.9 2.3 Low & middle income 41.9 48.8 27.0 26.1 11.5 8.0 13.6 14.1 5.9 2.9 East Asia & Pacific 32.0 50.0 39.8 33.1 14.8 6.0 8.1 8.9 5.3 1.9 Europe & Central Asia 51.7 53.4 19.2 16.9 10.1 11.1 11.3 14.1 7.7 4.4 Latin America & Carib. 29.1 31.9 24.4 22.4 9.2 6.9 33.6 35.6 3.7 3.2 Middle East & N. Africa 31.0 36.4 24.9 20.6 15.6 17.0 24.1 23.2 4.3 2.9 South Asia 42.4 56.8 29.1 20.8 10.3 6.8 14.9 10.9 3.2 4.7 Sub-Saharan Africa 48.1 53.3 25.8 13.8 6.0 7.6 18.0 23.6 2.0 1.7 High income 41.8 46.1 18.4 15.1 13.6 11.5 24.3 25.9 1.9 1.4 Euro area 36.7 37.8 21.0 16.8 18.5 17.2 21.4 25.8 2.4 2.3 Note: Shares may not sum to 100 percent because of rounding. 180 2012 World Development Indicators 3.11 ENVIRONMENT Carbon dioxide emissions by sector About the data De�nitions Carbon dioxide emissions account for the largest Carbon dioxide emissions from transport are emis- •  Carbon dioxide emissions from electricity and share of greenhouse gases, which are associated sions from fuel combustion for all transport activity heat production are those from main activity pro- with global warming. In 2010 the International Energy (IPCC source/sink category 1A3), including domestic ducers of electricity and heat, unallocated auto- Agency (IEA) released data on carbon dioxide emis- aviation, domestic navigation, road, rail, and pipeline producers, and other energy industries. •  Carbon sions by sector for the first time, allowing a more transport but excluding international marine bunkers dioxide emissions from manufacturing industries comprehensive understanding of each sector’s con- and international aviation. The IEA data do not allow and construction are those from fuel combustion tribution to total emissions. The sectoral approach energy consumption to be categorized by end-use, in industry (IPCC source/sink category 1A2). • Car- yields data on carbon dioxide emissions from fuel and thus emissions from autoproducers are listed bon dioxide emissions from residential buildings combustion (Intergovernmental Panel on Climate separately under unallocated autoproducers. and commercial and public services are those from Change [IPCC] source/sink category 1A) as calcu- Carbon dioxide emissions from other sectors are fuel combustion in household and all activities of lated using the IPCC tier 1 sectoral approach. The emissions from commercial and institutional activi- ISIC divisions 41, 50–52, 55, 63–67, 70–75, 80, table does not include all sectors. ties and from residential, agriculture and forestry, 85, 90–93, and 99. •  Carbon dioxide emissions Carbon dioxide emissions from electricity and heat fishing, and other processes not specified elsewhere from transport are those from fuel combustion for all production are the sum of emissions from main activ- that are included in IPCC source/sink categories 1A4 transport activities (IPCC source/sink category 1A3). ity producers of electricity and heat, unallocated auto- and 1A5. Although in the 1996 IPCC guidelines, this • Carbon dioxide emissions from other sectors are producers, and other energy industries. Main activity category included emissions from autoproducers in those from commercial and institutional activities producers (formerly known as public supply under- the commercial, residential, and agricultural sectors and from residential, agriculture and forestry, fish- takings) generate electricity or heat for sale to third that generate electricity or heat, the IEA data do not ing, and other emissions not specified elsewhere parties as their primary activity and may be privately allow energy consumption to be classified by end- that are included in IPCC source/sink categories or publicly owned. Emissions from own onsite use use, and thus emissions from autoproducers are 1A4 and 1A5. of fuel in power plants are also included in this cat- listed separately under unallocated autoproducers. egory. Unallocated autoproducers are undertakings that generate electricity or heat, wholly or partly for their own use as an activity that supports their primary activity and may be privately or publicly owned. In the 1996 IPCC guidelines these emissions were allocated among industry, transport, and “other� sectors. Emis- sions from other energy industries are emissions from fuel combusted in petroleum refineries, the manufac- ture of solid fuels, coal mining, oil and gas extraction, and other energy-producing industries. Carbon dioxide emissions from manufacturing industries and construction are the emissions from fuel combustion in industry (IPCC source/sink Cate- gory 1A2). Although in the 1996 IPCC guidelines, this category included emissions from industry autopro- ducers that generate electricity or heat, the IEA data do not allow energy consumption to be categorized by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers. Emissions from manufacturing industries and con- struction include those from coke inputs into blast furnaces, which may be reported under the trans- formation sector, the industry sector, or industrial processes (IPCC source/sink category 2). Carbon emissions from residential buildings and commercial and public services are the sum of emis- sions from fuel combustion in households (IPCC Data sources source/sink category 1A4b) and emissions from all activities of International Standard Industrial Clas- Data on carbon dioxide emissions by sector are sification divisions 41, 50–52, 55, 63–67, 70–75, from IEA electronic files. 80, 85, 90–93, and 99. 2012 World Development Indicators 181 3.12 Climate variability, exposure to impact, and resilience Climate variability Exposure to impact Resilience % of population Land area Population Population Disaster risk Average daily change in average number with an living in affected by reduction minimum/ of days/nights elevation areas with droughts, progress maximum Projected annual Projected annual Projected annual Projected annual of 5 meters an elevation floods, and score temperature temperature cool days/ hot days/ precipitation or less of 5 meters extreme 1, worst, degrees Celsius degrees Celsius cold nights warm nights millimeters % of land area or less temperature to 5, best 1961–1990 2045–2065 2045–2065 2045–2065 2045–2065 2000 2000 1990–2009 2011 Afghanistan 5.5 / 19.7 2.3 to 3.6 –1.4 / –1.5 3.1 / 7.0 –58 to 13 0.0 0.0 1.1 .. Albania 6.3 / 16.5 1.9 to 2.9 –1.5 / –1.4 3.2 / 6.5 –147 to –44 5.0 8.2 5.3 .. Algeria 15.4 / 29.6 2.4 to 3.0 –1.7 / –2.0 3.9 / 7.8 –39 to 4 0.4 3.5 0.0 3.5 Angola 14.8 / 28.3 2.1 to 2.7 –2.2 / –2.6 7.0 / 20.2 –89 to 75 0.2 2.1 1.0 .. Argentina 8.2 / 21.4 1.4 to 2.1 –1.2 / –1.3 2.9 / 6.5 –57 to 52 1.2 4.5 0.2 3.3 Armenia 1.3 / 13.0 2.1 to 3.3 –1.3 / –1.4 2.8 / 6.3 –75 to –7 0.0 0.0 0.5 3.0 Australia 14.9 / 28.4 1.7 to 2.4 –1.8 / –1.9 3.6 / 9.3 –67 to 54 1.1 7.2 3.0 4.0 Austria 2.3 / 10.4 1.9 to 2.9 –1.4 / –1.5 2.9 / 5.2 –70 to 27 0.0 0.0 0.0 .. Azerbaijan 6.7 / 17.2 1.9 to 3.1 –1.3 / –1.3 2.7 / 6.2 –58 to 3 20.0 29.8 1.1 .. Bahrain 20.5 / 33.8 2.1 to 3.0 –1.5 / –1.9 4.6 / 8.6 –19 to 4 39.0 66.6 .. .. Bangladesh 20.4 / 29.6 1.7 to 2.4 –1.7 / –2.1 3.4 / 11.8 –126 to 120 14.1 14.0 4.6 4.0 Belarus 2.0 / 10.3 2.2 to 3.1 –1.5 / –1.7 2.1 / 4.5 8 to 70 0.0 0.0 0.0 .. Belgium 5.5 / 13.6 1.4 to 2.4 –1.4 / –1.5 2.9 / 5.5 –64 to 29 9.2 14.3 0.0 .. Benin 21.7 / 33.4 2.0 to 2.5 –2.3 / –2.6 5.5 / 19.2 –207 to 97 1.2 14.1 0.9 .. Bolivia 15.2 / 27.9 2.1 to 3.0 –2.0 / –2.1 5.1 / 16.9 –91 to 137 0.0 0.0 1.3 2.3 Bosnia and Herzegovina 5.0 / 14.7 1.9 to 3.0 –1.5 / –1.5 3.1 / 6.1 –116 to –7 0.1 0.1 0.5 .. Botswana 13.6 / 29.4 2.5 to 3.3 –1.8 / –2.1 4.7 / 12.8 –106 to 24 0.0 0.0 0.7 3.0 Brazil 19.6 / 30.3 1.9 to 2.6 –2.1 / –2.5 5.6 / 19.6 –95 to 136 1.2 4.9 0.5 4.5 Bulgaria 5.6 / 15.5 1.9 to 3.1 –1.4 / –1.3 2.9 / 6.0 –127 to –21 0.4 1.5 0.0 3.5 Burkina Faso 21.5 / 35.0 2.2 to 2.8 –2.1 / –2.6 5.3 / 16.6 –229 to 88 0.0 0.0 1.3 .. Burundi 13.8 / 25.8 2.1 to 2.4 –2.1 / –2.8 7.8 / 26.2 –21 to 206 0.0 0.0 2.4 3.3 Cambodia 22.3 / 31.3 1.6 to 2.0 –1.8 / –1.7 4.5 / 18.2 –109 to 95 3.8 10.6 6.6 .. Cameroon 19.1 / 30.1 2.1 to 2.4 –2.4 / –2.4 6.2 / 22.0 –71 to 115 0.1 0.3 0.1 .. Canada –10.1 / –0.6 2.6 to 3.5 –1.6 / –1.7 1.9 / 4.4 17 to 88 2.4 4.0 0.0 4.3 Central African Republic 18.3 / 31.5 2.1 to 2.4 –2.3 / –2.4 4.7 / 22.5 –73 to 100 0.0 0.0 0.2 .. Chad 18.6 / 34.5 2.3 to 2.6 –1.8 / –2.3 5.1 / 13.7 –79 to 41 0.0 0.0 2.7 .. Chile 3.5 / 13.4 1.2 to 1.9 –1.7 / –1.7 3.8 / 7.3 –118 to 24 3.1 1.6 0.3 2.8 China 0.9 / 13.0 2.1 to 3.0 –1.5 / –1.6 2.9 / 5.6 –37 to 86 1.4 8.1 8.0 .. Hong Kong SAR, China 19.3 / 26.0 1.4 to 1.9 –1.2 / –1.4 7.4 / 10.3 –83 to 73 24.6 26.2 0.0 .. Colombia 19.6 / 29.4 1.8 to 2.5 –2.4 / –2.9 7.9 / 26.6 –80 to 199 0.9 2.0 0.7 3.8 Congo, Dem. Rep. 18.4 / 29.6 2.1 to 2.4 –2.1 / –2.7 6.4 / 24.7 –48 to 128 0.0 0.0 0.0 .. Congo, Rep. 19.8 / 29.3 2.0 to 2.3 –2.3 / –2.7 9.1 / 26.5 –40 to 134 0.1 1.0 0.3 .. Costa Rica 19.6 / 30.0 1.6 to 2.3 –2.8 / –3.0 11.8 / 26.8 –239 to 60 2.1 0.8 0.7 4.5 Côte d’Ivoire 21.1 / 31.6 1.8 to 2.4 –2.4 / –2.5 7.1 / 22.9 –125 to 73 0.2 3.2 0.0 2.5 Croatia 6.2 / 15.6 1.9 to 2.9 –1.6 / –1.6 3.2 / 6.0 –112 to –2 3.0 3.4 0.0 .. Cuba 20.4 / 30.0 1.4 to 1.8 –1.9 / –1.8 10.5 / 17.6 –108 to 39 12.7 10.0 0.7 4.5 Cyprus 13.1 / 23.8 1.7 to 2.3 –1.9 / –1.8 4.0 / 7.3 –98 to –44 6.4 9.7 0.0 .. Czech Republic 3.3 / 11.8 1.9 to 2.8 –1.5 / –1.6 2.5 / 4.7 –43 to 55 0.0 0.0 0.2 2.8 Denmark 4.3 / 10.7 1.6 to 2.5 –1.6 / –1.7 2.6 / 5.2 21 to 87 17.7 18.5 0.0 .. Dominican Republic 19.9 / 29.2 1.5 to 1.8 –2.8 / –2.9 12.1 / 23.6 –128 to 30 4.1 3.0 0.1 3.0 Ecuador 15.6 / 28.1 1.8 to 2.3 –2.6 / –2.8 9.5 / 24.4 22 to 273 2.0 7.3 0.3 4.8 Egypt, Arab Rep. 14.8 / 29.4 1.9 to 2.4 –1.7 / –2.1 4.0 / 8.1 –20 to 1 4.0 25.6 0.0 .. El Salvador 18.4 / 30.5 1.7 to 2.9 –2.2 / –2.3 8.9 / 24.5 –205 to 14 2.5 1.7 0.4 3.3 Eritrea 18.9 / 32.1 2.1 to 2.7 –2.0 / –2.4 6.6 / 14.4 –55 to 26 3.1 1.2 7.3 .. Estonia 1.5 / 8.7 2.2 to 3.2 –1.7 / –1.8 2.1 / 4.8 33 to 93 3.4 7.2 0.0 .. Ethiopia 15.5 / 28.9 2.1 to 2.5 –2.0 / –2.7 6.6 / 21.4 –42 to 79 0.7 0.4 3.3 .. Finland –2.4 / 5.8 2.4 to 3.7 –1.7 / –1.9 1.7 / 4.1 37 to 103 1.1 4.4 0.0 3.5 France 6.5 / 14.9 1.6 to 2.6 –1.5 / –1.5 3.6 / 6.3 –112 to 2 2.1 4.0 0.0 .. Gabon 20.9 / 29.2 1.8 to 2.2 –2.6 / –2.9 13.7 / 26.9 –51 to 148 0.5 5.9 .. .. Gambia, The 20.4 / 34.6 2.1 to 2.7 –2.3 / –2.6 6.7 / 16.8 –87 to 26 16.6 33.4 0.2 .. Georgia 0.4 / 11.2 1.9 to 3.0 –1.3 / –1.3 2.8 / 5.9 –77 to –3 1.4 3.3 0.8 2.8 Germany 4.6 / 12.4 1.7 to 2.6 –1.5 / –1.5 2.6 / 5.0 –38 to 57 4.9 4.4 0.0 4.3 Ghana 22.1 / 32.3 1.8 to 2.4 –2.4 / –2.5 6.6 / 22.1 –159 to 83 0.8 2.3 1.0 3.3 Greece 10.5 / 20.3 1.7 to 2.6 –1.7 / –1.7 3.5 / 6.5 –110 to –40 6.3 9.9 0.0 .. Guatemala 18.2 / 28.7 1.7 to 2.8 –1.7 / –1.7 7.8 / 21.6 –186 to 22 0.6 0.3 1.3 3.3 Guinea 19.6 / 31.8 2.0 to 2.6 –2.4 / –2.5 6.9 / 20.2 –122 to 104 1.1 3.6 0.2 .. Guinea-Bissau 20.9 / 32.6 1.8 to 2.2 –2.7 / –2.7 9.9 / 22.2 –69 to 130 9.5 18.8 0.5 1.0 Haiti 20.1 / 29.7 1.5 to 1.8 –2.8 / –2.8 12.6 / 23.1 –125 to 34 3.9 5.4 0.8 .. 182 2012 World Development Indicators 3.12 ENVIRONMENT Climate variability, exposure to impact, and resilience Climate variability Exposure to impact Resilience % of population Land area Population Population Disaster risk Average daily change in average number with an living in affected by reduction minimum/ of days/nights elevation areas with droughts, progress maximum Projected annual Projected annual Projected annual Projected annual of 5 meters an elevation floods, and score temperature temperature cool days/ hot days/ precipitation or less of 5 meters extreme 1, worst, degrees Celsius degrees Celsius cold nights warm nights millimeters % of land area or less temperature to 5, best 1961–1990 2045–2065 2045–2065 2045–2065 2045–2065 2000 2000 1990–2009 2011 Honduras 18.9 / 28.1 1.6 to 2.6 –2.2 / –2.5 10.2 / 24.5 –204 to 15 3.0 2.2 1.3 3.8 Hungary 4.8 / 14.7 2.0 to 3.0 –1.5 / –1.7 3.0 / 5.3 –75 to 23 0.0 0.0 0.1 .. India 17.7 / 29.6 1.9 to 2.6 –2.0 / –2.2 4.6 / 13.3 –91 to 135 1.4 3.8 4.4 3.3 Indonesia 21.1 / 30.6 1.5 to 1.8 –2.8 / –2.9 16.5 / 26.7 –160 to 234 5.5 11.2 0.2 3.3 Iran, Islamic Rep. 10.1 / 24.4 2.2 to 3.3 –1.4 / –1.4 3.3 / 7.1 –51 to 6 1.6 5.0 3.1 .. Iraq 14.1 / 28.7 2.3 to 3.2 –1.5 / –1.5 3.4 / 6.8 –38 to –2 4.0 6.5 0.0 .. Ireland 5.9 / 12.7 0.9 to 1.4 –1.3 / –1.4 3.0 / 5.6 –13 to 78 4.0 6.6 0.0 .. Israel 13.4 / 25.0 1.9 to 2.7 –1.6 / –1.8 3.7 / 7.7 –41 to –11 7.8 8.3 0.0 .. Italy 9.5 / 17.4 1.8 to 2.6 –1.8 / –1.8 4.0 / 6.7 –108 to –11 5.2 7.5 0.0 3.5 Jamaica 20.6 / 29.3 1.3 to 1.8 –2.9 / –2.8 14.0 / 23.7 –102 to 28 7.1 5.8 1.1 3.8 Japan 7.0 / 15.3 1.8 to 2.3 –1.9 / –2.2 3.5 / 6.0 –47 to 80 5.9 16.2 0.0 4.5 Jordan 11.2 / 25.4 2.1 to 2.9 –1.5 / –1.6 3.7 / 7.4 –28 to –3 2.0 4.2 0.4 .. Kazakhstan 0.6 / 12.2 2.2 to 3.1 –1.2 / –1.4 2.3 / 5.0 –1 to 37 6.7 3.9 0.2 .. Kenya 18.9 / 30.6 1.9 to 2.1 –2.0 / –2.9 8.1 / 25.0 0 to 144 0.2 1.4 6.5 4.0 Korea, Dem. Rep. 0.3 / 11.1 2.1 to 2.7 –1.4 / –1.8 2.6 / 4.6 –9 to 129 2.4 5.3 2.5 .. Korea, Rep. 6.5 / 16.5 1.8 to 2.3 –1.4 / –1.8 3.2 / 5.2 0 to 181 4.3 5.0 0.1 .. Kosovo .. 2.0 to 3.1 –1.5 / –1.3 3.1 / 6.3 –148 to –34 .. .. .. .. Kuwait 18.5 / 32.2 2.2 to 3.1 –1.5 / –1.7 3.9 / 7.4 –19 to 4 8.9 22.8 0.0 .. Kyrgyz Republic –4.6 / 7.7 2.3 to 3.2 –1.2 / –1.4 3.1 / 6.6 –32 to 35 0.0 0.0 2.1 .. Lao PDR 18.2 / 27.4 1.6 to 2.4 –1.1 / –1.3 3.1 / 12.7 –95 to 70 0.0 0.0 2.7 2.3 Latvia 1.9 / 9.3 2.1 to 3.1 –1.7 / –1.8 2.1 / 5.0 31 to 89 3.0 23.9 0.0 .. Lebanon 10.7 / 22.1 2.0 to 2.9 –1.7 / –1.6 3.6 / 7.5 –75 to –27 1.7 9.1 0.0 3.0 Lesotho 5.2 / 18.5 2.0 to 2.5 –1.6 / –1.9 3.7 / 10.1 –66 to 89 0.0 0.0 3.4 2.5 Liberia 20.0 / 30.6 1.7 to 2.2 –2.7 / –2.7 10.2 / 25.3 –115 to 81 0.4 3.3 1.9 .. Libya 14.4 / 29.2 2.0 to 2.4 –1.7 / –2.1 3.5 / 7.6 –18 to 5 0.8 4.7 0.0 .. Lithuania 2.3 / 10.1 2.0 to 3.0 –1.6 / –1.8 2.0 / 4.8 22 to 83 1.9 4.0 0.0 .. Macedonia, FYR 4.5 / 15.1 1.9 to 3.0 –1.4 / –1.3 3.2 / 6.4 –150 to –43 0.0 0.0 0.3 3.3 Madagascar 18.0 / 27.3 1.7 to 2.1 –2.5 / –2.7 7.6 / 15.7 –104 to 56 1.3 2.0 0.9 3.8 Malawi 16.6 / 27.2 2.0 to 2.5 –2.1 / –2.5 3.6 / 15.9 –53 to 91 0.0 0.0 8.8 1.8 Malaysia 21.2 / 29.6 1.6 to 1.9 –2.5 / –2.8 13.0 / 26.3 –160 to 117 3.0 9.5 0.1 3.8 Mali 20.9 / 35.6 2.5 to 3.1 –2.0 / –2.5 5.5 / 12.8 –142 to 43 0.0 0.0 0.7 .. Mauritania 20.2 / 35.1 2.4 to 3.1 –2.0 / –2.4 5.1 / 10.6 –113 to 9 1.0 20.4 3.1 .. Mauritius 18.9 / 25.9 1.3 to 1.7 –2.9 / –2.9 8.5 / 13.7 –165 to 67 7.1 5.6 0.0 3.5 Mexico 13.5 / 28.5 1.7 to 2.8 –1.6 / –1.5 5.6 / 12.1 –178 to 10 2.9 2.7 0.1 4.3 Moldova 5.0 / 13.9 2.0 to 3.3 –1.5 / –1.6 2.8 / 5.2 –72 to 11 1.3 0.9 0.3 .. Mongolia –7.7 / 6.3 2.3 to 3.1 –1.3 / –1.4 2.2 / 4.2 1 to 50 0.0 0.0 2.6 2.8 Morocco 11.1 / 23.1 2.1 to 3.0 –1.8 / –2.0 3.6 / 7.9 –62 to –5 0.7 3.8 0.1 3.0 Mozambique 18.5 / 29.1 1.9 to 2.4 –2.2 / –2.5 5.0 / 15.0 –69 to 61 1.8 6.5 3.7 4.0 Myanmar 17.7 / 8.4 1.7 to 2.3 –1.7 / –1.8 3.8 / 13.9 –84 to 97 4.6 14.0 0.1 .. Namibia 12.7 / 27.2 2.2 to 2.9 –1.9 / –2.2 5.8 / 13.1 –67 to 23 0.3 2.9 3.4 .. Nepal 1.5 / 14.7 2.2 to 3.4 –2.1 / –2.1 2.5 / 8.0 –116 to 231 0.0 0.0 0.7 2.8 Netherlands 5.5 / 13.0 1.4 to 2.3 –1.6 / –1.6 2.9 / 5.3 –18 to 50 58.5 61.3 0.0 .. New Zealand 6.0 / 15.1 1.1 to 1.7 –2.2 / –2.1 4.1 / 7.9 –44 to 53 2.7 12.6 0.0 3.8 Nicaragua 20.3 / 29.5 1.6 to 2.4 –2.6 / –2.8 10.4 / 26.0 –220 to 19 3.6 1.5 0.8 3.8 Niger 19.4 / 34.9 2.4 to 2.8 –1.8 / –2.4 5.4 / 12.2 –71 to 36 0.0 0.0 7.5 .. Nigeria 20.8 / 32.8 2.0 to 2.5 –2.2 / –2.5 5.5 / 19.0 –128 to 89 0.5 3.0 0.1 4.0 Norway –2.0 / 5.0 1.8 to 3.3 –1.8 / –1.8 2.2 / 4.8 41 to 131 4.9 9.3 0.0 3.8 Oman 20.8 / 30.4 2.0 to 2.4 –1.9 / –2.1 4.4 / 11.8 –16 to 31 1.1 5.5 .. .. Pakistan 13.2 / 27.2 2.4 to 3.4 –1.8 / –1.9 3.4 / 8.1 –60 to 36 1.4 1.3 1.1 3.5 Panama 21.3 / 29.5 1.6 to 2.2 –2.9 / –3.0 14.0 / 26.9 –228 to 107 3.7 4.0 0.2 3.0 Papua New Guinea 20.1 / 30.4 1.4 to 1.8 –2.8 / –2.9 15.5 / 25.5 –163 to 421 1.8 2.0 0.7 .. Paraguay 17.7 / 29.4 1.9 to 2.6 –0.8 / –1.2 3.5 / 11.8 –48 to 112 0.0 0.0 0.7 3.8 Peru 13.5 / 25.7 2.1 to 2.7 –2.5 / –2.7 7.7 / 22.9 –57 to 181 0.4 1.7 2.0 3.0 Philippines 21.6 / 30.1 1.4 to 1.8 –2.5 / –2.5 13.5 / 23.8 –136 to 140 6.0 10.5 0.8 .. Poland 3.8 / 11.9 1.9 to 2.8 –1.5 / –1.7 2.3 / 4.8 –12 to 64 1.7 2.5 0.0 3.3 Portugal 10.2 / 20.1 1.3 to 2.3 –2.0 / –1.9 4.5 / 8.7 –113 to –7 2.3 5.2 0.0 .. Puerto Rico 21.2 / 29.3 1.4 to 1.8 –3.0 / –2.9 12.8 / 24.2 –133 to 37 7.7 11.3 0.0 .. Qatar 21.3 / 33.0 2.3 to 2.9 –1.6 / –1.9 4.6 / 8.8 –23 to 8 13.4 23.1 .. .. 2012 World Development Indicators 183 3.12 Climate variability, exposure to impact, and resilience Climate variability Exposure to impact Resilience % of population Land area Population Population Disaster risk Average daily change in average number with an living in affected by reduction minimum/ of days/nights elevation areas with droughts, progress maximum Projected annual Projected annual Projected annual Projected annual of 5 meters an elevation floods, and score temperature temperature cool days/ hot days/ precipitation or less of 5 meters extreme 1, worst, degrees Celsius degrees Celsius cold nights warm nights millimeters % of land area or less temperature to 5, best 1961–1990 2045–2065 2045–2065 2045–2065 2045–2065 2000 2000 1990–2009 2011 Romania 3.8 / 13.8 2.0 to 3.1 –1.5 / –1.6 3.0 / 5.5 –92 to 2 2.9 2.9 0.1 3.3 Russian Federation –10.1 / –0.1 2.6 to 3.7 –1.6 / –1.8 1.7 / 4.0 24 to 79 1.9 2.9 0.1 .. Rwanda 11.9 / 23.8 2.1 to 2.4 –2.2 / –2.8 8.3 / 26.4 –12 to 211 0.0 0.0 1.3 .. Saudi Arabia 18.2 / 31.1 2.3 to 2.9 –1.5 / –1.7 4.6 / 9.5 –27 to 9 0.5 1.0 0.0 .. Senegal 20.8 / 34.9 2.0 to 2.7 –2.3 / –2.6 7.1 / 16.9 –124 to 37 4.5 14.8 0.6 2.8 Serbia .. 2.0 to 3.1 –1.5 / –1.4 3.2 / 6.1 –125 to –15 0.2 0.1 0.0 .. Sierra Leone 21.0 / 31.1 1.8 to 2.3 –2.7 / –2.6 9.2 / 24.0 –96 to 128 3.0 5.1 0.2 3.0 Singapore 22.6 / 30.3 1.5 to 1.8 –2.6 / –2.7 12.4 / 25.9 –134 to 49 8.1 12.1 .. .. Slovak Republic 2.1 / 11.5 2.0 to 2.9 –1.5 / –1.8 2.6 / 4.7 –44 to 45 0.0 0.0 0.0 .. Slovenia 4.2 / 13.6 2.0 to 3.0 –1.4 / –1.5 3.1 / 5.6 –93 to 5 0.2 1.3 0.0 .. Somalia 21.2 / 32.9 1.9 to 2.2 –2.2 / –2.8 9.5 / 21.2 –4 to 111 0.6 2.2 4.6 .. South Africa 10.6 / 24.9 1.9 to 2.7 –1.7 / –2.0 4.1 / 10.4 –78 to 33 0.1 0.5 1.8 .. South Sudan .. .. .. .. .. .. .. Spain 7.9 / 18.7 1.5 to 2.6 –1.9 / –1.8 4.0 / 8.0 –112 to –18 1.3 6.6 0.7 .. Sri Lanka 23.3 / 30.6 1.5 to 1.8 –2.8 / –2.9 7.9 / 23.9 11 to 196 3.9 5.4 2.2 3.5 Sudan 19.4 / 34.4 2.1 to 2.6 –1.8 / –2.2 5.2 / 16.6 –69 to 44 0.1 0.2 2.8 .. Swaziland 15.5 / 27.3 2.0 to 2.4 –1.6 / –2.1 3.8 / 10.9 –82 to 34 0.0 0.0 9.2 .. Sweden –2.1 / 6.3 2.1 to 3.1 –1.7 / –1.8 2.0 / 4.4 35 to 107 1.5 6.3 0.0 3.8 Switzerland 2.0 / 9.0 1.7 to 2.8 –1.4 / –1.5 3.1 / 5.6 –89 to 11 0.0 0.0 0.0 4.8 Syrian Arab Republic 10.8 / 24.7 2.2 to 3.1 –1.6 / –1.3 3.3 / 6.7 –57 to –13 0.1 0.3 0.5 3.5 Tajikistan –3.9 / 7.9 2.3 to 3.4 –1.4 / –1.5 3.2 / 7.0 –46 to 27 0.0 0.0 5.4 .. Tanzania 16.7 / 28.0 1.9 to 2.2 –2.1 / –2.8 6.2 / 21.6 –9 to 167 0.2 1.3 1.5 3.5 Thailand 21.2 / 31.4 1.7 to 2.1 –1.6 / –1.6 3.6 / 17.0 –109 to 76 4.2 13.8 3.8 3.8 Timor-Leste .. 1.3 to 1.7 –3.0 / –3.0 16.1 / 25.9 –123 to 206 2.9 4.4 0.0 .. Togo 21.8 / 32.5 1.9 to 2.5 –2.4 / –2.5 6.0 / 21.5 –175 to 100 0.6 6.1 0.5 .. Trinidad and Tobago 21.5 / 30.0 1.5 to 2.1 –2.9 / –3.0 14.7 / 26.5 –266 to 8 8.0 7.5 0.0 .. Tunisia 13.5 / 24.9 1.9 to 2.6 –1.9 / –2.0 3.4 / 6.8 –57 to –16 2.8 9.5 0.1 .. Turkey 5.4 / 16.8 1.9 to 2.9 –1.5 / –1.4 3.1 / 6.3 –103 to –29 1.0 2.4 0.1 .. Turkmenistan 8.7 / 21.5 1.8 to 3.0 –1.0 / –1.1 2.7 / 5.7 –32 to 12 5.3 5.6 0.0 .. Uganda 16.6 / 29.0 2.1 to 2.3 –2.1 / –2.8 6.4 / 25.6 –13 to 224 0.0 0.0 0.9 .. Ukraine 4.0 / 12.6 2.1 to 3.0 –1.4 / –1.6 2.6 / 4.8 –43 to 31 1.5 2.1 0.3 .. United Arab Emirates 21.0 / 33.0 2.2 to 2.8 –1.6 / –2.0 4.2 / 9.4 –26 to 17 4.6 7.3 .. .. United Kingdom 5.1 / 11.8 1.1 to 1.8 –1.4 / –1.5 3.0 / 5.4 –10 to 66 7.6 8.6 0.0 .. United States 2.2 / 14.9 2.0 to 3.0 –1.4 / –1.5 3.1 / 6.4 –26 to 98 1.7 4.1 0.2 3.5 Uruguay 12.2 / 22.9 1.4 to 1.8 –0.8 / –1.0 2.0 / 5.7 0 to 130 1.9 4.7 0.3 .. Uzbekistan 5.8 / 18.3 2.0 to 3.0 –1.1 / –1.3 2.6 / 5.7 –26 to 17 0.1 0.0 0.1 .. Venezuela, RB 20.3 / 30.4 1.8 to 2.9 –2.5 / –2.9 9.1 / 26.5 –150 to 85 2.3 3.7 0.2 2.8 Vietnam 20.6 / 28.3 1.4 to 2.1 –1.5 / –1.5 5.7 / 14.4 –107 to 61 17.5 42.8 1.6 .. West Bank and Gaza .. 2.0 to 2.6 –1.7 / –1.8 3.8 / 7.9 –40 to –13 14.8 11.2 .. .. Yemen, Rep. 18.6 / 29.1 2.0 to 2.6 –2.0 / –2.2 5.6 / 13.1 –24 to 51 0.6 1.8 0.1 2.3 Zambia 14.5 / 28.3 2.1 to 2.7 –2.0 / –2.5 4.0 / 17.7 –54 to 94 0.0 0.0 4.2 3.8 Zimbabwe 14.2 / 27.8 2.2 to 2.9 –1.9 / –2.3 3.8 / 13.2 –81 to 24 0.0 0.0 .. .. World 1.8 t 6.5 t .. .. Low income 0.7 5.1 .. .. Middle income 1.8 6.5 .. .. Lower middle income 1.7 6.4 .. .. Upper middle income 1.8 6.5 .. .. Low & middle income 1.6 6.3 .. .. East Asia & Pacific 2.5 10.3 .. .. Europe & Central Asia 2.5 3.0 .. .. Latin America & Carib. 1.5 3.8 .. .. Middle East & N. Africa 1.4 9.7 .. .. South Asia 1.5 4.4 .. .. Sub-Saharan Africa 0.4 2.0 .. .. High income 2.2 7.7 .. .. Euro area 3.6 8.5 .. .. 184 2012 World Development Indicators 3.12 ENVIRONMENT Climate variability, exposure to impact, and resilience About the data Scientists use the terms climate change and global wave can be both a prolonged period of excessively at an elevation of 5 meters or less is the percent- warming to refer to the gradual increase in the cold weather and the sudden invasion of very cold age of the population living in areas with an eleva- Earth’s surface temperature that has accelerated air over a large area. Accompanied by frost, it can tion of 5 meters or less. • Population affected by since the industrial revolution and especially over damage agriculture, infrastructure, and property. droughts, floods, and extreme temperature is the the past two decades. Most global warming has been A heat wave is a prolonged period of excessively average annual percentage of the population that is caused by human activities that have changed the hot and sometimes humid weather. Population affected by natural disasters classified as droughts, chemical composition of the atmosphere through affected by these natural disasters is the num- floods, or extreme temperature events. • Disaster a buildup of greenhouse gases—primarily carbon ber of people injured, left homeless, or requiring risk reduction progress score is the average of self- dioxide, methane, and nitrous oxide. Rising global immediate assistance and can include displaced assessment scores, ranging from 1 to 5, where 1 is temperatures will cause sea level rise and alter local or evacuated people. the worst and 5 is the best, submitted by countries climate conditions, affecting forests, crop yields, • Resilience is measured by the disaster risk reduc- under Priority 1 of the Hyogo Framework National and water supplies, and may affect human health, tion progress score, an average of self-assess- Progress Reports. animals, and many types of ecosystems. The 2007 ment scores submitted by countries under Prior- Intergovernmental Panel on Climate Change’s (IPCC) ity 1 of the Hyogo Framework National Progress assessment report concluded that global warming Reports. The Hyogo Framework is a global blueprint is “unequivocal� and gave the strongest warning yet for disaster risk reduction efforts that was adopted about the role of human activities. The report esti- by 168 countries in 2005. Assessments of Priority mated that sea levels would rise approximately 49 1 include four indicators that reflect the degree to centimeters over the next 100 years, with a range of which countries have prioritized disaster risk reduc- uncertainty of 20–86 centimeters. That will lead to tion and the strengthening of relevant institutions. increased coastal flooding through direct inundation De�nitions and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model • Average daily minimum and maximum tempera- simulations predict an increase in average surface ture are the minimum and maximum daily tem- air temperature of about 2.5°C by 2100 (Kattenberg peratures in the country averaged over the period and others 1996) and increase of “killer� heat waves specified, based on gridded climatologies from the during the warm season (Karl and others 1997). Climatic Research Unit of the University of East Data sources Data in the table are categorized into three climate Anglia. • Projected change in annual temperature change topics. is the projected change in annual temperature during Data on average daily minimum and maximum • Climate variability contains such indicators as aver- the period specified, relative to the control period temperature are from Mitchell and others (2003). age daily minimum and maximum temperature, 1961–2000. •  Projected change in annual cool Data on projected change in annual temperature projected change in annual temperature, projected days and cold nights are the projected change in are from IPCC (2007). Data on projected change change in annual cool days and cold nights and the annual number of cool days and cold nights dur- in annual cool days and cold nights and projected hot days and warm nights, and projected change ing the period specified, relative to the control period change in annual hot days and warm nights are in annual precipitation. These indicators are use- 1961–2000. Cool days are those that fall below the from University of Cape Town Climate Systems ful for understanding critical thresholds related 10th percentile of maximum temperature in the con- Analysis Group calculations based on data from to heat stress in such sectors as agriculture and trol period, and cold nights are those that fall below Meehl and others (2007). Data on projected energy. the 10th percentile of minimum temperature in the change in annual precipitation are from Meehl and • Exposure to impact contains indicators that mea- control period. •  Projected change in annual hot others (2007). Data on land area with an elevation sure vulnerability to the impacts of climate change, days and warm nights are the projected change in of 5 meters or less and population living in areas such as land area with an elevation of 5 meters the annual number of hot days and warm nights dur- with an elevation 5 meters or less are from CIESIN or less and population living in such areas as well ing the period specified, relative to the control period (2007). Data on population affected by droughts, as population affected by droughts, floods, and 1961–2000. Hot days are those that exceed the floods, and extreme temperature are from the extreme temperature. A drought is an extended 90th percentile of maximum temperatures in the con- United States Agency for International Develop- period of deficiency in a region’s water supply as trol period, and warm nights are those that exceed ment Office of Foreign Disaster Assistance/Center a result of below average precipitation. A drought the 90th percentile of minimum temperatures in the for Research on the Epidemiology of Disasters can lead to losses in agriculture, affect inland control period. • Projected change in annual precipi- International Disaster Database (www.emdat.be), navigation and hydropower plants, reduce access tation is the projected change in annual precipitation Université Catholique de Louvain, and the World to drinking water, and cause famines. A flood is during the period specified, relative to the control Bank. Data on disaster risk reduction progress a significant rise of water level in a stream, lake, period 1961–2000. •  Land area at an elevation score are from UNISDR (2009, 2010, 2011) Prog- reservoir, or coastal region. Extreme temperature of 5 meters or less is the percentage of land area ress Reports. events are either cold waves or heat waves. A cold with an elevation of 5 meters or less. • Population 2012 World Development Indicators 185 3.13 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 2010 1990 2010 1990–2010 1990 2010 1990 2010 1990 2010 1990 2010 Afghanistan 3 9 18 25 4.3 7 11 37 44 .. 60 .. 30 Albania 1 2 36 48 1.7 .. .. 21 29 94 95 66 93 Algeria 13 24 52 67 2.4 7 8 14 12 99 98 77 88 Angola 4 11 37 59 4.3 15 25 41 43 67 85 6 19 Argentina 28 37 87 92 1.1 39 39 37 35 93 91 73 77 Armenia 2 2 68 64 0.1 33 36 49 56 95 95 .. 80 Australia 15 20 85 89 1.8 60 58 25 22 100 100 100 100 Austria 5 6 66 68 0.6 20 20 30 30 100 100 100 100 Azerbaijan 4 5 54 52 1.5 24 22 45 42 .. 86 .. 78 Bahrain 0 1 88 89 7.6 .. .. 29 16 100 100 .. .. Bangladesh 21 42 20 28 2.8 9 14 32 35 58 57 34 55 Belarus 7 7 66 74 0.4 16 20 24 26 91 91 96 97 Belgium 10 11 96 97 0.9 17 17 17 18 100 100 100 100 Benin 2 4 35 42 3.8 .. .. 31 23 14 25 0 5 Bolivia 4 7 56 67 2.3 25 33 29 25 28 35 6 10 Bosnia and Herzegovina 2 2 39 49 1.0 .. .. 24 22 98 99 .. 92 Botswana 1 1 42 61 2.5 .. .. 21 16 61 75 22 41 Brazil 112 169 75 87 1.4 35 41 13 12 80 85 33 44 Bulgaria 6 5 66 72 –0.3 14 16 21 22 100 100 98 100 Burkina Faso 1 3 14 20 5.1 6 12 42 57 43 50 2 6 Burundi 0 1 6 11 5.3 .. .. 66 52 41 49 44 46 Cambodia 1 3 13 23 3.9 6 11 51 48 36 73 5 20 Cameroon 5 11 41 58 3.6 14 20 19 19 63 58 37 36 Canada 21 28 77 81 1.3 40 44 18 20 100 100 99 99 Central African Republic 1 2 37 39 2.3 .. .. 43 42 21 43 5 28 Chad 1 3 21 28 4.3 .. .. 38 27 21 30 4 6 Chile 11 15 83 89 1.2 35 35 42 39 91 98 48 83 China 311 601 27 45 2.5 9 18 3 3 48 74 15 56 Hong Kong SAR, China 6 7 100 100 0.9 100 105 100 100 .. .. .. .. Colombia 23 35 68 75 1.8 31 38 21 24 79 82 40 63 Congo, Dem. Rep. 10 23 28 35 4.5 13 18 35 38 23 24 4 24 Congo, Rep. 1 3 54 62 3.2 29 33 54 53 .. 20 .. 15 Costa Rica 2 3 51 64 2.3 24 31 47 49 94 95 91 96 Côte d’Ivoire 5 10 40 50 3.3 17 21 42 42 38 36 8 11 Croatia 3 3 54 58 0.2 .. .. 27 27 99 99 98 98 Cuba 8 9 73 76 0.0 20 19 27 25 86 94 64 81 Cyprus 1 1 67 70 1.5 .. .. 33 31 100 100 100 100 Czech Republic 8 8 75 74 0.3 12 11 16 15 100 99 98 97 Denmark 4 5 85 87 0.7 20 21 24 25 100 100 100 100 Dominican Republic 4 7 55 71 2.4 21 22 38 31 83 87 61 75 Ecuador 6 10 55 67 2.4 26 31 28 28 86 96 48 84 Egypt, Arab Rep. 25 35 44 43 1.8 21 19 37 32 91 97 57 93 El Salvador 3 4 49 61 1.0 18 25 37 41 88 89 62 83 Eritrea 0 1 16 22 5.1 .. .. 72 60 58 52 0 4 Estonia 1 1 71 70 0.0 .. .. 43 43 96 96 94 94 Ethiopia 6 15 13 18 3.9 4 4 29 20 20 29 1 19 Finland 3 3 61 64 0.9 17 21 28 33 100 100 100 100 France 43 50 74 78 0.8 22 22 22 21 100 100 100 100 Gabon 1 1 69 86 2.4 .. .. 62 49 .. 33 .. 30 Gambia, The 0 1 38 58 4.2 .. .. 62 45 .. 70 .. 65 Georgia 3 2 55 53 1.1 25 25 46 48 97 96 95 93 Germany 58 60 73 74 0.0 8 8 6 6 100 100 100 100 Ghana 5 13 36 52 3.8 13 17 22 19 12 19 4 8 Greece 6 7 59 61 0.6 30 29 51 47 100 99 93 97 Guatemala 4 7 41 50 3.4 9 8 22 16 81 87 48 70 Guinea 2 4 28 35 3.6 16 17 55 47 19 32 6 11 Guinea-Bissau 0 0 28 30 2.3 .. .. 54 68 .. 44 4 9 Haiti 2 5 29 50 4.1 16 21 56 43 44 24 19 10 186 2012 World Development Indicators 3.13 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 2010 1990 2010 1990–2010 1990 2010 1990 2010 1990 2010 1990 2010 Honduras 2 4 40 49 2.9 12 14 29 28 71 85 36 69 Hungary 7 7 66 68 0.4 19 17 29 25 100 100 100 100 India 223 369 26 30 2.3 10 12 4 6 51 58 7 23 Indonesia 56 129 31 54 3.1 10 9 14 7 56 73 21 39 Iran, Islamic Rep. 31 51 56 70 1.9 24 24 21 14 83 100 74 100 Iraq 13 21 70 66 2.8 27 23 32 28 .. 76 .. 67 Ireland 2 3 57 62 0.8 26 25 46 40 100 100 98 98 Israel 4 7 90 92 1.8 56 57 48 47 100 100 100 100 Italy 38 41 67 68 0.7 19 17 9 8 .. .. .. .. Jamaica 1 1 49 54 0.6 .. .. 49 40 78 78 81 82 Japan 78 85 63 67 0.2 46 49 42 43 100 100 100 100 Jordan 2 5 72 79 2.3 27 18 37 23 98 98 95 98 Kazakhstan 9 10 56 59 1.9 7 8 12 14 96 97 97 98 Kenya 4 9 18 22 4.0 6 9 32 39 27 32 25 32 Korea, Dem. Rep. 12 15 58 63 1.0 13 12 21 18 .. 86 .. 71 Korea, Rep. 32 40 74 82 0.5 51 48 33 24 100 100 100 100 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 2 3 98 98 3.4 67 84 68 86 100 100 100 100 Kyrgyz Republic 2 2 38 37 1.6 .. .. 38 43 94 94 .. 93 Lao PDR 1 2 15 33 5.0 .. .. 70 40 .. 89 .. 50 Latvia 2 2 69 68 –0.6 .. .. 49 46 .. 82 .. 71 Lebanon 2 4 83 87 0.9 44 46 53 53 100 100 .. .. Lesotho 0 1 14 27 3.7 .. .. 49 39 .. 32 .. 24 Liberia 1 2 45 62 5.2 .. .. 108 34 .. 29 .. 7 Libya 3 5 76 78 1.7 20 17 26 22 97 97 96 96 Lithuania 2 2 68 67 –1.4 .. .. 23 24 95 95 .. .. Macedonia, FYR 1 1 58 68 0.9 .. .. 40 35 92 92 .. 82 Madagascar 3 6 24 30 4.0 8 9 36 30 15 21 7 12 Malawi 1 3 12 20 5.7 .. .. 24 29 48 49 38 51 Malaysia 9 21 50 72 2.9 8 9 12 7 88 96 81 95 Mali 2 5 23 33 4.7 9 11 37 33 33 35 10 14 Mauritania 1 1 40 41 2.9 .. .. 53 51 29 51 8 9 Mauritius 0 1 44 43 0.6 .. .. 30 28 91 91 88 88 Mexico 60 88 71 78 1.6 33 35 25 22 76 87 34 79 Moldova 2 1 47 41 –0.8 .. .. 38 44 .. 89 .. 82 Mongolia 1 2 57 58 1.9 .. .. 46 61 .. 64 .. 29 Morocco 12 18 48 57 1.6 18 19 22 18 81 83 27 52 Mozambique 3 9 21 38 4.4 6 7 27 18 36 38 4 5 Myanmar 10 16 25 34 2.7 9 11 30 27 .. 83 .. 73 Namibia 0 1 28 38 3.4 .. .. 36 41 62 57 9 17 Nepal 2 5 9 18 4.4 .. .. 23 19 37 48 7 27 Netherlands 10 14 69 83 1.2 13 12 9 8 100 100 100 100 New Zealand 3 4 85 87 1.3 25 32 30 37 .. .. 88 .. Nicaragua 2 3 52 57 1.8 18 23 34 28 59 63 26 37 Niger 1 3 15 17 4.0 6 7 36 40 19 34 2 4 Nigeria 34 79 35 50 4.0 12 15 14 13 39 35 36 27 Norway 3 4 72 78 1.3 .. .. 22 23 100 100 100 100 Oman 1 2 66 72 2.6 .. .. 27 33 96 100 55 95 Pakistan 34 64 31 37 2.9 15 18 21 20 72 72 7 34 Panama 1 3 54 75 2.6 35 39 65 52 73 75 40 51 Papua New Guinea 1 1 15 13 2.1 .. .. 31 37 78 71 42 41 Paraguay 2 4 49 62 2.7 26 31 53 51 61 90 15 40 Peru 15 21 69 72 1.2 27 31 39 43 71 81 17 37 Philippines 30 62 49 66 2.8 14 14 27 19 69 79 45 69 Poland 23 23 61 61 0.0 4 4 7 7 96 96 .. 80 Portugal 5 6 48 61 1.1 37 39 53 44 97 100 87 100 Puerto Rico 3 4 72 99 0.5 44 69 60 70 .. .. .. .. Qatar 0 2 92 96 9.7 .. .. 54 28 100 100 100 100 2012 World Development Indicators 187 3.13 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 2010 1990 2010 1990–2010 1990 2010 1990 2010 1990 2010 1990 2010 Romania 12 12 53 55 0.1 9 9 17 17 88 88 52 54 Russian Federation 109 103 73 73 –0.1 17 18 8 10 80 74 58 59 Rwanda 0 2 5 19 4.5 .. .. 57 57 69 52 34 56 Saudi Arabia 12 23 77 84 4.0 34 39 19 21 100 100 .. .. Senegal 3 5 39 43 3.3 19 23 50 54 62 70 22 39 Serbia 4 4 50 52 –0.1 15 15 30 29 96 96 .. 88 Sierra Leone 1 2 33 38 3.0 .. .. 40 40 22 23 5 6 Singapore 3 5 100 100 1.8 99 95 99 95 99 100 .. .. Slovak Republic 3 3 57 57 0.4 .. .. .. .. 100 100 100 99 Slovenia 1 1 50 48 –0.2 .. .. 27 26 100 100 100 100 Somalia 2 3 30 37 3.5 16 16 53 43 .. 52 .. 6 South Africa 18 31 52 62 2.1 28 34 10 12 82 86 60 67 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. .. Spain 29 36 75 77 0.5 22 24 15 16 100 100 100 100 Sri Lanka 3 3 17 15 0.9 .. .. 21 22 85 88 67 93 Sudan 7 20 27 45 4.5 9 12 33 26 51 44 18 14 Swaziland 0 0 23 26 2.6 .. .. 22 25 62 64 44 55 Sweden 7 8 83 85 0.9 12 14 15 16 100 100 100 100 Switzerland 5 6 73 74 1.1 15 15 20 20 100 100 100 100 Syrian Arab Republic 6 11 49 55 2.6 31 34 26 27 95 96 75 93 Tajikistan 2 2 32 27 1.5 .. .. 35 39 93 95 .. 94 Tanzania 5 12 19 26 4.7 5 7 27 28 10 20 6 7 Thailand 17 24 29 34 1.6 10 10 35 30 94 95 80 96 Timor-Leste 0 0 21 28 3.6 .. .. 79 55 .. 73 .. 37 Togo 1 3 30 43 3.7 17 28 56 64 26 26 8 3 Trinidad and Tobago 0 0 9 14 2.9 .. .. 45 32 93 92 93 92 Tunisia 5 7 58 67 1.6 .. .. 14 11 95 96 44 64 Turkey 32 51 59 70 1.9 24 29 20 21 96 97 66 75 Turkmenistan 2 2 45 50 2.1 .. .. 25 26 99 99 97 97 Uganda 2 4 11 13 4.4 4 5 38 36 32 34 26 34 Ukraine 35 31 67 68 –0.3 12 14 7 9 97 96 .. 89 United Arab Emirates 1 6 79 78 8.0 26 21 33 27 98 98 95 95 United Kingdom 51 56 89 90 0.8 26 26 15 15 100 100 100 100 United States 188 255 75 82 1.2 42 45 9 8 100 100 99 99 Uruguay 3 3 89 93 0.5 50 49 56 53 95 100 83 99 Uzbekistan 8 10 40 37 1.8 10 8 26 21 95 100 76 100 Venezuela, RB 17 27 84 94 1.9 34 32 17 11 89 .. 45 .. Vietnam 13 25 20 29 2.7 9 13 25 25 63 94 30 68 West Bank and Gaza 1 3 68 72 2.8 .. .. .. .. 91 92 .. 92 Yemen, Rep. 2 8 21 32 4.9 5 10 26 31 70 93 12 34 Zambia 3 5 39 36 2.0 10 11 24 31 61 57 37 43 Zimbabwe 3 5 29 38 2.0 10 13 35 34 54 52 35 32 World 2,262 s 3,482 s 43 w 51 w 2.0 w 17 w 20 w 17 w 16 w 76 w 79 w 27 w 47 w Low income 108 225 21 28 3.6 8 11 34 33 38 47 17 32 Middle income 1,456 2,401 38 48 2.2 14 18 15 13 68 75 22 45 Lower middle income 560 992 32 39 2.7 12 14 16 14 61 66 17 34 Upper middle income 896 1,409 44 57 1.9 16 22 14 12 71 82 27 62 Low & middle income 1,564 2,626 36 46 2.3 13 17 16 15 66 72 21 43 East Asia & Pacific 463 901 29 46 2.7 .. .. 9 7 54 76 20 57 Europe & Central Asia 246 259 63 64 0.5 16 18 15 16 88 87 68 80 Latin America & Carib. 309 462 71 79 1.6 32 35 24 22 80 84 38 59 Middle East & N. Africa 117 192 52 58 2.2 20 20 26 22 89 94 57 80 South Asia 286 492 25 30 2.5 10 13 9 11 54 60 11 28 Sub-Saharan Africa 144 319 28 37 3.9 11 14 27 26 42 42 19 23 High income 698 856 73 78 1.0 .. .. 20 19 100 100 99 99 Euro area 215 244 71 74 0.6 18 18 16 15 100 100 99 100 188 2012 World Development Indicators 3.13 ENVIRONMENT Urbanization About the data De�nitions There is no consistent and universally accepted • Urban population is the midyear population of standard for distinguishing urban from rural areas, areas defined as urban in each country and reported in part because of the wide variety of situations to the United Nations (see About the data). • Popula- across countries (see About the data for table 3.1). tion in urban agglomerations of more than 1 million Most countries use an urban classification related is the percentage of a country’s population living in to the size or characteristics of settlements. Some metropolitan areas that in 2005 had a population of define urban areas based on the presence of cer- more than 1 million. • Population in largest city is tain infrastructure and services. And other countries the percentage of a country’s urban population living designate urban areas based on administrative in that country’s largest metropolitan area. • Access arrangements. to improved sanitation facilities is the percentage The population of a city or metropolitan area of the urban or rural population with access to at depends on the boundaries chosen. For example, in least adequate excreta disposal facilities (private or 1990 Beijing, China, contained 2.3 million people in shared but not public) that can effectively prevent 87 square kilometers of “inner city� and 5.4 million human, animal, and insect contact with excreta. in 158 square kilometers of “core city.� The popula- Improved facilities range from simple but protected tion of “inner city and inner suburban districts� was pit latrines to flush toilets with a sewerage connec- 6.3 million and that of “inner city, inner and outer tion. To be effective, facilities must be correctly con- suburban districts, and inner and outer counties� structed and properly maintained. was 10.8 million. (Most countries use the last defini- tion.) For further discussion of urban-rural issues see box 3.1a in About the data for table 3.1. Estimates of the world’s urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China’s State Statis- tical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China’s popula- tion, more than double the 20 percent considered urban in 1994. In addition to the continuous migra- tion of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years. Because the estimates in the table are based on national definitions of what constitutes a city or met- ropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank’s estimates of total population (see table 2.1). The table shows access to improved sanitation Data sources facilities for both urban and rural populations to allow comparison of access. Definitions of access Data on urban population and the population in and urban areas vary, however, so comparisons urban agglomerations and in the largest city are between countries can be misleading. from the United Nations Population Division’s World Urbanization Prospects: The 2010 Revi- sion. Data on total population are World Bank estimates. Data on access to sanitation are from the World Health Organization and United Nations Children’s Fund’s Progress on Drinking Water and Sanitation (2012). 2012 World Development Indicators 189 3.14 Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units 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 .. .. Bahrain   .. .. .. .. .. .. .. .. .. .. .. .. 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 .. .. .. .. .. .. .. .. .. Cyprus .. .. .. .. .. .. .. .. .. .. .. .. 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, The 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 19 190 2012 World Development Indicators 3.14 ENVIRONMENT Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units 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 Honduras 2001 4.4 .. .. .. 69 85 .. .. .. .. 14 .. 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 .. .. .. .. .. .. .. .. .. .. Lao PDR 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 3.6b 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 .. 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   .. .. .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 191 3.14 Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units 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 .. .. .. .. South Sudan .. .. .. .. .. .. .. .. .. .. .. .. 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 4.5b 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 3.4b 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, Rep. 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. 192 2012 World Development Indicators 3.14 ENVIRONMENT Urban housing conditions About the data De�nitions Urbanization can yield important social benefi ts, •  Census year is the year in which the underlying improving access to public services and the job mar- data were collected. • Household size is the aver- ket. It also leads to significant demands for services. age number of people within a household, calcu- Inadequate living quarters and demand for housing lated by dividing total population by the number and shelter are major concerns for policymakers. of households in the country and in urban areas. The unmet demand for affordable housing, along • Overcrowding refers to the number of households with urban poverty, has led to the emergence of living in dwellings with two or more people per room slums in many poor countries. Improving the shel- as a percentage of total households in the country ter situation requires a better understanding of the and in urban areas. •  Durable dwelling units are mechanisms governing housing markets and the pro- the number of housing units in structures made of cesses governing housing availability. That requires durable building materials (concrete, stone, cement, good data and adequate policy-oriented analysis so brick, asbestos, zinc, and stucco) expected to main- that housing policy can be formulated in a global 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 There is a strong demand for quantitative indi- units are included, depending on the definition used cators that can measure housing conditions on a in the census data. State- and community-owned regular basis to monitor progress. However, data units and rented, squatted, and rent-free units are deficiencies and lack of rigorous quantitative analy- excluded. •  Multiunit dwellings are the number sis hamper informed decisionmaking on desirable of multiunit dwellings, such as apartments, flats, policies to improve housing conditions. The data condominiums, barracks, boardinghouses, orphan- in the table are from housing and population cen- ages, retirement houses, hostels, hotels, and col- suses, collected using similar definitions. The table lective dwellings, as a percentage of total dwellings. will incorporate household survey data in future edi- • Vacancy rate is the percentage of completed dwell- tions. The table focuses attention on urban areas, ing units that are currently unoccupied. It includes where housing conditions are typically most severe. all vacant units, whether on the market or not (such Not all the compiled indicators are presented in the as second homes). table because of space limitations. Data sources Data on urban housing conditions are from national population and housing censuses. 2012 World Development Indicators 193 3.15 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 Super grade micrograms per people of road people land area consumption Total Diesel fuel Gasoline fuel gasoline Diesel cubic meter 2009 2009 2009 2009 2009 2009 2009 2009 2010 2010 1990 2009 Afghanistan 29 11 21 6 .. .. .. .. 1.15 1.00 69 32 Albania 120 .. 89 63 23 123 120 15 1.46 1.40 92 37 Algeria 112 35 74 5 23 265 177 67 0.32 0.19 112 75 Angola 38 .. 8 4 13 85 45 36 0.65 0.43 112 56 Argentina 314 .. .. 8 17 307 140 105 0.96 1.05 104 60 Armenia 103 42 94 26 22 182 38 60 1.08 0.99 367 61 Australia 688 18 550 11 18 1,103 433 610 1.27 1.23 22 14 Austria 569 45 521 127 23 878 594 208 1.63 1.55 39 27 Azerbaijan 104 13 86 61 12 155 45 109 0.75 0.56 133 29 Bahrain 537 104 451 575 12 955 369 535 0.21 0.13 75 45 Bangladesh 3 .. 2 166 7 14 10 3 1.09 0.63 228 121 Belarus 282 .. 240 46 7 192 107 50 1.08 0.86 24 7 Belgium 552 39 483 504 15 814 657 125 1.87 1.62 31 21 Benin 22 .. 18 17 27 110 41 63 1.04 1.21 77 48 Bolivia 68 9 18 7 32 206 98 68 0.70 0.54 105 60 Bosnia and Herzegovina 138 20 121 43 15 234 148 81 1.42 1.42 36 19 Botswana 133 7 69 4 31 318 131 174 0.93 0.97 92 66 Brazil 209 18 178 21 24 298 149 73 1.58 1.14 39 19 Bulgaria 375 72 330 36 15 336 192 81 1.51 1.58 108 45 Burkina Faso 11 1 7 34 .. .. .. .. 1.44 1.28 143 63 Burundi 6 .. 2 44 .. .. .. .. 1.43 1.42 60 28 Cambodia 21 7 18 21 7 26 13 12 1.15 0.98 107 37 Cameroon 14 .. 10 6 12 42 23 18 1.20 1.10 122 58 Canada 607 15 420 14 17 1,313 405 886 1.21 1.08 25 16 Central African Republic 0 .. 0 4 .. .. .. .. 1.71 1.69 60 33 Chad 6 1 2 3 .. .. .. .. 1.32 1.31 209 82 Chile 174 38 118 10 21 355 208 147 1.38 1.02 89 53 China 47 16 34 40 5 92 56 45 1.11 1.04 115 60 Hong Kong SAR, China 74 254 56 188 13 285 224 50 1.92 1.32 .. .. Colombia 71 25 53 15 21 144 77 54 1.41 0.95 38 20 Congo, Dem. Rep. 5 .. .. 7 1 4 0 3 1.28 1.27 71 38 Congo, Rep. 27 .. 16 5 27 98 68 26 1.27 0.84 130 60 Costa Rica 166 20 130 76 30 319 156 147 1.14 0.97 43 29 Côte d’Ivoire 20 5 16 25 4 21 17 6 1.68 1.30 87 29 Croatia 384 58 346 52 22 435 268 153 1.59 1.49 45 25 Cuba 38 .. 21 55 4 40 16 22 1.72 1.24 31 15 Cyprus 675 48 529 134 30 682 294 351 1.47 1.47 60 28 Czech Republic 482 39 423 166 14 557 338 186 1.75 1.69 41 17 Denmark 478 36 380 170 22 727 456 292 2.00 1.79 28 16 Dominican Republic 128 .. 87 26 17 144 59 78 1.23 1.03 43 16 Ecuador 57 18 36 17 38 305 174 148 0.53 0.28 36 20 Egypt, Arab Rep. 45 37 33 10 18 159 86 61 0.48 0.32 214 88 El Salvador 54 .. 49 48 17 140 62 71 0.92 0.89 44 28 Eritrea 11 .. 6 3 5 7 6 1 2.54 1.07 196 64 Estonia 471 11 407 129 14 493 292 218 1.54 1.57 44 9 Ethiopia 3 5 1 4 4 17 14 2 0.91 0.78 108 51 Finland 532 36 459 23 12 728 409 299 1.94 1.60 22 15 France 598 39 496 173 16 642 471 123 1.98 1.72 18 12 Gabon .. .. .. 3 6 78 53 22 .. .. 9 5 Gambia, The 8 3 5 33 .. .. .. .. .. .. 136 61 Georgia 151 28 126 29 22 156 69 79 1.13 1.13 224 54 Germany 564 72 510 180 16 612 323 236 1.90 1.68 27 16 Ghana 30 6 18 46 16 62 37 27 0.82 0.83 38 21 Greece 573 55 455 89 24 632 269 358 2.05 1.78 64 31 Guatemala 98 .. 37 13 21 145 70 68 0.95 0.85 69 68 Guinea .. .. .. 18 .. .. .. .. 0.95 0.95 103 54 Guinea-Bissau 33 .. 27 12 .. .. .. .. .. .. 114 45 Haiti .. .. .. 15 9 25 19 23 .. .. 68 33 194 2012 World Development Indicators 3.15 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 Super grade micrograms per people of road people land area consumption Total Diesel fuel Gasoline fuel gasoline Diesel cubic meter 2009 2009 2009 2009 2009 2009 2009 2009 2010 2010 1990 2009 Honduras 95 .. 29 12 23 136 65 64 1.04 0.92 43 34 Hungary 347 18 301 212 17 433 259 149 1.67 1.61 34 15 India 18 5 12 125 7 37 26 11 1.15 0.82 110 57 Indonesia 79 38 45 25 14 119 44 75 0.79 0.51 132 68 Iran, Islamic Rep. 128 51 113 11 18 531 223 237 0.10 0.02 92 55 Iraq 77 55 27 9 32 334 185 134 0.78 0.56 168 110 Ireland 513 24 434 137 28 900 519 347 1.78 1.69 23 12 Israel 316 128 265 83 27 766 347 380 1.85 1.87 66 23 Italy 672 80 596 162 22 602 376 172 1.87 1.69 41 21 Jamaica 120 15 84 201 16 189 65 177 0.98 0.98 56 29 Japan 589 63 454 320 14 530 173 332 1.60 1.37 42 25 Jordan 154 117 113 9 23 290 108 173 1.04 0.73 107 29 Kazakhstan 199 33 167 4 6 235 39 205 0.71 0.51 43 17 Kenya 23 14 13 11 7 31 21 11 1.33 1.27 64 30 Korea, Dem. Rep. .. .. .. 21 2 13 7 5 .. .. 169 56 Korea, Rep. 355 165 267 105 12 569 286 158 1.52 1.35 51 33 Kosovo .. .. .. .. .. .. .. .. 1.63 1.60 .. .. Kuwait 495 212 412 37 13 1,492 491 920 0.23 0.21 113 95 Kyrgyz Republic 59 9 44 17 28 160 103 51 0.85 0.79 79 35 Lao PDR 20 3 2 17 .. .. .. .. 1.26 0.97 92 45 Latvia 459 15 401 107 20 370 245 137 1.48 1.49 38 12 Lebanon .. .. .. 67 26 418 5 386 1.13 0.77 39 28 Lesotho .. .. .. 20 .. .. .. .. 0.97 1.07 108 43 Liberia 3 .. 2 10 .. .. .. .. 0.98 0.96 68 31 Libya 290 .. 225 5 20 640 405 206 0.17 0.13 101 81 Lithuania 555 23 508 125 16 405 242 103 1.59 1.42 52 15 Macedonia, FYR 155 23 138 54 15 203 116 59 1.52 1.27 45 18 Madagascar 26 .. 7 8 .. .. .. .. 1.52 1.26 80 30 Malawi 8 .. 4 13 .. .. .. .. 1.71 1.54 82 33 Malaysia 350 69 313 30 21 492 178 295 0.59 0.56 35 19 Mali 14 8 8 2 .. .. .. .. 1.42 1.25 258 106 Mauritania .. .. .. 1 .. .. .. .. 1.16 0.99 145 68 Mauritius 166 102 129 101 .. .. .. .. 1.55 1.23 21 14 Mexico 276 81 191 19 28 439 123 295 0.81 0.72 66 33 Moldova 146 41 107 38 11 75 52 23 1.21 1.08 118 33 Mongolia 72 2 48 3 11 135 42 119 1.11 1.04 181 101 Morocco 70 38 53 13 25 119 98 16 1.23 0.88 39 23 Mozambique 12 9 9 4 5 20 17 4 1.11 0.86 112 24 Myanmar 7 10 5 4 6 19 8 9 0.80 0.80 107 41 Namibia 103 5 46 5 37 281 89 174 1.06 1.09 50 45 Nepal 5 8 3 14 6 19 13 4 1.18 0.91 67 30 Netherlands 522 63 459 329 14 673 380 240 2.13 1.71 44 30 New Zealand 718 33 603 35 24 949 394 531 1.47 0.97 14 12 Nicaragua 58 15 18 17 17 90 49 38 1.09 0.99 46 24 Niger 8 6 6 1 .. .. .. .. 1.07 1.16 199 92 Nigeria 31 .. 31 21 8 53 4 45 0.44 0.77 195 42 Norway 578 30 465 29 12 704 549 258 2.12 2.01 22 15 Oman 215 12 166 18 13 721 59 617 0.31 0.38 133 82 Pakistan 13 8 10 32 13 63 38 11 0.86 0.92 216 101 Panama 141 35 101 19 18 162 124 151 0.85 0.77 55 33 Papua New Guinea 9 .. 6 4 .. .. .. .. .. .. 35 16 Paraguay 91 16 39 8 27 200 150 37 1.28 1.01 107 65 Peru 68 16 41 10 29 162 113 34 1.41 1.10 96 43 Philippines 33 .. 8 67 19 79 47 29 1.05 0.84 56 17 Poland 508 50 432 123 16 400 225 105 1.57 1.50 58 34 Portugal 509 67 495 90 25 572 406 137 1.85 1.58 49 20 Puerto Rico 596 92 582 301 .. .. .. .. .. .. 23 14 Qatar 532 .. 335 67 9 1,331 675 591 0.19 0.19 54 31 2012 World Development Indicators 195 3.15 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 Super grade micrograms per people of road people land area consumption Total Diesel fuel Gasoline fuel gasoline Diesel cubic meter 2009 2009 2009 2009 2009 2009 2009 2009 2010 2010 1990 2009 Romania 230 20 198 83 14 223 147 67 1.46 1.46 42 14 Russian Federation 271 39 233 6 7 330 114 221 0.84 0.72 41 16 Rwanda 5 .. 2 53 .. .. .. .. 1.63 1.62 53 23 Saudi Arabia 192 20 139 11 21 1,241 543 635 0.16 0.07 160 103 Senegal 22 16 16 8 21 50 41 9 1.57 1.34 94 80 Serbia 252 42 224 50 13 265 162 70 1.50 1.48 .. .. Sierra Leone 6 2 5 .. .. .. .. .. 0.94 0.94 87 37 Singapore 156 232 121 473 15 545 347 174 1.42 1.04 108 23 Slovak Republic 348 43 293 89 11 339 194 105 1.70 1.53 46 12 Slovenia 566 30 522 192 24 836 514 288 1.67 1.62 39 26 Somalia .. .. .. 3 .. .. .. .. .. .. 82 28 South Africa 162 .. 110 30 11 320 134 176 1.19 1.14 39 26 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 596 41 478 132 24 660 528 126 1.56 1.47 41 25 Sri Lanka 47 .. 19 148 18 79 54 22 1.19 0.66 93 71 Sudan 27 .. 19 1 16 58 39 17 0.62 0.43 285 137 Swaziland 89 25 45 21 .. .. .. .. 1.07 1.10 30 38 Sweden 519 8 462 129 16 773 353 357 1.87 1.82 15 10 Switzerland 563 61 519 173 21 737 286 424 1.66 1.77 34 22 Syrian Arab Republic 63 20 30 37 22 248 154 83 0.96 0.45 135 71 Tajikistan 38 .. 29 19 4 14 0 12 1.02 0.91 84 30 Tanzania 7 .. 4 11 6 26 19 6 1.22 1.19 56 20 Thailand 134 50 57 35 18 275 163 74 1.41 0.95 77 53 Timor-Leste .. .. .. .. .. .. .. .. 1.40 0.90 .. .. Togo 2 1 2 21 11 51 17 31 1.18 1.17 56 28 Trinidad and Tobago 353 .. .. 162 5 704 313 355 .. .. 136 98 Tunisia 114 61 76 12 16 145 96 41 0.94 0.82 71 23 Turkey 142 29 95 46 14 185 120 34 2.52 2.03 77 37 Turkmenistan 106 .. 80 5 4 172 0 164 0.22 0.20 196 41 Uganda 8 .. 3 29 .. .. .. .. 1.42 1.11 29 11 Ukraine 167 45 142 28 6 153 46 102 1.01 0.92 71 17 United Arab Emirates 313 271 293 5 14 1,229 606 563 0.47 0.71 273 62 United Kingdom 523 77 460 172 19 616 351 255 1.92 1.98 24 13 United States 802 38 439 67 23 1,640 384 1,134 0.76 0.84 30 18 Uruguay 200 .. 179 44 23 281 165 99 1.49 1.44 229 142 Uzbekistan .. .. .. 18 4 67 11 53 0.92 0.83 110 37 Venezuela, RB 147 .. 107 11 27 626 102 459 0.02 0.01 21 9 Vietnam 13 7 13 48 14 108 59 46 0.88 0.77 123 50 West Bank and Gaza 30 22 25 93 .. .. .. .. 1.71 1.54 .. .. Yemen, Rep. 35 .. 16 14 24 77 2 66 0.35 0.23 133 43 Zambia 21 .. 13 12 2 10 4 10 1.66 1.52 125 30 Zimbabwe 114 .. 98 25 4 27 17 10 1.29 1.15 55 37 World 170 w 30 w 125 w 30 w 15 w 259 w 110 w 134 w 1.21 w 1.07 w 79 w 43 w Low income 10 .. 7 .. 6 20 13 7 1.18 1.15 127 56 Middle income 66 18 54 23 11 132 65 61 1.08 0.97 96 49 Lower middle income 33 10 21 48 11 70 37 30 1.08 0.91 124 58 Upper middle income 99 25 84 17 11 195 93 92 1.13 1.02 78 43 Low & middle income 61 15 50 21 10 120 60 55 1.12 0.98 98 50 East Asia & Pacific 54 18 38 37 7 105 58 51 1.08 0.93 112 56 Europe & Central Asia 219 36 183 9 8 230 100 125 1.21 1.13 57 23 Latin America & Carib. 178 23 143 18 24 299 123 137 1.04 0.98 57 30 Middle East & N. Africa 87 40 67 9 20 283 142 115 0.94 0.56 125 66 South Asia 17 5 11 102 7 38 26 10 1.12 0.83 130 68 Sub-Saharan Africa 28 .. 23 .. 9 61 26 33 1.22 1.15 119 46 High income 618 37 447 46 19 939 357 519 1.63 1.55 37 23 Euro area 593 47 418 140 18 646 418 186 1.78 1.62 33 19 196 2012 World Development Indicators 3.15 ENVIRONMENT Traffic and congestion About the data De�nitions Traffic congestion in urban areas constrains eco- Considerable uncertainty surrounds estimates •  Motor vehicles include cars, buses, and freight nomic productivity, damages people’s health, and of particulate matter concentrations, and caution vehicles but not two-wheelers. Population figures are degrades the quality of life. In recent years owner- should be used in interpreting them. They allow for midyear population in the year for which data are ship of passenger cars has increased, and the expan- cross-country comparisons of the relative risk of available. Roads refer to motorways (a road designed sion of economic activity has led to more goods and particulate matter pollution facing urban residents. and built for motor traffic that separates the traf- services being transported by road over greater dis- Major sources of urban outdoor particulate matter fic flowing in opposite directions), highways, main tances (see table 5.10). These developments have pollution are traffic and industrial emissions, but or national roads, and secondary or regional roads. increased demand for roads and vehicles, adding to nonanthropogenic sources such as dust storms may • Passenger cars are road motor vehicles, other than urban congestion, air pollution, health hazards, and also be a substantial contributor for some cities. two-wheelers, intended for the carriage of passen- traffic accidents and injuries. Country technology and pollution controls are impor- gers and designed to seat no more than nine people The data in the table on motor vehicles, passenger tant determinants of particulate matter. Data on par- (including the driver). • Road density is the ratio of cars, and road density are compiled by the Interna- ticulate matter for selected cities are in table 3.16. the length of the country’s total road network to the tional Road Federation (IRF) through questionnaires country’s land area. The road network includes all sent to national organizations. The IRF uses a hier- roads in the country— motorways, highways, main archy of sources to gather as much information as or national roads, secondary or regional roads, and possible. Primary sources are national road asso- other urban and rural roads. • Road sector energy ciations. If they lack data or do not respond, other consumption is the total energy used in the road agencies are contacted, including road directorates, sector, including energy from petroleum products, ministries of transport or public works, and central natural gas, combustible and renewable waste, statistical offices. As a result, data quality is uneven. and electricity (see table 3.7). • Total energy con- Coverage of each indicator may differ across coun- sumption is the country’s total energy consumption tries because of different definitions. Comparability from all sources (see table 3.7). • Diesel is heavy is also limited when time series data are reported. oils used in internal combustion in diesel engines. The IRF is taking steps to improve the quality of the • Gasoline fuel is light hydrocarbon oil used in inter- data in its World Road Statistics. Because this effort nal combustion engines such as motor vehicles, covers only 2003–09, time series data may not be excluding aircraft. • Fuel price is the pump price of comparable. Another reason is coverage. Road den- super grade gasoline and of diesel fuel, converted sity is a rough indicator of accessibility and does not from the local currency to U.S. dollars (see About capture road width, type, or condition. Thus com- the data). • Particulate matter concentration is fine parisons over time and across countries should be suspended particulates of less than 10 microns in made with caution. diameter (PM10) that are capable of penetrating Road sector energy consumption includes energy deep into the respiratory tract and causing severe from petroleum products, natural gas, renewable and health damage. Data are urban-population-weighted combustible waste, and electricity. Biodiesel and bio- PM10 levels in residential areas of cities with more gasoline, forms of renewable energy, are biodegrad- than 100,000 residents. The estimates represent able and emit less sulfur and carbon monoxide than the average annual exposure level of the average petroleum-derived ones. They can be produced from urban resident to outdoor particulate matter. vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine. Data on fuel prices are compiled by the German Agency for International Cooperation (GIZ), from Data sources its global network and other sources, including the Allgemeiner Deutscher Automobile Club (for Europe) Data on vehicles and road density are from the and the Latin American Energy Organization (for Latin IRF’s electronic files and its annual World Road America). Local prices are converted to U.S. dollars Statistics, except where noted. Data on road sec- using the exchange rate in the Financial Times inter- tor energy consumption are from the IRF and the national monetary table on the survey date. When International Energy Agency. Data on fuel prices multiple exchange rates exist, the market, parallel, are from the GIZ’s electronic files. Data on par- or black market rate is used. Prices were compiled ticulate matter concentrations are from Pandey in mid-November 2010, based on the crude oil price and others (2006b). of $81 a barrel Brent. 2012 World Development Indicators 197 3.16 Air pollution City City Particulate matter Sulfur Nitrogen About the data population concentration dioxide dioxide Indoor and outdoor air pollution place a major burden Urban- on world health. More than half the world’s people population-weighted PM10 micrograms micrograms rely on dung, wood, crop waste, or coal to meet basic micrograms per per per thousands cubic meter cubic meter cubic meter energy needs. Cooking and heating with these fuels on 2010 1990 2009 2001 a 2001 a open fires or stoves without chimneys lead to indoor air Argentina Buenos Aires 13,074 159 92 .. .. pollution, which is responsible for 1.6 million deaths a   Córdoba 1,493 78 45 .. 97 year—one every 20 seconds. In many urban areas air Australia Melbourne 3,853 17 11 .. 30 pollution exposure is the main environmental threat to   Perth 1,599 16 11 5 19 health. Long-term exposure to high levels of soot and   Sydney 4,429 27 17 28 81 small particles contributes to a range of health effects, Austria Vienna 1,706 45 32 14 42 including respiratory diseases, lung cancer, and heart Belgium Brussels 1,904 33 23 20 48 Brazil Rio de Janeiro 11,950 50 25 129 .. disease. Particulate pollution, alone or with sulfur diox-   São Paulo 20,262 57 28 43 83 ide, creates an enormous burden of ill health. Bulgaria Sofia 1,196 118 49 39 122 Sulfur dioxide and nitrogen dioxide emissions Canada Montréal 3,783 24 15 10 42 lead to deposition of acid rain and other acidic com-   Toronto 5,449 29 18 17 43 pounds over long distances, which can lead to the   Vancouver 2,220 17 11 14 37 leaching of trace minerals and nutrients critical to Chile Santiago 5,952 100 60 29 81 trees and plants. Sulfur dioxide emissions can dam- China Anshan 1,663 132 68 115 88   Beijing 12,385 141 73 90 122 age human health, particularly that of the young and   Changchun 3,597 117 61 21 64 old. Nitrogen dioxide is emitted by bacteria, motor   Chengdu 4,961 136 71 77 74 vehicles, industrial activities, nitrogen fertilizers, fuel   Chongqing 9,401 194 101 340 70 and biomass combustion, and aerobic decomposi-   Dalian 3,306 79 41 61 100 tion of organic matter in soils and oceans.   Foshan 4,969 107 56 .. .. Where coal is the primary fuel for power plants   Guangzhou 8,884 99 52 57 136   Guiyang 2,154 111 58 424 53 without effective dust controls, steel mills, industrial   Harbin 4,251 121 63 23 30 boilers, and domestic heating, high levels of urban   Jinan 3,237 148 77 132 45 air pollution are common— especially particulates   Kunming 3,116 111 58 19 33 and sulfur dioxide. Elsewhere the worst emissions   Lanzhou 2,285 145 75 102 104 are from petroleum product combustion.   Liupanshui 1,221b 94 49 102 .. Sulfur dioxide and nitrogen dioxide concentration   Nanchang 2,701 124 65 69 29 data are based on average observed concentrations   Shanghai 16,575 115 60 53 73   Shenyang 5,166 160 83 99 73 at urban monitoring sites, which not all cities have.   Shenzhen 9,005 89 46 .. .. The data on particulate matter are estimated aver-   Tianjin 7,884 198 103 82 50 age annual concentrations in residential areas away   Wuhan 7,681 125 65 40 43 from air pollution “hotspots,� such as industrial   Xi’an 4,747 221 115 .. .. districts and transport corridors. The data are from   Zhengzhou 2,966 154 80 63 95 the World Bank’s Development Research Group and   Zibo 2,456 117 61 198 43 Environment Department estimates of annual ambi- Colombia Bogotá 8,500 51 27 .. .. Croatia Zagreb 779 b 48 26 31 .. ent concentrations of particulate matter in cities Cuba Havana 2,130 35 17 1 5 with populations exceeding 100,000 (Pandey and Czech Republic Prague 1,162 42 17 14 33 others 2006b). A country’s technology and pollution Denmark Copenhagen 1,186 30 17 7 54 controls are important determinants of particulate Ecuador Guayaquil 2,690 33 18 15 .. matter concentrations.   Quito 1,846 44 24 22 .. Pollutant concentrations are sensitive to local con- Egypt, Arab Rep. Cairo 11,001 274 112 69 .. Finland Helsinki 1,117 24 17 4 35 ditions, and even monitoring sites in the same city France Paris 10,485 14 10 14 57 may register different levels. Thus these data should Germany Berlin 3,450 30 18 18 26 be considered only a general indication of air qual-   Frankfurt 680 b 27 16 11 45 ity, and comparisons should be made with caution.   Munich 1,349 27 16 8 53 Current World Health Organization (WHO) air quality Ghana Accra 2,342 37 21 .. .. guidelines are annual mean concentrations of 20 Greece Athens 3,257 69 33 34 64 micrograms per cubic meter for particulate matter Hungary Budapest 1,706 35 16 39 51 Iceland Reykjavik 319 b 23 16 5 42 less than 10 microns in diameter and 40 micrograms India Ahmadabad 5,717 126 66 30 21 for nitrogen dioxide and daily mean concentrations   Bengaluru 7,218 68 35 .. .. of 20 micrograms per cubic meter for sulfur dioxide. 198 2012 World Development Indicators 3.16 ENVIRONMENT Air pollution City City Particulate matter Sulfur Nitrogen De�nitions population concentration dioxide dioxide • City population is the number of residents of Urban- the city or metropolitan area as defined by national population-weighted PM10 micrograms micrograms micrograms per per per authorities and reported to the United Nations. thousands cubic meter cubic meter cubic meter •  Particulate matter concentration is fi ne sus- 2010 1990 2009 2001 a 2001 a pended particulates of less than 10 microns in  India (continued) Chennai 7,547 56 29 15 17 diameter (PM10) that are capable of penetrating   Delhi 22,157 227 118 24 41 deep into the respiratory tract and causing severe   Hyderabad 6,751 62 32 12 17 health damage. Data are urban- population-weighted   Kanpur 3,364 165 86 15 14   Kolkata 15,552 193 101 49 34 PM10 levels in residential areas of cities with more   Lucknow 2,873 165 86 26 25 than 100,000 residents. The estimates represent   Mumbai 20,041 95 50 33 39 the average annual exposure level of the average   Nagpur 2,607 84 44 6 13 urban resident to outdoor particulate matter. • Sulfur   Pune 5,002 71 37 .. .. dioxide is an air pollutant produced when fossil fuels Indonesia Jakarta 9,210 137 70 .. .. Iran, Islamic Rep. Tehran 7,241 92 55 209 .. containing sulfur are burned. • Nitrogen dioxide is Ireland Dublin 1,099 24 13 20 .. a poisonous, pungent gas formed when nitric oxide Italy Milan 2,967 46 24 31 248 combines with hydrocarbons and sunlight, producing   Rome 3,362 44 23 .. .. a photochemical reaction. These conditions occur in   Turin 1,665 66 35 .. .. both natural and anthropogenic activities. Japan Osaka-Kobe 11,337 48 28 19 63   Tokyo 36,669 54 32 18 68   Yokohama 3,654b 42 25 100 13 Kenya Nairobi 3,523 67 31 .. .. Korea, Rep Busan 3,425 52 33 60 51   Seoul 9,773 55 35 44 60   Daegu 2,458 59 38 81 62 Malaysia Kuala Lumpur 1,519 36 19 24 .. Mexico Mexico City 19,460 88 43 74 130 Netherlands Amsterdam 1,049 45 30 10 58 New Zealand Auckland 1,404 13 11 3 20 Norway Oslo 888 27 19 8 43 Philippines Manila 11,628 78 24 33 .. Poland Katowice 309b 60 35 83 79   Lódz 742b 60 34 21 43   Warsaw 1,712 65 38 16 32 Portugal Lisbon 2,824 44 18 8 52 Romania Bucharest 1,934 47 16 10 71 Russian Federation Moscow 10,550 42 16 109 ..   Omsk 1,124 44 17 20 34 Singapore Singapore 4,837 108 23 20 30 Slovak Republic Bratislava 500 b 44 12 21 27 South Africa Cape Town 3,405 24 16 21 72   Durban 2,879 47 32 31 ..   Johannesburg 3,670 49 33 19 31 Spain Barcelona 5,083 43 27 11 43   Madrid 5,851 37 23 24 66 Sweden Stockholm 1,285 14 9 3 20 Switzerland Zurich 1,150 32 21 11 39 Thailand Bangkok 6,976 88 60 11 23 Data sources Turkey Ankara 3,906 75 35 55 46   Istanbul 10,525 88 42 120 .. Data on city population are from the United Ukraine Kiev 2,805 91 21 14 51 Nations Population Division’s World Urbanization United Kingdom Birmingham 2,302 33 20 9 45 Prospects: The 2010 Revision. Data on particulate   London 8,631 27 17 25 77 matter concentrations are from Pandey and others   Manchester 2,253 24 12 26 49 (2006b). Data on sulfur dioxide and nitrogen diox- United States Chicago 9,204 33 20 14 57   Los Angeles 12,762 46 28 9 74 ide concentrations are from the WHO’s Healthy   New York 19,425 28 17 26 79 Cities Air Management Information System and Venezuela, RB Caracas 3,090 31 13 33 57 the World Resources Institute. a. Data are for the most recent year available. b. Data are from national sources. 2012 World Development Indicators 199 3.17 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 Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Afghanistan   2002 2004 d 2004 d   2002   1985d 1995d Albania 1993   1994 d 1999d 1999d 2003 1994 d 2005d 2003d 2000 d 2004 Algeria 2001   1993 1992d 1992d 1996 1995 2005d 1983d 1996 2006 Angola     2000 2000 d 2000 d 1990 1998 2007   1997 2006d Argentina 1992   1994 1990 1990 1995 1994 2001 1981 1997 2005 Armenia     1993e 1999d 1999d 2002 1993e 2003d 2008d 1997 2003 Australia 1992 1994 1992 1987d 1989 1994 1993 2007 1976 2000 2004 Austria     1994 1987 1989 1995 1994 2002 1982d 1997d 2002 Azerbaijan 1998   1995 1996d 1996d   2000 f 2000 d 1998d 1998d 2004 d Bahrain     1994 1990d 1990 d 1985 1996 2006d   1997d 2006 Bangladesh 1991 1990 1994 1990 d 1990 d 2001 1994 2001d 1981 1996 2007 Belarus     2000 f 1986e 1988f 2006 1993 2005d 1995d 2001d 2004 d Belgium     1996 1988 1988 1998 1996 2002 1983 1997d 2006 Benin 1993   1994 1993d 1993d 1997 1994 2002d 1984 d 1996 2004 Bolivia 1994 1988 1994 1994 d 1994 d 1995 1994 1999 1979 1996 2003 Bosnia and Herzegovina     2000 d 1993g 1992g 1994 2002e 2007d 2009d 2002d 2010 Botswana 1990 1991 1994 1991d 1991d 1990 1995 2003d 1977d 1996 2002d Brazil   1988 1994 1990 d 1990 d 1988 1994 2002 1975 1997 2004 Bulgaria   1994 1995 1990d 1990 d 1996 1996 2002 1991d 2001d 2004 Burkina Faso 1993   1993 1989 1989 2005 1993 2005d 1989d 1996 2004 Burundi 1994 1989 1997 1997d 1997d   1997 2001d 1988d 1997 2005 Cambodia 1999   1995d 2001d 2001d   1995d 2002d 1997 1997 2006 Cameroon   1989 1994 1989d 1989d 1985 1994 2002d 1981d 1997 2009 Canada 1990 1994 1992 1986 1988 2003 1992 2002 1975 1995 2001 Central African Republic     1995 1993d 1993d   1995 2008d 1980 d 1996 2008 Chad 1990   1994 1989d 1994 2009 1994 2009d 1989d 1996 2004 Chile   1993 1995 1990 1990 1997 1994 2002 1975 1997 2005 China 1994 1994 1993 1989d 1991d 1996 1993 2002f 1981d 1997 2004 Hong Kong SAR, China Colombia 1998 1988 1995 1990 d 1993d   1994 2001d 1981 1999 2008 Congo, Dem. Rep.   1990 1995 1994 d 1994 d 1989 1994 2005d 1976d 1997 2005d Congo, Rep.   1990 1996 1994 d 1994 2008 1996 2007d 1983d 1999 2007 Costa Rica 1990 1992 1994 1991d 1991d 1992 1994 2002 1975 1998 2007 Côte d’Ivoire 1994 1991 1994 1993d 1993d 1984 1994 2007d 1994 d 1997 2004 Croatia 2001 2000 1996e 1992g 1992g 1995 1996 2007 2000 d 2000 d 2007 Cuba     1994 1992d 1992d 1984 1994 2002 1990 d 1997 2007 Cyprus 1997 1992d 1992d 1988 1996 1992d 1974 2000 d 2005d Czech Republic 1994   1993f 1993g 1993g 1996 1993f 2001f 1993 2000 d 2002 Denmark 1994   1995 1988 1988 2004 1993 2002 1977 1995 2003 Dominican Republic   1995 1998 1993d 1993d 2009 1996 2002d 1986d 1997d 2007 Ecuador 1993 1995 1993 1990d 1990 d   1993 2000 1975 1995 2004 Egypt, Arab Rep. 1992 1988 1994 1988 1988 1983 1994 2005 1978 1995 2003 El Salvador 1994 1988 1995 1992 1992d   1994 1998 1987d 1997d 2008 Eritrea 1995   1995d 2005d 2005d   1996d 2005d 1994 d 1996 2005d Estonia 1998   1994 1996d 1996d 2005 1994 2002 1992d   2008d Ethiopia 1994 1991 1994 1994 d 1994 d     2005d 1989d 1997 2003 Finland 1995   1994 e 1986 1988 1996 1994 e 2002 1976d 1995d 2002d France 1990   1994 1987f 1988f 1996 1994 2002f 1978f 1997 2004f Gabon   1990 1998 1994 d 1994 d 1998 1997 2006d 1989d 1996d 2007 Gambia, The 1992 1989 1994 1990d 1990 d 1984 1994 2001d 1977d 1996 2006 Georgia 1998   1994 d 1996d 1996d 1996 1994 d 1999d 1996d 1999 2006 Germany     1993 1988 1988 1994 1993 2002 1976 1996 2002 Ghana 1992 1988 1995 1989d 1992 1983 1994 2003d 1975 1996 2003 Greece     1994 1988 1988 1995 1994 2002 1992d 1997 2006 Guatemala 1994 1988 1995 1987d 1989d 1997 1995 1999 1979 1998d 2008 Guinea 1994 1988 1993 1992d 1992d 1985 1993 2000 d 1981d 1997 2007 Guinea-Bissau 1993 1991 1995 2002d 2002d 1986 1995 2005d 1990 d 1995 2008 Haiti 1999   1996 2000 d 2000 d 1996 1996 2005d   1996 200 2012 World Development Indicators 3.17 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 Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Honduras 1993   1995 1993d 1993d 1993 1995 2000 1985d 1997 2005 Hungary 1995   1994 1988d 1989d 2002 1994 2002d 1985d 1999d 2008 India 1993 1994 1993 1991d 1992d 1995 1994 2002d 1976 1996 2006 Indonesia 1993 1993 1994 1992d 1992 1986 1994 2004 1978d 1998 2009 Iran, Islamic Rep.     1996 1990 d 1990 d   1996 2005d 1976 1997 2006 Iraq     2009d 2008d 2008d 1985 2009d 2009d   2010 d Ireland     1994 1988d 1988 1996 1996 2002 2002 1997 2010 Israel     1996 1992d 1992   1995 2004 1979 1996 Italy     1994 1988 1988 1995 1994 2002 1979 1997 Jamaica 1994   1995 1993d 1993d 1983 1995 1999d 1997d 1997d 2007 Japan     1993e 1988d 1988e 1996 1993e 2002d 1980e 1998e 2002d Jordan 1991   1993 1989d 1989d 1995 1993 2003d 1978d 1996 2004 Kazakhstan     1995 1998d 1998d   1994 2009 2000 d 1997 2007 Kenya 1994 1992 1994 1988d 1988 1989 1994 2005d 1978 1997 2004 Korea, Dem. Rep.     1994f 1995d 1995d   1994f 2005d   2003d 2002d Korea, Rep.     1993 1992d 1992 1996 1994 2002 1993d 1999 2007 Kosovo Kuwait     1994 d 1992d 1992d 1986 2002 2005d 2002 1997 2006 Kyrgyz Republic 1995   2000 2000 d 2000 d   1996d 2003d 2007d 1997d 2006 Lao PDR 1995   1995d 1998d 1998d 1998 1996d 2003d 2004 d 1996d 2006 Latvia     1995 1995d 1995d 2004 1995 2002 1997d 2002d 2004 Lebanon     1994 1993d 1993d 1995 1994 2006d   1996 2003 Lesotho 1989   1995 1994 d 1994 d 2007 1995 2000d 2003 1995 2002 Liberia     2002 1996d 1996d 2008 2000 2002d 1981d 1998d 2002d Libya     1999 1990 d 1990 d   2001 2006d 2003d 1996 2005d Lithuania     1995 1995d 1995d 2003 1996 2003 2001d 2003d 2006 Macedonia, FYR     1998d 1994g 1994g 1994 1997d 2004 d 2000 d 2002d 2004 Madagascar 1988 1991 1999 1996d 1996d 2001   2003d 1975 1997 2005 Malawi 1994   1994 1991d 1991d 2010 1996 2001d 1982d 1996 2009 Malaysia 1991 1988 1994 1989d 1989d 1996 1994 2002 1977d 1997 Mali   1989 1995 1994d 1994 d 1985 1995 2002 1994 d 1995 2003 Mauritania 1988   1994 1994 d 1994 d 1996 1996 2005d 1998d 1996 2005 Mauritius 1990   1992 1992d 1992d 1994 1992 2001d 1975 1996 2004 Mexico   1988 1993 1987 1988e 1983 1993 2000 1991d 1995 2003 Moldova 2002   1995 1996d 1996d 2007 1995 2008d 2001d 1999d 2004 Mongolia 1995   1993 1996d 1996d 1996 1993 1999d 1996d 1996 2004 Morocco   1988 1995 1995 1995 2007 1995 2002d 1975 1996 2004 Mozambique 1994   1995 1994 d 1994 d 1997 1995 2005d 1981d 1997 2005 Myanmar   1989 1994 1993d 1993d 1996 1994 2003d 1997d 1997d 2004 d Namibia 1992   1995 1993d 1993d 1983 1997 2003d 1990 d 1997 2005d Nepal 1993   1994 1994 d 1994 d 1998 1993 2005d 1975d 1996 2007 Netherlands 1994   1993e 1988e 1988e 1996 1994 e 2002e 1984 1995e 2002e New Zealand 1994   1993 1987 1988 1996 1993 2002 1989d 2000 d 2004 Nicaragua 1994   1995 1993d 1993d 2000 1995 1999 1977d 1998 2005 Niger   1991 1995 1992d 1992d   1995 2004 1975 1996 2006 Nigeria 1990 1992 1994 1988d 1988d 1986 1994 2004 d 1974 1997 2004 Norway   1994 1993 1986 1988 1996 1993 2002 1976 1996 2002 Oman     1995 1999d 1999d 1989 1995 2005d 2008d 1996d 2005 Pakistan 1994 1991 1994 1992d 1992d 1997 1994 2005d 1976d 1997 2008 Panama 1990   1995 1989d 1989 1996 1995 1999 1978 1996 2003 Papua New Guinea 1992 1993 1993 1992d 1992d 1997 1993 2002 1975d 2000 d 2003 Paraguay     1994 1992d 1992d 1986 1994 1999 1976 1997 2004 Peru   1988 1993 1989 1993d   1993 2002 1975 1995 2005 Philippines 1989 1989 1994 1991d 1991 1984 1993 2003 1981 2000 2004 Poland 1993 1991 1994 1990 d 1990 d 1998 1996 2002 1989 2001d 2008 Portugal 1995   1993 1988d 1988 1997 1993 2002f 1980 1996 2004 e Puerto Rico Qatar     1996d 1996d 1996d 2002 1996 2005a 2001d 1999d 2004 d 2012 World Development Indicators 201 3.17 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 Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Romania 1995   1994 1993d 1993d 1996 1994 2001 1994 d 1998d 2004 Russian Federation 1999 1994 1994 1986e 1988e 1997 1995 2008 1992h 2003d 2011 Rwanda 1991   1998 2001d 2001d   1996 2004 d 1980 d 1998 2002d Saudi Arabia     1994 d 1993d 1993d 1996 2001d 2005d 1996d 1997d Senegal 1984 1991 1994 1993d 1993 1984 1994 2001d 1977d 1995 2003 Serbia     2001a 2001g 2001g 2001 2002 2007d 2006h 2007a 2009 Sierra Leone 1994   1995 2001d 2001d 1994 1994 d 2006d 1994 d 1997 2003d Singapore 1993 1995 1997 1989d 1989d 1994 1995 2006d 1986d 1999d 2005 Slovak Republic     1994f 1993g 1993g 1996 1994f 2002 1993g 2002d 2002 Slovenia 1994   1995 1992g 1992g 1995 1996 2002 2000 d 2001d 2004 Somalia     2009d 2001d 2001d 1989 2009d 2010 1985d 2002d 2010 d South Africa 1993   1997 1990d 1990 d 1997 1995 2002d 1975 1997 2002 South Sudan Spain     1993 1988d 1988 1997 1993 2002 1986d 1996 2004 Sri Lanka 1994 1991 1993 1989d 1989d 1994 1994 2002d 1979d 1998d 2005 Sudan     1993 1993d 1993d 1985 1995 2004 d 1982 1995 2006 Swaziland     1996 1992d 1992d 2009 1994 2006d 1997d 1996 2006d Sweden     1993 1986 1988 1996 1993 2002 1974 1995 2002 Switzerland     1993 1987 1988   1994 2003 1974 1996 2003 Syrian Arab Republic 1999   1996d 1989d 1989d   1996 2006d 2003d 1997 2005 Tajikistan     1998d 1996d 1998d   1997d 2008d   1997d 2007 Tanzania 1994 1988 1996 1993d 1993d 1985 1996 2002d 1979 1997 2004 Thailand     1994 1989d 1989 2011 2003 2002 1983 2001d 2005 Togo 1991   1995e 1991d 1991 1985 1995e 2004 d 1978 1995e 2004 Trinidad and Tobago     1994 1989d 1989d 1986 1996 1999 1984 d 2000 d 2002d Tunisia 1994 1988 1993 1989d 1989d 1985 1993 2003d 1974 1995 2004 Turkey 1998   2004 d 1991d 1991d   1997 2006d 1996d 1998 2009 Turkmenistan     1995d 1993d 1993d   1996d 1999d   1996 Uganda 1994 1988 1993 1988d 1988 1990 1993 2002d 1991d 1997 2004 d Ukraine 1999   1997 1986e 1988e 1999 1995 2004 1999d 2002d 2007 United Arab Emirates     1995d 1989d 1989d   2000 2005d 1990 d 1998d 2002 United Kingdom 1995 1994 1993 1987 1988 1997 1994 2002 1976 1996 2005 United States 1995 1995 1992 1986 1988       1974 2000 Uruguay     1994 1989d 1991d 1992 1993 2001 1975 1999d 2004 Uzbekistan     1993d 1993d 1993d   1995d 1999 1997d 1995 Venezuela     1994 1988d 1989   1994 2005d 1977 1998d 2005 Vietnam   1993 1994 1994 d 1994 d 1994 1994 2002 1994 d 1998d 2002 West Bank and Gaza Yemen, Rep. 1996 1992 1996 1996d 1996d 1987 1996 2004 d 1997d 1997d 2004 Zambia 1994   1993 1990 d 1990 d 1983 1993 2006 1980 d 1996 2006 Zimbabwe 1987   1992 1992d 1992d 1993 1994 2009d 1981d 1997 a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention of December 10, 1982. d. Accession. e. Acceptance. f. Approval. g. Succession. h. Continuation. 202 2012 World Development Indicators 3.17 ENVIRONMENT Government commitment About the data De�nitions 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 2012 World Development Indicators 203 3.18 Contribution of natural resources to gross domestic product Total natural Oil rents Natural gas rents Coal rents Mineral rents Forest rents resources rents % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 2010 2010 2010 2010 2010 2010 Afghanistan 2.3 .. .. 0.0 0.0 2.3 Albania 3.6 3.0 0.0 0.0 0.5 0.1 Algeria 25.7 17.6 7.8 0.0 0.3 0.1 Angola 46.3 46.1 0.1 .. 0.0 0.2 Argentina 6.1 4.0 1.4 0.0 0.6 0.1 Armenia 1.7 0.0 0.0 .. 1.7 0.0 Australia 8.3 0.9 0.7 1.8 6.6 0.1 Austria 0.3 0.1 0.1 0.0 0.0 0.1 Azerbaijan 46.5 42.6 3.9 .. 0.0 0.0 Bahrain 23.2 16.4 6.9 .. 0.0 0.0 Bangladesh 3.3 0.0 2.8 0.1 0.0 0.5 Belarus 1.9 1.3 0.0 .. 0.0 0.5 Belgium 0.0 0.0 0.0 0.0 0.0 0.0 Benin 1.7 0.0 0.0 .. 0.0 1.7 Bolivia 18.1 4.6 9.0 .. 4.2 0.4 Bosnia and Herzegovina 2.8 0.0 0.0 2.4 2.3 0.6 Botswana 4.7 0.0 0.0 0.4 4.6 0.1 Brazil 5.3 2.2 0.1 0.0 2.7 0.3 Bulgaria 2.3 0.0 0.0 1.1 2.0 0.3 Burkina Faso 7.1 .. .. .. 4.1 2.9 Burundi 13.3 .. .. .. 1.3 12.0 Cambodia 1.2 0.0 0.0 .. 0.0 1.2 Cameroon 9.3 7.1 0.2 0.0 0.2 1.8 Canada 3.8 2.3 0.3 0.1 0.8 0.5 Central African Republic 5.9 .. .. .. 0.0 5.8 Chad 43.3 41.2 .. .. 0.0 2.1 Chile 18.9 0.0 0.1 0.0 18.3 0.5 China 4.0 1.5 0.1 3.8 2.2 0.2 Hong Kong SAR, China 0.0 0.0 0.0 .. 0.0 0.0 Colombia 7.9 6.5 0.5 1.4 0.8 0.1 Congo, Dem. Rep. 29.8 3.9 0.0 0.1 16.4 9.5 Congo, Rep. 64.1 61.6 0.1 .. 0.0 2.5 Costa Rica 0.4 0.0 0.0 .. 0.1 0.3 Côte d’Ivoire 7.1 4.3 1.0 .. 0.7 1.1 Croatia 1.2 0.5 0.5 0.0 0.0 0.2 Cuba 5.1 3.0 0.6 .. 1.5 0.1 Cyprus 0.0 0.0 0.0 .. 0.0 0.0 Czech Republic 0.3 0.0 0.0 0.7 0.0 0.3 Denmark 2.1 1.8 0.3 0.0 0.0 0.0 Dominican Republic 0.3 0.0 0.0 .. 0.2 0.0 Ecuador 20.6 20.2 0.1 .. 0.0 0.3 Egypt, Arab Rep. 10.1 5.9 3.9 0.0 0.3 0.1 El Salvador 0.5 0.0 0.0 .. 0.0 0.5 Eritrea 0.6 0.0 0.0 .. 0.0 0.6 Estonia 1.1 0.0 0.0 1.9 0.0 1.1 Ethiopia 4.7 0.0 0.0 .. 0.3 4.4 Finland 0.8 0.0 0.0 .. 0.2 0.7 France 0.1 0.0 0.0 0.0 0.0 0.1 Gabon 49.8 46.4 0.2 .. 0.1 3.1 Gambia, The 2.5 .. .. .. 0.0 2.5 Georgia 0.8 0.2 0.0 0.1 0.6 0.1 Germany 0.1 0.0 0.0 0.1 0.0 0.0 Ghana 10.5 0.0 0.0 .. 8.8 1.6 Greece 0.2 0.0 0.0 0.3 0.1 0.0 Guatemala 2.4 0.8 0.0 .. 0.8 0.8 Guinea 21.2 .. .. .. 17.0 4.2 Guinea-Bissau 4.6 .. .. .. 0.0 4.6 Haiti 0.6 0.0 0.0 .. 0.0 0.6 204 2012 World Development Indicators 3.18 ENVIRONMENT Contribution of natural resources to gross domestic product Total natural Oil rents Natural gas rents Coal rents Mineral rents Forest rents resources rents % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 2010 2010 2010 2010 2010 2010 Honduras 1.8 0.0 0.0 .. 0.7 1.1 Hungary 0.6 0.3 0.2 0.1 0.0 0.1 India 4.0 1.0 0.4 2.6 2.1 0.6 Indonesia 6.0 2.4 0.9 3.4 1.9 0.7 Iran, Islamic Rep. 31.6 23.7 6.8 0.0 1.0 0.0 Iraq 69.3 69.1 0.2 .. 0.0 0.0 Ireland 0.2 0.0 0.0 0.0 0.1 0.0 Israel 0.3 0.0 0.2 0.0 0.1 0.0 Italy 0.2 0.1 0.0 0.0 0.0 0.0 Jamaica 1.1 0.0 0.0 .. 1.0 0.1 Japan 0.0 0.0 0.0 0.0 0.0 0.0 Jordan 1.6 0.0 0.1 .. 1.5 0.0 Kazakhstan 27.6 22.4 2.7 5.5 2.5 0.0 Kenya 1.3 0.0 0.0 .. 0.1 1.2 Korea, Dem. Rep. .. .. .. .. .. .. Korea, Rep. 0.0 0.0 0.0 0.0 0.0 0.0 Kosovo 1.5 .. .. .. 1.5 .. Kuwait 43.0 41.2 1.8 .. 0.0 0.0 Kyrgyz Republic 9.2 0.7 0.0 0.4 8.5 0.0 Lao PDR 14.1 .. .. 0.0 12.6 1.5 Latvia 1.5 0.0 0.0 .. 0.0 1.5 Lebanon 0.0 0.0 0.0 .. 0.0 0.0 Lesotho 1.3 .. .. .. 0.0 1.3 Liberia 14.6 .. .. .. 2.1 12.5 Libya 46.1 42.3 3.8 .. 0.0 0.0 Lithuania 1.8 0.1 0.0 .. 0.0 1.6 Macedonia, FYR 7.4 0.0 0.0 1.5 7.3 0.1 Madagascar 3.1 .. .. .. 1.3 1.8 Malawi 4.1 .. .. .. 0.0 4.1 Malaysia 10.9 6.3 3.9 0.1 0.1 0.6 Mali 15.0 .. .. .. 14.0 1.1 Mauritania 54.7 .. .. .. 54.2 0.5 Mauritius 0.0 .. .. .. 0.0 0.0 Mexico 7.3 6.0 0.5 0.1 0.6 0.1 Moldova 0.2 0.1 0.0 .. 0.0 0.1 Mongolia 23.4 2.0 0.0 22.9 21.2 0.2 Morocco 2.6 0.0 0.0 0.0 2.5 0.1 Mozambique 8.5 0.0 5.0 0.0 0.1 3.3 Myanmar .. .. .. .. .. .. Namibia 1.0 0.0 0.0 .. 1.0 0.1 Nepal 3.4 0.0 0.0 0.0 0.0 3.4 Netherlands 1.2 0.1 1.1 0.0 0.0 0.0 New Zealand 2.4 0.7 0.5 0.1 0.4 0.8 Nicaragua 3.0 0.0 0.0 .. 1.3 1.7 Niger 3.3 .. .. 0.0 1.3 2.0 Nigeria 32.6 29.5 2.2 0.0 0.0 0.9 Norway 13.1 10.1 2.9 0.0 0.0 0.1 Oman 39.2 31.6 7.6 .. 0.0 0.0 Pakistan 3.9 0.8 2.3 0.1 0.1 0.7 Panama 0.1 0.0 0.0 .. 0.0 0.1 Papua New Guinea 36.3 18.4 .. .. 32.2 4.1 Paraguay 1.3 0.0 0.0 .. 0.0 1.3 Peru 11.3 1.1 0.7 0.0 9.3 0.1 Philippines 2.6 0.1 0.3 0.2 2.0 0.2 Poland 1.0 0.1 0.1 1.3 0.6 0.2 Portugal 0.3 0.0 0.0 0.0 0.2 0.1 Puerto Rico 0.0 .. .. .. 0.0 .. Qatar 27.9 13.4 14.5 .. 0.0 .. 2012 World Development Indicators 205 3.18 Contribution of natural resources to gross domestic product Total natural Oil rents Natural gas rents Coal rents Mineral rents Forest rents resources rents % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 2010 2010 2010 2010 2010 2010 Romania 2.2 1.2 0.7 0.3 0.0 0.2 Russian Federation 19.9 14.2 3.6 1.4 1.7 0.3 Rwanda 3.4 .. .. .. 0.1 3.3 Saudi Arabia 53.7 50.5 3.2 .. 0.0 0.0 Senegal 2.4 0.0 0.0 .. 1.2 1.1 Serbia 0.9 0.8 0.1 2.0 0.0 .. Sierra Leone 4.5 .. .. .. 0.9 3.6 Singapore 0.0 0.0 0.0 .. 0.0 0.0 Slovak Republic 0.4 0.0 0.0 0.0 0.0 0.3 Slovenia 0.2 0.0 0.0 0.2 0.0 0.2 Somalia .. .. .. .. .. .. South Africa 4.6 0.0 0.0 5.1 3.8 0.8 South Sudan .. .. .. .. .. .. Spain 0.1 0.0 0.0 0.0 0.0 0.0 Sri Lanka 0.5 0.0 0.0 .. 0.0 0.5 Sudan 19.3 18.5 0.0 .. 0.1 0.7 Swaziland 1.7 .. .. 0.0 0.0 1.7 Sweden 1.2 0.0 0.0 0.0 0.7 0.5 Switzerland 0.0 0.0 0.0 .. 0.0 0.0 Syrian Arab Republic 16.5 14.4 1.9 .. 0.2 0.0 Tajikistan 0.9 0.2 0.1 0.3 0.6 0.0 Tanzania 6.8 0.0 0.5 0.0 4.2 2.1 Thailand 2.7 1.1 1.2 0.1 0.1 0.3 Timor-Leste 0.3 .. .. .. 0.0 0.3 Togo 3.9 0.0 0.0 0.0 1.9 2.0 Trinidad and Tobago 37.3 11.5 25.8 .. 0.0 0.0 Tunisia 6.7 4.4 1.0 .. 1.1 0.1 Turkey 0.5 0.2 0.0 0.2 0.2 0.1 Turkmenistan 43.9 19.7 24.2 .. 0.0 .. Uganda 4.9 .. .. .. 0.0 4.9 Ukraine 2.9 0.9 1.7 3.1 0.1 0.3 United Arab Emirates 20.5 18.0 2.4 .. 0.0 .. United Kingdom 1.5 1.2 0.3 0.0 0.0 0.0 United States 1.0 0.7 0.1 0.4 0.1 0.1 Uruguay 1.0 0.0 0.0 .. 0.2 0.9 Uzbekistan 29.4 3.3 18.1 0.2 8.1 0.0 Venezuela, RB 19.6 18.0 0.9 0.1 0.7 0.0 Vietnam 10.4 8.0 1.3 3.1 0.3 0.7 West Bank and Gaza .. .. .. .. .. .. Yemen, Rep. 21.9 19.0 3.0 .. 0.0 0.0 Zambia 28.1 0.0 0.0 0.0 26.7 1.5 Zimbabwe 3.4 0.0 0.0 2.9 1.2 2.2 World 4.0 w 1.8 w 0.4 w 0.8 w 0.8 w 0.2 w Low income 6.5 1.2 1.3 .. 1.8 2.3 Middle income 9.8 4.6 0.9 2.0 2.0 0.3 Lower middle income 11.4 5.7 1.2 2.2 1.8 0.6 Upper middle income 9.3 4.3 0.8 2.0 2.1 0.2 Low & middle income 9.8 4.5 0.9 2.0 2.0 0.3 East Asia & Pacific 7.9 1.7 0.4 3.4 2.1 0.2 Europe & Central Asia 14.2 8.9 2.5 1.3 1.2 0.3 Latin America & Carib. 8.0 4.7 0.4 0.1 2.6 0.2 Middle East & N. Africa 18.7 14.9 3.2 0.0 0.6 0.1 South Asia 6.2 0.9 0.6 2.3 1.7 0.7 Sub-Saharan Africa 16.7 12.0 0.5 .. 2.8 1.3 High income 1.4 0.6 0.2 0.2 0.3 0.1 Euro area 0.3 0.0 0.1 0.0 0.0 0.1 Note: Components may not sum to 100 percent because of rounding. 206 2012 World Development Indicators 3.18 ENVIRONMENT Contribution of natural resources to gross domestic product About the data De�nitions Accounting for the contribution of natural resources • Total natural resources rents are the sum of oil to economic output is important in building an ana- rents, natural gas rents, coal rents (hard and soft), lytical framework for sustainable development. In mineral rents, and forest rents. • Oil rents are the some countries earnings from natural resources, difference between the value of crude oil produc- especially from fossil fuels and minerals, account tion at world prices and total costs of production. for a sizable share of GDP, and much of these earn- • Natural gas rents are the difference between the ings come in the form of economic rents—revenues value of natural gas production at world prices and above the cost of extracting the resources. Natural total costs of production. • Coal rents are the dif- resources give rise to economic rents because they ference between the value of both hard and soft are not produced. For produced goods and services coal production at world prices and their total costs competitive forces expand supply until economic of production. •  Mineral rents are the difference profits are driven to zero, but natural resources in between the value of production for a stock of miner- fixed supply often command returns well in excess als at world prices and their total costs of production. of their cost of production. Rents from nonrenew- Minerals included in the calculation are tin, gold, able resources—fossil fuels and minerals—as well lead, zinc, iron, copper, nickel, silver, bauxite, and as rents from overharvesting of forests indicate the phosphate. • Forest rents are roundwood harvest liquidation of a country’s capital stock. When coun- times the product of average prices and a region- tries use such rents to support current consumption specific rental rate (based on a number of reviews; rather than to invest in new capital to replace what is World Bank 2011c). being used up, they are, in effect, borrowing against their future. The estimates of natural resources rents shown in the table are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the world price of units of specific commodities and subtract- ing estimates of average unit costs of extraction or harvesting costs (including a normal return on capital). These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP). This definition of economic rent differs from that used in the System of National Accounts, where rents are a form of property income, consisting of payments to landowners by a tenant for the use of the land or payments to the owners of subsoil assets by institutional units permitting them to extract sub- soil deposits. The Environment section of previous editions of World Development Indicators included a table “Toward a broader measure of savings,� which showed the derivation of adjusted net savings, tak- ing into account consumption of fixed and natural capital, pollution damage, and additions to human capital. Adjusted net savings measures the net addi- tions or subtractions from a country’s stock of tan- Data sources gible and intangible capital. This table is included in the Economy section as table 4.11, along with Data on contributions of natural resources to GDP the closely related table 4.10, “Toward a broader are estimates based on sources and methods measure of income.� described in World Bank (2011c). 2012 World Development Indicators 207 ECONOMY T 4 he data in the Economy section provide a pic- The accuracy of GDP estimates and their ture of the global economy and the economic comparability across countries depend on activity of more than 200 countries and ter- timely revisions to data on GDP and its compo- ritories that produce, trade, and consume the nents. The frequency of revisions to GDP data world’s output. The indicators measure changes varies: some countries revise numbers monthly, in the size and structure of the global economy others quarterly or annually, and others less and the effects of these changes on national frequently. Such revisions are usually small economies. They include measures of macro- and based on additional information received economic performance (gross domestic product during the year. However, in some cases larger [GDP], consumption, investment, and interna- revisions are required because of new method- tional trade), stability (central government bud- ologies, changes to the base year, or changes gets, prices, the money supply, and the balance in coverage. Comprehensive revisions of GDP of payments), and broader measures of income data usually result in upward adjustments as and savings adjusted for pollution, depreciation, improved data sources increase the coverage and depletion of resources. of the economy and as new weights for growing In 2010 the world economy grew 4.2 per- industries more accurately reflect their contribu- cent, a quick rebound from 2.3 percent in 2009 tions to the economy. Revisions to data can lead and well above the annual average of 2.9 per- to changes in income or lending classification, cent since 2000. Total output measured in GDP but such changes have been rare. at current prices increased more than $10 tril- Revisions to GDP data may cause breaks lion. Upper middle-income economies, includ- in series unless they are applied consistently ing China, were affected by slowing investment to historical data. For constant price series a and widespread uncertainty in financial markets break caused by rebasing can be eliminated by but still grew 7.8 percent. Lower middle-income linking the old series to the new using historical economies grew 6.9 percent, and low-income growth rates. But for nominal GDP data a break economies grew 5.9  percent. High-income in the time series cannot be avoided unless the economies, accounting for 68 percent of the statistics office revises historical series. Other world’s GDP, grew 3.1 percent in 2010. Devel- data series are affected by revisions to GDP. oping economies grew faster over the last Because rebasing real GDP and its components decade than in the previous two and faster than leaves the pre−base year current price series high-income economies. World output in 2010 unchanged, the GDP deflator calculated from reached $63 trillion, measured in GDP at cur- these two series is skewed for pre−base years. rent prices—a nominal increase of 96 percent Other series affected by the break in GDP are increase over 2000. Developing economies’ fiscal indicators expressed as a percentage of share of global output increased from 18 per- GDP. When nominal GDP is revised upward, the cent to 31 percent. The developing economies ratio of revenue and expenditure to GDP look in East Asia and Pacific grew the most, qua- smaller than previously reported. Information drupling their output and more than doubling on significant revisions and breaks in series will their share of global output from 5 percent to be included in the next release of the World 12 percent. Development Indicators database. 2012 World Development Indicators 209 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 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2011 a 2011 a Albania 3.5 3.0 4.2 5.0 –8.5 3.2 3.5 1.9 –11.9 –11.7 2,421 4.1 Algeria 3.3 3.0 .. .. .. .. 16.2 9.6 .. .. 183,122 36.2 Angola 2.3 7.0 .. .. .. .. 26.6 12.4 8.8 7.0 28,348 6.3 Antigua and Barbuda –5.2 3.0 .. .. .. .. 2.0 –3.9 –13.7 –13.9 148 2.5 Argentina 9.2 7.5 14.6 5.5 34.0 9.9 15.4 23.4 0.8 0.0 43,321 6.1 Armenia 2.1 4.2 21.7 6.0 13.8 7.5 9.2 6.6 –14.7 –12.8 1,959 4.8 Australia 2.3 1.7 5.3 5.2 5.1 9.2 0.1 4.1 –2.8 –2.2 42,921 1.7 Austria 2.3 2.8 8.3 6.6 8.0 5.9 1.8 2.0 3.0 2.5 11,482 0.6 Azerbaijan 5.0 0.2 24.2 1.0 1.3 2.5 11.2 17.7 29.1 26.6 10,274 9.3 Bangladesh 6.1 6.5 0.9 12.3 0.7 10.5 6.5 9.9 2.1 0.7 8,533 2.9 Belarus 7.6 3.8 7.1 7.2 11.9 2.0 10.2 35.2 –15.2 –15.7 6,076 1.7 Belgium 2.3 2.1 9.9 4.5 8.7 5.0 1.8 1.9 1.4 –0.5 18,311 0.5 Belize 2.9 2.1 .. .. .. .. 0.9 5.2 –3.3 –3.3 237 3.1 Benin 3.0 3.4 .. .. .. .. 1.8 1.3 .. .. 976 4.3 Bolivia 4.1 4.5 9.9 5.6 11.0 5.4 8.8 11.9 4.4 4.7 9,984 17.4 Botswana 7.2 6.8 1.2 7.2 5.0 5.1 14.7 12.0 0.3 –3.3 8,337 16.7 Brazil 7.5 2.9 11.5 4.4 36.2 9.7 7.3 6.9 –2.3 –2.5 350,414 14.0 Bulgaria 0.2 2.0 16.2 5.0 4.5 5.0 2.9 –0.3 –1.5 1.3 15,321 5.6 Burkina Faso 9.2 5.8 .. .. .. .. 4.0 9.5 .. .. 843 3.1 Burundi 3.9 4.4 .. .. .. .. 7.8 4.6 –18.7 –13.4 282 5.3 Cambodia 6.0 6.1 20.6 5.2 16.8 3.4 3.1 4.6 –7.8 –12.1 3,471 4.6 Cameroon 2.6 3.8 –0.3 7.4 4.6 7.3 3.2 4.5 –3.8 –2.9 3,199 5.2 Canada 3.2 2.3 6.4 4.2 13.1 6.0 2.9 4.1 –3.1 –2.7 65,658 1.4 Cape Verde 5.4 5.8 .. .. .. .. 3.3 –4.4 –11.2 –16.7 278 2.4 Central African Republic 3.3 4.0 .. .. .. .. 3.2 2.6 .. .. 155 5.6 Chad 4.3 6.0 .. .. .. .. 11.6 19.9 .. .. 951 4.1 Chile 5.2 6.2 –0.3 5.7 26.3 10.5 14.4 4.6 1.8 –0.4 41,932 6.0 China 10.4 9.1 28.4 11.3 20.1 14.4 6.6 4.9 5.2 3.7 3,204,610 19.7 Hong Kong SAR, China 7.0 4.7 16.8 4.9 17.3 5.0 0.5 12.1 5.7 –0.5 285,306 6.2 Colombia 4.3 4.9 1.2 7.0 18.1 6.8 3.1 3.8 –3.1 –2.5 30,504 6.7 Comoros 2.1 2.3 .. .. .. .. 3.8 5.0 .. .. 161 11.6 Congo, Dem. Rep. 7.2 6.7 9.4 16.0 17.0 13.6 22.4 15.2 .. .. 1,273 2.3 Congo, Rep. 8.8 5.1 .. .. .. .. 19.8 14.7 .. .. 5,641 6.2 Costa Rica 4.2 3.8 5.4 6.1 15.2 7.6 7.8 4.9 –4.0 –5.2 4,756 3.2 Côte d’Ivoire 3.0 –5.8 –0.5 –2.0 7.6 5.1 0.7 –6.5 .. .. 4,192 4.7 Croatia –1.2 1.2 6.0 5.8 –1.3 5.5 1.0 2.0 –1.6 –2.8 14,484 7.0 Czech Republic 2.3 2.1 18.0 5.0 18.0 6.8 –1.2 2.4 –3.1 –3.7 39,692 2.9 210 2012 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 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2011 a 2011 a Denmark 1.3 1.3 3.2 6.6 3.5 5.4 3.9 3.2 5.5 5.8 81,794 6.1 Dominica 0.1 0.9 .. .. .. .. 0.0 4.9 –15.3 –21.9 81 3.5 Dominican Republic 7.8 4.9 11.6 6.8 14.4 5.1 5.1 8.4 –8.6 –8.2 3,755 2.4 Ecuador 3.6 5.1 2.3 4.6 16.3 7.0 7.6 7.2 –3.1 –2.7 1,710 0.9 Egypt, Arab Rep. 5.1 0.5 –3.0 –4.8 –3.2 –11.7 10.1 15.1 –2.1 1.8 15,046 3.3 El Salvador 1.4 1.5 12.3 7.5 11.1 7.0 1.2 3.4 –2.3 –3.8 2,165 2.4 Equatorial Guinea 0.9 2.8 .. .. .. .. 19.1 6.9 .. .. 3,054 7.8 Eritrea 2.2 8.2 .. .. .. .. 11.6 14.7 .. .. 113 1.8 Estonia 3.1 7.6 10.7 6.1 8.8 6.5 1.5 5.4 3.5 0.9 195 0.1 Ethiopia 10.1 7.7 14.4 9.0 15.9 11.0 3.8 10.8 –1.4 –10.6 .. .. Fiji 0.3 1.3 .. .. .. .. 8.1 0.4 –12.9 –7.2 832 4.6 Finland 3.7 3.1 8.6 0.0 7.4 3.5 0.4 2.2 1.9 1.0 7,942 0.9 France 1.5 1.6 9.7 5.7 8.8 5.0 0.8 1.5 –1.7 –2.0 52,819 0.8 Gabon 5.7 6.0 3.0 3.7 9.3 9.6 18.0 11.4 .. .. 2,157 5.0 Gambia, The 5.0 5.3 4.1 4.7 7.0 4.3 8.4 5.5 6.5 1.9 223 8.2 Georgia 6.4 5.4 .. .. .. .. 8.7 –1.7 –11.5 –10.4 2,818 4.6 Germany 3.7 3.0 13.7 5.9 11.7 6.8 0.6 1.9 5.7 5.3 72,796 0.5 Ghana 6.6 13.6 53.7 41.0 69.4 35.9 14.0 22.7 –8.6 –7.0 .. .. Greece –3.5 –5.4 4.2 –1.0 –7.2 –10.0 1.7 2.3 –10.3 –8.6 1,442 0.2 Guatemala 2.8 2.8 4.4 4.5 12.4 5.5 5.0 8.7 –1.5 –2.2 5,847 3.8 Guinea 1.9 4.3 1.5 1.8 0.1 5.1 19.7 10.1 –7.2 –14.2 .. .. Guinea-Bissau 3.5 4.8 .. .. .. .. 1.7 –9.2 .. .. 225 9.4 Guyana 3.6 4.6 .. .. .. .. 5.9 3.9 –7.2 –10.6 798 5.4 Haiti –5.1 6.7 –7.3 14.5 19.7 0.2 5.4 3.2 –2.5 –13.4 1,389 3.5 Honduras 2.8 3.4 6.0 2.7 10.2 2.9 5.7 7.3 –6.2 –6.4 2,751 2.9 Hungary 1.3 3.4 14.3 8.7 12.8 7.9 3.1 5.3 1.1 2.8 48,686 5.4 Iceland –4.0 2.8 0.4 6.2 4.0 6.7 6.9 1.7 –8.0 –8.1 8,454 14.9 India 8.8 7.0 17.9 23.0 9.2 14.0 10.5 9.1 –3.0 –3.4 272,249 5.7 Indonesia 6.1 6.5 14.9 15.9 17.3 13.6 8.0 7.1 0.8 0.4 106,665 6.4 Ireland –0.4 2.2 6.3 7.8 2.7 6.8 –2.4 2.0 0.5 0.6 1,410 0.1 Israel 4.7 4.5 13.4 4.3 12.5 12.5 1.1 2.6 2.9 0.6 74,874 9.5 Italy 1.5 0.7 12.2 6.2 12.7 4.8 0.4 2.0 –3.5 –3.9 53,421 0.9 Jamaica –0.6 1.3 .. .. .. .. 10.6 7.3 –6.6 –11.2 2,273 3.8 Japan 4.0 –0.2 23.9 0.5 9.8 3.5 –2.2 –1.2 3.6 2.2 1,259,494 16.0 Jordan 3.1 2.5 7.6 –3.7 7.1 –0.5 6.3 1.4 –4.8 –8.5 11,489 6.8 Kazakhstan 7.3 6.6 1.9 11.0 –4.0 11.0 19.5 12.9 2.0 6.3 25,316 5.7 Kenya 5.3 4.3 6.1 8.9 3.0 8.6 3.9 10.1 –8.0 –9.2 4,264 3.4 2012 World Development Indicators 211 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 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2011 a 2011 a Korea, Rep. 6.2 3.8 14.5 9.1 16.9 6.3 3.7 1.5 2.8 2.5 304,349 5.8 Kyrgyz Republic –1.4 5.5 –4.2 6.5 1.6 5.5 6.9 17.8 –8.3 –6.4 1,707 4.2 Lao PDR 9.4 7.9 29.3 6.5 4.1 5.0 9.4 7.7 0.4 –14.0 .. .. Latvia –0.3 3.8 10.3 7.0 8.6 5.0 –2.3 6.4 3.0 –0.7 6,011 4.5 Lebanon 7.0 3.0 0.4 –9.1 –7.8 –4.1 4.4 2.4 –22.8 –20.6 34,236 12.7 Lesotho 3.3 3.1 2.2 5.9 9.6 5.4 3.7 –0.7 –19.8 –24.5 .. .. Lithuania 1.3 5.8 16.3 5.8 17.6 9.0 2.0 10.0 1.5 –2.3 7,925 3.4 Luxembourg 2.7 1.9 2.8 6.0 4.6 6.2 4.9 2.4 7.7 6.1 904 0.2 Macedonia, FYR 1.8 3.8 23.4 7.2 10.9 5.8 2.2 0.7 –2.2 –5.1 2,343 4.0 Madagascar 1.6 2.6 .. .. .. .. 8.1 9.2 .. .. 1,279 4.2 Malawi 7.1 5.6 .. .. .. .. 7.7 9.2 .. .. 212 1.1 Malaysia 7.2 4.8 9.9 4.1 15.1 5.2 5.1 4.1 11.5 9.7 131,867 7.0 Mali 4.5 5.4 .. .. .. .. 3.6 2.1 .. .. 1,418 5.2 Mauritania 5.0 5.1 .. .. .. .. 19.3 5.6 .. .. 485 3.3 Mauritius 4.0 4.1 –4.2 1.9 –0.6 13.5 1.6 1.6 –8.2 –11.1 2,589 4.3 Mexico 5.4 4.0 25.6 8.6 23.5 7.2 4.4 2.7 –0.6 –0.8 144,174 4.5 Moldova 6.9 6.0 12.8 20.0 13.7 10.0 11.2 3.7 –8.3 –9.4 1,965 4.1 Morocco 3.7 4.3 16.3 7.3 3.3 8.8 0.6 –0.7 –4.3 –6.7 19,572 4.7 Mozambique 7.2 7.4 2.2 7.3 1.7 6.0 12.7 9.5 –11.6 –13.6 2,473 6.5 Namibia 4.8 3.9 –42.3 10.9 –60.0 1.9 9.3 3.9 0.3 –0.5 1,796 3.3 Nepal 4.6 4.0 –13.7 22.1 26.7 13.5 13.4 11.6 .. .. 3,567 6.1 Netherlands 1.7 1.4 10.8 6.2 10.6 5.7 1.3 1.8 6.6 7.3 21,322 0.4 Nicaragua 7.6 4.1 13.2 6.1 10.8 7.9 2.9 7.9 –14.7 –16.3 1,892 3.1 Niger 8.8 6.0 .. .. .. .. 1.7 3.7 .. .. 659 3.3 Nigeria 7.9 7.0 .. .. .. .. 7.5 23.5 1.3 14.3 35,249 4.8 Norway 0.7 1.6 1.8 6.3 9.9 6.7 6.4 6.3 12.3 16.9 49,273 4.3 Pakistan 4.1 3.2 15.8 11.7 4.4 8.8 12.0 11.2 –0.8 0.5 14,636 3.7 Panama 4.8 8.1 6.2 7.1 21.2 7.7 3.0 5.3 –10.7 –12.3 1,792 1.0 Papua New Guinea 8.0 9.0 .. .. .. .. 9.3 9.8 –6.7 –24.0 4,172 6.2 Paraguay 15.0 4.9 34.3 4.0 29.3 5.1 6.7 10.6 –3.5 –3.1 4,951 4.8 Peru 8.8 6.3 2.5 7.2 23.8 9.1 6.9 4.2 –1.5 –2.7 47,266 12.9 Philippines 7.6 3.7 21.0 –7.7 22.5 1.6 4.2 5.0 4.5 2.1 67,565 9.7 Poland 3.9 4.0 12.1 6.1 13.9 7.1 1.4 2.9 –4.7 –5.1 92,824 4.6 Portugal 1.4 –1.5 8.8 6.0 5.4 –1.5 1.0 2.3 –10.0 –7.6 2,635 0.3 Romania 0.9 2.4 10.5 14.7 10.5 11.9 3.6 5.4 –4.0 –4.5 43,118 6.0 Russian Federation 4.0 4.1 7.1 5.0 25.6 10.0 11.4 11.7 4.7 5.1 455,474 13.9 212 2012 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 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2010 2011 a 2011 a 2011 a Rwanda 7.5 7.2 .. .. .. .. 2.1 3.8 –7.5 –6.1 825 4.8 Saudi Arabia 3.8 5.0 .. .. .. .. 12.4 15.3 15.4 23.3 541,234 34.3 Senegal 4.2 4.2 5.7 5.2 3.5 4.4 1.4 4.4 .. .. 2,536 4.2 Seychelles 6.2 4.0 .. .. .. .. –0.8 2.0 –24.0 –33.8 252 2.0 Sierra Leone 4.9 5.6 .. .. .. .. 14.4 9.1 –16.8 –13.6 432 7.0 Singapore 14.5 4.8 19.2 4.0 16.6 5.0 –0.5 4.0 23.7 20.5 237,874 5.9 Slovak Republic 4.2 3.0 16.5 8.1 16.3 8.7 0.5 4.0 –3.4 –2.3 908 0.1 Slovenia 1.4 2.8 9.5 3.0 7.2 –2.6 –1.1 –3.0 –0.8 –0.6 836 0.3 South Africa 2.8 3.2 16.5 5.0 5.5 11.0 8.1 6.8 –2.8 –3.0 42,811 4.1 Spain –0.1 0.7 10.3 5.9 5.4 0.5 1.0 2.1 –4.6 –3.5 33,330 0.9 Sri Lanka 8.0 7.7 5.8 15.0 13.0 16.5 7.3 7.0 –2.9 –3.8 6,095 3.8 St. Lucia 3.1 2.7 .. .. .. .. 5.1 –3.5 –14.6 –21.4 213 3.0 St. Vincent & Grenadines –1.3 –0.2 .. .. .. .. 2.1 6.2 –29.2 –29.1 90 2.6 Sudan 4.5 5.3 .. .. .. .. 17.6 4.1 0.3 –7.2 220 0.2 Swaziland 1.1 –2.1 –2.4 5.3 0.9 4.7 6.1 5.0 –10.7 –15.8 601 2.5 Sweden 5.6 4.1 11.1 6.8 12.7 7.0 1.2 0.9 6.6 6.6 44,243 2.3 Switzerland 2.7 1.7 8.4 7.4 7.3 5.9 0.1 –0.1 14.0 13.0 281,187 10.0 Syrian Arab Republic 3.2 –3.0 5.7 –3.5 8.3 5.8 6.3 7.5 –0.6 –2.2 16,714 9.5 Tanzania 7.0 6.4 10.7 3.9 10.9 6.7 7.7 8.3 –8.6 –9.1 3,726 4.9 Thailand 7.8 2.0 14.7 8.8 21.5 13.1 3.7 1.9 4.1 0.7 167,652 7.7 Togo 3.4 3.7 .. .. .. .. 1.4 3.3 .. .. 746 4.6 Trinidad and Tobago 0.1 2.8 .. .. .. .. 4.4 10.3 .. .. 10,106 13.7 Tunisia 3.7 –0.5 4.8 –1.2 3.8 –2.2 4.0 –5.9 –4.8 –5.8 7,652 3.5 Turkey 9.0 7.9 3.4 6.0 20.7 13.0 6.3 8.9 –6.4 –9.8 78,660 3.8 Uganda 5.2 6.3 5.6 6.4 7.8 16.0 9.1 4.3 –10.2 –12.1 2,617 4.0 Ukraine 4.2 4.5 4.5 6.5 11.1 9.0 15.0 6.5 –2.2 –4.4 30,458 4.0 United Kingdom 2.1 1.0 7.4 6.0 8.6 0.3 2.9 3.8 –3.3 –1.5 79,808 1.2 United States 3.0 1.7 11.3 6.6 12.5 3.9 0.8 2.5 –3.2 –3.4 150,964 0.7 Uruguay 8.5 5.5 8.5 7.1 16.2 7.1 5.1 6.8 –0.4 –2.0 10,289 10.3 Uzbekistan 8.5 7.5 .. .. .. .. 18.5 19.6 .. .. .. .. Vanuatu 3.0 3.9 .. .. .. .. 2.8 5.5 .. .. 176 5.8 Venezuela, RB –1.5 3.1 –12.9 6.1 –2.9 7.9 46.7 59.8 3.1 7.9 10,562 2.4 Vietnam 6.8 5.6 14.7 18.2 14.1 14.5 11.9 12.0 –4.0 –4.9 .. .. Yemen, Rep. 8.0 –6.0 15.8 0.2 –6.8 –1.0 24.7 19.8 –3.9 9.6 4,519 4.4 Zambia 7.6 6.8 .. .. .. .. 11.7 16.9 3.8 4.9 2,324 4.3 Zimbabwe 9.0 5.0 21.5 14.1 6.6 8.0 17.5 3.3 .. .. .. .. a. Data are preliminary estimates based on World Bank staff estimates and national sources. Source: World Development Indicators data files, the World Bank’s Global Economic Prospects 2012, and the International Monetary Fund’s International Financial Statistics. 2012 World Development Indicators 213 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 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Afghanistan .. 11.3 .. 6.3 .. 14.5 .. 9.5 .. 14.2 Albania 3.8 5.4 4.3 1.4 –0.5 4.7 .. .. 6.9 8.1 Algeria 1.9 3.9 3.6 4.2 1.8 3.2 –2.1 2.0 1.8 5.2 Angolaa 1.6 12.9 –1.4 14.2 4.4 12.7 –0.3 19.3 –2.2 12.9 Argentina 4.3 5.6b 3.5 2.9 3.8 6.0 2.7 5.9 4.5 5.0 Armenia –1.9 9.2 0.5 6.0 –7.4 9.2 –4.3 5.8 6.7 10.4 Australia 3.7 3.2 3.4 1.7 2.8 2.8 1.7 0.9 4.0 3.6 Austria 2.5 1.8 –0.1 1.3 2.5 2.0 2.5 2.6 2.7 2.0 Azerbaijan –6.3 17.1 –1.7 4.7 –2.1 22.4 –15.7 8.3 –1.6 14.5 Bahrain 5.0 6.6 .. .. .. .. .. .. .. .. Bangladesh 4.8 5.9 2.9 3.5 7.3 7.7 7.2 7.8 4.5 6.1 Belarus –1.6 8.0 –4.0 5.5 –1.8 12.0 –0.7 10.7 –0.4 5.4 Belgium 2.2 1.6 .. .. 1.8 0.6 .. .. .. .. 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 2.9 4.1 5.4 3.8 4.4 4.3 3.1 Bosnia and Herzegovina .. 4.6 .. 4.3 .. 6.3 .. 6.5 .. 3.9 Botswana 5.7 4.1 –0.7 0.9 5.4 3.2 3.5 5.1 7.5 4.7 Brazil 2.7 3.7 3.6 3.6 2.4 2.8 2.0 2.5 3.8 3.9 Bulgaria –1.1 4.8 –3.9 –2.3 –19.5 5.3 .. 5.8 .. 5.6 Burkina Faso 5.5 5.5 5.9 6.2 5.9 7.3 5.9 6.3 3.9 5.5 Burundi –2.9 3.2 –1.9 –1.5 –4.3 –6.2 .. .. –2.8 10.4 Cambodia 7.0 8.7 3.7 5.6 14.3 10.6 18.6 10.2 7.1 9.2 Cameroon 1.7 3.2 5.4 3.4 –0.9 –0.4 1.4 .. 0.2 6.2 Canada 3.1 2.0 1.1 1.5 3.2 0.1 4.5 –1.6 3.1 3.0 Central African Republic 2.0 1.0 3.8 0.3 0.7 –0.4 –0.2 –0.1 0.2 –2.5 Chad 2.2 9.0 4.9 .. 0.6 .. .. .. 0.8 .. Chile 6.6 4.0 2.2 4.2 5.6 2.3 4.4 2.7 6.9 4.4 Chinaa 10.6 10.8 4.1 4.4 13.7 11.8 12.9 11.6 11.0 11.5 Hong Kong SAR, China 3.6 4.6 .. –3.9 .. –2.7 .. –3.6 .. 4.9 Colombia 2.8 4.5 –2.7 2.2 1.4 4.5 –2.5 3.9 4.1 4.7 Congo, Dem. Rep. –4.9 5.3 1.4 1.7 –8.0 8.7 –8.7 6.3 –13.0 11.2 Congo, Rep.a 1.0 4.3 .. .. .. .. .. .. .. .. Costa Rica 5.3 4.9 4.1 3.3 6.2 4.8 6.8 4.4 4.7 5.4 Côte d’Ivoirea 3.2 1.1 3.5 1.6 6.3 0.3 5.5 –1.1 2.0 1.2 Croatia 0.5 3.2 –5.5 1.9 –2.2 2.8 –3.5 1.6 2.2 3.9 Cuba –0.7 6.7 –3.3 –0.9 –1.0 2.3 0.8 –1.5 –0.7 8.3 Cyprus 4.2 3.1 1.4 –3.3 0.6 3.0 0.2 0.1 6.5 3.9 Czech Republic 1.1 3.8 0.0 0.4 0.2 5.5 4.3 6.9 1.2 3.2 Denmark 2.7 0.9 4.6 –0.7 2.4 –0.8 2.2 0.2 2.7 1.2 Dominican Republica 6.3 5.6 1.9 3.4 7.1 2.6 7.0 2.8 5.9 7.1 Ecuador 1.9 4.8 –1.7 4.4 2.6 5.4 1.5 5.2 2.4 2.8 Egypt, Arab Rep. 4.4 5.1 3.1 3.3 5.1 5.5 6.3 4.9 4.1 5.4 El Salvador 4.8 2.2 1.2 3.1 5.1 1.4 5.2 1.7 4.0 2.4 Eritrea 5.7 0.2 1.5 2.7 15.0 0.6 10.6 –6.0 5.7 0.6 Estonia 0.4 4.6 –6.2 –2.9 –2.4 8.6 7.3 8.9 3.2 7.1 Ethiopia 3.8 8.8 2.6 7.1 4.1 9.3 3.9 7.6 5.2 10.9 Finland 2.7 2.1 –0.3 2.7 3.4 3.0 6.4 3.3 2.8 1.5 France 1.9 1.3 2.0 0.3 0.9 0.5 .. 0.1 2.2 1.7 Gabona 2.3 2.2 2.0 1.5 1.6 0.8 3.0 3.0 3.1 3.3 Gambia, The 3.0 3.7 3.3 3.2 1.0 7.3 0.9 .. 3.7 6.2 Georgia –7.1 6.9 –11.0 0.0 –8.1 9.3 .. 10.2 –0.3 8.4 Germany 1.6 1.0 0.1 –0.2 –0.1 0.1 0.1 0.5 2.6 1.7 Ghana 4.3 5.9 .. .. .. .. .. .. .. .. Greece 2.2 2.6 .. .. 1.0 0.8 .. .. .. .. Guatemala 4.2 3.6 2.8 2.9 4.3 2.5 2.8 2.6 4.7 4.4 Guinea 4.4 2.9 4.3 5.0 4.9 4.6 4.0 3.1 3.6 –2.1 Guinea-Bissau 1.2 1.5 .. .. .. .. .. .. .. .. Haiti 0.5 0.6 .. .. .. .. .. .. .. .. 214 2012 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 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Honduras 3.2 4.6 2.2 3.2 3.6 3.6 4.0 4.1 3.8 5.9 Hungary 1.0 2.2 –1.9 3.4 3.7 2.1 7.9 3.4 0.5 2.1 India 5.9 8.0 3.2 3.0 6.1 8.5 6.7 8.7 7.7 9.6 Indonesiaa 4.2 5.3 2.0 3.5 5.2 4.1 6.7 4.6 4.0 7.3 Iran, Islamic Rep. 3.1 5.4 3.2 5.9 2.6 6.9 5.1 9.9 3.8 5.3 Iraq .. 0.4 .. .. .. .. .. .. .. .. Ireland 7.4 2.8 0.0 –4.6 11.5 3.7 .. .. .. 3.8 Israela 5.5 3.6 .. .. .. .. .. .. .. .. Italy 1.5 0.5 2.1 –0.1 1.0 –0.8 1.6 –1.4 1.7 1.0 Jamaica 1.6 1.2 –0.6 –0.2 –0.8 –0.3 –1.8 –1.5 3.8 1.7 Japan 1.0 0.9 –0.4 –0.8 –0.4 0.5 0.5 1.6 1.7 1.3 Jordan 5.0 6.7 –3.0 8.2 5.2 7.9 5.6 8.9 5.0 6.1 Kazakhstan –4.1 8.3 –8.0 3.8 –8.6 9.0 .. 6.3 1.1 8.3 Kenya 2.2 4.3 1.9 1.9 1.2 4.9 1.3 4.3 3.2 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 5.8 4.1 1.6 2.0 6.0 5.4 7.3 6.3 5.6 3.6 Kosovo .. 5.3 .. .. .. .. .. .. .. .. Kuwait a 4.9 8.4 1.0 .. 0.3 .. –0.1 .. 3.5 .. Kyrgyz Republic –4.1 4.4 1.5 0.0 –10.3 1.1 –7.5 –0.6 –5.2 10.9 Lao PDR 6.4 7.2 4.8 3.4 11.1 13.1 11.7 –0.2 6.6 7.3 Latvia –1.5 4.8 –5.2 2.8 –8.3 3.7 –7.3 2.5 2.7 5.7 Lebanon 5.3 4.9 2.9 0.8 –0.2 4.3 1.9 0.9 1.5 4.9 Lesotho 4.0 3.5 2.8 –1.9 5.5 3.4 7.9 4.7 4.5 3.8 Liberia 4.1 0.9 .. .. .. .. .. .. .. .. Libya .. 5.4 .. .. .. .. .. .. .. .. Lithuania –2.5 5.3 –0.4 1.9 3.3 5.8 6.6 7.1 5.8 5.3 Macedonia, FYR –0.8 3.3 0.2 1.9 –2.3 3.3 –5.3 1.9 0.5 3.6 Madagascar 2.0 3.4 1.8 2.4 2.4 4.2 2.0 5.1 2.3 3.6 Malawi 3.7 5.2 8.6 2.9 2.0 6.2 0.5 5.7 1.6 6.5 Malaysiaa 7.0 5.0 0.3 3.3 8.6 3.3 9.5 4.1 7.3 6.9 Mali 4.1 5.2 2.6 4.8 6.4 4.5 –1.4 5.1 3.0 6.5 Mauritania 2.9 4.4 –0.2 1.3 3.4 4.4 5.8 –0.9 4.9 5.2 Mauritius 5.2 3.9 0.0 0.1 5.4 1.8 5.3 0.6 6.3 5.6 Mexico 3.1 2.1 1.5 1.7 3.8 1.3 4.3 1.1 2.9 2.5 Moldova –9.6 5.2 –11.2 –0.9 –13.6 –1.7 –7.1 1.0 0.7 10.1 Mongolia 1.0 7.2 0.3 4.6 1.5 5.8 –6.6 6.1 –0.9 8.9 Morocco 2.4 4.9 –0.4 5.9 3.2 3.8 2.6 3.0 3.1 4.9 Mozambique 6.1 7.8 5.2 8.3 12.3 8.5 10.2 7.1 5.0 6.9 Myanmar a .. .. .. .. .. .. .. .. .. .. Namibia 4.0 5.0 3.8 –2.4 2.4 1.1 7.4 0.2 4.2 6.9 Nepal 4.9 3.8 2.5 3.2 7.1 2.5 8.9 0.7 6.2 4.4 Netherlands 3.2 1.6 1.8 1.6 1.7 0.8 2.6 1.1 3.5 2.1 New Zealand 3.3 2.6 2.9 2.0 2.5 1.1 .. .. 3.6 3.1 Nicaragua 3.7 3.6 4.7 2.9 5.5 3.4 5.3 4.7 5.0 4.3 Niger a 2.4 4.2 3.0 .. 2.0 .. 2.6 .. 1.9 .. Nigeria 2.5 6.7 .. .. .. .. .. .. .. .. Norway 3.9 1.7 2.6 2.6 3.8 –0.6 1.5 2.3 3.9 3.1 Omana 4.5 4.7 5.0 .. 3.9 .. 6.0 .. 5.0 .. Pakistan 3.8 5.1 4.4 3.4 4.1 6.7 3.8 8.0 4.4 5.7 Panama 4.7 6.8 3.1 2.9 6.0 6.0 2.7 1.7 4.5 7.3 Papua New Guinea 3.8 3.8 4.5 2.4 5.4 4.3 4.6 3.9 –0.6 3.9 Paraguaya 2.2 3.8 3.3 5.2 0.6 2.1 1.4 1.3 2.5 3.8 Peru 4.7 6.1 5.5 4.1 5.4 6.6 3.8 6.2 4.0 6.2 Philippinesa 3.3 4.9 1.9 3.2 3.2 4.2 3.0 3.7 3.7 5.8 Poland 4.7 4.3 0.5 1.1 6.7 5.8 8.1 8.7 5.2 3.6 Portugal 2.9 0.7 –0.6 –0.2 3.1 –0.9 2.7 –0.4 2.4 1.6 Puerto Ricoa 4.3 0.0 .. .. .. .. .. .. .. .. Qatar .. 14.2 .. .. .. .. .. .. .. .. 2012 World Development Indicators 215 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 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Romania –0.6 5.0 –1.9 7.0 –1.2 5.8 .. .. –0.3 6.0 Russian Federation –4.7 5.4 –4.9 1.5 –7.1 4.1 .. .. –4.7 6.5 Rwandaa –0.2 7.6 2.5 .. –3.8 .. –5.8 .. –0.9 .. Saudi Arabiaa 2.1 3.6 1.6 1.2 2.2 3.2 5.6 5.6 2.2 4.3 Senegal 3.0 4.2 2.4 2.5 3.8 3.2 3.1 1.4 3.0 5.9 Serbia –4.2 4.1 .. 1.1 .. 2.0 .. .. .. 5.4 Sierra Leone –5.0 8.8 .. .. .. .. .. .. .. .. Singapore 7.2 6.0 –2.8 –3.2 7.7 5.6 7.0 6.1 7.2 6.3 Slovak Republic 1.9 5.4 0.4 6.2 4.1 7.9 .. 9.7 4.4 4.2 Slovenia 2.7 3.3 0.4 –0.6 1.6 3.3 1.8 3.2 3.0 3.6 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.1 3.9 1.0 1.5 1.0 2.9 1.6 3.1 3.0 4.1 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 2.7 2.4 3.1 –0.1 2.2 0.8 5.2 –0.2 2.7 3.2 Sri Lankaa 5.3 5.6 1.9 3.1 6.9 5.7 8.1 4.6 6.0 6.2 Sudan 5.5 6.7 7.4 2.6 8.5 10.6 7.5 4.7 1.9 7.9 Swaziland 3.4 2.4 0.9 1.4 3.2 1.7 2.8 1.8 3.9 3.4 Sweden 2.3 2.2 –0.8 3.3 4.3 2.2 8.9 2.7 2.0 2.2 Switzerland 1.0 1.9 –0.9 0.5 0.2 2.0 1.0 2.3 1.2 1.9 Syrian Arab Republic 5.1 5.0 6.0 .. 9.2 .. .. .. 1.5 .. Tajikistan –10.4 8.6 –6.8 7.4 –11.4 8.9 –12.6 8.2 –10.8 7.2 Tanzaniac 3.0 7.1 3.2 4.4 3.1 9.3 2.8 8.7 2.6 7.8 Thailanda 4.2 4.5 1.0 2.2 5.7 5.4 6.9 5.6 3.7 4.0 Timor-Lestea .. 3.4 .. .. .. .. .. .. .. .. Togoa 3.5 2.7 4.0 2.8 1.8 8.1 1.8 7.5 3.9 –0.7 Trinidad and Tobago 3.2 6.5 2.7 –8.3 3.2 9.2 4.9 8.9 3.2 5.6 Tunisiaa 4.7 4.7 2.6 2.5 4.4 3.0 5.7 2.8 5.5 6.6 Turkey 3.9 4.7 1.3 1.6 4.7 5.2 4.7 5.1 4.0 5.0 Turkmenistan –4.9 13.6 –4.7 12.3 –2.7 20.2 .. .. –5.8 21.8 Uganda 7.0 7.7 3.4 2.2 12.3 9.2 14.2 6.8 8.2 8.4 Ukraine –9.3 4.8 –5.6 2.9 –12.6 3.2 –11.2 6.2 –8.1 4.8 United Arab Emirates 4.8 5.1 .. .. .. .. .. .. .. .. United Kingdom 3.2 1.8 –0.3 0.2 1.5 –0.7 .. .. 4.1 2.6 United States 3.6 1.8 3.8 1.9 3.7 0.3 .. 1.9 3.6 2.2 Uruguay 3.9 3.6 3.9 2.1 1.3 3.2 –0.4 4.9 1.7 3.6 Uzbekistan –0.2 7.1 0.5 6.4 –3.4 5.0 0.7 2.6 0.4 9.0 Venezuela, RB 1.6 4.7 1.2 2.5 1.2 2.2 4.5 2.4 –0.1 6.4 Vietnama 7.9 7.5 4.3 3.7 11.9 9.3 11.2 10.9 7.5 7.5 West Bank and Gaza 7.3 –0.9 .. .. .. .. .. .. .. .. Yemen, Rep.a 5.6 4.1 5.1 2.7 5.2 2.0 1.8 5.1 6.1 6.0 Zambia 0.5 5.6 4.2 1.1 –4.2 9.4 0.8 5.1 2.5 5.5 Zimbabwe 2.3 –6.0 4.3 –9.6 0.4 –4.9 0.4 –5.9 3.0 –4.2 World 2.9 w 2.7 w 2.0 w 2.5 w 2.4 w 2.5 w .. w 3.1 w 3.1 w 2.8 w Low income 3.0 5.5 2.9 3.7 3.6 7.2 3.6 6.6 2.8 6.1 Middle income 3.9 6.4 2.4 3.5 4.4 7.2 6.2 7.5 4.0 6.6 Lower middle income 3.8 6.3 2.6 3.3 4.1 6.0 4.7 6.3 4.5 7.6 Upper middle income 3.9 6.5 2.3 3.6 4.5 7.5 6.5 7.8 3.9 6.4 Low & middle income 3.9 6.4 2.4 3.5 4.4 7.2 6.2 7.5 4.0 6.6 East Asia & Pacific 8.5 9.4 3.4 4.1 10.9 10.2 10.9 10.1 8.4 10.0 Europe & Central Asia –1.8 5.4 –2.1 2.7 –4.4 5.4 .. .. –1.2 5.9 Latin America & Carib. 3.2 3.8 2.0 2.9 3.0 3.1 2.9 2.8 3.5 4.0 Middle East & N. Africa 3.8 4.7 2.9 4.6 4.1 3.5 4.3 5.4 3.3 5.1 South Asia 5.5 7.4 3.3 3.1 6.0 8.1 6.4 8.4 6.9 8.8 Sub-Saharan Africa 2.5 5.0 3.2 3.2 1.9 4.9 2.2 3.4 2.6 4.8 High income 2.7 1.8 1.5 0.7 1.9 0.7 .. 1.9 3.0 2.1 Euro area 2.0 1.3 1.6 0.2 1.0 0.4 1.2 0.3 2.4 1.8 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 lower than official reports indicate since the last quarter of 2008. c. Covers mainland Tanzania only. 216 2012 World Development Indicators 4.1 ECONOMY Growth of output About the data De�nitions 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 World Development Indicators adopted the termi- organizations and central banks by visiting and measurement problem, especially in developing nology of the 1993 SNA in 2001. Although many resident World Bank missions. Data for high- countries, where much economic activity is unre- countries continue to compile their national accounts income economies are from Organisation for corded. A complete picture of the economy requires according to the SNA version 3 (referred to as the Economic Co-operation and Development (OECD) estimating household outputs produced for home 1968 SNA), more and more are adopting the 1993 data files. The United Nations Statistics Division use, sales in informal markets, barter exchanges, SNA. Some low-income countries still use concepts publishes detailed national accounts for UN mem- and illicit or deliberately unreported activities. The from the even older 1953 SNA guidelines, including ber countries in National Accounts Statistics: Main consistency and completeness of such estimates valuations such as factor cost, in describing major Aggregates and Detailed Tables and publishes depend on the skill and methods of the compiling economic aggregates. Countries that use the 1993 updates in the Monthly Bulletin of Statistics. statisticians. SNA are identified in Primary data documentation. 2012 World Development Indicators 217 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ billions % of GDP % of GDP % of GDP % of GDP 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan 2.5 17.2 45 30 20 22 15 13 35 48 Albania 3.7 11.8 29 20 19 19 11 19 52 61 Algeria 54.8 162.0 9 7 59 62 7 6 33 31 Angolaa 9.1 84.4 6 10 72 63 3 6 22 27 Argentina 284.2 368.7 5 10 28 31 18 21 67 59 Armenia 1.9 9.4 26 20 39 36 19 11 35 44 Australia 416.9 1,131.6 4 2 27 20 13 9 70 78 Austria 192.1 379.1 2 2 31 29 20 19 67 69 Azerbaijan 5.3 51.8 17 6 45 65 6 6 38 30 Bahrain 8.0 20.6 .. .. .. .. .. .. .. .. Bangladesh 47.1 100.4 26 19 25 28 15 18 49 53 Belarus 12.7 54.7 14 9 39 44 32 31 47 47 Belgium 232.7 469.4 1 1 27 22 19 14 72 78 Benina 2.3 6.6 37 .. 14 .. 9 .. 50 .. Bolivia 8.4 19.6 15 13 30 37 15 14 55 50 Bosnia and Herzegovina 5.5 16.6 11 8 23 28 10 14 66 64 Botswana 5.6 14.9 3 3 53 45 5 3 45 52 Brazil 644.7 2,087.9 6 6 28 27 17 16 67 67 Bulgaria 12.9 47.7 14 5 26 31 18 16 61 63 Burkina Faso 2.6 8.8 29 .. 24 .. 16 .. 47 .. Burundi 0.7 1.6 40 .. 19 .. 9 .. 41 .. Cambodia 3.7 11.2 38 36 23 23 17 16 39 41 Cameroon 10.1 22.4 22 .. 36 .. 21 .. 42 .. Canada 724.9 1,577.0 2 .. 33 .. 19 .. 65 .. Central African Republic 1.0 2.0 53 56 16 15 7 .. 31 29 Chad 1.4 7.6 42 14 11 49 9 7 46 38 Chile 75.2 212.7 6 3 38 43 19 12 55 54 Chinaa 1,198.5 5,926.6 15 10 46 47 32 30 39 43 Hong Kong SAR, China 169.1 224.5 0 0 12 7 3 2 88 93 Colombia 100.4 288.2 9 7 29 36 15 15 62 57 Congo, Dem. Rep. 4.3 13.1 50 43 20 24 5 5 30 33 Congo, Rep.a 3.2 11.9 5 4 72 80 3 4 23 16 Costa Rica 15.9 35.8 9 7 32 26 25 17 58 67 Côte d’Ivoirea 10.4 22.8 24 23 25 27 22 19 51 50 Croatia 21.5 60.9 6 6 29 27 20 18 65 67 Cuba 30.6 62.7 8 5 28 20 18 10 64 75 Cyprus 9.3 23.1 4 2 19 20 10 8 77 78 Czech Republic 56.7 192.0 4 2 38 38 27 24 58 60 Denmark 160.1 312.0 3 1 27 22 16 12 71 77 Dominican Republica 24.0 51.8 7 6 36 32 26 24 57 62 Ecuador 15.9 58.0 9 7 32 38 11 10 59 55 Egypt, Arab Rep. 99.8 218.9 17 14 33 38 19 16 50 48 El Salvador 13.1 21.2 10 13 32 27 25 21 58 60 Eritrea 0.6 2.1 15 15 23 22 11 6 62 63 Estonia 5.7 19.2 5 3 28 29 18 17 68 68 Ethiopia 8.2 29.7 50 48 12 14 6 5 38 38 Finland 121.8 238.0 3 3 35 29 26 19 62 68 France 1,326.3 2,560.0 3 2 23 19 16 11 74 79 Gabona 5.1 13.0 6 4 56 54 4 4 38 42 Gambia, The 0.4 0.8 36 27 13 16 5 5 51 57 Georgia 3.1 11.7 22 8 22 23 9 13 56 68 Germany 1,886.4 3,280.5 1 1 30 28 23 21 68 71 Ghana 5.0 31.3 39 30 28 19 10 6 32 51 Greece 124.4 301.1 .. .. 21 18 .. .. .. .. Guatemala 19.3 41.2 15 13 29 19 21 19 56 68 Guinea 3.1 4.5 20 13 33 47 4 5 47 40 Guinea-Bissau 0.2 0.9 56 .. 13 .. 11 .. 31 .. Haiti 3.7 6.7 .. .. .. .. .. .. .. .. 218 2012 World Development Indicators 4.2 ECONOMY Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ billions % of GDP % of GDP % of GDP % of GDP 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 7.1 15.4 16 13 32 27 23 18 52 61 Hungary 46.4 128.6 6 4 32 31 24 23 62 65 India 460.2 1,727.1 23 19 26 26 16 14 50 55 Indonesiaa 165.0 706.6 16 15 46 47 28 25 38 38 Iran, Islamic Rep. 101.3 331.0 14 .. 37 .. 13 .. 50 .. Iraq 25.9 82.2 5 .. 84 .. 1 .. 10 .. Ireland 97.5 206.6 3 1 42 32 33 24 55 67 Israela 124.7 217.3 .. .. .. .. .. .. .. .. Italy 1,104.0 2,061.0 3 2 28 25 21 17 69 73 Jamaica 9.0 14.3 7 6 26 22 11 9 67 71 Japan 4,667.4 5,458.8 2 1 32 27 22 18 66 72 Jordan 8.5 27.6 2 3 26 31 16 19 72 66 Kazakhstan 18.3 149.1 9 5 40 42 18 13 51 53 Kenya 12.7 31.4 32 19 17 14 12 8 51 67 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 533.4 1,014.5 5 3 38 39 28 31 57 58 Kosovo 1.8 5.6 .. 12 .. 20 .. 17 .. 68 Kuwait a 37.7 109.5 0 .. 59 .. 3 .. 40 .. Kyrgyz Republic 1.4 4.6 37 21 31 28 19 18 32 51 Lao PDR 1.7 7.3 53 33 23 30 17 8 25 37 Latvia 7.8 24.0 5 4 24 22 14 12 72 74 Lebanon 17.3 39.0 7 6 23 21 13 8 70 72 Lesotho 0.7 2.1 12 8 32 34 14 16 56 58 Liberia 0.6 1.0 72 61 12 17 9 13 16 22 Libya 33.9 62.4 5 2 66 78 3 4 29 20 Lithuania 11.4 36.3 6 4 30 28 19 16 64 68 Macedonia, FYR 3.6 9.2 12 11 34 28 21 16 54 61 Madagascar 3.9 8.7 29 29 14 16 12 14 57 55 Malawi 1.7 5.1 40 31 18 16 13 10 43 53 Malaysiaa 93.8 237.8 9 11 48 44 31 26 43 45 Mali 2.4 9.3 42 .. 21 .. 4 .. 38 .. Mauritania 1.1 3.6 28 20 30 37 9 4 43 43 Mauritius 4.6 9.7 7 4 31 29 23 19 62 67 Mexico 581.4 1,034.8 4 4 28 34 20 18 68 62 Moldova 1.3 5.8 29 14 22 13 16 13 49 73 Mongolia 1.1 6.2 31 16 25 38 8 7 44 46 Morocco 37.0 90.8 15 15 29 30 17 15 56 55 Mozambique 4.2 9.6 24 32 25 23 12 13 51 45 Myanmar a .. .. 57 36 10 26 7 20 33 38 Namibia 3.9 12.2 12 8 28 20 13 8 60 73 Nepal 5.5 15.7 41 36 22 15 9 7 37 48 Netherlands 385.1 779.4 3 2 25 24 16 13 72 74 New Zealand 51.6 126.7 9 .. 25 .. 17 .. 66 .. Nicaragua 3.9 6.6 21 21 28 30 17 20 51 49 Niger a 1.8 5.5 38 .. 18 .. 7 .. 44 .. Nigeria 46.0 193.7 49 .. 31 .. 3 .. 21 .. Norway 168.3 417.5 2 2 42 40 11 9 56 58 Omana 19.9 46.9 2 .. 57 .. 5 .. 41 .. Pakistan 74.0 176.9 26 21 23 25 15 17 51 53 Panama 11.6 26.7 7 5 19 17 10 6 74 78 Papua New Guinea 3.5 9.5 36 36 41 45 8 6 23 19 Paraguaya 7.1 18.3 17 22 22 20 15 12 61 57 Peru 53.3 157.1 8 8 30 34 16 17 62 57 Philippinesa 81.0 199.6 14 12 34 33 24 21 52 55 Poland 171.3 469.4 5 4 32 32 19 18 63 65 Portugal 117.3 228.6 4 2 28 23 18 13 68 74 Puerto Ricoa 61.7 96.3 1 1 42 50 39 46 57 49 Qatar 17.8 98.3 .. .. .. .. .. .. .. .. 2012 World Development Indicators 219 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ billions % of GDP % of GDP % of GDP % of GDP 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 37.1 161.6 13 7 36 26 15 22 51 67 Russian Federation 259.7 1,479.8 6 4 38 37 17 16 56 59 Rwandaa 1.7 5.6 37 34 14 14 7 6 49 52 Saudi Arabiaa 188.4 434.7 5 3 54 62 10 10 41 35 Senegal 4.7 13.0 19 17 23 22 15 13 58 61 Serbia 6.1 38.4 20 9 30 27 24 16 50 64 Sierra Leone 0.6 1.9 58 49 28 21 4 .. 13 30 Singapore 95.9 208.8 0 0 35 28 27 22 65 72 Slovak Republic 28.7 87.3 4 4 36 35 25 21 59 61 Slovenia 20.0 46.9 3 2 36 32 26 21 61 66 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 132.9 363.7 3 3 32 31 19 15 65 66 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 580.7 1,407.4 4 3 29 26 19 13 66 72 Sri Lankaa 16.3 49.6 20 13 27 29 17 18 53 58 Sudan 12.4 62.0 42 24 22 33 9 6 37 43 Swaziland 1.5 3.6 12 7 45 50 39 45 43 42 Sweden 247.3 458.6 2 2 29 27 22 16 69 71 Switzerland 249.9 527.9 2 1 27 27 19 19 71 72 Syrian Arab Republic 19.3 59.1 24 23 38 31 7 .. 38 46 Tajikistan 0.9 5.6 27 21 39 22 34 10 34 57 Tanzaniab 10.2 23.1 33 28 19 25 9 10 47 47 Thailanda 122.7 318.5 9 12 42 45 34 36 49 43 Timor-Lestea 0.3 0.7 26 .. 19 .. 3 .. 56 .. Togoa 1.3 3.2 34 .. 18 .. 8 .. 48 .. Trinidad and Tobago 8.2 20.6 1 1 49 52 7 5 49 47 Tunisiaa 21.5 44.3 11 8 30 32 18 18 58 60 Turkey 266.6 734.4 11 10 31 27 23 18 57 64 Turkmenistan 2.9 20.0 24 12 44 54 11 .. 31 34 Uganda 6.2 17.0 29 24 23 25 8 8 48 50 Ukraine 31.3 137.9 17 8 36 31 19 17 47 61 United Arab Emirates 104.3 297.6 2 1 50 54 13 10 48 46 United Kingdom 1,477.2 2,261.7 1 1 27 22 17 11 72 78 United States 9,898.8 14,586.7 1 1 23 20 16 13 75 79 Uruguay 22.8 39.1 7 9 25 26 14 14 69 65 Uzbekistan 13.8 39.0 34 20 23 35 9 9 43 45 Venezuela, RB 117.1 391.8 4 .. 50 .. 20 .. 46 .. Vietnama 31.2 106.4 25 21 37 41 19 20 39 38 West Bank and Gaza 4.1 .. .. .. .. .. .. .. .. .. Yemen, Rep.a 9.6 31.3 14 8 46 29 6 6 40 63 Zambia 3.2 16.2 22 9 25 37 11 9 52 54 Zimbabwe 6.6 7.5 18 17 25 29 16 15 57 53 World 32,248.5 w 63,242.1 w 4w 3w 29 w 25 w 19 w 16 w 68 w 72 w Low income 164.8 416.5 34 25 21 25 12 14 45 50 Middle income 5,708.1 19,632.1 11 10 36 36 21 20 53 55 Lower middle income 1,258.9 4,312.3 20 17 34 31 17 16 46 52 Upper middle income 4,449.2 15,317.0 9 8 36 37 23 22 55 55 Low & middle income 5,874.8 20,071.7 12 10 35 35 21 20 53 54 East Asia & Pacific 1,727.2 7,630.5 15 11 44 45 31 29 41 43 Europe & Central Asia 709.9 3,059.0 11 7 35 32 18 17 55 61 Latin America & Carib. 2,054.4 4,980.8 6 6 30 31 18 17 65 63 Middle East & N. Africa 433.5 1,207.0 13 .. 43 .. 13 .. 44 .. South Asia 608.2 2,090.4 24 19 26 26 15 15 50 54 Sub-Saharan Africa 342.1 1,097.9 16 13 29 30 15 13 54 57 High income 26,375.3 43,240.0 2 1 28 24 19 15 71 75 Euro area 6,256.1 12,149.1 2 2 28 26 20 16 70 72 a. Components are at producer prices. b. Covers mainland Tanzania only. 220 2012 World Development Indicators 4.2 ECONOMY Structure of output About the data De�nitions 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. 2012 World Development Indicators 221 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ billions % of total % of total % of total % of total % of total 2000 2010 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Afghanistan 0.64 1.96 .. .. .. .. .. .. .. .. .. .. Albania 0.37 1.95 20 15 27 23 3 3 5 11 46 48 Algeria 3.86 7.32 .. .. .. .. .. .. .. .. .. .. Angola 0.26 4.89 .. .. .. .. .. .. .. .. .. .. Argentina 46.88 69.44 29 .. 8 .. 11 .. 15 .. 37 .. Armenia 0.32 0.89 .. .. .. .. .. .. .. .. .. .. Australia 49.08 98.34 21 19 4 3 13 14 8 7 55 58 Austria 35.36 65.50 10 9 4 2 25 26 7 7 54 56 Azerbaijan 0.28 2.81 42 15 3 1 11 6 6 4 37 75 Bahrain .. .. .. .. .. .. .. .. .. .. .. .. Bangladesh 6.92 17.36 24 .. 40 .. 3 .. 11 .. 21 .. Belarus 3.44 14.90 .. .. .. .. .. .. .. .. .. .. Belgium 39.90 59.03 12 13 5 4 19 17 20 21 44 45 Benin 0.20 .. .. .. .. .. .. .. .. .. .. .. Bolivia 1.11 2.21 37 .. 5 .. 0 .. 4 .. 54 .. Bosnia and Herzegovina 0.47 1.87 .. .. .. .. .. .. .. .. .. .. Botswana 0.25 0.44 20 22 5 5 .. .. .. .. 75 73 Brazil 96.17 280.65 17 18 7 6 19 21 12 11 45 44 Bulgaria 1.98 6.67 20 20 14 14 14 18 11 5 41 44 Burkina Faso 0.40 .. .. .. .. .. .. .. .. .. .. .. Burundi 0.05 .. .. .. .. .. .. .. .. .. .. .. Cambodia 0.59 1.65 7 .. 87 .. 0 .. 0 .. 7 .. Cameroon 1.94 .. 36 .. 19 .. 1 .. 2 .. 43 .. Canada 129.47 .. 12 1 4 2 32 26 8 8 45 64 Central African Republic 0.06 .. .. .. .. .. .. .. .. .. .. .. Chad 0.12 0.38 .. .. .. .. .. .. .. .. .. .. Chile 13.25 22.65 32 14 4 2 4 2 14 14 46 69 China 384.94 1,756.82 14 12 11 10 14 24 12 11 48 43 Hong Kong SAR, China 5.54 3.76 7 14 20 11 12 21 4 5 58 48 Colombia 14.44 40.07 30 .. 11 .. 4 .. 16 .. 39 .. Congo, Dem. Rep. 0.21 0.59 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 0.11 0.46 .. .. .. .. .. .. .. .. .. .. Costa Rica 3.68 5.68 45 42 6 4 3 3 12 9 33 43 Côte d’Ivoire 2.26 4.38 .. .. .. .. .. .. .. .. .. .. Croatia 3.62 9.43 .. .. .. .. .. .. .. .. .. .. Cuba 4.57 4.96 .. .. .. .. .. .. .. .. .. .. Cyprus 0.85 1.66 37 32 7 3 4 5 6 6 46 54 Czech Republic 13.79 42.25 12 9 6 3 24 30 7 5 51 53 Denmark 22.25 32.86 18 16 2 2 20 19 10 12 48 51 Dominican Republic 5.64 11.49 .. .. .. .. .. .. .. .. .. .. Ecuador 2.17 5.41 60 40 3 3 1 2 5 4 31 51 Egypt, Arab Rep. 17.97 32.98 20 18 10 12 7 6 22 14 41 50 El Salvador 3.03 4.04 29 .. 28 .. 2 .. 16 .. 25 .. Eritrea 0.07 0.10 60 .. 12 31 1 1 6 11 21 56 Estonia 0.90 2.39 17 14 15 8 10 18 4 6 53 55 Ethiopia 0.42 1.46 54 46 12 7 7 2 5 6 22 39 Finland 28.07 39.11 6 6 2 1 33 37 5 6 54 50 France 190.45 253.61 13 14 4 3 26 25 12 13 45 44 Gabon 0.19 0.49 .. .. .. .. .. .. .. .. .. .. Gambia, The 0.02 0.03 .. .. .. .. .. .. .. .. .. .. Georgia 0.26 1.31 37 25 1 1 12 8 7 7 43 59 Germany 392.47 614.23 8 7 2 2 33 37 10 10 47 44 Ghana 0.45 1.95 .. .. .. .. .. .. .. .. .. .. Greece .. .. 24 22 12 8 11 10 10 6 43 54 Guatemala 2.54 7.52 .. .. .. .. .. .. .. .. .. .. Guinea 0.12 0.20 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 0.02 .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 222 2012 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 $ billions % of total % of total % of total % of total % of total 2000 2010 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Honduras 1.46 2.63 .. .. .. .. .. .. .. .. .. .. Hungary 9.28 25.26 15 11 5 3 27 30 10 10 43 46 India 65.75 226.79 13 9 13 8 16 16 21 14 38 54 Indonesia 45.79 175.39 18 26 17 12 20 18 11 6 35 38 Iran, Islamic Rep. 13.24 .. 10 .. 6 .. 20 .. 19 .. 45 .. Iraq 0.24 .. .. .. .. .. .. .. .. .. .. .. Ireland 28.22 48.71 14 17 1 1 20 14 36 36 29 33 Israel .. .. 12 10 5 3 32 22 10 20 41 44 Italy 205.51 308.22 9 9 12 10 23 23 8 7 48 51 Jamaica 0.85 1.16 .. .. .. .. .. .. .. .. .. .. Japan 1,034.09 905.54 11 11 3 2 34 37 10 10 41 40 Jordan 1.14 4.78 30 21 7 10 3 4 18 18 43 47 Kazakhstan 3.02 18.54 .. .. .. .. .. .. .. .. .. .. Kenya 1.31 3.02 29 30 8 4 2 2 5 4 55 62 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 134.56 279.44 8 6 8 5 41 46 10 8 33 35 Kosovo .. 0.77 .. .. .. .. .. .. .. .. .. .. Kuwait 0.97 .. 8 .. 4 .. 2 .. 3 .. 82 .. Kyrgyz Republic 0.25 0.74 33 19 2 10 1 3 1 2 63 66 Lao PDR 0.29 0.52 46 .. 22 .. 8 .. 3 .. 22 .. Latvia 0.96 2.62 27 23 11 6 8 16 3 5 51 50 Lebanon 1.97 2.85 26 .. 10 .. 3 .. 6 .. 55 .. Lesotho 0.10 0.30 .. .. .. .. .. .. .. .. .. .. Liberia 0.05 0.10 .. .. .. .. .. .. .. .. .. .. Libya 0.64 3.88 .. .. .. .. .. .. .. .. .. .. Lithuania 1.96 5.43 27 22 18 9 12 13 3 10 40 46 Macedonia, FYR 0.62 1.25 32 18 15 17 9 4 6 6 38 55 Madagascar 0.43 1.11 0 0 35 30 0 1 2 2 62 67 Malawi 0.20 0.48 71 .. 5 .. 0 .. 9 .. 15 .. Malaysia 28.95 62.10 8 9 4 2 38 30 8 12 42 47 Mali 0.09 .. .. .. .. .. .. .. .. .. .. .. Mauritania 0.09 0.12 .. .. .. .. .. .. .. .. .. .. Mauritius 0.94 1.60 20 28 52 31 1 1 .. .. 26 39 Mexico 107.20 179.11 25 25 4 3 24 18 15 19 31 35 Moldova 0.18 0.62 65 40 9 14 5 5 .. .. 22 41 Mongolia 0.08 0.40 49 43 40 17 0 0 2 5 9 35 Morocco 5.74 12.50 34 29 18 13 4 6 12 18 32 34 Mozambique 0.45 1.09 .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 0.46 0.88 .. .. .. .. .. .. .. .. .. .. Nepal 0.49 0.85 45 .. 19 .. 1 .. 10 .. 24 .. Netherlands 53.51 92.47 17 19 2 2 21 20 14 14 46 45 New Zealand 7.99 .. 33 27 .. 2 .. 13 .. .. 67 58 Nicaragua 0.59 1.14 .. .. .. .. .. .. .. .. .. .. Niger 0.12 .. .. .. .. .. .. .. .. .. .. .. Nigeria 1.97 .. .. .. .. .. .. .. .. .. .. .. Norway 15.70 34.12 21 20 1 1 21 25 12 9 45 45 Oman 1.08 .. 12 8 5 0 1 1 5 12 76 79 Pakistan 10.10 28.24 21 22 33 29 5 8 17 14 25 26 Panama 1.10 1.57 56 .. 5 .. 3 .. 7 .. 32 .. Papua New Guinea 0.25 0.50 .. .. .. .. .. .. .. .. .. .. Paraguay 1.09 2.24 66 .. 6 .. 0 .. 10 .. 18 .. Peru 7.69 23.56 33 30 16 12 3 2 11 12 37 44 Philippines 19.83 42.80 29 22 7 5 27 33 9 6 28 33 Poland 28.21 76.44 5 17 6 4 15 19 8 8 66 53 Portugal 17.99 26.97 13 14 18 11 16 7 5 6 48 61 Puerto Rico 24.08 44.64 8 9 3 1 9 9 60 62 20 20 Qatar .. .. 4 1 8 2 0 0 21 17 67 80 2012 World Development Indicators 223 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ billions % of total % of total % of total % of total % of total 2000 2010 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Romania 4.77 32.50 32 16 12 12 13 24 5 5 38 43 Russian Federation 52.13 209.23 19 15 2 2 19 10 8 10 51 63 Rwanda 0.12 0.33 75 .. 2 .. .. .. 6 .. 17 .. Saudi Arabia 18.21 43.85 .. 19 .. 5 .. 6 .. 27 .. 43 Senegal 0.61 1.49 22 .. 4 .. 2 .. 29 .. 43 .. Serbia 1.32 5.04 .. .. .. .. .. .. .. .. .. .. Sierra Leone 0.02 .. .. .. .. .. .. .. .. .. .. .. Singapore 24.01 43.63 3 3 1 1 57 54 14 21 25 21 Slovak Republic 6.32 16.44 10 7 4 3 20 30 7 4 59 56 Slovenia 4.48 8.60 10 7 10 5 18 20 11 15 52 53 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 22.93 38.85 15 19 5 3 14 13 7 6 59 58 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 97.78 172.43 14 15 7 4 19 18 10 9 51 54 Sri Lanka 2.46 8.92 39 30 31 31 4 7 4 4 22 27 Sudan 1.02 3.30 66 .. 4 .. 4 .. 4 .. 21 .. Swaziland 0.48 1.37 .. .. .. .. .. .. .. .. .. .. Sweden 47.75 65.64 7 7 1 1 35 35 9 11 48 47 Switzerland 44.62 95.68 .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 1.29 .. .. .. .. .. .. .. .. .. .. .. Tajikistan 0.27 0.48 .. .. .. .. .. .. .. .. .. .. Tanzaniab 0.89 2.05 45 62 0 8 2 1 7 .. 46 29 Thailand 41.23 113.47 18 16 12 9 26 35 6 6 38 34 Timor-Leste 0.01 .. .. .. .. .. .. .. .. .. .. .. Togo 0.11 .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 0.58 1.10 21 11 1 1 0 0 27 39 51 49 Tunisia 3.50 7.27 19 17 35 24 3 .. 10 9 33 49 Turkey 53.51 113.76 18 12 16 19 15 20 10 7 42 42 Turkmenistan 0.29 .. .. .. .. .. .. .. .. .. .. .. Uganda 0.44 1.33 64 .. 4 .. .. .. 11 .. 21 .. Ukraine 5.10 21.06 .. .. .. .. .. .. .. .. .. .. United Arab Emirates 9.47 28.93 .. .. .. .. .. .. .. .. .. .. United Kingdom 226.97 230.62 14 15 4 2 25 24 10 12 47 48 United States 1,468.08 1,814.34 13 13 3 2 30 25 12 16 42 44 Uruguay 2.86 4.81 39 42 9 7 3 4 8 8 41 39 Uzbekistan 1.14 3.10 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 21.71 .. 22 .. 2 .. .. .. 34 .. 41 .. Vietnam 5.79 20.94 30 .. 21 .. 12 .. 6 .. 31 .. West Bank and Gaza .. .. 32 27 21 13 1 1 4 4 41 55 Yemen, Rep. 0.55 1.88 43 60 4 9 1 0 5 4 48 27 Zambia 0.33 1.43 .. .. .. .. .. .. .. .. .. .. Zimbabwe 0.90 0.88 .. .. .. .. .. .. .. .. .. .. World 5,737.81 t 9,989.14 t .. .. .. .. .. .. .. .. .. .. Low income 18.46 46.11 .. .. .. .. .. .. .. .. .. .. Middle income 1,188.59 3,868.14 .. .. .. .. .. .. .. .. .. .. Lower middle income 203.74 639.71 .. .. .. .. .. .. .. .. .. .. Upper middle income 985.43 3,226.81 .. .. .. .. .. .. .. .. .. .. Low & middle income 1,207.17 3,916.69 .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 530.45 2,185.11 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. .. Latin America & Carib. 338.85 712.08 .. .. .. .. .. .. .. .. .. .. Middle East & N. Africa 51.72 115.45 .. .. .. .. .. .. .. .. .. .. South Asia 86.21 283.82 .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa 41.20 82.72 .. .. .. .. .. .. .. .. .. .. High income 4,535.77 5,562.68 .. .. .. .. .. .. .. .. .. .. Euro area 1,115.92 1,754.91 .. .. .. .. .. .. .. .. .. .. a. Includes unallocated data. b. Covers mainland Tanzania only. 224 2012 World Development Indicators 4.3 ECONOMY Structure of manufacturing About the data De�nitions The data on the distribution of manufacturing value resulting in a homogeneous set of products� (United • Manufacturing value added is the sum of gross added by industry are provided by the United Nations Nations 1990 [ISIC, series M, no. 4, rev. 3], p. 9). output less the value of intermediate inputs used in Industrial Development Organization (UNIDO). UNIDO Firms typically use multiple processes to produce production for industries classified in ISIC major divi- obtains the data from a variety of national and inter- a product. For example, an automobile manufac- sion D. • Food, beverages, and tobacco correspond national sources, including the United Nations Sta- turer engages in forging, welding, and painting as to ISIC divisions 15 and 16. • Textiles and clothing tistics Division, the World Bank, the Organisation for well as advertising, accounting, and other service correspond to ISIC divisions 17–19. • Machinery and Economic Co-operation and Development, and the activities. Collecting data at such a detailed level transport equipment correspond to ISIC divisions International Monetary Fund. To improve comparabil- is not practical, nor is it useful to record produc- 29, 30, 32, 34, and 35. • Chemicals correspond to ity over time and across countries, UNIDO supple- tion data at the highest level of a large, multiplant, ISIC division 24. • Other manufacturing, a residual, ments these data with information from industrial multiproduct firm. The ISIC has therefore adopted as covers wood and related products (ISIC division 20), censuses, statistics from national and international the definition of an establishment “an enterprise or paper and related products (ISIC divisions 21 and organizations, unpublished data that it collects in the part of an enterprise which independently engages in 22), petroleum and related products (ISIC division field, and estimates by the UNIDO Secretariat. Nev- one, or predominantly one, kind of economic activity 23), basic metals and mineral products (ISIC divi- ertheless, coverage may be incomplete, particularly at or from one location . . . for which data are avail- sion 27), fabricated metal products and professional for the informal sector. When direct information on able . . .� (United Nations 1990, p. 25). By design, goods (ISIC division 28), and other industries (ISIC inputs and outputs is not available, estimates may this definition matches the reporting unit required divisions 25, 26, 31, 33, 36, and 37). be used, which may result in errors in industry totals. for the production accounts of the United Nations Moreover, countries use different reference periods System of National Accounts. The ISIC system is (calendar or fiscal year) and valuation methods (basic described in the United Nations’ International Stan- or producer prices) to estimate value added. (See dard Industrial Classi�cation of All Economic Activi- About the data for table 4.2.) ties, Third Revision (1990). The discussion of the ISIC The data on manufacturing value added in U.S. dol- draws on Ryten (1998). lars are from the World Bank’s national accounts files and may differ from those UNIDO uses to calculate shares of value added by industry, in part because of differences in exchange rates. Thus value added in a particular industry estimated by applying the shares to total manufacturing value added will not match those from UNIDO sources. Classification of manufacturing industries in the table accords with the United Nations International Standard Industrial Classifi cation (ISIC) revision  3. Editions of World Development Indicators prior to 2008 used revision 2, first published in 1948. Revision 3 was completed in 1989, and many countries now use it. But revi- sion 2 is still widely used for compiling cross-country data. UNIDO has converted these data to accord with revision 3. Concordances matching ISIC categories to national classifi cation systems and to related systems such as the Standard International Trade Classification are available. In establishing classifi cations systems compil- ers must define both the types of activities to be Data sources described and the units whose activities are to be reported. There are many possibilities, and the Data on manufacturing value added are from the choices affect how the statistics can be interpreted World Bank’s national accounts files. Data used and how useful they are in analyzing economic to calculate shares of industry value added are behavior. The ISIC emphasizes commonalities in the provided to the World Bank in electronic files production process and is explicitly not intended to by UNIDO. The most recent published source is measure outputs (for which there is a newly devel- UNIDO’s International Yearbook of Industrial Sta- oped Central Product Classification). Nevertheless, tistics 2011. the ISIC views an activity as defined by “a process 2012 World Development Indicators 225 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan 137 430 .. 40 .. 11 .. .. .. 0 .. 20 Albania 258 1,550 7 4 6 2 2 18 4 13 82 62 Algeria 22,031 57,053 0 1 0 0 97 97 0 0 2 2 Angola 7,921 53,500 .. .. .. .. .. .. .. .. .. .. Argentina 26,341 68,133 44 51 2 1 18 8 3 4 32 33 Armenia 294 1,011 14 17 5 1 11 3 27 52 43 24 Australia 63,870 212,554 21 11 6 2 22 31 17 34 29 17 Austria 67,710 152,313 5 7 2 2 1 3 3 4 80 80 Azerbaijan 1,745 26,476 3 3 2 0 85 95 2 0 8 2 Bahrain 6,195 13,647 1 7 0 0 0 0 16 70 10 22 Bangladesh 6,389 19,191 8 .. 1 .. 0 .. 0 .. 91 .. Belarus 7,326 25,226 7 13 4 2 20 28 1 1 65 53 Belgium 188,371 412,223 9 9 2 1 4 9 3 4 78 75 Benin 392 1,200 21 .. 72 .. 0 .. 0 .. 7 .. Bolivia 1,230 6,290 30 15 3 1 13 44 25 34 29 6 Bosnia and Herzegovina 1,069 4,803 .. 7 .. 6 .. 15 .. 12 .. 57 Botswana 2,675 4,693 3 5 0 0 0 0 7 15 90 80 Brazil 55,086 201,915 23 31 5 4 2 10 10 18 58 37 Bulgaria 4,852 20,666 10 16 3 1 12 13 13 17 57 49 Burkina Faso 209 1,288 19 33 59 56 3 0 0 2 18 9 Burundi 50 100 91 81 8 5 .. 2 1 5 0 6 Cambodia 1,389 5,030 1 1 3 2 0 0 0 0 96 96 Cameroon 1,833 4,000 15 24 9 15 54 50 6 3 3 8 Canada 276,635 388,019 6 10 6 4 13 26 4 8 64 49 Central African Republic 161 140 11 4 13 32 0 0 8 62 68 3 Chad 183 3,450 .. .. .. .. .. .. .. .. .. .. Chile 19,210 71,028 25 17 10 5 1 0 45 65 16 13 China† 249,203 1,577,824 5 3 1 0 3 2 2 1 88 94 Hong Kong SAR, Chinaa 202,683 401,022 2 7 0 3 0 3 2 9 95 77 Colombia 13,040 39,820 19 12 5 4 43 60 1 2 32 23 Congo, Dem. Rep. 807 5,300 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 2,489 8,200 .. .. .. .. .. .. .. .. .. .. Costa Rica 5,865 9,385 30 35 3 3 1 1 1 1 66 61 Côte d’Ivoire 3,888 10,320 50 50 14 10 21 24 0 0 14 16 Croatia 4,432 11,807 9 11 5 4 11 12 3 5 73 68 Cuba 1,676 3,900 50 .. 0 .. 3 .. 37 .. 9 .. Cyprus 951 1,412 33 34 1 2 6 0 5 14 49 50 Czech Republic 29,094 132,852 4 4 2 1 3 4 2 2 88 86 Denmark 51,292 97,681 20 18 3 3 7 8 1 2 64 60 Dominican Republic 5,737 6,598 41 27 2 1 16 2 2 4 34 65 Ecuador 4,927 17,490 37 30 4 4 49 55 0 1 10 10 Egypt, Arab Rep. 5,276 26,438 8 17 5 3 42 30 4 6 38 43 El Salvador 2,941 4,499 19 22 0 1 3 3 1 2 21 73 Eritrea 37 12 54 .. 10 .. 0 .. 8 .. 28 .. Estonia 3,830 11,605 8 10 9 5 4 16 5 3 73 62 Ethiopia 486 2,238 71 79 19 9 0 0 1 1 10 9 Finland 46,102 69,630 2 3 6 6 3 8 3 5 85 77 France 327,611 520,661 11 12 1 1 3 4 2 2 81 78 Gabon 2,598 9,371 1 1 12 9 83 83 2 3 2 4 Gambia, The 15 15 81 78 1 2 0 0 0 10 17 10 Georgia 323 1,583 28 22 3 1 8 6 29 21 31 50 Germany 551,810 1,268,874 4 5 1 1 1 2 2 3 84 82 Ghana 1,671 7,896 48 61 10 7 8 0 19 11 15 21 Greece 11,751 21,409 22 24 3 3 15 11 7 9 50 50 Guatemala 2,696 8,466 56 42 4 4 6 5 2 6 32 43 Guinea 666 1,250 3 2 3 5 0 2 63 59 30 32 Guinea-Bissau 62 125 .. .. .. .. .. .. .. .. .. .. Haiti 318 580 .. .. .. .. .. .. .. .. .. .. †Data for Taiwan, China 151,357 203,675 1 1 1 1 1 6 1 2 95 89 226 2012 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 3,343 5,742 72 54 5 1 0 4 6 4 17 35 Hungary 28,192 95,437 7 7 1 1 2 3 2 2 86 82 India 42,379 219,959 13 8 1 2 3 17 3 7 78 64 Indonesia 65,403 157,818 9 16 4 7 25 30 5 10 57 37 Iran, Islamic Rep. 28,739 100,524 3 6 0 0 89 71 1 3 7 16 Iraq 20,603 52,800 1 0 0 0 97 99 0 0 0 0 Ireland 77,413 116,801 8 9 0 1 0 1 0 1 86 85 Israel 31,404 58,393 2 3 1 1 0 1 1 1 82 93 Italy 240,518 447,535 6 8 1 1 2 5 1 2 89 82 Jamaica 1,304 1,337 23 25 0 0 0 23 4 12 73 40 Japan 479,249 769,839 0 1 0 1 0 2 1 3 94 89 Jordan 1,899 7,028 16 17 0 0 0 1 15 9 69 74 Kazakhstan 8,812 59,217 7 4 1 0 54 71 19 11 19 14 Kenya 1,734 5,151 59 48 9 11 8 4 3 2 21 35 Korea, Dem. Rep. 708 3,010 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 172,267 466,384 2 1 1 1 5 7 1 2 91 89 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 19,436 67,014 0 0 0 0 94 93 0 0 4 6 Kyrgyz Republic 505 1,760 17 30 14 5 27 15 11 4 31 38 Lao PDR 330 1,600 .. .. .. .. .. .. .. .. .. .. Latvia 1,868 9,489 6 17 29 12 2 5 6 4 56 59 Lebanon 715 5,021 20 15 2 1 0 0 7 11 71 64 Lesotho 220 820 5 14 0 9 0 0 0 2 95 74 Liberia 329 231 .. .. .. .. .. .. .. .. .. .. Libya 13,380 47,400 1 .. 0 .. 93 .. 0 .. 7 .. Lithuania 3,810 20,835 12 17 5 2 21 23 2 1 60 54 Macedonia, FYR 1,323 3,302 15 18 2 1 5 1 9 3 69 51 Madagascar 824 1,090 38 27 3 3 4 7 2 9 52 48 Malawi 379 1,066 89 76 3 3 0 0 0 11 7 9 Malaysia 98,229 198,801 6 12 3 3 10 16 1 2 80 67 Mali 545 2,350 4 30 91 48 0 0 0 1 5 20 Mauritania 355 2,033 21 58 0 0 .. 0 46 30 0 0 Mauritius 1,557 2,239 18 37 1 1 0 0 0 0 81 60 Mexico 166,367 298,305 5 6 1 0 10 14 1 3 84 76 Moldova 472 1,582 62 72 3 1 0 0 1 4 33 23 Mongolia 536 2,899 4 .. 28 .. 0 .. 41 .. 26 .. Morocco 7,432 17,579 21 19 2 2 4 1 9 12 64 66 Mozambique 364 3,200 42 16 11 4 21 20 17 54 7 2 Myanmar 1,646 8,749 .. .. .. .. .. .. .. .. .. .. Namibia 1,320 4,052 29 23 1 0 2 0 11 31 56 45 Nepal 804 860 10 19 0 4 0 0 0 5 67 72 Netherlands 233,130 573,360 13 14 3 3 8 10 2 2 59 57 New Zealand 13,272 31,396 46 56 14 11 3 5 5 4 31 21 Nicaragua 645 1,851 88 88 2 1 2 1 0 2 8 7 Niger 283 930 44 21 3 3 2 2 41 60 9 14 Nigeria 20,975 82,000 0 3 0 2 100 87 0 1 0 7 Norway 60,058 131,395 6 7 1 1 64 64 6 6 18 18 Oman 11,319 36,601 4 3 0 0 83 81 1 3 12 12 Pakistan 9,028 21,410 11 17 3 2 1 6 0 2 85 74 Panama 859 832 74 73 1 2 7 0 2 11 16 13 Papua New Guinea 2,096 5,612 15 .. 2 .. 29 .. 51 .. 2 .. Paraguay 869 4,534 65 86 15 3 0 0 0 1 19 11 Peru 7,028 35,565 30 20 3 1 7 12 39 52 20 14 Philippines 39,783 51,496 5 7 1 1 1 2 2 4 92 86 Poland 31,747 155,752 8 11 2 1 5 4 5 5 80 79 Portugal 24,363 48,748 7 11 3 3 2 6 2 4 85 74 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 11,594 62,000 0 0 0 0 91 73 0 0 9 5 2012 World Development Indicators 227 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 10,412 49,401 3 8 5 2 7 5 7 4 77 79 Russian Federation 105,565 400,132 1 2 3 2 51 64 9 6 24 15 Rwanda 52 297 57 52 3 3 0 0 37 37 3 8 Saudi Arabia 77,583 249,700 1 1 0 0 92 87 0 0 7 11 Senegal 920 2,161 52 29 2 1 14 26 5 4 27 40 Serbia .. 9,795 17 .. 6 .. 0 .. 16 .. 61 .. Sierra Leone 13 338 19 .. 1 .. .. .. 1 .. 10 .. Singaporea 137,804 351,867 2 2 0 0 7 16 1 1 86 73 Slovak Republic 11,832 65,345 3 4 2 1 7 5 3 3 84 87 Slovenia 8,770 29,446 4 4 2 2 1 4 4 4 90 85 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 29,983 81,821 8 9 3 2 10 10 11 33 54 47 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 115,251 245,637 14 15 1 1 4 5 2 3 78 73 Sri Lanka 5,430 8,500 21 27 2 4 0 0 0 1 77 67 Sudan 1,807 11,443 17 6 5 1 69 92 0 0 8 0 Swaziland 910 1,550 34 .. 11 .. 1 .. 0 .. 54 .. Sweden 87,132 158,314 2 5 5 4 3 7 2 5 82 74 Switzerland 80,500 195,392 3 4 1 0 0 3 6 4 91 89 Syrian Arab Republic 4,634 13,500 9 22 5 1 76 39 1 4 8 33 Tajikistan 785 1,195 4 .. 13 .. 14 .. 56 .. 13 .. Tanzania 734 3,687 66 32 13 7 0 3 1 34 20 24 Thailand 69,057 195,319 14 13 3 5 3 5 1 1 75 75 Timor-Leste .. 17 .. .. .. .. .. .. .. .. .. .. Togo 363 800 20 15 23 5 1 0 26 6 31 74 Trinidad and Tobago 4,274 10,590 6 3 0 0 65 66 0 0 29 31 Tunisia 5,850 16,427 9 8 1 1 12 14 2 2 77 76 Turkey 27,775 113,981 13 11 1 0 1 4 3 4 81 79 Turkmenistan 2,506 6,500 0 .. 10 .. 81 .. 0 .. 7 .. Uganda 403 1,612 71 67 15 7 6 1 5 2 3 23 Ukraine 14,573 51,478 9 19 2 1 5 7 12 7 69 65 United Arab Emirates 49,835 220,000 1 1 0 0 94 65 3 1 2 4 United Kingdom 285,425 405,666 5 6 0 1 8 13 2 4 77 70 United States 781,918 1,278,263 7 10 2 3 2 7 2 4 83 66 Uruguay 2,295 6,733 47 64 9 8 2 1 0 0 42 26 Uzbekistan 2,817 11,857 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 33,529 65,786 1 0 0 0 86 93 3 2 9 4 Vietnam 14,483 72,192 25 21 2 2 26 15 0 1 43 60 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 4,079 8,700 2 6 0 0 97 92 0 0 0 2 Zambia 892 7,200 9 6 4 1 1 1 74 86 11 6 Zimbabwe 1,925 2,500 47 20 13 7 1 2 11 35 28 36 World 6,456,422 t 15,211,311 t 7w 8w 2w 2w 10 w 12 w 3w 5w 75 w 69 w Low income 23,852 79,667 26 .. 11 .. 3 .. 8 .. 51 .. Middle income 1,350,497 4,848,882 9 10 2 2 21 22 4 6 61 59 Lower middle income 299,735 960,037 12 15 2 3 29 22 4 7 52 52 Upper middle income 1,050,746 3,889,297 9 9 2 2 19 21 5 6 63 60 Low & middle income 1,374,348 4,928,568 10 11 2 2 21 21 4 6 61 58 East Asia & Pacific 544,009 2,281,768 8 8 2 2 7 8 2 3 80 79 Europe & Central Asia 201,167 830,383 5 7 3 2 34 42 9 6 42 36 Latin America & Carib. 356,697 862,436 16 16 2 2 17 21 6 10 58 51 Middle East & N. Africa 114,670 352,565 4 .. 1 .. 77 .. 2 .. 16 .. South Asia 64,379 271,099 12 12 2 2 3 13 2 6 80 66 Sub-Saharan Africa 93,392 332,645 15 15 5 4 37 32 7 18 31 31 High income 5,082,101 10,278,808 6 8 2 2 8 9 2 4 78 72 Euro area 1,920,244 4,007,130 8 9 1 1 3 5 2 3 80 77 Note: Components may not sum to 100 percent because of unclassified trade. Exports of gold are excluded. a. Includes re-exports. 228 2012 World Development Indicators 4.4 ECONOMY Structure of merchandise exports About the data De�nitions Data on merchandise trade are from customs data for countries in Europe and Central Asia has • Merchandise exports are the f.o.b. value of goods reports of goods moving into or out of an economy also improved. provided to the rest of the world. • Food corresponds or from reports of financial transactions related to Export shares by major commodity group are from to the commodities in SITC sections 0 (food and live merchandise trade recorded in the balance of pay- Comtrade. The values of total exports reported animals), 1 (beverages and tobacco), and 4 (animal ments. Because of differences in timing and defi - here have not been fully reconciled with the esti- and vegetable oils and fats) and SITC division 22 nitions, trade flow estimates from customs reports mates from the national accounts or the balance (oil seeds, oil nuts, and oil kernels). • Agricultural and balance of payments may differ. Several inter- of payments. raw materials correspond to SITC section 2 (crude national agencies process trade data, each correct- The classification of commodity groups is based materials except fuels), excluding divisions 22, 27 ing unreported or misreported data, leading to other on the Standard International Trade Classification (crude fertilizers and minerals excluding coal, petro- differences. (SITC) revision 3. Previous editions contained data leum, and precious stones), and 28 (metalliferous The most detailed source of data on international based on the SITC revision 1. Data for earlier years in ores and scrap). • Fuels correspond to SITC section trade in goods is the United Nations Statistics Divi- previous editions may differ because of this change 3 (mineral fuels). • Ores and metals correspond to sion’s Commodity Trade Statistics (Comtrade) data- in methodology. Concordance tables are available the commodities in SITC divisions 27, 28, and 68 base. The International Monetary Fund (IMF) also col- to convert data reported in one system to another. (nonferrous metals). • Manufactures correspond to lects customs-based data on trade in goods. Exports the commodities in SITC sections 5 (chemicals), 6 are recorded as the cost of the goods delivered to the (basic manufactures), 7 (machinery and transport frontier of the exporting country for shipment—the equipment), and 8 (miscellaneous manufactured free on board (f.o.b.) value. Many countries report goods), excluding division 68. trade data in U.S. dollars. When countries report in local currency, 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 gen- eral or special system of trade. Under the general system exports comprise outward-moving goods that are (a) goods wholly or partly produced in the country; (b) foreign goods, neither transformed nor declared for domestic consumption in the country, that move outward from customs storage; and (c) goods previ- ously included as imports for domestic consumption but subsequently exported without transformation. Under the special system exports comprise catego- ries a and c. In some compilations categories b and c are classified as re-exports. Because of differences in reporting practices, data on exports may not be fully comparable across economies. Data sources The data on total exports of goods (merchandise) are from the World Trade Organization (WTO), which Data on merchandise exports are from the WTO. obtains data from national statistical offices and the Data on shares of exports by major commodity IMF’s International Financial Statistics, supplemented group are from the United Nations Statistics Divi- by the Comtrade database and publications or data- sion’s Comtrade database. The WTO publishes bases of regional organizations, specialized agen- data on world trade in its Annual Report. The IMF cies, economic groups, and private sources (such as publishes estimates of total exports of goods in Eurostat, the Food and Agriculture Organization, and its International Financial Statistics and Direction country reports of the Economist Intelligence Unit). of Trade Statistics, as does the United Nations Sta- Country websites and email contact have improved tistics Division in its Monthly Bulletin of Statistics. collection of up-to-date statistics, reducing the pro- And the United Nations Conference on Trade and portion of estimates. The WTO database now covers Development publishes data on the structure of most major traders in Africa, Asia, and Latin America, exports in its Handbook of Statistics. Tariff line which together with high-income countries account records of exports are compiled in the United for nearly 95 percent of world trade. Reliability of Nations Statistics Division’s Comtrade database. 2012 World Development Indicators 229 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan 1,176 4,400 .. 14 .. 0 .. 21 .. 0 .. 19 Albania 1,090 4,601 22 18 1 1 9 14 2 4 67 64 Algeria 9,171 40,212 28 16 3 2 1 2 1 2 67 78 Angola 3,040 21,500 .. .. .. .. .. .. .. .. .. .. Argentina 25,154 56,503 5 3 1 1 4 7 2 3 87 85 Armenia 882 3,783 25 18 1 1 21 18 1 3 52 56 Australia 71,529 201,640 5 5 1 1 8 14 1 2 84 75 Austria 72,394 158,752 5 7 3 2 5 11 3 5 81 74 Azerbaijan 1,172 6,746 19 19 2 2 5 1 4 2 71 76 Bahrain 4,633 10,000 10 12 1 1 1 2 3 19 41 65 Bangladesh 8,883 27,819 16 .. 6 .. 7 .. 2 .. 68 .. Belarus 8,646 34,868 12 8 2 1 30 35 4 3 48 47 Belgium 177,511 390,443 9 8 2 1 9 14 4 4 76 71 Benin 613 2,200 22 .. 5 .. 19 .. 1 .. 53 .. Bolivia 1,830 5,361 14 8 2 1 5 12 1 1 79 78 Bosnia and Herzegovina 3,107 9,223 .. 18 .. 2 .. 19 .. 2 .. 58 Botswana 2,081 5,657 14 12 1 1 5 15 2 2 75 68 Brazil 59,053 191,491 7 5 2 1 15 17 3 3 73 74 Bulgaria 6,544 25,403 5 10 1 1 26 22 6 9 59 55 Burkina Faso 611 2,048 13 15 1 1 25 22 1 1 61 61 Burundi 148 509 23 14 2 1 12 2 2 1 60 82 Cambodia 1,939 7,500 10 7 3 2 13 7 0 2 73 82 Cameroon 1,489 4,850 18 18 2 2 23 27 1 1 56 52 Canada 244,786 402,280 5 7 1 1 5 10 2 3 84 77 Central African Republic 117 340 29 39 4 2 8 1 4 2 54 56 Chad 317 2,600 .. .. .. .. .. .. .. .. .. .. Chile 18,507 58,956 7 7 1 1 18 21 1 2 71 69 China† 225,094 1,395,099 4 5 5 4 9 15 6 14 75 61 Hong Kong SAR, China 214,042 442,035 4 4 1 1 2 4 2 2 91 90 Colombia 11,539 40,683 12 10 3 1 2 5 2 2 80 80 Congo, Dem. Rep. 683 4,500 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 465 2,900 .. .. .. .. .. .. .. .. .. .. Costa Rica 6,372 13,570 7 9 1 1 8 12 2 2 82 73 Côte d’Ivoire 2,482 7,830 17 19 1 1 34 24 1 1 46 55 Croatia 7,887 20,054 8 10 2 1 15 19 2 2 73 67 Cuba 4,843 11,300 16 .. 1 .. 24 .. 1 .. 58 .. Cyprus 31,974 126,222 5 5 2 1 10 9 4 4 80 77 Czech Republic 3,846 8,499 19 15 1 1 13 20 1 1 65 61 Denmark 45,557 84,848 11 14 3 2 6 8 2 2 76 73 Dominican Republic 9,479 15,299 12 14 2 1 23 25 1 1 62 60 Ecuador 3,721 20,591 9 8 3 1 7 17 2 1 77 72 Egypt, Arab Rep. 14,578 52,923 25 19 5 3 8 13 2 4 56 60 El Salvador 4,947 8,498 12 17 2 2 12 16 1 1 43 64 Eritrea 471 690 37 .. 1 .. 2 .. 1 .. 58 .. Estonia 5,052 12,252 10 11 3 2 7 16 3 1 76 63 Ethiopia 1,260 8,552 7 11 1 0 20 19 1 1 71 69 Finland 34,443 68,510 5 7 2 2 12 18 6 8 73 61 France 338,940 605,706 8 8 2 1 10 14 3 3 77 73 Gabon 950 2,983 18 17 1 0 4 7 1 1 76 74 Gambia, The 187 276 35 36 1 1 12 20 1 1 51 41 Georgia 709 5,096 23 18 1 1 20 18 1 2 55 60 Germany 497,197 1,066,839 7 7 2 2 9 11 4 5 68 68 Ghana 2,973 10,703 13 15 2 1 21 1 1 1 62 81 Greece 33,480 63,173 11 12 2 1 13 24 3 3 70 60 Guatemala 4,791 13,837 12 13 2 1 13 18 1 1 72 66 Guinea 612 1,100 24 13 1 0 25 33 1 0 49 53 Guinea-Bissau 59 220 .. .. .. .. .. .. .. .. .. .. Haiti 1,036 3,150 .. .. .. .. .. .. .. .. .. .. †Data for Taiwan, China 140,642 174,371 4 5 2 1 9 21 5 7 79 65 230 2012 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 3,988 8,550 22 19 2 1 18 19 1 1 50 60 Hungary 32,172 88,120 3 5 1 1 5 11 3 3 84 72 India 51,523 327,230 5 4 3 2 39 36 5 5 47 51 Indonesia 43,595 131,737 10 8 7 3 19 20 3 3 61 65 Iran, Islamic Rep. 13,898 65,021 19 15 3 2 2 3 2 2 73 71 Iraq 13,384 42,500 1 .. 0 .. 0 .. 0 .. 8 .. Ireland 51,041 60,032 6 12 1 1 4 12 1 2 82 66 Israel 37,686 61,209 5 7 1 1 10 18 2 2 81 71 Italy 238,757 483,814 9 9 4 2 10 19 4 5 69 64 Jamaica 3,326 5,195 15 18 2 1 18 30 1 0 61 49 Japan 379,511 694,052 13 9 3 2 20 29 6 8 57 51 Jordan 4,597 15,402 21 16 2 1 5 22 2 2 66 56 Kazakhstan 5,040 29,760 9 9 1 1 12 10 3 1 74 80 Kenya 3,105 12,090 14 12 2 2 22 22 1 2 60 63 Korea, Dem. Rep. 1,686 4,420 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 160,481 425,212 5 5 3 2 24 29 6 8 62 57 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 7,157 22,446 14 15 0 1 1 1 1 3 34 81 Kyrgyz Republic 554 3,223 15 17 2 1 23 27 2 1 59 54 Lao PDR 535 1,800 .. .. .. .. .. .. .. .. .. .. Latvia 3,202 11,593 12 15 2 1 12 15 2 2 71 59 Lebanon 6,230 18,460 19 16 2 1 18 21 2 2 59 59 Lesotho 809 2,200 18 20 1 2 19 11 2 1 49 53 Liberia 668 700 .. .. .. .. .. .. .. .. .. .. Libya 3,732 10,500 28 .. 1 .. 0 .. 1 .. 70 .. Lithuania 5,457 23,399 10 12 3 2 22 32 2 2 61 50 Macedonia, FYR 2,094 5,451 12 13 2 1 14 5 2 1 45 62 Madagascar 1,097 2,650 13 14 0 1 23 15 0 0 63 70 Malawi 532 1,900 10 14 2 1 16 10 1 1 72 74 Malaysia 81,963 164,733 4 8 1 2 5 10 3 5 85 74 Mali 806 2,850 15 12 1 0 24 26 1 1 59 61 Mauritania 454 1,822 19 19 0 0 23 26 0 0 41 53 Mauritius 2,093 4,402 14 21 2 2 12 19 1 1 70 56 Mexico 179,464 310,618 5 6 1 1 3 8 2 3 83 80 Moldova 777 3,855 13 15 2 1 32 21 1 1 51 62 Mongolia 615 3,278 17 .. 1 .. 19 .. 0 .. 63 .. Morocco 11,534 35,277 14 11 3 2 18 23 3 3 63 59 Mozambique 1,158 4,500 14 12 1 1 13 20 1 1 68 50 Myanmar 2,401 4,807 7 .. 0 .. 19 .. 1 .. 72 .. Namibia 1,550 5,360 17 14 1 1 3 14 1 1 78 70 Nepal 1,573 5,280 13 14 4 2 16 18 3 4 49 64 Netherlands 218,267 516,927 9 10 2 1 10 16 2 3 65 57 New Zealand 13,906 30,617 8 11 1 1 10 15 2 2 79 71 Nicaragua 1,805 4,173 16 16 1 1 18 22 1 0 65 61 Niger 395 2,150 39 15 4 2 15 13 2 1 41 69 Nigeria 8,721 44,235 20 10 1 1 2 1 2 1 75 86 Norway 34,392 77,252 6 8 2 1 4 7 5 7 81 76 Oman 5,131 19,870 22 12 1 1 2 7 3 4 70 51 Pakistan 10,864 39,044 14 13 3 5 33 30 2 3 47 49 Panama 3,379 9,145 12 8 0 0 19 1 1 0 68 90 Papua New Guinea 1,151 3,850 18 .. 1 .. 22 .. 1 .. 58 .. Paraguay 2,193 10,040 17 7 1 1 16 12 1 1 66 79 Peru 7,415 30,126 12 10 2 2 16 14 1 1 70 72 Philippines 37,027 58,229 7 11 1 1 11 17 2 4 78 67 Poland 49,029 173,648 6 8 2 2 11 11 3 3 78 74 Portugal 39,952 75,648 11 13 3 1 10 14 2 3 73 67 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 3,252 23,240 12 8 1 0 0 1 3 4 84 84 2012 World Development Indicators 231 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 13,148 61,995 7 8 1 1 12 10 4 3 76 75 Russian Federation 44,659 248,738 20 13 2 1 4 2 3 1 59 69 Rwanda 211 1,431 21 13 3 2 14 8 2 1 60 76 Saudi Arabia 30,238 97,077 18 16 1 1 0 0 3 5 76 77 Senegal 1,519 4,782 23 22 2 2 23 30 1 2 51 44 Serbia .. 16,734 9 .. 4 .. 20 .. 4 .. 63 .. Sierra Leone 149 770 33 .. 4 .. 28 .. 1 .. 35 .. Singapore 134,545 310,791 3 3 0 0 12 26 2 2 82 66 Slovak Republic 12,760 66,557 6 6 2 1 18 13 3 4 72 76 Slovenia 10,147 30,037 6 8 4 3 9 13 5 6 76 70 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 29,695 94,040 5 6 1 1 14 20 2 2 69 65 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 156,143 314,320 9 10 2 1 12 18 3 4 73 65 Sri Lanka 7,177 13,512 14 15 1 1 9 17 1 2 74 65 Sudan 1,553 10,045 23 15 1 1 7 4 1 1 68 78 Swaziland 1,046 1,700 19 .. 2 .. 13 .. 1 .. 64 .. Sweden 72,880 148,710 6 9 2 1 9 13 3 4 74 69 Switzerland 82,521 175,933 6 6 1 1 5 8 6 4 83 81 Syrian Arab Republic 3,815 16,900 19 14 3 3 4 31 2 4 65 47 Tajikistan 675 2,900 10 .. 1 .. 37 .. 0 .. 51 .. Tanzania 1,524 7,830 15 10 3 1 19 28 1 1 63 60 Thailand 61,924 182,400 4 5 3 2 12 18 3 5 77 70 Timor-Leste .. 298 .. .. .. .. .. .. .. .. .. .. Togo 562 1,550 18 16 2 1 19 14 2 2 59 67 Trinidad and Tobago 3,308 6,575 8 11 1 1 32 33 2 5 56 50 Tunisia 8,567 22,218 8 9 3 2 11 13 2 4 76 72 Turkey 54,503 185,542 4 4 4 3 14 15 4 8 70 63 Turkmenistan 1,786 5,600 12 .. 0 .. 1 .. 1 .. 80 .. Uganda 1,536 4,550 14 12 2 1 17 20 2 1 65 65 Ukraine 13,956 60,911 6 9 2 1 43 32 5 4 41 53 United Arab Emirates 35,009 160,000 11 7 1 0 1 1 2 5 85 73 United Kingdom 348,058 560,097 8 10 2 1 4 11 3 4 78 68 United States 1,259,300 1,969,184 4 5 1 1 11 19 2 2 77 70 Uruguay 3,466 8,622 11 10 3 2 15 24 1 1 69 62 Uzbekistan 2,697 8,386 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 16,213 40,800 12 15 2 1 4 1 2 1 81 81 Vietnam 15,638 84,801 5 8 3 3 14 11 2 4 73 74 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2,324 9,700 36 28 2 1 12 21 1 1 49 50 Zambia 888 5,321 8 5 3 1 12 12 3 21 73 62 Zimbabwe 1,863 3,800 4 19 1 3 42 11 3 14 48 52 World 6,659,168 t 15,264,186 t 7w 7w 2w 1w 10 w 16 w 3w 4w 74 w 68 w Low income 38,879 134,286 16 .. 3 .. 16 .. 2 .. 63 .. Middle income 1,250,130 4,613,566 8 8 3 2 11 15 3 6 72 68 Lower middle income 282,691 1,083,885 10 10 3 2 20 23 3 4 57 60 Upper middle income 967,323 3,529,396 7 7 3 2 9 13 3 6 76 70 Low & middle income 1,289,014 4,747,794 8 8 3 2 11 15 3 6 71 68 East Asia & Pacific 475,589 2,046,331 5 6 4 3 10 15 4 10 76 66 Europe & Central Asia 182,530 786,004 10 9 3 2 15 14 4 4 63 63 Latin America & Carib. 375,652 872,376 7 7 2 1 7 11 2 3 79 77 Middle East & N. Africa 92,037 329,532 17 15 3 2 7 13 2 3 56 66 South Asia 81,760 419,140 8 7 4 2 34 32 4 5 49 52 Sub-Saharan Africa 81,357 302,642 12 10 2 1 14 17 1 2 66 67 High income 5,370,223 10,519,458 7 7 2 1 10 16 3 4 75 68 Euro area 1,904,622 3,949,668 8 9 2 2 9 15 3 4 72 67 Note: Components may not sum to 100 percent because of unclassified trade. 232 2012 World Development Indicators 4.5 ECONOMY Structure of merchandise imports About the data De�nitions Data on imports of goods are derived from the • Merchandise imports are the c.i.f. value of goods same sources as data on exports. In principle, world purchased from the rest of the world valued in U.S. exports and imports should be identical. Similarly, dollars. • Food corresponds to the commodities in exports from an economy should equal the sum of SITC sections 0 (food and live animals), 1 (beverages imports by the rest of the world from that economy. and tobacco), and 4 (animal and vegetable oils and But differences in timing and definitions result in dis- fats) and SITC division 22 (oil seeds, oil nuts, and oil crepancies in reported values at all levels. For further kernels). • Agricultural raw materials correspond to discussion of indicators of merchandise trade, see SITC section 2 (crude materials except fuels), exclud- About the data for tables 4.4 and 6.1. ing divisions 22, 27 (crude fertilizers and minerals The value of imports is generally recorded as the excluding coal, petroleum, and precious stones), cost of the goods when purchased by the importer and 28 (metalliferous ores and scrap). • Fuels cor- plus the cost of transport and insurance to the fron- respond to SITC section 3 (mineral fuels). •  Ores tier of the importing country—the cost, insurance, and metals correspond to the commodities in SITC and freight (c.i.f.) value, corresponding to the landed divisions 27, 28, and 68 (nonferrous metals). • Man- cost at the point of entry of foreign goods into the ufactures correspond to the commodities in SITC country. A few countries, including Australia, Canada, sections 5 (chemicals), 6 (basic manufactures), 7 and the United States, collect import data on a free (machinery and transport equipment), and 8 (miscel- on board (f.o.b.) basis and adjust them for freight and laneous manufactured goods), excluding division 68. insurance costs. Many countries report trade data in U.S. dollars. When countries report in local currency, 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 and free trade zones. Goods transported through a country en route to another are excluded. The data on total imports of goods (merchandise) in the table come from the World Trade Organization Data sources (WTO). For further discussion of the WTO’s sources and methodology, see About the data for table 4.4. Data on merchandise imports are from the WTO. The import shares by major commodity group are Data on shares of imports by major commodity from the United Nations Statistics Division’s Com- group are from the United Nations Statistics Divi- modity Trade Statistics (Comtrade) database. The sion’s Comtrade database. The WTO publishes values of total imports reported here have not data on world trade in its Annual Report. The Inter- been fully reconciled with the estimates of imports national Monetary Fund publishes estimates of of goods and services from the national accounts total imports of goods in its International Finan- (shown in table 4.8) or those from the balance of cial Statistics and Direction of Trade Statistics, payments (table 4.17). as does the United Nations Statistics Division in The classification of commodity groups is based its Monthly Bulletin of Statistics. And the United on the Standard International Trade Classification Nations Conference on Trade and Development (SITC) revision 3. Previous editions contained data publishes data on the structure of imports in based on the SITC revision 1. Data for earlier years in its Handbook of Statistics. Tariff line records of previous editions may differ because of this change imports are compiled in the United Nations Sta- in methodology. Concordance tables are available tistics Division’s Comtrade database. to convert data reported in one system to another. 2012 World Development Indicators 233 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports �nancial services communications, and other commercial services $ millions % of total % of total % of total % of total 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 429 2,192 4 11 91 74 0 0 5 15 Algeria .. 2,794 .. 28 .. 10 .. 9 .. 54 Angola 267 857 6 5 18 84 13 .. 81 11 Argentina 4,775 12,931 24 16 61 38 0 0 15 46 Armenia 130 750 49 21 29 54 3 3 19 22 Australia 19,413 48,490 22 18 48 56 5 3 25 22 Austria 22,865 54,161 18 24 43 35 8 4 31 37 Azerbaijan 234 2,017 51 32 27 33 1 1 22 35 Bahrain 933 4,047 30 20 61 34 .. 22 8 24 Bangladesh 283 1,209 32 14 18 7 6 4 44 75 Belarus 989 4,470 59 67 9 10 1 0 31 23 Belgium 36,285 85,339 24 30 19 12 7 5 50 53 Benin 126 204 14 9 61 64 2 2 22 24 Bolivia 207 530 24 13 33 58 22 13 21 16 Bosnia and Herzegovina 448 1,280 6 23 52 46 4 1 38 30 Botswana 306 385 17 10 73 57 4 1 7 32 Brazil 8,961 30,294 16 16 20 20 8 8 56 56 Bulgaria 2,129 6,750 30 19 50 53 1 3 18 25 Burkina Faso 28 142 13 26 67 47 0 2 19 26 Burundi 2 7 43 10 37 24 0 22 20 44 Cambodia 423 1,671 17 14 72 75 0 0 11 11 Cameroon 666 1,105 21 43 9 14 3 4 68 38 Canada 39,271 67,432 19 17 27 23 7 11 46 48 Central African Republic .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile 3,995 10,685 55 61 21 15 3 3 22 21 China 30,146 170,249 12 20 54 27 1 2 33 51 Hong Kong SAR, China 40,362 106,432 32 27 15 19 12 14 42 40 Colombia 1,984 4,357 30 28 52 48 4 1 15 23 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 130 303 26 4 9 18 2 31 63 47 Costa Rica 1,911 4,149 13 7 68 48 0 1 18 44 Côte d’Ivoire 415 816 20 29 12 14 13 13 55 57 Croatia 4,056 11,034 14 10 68 73 1 1 17 16 Cuba .. .. .. .. .. .. .. .. .. .. Cyprus 6,751 20,911 21 24 44 34 6 2 30 40 Czech Republic 3,798 8,044 19 24 51 27 6 15 25 34 Denmark 23,721 60,405 45 .. 15 .. .. .. 39 .. Dominican Republic 3,143 4,998 2 8 91 84 0 1 7 7 Ecuador 793 1,371 37 26 51 57 0 .. 13 17 Egypt, Arab Rep. 9,687 23,618 27 34 45 53 1 1 27 12 El Salvador 673 944 37 32 32 41 10 3 20 23 Eritrea 54 .. 18 .. 64 .. 1 .. 17 .. Estonia 1,458 4,485 49 40 35 24 1 2 15 34 Ethiopia 387 1,991 56 59 15 26 1 0 28 14 Finland 7,669 27,729 22 11 18 10 0 2 60 76 France 82,115 143,896 22 25 40 32 3 3 34 40 Gabon 171 .. 56 .. 12 .. 0 .. 32 .. Gambia, The 73 88 27 42 67 36 1 0 6 22 Georgia 320 1,514 48 46 44 44 3 4 5 7 Germany 79,659 233,338 25 25 23 15 5 8 46 53 Ghana 490 1,344 20 27 68 46 1 1 11 26 Greece 19,181 37,336 41 55 48 33 1 2 9 10 Guatemala 702 2,192 12 13 69 63 3 2 16 22 Guinea 27 61 58 6 7 3 0 16 34 74 Guinea-Bissau 4 32 2 0 77 38 2 9 21 53 Haiti 158 183 2 .. 81 91 .. .. 19 9 234 2012 World Development Indicators 4.6 ECONOMY Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports �nancial services communications, and other commercial services $ millions % of total % of total % of total % of total 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 487 1,003 12 5 53 65 3 2 32 28 Hungary 5,836 19,288 9 20 64 28 3 1 24 52 India 16,031 123,277 12 11 22 11 3 6 63 71 Indonesia 5,061 16,211 16 16 98 43 0 2 2 38 Iran, Islamic Rep. 1,357 .. 49 .. 37 .. 11 .. 3 .. Iraq .. 1,721 .. 22 .. 0 .. 0 .. 78 Ireland 18,326 97,833 8 5 14 4 17 19 61 72 Israel 15,619 24,209 16 18 26 20 0 0 58 63 Italy 55,998 97,368 17 15 49 39 2 6 32 40 Jamaica 1,988 2,600 17 11 67 77 1 2 15 10 Japan 68,303 138,875 37 28 5 10 4 4 53 59 Jordan 1,602 4,782 19 18 45 71 0 .. 36 11 Kazakhstan 905 3,890 51 58 39 26 2 3 8 13 Kenya 727 2,920 57 54 39 27 1 5 4 14 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 30,650 81,556 45 47 22 12 3 4 31 37 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 1,571 7,137 88 45 6 3 6 2 0 50 Kyrgyz Republic 57 679 29 22 27 42 3 1 42 35 Lao PDR 134 489 15 10 85 78 0 2 0 9 Latvia 1,131 3,657 68 50 12 17 4 7 17 26 Lebanon 4,412 15,706 0 4 97 50 1 14 1 32 Lesotho 20 44 3 2 88 79 0 0 9 19 Liberia .. 40 .. 56 .. 31 .. .. .. 13 Libya 119 410 13 64 63 15 17 18 7 3 Lithuania 1,052 4,064 47 60 37 25 1 1 16 14 Macedonia, FYR 290 903 42 32 13 22 2 1 44 45 Madagascar 314 .. 16 .. 39 .. 1 .. 44 .. Malawi 34 126 26 18 74 55 2 0 0 26 Malaysia 13,812 32,760 20 14 36 56 2 2 41 30 Mali 92 335 36 6 44 57 3 2 18 35 Mauritania 24 .. 3 .. 83 .. .. .. 15 .. Mauritius 1,066 2,656 21 14 51 48 6 3 22 34 Mexico 13,291 14,935 8 7 62 79 14 12 16 2 Moldova 155 663 54 37 25 26 2 1 19 36 Mongolia 74 483 41 36 49 51 0 2 10 11 Morocco 2,854 12,138 17 18 71 55 1 2 10 25 Mozambique 325 576 30 28 23 34 3 1 47 36 Myanmar 459 334 17 45 35 22 .. .. 48 34 Namibia 163 835 23 16 68 53 0 1 8 30 Nepal 410 584 15 7 38 59 0 0 47 34 Netherlands 48,361 93,361 35 27 15 14 2 2 48 57 New Zealand 4,352 8,908 28 20 52 55 1 1 19 24 Nicaragua 187 430 16 11 69 72 2 1 13 16 Niger 35 100 24 8 64 66 4 3 8 23 Nigeria 1,833 2,613 12 75 6 22 1 1 82 3 Norway 17,528 39,506 55 40 12 12 3 4 30 45 Oman 452 1,761 43 36 49 44 3 1 5 18 Pakistan 1,284 2,949 65 48 6 10 1 3 27 38 Panama 1,961 5,659 59 53 23 30 9 9 9 8 Papua New Guinea 243 279 5 7 3 1 2 3 90 89 Paraguay 573 1,324 12 17 13 16 5 2 70 64 Peru 1,445 3,816 17 22 58 60 9 6 17 12 Philippines 3,377 14,358 14 9 64 18 3 1 20 71 Poland 10,395 32,700 24 27 55 29 3 2 19 42 Portugal 8,905 22,957 16 27 59 44 3 1 22 28 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 235 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports �nancial services communications, and other commercial services $ millions % of total % of total % of total % of total 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 1,720 8,728 37 29 21 13 8 2 35 56 Russian Federation 9,565 44,605 37 33 36 20 1 3 26 43 Rwanda 41 243 34 11 57 83 .. 0 8 6 Saudi Arabia 4,779 10,346 .. 20 .. 65 .. 12 .. 3 Senegal 330 909 10 5 44 51 2 2 45 42 Serbia .. 3,525 .. 22 .. 23 .. 2 .. 53 Sierra Leone 39 60 46 40 27 43 0 1 27 15 Singapore 28,420 112,061 41 29 18 13 8 13 32 45 Slovak Republic 2,218 5,817 45 31 20 38 2 1 33 30 Slovenia 1,883 6,120 26 26 51 42 1 2 22 30 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 4,888 13,617 24 12 55 67 9 8 12 13 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 52,112 122,773 16 17 57 43 3 5 24 36 Sri Lanka 915 2,448 44 47 27 24 4 3 25 26 Sudan 24 224 63 2 22 42 8 6 15 49 Swaziland 271 250 7 8 8 20 0 16 86 55 Sweden 22,193 64,835 21 15 18 17 6 3 55 65 Switzerland 29,443 81,649 15 7 23 18 41 25 21 49 Syrian Arab Republic 1,480 7,040 17 8 73 88 2 1 10 3 Tajikistan 60 182 76 27 3 2 2 5 20 65 Tanzania 575 2,047 10 22 65 61 3 2 22 15 Thailand 13,785 34,058 24 17 54 59 1 1 22 23 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 46 265 23 34 18 26 9 5 51 35 Trinidad and Tobago 543 758 38 28 39 48 8 14 14 9 Tunisia 2,680 5,471 22 28 63 48 2 3 13 21 Turkey 19,267 34,247 15 28 40 61 2 4 43 8 Turkmenistan 269 .. 50 .. 12 .. .. .. 38 .. Uganda 205 984 15 5 81 74 1 3 4 18 Ukraine 3,800 16,466 77 47 10 23 1 3 12 27 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 118,567 253,287 16 12 18 13 22 24 43 51 United States 275,881 522,510 16 14 37 26 7 16 40 45 Uruguay 1,249 2,458 30 18 57 61 8 5 5 16 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 1,057 1,626 35 35 40 45 0 0 24 19 Vietnam 2,702 7,460 .. .. .. .. .. .. .. .. West Bank and Gaza 453 554 8 5 62 74 0 0 29 21 Yemen, Rep. 174 1,460 12 13 42 80 .. .. 46 7 Zambia 114 312 37 49 58 40 4 2 0 9 Zimbabwe .. .. .. .. .. .. .. .. .. .. World 1,530,963 t 3,806,462 t 24 w 22 w 32 w 26 w 6w 8w 38 w 45 w Low income 6,240 20,076 30 24 35 26 4 4 33 47 Middle income 223,261 773,203 21 21 48 41 4 4 28 33 Lower middle income 63,035 276,733 23 23 50 32 2 3 32 42 Upper middle income 161,130 504,513 21 21 48 44 4 5 27 31 Low & middle income 229,067 792,143 22 22 48 41 4 4 28 33 East Asia & Pacific 70,928 280,031 16 17 56 38 1 2 29 43 Europe & Central Asia 44,988 144,429 39 36 33 27 2 3 27 34 Latin America & Carib. 51,355 109,176 18 17 51 57 9 8 23 19 Middle East & N. Africa .. .. .. 28 .. 29 .. 3 .. 40 South Asia 19,358 131,836 24 19 20 13 3 5 52 63 Sub-Saharan Africa 15,245 40,508 21 31 35 50 6 5 41 14 High income 1,305,653 3,018,606 25 22 27 21 7 9 41 48 Euro area 472,024 1,145,715 23 23 32 23 4 5 40 49 236 2012 World Development Indicators 4.6 ECONOMY Structure of service exports About the data De�nitions Balance of payments statistics, the main source of lodging, and transport (within the economy visited), •  Commercial service exports are total service information on international trade in services, have including car rental. exports minus exports of government services not many weaknesses. Disaggregation of important included elsewhere. • Transport covers all transport components may be limited and varies considerably services (sea, air, land, internal waterway, space, across countries. There are inconsistencies in the and pipeline) performed by residents of one economy methods used to report items. And the recording of for those of another and involving the carriage of major flows as net items is common (for example, passengers, movement of goods (freight), rental of insurance transactions are often recorded as premi- carriers with crew, and related support and auxiliary ums less claims). These factors contribute to a down- services. Excluded are freight insurance, which is ward bias in the value of the service trade reported included in insurance services; goods procured in in the balance of payments. ports by nonresident carriers and repairs of trans- Efforts are being made to improve the coverage, port equipment, which are included in goods; repairs quality, and consistency of these data. Eurostat and of harbors, railway facilities, and airfield facilities, the Organisation for Economic Co-operation and which are included in construction services; and Development, for example, are working together rental of carriers without crew, which is included to improve the collection of statistics on trade in in other services. •  Travel covers goods and ser- services in member countries. In addition, the Inter- vices acquired from an economy by travelers in that national Monetary Fund (IMF) has implemented economy for their own use during visits of less than the new classifi cation of trade in services intro- one year for business or personal purposes. • Insur- duced in the fifth edition of its Balance of Payments ance and �nancial services cover freight insurance Manual (1993). on goods exported and other direct insurance such Still, difficulties in capturing all the dimensions of as life insurance; financial intermediation services international trade in services mean that the record such as commissions, foreign exchange transac- is likely to remain incomplete. Cross-border intrafirm 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 affiliates. Another important dimension of service nonresidents; construction services; royalties and trade not captured by conventional balance of pay- license fees; miscellaneous business, professional, ments statistics is establishment trade—sales in and technical services; and personal, cultural, and the host country by foreign affiliates. By contrast, recreational services. cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments. The data on exports of services in the table and on imports of services in table 4.7, unlike those in edi- tions before 2000, include only commercial services and exclude the category “government services not included elsewhere.� The data are compiled by the IMF based on returns from national sources. Data on total trade in goods and services from the IMF’s Bal- ance of Payments database are shown in table 4.17. International transactions in services are defined Data sources by the IMF’s Balance of Payments Manual (1993) as the economic output of intangible commodities that Data on commercial service exports are from the may be produced, transferred, and consumed at the IMF, which publishes balance of payments data in same time. Definitions may vary among reporting its International Financial Statistics and Balance of economies. Travel services include the goods and Payments Statistics Yearbook. services consumed by travelers, such as meals, 2012 World Development Indicators 237 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports �nancial services communications, and other commercial services $ millions % of total % of total % of total % of total 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 413 1,992 23 16 66 68 8 5 3 11 Algeria .. 11,203 .. 26 .. 4 .. 2 .. 67 Angola 2,271 16,028 14 19 6 1 3 7 78 73 Argentina 8,960 13,769 27 27 49 35 4 5 20 33 Armenia 177 985 66 45 22 41 6 7 6 7 Australia 18,554 51,470 34 31 34 39 5 3 27 27 Austria 16,383 36,926 23 33 38 27 7 4 32 36 Azerbaijan 475 3,762 30 21 28 21 2 3 42 55 Bahrain 757 1,905 57 40 30 27 3 23 10 11 Bangladesh 1,523 4,128 66 83 19 6 8 2 6 9 Belarus 524 2,878 21 48 41 21 3 3 35 28 Belgium 35,288 78,377 22 25 29 24 8 4 41 47 Benin 186 488 67 59 7 11 9 5 17 25 Bolivia 450 1,128 60 41 17 28 14 13 9 18 Bosnia and Herzegovina 256 581 48 39 28 33 10 5 14 22 Botswana 538 867 42 51 37 3 4 5 17 42 Brazil 15,573 59,746 28 19 25 27 6 5 41 48 Bulgaria 1,660 4,164 44 22 32 30 5 6 18 43 Burkina Faso 132 545 64 56 15 12 18 14 3 18 Burundi 36 156 53 71 38 13 4 3 4 14 Cambodia 321 1,084 53 57 10 18 4 6 32 19 Cameroon 994 1,717 22 36 21 11 4 7 53 46 Canada 43,597 89,963 21 23 29 33 10 11 40 33 Central African Republic .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile 4,664 11,568 47 58 13 17 9 8 31 17 China 35,858 192,174 29 33 37 29 7 9 27 30 Hong Kong SAR, China 24,588 50,869 25 28 51 36 5 9 18 27 Colombia 3,242 7,893 40 36 33 23 10 9 17 32 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 728 3,523 11 15 7 5 5 5 77 75 Costa Rica 1,261 1,769 33 38 38 24 4 8 25 30 Côte d’Ivoire 1,142 2,324 45 58 17 15 11 9 27 27 Croatia 1,782 3,389 21 16 32 25 5 6 42 53 Cuba .. .. .. .. .. .. .. .. .. .. Cyprus 1,563 3,114 56 41 26 37 7 8 10 14 Czech Republic 5,364 16,925 13 24 24 24 9 3 54 49 Denmark 21,063 51,894 44 .. 22 .. .. .. 33 .. Dominican Republic 1,340 2,044 62 59 23 19 7 9 8 13 Ecuador 1,225 2,950 36 59 24 19 3 8 36 14 Egypt, Arab Rep. 7,161 12,991 31 51 15 17 7 11 48 21 El Salvador 912 1,024 44 45 18 21 17 13 21 21 Eritrea 24 .. 28 .. 50 .. 2 .. 21 .. Estonia 870 2,770 48 35 23 23 1 2 27 40 Ethiopia 479 2,534 60 65 15 6 4 4 21 25 Finland 8,323 27,650 31 20 22 15 0 3 46 61 France 64,400 131,391 28 27 35 29 2 4 36 40 Gabon 846 .. 32 .. 10 .. 8 .. 50 .. Gambia, The 32 72 78 48 11 15 10 7 1 30 Georgia 271 996 38 56 41 20 8 14 13 11 Germany 135,812 262,245 19 24 39 30 2 4 40 42 Ghana 514 2,444 53 46 20 24 6 5 22 25 Greece 10,918 19,892 37 54 42 14 3 10 18 22 Guatemala 786 2,362 54 48 23 33 8 9 15 10 Guinea 183 381 61 60 5 2 4 7 30 31 Guinea-Bissau 30 85 90 36 9 30 0 5 1 28 Haiti 270 1,223 91 47 7 5 .. 0 3 47 238 2012 World Development Indicators 4.7 ECONOMY Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports �nancial services communications, and other commercial services $ millions % of total % of total % of total % of total 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 688 1,312 60 45 17 24 2 8 21 23 Hungary 4,708 15,376 15 21 35 16 7 2 43 61 India 18,898 116,140 46 40 14 9 11 10 29 41 Indonesia 15,381 25,601 26 34 21 25 2 6 51 35 Iran, Islamic Rep. 1,577 .. 72 .. 13 .. 14 .. 1 .. Iraq .. 7,565 .. 53 .. 10 .. 27 .. 10 Ireland 31,212 107,270 8 2 8 7 9 13 75 78 Israel 11,849 17,787 36 33 24 19 3 2 38 46 Italy 54,632 108,616 24 24 29 25 3 8 44 44 Jamaica 1,391 1,767 42 41 15 11 8 11 35 37 Japan 115,686 155,800 30 30 28 18 3 6 39 46 Jordan 1,463 4,164 47 51 24 34 6 7 22 7 Kazakhstan 1,831 11,142 18 17 22 11 3 5 57 67 Kenya 665 1,816 51 51 20 12 10 10 19 27 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 33,128 92,936 33 31 22 19 1 2 44 48 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 4,115 12,260 37 40 61 55 1 3 1 2 Kyrgyz Republic 144 915 35 46 11 30 9 3 46 21 Lao PDR 13 258 38 6 62 79 –3 5 0 10 Latvia 680 2,211 31 30 36 29 10 6 22 35 Lebanon 3,340 13,262 14 14 80 36 4 10 2 41 Lesotho 247 479 7 14 78 65 3 4 12 17 Liberia .. 234 .. 54 .. 27 .. 1 .. 18 Libya 815 5,251 43 45 49 39 1 12 8 4 Lithuania 655 2,718 34 52 39 29 2 2 26 17 Macedonia, FYR 260 816 50 39 13 11 3 6 34 44 Madagascar 395 .. 48 .. 29 .. 2 .. 21 .. Malawi 167 79 53 19 30 59 0 7 17 15 Malaysia 16,603 32,216 35 37 12 25 3 4 49 38 Mali 324 813 74 57 12 12 4 6 10 25 Mauritania 130 .. 37 .. 33 .. .. .. 30 .. Mauritius 748 1,956 35 28 24 20 7 8 34 44 Mexico 16,242 21,818 38 49 34 33 14 15 14 3 Moldova 190 729 32 39 38 36 2 3 28 21 Mongolia 158 760 54 38 33 35 1 4 13 24 Morocco 1,520 5,724 41 46 28 21 2 4 29 29 Mozambique 439 1,102 38 28 25 23 6 3 32 46 Myanmar 310 761 82 60 8 7 .. .. 10 33 Namibia 308 697 29 32 24 21 6 7 40 40 Nepal 193 846 34 33 38 48 7 4 29 16 Netherlands 49,941 84,384 26 22 24 23 3 3 47 52 New Zealand 4,404 9,227 32 30 33 33 3 1 32 36 Nicaragua 334 660 46 50 23 31 7 11 23 8 Niger 125 735 67 73 21 7 3 4 9 17 Nigeria 3,144 20,163 20 43 19 28 3 3 59 26 Norway 14,832 42,358 35 28 31 33 6 3 28 36 Oman 1,758 6,525 37 41 27 15 6 11 30 33 Pakistan 2,109 6,481 72 58 12 14 4 4 12 24 Panama 1,096 2,569 55 60 17 15 8 16 20 9 Papua New Guinea 772 2,737 21 22 7 4 5 13 67 61 Paraguay 390 707 61 68 21 22 15 8 4 3 Peru 2,165 5,843 40 42 20 22 8 9 33 27 Philippines 5,175 11,188 40 44 32 31 4 3 25 21 Poland 8,862 29,473 17 21 37 29 6 4 39 46 Portugal 6,787 14,237 29 30 33 27 5 4 33 38 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 239 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports �nancial services communications, and other commercial services $ millions % of total % of total % of total % of total 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 1,948 9,341 32 30 22 18 7 7 39 46 Russian Federation 16,230 72,278 14 17 55 37 3 4 28 43 Rwanda 113 442 73 73 19 17 .. 1 8 9 Saudi Arabia 10,927 50,996 21 25 .. 41 2 5 77 28 Senegal 396 1,110 61 54 12 14 10 12 18 21 Serbia .. 3,477 .. 29 .. 27 .. 3 .. 41 Sierra Leone 82 134 21 70 39 10 4 5 36 15 Singapore 29,968 96,255 42 30 16 17 7 6 34 47 Slovak Republic 1,779 6,781 24 28 17 29 5 8 54 35 Slovenia 1,423 4,305 25 22 36 28 2 4 37 46 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 5,657 18,023 43 39 37 31 7 4 13 26 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 32,837 86,752 31 24 18 19 4 7 47 49 Sri Lanka 1,592 3,084 62 66 15 15 6 6 17 13 Sudan 632 2,195 88 46 9 51 0 1 3 2 Swaziland 300 650 12 11 10 9 2 6 76 74 Sweden 24,127 47,316 14 17 34 28 3 1 50 54 Switzerland 14,533 39,435 36 21 37 28 7 7 20 43 Syrian Arab Republic 1,468 3,377 48 47 46 45 2 4 7 4 Tajikistan 103 389 79 51 2 5 6 7 13 37 Tanzania 620 1,840 33 39 54 45 3 4 9 12 Thailand 15,329 44,592 44 50 18 12 5 5 33 32 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 116 374 72 62 2 13 15 9 12 16 Trinidad and Tobago 363 335 48 39 41 31 0 7 12 22 Tunisia 1,119 3,165 49 50 23 17 7 9 20 23 Turkey 7,624 18,343 32 45 22 26 13 10 32 18 Turkmenistan 669 .. 23 .. 19 .. 2 .. 57 .. Uganda 459 1,809 33 55 13 14 3 13 64 18 Ukraine 2,590 12,137 15 34 18 31 6 10 61 26 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 96,893 162,086 25 18 39 31 6 7 30 44 United States 203,169 367,016 30 21 33 23 8 21 29 36 Uruguay 842 1,365 47 45 33 31 6 4 13 20 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 4,236 10,548 43 38 25 17 5 5 27 39 Vietnam 3,252 9,921 .. .. .. .. .. .. .. .. West Bank and Gaza 459 777 12 11 66 62 2 2 20 25 Yemen, Rep. 757 2,263 45 47 9 8 7 10 38 35 Zambia 322 901 63 56 14 7 6 10 18 26 Zimbabwe .. .. .. .. .. .. .. .. .. .. World 1,485,017 t 3,472,677 t 29 w 27 w 31 w 25 w 6w 9w 34 w 39 w Low income 9,090 29,773 58 61 20 14 7 5 18 21 Middle income 264,736 906,395 36 39 28 26 8 8 28 27 Lower middle income 82,866 296,871 40 43 20 22 6 7 34 29 Upper middle income 182,790 613,085 35 38 30 27 8 9 27 27 Low & middle income 273,647 935,741 36 39 28 26 8 8 28 27 East Asia & Pacific 94,144 323,841 33 37 28 25 5 7 34 31 Europe & Central Asia 39,655 160,613 26 33 33 28 7 7 33 32 Latin America & Carib. 67,954 155,546 38 42 30 29 10 11 21 18 Middle East & N. Africa 23,663 70,149 48 42 22 20 .. 7 23 31 South Asia 24,653 132,213 53 49 15 11 9 8 23 32 Sub-Saharan Africa 25,073 97,316 41 40 27 25 6 5 27 30 High income 1,211,620 2,536,783 27 24 32 25 5 10 36 42 Euro area 477,111 1,044,273 24 25 31 25 3 5 41 45 240 2012 World Development Indicators 4.7 ECONOMY Structure of service imports About the data De�nitions 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 �nancial 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 recreational services. Data sources Data on commercial service imports are from the IMF, which publishes balance of payments data in its International Financial Statistics and Balance of Payments Statistics Yearbook. 2012 World Development Indicators 241 4.8 Structure of demand Household General Gross Exports Imports Gross �nal consumption government capital of goods and of goods and savings expenditure �nal consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan 112 111 8 11 12 16 31 15 62 54 .. .. Albania 85 88 9 8 25 26 19 30 37 52 24 13 Algeria 42 35 14 14 25 41 41 31 21 21 .. 54 Angola .. .. .. .. 15 15 90 58 63 44 24 16 Argentina 71 60 14 15 16 22 11 22 12 18 13 22 Armenia 97 78 12 13 19 33 23 21 51 45 4 19 Australia 58 54 18 18 26 28 19 20 21 20 21 24 Austria 55 55 19 19 25 22 46 54 44 50 24 25 Azerbaijan 69 37 9 11 21 17 39 55 38 20 17 46 Bahrain 47 31 18 13 10 33 89 97 64 74 20 43 Bangladesh 78 77 5 5 23 24 14 18 19 25 27 38 Belarus 58 57 19 16 25 41 69 55 72 68 23 25 Belgium 53 53 21 24 23 20 78 80 75 77 25 23 Benin 82 .. 12 .. 19 26 15 14 28 28 10 13 Bolivia 76 62 15 14 18 17 18 41 27 34 11 25 Bosnia and Herzegovina 104 80 25 21 21 20 29 36 76 57 13 15 Botswana 31 46 25 21 32 36 53 29 41 32 41 26 Brazil 64 61 19 21 18 19 10 11 12 12 14 17 Bulgaria 68 61 19 16 18 25 50 58 56 60 12 24 Burkina Faso 79 .. 21 .. 17 .. 9 .. 25 .. 5 .. Burundi 88 .. 18 .. 6 .. 8 .. 20 .. 4 .. Cambodia 89 82 5 6 18 17 50 54 62 60 14 13 Cameroon 70 .. 9 .. 17 .. 23 28 20 33 15 .. Canada 55 58 19 22 20 22 46 29 40 31 23 18 Central African Republic 81 93 14 5 10 11 20 15 24 23 .. .. Chad 87 73 8 15 23 37 17 44 35 69 .. .. Chile 64 59 12 12 22 21 32 39 30 32 21 23 China 47 35 16 13 35 48 23 30 21 26 37 53 Hong Kong SAR, China 59 62 9 8 27 24 143 223 139 217 32 30 Colombia 69 62 17 16 15 24 16 16 17 18 14 19 Congo, Dem. Rep. 88 75 8 5 3 29 22 15 21 34 .. .. Congo, Rep. 29 33 12 10 23 25 80 82 44 50 31 .. Costa Rica 67 65 13 18 17 20 49 38 46 41 13 15 Côte d’Ivoire 75 73 7 9 11 14 40 41 33 36 8 15 Croatia 60 56 24 22 19 23 42 38 45 39 18 22 Cuba 61 54 29 33 13 11 14 20 17 18 .. .. Cyprus 65 68 16 20 18 18 55 40 55 47 15 9 Czech Republic 52 51 21 22 29 23 63 79 66 75 25 21 Denmark 48 48 25 29 21 17 47 50 40 45 23 23 Dominican Republic 78 88 8 8 23 16 37 22 46 34 18 7 Ecuador 64 68 10 12 20 26 37 33 31 39 26 23 Egypt, Arab Rep. 76 75 11 11 20 19 16 21 23 26 18 18 El Salvador 88 93 10 11 17 13 27 26 42 44 14 11 Eritrea 79 .. 64 .. 24 .. 15 5 82 20 4 .. Estonia 55 53 20 21 28 20 85 78 88 72 23 24 Ethiopia 74 89 18 10 20 21 12 11 24 32 16 17 Finland 49 55 21 25 21 19 44 40 34 39 29 21 France 56 58 23 25 20 19 29 25 28 28 22 17 Gabon 32 43 10 10 22 26 69 52 33 31 42 .. Gambia, The 78 78 14 15 17 26 48 29 57 49 .. 13 Georgia 82 77 9 21 27 20 23 35 40 52 22 10 Germany 58 57 19 20 22 17 33 47 33 41 20 23 Ghana 84 80 10 11 24 22 49 25 67 38 15 20 Greece 70 75 19 18 25 16 26 22 40 30 14 5 Guatemala 84 86 7 10 18 15 20 25 29 36 12 13 Guinea 78 77 7 8 20 20 24 35 28 39 15 10 Guinea-Bissau 95 .. 14 .. 11 .. 32 .. 52 .. –15 .. Haiti 86 .. 8 .. 27 25 13 12 33 57 .. 23 242 2012 World Development Indicators 4.8 ECONOMY Structure of demand Household General Gross Exports Imports Gross �nal consumption government capital of goods and of goods and savings expenditure �nal consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 71 80 13 18 28 23 54 44 66 65 21 16 Hungary 55 53 21 22 27 18 75 87 78 80 19 20 India 64 57 13 12 24 35 13 22 14 25 25 34 Indonesia 61 57 7 9 22 32 41 25 30 23 25 32 Iran, Islamic Rep. 48 .. 14 .. 33 .. 23 .. 17 .. 39 .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 49 51 14 19 24 11 97 101 84 82 24 12 Israel 53 58 26 24 21 16 37 37 37 35 17 18 Italy 60 60 18 21 21 20 27 27 26 29 21 17 Jamaica 74 80 14 17 .. 21 .. 26 .. 43 17 12 Japan 56 59 17 20 25 20 11 15 10 14 28 24 Jordan 81 85 24 21 22 15 42 45 68 66 23 9 Kazakhstan 62 49 12 11 19 25 57 44 49 29 21 28 Kenya 78 78 15 13 17 21 22 26 32 39 14 16 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 55 53 12 15 31 29 39 52 36 50 33 32 Kosovo .. 95 .. 18 .. 30 .. 19 .. 62 .. .. Kuwait 42 28 21 21 11 14 56 56 30 28 50 59 Kyrgyz Republic 66 84 20 19 20 28 42 58 48 89 15 20 Lao PDR 93 69 7 9 14 26 30 36 44 41 2 20 Latvia 63 63 21 17 24 21 42 53 49 54 19 24 Lebanon 84 78 17 12 20 33 14 21 36 44 –4 12 Lesotho 83 87 42 44 44 34 34 49 103 114 37 34 Liberia 89 202 14 19 5 20 21 31 26 173 .. –2 Libya 46 23 21 9 13 28 36 67 15 27 22 67 Lithuania 65 65 23 20 19 17 45 68 51 70 13 19 Macedonia, FYR 74 75 18 18 22 25 49 47 64 66 22 24 Madagascar 83 79 9 12 15 33 31 29 38 53 9 .. Malawi 82 66 15 19 14 24 26 26 35 36 10 13 Malaysia 44 48 10 13 27 21 120 97 101 79 36 33 Mali 79 .. 9 .. 25 .. 27 .. 39 .. 16 .. Mauritania 83 70 26 18 19 28 46 48 74 63 21 .. Mauritius 60 76 14 14 26 22 61 45 62 58 26 16 Mexico 67 65 11 12 24 25 31 30 33 32 20 24 Moldova 91 91 10 24 24 24 50 40 75 78 16 16 Mongolia 70 53 15 14 29 41 54 55 68 62 23 27 Morocco 61 57 18 18 26 35 28 33 33 43 24 31 Mozambique 81 82 9 12 31 24 16 25 37 43 10 11 Myanmar .. .. .. .. 12 23 0 0 1 0 .. .. Namibia 63 53 24 22 17 23 41 39 45 38 25 34 Nepal 76 82 9 11 24 35 23 10 32 37 22 37 Netherlands 50 45 22 28 22 19 70 78 65 71 28 23 New Zealand 60 58 17 21 21 20 35 29 33 27 18 16 Nicaragua 84 91 12 10 30 28 24 41 51 70 8 13 Niger 83 .. 13 .. 11 .. 18 .. 26 .. 5 .. Nigeria .. .. .. .. .. .. 54 39 32 27 .. .. Norway 43 43 19 22 20 22 47 41 29 29 35 36 Oman 40 40 21 20 12 30 59 53 31 41 29 38 Pakistan 75 82 9 8 17 15 13 14 15 19 20 22 Panama 60 71 13 6 24 27 73 65 70 69 23 18 Papua New Guinea 45 70 17 9 22 18 66 56 49 53 32 20 Paraguay 79 69 13 9 19 19 38 57 49 55 11 23 Peru 71 63 11 10 20 24 16 25 18 22 17 23 Philippines 72 72 11 10 18 21 51 35 53 37 23 27 Poland 64 61 17 19 25 21 27 42 34 43 19 17 Portugal 64 66 19 22 28 20 29 31 40 38 18 10 Puerto Rico 91 94 11 11 18 9 75 78 98 92 .. .. Qatar 15 21 20 25 20 39 67 47 22 31 .. .. 2012 World Development Indicators 243 4.8 Structure of demand Household General Gross Exports Imports Gross �nal consumption government capital of goods and of goods and savings expenditure �nal consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 78 60 7 15 20 31 33 23 38 30 16 26 Russian Federation 46 49 15 19 19 23 44 30 24 22 36 28 Rwanda 88 81 11 15 18 22 9 12 26 29 13 15 Saudi Arabia 37 34 26 23 19 22 44 57 25 35 29 33 Senegal 76 82 13 9 20 29 28 25 37 44 14 19 Serbia 88 74 20 19 9 23 24 35 40 51 .. 16 Sierra Leone 100 84 14 12 7 16 18 17 39 30 –4 13 Singapore 43 37 11 11 33 24 192 211 180 183 44 46 Slovak Republic 56 58 20 20 26 23 70 81 73 82 23 20 Slovenia 57 56 19 21 27 23 54 65 57 65 25 22 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 63 57 18 20 16 25 28 26 25 27 16 16 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 60 58 17 21 26 23 29 26 32 28 23 19 Sri Lanka 72 66 11 16 28 28 39 22 50 31 22 25 Sudan 76 61 8 15 18 23 15 20 18 19 9 18 Swaziland 78 77 19 25 17 17 76 58 90 77 13 3 Sweden 49 49 26 27 19 18 47 50 40 44 23 25 Switzerland 60 58 11 11 23 19 46 54 41 42 35 36 Syrian Arab Republic 64 71 12 10 17 19 35 35 29 36 22 17 Tajikistan 84 94 8 29 9 23 99 15 101 61 9 3 Tanzaniaa 78 66 12 18 17 31 13 24 20 38 13 21 Thailand 57 54 11 13 23 26 67 71 58 64 30 31 Timor-Leste 112 .. 35 .. 26 .. .. .. .. .. .. .. Togo 92 .. 10 .. 18 .. 31 .. 51 .. 1 .. Trinidad and Tobago 57 51 9 10 20 11 59 65 45 38 26 35 Tunisia 61 63 17 16 26 26 40 49 43 54 22 20 Turkey 71 71 12 14 21 20 20 21 23 27 18 14 Turkmenistan 36 33 14 11 35 59 96 52 81 55 .. .. Uganda 77 75 15 12 19 24 11 24 22 34 14 19 Ukraine 54 63 21 20 20 19 62 50 57 53 24 17 United Arab Emirates 61 57 9 8 22 25 49 78 41 69 .. .. United Kingdom 66 64 19 23 18 15 28 30 29 33 15 12 United States 69 71 14 17 21 15 11 13 15 16 18 11 Uruguay 77 67 12 13 14 19 17 27 20 26 11 17 Uzbekistan 62 55 19 18 16 26 25 31 22 31 .. .. Venezuela, RB 52 57 12 11 24 21 30 29 18 17 34 31 Vietnam 66 65 6 7 30 39 55 78 57 88 31 32 West Bank and Gaza 95 .. 27 .. 33 .. 16 .. 71 .. 9 .. Yemen, Rep. 60 81 14 12 19 12 41 30 34 34 33 9 Zambia 87 55 10 13 17 22 27 44 41 35 –1 22 Zimbabwe 60 101 24 17 14 1 39 37 36 56 .. .. World 61 w 62 w 16 w 19 w 22 w 20 w 25 w 28 w 25 w 28 w 22 w 19 w Low income 79 80 10 10 19 23 17 20 25 32 19 27 Middle income 60 56 14 14 24 29 27 29 26 28 25 30 Lower middle income 67 64 12 11 22 28 27 28 27 31 23 28 Upper middle income 58 54 15 15 24 30 27 29 25 27 25 30 Low & middle income 61 56 14 14 24 29 27 28 25 28 25 30 East Asia & Pacific 50 42 14 13 31 41 35 37 31 33 34 46 Europe & Central Asia 60 60 14 17 20 23 37 31 31 31 26 22 Latin America & Carib. 66 63 15 15 20 22 20 22 21 22 17 21 Middle East & N. Africa 60 .. 15 .. 24 .. 27 .. 25 .. .. .. South Asia 67 62 11 11 23 32 14 20 16 25 25 33 Sub-Saharan Africa 69 64 16 17 17 24 32 30 31 32 16 17 High income 61 63 17 19 22 18 24 28 25 28 22 17 Euro area 57 58 20 22 22 19 37 41 36 39 22 20 a. Covers mainland Tanzania only. 244 2012 World Development Indicators 4.8 ECONOMY Structure of demand About the data De�nitions Gross domestic product (GDP) from the expenditure 1993 SNA guidelines are capital outlays on defense • Household �nal 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 � 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 2012 World Development Indicators 245 4.9 Growth of consumption and investment Household �nal General government Gross capital Goods and consumption �nal consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 1.3 5.2 2.2 4.8 14.5 6.3 25.8 5.1 18.9 9.2 15.7 11.8 Algeria –0.1 3.6 –1.9 2.1 3.6 4.8 –0.6 8.8 3.2 2.3 –1.0 7.8 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2.8 5.0 1.5 4.1 2.2 4.2 7.4 11.0 8.7 6.2 15.6 9.7 Armenia –0.5 7.3 1.1 7.2 –1.5 8.2 –1.9 16.3 –18.4 5.3 –12.7 7.7 Australia 3.3 3.7 2.1 2.1 3.0 3.1 5.3 6.9 7.8 2.5 7.6 8.4 Austria 1.7 1.4 1.4 0.9 2.6 1.6 2.6 0.6 5.8 4.4 4.8 3.5 Azerbaijan 2.0 12.4 1.0 11.1 7.4 22.4 41.7 13.9 13.4 22.5 15.5 17.1 Bahrain .. .. .. .. .. .. .. .. .. .. .. .. Bangladesh 2.6 4.6 0.5 3.2 4.7 8.5 9.2 7.7 13.1 10.6 9.7 8.0 Belarus –0.5 11.3 –0.3 11.9 –1.9 0.1 –7.5 18.1 –4.8 5.1 –8.7 10.3 Belgium 1.9 1.4 1.6 0.8 1.6 1.5 2.4 2.1 5.3 2.6 5.0 2.7 Benin 2.6 2.3 –0.6 –0.9 4.4 8.3 12.2 7.7 1.8 2.7 2.1 1.8 Bolivia 3.6 3.4 1.3 1.6 3.6 3.5 8.5 4.8 4.5 6.9 6.0 5.4 Bosnia and Herzegovina .. .. .. .. .. .. .. 5.3 .. 8.7 .. 3.4 Botswana 3.2 8.5 0.8 7.1 6.1 4.8 4.6 3.1 4.4 1.6 4.3 4.8 Brazil 3.7 3.9 2.2 2.8 1.0 3.3 4.2 4.7 5.9 6.4 11.6 8.5 Bulgaria –2.6 5.2 –2.0 5.9 –8.0 1.4 –5.3 11.2 4.3 7.6 2.9 9.0 Burkina Faso 5.7 4.5 2.8 1.5 2.9 8.7 3.1 9.0 4.4 10.9 1.9 7.2 Burundi –4.9 .. .. .. –2.6 .. –0.5 .. –1.2 .. –1.6 .. Cambodia 6.0 8.0 3.5 6.7 7.2 9.9 10.3 12.6 21.7 14.2 14.8 13.8 Cameroon 3.1 4.5 0.6 2.2 0.7 2.8 0.4 4.4 3.2 –0.7 5.1 3.6 Canada 2.7 3.3 1.7 2.3 0.3 2.8 4.6 4.3 8.7 –0.6 7.1 3.2 Central African Republic .. –0.9 .. –2.5 .. –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 8.1 9.4 4.9 11.7 10.3 China 8.9 7.7 7.7 7.1 9.6 9.2 10.8 13.3 15.5 19.1 16.7 16.2 Hong Kong SAR, China 3.8 3.6 2.0 3.0 3.7 1.2 4.8 2.7 7.9 7.8 8.4 7.1 Colombia 2.4 4.0 0.6 2.4 10.9 4.1 2.1 9.6 5.0 5.2 9.3 9.6 Congo, Dem. Rep. –1.1 .. –3.7 .. –20.4 .. 2.6 .. –0.5 6.4 –2.4 15.0 Congo, Rep. –1.8 .. .. .. –4.4 .. 10.4 .. 3.0 .. 2.0 .. Costa Rica 5.1 4.2 2.5 2.4 2.0 2.4 5.1 5.6 10.9 5.9 9.2 4.6 Côte d’Ivoire 4.1 .. 1.2 .. 0.8 2.9 8.1 4.3 1.9 2.0 8.2 4.1 Croatia 1.9 2.9 2.7 2.9 2.6 2.6 7.2 7.1 6.3 3.0 4.9 4.3 Cuba 4.0 5.0 3.5 4.8 –2.9 7.6 0.7 8.8 –9.0 12.2 –2.9 10.1 Cyprus 6.1 4.3 4.2 2.6 2.3 3.6 –2.6 4.7 6.3 1.4 4.6 3.7 Czech Republic 3.0 3.3 3.0 3.1 –0.9 1.9 4.6 2.5 8.7 10.0 12.0 8.8 Denmark 2.2 1.9 1.7 1.5 2.4 1.7 5.7 0.4 5.0 3.0 6.0 4.7 Dominican Republic 6.1 6.9 4.2 5.3 7.0 4.8 11.7 2.4 8.3 1.2 9.9 2.7 Ecuador 2.1 5.2 0.2 3.5 –1.5 4.3 –0.6 8.2 5.3 5.4 2.8 8.5 Egypt, Arab Rep. 3.7 4.6 1.9 2.7 4.4 2.7 5.8 7.2 3.5 15.1 3.0 12.9 El Salvador 5.3 2.4 4.2 2.0 2.8 1.6 7.1 0.4 13.4 3.0 11.6 2.5 Eritrea –5.0 1.6 –6.7 –2.2 22.6 1.2 19.1 –1.0 –2.5 –6.3 7.5 –3.7 Estonia 0.6 5.5 2.1 5.8 5.7 2.6 0.5 3.9 11.0 4.8 12.0 4.7 Ethiopia 3.6 10.8 0.4 8.2 9.0 1.8 6.5 11.1 7.1 9.7 5.8 16.7 Finland 1.8 2.8 1.4 2.4 0.9 1.4 3.2 1.5 10.3 3.7 6.7 4.7 France 1.6 1.7 1.2 1.0 1.4 1.6 1.9 1.4 6.8 1.4 5.7 2.7 Gabon –0.3 4.2 –3.1 2.2 3.7 2.7 3.0 5.5 2.1 –1.8 0.1 4.0 Gambia, The 3.6 .. 0.7 .. –2.2 .. 1.9 .. 0.1 1.6 0.1 1.7 Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 1.7 0.4 1.3 0.4 2.0 1.1 1.1 0.2 5.8 5.7 5.9 4.7 Ghana .. .. .. .. .. .. .. .. .. .. .. .. Greece 2.2 3.3 1.4 2.9 2.1 2.2 4.1 0.0 7.6 2.0 7.4 1.8 Guatemala 4.2 3.7 1.9 1.2 5.1 3.9 6.1 –0.1 6.1 2.3 9.2 2.1 Guinea 5.2 4.2 1.4 2.4 –0.5 0.9 0.1 1.8 0.3 2.4 –1.1 1.2 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. 9.0 1.2 10.1 4.1 19.4 3.1 246 2012 World Development Indicators 4.9 ECONOMY Growth of consumption and investment Household �nal General government Gross capital Goods and consumption �nal consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Honduras 3.0 4.6 0.6 2.5 2.0 6.0 6.9 2.8 1.6 4.1 3.8 3.8 Hungary 0.3 1.9 0.5 2.2 0.3 1.1 8.2 –0.6 9.3 9.5 11.0 7.9 India 4.8 7.1 2.8 5.5 6.6 6.2 6.9 12.9 12.3 14.7 14.4 15.9 Indonesia 6.6 4.3 5.0 3.1 0.1 8.1 –0.6 6.1 5.9 7.6 5.7 8.2 Iran, Islamic Rep. 3.2 7.4 1.5 6.1 1.6 3.6 –0.1 8.3 1.2 5.0 –6.8 13.2 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 5.6 3.1 4.8 1.2 4.1 3.5 9.7 –1.1 15.7 3.9 14.5 3.5 Israel 5.0 3.4 2.5 1.5 2.7 1.7 2.0 2.5 10.9 4.9 7.6 2.9 Italy 1.6 0.6 1.6 –0.1 –0.3 1.5 1.6 0.1 5.9 1.5 4.5 2.2 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 1.4 0.9 1.1 0.8 2.9 1.7 –0.8 –1.6 4.3 5.1 4.3 2.2 Jordan 4.9 7.6 1.1 5.2 4.7 6.7 0.3 5.1 2.6 5.0 1.5 6.3 Kazakhstan –7.5 9.2 –6.4 8.2 –7.1 7.1 –19.0 15.2 –1.9 4.7 –12.7 4.3 Kenya 3.6 3.9 0.6 1.3 6.9 2.5 6.1 9.1 1.0 6.1 9.4 7.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 4.9 2.9 3.9 2.5 4.7 4.8 3.4 2.1 16.0 10.3 10.0 8.1 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 4.5 .. 0.6 .. –2.4 .. 1.0 .. –1.6 .. 0.8 .. Kyrgyz Republic –6.4 8.1 –7.3 7.0 –8.8 1.0 –4.5 9.1 –1.6 5.9 –8.2 11.4 Lao PDR .. 5.0 .. 3.4 .. 4.2 .. 16.6 .. 9.4 .. 8.8 Latvia –3.9 6.1 –2.7 6.7 1.8 1.1 –3.7 3.7 4.3 6.7 7.6 6.3 Lebanon –0.2 3.6 –1.9 2.3 10.9 2.4 –5.8 10.3 18.6 9.6 –1.1 6.8 Lesotho 1.8 8.2 –0.1 7.1 8.1 6.3 0.2 1.6 10.3 7.9 2.7 11.3 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 5.3 6.5 6.1 7.1 1.9 3.6 11.1 5.6 4.9 8.9 7.5 9.5 Macedonia, FYR 2.2 4.6 1.7 4.3 –0.4 0.5 3.6 4.8 4.2 2.6 7.5 3.8 Madagascar 2.2 2.2 –0.9 –0.9 0.0 5.5 3.3 14.1 3.8 6.7 4.1 9.3 Malawi 5.4 .. 3.7 .. –4.4 .. –8.4 .. 4.0 .. –1.1 .. Malaysia 5.3 7.4 2.6 5.3 4.8 7.4 5.3 3.0 12.0 4.8 10.3 5.8 Mali 3.0 0.9 0.3 –2.2 3.2 .. 0.4 6.2 9.9 6.3 3.5 3.9 Mauritania .. 7.4 .. 4.4 .. 3.1 .. 23.8 –1.3 –2.1 0.6 14.1 Mauritius 5.1 5.6 3.9 4.7 3.6 4.0 4.8 4.7 5.6 1.6 5.1 2.0 Mexico 3.9 2.8 2.1 1.6 1.8 1.1 4.7 0.4 14.6 4.6 12.3 4.7 Moldova 9.9 7.2 10.1 7.5 –12.4 4.9 –15.5 7.5 0.7 8.9 5.6 10.0 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 1.8 4.7 0.3 3.5 3.9 3.9 2.5 8.3 5.9 6.0 5.1 7.8 Mozambique 5.8 5.8 2.6 3.2 3.2 –2.9 8.6 8.1 13.1 14.3 7.6 6.2 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 4.8 4.9 1.8 3.0 3.3 4.6 7.3 8.5 3.8 2.5 5.4 5.1 Nepal .. 4.2 .. .. .. 5.7 .. 10.2 .. –1.6 .. 6.1 Netherlands 3.1 0.5 2.5 0.1 2.0 3.3 4.4 0.8 7.3 3.9 7.6 3.7 New Zealand 3.2 3.1 2.0 1.8 2.4 3.9 6.1 3.0 5.2 2.4 6.2 4.4 Nicaragua 6.1 3.7 3.9 2.4 –1.5 1.6 11.3 4.3 9.3 8.4 12.2 5.2 Niger 1.8 .. .. .. 0.8 .. 4.0 .. 3.1 .. –2.1 .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 3.6 3.7 3.0 2.9 2.7 2.3 6.0 4.6 5.5 0.2 5.8 4.8 Oman 5.4 .. 3.4 .. 2.4 .. 4.0 .. 6.2 .. 5.9 .. Pakistan 4.9 4.7 2.2 2.8 0.7 7.5 1.8 5.0 1.7 6.7 2.5 6.3 Panama 6.4 7.2 4.2 5.4 1.7 3.6 10.4 10.6 –0.4 7.8 1.2 7.4 Papua New Guinea 2.5 .. –0.1 .. 2.5 .. 1.9 .. 5.1 .. 3.4 .. Paraguay 2.6 3.3 0.3 1.4 2.5 4.2 0.7 3.6 3.1 7.6 2.9 6.7 Peru 4.0 5.3 2.2 4.1 5.2 5.7 7.4 10.7 8.5 7.2 9.0 9.2 Philippines 3.9 4.5 1.6 2.5 2.6 3.9 2.1 2.8 8.2 5.2 8.5 3.5 Poland 5.4 3.7 5.3 3.8 3.2 4.3 10.6 5.8 11.3 8.7 16.7 7.9 Portugal 3.0 1.3 2.7 0.9 3.0 1.6 5.9 –2.1 5.7 3.1 7.6 2.5 Puerto Rico .. 1.9 .. .. .. 0.3 .. –3.6 1.6 1.1 4.5 1.6 Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 247 4.9 Growth of consumption and investment Household �nal General government Gross capital Goods and consumption �nal consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Romania 1.3 6.4 1.6 6.8 0.8 3.8 –5.1 10.6 8.1 9.0 6.0 12.1 Russian Federation –0.9 9.3 –0.7 9.7 –2.2 2.0 –19.1 7.6 0.8 6.5 –6.1 14.5 Rwanda 0.4 .. .. .. –2.6 .. 0.4 .. –6.4 .. 6.1 .. Saudi Arabia .. 5.3 .. 1.6 .. 7.6 .. 11.4 .. 6.9 .. 16.9 Senegal 2.6 4.9 –0.2 2.1 0.9 –0.9 3.5 9.6 4.1 3.9 2.0 7.1 Serbia .. 1.0 .. 1.3 .. 5.9 .. 18.1 .. 9.7 .. 8.6 Sierra Leone –4.4 .. .. .. 10.4 .. –5.6 .. –11.2 .. –0.2 .. Singapore 5.8 3.9 2.7 1.5 9.3 4.7 6.6 5.4 11.4 9.2 11.4 8.7 Slovak Republic 4.9 4.7 4.7 4.6 1.1 3.6 7.4 4.2 8.0 8.8 8.5 7.3 Slovenia 4.0 2.9 4.1 2.6 2.8 3.2 10.4 3.4 1.7 6.7 5.2 6.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2.9 4.1 0.6 2.8 0.3 5.6 4.7 7.7 5.8 2.6 7.1 7.0 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 2.4 2.4 2.0 0.9 2.7 4.8 3.2 1.9 10.5 2.7 9.4 3.8 Sri Lanka .. .. .. .. 7.6 6.8 6.6 8.2 7.5 3.1 8.6 4.7 Sudan 3.7 5.9 1.1 3.4 5.5 8.4 22.0 11.2 11.6 14.3 8.4 12.0 Swaziland 7.3 2.2 5.5 1.8 7.1 6.6 –4.7 0.2 6.4 4.2 6.2 4.2 Sweden 1.5 2.2 1.2 1.7 0.6 0.9 2.0 3.0 8.6 4.2 6.4 4.0 Switzerland 1.1 1.5 0.5 0.6 0.5 1.4 0.7 0.2 4.1 4.5 4.3 3.5 Syrian Arab Republic 3.0 .. 0.3 .. 2.0 9.1 3.3 7.9 12.0 1.9 4.4 7.5 Tajikistan –11.8 6.0 –13.1 4.9 –15.7 1.6 –17.6 5.8 –5.3 9.2 –6.0 10.3 Tanzaniaa 5.1 6.3 2.0 3.4 –8.8 12.8 –1.1 12.4 11.7 11.8 4.7 15.5 Thailand 3.7 3.8 2.7 2.9 5.1 5.4 –4.0 4.5 9.5 5.6 4.5 5.4 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 5.0 0.5 2.2 –1.9 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 .. 6.9 5.8 9.9 9.5 Tunisia 4.2 4.5 2.5 3.5 4.5 5.1 3.1 3.9 5.3 3.4 3.7 3.0 Turkey 3.8 5.0 2.2 3.6 4.6 4.1 4.7 6.7 11.1 5.8 10.8 8.2 Turkmenistan .. .. .. .. .. .. .. 5.7 –2.4 17.5 7.2 9.4 Uganda 6.6 4.4 3.4 1.1 6.4 3.7 8.4 11.0 13.8 19.7 10.2 11.8 Ukraine –6.9 11.2 –6.4 12.0 –4.1 2.0 –18.5 1.6 –3.6 0.9 –6.6 4.3 United Arab Emirates .. 7.0 .. .. .. 4.7 .. 11.9 .. 12.5 .. 18.7 United Kingdom 3.6 1.7 3.3 1.2 1.7 2.2 5.0 1.6 7.8 3.2 8.2 3.1 United States 3.8 2.1 2.5 1.1 0.7 2.0 7.6 –0.6 7.3 4.5 9.8 3.0 Uruguay 5.0 3.5 4.3 3.4 2.3 1.1 6.1 6.6 6.0 7.8 9.9 7.2 Uzbekistan .. .. .. .. .. .. –2.5 4.7 2.5 4.9 –0.4 4.2 Venezuela, RB 0.6 7.6 –1.5 5.7 3.7 7.7 11.0 10.7 1.0 –2.9 8.2 12.2 Vietnam 5.4 7.8 3.8 6.6 3.2 7.9 19.8 12.0 19.2 11.2 19.5 13.2 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. .. .. .. .. 3.8 3.6 10.0 –0.6 22.9 1.8 11.9 5.4 Zambia 2.4 0.1 –0.3 –2.2 –8.1 24.9 3.9 6.6 6.7 21.9 15.5 15.6 Zimbabwe .. .. .. .. .. .. .. .. 3.9 –9.2 3.1 –3.8 World 3.0 w 2.5 w 1.6 w 1.3 w 1.8 w 2.7 w 3.3 w 2.8 w 7.2 w 5.8 w 7.2 w 5.5 w Low income 2.9 5.4 0.4 3.1 –1.0 6.0 5.5 8.4 5.6 9.0 5.2 8.9 Middle income 4.0 5.6 2.5 4.4 3.3 5.7 2.6 9.6 7.5 9.9 6.5 10.2 Lower middle income 4.1 5.9 2.2 4.2 3.5 5.7 2.8 9.5 6.0 9.1 5.9 9.7 Upper middle income 4.0 5.6 2.9 4.8 3.3 5.7 2.6 9.6 7.9 10.1 6.6 10.4 Low & middle income 4.0 5.6 2.4 4.3 3.3 5.7 2.6 9.6 7.5 9.9 6.4 10.2 East Asia & Pacific 7.4 6.9 6.1 6.0 7.9 8.7 7.7 12.0 11.8 13.9 11.0 12.1 Europe & Central Asia 0.5 7.2 0.3 7.0 –0.8 3.2 –11.2 8.0 1.8 6.5 –2.3 10.6 Latin America & Carib. 3.6 4.1 1.9 2.8 1.9 3.5 5.4 5.2 8.1 4.8 10.5 6.8 Middle East & N. Africa 2.8 4.9 0.7 3.1 3.6 3.7 1.2 7.6 4.1 6.9 0.0 9.6 South Asia 4.6 6.6 2.5 4.9 5.8 6.5 6.5 11.9 10.0 13.0 11.2 13.7 Sub-Saharan Africa 3.3 4.6 0.6 2.1 0.3 5.5 4.6 7.8 .. .. 5.7 8.8 High income 2.9 1.8 2.1 1.1 1.5 2.1 3.5 0.4 7.1 4.6 7.4 4.1 Euro area 1.9 1.2 1.6 0.7 1.5 1.9 2.2 0.8 6.8 3.7 6.3 3.6 a. Covers mainland Tanzania only. 248 2012 World Development Indicators 4.9 ECONOMY Growth of consumption and investment About the data De�nitions Measures of growth in consumption and capital for- • Household �nal consumption expenditure is the mation are subject to two kinds of inaccuracy. The market value of all goods and services, including first stems from the difficulty of measuring expendi- durable products (such as cars and computers), tures at current price levels, as described in About purchased by households. It excludes purchases the data for table 4.8. The second arises in deflat- of dwellings but includes imputed rent for owner- ing current price data to measure volume growth, occupied dwellings. It also includes government fees where results depend on the relevance and reliabil- for permits and licenses. Expenditures of nonprofit ity of the price indexes and weights used. Measur- institutions serving households are included, even ing price changes is more difficult for investment when reported separately. Household consumption goods than for consumption goods because of the expenditure may include any statistical discrepancy one-time nature of many investments and because in the use of resources relative to the supply of the rate of technological progress in capital goods resources. • Household �nal 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 �nal 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- the change in government employment. Neither ance, transport, travel, royalties, license fees, and technique captures improvements in productivity other services (communication, construction, finan- or changes in the quality of government services. cial, information, business, personal, government Deflators for household consumption are usually cal- services, and so on). They exclude compensation of culated on the basis of the consumer price index. employees and investment income (factor services in Many countries estimate household consumption the 1968 System of National Accounts) and transfer as a residual that includes statistical discrepancies payments. associated with the estimation of other expenditure items, including changes in inventories; thus these estimates lack detailed breakdowns of household Data sources consumption expenditures. Data on national accounts indicators for most developing countries are collected from national statistical organizations and central banks by vis- iting and resident World Bank missions. Data for high-income economies are from Organisation for Economic Co-operation and Development (OECD) data files. 2012 World Development Indicators 249 4.10 Toward a broader measure of national income Gross Gross Adjustments Adjusted Gross Gross Adjusted domestic national net national domestic national net national product income income product income income % of GNI Consumption of Natural resource $ billions $ billions fi xed capital depletion $ billions % growth % growth % growth 2010 2010 2010 2010 2010 2000–10 2000–10 2000–10 Afghanistan 17.2 15.2 8.9 2.6 13.4 .. .. .. Albania 11.8 11.7 10.6 2.5 10.1 5.4 5.8 6.6 Algeria 162.0 155.5 11.3 18.1 109.8 3.9 4.4 5.0 Angola 84.4 75.5 12.1 35.1 39.9 12.9 .. .. Argentina 368.7 358.6 12.1 4.9 297.6 5.6 5.3 5.8 Armenia 9.4 9.7 9.9 1.0 8.6 9.2 9.2 9.6 Australia 1,131.6 1,094.5 14.6 6.5 863.2 3.2 3.3 4.0 Austria 379.1 377.1 14.1 0.2 323.4 1.8 1.7 1.8 Azerbaijan 51.8 48.3 12.0 34.5 25.9 17.1 18.1 21.6 Bahrain 20.6 21.0 6.7 30.0 13.3 6.6 .. .. Bangladesh 100.4 109.7 7.5 2.3 99.0 5.9 5.3 5.9 Belarus 54.7 53.4 11.4 1.0 46.7 8.0 8.3 9.9 Belgium 469.4 477.6 13.7 0.0 412.3 1.6 1.7 1.2 Benin 6.6 6.6 8.4 0.3 6.1 4.0 3.9 3.6 Bolivia 19.6 18.8 10.2 12.3 14.6 4.1 4.2 3.2 Bosnia and Herzegovina 16.6 17.0 10.5 .. .. 4.6 5.3 .. Botswana 14.9 14.8 11.6 3.4 12.6 4.1 3.5 3.5 Brazil 2,087.9 2,049.2 12.2 3.3 1,729.7 3.7 3.6 3.8 Bulgaria 47.7 46.0 14.3 2.0 38.5 4.8 5.4 4.4 Burkina Faso 8.8 8.8 7.9 4.3 7.7 5.5 6.0 5.2 Burundi 1.6 1.6 6.6 12.7 1.3 3.2 .. .. Cambodia 11.2 10.7 8.9 0.1 9.7 8.7 8.9 9.5 Cameroon 22.4 22.0 9.1 4.8 19.0 3.2 2.7 4.4 Canada 1,577.0 1,549.7 14.3 2.3 1,292.3 2.0 1.8 2.6 Central African Republic 2.0 2.0 7.7 0.0 1.9 1.0 –0.9 –1.2 Chad 7.6 6.7 9.3 29.0 4.2 9.0 20.2 –2.5 Chile 212.7 197.3 13.2 12.4 146.8 4.0 4.7 4.9 China 5,926.6 5,957.0 10.8 5.1 5,013.1 10.8 10.6 9.6 Hong Kong SAR, China 224.5 229.2 13.2 0.0 198.9 4.6 4.4 3.8 Colombia 288.2 276.1 11.8 7.7 222.2 4.5 4.7 4.5 Congo, Dem. Rep. 13.1 12.3 7.0 13.7 9.7 5.3 5.6 7.4 Congo, Rep. 11.9 8.6 14.1 59.6 2.3 4.3 .. .. Costa Rica 35.8 34.9 11.9 0.1 30.7 4.9 4.5 4.0 Côte d’Ivoire 22.8 21.7 9.4 3.9 18.8 1.1 0.9 1.1 Croatia 60.9 58.8 12.8 0.9 50.8 3.2 3.4 4.3 Cuba 62.7 61.8 11.3 3.2 52.8 6.7 6.6 6.7 Cyprus 23.1 22.5 13.3 0.0 19.5 3.1 3.0 3.3 Czech Republic 192.0 179.4 13.6 0.5 154.0 3.8 4.1 4.2 Denmark 312.0 319.3 14.0 1.7 269.3 0.9 0.4 1.7 Dominican Republic 51.8 50.0 11.4 0.2 44.2 5.6 5.5 5.4 Ecuador 58.0 56.9 10.9 12.9 43.4 4.8 4.3 5.7 Egypt, Arab Rep. 218.9 214.5 10.3 7.1 177.2 5.1 5.2 3.2 El Salvador 21.2 20.8 10.6 0.4 18.5 2.2 2.2 1.7 Eritrea 2.1 2.1 7.6 0.0 1.9 0.2 1.4 4.1 Estonia 19.2 18.4 13.0 1.6 15.7 4.6 4.6 5.3 Ethiopia 29.7 29.6 7.4 4.2 26.2 8.8 8.7 10.8 Finland 238.0 242.0 16.0 0.1 203.1 2.1 1.9 1.6 France 2,560.0 2,606.8 13.6 0.0 2,253.0 1.3 1.2 1.1 Gabon 13.0 11.5 13.3 33.1 6.1 2.2 2.3 4.3 Gambia, The 0.8 0.7 8.4 0.8 0.7 3.7 3.5 2.3 Georgia 11.7 11.5 9.7 0.6 10.3 6.9 .. .. Germany 3,280.5 3,341.4 13.6 0.1 2,883.3 1.0 0.6 1.5 Ghana 31.3 30.8 9.3 8.0 25.5 5.9 .. .. Greece 301.1 292.9 13.6 0.3 252.0 2.6 3.1 2.0 Guatemala 41.2 40.0 10.5 1.7 35.1 3.6 3.8 3.1 Guinea 4.5 4.2 8.2 14.3 3.3 2.9 3.7 0.3 Guinea-Bissau 0.9 0.9 8.0 0.5 0.8 1.5 .. .. Haiti 6.7 6.5 8.2 0.6 5.9 0.6 .. .. 250 2012 World Development Indicators 4.10 ECONOMY Toward a broader measure of national income Gross Gross Adjustments Adjusted Gross Gross Adjusted domestic national net national domestic national net national product income income product income income % of GNI Consumption of Natural resource $ billions $ billions fi xed capital depletion $ billions % growth % growth % growth 2010 2010 2010 2010 2010 2000–10 2000–10 2000–10 Honduras 15.4 14.8 10.1 0.5 13.2 4.6 4.6 2.5 Hungary 128.6 122.4 12.9 0.5 106.0 2.2 2.2 2.4 India 1,727.1 1,712.6 9.3 4.3 1,478.5 8.0 7.9 7.7 Indonesia 706.6 686.6 10.5 6.6 568.9 5.3 5.1 4.7 Iran, Islamic Rep. 331.0 328.6 10.9 19.9 227.5 5.4 6.2 6.7 Iraq 82.2 77.8 10.6 45.7 34.0 0.4 .. .. Ireland 206.6 171.3 16.9 0.2 142.0 2.8 2.8 1.8 Israel 217.3 210.4 13.8 0.2 180.9 3.6 2.8 3.6 Italy 2,061.0 2,051.4 13.7 0.1 1,768.9 0.5 0.5 0.3 Jamaica 14.3 13.6 11.6 0.6 11.9 1.2 .. .. Japan 5,458.8 5,601.6 13.6 0.0 4,841.4 0.9 0.7 1.2 Jordan 27.6 27.8 10.8 1.0 24.6 6.7 6.5 6.9 Kazakhstan 149.1 131.9 13.3 23.4 83.4 8.3 9.3 9.2 Kenya 31.4 31.3 7.3 1.1 28.6 4.3 4.3 4.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1,014.5 1,014.8 12.9 0.0 883.5 4.1 4.0 3.3 Kosovo 5.6 5.7 10.0 .. .. 5.3 .. .. Kuwait 109.5 117.2 7.2 25.1 79.3 8.4 .. .. Kyrgyz Republic 4.6 4.3 9.2 6.9 3.6 4.4 4.4 2.7 Lao PDR 7.3 7.0 9.4 8.3 5.7 7.2 7.1 6.2 Latvia 24.0 24.1 18.0 0.5 19.6 4.8 4.6 6.0 Lebanon 39.0 39.1 11.8 0.0 34.4 4.9 4.4 5.0 Lesotho 2.1 2.6 7.3 1.0 2.3 3.5 13.4 12.7 Liberia 1.0 0.8 8.4 6.4 0.7 0.9 .. .. Libya 62.4 62.0 12.0 29.0 36.6 5.4 .. .. Lithuania 36.3 35.7 12.3 0.6 31.1 5.3 5.2 6.5 Macedonia, FYR 9.2 9.0 11.1 5.9 7.5 3.3 3.4 2.6 Madagascar 8.7 8.6 7.7 1.0 7.9 3.4 3.2 2.3 Malawi 5.1 5.0 7.5 1.8 4.5 5.2 .. .. Malaysia 237.8 229.6 12.1 6.9 185.9 5.0 4.5 7.0 Mali 9.3 8.9 8.4 9.8 7.3 5.2 5.9 4.9 Mauritania 3.6 3.7 8.8 34.3 2.1 4.4 6.3 6.9 Mauritius 9.7 9.8 11.5 0.0 8.7 3.9 3.6 2.0 Mexico 1,034.8 1,020.3 12.0 5.7 839.9 2.1 2.0 1.7 Moldova 5.8 6.3 8.7 0.2 5.7 5.2 4.9 5.6 Mongolia 6.2 5.6 10.9 32.3 3.2 7.2 .. .. Morocco 90.8 88.6 10.5 1.6 77.9 4.9 4.8 4.3 Mozambique 9.6 9.4 7.7 3.3 8.4 7.8 7.4 6.5 Myanmar .. .. .. .. .. .. .. .. Namibia 12.2 12.1 11.1 0.7 10.7 5.0 5.4 .. Nepal 15.7 15.8 7.8 2.5 14.2 3.8 .. .. Netherlands 779.4 772.7 14.2 0.8 656.9 1.6 2.0 1.2 New Zealand 126.7 121.4 14.0 1.0 103.3 2.6 2.7 2.6 Nicaragua 6.6 6.3 9.3 1.6 5.6 3.6 3.3 2.8 Niger 5.5 5.5 3.2 2.4 5.2 4.2 .. .. Nigeria 193.7 176.8 9.9 22.0 120.4 6.7 .. .. Norway 417.5 427.2 14.5 10.2 321.5 1.7 1.6 3.6 Oman 46.9 44.1 13.5 28.5 25.6 4.7 .. .. Pakistan 176.9 183.6 8.5 2.8 162.9 5.1 4.7 4.3 Panama 26.7 25.0 12.3 0.0 22.0 6.8 7.1 6.1 Papua New Guinea 9.5 9.3 9.4 22.2 6.3 3.8 .. .. Paraguay 18.3 18.0 10.4 0.0 16.1 3.8 4.0 3.9 Peru 157.1 147.0 11.8 8.1 117.7 6.1 6.7 5.3 Philippines 199.6 199.9 9.8 2.1 176.2 4.9 4.9 4.1 Poland 469.4 452.3 12.7 1.4 388.7 4.3 4.7 4.3 Portugal 228.6 221.1 18.4 0.1 180.1 0.7 0.9 0.3 Puerto Rico 96.3 63.3 20.0 .. .. 0.0 0.3 .. Qatar 98.3 .. .. .. .. 14.2 .. .. 2012 World Development Indicators 251 4.10 Toward a broader measure of national income Gross Gross Adjustments Adjusted Gross Gross Adjusted domestic national net national domestic national net national product income income product income income % of GNI Consumption of Natural resource $ billions $ billions fi xed capital depletion $ billions % growth % growth % growth 2010 2010 2010 2010 2010 2000–10 2000–10 2000–10 Romania 161.6 159.0 11.7 1.6 137.8 5.0 5.4 6.5 Russian Federation 1,479.8 1,431.1 12.4 14.3 1,049.8 5.4 5.6 8.4 Rwanda 5.6 5.6 8.0 3.1 5.0 7.6 .. .. Saudi Arabia 434.7 381.3 12.1 29.1 224.0 3.6 3.4 6.0 Senegal 13.0 12.9 8.9 0.8 11.6 4.2 4.1 4.3 Serbia 38.4 37.5 11.3 .. .. 4.1 4.3 .. Sierra Leone 1.9 1.9 7.2 2.1 1.7 8.8 .. .. Singapore 208.8 201.1 14.4 0.0 172.1 6.0 6.1 5.4 Slovak Republic 87.3 86.1 12.7 0.4 74.8 5.4 5.5 5.6 Slovenia 46.9 46.2 13.3 0.3 40.0 3.3 3.5 3.3 Somalia .. .. .. .. .. .. .. .. South Africa 363.7 356.5 13.5 6.0 286.8 3.9 3.9 3.8 South Sudan .. .. .. .. .. .. .. .. Spain 1,407.4 1,388.7 13.6 0.0 1,198.9 2.4 2.5 2.2 Sri Lanka 49.6 48.9 10.1 0.3 43.8 5.6 5.6 5.5 Sudan 62.0 55.9 10.3 12.9 43.0 6.7 6.7 5.6 Swaziland 3.6 3.6 10.6 0.1 3.2 2.4 3.2 2.1 Sweden 458.6 466.9 13.2 0.4 403.3 2.2 1.9 2.3 Switzerland 527.9 568.6 13.5 0.0 491.9 1.9 2.3 1.8 Syrian Arab Republic 59.1 57.3 10.6 11.9 44.4 5.0 4.8 7.4 Tajikistan 5.6 5.6 8.6 0.8 5.0 8.6 8.1 6.2 Tanzaniaa 23.1 23.0 7.9 3.2 20.5 7.1 6.9 6.4 Thailand 318.5 304.8 11.4 2.4 263.0 4.5 4.6 4.5 Timor-Leste 0.7 2.7 2.1 .. .. 3.4 .. .. Togo 3.2 2.8 8.8 3.4 2.5 2.7 .. 1.1 Trinidad and Tobago 20.6 19.3 13.4 32.0 10.5 6.5 8.3 5.4 Tunisia 44.3 42.0 11.3 5.1 35.1 4.7 4.9 4.1 Turkey 734.4 727.1 12.1 0.4 636.8 4.7 4.6 3.9 Turkmenistan 20.0 18.1 11.8 .. .. 13.6 16.1 .. Uganda 17.0 16.7 8.0 4.5 14.6 7.7 7.7 7.6 Ukraine 137.9 135.9 10.4 3.7 116.7 4.8 4.7 6.8 United Arab Emirates 297.6 273.5 13.6 .. .. 5.1 .. .. United Kingdom 2,261.7 2,271.6 13.6 1.3 1,931.7 1.8 1.7 1.7 United States 14,586.7 14,635.6 14.0 0.9 12,453.3 1.8 1.9 1.2 Uruguay 39.1 37.7 12.6 0.6 32.7 3.6 3.9 3.1 Uzbekistan 39.0 39.0 9.2 19.2 27.9 7.1 5.0 –8.7 Venezuela, RB 391.8 389.0 12.4 12.4 292.4 4.7 4.4 8.1 Vietnam 106.4 102.0 9.4 9.4 82.8 7.5 7.8 7.3 West Bank and Gaza .. .. .. .. .. –0.9 0.2 .. Yemen, Rep. 31.3 29.5 9.7 14.5 22.3 4.1 3.8 5.6 Zambia 16.2 14.3 10.3 18.9 10.1 5.6 7.7 5.1 Zimbabwe 7.5 7.0 8.6 2.7 6.2 –6.0 –6.0 –7.1 World 63,242.1 w 63,087.6 w 13.0 w 2.6 w 53,010.4 w 2.7 w 2.6 w 2.5 w Low income 416.5 422.4 7.7 3.8 373.6 5.5 5.6 5.7 Middle income 19,632.1 19,525.6 11.2 6.5 16,053.8 6.4 6.4 6.2 Lower middle income 4,312.3 4,370.0 9.8 7.4 3,663.4 6.3 6.3 5.9 Upper middle income 15,317.0 15,152.4 11.6 6.2 12,388.6 6.5 6.4 6.3 Low & middle income 20,071.7 19,971.7 11.1 6.4 16,442.8 6.4 6.4 6.2 East Asia & Pacific 7,630.5 7,614.8 10.7 5.2 6,401.9 9.4 9.3 8.5 Europe & Central Asia 3,059.0 2,965.9 12.2 9.4 2,320.4 5.4 5.4 6.2 Latin America & Carib. 4,980.8 4,867.8 12.1 5.5 4,016.0 3.8 3.7 3.9 Middle East & N. Africa 1,207.0 1,334.2 10.7 12.8 1,023.9 4.7 4.9 4.9 South Asia 2,090.4 2,089.3 9.2 4.0 1,814.7 7.4 7.3 7.2 Sub-Saharan Africa 1,097.9 1,044.6 10.9 11.8 807.6 5.0 4.4 4.2 High income 43,240.0 43,247.5 13.9 0.9 36,615.4 1.8 1.7 1.5 Euro area 12,149.1 12,161.9 13.8 0.1 10,459.5 1.3 1.3 1.3 a. Covers mainland Tanzania only. 252 2012 World Development Indicators 4.10 ECONOMY Toward a broader measure of national income About the data De�nitions An economy’s growth is typically measured by the economic growth that is strikingly different from the • Gross domestic product is the sum of value added change in the volume of its output, as shown in one provided by GDP. by all resident producers plus any product taxes (less table 4.1. But gross domestic product (GDP), though The key to increasing future consumption and subsidies) not included in the valuation of output. widely tracked, may not always be the most relevant thus the standard of living lies in increasing national • Gross national income is GDP plus net receipts summary of aggregated economic performance for wealth—including not only the traditional measures of primary income (compensation of employees all economies, especially when production occurs at of capital (such as produced and human capital), and property income) from abroad. • Consumption the expense of consuming capital stock. For coun- but also natural capital. Natural capital comprises of �xed capital is the replacement value of capital tries with significant exhaustible natural resources such assets as land, forests, and subsoil resources. used up in production. •  Natural resource deple- and important foreign-investor presence, adjusted All three types of capital are key to sustaining eco- tion is the sum of net forest depletion, energy deple- net national income complements GDP in assessing nomic growth. By accounting for the consumption tion, and mineral depletion. Net forest depletion is economic progress (Hamilton and Ley 2010). of fixed and natural capital depletion, adjusted net unit resource rents times the excess of roundwood The table presents three measures of economic national income better measures the income avail- harvest over natural growth. Energy depletion is the progress: GDP, gross national income (GNI), and able for consumption or for investment to increase ratio of the value of the stock of energy resources to adjusted net national income. GDP accounts for a country’s future consumption. For a measure of the remaining reserve lifetime (capped at 25 years). all domestic production, regardless of whether the how comprehensive wealth is changing over time, It covers coal, crude oil, and natural gas. Mineral income accrues to domestic or foreign institutions. see table 4.11. depletion is the ratio of the value of the stock of GNI accounts for the operation of foreign inves- Methods of computing growth are described in Sta- mineral resources to the remaining reserve lifetime tors, who may be repatriating some of the income tistical methods. For a detailed note on methodology, (capped at 25 years). It covers tin, gold, lead, zinc, produced domestically. GNI comprises GDP plus see http://data.worldbank.org. iron, copper, nickel, silver, bauxite, and phosphate. net receipts of primary income from nonresident • Adjusted net national income is GNI minus con- sources. Adjusted net national income goes a step sumption of fi xed capital and natural resources further by subtracting from GNI a charge for the con- depletion. sumption of fixed capital (a calculation that yields net national income) and for the depletion of natural resources. The deduction for the depletion of natural resources, which covers net forest depletion, energy depletion, and mineral depletion, reflects the decline in asset values associated with the extraction and harvest of natural resources. For more discussion of the estimates and methodology of produced capi- tal consumption and natural capital depletion, see About the data for table 4.11. The United Nations System of National Accounts includes nonproduced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, Data sources energy, and mineral depletion, thus remains within the System of National Accounts boundaries. This GNI and GDP are estimated by World Bank staff point is critical because it allows for comparisons based on national accounts data collected by across GDP, GNI, and adjusted net national income; World Bank staff during economic missions or such comparisons reveal the impact of natural reported by national statistical offices to other resource depletion, which is otherwise ignored by international organizations such as the Organisa- the popular economic indicators. tion for Economic Co-operation and Development. Adjusted net national income is particularly useful Data on consumption of fi xed capital are from in monitoring low-income, resource-rich economies, the United Nations Statistics Division’s National like many countries in Sub-Saharan Africa, because Accounts Statistics: Main Aggregates and Detailed such economies often see large natural resources Tables, extrapolated to 2010. Data on energy, min- depletion as well as substantial exports of resource eral, and forest depletion are estimates based rents to foreign mining companies. For recent years on sources and methods in World Bank (2011a). adjusted net national income gives a picture of 2012 World Development Indicators 253 4.11 Toward a broader measure of savings Gross Consumption Education Net Energy Mineral Carbon Local Adjusted savings of �xed expenditure forest depletion depletion dioxide pollution net capital depletion damage savings % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI 2010 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan .. 8.9 .. 2.6 0.0 0.0 0.1 0.8 .. Albania 13.6 10.6 2.8 0.0 2.2 0.3 0.3 0.2 2.7 Algeria 53.6 11.3 4.5 0.1 17.8 0.2 0.6 0.3 29.8 Angola 17.9 12.1 2.3 0.0 35.1 0.0 0.3 1.8 –29.2 Argentina 23.1 12.1 6.0 0.0 4.5 0.4 0.4 1.6 10.1 Armenia 18.1 9.9 2.2 0.0 0.0 1.0 0.4 2.2 6.8 Australia 25.1 14.6 4.5 0.0 2.2 4.3 0.3 0.0 8.2 Austria 24.9 14.1 5.3 0.0 0.1 0.0 0.1 0.2 15.6 Azerbaijan 49.5 12.0 3.4 0.0 34.5 0.0 0.9 0.4 5.1 Bahrain 45.4 6.7 3.0 0.0 30.0 0.0 0.8 0.3 10.6 Bangladesh 35.2 7.5 1.8 0.4 1.8 0.0 0.4 0.6 26.2 Belarus 25.7 11.4 4.4 0.0 1.0 0.0 1.0 0.0 16.6 Belgium 22.5 13.7 6.1 0.0 0.0 0.0 0.2 0.1 14.6 Benin 12.8 8.4 4.3 0.3 0.0 0.0 0.5 0.5 7.4 Bolivia 26.1 10.2 5.2 0.0 9.4 2.9 0.5 1.1 7.3 Bosnia and Herzegovina 14.5 10.5 .. .. 1.4 1.4 1.3 0.1 .. Botswana 26.8 11.6 7.6 0.0 0.2 3.2 0.2 0.3 18.6 Brazil 16.8 12.2 5.2 0.0 1.6 1.7 0.2 0.2 6.1 Bulgaria 24.6 14.3 4.1 0.0 0.7 1.3 0.8 1.2 10.4 Burkina Faso .. 7.9 4.3 1.5 0.0 2.8 0.2 0.8 .. Burundi .. 6.6 8.7 11.8 0.0 0.9 0.1 0.1 .. Cambodia 13.2 8.9 1.6 0.1 0.0 0.0 0.3 0.4 5.1 Cameroon .. 9.1 3.1 0.0 4.6 0.1 0.2 0.7 .. Canada 18.8 14.3 4.5 0.0 1.8 0.6 0.3 0.1 6.3 Central African Republic .. 7.7 1.1 0.0 0.0 0.0 0.1 0.2 .. Chad .. 9.3 2.3 0.0 29.0 0.0 0.0 1.4 .. Chile 24.9 13.2 4.6 0.0 0.1 12.3 0.3 0.6 3.0 China 52.7 10.8 1.8 0.0 3.7 1.4 1.1 1.2 36.3 Hong Kong SAR, China 29.3 13.2 3.1 0.0 0.0 0.0 0.1 .. .. Colombia 19.8 11.8 3.9 0.0 7.2 0.5 0.2 0.1 3.8 Congo, Dem. Rep. .. 7.0 0.9 0.0 2.7 11.0 0.2 0.7 .. Congo, Rep. .. 14.1 2.5 0.0 59.6 0.0 0.2 0.9 .. Costa Rica 15.7 11.9 6.2 0.1 0.0 0.0 0.2 0.1 9.7 Côte d’Ivoire 15.4 9.4 4.3 0.0 3.4 0.5 0.3 0.3 5.3 Croatia 22.6 12.8 4.3 0.2 0.7 0.0 0.3 0.3 12.7 Cuba .. 11.3 13.4 0.0 2.3 0.9 0.4 0.0 .. Cyprus 9.5 13.3 6.9 0.0 0.0 0.0 0.3 0.3 2.5 Czech Republic 22.5 13.6 3.8 0.0 0.5 0.0 0.5 0.0 11.6 Denmark 22.4 14.0 7.3 0.0 1.7 0.0 0.1 0.0 13.9 Dominican Republic 7.5 11.4 1.9 0.0 0.0 0.2 0.4 0.0 –2.6 Ecuador 23.1 10.9 1.4 0.0 12.9 0.0 0.4 0.1 0.2 Egypt, Arab Rep. 18.2 10.3 4.4 0.1 6.8 0.2 0.8 0.7 3.6 El Salvador 11.2 10.6 3.0 0.4 0.0 0.0 0.2 0.2 2.7 Eritrea .. 7.6 1.7 0.0 0.0 0.0 0.2 0.4 .. Estonia 25.3 13.0 5.5 0.3 1.3 0.0 0.7 0.0 15.6 Ethiopia 16.7 7.4 2.9 4.0 0.0 0.2 0.2 0.2 7.5 Finland 20.3 16.0 5.7 0.0 0.0 0.1 0.2 0.0 9.7 France 17.2 13.6 5.1 0.0 0.0 0.0 0.1 0.0 8.5 Gabon .. 13.3 3.1 0.0 33.0 0.0 0.1 0.0 .. Gambia, The 13.7 8.4 3.1 0.8 0.0 0.0 0.4 0.5 6.7 Georgia 10.0 9.7 2.8 0.0 0.2 0.4 0.4 1.5 0.7 Germany 22.7 13.6 4.4 0.0 0.1 0.0 0.2 0.0 13.2 Ghana 20.8 9.3 4.7 1.5 0.0 6.5 0.3 0.0 8.6 Greece 4.8 13.6 3.2 0.0 0.2 0.1 0.2 0.5 –6.7 Guatemala 13.3 10.5 2.9 0.7 0.5 0.6 0.3 0.2 3.4 Guinea 10.5 8.2 2.3 2.3 0.0 12.0 0.3 0.6 –10.6 Guinea-Bissau .. 8.0 2.3 0.5 0.0 0.0 0.3 0.8 .. Haiti 23.2 8.2 1.5 0.6 0.0 0.0 0.3 0.5 15.1 254 2012 World Development Indicators 4.11 ECONOMY Toward a broader measure of savings Gross Consumption Education Net Energy Mineral Carbon Local Adjusted savings of �xed expenditure forest depletion depletion dioxide pollution net capital depletion damage savings % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI 2010 2010 2010 2010 2010 2010 2010 2010 2010 Honduras 16.9 10.1 3.5 0.0 0.0 0.5 0.4 0.2 9.2 Hungary 21.5 12.9 5.2 0.0 0.5 0.0 0.3 0.0 12.9 India 34.0 9.3 3.1 0.5 2.5 1.3 0.9 0.7 21.8 Indonesia 32.9 10.5 4.3 0.0 5.3 1.3 0.5 0.8 18.8 Iran, Islamic Rep. .. 10.9 4.1 0.0 19.2 0.6 1.2 0.8 .. Iraq .. 10.6 .. 0.0 45.7 0.0 1.1 4.0 .. Ireland 14.8 16.9 5.9 0.0 0.0 0.1 0.2 0.0 3.5 Israel 18.9 13.8 5.7 0.0 0.1 0.1 0.2 0.1 10.3 Italy 17.1 13.7 4.4 0.0 0.1 0.0 0.2 0.1 7.5 Jamaica 12.9 11.6 5.8 0.0 0.0 0.6 0.6 0.2 5.6 Japan 23.2 13.6 3.2 0.0 0.0 0.0 0.2 0.3 12.3 Jordan 9.1 10.8 5.6 0.0 0.1 0.9 0.7 0.2 2.0 Kazakhstan 31.6 13.3 4.4 0.0 21.6 1.8 1.4 0.2 –2.3 Kenya 15.6 7.3 5.9 1.1 0.0 0.1 0.3 0.1 13.1 Korea, Dem. Rep. .. .. .. .. .. .. .. 0.8 .. Korea, Rep. 31.6 12.9 4.3 0.0 0.0 0.0 0.4 0.5 22.1 Kosovo .. 10.0 .. .. 0.0 0.9 .. .. .. Kuwait 54.9 7.2 3.2 0.0 25.1 0.0 0.5 0.5 14.9 Kyrgyz Republic 21.3 9.2 6.0 0.0 0.7 6.2 1.1 0.5 9.5 Lao PDR 20.5 9.4 1.1 0.0 0.0 8.3 0.2 0.8 2.9 Latvia 23.7 18.0 5.6 0.5 0.0 0.0 0.2 0.0 10.5 Lebanon 12.0 11.8 1.6 0.0 0.0 0.0 0.4 0.2 1.2 Lesotho 27.4 7.3 9.8 1.0 0.0 0.0 .. 0.1 .. Liberia –2.7 8.4 3.1 4.7 0.0 1.7 0.8 0.4 –14.0 Libya 66.8 12.0 .. 0.0 29.0 0.0 0.7 1.8 .. Lithuania 18.8 12.3 4.7 0.5 0.1 0.0 0.3 0.1 10.4 Macedonia, FYR 24.8 11.1 4.9 0.1 1.0 4.8 1.0 0.1 11.6 Madagascar .. 7.7 2.7 0.2 0.0 0.8 0.2 0.1 .. Malawi 13.0 7.5 4.4 1.8 0.0 0.0 0.2 0.1 7.5 Malaysia 34.1 12.1 4.1 0.0 6.9 0.1 0.7 0.0 18.5 Mali .. 8.4 3.9 0.0 0.0 9.8 0.1 1.7 .. Mauritania .. 8.8 3.7 0.4 0.0 33.9 0.4 0.6 .. Mauritius 16.1 11.5 3.1 0.0 0.0 0.0 0.3 0.0 7.6 Mexico 24.6 12.0 4.8 0.0 5.4 0.3 0.3 0.4 11.0 Moldova 14.7 8.7 7.7 0.1 0.1 0.0 0.6 0.9 11.9 Mongolia 29.8 10.9 5.1 0.0 17.5 14.8 1.6 2.8 –12.6 Morocco 31.5 10.5 5.2 0.0 0.0 1.6 0.4 0.1 24.1 Mozambique 11.0 7.7 4.0 0.0 3.2 0.1 0.2 0.1 3.7 Myanmar .. .. 0.8 .. .. .. .. 0.5 .. Namibia 34.2 11.1 8.0 0.0 0.0 0.7 0.2 0.1 30.1 Nepal 36.8 7.8 4.2 2.5 0.0 0.0 0.2 0.1 29.2 Netherlands 23.6 14.2 4.7 0.0 0.8 0.0 0.2 0.4 12.8 New Zealand 17.1 14.0 7.2 0.0 0.7 0.3 0.2 0.0 8.5 Nicaragua 13.4 9.3 3.0 0.7 0.0 0.9 0.6 0.1 4.9 Niger .. 3.2 3.5 1.5 0.0 0.9 0.1 1.4 .. Nigeria .. 9.9 0.9 0.3 21.7 0.0 0.5 0.7 .. Norway 35.2 14.5 6.0 0.0 10.2 0.0 0.1 0.0 16.4 Oman 39.7 13.5 4.2 0.0 28.5 0.0 0.8 0.0 –8.5 Pakistan 21.0 8.5 1.6 0.7 2.1 0.1 0.7 1.1 9.4 Panama 19.5 12.3 3.5 0.0 0.0 0.0 0.2 0.2 10.3 Papua New Guinea 20.8 9.4 .. 0.0 0.0 22.2 0.4 0.0 .. Paraguay 23.0 10.4 3.7 0.0 0.0 0.0 0.2 1.1 15.1 Peru 24.4 11.8 2.1 0.0 1.3 6.8 0.2 0.4 5.9 Philippines 27.2 9.8 2.4 0.1 0.4 1.5 0.3 0.1 17.3 Poland 17.5 12.7 4.9 0.1 1.0 0.4 0.6 0.4 7.4 Portugal 10.8 18.4 4.9 0.0 0.0 0.1 0.2 0.0 –3.1 Puerto Rico .. 20.0 .. .. 0.0 0.0 .. .. .. Qatar .. .. 1.8 .. .. .. .. 0.1 .. 2012 World Development Indicators 255 4.11 Toward a broader measure of savings Gross Consumption Education Net Energy Mineral Carbon Local Adjusted savings of �xed expenditure forest depletion depletion dioxide pollution net capital depletion damage savings % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI 2010 2010 2010 2010 2010 2010 2010 2010 2010 Romania 26.7 11.7 3.4 0.0 1.6 0.0 0.5 0.0 16.2 Russian Federation 28.6 12.4 3.5 0.0 13.2 1.1 0.9 0.1 4.5 Rwanda 15.1 8.0 4.2 3.0 0.0 0.1 0.1 0.1 7.7 Saudi Arabia 32.5 12.1 7.2 0.0 29.1 0.0 0.8 1.2 –3.6 Senegal 19.7 8.9 5.2 0.0 0.0 0.8 0.3 0.7 13.7 Serbia 15.9 11.3 5.0 .. 1.9 0.0 1.0 .. .. Sierra Leone 13.0 7.2 3.4 1.5 0.0 0.6 0.6 1.1 5.4 Singapore 47.7 14.4 3.0 0.0 0.0 0.0 0.2 0.4 35.7 Slovak Republic 20.4 12.7 3.5 0.3 0.0 0.0 0.4 0.0 10.4 Slovenia 22.4 13.3 4.9 0.2 0.1 0.0 0.3 0.1 13.3 Somalia .. .. .. .. .. .. .. 0.5 .. South Africa 16.8 13.5 5.4 0.2 3.3 2.6 0.9 0.2 1.6 South Sudan .. .. .. .. .. .. .. .. .. Spain 19.1 13.6 4.2 0.0 0.0 0.0 0.2 0.2 9.2 Sri Lanka 25.1 10.1 1.7 0.3 0.0 0.0 0.2 0.3 15.9 Sudan 19.7 10.3 0.9 0.0 12.9 0.0 0.2 0.9 –3.6 Swaziland 2.7 10.6 6.9 0.1 0.0 0.0 0.2 0.1 –1.4 Sweden 24.5 13.2 6.2 0.0 0.0 0.4 0.1 0.0 17.0 Switzerland 33.4 13.5 5.3 0.0 0.0 0.0 0.1 0.1 25.0 Syrian Arab Republic 17.4 10.6 2.6 0.0 11.7 0.1 1.0 1.3 –4.8 Tajikistan 2.6 8.6 3.2 0.0 0.4 0.4 0.5 0.2 –4.3 Tanzaniaa 21.1 7.9 2.4 0.0 0.3 2.9 0.2 0.1 12.1 Thailand 32.3 11.4 4.1 0.1 2.2 0.1 0.8 0.3 21.5 Timor-Leste .. 2.1 3.3 .. 0.0 0.0 0.1 .. .. Togo .. 8.8 4.4 2.1 0.0 1.3 0.4 0.1 .. Trinidad and Tobago 36.4 13.4 4.0 0.0 32.0 0.0 1.5 0.3 –26.0 Tunisia 21.4 11.3 6.0 0.1 4.1 0.9 0.5 0.1 10.3 Turkey 13.7 12.1 2.6 0.0 0.2 0.1 0.3 0.9 2.7 Turkmenistan .. 11.8 .. .. 35.1 0.0 2.5 0.8 .. Uganda 19.1 8.0 3.0 4.5 0.0 0.0 0.2 0.0 9.4 Ukraine 17.5 10.4 5.9 0.0 3.7 0.0 1.7 0.1 7.4 United Arab Emirates .. 13.6 .. .. 10.9 0.0 0.4 0.8 .. United Kingdom 11.9 13.6 5.1 0.0 1.3 0.0 0.2 0.0 1.8 United States 10.9 14.0 4.8 0.0 0.8 0.1 0.3 0.1 0.4 Uruguay 17.3 12.6 2.3 0.5 0.0 0.1 0.2 1.8 4.4 Uzbekistan .. 9.2 9.4 0.0 13.7 5.5 2.9 0.4 .. Venezuela, RB 31.7 12.4 3.6 0.0 12.0 0.4 0.3 0.0 10.1 Vietnam 33.2 9.4 2.8 0.2 9.0 0.2 1.0 0.5 15.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. Yemen, Rep. 9.6 9.7 4.2 0.0 14.5 0.0 0.6 0.4 –11.5 Zambia 25.4 10.3 1.3 0.0 0.0 18.9 0.1 0.3 –2.8 Zimbabwe .. 8.6 2.5 0.0 1.9 0.8 1.0 0.2 .. World 22.5 w 13.0 w 4.2 w 0.0 w 2.1 w 0.5 w 0.4 w 0.3 w 6.4 w Low income 24.6 7.7 2.9 1.2 1.4 1.2 0.3 0.5 .. Middle income 33.8 11.2 3.3 0.1 5.1 1.3 0.7 0.7 14.4 Lower middle income 29.2 9.8 3.3 0.3 6.0 1.2 0.7 0.7 13.3 Upper middle income 35.1 11.6 3.3 0.0 4.8 1.3 0.7 0.7 14.7 Low & middle income 33.7 11.1 3.3 0.1 5.0 1.3 0.7 0.7 14.5 East Asia & Pacific 48.4 10.7 2.2 0.0 3.8 1.3 1.0 1.1 31.9 Europe & Central Asia 23.9 12.2 3.6 0.0 8.6 0.8 0.8 0.4 4.5 Latin America & Carib. 20.9 12.1 4.7 0.0 3.8 1.6 0.3 0.4 7.0 Middle East & N. Africa 19.2 10.7 4.3 0.1 12.4 0.4 0.7 0.9 .. South Asia 32.7 9.2 2.8 0.6 2.4 1.1 0.8 0.7 20.1 Sub-Saharan Africa 18.0 10.9 3.6 0.5 9.4 1.9 0.5 0.5 –1.0 High income 17.6 13.9 4.6 0.0 0.7 0.2 0.2 0.1 5.3 Euro area 19.4 13.8 4.7 0.0 0.1 0.0 0.2 0.1 7.3 a. Covers mainland Tanzania only. 256 2012 World Development Indicators 4.11 ECONOMY Toward a broader measure of savings About the data De�nitions Adjusted net savings measure the change in value of of production. Natural resources give rise to rents • Gross savings are the difference between gross a specified set of assets, excluding capital gains. If because they are not produced; in contrast, for pro- national income and public and private consump- a country’s net savings are positive and the account- duced goods and services competitive forces will tion, plus net current transfers. • Consumption of ing includes a sufficiently broad range of assets, expand supply until economic profits are driven to � xed capital is the replacement value of capital economic theory suggests that the present value zero. For each type of resource and each country, unit used up in production. • Education expenditure is of social welfare is increasing. Conversely, persis- resource rents are derived by taking the difference public current operating expenditures in education, tently negative adjusted net savings indicate that an between world prices (to reflect the social oppor- including wages and salaries and excluding capi- economy is on an unsustainable path. tunity cost of resource extraction) and the average tal investments in buildings and equipment. • Net The table shows the extent to which today’s rents unit extraction or harvest costs (including a “normal� forest depletion is unit resource rents times the from a number of natural resources and changes return on capital). Unit rents are then multiplied by excess of roundwood harvest over natural growth. in human capital are balanced by net savings —that the physical quantity extracted or harvested to arrive • Energy depletion is the ratio of the value of the is, this generation’s bequest to future generations. at total rent. To estimate the value of the resource, stock of energy resources to the remaining reserve Adjusted net savings are derived from standard rents are assumed to be constant over the life of the lifetime (capped at 25 years). It covers coal, crude national accounting measures of gross savings resource (the El Serafy approach), and the present oil, and natural gas. • Mineral depletion is the ratio by making four adjustments. First, estimates of value of the rent flow is calculated using a 4 percent of the value of the stock of mineral resources to the fixed capital consumption of produced assets are social discount rate. For details on the estimation of remaining reserve lifetime (capped at 25 years). It deducted to obtain net savings. Second, current natural wealth see World Bank (2011c). covers tin, gold, lead, zinc, iron, copper, nickel, silver, public expenditures on education are added to net A positive net depletion figure for forest resources bauxite, and phosphate. • Carbon dioxide damage is savings (in standard national accounting these implies that the harvest rate exceeds the rate of estimated at $20 per ton of carbon (the unit damage expenditures are treated as consumption). Third, natural growth; this is not the same as deforesta- in 1995 U.S. dollars) times tons of carbon emitted. estimates of the depletion of a variety of natural tion, which represents a change in land use (see • Local pollution damage is the willingness to pay resources are deducted to reflect the decline in asset De�nitions for table 3.4). In principle, there should to avoid illness and death attributable to particulate values associated with their extraction and harvest. be an addition to savings in countries where growth emissions.• Adjusted net savings are net savings And fourth, deductions are made for damages from exceeds harvest, but empirical estimates suggest plus education expenditure minus energy depletion, carbon dioxide emissions and local pollution. that most of this net growth is in forested areas that mineral depletion, net forest depletion, and carbon The exercise treats public education expenditures cannot currently be exploited economically. Because dioxide and particulate emissions damage. as an addition to savings. However, because of the the depletion estimates reflect only timber values, 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 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. tion should also consider private education expen- The unit damage figure represents the present value Data sources diture, but data are not available for a large number of global damage to economic assets and to human of countries. welfare over the time the unit of pollution remains Data on gross savings are from World Bank While extensive, the accounting of natural resource in the atmosphere. national accounts data files (see table 4.8). depletion and pollution costs still has some gaps. Local pollution damage is estimated by valuing the Data on consumption of fi xed capital are from Key estimates missing on the resource side include human health effects from exposure to particulate the United Nations Statistics Division’s National the value of fossil water extracted from aquifers, net matter pollution in urban areas. The estimates are Accounts Statistics: Main Aggregates and Detailed depletion of fish stocks, and depletion and degrada- calculated as willingness to pay to avoid illness and Tables, extrapolated to 2010. Data on education tion of soils. Important pollutants affecting human death from cardiopulmonary disease and lung cancer expenditure are from the United Nations Educa- health and economic assets are excluded because no in adults and acute respiratory infections in children tional, Scientific, and Cultural Organization Insti- internationally comparable data are widely available that is attributable to particulate emissions. tute for Statistics online database; missing data on damage from ground-level ozone or sulfur oxides. Adjusted net savings aims to be as comprehensive are estimated by World Bank staff. Data on for- Estimates of resource depletion are based on the a measure as possible to provide a better under- est, energy, and mineral depletion are estimates “change in real wealth� method described in Hamil- standing of the rate of county wealth creation or based on sources and methods in World Bank ton and Ruta (2008), which estimates depletion as depletion. To do so, it treats education as investment (2011c). Data on carbon dioxide damage are the ratio between the total value of the resource and accounts for pollution damages to assets and from Fankhauser (1995). Data on local pollution and the remaining reserve lifetime. The total value human welfare, which goes outside the boundaries damage are from Pandey and others (2006c). The of the resource is the present value of current and of the United Nations System of National Accounts. conceptual underpinnings of the savings measure future rents from resource extractions. An economic For a detailed note on methodology, see http:// appear in Hamilton and Clemens (1999). rent represents an excess return to a given factor data.worldbank.org. 2012 World Development Indicators 257 4.12 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or de�cit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2010 2010 Afghanistanb .. 10.1 .. 46.1 .. 1.4 .. 0.2 .. 0.7 .. 0.0 Albaniab 23.4 .. 24.3 .. –6.7 .. 3.1 .. 2.6 .. .. .. Algeria .. 37.3 .. 25.5 .. –4.5 .. 6.0 .. 0.0 .. 1.0 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 14.1 .. 19.7 .. –5.7 .. 1.9 .. 2.0 .. .. .. Armeniab 17.7 22.4 16.4 22.7 –0.7 –4.9 0.1 0.8 2.4 2.8 .. 3.7 Australia 25.8 24.6 24.0 26.6 2.0 –2.4 .. .. .. .. 24.1 3.7 Austria 37.7 36.6 40.4 39.6 –2.0 –2.6 .. .. .. .. 70.5 7.0 Azerbaijanb .. 25.8 .. 14.6 .. 0.3 .. 0.0 .. 0.2 .. 0.3 Bahrain 32.9 28.8 22.6 18.8 8.5 4.0 4.8 .. 0.7 .. 19.2 3.2 Bangladeshb 9.8 11.1 9.0 11.3 –0.7 –1.7 3.3 3.1 1.1 0.4 .. 21.7 Belarusb 28.7 31.7 25.3 31.6 0.1 –1.5 0.3 –0.2 –0.5 2.8 18.7 1.8 Belgium 42.8 40.7 42.9 44.1 0.0 –3.3 –8.2 1.8 8.9 1.7 91.8 7.8 Beninb 16.5 18.2 11.9 15.0 0.7 –1.0 –3.3 –0.3 3.1 2.0 .. 2.7 Bolivia 18.4 23.3 28.8 21.8 –8.7 1.2 2.4 –0.2 4.2 –0.1 .. 8.0 Bosnia and Herzegovina 37.1 39.7 35.6 40.6 0.7 –2.3 1.0 –1.4 0.5 3.3 .. 1.1 Botswanab .. .. .. .. .. .. .. .. .. .. .. .. Brazilb 19.9 23.1 21.7 25.6 –1.8 –3.5 .. 8.3 .. –0.1 61.0 20.7 Bulgariab 32.9 32.4 31.6 31.7 –0.4 –0.1 0.0 –0.4 –1.6 0.5 .. 2.2 Burkina Faso 11.4 15.6 10.9 12.1 –4.5 –5.7 0.6 2.6 4.1 3.4 .. 2.4 Burundib 15.8 .. 20.6 .. –2.4 .. 3.3 .. 2.9 .. .. .. Cambodia 10.3 12.2 9.4 11.3 –3.4 –3.7 –0.2 1.1 3.6 2.1 .. 1.4 Cameroonb 14.1 .. 12.0 .. 0.1 .. .. .. .. .. .. .. Canadab 20.9 17.2 18.7 19.2 2.2 –2.0 .. .. .. .. 52.6 9.8 Central African Republicb .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 21.6 22.0 21.0 21.0 –0.7 –0.4 –0.4 1.8 –0.3 0.7 .. 2.1 Chinab 7.1 11.9 10.7 .. –2.6 .. 4.2 2.0 0.0 0.0 .. .. Hong Kong SAR, China 14.9 20.7 21.3 19.2 –6.8 1.1 1.6 2.0 .. –0.1 34.0 0.3 Colombia 15.1 18.2 19.6 18.3 –5.8 –3.5 3.5 8.2 4.3 0.0 62.9 15.3 Congo, Dem. Rep.b 3.7 23.4 9.1 13.7 –4.2 3.8 4.1 –4.7 –0.1 5.5 .. 1.3 Congo, Rep.b 28.6 .. 19.9 .. 1.9 .. .. .. .. .. .. .. Costa Rica .. 24.7 .. 26.0 .. –3.4 .. .. .. .. .. 8.8 Côte d’Ivoire 16.9 18.9 17.4 17.8 –2.5 0.9 2.2 .. 1.1 .. .. 7.1 Croatiab 35.7 32.9 39.1 36.6 –5.3 –4.3 0.5 3.9 3.9 1.3 .. 5.9 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Cyprus .. 39.7 61.7 42.7 .. –5.9 .. .. .. .. 97.3 7.7 Czech Republicb 30.5 29.6 33.4 36.7 –3.6 –4.9 2.5 2.0 0.0 2.6 36.2 3.3 Denmark 36.2 39.8 34.8 42.2 1.6 –2.1 .. .. .. .. 40.8 4.9 Dominican Republic .. 14.5 .. 15.6 .. –3.6 .. 1.9 .. 2.2 .. 12.9 Ecuador b .. .. .. .. .. .. .. .. .. .. .. .. Egypt, Arab Rep.b 24.3 24.8 27.2 28.9 –6.7 –7.7 14.4 9.2 1.7 0.2 85.8 20.5 El Salvador 16.0 19.2 17.9 21.0 –4.7 –2.7 –2.1 –0.8 9.4 2.0 50.0 11.7 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 31.0 36.8 29.5 36.5 0.2 –1.3 .. .. .. .. 9.0 0.6 Ethiopiab 11.9 .. 14.6 .. –4.2 .. 0.8 .. 3.4 .. .. .. Finland 40.9 38.8 34.9 34.8 6.7 4.6 –5.2 –0.2 0.9 –0.6 36.0 3.2 France 42.9 40.9 44.9 48.1 –1.7 –7.3 .. .. .. .. 83.5 5.4 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Theb .. .. .. .. .. .. .. .. .. .. .. .. Georgiab 10.4 23.8 11.6 26.3 –1.6 –4.4 1.9 0.6 –0.8 5.5 36.7 3.7 Germany 30.6 29.7 32.0 32.0 1.4 –2.2 –0.8 3.1 0.0 –0.2 47.6 5.5 Ghanab 18.1 15.4 18.7 18.0 –6.5 –5.6 –0.3 2.8 5.0 2.6 .. 15.2 Greece 41.9 37.1 44.7 52.0 –3.8 –15.6 .. .. .. .. 142.0 14.3 Guatemalab 10.2 10.9 10.9 12.4 –1.8 –3.1 0.5 1.4 1.2 1.3 23.0 12.6 Guineab 12.0 .. 13.5 .. –2.4 .. 0.2 .. 2.4 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 258 2012 World Development Indicators 4.12 ECONOMY Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or de�cit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2010 2010 Honduras 20.0 21.1 20.9 23.4 –3.0 –3.1 1.7 0.4 2.0 2.6 .. 3.9 Hungary 38.6 41.3 42.0 46.2 –2.8 –4.1 –1.7 –1.9 2.8 6.0 83.2 10.6 Indiab 11.9 11.4 15.7 15.0 –3.9 –3.7 5.1 4.2 0.4 0.3 46.1 27.1 Indonesiab 18.3 15.1 16.2 14.4 –3.7 –0.6 .. 1.0 1.4 0.1 26.1 9.4 Iran, Islamic Rep.b 23.4 31.9 16.9 24.7 1.8 0.6 1.2 .. 0.0 .. .. 0.6 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 33.0 31.0 27.6 44.2 4.9 –14.1 .. .. .. .. 70.5 6.9 Israel 40.7 35.3 47.1 41.1 –3.5 –4.0 .. .. .. .. .. 12.7 Italy 36.9 38.4 38.9 43.8 –0.7 –4.9 .. .. .. .. 118.4 11.1 Jamaica 31.9 25.9 34.5 39.8 –2.4 –15.3 .. 7.1 .. 4.6 115.8 64.5 Japan .. .. .. .. .. .. .. .. .. .. 174.4 .. Jordanb 25.1 21.8 27.1 25.9 –2.0 –5.4 1.8 3.3 –1.7 2.8 59.0 8.5 Kazakhstanb 11.3 9.9 13.7 16.2 0.1 –1.1 –0.7 1.7 1.2 1.0 10.2 2.6 Kenyab 19.7 20.3 16.8 22.4 2.0 –5.9 0.1 4.9 1.2 1.9 .. 10.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.b 22.3 22.7 16.6 19.9 4.4 1.7 –0.9 1.4 –0.1 –0.1 .. 5.0 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwaitb 48.3 55.5 39.4 32.6 4.9 18.7 .. .. .. .. .. 0.0 Kyrgyz Republicb 14.2 20.2 15.8 22.0 –2.9 –5.0 .. –0.1 .. 3.1 .. 3.5 Lao PDR .. 14.2 .. 10.8 .. –0.8 .. 0.2 .. 1.7 .. 3.3 Latviab 26.1 25.0 28.1 35.6 –2.2 –6.8 1.5 0.5 –0.2 5.9 49.9 4.5 Lebanon 16.0 22.2 30.5 29.2 –18.4 –8.2 13.3 11.6 9.3 0.3 .. 48.7 Lesothob 50.7 66.2 43.7 51.9 –2.8 5.7 0.0 –0.4 9.0 1.6 .. 1.3 Liberiab .. 0.4 .. 0.3 .. 0.0 .. 0.0 .. 0.0 .. 2.1 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 25.9 27.4 26.9 37.5 –2.8 –7.2 0.7 1.0 2.0 8.6 43.2 6.1 Macedonia, FYRb .. 32.9 .. 30.3 .. –0.8 .. –0.6 .. 0.2 .. 1.9 Madagascar 11.7 14.2 10.6 11.8 –2.0 –1.9 1.3 0.6 1.7 3.0 .. 3.9 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiab 17.5 20.8 16.5 19.6 –4.1 –5.4 1.6 0.5 2.1 4.8 53.1 9.8 Mali 13.4 17.1 11.6 14.7 –3.4 –2.1 –1.0 –4.4 3.0 2.6 .. 1.7 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. 22.7 .. 22.7 .. –2.4 .. –0.2 .. 1.8 37.8 10.6 Mexicob 14.7 .. 15.4 .. –1.2 .. .. .. –0.7 .. .. .. Moldovab 24.5 31.6 28.9 35.0 –1.5 –2.6 1.5 0.4 –0.2 2.9 26.3 2.2 Mongoliab 24.4 33.7 22.4 26.6 0.2 3.0 1.3 1.2 4.3 –0.9 45.0 1.4 Moroccob 30.0 31.8 30.4 30.6 –3.1 –2.6 1.7 2.3 –2.8 2.1 50.3 3.8 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar b 5.3 .. 3.1 .. –2.7 .. 2.7 .. 0.0 .. .. .. Namibiab 30.1 29.2 28.5 24.1 –1.6 2.0 1.0 –0.8 0.7 –0.1 .. 6.3 Nepalb 10.6 15.2 .. .. .. .. 1.0 2.0 2.1 0.1 43.8 4.6 Netherlands 40.7 40.9 39.3 45.5 2.0 –4.8 .. .. .. .. 58.2 4.6 New Zealand 33.7 36.1 32.1 32.1 1.7 3.1 1.4 .. –1.0 .. 37.9 3.4 Nicaraguab 15.1 19.7 16.5 20.0 –3.6 –1.0 .. .. .. .. .. 6.5 Niger .. 13.5 .. 11.6 .. –0.9 .. –1.9 .. 2.4 .. 1.8 Nigeriab .. 9.7 .. 7.2 .. –1.7 .. 0.1 .. .. 3.0 6.6 Norway 48.4 47.7 32.6 35.7 15.7 11.7 0.0 –2.5 7.1 4.7 35.4 1.8 Omanb 23.9 .. 26.2 .. –4.4 .. –1.3 .. –0.7 .. .. .. Pakistanb 13.9 13.8 17.2 17.5 –4.1 –5.0 .. .. .. .. .. 37.4 Panamab 23.1 .. 22.1 .. –0.8 .. .. .. .. .. .. .. Papua New Guineab 24.2 .. 30.0 .. –1.9 .. 1.6 .. 1.8 .. .. .. Paraguay b 17.0 18.1 17.5 15.1 –3.9 1.4 2.7 0.1 0.9 0.3 .. 2.1 Perub 17.4 18.5 17.9 16.4 –2.1 0.3 0.6 1.6 2.3 –0.6 21.6 6.0 Philippinesb 14.2 13.4 16.3 16.9 –3.7 –3.5 1.3 2.5 2.4 1.5 .. 25.1 Poland 31.5 30.0 35.6 35.7 –2.8 –6.1 4.9 1.6 –1.7 3.6 48.1 8.1 Portugal 34.6 34.5 36.7 43.0 –2.6 –8.7 –0.2 3.3 2.0 5.8 84.0 7.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar b .. 47.2 .. 19.3 .. 15.2 .. .. .. .. .. 3.6 2012 World Development Indicators 259 4.12 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or de�cit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2010 2010 Romania 25.8 30.9 25.9 33.8 –2.0 –4.6 0.4 2.4 1.7 0.9 .. 2.0 Russian Federation 31.8 26.8 22.6 28.2 7.0 –1.9 –4.1 1.4 –1.9 0.2 9.4 1.6 Rwandab .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegalb 16.9 .. 12.8 .. –0.9 .. 0.3 .. 0.5 .. .. .. Serbiab .. 37.5 .. 39.8 .. –3.9 .. 2.3 .. 1.1 38.7 2.9 Sierra Leoneb 11.4 11.8 28.7 22.9 –9.3 –3.2 4.8 .. .. .. .. 8.3 Singaporeb 26.2 18.1 16.0 13.4 11.2 8.0 7.8 10.3 .. .. 109.2 0.0 Slovak Republic 35.3 28.6 39.4 37.7 –3.2 –7.3 2.9 2.9 –0.2 3.0 38.2 4.7 Sloveniab 38.9 36.9 38.8 42.7 –1.1 –5.5 –0.4 3.7 1.6 –1.4 .. 4.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 26.3 28.4 27.9 33.2 –2.0 –4.9 1.6 7.0 0.3 1.0 .. 8.4 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 31.1 24.9 31.3 30.5 –0.5 –5.2 .. 2.4 .. 2.2 47.8 5.8 Sri Lankab 16.8 14.9 23.0 19.2 –8.4 –6.6 9.5 6.9 0.0 –0.1 85.0 31.0 Sudanb 8.0 .. 7.6 .. –0.4 .. 1.0 .. .. .. .. .. Swazilandb 25.6 .. 22.1 .. –0.8 .. .. .. .. .. .. .. Sweden 42.2 34.8 .. .. .. .. .. .. .. .. 44.2 .. Switzerlandb 24.3 18.3 25.5 17.0 2.2 1.3 –2.8 2.0 .. .. 28.8 3.5 Syrian Arab Republicb 24.0 .. .. .. .. .. .. .. .. .. .. .. Tajikistanb 10.6 .. 9.0 .. –0.8 .. –0.5 .. 0.5 .. .. .. Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand 19.5 20.3 15.9 18.6 1.5 –0.6 0.9 2.6 –0.6 –0.1 28.8 6.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. 17.6 .. 14.5 .. 0.6 .. –2.7 .. 1.9 .. 4.5 Trinidad and Tobagob 27.1 33.5 23.9 32.3 2.0 –4.8 .. –0.4 .. 0.3 21.4 8.4 Tunisiab 26.5 29.0 25.0 27.0 –2.4 –1.3 0.5 –0.6 –0.2 –0.4 40.5 6.2 Turkey b .. 24.4 .. 25.4 .. –2.2 .. 2.8 .. 0.9 50.5 18.3 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandab 10.8 12.6 15.5 13.9 –1.9 –0.9 0.6 1.6 2.0 1.9 33.1 7.7 Ukraineb 26.8 34.6 26.9 40.7 –0.6 –5.6 1.5 6.8 –0.3 4.9 .. 3.1 United Arab Emiratesb 6.5 .. 6.2 .. 0.1 .. .. .. .. .. .. .. United Kingdom 37.1 36.0 36.2 46.4 1.6 –10.9 .. .. .. .. 73.3 5.3 United States 20.1 16.9 19.6 26.8 0.5 –10.0 0.9 6.5 1.9 5.1 76.1 11.4 Uruguay b 24.7 30.9 26.6 30.3 –3.0 –0.9 –7.4 –0.8 2.7 –0.4 45.6 7.9 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBb 21.2 .. 21.6 .. –1.2 .. 3.9 .. –0.5 .. .. .. Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. 1.1 Yemen, Rep.b 23.4 .. 21.6 .. –2.3 .. .. .. .. .. .. .. Zambiab 19.7 17.4 15.9 17.2 1.8 –1.5 .. .. 4.6 .. .. 9.0 Zimbabweb 29.1 .. 33.2 .. –5.2 .. –0.6 .. –0.1 .. .. .. World 25.8 m 23.8 m 26.7 m 31.1 m –0.2 m –7.0 m .. m .. m .. m .. m .. m 5.5 m Low income 10.4 .. 11.0 .. –1.7 .. .. .. .. .. .. .. Middle income 14.6 18.8 18.2 .. –2.3 .. .. 2.8 1.0 1.2 .. 7.0 Lower middle income 13.8 14.9 18.7 17.6 –4.0 –3.6 .. .. .. .. .. 6.0 Upper middle income 14.9 19.7 18.1 .. –1.8 .. 0.9 3.0 0.8 0.9 .. 7.0 Low & middle income 14.5 18.6 18.1 .. –2.3 .. .. 0.7 .. 0.4 .. 5.8 East Asia & Pacific 8.2 13.5 11.6 .. –2.6 .. 2.7 1.1 2.4 1.5 .. 6.1 Europe & Central Asia .. 25.6 .. 27.3 .. –2.2 0.7 0.7 0.4 2.9 .. 3.2 Latin America & Carib. 18.1 .. 19.2 .. –1.5 .. 1.6 2.0 2.0 1.2 .. 9.3 Middle East & N. Africa .. 30.5 .. 27.2 .. –3.0 2.6 6.8 0.7 0.1 .. 6.9 South Asia 12.3 11.7 16.1 15.5 –4.0 –3.8 5.1 2.0 0.4 0.3 56.5 15.9 Sub-Saharan Africa .. 24.3 .. 24.2 .. –1.0 .. .. .. .. .. .. High income 27.5 24.9 27.3 32.2 0.1 –7.5 .. .. .. .. 58.2 5.4 Euro area 36.3 34.7 37.5 40.0 0.1 –5.2 .. .. .. .. 70.5 6.5 a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework. 260 2012 World Development Indicators 4.12 ECONOMY Central government finances About the data De�nitions Tables 4.12–4.14 present an overview of the size data documentation. Because budgetary accounts • Revenue is cash receipts from taxes, social con- and role of central governments relative to national may not include all central government units (such tributions, and other revenues such as fines, fees, economies. The tables are based on the concepts as social security funds), they usually provide an rent, and income from property or sales. Grants, usu- and recommendations of the second edition of the incomplete picture. ally considered revenue, are excluded. • Expense is International Monetary Fund’s (IMF) Government Data on government revenue and expense are col- cash payments for government operating activities in Finance Statistics Manual 2001. Before 2005 World lected by the IMF through questionnaires to mem- providing goods and services. It includes compensa- Development Indicators reported data derived on the ber countries and by the Organisation for Economic tion of employees, interest and subsidies, grants, basis of the 1986 manual’s cash-based method. The Co- operation and Development. Despite IMF efforts social benefi ts, and other expenses such as rent 2001 manual, harmonized with the 1993 United to standardize data collection, statistics are often and dividends. • Cash surplus or de�cit is revenue Nations System of National Accounts, recommends incomplete, untimely, and not comparable across (including grants) minus expense, minus net acquisi- an accrual accounting method, focusing on all eco- countries. tion of nonfinancial assets. In editions before 2005 nomic events affecting assets, liabilities, revenues, Government fi nance statistics are reported in nonfinancial assets were included under revenue and expenses, not only those represented by cash local currency. The indicators here are shown as and expenditure in gross terms. This cash surplus transactions. It takes all stocks into account, so percentages of GDP. Many countries report govern- or deficit is close to the earlier overall budget balance that stock data at the end of an accounting period ment finance data by fiscal year; see Primary data (still missing is lending minus repayments, which are equal stock data at the beginning of the period plus documentation for information on fiscal year end by included as a financing item under net acquisition flows over the period. The 1986 manual considered country. of financial assets). • Net incurrence of liabilities only the debt stock data. Further, the new manual no is domestic financing (obtained from residents) and longer distinguishes between current and capital rev- foreign financing (obtained from nonresidents), or enue or expenditures, and it introduces the concepts the means by which a government provides financial of nonfinancial and financial assets. Most countries resources to cover a budget deficit or allocates finan- still follow the 1986 manual, however. The IMF has cial resources arising from a budget surplus. The net reclassified historical Government Finance Statistics incurrence of liabilities should be offset by the net Yearbook data to conform to the 2001 manual’s for- acquisition of financial assets (a third financing item). mat. Because of reporting differences, the reclas- The difference between the cash surplus or deficit sified data understate both revenue and expense. and the three financing items is the net change in The 2001 manual describes government’s eco- the stock of cash. • Total debt is the entire stock of nomic functions as the provision of goods and ser- direct government fixed-term contractual obligations vices on a nonmarket basis for collective or individual to others outstanding on a particular date. It includes consumption, and the redistribution of income and domestic and foreign liabilities such as currency and wealth through transfer payments. Government money deposits, securities other than shares, and activities are financed mainly by taxation and other loans. It is the gross amount of government liabili- income transfers, though other financing such as ties reduced by the amount of equity and financial borrowing for temporary periods can also be used. derivatives held by the government. Because debt Government excludes public corporations and quasi is a stock rather than a flow, it is measured as of corporations (such as the central bank). a given date, usually the last day of the fiscal year. Units of government at many levels meet this defini- • Interest payments are interest payments on gov- tion, from local administrative units to the national ernment debt—including long-term bonds, long-term government, but inadequate statistical coverage pre- loans, and other debt instruments —to domestic and cludes presenting subnational data. Although data foreign residents. for general government under the 2001 manual are available for a few countries, only data for the cen- Data sources tral government are shown to minimize disparities. Still, different accounting concepts of central govern- Data on central government finances are from the ment make cross-country comparisons potentially IMF’s Government Finance Statistics database. misleading. Each country’s accounts are reported using the Central government can refer to consolidated or system of common definitions and classifications budgetary accounting. For most countries central in the IMF’s Government Finance Statistics Manual government finance data have been consolidated 2001. See these sources for complete and author- into one account, but for others only budgetary itative explanations of concepts, definitions, and central government accounts are available. Coun- data sources. tries reporting budgetary data are noted in Primary 2012 World Development Indicators 261 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistana .. 75 .. 23 .. 0 .. 2 .. 0 Albaniaa 15 .. 31 .. 16 .. 36 .. 3 .. Algeria .. 11 .. 34 .. 1 .. 45 .. 8 Angola .. .. .. .. .. .. .. .. .. .. Argentina 4 .. 11 .. 34 .. 43 .. 7 .. Armeniaa 53 13 5 24 4 4 34 39 4 20 Australia 11 10 11 10 7 3 67 73 6 6 Austria 5 6 13 14 9 7 70 71 5 5 Azerbaijana .. 9 .. 12 .. 1 .. 18 .. 61 Bahrain 8 26 58 54 7 5 8 10 .. 5 Bangladesha 14 12 27 19 18 22 26 35 14 12 Belarusa 14 10 11 12 3 2 68 69 4 7 Belgium 3 3 7 7 14 7 74 82 3 3 Benina 33 16 38 48 6 4 2 30 21 2 Bolivia 16 14 25 22 8 10 45 47 6 7 Bosnia and Herzegovina 26 24 31 28 2 1 38 42 3 5 Botswanaa .. .. .. .. .. .. .. .. .. .. Brazila 18 13 21 19 17 19 45 49 0 0 Bulgariaa 24 9 9 19 12 2 53 64 2 6 Burkina Faso 25 17 41 46 7 4 8 12 19 21 Burundia .. .. .. .. .. .. .. .. 14 .. Cambodia 35 29 37 39 2 2 15 19 11 11 Cameroona 23 .. 37 .. 22 .. 17 .. .. .. Canadaa 8 8 11 12 22 9 59 69 1 4 Central African Republica .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile 9 10 21 20 6 2 59 51 10 19 Chinaa .. .. .. .. 5 .. 65 .. 0 .. Hong Kong SAR, China 23 28 26 23 0 0 27 19 26 33 Colombia 7 1 21 11 20 15 3 61 1 16 Congo, Dem. Rep.a 56 19 27 43 6 3 1 34 16 1 Congo, Rep.a 26 .. 28 .. 35 .. 11 .. 1 .. Costa Rica .. 11 .. 46 .. 8 .. 21 .. 14 Côte d’Ivoire 30 29 39 38 16 9 16 16 .. 7 Croatiaa 24 8 26 26 4 5 43 55 3 6 Cuba .. .. .. .. .. .. .. .. .. .. Cyprus 11 12 36 35 15 7 30 33 1 1 Czech Republica 7 6 10 8 3 3 74 74 7 10 Denmark 9 9 14 13 10 5 24 17 2 2 Dominican Republic .. 17 .. 36 .. 12 .. 28 .. 7 Ecuador a .. .. .. .. .. .. .. .. .. .. Egypt, Arab Rep.a 8 8 30 25 20 18 24 42 18 8 El Salvador 15 17 43 38 11 11 3 24 27 12 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia 19 13 26 21 0 1 42 48 3 4 Ethiopiaa 22 .. 23 .. 11 .. 44 .. 0 .. Finland 9 10 11 10 8 4 68 71 8 8 France 7 6 23 21 6 5 60 54 6 2 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, Thea .. .. .. .. .. .. .. .. .. .. Georgiaa 17 16 11 18 24 4 48 51 .. 11 Germany 4 5 6 5 7 5 81 81 4 4 Ghanaa 14 16 36 40 39 16 4 28 .. 12 Greece 13 12 22 24 17 10 42 50 10 7 Guatemalaa 15 15 31 29 10 11 22 33 21 12 Guineaa 15 .. 30 .. 32 .. 12 .. 1 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 262 2012 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 13 14 51 56 7 4 18 6 12 21 Hungary 9 10 13 13 13 10 57 63 13 8 Indiaa 12 10 10 9 30 21 43 60 7 1 Indonesiaa 11 11 11 16 24 10 53 55 0 9 Iran, Islamic Rep.a 18 11 57 40 0 1 24 34 1 14 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 6 10 14 23 7 5 29 40 1 1 Israel 24 26 23 24 17 11 30 31 7 9 Italy 4 4 16 15 16 10 61 66 5 6 Jamaica 16 6 33 14 47 43 2 6 2 31 Japan .. .. .. .. .. .. .. .. .. .. Jordana 6 9 70 52 13 8 8 29 3 2 Kazakhstana 25 22 9 7 10 3 55 66 1 2 Kenyaa 21 14 55 38 18 10 3 37 2 1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 14 11 11 10 7 6 53 58 15 15 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait a 23 15 32 24 1 0 32 30 11 19 Kyrgyz Republica 40 25 37 28 9 4 14 42 .. 2 Lao PDR .. 29 .. 43 .. 6 .. 13 .. 9 Latviaa 13 9 12 12 3 4 67 72 4 3 Lebanon 3 3 24 21 50 38 21 36 3 2 Lesothoa 30 42 38 35 8 2 21 14 .. 6 Liberiaa .. 37 .. 36 .. 2 .. 24 .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania 16 10 21 15 6 5 55 68 2 6 Macedonia, FYRa .. 28 .. 17 .. 2 .. 49 .. 4 Madagascar 18 15 41 40 13 7 10 25 19 14 Malawi .. .. .. .. .. .. .. .. .. .. Malaysiaa 15 16 28 31 15 10 48 42 3 1 Mali 38 31 37 34 8 2 0 15 17 17 Mauritania .. .. .. .. .. .. .. .. .. .. Mauritius .. 12 .. 35 .. 11 .. 31 .. 10 Mexicoa 8 .. 17 .. 13 .. .. .. .. .. Moldovaa 10 20 10 14 22 2 55 59 3 5 Mongoliaa 23 20 13 29 7 2 56 42 2 7 Moroccoa 13 9 42 40 13 4 29 33 3 14 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar a .. .. .. .. .. .. .. .. .. .. Namibiaa 21 20 51 45 7 8 10 13 11 14 Nepala .. .. .. .. .. .. .. .. .. .. Netherlands 7 8 8 7 8 4 76 79 4 4 New Zealand 31 30 25 25 6 4 37 38 4 7 Nicaraguaa 17 13 23 38 13 7 35 37 12 5 Niger .. 30 .. 30 .. 3 .. 9 .. 28 Nigeriaa .. 15 .. 24 .. 9 .. 53 .. .. Norway 10 11 11 16 3 2 74 68 4 5 Omana 50 .. 34 .. 5 .. 11 .. 0 .. Pakistana 48 22 4 4 36 31 3 25 .. 17 Panamaa 15 .. 37 .. 22 .. 26 .. 1 .. Papua New Guineaa 37 .. 25 .. 14 .. 24 .. .. .. Paraguaya 8 10 54 53 7 3 31 27 1 7 Perua 23 22 22 18 13 7 37 48 5 6 Philippinesa 26 27 31 31 24 20 19 20 2 3 Poland 7 5 11 12 8 7 70 71 8 7 Portugal 9 7 32 24 8 6 46 51 7 1 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar a .. 28 .. 28 .. 7 .. 16 .. 22 2012 World Development Indicators 263 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 22 13 16 19 8 2 43 60 12 8 Russian Federation 19 13 17 16 9 2 54 67 1 9 Rwandaa .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegala 26 .. 41 .. 11 .. 19 .. .. .. Serbiaa .. 13 .. 25 .. 3 .. 58 .. 1 Sierra Leonea 15 24 23 28 22 7 5 23 34 18 Singaporea 33 36 29 30 2 0 36 0 .. .. Slovak Republic 11 7 12 12 6 4 64 68 12 14 Sloveniaa 16 13 18 19 4 4 59 62 3 3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 11 13 16 13 18 7 53 63 2 4 South Sudan .. .. .. .. .. .. .. .. .. .. Spain 4 4 11 8 9 5 44 81 2 5 Sri Lankaa 22 14 24 28 25 25 21 23 9 10 Sudana 41 .. 41 .. 10 .. 8 .. .. .. Swazilanda 26 .. 45 .. 2 .. 27 .. 21 .. Sweden .. .. .. .. .. .. .. .. .. .. Switzerlanda 23 6 5 6 3 4 67 83 2 3 Syrian Arab Republica .. .. .. .. .. .. .. .. .. .. Tajikistana 31 .. 22 .. 4 .. 43 .. 0 .. Tanzania .. .. .. .. .. .. .. .. .. .. Thailand 27 32 35 40 7 7 24 21 6 3 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo .. 26 .. 36 .. 7 .. 26 .. 4 Trinidad and Tobagoa 15 19 37 26 18 9 29 45 1 2 Tunisiaa 9 6 40 36 12 7 36 39 5 12 Turkeya .. 10 .. 25 .. 18 .. 44 .. 6 Turkmenistan .. .. .. .. .. .. .. .. .. .. Ugandaa 55 31 12 14 5 9 27 45 .. 1 Ukrainea 19 12 15 13 9 3 56 70 1 2 United Arab Emiratesa 46 .. 36 .. .. .. .. .. .. .. United Kingdom 17 18 14 14 7 4 55 53 9 12 United States 13 15 12 12 13 7 62 64 2 4 Uruguaya 12 13 15 23 7 8 66 46 0 10 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 6 .. 21 .. 12 .. 59 .. 2 .. Vietnam .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 12 .. 67 .. 1 .. 18 .. 1 Yemen, Rep.a 17 .. 49 .. 11 .. 21 .. .. .. Zambiaa 35 24 39 43 7 10 3 15 16 8 Zimbabwea 13 .. 38 .. 21 .. 28 .. .. .. World 15 m 12 m 21 m 23 m 8m 5m 43 m 45 m 6m 6m Low income .. .. .. .. .. .. .. .. .. .. Middle income 15 13 22 26 12 7 41 44 4 7 Lower middle income 15 15 29 31 15 8 29 33 .. 8 Upper middle income 13 11 21 22 9 7 48 46 5 6 Low & middle income 17 14 27 28 10 6 34 36 .. 7 East Asia & Pacific 23 27 31 31 10 7 44 21 0 7 Europe & Central Asia 19 13 17 17 6 3 50 58 5 6 Latin America & Carib. 13 13 23 29 13 9 43 33 4 12 Middle East & N. Africa 8 9 41 36 12 7 .. 36 .. 9 South Asia 22 22 10 9 30 21 26 25 7 1 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. High income 9 10 13 15 7 5 58 57 4 5 Euro area 7 7 14 17 8 5 60 64 4 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. 264 2012 World Development Indicators 4.13 ECONOMY Central government expenses About the data De�nitions The term expense has replaced expenditure in the • Goods and services are all government payments table since the 2005 edition of World Development in exchange for goods and services used for the Indicators in accordance with use in the International production of market and nonmarket goods and ser- Monetary Fund’s (IMF) Government Finance Statis- vices. Own-account capital formation is excluded. tics Manual 2001. Government expenses include all • Compensation of employees is all payments in nonrepayable payments, whether current or capital, cash, as well as in kind (such as food and hous- requited or unrequited. The concept of total central ing), to employees in return for services rendered, government expense as presented in the IMF’s Gov- 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 to households are shown as subsidies and other transfers, and other expenses. The economic clas- sification can be problematic. For example, subsidies to public corporations or banks may be disguised as capital financing or hidden in special contractual pricing for goods and services. For further discussion of government finance statistics, see About the data for tables 4.12 and 4.14. Data sources Data on central government expenses are from the IMF’s Government Finance Statistics database. Each country’s accounts are reported using the system of common definitions and classifications in the IMF’s Government Finance Statistics Manual 2001. See these sources for complete and author- itative explanations of concepts, definitions, and data sources. 2012 World Development Indicators 265 4.14 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and pro�ts, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistana .. 3 .. 3 .. 5 .. 0 .. 0 .. 89 Albaniaa 14 .. 43 .. 9 .. 1 .. 17 .. 17 .. Algeria .. 60 .. 28 .. 4 .. 1 .. .. .. 6 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 13 .. 28 .. 14 .. 15 .. 20 .. 11 .. Armeniaa 10 19 43 43 3 4 10 8 13 13 20 15 Australia 67 65 18 23 2 2 2 0 .. .. 11 10 Austria 24 23 24 23 0 0 4 5 41 42 7 7 Azerbaijana .. 33 .. 23 .. 4 .. 1 .. .. .. 39 Bahrain 11 19 25 29 31 24 4 3 .. .. 28 24 Bangladesha 3 1 2 0 6 4 2 .. .. .. 87 95 Belarusa 11 7 39 32 5 11 3 3 35 37 7 9 Belgium 37 35 24 25 .. .. 2 1 34 36 2 4 Benina 20 16 35 38 21 22 7 6 .. 2 18 15 Bolivia 7 10 41 43 4 3 8 9 9 7 32 28 Bosnia and Herzegovina 2 6 28 45 18 0 5 0 34 39 13 10 Botswanaa .. .. .. .. .. .. .. .. .. .. .. .. Brazila 25 30 34 33 4 2 8 2 25 26 5 6 Bulgariaa 11 16 38 45 2 1 1 0 27 23 20 16 Burkina Faso 18 15 37 36 13 11 2 2 .. .. 30 36 Burundia 18 .. 38 .. 17 .. 1 .. 6 .. 19 .. Cambodia 6 10 30 35 22 14 0 0 .. .. 42 41 Cameroona 21 .. 26 .. 28 .. 4 .. 2 .. 18 .. Canadaa 54 53 16 15 3 1 .. .. 19 23 8 8 Central African Republica .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 22 27 47 45 6 1 2 8 7 6 16 13 Chinaa 8 25 65 59 10 4 6 1 .. .. 11 12 Hong Kong SAR, China 38 36 13 9 0 0 10 17 0 0 39 38 Colombia 28 21 32 32 6 6 7 5 0 0 32 36 Congo, Dem. Rep.a 10 12 14 14 14 14 20 0 .. .. 41 60 Congo, Rep.a 7 .. 15 .. 5 .. 0 .. 3 .. 76 .. Costa Rica .. 17 .. 32 .. 4 .. 3 .. 34 .. 10 Côte d’Ivoire 18 15 18 20 46 33 3 8 8 6 7 18 Croatiaa 9 7 46 46 6 1 1 2 32 35 5 8 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Cyprus .. 26 .. 34 .. 1 .. 4 .. 22 .. .. Czech Republica 13 15 34 28 2 0 1 1 47 44 3 12 Denmark 37 45 42 36 .. .. 6 5 6 3 9 .. Dominican Republic .. 22 .. 53 .. 9 .. 4 .. 3 .. 9 Ecuador a .. .. .. .. .. .. .. .. .. .. .. .. Egypt, Arab Rep.a 20 25 22 22 8 5 3 4 .. .. 47 44 El Salvador 20 25 39 40 7 5 1 0 15 12 19 17 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 13 8 38 39 0 .. .. .. 35 36 .. .. Ethiopiaa 14 .. 10 .. 26 .. 1 .. 0 .. 49 .. Finland 26 20 32 32 0 .. 2 2 29 31 12 15 France 26 22 25 23 0 0 3 4 41 45 5 .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Thea .. .. .. .. .. .. .. .. .. .. .. .. Georgiaa 8 31 57 51 7 1 .. 1 21 17 7 15 Germany 18 16 20 24 .. .. .. .. 57 55 4 4 Ghanaa 22 23 15 29 32 16 .. .. .. .. 31 32 Greece 22 21 29 29 0 0 4 3 30 36 15 12 Guatemalaa 24 29 60 56 12 7 1 2 2 3 6 4 Guineaa 6 .. 3 .. 48 .. 2 .. 1 .. 40 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 266 2012 World Development Indicators 4.14 ECONOMY Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and pro�ts, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 15 20 44 40 6 4 1 2 12 13 22 21 Hungary 20 23 34 32 3 0 1 1 34 32 8 12 Indiaa 27 47 29 23 19 13 0 0 0 0 25 16 Indonesiaa 31 36 26 29 3 2 3 4 .. .. 37 28 Iran, Islamic Rep.a 13 19 7 3 6 6 1 1 11 19 61 52 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 39 33 .. .. 0 0 4 2 15 22 .. .. Israel 34 26 26 33 1 1 5 5 15 18 20 17 Italy 35 32 23 20 .. .. 5 7 33 36 4 5 Jamaica 15 25 33 37 8 7 20 10 2 3 21 18 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordana 9 13 31 43 17 6 8 2 1 0 35 36 Kazakhstana 24 24 40 20 5 10 7 0 0 .. 24 46 Kenyaa 29 41 41 40 15 11 0 1 0 .. 15 7 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 26 28 28 27 4 4 10 8 13 16 18 17 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait a 0 1 0 0 2 1 0 0 .. .. 98 98 Kyrgyz Republica 14 21 61 37 3 9 0 .. .. .. 22 34 Lao PDR .. 13 .. 42 .. 7 .. 1 .. .. .. 37 Latviaa 12 7 40 35 1 0 0 0 34 29 13 28 Lebanon 12 15 26 44 26 6 11 10 2 1 24 23 Lesothoa 17 17 12 12 41 57 0 3 .. .. 29 11 Liberiaa .. 28 .. 15 .. 39 .. 1 .. .. .. 18 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 11 6 44 37 1 .. 0 0 36 39 8 18 Macedonia, FYRa .. 13 .. 40 .. 5 .. 0 .. 29 .. 13 Madagascar 12 12 22 15 40 31 1 6 .. 4 26 32 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiaa 43 46 25 17 7 2 3 4 .. .. 22 31 Mali 12 19 42 29 11 10 5 10 .. .. 30 31 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. 20 .. 50 .. 2 .. 7 .. 7 .. 14 Mexicoa 34 .. 62 .. 4 .. 1 .. 10 .. 10 .. Moldovaa 3 1 45 48 5 4 0 0 23 30 23 17 Mongoliaa 12 27 36 29 7 6 1 2 18 13 26 23 Moroccoa 22 26 30 36 11 6 3 6 13 13 22 14 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar a 19 .. 33 .. 4 .. .. .. .. .. 44 .. Namibiaa 32 28 23 19 35 44 1 1 1 0 9 7 Nepala 15 14 30 38 23 16 3 5 .. .. 28 27 Netherlands 25 26 26 27 .. .. 4 2 39 35 6 10 New Zealand 54 57 29 26 3 3 0 0 0 0 14 15 Nicaraguaa 13 27 54 52 7 4 0 0 .. .. 27 16 Niger .. 12 .. 18 .. 26 .. 3 .. .. .. 41 Nigeriaa .. 1 .. 2 .. .. .. .. .. .. .. 97 Norway 28 30 28 24 0 0 1 1 18 20 25 24 Omana 24 .. 1 .. 3 .. 2 .. .. .. 70 .. Pakistana 19 25 30 31 11 8 8 5 .. .. 32 32 Panamaa 18 .. 9 .. 9 .. 4 .. 18 .. 38 .. Papua New Guineaa 31 .. 9 .. 24 .. 2 .. 0 .. 33 .. Paraguaya 11 13 37 47 11 9 3 1 7 7 30 23 Perua 19 31 40 39 8 2 3 6 10 9 20 14 Philippinesa 40 41 27 29 19 21 4 .. .. .. 10 9 Poland 15 14 33 37 2 0 0 1 36 37 14 10 Portugal 25 23 32 31 0 0 2 2 30 33 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar a .. 40 .. .. .. 3 .. .. .. .. .. 57 2012 World Development Indicators 267 4.14 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and pro�ts, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 9 22 33 35 3 0 1 0 42 33 13 10 Russian Federation 5 2 28 21 9 26 0 0 37 20 20 31 Rwandaa .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegala 21 .. 34 .. 30 .. 3 .. .. .. 12 .. Serbiaa .. 9 .. 45 .. 4 .. 0 .. 34 .. 8 Sierra Leonea 15 17 8 25 29 14 0 .. .. .. 48 44 Singaporea 29 34 18 26 1 0 9 16 .. .. 42 24 Slovak Republic 17 9 31 33 1 0 1 0 39 43 13 15 Sloveniaa 13 10 34 34 2 0 3 0 39 42 9 14 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 52 53 33 32 3 3 3 2 2 2 7 8 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 27 23 25 20 0 .. 0 0 41 51 .. 6 Sri Lankaa 13 18 57 45 11 14 4 8 2 1 14 14 Sudana 15 .. 35 .. 29 .. 1 .. .. .. 21 .. Swazilanda 24 .. 13 .. 50 .. 4 .. .. .. 9 .. Sweden 17 11 29 37 .. .. 6 13 30 25 .. .. Switzerlanda 14 24 22 26 1 6 3 3 41 36 19 5 Syrian Arab Republica 30 .. 21 .. 11 .. 6 .. 0 .. 32 .. Tajikistana 3 .. 55 .. 14 .. 1 .. 20 .. 7 .. Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand 29 33 40 40 10 5 0 1 4 6 17 15 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. 11 .. 37 .. 19 .. 6 .. .. .. 27 Trinidad and Tobagoa 36 47 23 15 5 4 17 11 5 6 13 16 Tunisiaa 20 27 37 31 11 6 4 4 17 21 10 10 Turkeya .. 24 .. 51 .. 1 .. 8 .. .. .. 16 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandaa 10 22 29 47 22 10 0 0 .. .. 39 22 Ukrainea 12 10 33 34 4 2 2 0 30 37 19 17 United Arab Emiratesa .. .. 17 .. .. .. .. .. 0 .. 82 .. United Kingdom 38 36 30 28 .. .. 8 7 20 23 4 6 United States 57 50 3 3 1 1 1 1 35 39 3 6 Uruguaya 15 18 34 40 3 4 8 2 29 30 12 7 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 27 .. 25 .. 7 .. 4 .. 4 .. 34 .. Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 2 .. 21 .. 11 .. 0 .. 0 .. 66 Yemen, Rep.a 18 .. 9 .. 10 .. 2 .. .. .. 61 .. Zambiaa 36 44 45 32 13 8 0 0 0 .. 6 15 Zimbabwea 43 .. 23 .. 20 .. 2 .. 3 .. 9 .. World 20 m 23 m 30 m 32 m 6m 4m 2m 2m .. m .. m 15 m 17 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income 20 24 34 36 7 5 1 2 .. .. 17 17 Lower middle income 18 23 32 33 13 7 1 1 .. .. 20 18 Upper middle income 14 22 36 37 6 4 3 2 16 19 14 14 Low & middle income 18 21 32 36 9 6 2 1 .. .. 20 18 East Asia & Pacific 25 33 30 29 9 6 3 1 .. .. 24 28 Europe & Central Asia 10 8 37 40 6 4 0 0 30 30 17 16 Latin America & Carib. 18 26 40 39 6 4 4 2 9 9 16 16 Middle East & N. Africa 17 27 30 31 10 6 4 3 .. 6 30 23 South Asia 17 19 30 27 15 10 4 3 .. 0 26 30 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 26 26 27 26 1 0 3 2 33 35 8 12 Euro area 25 23 25 27 0 0 3 3 35 37 6 7 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. 268 2012 World Development Indicators 4.14 ECONOMY Central government revenues About the data De�nitions The International Monetary Fund (IMF) classifi es • Taxes on income, pro�ts, and capital gains are government revenues as taxes, grants, and property levied on the actual or presumptive net income income. Taxes are classified by the base on which of individuals, on the profi ts of corporations and the tax is levied, grants by the source, and property enterprises, and on capital gains, whether real- income by type (for example, interest, dividends, ized or not, on land, securities, and other assets. or rent). The most important source of revenue is Intragovernmental payments are eliminated in con- taxes. Grants are unrequited, nonrepayable, non- solidation. • Taxes on goods and services include compulsory receipts from other government units general sales and turnover or value added taxes, and foreign governments or from international orga- selective excises on goods, selective taxes on ser- nizations. Transactions are generally recorded on an vices, taxes on the use of goods or property, taxes accrual basis. on extraction and production of minerals, and prof- The IMF’s Government Finance Statistics Manual its of fiscal monopolies. • Taxes on international 2001 describes taxes as compulsory, unrequited trade include import duties, export duties, profi ts payments made to governments by individuals, busi- of export or import monopolies, exchange profi ts, nesses, or institutions. Taxes are classified in six and exchange taxes. • Other taxes include employer major groups by the base on which the tax is levied: payroll or labor taxes, taxes on property, and taxes income, profits, and capital gains; payroll and work- not allocable to other categories, such as penalties force; property; goods and services; international for late payment or nonpayment of taxes. • Social trade and transactions; and other. However, the dis- contributions include social security contributions by tinctions are not always clear. Taxes levied on the employees, employers, and self-employed individu- income and profits of individuals and corporations als, and other contributions whose source cannot are classified as direct taxes, and taxes and duties be determined. They also include actual or imputed levied on goods and services are classified as indi- contributions to social insurance schemes operated rect taxes. This distinction may be a useful simplifica- by governments. • Grants and other revenue include tion, but it has no particular analytical significance grants from other foreign governments, international except with respect to the capacity to fix tax rates. organizations, and other government units; interest; Direct taxes tend to be progressive, whereas indirect dividends; rent; requited, nonrepayable receipts taxes are proportional. for public purposes (such as fines, administrative Social security taxes do not reflect compulsory fees, and entrepreneurial income from government payments made by employers to provident funds ownership of property); and voluntary, unrequited, or other agencies with a like purpose. Similarly, nonrepayable receipts other than grants. expenditures from such funds are not reflected in government expenses (see table 4.13). For further discussion of taxes and tax policies, see About the data for table 5.6. For further discussion of govern- ment revenues and expenditures, see About the data for tables 4.12 and 4.13. Data sources Data on central government revenues are from the IMF’s Government Finance Statistics database. Each country’s accounts are reported using the system of common definitions and classifications in the IMF’s Government Finance Statistics Manual 2001. The IMF receives additional information from the Organisation for Economic Co-operation and Development on the tax revenues of some of its members. See the IMF sources for complete and authoritative explanations of concepts, defini- tions, and data sources. 2012 World Development Indicators 269 4.15 Monetary indicators Broad Claims on Claims on Interest rate money domestic central economy government Annual growth Annual growth % annual % growth % of broad money % of broad money Deposit Lending Real 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan .. 26.9 .. 9.0 .. –6.0 .. .. .. 15.7 .. 11.6 Albania 12.0 12.5 0.9 4.9 4.8 0.2 8.3 6.4 22.1 12.8 17.0 9.0 Algeria 14.1 10.5 8.4 2.4 –11.6 –2.7 7.5 1.8 10.0 8.0 –11.7 –7.1 Angolaa 303.7 14.0 .. .. –413.7 –13.9 39.6 12.8 103.2 22.5 –60.8 –3.2 Argentinaa 1.5 33.1 .. .. –0.8 13.1 8.3 9.2 11.1 10.6 9.9 –4.2 Armenia 38.6 10.6 0.3 24.9 –5.7 8.3 18.1 9.0 31.6 19.2 33.4 9.2 Australiaa 3.7 9.4 .. .. –1.8 0.2 5.1 4.2 9.3 7.3 6.6 7.2 Austriab .. .. .. .. .. .. 2.2 .. 5.6 .. 5.3 .. Azerbaijan 73.4 24.3 –23.9 12.9 15.4 8.2 12.9 11.6 19.7 20.7 6.4 8.5 Bahrain 10.2 10.5 .. .. –0.4 –0.5 5.8 1.2 11.6 7.2 –2.4 –3.0 Bangladesh 19.3 21.1 10.7 19.7 5.6 2.7 8.6 7.1 15.5 13.0 13.4 6.1 Belarus 219.3 31.9 59.9 79.2 22.2 –4.7 37.6 9.1 67.7 9.2 –41.2 –0.9 Belgiumb .. .. .. .. .. .. 3.6 .. 8.0 9.5 5.9 8.2 Benina 26.0 7.1 .. .. 0.6 –5.5 3.5 3.5 .. .. .. .. Bolivia 1.6 14.8 –1.3 9.6 3.1 –6.5 11.0 1.0 34.6 9.9 27.9 1.0 Bosnia and Herzegovinaa 11.3 7.2 19.9 3.6 –0.4 2.3 14.7 3.2 30.5 7.9 1.3 6.6 Botswana 1.4 10.7 10.3 6.3 –56.2 20.2 9.4 5.6 15.5 11.5 15.4 –2.8 Brazil 19.7 15.4 8.3 20.7 13.5 4.9 17.2 8.9 56.8 40.0 47.7 30.4 Bulgaria 30.8 6.3 6.5 1.5 8.5 3.6 3.1 4.1 11.3 11.1 4.4 8.0 Burkina Fasoa 7.5 19.3 .. .. 6.6 3.4 3.5 3.5 .. .. .. .. Burundi 15.5 19.9 15.0 15.7 –22.6 5.3 .. .. 15.8 12.4 2.3 4.3 Cambodia 26.9 21.3 5.4 14.8 –6.9 0.4 6.8 1.3 .. .. .. .. Cameroona 19.1 12.8 6.6 7.1 –12.3 3.2 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 2.6 3.0 –0.3 Central African Republica 2.4 14.2 .. .. 6.8 11.6 5.0 3.3 22.0 15.0 18.3 13.3 Chada 19.4 26.1 .. .. 15.1 8.7 5.0 3.3 22.0 15.0 15.9 9.4 Chile 9.1 10.6 4.1 2.1 4.0 0.4 9.2 1.8 14.8 4.8 9.8 –8.4 Chinaa 12.3 18.9 .. .. 0.0 0.3 2.3 2.8 5.9 5.8 3.7 –0.7 Hong Kong SAR, Chinaa 9.3 7.4 .. .. 0.4 0.6 4.8 0.0 9.5 5.0 13.6 4.5 Colombia 3.6 11.5 8.9 16.2 6.0 –0.7 12.1 3.7 18.8 9.4 –10.3 6.1 Congo, Dem. Rep.a 51.2 30.8 .. .. –1.6 –44.7 .. 16.8 .. 56.5 .. 27.9 Congo, Rep.a 58.5 37.6 .. .. –11.7 –34.4 5.0 3.3 22.0 15.0 –17.0 14.4 Costa Rica 24.0 0.8 14.1 4.7 –0.2 0.2 13.4 5.3 24.9 17.1 16.7 8.6 Côte d’Ivoirea –1.8 18.2 .. .. –7.6 4.7 3.5 3.5 .. .. .. .. Croatia 29.1 4.3 21.3 7.0 2.0 1.4 3.7 1.8 12.1 10.4 7.1 9.3 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Cyprus 10.4 19.8 11.1 35.6 0.1 3.5 6.5 3.4 8.0 6.7 4.0 2.0 Czech Republic 16.0 1.9 –11.0 2.1 2.6 1.9 3.4 1.1 7.2 5.9 5.6 7.1 Denmark –12.1 –3.1 26.1 5.0 3.0 0.0 3.2 .. 8.1 .. 4.9 .. Dominican Republic 16.8 12.2 13.2 13.0 2.8 –0.1 17.7 4.9 26.8 12.1 18.6 6.7 Ecuador 47.0 18.6 –11.4 20.2 –28.1 –5.9 8.8 3.9 17.1 .. 26.0 .. Egypt, Arab Rep. 11.6 12.4 4.1 3.9 7.7 2.5 9.5 6.2 13.2 11.0 7.9 0.8 El Salvador 1.6 –0.1 2.6 –0.4 2.3 2.0 9.3 .. 14.0 .. 10.5 .. Eritrea 17.3 15.6 3.7 1.6 25.7 12.6 .. .. .. .. .. .. Estonia 30.5 2.0 39.4 –8.6 –4.1 3.4 3.8 1.1 7.4 7.8 2.4 6.2 Ethiopiaa 13.1 23.4 .. .. 19.8 2.5 6.0 4.7 10.9 8.0 3.8 –17.1 Finlandb .. .. .. .. .. .. 1.6 .. 5.6 .. 2.9 .. Franceb .. .. .. .. .. .. 2.6 1.5 6.7 .. 5.0 .. Gabona 18.3 19.2 .. .. –42.2 21.4 5.0 3.3 22.0 15.0 –4.8 9.3 Gambia, Thea 34.8 13.7 .. .. 2.7 15.4 12.5 14.6 24.0 27.0 19.6 17.2 Georgia 39.2 34.8 18.7 24.7 19.8 –2.0 10.2 9.2 32.8 24.2 26.8 14.3 Germany b .. .. .. .. .. .. 3.4 .. 9.6 .. 10.4 .. Ghana 54.2 31.9 7.5 13.0 32.9 11.7 28.6 17.1 .. .. .. .. Greeceb .. .. .. .. .. .. 6.1 .. 12.3 .. 8.6 .. Guatemala 21.4 9.1 4.2 2.6 10.2 2.3 10.2 5.5 20.9 13.3 13.2 8.0 Guineaa 12.9 .. .. .. 7.9 .. 7.5 .. 19.4 .. 7.4 .. Guinea-Bissaua 60.7 24.4 .. .. 16.2 9.4 3.5 3.5 .. .. .. .. 270 2012 World Development Indicators 4.15 ECONOMY Monetary indicators Broad Claims on Claims on Interest rate money domestic central economy government Annual growth Annual growth % annual % growth % of broad money % of broad money Deposit Lending Real 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Haiti 20.3 26.1 12.3 –0.2 13.8 –9.0 12.1 0.7 31.4 17.5 18.3 11.5 Honduras 15.4 9.8 7.7 5.1 –2.6 1.4 15.9 9.8 26.8 18.9 –3.1 12.4 Hungary 12.6 4.2 14.5 5.0 –2.0 1.3 9.5 4.9 12.6 7.6 2.6 4.4 Indiaa 15.2 17.8 9.9 15.8 4.7 5.3 .. .. 12.3 12.2 8.5 12.6 Indonesia 16.6 15.4 7.2 17.1 17.2 –3.7 12.5 7.0 18.5 13.3 –1.7 4.8 Iran, Islamic Rep.a 22.4 27.7 .. .. –7.9 2.0 11.7 11.9 .. 12.0 .. 11.3 Iraq .. 31.2 .. 8.1 .. 14.4 .. 6.1 .. 14.3 .. –8.5 Irelandb .. .. .. .. .. .. 0.1 .. 4.8 .. –2.2 .. Israela 8.0 5.7 .. .. –4.8 1.3 8.6 1.6 12.9 4.5 11.1 3.4 Italy b .. .. .. .. .. .. 1.8 .. 7.0 4.0 5.0 3.6 Jamaica –7.0 5.8 9.1 –0.7 –2.3 –8.2 11.6 6.3 23.3 20.5 11.5 8.9 Japan 1.3 2.0 –5.4 –1.4 2.6 2.8 0.1 0.5 2.1 1.6 3.9 3.8 Jordana 7.6 9.2 .. .. –1.2 0.4 7.0 3.5 11.8 9.0 12.2 2.6 Kazakhstan 45.0 13.3 32.2 7.1 –3.2 1.2 .. .. .. .. .. .. Kenya 4.9 22.4 4.7 14.3 –2.1 12.8 8.1 4.6 22.3 14.4 15.3 10.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 25.4 14.9 .. .. –1.1 0.4 7.9 3.9 8.5 5.5 3.4 1.7 Kosovo –12.2 13.5 12.1 10.9 –37.7 1.4 .. 3.4 .. 14.3 .. 10.9 Kuwait 6.3 3.0 8.5 2.0 –7.4 –12.9 5.9 2.3 8.9 4.9 –9.7 2.5 Kyrgyz Republica 11.7 33.2 .. .. 7.8 –8.8 18.4 4.1 51.9 31.5 19.4 23.1 Lao PDRa 46.0 39.1 .. .. –17.6 1.5 12.0 3.0 32.0 22.6 5.5 12.0 Latvia 27.0 11.5 31.2 –18.1 7.8 2.4 4.4 1.9 11.9 9.6 7.4 12.2 Lebanona 9.8 12.1 .. .. 10.5 1.1 11.2 6.2 18.2 8.3 20.7 3.8 Lesotho 1.4 14.5 6.6 5.9 14.9 13.9 4.9 3.7 17.1 11.2 14.4 7.3 Liberiaa 18.3 43.4 .. .. 197.0 47.7 6.2 4.1 20.5 14.2 22.1 5.9 Libyaa 3.1 –0.6 .. .. –10.4 –19.6 3.0 2.5 7.0 6.0 –9.6 57.8 Lithuania 16.5 8.4 14.4 –6.0 0.5 –0.6 3.9 4.8 12.1 8.4 11.1 12.6 Macedonia, FYR 22.2 12.1 2.7 6.3 –15.9 6.6 11.2 7.1 18.9 9.5 9.9 7.2 Madagascara 17.2 9.7 .. .. 0.1 –6.4 15.0 10.5 26.5 49.0 18.0 37.9 Malawia 45.5 17.2 .. .. 7.7 –13.1 33.3 3.6 53.1 24.6 17.3 15.7 Malaysia 10.0 7.3 5.5 8.1 2.1 –0.2 3.4 2.5 7.7 5.0 –1.1 –0.1 Malia 12.5 12.2 .. .. –4.2 1.6 3.5 3.5 .. .. .. .. Mauritaniaa .. 15.5 .. .. .. 12.6 9.4 8.0 25.6 17.0 23.9 –2.0 Mauritius 9.2 7.6 5.8 9.3 –4.7 1.0 9.6 8.4 20.8 8.9 18.3 7.2 Mexico –4.5 12.8 10.1 8.7 3.5 0.8 8.3 1.2 16.9 5.3 4.3 0.9 Moldova 41.7 13.4 24.4 9.3 –5.7 –4.0 24.9 7.7 33.8 16.4 5.1 4.7 Mongolia 17.6 62.5 29.6 23.7 –7.1 –4.1 16.8 11.9 37.0 20.1 22.3 0.0 Morocco 8.4 7.9 4.0 8.8 3.6 0.3 5.2 3.7 13.3 .. 14.0 .. Mozambique 38.3 22.8 11.9 18.3 6.9 –1.3 9.7 9.7 19.0 16.3 6.3 3.1 Myanmar a 42.4 42.5 .. .. 24.9 32.6 9.8 12.0 15.3 17.0 12.5 8.1 Namibia 13.2 9.6 19.4 10.1 –4.0 7.1 7.4 5.0 15.3 9.7 –9.0 0.4 Nepal 18.8 9.6 –10.4 8.9 2.6 6.2 6.0 3.6 9.5 8.0 4.8 –4.8 Netherlandsb .. .. .. .. .. .. 2.9 2.3 4.8 1.8 0.6 0.4 New Zealanda 1.5 8.4 .. .. –1.0 2.1 6.4 4.6 7.8 6.3 4.7 4.9 Nicaragua 9.4 21.7 7.0 4.8 10.0 3.9 10.8 3.0 18.1 13.3 8.8 10.1 Niger a 12.8 21.6 .. .. –13.8 0.8 3.5 3.5 .. .. .. .. Nigeria 48.1 9.3 5.8 –11.2 –43.0 24.0 11.7 6.5 21.3 17.6 –12.2 9.4 Norwaya 8.7 .. .. .. –4.7 .. 6.7 2.3 8.9 4.3 –5.8 11.4 Oman 6.0 11.3 1.1 16.1 9.5 –4.1 7.6 3.4 10.1 6.8 –8.3 40.4 Pakistan 12.1 15.1 2.0 2.3 2.6 10.9 .. 8.1 .. 14.0 .. 1.9 Panama 9.3 11.1 –8.4 13.6 0.2 1.6 7.1 3.0 10.5 7.7 11.9 4.6 Papua New Guinea 5.0 10.2 1.2 9.3 –4.6 –6.6 8.5 1.4 17.5 10.4 3.9 1.1 Paraguay 2.8 19.0 1.7 27.2 4.7 –6.3 15.7 1.2 26.8 26.0 13.1 18.1 Perua –0.4 21.5 .. .. 2.3 –3.4 9.8 1.5 30.0 19.0 25.4 11.3 Philippines 8.1 10.9 2.2 7.5 1.5 2.9 8.3 3.2 10.9 7.7 4.9 3.3 Poland 11.6 8.7 .. 9.0 –5.8 1.2 14.2 .. 20.0 .. 11.9 .. Portugalb .. .. .. .. .. .. 2.4 .. 5.2 .. 1.8 .. 2012 World Development Indicators 271 4.15 Monetary indicators Broad Claims on Claims on Interest rate money domestic central economy government Annual growth Annual growth % annual % growth % of broad money % of broad money Deposit Lending Real 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 10.7 23.1 –1.7 21.6 –23.1 4.2 0.0 2.9 .. 7.3 .. 31.0 Romania 40.8 6.8 20.0 5.8 –1.1 8.0 33.1 7.3 53.9 14.1 6.7 10.1 Russian Federation 58.5 24.6 33.1 12.4 –18.0 9.7 6.5 6.0 24.4 10.8 –9.6 –0.5 Rwandaa 15.6 .. .. .. –11.4 .. 10.1 7.1 17.0 16.7 20.6 14.3 Saudi Arabiaa 4.5 5.2 .. .. –3.5 –5.4 .. .. .. .. .. .. Senegala 10.8 13.7 .. .. –3.9 3.8 3.5 3.5 .. .. .. .. Serbia 160.8 13.1 –71.0 29.5 22.5 5.9 78.7 11.3 6.3 17.3 –40.1 7.6 Sierra Leonea 12.1 32.7 .. .. 54.6 30.0 9.2 8.9 26.3 21.3 19.0 6.0 Singaporea –2.0 8.6 .. .. –1.6 –4.9 1.7 0.2 5.8 5.4 2.1 5.9 Slovak Republicb 15.2 5.0 8.2 11.7 4.1 1.1 8.5 3.8 14.9 5.8 5.0 2.8 Sloveniab .. .. .. .. .. .. 10.0 1.4 15.8 5.9 10.0 2.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 7.2 6.9 –11.8 6.8 0.2 –0.2 9.2 6.5 14.5 9.8 5.2 1.6 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spainb .. .. .. .. .. .. 3.0 .. 5.2 .. 1.7 .. Sri Lankaa 12.9 15.8 .. .. 12.5 –0.6 9.2 6.9 16.2 10.2 8.3 2.7 Sudan 36.9 25.4 16.9 10.7 33.9 12.5 .. .. .. .. .. .. Swaziland –6.6 7.9 16.9 5.0 1.7 26.0 6.5 3.9 14.0 9.8 13.8 3.4 Sweden 1.9 –8.5 8.5 9.5 2.4 –0.9 2.2 .. 5.8 .. 4.3 .. Switzerlanda –16.9 5.5 .. .. 2.1 0.2 3.0 0.1 4.3 2.7 3.1 2.7 Syrian Arab Republic 19.0 13.5 –4.1 10.3 –6.1 1.9 4.0 6.2 9.0 9.9 –0.6 3.4 Tajikistana 63.3 –3.6 .. .. 36.6 –9.8 1.3 6.1 25.6 24.1 2.4 7.6 Tanzania 14.8 25.4 12.2 11.4 0.7 7.7 7.4 6.6 21.6 14.5 13.0 6.4 Thailand 4.9 10.9 6.2 10.7 0.5 –1.2 3.3 1.0 7.8 5.9 6.4 2.2 Timor-Leste 41.1 9.9 45.7 2.3 –36.8 –46.5 0.8 0.8 16.7 11.0 11.4 1.7 Togoa 15.4 16.3 .. .. –0.3 4.3 3.5 3.5 .. .. .. .. Trinidad and Tobagoa 11.7 30.5 .. .. –13.2 25.3 8.2 1.5 16.5 9.3 3.2 4.6 Tunisiaa 14.1 11.3 .. .. 5.6 –1.2 .. .. .. .. .. .. Turkey 40.7 18.5 16.2 27.6 26.8 4.0 47.2 15.3 .. .. .. .. Turkmenistana 83.3 .. .. .. –53.4 .. .. .. .. .. .. .. Uganda 18.1 37.8 8.2 20.7 29.4 14.0 9.8 7.7 22.9 20.2 10.6 10.2 Ukraine 44.5 22.7 30.9 2.1 –1.7 8.4 13.7 10.6 41.5 15.9 15.0 0.7 United Arab Emiratesa 15.3 6.2 .. .. –9.6 2.6 6.2 .. 9.7 .. –1.5 .. United Kingdoma 11.1 4.0 .. .. –2.4 2.9 4.5 .. 6.0 0.5 5.3 –2.3 United States 8.1 –2.1 5.0 –1.5 0.5 0.6 .. .. 9.2 3.3 6.9 2.4 Uruguay 9.5 22.1 45.1 11.3 –1.8 8.6 18.3 4.2 46.1 10.3 41.1 4.9 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 33.7 28.0 .. .. –6.4 –1.5 16.3 14.8 25.2 18.3 –3.3 –19.3 Vietnama 35.4 29.7 .. .. –2.4 2.3 3.7 11.2 10.6 13.1 6.9 1.1 West Bank and Gaza .. .. .. .. .. .. 1.5 0.3 .. .. .. .. Yemen, Rep.a 25.3 11.8 .. .. –45.6 12.9 14.0 18.7 19.5 23.8 –3.1 –0.7 Zambia 73.8 29.9 –11.4 8.1 162.0 11.5 20.2 7.4 38.8 20.9 6.7 8.2 Zimbabwea 45.7 .. .. .. 29.5 .. 50.2 121.5 68.2 579.0 67.8 605.4 a. Includes claims on the private sector only. b. As members of the European Monetary Union, these countries share a single currency, the euro. 272 2012 World Development Indicators 4.15 ECONOMY Monetary indicators About the data De�nitions 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 cur- supply of money lie at the heart of a country’s tives and the net liabilities of the banking system rency outside banks; demand deposits other than financial system. There are several commonly used can also be difficult. The quality of commercial bank those of the central government; the time, savings, defi nitions of the money supply. The narrowest, reporting also may be adversely affected by delays in and foreign currency deposits of resident sectors M1, encompasses currency held by the public and reports from bank branches, especially in countries other than the central government; bank and travel- demand deposits with banks. M2 includes M1 plus where branch accounts are not computerized. Thus er’s checks; and other securities such as certificates time and savings deposits with banks that require the data in the balance sheets of commercial banks of deposit and commercial paper. Change in broad prior notice for withdrawal. M3 includes M2 as well may be based on preliminary estimates subject to money is measured as the difference in end-of-year as various money market instruments, such as cer- constant revision. This problem is likely to be even totals relative to the preceding year. Data for 2010 tificates of deposit issued by banks, bank deposits more serious for nonbank financial intermediaries. for countries reporting under the old presentation denominated in foreign currency, and deposits with Many interest rates coexist in an economy, reflect- of monetary statistics and data for 2000 for all fi nancial institutions other than banks. However ing competitive conditions, the terms governing countries are based on money plus quasi money. defined, money is a liability of the banking system, loans and deposits, and differences in the position • Claims on domestic economy (IFS line 32S..ZK) distinguished from other bank liabilities by the spe- and status of creditors and debtors. In some econo- are gross credit from the financial system to house- cial role it plays as a medium of exchange, a unit of mies interest rates are set by regulation or adminis- holds, nonprofit institutions serving households, non- account, and a store of value. trative fiat. In economies with imperfect markets, or financial corporations, state and local governments, The banking system’s assets include its net for- where reported nominal rates are not indicative of and social security funds. Data for countries where eign assets and net domestic credit. Net domestic effective rates, it may be difficult to obtain data on claims on the domestic economy are not available credit includes credit extended to the private sector interest rates that reflect actual market transactions. are claims on the private sector (IFS line 32D..ZK and general government and credit extended to the Deposit and lending rates are collected by the Inter- or 32D.ZF) and are footnoted as such. • Claims on nonfinancial public sector in the form of investments national Monetary Fund (IMF) as representative inter- central government (IFS line 32AN..ZK) are loans in short- and long-term government securities and est rates offered by banks to resident customers. to central government institutions less 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 tral bank lending to the government often playing the (i – P) / (1 + P), where i is the nominal lending inter- most important role. The central bank can regulate est rate and P is the inflation rate (as measured by lending to the private sector in several ways—for the 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 its Monetary Financial Sta- controlling the availability of credit through changes tistical Manual 2000. The presentation for countries Data sources in the reserve requirements imposed on banks and that report data in accordance with its International ceilings on the credit provided by banks to the pri- Financial Statistics (IFS) remains the same. Data on monetary and financial statistics are vate sector. published by the IMF in its monthly International Monetary accounts are derived from the balance Financial Statistics and annual International Finan- sheets of financial institutions—the central bank, cial Statistics Yearbook. The IMF collects data on commercial banks, and nonbank financial interme- the financial systems of its member countries. diaries. Although these balance sheets are usually The World Bank receives data from the IMF in reliable, they are subject to errors of classification, electronic files that may contain more recent revi- valuation, and timing and to differences in account- sions than the published sources. The discussion ing practices. For example, whether interest income of monetary indicators draws from Caiola (1995). is recorded on an accrual or a cash basis can make Also see the IMF’s Monetary and Financial Statis- a substantial difference, as can the treatment of non- tics Manual (2000) for guidelines for the presenta- performing assets. Valuation errors typically arise tion of monetary and financial statistics. Data on for foreign exchange transactions, particularly in real interest rates are derived from World Bank countries with flexible exchange rates or in countries data on the GDP deflator. that have undergone currency devaluation during the 2012 World Development Indicators 273 4.16 Exchange rates and prices Of�cial 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 $ 2005 = 100 % growth % growth % growth 2010 2011 a 2000 2010 2010 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Afghanistan 46.45 46.58 13.7 19.3 0.4 .. .. 7.8 .. 8.9 .. .. Albania 103.94 100.51 40.6 44.5 0.4 .. 37.7 3.3 27.8 2.8 .. 4.1 Algeria 74.39 72.94 25.1 40.3 0.5 102.4 18.5 8.3 17.3 3.4 .. 4.0 Angola 91.91 93.74 2.8 66.9 0.7 .. 739.4 36.1 711.0 36.7 .. .. Argentina 3.90 4.11 0.8 2.2 0.6 .. 5.2 13.0 8.9 9.8 0.1 15.1 Armenia 373.66 372.50 164.8 207.3 0.6 126.2 212.5 4.6 70.5 4.3 .. 2.4 Australia 1.09 0.97 1.3 1.5 1.3 115.2 1.4 3.9 2.1 2.9 1.1 3.4 Austriab 0.76 0.72 0.9 0.9 1.1 98.7 1.5 1.7 2.2 1.9 0.3 2.3 Azerbaijan 0.80 0.79 0.3 0.5 0.6 .. 203.0 9.8 179.7 8.4 .. .. Bahrain 0.38 0.38 0.2 0.3 0.8 89.5 0.5 7.4 0.8 2.1 .. .. Bangladesh 69.65 74.15 21.3 28.1 0.4 .. 4.1 5.4 5.5 6.8 .. .. Belarus 2,978.51 4,974.63 177.3 1,232.9 0.4 .. 355.1 21.5 271.3 17.4 267.8 21.2 Belgiumb 0.76 0.72 0.9 0.9 1.1 100.7 1.8 2.1 1.9 2.1 1.2 2.6 Benin 495.28 471.87 212.7 233.9 0.5 .. 8.7 3.3 8.7 3.2 .. .. Bolivia 7.02 6.94 2.0 2.9 0.4 122.1 8.6 6.9 8.7 5.4 .. .. Bosnia and Herzegovina 1.48 1.41 0.7 0.8 0.5 .. 4.1 3.8 .. 3.3 .. .. Botswana 6.79 6.84 1.9 3.6 0.5 .. 9.7 9.0 10.4 8.8 .. .. Brazil 1.76 1.67 1.0 1.7 1.0 .. 211.8 8.1 199.5 6.6 204.9 9.3 Bulgaria 1.48 1.41 0.5 0.7 0.5 121.1 102.1 5.9 117.5 6.2 85.7 6.1 Burkina Faso 495.28 471.87 196.9 211.2 0.4 .. 3.7 2.7 5.5 3.0 .. .. Burundi 1,230.75 1,261.07 255.8 535.3 0.5 113.3 13.4 10.7 16.1 9.4 .. .. Cambodia 4,184.92 4,058.50 1,232.7 1,516.7 0.4 .. 4.4 4.9 6.6 6.2 .. .. Cameroon 495.28 471.87 256.8 248.1 0.5 101.1 6.3 2.1 6.5 2.5 .. .. Canada 1.03 0.99 1.2 1.2 1.2 111.8 1.5 2.5 1.7 2.1 2.7 1.3 Central African Republic 495.28 471.87 271.9 287.2 0.6 110.3 4.5 2.8 5.3 3.3 6.0 4.4 Chad 495.28 471.87 180.3 244.3 0.5 .. 7.1 5.4 6.9 2.7 .. .. Chile 510.25 483.67 284.0 402.0 0.8 108.4 7.9 6.4 .. .. 7.0 6.1 China 6.77 6.46 3.3 4.0 0.6 118.7 7.9 4.4 8.6 2.4 .. .. Hong Kong SAR, China 7.77 7.78 7.5 5.3 0.7 .. 4.5 –1.1 5.9 0.6 0.6 0.2 Colombia 1,898.57 1,848.14 898.0 1,248.6 0.7 120.3 22.6 5.9 20.2 5.6 16.4 4.6 Congo, Dem. Rep. 905.91 919.49 26.0 519.9 0.6 1,025.3 964.9 26.7 930.2 26.9 .. .. Congo, Rep. 495.28 471.87 264.4 343.3 0.7 .. 9.0 7.4 9.3 3.4 .. .. Costa Rica 525.83 505.66 173.9 349.6 0.7 121.8 15.9 10.1 15.6 10.8 14.1 12.5 Côte d’Ivoire 495.28 471.87 278.7 301.0 0.6 99.6 9.2 3.2 7.2 2.9 .. .. Croatia 5.50 5.34 3.7 3.9 0.7 105.9 90.0 3.9 86.3 2.9 69.8 3.1 Cuba .. .. .. .. .. .. 6.4 3.3 .. .. .. .. Cyprusb 0.76 0.72 0.7 0.7 0.9 102.3 4.3 3.1 3.7 2.7 2.8 3.8 Czech Republic 19.10 17.70 14.2 14.2 0.7 122.5 12.8 2.0 7.8 2.5 8.2 2.2 Denmark 5.62 5.37 8.4 7.9 1.4 102.0 1.6 2.5 2.1 2.0 1.1 2.4 Dominican Republic 36.88 38.11 8.8 20.5 0.6 97.4 9.8 12.6 8.7 13.4 .. .. Ecuador .. .. 0.3 0.5 0.5 96.7 4.4 8.0 37.1 6.3 .. 7.6 Egypt, Arab Rep. 5.62 5.93 1.4 2.4 0.4 .. 8.7 8.7 8.8 8.5 6.1 9.4 El Salvador 8.75 8.75 0.5 0.5 0.5 .. 6.2 3.3 8.5 3.8 .. 4.4 Eritrea 15.38 15.38 2.9 11.4 0.7 .. 7.9 16.7 .. .. .. .. Estonia 11.81 0.72 7.1 8.2 0.7 .. 53.7 5.4 21.6 4.3 8.1 3.4 Ethiopia 14.41 16.90 2.2 4.4 0.3 .. 6.5 11.5 5.5 12.8 .. .. Finlandb 0.76 0.72 1.0 0.9 1.2 98.3 1.9 1.2 1.5 1.5 0.9 2.1 Franceb 0.76 0.72 0.9 0.9 1.2 98.8 1.3 1.9 1.6 1.8 .. 1.7 Gabon 495.28 471.87 248.7 284.7 0.6 101.3 7.0 4.9 4.6 2.1 .. .. Gambia, The 28.01 29.46 4.1 9.1 0.3 101.0 4.2 10.9 4.0 7.2 .. .. Georgia 1.78 1.69 0.6 0.9 0.5 118.5 356.7 6.9 24.7 7.0 .. 6.6 Germany b 0.76 0.72 1.0 0.8 1.1 97.3 1.8 1.0 2.1 1.6 0.4 2.3 Ghana 1.43 1.51 0.2 1.1 0.8 97.6 26.7 26.2 28.4 15.9 .. .. Greeceb 0.76 0.72 0.7 0.7 0.9 106.7 9.2 3.2 9.0 3.3 3.6 4.2 Guatemala 8.06 7.79 3.8 4.8 0.6 .. 10.4 5.5 10.1 7.1 .. .. Guinea 5,726.07 6,620.84 829.0 2,480.8 0.4 .. 5.5 16.2 .. 20.5 .. .. Guinea-Bissau 495.28 471.87 121.8 230.2 0.5 .. 32.5 10.2 34.0 2.5 .. .. 274 2012 World Development Indicators 4.16 ECONOMY Exchange rates and prices Of�cial 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 $ 2005 = 100 % growth % growth % growth 2010 2011 a 2000 2010 2010 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Haiti 39.80 40.52 8.9 24.1 0.6 .. 18.1 14.2 21.9 15.2 .. .. Honduras 18.90 18.90 6.7 9.8 0.5 .. 19.9 6.3 18.8 7.7 .. .. Hungary 207.94 201.06 107.9 130.2 0.6 106.1 20.0 5.0 20.3 5.5 16.8 3.7 India 45.73 46.67 13.2 18.8 0.4 .. 8.1 5.9 9.1 6.0 7.4 5.3 Indonesia 9,090.43 8,770.43 2,801.1 6,190.8 0.7 .. 15.8 11.1 13.7 8.7 15.4 10.9 Iran, Islamic Rep. 10,254.18 10,616.31 1,335.8 3,858.8 0.4 145.9 27.7 16.4 26.0 15.3 28.4 10.8 Iraq 1,170.00 1,170.00 501.8 842.5 0.7 .. .. 11.1 97.6 21.0 .. .. Irelandb 0.76 0.72 1.0 0.9 1.1 100.2 3.7 1.7 2.3 2.8 1.6 –0.1 Israel 3.74 3.58 3.4 3.7 1.0 115.4 11.0 1.4 9.7 2.0 8.1 4.2 Italy b 0.76 0.72 0.8 0.8 1.1 99.4 3.8 2.3 3.7 2.2 2.9 2.6 Jamaica 87.20 85.89 25.7 59.2 0.7 .. 24.8 11.5 23.5 11.9 .. .. Japan 87.78 79.81 154.8 111.5 1.3 102.7 0.0 –1.1 0.8 –0.1 –1.0 0.6 Jordan 0.71 0.71 0.4 0.6 0.8 .. 3.2 6.5 3.5 4.6 .. 8.7 Kazakhstan 147.36 146.62 36.5 109.8 0.8 .. 204.7 15.1 67.8 8.8 16.3 12.6 Kenya 79.23 88.81 27.2 37.3 0.5 .. 16.6 6.1 15.6 11.2 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 1,156.06 1,108.29 746.2 824.6 0.7 .. 5.9 2.3 5.1 3.1 3.7 2.6 Kosovo .. .. .. .. .. .. .. 0.5 .. 1.7 .. .. Kuwait 0.29 0.28 0.2 .. .. .. 1.5 9.8 2.0 3.7 1.4 2.9 Kyrgyz Republic 45.96 46.14 10.0 17.4 0.4 .. 110.6 8.6 23.3 7.4 35.6 11.2 Lao PDR 8,258.77 8,058.40 2,155.5 3,777.2 0.5 .. 27.2 8.3 28.3 7.8 .. .. Latvia 0.53 0.50 0.3 0.4 0.7 .. 48.0 8.1 29.2 6.3 12.0 7.0 Lebanon 1,507.50 1,507.50 909.2 988.7 0.7 .. 19.0 3.1 .. .. .. .. Lesotho 7.32 7.26 2.7 4.7 0.6 106.1 9.7 8.0 5.9 7.6 .. .. Liberia 71.32 72.38 19.1 42.2 0.6 .. 51.8 10.3 .. 10.6 .. .. Libya 1.27 1.22 0.3 0.7 0.6 .. .. 17.9 5.6 0.4 .. .. Lithuania 2.61 2.48 1.4 1.6 0.6 .. 75.0 3.9 32.6 3.3 24.8 4.7 Macedonia, FYR 46.49 44.23 20.0 18.6 0.4 100.3 79.3 3.7 10.6 2.4 8.5 2.7 Madagascar 2,089.95 2,025.12 426.5 908.3 0.4 .. 19.1 10.9 18.7 10.6 .. .. Malawi 150.49 155.78 15.6 58.6 0.4 101.1 33.6 15.8 33.8 11.7 .. .. Malaysia 3.22 3.06 1.7 1.8 0.6 108.8 4.1 3.8 3.6 2.4 3.4 4.6 Mali 495.28 471.87 227.0 280.0 0.6 .. 7.0 4.5 5.2 2.5 .. .. Mauritania 275.89 281.12 71.1 148.0 0.5 .. 8.7 10.7 6.1 7.1 .. .. Mauritius 30.78 28.71 12.5 17.1 0.6 .. 6.3 6.0 6.9 6.2 .. .. Mexico 12.64 12.42 6.1 7.9 0.6 92.7 19.0 7.4 19.5 4.5 18.4 5.8 Moldova 12.37 11.74 3.0 6.5 0.5 126.6 119.6 10.8 21.4 10.4 .. .. Mongolia 1,357.06 1,265.52 259.3 756.5 0.6 .. 56.3 14.7 35.7 9.1 .. .. Morocco 8.42 8.09 5.2 5.0 0.6 98.0 4.0 2.0 3.9 1.9 2.9 .. Mozambique 33.96 29.07 8.0 14.8 0.4 .. 34.1 8.2 31.8 10.6 .. .. Myanmar 5.58 5.39 107.2 433.1 .. .. 25.3 17.6 25.9 21.1 .. .. Namibia 7.32 7.26 3.6 6.0 0.8 .. 11.1 7.3 .. 6.1 .. .. Nepal 73.16 74.02 19.4 32.6 0.4 .. 8.0 7.0 8.7 6.7 .. .. Netherlandsb 0.76 0.72 0.9 0.8 1.1 99.0 2.1 1.9 2.4 1.9 1.3 2.6 New Zealand 1.39 1.27 1.4 1.5 1.0 94.9 1.7 3.0 1.8 2.7 1.5 3.4 Nicaragua 21.36 22.42 5.2 8.3 0.4 104.3 42.4 7.8 .. 8.8 .. .. Niger 495.28 471.87 221.5 243.4 0.5 .. 6.0 3.3 6.1 2.9 .. .. Nigeria 150.30 153.90 29.0 77.2 0.5 117.9 29.5 13.7 32.5 12.4 .. .. Norway 6.04 5.60 9.1 9.0 1.5 102.8 2.7 4.6 2.2 1.9 1.6 8.4 Oman 0.38 0.38 0.2 0.3 0.7 .. 0.1 8.5 .. 3.2 .. .. Pakistan 85.19 86.34 16.2 31.8 0.4 103.4 11.1 9.2 9.7 8.8 10.4 9.8 Panama 1.00 1.00 0.6 0.6 0.6 .. 3.6 2.6 1.1 2.7 1.0 3.8 Papua New Guinea 2.72 2.37 1.1 1.5 0.6 113.0 7.6 6.2 9.3 5.9 .. .. Paraguay 4,735.46 4,176.07 1,372.0 2,595.9 0.6 139.6 11.5 9.8 13.1 8.1 .. 9.6 Peru 2.83 2.75 1.5 1.6 0.6 .. 26.7 3.4 27.3 2.5 23.7 2.8 Philippines 45.11 43.31 19.4 24.3 0.5 126.8 9.4 4.7 7.7 5.4 6.3 5.9 Poland 3.02 2.96 1.8 1.9 0.6 104.7 23.2 2.7 25.3 2.6 19.8 2.7 Portugalb 0.76 0.72 0.7 0.6 0.9 99.9 5.2 2.5 4.5 2.5 .. 2.5 2012 World Development Indicators 275 4.16 Exchange rates and prices Of�cial 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 $ 2005 = 100 % growth % growth % growth 2010 2011 a 2000 2010 2010 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10 Puerto Rico .. .. .. .. .. .. 3.0 4.3 .. .. .. .. Qatar 3.64 3.64 1.9 2.8 0.8 .. .. 10.6 2.8 6.7 .. .. Romania 3.18 3.05 0.6 1.7 0.5 104.2 98.0 14.7 100.5 10.7 93.8 14.1 Russian Federation 30.37 29.38 7.3 15.9 0.5 125.9 161.5 15.1 99.1 12.1 99.8 14.7 Rwanda 583.13 600.31 142.5 265.5 0.5 .. 14.3 10.4 16.2 8.9 .. .. Saudi Arabia 3.75 3.75 2.0 2.6 0.7 104.8 1.6 7.0 1.0 2.7 1.3 2.6 Senegal 495.28 471.87 259.3 264.9 0.5 .. 6.0 2.7 5.4 2.2 .. .. Serbia 77.73 73.33 8.9 36.1 0.5 .. .. 15.3 50.2 14.3 .. .. Sierra Leone 3,978.09 4,230.53 853.8 1,566.0 0.4 99.7 31.9 9.6 .. .. .. .. Singapore 1.36 1.26 1.2 1.0 0.7 111.3 1.4 1.4 1.7 1.6 –1.0 2.4 Slovak Republicb 0.76 0.72 0.5 0.5 0.7 132.2 11.4 3.0 8.4 4.5 9.5 4.1 Sloveniab 0.76 0.72 0.5 0.6 0.9 .. 29.4 3.7 12.0 3.9 9.1 3.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 7.32 7.26 3.1 5.0 0.7 101.2 9.9 7.2 8.7 5.8 7.7 6.7 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spainb 0.76 0.72 0.7 0.7 1.0 103.7 3.9 3.4 3.8 2.9 2.4 3.1 Sri Lanka 113.06 110.57 24.7 52.9 0.5 .. 9.1 10.6 9.9 10.9 8.1 12.2 Sudan 2.31 2.67 0.8 1.6 0.6 .. 65.5 10.6 72.0 9.0 .. .. Swaziland 7.32 7.26 2.7 4.4 0.6 .. 10.5 7.8 9.5 7.0 .. .. Sweden 7.21 6.49 9.1 9.0 1.3 95.5 2.2 1.7 1.9 1.5 2.3 3.2 Switzerland 1.04 0.89 1.9 1.5 1.5 107.5 1.1 1.2 1.6 0.9 –0.4 1.0 Syrian Arab Republic 11.23 11.23 17.4 25.6 0.6 .. 7.9 7.3 6.4 6.3 4.7 4.5 Tajikistan 4.38 4.61 0.3 1.7 0.4 .. 235.0 19.7 .. 12.3 .. .. Tanzania 1,409.27 1,572.12 320.4 518.2 0.4 .. 23.0 7.4 20.9 6.8 .. .. Thailand 31.69 30.49 16.0 17.1 0.5 .. 4.2 3.2 4.9 2.9 3.8 5.6 Timor-Leste 1.00 1.00 0.5 0.7 0.7 .. .. 5.3 .. 5.2 .. .. Togo 495.28 471.87 255.9 259.6 0.5 98.1 7.0 2.4 8.5 2.9 .. .. Trinidad and Tobago 6.38 6.41 3.2 3.8 0.6 130.7 5.4 5.6 5.7 6.9 2.8 4.0 Tunisia 1.43 1.41 0.6 0.6 0.4 93.5 5.7 3.3 4.4 3.4 3.6 4.6 Turkey 1.50 1.67 0.3 1.0 0.6 .. 81.7 14.1 79.9 15.6 75.2 15.3 Turkmenistan .. .. 0.6 1.4 0.5 .. 408.2 12.6 .. .. .. .. Uganda 2,177.56 2,522.75 564.1 811.9 0.4 111.4 12.1 5.9 8.3 6.9 .. .. Ukraine 7.94 7.97 1.1 3.6 0.5 99.1 271.0 16.6 155.7 11.4 161.6 15.2 United Arab Emirates 3.67 3.67 2.1 3.1 0.8 .. 2.2 8.2 .. .. .. .. United Kingdom 0.65 0.62 0.6 0.7 1.0 83.7 2.4 2.5 2.5 2.1 2.4 2.1 United States 1.00 1.00 1.0 1.0 1.0 91.4 2.0 2.5 2.7 2.6 1.2 4.0 Uruguay 20.06 19.31 9.8 16.5 0.8 137.2 32.6 7.9 33.9 8.8 27.2 12.7 Uzbekistan .. .. 91.3 705.2 0.4 .. 245.8 24.0 .. .. .. .. Venezuela, RB 2.58 4.29 0.4 2.9 1.1 195.3 45.3 25.1 49.0 21.9 44.1 25.7 Vietnam 18,612.92 20,450.10 4,018.5 7,109.8 0.4 .. 15.2 8.7 4.1 8.2 .. .. West Bank and Gaza .. .. 2.0 .. .. .. 5.7 3.4 .. 4.0 .. .. Yemen, Rep. 219.59 213.80 46.8 107.6 0.5 .. 20.9 11.9 26.3 11.4 .. .. Zambia 4,797.14 4,860.67 1,082.7 3,847.2 0.8 126.0 52.1 15.9 57.0 15.2 101.4 .. Zimbabwe .. .. .. .. .. .. –3.9 5.1 29.0 497.7 25.9 .. Note: The differences in the growth rates of the GDP deflator and 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. As members of the euro area, these countries share a single currency, the euro. 276 2012 World Development Indicators 4.16 ECONOMY Exchange rates and prices About the data De�nitions In a market-based economy, household, producer, effective exchange rate index and a cost indicator • Of�cial exchange rate is the exchange rate deter- and government choices about resource allocation of relative normalized unit labor costs in manufactur- mined by national authorities or the rate determined are influenced by relative prices, including the real ing. For selected other countries the nominal effec- in the legally sanctioned exchange market. It is cal- exchange rate, real wages, real interest rates, and tive exchange rate index is based on manufactured culated as an annual average based on monthly other prices in the economy. Relative prices also goods and primary products trade with partner or averages (local currency units relative to the U.S. largely reflect these agents’ choices. Thus relative competitor countries. For these countries the real dollar). • Purchasing power parity (PPP) conversion prices convey vital information about the interaction effective exchange rate index is the nominal index factor is the number of units of a country’s currency of economic agents in an economy and with the rest adjusted for relative changes in consumer prices; an required to buy the same amount of goods and ser- of the world. increase represents an appreciation of the local cur- vices in the domestic market that a U.S. dollar would The exchange rate is the price of one currency rency. Because of conceptual and data limitations, buy in the United States. •  Ratio of PPP conver- in terms of another. Offi cial exchange rates and changes in real effective exchange rates should be sion factor to market exchange rate is the result exchange rate arrangements are established by interpreted with caution. obtained by dividing the PPP conversion factor by the governments. Other exchange rates recognized by Inflation is measured by the rate of increase in a market exchange rate. •  Real effective exchange governments include market rates, which are deter- price index, but actual price change can be nega- rate is the nominal effective exchange rate (a mea- mined largely by legal market forces, and for coun- tive. The index used depends on the prices being sure of the value of a currency against a weighted tries with multiple exchange arrangements, principal examined. The GDP deflator reflects price changes average of several foreign currencies) divided by rates, secondary rates, and tertiary rates. for total GDP. The most general measure of the over- a price deflator or index of costs. •  GDP implicit Official or market exchange rates are often used all price level, it accounts for changes in government deflator measures the average annual rate of price to convert economic statistics in local currencies to consumption, capital formation (including inventory change in the economy as a whole for the periods a common currency in order to make comparisons appreciation), international trade, and the main com- shown. •  Consumer price index reflects changes across countries. Since market rates reflect at best ponent, household final consumption expenditure. in the cost to the average consumer of acquiring a the relative prices of tradable goods, the volume of The GDP deflator is usually derived implicitly as the basket of goods and services that may be fixed or goods and services that a U.S. dollar buys in the ratio of current to constant price GDP—or a Paasche may change at specified intervals, such as yearly. United States may not correspond to what a U.S. index. It is defective as a general measure of inflation The Laspeyres formula is generally used. • Whole- dollar converted to another country’s currency at for policy use because of long lags in deriving esti- sale price index refers to a mix of agricultural and the official exchange rate would buy in that country, mates and because it is often an annual measure. industrial goods at various stages of production and particularly when nontradable goods and services Consumer price indexes are produced more fre- distribution, including import duties. The Laspeyres account for a significant share of a country’s output. quently and so are more current. They are also con- formula is generally used. An alternative exchange rate—the purchasing power structed explicitly, based on surveys of the cost of parity (PPP) conversion factor—is preferred because a defined basket of consumer goods and services. it reflects differences in price levels for both tradable Nevertheless, consumer price indexes should be and nontradable goods and services and therefore interpreted with caution. The definition of a house- provides a more meaningful comparison of real out- hold, the basket of goods, and the geographic (urban put. See table 1.1 for further discussion. or rural) and income group coverage of consumer The ratio of the PPP conversion factor to the official price surveys can vary widely by country. In addi- exchange rate—the national price level or compara- tion, weights are derived from household expendi- tive price level—measures differences in the price ture surveys, which, for budgetary reasons, tend to level at the gross domestic product (GDP) level. The be conducted infrequently in developing countries, price level index tends to be lower in poorer countries impairing comparability over time. Although useful for and to rise with income. measuring consumer price inflation within a country, The real effective exchange rate is a nominal consumer price indexes are of less value in compar- effective exchange rate index adjusted for relative ing countries. movements in national price or cost indicators of Wholesale price indexes are based on the prices at the home country, selected countries, and the euro the first commercial transaction of commodities that Data sources area. A nominal effective exchange rate index is the are important in a country’s output or consumption. ratio (expressed on the base 2005 = 100) of an Prices are farm-gate for agricultural commodities and Data on official and real effective exchange rates index of a currency’s period-average exchange rate ex-factory for industrial goods. Preference is given to and consumer and wholesale price indexes are to a weighted geometric average of exchange rates indexes with the broadest coverage of the economy. from the International Monetary Fund’s Interna- for currencies of selected countries and the euro The least squares method is used to calculate tional Financial Statistics. PPP conversion factors area. For most high-income countries weights are growth rates of the GDP implicit deflator, consumer and GDP deflators are from the World Bank’s data derived from industrial country trade in manufac- price index, and wholesale price index. files. tured goods. Data are compiled from the nominal 2012 World Development Indicators 277 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 704 3,791 1,499 6,316 107 –101 533 1,223 –156 –1,403 646 2,541 Algeria .. 48,171 .. 49,082 .. –1,319 .. 2,632 .. 402 13,556 170,461 Angola 8,188 51,452 5,739 35,421 –1,681 –8,172 28 –438 796 7,421 1,198 19,749 Argentina 31,277 81,251 33,108 67,992 –7,548 –9,938 399 –388 –8,981 2,932 25,152 52,208 Armenia 447 1,937 966 4,212 53 339 188 563 –278 –1,373 314 1,866 Australia 83,898 261,340 87,799 246,140 –10,814 –45,803 –47 –1,388 –14,763 –31,990 18,822 42,268 Austria 87,777 202,416 85,125 189,033 –2,259 737 –1,732 –2,658 –1,339 11,461 17,650 22,242 Azerbaijan 2,118 28,590 2,024 10,592 –335 –3,467 73 509 –168 15,040 680 6,409 Bahrain 7,176 17,880 5,132 13,095 –224 –2,373 –990 –1,642 830 770 1,605 .. Bangladesh 7,214 21,661 9,673 29,477 –266 –1,454 2,420 11,379 –306 2,109 1,516 11,175 Belarus 7,641 29,909 8,087 37,367 –47 –1,163 155 304 –338 –8,317 350 5,025 Belgium 206,988 366,855 195,511 363,040 4,475 10,958 –4,341 –8,423 11,611 6,349 12,272 26,779 Benin 528 1,446 708 2,234 –12 –33 111 172 –81 –649 459 1,200 Bolivia 1,470 6,840 2,078 6,159 –225 –889 387 1,081 –446 874 1,184 9,731 Bosnia and Herzegovina 1,580 6,221 4,157 9,819 590 331 1,591 2,258 –396 –1,008 497 4,411 Botswana 3,000 5,028 2,321 5,718 –351 –243 217 979 545 46 6,318 7,885 Brazil 64,584 233,736 72,444 244,360 –17,886 –39,486 1,521 2,788 –24,225 –47,323 33,015 288,575 Bulgaria 7,000 27,326 7,670 28,421 –323 –1,680 290 2,038 –703 –736 3,507 17,223 Burkina Faso 237 1,053 658 1,942 –20 –5 122 514 –319 –380 243 1,068 Burundi 53 181 150 607 –12 –11 59 136 –50 –301 38 332 Cambodia 1,826 6,887 2,263 7,879 –123 –533 425 646 –136 –879 611 3,817 Cameroon 2,668 5,645 2,501 6,408 –493 –239 109 146 –218 –856 220 3,643 Canada 329,252 462,349 288,093 493,120 –22,291 –15,968 754 –2,569 19,622 –49,307 32,427 57,151 Central African Republic .. .. .. .. .. .. .. .. .. .. 136 181 Chad .. .. .. .. .. .. .. .. .. .. 114 632 Chile 23,293 81,826 21,893 66,990 –2,856 –15,424 558 4,390 –898 3,802 15,055 27,827 China† 279,561 1,752,621 250,688 1,520,559 –14,666 30,380 6,311 42,932 20,518 305,374 171,763 2,913,712 Hong Kong SAR. China 243,127 500,447 235,589 487,850 1,125 3,644 –1,670 –3,443 6,993 12,798 107,560 268,743 Colombia 15,808 45,224 14,397 46,608 –2,289 –11,945 1,673 4,475 795 –8,855 9,006 28,076 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 83 1,300 Congo, Rep. 2,628 .. 1,194 .. –805 .. 19 .. 648 .. 225 4,447 Costa Rica 7,750 13,662 7,297 14,723 –1,252 –748 93 370 –707 –1,439 1,318 4,630 Côte d’Ivoire 4,370 11,478 3,629 8,803 –653 –890 –330 –115 –241 1,670 674 3,624 Croatia 8,645 23,105 9,592 23,409 –466 –2,080 880 1,436 –533 –947 3,524 14,133 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Cyprus 5,019 9,745 5,142 11,235 –542 –1,267 177 –45 –488 –2,803 1,869 1,142 Czech Republic 35,858 137,665 37,550 130,932 –1,371 –13,198 373 472 –2,690 –5,992 13,142 42,483 Denmark 73,805 156,176 64,506 139,022 –4,023 5,771 –3,014 –5,792 2,262 17,134 15,696 76,510 Dominican Republic 8,964 11,697 10,852 17,462 –1,041 –1,788 1,902 3,118 –1,027 –4,435 632 3,501 Ecuador 5,906 19,610 4,927 22,651 –1,405 –1,054 1,352 2,310 926 –1,785 1,179 2,622 Egypt, Arab Rep. 16,864 48,831 22,895 59,862 888 –5,912 4,172 12,439 –971 –4,504 13,785 37,029 El Salvador 3,662 5,553 5,636 9,259 –253 –381 1,797 3,599 –431 –488 1,901 2,897 Eritrea 98 .. 500 .. –1 .. 299 .. –105 .. 36 114 Estonia 4,784 16,169 4,965 14,771 –203 –1,067 86 341 –299 673 923 2,567 Ethiopia 992 4,644 1,621 9,911 –36 –64 678 4,905 13 –425 363 1,781 Finland 53,431 97,979 40,459 93,836 –1,724 2,523 –723 –2,207 10,526 4,459 8,410 9,547 France 380,260 662,123 366,239 720,573 19,425 48,892 –13,771 –34,941 19,674 –44,499 63,728 165,852 Gabon 3,498 .. 1,656 .. –779 .. –63 .. 1,001 .. 194 1,736 Gambia, The .. 256 .. 308 .. –8 .. 113 .. 52 109 202 Georgia 859 4,061 1,323 6,134 37 –363 250 1,098 –177 –1,337 116 2,264 Germany 627,879 1,541,139 626,519 1,362,052 –7,662 59,648 –25,976 –50,792 –32,279 187,943 87,497 215,978 Ghana 2,441 9,437 3,350 13,925 –108 –535 631 2,322 –387 –2,700 309 .. Greece 29,440 60,094 41,727 80,353 –885 –10,756 3,352 118 –9,820 –30,897 14,594 6,352 Guatemala 3,862 10,827 5,567 15,188 –210 –1,211 865 4,946 –1,050 –626 1,806 5,949 Guinea 734 1,534 872 1,800 –78 –77 75 17 –140 –327 168 .. Guinea-Bissau 67 155 92 289 –12 –11 26 98 –11 –48 67 156 Haiti 504 799 1,369 4,084 –9 22 760 3,097 –114 –166 183 1,337 †Data for Taiwan, China 171,909 314,339 164,874 285,177 4,468 13,447 –2,604 –2,710 8,899 39,899 110,464 401,148 278 2012 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 3,850 6,764 4,681 9,881 –215 –598 538 2,760 –508 –955 1,319 2,701 Hungary 34,662 110,843 36,449 102,710 –2,575 –7,207 357 490 –4,004 1,417 11,217 44,988 India 59,932 349,264 73,075 440,277 –4,892 –12,926 13,434 52,158 –4,601 –51,781 41,059 300,480 Indonesia 70,622 174,840 56,002 153,537 –8,443 –20,291 1,816 4,630 7,992 5,643 29,353 96,211 Iran, Islamic Rep. 29,727 .. 17,503 .. –200 .. 457 .. 12,481 .. .. .. Iraq .. 65,695 .. 37,731 .. 2,106 .. –2,936 .. 27,133 7,882 50,642 Ireland 92,068 207,689 79,792 168,853 –13,547 –36,293 915 –1,589 –356 954 5,408 2,114 Israel 46,591 80,323 46,794 76,094 –8,323 –6,312 6,470 8,426 –2,056 6,342 23,281 70,907 Italy 297,030 546,949 286,526 586,410 –12,010 –11,410 –4,276 –21,144 –5,781 –72,015 47,201 158,478 Jamaica 3,589 4,004 4,427 6,454 –350 –495 821 2,010 –367 –934 1,054 2,501 Japan 528,751 871,533 459,660 796,674 60,401 133,291 –9,831 –12,395 119,660 195,755 361,639 1,096,069 Jordan 3,539 12,189 5,796 17,949 100 507 2,184 3,941 27 –1,312 3,441 13,633 Kazakhstan 10,341 65,086 8,970 43,268 –1,254 –18,325 249 –481 366 3,013 2,099 28,265 Kenya 2,776 8,900 3,763 13,543 –133 –155 921 2,286 –199 –2,512 898 4,321 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 209,589 547,006 192,970 516,332 –2,383 768 566 –3,229 14,803 28,214 96,251 292,143 Kosovo .. .. .. .. .. .. .. .. .. .. .. 846 Kuwait 21,301 74,689 11,372 32,682 6,699 7,818 –1,956 –13,003 14,672 36,822 7,779 24,805 Kyrgyz Republic 573 2,472 654 3,905 –82 –343 87 1,391 –76 –385 262 1,720 Lao PDR 506 2,257 578 2,324 –52 –83 116 179 –8 29 144 1,105 Latvia 3,229 12,800 3,813 13,025 17 85 195 871 –371 731 919 7,606 Lebanon 5,849 21,240 9,600 31,010 –868 76 78 785 –4,541 –8,909 8,475 44,476 Lesotho 269 900 1,044 2,515 467 532 237 661 –71 –421 418 .. Liberia .. 400 .. 1,800 .. 24 .. 960 .. –416 0 372 Libya 12,210 49,345 5,024 30,686 –429 –30 –487 –1,828 6,270 16,801 13,730 106,144 Lithuania 5,109 24,849 5,833 25,245 –194 –828 243 1,758 –675 534 1,363 6,598 Macedonia, FYR 1,637 4,213 2,280 6,094 –70 –124 609 1,805 –103 –200 460 2,277 Madagascar 1,188 .. 1,520 .. –42 .. 113 .. –260 .. 285 1,172 Malawi 437 1,399 629 2,105 –17 –110 135 253 –73 –563 247 325 Malaysia 112,370 231,714 94,350 189,499 –7,608 –8,142 –1,924 –6,783 8,488 27,290 28,651 106,528 Mali 644 2,128 927 2,812 –98 –457 126 486 –255 –655 382 1,344 Mauritania 393 .. 471 .. –32 .. 187 .. 77 .. 49 288 Mauritius 2,622 4,957 2,707 6,141 –16 202 64 183 –37 –800 914 2,619 Mexico 179,876 313,797 191,818 327,077 –13,795 –13,948 6,994 21,504 –18,743 –5,724 35,577 120,584 Moldova 641 2,292 972 4,581 22 487 211 1,319 –98 –484 222 1,718 Mongolia 614 3,394 771 3,869 –7 –599 94 187 –70 –887 202 2,288 Morocco 10,453 30,129 12,546 40,083 –864 –1,242 2,483 7,270 –475 –3,925 5,017 23,609 Mozambique 689 2,980 1,492 4,666 –192 –85 231 657 –764 –1,113 742 2,265 Myanmar 2,139 8,198 2,493 5,173 –133 –1,740 276 241 –212 1,527 286 .. Namibia 1,483 4,982 1,630 5,620 –98 –564 436 1,232 192 30 260 1,696 Nepal 1,282 1,574 1,790 5,887 37 94 340 4,092 –131 –128 987 2,925 Netherlands 254,590 576,108 238,810 513,539 –2,297 3,570 –6,219 –14,504 7,264 51,635 17,688 46,147 New Zealand 17,864 40,916 17,306 38,882 –3,202 –6,999 237 –29 –2,407 –4,994 3,952 16,723 Nicaragua 1,102 3,628 2,152 5,486 –296 –278 410 1,173 –936 –963 492 1,799 Niger 321 1,097 456 2,529 –16 –39 47 151 –104 –1,320 81 760 Nigeria 20,965 76,774 12,017 75,768 –3,148 –18,623 1,627 20,093 7,427 2,476 10,099 35,885 Norway 78,111 172,425 49,476 117,143 –2,305 872 –1,250 –4,711 25,079 51,444 27,922 52,798 Oman 11,770 38,362 6,351 24,400 –838 –3,162 –1,451 –5,704 3,129 5,096 2,460 13,025 Pakistan 10,119 28,062 12,148 40,021 –2,218 –3,187 4,162 13,778 –85 –1,368 2,087 17,256 Panama 7,833 18,402 8,122 19,882 –560 –1,859 177 477 –673 –2,862 723 2,714 Papua New Guinea 2,337 6,055 1,771 6,286 –210 –592 –5 190 351 –633 304 3,122 Paraguay 2,924 9,989 3,286 10,671 22 –502 177 542 –163 –641 772 4,167 Peru 8,510 39,521 9,648 34,809 –1,410 –10,053 1,001 3,026 –1,546 –2,315 8,676 44,215 Philippines 40,724 65,106 48,565 73,133 –30 347 5,643 16,604 –2,228 8,924 15,074 62,326 Poland 46,300 198,427 57,204 207,139 –731 –16,923 1,292 3,762 –10,343 –21,873 27,469 93,472 Portugal 34,102 72,125 47,262 87,411 –2,371 –10,423 3,342 2,858 –12,189 –22,850 14,262 20,937 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 1,163 31,182 2012 World Development Indicators 279 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 12,113 58,268 14,043 66,807 –285 –2,532 860 4,557 –1,355 –6,514 3,396 48,048 Russian Federation 114,598 445,539 61,091 323,070 –6,736 –48,615 69 –3,600 46,839 70,255 27,656 479,222 Rwanda 128 608 423 1,641 –15 –46 216 657 –94 –421 191 813 Saudi Arabia 82,259 261,832 52,932 174,204 479 7,044 –15,490 –27,921 14,317 66,751 20,847 459,313 Senegal 1,307 3,119 1,742 5,276 –111 –181 214 1,473 –332 –865 388 2,047 Serbia .. 13,351 .. 19,689 .. –899 .. 4,417 .. –2,819 517 13,308 Sierra Leone 55 423 250 879 –5 –49 88 185 –112 –320 49 409 Singapore 181,346 470,793 169,223 408,190 –784 –8,230 –1,095 –4,815 10,244 49,558 80,170 225,715 Slovak Republic 14,137 70,494 14,596 71,300 –355 –1,658 120 –544 –694 –3,009 4,376 2,156 Slovenia 10,696 30,489 11,385 30,360 26 –662 116 146 –548 –388 3,196 1,071 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 36,995 99,703 33,075 100,318 –3,184 –7,224 –926 –2,278 –191 –10,117 7,702 43,820 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 168,221 376,601 186,027 402,450 –6,849 –28,985 1,469 –9,508 –23,185 –64,342 35,608 31,872 Sri Lanka 6,378 10,776 8,105 15,282 –300 –572 983 3,660 –1,044 –1,418 1,131 7,195 Sudan 1,834 11,658 2,014 11,161 –575 –2,472 237 2,131 –518 157 138 1,036 Swaziland 1,240 2,063 1,438 2,625 51 –226 101 400 –46 –388 352 756 Sweden 111,275 225,662 97,042 196,830 –1,231 7,781 –3,142 –6,204 9,860 30,408 16,499 48,246 Switzerland 125,517 342,153 107,391 285,841 19,187 29,967 –4,483 –12,263 32,830 74,015 53,620 270,480 Syrian Arab Republic 6,845 19,606 5,390 19,409 –879 –1,514 485 949 1,061 –367 355 20,632 Tajikistan 768 1,512 928 3,329 –41 –79 186 1,513 –15 –383 94 .. Tanzania 1,361 6,388 2,050 8,975 –130 –216 391 824 –428 –1,978 974 3,905 Thailand 81,762 227,908 71,653 206,780 –1,381 –14,061 586 6,031 9,313 13,099 32,665 172,028 Timor-Leste .. .. .. .. .. .. .. .. .. .. 43 406 Togo 424 1,197 602 1,690 –29 –19 68 336 –140 –177 141 715 Trinidad and Tobago 4,844 9,940 3,709 7,356 –629 –997 38 27 544 1,614 1,403 9,692 Tunisia 8,607 22,236 9,311 24,351 –942 –1,925 825 1,935 –821 –2,104 1,871 9,764 Turkey 50,353 155,632 61,035 197,042 –4,002 –7,137 4,764 1,448 –9,920 –47,099 23,515 85,959 Turkmenistan .. .. .. .. .. .. .. .. .. .. 1,513 .. Uganda 663 3,474 1,409 6,099 –112 –305 499 1,190 –359 –1,740 808 2,706 Ukraine 19,522 69,255 17,947 73,239 –942 –2,009 848 2,975 1,481 –3,018 1,477 34,571 United Arab Emirates .. .. .. .. .. .. .. .. .. .. 13,632 42,785 United Kingdom 404,775 667,596 433,976 731,828 5,156 20,679 –14,756 –31,676 –38,800 –75,229 43,075 82,365 United States 1,072,780 1,837,576 1,449,535 2,337,607 19,179 165,224 –58,767 –136,095 –416,343 –470,902 128,400 488,928 Uruguay 3,660 10,555 4,193 9,743 –61 –1,093 27 122 –566 –160 2,776 7,656 Uzbekistan .. .. .. .. .. .. .. .. .. .. 1,242 .. Venezuela, RB 34,711 67,603 21,300 49,661 –1,388 –5,302 –170 –568 11,853 12,072 15,899 29,665 Vietnam 17,150 79,652 17,325 87,260 –451 –4,564 1,732 7,885 1,106 –4,287 3,417 12,467 West Bank and Gaza 1,012 1,224 3,270 5,008 628 808 639 2,239 –990 –737 .. .. Yemen, Rep. 4,008 9,329 3,294 11,017 –777 –1,812 1,399 2,291 1,337 –1,209 2,914 5,939 Zambia 872 7,725 1,312 5,650 –164 –1,893 14 432 –591 615 245 2,094 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World 7,977,777 t 18,856,365 t 7,950,280 t 18,328,230 t .. .. .. .. .. .. .. .. Low income 32,005 105,896 45,127 156,378 .. .. .. .. .. .. .. .. Middle income 1,592,025 5,719,672 1,495,508 5,389,112 .. .. .. .. .. .. .. .. Lower middle income 369,018 1,288,993 375,652 1,374,029 .. .. .. .. .. .. .. .. Upper middle income 1,224,283 4,435,558 1,121,580 4,021,226 .. .. .. .. .. .. .. .. Low & middle income 1,623,276 5,824,525 1,540,600 5,547,163 .. .. .. .. .. .. .. .. East Asia & Pacific 612,105 2,566,113 550,904 2,271,625 .. .. .. .. .. .. .. .. Europe & Central Asia 251,713 993,266 223,344 944,420 .. .. .. .. .. .. .. .. Latin America & Carib. 418,299 996,199 438,522 1,009,026 .. .. .. .. .. .. .. .. Middle East & N. Africa .. .. 122,648 384,516 .. .. .. .. .. .. .. .. South Asia 85,777 414,188 106,235 537,246 .. .. .. .. .. .. .. .. Sub-Saharan Africa 111,058 374,373 102,161 394,903 .. .. .. .. .. .. .. .. High income 6,357,262 13,051,331 6,409,545 12,801,007 .. .. .. .. .. .. .. .. Euro area 2,298,517 4,964,584 2,262,865 4,779,448 .. .. .. .. .. .. .. .. a. International reserves including gold valued at London gold price. 280 2012 World Development Indicators 4.17 ECONOMY Balance of payments current account About the data De�nitions The balance of payments records an economy’s collection systems according to the fourth edition • Exports and imports of goods and services are transactions with the rest of the world. Balance of of the Balance of Payments Manual (1977). Where all transactions between residents of an economy payments accounts are divided into two groups: necessary, the IMF converts such reported data to and the rest of the world involving a change in the current account, which records transactions conform to the fifth edition (see Primary data docu- ownership of general merchandise, goods sent for in goods, services, income, and current transfers, mentation). Values are in U.S. dollars converted at processing and repairs, nonmonetary gold, and ser- and the capital and financial account, which records market exchange rates. vices. •  Net income is receipts and payments of capital transfers, acquisition or disposal of non- employee compensation for nonresident workers, produced, nonfinancial assets, and transactions in and investment income (receipts and payments on financial assets and liabilities. The table presents direct investment, portfolio investment, and other data from the current account plus gross interna- investments and receipts on reserve assets). Income tional reserves. derived from the use of intangible assets is recorded The balance of payments is a double-entry under business services. •  Net current transfers accounting system that shows all flows of goods and are recorded in the balance of payments whenever services into and out of an economy; all transfers an economy provides or receives goods, services, that are the counterpart of real resources or financial income, or financial items without a quid pro quo. claims provided to or by the rest of the world without All transfers not considered to be capital are cur- a quid pro quo, such as donations and grants; and rent. • Current account balance is the sum of net all changes in residents’ claims on and liabilities to exports of goods and services, net income, and net nonresidents that arise from economic transactions. current transfers. • Total reserves are holdings of All transactions are recorded twice—once as a credit monetary gold, special drawing rights, reserves of and once as a debit. In principle the net balance IMF members held by the IMF, and holdings of foreign should be zero, but in practice the accounts often do exchange under the control of monetary authorities. not balance, requiring inclusion of a balancing item, The gold component of these reserves is valued at net errors and omissions. year-end (December 31) London prices ($386.75 an Discrepancies may arise in the balance of pay- ounce in 1995 and $1,087.50 an ounce in 2009). ments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transac- tions, and foreign exchange records. Differences in collection methods—such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions—contribute to net errors and omissions. In addition, smuggling and other ille- gal or quasi-legal transactions may be unrecorded or misrecorded. For further discussion of issues relat- ing to the recording of data on trade in goods and Data sources services, see About the data for tables 4.4–4.7. The concepts and definitions underlying the data Data on the balance of payments are published in in the table are based on the fi fth edition of the the IMF’s Balance of Payments Statistics Yearbook International Monetary Fund’s (IMF) Balance of Pay- and International Financial Statistics. The World ments Manual (1993). That edition redefined as capi- Bank exchanges data with the IMF through elec- tal transfers some transactions previously included tronic files that in most cases are more timely and in the current account, such as debt forgiveness, cover a longer period than the published sources. migrants’ capital transfers, and foreign aid to acquire More information about the design and compila- capital goods. Thus the current account balance now tion of the balance of payments can be found in reflects more accurately net current transfer receipts the IMF’s Balance of Payments Manual, fifth edition in addition to transactions in goods, services (pre- (1993), Balance of Payments Textbook (1996), and viously nonfactor services), and income (previously Balance of Payments Compilation Guide (1995). factor income). Many countries maintain their data 2012 World Development Indicators 281 STATES AND MARKETS S 5 tates and markets includes indicators of pri- and jobs is crucial to breaking cycles of violence vate sector investment and performance, and underdevelopment. the role of the public sector in nurturing Although civil wars and wars between states investment and growth, and the quality and are less common than in the past, insecurity availability of infrastructure essential for growth remains a primary development challenge. and development. Some 1.5 billion people live in areas affected by When private firms make investments, cre- fragility, conflict, or large-scale organized crimi- ate jobs, and improve productivity, they pro- nal violence, and no fragile or conflict-affected mote growth and expand opportunities for peo- country has achieved a single Millennium Devel- ple. Tables 5.1–5.6 cover private investment in opment Goal. People in these countries are infrastructure, the business environment, and more than twice as likely to be undernourished the development of financial systems. Just as people in other developing countries, more as a vibrant private sector is essential for job than three times as likely to be unable to send creation and growth, so are capable govern- their children to school, twice as likely to see ments and high-quality institutions essential their children die before age 5, and more than for promoting growth, raising incomes, and twice as likely to lack access to clean water. reducing poverty. Tables 5.7–5.9 cover these A major episode of violence can wipe out functions of governments, from tax policies an entire generation of economic progress. The and public institutions to crime statistics, and average cost of a civil war is equivalent to more military expenditures. Tables 5.10–5.12 cover than 30 years of GDP growth for a medium-size infrastructure —the systems for delivering developing country. World Development Report energy, transport, water and sanitation, and 2011 found that countries and areas with the information and communication technology ser- weakest institutional legitimacy (both formal vices to people. Table 5.13 covers innovation and informal) and poor governance are the most in science and technology. vulnerable to violence and instability and the World Development Indicators 2010 intro- least able to respond to internal and external duced a new table, continued this year as stresses. table 5.8, Fragile situations, with indicators for The World Bank manages the State and countries that have been identified as fragile Peace Building Fund, a multidonor trust fund or conflict- affected. World Development Report that provides grants to scale up Bank engage- 2011: Conflict, Security, and Development (World ment in fragile and conflict-affected countries; to Bank 2011d, p. xvi) defined fragility or fragile sit- promote cross-cutting, innovative approaches; uations as “periods when states or institutions and to foster strategic partnerships. As state- lack the capacity, accountability, or legitimacy building and peace, security, and institution- to mediate relations between citizen groups and building become central objectives for develop- between citizens and the state, making them ing countries and the development community, vulnerable to violence.� The report provided a new indicators are being developed to measure framework and practical recommendations on progress. The International Dialogue on Peace- how to move beyond fragility and conflict to building and Statebuilding, with more than 40 secure development. Its central message was participating countries and eight international that strengthening legitimate institutions and organizations (including the World Bank) is con- governance to provide citizens security, justice, tributing to this work. 2012 World Development Indicators 283 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 2006–10 2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2010 2009 2009 Afghanistan 466.1 1,040.4 1.6 .. .. .. .. .. 10.5 .. .. Albania 569.2 778.8 790.6 692.0 308.0 .. 8.0 0.0 38.0 2,045 0.84 Algeria 3,422.5 2,162.0 962.0 2,320.0 120.9 269.0 510.0 1,572.0 15.6 10,544 0.44 Angola 278.7 1,663.0 45.0 9.4 .. 53.0 .. .. 20.3 .. .. Argentina 5,836.8 5,993.9 3,826.9 3,801.9 203.6 1,402.6 791.6 .. 14.6 11,924 0.46 Armenia 317.1 586.7 74.0 127.0 63.0 715.0 0.0 0.0 26.5 2,698 1.28 Australia .. .. .. .. .. .. .. .. 131.1 89,960 6.38 Austria .. .. .. .. .. .. .. .. 122.4 3,228 0.58 Azerbaijan 355.6 1,407.8 375.2 .. .. .. 0.0 .. 18.3 5,314 0.93 Bahrain .. .. .. .. .. .. .. .. 79.6 .. .. Bangladesh 1,294.3 4,250.3 501.5 340.4 0.0 0.0 .. .. 47.1 .. .. Belarus 735.4 2,638.7 .. 2,500.0 .. 4.0 .. .. 44.8 5,508 0.80 Belgium .. .. .. .. .. .. .. .. 94.9 29,548 4.28 Benin 116.9 793.7 590.0 .. .. .. .. .. 23.1 .. .. Bolivia 520.5 284.7 884.4 137.3 16.6 .. .. .. 40.3 2,504 0.43 Bosnia and Herzegovina 0.0 1,102.5 .. 908.6 .. .. .. .. 66.0 1,896 0.58 Botswana 104.0 242.9 .. .. .. .. .. .. 23.4 .. .. Brazil 41,053.8 40,063.0 26,034.6 52,825.6 3,156.5 23,527.7 1,234.4 1,581.0 57.0 315,645 2.38 Bulgaria 2,179.1 2,211.3 3,253.5 2,454.1 2.1 536.2 152.0 .. 74.6 35,545 7.20 Burkina Faso 41.9 979.6 .. .. .. .. .. .. 17.6 610 0.08 Burundi 53.6 0.0 .. .. .. .. .. .. 25.5 .. .. Cambodia 136.1 446.7 82.1 2,452.3 125.3 40.1 .. .. 27.6 2,003 0.22 Cameroon 394.4 934.4 91.8 908.0 0.0 .. .. 0.0 11.9 .. .. Canada .. .. .. .. .. .. .. .. 128.2 174,000 7.56 Central African Republic 0.0 30.8 .. .. .. .. .. .. 8.7 .. .. Chad 11.0 591.4 0.0 .. .. .. .. .. 5.7 .. .. Chile 1,260.7 1,326.7 1,393.2 1,567.2 4,830.7 1,943.1 1,495.2 3.1 86.3 23,541 2.12 China 8,548.0 0.0 10,970.9 7,477.5 15,454.0 15,795.0 3,505.2 4,626.9 130.0 .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. 189.0 101,023 19.19 Colombia 1,570.9 6,347.9 351.6 1,080.9 1,497.4 4,461.4 314.3 305.0 43.5 31,132 1.07 Congo, Dem. Rep. 473.4 1,054.0 .. .. .. .. .. .. 6.6 .. .. Congo, Rep. 61.8 407.7 .. .. .. 735.0 0.0 .. 5.5 .. .. Costa Rica .. .. 80.0 190.0 465.2 407.0 .. .. 45.9 26,765 8.78 Côte d’Ivoire 134.9 1,204.4 0.0 0.0 176.4 .. .. 0.0 18.1 .. .. Croatia 1,205.7 3,035.0 7.1 85.0 451.0 492.0 298.7 .. 70.1 7,800 2.57 Cuba 60.0 0.0 116.0 60.0 0.0 .. 600.0 .. .. .. .. Cyprus .. .. .. .. .. .. .. .. 283.6 16,101 20.30 Czech Republic .. .. .. .. .. .. .. .. 56.2 21,717 3.00 Denmark .. .. .. .. .. .. .. .. 223.5 16,519 4.57 Dominican Republic 393.0 220.1 1,306.6 0.0 898.9 948.9 .. .. 22.7 12,881 2.13 Ecuador 357.8 2,003.3 302.0 129.0 695.0 766.0 510.0 .. 30.8 .. .. Egypt, Arab Rep. 3,471.9 10,977.0 678.0 469.0 821.5 1,370.0 .. 475.0 33.1 6,291 0.13 El Salvador 1,110.6 1,037.6 85.0 16.0 .. .. .. .. 41.0 4,400 1.19 Eritrea 40.0 0.0 .. .. .. .. .. .. 16.0 .. .. Estonia .. .. .. .. .. .. .. .. 97.2 7,199 8.10 Ethiopia .. 0.0 .. 4.0 .. .. .. .. 17.8 1,327 0.03 Finland .. .. .. .. .. .. .. .. 95.2 11,820 3.37 France .. .. .. .. .. .. .. .. 114.4 128,906 3.08 Gabon 26.6 403.8 0.0 0.0 177.4 3.9 .. .. 8.2 3,490 4.27 Gambia, The 6.6 35.0 .. 0.0 .. .. .. .. 19.1 .. .. Georgia 201.3 722.0 40.0 634.2 .. 573.0 .. 435.0 32.4 7,226 2.32 Germany .. .. .. .. .. .. .. .. 107.8 64,840 1.19 Ghana 156.5 3,206.0 590.0 100.0 10.0 .. 0.0 .. 15.2 9,606 0.72 Greece .. .. .. .. .. .. .. .. 115.9 8,426 1.18 Guatemala 560.1 1,724.4 110.0 1,021.8 .. .. .. 6.7 23.4 5,133 0.68 Guinea 50.6 313.2 .. .. .. 159.0 .. .. .. .. .. Guinea-Bissau 9.7 107.2 .. .. .. .. .. .. 6.2 .. .. Haiti 18.0 306.0 5.5 0.0 .. .. .. 1.0 14.0 .. .. 284 2012 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 2006–10 2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2010 2009 2009 Honduras 135.0 1,001.0 358.8 250.0 120.0 .. 207.9 .. 50.2 .. .. Hungary 5,172.8 1,523.3 851.6 1,707.0 3,297.5 1,588.0 0.0 0.0 72.6 42,951 6.26 India 20,551.8 53,090.4 8,663.6 90,973.0 4,413.4 38,036.2 112.9 241.7 49.0 84,800 0.12 Indonesia 6,557.2 11,009.5 1,280.5 6,079.3 159.2 1,731.5 44.8 20.2 29.1 28,998 0.18 Iran, Islamic Rep. 695.0 1,992.0 650.0 .. .. .. .. .. 36.7 .. .. Iraq 984.0 4,977.0 .. 590.0 .. 500.0 .. .. 9.1 .. .. Ireland .. .. .. .. .. .. .. .. 215.0 13,188 4.67 Israel .. .. .. .. .. .. .. .. 95.7 19,758 4.46 Italy .. .. .. .. .. .. .. .. 122.0 68,508 1.78 Jamaica 612.0 157.9 201.0 210.0 565.0 .. .. .. 24.8 2,003 1.16 Japan .. .. .. .. .. .. .. .. 169.3 105,698 1.28 Jordan 1,589.0 949.6 .. 989.0 0.0 1,380.0 169.0 951.0 70.3 2,737 0.74 Kazakhstan 1,153.7 3,766.5 300.0 0.0 231.0 31.0 .. .. 39.3 27,978 2.59 Kenya 1,434.0 3,465.8 .. 437.7 .. 404.0 .. .. 33.8 17,896 0.85 Korea, Dem. Rep. .. 474.0 .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. 100.8 60,039 1.72 Kosovo .. 385.1 .. .. .. .. 0.0 .. 37.3 141 0.12 Kuwait .. .. .. .. .. .. .. .. 82.4 .. .. Kyrgyz Republic 11.5 130.2 .. .. .. .. 0.0 .. .. 4,412 1.26 Lao PDR 87.7 135.0 1,250.0 5,285.0 1.5 1.5 .. .. 20.4 .. .. Latvia 700.0 468.1 158.1 184.0 .. 135.0 .. .. 103.7 7,175 4.62 Lebanon 138.1 0.0 .. .. 153.0 .. 0.0 .. 81.3 .. .. Lesotho 88.4 41.6 0.0 .. .. .. .. .. 13.6 .. .. Liberia 70.3 88.8 .. 170.0 .. 120.0 .. .. 16.0 .. .. Libya .. .. .. .. .. .. .. .. 10.9 .. .. Lithuania 993.0 548.2 514.3 464.1 .. .. .. .. 66.4 5,399 2.18 Macedonia, FYR 706.6 575.4 .. 655.0 .. 295.0 .. .. 45.3 8,074 5.63 Madagascar 12.6 436.8 0.0 17.8 61.0 17.5 .. .. 11.7 724 0.07 Malawi 36.3 313.7 0.0 .. .. .. .. .. 16.0 619 0.08 Malaysia 3,294.9 3,689.9 6,637.6 384.5 4,263.0 1,632.3 6,502.2 0.0 114.9 41,638 2.55 Mali 82.6 837.0 365.9 .. 55.4 .. .. .. 18.4 .. .. Mauritania 92.1 266.1 .. .. .. .. .. .. 30.4 .. .. Mauritius 393.0 63.1 0.0 .. .. .. .. 0.0 87.8 6,626 7.33 Mexico 18,758.0 16,290.6 6,749.3 2,282.7 2,970.4 12,651.5 523.7 1,096.8 24.6 44,084 0.61 Moldova 46.1 426.8 227.2 68.0 0.0 60.0 .. .. 33.3 4,180 1.32 Mongolia 22.1 0.0 .. .. .. .. .. .. 39.6 .. .. Morocco 6,139.5 3,673.6 1,049.0 .. 200.0 200.0 .. .. 68.7 26,166 1.28 Mozambique 123.0 236.2 1,205.8 .. 334.6 0.0 .. 0.0 25.8 .. .. Myanmar .. .. .. 556.1 .. .. .. .. 4.7 .. .. Namibia 35.0 8.5 1.0 .. .. .. 0.0 .. 45.6 .. .. Nepal 109.3 26.0 15.1 34.1 .. .. .. 0.0 55.6 .. .. Netherlands .. .. .. .. .. .. .. .. 199.3 35,100 3.10 New Zealand .. .. .. .. .. .. .. .. 149.0 47,897 17.08 Nicaragua 218.5 512.2 126.3 510.0 104.0 .. .. .. 32.5 .. .. Niger 85.5 358.7 .. .. .. .. 3.4 .. 12.6 24 0.00 Nigeria 6,949.7 14,384.1 1,920.0 280.0 2,355.4 644.1 .. .. 29.4 65,089 0.79 Norway .. .. .. .. .. .. .. .. .. 13,805 4.49 Oman .. .. .. .. .. .. .. .. 48.2 3,165 1.67 Pakistan 6,594.9 9,006.5 375.4 4,120.5 112.8 923.7 .. .. 21.5 2,759 0.03 Panama 211.4 1,262.2 449.3 877.0 51.4 0.0 .. .. 91.5 548 0.26 Papua New Guinea .. 150.0 .. .. .. .. .. .. 31.8 .. .. Paraguay 199.0 636.4 .. .. .. .. .. .. 37.8 .. .. Peru 2,241.4 3,191.1 2,498.9 2,191.0 522.5 3,289.6 152.0 119.8 24.3 51,151 2.65 Philippines 4,616.4 5,172.0 3,428.4 10,942.5 943.5 968.9 0.0 530.5 29.6 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 54.8 14,434 0.52 Portugal .. .. .. .. .. .. .. .. 191.0 27,759 3.92 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. 51.5 .. .. 2012 World Development Indicators 285 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 2006–10 2000–05 2006–10 2000–05 2006–10 2000–05 2006–10 2010 2009 2009 Romania 3,906.9 4,996.4 1,240.8 6,288.7 .. 116.8 116.0 41.0 46.1 56,698 3.66 Russian Federation 22,049.4 30,982.6 1,726.0 32,401.2 109.4 4,786.9 904.7 1,241.7 45.1 261,633 2.61 Rwanda 72.3 414.0 1.6 .. .. .. .. .. .. 3,028 0.51 Saudi Arabia .. .. .. .. .. .. .. .. 47.6 .. .. Senegal 593.1 1,569.0 93.3 22.0 55.4 398.0 0.0 0.0 25.9 1,636 0.22 Serbia 563.5 3,024.1 .. .. .. .. 0.0 .. 51.5 9,715 1.94 Sierra Leone 48.8 149.2 .. 1.2 .. 130.0 .. .. 10.4 .. .. Singapore .. .. .. .. .. .. .. .. 102.1 26,416 7.40 Slovak Republic .. .. .. .. .. .. .. .. 44.9 15,825 4.04 Slovenia .. .. .. .. .. .. .. .. 94.4 5,836 4.16 Somalia 13.4 0.0 .. .. .. .. .. .. .. .. .. South Africa 10,519.5 9,815.0 1,251.3 15.9 504.7 3,483.0 31.3 0.0 145.5 24,700 0.77 South Sudan .. .. .. .. .. .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. 211.6 79,757 2.92 Sri Lanka 714.6 1,283.5 270.8 .. .. .. .. .. 26.6 4,223 0.29 Sudan 747.7 2,226.3 .. .. .. 30.0 .. 120.7 11.6 .. .. Swaziland 27.7 63.3 .. .. .. .. .. .. 23.0 .. .. Sweden .. .. .. .. .. .. .. .. 140.2 24,228 4.09 Switzerland .. .. .. .. .. .. .. .. 174.6 25,250 4.88 Syrian Arab Republic 583.0 372.7 .. .. .. 82.0 .. .. 22.5 .. .. Tajikistan 8.5 196.4 16.0 .. .. .. .. .. .. 2,171 0.48 Tanzania 515.3 2,109.5 348.0 28.4 27.7 134.0 8.5 .. 16.2 .. .. Thailand 5,602.7 3,526.0 4,693.3 6,641.4 939.0 .. 524.7 18.8 116.6 27,520 0.59 Timor-Leste 0.0 0.0 .. .. .. .. .. .. 15.6 .. .. Togo 0.0 67.0 657.7 190.0 .. .. .. .. 23.0 125 0.04 Trinidad and Tobago .. .. .. .. .. .. .. .. 39.2 .. .. Tunisia 751.0 3,771.0 30.0 .. .. 840.0 .. 95.0 68.8 9,079 1.23 Turkey 12,788.6 13,751.7 5,854.8 13,504.2 3,203.6 4,471.2 .. .. 44.0 44,472 0.87 Turkmenistan 20.0 202.5 .. .. .. .. .. .. .. .. .. Uganda 387.6 1,720.0 113.9 1,000.6 .. 404.0 0.0 .. 15.8 11,152 0.72 Ukraine 3,162.9 4,921.4 160.0 64.9 .. 130.0 100.0 102.0 61.7 19,300 0.60 United Arab Emirates .. .. .. .. .. .. .. .. 72.5 .. .. United Kingdom .. .. .. .. .. .. .. .. 202.9 330,100 8.05 United States .. .. .. .. .. .. .. .. 202.2 .. .. Uruguay 114.2 200.2 330.0 .. 251.1 .. 368.0 .. 23.0 4,664 2.08 Uzbekistan 285.6 2,046.8 .. .. .. 25.0 0.0 .. .. 14,428 0.78 Venezuela, RB 3,337.0 3,042.8 39.5 .. 34.0 .. 15.0 .. 18.9 .. .. Vietnam 430.0 1,593.7 2,360.6 297.0 20.0 1,120.0 266.0 .. 125.0 .. .. West Bank and Gaza 279.8 644.0 150.0 .. .. .. .. .. .. .. .. Yemen, Rep. 376.8 451.2 .. 15.8 .. 220.0 .. .. 6.2 .. .. Zambia 208.3 1,077.0 3.0 .. 15.6 .. 0.0 .. 11.5 5,509 0.88 Zimbabwe 72.0 644.0 .. .. .. .. .. .. .. .. .. World .. s .. s .. s .. s .. s .. s .. s .. s 134.8 w Low income 4,646.3 21,615.6 .. .. .. .. .. .. 28.3 Middle income 226,353.6 317,609.7 107,559.1 266,780.1 51,413.6 106,204.4 16,177.1 6,654.9 73.7 Lower middle income 68,257.9 144,334.1 19,986.8 123,341.9 1,781.0 34,127.5 192.0 .. 40.2 Upper middle income 158,095.7 34,212.5 83,156.9 143,438.2 41,607.7 72,076.9 18,427.2 9,705.8 83.3 Low & middle income 232,154.6 339,225.4 85,938.8 272,012.7 5,437.5 86,673.9 2,481.0 .. 73.0 East Asia & Pacific 29,380.1 4,662.0 30,710.4 40,115.6 21,905.4 20,538.0 10,842.9 5,196.4 116.3 Europe & Central Asia 51,002.1 76,087.4 4,439.1 58,924.3 .. .. .. .. 46.2 Latin America & Carib. 78,935.4 85,698.9 45,347.6 67,445.3 16,419.9 49,397.7 2,516.1 .. 41.9 Middle East & N. Africa 13,435.4 29,970.1 .. .. .. .. .. .. 35.0 South Asia 29,784.3 68,726.2 9,828.0 95,669.0 4,526.2 39,437.9 112.9 241.7 45.7 Sub-Saharan Africa 24,622.2 52,365.4 .. .. .. .. .. .. 65.4 High income .. .. .. .. .. .. .. .. 164.6 Euro area .. .. .. .. .. .. .. .. 133.6 a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached financial closure in 1990–2010. 286 2012 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy About the data De�nitions 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 Statis- ments are the sum of investments in physical assets easing fiscal constraints, improving the efficiency tics or, when unavailable, from its monetary survey. and payments to the government. Investments in of infrastructure services, and extending delivery The monetary survey includes monetary authorities physical assets are resources the project company to poor people. Developing countries have been in (the central bank), deposit money banks, and other commits to invest during the contract period in new the forefront, pioneering better approaches to infra- banking institutions, such as finance companies, facilities or in expansion and modernization of exist- structure services and reaping the benefits of greater development banks, and savings and loan institu- ing facilities. Payments to the government are the competition and customer focus. tions. Credit to the private sector may sometimes resources the project company spends on acquir- The data on investment in infrastructure projects include credit to state-owned or partially state-owned ing government assets such as state-owned enter- with private participation refer to all investment (pub- enterprises. prises, rights to provide services in a specific area, or lic and private) in projects in which a private com- Entrepreneurship is essential to the dynamism of use of specific radio spectrums. • Domestic credit pany assumes operating risk during the operating the modern market economy, and a greater entry to private sector is financial resources provided period or development and operating risk during the density of new businesses can foster competition to the private sector—such as through loans, pur- contract period. Investment refers to commitments and economic growth. The table includes data on chases of nonequity securities, and trade credits and not disbursements. Foreign state-owned companies business registrations from the 2008 and 2010 other accounts receivable—that establish a claim for are considered private entities for the purposes of World Bank Group Entrepreneurship Survey, which repayment. For some countries these claims include this measure. includes entrepreneurial activity in more than 100 credit to public enterprises. • New businesses regis- Investments are classified into two types: invest- countries for 2000–09. Survey data are used to tered are the number of limited liability corporations ments in physical assets—the resources a com- analyze firm creation, its relationship to economic registered in the calendar year. • Entry density is the pany commits to invest in expanding and modern- growth and poverty reduction, and the impact of number of newly registered limited liability corpora- izing facilities—and payments to the government to regulatory and institutional reforms. The 2010 sur- tions per 1,000 people ages 15–64. acquire state-owned enterprises or rights to provide vey improves on earlier surveys’ methodology and services in a specific area or to use part of the radio country coverage for better cross-country compara- spectrum. bility; the database will be updated in 2012. Data on The data are from the World Bank’s Private Par- total registered businesses were collected directly ticipation in Infrastructure (PPI) Project database, from national registrars of companies. For cross- which tracks infrastructure projects with private par- country comparability, only limited liability corpora- ticipation in developing countries. It provides infor- tions that operate in the formal sector are included. Data sources mation on more than 4,800 infrastructure projects For additional information on sources, methodol- in 139 developing economies from 1984 to 2010. ogy, calculation of entrepreneurship rates, and data Data on investment commitments in infra- The database contains more than 30 fields per proj- limitations see http://econ.worldbank.org/research/ structure projects with private participation are ect record, including country, financial closure year, entrepreneurship. 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 Finan- location, contract duration, private sponsors, bidding cial Statistics. Data on business registration process, and development bank support. Data on the are from the World Bank’s Entrepreneurship projects are compiled from publicly available infor- Snapshots (http://econ.worldbank.org/research/ mation. The database aims to be as comprehensive entrepreneurship). as possible, but some projects—particularly those 2012 World Development Indicators 287 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 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 19.9a Algeria 2007 25.1 2.3 19.3 66.6 0.9 98.3 15.0 8.9 4.0 5.0 14.1 17.3a Angola 2010 12.2 2.5 34.7 48.9 1.5 62.7 56.6 13.1 12.6 21.7 6.7 23.5 Argentina 2010 20.8 2.7 176.1 18.2 0.6 92.4 38.0 30.3 3.5 18.2 7.3 63.6 Armenia 2009 10.3 2.1 20.0 16.0 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 52.2 0.3 85.1 10.8 19.0 1.8 18.2 1.9 10.5 Bahrain   .. .. .. .. .. .. .. .. .. .. .. .. Bangladesh 2007 3.2 1.3 6.0 85.1 0.1 .. 16.1 24.7 10.6 7.8 8.4 27.2a Belarus 2008 13.6 1.1 38.2 26.1 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 2010 28.5 1.6 37.3 17.6 0.8 72.4 41.3 27.8 2.5 22.4 12.4 57.1 Bosnia and Herzegovina 2009 11.2 1.0 21.4 10.3 0.4 98.6 32.8 59.7 1.9 30.1 1.3 66.5 Botswana 2010 10.2 1.0 27.2 7.3 1.5 93.9 55.3 32.8 3.7 21.3 6.2 51.9 Brazil 2009 18.7 1.2 83.5 11.9 1.7 95.8 59.3 48.4 3.0 25.7 15.9 52.9 Bulgaria 2009 10.6 2.2 20.8 22.4 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 .. 22.1a Cambodia 2007 5.6 1.0 .. 61.2 0.4 87.5 .. 11.3 2.4 2.8 1.5 48.4a Cameroon 2009 7.0 4.4 30.0 51.2 1.6 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 2010 9.9 2.9 68.5 0.7 0.8 96.1 29.6 44.8 1.3 22.1 10.8 57.5 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 2010 12.9 0.9 25.6 2.8 0.3 94.3 35.3 35.0 1.8 20.8 8.6 65.2 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 .. 81.8 3.3 84.3 31.8 7.7 16.4 19.6 .. 37.5 Costa Rica 2010 8.4 0.8 35.6 3.7 0.4 80.8 43.5 22.2 1.7 13.3 10.0 54.7 Côte d’Ivoire 2009 1.6 3.7 44.1 38.5 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 28.0a Cuba   .. .. .. .. .. .. .. .. .. .. .. .. Cyprus   .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 2009 10.4 1.5 19.9 12.8 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 2010 22.5 0.8 30.0 11.8 1.0 85.1 24.1 17.0 3.9 9.7 18.2 65.9 Egypt, Arab Rep. 2008 8.8 3.4 42.7 15.2 3.0 17.9 34.0 5.6 3.2 21.1 6.2 21.7 El Salvador 2010 19.7 3.0 44.4 12.7 1.6 75.7 40.2 31.7 7.0 14.5 3.7 61.0 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 3.7 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 38.2a Finland   .. .. .. .. .. .. .. .. .. .. .. .. France   .. .. .. .. .. .. .. .. .. .. .. .. Gabon 2009 2.8 15.2 12.1 41.8 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 25.6a Georgia 2008 2.1 0.6 11.8 14.7 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 3.2 4.3 6.4 38.8 0.9 66.4 44.0 16.0 5.6 6.8 7.8 33.0a Greece 2005 1.8 1.7 .. 21.6 0.0 .. 24.4 25.9 .. 11.7 5.5 20.0 Guatemala 2010 10.2 2.7 41.1 6.3 1.3 90.0 44.2 26.6 2.8 11.9 4.7 51.9 Guinea 2006 2.7 2.8 13.0 84.8 2.0 .. 25.4 0.9 14.0 5.2 4.3 21.1a Guinea-Bissau 2006 2.9 3.4 30.4 63.1 1.1 .. 19.9 0.7 5.3 8.4 5.6 12.4a Haiti   .. .. .. .. .. .. .. .. .. .. .. .. 288 2012 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 management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Honduras 2010 17.0 2.2 28.8 6.1 2.2 81.3 43.3 17.0 9.2 16.3 10.1 35.8 Hungary 2009 13.5 0.8 35.6 5.4 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 46.6 6.6 22.5 15.1 15.9a Indonesia 2009 1.6 0.2 21.1 14.9 0.4 29.1 42.8 11.7 2.2 2.9 2.3 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 2010 1.7 0.6 9.3 10.6 0.4 90.0 38.2 44.4 0.2 16.5 13.1 25.9 Japan   .. .. .. .. .. .. .. .. .. .. .. .. Jordan 2006 6.7 1.7 6.4 18.1 0.1 .. 13.1 8.6 1.7 15.5 3.8 23.9a Kazakhstan 2009 4.7 2.6 30.8 34.1 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 48.5a 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 7.5 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 47.8 0.3 95.9 60.4 17.9 10.5 16.2 15.8 29.7 Lao PDR 2009 1.2 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 13.4 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 28.1 2.9 86.8 18.4 32.7 6.7 24.7 5.4 42.5 Liberia 2009 7.5 6.5 16.0 55.4 2.8 73.8 53.0 10.1 2.9 2.4 .. 17.0 Libya   .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 2009 9.3 0.8 65.5 10.7 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 16.9 0.7 99.2 36.4 47.0 5.9 21.5 2.5 19.0 Madagascar 2009 17.1 0.9 41.3 21.8 1.2 97.5 50.0 12.2 7.7 8.7 14.2 27.0 Malawi 2009 3.5 2.6 15.0 10.8 5.7 78.6 23.9 20.6 13.3 17.9 9.9 48.4 Malaysia 2007 7.8 2.1 22.4 .. 1.0 53.0 13.1 48.6 3.0 54.1 2.7 50.1a Mali 2010 2.0 1.2 41.0 19.4 0.5 79.2 58.3 29.3 4.1 24.8 12.9 32.1 Mauritania 2006 5.8 1.8 10.7 82.1 0.6 .. 17.3 3.2 1.6 5.9 3.9 25.5a Mauritius 2009 9.4 0.5 19.1 5.9 1.4 84.2 16.9 37.5 2.2 11.1 10.3 25.6 Mexico 2010 13.6 1.1 54.0 11.6 1.4 84.7 25.7 16.2 3.4 24.0 7.1 50.8 Moldova 2009 7.0 1.9 13.9 33.5 0.4 97.9 53.1 30.8 2.0 9.1 2.4 33.1 Mongolia 2009 12.1 2.0 43.5 33.4 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 24.7a Mozambique 2007 3.3 1.9 35.2 14.8 1.8 85.9 24.4 10.5 2.4 18.7 10.1 22.1a Myanmar   .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2006 2.9 0.3 9.6 11.4 1.3 .. 33.4 8.1 0.7 17.6 1.4 44.5a 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 2010 20.2 1.5 17.6 8.3 2.2 74.0 61.9 21.9 18.2 15.5 4.7 47.2 Niger 2009 22.9 1.2 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 25.7a Norway   .. .. .. .. .. .. .. .. .. .. .. .. Oman   4.4 11.8 33.2 .. .. .. 31.0 4.2 10.8 3.4 20.9 .. Pakistan 2007 1.9 1.5 10.2 48.0 0.4 .. 6.7 9.7 9.2 9.6 2.6 6.7a Panama 2010 33.3 0.8 66.3 30.5 0.3 99.7 24.7 1.2 2.1 22.5 7.6 11.0 Papua New Guinea   .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 2010 20.7 1.0 81.3 17.5 1.3 98.7 51.6 30.1 1.4 15.0 21.7 54.9 Peru 2010 14.1 1.7 46.5 21.4 0.6 82.6 28.7 45.9 3.2 14.2 16.1 60.1 Philippines 2009 9.1 1.5 10.6 18.6 1.1 97.5 69.4 22.0 3.9 15.7 10.0 31.1 Poland 2009 12.8 0.6 14.6 14.7 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   .. .. .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 289 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 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 22.2 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 39.6 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 27.6a 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 16.3a Serbia 2009 12.2 1.4 28.0 21.4 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 20.4 0.8 89.2 7.9 6.9 6.6 13.8 .. 18.6 Singapore   .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 2009 6.7 0.9 32.1 15.7 0.7 100.0 29.6 33.5 0.3 28.6 2.4 33.1 Slovenia 2009 7.3 0.3 56.1 5.8 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 36.8a South Sudan   .. .. .. .. .. .. .. .. .. .. .. .. 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 51.0a 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 44.6 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 36.5a Thailand 2006 0.4 1.0 32.1 .. 0.1 .. .. 74.4 1.5 39.0 1.3 75.3a Timor-Leste 2009 3.8 0.9 16.6 19.4 2.7 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 18.0 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 35.0a Ukraine 2008 11.3 2.1 31.0 31.8 0.6 95.8 47.1 32.1 4.4 13.0 3.4 24.8 United Arab Emirates   .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom   .. .. .. .. .. .. .. .. .. .. .. .. United States   .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 2010 11.6 1.0 108.0 8.1 0.3 94.6 23.1 13.7 0.3 10.8 6.2 48.6 Uzbekistan 2008 11.1 0.7 9.1 59.5 0.7 100.0 39.8 8.2 5.4 1.3 5.1 9.6 Venezuela, RB 2010 27.6 3.0 117.2 23.7 1.4 95.6 30.7 35.3 8.3 25.9 18.4 56.0 Vietnam 2009 4.6 0.9 15.9 52.5 0.3 87.5 59.2 21.5 3.6 16.7 4.2 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 26.5a 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 26.0a Zimbabwe   .. .. .. .. .. .. .. .. .. .. .. Note: Enterprise surveys are updated several times a year; see www.enterprisesurveys.org for the most recent updates. a. The sample was drawn from the manufacturing sector only. 290 2012 World Development Indicators 5.2 STATES AND MARKETS Business environment: enterprise surveys About the data De�nitions The World Bank Enterprise Surveys gather firm-level distortions limit access to credit and thus restrain •  Survey year is the year in which the underlying data to benchmark the business environment of growth. data were collected. • Time dealing with tax of� - economies and assess how business environment The reliability and availability of infrastructure ben- cials is the average percentage of senior manage- constraints affect productivity and job creation. Stan- efit households and support development. Firms with ment’s time that is spent in a typical week dealing dardized surveys are conducted all over the world, access to modern and efficient infrastructure—tele- with requirements imposed by government regula- and data are available on more than 130,000 firms communications, electricity, and transport—can be tions. • Average number of times meeting with tax in 128 countries. The survey covers 11 dimensions more productive. Firm-level innovation and use of of�cials is the average number of visits or required of the business environment, including regulation, modern technology may help firms compete. meetings with tax officials. • Time required to obtain corruption, crime, informality, finance, infrastructure, Delays in clearing customs can be costly, deterring operating license is the average wait to obtain an and trade. For several countries firm-level panel firms from engaging in trade or making them uncom- operating license from the day applied for to the day data are also available, making it possible to track petitive globally. Ill-considered labor regulations dis- granted. • Informal payments to public of�cials are changes in the business environment over time. courage firms from creating jobs, and while employed the percentage of firms that answered positively Firms evaluating investment options, governments workers may benefit, unemployed, low-skilled, and to the question “Was a gift or informal payment interested in improving business conditions, and informally employed workers will not. A trained labor expected or requested during a meeting with tax economists seeking to explain economic perfor- force enables firms to thrive, compete, innovate, and officials?� • Losses due to theft, robbery, vandal- mance have all grappled with defining and measur- adopt new technology. ism, and arson are the estimated losses from those ing the business environment. The firm-level data The data in the table are from Enterprise Surveys causes that occurred on establishments’ premises from Enterprise Surveys provide a useful tool for implemented by the World Bank’s Financial and Pri- as a percentage of annual sales. • Firms formally benchmarking economies across a large number of vate Sector Development Enterprise Analysis Unit. All registered when operations started are firms for- indicators measured at the firm level. economies in East Asia and Pacific, Europe and Cen- mally registered when they started operations in the Most countries can improve regulation and taxa- tral Asia, Latin America and the Caribbean, Middle country. Firms not formally registered (the residual) tion without compromising broader social interests. East and North Africa, and Sub-Saharan Africa (for are in the informal sector of the economy. • Firms Excessive regulation may harm business perfor- 2009) and Afghanistan, Bangladesh, and India draw with female participation in ownership are firms with mance and growth. For example, time spent with a sample of registered nonagricultural businesses, a woman among the owners. • Firms using banks to tax officials is a burden firms may face in paying excluding those in the financial and public sectors. �nance investment are firms that invested in fixed taxes. The business environment suffers when gov- Samples for other economies are drawn only from assets during the last fiscal year that used banks to ernments increase uncertainty and risks or impose the manufacturing sector and are footnoted in the finance fixed assets. • Value lost due to electrical unnecessary costs and unsound regulation and taxa- table. Typical Enterprise Survey sample sizes range outages is losses that resulted from power outages tion. Time to obtain licenses and permits and the from 150 to 1,800, depending on the size of the as a percentage of annual sales. • Internationally associated red tape constrain firm operations. economy. In each country samples are selected by recognized quality certi� cation ownership is the In some countries doing business requires informal stratified random sampling, unless otherwise noted. percentage of firms that have an internationally rec- payments to “get things done� in customs, taxes, Stratified random sampling allows indicators to be ognized quality certification, such as International licenses, regulations, services, and the like. Such computed by sector, firm size, and location and Organization for Standardization 9000, 9001, 9002, corruption can harm the business environment by increases the precision of economywide indicators or 14000 or Hazard Analysis and Critical Control distorting policymaking, undermining government compared with alternative simple random sampling. Points. •  Average time to clear direct exports credibility, and diverting public resources. Crime, Stratification by sector of activity divides the econ- through customs is the average number of days to theft, and disorder also impose costs on businesses omy into manufacturing and retail and other services clear direct exports through customs. • Firms offer- and society. sectors. For medium-size and large economies the ing formal training are firms offering formal training In many developing countries informal businesses manufacturing sector is further stratified by industry. programs for their permanent, full-time employees. operate without formal registration. These firms have Firm size is stratified into small (5–19 employees), less access to financial and public services and can medium (20–99 employees), and large (more than engage in fewer types of contracts and investments, 99 employees). Geographic stratification divides the constraining growth. national economy into the main centers of economic Equal opportunities for men and women contribute activity. to development. Female participation in firm owner- ship and in management measures women’s integra- tion as decisionmakers. Data sources Financial markets connect firms to lenders and investors, allowing firms to grow their businesses: Data on the business environment are from the creditworthy firms can obtain credit from financial World Bank Enterprise Surveys website (www. intermediaries at competitive prices. But too often enterprisesurveys.org). market imperfections and government-induced 2012 World Development Indicators 291 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Getting Enforcing Protecting Resolving business property construction electricity contracts investors insolvency permits Time Disclosure Cost Number of required index Time % of per Time procedures to build a Time Time 0–10 (least Time Number of required capita Number of required to build a warehouse required Number of required to most required procedures days income procedures days warehouse days days procedures days disclosure) years June June June June June June June June June June June June 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 Afghanistan 4 7 25.8 9 250 12 334 109 47 1,642 1 2.0 Albania 5 5 29.0 6 33 .. .. 177 39 390 8 2.0 Algeria 14 25 12.1 10 48 19 281 159 45 630 6 2.5 Angola 8 68 118.9 7 184 11 321 48 46 1,011 5 6.2 Argentina 14 26 11.9 7 53 25 365 67 36 590 6 2.8 Armenia 3 8 2.9 3 7 18 79 242 49 440 5 1.9 Australia 2 2 0.7 5 5 15 147 81 28 395 8 1.0 Austria 8 28 5.2 3 21 13 194 23 25 397 3 1.1 Azerbaijan 6 8 2.7 4 11 30 212 241 39 237 7 2.7 Bahrain 7 9 0.7 2 31 12 43 90 48 635 8 2.5 Bangladesh 7 19 30.6 8 245 11 201 372 41 1,442 6 4.0 Belarus 5 5 1.3 2 10 13 140 254 29 275 7 5.8 Belgium 3 4 5.2 8 64 12 169 88 26 505 8 0.9 Benin 6 29 149.9 4 120 12 372 158 42 795 6 4.0 Bolivia 15 50 90.4 7 92 14 249 42 40 591 1 1.8 Bosnia and Herzegovina 12 40 17.0 7 33 18 181 125 37 595 3 3.3 Botswana 10 61 1.8 5 16 22 145 121 28 625 7 1.7 Brazil 13 119 5.4 13 39 17 469 34 45 731 6 4.0 Bulgaria 4 18 1.5 8 15 23 120 130 39 564 10 3.3 Burkina Faso 3 13 47.7 4 59 12 98 158 37 446 6 4.0 Burundi 9 14 116.8 5 94 22 135 188 44 832 8 .. Cambodia 9 85 109.7 7 56 21 652 183 44 401 5 6.0 Cameroon 5 15 45.5 5 93 11 147 67 43 800 6 3.2 Canada 1 5 0.4 6 17 12 73 168 36 570 8 0.8 Central African Republic 7 21 175.5 5 75 18 203 102 43 660 6 4.8 Chad 11 66 208.5 6 44 13 154 67 41 743 6 4.0 Chile 7 7 5.1 6 31 17 155 31 36 480 8 4.5 China 14 38 3.5 4 29 33 311 145 34 406 10 1.7 Hong Kong SAR, China 3 3 1.9 5 36 6 67 43 26 280 10 1.1 Colombia 9 14 8.0 7 15 8 46 165 34 1,346 8 1.3 Congo, Dem. Rep. 10 65 551.4 6 54 11 117 58 43 610 3 5.2 Congo, Rep. 10 160 85.2 6 55 14 186 129 44 560 6 3.3 Costa Rica 12 60 11.1 5 20 20 188 62 40 852 2 3.5 Côte d’Ivoire 10 32 132.6 6 62 18 583 33 33 770 6 2.2 Croatia 6 7 8.6 5 104 12 317 70 38 561 1 3.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Cyprus 6 8 13.1 6 42 9 677 247 43 735 8 1.5 Czech Republic 9 20 8.4 4 25 33 120 279 27 611 2 3.2 Denmark 4 6 0.0 3 16 5 67 38 35 410 7 1.0 Dominican Republic 7 19 18.2 7 60 14 216 87 34 460 5 3.5 Ecuador 13 56 28.8 9 16 16 128 89 39 588 1 5.3 Egypt, Arab Rep. 6 7 5.6 7 72 22 218 54 41 1,010 8 4.2 El Salvador 8 17 45.1 5 31 33 157 78 34 786 3 4.0 Eritrea 13 84 62.6 11 78 .. .. 59 39 405 4 .. Estonia 5 7 1.8 3 18 13 148 111 35 425 8 3.0 Ethiopia 5 9 12.8 10 41 9 128 95 37 620 4 3.0 Finland 3 14 1.0 3 14 16 66 53 33 375 6 0.9 France 5 7 0.9 8 59 10 184 123 29 331 10 1.9 Gabon 9 58 17.3 7 39 13 201 160 38 1,070 6 5.0 Gambia, The 8 27 206.1 5 66 14 143 78 33 434 2 3.0 Georgia 2 2 4.3 1 2 9 74 97 36 285 9 3.3 Germany 9 15 4.6 5 40 9 97 17 30 394 5 1.2 Ghana 7 12 17.3 5 34 16 218 78 36 487 7 1.9 Greece 10 10 20.1 11 18 14 169 77 39 819 1 2.0 Guatemala 12 37 52.5 4 23 19 165 39 31 1,459 3 3.0 Guinea 12 40 118.0 6 59 29 287 69 49 276 6 3.8 Guinea-Bissau 9 9 49.8 8 210 12 170 455 40 1,715 6 .. Haiti 12 105 314.2 5 301 9 1,129 66 35 530 2 5.7 292 2012 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators Starting a Registering Dealing with Getting Enforcing Protecting Resolving business property construction electricity contracts investors insolvency permits Time Disclosure Cost Number of required index Time % of per Time procedures to build a Time Time 0–10 (least Time Number of required capita Number of required to build a warehouse required Number of required to most required procedures days income procedures days warehouse days days procedures days disclosure) years June June June June June June June June June June June June 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 Honduras 13 14 46.7 7 23 14 94 33 47 920 0 3.8 Hungary 4 4 7.6 4 17 29 102 252 35 395 2 2.0 India 12 29 46.8 5 44 34 227 67 46 1,420 7 7.0 Indonesia 8 45 17.9 6 22 13 158 108 40 570 10 5.5 Iran, Islamic Rep. 6 8 3.8 9 36 16 320 140 39 505 5 4.5 Iraq 11 77 115.7 5 51 13 187 47 51 520 4 .. Ireland 4 13 0.4 5 38 10 141 205 21 650 10 0.4 Israel 5 34 4.4 7 144 19 212 132 35 890 7 4.0 Italy 6 6 18.2 7 27 11 258 192 41 1,210 7 1.8 Jamaica 6 7 7.2 6 37 8 145 96 35 655 4 1.1 Japan 8 23 7.5 6 14 14 193 117 30 360 7 0.6 Jordan 7 12 13.9 7 21 17 70 43 38 689 5 4.3 Kazakhstan 6 19 0.8 4 40 32 189 88 36 390 9 1.5 Kenya 11 33 37.8 8 64 8 125 163 40 465 3 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 5 7 14.6 7 11 12 30 49 33 230 7 1.5 Kosovo 10 58 26.7 8 33 17 301 60 53 420 3 2.0 Kuwait 12 32 1.2 8 47 24 130 42 50 566 7 4.2 Kyrgyz Republic 2 10 3.5 4 5 12 142 337 38 260 8 4.0 Lao PDR 7 93 7.6 5 98 23 108 134 42 443 2 .. Latvia 4 16 2.6 5 18 23 205 108 27 369 5 3.0 Lebanon 5 9 67.1 8 25 19 219 75 37 721 9 4.0 Lesotho 7 40 24.9 6 101 12 510 140 40 785 2 2.6 Liberia 4 6 68.4 10 50 23 75 586 41 1,280 4 3.0 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 6 22 2.8 3 3 15 142 148 30 275 7 1.5 Macedonia, FYR 3 3 2.4 4 40 10 117 151 37 370 9 2.0 Madagascar 3 8 12.1 6 74 16 172 450 38 871 5 2.0 Malawi 10 39 90.9 6 69 18 200 244 42 312 4 2.6 Malaysia 4 6 16.4 5 48 22 260 51 29 425 10 1.5 Mali 4 8 90.5 5 29 11 179 120 36 620 6 3.6 Mauritania 9 19 48.3 4 49 18 119 75 46 370 5 8.0 Mauritius 5 6 3.6 4 22 16 136 91 36 645 6 1.7 Mexico 6 9 11.2 7 74 10 81 114 38 415 8 1.8 Moldova 7 9 9.1 5 5 27 291 140 30 352 7 2.8 Mongolia 7 13 2.9 5 11 19 208 156 32 314 5 4.0 Morocco 6 12 15.7 8 75 15 97 71 40 510 7 1.8 Mozambique 9 13 11.7 8 42 13 370 117 30 730 5 5.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 10 66 17.2 7 39 12 139 55 33 270 5 1.5 Nepal 7 29 37.4 3 5 13 222 70 39 910 6 5.0 Netherlands 6 8 5.5 5 7 15 176 143 26 514 4 1.1 New Zealand 1 1 0.4 2 2 6 64 50 30 216 10 1.3 Nicaragua 8 39 107.9 8 49 16 218 70 37 409 4 2.2 Niger 9 17 114.4 4 35 12 326 120 39 545 6 5.0 Nigeria 8 34 70.6 13 82 15 85 260 40 457 5 2.0 Norway 5 7 1.8 1 3 11 250 66 34 280 7 0.9 Oman 5 8 3.1 2 16 14 174 62 51 598 8 4.0 Pakistan 10 21 11.2 6 50 11 222 206 46 976 6 2.8 Panama 6 8 9.9 8 32 17 113 35 31 686 1 2.5 Papua New Guinea 6 51 15.6 4 72 21 219 66 42 591 5 3.0 Paraguay 7 35 47.2 6 46 12 137 53 38 591 6 3.9 Peru 5 26 11.9 4 7 16 188 100 41 428 8 3.1 Philippines 15 35 19.1 8 39 30 85 50 37 842 2 5.7 Poland 6 32 17.3 6 152 30 301 143 37 830 7 3.0 Portugal 5 5 2.3 1 1 14 255 64 31 547 6 2.0 Puerto Rico 6 6 0.6 8 194 18 189 32 39 620 7 3.8 Qatar 8 12 8.3 7 13 17 70 90 43 570 5 2.8 2012 World Development Indicators 293 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Getting Enforcing Protecting Resolving business property construction electricity contracts investors insolvency permits Time Disclosure Cost Number of required index Time % of per Time procedures to build a Time Time 0–10 (least Time Number of required capita Number of required to build a warehouse required Number of required to most required procedures days income procedures days warehouse days days procedures days disclosure) years June June June June June June June June June June June June 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 Romania 6 14 3.0 8 26 16 287 223 31 512 9 3.3 Russian Federation 9 30 2.0 5 43 51 423 281 36 281 6 2.0 Rwanda 2 3 4.7 5 25 12 164 30 24 230 7 3.0 Saudi Arabia 3 5 5.9 2 2 9 75 71 43 635 9 1.5 Senegal 3 5 68.0 6 122 13 210 125 43 780 6 3.0 Serbia 7 13 7.8 6 11 19 279 131 36 635 7 2.7 Sierra Leone 6 12 93.3 7 86 20 238 137 39 515 6 2.6 Singapore 3 3 0.7 3 5 11 26 36 21 150 10 0.8 Slovak Republic 6 18 1.8 3 17 11 286 177 32 565 3 4.0 Slovenia 2 6 0.0 5 110 13 199 38 32 1,290 3 2.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 5 19 0.3 6 23 13 127 226 29 600 8 2.0 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 10 28 4.7 5 13 8 182 101 39 515 5 1.5 Sri Lanka 4 35 4.7 8 83 18 217 132 40 1,318 6 1.7 Sudan 10 36 31.4 6 9 16 270 70 53 810 0 2.0 Swaziland 12 56 29.2 9 21 13 95 137 40 972 2 2.0 Sweden 3 15 0.6 1 7 7 116 52 30 508 8 2.0 Switzerland 6 18 2.1 4 16 13 154 39 32 390 0 3.0 Syrian Arab Republic 7 13 17.1 4 19 23 104 71 55 872 7 4.1 Tajikistan 5 24 33.3 6 37 26 228 238 35 430 8 1.7 Tanzania 12 29 28.8 9 73 19 303 109 38 462 3 3.0 Thailand 5 29 6.2 2 2 8 157 35 36 479 10 2.7 Timor-Leste 10 103 4.5 .. .. 19 238 63 51 1,285 3 .. Togo 7 84 177.2 5 295 12 309 74 41 588 6 3.0 Trinidad and Tobago 9 43 0.9 8 162 17 297 61 42 1,340 4 4.0 Tunisia 10 11 4.2 4 39 17 88 65 39 565 5 1.3 Turkey 6 6 11.2 6 6 24 189 70 36 420 9 3.3 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 16 34 84.5 13 48 15 125 91 38 490 2 2.2 Ukraine 9 24 4.4 10 117 21 375 274 30 343 5 2.9 United Arab Emirates 7 13 5.6 1 2 14 46 55 49 537 4 5.1 United Kingdom 6 13 0.7 6 29 9 99 109 28 399 10 1.0 United States 6 6 1.4 4 12 15 26 68 32 300 7 1.5 Uruguay 5 7 24.9 8 66 27 234 48 41 720 3 2.1 Uzbekistan 6 14 6.4 12 78 25 243 117 42 195 4 4.0 Venezuela, RB 17 141 26.1 8 38 10 381 125 30 510 3 4.0 Vietnam 9 44 10.6 4 57 10 200 142 34 295 6 5.0 West Bank and Gaza 11 49 96.0 7 47 18 119 63 44 540 6 .. Yemen, Rep. 6 12 83.8 6 19 12 116 35 36 520 6 3.0 Zambia 6 18 27.4 5 40 14 196 117 35 471 3 2.7 Zimbabwe 9 90 148.9 5 31 12 614 125 38 410 8 3.3 World 7u 31 u 36.2 u 6u 55 u 16 u 193 u 111 u 38 u 610 u 5u 2.9 u Low income 8 33 106.0 6 86 15 260 167 39 662 5 3.7 Middle income 8 36 28.0 6 51 17 188 102 39 626 5 3.1 Lower middle income 8 34 40.9 6 66 16 181 98 40 662 4 3.3 Upper middle income 8 38 13.9 6 36 17 195 106 38 588 6 2.8 Low & middle income 8 35 46.3 6 60 16 205 117 39 634 5 3.2 East Asia & Pacific 8 39 26.4 5 80 17 172 98 37 548 5 3.2 Europe & Central Asia 6 16 8.0 6 29 21 214 168 37 390 7 2.8 Latin America & Carib. 9 57 40.8 7 56 14 221 66 40 699 4 3.1 Middle East & N. Africa 8 23 50.4 7 41 17 166 84 42 692 6 3.5 South Asia 7 23 21.6 6 103 16 222 145 43 1,075 5 3.4 Sub-Saharan Africa 8 34 80.8 6 66 15 212 138 39 657 5 3.4 High income 6 17 6.8 5 44 14 159 93 35 539 6 2.1 Euro area 6 12 5.4 5 32 12 210 111 32 600 6 1.7 Note: Regional aggregates differ from those reported on the Doing Business website because the regional aggregates reported on the Doing Business website include developed countries. 294 2012 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators About the data De�nitions The economic health of a country is measured not only The Doing Business project encompasses two • Number of procedures for starting a business is in macroeconomic terms but also by other factors that types of data: data from readings of laws and regu- the number of procedures required to start a busi- shape daily economic activity such as laws, regula- lations and data on time and motion indicators that ness, including interactions to obtain necessary per- tions, and institutional arrangements. The Doing Busi- measure efficiency in achieving a regulatory goal. mits and licenses and to complete all inscriptions, ness indicators measure business regulation, gauge Within the time and motion indicators cost estimates verifications, and notifications to start operations regulatory outcomes, and measure the extent of legal are recorded from official fee schedules where appli- for businesses with specific characteristics of own- protection of property, the flexibility of employment cable. The data from surveys are subjected to numer- ership, size, and type of production. • Time required regulation, and the tax burden on businesses. ous tests for robustness, which lead to revision or for starting a business is the number of calendar The table presents a subset of Doing Business expansion of the information collected. days to complete the procedures for legally operating indicators covering 7 of the 11 sets of indicators: The Doing Business methodology has limitations a business using the fastest procedure, independent starting a business, registering property, dealing with that should be considered when interpreting the of cost. • Cost for starting a business is normalized construction permits, getting electricity, enforcing data. First, the data collected refer to businesses as a percentage of gross national income (GNI) per contracts, protecting investors, and resolving insol- in the economy’s largest city and may not represent capita. It includes all official fees and fees for legal vency. Table 5.5 includes Doing Business measures regulations in other locations of the economy. To or professional services if they are required by law. of getting credit, and table 5.6 presents data on pay- address this limitation, subnational indicators are • Number of procedures for registering property is ing business taxes. being collected for selected economies. These sub- the number of procedures required for a business to The fundamental premise of the Doing Business national studies point to significant differences in legally transfer property. • Time required for register- project is that economic activity requires good rules the speed of reform and the ease of doing business ing property is the number of calendar days for a busi- and regulations that are efficient, accessible to all across cities in the same economy. Second, the data ness to legally transfer property. • Number of proce- who need to use them, and simple to implement. often focus on a specific business form—generally dures for dealing with construction permits to build Thus some Doing Business indicators give a higher a limited liability company of a specified size—and a warehouse is the number of interactions of a com- score for more regulation, such as stricter disclosure may not represent regulation for other types of busi- pany’s employees or managers with external parties, requirements in related-party transactions, and oth- nesses such as sole proprietorships. Third, transac- including government staff, public inspectors, nota- ers give a higher score for simplified regulations, tions described in a standardized business case refer ries, land registry and cadastre staff, and technical such as a one-stop shop for completing business to a specific set of issues and may not represent the experts apart from architects and engineers. • Time startup formalities. full set of issues a business encounters. Fourth, the required for dealing with construction permits to In constructing the indicators, it is assumed that time measures involve an element of judgment by the build a warehouse is the number of calendar days entrepreneurs know about all regulations and comply expert respondents. When sources indicate different to complete the required procedures for building a with them; in practice, entrepreneurs may not be estimates, the Doing Business time indicators repre- warehouse using the fastest procedure, independent aware of all required procedures or may avoid legally sent the median values of several responses given of cost. • Time required for getting electricity is the required procedures altogether. But where regula- under the assumptions of the standardized case. number of calendar days required for a business to tion is particularly onerous, levels of informality are Fifth, the methodology assumes that a business has obtain a permanent electricity connection and supply. higher, which comes at a cost: firms in the informal full information on what is required and does not • Number of procedures for enforcing contracts is sector usually grow more slowly, have less access waste time when completing procedures. the number of independent actions, mandated by law to credit, and employ fewer workers—and those or court regulation, that demand interaction between workers remain outside the protections of labor law. the parties to a contract or between them and the The indicators in the table can help policymakers judge or court officer. • Time required for enforcing understand the business environment in a country contracts is the number of calendar days from the and—along with information from other sources such time of the filing of a lawsuit in court to the final deter- as the World Bank’s Enterprise Surveys—provide mination and payment. • Disclosure index measures insights into potential areas of reform. the degree to which investors are protected through Doing Business data are collected with a stan- disclosure of ownership and financial information. dardized survey that uses a simple business case Higher values indicate more disclosure. •  Time to ensure comparability across economies and over required to resolve insolvency is the number of years time—with assumptions about the legal form of the from time of filing for insolvency in court until resolu- business, its size, its location, and nature of its oper- tion of distressed assets and payment of creditors. ation. Surveys in 183 countries are administered Data sources through more than 9,000 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 (www. sionals who routinely administer or advise on legal doingbusiness.org). and regulatory requirements. 2012 World Development Indicators 295 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 2005 2011 2005 2010 2005 2010 2005 2011 2005 2011 2010 2011 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 61,478 43,580 33.6 17.3 9.0 0.7 30.4 4.8 101 99 55.3a –30.2a Armenia 43 44 0.9 0.3 0.0 0.0 3.7 1.5 198 12 .. .. Australia 804,074 1,198,164 115.4 128.5 88.5 108.0 78.0 94.0 1,643 1,922 12.5 –15.7 Austria 124,390 82,374 40.8 17.9 15.1 12.7 43.6 51.6 92 73 10.9 –35.8 Azerbaijan .. .. .. .. .. .. .. .. .. .. .. .. Bahrain 17,364 17,152 129.0 82.2 5.3 4.2 4.6 1.5 47 44 10.0a –14.4 a Bangladesh 3,035 23,546 5.0 15.6 1.7 14.6 31.5 92.6 262 216 37.6a –42.3a Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 288,515 229,896 76.5 57.4 33.3 23.7 44.8 43.0 222 158 0.5 –15.1 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 2,200 4,125 23.0 17.2 0.0 0.1 0.1 0.4 36 40 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 2,437 4,107 23.8 27.4 0.4 0.9 1.8 3.6 18 23 –6.8a –7.5a Brazil 474,647 1,228,969 53.8 74.0 17.5 43.2 38.3 69.3 381 366 6.5 –24.4 Bulgaria 5,086 8,253 17.6 15.2 4.8 0.4 35.2 3.4 331 393 –15.2a –22.1a Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 1,480,891 1,906,589 130.6 137.0 74.5 86.6 63.6 74.8 3,721 3,932 22.0 –14.7 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 136,446 270,289 115.4 160.6 16.0 25.5 14.9 18.6 245 229 47.2 –24.1 China 780,763 3,389,098 34.6 80.4 26.0 135.5 82.5 188.2 1,387 2,342 6.9 –21.7 Hong Kong SAR, China 693,486 889,597 390.1 481.0 165.4 711.7 43.3 157.6 1,020 1,472 21.3 –20.2 Colombia 46,016 201,296 31.4 72.3 4.3 8.0 17.9 13.3 114 79 44.1 –12.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 1,478 1,443 7.4 4.0 0.1 0.1 1.7 2.6 19 9 .. .. Côte d’Ivoire 2,327 6,288 14.2 31.2 0.2 0.6 1.4 1.8 39 33 19.3a –15.2a Croatia 12,918 21,796 28.8 40.9 1.8 1.7 6.7 4.1 145 209 –0.4 a –30.3a Cuba .. .. .. .. .. .. .. .. .. .. .. .. Cyprus 6,583 2,853 38.7 29.5 2.4 2.7 7.1 10.0 144 117 .. –71.9a Czech Republic 38,345 38,352 30.8 22.4 33.0 7.3 118.6 38.0 36 15 0.2 –15.0 Denmark 178,038 179,529 69.1 74.3 59.0 46.3 92.3 73.2 179 186 25.1 –17.3 Dominican Republic .. .. .. .. .. .. .. .. .. .. .. .. Ecuador 3,214 5,779 8.7 9.1 0.4 0.2 5.0 1.9 32 41 9.7a –9.6a Egypt, Arab Rep. 79,672 48,683 88.8 37.7 28.3 17.0 43.0 33.5 744 231 11.5 –49.1 El Salvador 3,623 5,474 21.2 19.9 0.4 0.2 2.3 1.6 35 65 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3,495 1,611 25.1 11.8 17.8 1.7 51.1 12.6 15 15 56.0a –23.3a Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 209,504 143,081 107.0 49.6 139.7 42.8 139.1 133.5 134 121 10.7 –33.1 France 1,758,721 1,568,730 82.3 75.3 71.4 57.3 92.0 84.4 885 893 –9.9b –19.5b Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 355 796 5.5 9.1 0.6 0.0 13.6 0.2 257 135 .. .. Germany 1,221,250 1,184,459 44.1 43.6 63.7 42.8 146.0 134.5 648 670 7.4 c –16.6c Ghana 1,661 3,097 15.5 11.3 0.6 0.3 3.2 4.1 30 36 94.1a –22.8a Greece 145,013 33,648 60.4 24.1 27.2 14.3 48.3 46.5 307 275 –43.8 –58.3 Guatemala .. .. .. .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 296 2012 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 2005 2011 2005 2010 2005 2010 2005 2011 2005 2011 2010 2011 Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary 32,576 18,773 29.5 21.5 21.7 20.6 78.0 83.9 44 52 –10.8 –35.3 India 553,074 1,015,370 66.3 93.6 52.0 61.2 92.2 56.3 4,763 5,112 18.7 –38.0 Indonesia 81,428 390,107 28.5 51.0 14.7 18.3 54.2 37.2 335 440 37.9 1.1 Iran, Islamic Rep. 38,724 107,249 20.2 19.1 4.3 5.2 19.1 20.7 420 347 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 114,134 35,363 56.1 16.3 31.8 8.1 56.7 45.3 53 48 –7.7 –1.5 Israel 120,114 144,970 89.5 100.3 44.6 61.4 55.5 64.7 572 576 7.4 –29.7 Italy 798,167 431,471 44.7 15.4 62.4 26.2 140.5 236.8 275 287 –17.4 –27.6 Jamaica 13,028 7,223 116.8 46.5 3.9 1.5 3.1 3.1 39 37 22.4a 28.4 a Japan 4,736,513 3,540,685 104.0 75.1 109.8 78.4 118.8 108.9 3,279 3,961 9.6d –12.2d Jordan 37,639 27,183 299.0 111.9 189.1 34.3 85.0 13.9 201 247 –8.6a –16.2a Kazakhstan 10,521 43,301 18.4 40.8 1.9 1.5 14.9 2.1 62 63 –1.0a –36.4 a Kenya 6,384 10,203 34.1 46.0 2.7 3.5 9.8 7.1 47 58 33.8a –31.6a Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 718,180 994,302 85.0 107.4 142.4 160.3 209.8 195.1 1,620 1,792 25.3 –10.9 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 130,080 100,869 161.0 87.6 116.4 63.9 94.3 19.4 143 206 29.1a –21.4 a Kyrgyz Republic 42 165 1.7 1.7 0.5 0.2 34.1 2.7 8 34 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 2,527 1,076 15.8 5.2 0.6 0.1 4.6 4.4 45 32 39.4a –17.3a Lebanon 4,929 10,164 22.5 32.3 4.2 4.8 25.5 4.5 11 10 –8.7a –22.2a Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 8,183 4,075 31.5 15.6 2.9 0.8 10.1 5.0 43 33 44.0a –16.1a Macedonia, FYR 646 2,504 10.8 28.8 1.6 0.4 18.3 2.0 57 32 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi 230 1,384 8.4 26.7 0.3 0.4 4.1 3.9 9 13 .. .. Malaysia 181,236 395,083 131.4 172.6 36.2 37.9 26.9 32.0 1,020 941 35.1 –1.1 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 2,617 6,540 41.7 66.9 2.4 3.7 6.0 8.0 42 86 8.2a –2.5a Mexico 239,128 408,691 28.2 43.9 6.2 10.5 25.7 26.0 151 128 26.6 –14.8 Moldova .. .. .. .. 0.6 0.2 .. .. .. .. .. .. Mongolia 46 1,579 1.8 17.6 0.1 0.8 6.1 3.4 392 332 .. .. Morocco 27,220 60,088 45.7 76.2 7.0 11.8 15.9 9.8 56 75 13.1 –17.7 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 415 1,152 5.7 9.7 0.1 0.2 1.5 1.2 13 7 24.2a 6.2a Nepal 1,344 4,529 16.5 30.8 0.6 0.6 4.4 1.7 125 181 .. .. Netherlands 592,906 594,732 92.9 84.8 130.9 76.0 147.7 88.3 237 108 1.2 –16.4 New Zealand 43,409 71,657 39.1 52.9 15.7 14.2 40.0 39.6 154 144 5.2 –3.8 Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 19,356 39,270 17.2 26.3 1.7 2.7 11.5 9.2 214 196 20.3a –29.5a Norway 190,952 219,245 62.8 60.1 64.1 52.0 117.2 88.6 191 192 13.7 –18.1 Oman 15,269 19,719 49.4 36.9 10.4 12.4 29.8 12.9 96 136 12.2a –14.1a Pakistan 45,937 32,764 41.9 21.6 128.6 7.3 376.3 28.6 661 638 15.3a –18.8a Panama 5,074 10,682 32.8 40.9 0.5 0.7 1.8 0.6 24 21 12.8 a 18.0a Papua New Guinea 3,166 8,999 64.6 102.8 0.4 0.2 0.6 0.6 9 11 .. .. Paraguay 257 958 3.4 0.2 0.0 0.1 0.6 3.0 54 66 .. .. Peru 35,995 79,329 45.3 63.6 2.5 2.5 7.2 5.5 196 202 51.3 –21.3 Philippines 40,153 165,380 39.0 78.8 6.7 13.4 20.1 20.4 235 251 56.7 0.2 Poland 93,873 138,246 30.9 40.5 9.9 16.5 36.3 58.4 248 757 11.3 –33.4 Portugal 66,981 61,688 34.9 35.9 21.7 13.7 60.7 50.3 48 46 –16.6 –31.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 87,316 125,413 202.9 89.4 65.2 25.9 40.0 18.6 31 42 27.7a 3.3a 2012 World Development Indicators 297 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 2005 2011 2005 2010 2005 2010 2005 2011 2005 2011 2010 2011 Romania 20,588 21,197 20.8 20.0 3.4 1.1 21.0 12.0 3,747 1,267 –6.6a –18.2a Russian Federation 548,579 796,376 71.8 67.9 20.9 54.0 39.0 127.3 296 327 21.7 –23.4 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 646,104 338,873 204.7 81.3 349.7 46.7 231.7 84.6 77 150 9.0 e –3.9e Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia 5,409 8,365 21.4 25.2 2.6 0.6 15.3 3.7 864 1,322 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 316,658 308,320 256.4 177.3 97.0 135.1 40.4 74.8 685 462 18.4 –21.2 Slovak Republic 4,393 4,736 7.2 4.8 0.1 0.2 1.6 10.2 209 81 5.4 a 3.0a Slovenia 7,899 6,326 22.1 20.1 2.2 0.6 9.0 6.5 116 66 –20.3a –30.7a Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 565,408 855,711 228.9 278.4 81.2 93.5 39.3 39.8 388 355 32.1 –17.4 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 960,024 1,030,951 84.9 83.2 137.8 96.7 163.9 128.9 3,300 3,241 –24.5 –16.8 Sri Lanka 5,720 19,437 23.4 40.2 4.7 6.7 24.3 25.1 239 253 84.6a –23.0a Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 197 .. 7.8 .. 0.0 .. 0.0 .. 6 5 .. .. Sweden 403,948 470,122 109.0 126.7 125.2 95.9 118.9 96.2 252 340 32.6 –18.5 Switzerland 938,624 932,207 252.0 232.9 237.1 164.7 100.1 85.9 263 246 11.0 –9.4 Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 588 1,539 4.2 5.5 0.1 0.1 2.3 2.5 6 17 .. .. Thailand 124,864 268,489 70.8 87.2 50.6 68.4 73.9 85.1 504 545 52.1 –4.7 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 16,972 14,725 106.2 59.0 3.9 0.7 3.7 1.2 37 37 0.8a 26.7a Tunisia 2,876 9,662 8.9 24.1 1.4 3.8 16.5 11.0 46 57 11.7a –13.4 a Turkey 161,537 201,817 33.4 41.8 41.7 57.4 154.9 162.7 302 362 21.4 –37.0 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 103 7,727 1.1 10.5 0.0 0.1 3.1 .. 5 8 .. .. Ukraine 24,976 25,558 29.0 28.6 0.8 1.5 3.6 14.1 221 195 53.8a –36.3a United Arab Emirates 225,568 93,767 124.9 35.2 79.2 9.2 89.6 15.9 79 104 –6.8 a –16.5a United Kingdom 3,058,182 1,202,031 134.1 137.4 182.7 132.9 141.9 137.9 2,759 2,001 5.2 f –6.1f United States 16,970,865 15,640,707 134.9 117.5 171.0 208.8 129.2 187.6 5,143 4,171 12.8g 0.0 g Uruguay 96 175 0.6 0.4 0.0 0.0 1.1 0.4 9 6 .. .. Uzbekistan 37 .. 0.3 .. 0.3 0.1 184.7 .. 114 132 .. .. Venezuela, RB 5,017 5,143 3.4 1.0 0.2 0.0 4.5 0.9 50 36 .. .. Vietnam 461 18,316 0.9 19.2 0.2 16.2 24.8 29.5 33 301 0.5a –26.8a West Bank and Gaza 4,461 2,532 111.1 .. 52.2 .. 75.4 14.7 28 45 .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 989 4,009 13.8 17.4 0.2 1.6 2.0 .. 15 20 17.4 a –1.3a Zimbabwe 2,402 10,903 43.0 153.6 5.9 15.3 15.3 .. 79 75 .. .. World 43,319,352 s 45,082,821s 96.6 w 88.7 w 105.7 w 106.4 w 116.3 w 133.4 w 50,936 s 49,553 s Low income .. 59,995 .. 26.6 .. .. 15.3 48.6 .. 602 Middle income 4,426,053 10,282,607 49.3 72.6 24.9 67.7 58.4 103.0 19,925 18,737 Lower middle income 898,133 1,854,685 48.0 65.9 35.2 34.2 86.4 44.8 8,741 8,653 Upper middle income 3,527,921 8,427,921 49.6 74.4 22.2 76.5 51.4 116.1 11,184 10,084 Low & middle income 4,440,182 10,342,602 48.8 72.1 24.6 67.0 58.3 102.8 20,466 19,339 East Asia & Pacific 1,212,704 4,638,422 40.1 79.9 25.6 113.3 68.4 154.3 3,931 5,181 Europe & Central Asia 789,576 1,116,849 48.7 51.8 22.7 42.7 61.6 121.1 6,564 4,368 Latin America & Carib. 1,028,157 2,274,194 40.5 57.6 9.9 22.9 28.4 46.4 1,504 1,446 Middle East & N. Africa 135,018 265,561 36.8 34.6 7.2 7.5 39.3 19.4 1,531 1,012 South Asia 609,110 1,095,645 58.8 81.9 55.7 52.6 111.6 55.4 6,050 6,400 Sub-Saharan Africa 605,113 951,930 128.6 149.5 43.3 46.6 37.3 37.2 911 932 High income 38,879,170 34,740,219 108.8 95.9 126.3 123.5 122.4 143.0 30,470 30,214 Euro area 6,357,326 5,482,967 62.7 51.7 73.1 47.1 120.5 110.4 6,737 6,250 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 the Saudi Arabia country index. f. Refers to the FTSE 100 index. g. Refers to the S&P 500 index. 298 2012 World Development Indicators 5.4 STATES AND MARKETS Stock markets About the data De�nitions The development of an economy’s financial markets is size of the stock market in U.S. dollars and as a •  Market capitalization (also known as market closely related to its overall development. Well function- percentage of GDP. The number of listed domestic value) is the share price times the number of shares ing financial systems provide good and easily acces- companies is another measure of market size. Mar- outstanding. •  Market liquidity is the total value sible information. That lowers transaction costs, which ket size is positively correlated with the ability to of shares traded during the period divided by gross in turn improves resource allocation and boosts eco- mobilize capital and diversify risk. domestic product (GDP). This indicator complements nomic growth. Both banking systems and stock mar- Market liquidity, the ability to easily buy and sell secu- the market capitalization ratio by showing whether kets enhance growth, the main factor in poverty reduc- rities, is measured by dividing the total value of shares market size is matched by trading. • Turnover ratio tion. At low levels of economic development commercial traded by GDP. The turnover ratio—the value of shares is the total value of shares traded during the period banks tend to dominate the financial system, while at traded as a percentage of market capitalization—is divided by the average market capitalization for the higher levels domestic stock markets tend to become also a measure of liquidity as well as of transaction period. Average market capitalization is calculated as more active and efficient relative to domestic banks. costs. (High turnover indicates low transaction costs.) the average of the end-of-period values for the cur- Open economies with sound macroeconomic poli- The turnover ratio complements the ratio of value rent period and the previous period. • Listed domes- cies, good legal systems, and shareholder protection traded to GDP, because the turnover ratio is related to tic companies are the domestically incorporated attract capital and therefore have larger financial mar- the size of the market and the value traded ratio to the companies listed on the country’s stock exchanges kets. Recent research on stock market development size of the economy. A small, liquid market will have at the end of the year. This indicator does not include shows that modern communications technology and a high turnover ratio but a low value of shares traded investment companies, mutual funds, or other col- increased financial integration have resulted in more ratio. Liquidity is an important attribute of stock mar- lective investment vehicles. •  S&P/Global Equity cross-border capital flows, a stronger presence of kets because, in theory, liquid markets improve the allo- Indices measure the U.S. dollar price change in the financial firms around the world, and the migration of cation of capital and enhance prospects for long-term stock markets. stock exchange activities to international exchanges. economic growth. A more comprehensive measure of Many firms in emerging markets now cross-list on inter- liquidity would include trading costs and the time and national exchanges, which provides them with lower uncertainty in finding a counterpart in settling trades. cost capital and more liquidity-traded shares. However, S&P Indices, the source for all the data in the this also means that exchanges in emerging markets table, provides regular updates on 21 emerging stock may not have enough financial activity to sustain them, markets and 36 frontier markets. S&P Indices main- putting pressure on them to rethink their operations. tains a series of indexes for investors interested in The indicators in the table are from Standard & investing in stock markets in developing countries. Poor’s Emerging Markets Data Base. They include The S&P/IFCI index, S&P Indices’s leading emerging measures of size (market capitalization, number markets index, is designed to be sufficiently invest- of listed domestic companies) and liquidity (value able to support index tracking portfolios in emerging of shares traded as a percentage of gross domes- market stocks that are legally and practically open to tic product, value of shares traded as a percentage foreign portfolio investment. The S&P Frontier BMI of market capitalization). The comparability of such measures the performance of 36 smaller and less indicators across countries may be limited by concep- liquid markets. The S&P Frontier BMI country indexes tual and statistical weaknesses, such as inaccurate aim to include all publicly listed equities represent- reporting and differences in accounting standards. ing an aggregate of at least 80 percent of the total The percentage change in stock market prices in U.S. market capitalization in each market, subject to dollars for developing economies is from Standard & the securities meeting size and liquidity thresholds Poor’s Global Equity Indices (S&P IFCI) and Standard & defined by three market size tiers. These indexes are Poor’s Frontier Broad Market Index (BMI). The percent- widely used benchmarks for international portfolio age change for France, Germany, Japan, the United management. See www.standardandpoors.com for Data sources Kingdom, and the United States is from local stock further information on the indexes. market prices. The indicator is an important measure Because markets included in Standard & Poor’s Data on stock markets are from Standard & Poor’s of overall performance. Regulatory and institutional emerging markets category vary widely in level of Global Stock Markets Factbook 2011, which draws factors that can affect investor confidence, such as development, it is best to look at the entire category on the Emerging Markets Data Base, supple- entry and exit restrictions, the existence of a securi- to identify the most significant market trends. And it mented by other data from Standard & Poor’s. ties and exchange commission, and the quality of laws is useful to remember that stock market trends may The firm collects data through an annual survey to protect investors, may influence the functioning of be distorted by currency conversions, especially when of the world’s stock exchanges, supplemented by stock markets but are not included in the table. a currency has registered a significant devaluation. information provided by its network of correspon- Stock market size can be measured in various About the data is based on Demirgüç-Kunt and dents and by Reuters. Data on GDP are from the ways, and each may produce a different ranking of Levine (1996), Beck and Levine (2001), and Claes- World Bank’s national accounts data files. countries. Market capitalization shows the overall sens, Klingebiel, and Schmukler (2002). 2012 World Development Indicators 299 5.5 Financial access, stability, and efficiency Getting Financial access Bank Ratio of bank Domestic Interest Risk premium credit and outreach capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Depth of Strength of credit per 1,000 adults Lending Prime lending legal rights information Depositors Borrowers per 100,000 adults rate minus rate minus index index with from Commercial Automated deposit rate treasury bill rate 0–10 (weak 0–6 (low commercial commercial bank teller percentage percentage to strong) to high) banks banks branches machines % % % of GDP points points June 2011 June 2011 2010 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan 6 0 100 4 2.2 0.50 .. .. 2.1 .. .. Albania 9 4 .. 118 22.6 31.96 8.5 13.9 67.5 6.4 7.0 Algeria 3 3 346 23 5.2 6.07 .. .. –7.4 6.3 7.7 Angola 3 4 97 86 1.3 12.66 .. .. 22.9 9.8 .. Argentina 4 6 702 285 13.3 42.45 11.9 1.8 29.2 1.4 .. Armenia 6 6 589 202 17.5 32.11 20.4 3.0 25.8 10.3 8.6 Australia 9 5 .. .. 31.6 162.38 5.7 2.2 147.6 3.1 2.8 Austria 7 6 1,376 .. 11.4 48.16 7.5 2.8 137.5 .. .. Azerbaijan 6 5 41 256 10.4 28.43 .. .. 23.5 9.1 18.9 Bahrain 4 3 .. .. .. .. .. .. 79.3 6.0 6.4 Bangladesh 7 2 418 81 6.9 1.93 6.5 11.2 65.9 5.9 .. Belarus 3 5 .. .. 3.1 37.17 13.7 3.5 45.7 0.1 .. Belgium 7 4 .. .. 48.0 86.37 5.0 2.8 117.1 .. 8.9 Benin 6 1 .. .. .. .. .. .. 18.1 .. .. Bolivia 1 6 .. .. .. .. 8.4 2.2 49.4 8.9 9.8 Bosnia and Herzegovina 5 5 914 251 30.9 34.47 17.6 11.4 67.2 4.7 .. Botswana 7 4 496 238 8.6 .. .. .. 9.4 5.9 .. Brazil 3 5 .. 194 13.8 120.62 11.1 3.1 97.8 31.1 29.1 Bulgaria 8 6 1,958 421 91.2 80.53 10.5 11.9 71.0 7.1 8.6 Burkina Faso 6 1 .. .. .. .. .. .. 16.4 .. .. Burundi 3 1 .. .. .. .. .. .. 40.1 .. .. Cambodia 8 0 108 29 4.0 5.07 .. .. 22.7 .. .. Cameroon 6 2 72 17 1.4 1.40 .. .. 9.0 .. .. Canada 7 6 .. .. 24.3 220.02 4.7 1.2 177.6 2.6 2.0 Central African Republic 6 2 3 1 0.6 .. .. .. 18.8 .. .. Chad 6 2 24 3 0.6 0.36 .. .. 9.3 .. .. Chile 6 5 2,134 310 17.6 62.51 8.3 2.7 90.3 3.0 .. China 6 4 .. .. .. .. 6.1 1.1 146.4 3.1 .. Hong Kong SAR, China 10 5 .. .. 23.6 .. 12.3 0.8 199.0 5.0 4.7 Colombia 5 5 .. .. .. .. 14.3 2.9 65.7 5.7 .. Congo, Dem. Rep. 3 0 .. .. .. .. .. .. 1.0 39.7 .. Congo, Rep. 6 2 20 3 2.4 1.23 .. .. –16.4 .. .. Costa Rica 3 5 .. .. 19.2 .. 14.4 1.9 51.3 11.8 .. Côte d’Ivoire 6 1 .. .. .. .. .. .. 25.1 .. .. Croatia 6 5 .. .. .. 100.86 13.9 11.2 82.2 8.6 .. Cuba .. .. .. .. .. .. .. .. .. .. .. Cyprus 9 0 .. .. 151.5 87.58 5.9 5.6 315.8 .. .. Czech Republic 6 5 .. .. 22.5 41.65 6.5 6.2 64.7 4.8 5.0 Denmark 9 4 .. .. 41.1 63.55 5.5 4.1 215.3 .. .. Dominican Republic 3 6 .. .. .. .. 9.3 2.9 39.6 7.3 .. Ecuador 3 6 .. .. .. .. 8.9 3.5 26.4 .. .. Egypt, Arab Rep. 3 6 .. .. .. .. 6.2 11.0 69.4 4.8 1.7 El Salvador 5 6 .. .. .. .. 13.9 3.9 45.1 .. .. Eritrea 2 0 .. .. .. .. .. .. 114.4 .. .. Estonia 7 5 1,993 542 19.3 88.09 9.3 5.4 97.6 6.7 .. Ethiopia 4 2 107 2 1.8 0.31 .. .. 37.1 3.3 7.3 Finland 8 4 .. .. 15.6 91.72 5.5 0.6 100.8 .. .. France 7 4 .. .. 43.1 110.07 4.4 4.2 134.4 .. .. Gabon 6 2 95 46 4.7 8.62 11.3 9.9 10.2 .. .. Gambia, The 5 0 .. .. .. .. .. .. 45.4 12.4 .. Georgia 8 6 697 377 18.6 42.34 16.9 12.5 33.8 15.0 14.7 Germany 7 6 .. .. 17.6 116.80 4.3 3.3 132.0 .. .. Ghana 8 3 324 33 5.0 .. 7.9 17.6 28.3 .. .. Greece 4 5 .. .. 41.2 78.24 6.9 10.4 145.5 .. .. Guatemala 8 6 .. .. 36.5 .. 10.3 2.1 38.5 7.9 .. Guinea 6 0 .. .. .. .. .. .. .. .. .. Guinea-Bissau 6 1 .. .. .. .. .. .. 7.7 .. .. Haiti 3 2 339 12 2.8 .. .. .. 19.9 16.7 .. 300 2012 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency Getting Financial access Bank Ratio of bank Domestic Interest Risk premium credit and outreach capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Depth of Strength of credit per 1,000 adults Lending Prime lending legal rights information Depositors Borrowers per 100,000 adults rate minus rate minus index index with from Commercial Automated deposit rate treasury bill rate 0–10 (weak 0–6 (low commercial commercial bank teller percentage percentage to strong) to high) banks banks branches machines % % % of GDP points points June 2011 June 2011 2010 2010 2010 2010 2010 2010 2010 2010 2010 Honduras 8 6 .. .. .. .. .. .. 52.3 9.0 .. Hungary 7 4 1,072 .. 16.6 56.73 9.8 9.7 81.7 2.7 2.2 India 8 4 747 137 10.9 .. 7.1 2.4 71.1 .. .. Indonesia 3 4 .. 275 8.3 13.37 11.4 2.6 36.5 6.2 .. Iran, Islamic Rep. 4 4 .. .. 26.6 30.95 .. .. 37.2 0.1 .. Iraq 3 0 .. .. 4.2 1.09 .. .. –2.1 8.3 8.0 Ireland 9 5 .. .. 28.6 92.47 4.4 8.6 233.2 .. .. Israel 9 5 .. 927 19.9 102.35 6.8 1.4 85.7 2.9 2.3 Italy 3 5 1,307 480 66.9 98.56 9.3 7.8 154.6 .. 2.9 Jamaica 8 0 .. .. 6.7 25.98 .. .. 48.1 14.1 11.2 Japan 7 6 7,169 171 34.0 132.96 4.8 1.8 326.6 1.1 1.5 Jordan 4 2 .. .. 18.1 .. 10.5 7.9 96.0 5.5 .. Kazakhstan 4 5 874 .. 3.3 62.75 10.9 23.8 45.4 .. .. Kenya 10 4 370 73 4.4 7.27 13.2 6.3 51.0 9.8 10.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 8 6 4,522 .. 18.6 250.29 7.6 1.9 103.2 1.7 .. Kosovo 8 5 770 54 17.5 22.86 .. 4.4 17.8 10.9 .. Kuwait 4 4 .. .. .. .. 12.6 8.9 84.0 2.6 4.3 Kyrgyz Republic 10 4 181 26 6.2 7.45 .. .. .. 27.4 27.0 Lao PDR 4 0 44 4 2.6 4.33 .. .. 26.1 19.6 14.6 Latvia 10 5 1,286 273 31.7 69.98 7.3 19.0 89.6 7.7 7.1 Lebanon 4 5 873 276 29.7 38.25 7.3 4.3 164.4 2.1 4.2 Lesotho 6 0 291 39 3.5 7.28 8.4 3.7 –6.2 7.5 5.0 Liberia 7 1 .. .. .. .. .. .. 148.9 10.1 .. Libya .. .. .. .. 10.5 3.61 .. .. –65.9 3.5 .. Lithuania 5 6 .. .. 27.3 55.29 8.9 19.7 64.6 3.6 –0.1 Macedonia, FYR 7 6 .. 291 26.6 51.88 10.6 9.0 48.2 2.4 .. Madagascar 2 0 45 18 1.6 1.49 .. .. 10.5 38.5 39.7 Malawi 7 0 .. .. .. .. .. .. 28.4 21.0 17.5 Malaysia 10 6 1,458 280 10.4 46.38 9.1 3.4 132.2 2.5 2.4 Mali 6 1 .. .. .. .. .. .. 12.5 .. .. Mauritania 3 1 .. 32 4.3 .. .. .. 52.9 9.0 8.5 Mauritius 6 3 465 2,168 23.0 1,009.32 7.3 2.8 110.8 0.5 .. Mexico 6 6 1,205 344 15.2 47.28 10.4 2.0 45.0 4.1 0.9 Moldova 8 4 1,197 38 10.3 143.38 16.0 13.3 37.2 8.7 9.2 Mongolia 6 4 1,339 706 53.6 .. .. .. 29.9 8.2 .. Morocco 3 5 694 .. 21.0 .. 8.4 4.4 106.0 .. .. Mozambique 2 4 .. .. 3.4 5.70 8.0 1.9 24.1 6.6 4.3 Myanmar .. .. .. .. .. .. .. .. 25.1 5.0 .. Namibia 8 5 624 213 7.6 30.49 8.4 2.0 48.4 4.7 3.3 Nepal 7 3 .. .. .. .. .. .. 68.6 4.4 1.2 Netherlands 6 5 1,769 832 23.2 58.27 4.4 2.8 212.1 –0.6 .. New Zealand 10 5 .. .. 34.7 72.75 .. .. 157.8 1.7 3.5 Nicaragua 3 5 .. .. .. .. .. .. 63.4 10.3 .. Niger 6 1 .. .. .. .. .. .. 12.8 .. .. Nigeria 9 0 .. .. .. .. 3.2 17.2 36.3 11.1 13.7 Norway 7 4 529 .. 7.6 56.07 6.4 1.5 .. 2.0 .. Oman 4 4 1,012 401 22.9 .. 13.5 3.3 41.3 3.5 .. Pakistan 6 4 249 28 8.8 4.40 9.8 14.7 46.3 5.9 1.5 Panama 5 6 .. .. .. .. 12.5 1.1 88.0 4.7 .. Papua New Guinea 5 3 178 23 1.8 5.25 .. .. 35.7 9.1 5.8 Paraguay 3 6 .. .. .. .. 9.4 1.3 32.5 24.8 .. Peru 7 6 436 139 49.5 25.21 9.5 2.6 18.0 17.4 .. Philippines 4 3 488 .. 7.7 14.88 11.7 3.8 49.2 4.5 4.2 Poland 9 5 .. .. 45.8 52.10 9.1 8.8 63.6 .. .. Portugal 3 4 2,806 413 75.9 197.05 6.4 3.3 209.2 .. .. Puerto Rico 8 5 .. .. .. .. .. .. .. .. .. Qatar 4 4 770 315 23.4 67.00 .. .. 75.7 4.4 .. 2012 World Development Indicators 301 5.5 Financial access, stability, and efficiency Getting Financial access Bank Ratio of bank Domestic Interest Risk premium credit and outreach capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Depth of Strength of credit per 1,000 adults Lending Prime lending legal rights information Depositors Borrowers per 100,000 adults rate minus rate minus index index with from Commercial Automated deposit rate treasury bill rate 0–10 (weak 0–6 (low commercial commercial bank teller percentage percentage to strong) to high) banks banks branches machines % % % of GDP points points June 2011 June 2011 2010 2010 2010 2010 2010 2010 2010 2010 2010 Romania 9 5 .. 222 33.2 55.48 8.9 11.9 54.9 6.8 6.9 Russian Federation 3 5 .. .. .. 76.58 14.0 8.2 38.6 4.8 .. Rwanda 8 6 218 29 2.2 0.81 11.4 10.8 .. 9.6 9.2 Saudi Arabia 5 6 780 181 9.0 57.97 12.6 3.0 –0.2 .. .. Senegal 6 1 .. .. .. .. 10.0 20.2 29.0 .. .. Serbia 8 5 .. 186 10.2 47.43 19.7 16.9 57.8 6.0 3.1 Sierra Leone 7 0 190 12 2.9 0.43 17.5 15.6 18.4 12.3 4.0 Singapore 10 4 2,134 968 10.3 58.57 9.6 1.8 85.7 5.2 5.0 Slovak Republic 9 4 .. .. 26.5 50.38 9.7 5.8 54.0 2.0 .. Slovenia 4 4 .. .. 39.5 103.04 8.2 3.6 97.4 4.5 4.8 Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 10 6 978 415 10.1 59.58 7.0 5.8 182.2 3.4 3.4 South Sudan .. .. .. .. .. .. .. .. .. .. .. Spain 6 5 775 337 38.3 153.63 6.2 4.6 231.4 .. .. Sri Lanka 4 5 .. .. .. .. .. .. 40.5 3.3 1.7 Sudan 4 0 .. .. .. .. .. .. 20.5 .. .. Swaziland 6 5 455 185 5.7 21.43 12.2 8.0 17.2 5.9 3.1 Sweden 7 4 .. .. .. .. 5.0 2.0 143.5 .. .. Switzerland 8 5 .. .. 52.8 97.47 5.4 0.4 191.1 2.7 2.7 Syrian Arab Republic 1 2 220 67 3.8 7.35 .. .. 47.7 3.7 .. Tajikistan 2 0 .. .. .. .. .. .. .. 18.0 .. Tanzania 8 0 131 30 1.8 3.30 .. .. 21.1 8.0 10.7 Thailand 5 5 1,120 237 11.2 77.69 11.3 3.9 135.5 4.9 4.5 Timor-Leste 2 3 .. .. .. .. .. .. –31.6 10.2 .. Togo 6 1 181 0 3.7 .. .. .. 31.9 .. .. Trinidad and Tobago 8 4 .. .. .. .. .. .. 32.4 7.8 8.4 Tunisia 3 5 .. 148 16.6 20.84 .. 12.1 73.7 .. .. Turkey 4 5 1,265 719 17.4 43.74 13.4 3.8 69.3 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 7 4 192 18 2.5 3.58 13.9 2.1 17.1 12.5 15.2 Ukraine 9 4 3,220 .. 2.3 76.13 14.6 15.3 78.6 5.3 .. United Arab Emirates 4 5 .. .. 21.7 96.81 17.7 5.6 92.3 .. .. United Kingdom 10 6 .. .. 25.5 64.58 5.4 4.0 222.6 .. 0.0 United States 9 6 .. .. 35.7 173.75 11.1 4.9 231.4 .. 3.1 Uruguay 4 6 538 426 13.5 30.38 9.5 1.0 32.4 6.2 1.3 Uzbekistan 2 3 957 37 47.5 2.52 .. .. .. .. .. Venezuela, RB 1 0 .. .. .. .. 9.9 3.4 22.6 3.5 .. Vietnam 8 5 .. .. 3.3 17.64 .. .. 135.8 1.9 2.0 West Bank and Gaza 1 3 543 68 10.3 13.68 .. .. .. .. .. Yemen, Rep. 3 2 101 2 2.0 2.75 .. .. 19.3 5.2 2.9 Zambia 9 5 .. .. .. .. .. .. 18.9 13.5 14.6 Zimbabwe 7 0 .. .. .. .. .. .. .. .. .. World 5.9 u 3.2 u .. w .. w 13.7 w 44.00 w 9.4 m 4.0 m 167.9 w 6.2 m Low income 5.8 1.3 .. .. .. .. .. .. 37.2 11.4 Middle income 5.5 3.3 .. 167 12.2 29.02 10.2 3.9 90.8 6.3 Lower middle income 5.3 2.8 .. .. 5.6 9.77 .. .. 57.1 8.7 Upper middle income 5.7 3.7 .. 238 16.6 47.36 10.4 3.7 100.5 5.8 Low & middle income 5.6 2.8 .. .. 8.1 18.23 .. 3.9 90.0 7.3 East Asia & Pacific 6.2 2.0 .. 221 7.4 14.09 .. .. 131.8 6.9 Europe & Central Asia 6.5 4.7 894 212 18.1 44.89 13.6 12.2 50.8 6.9 Latin America & Carib. 5.3 3.4 .. .. .. .. 10.1 2.4 66.7 7.3 Middle East & N. Africa 2.8 3.2 443 66 10.9 6.84 .. .. 51.9 5.0 South Asia 5.6 2.8 249 28 7.8 1.93 7.3 10.5 67.4 5.9 Sub-Saharan Africa 5.8 1.8 167 27 2.7 .. .. .. 81.6 9.7 High income 6.9 4.3 .. .. 23.9 90.08 6.8 4.1 205.6 .. Euro area 6.4 4.1 .. .. 24.9 91.72 6.2 4.4 156.1 .. 302 2012 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency About the data Access to finance can expand opportunities for all identifying problems with asset quality in the loan nonfinancial corporations (public and private) and with higher levels of access and use of banking ser- portfolio. A high ratio may signal deterioration of the households that obtained loans from commercial vices associated with lower financing obstacles for credit portfolio. International guidelines recommend banks and other banks functioning as commercial people and businesses. A stable financial system that loans be classified as nonperforming when pay- banks. For many countries data cover the total num- that promotes efficient savings and investment is ments of principal and interest are 90 days or more ber of loan accounts due to lack of information on also crucial for a thriving democracy and market past due or when future payments are not expected loan account holders. • Commercial bank branches economy. to be received in full. are retail locations of resident commercial banks and There are several aspects of access to financial Domestic credit provided by the banking sector as other resident banks that function as commercial services: availability, cost, and quality of services. a share of GDP measures banking sector depth and banks that provide financial services to customers The development and growth of credit markets financial sector development in terms of size. In a and are physically separated from the main office depend on access to timely, reliable, and accurate few countries governments may hold international but not legally separated subsidiaries. • Automated data on borrowers’ credit experiences. Access to reserves as deposits in the banking system rather teller machines are computerized telecommunica- credit can be improved by making it easy to create than in the central bank. Since claims on the central tions devices that provide clients of a financial insti- and enforce collateral agreements and by increasing government are a net item (claims on the central tution access to financial transactions in a public information about potential borrowers’ creditworthi- government minus central government deposits), the place. • Bank capital to asset ratio is the ratio of ness. Lenders look at a borrower’s credit history figure may be negative, resulting in a negative figure bank capital and reserves to total assets. Capital and collateral. Where credit registries and effective for domestic credit provided by the banking sector. and reserves include funds contributed by owners, collateral laws are absent—as in many developing The interest rate spread—the margin between retained earnings, general and special reserves, pro- countries —banks make fewer loans. Indicators that the cost of mobilizing liabilities and the earnings on visions, and valuation adjustments. • Ratio of bank cover getting credit include the strength of legal assets—measures financial sector efficiency in inter- nonperforming loans to total gross loans is the value rights index and the depth of credit information index. mediation. A narrow spread means low transaction of nonperforming loans (gross value of the loan as The “unbanked� have to resort to informal services costs, which reduces the cost of funds for invest- recorded on the balance sheet) divided by the total to manage their money—saving under the mattress, ment, crucial to economic growth. value of the loan portfolio (including nonperforming borrowing from family and friends, or using money The risk premium on lending is the spread between loans before the deduction of loan loss provisions). lenders—that are usually less reliable and more the lending rate to the private sector and the “risk- • Domestic credit provided by banking sector is all costly than formal banking institutions. The table free� government rate. Spreads are expressed as an credit to various sectors on a gross basis, except to presents data on financial access covering deposi- annual average. A small spread indicates that the the central government, which is net. The banking tors and borrowers and on outreach indicators such market considers its best corporate customers to be sector includes monetary authorities, deposit money as the number of branches and automated teller low risk; a negative value indicates that the market banks, and other banking institutions for which data machines. Data for these financial access and out- considers its best corporate clients to be lower risk are available. • Interest rate spread is the interest reach indicators are from the International Monetary than the government. rate charged by banks on loans to prime customers Fund’s (IMF) Financial Access Survey database; this minus the interest rate paid by commercial or similar De�nitions sources differs from that in the 2011 edition. banks for demand, time, or savings deposits. • Risk The size and mobility of international capital • Strength of legal rights index measures the degree premium on lending is the interest rate charged by flows make it increasingly important to monitor the to which collateral and bankruptcy laws protect the banks on loans to prime private sector customers strength of financial systems. Robust financial sys- rights of borrowers and lenders and thus facilitate minus the “risk-free� treasury bill interest rate at tems can increase economic activity and welfare, but lending. Higher values indicate that the laws are bet- which short-term government securities are issued instability can disrupt financial activity and impose ter designed to expand access to credit. • Depth of or traded in the market. widespread costs on the economy. The ratio of bank credit information index measures rules affecting capital to assets, a measure of bank solvency and the scope, accessibility, and quality of information resiliency, shows the extent to which banks can deal available through public or private credit registries. Data sources with unexpected losses. Capital includes tier 1 capi- Higher values indicate the availability of more credit tal (paid-up shares and common stock), a common information. •  Depositors with commercial banks Data on getting credit are from the World Bank’s feature in all countries’ banking systems, and total are deposit account holders at commercial banks Doing Business project (www.doingbusiness. regulatory capital, which includes several types of and other resident banks functioning as commercial org). Data on financial access and outreach are subordinated debt instruments that need not be banks that are resident nonfinancial corporations from the IMF’s Financial Access Survey database repaid if the funds are required to maintain minimum (public and private) and households. For many coun- (http://fas.imf.org). Data on bank capital and capital levels (tier 2 and tier 3 capital). Total assets tries data cover the total number of deposit accounts nonperforming loans are from the IMF’s Financial include all nonfinancial and financial assets. Data due to lack of information on account holders. The Soundness Indicators database (http://fsi.imf. are from internally consistent financial statements. major types of deposits are checking accounts, sav- org). Data on credit and interest rates are from The ratio of bank nonperforming loans to total ings accounts, and time deposits. • Borrowers from the IMF’s International Financial Statistics. gross loans measures bank health and efficiency by commercial banks are resident customers that are 2012 World Development Indicators 303 5.6 Tax policies Tax revenue collected Taxes payable by central government by businesses Time to prepare, % of commercial profi ts Number file, and pay taxes Profi t Labor tax and Other Total % of GDP of payments hours tax contributions taxes tax rate 2005 2010 June 2011 June 2011 June 2011 June 2011 June 2011 June 2011 Afghanistan 6.3 8.3 8 275 0.0 0.0 36.4 36.4 Albania 17.3 .. 44 371 8.7 25.0 4.8 38.5 Algeria 40.7a 34.9a 29 451 6.6 29.7 35.7 72.0 Angola .. .. 31 282 24.6 9.0 19.5 53.2 Argentina 14.2a .. 9 415 2.8 29.4 76.1 108.2 Armenia 14.3 16.9 34 500 16.8 23.0 1.1 40.9 Australia 24.7a 22.1a 11 109 26.0 20.4 1.3 47.7 Austria 20.1a 18.7a 14 170 15.0 34.8 3.4 53.1 Azerbaijan .. 15.9 18 225 12.9 24.8 2.2 40.0 Bahrain 1.4 a .. 25 36 0.0 14.7 0.4 15.0 Bangladesh 8.2 8.6 21 302 25.7 0.0 9.2 35.0 Belarus 20.1 17.1 18 654 20.2 39.0 3.5 62.8 Belgium 26.0a 24.6a 11 156 5.2 50.4 1.7 57.3 Benin 15.5a 16.2a 55 270 14.8 27.3 23.9 66.0 Bolivia 16.2a .. 42 1,080 0.0 15.5 64.6 80.0 Bosnia and Herzegovina 20.7a 20.4 a 40 422 7.1 12.6 5.3 25.0 Botswana .. .. 19 152 15.9 0.0 3.6 19.4 Brazil 16.7 15.6 9 2,600 22.4 40.9 3.8 67.1 Bulgaria 21.6 21.0 17 500 4.9 19.2 4.1 28.1 Burkina Faso 11.8a 13.0a 46 270 14.8 22.6 6.2 43.6 Burundi .. .. 24 274 37.4 7.8 1.0 46.2 Cambodia 7.9a 10.1a 39 173 18.9 0.1 3.5 22.5 Cameroon .. .. 44 654 29.9 18.3 0.9 49.1 Canada 13.7a 11.9a 8 131 9.3 12.6 6.8 28.8 Central African Republic 6.2 .. 54 504 0.0 19.8 34.8 54.6 Chad .. .. 54 732 31.3 28.4 5.7 65.4 Chile 18.7a 17.8a 9 316 18.0 3.8 3.2 25.0 China 8.7 10.5 7 398 5.9 49.6 7.9 63.5 Hong Kong SAR, China 12.7a 12.8a 3 80 17.6 5.3 0.1 23.0 Colombia 12.8a 11.5a 9 193 18.9 28.8 27.1 74.8 Congo, Dem. Rep. 10.0 13.7 32 336 58.9 7.9 272.8 339.7 Congo, Rep. 6.2 .. 61 606 18.1 32.5 15.4 65.9 Costa Rica .. 13.9a 31 246 18.9 29.5 6.6 55.0 Côte d’Ivoire 9.8 a 16.6a 62 270 8.8 20.1 15.4 44.3 Croatia 20.0 18.8 17 196 11.5 19.4 1.5 32.3 Cuba .. .. .. .. .. .. .. .. Cyprus 45.4 a 25.8a 27 149 9.1 11.8 2.2 23.1 Czech Republic 15.6 13.9 8 557 7.5 38.4 3.2 49.1 Denmark 32.6a 34.3a 10 135 20.1 3.6 3.8 27.5 Dominican Republic 14.6a 13.1a 9 324 21.3 18.6 1.8 41.7 Ecuador .. .. 8 654 18.4 14.2 2.7 35.3 Egypt, Arab Rep. 14.1 14.1 29 433 13.0 27.1 3.6 43.6 El Salvador 12.5a 13.7a 53 320 16.5 17.2 1.3 35.0 Eritrea .. .. 18 216 8.8 0.0 75.8 84.5 Estonia 16.1a 17.4a 8 85 8.0 39.4 11.2 58.6 Ethiopia 8.8 .. 19 198 26.8 0.0 4.3 31.1 Finland 22.6a 21.2a 8 93 13.7 24.2 1.2 39.0 France 22.4 a 19.8a 7 132 8.2 51.7 5.7 65.7 Gabon .. .. 26 488 18.4 22.7 2.3 43.5 Gambia, The .. .. 50 376 6.1 12.8 264.6 283.5 Georgia 12.1 22.1 4 387 14.3 0.0 2.2 16.5 Germany 11.1a 12.2a 12 221 19.0 21.8 5.9 46.7 Ghana 21.3 12.6 33 224 18.4 14.7 0.5 33.6 Greece 20.3a 19.6a 10 224 13.4 31.7 1.4 46.4 Guatemala 11.2 10.3 24 344 25.9 14.3 0.7 40.9 Guinea .. .. 56 416 20.9 22.8 10.6 54.3 Guinea-Bissau .. .. 46 208 14.9 24.8 6.1 45.9 Haiti .. .. 46 184 24.1 12.4 4.3 40.8 304 2012 World Development Indicators 5.6 STATES AND MARKETS Tax policies Tax revenue collected Taxes payable by central government by businesses Time to prepare, % of commercial profi ts Number file, and pay taxes Profi t Labor tax and Other Total % of GDP of payments hours tax contributions taxes tax rate 2005 2010 June 2011 June 2011 June 2011 June 2011 June 2011 June 2011 Honduras 14.5a 14.8a 47 224 24.7 10.7 8.6 44.0 Hungary 20.3a 23.9a 13 277 14.8 34.1 3.5 52.4 India 9.9 9.5 33 254 24.7 18.2 19.0 61.8 Indonesia 12.5 10.9 51 266 23.7 10.6 0.1 34.5 Iran, Islamic Rep. 7.9 9.3 20 344 17.8 25.9 0.4 44.1 Iraq .. .. 13 312 14.9 13.5 0.0 28.4 Ireland 24.9a 21.2a 8 76 11.9 11.6 2.7 26.3 Israel 26.9a 24.3a 33 235 22.8 5.3 3.1 31.2 Italy 21.1a 22.9a 15 285 22.8 43.4 2.2 68.5 Jamaica 25.5a 21.0a 72 414 25.6 13.0 7.0 45.6 Japan 10.9a 8.7a 14 330 27.0 16.5 5.7 49.1 Jordan 24.4 15.3 25 116 13.0 12.4 2.3 27.7 Kazakhstan 17.1 8.9 7 188 15.9 11.2 1.6 28.6 Kenya 18.7 19.5 41 393 33.1 6.8 9.7 49.6 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 14.7 15.2 12 225 15.1 13.0 1.5 29.7 Kosovo .. .. 33 164 9.1 5.6 0.6 15.4 Kuwait 1.0 0.9 15 118 4.7 10.7 0.0 15.5 Kyrgyz Republic 14.2 15.6 52 210 6.2 19.5 43.4 69.0 Lao PDR 10.4 12.7 34 362 24.8 5.6 2.9 33.3 Latvia 15.1 12.8 7 290 6.1 27.2 4.7 37.9 Lebanon 14.9a 17.1a 19 180 6.1 24.1 0.0 30.2 Lesotho 46.6 59.8 21 324 13.1 0.0 3.0 16.0 Liberia 0.2 0.3 33 158 0.0 5.4 38.3 43.7 Libya .. .. .. .. .. .. .. .. Lithuania 17.3a 13.4 a 11 175 5.7 35.1 3.1 43.9 Macedonia, FYR 19.3 19.1 28 119 6.3 0.0 3.4 9.7 Madagascar 10.1a 13.0a 23 201 14.7 20.3 1.6 36.6 Malawi .. .. 19 157 23.6 1.1 3.5 28.2 Malaysia 15.4 14.3 13 133 17.0 15.6 1.4 34.0 Mali 15.7a 14.7a 59 270 10.8 34.3 6.6 51.8 Mauritania .. .. 37 696 0.0 17.6 50.7 68.3 Mauritius .. 18.5a 7 161 11.6 6.1 7.3 25.0 Mexico .. .. 6 347 24.5 26.8 1.4 52.7 Moldova 18.5 18.2 48 228 0.0 30.6 0.7 31.3 Mongolia 28.7 22.7 41 192 10.2 12.4 2.0 24.6 Morocco 22.0a 23.4 a 17 238 25.2 22.7 1.8 49.6 Mozambique .. .. 37 230 27.7 4.5 2.1 34.3 Myanmar 3.9 .. .. .. .. .. .. .. Namibia 25.8 .. 37 375 4.0 1.0 4.8 9.8 Nepal 9.2 13.3 34 326 17.2 11.3 3.0 31.5 Netherlands 22.6a 22.6a 9 127 20.9 18.1 1.5 40.5 New Zealand 30.8b .. 8 172 29.9 2.9 1.7 34.4 Nicaragua 16.7 18.3 42 207 24.5 20.3 22.0 66.8 Niger 10.1b .. 41 270 17.3 20.1 6.3 43.8 Nigeria 0.2 0.3 35 938 22.3 9.7 0.7 32.7 Norway 28.7a 26.9a 4 87 24.4 15.9 1.3 41.6 Oman .. .. 14 62 10.0 11.8 0.1 22.0 Pakistan 9.6 10.0 47 560 17.9 15.1 2.3 35.3 Panama .. .. 53 482 13.7 21.7 9.7 45.2 Papua New Guinea .. .. 33 194 22.0 11.7 8.6 42.3 Paraguay 11.8 13.1 35 387 9.6 18.6 6.7 35.0 Peru 13.5 14.5 9 309 26.6 11.0 3.1 40.7 Philippines 12.4 12.1 47 195 21.0 11.3 14.2 46.5 Poland 16.7a 16.3a 29 296 17.4 23.6 2.6 43.6 Portugal 20.6a 19.6a 8 275 15.1 26.8 1.5 43.3 Puerto Rico .. .. 16 218 28.3 14.4 20.5 63.1 Qatar 21.0 19.8 3 36 0.0 11.3 0.0 11.3 2012 World Development Indicators 305 5.6 Tax policies Tax revenue collected Taxes payable by central government by businesses Time to prepare, % of commercial profi ts Number file, and pay taxes Profi t Labor tax and Other Total % of GDP of payments hours tax contributions taxes tax rate 2005 2010 June 2011 June 2011 June 2011 June 2011 June 2011 June 2011 Romania 12.2a 17.9a 113 222 10.4 31.8 2.2 44.4 Russian Federation 16.6a 13.4 a 9 290 8.9 32.1 5.8 46.9 Rwanda .. .. 18 148 21.2 5.7 4.4 31.3 Saudi Arabia .. .. 14 79 2.1 12.4 0.0 14.5 Senegal .. .. 59 666 14.8 24.1 7.0 46.0 Serbia 23.6 22.0 66 279 11.6 20.2 2.2 34.0 Sierra Leone 10.8 11.0 29 357 17.6 11.3 3.3 32.1 Singapore 11.8 13.8 5 84 6.5 15.9 4.7 27.1 Slovak Republic 14.9a 12.4a 31 231 7.2 39.6 2.0 48.8 Slovenia 20.5 17.1 22 260 14.1 18.2 2.4 34.7 Somalia .. .. .. .. .. .. .. .. South Africa 26.9a 25.5a 9 200 24.4 4.1 4.6 33.1 South Sudan .. .. .. .. .. .. .. .. Spain 12.9a 11.2a 8 187 1.2 36.7 0.7 38.7 Sri Lanka 13.7 13.3 71 256 26.7 16.9 61.6 105.2 Sudan .. .. 42 180 13.8 19.2 3.1 36.1 Swaziland 26.1 .. 33 104 28.1 4.0 4.7 36.8 Sweden 22.6a 21.6a 4 122 15.7 35.5 1.6 52.8 Switzerland 10.3 10.9 19 63 8.9 17.5 3.6 30.1 Syrian Arab Republic .. .. 19 336 20.0 19.3 0.5 39.7 Tajikistan 9.8 .. 69 224 0.0 28.5 56.0 84.5 Tanzania .. .. 48 172 20.1 18.0 7.3 45.5 Thailand 17.2a 16.0a 23 264 28.8 5.7 3.0 37.5 Timor-Leste .. .. 6 276 0.0 0.0 0.2 0.2 Togo 13.9a 15.8a 53 270 9.3 26.5 13.7 49.5 Trinidad and Tobago 26.4 26.2 39 210 21.6 5.8 1.8 29.1 Tunisia 18.9 20.1 8 144 15.2 25.2 22.5 62.9 Turkey 19.7a 20.5a 15 223 17.9 18.8 4.4 41.1 Turkmenistan .. .. .. .. .. .. .. .. Uganda 11.8 12.2 32 213 23.3 11.3 1.1 35.7 Ukraine 17.1 16.4 135 657 12.2 43.3 1.6 57.1 United Arab Emirates .. .. 14 12 0.0 14.1 0.0 14.1 United Kingdom 27.2a 26.0a 8 110 23.1 11.0 3.2 37.3 United States 11.2a 9.3a 11 187 27.6 10.0 9.1 46.7 Uruguay 17.9 19.5 53 336 23.6 15.6 2.9 42.0 Uzbekistan .. .. 41 205 1.1 28.2 68.1 97.5 Venezuela, RB 15.5 .. 70 864 6.9 18.0 38.5 63.5 Vietnam .. .. 32 941 17.2 22.6 0.3 40.1 West Bank and Gaza .. .. 27 154 16.2 0.0 0.6 16.8 Yemen, Rep. .. .. 44 248 20.0 11.3 1.5 32.9 Zambia 17.2 16.6 37 132 1.5 10.4 2.5 14.5 Zimbabwe .. .. 49 242 20.3 5.1 10.1 35.6 World 15.4 w 13.5 w 29 u 277 u 16.0 u 16.2 u 12.6 u 44.8 u Low income 10.7 11.8 38 271 18.7 13.0 36.1 67.8 Middle income 12.9 13.0 32 327 16.5 15.5 9.2 41.1 Lower middle income 11.1 10.9 36 324 16.0 13.7 10.7 40.4 Upper middle income 13.5 13.4 26 330 17.0 17.3 7.5 41.9 Low & middle income 12.8 13.0 33 314 17.0 14.9 15.5 47.4 East Asia & Pacific 10.0 11.0 27 232 17.9 10.5 7.9 36.2 Europe & Central Asia 16.5 15.5 39 314 9.2 22.2 10.2 41.6 Latin America & Carib. .. .. 33 404 20.2 15.2 12.4 47.8 Middle East & N. Africa 18.7 17.5 24 253 15.5 19.1 6.0 40.6 South Asia 9.9 9.5 28 281 18.6 7.7 18.2 44.4 Sub-Saharan Africa 17.5 .. 37 314 18.5 13.2 25.6 57.3 High income 16.0 13.6 15 169 13.0 20.2 4.2 37.4 Euro area 18.1 17.2 14 171 11.8 29.7 3.0 44.5 Note: Regional aggregates differ from those reported on the Doing Business website because the regional aggregates reported on the Doing Business website include developed countries. 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. 306 2012 World Development Indicators 5.6 STATES AND MARKETS Tax policies About the data De�nitions 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 2012. 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 PwC, which measures the on a cash basis, the IMF adjusts reported data to the The level of taxation is typically measured by tax taxes that are borne by companies and that affect GFSM 2001 accrual framework. These countries are revenue as a share of gross domestic product (GDP). their income statements. However, PwC bases its footnoted in the table. • Number of tax payments Comparing levels of taxation across countries pro- calculation on data from the largest companies in by businesses is the total number of taxes paid by vides a quick overview of the fiscal obligations and the economy, while Doing Business focuses on a businesses during one year. When electronic filing is incentives facing the private sector. The table shows standardized medium-size company. available, the tax is counted as paid once a year even only central government data, which may significantly if payments are more frequent. • Time to prepare, understate the total tax burden, particularly in coun- �le, 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 • Pro�t tax is the amount of taxes on profits paid economy with unrecorded and undisclosed incomes. by businesses. • Labor tax and contributions are Tax revenue ratios tend to rise with income, with the amount of taxes and mandatory contributions higher income countries relying on taxes to finance on labor paid by businesses. • Other taxes are the a much broader range of social services and social amounts paid by businesses for property taxes, turn- security than lower income countries are able to. over taxes, and other small taxes such as municipal The total tax rate payable by businesses provides fees and vehicle and fuel taxes. • Total tax rate is 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 businesses 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 Taxes withheld (such as sales or value added tax divided by commercial profit. or personal income tax) but not paid by businesses The indicators covering taxes payable by busi- are excluded. For further details on the method used nesses measure all taxes and contributions that for assessing the total tax payable, see the World are government mandated (at any level—federal, Bank’s Doing Business 2012. state, or local), apply to standardized businesses, and have an impact in their income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unre- quited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions Data sources and value added taxes. The indicators account for government-mandated contributions paid by the Data on central government tax revenue are from employer to a requited private pension fund or work- print and electronic editions of the IMF’s Govern- ers insurance fund but exclude value added taxes ment Finance Statistics Yearbook. Data on taxes because they do not affect the accounting profits of payable by businesses are from Doing Business the business —that is, they are not reflected in the 2012 (www.doingbusiness.org). income statement. 2012 World Development Indicators 307 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers Trend indicator values % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 Afghanistan 1.6 1.8 8.7 4.6 27 307 0.4 3.4 .. .. 42 407 Albania 1.3 1.6 6.2 .. 23 15 1.6 1.0 .. .. 42 13 Algeria 2.9 3.5 13.8 15.0 319 317 3.2 2.8 .. .. 155 791 Angola 4.5 4.4 .. .. 118 117 1.9 1.6 .. .. 40 20 Argentina 0.9 0.9 5.2 .. 102 104 0.6 0.6 2 .. 3 17 Armenia 2.9 4.5 15.8 19.8 49 56 3.4 3.9 .. .. 120 36 Australia 1.9 2.0 7.3 7.3 53 57 0.5 0.5 50 119 467 1,677 Austria 0.9 0.9 2.2 2.2 40 26 1.0 0.6 3 33 22 5 Azerbaijan 2.3 2.9 .. 22.5 82 82 2.0 1.8 .. .. 45 62 Bahrain 3.6 3.7 17.6 .. 21 19 6.2 2.7 .. .. 63 71 Bangladesh 1.2 1.2 12.4 10.2 252 221 0.4 0.3 .. .. 9 45 Belarus 1.5 1.3 5.0 4.2 183 183 3.9 4.1 24 42 6 3 Belgium 1.1 1.1 2.5 2.5 37 36 0.8 0.7 161 7 0 30 Benin 1.1 1.0 8.1 6.8 8 7 0.3 0.2 .. .. 2 0 Bolivia 1.9 1.6 7.2 .. 70 83 1.7 1.8 .. .. 1 1 Bosnia and Herzegovina 1.6 1.4 4.7 3.4 12 11 0.9 0.7 .. .. .. .. Botswana 2.9 2.7 .. .. 11 11 1.2 1.0 .. .. 8 10 Brazil 1.5 1.6 6.0 6.3 673 713 0.7 0.7 1 179 223 314 Bulgaria 2.4 1.4 7.6 6.3 85 65 2.5 1.9 66 14 149 17 Burkina Faso 1.2 1.5 10.2 12.4 11 11 0.2 0.2 .. .. 19 2 Burundi 6.2 3.8 .. .. 82 51 2.3 1.2 .. .. .. 2 Cambodia 1.1 1.8 14.8 16.6 191 191 2.8 2.4 .. .. 12 28 Cameroon 1.3 1.6 .. .. 23 23 0.3 0.3 .. .. 5 9 Canada 1.1 1.4 6.3 7.5 71 66 0.4 0.3 226 258 116 373 Central African Republic 1.1 2.6 12.3 .. 3 3 0.2 0.2 .. .. 9 0 Chad 1.0 3.0 .. .. 35 35 0.9 0.8 .. .. 9 17 Chile 3.6 3.2 20.0 15.5 116 104 1.7 1.3 .. 133 460 434 China 2.1a 2.0a 15.3a 16.1a 3,755 2,945 0.5 0.4 303 1,423 3,536 559 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 3.4 3.7 12.7 20.3 336 442 1.7 2.0 .. .. 15 172 Congo, Dem. Rep. 2.3 1.4 10.5 10.2 65 159 0.3 0.6 .. .. 17 25 Congo, Rep. 1.3 1.1 8.8 .. 12 12 0.8 0.7 .. .. 4 1 Costa Rica .. .. .. .. 10 10 0.5 0.4 .. 0 .. .. Côte d’Ivoire 1.5 1.6 9.0 8.8 19 19 0.3 0.2 .. .. 10 .. Croatia 1.6 1.7 4.7 4.6 31 22 1.6 1.1 .. .. 8 10 Cuba 3.8 3.2 .. .. 76 76 1.6 1.4 .. .. .. .. Cyprus 2.2 2.2 3.1 4.7 11 13 2.1 2.2 .. .. 20 .. Czech Republic 2.0 1.3 5.4 3.6 28 29 0.5 0.5 66 3 621 73 Denmark 1.3 1.4 4.1 3.3 21 19 0.7 0.6 9 11 124 16 Dominican Republic 0.8 0.6 5.6 4.4 40 40 1.0 0.9 .. .. 2 33 Ecuador 2.6 3.8 .. .. 47 59 0.7 0.9 .. .. 48 116 Egypt, Arab Rep. 2.9 2.0 10.4 6.9 799 836 3.3 3.1 .. .. 641 681 El Salvador 0.6 0.6 3.6 3.0 16 33 0.7 1.3 .. .. 9 4 Eritrea 20.9 .. .. .. 202 202 9.3 7.8 .. .. 69 .. Estonia 1.9 1.7 7.2 6.2 8 6 1.2 0.8 .. .. 17 1 Ethiopia 2.8 1.1 18.6 .. 183 138 0.5 0.3 .. .. 240 .. Finland 1.4 1.5 3.9 3.8 31 25 1.2 0.9 27 34 99 72 France 2.5 2.3 5.4 5.3 359 342 1.2 1.1 1,724 834 60 120 Gabon 1.3 1.0 .. .. 7 7 1.4 1.1 .. .. 17 17 Gambia, The 0.6 .. .. .. 1 1 0.1 0.1 .. .. 5 .. Georgia 3.3 3.9 19.3 14.8 23 32 1.0 1.4 17 .. 74 34 Germany 1.4 1.4 4.4 4.5 285 251 0.7 0.6 2,080 2,340 195 101 Ghana 0.6 0.4 2.6 2.4 7 16 0.1 0.1 .. .. 0 6 Greece 2.9 3.1 6.9 6.3 168 150 3.3 2.8 13 .. 389 703 Guatemala 0.4 0.4 2.9 3.1 48 34 1.0 0.6 .. .. .. 0 Guinea 2.2 .. .. .. 13 19 0.4 0.5 .. .. 1 .. Guinea-Bissau 2.1 .. .. .. 9 6 1.6 1.0 .. .. .. .. Haiti .. .. .. .. 5 0 0.1 0.0 .. .. .. .. 308 2012 World Development Indicators 5.7 STATES AND MARKETS Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers Trend indicator values % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 Honduras 0.8 1.6 3.9 6.8 20 20 0.8 0.7 .. .. .. 0 Hungary 1.4 1.0 3.4 2.5 44 35 1.0 0.8 82 .. 13 18 India 2.7 2.4 18.3 16.0 3,047 2,626 0.7 0.6 19 4 1,059 3,337 Indonesia 1.1 1.0 6.9 7.1 582 582 0.5 0.5 8 .. 32 198 Iran, Islamic Rep. 3.5 1.9 15.3 8.6 585 563 2.4 2.2 2 5 66 88 Iraq 3.6 6.0 .. .. 227 802 3.6 10.6 .. .. 165 464 Ireland 0.6 0.6 1.8 1.4 10 10 0.5 0.5 .. 4 4 1 Israel 7.6 6.5 16.7 15.7 176 185 6.4 5.8 368 472 1,133 43 Italy 1.9 1.8 4.8 4.1 445 359 1.8 1.4 774 627 150 85 Jamaica 0.5 0.8 1.7 2.3 3 3 0.2 0.2 .. .. 13 2 Japan 1.0 1.0 .. .. 272 260 0.4 0.4 .. .. 375 369 Jordan 4.8 5.2 13.5 20.1 111 111 8.0 7.1 17 88 35 114 Kazakhstan 1.0 0.9 5.7 5.5 101 81 1.3 0.9 12 .. 42 57 Kenya 1.7 1.9 9.3 8.3 29 29 0.2 0.2 .. .. 8 73 Korea, Dem. Rep. .. .. .. .. 1,295 1,379 9.2 9.5 40 .. 5 1 Korea, Rep. 2.6 2.7 13.3 13.7 693 660 2.9 2.7 48 95 796 1,131 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 4.3 4.0 16.7 10.2 23 23 2.0 1.7 .. .. 16 17 Kyrgyz Republic 3.1 3.6 20.0 18.9 18 20 0.8 0.8 92 14 3 .. Lao PDR 0.4 0.3 3.8 2.7 129 129 4.7 4.1 .. .. 4 26 Latvia 1.7 1.1 5.9 3.0 5 5 0.4 0.4 .. .. 7 15 Lebanon 4.4 4.2 16.7 14.0 85 79 6.5 5.4 .. .. 1 60 Lesotho 2.5 2.7 5.9 3.1 2 2 0.2 0.2 .. .. 1 .. Liberia 1.5 0.8 .. .. 15 2 1.4 0.1 .. .. .. .. Libya 1.5 1.2 .. .. 76 76 3.6 3.2 113 28 2 7 Lithuania 1.6 1.3 5.7 3.4 29 25 1.8 1.5 .. .. 15 81 Macedonia, FYR 2.1 1.5 7.0 5.8 19 8 2.2 0.8 .. .. 0 .. Madagascar 1.1 0.7 9.7 9.3 22 22 0.3 0.2 .. .. .. .. Malawi 1.2 .. .. .. 7 5 0.1 0.1 .. .. .. 2 Malaysia 2.3 1.5 12.3 7.8 135 134 1.2 1.1 .. 0 49 411 Mali 2.3 1.9 15.0 13.3 12 12 0.3 0.3 .. .. 13 9 Mauritania 3.1 3.8 .. .. 21 21 2.2 1.9 .. .. 7 4 Mauritius 0.2 0.2 .. .. 2 2 0.4 0.3 .. .. 6 .. Mexico 0.4 0.5 .. .. 204 332 0.5 0.7 .. .. 36 188 Moldova 0.4 0.3 1.4 0.9 10 8 0.7 0.6 18 20 .. .. Mongolia 1.2 0.9 4.9 3.3 16 17 1.5 1.5 .. .. .. 13 Morocco 3.4 3.5 11.4 11.4 251 246 2.3 2.2 .. .. 87 138 Mozambique 0.9 0.9 .. .. 11 11 0.1 0.1 .. .. 1 0 Myanmar .. .. .. .. 483 513 1.9 1.8 .. .. 65 76 Namibia 2.6 3.3 10.3 .. 15 15 1.9 1.6 .. .. 72 14 Nepal 1.9 1.5 12.8 .. 131 158 0.9 1.0 .. .. 5 1 Netherlands 1.5 1.4 3.8 3.4 60 43 0.7 0.5 583 503 96 162 New Zealand 1.0 1.1 3.1 .. 9 10 0.4 0.4 0 .. 8 71 Nicaragua 0.7 0.7 4.0 3.4 14 12 0.7 0.5 .. .. .. .. Niger 1.0 0.9 10.6 .. 10 11 0.2 0.2 .. .. 14 1 Nigeria 0.6 1.0 12.1 10.8 161 162 0.4 0.3 .. .. 14 189 Norway 1.6 1.6 4.9 4.5 47 24 1.9 0.9 12 141 14 205 Oman 11.8 9.6 .. .. 46 47 5.1 3.9 1 .. 164 36 Pakistan 4.0 3.2 27.6 18.5 921 946 1.8 1.6 22 .. 406 2,580 Panama .. .. .. .. 12 12 0.8 0.7 .. .. .. 5 Papua New Guinea 0.6 0.5 .. .. 3 3 0.1 0.1 .. .. .. .. Paraguay 0.8 0.9 4.8 5.8 25 25 0.9 0.8 .. .. 1 3 Peru 1.5 1.4 8.5 8.3 157 192 1.2 1.2 5 .. 368 60 Philippines 0.8 0.8 4.9 4.8 147 166 0.4 0.4 4 .. 14 8 Poland 1.9 1.9 5.3 5.1 162 121 0.9 0.7 17 8 95 142 Portugal 2.1 2.2 5.1 4.8 93 90 1.7 1.6 .. 0 172 941 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 2.5 2.3 12.1 15.4 12 12 2.3 0.9 6 .. 11 20 2012 World Development Indicators 309 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers Trend indicator values % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 2005 2010 Romania 2.0 1.4 8.3 4.4 177 154 1.8 1.5 2 4 491 109 Russian Federation 3.7 4.0 18.7 14.1 1,452 1,430 2.0 1.9 5,134 6,039 5 19 Rwanda 1.7 1.4 .. .. 53 35 1.2 0.7 .. .. 3 13 Saudi Arabia 8.0 10.4 .. .. 216 249 2.7 2.6 .. 58 150 787 Senegal 1.4 1.6 .. .. 19 19 0.4 0.3 .. .. 15 4 Serbia 2.5 2.2 6.5 5.5 110 29 2.8 0.8 4 5 .. 14 Sierra Leone 1.9 2.3 8.3 11.2 13 11 0.7 0.5 .. .. 10 .. Singapore 4.4 3.8 33.9 28.2 167 148 7.4 5.3 3 27 537 1,078 Slovak Republic 1.7 1.1 4.9 3.7 20 16 0.8 0.6 7 8 4 8 Slovenia 1.4 1.6 3.6 3.8 12 12 1.2 1.2 .. .. 2 73 Somalia .. .. .. .. .. 2 .. 0.1 .. .. .. .. South Africa 1.5 1.2 5.1 4.1 56 77 0.3 0.4 26 80 262 183 South Sudan .. .. .. .. .. 140 .. .. .. .. 37 1 Spain 1.0 1.1 4.2 3.6 220 223 1.0 1.0 108 513 339 313 Sri Lanka 2.6 2.8 13.1 19.3 200 223 2.5 2.6 .. .. 33 5 Sudan 4.3 .. .. .. 123 127 1.0 0.9 .. .. 104 14 Swaziland 2.3 3.4 7.0 .. .. .. .. .. .. .. .. .. Sweden 1.5 1.2 .. .. 29 21 0.6 0.4 538 806 82 35 Switzerland 0.9 0.9 4.9 4.8 109 25 2.6 0.6 246 137 164 34 Syrian Arab Republic 5.0 3.9 .. .. 416 403 8.0 7.4 3 25 7 167 Tajikistan 2.2 .. 15.8 .. 13 16 0.5 0.6 .. .. 13 .. Tanzania 1.0 1.0 .. .. 28 28 0.1 0.1 .. .. 9 9 Thailand 1.1 1.5 6.7 8.2 421 420 1.1 1.1 8 .. 63 83 Timor-Leste 2.5 2.8 .. .. 1 1 0.3 0.4 .. .. .. 20 Togo 1.6 1.8 9.8 13.0 10 9 0.4 0.3 .. .. .. 1 Trinidad and Tobago .. .. .. .. 3 4 0.4 0.6 .. .. 6 45 Tunisia 1.5 1.2 5.6 4.5 47 48 1.4 1.2 .. .. 168 2 Turkey 2.5 2.4 11.5 9.4 617 613 2.7 2.3 47 31 1,065 468 Turkmenistan .. .. .. .. 26 22 1.3 1.0 .. .. 22 29 Uganda 2.4 1.7 14.4 15.3 47 47 0.4 0.3 .. .. 9 1 Ukraine 2.8 2.7 7.7 7.0 273 215 1.2 0.9 290 201 .. .. United Arab Emirates 4.2 5.4 .. .. 51 51 2.0 1.0 11 37 2,199 493 United Kingdom 2.4 2.6 5.9 5.7 217 174 0.7 0.5 1,039 1,054 27 518 United States 4.0 4.8 18.9 17.9 1,546 1,569 1.0 1.0 6,700 8,641 520 893 Uruguay 1.3 1.5 5.0 5.0 25 25 1.6 1.5 .. .. 20 36 Uzbekistan 0.5 .. .. .. 91 87 0.9 0.7 4 90 .. .. Venezuela, RB 1.4 0.9 5.6 .. 82 115 0.7 0.9 7 40 23 365 Vietnam 1.9 2.2 .. .. 495 522 1.1 1.0 12 .. 328 515 West Bank and Gaza .. .. .. .. 56 56 7.1 5.7 .. .. 2 4 Yemen, Rep. 4.9 4.4 .. .. 138 138 2.6 2.1 .. .. 306 7 Zambia 2.0 1.7 8.5 10.0 16 17 0.3 0.3 .. .. 0 .. Zimbabwe 2.3 1.3 .. .. 51 51 0.8 0.8 .. .. 25 .. World 2.5 w 2.6 w 10.8 w 10.0 w 28,539 s 27,994 s 0.9 w 0.9 w .. s .. s 20,973 s 24,960 s Low income 1.6 1.4 12.0 .. 3,330 3,714 1.1 1.0 .. .. .. .. Middle income 2.1 2.0 12.5 12.3 19,030 18,527 0.9 0.8 .. .. 10,757 13,328 Lower middle income 2.4 2.1 13.8 12.7 8,635 8,858 0.9 0.9 .. .. 3,334 8,426 Upper middle income 2.0 2.0 12.3 12.4 10,395 9,669 0.8 0.7 6,188 7,020 7,423 4,902 Low & middle income 2.1 2.0 12.5 12.2 22,360 22,241 0.9 0.8 .. .. 11,031 14,002 East Asia & Pacific 1.9 1.9 13.9 14.6 7,656 7,005 0.7 0.6 311 1,423 4,096 1,912 Europe & Central Asia 2.9 3.0 15.2 11.9 3,398 3,169 1.9 1.6 5,585 6,380 1,152 920 Latin America & Carib. 1.3 1.4 .. .. 2,072 2,439 0.8 0.9 .. .. 1,213 1,739 Middle East & N. Africa 3.2 3.3 12.5 .. 3,123 3,611 3.3 3.5 .. .. 1,639 2,523 South Asia 2.8 2.4 18.7 16.2 4,578 4,480 0.7 0.7 41 4 1,554 6,378 Sub-Saharan Africa 1.7 1.6 .. .. 1,533 1,536 0.5 0.5 .. .. 986 530 High income 2.6 2.9 10.5 10.0 6,179 5,753 1.1 1.0 14,880 16,758 9,942 10,958 Euro area 1.7 1.6 4.5 4.3 1,802 1,606 1.2 1.0 5,473 4,891 1,527 2,620 Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2011). a. Estimates differ from official statistics of the government of China, which has published the following estimates: military expenditure as 1.4 percent of GDP in 2005 and 1.5 percent in 2009 and 7.3 percent of national government expenditure in 2005 and 6.5 percent in 2009 (see National Bureau of Statistics of China, www.stats.gov.cn). 310 2012 World Development Indicators 5.7 STATES AND MARKETS Military expenditures and arms transfers About the data De�nitions Although national defense is an important function provided. Because of the differences in definitions • Military expenditures are SIPRI data derived from of government and security from external threats and the difficulty in verifying the accuracy and com- NATO’s former definition, (in use until 2002) which that contributes to economic development, high pleteness of data, data on military expenditures are includes all current and capital expenditures on military expenditures for defense or civil conflicts not always comparable across countries. However, the armed forces, including peacekeeping forces; burden the economy and may impede growth. Data SIPRI puts a high priority on ensuring that the data defense ministries and other government agencies on military expenditures as a share of gross domes- series for each country is comparable over time. engaged in defense projects; paramilitary forces, if tic product (GDP) are a rough indicator of the portion More information on SIPRI’s military expenditure proj- judged to be trained and equipped for military opera- of national resources used for military activities and ect can be found at www.sipri.org/contents/milap/. tions; and military space activities. Such expendi- of the burden on the economy. As an “input� mea- Data on armed forces refer to military personnel on tures include military and civil personnel, including sure military expenditures are not directly related active duty, including paramilitary forces. Because retirement pensions and social services for military to the “output� of military activities, capabilities, or data exclude personnel not on active duty, they personnel; operation and maintenance; procurement; security. Comparisons of military spending among underestimate the share of the labor force working military research and development; and military aid countries should take into account the many fac- for the defense establishment. Governments rarely (in the military expenditures of the donor country). tors that influence perceptions of vulnerability and report the size of their armed forces, so such data Excluded are civil defense and current expenditures risk, including historical and cultural traditions, the typically come from intelligence sources. for previous military activities, such as for veterans length of borders that need defending, the quality of SIPRI’s Arms Transfers Programme collects data benefits, demobilization, and weapons conversion relations with neighbors, and the role of the armed on arms transfers from open sources. Since publicly and destruction. This definition cannot be applied forces in the body politic. available information is inadequate for tracking all for all countries, however, since that would require Data on military expenditures are reported to weapons and other military equipment, SIPRI covers more detailed information than is available about mili- the United Nations Office for Disarmament Affairs only what it terms major conventional weapons. Data tary budgets and off-budget military expenditures (for (UNODA) by about 60 governments. Data on military cover the supply of weapons through sales, aid, gifts, example, whether military budgets cover civil defense, expenditures are not compiled using standard defini- and manufacturing licenses; therefore the term arms reserves and auxiliary forces, police and paramilitary tions and are often incomplete and unreliable due transfers rather than arms trade is used. SIPRI data forces, and military pensions). • Armed forces per- to countries’ reluctance to disclose military informa- also cover weapons supplied to or from rebel forces sonnel are active duty military personnel, including tion. Even in countries where the parliament vigilantly in an armed conflict as well as arms deliveries for paramilitary forces if the training, organization, equip- reviews budgets and spending, military expenditures which neither the supplier nor the recipient can be ment, and control suggest they may be used to sup- and arms transfers rarely receive close scrutiny or identified with acceptable certainty; these data are port or replace regular military forces. Reserve forces, full, public disclosure (see Ball 1984 and Happe available in SIPRI’s database. which are not fully staffed or operational in peace and Wakeman-Linn 1994). However, the Stockholm SIPRI’s estimates of arms transfers are designed time, are excluded. The data also exclude civilians in International Peace Research Institute (SIPRI) has as a trend-measuring device in which similar weap- the defense establishment and so are not consistent adopted a definition of military expenditure derived ons have similar values, reflecting both the quantity with the data on military expenditures on personnel. from the North Atlantic Treaty Organization’s (NATO) and quality of weapons transferred. The estimated • Arms transfers cover the supply of military weapons former definition (in use until 2002; see De�nitions). values do not reflect financial value (payments for through sales, aid, gifts, and manufacturing licenses The data on military expenditures as a share of GDP weapons transferred) because reliable data on the and are based on actual deliveries only. Weapons are SIPRI estimates. Data on military expenditures value of the transfer are not available, and even must be transferred voluntarily by the supplier, have as a share of central government expenditures use when values are known, the transfer usually includes a military purpose, and be destined for the armed data on central government expenditures from the more than the actual conventional weapons, such as forces, paramilitary forces, or intelligence agencies International Monetary Fund (IMF). Therefore the spares, support systems, and training, and details of of another country. Data cover major conventional data in the table may differ from comparable data the financial arrangements (such as credit and loan weapons such as aircraft, armored vehicles, artil- published by national governments. conditions and discounts) are usually not known. lery, radar systems and other sensors, missiles, and SIPRI’s primary source of military expenditure data Given these measurement issues, SIPRI’s method ships designed for military use as well as some major is official data provided by national governments. of estimating the transfer of military resources components such as turrets for armored vehicles and These data are derived from budget documents, includes an evaluation of the technical parameters engines. Excluded are other military equipment such defense white papers, and other public documents of the weapons. Weapons for which a price is not as most small arms and light weapons, trucks, small from official government agencies, including govern- known are compared with the same weapons for artillery, ammunition, support equipment, technology ment responses to questionnaires sent by SIPRI, which actual acquisition prices are available (core transfers, and other services. the UNODA, or the Organization for Security and weapons) or for the closest match. These weapons Data sources Co-operation in Europe. Secondary sources include are assigned a value in an index that reflects their Data on military expenditures are from SIPRI’s Mili- international statistics, such as those of NATO and military resource value in relation to the core weap- tary Expenditure Database (www.sipri.org/data- the IMF’s Government Finance Statistics Yearbook. ons. These matches are based on such characteris- bases/milex). Data on armed forces personnel Other secondary sources include country reports of tics as size, performance, and type of electronics, are from the International Institute for Strategic the Economist Intelligence Unit, country reports by and adjustments are made for secondhand weap- Studies’ The Military Balance 2012. Data on arms IMF staff, and specialist journals and newspapers. ons. More information on SIPRI’s Arms Transfers transfers are from SIPRI’s Arms Transfers Data- In the many cases where SIPRI cannot make Programme is available at www.sipri.org/research/ base (www.sipri.org/databases/armstransfers). independent estimates, it uses the national data armaments/transfers. 2012 World Development Indicators 311 5.8 Fragile situations International Peacebuilding and Battle- Intentional Military Business environment Development peacekeeping related homicides expenditures Association deaths Resource per 100,000 Allocation Troops, police, people Losses due to Firms formally and military Index Combined theft, robbery, registered when observers Operation 1–6 source vandalism, operations namea number (low to high) number estimatesb % of GDP and arson started December December Survey 2010 2011 2011 2000–10c 2008 2010 year % of sales % of firms Afghanistan 2.6 UNAMA 15 35,460 2.4 1.8 2008 1.5 88.0 Angola 2.8 .. 2,620 19.0 4.4 2010 1.5 62.7 Bosnia and Herzegovina 3.7 .. .. 1.7 1.4 2009 0.4 98.6 Burundi 3.1 BNUB 1 6,413 21.7 3.8 2006 1.1 .. Central African Republic 2.8 BINUCA 4 544 29.3 2.6   .. .. Chad 2.4 .. 4,238 15.8 3.0 2009 2.5 77.1 Comoros 2.5 .. .. 12.2 .. .. .. Congo, Dem. Rep. 2.7 MONUSCO 18,928 6,348 21.7 1.4 2010 1.8 61.9 Congo, Rep. 2.9 .. 167 30.8 1.1 2009 3.3 84.3 Côte d’Ivoire 2.7 UNOCI 10,999 809 56.9 1.6 2009 3.4 56.4 Eritrea 2.2 .. 25,057 17.8 .. 2009 0.0 100.0 Georgia 4.4 .. 648 4.1h 3.9 2008 0.7 99.6 Guinea 2.8 .. 647 22.5 .. 2006 2.0 .. Guinea-Bissau 2.7 UNIOGBIS 17 0 20.2 .. 2006 1.1 .. Haiti 2.9 MINUSTAH 11,611 244 6.9h ..   .. .. Iraq .. UNAMI 361 24,088 2.0 6.0   .. .. Kiribati 3.0 .. .. 7.3 .. .. .. Kosovo 3.4 UNMIK 16 .. .. .. 2009 0.3 89.2 Liberia 2.9 UNMIL 9,206 2,427 10.1 0.8 2009 2.8 73.8 Marshall Islands .. .. .. .. .. .. .. Micronesia, Fed. Sts. .. .. .. 0.9 .. 2009 2.1 96.9 Myanmar .. .. 1,590 10.2 .. .. .. Nepal 3.3 .. 9,418 2.8i 1.5 2009 0.9 94.0 Sierra Leone 3.3 .. 156 14.9 2.3 2009 0.8 89.2 Solomon Islands 2.8 RAMSI 468 .. 3.7 ..   .. .. Somalia .. UNPOS 6 7,574 1.5 ..   .. .. Sudan 2.4   .. 17,658 24.2 .. .. .. Timor-Leste 3.0 UNMIT 1,216 .. 6.9 2.8 2009 2.7 91.8 Togo 2.9 .. .. 10.9 1.8 2009 2.4 75.8 West Bank and Gaza .. .. .. .. .. 2006 1.2 .. Western Saharam .. MINURSO 228 .. .. .. .. .. Yemen, Rep. 3.2 .. 259 4.2i 4.4 2010 0.6 81.7 Zimbabwe 2.0 .. .. 14.3 1.3 .. .. Fragile situations .. 82,763 s 15.8 w 3.8 w Low income   .. .. 17.5 1.4 Note: The countries and territories with fragile situations in the table are primarily International Development Association-eligible countries and nonmember or inactive countries and territories that have 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 among 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 imperfect and is used here to reflect a complex concept. The World Bank continues to work with partners and client countries to refine the concept. a. UNAMA is United Nations Assistance Mission in Afghanistan, BNUB is United Nations Office in Burundi, BINUCA is United Nations Integrated Peacebuilding Office in the Central African Republic, MONUSCO is United Nations Organization Stabilization Mission in the Democratic Republic of the Congo, UNOCI is United Nations Operation in Côte d’Ivoire, UNIOGBIS is United Nations Integrated Peacebuilding Office in Guinea-Bissau, 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, RAMSI is Regional Assistance Mission to Solomon Islands, UNPOS is United Nations Political Office for Somalia, UNMIT is United Nations Integrated Mission in Timor-Leste, and MINURSO is United Nations Mission for the Referendum in Western Sahara. b. Best estimate from the UN Office on Drug and Crime’s Intentional Homicide Statistics database. The combined source estimate uses both public health and law enforcement and criminal justice sources. c. Total over the period. d. Data are for the most recent year available. e. Average over the period. f. Covers only Angola-secured territory. g. Covers children ages 10–14. h. Data are for 2010. i. Data are for 2009. j. Northern Sudan only. k. Figure represents the Internal Displacement Monitoring Centre’s (IDMC) high estimate; the low estimates is 4,500,000. l. 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 Office of the United Nations High Commissioner for Refugees. m. The designation Western Sahara is used instead of Former Spanish Sahara (the designation used on the maps on the inside 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. n. Figure represents IDMC’s high estimate; the low estimate is 570,000. 312 2012 World Development Indicators 5.8 STATES AND MARKETS Fragile situations Children in Refugees Internally Access Access to Maternal mortality Under-�ve Depth of Primary employment displaced to an improved ratio mortality hunger gross persons improved sanitation rate enrollment water facilities ratio source per 100,000 live births kilocalories % of By country By country % of % of National Modeled per 1,000 per person % of relevant Survey children of origin of asylum number population population estimates estimates live births per day age group year ages 7–14 2010 2010 2010 2010 2010 2005–10 d 2008 2010 2006–08e 2010 Afghanistan .. 3,054,709 6,434 352,000 50 37 .. 1,400 149 .. 97 Angola 2001 30.1f 134,858 15,155 .. 51 58 .. 610 161 320 124 Bosnia and Herzegovina 2006 10.6 63,004 7,016 113,400 99 95 3 9 8 140 88 Burundi 2005 11.7 84,064 29,365 100,000 72 46 620 970 142 390 156 Central African Republic 2000 67.0 164,905 21,574 192,000 67 34 540 850 159 300 93 Chad 2004 60.4 53,733 347,939 171,000 51 13 .. 1,200 173 320 90 Comoros .. 368 .. .. 95 36 .. 340 86 300 104 Congo, Dem. Rep. 2000 39.8g 476,693 166,336 1,700,000 45 24 550 670 170 .. 94 Congo, Rep. 2005 30.1 20,679 133,112 7,800 71 18 780 580 93 230 115 Côte d’Ivoire 2006 45.7 41,758 26,218 621,000 80 24 540 470 123 230 88 Eritrea .. 222,460 4,809 10,000 61 14 .. 280 61 350 45 Georgia 2006 31.8 10,640 639 258,000 98 95 52 48 22 160 109 Guinea 1994 48.3 11,985 14,113 .. 74 18 980 680 130 260 94 Guinea-Bissau 2006 50.5 1,127 7,679 .. 64 20 410 1,000 150 250 123 Haiti 2005 33.4 25,892 3 .. 69 17 630 300 165 420 .. Iraq 2006 14.7 1,683,579 34,655 2,800,000 79 73 84 75 39 .. .. Kiribati .. 33 .. .. .. .. .. .. 49 180 113 Kosovo .. .. .. 18,300 .. .. .. .. .. .. .. Liberia 2007 37.4 70,129 24,743 .. 73 18 990 990 103 330 96 Marshall Islands .. .. .. .. 94 75 .. .. 26 .. 102 Micronesia, Fed. Sts. .. .. 1 .. .. .. .. .. 42 .. .. Myanmar .. 415,670 .. 446,000 83 76 320 240 66 .. 126 Nepal 1999 47.2 5,889 89,808 50,000 89 31 280 380 50 220 .. Sierra Leone 2007 14.9 11,275 8,363 .. 55 13 860 970 174 340 125 Solomon Islands .. 75 .. .. .. .. .. 100 27 190 .. Somalia 2006 43.5 770,154 1,937 1,500,000 29 23 1,000 1,200 180 .. .. Sudan 2000 19.1j 387,288 178,308 5,200,000k 58 26 1,100 750 103 240 73 Timor-Leste .. 8 1 400 69 47 560 370 81 260 117 Togo 2006 38.7 18,330 14,051 .. 61 13 .. 350 103 280 140 West Bank and Gaza .. 93,323 1,910,677l 160,000 85 92 .. .. 22 190 91 Western Saharam .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2006 18.3 2,076 190,092 250,000 55 53 .. 210 77 260 87 Zimbabwe 1999 14.3 24,089 4,435 1,000,000n 80 40 730 790 80 300 .. Fragile situations 7,848,793 s 3,237,459 s 14,328,500 s 65 w 41 w .. 650 w 120 w 271 w 102 w Low income 5,650,811 1,873,979 7,113,000 65 37 .. 590 108 281 104 About the data The table focuses on countries and territories with According to the Geneva Declaration on Armed Vio- fragile situations have to build their own institutions fragile situations and highlights the links among weak lence and Development, more than 526,000 people tailored to their own needs. Peacekeeping opera- institutions, poor development outcomes, fragility, die each year because of the violence associated with tions in post-conflict situations have been effective and risk of conflict. Many of these countries and ter- armed conflict and large- and small-scale criminal- in reducing the risks of reversion to conflict. ritories have weak institutions that are ill-equipped ity. Recovery and rebuilding can take years, and the The countries and territories with fragile situations to handle economic shocks, natural disasters, and challenges are numerous: infrastructure to be rebuilt, in the table are primarily International Development illegal trade or to resist conflict, which increasingly persistently high crime, widespread health problems, Association– eligible countries and nonmember or spills across borders. Organized violence, including education systems in disrepair, and unexploded ord- inactive countries or territories of the World Bank that violent crime, interrupts economic and social devel- nance to be cleared. Most countries emerging from have a 3.2 or lower harmonized average of the World opment through lost human and social capital, dis- conflict lack the capacity to rebuild the economy. Bank’s Country Policy and Institutional Assessment rupted services, displaced populations, and reduced Thus, capacity building is one of the first tasks for rating and the corresponding rating by a regional confidence for future investment. As a result, coun- restoring growth and is linked to building peace and development bank or that have had a UN or regional tries with fragile situations achieve lower develop- creating the conditions that lead to sustained poverty peacebuilding mission (for example, by the African ment outcomes and make slower progress toward reduction. The World Bank and other international Union, European Union, or Organization of American the Millennium Development Goals. development agencies can help, but countries with States) or peacekeeping mission (for example, by the 2012 World Development Indicators 313 5.8 Fragile situations About the data (continued) African Union, European Union, North Atlantic Treaty governments. For a more detailed discussion of mili- peace and development) or peacekeeping (providing Organization [NATO], or Organization of American tary expenditures, see About the data for table 5.7. essential security to preserve the peace where fight- States) during the last three years. Peacebuilding Along with public sector efforts, private sector devel- ing has been halted and to assist in implementing and peacekeeping involve many elements—military, opment and investment, especially in competitive mar- agreements achieved by the peacemakers). UN police, and civilian—working together to lay the foun- kets, has tremendous potential to contribute to growth peacekeeping operations are authorized by the UN dations for sustainable peace. The list of countries and poverty reduction. The World Bank’s Enterprise Secretary-General and planned, managed, directed, and territories with fragile situations is imperfect and Surveys review the business environment, assessing and supported by the United Nations Department of is used here to reflect a complex concept. The World constraints to private sector growth and enterprise Peacekeeping Operations and the Department of Bank continues to work with partners and client coun- performance. Crime, theft, and disorder impose Field Support. The UN Charter gives the Security tries to refine the concept. costs on businesses and society. And in many devel- Council primary responsibility for maintaining interna- An armed conflict is a contested incompatibility oping countries informal businesses operate without tional peace and security, including the establishment that concerns a government or territory where the licenses. These firms have less access to financial of a UN peacekeeping operation. • Troops, police, use of armed force between two parties (one of and public services and can engage in fewer types of and military observers in peacebuilding and peace- them the government) results in at least 25 battle- contracts and investments, constraining growth. The keeping are people active in peacebuilding and related deaths in a calendar year. There were 30 table presents data on the loss of sales due to theft, peacekeeping as part of an official operation. Peace- active armed conflicts in 2010. This table presents robbery, vandalism, and arson and on the percentage keepers deploy to war-torn regions where no one else data from the Uppsala Conflict Data Program (UCDP) of firms operating informally. For further information on is willing or able to go to prevent conflict from return- Battle-Related Deaths Dataset (v.5 2011), which enterprise surveys, see About the data for table 5.2. ing or escalating. • Battle-related deaths are deaths focuses on the incompatibility and lists the country, As the table shows, the human toll of armed vio- of members of warring parties in battle-related con- as well as the battle location and territory where lence across various contexts is severe. Additionally, flicts. Typically, battle-related deaths occur in warfare battle-related deaths are reported. When more than in countries with fragile situations weak institutional involving the armed forces of the warring parties one country is listed in the dataset, the assignment capacity often results in poor performance and fail- (battlefield fighting, guerrilla activities, and all kinds of battle-related deaths in the table is determined by ure to meet expectations of effective service deliv- of bombardments of military units, cities, and vil- the battle location. See country footnotes for addi- ery. Failure to deliver water, health, and education lages). The targets are usually the military and its tional details. services can weaken struggling governments. The installations or state institutions and state represen- Data on intentional homicides are from the United table includes several indicators related to living con- tatives, but there is often substantial collateral dam- Nations Office on Drugs and Crime (UNODC), which ditions in fragile situations: children in employment, age of civilians killed in crossfire, indiscriminate uses a variety of national and international sources refugees, internally displaced persons, access to bombings, and other military activities. All deaths— on homicides—primarily criminal justice sources water and sanitation, maternal and under-five mortal- civilian as well as military— incurred in such situa- as well as public health data from the World Health ity, depth of hunger, and primary school enrollment. tions are counted as battle-related deaths. • Inten- Organization (WHO) and the Pan American Health For more detailed information on these indicators, tional homicides are estimates of unlawful homicides Organization—and the United Nations Survey of see About the data for table 2.6 (children in employ- purposely inflicted as a result of domestic disputes, Crime Trends and Operations of Criminal Justice ment), table 6.14 (refugees), table 2.18 (access to interpersonal violence, violent conflicts over land Systems to present accurate and comparable sta- improved water and sanitation), table 2.19 (maternal resources, intergang violence over turf or control, and tistics. The UNODC defines homicide as “unlawful mortality), table 2.23 (under-five mortality), and table predatory violence and killing by armed groups. Inten- death purposefully inflicted on a person by another 2.12 (primary school enrollment). tional homicide does not include all intentional killing; person.� This definition excludes deaths arising from the difference is usually in the organization of the De�nitions armed conflict. For additional information, see the killing. Individuals or small groups usually commit UNODC Homicide Statistics database at www.unodc. • International Development Association Resource homicide, whereas killing in armed conflict is usually org/unodc/en/data-and-analysis/homicide.html. Allocation Index is from the Country Policy and Insti- committed by fairly cohesive groups of up to several Data on military expenditures reported by govern- tutional Assessment rating, which is the average hundred members and is thus usually excluded. • Mil- ments are not compiled using standard definitions score of four clusters of indicators designed to mea- itary expenditures are SIPRI data derived from NATO’s and are often inconsistent across countries, incom- sure macroeconomic, governance, social, and struc- former definition (in use until 2002), which includes plete, and unreliable. Even in countries where the tural dimensions of development: economic manage- all current and capital expenditures on the armed parliament vigilantly reviews budgets and spend- ment, structural policies, policies for social inclusion forces, including peacekeeping forces; defense min- ing, military expenditures and arms transfers rarely and equity, and public sector management and insti- istries and other government agencies engaged in receive close scrutiny or full public disclosure. tutions (see table 5.9). Countries are rated on a scale defense projects; paramilitary forces, if judged to be Data are from the Stockholm International Peace of 1 (low) to 6 (high). • Peacebuilding and peacekeep- trained and equipped for military operations; and mili- Research Institute (SIPRI), which uses NATO’s former ing refer to operations that engage in peacebuilding tary space activities. Such expenditures include mili- definition of military expenditure (in use until 2002; (reducing the risk of lapsing or relapsing into conflict tary and civil personnel, including retirement pensions see De�nitions). Therefore, the data in the table may by strengthening national capacities for conflict man- and social services for military personnel; operation differ from comparable data published by national agement and laying the foundation for sustainable and maintenance; procurement; military research and 314 2012 World Development Indicators 5.8 STATES AND MARKETS Fragile situations development; and military aid (in the military expen- Reasonable access is the availability of at least 20 from the UNODC’s International Homicide Statis- ditures of the donor country). Excluded are civil liters a person a day from a source within 1 kilometer tics database (www.unodc.org/unodc/en/data- defense and current expenditures for previous military of the dwelling. • Access to improved sanitation and-analysis/homicide.html). Data on military activities, such as for veterans benefits, demobiliza- facilities refers to people with at least adequate expenditures are from SIPRI’s Military Expendi- tion, and weapons conversion and destruction. This access to excreta disposal facilities that can effec- ture database (www.sipri.org/databases/milex). definition cannot be applied to all countries, however, tively prevent human, animal, and insect contact with Data on the business environment are from the since the necessary detailed information is missing excreta. Improved facilities range from protected pit World Bank’s Enterprise Surveys (www.enterprise- in some cases for military budgets and off-budget latrines to flush toilets. • Maternal mortality ratio is surveys.org). Data on children in employment are military expenditures (for example, whether military the number of women who die from pregnancy-related estimates produced by the Understanding Chil- budgets cover civil defense, reserves and auxiliary causes during pregnancy and childbirth per 100,000 dren’s Work project based on household survey forces, police and paramilitary forces, and military live births. National estimates are based on national data sets made available by the International pensions). • Survey year is the year in which the surveys, vital registration records, and surveillance Labour Organization’s International Programme underlying data were collected. • Losses due to theft, data or are derived from community and hospital on the Elimination of Child Labour under its Sta- robbery, vandalism, and arson are the estimated records. Modeled estimates are based on an exercise tistical Monitoring Programme on Child Labour, losses from those causes that occurred on business by the WHO, United Nations Children’s Fund (UNICEF), UNICEF under its Multiple Indicator Cluster Survey establishment premises calculated as a percentage United Nations Population Fund (UNFPA), and the program, the World Bank under its Living Stan- of annual sales. • Firms formally registered when World Bank. See About the data for table 2.19 for dards Measurement Study program, and national operations started are firms formally registered when further details. • Under-�ve mortality rate is the prob- statistical offices (see table 2.6). Data on refu- they started operations in the country. • Children in ability of a child born in a specific year dying before gees are from the UNHCR’s Statistical Yearbook employment are children involved in any economic reaching age 5, if subject to the age-specific mortality 2010, complemented by statistics on Palestinian activity for at least one hour in the reference week of rate of that year. The probability is derived from life refugees under the mandate of the United Nations the survey. • Refugees are people who are recognized tables and expressed as rate per 1,000 live births. Relief and Works Agency for Palestine Refugees in as refugees under the 1951 Convention Relating to • Depth of hunger, or the intensity of food depriva- the Near East as published on its website (www. the Status of Refugees or its 1967 Protocol, the 1969 tion, indicates how much people who are food- unrwa.org). Data on internally displaced persons Organization of African Unity Convention Governing deprived fall short of minimum food needs in terms of are from the Internal Displacement Monitoring the Specific Aspects of Refugee Problems in Africa, dietary energy. It is measured by comparing the aver- Centre. Data on access to water and sanitation people recognized as refugees in accordance with the age amount of dietary energy that undernourished are from the WHO and UNICEF’s Progress on Drink- UN Refugee Agency (UNHCR) statute, people granted people get from the foods they eat with the minimum ing Water and Sanitation (2012). National esti- refugee-like humanitarian status, and people pro- amount of dietary energy they need to maintain body mates of maternal mortality are from UNICEF’s vided temporary protection. Asylum seekers—people weight and undertake light activity. Depth of hunger The State of the World’s Children 2009 and Child- who have applied for asylum or refugee status and is low when it is less than 200 kilocalories per person info and MEASURE DHS Demographic and Health who have not yet received a decision, or who are reg- per day and high when it is above 300. • Primary Surveys by ICF International. Modeled estimates istered as asylum seekers—are excluded. Palestinian gross enrollment ratio is the ratio of total enrollment, for maternal mortality are from WHO, UNICEF, refugees are people (and their descendants) whose regardless of age, to the population of the age group UNFPA, and the World Bank’s Trends in Maternal residence was Palestine between June 1946 and May that officially corresponds to the primary level of edu- Mortality: 1990–2008 (2010). Data on under-fi ve 1948 and who lost their homes and means of liveli- cation. Primary education provides children with basic mortality estimates by the UN Inter-agency Group hood as a result of the 1948 Arab-Israeli conflict. reading, writing, and mathematics skills along with an for Child Mortality Estimation (which comprises • Country of origin refers to the nationality or country elementary understanding of such subjects as his- UNICEF, WHO, the World Bank, United Nations of citizenship of a claimant. • Country of asylum is tory, geography, natural science, social science, art, Population Division, and other universities and the country where an asylum claim was filed and and music. research institutes) and are based mainly on granted. • Internally displaced persons are people or household surveys, censuses, and vital regis- Data sources groups of people who have been forced or obliged to tration data, supplemented by the World Bank’s flee or to leave their homes or places of habitual resi- Data on the International Development Associa- Human Development Network estimates based dence, in particular as a result of armed conflict, or to tion Resource Allocation Index are from the World on vital registration and sample registration data avoid the effects of armed conflict, situations of gen- Bank Group’s International Development Associa- (see table 2.23). Data on depth of hunger are eralized violence, violations of human rights, or natu- tion database (www.worldbank.org/ida). Data on from the Food and Agriculture Organization’s Food ral or human-made disasters and who have not peacebuilding and peacekeeping operations are Security Statistics database (www.fao.org/eco- crossed an international border. •  Access to an from the UN Department of Peacekeeping Opera- nomic/ess/ess-fs/fs-data/ess-fadata/en/). Data improved water source refers to people with reason- tions. Data on battle-related deaths are from the on primary gross enrollment are from the United able access to water from an improved source, such UCDP Battle-Related Deaths Dataset (v.5-2011) Nations Educational, Scientific, and Cultural Orga- as piped water into a dwelling, public tap, tubewell, 1989-2010. Data on intentional homicides are nization’s Institute for Statistics. protected dug well, and rainwater collection. 2012 World Development Indicators 315 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 2010 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan 2.6 3.5 3.0 2.5 3.0 3.0 2.0 2.5 2.5 Angola 2.8 3.0 3.0 3.0 3.0 4.0 2.5 2.0 2.8 Armenia 4.1 4.5 5.0 4.5 4.7 4.5 3.5 4.0 4.0 Azerbaijan 3.7 4.0 4.5 4.5 4.3 3.5 3.0 4.0 3.5 Bangladesh 3.5 4.0 4.0 4.0 4.0 3.0 3.5 3.5 3.3 Benin 3.5 4.0 3.0 3.5 3.5 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 3.7 4.0 4.0 4.5 4.2 5.0 3.5 2.5 3.7 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 3.4 4.5 4.0 3.5 4.0 4.0 2.5 3.5 3.3 Cameroon 3.2 4.0 3.5 3.0 3.5 3.5 3.0 3.0 3.2 Cape Verde 4.1 4.5 4.5 4.0 4.3 4.0 4.0 3.5 3.8 Central African Republic 2.8 3.5 3.5 3.0 3.3 3.5 2.5 2.0 2.7 Chad 2.4 2.5 2.5 2.5 2.5 3.0 2.5 2.0 2.5 Comoros 2.5 3.0 2.0 2.0 2.3 3.5 2.5 2.5 2.8 Congo, Dem. Rep. 2.7 3.5 3.5 3.0 3.3 3.0 2.0 2.0 2.3 Congo, Rep. 2.9 3.5 3.0 3.0 3.2 3.5 3.0 2.5 3.0 Côte d’Ivoire 2.7 3.5 2.5 2.0 2.7 4.0 3.0 3.0 3.3 Djibouti 3.2 3.5 3.0 2.5 3.0 4.0 3.0 3.5 3.5 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 3.4 4.0 3.5 3.0 3.5 3.5 3.5 3.5 3.5 Georgia 4.4 4.5 4.5 5.0 4.7 6.0 3.5 5.5 5.0 Ghana 3.9 3.5 3.5 4.0 3.7 4.0 4.0 4.5 4.2 Grenada 3.8 3.5 3.0 3.0 3.2 4.5 4.0 4.5 4.3 Guinea 2.8 2.5 2.5 2.0 2.3 4.0 3.0 2.5 3.2 Guinea-Bissau 2.7 3.0 2.5 2.0 2.5 4.0 2.5 2.5 3.0 Guyana 3.4 3.5 3.5 4.0 3.7 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 3.6 3.5 3.5 4.0 3.7 4.5 3.0 3.5 3.7 India 3.7 4.5 3.5 4.0 4.0 3.5 4.0 3.5 3.7 Kenya 3.8 4.5 4.0 4.0 4.2 4.0 4.0 4.0 4.0 Kiribati 3.0 2.5 3.0 3.5 3.0 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 a higher IDA allocation in per capita terms. The IRAI part of the World Bank Group that helps the poorest the mid-1970s by World Bank staff. Over time the is a key element in the country performance rating. countries reduce poverty by providing concessional loans criteria have been revised from a largely macro- The CPIA exercise is intended to capture the quality and grants for programs aimed at boosting economic economic focus to include governance aspects and of a country’s policies and institutional arrangements, growth and improving living conditions. IDA funding helps a broader coverage of social and structural dimen- focusing on key elements that are within the country’s these countries deal with the complex challenges they sions. Country performance is assessed against a control, rather than on outcomes (such as economic face in meeting the Millennium Development Goals. set of 16 criteria grouped into four clusters: eco- growth rates) that are influenced by events beyond The World Bank’s IDA Resource Allocation Index nomic management, structural policies, policies the country’s control. More specifically, the CPIA (IRAI), presented in the table, is based on the results for social inclusion and equity, and public sector measures the extent to which a country’s policy and of the annual Country Policy and Institutional Assess- management and institutions. IDA resources are institutional framework supports sustainable growth ment (CPIA) exercise, which covers the IDA-eligible allocated to a country on per capita terms based and poverty reduction and, consequently, the effective countries. The table does not include Myanmar and on its IDA country performance rating and, to a lim- use of development assistance. Somalia because they were not rated in the 2010 ited extent, based on its per capita gross national All criteria within each cluster receive equal weight, exercise even though they are IDA eligible. Country income. This ensures that good performers receive and each cluster has a 25 percent weight in the 316 2012 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 2010 2010 2010 2010 2010 2010 2010 2010 2010 Lao PDR 3.3 4.0 4.0 3.0 3.7 3.5 2.0 3.0 2.8 Lesotho 3.5 4.0 3.5 4.0 3.8 3.5 3.0 3.0 3.2 Liberia 2.9 3.5 3.5 3.0 3.3 3.0 2.5 3.0 2.8 Madagascar 3.4 3.5 3.0 4.0 3.5 4.0 3.0 3.0 3.3 Malawi 3.3 3.0 3.5 3.0 3.2 3.5 3.0 3.0 3.2 Maldives 3.4 2.5 2.0 2.5 2.3 4.0 3.0 4.0 3.7 Mali 3.6 4.5 4.0 4.0 4.2 4.0 3.0 3.5 3.5 Mauritania 3.2 3.5 3.0 3.5 3.3 4.0 2.5 3.0 3.2 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 3.5 3.3 4.5 2.5 3.5 3.5 Mozambique 3.7 4.5 4.5 4.5 4.5 4.5 3.5 3.0 3.7 Nepal 3.3 3.5 4.0 3.0 3.5 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 3.4 4.0 3.5 4.0 3.8 4.0 3.0 3.0 3.3 Nigeria 3.4 4.0 4.0 4.5 4.2 3.5 3.5 3.5 3.5 Pakistan 3.1 2.5 2.5 3.5 2.8 3.5 3.5 3.5 3.5 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 3.0 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 3.3 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 4.0 4.0 3.8 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 2.4 3.5 3.0 1.5 2.7 2.5 2.5 2.5 2.5 Tajikistan 3.3 4.0 3.5 3.5 3.7 4.0 2.5 3.0 3.2 Tanzania 3.8 4.5 4.0 4.0 4.2 4.0 4.0 3.5 3.8 Timor-Leste 3.0 3.0 3.5 4.0 3.5 4.5 2.5 1.5 2.8 Togo 2.9 3.0 3.0 3.0 3.0 4.0 2.5 3.0 3.2 Tonga 3.5 3.0 3.0 2.5 2.8 5.0 3.5 3.0 3.8 Uganda 3.8 4.5 4.0 4.5 4.3 4.0 3.5 4.0 3.8 Uzbekistan 3.4 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 3.0 3.0 3.5 3.2 Vietnam 3.8 4.0 4.5 4.0 4.2 3.5 3.0 3.5 3.3 Yemen, Rep. 3.2 3.5 3.0 3.0 3.2 4.5 2.5 3.5 3.5 Zambia 3.4 4.0 3.0 3.5 3.5 4.0 3.5 3.5 3.7 Zimbabwe 2.0 2.0 2.0 1.0 1.7 3.0 2.0 2.0 2.3 over all score, which is obtained by averaging the should be noted that the criteria are designed in a consistent across countries, the process involves average scores of the four clusters. For each of the developmentally neutral manner. Accordingly, higher two key phases. In the benchmarking phase a small 16 criteria countries are rated on a scale of 1 (low) scores can be attained by a country that, given its representative sample of countries drawn from all to 6 (high). The scores depend on the level of perfor- stage of develop ment, has a policy and institutional regions is rated. Country teams prepare proposals mance in a given year assessed against the criteria, framework that more strongly fosters growth and that are reviewed first at the regional level and then rather than on changes in performance compared poverty reduction. in a Bankwide review process. A similar process is with the previous year. All 16 CPIA criteria contain a The country teams that prepare the ratings are very followed to assess the performance of the remaining detailed description of each rating level. In assess- familiar with the country, and their assessments are countries, using the benchmark countries’ scores as ing country performance, World Bank staff evaluate based on country diagnostic studies prepared by the guideposts. The final ratings are determined following the country’s performance on each of the criteria World Bank or other development organizations and a Bankwide review. The overall numerical IRAI score and assign a rating. The ratings reflect a variety of on their own professional judgment. An early con- and the separate criteria scores were first publicly indicators, observations, and judgments based on sultation is conducted with country authorities to disclosed in June 2006. country knowledge and on relevant publicly available make sure that the assessments are informed by See IDA’s website at www.worldbank.org/ida for indicators. In interpreting the assessment scores, it up-to-date information. To ensure that scores are more information. 2012 World Development Indicators 317 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 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 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 2.5 3.0 2.8 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 3.5 3.5 3.0 2.5 3.1 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 3.5 3.5 3.0 3.5 3.4 3.0 3.5 3.5 3.0 3.5 3.3 Bhutan 4.0 4.0 4.5 3.5 4.5 4.1 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 3.5 3.0 3.0 3.4 2.5 3.0 3.0 2.5 2.0 2.6 Cambodia 4.0 3.5 3.5 3.0 3.0 3.4 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 3.0 2.5 2.5 2.5 2.5 Chad 2.5 2.0 2.5 2.5 2.0 2.3 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 3.5 2.5 2.5 2.8 2.0 2.5 2.5 2.0 2.0 2.2 Congo, Rep. 3.0 2.5 3.5 2.5 2.5 2.8 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 3.5 2.0 2.0 2.4 Djibouti 3.0 3.0 3.5 3.5 3.5 3.3 2.5 3.0 3.5 2.5 2.5 2.8 Dominica 3.0 3.5 4.0 3.5 3.5 3.5 4.0 4.0 4.0 3.5 4.0 3.9 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 4.0 3.5 2.5 3.5 3.4 3.0 3.5 3.5 3.0 2.0 3.0 Georgia 4.5 4.5 4.5 4.5 3.0 4.2 3.5 4.0 4.5 4.0 3.5 3.9 Ghana 4.0 4.0 4.5 4.0 3.5 4.0 3.5 3.5 4.0 3.5 4.0 3.7 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 2.5 2.5 3.0 2.7 2.5 2.5 3.0 2.5 2.5 2.6 Guyana 3.5 3.5 4.0 3.0 3.0 3.4 3.0 3.5 3.5 2.5 2.5 3.0 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 4.0 3.0 3.5 3.7 3.0 3.5 4.0 3.0 3.0 3.3 India 3.5 4.0 4.0 3.5 3.5 3.7 3.5 3.5 4.0 3.5 3.5 3.6 Kenya 3.5 4.0 4.0 3.5 3.5 3.7 2.5 3.5 4.0 3.5 3.0 3.3 Kiribati 2.5 4.0 2.5 3.0 3.0 3.0 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 De�nitions • 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 318 2012 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 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Lao PDR 3.5 4.0 3.5 2.5 4.0 3.5 3.0 3.5 3.5 3.0 2.5 3.1 Lesotho 4.0 3.0 3.5 3.0 3.0 3.3 3.5 3.5 4.0 3.0 3.5 3.5 Liberia 2.5 3.5 2.5 2.5 2.5 2.7 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 3.0 2.5 3.5 3.5 2.5 3.0 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 4.0 3.5 4.0 3.9 3.5 3.0 4.5 3.5 3.0 3.5 Mali 3.5 4.0 3.5 3.5 3.0 3.5 3.5 3.5 3.5 3.0 3.5 3.4 Mauritania 4.0 3.5 3.0 2.5 3.0 3.2 3.0 3.0 3.5 3.0 2.5 3.0 Moldova 5.0 3.5 4.0 3.5 4.0 4.0 3.5 4.0 3.5 3.0 3.0 3.4 Mongolia 3.5 3.5 3.5 3.5 3.0 3.4 3.0 4.0 3.5 3.5 3.0 3.4 Mozambique 3.5 3.5 3.5 3.0 3.5 3.4 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 2.5 3.5 3.0 2.5 2.8 Nicaragua 3.5 4.0 3.5 3.5 3.5 3.6 2.5 4.0 4.0 3.0 2.5 3.2 Niger 2.5 3.5 3.5 3.0 3.5 3.2 3.0 3.5 3.5 3.0 3.0 3.2 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 3.5 2.5 2.9 3.0 3.5 3.0 3.5 2.5 3.1 Papua New Guinea 2.5 4.0 2.5 3.0 2.0 2.8 2.0 3.0 3.5 3.0 3.0 2.9 Rwanda 4.0 4.5 4.5 3.5 3.5 4.0 3.5 4.0 3.5 4.0 3.5 3.7 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 3.5 2.5 3.0 3.0 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 3.5 4.0 3.5 3.0 3.5 Sierra Leone 3.0 3.5 3.5 3.5 2.5 3.2 2.5 3.5 3.0 3.0 3.0 3.0 Solomon Islands 3.0 2.5 3.0 2.5 2.0 2.6 3.0 2.5 3.0 2.0 3.0 2.7 Sri Lanka 4.0 3.5 4.5 3.5 3.0 3.7 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 2.5 2.5 2.5 2.5 2.0 2.4 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 3.5 3.0 3.0 2.0 2.8 Tanzania 3.5 4.0 3.5 4.0 3.5 3.7 3.5 3.5 4.0 3.0 2.5 3.3 Timor-Leste 3.5 3.0 3.0 2.5 2.5 2.9 2.0 3.0 3.0 2.5 3.0 2.7 Togo 3.0 2.5 3.0 3.0 2.5 2.8 2.5 3.0 3.0 2.0 2.5 2.6 Tonga 3.0 4.0 4.0 3.0 3.0 3.4 4.0 3.5 4.5 3.5 3.5 3.8 Uganda 3.5 4.0 3.5 3.5 4.0 3.7 3.5 3.5 3.5 3.0 2.5 3.2 Uzbekistan 4.0 4.0 4.0 3.5 3.5 3.8 2.5 3.5 3.5 3.0 1.5 2.8 Vanuatu 3.5 3.5 2.5 2.5 3.0 3.0 3.5 4.0 3.5 3.0 3.0 3.4 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 2.5 2.9 Zambia 3.5 3.5 4.0 3.0 3.5 3.5 3.0 3.5 3.5 3.0 2.5 3.1 Zimbabwe 2.5 2.0 1.5 1.0 2.0 1.8 1.5 2.0 3.5 2.0 1.5 2.1 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- • Ef�ciency 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 �nancial 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 (www. a comprehensive and credible budget linked to policy use of funds and for the results of its actions by the worldbank.org/ida). priorities, effective financial management systems, elector ate, the legislature, and the judiciary and the 2012 World Development Indicators 319 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 2000–09a 2000–09a 2000–09a 2000–09a 2000–10a 2000–10a 2000–10a 2010 2010 2010 2010 Afghanistan 42,150 29.3 232 6,575 .. .. .. .. .. .. .. Albania 18,000 39.0 197 2,200 423 32 46 87 10 920 0 Algeria 112,039 74.0 .. .. 3,512 1,045 1,281 266 44 3,686 6 Angola 51,429 10.4 166,045 4,709 .. .. .. .. 14 1,283 91 Argentina 231,374 30.0 .. .. 25,023 6,979 12,025 2,018 100 10,030 178 Armenia 7,705 93.6 2,356 182 826 50 346 .. 9 757 6 Australia 817,089 43.5 301,524 189,847 8,615 1,500 64,172 6,536 418 45,268 2,380 Austria 106,840 100.0 69,000 16 5,066 10,306 23,104 350 184 13,869 430 Azerbaijan 52,942 50.6 15,291 10,634 2,079 917 8,250 .. 10 801 8 Bahrain 4,083 82.1 .. .. .. .. .. 290 58 5,339 843 Bangladesh 239,226 9.5 .. .. 2,835 7,305 710 1,356 12 2,177 85 Belarus 94,797 88.6 8,184 22,767 5,503 7,578 46,224 .. 13 888 2 Belgium 153,872 78.2 131,470 36,174 3,578 10,493 5,439 10,985 143 7,263 1,274 Benin 19,000 9.5 .. .. 758 .. 36 278 .. .. .. Bolivia 80,294 7.9 .. .. 2,866 313 1,060 .. 36 1,916 14 Bosnia and Herzegovina 21,846 52.3 1,959 1,711 1,026 59 1,227 .. 2 133 0 Botswana 25,798 32.6 .. .. 888 94 674 .. 8 290 0 Brazil 1,751,868 5.5 .. .. 29,817 .. 267,700 8,121 885 77,255 976 Bulgaria 40,231 98.4 13,839 17,742 4,098 2,100 3,061 143 31 2,243 2 Burkina Faso 92,495 4.2 .. .. 622 .. .. .. 4 254 0 Burundi 12,322 10.4 .. .. .. .. .. .. .. .. .. Cambodia 38,257 6.3 201 .. 650 45 92 224 5 312 18 Cameroon 28,857 17.0 .. .. 977 377 978 250 10 466 23 Canada 1,409,000 39.9 493,814 129,600 58,345 2,875 322,741 4,813 1,235 67,325 2,011 Central African Republic 24,307 .. .. .. .. .. .. .. .. .. .. Chad 40,000 0.8 .. .. .. .. .. .. .. .. .. Chile 78,425 22.5 .. .. 5,352 840 4,032 3,172 109 10,234 1,400 China 3,860,823 53.5 1,351,144 3,718,882 66,239 791,158 2,451,185 129,611 2,391 267,691 17,441 Hong Kong SAR, China 2,050 100.0 .. .. .. .. .. 23,699 151 25,383 7,970 Colombia 129,485 .. 157 39,726 1,672 .. 9,049 2,444 180 15,111 1,576 Congo, Dem. Rep. 153,497 1.8 .. .. 3,641 37 193 .. .. .. .. Congo, Rep. 17,000 7.1 .. .. 795 211 234 297 .. .. .. Costa Rica 39,039 26.0 27 1 .. .. .. 1,013 49 1,747 20 Côte d’Ivoire 81,996 7.9 .. .. 639 10 675 608 .. .. .. Croatia 29,343 90.5 3,438 9,429 2,722 1,742 2,618 137 26 1,641 2 Cuba .. 49.0 6,634 2,315 5,076 1,285 1,351 228 10 862 16 Cyprus 12,380 64.9 .. 944 .. .. .. 349 22 2,363 38 Czech Republic 130,573 100.0 88,352 44,955 9,569 6,553 13,592 .. 94 7,103 23 Denmark 73,330 100.0 68,907 10,003 2,131 7,405 2,030 709 4b 519b 0b Dominican Republic 12,600 49.4 .. .. .. .. .. 1,383 .. .. .. Ecuador 43,670 14.8 11,819 1,193 .. .. .. 1,222 50 4,461 115 Egypt, Arab Rep. 100,472 89.4 12,793 .. 5,195 40,837 3,840 6,709 113 9,637 188 El Salvador 10,029 19.8 .. .. .. .. .. 146 20 2,150 17 Eritrea 4,010 21.8 .. .. .. .. .. .. .. .. .. Estonia 58,382 28.6 2,453 5,249 787 248 6,261 152 11 809 1 Ethiopia 44,359 13.7 219,113 2,456 .. .. .. .. 48 3,141 672 Finland 78,925 65.5 72,700 25,200 5,919 3,959 9,760 1,247 183 11,578 736 France 951,260 100.0 773,000 265,000 33,608 86,853 22,840 5,323 751 65,401 5,081 Gabon 9,170 12.0 .. .. 810 111 2,238 136 1 96 2 Gambia, The 3,742 19.3 16 .. .. .. .. .. .. .. .. Georgia 20,329 94.1 5,724 611 1,566 655 6,228 226 6 328 15 Germany 643,969 100.0 949,306 427,300 33,708 78,582 105,794 14,625 1,086 112,399 9,245 Ghana 109,515 12.6 .. .. 953 85 181 514 .. .. .. Greece 116,929 91.8 .. 28,585 2,552 1,413 538 1,165 148 15,588 37 Guatemala 14,095 34.5 .. .. .. .. .. 1,012 .. .. .. Guinea 44,348 9.8 .. .. .. .. .. .. .. .. .. Guinea-Bissau 3,455 27.9 .. .. .. .. .. .. .. .. .. Haiti 4,160 24.3 .. .. .. .. .. .. .. .. .. 320 2012 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 2000–09a 2000–09a 2000–09a 2000–09a 2000–10a 2000–10a 2000–10a 2010 2010 2010 2010 Honduras 13,600 20.4 .. .. .. .. .. 620 .. .. .. Hungary 197,519 38.0 20,449 35,373 7,893 5,398 1,000 .. 118 11,829 8 India 4,109,592 49.5 .. .. 63,974 903,465 600,548 9,753 630 64,144 1,720 Indonesia 476,337 56.9 .. .. 3,370 14,344 4,390 8,371 405 35,321 660 Iran, Islamic Rep. 192,685 73.3 .. .. 6,073 16,814 20,247 2,593 152 17,585 131 Iraq 40,988 84.3 .. .. 2,025 54 121 .. .. .. .. Ireland 96,424 100.0 .. 12,787 1,919 1,678 92 773 683 76,488 350 Israel 18,318 100.0 .. .. 1,034 1,986 1,062 2,282 54 6,141 865 Italy 487,700 100.0 97,560 192,700 18,011 44,535 12,037 9,787 364 48,748 1,164 Jamaica 22,121 73.3 .. .. .. .. .. 1,892 8 974 6 Japan 1,207,867 80.1 905,907 334,667 20,035 244,235 20,432 18,060 650 94,212 8,380 Jordan 7,878 100.0 .. .. 294 .. 353 619 39 2,972 219 Kazakhstan 96,846 88.5 110,475 66,254 14,202 15,448 213,174 .. 27 2,426 48 Kenya 61,945 14.3 .. 22 1,917 226 1,399 696 36 3,284 258 Korea, Dem. Rep. 25,554 2.8 .. .. .. .. .. .. 1 84 7 Korea, Rep. 104,983 79.3 100,617 12,545 3,379 33,027 9,452 18,538 483 42,763 12,802 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 6,524 85.0 .. .. .. .. .. 888 22 2,741 274 Kyrgyz Republic 34,000 91.1 6,745 912 417 99 738 .. 5 270 1 Lao PDR 39,568 13.7 2,113 287 .. .. .. .. 11 645 0 Latvia 69,148 20.9 14,625 8,115 1,897 79 17,164 257 66 4,466 23 Lebanon 6,970 .. .. .. .. .. .. 949 17 1,600 43 Lesotho 5,940 18.3 .. .. .. .. .. .. .. .. .. Liberia 10,600 6.2 .. .. .. .. .. .. .. .. .. Libya 83,200 57.2 .. .. .. .. .. 162 25 2,345 17 Lithuania 81,331 29.4 20,376 17,757 1,767 373 13,431 295 5 167 120 Macedonia, FYR 13,940 57.6 1,239 3,978 699 155 497 .. 2 137 0 Madagascar 49,827 11.6 .. .. 854 10 12 141 12 417 22 Malawi 15,451 45.0 .. .. 797 44 33 .. 3 172 4 Malaysia 98,722 81.3 .. .. 1,665 1,527 1,384 18,247 240 26,255 2,451 Mali 22,474 24.6 .. .. 733 196 189 .. .. .. .. Mauritania 11,066 26.8 .. .. 728 47 7,566 66 1 154 0 Mauritius 2,066 98.0 .. .. .. .. .. 445 12 1,268 179 Mexico 366,807 35.3 436,900 211,600 26,704 178 71,136 3,694 186 13,608 1,380 Moldova 12,779 85.8 2,268 2,714 1,157 399 927 .. 6 455 1 Mongolia 49,250 3.5 1,215 782 1,814 1,220 10,287 .. 8 417 3 Morocco 58,216 70.3 .. 800 2,109 4,398 5,572 2,058 92 8,971 11 Mozambique 30,331 20.8 .. .. 3,116 114 695 223 14 553 8 Myanmar 27,000 11.9 .. .. .. 4,163 885 167 8 396 2 Namibia 42,100 14.7 47 591 .. .. .. 256 9 488 0 Nepal 19,875 53.9 .. .. .. .. .. .. 2 288 4 Netherlands 136,827 90.0 .. 72,675 3,016 15,400 4,331 11,331 243 27,554 5,028 New Zealand 94,301 66.2 .. .. .. .. 4,078 2,427 186 10,128 668 Nicaragua 21,975 11.6 133 .. .. .. .. 68 .. .. .. Niger 18,948 20.7 .. .. .. .. .. .. .. .. .. Nigeria 193,200 15.0 .. .. 3,528 174 77 90 17 1,555 1 Norway 93,853 80.7 64,014 16,109 4,114 2,674 2,092 331 3b 399b 0b Oman 56,361 46.0 .. .. .. .. .. 3,893 35 4,068 27 Pakistan 258,350 65.4 263,788 129,249 7,791 24,731 6,187 2,149 50 6,012 310 Panama 13,974 42.0 .. .. .. .. .. 5,906 81 7,366 2 Papua New Guinea 19,600 3.5 .. .. .. .. .. 269 20 1,158 26 Paraguay 31,531 50.8 .. .. .. .. .. 7 6 606 0 Peru 126,500 13.9 .. .. 2,020 76 900 1,534 68 6,130 248 Philippines 200,037 9.9 .. .. 479 83 1 4,947 169 18,933 472 Poland 384,104 69.9 24,386 191,484 19,702 15,715 34,266 1,045 91 5,466 91 Portugal 82,900 86.0 .. 35,808 2,843 3,718 1,932 1,610 171 11,124 375 Puerto Rico 26,677 95.0 .. 10 .. .. .. 1,526 .. .. .. Qatar 7,790 90.0 .. .. .. .. .. 346 91 12,391 2,946 2012 World Development Indicators 321 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 2000–09a 2000–09a 2000–09a 2000–09a 2000–10a 2000–10a 2000–10a 2010 2010 2010 2010 Romania 198,817 30.2 12,805 20,878 13,620 5,248 9,134 557 66 3,996 5 Russian Federation 982,000 80.1 139,034 180,135 85,292 139,028 2,011,308 3,130 673 56,848 4,614 Rwanda 14,008 19.0 .. .. .. .. .. .. .. .. .. Saudi Arabia 221,372 21.5 .. .. 1,020 337 1,748 5,313 163 18,998 1,336 Senegal 14,825 32.0 .. .. 906 129 384 349 0 573 0 Serbia 44,334 63.2 4,169 1,184 4,058 658 3,868 .. 18 1,059 2 Sierra Leone 11,300 8.0 .. .. .. .. .. .. 0 24 9 Singapore 3,356 100.0 5,762 .. .. .. .. 29,179 81 25,319 4,004 Slovak Republic 43,879 87.1 31,093 27,484 3,587 2,291 7,669 .. 14 991 0 Slovenia 38,927 100.0 777 14,762 1,228 813 3,283 477 25 1,170 2 Somalia 22,100 11.8 .. .. .. .. .. .. .. .. .. South Africa 362,099 17.3 .. 434 22,051 18,865 113,342 3,806 210 16,779 1,107 South Sudan .. .. .. .. .. .. .. .. .. .. .. Spain 667,064 99.0 410,192 211,891 15,317 22,304 7,844 12,608 606 58,563 1,684 Sri Lanka 97,286 81.0 21,067 .. 1,463 4,767 135 4,080 17 2,800 329 Sudan 11,900 36.3 .. .. 4,508 34 766 439 6 602 13 Swaziland 3,594 30.0 .. .. 300 0 776 .. .. .. .. Sweden 582,950 24.4 109,100 35,000 9,957 6,774 11,500 1,391 3b 363b 0b Switzerland 71,371 100.0 95,090 16,734 3,543 17,609 8,725 99 220 21,477 1,275 Syrian Arab Republic 68,157 90.3 589 .. 2,139 1,120 2,370 649 11 1,018 3 Tajikistan 27,767 .. 8,591 5,013 621 33 808 .. 5 951 5 Tanzania 103,706 6.7 8 7 2,600 c 475c 728c 427 21 749 2 Thailand 180,053 98.5 .. .. 4,429 8,037 3,161 6,649 122 20,303 3,133 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 11,652 21.0 .. .. .. .. .. .. .. .. .. Trinidad and Tobago 8,320 51.1 .. .. .. .. .. 573 25 1,721 13 Tunisia 19,371 75.2 .. 16,611 1,119 1,493 2,073 466 44 5,464 21 Turkey 362,660 88.7 212,464 176,455 9,594 5,491 11,030 5,547 400 51,590 1,101 Turkmenistan 24,000 81.2 .. .. 3,115 1,811 11,992 .. 3 399 6 Uganda 70,746 23.0 .. .. 259 .. 218 .. 0 70 32 Ukraine 169,495 97.8 54,631 33,193 21,705 50,240 218,091 660 84 5,679 598 United Arab Emirates 4,080 100.0 .. .. .. .. .. 15,174 261 42,555 10,126 United Kingdom 419,665 100.0 736,000 143,453 31,471 55,019 12,512 7,389 1,173 123,972 8,555 United States 6,545,839 67.4 7,874,329 1,889,923 228,513 9,518 2,468,738d 42,190 8,934 e 707,426e 50,743e Uruguay 77,732 10.0 2,588 .. 2,993 15 284 672 19 744 0 Uzbekistan 81,600 87.3 56,674 21,038 4,227 2,905 22,282 .. 24 2,167 166 Venezuela, RB 96,155 33.6 .. .. 336 .. 81 1,216 125 5,881 5 Vietnam 160,089 47.6 59,735 30,261 2,347 4,378 3,901 5,984 103 14,099 428 West Bank and Gaza 5,588 91.7 .. .. .. .. .. .. .. .. .. Yemen, Rep. 71,300 8.7 .. .. .. .. .. 370 11 1,134 28 Zambia 66,781 22.0 .. .. 1,273 183 .. .. 4 62 .. Zimbabwe 97,267 19.0 .. .. 2,583 .. 1,580 .. 5 302 12 World 64.9 m .. m .. m .. s 2,134 m 4,532 m 538,284 s 28,078 s 2,595,449 s 189,325 s Low income 20.7 .. .. .. .. .. .. 182 13,444 1,142 Middle income 53.5 .. .. .. 1,220 4,802 260,211 8,539 834,810 41,820 Lower middle income 48.6 .. .. .. 1,120 3,910 51,323 1,973 185,587 5,190 Upper middle income 57.6 .. .. .. 1,269 4,032 208,887 6,566 649,224 36,630 Low & middle income 44.8 .. .. .. .. 3,127 263,722 8,721 848,254 42,962 East Asia & Pacific 30.7 .. .. .. 4,248 3,384 174,467 3,572 388,023 24,716 Europe & Central Asia 85.8 14,625 9,375 177,892 658 8,250 10,901 1,464 136,681 6,725 Latin America & Carib. 22.5 .. .. .. .. .. 36,495 1,974 160,869 5,967 Middle East & N. Africa 75.2 .. .. .. 1,493 2,222 15,441 547 54,412 668 South Asia 53.9 .. .. .. 24,731 6,187 17,396 719 75,735 2,453 Sub-Saharan Africa 18.8 .. .. .. .. .. .. 445 32,534 2,432 High income 81.1 .. 28,585 .. 7,090 8,285 274,561 19,357 1,747,195 146,364 Euro area 87.3 69,000 28,035 131,414 10,306 7,669 73,233 4,711 457,356 30,754 a. Data are for the most recent year available in the period shown. b. Covers international nonscheduled carriers only. Total for scheduled international and domestic carriers for Scandinavian countries are 621,715 registered carrier departures worldwide, 48,916,632 passengers carried, and 583,140,000 ton-kilometers of air freight. c. Includes Tazara railway. d. Refers to class 1 railways only. e. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers. 322 2012 World Development Indicators 5.10 STATES AND MARKETS Transport services About the data De�nitions Transport infrastructure—highways, railways, ports Measures of port container traffi c, much of it • Total road network covers motorways, highways, and waterways, and airports and air traffic control commodities of medium to high value added, give main or national roads, secondary or regional roads, systems—and the services that fl ow from it are some indication of economic growth in a country. and all other roads in a country. • Paved roads are crucial to the activities of households, producers, But when traffic is merely transshipment, much of roads surfaced with crushed stone (macadam) and and governments. Because performance indicators the economic benefit goes to the terminal operator hydrocarbon binder or bituminized agents, with con- vary widely by transport mode and focus (whether and ancillary services for ships and containers rather crete, or with cobblestones. • Passengers carried physical infrastructure or the services flowing from than to the country more broadly. In transshipment by road are the number of passengers transported that infrastructure), highly specialized and carefully centers empty containers may account for as much by road times kilometers traveled. • Goods hauled specified indicators are required. The table provides as 40 percent of traffic. by road are the volume of goods transported by road selected indicators of the size, extent, and produc- The air transport data represent the total (interna- vehicles, measured in millions of metric tons times tivity of roads, railways, and air transport systems tional and domestic) scheduled traffic carried by the kilometers traveled. • Rail lines are the length of rail- and of the volume of traffic in these modes as well air carriers registered in a country. Countries submit way route available for train service, irrespective of as in ports. Indicators on traffic and congestion are air transport data to ICAO on the basis of standard the number of parallel tracks. • Passengers carried presented in table 3.15, and indicators on logistics instructions and definitions issued by ICAO. In many by railway are the number of passengers transported performance are presented in table 6.8. cases, however, the data include estimates by ICAO by rail times kilometers traveled. •  Goods hauled Data for transport sectors are not always inter- for nonreporting carriers. Where possible, these esti- by railway are the volume of goods transported by nationally comparable. Unlike for demographic sta- mates are based on previous submissions supple- railway, measured in metric tons times kilometers tistics, national income accounts, and international mented by information published by the air carriers, traveled. • Port container traf�c measures the flow trade data, the collection of infrastructure data has such as flight schedules. of containers from land to sea transport modes and not been “internationalized.� But data on roads are The data cover the air traffic carried on scheduled vice versa in twenty-foot-equivalent units (TEUs), a collected by the International Road Federation (IRF), services, but changes in air transport regulations standard-size container. Data cover coastal shipping and data on air transport by the International Civil in Europe have made it more diffi cult to classify as well as international journeys. Transshipment traf- Aviation Organization (ICAO). traffic as scheduled or nonscheduled. Thus recent fic is counted as two lifts at the intermediate port National road associations are the primary source increases shown for some European countries may (once to off-load and again as an outbound lift) and of IRF data. In countries where a national road asso- be due to changes in the classification of air traffic includes empty units. •  Registered carrier depar- ciation is lacking or does not respond, other agencies rather than actual growth. For countries with few air tures worldwide are domestic takeoffs and takeoffs are contacted, such as road directorates, ministries carriers or only one, the addition or discontinuation abroad of air carriers registered in the country. • Pas- of transport or public works, or central statistical of a home-based air carrier may cause significant sengers carried by air include both domestic and offices. As a result, definitions and data collection changes in air traffic. international passengers of air carriers registered methods and quality differ, and the compiled data in the country. • Air freight is the volume of freight, are of uneven quality. Moreover, the quality of trans- express, and diplomatic bags carried on each flight port service (reliability, transit time, and condition of stage (operation of an aircraft from takeoff to its next goods delivered) is rarely measured, though it may be landing), measured in metric tons times kilometers as important as quantity in assessing an economy’s traveled. transport system. Unlike the road sector, where numerous qualified Data sources motor vehicle operators can operate anywhere on the road network, railways are a restricted transport Data on roads are from the IRF’s World Road system with vehicles confined to a fixed guideway. Statistics, supplemented by World Bank staff Considering the cost and service characteristics, estimates. Data on railways are from a database railways generally are best suited to carry—and can maintained by the World Bank’s Transport, Water, effectively compete for—bulk commodities and con- and Information and Communication Technologies tainerized freight for distances of 500–5,000 kilo- Department, Transport Division, based on data meters, and passengers for distances of 50–1,000 from the International Union of Railways. Data kilometers. Below these limits road transport on port container traffic are from Containerisa- tends to be more competitive, while above these tion International’s Containerisation International limits air transport for passengers and freight and Yearbook and the United Nations Conference on sea transport for freight tend to be more competi- Trade and Development’s UNCTADstat database tive. The railways indicators in the table focus on (http://unctadstat.unctad.org). Data on air trans- scale and output measures: total route-kilometers, port are from the ICAO’s Civil Aviation Statistics of passenger- kilometers, and goods (freight) hauled in the World and ICAO staff estimates. ton-kilometers. 2012 World Development Indicators 323 5.11 Power and communications Electric power Telephonesa Access and use Quality Affordability and efficiency International Transmission voice traffic Population $ per month Mobile cellular and Subscriptions minutes per person covered by Residential Mobile Telecom- and fi xed- Consumption distribution per 100 people Mobile mobile cellular fi xed- cellular munications telephone per capita losses Fixed Mobile Fixed cellular network telephone prepaid revenue subscriptions kWh % of output telephone cellular telephone network % tariff tariff % of GDP per employee 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan .. .. 0 38 .. .. 75 .. .. .. .. Albania 1,747 21 10 142 46 201 98 6.4 25.8 4.7 1,151 Algeria 971 21 8 92 17 36 .. 5.4 12.5 3.6 .. Angola 202 10 2 47 .. .. .. 16.5 19.2 .. .. Argentina 2,759 15 25 142 44 .. .. 3.8 30.7 3.2 .. Armenia 1,550 15 19 125 185 233 99 4.2 8.8 4.4 969 Australia 11,113 7 39 101 .. .. 99 27.6 27.7 .. 315 Austria 7,944 5 39 146 .. .. 99 25.3 13.9 1.8 961 Azerbaijan 1,620 22 17 101 20 74 100 2.5 6.9 2.7 645 Bahrain 9,214 12 18 124 .. .. 100 4.8 15.0 4.4 698 Bangladesh 252 2 1 46 .. .. .. 1.3 2.0 .. .. Belarus 3,299 11 44 109 98 25 100 1.3 7.9 3.1 .. Belgium 7,903 5 43 112 .. .. 100 31.4 40.2 2.6 907 Benin 91 .. 2 80 12 52 90 9.0 13.0 5.4 3,146 Bolivia 558 11 9 72 84 .. .. 23.7 11.3 7.3 .. Bosnia and Herzegovina 2,867 12 27 83 190 73 100 9.3 15.4 5.3 499 Botswana 1,503 79 7 118 113 .. 99 18.7 13.2 3.5 4,031 Brazil 2,206 17 22 104 .. .. 100 23.0 57.2 4.9 .. Bulgaria 4,401 11 30 135 70 84 100 12.5 30.6 3.2 543 Burkina Faso .. .. 1 35 .. .. .. 10.9 21.2 .. 2,306 Burundi .. .. 0 14 .. .. 83 .. .. 3.1 838 Cambodia 131 18 3 58 .. .. 99 7.4 6.7 5.5 1,333 Cameroon 271 9 3 44 3 26 .. 15.0 20.1 3.8 2,251 Canada 15,471 8 50 70 .. .. 99 19.4 34.3 2.7 .. Central African Republic .. .. 0 22 0 12 55 10.1 12.9 2.9 .. Chad .. .. 0 24 .. .. .. 16.7 15.4 .. .. Chile 3,283 11 20 116 30 9 100 25.0 23.7 .. 646 China 2,631 5 22 64 9 .. 99 4.7 6.0 2.0 1,254 Hong Kong SAR, China 5,925 13 62 195 1,463 504 100 8.5 1.4 3.5 1,066 Colombia 1,047 15 16 96 83 .. .. 5.9 16.9 4.2 466 Congo, Dem. Rep. 104 5 0 18 0 7 50 .. .. 4.9 5,538 Congo, Rep. 146 73 0 94 .. .. .. .. .. .. .. Costa Rica 1,813 11 32 65 141 77 70 6.9 3.4 2.1 498 Côte d’Ivoire 203 25 1 76 30 47 92 19.6 13.6 6.7 3,912 Croatia 3,712 16 42 144 248 66 100 17.0 17.1 4.6 945 Cuba 1,348 14 10 9 19 16 78 0.3 33.9 .. 129 Cyprus 4,620 4 37 94 367 504 100 23.8 7.7 3.6 458 Czech Republic 6,114 5 23 137 90 80 100 26.8 28.1 3.4 778 Denmark 6,246 6 47 125 189 186 .. 27.8 9.8 2.3 616 Dominican Republic 1,358 11 10 90 322 .. 81 15.5 15.6 2.2 1,394 Ecuador 1,115 14 14 102 55 20 93 14.1 13.8 .. .. Egypt, Arab Rep. 1,549 11 12 87 11 22 100 2.9 7.0 3.2 1,217 El Salvador 845 12 16 124 .. .. .. 10.2 9.5 3.5 2,748 Eritrea 51 12 1 4 6 18 90 .. .. 3.2 149 Estonia 5,950 10 36 123 85 99 100 11.5 22.3 4.9 631 Ethiopia 46 10 1 8 6 4 .. 1.0 4.1 1.5 604 Finland 15,242 4 23 156 .. .. 100 16.8 13.1 2.7 708 France 7,488 6 54 97 288 74 99 26.6 48.6 2.3 789 Gabon 922 18 2 107 26 23 .. .. .. .. .. Gambia, The .. .. 3 86 .. .. .. 2.4 6.3 .. .. Georgia 1,585 13 25 89 56 64 99 2.3 11.6 3.7 697 Germany 6,779 4 56 128 .. 43 99 27.0 13.1 2.4 851 Ghana 265 23 1 71 10 60 77 7.3 7.4 3.7 3,764 Greece 5,540 5 46 109 185 114 100 23.8 38.5 3.0 804 Guatemala 548 14 10 126 .. .. .. 5.6 7.8 .. .. Guinea .. .. 0 40 .. .. 80 1.6 4.1 .. .. Guinea-Bissau .. .. 0 39 .. .. .. .. .. .. .. Haiti 36 51 1 40 .. .. .. .. .. .. .. 324 2012 World Development Indicators 5.11 STATES AND MARKETS Power and communications Electric power Telephonesa Access and use Quality Affordability and efficiency International Transmission voice traffic Population $ per month Mobile cellular and Subscriptions minutes per person covered by Residential Mobile Telecom- and fi xed- Consumption distribution per 100 people Mobile mobile cellular fi xed- cellular munications telephone per capita losses Fixed Mobile Fixed cellular network telephone prepaid revenue subscriptions kWh % of output telephone cellular telephone network % tariff tariff % of GDP per employee 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2010 Honduras 678 22 9 125 39 197 .. 6.3 8.9 6.2 .. Hungary 3,773 10 30 120 .. .. 99 21.2 25.7 3.8 1,127 India 571 24 3 61 .. .. 83 3.3 3.4 2.3 .. Indonesia 590 10 16 92 .. .. .. 5.0 7.8 .. .. Iran, Islamic Rep. 2,238 17 36 91 .. .. .. 0.2 3.6 .. .. Iraq 1,069 40 5 75 0 .. .. .. .. .. 1,466 Ireland 6,034 8 46 105 .. .. 99 26.3 37.3 2.5 534 Israel 6,608 3 43 130 413 .. 100 15.7 34.1 4.0 .. Italy 5,271 7 36 150 .. .. 99 25.6 29.8 2.1 1,341 Jamaica 1,902 7 10 118 119 978 .. 11.6 12.0 5.4 .. Japan 7,819 5 32 95 .. .. 100 26.4 55.9 3.0 1,304 Jordan 2,112 14 8 109 23 217 99 9.5 10.5 5.9 1,499 Kazakhstan 4,448 8 25 119 23 54 95 2.4 14.4 2.2 463 Kenya 147 16 1 62 1 23 89 14.2 10.8 6.3 2,546 Korea, Dem. Rep. 733 16 5 2 .. .. .. .. .. .. .. Korea, Rep. 8,980 4 58 104 24 45 100 5.3 14.4 4.6 703 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 17,610 12 21 161 .. .. 100 8.6 7.8 .. .. Kyrgyz Republic 1,386 30 9 97 40 19 96 1.2 3.6 7.9 442 Lao PDR .. .. 2 65 .. .. 80 4.0 6.3 3.8 1,283 Latvia 2,875 13 24 103 .. .. .. 10.1 10.1 3.6 661 Lebanon 3,130 13 21 68 .. .. 95 10.3 27.3 .. .. Lesotho .. .. 2 45 .. 26 .. 13.0 24.1 3.1 6,933 Liberia .. .. 0 39 .. 52 .. .. .. .. .. Libya 4,170 14 19 172 .. .. 98 .. .. .. .. Lithuania 3,431 7 22 149 49 61 100 13.1 9.6 2.7 .. Macedonia, FYR 3,442 17 20 105 165 86 100 11.9 23.4 5.7 329 Madagascar .. .. 1 37 1 6 .. 18.2 15.4 2.8 3,203 Malawi .. .. 1 20 5 1 85 4.3 21.2 3.6 .. Malaysia 3,614 4 16 119 85 .. 95 5.1 7.5 .. .. Mali .. .. 1 48 2 21 .. 8.5 14.4 6.1 4,091 Mauritania .. .. 2 79 4 .. 62 18.0 14.6 6.9 2,842 Mauritius .. .. 30 93 105 148 99 5.1 6.8 2.4 .. Mexico 1,943 16 18 81 159 .. 100 18.9 17.4 3.0 918 Moldova 1,018 40 33 89 126 133 .. 1.9 12.6 8.5 461 Mongolia 1,411 12 7 91 4 48 85 0.7 3.6 5.6 372 Morocco 756 12 12 100 66 63 98 21.3 33.1 4.7 2,770 Mozambique 453 9 0 31 .. 9 32 12.4 17.0 3.9 2,430 Myanmar 104 16 1 1 .. .. .. 0.9 12.8 .. 83 Namibia 1,576 15 7 67 .. .. .. 15.1 17.0 .. .. Nepal 91 31 3 31 .. .. 35 3.1 2.7 .. 981 Netherlands 6,896 4 44 115 .. 84 98 22.4 33.2 2.3 .. New Zealand 9,346 7 43 115 .. .. 97 34.1 47.2 3.2 600 Nicaragua 460 24 4 65 39 .. 100 4.5 13.2 .. .. Niger .. .. 1 25 .. .. .. 11.7 20.8 .. .. Nigeria 121 6 1 55 1 30 90 14.0 13.7 3.4 .. Norway 23,550 8 34 116 .. .. .. 32.7 20.5 1.3 .. Oman 5,724 13 10 166 24 406 98 13.1 9.1 3.5 1,275 Pakistan 449 20 2 57 5 33 92 3.3 2.5 2.4 1,888 Panama 1,735 13 16 185 53 77 91 12.0 8.5 3.3 712 Papua New Guinea .. .. 2 28 .. .. .. 4.5 23.3 .. .. Paraguay 1,056 6 6 92 41 21 94 6.8 8.6 4.0 .. Peru 1,136 8 11 100 .. .. 97 14.6 43.2 2.9 2,512 Philippines 593 12 7 86 .. .. 99 15.3 10.1 .. 4,504 Poland 3,591 8 20 123 .. .. 99 20.2 15.8 3.0 945 Portugal 4,815 8 42 143 .. 121 99 25.3 22.9 4.5 1,507 Puerto Rico .. .. 22 74 .. .. 68 .. .. .. 411 Qatar 14,421 7 17 132 .. .. 100 9.1 18.4 1.6 927 2012 World Development Indicators 325 5.11 Power and communications Electric power Telephonesa Access and use Quality Affordability and efficiency International Transmission voice traffic Population $ per month Mobile cellular and Subscriptions minutes per person covered by Residential Mobile Telecom- and fi xed- Consumption distribution per 100 people Mobile mobile cellular fi xed- cellular munications telephone per capita losses Fixed Mobile Fixed cellular network telephone prepaid revenue subscriptions kWh % of output telephone cellular telephone network % tariff tariff % of GDP per employee 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2010 Romania 2,267 12 21 115 49 97 100 13.3 21.8 3.0 883 Russian Federation 6,136 11 32 168 .. .. .. 6.2 9.2 2.8 820 Rwanda .. .. 0 33 0 15 96 13.2 13.9 3.0 3,892 Saudi Arabia 7,427 8 15 188 .. .. 99 9.2 14.1 3.8 2,381 Senegal 196 17 3 67 12 101 90 10.3 12.7 9.9 3,710 Serbia 4,224 16 43 136 125 59 97 5.4 11.6 5.1 865 Sierra Leone .. .. 0 34 .. .. .. .. .. .. .. Singapore 7,949 5 39 145 .. .. 100 8.2 8.1 4.0 .. Slovak Republic 4,925 3 20 109 170 109 100 20.4 38.2 3.0 688 Slovenia 6,103 5 44 104 81 117 100 17.9 20.8 3.6 552 Somalia .. .. 1 7 .. .. .. .. .. .. .. South Africa 4,532 10 8 101 .. .. .. 25.0 23.3 .. .. South Sudan .. .. .. .. .. .. .. .. .. .. .. Spain 6,006 3 44 112 163 .. 100 27.0 53.2 2.7 923 Sri Lanka 408 15 17 83 .. .. 98 4.9 1.9 .. 1,524 Sudan 114 28 1 41 6 .. 66 3.9 3.4 3.2 2,168 Swaziland .. .. 4 69 .. .. 91 5.0 24.2 4.5 2,000 Sweden 14,142 7 53 116 .. .. 99 25.5 17.2 1.6 813 Switzerland 8,021 6 56 122 .. .. 100 29.4 57.0 3.2 599 Syrian Arab Republic 1,563 28 20 58 35 74 98 1.3 19.9 11.7 .. Tajikistan 1,985 17 5 86 .. .. .. 0.9 1.8 .. .. Tanzania 86 19 0 47 0 7 85 8.8 9.7 .. .. Thailand 2,045 6 10 104 .. .. .. 8.8 8.7 .. .. Timor-Leste .. .. 0 53 98 22 69 17.3 16.1 8.1 1,751 Togo 111 53 4 41 4 42 75 11.0 19.9 9.5 1,896 Trinidad and Tobago 5,662 2 22 141 .. .. 100 19.5 12.1 2.8 1,862 Tunisia 1,311 13 12 105 21 52 100 2.5 10.0 3.9 1,130 Turkey 2,298 15 22 85 73 29 100 16.8 43.9 2.0 2,069 Turkmenistan 2,446 14 10 63 .. .. .. .. .. .. .. Uganda .. .. 1 38 .. .. 100 8.8 12.2 4.3 2,046 Ukraine 3,200 12 28 118 .. .. 100 3.0 7.5 4.0 .. United Arab Emirates 11,464 12 20 145 .. .. 100 4.1 8.6 3.6 1,094 United Kingdom 5,692 7 54 130 .. .. 100 21.0 31.0 4.5 .. United States 12,914 6 49 90 .. .. 100 12.8 32.7 2.0 478 Uruguay 2,671 13 29 132 .. .. 100 13.3 17.8 3.1 765 Uzbekistan 1,636 9 7 74 .. .. 93 1.1 3.0 .. 739 Venezuela, RB 3,152 27 25 97 46 .. .. 1.7 22.3 3.3 1,490 Vietnam 918 10 19 177 .. .. .. 2.4 5.4 7.1 .. West Bank and Gaza .. .. 9 45 .. .. .. .. .. .. .. Yemen, Rep. 219 24 4 46 20 61 84 1.1 8.1 3.3 1,273 Zambia 635 23 1 42 .. .. 90 24.1 16.9 .. .. Zimbabwe 1,026 7 3 61 15 23 80 9.1 20.5 .. 2,369 World 2,803 w 8w 17 w 78 w .. w .. w 93 w 11.3 m 14.4 m 2.7 w 887 m Low income 229 12 1 33 .. .. .. 8.8 13.0 .. 2,306 Middle income 1,675 11 14 78 .. .. 92 7.1 12.6 3.2 1,044 Lower middle income 644 19 6 72 .. .. 86 5.3 10.7 3.4 .. Upper middle income 2,714 9 22 84 30 .. 99 9.8 14.9 2.4 765 Low & middle income 1,525 11 12 72 .. .. 91 8.8 12.7 3.2 1,168 East Asia & Pacific 2,095 5 19 73 11 .. 99 5.1 7.7 2.1 1,283 Europe & Central Asia 3,859 12 26 124 .. .. .. 4.8 10.8 2.7 697 Latin America & Carib. 1,892 16 18 98 .. .. 98 10.9 14.7 4.2 .. Middle East & N. Africa 1,497 18 16 86 17 .. .. 4.1 11.5 .. 1,131 South Asia 517 23 3 59 .. .. 84 3.3 2.5 2.3 1,363 Sub-Saharan Africa 511 11 1 45 .. 21 .. 12.0 15.0 .. 1,840 High income 9,064 6 44 111 .. .. 100 21.2 22.3 2.6 784 Euro area 6,592 5 46 122 .. .. 99 25.3 29.8 2.4 789 a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators database. Please cite the ITU for third-party use of these data. 326 2012 World Development Indicators 5.11 STATES AND MARKETS Power and communications About the data De�nitions The quality of an economy’s infrastructure, includ- growth was driven primarily by wireless technologies • Electric power consumption per capita is the pro- ing power and communications, is an important ele- and liberalization of telecommunications markets, duction of power plants and combined heat and power ment in investment decisions for both domestic and which have enabled faster and less costly network plants less transmission, distribution, and transfor- foreign investors. Government effort alone is not rollout. Developing countries’ share of world mobile mation losses and own use by heat and power plants, enough to meet the need for investments in modern subscriptions rose from 53 percent in 2005 to 73 divided by midyear population. •  Electric power infrastructure; public-private partnerships, especially percent in 2010. And the number of short message transmission and distribution losses are losses in those involving local providers and financiers, are service texts sent globally tripled between 2007 and transmission between sources of supply and points critical for lowering costs and delivering value for 2010, from 1.8 trillion to 6.1 trillion. The Interna- of distribution and in distribution to consumers, money. In telecommunications, competition in the tional Telecommunication Union (ITU) estimates that including pilferage. • Fixed-telephone subscriptions marketplace, along with sound regulation, is lower- there were 5.9 billion mobile subscriptions globally in are the sum of the active number of analog fixed- ing costs, improving quality, and easing access to 2011. No technology has ever spread faster around telephone lines, voice-over-IP subscriptions, fixed services around the globe. the world. Mobile communications have a particu- wireless local loop subscriptions, Integrated Ser- An economy’s production and consumption of elec- larly important impact in rural areas. The mobility, vices Digital Network voice-channel equivalents, and tricity are basic indicators of its size and level of ease of use, flexible deployment, and relatively low fixed public payphones. • Mobile cellular telephone development. Although a few countries export elec- and declining rollout costs of wireless technologies subscriptions are subscriptions to a public mobile tric power, most production is for domestic consump- enable them to reach rural populations with low lev- telephone service using cellular technology, which tion. Expanding the supply of electricity to meet the els of income and literacy. The next billion mobile provide access to the public switched telephone growing demand of increasingly urbanized and indus- subscribers will consist mainly of the rural poor. network. Post-paid and prepaid subscriptions are trialized economies without incurring unacceptable Access is the key to delivering telecommunications included. • International voice traf�c is the sum of social, economic, and environmental costs is one services to people. If the service is not affordable international incoming and outgoing telephone traffic of the great challenges facing developing countries. to most people, goals of universal usage will not be (in minutes) divided by total population. • Population Data on electric power production and consumption met. Two indicators of telecommunications afford- covered by mobile cellular network is the percentage are collected from national energy agencies by the ability are presented in the table: fi xed- telephone of people that live in areas served by a mobile cellular International Energy Agency (IEA) and adjusted by the service tariff and prepaid mobile cellular service signal regardless of whether they use it. • Residen- IEA to meet international definitions (for data on elec- tariff. Telecommunications efficiency is measured tial �xed-telephone tariff is the monthly subscription tricity production, see table 3.8). Electricity consump- by total telecommunications revenue divided by GDP charge plus the cost of 30 three-minute local calls tion is equivalent to production less power plants’ own and by mobile cellular and fixed- telephone subscrip- (15 peak and 15 off-peak). • Mobile cellular prepaid use and transmission, distribution, and transformation tions per employee. tariff is based on the Organisation for Economic Co- losses less exports plus imports. It includes consump- Operators have traditionally been the main source operation and Development’s low-user definition, tion by auxiliary stations, losses in transformers that of telecommunications data, so information on which includes the cost of monthly mobile use for are considered integral parts of those stations, and subscriptions has been widely available for most 25 outgoing calls per month spread over the same electricity produced by pumping installations. Where countries. This gives a general idea of access, but a mobile network, other mobile networks, and mobile data are available, it covers electricity generated by more precise measure is the penetration rate—the to fixed- telephone calls and during peak, off-peak, primary sources of energy—coal, oil, gas, nuclear, share of households with access to telecommunica- and weekend times as well as 30 text messages per hydro, geothermal, wind, tide and wave, and combus- tions. During the past few years more information month. • Telecommunications revenue is the rev- tible renewables. Neither production nor consumption on information and communication technology use enue from the provision of telecommunications ser- data capture the reliability of supplies, including break- has become available from household and business vices such as fixed telephone, mobile, and Internet downs, load factors, and frequency of outages. surveys. Also important are data on actual use of divided by GDP. • Mobile cellular and �xed-telephone Over the past decade new financing and technol- telecommunications services. Ideally, statistics on subscriptions per employee are telephone subscrip- ogy, along with privatization and market liberalization, telecommunications (and other information and com- tions (fixed telephone plus mobile) divided by the total have spurred dramatic growth in telecommunications munications technologies) should be compiled for all number of telecommunications employees. in many countries. With the rapid development of three measures: subscriptions, access, and use. The Data sources mobile telephony and the global expansion of the quality of data varies among reporting countries as Internet, information and communication technolo- a result of differences in regulations covering data Data on electricity consumption and losses are gies are increasingly recognized as essential tools of provision and availability. from the IEA’s Energy Statistics of Non-OECD development, contributing to global integration and Countries 2011, Energy Balances of Non-OECD enhancing public sector effectiveness, efficiency, Countries 2011, and Energy Statistics of OECD and transparency. The table presents telecommuni- Countries 2011 and from the United Nations Sta- cations indicators covering access and use, quality, tistics Division’s Energy Statistics Yearbook. Data and affordability and efficiency. on telecommunications are from the ITU’s World Access to telecommunication services rose on an Telecommunication/ICT Indicators database and unprecedented scale over the past 15 years. This TeleGeography. 2012 World Development Indicators 327 5.12 The information society Daily Households Personal computers and the Internet Information and communications newspapers with technology trade televisiona Use Quality Affordability Application Fixed International broadband Internet Fixed Secure Goods Services Internet bandwidtha broadband Internet Exports Imports Exports per 100 people a subscriptions bits per Internet servers % of total % of total % of total per 1,000 Computers Internet per 100 second per access tariffa per million goods goods services people % usersa usersa people capita $ per month people exports imports exports 2000–05b 2010 2010 2010 2010 2010 2010 December 2011 2010 2010 2010 Afghanistan .. .. .. 3.7 0.00 58 .. 1 .. 0.4 .. Albania 24 .. .. 45.0 3.29 5,304 11 14 0.8 4.1 4.8 Algeria .. 98 .. 12.5 2.54 1,015 15 1 0.0 3.0 3.5 Angola 2 38 .. 10.0 0.10 63 133 3 .. .. 5.4 Argentina 36 .. .. 36.0 9.56 9,898 26 34 0.1 9.1 11.7 Armenia 8 97 .. 44.0 2.75 3,411 32 28 0.8 4.7 16.8 Australia 155 .. .. 75.9 24.15 31,392 37 2,006 1.0 10.6 4.9 Austria 311 .. 77.8 72.7 23.86 53,635 26 996 3.9 5.8 6.2 Azerbaijan 16 100 38.6 46.7 5.08 4,524 12 5 0.0 3.5 4.0 Bahrain .. .. 72.0 55.0 5.36 7,924 27 118 0.9 4.4 .. Bangladesh .. 36 .. 3.7 0.04 103 15 1 .. .. 13.0 Belarus 81 98 .. 32.1 17.55 7,060 18 12 0.5 2.4 8.8 Belgium 165 .. 78.8 73.7 30.96 82,599 25 604 2.3 3.5 9.2 Benin 0 25 .. 3.1 0.04 70 50 1 .. .. .. Bolivia .. 69 .. 20.0 0.97 854 35 10 0.0 3.4 11.8 Bosnia and Herzegovina .. 97 .. 52.0 8.18 8,138 14 20 0.1 2.9 .. Botswana 41 .. .. 6.0 0.60 386 30 9 0.3 3.1 5.5 Brazil 36 98 44.1 40.7 6.81 5,130 17 54 1.0 9.5 2.0 Bulgaria 79 99 48.0 46.0 14.44 29,520 13 139 2.5 5.6 8.9 Burkina Faso .. 18 .. 1.4 0.09 49 83 1 0.0 2.5 10.5 Burundi .. .. .. 2.1 0.00 2 .. 0 0.4 5.8 .. Cambodia .. 60 .. 1.3 0.25 354 47 3 0.1 2.5 5.5 Cameroon .. 33 .. 4.0 0.01 16 80 1 0.0 2.6 3.6 Canada 175 99 .. 81.3 29.71 43,955 26 1,369 2.8 8.4 11.4 Central African Republic .. .. .. 2.3 0.00 4 1,329 0 0.0 6.8 .. Chad .. .. .. 1.7 0.00 2 12 .. .. .. .. Chile 51 .. 42.8 45.0 10.45 8,613 39 67 0.4 8.2 2.2 China 74 .. .. 34.4 9.44 821 18 2 29.1 20.4 6.1 Hong Kong SAR, China 222 .. 70.0 71.8 29.87 557,998 19 568 44.2 42.8 1.8 Colombia 23 91 43.1 36.5 5.60 3,739 35 21 0.1 9.6 6.2 Congo, Dem. Rep. .. .. .. 0.7 0.01 2 .. 0 .. .. .. Congo, Rep. .. 38 .. 5.0 0.00 2 .. 1 .. .. .. Costa Rica 65 96 42.6 36.5 6.19 4,630 7 111 19.9 17.7 26.6 Côte d’Ivoire .. .. .. 2.6 0.04 203 40 1 0.2 3.3 11.0 Croatia .. 97 58.0 60.1 18.19 25,804 18 225 2.1 5.5 3.6 Cuba 65 .. .. 15.9 0.03 35 1,753 0 .. .. .. Cyprus .. 100 57.2 53.0 17.63 27,380 21 1,121 9.1 4.8 2.3 Czech Republic 183 .. 70.7 68.6 14.46 47,529 31 387 15.0 17.8 8.6 Denmark 353 98 89.9 88.8 37.72 126,194 44 2,185 3.6 7.9 .. Dominican Republic 39 86 .. 39.5 3.63 4,029 19 20 2.3 4.8 4.5 Ecuador 99 85 37.5 29.0 1.37 2,394 20 20 0.1 6.3 .. Egypt, Arab Rep. .. 94 21.6 26.7 1.79 1,762 8 3 0.1 3.7 4.2 El Salvador 38 83 21.0 15.9 2.83 242 25 17 0.3 5.6 17.7 Eritrea .. .. .. 5.4 0.00 6 .. .. .. .. .. Estonia 191 99 75.6 74.2 25.10 17,164 21 534 7.8 9.5 8.9 Ethiopia 5 .. .. 0.7 0.00 40 294 0 0.2 8.4 4.5 Finland 431 .. 88.7 86.9 28.57 93,214 35 1,489 6.4 8.2 24.5 France 164 99 78.0 77.5 32.89 53,933 30 356 4.4 7.3 4.2 Gabon .. .. .. 7.2 0.27 3,767 .. 8 0.0 3.5 .. Gambia, The .. 76 .. 9.2 0.02 98 307 3 0.4 1.5 17.8 Georgia 4 94 .. 26.3 5.70 5,615 39 19 0.2 5.3 2.2 Germany 267 95 85.4 82.5 31.90 61,142 39 1,026 5.1 9.2 9.1 Ghana .. 47 .. 9.5 0.21 139 32 2 0.0 7.1 .. Greece .. 100 49.5 44.6 19.95 13,816 19 154 2.5 5.0 2.4 Guatemala .. 71 .. 10.5 1.80 417 33 14 0.9 6.8 14.3 Guinea .. .. .. 1.0 0.01 15 800 0 0.0 5.2 21.6 Guinea-Bissau .. .. .. 2.5 0.00 1 .. 1 .. .. .. Haiti .. .. .. 8.4 0.00 9 .. 1 .. .. .. 328 2012 World Development Indicators 5.12 STATES AND MARKETS The information society Daily Households Personal computers and the Internet Information and communications newspapers with technology trade televisiona Use Quality Affordability Application Fixed International broadband Internet Fixed Secure Goods Services Internet bandwidtha broadband Internet Exports Imports Exports per 100 people a subscriptions bits per Internet servers % of total % of total % of total per 1,000 Computers Internet per 100 second per access tariffa per million goods goods services people % usersa usersa people capita $ per month people exports imports exports 2000–05b 2010 2010 2010 2010 2010 2010 December 2011 2010 2010 2010 Honduras .. 69 .. 11.1 1.00 658 22 8 0.1 6.3 25.2 Hungary 217 99 67.1 65.2 19.56 6,500 21 220 25.6 21.3 8.5 India 71 60 .. 7.5 0.90 437 5 3 2.0 6.6 47.0 Indonesia .. 72 .. 9.9 0.79 292 22 3 5.1 8.6 7.4 Iran, Islamic Rep. .. 97 .. 13.0 0.68 406 30 1 0.0 3.6 .. Iraq .. 98 13.5 2.5 0.00 2 .. 0 .. .. 0.6 Ireland 182 .. 72.0 69.8 21.04 44,693 33 1,145 7.5 10.2 39.0 Israel .. 90 65.6 65.4 24.46 5,247 8 471 12.3 9.2 33.0 Italy 137 .. 55.7 53.7 21.92 33,067 26 192 2.1 7.4 8.9 Jamaica .. 88 .. 26.5 4.32 5,551 25 48 0.4 3.9 6.4 Japan 551 99 66.9 77.6 26.71 12,293 23 743 10.7 12.0 1.3 Jordan .. 98 56.0 38.9 3.24 2,481 19 25 1.3 4.3 .. Kazakhstan .. 87 .. 33.4 8.74 2,868 13 6 0.1 2.9 2.4 Kenya .. 28 .. 25.9 0.01 499 38 3 1.4 7.2 9.8 Korea, Dem. Rep. .. .. .. 0.0 0.00 .. .. 0 .. .. .. Korea, Rep. .. .. 81.5 82.5 35.18 9,802 24 2,536 21.4 11.9 1.2 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. 38.3 1.68 3,655 19 180 0.3 6.4 .. Kyrgyz Republic 1 .. .. 19.6 0.28 55 55 3 0.6 2.7 3.3 Lao PDR 3 .. .. 7.0 0.19 161 140 1 .. .. .. Latvia 154 .. 69.7 71.5 19.42 21,438 13 205 5.8 6.4 6.2 Lebanon 54 .. .. 31.0 4.73 591 23 41 7.1 2.8 3.0 Lesotho .. .. .. 3.9 0.02 12 51 0 20.9 2.7 4.5 Liberia .. 9 .. 7.0 0.00 14 .. 1 .. .. .. Libya .. .. .. 14.0 1.15 1,574 .. 1 .. .. .. Lithuania 108 99 64.2 62.8 20.81 28,533 10 237 2.7 4.1 3.7 Macedonia, FYR 89 .. 57.5 51.9 12.47 8,738 13 29 0.4 4.7 14.1 Madagascar .. 16 .. 1.7 0.03 94 91 1 0.2 3.1 .. Malawi .. .. .. 2.3 0.03 1 562 0 0.4 5.2 .. Malaysia 109 .. .. 56.3 7.32 6,443 20 54 34.0 29.8 7.0 Mali .. 31 .. 2.7 0.02 50 50 1 0.1 2.6 23.2 Mauritania .. 25 .. 3.0 0.19 81 24 2 .. 0.9 .. Mauritius 77 97 35.8 28.7 6.18 2,646 16 117 1.1 5.1 3.8 Mexico 93 95 40.1 31.1 9.98 2,272 17 27 20.2 19.2 .. Moldova .. 93 42.4 40.1 7.55 26,390 6 20 0.7 3.9 22.7 Mongolia 20 89 .. 12.9 2.60 6,237 8 14 .. .. 2.0 Morocco 12 93 50.9 49.0 1.56 2,347 12 4 3.8 5.9 8.0 Mozambique 3 .. .. 4.2 0.06 55 22 1 0.1 1.8 7.0 Myanmar .. .. .. 0.2 0.03 21 28 0 .. .. .. Namibia 28 42 .. 6.5 0.42 287 95 20 0.5 4.0 1.7 Nepal .. 36 .. 7.9 0.20 134 23 2 0.4 7.2 .. Netherlands 307 99 91.7 90.7 38.10 139,986 33 2,757 12.5 14.5 11.7 New Zealand 182 97 82.7 83.0 24.93 16,026 29 1,597 1.2 8.3 4.6 Nicaragua .. 66 .. 10.0 0.82 864 34 10 0.1 4.8 .. Niger 0 10 0.9 0.8 0.02 19 60 0 0.3 1.9 12.8 Nigeria .. 41 .. 28.4 0.06 32 53 2 0.0 6.6 .. Norway 516 .. 93.8 93.3 35.26 102,270 49 1,822 1.4 7.5 9.5 Oman .. .. .. 62.0 1.63 3,786 31 53 0.1 3.1 .. Pakistan 50 68 .. 16.8 0.31 435 14 1 0.2 3.3 6.6 Panama 65 92 .. 42.7 7.84 9,096 17 143 9.6 9.6 4.8 Papua New Guinea 9 .. .. 1.3 0.09 41 140 7 .. .. 0.7 Paraguay .. 88 .. 19.8 0.44 1,644 19 10 0.1 27.0 1.4 Peru .. 77 .. 34.3 3.14 2,911 42 19 0.1 7.4 3.1 Philippines 79 74 .. 25.0 1.85 2,681 22 8 35.6 31.6 12.6 Poland 114 .. 65.4 62.5 12.99 23,570 18 270 9.5 9.7 6.4 Portugal .. .. 58.2 51.3 19.30 75,202 26 224 4.0 5.7 4.2 Puerto Rico .. .. 61.3 42.7 13.86 57,818 .. 98 .. .. .. Qatar .. 95 84.6 81.6 8.19 13,930 55 126 0.0 4.3 .. 2012 World Development Indicators 329 5.12 The information society Daily Households Personal computers and the Internet Information and communications newspapers with technology trade televisiona Use Quality Affordability Application Fixed International broadband Internet Fixed Secure Goods Services Internet bandwidtha broadband Internet Exports Imports Exports per 100 people a subscriptions bits per Internet servers % of total % of total % of total per 1,000 Computers Internet per 100 second per access tariffa per million goods goods services people % usersa usersa people capita $ per month people exports imports exports 2000–05b 2010 2010 2010 2010 2010 2010 December 2011 2010 2010 2010 Romania 70 .. 45.9 40.0 13.90 20,571 5 54 8.4 9.3 18.0 Russian Federation 92 99 40.3 43.4 11.08 13,346 10 27 0.2 7.9 6.0 Rwanda .. 3 .. 13.0 0.02 155 86 1 0.6 11.7 4.4 Saudi Arabia .. .. .. 41.0 5.45 11,584 27 22 0.1 7.2 .. Senegal 9 57 29.9 16.0 0.63 386 36 1 0.4 3.3 12.9 Serbia .. .. 54.5 43.1 11.77 20,241 15 29 1.6 4.2 9.2 Sierra Leone .. 10 .. 0.3 .. 29 .. 1 .. .. .. Singapore 361 .. 69.1 71.1 24.99 122,454 27 607 34.3 27.9 2.8 Slovak Republic 126 .. 82.4 79.9 12.79 9,208 26 164 19.3 15.4 9.2 Slovenia 173 .. 71.0 69.3 24.02 48,804 34 433 2.2 4.6 7.3 Somalia .. .. .. 1.2 0.00 .. .. 0 .. .. .. South Africa 30 75 .. 12.3 1.49 211 27 74 1.0 9.4 3.7 South Sudan .. .. .. .. .. .. .. .. .. .. .. Spain 144 .. 69.7 65.8 22.87 36,900 26 284 2.2 6.7 6.9 Sri Lanka 26 .. .. 12.0 1.09 398 5 6 0.5 2.9 14.1 Sudan .. .. .. 10.2 0.38 305 23 0 0.0 3.3 25.8 Swaziland 24 .. .. 9.0 0.15 52 875 15 .. .. 9.0 Sweden 481 .. 92.8 90.0 31.85 213,265 35 1,455 9.8 11.3 13.9 Switzerland 420 .. .. 82.2 37.16 127,779 33 2,153 1.6 5.9 .. Syrian Arab Republic .. .. .. 20.7 0.33 280 22 0 0.0 1.1 1.9 Tajikistan .. .. .. 11.5 0.07 .. 362 1 .. .. 37.0 Tanzania 2 10 .. 11.0 0.01 77 21 0 0.4 3.8 2.1 Thailand .. 97 30.9 21.2 4.61 2,296 19 17 18.9 14.2 .. Timor-Leste .. .. .. 0.2 0.04 28 99 3 .. .. .. Togo 2 .. .. 5.4 0.06 230 166 2 0.2 5.1 18.6 Trinidad and Tobago 149 .. .. 48.5 10.81 8,656 13 85 0.2 3.0 .. Tunisia 23 .. .. 36.6 4.57 4,854 11 19 6.5 6.3 5.9 Turkey .. .. 41.0 39.8 9.73 7,601 19 143 1.8 4.5 1.6 Turkmenistan 9 .. .. 2.2 0.01 77 .. 0 .. .. .. Uganda .. 6 .. 12.5 0.16 108 14 2 5.7 7.4 5.2 Ukraine 131 95 .. 44.6 6.44 2,616 8 18 1.1 3.2 5.5 United Arab Emirates .. 96 74.0 78.0 10.47 13,991 41 180 2.0 4.5 .. United Kingdom 290 .. 86.7 84.7 31.46 112,482 25 1,594 5.9 9.3 8.8 United States 193 .. .. 74.2 27.71 29,093 20 1,562 10.5 14.2 4.6 Uruguay .. 94 49.1 47.9 10.95 11,004 19 70 0.1 6.4 8.2 Uzbekistan .. .. .. 19.4 0.32 53 200 0 .. .. .. Venezuela, RB 93 96 .. 35.9 5.40 2,428 16 8 0.0 7.6 8.2 Vietnam .. 87 .. 27.9 4.18 1,546 10 5 5.8 8.6 .. West Bank and Gaza 10 96 55.6 36.4 .. .. .. 4 0.9 3.2 6.0 Yemen, Rep. 4 .. .. 12.3 0.35 133 119 0 0.0 2.0 .. Zambia 5 27 .. 10.1 0.08 39 59 2 0.0 2.3 8.0 Zimbabwe .. .. .. 11.5 0.26 35 406 1 0.0 3.6 .. World 104 w .. m .. w 30.2 w 7.75 w 8,662 w 26 m 184 w 11.1 w 12.7 w 9.3 w Low income .. .. .. 5.6 0.05 93 55 1 .. .. .. Middle income 68 87 .. 23.8 4.65 1,856 20 12 14.2 14.2 13.6 Lower middle income .. 72 .. 13.5 1.04 656 32 3 8.3 10.4 27.9 Upper middle income 69 96 .. 34.1 8.36 3,084 18 20 15.5 15.0 5.8 Low & middle income 65 84 .. 21.5 4.05 1,628 23 10 14.1 14.0 13.5 East Asia & Pacific 74 .. .. 29.8 7.18 992 22 4 26.2 21.3 7.1 Europe & Central Asia .. .. 40.9 39.3 9.12 9,777 13 48 1.2 5.5 5.9 Latin America & Carib. 64 87 .. 34.0 6.59 4,062 23 36 10.3 14.0 .. Middle East & N. Africa .. 97 .. 20.9 1.36 1,139 17 3 .. 4.2 .. South Asia 68 48 .. 8.1 0.73 394 14 2 1.6 5.7 43.8 Sub-Saharan Africa .. 26 .. 11.3 0.18 116 52 6 0.5 7.0 .. High income 255 .. .. 73.4 26.46 44,235 26 1,068 10.4 12.5 8.1 Euro area 201 .. 73.7 71.2 27.65 53,594 26 647 5.2 8.2 10.7 a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators database. Please cite the ITU for third-party use of these data. b. Data are for the most recent year available. 330 2012 World Development Indicators 5.12 STATES AND MARKETS The information society About the data The digital and information revolution has changed of upstream and downstream capacity and includes high-speed technology (excluding wireless). • Inter- the way the world learns, communicates, does digital subscriber lines, cable modems, satellite national Internet bandwidth is the contracted capac- business, and treats illnesses. New information broadband Internet, fiber-to-home Internet access, ity of international connections between countries and communications technologies (ICT) offer vast Ethernet local access networks, and wireless area for transmitting Internet traffic. • Fixed broadband opportunities for progress in all walks of life in all networks. Bandwidth refers to the range of frequen- Internet access tariff is the lowest sampled cost per countries —opportunities for economic growth, cies available for signals. The higher the bandwidth, 100 kilobits a second per month and are calculated improved health, better service delivery, learning the more information that can be transmitted at from low- and high-speed monthly service charges. through distance education, and social and cultural one time. Reporting countries may have different Monthly charges do not include installation fees or advances. definitions of broadband, so data are not strictly modem rentals. • Secure Internet servers are serv- Comparable statistics on access, use, quality, comparable. ers using encryption technology in Internet transac- and affordability of ICT are needed to formulate The number of secure Internet servers, from the tions. • Information and communication technology growth-enabling policies for the sector and to moni- Netcraft Secure Server Survey, indicates how many goods exports and imports include telecommunica- tor and evaluate the sector’s impact on develop- companies conduct encrypted transactions over the tions, audio and video, computer and related equip- ment. Although basic access data are available for Internet. The survey examines the use of encrypted ment; electronic components; and other information many countries, in most developing countries little transactions through extensive automated explora- and communication technology goods. Software is is known about who uses ICT; what they are used for tion, tallying the number of Web sites using a secure excluded. • Information and communication tech- (school, work, business, research, government); and socket layer (SSL). The country of origin of more than nology services exports include computer and how they affect people and businesses. The global a third of the 1.5  million distinct valid third-party communications services (telecommunications and Partnership on Measuring ICT for Development is certificates is unknown. Some countries, such as the postal and courier services) and information services helping to set standards, harmonize information and Republic of Korea, use application layers to estab- (databases, data processes, software design and communications technology statistics, and build sta- lish the encryption channel, which is SSL equivalent; development, maintenance and repair, and news- tistical capacity in developing countries. For more these data are reported in the table. related service transactions). information see www.itu.int/ITU-D/ict/partnership/. Information and communication technology goods Data on daily newspaper circulation are from exports and imports are defined by the Working United Nations Educational, Scientific, and Cultural Party on Indicators for the Information Society and Organization (UNESCO) Institute for Statistics sur- are reported in the Organisation for Economic Co- veys on newspaper statistics. operation and Development’s Guide to Measuring the Estimates of households with television are derived Information Society (2005). Information and commu- from household surveys. Some countries report only nication technology service exports data are based the number of households with a color television set, on the International Monetary Fund’s (IMF) Balance and so the true number may be higher than reported. of Payments Statistics Yearbook classification. Data on computer users, Internet users, and De�nitions related indicators (broadband and bandwidth) are col- lected by national statistics offices through house- •  Daily newspapers are newspapers published at hold surveys. Since survey questions and definitions least four times a week that report mainly on events Data sources differ, the estimates may not be strictly comparable since the previous issue. The indicator is average across countries. In particular, in the “post-PC age� circulation per 1,000 people. •  Households with Data on newspapers are compiled by the UNESCO what constitutes a computer is becoming harder television are the percentage of households with a Institute for Statistics. Data on televisions, com- to define. Today’s smartphones and tablets have television set. • Computer users are individuals who puter users, Internet users, Internet broadband computer power equivalent to that of yesterday’s have used a computer (in any location) in the last users and cost, and Internet bandwidth are from computers and provide a similar range of functions. 12 months. Computers include desktop, portable, the ITU’s World Telecommunication/ICT Indicators Device convergence is thus rendering the conven- or handheld computers (such as a personal digital database and TeleGeography. Data on secure tional definition obsolete. Countries without surveys assistant) and exclude equipment with some embed- Internet servers are from Netcraft (www.netcraft. generally derive their estimates by multiplying sub- ded computing abilities (such as mobile phones or com) and official government sources. Data on scriber counts reported by Internet service providers television sets). • Internet users are individuals who information and communication technology goods by a multiplier. This method may undercount actual have used the Internet (in any location) with a device trade are from the United Nations Conference on users, particularly in developing countries, where such as a computer, smartphone, or digital television Trade and Development’s UNCTADstat database many commercial subscribers rent out computers in the last 12 months via a fixed or mobile network. (http://unctadstat.unctad.org). Data on informa- connected to the Internet or prepaid cards are used The Internet provides access to the worldwide net- tion and communication technology services to access the Internet. work. • Fixed broadband Internet subscriptions are exports are from the IMF’s Balance of Payments Broadband refers to technologies that provide the number of fixed broadband subscriptions with Statistics database. Internet speeds of at least 256 kilobits a second a digital subscriber line, cable modem, or other 2012 World Development Indicators 331 5.13 Science and technology Research and Scienti�c Expenditures High-technology Royalty and Patent Trademark development and for R&D exports license fees applications applications (R&D) technical �leda,b �leda,c journal articles full-time equivalent % of manu- per million people factured $ millions Non- Researchers Technicians % of GDP $ millions exports Receipts Payments Residents residents Total 2005–09d 2005–09d 2009 2005–09d 2010 2010 2010 2010 2010 2010 2010 Afghanistan .. .. 12 .. .. .. .. .. .. .. .. Albania 147 38 8 0.15 9 0.9 1 12 .. 361 2,920 Algeria 170 34 607 0.07 5 0.5 2 17 76 730 5,632 Angola .. .. 6 .. .. .. 12 6 .. .. .. Argentina 1,046 207 3,655 0.52 1,635 7.5 119 1,541 801 4,781 69,565 Armenia .. .. 164 0.27 4 1.8 .. .. 136 6 4,620 Australia 4,259 1,144 18,923 2.35 3,826 11.9 703 3,026 2,409 22,478 59,459 Austria 4,122 1,959 4,832 2.75 13,721 11.9 646 1,403 2,424 249 10,375 Azerbaijan .. .. 151 0.26 6 1.1 0 17 222 5 3,310 Bahrain .. .. 36 .. 1 0.1 .. .. .. .. 2,044 Bangladesh .. .. 260 .. 134 1.2 1 18 66 276 10,231 Belarus .. .. 380 0.64 408 3.0 9 101 1,759 174 10,695 Belgium 3,491 1,406 7,218 1.96 32,227 10.5 2,138 1,904 620 140 25,799e Benin .. .. 48 .. .. .. 0 3 .. .. .. Bolivia .. .. 45 .. 37 8.6 3 20 .. .. 6,081 Bosnia and Herzegovina 197 71 64 0.02 71 2.6 15 5 56 9 4,730 Botswana .. .. 45 0.52 15 0.4 0 11 .. .. 674 Brazil 696 560 12,306 1.08 8,122 11.2 397 2,850 2,705 19,981 125,654 Bulgaria 1,587 492 735 0.53 802 7.9 34 115 243 17 7,140 Burkina Faso .. .. 50 0.21 3 7.8 0 1 2 .. 34 Burundi .. .. 3 .. 1 8.5 .. .. .. .. .. Cambodia .. .. 27 .. 5 0.1 0 6 .. .. 2,866 Cameroon .. .. 145 .. 14 4.9 0 12 .. .. .. Canada 4,335 1,740 29,017 1.95 23,966 14.1 3,813 8,665 4,550 30,899 45,220 Central African Republic .. .. 4 .. .. .. .. .. .. .. .. Chad .. .. 2 .. .. .. .. .. .. .. .. Chile 355 293 1,868 0.39 483 5.5 64 496 328 748 45,104 China 1,199 .. 74,019 1.47 406,090 27.5 830 13,040 293,066 98,111 1,057,480 Hong Kong SAR, China 2,759 352 .. 0.79 1,106 16.1 383 1,700 133 11,569 28,872 Colombia 157 .. 608 0.16 425 5.1 56 362 133 1,739 25,990 Congo, Dem. Rep. .. .. 19 0.48 .. .. .. .. .. .. .. Congo, Rep. .. .. 18 .. .. .. .. .. .. .. .. Costa Rica 257 .. 98 0.40 2,193 40.0 8 64 8 1,212 11,265 Côte d’Ivoire 70 .. 56 .. 126 8.2 0 21 .. .. .. Croatia 1,571 636 1,164 0.83 732 9.2 32 225 257 21 7,950 Cuba .. .. 222 0.49 .. .. .. .. 59 172 1,397 Cyprus 752 216 195 0.46 140 37.1 8 31 4 4 2,381 Czech Republic 2,755 1,533 3,946 1.53 17,469 15.3 105 771 868 114 11,048 Denmark 6,390 2,628 5,306 3.02 8,291 14.2 .. .. 1,626 142 5,788 Dominican Republic .. .. 6 .. 102 2.9 .. 63 .. .. 6,453 Ecuador 106 31 68 0.26 145 8.4 .. 54 4 690 16,195 Egypt, Arab Rep. 420 394 2,247 0.21 96 0.9 122 226 605 1,625 3,955 El Salvador .. .. 6 0.11 186 5.8 0 31 .. .. .. Eritrea .. .. 4 .. .. .. .. .. .. .. .. Estonia 3,210 627 518 1.44 721 9.1 20 60 84 13 3,140 Ethiopia 21 13 175 0.17 6 3.1 0 1 12 25 719 Finland 7,647 .. 4,949 3.96 5,776 10.8 2,340 1,236 1,731 102 5,504 France 3,690 1,872 31,748 2.23 99,736 24.9 10,407 5,559 14,748 1,832 93,187 Gabon .. .. 18 0.64 7 3.0 .. .. .. .. .. Gambia, The .. .. 20 0.02 0 1.1 .. .. .. .. 327 Georgia .. .. 129 0.18 11 1.8 5 7 179 180 4,301 Germany 3,780 1,329 45,003 2.82 158,507 15.3 14,384 13,051 47,047 12,198 74,339 Ghana 17 15 102 0.23 8 2.0 .. .. .. .. 884 Greece 1,849 756 4,881 0.58 1,090 10.2 69 627 728 16 6,559 Guatemala 39 35 22 0.06 205 5.7 13 94 7 374 9,175 Guinea .. .. 3 .. 0 0.1 0 1 .. .. .. Guinea-Bissau .. .. 6 .. .. .. .. 0 .. .. 6 Haiti .. .. 7 .. .. .. .. 0 .. .. .. 332 2012 World Development Indicators 5.13 STATES AND MARKETS Science and technology Research and Scienti�c Expenditures High-technology Royalty and Patent Trademark development and for R&D exports license fees applications applications (R&D) technical �leda,b �leda,c journal articles full-time equivalent % of manu- per million people factured $ millions Non- Researchers Technicians % of GDP $ millions exports Receipts Payments Residents residents Total 2005–09d 2005–09d 2009 2005–09d 2010 2010 2010 2010 2010 2010 2010 Honduras .. .. 6 .. 12 1.3 .. 30 .. .. 7,403 Hungary 2,006 553 2,397 1.15 18,771 24.2 1,051 1,386 649 47 6,298 India 136 93 19,917 0.76 10,087 7.2 129 2,438 7,262 27,025 141,943 Indonesia 90 .. 262 0.08 6,673 11.4 60 1,616 .. .. 47,794 Iran, Islamic Rep. 751 .. 6,313 0.79 584 4.5 .. .. .. .. 3,096 Iraq 49 26 70 .. 0 0.1 1,312 396 .. .. .. Ireland 3,373 742 2,799 1.77 21,232 21.2 2,252 37,823 733 59 3,769 Israel .. .. 6,304 4.27 7,979 14.7 849 860 1,450 5,856 8,614 Italy 1,690 .. 26,755 1.27 26,366 7.2 3,603 6,986 8,814 903 4,387 Jamaica .. .. 51 .. 3 0.6 5 36 .. .. 1,708 Japan 5,189 597 49,627 3.45 122,047 18.0 26,680 18,769 290,081 54,517 124,726 Jordan .. .. 383 0.42 122 2.9 .. .. 45 429 5,971 Kazakhstan .. .. 99 0.23 2,110 29.9 .. 86 11 162 3,615 Kenya 56 63 291 0.42 100 5.7 54 18 77 120 4,321 Korea, Dem. Rep. .. .. 8 .. .. .. .. .. 8,018 39 1,231 Korea, Rep. 4,947 825 22,271 3.36 92,856 28.7 3,146 8,965 131,805 38,296 129,486 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 152 35 214 0.11 16 0.5 .. .. .. .. .. Kyrgyz Republic .. .. 15 0.16 2 1.0 1 3 134 6 2,535 Lao PDR .. .. 12 .. .. .. .. .. .. .. .. Latvia 1,601 561 162 0.46 396 7.6 12 33 178 7 3,589 Lebanon .. .. 256 .. 279 12.8 7 13 .. .. .. Lesotho 21 22 4 0.03 0 0.2 .. 3 .. .. 566 Liberia .. .. 0 .. .. .. .. .. .. .. 612 Libya .. .. 34 .. .. .. .. .. .. .. .. Lithuania 2,541 445 388 0.84 1,190 10.6 1 35 108 6 4,351 Macedonia, FYR 472 82 57 0.23 40 2.9 7 18 34 406 3,436 Madagascar 46 25 35 0.15 5 1.0 .. .. 9 34 1,773 Malawi 30 58 53 .. 1 1.3 .. 0 .. .. .. Malaysia 365 43 1,351 0.63 59,332 44.5 266 1,133 1,233 5,230 26,370 Mali 38 12 25 0.25 2 2.4 0 3 .. .. .. Mauritania .. .. 3 .. .. .. .. .. .. .. .. Mauritius .. .. 22 0.37 6 0.7 1 12 2 22 2,032 Mexico 347 183 4,128 0.37 37,657 16.9 .. .. 951 13,625 94,457 Moldova 794 78 80 0.53 17 8.3 5 13 134 5 5,459 Mongolia .. .. 42 0.24 7 7.4 0 3 110 69 2,403 Morocco 661 61 391 0.64 897 7.7 4 30 152 882 11,030 Mozambique 16 35 29 0.21 1 1.3 0 4 18 22 891 Myanmar .. .. 10 .. .. .. .. .. .. .. .. Namibia .. .. 14 .. 19 0.9 0 8 .. .. 804 Nepal .. .. 56 .. 4 0.6 .. .. .. .. 1,132 Netherlands 2,818 1,131 14,866 1.84 59,510 21.3 5,491 3,707 2,575 279 .. New Zealand 4,324 886 3,188 1.17 548 9.0 183 669 1,585 5,051 17,124 Nicaragua .. .. 12 .. 6 4.8 .. .. .. .. 5,975 Niger 8 11 16 .. 4 6.6 0 2 .. .. .. Nigeria 39 13 462 0.22 63 1.1 .. 224 .. .. .. Norway 5,504 .. 4,440 1.80 3,830 16.1 498 536 1,117 696 13,835 Oman .. .. 114 .. 18 0.6 .. .. .. .. 1,913 Pakistan 162 64 1,043 0.46 262 1.7 4 123 170 1,375 15,734 Panama 111 .. 73 0.21 1 0.8 .. 46 .. 468 9,629 Papua New Guinea .. .. 17 .. .. .. .. .. 1 45 612 Paraguay 75 .. 11 0.06 32 6.6 254 3 18 347 22,102 Peru .. .. 159 .. 252 6.6 3 197 39 261 23,120 Philippines 78 11 223 0.11 29,792 67.8 4 445 166 3,223 16,838 Poland 1,598 189 7,355 0.68 8,378 6.6 237 2,248 3,203 227 18,251 Portugal 4,308 383 4,157 1.66 1,213 3.4 41 548 499 46 19,636 Puerto Rico 668 .. .. 0.49 .. .. .. .. .. .. .. Qatar .. .. 64 .. 1 0.0 .. .. .. .. .. 2012 World Development Indicators 333 5.13 Science and technology Research and Scienti�c Expenditures High-technology Royalty and Patent Trademark development and for R&D exports license fees applications applications (R&D) technical �leda,b �leda,c journal articles full-time equivalent % of manu- per million people factured $ millions Non- Researchers Technicians % of GDP $ millions exports Receipts Payments Residents residents Total 2005–09d 2005–09d 2009 2005–09d 2010 2010 2010 2010 2010 2010 2010 Romania 895 185 1,367 0.48 4,249 10.9 466 453 1,382 36 12,063 Russian Federation 3,091 475 14,016 1.25 5,193 8.8 625 5,066 28,722 13,778 56,856 Rwanda 12 .. 12 .. 1 5.9 0 0 .. .. 238 Saudi Arabia .. .. 710 0.08 201 0.7 .. .. 288 643 .. Senegal 384 53 56 0.37 9 1.2 1 12 .. .. .. Serbia 1,060 224 1,173 0.89 .. .. 39 156 290 39 7,005 Sierra Leone .. .. 3 .. .. .. 1 0 .. .. 676 Singapore 5,834 597 4,187 2.66 126,982 49.9 1,867 15,857 895 8,878 17,504 Slovak Republic 2,438 345 1,000 0.48 3,921 7.0 45 147 234 48 5,027 Slovenia 3,679 1,863 1,234 1.86 1,131 5.5 69 369 442 11 3,894 Somalia .. .. 1 .. .. .. .. .. .. .. .. South Africa 396 124 2,864 0.93 1,420 4.3 59 1,941 821 5,562 30,549 South Sudan .. .. .. .. .. .. .. .. .. .. .. Spain 2,932 1,148 21,543 1.38 11,290 6.4 877 2,649 3,566 213 47,120 Sri Lanka 96 77 135 0.11 57 1.0 .. .. 225 235 6,244 Sudan .. .. 63 0.29 10 29.4 3 11 3 13 1,026 Swaziland .. .. 8 .. 1 0.1 0 16 .. .. 659 Sweden 5,018 2,006 9,478 3.62 16,133 13.9 6,133 1,383 2,196 353 12,662 Switzerland 3,320 2,874 9,469 3.00 42,820 24.8 .. .. 1,622 533 27,972 Syrian Arab Republic .. .. 72 .. 86 1.8 1 37 .. .. 2,362 Tajikistan .. .. 12 0.09 .. .. 1 0 7 3 2,293 Tanzania .. .. 152 0.43 25 3.5 0 0 .. .. 556 Thailand 316 140 2,033 0.21 34,156 24.0 153 3,084 1,214 723 37,656 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 38 18 7 .. 0 0.1 .. 5 .. .. .. Trinidad and Tobago .. .. 48 0.04 3 0.2 .. .. 1 280 .. Tunisia 1,863 43 1,022 1.10 611 4.9 25 15 .. .. .. Turkey 804 122 8,301 0.85 1,714 1.9 .. 816 2,555 177 8,241 Turkmenistan .. .. 1 .. .. .. .. .. .. .. 2,245 Uganda .. .. 143 0.41 6 2.4 4 4 6 1 .. Ukraine 1,353 288 1,639 0.86 1,441 4.3 132 744 2,556 2,756 28,915 United Arab Emirates .. .. 265 .. 50 3.2 .. .. .. .. .. United Kingdom 3,947 871 45,649 1.87 59,447 20.9 13,822 8,499 15,490 6,439 36,484 United States 4,673 .. 208,601 2.79 145,498 19.9 105,583 33,450 241,977 248,249 281,867 Uruguay 346 .. 246 0.66 79 5.8 0 17 23 761 5,730 Uzbekistan .. .. 139 .. .. .. .. .. 370 262 4,863 Venezuela, RB 183 .. 354 .. 145 5.1 .. 340 .. .. .. Vietnam .. .. 326 .. 2,101 6.2 .. .. 306 3,276 32,289 West Bank and Gaza 144 27 .. .. .. .. 0 0 .. .. .. Yemen, Rep. .. .. 25 .. 0 0.4 33 5 20 55 4,165 Zambia 43 67 35 0.34 4 1.0 .. 1 .. .. 765 Zimbabwe .. .. 56 .. 9 0.8 .. .. .. .. .. World 1,269 w .. w 788,333 s 2.15 w 1,572,076 s 17.5 w 210,577 s 216,352 s 1,060,313 s 621,207 s 3,023,628 s Low income .. .. 1,561 .. .. 3.1 62 56 .. .. .. Middle income 591 .. 167,803 1.07 490,375 17.9 3,612 37,290 281,357 190,556 1,826,974 Lower middle income .. .. 28,049 0.61 46,413 11.0 665 6,170 11,816 44,547 352,071 Upper middle income 1,197 .. 139,753 1.10 569,935 19.5 2,947 31,120 333,407 164,472 1,737,397 Low & middle income 574 .. 169,364 1.07 527,339 17.8 3,674 37,346 289,428 191,053 1,850,304 East Asia & Pacific 1,199 .. 78,373 1.47 .. 28.7 1,049 18,196 304,113 110,671 1,222,061 Europe & Central Asia 2,006 329 29,089 0.96 17,622 6.7 1,352 7,678 36,143 17,415 187,119 Latin America & Carib. 482 346 23,970 0.65 51,633 10.9 970 6,291 4,216 40,206 466,522 Middle East & N. Africa .. .. 11,421 .. 1,572 3.2 37 326 .. .. 36,211 South Asia 129 86 21,432 0.75 .. 6.7 142 2,578 7,519 27,500 172,509 Sub-Saharan Africa .. .. 5,079 0.58 2,570 2.8 124 2,277 .. .. .. High income 3,982 .. 618,970 2.44 1,081,514 17.4 206,903 179,006 775,219 450,049 1,183,907 Euro area 3,119 1,355 171,873 2.09 439,190 14.9 42,900 76,676 72,951 14,959 305,982 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 European Patent Office (150,961 by nonresidents) and the Eurasian Patent Organization (3,329 by nonresidents). c. Excludes applications filed under the auspices of the Office for Harmonization in the Internal Market (98,616). d. Data are for the most recent year available. e. Includes Luxembourg and the Netherlands. 334 2012 World Development Indicators 5.13 STATES AND MARKETS Science and technology About the data De�nitions The United Nations Educational, Scientifi c, and of high technology are also important, such as know- •  Researchers in research and development (R&D) Cultural Organization (UNESCO) Institute for Statis- how, scientific personnel, and technology embodied are professionals engaged in conceiving of or creating tics collects data on researchers, technicians, and in patents. Considering these characteristics would new knowledge, products, processes, methods, and expenditure on research and development (R&D) yield a different list (see Hatzichronoglou 1997). systems and in managing the projects concerned. through its biennial R&D survey and from other A patent is an exclusive right granted for a specified Postgraduate doctoral students (International Stan- international partners such as the Organisation for period (generally 20 years) for a new way of doing dard Classification of Education 1997 level 6) engaged Economic Co-operation and Development (OECD), something or a new technical solution to a problem— in R&D are considered researchers. • Technicians in Eurostat, and the Network for Science and Technol- an invention. The invention must be of practical use R&D and equivalent staff are people whose main tasks ogy Indicators—Ibero-American and Inter-American. and display a characteristic unknown in the existing require technical knowledge and experience in engi- The OECD’s Frascati Manual 2002 (OECD 2002) body of knowledge in its field. Most countries have neering, physical and life sciences (technicians), and defines research and experimental development as systems to protect patentable inventions. The Patent social sciences and humanities (equivalent staff). They “creative work undertaken on a systemic basis in Cooperation Treaty (www.wipo.int/pct) provides a two- engage in R&D by performing scientific and technical order to increase the stock of knowledge, including phase system for filing patent applications in 144 eli- tasks involving the application of concepts and opera- knowledge of man, culture and society, and the use gible countries (as of November 2011). International tional methods, normally under researcher supervision. of this stock of knowledge to devise new applica- applications under the treaty provide for a national • Scienti�c and technical journal articles are published tions.� R&D covers basic research, applied research, patent grant only—there is no international patent. articles in physics, biology, chemistry, mathematics, and experimental development. Data on research- The national filing represents the applicant’s seek- clinical medicine, biomedical research, engineering and ers and technicians in R&D are measured in both ing of patent protection for a given territory, whereas technology, and earth and space sciences. • Expen- full-time equivalent and headcount but are shown in international filings, while representing a legal right, ditures for R&D are current and capital expenditures full-time equivalent only. do not accurately reflect where patent protection is on creative work undertaken on a systematic basis to Scientific and technical article counts are from jour- sought. Resident filings are those from residents of increase the stock of knowledge, including knowledge nals classified by the Institute for Scientific Informa- the country concerned. Nonresident filings are from on humanity, culture, and society, and the use of knowl- tion’s Science Citation Index (SCI) and Social Sciences applicants abroad. For regional offices applications edge to devise new applications. •  High-technology Citation Index (SSCI). Counts are based on fractional from residents of any member state of the regional exports are products with high R&D intensity, such assignments; articles with authors from different patent convention are considered nonresident filings. as in aerospace, computers, pharmaceuticals, scien- countries are allocated proportionately to each country Some offices (notably the U.S. Patent and Trademark tific instruments, and electrical machinery. • Royalty (see De�nitions for fields covered). The SCI and SSCI Office) use the residence of the inventor rather than and license fees are payments and receipts between databases cover the core set of scientific journals but the applicant to classify filings. residents and nonresidents for authorized use of intan- may exclude some of local importance and may reflect A trademark is a distinctive sign identifying goods gible, nonproduced, nonfinancial assets and proprietary some bias toward English-language journals. or services as produced or provided by a specific per- rights (such as patents, copyrights, trademarks, and R&D expenditures include expenditures from all son or enterprise. A trademark protects the owner of industrial processes) and for the use, through licens- sources for R&D performed within a country, includ- the mark by ensuring exclusive right to use it to iden- ing, of produced originals of prototypes (such as films ing capital expenditures and current costs (wages tify goods or services or to authorize another to use and manuscripts). • Patent applications �led are pat- and associated costs of researchers, technicians, it. The period of protection varies, but a trademark ent applications at a national or regional patent office; and other supporting staff and other current costs, can be renewed indefinitely for an additional fee. an international patent application (Patent Coopera- including noncapital purchases of materials, supplies, Detailed components of trademark filings, available tion Treaty filing) provides a national patent grant only. and minor equipment to support R&D such as utilities, on the World Development Indicators CD-ROM and • Trademark applications �led are applications to reg- reference materials, subscriptions to libraries and at http://data.worldbank.org, include applications ister a trademark with a national or regional IP office. scientific societies, and materials for laboratories). filed by direct residents (domestic applicants filing Data sources The method for determining high- technology directly at a given national or regional intellectual exports was developed by the Organisation for Eco- property [IP] office); direct nonresident (foreign appli- Data on R&D are provided by the UNESCO Insti- nomic Co-operation and Development in collabora- cants filing directly at a given national or regional IP tute for Statistics. Data on scientific and technical tion with Eurostat. It takes a “product approach� office); aggregate direct (applicants not identified as journal articles are from the U.S. National Science (rather than a “sectoral approach�) based on R&D direct resident or direct nonresident by the national Board’s Science and Engineering Indicators 2012. intensity (expenditure divided by total sales) for or regional office); and Madrid (designations received Data on high-technology exports are from the United groups of products from Germany, Italy, Japan, by the national or regional IP office based on inter- Nations Statistics Division’s Commodity Trade the Netherlands, Sweden, and the United States. national applications filed via the World Intellectual (Comtrade) database. Data on royalty and license Because industrial sectors specializing in a few high- Property Organization–administered Madrid System). fees are from the International Monetary Fund’s technology products may also produce low-technol- Data are based on information supplied to WIPO by Balance of Payments Statistics Yearbook. Data on ogy products, the product approach is more appro- IP offices in annual surveys, supplemented by data patents and trademarks are from the World Intellec- priate for international trade. The method takes only in national IP office reports. Data may be missing for tual Property Organization (www.wipo.int/ipstats). R&D intensity into account, but other characteristics some offices or periods. 2012 World Development Indicators 335 GLOBAL LINKS T 6 he world economy is bound together by extractive industries and infrastructure devel- trade in goods and services, financial flows, opment in other developing countries fueled the and movements of people. As economies rebound. China was a key driver, with net FDI develop, their links expand and grow more com- inflows up 62 percent in 2010 (to $185 billion). plex. The indicators in Global links measure the Portfolio equity flows recovered rapidly as size and direction of these flows and document the global financial crisis ebbed, particularly in policy interventions, such as tariffs, trade facili- emerging markets with good growth prospects. tation, and aid flows. Low- and middle-income economies recorded During economic crises, declining trade and a 19 percent increase (to $130 billion), under- financing and changes in migration patterns in pinned by a 89  percent rise in net inflows to one country or region can transmit shocks to India. For high-income economies the story was others. An example is the financial crisis that mixed—particularly in the euro area, where net began in the United States in 2007 and spread inflows rose 13 percent. rapidly to Europe. But the same links that Debt-related flows to low- and middle- transfer shocks also allow expansion into new income economies from private creditors rose markets and access to new sources of finance, in 2010 to $111 billion, more than twice their reducing the impacts of shocks. In an increas- 2009  level, underpinned by a resurgence of ingly multipolar world, developing countries are bond issuance by public and private sector bor- trading and investing more with each other, rowers. Similarly, net inflows from banks and reducing their dependence on high-income other private creditors showed signs of recovery, economies. Evidence of this shift can be found rising 123 percent, to $44 billion, albeit from in the tables of Global links. a low base of $20 billion in 2009. Net inflows Capital inflows rebounded strongly in 2010, from bilateral and multilateral creditors (loans especially in low- and middle-income econo- and grants) fell 11 percent, due largely to the mies, where combined net debt and equity redirection of International Monetary Fund flows inflows rose 37  percent, to $799  billion. For- to countries in the euro zone. eign direct investment (FDI) inflows rose Low- and middle-income economies’ com- 3 percent—with wide disparity across income bined external debt rose $437 billion in 2010, groups. In high-income economies FDI inflows to $4  trillion, but it remained within sustain- fell 7 percent, dragged down by a 13 percent able bounds, an average of 21 percent of gross drop in net inflows to the euro area. But inflows national income and 69 percent of export earn- to the United States—the world’s largest FDI ings. Short-term external debt was the fast- recipient—surged. And low- and middle-income est growing component, rising 34  percent in economies saw net FDI inflows jump 27  per- 2010 compared with 6 percent for outstanding cent, to $514 billion, and their share of global long-term external debt. Risks associated with a FDI inflows increase to almost 36 percent, up high proportion of short-term debt—25 percent from 29 percent in 2009. A better investment of total debt stock at end-2010—were mitigated environment, corporate earnings revival, and by international reserves equivalent to 137 per- increased developing country investment in cent of total outstanding external debt. 2012 World Development Indicators 337 6.1 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 2000–10 2000–10 2000–10 2000–10 2010 Afghanistan 9.9 4.4 20.1 11.0 146.5 Albania 15.0 11.6 20.2 17.5 95.3 Algeria 0.0 12.2 14.4 17.9 177.5 Angola 11.4 22.0 27.6 26.4 210.9 Argentina 5.9 11.7 11.9 14.6 126.6 Armenia 3.6 12.6 12.3 19.4 126.2 Australia 2.3 8.3 14.0 13.1 178.9 Austria 5.9 4.5 9.9 9.7 91.1 Azerbaijan 22.3 14.1 38.6 20.8 160.3 Bahrain 0.6 3.5 11.7 11.9 114.5 Bangladesh 11.4 5.0 13.0 13.3 59.0 Belarus 6.8 8.7 16.4 18.3 102.8 Belgium 2.5 3.0 9.7 10.1 100.8 Benin 6.6 7.1 15.5 16.2 103.5 Bolivia 9.4 8.2 21.2 13.7 152.4 Bosnia and Herzegovina 13.4 5.1 20.4 11.5 101.1 Botswana 3.5 5.7 7.1 11.6 84.7 Brazil 6.6 8.1 15.5 14.9 125.1 Bulgaria 7.9 9.3 18.0 18.4 108.8 Burkina Faso 11.8 7.9 19.3 14.5 120.9 Burundi –3.1 9.7 7.9 15.8 153.1 Cambodia 12.7 9.3 15.0 15.3 75.9 Cameroon –2.5 5.2 9.8 13.9 143.8 Canada –0.9 2.5 4.9 6.4 119.9 Central African Republic –4.3 6.6 –0.4 13.1 85.9 Chad 23.9 11.6 42.2 17.4 180.0 Chile 4.3 11.2 17.1 14.9 204.0 China† 20.5 15.0 22.4 20.9 77.4 Hong Kong SAR, China 6.7 6.6 7.8 8.3 96.0 Colombia 6.2 11.0 14.7 15.3 133.9 Congo, Dem. Rep. 10.1 14.9 20.9 21.8 137.9 Congo, Rep. 1.4 16.6 15.9 22.4 182.3 Costa Rica 7.3 6.8 7.0 9.2 78.2 Côte d’Ivoire –0.1 5.1 11.6 13.7 161.6 Croatia 6.3 6.0 12.5 12.2 100.7 Cuba 0.5 8.9 11.2 12.4 .. Cyprus 0.3 5.5 5.7 10.9 103.5 Czech Republic 9.9 8.0 18.5 15.7 106.4 Denmark 2.1 2.9 8.3 8.7 106.6 Dominican Republic –0.8 3.3 2.1 6.7 98.4 Ecuador 7.5 12.2 16.4 17.5 121.9 Egypt, Arab Rep. 9.0 10.2 23.5 17.9 152.4 El Salvador 2.6 3.4 4.8 6.6 91.3 Eritrea –10.9 –3.8 –6.7 3.6 77.2 Estonia 3.3 4.1 13.3 11.4 144.4 Ethiopia 9.1 16.5 19.2 23.6 127.5 Finland 2.6 2.9 6.7 10.0 77.1 France 1.5 2.9 6.2 8.0 98.2 Gabon 0.1 7.2 14.4 11.9 195.8 Gambia, The –3.7 0.9 2.7 8.4 93.3 Georgia 8.9 19.5 19.8 27.5 132.2 Germany 5.2 4.9 10.3 9.9 103.3 Ghana 5.2 9.2 17.2 16.3 175.4 Greece 0.8 1.2 9.1 10.3 92.7 Guatemala 7.9 5.1 12.4 10.5 92.6 Guinea –0.2 2.8 8.5 9.6 110.2 Guinea-Bissau 1.8 7.9 9.0 17.5 83.5 Haiti 4.9 4.0 8.2 11.2 77.9 †Data for Taiwan, China 7.5 2.4 8.0 8.0 66.0 338 2012 World Development Indicators 6.1 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 2000–10 2000–10 2000–10 2000–10 2010 Honduras 3.5 4.7 6.2 9.7 83.4 Hungary 10.5 7.6 15.1 12.8 95.4 India 11.4 19.5 19.9 24.2 127.2 Indonesia 0.8 6.5 11.0 14.4 127.3 Iran, Islamic Rep. 2.9 7.6 17.5 14.8 157.9 Iraq 1.7 9.2 16.8 15.1 184.3 Ireland 1.7 0.0 4.7 3.8 95.4 Israel 3.3 1.5 8.1 6.4 98.1 Italy –0.1 0.4 8.1 9.1 99.0 Jamaica 0.0 0.0 4.0 7.7 70.7 Japan 5.2 1.8 6.3 8.0 67.8 Jordan 3.9 5.1 15.0 15.6 85.4 Kazakhstan 8.6 14.2 25.3 20.7 192.6 Kenya 5.1 8.9 12.2 17.1 91.7 Korea, Dem. Rep. 7.1 –1.7 13.5 7.5 77.0 Korea, Rep. 12.1 7.1 12.4 12.6 68.0 Kosovo .. .. .. .. .. Kuwait 4.1 9.2 18.7 13.8 187.3 Kyrgyz Republic 7.7 16.4 16.8 26.0 107.6 Lao PDR 11.1 9.3 19.6 16.0 119.5 Latvia 9.6 7.3 20.5 17.1 105.2 Lebanon 13.4 4.4 20.6 12.1 95.5 Lesotho 12.8 7.3 13.5 12.0 66.2 Liberia –7.4 4.5 0.5 10.4 146.7 Libya 4.2 13.5 19.4 20.8 162.5 Lithuania 12.7 10.7 21.0 18.3 103.8 Macedonia, FYR 6.2 6.0 13.4 14.3 89.1 Madagascar 1.5 8.6 4.8 15.2 76.3 Malawi 6.9 9.9 12.2 16.6 87.7 Malaysia 5.5 5.1 9.2 8.6 100.2 Mali 2.9 7.7 13.7 14.6 158.7 Mauritania 11.2 11.3 23.6 18.0 132.7 Mauritius 3.2 6.0 2.9 9.1 73.2 Mexico 2.9 3.4 7.0 6.8 104.5 Moldova 11.3 18.8 12.6 20.2 104.8 Mongolia 3.9 12.4 21.2 20.5 215.6 Morocco 0.6 8.1 10.9 15.4 134.2 Mozambique 10.0 7.3 19.1 14.6 108.9 Myanmar 7.4 0.3 17.3 7.6 110.2 Namibia 6.2 10.6 14.5 15.4 120.3 Nepal –2.9 4.5 2.5 14.0 78.3 Netherlands 5.2 5.7 11.5 11.3 101.8 New Zealand 3.3 5.5 9.5 9.5 124.4 Nicaragua 9.5 5.6 12.9 10.7 81.7 Niger 4.0 14.4 16.4 21.2 150.1 Nigeria 3.0 13.9 18.4 20.3 186.9 Norway –0.1 5.0 11.3 11.1 140.4 Oman –0.2 11.1 14.8 17.1 193.1 Pakistan 5.8 4.6 9.6 16.8 51.9 Panama 0.0 9.7 1.7 13.7 87.5 Papua New Guinea –1.1 6.0 14.0 15.1 150.4 Paraguay 14.8 16.1 19.1 19.9 102.9 Peru 7.2 9.8 20.6 17.4 152.5 Philippines 2.8 –0.1 3.4 4.9 68.6 Poland 10.0 7.4 19.8 16.5 101.9 Portugal 5.1 3.4 9.0 9.0 88.0 Puerto Rico .. .. .. .. .. Qatar 8.1 23.0 23.3 27.9 187.8 2012 World Development Indicators 339 6.1 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 2000–10 2000–10 2000–10 2000–10 2010 Romania 12.4 13.7 18.5 19.9 99.9 Russian Federation 3.6 16.8 17.7 21.1 197.3 Rwanda 1.0 17.0 18.3 23.4 234.4 Saudi Arabia 0.3 10.8 16.1 16.3 222.6 Senegal 1.1 5.7 9.3 14.4 98.9 Serbia .. .. .. .. .. Sierra Leone 25.9 4.5 32.7 15.2 70.2 Singapore 10.2 7.5 12.0 11.4 83.2 Slovak Republic 17.1 13.8 21.7 19.6 90.0 Slovenia 11.2 8.6 15.5 14.5 89.3 Somalia –0.2 4.0 7.7 11.6 106.2 South Africa 1.5 7.4 12.2 14.6 139.4 South Sudan .. .. .. .. .. Spain 3.1 3.9 9.5 9.9 104.5 Sri Lanka 2.5 2.1 5.9 9.2 75.7 Sudan 8.0 17.0 23.5 22.1 196.8 Swaziland –2.2 –0.9 5.0 5.8 110.1 Sweden 2.8 3.0 8.3 9.9 88.6 Switzerland 6.8 3.3 10.4 9.1 81.0 Syrian Arab Republic 0.7 11.7 12.5 19.2 139.5 Tajikistan –1.0 9.3 7.0 18.9 96.8 Tanzania 7.0 12.3 18.2 19.9 139.2 Thailand 7.2 7.5 12.6 12.7 98.0 Timor-Leste .. .. .. .. .. Togo 2.0 –2.2 10.1 12.4 30.7 Trinidad and Tobago 0.7 1.4 14.4 10.1 133.5 Tunisia 6.6 4.8 12.4 11.2 95.2 Turkey 10.4 9.2 17.4 17.0 91.8 Turkmenistan –0.6 8.4 14.0 13.1 195.6 Uganda 13.0 8.9 21.7 15.7 111.1 Ukraine 4.7 9.9 15.1 18.7 118.9 United Arab Emirates 6.5 14.6 19.3 20.2 163.3 United Kingdom 0.4 2.0 4.9 6.1 103.3 United States 3.9 2.5 6.6 5.9 97.1 Uruguay 8.5 7.6 14.3 14.3 99.6 Uzbekistan 7.6 10.6 18.9 16.0 151.6 Venezuela, RB –2.5 10.8 12.2 14.3 216.3 Vietnam 11.4 12.6 19.4 20.6 100.6 West Bank and Gaza .. .. .. .. .. Yemen, Rep. –4.1 9.7 9.7 18.2 149.6 Zambia 9.4 14.1 25.9 20.8 189.0 Zimbabwe –4.9 –1.3 4.5 7.5 106.7 340 2012 World Development Indicators 6.1 GLOBAL LINKS Growth of merchandise trade About the data De�nitions Data on international trade in goods are available from national and international sources such as •  Export and import volumes are indexes of the from each country’s balance of payments and the IMF’s International Financial Statistics data- quantity of goods traded. They are derived from UNC- customs records. While the balance of payments base, the United Nations Economic Commission for TAD’s volume index series and are the ratio of the focuses on the financial transactions that accom- Latin America and the Caribbean, the U.S. Bureau export or import value indexes to the corresponding pany trade, customs data record the direction of of Labor Statistics, Japan Customs, Bank of Japan, unit value indexes. Unit value indexes are based on trade and the physical quantities and value of goods and UNCTAD’s Commodity Price Statistics and Mer- data reported by countries that demonstrate consis- entering or leaving the customs area. Customs data chandise Trade Matrix. The IMF also compiles data tency under UNCTAD quality controls, supplemented may differ from data recorded in the balance of pay- on trade prices and volumes in its International by UNCTAD’s estimates using the previous year’s ments because of differences in valuation and time Financial Statistics (IFS) database. trade values at the Standard International Trade of recording. The 2008 United Nations System of The growth rates and terms of trade in the table Classification three-digit level as weights. To improve National Accounts and the fifth edition of the Inter- were calculated from index numbers compiled by data coverage, especially for the most recent peri- national Monetary Fund’s (IMF) Balance of Payments UNCTAD. ods, UNCTAD constructs a set of average price Manual (1993) attempted to reconcile definitions and The terms of trade index measures the relative indexes at the three-digit product classification of the reporting standards for international trade statistics, prices of a country’s exports and imports. There are Standard International Trade Classification revision but differences in sources, timing, and national prac- several ways to calculate it. The most common is the 3 using its Commodity Price Statistics database, tices limit comparability. Real growth rates derived net barter (or commodity) terms of trade index, or international and national sources, and estimates from trade volume indexes and terms of trade based the ratio of the export price index to the import price by the UNCTAD secretariat and calculates unit value on unit price indexes may therefore differ from those index. When a country’s net barter terms of trade indexes at the country level using the current year’s derived from national accounts aggregates. index increases, its exports become more expensive trade values as weights. • Export and import values Trade in goods, or merchandise trade, includes all or its imports become cheaper. are the current value of exports (free on board, f.o.b.) goods that add to or subtract from an economy’s or imports (cost, insurance, and freight, c.i.f.), con- material resources. Trade data are collected on the verted to U.S. dollars and expressed as a percentage basis of a country’s customs area, which in most of the average for the base period (2000). UNCTAD’s cases is the same as its geographic area. Goods export or import value indexes are reported for most provided as part of foreign aid are included, but economies. •  Net barter terms of trade index is goods destined for extraterritorial agencies (such calculated as the percentage ratio of the export unit as embassies) are not. value indexes to the import unit value indexes, mea- Collecting and tabulating trade statistics are dif- sured relative to the base year 2000. ficult. Some developing countries lack the capacity to report timely data, especially landlocked coun- tries and countries whose territorial boundaries are porous. Their trade has to be estimated from the data reported by their partners. (For further discussion of the use of partner country reports, see About the data for table 6.2.) Countries that belong to common customs unions may need to collect data through direct inquiry of companies. Economic or political 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 over- invoicing 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 reported to the United Nations Statistics Division, which maintains the Commodity Trade (Comtrade) Data sources and Monthly Bulletin of Statistics databases. The United Nations Conference on Trade and Develop- Data on trade indexes are from UNCTAD’s annual ment (UNCTAD) compiles international trade sta- Handbook of Statistics. tistics, including price, value, and volume indexes, 2012 World Development Indicators 341 6.2 Direction and growth of merchandise trade Direction of trade Low- and middle-income importers High-income importers % of world trade, 2010 Europe Latin Middle % of world East Asia & Central America East & South Sub-Saharan trade, 2010 Source of exports & Paci�c Asia & Caribbean N. Africa Asia Africa Total Total High-income economies 9.2 2.6 3.5 1.4 1.6 1.0 19.3 47.6 European Union 1.2 1.9 0.7 0.7 0.3 0.5 5.3 26.1 Japan 1.6 0.1 0.3 0.0 0.1 0.1 2.1 3.0 United States 0.9 0.1 1.9 0.1 0.2 0.1 3.4 5.2 Other high-income economies 5.6 0.5 0.6 0.5 1.0 0.3 8.5 13.3 Low- and middle-income economies 3.3 1.9 2.0 0.9 1.2 0.9 10.5 20.9 East Asia & Pacific 1.9 0.5 0.7 0.3 0.6 0.4 4.4 10.8 China 0.7 0.5 0.6 0.3 0.4 0.3 2.8 7.8 Europe & Central Asia 0.3 1.1 0.0 0.3 0.1 0.0 1.8 2.9 Russian Federation 0.2 0.4 0.0 0.1 0.0 0.0 0.7 1.5 Latin America & Caribbean 0.6 0.1 1.1 0.1 0.1 0.1 2.0 3.6 Brazil 0.2 0.0 0.3 0.1 0.0 0.0 0.7 0.6 Middle East & N. Africa 0.2 0.1 0.0 0.2 0.2 0.1 0.8 1.4 Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.3 South Asia 0.2 0.1 0.1 0.1 0.1 0.1 0.6 1.2 India 0.2 0.0 0.0 0.1 0.1 0.1 0.5 0.9 Sub-Saharan Africa 0.1 0.0 0.1 0.0 0.2 0.2 0.9 1.1 South Africa 0.1 0.0 0.0 0.0 0.0 0.1 0.2 0.3 World 12.5 4.5 5.5 2.3 2.9 1.8 29.7 68.5 Nominal growth of trade Low- and middle-income importers High-income importers average annual % growth, 2000–10 average annual Europe Latin Middle % growth, East Asia & Central America East & South Sub-Saharan 2000–10 Source of exports & Paci�c Asia & Caribbean N. Africa Asia Africa Total Total High-income economies 15.1 17.3 8.8 13.7 21.4 12.5 14.1 7.5 European Union 15.0 16.7 9.5 11.4 15.5 11.3 13.8 8.2 Japan 12.5 24.8 11.1 12.5 14.1 11.2 12.7 3.4 United States 12.1 14.0 7.2 12.9 21.1 13.2 9.4 5.2 Other high-income economies 16.5 21.0 13.4 20.1 25.7 15.0 17.4 8.2 Low- and middle-income economies 22.3 20.8 18.1 22.7 27.0 21.5 21.7 13.9 East Asia & Pacific 20.9 34.8 27.7 25.9 28.1 27.1 24.9 15.5 China 26.1 37.2 31.5 30.3 35.0 31.0 31.0 20.4 Europe & Central Asia 19.4 17.8 20.5 22.4 24.3 21.9 19.0 16.7 Russian Federation 17.4 15.8 21.0 19.8 20.5 14.0 16.9 16.4 Latin America & Caribbean 30.5 19.5 14.5 18.0 29.4 20.9 18.2 8.6 Brazil 31.8 20.4 16.6 21.8 25.6 22.4 20.9 12.1 Middle East & N. Africa 22.0 16.4 14.4 22.5 37.9 18.5 23.4 13.9 Algeria 32.5 11.2 8.6 19.0 64.1 15.9 16.0 14.3 South Asia 25.8 17.5 23.3 21.5 19.2 23.7 22.5 15.4 India 27.2 16.0 25.2 23.8 19.8 24.7 23.8 18.2 Sub-Saharan Africa 22.6 24.5 20.4 13.8 21.7 16.1 21.8 13.5 South Africa 31.2 23.9 12.3 19.5 29.0 13.8 19.8 12.4 World 16.5 18.6 11.4 16.6 23.5 15.8 16.3 9.1 342 2012 World Development Indicators 6.2 GLOBAL LINKS Direction and growth of merchandise trade About the data De�nitions The table provides estimates of the flow of trade in •  Merchandise trade includes all trade in goods; goods between groups of economies. The data are trade in services is excluded. • High-income econo- from the International Monetary Fund’s (IMF) Direc- mies are those classified as such by the World Bank tion of Trade database. All high-income economies (see front cover flap). • European Union is defined and major developing economies report trade on as all high-income EU members: Austria, Belgium, a timely basis, covering about 85 percent of trade Cyprus, Czech Republic, Denmark, Estonia, Finland, for recent years. Trade by less timely reporters and France, Germany, Greece, Hungary, Ireland, Italy, Lux- by countries that do not report is estimated using embourg, Malta, the Netherlands, Portugal, Slovak reports of trading partner countries and extrapola- Republic, Slovenia, Spain, Sweden, and the United tion. Because the largest exporting and importing Kingdom. • Other high-income economies include countries are reliable reporters, a large portion of all high-income economies (both Organisation for the missing trade flows can be estimated from part- Economic Co-operation and Development members ner reports. Partner country data may introduce dis- and others) except the high-income European Union, crepancies due to confidentiality, different exchange Japan, and the United States. • Low- and middle- rates, overreporting of transit trade, inclusion or income regional groupings are based on World Bank exclusion of freight rates and insurance, and differ- classifications (see back cover flap) and may differ ent points of valuation and times of recording. from those used by other organizations. Most countries report their trade data in national currencies, which are converted into U.S. dollars using the IMF’s published period average exchange rate (series rf or rh, monthly averages of the market or official rates) for the reporting country. Because imports are reported at cost, insurance, and freight (c.i.f.) valuations, and exports at free on board (f.o.b.) valuations, the IMF adjusts country reports of import values by dividing them by 1.10 to estimate equiva- lent export values. The accuracy of this approxima- tion depends on the set of partners and the items traded. Other factors affecting the accuracy of trade data include lags in reporting, recording differences across countries, and whether the country reports trade according to the general or special system of trade. (For further discussion of the measurement of exports and imports, see About the data for tables 4.4 and 4.5.) The regional trade flows in the table are calculated from current price values. The growth rates are in nominal terms; that is, they include the effects of changes in both volumes and prices. Data sources Data on the direction and growth of merchandise trade were calculated using the IMF’s Direction of Trade database. Regional and income group classifications are according to the World Bank classification of economies as of July 1, 2011, and are as shown on the cover flaps of this report. 2012 World Development Indicators 343 6.3 High-income economy trade with low- and middle-income economies High-income economies European Union Japan United States 2000 2010 2000 2010 2000 2010 2000 2010 Exports to low-income economies Total ($ billions) 19.5 53.9 8.8 19.3 2.3 5.3 1.9 7.3 % of total exports Food 11.8 9.8 13.8 12.0 0.5 0.3 27.5 15.1 Agricultural raw materials 2.5 2.1 2.1 2.1 1.3 1.4 7.2 4.5 Ores and nonferrous metals 1.0 0.9 0.6 0.7 0.5 1.1 0.4 0.5 Fuels 5.6 8.7 2.9 10.2 0.4 0.2 1.3 1.7 Manufactured goods 74.1 64.8 77.7 71.0 94.7 95.2 54.6 56.7 Miscellaneous goods 5.1 13.7 2.9 4.0 2.6 1.8 9.0 21.5 Imports from low-income economies Total ($ billions) 19.6 45.9 10.5 23.9 1.1 1.5 5.6 11.7 % of total imports Food 20.9 14.0 24.2 17.5 42.7 26.3 8.0 5.0 Agricultural raw materials 6.1 3.6 8.6 4.9 3.3 2.7 1.4 1.4 Ores and nonferrous metals 5.7 11.5 5.3 11.7 21.6 12.2 2.8 1.7 Fuels 2.9 7.6 0.4 2.5 1.1 0.0 3.3 22.8 Manufactured goods 62.8 58.1 59.4 61.8 30.8 58.5 83.6 67.5 Miscellaneous goods 1.6 5.2 2.2 1.6 0.5 0.2 0.9 1.6 Simple applied tariff rates on imports from low-income economies (%)a Average 4.2 2.5 0.8 0.9 3.1 0.5 5.2 3.3 Food 5.9 4.4 3.2 0.3 10.9 1.2 2.8 1.9 Agricultural raw materials 5.2 1.5 0.0 0.2 0.4 0.1 0.2 0.1 Ores and nonferrous metals 1.2 0.7 0.3 0.2 1.4 0.2 0.0 0.1 Fuels 3.2 1.5 0.0 1.2 0.0 0.0 0.0 0.2 Manufactured goods 4.0 2.3 0.5 1.1 1.9 0.4 5.8 3.8 Miscellaneous goods 0.6 0.8 0.1 0.2 0.0 0.0 0.0 0.1 Exports to middle-income economies Total ($ billions) 737.3 2,225.7 252.8 846.5 107.1 314.4 216.6 436.2 % of total exports Food 6.1 6.3 7.0 6.1 0.3 0.4 7.8 12.0 Agricultural raw materials 1.9 2.2 1.4 1.5 1.0 1.1 2.4 4.1 Ores and nonferrous metals 2.1 5.1 1.8 3.0 1.9 3.4 1.6 4.2 Fuels 3.9 6.9 2.2 3.8 0.5 1.5 3.1 9.6 Manufactured goods 81.5 74.3 83.4 82.0 93.5 89.5 80.3 61.3 Miscellaneous goods 4.3 5.3 4.3 3.5 2.8 4.1 4.9 8.8 Imports from middle-income economies Total ($ billions) 1,231.2 3,446.5 362.3 1,229.0 136.2 318.9 453.1 1,004.2 % of total imports Food 8.0 6.8 10.3 7.9 12.9 8.0 5.4 5.3 Agricultural raw materials 2.1 1.3 3.2 1.7 3.6 2.1 1.0 0.9 Ores and nonferrous metals 4.7 4.2 5.9 4.5 8.8 10.5 2.7 1.9 Fuels 17.0 20.5 24.2 26.4 16.4 18.3 15.5 20.3 Manufactured goods 65.9 64.4 53.1 56.8 56.9 59.5 72.8 69.2 Miscellaneous goods 2.3 2.9 3.3 2.7 1.5 1.6 2.6 2.4 Simple applied tariff rates on imports from middle-income economies (%)a Average 5.1 3.7 2.5 1.6 2.7 2.3 3.4 2.5 Food 8.8 6.0 10.4 3.2 12.9 7.1 3.7 2.9 Agricultural raw materials 2.7 2.1 0.5 0.4 1.1 0.5 0.4 0.4 Ores and nonferrous metals 1.8 1.4 0.9 0.8 0.1 0.1 0.3 0.4 Fuels 3.2 1.7 0.0 0.3 0.8 0.2 0.6 1.3 Manufactured goods 4.8 3.5 1.7 1.5 1.4 1.9 3.6 2.6 Miscellaneous goods 1.9 1.0 0.9 0.5 0.0 0.0 0.5 0.3 a. Includes ad valorem equivalents of specific rates. 344 2012 World Development Indicators 6.3 GLOBAL LINKS High-income economy trade with low- and middle-income economies About the data De�nitions Developing economies are becoming increasingly table may not be fully comparable with those used The product groups in the table are defined in accor- important in the global trading system. Since the to calculate the direction of trade statistics in tables dance with SITC revision 2: food (0, 1, 22, and 4), early 1990s trade between high-income economies 6.2 and 6.4 or the aggregate flows in tables 4.4, agricultural raw materials (2 excluding 22, 27, and and low- and middle-income economies has grown 4.5, and 6.1. Tariff data are from the United Nations 28), ores and nonferrous metals (27, 28, and 68), faster than trade among high-income economies. The Conference on Trade and Development’s (UNCTAD) fuels (3), manufactured goods (5–8 excluding 68), increased trade benefits consumers and producers. Trade Analysis and Information System database. For and miscellaneous goods (9). • Exports are all mer- But as was apparent at the World Trade Organiza- further discussion of merchandise trade statistics, chandise exports by high-income economies to low- tion’s (WTO) Ministerial Conferences in Doha, Qatar, see About the data for tables 4.4, 4.5, 6.1, 6.2, and income and middle-income economies as recorded in October 2001; Cancun, Mexico, in September 6.4, and for information about tariff barriers, see in the United Nations Statistics Division’s Comtrade 2003; Hong Kong SAR, China, in December 2005; table 6.7. database. Exports are recorded free on board and Geneva, Switzerland, in December 2009 and (f.o.b.). •  Imports are all merchandise imports by December 2011, achieving a more pro-development high-income economies from low-income and middle- outcome from trade remains a challenge. Doing so income economies as recorded in the United Nations will require strengthening international consultation. Statistics Division’s Comtrade database. Imports After the Doha meetings, negotiations were launched include insurance and freight charges (c.i.f.). • High-, on services, agriculture, manufactures, WTO rules, middle-, and low-income economies are those the environment, dispute settlement, intellec- classified as such by the World Bank as of July 1, tual property rights protection, and disciplines on 2011 (see front cover flap). •  European Union is regional integration. At the most recent negotiations defined as all high-income EU members: Austria, Bel- in Geneva, Switzerland, trade ministers reaffirmed gium, Cyprus, Czech Republic, Denmark, Estonia, that development is a core element of the WTO’s Finland, France, Germany, Greece, Hungary, Ireland, work and that the WTO needs to assist in further Italy, Luxembourg, Malta, the Netherlands, Poland, integrating developing countries into the multilateral Portugal, Slovak Republic, Slovenia, Spain, Sweden, trading system. and the United Kingdom. Trade flows between high-income and low- and middle-income economies reflect the changing mix of exports to and imports from developing economies. While food and primary commodities have continued to fall as a share of high-income economies’ imports, manufactures as a share of goods imports from both low- and middle-income economies have grown. And trade between developing economies has grown substantially over the past decade, a result of their increasing share of world output and liberalization of trade, among other influences. Yet trade barriers remain high. The table includes information about tariff rates by selected product groups. Applied tariff rates are the tariffs in effect for partners in preferential trade agreements such as the North American Free Trade Agreement. When these rates are unavailable, most favored nation rates are used. The difference between most favored nation and applied rates can be substantial. Simple averages of applied rates are shown because they Data sources are generally a better indicator of tariff protection than weighted average rates are. Data on trade fl ows are from United Nations The data on trade flows are from the United Nations Statistics Division’s Comtrade database. Data Statistics Division’s Commodity Trade (Comtrade) on tariffs are from UNCTAD’s Trade Analysis and database. Partner country reports by high-income Information System database and are calculated economies were used for both exports and imports. by World Bank staff using the World Integrated Because of differences in sources of data, timing, Trade Solution system. and treatment of missing data, the numbers in the 2012 World Development Indicators 345 6.4 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 East Asia & Paci�c 8.9 w 12.4 w 7.3 w 16.6 w 83.3 w 70.3 w 11.7 w 16.3 w 8.5 w 18.5 w 78.1 w 62.8 w Cambodia 7.4 6.1 0.2 2.3 91.6 91.6 39.7 55.4 1.0 2.1 58.5 42.5 China 4.9 7.0 8.9 19.0 86.2 73.7 7.8 9.6 10.4 18.0 78.4 63.4 Fiji 10.9 21.1 2.0 2.2 74.5 48.2 8.3 16.7 2.6 3.2 88.1 78.1 Indonesia 11.5 22.6 7.6 14.3 80.8 63.1 14.1 28.4 7.5 9.7 77.4 61.8 Korea, Dem. Rep. 7.5 50.8 43.9 36.7 48.6 12.5 34.9 62.3 32.9 30.3 32.3 7.4 Lao PDR .. .. 0.5 1.1 34.6 15.4 78.4 87.1 1.3 0.5 18.7 11.0 Malaysia 11.1 24.6 5.9 11.6 83.1 63.8 13.7 28.3 2.8 7.1 81.6 64.0 Mongolia .. .. 9.5 3.4 40.6 14.7 19.1 42.7 37.2 30.3 43.7 27.0 Myanmar 22.6 59.4 10.6 20.2 56.6 14.5 48.0 70.0 2.7 4.6 49.3 25.4 Papua New Guinea 9.0 12.1 0.1 1.5 58.5 48.0 11.2 19.8 1.8 1.1 85.8 77.9 Philippines 9.2 19.3 1.6 2.9 88.8 77.0 11.2 27.6 4.2 4.4 83.9 68.1 Thailand 15.0 29.1 6.2 11.8 78.3 58.5 15.9 26.5 7.2 7.3 74.8 64.8 Vietnam 22.6 22.3 6.1 8.3 70.6 69.4 20.2 38.8 4.6 6.7 74.4 54.5 Europe & Central Asia 24.7 w 20.7 w 9.6 w 14.0 w 63.0 w 56.6 w 31.3 w 25.6 w 11.8 w 14.7 w 57.9 w 52.5 w Albania 3.9 10.7 0.0 7.4 96.0 81.9 14.0 16.1 2.7 10.6 83.3 73.3 Armenia 25.0 38.7 10.5 13.1 54.2 47.1 25.2 43.0 13.6 22.6 55.8 34.3 Azerbaijan 21.0 13.2 2.3 13.9 76.2 73.0 44.4 44.6 11.4 16.4 44.1 39.0 Belarus 72.2 60.3 7.5 11.9 20.0 26.8 71.8 61.0 3.2 11.1 23.8 24.4 Bosnia and Herzegovina 6.6 6.5 .. .. 90.3 90.3 5.4 12.6 .. .. 94.4 86.4 Bulgaria 29.0 29.9 5.3 7.1 56.6 61.2 36.1 36.4 6.0 7.3 56.0 55.7 Cyprus .. .. 35.1 21.1 52.9 66.9 .. .. 20.3 14.9 79.7 83.3 Georgia 64.7 61.3 5.9 6.5 29.2 32.2 52.6 54.1 3.7 11.9 42.0 34.0 Kazakhstan 25.2 18.8 11.3 22.8 42.3 47.1 57.6 33.2 7.0 36.3 35.2 30.5 Kyrgyz Republic 45.3 80.9 .. .. 43.1 7.0 59.1 29.2 10.1 63.5 30.8 7.2 Lithuania 33.2 37.6 1.2 1.6 65.5 60.8 34.1 43.2 3.9 4.4 60.5 52.4 Macedonia, FYR 31.7 27.7 1.0 3.2 67.1 55.9 36.5 34.1 4.1 11.5 59.4 54.0 Moldova 69.4 64.3 1.8 2.7 28.8 32.2 54.0 50.1 1.5 12.3 44.4 37.5 Romania 14.3 18.3 7.6 6.1 77.6 75.5 16.4 16.6 5.5 8.9 74.4 74.4 Russian Federation 21.6 14.1 10.1 12.0 68.1 60.3 36.8 13.3 10.5 26.6 52.5 58.1 Serbia .. 32.5 .. 2.3 .. 56.5 .. 21.2 .. 5.3 .. 61.0 Tajikistan 56.2 46.9 .. .. 41.0 4.7 91.2 65.3 .. .. 5.8 14.5 Turkey 9.2 14.5 9.8 24.5 75.4 57.9 12.8 19.7 13.7 25.4 70.4 53.9 Turkmenistan 60.5 26.9 .. .. 26.2 24.5 54.3 51.0 .. .. 32.2 32.8 Ukraine 42.6 47.2 18.5 22.2 37.8 29.7 60.8 48.7 4.7 13.6 34.2 36.7 Uzbekistan 57.0 56.5 .. .. 36.4 10.2 45.3 40.2 .. .. 51.2 42.6 Latin America & Carib. 15.4 w 18.9 w 3.8 w 15.6 w 77.6 w 63.0 w 15.2 w 19.4 w 3.4 w 14.8 w 75.0 w 58.4 w Argentina 48.1 41.9 14.5 26.0 35.6 30.3 34.3 39.9 9.9 19.3 53.5 36.5 Bolivia 44.5 64.4 1.0 3.7 53.4 31.5 51.2 73.3 4.5 4.4 44.1 22.2 Brazil 22.9 22.7 8.6 29.8 57.3 45.5 20.8 16.6 12.5 28.5 66.4 54.5 Chile 21.1 18.2 8.6 30.4 65.3 51.3 32.2 29.3 10.9 22.1 46.4 47.9 Colombia 27.2 25.0 1.2 8.3 70.7 66.1 28.0 27.4 3.9 18.4 66.8 51.0 Costa Rica 17.9 28.5 2.7 6.3 79.4 65.2 22.3 22.8 3.9 10.1 38.9 65.5 Cuba 8.8 21.6 31.9 35.2 59.4 43.2 40.3 47.4 12.7 21.8 47.0 30.8 Dominican Republic 3.9 24.9 0.7 3.0 95.3 65.3 20.7 25.2 3.0 8.5 76.2 63.8 Ecuador 31.4 39.8 3.0 7.4 62.1 52.4 41.4 37.6 3.7 11.9 51.5 49.6 El Salvador 27.7 42.8 0.9 1.1 70.8 56.1 30.8 40.3 2.0 7.1 64.2 52.6 Guatemala 40.4 43.3 2.7 3.7 56.4 52.7 36.2 33.8 2.6 10.7 60.2 54.5 Haiti 4.4 5.1 0.8 3.3 94.7 91.3 21.8 38.7 4.5 10.8 72.1 50.4 Honduras 24.3 29.5 0.8 3.4 69.7 67.1 31.9 40.6 3.5 6.6 62.8 52.8 Jamaica 3.5 10.1 2.5 6.8 93.5 81.8 15.3 24.8 3.4 7.4 78.3 65.3 Mexico 3.1 6.9 0.3 2.1 96.1 90.2 2.5 4.2 4.3 20.2 91.1 74.2 Nicaragua 30.5 20.3 0.0 1.4 64.0 77.9 45.8 52.7 0.0 9.5 42.6 37.5 Panama 22.3 20.3 0.4 7.5 70.3 70.0 30.6 21.3 1.4 7.5 55.2 42.9 Paraguay 74.5 67.8 1.9 13.7 20.7 17.3 49.5 46.1 12.5 37.2 38.0 16.5 Peru 18.0 18.4 11.6 21.9 68.8 59.7 38.0 32.3 8.1 21.1 53.1 46.5 Uruguay 53.9 43.5 9.7 24.6 35.4 29.0 51.7 48.1 12.3 23.7 35.6 28.0 Venezuela, RB 19.7 12.0 0.5 16.0 74.2 52.7 21.1 34.8 2.4 11.5 57.1 52.2 346 2012 World Development Indicators 6.4 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 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Middle East & N. Africa 3.9 w 7.4 w 15.6 w 27.0 w 76.0 w 60.4 w 5.1 w 7.9 w 18.2 w 26.5 w 71.6 w 61.3 w Algeria 1.4 3.2 15.3 14.7 83.4 82.1 1.4 2.9 16.7 28.2 81.8 68.2 Bahrain .. .. 13.6 11.4 24.4 17.1 .. .. 12.8 26.4 86.7 73.0 Egypt, Arab Rep. 6.9 19.2 9.8 24.0 73.5 53.1 1.1 3.2 19.6 35.2 72.7 61.2 Iran, Islamic Rep. 1.4 1.3 22.8 42.3 63.3 40.9 0.9 0.5 30.1 24.7 68.3 73.2 Iraq 7.3 3.7 7.4 31.3 85.3 65.0 10.1 24.1 38.5 48.2 51.4 27.7 Jordan 22.8 30.1 35.9 25.6 38.9 42.9 18.0 10.0 18.1 28.0 61.0 61.9 Lebanon 17.8 39.6 13.0 14.4 68.2 45.1 7.9 15.5 20.1 26.6 70.6 56.0 Libya 3.5 3.3 7.5 13.7 89.1 83.0 12.1 13.4 10.0 29.5 77.7 57.0 Morocco 2.8 3.8 10.9 22.4 83.1 70.2 10.2 7.1 13.0 23.5 75.2 69.2 Syrian Arab Republic 8.7 49.5 12.5 6.6 75.9 43.9 3.6 18.4 23.6 35.7 54.5 45.9 Tunisia 7.5 11.3 5.6 9.5 83.2 79.2 6.6 7.4 9.4 16.6 82.4 74.7 Yemen, Rep. 0.5 2.2 56.3 70.2 40.4 26.8 4.4 4.0 22.9 39.6 70.1 55.4 South Asia 4.6 w 5.8 w 16.0 w 28.4 w 75.3 w 63.9 w 4.3 w 4.0 w 12.8 w 17.0 w 58.2 w 56.9 w Afghanistan 42.4 52.6 18.2 16.7 39.4 30.6 27.3 28.4 35.5 26.9 37.2 44.7 Bangladesh 1.7 2.9 4.1 9.6 79.7 83.5 11.8 15.8 18.3 37.0 53.1 40.4 India 4.3 5.0 18.8 30.4 73.4 63.2 0.9 0.6 20.0 39.2 55.6 59.7 Nepal 42.9 65.9 .. .. 55.3 27.8 37.4 57.5 .. .. 40.5 15.1 Pakistan 4.6 12.4 14.3 27.2 80.6 58.5 2.7 6.3 22.3 36.0 74.7 57.0 Sri Lanka 3.5 7.4 10.5 16.4 79.9 65.5 10.6 25.7 16.7 32.3 64.2 39.8 Sub-Saharan Africa 10.5 w 12.4 w 14.7 w 32.2 w 63.2 w 53.5 w 12.0 w 13.0 w 18.2 w 31.0 w 70.0 w 50.0 w Angola 0.2 3.7 25.0 54.1 74.7 42.1 19.8 7.3 13.8 31.8 66.2 60.7 Benin 10.7 29.9 65.9 57.1 23.4 13.0 22.2 7.7 15.8 57.5 61.5 34.8 Burkina Faso 22.0 13.7 0.0 43.0 76.5 39.2 31.0 43.4 11.7 10.8 52.6 37.3 Burundi 17.0 12.0 0.0 20.4 61.9 51.3 23.8 28.6 8.1 17.4 46.7 44.4 Cameroon 7.0 13.6 9.4 20.5 77.9 62.8 27.3 21.2 8.8 29.5 54.7 48.7 Central African Republic 1.5 10.2 7.9 40.7 90.7 49.1 12.2 12.3 7.0 11.2 57.0 55.3 Chad 6.0 0.4 .. .. 82.3 82.2 13.7 19.2 .. .. 81.7 45.4 Comoros .. .. .. .. 96.2 59.3 .. .. .. .. 52.4 46.4 Congo, Dem. Rep. 2.2 25.7 0.4 48.3 97.2 25.8 43.2 50.3 6.8 18.8 49.4 30.8 Congo, Rep. 3.0 1.1 17.4 38.7 79.5 60.0 13.2 6.3 11.2 31.4 68.5 60.4 Côte d’Ivoire 27.5 30.6 10.4 13.0 50.2 51.6 28.1 29.1 13.1 28.0 46.2 34.7 Ethiopia 0.6 5.3 18.3 26.0 81.1 55.2 1.8 2.8 39.6 28.6 48.9 35.1 Gabon 1.1 2.4 9.3 28.1 83.7 55.8 4.8 10.9 3.4 18.8 90.6 68.3 Gambia, The 18.0 7.0 10.7 48.4 71.3 44.7 16.0 16.9 37.2 56.1 46.8 27.0 Ghana 5.3 10.7 11.6 24.4 74.8 52.8 22.1 24.9 15.7 33.5 61.3 40.7 Guinea 6.4 2.3 13.0 46.9 80.6 35.1 24.4 6.4 13.8 21.1 61.5 26.2 Guinea-Bissau 1.2 30.5 .. .. 6.9 4.0 13.6 22.1 .. .. 48.5 38.8 Kenya 40.0 37.4 18.7 15.6 39.6 38.2 8.4 12.3 16.0 38.4 75.0 48.3 Liberia 1.2 32.2 14.6 9.4 84.2 58.4 1.3 0.9 5.7 29.6 93.0 69.6 Madagascar 3.0 4.9 3.5 13.1 88.4 72.1 6.2 12.9 33.0 27.3 52.6 48.2 Malawi 15.6 22.3 14.7 28.1 69.2 49.1 71.8 61.8 6.9 16.0 20.0 21.5 Mali 6.4 6.6 40.5 41.5 50.7 43.9 26.0 30.9 7.5 12.5 32.7 28.1 Mauritania 20.4 14.4 8.3 44.4 70.2 40.0 4.4 4.7 21.0 33.5 65.8 51.9 Mauritius 7.6 14.6 0.9 4.6 91.5 80.8 18.3 11.5 26.8 48.6 54.9 39.8 Mozambique 36.0 25.9 7.0 8.3 36.7 64.0 51.8 38.1 7.4 15.9 28.6 39.6 Niger 48.5 77.6 0.5 5.6 51.0 16.7 13.2 21.1 5.8 29.8 80.5 49.1 Nigeria 7.0 9.6 22.3 25.1 70.4 64.3 4.1 4.9 20.1 28.5 75.2 49.6 Rwanda 7.3 58.4 16.1 21.2 42.6 19.7 33.9 50.1 5.4 12.7 37.6 36.1 Senegal 27.7 50.2 15.5 14.9 48.7 26.9 19.2 15.2 17.4 32.8 58.8 51.7 Sierra Leone 4.2 8.0 2.4 25.8 91.2 63.1 6.4 24.8 8.8 34.9 81.3 35.5 Somalia 4.0 4.1 21.2 23.9 74.9 71.9 13.4 8.8 50.9 64.8 24.7 13.7 South Africa 12.9 14.9 7.2 28.9 52.9 56.1 2.0 7.9 15.5 32.3 82.1 59.8 Sudan 0.2 1.6 50.6 78.6 49.2 19.7 3.5 7.4 36.6 49.6 59.9 39.2 Tanzania 18.2 19.6 17.8 33.3 63.1 38.4 20.2 16.6 21.1 42.1 58.6 37.1 Togo 40.4 63.2 30.2 25.2 25.1 11.2 22.1 13.6 12.1 34.1 62.6 50.9 Uganda 30.9 50.9 2.2 6.8 63.8 39.3 39.9 27.1 15.6 23.3 44.4 49.5 Zambia 41.8 19.1 1.8 21.5 49.8 59.4 69.0 63.6 3.7 9.6 26.4 26.8 Zimbabwe 17.4 47.3 8.9 23.1 29.4 29.4 49.6 69.2 6.3 13.0 33.0 13.6 Note: Bilateral trade data are not available for Timor-Leste, Kosovo, West Bank and Gaza, Botswana, Eritrea, Lesotho, Namibia, South Sudan, and Swaziland. Components may not sum to 100 percent because of trade with unspecified partners or with economies not covered by World Bank classification. 2012 World Development Indicators 347 6.4 Direction of trade of developing economies About the data De�nitions Developing economies are an increasingly impor- 2010. This is due partly to trade-related advantages, •  Exports to developing economies within region tant part of the global trading system. From 2009 to such as proximity, lower transport costs, increased are the sum of merchandise exports from the report- 2010 the volume of merchandise exports increased knowledge from repeated interaction, and cultural ing economy to other developing economies in the 14.5 percent globally and 12.9 percent in develop- and historical affinity. The direction of trade is also same World Bank region as a percentage of total ing countries; the volume of merchandise imports influenced by preferential trade agreements that a merchandise exports by the economy. • Exports to increased 13.5  percent globally and 10.7  percent country has made with other economies. Though developing economies outside region are the sum in developing countries. Trade between high-income formal agreements on trade liberalization do not of merchandise exports from the reporting econ- economies and low- and middle-income economies automatically increase trade, they nevertheless omy to other developing economies in other World has grown faster than trade between high-income affect the direction of trade between the participat- Bank regions as a percentage of total merchandise economies. This increased trade benefi ts both ing economies. Table 6.6 illustrates the size of exist- exports by the economy. • Exports to high-income producers and consumers in developing and high- ing regional trade blocs that have formal preferential economies are the sum of merchandise exports from income economies. trade agreements. the reporting economy to high-income economies as The table shows trade in goods between develop- Although global integration has increased, develop- a percentage of total merchandise exports by the ing economies in the same region and other regions ing economies still face trade barriers when access- economy. •  Imports from developing economies and between developing economies and high-income ing other markets (see table 6.7). within region are the sum of merchandise imports economies. Data on exports and imports are from by the reporting economy from other developing the International Monetary Fund’s (IMF) Direction of economies in the same World Bank region as a per- Trade database and should be broadly consistent centage of total merchandise imports by the econ- with data from other sources, such as the United omy. • Imports from developing economies outside Nations Statistics Division’s Commodity Trade (Com- region are the sum of merchandise imports by the trade) database. All high-income economies and reporting economy from other developing economies major developing economies report trade data to the in other World Bank regions as a percentage of total IMF on a timely basis, covering about 85 percent of merchandise imports by the economy. •  Imports trade for recent years. Trade data for less timely from high-income economies are the sum of mer- reporters and for countries that do not report are chandise imports by the reporting economy from estimated using reports of trading partner countries. high-income economies as a percentage of total Therefore, data on trade between developing and merchandise imports by the economy. high-income economies shown in the table should be generally complete. But trade fl ows between many developing economies—particularly those in Sub-Saharan Africa—are not well recorded, and the value of trade among developing economies may be understated. The table does not include some devel- oping economies because data on their bilateral trade flows are not available. Data on the direction of trade between selected high-income economies are presented and discussed in tables 6.2 and 6.3. At the regional level most exports from developing economies are to high-income economies, but the share of intraregional trade is increasing. Geographic patterns of trade vary widely by country and commod- ity. Larger shares of exports from oil- and resource- Data sources rich economies are to high-income economies. The relative importance of intraregional trade Data on merchandise trade flows are published in is higher for both landlocked countries and small the IMF’s Direction of Trade Statistics Yearbook and countries with close trade links to the largest Direction of Trade Statistics Quarterly; the data in regional economy. For most developing economies— the table were calculated using the IMF’s Direction especially smaller ones—there is a “geographic of Trade database. Regional and income group bias� favoring intraregional trade. Despite the broad classifications are according to the World Bank trend toward globalization and the reduction of trade classification of economies as of July 1, 2011, barriers, the relative share of intraregional trade and are as shown on the cover flaps of this report. increased for most economies between 1999 and 348 2012 World Development Indicators 6.5 GLOBAL LINKS Primary commodity prices 1970 1980 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 World Bank commodity price index (2005 = 100) Energy 11 87 45 32 60 100 115 120 156 105 128 153 Nonenergy commodities 138 134 87 92 81 100 122 139 156 130 154 171 Agriculture 155 157 94 104 88 100 110 124 146 136 151 170 Beverages 182 207 94 113 86 100 105 114 129 144 161 169 Food 165 163 94 100 86 100 108 128 159 142 150 171 Fats and oils 190 158 85 105 86 100 102 145 178 151 163 181 Grains 176 166 103 110 89 100 116 139 190 155 152 194 Other food 124 168 97 83 83 100 110 96 106 120 131 136 Raw materials 115 116 93 109 95 100 115 119 122 118 147 168 Timber 94 89 85 107 102 100 112 115 117 116 116 125 Other raw materials 138 144 102 111 87 100 119 123 128 121 182 215 Fertilizers 61 117 68 75 75 100 102 137 341 186 166 217 Metals and minerals 112 89 75 69 67 100 151 171 154 110 159 167 Base metals 125 97 81 76 71 100 162 171 142 109 150 157 Steel productsa .. 77 76 71 61 100 95 90 133 112 110 117 Commodity prices (2005 prices) Energy Coal, Australian ($/mt) 29 53 41 37 29 48 48 61 109 66 88 98 Natural gas, Europe ($/mmBtu) 2 6 3 3 4 6 8 8 11 8 7 9 Natural gas, U.S. ($/mmBtu) 1 2 2 2 5 9 7 6 8 4 4 3 Natural gas, liquefied, Japan ($/mmBtu) .. 7 4 3 5 6 7 7 11 8 10 12 Petroleum, avg, spot ($/bbl) 4 48 24 16 32 53 63 66 83 56 70 85 Beverages (cents/kg) Cocoa 249 342 131 133 101 154 156 180 220 264 277 242 Coffee, Arabica 423 455 204 309 215 253 247 251 263 290 383 486 Coffee, robusta 337 426 122 257 102 111 146 176 198 150 154 196 Tea, avg., 3 auctions 308 218 213 138 210 165 183 188 207 249 255 238 Tea, Colombo auctions 231 146 194 132 201 184 187 232 238 287 291 266 Tea, Kolkata auctions 365 269 290 162 202 162 172 177 193 230 248 226 Tea, Mombasa auctions 327 238 154 121 227 148 191 153 189 230 227 221 Food Fats and oils ($/mt) Coconut oil 1,464 884 348 621 504 617 594 846 1,046 664 995 1,407 Copraa 829 594 239 407 341 414 394 559 697 439 664 941 Groundnut oil 1,395 1,127 997 920 799 1,060 950 1,245 1,821 1,083 1,243 1,615 Palm oil 959 766 300 583 347 422 468 719 810 625 798 915 Palm kernel oila .. .. .. .. 496 627 569 818 965 640 1,049 1,341 Soybeans 431 389 255 241 237 275 263 354 447 400 398 440 Soybean meal 378 344 207 183 212 214 205 284 363 373 335 324 Soybean oil 1,056 784 463 580 378 545 586 812 1,075 776 890 1,057 Grains ($/mt) Barley .. 103 83 97 86 95 114 159 171 117 140 169 Maize 215 164 113 115 99 99 119 151 191 151 165 237 Rice, Thailand, 5% 466 539 280 298 227 286 298 301 555 508 433 442 Rice, Thailand, 25% a .. .. 270 276 193 265 271 282 289 419 391 412 Rice, Thailand, A1a .. .. 209 269 187 218 215 251 412 299 340 373 Rice, Vietnam, 5% a .. .. .. .. .. 258 255 284 485 .. 380 418 Sorghuma 191 169 107 110 99 96 120 150 178 138 146 219 Wheat, Canadaa 231 250 162 192 165 198 212 277 388 275 277 358 Wheat, U.S., soft red winter a 210 221 133 155 111 136 156 220 232 170 203 233 Wheat, U.S., hard red winter 202 227 140 164 128 152 188 235 279 205 198 257 2012 World Development Indicators 349 6.5 Primary commodity prices 1970 1980 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 Commodity prices (continued) (2005 prices) Food (continued) Other food Bananas, U.S. ($/mt) 612 495 559 413 475 603 663 622 721 775 769 787 Beef (cents/kg) 481 362 265 177 216 262 249 240 268 241 297 329 Chicken meat (cents/kg) .. 99 112 114 147 163 149 159 159 173 168 157 Fishmeal ($/mt)a 726 662 426 459 462 731 1,142 1,084 968 1,125 1,494 1,251 Oranges ($/mt) 619 513 549 493 407 875 812 881 946 832 915 725 Shrimp, Mexico (cents/kg)a .. 1,511 1,106 1,401 1,696 1,034 1,002 930 913 865 890 971 Sugar, EU domestic (cents/kg) 41 64 60 64 62 67 63 63 60 48 39 37 Sugar, U.S. domestic (cents/kg) 61 87 53 47 48 47 48 42 40 50 70 68 Sugar, world (cents/kg) 30 83 29 27 20 22 32 20 24 37 42 47 Agricultural raw materials Cotton A Index (cents/kg) 249 271 188 197 146 122 124 129 134 126 202 271 Cotton, Memphis (cents/kg) .. .. 191 200 164 130 131 132 138 133 207 59 Logs, Cameroon ($/cu. m)a 159 330 355 315 308 334 312 351 450 386 380 394 Logs, Malaysian ($/cu. m) 159 257 183 237 213 203 234 247 250 263 246 318 Rubber, Singapore (cents/kg) 150 187 89 147 75 149 203 208 221 176 324 392 Rubber, TSR 20 (cents/kg)a 0 0 0 0 70 139 190 199 216 165 299 368 Rubber, US (cents/kg)a 170 213 106 168 93 166 226 228 243 196 342 421 Plywood (cents/sheet)a 380 359 367 542 502 509 583 590 551 517 504 494 Sawnwood, Malaysian ($/cu. m) 647 520 551 687 666 659 734 743 760 737 751 764 Sawnwood, Cameroon ($/cu. m)a .. .. .. 553 547 559 610 700 819 685 720 672 Tobacco ($/mt)a 3,967 2,986 3,508 2,453 3,332 2,790 2,906 3,054 3,066 3,880 3,812 3,641 Woodpulp ($/mt)a 654 704 842 792 744 635 684 706 701 562 768 732 Fertilizers ($/mt) Diammonium phosphate 199 292 177 201 173 247 255 398 826 296 443 503 Phosphate rock 41 61 42 32 49 42 43 65 295 111 109 150 Potassium chloride 116 152 101 109 137 158 171 184 487 577 294 354 Triple superphosphate 157 237 136 139 154 201 197 312 751 235 338 438 Urea 67 252 123 174 113 219 218 285 421 228 256 342 Metals and minerals Aluminum ($/mt) 2,050 1,910 1,695 1,676 1,734 1,898 2,516 2,430 2,198 1,523 1,924 1,953 Copper ($/mt) 5,219 2,863 2,752 2,724 2,030 3,679 6,580 6,557 5,943 4,711 6,672 7,181 Gold ($/toz)a 132 798 397 357 312 445 592 642 745 890 1,084 1,276 Iron ore, spot, cfr China ($/dmt)a .. .. .. .. .. .. 68 113 133 73 129 136 Lead (cents/kg) 112 119 84 59 51 98 126 238 179 157 190 195 Nickel ($/mt) 10,492 8,553 9,167 7,636 9,669 14,744 23,742 34,293 18,035 13,406 19,313 18,637 Silver (cents/toz)a 653 2,708 498 482 560 734 1,132 1,235 1,281 1,344 1,789 2,868 Tin (cents/kg) 1,354 2,201 629 577 608 738 860 1,339 1,581 1,242 1,807 2,119 Zinc (cents/kg) 109 100 157 96 126 138 321 299 160 151 191 178 MUV G-15 index (2005 = 100) 27 76 97 108 89 100 102 109 117 109 113 123 MUV G-5 index (2005 = 100) 26 74 93 109 91 100 102 106 114 107 110 115 Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal units, mt = metric ton, toz = troy ounce. a. Series not included in the nonenergy index. 350 2012 World Development Indicators 6.5 GLOBAL LINKS Primary commodity prices About the data De�nitions Primary commodities—raw or partially processed economies (France, Germany, Japan, the United • Energy price index is the composite price index for materials that will be transformed into fi nished Kingdom, and the United States) and is included in coal, petroleum, and natural gas, weighted by exports goods—are often developing countries’ most impor- the table for comparison purposes. of each commodity from low- and middle-income tant exports, and commodity revenues can affect liv- countries. • Nonenergy commodity price index cov- ing standards. Price data are collected from various ers the 38 nonenergy primary commodities that sources, including international commodity study make up the agriculture, fertilizer, and metals and groups, government agencies, industry trade jour- minerals indexes. • Agriculture includes beverages, nals, and Bloomberg and Datastream. Prices are food, and agricultural raw materials. •  Beverages compiled in U.S. dollars or converted to U.S. dollars include cocoa, coffee, and tea. • Food includes fats when quoted in local currencies. and oils, grains, and other food items. Fats and oils The table is based on frequently updated price include coconut oil, copra, groundnut oil, palm oil, reports. Prices are those received by exporters when palm kernel oil, soybeans, soybean meal, and soy- available, or the prices paid by importers or trade bean oil. Grains include barley, maize, rice, sorghum, unit values. Annual price series are generally simple and wheat. Other food items include bananas, beef, averages based on higher frequency data. The con- chicken meat, fishmeal, oranges, shrimp, and sugar. stant price series in the table are deflated by the •  Agricultural raw materials include timber and manufactures unit value (MUV) index for the Group other raw materials. Timber includes tropical hard of Fifteen (G-15) countries (see below). logs and sawnwood. Other raw materials include cot- Commodity price indexes are calculated as ton, natural rubber, and tobacco. • Fertilizers include Laspeyres index numbers; the fixed weights are the phosphate, phosphate rock, potassium, and nitrog- 2002–04 average export values for low- and middle- enous products. • Metals and minerals include base income economies (based on 2001 gross national metals (aluminum, copper, lead, nickel, tin, and zinc) income) rebased to 2005. Data for exports are from and iron ore. •  Steel products price index is the the United Nations Statistics Division’s Commod- composite price index for eight steel products based ity Trade Statistics (Comtrade) database Standard on quotations free on board (f.o.b.) Japan excluding International Trade Classification (SITC) revision 3, shipments to the United States for all years and to the Food and Agriculture Organization’s FAOSTAT China prior to 2002, weighted by product shares of database, the International Energy Agency data- apparent combined consumption (volume of deliv- base, BP’s Statistical Review of World Energy, the eries) for Germany, Japan, and the United States. World Bureau of Metal Statistics, and World Bank • Commodity prices—for definitions and sources, staff estimates. see “Commodity price data� (also known as the “Pink Each index in the table represents a fixed basket of Sheet�) at the World Bank Prospects for Develop- primary commodity exports over time. The nonenergy ment website (www.worldbank.org/prospects, click commodity price index contains 39 price series for on Products). • MUV G-15 index is the manufactures 38 nonenergy commodities. Separate indexes are unit value index for G-15 country exports to low- and compiled for energy and steel products, which are middle-income economies. not included in the nonenergy commodity price index. The MUV G-15 index is a composite index of prices for manufactured exports from the 15 major (G-15) developed and emerging economies (Brazil, Canada, China, France, Germany, India, Italy, Japan, the Republic of Korea, Mexico, South Africa, Spain, Thailand, the United Kingdom, and the United States) to low- and middle-income economies, valued in U.S. dollars. For the MUV G-15 index, unit value indexes in local currency for each country are converted to Data sources U.S. dollars using market exchange rates and are combined using weights determined by the share of Data on commodity prices and the MUV G-15 index each country’s exports to low-  and middle-income are compiled by the World Bank’s Development countries in the base year (2005). The MUV G-5 Prospects Group. Monthly updates of commodity index is a composite index of prices for manufac- prices are available at http://data.worldbank.org. tured exports from the fi ve major (G-5) industrial 2012 World Development Indicators 351 6.6 Regional trade blocs Year of Year of entry Type of most Merchandise exports Merchandise creation into force of the recent within bloc exports by bloc most recent agreementa agreement % of total bloc $ millions exports % of world exports 2010 2010 2010 High-income and low- and middle-income economies APECb 1989 None 4,868,838 67.5 47.3 EEA 1994 1994 EIA 3,519,827 68.7 33.6 EFTA 1960 2002 EIA 2,096 0.6 2.2 European Union 1957 1958 EIA, CU 3,356,310 67.3 32.7 NAFTA 1994 1994 FTA 955,598 48.7 12.9 SPARTECA 1981 1981 PTA 20,717 8.1 1.7 Trans-Pacific SEP 2006 2006 EIA, FTA 3,969 0.9 3.0 East Asia and Paci�c and South Asia APTA 1975 1976 PTA 278,451 12.1 15.1 ASEAN 1967 1992 FTA 262,270 25.0 6.9 MSG 1993 1994 PTA 99 0.8 0.1 PICTA 2001 2003 FTA 308 2.6 0.1 SAARC 1985 2006 FTA 15,702 5.8 1.8 Europe, Central Asia, and Middle East Agadir agreement 2004 NNA 2,068 3.2 0.4 CEFTA 1992 1994 FTA 5,229 17.5 0.2 CEZ 2003 2004 FTA 18,065 4.0 2.9 CIS 1991 1994 FTA 68,596 12.9 3.5 Customs Union of Belarus, Kazakhstan, and Russian Federation 2010 CU 18,065 4.0 2.9 EAEC 1997 2000 CU 21,200 4.7 2.9 ECO 1985 2003 PTA 27,654 8.8 2.1 GCC 1981 2003c CU 28,623 4.8 3.9 PAFTA 1997 1998 FTA 81,816 9.7 5.5 UMA 1989 1994 c NNA 3,926 2.9 0.9 Latin America and the Caribbean Andean Community 1969 1988 CU 7,825 8.5 0.6 CACM 1961 1961 CU 6,330 22.5 0.2 CARICOM 1973 1997 EIA 3,356 15.2 0.1 LAIA 1980 1981 PTA 128,829 15.9 5.3 MERCOSUR 1991 2005 EIA 44,239 15.7 1.9 OECS 1981 1981c NNA 132 17.5 0.0 Sub-Saharan Africa CEMAC 1994 1999 CU 383 1.2 0.2 CEPGL 1976 NNA 81 1.5 0.0 COMESA 1994 1994 CU 8,158 7.7 0.7 EAC 1996 2000 CU 1,997 20.3 0.1 ECCAS 1983 2004 c NNA 483 0.6 0.6 ECOWAS 1975 1993 PTA 8,911 8.8 0.7 Indian Ocean Commission 1984 2005c NNA 184 5.3 0.0 SADC 1992 2000 FTA 14,576 9.8 1.0 UEMOA 1994 2000 CU 2,250 14.6 0.1 Note: APEC is Asia Pacific Economic Cooperation, EEA is European Economic Area, EFTA is European Free Trade Association, NAFTA is North American Free Trade Agreement, SPARTECA is South Pacific Regional Trade and Economic Cooperation Agreement, Trans-Pacific SEP is Trans-Pacific Strategic Economic Partnership, APTA is Asia-Pacific Trade Agreement, ASEAN is Association of South East Asian Nations, MSG is Melanesian Spearhead Group, PICTA is Pacific Island Countries Trade Agreement, SAARC is South Asian Association for Regional Cooperation, CEFTA is Central European Free Trade Area, CEZ is Common Economic Zone, CIS is Commonwealth of Independent States, EAEC is Eurasian Economic Community, ECO is Economic Cooperation Organization, GCC is Gulf Cooperation Council, PAFTA is Pan-Arab Free Trade Area, UMA is Arab Maghreb Union, CACM is Central American Common Market, CARICOM is Caribbean Community and Common Market, LAIA is Latin American Integration Association, MERCOSUR is Southern Common Market, OECS is Organization of Eastern Caribbean States, CEMAC is Economic and Monetary Community of Central Africa, CEPGL is Economic Community of the Great Lakes Countries, COMESA is Common Market for Eastern and Southern Africa, EAC is East African Community, ECCAS is Economic Community of Central African States, ECOWAS is Economic Community of West African States, SADC is Southern African Development Community, UEMOA is West African Economic and Monetary Union. For regional bloc memberships, see the World Trade Organization’s Regional Trade Agreements Information System (http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx). 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. Collected from the official website of the trade bloc. 352 2012 World Development Indicators 6.6 GLOBAL LINKS Regional trade blocs About the data De�nitions Trade blocs are groups of countries that have estab- important regional trade blocs and the size of intrare- • Type of most recent agreement includes customs lished preferential arrangements governing trade gional trade relative to each bloc’s exports of goods union, under which members substantially eliminate between members. Although in some cases the pref- and the share of the bloc’s exports in world exports. all tariff and nontariff barriers among themselves erences—such as lower tariff duties or exemptions Although the Asia Pacific Economic Cooperation has and establish a common external tariff for nonmem- from quantitative restrictions—may be no greater no preferential arrangements, it is included because bers; economic integration agreement, which liberal- than those available to other trading partners, such of the volume of trade between its members. izes trade in services among members and covers a arrangements are intended to encourage exports by The data on country exports are from the Interna- substantial number of sectors, affects a sufficient bloc members to one another—sometimes called tional Monetary Fund’s (IMF) Direction of Trade data- volume of trade, includes substantial modes of intraregional trade. Most countries are members base and should be broadly consistent with those supply, and is nondiscriminatory (in the sense that of a regional trade bloc, and the surge of countries from sources such as the United Nations Statistics similarly situated service suppliers are treated the participating in such arrangements has continued Division’s Commodity Trade Statistics (Comtrade) data- same); free trade agreement, under which members unabated since the early 1990s. While trade blocs base. All high-income economies and major developing substantially eliminate all tariff and nontariff barriers vary in structure, they all have the same objective: to economies report trade to the IMF on a timely basis, but set tariffs on imports from nonmembers; pref- reduce trade barriers among member countries. But covering about 85 percent of trade for recent years. erential trade agreement, which is an agreement effective integration requires more than reducing tar- Trade by less timely reporters and by countries that do notified to the WTO that is not a free trade agree- iffs and quotas. Economic gains from competition and not report is estimated using reports of trading partner ment, a customs union, or an economic integration scale may not be achieved unless other barriers that countries. Therefore, data on trade between developing agreement; and not notified agreement, which is a divide markets and impede the free flow of goods, ser- and high-income economies shown in the table should preferential trade arrangement established among vices, and investments are lifted. For example, many be generally complete. However, trade flows between member countries that is not notified to the World regional trade blocs retain contingent protections on many developing countries—particularly those in Sub- Trade Organization (the agreement may be func- intrabloc trade, including antidumping, countervailing Saharan Africa—are not well recorded, and the value of tionally equivalent to any of the other agreements). duties, and “emergency protection� to address bal- trade among developing countries may be understated. • Merchandise exports within bloc are the sum of ance of payments problems or protect an industry from Membership in the trade blocs shown is based merchandise exports by members of a trade bloc to import surges. Other barriers include differing product on the most recent information available (see Data other members of the bloc. They are shown both in standards, discrimination in public procurement, and sources). Other types of preferential trade agree- U.S. dollars and as a percentage of total merchan- cumbersome border formalities. In addition, becoming ments may have entered into force earlier than dise exports by the bloc. • Merchandise exports by a member of a trade bloc can result in trade diversion, those shown in the table and may still be effective. bloc as a share of world exports are the bloc’s total where a member switches from being a relatively effi - Unless otherwise footnoted, information on the type merchandise exports (within the bloc and to the rest cient, low-cost producer outside a trade bloc to a less of agreement and date of enforcement are based on of the world) as a share of total merchandise exports efficient, higher cost producer within a trade bloc. On a the World Trade Organization’s (WTO) list of regional by all economies in the world. global scale this could lead to misallocated resources. trade agreements. Information on trade agreements Membership in a regional trade bloc may reduce not notified to the WTO was collected from the Global the frictional costs of trade, increase the credibility Preferential Trade Agreements database (box 6.6a) of reform initiatives, and strengthen security among and from official websites of the trade blocs. partners. But making it work effectively is challeng- Some countries belong to more than one trade bloc, ing. All economic sectors may be affected, and some so shares of world exports exceed 100 percent. Exports may expand while others contract, so it is important include all commodity trade, which may include items to weigh the potential costs and benefits of mem- not specified in trade bloc agreements. Differences bership. The table shows the value of intraregional from previously published estimates may be due to merchandise trade (service exports are excluded) for changes in membership or revisions in underlying data. Global Preferential Trade Agreement Database 6.6a Data sources The Global Preferential Trade Agreement Database provides information on preferential trade agreements Data on merchandise trade flows are published in the IMF’s Direction of Trade Statistics Yearbook around the world, including agreements that have not been notified to the World Trade Organization and Direction of Trade Statistics Quarterly; the (WTO). It is designed to help trade policymakers, scholars, and business operators better understand data in the table were calculated using the IMF’s and navigate the world of preferential trade agreements. Updated regularly, the database currently covers Direction of Trade database. Data on trade bloc more than 330 preferential trade agreements in their original language, which have been indexed by WTO membership are from World Bank (2000b), UNC- criteria and can be downloaded as PDFs. Users can search by provision or keyword; compare provisions TAD’s Trade and Development Report 2007, WTO’s across multiple agreements; and sort agreements by membership, date of signature, in-force status, Regional Trade Agreements Information System and other criteria. The database was developed jointly by the World Bank and the Center for International (http://rtais.wto.org/UI/PublicMaintainRTAHome. Business, Tuck School of Business at Dartmouth College. It was supported by the Multidonor Trust Fund aspx), and the World Bank and the Center for Inter- for Trade and Development, with financing from the governments of Finland, Norway, Sweden, and the national Business at the Tuck School of Business United Kingdom. The database is integrated with the World Integrated Trade Solution database and is at Dartmouth College’s Global Preferential Trade part of the World Bank’s Open Data initiative (http://wits.worldbank.org/gptad/). Agreements Database. 2012 World Development Indicators 353 6.7 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 0.7 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 5.6 5.0 Algeria 2009 .. .. 14.2 8.6 53.2 0.0 14.5 7.8 14.2 8.9 Angola 2009 100.0 59.2 7.4 7.4 23.4 0.6 11.6 13.9 6.8 5.9 Antigua and Barbuda 2009 97.9 58.7 13.8 14.6 49.4 0.1 17.2 14.8 13.1 14.5 Argentina 2010 100.0 31.9 11.4 6.2 24.3 0.0 7.5 1.6 11.9 7.0 Armenia 2008 100.0 8.5 3.7 2.3 0.0 0.4 5.6 2.2 3.5 2.4 Australia 2010 97.0 10.0 2.8 1.9 0.0 0.2 1.3 0.4 3.0 2.5 Azerbaijan 2009 .. .. 8.3 3.9 46.5 2.3 9.5 3.8 8.1 4.0 Bahamas, The 2010 .. .. 33.2 21.5 68.8 0.5 21.9 7.6 36.1 32.6 Bahrain 2009 73.6 34.8 4.4 3.6 0.3 0.2 7.0 6.9 4.0 3.1 Bangladesh 2008 15.9 169.9 13.9 13.0 38.0 0.9 16.3 8.8 13.6 14.0 Barbados 2007 97.8 78.1 15.1 14.8 44.9 1.0 26.3 21.9 13.4 12.3 Belarus 2010 .. .. 6.7 2.1 4.5 14.0 6.3 0.9 6.8 3.4 Belize 2010 97.9 58.4 11.5 6.4 31.9 1.9 17.5 4.0 10.6 10.1 Benin 2010 39.5 28.7 13.3 15.4 50.2 0.0 15.5 12.4 12.9 17.0 Bermuda 2010 .. .. 18.0 26.1 64.3 2.8 10.0 14.3 19.4 27.7 Bhutan 2007 .. .. 18.2 17.8 50.7 0.0 43.5 44.9 15.5 16.0 Bolivia 2010 100.0 40.0 9.6 5.4 11.9 0.0 8.4 5.8 9.7 5.2 Bosnia and Herzegovina 2010 .. .. 3.3 1.8 0.0 4.2 1.4 1.1 3.6 2.3 Botswana 2010 96.1 19.0 8.8 5.2 20.2 1.0 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 14.0 9.8 Brunei Darussalam 2010 95.3 24.1 3.8 4.1 20.8 1.2 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 0.1 15.4 9.4 9.1 4.6 Cambodia 2008 100.0 19.1 12.4 9.9 19.7 0.0 13.8 11.8 12.2 9.6 Cameroon 2009 13.7 79.9 18.4 15.0 52.5 0.0 20.5 12.9 18.1 15.9 Canada 2010 99.7 5.2 2.9 0.9 6.5 3.4 1.7 0.3 3.2 1.1 Cape Verde 2010 100.0 15.8 14.7 11.6 44.3 0.0 16.2 12.2 14.4 11.0 Central African Republic 2007 62.5 36.0 17.5 13.6 47.4 0.1 18.9 13.8 17.3 13.3 Chad 2009 13.9 79.9 17.6 14.7 47.4 0.3 22.5 17.2 16.8 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† 2010 100.0 10.0 7.7 4.0 11.2 0.2 8.1 1.8 7.7 5.6 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 11.3 8.9 Comoros 2010 .. .. 7.6 8.1 1.5 0.3 4.5 3.9 8.4 11.2 Congo, Dem. Rep. 2009 100.0 96.2 12.9 11.0 42.5 0.2 14.2 10.8 12.6 11.1 Congo, Rep. 2007 16.5 27.4 18.6 14.7 52.6 0.2 21.9 18.6 18.2 14.1 Costa Rica 2009 100.0 43.2 4.8 2.4 0.7 0.0 6.3 3.3 4.6 2.1 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 5.2 4.5 1.9 2.1 1.0 Cuba 2010 31.7 21.4 10.5 8.7 11.6 0.0 11.1 6.2 10.4 9.8 Cyprus 2010 100.0 4.2 1.9 1.6 1.9 7.8 2.5 0.5 1.9 2.3 Djibouti 2009 100.0 41.2 20.6 15.2 69.4 0.5 15.9 8.7 21.3 18.6 Dominica 2007 94.7 58.7 11.9 7.9 43.3 0.0 19.2 5.7 10.6 9.3 Dominican Republic 2010 100.0 34.9 8.3 6.1 30.1 0.0 11.8 4.6 7.8 6.9 Ecuador 2010 100.0 21.7 9.3 6.0 20.2 0.0 9.0 4.3 9.4 6.7 Egypt, Arab Rep. 2009 99.3 37.3 12.6 8.1 18.4 0.2 37.6 6.4 9.4 9.5 El Salvador 2010 100.0 36.9 5.1 5.5 1.9 0.0 8.4 7.4 4.7 4.3 Equatorial Guinea 2007 .. .. 18.3 15.6 52.3 0.2 21.5 21.4 17.8 14.3 Eritrea 2006 .. .. 9.6 5.4 22.4 0.0 9.2 3.5 9.6 7.2 Ethiopia 2010 .. .. 18.1 10.5 55.6 0.0 19.7 5.1 18.0 13.2 European Union 2010 100.0 4.2 1.9 1.6 1.9 7.8 2.5 0.5 1.9 2.3 Fiji 2010 51.4 40.1 11.9 11.0 23.3 1.9 13.5 9.8 11.6 12.2 French Polynesia 2009 .. .. 6.8 4.2 28.0 0.0 4.1 2.7 7.3 5.2 Gabon 2009 100.0 21.4 18.7 14.5 53.1 0.1 21.2 15.1 18.4 14.3 Gambia, The 2009 13.7 101.8 18.7 14.8 91.2 0.1 16.9 12.8 19.2 17.0 Georgia 2010 100.0 7.2 0.5 0.4 0.0 1.0 4.0 1.0 0.1 0.0 Ghana 2009 14.4 92.5 13.0 8.6 40.5 1.2 16.6 8.9 12.5 8.4 †Data for Taiwan, China 2010 100.0 6.0 5.3 2.5 6.0 1.7 8.4 2.0 4.8 2.9 354 2012 World Development Indicators 6.7 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 2010 100.0 56.8 8.9 7.7 36.7 0.0 10.1 7.0 8.6 8.3 Guatemala 2010 100.0 42.3 4.1 2.4 16.0 0.0 4.8 1.7 4.0 2.8 Guinea 2010 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 2010 100.0 56.8 10.1 6.9 39.2 0.0 15.5 5.9 9.4 7.7 Haiti 2009 89.8 17.6 3.0 5.1 5.1 0.3 5.8 4.1 2.5 5.9 Honduras 2009 100.0 32.5 6.4 6.5 0.5 0.1 9.9 8.1 6.0 5.5 Iceland 2010 95.0 13.5 1.8 1.1 5.2 3.4 2.3 1.1 1.7 1.1 India 2009 74.5 50.2 11.5 8.2 10.4 4.7 20.1 7.5 10.3 8.3 Indonesia 2010 96.6 37.5 4.8 2.5 7.0 0.5 3.2 1.6 5.0 2.9 Iran, Islamic Rep. 2008 .. .. 24.8 19.6 56.5 0.3 21.7 12.5 25.1 21.2 Iraq .. .. .. .. .. .. .. .. .. .. Israel 2009 75.2 22.0 6.0 3.5 2.9 4.1 8.8 3.3 5.6 3.7 Jamaica 2010 100.0 49.7 8.4 7.5 42.6 0.0 13.9 6.1 7.7 8.9 Japan 2010 99.7 3.0 2.6 1.6 8.6 3.3 5.1 1.6 2.1 1.7 Jordan 2009 100.0 16.3 9.7 5.2 29.5 0.6 14.2 3.9 9.0 6.1 Kazakhstan 2010 .. .. 6.4 3.4 19.6 16.4 6.1 0.9 6.4 4.0 Kenya 2010 15.2 95.3 12.1 9.2 36.6 0.1 16.0 12.6 11.7 6.7 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2010 95.1 16.1 10.3 8.7 7.0 0.6 26.3 12.7 7.4 5.1 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 2009 99.9 100.0 4.1 4.1 0.0 0.3 3.4 3.0 4.2 4.4 Kyrgyz Republic 2010 99.9 7.5 3.3 2.3 1.8 1.7 4.5 0.9 3.1 3.6 Lao PDR 2008 .. .. 9.3 13.2 20.4 0.1 16.0 14.2 8.4 12.6 Lebanon 2007 .. .. 5.6 4.8 11.6 10.9 8.2 5.0 5.2 5.1 Lesotho 2010 100.0 78.9 9.5 10.5 21.6 1.4 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 2010 100.0 6.9 3.6 2.7 14.5 2.5 7.4 5.7 3.2 1.8 Madagascar 2010 30.5 27.3 10.6 7.7 38.3 0.2 12.6 3.3 10.3 8.9 Malawi 2010 32.0 75.9 11.7 6.6 42.7 0.3 13.4 6.0 11.5 6.8 Malaysia 2009 83.9 14.6 6.8 4.0 16.9 0.8 10.1 5.0 6.1 3.7 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 12.9 11.0 Mauritius 2010 17.7 98.3 2.0 1.1 10.4 8.7 1.2 0.4 2.1 1.6 Mayotte 2010 .. .. 5.1 1.8 1.4 0.0 3.7 1.5 5.3 1.9 Mexico 2010 100.0 35.1 7.4 2.2 6.1 0.1 9.3 1.5 7.2 2.4 Moldova 2010 99.9 6.7 4.6 2.5 8.4 2.8 7.0 2.3 4.3 2.6 Mongolia 2009 100.0 17.5 4.9 5.1 0.1 0.0 5.1 5.4 4.9 4.9 Montenegro 2010 .. .. 3.0 3.5 4.5 2.9 5.8 4.8 2.7 2.8 Morocco 2009 100.0 41.3 9.1 7.1 23.6 0.0 18.0 8.9 8.2 5.8 Mozambique 2010 14.0 97.4 7.7 4.8 23.9 0.1 8.7 4.7 7.5 4.7 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 2.0 4.1 2.1 6.7 1.6 Nepal 2010 99.4 26.2 12.6 12.1 46.3 1.2 12.9 10.3 12.6 14.4 New Zealand 2010 100.0 10.0 2.5 1.6 0.0 2.9 1.4 0.4 2.6 2.1 Nicaragua 2010 100.0 41.7 4.2 2.3 16.2 0.0 5.6 2.8 4.0 1.9 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 2010 100.0 3.0 0.5 0.4 0.5 6.9 2.0 1.2 0.3 0.2 Oman 2009 100.0 13.9 3.7 3.2 0.3 0.2 5.0 3.3 3.5 3.2 Pakistan 2009 98.6 60.0 14.8 9.5 45.3 0.3 14.5 6.5 14.8 12.3 Palau 2010 .. .. 2.7 1.0 1.3 6.6 0.7 0.1 3.3 3.1 Panama 2009 99.9 23.5 7.6 7.6 2.8 0.0 11.5 8.4 7.1 7.3 Papua New Guinea 2010 100.0 31.5 4.4 2.5 23.3 1.0 11.8 1.8 3.4 2.8 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 5.1 4.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 355 6.7 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 0.2 5.1 4.0 4.1 3.8 Russian Federation 2010 .. .. 6.0 3.8 17.4 15.4 5.8 3.4 6.0 3.9 Rwanda 2010 100.0 89.3 9.9 6.0 31.4 0.1 11.5 6.4 9.7 5.9 Saudi Arabia 2009 100.0 10.8 4.1 3.9 0.0 0.3 3.5 2.8 4.1 4.2 Senegal 2010 100.0 30.0 13.4 8.9 50.5 0.0 14.1 7.7 13.3 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 2.0 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 0.1 0.0 0.0 0.0 0.0 Solomon Islands 2010 100.0 78.8 9.2 8.7 0.0 4.4 9.3 8.5 9.2 8.8 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2010 96.1 19.4 7.6 4.4 17.9 1.8 5.4 1.9 7.9 5.7 South Sudan .. .. .. .. .. .. .. .. .. .. Sri Lanka 2010 38.1 30.1 9.4 6.9 23.8 1.0 15.3 9.6 8.8 5.7 St. Kitts and Nevis 2010 97.8 76.1 9.6 10.5 36.1 0.5 9.9 7.0 9.6 11.8 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 0.5 15.1 7.8 10.6 8.6 Sudan 2010 .. .. 13.3 14.8 28.3 0.1 16.6 10.9 12.9 15.8 Suriname 2010 27.6 18.1 11.6 11.9 36.2 0.1 18.3 15.0 10.4 10.4 Swaziland 2010 96.1 19.4 10.9 10.2 26.2 3.2 9.7 1.3 11.1 16.2 Switzerland 2010 99.8 0.0 0.0 0.0 0.0 26.9 0.0 0.0 0.0 0.0 Syrian Arab Republic 2010 .. .. 6.7 6.1 27.6 0.0 6.5 6.1 6.7 6.1 Tajikistan 2010 .. .. 4.4 5.9 8.2 0.8 3.5 1.1 4.5 7.8 Tanzania 2010 13.8 120.0 12.9 8.2 39.9 0.1 17.5 8.7 12.4 8.0 Thailand 2009 74.7 26.1 11.2 4.9 20.5 5.2 15.9 2.9 10.5 6.1 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 2010 100.0 17.6 10.9 7.2 65.6 0.3 11.9 5.1 10.6 9.0 Trinidad and Tobago 2008 100.0 55.8 8.7 10.0 43.6 0.5 16.6 3.1 7.6 17.4 Tunisia 2008 58.3 58.0 21.9 16.0 57.8 0.0 26.8 12.0 21.4 18.2 Turkey 2010 50.3 29.2 2.5 2.4 5.4 0.2 14.4 4.8 1.2 1.3 Turkmenistan 2002 .. .. 5.4 2.9 14.8 3.2 14.7 12.6 3.8 1.1 Uganda 2010 16.1 73.5 12.1 8.2 37.5 0.2 15.7 8.8 11.6 7.9 Ukraine 2010 100.0 5.8 4.5 2.8 1.1 0.4 5.9 2.5 4.3 3.1 United Arab Emirates 2009 100.0 14.8 4.5 3.7 0.3 0.3 6.0 2.8 4.2 4.4 United States 2010 100.0 3.7 2.9 1.8 3.4 6.9 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 10.0 5.3 Uzbekistan 2009 .. .. 11.8 6.9 20.1 10.9 12.6 3.9 11.7 7.4 Vanuatu 2009 .. .. 18.2 18.5 66.6 3.4 26.7 27.8 16.5 14.6 Venezuela, RB 2010 100.0 36.5 13.1 10.6 21.9 0.0 12.2 10.0 13.2 10.8 Vietnam 2010 100.0 11.5 7.1 5.7 23.6 0.5 8.6 6.0 6.9 5.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2009 .. .. 5.5 4.2 1.4 0.8 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 11.0 4.2 Zimbabwe 2007b 22.2 91.4 16.7 17.3 38.8 6.5 19.6 20.4 16.3 15.1 World 78.3 w 31.2 6.9 3.1 15.0 2.0 9.0 2.7 6.6 3.3 w Low income 48.3 52.3 12.5 9.2 41.4 0.2 14.3 7.9 12.3 9.8 Middle income 87.7 32.9 8.5 5.0 20.1 0.8 10.8 3.4 8.2 5.7 Lower middle income 80.6 37.3 9.1 6.3 20.0 0.8 13.2 5.6 8.6 6.6 Upper middle income 90.3 29.3 8.3 4.6 20.7 0.8 9.3 2.7 8.1 5.5 Low & middle income 76.5 36.5 9.0 5.1 21.5 0.8 11.3 3.5 8.7 5.8 East Asia & Pacific 84.2 22.3 8.3 4.2 15.8 0.9 10.6 2.3 7.9 5.4 Europe & Central Asia 91.7 8.9 4.9 4.1 13.1 0.9 7.7 3.1 4.6 4.5 Latin America & Carib. 96.1 36.6 8.1 4.9 18.1 0.4 7.9 3.1 8.1 5.5 Middle East & N. Africa 99.9 30.4 10.6 7.4 25.0 0.0 17.2 6.8 9.7 7.8 South Asia 81.5 41.6 13.7 8.5 39.6 2.7 17.8 7.6 13.1 8.7 Sub-Saharan Africa 53.4 48.4 11.4 7.2 36.7 1.0 12.8 6.1 11.3 7.7 High income 90.3 17.3 3.6 2.2 5.0 4.2 6.4 2.4 3.1 2.1 OECD 89.6 7.4 3.3 2.2 5.1 5.4 7.1 2.6 2.6 2.0 Non-OECD 73.1 9.1 2.9 0.8 4.9 0.0 3.2 0.8 2.8 0.8 a. Includes Montenegro. b. Rates are most favored nation rates. 356 2012 World Development Indicators 6.7 GLOBAL LINKS Tariff barriers About the data De�nitions 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, sec- Bound rates result from trade negotiations incorpo- lines with an agreed bound rate. •  Simple mean tors that confront the greatest trade barriers. Remov- rated into a country’s schedule of concessions and bound rate is the unweighted average of all the lines ing barriers to merchandise trade could increase are thus enforceable. in the tariff schedule in which bound rates have been growth in these countries—even more if trade in Some countries set fairly uniform tariff rates set. • Simple mean tariff is the unweighted average services were also liberalized. 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 ci�c 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 line data were and quarantine measures. Because of the difficulty matched to Standard International Trade Classifi - of combining nontariff barriers into an aggregate indi- cation (SITC) revision 2 codes to define commodity cator, they are not included in the table. groups and import weights. Import weights were cal- Unless specified as most favored nation rates, the culated using the United Nations Statistics Division’s tariff rates used in calculating the indicators in the Commodity Trade Statistics (Comtrade) database. table are effectively applied rates. Effectively applied The table shows tariff rates for three commodity rates are those in effect for partners in preferen- groups: all products, primary products, and manu- tial trade arrangements such as the North Ameri- factured products. Effectively applied rates at the can Free Trade Agreement. The difference between six- and eight-digit product level are averaged for most favored nation and applied rates can be sub- products in each commodity group. When an effec- stantial. Because more countries now report their tively applied rate is not available, the most favored free trade agreements, suspensions of tariffs, and nation rate is used instead. other special preferences, this year’s World Develop- Data are shown only for the last year for which ment Indicators includes effectively applied rates for complete data are available. EU member countries most countries. All estimates are calculated using apply a common tariff schedule that is listed under the most recent information, which is not necessarily European Union and are thus not listed separately. Data sources revised every year. As a result, data for the same year may differ from data in last year’s edition. All indicators in the table were calculated by World Three measures of average tariffs are shown: sim- Bank staff using the World Integrated Trade Solu- ple bound rates and the simple and the weighted tion system (http://wits.worldbank.org). Data on tariffs. Bound rates are based on all products in a tariffs are from the United Nations Conference country’s tariff schedule, while the most favored on Trade and Development’s Trade Analysis and nation or applied rates are calculated using all traded Information System database and the World Trade items. Weighted mean tariffs are weighted by the Organization’s Integrated Data Base and Consoli- value of the country’s trade with each trading part- dated Tariff Schedules database. Data on global ner. Simple averages are often a better indicator of imports are from the United Nations Statistics tariff protection than weighted averages, which are Division’s Comtrade database. biased downward because higher tariffs discourage 2012 World Development Indicators 357 6.8 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 1 kilogram DHL days number 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) $ 2010 2010–11b 2010 2010 June 2011 June 2011 2011 2010–11 2012 Afghanistan 2.24 .. 2.0 4.0 10 10 .. .. 148.25 Albania 2.46 4.2 1.7 2.0 7 8 4.5 3.9 163.10 Algeria 2.36 2.8 4.6 7.1 8 9 31.1 3.0 162.00 Angola 2.25 2.7 6.0 8.0 11 8 11.3 2.3 162.00 Argentina 3.10 2.7 3.7 3.8 7 7 30.6 3.7 92.30 Armenia 2.52 2.9 .. .. 5 8 .. 2.7c 148.25 Australia 3.84 5.1 2.6 2.8 6 5 28.3 5.1 102.10 Austria 3.76 5.0 2.0 3.7 4 5 .. 4.7c 142.10 Azerbaijan 2.64 3.5 7.0 3.0 8 10 .. 4.1c 163.10 Bahrain 3.37 5.5 1.0 2.0 6 7 9.8 6.0 148.25 Bangladesh 2.74 3.4 1.4 1.4 6 8 8.2 3.4 102.10 Belarus 2.53 .. .. .. 9 10 .. .. 163.10 Belgium 3.94 4.6 1.7 1.6 4 5 88.5 6.5 116.75 Benin 2.79 3.7 3.0 7.0 7 8 12.7 3.9 162.00 Bolivia 2.51 3.0 15.0 28.3 8 7 .. 3.1c 92.30 Bosnia and Herzegovina 2.66 3.4 2.0 2.0 8 9 .. 1.7 163.10 Botswana 2.32 4.7 .. .. 6 8 .. 3.9c 162.00 Brazil 3.20 3.1 2.8 3.9 7 8 34.6 2.7 92.30 Bulgaria 2.83 3.5 2.0 3.9 5 6 5.4 3.8 142.10 Burkina Faso 2.23 4.0 4.0 14.0 10 10 .. 3.7c 162.00 Burundi 2.29 2.9 .. .. 9 10 .. 3.0 c 162.00 Cambodia 2.37 3.7 1.3 4.0 9 10 5.4 4.0 111.40 Cameroon 2.55 4.0 3.4 8.9 11 12 11.4 3.5 162.00 Canada 3.87 4.9 2.8 3.7 3 4 38.4 5.8 76.15 Central African Republic .. .. .. .. 9 17 .. .. 162.00 Chad 2.49 2.8 74.0 35.0 8 11 .. 2.7c 162.00 Chile 3.09 5.5 3.5 3.0 6 6 22.8 5.2 92.30 China 3.49 4.4 2.8 2.6 8 5 152.1 4.5 86.75 Hong Kong SAR, China 3.88 6.2 1.7 1.6 4 4 115.3 6.6 92.75 Colombia 2.77 4.0 7.0 7.0 5 6 27.3 3.4 92.30 Congo, Dem. Rep. 2.68 .. 2.0 3.0 8 9 3.7 .. 162.00 Congo, Rep. 2.48 .. .. .. 11 10 10.8 .. 162.00 Costa Rica 2.91 3.9 2.0 2.0 6 7 10.7 2.3 92.30 Côte d’Ivoire 2.53 3.9 1.0 1.0 10 9 17.4 4.9 162.00 Croatia 2.77 4.1 1.0 1.0 7 8 21.8 4.0 163.10 Cuba 2.07 .. .. .. .. .. 6.6 .. 80.30 Cyprus 3.13 4.9 1.0 2.0 5 7 17.1 5.1 142.10 Czech Republic 3.51 4.4 2.5 3.5 4 7 0.4 4.7c 142.10 Denmark 3.85 5.7 1.0 1.0 4 3 26.4 6.2 142.10 Dominican Republic 2.82 4.4 2.2 3.5 6 7 22.9 4.4 80.30 Ecuador 2.77 3.5 2.1 3.4 8 7 22.5 3.8 92.30 Egypt, Arab Rep. 2.61 4.1 1.3 3.1 8 9 51.2 4.0 148.25 El Salvador 2.67 3.9 2.0 2.0 8 8 12.0 3.8 92.30 Eritrea 1.70 .. 3.0 3.0 10 12 4.0 .. 162.00 Estonia 3.16 5.2 4.0 4.0 3 4 5.8 5.6 142.10 Ethiopia 2.41 3.5 5.0 6.0 7 9 .. 3.9c 162.00 Finland 3.89 6.0 1.6 1.8 4 5 11.3 6.2 142.10 France 3.84 4.9 3.2 4.5 2 2 71.8 5.6 116.75 Gabon 2.41 .. 4.3 13.0 7 8 8.0 .. 162.00 Gambia, The 2.49 5.2 4.6 3.5 6 7 5.4 4.9 162.00 Georgia 2.61 4.9 .. .. 4 4 3.8 4.2 163.10 Germany 4.11 4.7 3.6 2.4 4 5 93.3 6.1 116.75 Ghana 2.47 3.6 2.9 6.8 6 7 18.0 4.2 162.00 Greece 2.96 4.0 3.0 3.5 5 6 32.2 4.1 142.10 Guatemala 2.63 4.2 2.6 3.4 10 9 20.9 4.3 92.30 Guinea 2.60 .. 3.5 3.9 7 9 6.2 .. 162.00 Guinea-Bissau 2.10 .. .. .. 6 6 4.1 .. 162.00 Haiti 2.59 2.5 4.2 5.3 8 10 4.8 1.8 80.30 358 2012 World Development Indicators 6.8 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 1 kilogram DHL days number 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) $ 2010 2010–11b 2010 2010 June 2011 June 2011 2011 2010–11 2012 Honduras 2.78 4.0 2.4 3.2 6 8 9.4 5.1 92.30 Hungary 2.99 4.5 3.5 5.0 6 7 .. 4.0 c 142.10 India 3.12 3.8 2.3 5.3 8 9 41.5 3.9 102.10 Indonesia 2.76 3.9 2.1 5.4 4 7 25.9 3.6 102.10 Iran, Islamic Rep. 2.57 3.5 2.6 28.3 7 8 30.3 3.9 148.25 Iraq 2.11 .. .. .. 10 10 4.2 .. 148.25 Ireland 3.89 5.2 1.0 1.0 4 4 5.9 5.2 116.75 Israel 3.41 4.7 2.0 2.0 5 4 28.5 4.2 148.25 Italy 3.64 4.0 2.6 3.0 4 4 70.2 3.9 116.75 Jamaica 2.53 3.7 10.0 10.0 6 6 28.2 5.3 80.30 Japan 3.97 4.7 1.0 1.0 3 5 67.8 5.2 124.90 Jordan 2.74 4.4 3.2 4.6 6 7 16.7 4.3 148.25 Kazakhstan 2.83 3.5 2.8 11.5 9 12 .. 3.6c 163.10 Kenya 2.59 3.3 3.0 5.9 8 7 12.0 3.8 162.00 Korea, Dem. Rep. .. .. .. .. .. .. .. .. 111.40 Korea, Rep. 3.64 4.4 1.6 2.0 3 3 92.0 5.5 102.10 Kosovo .. .. .. .. 8 8 .. .. 163.10 Kuwait 3.28 4.1 2.0 3.0 7 10 5.6 4.2 148.25 Kyrgyz Republic 2.62 2.8 2.0 .. 8 9 .. 1.5c 163.10 Lao PDR 2.46 .. .. .. 9 10 .. .. 111.40 Latvia 3.25 4.1 1.3 1.6 5 6 5.5 4.7 142.10 Lebanon 3.34 3.5 3.4 2.2 5 7 35.1 4.3 148.25 Lesotho 2.30 3.7 .. .. 8 8 .. 3.4 c 162.00 Liberia 2.38 .. 4.0 5.0 10 9 6.2 .. 162.00 Libya 2.33 3.5 3.2 10.0 .. .. 6.6 3.2 162.00 Lithuania 3.13 4.5 2.0 2.3 6 6 9.8 4.9 142.10 Macedonia, FYR 2.77 4.2 .. .. 6 6 .. 4.1c 163.10 Madagascar 2.66 3.4 .. .. 4 9 7.7 3.3 162.00 Malawi 2.42 3.8 4.2 3.7 10 9 .. 3.6c 162.00 Malaysia 3.44 5.0 2.6 2.8 6 7 91.0 5.7 102.10 Mali 2.27 4.1 5.0 4.0 6 9 .. 3.7c 162.00 Mauritania 2.63 4.1 2.0 3.0 8 8 5.6 3.3 162.00 Mauritius 2.72 4.6 3.0 2.4 5 6 15.4 4.7 162.00 Mexico 3.05 4.1 2.1 2.5 5 4 36.1 4.0 51.15 Moldova 2.57 3.5 .. .. 6 7 .. 2.9 163.10 Mongolia 2.25 3.3 14.0 12.0 8 8 .. 2.8 c 111.40 Morocco 2.38 4.4 2.0 3.2 6 8 55.1 4.5 162.00 Mozambique 2.29 3.7 .. .. 7 10 10.1 3.4 162.00 Myanmar 2.33 .. 4.6 8.4 .. .. 3.2 .. 111.40 Namibia 2.02 4.1 3.0 3.0 9 7 12.0 5.5 162.00 Nepal 2.20 3.4 1.8 6.3 9 9 .. 2.6c 111.40 Netherlands 4.07 5.2 1.8 1.9 4 5 92.1 6.6 116.75 New Zealand 3.65 5.8 1.3 1.6 7 5 18.5 5.5 102.10 Nicaragua 2.54 3.0 3.2 3.2 5 5 8.4 2.7 92.30 Niger 2.54 .. .. .. 8 10 .. .. 162.00 Nigeria 2.59 3.5 2.5 4.1 10 9 19.9 3.3 162.00 Norway 3.93 5.2 1.0 2.0 4 4 7.3 5.5 142.10 Oman 2.84 5.0 .. .. 8 8 49.3 5.4 148.25 Pakistan 2.53 3.6 2.3 1.6 7 8 30.5 4.1 148.25 Panama 3.02 4.2 1.4 1.4 3 4 37.5 6.4 92.30 Papua New Guinea 2.41 .. .. .. 7 9 8.8 .. 111.40 Paraguay 2.75 3.9 1.0 4.0 8 10 0.0 3.4 c 92.30 Peru 2.80 4.4 2.0 3.8 6 8 21.2 3.5 92.30 Philippines 3.14 3.0 1.8 5.0 7 8 18.6 3.0 102.10 Poland 3.44 4.4 3.0 3.6 5 5 26.5 3.4 142.10 Portugal 3.34 4.7 2.5 5.0 4 5 21.1 4.9 116.75 Puerto Rico .. 4.6 .. .. 6 10 .. 5.3 .. Qatar 2.95 4.8 3.8 2.3 5 7 3.6 5.4 148.25 2012 World Development Indicators 359 6.8 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 1 kilogram DHL days number 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) $ 2010 2010–11b 2010 2010 June 2011 June 2011 2011 2010–11 2012 Romania 2.84 3.3 2.0 2.0 5 6 21.4 2.8 142.10 Russian Federation 2.61 2.8 4.0 2.9 8 10 20.6 3.7 163.10 Rwanda 2.04 5.3 .. .. 8 8 .. 3.2 162.00 Saudi Arabia 3.22 5.0 2.3 6.3 5 5 60.0 5.4 148.25 Senegal 2.86 4.7 1.4 2.7 6 5 12.3 4.5 162.00 Serbia 2.69d 3.7 2.0 d 3.0 d 6 6 .. 2.7 163.10 Sierra Leone 1.97 .. 2.0 32.0 7 7 5.4 .. 162.00 Singapore 4.09 6.2 2.2 1.8 4 4 105.0 6.8 92.75 Slovak Republic 3.24 4.3 3.0 5.0 6 7 .. 3.9c 142.10 Slovenia 2.87 5.0 1.0 2.0 6 8 21.9 5.2 142.10 Somalia 1.34 .. .. .. .. .. 4.2 .. 162.00 South Africa 3.46 4.2 2.3 3.3 8 8 35.7 4.7 162.00 South Sudan .. .. .. .. .. .. .. .. .. Spain 3.63 4.5 4.0 7.1 6 7 76.6 5.8 116.75 Sri Lanka 2.29 4.4 1.3 2.5 6 6 41.1 4.9 102.10 Sudan 2.21 .. 39.0 5.0 7 7 9.3 .. 162.00 Swaziland .. 3.4 .. .. 9 9 .. 4.2 162.00 Sweden 4.08 5.8 1.0 2.6 3 3 30.0 6.0 142.10 Switzerland 3.97 5.1 2.6 2.6 4 5 1.9 5.2c 142.10 Syrian Arab Republic 2.74 2.9 2.5 3.2 8 9 16.8 3.4 148.25 Tajikistan 2.35 3.6 7.0 .. 11 9 .. 1.8 c 163.10 Tanzania 2.60 3.6 3.2 7.1 6 6 11.5 3.3 162.00 Thailand 3.29 3.9 1.6 2.6 5 5 36.7 4.7 102.10 Timor-Leste 1.71 3.4 .. .. 6 7 .. 2.6 111.40 Togo 2.60 .. .. .. 6 8 14.1 .. 162.00 Trinidad and Tobago .. 3.0 .. .. 5 6 17.9 3.9 80.30 Tunisia 2.84 4.6 1.7 7.0 4 7 6.3 4.6 162.00 Turkey 3.22 3.7 2.2 3.8 7 8 39.4 4.2 148.25 Turkmenistan 2.49 .. 3.0 .. .. .. .. .. 163.10 Uganda 2.82 4.4 5.5 14.0 7 9 .. 3.7c 162.00 Ukraine 2.57 2.8 1.7 7.0 6 8 21.4 3.7 163.10 United Arab Emirates 3.63 5.6 2.5 2.0 4 5 62.5 6.2 148.25 United Kingdom 3.95 4.9 3.3 1.9 4 4 87.5 5.6 116.75 United States 3.86 4.3 2.8 4.0 4 5 81.6 5.5 .. Uruguay 2.75 4.2 3.0 3.0 9 9 24.4 5.1 92.30 Uzbekistan 2.79 .. 1.4 2.0 10 11 .. .. 163.10 Venezuela, RB 2.68 2.3 9.4 12.1 8 9 20.0 2.5 92.30 Vietnam 2.96 3.4 1.4 1.7 6 8 49.7 3.4 102.10 West Bank and Gaza .. .. .. .. 6 6 .. .. .. Yemen, Rep. 2.58 2.9 3.1 3.6 6 9 11.9 2.9 148.25 Zambia 2.28 4.1 9.2 4.0 6 8 .. 4.0 c 162.00 Zimbabwe 2.29 3.8 25.0 18.0 8 9 .. 4.4 c 162.00 World 2.87e u 4.1 u 3.8e u 4.6e u 7u 7u .. 4.3 u Low income 2.38 3.7 7.3 7.8 8 9 .. 3.3 Middle income 2.68 3.8 3.7 4.9 7 7 .. 3.9 Lower middle income 2.54 3.6 4.8 5.3 7 8 .. 3.6 Upper middle income 2.80 3.9 2.9 4.6 6 7 .. 4.1 Low & middle income 2.60 3.7 4.5 5.5 7 8 .. 3.7 East Asia & Pacific 2.73 3.8 3.6 4.9 7 7 .. 3.8 Europe & Central Asia 2.71 3.7 2.8 3.0 7 8 .. 3.4 Latin America & Carib. 2.74 3.7 3.9 5.5 6 7 .. 3.8 Middle East & N. Africa 2.60 3.7 2.7 7.2 7 8 .. 3.9 South Asia 2.49 3.7 1.9 3.3 8 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 3.55 4.9 2.3 3.0 4 5 .. 5.3 a. Transportation charges only; excludes fuel, surcharges, duties, and taxes. b. Average of the 2010 and 2011 survey ratings. c. Landlocked country. d. Includes Montenegro. e. Aggregates are computed according to the World Bank classification of economies as of July 1, 2011, and may differ from data published in the original source. 360 2012 World Development Indicators 6.8 GLOBAL LINKS Trade facilitation About the data De�nitions Trade facilitation encompasses customs efficiency include the value of time to import or export and the •  Logistics Performance Index refl ects percep- and other physical and regulatory environments risk of delay or loss of shipments. Long lead times tions of a country’s logistics based on efficiency of where trade takes place, harmonization of stan- and burdensome regulatory procedures may lower customs clearance process, quality of trade- and dards and conformance to international regulations, competitiveness. Data on lead time are from the transport-related infrastructure, ease of arranging and the logistics of moving goods and associated Logistics Performance Index survey. Respondents competitively priced shipments, quality of logistics documentation through countries and ports. Though provided separate values for the best case (10 per- services, ability to track and trace consignments, and collection of trade facilitation data has improved cent of shipments) and the median case (50 percent frequency with which shipments reach the consignee over the last decade, data that allow meaningful of shipments). The data are exponentiated averages within the scheduled time. The index ranges from 1 evaluation, especially for developing economies, are of the logarithm of single value responses and of mid- to 5, with a higher score representing better perfor- lacking. Data on trade facilitation are drawn from point values of range responses for the median case. mance. • Burden of customs procedure measures research by private and international agencies. Most Data on the number of documents needed to business executives’ perceptions of their country’s data are perception-based evaluations by business export or import are from the World Bank’s Doing efficiency of customs procedures. Values range from executives and professionals. Because of different Business surveys, which compile procedural require- 1 to 7, with a higher rating indicating greater effi - backgrounds, values, and personalities, those sur- ments for exporting and importing a standardized ciency. • Lead time to export is the median time (the veyed may evaluate the same situation differently. cargo of goods by ocean transport from local freight value for 50 percent of shipments) from shipment Caution should thus be used when interpreting per- forwarders, shipping lines, customs brokers, port point to port of loading. • Lead time to import is the ception-based indicators. Nevertheless, they convey officials, and banks. To make the data comparable median time (the value for 50 percent of shipments) much needed information on trade facilitation. across economies, several assumptions about the from port of discharge to arrival at the consignee. The table presents data from Logistics Perfor- business and the traded goods are used (see www. • Documents to export and documents to import are mance Surveys conducted by the World Bank in part- doingbusiness.org). all documents required per shipment by government nership with academic and international institutions Access to global shipping and air freight networks ministries, customs authorities, port and container and private companies and individuals engaged in and the quality and accessibility of ports and roads terminals, health and technical control agencies, and international logistics. The Logistics Performance affect logistics performance. The table shows two banks to export or import goods. Documents renewed Index assesses performance across six aspects of indicators related to trade and transport service annually and not requiring renewal per shipment are the logistics environment (see De�nitions), based infrastructure: the Liner Shipping Connectivity Index excluded. • Liner Shipping Connectivity Index indi- on more than 5,000 country assessments by nearly and the quality of port infrastructure rating. The Liner cates how well countries are connected to global ship- 1,000 international freight forwarders. Respondents Shipping Connectivity Index captures how well coun- ping networks based on the status of their maritime evaluate eight markets on six core dimensions. The tries are connected to global shipping networks. It transport sector. The highest value in 2004 is 100. markets are chosen based on the most important is computed by the United Nations Conference on • Quality of port infrastructure measures business export and import markets of the respondent’s coun- Trade and Development (UNCTAD) based on five com- executives’ perceptions of their country’s port facili- try, random selection, and, for landlocked countries, ponents of the maritime transport sector: number of ties. Values range from 1 to 7, with a higher rating neighboring countries that connect them with inter- ships, their container-carrying capacity, maximum indicating better development of port infrastructure. national markets. Scores for the six dimensions are vessel size, number of services, and number of • Freight costs to the United States is the DHL inter- averaged across all respondents and aggregated to companies that deploy container ships in a coun- national U.S. inbound worldwide priority express rate a single score. Details of the survey methodology try’s ports. For each component a country’s value is for a 1 kilogram nondocument air package. and index construction methodology are in Arvis and divided by the maximum value of each component Data sources others (2010). in 2004, the five components are averaged for each Data on the burden of customs procedures are country, and the average is divided by the maximum Data on the Logistics Performance Index and from the World Economic Forum’s Executive Opinion average for 2004 and multiplied by 100. The index lead time to export and import are from Arvis and Survey. The 2011 round included more than 15,000 generates a value of 100 for the country with the others (2010). Data on the burden of customs respondents from 142 countries. Sampling follows a highest average index in 2004. procedure and quality of port infrastructure rat- dual stratification based on company size and sec- The quality of port infrastructure measures busi- ings are from the World Economic Forum’s Global tor of activity. Data are collected online, through in- ness executives’ perception of their country’s port Competitiveness Report 2011–2012. Data on num- person interviews, and through mail and telephone facilities. Values range from 1 (extremely underde- ber of documents to export and import are from interviews. Responses are aggregated using sector- veloped) to 7 (efficient). Respondents in landlocked the World Bank’s Doing Business project (www. weighted averaging. Data are a two-year moving aver- countries were asked: “How accessible are port doingbusiness.org). Data on the Liner Shipping age. Respondents evaluated the efficiency of cus- facilities (1 = extremely inaccessible; 7 = extremely Connectivity Index are from UNCTAD’s Review of toms procedures in their country. The lowest value (1) accessible.)� Maritime Transport (2011). Freight costs to the rates the customs procedure as extremely inefficient, The costs of transport services are a crucial deter- United States are based on DHL’s “DHL Rate and and the highest score (7) as extremely efficient. minant of export competitiveness. The proxy indica- Transit Guide 2012� (2012). The direct costs of cross-border trade include tor in the table is the shipping rates to the United freight, customs, and storage fees. Indirect costs States of an international freight moving business. 2012 World Development Indicators 361 6.9 External debt Total external Long-term Short-term Total Present value of debt debt debt debt debt service $ millions % of exports % of exports Public and of goods and of goods and publicly Private % of total services and services and $ millions % of GNI guaranteed nonguaranteed $ millions % of total debt reserves incomea % of GNIa incomea 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan 2,297 .. 2,076 .. 102 4.4 .. .. 6.5 21.1 Albania 4,736 40.5 2,972 1,133 573 12.1 22.6 11.1 29.5 87.6 Algeria 5,276 3.4 2,530 968 1,778 33.7 1.0 1.0 3.0 5.4 Angola 18,562 24.6 15,440 .. 2,241 12.1 11.3 4.5 22.1 22.3 Argentina 127,849 36.1 67,331 25,514 35,005 27.4 67.1 16.7 37.5 150.3 Armenia 6,103 64.8 2,557 2,187 618 10.1 33.1 33.4 46.5 182.6 Australia .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. Azerbaijan 6,974 14.9 3,892 2,158 878 12.6 13.7 1.4 9.1 13.7 Bahrain .. .. .. .. .. .. .. .. .. .. Bangladesh 24,963 22.8 21,371 .. 2,974 11.9 26.6 4.7 16.2 84.3 Belarus 25,726 46.8 7,850 2,401 11,980 46.6 238.4 4.6 41.8 74.8 Belgium .. .. .. .. .. .. .. .. .. .. Benin 1,221 18.4 1,134 .. 32 2.7 2.7 .. 12.8b 70.1b Bolivia 5,267 27.8 2,806 2,358 103 2.0 1.1 9.3 17.4b 40.9b Bosnia and Herzegovina 8,457 48.8 3,751 3,149 1,037 12.3 23.7 19.9 37.3 95.0 Botswana 1,709 11.6 1,352 .. 357 20.9 4.5 1.5 8.7 18.9 Brazil 346,978 16.9 96,542 184,940 65,496 18.9 22.7 19.0 18.8 146.0 Bulgaria 48,077 104.8 4,466 28,238 15,373 32.0 89.3 14.2 94.9 159.1 Burkina Faso 2,053 23.3 1,925 .. 0 0.0 0.0 .. 18.6b 168.7b Burundi 537 33.8 412 .. 16 2.9 4.7 .. 14.3b 151.5b Cambodia 4,676 43.4 4,414 .. 262 5.6 6.9 .. 35.8 58.6 Cameroon 2,964 13.5 2,185 577 31 1.0 0.8 3.6 5.3b 19.1b Canada .. .. .. .. .. .. .. .. .. .. Central African Republic 385 19.2 218 .. 77 19.9 42.3 .. 12.4b 75.9b Chad 1,733 25.7 1,708 .. 8 0.5 1.3 .. 28.3b 57.3b Chile 86,349 45.9 12,929 47,541 25,879 30.0 93.0 15.2 47.7 98.9 China 548,551 9.3 90,180 110,847 347,524 63.4 11.9 3.3 10.1 31.2 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 63,064 22.8 36,777 18,078 8,209 13.0 29.2 21.0 37.5 211.9 Congo, Dem. Rep. 5,774 47.1 4,957 .. 494 8.6 38.0 3.8 27.0 b 77.8b Congo, Rep. 3,781 43.9 3,531 .. 222 5.9 5.0 .. 19.5b 19.2b Costa Rica 8,849 26.8 3,725 2,693 2,431 27.5 52.5 7.7 26.8 59.1 Côte d’Ivoire 11,430 52.6 10,416 280 351 3.1 9.7 .. 48.0 b 88.2b Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. Cyprus .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. Dominican Republic 13,045 26.2 9,115 843 1,948 14.9 55.6 11.0 23.7 89.1 Ecuador 14,815 23.1 8,598 5,848 369 2.5 14.1 9.4 22.1 64.3 Egypt, Arab Rep. 34,844 16.2 31,641 54 3,149 9.0 8.5 6.0 15.2 46.0 El Salvador 11,069 53.2 6,394 3,569 1,105 10.0 38.1 19.0 46.5 177.1 Eritrea 1,010 48.2 1,003 .. 7 0.7 6.3 .. 33.8b 731.6b Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia 7,147 24.1 6,545 .. 314 4.4 .. .. 13.3b 106.7b Finland .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. Gabon 2,331 20.3 2,158 .. 174 7.5 10.0 .. 18.7 16.2 Gambia, The 470 63.3 395 .. 44 9.4 21.9 7.2 29.5b 72.6b Georgia 9,238 80.4 4,081 3,143 963 10.4 42.6 18.1 65.0 183.9 Germany .. .. .. .. .. .. .. .. .. .. Ghana 8,368 27.2 5,727 0 2,249 26.9 .. 3.4 17.5b 61.0 b Greece .. .. .. .. .. .. .. .. .. .. Guatemala 14,340 35.9 5,527 7,218 1,595 11.1 26.8 14.3 31.7 117.6 Guinea 2,923 69.1 2,752 .. 123 4.2 .. 5.6 53.6b 145.2b Guinea-Bissau 1,095 124.8 963 .. 128 11.7 81.6 .. 14.6b 86.6b Haiti 492 7.3 479 .. 0 0.0 0.0 15.7 4.3b 31.7b 362 2012 World Development Indicators 6.9 GLOBAL LINKS External debt Total external Long-term Short-term Total Present value of debt debt debt debt debt service $ millions % of exports % of exports Public and of goods and of goods and publicly Private % of total services and services and $ millions % of GNI guaranteed nonguaranteed $ millions % of total debt reserves incomea % of GNIa incomea 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Honduras 4,168 28.2 2,798 942 398 9.6 14.7 7.6 14.0b 26.9b Hungary .. .. .. .. .. .. .. .. .. .. India 290,282 16.9 106,205 127,629 56,448 19.4 18.8 5.6 17.7 79.4 Indonesia 179,064 26.1 91,024 56,785 31,255 17.5 32.5 16.6 28.2 102.0 Iran, Islamic Rep. 12,570 .. 6,411 0 6,159 49.0 .. 2.0 3.3 .. Iraq .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. Jamaica 13,865 104.2 7,593 4,307 1,180 8.5 47.2 27.9 102.2 221.3 Japan .. .. .. .. .. .. .. .. .. .. Jordan 7,822 27.9 6,504 .. 1,310 16.7 9.6 4.9 25.9 51.2 Kazakhstan 118,723 94.3 3,842 105,844 9,037 7.6 32.0 71.4 89.1 154.7 Kenya 8,400 26.9 6,978 0 1,005 12.0 23.3 4.4 19.6 71.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. Kosovo 342 6.0 342 .. 0 0.0 0.0 1.6 4.2 24.8 Kuwait .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 3,984 89.2 2,442 1,171 195 4.9 11.3 21.9 37.8 b 66.0 b Lao PDR 5,559 79.0 2,939 2,610 0 0.0 0.0 .. 65.4 210.8 Latvia 39,555 164.3 6,891 18,428 12,723 32.2 167.3 76.4 129.1 260.0 Lebanon 24,293 60.7 20,213 500 3,482 14.3 7.8 19.1 67.1 98.4 Lesotho 726 28.4 698 .. 0 0.0 .. 1.9 17.1 23.6 Liberia 228 28.3 184 .. 0 0.0 .. 1.3 11.7b 13.8b Libya .. .. .. .. .. .. .. .. .. .. Lithuania 29,602 83.0 11,664 12,468 5,469 18.5 80.0 34.3 68.7 107.1 Macedonia, FYR 5,804 65.1 1,865 1,885 2,054 35.4 90.2 15.2 57.0 118.4 Madagascar 2,295 26.6 1,981 3 214 9.3 18.3 2.6 20.8 b 60.2b Malawi 922 18.5 715 .. 61 6.6 18.7 .. 15.7b 63.4b Malaysia 81,497 35.4 25,795 20,626 35,076 43.0 32.9 5.2 35.7 33.1 Mali 2,326 26.1 2,271 .. 6 0.3 0.5 2.5 16.5b 57.6b Mauritania 2,461 67.0 2,174 .. 237 9.6 82.4 .. 68.4b 126.1b Mauritius 1,076 11.0 972 100 3 0.3 0.1 2.4 8.5 15.2 Mexico 200,081 19.5 111,467 49,600 39,013 19.5 32.4 9.8 18.0 60.5 Moldova 4,615 73.5 817 1,906 1,564 33.9 91.1 12.8 65.5 135.9 Mongolia 2,444 44.3 1,795 221 230 9.4 10.1 5.0 33.1 57.9 Morocco 25,403 28.1 21,015 2,589 1,800 7.1 7.6 10.7 23.4 67.1 Mozambique 4,124 43.8 2,960 .. 975 23.6 43.0 2.9 20.9b 65.6b Myanmar 6,352 .. 4,395 .. 1,956 30.8 .. .. .. .. Namibia .. .. .. .. .. .. .. .. .. .. Nepal 3,702 23.4 3,527 .. 61 1.6 2.1 10.5 19.9b 150.6b Netherlands .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. Nicaragua 4,786 76.9 2,668 1,255 697 14.6 38.7 14.3 36.8b 70.7b Niger 1,127 20.5 972 0 94 8.4 12.4 .. 10.5b 53.2b Nigeria 7,883 4.5 4,686 .. 3,197 40.6 8.9 0.4 3.2 7.4 Norway .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. Pakistan 56,773 31.3 43,202 2,544 2,291 4.0 13.3 15.2 24.1 158.8 Panama 11,412 45.8 10,421 991 0 0.0 0.0 5.7 43.7 54.7 Papua New Guinea 5,822 62.9 1,030 4,400 392 6.7 12.6 12.9 54.9 79.1 Paraguay 4,938 25.3 2,369 1,421 1,147 23.2 27.5 4.6 25.7 47.7 Peru 36,271 24.6 20,027 10,189 6,055 16.7 13.7 16.7 24.9 88.9 Philippines 72,337 36.2 44,641 21,402 6,295 8.7 10.1 18.4 34.6 99.6 Poland .. .. .. .. .. .. .. .. .. .. Portugal .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 363 6.9 External debt Total external Long-term Short-term Total Present value of debt debt debt debt debt service $ millions % of exports % of exports Public and of goods and of goods and publicly Private % of total services and services and $ millions % of GNI guaranteed nonguaranteed $ millions % of total debt reserves incomea % of GNIa incomea 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Romania 121,505 76.4 20,557 60,826 25,029 20.6 52.1 31.2 58.1 170.9 Russian Federation 384,740 26.9 162,924 183,059 38,756 10.1 8.1 12.8 24.7 72.1 Rwanda 795 14.2 766 .. 14 1.8 1.7 2.3 11.5b 102.7b Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegal 3,677 28.5 3,155 308 0 0.0 0.0 .. 20.0 b 67.3b Serbia 32,222 84.3 9,477 17,912 2,798 8.7 21.0 30.9 67.1 201.2 Sierra Leone 778 40.8 661 .. 4 0.5 1.0 2.6 22.8b 115.1b Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. Somalia 2,942 .. 1,990 .. 780 26.5 .. .. .. .. South Africa 45,165 12.7 17,753 15,107 12,305 27.2 28.1 4.9 14.7 45.4 South Sudan .. .. .. .. .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. .. .. Sri Lanka 20,452 41.8 16,449 919 1,773 8.7 24.6 13.0 36.6 156.1 Sudan 21,846 39.1 14,444 0 7,012 32.1 676.7 4.2 70.2b 339.2b Swaziland 616 17.2 385 .. 231 37.5 30.5 .. 19.5 26.3 Sweden .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 4,729 8.2 4,171 0 558 11.8 2.7 .. 7.9 23.1 Tajikistan 2,955 53.1 1,806 927 122 4.1 .. 44.8 42.4 145.9 Tanzania 8,664 37.7 5,572 1,224 1,515 17.5 38.8 3.0 22.8 b 84.7b Thailand 71,263 23.4 11,357 21,434 38,471 54.0 22.4 4.8 24.2 31.2 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 1,728 61.1 1,534 .. 61 3.5 8.5 .. 13.9b 34.9b Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. Tunisia 21,584 51.1 14,609 1,996 4,979 23.1 51.0 10.4 47.5 77.1 Turkey 293,872 40.4 93,088 117,035 78,123 26.6 90.9 36.7 39.4 165.0 Turkmenistan 422 2.1 359 7 55 13.0 .. .. 2.2 2.5 Uganda 2,994 17.9 2,671 .. 314 10.5 11.1 1.8 7.1b 33.1b Ukraine 116,808 85.9 16,246 59,858 26,459 22.7 76.5 40.7 75.0 144.0 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. Uruguay 11,347 29.0 9,704 93 1,550 13.7 20.2 12.4 30.6 101.6 Uzbekistan 7,404 19.0 3,426 3,741 238 3.2 .. .. 18.1 45.3 Venezuela, RB 55,572 14.3 37,086 3,060 15,426 27.8 52.0 .. .. .. Vietnam 35,139 36.5 28,145 .. 6,949 19.8 55.7 1.7 28.9 37.1 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,324 .. 5,933 .. 313 5.0 5.3 2.8 15.2 44.4 Zambia 3,689 25.8 1,309 794 1,191 32.3 56.9 1.9 12.0 b 26.6b Zimbabwe 5,016 71.8 3,686 378 843 16.8 .. .. 110.3 275.3 World .. .. .. .. .. .. .. .. .. .. Low income 116,593 28.5 95,929 3,703 12,806 11.0 19.0 .. .. .. Middle income 3,959,705 20.8 1,486,657 1,388,971 1,023,572 25.8 18.3 .. .. .. Lower middle income 1,021,016 24.7 518,320 308,804 163,917 16.1 21.9 .. .. .. Upper middle income 2,938,688 19.7 968,337 1,080,167 859,655 29.3 17.7 .. .. .. Low & middle income 4,076,298 21.0 1,582,586 1,392,674 1,036,378 25.4 18.3 .. .. .. East Asia & Pacific 1,013,971 13.5 306,774 238,402 468,525 46.2 13.8 .. .. .. Europe & Central Asia 1,273,418 43.0 366,663 627,496 234,232 18.4 31.1 .. .. .. Latin America & Carib. 1,038,725 21.7 457,714 370,470 208,277 20.1 32.3 .. .. .. Middle East & N. Africa 143,595 14.1 113,766 6,106 23,528 16.4 5.3 .. .. .. South Asia 400,596 19.2 194,376 131,428 63,879 15.9 18.7 .. .. .. Sub-Saharan Africa 205,992 20.0 143,293 18,772 37,936 18.4 21.8 .. .. .. High income .. .. .. .. .. .. .. .. .. .. Euro area .. .. .. .. .. .. .. .. .. .. a. The numerator refers to 2010, whereas the denominator is a three-year average of 2008–10 data. b. Data are from debt sustainability analyses for low-income countries and include the effects of traditional relief, debt relief under the Heavily Indebted Poor Country Initiative, and relief under the Multilateral Debt Relief Initiative. Present value estimates for these countries are for public and publicly guaranteed debt only. 364 2012 World Development Indicators 6.9 GLOBAL LINKS External debt About the data De�nitions External indebtedness affects a country’s credit- Where such information is not available from the •  Total external debt is debt owed to nonresident worthiness and investor perceptions. Data on exter- debtor country, data are derived from BIS data on creditors and repayable in foreign currencies, goods, or nal debt are gathered through the World Bank’s international bank lending based on time remaining services by public and private entities in the country. It Debtor Reporting System (DRS). Indebtedness is to original maturity. The data are reported based is the sum of long-term external debt, short-term debt, calculated using loan-by-loan reports submitted by on residual maturity, but an estimate of short-term and use of IMF credit. Debt repayable in domestic cur- countries on long-term public and publicly guaran- external liabilities by original maturity can be derived rency is excluded. • Long-term debt is debt that has teed borrowing and information on short-term debt by deducting from claims due in one year those that an original or extended maturity of more than one year. collected by the countries or from creditors through have a maturity of between one and two years. It has three components: public, publicly guaranteed, the reporting systems of the Bank for International However, BIS data include liabilities reported only and private nonguaranteed debt. • Public and publicly Settlements (BIS). These data are supplemented by by banks within the BIS reporting area. The results guaranteed debt is the long-term external obligations information from major multilateral banks and official should thus be interpreted with caution. Because of public debtors, including the national government lending agencies in major creditor countries and by short-term debt poses an immediate burden and is and political subdivisions (or an agency of either) and estimates by World Bank and International Monetary particularly important for monitoring vulnerability, it autonomous public bodies, and the external obliga- Fund (IMF) staff. is compared with total debt and foreign exchange tions of private debtors that are guaranteed for repay- Currently, 129 developing countries report to the reserves, which are instrumental in providing cover- ment by a public entity. • Private nonguaranteed debt DRS. Nonreporting countries might have outstanding age for such obligations. is the long-term external obligations of private debt- debt with the World Bank, other international finan- Total debt service is contrasted with countries’ ors that are not guaranteed for repayment by a public cial institutions, or private creditors. ability to obtain foreign exchange through exports of entity. • Short-term debt is debt owed to nonresidents Debt data, normally reported in the currency of goods, services, income, and workers’ remittances. having an original maturity of one year or less and inter- repayment, are converted into U.S. dollars to pro- The present value of external debt provides a mea- est in arrears on long-term debt and on the use of IMF duce summary tables. Stock fi gures (amount of sure of future debt service obligations. It is calcu- credit. • Total reserves are holdings of monetary gold, debt outstanding) are converted using end-of-period lated by discounting the debt service (interest plus special drawing rights, reserves of IMF members held exchange rates, as published in the IMF’s Interna- amortization) due on long-term external debt over by the IMF, and holdings of foreign exchange under tional Financial Statistics. Flow figures are converted the life of existing loans. Short-term debt is included the control of monetary authorities. • Total debt ser- at annual average exchange rates. Projected debt at face value. The discount rate on long-term debt vice is the sum of principal repayments and interest service is converted using end-of-period exchange depends on the currency of repayment and is based actually paid in foreign currency, goods, or services rates. Debt repayable in multiple currencies, goods, on commercial interest reference rates established on long-term debt, interest paid on short-term debt, or services and debt with a provision for maintenance by the Organisation for Economic Co-operation and and repayments (repurchases and charges) to the of the value of the currency of repayment are shown Development. Loans from the International Bank IMF. • Exports of goods and services and income at book value. for Reconstruction and Development (IBRD), cred- are the total value of exports of goods and services, A country’s external debt burden, both debt out- its from the International Development Association receipts of compensation of nonresident workers, and standing and debt service, affects its creditworthi- (IDA), and obligations to the IMF are discounted using investment income from abroad. • Present value of ness and vulnerability. The table shows total exter- a special drawing rights reference rate. When the debt is the sum of short-term external debt plus the nal debt relative to a country’s size—gross national discount rate is greater than the loan interest rate, discounted sum of total debt service payments due on income (GNI). While data related to public and pub- the present value is less than the nominal sum of public, publicly guaranteed, and private nonguaranteed licly guaranteed debt are reported to the DRS on a future debt service obligations. long-term external debt over the life of existing loans. loan-by-loan basis. Aggregate data on long-term pri- Debt ratios are used to assess the sustainability of Data sources vate nonguaranteed debt are reported annually and a country’s debt service obligations, but no absolute are reported by the country or estimated by World rules determine what values are too high. Empirical Data on external debt are mainly from reports Bank staff for countries where this type of external analysis of developing countries’ experience and to the World Bank through its DRS from member debt is known to be significant. Estimates are based debt service performance shows that debt service countries that have received IBRD loans or IDA on national data from the World Bank’s Quarterly difficulties become increasingly likely when the pres- credits, with additional information from the files External Debt Statistics. ent value of debt reaches 200 percent of exports. of the World Bank, the IMF, the African Develop- The DRS encourages debtor countries to volun- Still, what constitutes a sustainable debt burden var- ment Bank and African Development Fund, the tarily provide information on their short-term external ies by country. Countries with fast-growing econo- Asian Development Bank and Asian Development obligations. By its nature, short-term external debt mies and exports are likely to be able to sustain Fund, and the Inter-American Development Bank. is diffi cult to monitor: loan-by-loan registration is higher debt levels. Summary tables of the external debt of develop- normally impractical, and monitoring systems typi- ing countries are published annually in the World cally rely on information requested periodically by Bank’s Global Development Finance, on its Global the central bank from the banking sector. The World Development Finance CD-ROM, and in its Global Bank regards the debtor country as the authorita- Development Finance database. tive source of information on its short-term debt. 2012 World Development Indicators 365 6.10 Global private financial flows Equity net flows Debt flows Foreign direct investment $ millions Net inflow Portfolio equity Commercial bank and $ millions % of GDP $ millions Bonds other lending 2010 2010 2010 2010 2010 Afghanistan 76 0.4 .. 0 0 Albania 1,110 9.4 8 398 –35 Algeria 2,291 1.4 .. 0 –398 Angola –3,227 –3.8 0 0 –1,553 Argentina 7,055 1.9 –208 –1,660 –2,477 Armenia 570 6.1 0 0 703 Australia 30,576 2.7 9,974 .. .. Austria –25,636 –6.8 –385 .. .. Azerbaijan 563 1.1 1 0 2,021 Bahrain 156 .. 1,653 .. .. Bangladesh 917 0.9 0 0 –11 Belarus 1,403 2.6 1 1,777 702 Belgium 72,914 15.5 –1,837 .. .. Benin 111 1.7 .. 0 0 Bolivia 622 3.2 0 0 –359 Bosnia and Herzegovina 232 1.4 0 –25 –913 Botswana 265 1.8 18 0 –1 Brazil 48,506 2.3 37,684 24,086 19,981 Bulgaria 2,355 4.9 9 0 –1,820 Burkina Faso 37 0.4 .. 0 –2 Burundi 1 0.1 0 0 0 Cambodia 783 7.0 0 0 0 Cameroon –1 0.0 0 0 –32 Canada 23,587 1.5 17,775 .. .. Central African Republic 72 3.6 .. 0 0 Chad 781 10.3 .. 0 –2 Chile 15,095 7.1 1,748 4,867 3,813 China 185,081 3.1 31,357 11,112 2,066 Hong Kong SAR, China 71,066 31.7 18,534 .. .. Colombia 6,914 2.4 1,351 972 4,302 Congo, Dem. Rep. 2,939 22.4 .. 0 –4 Congo, Rep. 2,816 23.7 .. 0 –24 Costa Rica 1,466 4.1 0 0 313 Côte d’Ivoire 418 1.8 .. 0 –58 Croatia 408 0.7 112 .. .. Cuba 86 .. .. .. .. Cyprus 1,886 8.2 440 .. .. Czech Republic 6,720 3.5 287 .. .. Denmark –7,697 –2.5 7,262 .. .. Dominican Republic 1,626 3.1 0 645 –85 Ecuador 167 0.3 0 –6 –3 Egypt, Arab Rep. 6,386 2.9 1,724 1,500 19 El Salvador –6 0.0 0 0 131 Eritrea 56 2.6 .. 0 0 Estonia 1,539 8.0 15 .. .. Ethiopia 288 1.0 0 0 647 Finland 7,072 3.0 1,980 .. .. France 33,672 1.3 –8,442 .. .. Gabon 170 1.3 .. –23 189 Gambia, The 37 4.6 0 0 6 Georgia 817 7.0 –20 250 100 Germany 46,127 1.4 –1,991 .. .. Ghana 2,527 8.1 0 0 251 Greece 430 0.1 –1,459 .. .. Guatemala 881 2.1 0 0 –88 Guinea 101 2.3 0 0 4 Guinea-Bissau 9 1.0 .. 0 0 Haiti 150 2.2 0 0 0 366 2012 World Development Indicators 6.10 GLOBAL LINKS Global private financial flows Equity net flows Debt flows Foreign direct investment $ millions Net inflow Portfolio equity Commercial bank and $ millions % of GDP $ millions Bonds other lending 2010 2010 2010 2010 2010 Honduras 797 5.2 0 0 30 Hungary –42,283 –32.9 –143 .. .. India 24,159 1.4 39,972 10,339 12,971 Indonesia 13,371 1.9 2,132 2,329 3,563 Iran, Islamic Rep. 3,617 .. .. 0 –1,084 Iraq 1,426 1.7 .. .. .. Ireland 27,085 13.1 152,236 .. .. Israel 5,152 2.4 –612 .. .. Italy 9,594 0.5 3,826 .. .. Jamaica 228 1.6 0 1,007 146 Japan –1,359 0.0 40,328 .. .. Jordan 1,701 6.2 –20 733 2 Kazakhstan 10,677 7.2 131 –1,053 7,156 Kenya 186 0.6 33 0 8 Korea, Dem. Rep. 38 .. .. .. .. Korea, Rep. –150 0.0 23,026 .. .. Kosovo 481 8.7 0 0 0 Kuwait 81 .. –815 .. .. Kyrgyz Republic 438 9.5 –18 0 97 Lao PDR 279 3.8 54 0 –14 Latvia 369 1.5 9 0 –4,045 Lebanon 4,280 11.0 163 –396 5 Lesotho 117 5.5 0 0 0 Liberia 453 45.9 0 0 0 Libya 1,784 .. 0 .. .. Lithuania 748 2.1 37 2,458 –3,897 Macedonia, FYR 207 2.3 –4 0 36 Madagascar 860 9.9 .. 0 –1 Malawi 140 2.7 .. 0 0 Malaysia 9,167 3.9 .. 2,024 2,017 Mali 148 1.6 .. 0 –1 Mauritania 14 0.4 .. 0 –11 Mauritius 431 4.4 –40 0 27 Mexico 19,792 1.9 641 13,338 643 Moldova 193 3.3 6 0 119 Mongolia 1,455 23.5 680 –75 151 Morocco 1,241 1.4 132 1,327 77 Mozambique 789 8.2 0 0 71 Myanmar 910 .. .. 0 –546 Namibia 796 6.5 4 .. .. Nepal 88 .. .. 0 0 Netherlands –15,597 –2.0 11,327 .. .. New Zealand 701 .. –298 .. .. Nicaragua 508 7.8 0 0 –63 Niger 947 17.1 .. 0 –7 Nigeria 6,049 3.1 2,161 0 –33 Norway 11,747 2.8 1,993 .. .. Oman 2,333 .. 703 .. .. Pakistan 2,022 1.1 524 –1,200 –227 Panama 2,350 8.8 0 –150 0 Papua New Guinea 29 0.3 .. 0 2,418 Paraguay 345 1.9 0 0 426 Peru 7,328 4.7 87 4,635 971 Philippines 1,713 0.9 503 2,712 3,767 Poland 9,104 1.9 7,875 .. .. Portugal 1,476 0.7 –1,628 .. .. Puerto Rico .. .. .. .. .. Qatar 5,534 .. .. .. .. 2012 World Development Indicators 367 6.10 Global private financial flows Equity net flows Debt flows Foreign direct investment $ millions Net inflow Portfolio equity Commercial bank and $ millions % of GDP $ millions Bonds other lending 2010 2010 2010 2010 2010 Romania 2,941 1.8 –25 –929 –858 Russian Federation 43,288 2.9 –4,808 14,900 –6,734 Rwanda 42 0.8 21 0 0 Saudi Arabia 21,560 5.0 .. .. .. Senegal 237 1.8 .. 0 –63 Serbia 1,340a 3.5 84 0 312 Sierra Leone 87 4.6 0 0 0 Singapore 38,638 18.5 3,559 .. .. Slovak Republic 553 0.6 25 .. .. Slovenia 366 0.8 169 .. .. Somalia 112 .. .. 0 0 South Africa 1,224 0.3 5,826 1,422 795 South Sudan .. .. .. .. .. Spain 24,658 1.8 –4,790 .. .. Sri Lanka 478 1.0 –1,049 1,000 72 Sudan 2,064 3.3 1 0 0 Swaziland 136 3.7 5 0 0 Sweden 5,847 1.3 5,474 .. .. Switzerland 21,707 4.1 –7,210 .. .. Syrian Arab Republic 1,469 2.5 .. 0 0 Tajikistan 16 0.3 0 0 50 Tanzania 433 1.9 3 0 137 Thailand 9,679 3.0 2,606 1,730 –452 Timor-Leste 280 39.9 .. .. .. Togo 41 1.3 .. 0 0 Trinidad and Tobago 549 2.7 .. .. .. Tunisia 1,401 3.2 –26 0 –550 Turkey 9,084 1.2 3,468 5,961 –8,310 Turkmenistan 2,083 10.4 .. 0 –39 Uganda 817 4.8 –70 0 0 Ukraine 6,495 4.7 290 3,089 6,892 United Arab Emirates 3,948 1.3 .. .. .. United Kingdom 52,968 2.3 –11,488 .. .. United States 236,226 1.6 172,376 .. .. Uruguay 1,627 4.2 –12 –93 7 Uzbekistan 822 2.1 .. 0 534 Venezuela, RB 1,209 0.3 10 1,141 –264 Vietnam 8,000 7.5 2,383 981 129 West Bank and Gaza .. .. .. .. .. Yemen, Rep. 56 0.2 0 0 –1 Zambia 1,041 6.4 101 0 –21 Zimbabwe 105 1.4 .. 0 289 World 1,430,438 s 2.3 s 779,547 s .. s .. s Low income 13,017 3.4 –31 0 733 Middle income 501,236 2.6 129,690 111,383 43,393 Lower middle income 90,233 2.1 49,598 22,252 29,824 Upper middle income 411,003 2.8 80,092 89,130 13,569 Low & middle income 514,253 2.6 129,660 111,383 44,126 East Asia & Pacific 231,299 3.1 39,715 20,813 13,127 Europe & Central Asia 86,991 2.8 –840 27,091 –7,863 Latin America & Carib. 117,368 2.4 41,302 48,776 27,376 Middle East & N. Africa 25,688 2.7 1,973 3,164 –1,933 South Asia 27,923 1.3 39,447 10,139 12,751 Sub-Saharan Africa 24,984 2.3 8,063 1,400 669 High income 916,185 2.1 649,887 .. .. Euro area 395,004 3.3 359,369 .. .. a. Includes Montenegro. 368 2012 World Development Indicators 6.10 GLOBAL LINKS Global private financial flows About the data De�nitions Private financial flows—equity and debt—account for from the International Development Association. The • Foreign direct investment is net inflows of invest- the bulk of development finance. Equity flows com- reports are cross-checked with data from market ment to acquire a lasting interest in or management prise foreign direct investment (FDI) and portfolio sources that include transactions data. Information control over an enterprise operating in an economy equity. Debt flows are financing raised through bond on private nonguaranteed bonds and bank lending other than that of the investor. It is the sum of equity issuance, bank lending, and supplier credits. Data is collected from market sources when data are not capital, reinvested earnings, other long-term capital, on equity flows are based on balance of payments reported to the Debtor Reporting System. and short-term capital, as shown in the balance of data reported by the International Monetary Fund Data on equity flows are shown for all countries payments. Net inflows are new investments less dis- (IMF). FDI data are supplemented by staff estimates for which data are available. Debt flows are shown investments. • Portfolio equity includes net inflows using data from the United Nations Conference on only for 129 developing countries that report to the from equity securities other than those recorded Trade and Development and official national sources. Debtor Reporting System; nonreporting countries as direct investment and including shares, stocks, The internationally accepted definition of FDI (from may also receive debt flows. depository receipts, and direct purchases of shares the fifth edition of the IMF’s Balance of Payments The volume of global private fi nancial fl ows in local stock markets by foreign investors • Bonds Manual [1993]), includes three components: equity reported by the World Bank generally differs from are securities issued with a fixed rate of interest for a investment, reinvested earnings, and short- and that reported by other sources because of differ- period of more than one year. They include net flows long-term loans between parent firms and foreign ences in sources, classification of economies, and through cross-border public and publicly guaranteed affiliates. Distinguished from other kinds of interna- method used to adjust and disaggregate reported and private nonguaranteed bond issues. • Commer- tional investment, FDI is made to establish a lasting information. In addition, particularly for debt financ- cial bank and other lending includes net commercial interest in or effective management control over an ing, differences may also reflect how some install- bank lending (public and publicly guaranteed and pri- enterprise in another country. A lasting interest in an ments of the transactions and certain offshore issu- vate nonguaranteed) and other private credits. investment enterprise typically involves establish- ances are treated. 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 Global Development Finance. 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 International Financial Statistics and Balance of of public and publicly guaranteed debt and private Payments databases, and Dealogic. These data nonguaranteed debt. Data on public and publicly are also published annually in the World Bank’s guaranteed debt are reported through the Debtor Global Development Finance, on its Global Develop- Reporting System by World Bank member econo- ment Finance CD-ROM, and in the Global Develop- mies that have received loans from the International ment Finance database. Bank for Reconstruction and Development or credits 2012 World Development Indicators 369 6.11 Net official financial flows Total International �nancial institutions $ millions $ millions From From World Bank IMF Regional development banksa bilateral multilateral Non- Non- Other sources sourcesa IDA IBRD Concessional concessional Concessional concessional institutions 2010 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan 0.0 77.8 8.4 .. 8.6 .. 64.9 .. 4.5 Albania –7.1 102.5 15.1 3.1 –12.1 –0.2 .. 1.9 82.3 Algeria –142.4 –2.3 .. –0.5 .. .. .. .. –1.7 Angola 3,372.3 1.5 –0.7 .. .. 524.2 3.0 –0.4 –0.5 Argentina –249.8 788.3 .. 46.2 .. .. .. 609.9 136.7 Armenia 64.9 109.5 20.3 52.4 35.5 127.4 22.3 8.7 5.9 Australia .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. Azerbaijan 147.7 310.4 35.9 101.2 –14.7 .. 5.1 33.6 134.6 Bahrain .. .. .. .. .. .. .. .. .. Bangladesh –112.3 352.1 83.5 .. –45.6 0.0 75.0 166.1 27.6 Belarus 1,589.5 33.6 .. 35.7 .. 668.3 .. –2.1 .. Belgium .. .. .. .. .. .. .. .. .. Benin 19.2 144.2 78.3 .. 16.2 .. 40.1 .. 25.7 Bolivia 45.8 297.4 44.0 0.0 .. .. 86.2 29.7 140.8 Bosnia and Herzegovina 65.5 277.4 97.2 25.7 .. 237.4 .. 58.6 100.7 Botswana –13.6 –24.8 –0.5 6.5 .. .. –1.8 –3.0 –26.0 Brazil 3,615.0 5,069.6 .. 3,511.7 .. .. .. 951.3 589.8 Bulgaria –52.6 –8.6 .. –69.9 .. .. .. –11.8 73.0 Burkina Faso 16.5 228.4 66.6 .. 20.2 .. 63.4 .. 98.5 Burundi .. 33.9 17.8 .. 20.1 .. 1.6 .. 14.4 Cambodia 262.7 50.6 9.0 .. .. .. 35.1 .. 6.6 Cameroon 34.8 119.4 80.6 –5.4 0.0 .. 50.3 –9.7 3.6 Canada .. .. .. .. .. .. .. .. .. Central African Republic –0.1 5.1 5.0 .. 13.2 .. 0.0 .. 0.1 Chad –4.1 17.9 –16.0 .. –11.1 .. 3.8 .. 30.2 Chile 36.9 –44.8 –0.7 –25.8 .. .. .. –18.3 .. China –123.3 828.7 –348.4 195.6 .. .. .. 1,033.3 –51.8 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. Colombia –81.8 1,094.1 –0.7 937.2 .. .. –1.6 188.0 –28.7 Congo, Dem. Rep. –23.4 –15.8 17.5 .. 18.9 .. 17.5 –38.1 –12.7 Congo, Rep. –42.4 –11.2 1.5 .. 1.8 .. –0.2 –8.3 0.2 Costa Rica 10.1 527.6 –0.2 512.3 .. .. –8.2 18.6 5.0 Côte d’Ivoire –36.3 –81.3 –0.9 –24.9 44.4 .. –2.2 –32.5 –5.0 Croatia .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. Cyprus .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. Dominican Republic 362.0 470.1 –0.7 116.2 .. 382.9 –21.2 359.5 16.2 Ecuador 890.3 826.1 –1.1 –85.5 .. .. –26.7 64.0 455.4 Egypt, Arab Rep. –1,015.2 772.7 –44.1 693.6 .. .. –6.0 140.5 –11.2 El Salvador –44.4 365.8 –0.8 345.5 .. .. –23.2 26.0 18.3 Eritrea –4.1 0.1 –0.5 .. .. .. 3.7 .. –3.1 Estonia .. .. .. .. .. .. .. .. .. Ethiopia 510.7 472.1 384.7 .. 122.4 .. 80.8 –6.7 13.3 Finland .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. Gabon 15.9 –7.4 .. 5.6 .. .. –0.2 2.6 –15.4 Gambia, The 6.5 18.5 1.9 .. 3.0 .. 2.3 .. 14.3 Georgia 36.1 294.0 52.9 72.8 –21.5 296.9 38.3 118.7 11.2 Germany .. .. .. .. .. .. .. .. .. Ghana 357.9 427.0 304.8 .. 124.4 .. 130.0 0.0 –7.1 Greece .. .. .. .. .. .. .. .. .. Guatemala –14.6 631.0 .. 260.0 .. .. –18.3 320.8 68.5 Guinea –9.0 –11.0 0.0 .. –10.2 .. 6.3 –4.6 –12.7 Guinea-Bissau .. –2.5 –1.0 .. 15.7 –8.1 –2.4 .. 0.8 Haiti 121.4 42.3 –35.9 .. 124.8 .. 69.5 .. 8.8 370 2012 World Development Indicators 6.11 GLOBAL LINKS Net official financial flows Total International �nancial institutions $ millions $ millions From From World Bank IMF Regional development banksa bilateral multilateral Non- Non- Other sources sourcesa IDA IBRD Concessional concessional Concessional concessional institutions 2010 2010 2010 2010 2010 2010 2010 2010 2010 Honduras 2.4 388.9 108.7 .. –1.6 .. 101.0 36.1 143.2 Hungary .. .. .. .. .. .. .. .. .. India 826.1 4,575.8 231.5 2,795.2 .. .. .. 1,440.1 97.2 Indonesia –8.2 1,367.1 110.0 1,177.4 .. .. 66.1 13.6 0.0 Iran, Islamic Rep. –289.5 73.4 .. 64.2 .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. Jamaica 32.1 835.2 .. 180.6 .. 778.1 –4.2 559.8 99.1 Japan .. .. .. .. .. .. .. .. .. Jordan 13.1 153.9 –2.6 –68.5 .. –3.9 .. .. 225.0 Kazakhstan –195.0 1,436.1 .. 1,283.0 .. .. –0.2 101.9 51.3 Kenya 40.4 223.3 132.4 .. –25.5 .. 83.7 –2.4 9.6 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. Kosovo .. –16.3 .. –16.3 .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. Kyrgyz Republic 101.3 7.7 5.4 .. 12.4 .. –8.5 2.3 8.5 Lao PDR 57.3 –31.2 –12.6 .. –5.5 .. –11.1 0.2 –7.6 Latvia –17.6 1,086.1 .. 117.6 .. 409.6 .. –1.8 970.3 Lebanon –71.8 –26.3 .. 3.0 .. –19.4 .. .. –29.3 Lesotho 0.6 20.9 22.3 –0.7 4.4 .. –1.6 .. 0.9 Liberia 3.7 –3.7 –1.7 .. 13.6 .. –2.1 0.0 .. Libya .. .. .. .. .. .. .. .. .. Lithuania .. 446.0 .. –3.1 .. .. .. –4.9 454.0 Macedonia, FYR 0.0 85.4 –8.2 61.6 .. .. .. –5.8 37.9 Madagascar 68.9 106.4 74.6 .. –1.7 .. 12.5 .. 19.3 Malawi 22.8 44.8 33.8 .. 21.2 .. 4.9 –2.1 8.2 Malaysia –599.4 –69.9 .. –38.5 .. .. .. –21.5 –10.0 Mali 42.9 230.6 151.3 .. 5.9 .. 65.5 .. 13.8 Mauritania 97.6 249.4 38.7 .. 33.7 .. 9.9 –8.1 209.0 Mauritius 110.7 136.8 –0.6 –3.0 .. .. –0.2 153.9 –13.3 Mexico 464.4 3,580.4 .. 2,255.6 .. .. .. 1,299.1 25.7 Moldova –6.8 48.8 59.4 –17.8 113.6 61.0 .. –3.1 10.2 Mongolia 11.6 20.2 17.3 .. –4.6 23.4 0.5 .. 2.3 Morocco 504.5 625.7 –1.4 69.2 .. .. –1.1 160.3 398.7 Mozambique 7.8 237.6 157.7 .. 21.4 .. 68.4 .. 11.4 Myanmar –103.6 –0.8 .. .. .. .. 0.0 .. –0.8 Namibia .. .. .. .. .. .. .. .. .. Nepal –4.2 –11.1 –32.6 .. 39.4 .. 15.8 .. 5.7 Netherlands .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. Nicaragua –3.1 215.0 36.2 .. 19.5 .. 97.3 43.7 37.4 Niger 14.7 64.1 14.1 .. 4.9 .. 16.4 .. 33.7 Nigeria –28.7 866.5 975.4 –70.8 .. .. 21.9 –65.2 5.1 Norway .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. Pakistan –8.4 439.4 204.9 –127.1 –264.6 1,633.8 87.5 148.2 126.0 Panama 87.2 188.1 .. –14.8 .. .. –5.6 214.9 –6.5 Papua New Guinea –21.9 –2.1 4.6 –10.6 .. .. –5.5 13.1 –3.8 Paraguay –62.9 142.4 –1.5 –25.0 .. .. –11.3 140.4 43.3 Peru –841.9 –527.3 .. 118.9 .. .. .. –755.5 109.3 Philippines –292.0 –179.9 –6.9 58.9 .. .. –41.1 –203.2 12.5 Poland .. .. .. .. .. .. .. .. .. Portugal .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 371 6.11 Net official financial flows Total International �nancial institutions $ millions $ millions From From World Bank IMF Regional development banksa bilateral multilateral Non- Non- Other sources sourcesa IDA IBRD Concessional concessional Concessional concessional institutions 2010 2010 2010 2010 2010 2010 2010 2010 2010 Romania 84.4 3,374.6 .. –74.6 .. 5,664.3 4.7 –51.5 3,496.0 Russian Federation –297.2 –659.5 .. –596.4 .. .. .. –61.1 –2.1 Rwanda 17.1 36.0 9.4 .. –0.1 .. 14.8 .. 11.9 Saudi Arabia .. .. .. .. .. .. .. .. .. Senegal 68.0 204.8 108.5 .. 48.9 .. 63.0 –12.7 46.2 Serbia 142.5 708.5 16.4 172.0 .. 457.6 .. 181.4 326.2 Sierra Leone 6.1 55.5 32.1 .. 40.7 .. 14.9 .. 8.6 Singapore .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. Somalia 0.0 0.0 0.0 .. .. .. 0.0 0.0 0.0 South Africa .. 872.8 .. 363.1 .. .. .. 509.8 .. South Sudan .. .. .. .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. .. Sri Lanka 1,002.8 293.8 82.9 .. –11.7 608.5 18.0 184.6 8.3 Sudan 471.9 65.9 0.0 .. .. –5.8 0.0 0.0 65.9 Swaziland 0.9 –19.8 –0.3 –5.2 .. .. –1.4 –7.8 –5.1 Sweden .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. Syrian Arab Republic –309.3 98.3 –1.5 0.0 0.0 0.0 0.0 0.0 99.8 Tajikistan 83.7 129.8 10.7 .. 59.8 .. 33.0 –0.9 17.1 Tanzania 143.5 817.7 650.0 .. 29.9 .. 138.6 –1.0 30.2 Thailand –352.5 –21.2 –3.4 –1.7 .. .. .. –4.8 –11.3 Timor-Leste .. .. .. .. .. .. .. .. .. Togo 36.7 20.9 –22.0 .. 43.6 .. –2.0 .. 34.7 Trinidad and Tobago .. .. .. .. .. .. .. .. .. Tunisia 83.8 547.3 –1.9 76.4 .. .. .. 103.6 369.3 Turkey 174.9 3,657.1 –5.9 2,094.3 .. –2,170.8 .. .. 1,533.7 Turkmenistan –75.8 –2.9 .. –1.4 .. .. .. .. –1.5 Uganda 25.5 421.2 323.0 .. –0.3 .. 92.0 0.0 6.3 Ukraine –148.3 –6.1 .. –84.7 .. 3,433.3 .. –11.9 90.5 United Arab Emirates .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. Uruguay –20.6 –175.0 .. –42.0 .. .. –1.8 –408.4 279.2 Uzbekistan 72.7 106.3 32.1 –27.6 .. .. 36.3 55.0 10.5 Venezuela, RB –78.7 895.0 .. .. .. .. .. 425.5 469.4 Vietnam 588.6 1,924.7 800.9 700.0 –37.9 .. 171.4 170.3 82.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. Yemen, Rep. –15.2 148.6 28.2 .. 26.0 .. .. .. 120.4 Zambia 59.3 70.6 29.9 .. 55.3 .. 51.7 –3.2 –7.9 Zimbabwe 18.7 –0.2 0.0 –0.1 –4.0 .. 0.0 –0.1 0.0 World .. s .. s .. s .. s .. s .. s .. s .. s .. s Low income 1,525.0 3,790.5 2,155.6 –0.1 559.7 –8.1 1,008.3 112.5 434.0 Middle income 10,366.5 41,745.3 3,190.5 17,081.8 143.2 13,125.0 986.7 8,277.1 11,723.0 Lower middle income 5,826.9 14,764.8 3,365.2 5,803.1 163.2 6,702.9 1,026.0 2,747.0 1,839.5 Upper middle income 4,539.6 26,980.5 –174.8 11,278.7 –20.0 6,422.2 –39.2 5,530.1 9,883.5 Low & middle income 11,891.5 45,535.8 5,346.1 17,081.7 702.8 13,116.9 1,995.0 8,389.7 12,157.0 East Asia & Pacific –498.0 3,926.3 589.6 2,081.2 –38.5 23.4 230.9 1,012.0 12.7 Europe & Central Asia 1,760.8 11,516.2 334.3 3,121.8 172.9 9,184.9 130.9 408.2 7,408.4 Latin America & Carib. 4,312.3 15,755.4 163.8 8,090.7 146.7 1,161.0 276.8 4,120.5 2,659.8 Middle East & N. Africa –1,234.4 2,401.1 –24.0 837.3 22.7 –23.2 –5.4 404.3 1,179.7 South Asia 1,841.2 5,802.0 594.6 2,668.1 –272.4 2,247.8 301.2 1,960.1 266.3 Sub-Saharan Africa 5,709.6 6,134.8 3,687.8 282.7 671.5 523.1 1,060.6 484.6 630.0 High income .. .. .. .. .. .. .. .. .. Euro area .. .. .. .. .. .. .. .. .. a. Aggregates include amounts for economies not specified elsewhere. 372 2012 World Development Indicators 6.11 GLOBAL LINKS Net official financial flows About the data De�nitions The table shows concessional and nonconcessional and the Rapid Credit Facility. Eligibility is based prin- • Total net of�cial �nancial flows are disbursements financial flows from official bilateral sources and the cipally on a country’s per capita income and eligibility of public or publicly guaranteed loans and credits, major international financial institutions. The inter- under IDA. Nonconcessional lending from the IMF less repayments of principal. • IDA is the Interna- national financial institutions fund nonconcessional is provided mainly through Stand-by Arrangements, tional Development Association, the concessional lending operations primarily by selling low-interest, the Flexible Credit Line, and the Extended Fund Facil- arm of the World Bank Group. • IBRD is the Inter- highly rated bonds backed by prudent lending and ity. The IMF’s loan instruments have changed over national Bank for Reconstruction and Development, financial policies and the strong financial support of time to address the specific circumstances of its the founding and largest member of the World Bank their members. Funds are then on-lent to developing members. Group. •  IMF is the International Monetary Fund, countries at slightly higher interest rates with 15- to Regional development banks also maintain conces- which provides concessional lending through its 20-year maturities. Lending terms vary with market sional windows. Their loans are recorded in the table Extended Credit Facility, Standby Credit Facility, and conditions and institutional policies. according to each institution’s classification and not Rapid Credit Facility and nonconcessional lending Concessional fl ows from international financial according to the DAC definition. through credit to members, mainly for balance of institutions are credits provided through conces- Data for flows from international financial institu- payments needs. • Regional development banks are sional lending facilities. Subsidies from donors or tions are available for 129 countries that report to the African Development Bank, which serves Africa, other resources reduce the cost of these loans. the World Bank’s Debtor Reporting System. Non- including North Africa; the Asian Development Bank, Grants are not included in net flows. The Organisa- reporting countries may have net flows from other which serves South and Central Asia and East Asia tion for Economic Co-operation and Development’s international financial institutions. and Pacific; the European Bank for Reconstruction (OECD) Development Assistance Committee (DAC) and Development, which serves Europe and Central defines concessional flows from bilateral donors as Asia; and the Inter-American Development Bank, flows with a grant element of at least 25 percent; which serves the Americas. • Concessional financial they are evaluated assuming a 10 percent nominal flows are disbursements through concessional lend- discount rate. ing facilities. • Nonconcessional financial flows are World Bank concessional lending is done by the all disbursements that are not concessional. • Other International Development Association (IDA) based institutions, a residual category, includes such insti- on gross national income (GNI) per capita and per- tutions as the Caribbean Development Fund, Coun- formance standards assessed by World Bank staff. cil of Europe, European Development Fund, Islamic The cutoff for IDA eligibility, set at the beginning of Development Bank, and Nordic Development Fund. the World Bank’s fiscal year, has been $1,175 since July 1, 2011, measured in 2010 U.S. dollars using the World Bank Atlas method (see Users guide). In exceptional circumstances IDA extends temporary eligibility to countries above the cutoff that are undertaking major adjustments but are not cred- itworthy for International Bank for Reconstruction and Development (IBRD) lending. Exceptions are also made for small island economies. The IBRD lends to creditworthy countries at a variable base rate of six-month LIBOR plus a spread, either variable or fixed, for the life of the loan. The rate is reset every six months and applies to the interest period begin- ning on that date. Although some outstanding IBRD loans have a low enough interest rate to be classified as concessional under the DAC definition, all IBRD loans in the table are classified as nonconcessional. Data sources Lending by the International Finance Corporation, the Multilateral Investment Guarantee Agency, and the Data on net financial flows from international finan- International Centre for Settlement of Investment cial institutions are from the World Bank’s Debtor Disputes is excluded. Reporting System and published in the World The International Monetary Fund (IMF) makes con- Bank’s Global Development Finance 2012, on its cessional funds available through its Extended Credit Global Development Finance CD-ROM, and in its Facility (which replaced the Poverty Reduction and Global Development Finance database. Growth Facility in 2010), the Standby Credit Facility, 2012 World Development Indicators 373 6.12 Aid dependency Net of�cial Aid dependency development assistance ratios Net official development assistance $ millions % of imports of % of Total Per capita Technical % of gross capital goods, services, central government $ millions $ Grants cooperation % of GNI formation and income expense 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan 6,374 185 5,476 982 42.0 227.0 .. 80.2 Albania 357 112 160 101 2.9 11.1 5.0 .. Algeria 319 9 74 185 0.1 0.3 .. .. Angola 239 13 207 42 0.3 1.9 0.5 .. Argentina 127 3 36 69 0.0 0.2 0.2 .. Armenia 526 170 159 32 3.5 10.9 7.0 15.9 Australia Austria Azerbaijan 232 26 84 53 0.3 1.8 1.1 .. Bahrain .. .. .. .. .. .. .. .. Bangladesh 1,226 8 1,082 218 1.3 5.8 4.6 .. Belarus 98 10 105 37 0.3 0.6 0.4 0.8 Belgium Benin 682 79 462 92 10.4 40.4 .. 69.7 Bolivia 725 74 527 133 3.6 20.2 9.5 .. Bosnia and Herzegovina 414 110 214 138 2.9 15.2 4.9 7.3 Botswana 279 141 141 21 1.1 2.9 2.5 .. Brazil 337 2 413 237 0.0 0.2 0.2 .. Bulgaria .. .. .. .. .. .. .. .. Burkina Faso 1,083 68 766 125 12.1 .. .. 99.6 Burundi 561 69 525 74 39.8 .. 102.2 .. Cambodia 721 52 453 186 6.9 37.7 8.7 58.1 Cameroon 648 34 358 155 2.4 .. 8.0 .. Canada Central African Republic 242 56 241 17 13.1 .. .. .. Chad 561 51 453 40 7.3 17.6 .. .. Chile 79 5 83 65 0.1 0.4 0.2 0.4 China 1,129 1 459 960 0.0 0.0 0.0 .. Hong Kong SAR, China .. .. .. .. .. .. .. .. Colombia 1,059 23 669 141 0.3 1.3 1.5 1.7 Congo, Dem. Rep. 2,357 37 5,480 234 27.8 .. .. 189.1 Congo, Rep. 283 72 1,574 24 15.2 44.3 .. .. Costa Rica 109 24 75 29 0.3 1.3 0.6 .. Côte d’Ivoire 2,402 124 646 54 3.9 26.9 .. .. Croatia 169 38 106 39 0.3 1.0 0.6 0.7 Cuba 115 10 96 33 .. .. .. .. Cyprus .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. Denmark Dominican Republic 119 12 197 41 0.4 2.1 0.9 .. Ecuador 208 15 166 81 0.3 1.0 0.6 .. Egypt, Arab Rep. 999 13 655 165 0.3 1.4 0.9 0.9 El Salvador 276 45 331 59 1.4 10.1 2.9 6.4 Eritrea 144 28 147 7 7.7 .. .. .. Estonia .. .. .. .. .. .. .. .. Ethiopia 3,819 47 2,628 264 11.9 55.3 35.3 .. Finland France Gabon 77 52 56 41 0.9 3.1 .. .. Gambia, The 127 76 94 9 16.3 57.8 36.6 .. Georgia 907 206 377 122 5.5 27.5 9.1 20.4 Germany Ghana 1,582 66 920 123 5.5 24.2 11.7 .. Greece Guatemala 376 27 297 119 1.0 6.6 2.4 .. Guinea 214 22 167 62 5.1 23.6 11.3 .. Guinea-Bissau 147 99 270 18 16.0 .. .. .. Haiti 1,120 114 3,496 269 .. 183.4 75.1 .. 374 2012 World Development Indicators 6.12 GLOBAL LINKS Aid dependency Net of�cial Aid dependency development assistance ratios Net official development assistance $ millions % of imports of % of Total Per capita Technical % of gross capital goods, services, central government $ millions $ Grants cooperation % of GNI formation and income expense 2010 2010 2010 2010 2010 2010 2010 2010 Honduras 456 61 279 55 3.9 16.3 5.5 16.0 Hungary .. .. .. .. .. .. .. .. India 2,500 2 1,007 283 0.2 0.5 0.6 1.1 Indonesia 1,047 4 851 520 0.2 0.6 0.8 1.4 Iran, Islamic Rep. 92 1 78 73 .. .. .. .. Iraq 2,791 90 1,881 126 2.8 .. .. .. Ireland Israel .. .. .. .. .. .. .. .. Italy Jamaica 149 55 152 15 1.0 4.8 2.0 .. Japan Jordan 740 125 762 73 3.4 22.6 5.2 13.4 Kazakhstan 298 18 186 50 0.2 0.6 0.3 0.9 Kenya 1,776 45 1,228 153 5.2 24.4 11.8 22.6 Korea, Dem. Rep. 65 3 72 5 .. .. .. .. Korea, Rep. Kosovo 781 433 318 275 10.3 35.0 .. .. Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 313 58 296 58 8.7 28.4 8.6 36.8 Lao PDR 419 69 281 110 6.0 21.9 16.9 52.8 Latvia .. .. .. .. .. .. .. .. Lebanon 580 138 343 90 1.2 3.5 1.4 .. Lesotho 122 57 219 12 10.1 35.7 9.0 .. Liberia 513 134 1,698 30 176.8 .. 78.7 .. Libya 41 7 12 9 .. .. 0.0 .. Lithuania .. .. .. .. .. .. .. .. Macedonia, FYR 192 94 97 68 2.0 7.6 2.8 .. Madagascar 444 22 307 67 5.5 .. .. .. Malawi 771 53 880 85 20.6 82.2 .. .. Malaysia 143 5 50 49 0.0 0.0 0.0 0.0 Mali 984 66 741 128 12.3 .. .. .. Mauritania 373 111 120 53 10.2 36.6 .. .. Mauritius 155 122 73 20 1.3 5.7 2.0 5.7 Mexico 184 2 286 112 0.0 0.2 0.1 .. Moldova 244 68 241 54 7.5 34.1 9.7 23.0 Mongolia 371 137 189 96 5.4 12.0 6.7 18.4 Morocco 930 29 424 291 1.1 3.1 2.4 3.6 Mozambique 2,012 88 1,443 171 20.8 86.2 39.9 .. Myanmar 356 7 339 57 .. .. 5.1 .. Namibia 326 145 194 44 2.1 9.1 4.0 .. Nepal 854 29 684 125 .. .. 13.7 .. Netherlands New Zealand Nicaragua 773 135 316 98 10.0 34.9 10.9 47.8 Niger 469 31 589 105 13.6 .. .. .. Nigeria 1,657 11 827 198 1.2 .. 2.2 .. Norway Oman 154 57 18 3 .. .. –0.1 .. Pakistan 2,769 16 2,743 232 1.6 11.1 6.9 9.7 Panama 65 19 27 18 0.5 1.8 0.6 .. Papua New Guinea 412 61 221 313 5.5 30.4 7.4 .. Paraguay 148 23 104 53 0.6 2.9 0.9 3.8 Peru 441 15 327 173 –0.2 –0.7 –0.6 –1.0 Philippines 309 3 429 212 0.3 1.3 0.7 1.6 Poland .. .. .. .. .. .. .. .. Portugal Puerto Rico .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. 2012 World Development Indicators 375 6.12 Aid dependency Net of�cial Aid dependency development assistance ratios Net official development assistance $ millions % of imports of % of Total Per capita Technical % of gross capital goods, services, central government $ millions $ Grants cooperation % of GNI formation and income expense 2010 2010 2010 2010 2010 2010 2010 2010 Romania .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. Rwanda 934 91 848 153 18.5 .. 60.9 .. Saudi Arabia .. .. .. .. .. .. .. .. Senegal 1,016 84 449 195 7.2 24.8 .. .. Serbia 614 84 444 127 1.7 7.4 3.1 4.3 Sierra Leone 448 78 301 75 24.9 158.2 50.6 .. Singapore .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. Somalia 662 73 470 30 .. .. .. .. South Africa 1,075 22 841 161 0.3 1.1 0.9 .. South Sudan .. .. .. .. .. .. .. .. Spain Sri Lanka 703 34 332 78 1.2 4.2 3.6 .. Sudan 2,351 55 1,760 228 3.7 14.2 14.9 .. Swaziland 56 54 90 8 2.6 15.3 3.0 .. Sweden Switzerland Syrian Arab Republic 208 10 203 119 0.2 1.2 0.6 .. Tajikistan 408 60 267 40 7.7 33.4 12.6 .. Tanzania 2,933 67 1,814 214 12.9 42.0 31.7 .. Thailand –78 –1 167 115 0.0 0.0 0.0 0.0 Timor-Leste 216 197 144 149 10.8 .. .. .. Togo 499 84 455 52 14.9 .. .. 92.1 Trinidad and Tobago 7 5 3 2 0.0 .. .. .. Tunisia 503 48 142 162 1.3 4.7 2.1 4.6 Turkey 1,362 19 340 154 0.1 0.7 0.5 0.6 Turkmenistan 40 8 24 20 0.2 0.4 .. .. Uganda 1,785 55 1,189 105 10.3 42.8 26.9 .. Ukraine 666 14 338 239 0.5 2.3 0.8 .. United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom United States Uruguay 50 15 30 24 0.1 0.7 0.4 0.4 Uzbekistan 190 7 70 53 0.6 2.2 .. .. Venezuela, RB 66 2 36 19 0.0 0.1 0.1 .. Vietnam 3,732 43 656 378 2.9 7.1 3.2 .. West Bank and Gaza 2,817 697 2,345 169 .. .. .. .. Yemen, Rep. 558 24 523 63 2.3 18.3 5.2 .. Zambia 1,267 100 706 72 6.4 25.2 12.1 32.9 Zimbabwe 736 59 669 78 10.6 1,860.4 .. .. World 126,968 s 19 w .. .. 0.2 w 0.9 w 0.6 w .. Low income 36,252 46 .. .. 9.6 40.9 24.6 .. Middle income 54,621 11 .. .. 0.3 0.8 0.9 .. Lower middle income 40,510 16 .. .. 0.9 3.0 2.6 .. Upper middle income 13,151 5 .. .. 0.1 0.2 0.3 .. Low & middle income 126,593 22 95,651 19,564 0.7 2.0 2.1 .. East Asia & Pacific 10,165 5 .. .. 0.1 0.3 0.4 .. Europe & Central Asia 8,087 20 .. .. 0.2 1.0 0.7 .. Latin America & Carib. 9,036 16 .. .. 0.2 1.0 0.9 .. Middle East & N. Africa 13,383 41 .. .. 0.9 .. 3.1 .. South Asia 14,591 9 .. .. 0.7 2.3 2.7 .. Sub-Saharan Africa 44,582 54 .. .. 4.3 18.8 9.9 .. High income 374 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. 376 2012 World Development Indicators 6.12 GLOBAL LINKS Aid dependency About the data De�nitions The flows of official and private financial resources Saudi Arabia) are shown in the table as aid recipients • Net of�cial development assistance is flows (net from the members of the Development Assistance (see table 6.13a). of repayment of principal) that meet the DAC defini- Committee (DAC) of the Organisation for Economic The table does not distinguish types of aid (pro- tion of ODA and are made to countries and territo- Co-operation and Development (OECD) to developing gram, project, or food aid; emergency assistance; or ries on the DAC list of aid recipients. • Net of�cial economies are compiled by DAC, based principally on post-conflict peacekeeping assistance), which may development assistance per capita is net ODA reporting by DAC members using standard question- have different effects on the economy. divided by midyear population. • Grants are legally naires issued by the DAC Secretariat. Ratios of aid to gross national income (GNI), gross binding commitments that obligate a specific value DAC exists to help its members coordinate their capital formation, imports, and government spending of funds available for disbursement for which there development assistance and to encourage the provide measures of recipient country dependency is no payment requirement. •  Technical coopera- expansion and improve the effectiveness of the on aid. But care must be taken in drawing policy con- tion is the provision of resources whose main aim is aggregate resources flowing to recipient economies. clusions. For foreign policy reasons some countries to augment the stock of human intellectual capital, In this capacity DAC monitors the flow of all financial have traditionally received large amounts of aid. Thus such as the level of knowledge, skills, and techni- resources, but its main concern is official develop- aid dependency ratios may reveal as much about cal know-how in the recipient country (including the ment assistance (ODA). Grants or loans to countries a donor’s interests as about a recipient’s needs. cost of associated equipment). Contributions take and territories on the DAC list of aid recipients have Ratios are generally much higher in Sub-Saharan the form mainly of the supply of human resources to meet three criteria to be counted as ODA. They Africa than in other regions, and they increased in from donors or action directed to human resources are provided by official agencies, including state and the 1980s. High ratios are due only in part to aid (such as training or advice). Also included are aid for local governments, or by their executive agencies. flows. Many African countries saw severe erosion promoting development awareness and aid provided They promote economic development and welfare as in their terms of trade in the 1980s, which, along to refugees in the donor economy. Assistance spe- the main objective. And they are provided on conces- with weak policies, contributed to falling incomes, cifically to facilitate a capital project is not included. sional financial terms (loans must have a grant ele- imports, and investment. Thus the increase in aid • Aid dependency ratios are calculated using values ment of at least 25 percent, calculated at a discount dependency ratios reflects events affecting both the in U.S. dollars converted at official exchange rates. rate of 10 percent). The DAC Statistical Reporting numerator (aid) and the denominator (GNI). Imports of goods, services, and income refer to inter- Directives provide the most detailed explanation of Because the table relies on information from national transactions involving a change in owner- this definition and all ODA-related rules. donors, it is not necessarily consistent with infor- ship of general merchandise, goods sent for process- This definition excludes nonconcessional fl ows mation recorded by recipients in the balance of pay- ing and repairs, nonmonetary gold, services, receipts from official creditors, which are classified as “other ments, which often excludes all or some technical of employee compensation for nonresident workers, official flows,� and aid for military and anti-terrorism assistance—particularly payments to expatriates and investment income. For definitions of GNI, gross purposes. Transfer payments to private individuals, made directly by the donor. Similarly, grant commod- capital formation, and central government expense, such as pensions, reparations, and insurance pay- ity aid may not always be recorded in trade data or in see De�nitions for tables 1.1, 4.8, and 4.10. outs, are in general not counted. In addition to finan- the balance of payments. Moreover, DAC statistics cial flows, ODA includes technical cooperation, most exclude aid for military and antiterrorism purposes. expenditures for peacekeeping under UN mandates The nominal values used here may overstate the and assistance to refugees, contributions to multi- real value of aid to recipients. Changes in interna- lateral institutions such as the United Nations and tional prices and exchange rates can reduce the pur- its specialized agencies, and concessional funding chasing power of aid. Tying aid, still prevalent though to multilateral development banks. declining in importance, also tends to reduce its pur- Flows are transfers of resources, either in cash or chasing power. Tying requires recipients to purchase Data sources in the form of commodities or services measured on goods and services from the donor country or from a cash basis. Short-term capital transactions (with a specified group of countries. Such arrangements Data on financial flows are compiled by OECD DAC one year or less maturity) are not counted. Repay- prevent a recipient from misappropriating or misman- and published in its annual statistical report, Geo- ments of the principal (but not interest) of ODA loans aging aid receipts, but they may also be motivated by graphical Distribution of Financial Flows to Develop- are recorded as negative flows. Proceeds from offi - a desire to benefit donor country suppliers. ing Countries, and in its annual Development Co- cial equity investments in a developing country are The aggregates refer to World Bank classifications operation Report. Data are available electronically reported as ODA, while proceeds from their later sale of economies and therefore may differ from those on the OECD DAC International Development Sta- are recorded as negative flows. of the OECD. tistics CD-ROM and at www.oecd.org/dac/stats/ The table shows data on ODA for aid-receiving idsonline. Data on population, GNI, gross capital countries. The data cover loans and grants from formation, imports of goods and services, and DAC member countries, multilateral organizations, central government expense used in computing and non-DAC donors. They do not reflect aid given by the ratios are from World Bank and International recipient countries to other developing countries. As Monetary Fund databases. a result, some countries that are net donors (such as 2012 World Development Indicators 377 6.13 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United EU United DAC donors $ millions States institutions Germany Kingdom France Japan Netherlands Spain Canada Norway $ millions 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Afghanistan 5,701.3 2,893.4 285.0 469.8 234.8 58.6 745.7 59.2 60.8 267.1 120.2 506.8 Albania 301.6 30.1 75.0 35.4 0.9 4.3 2.4 4.0 4.8 0.0 2.5 142.2 Algeria 194.7 8.6 51.8 10.2 2.2 69.8 15.5 0.2 9.5 –0.5 1.0 26.5 Angola 174.0 54.8 24.4 7.1 16.7 4.1 37.6 –2.7 5.5 0.7 13.2 12.7 Argentina 149.4 3.9 8.0 21.9 0.5 13.5 73.8 0.5 23.0 1.0 0.1 3.2 Armenia 238.8 91.6 33.4 16.7 0.5 4.5 77.5 1.2 0.3 0.0 3.6 9.7 Australia Austria Azerbaijan 81.2 35.9 20.8 15.9 0.9 5.1 –11.0 0.0 0.1 0.0 3.4 10.2 Bahrain .. .. .. .. .. .. .. .. .. .. .. .. Bangladesh 1,071.6 124.7 188.7 65.1 228.3 –3.0 24.2 78.0 5.5 86.1 16.9 257.2 Belarus 96.0 27.2 15.2 18.0 0.4 4.1 1.4 0.0 0.3 0.0 2.2 27.2 Belgium Benin 461.8 98.9 122.8 34.7 0.0 48.8 29.1 31.3 1.1 6.4 0.1 88.7 Bolivia 521.4 86.1 64.7 42.5 0.1 12.2 54.2 46.9 69.0 19.0 5.9 120.8 Bosnia and Herzegovina 348.2 28.3 105.1 30.0 9.7 3.5 2.2 14.3 20.2 0.4 18.2 116.4 Botswana 145.4 77.0 39.3 2.4 1.1 6.5 10.7 0.0 0.0 1.5 0.6 6.3 Brazil 632.3 24.4 21.3 247.5 40.7 46.6 –62.7 0.4 26.4 7.1 245.4 35.2 Bulgaria .. .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 619.9 62.1 164.1 52.5 0.1 63.8 41.6 51.4 12.3 30.8 0.8 140.4 Burundi 413.8 43.5 131.2 29.5 20.1 15.2 39.1 19.1 1.2 4.7 19.5 90.8 Cambodia 544.8 84.7 27.3 41.3 26.0 26.6 147.5 1.3 23.1 8.2 4.7 154.3 Cameroon 340.7 18.0 74.2 90.5 1.0 82.1 42.0 0.2 9.0 7.2 0.4 16.0 Canada Central African Republic 197.6 20.5 84.8 3.1 3.0 24.5 8.1 2.0 3.3 1.4 0.0 46.9 Chad 386.9 134.6 101.9 20.1 2.9 40.7 13.8 4.9 7.8 11.7 1.7 47.1 Chile 173.4 13.3 16.2 71.8 0.7 10.1 15.9 0.3 11.3 2.7 13.1 18.2 China 745.4 86.5 42.6 321.5 86.7 316.7 –192.7 4.0 1.4 9.0 22.6 47.1 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 840.7 424.0 53.7 45.3 2.6 160.3 –26.2 26.3 56.2 22.6 14.2 61.8 Congo, Dem. Rep. 2,750.9 277.9 364.3 77.1 250.8 13.5 80.0 420.5 306.2 26.5 28.3 905.9 Congo, Rep. 1,247.5 21.4 32.2 9.4 78.8 909.4 6.0 0.0 0.5 21.9 0.6 167.4 Costa Rica 94.2 0.7 4.2 21.9 0.8 4.8 63.7 3.4 5.2 0.7 0.5 –11.8 Côte d’Ivoire 504.5 76.3 66.9 92.6 26.0 138.5 81.3 5.5 –7.6 6.9 1.7 16.5 Croatia 142.2 0.2 105.4 22.5 1.1 3.8 1.9 0.2 0.4 0.0 3.5 3.3 Cuba 112.4 16.4 24.9 2.5 0.4 2.7 5.2 0.1 42.8 5.7 1.1 10.6 Cyprus .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark Dominican Republic 172.5 35.5 80.7 0.9 0.1 1.0 –1.9 0.2 49.9 1.3 0.3 4.6 Ecuador 160.8 33.0 24.7 27.8 0.0 0.5 –5.2 0.5 55.3 1.4 3.1 19.7 Egypt, Arab Rep. 502.8 52.7 136.9 104.5 9.0 140.1 –17.7 11.0 7.2 8.9 0.7 49.7 El Salvador 291.1 151.3 52.5 17.1 –48.8 3.3 8.8 0.1 85.5 2.0 1.1 18.2 Eritrea 70.8 0.9 37.1 1.1 5.5 0.7 9.9 0.5 0.2 0.0 9.6 5.4 Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2,164.3 875.3 237.6 96.5 407.0 13.3 93.9 53.2 39.5 140.4 32.6 175.1 Finland France Gabon 96.9 1.5 13.1 –1.3 0.2 58.1 24.8 0.0 0.2 0.1 0.0 0.3 Gambia, The 56.0 6.5 22.7 0.6 2.0 0.4 17.2 0.0 4.0 0.5 0.1 2.1 Georgia 504.7 202.2 154.7 82.0 3.4 6.2 6.5 3.2 0.1 0.0 10.0 36.4 Germany Ghana 1,005.2 208.1 105.6 58.2 166.6 33.8 70.0 72.9 14.3 114.2 3.6 158.1 Greece Guatemala 390.3 105.0 37.4 13.5 0.2 3.3 41.2 19.6 92.9 10.3 9.4 57.6 Guinea 164.1 21.7 72.4 13.3 0.0 36.0 10.8 0.0 2.1 2.2 0.0 5.6 Guinea-Bissau 70.4 6.5 16.6 1.3 0.1 1.8 16.1 0.0 8.3 0.5 0.0 19.1 Haiti 2,612.1 1,106.9 284.3 43.6 26.2 144.1 72.0 19.2 155.8 458.9 66.8 234.6 378 2012 World Development Indicators 6.13 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 Germany Kingdom France Japan Netherlands Spain Canada Norway $ millions 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Honduras 339.0 102.9 58.5 14.1 25.3 1.4 16.0 0.3 69.1 17.6 1.5 32.4 Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 2,313.5 57.4 94.3 396.9 650.3 2.8 981.1 2.5 11.4 7.9 24.0 84.8 Indonesia 1,093.5 180.3 105.5 –12.6 26.9 262.5 61.1 34.1 –10.6 10.9 41.9 393.6 Iran, Islamic Rep. 76.1 1.6 3.4 45.8 0.0 14.2 –7.1 3.4 0.2 0.1 7.3 7.3 Iraq 2,059.3 1,622.9 54.1 36.9 31.0 9.6 144.4 2.6 0.4 6.3 7.7 143.5 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica 104.5 –3.6 106.4 –6.2 3.9 –0.4 –2.1 –2.9 0.5 4.1 0.1 4.7 Japan Jordan 544.4 371.6 129.9 39.4 2.6 6.2 –50.9 1.0 9.2 7.1 0.4 27.9 Kazakhstan 112.7 68.1 17.4 13.6 0.3 4.1 –1.8 0.4 0.1 0.1 4.3 6.2 Kenya 1,260.5 565.9 101.6 79.8 105.2 123.4 36.7 17.6 8.3 25.9 13.4 182.6 Korea, Dem. Rep. 42.6 5.4 14.8 2.5 0.4 0.7 0.0 0.1 0.0 0.0 2.5 16.2 Korea, Rep. Kosovo 558.2 101.0 279.3 30.6 9.5 1.7 1.1 3.2 0.1 0.0 24.2 107.5 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 182.9 56.0 24.4 25.3 7.3 1.5 23.2 0.6 0.9 2.0 6.3 35.6 Lao PDR 301.9 12.8 16.0 24.8 0.1 15.0 121.5 0.1 0.0 0.6 2.6 108.5 Latvia .. .. .. .. .. .. .. .. .. .. .. .. Lebanon 316.9 84.1 53.4 28.1 4.0 59.7 3.2 1.0 23.4 5.3 9.3 45.7 Lesotho 168.4 57.5 74.3 5.0 4.8 –1.4 8.8 0.0 0.0 0.4 1.1 17.9 Liberia 793.4 131.4 90.9 50.1 25.6 232.0 134.3 40.0 1.8 1.1 22.8 63.3 Libya 18.4 6.6 1.1 3.5 1.6 3.8 0.1 0.1 0.2 0.0 0.0 1.5 Lithuania .. .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 150.1 20.4 55.1 14.1 1.2 2.9 23.1 2.3 0.4 0.0 7.5 23.2 Madagascar 254.5 77.0 40.1 13.1 –0.3 84.0 9.6 0.0 0.3 1.9 13.0 15.7 Malawi 725.5 126.3 208.3 41.9 148.0 –1.0 69.5 0.0 0.6 16.5 64.7 50.5 Malaysia –13.7 18.6 1.2 11.2 –0.8 1.0 –53.2 0.2 0.1 0.0 0.8 7.2 Mali 782.4 197.9 98.5 60.3 0.1 77.6 38.3 56.1 28.4 96.0 16.0 113.2 Mauritania 130.9 11.4 25.3 7.7 0.0 32.2 14.6 0.0 34.7 0.5 0.6 4.0 Mauritius 126.0 0.5 67.9 –0.2 5.5 54.0 –2.9 0.0 0.0 0.0 0.3 0.8 Mexico 428.6 205.6 7.5 35.5 9.4 205.8 –46.7 –0.3 5.3 1.4 0.1 5.0 Moldova 228.4 19.4 138.0 8.7 14.5 6.4 0.9 5.6 0.1 0.2 3.0 31.7 Mongolia 232.1 47.2 13.4 29.1 0.8 5.0 53.9 8.8 –0.3 8.3 1.7 64.2 Morocco 822.3 47.7 223.4 38.9 3.2 254.4 121.2 1.1 90.6 3.1 0.0 38.6 Mozambique 1,549.7 277.9 192.3 76.9 104.4 38.1 62.9 81.8 43.9 82.0 73.7 515.7 Myanmar 304.1 31.3 55.9 18.3 44.2 2.0 46.8 2.7 0.0 0.6 21.7 80.5 Namibia 222.1 117.2 10.8 24.0 0.6 0.4 40.6 1.6 8.5 0.5 –3.0 20.9 Nepal 521.9 51.9 46.2 42.0 105.2 –3.2 81.2 0.0 0.2 11.8 47.2 139.4 Netherlands New Zealand Nicaragua 416.2 54.5 21.9 27.7 7.3 1.0 34.4 26.3 106.2 12.5 18.5 106.1 Niger 531.6 102.6 150.8 22.6 3.2 50.0 25.2 2.8 25.9 53.7 3.5 91.4 Nigeria 909.5 445.9 60.3 39.0 264.6 8.9 23.9 9.1 0.5 12.6 14.1 30.7 Norway Oman 7.6 2.9 0.0 1.0 0.9 0.8 1.5 0.4 0.0 0.0 0.0 0.0 Pakistan 2,586.5 1,196.8 172.3 142.1 298.5 14.4 207.9 52.1 22.7 101.9 83.1 294.8 Panama 125.3 11.9 1.5 1.4 0.0 0.1 101.8 0.0 5.9 0.5 2.4 –0.3 Papua New Guinea 490.8 2.3 50.1 0.8 1.0 0.2 22.2 0.0 0.2 0.1 1.8 412.2 Paraguay 97.4 28.0 29.9 5.4 0.0 0.1 –3.7 0.0 21.8 0.5 1.0 14.4 Peru –288.2 130.6 25.8 51.9 1.3 10.1 –711.6 0.3 118.1 22.2 3.8 59.5 Philippines 505.0 114.8 51.9 26.3 0.6 189.4 –87.7 0.4 27.0 16.7 17.6 148.1 Poland .. .. .. .. .. .. .. .. .. .. .. .. Portugal Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2012 World Development Indicators 379 6.13 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United EU United DAC donors $ millions States institutions Germany Kingdom France Japan Netherlands Spain Canada Norway $ millions 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 Romania .. .. .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 652.1 140.6 104.4 48.3 106.2 4.1 22.8 39.4 1.8 58.7 4.4 121.5 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 618.4 101.4 84.1 23.1 0.9 157.2 55.2 30.0 45.6 56.7 0.3 64.0 Serbia 603.2 57.9 290.1 126.3 5.4 14.0 5.2 3.8 0.4 0.8 20.2 79.2 Sierra Leone 279.9 29.8 80.3 13.3 84.8 0.3 12.2 1.1 1.9 33.0 3.0 20.1 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 435.5 59.4 127.1 12.5 62.3 3.5 29.1 9.3 5.9 4.4 31.6 90.5 South Africa 974.7 529.5 153.1 39.5 39.3 47.6 7.1 36.1 1.1 16.5 24.7 80.1 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain Sri Lanka 433.8 26.1 48.1 –6.4 –8.5 45.1 155.4 3.6 –0.7 11.5 29.0 130.6 Sudan 1,793.4 726.4 284.2 39.2 119.1 10.1 119.1 57.6 23.0 108.3 116.7 189.9 Swaziland 52.6 23.6 21.5 –0.6 0.0 0.2 4.4 0.0 0.0 0.8 1.6 1.3 Sweden Switzerland Syrian Arab Republic 95.7 6.9 51.6 45.9 2.0 23.1 –54.7 0.1 6.1 0.1 1.3 13.4 Tajikistan 201.2 45.9 36.6 34.7 12.5 0.2 43.4 0.6 0.0 0.7 3.2 23.3 Tanzania 1,848.1 457.4 192.6 134.5 240.9 21.3 104.6 59.2 3.2 111.6 124.0 398.8 Thailand –76.1 47.2 24.1 –23.2 7.2 –13.6 –143.5 0.3 0.6 –0.6 0.3 25.3 Timor-Leste 272.7 27.3 14.6 9.4 0.0 0.1 27.7 0.0 6.0 1.1 7.8 178.7 Togo 301.7 4.0 48.9 8.8 –0.1 168.0 7.5 17.6 1.6 23.1 0.1 22.1 Trinidad and Tobago 4.1 1.3 0.6 0.4 0.2 0.5 0.1 0.0 0.1 0.3 0.0 0.6 Tunisia 447.6 –3.3 92.3 23.9 2.5 126.8 35.9 –0.7 158.0 –2.9 0.0 15.1 Turkey 1,029.7 6.4 295.2 –10.3 3.8 88.4 543.5 0.3 56.0 –2.6 0.1 49.0 Turkmenistan 17.0 8.4 5.7 1.8 0.1 0.1 –0.9 0.0 0.0 0.0 0.6 1.4 Uganda 1,162.0 378.1 128.9 40.9 179.3 1.8 71.2 36.7 4.3 5.7 71.5 243.6 Ukraine 545.6 140.2 153.0 89.1 0.8 21.5 53.2 0.1 0.3 20.2 3.8 63.4 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay 40.0 1.2 7.1 0.8 0.1 1.2 11.4 0.1 8.4 0.5 0.1 9.4 Uzbekistan 90.8 13.4 6.5 25.0 1.2 2.3 7.0 0.0 0.2 0.0 0.3 34.9 Venezuela, RB 43.4 8.6 5.9 7.0 1.1 6.9 3.1 0.1 8.2 0.4 0.1 2.0 Vietnam 1,866.5 93.1 41.9 96.4 82.2 242.4 807.8 21.2 16.0 25.0 20.2 420.3 West Bank and Gaza 2,069.2 720.8 441.1 104.6 97.6 69.3 78.6 33.7 97.6 65.1 109.5 251.4 Yemen, Rep. 322.8 45.4 40.7 82.1 63.9 7.0 26.7 26.5 1.1 2.6 0.4 26.4 Zambia 685.5 225.1 92.5 33.3 79.3 0.8 46.1 36.1 0.2 8.7 54.1 109.3 Zimbabwe 630.8 175.2 109.5 33.1 108.0 3.0 18.9 11.1 3.6 9.3 24.5 134.6 World 103,174.7 s 26,586.4 s 12,428.0 s 8,035.5 s 8,016.8 s 7,786.7 s 7,331.1 s 4,644.2 s 3,998.9 s 3,919.6 s 3,560.9 s 16,866.6 s Low income 29,779.4 8,672.4 4,003.4 1,708.5 2,544.1 1,313.1 2,186.9 1,117.1 763.8 1,583.6 848.0 5,038.5 Middle income 41,152.0 10,211.0 6,373.5 3,372.7 2,351.5 4,952.3 3,460.9 724.8 1,637.4 844.5 1,103.9 6,119.4 Lower middle income 29,587.9 7,602.4 3,681.5 1,899.4 2,088.0 2,899.8 3,710.9 519.5 868.2 696.2 671.5 4,950.4 Upper middle income 10,377.3 2,549.0 2,088.8 1,312.3 254.1 1,954.3 –252.0 178.7 715.0 107.2 410.5 1,059.4 Low & middle income 102,918.5 26,581.7 12,301.2 8,011.3 8,014.4 7,778.5 7,326.1 4,643.7 3,978.6 3,918.5 3,557.5 16,807.2 East Asia & Pacific 7,875.1 1,032.2 530.7 565.1 281.2 1,221.0 973.2 73.1 88.4 116.2 151.5 2,842.4 Europe & Central Asia 5,556.1 979.9 1,754.1 664.7 72.5 173.7 780.3 41.4 97.7 37.0 120.8 834.1 Latin America & Carib. 9,201.1 2,720.6 1,268.2 912.0 179.2 664.2 –311.4 223.2 1,369.9 807.1 434.2 933.9 Middle East & N. Africa 8,009.6 3,006.8 1,441.4 622.9 229.7 859.7 335.8 80.1 462.6 123.5 150.0 697.3 South Asia 12,867.1 4,383.0 850.7 1,127.7 1,510.9 115.4 2,277.8 200.0 108.8 498.1 324.5 1,470.1 Sub-Saharan Africa 32,263.2 7,637.2 4,788.9 1,736.0 3,003.3 3,526.9 1,694.5 1,313.7 912.0 1,512.0 945.2 5,193.7 High income 256.2 4.7 126.9 24.3 2.4 8.2 5.0 0.5 20.3 1.0 3.5 59.4 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. 380 2012 World Development Indicators 6.13 GLOBAL LINKS Distribution of net aid by Development Assistance Committee members About the data De�nitions The table shows net bilateral aid to low- and middle- controls the use of the funds) and are included in the • Net aid refers to net bilateral official development income economies from members of the Development data reported in the table. assistance that meets the DAC definition of official Assistance Committee (DAC) of the Organisation for The data include aid to some countries and terri- development assistance and is made to countries Economic Co-operation and Development (OECD). tories not shown in the table and aid to unspecified and territories on the DAC list of aid recipients. DAC has 24 members—23 individual economies and economies recorded only at the regional or global • Other DAC donors are Australia, Austria, Belgium, 1 multilateral institution (European Union institutions). level. Aid to countries and territories not shown in Denmark, Finland, Greece, Ireland, Italy, the Republic The table is based on donor country reports of the table has been assigned to regional totals based of Korea, Luxembourg, New Zealand, Portugal, Swe- bilateral programs, which may differ from reports on the World Bank’s regional classification system. den, and Switzerland. by recipient countries. Recipients may lack Aid to unspecified economies is included in regional access to information on such aid expenditures as totals and, when possible, income group totals. Aid development- oriented research, stipends and tuition not allocated by country or region—including admin- costs for aid-financed students in donor countries, istrative costs, research on development, and aid to and payment of experts hired by donor countries. nongovernmental organizations—is included in the Moreover, a full accounting would include donor world total. Thus regional and income group totals country contributions to multilateral institutions, do not sum to the world total. the flow of resources from multilateral institutions Some of the aid recipients shown in the table are to recipient countries, and flows from countries that also aid donors. Development cooperation activities are not members of DAC. by non-DAC members have increased in recent years Data in the table exclude DAC members’ multi- and in some cases surpass those of individual DAC lateral aid (contributions to the regular budgets of the members. Some non-DAC donors report their devel- multilateral institutions). However, projects executed opment cooperation activities to DAC on a voluntary by multilateral institutions or nongovernmental orga- basis, but many do not yet report their aid flows to nizations on behalf of DAC members are classified DAC. See table 6.13a for a summary of ODA from as bilateral aid (since the donor country effectively non-DAC countries. Of�cial development assistance from non-DAC donors, 2006–10 6.13a Net disbursements ($ millions) 2006 2007 2008 2009 2010 OECD members (non-DAC) Czech Republic 161 179 249 215 228 Hungary 149 103 107 117 114 Iceland 42 48 48 34 29 Poland 297 363 372 375 378 Slovak Republic 55 67 92 75 74 Turkey 714 602 780 707 967 Arab countries Kuwait 158 110 283 221 211 Saudi Arabia 2,025 1,551 4,979 3,134 3,480 United Arab Emirates 783 2,426 1,266 834 412 Other donors Israela 90 111 138 124 145 Data sources Taiwan, China 513 514 435 411 381 Thailand 74 67 178 40 10 Data on financial fl ows are compiled by OECD Othersb 121 188 343 385 808 DAC and published in its annual statistical report, Total 5,181 6,329 9,271 6,672 7,235 Geographical Distribution of Financial Flows to Aid Recipients, and its annual Development Note: The above table does not reflect aid provided by several major emerging non–Organisation for Economic Co-operation and Development (OECD) donors, as information on their aid has not been disclosed. Co-operation Report. Data are available electroni- a. Data are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the cally on the DAC’s International Development Sta- 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. b. Includes Cyprus, Estonia, Latvia, Liechtenstein, Lithuania, Malta, Romania, the tistics CD-ROM and at www.oecd.org/dac/stats/ Russian Federation, and Slovenia. idsonline. 2012 World Development Indicators 381 6.14 Movement of people across borders Net migration International Emigration of Refugees Workers’ remittances and migrant stock tertiary educated compensation of employees population to OECD countries % of tertiary educated population thousands $ millions thousands thousands ages 25 and older By country of origin By country of asylum Received Paid 2005–10 2010 2000 2010 2010 2010 2010 Afghanistan –381 91 22.6 3,054.7 6.4 .. .. Albania –48 89 17.5 14.8 0.1 1,156 24 Algeria –140 242 9.5 6.7 94.1 2,044 a 46 Angola 82 65 3.7 134.9 15.2 82 716 Argentina –200 1,449 2.8 0.6 3.3 641 993 Armenia –75 324 8.9 17.5 3.3 996 157 Australia 1,125 4,711 2.7 0.0 21.8 4,840a 3,776a Austria 160 1,310 13.5 0.0 42.6 3,220 3,453 Azerbaijan 53 264 1.8 16.8 1.9 1,432 961 Bahrain 448 315 5.1 0.1 0.2 .. 1,642 Bangladesh –2,908 1,085 4.4 10.0 229.3 10,852 9 Belarus –50 1,090 3.2 5.7 0.6 376 105 Belgium 200 975 5.5 0.1 17.9 10,178 4,040 Benin 50 232 8.7 0.4 7.1 248a 88 Bolivia –165 146 5.8 0.6 0.7 1,088 104 Bosnia and Herzegovina –10 28 20.3 63.0 7.0 1,905 54 Botswana 19 115 5.1 0.1 3.0 100 102 Brazil –500 688 2.0 1.0 4.4 4,000 1,198 Bulgaria –50 107 9.6 2.6 5.5 1,387 25 Burkina Faso –125 1,043 2.6 1.1 0.5 95a 100 Burundi 370 61 9.3 84.1 29.4 28 1 Cambodia –255 336 21.5 16.3 0.1 369 215 Cameroon –19 197 17.3 15.0 104.3 195 54 Canada 1,098 7,202 4.7 0.1 165.5 .. .. Central African Republic 5 80 7.3 164.9 21.6 .. .. Chad –75 388 9.1 53.7 347.9 .. .. Chile 30 320 6.0 1.2 1.6 3 5 China –1,884b 686b 3.8 199.7c 301.0 53,038a 1,754 Hong Kong SAR, China 176 2,742 29.6 0.0 0.2 347 433 Colombia –120 110 10.4 395.6 0.2 4,058 112 Congo, Dem. Rep. –24 445 14.9 476.7 166.3 .. .. Congo, Rep. 50 143 28.2 20.7 133.1 15a 102 Costa Rica 76 489 7.1 0.4 19.5 552 271 Côte d’Ivoire –360 2,407 6.2 41.8 26.2 179 754 Croatia 10 700 24.6 65.9 0.9 1,315 164 Cuba –190 15 28.8 7.5 0.4 .. .. Cyprus 44 154 34.2 0.0 3.4 146 404 Czech Republic 240 453 8.5 0.8 2.4 1,122 1,812 Denmark 90 484 7.8 0.0 17.9 633 3,184 Dominican Republic –140 434 22.4 0.2 0.6 3,369 29 Ecuador –120 394 9.5 0.9 121.2 2,569 81 Egypt, Arab Rep. –347 245 4.7 6.9 95.1 7,725 255 El Salvador –292 40 31.7 5.0 0.0 3,449 23 Eritrea 55 16 35.2 222.5 4.8 .. .. Estonia 0 182 9.9 0.2 0.0 322 94 Ethiopia –300 548 9.8 68.8 154.3 225 27 Finland 73 226 7.2 0.0 8.7 826 437 France 500 6,685 3.5 0.1 200.7 15,629 5,264 Gabon 5 284 14.6 0.2 9.0 .. .. Gambia, The –14 290 67.8 2.2 8.4 116 58 Georgia –150 167 2.8 10.6 0.6 806 50 Germany 550 10,758 5.8 0.2 594.3 11,338 15,908 Ghana –51 1,852 44.7 20.2 13.8 136 .. Greece 154 1,133 12.2 0.1 1.4 1,499 1,932 Guatemala –200 59 23.9 5.7 0.1 4,229 21 Guinea –300 395 4.7 12.0 14.1 60 43 Guinea-Bissau –10 19 27.7 1.1 7.7 48 17a Haiti –240 35 83.4 25.9 0.0 1,499 135 382 2012 World Development Indicators 6.14 GLOBAL LINKS Movement of people across borders Net migration International Emigration of Refugees Workers’ remittances and migrant stock tertiary educated compensation of employees population to OECD countries % of tertiary educated population thousands $ millions thousands thousands ages 25 and older By country of origin By country of asylum Received Paid 2005–10 2010 2000 2010 2010 2010 2010 Honduras –100 24 24.8 1.3 0.0 2,649 12 Hungary 75 368 12.8 1.4 5.4 2,265 1,265 India –3,000 5,436 4.3 17.8 184.8 54,035 3,888 Indonesia –1,293 123 2.9 16.9 0.8 6,916 2,840 Iran, Islamic Rep. –186 2,129 14.3 68.8 1,073.4 1,181a .. Iraq –150 83 10.9 1,683.6 34.7 71 32a Ireland 100 899 33.7 0.0 9.1 601 1,751 Israel 274 2,940 7.8 1.3 25.5 1,411 3,739 Italy 1,999 4,463 9.7 0.1 56.4 6,803 12,201 Jamaica –100 30 84.7 1.1 0.0 2,011 314 Japan 270 2,176 1.2 0.2 2.6 1,802 4,474 Jordan 203 2,973 7.4 2.3 2,455.7d 3,641 495 Kazakhstan 7 3,079 1.2 3.6 4.4 291 3,021 Kenya –189 818 38.5 8.6 402.9 1,777a 61 Korea, Dem. Rep. 0 37 .. 0.9 .. .. .. Korea, Rep. –30 535 7.5 0.6 0.4 8,708 11,385 Kosovo .. .. .. .. .. 932 146 Kuwait 278 2,098 7.1 1.0 0.2 .. 11,770 Kyrgyz Republic –132 223 0.9 2.7 2.5 1,275a 297 Lao PDR –75 19 37.2 8.4 .. 41 8 Latvia –10 335 8.5 0.7 0.1 614 43 Lebanon –13 758 43.9 15.9 435.1d 7,558 3,737 Lesotho –20 6 4.1 0.0 .. 746 19 Liberia 300 96 44.3 70.1 24.7 27a 1 Libya –20 682 4.3 2.3 7.9 17a 1,361 Lithuania –35 129 8.4 0.5 0.8 1,575 538 Macedonia, FYR 2 130 29.4 7.9 1.4 388 23 Madagascar –5 38 7.7 0.3 .. .. .. Malawi –20 276 20.9 0.2 5.7 .. .. Malaysia 84 2,358 10.5 0.6 81.5 1,301 6,528 Mali –101 163 14.8 3.7 13.6 436a 167 Mauritania 10 99 8.6 37.7 26.7 .. .. Mauritius 0 43 56.0 0.0 .. 226a 13 Mexico –1,805 726 15.5 6.8 1.4 22,048 .. Moldova –172 408 4.1 6.2 0.1 1,370 117 Mongolia –15 10 7.4 1.7 0.0 277 169 Morocco –675 49 18.6 2.3 0.8 6,423 62 Mozambique –20 450 22.6 0.1 4.1 132 80 Myanmar –500 89 3.9 415.7 .. 133a .. Namibia –1 139 3.4 1.0 7.3 15 16 Nepal –100 946 4.0 5.9 89.8 3,468 32 Netherlands 50 1,753 9.6 0.1 75.0 3,834 12,923 New Zealand 65 962 21.8 0.0 2.3 843 1,167 Nicaragua –200 40 30.2 1.4 0.1 823 .. Niger –28 202 5.5 0.8 0.3 88 22a Nigeria –300 1,128 10.5 15.6 8.7 10,045a 48 Norway 171 485 6.2 0.0 40.3 680 4,045 Oman 153 826 0.4 0.1 0.1 39 5,704 Pakistan –2,000 4,234 12.7 40.0 1,900.6 9,690 19 Panama 11 121 16.7 0.1 17.1 231 248 Papua New Guinea 0 25 27.8 0.1 9.7 15 323 Paraguay –40 161 3.8 0.1 0.1 673 .. Peru –725 38 5.8 5.8 1.1 2,534 122 Philippines –1,233 435 13.6 1.0 0.2 21,423 62 Poland 56 827 14.3 1.8 15.6 7,614 1,575 Portugal 150 919 19.0 0.0 0.4 3,540 1,406 Puerto Rico –145 324 .. .. .. .. .. Qatar 857 1,305 2.1 0.1 0.1 .. .. 2012 World Development Indicators 383 6.14 Movement of people across borders Net migration International Emigration of Refugees Workers’ remittances and migrant stock tertiary educated compensation of employees population to OECD countries % of tertiary educated population thousands $ millions thousands thousands ages 25 and older By country of origin By country of asylum Received Paid 2005–10 2010 2000 2010 2010 2010 2010 Romania –100 133 11.3 3.9 1.0 3,883 355 Russian Federation 1,136 12,270 1.4 111.9 4.9 5,264 18,796 Rwanda 15 465 31.7 114.8 55.4 92 71 Saudi Arabia 1,056 7,289 0.9 0.7 0.6 236 27,069 Senegal –133 210 17.2 16.3 20.7 1,346 144 a Serbia 0 525 .. 183.3 73.6 3,351a 70 Sierra Leone 60 107 49.2 11.3 8.4 58 6 Singapore 722 1,967 14.5 0.1 0.0 .. .. Slovak Republic 37 131 14.3 0.2 0.5 1,591 70 Slovenia 22 164 11.0 0.0 0.3 309 158 Somalia –300 23 34.5 770.2 1.9 .. .. South Africa 700 1,863 7.4 0.4 57.9 1,119 1,372 South Sudan .. .. .. .. .. .. .. Spain 2,250 6,378 4.2 0.0 3.8 10,507 12,227 Sri Lanka –250 340 28.2 141.1 0.2 4,155 545 Sudan 135 753 6.8 387.3 178.3 1,974a 1a Swaziland –6 40 5.4 0.0 0.8 109 11 Sweden 266 1,306 4.5 0.0 82.6 688 695 Switzerland 183 1,763 9.6 0.0 48.8 2,619 21,668 Syrian Arab Republic –56 2,206 6.2 18.5 1,483.2d 1,646a 214 a Tajikistan –296 284 0.6 0.6 3.1 2,254 856 Tanzania –300 659 12.1 1.1 109.3 25 127 Thailand 492 1,157 2.2 0.4 96.7 1,764 .. Timor-Leste –50 14 16.5 0.0 0.0 .. .. Togo –5 185 16.5 18.3 14.1 333a 72a Trinidad and Tobago –20 34 78.9 0.3 0.0 120a .. Tunisia –20 34 12.6 2.2 0.1 1,970 13 Turkey –50 1,411 5.8 146.8 10.0 874 175 Turkmenistan –54 208 0.4 0.7 0.1 .. .. Uganda –135 647 36.0 6.4 135.8 915 602 Ukraine –40 5,258 4.3 25.1 3.0 5,607 24 United Arab Emirates 3,077 3,293 0.7 0.4 0.5 .. .. United Kingdom 1,020 6,452 17.1 0.2 238.2 7,532 3,528 United States 4,955 42,813 0.5 3.0 264.6 5,277 51,597 Uruguay –50 80 9.0 0.2 0.2 103 7 Uzbekistan –518 1,176 0.8 8.8 0.3 .. .. Venezuela, RB 40 1,007 3.8 6.7 201.5 143 805 Vietnam –431 69 27.0 338.7 1.9 8,260a .. West Bank and Gaza –90 1,924 12.0 93.3 1,910.7d 1,151a 9a Yemen, Rep. –135 518 6.0 2.1 190.1 1,240 337 Zambia –85 233 16.4 0.2 47.9 44 68 Zimbabwe –900 372 13.1 24.1 4.4 .. .. World ..e s 213,397f s 5.4 w 15,369.9d,g s 15,369.9d s 449,197 s 303,799 s Low income –6,818 11,158 11.8 5,650.8 1,874.0 24,553 3,088 Middle income –16,342 70,369 6.8 4,518.3 11,535.2 300,725 55,580 Lower middle income –12,613 31,148 8.0 3,223.5 6,412.2 161,464 11,579 Upper middle income –3,729 39,220 6.1 1,294.8 5,123.0 139,262 44,001 Low & middle income –23,160 81,527 7.1 10,169.1 13,409.2 325,278 58,668 East Asia & Pacific –5,221 5,434 7.0 1,002.3 492.0 93,957 11,945 Europe & Central Asia –595 27,681 3.5 637.2 140.8 36,037 25,865 Latin America & Carib. –5,088 6,569 10.6 470.4 373.8 57,275 4,603 Middle East & N. Africa –1,628 11,957 10.5 1,905.3 7,795.9 34,700 6,566 South Asia –8,622 12,175 5.3 3,344.6 2,411.2 82,209 4,687 Sub-Saharan Africa –2,006 17,710 12.6 2,809.5 2,195.6 21,101 5,002 High income 22,906 131,871 4.1 79.4 1,960.7 123,919 245,131 Euro area 6,336 36,317 7.1 1.0 1,023.9 71,976 82,745 a. World Bank estimates. 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. 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. f. 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. g. 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. 384 2012 World Development Indicators 6.14 GLOBAL LINKS Movement of people across borders About the data De�nitions Movement of people, most often through migration, an international border to find sanctuary and have • Net migration is the net total of migrants (immi- is a significant part of global integration. Migrants been granted refugee or refugee-like status or tem- grants less emigrants, including both citizens and contribute to the economies of both their host coun- porary protection. Asylum seekers— people who noncitizens) during the period. Data are five-year esti- try and their country of origin. Yet reliable statistics have applied for asylum or refugee status and who mates. • International migrant stock is the number on migration are difficult to collect and are often have not yet received a decision or who are regis- of people, including refugees, born in a country other incomplete, making international comparisons a tered as asylum seekers—and internally displaced than that in which they live. • Emigration of tertiary challenge. people—who are often confused with refugees—are educated population to OECD countries is the stock The United Nations Population Division provides not included. Unlike refugees, internally displaced of emigrants ages 25 and older with at least one year data on net migration and migrant stock. Because people remain under the protection of their own gov- of tertiary education who reside in an OECD country data on migrant stock is difficult for countries to col- ernment, even if their reason for fleeing was similar other than that in which they were born. • Refugees lect, the United Nations Population Division takes to that of refugees. Palestinian refugees are people are people recognized as refugees under the 1951 into account the past migration history of a country (and their descendants) whose residence was Pal- Convention Relating to the Status of Refugees or or area, the migration policy of a country, and the estine between June 1946 and May 1948 and who its 1967 Protocol, the 1969 Organization of African influx of refugees in recent periods when deriving lost their homes and means of livelihood as a result Unity Convention Governing the Specific Aspects of estimates of net migration. The data to calculate of the 1948 Arab-Israeli conflict. Refugee Problems in Africa; recognized as refugees these estimates come from a variety of sources, Registrations, together with other sources— in accordance with the UNHCR statute; granted refu- including border statistics, administrative records, including estimates and surveys—are the main gee-like humanitarian status; or provided temporary surveys, and censuses. When there is insufficient sources of refugee data. There are diffi culties in protection. Asylum seekers and internally displaced data, net migration is derived through the difference collecting accurate statistics. Many refugees may people are excluded. • Country of origin refers to between the overall population growth rate and the not be aware of the need to register or may choose the nationality or country of citizenship of a claimant. rate of natural increase (the difference between the not to do so, and administrative records tend to • Country of asylum is the country where an asylum birth rate and the death rate) during the same period. overestimate the number of refugees because it is claim was filed and granted. • Workers’ remittances Such calculations are usually made for intercensal easier to register than to de-register. The UN Refu- and compensation of employees, received and paid, periods. The estimates are also derived from the gee Agency (UNHCR) collects and maintains data on are current transfers by migrant workers and wages data on foreign-born population —people who have refugees, except for Palestinian refugees residing in and salaries earned by nonresident workers. Remit- residence in one country but were born in another areas under the mandate of the United Nations Relief tances are classified as current private transfers country. When data on the foreign-born population and Works Agency for Palestine Refugees in the Near from migrant workers resident in the host country are not available, data on foreign population —that East (UNRWA). Registration is voluntary, and esti- for more than a year, irrespective of their immigra- is, people who are citizens of a country other than the mates by the UNRWA are not an accurate count of the tion status, to recipients in their country of origin. country in which they reside—are used as estimates. Palestinian refugee population. The table shows esti- Migrants’ transfers are defined as the net worth of For countries with information on the interna- mates of refugees collected by the UNHCR, comple- migrants who are expected to remain in the host tional migrant stock for at least two points in time, mented by estimates of Palestinian refugees under country for more than one year that is transferred to interpolation or extrapolation was used to estimate the UNRWA mandate. Thus, the aggregates differ another country at the time of migration. Compen- the international migrant stock on July 1 of the refer- from those published by the UNHCR. sation of employees is the income of migrants who ence years. For countries with only one observation, Workers’ remittances and compensation of have lived in the host country for less than a year. estimates for the reference years were derived using employees are from the International Monetary Data sources rates of change in the migrant stock in the years pre- Fund’s (IMF) Balance of Payments Statistics Yearbook. ceding or following the single observation available. The IMF data are supplemented by World Bank staff Data on net migration are from the United Nations A model was used to estimate migrants for countries estimates for missing data for countries where work- Population Division’s World Population Prospects: that had no data. ers’ remittances are important. The data reported The 2010 Revision. Data on international migra- One negative effect of migration is “brain drain�— here are the sum of three items defined in the fifth tion stock are from the United Nations Population emigration of highly educated people. The table edition of the IMF’s Balance of Payments Manual: Division’s Trends in Total Migrant Stock: The 2008 shows data on emigration of people with tertiary workers’ remittances, compensation of employees, Revision. Data on migration of tertiary educated education, drawn from Docquier, Marfouk, and Low- and migrants’ transfers. population are from Docquier, Lowell, and Marfouk ell (2007), which analyzes skilled migration using (2009). Data on refugees are from the UNHCR’s data from censuses and registers of Organisation Statistical Yearbook 2010, complemented by sta- for Economic Development and Co-operation (OECD) tistics on Palestinian refugees under the mandate countries and provides data disaggregated by gender of the UNRWA as published on its website. Data for 1990 and 2000. on remittances are from the IMF’s Balance of Pay- The table also shows data on refugees because ments Statistics Yearbook supplemented by World they are an important part of migrant stock. The Bank staff estimates. refugee data refer to people who have crossed 2012 World Development Indicators 385 6.15 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 317a,b 2,417a,b .. 3,443 398 1,780 56.6 47.0 290 1,454 19.3 23.0 Algeria 866a,c .. 1,006 .. 102d .. .. .. 193e .. .. .. Angola 51 425 .. .. 34 e 726 0.4 1.4 146 275 2.5 0.8 Argentina 2,909 5,325 4,953 5,307 3,195 5,629 10.2 6.9 5,460 6,375 16.5 9.4 Armenia 45 .. 111 .. 52 456 11.6 23.5 56 466 5.8 11.1 Australia 4,931a,b 5,885a,b,e 3,498 7,111 13,016 .. 15.5 .. 8,780 .. 10.0 .. Austria 17,982f 22,004f 7,528 9,882 11,382 20,931 13.0 10.3 7,001 12,215 8.2 6.5 Azerbaijan .. 1,280 1,326 3,176 68 792 3.2 2.8 138 856 6.8 8.1 Bahrain 2,420 .. .. .. 854 2,163 11.9 12.1 425 684 8.3 5.2 Bangladesh 199 .. 1,128 .. 50d 103d 0.7 0.5 471 835 4.9 2.8 Belarus 60 119 1,289 415 188 662 2.5 2.2 247 738 3.1 2.0 Belgium 6,457f 7,186f 7,932 10,170 6,592d 11,431 .. 3.1 9,429d,e 20,558 .. 5.7 Benin 96 199 .. .. 77 134 14.6 .. 50 97 7.1 .. Bolivia 319 807 201 708 101 339 6.9 5.0 116 421 5.6 6.8 Bosnia and Herzegovina 171f 365f .. .. 246 668 15.6 10.7 92 244 2.2 2.5 Botswana 1,104 2,145 .. .. 227 222 7.6 4.4 209 26 9.0 0.5 Brazil 5,313 5,161 3,228 5,305 1,969 6,181 3.0 2.6 4,548 19,340 6.3 7.9 Bulgaria 2,785 6,047 2,337 3,676 1,364 4,035 19.5 14.8 764 1,382 10.0 4.9 Burkina Faso 126g 274g .. .. 23 .. 9.7 .. 30 .. 4.6 .. Burundi 29c .. 28 .. 1 2 2.6 1.2 14 d 35 9.3 5.8 Cambodia .. 2,399 41 505 345 1,412 18.9 20.5 52 268 2.3 3.4 Cameroon 277g .. .. .. 132 171 4.9 3.0 241 265 9.6 4.1 Canada 19,627 16,097 19,182 28,678 13,035 18,281 4.0 4.0 15,125 36,677 5.3 7.4 Central African Republic 11h .. .. .. 5e .. .. .. 33e .. .. .. Chad 43g .. 27 .. 14 e .. .. .. 56e .. .. .. Chile 1,742 2,766 1,830 3,348 1,179 2,413 5.1 2.9 904 2,339 4.1 3.5 China 31,229 55,664 10,473 57,386 17,318 50,154 6.2 2.9 14,169 59,840 5.7 3.9 Hong Kong SAR, China 8,814 20,085 .. 84,442 8,198 e 27,028e 3.4 5.4 12,502d,e 17,461d,e 5.3 3.6 Colombia 557 .. 1,235 .. 1,313 2,797 8.3 6.2 1,452 2,368 10.1 5.1 Congo, Dem. Rep. 103 .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 19g .. .. .. 12 .. 0.5 .. 59 .. 4.9 .. Costa Rica 1,088 2,100 381 662 1,477 2,189 19.1 16.0 551 534 7.6 3.6 Côte d’Ivoire .. .. .. .. 53 .. 1.2 .. 291 .. 8.0 .. Croatia 5,831f 9,111f .. 1,873 2,871 8,209 33.2 35.5 634 853 6.6 3.6 Cuba 1,741h 2,507h 139 251 1,948e 2,396e .. .. .. .. .. .. Cyprus 2,686 2,173 503 1,067 2,137 2,416 42.6 24.8 543 1,436 10.6 12.8 Czech Republic .. 8,185 .. 6,429 2,973d 8,017 8.3 5.8 1,276d 4,166 3.4 3.2 Denmark 3,535f 8,744f 5,011 .. 3,671d 5,704 e 5.0 3.7 4,669d 9,082d 7.2 6.5 Dominican Republic 2,978 c,h 4,125c,h 360 401 2,860 d 4,209d 31.9 36.0 440 542 4.1 3.1 Ecuador 627a,b 1,047a,b 520 899 451 786 7.6 4.0 416 862 8.4 3.8 Egypt, Arab Rep. 5,116 14,051 2,964 .. 4,657 13,633 27.6 27.9 1,206 2,696 5.3 4.5 El Salvador 795 1,150 923 .. 437 646 11.9 11.6 219 280 3.9 3.0 Eritrea 70a,c 84a,c .. .. 36e .. 36.8 .. .. .. .. .. Estonia 1,220 2,120 1,800 955 657 1,412 13.7 8.7 253 719 5.1 4.9 Ethiopia 136c .. .. .. 205 1,434 20.7 30.9 80 143d 4.9 1.4 Finland 2,714 3,670 5,914 6,633 2,035 4,362 3.8 4.5 2,293 5,202 5.7 5.5 France 77,190 77,148 19,886 21,609 38,534 56,654 10.1 8.6 26,703 46,227 7.3 6.4 Gabon 155h .. 168 .. 99 .. 2.8 .. 183 .. 11.1 .. Gambia, The 79 91 .. .. .. 38 .. 14.9 .. 11d .. 3.6 Georgia 387a 2,033a 315 2,089 107 738 12.5 18.2 129 328 9.8 5.3 Germany 18,983f 26,875f 74,400 .. 24,943 49,133 4.0 3.2 57,601 91,208 9.2 6.7 Ghana 399c .. .. .. 357 706 14.6 7.5 162 882 4.8 6.3 Greece 13,096 15,007 .. .. 9,262 12,579 31.5 20.9 4,564 2,874 10.9 3.6 Guatemala 826a 1,876a 488 1,136 498 1,378 12.9 12.7 216 1,033 3.9 6.8 Guinea 33h .. .. .. 8 2 1.1 0.1 13 17 1.5 0.9 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 140h .. .. .. 128d 167d 25.4 20.9 173 431 12.6 10.6 386 2012 World Development Indicators 6.15 GLOBAL LINKS Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Honduras 471 896 274 429 263 652 6.8 9.6 198 406 4.2 4.1 Hungary .. 9,510 11,065 16,082 3,809 6,346 11.0 5.7 1,722 2,867 4.7 2.8 India 2,649 b 5,776b 4,416 12,988 3,598 14,673 6.0 4.2 3,686 13,746 5.0 3.1 Indonesia 5,064 7,003 2,205 6,235 4,975d 7,618 7.0 4.4 3,197d 8,432 5.7 5.5 Iran, Islamic Rep. 1,342 .. 2,286 .. 677 .. 2.3 .. 671 .. 3.8 .. Iraq 78a 1,518a .. .. 2d .. .. .. 9d .. .. .. Ireland 6,646 .. 3,783 .. 3,517 8,071 3.8 3.9 2,626 7,798 3.3 4.6 Israel 2,417b 2,803b 3,530 4,269 4,611 5,474 9.9 6.8 3,733 4,433 8.0 5.8 Italy 41,181 43,626 21,993 29,823 28,706 40,058 9.7 7.3 18,169 33,053 6.3 5.6 Jamaica 1,323c,h 1,922c,h .. .. 1,577 2,095 43.9 52.3 238 235 5.4 3.6 Japan 4,757a,b 8,611a,b 17,819 16,637 5,970 15,356 1.1 1.8 42,643 39,306 9.3 4.9 Jordan 1,580 c 4,557c 1,625 2,917 935 4,018 26.4 33.0 387 1,605 6.7 8.9 Kazakhstan 1,471 3,393 1,247 .. 403 1,236 3.9 1.9 483 1,437 5.4 3.3 Kenya 899 1,469 .. .. 500 1,620 18.0 18.2 156 212d 4.1 1.6 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 5,322a,c 8,798a,c 5,508 .. 8,527 13,805 4.1 2.5 7,945 19,695 4.1 3.8 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 78g 207g 1,236 .. 394 510 1.8 0.7 2,852 7,419 25.1 22.7 Kyrgyz Republic 59 1,316 47 1,296 20 336 3.5 13.6 28 398 4.3 10.2 Lao PDR 191 1,670 .. .. 114 d 385d 22.5 17.1 8 215d 1.4 9.3 Latvia 509 1,373 2,596 3,332 172 963 5.3 7.5 281 771 7.4 5.9 Lebanon 742 2,168 .. .. 742 8,174 .. 38.5 .. 5,080 .. 16.4 Lesotho .. 414 .. .. 18d 34 d 6.7 3.8 12 .. 1.1 .. Liberia .. .. .. .. .. 12d .. 3.0 .. 134 .. 7.4 Libya .. .. .. .. 84 170 0.7 0.3 495 2,184 9.9 7.1 Lithuania 1,083 1,507 3,632 1,411 430 1,097 8.4 4.4 261 786 4.5 3.1 Macedonia, FYR 224f 262f .. .. 88 209 5.4 5.0 58 141 2.5 2.3 Madagascar 160h 196h .. .. 152 633 12.8 .. 139 110 d 9.1 .. Malawi 228 746 .. .. 29 .. 6.6 .. 53 .. 8.4 .. Malaysia 10,222 24,577 30,532 .. 5,873 18,315 5.2 7.9 2,543 7,943d 2.7 4.2 Mali 86g,h 169g .. .. 47 296 7.3 .. 66 235 7.1 .. Mauritania 30 .. .. .. .. .. .. .. .. .. .. .. Mauritius 656 935 163 212 732 1,585 27.9 32.0 203 423 7.5 6.9 Mexico 20,641c 22,260c 11,079 14,395 9,133 12,417 5.1 4.0 6,365 9,075 3.3 2.8 Moldova 18 8 32 117 57 233 8.9 10.2 86 329 8.8 7.2 Mongolia 137 457 .. .. 43 288 7.0 8.5 54 319 7.0 8.2 Morocco 4,278c 9,288c 1,508 .. 2,280 8,176 21.8 27.1 506 1,879 4.0 4.7 Mozambique .. .. .. .. 74 d 230 10.7 7.7 122 285 8.2 6.1 Myanmar 208 311 .. .. 195 92 9.1 1.1 30 d 54 1.2 1.0 Namibia 656 984 .. .. 193d 560 d 13.0 11.2 86d 145 5.3 2.6 Nepal 464 603 155 765 219 378 17.1 24.0 109 528 6.1 9.0 Netherlands 10,003f 10,883f 13,896 18,430 11,285 18,690 4.4 3.2 13,649 19,772 5.7 3.9 New Zealand 1,780 2,492 1,283 2,026 2,272d 4,907d 12.7 12.0 1,235d 3,038d 7.1 7.8 Nicaragua 486 1,011c 486 908 129d 309d 11.7 8.5 126 323 5.9 5.9 Niger 50 .. .. .. 23d .. 7.2 .. 32 .. 7.0 .. Nigeria 813 .. .. .. 186 738 0.9 1.0 610 8,379 5.1 11.1 Norway 3,104 4,767 2,394 .. 2,521 5,083 3.2 2.9 4,893 13,971 9.9 11.9 Oman 571g 1,048g .. .. 377 1,251 3.2 3.3 629d 1,768 9.9 7.2 Pakistan 557 .. .. .. 551 998 5.4 3.6 574 1,370 4.7 3.4 Panama 484 1,324 216 392 628 2,552 8.0 13.9 241 575 3.0 2.9 Papua New Guinea 58 .. 52 .. 7d 2d 0.3 0.0 50 d 138 2.8 2.2 Paraguay 289b 465 175 313 88 243 3.0 2.4 154 269 4.7 2.5 Peru 800 2,299 730 2,058 861 2,741 10.1 6.9 641 1,646 6.6 4.7 Philippines 1,992c 3,520c 1,670 .. 2,334 3,228 5.7 5.0 1,841 4,253 3.8 5.8 Poland 17,400 12,470 56,677 42,760 6,128 9,986 13.2 5.0 3,417 9,100 6.0 4.4 Portugal 5,599 f 6,756f .. .. 6,027 12,969 17.7 18.0 2,754 4,691 5.8 5.4 Puerto Rico 3,341h 3,679h 1,259 1,357 2,388e 3,598e .. .. 1,333e 1,723e .. .. Qatar 378g,i 1,866g,i .. .. 128 ..e .. .. 307d,e .. .. .. 2012 World Development Indicators 387 6.15 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 2000 2010 Romania 5,264 a .. 6,388 .. 394 1,653 3.3 2.8 447 1,897 3.2 2.8 Russian Federation 21,169a 22,281a 18,371 39,323 3,429d 13,379 3.0 3.0 8,848d 29,993 14.5 9.3 Rwanda .. 666 .. .. 27 218d 21.1 35.9 35 94 8.3 5.7 Saudi Arabia 6,585 10,850 .. 7,233 .. 7,655 .. 2.9 .. 22,803 .. 13.1 Senegal .. .. .. .. 152 .. 11.6 .. 125 .. 7.2 .. Serbia .. 683 .. .. .. 951 .. 7.1 .. 1,106 .. 5.6 Sierra Leone 16h 39f,h 13 76 10 d 26d 18.2 6.1 35 22 13.8 2.5 Singapore 6,062 9,161 4,444 7,342 5,142d 14,181d 2.8 3.0 4,535d 16,770 d 2.7 4.1 Slovak Republic 1,053f .. .. .. 441 2,335 3.1 3.3 341 2,146 2.3 3.0 Slovenia 1,090 f 1,869f 1,965 2,874 1,016 2,735 9.5 9.0 544 1,377 4.8 4.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 5,872 8,074 3,834 5,165 3,338 10,308 9.0 10.3 2,684 8,139 8.1 8.1 South Sudan .. .. .. .. .. .. .. .. .. .. .. .. Spain 46,403 52,677 4,100 .. 32,656 58,810 19.4 15.6 7,710 22,800 4.1 5.7 Sri Lanka 400 b 654b 524 1,122 388 1,044 6.1 9.7 383 828 4.7 5.4 Sudan 38 .. .. .. 5d 94 d 0.3 0.8 55d 1,116d 2.7 10.0 Swaziland 281i 868g .. 1,141 24 51 1.9 2.5 32 87 2.2 3.3 Sweden 3,828f 4,951f 10,147 13,042 4,825 13,316 4.3 5.9 8,959 14,878 9.2 7.6 Switzerland 7,821g 8,628g 12,240 .. 8,988 17,847 7.2 5.2 7,360 13,317 6.9 4.7 Syrian Arab Republic 2,100 c,f 8,546 3,863 6,259 1,082d 6,308 15.8 32.2 669d 1,598 12.4 8.2 Tajikistan .. .. 6 .. .. 32 .. 2.1 .. 18d .. 0.5 Tanzania 459 783 .. .. 381 1,279 28.0 20.0 369 861 18.0 9.6 Thailand 9,579c 15,936 1,909 5,451 9,935 23,407 12.2 10.3 3,218 6,582 4.5 3.2 Timor-Leste .. 40 .. .. .. 21 .. .. .. 72 .. .. Togo 60 g .. .. .. 11 .. 2.6 .. 15 .. 2.5 .. Trinidad and Tobago 399h .. .. .. 371 .. 7.7 .. 190 .. 5.1 .. Tunisia 5,058b 6,903b 1,632 2,250 1,977 3,477 23.0 15.6 310 611 3.3 2.5 Turkey 9,586 27,000 5,284 11,002 7,636d 24,784 15.2 15.9 1,713d 5,451 2.8 2.8 Turkmenistan 3 .. 78 .. .. .. .. .. .. .. .. .. Uganda 193 946 153 324 165d 762 24.9 21.9 .. 389 .. 6.4 Ukraine 6,431 21,203 13,422 17,180 563 4,696 2.9 6.8 561 4,134 3.1 5.6 United Arab Emirates 3,907c,i .. .. .. 1,063e 8,577e .. .. 3,019e 11,818e .. .. United Kingdom 23,212 28,295 56,837 55,562 29,978 39,945 7.4 6.0 47,009 60,291 10.8 8.2 United States 51,238 59,791 61,327 .. 120,912 165,777 11.3 9.0 91,473 109,975 6.3 4.7 Uruguay 1,968 2,353 667 1,027 827 1,607 22.6 15.2 381 534 9.1 5.5 Uzbekistan 302 975 217 1,610 63e 121d,e .. .. .. .. .. .. Venezuela, RB 469 .. 954 .. 469 672 1.4 1.0 1,647 2,196 7.7 4.4 Vietnam 2,140a .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 310 g 522g .. .. 283d .. 28.0 .. 316 .. 9.7 .. Yemen, Rep. 73 536 .. .. 73d 622d 1.8 6.7 127 252 3.9 2.3 Zambia 457 815 .. .. 67d 125d 7.7 1.6 102 128 7.8 2.3 Zimbabwe 1,967a 2,239a .. 650 125e 634 e .. .. .. .. .. .. World 681,151 t 941,666 t 730,907 t .. t 572,408 t 1,111,919 t 7.4 w 5.9 w 531,843 t 996,273 t 6.8 w 5.4 w Low income 7,122 16,234 .. .. 3,304 11,469 11.0 11.2 2,837 7,086 6.0 4.3 Middle income 196,996 360,007 179,453 370,724 112,754 304,678 7.2 5.5 82,078 261,770 5.6 4.8 Lower middle income 39,368 98,178 45,422 86,323 26,019 77,617 7.7 6.8 17,992 65,963 5.0 4.7 Upper middle income 157,416 261,779 124,379 .. 86,796 227,263 7.0 5.2 64,076 196,511 5.8 4.8 Low & middle income 207,153 380,843 196,477 409,004 115,911 315,502 7.2 5.6 84,837 269,032 5.6 4.8 East Asia & Pacific 62,673 117,829 51,581 .. 42,855 109,418 7.0 4.3 26,308 91,275 4.8 4.1 Europe & Central Asia 52,830 106,138 59,247 114,685 16,274 60,832 6.5 6.1 15,983 56,988 7.2 5.9 Latin America & Carib. 47,146 64,289 29,567 43,711 30,617 53,813 6.9 5.2 25,096 50,734 5.7 5.0 Middle East & N. Africa 21,987 53,132 18,577 .. 12,902 48,730 12.9 20.9 5,237 23,279 5.5 6.8 South Asia 4,839 9,330 7,137 20,157 5,152 18,026 6.0 4.3 5,333 17,625 5.0 3.3 Sub-Saharan Africa 17,703 31,646 .. .. 8,000 24,873 7.2 7.0 7,133 32,518 6.9 6.9 High income 470,103 558,474 481,718 .. 456,489 796,198 7.4 6.0 446,402 730,544 7.1 5.7 Euro area 254,371 282,908 179,060 .. 181,607 307,946 8.4 6.2 155,713 275,992 7.1 5.8 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. Expenditure of travel-related items only; excludes passenger transport items. e. Data are from national sources. f. Arrivals in all types of accommodation establishments. g. Arrivals in hotels and similar establishments. h. Arrivals in hotels only. i. Arrivals by air only. 388 2012 World Development Indicators 6.15 GLOBAL LINKS Travel and tourism About the data De�nitions Tourism is defined as the activities of people trav- residing abroad while others do not. Caution should • International inbound tourists (overnight visitors) eling to and staying in places outside their usual thus be used in comparing arrivals across countries. are tourists who travel to a country other than that environment for no more than one year for leisure, The World Tourism Organization is improving its in which they usually reside, and outside their usual business, and other purposes not related to an activ- coverage of tourism expenditure data, using balance environment, for a period not exceeding 12 months ity remunerated from within the place visited. The of payments data from the International Monetary and whose main purpose in visiting is other than social and economic phenomenon of tourism has Fund (IMF) supplemented by data from individual an activity remunerated in the country visited. When grown substantially over the past quarter century. countries. These data, shown in the table, include number of tourists are not available, data on visitors, Statistical information on tourism is based mainly travel and passenger transport items as defined in which include tourists, same-day visitors, cruise pas- on data on arrivals and overnight stays along with the IMF’s Balance of Payments Manual, 5th edition. sengers, and crew members, are shown. • Interna- balance of payments information. These data do not When the IMF does not report data on passenger tional outbound tourists are departures that people completely capture the economic phenomenon of transport items, expenditure data for travel items make from their country of usual residence to any tourism or provide the information needed for effec- are shown. other country for any purpose other than an activity tive public policies and efficient business operations. Tourism can be either domestic or international. remunerated in the country visited. • Inbound tour- Data are needed on the scale and significance of The table shows data relevant to international tour- ism expenditure is expenditures by international tourism. Information on the role of tourism in national ism, where the traveler’s country of residence differs inbound visitors, including payments to national economies is particularly defi cient. Although the from the visiting country. International tourism con- carriers for international transport and any other pre- World Tourism Organization reports progress in har- sists of inbound and outbound tourism. payment made for goods or services received in the monizing definitions and measurement, differences The aggregates are calculated using the World destination country. They may include receipts from in national practices still prevent full comparability. Bank’s weighted aggregation methodology (see Sta- same-day visitors, except when these are important The usual environment of an individual is a key tistical methods) and differ from the World Tourism enough to justify separate classification. For some concept in tourism statistics and is defined as the Organization’s aggregates. countries they do not include receipts for passenger geographical area within which an individual con- transport items. Their share in exports is calculated ducts regular life routines. This concept excludes as a ratio to exports of goods and services (all trans- travelers who commute regularly between their place actions between residents of a country and the rest of usual residence and place of work or study or of the world involving a change of ownership from who frequently visit places within their current life residents to nonresidents of general merchandise, routine—for instance, homes of friends or relatives; goods sent for processing and repairs, nonmonetary shopping centers; and religious, health care, or other gold, and services). • Outbound tourism expenditure facilities a substantial distance away or in a different is expenditures of international outbound visitors in administrative area. other countries, including payments to foreign carri- The data in the table are from the World Tourism ers for international transport. These expenditures Organization, a United Nations agency. The data on may include those by residents traveling abroad as inbound and outbound tourists refer to the number of same-day visitors, except when these are important arrivals and departures, not to the number of people enough to justify separate classification. For some traveling. Thus a person who makes several trips to countries they do not include expenditures for pas- a country during a given period is counted each time senger transport items. Their share in imports is cal- as a new arrival. Unless otherwise indicated in the culated as a ratio to imports of goods and services footnotes, the data on inbound tourism show the (all transactions between residents of a country and arrivals of nonresident tourists (overnight visitors) the rest of the world involving a change of ownership at national borders. When data on international tour- from nonresidents to residents of general merchan- ists are unavailable or incomplete, the table shows dise, goods sent for processing and repairs, nonmon- the arrivals of international visitors, which include etary gold, and services). tourists, same-day visitors, cruise passengers, and Data sources crew members. Sources and collection methods for arrivals differ Data on visitors and tourism expenditure are from across countries. In some cases data are from bor- the World Tourism Organization’s Yearbook of Tour- der statistics (police, immigration, and the like) and ism Statistics and Compendium of Tourism Statistics supplemented by border surveys. In other cases data 2011. Data in the table are updated from electronic are from tourism accommodation establishments. files provided by the World Tourism Organization. For some countries number of arrivals is limited to Data on exports and imports are from the IMF’s Bal- arrivals by air and for others to arrivals staying in ance of Payments Statistics Yearbook and data files. hotels. Some countries include arrivals of nationals 2012 World Development Indicators 389 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 environmental indicators in World Development Indicators. Additional documentation is avail- able from the World Bank’s Bulletin Board on Statistical Capacity at http://data. worldbank.org. The demand for good-quality statistical data is ever increasing. Statistics provide the evidence needed to improve decisionmaking, document results, and heighten public accountability. The need for improved statistics to monitor the Millennium Development Goals and the parallel effort to support a culture of results-based management has stimulated a decade-long effort to improve statistics. The results has been impressive, but more needs to done. Thus a new action plan for statistics, “Statistics for Transparency, Account- ability, and Results: A Busan Action Plan for Statistics,� was endorsed by the Busan Partnership for Effective Development Cooperation at the Fourth High-level Forum for Aid Effectiveness held November 29–December 1, 2011, in Busan, Republic of Korea. This new action plan builds on the progress made under the first global plan to improve national and international statistics, the 2004 Mar- rakech Action Plan for Statistics from 2004, but goes beyond it in many ways. The main objectives of the new plan are to integrate statistics into decisionmaking, promote open access to statistics within government and for all other uses, and increase resources for statistical systems, both for investment in new capacity and for maintaining current operations. 2012 World Development Indicators 391 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 G C G Algeria Algerian dinar 1980 VAB BPM5 Actual S B G American Samoa U.S. dollar S Andorra Euro S Angola Angolan kwanza 1997 VAP 1991–96 2005 BPM5 Actual S G Antigua and Barbuda East Caribbean dollar 2006 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 S C S Aruba Aruban florin 1995 BPM5 S Australia Australian dollar a 2009 b VAB 2005 BPM5 G C S Austria Euro 2005 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 2005 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 S B G Bermuda Bermuda dollar 1996 VAB BPM5 G Bhutan Bhutanese ngultrum 2000 b VAB 2005 BPM5 Actual G C G Bolivia Bolivian Boliviano 1990 b VAB 1960–85 2005 BPM5 Actual G 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 G B G Brazil Brazilian real 2000 b VAB 2005 BPM5 Actual G 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 BPM5 Actual G B G Burundi Burundi franc 1980 VAB 2005 BPM5 Preliminary S C G Cambodia Cambodian riel 2000 VAB 2005 BPM5 Actual S C G Cameroon CFA franc 2000 b VAB 2005 BPM5 Actual S B G Canada Canadian dollar 2005 b VAB 2005 BPM5 G C S Cape Verde Cape Verde escudo 1980 VAP 2005 BPM5 Actual G C G Cayman Islands Cayman Islands dollar G Central African Republic CFA franc 2000 VAB 2005 BPM5 Preliminary S B G Chad CFA franc 1995 b VAB 2005 BPM5 Actual S G Channel Islands Pound sterling 2007, 2007 b VAB 2003 Chile Chilean peso 2003 b VAB 2005 BPM5 Actual S C S China Chinese yuan 2000 b VAP 1978–93 2005 BPM5 Preliminary S B G Hong Kong SAR, China Hong Kong dollar 2009 b VAB 2005 BPM5 G C S Macao SAR, China Macao pataca 2009 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 Actual S Congo, Dem. Rep. Congolese franc 1987 VAB 1999–2001 2005 BPM4 Estimate S C G Congo, Rep. CFA franc 1978 VAP 1993 2005 BPM5 Estimate S C G Costa Rica Costa Rican colon 1991 b VAB BPM5 Actual S C S Côte d’Ivoire CFA franc 1996 VAP 2005 BPM5 Estimate S C G Croatia Croatian kuna a 2000 b VAB 2005 BPM5 G C S Cuba Cuban peso 1990 VAB S Curaçao Netherlands Antilles guilder 392 2012 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; Special IHS, 2008 2010 2010 2000 Survey, 2010 Albania 2001 DHS, 2008/09 LSMS, 2008 Yes 1998 2010 2010 2000 Algeria 2008 MICS, 2006 IHS, 1995 2001 2009 2010 2000 American Samoa 2010 Yes 2007 2009 Andorra 1989 Yes 2006 Angola 1970 MICS, 2001; MIS, IHS, 2000 1964–65 2010 1991 2000 2006/07 Antigua and Barbuda 2011 Yes 2007 2010 2010 1990 Argentina 2010 IHS, 2010 Yes 2007 2010 2010 2000 Armenia 2001 DHS, 2005 IHS, 2010 Yes 2010 2010 2006 Aruba 2010 Yes 2010 Australia 2006 ES/BS, 1994 Yes 2011 2010 2010 2000 Austria 2011 IS, 2000 Yes 2010 2010 2010 2000 Azerbaijan 2009 DHS, 2006 ES/BS, 2008 Yes 2010 2010 2005 Bahamas, The 2010 2006 2010 Bahrain 2010 Yes 1995 2010 2003 Bangladesh 2011 DHS, 2007 IHS, 2010 2008 2010 2007 2008 Barbados 2010 Yes 2005 2010 2000 Belarus 2009 MICS, 2005 ES/BS, 2009 Yes 1994 2010 2010 2000 Belgium 2011 IHS, 2000 Yes 1999–2000 2009 2010 2007 Belize 2010 MICS, 2006 ES/BS, 1999 2008 2010 2000 Benin 2002 DHS, 2006 CWIQ, 2003 2011–12 2005 2006 2001 Bermuda 2010 Yes 2009 Bhutan 2005 MICS, 2010 IHS, 2007 2008 2009 2010 2008 Bolivia 2001 DHS, 2008 IHS, 2008 2008 2010 2010 2000 Bosnia and Herzegovina 1991 MICS, 2006 LSMS, 2007 Yes 2010 2010 2009 Botswana 2011 MICS, 2000 ES/BS, 2003 1993 2010 2010 2000 Brazil 2010 DHS, 1996 LFS, 2009 2006 2010 2011 2006 Brunei Darussalam 2001 Yes 2008 2006 1994 Bulgaria 2011 ES/BS, 2007 Yes 2010 2010 2010 2009 Burkina Faso 2006 MICS, 2006 CWIQ, 2009 2010 2006 2010 2000 Burundi 2008 MICS, 2005 CWIQ, 2006 2005 2010 2000 Cambodia 2008 DHS, 2010 IHS, 2008 2012 2010 2010 2006 Cameroon 2005 MICS, 2006 PS, 2007 1984 2007 2010 2000 Canada 2011 LFS, 2000 Yes 2011 2007 2010 2000 Cape Verde 2010 DHS, 2005 ES/BS, 2007 Yes 2004 2009 2010 Cayman Islands 2010 Yes Central African Republic 2003 MICS, 2006 PS, 2008 1985 2006 2009 2000 Chad 2009 DHS, 2004 PS, 2002/03 2011 2008 1995 2000 Channel Islands 2001 Chile 2002 IHS, 2009 Yes 2007 2010 2010 2000 China 2010 NSS, 2007 IHS, 2008 2007 2010 2010 2005 Hong Kong SAR, China 2006 Yes 2010 2010 Macao SAR, China 2011 Yes 2009 2010 Colombia 2006 DHS, 2010 IHS, 2010 2001 2010 2010 2000 Comoros 2003 MICS, 2000 IHS, 2004 2009 2007 1999 Congo, Dem. Rep. 1984 MICS, 2010 1–2-3, 2005/06 1990 2009 1986 2000 Congo, Rep. 2007 AIS, 2009; DHS, 2005 CWIQ/PS, 2005 1985–86 2010 2005 2002 Costa Rica 2011 RHS, 1993 LFS, 2010 Yes 1973 2010 2010 2000 Côte d’Ivoire 1998 MICS, 2006 IHS, 2008 2001 2010 2010 2000 Croatia 2011 ES/BS, 2008 Yes 2003 2010 2011 2009 Cuba 2002 MICS, 2006 Yes 2008 2006 2000 Curaçao 2012 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 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 2005 b VAB 2005 BPM5 S C S Djibouti Djibouti franc 1990 VAB 2005 BPM5 Actual G G Dominica East Caribbean dollar 2006 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 G 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 S C S Equatorial Guinea CFA franc 2000 VAB 1965–84 2005 G Eritrea Eritrean nakfa 1992 VAB BPM4 Actual Estonia Estonian kroon 2000 b VAB 1987–95 2005 BPM5 S 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 2005 b VAB 2005 BPM5 G C S France Euro a 2005 b VAB 2005 BPM5 S C S French Polynesia CFP franc S Gabon CFA franc 1991 VAP 1993 2005 BPM5 Actual S G Gambia, The Gambian dalasi 1987 VAB 2005 BPM5 Actual G C G Georgia Georgian lari a 1996 b VAB 1990–95 2005 BPM5 Actual G C S Germany Euro 2005 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 2005 VAB 2005 BPM5 S C S Greenland Danish krone G Grenada East Caribbean dollar 2006 VAB BPM5 Actual S B G Guam U.S. dollar G Guatemala Guatemalan quetzal 2001 b VAB BPM5 Actual S B G Guinea Guinean franc 1996 VAB 2005 BPM5 Estimate S B G Guinea-Bissau CFA franc 2005 VAB 2005 BPM5 Estimate G G Guyana Guyana dollar 2006 VAB BPM5 Actual S G Haiti Haitian gourde 1986/87 VAB 1991 BPM5 Actual G G Honduras Honduran lempira 2000 b VAB 1988–89 BPM5 Actual S C G Hungary Hungarian forint a 2005 b VAB 2005 BPM5 S C S Iceland Iceland krona 2005 VAB 2005 BPM5 G C S India Indian rupee 2004/05 b VAB 2005 BPM5 Actual G C S Indonesia Indonesian rupiah 2000 VAP 2005 BPM5 Actual S 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 2005 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 2005 b VAB 2005 BPM5 S C S Jamaica Jamaican dollar 2003 VAB BPM5 Actual G C G Japan Japanese yen 2005 VAB 2005 BPM5 G C S Jordan Jordanian dinar 1994 VAB 2005 BPM5 Actual G 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 BPM5 G G Korea, Dem. Rep. Democratic People’s BPM4 Republic of Korean won Korea, Rep. Korean won 2005 b VAB 2005 BPM5 G C S Kosovo Euro Actual G Kuwait Kuwaiti dinar 1995 VAP 2005 BPM5 S B G Kyrgyz Republic Kyrgyz som a 1995 b VAB 1990–95 2005 BPM5 Actual S B S Lao PDR Lao kip 1990 VAB 2005 BPM5 Preliminary S B Latvia Latvian lats 2000 b VAB 1987–95 2005 BPM5 Actual S C S Lebanon Lebanese pound 1997 VAB 2005 BPM5 Actual G B G 394 2012 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 Cyprus 2001 Yes 2008 2010 2009 Czech Republic 2011 RHS, 1993 IS, 1996 Yes 2010 2010 2010 2007 Denmark 2011 ITR, 1997 Yes 2010 2010 2010 2009 Djibouti 2009 MICS, 2006 PS, 2002 2007 2009 2000 Dominica 2011 Yes 2010 2010 2004 Dominican Republic 2010 DHS, 2007 IHS, 2010 1971 2010 2010 2000 Ecuador 2010 RHS, 2004 LFS, 2010 2012 2010 2010 2000 Egypt, Arab Rep. 2006 DHS, 2008 ES/BS, 2008 Yes 2010 2010 2010 2000 El Salvador 2007 RHS, 2008 IHS, 2009 Yes 2007–08 2010 2010 2000 Equatorial Guinea 2002 2009 2000 Eritrea 1984 DHS, 2002 2009 2005 2004 Estonia 2000 ES/BS, 2004 Yes 2010 2009 2011 2007 Ethiopia 2007 DHS, 2005 ES/BS, 2005 2001–02 2010 2011 2002 Faeroe Islands 2011 Yes 2009 Fiji 2007 ES/BS, 2009 Yes 2009 2010 2010 2000 Finland 2010 IS, 2000 Yes 2010 2010 2010 2005 France 2006c ES/BS, 1994/95 Yes 2010 2009 2010 2007 French Polynesia 2007 Yes 2010 Gabon 2003 DHS, 2000 CWIQ/IHS, 2005 1974–75 2010 2010 2000 Gambia, The 2003 MICS, 2005/06 IHS, 2003 2001–02 2010 2010 2000 Georgia 2002 MICS, 2005; RHS, 2005 IHS, 2009 Yes 2004 2010 2010 2005 Germany 2011 IHS, 2000 Yes 2010 2010 2010 2007 Ghana 2010 DHS, 2008 LSMS, 2006 2011 2010 2010 2000 Gibraltar 2001 Yes Greece 2011 IHS, 2000 Yes 2009 2009 2010 2007 Greenland 2010 Yes 2007 Grenada 2011 Yes 2011 2010 2009 2005 Guam 2010 Yes Guatemala 2002 RHS, 2002 LSMS, 2006 Yes 2008 2010 2010 2000 Guinea 1996 DHS, 2005 CWIQ, 2007 2000–01 2010 2008 2000 Guinea-Bissau 2009 MICS, 2010 CWIQ, 2002 1988 2002 2005 2000 Guyana 2002 DHS, 2009 IHS, 1998 2010 2010 2000 Haiti 2003 DHS, 2005/06 IHS, 2001 2009 1997 2000 Honduras 2001 DHS, 2005/06 IHS, 2009 2013 2010 2009 2000 Hungary 2001 ES/BS, 2007 Yes 2007 2010 2010 2007 Iceland d Yes 2010 2009 2010 2005 India 2011 DHS, 2005/06 IHS, 2010 2011 2010 2010 2010 Indonesia 2010 DHS, 2007 IHS, 2011 2003 2010 2010 2000 Iran, Islamic Rep. 2006 DHS, 2000 ES/BS, 2005 Yes 2003 2007 2010 2004 Iraq 1997 MICS, 2006 IHS, 2007 2011 2003 2009 2000 Ireland 2011 IHS, 2000 Yes 2010 2009 2010 2000 Isle of Man 2006 Yes Israel 2009 ES/BS, 2001 Yes 1981 2010 2004 Italy 2012 ES/BS, 2000 Yes 2010 2010 2010 2000 Jamaica 2011 MICS, 2005 LSMS, 2007 2007 2010 2010 2000 Japan 2010 IS, 1993 Yes 2010 2009 2011 2001 Jordan 2004 DHS, 2009 ES/BS, 2010 2007 2010 2010 2005 Kazakhstan 2009 MICS, 2006 ES/BS, 2009 Yes 2010 2009 2000 Kenya 2009 DHS, 2008/09; MIS, 2010 IHS, 2005/06 1977–79 2010 2010 2003 Kiribati 2005 2009 2009 Korea, Dem. Rep. 2009 MICS, 2010 2005 Korea, Rep. 2010 ES/BS, 1998 Yes 2000 2010 2010 2002 Kosovo 1981 IHS, 2009 2010 Kuwait 2010 FHS, 1996 Yes 1970 2003 2009 2002 Kyrgyz Republic 2009 MICS, 2005/06 ES/BS, 2010 Yes 2002 2010 2010 2000 Lao PDR 2005 MICS, 2006 ES/BS, 2008 2010–11 2010 1975 2005 Latvia 2011 IHS, 2008 Yes 2010 2010 2010 2000 Lebanon 1970 MICS, 2000 Yes 2010 2010 2010 2005 2012 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 Lesotho Lesotho loti 1995 b VAB 2005 BPM5 Actual G C G Liberia Liberian dollar 1992 VAP 2005 BPM5 Actual S B G Libya Libyan dinar 1999 VAB 1986 BPM5 G G Liechtenstein Swiss franc VAB S Lithuania Lithuanian litas 2000 b VAB 1990–95 2005 BPM5 Actual G C S Luxembourg Euro 2005 VAB 2005 BPM5 S C S Macedonia, FYR Macedonian denar 1997 1995 b VAB 2005 BPM5 Actual S S Madagascar Malagasy ariary 1984 VAB 2005 BPM5 Actual S 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 2003 VAB 2005 BPM5 Actual G C G Mali CFA franc 1987 VAB 2005 BPM5 Actual S B G Malta Euro 2005 VAB 2005 BPM5 G C S Marshall Islands U.S. dollar 2004 VAB G Mauritania Mauritanian ouguiya 2004 VAB 2005 BPM4 Actual S G Mauritius Mauritian rupee 2006 VAB 2005 BPM5 Actual G C G Mayotte Euro G Mexico Mexican peso 2003 b VAB 2005 BPM5 Actual G C S Micronesia, Fed. Sts. U.S. dollar 2004 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 S G Morocco Moroccan dirham 1998 VAB 2005 BPM5 Actual S C S Mozambique New Mozambican 2003 VAB 1992–95 2005 BPM5 Actual S G metical Myanmar Myanmar kyat 2005/06 VAP BPM5 Estimate G C Namibia Namibian dollar 2004/05 b VAB 2005 BPM5 G B G Nepal Nepalese rupee 2000/01 VAB 2005 BPM5 Actual G C G Netherlands Euro a 2005 b VAB 2005 BPM5 S C S New Caledonia CFP franc S New Zealand New Zealand dollar 2005/06 VAB 2005 BPM5 G C Nicaragua Nicaraguan gold 1994 b VAB 1965–95 BPM5 Actual G B G cordoba Niger CFA franc 1987 VAP 1993 2005 BPM5 Actual S 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 2005 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 S 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 G Paraguay Paraguayan guarani 1994 VAP 2005 BPM5 Actual S B G Peru Peruvian new sol 1994 VAB 1985–90 2005 BPM5 Actual S C S Philippines Philippine peso 2000 VAP 2005 BPM5 Actual G B S Poland Polish zloty a 2005 b VAB 2005 BPM5 S C S Portugal Euro 2005 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 Actual G C G Samoa Samoan tala 2002 VAB BPM5 Actual S San Marino Euro 1995 2000 b VAB C G São Tomé and Príncipe São Tomé and 2001 VAP 2005 BPM4 Actual S G Príncipe dobra 396 2012 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 Lesotho 2006 DHS, 2009/10 ES/BS, 2002/03 2010 2010 2008 2000 Liberia 2008 DHS, 2007; MIS, 2009 CWIQ, 2007 2008 1985 2000 Libya 2006 MICS, 2000 2001 2008 2004 2000 Liechtenstein 2010 Yes Lithuania 2011 ES/BS, 2008 Yes 2010 2009 2010 2007 Luxembourg 2011 Yes 2010 2010 2010 1999 Macedonia, FYR 2010 MICS, 2005 ES/BS, 2009 Yes 2007 2010 2009 2007 Madagascar 1993 DHS, 2008/09 PS, 2010 2004 2009 2010 2000 Malawi 2008 DHS, 2010 LSMS, 2004/05 2006–07 2010 2010 2000 Malaysia 2010 ES/BS, 2009 Yes 2012 2010 2010 2005 Maldives 2011 DHS, 2009 IHS, 2004 Yes 2010 2010 2008 Mali 2009 DHS, 2006; Special, 2010 IHS, 2010 2007 2010 2000 Malta 2005 Yes 2010 2009 2010 2002 Marshall Islands 2011 Mauritania 2000 MICS, 2007 IHS, 2008 2010 2010 2000 Mauritius 2011 Yes 2010 2010 2003 Mayotte 2007 Yes 2009 Mexico 2010 ENPF, 1995 LFS, 2010 2007 2010 2010 2008 Micronesia, Fed. Sts. 2000 IHS, 2000 1983 Moldova 2004 DHS, 2005 ES/BS, 2010 Yes 2011 2010 2010 2000 Monaco 2008 Yes Mongolia 2010 MICS, 2010 LSMS, 2007/08 Yes 2011 2010 2007 2005 Montenegro 2011 MICS, 2005/06 ES/BS, 2008 Yes 2010 2010 Morocco 2004 MICS, 2006 ES/BS, 2007 2012 2010 2010 2000 Mozambique 2007 DHS, 2003; AIS, 2009 ES/BS, 2008 2009–10 2010 2010 2000 Myanmar 1983 MICS, 2000 2010–11 2010 2001 2000 Namibia 2001 DHS, 2006/07; ES/BS, 2004 1996–97 2010 2008 2000 HIV/MCH SPA, 2009 Nepal 2001 MICS, 2010 LSMS, 2010 2011–12 2010 2010 2005 Netherlands 2011 IHS, 1999 Yes 2010 2010 2010 2008 New Caledonia 2009 Yes 1997 2010 New Zealand 2006 IS, 1997 Yes 2012 2006 2010 2002 Nicaragua 2005 RHS, 2006/07 LSMS, 2005 2011 2010 2010 2000 Niger 2001 DHS, 2006 CWIQ/PS, 2008 2005–07 2003 2010 2000 Nigeria 2006 DHS, 2008 IHS, 2010 2007 2006 2010 2000 Northern Mariana Islands 2010 Norway 2001 IS, 2000 Yes 2010 2010 2010 2006 Oman 2010 FHS, 1995 1978–79 2004 2010 2003 Pakistan 1998 MICS, 2010 IHS, 2008 2000 2010 2010 2008 Palau 2010 Yes 2007 Panama 2010 LSMS, 2003 LFS, 2010 2011 2010 2010 2000 Papua New Guinea 2000 DHS, 1996 IHS, 1996 2010 2005 2005 Paraguay 2002 RHS, 2004 IHS, 2010 2008 2010 2011 2000 Peru 2007 DHS, 2007/08 IHS, 2010 2012 2010 2010 2000 Philippines 2010 DHS, 2008 ES/BS, 2009 Yes 2012 2010 2010 2009 Poland 2011 ES/BS, 2009 Yes 2010 2010 2010 2009 Portugal 2011 IS, 1997 Yes 2009 2010 2010 2002 Puerto Rico 2010 RHS, 1995/96 Yes 2007 2010 2005 Qatar 2010 Yes 2000–01 2009 2005 Romania 2011 RHS, 1999 LFS, 2009 Yes 2010 2010 2010 2009 Russian Federation 2010 RHS, 1996 IHS, 2009 Yes 2006 2010 2010 2001 Rwanda 2002 DHS, 2007/08 IHS, 2011 2008 2009 2010 2000 Samoa 2006 DHS, 2009 2009 2010 2010 San Marino 2010 Yes São Tomé and Príncipe 2001 DHS, 2008/09 PS, 2000/01 2011 2005 2010 1993 2012 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 Saudi Arabia Saudi Arabian riyal 1999 VAP 2005 BPM5 S G Senegal CFA franc 1999 1987 b VAB 2005 BPM5 Actual G B G Serbia New Serbian dinar a 2002 b VAB 2005 BPM5 Actual S C G Seychelles Seychelles rupee 1986 VAP BPM5 Actual G C G Sierra Leone Sierra Leonean leone 1990 b VAB 2005 BPM5 Actual S B G Singapore Singapore dollar 2005 b VAB 2005 BPM5 G C S Sint Maarten Netherlands Antilles guilder Slovak Republic Euro 2005 b VAB 2005 BPM5 S C S Slovenia Euro a 2005 b VAB 2005 BPM5 S C S Solomon Islands Solomon Islands dollar 2004 VAB BPM5 Actual S G Somalia Somali shilling 1985 VAB 1977–90 Estimate South Africa South African rand 2005 b VAB 2005 BPM5 Preliminary G C S South Sudan South Sudanese Pound Spain Euro 2005 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 2006 b VAB BPM5 Actual S C G St. Lucia East Caribbean dollar 2006 VAB BPM5 Actual S G St. Martin Euro St. Vincent & Grenadines East Caribbean dollar 2006 VAB BPM5 Actual S B G Sudan Sudanese pound 1981/82e 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 2005 VAB 2005 BPM5 G C S Switzerland Swiss franc 2005 VAB 2005 BPM5 S C S Syrian Arab Republic Syrian pound 2000 VAB 1970–2010 2005 BPM5 Actual S C G Tajikistan Tajik somoni a 2000 b VAB 1990–95 2005 BPM5 Actual G 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 VAB 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 G 1997–2007 Turks and Caicos Islands U.S. dollar G Tuvalu Australian dollar VAP G 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 2007 VAP BPM5 G B G United Kingdom Pound sterling 2005 b VAB 2005 BPM5 G C S United States U.S. dollar a 2005 VAB 2005 BPM5 G C S Uruguay Uruguayan peso 2005 VAB 2005 BPM5 Actual G C S Uzbekistan Uzbek sum a 1997 b VAB 1990–95 BPM4 Actual G Vanuatu Vanuatu vatu 2006 VAB BPM5 Estimate 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 BPM5 Preliminary G 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 398 2012 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 Saudi Arabia 2010 Demographic survey, 2007 2010 2010 2010 2006 Senegal 2002 DHS, 2005; MIS, 2008/09 PS, 2005 2011–12 2010 2011 2002 Serbia 2011 MICS, 2005/06 IHS, 2009 Yes 2012 2010 2008 2009 Seychelles 2010 IHS, 2007 Yes 2011 2009 2008 2005 Sierra Leone 2004 DHS, 2008 IHS, 2003 1984–85 2003 2002 2000 Singapore 2010 General household, 2005 Yes 2010 2010 Sint Maarten Slovak Republic 2011 IS, 2009 Yes 2001 2010 2010 2007 Slovenia 2011 ES/BS, 2004 Yes 2010 2010 2010 2009 Solomon Islands 2009 2012 2009 2007 Somalia 1987 MICS, 2006 1990 1982 2003 South Africa 2001 DHS, 2003 ES/BS, 2009 2012 2010 2010 2000 South Sudan 2008 Spain 2001 IHS, 2000 Yes 1999 2009 2010 2008 Sri Lanka 2001 DHS, 2006/07 ES/BS, 2007 Yes 2012 2010 2010 2005 St. Kitts and Nevis 2011 Yes 2010 2008 St. Lucia 2010 IHS, 1995 Yes 2010 2008 2005 St. Martin St. Vincent & Grenadines 2011 Yes 2010 2010 1995 Sudan 2008 MICS, 2010 ES/BS, 2009 2010 2009 2000 f Suriname 2004 MICS, 2006 ES/BS, 1999 Yes 2008 2009 2010 2000 Swaziland 2007 MICS, 2010 ES/BS, 2010 2003 2010 2007 2000 Sweden d IS, 2000 Yes 2010 2010 2010 2007 Switzerland 2010 ES/BS, 2000 Yes 2008 2010 2010 2000 Syrian Arab Republic 2004 MICS, 2006 ES/BS, 2004 2014 2002 2008 2005 Tajikistan 2010 MICS, 2005 LSMS, 2009 2013 2010 2000 2000 Tanzania 2002 DHS, 2010 ES/BS, 2007 2007–08 2010 2011 2002 Thailand 2010 MICS, 2005/06 IHS, 2009 2013 2010 2010 2007 Timor-Leste 2010 DHS, 2009/10 LSMS, 2007 2012 2000 2005 2004 Togo 2010 MICS, 2010 CWIQ, 2006 2011–12 2005 2010 2002 Tonga 2006 Yes 2011–12 2010 2010 Trinidad and Tobago 2011 MICS, 2006 IHS, 1992 Yes 2004 2010 2010 2000 Tunisia 2004 MICS, 2006 IHS, 2005/06 2004 2010 2010 2001 Turkey 2000 DHS, 2003 LFS, 2008 2001 2010 2010 2003 Turkmenistan 1995 MICS, 2006 LSMS, 1998 Yes 2004 2000 2000 Turks and Caicos Islands 2001 Yes 2009 Tuvalu 2002 2008 2008 Uganda 2002 DHS, 2006; MIS, 2009/10 PS, 2009 2008 2010 2010 2002 Ukraine 2001 DHS, 2007 ES/BS, 2009 Yes 2011 2010 2010 2000 United Arab Emirates 2010 2012 2010 2009 2005 United Kingdom 2011 IS, 1999 Yes 2010 2010 2010 2006 United States 2010 CPS (monthly) LFS, 2000 Yes 2007 2010 2010 2005 Uruguay 2004 IHS, 2010 Yes 2011 2010 2009 2000 Uzbekistan 1989 MICS, 2006 ES/BS, 2003 Yes 2010 2000 Vanuatu 2009 MICS, 2007 2007 2009 2007 Venezuela, R.B. 2001 MICS, 2000 IHS, 2009 Yes 2007 2007 2010 2000 Vietnam 2009 MICS, 2006 IHS, 2008 Yes 2011 2010 2009 2005 Virgin Islands (U.S.) 2010 Yes 2007 West Bank and Gaza 2007 PAPFAM, 2006 IHS, 2009 1971 2008 Yemen, Rep. 2004 MICS, 2006 ES/BS, 2005 2002 2010 2009 2005 Zambia 2010 DHS, 2007 IHS, 2006 1990 2010 2010 2000 Zimbabwe 2002 DHS, 2005/06 IHS, 2003 1960 2010 2010 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. Rolling. e. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year. 2012 World Development Indicators 399 Primary data documentation notes • Base year is the base or pricing period used for staff estimation, and estimate that data are World from the very recent censuses could be reflected in constant price calculations in the country’s national Bank staff estimates. • System of trade refers to timely revisions if basic data are available, such as accounts. Price indexes derived from national the United Nations general trade system (G) or spe- population by age and sex, as well as the detailed accounts aggregates, such as the implicit deflator cial trade system (S). Under the general trade system definition of counting, coverage, and completeness. for gross domestic product (GDP), express the price goods entering directly for domestic consumption Countries that hold register-based censuses produce level relative to base year prices. • Reference year and goods entered into customs storage are similar census tables every 5 or 10 years. Germany’s is the year in which the local currency, constant price recorded as imports at arrival. Under the special 2001 census is a register-based test census using series of a country is valued. The reference year is trade system goods are recorded as imports when a sample of 1.2  percent of the population. A rare usually the same as the base year used to report the declared for domestic consumption whether at time case, France has been conducting a rolling census constant price series. However, when the constant of entry or on withdrawal from customs storage. every year since 2004; the 1999 general population price data are chain linked, the base year is changed Exports under the general system comprise outward- census was the last to cover the entire population annually, so the data are rescaled to a specific refer- moving goods: (a)  national goods wholly or partly simultaneously (www.insee.fr/en/recensement/ ence year to provide a consistent time series. When produced in the country; (b) foreign goods, neither page_accueil_rp.htm). • Latest demographic, edu- the country has not rescaled following a change in transformed nor declared for domestic consumption cation, or health household survey indicates the base year, World Bank staff rescale the data to main- in the country, that move outward from customs stor- household surveys used to compile the demographic, tain a longer historical series. To allow for cross- age; and (c)  nationalized goods that have been education, and health data in section 2. AIS is HIV/ country comparison and data aggregation, constant declared for domestic consumption and move out- AIDS Indicator Survey, CPS is Current Population price data reported in World Development Indicators ward without being transformed. Under the special Survey, DGHS is Demographic and General Health are rescaled to a common reference year (2000) and system of trade, exports are categories a and c. In Survey, DHS is Demographic and Health Survey, currency (U.S. dollars). •  System of National some compilations categories b and c are classified ENPF is National Family Planning Survey (Encuesta Accounts identifies countries that use the 1993 as re-exports. Direct transit trade—goods entering Nacional de Planificacion Familiar), FHS is Family System of National Accounts (1993 SNA), the termi- or leaving for transport only—is excluded from both Health Survey, LSMS is Living Standards Measure- nology applied in World Development Indicators since import and export statistics. See About the data for ment Survey, MICS is Multiple Indicator Cluster Sur- 2001, to compile national accounts. Although more tables 4.4, 4.5, and 6.2 for further discussion. vey, MIS is Malaria Indicator Survey, NSS is National countries are adopting the 1993 SNA, many still fol- •  Government �nance accounting concept is the Sample Survey on Population Change, PAPFAM is Pan low the 1968 SNA, and some low-income countries accounting basis for reporting central government Arab Project for Family Health, RHS is Reproductive use concepts from the 1953 SNA. • SNA price valu- fi nancial data. For most countries government Health Survey, and SPA is Service Provision Assess- ation shows whether value added in the national finance data have been consolidated (C) into one set ments. Detailed information for AIS, DHS, MIS, and accounts is reported at basic prices (VAB) or pro- of accounts capturing all central government fiscal SPA are available at www.measuredhs.com/ ducer prices (VAP). Producer prices include taxes activities. Budgetary central government accounts aboutsurveys; for MICS at www.childinfo.org; and for paid by producers and thus tend to overstate the (B) exclude some central government units. See RHS at www.cdc.gov/reproductivehealth/surveys. actual value added in production. However, VAB can About the data for tables 4.12, 4.13, and 4.14 for • Source of most recent income and expenditure be higher than VAP in countries with high agricultural further details. • IMF data dissemination standard data shows household surveys that collect income subsidies. See About the data for tables 4.1 and 4.2 shows the countries that subscribe to the IMF’s Spe- and expenditure data. Names and detailed informa- for further discussion of national accounts valuation. cial Data Dissemination Standard (SDDS) or General tion on household surveys can be found on the • Alternative conversion factor identifies the coun- Data Dissemination System (GDDS). S refers to coun- website of the International Household Survey Net- tries and years for which a World Bank–estimated tries that subscribe to the SDDS and have posted work (www.surveynetwork.org). Core Welfare Indica- conversion factor has been used in place of the offi - data on the Dissemination Standards Bulletin Board tor Questionnaire Surveys (CWIQ), developed by the cial exchange rate (line rf in the International Mon- at http://dsbb.imf.org. G refers to countries that sub- World Bank, measure changes in key social indica- etary Fund’s [IMF] International Financial Statistics). scribe to the GDDS. The SDDS was established for tors for different population groups— specifi cally See Statistical methods for further discussion of member countries that have or might seek access indicators of access, utilization, and satisfaction alternative conversion factors. • Purchasing power to international capital markets to guide them in pro- with core social and economic services. Expenditure parity (PPP) survey year is the latest available sur- viding their economic and financial data to the public. survey/budget surveys (ES/BS) collect detailed infor- vey year for the International Comparison Program’s The GDDS helps countries disseminate comprehen- mation on household consumption as well as on estimates of PPPs. See About the data for table 1.1 sive, timely, accessible, and reliable economic, finan- general demographic, social, and economic charac- for a more detailed description of PPPs. • Balance cial, and sociodemographic statistics. IMF member teristics. Integrated household surveys (IHS) collect of Payments Manual in use refers to the classifica- countries elect to participate in either the SDDS or detailed information on a wide variety of topics, tion system used to compile and report data on bal- the GDDS. Both standards enhance the availability including health, education, economic activities, ance of payments items in table 4.17. BPM4 refers of timely and comprehensive data and therefore con- housing, and utilities. Income surveys (IS) collect to the 4th edition of the IMF’s Balance of Payments tribute to the pursuit of sound macroeconomic poli- information on the income and wealth of households Manual (1977), and BPM5 to the 5th edition (1993). cies. The SDDS is also expected to improve the as well as various social and economic characteris- •  External debt shows debt reporting status for functioning of financial markets. • Latest population tics. Labor force surveys (LFS) collect information on 2010 data. Actual indicates that data are as census shows the most recent year in which a cen- employment, unemployment, hours of work, income, reported, preliminary that data are based on reported sus was conducted and in which at least preliminary and wages. Living Standards Measurement Studies or collected information but include an element of results have been released. The preliminary results (LSMS), developed by the World Bank, provide a 400 2012 World Development Indicators Primary data documentation notes comprehensive picture of household welfare and the the fiscal period; if the fiscal year ends on or after line with State Statistical Committee data that were factors that affect it; they typically incorporate data June 30, data are shown in the second year of the not previously available. •  Botswana. The Central collection at the individual, household, and commu- period. Balance of payments data are reported in Statistical Offi ce has revised national accounts nity levels. Priority surveys (PS) are a light monitoring World Development Indicators by calendar year. series for 2004 onward. • Dominica. Based on offi - survey, designed by the World Bank, for collecting cial government statistics, national accounts data data from a large number of households cost-effec- Economies with exceptional reporting periods have been revised for 2000 onward; the new base tively and quickly. Income tax registers (ITR) provide Reporting period year is 2006. • Grenada. Based on official govern- Fiscal for national information on a population’s income and allowance, Economy year end accounts data ment statistics, national accounts data have been such as gross income, taxable income, and taxes by revised for 2000 onward; the new base year is 2006. Afghanistan Mar. 20 FY socioeconomic group. 1-2-3 surveys (1-2-3) are imple- • Hungary. Based on data from the Organisation for Australia Jun. 30 FY mented in three phases and collect sociodemo- Economic Co-operation and Development, national Bangladesh Jun. 30 FY graphic and employment data, data on the informal accounts data have been revised for 1991 onward. Botswana Jun. 30 FY sector, and information on living conditions and • Macedonia. Based on official statistics, national Canada Mar. 31 CY household consumption. • Vital registration com- accounts data have been revised for 2003 onward. Egypt, Arab Rep. Jun. 30 FY plete identifies countries that report at least 90 •  Maldives. The Department of National Plan- Ethiopia Jul. 7 FY percent complete registries of vital (birth and death) ning has revised national accounts data for 2000 Gambia, The Jun. 30 CY statistics to the United Nations Statistics Division onward; the new base year is 2003. • Mauritania. and reported in Population and Vital Statistics Haiti Sep. 30 FY Based on official government statistics, data have Reports. Countries with complete vital statistics reg- India Mar. 31 FY been revised for 1991 onward; the new base year istries may have more accurate and more timely Indonesia Mar. 31 CY for constant price series is 2004. •  Philippines. demographic indicators than other countries. • Lat- Iran, Islamic Rep. Mar. 20 FY National accounts data have been revised for 1998 est agricultural census shows the most recent year Japan Mar. 31 CY onward. Because intellectual property products are in which an agricultural census was conducted and Kenya Jun. 30 CY now reported as a part of gross fixed capital forma- reported to the Food and Agriculture Organization of Kuwait Jun. 30 CY tion, gross domestic product (GDP) in current prices the United Nations. • Latest industrial data show Lesotho Mar. 31 CY averages 4 percent higher than previous estimates. the most recent year for which manufacturing value Malawi Mar. 31 CY • Puerto Rico. Based on data from the Instituto de added data at the three-digit level of the International Myanmar Mar. 31 FY Estadísticas de Puerto Rico, national accounts data Standard Industrial Classification (ISIC, revision 2 or have been revised for 2001 onward. • Serbia. The Namibia Mar. 31 CY 3) are available in the United Nations Industrial Statistical Office has improved the methodology of Nepal Jul. 14 FY Development Organization database. • Latest trade national accounts data for 2003 onward. Specifically, New Zealand Mar. 31 FY data show the most recent year for which structure the classification of sectors has been revised, and Pakistan Jun. 30 FY of merchandise trade data from the United Nations GDP is now calculated using chain linked volumes in Puerto Rico Jun. 30 FY Statistics Division’s Commodity Trade (Comtrade) 2005 prices. • Singapore. National accounts time Sierra Leone Jun. 30 CY database are available. • Latest water withdrawal series have been replaced with official government Singapore Mar. 31 CY data show the most recent year for which data on statistics. •  St. Kitts and Nevis, St. Lucia, and South Africa Mar. 31 CY freshwater withdrawals have been compiled from a St. Vincent and the Grenadines. Based on official variety of sources. See About the data for table 3.5 Swaziland Mar. 31 CY government statistics, national accounts data have for more information. Sweden Jun. 30 CY been revised for 2000 onward; the new base year Thailand Sep. 30 CY is 2006. • Swaziland. The Central Statistical Office Exceptional reporting periods Uganda Jun. 30 FY has revised national accounts data for 1990 onward. In most economies the �scal year is concurrent with United States Sep. 30 CY • Syria. The Central Bureau of Statistics has revised the calendar year. Exceptions are shown in the table Zimbabwe Jun. 30 CY national accounts data for 2003 onward. • Tunisia. at right. The ending date reported here is for the fis- Based on data from the Central Bank and its Sta- cal year of the central government. Fiscal years for Revisions to national accounts data tistical Bulletin, national accounts data have been other levels of government and reporting years for National accounts data are revised by national statisti- revised for 1997 onward. • Uganda. The Bureau of statistical surveys may differ. cal offices when methodologies change or data sources Statistics has revised national accounts series for The reporting period for national accounts data improve. National accounts data in World Develop- 1998 onward; the new base year for constant price is designated as either calendar year basis (CY) or ment Indicators are also revised when data sources series is 2001/02. •  United Arab Emirates. The fiscal year basis (FY). Most economies report their change. The following notes, while not comprehensive, National Bureau of Statistics has revised national national accounts and balance of payments data provide information on revisions from previous data. accounts data for 2001 onward; the new base year using calendar years, but some use fiscal years. In •  Antigua and Barbuda. Based on official govern- is 2007. • Uruguay. The Central Bank has revised World Development Indicators fiscal year data are ment statistics, national accounts data have been national accounts data for 2006 onward. • Yemen. assigned to the calendar year that contains the larger revised for 2000 onward; the new base year is 2006. Based on official government statistics and Interna- share of the fiscal year. If a country’s fiscal year ends • Azerbaijan. National accounts historical expendi- tional Monetary Fund data, national accounts data before June 30, data are shown in the first year of ture series in constant prices have been revised in have been revised for 1990 onward. 2012 World Development Indicators 401 STATISTICAL METHODS This section describes some of the statistical procedures used in preparing World indicator as a weight) and denoted by a u when calculated as unweighted Development Indicators. It covers the methods employed for calculating regional averages. The aggregate ratios are based on available data, including data and income group aggregates and for calculating growth rates, and it describes the for economies not shown in the main tables. Missing values are assumed World Bank Atlas method for deriving the conversion factor used to estimate gross to have the same average value as the available data. No aggregate is cal- national income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures culated if missing data account for more than a third of the value of weights and calculations are described in the About the data sections following each table. in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing values for missing data according Aggregation rules to the above rules for computing totals. Aggregates based on the World Bank’s regional and income classifications of econo- • Aggregate growth rates are denoted by a w when calculated as a weighted mies appear at the end of most tables. The countries included in these classifications average of growth rates. In a few cases growth rates may be computed from are shown on the flaps on the front and back covers of the book. Most tables also time series of group totals. Growth rates are not calculated if more than half include the aggregate euro area. This aggregate includes the member states of the the observations in a period are missing. For further discussion of methods Economic and Monetary Union (EMU) of the European Union that have adopted the of computing growth rates see below. euro as their currency: Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, • Aggregates denoted by an m are medians of the values shown in the table. Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic, No value is shown if more than half the observations for countries with a Slovenia, and Spain. Other classifications, such as the European Union and regional population of more than 1 million are missing. trade blocs, are documented in About the data for the tables in which they appear. Exceptions to the rules occur throughout the book. Depending on the judgment Because of missing data, aggregates for groups of economies should be of World Bank analysts, the aggregates may be based on as little as 50 percent of treated as approximations of unknown totals or average values. Regional and the available data. In other cases, where missing or excluded values are judged to income group aggregates are based on the largest available set of data, includ- be small or irrelevant, aggregates are based only on the data shown in the tables. ing values for the 158 economies shown in the main tables, other economies shown in table 1.6, and Taiwan, China. The aggregation rules are intended to Growth rates yield estimates for a consistent set of economies from one period to the next and Growth rates are calculated as annual averages and represented as percentages. for all indicators. Small differences between sums of subgroup aggregates and Except where noted, growth rates of values are computed from constant price overall totals and averages may occur because of the approximations used. In series. Three principal methods are used to calculate growth rates: least squares, addition, compilation errors and data reporting practices may cause discrepan- exponential endpoint, and geometric endpoint. Rates of change from one period cies in theoretically identical aggregates such as world exports and world imports. to the next are calculated as proportional changes from the earlier period. Five methods of aggregation are used in World Development Indicators: • For group and world totals denoted in the tables by a t, missing data are Least squares growth rate. Least squares growth rates are used wherever imputed based on the relationship of the sum of available data to the total there is a sufficiently long time series to permit a reliable calculation. No growth in the year of the previous estimate. The imputation process works forward rate is calculated if more than half the observations in a period are missing. and backward from 2000. Missing values in 2000 are imputed using one of The least squares growth rate, r, is estimated by fitting a linear regression trend several proxy variables for which complete data are available in that year. The line to the logarithmic annual values of the variable in the relevant period. The imputed value is calculated so that it (or its proxy) bears the same relation- regression equation takes the form ship to the total of available data. Imputed values are usually not calculated if missing data account for more than a third of the total in the benchmark ln Xt = a + bt 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 which is the logarithmic transformation of the compound growth equation, in agriculture, industry, manufacturing, and services in U.S. dollars. Xt = Xo (1 + r ) t. • Aggregates marked by an s are sums of available data. Missing values are not imputed. Sums are not computed if more than a third of the observations In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are in the series or a proxy for the series are missing in a given year. parameters to be estimated. If b* is the least-squares estimate of b, then the • Aggregates of ratios are denoted by a w when calculated as weighted averages average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by of the ratios (using the value of the denominator or, in some cases, another 100 for expression as a percentage. The calculated growth rate is an average 402 2012 World Development Indicators 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) for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. World Bank Atlas method dollar terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is In calculating GNI and GNI per capita in U.S. dollars for certain operational current GNI (local currency) for year t, and Nt is the midyear population for year t. purposes, the World Bank uses the Atlas conversion factor. The purpose of the Atlas conversion factor is to reduce the impact of exchange rate fluctuations in Alternative conversion factors the cross-country comparison of national incomes. The World Bank systematically assesses the appropriateness of official exchange The Atlas conversion factor for any year is the average of a country’s exchange rates as conversion factors. An alternative conversion factor is used when the rate (or alternative conversion factor) for that year and its exchange rates for the official exchange rate is judged to diverge by an exceptionally large margin from two preceding years, adjusted for the difference between the rate of inflation in the rate effectively applied to domestic transactions of foreign currencies and the country and that in Japan, the United Kingdom, the United States, and the traded products. This applies to only a small number of countries, as shown euro area. A country’s inflation rate is measured by the change in its GDP deflator. in Primary data documentation. Alternative conversion factors are used in the The inflation rate for Japan, the United Kingdom, the United States, and the Atlas methodology and elsewhere in World Development Indicators as single-year euro area, representing international inflation, is measured by the change in the conversion factors. 2012 World Development Indicators 403 CREDITS 1. World view and Edward Gillin of the Food and Agriculture Organization of the United Nations; Section 1 was prepared by a team led by Eric Swanson. Eric Swanson wrote the Ricardo Quercioli and Karen Treanton of the International Energy Agency; Laura introduction with input from Neil Fantom, Shota Hatakeyama, Masako Hiraga, Battlebury of the World Conservation Monitoring Centre; Gerhard Metchies and Wendy Huang, Hiroko Maeda, Johan Mistiaen, Sulekha Patel, William Prince, Evis Armin Wagner of German International Cooperation; and Craig Hilton-Taylor and Rucaj, and Emi Suzuki and with valuable suggestions from Jose Alejandro Quijada, Caroline Pollock of the International Union for Conservation of Nature. The World Sachin Shahria, Jos Verbeek, and participants of the Global Monitoring Report Bank’s Environment Department devoted substantial staff resources. 2012 seminar series. Bala Bhaskar Naidu Kalimili coordinated tables 1.1 and 1.6. Shota Hatakeyama, Masako Hiraga, Hiroko Maeda, and Johan Mistiaen prepared 4. Economy tables 1.2 and 1.5, and Mahyar Eshragh-Tabary, Shota Hatakeyama, Masako Section 4 was prepared by Bala Bhaskar Naidu Kalimili in close collaboration Hiraga, Buyant Erdene Khaltarkhuu, and Hiroko Maeda prepared table 1.3. Wendy with the Sustainable Development and Economic Data Team of the World Bank’s Huang prepared table 1.4 with input from Azita Amjadi. Signe Zeikate of the Development Data Group and with valuable suggestions from Liu Cui and William World Bank’s Economic Policy and Debt Department provided the estimates of Prince. Bala Bhaskar Naidu Kalimili wrote the introduction with valuable sugges- debt relief for the Heavily Indebted Poor Countries Debt Initiative and Multilateral tions from Eric Swanson. Azita Amjadi and Esther G. Naikal also contributed to Debt Relief Initiative. the section. The national accounts data for low- and middle-income economies were gathered by the World Bank’s regional staff through the annual Unified 2. People Survey. Maja Bresslauer, Mahyar Eshragh-Tabary, Bala Bhaskar Naidu Kalimili, Section 2 was prepared by Shota Hatakeyama, Masako Hiraga, Hiroko Maeda, and Buyant Erdene Khaltarkhuu, and Maurice Nsabimana updated, estimated, and Sulekha Patel in partnership with the World Bank’s Human Development Network validated the databases for national accounts. The team is grateful to Eurostat, and the Development Research Group in the Development Economics Vice Presi- the International Monetary Fund, Organisation for Economic Co-operation and dency. Masako Hiraga, Sulekha Patel, and Johan Mistiaen wrote the introduction Development, United Nations Industrial Development Organization, and World with valuable input and comments from Eric Swanson. Emi Suzuki prepared the Trade Organization for access to their databases. demographic estimates and projections. The poverty estimates at national poverty lines were compiled by the Global Poverty Working Group, a team of poverty experts 5. States and markets from the Poverty Reduction and Equality Network, the Development Research Section 5 was prepared by Buyant Erdene Khaltarkhuu in partnership with the Group, and the Development Data Group. Shaohua Chen and Prem Sangraula of World Bank’s Financial and Private Sector Development Network, Poverty Reduc- the World Bank’s Development Research Group prepared the poverty estimates at tion and Economic Management Network, and Sustainable Development Net- international poverty lines. Lorenzo Guarcello and Furio Rosati of the Understanding work; the International Finance Corporation; and external partners. David Cies- Children’s Work project prepared the data on children at work. Other contributions likowski and Buyant Erdene Khaltarkhuu wrote the introduction with input from were provided by Emi Suzuki (health and nutrition); Montserrat Pallares-Miralles and Eric Swanson. Other contributors include Ada Karina Izaguirre (privatization and Carolina Romero Robayo (vulnerability and security); Theo door Sparreboom and infrastructure projects); Leora Klapper (business registration); Federica Saliola Alan Wittrup of the International Labour Organization (labor force); Amelie Gagnon, and Joshua Wimpey (Enterprise Surveys); Carolin Geginat and Frederic Meunier Said Ould Voffal, and Weixin Lu of the United Nations Educational, Scientific, and (Doing Business); Alka Banerjee, Trisha Malinky, and Michael Orzano (Standard Cultural Organization Institute for Statistics (education and literacy); the World & Poor’s global stock market indexes); Oya Pinar Ardic Alper (financial access); Health Organization’s Chandika Indikadahena (health expenditure), Charu Garg Gary Milante, Holly Benner, and Kenneth Anya (fragile situations); Satish Mannan (national health account), Monika Bloessner and Mercedes de Onis (malnutrition (public policies and institutions); James Hackett of the International Institute and overweight), Teena Kunjument (health workers), Jessica Ho (hospital beds), for Strategic Studies (military personnel); Sam Perlo-Freeman of the Stockholm Rifat Hossain (water and sanitation), and Hazim Timimi (tuberculosis); Leonor Guar- International Peace Research Institute (military expenditures and arms transfers); iguata of the International Diabetes Federation (diabetes); and Colleen Murray Christian Gonzalez of the International Road Federation, Narjess Teyssier and of the United Nations Children’s Fund (health). Eric Swanson provided valuable Zubair Anwar of the International Civil Aviation Organization, and Hélène Stephan comments and suggestions on the introduction and at all stages of production. and Marc Juhel (transport); Jane Degerlund of Containerisation International and Vincent Valentine of the United Nations Conference on Trade and Development 3. Environment (ports); Azita Amjadi (high-tech exports); Vanessa Grey, Esperanza Magpantay, and Section 3 was prepared by Mahyar Eshragh-Tabary in partnership with the Environ- Susan Teltscher of the International Telecommunication Union; Torbjörn Fredriks- ment Department of the Sustainable Development Vice Presidency of the World son and Rémi Lang of the United Nations Conference on Trade and Development Bank. Mahyar Eshragh-Tabary wrote the introduction with input from Neil Fantom (information and communication technology goods trade); Martin Schaaper of and Eric Swanson and with valuable suggestions from Jane Ebinger, Tim Herzog, the United Nations Educational, Scientific, and Cultural Organization Institute for Glenn-Marie Lange, and Soong Sup Lee. Esther G. Naikal and William Prince also Statistics (research and development, researchers, and technicians); and Ryan contributed to the section. Other contributors include Kirk Hamilton; Carola Fabi Lamb of the World Intellectual Property Organization (patents and trademarks). 404 2012 World Development Indicators 6. Global links Administrative assistance, of�ce technology, and systems Section 6 was prepared by Wendy Huang and Evis Rucaj with valuable input development support from Uranbileg Batjargal and in partnership with the Financial Data Team of Awatif Abuzeid, Elysee Kiti, Premi Ratham Raj, and Estela Zamora provided admin- the World Bank’s Development Data Group, Development Research Group istrative assistance. Jean-Pierre Djomalieu, Gytis Kanchas, and Nacer Megherbi (trade), Development Prospects Group (commodity prices and remittances), provided information technology support. Ramvel Chandrasekaran, Ugendran International Trade Department (trade facilitation), and external partners. Machakkalai, Shanmugam Natarajan, Atsushi Shimo, and Malarvizhi Veerappan Malvina Pollock and Evis Rucaj wrote the introduction with substantial input provided software support on the Development Data Platform application. from Eric Swanson. Azita Amjadi (trade and tariffs) and Rubena Sukaj (external debt and fi nancial data) provided substantial input on the data and tables. Publishing and dissemination Other contributors include Frédéric Docquier (emigration rates); Flavine Creppy The Office of the Publisher, under the direction of Carlos Rossel, provided valu- and Yumiko Mochizuki of the United Nations Conference on Trade and Devel- able assistance throughout the production process. Denise Bergeron, Dina Tow- opment and Mondher Mimouni of the International Trade Centre (trade); Cris- bin, Stephen McGroarty, and Nora Ridolfi coordinated printing and supervised tina Savescu (commodity prices); Jeff Reynolds and Joseph Siegel of DHL marketing and distribution. Merrell Tuck-Primdahl of the Development Economics (freight costs); Yasmin Ahmad and Elena Bernaldo of the Organisation for Vice President’s Office managed the communications strategy. Economic Co-operation and Development (aid); Ibrahim Levent, Hiroko Maeda, and Maryna Taran (external debt); Sanket Mohapatra and Ani Rudra Silwal World Development Indicators CD-ROM (remittances); and Teresa Ciller of the World Tourism Organization (tourism). Software preparation and testing was managed by Vilas Mandlekar with the Ramgopal Erabelly, Shelley Lai Fu, and William Prince provided valuable tech- assistance of Ramgopal Erabelly, Parastoo Oloumi, and William Prince. Systems nical assistance. development was undertaken by the Data and Information Systems Team led by Reza Farivari. William Prince coordinated user interface design and overall Other parts of the book production and provided quality assurance, with assistance from Jomo Tariku. Jeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of the maps on the inside covers. William Prince prepared Users guide. Eric Swanson World Development Indicators mobile applications wrote Statistical methods. Maja Bresslauer, Federico Escaler, and Buyant Erdene Software preparation and testing was managed by Vilas Mandlekar and Shelley Khaltarkhuu, prepared Primary data documentation. Alison Kwong prepared Part- Fu with assistance from Azita Amjadi, Prashant Chaudhari, Ying Chi, Liu Cui, ners and Index of indicators. Ghislaine Delaine, Ramgopal Erabelly, Federico Escaler, Buyant Erdene Khal- tarkhuu, Maurice Nsabimana Parastoo Oloumi, Beatriz Prieto Oramas, William Database management Prince, Virginia Romand, Jomo Tariku, and Vera Wen. Systems development was William Prince coordinated management of the World Development Indicators undertaken in the Data and Information Systems Team led by Reza Farivari. Wil- database, with assistance from Liu Cui. Operation of the database management liam Prince provided data quality assurance. system was made possible by Ramgopal Erabelly and Shelley Fu in the Data and Information Systems Team under the leadership of Reza Farivari. Open Data and online access Coordination of the Open Data website (http://data.worldbank.org) was provided Design, production, and editing by Neil Fantom. Design, programming, and testing were carried out by Reza Azita Amjadi and Alison Kwong coordinated all stages of production with Com- Farivari and his team: Azita Amjadi, Ramvel Chandrasekaran, Ying Chi, Federico munications Development Incorporated, which provided overall design direc- Escaler, Shelley Fu, Buyant Erdene Khaltarkhuu, Ugendran Machakkalai, Shan- tion, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, mugam Natarajan, Atsushi Shimo, Sun Hwa Song, Lakshmikanthan Subrama- and Christopher Trott and assisted by Rob Elson. Elaine Wilson created the nian, Maryna Taran, Jomo Tariku, Malarvizhi Veerappan, and Vera Wen. William cover and graphics and typeset the book. Joseph Caponio provided produc- Prince coordinated production and provided quality assurance. Support from tion assistance. Peter Grundy, of Peter Grundy Art & Design, designed the the Corporate Communications Unit in External Affairs was provided by a team report. including George Gongadze and Jeffrey McCoy. The multilingual web team was led by James Edward Rosenberg. Client services The Development Data Group’s Client Services and Communications Team (Fed- Client feedback erico Escaler, Alison Kwong, Beatriz Prieto-Oramas, Maryna Taran, Jomo Tariku, The team is grateful to the many people who have taken the time to provide and Vera Wen) contributed to the design and planning and helped coordinate work feedback and suggestions, which have helped improve this year’s edition. Please with the Office of the Publisher under the leadership of Azita Amjadi. contact us at data@worldbank.org. 2012 World Development Indicators 405 BIBLIOGRAPHY African Union and United Nations Economic Commission for Africa. 2005. “Trans- Containerisation International. 2010. 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Washington, D.C. 2012 World Development Indicators 413 INDEX OF INDICATORS References are to table numbers. Aid A Agriculture by recipient aid dependency ratios grants 6.12 6.12 agricultural raw materials per capita 6.12 commodity prices 6.5 technical cooperation 6.12 exports total 6.12 as share of total exports 4.4 net disbursements from high-income economies as share of total exports 6.3 as share of donor GNI 1.4 imports from major donors, by recipient 6.13 as share of total imports 4.5 net flows by high-income economies as share of total imports 6.3 bilateral aid 6.13 tariff rates applied by high-income countries 6.3 from bilateral sources 6.11 cereal from international financial institutions 6.11 area under production 3.2 from multilateral sources 6.11 yield 3.3 for basic social services, as share of sector-allocable bilateral employment, as share of total 3.2 ODA commitments 1.4 fertilizer commodity prices 6.5 AIDS—see HIV, prevalence consumption, per hectare of arable land 3.2 food Air pollution—see Pollution commodity prices 6.5 exports 4.4 Air transport from high-income economies as share of total exports 6.3 air freight 5.10 imports 4.5 passengers carried 5.10 by high-income economies as share of total imports 6.3 registered carrier departures worldwide 5.10 tariff rates applied by high-income countries 6.3 freshwater withdrawals for, as share of total 3.5 Antiretroviral therapy coverage 2.22 land agricultural, as share of land area 3.2 Asylum seekers—see Migration; Refugees arable as share of land area per 100 people area under cereal production 3.1 3.1 3.2 B Balance of payments Irrigated 3.2 current account balance 4.17 permanent cropland, as share of land area 3.1 as share of GDP 4.a machinery exports and imports of goods and services 4.17 tractors per 100 square kilometers of arable land 3.2 net current transfers 4.17 production indexes net income 4.17 crop 3.3 total reserves 4.a, 4.17 food 3.3 See also Exports; Imports; Investment; Private financial flows; Trade livestock 3.3 value added Base metals annual growth 4.1 commodity prices and price index 6.5 as share of GDP 4.2 414 2012 World Development Indicators Battle-related deaths 5.8 time required 5.3 finance Beverages firms using banks to finance investment 5.2 commodity prices 6.5 gender female participation in ownership 5.2 Biodiversity—see Biological diversity getting electricity time required 5.3 Biological diversity informality assessment, date prepared, by country 3.17 firms formally registered when operations started 5.2, 5.8 GEF benefits index 3.4 infrastructure threatened species 3.4 value lost due to electrical outages 5.2 birds 3.4 innovation fish 3.4 internationally recognized quality certification ownership 5.2 higher plants 3.4 permits and licenses mammals 3.4 time required to obtain operating license 5.2 treaty 3.17 protecting investors disclosure, index 5.3 Birth rate, crude 2.1 registering property See also Fertility rate number of procedures 5.3 time to register 5.3 Births attended by skilled health staff 2.19 regulation and tax average number of times firms spend meeting with tax officials 5.2 Birthweight, low 2.21 time dealing with officials 5.2 resolving insolvency Bonds—see Debt flows; Private financial flows time required 5.3 starting a business Brain drain—see Emigration of people with tertiary education to OECD cost to start a business 5.3 countries number of start-up procedures 5.3 time to start a business 5.3 Breastfeeding, exclusive 2.21 trade average time to clear direct exports 5.2 Broad money 4.15 workforce firms offering formal training 5.2 Business environment businesses registered entry density new 5.1 5.1 C Carbon dioxide corruption damage, as share of GNI 4.11 informal payments to public officials 5.2 emissions crime by sector 3.11 losses due to theft, robbery, vandalism, and arson 5.2, 5.8 from electricity and heat production, as share of total fuel dealing with construction permits to build a warehouse combustion 3.11 number of procedures 5.3 from manufacturing industries and construction, as share of time required 5.3 total fuel combustion 3.11 enforcing contracts from other sectors, as share of total fuel combustion 3.11 number of procedures 5.3 2012 World Development Indicators 415 INDEX OF INDICATORS from residential buildings and commercial and public Computer users per 100 people 5.12 services, as share of total fuel combustion 3.11 from transport as share of total fuel combustion 3.11 Consumption per capita 1.3, 3.9 as share of GDP 4.8 per unit of GDP 3.9 average annual growth 4.9 total 1.6, 3.9 per capita 4.9 intensity 3.9 distribution—see Income distribution fixed capital 4.10, 4.11 Cause of death government, general final expenditure communicable diseases and maternal, prenatal, and nutritional annual growth 4.9 conditions 2.22 as share of GDP 4.8 injuries 2.22 See also Purchasing power parity (PPP) noncommunicable disease 2.22 Contraceptives Children at work prevalence rate 1.3, 2.19 by economic activity 2.6 unmet need for 2.19 male and female 2.6 status in employment 2.6 Contract enforcement study and work 2.6 number of procedures 5.3 total 2.6, 5.8 time required for 5.3 work only 2.6 Corruption, informal payments to public officials 5.2 Cities—see Urban environment; Country Policy and Institutional Assessment (CPIA)—see Economic Climate variability management; Social inclusion and equity policies; Public sector management average daily minimum/maximum temperature 3.12 and institutions; Structural policies projected annual precipitation change 3.12 projected annual temperature change 3.12 Credit projected change in annual cool days/cold nights 3.12 getting credit projected change in annual hot days/warm nights 3.12 depth of credit information index 5.5 strength of legal rights index 5.5 Closing a business—see Business environment provided by banking sector 5.5 to private sector 5.1 Commercial bank and other lending 6.10 See also Debt flows; Private financial flows Crime business losses due to 5.2 Commodity prices and price indexes 6.5 intentional homicide rate 5.8 Communications—see Internet; Newspapers, daily; Telephones; Television, Current account balance 4.17 households with See also Balance of payments Compensation Customs of central government employees 4.13 average time to clear exports 5.2 See also Remittances burden of procedures 6.8 416 2012 World Development Indicators D DAC (Development Assistance Committee)—see Aid economic management cluster average fiscal policy macroeconomic management 5.9 5.9 5.9 Death rate, crude 2.1 Education See also Mortality rate children out of school male and female 2.12 Debt, external poorest and richest wealth quintile 2.15 as share of GNI 6.9 cohort survival rate debt service to grade 5, male and female 2.13 total, as share of exports of goods and services and income 6.9 to last grade of primary education, male and female 2.13 long-term completion rate, primary private nonguaranteed 6.9 male and female 2.14, 2.15 public and publicly guaranteed 6.9 poorest and richest wealth quintiles 2.15 present value total 1.2, 2.14 as share of GNI 6.9 enrollment ratio as share of exports of goods and services and income 6.9 girls to boys enrollment in primary and secondary education 1.2 short-term gross as share of total debt 6.9 by level 2.12 as share of total reserves 6.9 primary 5.8 total 6.9 secondary 2.4 net Debt flows by level 2.12 bonds 6.10 primary, adjusted 2.12 commercial banks and other lending 6.10 intake ratio, gross See also Private financial flows first grade of primary education 2.13 grade 1 2.15 Deforestation, average annual 3.4 primary participation rate, gross 2.15 public expenditure on Demand—see Consumption; Imports; Exports; Savings as share of GDP 2.11 as share of GNI 4.11 Density—see Population, density as share of total government expenditure 2.11 per student, as share of GDP per capita, by level 2.11 Dependency ratio—See Population, age dependency ratio pupil–teacher ratio, primary 2.11 repeaters, primary, male and female 2.13 Development assistance—see Aid teachers, primary, trained 2.11 transition to secondary school, male and female 2.13 Disaster risk reduction progress score—see Resilience unemployment by level of educational attainment 2.5 years of schooling, average, poorest and richest wealth quintiles 2.15 Disease—see Health risks Electricity Distribution of income or consumption—see Income distribution access to 3.8 business lost due to outages 5.2 E Economic management (Country Policy and Institutional Assessment) consumption production total 5.11 3.8 debt policy 5.9 sources 3.8 2012 World Development Indicators 417 INDEX OF INDICATORS time required 5.3 GDP per unit of energy use 3.9 transmission and distribution losses 5.11 per capita 3.7 total 3.7 Emissions See also Electricity; Fuels carbon dioxide average annual growth 3.10 Enforcing contracts—see Business environment intensity 3.9 per capita 1.3, 3.9 Enrollment—see Education per unit of GDP 3.9 total 1.6, 3.9 Entry regulations for business—see Business environment See also Carbon dioxide, emissions methane Environmental strategy or action plans, year adopted 3.17 agricultural, as share of total 3.10 from energy processes, as share of total 3.10 Equity flows total 3.10 foreign direct investment 6.10 nitrous oxide portfolio equity 6.10 agricultural, as share of total 3.10 See also Private financial flows energy and industry, as share of total 3.10 total 3.10 European Commission other greenhouse gases 3.10 distribution of net aid from 6.13 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 Exports to population ratio 2.4 arms 5.7 vulnerable 1.2, 2.4 documents required for 6.8 See also Labor force; Unemployment goods and services as share of GDP 4.8 Endangered species—see Biological diversity; Plants, higher average annual growth 4.a, 4.9 total 4.17 Energy high-technology commodity price index 6.5 share of manufactured exports 5.13 consumption, road sector 3.15 total 5.13 depletion, as share of GNI 4.11 information and communications technology 5.12 emissions—see Pollution lead time 6.8 imports, net 3.9 merchandise production 3.7 annual growth 6.1, 6.2 use by regional trade blocs 6.6 alternative and nuclear energy 3.7 direction of trade 6.2 average annual growth 3.7 from high-income countries, by product 6.3 combustible renewables and waste, as share of total 3.7 from developing countries, by recipient 6.4 fossil fuel consumption, as share of total 3.7 structure 4.4 418 2012 World Development Indicators total 4.4 Foreign direct investment, net—see Investment; Private financial flows value, average annual growth 6.1 volume, average annual growth 6.1 Forest services area structure 4.6 as share of total land area 3.1 total 4.6 total 3.4 travel 4.6, 6.15 deforestation, average annual 3.4 See also Trade net depletion, as share of GNI 4.11 Exposure to impact Fuels land area with elevation of 5 meters or less 3.12 consumption population affected by droughts, floods, and extreme temperature 3.12 road sector 3.15 population living in areas with elevation of five meters or less 3.12 exports as share of total merchandise exports 4.5 F Female-headed households 2.10 from high-income economies, as share of total exports imports as share of total imports 6.3 4.4 by high-income economies, as share of total imports 6.3 Female participation in ownership 5.2 prices 3.15 tariff rates applied by high-income countries 6.3 Fertility rate adolescent crude birth rate desired 2.19 2.1 2.19 G GEF benefits index for biodiversity 3.4 poorest and richest wealth quintile 2.24 total 2.19 Gender differences in children in employment 2.6, 5.8 Finance, firms using banks to finance investment 5.2 in education 1.2, 2.12, 2.13, 2.14 in employment 2.3 Financial access, stability, and efficiency by economic activity 2.3 automated teller machines 5.5 in HIV prevalence 2.22 bank capital to asset ratio 5.5 in labor force participation 2.2 bank nonperforming loans, ratio to total gross loans 5.5 in life expectancy at birth 1.5 borrowers from commercial banks 5.5 in literacy commercial bank branches 5.5 adult 2.14 depositors with commercial banks 5.5 youth 2.14 in mortality Financial flows, net adult 2.23 official child 2.23 from bilateral sources 6.11 in ownership of firms 5.2 from international financial institutions 6.11 in parliaments 1.5 from multilateral sources 6.11 in smoking 2.22 total 6.11 unemployment See also Aid total 2.5 youth 2.10 Food—see Agriculture, production indexes; Commodity prices and price nonagricultural wage employment 1.5 indexes unpaid family workers 1.5 2012 World Development Indicators 419 INDEX OF INDICATORS vulnerable employment 2.4 U.S. dollars 1.1 total Gini index 2.9 PPP dollars 1.1, 1.6 U.S. dollars 1.1, 1.6, 4.10 Government, central cash surplus or deficit debt as share of GDP 4.12 4.12 H Health care interest, as share of revenue 4.12 antiretroviral therapy coverage 2.22 expense children sleeping under treated nets 2.18 as share of GDP 4.12 children with acute respiratory infection taken to health provider 2.18 by economic type 4.13 children with diarrhea who received oral rehydration and continued net incurrence of liabilities, as share of GDP, domestic and foreign 4.12 feeding 2.18 revenue children with fever receiving antimalarial drugs 2.18 as share of GDP 4.12 HIV, prevalence 1.3 grants and other 4.14 hospital beds per 1,000 people 2.16 social contributions 4.14 immunization rate, child 2.18 taxes nurses and midwives per 1,000 people 2.16 as share of GDP 5.6 outpatient visits per capita 2.16 by source, as share of revenue 4.14 physicians per 1,000 people 2.16 reproductive Greenhouse gases—see Emissions anemia, prevalence of, pregnant women 2.21 births attended by skilled health staff 2.19 Gross capital formation contraceptive prevalence rate 1.3, 2.19 annual growth 4.9 fertility rate as share of GDP 4.8 adolescent 2.19 total 2.19 Gross domestic product (GDP) lifetime risk of maternal death 2.19 annual growth 1.1, 1.6, 4.1, 4.10 low-birthweight babies 2.21 contribution of natural resources 3.18 maternal mortality ratio 1.3, 2.19, 5.8 implicit deflator—see Prices pregnant women receiving prenatal care 1.5, 2.19 per capita, annual growth 1.1, 1.6 unmet need for contraception 2.19 total 4.2, 4.10 tuberculosis incidence 1.3, 2.22 Gross enrollment—see Education treatment success rate 2.18 Gross national income (GNI) Health expenditure adjusted net national income as share of GDP 2.16 annual growth 4.10 external resources 2.16 total 4.10 out of pocket 2.16 annual growth 4.10 per capita 2.16 per capita public 2.16 PPP dollars 1.1, 1.6 rank 1.1 Health information U.S. dollars 1.1, 1.6 census, year last completed 2.17 rank completeness of vital registration PPP dollars 1.1 birth registration 2.17 420 2012 World Development Indicators 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 Immunization rate, child year last completed 2.17 DPT, share of children ages 12–23 months 2.18 measles, share of children ages 12–23 months 2.18 Health risks anemia, prevalence of Imports children under age 5 2.21 arms 5.7 pregnant women 2.21 documents required for 6.8 cause of death 2.22 energy, net, as share of total energy use 3.9 child malnutrition, prevalence goods and services stunting 2.20 as share of GDP 4.8 underweight 1.2, 2.20 average annual growth 4.9 wasting 2.20 total 4.17 diabetes, prevalence 2.22 information and communications technology goods 5.12 HIV, prevalence 1.3, 2.22 lead time 6.8 low-birthweight babies 2.21 merchandise overweight children, prevalence 2.20 annual growth 6.2 smoking, prevalence, male and female 2.22 by developing countries, by partner 6.4 tuberculosis, incidence 1.3, 2.22 by high-income countries, by product 6.3 undernourishment, prevalence 2.20 structure 4.5 tariffs 6.3, 6.7 Heavily indebted poor countries (HIPCs) total 4.5 assistance 1.4 value, average annual growth 6.1 completion point 1.4 volume, average annual growth 6.1 decision point 1.4 services Multilateral Debt Relief Initiative (MDRI) assistance 1.4 structure 4.7 total 4.7 HIV travel 4.7, 6.14 prevalence See also Trade female 2.22 population ages 15–24, male and female 2.22 Income distribution total 1.3, 2.22 Gini index 2.9 percentage of 1.2, 2.9 Homicide rate, intentional 5.8 Industry Hospital beds—see Health care annual growth 4.1 as share of GDP 4.2 Housing conditions, national and urban employment, male and female 2.3 durable dwelling units 3.14 freshwater withdrawals for output 3.5 home ownership 3.14 household size 3.14 Inflation—see Prices multiunit dwellings 3.14 overcrowding 3.14 Informal economy, firms formally registered when operations started 5.2 vacancy rate 3.14 2012 World Development Indicators 421 INDEX OF INDICATORS Information and communications technology trade Innovation, internationally recognized certification ownership 5.11 5.2 L Labor force annual growth 2.2 Interest payments—see Government, central, debt armed forces 5.7 children at work 2.6 Interest rates female 2.2 deposit 4.15 nonagricultural 1.5 lending 4.15 part-time 1.5 real 4.15 participation of population ages 15 and older, male and female 2.2 risk premium on lending 5.5 total 2.2 spread 5.5 See also Employment; Migration; Unemployment Internally displaced persons 5.8 Land area arable—see Agriculture, land; Land use International Bank for Reconstruction and Development (IBRD) See also Protected areas; Surface area; and Exposure to impact net financial flows from 6.11 Land use International Development Association (IDA) arable land net concessional flows from 6.11 as share of total land 3.1 Resource Allocation Index 5.8, 5.9 per 100 people 3.1 area under cereal production 3.2 International Monetary Fund (IMF) by type 3.1 net financial flows from 6.11 forest area, as share of total land 3.1 irrigated land 3.2 Internet permanent cropland, as share of total land 3.1 fixed broadband total area 3.1 access tariff 5.12 subscriptions 5.12 Life expectancy at birth international Internet bandwidth 5.12 male and female 1.5 secure servers 5.12 total 1.6, 2.23 users 5.12 Literacy Investment adult foreign direct, net inflows male and female 2.14 as share of GDP 6.10 total 1.6 total 6.10 mathematics, students at the lowest level of proficiency on PISA 2.14 infrastructure, private participation in youth, male and female 2.14 energy 5.1 telecommunications 5.1 Logistics Performance Index 6.8 transport 5.1 water and sanitation See also Gross capital formation; Private financial flows 5.1 M Malaria Iodized salt, consumption of 2.21 children sleeping under treated nets 2.18 children with fever receiving antimalarial drugs 2.18 422 2012 World Development Indicators Malnutrition, in children under age 5 1.2, 2.20 by developing countries, by partner 6.4 food 4.5, 6.3 Management time dealing with officials 5.2 fuels 4.5, 6.3 information and communications technology goods 5.12 Manufacturing manufactures 4.5 annual growth 4.1 ores and metals 4.5 as share of GDP 4.2 ores and nonferrous materials 6.3 value added to low-income economies by high-income economies, by product 6.3 chemicals 4.3 to middle-income economies by high-income economies, by product 6.3 food, beverages, and tobacco 4.3 total 4.5 machinery and transport equipment 4.3 value, average annual growth 6.1 other 4.3 volume, average annual growth 6.1 structure 4.3 trade textiles and clothing 4.3 by developing countries, by partner 6.4 total 4.3 direction 6.2 See also Merchandise growth 6.2 regional trade blocs 6.6 Market access to high-income countries goods admitted free of tariffs 1.4 Metals and minerals support to agriculture 1.4 commodity prices and price index 6.5 tariffs on exports from least developed countries agricultural products 1.4 Methane emissions clothing 1.4 agricultural as share of total 3.10 textiles 1.4 industrial as share of total 3.10 total 3.10 Merchandise exports Migration agricultural raw materials 4.4, 6.3 emigration of people with tertiary education to OECD countries 6.14 food 4.4, 6.3 international migrant stock, total 6.14 from developing countries, by partner 6.4 net 6.14 from regional trade blocs 6.6 See also Refugees; Remittances fuels 4.4, 6.3 information and communications technology goods 5.12 Military information and communications technology services 5.12 armed forces personnel manufactures 4.4 as share of labor force 5.7 ores and metals 4.4 total 5.7 ores and nonferrous materials 6.3 arms transfers structure 4.4 exports 5.7 to low-income economies from high-income economies, by product 6.3 imports 5.7 to middle-income economies from high-income economies, military expenditure by product 6.3 as share of central government expenditure 5.7 total 4.4 as share of GDP 5.7, 5.8 value, average annual growth 6.1 volume, average annual growth 6.1 Millennium Development Goals, indicators for within regional trade blocs 6.6 access to improved sanitation facilities 1.3, 2.18, 3.13, 5.8 imports access to improved water source 2.18, 3.5, 5.8 agricultural raw materials 4.5, 6.3 2012 World Development Indicators 423 INDEX OF INDICATORS average tariff imposed by developed countries on exports of least incidence 1.3, 2.22 developed countries 1.4 treatment success rate 2.18 births attended by skilled health staff 2.19 under-five mortality rate 1.2, 2.22, 5.8 carbon dioxide emissions per capita 1.3, 3.9 poorest and richest wealth quintile 2.24 children sleeping under treated nets 2.18 total 1.2, 2.23, 5.8 contraceptive prevalence rate 1.3, 2.19 undernourishment, prevalence 2.20 employment to population ratio 2.4 unmet need for contraception 2.19 enrollment ratio, net, primary 2.12 vulnerable employment 1.2, 2.4 female to male enrollments, primary and secondary 1.2 women in wage employment in the nonagricultural sector 1.5 fertility rate, adolescent 2.19 goods admitted free of tariffs from least developed countries 1.4 Minerals depletion, as share of GNI 4.11 heavily indebted poor countries (HIPCs) assistance 1.4 Monetary indicators completion point 1.4 broad money 4.15 decision point 1.4 claims central government 4.15 Multilateral Debt Relief Initiative (MDRI) assistance nominal debt other claims on domestic economy 4.15 service relief committed 1.4 immunization rate, child Mortality rate DPT 2.18 adult, male and female 2.23 measles 2.18 child, male and female 2.23 income or consumption, national share of poorest quintile 1.2, 2.9 children under age 5 1.2, 2.23, 5.8 infant mortality rate 2.22 crude death rate 2.1 poorest and richest wealth quintile 2.24 infant 2.23 total 2.23 life expectancy at birth 2.23 Internet users per 100 people 1.3, 5.12 maternal 1.3, 2.19, 5.8 labor productivity, GDP per person employed 2.4 lifetime risk of maternal death 2.19 literacy rate of 15- to 24-year-olds 2.14 neonatal 2.23 malnutrition, prevalence 1.2, 2.20 malaria Motor vehicles children under age 5 sleeping under treated nets 2.18 passenger cars 3.15 children under age 5 with fever who are treated with appropriate per 1,000 people 3.15 antimalarial drugs 2.18 per kilometer of road 3.15 maternal mortality ratio 1.3, 2.19, 5.8 road density 3.15 mobile cellular subscriptions per 100 people 5.11 See also Roads; Traffic national parliament seats held by women 1.5 official development assistance MUV G-5 index 6.5 for basic social services as share of total sector allocable ODA commitments 1.4 MUV G-15 index 6.5 net disbursements, as share of donor GNI 1.4 poverty gap pregnant women receiving prenatal care share of cohort reaching last grade of primary education 2.7, 2.8 1.5, 2.19 2.13 N Natural resource depletion, as share of GNI 4.10 support to agriculture 1.4 telephone lines, fixed, per 100 people 5.11 Net enrollment—see Education tuberculosis case detection rate 2.18 Newspapers, daily 5.12 424 2012 World Development Indicators Nitrous oxide emissions Plants, higher, threatened species 3.4 agricultural as share of total 3.10 Industrial as share of total 3.10 Pollution total 3.10 carbon dioxide damage, as share of GNI 4.11 Nutrition emissions anemia, prevalence of average annual growth 3.10 children ages under 5 2.21 intensity 3.9 pregnant women 2.21 per capita 1.3, 3.9 breastfeeding, exclusive 2.21 per unit of GDP 3.9 iodized salt consumption 2.21 total 1.6, 3.9 malnutrition, child 1.2, 2.20 local damage 4.11 overweight children, prevalence 2.20 methane emissions undernourishment, prevalence 2.20 agricultural, as share of total 3.10 vitamin A supplementation 2.21 from energy processes, as share of total 3.10 total 3.10 O Official development assistance—see Aid nitrogen dioxide, selected cities nitrous oxide emissions agricultural, as share of total 3.16 3.10 energy and industry, as share of total 3.10 Official flows—see Aid; Financial flows, net total 3.10 organic water pollutants, emissions P Particulate matter by industry per day per worker 3.6 3.6 3.6 selected cities 3.16 particulate matter concentration urban-population-weighted PM10 3.15 selected cities 3.16 total 3.15 Passenger cars per 1,000 people 3.15 sulfur dioxide, selected cities 3.16 Patent applications filed 5.13 Population age dependency ratio, young and old 2.1 Peacebuilding and peacekeeping operations average annual growth 2.1 operation name 5.8 by age group, as share of total troops, police, and military observers 5.8 0–14 2.11 5–64 2.1 Pension 65 and older 2.1 average, as share of per capita income 2.10 density 1.1, 1.6 contributors female, as share of total 1.5 as share of labor force 2.10 rural as share of working-age population 2.10 annual growth 3.1 public expenditure on, as share of GDP 2.10 as share of total 3.1 total 1.1, 1.6, 2.1 Permits and licenses, time required to obtain operating license 5.2 urban as share of total 3.13 Physicians—see Health care average annual growth 3.13 in largest city 3.13 2012 World Development Indicators 425 INDEX OF INDICATORS in selected cities 3.16 See also Investment in urban agglomerations 3.13 total 3.13 Productivity See also Migration; Electricity; and Exposure to impact agricultural 3.3 labor 2.4 Portfolio—see Equity flows; Private financial flows water 3.5 Ports Protected areas container traffic in 5.9 marine quality of infrastructure 6.8 as share of total surface area 3.4 total 3.4 Poverty terrestrial international poverty line as share of total surface area 3.4 local currency 2.8 total 3.4 population living below $1.25 a day 2.8 Protecting investors disclosure index 5.3 $2 a day 2.8 national poverty line Public sector management and institutions (Country Policy and Institutional population living below, national, rural, and urban 2.7 Assessment) poverty gap, national, rural, and urban 2.7 efficiency of revenue mobilization 5.9 property rights and rule-based governance 5.9 Power—see Electricity, production public sector management and institutions cluster average 5.9 quality of budgetary and financial management 5.9 Precipitation quality of public administration 5.9 average annual 3.2, 3.12 transparency, accountability, and corruption in the public sector 5.9 see also Climate variability Purchasing power parity (PPP) Prenatal care, pregnant women receiving 1.5, 2.19 conversion factor 4.16 gross national income 1.1, 1.6 Prices commodity prices and price indexes consumer, annual growth gasoline fuel 6.5 4.16 3.15 R Railways GDP implicit deflator, annual growth 4.16 goods hauled by 5.10 net barter terms of trade 6.1 lines, total 5.10 wholesale, annual growth 4.16 passengers carried 5.10 Primary education—see Education Refugees by country of asylum 5.8, 6.14 Private financial flows by country of origin 5.8, 6.14 debt flows internally displaced persons 5.8 bonds 6.10 commercial bank and other lending 6.10 Regional development banks, net financial flows from 6.11 equity flows foreign direct investment, net inflows 6.10 Regional trade agreements—see Trade blocs, regional portfolio equity 6.10 426 2012 World Development Indicators Registering property Sanitation, access to improved facilities, population with number of procedures 5.3 total 1.3, 2.18, 5.8 time to register 5.3 urban and rural 3.13 Regulation and tax administration Savings management time dealing with officials 5.2 adjusted net 4.11 meeting with tax officials, number of times 5.2 gross, as share of GDP 4.8 gross, as share of GNI 4.11 Relative prices (PPP)—see Purchasing power parity (PPP) Schooling—see Education Remittances workers’ remittances and compensation of employees Science and technology paid 6.14 scientific and technical journal articles 5.13 received 6.14 See also Research and development Research and development Secondary education—see Education expenditures 5.13 researchers 5.13 Services technicians 5.13 employment, male and female 2.3 exports Reserves, gross international—see Balance of payments computer, information and communications, and other commercial services 4.6 Resilience insurance and financial services 4.6 disaster risk reduction progress score 3.12 structure 4.6 See also Electricity, access to total commercial 4.6 transport 4.6 Roads travel 4.6 goods hauled by 5.10 imports passengers carried 5.10 computer, information and communications, and other paved, as share of total 5.10 commercial services 4.7 sectoral energy consumption 3.15 insurance and financial services 4.7 total network 5.10 structure 4.7 total commercial 4.7 Royalty and license fees transport 4.7 payments 5.13 travel 4.7 receipts 5.13 value added annual growth 4.1 Rural environment as share of GDP 4.2 access to improved sanitation facilities 3.13 access to an improved water source 3.5 Smoking, prevalence, male and female 2.22 population annual growth 3.1 Social inclusion and equity policies (Country Policy and Institutional as share of total 3.1 Assessment) building human resources 5.9 S S&P/Global Equity Indices 5.4 equity of public resource use gender equity policy and institutions for environmental sustainability 5.9 5.9 5.9 2012 World Development Indicators 427 INDEX OF INDICATORS social inclusion and equity cluster average 5.9 weighted mean tariff 6.7 social protection and labor 5.9 share of tariff lines with international peaks 6.7 share of tariff lines with specific rates 6.7 Starting a business—see Business environment Taxes and tax policies Stock markets business taxes listed domestic companies 5.4 average number of times firms meet with tax officials 5.2 market capitalization labor tax, as share of commercial profits 5.6 as share of GDP 5.4 number of payments 5.6 total 5.4 other taxes, as share of commercial profits 5.6 market liquidity 5.4 profit tax, as share of commercial profits 5.6 S&P/Global Equity Indices 5.4 time to prepare, file, and pay 5.6 turnover ratio 5.4 total tax rate, as share of commercial profits 5.6 goods and services taxes, domestic 4.14 Steel products, commodity prices and price index 6.5 income, profit, and capital gains taxes 4.14 international trade taxes 4.14 Structural policies (Country Policy and Institutional Assessment) other taxes 4.14 business regulatory environment 5.9 social contributions 4.14 financial sector 5.9 tax revenue collected by central government, as share of GDP 5.6 structural policies cluster average 5.9 trade 5.9 Technology—see Computers; Exports, high-technology; Internet; Research and development; Science and technology Stunting, prevalence of 2.20 Telephones Sulfur dioxide emissions—see Pollution fixed international voice traffic 5.11 Surface area 1.1, 1.6 per 100 people 5.11 See also Land use residential tariff 5.11 mobile cellular Suspended particulate matter—see Pollution international voice traffic 5.11 population covered 5.11 T Tariffs prepaid tariff subscriptions per 100 people mobile cellular and fixed subscriptions per employee 5.11 5.11 5.11 all products total revenue 5.11 binding coverage 6.7 simple mean bound rate 6.7 Television, households with 5.12 simple mean tariff 6.7 weighted mean tariff 6.7 Temperature—see Climate variability applied rates on imports from low- and middle-income economies 6.3 manufactured products Terms of trade index, net barter 6.1 simple mean tariff 6.7 weighted mean tariff 6.7 Tertiary education—see Education on exports of least developed countries 1.4 primary products Threatened species—see Animal species; Biological diversity; Plants, higher simple mean tariff 6.7 428 2012 World Development Indicators Tourism, international year of creation 6.6 tourism expenditure year of entry into force of the most recent agreement 6.6 inbound as share of exports 6.15 Trademark applications filed 5.13 total 6.15 outbound Trade policies—see Tariffs as share of imports 6.15 total 6.15 Traffic—see Fuels; Motor vehicles; Roads tourists inbound 6.15 Transport—see Air transport; Ports; Railways; Roads outbound 6.15 Travel—see Tourism, international Trade arms 5.7 Treaties, participation in barriers 6.7 biological diversity 3.17 facilitation CFC control 3.17 burden of customs procedures 6.8 climate change 3.17 documents Convention on International Trade on Endangered Species (CITES) 3.17 to export 6.8 Convention to Combat Desertification (CCD) 3.17 to import 6.8 Kyoto Protocol 3.17 freight costs to the United States 6.8 Law of the Sea 3.17 lead time Ozone layer 3.17 to export 6.8 Stockholm Convention on Persistent Organic Pollutants 3.17 to import 6.8 liner shipping connectivity index 6.8 Tuberculosis logistics performance index 6.8 case detection rate 2.18 quality of port infrastructure 6.8 incidence 1.3, 2.22 information and communications technology 5.12 treatment rate 2.18 merchandise direction of, by developing countries direction of, by region high-income economy with low- and middle-income economies, 6.4 6.2 U Undernourishment, prevalence of 2.20 by product 6.3 nominal growth, by region 6.2 Underweight, prevalence of 2.20 regional trading blocs 6.6 structure 4.4, 4.5 Unemployment total 4.4, 4.5 by level of educational attainment, primary, secondary, tertiary 2.5 services incidence of long-term, total, male, and female 2.5 structure 4.6, 4.7 total, male, and female 2.5 total 4.6, 4.7 youth, male, and female 1.3, 2.10 See also Balance of payments; Exports; Imports; Manufacturing; Merchandise; Terms of trade; Trade blocs Urban environment access to improved sanitation facilities 3.13 Trade blocs, regional access to an improved water source 3.5 exports within bloc 6.6 emissions, selected cities total exports, by bloc 6.6 nitrogen dioxide 3.16 type of agreement 6.6 particulate matter 3.16 2012 World Development Indicators 429 INDEX OF INDICATORS sulfur dioxide 3.16 urban and rural 3.5 housing conditions freshwater durable dwelling units 3.14 annual withdrawals home ownership 3.14 as share of internal resources 3.5 household size 3.14 for agriculture 3.5 multiunit dwellings 3.14 for domestic use 3.5 overcrowding 3.14 for industry 3.5 vacancy rate 3.14 total 3.5 population internal renewable resources as share of total 3.13 flows 3.5 average annual growth 3.13 per capita 3.5 in largest city 3.13 pollution—see Pollution, organic water pollutants in selected cities 3.16 productivity 3.5 in urban agglomerations 3.13 total 3.13 Women in development See also Pollution; Population; Sanitation; Water female-headed households 2.10 female population, as share of total 1.5 V Value added life expectancy at birth pregnant women receiving prenatal care teenage mothers 1.5 1.5, 2.19 1.5 as share of GDP poorest and richest wealth quintile 2.24 in agriculture 4.2 total 1.5 in industry 4.2 unpaid family workers 1.5 in manufacturing 4.2 vulnerable employment 2.4 in services 4.1, 4.2 women in nonagricultural sector 1.5 per worker women in parliaments 1.5 in agriculture 3.3 Workforce, firms offering formal training 5.2 W Wasting, prevalence of 2.20 World Bank, net financial flows from See also International Bank for Reconstruction and Development; 6.11 International Development Association Water access to improved source of, population with total 2.18, 5.8 430 2012 World Development Indicators REGION MAP The world by region East Asia and Paci�c Chile Cape Verde Italy* American Samoa Colombia Central African Republic Japan Cambodia Costa Rica Chad Korea, Rep. China Cuba Comoros Luxembourg* Fiji Dominica Congo, Dem. Rep. Netherlands* Indonesia Dominican Republic Congo, Rep. New Zealand Kiribati Ecuador Côte d'Ivoire Norway Korea, Dem. Rep. El Salvador Eritrea Poland Lao PDR Grenada Ethiopia Portugal* Malaysia Guatemala Gabon Slovak Republic* Marshall Islands Guyana Gambia, The Slovenia* Micronesia, Fed. Sts. Haiti Ghana Spain* Mongolia Honduras Guinea Sweden Myanmar Jamaica Guinea-Bissau Switzerland Palau Mexico Kenya United Kingdom Papua New Guinea Nicaragua Lesotho United States Philippines Panama Liberia Samoa Paraguay Madagascar Other high income Solomon Islands Peru Malawi Andorra Thailand St. Kitts and Nevis Mali Aruba Timor-Leste St. Lucia Mauritania Bahamas, The Tonga St. Vincent & Grenadines Mauritius Bahrain Tuvalu Suriname Mayotte Barbados Vanuatu Uruguay Mozambique Bermuda Vietnam Venezuela, RB Namibia Brunei Darussalam Niger Cayman Islands Europe and Middle East and Nigeria Channel Islands Central Asia North Africa Rwanda Croatia Albania Algeria São Tomé and Príncipe Curaçao Armenia Djibouti Senegal Cyprus* Azerbaijan Egypt, Arab Rep. Seychelles Equatorial Guinea Belarus Iran, Islamic Rep. Sierra Leone Faeroe Islands Bosnia and Herzegovina Iraq Somalia French Polynesia Bulgaria Jordan South Africa Gibraltar Georgia Lebanon South Sudan Greenland Kazakhstan Libya Sudan Guam Kosovo Morocco Swaziland Hong Kong SAR, China Kyrgyz Republic Syrian Arab Republic Tanzania Isle of Man Latvia Tunisia Togo Kuwait Lithuania West Bank and Gaza Uganda Liechtenstein Macedonia, FYR Yemen, Rep. Zambia Macao SAR, China Moldova Zimbabwe Malta* Montenegro South Asia Monaco Romania Afghanistan High-income OECD New Caledonia Russian Federation Bangladesh Australia Northern Mariana Islands Serbia Bhutan Austria* Oman Tajikistan India Belgium* Puerto Rico Turkey Maldives Canada Qatar Turkmenistan Nepal Czech Republic San Marino Ukraine Pakistan Denmark Saudi Arabia Uzbekistan Sri Lanka Estonia* Singapore Finland* Sint Maarten Latin America and Sub-Saharan Africa France* St. Martin the Caribbean Angola Germany* Trinidad and Tobago Antigua and Barbuda Benin Greece* Turks and Caicos Islands Argentina Botswana Hungary United Arab Emirates Belize Burkina Faso Iceland Virgin Islands (U.S.) Bolivia Burundi Ireland* Brazil Cameroon Israel *Member of the Euro area The world by region Classi�ed according to Low- and middle-income economies World Bank analytical East Asia and Pacific Middle East and North Africa High-income economies grouping Europe and Central Asia South Asia OECD Latin America and the Caribbean Sub-Saharan Africa Other No data Greenland (Den) Iceland Faeroe Norway Islands (Den) Sweden Finland Russian Federation The Netherlands Estonia Isle of Man (UK) Canada Denmark Russian Latvia Fed. Lithuania United Ireland Kingdom Germany Poland Belarus Channel Islands (UK) Belgium Ukraine Luxembourg Moldova Kazakhstan Mongolia Liechtenstein France Italy Romania Switzerland Bulgaria Georgia Uzbekistan Kyrgyz Andorra Armenia Azer- Rep. Dem.People’s United States Spain baijan Turkmenistan Rep.of Korea Portugal Turkey Tajikistan Monaco Greece Japan Cyprus Syrian Rep.of Gibraltar (UK) Arab Islamic Rep. Korea Bermuda Malta Lebanon China Tunisia Rep. of Iran Afghanistan (UK) Israel Iraq Morocco Kuwait West Bank and Gaza Jordan Algeria Bahrain Pakistan Bhutan Libya Arab Rep. Qatar Nepal The Bahamas Former Spanish of Egypt Saudi Sahara Arabia Bangladesh Cayman Is.(UK) United Arab Turks and Caicos Is. (UK) Emirates India Mexico Cuba Myanmar Mauritania Oman Lao Haiti Cape Verde P.D.R. Mali N. Mariana Islands (US) Belize Jamaica Niger Chad Eritrea Rep. of Yemen Thailand Guatemala Honduras Senegal Sudan The Gambia Vietnam Guam (US) El Salvador Nicaragua Burkina Cambodia Guinea-Bissau Faso Djibouti Philippines Guinea Federated States of Micronesia Costa Rica Benin Marshall Islands Panama Nigeria Central Ethiopia Sri R.B. de Guyana Sierra Leone Côte Ghana Lanka Venezuela d’Ivoire African South Suriname Republic Sudan Brunei Darussalam Liberia Palau French Guiana (Fr) Cameroon Malaysia Colombia Togo Somalia Equatorial Guinea Maldives Uganda São Tomé and Príncipe Kenya Nauru Kiribati Congo Singapore Ecuador Gabon Rwanda Kiribati Dem.Rep.of Burundi Seychelles Congo Solomon Tanzania Papua New Guinea Islands Comoros Indonesia Tuvalu Peru Brazil Timor-Leste Samoa French Polynesia (Fr) Angola Malawi Zambia Mayotte American (Fr) Vanuatu Fiji Samoa (US) Bolivia Mozambique Fiji Zimbabwe Madagascar Tonga Mauritius Namibia Botswana New Paraguay Réunion (Fr) Caledonia Australia (Fr) Swaziland Dominican St. Martin (Fr) Germany South Republic Puerto Poland Lesotho Rico (US) St. Maarten (Neth) Africa Czech Republic Ukraine Uruguay Slovak Republic Antigua and Barbuda Chile U.S. Virgin Argentina Islands (US) Guadeloupe (Fr) Austria St. Kitts Hungary New and Nevis Zealand Dominica Slovenia Romania Croatia Martinique (Fr) Bosnia and St. Lucia Herzegovina Serbia Aruba (Neth) St. Vincent and San Curaçao (Neth) the Grenadines Barbados Marino Kosovo Bulgaria Grenada Italy Montenegro FYR Macedonia Trinidad Vatican Albania and Tobago City Greece R.B. de Venezuela Antarctica IBRD 39126 MARCH 2012 The World Bank 1818 H Street N.W. ISBN 978-0-8213-8985-0 Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: data.worldbank.org SKU 18985 Email: data@worldbank.org The World Development Indicators • Includes more than 1,000 indicators for 158 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: 64 trees 26 million Btu of total energy 6,503 pounds of net greenhouse gases 29,321 gallons of waste water 1,859 pounds of solid waste