IW-ORLD--DEVELOPMENT IINDICATORS 11~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1 I I - M | ~~~- .:l. I= The world by income Low Income Ukraine Upper middle Income Greenland Afghanistan Uzbekistan American Samoa Guam Angola Vietnam Antigua and Barbuda Hong Kong, China Armenia Yemen, Rep Argentina Iceland Azerbaijan Zambia Barbados Ireland Bangladesh Zimbabwe Botswana Israel Benin Brazil Italy Bhutan Lower middle Income Chile Japan Burkina Faso Albania Costa Rica Korea, Rep Burundi Algeria Croatia Kuwait Cambodia Belarus Czech Republic Liechtenstein Cameroon Belize Dominica Luxembourg Central African Republic Bolivia Estonia Macao, China Chad Bosnia and Herzegovina Gabon Monaco Coneoros Bulgaria Grenada Netherlands Congo, Dem Rep Cape Verde Hungary Netherlands Antilles Congo, Rep China Isle of Man Now Caledonia Cote d'lvoire Colombia Latvia New Zealand Equatorial Guinea Cuba Lithuania Northern Mariana Eritrea Djibouti Lebanon Islands Ethiopia Dominican Republic Libya Norway Gambia, The Ecuador Malaysia Portugal Georgia Egypt, Arab Rep Malta Qatar Ghana El Salvador Mauritius San Marino Guinea Fiji Mayotte Singapore Guinea-Bissau Guatemala Mexico Slovenia Haiti Guyana Oman Spain India Honduras Palau Sweden Indonesia Iran, Islamic Rep Panama Switzerland Kenya Iraq Poland United Arab Emirates Korea, Dem Rep Jamaica Puerto Rico United Kingdom Kyrgyz Republic Jordan Saudi Arabia United States Lao PDR Kazakhstan Seychelles Virgin Islands (U S I Lesotho Kiribati Slovak Republic Liberia Macedonia, FYR St Kitts and Nevis Madagascar Maldives St Lucia Malawi Marshall Islands Trinidad and Tobago Mail Micronesia, Fed Sts Uruguay Mauritania Morocco Venezuela, RB Moldova Namibia Mongolia Paraguay High Income Mozambique Peru Andorra Myanmar Philippines Aruba Nepal Romania Australia Nicaragua Russian Federation Austria Niger Samoa Bahamas, The Nigeria South Africa Bahrain Pakistan Sri Lanka Belgium Papua New Guinea St Vincent and the Bermuda Rwanda Grenadines Brunei Sao Tomb and Principe Suriname Canada Senegal Swaziland Cayman Islands Sierra Leone Syrian Arab Republic Channel Islands Solomon Islands Thailand Cyprus Somalia Tonga Denmark Sudan Tunisia Faeroe Islands Tajikistan Turkey Finland Tanzania Turkmenistan France Timor-Leste Vanuatu French Polynesia Togo West Bank and Gaza Germany Uganda Yugoslavia, Fed Rep Greece c~~~~~~~~~~~~~~~A 3~IA < a) IIt X-> 0X0 0~~~~~~~~ v . s ! |~~~~~~~~~~~~~~~~~~~~~~~C f aS#.'A'9 tA S 00000 1X /1 S; X~~r I X W OtUBUl"" A W~~~~~~~~~~~~~ XA X ml mN^ll u i~~~~~~~~~~~~~~~~~~~~~~~~~~ Copyright 2003 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, DC 20433, USA All rights reserved Manufactured in the United States of America Frst printing April 2003 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 Executive Directors or the countries they represent The World Bank does not guarantee the accuracy of the data included tn this publication and accepts no responsibil- ity 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 terri- tory 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 distor- tions 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 dissemination 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, Massachusetts 01923, USA Photo credits Front cover, from top to bottom and left to right, PhotoDisc, Alex Baluyut/World Bank, PhotoDisc, Demi/ UNEP/Still Pictures, PhotoDisc, PhotoDisc, Back cover, Curt Carnemark/World Bank, PhotoDisc, Page 178, Curt Carnemark/World Bank If you have questions or comments about this product, please contact Development Data Group The World Bank 1818 H Street NW, Room MC2-812, Washington, DC 20433, USA Hotline 800 590 1906 or 202 473 7824, fax 202 522 1498 Email data@worldbank org Web site www worldbank org or www worldbank org/data ISBN 0-8213-5422-1 08I WORLD DEVELOPMENT I __ I INDICATOR 1 The World Bank When the world's nations adopted the Millennium Declaration in September 2000, they made a commitment to quicken the pace of devel- opment and ensure that its benefits reach all people We at the World Bank share that commitment The challenge now is to scale up our efforts to meet the goals that we have set for ourselves The Millennium Development Goals set specific targets for achieving development progress We know what this effort will require First, good development outcomes require good policies and institutions that are country owned and driven Second, the global economic envi- ronment, including trade and financial systems, must be open, fair, and supportive Third, when these conditions are met, development assistance can be highly effective But the poorest countries will need substantial increases in assistance if they are to reach the Millennium Development Goals In the past three years our partners-developing countries, high-income countries, and international agencies-have shaped an unprece- dented consensus on how to achieve progress In Doha, Qatar, in November 2001 the members of the World Trade Organization declared that the next round of trade negotiations would place the interests of the developing countries at the top of the agenda In Monterrey, Mexico, in March 2002 developing countries committed to improving their governance, institutions, and policies, and rich countries com- mitted to increasing aid, opening trade, and supporting capacity building And in Johannesburg, South Africa, in August 2002 the inter- national community addressed the challenges of achieving sustainable development and protecting the environment Together, these agreements provide the foundations for a global partnership for development Over the past 40 years the developing world has seen tangible but uneven growth and poverty reduction The World Development Indicators documents much of that story In 1962 the list of the 20 poorest countries in the world included the Republic of Korea, Botswana, China, and India Today Korea has Joined the ranks of the high-income economies Botswana's average real income has doubled almost four times, placing it among upper-middle-income countries China, where poverty is falling rapidly, is now the sixth largest economy in the world And India, after opening its economy and accelerating growth, has moved well above the 20 poorest Still, the World Development Indicators reminds us that growth alone will not be enough to achieve the Millennium Development Goals Hundreds of millions of people suffer illnesses and early deaths that could be prevented if basic health services were widely available Each year more than 10 million children die before their fifth birthday More than 100 million children do not attend primary school And more than a billion people lack access to a safe source of water These figures pose enormous challenges Speaking plainly, many of the poor- est countries will not reach the Millennium Development Goals unless all development partners take decisive action without further delay One more thing we need to do continue to measure our efforts using the best statistics available and to use that information wisely to set policies, guide programs, and monitor outcomes The World Development Indicators is the result of a worldwide effort shared by many people and agencies It is another example of development partnership, something very important to us Our focus is on scaling up sup- port for capacity building in statistics and working with our partners to improve the internationally comparable data set that is needed to monitor development policies, actions, and outcomes Statistics are not the most glamorous work But without them we would not know how far we have come-or how far we have to go James D Wolfensohn President The World Bank Group 2003 World Development Indicators I V This book and its companion volumes, The World Bank Atlas and The Little Data Book, were prepared by a team coordinated by David Cieslikowski The team consisted of Mehdi Akhlaghi, Mona Fetouh, Richard Fix, Amy Heyman, Masako Hiraga, M H Saeed Ordoubadi, Sulekha Patel, Eric Swanson, K M Vijayalakshmi, Vivienne Wang, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency's Development Data Group The CD-ROM development team included Azita Amjadi, Elizabeth Crayford, Ramgopal Erobelly, Reza Farivari, and William Prince The work was carried out under the management of Shaida Badiee The choice of indicators and textual content was shaped through close consultation with and substantial contributions from staff in the World Bank's four thematic networks-Environmentally and Socially Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Private Sector Development and Infrastructure-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 the Credits section For a listing of our key partners, see the Partners section Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont and Bruce Ross-Larson The editing and production team consisted of Joseph Costello, Wendy Guyette, Paul Holtz, Elizabeth McCrocklin, Alison Strong, and Elaine Wilson Communications Development's London partner, Grundy & Northedge, provided art direction and design Staff from External Affairs oversaw pub- lication and dissemination of the book Vii II 2003 World Development Indicators When launched more than a quarter century ago, the World Development Indicators presented a statistical snapshot of the world as seen by development economists As our understanding of the development process has grown, so has the World Development Indicators It now encompasses more than 500 indicators covering 152 counries, selected from a database spanning 40 years with more than 800 indicators for 208 countries It provides a larger picture of poverty trends and social welfare, the use of environmental resources, the per- formance of the public sector, and the integration of the global economy The availability of internationally comparable statistics has encouraged a new focus on measuring development outcomes The Millennium Development Goals, adopted by all members of the United Nations, set specific, quantified targets for reducing poverty and achieving progress in health, education, and the use of environmental resources (The World view section reports on these goals and on the com- mitments by richer countries to help poorer countries achieve them ) These goals and the growing emphasis on results-focused develop- ment strategies have in turn increased the demand for timely, reliable, and relevant data But many countries still lack the capacity to produce and use reliable statistical information A shortage of skills, resources, and tech- nology has often led to incomplete or erroneous data And the unreliability of data has meant less demand from potential users and fewer resources for statistical agencies The result a long history of underinvestment in the most public of all goods-information In the past several years the World Bank has expanded its efforts to help developing countries break out of this vicious cycle Working in partnership with national agencies and international donors, we are helping to build the capacity of statistical systems to collect, com- pile, and disseminate reliable statistics We also support training programs to increase the use of statistics to inform public choices And by participating in the international statistical system, we help to provide frameworks and standards to guide practitioners Improving statistics requires real resources The World Banks Trust Fund for Statistical Capacity Building, supported by contributions from bilateral aid agencies, provides grants to support planning of statistical systems For larger, long-term projects a new lending facility will provide funding for reforming and expanding statistical services But making long-term improvements in statistics requires more than money We work closely with the International Monetary Fund on the implementation of the General Data Dissemination System and Data Quality Assessment Framework, both of which encourage countries to improve the quality of official statistics The World Bank is an active member of the Partnership in Statistics for the 21st Century-the PARIS21 consortium-which helps to build awareness of statistics and provides a forum where statisticians, policymakers, and other users of data can interact and articulate needs And we are leading the work of the International Comparison Programme in non-OECD countries to produce a new set of international price data The World Development Indicators reflects the efforts of many people and organizations We have tried to acknowledge our debts in the Partners section and the About the data pages that accompany each table Our purpose is to serve you, the user of statistics, whether a policymaker, researcher, commentator, or interested citizen We hope that the World Development Indicators goes some way toward meeting your needs You can find out more about our products at http //www worldbank org/data And you can send queries and com- ments to data@worldbank org Shaida Badiee Director Development Data Group 2003 World Development Indicators I Vii H3 1. WORID VIEW Foreword v Introduction 3 Acknowledgments vi Preface vii Tables Partners xi 1. Size of the economy 14 Users guide xxiv .2 Millennium Development Goals eradicating poverty and improving lives 18 1.3 Millennium Development Goals protecting our common environment 22 L4 Millennium Development Goals overcoming obstacles 26 1.5 Women in development 28 1.E Key indicators for other economies 32 Text figures and boxes la Asia has reduced poverty the most over the past decade 4 lb Measuring poverty 5 le Despite progress, millions remain in extreme poverty 5 Id And millions more live on less than $2 a day 5 le Undernourishment is rising in Sub-Saharan Africa 6 if Child malnutrtion remains highest in South Asia 6 lg Education for all can be achieved, but sustained effort is required 7 lb Recent estimates show more girls in school 8 Slow progress toward the child mortality goal 9 lj Skilled attendants reduce maternal deaths 10 lIh Young mothers at risk 10 II No end in sight for the HIV/AIDS epidemic 11 WIm More global cooperation needed against tuberculosis 11 Ini Water is reaching more people 12 10 Many still lack access to sanitation 12 Ip Aid to the poorest countries has increased 13 Iq HIPCs have improved debt service ratios 13 1.2a Location of indicators for Millennium Development Goals 1-5 21 iSa Location of indicators for Millennium Development Goals 6-7 25 1.5a Location of indicators for Millennium Development Goal 8 27 viii I 2003 World Development Indicators 2. PEOPIE 3 S. ENVIRONMENT Introduction 35 Introduction 117 Tables Tables 21 Population dynamics 38 2.1 Rural environment and land use 120 2.2 Labor force structure 42 3.2 Agricultural inputs 124 2.3 Employment by economic activity 46 3.3 Agricultural output and productivity 128 2.4 Unemployment 50 3.4 Deforestation and biodiversity 132 2 5 Wages and productivity 54 3.5 Freshwater 136 2.8 Poverty 58 2.0 Water pollution 140 2.1 Social indicators of poverty 62 3.7 Energy production and use 144 2.8 Distribution of income or consumption 64 2.8 Energy efficiency and emissions 148 2.0 Assessing vulnerability 68 3.9 Sources of electricity 152 2.10 Enhancing security 72 3.10 Urbanization 156 2.11 Education inputs 76 3.11 Urban environment 160 212 Participation in education 80 3.12 Traffic and congestion 164 2.13 Education efficiency 84 9.l8 Air pollution 168 2.14 Education outcomes 88 314 Government commitment 170 2.15 Health expenditures, services, and use 92 3.15 Understanding savings 174 218 Disease prevention coverage and quality 96 2.17 Reproductive health 100 Text figures and boxes 2.18 Nutrition 104 3a Adjusted net savings tend to be small in low- and 2.19 Health risk factors and future challenges 108 middle-income countries 119 2.20 Mortality 112 3.1a The 10 countries with the largest shares of rural population in 2001-and the 10 with the smallest 123 Text figures and boxes 3.2a In low-income countries fertilizer consumption has more 2a The world population boomed in the second half of the than doubled but cereal yields remain less 20th century 36 than a third of those in high-income countries 127 2b Thailand's child dependency ratio fell quickly in the 1970s and 3.3a Food production has grown in all country income groups 1980s, before the old-age dependency rose 36 and regions in the past two decades 131 2e In India educational attainment is sharply lower for the poor- 3.5a Agriculture accounts for most freshwater withdrawals in and for girls 37 developing countries 139 2d Where per capita income is low, so is life expectancy 37 3.6a Top five emitters of organic water pollutants 143 2.2a Women are clustered in unpaid family work 45 3.7a High-income countries consume a disproportionate share of 2.4a Youth unemployment does not always exceed the world's energy 147 adult unemployment 53 S.7b People in high-income countries use almost 10 times as much 2.7a Poor women lack adequate access to reproductive health care commercial energy as do people in low-income countries 147 in urban as well as rural areas 63 3.81 Per capita emissions of carbon dioxide vary 151 2.19! Public health spending often far exceeds public 75 3S8h but emissions per unit of GDP have declined 151 2.111 Even the poorest households in Indonesia contribute a 29a Electricity sources have shifted over the past two decades 155 significant share of education spending 79 3.9b The shift in electricity sources has been more profound in 2.12a Nearly 40 million African children were out of school in 1998 83 low-income countries 155 2.11a In some regions more young women than men are living with 3.10a The urban population in low-income countries has doubled HIV/AIDS 111 in the past two decades 159 2.20a Infant mortality rates reflect wide disparities between rich 3.111 The use of public transportation for work trips varied widely and poor 115 across cities in 1998 163 312a The 10 countries with the fewest passenger cars per 1,000 people in 2000-and the 10 with the most 167 3.14i - Status of national environmental action plans 170 35b States that have signed the Kyoto Protocol 171 3.140 Global focus on biodiversity and climate change 172 2003 World Development Indicators I ix X E iC DNOMS X So MAN AND MARKET$ Introduction Introduction 255 Tables Tables 4.1 Growth of output 186 5.1 Private sector development 258 4.2 Structure of output 190 5.2 Investment climate 262 4.3 Structure of manufacturing 194 5.3 Business environment 266 4.4 Growth of merchandise trade 198 5.4 Stock markets 270 4.5 Structure of merchandise exports 202 5.5 Financial depth and efficiency 274 4.0 Structure of merchandise imports 206 5.0 Tax policies 278 4.1 Structure of service exports 210 5.1 Relative prices and exchange rates 282 4.0 Structure of service imports 214 5.0 Defense expenditures and trade in arms 286 4.9 Structure of demand 218 5.0 Transport infrastructure 290 4.10 Growth of consumption and investment 222 5.10 Power and communications 294 4.11 Central government finances 226 5.11 The information age 298 4.12 Central government expenditures 230 5.12 Science and technology 302 4.13 Central government revenues 234 4.14 Monetary indicators and prices 238 Text figures and boxes 4.15 Balance of payments current account 242 5a Investment in infrastructure projects with private participation 4.10 External debt 246 grew dramatically in developing countries in the 1990's 257 4.17 External debt management 250 5.1a In 1990-2001 telecommunications and electricity captured most of the investment in Infrastructure projects with private Text figures and boxes participation in developing countries 261 4a Measuring national income 180 5.4a Top five emerging stock markets in 2002 273 4J8 Recent economic performance 182 5.10D In many countries mobile phone subscribers now outnumber 4.b Key macroeconomic indicators 183 fixed-line subscribers 297 4.3a Manufacturing continues to show strong growth in East Asia 197 5.11a Latin America and the Caribbean leads the developing regions 4.5a Top 10 developing country exporters in 2001 205 in personal computers, with almost 60 per 1,000 people 301 4.8a Manufactures account for the biggest share of merchandise imports 209 4.78 Top 10 developing country exporters of commercial services 213 4.8a Developing economies are consuming more international travel services 217 4.10D Per capita consumption has risen in Asia, fallen in Africa 225 4.12a Some developing and high-income economies direct more than half their central government spending to subsidies and other current transfers 233 4.13a The level of a country's income tends to determine its method of taxation 237 4.15a Among the top recipients of workers' remittances, India and Mexico have seen their share grow substantially.. 245 4.10a Thanks to traditional debt relief and the HIPC Debt Initiative, the total debt burden of heavily indebted poor countries, most in Sub-Saharan Africa, has declined since 1999 249 X 2003 World Development Indicators *3 S. GIOBAL [INKS 3y u Introduction 307 Pnmary data documentation 357 Acronyms and abbreviations 365 Tables Statistical methods 366 B1 Integration with the global economy - 310 Credits 368 0.2 Direction and growth of merchandise trade 314 Bibliography 370 B3 OECD trade with low-and middle-income economies 317 Index of indicators 378 8.4 Primary commodity prices 320 6.5 Regional trade blocs 322 0.0 Tariff barriers 326 0.7 Global financial flows 330 0.0 Net financial flows from Development Assistance Committee members 334 0 9 Aid flows from Development Assistance Committee members 336 0.10 Aid dependency 338 0.11 Distribution of net aid by Development Assistance Committee members 342 B 12 Net financial flows from multilateral institutions 346 B.13 Foreign labor and population in selected OECD countries 350 0.14 - Travel and tourism 352 Text figures and boxes ea A global trade agenda focusing on development 308 Sb Aid after Monterrey 309 B.la Top ten low- and middle-income recipients of gross private capital flows in 2001 313 B.3a Manufactures account for a growing share of high-income OECD countries' imports from low- and middle-income economies 319 8.5a Exports within several trade blocs have remained a steady share of world exports 325 B Ba Official development assistance is one of several sources of financing to developing countries 335 B.9a Official development assistance from selected non-DAC donors, 1997-2001 337 O lla A large share of bilateral aid goes to middle-income countries 345 B.12a The International Monetary Fund responds to financial crises in developing countries 349 0.13a Foreign labor levels have remained steady over the past decade in many OECD countries 351 B.14a Tourism continues to rise 355 2003 World Development Indicators I Xi Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations The indicators presented in the 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 com- mittees and working parties of the national and international statistical agencies that develop the nomen- clature, classifications, and standards fundamental to an international statistical system. Nongovernmental organizations 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 devel- oping statistical methods and carrying on a continuing dialogue about the quality and interpretation of sta- tistical 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 the 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 your easy reference we have included URLs (Web addresses) for organizations that maintain Web sites. The addresses shown were active on 1 March 2003. Information about the World Bank is also provided. llneirnatlonal and government agGncies Buireau of Verlflcatlon and CompgDance, U.S. Department of $Ste The Bureau of Verification and Compliance, U.S. Department of State, is responsible for international agree- ments on conventional, chemical, and biological weapons and on strategic forces; treaty verification and compliance; and support to ongoing negotiations, policymaking, and interagency implementation efforts. For information, contact the Public Affairs Officer, Bureau of Verification and Compliance, U.S. Department of State, 2201 C Street NW, Washington, DC 20520, USA; telephone: 202 647 6946; Web site: www.state.gov/t/vc. Cabon Dloxlde Unformatlon AnElysls CentGr The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global change data and information analysis center of the U.S. Department of Energy. The CDIAC's scope includes potentially anything that would be of value to those concerned with the greenhouse effect and global climate change, including con- centrations of carbon dioxide and other radiatively active gases in the atmosphere; the role of the terres- trial biosphere and the oceans in the biogeochemical cycles of greenhouse gases; emissions of carbon diox- ide 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 information, contact the CDIAC, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831- 6335, USA; telephone: 865 574 0390; fax: 865 574 2232; email: cdiac@ornl.gov; Web site: cdiac.esd.ornl.gov. XDD 0 2003 Worid Development Indicators Deutsche Gesellschaft fur Technische Zusammenarbeit The Deutsche Gesellschaft fur Technische Zusammenarbeit (GTZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GTZ's aim is to positively shape the political, economic, ecological and social development in partner countries, thereby improving people's liv- ing conditions and prospects. The organization has more than 10,000 employees in some 130 countries of Africa, Asia, Latin America, and Eastern Europe For publications, contact Deutsche Gesellschaft fur Technische Zusammenarbeit (GTZ) GmbH Corporate Communications, Dag-Hammarskj6ld-Weg 1-5, 65760 Eschborn, Germany; telephone: + 49 (0) 6196 79 1174, fax: + 49 (0) 6196 79 6196, email: presse@gtz.de, Web site: www.gtz.de Food and Agriculture Organization The Food and Agriculture Organization (FAO), 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 pro- ductivity, 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 govern- F ments; and serves as an international forum for debate on food and agricultural issues. Statistical publications of the FAO include the Production Yearbook, Trade Yearbook, and Fertilizer Yearbook. The FAO makes much of its data available online through its FAOSTAT and AQUASTAT systems. FAO publications can be ordered from national sales agents or directly from the FAO Sales and Marketing Group, Viale delle Terme di Caracalla, 00100 Rome, Italy, telephone: 39 06 5705 5727; fax: 39 06 5705 3360; email: Publications-sales@fao.org; Web site: www.fao.org. International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, was found- ed on December 7, 1944. It is responsible for establishing international standards and recommended prac- tices and procedures for the technical, economic, and legal aspects of international civil aviation opera- tions. The ICAO works to achieve the highest practicable degree of uniformity worldwide in civil aviation issues whenever this will facilitate and improve air safety, efficiency, and regularity. MOX To obtain ICAO publications, contact the ICAO, Document Sales Unit, 999 University Street, Montreal, Quebec H3C 5H7, Canada; telephone 514 954 8022; fax: 514 954 6769; email: sales_unit@icao.int, Web site: www.icao.int. International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promo- tion of social justice and internationally recognized human and labor rights. Founded in 1919, it is the only surviving major creation of the Treaty of Versailles, which brought the League of Nations into being. It became the first specialized agency of the United Nations in 1946. Unique within the United Nations sys- ISr tem, the ILO's tripartite structure has workers and employers participating as equal partners with govern- ments in the work of its governing organs. As part of its mandate, the ILO maintains an extensive statistical publication program. The Yearbook of Labour Statistics is its most comprehensive collection of labor force data. Publications can be ordered from the International Labour Organization Publications, 4 route des 2003 World Development Indicators Xiii Morillons, CH-1211 Geneva 22, Switzerland, or from sales agents and major booksellers throughout the world and ILO offices in many countries. telephone: 41 22 799 6111; fax: 41 22 798 8685; email: pubins@ilo.org; Web site: www ilo.org. DnternationaD iWonetavy Faund The International Monetary Fund (IMF) was established at a conference in Bretton Woods, New Hampshire, United States, on July 1-22, 1944. (The conference also established the World Bank.) The IMF came into official existence on December 27, 1945, and commenced financial operations on March 1, 1947. It cur- rently has 184 member countries The statutory purposes of the IMF are to promote international monetary cooperation, facilitate the o expansion and balanced growth of international trade, promote exchange rate stability, help to establish a multilateral payments system, make the general resources of the IMF temporarily available to its members under adequate safeguards, and shorten the duration and lessen the degree of disequilibrium in the inter- national balances of payments of members. The IMF maintains an extensive program for the development and compilation of international statis- tics and is responsible for collecting and reporting statistics on international financial transactions and the balance of payments. In April 1996 it undertook an important initiative to improve the quality of inter- national statistics, establishing the Special Data Dissemination Standard (SDDS) to guide members that have, or seek, access to international capital markets in providing economic and financial data to the pub- lic. In 1997 the IMF established the General Data Dissemination System (GDDS) to guide countries in pro- viding the public with comprehensive, timely, accessible, and reliable economic, financial, and sociode- mographic data. Building on this work, the IMF established the Data Quality Assessment Framework (DQAF) to assess data quality in subject areas such as debt and poverty. The DQAF comprises dimensions of data quality such as methodological soundness, accuracy, serviceability, and accessibility. In 1999 work began on Reports on the Observance of Standards and Codes (ROSC), which summarize the extent to which countries observe certain internationally recognized standards and codes in areas including data, monetary and financial policy transparency, fiscal transparency, banking supervision, securities, insur- ance, payments systems, corporate governance, accounting, auditing, and insolvency and creditor rights. The IMF's major statistical publications include International Financial Statistics, Balance of Payments Statistics Yearbook, Government Finance Statistics Yearbook, and Direction of Trade Statistics Yearbook. For more information on IMF statistical publications, contact the International Monetary Fund, Publications Services, Catalog Orders, 700 19th Street NW, Washington, DC 20431, USA; telephone: 202 623 7430; fax: 202 623 7201; telex RCA 248331 IMF UR; email: pub-web@lmf.org; Web site: www imf.org; SDDS and GDDS bulletin board: dsbb.imf.org. Daternationag Teflcommunficatlon Union Founded in Paris in 1865 as the International Telegraph Union, the International Telecommunication Union (ITU) took its current name in 1934 and became a specialized agency of the United Nations in 1947. The ITU is an intergovernmental organization in which the public and private sectors cooperate for the develop- ment of telecommunications. The ITU adopts international regulations and treaties governing all terrestrial and space uses of the frequency spectrum and the use of the geostationary satellite orbit. It also develops standards for the interconnection of telecommunications systems worldwide. 1< The ITU fosters the development of telecommunications in developing countries by establishing medium- ;%Dv H 2003 World Development Indicators term development policies and strategies in consultation with other partners in the sector and providing specialized technical assistance in management, telecommunications policy, human resource manage- ment, research and development, technology choice and transfer, network installation and maintenance, and investment financing and resource mobilization. The ITU's main statistical publication is the ITU Yearbook of Statistics. Publications can be ordered from ITU Sales and Marketing Service, Web site: www.itu.int/ITU-D/ict/pub- lications/index.htm; telephone, 41 22 730 6141 (English), 41 22 730 6142 (French), and 41 22 730 6143 (Spanish), fax: 41 22 730 5194; email: sales@itu.int; telex: 421 000 uit ch; telegram ITU GENEVE; Web site: www.itu.int. National Science Foundation The National Science Foundation (NSF) is an independent U.S government agency whose mission is to pro- mote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. It is responsible for promoting science and engineering through almost 20,000 research and education projects. In addition, the NSF fosters the exchange of scientific information among scientists and engineers in the United States and other countries, supports programs to strengthen scientific and engineering research potential, and evaluates the impact of research on industrial development and gen- eral welfare. As part of its mandate, the NSF biennially publishes Science and Engineering Indicators, which tracks national and international trends in science and engineering research and education. Electronic copies of NSF documents can be obtained from the NSF's online document system (www.nsf.gov/pubsys/ods/index.html) or requested by email from its automated mailserver (getpub@nsf.gov). Documents can also be requested from the NSF Publications Clearinghouse by mail, at PO Box 218, Jessup, MD 20794-0218, USA, or by telephone, at 301 947 2722. For more information, contact the National Science Foundation, 4201 Wilson Boulevard, Arlington, VA 22230, USA; telephone: 703 292 5111; Web site: www.nsf.gov. Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) was set up in 1948 as the Organisation for European Economic Co-operation (OEEC) to administer Marshall Plan funding in Europe. In 1960, when the Marshall Plan had completed its task, the OEEC's member countries agreed to bring in Canada and the United States to form an organization to coordinate policy among industrial countries The OECD OECD is the international organization of the industrialized, market economy countries. Representatives of member countries meet at the OECD to exchange information and harmonize policy with a view to maximizing economic growth in member countries and helping nonmember countries devel- OCDE op more rapidly The OECD has set up a number of specialized committees to further its aims. One of these is the Development Assistance Committee (DAC), whose members have agreed to coordinate their policies on assistance to developing and transition economies. Also associated with the OECD are several agencies or bodies that have their own governing statutes, including the International Energy Agency and the Centre for Co-operation with Economies in Transition. The OECD's main statistical publications include Geographical Dlstribution of Financial Flows to Aid Recipients, National Accounts of OECD Countries, Labour Force Statistics, Revenue Statistics of OECD Member Countnes, International Direct Investment Statistics Yearbook, Basic Science and Technology 2003 World Development indicators l Xv Statistics, Industrial Structure Statistics, and Services: Statistics on International Transactions. For information on OECD publications, contact the OECD, 2, rue Andre Pascal, F-75775 Paris Cedex 16, France; telephone: 33 1 45 24 81 67; fax: 33 1 45 24 19 50; email: sales@oecd.org; Web sites: www.oecd.org and www.oecd.org/bookshop. United Nations The United Nations and its specialized agencies maintain a number of programs for the collection of inter- national statistics, some of which are described elsewhere in this book At United Nations headquarters the Statistics Division provides a wide range of statistical outputs and services for producers and users of statistics worldwide. The Statistics Division publishes statistics on international trade, national accounts, demography and population, gender, industry, energy, environment, human settlements, and disability. Its major statistical publications include the Intemational Trade Statistics Yearbook, Yearbook of National Accounts, and Monthly Bulletin of Statistics, along with general statistics compendiums such as the Statistical Yearbook and World Statistics Pocketbook. For publications, contact United Nations Publications, Room DC2-853, Department 1004, 2 UN Plaza, New York, NY 10017, USA; telephone: 212 963 8302 or 800 253 9646 (toll free); fax: 212 963 3489; email: publications@un.org; Web site: www un.org. United Nations Centre for Human Settlements (Habitat), Global Urban Observatory The Urban Indicators Programme of the United Nations Centre for Human Settlements (Habitat) was estab- lished 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 urban development at the city level. In 1997 the Urban Indicators Programme was integrated into the Global Urban Observatory, the principal United Nations program for monitoring urban conditions and trends and for tracking progress in imple- menting the goals of the Habitat Agenda With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity building net- work to help governments, local authorities, the private sector, and nongovernmental and other civil soci- ety organizations. Contact the Co-ordinator, Global Urban Observatory and Statistics, Urban Secretariat, UN-HABITAT, PO Box 30030, Nairobi, Kenya; telephone: 254 2 623119; fax: 254 2 623080; email: habitat.publications@ unhabitat.org or guo@unhabitat.org; Web site: www.unhabitat.org. United Nations Children's Fund The United Nations Children's Fund (UNICEF), the only organization of the United Nations dedicated exclu- sively to children, works with other United Nations bodies and with governments and non-governmental organizations to improve children's lives in more than 140 developing countries through community-based services in primary health care, basic education, and safe water and sanitation. UNICEF's major publications include The State of the World's Children and The Progress of Nations. For information on UNICEF publications, contact the Chief, EPS, Division of Communication, UNICEF, 3 United Nations Plaza, New York, NY 10017, USA; telephone: 212 326 7000; fax: 212 303 7985; email: unicef pubdoc@unicef.org; Web site: www.unicef.org and www.un.org/Publications. XVi 1 2003 World Development Indicators 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. It was established as a permanent intergovernmental body in 1964 in Geneva with a view to accelerating economic growth and development, particularly in develop- I00 ing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, con- \< / sensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. ' & UNCTAD produces a number of publications containing trade and economic statistics, including the UNCTAD Handbook of International Trade and Development Statistics For information, contact UNCTAD, Palais des Nations, 8-14, Avenue de la Paix, 1211 Geneva 10, Switzerland; telephone: 41 22 907 1234; fax: 41 22 907 0043, email: info@unctad org; Web site: www unctad.org. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics (UIS) The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations established in 1945 to promote "collaboration among nations through education, science, and culture in order to further universal respect for justice, for the rule of law, and for the human rights and fun- damental freedoms . .. for the peoples of the world, without distinction of race, sex, language, or religion." UNESCO's principal statistical publications are the World Education Report (biennial) and Basic Education U EaSCO and Literacy: World Statistical Indicators They are produced by the UNESCO Institute for Statistics. For publications, contact the UNESCO Institute for Statistics, C.P. 6128, Succursale Centre-ville, Montreal, Quebec, H3C 3J7, Canada; telephone: 1 514 343 6880; fax 1 514 343 6882; email: uis@unesco.org; Web site www.unesco.org, and for the Institute for Statistics: www.uis.unesco.org/. United Nations Environment Programme The mandate of the United Nations Environment Programme (UNEP) is to provide leadership and encourage partnership 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. 0 NA UNEP publications include Global Environment Outlook and Our Planet (a bimonthly magazine). 'IV For information, contact the UNEP, PO Box 30552, Nairobi, Kenya, telephone: 254 2 621234; fax. 254 2 624489/90; email. eisinfo@unep.org; Web site. www.unep.org. United Nations Industrial Development Organization The United Nations Industrial Development Organization (UNIDO) was established in 1966 to act as the cen- tral coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels In 1985 UNIDO became the 16th specialized agency of the United Nations, with a mandate to help develop scientific and technological plans and programs for indus- UNIDO trialization in the public, cooperative, and private sectors. UNIDO's databases and information services include the Industrial Statistics Database (INDSTAT), Commodity Balance Statistics Database (COMBAL), Industrial Development Abstracts (IDA), and the International Referral System on Sources of Information. Among its publications is the International Yearbook of Industrial Statistics. For information, contact UNIDO Public Information Section, Vienna International Centre, PO Box 300, A-1400 Vienna, Austria; telephone: 43 1 26026 5031; fax: 43 1 21346 5031 or 26026 6843; email: publications@unido.org; Web site www.unido.org. 2003 World Development Indicators l Xvi World Bank Group The World Bank Group is made up of five organizations the International Bank for Reconstruction and Development (IBRD), the International Development Association (IDA), the International Finance Corporation (IFC), the Multilateral Investment Guarantee Agency (MIGA), and the International Centre for Settlement of Investment Disputes (ICSID) Established in 1944 at a conference of world leaders in Bretton Woods, New Hampshire, United States, U the World Bank is the world's largest source of development assistance, providing $19.5 billion in loans to its client countries. It uses its financial resources, trained staff, and extensive knowledge base to help each developing country onto a path of stable, sustainable, and equitable growth in the fight against poverty. The World Bank Group has 184 member countries. For information about the World Bank, visit its Web site at www.worldbank.org. For more information about development data, contact the Development Data Group, World Bank, 1818 H Street NW, Washington, DC 20433, USA; telephone: 800 590 1906 or 202 473 7824; fax: 202 522 1498; email: data@worldbank.org; Web site: www.worldbank.org/data. World Health Organization The constitution of the World Health Organization (WHO) was adopted on July 22, 1946, by the International Health Conference, convened in New York by the Economic and Social Council of the United Nations. The objec- tive of the WHO, a specialized agency of the United Nations, is the attainment by all people of the highest pos- i sible level of health. The WHO carries out a wide range of functions, including coordinating international health work; helping gov- ernments strengthen health services; providing technical assistance and emergency aid; working for the pre- vention and control of disease; promoting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promoting and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international stan- dards for biological, pharmaceutical, and similar products; and standardizing diagnostic procedures. The WHO publishes the World Health Statistics Annual and many other technical and statistical publications. For publications, contact the World Health Organization, Marketing and Dissemination, CH-1211 Geneva 27, Switzerland; telephone: 41 22 791 2476; fax: 41 22 791 4857; email: publications@who.int; Web site: www.who.int. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations based in Geneva, Switzerland. The objectives of WIPO are to promote the protection of intellectual property through- out the world through cooperation among states and, where appropriate, in collaboration with other inter- national organizations and to ensure administrative cooperation among the intellectual property unions- that is, the "unions" created by the Paris and Berne Conventions and several subtreaties concluded by HP members of the Paris Union. WIPO is responsible for administering various multilateral treaties dealing with the legal and administrative aspects of intellectual property. A substantial part of its activities and resources is devoted to development cooperation with developing countries. For information, contact the World Intellectual Property Organization, 34, chemin des Colombettes, CH-1211 Geneva 20, Switzerland; telephone: 41 22 338 9734; fax: 41 22 740 1812; email: ebookshop@wipo.int; Web site: www wipo int. xviiiI 2003 World Development indrcators World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with pro- moting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. The organization began as the International Union of Official Tourist Publicity Organizations, set up in 1925 in The Hague. Renamed the World Tourism Organization, it held its first gen- eral assembly in Madrid in May 1975. Its membership includes 139 countries and territories and more than 350 affiliate members representing local governments, tourism associations, and private companies, including airlines, hotel groups, and tour operators. The World Tourism Organization publishes the Yearbook of Tourism Statistics, Compendium of Tourism Statistics, and Travel and Tourism Barometer (triannual). For information, contact the World Tourism Organization, Calle Capit6n Haya, 42, 28020 Madrid, Spain; telephone: 34 91 567 8100; fax- 34 91 571 3733; email. infoshop@world-tourism.org; Web site, www.world-tourism .org. World Trade Organization The World Trade Organization (WTO), established on January 1, 1995, is the successor to the General Agreement on Tariffs and Trade (GATT) The WTO has 144 member countries, and 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 possible It does this by administering trade agreements, act- ing 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 WTO's agreements, negotiated and signed by a large majority of the world's trading nations and ratified by their parliaments. The WTO's International Trade Statistics is its main statistical publication, providing comprehensive, comparable, and up-to-date statistics on trade. For publications, contact the World Trade Organization, Publications Services, Centre William Rappard, rue de Lausanne 154, CH-1211, Geneva 21, Switzerland; telephone 41 22 739 5208 or 5308; fax: 41 22 739 5792; email: publications@wto org; Web site: www.wto.org. Private and nongovernmental organizations Containerisation International Containensation Intemational Yearbook is the most concise, yet comprehensive, single reference source on the container transport industry. Now in its 34th year, the Yearbook is known worldwide as "the bible of the industry." It has more than 850 pages of data, including detailed information on more than 560 con- tainer ports in more than 150 countries and a review section that features two-year rankings for 350 ports. CONTRItERISfTION The information can be accessed on the Web at www.ci-online.co.uk, which also provides a comprehensive YEARBOOK online daily business news and information service for the container industry. For more information, contact Informa UK at 69-77 Paul Street, London, EC2A 4LQ, UK; telephone: 44 1206 772061, fax: 44 1206 772563; email: webtechhelp@informa.com. 2003 World Development Indicators l Xix Euromoney Publications PLC Euromoney Publications PLC provides a wide range of financial, legal, and general business information. The monthly magazine Euromoney carries a semiannual rating of country creditworthiness. For information, contact Euromoney Publications PLC, Nestor House, Playhouse Yard, London EC4V 5EX, [J UK; telephone: 44 870 90 62 600; email: customerservice@euromoney.com; Web site: www.euromoney.com. Institutional Investor, Inc. Institutional Investor, Inc., develops country credit ratings every six months based on information provided by leading international banks. It publishes the magazine Institutional Investor monthly. For information, contact Institutional Investor, Inc., 225 Park Avenue South, New York, NY 10003, USA; telephone: 212 224 3800; email: info@iiplatinum.com; Web site: www.institutionalinvestor.com. International Road Federation The International Road Federation (IRF) is a not-for-profit, nonpolitical service organization. Its purpose is to encourage better road and transport systems worldwide and to help apply technology and management practices that will maximize economic and social returns from national road investments. The IRF has led global road infrastructure developments and is the international point of affiliation for about 600 member companies, associations, and governments. S The IRF's mission is to promote road development as a key factor in social and economic growth, to pro- vide governments and financial institutions with professional ideas and expertise, to facilitate business exchange among members, to establish links between members and external institutions and agencies, to support national road federations, and to give information to professional groups. The IRF publishes World Road Statistics. Contact the Geneva office at chemin de Blandonnet 2, CH-1214 Vernier, Geneva, Switzerland; tele- phone: 41 22 306 0260; fax: 41 22 306 0270; or the Washington, DC, office at 1010 Massachusetts Avenue NW, Suite 410, Washington, DC 20001, USA; telephone: 202 371 5544; fax: 202 371 5565; email: info@irfnet.com; Web site: www.irfnet.org. Moody's Investors Service Moody's Investors Service is a global credit analysis and financial opinion firm. It provides the internation- al investment community with globally consistent credit ratings on debt and other securities issued by North American state and regional government entities, by corporations worldwide, and by some sovereign issuers. It also publishes extensive financial data in both print and electronic form. Its clients include invest- mooyrivstersserVn ment banks, brokerage firms, insurance companies, public utilities, research libraries, manufacturers, and government agencies and departments. Moody's publishes Sovereign, Subnational and Sovereign-Guaranteed Issuers. For information, contact Moody's Investors Service, 99 Church Street, New York, NY 10007, USA; telephone: 212 553 0377; fax: 212 553 0882; Web site: www.moodys.com. Notcraft Netcraft is an Internet consultancy based in Bath, England. Most of its work relates to the development of Internet services for its clients or for itself acting as principal. For information, visit its Web site: www.netcraft.com. XX 1 2003 World Development Indicators PricewaterhouseCoopers Drawing on the talents of 125,000 people in more than 142 countries, PricewaterhouseCoopers provides a full range of business advisory services to leading global, national, and local companies and public institutions. Its service offerings have been organized into four lines of service, each staffed with high- ly qualified, experienced professionals and leaders. These services include audit, assurance, and busi- ness advisory services; business process outsourcing; corporate finance and recovery services; and global tax services. PricewaterhouseCoopers publishes Corporate Taxes: Worldwide Summaries and Individual Taxes: Worldwide Summanes. For information, contact PricewaterhouseCoopers, 1177 Avenue of the Americas, New York, NY 10036, USA; telephone: 646 471 4000; fax: 646 471 3188; Web site: www.pwcglobal.com. The PRS Group, Inc. The PRS Group, Inc., is a global leader in political and economic risk forecasting and market analysis and has served international companies large and small for over 20 years. The data it contributed to this year's World Development Indicators come from the Intemational Country Risk Guide monthly publication that A-"-^D monitors and rates political, financial, and economic risk in 140 countries. '-F A. C The guide's data series and commitment to independent and unbiased analysis make it the standard for any organization practicing effective risk management. For information, contact The PRS Group, Inc., 6320 Fly Road, Suite 102, P0 Box 248, East Syracuse, NY 13057-0248, USA; telephone: 315 431 0511; fax: 315 431 0200; email: custserv@PRSgroup.com; Web site: www prsgroup.com or www.lCRGOnline.com. Standard & Poor's Equity Indexes and Rating Services Standard & Poor's, a division of the McGraw-Hill Companies, has provided independent and objective finan- cial information, analysis, and research for more than 140 years. The S&P 500 index, one of its most pop- ular products, is calculated and maintained by Standard & Poor's Index Services, a leading provider of equi- ty indexes. Standard & Poor's indexes are used by investors around the world for measuring investment performance and as the basis for a wide range of financial instruments. Standard & Poor's Sovereign Ratings provides issuer and local and foreign currency debt ratings for sov- ereign governments and for sovereign-supported and supranational issuers worldwide. Standard & Poor's Rating Services monitors the credit quality of $1.5 trillion worth of bonds and other financial instruments and offers investors global coverage of debt issuers Standard & Poor's also has ratings on commercial paper, mutual funds, and the financial condition of insurance companies worldwide. For information on equity indexes, contact Standard & Poor's Index Services, 22 Water Street, New York, NY 10041, USA; telephone: 212 438 7280; fax 212 438 3523; email. index_services@sandp.com, Web site: www.spglobal.com. For information on ratings contact the McGraw-Hill Companies, Inc., Executive Offices, 1221 Avenue of the Americas, New York, NY 10020, USA, telephone: 212 512 4105 or 800 352 3566 (toll free); fax. 212 512 4105; email- ratingsdirect@standardandpoors.com; Web site: http://www.ratingsdirect.com. 2003 World Development Indicators l Xxi Would Conseirvation ionitoring Centre The World Conservation Monitoring Centre (WCMC) provides information on the conservation and sustain- able 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 infor- mation needed for wise management of the world's living resources. Committed to the principle of data exchange with other centers and noncommercial users, the WCMC, whenever possible, places the data it manages in the public domain. WORLD CONSERVATION For information, contact the World Conservation Monitoring Centre, 219 Huntington Road, Cambridge CB3 ODL, UK; telephone: 44 12 2327 7314; fax: 44 12 2327 7136; email: info@unep-wcmc.org; Web site- www.unep-wcmc org. Worgd Onformatlon Teclhnology and Services Agliance The World Information Technology and Services Alliance (WITSA) is a consortium of 41 information tech- nology industry associations from around the world. WITSA members represent more than 97 percent of the world information technology market As the global voice of the information technology industry, WITSA is dedicated to advocating policies that advance the industry's growth and development; facilitating inter- national trade and investment in information technology products and services; strengthening WITSA's national industry associations by sharing knowledge, experience, and information; providing members with a network of contacts in nearly every region; and hosting the World Congress on Information Technology. WITSA's publication, Digital Planet 2002: The Global Information Economy, uses data provided by the International Data Corporation. For information, contact WITSA, 1401 Wilson Boulevard, Suite 1100, Arlington, VA 22209, USA; tele- phone: 703 284 5333; fax: 617 687 6590; email: ahalvorsen@itaa.org; Web site: wwwwitsa.org. Woirgd lesouirces Onstitute The World Resources Institute is an independent center for policy research and technical assistance on glob- al environmental and development issues. The institute provides-and helps other institutions provide-objec- tive information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development. The institute's current areas of work include trade, forests, energy, eco- nomics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and envi- S ronmental information, and national strategies for environmental and resource management. For information, contact the World Resources Institute, Suite 800, 10 G Street NE, Washington, DC 20002, USA; telephone. 202 729 7600; fax 202 729 7610; email: front@wri.org; Web site: www wri.org. ;xID [ 2003 World Development Indicators I rabOos further discussion of aggregation methods, see be construed only as indicating trends and character- The tables are numbered by section and display the Statistical methods izing major differences among economies rather than identifying icon of the sectton. Countries and offering precise quantitative measures of those differ- economies are listed alphabetically (except for Hong Agregate measures for regions ences Discrepancies in data presented in different Kong, China, which appears after China) Data are The aggregate measures for regions include only low- editions of the World Development Indicators reflect shown for 152 economies with populations of more and middle-income economies (note that these meas- updates by countnes as well as revisions to historical than 1 million, as well as for Taiwan, China, in selected ures include developing economies with populations of series and changes in methodology Thus readers are tables Selected indicators for 56 other economies- less than 1 million, including those listed in table 1 6) advised not to compare data senes between editions small economies with populations between 30,000 The country composition of regions is based on of the World Development Indicators or between dif- and 1 million and smaller economies if they are mem- the World Bank's analytical regions and may differ ferent World Bank publications Consistent time-series bers of the International Bank for Reconstruction and from common geographic usage For regional classi- data for 1960-2001 are available on the World Devel- Development (IBRD) or, as it is commonly known, the fications, see the map on the inside back cover and opment Indicators CD-ROM. Word Bank-are shown in table 1 6 The term country, the list on the back cover flap. For further discussion Except where otherwise noted, growth rates are used interchangeably with economy does not imply of aggregation methods, see Statistical methods. in real terms (See Statistical methods for informa- political independence, but refers to any territory for tion on the methods used to calculate growth rates ) which authorities report separate social or economic statNotocs Data for some economic indicators for some statistics When available, aggregate measures for Data are shown for economies as they were consti- economies are presented in fiscal years rather than income and regional groups appear at the end of tuted in 2001, and historical data are revised to calendar years, see Primary data documentation All each table reflect current political arrangements Exceptions are dollar figures are current U S dollars unless other- Indicators are shown for the most recent year or noted throughout the tables wise stated The methods used for converting nation- period for which data are available and, in most Additional information about the data is provided al currencies are described in Statistical methods tables, for an earlier year or period (usually 1990 in in Pnmary data documentation That section sum- this edition) Time-series data are available on the marizes national and international efforts to improve China World Development Indicators CD-ROM basic data collection and gives information on pri- On July 1, 1997, China resumed its exercise of sov- Known deviations from standard definitions or mary sources, census years, fiscal years, and other ereignty over Hong Kong, and on December 20, breaks in comparability over time or across countries background Statistical methods provides technical 1999, it resumed its exercise of sovereignty over are either footnoted in the tables or noted in About information on some of the general calculations and Macao. Unless otherwise noted, data for China do the data When available data are deemed to be too formulas used throughout the book not include data for Hong Kong, China, Taiwan, China, weak to provide reliable measures of levels and or Macao, China trends or do not adequately adhere to international Data consistency and reliability standards, the data are not shown Considerable effort has been made to standardize the Democratic Republic of Congo data, but full comparability cannot be assured, and Data for the Democratic Republic of Congo (Congo, Aggregate measures for Income groups care must be taken in interpreting the indicators Dem Rep., in the table listings) refer to the former The aggregate measures for income groups include Many factors affect data availability, comparability, Zaire The Republic of Congo is referred to as Congo, 208 economies (the economies listed in the main and reliability. statistical systems in many developing Rep , in the table listings tables plus those in table 1.6) wherever data are economies are still weak, statistical methods, cover- available To maintain consistency in the aggregate age, practices, and definitions differ widely, and cross- Czech Republic and Slovak Republic measures over time and between tables, missing country and inter-temporal comparisons involve com- Data are shown whenever possible for the individual data are imputed where possible The aggregates plex technical and conceptual problems that cannot countries formed from the former Czechoslovakia- are totals (designated by a t if the aggregates include be unequivocally resolved Data coverage may not be the Czech Republic and the Slovak Republic gap-filled estimates for missing data and by an s, for complete for economies expenencing problems (such simple totals, where they do not), median values (m), as those stemming from internal or external conflicts) Ertresa or weighted averages (w) Gap filling of amounts not affecting the collection and reporting of data For Data are shown for Eritrea whenever possible, but in allocated to countries may result in discrepancies these reasons, although data are drawn from the most cases before 1992 Eritrea is included in the between subgroup aggregates and overall totals For sources thought to be most authoritative, they should data for Ethiopia Xzr,v[ 2003 World Development Indicators Germany Former Socialist Federal Republic of Indicators to the next Once the classification is Data for Germany refer to the unified Germany Yugoslavia fixed for an edition, based on GNI per capita in the unless otherwise noted Available data are shown for the individual coun- most recent year for which data are available (2001 tries formed from the former Socialist Federal in this edition), all historical data presented are Jordan Republic of Yugoslavia-Bosnia and Herzegovina, based on the same country grouping Data for Jordan refer to the East Bank only unless Croatia, the former Yugoslav Republic of Low-income economies are those with a GNI per otherwise noted Macedonia, Slovenia, and the Federal Republic of capita of $745 or less in 2001 Middle-income Yugoslavia Note that on February 4, 2003, the economies are those with a GNI per capita of more Timor-Leste Federal Republic of Yugoslavia changed its name than $745 but less than $9,206 Lower-middle- On May 20, 2002, Timor-Leste became an inde- to Serbia and Montenegro income and upper-middle-income economies are sep- pendent country Data for Indonesia include Timor- arated at a GNI per capita of $2,975 High-income Leste through 1999 unless otherwise noted Changes In the System of National Accounts economies are those with a GNI per capita of $9,206 The World Development Indicators uses terminology or more The 12 participating member countries of Union of Soviet Socialist Republics in line with the 1993 System of National Accounts the European Monetary Union (EMU) are presented In 1991 the Union of Soviet Socialist Republics (SNA) For example, in the 1993 SNA gross national as a subgroup under high-income economies came to an end Available data are shown for the income replaces gross national product See About individual countries now existing on its former tern- the data for tables 1 1 and 4 9 Symbols tory (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Most countries continue to compile their nation- .. Kazakhstan, the Kyrgyz Republic, Latvia, Lithuania, al accounts according to the 1968 SNA, but more means that data are not available or that aggregates Moldova, the Russian Federation, Tajikistan, and more are adopting the 1993 SNA Countries that cannot be calculated because of missing data in the Turkmenistan, Ukraine, and Uzbekistan) External use the 1993 SNA are identified in Primary data doc- years shown debt data presented for the Russian Federation prior umentation A few low-income countries still use con- 0 or 0.0 to 1992 are for the former Soviet Union The debt of cepts from older SNA guidelines, including valuations means zero or less than half the unit shown the former Soviet Union is included in the Russian such as factor cost, in describing major economic / Federation data after 1992 on the assumption that aggregates in dates, as in 1990/91, means that the period of 100 percent of all outstanding external debt as of time, usually 12 months, straddles two calendar December 1991 has become a liability of the Classiflcation of economies years and refers to a crop year, a survey year, or a Russian Federation Beginning in 1993, the data for For operational and analytical purposes the World fiscal year the Russian Federation have been revised to include Bank's main criterion for classifying economies is $ obligations to members of the former Council for gross national income (GNI) per capita Every econ- means current U S dollars unless otherwise noted Mutual Economic Assistance and other countries in omy is classified as low income, middle income > the form of trade-related credits amounting to $15 4 (subdivided into lower middle and upper middle), or means more than billion as of the end of 1996 high income For income classifications, see the < map on the inside front cover and the list on the means less than Republica Bolivarlana de Venezuela front cover flap Low- and middle-income economies In December 1999 the official name of Venezuela are sometimes referred to as developing Data presentation conventions was changed to Repiblica Bolivaniana de Venezuela economies The use of the term is convenient, it is * A blank means not applicable or, for an aggre- (Venezuela, RB, in the table listings) not intended to imply that all economies in the gate, not analytically meaningful group are experiencing similar development or that * A billion is 1,000 million Repubilc of Yemen other economies have reached a preferred or final * A trillion is 1,000 billion Data for the Republic of Yemen refer to that country stage of development Note that classification by * Figures in italics refer to years or periods other from 1990 onward, data for previous years refer to income does not necessarily reflect development than those specified aggregated data for the former People's Democratic status Because GNI per capita changes over time, * Data for years that are more than three years Republic of Yemen and the former Yemen Arab the country composition of income groups may from the range shown are footnoted Republic unless otherwise noted change from one edition of the World Development The cutoff date for data is February 1, 2003 2003 World Development Indicators I XXV L i I I ''I I I I , -1 i- I - -7*. I 7f i R. ov - he Millennium Development Goals summarize and give substance to the commitments embodied in the Millennium Declaration, adopted unanimously by the members of the United Nations in September 2000. They reinforce the paramount task of development as improving the welfare of all people on earth-to help them realize their human potential, to reduce insecurity and increase opportunity, and to ensure that the benefits secured in the current generation are sustained and augmented in the next. The Millennium Development Goals set specific targets for improving income poverty, education, the status of women, health, the environment, and global development cooperation. Now widely accepted as a framework for measuring development progress, the goals focus the efforts of the world community on achieving significant, measurable improvements in people's lives. They establish yardsticks for measuring results-not just for developing countries but for rich countries that help to fund development programs and for the multilateral institutions that help countries implement these programs. Each of the goals is important by itself, but they should be viewed together because they are mutually reinforcing. Better health care increases school enrollment and reduces poverty. Better education leads to better health. And increasing income gives people more resources to pursue better education and health care and a cleaner environment. __q_ (X) Eirradicao entireme povevty ... beginning. And if projected growth remains on track, global pover- The first Millennium Development Goal calls for cutting in half ty rates will fall to 13 percent-less than half the 1990 level- the proportion of people living in extreme poverty-and those and 360 million more people will avert extreme poverty. But rapid suffering from hunger-between 1990 and 2015. In 1990, 30 progress in Asia and a return to pretransition poverty levels in percent of the people in low- and middle-income countries lived Europe and Central Asia will do nothing to alleviate the crushing on less than $1 a day By 1999 the share had fallen to 23 per- burden of poverty in Sub-Saharan Africa, where more than 400 cent, representing 1,170 million people living in extreme pover- million people will continue to live on less than $1 a day. ty. During the same period the population of low- and A poverty line set at $1 a day ($1.08 in 1993 purchasing middle-income countries grew by 15 percent to 5 billion, and power parity terms), has been accepted as the working defini- their gross domestic product (GDP) grew by 31 percent. tion of extreme poverty in low-income countries. Although Progress was far from uniform. The fastest economic growth many people in low-income countries live on less than $1 a and the greatest poverty reduction were in East Asia and day, in middle-income countries a poverty line of $2 a day Pacific, where GDP per capita rose by 75 percent while the ($2.15 in 1993 purchasing power parity terms) is closer to a share of people in extreme poverty fell from 31 percent to 16 practical minimum, and national poverty lines may be set even percent. But in Sub-Saharan Africa, where GDP per capita fell higher. In 1999 an estimated 2.8 billion people were living on by 5 percent, the poverty rate rose from 47 percent in 1990 to less than $2 a day-more than half the population of the 49 percent in 1999, and the number of people living in extreme developing world. The numbers living on less than $2 a day will poverty increased by 74 million. The transition economies of continue to rise in South Asia and Sub-Saharan Africa. Europe and Central Asia experienced an even sharper drop in Improvements will be greatest in East Asia and Pacific. But by income, and their poverty rate more than doubled. 2015, if present trends continue, the poverty rate measured Despite these setbacks, there were at least 123 million fewer at this higher line will have fallen by no more than 40 percent people living in extreme poverty at the decade's end than at its from its 1990 level. 1I~i Share of people living on less than $1 (or $2) a day (%) East Asia & Paciflc Europe a Central Asia Latin America & the Caribbean 20 SC - ---~~~~~~~~~~_ 1 -- -- - 07____________________ 40 _ _ 1990 1995 2000 20000 20051 2010 2015 990 1995 2000 2005 2010 2015 Middle East & Niorth Africa South Asia Sub-Saharan Africa 5o 45-- --- ------------------ -- _ -__ -__ -__- __- 20t, 1__1, Oc ____-__ 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 20 5 1990 1995 2000 2005 2010 2015 Goal Poverty rate at 51 a day -Actual Poverty rate at S2 a day -Actual 1990 0999 2015 --- P --- rojecte-ProJected Source World Bank staff estimates O0 13 ( Path to goal 16 0 2003 World Development Indicators lb There is almost never just one way to measure an economic indicator and power of a dollar than market exchange rates, especially in very poor coun- poverty is no exception Judgments are required about the design and con- tries However, PPP rates are themselves a product of a complex and error- duct of the household survey, the processing of the data, and the subsequent prone data collection process Furthermore, different methods of deriving data analysis that results in estimates of the level and distribution of income PPP rates can change the relative value of expenditures between countries. or consumption Compansons over time or between countries or even The international poverty line is applied to distributions of consumption per between regions in the same country require additional judgments and data person (or income per person if consumption is not available) constructed The World Bank's poverty measure, based on a 'dollar a day" poverty line from the household survey data Adjustments to the data are often required began with the 1990 World Development Report (World Bank 1990) That For example, population weights are needed to obtain an estimate of the dis- report provided estimates for developing countries taken as a whole and by tnbution of individual consumption per person from household consumption region centered on 1985 Those estimates were based on 22 household sur- data Because surveys are not conducted at the same time or at regular inter- veys, one for each of 22 countries, and model-based extrapolations for other vals in all countries, it is necessary to adjust consumption estimates to a countries (Ravallion, Datt, and van de Walle 1991) Since then the data set common, reference year when calculating regional and global aggregates has expanded to include more than 300 representative household surveys Aggregate poverty measures based on international poverty lInes from more than 90 countries. The surveys all have national coverage Most should not be confused with estimates based national poverty lines Most measure consumption, including consumption from own-production-a key of the poverty analysis work done at the World Bank is based on national feature in many developing countnes Consumption is preferred to income for poverty lines The PPP-based international poverty line is required only to measuring poverty, but income is used when consumption is not available. form aggregate poverty estimates across countries, for which the judg- Most countries set their own poverty lines. But to measure poverty ment is made than people with the same command over the purchase of between countries, an international poverty line is needed The dollar-a-day goods and services should be treated the same no matter where they live poverty line was originally chosen as representative of typical poverty lines National poverty lines are set in a variety of ways some are calculated prevailing in a sample of low-income countries It has since been updated to from minimum consumption levels and some are based on relative con- $1 08 a day in 1993 prices Poverty measured at this level is sometimes sumption levels As a general rule, national poverty lines tend to increase called "extreme poverty " To estimate poverty in a country, the dollar-day-line in purchasing power with the average level of income of a country So the is converted to local currency units using the purchasing power parity (PPP) dollar-a-day line, while representative of poverty lines in very poor coun- exchange rates The PPP rates, based on the relative prices of consumption tries, underestimates the national poverty lines of richer countries, which goods in each country, are more representative of the actual purchasing may be set at the equivalent of two or three dollars-a-day or higher Source Adapted from Ravailion (2002) lc~~~~~~~M People living on less than $1 a day (millions) Share of people living on less than $1 a day (%) 1990 1999 2015 1990 1999 2015 iEast Asia &1Pacific 486_ ___ 279 80 305 15 6 3 9 Excluding China 110 57 7 24 2 10 6 1 1 LEu.rop Centra_l As_ a _ - 6 ___ 24 7 14 5 1 1.4 Latin America & Caribbean 48 57 47 11 0 11 1 7 5 ~~~Nrt Afic 5 _ 8 212_. South Asia 506 488 264 45 0 36 6 15 7 Su~b-Saharb ~fn Afa _241 _ 31_ 5 404 474__ 49 0 46 01 Total 1,292 1,169 809 29.6 23.2 13.3 Excluding China 917 945 735 28 5 25.0 Source Worid Bank 2002d ld People living on less than $2 a day (millions) Share of people living on less than $2 a day (%) 1990 1999 2015 1990 1 999 2015 ______ __________________ 897 ~~~~~~~~339 69 7 50 1 _ __ 16 6§j Excluding China 295 269 120 64 9 50 2 18 4 FEurope & Central Asia 31 97 45 6 8 20.3 9.3 Latin America & Caribbean 121 132 117 27 6 26 0 18 9 Middle East & North Africa 50 68 62 21.0 23 3 16___ South Asia 1,010 1,128 1,139 89 8 84 8 68 0 [/u ~ShW_WArAfnca 386 480 618 76 0 74 7 -70.4 Total 2,712 2,802 2,320 62.1 55.6 38.1 CxcludingChina - 1,892 2,173 2,101 58 7 57 5 Source World Bank 2002d 2003 worid Development Indicators I 5 ) ... and reduce hungeir and magnutiition Eastern Europe and the former Soviet Union and the 11 mil- The Millennium Development Goals also call for halving the pro- lion more in high-income countries. Since 1990-92 the num- portion of people who suffer from hunger. Of the many ways to ber of undernourished people in developing countries has measure hunger, the goals refer to two. the prevalence of fallen by 20 million, and the prevalence of undernourishment undernourishment in the general population and the prevalence by 3 percentage points. Regional trends show the greatest of underweight children under five. progress in East Asia and Pacific, but the rates of malnutri- Undernourishment means consuming too little food to tion remain high in South Asia, and they are rising in Sub- maintain normal levels of activity. The Food and Agriculture Saharan Africa. Organization (FAO) sets the average requirement at 1,900 calo- Malnutrition among children is measured by comparing their ries a day. Among the less severely affected, the average daily weight and height with those of a well-nourished reference shortfall is less than 200 calories a person. In the FAO's esti- population. Such data must be obtained from surveys, which mation, extreme hunger occurs with a shortfall of more than are costly and infrequently carried out. So it is difficult to 300 calories, but the needs of individuals vary with age, sex, assess progress toward the malnutrition target. A comparison and height. Adding to the problems of undernourishment are of median malnutrition rates in 1990-95 and 1996-2001 diets that lack essential nutrients and illnesses that deplete shows small signs of progress in all regions except South those nutrients. Asia. But some large countries, such as Brazil, India, Pakistan, For 1998-2000 the FAO estimates that 799 million peo- and the Russian Federation, are not included in the analysis. ple, or 17 percent of the population in developing countries, Coverage of countries in Africa, where donors have taken a were undernourished. This does not include the 30 million greater interest in measuring child malnutrition, tends to be undernourished people in the transition economies of better than in other regions. Prevalence of undernourishment (% of population) Median child malnutrition rate (% of children under five) Sub-Saharan Sub-Saharan .. ] Africa _____________________________Africa j j j jj South Asia South Asia East Asia j East Asia & Pacific & Pacific Latin America Latin America & Caribbean & Caribbean Middle East Middle East & North Africa & North Africa Europe & Centrai Asia 0 5 10 15 20 25 30 35 0 10 20 30 40 50 0 1992 J 2000 0 1990-95 C 1996-2001 Source FAQ 2001. The State of Food insecunty in the World Rome Source World Health Organization and World Bank staff estimates (8 0 2003 World Development Indicators (2) Achieve universal primary education The charts here show the primary school completion rate, In 1990 the United Nations Conference on Education for All the number of students successfully completing the last year of called for universal primary education. The original target date (or graduating from) primary school divided by the number of of 2000 has come and gone-and an estimated 115 million children of official graduation age in the population. This indi- children remain out of school. In 2000 the Millennium cator directly measures progress toward the primary education Declaration resolved to ensure, by 2015, that all children would target. be able to complete a course of primary education. This target Three regions-East Asia and the Pacific, Europe and Central can be achieved-and it must be, if all developing countries are Asia, and Latin America and the Caribbean-are on track to to compete in the global economy. achieving the goal. But three more, with 150 million primary- Progress toward the primary education target is commonly school-age children, are in danger of falling short. Sub-Saharan measured by the net enrollment ratio-the ratio of enrolled chil- Africa lags farthest behind, with little progress since 1990 dren of official school age to the number of children of the South Asia is the other region with chronically low enrollment same age in the population. Net enrollment ratios at or near and completion rates Some countries have made large gains. 100 percent imply that all children will receive a full primary Azerbaijan, Guinea, Haiti, and Malawi doubled their completion education, though repetition may delay completion of their rates in the 1990s. Removing impediments and reducing costs schooling But lower ratios are ambiguous. They may show that can help boost enrollments. Malawi and Uganda lowered schools fail to enroll all students in the first grade or that many school fees but lost part of their gains when they could not pro- students drop out in later grades For example, Bangladesh has vide spaces for all the new students Many countries face the increased its enrollment ratio to 96 percent, but only 45 per- challenge of improving school quality while attracting and keep- cent complete the final year of primary education. ing more children in school. Io Primary completion rate (% of relevant age group) East Asia & Pacific Europe & Central Asia Latin America & the Caribbean 100icc 3 __ _______ 90 ___ __ _ 10__ ___90_ __ __ __ __ 9 o0- ---------____ _____--- - - - … --___ -___________ _________-___8 80 - -_____ _ ___ __________ _ _- 70 -_ __---_______ 40 ____ ___ ___ 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 201O 2015 1990 1995 2000 2005 2010 2015 Middle East & North Africa South Asia Sub-Saharan Africa 100 ----- _ _ _- -__ _ _ _ _ _ __---_ _ _ _ -_ - -- -- -----------_ __ _ __ __ t ~~~~~10l_100 0 90 _e ______ _ 70 4 ___________= - 4 60 _-- 40 ------ _____ _ - _ 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Goal Primary completion rate - Actual 1990 2000 2015 - Path to goal Source World Bank staff estimates * * S 2003 World Development Indicators 7 7 (a) pIromoto gendeir equialfty and onmpower womem decade. Gender differences at the primary level have been Gender disparities exist everywhere in the world. Women are eliminated or greatly reduced in Algeria, Angola, Bangladesh, underrepresented in local and national decisionmaking bodies. China, the Arab Republic of Egypt, The Gambia, and India. In They earn less than men and are less likely to participate in some countries girls' secondary school enrollments now wage employment. And in many low-income countries girls are exceed those of boys. less likely to attend school. What does improving girls' enrollments require? Mainly over- The Millennium Development Goals call for eliminating gen- coming the social and economic obstacles that stop parents der disparities in primary and secondary education by 2005 from sending their daughters to school. Concerns about girls' and at all levels of education by 2015. But all regions except safety and lack of suitable toilet facilities inhibit attendance. And Latin America are still short of the first target. The differences for many poor families the economic value of girls' work at home between boys' and girls' schooling are greatest in regions with exceeds the perceived returns to schooling. Improving the quali- the lowest primary school completion rates and lowest average ty of schools is a first step. Overcoming women's disadvantages incomes. In Sub-Saharan Africa the ratio of girls' to boys' enroll- in the labor force and increasing their representation in public life ments in primary and secondary school has barely changed will also help encourage girls to attend and stay in school. since 1990, and in 1998 it stood at 80 percent. In South Asia Increasing opportunities for women will also contribute progress has been greater, but girls' enrollments reached only toward other goals for reducing poverty, educating children, 78 percent of boys' in 1998. improving health, and managing environmental resources For The failure to enroll girls and keep them in school has long- example, there is strong evidence that the children of mothers term effects. In South Asia, where only 61 percent of girls with less education are more likely to be malnourished and complete primary school, the average woman has 3.4 years of have higher mortality rates-and that educated women make schooling, almost 2.5 years less than a man. Even so, there better decisions in seeking health care for themselves and has been remarkable progress in many countries over the past their families. Girls' enrollments in primary and secondary education as % of boys' East Asia & Pacific Europe & Central Asia Latin America a the Caribbean 110 _ _ _- 102 100 98 too 100 100 _ W~:M 80 _ __ _ _ _ _ _ _ 70 60 ____________ _- _ _ _ 1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005 Middle East & North Africa South Asia Sub-Saharan Africa 110 ___________________; ___ _1__ 100 100 100 ___ ________ 80 - - - 7__ 0 90 60 _ __ 6__ _ _ 1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005 Note A break in the series between 1997 and 1998. due to the change from the International Standard Goal Girls' enrollments as - Actual Classification of Education, 1976 (ISCED76), to ISCED97, may affect comparability over time 1990 1999 2005 % of boys - Path to goal Source United Nations and UNESCO data 0 0 @ 0 0 2003 World Development Indicators (4) Reduce child mortality tality rates by 36 percent. But even this falls short of the rate Rapid improvements before 1990 gave hope that mortality needed to reach the target. Within countries there is evidence rates for infants and children under five could be cut by two- that improvements in child mortality have been greatest among thirds in the following 25 years. But progress slowed almost the better off. In Bolivia, which is nearly on track to achieve the everywhere in the 1990s. And no region, except possibly Latin target, under-five mortality rates fell by 34 percent in the America and the Caribbean, is on track to achieve that target. wealthiest quintile but by only 8 percent in the poorest. In Progress has been particularly slow in Sub-Saharan Africa, Vietnam mortality rates also fell among the better off but where civil disturbances and the HIV/AIDS epidemic have driv- scarcely changed for the poor Trends such as these raise the en up rates of infant and child mortality in several countries. possibility that without greater effort to ensure that health care Child mortality is closely linked to poverty. In 2001 the aver- and other public services reach the poor, success in reaching age under-five mortality rate was 121 deaths per 1,000 live the Millennium Development Goals will make little difference births in low-income countries, 41 in lower-middle-income coun- for many of the poor tries, and 27 in upper-middle-income countries. In high-income Just as child deaths are the result of many causes, reducing countries the rate was less than 7. For 70 percent of the child mortality will require multiple, complementary interven- deaths before age five, the cause is a disease or a combina- tions Raising incomes will help So will increasing public tion of diseases and malnutrition that would be preventable in spending on health services. But more is needed. Access to a high-income country: acute respiratory infections, diarrhea, safe water, better sanitation facilities, and improvements in measles, and malaria. education, especially for girls and mothers, are closely linked Improvements in infant and child mortality have come slowly to reduced mortality Also needed are roads to improve access in low-income countries, where mortality rates have fallen by to health facilities and modern forms of energy to reduce only 12 percent since 1990 Upper-middle-income countries dependence on traditional fuels, which cause damaging indoor have made the greatest improvement, reducing average mor- air pollution li Under-five mortaiity rate (per 1,000 kve births) East Asia & Pacific Europe & Central Asia Latin America & the Caribbean 150c - -----------------_ - - - --------- 60 ~~~~~44 44 -38 34__ _ - -- - - - - -- - - - - - 30 --- -4 2__ 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Middle East & North Africa South Asia Sub-Saharan Africa 150 ------ -- ------ ----------------- ---------… 120 - - - --- - - - - - - -- - -- - - -…- - - - - - - I :2 ------------- C- i 301---------- - ------------- - --- -------… ----- C)L -- - - __ _ _ _ _ __ _ _ _ -__ --_- - --_-__-_… - -- -- -- -- - 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Goal Under-five mortality - Actual 1990 2001 2015 - Path to goal Source World Bank staff estimates * * O 2003 World Development Indicators 1 9 ( I hmprove the healOth of mothers tality is relatively low. But in Africa, where skilled attendants and The most recent global estimates of maternal mortality suggest health facilities are not readily available, it is very high. that about 500,000 women died during pregnancy and child- Maternal mortality is difficult to measure accurately. Deaths birth in 2000, most of them in developing countries. What resulting from pregnancy or childbirth are relatively rare and may makes maternal mortality such a compelling problem is that it not be captured in general-purpose surveys or those with small strikes young women undergoing what should be a normal sample sizes. Moreover, maternal deaths may be underreport- process. The difference in outcomes is enormous between ed in countries that lack good administrative statistics or where those who live in rich countries, where the average maternal many births take place outside the formal health system. mortality ratio is around 21 deaths per 100,000 live births- Significant progress in reducing maternal mortality will and those who live in poor countries, where the ratio may be as require a comprehensive approach to health care: deaths in high as 1,000 deaths per 100,000 live births. childbirth often involve complications, such as hemorrhaging, The Millennium Development Goals call for reducing the mater- that require fully equipped medical facilities. Causes of compli- nal mortality ratio by three-quarters between 1990 and 2015. For cations during pregnancy and childbirth include inadequate this to be possible, women need access to modern health serv- nutrition, unsafe sex, and poor health care. Gender inequality ices. The share of births attended by skilled health staff provides in controlling household resources and making decisions also a good index of where the need is greatest. Only 58 percent of contributes to poor maternal health. Early childbearing and women in developing countries give birth with the assistance of a closely spaced pregnancies increase the risks for mothers and trained midwife or doctor. In Latin America, where the share of children Access to family planning services helps women plan births attended by skilled health personnel is high, maternal mor- whether and when to have children. nI~i . iI * Births attended by skilled health personnel (% of total) Adolescent fertility rate (births per 1,000 women ages 15-19), 2000 South Asia South Asia Sub-Saharan Sub-Saharan Africa 1 Africa Latin America Latin America l & Caribbean & Caribbean Middle East Middle East & North Africa & North Africa East Asia East Asia & Pacific & Pacific Europe & Europe & Central Asia Central Asia 0 20 40 60 80 100 0 30 60 90 120 150 E 1990-95 fl 1996-2001 Source World Health Organization and World Bank staff estimates Source World Bank staff estimates 10O 0 2003 World Development Indicators (V Combat HIV/AIDS, malaria, and other diseases increased the urgency of finding new and effective means of Epidemic diseases exact a huge toll in human suffering and treatment and prevention. lost opportunities for development. Poverty, civil disturbances, Because children bear the greatest burden of the disease, and natural disasters all contribute to, and are made worse by, the Millennium Development Goals call for a monitoring effort the spread of diseases. In Africa the spread of HIV/AIDS has focusing on children under five. An effective means of prevent- reversed decades of improvements in life expectancy and left ing new infections is the use of insecticide-treated bed nets. millions of children orphaned. It is draining the supply of teach- Vietnam, where more than 25 percent of children sleep under ers and eroding the quality of education. treated bed nets, has made significant strides in controlling In 2002, 42 million adults and 5 million children were living malaria. But in Africa, only 3 of 24 countries with survey data with HIV/AIDS-more than 95 percent of them in developing reported rates of bed net use greater than 5 percent countries and 70 percent in Sub-Saharan Africa There were Tuberculosis kills around 2 million people a year. The emer- almost a million new cases in South and East Asia, where more gence of drug-resistant strains of tuberculosis, the spread of than 7 million people are now living with HIV/AIDS. Current pro- HIV/AIDS, which reduces resistance to tuberculosis, and the jections suggest that by 2010, 45 million more people in low- growing number of refugees and displaced persons have and middle-income countries will become infected unless the allowed the disease to spread. Each year there are about 8 mil- world mounts an effective campaign to halt the disease's lion new cases-2 million in Sub-Saharan Africa, 3 million in spread Southeast Asia, and more than a quarter million in Eastern Malaria is endemic in large parts of the developing world, Europe and the former Soviet Union particularly in tropical and subtropical regions. Because many Poorly managed tuberculosis programs allow drug-resistant cases of malaria are not clinically diagnosed or reported strains to spread The World Health Organization has devel- to official agencies, it is hard to gauge the full extent of the oped a treatment strategy-directly observed treatment, short epidemic. The World Health Organization estimates that course (DOTS)-that emphasizes positive diagnosis followed 300-500 million cases occur each year, leading to 1.1 million by an effective course of treatment and follow-up care DOTS deaths (WHO 2002). Almost 90 percent of all cases occur in produces cure rates of up to 95 percent, even in poor coun- Sub-Saharan Africa, where children are the most affected and tries. That is why the Millennium Development Goals include an malaria may account for as much as 25 percent of child mor- indicator of the proportion of tuberculosis cases detected and tality. The emergence of drug-resistant strains of malaria has cured under DOTS ii lm ^~~~. * v,. .,, i . :I .............I Adults and children newly infected with HIV (miilions), 2002 Incidence of tuberculosis (per 100,000 people), 2000 Sub-Saharan Sub-Saharan Africa -j _ Africa South & South Asia Southeast Asia East Asia East Asia & Pacific & Pacific Europe & Europe & Central Asia Central Asia Latin America Latin America & Caribbean & Caribbean Middle East Middle East & North Africa & North Africa High-income countries 0 1 2 3 4 0 50 100 150 200 250 300 350 400 Note UNAIDS regions differ from World Bank definitions Source UNAIDS 2002 Source WHO 2002, World Health Report 2002 Geneva 2003 World Development Indicators l @) IEnsure envivonmenstal sustainabDiDty disease. A basic sanitation system provides disposal facilities Sustainable development can be ensured only by protecting the that can effectively prevent human, animal, and insect contact environment and using its resources wisely. The Millennium with excreta. Such systems do not, however, ensure that efflu- Development Goals draw attention to some of the environmental ents are treated to remove harmful substances before they are conditions that need to be closely monitored-changes in forest released into the environment. coverage and biological diversity, energy use and the emission of In 2000, 1.2 billion people still lacked access to an improved greenhouse gases, the plight of slum dwellers in rapidly growing water source, 40 percent of them in East Asia and Pacific and cities, and the availability of adequate water and sanitation serv- 25 percent in Sub-Saharan Africa. Meeting the Millennium ices. But the Millennium Development Goals cannot cover all Development Goals will require providing about 1.5 billion peo- aspects of the environment. Nor can they capture all the ways ple with access to safe water and 2 billion with access to basic environmental factors interact with the other development goals. sanitation facilities between 2000 and 2015. Lack of clean water and basic sanitation is the main reason Rapid urbanization is exposing more people in developing diseases transmitted by feces are so common in developing countries to polluted air. Poor people, who live in crowded countries In 1990 diarrhea led to 3 million deaths, 85 percent neighborhoods close to traffic corridors and industrial plants, of them among children. Between 1990 and 2000 about 900 are likely to suffer the most. Every year an estimated 0.5-1 0 million people obtained access to improved water sources, million people die prematurely from respiratory and other ill- gains just sufficient to keep pace with population growth. nesses associated with urban air pollution (World Bank 2002i). An improved water source is any form of water collection But not all sources of air pollution are outside the home. The or piping used to make water regularly available. It is not the use of traditional fuels for cooking and heating-wood, dung, same as 'safe water," but there is no practical measure of charcoal, crop residues-is associated with blindness, chronic whether water supplies are safe. Connecting all households lung disease, complications during pregnancy, and acute respi- to a reliable source of water that is reasonably protected ratory infections in children. from contamination would be an important step toward Because poor people are often those most dependent on envi- improving health and reducing the time spent collecting ronmental resources for their livelihood, they are most affected water. by environmental degradation and by natural disasters, such as Along with safe water sources, improved sanitation services fires, storms, and earthquakes, whose effects are worsened by and good hygiene practices are needed to reduce the risk of environmental mismanagement I -- Population with access to an improved water source (%) % of population with access to improved sanitation facilities Sub-Saharan u Africa Sub-Saharanl Africa l East Asia l I & Pacific East Asia I ~~~~~~~& Pacific South Asia South Asia Latin America Latin America & Caribbean _& Caribbean _ Middle East & Middle East & North Africa North Africa Europe & Central Asia 0 20 40 60 80 100 0 20 40 60 80 100 al 1990 C 2000 l 1990 E 2000 Source Woed Health Organization, UNICEF, and World Bank staff estimates Source World Health Organization 22 0 2003 World Oevelopment indicators @Iw Develop a global partnership for development * Providing effective development assistance Aid is most To achieve the Millennium Development Goals, economies effective in reducing poverty when it goes to poor coun- need to grow to provide more jobs and more income for poor tries with good economic policies and sound governance people. And growth requires investment in plants and equip- and advances country-owned poverty reduction pro- ment, in energy and transport systems, in human skills and grams Aid levels have been falling, both in comparison knowledge Growth is fastest in a good investment climate with the size of donor country economies and in nominal where good economic policies and good governance assure terms. In 2001 only 56 percent of all aid went to low- investors and workers of the rewards for their efforts. income economies with per capita income of $745 or But growth alone will not be enough to achieve the Millennium less. To help the poorest countries reach the Millennium Development Goals. Also needed are health and education sys- Development Goals, official development assistance will tems that deliver services to everyone, men and women, rich need to double from its current level of $52 billion a year and poor. Infrastructure that works and is accessible to all. And and developing counties will have to supply several times policies that empower people to participate in the development more than that process. While success depends on the actions of developing * Easing the burden of debt The Debt Initiative for Heavily countries, which must direct their own development, there is Indebted Poor Countries (HIPCs) provides debt relief to the also much that rich countries must do to help. world's poorest and most heavily indebted countries. By Goal 8 complements the first seven. It commits wealthy November 2002, 26 countries had qualified for debt relief countries to work with developing countries to create an envi- amounting to about $40 billion. The savings in debt serv- ronment in which rapid, sustainable development is possible. It ice have allowed average annual social spending in these calls for an open, rule-based trading and financial system, more countries to rise from 6 percent of GDP to 8 percent generous aid to countries committed to poverty reduction, and * Increasing market access. Tariffs and quotas on textile relief for the debt problems of developing countries. It draws exports to high-income countries cost developing countries attention to the problems of the least developed countries and 27 million jobs And rich countries' agricultural subsidies, of landlocked countries and small island developing states, more than $300 billion a year in 2001, hurt growth in the which have greater difficulty competing in the global economy agricultural sector, where many of the poorest people And it calls for cooperation with the private sector to address work. The World Bank estimates that full liberalization of youth unemployment, ensure access to affordable, essential trade could increase growth enough to lift 300 million drugs, and make available the benefits of new technologies. more people out of poverty by 2015. lp lq Aid to low- and middle-income economies (2000 $ biilions) Ratio of debt service to exports for heavily indebted poor countries (%) 40 50 Low incomeA 40 Latin America & Caribbean 3030 Middle income 20 20 Sub-Saharan Africa 10 10 0 O 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Source OECD, Development Assistance Committee data Source World Bank staff estimates 2003 World Development Indicators I 13 U0LSize of the economy Population Surface Population Gross national Gross national PPP gross national Gross area density Incomo Income per capita income" domestic product Per thousand people capita Per capita millions sq km per sq km $ billions Reek $ Rank $ billions $ Rank % growth % growth 2001 2001. 2001 2001 b 2001 2001b 2001 2001 2001 2001 2000-01 2000-01 Afghanistan 27c C 652 42 d Albania 3 29 ~115 4 2 119 1,340 123 12 3,810 130 6 5 5 5 Algeria 31 2,382 13 51 0 48 1,650 114 182e 5,9100 e 99 2 1 0 6 Angola -14 1,247 11 6 7 102 500 158 23e 1,690 e 171 3 2 0 3 Argentina 37 2,780 14 260 3 17 6,940 60 412 10,980 63 -4 5 -5 6 Armenia 4 30 135 2 2 143 570 154 10 2,730 145 9 6 9 4 Australia 19 7,741 3 385 9 15 19,900 29 478 24,630 24 3 9 2 8 Austria 8 84 98 194 7 21 23,940 17 215 26,380 17 1 0 0 8 AzerbaiUan 8 87 94 5 3 ill 650 146 23 2,890 141 9 9 9 0 Bangladesh 133 144 1,024 48 6 51 360 172 213 -1,600 173 5 3 3 5 Belarus 10 208 48 12 9 81 1,290 126 76 7,630 83 4 1 4 5 Belgium 10 31 313 245 3 19 23,850 18 269 26,150 18 1 0 0 7 Benin 6 113 58 2 4 142 380 169 6 970 190 5 0 2 3 Bolivia 9 1,099 8 8.1 96 950 134 19 2,240 155 1 2 -1 0 Bosnia and Herzegovina 4 51 80 5 0 114 1,240 127 25 6,250 92 60 38 Botswana 2 582 3 53 112 3,100 89 13 7,410 84 6 3 5 1 Brazil 172 8,547 20 528 9 11 3,070 90 1,219 7,070 86 1 5 0 2 Bulgaria 8 111 73 13 2 79 1,650 114 54 6,740 89 4 0 5 9 Burkina Faso 12 274 42 2 5 141 220 192 13e 1,1200e 185 5 6 3 1 Burundi 7 28 270 0 7 178 100 206 5e 6800e 203 3 2 1 3 Cambodia 12 181 69 3 3 131 270 184 22 1,790 168 6 3 4 2 Cameroon 15 475 33 8 7 91 580 152 24 1,580 174 5 3 3 1 Canada 31 9,971 3 681 6 8 21,930 25 825 e 26,530 e 15 1 5 0 4 Central African Republic 4 623 6 1 0 170 260 187 5e 1,300e 181 1 5 0 1 Chad 8 1,284 6 1 6 154 200 195 8 1,060 187 8 5 5 5 Chile 15 757 21 70 6 43 4,590 73 136 8,840 76 2 8 1 5 China 1,272 9,5981 136 1,131 2 6 890 138 5,027 3,950 127 7 3 6 5 Hong Kong, China 7 .. 170 3 23 25,330 13 172 25,560 19 0 1 -0 7 Colombia 43 1,139 41 81 6 40 1,890 106 292 6,790 88 1 4 -0 3 Congo, Dem Rep 52 2,345 23 4 2 122 80 208 33 630 205 -45 -7 1 Congo, Rep 3 342 9 2 0 146 640 147 2 680 203 2 9 0 1 Costa Rica 4 51 76 15 7 74 4,060 76 36 9,260 74 0 9 -0 7 M6e dIlvoire 16 322 52 10 3 85 630 149 23 1,400 179 -0 9 -3 3 Croatia 4 57 78 19 9 64 4,550 74 39 8,930 75 4 1 4 1 Cuba 11 111 102 g Czech Republic 10 79 132 54 3 45 5,310 70 146 14,320 55 3.3 3 8 Denmark 5 43 126 164 0 25 30,600 8 153 28,490 9 1 0 0 6 Dominican Republic 9 49 176 19 0 68 2,230 96 57 6,650 90 2 7 1 1 Ecuador 13 284 47 14.0 77 1,080 129 38 2,960 140 5 6 3 7 Egypt, Arab Rep 65 1,001 65 99 6 37 1,530 116 232 3,560 131 2 9 1 0 El Salvador 6 21 309 13 0 80 2,040 101 33 5,160 107 1 8 -0 1 Eritrea 4 118 42 0 7 179 160 199 4 1,030 189 9 7 6 9 Estonia 1 45 32 5 3 110 3,870 79 13 9,650 71 5.0 5 5 Ethiopia 66 1.104 66 6 7 103 100 206 53 800 198 7 _7 5 2 Finland 5 338 17 123 4 -29 23,780 19 125 24,030 28 0 7 0 4 France 59 552 108 1,3807 h 5 22,730 h 23 1,425 24,080 27 1 8 1 3 Gabon 1 268 5 4 0 125 3,160 88 7 5,190 105 2 5 0 0 Gambia, The 1 11 134 0 4 191 320 176 30 2,0100e 160 6 0 3 0 Georgia 5 70 ~76 3 1 136 590 -150 14 2,580 148 4 5 6 2 Germany 82 357 231 1,939 6 3 23,560 20 2,078 25,240 21 06 04 Ghana 20 239 87 5 7 -109- 290 179 43e 2,1700 157 40 1 9 Greece 11 132 82 121 0 31 11,430 47 186 17,520 47 4 1 38 Guatemala -12 109- 108 _19 6 _65 1,680 112 51 4,380 120 2 1 -0 5 Guinea 8 246 31 3 1 135 410 165 14 1,900 -164 3 6 1 3 Guinea-Bissau 1 36 44 0 2 203 160 199 1 890 193 0 2 -2 0 Haiti 8 28 295 3 9 126 480 160 150 1,8700 166 -1 7 -3 8 1 H 2003 World Development indicators Size of the economy . Population Surface Population Gross national Gross national PPP gross national Gross area density Income Income per capita income a domestic product Per thousand people capita Per capita millions so km per sq km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2001 2001 2001 2001 b 2001 2001 b 2001 2001 2001 2001 2000-01 2000-01 Honduras 7 112 59 5 9 106 900 137 18 2,760 144 2 6 0 0 Hungary 10 93 110 49 2 50 4,830 71 122 11,990 59 3 8 3 1 India 1,032 3,287 347 477 4 12 460 162 2,913 2,820 143 5 4 3 7 Indonesia 209 1,905 115 144 7 28 690 145 591 2,830 142 3 3 2 0 Iran, Islamic Rep 65 1,648 39 108 7 35 1,680 112 383 5,940 98 4 8 3 4 Iraq 24 438 -54 Ireland 4 70 56 87 7 39 22,850 22 104 27,170 14 5 8 4 6 Israel 6 21 309 106 6 36 16,750 35 125 19,630 40 -0 9 -2 9 Italy 58 301 197 1,123 8 7 19,390 30 1,422 24,530 25 1 8 1 3 Jamaica 3 11- 239 7 3 100 -2,800- 93 9 3,490 133 1 7 1 1 Japan 127 378 349 4,523 3 2 35,610 5 3,246 25,550 20 -0 6 -0 7 Jordan 5 89 57 8 8 90 1,750 108 20 3,880 128 4 2 1 2 Kazakhstan 15 2,725 6 20 1 62 1,350 12-0 92 6.150 94 13 2 14 4 Kenya 31 580 54 10 7 84 350 174 30 970 190 1 1 -1 0 Korea, Dem Rep 22 121 186 d Korea, Rep 47 99 480 447 6 13 9,460 54 713 15,060 54 3 0 2 3 Kuwait 2 18 1-15 37 4 54 18,270 31 44 21,530 35 -1 0 -3 9 Kyrgyz Republic 5 200 26 1 4 158 280 182 13 2,630 147 5 3 4 5 Lao PDR 5 237 23 1 6 153 300 178 8e 1,540e 175 5 7 3 3 Latvia 2 65 38 7 6 98 3,230 86 18 7.760 82 7 6 8 2 Lebanon 4 10 429 17 6 69 4,010 77 19 4.400 119 1 3 0 0 Lesotho 2 -30 68_ 1 1 166 530_ 156 6e 2,980 e 139 4 0 2 6 Liberia 3 ill 33 0 5 190 140 203 196 5 3 2 6 Libya 5 1,760 3 Lithuania 3 65 54 11 7 82 3,350 83 29 8,350 78 5 9 66 Macedonia, FYR -2 26 80 3 5 130 1,690 ill 12 6,040 97 -4 1 -4 7 Madagascar 16 587 27 4 2 120 260 187 13 820 197 6 0 3 0 Malawi 11 118 112 1 7 151 160 199 -6 560 206 -1 5 -3 5 Malaysia 24_ 330 72 79 3 42 3,330 84_ 188 7,910 81 0 4 -1 9 Mali 11 1,240 9 2 5 139 230 191 -9 770 200 1 4 -0 9 Mauritania 3 1,026 3 1 0 169 360 172 5 1,940 162 4 6 1 4 Mauritius -1 2 591 4 6 117 3,830 80 -12 9,860 70 7 2 6 0 Mexico 99 1,958 52 550 2 10 5,530 69 820 8,240 80 -0 3 -1 8 Moldova 4_ 34 130 1 5 156 400 167 10 -2,300 154 6 1 6 3 Mongolia 2 1,567 2 1 0 172 400 167 4 1,710 170 1 4 0 4 Mo-rocco 29 -447 ~ 65 34 7 57 1,190 -128 102 3,500 132 6 5 4 8 Mozambique 18 802 23 3 8 127 210 194 I9e 1,050e 188 13 9 11 5 Myanmar 48 677 73 d Namibia 2 824 --2 3 5 129 1,9-60 _104 13-e 7,410 e 85 _2 7 0 7 Nepal 24 147 165 5 8 108 250 190 32 1,360 180 4 8 2 4 Netherlands 16 42 473 390 3 -14 24,330 16 439 27,390 13 1 1 0 4 New Zealand 4 271 14 51.0 49 13,250 44 70 18,250 43 3 2 2 7 Nicaragua 5 130 43 148 171 .158 Niger 11 1,267 9 2 0 147 180 197 jo 880 e 194 7 6 4 2 Nigeria 130 -924 143 37 1 55 290 179 102 790 199 3 9 1 5 Norway 5 324 - 15 160 8 27 35,63-0 4 132 29,340 7 1 4 0 9 Oman 2 310 8 Pakistan 141 796 183 60 0 44 42-0 _164 -263 -1,860 167 2 7 0 3 Panama 3 76 39 9 5 87 3,260 85 16e 5,440 e 104 0 3 -1 2 Papua New Guinea 5 463 12 3 0 137 580 152 13e 2,450 e 149 _-3 5 -5 8 Paraguay 6 407 14 7 6 99 1,350 120 29e 5,180 e 106 2 7 0 2 Peru 26 1,285 21 52 2 47 1,980 103 118 4,470 117 0 2 -1 3 Philippines 78 300 263 80 8 41 1,030 132 319 4,070 125 3 4 1 2 Poland 39 323 127 163 6 26 4,230 75~ 362 9,370 73 1 0 1 0 Portugal 10 92 110 109 3 3-4 10.900 51 178 17,710 46 1 7 1 5 Puerto Rico 4 9 433 42 1 52 10,950' 50 69 18,090 44 5 6 4 9 2003 World Development Indicators I 15 LKUSize of the economy Population Surface Population Gross national Gross national PPP gross national Gross area density Income Income per capita Income a domestic product Per thousand people capita Per capita millions sq km per sq km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2001 2001 2001 2001 b 2001 2001 b 2001 2001 2001 2001 2000-01 2000-01 Romania 22 238 97 38 6 53 1,720 110 130 5,780 101 5 3 5 4 Russian Federation 145 17,075 9 253 4 18 1,750 108 995 6,880 87 5 0 5 6 Rwanda 9 26 352 1 9 149 220 192 11 1,240 183 6 7 4 5 Saudi Arabia 21 2,150 10 181 1 22 8,460 57 284 13,290 56 1 2 -2 0 Senegal 10 197 -51 4 7 115 490 159 14 1,480 176 5 7 3 2 Sierra Leone 5 72 72 0 7 177 140 203 2 460 208 5 4 3 3 Singapore 4 1 6,772 88 8 38 21,500 26 94 22,850 -32 -2 0 -4 7 Slovak Republi-c 5 49 112 20 3 61 3,760 81 -64 11,780 60 3 3 3 2 Slovenia 2 20 99 19 4 66 9,760 52 34 17,060 49 3 0 2 8 Somalia 9 638 14 d South Africa 43 1,221 35 121 9 30 2,820 92 472e 10,910e 64 2 2 1 2 Spain 41 506 82 588 0 9 14,300 41 816 19,860 39 2 8 1 2 Sri Lanka 19 66 290 16_4 _ 73 880 140 61 3,260 134 -1 4 -2 8 Sudan 32 2,506 13 10 7 83 340 175 56 1,750 169 6 9 4 9 Swaziland 1 17_ 62 1 4 157 1,300 125 5_ 4,430 118 1 6 -0 6 Sweden 9 450 22 225 9 20 25,400 12 212 23,800 29 1 2 0.9 Switzerland 7 41 183 277 2 16 38,330 3 224 30,970 5 1 3 0 6 Syrian Arab Republic 17 185 90 17 3 71 1,040 131 52 3,160 136 28_ 0 3 Tajikistan 6 143 44 1 1 165 180 197 7 1,140 184 10 2 9 3 Tanzania 34 945 39 9 4k 88 270k 184 18 520 207 5 7 34 Thailand 61 513 120 118 5 32 1,940 105 381 6,230 93 1 8 1 0 Togo 5 57 86 1 3 159 270 184 8 1,620 172 2 7 -01I Trinidad and Tobago 1 5 255 7 8 97 5,960 66 11 8,620 77 5 0 4 3 Tunisia 10 164 62 20 0 63 2,070 100 59 6,090 96 4 9 3 7 Turkey 66 775 86 167 3 24 2,530 95 386 5,830 -100 -7 4 -8 7 Turkmenistan 5 488 12 5 1 113 950 134 23 4,240 124 20 5 17 2 Uganda 23 241 116 5 9 107 260 187 33e 1,460e 177 4 6 2 0 Ukraine 49 604 85 35 2 -56 720 143 210 4,270 123 9 1 10 0 United Arab Emirates 3 84 36 United Kingdom 59 243 244 1,476 8 4 25,120 14 1.431 24,340 26 2 2 2 1 United States 285 9,629 31 9,780 8 1 34,280 7 9,781 34,280 3 0 3 -0 8 Uruguay 3 176 19 19 2 67 5,710 68 28 8,250 79 -3 1 -3 8 Uzbekistan 25 447 61 13 8 78 550 155 60 2,410 152 4 5 3 2 Venezuela. RB 25 912 28 117 2 33 4,760 72 138 5,590 102 2 7 0 7 Vietnam 80 332 244 32 8 58 410 165 164 2,070 159 6 8 5 5 West Bank and Gaza 3 4 2 121 1,350 120 129 -11 9 -15 4 Yemen, Rep 18 528 34 8 2 93 450 163 13 730 202 3 1 0 0 Yugoslavia, Fed Rep 11 102 108 9 9 86 930 136 Zambia 10 753 14 3 3 132 320 176 8 750 201 4 9 2 9 Zimbabwe 13 391 33 6 2 105 480 160 28 2,220 156 -8 4 -9 8 Ja6.-o - - mma - . Low Income -2,506 34,246 76 1,069 430 5,494 2,190 4 7 2 8 Middle Income - 2,667 67,224 - 40 4,957 1,860 14,373 5,390 -2 5 1 6 Lower middle income 2,164 45,811 48 2,672 1-230 10,178 4,700 4 1 3 2 Upper middle income 504 21,413 24 2,291 - 4,550 4,282 8,500 0 7 -0 6 Low & middle Income 5.172 101,470 52 6,025 1,160 19,823 3,830 2 9 1 5 East Asia & Pacific -1.823 16,301 115 1.640 900 6,899 3,790 5 5 4 5 Europe & Central Asia 475 24,168 20 935 1,970 2,998 6,320 2 3 2 3 Latin America & Carib 524 20,460 26 1,876 3.580 3,613 6,900 0 4 -1 1 Middle East & N Africa 301 11,135 27 669 2,220 1,631 5,430 3 0 1 0 South Asia 1,378 5,140 288 618 450 3,535 2,570 4 9 3 1 Sub-Saharan Africa 674 24,267 29 311 460 1,178 1,750 2 9 0 7 High Income 957 32,414 31 25.372 26,510 25,506 26,650 0 7 0 0 Europe EMU 307 2,569 121 6,339 20,670 7,298 23,800- 1 4 0 9 a PPP is purchasing power parity, see Definitions b Calculated using the World Bank Atlas method c Estimate does not account for recent tefugee tlows d Estimated to be low income ($745 or less) e The estimate is based on regression, others are estrapolated from the latest International Comparison Programme benchmark estimates f Includes Taiwan, China, Macan. China, and Hong Kong, China g Estimated to be lower middle income ($746-.S2,975) h GNI and GNI per capita estimates include the French overseas departments of French Guiana. Guadeloupe, Martinique, and Reunion i Estimated to be upper middle income ($2,976-.$9,205) jIncluded in tee aggregates for upper-middle-income economies on the basis of earlier data k Data refer to mainland Tanzania only I Estimated to be high income ($9,206 or more) 16 Li 2003 World Development indicators Size of the economy 0 Population, land area, income, and output are basic shows GNI and GNI per capita estimates converted * Population is based on the de facto definition of measures of the size of an economy They also pro- into international dollars using purchasing power parity population, which counts all residents regardless of vide a broad indication of actual and potential (PPP) rates PPP rates provide a standard measure legal status or citizenship-except for refugees not resources Population, land area, income-as meas- allowing comparison of real price levels between coun- permanently settled in the country of asylum, who ured by gross national income (GNI)-and output- tries, Just as conventional price indexes allow compan- are generally considered part of the population of as measured by gross domestic product (GDP)-are son of real values over time The PPP conversion their country of origin The values shown are midyear therefore used throughout the World Development factors used here are derived from price surveys estimates for 2001 See also table 2 1 * Surface Indicators to normalize other indicators covering 118 countries conducted by the International area is a country's total area, including areas under Population estimates are generally based on Comparison Programme For Organisation for Econom- inland bodies of water and some coastal waterways extrapolations from the most recent national census ic Co-operation and Development countries data come * Population density is midyear population divided For further discussion of the measurement of popu- from the most recent round of surveys, completed in by land area in square kilometers * Gross national lation and population growth, see About the data for 1999, the rest are either from the 1996 survey, or Income (GNI) is the sum of value added by all resi- table 2 1 and Statistical methods data from the 1993 or earier round, which have been dent producers plus any product taxes (less subsi- The surface area of a country or economy includes extrapolated to the 1996 benchmark Estimates for dies) not included in the valuation of output plus net inland bodies of water and some coastal waterways countries not included in the surveys are derived from receipts of primary income (compensation of employ- Surface area thus differs from land area, which statistical models using available data ees and property income) from abroad Data are in excludes bodies of water, and from gross area, which All economies shown in the World Development current U S dollars converted using the World Bank may include offshore territorial waters Land area is Indicators are ranked by size, including those that Atlas method (see Statistical methods) * GNI per particularly important for understanding the agricul- appear in table 1 6 Ranks are shown only in table capita is gross national income divided by midyear tural capacity of an economy and the effects of 1 1 (The World Bank Atlas includes a table compar- population GNI per capita in U S dollars is convert- human activity on the environment (For measures of ing the GNI per capita rankings based on the Atlas ed using the World Bank Atlas method * PPP GNI is land area and data on rural population density, land method with those based on the PPP method for all gross national income converted to international dol- use, and agricultural productivity, see tables economies with available data ) No rank is shown for lars using purchasing power parity rates An interna- 3 1-3 3 ) Recent innovations in satellite mapping economies for which numerical estimates of GNI per tional dollar has the same purchasing power over techniques and computer databases have resulted in capita are not published Economies with missing GNI as a U S dollar has in the United States more precise measurements of land and water areas data are included in the ranking process at their * Gross domestic product (GDP) is the sum of value GNI (gross national product, or GNP, in the termi- approximate level, so that the relative order of other added by all resident producers plus any product nology of the 1968 United Nations System of economies remains consistent Where available, taxes (less subsidies) not included in the valuation of National Accounts) measures the total domestic and rankings for small economies are shown in the World output Growth is calculated from constant price GDP foreign value added claimed by residents GNI com- Bank Atlas data in local currency * GDP per capita is gross prises GDP plus net receipts of primary income Growth in GDP and growth in GDP per capita are domestic product divided by midyear population (compensation of employees and property income) based on GDP measured in constant prices Growth from nonresident sources in GDP is considered a broad measure of the growth The World Bank uses GNI per capita in U S dollars of an economy, as GDP In constant prices can be to classify countries for analytical purposes and to estimated by measuring the total quantity of goods determine borrowing eligibility See the Users guide and services produced in a period, valuing them at for definitions of the income groups used in the an agreed set of base year prices, and subtracting Population estimates are prepared by World Bank World Development Indicators For further discussion the cost of intermediate inputs, also in constant staff from a variety of sources (see Data sources of the usefulness of national income as a measure prices For further discussion of the measurement of for table 2 1) The data on surface and land area of productivity or welfare, see About the data for economic growth, see About the data for table 4 1 are from the Food and Agriculture Organization tables 4 1 and 4 2 (see Data sources for table 31) GNI, GNI per When calculating GNI in U S dollars from GNI capita, GDP growth, and GDP per capita growth are reported in national currencies, the World Bank fol- estimated by World Bank staff based on national lows its Atlas conversion method This involves using accounts data collected by Bank staff during ecc- a three-year average of exchange rates to smooth the nomic missions or reported by national statistical effects of transitory exchange rate fluctuations (For offices to other international organizations such further discussion of the Atlas method, see as the Organisation for Economic Co-operation and Statistical methods ) Note that growth rates are cal- Development Purchasing power parity conversion culated from data in constant prices and national factors are estimates by World Bank staff based currency units, not from the Atlas estimates on data collected by the International Comparison Because exchange rates do not always reflect inter- Program national differences in relative prices, this table also 2003 World Development Indicators 1 17 rf~Millennium Development Goals: U~~(~~ eradicating poverty and improving Gives Eradicate extreme poverty Achieve Promote Reduce child Improve matemal health and hunger universal gender mortalty primary equality Maternal Share of poorest Prevalence of education Ratio of female to mortality ratio quintile in child malnutrition Primary male enrollments per 100,000 Births attended national income % of completion in primary and Under-five live births by skilled or consumption children rate secondary school v.ad mortality rate Modeled health staff %under 5 % per 1,000 estimates % of total 1987-200jb' 1990 2001 1990 2001 1990 2000 1990 2001 1995 1990 2000 Afghanistan 22 8 50 260 257 9 Albania 14 101 90 102 42 25 31 99 Algeria 7 0 9 6 82 80 98 69 49 150 77 92 Angola 20 28 84 260 260 1,300 17 Argentina 96 103 28 19 85 98 Armenia 6 7 3 106 58 35 29 97 Australia 5 9 96 100 10 6 6 100 100 Austria 7 0 90 97 9 5 11 Azerbaijan 7 4 17 47 100 94 101 106 96 37 88 Bangladesh 9 0 66 48 50 70 72 103 144 77 600 7 12 Belarus 8 4 97 101 21 20 33 Belgium 8 3 97 106 9 6 8 Benin 23 23 39 62 185 158 880 38 Bolivia 4 0 11 8 55 72 89 97 122 77 550 43 59 Bosnia and Herzegovina 4 88 22 18 15 100 Botswana 2 2 13 114 107 102 58 110 480 79 99 Brazil 2 0 7 48 71 103 60 36 260 Bulgaria 6 7 90 94 97 19 16 23 99 Burkina Faso 4 5 34 19 25 61 70 210 197 1.400 30 27 Burundi 5 1 45 46 43 82 79 190 190 1,900 20 25 Cambodia 6 9 45 71 70 83 115 138 590 47 34 Cameroon 4 6 15 22 57 43 82 81 139 155 720 58 56 Canada 7 3 94 101 8 7 6 Central African Republic 2 0 28 19 61 180 180 1,200 66 44 Chad 28 19 19 56 203 200 1,500 15 16 Chile 3 2 1 94 99 98 88 19 12 33 China 5 9 17 10 99 81 98 49 39 60 Hong Kong, China 5 3 ..100 100 Colombia .1 4 10 7 72 85 104 104 36 23 120 94 86 Congo, Dem Rep . 48 40 69 80 205 205 940 70 Congo, Rep ..61 44 88 89 110 108 1,100 Costa Rica 2 6 3 73 89 _96 101 17 11 35 97 98 CMe dilvoire 7 1 -21 44 40 71 155 175 1,200 50 47 Croatia 8 3 86 97 13 8 18 Cuba 101 100 13 9 24 100 Czech Republic 10 3 1 89 94 101 12 5 14 Denmark 8 3 96 103 9 4 15 Dominican Republic 5 1 10 5 82 106 65 47 110 92 Ecuador 3 3 14 99 96 97 100 57 30 210 56 69 Egypt, Arab Rep 8 6 10 4 77 . 78 94 104 41 170 37 61 El Salvador 3 3 15 12 61 80 100 98 60 39 180 90 90 Eritrea 22 35 82 77 155 1l1 1,100 Estonia 7 0 93 99 99 17 12 80 Ethiopia 2 4 48 47 22 24 68 68 193 172 1,800 8 10 Finland 10 1 105 106 7 5 6 France 7 2 . 98 100 10 6 20 Gabon . 1-2 71 98 90 90 620 79 86 Gambia, The 4 0 17 40 70 64 85 154 126 1,100 44 51 Georgia 6 0 3 .. 90 94 102 29 29 22 96 Germany 5 7 .. 94 99 9 5 12 Ghana 5 6 30 25 63 64 88 126 100 590 55 44 Greece 7.1 . .93 101 11 5 2 Guatemala 2 6 24 43 52 92 82 58 270 30 41 Guinea 6 4 33 16 34 43 57 240 169 1,200 31 35 Guinea-Bissau 5 2 25 16 31 . 65 253 211 910 35 Haiti 27 17 28 70 150 123 1,100 78 24 13 III 2003 World Development Indicators Millennium Development Goals:1) eradicating poverty and improving lives I Eradicate extreme poverty Achieve Promote Reduce child Improve maternal health and hunger universal gender mortality primary equality Maternal Share of poorest Prevalence of education Ratio of female te mortality ratio quintile in child malnutrition Primary male enrollments per 100,000 Births attended national income % of completion in primary and Under-five live births by skilled or consumption a children rate secondary school cd mortality rate Modeled health staft %under 5 % per 1 000 estimates % of total 1987-2001b 1990 2001 1990 2001 1990 2000 1990 2001 1995 1990 2000 Honduras 2 0 18 17 66 67 103 61 38 220 47 Hungary 10 0 2 93 -96 100 17 9 23 India 8 1 64 70 76 68 78 123 93 440 44 42 Indonesia 8 4 25 92 91 91 98 91 45 470 47 56 Iran, Islamic Rep 5 1 11 -94 80 95 72 42 130 78 Iraq 12 63 75 77 50 133 370 50 Ireland 6 7 -99 9 6 9 Israel 6 9 99 100 12 6 B Italy 6 0 - 95 .98 10 6 11 Jamaica 6 7 5 4 90 94 97 101 20 20 120 92 95 Japan 10 6 96 101 6 5 12 100 Jordan 7 6 6 102 104 93 _101 43 33 41 87 Kazakhstan 8 2 4 98 52 99 80 98 Kenya 5 6 -22 87 63_ 97 97 122 1,300 50 44 Korea, Dem Rep 28 55 55 35 Korea, Rep 7 9 96 96 93 100 9 5 20 95 Kuwait 56 97 101 16 10 25 Kyrgyz Republic 9 1 100 -100 99 81 61 80 98 Lao PDR 7 6 40 44 69 75 82 163 100 650 21 Latvia -7 6 76 96 101 18 21 70 Lebanon 102 37 32 130 95 95 Lesotho 1 4 16 18 -75 68 124 107 148 132 530 40 60 Liberia 70 235 235 Libya 103 42 19 120 76 Lithuania 7 9 88 93 99 14 9 27 Macedonia, FYR 8 4 6 89 94 98 33 26 17 88 Madagascar 6 4 41 34 26 97 168- 136 580 57 47 Malawi 4 9 28 25 33 64 79 94 241 183 580 50 56 Malaysia 4 4 25 91 98 105 218 39 96 Mali 4 6 11 23 57 66 254 231 630 Mauritania 6 4 48 32 34 46 67 93 183 183 - 870 40 57 Mauritius 136 98 97 25 19 45 92 Mexico 3 4 17 8 89 100- 96_ - 101_ ,46 29 65 Moldova 7 1 67 79 103 102 37 32 -65 - Mongolia 5 6 12 13 82 107 112 107 76 65 100 97 Morocco 6 5 10 47 67 83 85 44 390 31 Mozambique -6 5 30 36 73 75 235_ 197 980 Myanmar - 32 95 98_ 130 109 170 94 Namibia 1 4 26 70 ill1 104 84 67 370 68 76 Nepal 7 6 48 51 65 53 82_ 145 91 830 12 Netherlands 7 3 93 97 -8 6 -10 100 New Zealand 6 4 96 103 11 6 15 Nicaragua -2 3 12 45 65 105 66 43 250 61 Niger 2 6 43 40 18 20 54 67 320 265 920 15 16 Nigeria 4 4 35 31 72 67 -76 190 183 1,100 31 42 Norway 9 7 - - 97 101 9 4 9 Oman 24 67 86 97 30 13 120 87 Pakistan -- 8 8 - 40 - 44 59 47 61 -128 109 200 40 20 Panama 3 6 -6 - 87 94 96 100 34 25 100_ 90 Papua New Guinea 4 5 53 77 90 1-01 94 390 40 Paraguay 1 9 4 65 78 95- 9.9 37 30 170 71 71 Peru 4 4 11 7 85 98 93 97 75 39 240 78 Philippines 5 4 34 32 89~ 103 66 38 240 56 Poland 7 8 100 96 98 22 9 12 Portugal -5 8 -- -99 102 15 6 12 98 100 Puerto Rico 30 2003 World Development Indicators I 19 rfl~Mililennium Development GoaDs: L0(4 ~~eradicating poverty and improvi'ng liyes Eradicate extreme poverty Achieve Promote Reduce child Improve maternal health and hunger universal gender mortality primary equality Maternal Share of poorest Prevalence of education Ratio of female to mortality ratio qiuintile in child malnutrition Primary male enrollments per 100,000 Births attended national income % of completion in primary and Under-five live births by skilled or consumptione children rate secondary school ce mortality rate Modeled health staff %under 5 S per 1,000 estimates % of total 1987-2001b 1990 2001 1990 2001 1990 2000 1990 2001 1995 1990 2000 Romania 8 2 6 96 95 100 36 21 60 98 Russian Federation 4 9 96 21 21 75 99 Rwanda - 29 24 34 28 98 97 178 183 2,300 22 31 Saudi Arabia 60 82 94 44 28 23 88 91 Senegal 6 4 22 18 45 41 69 84 148 138 1,200 42 51 Sierra Leone 1 1 29 27 32 67 77 323 316 2,100 42 Singapore - 5 0 .89 8 4 -9 100 Slovak Republic 8 8 _ 96 98 101 14 9 14 Slovenia 9 1 .. 99 97 .. 10 5 17 Somalia 26 225 225 34 South Africa 2 0 76 . 103 100 60 71 340 84 Spain. - 7 5 -99 103 9 6 8 Sri Lanka 8 0 33 100 ill 99 102 23 19 60 85 Sudan 11 59 46 75_ 102 123 107 1,500 69 Swaziland 2 7 10 71 .96 110 149 55 Sweden 9.1 97 115 7 3 8 Switzerland 6 9 ..92 96 8 6 8 Syrian Arab Republic 98 82 92 44 28 200 64 Tajikistan 80_ . 95 87 127 116 120 77 Tanzania 6 8 29 29 65 60 97 99 163 165 1,100 44 35 Thailand 6 1 93 90 94 95 40 28 44 71 Togo 25 25, 41 63 59 70 152 141 980 32 51 Trinidad and Tobago. 5 5 100 81 98 102 24 20 65 Tunisia 5 7 10 4 75 82 100 52 27 70 80 90 Turkey 6 1 8 90 77 84 74 43 55 77 81 Turkmenistan -6 1 12 . 98 87_ 65_ 97 Uganda 7 1 23 23 49 65 89 165 124 1,100 38 U-krai-ne 8.8 3 58 ..92 -22 -20 45 - - 99 United Arab Emirates 94 96 105 14 9 .30 96 United Kingdom 6 1 . 97 111 9 7 10 100 99 United, States 5 2 95 100 11 8 12 99 Uruguay 4 45 6 95 98 105 24 16 50 Uzbekistan 9 2 100 65 68_ 60 . 9-6 Venezuela. RB 3 0 8 4 91 78 101 105 27 22 43 97 Vietnam 8 0 45 34 10-1 - 50 38_ _95 95 70 West Bank and Gaza .53 25 Yemen, Rep -7 4 30 58 50 142 107 850 16 22 Yugoslavia, Fed Rep -2 72 96 96 . 26 19 15 93 Zambia 3 3 25 91 73 92 192 202 870 41 Zimbabwe 4 6 12 13 97 96 94 80 123 610 62 84 Low Income 68 74 84 139 121 43 Middle Income 94 84 98 52 38 Lower middle income 18 10 95 . 82 97 54 41 Upper middle income .101 43 27 Low & middle Income .. 83 . 80 92 101 88 East Asia & Pacific 19 15 98 83 97 59 44 Europe & Central Asia . 98 44 38 Latin Amenica & Carib . . 102 53 34 Middle East & N. Africa 81 79 95 77 54 South Asia 64 70 74 68 81 129 99 39 42 Sub-Saharan Africa . 57 79 82 178 171 High Income . . 96 101 _ 1-0- 7 Europe EMU 97 100 10 6 a See table 2 B for survey year and whether share is based on income or consumption expenditure b Data are for the most recent year available c Break in series between 1997 and 1998 due to change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 d Data are provisional for Organisation for Economic Co-operation and Development and World Education Indicators (WEll tountries For a list of WEI countries, see About the data for table 2 10 20 II 2003 World Development Indicators Millennium Development Goals: 1' S eradicating poverty and improving lives I.L This table and the following two present indicators for 17 In previous editions of the World Development the indicators for the first five goals For more informa- of the 18 targets specified by the Millennium Indicators progress toward achieving universal primary tion about data collection methods and limitations, see Development Goals Each of the eight goals comprises education was measured by net enrollment ratios But About the data for the tables listed there For information one or more targets, and each target has associated with official enrollments sometimes differ significantly from about the indicators for goals 6, 7, and 8, see About the it several indicators by which progress toward the target actual attendance, and even school systems with high data for tables 1 3 and 14 can be monitored Most of the targets are set as a value average enrollment ratios may have poor completion of a specific indicator to be attained by a certain date In rates New estimates of primary school completion rates l some cases the target value is set relative to a level in have been calculated by World Bank staff using data pro- 1990 In others it is set at an absolute level Some of the vided by the United Nations Educational, Scientific, and * Share of poorest quintile In national consumption is targets for goals 7 and 8 have not yet been quantified Cultural Organization (UNESCO) and national sources the share of consumption (or, in some cases, income) The indicators in this table relate to goals 1-5 Goal 1 Eliminating gender disparities in education would help that accrues to the poorest 20 percent of the population has two targets between 1990 and 2015 to reduce by half to increase the status and capabilities of women The * Prevalence of child malnutrition is the percentage of the proportion of people whose income is less than $1 a ratio of girls' to boys' enrollments in primary and sec- children under five whose weight for age is more than two day and to reduce by half the proportion of people who suf- ondary school provides an imperfect measure of the rel- standard deviations below the median for the interna- fer from hunger Estimates of poverty rates can be found in ative accessibility of schooling for girls With a target tional reference population ages 0-59 months The ref- table 2 6 The indicator shown here, the share of the poor date of 2005, this is the first of the targets to fall due erence population, adopted by the World Health est quintile in national consumption, is a distnbutional The targets for reducing under-five and maternal mor- Organization in 1983, is based on children from the measure Countries with less equal distributions of con- tality are among the most challenging Although esti- United States, who are assumed to be well nourished sumption (or income) will have a higher rate of poverty for mates of under-five mortality rates are available at * Primary completion rate is the number of students a given average income No single indicator captures the regular intervals for most countries, maternal mortality is successfully completing (or graduating from) the last year concept of suffering from hunger Child malnutrition is a difficult to measure, in part because it is relatively rare of primary school in a given year, divided by the number symptom of inadequate food supply, lack of essential nutrF Most of the 48 indicators relating to the Millennium of children of official graduation age in the population ents, illnesses that deplete these nutrients, and under- Development Goals can be found in the World * Ratio of female to male enrollments In primary and nourished mothers who give birth to underweight children Development Indicators Table 1 2a shows where to find secondary school is the ratio of female students enrolled in primary and secondary school to male stu- 1.2a dents * Under-flve mortality rate is the probability that - . * ... . - - , , a newborn baby will die before reaching age five, if sub- Goal 1. Eradicate extreme poverty and hunger ject to current age-specific mortality rates The probabili- pori ppulaton b ew $1 adV ty is expressed as a rate per 1,000 * Matemal 2 Poverty gap ratio (table 2 6) mortality ratio is the number of women who die from Share of poorest umtle i atZI cn tables 12 nd 2 8) pregnancy-related causes during pregnancy and child- 4. Prevalence of underweight in children under five (tables 1 2 and 2 18) birth, per 100,000 live births The data shown here have V 5. Proporion =f p =pulation i6w)Bir~um1evel _f dietaryerlergyconsumption(table.2 18) -i been collected in various years and adjusted to a com- Goal 2. Achieve universal primary education mon 1995 base year The values are modeled estimates Nemollment , V __ _ (see About the data for table 2 17) * Births attended by 7 Proportion of pupils starting grade 1 who reach grade 5 (table 2 13) skilled health staff are the percentage of deliveries i - 1 Literacy rle 2ty1 attended by personnel trained to give the necessary Goal 3. Promote gender equality and empower women supervision, care, and advice to women during pregnan- r9: ,i girls ,oyl ('ma-send andAertiary education (see rabo-of girls to boys'in cy, labor, and the postpartum period, to conduct deliver- L~ynimary and-secoiVdary ~ r. , _- -' - -es on their own, and to care for newborns 10 Ratio of literate females to males among 15- to 24-year-olds (tables 1.5 and 2 14) 12 Proportion of seats held by women in national parliament (see women in decisionmaking postions _ in table 1 5) The indicators here and throughout the rest of the Goal 4. Reduce child mortality book have been compiled by World Bank staff from . ;v;;;tywtegtEle'si'iesnng ima p tionaEnidtreatrhentn easu3s line and mobile phone subscribers are telephone main- 23 Tuberculosis prevalence and death rates (see incidence of tuberculosis in tables 1 2 and 2.19) lines connecting a customer's equipment to the public 24 Prprtion of tuberculos~is case detctdand-cured under- directly observ,eid~tfreatm6nt;-short switched telephone network, and users of portable tele- course (tabler2 16) , X _ ' _o - phones subscribing to an automatic public mobile tele- Goal 7. Ensure environmental sustainability phone service using cellular technology that provides 725 Prop)orton -of land area cov access to the public switched telephone network 26 Ratio of area protected to maintain biological diversity to surface area (table 3 4) 27 Energy use (kilograrhs of oil eOu v e nt'en m,- tablne 3qu8)alent pe e ________________ _ The indicators here, and where they appear throughout 28 Carbon dioxide emissions per capita (table 3 8) and consumption of ozone-depleting the rest of the book, have been compiled by World Bank chlorofluorocarbons* staff from pnmary and secondary sources Efforts have § 29 Proportion of population using solid fuels-(see traditonallfuel use,lnmtable<3'8)#t-Su4e.n5Aj been made to harmonize these data senes with 30 Proportion of population with sustainable access to an improved water source (tables 2 16 and 3 5) those published on the United Nations Millennium , 31 Proportion of tirban -population-with access to improved sanitation (table 2 16) _ = _ - 16 D evelopment Goals Web site (http //www un org/ mu- 32 Proportion of population with access to secure tenure (table 3 11) lenniumgoals), but some differences in timing, sources, No data available in the World Development Indicators database and definitions remain 2003 World Development Indicators I 25 Millennium Development Goals: U0H overcoming obstacles Official development Market access to high4ncome countries Support to assistance (ODA) by donor agriculture Average tariff on exports ODA for of low- and middle-income basic social Goods economies servicesi' (excluding arms) Agricultural Textiles and Net ODA % of total ODA admitted free of tariffs products clothing % of donor GNI commitments % % % % of GDP 2001 2000-01 1990 2000 1990 2000 1990 2000 2001 Australia 0 25 19 0 38 8 42 7 1 9 1.6 29 3 14 6 0 3 Canada 0.22 19 4 278 65 2 3 6 2 7 20 0 11.5 0 7 European Union 48 2 72 9 11 1 4 9 6 3 4 3 1 4 Austria 0 29 20 5 Belgium 0 37 14 6 Denmark 1 03 8 7 Finland 0 32 11 6 France 0 32 Germany 0 27 9.7 Greece 0 17 4 6 Ireland 0 33 20 5 Italy 0 15 6 1 Luxembourg 0 82 21 2 Netherlands 0 82 22 5 Portugal 0 25 2 8 Spain 0 30 11 7 Sweden 0 81 13 6 United Kingdom 0 32 27 0 Japan 0.23 6 8 42.2 57 2 9 4 9 1 5 0 4 1 1 4 New Zealand 0 25 8 4 54 4 52 4 5 7 1 7 18 4 8 2 0 3 Norway 0 83 9 1 87.1 71.7 0 5 15 2 14 0 11 6 1 4 Switzerland 0 34 10 9 2.6 61 8 1 9 United States 0 11 21 5 20 3 56 2 3.7 4 4 11 8 10 2 0 9 HIPC HIPC Estimated total HIPC HIPC Estimated total decision completion nominal debt decision completion nominal debt point b pointc service relief point b point C service relief $ millions $ millions Benin Jul 2000 Floating 460 Malawi Dec 2000 Foating 1,000 Bolivia Feb 2000 Jun 2001 2,060 Mali Sep 2000 Floating 870 Burkina Faso Jul 2000 Apr 2002 930 Mauritania Feb 2000 Jun 2002 1,100 Cameroon Oct 2000 Floating 2,000 Mozambique Apr 2000 Sep 2001 4,300 Chad May 2001 Floating 260 Nicaragua Dec 2000 Floating 4,500 Cote d'lvoire Mar 1998 800 Niger Dec 2000 Floating 900 Ethiopia Nov 2001 Floating 1,930 Rwanda Dec 2000 Floating 810 Gambia, The Dec 2000 Floating 90 Sao Tome and Principe Dec 2000 Floating 200 Ghana Feb 2002 Floating 3,700 Senegal Jun 2000 Floating 850 Guinea Dec 2000 Floating 800 Sierra Leone Mar 2002 Floating 950 Guinea-Bissau Dec 2000 Floating 790 Tanzania Apr 2000 Nov 2001 3,000 Guyana Nov 2000 Floating 1,030 Uganda Feb 2000 May 2000 1,950 Honduras Jul 2000 Floating 900 Zambia Dec 2000 Floating 3,820 Madagascar Dec 2000 Floating 1,500 a Includes basic health, education, nutntion, and water and sanitation services b Except for C6te d'lvoire, Ghana, and Sierra Leone, the date refers to the enhanced framework The follow- ng countries also reached decision points under the original framework Bolivia in September 1997, Burkina Faso in September 1997, Guyana in December 1997, Mali in September 1998, Mozambique in April 1998, and Uganda in Apnl 1997 c Except for C6te d'lvoire, Ghana, and Sierra Leone. the date refers to the enhanced framework The following countries also reached completion points under the original framework Bolivia in September 1998. Burkina Faso in July 2000, Guyana in May 1999, Mall In September 2000, Mozambique in July 1999, and Uganda in April 1998 21 0 2003 World Development Indicators Millennium Development Goals: IA overcoming obstacles l. ! Achieving the Millennium Development Goals will require arms ' The data in the table reflect the tariff schedules and civil society have engaged in an intensive dialogue an open, rule-based global economy in which all coun- applied by high-income OECD members to exports of low- about its strengths and weaknesses A major review in tries, rich and poor, participate Many poor countries, and middle-income economies Agricultural commodities 1999 led to an enhancement of the original framework lacking the resources to finance their development, bur- and textiles and clothing are two of the most important dened by unsustainable levels of debt, and unable to categories of goods exported by developing economies = i_ compete in the global marketplace, need assistance Although average tariffs have been falling, averages may from nch countries For goal 8-develop a global part- disguise high tanffs targeted at specific goods (see table * Net official development assistance (ODA) comprises nership for development-many of the indicators there- 6 6 for estimates of the share of lines with "internation- grants and loans (net of repayments of principal) that fore monitor the actions of members of the Development al peaks" in each country's tariff schedule) The aver- meet the DAC definition of ODA and are made to coun- Assistance Committee (DAC) of the Organisation for ages in the table include only ad valorem duties No data tries and terntories in part I of the DAC list of recipient Economic Cooperation and Development (OECD) are shown for Switzerland, which applies specific duties countries * ODA for basic social services is aid report- Official development assistance (ODA) has declined almost exclusively The World Trade Organization is ed by DAC donors for basic health, education, nutrition, in recent years as a share of donor countries' gross preparing new estimates of trade flows and average tar- and water and sanitation services * Goods admitted national income (GNI) The poorest countries will iffs, the data shown here are from last year's edition of free of tariffs are the value of exports of goods (exclud- need additional assistance to achieve the Millennium the World Development Indicators ing arms) from developing countries admitted without tar- Development Goals Recent estimates suggest that Subsidies to agricultural producers and exporters in iff, as a share of total exports from developing countries $40-60 billion more a year, a doubling of current OECD countnes are another form of barrier to develop- * Average tariff is the simple mean tariff, the unweight- aid levels, would allow most of them to achieve the ing economies' exports The table shows the value of ed average of the effectively applied rates for all prod- goals, if the aid goes to countries with good policies total support to agriculture as a share of the economy's ucts subject to tariffs * Agricultural products comprise At the United Nations International Conference on gross domestic product (GDP) In 2001 agricultural sub- plant and animal products, including tree crops but Financing for Development in 2002 many donor coun- sidies in OECD economies totaled $311 billion excluding timber and fish products * Textiles and cloth- tries made new commitments that, if fulfilled, would The Debt Initiative for Heavily Indebted Poor Ing include natural and man-made fibers and fabrics and add $15 billion to ODA Countries (HIPCs) is the first comprehensive approach articles of clothing made from them * Support to agri- One of the most important things that high-income to reducing the external debt of the world's poorest, culture is the value of subsidies to the agricultural sec- economies can do to help is to reduce barriers to the most heavily indebted countries It represents an tor * HIPC decision point is the date at which a heavily exports of low- and middle-income economies The important step forward in placing debt relief within an indebted poor country with an established track record of European Union has announced a program to eliminate overall framework of poverty reduction While the initia- good performance under adjustment programs support- tariffs on developing country exports of -everything but tive yielded significant early progress, multilateral ed by the International Monetary Fund and the World organizations, bilateral creditors, HIPC governments, Bank commits to undertake additional reforms and to 1.4a develop and implement a poverty reduction strategy _LI=c = _=- - _ * HIPC completion point is the date at which the coun- Goal 8. Develop a global partnership for development try successfully completes the key structural reforms l 33. Net ODA as a percentage of DAC donors' gross national income (table 6.9) agreed on at the decision point, including developing and 34 Proportion of ODA for basic social services (table 1 4) implementing its poverty reduction strategy The country 3 oportwiri o tas (table 9 _T _ 3 then receives the bulk of debt relief under the HIPC Debt 36 Proportion of ODA received in landlocked countries as a percentage of GNI* Initiative without further policy conditions * Estimated 37 Proportion of ODA received In small Island developing states assa percentage of GNI*,-.I. total nominal debt service relief is the amount of debt 38 Proportion of developing country exports (by value, excluding arms) admitted free of duties and service relief, calculated at the decision point, that will quotas (table 1 4) allow the country to achieve debt sustainability at the 39 Average tariffs and quotas on agricultural products and textiles and clothing,(see related- completion point i ndicators in table 6 6) _ _ _________ 40 Agricultural support estimate for OECD countries as a percentage of GDP (table 1 4) -=_ 41 Proportion of ODA provided to help buld trad capacity* , _ The indicators here, and where they appear 42 Number of countries reaching HIPC decision and completion points (table 1 4) throughout the rest of the book, have been com- 43 Debt relief committed-under new HIP tnAitiative (table 14) - - -- piled by World Bank staff from primary and sec- 44 Debt service as a percentage of exports of goods and services (table 4 17) ondary sources Efforts have been made to §5 unemployment rate of 15- to 24-year-olds (see table 2.4 for related indicators) -1 harmonize these data series with those published 46 Proportion of population with access to affordable, essential drugs on a sustainable basis* on the United Nations Millennium Development -47 Telephone lines and mobilesubscribers per 1,000 p les 1 3 and 5 1_0)- Goals Web site (http //www un org/millennium_- 48 Personal computers and Internet users per 1,000 people (table 5 11) goals), but some differences in timing, sources, No data available in the World Development Indicators database and definitions remain 2003 World Development Indicators 1 27 & ~~ Women in development Female Life expectancy Pregnant Literacy Labor force Maternity Women In population at birth women gender gender parity leave decision- receiving parity Index benefits making prenatal Index positions care % of wages paid in % of total Male Female ages covered at ministerial % Of total years years % 15-24 period level 2001 2001 2001 1996 2001 1990 2001 1998 1994 1998 Afghanistan 49 0 43 43 0 5 0.6 Albania 48 9 72 76 1.0 0.7 0 7 0 11 Algeria 49 4 69 72 58 09 0 3 0 4 100 4 0 Angola 50 5 45 48 -25 0 9 09 100 7 14 Argentina 50 9 -71 78 . 1 0 04 0 5 100 0 8 Armenia 51 4 71 78 95 1 0 0 9 0 9 ..3 0 Australia 50 1 76 82 0.7 0.8 0 13 14 Austna 51 4 - 76 81 07 07 100 16 20 Azerbaijan 50 9 62 69 95 0.8 08 5 10 Bangladesh_ 49 6 61 62 23 07 07 0 7 100 8 5 Belarus 53 1 62 74 _ 10 1.0 1.0 100 3 -3 Belgium 50 9 - 75 82 07 0.7 82a 11 3 Benin 50 7 51 55 60 05 0 9 09 100 10 13 Bolivia 50 2 61 65 52 1 0 06 0.6 70b 0 6 Bosnia and_H-erzegovina 50 5 71 76 0 6 0 6 0 6 Botswana 50 2 39 38 92 1 1 0 9 0.8 25 6 14 Brazil - 50.6 64 72 74 1 0 0 5 0 6 100 5 4 Bulgaria 51 3 68 75 1 0 0 9 0 9 100 0 Burkina Faso 5-0 5 43 44 59 0.5 09 0 9 100 7 10 Burundi 51 0 41 42 88 1 0 1 0 0 9 50 7 8 Cambodia 51 3 52 55 52 0 9 1 2 1.1 50 0 Cameroon 50 0 48 50 73 1 0 .06 0 6 100 3 6 Canada 50 5 76 82 0 8 0 8 55c 14 Central African Republic 51 2 - 42 43 67 0 8 ..50 5 4 Chad 50 5 47 50 30 08 0.8 0 8 50 5 0 Chile 50-5 73 79 91 1 0 0.4 0 5 100 13 13 China 49 0 69 -72 79 1 0 0_8 0 8 100 6 Hong Kong, China 50 8 77 83 100 1 0 0.6 0 6 Colombia 50.5 69 75 83 1 0 06 0 6 100 11 18 Congo, Dem Rep 50_5 45_ 46 ~ 66 0 9 0 8 0 8 67 _6 Congo,Rep 51 0 49 54 55 1 0 0 8 0 8 100 6 6 Costa Rica 50 1 75 80 95 1 0 0 4 0.5 100- 10 -15 COte dIlvoire - - 49 2 45 46 83 0.8 0.5 0.5 100 8 3 Croatia 51.7 69 78 1 0 0 7 0 8 ..4 12 Cuba 50 0 75 79 100 1.0 0 6 0 7 100 0 5 Czech Republic 51.2 72 78 0 9 0 9 0 17 Denmark - 50-5 74 79 . 0.9 0 9 lood 29 41 Dominican Republic 49 2 65 70 97 1 0 0.4 0 5 100 4 10 Ecuador 49 8 - 69 72 75 1 0 _ 0 3 0.4 100 6 20 Egpt, Arab Rep 49.1 67 - 70 53 0 8 0 4 0.4 100 _ 4 6 El Salvador 50 9 67 73 69 1 0 0.5 0.6 -75 10 6 Eritrea 50 4 50 -52 19 0 8 0 9 0.9 7 5 Esto-nia 53 5 65 76 1 0 1 0 1 0 15 12 Ethiopia 49 8 41 4-3 20--- 0-8 0 7 0 7 100 10 5 Finland 51 2 75 82 0.9 0 9 80 39 29 France 51 4 76 83 0.8 0 8 100 7 12 Gabon -50 5 52 54 86 0.8 0.8 100 7 3 Gambia,The 50 5 52 55 _ 91 0.8 08 0 8 -100 -0 29 Georgia -52 5 69 77 95 0.9 0 9 0 4 Germany 50 9 75 81 . 0 7 0 7 100, 16 8 Ghana 50 2 ~ 55 57 86 1 0 1.0 1 0 50 11 9 Greece 50 8 75 81 1 0 0 5 0 6 75 4 5 Guatemala 49 6 62 68 ~ 53 0 9 0.3 0.4 100 19 0 Guinea 49 7 46 47 5-9 0 9 09 100 9 8 Guinea-Bissau 50 7 44 47- 50 0.6 0.7 0 7 I 00 4 18 Haiti 50 9 50 55 68 1.0 0 8 0 8 1ood 13 0 ~ l 2003 World Develop.ent Indicators Women in development 1.5 Female Life expectancy Pregnant Literacy Labor force Maternity Women In population at birth women gender gender parity leave decision- receiving parity Index benefits making prenatal Index positions care % of wages paid in % of total Male Female ages covered at ministerial % of total years years % 15-24 period level 2001 2001 2001 19se 2001 1990 2001 1998 1994 1998 Honduras 49 7 63 69 73 1,0 0 4 0 5 l00e 1 Hungary 52 3 67 76 1 0 0 8 0 8 100 0 5 India 48 4 62 64 62 0 8 0 5 0 5 100 3 Indonesia 50.1 65 68 82 1 0 0 6 0 7 100 6 3 Iran, Islamic Rep 49 8 68 70 -62 1 0 0 3 0 4 67 0 0 Iraq 49 2 - 61_ 63 59 0 5 0 2 0 3 100 0 0 Ireland 50 5 74 79 0 5 0 5 7Of 16 21 Israel 50 3 77 81 90 1.0 0 6 0-7 75 4 0 Italy 51 5 75 82 1 0 0 6 0 6 80 12 13 Jamaica 50 8 74 78 98 1 1 0 9 0 9 100g 5 12 Japan 51 1 78 85 0 7 0 7 60 6 0 Jordan 48 3 70 73 80_ 1I0 0 2 0 3 100 3 2 Kazaknhstan 51 6 58 68 92 -10 0 9 0 9 6 5 Kenya 49 9 46 47 95 1 0 0 8 0 9 100 0 0 Korea, Dem Rep 49 8 60 63 100 0-8 0 8 0 Korea,Rep 49 7 70 77 96 1 0 06 0 7 100 4 Kuwait 46 8 75 79 99 1 0 0 3 0 5 100 0 0 Kyrgyz Republic 51 1 62 70 90 .09 0 9 0 4 Lao PDR 50 0 53 55_ 25 0.8 100 0 0 Latvia 54 1 65 76 1 0 1 0 1 0 0 7 Lebanon 50 8 69 72 85 1 0 0 4 0 4 100 0 0 Lesotho 50 4 43 44 91 1 2 0 6 0 6 0 6 6 Liberia 49 7 46 48 0 0 6 0 6 07_ 0 0 0 Libya 48 2 70 74 100 0.9 0 2 0 3 50 0 7 Lithuania 52 8 68 78 1 0 0 9 0 9 0 6 Macedonia, FYR 50 0 71 -75 0 7 0 7 8 9 Madagascar 50 1 54 57 78 0 9 0 8 0 8 loot 0 19 Malawi 50 8 38 39 90 0 8 1 0 0 9 9 4 Malaysia 49 4 70 75 90 1 0 0 6 0 6 100 7 16 Maili 51 0 40 43 25 0_5 0 9 0 9 100 10 21 Mauritania 50 4 49 53 49 0 7 0 8 0 8 100 0- 4 Mauritius 50 5 69 76 99 -10 0 4 0 5 100 3 Mexico 51 4 70 76 -71 1 0 0,4 0 5 100 5 5 Moldova 52.4 64 71 1 0 0 9 0 9 0 0 Mongolia 50 4 64 67 90 1 0 0 9 0 9 - 0 0 Morocco 50 0 66 70 45 0 8 -05 05_ 100 0 0 Mozambique 51-4 41 43 -54 0 6 0 9 0 9 100 4 0 Myanmar - 50 3 54 60 80 1 0 0 8 0 8 67 0 0 Namibia 50 5 44 44 88 10_ 0 7 0 7 10 8 Nepal 48 7 60 59 15 06 07 0 7 100 -0 3 Netherlands 50 5 75 81 0 6 0 7 100 31 28 New Zealand 51 1 76 81 0 8 0 8 0 8 8 Nicaragua 50 2 66 71 71 ~ 10 0 5 0 6 60 10 5 Niger 50 6 44 48 30 0 4 0 8 0 8 50 5 10 Nigeria 50 6 45 47 60 0 9 0 5 0 6 50, 3 6 Norway 50 5 76 81 . 0 8 0 9 100 35 20 Oman 47 4 72 75 98 1 0 0 1 0 2 0 0 Pakistan 48 2 62 65 27 0 6 0 3 0 4 100 4 7 Panama 49. 6 72 77 72 1.0 0 5 0 6 100 13 6 Papua New Guinea 48 5 56 ~ 58 70 0.9 0 7 0 7 0 0 0 Paraguay 49 6 68 73 83 1 0 0 4 0 4 50h 0 7 Peru -49 7 67 72 64 1 0 0 4 0-5 100 6 10 Philippines 49 6 68 72 83 1 0 0 6 0 6 100 8 10 Poland 51 4 69 78 1 0 0 8 0 9 100 17 12 Portugal 52 0 73 79 1 0 0 7 0 8 100 10 10 Puerto Rico 51 9 72 81 99 1 0 0 5 0 6 2003 World Development Indicators 1 29 0 Women in deveGopment Female Life expectancy Pregnant Literacy Labor force Maternity Women In population at birth women gender gender parity leave decision- receiving parity Index beneflts making prenatai Index positions care 9k of wages paid in % of total Male Female ages covered at ministerial % of total years years 9k 15-24 period level 2001 2001 2001 1996 2001 1990 2001 1998 1994 1998 Romania 51 1 66 74 1 0 08 08 50-94 0 8 Russian Federation 53 3 59 72 1 0 0 9 1 0 100 0 8 Rwanda 50 5 39 40 94 1 0 1 0 1 0 67 9 5 Saudi Arabia 45 8 71 75 87 1 0 0 1 0 2 50-100 -0 0 Senegal 50 2 51 54 74 0 7 0 7 0 7 100 7 7 Sierra Leone 50 9 36 39 30 0 6 0 6 0 10 Singapore 48 7 76 80 100 1 0 0 6 0 6 100 0 0 Slovak Republic 51 3 69 77 0 9 0 9 5 19 Slovenia 51 3 72 79 1 0 0 9 0 9 5 0 Somalia 50 4 46 49 0 0 8 0 8 0 0 0 South Africa 51 7 46 48 89 1 0 0 6 0 6 45 6 Spain 51 1 75 82 1 0 0 5 0.6 100 14 18_ SriLanka 50 6 71 76 100 1 0 0 5 0 6 100 3 13 Sudan 49 7 57 59 54 0 9 0 4 0 4 100 0 0 Swaziland 51 8 44 45 0 1 0 0 6 0 6 0 0 0 Sweden 50 4 78 82 0 9 0 9 75 30 43 Switzerland 50 4 77 83 0 6 0 7 100 17 17 Syrian Arab Republic 49 5 68 72 33 0 8 0 3 0 4 100 7 8 Tajikistan 50 2 64 70 90 1 0 0 7 0 8 3 6 Tanzania 50 4 43 44 92 0.9 1 0 1 0 100 13 13 Thailand 50 8 67 71 77 1 0 0 9 0 9 1001 0 4 Togo 50 3 48 51 43 0 7 0 7 0 7 100 5 9 Trinidad and Tobago 50 1 70 75 98 1 0 0 5 0 5 60-100 19 14 Tunisia 49 5 70 74_ 71 0 9 0.4 0 5 67 4 3 Turkey 49 5 67 72 62 1 0 0 5 0 6 67 5 5 Turkmenistan 5-0 5 61 69 90 0 8 0 8 3 4 Uganda 50 0 43 43 87 0 9 0 9 0 9 1001 10 13 Ukraine 53 5 63 74 1 0 1 0 1 0 100 0 5 United Arab Emirates 34 2 74 77 95 1 1 0 1 0 2 100 0 0 United Kingdom 50 9 75 80 .0 7 0 8 90k 9 24 United States 51 1 75 81 .0.8 0 9 0 14 26 Uruguay 51 5 71 79 80 1 0 0 6 0 7 100 0 7 Uzbekistan 50 3 64 71 90 1 0 0 8 0.9 3 3 Venezuela, RB 49 7 71 77 74 1.0 0 5 0 5 100 11 3 Vietnam 50 6 67 72 78 1 0 1 0 1.0 100 5 0 West Bank and Gaza 49 3 70 75 Yemen, Rep 49 0 56 58 26_ 0 6 0.4 0 4 100 0 0 Yugoslavia, Fed Rep 50 2 70 75 0 7 0 8 5 Zambia 50 3 37 38 92 0 9 0 8 0.8 100 5 3 Zimbabwe 49.9 40 39 93 1 0 0 8 0 8 60-75 3 12 Low Income 49 3 58 60 62 0.9 0.6 0 6 4 6 Middle Income 49 5 67 72 77 1 0 0 7 0 7 5 Lower middle income 49 3 67 71 76 1.0 0 8 0 8 4 Upper middle income 50 5 68 75 80 1 0 0 5 0 6 6 8 Low &middie Income 49 4 63 66 70 0 9 0 7 0 7 5 East Asia & Pacific 489_ 67 71 80 1 0 0 8 0 8 3 Europe &Central Asia 51 8 64 73 1 0 0 8 0 9 3 7 Latin America &Carib 50 6 67 74 75 1 0 0 5 0 5 8 8 Middle East &N Africa 49 2 67 70 58 0 9 0 3 0 4 2 2 South Asia 48 5 62 63 55 0 8 0 5 0.5 4 Sub-Saharan Africa 50 3 45 47 65 0.9 0.7 0 7 5 8 High Income 50 7 75 81 .0 7 0 8 13 13 Europe Emu 51 0 75 82 0 7 0 7 16 15 a For 30 days, 75 percent thereafter b Benefit is 70 percent of wages above the minimum wage and 100 percent of the national miniMLM wage c For 15 weeks d For 6 weeks e For 84 days f Up to a ceiling g For 8 weeks h For 9 weeks i Benefit is 100 percent for the first 45 days, then 50 percent for 15 days For 1 month k For 6 weeks, flat rate thereafter 30 II 2003 Worid Development Indicators Women in development El Despite much progress in recent decades, gender Women who work outside the home continue to * Female population is the percentage of the popula- inequalities remain pervasive in many dimensions of bear a disproportionate share of the responsibility for tion that is female * Lfe expectancy at birth is the life-worldwide But while disparities exist throughout housework and child rearing They also face discrimi- number of years a newborn infant would live if prevail- the world, they are most prevalent in poor developing natory practices in the workplace, especially relating ing patterns of mortality at the time of its birth were to countries The differences in outcomes between men to equal pay and benefits The maternity benefits stay the same throughout its life * Pregnant women and women-and between boys and girls-are a con- data in the table relate only to legislated benefits and recelving prenatal care are the percentage of women sequence of differences in the opportunities and do not include contractual benefits negotiated attended at least once during pregnancy by skilled resources available to them Inequalities in the allo- through labor union contracts The benefits generally health personnel for reasons related to pregnancy cation of such resources as education, health care, apply only in the formal sector, leaving out the vast * Literacy gender parity Index is the ratio of the and nutrition matter because of the strong associa- majority of working women in developing countries As female literacy rate to the male rate, for the age group tion of these resources with well-being, productivity, a result, while the situation in the United States is 15-24 * Labor force gender parity Index is the ratio and economic growth This pattern of inequality much better than the data indicate, the situation in of the percentage of women who are economically begins at an early age, with boys routinely receiving a Thailand is likely to be much worse active to the percentage of men who are According to larger share of education and health spending than Women are vastly underrepresented in decision- the International Labour Organization's (ILO) defini- girls do, for example making positions in government, although there is tion, the economically active population is all those Life expectancy has increased for both men and some evidence of recent improvement While 6 per- who supply labor for the production of goods and serv- women in all regions, but female morbidity and mor- cent of the world's cabinet ministers were women in ices during a specified period It includes both the tality rates sometimes exceed male rates, particu- 1994, 8 percent were in 1998 Without representa- employed and the unemployed While national prac- larly during early childhood and the reproductive tion at this level, it is difficult for women to influence tices vary in the treatment of such groups as the years In high-income countries women tend to out- policy armed forces and seasonal or part-time workers, in live men by four to eight years on average, while in For information on other aspects of gender, see general the labor force includes the armed forces, the low-income countries the difference is narrower- tables 1 2 (Millennium Development Goals eradicat- unemployed, and first-time job seekers, but excludes about two to three years The female disadvantage is ing poverty and improving lives), 2 3 (employment by homemakers and other unpaid caregivers and workers best reflected in differences in child mortality rates economic activity), 2 4 (unemployment), 2 13 (edu- in the informal sector * Matemity leave benefits refer (see table 2 20) Child mortality captures the effect cation efficiency), 2 14 (education outcomes), 2 17 to the compensation provided to women dunng mater- of preferences for boys because adequate nutrition (reproductive health), 2.19 (health risk factors and nity leave, as a share of their full wages * Women In and medical interventions are particularly important future challenges), and 2 20 (mortality) decislonmaking positions are those in ministerial or for the age group 1-5 Because of the natural female equivalent positions in the government biological advantage, when female child mortality is as high as or higher than male child mortality, there is good reason to believe that girls are discriminated against Female disadvantage in mortality is carried into adolescence and the reproductive years Serious health risks for adolescents arise when they become sexually active And while in high-income countries women have universal access to health care during pregnancy, in developing countries it is estimated that 35 percent of pregnant women-some 45 mil- lion each year-receive no care at all (United Nations 2000b) Prenatal care is essential for recognizing, diagnosing, and promptly treating complications that arise during pregnancy Girls in many developing countries are allowed less _ - education by their families than boys are-a dispari- The data are from the World Bank's population data- ty reflected in lower female primary enrollment (see base, electronic databases of the United Nations table 1.2) and higher female illiteracy. As a result, Educational, Scientific, and Cultural Organization women have fewer employment opportunities, espe- (UNESCO), the ILO database Estimates and cially in the formal sector A labor force gender pan- Projections of the Economically Active Population, ty index of less than 1 0 shows that women's labor 1950-2010, and the United Nations' World's force participation in the formal sector is lower than Women. Trends and Statistics 2000 men's (A ratio of 1 0 indicates gender equality) 2003 World Development Indicators 1 31 Key indicators for other economDies Population Surface Population Gross national Income Gross domestic Life Adult Carbon area density product expectancy Illiteracy dioxide at rate emissions birth Per Per Per n thousand people capita capita capita ages 15 thousand thousands sq km per sq km $ millions $ $ millions $ % growth % growth years and above metric tons 2001 2001 2001 2001 b 2001 b 2001 2001 2000-01 2000-01 2001 2001 1999 American Samoa 70 0 2 350 c .. . 286 Andorra 70 0 5 140 d Antigua and Barbuda 68 0 4 156 627 9,150 654 9,550 0 2 -0 5 348 Aruba 90 0 2 474 d 1,905 Bahamas, The 310 13 9 31 d , 70 5 1,795 Bahrain 651 0 7 917 7,246 11,130 10,020 15,390 0 0 -0 4 73 12 19,012 Barbados 268 0 4 624 2,614 9,750e 4,052 15,110 1.5 1 0 75 0 2,034 Belize 247 23 0 11 727 2,940 1,273 5,150 5 1 2.1 74 7 619 Bermuda 60 0 1 1.200 d 462 Bhutan 828 47 0 18 529 640 7 0 4 0 63 385 Brunei 344 5 8 65 , d 76 8 4,668 Cape Verde 446 4 0 111 596 1,340 2,471 5,540f 3 3 0 6 69 25 139 Cayman Islands 35 0 3 135 d , 282 Channel Islands 149 0 2 768 d , 79 Comoros 572 2 2_ 256 219 380 1,080 1,890 1 9 -0 5 61 44 81 Cyprus 761 9 3 82 9,372 12,320 16,060f 21,110f 4 0 3 5 78 3 6,020 Djibouti 644 23 2 28 572 890 1,562 2,420 1 6 -0 4 45 35 385 Dominica 72 0 8 96 230 3,200 354 4,920 -4.3 -4 1 76 81 Equatorial Guinea 469 28 1 17 327 700 1 3 _ -1 3 51 16 649 Faeroe Islands 50 1 4 36 d ,, 649 Fiji 817 18 3 45 1,755 2,150 4,017 4,920 2 6 2 0 69 7 725 French Polynesia 237 4.0 65 ,, d 73 542 Greenland 60 341 7 0 d , 539 Grenada 100 0 3 _ 295 363 3,610 632 6,290 -4.7 -6 0 73 213 Guam 157 0 6 285 d 78 4,071 Guyana 766 215 0 4 641 840 3,280 4,280 1 5 0 8 63 1 1,685 Iceland 282 103 0 3 8,152 28,910 8,135 28,850 3 0 2 3 80 2,066 Isle of Man 80 0.6 133 c This table shows data for 56 economies-small e Populatlon is based on the de facto definition of U S dollars converted using the World Bank Atlas economies with populations between 30,000 and 1 population, which counts all residents regardless of method (see Statistical methods) * GNI per capita is million and smaller economies if they are members of legal status or citizenship-except for refugees not gross national income divided by midyear population the World Bank Where data on gross national income permanently settled in the country of asylum, who are GNI per capita in U S dollars is converted using the (GNI) per capita are not available, the estimated generally considered part of the population of their World Bank Atlas method o PPP GNI is gross nation- range is given For more information on the calcula- country of ongin The values shown are midyear esti- al income converted to international dollars using pur- tion of GNI (gross national product, or GNP, in the mates for 2001 See also table 2 1 ° Surface area is chasing power parity rates An international dollar has 1968 United Nations System of National Accounts) a country's total area, including areas under inland the same purchasing power over GNI as a U S dollar and purchasing power parity (PPP) conversion factors, bodies of water and some coastal waterways has in the United States. o Gross domestic product see About the data for table 1.1. Since 2000 this v Population density is midyear population divided by (GDP) is the sum of value added by all resident pro- table has excluded France's overseas departments- land area in square kilometers - Gross national ducers plus any product taxes (less subsidies) not French Guiana, Guadeloupe, Martinique, and Income (GNI) is the sum of value added by all resident included in the valuation of output Growth is calculat- Reunmon-for which GNI and other economic meas- producers plus any product taxes (less subsidies) not ed from constant price GDP data in local currency ures are now included in the French national included in the valuation of output plus net receipts of v GDP per capita is gross domestic product divided by accounts primary income (compensation of employees and midyear population * Ufe expectancy at birth is the property income) from abroad. Data are in current number of years a newborn infant would live if prevail- 39 0II 2003 World Development Indicators Key indicators for other economies 1.0 Population Surface Population Gross national income Gross domestic Ufe Aduit Carbon area density product expectancy Illiteracy dioxide at rate emissions birth Per Per Per n thousand people capita capita capita ages 15 thousand thousands sq km per sq km $ millions $ $ millions $ % growth % growth years and above metric tons 2001 2001 2001 2001 b 2001b 2001 2001 2000-01 2000-01 2001 2001 1999 Kiribati 93 0 7 127 77 830 1 6 -0 7 62 26 Liechtenstein 30 0 2 188 d Luxembourg 441 2 6 170 17,571 39,840 21,416 48,560 1 0 0 3 77 8,024 Macao, China 440 6,329g 14,380g 9,518 21,630 2 1 1 7 79 6 1,517 Maldives 280 0 3 934 562 2,000 2 1 -0 2 69 3 465 Malta 395 0 3 1,234 3,637 9,210e 5,192 13,140 -0 7 -2 0 78 8 3,422 Marshall Islands 53 0 2 263 115 2,190 0 6 -0 7 Mayotte 145 0 4 388 c Micronesia, Fed Sts 120 0 7 172 258 2,150 0 9 -0 9 68 Monaco 30 0 0 15,789 d Netherlands Antilles 220 0 8 275 d 3 5,606 New Caledonia 216 18 6 12 d 73 1,667 Northern Mariana Islands 80 0 5 160 d Palau 20 0 5 42 132 6,780 . 1 0 -1 1 242 Qatar 598 110 54 d 75 18 51,699 Samoa 174 2 8 61 260 1,490 1,067 6,130 100 8 7 69 1 139 Sao Tome and Principe 151 1 0 157 43 280 3 0 0 9 65 88 Seychelles 82 0 5 183 538 6,530 -8 1 -9 4 73 216 Solomon Islands 431 28 9 15 253 590 825f 1,910f -9 0 -11 5 69 165 San Manno 30 0 1 300 d St Kitts and Nevis 45 0 4 125 299 6,630 459 10,190 1 7 -0 7 71 103 St Lucia 157 0 6 257 619 3,950 778 4,960 -3 7 -4 6 72 322 St Vincent and the Grenadines 116 0 4 297 317 2,740 577 4,980 -0 6 -1 3 73 161 Suriname 420 163 3 3 761 1,810 5 9 5 2 70 2,151 Timor-Leste 753 14 9 51 391 520 Tonga 101 0 8 140 154 1,530 . 3.1 2.6 71 121 Vanuatu 201 12 2 17 212 1,050 626 3,110 -4 0 -6 0 68 81 Virgin Islands (U S) 109 0 3 322 d 78 13,106 a PPP is purchasing power parity, see Definitions b Calculated using the World Bank Atlas method c Estimated to be upper middle income ($2,976-9,205) d Estimated to be high income ($9,206 or more) e Included in the aggregates for upper.middle-income economies on the basis of earier data f The estimate is based on regression, others are extrapolated from the latest International Comparison Programme benchmark estimates g Refers to GDP or GDP per capita ing patterns of mortality at the time of its birth were to stay the same throughout its life * Aduit Iliteracy rate is the percentage of people ages 15 and above who cannot, with understanding, read and write a short, simple statement about their everyday life * Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring The indicators here and throughout the rest of the book have been compiled by World Bank staff from primary and secondary sources More infor- mation about the indicators and their sources can be found in About the data, Definitions, and Data sources for tables in subsequent sections 2003 World Development Indicators 1 33 I I I / I II I / i (t - L- - evelopment has often bypassed the poorest people-and sometimes increased their disadvantage. Attacking poverty directly has therefore become an urgent global priority. Poverty is commonly measured by income, but it has many other dimensions. Poor people not only lack money, but they also lack resources, opportunities, and access to services such as health and education. So to help people move out of poverty, countries need to take action in three areas: They need to stimulate growth and make markets work for the poor. They need to invest in health, nutrition, and education-increasing human capital. And they need to provide effective mechanisms for reducing vulnerability to economic shocks, natural disasters, ill health, and disability. This section measures income poverty and tracks the progress countries have made in developing their human capital and in reducing the vulnerability of their people. 35 IBoostfng growtlD to OlMt peopoe out of pove3rty A paradox of the second half of the 20th century is that the l - E -- world population underwent unprecedented growth-from 2.5 a . billion in 1950 to more than 6 billion in 2001-even as the % of population ages 15-64 population growth rate was declining (figure 2a). The decline was triggered largely by a drop in fertility rates. Between 1952 and 2001 fertility rates fell from 5.1 to 2.7 births per woman. Thus while the population grew by 1.5 percent a year in 80 1980-2001, the growth rate is expected to drop to 1 percent in 2001-15 (table 2.1). During the transition from high fertility and rapid population 60 growth to lower fertility and slower growth, the working-age pop- ulation expands relative to the dependent (younger and older) As0-14 population, opening a demographic window of opportunity for 40 economic growth. Countries can take advantage of this one-time opportunity if they invest appropriately in their human and physi- cal capital and create employment opportunities for youth and for 20 Ages 65+ those who have not been working for wages. Several countries in East Asia, such as the Republic of Korea and Thailand, and a few in Latin America, such as Brazil and Mexico, have done so (figure 0 ' ' t~~~~~~~~~~960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2b). But South Asian countries that are now moving into the later stage of their transition to low fertility may not benefit from the demographic transition if they do not encourage growth, invest- Source World Bank data ment, and human capital development. The demographic window for these countries will close within a generation. In many developing countries agriculture is still the main In developing countries gross domestic product (GDP) grew by economic activity (table 2.3). As economies grow, more people 3.3 percent a year in the 1990s, and the share of people living work for wages In most countries wages are rising steadily, on less than $1 a day fell from 29 percent to 23 percent. By increasing prosperity and raising standards of living (table 2.5). 1999, 125 million fewer people were living in extreme poverty. But the poorest are often excluded from all but the lowest level of economic activity. 22 Progress in reducing poverty has been uneven. Within coun- _. ;.... -. - . - tries, the large gaps in social indicators between rich and poor confirm the persistence of deprivation (table 2.7). Globally, much of the decline in income poverty took place in East Asia, Population size ibililons) 7 where sustained growth in China has lifted nearly 150 million people out of poverty since 1990. And faster growth in India has led to a modest decline in the number of poor people in South 6 _ Asia. But in other regions the number of poor people has increased even as their share in the population has declined- 5 _ and in Europe and Central Asia both the number and the share of poor people have risen. Unemployment is high in many of the 4 formerly centrally planned economies, with long-term unemploy- ment hovering around 50 percent of total unemployment in the 3 Czech Republic, Estonia, and Latvia in 1998-2001 (table 2.4). [Enhancing zocwviDty foir poor peopos 2 Poor people face many risks. They face labor market risks, often having to take precarious jobs in the informal sector and I put their children to work to increase household income. In Sub- Saharan Africa one in three children ages 10-14 was in the o _ labor force in 2001 (table 2.9). Poor people also face health 1927 1960 1974 1987 1999 risks, with illness and injury having both direct and opportunity costs. In South Asia nearly 80 percent of all spending on health Source World Bank staff estimates comes from private sources, much of it out of pocket, exposing 383 0 2003 World Development Indicators 2c low participation by poor people. But in many poor countries it lnt VANV.1r_TMr.AiI _ also has a gender dimension, reflecting traditional biases 07= 17M against girls' education and reliance on girls' contributions to the household (figure 2c; table 2.13) One consequence of this % of group attaining grade ievei imbalance: higher rates of illiteracy among women (table 2 14) Richest The public sector is the main provider of health care in devel- 20 _perent oping countries-training medical personnel, investing in hospi- 80 tals, and directly providing medical care (table 2.15). To reduce inequities, many countries have emphasized primary health care, including immunization, provision of sanitation, access to 60 safe drinking water, and safe motherhood initiatives (tables Males 2.16 and 2.17). Even so, much remains to be done. Child mal- nutrition remains a burden, with 22 countries having rates of 40 more than 30 percent in the 1990s (table 2.18). An estimated Femas 40 million people are living with HIV/AIDS, an unprecedented public health challenge (table 2.19). And the reemergence of old 20 diseases such as tuberculosis in Europe and Central Asia and Poorest parts of South and East Asia has put severe strains on health 40 percent budgets. A high prevalence of disease in a country goes hand- 0 in-hand with poor economic performance (figure 2d). 6 7 8 9 10 11 12 13 14 Grade level Source Demographic and Health Survey There are many ways to measure poverty and its effects on people's lives. The indicators reported here suffer from many many poor households to the impoverishing effects of needed shortcomings, noted in About the data for each table. But health care (table 2.9) taken together, the indicators provide a broad picture of how Enhancing security for poor people means reducing their vul- well different economies are doing in reducing poverty, enhanc- nerability to ill health and economic shocks. Market-based insur- ing human security, and building human capital-and how large ance and pension schemes can reduce risk significantly, but they a task still faces many developing countries. play only a minor role in many developing countries. In 16 devel- oping countries public spending on pensions amounted to less 2d than 0.5 percent of GDP in the 1990s (table 2.10). To increase = _ ! _ _ ET the security of poor people, national poverty reduction strategies must support their immediate consumption needs and protect GNI per capita I$ thousands), 2001 their assets by ensuring access to basic services. Literacy train- ing and health and nutrition services are often the most needed and most valued by poor people. Yet government spending in 8 these areas remains low in many countries. In 2000 low-income countries' public spending on health averaged 1 percent of GDP, 7 compared with 6 percent for high-income countries (table 2.10). 6 Building human capital through education and health 5 services Poor people lack the means to escape poverty. Increasing the 4 productivity of their labor through investments in education and * health is often the most effective way to improve their welfare. 2 Investments in education widen horizons, making it easier for 1 people to take advantage of new opportunities and helping them to participate in social and economic life. But despite increased 0 o lo 20 30 40 50 60 70 80 90 spending on education, particularly primary education (table 2.11), enrollment rates remain low in many countries. In Sub- Life expectancy (years), 2001 Saharan Africa primary enrollment rates declined between 1980 and 2000 (table 2.12) Low primary enrollment typically reflects Source World Bank data 2003 World Development Indicators I 37 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as proportion of Ages Ages Ages work~ing-age 0-14 15-64 65+ population per 1.000 per 1,000 millions % % Young Old people people 1980 2001 2015 1980-2001 2001-15 2001 2001 2001 2001 2001 2001 2001 Afghanistan 16 0 27 2a 38.8 2 6 2 5 43 7 53 5 2 8 0,8 0 1 -21 48 Albania 2 7 3 2 3 6 0.8 -1.0 28 6 64 5 6 9 0 5 0 1 6 17 Algeria 18 7 30 8 38 3 2 4 1 5 35 4 60 7 3 9 06 0 1 5 23 Angola 7 1 13 5 19 6 3 1 2 6 47 4 49 7 2 9 1 0 -01 19 47 Argentina 281_ 37 5 42 8 1 4 1 0 275_ 62 8 9 7 04 0 2 8 19 Armenia 3 1 3 8 4 0 1 0 0 3 23 0 67 7 9 3 0.4 0 1 7 11 Australia 14 7 19 4 21 5 1 3 0 7 20 5 67 5 12 0 0 3 0 2 7 13 Austria 7 6 8 1 80 0 4 -0 1 16 5 67.8 15 7 0 2 0 2 9 9 Azerbaijan 6 2 8 1 8 9 1 3 0 7 28 4 64 4 7 2 0.5 0 1 6 16 Bangladesh 85 4 133 3 166 0 2 1 1 6 37 0 59 7 3 3 0 6 0 1 9 28 Belarus 9 6 10 0 9 3 0 2 -0 5 18 0 68.3 13 6 0 3 0 2 14_ 9 Belgium 9 8 10 3 10 4 0 2 0 0 17 .2 66.1 16 6 0 3 0 3 10 11 Benin 3 5 6 4 9 0 3 0 2 4 45 9 51 4 2 7 0 9 0 1 13 39 Bolivia 5 4 8 5 10 9 2 2 1 8 39 1 56 5 44 0 7 0 1 8 30 Bosnia and Herzegovina 4 1 4 1 4 4 0 0 0 5 18 6 71.3 10 2 0 3 0 1 8 12 Botswana 0 9 1 7 1 8 3 0 0 5 42 0 55 7 2 2 0 8 00_ 21_ 31 Brazil 121 6 172 4 201 0 1.7 1 1 28.4 66 4 5 2 0 4 0 1 7 19 Bulgaria 8 9 8.0 7 3 -.0.5 -0 7 15.2 68 6 16.2 0 2 0 2 14 9 Burkina Faso 7 0 11 6 15 6 2 4 2 1 47.1 50 2 2 7 0 9 0 1 19 44 Burundi 4 1 6 9 8 8 2 5 1 7 46 0 51 4 2 6 0 9 0 1 20 39 Cambodia 6 8 12.3 15 1 2.8 1-5 43 0 54 2 2 8 0 8 0 1 12 29 Cameroon 8 7 15.2 19.4 2 6 1 7 41 6 54 8 3 7 0 8 0 1 15 36 Can ada 24 6 31 1 33 7 1 1 0 6 18 8 68 6 12 6 0 3 0 2 7 11 Central African Republic 2 3 3 8 4 6 2 3 1 5 42 3 54 2 3 5 0 8 0 1 20 36 Chad 4 5 7 9 11 8 2 7 2 9 49 6 47.4 3 0 1.1 0 1 16 45 Chile 11-1 15 4 17 7 1 5 1 0 27 8 65 0 7 2 0 4 0 1 6 17 China 981 2 1,271 8 1,392 6 1 2 0 6 24 8 68.1 71I 0 4 0 1 7 15 Hong Kong, China 5 0 6 7 7 0 1 4 0 3 -167 72 0 11 2 0 2 0 2 5 7 Colombia 28 4 43 0 51 5 2 0 1 3 32 4 62.9 4 7 0 5 0 1 6 22 Congo,Dem Rep 26 9 52 4 75 7 3 2 2 6 47 6 49.8 2 6 1 0 01 17 45 Congo,Rep 1 7 3 1 4 6 3 0 2 7 46 3 50 5 3 2 0 9 0 1 14 42 Costa Rica 2 3 3 9 4 7 2 5 1 4 31 2 63 1 5 7 0 5 0 1 4 20 C6ted'lvoire 8 2 16 4 20 5 3 3 1 6 42 1 55 3 2 6 0 8 0 0 17 37 Croatia- 4 6 4 4 4.2 -0 2 -0 3 16 7 68.1 15 2 0 2 0 2 11 9 Cuba 9 7 11 2 11 7 0 7 0 3 21 1 68 8 10 1 0 3 0 1 7 12 Czech Republic- 10 2 10 2 9 9 0 0 -0 2 16 1 70 1 13 8 0 2 0 2 11 9 Denmark 5 1 5 4 5 4 0 2 0.1 18 4 66.7 14 9 0 3 0 2 11 12 Dominican Republic 5 7 8 5 101I 1 9 1 3 33 0 62 7 4.4 0 5 0 1 7 23 Ecuador 80_ 12 9 15 8 2 3 1 5 33 4 _619 4 7 0 6 0 1 6 24 Egypt, Arab Rep 40 9 65 2 80 9 2 2 1 5 34 7 61.1 4 2 0.6 0 1 6 25 El Salvador 4 6 6 4 8 0 1 6 1 6 35 3 59 7 5 0 0 6 0 1 6 26 Eritrea 2 4 4 2 5 8 2 7 2 3 45 2 52.2 2 6 0 9 0 1 13 38 Estonia 1 5 1 4 1 3 -0 4 -0 5 17 1 68 0 14 9 0 3 0 2 14 9 Ethiopia 37 7 65 8 88 2 2 7 2 1 46 0 51 2 2 8 0 9 0 1 20 43 Finland 4 8 5 2 5 3 0.4 0 1 17 9 67 0 15 0 0 3 0 2 9 11 France 53 9 59 2 61 8 0 4 0 3 18 7 65 1 16 1 0 3 0 2 9 13 Gabon 0 7 1 3 1 7 2 9 2 2 40 3 54 1 5 7 0 7 0 1 15 35 Gambia,The 0 6 1 3 1 8 3 5 2 0 40 3 56.5 3 2 0 7 0 1 14 38 Georgia 5 1 5 3 4 8 0 2 -0 7 20 1 66 8 13 1 0 3 0 2 10 8 Germany 78 3 82 3 80 1 0 2 -0 2 15 3 68 3 16 4 0 2 0 2 10 9 Ghana 10 7 19 7 24 7 2 9 1 6 43 3 52 2 4 6 0 9 0 1 12 29 Greece 9 6 10 6 10 7 0 4 0 1 14 9 67 0 18 1 0 2 0 3 11 11 Guatemala 6 8 11 7 16 3 2 6 2 4 43 2 53 3 3 5 0 8 0 1 7 33 Guinea 4 5 7 6 9 8 2 5 1 9 44 3 53 1 2 6 0 8 0 1 17 38 Guinea-Bissau 0 8 1 2 1 7 2 3 2 2 43 6 52 9 3 6 0 8 0 1 20 40 Haiti 5 4 8 1 10 3 2 0 1 7 40 2 56 3 3 5 0 7 0 1 13 32 30 I 2003 Worid Development Indicators Population dynamics .1 Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as proportion of Ages Ages Ages working-age 0-14 15-64 65+ population per 1,000 per 1,000 milfions % % Young Old people people 1980 2001 2015 1980-2001 2001-15 2001 2001 2001 2001 2001 2001 2001 Honduras 3 6 6 6 8 9 2 9 2 1 414_ 55 2 3-4 0_8 0 1 6 30 Hungary 10 7 10 2 9 4 -0 2 -0 6 16 8 68 7 14 5 0 2 0 2 13 10 India 687 3 1,032 4 1,227 9 1 9 1 2 33 1 61 9 5 0 0 5 0 1 9 25 Indonesia 148 3 209 0 -245 5 1 6 1 1 30 2 65_1 4 7 -05 0 1 7 21 Iran, Islamic Rep 39 1 64 5 804 2 4 16 32 6 62 7 4 7 06 01 6 22 Iraq 13 0 23 8 31 2 2 9 1 9 40 9 56 2 2 9 0 8 0 1 8 30 Ireland 3 4 3 8 4 3 0 6 0 8 21 5 67 2 11 2 0 3 0 2 8 15 Israel 3 9 6 4 7 9 2 4 1 5 _276- 62 6 9 7 0 4 0 2 6 21 Italy 56 4 57 9 55 0 0 1 -0 4 14 2 _ 67 4 18 4 0 2 0 3 10 9 Jamaica 2 1 2 6 3 0 0 9 1 1 30 6 -624 7 0 0 5 0 1 7 21 Japan 116 8 127 0 124 1 0 4 -0 2 14 5 67 9 17 6 0 2 0 3 9 9 Jordan 2 2 5 0 6 8 4 0 2 2 38 2 58 7 3 1 0 7 0 1 4 29 Kazakhstan 14 9 14 9 15 1 00 0 1 26.1 66 4 7 5 04 0 1 10 15 Kenya 16 6 30 7 37 5 2 9 1 4 43 0 54 3 2 7 08 0 1 15 35 Korea,Dem Rep 17 2 22 4 24 2 1 3 0 6 27 1 67 1 5 8 0 4 0 1 11 18 Korea,Rep 38 1 47 3 50 3 1 0 0 4 21 3 71 8 7 0 0 3 0 1 6 13 Kuwait 1 4 2 0 2 7 1 9 2 1 32 2 65 5 2 3 0 5 0 0 2 20 Kyrgyz Republic 3.6 5 0 5 8 1 5 11 33 4 60 6 6 0 06 01 7 20 Lao PDR 3 2 5 4 7 3 2 5 2 2 42 4 54 1 3 5 0 8 0 1 13 36 Latvia -25 2 4 2 1 -0 4 -0 7 -165 686_ 149 03 02 14 8 Lebanon 3 0 4 4 5 2 1 8 1 2 31 5 62 6 5 9 0 5 0 1 6 20 Lesotho 1 4 2 1 2 3 2 0 0 8 39_7 56 1 -43 0 7 0 1 18 32 Liberia 1 9 32_ 4 4 2 6 2 3 44 5 52 8 2 8 0 8 0 1 19 44 Libya 3 0 5 4 7 0 2 7 1 9 33.6 62 9 3 5 0 5 0 1 4 27 Lithuania 3 4 3 5 3 4 0 1 -0 2 18 9 67 4 13 7 0 3 0 2 12 9 Macedonia, FYR 1 9 2 0 2 2 0 4 0 4 22 2 67 5 10 2 0 3 0 1 8 13 Madagascar -89 16 0 22 5 28_ 2 5 44 8 52 2 3 0 0 9 0 1 12 39 Malawi 6 2 10 5 13 6 2 5 1 8 44 4 52 0 -36 0 8 0 1 24 45 Malaysia 13 8 23 8 29 6 2 6 1 5 33 7 62 1 4 2 0 5 0 1 4 22 Mali 6 6 -111 -149 2 5 2 1 47 1 49 9 3 0 0 9 0 1 21 46 Mauritania 1 6 2 7 3 8 2 7 2 3 43 9 52 9 3 2 0 8 0 1 -15 41 Mauritius 1 0 1 2 1 4 1 0 0 9 255_ 68 3 6 2 0 4 0 1 7 16 Mesico 87 6 99 4 121 1 1 8 1 4 33 6 61 4 5 0 0 6 0 1 5 24 Moldova 4 0 4 3 4 1 0 3 -0 2 21.8 67.1 11 0 0 3 0 2 9 -9 Mongolia 17 _24_ 29 1 8 13 33 2 627 4 0 0 5 01 6 22 Morocco 19 4 29 2 35 4 1 9 1 4 34 1 61 7 4 2 0 6 01 6 22 Mozambique 12 1 18 1 22 7 1 9 1 6 42 8 53 5 3 7 0 8 0 1 21 40 Myanmar 33 7 48 3 55 9 1 7 1 0 32 7 62 7 4 6 0 5 0 1 12 24 Namibia 1 0 1.8 2.1 2 8 1 2 41 7 54 6 3 8 -08 0 1 19 35 Nepal 14 6 -236 311 23 2 0 40 7 55 5 3 8 07 01 10 32 Netherlands 14 2 16 0 16 9 0 6 0 4 18 5 67 9 13 7 0 3 0 2 9 13 New Zealand 3 1~ 38_ 41 1 0 05 22 3 65 9 11 8 03 02 7 15 Nicaragua 2 9 5 2 7 0 2 8 2 1 42 1 54 9 3 1 08 0 1 5 30 Niger 5 6 11.2 16 6 3 3 2 8 49 0 48 7 2 4 1 0 00 20 50 Nigeria 71 1 129 9 169 4 2 9 1 9 43 9 53 5 2 6 0 8 00 17 39 Norway 4 1 --45 48 0 5 04_ 200 65 0 -15.1 0 3 0 2 10 13 Oman I11 2 5 3 4 3 9 2 2 43 2 54 2 2 5 0 8 0 0 3 27 Pakistan 82 7 141 5 192 8 2 6 2 2 -4-12 55 5 3 3 0 8 0 1 8 33 Panama 2 0 2 9 3 5 1 9 1 3 30 9 635 5 6 0 5 0 1 5 21 Papua New Guinea 3 1 5 3 6 8 2 5 1 9 39 9 57 6 2 5 0 7 0 0 10 32 Paraguay 3 1 5 6 7 5 2 8 2 1 39 1 57 3 3 5 0 7 0 1 5 30 Peru 17 3 26 3 31 6 2 0 1 3 32 9 -62 2 4 9 0 5 0 1 6 24 Philippines 48 0 78 3 98 2 2 3 1 6 36 9 59 2 3 9 0 6 0 1 6 26 Poland 35 6 38 6 38 4 0 4 0 0 18 8 69 0 12 2 0 3 0 2 9 10 Portugal 9 8 10 0 9 9 0 1 -0 1 17 1 67 6 15 2 0 3 0 2 10 11 Puerto Rico 3 2 3 8 4 2 0 9 0 7 23 8 66 1 10 1 0 4 0 2 8 15 2003 World Development Indicators I39 PopuGat~on dynamics Total Average annual Population age Dependency Crude Crude population population composItlon ratio death birth growth rate rate rate dependents as proportion of Ages Ages Ages working-age 0-14 15-64 65+ population per 1,000 per 1,000 millions % % Young Old people people ±1980 2001 2015 1980-2001 2001-15 2001 2001 2001 2001 2001 2001 2001 Romania 22 2 22 4 21 4 0 0 -0 3 17 7 68 8 13 5 0 3 0 2 12 10 Rus sian Federation 139 0 144 8 134 5 0 2 -0 5 17_5 69 9 125_ 0 3 0 2 16 9 Rwanda --52 8 7 10 9 2 5 1 6 47.6 49 3 3 1 1 0 0 1 22 44 Saudi Arabia 9 4 21 4 321I 3 9 2 9 40.9 56 2 2 9 0 7 0 1 4 33 Senegal 5 5 9 8 13 0 2 7 2 0 44.4 52 9 2 7 0 9 0 1 13 36 Sierra-Leone_ 3 2 5 1 6 7 2 2 1 9 44 6 529_ 2.6 0 9 0 0 25 44 Singapore 2 4 4 1 4 8 2 6 1 1 21 5 71 2 7 3 0 3 0 1 4 12 Slovak Republic 5 0 5 4 5 4 0 4 0 0 19 3 69 3 11 3 0 3 0 2 10 10 Slovenia 1 9 2_0 1 9 0 2 -0 2 15 7 70 2 14 2 0 2 0 2 9 9 Somalia -65 9 1 14 0 1 6 3 1 47 9 49 7 24 1 0 00 17 50 South Africa 27 6 43 2 45 8 21i 0 4 32 3 63 1 4 6 0 5 0 1 18 25 Spain -37 4 41 1 414 0 5 00 150 6881 16 9 0 2 02 9 10 SriLanka 14 6 18 7 21 9 1 2 1 1 26 0 67 6 6 4 0 4 0 1 6 18 Sudan 19 3 31 7 421 2 4 -20 39 9 56 6 3 5 0 7 0 1 11 34 Swaziland- 06 11 1 3 3 0 12 42 2 55 0 2 8 08 01 17 35 Sweden 8 3 8 9 8 9 0 3 0 0 17 9 64 6 17 5 0 3 0 3 10 10 Switzerland 6 3 7 2 7 2 0 6 -0 1 16 8 67.8 15 4 0 3 0 2 8 10 Syrian Arab Republic 8 7 16 6 22 1 3 1 2 1 40 0 56 9 3.1 0 7 0 1 4 29 Tajikistan 4 0 6 2 7 7 2 2 1 5 38 6 57 0 4 4 0 7 0.1 6 21 Tanzania 18 6 34 4 43 9 2 9 1 7 45 2 52 4 2 4 0 9 0 0 18 39 Tha iland -46 7 61 2 66 3 1 3 0 6 23 6 70 1 6 3 0 3 0 1 8 15 Togo 2 5 4 7 6 0 2 9 1 9 43 9 53 0 3 2 0 8 0 1 15 35 Trinidad and Tobago 1 1 1 3 _ 1 5 0 9 0 8 24 9 68 8 6 3 0 4 0 1 7 15 Tunisia 6 4 9_7 11 6 2 0 1 3 -28.9 65.1 5 9 _ 0 5 0 1 6 17 Turkey 44 5 66 2 77 7 1 9 I1 283 65 9 58_ 0 4 01 7 20 Turkmenistan 2 9 5 4 6 4 3 1 1.1 36 6 59 1 4 3 0 6 0 1 7 20 Uganda 12 8 22 8 32 0 2 7 2 4 49 0 49 1 1 9 1 0 0 0 18 45 Ukraine 50 0 49 1 44 7 -0 1 -0 7 17 1 68 6 14 3 0 3 0 2 15 8 United Arab Emirates 1 0 3 0 3 8 5 0 1 8 26.1 -71 2 2 7 0 4 0 0 4 17 United Kingdom 56 3 58 8 58 9 0 2 0 0 18 6 65 4 16 1 0 3 0 2 11 11 United States- 227 2 285 3 319 9 1 1 0 8 21 2 66 2 12 6 0 3 0 2 9 15 Uruguay 2 9 3 4 3 7 0 7 0 6 24 7 62 7 12 6 0 4 0 2 10 16 Uzbekistan 16 0 25 1 30 0 2 2 1 3 36 5 59 0 4 5 0 6 0 1 6 21 Venezuela, RB 15 1 24 6 30 3 2 3 1 5 33 5 62 1 4 4 0 6 0 1 5 23 Vietnam 53 7 79 5 94 4 -19 1 2 32 4 62.3 5 3 0 5 0 1 6 19 West Bank and Gaza -31 4 8 3 2 46 6 50.1 3 3 1 0 0 1 4 37 Yemen, Rep 8 5 18 0 27 3 3.6- 3 0 46 2 51.0 2 8 0 9 0 1 11 41 Yugoslavia, Fed Rep 9 8 10 7 10 7 0 4 0 1 20 1 66 2 13 7 0 3 0 2 11 12 Zambia 5 7 10 3 12 2 2 8 1 2 45 1 52 7 2 2 0 9 0 0 22 39 Zimbabwe 7 1 12 8 14 0 2 8 0 6 44 6 52 2 3 2 0 9 0 1 20 29 P4:XIVMI* ll~~~~I I S Lovirncome 1,613 4 2,505 9 3,090 9 2 1 1 5 36 4 59 2 4 4 0 6 0 1 11 29 MiddlelIncome 1,99888 2,667 2 3,001.1 1 4 0 8 27.1 66 0 6 9 0 4 0 1 8 17 Lower middle income 1,626.4 2,163 5 2,413.0 1 4 0 8 26 7 66 4 6.9 0 4 0.1 8_ 17 Upper middle income 362 4 503 6 588 1 1 6 1 1 29 0 64 4 6 6 0 4 0 1 7 20 Low &middle Income 3,601 6 5, 172 3 6,091 9 1 7 1 2 31.6 62 7 5 7 0~.5 0 1 9 23 East Asia &Pacific 1,359 4 1,822.5 2,041 3 1.4 0 8 26 8 66 8 6 4 0 4 0 1 7 17 Europe &Central Asia 425 8 474 6 476 6 0 5 0 0 21 4 67 6 _110 0 3 02 12 12 Latin America &Carib 359 9 523 6 625.7 1 8 1 3 31.3 63 2 5 5 0 5 0 1 6 22 Middle East& N Africa 174 0 300 6 387 7 2 6 1 8 36.2 59.8 4 0 0.6 0 1 6 26 South Asia 901 3 1,377 8 1.680 0 2 0 1 4 34 6 60 8 4 6 0 6 0 1 9 26 Sub-Saharan Africa 381 7 673 9 880 6 2 7 1 9 44 0 53 0 3 0 0 8 0 1 17 39 High Income 827 4 957 0 1,001 9 0 7 0 3 18 4 67 3 14 3 0 3 0 2 9 12 Europe EMU 286 7 306 7 306 0 0 3 0 0 16 2 67 3 16 5 0 2 0 2 10 10 a Estimate does not account for recent refugee flows 40 N 2003 World Development Indicators Population dynamics 0 Population estimates are usually based on national Dependency ratios take into account the variations * Total population of an economy includes all res population censuses, but the frequency and quality of in the proportions of children, elderly people, and dents regardless of legal status or citizenship- these vary by country Most countries conduct a corn- working-age people in the population Separate cal- except for refugees not permanently settled in the plete enumeration no more than once a decade Pre- culations of young-age and old-age dependency sug- country of asylum, who are generally considered part and postcensus estimates are interpolations or extrap- gest the burden of dependency that the working-age of the population of their country of origin The val- olations based on demographic models Errors and population must bear in relation to children and the ues shown are midyear estimates for 1980 and undercounting occur even in high-income countnes, in elderly But dependency ratios show the age compo- 2001 and projections for 2015 * Average annual developing countries such errors may be substantial sition of a population, not economic dependency population growth rate is the exponential change for because of limits in the transport, communications, Some children and elderly people are part of the the period indicated See Statistical methods for and other resources required to conduct a full census labor force, and many working-age people are not more information * Population age composition The quality and reliability of official demographic data The vital rates shown in the table are based on refers to the percentage of the total population that are also affected by the public trust in the government, data derived from birth and death registration sys- is In specific age groups * Dependency ratio is the the government's commitment to full and accurate enu- tems, censuses, and sample surveys conducted by ratio of dependents-people younger than 15 or meration, the confidentiality and protection against national statistical offices. United Nations agencies, older than 64-to the working-age population-those misuse accorded to census data, and the independ- and other organizations The estimates for 2001 for ages 15-64 * Crude death rate and crude birth ence of census agencies from undue political influence many countries are based on extrapolations of levels rate are the number of deaths and the number of live Moreover, the international comparability of population and trends measured in earlier years births occurring during the year, per 1,000 popula- indicators is limited by differences in the concepts, def- Vital registers are the preferred source of these tion estimated at midyear Subtracting the crude initions, data collection procedures, and estimation data, but in many developing countries systems for death rate from the crude birth rate provides the rate methods used by national statistical agencies and registering births and deaths do not exist or are of natural increase, which is equal to the population other organizations that collect population data incomplete because of deficiencies in the coverage growth rate in the absence of migration Of the 152 economies listed in the table, 123 of events or of geographic areas Many developing (about 81 percent) conducted a census between countries carry out special household surveys that 1995 and 2002 The currentness of a census, along estimate vital rates by asking respondents about with the availability of complementary data from sur- births and deaths in the recent past Estimates veys or registration systems, is one of many objec- derived in this way are subject to sampling errors tive ways tojudge the quality of demographic data In as well as errors due to inaccurate recall by the some European countries registration systems offer respondents complete information on population in the absence The United Nations Statistics Division monitors the of a census See Pnmary data documentation for the completeness of vital registration systems The most recent census or survey year and for the com- share of countries with at least 90 percent complete pleteness of registration vital registration increased from 45 percent in 1988 Current population estimates for developing coun- to 55 percent in 2001 Still, some of the most pop- tries that lack recent census-based data, and pre- and ulous developing countries-China, India, Indonesia, The World Bank's population estimates are pro- postcensus estimates for countries with census data, Brazil, Pakistan, Bangladesh, Nigeria-do not have duced by its Human Development Network and are provided by national statistical offices, the United complete vital registration systems Fewer than 30 Development Data Group in consultation with its Nations Population Division, and other agencies The percent of births and 40 percent of deaths worldwide operational staff and country offices Important standard estimation method requires fertility, mortal- are thought to be registered and reported inputs to the World Bank's demographic work ity, and net migration data, which are often collected International migration is the only other factor come from the following sources' census reports from sample surveys, some of which may be small or besides birth and death rates that directly deter- and other statistical publications from national limited in coverage The population estimates are the mines a country's population growth In the high- statistical offices, Demographic and Health product of demographic modeling and so are suscep- income countries about 40 percent of annual popu- Surveys conducted by national agencies, Macro tible to biases and errors because of shortcomings in lation growth in 1990-95 was due to migration, while International, and the U S Centers for Disease the model as well as in the data Population projec- in the developing countries migration reduced popu- Control and Prevention, United Nations Statistics tions are made using the cohort component method lation growth by about 3 percent Estimating interna- Division, Population and Vital Statistics Report The growth rate of the total population conceals tional migration is difficult At any time many people (quarterly), United Nations Population Division, the fact that different age groups may grow at very are located outside their home country as tourists, World Population Prospects The 2000 Revision, different rates In many developing countries the workers, or refugees or for other reasons Standards Eurostat, Demographic Statistics (various years), population under 15 was earlier growing rapidly but relating to the duration and purpose of international Centro Latinoamericano de Demografia, Boletin is now starting to shrink Previously high fertility moves that qualify as migration vary, and accurate Oemografico (various years), and U S Bureau of rates and declining mortality rates are now reflected estimates require information on flows into and out the Census, International Database in the larger share of the working-age population of countries that is difficult to collect 2003 World Development Indicators 1 41 Labor force structure Population ages Labor force Average annual Total growth rate Female millions millions %% of labor force 1980 2001 ±980 2001 2010 1980-2001 2001-10 1980 2001 Afghanistan 8 5 14 6a 6 8 11.4 a 14.1 2.5 2 3 34 8 35 7 Albania 1 6 2 0 1 2 1 6 1 8 1 3 1 5 38 8 41 4 Algeria 9 3 18 7 4.8 10.6 14 3 3 7 3 4 21 4 28 3 Angola 3 7 6 7 3 5 6 2 -8.1 2 7 3 0 47 0 46 3 Argentina 17 2 23 5 10.7 15 4 18 5 1 7 2.1 27 6 33 8 Armenia 2 0 2 6 1.4 1.9 2 2 1 4 1 2 47 9 48 6 Australia 9_6 13 1 6.7 9 9 10 6 1 8 0 7 36 8 43 9 Austria 4 8 5 5 3 4 3 8 3 8 -05 0 0 40.5 40 4 AzerbaUjan 3 7 5 2 2.7 3 7 4 3 1 4 1 7 47 5 44 6 Bangladesh 44 8 79 6 40 3 70 8 86 0 2 7 2 2 42 3 42 4 Belarus 6 4 6 8 5 1 5 3 5 3 0 2 0 0 49 9 49 0 Belgium 6 5 6 8 3 9 4 3 4 2 0 4 -0 1 33 9 41 0 Benin 1 8 3 3 1 7 2 9 3 7 2 7 2 8 47 0 48 3 Bolivia 29_ 48 20 35 43_ 2.6 2 4 33 3 37 9 Bosnia and Herzegovina 2 7 2 9 1 6 1 9 2 0 0 8 0 7 32.8 38 1 Botswana 0 4 0 9 0 4 0 8 0 8 3 1 0 8 50 1 45 2 Brazil 70 3 114 3 47 7 80 7 89 9 2.5 1 2 28 4 35 5 Bulgaria 5 8 5 6 4 6 4.1 3.8 -0 6 -0 7 45 3 48.1 Burkina Faso 3 4 5 8 3 8 5 7 6.7 1 9 1 9 47 6 46 5 Burundi 2 1 3 6 2 3 3 8 4.6 2 5 2 2 50 2 48 6 Cambodia 3 9 6 6 3 7 6 5 7 9 2.7 2 2 55 4 51 6 Cameroon 4 5 84 ~ 3 6 6 2 7 5 2 5 2 1 36 8 38.1 Canada 16 7 21.3 12 2 16 7 17 5 1 5 0 6 39 5 45 9 Central African Republic 1 3 2 0 1 2 1 8 2 1 1.9 1.5 Chad 2 3 3 7 2 2 3 8 5.0 2 6 3 0 43-.4 44.8 Chile 6 8 10.0 3 8 6 3 7 5 2 4 1 9 26 3 34 1 China 586.3 865 4 538_7 763.2 818 3 1 7 0 8 43 2 45 2 Hong Kong, China 3 4 4 8 2 5 3 6 3 7 1 7 0 5 34 3 37 2 Colombia 15 8 27 0 9 4 18 9 23 0 3 3 2 1 26 2 38 9 Congo,Dem Rep 13.8 26.1 12 0 21 6 28 2 2 8 2 9 44 5 43 4 Congo, Rep 0 9 1 6 0 7 1 3 1 7 2 9 3 0 42 4 43 5 Costa Rica 1 3 2 4 0 8 1 6 1 9 3 2 2 0 20 8 31 4 M6e d'lvoire 4 2 - 9.0 3 3 6 6 8 0 3 3 2 2 32 2 33 5 Croatia 3 1 3 0 2 2 2 1 2 0 -0 2 -0_3 40 2 44 3 Cuba 5 9 7 7 3 7 5 6 5.9 1 9 0 7 31 4 39 7 Czech Republic 6 5 7 2 5 3 5 7 5 5 0 4 _-04 47 1 47.3 Denmark 3 3 3.6 2 7 2 9 2 8 0.4 -0 5 44 0 46 4 Dominican Republic 3 1 5 3 - 2 1 - 38 4 6 2 8 2 2 24.7 31 1 Ecuador 4 2 8 0 2.5 5 1 6 5 3.3 2 6 20.1 28.4 Egypt, Arab Rep 23 1 39 8 14 3 2~52 32 2 2 7 2 7 26.5 -30.7 El Salvador 2 4 3 8 1 6 2 8 3 6 2 8 2 8 26 5 36 9 Eritrea -1 3 2 2 1 2 2.1 2.7 2 6 2 6 47 4 47 4 Estonia 1 0 0 9 0.8 0 8 0 8 -0 3 -0 2 50 6 49 0 Ethiopia 19 9 33 6 16 9 28 3 34 6 2 4 2 2 42 3 40 9 Finland 3 2 3 5 2 4 2 6 2 5 0.3 _-0.6 46.5 48 1 France 34 4 38 5 23 8 26 8 27.6 0 6 0 3 40.1 45 2 Gabon 0 4 0 7 0 4 0 6 0 7 2 2 1 9 45 0 44 7 Gambia, The 0 3 0 8 0 3 0.7 0 8 3 4 2 2 44 8 45 1 Georgia 3 3 -33 26 25_ 49 3 46 8 Germany 51 6 56 1 37 5 41 0 40.9 0 4 0.0 40 1 42 4 Ghana 5 5 10 2 5 1 9 4 11.2_ 2.9 2 0 51 0 50 4 Greece 6 2 7 1 3 8 4 6 4 7 0 9 0 4 27.9 38 0 Guatemala 3 5 6 2 2 3 4 4 6 0 3 0 3 5 22 4 29 5 Guinea 2 3 4 0 -23 3 6 4 3 2 1 2 0 47.1 47 2 Guinea-Bissau 04_ 0.6 0 4 0 6 0.7 1 9 2 3 39 9 40.5 Haiti 2 9 4 6 2 5 3 6 4 2 1 6 17 44 6 42 9 I2II 2003 World Development Indicators Labor force structure2. Population ages Labor force 15-64 Average annual Total growth rate Female millions millions %% of labor force 1980 2001 1980 2001 2010 1980-2001 2001-10 1980 2001 Honduras 1 8 3 6 1 2 2.5 3 5 3 5 3 6 25 2 32 2 Hungary -69 6 9 5 1 4~9 4 6 -0 2 -0 8 43 3 44 7 India 394 5 637 9 299 5 460 5 543 6 2 0 1 8 33 7 32 4 Indonesia 83 2 136 1 58 6 102_0 122 0 2 6 2 0 35 2 41 0 Iran, Islamic Rep 20 5 40 5 11 7 20 4 27 7 2 6 3 4 20 4 27 8 Iraq 6 7 13 3 3 5 6.6 8 6 3 0 2 8 17 3 20 0 Ireland 2 0 2 6 1 3 1 6 1 9 1 3 1 4 28 1 34 8 Israel 2 3 4 0 1 5 -28 3 5 3 1 2 5 33 7 41 5 Italy 36 4 38 8 22 6 25 8 24 8 0 6 -0 4 32 9 38 6 Jamaica 1 1 1 6 1 0 1 4 1 6 1 7 1 5 46 3 46 2 Japan 78 7 86 2 57 2 68 2 66 0 0 8 -0 4 37 9 41 6 Jordan 1 0 2 9 0 5 1 5 2 0 5 1 3 4 14 7 25 1 Kazak 2 L makes available to the World Bank. Data on 0 __________________________________arabia land, irrigated land, and land under cereal Low income Lower middle income Upper middle income High income production are published in the FAO's Production Yearbook Source Tables 3 2 and 3 3 2003 World Development Indicators 1 127 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production production production yield productivlty Index Index Index Agnculture value added kilograms per worker 1989-91 = 100 1989-91 = 100 1989-91 = 100 per hectare 1995 $ 1979-al 1999-2001 1979-81 1999-2001 1979--81 1999-2001 1979-81 1999-2001 1979-81 1999-2001 Afghanistan 1,337 1,026 Albania 2,500 2.622 1,84 2.101 Algeria -77 5 127 9 67 3 133 1 54 6 128 3 656 929 -1,357 1,939- Angola 101 9 151 2 -90 0 146 3 83 8 136 8 526 630 131 Argentina 83 6 163 9 91 7 142 9 100 9 112 1 2,184 3,397 7,148 10,351 Armenia 98 5_ 73 9 60 2 1,675 5,435 Australia 79 9 171 2 91.3 145 7 85 6 114 8 1,321 2,038 - 20,872 33,225 Austria 92 8 102_6 92 2 107 6 94 5 106 3 4,131 5.629 11,082 31,091 Azerbaijan 57 4 78 0 . 79 6 2,37376 Bangladesh_ 80 0 132 4 79.2 135 6 81 3 140.9 1,938 3,322 217 311 Belarus 86 7 59_5 56 8 1,722 -2,180 Belgium0 84 9 146 1 88.5 113 6 88 8 109 7 4,861 7,679 21,861 29,098 Benin 53 8 -181 9 63 0 157_6 69 0 110.8 698 1,047 311 608 Bolivia 71 9 163 0 71 5 144 8 75 5 12-6 8 1,183 1,577 748 Bosnia and Herzegovina 3,034 7,811 Botswana 86 4 80 4 87 2 95 5 87 5 97.4 203 146 657 575 Brazil 75 4 129 1 69 5 145 9 67 9 162 2 1,496 -2,825 2.049 4,798 Bulgaria 107 7 59 8 105 5 66 0 96 3 60 7 3,853 2,696 __2,754 8,277 Burkina Faso 59 3 137 8 62.7 135 4 59 9 141 4 575 880 136 183 Burundi 79 9 92 3 79 9 91 8 82 3 77 3 1,081 1,290 177 150 Cambodia 55 2 149 0 48 9 153 3 27 3 166 1 1,025 2,050 . 363 Cameroon 86 7 132 9 80 2 130 8 61 3 122 0 849 1,842 826 1,189 Canada 77 6 123 7 79 8 126 7 _ 88 3 129 6 2,173 2,772 15,881 43,428 Central African Republic 102 8 133 3 79 7 139 4 48 9 137 3 529 1,217 380 490 Chad 66 9 144 5 79 9 138 0 _ 89 2 119 9 587 555 160 213 Chile 70 7 131 2 71 5 137 8 75 8 147 4 2,124 4,453 3,488 6,040 China 67 1 146 1 60 8 175 9 45 4 217 9 3,027 4,869 161 334 Hong Kong, China 133 6 59 3 99 8 58 0 194 3 57 0 1,712 C-olombia- 84 1 104 1 75 5 120 2 72 -6 122 9 2,452 3,236 3,034 3.590 Congo, Dem Rep 73 0 81 9 72 2 85 3 77 7 101.5 807 782 241 218 Congo, Rep 86 4 126 0 83 5 128 3 80.1 133.4 838 _ 782 385 471 Costa Rica 70 1 149 9 72 6 148 0 77 2 132 7 2,498 4,023 3,139 5,272 CMe dIlvoire 73 7 135 8 70 7 138 0 74.7 122 9 867 1,307 1,026 1,057 Croatia 86 6 67 3 50 0 4,355 9,44.9 Cuba 84 1 58 4 90 1 62 2 96 0 68 3 2,458 _ 2,601 Czech Republic 92 0 77 1 66.9 4,277 6,235 Denmark -65 2 91 9 83 3 104 2 95.0 116.8 4,040 6,032 19,350 57,896 Dominican Republic 96 5 87 3 85 2 110.1 68 8 141 0 3,024 4,105 2,020 3,179 Ecuador 78 2 157 6 77 4 156 1 73 0 153 1 1,633 2,212 1,206 1,716 Egypt, Arab Rep 75 5 151.2 68 4 155 9 67 0 161 4 4,053 7,238 721 1,324 El Salvador 120_4 104 0 88 9 117 0 86.5 120 2 1,702 _2,098 - 1,924 11710 Eritrea 148 9 126 8 ill 0 . 671 85 Estonia 69 5 43 4 37 2 . 1.704 4,265 Ethiopia 153 2 141_1 118 4 --- 1,164 141 Finland 76 3 94 7 93 8 89 8 107 5 91 7 2,511 3,071 18,547 40,463 France 87 4 107 9~ 93 6 105 3 97 8 104 6 -4,700 7,088 19,318 58,177 Gabon 76 2 119 9 79 0 115 9 86 5 119 0 1,718 -1,638 1,814 2,047 G-ambia, The 79 5 145 0 82 7 139 8 93 7 106 1 -1,284 1,298 325 298 Geor-gia 52 5 .. 78 7 9~4 9 1,576 Germany 90 0 119 4 91 4 98 2 98 7 87 8 4.166 6,749 9,061 32,814 Ghana 67 0 178 6 68 6 -169 6 78 7 117 7 807 1,305 671 569 Greece 86 8 Ill 6 91.2 103 4 99.9 96 1 3,090 3,527 8.600 14,079 Guatemala- 87 3 131 8 68.0 _134 7 76 3 134.1 1,578 1.778 2,143 2,115 Gui-nea 89 7 153 8 95 8 155 3 112 0 174 4 958 1,311 274 Guinea-B-issau 64 _9 144 7 68 3 140 0 78.0 126 2 711 1,271 237 322 Haiti 103 4 87 6 101 3 99.8 100 2 145 6 1,009 899 120 E 2003 World Development Indicators Agricultural output and productivityaa I Crop Food Livestock Cereal Agricultural production production production yield productivity Index Index Index Agriculture value added kilograms per worker 1989-91 = 100 1989-91 = 100 1989-91 = 100 per hectare 1995 $ 1979-81. 1999-2001 1979-8l 1999-2001 1979-81 1999-2001 ±979-81 1999-2001 1979-81 1999-2001 Honduras 90 4 105 1 88 2 112 1 80 8 149 5 1,170 1,327 696 990 Hungary 93 3 80 1 90 7 76 3 94 1 70 1 4,519 4,392 3,390 5,159 India 70 9 124 6 68 2 129 9 62.6 144 1 1,324 2,321 269 402 Indonesia 66 2 119 4 63 3_ 118_3 -51 0 116 4 2,837 3,947 604 744 Iran, Islamic Rep 57 5 135 9 61 2 139 0 68 0 145 1 1,108 1,806 2,165 3,698 Iraq 74 7 73 6 78 0 73 3 81 4 68 1 832 530 Ireland 93 9 -111 0 83 3 113 5 83 3 -115 2 4,733 7,241 Israel 99 8 1-00 3 85 0 115 0 78 4 122 5 1,840 2,411 Italy 106 1 104 7 101 4 104 9 93 0 106 6 3,548 4,920 11,090 26,690 Jamaica 98 6 118 1 86 0 118 8 -73 9 122 3 1,667 1,183 965 1,540 Japan -108 3 88 0 94 1 92 5 85 1 94 1 5,252 6,147 17,378 30,828 Jordan 54 6 114 9 57 4 134 1 51 5 176 6 521 1,949 1,141 825 Kazakhstan 84 1 70 3 45 3 1,162 1,649 Kenya 74 5 110 4 67 5 107 7 60 1 106 5 1,364 1,477 265 216 Korea, Dem Rep 3,694 2,753 Korea, Rep 87 8 113 0 77 4 131 0 52 3 161 6 4,986 6,500 3,765 13,782 Kuwait 37 1 166 2 91 4 208 1 106 6 210 0 3,124 2,260 Kyrgyz Republic 143 5 121 4 78 1 2,726 1,636 Lao PDR 73 5 153 7 70 3 163 4 56 0 185 7 1,402 2,978 614 Latvia 74 3 41 9 31 7 2,090 2,671 Lebanon 52 0 138 3 59 2 143 7 100 5 164 7 1,307 2,415 28,322 Lesotho 98 2 164 4 90 2 116 9 87.7- 87 5 977 1,337 636 553 Liberia 1,2_51 1,278 525 Libya 76 3 133.8 78 7 157 5 68 4 162 1 430 637 Lithuania 74 6 59 8 49 4 2,480 3,131 Macedonia, FYR 105 2 94 6 86 1 2,711 4,155 Madagascar 83 1 103 3 83 8 109.5 87 7 110 0 1,664 1,831 158 155 Malawi 85 7 149 4 93 2 160 8 78 2 113 2 1,161 1,634 96 123 Malaysia 75 3 119 7 55 6 143 2 41 0 155 1 2,828 3,075 3.939 6,843 Mali 54 5 135 0 76 7 119 5 94 5 111 8 804 1,113 242 290 Mauritania 62 1 133 9 86 5 109 4 89 4 106 0 384 718 299 500 Mauritius 93 3 90 4 89 7 101 7 64 0 141 3 2,536 4,793 2,891 5,580 Mexico 86 5 _121 9 83 8 133 6 83 -5 145 1 2,164 2.765_ 1.482 1,801 Moldova 56 8 45 9 34 5 2,437 1,661 Mongolia 44 6 30 9 88 1 103 4 93 2 109 2 -57~3 716 994 1,428 Morocco 54 8 -93 1 55 8 103 8 59 8 121 9 811 670 1,146 1,512 Mozambique 109 6 140 8 100 9 128 5 85 8 103 2 603 929 138 Myanmar 89 0 167 9 88 2 163 6 89 1 157 9 2,521 3,082 Namibia 80.1 121 5 107 2 118 0 115 6 117 7 377 347 1,003 1,618 Nepal 62 6 127 8 65 9 127 6 77 3. 125 5 1,615 2,089 156 200 Netherlan-ds 79 8 115 4 86 5 102 9 88.3 100 5 5,696 7,701 24,343 58,280 New Zealand 74 4 142 7 90 7 128 0 95 5 117 9 4,08 9 6,303 16,636 -28,791 Nicaragua 124 1 138 4 117 8 144 1 139 7 139 5 1,475 1,706 1,549 Niger -89 3 144 5 97 4 136 0 109 7 125 2 440 358 229 201 Nigeria 51 4 159 7 57 2 156 2 84 3 _127 4 1,265 1,197 417 714 Norway 94 8 79 2 93 9 91 9 96 2 99 1 3,634 3,928 17,013 34,535 Oman 60 1 166 1 62 1 162 5 61 5 133 6 982 2,266 Pakistan -65 6 126 0 66 3 145 2 59 5 155 1 1,608 2,305 416 712 Panama 97 1 94 0 85 6 106 7 71 3 126 1 1,524 2,732 2,122 2,738 Papua New Guinea 86 5 122 2 86 2 122 8 85 0 140 9 2,087 4,079 694 815 Paraguay 58 7 114.6 6-0 7 137 5 62 1 132 6 1,535 2,092 2,641 3,389 Peru 82 1 173 0 77 3 171 8 78 0 160 0 1,946 2,977 1,273 1,834 Philippines 88 3 119 9 86 1 131 1 73 8 162 7 1,611 2,571 1,381 1,428 Poland 84 6 85 3 87 9 85 9 98 0 83 4 2,345 2,860 1,601 Portugal 85 0 93 8 72 2 102 9 718 119 9 1,102 2,729 3,796 7,552 Puerto Rico 131 3 67 9 99 8 83 8 90 3 89 2 7,970 1,870 2003 World Development Indicators 129 AgricufturaO output and productivity Crop Food Livestock Cereal Agricuttural productlon production production yield productivity Index index Index Agriculture value added kilograms per worker 1989-91 = 100 1989-91 = 100 1989-91 = 100 per hectare 1995 $ 1979-81 1999-2001 1979-81 1999-2001 1979-81 1999-2001 1979-81 1999-2001 1979-81 1999-2001 Romania 1141 92 4 113 0 96 3 110 0 88 2 2,854 2,569 1,277 3,193 Russian Federation 78 7 63 7 51.5 . 1,767 . 2,648 Rwanda 84 3 100 4 85 3 103 8 80 9 116 8 1,134 891 271 251 Saudi Arabia 27 2 88 1 26 7 83 3 32 7 144.6 820 3,649 Senegal 77 2 129 5 74 1 136 6 65 7 147 4 690 854 336 334 Sierra Leone 80 3 72 9 84 5 81 9 84 1 126 0 1,249 1,092 674 359 Singapore 595 0 48 2 154 3 39 5 173.7 39 5 . 15,938 44,907 Slovak Republic Slovenia 94 4 108 9 104 7 . 4,912 34,697 Somalia . 474 544 South Africa 95 0 106 9 91 0 107 7 86 8 103 0 2,105 2,334 2,857 3,837 Spain 830 112 3 82 0 114 8 84 2 1284 1,986 3,047 7,556 22,088 Sri Lanka 99 3 120 3 98 3 122 2 93 2 135 0 2,462 3,270 642 734 Sudan 130 2 159 6 105 1 1617 89 3 158 3 645 484 Swaziland 72 5 87 9 80 2 89 0 96.5 82 5 1,345 1,620 1,752 1,922 Sweden 93 1 89 2 100 6 97 1 103 8 102.5 3,595 4,557 18,020 36,365 Switzerland 95 5 90 1 95 8 94 1 98 8 93 5 4,883 6,204 Syrian Arab Republic 100 5 156 3 94 2 147 0 72 2 134.6 1,156 1,304 2,206 2,618 Tajlikistan 51 6 51 5 39 5 1,025 1,332 Tanzania 818 97 2 76 7 103 8 69 3 121 8 1,063 1,273 185 Thailand 79 1 118.5 80 2 118 3 64 6 130 1 1,911 2,659 626 904 Togo 70 6 140 7 78 3 132 9 56 2 107 5 729 1,096 365 531 Trinidad and Tobago 119 9 108 2 1019 114 7 84 3 100 8 3,167 2,928 3,536 3,036 Tunisia 68 1 122 1 66 3 132 4 60 3 162.8 828 1,109 1,743 3.168 Turkey 76 6 113 8 75 8 ill 5 80 4 106 8 1,869 2,187 1,872 1,852 Turkmenistan 91 8 134 1 1368 1,771 1,518 Uganda 67 5 135 5 69 9 131 8 81 9 127 3 1,555 1,605 342 Ukraine 63 9 49 5 46 4 2,226 1,521 United Arab Emirates 38 9 284 6 48 8 270 7 45 3 203 4 2,224 598 United Kingdom 80 1 97 9 92.2 95 5 98 1 95 7 4,792 6,836 20,326 33,520 United States 98 6 119 8 94 5 122 4 89 0 122 1 4,151 5,824 20,634 50,777 Uruguay 86 8 150 4 87.1 136 6 85 9 119 9 1,644 3,796 6,240 8,010 Uzbekistan 88 8 118 0 116 3 2,603 1,088 Venezuela, RB 76 3 118 2 80 2 123 2 84 9 118 6 1,904 3,341 3,935 5,304 Vietnam 66 6 166 8 630 1539 49 5 154 9 2,049 4,075 . 253 West Bank and Gaza Yemen, Rep 82 3 129 6 75 0 134 1 68 9 145 9 1,038 1,094 .. 406 Yugoslavia, Fed Rep 96 3 94 3 94 2 3,601 Zambia 64 5 100 7 72 9 106 0 86 2 117 5 1,676 1,437 186 195 Zimbabwe 77 8 1213 83 3 110 0 89 7 114 5 1,359 1,221 310 361 Low income 717 125 8 70 7 127 8 68 4 132 1 1,090 1,309 415 Middle Income 74 3 128 7 71 8 143 2 69 6 155 8 1,811 2,357 737 Lower middle income 72 5 131 9 68 8 150 8 60 8 176 0 1.771 2,004 558 Upper middle Income 79 4 119 3 78 8 125 7 82 8 124 4 1,892 2,803 Low & middle Income 73 3 127 6 71 5 137 9 69 3 149 7 1,422 1,818 583 East Asia & Pacific 68 5 136 9 63 4 159 7 47 9 202 7 2,034 2,978 Europe & Central Asia . 2,854 2,388 2,049 Latin America & Carib 80 3 125 9 78 3 133.0 79 8 133 4 1,842 2,545 2,209 3,680 Middle East & N Africa 66 0 128 2 64.8 132 2 64 1 137 9 965 1,595 South Asia 71 9 122 8 69 6 127 1 64 0 137 1 1,510 2,182 284 568 Sub-Saharan Africa 75 4 129 4 78 3 125 8 84 1 114 9 895 1,188 421 675 High income 93 4 116 4 91 9 113 6 90 6 110.9 3,400 4,246 Europe EMU 90 7 109 6 91 4 104 0 93 9 101 1 4,035 5,629 a Includes Luxembourg 13O 0 2003 World Development Indicators Agricultural output and productivity { ON The agricultural production indexes in the table are pre- price regardless of where it was produced The use of * Crop production Index shows agricultural production pared by the Food and Agriculture Organization (FAQ) The international prices eliminates fluctuations in the value for each period relative to the base period 1989-91 It FAO obtains data from official and semiofficial reports of of output due to transitory movements of nominal includes all crops except fodder crops The regional and crop yields, area under production, and livestock num- exchange rates unrelated to the purchasing power of the income group aggregates for the FAO's production ndex- bers If data are not available, the FAQ makes estimates domestic currency es are calculated from the underlying values in interna- The indexes are calculated using the Laspeyres formula Data on cereal yield may be affected by a variety of tional dollars, normalized to the base period 1989-91 production quantities of each commodity are weighted by reporting and timing differences The FAO allocates pro- The data in this table are three-year averages Missing average international commodity prices in the base peri- duction data to the calendar year in which the bulk of observations have not been estimated or imputed od and summed for each year Because the FAO's index- the harvest took place But most of a crop harvested * Food production Index covers food crops that are es are based on the concept of agriculture as a single near the end of a year will be used in the following year considered edible and that contain nutrients Coffee enterprise, estimates of the amounts retained for seed Cereal crops harvested for hay or harvested green for and tea are excluded because, although edible, they and feed are subtracted from the production data to avoid food, feed, or silage, and those used for grazing, are have no nutritive value * Livestock production index double counting The resulting aggregate represents pro- generally excluded But millet and sorghum, which are includes meat and milk from all sources, dairy products duction available for any use except as seed and feed grown as feed for livestock and poultry in Europe and such as cheese, and eggs, honey, raw silk, wool, and The FAO's indexes may differ from other sources because North America, are used as food in Africa, Asia, and hides and skins * Cereal yield, measured In kilograms of differences in coverage, weights, concepts, time peri- countries of the former Soviet Union So some cereal per hectare of harvested land, includes wheat, rice, ods, calculation methods, and use of international prices crops are excluded from the data for some countries maize, barley, oats, rye, millet, sorghum, buckwheat, To ease cross-country comparisons, the FAO uses and included elsewhere, depending on their use and mixed grains Production data on cereals refer to international commodity prices to value production Agricultural productivity is measured by value added per crops harvested for dry grain only Cereal crops har- These prices, expressed in international dollars (equiva- unit of input (For further discussion of the calculation of vested for hay or harvested green for food, feed, or lent in purchasing power to the U S dollar), are derived value added in national accounts, see About the data for silage, and those used for grazing, are excluded using a Geary-Khamis formula applied to agricultural tables 4 1 and 4 2 ) Agricultural value added includes * Agricultural productivity refers to the ratio of agricul- outputs (see Inter-Secretariat Working Group on that from forestry and fishing Thus interpretations of land tural value added, measured in constant 1995 U S dol- National Accounts 1993, sections 16 93-96) This productivity should be made with caution To smooth lars, to the number of workers in agriculture method assigns a single price to each commodity so annual fluctuations in agricultural activity, the indicators in that, for example, one metric ton of wheat has the same the table have been averaged over three years 3.3a Food production ixdex (1980 = 100) 250 Lower middle income 200 ~: 200 ~~~~~~~~~~~~~~~~~~~~~Low income 150 150 ~~~~~~~~~~~~~~~~~~~~Upper middle Income 100 High income 50 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Food production index (1980 = 100) East Asia & Pacific 250 Middle East & North Africa | QiXD | 200 South Asia 2 =The agricultural production indexes are prepared 150 by the FAO and published annually in its _ ' 7 Sub S~~~~~~~~~~~~aharan Africa Latin America Production Yearbook The FAQ makes these data 100 & Caribbean and the data on cereal yield and agricultural 50 employment available to the World Bank in elec- 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 tronic files that may contain more recent informa- Food production has outpaced population growth in the past two decades, but the progress has been uneven And despite tion than the published versions For sources of the more than 80 percent increase in food production in low-income countries, hunger persists in parts of the world Amnong data on agricultural value added, see Data developing regions, East Asia and Pacific has had the highest growth in food production, Sub Saharan Africa the lowest sources for table 4 2 Source Table 3 3 2003 World Development indicators 1 131 Deforestation and biodiversity Forest area Average Mammals Birds Hlghier plants aNationally annual protected deforestation areas % of % of thousand total Threatened Threatened Threatened thousand total sq km land area sq km % Species species Species species Species species sq km land area 2000 2000 1990-2000 1990-2000 1996 2002b 1996 2002b 1997 1997 2002C 20021 Afghanistan 14 2 1 0 00 123 13- 235 11 4,000 4 2 2 0 3 Albania 10 36 2 78 08 68 3 230 3 3,031 79 1 0 3 8 Algeria 21 0 9 -266 -1 3 92 13 192 6 3.164 141 119 5 _50 Angola 698 56 0 ~1,242~ 276 19 765 15 5.185 30 81 8 6 6 Argentina -346 -12 7 2,851 0 8 320 34 897 39 9,372 247- 181 6 6 6 Armenia 4 12 4 -42 -1 3 11 . 4 31 2 1 7 6 Australia 1,581 20 6 0 0 0 252 63 649 37 15,638 2,245 1,017 7 13 2 Austria 39 47 0 -77 -0 2 83 7 213 3 3,100 23 28 1 33 9 Azerbaijan 11 12 6 -130 -1 3 13 . 8 .. 28 4 8 5 5 Bangladesh 13 10 2 -165 -1 3 109 23 295 23 5,000 24 -1 0 0 8 Belarus 94 _45 3 -2,562 -32_ 7 221 3 1 13 0 6 3 Belgium 58 11 180 2 1,550 2 0 9 2 6 Benin _ _27 24-0 699 2 3- 188 8 307 2_ 2,201 4 12 6 11 4 Bolivia 531 48 9 1,611 0 3 _316 24 28 17,367 227 151 0 13 9 Bosnia and Herzegovina 23 44 6 0 0 0 10 3 64 0 3 0 5 Botswana- 124 21 9 1,184 0 9 164 6 386 7 2,151 -7 105 0 18 5 Brazil 5,325 63 0 22.264 0 4 394 81 1,492 114 56,215 1,358 568 6 6 7 Bulgaria 37 33 4 -204 -0 6 81 14 240 10 3,572 106 5 0 4.5 Burkina Faso 71 25 9 152 0 2 147 7 335 2 1,100 0 28 6 10 4 Burundi 1 3 7 147 9 0 107 6 451 7 2,500 1 1 5 5 7 Cambodia 93 52 9 561 0.6 123 24 307 19 5 32 7 18 5 Cameroon 239 51 3 2,218 0 9 297 40 690 15 8,260 89 21 0 4 5 Canada 2,446 26 5 0 0 0 193 14 426 8 3,270 278 1,077 6 11 7 Central African Republic 229 36 8 300 0 1 209 14 537 3 3,602 1 55 2 8 9 Chad 127 10 1 817 0.6 134 17 370 5 1,600 12 114 9 9 1 Chi-le _ 155 20 7 203 0.1 91 21 296 22 5,284 329 141 4 18 9 China 1,589 17 0 -13,483 -0 9 499 94 1,258 183 3-2,200 312 730 4 7 8 Hong Kong, China 24 1 76 11 1,984 9 0 5 Colombia 496 47 8 1,905 0 4 359 41 1,695 78 51,220 712 94 7 9 1 Congo, Dem Rep. 1,352 59 6 5,324 0 4_ 415 40 929 28 11,007 78 146 4 6 5 Congo, Rep 221 64 6 175 0 1 200 15 449 3 6,000 3 17 0 5 0 Costa Rica 20 38 5 158 0 8 205 14 600 13 12,119 527 11 8 23 0 CMe dIlvoire 71 22 4 2,649 3 1 230 19 535 12 3,660 94 20 5 6 4 Croatia 18 31 9 -20 -0 1 9 224 4 6 4 2 7 5 Cuba 23 21 4 -277 -1 3 31 11 137 18 6,522 888 74 1 67 5 Czech Republic 26 34 1 -5 0.0 8 199 2 _81 12 5 16 1 Denmark 5 10 7 -10 -0 2 -43 5 196 1 1,450 2 14 4 34 0 Dominican Republic 14 28 4 0 0 0 20 5 136 15 5,657 136 15 5 32 0 Ecuador 106 38.1 1,372 1 2 302 33 1,388 62 19,362 824 128 5 46 4 Egypt, Arab Rep I 0 1 -20 -3 4 98 13 153 7 2,076 82 101 10 El Salvador 1 5 8 72 4 6 135 2 251 0 2,911 42 0 1 0 4 Eritrea 16 15 7 54 0 3 112 12 319 7 0 5 0 5 0 Estonia 21 48 7 -125 -0 6 -65 4 213 3 1,630 2 5 0 11 8 Ethiopia 46 4 6 403 0 8- 255 35_ 626 16 6,603 163 227 7 22 8 Finland 219 72 0 -80 0 0 60 5 248 3 1,102 6 28 4 9.3 France 153 27 9 -616 -0 4 93 18 269 5- 4,630 195 73 2 13 3 Gabon 218 84 7 101 0 0 190 15 466 5 6,651 91 7.2 2 8_ Gambia, The -5 48 1 -45 -1 0 108 3 280 2 974 1 0 2 2 3 Georgia 30 43 0 0 0 0 13 3 -29 2 0 28_ Germany 107 30 1 0- 0 0 76 11 239 5 2,682 14 111 6 313 Ghana 63 27 8 1,200 1 7 222 14 529 8 3,725 103 12 7 5 6 Greece 36 27.9 -300 -O 9 95 13 251 7 4,992 571 4 7 3 6 Guatemala 29 26 3 537 1 7 250 6 458 6 8,681 355 21.7 20 0 Guinea 69 28 2 347 0 5 190 12 409 10 3,000 39 1 6 0 7 Guinea-Bissau 22 77 8 216 0 9 108 3 243 0 1,000 0 Haiti 1 3 2 70 5 7 3 4 75 14 5,242 100 0 1 0 4 132 H 2003 World Development indicators Deforestation and biodivers ity3. Forest area Average Mammals Birds Higher plants aNationally annual protected deforestation areas % of % of thousand total Threatened Threatened Threatened thousand total sq km land area sq km % Species species Species species Species species sq km land area 2000 2000 1990-2000 1990-2000 1996 2002b 1996 2002 b 1997 1997 2002 C 20021 Honduras 54 48 1 590 1 0 173 10 422 5 5,680 96 7 2 6 4 Hungary 18 19 9 -72 -0O4 -72 9 205 8 2,214 30 6 5 7 0 India 641 21 6 -381 -0 1 316 88 923 72 16,000 1,236 154 7 5 2 Indonesia 1,050 58 0 13,124 1 2 436 147 1,519 114 29,375 264 357 4 19 7 Iran, Islamic Rep 73 4 5 0 0 0 140 22 323 13 8,000 2 83 0 5 1 Iraq 8 1 8 0 0 0 81 11 172 11 2 0 0 0 0 Ireland 7 9 6 -170 -3 0 25 5- 142 1 950 1 0 7 1 0 Israel 1 6 4 -50 -4 9 92 14 180 12 2,317 32 3 3 15 8 Italy 100 34 0 --295 -0 3 90 14 -234 5 5,599 311 23 1 7 9 Jamaica 3 30 0 54 1 5 24 5 113 12 3,308 744 Japan 241 66 1 -34 00 132 37 250 34 5,565 707 25 6 7 0 Jordan i 1 0 0 00 71_ 10 141 8 2,100 9 30 34 Kazakhstan 121 4 5 -2,390 -2 2 16 15 71 73 4 2 7 Kenya 171 30 0 931 0 5 359 51 844 24 6,506 240 45 5 80 Korea, Dem Rep 82 68 2 0 00 13 115 19 2,898 4 32 2 6 Korea, Rep 63 63 3 49 0 1 49 13 112 25 2,898 66 6 8 6 9 Kuwait 0 0 3 -2 -5 2 21 1 20 7 234 0 0 3 1 5 Kyrgyz Republic 10 5 2 -228 -2 6 7 4 34 6 9 3 6 Lao PDR 126 54 4 527 0 4 172 31 487 20 2 30 3 13 1 Latvia 29 47 1 -127 -0 4 83 4 217 3 1,153 0 8 3 13 4 Lebanon 0 3 5 1 0 3 54 5 154 7 3,000 5 0 0 0 5 Lesotho 0 0 5 0 0 0 33 3 58 7 1,591 21 0 1 0 2 Liberia 35 36 1 760 2 0 193 17 372 11 2,200 25 2 5 2 6 Libya 4 -02 -47 _-14 76 8 91 1 1,825 57 1 7 0 1 Lithuania 20- 30 8 -48 -0 2 68 5 202 4 1,796 1 6 5 10 0 Macedonia, FYR 9 35 6 0 0 0 11 3 0 1 8 7 1 Madagascar 117 20 2 1,174 0 9 105 50 202 27 9,505 306 12 3 2 1 Malawi 26 27 6 707 2 4 195 8 521 11 3,765 61 10 6 11 3 Malaysia 193 58 7 2.377 1 2 286 50 501 37 15,500 490 17 4 5 3 Mali 13-2 10 8 993 0 7 137 13 397 -4 1,741 15_ 45_3 3 7 Mauritania 3 03 98 2 7 61 10 273 2 1,100 3 17 5 1 7 Mauritius 0 7 9 1 06 4 3 27 9 750 294 0 2 7 8 Mexico 552 28 9 6,306 1 1 450 70 769 39 26,071 1,593 195 2 10 2 Moldova 3 99 -7 -0 2 68 6 177 5 5 0 5 1 4 Mongolia 106 6 8 600 0 5 134 14 16 2,272 0 179 9 11 5 Morocco 30 6 8 12 00 105 16 210 9 3,675 186 3 2 0 7 Mozambique 306 _39 0 637 0 2 179 14 498 16 5,692 89 66 0 8 4 Myanmar 344 52 3 5,169 1 4 251 39 867 35 7,000 32 5 6 0 9 Namibia 80 9 8 734 09 154 15 469 11 3,174 75 112 2 13 6 Nepal 39 27 3 783 1 8 167 31 611 25 6,973 20 12 7 8 9 Netherlands 4 11 1 -10 -0 3 55 10 191 4 1,221 1 4 8 14 2 New Zealand 79 29 7 -390 -0 5 10 8 150 63 2,382 211~ 63 4 23 7 Nicaragua 33 27 0 1,172 3 0 200 6 482 5 7.590 98 21 6 17 8 Niger 13 1 0 617 3 7 131 11 299 3 1,1 70 0 96 9 7 7 Nigeria 135 14 8 3,984 2 6 274 27 681 9 4,715 37 30 2 3 3 Norway 89 28 9 -310 -0 4 54 - 10 243 2 1,715 12 20 9 6 8 Oman 0 00 0 0 0 56 9 107 -10 1,204 30 39 1 12 6 Pakistan -25 3 2- 304 1 1 151 19 375 17 4,950 14 37 4 -4 9 Panama 29 38 6 519 1 6 218 20 732 - 16 9,915 1,302 17 1 22 9 Papua New Guinea 306 67 6 1,129 0 4 -214 58 644 32 11,544 92 10 5 2 3 Paraguay 234 58 8 1,230 0 5 305 10 556 26 7,851 129 14 0 3 5 Peru 652 509 2,688 0 4 344 49 1,538 76 18,245 906 78 3 6 1 Philippines 58 19 4 887 1 4 153 50 395 67 8,931 360 17 0 5 7 Poland -93 30_6 -110 -0 1 84 15 227 4 2,450 27 37 9 12 4 Portugal 37 40 1 -570 -1 7 63 17 207 7 5,050 269 6 0 6 6 Puerto Rico 2 25 8 5 0 2 16 2 105 8 2,493 223 0 3 3 5 2003 World Development indicators 133 Deforestation and biodiversity Forest area Average Mammals Birds Higher plants a Nationally annual protected deforestation areas % of % of thousand total Threatened Threatened Threatened thousand total sq km land area sq km % Species species Species species Species species sq km land area 2000 2000 1990-2000 1990-2000 1996 2002 b 199e 2002 b 1997 1997 20021 20021 Romania 64 28 0 -447 -0 2 84 17 247 8 3,400 99 10 9 4 7 Russian Federation 8,514 50 4 -1,353 0 0 269 45 628 38 214 1.395 1 8 3 Rwanda 3 12 4 150 3 9 151 9 513 -9 2,288 0 36 14 7 Saudi Arabia 15 0 7 0 0 0 77 8 155 15 2,028 7 825 7 38 4 Senegal 62 ~32 2 -450 0 7 155 12 384 4 2,086 31 22 4 11 6 Sierra Leone 11 14 7 361 2 9 147 12 466 10 2.090 -29 -1 5 2.1 Singapore 0 3 3 0 0 0 45 3 118 7 2,168 29 0 0 4 9 Slovak Repu blic 20 42 5 -69 -0 3 9 209 4 65 11 0 Slovenia 11 55 0 -22 -0 2 69 9 207 1 . 13 1.2 6 0 Somalia 75 12 0 769 1 0 171 19 422 10 3,028 103 5 2 0 8 South Africa 89 7 3 80 0 1 247 42 596 28 23,420- 2,215- 67 3 5 5 Spain 144 28 8 -860 -0 6 82 24_ 278 7 5,050 985, 42 4 8 5 Sri Lanka 19 30 0 348 1 6 88 22 250 14 3,314 45-5 8 7 13 5 Sudan 616 25 9 9,589 1 4 267 23 680 6 3,137 10 122 5- 5 2 Swaziland 5~ 30 3 -58 -1 2 47 4 364 5 2,715 42 0 6 3 5 Sweden 271 65 9 -6 0 0 60 7 249 2 1,750 13- 54 2 13 2 Switzerland 12 30 3 -43 -0.4 75 5 193 2 3,030 30 11 9 30 0 Syrian Arab -Republic- 5 2_ 5 0 0 0 63 4 204 8 3,000 8 Tajik-istan 4 2 8 -20 -0 5 9 7 -50 5.9 4 2 Tanzania 388 43 9 913 0 2 316 42 822 33 10,008 436 263 4 29 8 Thailand 148 28 9 1,124 0 7 265 37 616 37 11,625 385 70 8 13 9 Togo 5 9 4 209 3 4 196 9 391 0 2,201 4 4_ Trinidad and Tobago 3 50 5- 22 0 8 100 1 260 1 2,259 21 0 3 6 0 Tunisia 5 3 3 -11 -0 2 78 11 173 5 2,196 24 0 4 0 3 Turkey 102 13 3 -220 -0 2 116 17 302 11 8,650 1,876 12 0 1 6 Turkmenistan 38 8 0 0 0 0 13 6 17 19 8 4.2 Uganda ~ 4-2 21 3 913 2 0 338 20 830 13 5,406 15 49 2 24 9 Ukraine 96 16 5 -310 -0 3 16 263 8 2,927 52 22 9 3 9 United Arab Emirates 3 38_ -78 -2.8 25 3 67 8 0 0 0 0 0 United Kingdom 26 10 7 -200 -0 8 50 12 230 2 1,623 18 54 8 22 8 United States 2,260 24 7 -3,880 -0 2 428 37 650 55 19,473 4,669 2.373 9 25 9 Uruguay 13 7 4 -501 -5 0 81 6 -237 11 2,278 -15 05 03 Uzbekistan 20 48 -46 -0 2 9 . 9 41 8 2 20 Venezuela, RB 495 56 1 2,175 0 4_ 305 26 1,181 24 21,07-3 426 563 1 63 8 Vietnam 98 30 2 -516 -0 5 213 40 535 37 10,500 341 11 6 3 5 West Bank and Gaza 1 1 Yemen, Rep 4 0 9 92 1 8 66 5 143 12 149 Yugoslavia, Fed Rep 29 14 0.0- 12 5 5,351 155 Zamb ia 312 42 0 8. 509 2 4 229 11 605 11 4,747 12 453 2 61 0 Zimbabwe 190 49 2 3,199 1 5 270 11 532 10 4,440 100 50 0 12 9 % Low Income 9,131 27 1 72,735 0 0.8 2,974 8 9 2 Middle Income 21,442 32 7 25,646 0 0 1 6,141 4 9 3 Lower middle income 13,700 31 8 -11,406 0 -0 1 3,372.5 7 5 upper middle income 7,742 34.5 37,052 0 0 5 2,768 9 13 0 Low & middle Income 30,568 30 9 98.347 0 -0 3 9,116 2 9 3 East Asia & Pacific 4,284 27 2 7,033 0 0 2 1,467 5 9 2 Europe & Central Asia 9,464 39 7 -8,143.0 -0 1 1,677 2 7 0 Latin America & Carib 9,440 47 1 45,878 0 0 5 2,315 2 11 5 M iddle East & N Africa 168 1 5 -239 0 -01I 1,086 0 10 4 South Asia 782 16 3 889 0 0.1 228 6 48_ Sub-Saharan Africa 6,436 27 3 52,963 0 0 8 2,341 8 9 9 High Income 8,034 26 1 -7,962 0 -0 1 6,060 B 19 5 Europe EMU 927 37 0 -2,988 0 -0 3 332 6 13 1 a Flowering plants oniy b Data may be far earlier years They are the most recent reported by the World Conservation Monitoring Centre in 2002 c These are tentative data and are being finalized 1306 II 2003 World Development Indicators Deforestation and biodiversity The estimates of forest area are from the Food and * Scientific reserves and strict nature reserves * Forest area is land under natural or planted stands Agriculture Organization's (FAO) State of the World's with limited public access of trees, whether productive or not * Average annual Forests 2001, which provides information on forest * National parks of national or international sig- deforestation refers to the permanent conversion of cover in 2000 and a revised estimate of forest cover nificance (not materially affected by human natural forest area to other uses, including shifting in 1990 The current survey is the latest global for- activity) cultivation, permanent agriculture, ranching, settle- est assessment and the first to use a uniform glob- * Natural monuments and natural landscapes ments, and infrastructure development Deforested al definition of forest According to this assessment, with unique aspects areas do not include areas logged but intended for the global rate of net deforestation has slowed to 9 * Managed nature reserves and wildlife sanctu- regeneration or areas degraded by fuelwood gather- million hectares a year, a rate 20 percent lower than aries ing, acid precipitation, or forest fires Negative num- that previously reported * Protected landscapes and seascapes (which bers indicate an increase in forest area * Mammals No breakdown of forest cover between natural for- may include cultural landscapes) exclude whales and porpoises * Birds are listed for est and plantation is shown in the table because of Designating land as a protected area does not nec- countries included within their breeding or wintering space limitations (This breakdown is provided by the essarily mean that protection is in force For small ranges * Higher plants refer to native vascular plant FAO only for developing countries ) For this reason countries that may only have protected areas small- species * Threatened species are the number of the deforestation data in the table may underesti- er than 1,000 hectares, this size limit in the defini- species classified by the IUCN as endangered, vul- mate the rate at which natural forest is disappearing tion will result in an underestimate of the extent and nerable, rare, indeterminate, out of danger, or insuffi in some countries number of protected areas ciently known * Nationally protected areas are Deforestation is a major cause of loss of biodiver- Threatened species are defined according to the totally or partially protected areas of at least 1,000 sity, and habitat conservation is vital for stemming IUCN's classification categories- endangered (in dan- hectares that are designated as national parks, natu- this loss Conservation efforts traditionally have ger of extinction and unlikely to survive if causal fac- ral monuments, nature reserves or wildlife sanctuar- focused on protected areas, which have grown sub- tors continue operating), vulnerable (likely to move ies, protected landscapes and seascapes, or stantially in recent decades Measures of species into the endangered category in the near future if scientific reserves with limited public access The richness are among the most straightforward ways causal factors continue operating), rare (not endan- data do not include sites protected under local or to indicate the importance of an area for biodiversi- gered or vulnerable but at risk), indeterminate provincial law Total land area is used to calculate the ty The number of small plants and animals is usu- (known to be endangered, vulnerable, or rare but not percentage of total area protected (see table 3 1) ally estimated by sampling plots It is also important enough information is available to say which), out of to know which aspects are under the most immedi- danger (formerly included in one of the above cate- ate threat This, however, requires a large amount of gones but now considered relatively secure because data and time-consuming analysis For this reason appropriate conservation measures are in effect), global analyses of the status of threatened species and insufficiently known (suspected but not definite- have been carried out for few groups of organisms ly known to belong to one of the above categories) Only for birds has the status of all species been Figures on species are not necessarily comparable assessed An estimated 45 percent of mammal across countries because taxonomic concepts and species remain to be assessed For plants the coverage vary And while the number of birds and World Conservation Union's (IUCN) 1997 IUCN Red mammals is fairly well known, it is difficult to make List of Threatened Plants provides the first-ever an accurate count of plants Although the data in the comprehensive listing of threatened species on a table should be interpreted with caution, especially global scale, the result of more than 20 years' work for numbers of threatened species (where our knowl- by botanists from around the world Nearly 34,000 edge is very incomplete), they do identify countries plant species, 12 5 percent of the total, are threat- that are major sources of global biodiversity and ened with extinction show national commitments to habitat protection The table shows information on protected areas, numbers of certain species, and numbers of those species under threat The World Conservation - Monitoring Centre (WCMC) compiles these data from The forestry data are from the FAO's State of the a variety of sources Because of differences in defi- World's Forests 2001 The data on species are nitions and reporting practices, cross-country com- from the WCMC's electronic files and the IUCN's parability is limited Compounding these problems, 2002 IUCN Red List of Threatened Animals and available data cover different periods 1997 IUCN Red List of Threatened Plants The Nationally protected areas are areas of at least data on protected areas are from the WCMC's 1,000 hectares that fall into one of five management Protected Areas Data Unit categories defined by the WCMC 2003 World Development Indicators 1 135 15 Freshwater Freshwater Annual freshwater withdrawals Access to improved resources water source Flows from Total Internal other renewable flows countries resources Urban Rural billion billion per capita % of total % of % of Cu m cu m cu ml billion renewable % for % for % for population population 2000 2000 2000 CU Mb resourceSb agncultureb industryb domestic b 1990 2000 1990 2000 Afghanistan 55 10.0 2,448 26 1 402_ 99 0 1 19 Il- Albania 27 15 7 13,593 1 4 3 3 71 0 -29 99 95 Algeria 14 0 4 471 5.0 35 0 52_ 14 34_ 94 82 Angola 184 14,009 0 5 0 3 76 10 14 34 40 Argentina 276 623 0 24,276 28 6 3 2 75 9 16 97 73 Armenia 9 1 5 2,787 2 9 27 4 66 4 30 Australia -492 0 0 25,649 14 6 3 0 33 2 65_ 100 100 100 100 Austria 55 29 0 10, 357 2 4 2 9 9 58 33 100 100 100 100 AzerbaUjan 8 21 0 3,615 16 5 56 7 70 25 5 93 58 Bangladesh 105 1,105 6 9,238 14 6 1 2 86 _2 12 99 99 93 97 Belarus 37 20 8 5,797 2 7 4.7 35 43 22 . 100 100 Belgium 12 4.0- 1,561 Benin 10 15 5 4,114 0 1 0 4 67 10- 23 74 55 Bolivia 304 7 2 37,305 1 2 0 4 87 3 10 91 95 47 64 Bosnija and Herzegovina - 36 2 0 -9,429 190 2 7 60 10- 30-- Botswana 3 11 8 _ , 8776 0 1 0 7 48 20 _32 100 100 88 90 Brazil 5,418 1,900 0 43,022 54 9 0 8 61 18 21 93 95 54 53 Bulgana 21 0 2 2,595 13 9 65 6 22 75 _ 3 100 100 Burkina Faso 13 2 0 1,286 0 4 2 8 81 0 19 66 37 Burundi 4 529 0 1 2 8 64 0 36 96 91 67 77 Cambodia 121 355 6 39,613 0 5 0 1 94 1 554 . 26 Cameroon 273 -00 18,352 0 4 0 1 35 19 46 78 78 32 39 Canada 2,850 52 0 94,314 45 1 1 6 12 70 18 100 100 99 99 Central African Republic 141 37,934 0 1 0 1 -74 5 21 71 89 35 57 Chad 15 28 0 5,589 0 2 0.5 82 2 16 31 26 Chile 884 0 0 58,115 -20.3 2 3 84 11 5 98 99 49 58 China 2,812 17 2 2,241 525 5 18 6 78 18 5 99 94 60 66 Hong Kong, China Colombia 2,112 0 0 49,930 8 9 0 4 37 4 59 98 99 84 70 Congo, Dem Rep 900 313 0 23,809 0 4 0 0 23 16 61 89 26 Congo, Rep 222 610 0 275,679 0 0 0 0 11 27 62_ 71 17 Costa Rica 112 29,501 5.8 5 2 80 7 13 99 92 CMe dIlvoire 77 .. 4,790 0.7 _09 67 11 22 97 92 69 72 Croatia -38 33 7 16,301 0 8 1.1 0 50 50 Cuba 38 0 0 3,405 5.2 13 6 51 0 49 95 77 Czech Republic 13 1 0 1,382 2 7 19 0 2 57 41 Denmark 6 1,124 1 2 20.0 43 27 30 100 100 Dominican Republic- 21 2,508 8 3 39 5 89 0 11 92 90 71 78 Ecuador 432 0.0 34,161 17.0 39 82 _ 6 12 82 90 5-8 75 Egypt, Arab Rep - 2 66 7 1,071 66 0 96 4 82 11 7 97 99 92 96 El Salvador 18 2,836 0 7 3 9 46 20 34 88 91 48 64 Eritrea 3 6 0 2,148 ..._ 63 . 42 Estonia 13 0 1 9,346 0 2 1 6 5 39 56 Ethiopia 110 0 0 1.711 2.2 2 0 86 3 11 80 81 17 12 Finland 107 3 0 21,268 2.2 2 0 3 85 12 100 100 100 100 France 179 11 0 3,218 32 3 17.0 10 72 18 Gabon 164 0 0 133,333 0 1 0 1 6 22 72 95 47 Gambia, The 3 5.0 6,140 0.0 0 0 91 2 7 80 53 Georgia -58 8 4 12,395 3 5 5 3 59 20 21 90 61 Germany 107 71 0 2,165 46 3 26 0 20 69 11 Ghana 30 22 9 2,756 0 3 0 6 52 13 35 85 91 36 62 Greece 58 15 0 6,913 8.7 11 9 87 3 10 Guatemala 109 0 0 9,591 1 2 1 1 74 17 9 88 98 69 88 Guinea 226 0 0 30,479 0 7 0 3 87 3 10 72 -72 36 36 Guinea-Bissau 16 11 0 22,519 0 0 0 0 36 4 60 79 49 Haiti 13 1,633 1.0 7 7 94 1 5 59 49 50 45 136 I 2003 world Development Indicators Freshwater 35 Freshwater Annual freshwater withdrawals Access to Improved resources water source Flows from Total Internal other renewable flows countries resources Urban Rural billion billion per capita % of total % of % of Cu m Cu m cu mu billion renewable % for % for % for population population 2000 2000 2000 CU Mb resources' agiulue inuty doesi b 990 2000 ±990 2000 Honduras 96 0 0 14.945 1 5 1 6 91 5 4 89 95 78 81 Hungary 6 114 0 11,855 6 8 5 7 36 55 9 100 100 98 98 India 1,261 647 2 1,878 500 0 26 2 92 3 5 88 95 61 79 Indonesia 2,838 13,759 74 3 2 6 93 1 6 92 90 62 69 Iran, Islamic Rep 129 2,018 70 0 54 5 92_ 2 6 98 83 Iraq 35 75 9 4,776 42 8 38 5 92 -5 3 96 48 Ireland 49 3 0 13,706 0 8 1 5 -10 74 16 Israel 1 0 9 273 1 6 94 1 54 7 39 Italy 183 6 8 3,281 42 0 22 2 48 _34 19 Jamaica 9 - -3,653 0 9 9 6 77 7 15 98 98 87 85 Japan 430 0 0 3,389 91 4 21 3 64 17 19 Jordan 1 143 1.0 7-5 3 22 99 100 92 84 Kazakhstan 75 34 2 7,278 33 7 30 7 81 17 2 98 82 Kenya 20 10 0 1,004 2 0 6 6 76 4 20 91 88 31 42 Korea, Dem Rep 67 _10 1 3.462 14 2 18 4 73 16 11 100 100 Korea, Rep 65 4 9 1,485 23 7 34 0 ~ 63 11 26 97 71 Kuwait 0 0 0 0 0 5 60 2 37 Kyrgyz Republic 47 0 0 9,461 10 1 21 7 94 3 3 98 66 Lao PDR 190 143 1 63,175 1 0 0 3 82 10 8 61 29 Latvia 17 18 7 14,924 0 3 0 8 13 32 55 Lebanon 5 0 0 1,109 1 3 27.1 68 6 27 100 100 Lesotho 5 -00 2,555 0 1 1 9 56 22 22 88 74 Liberia 200 32 0 74,1-21 0 1 0 0 60 13 27 Libya I 0 0 113 4 5 84- 3 13 72 72 68 68 Lithuania -16 9 3 7,102 0 3 1 2 3 16 81 Macedonia, FYR 5 1 0 3,151 1 9 29.7 74_ 15 12 Madagascar 337 0 00 21,710 16 3 4 8 99 1 85 85 31 31 Malawi 16 1 1 1,668 0 9 5 2 86 3 10 90 95 43 44 Malaysia 580 24,925 12.7 2 2 77 13 11 94 Mali 60 40 0 9,225 1 4 1 4 97 1 2 65 74 52 61 Mauritania 0 11.0 4,278 1 6 14 0 92 2 6 34 34 40 40 Mauritius 2 0 0 1,853 77 7 16 100 100 100 100 Mexico 409 49 0 4,675 77 8 17 0 78 5 17 90 95 52 69 Moldova 1 _10 7 2,735 3 0 25 6 26 65 9 97 88 Mongolia 35 14,512 0 4 1 1 53 27 20 77 30 Morocco 29 0 0 1,010 11 5 39.7 89 2 10 94 98 58 56 Mozambique 99 ill 0 11,870 0 6 0 3 89 2 9 81 41 Myanmar 881 165 0 21.898 4 0 0 4 90 3 7 89- 66 Namibia 6 39 3 25,896 0 2 0 4 68 3 29 98 100 63 67 Nepal 198 12 0 9,122 29 0 13 8 99 0 1-9-9 64 87 Netherlands 11 80 0 5,716 7 8 8 6 34 61 5 100 100 100 100 New Zealand 327 0 0 85,361 2 0 0 6 44 10 46 100 100 Nicaragua 190 0 0 37,409 1 3 0 7 84 2 14 93 91 44 59 Niger 4 29 0 3,000 0 5 1 5 82 2 16 65 70 51 56 Nigeria 221 59 0 2,206 3 6 1 3 54 15 31 83 78 37 49 Norway 382 11 0 87,508 2 0 0 5 8 72 20 100 100 100 100 Oman 1 415 1 2 -94 2 5 41 41 30 30 Pakistan 52 170 3 1,610 155 6 70 0 97 2 2 96 95 77 87 Panama -147 51,647 1 6 1 1 70 2 28' 99 79 Papua New Guinea 801 156,140 0 1 0 0 49 22 29 88 -88 32 32 Paraguay 94 17,103 0 4 0 4 78_ 7 15 80 93 46 59 Peru 1,616 144 0 67,852 19 0 1 1 86 7 7 88 87 42 62 Philippines 479 0 0 6,251 55 4 11 6 ~ 88 4 8 93 91 82 79 Poland 54 8 0 1,594 12 3 20 0 11 76 13 Portugal 38 35 0 7,294 7 3 10.0 48 37- 15 Puerto Rico 2003 World Development indicators I137 Freshwater Freshwater Annual freshwater withdrawals Access to Improved resources water source Flows from Total Internal other renewable flows countries resources Urban Rural billion billion per capita % of total % of % of Cu m cor C u mu billion renewable % for % for % for population population 2000 2000 2000 CU M b rsueSb agricultureb industry b domestic5b 1990 2000 1990 2000 Romania 42 170 0 9,463 26 0 12 2 59 33 8 91 16 Russian Federation- 4,313- 185 5 3094 77 1 1 7 20- 62 19 100 96 Rwanda 5 611 0 8 15 4 94 2 5 .. 60 40 Saudi Arabia 2 116 17 0 90 1 9 100 64 Senegal 26 13 0 4,134 1 4 3 6 92 3 5 90 92 60 65 Sierra Leone 160 0 0 31,803 0 4 0 3 89 4 7 75 . 46 Singapore -~~ ~~~~~~~~4- 51 45- 100 100 100 Slovak Republic 13 70.0 15,293 -1 8 2 2 ... 100 100 Slovenia -19 0 0 9,40 2- 1 3 7 0 1 80 20 100 100 100 -100 Somalia 6 9 7 1,789 0.8 5 1 97 0 3 South Africa 45 5 2 1,168 13 3 26 6 72 11 17 99 99 73 73 Spain ill 0 3 2,753 35 2 31 6 68 ~ 19 13 Sri Lanka 50 0 0 2,708 9 8 1-9.6 96 2 2 91 -98 62 70 Sudan 30~ 119 0 4,792 17 8 11 9 94 1 4 86 86 60 69 Swaziland 3 -1 9 4,306 96 2 2 Sweden 171 12 2 20,656 2 9 1 6 9 55 36 100 100~ 100 100 Switzerland 40 13 0 7,437 1 2 2 2 4 73 23 100 100 100 100 Syrian Arab Republic 7 37.7 2,761 12 0 26 8 90 2 8 94 64 Tajikistan -66 13 3 12,853 11 9 14 9 92 4 3 . 93 47 Tanzania 82 90 2,701 1 2 1 3 89 2 9 76_ 90 28 57 Thailand 210 199 9 6,750 33 1 8 1 91 4 5 87 95 78 81 Togo 12 05 2,651 0 1 0 8 25 13 62 -82_ 85 38 38 Trinidad and Tobago 4 2,921 0 3 7.9 6 26 68 Tunisia 4 0 4 481 2_8 60 9 86_ 1 13 91 92 54 58 Turkey 227 7 6 3,593 35 5 15 1 73 12 16 83 81 72 86 Turkmenistan 1 59 5 11,523 23 8 39 1 98 1 1- Uganda 39 27 0 2,972 0 2 0 3 60 8 32 81 80 40 47 Ukraine 53 86 5 2,820 26 0 18 6 30 52 18 100 94 United Arab Emirates 0 0 0 69 2 1 67 9 24 United Kingdom 145 2 0 2,503 11 8 80 3 77 20 100 100 100 100 United States 2,800 18 0 9,985 467 3 16 6 42 45 13 100 100 100 100 Uruguay 59 74 0 39,856 0 7 0 5 91 3 6 98 93 Uzbekistan 16 98 1 4,623 58 1 50 8 94 2 4 94 79 Venezuela, RB 723 29,892 4 1 0 6 46 10 44 85 70 Vietnam 367 524 7 1-1,350 54 3 6 1 87 10 4 86 -95 48 72 West Bank and Gaza Yemen, Rep 4 234 2 9 70 7 92 1 7 .. 74 68 Yugoslavia, Fed Rep ~ 44 144 0- 17,674 13 0 6 9 8 86 6 99 97 Zambia 80 35 8 11,498 1 7 1 5 77 7 16 88 88 28 48 Zimbabwe 14 1,117 1 2 8 5 79 7 14 99 100 69 73 TlY~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Low Income 11,247 4,903 6 6,559 90 5 5 88 90 59 70 Middle Income 22,836 4,187 4 10,230 74 17 9 95 95 63 70 Lower middle income 13.877 1,274 8 7,066 75 18 8 96 95 63 70 Upper middle income 8,958 2.912 6 23,872 69 14 16 93 94 57 69 Low & middle Income 34,082 9,091 0 8,460 81 12 7 93 93 61 70 East Asia & Pacific 9,454 1,415 6 6,020 81 14 5 97 93 61 67 Europe & Central Asia 5,255 1,134 8 13,465 57 33 10 96 83 Latin America & Carib 13,429 2,833 8 3-1,530 74 9 18 ~ 92 94 58 65 Middle East & N Africa 234 183 1 1,413 88 5 7 96 78 South Asia 1,816 1,945 1 2,777 94 3 4 90 94 66 80 Sub-Saharan Africa 3,895 1,578 7 8,306 85 6 10 86 83 40 46 High Income 8,818 372 8 9,672 42 42 16 Europe EMU 910 258 8 3,832 38 47 15 a Riser flows from other countries are included when available, but river outflows are not because or data unreliability b Most data are for yearS between 1980 and 2000 For specific year, please refer to the Primary data documentation 138 2003 World Development Indicators Freshwater X The data on freshwater resources are based on The table shows both internal freshwater * Freshwater resources refer to total renewable estimates of runoff into rivers and recharge of resources and river flows arising outside countries resources, broken down between internas flows (inter- groundwater These estmates are based on differ- However, river outflows are not shown because they nal nver flows and groundwater from rainfall) in the ent sources and refer to different years, so cross- are of different vintage and are deemed unreliable country and river flows from other countnes country comparisons should be made with caution Because the data on total freshwater resources Freshwater resources per capita are calculated using Because the data are collected intermittently, they include river flows entering a country without river the World Bank's population estimates (see table may hide significant variations in total renewable flows out of the country being deducted, they over- 2 1) * Annual freshwater withdrawals refer to total water resources from one year to the next The estimate the availability of water from international water withdrawals, not counting evaporation losses data also fail to distinguish between seasonal and river ways This can be important in water-short from storage basins Withdrawals also include water geographic variations in water availability within countries, notably in the Middle East from desalination plants in countnes where they are a countres Data for small countries and countries The data on access to an improved water source significant source Data on total withdrawals are for in arid and semiarid zones are less reliable than measure the share of the population with reason- single years between 1980 and 2000 unless other- those for larger countries and countries with able and ready access to an adequate amount of wise indicated Withdrawals can exceed 100 percent greater rainfall Finally, caution is also needed in safe water for domestic purposes An improved of total renewable resources where extraction from comparing data on annual freshwater withdrawals, source can be any form of collection or piping used nonrenewable aquifers or desalination plants is con- which are subject to variations in collection and to make water regularly available While information siderable or where there is significant water reuse estimation methods on access to an improved water source is widely Withdrawals for agnculture and industry are total with- used, it is extremely subjective, and such terms as drawals for irrigation and livestock production and for 3.5a safe, improved, adequate, and reasonabie may direct industrial use (including withdrawals for cooling have very different meanings in different countries thermoelectnc plants) Withdrawals for domestic uses - ~~ despite official World Health Organization defini- include drinking water, municipal use or supply, and Low-income countries tions (see Definitions) Even in high-income coun- use for public services, commercial establishments, Domestic tries treated water may not always be safe to drink and homes For most countnes sectoral withdrawal Industry 5% While access to an improved water source is equat- data are estimated for 1987 * Access to an \% ed with connection to a public supply system, this Improved water source refers to the percentage of the does not take into account variations in the quality population with reasonable access to an adequate and cost (broadly defined) of the service once con- amount of water from an improved source, such as a nected Thus cross-country comparisons must be household connection, public standpipe, borehole, made cautiously Changes over time within coun- protected well or spring, or rainwater collection tries may result from changes in definitions or Unimproved sources include vendors, tanker trucks, measurements and unprotected wells and springs Reasonable Middle-income countries access is defined as the availability of at least 20 Domestic liters a person a day from a source within one kilome- 99%/\ ter of the dwelling /Indust 17% Nigh-income countries The data on freshwater resources and with- Domest drawals are compiled by the World Resources 16% - Institute from vanous sources and published in World Resources 2000-01 and World Resources 2002-03 (produced in collaboration with the n ustry United Nations Environment Programme, United \ 42% i ' Nations Development Programme. and World Bank) These are supplemented by the Food and Agriculture Organization's AQUASTAT data The Note Data are for the most recent year availabte data on access to an improved water source (see table 3 51 come from the World Health Organization Source Table 3 5 2003 Worlc Development Inticators I 139 N1 t 1 gJ]Water poI ution Emissions Industry shares of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics, kilograms per day metals and pulp Chemicals beverages and glass Textiles Wood Other per day per worker 96 % 9i % % % % % 1980 2000' 1980 20000 2000' 2000' 20000 2000' 20000 200 20a 2000' Afghanistan 6,680 017 .. 0 2 Albania 6,512 0.29 14 3 0 9 6 0 735 0 3 4 6 0 7 15 Algeria 60,290 45,645 0.19 0 24 19 3 90 6 0 484 03 13 9 17 4 0 Angola 1,472 .. 020 7.6 30 9.0 659 03 55 44 41 Argentina 244,711 177,882 018 0 21 7 1 11.6 8.0 59 0 0 2 8 4 18 3 9 Armenia 10,014 0 25 Australia 204,333 95,369 0 18 0 21 Austria 108,416 80,789 016 013 14 9 18 2 110 32 8 0 4 51 5 3 121 Azerbaijan 45,025 017 116 2.5 12 0 49 0 0 2 181 1.0 5 6 Bangladesh 66,713 273,082 016 014 4 5 8 2 3 0 30 3 01 48 6 0 3 15 Belarus Belgium 136,452 113,460 0.16 016 16.2 16 9 12 0 34 5 0 2 9 9 19 9 9 Benin 1,646 0 28 2 9 . 826 0 2 12.2 0.5 Bolivia 9,343 12,759 0 22 0_25 0 9 20 5 7 0 614 0 3 71 2 4 0 9 BosniaandHerzegovina 8,903 .. 018 205 131 70 333 0.2 17.6 58 28 Botswana 1,307 4,635 0 24 020 17 5 2 5 0 801 02 10 7 18 18 Brazil 866,790 629,406 016 0 20 10 5 141 9 0 42.7 0.3 14 5 3 5 6 9 Bulgana 152,125 107,945 013 017 106 69 7.0 467 04 157 2.3 93 Burkina Faso 2,385 2,598 0 29 022 1 6 2 8 50 81.5 0 0 6 5 0 6 13 Burundi 769 1,644 0.22 024 00 79 50 721 02 95 1 7 08 Cambodia 12,078 016 0.0 34 30 592 06 24 7 58 31 Cameroon 14,569 10,714 0 29 0 20 31 4 7 28 0 78 9 01 4 5 2 9 0 4 Canada 330,241 307,325 0.18 015 10 8 23 9 100 34 8 0 1 5 4 5 1 10 0 Central African Republic 861 670 026 017 00 40 620 00 138 196 Chad Chile 44,371 72,850 0 21 0 24 6 9 113 9 0 62 7 01 5 0 2 6 2 5 China 3,377,105 6,519,911 0 14 014 20 5 10 9 15.0 28 7 0 5 14 7 0.8 8 7 HongKong, China 102,002 31,725 011 017 16 425 40 21.9 01 229 02 67 Colombia 96,055 100,752 019 0 21 3 9 16.2 10 0 51 1 0 2 14 8 0 7 2 7 Congo, Dem Rep Congo, Rep 1,039 0 21 Costa Rica 35,164 022 14 9 5 7 0 64 3 01 13 4 16 2 6 C6te d'lvoire 15,414 12,401 0 23 0.24 0 5 3 9 70 73.5 01 10 3 5 2 16 Croatia 48,447 017 72 144 90 45,2 02 146 38 60 Cuba 120,703 0 24 Czech Republic 158,462 014 15 6 70 80 436 03 104 39 114 Denmark 65,465 83,591 0.17 017 36 201 80 53.4 02 50 22 8.5 Dominican Republic 54,935 0 38 0 6 2.8 . 92 0 19 0 2 0 3 Ecuador 25,297 32,266 0 23 0 27 2 0 10.8 6 0 65 5 0 2 9 6 2 2 2 5 Egypt, Arab Rep 169,146 210,242 0 19 0 19 13 5 7 8 10.0 43.9 0 2 22 1 0.4 3 8 El Salvador 9,390 22.760 0 24 018 3 5 13 2 8.0 57 9 0 1 16 4 0 5 1 2 Eritrea 16,754 Estonia Ethiopia 16.754 0.22 8 8 . 58 5 24 9 2 1 Finland 92,275 62,610 0 17 0 19 9 8 43 3 2.0 30.2 0.2 2 8 4 4 7 0 France 729,776 278,878 014 010 15 7 18 0 23.0 31.7 0 2 10.4 2 1 116 Gabon 2,661 1,886 015 0 26 51 5 8 5 0 54 5 0.2 3 4 22 3 3 7 Gambia, The 549 832 O.0 0 34 00 2 3 2 0 89 3 0 0 21 3 9 0 4 Georgia Germany 792,194 0.13 112 223 10 0 34.4 0 2 3 2 2 3 16 5 Ghana 15,868 14,449 020 0.17 98 169 100 39.5 02 91 124 1 7 Greece 65,304 57,178 017 0 20 6 3 118 9 0 54 0 0 2 13 2 15 3 8 Guatemala 20,856 19,253 0 25 0 28 2 3 101 6 0 72 8 0 2 9 8 13 10 Guinea Guinea-Bissau Haiti 4,734 019 . 715 18 4 0 8 140 D11 2003 World Oevelopment Indicators Water pollution S. I Emissions Industry shares of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics, kilograms per day metals and pulp Chemicals beverages and glass Textiles Wood Other per day per worker % % % % % % % % 1980 2000a 1980 2000a 2000a 20000 2000* 2000n 20000 2000n 2000n 2000a Honduras 13,067 34,036 0 23 0 20 11 78 4 0 55 5 01 26 8 4 0 0 8 Hungary 201,888 152,531 015 017 8 0 121 80 48 0 02 141 24 73 India 1,422,5641,651,250 0 21 019 13 5 68 10 0 510 02 13 3 03 53 Indonesia 214,010 783,207 0 22 018 27 59 90 52 8 01 218 56 45 Iran, Islamic Rep 72,334 101,900 015 017 206 80 80 397 05 173 07 54 Iraq 32,986 19,617 019 016 88 141 150 394 0 7 16 7 03 48 Ireland 43,544 49,144 019 015 1 3 142 110 56 4 0 2 31 16 118 Israel 39,113 54,149 015 016 3 7 19 7 9 0 43 9 0 2 121 18 9 3 Italy 442,712 495,411 0 13 0 13 9 5 16 9 110 30 3 0 3 16 0 3 7 12 5 Jamaica 11,123 17,507 0 25 0 29 6 9 7 2 4.0 70 8 0 1 9 8 1 3 0 0 Japan 1,456,016 1,369,931 0 14 015 7 9 218 9 0 410 0 2 5 6 17 12 9 Jordan 4,146 16,142 017 018 3 9 16 2 15 0 514 0 5 7 2 3 3 3 0 Kazakhstan Kenya 26,834 52,945 019 0 25 41 118 6 0 699 01 8 5 18 2 7 Korea, Dem Rep Korea, Rep 281,900 303,091 014 012 12 2 17 0 12 0 260 0 2 15 7 13 15 3 Kuwait 6,921 11,050 016 017 2.6 166 110 477 04 137 28 54 Kyrgyz Republic 20,700 016 13 7 0 2 10 54 8 0 4 210 10 8 0 Lao PDR Latvia 22,491 . 021 28 7 5 10 690 01 110 9 6 5 7 Lebanon 14,586 14,899 0 20 019 31 17 8 4 0 57 6 0 5 118 3 7 2 2 Lesotho 993 3,123 0 24 016 1 2 4 0 1 0 39.7 01 51 3 0 6 2 3 Liberia Libya 3,532 0 21 1.4 . 770 0 7 9 6 Lithuania 37,125 018 1 4 12 5 5 0 54 6 0 2 17 8 4 1 4 4 Macedonia, FYR 23,490 0 18 11 7 9 6 6.0 45 0 01 20 9 1 7 4 9 Madagascar 9,131 0 23 71 73 4 15 4 0 6 Malawi 12,224 11,805 0 32 029 0 0 16 0 4 0 700 0 0 7 8 1 7 0 7 Malaysia 77,215 180,641 015 011 7 5 13 3 17.0 30 2 0 3 8 3 6 6 17 4 Mali Mauritania Mauritius 9,224 17,700 021 0.15 09 66 30 328 01 554 06 11 Mexico 130,993 296,093 0 22 0 20 7.8 12.5 10 0 55 6 0 2 7 5 0 9 5 1 Moldova 34,234 0.29 0 2 4 0 1 0 81 7 0 2 10 8 13 0 6 Mongolia 9,254 7,939 019 018 18 8 5 10 54 5 0 0 25 3 6 7 0 8 Morocco 26,598 89,200 015 017 2.0 12 1 7.0 39 3 0 4 301 19 4 5 Mozambique 495 016 3 1 414 4 0 10 9 01 30 3 17 4 11 Myanmar 3,356 . 013 14 0 90 40 0 270 0 5 4 9 2 9 12 Namibia 7,350 0 35 0 0 5 0 2 0 90 4 01 1.2 0 9 0 8 Nepal 18,692 26,550 025 014 15 81 40 433 12 393 17 10 Netherlands 165,416 124,182 0 18 0 18 7.3 26 7 110 430 0 2 2 3 1 2 8 2 New Zealand 59,012 50,706 0 21 0 22 4 0 19 1 5 0 58 6 0 1 4 9 31 4 2 Nicaragua 9,647 0 28 0 2 5 4 79 7 01 7 6 10 0 9 Niger 372 019 50 3 Nigeria 72,082 82,477 017 017 14 154 110 40.2 01 235 47 35 Norway 67,897 55,439 019 0 20 8 7 317 5 0 42 9 01 14 3 0 7 2 Oman 5,560 017 6.0 140 70 522 08 12 7 39 35 Pakistan 75,125 100,821 017 018 8.2 5 9 8 0 39 2 0 2 35 0 0 2 3 2 Panama 8,121 11,461 0 26 0 31 2 2 141 5 0 68 0 0 2 9 0 18 0 9 Papua New Guinea 4,365 - 0 22 2 7 9 5 733 01 0 7 8 7 4 0 Paraguay 3,250 0 28 2 3 9 9 6 0 73 6 0 3 6 7 0 3 0 9 Peru 50,367 52,644 018 0 21 7 9 14 0 9 0 47 2 0 2 141 2 2 3 8 Philippines 182,052 201,952 019 018 5 2 9 8 7 0 58 2 0 2 15 9 3 4 4 6 Poland 580,869 388,153 014 016 13 8 6 2 7 0 48 8 0 4 13 6 2 6 7 9 Portugal 105,441 121,013 015 014 4 0 17 4 5.0 33 6 0 4 27 4 5 4 7 1 Puerto Rico 24,034 16,207 016 014 10 135 190 376 02 173 16 94 2003 World Development Indicators 1 141 Water pollution Emissions Industry shares of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics. kilograms per day metals and pulp Chemicals beverages and glass Textiles Wood Other per day per worker % % % % % % % % 1980 20000 1980 2000* 2000a 2000 a 2000 0 2000* 2000 2000 2000 2000a Romania 343,145 333,168 012 014 23 9 5 5 9 0 25 5 0 3 20 2 5 4 10 9 Russian Federation 1,485,833 016 17 7 74 90 46.8 03 6.9 21 95 Rwanda Saudi Arabia 18,181 24,436 012 014 4 7 172 210 413 1 4 14 4 4 3 108 Senegal 9,865 10,488 0 31 0 30 0 0 2 7 9.0 87 1 0 0 4 8 0 2 13 Sierra Leone 1,612 4,170 024 032 78 30 72.1 01 71 64 Singapore 28,558 33,331 0.10 0 09 13 26 8 16.0 20 9 01 4 0 13 29 5 Slovak Republic 57,970 015 172 127 80 375 03 119 27 99 Slovenia 38,213 . 017 301 15 8 8.0 25 6 0 2 119 2 0 5 8 Somalia South Africa 237,599 234,012 0 17 0 17 13.7 16 3 9 0 40 3 0 2 10 2 3 4 6 8 Spain 376,253 374,589 016 0.15 6 7 198 90 42 5 0 3 9 3 40 86 Sri Lanka 30,086 83,885 0 18 018 3 5 13 9 7 0 501 0 5 332 1 6 1 2 Sudan Swaziland 2,826 2,009 0 26 0 23 79 8 0.0 02 16 5 2 0 Sweden 130,439 103,913 015 014 113 35 0 8 0 26 6 01 13 3 0 14 9 Switzerland 123,752 017 24 9 23 6 100 25 0 02 32 42 87 Syrian Arab Republic 36,262 15,115 019 0 20 14 4 4 4 0 69 8 0 4 20 3 3 5 0 2 Tajikistan Tanzania 21,084 35,155 0 21 025 2 1 81 30 58 6 01 22 7 17 18 Thailand 213,271 355,819 022 016 4 8 5 3 5 0 422 02 35 4 15 3 9 Togo 963 027 44 221 450 01 241 19 00 Trinidad and Tobago 7,835 11,787 018 028 44 146 70 516 03 88 22 12 Tunisia 20,294 46,025 016 0 16 5 9 8 0 6.0 45 8 0 4 22 7 19 3 4 Turkey 160,173 170,685 0 20 017 110 71 8 0 44 5 0 3 23 6 11 5 0 Turkmenistan Uganda Ukraine 499,886 018 22 8 3 4 7 0 51.6 0 3 5 8 16 7 9 United Arab Emirates 4,524 0 15 United Kingdom 964,510 569,736 015 015 7 2 30 4 10 0 321 0 2 5 6 2 5 12 0 United States 2,742,993 1,968,196 0.14 0 12 10 5 11 0 14 0 38 4 0 2 7 1 4 1 14 9 Uruguay 34,270 23,109 0 21 0 27 3 4 112 6 0 72 3 0 2 6 6 0 7 18 Uzbekistan Venezuela, RB 84,797 94,175 020 021 137 139 100 469 02 99 17 39 Vietnam West Bank and Gaza Yemen, Rep 7,823 025 54 91 130 711 03 49 10 00 Yugoslavia, Fed Rep 106,409 016 9 3 123 8.0 45 9 03 141 21 81 Zambia 13,605 11,433 0 23 022 31 7 8 70 68 0 0 2 9.8 17 2 5 Zimbabwe 32,681 29,617 0 20 0 20 15 9 9 2 6 0 50 5 0 2 13 3 2 9 3 5 Note: Industry shares may not sum to 100 percent because data may be for different years a Data are for any year from 1993 to 2000 S142 0 2003 World Development Indicators Water pollution l. i Emissions of organic pollutants from industrial activ- cross-country comparisons of emissions BOD ment were multiplied by sectoral employment num- ties are a major cause of degradation of water qual- measures the strength of an organic waste in terms bers from UNIDO's industry database for 1980-98 ity Water quality and pollution levels are generally of the amount of oxygen consumed in breaking it The estimates of sectoral emissions were then measured in terms of concentration or load-the down A sewage overload in natural waters exhausts totaled to get daily emissions of organic water pollu- rate of occurrence of a substance in an aqueous the water's dissolved oxygen content Wastewater tants in kilograms per day for each country and year solution Polluting substances include organic mat- treatment, by contrast, reduces BOD. The data in the table were derived by updating these ter, metals, minerals, sediment, bacteria, and toxic Data on water pollution are more readily available estimates through 2000 chemicals This table focuses on organic water pol- than other emissions data because most industrial lution resulting from industrial activities Because pollution control programs start by regulating emis- water pollution tends to be sensitive to local condi- sions of organic water pollutants Such data are fairly tions, the national-level data in the table may not reliable because sampling techniques for measuring * Emissions of organic water pollutants are meas- reflect the quality of water in specific locations water pollution are more widely understood and much ured in terms of biochemical oxygen demand, which The data in the table come from an international less expensive than those for air pollution refers to the amount of oxygen that bacteria in water study of industrial emissions that may be the first to Hettige, Mani, and Wheeler (1998) used plant- and will consume in breaking down waste This Is a stan- include data from developing countries (Hettige, sector-level information on emissions and employ- dard water treatment test for the presence of organic Mani, and Wheeler 1998) These data were updated ment from 13 national environmental protection pollutants Emissions per worker are total emissions through 2000 by the World Bank's Development agencies and sector-level information on output and divided by the number of industrial workers Research Group Unlike estimates from earlier stud- employment from the United Nations Industrial * Industry shares of emissions of organic water pol- ies based on engineering or economic models, these Development Organization (UNIDO) Their economet- lutants refer to emissions from manufacturing activ- estimates are based on actual measurements of nc analysis found that the ratio of BOD to employ- ities as defined by two-digit divisions of the plant-level water pollution The focus is on organic ment in each industrial sector is about the same International Standard Industrial Classification (ISIC) water pollution, measured in terms of biochemical across countries This finding allowed the authors to revision 2 primary metals (ISIC division 37), paper oxygen demand (BOD), because the data for this indi- estimate BOD loads across countries and over time and pulp (34), chemicals (35), food and beverages cator are the most plentiful and the most reliable for The estimated BOD intensities per unit of employ- (31), stone, ceramics, and glass (36), textiles (32), wood (33), and other (38 and 39) 3.6a Emissions of organic water pollutants (thousands of tons per dayl 8 7 * 1980 U 2000 6 5 4 China United States India Russian Federation Japan Emissions of organic water pollutants per worker (kilograms per day) 0 25 0 20 ___ o 15 The data come from a 1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, o 1o 'industrial Pollution in Economic Development 005 Kuznets Revisited' (available on the Web 000 at http //www worldbank org/nipr) These data China United States India Russian Federation Japan were updated through 2000 by the World Bank's Development Research Group using the same Total emissions of organic water pollutants have increased in most developing countries while they have fallen in several methodology as the initial study Sectoral high-income countries Relative to the number of industrial workers, emissions have generally deciined employment numbers are from UNIDO's Industry Note No data are available for the Russian Federation for 1980 database Source Table 3 6 2003 World Development Indicators 1 143 Energy production and use Commercial Commercial energy use Commercial energy use Net energy energy per capita Imports a production thousand thousand average average % of metric tons of metric tons of annual kg of oil annual commercial oil equivalent oil equivalent % growth equivalent % growth energy use 1980 2000 1980 2000 1980-2000 1980 2000 1980-2000 1980 2000 Afghanistan Albania 3,428 814 3,049 1,634 -5 6 1,142 521 -6 3 -12 50 Algeria 67,103 149,629 12,185 29,060 3 5 653 956 10 -451 -415 Angola 11,301 43,669 4,437 7,667 2 8 628 584 -0 3 -155 -470 Argentina 38,813 81,221 41,868 61,469 2 2 1,490 1,660 0 8 7 -32 Armenia 1,263 632 1,070 2,061 . 346 542 -18 69 Australia 86,096 232,552 70,372 110,174 2 4 4,790 5,744 1 0 -22 -111 Austria 7,561 9,686 22,823 28,582 1 6 3,022 3,524 1 1 67 66 Azerbaijan 14,821 18,951 15,001 11,703 .. 2,433 1,454 1 -62 Bangladesh 6,745 15,053 8,441 18,666 4 1 99 142 1 9 20 19 Belarus 2,566 3,466 2,385 24,330 247 2,432 -8 86 Belgium 7,445 13,233 46,100 59,217 1 8 4,682 5,776 1 6 84 78 Benin 1,212 1,821 1,363 2,362 2 3 394 377 -0 8 11 23 Bolivia 4,372 5,901 2,436 4,929 3 8 455 592 1 5 -79 -20 Bosnia and Herzegovina 3,277 4,359 1,096 25 Botswana Brazil 62,372 142,078 111,471 183,165 2 7 917 1,077 1 0 44 22 Bulgaria 7,737 10,005 28,673 18,784 -2 6 3,235 2,299 -2 2 73 47 Burkina Faso Burundi Cambodia Cameroon 6,707 12,729 3,676 6,355 2 5 421 427 -0 2 -82 -100 Canada 207,417 374,864 193,000 250,967 1.6 7,848 8,156 0 4 -7 -49 Central African Republic Chad Chile 5,801 8,299 9,662 24,403 5 7 867 1,604 4 0 40 66 China 615,475 1,107,636 598,498 1,142,439 3 7 610 905 2 4 -3 3 Hong Kong, China 39 48 5,439 15,453 5 6 1,079 2,319 4 2 99 100 Colombia 18,040 74,584 19,348 28,786 2 5 680 681 0 5 7 -159 Congo, Dem Rep 8,697 15,446 8,706 14,888 2 7 324 292 -0 6 0 -4 Congo, Rep 4,024 14,656 862 895 -1 1 516 296 -4 0 -367 -1,538 Costa Rica 767 1,591 1,527 3,281 4 2 669 861 1 5 50 52 CMte d'lvoire 2,419 6,097 3,662 6,928 3 3 447 433 -0 1 34 12 Croatia 3,582 7,775 1,775 54 Cuba 4,227 6,051 14,910 13,203 -1 6 1,536 1,180 -2 4 72 54 Czech Republic 41,185 29,869 47,254 40,383 -1 2 4,618 3,931 -1.2 13 26 Denmark 952 27,831 19,783 19,456 0 6 3,862 3,643 0 4 95 -43 Dominican Republic 1,327 1,421 3,491 7,804 4 0 613 932 2 1 62 82 Ecuador 11,744 _ 22,520 5,180 8,187 2 1 651 647 -0 3 -127 -175 Egypt, Arab Rep 34,168 57,599 15,970 46,423 4 6 391 726 2 3 -114 -24 El Salvador 1,913 2,157 2,537 4,083 2 2 553 651 0 6 25 47 Eritrea Estonia 6,951 2,917 6,275 4,523 4,248 3,303 -11 36 Ethiopia 10,575 17,583 11,145 18,732 2 6 295 291 -0 1 5 6 Finland 6,912 15,134 25,413 33,147 1 7 5,316 6,409 1 3 73 54 France 45,544 130,730 187,737 257,128 1 9 3,484 4,366 1 5 76 49 Gabon 9,441 16,800 1,493 1,563 -0 2 2,157 1,271 -3 1 -532 -975 Gambia, The Georgia 1,504 737 4,474 2,860 .. 882 533 66 74 Germany 185,628 134,317 360,385 339,640 -0 2 4,602 4,131 -0 5 48 60 Ghana 3,305 5,883 4,063 7,720 3 6 378 400 0 5 19 24 Greece 3,696 9,987 15,695 27,822 3 0 1,628 2,635 2 5 76 64 Guatemala 2,583 5,241 3,847 7,146 3 5 564 628 0 9 33 27 Guinea Guinea-Bissau Haiti 1,877 1,542 2,099 2,039 0 4 392 256 -1 6 11 24 144 H 2003 World Development Indicators Energy production and use 31 Commercial Commercial energy use Commercial energy use Net energy energy per capita Imports 1 production thousand thousand average average % of metric tons of metric tons of annual kg of oil annual commercial oil equivalent oil equivalent % growth equivalent % growth energy use 1980 2000 1980 2000 1980-2000 1980 2000 1980-2000 i980 2000 Honduras 1,315 1,522 1,892 3,0 12 2 8 530 469 -O 2 31 49 Hungary 14,935 11,090 28,940 24,783 -1 0 2,703 2,448 -0 7 48 55 India 221,322 421,565 24 1,016 501,894 3 8 351 494 1 8 8 16 Indonesia 128,996 229.478 59,933 145,575 4 48 404 706 3 1 -115 -58 Iran, Islamic Rep 81,142 -242,146 38,987 112,725 5 6 997 1,771 3 1 -108 -115 I raq 136,.643 134,089 12,030 27,878 4 3 925 1,190 1 3 -1,036 -384 Ireland 1,894 2.197 8,485 14,623 2 7 2,495 3,854 2 3 7 8 8 5 Israel -153 654 8,563 2 0200 5 2 2,208 3,241 - 2 6 98 9 7 Italy 19,644 26.858 1-38,629 171,567 13 2,456 2,974 1 2 86 84 Jamaica 22 4 486 2,378 3,920 3 5 1,115 1,524 2 6 91 88 Japan 43,204 105,505 346,538 524,715 2 6 2,967 4,136 2 2 88 80 Jordan 1 286 1,71 4 5,185 4 9 786 1,061 0 5 100 94 Kazakhstan 76,799 78,102 78,799 39,063 5,163 2,594 0 -100 Kenya 7,891 12,260 9,791 15,482 2 2 589 515 -0 7 19 21 Korea, Dem Rep 29,669 -42.576 32,631 46,112 1 9 1,898 2,071 0 5 9 8 Korea, Rep 9,272 33,615 41,372 193,626 9 1 1,085 4,119 8 0 78 83 Kuwait 91,636 111,469 12,249 20,894 1 0 8,908 10,529 0 2 -648 -434 Kyrgyz Republic 2,190 1,443 1,717 2,445 473 497 -28 41 Lao PDR Latvia 261 1,250 566 3,655 222 1,541 , 54 66 Lebanon 178 ~ 171 2,524 5,058 4 8 841 1,169 2 8 93 97 Lesotho Liberia Libya 96,550 73,904 7,193 16,438 36 2,364 3,107 1 0 -1,242 -350 Lithuania 3,2_12 7,124- 2,032 55 Macedonia, FYR Madagascar Malawi Malaysia 18,202 76,759 12,162 49,472 77 884 2,126 4 9 -50 -55 Mali Mauritania Mauritius Mexico 149,359 229,653 98,898 153,513 2 1 1,464 1,567 0 2 -51 -50 Moldova - 35 60 -2,871 671 98 Mongolia Morocco 877 572 4,778 10,293 4 3 247 359 2 2 82 94 Mozambique 7,413 7,219 8,074 7,126 -0 8 668 403 -2 6 8 -1 Myanmar 9,513 15,144 9,430 12,522 1 3 280 262 -0 4 -1 -21 Namibr-a 292 1,03 1 587 72 Nepal 4,403 6,872 4,576 7,900 2 7 314 343 0 4 4 13 Netherlands 71,821 57,239 64,984 75,799 1 4 4,593 4,762 0 8 -11 24 New Zealand 5,488 15,379 9,213 18,633 3 8 2,959 4,864 2 7 40 17 Nicaragua 907 1,553 1,553 2,746 2 7 531 542 0 0 42 43 Niger -- - -- - Nigeria 148,479 197,726 52,846 90,169 2 6 743 710 -0 4 -181 -119 Nor way 55,675 224,993 18,768 25,61 7 1 8 458 5,704 1 3 -197 -778 Oman 15,090 60,08'4 996 9,750 11 0 905 4,046 6 6 -1,415 -516 Pakistan 20,997 47,124 25,472 63, 951 4 8 308 463 2 2 18 26 Panama -526 732 1,399 2,546 2 7 717 892 0 -7 62 71 Papua New Guinea Paraguay 1,605 6,886 2,089 3,-930 4 2 671 715 1 2 23 -75 Peru -14,656 9,477 11,752 12,695- 0 2 678 489 -1 8 -25 25 Philippines 10,670 20,922 21,212 42,424 3 9 442 554 1 5 50 51 Poland 122,222 78,960 123,031 89,975 -1 4 3,458 2,328 -1 8 1 12 Portugal 1,481 3,129 10,291 24,613 4 7 1,054 2,459 4 7 86 87 Puerto Rico 2003 World Development Indicators 1 145 Energy production and use Commercial Commercial energy use Commercial energy use Net energy energy per capfta Imports8 production thousand thousand average average % of metric toss of metrnc tons of annual kg of oil annual commercial oil equivalent oil equivalent % growth equivalent % growth energy use 1980 2000 1980 2000 1980-2000 ±980 2000 1980-2000 1980 2000 Romania 52,587 28,290 65,123 36,330 -3 1 2,933 1,619 -3 2 19 22 Russian Federation 748,647 966,512 -763,707 613.969 5,494 4,218 2 -57 Rwanda Saudi Arabia 533,07-1 487,889 31,108 105,303 5 0 3,319 5,081 1 0 -1,614 -363 Senegal 1,046 1,723 1,919 3,086 24 346 324 -O 3 45 44 Sierra Leone Singapore 64 6,062 24,591 8 7 2,511 6.120 6 0 100 Slovak Republic 3,418 5,994 21,056 17,466 -1 4 4,224 3,234 -_1 7 84 66 Slovenia 3,098 6,540 .3.288 53 Somalia South Africa 73,169 144,469 65,417 107,595 2 1 2,372 2,514 -0 2 -12 -34 Spain 15,636 31,865 68,576 124,881 3 2 1,834 3,084 2 9 77 74 Sri L anka 3,209 4,530 4,536 8,063 2.5 311 437 14 29 44 Sudan 7,089 23,664 8,406 16,216 3 1 435 -521 0 7 16 -46 Swaziland Sweden 16,132 30,681 39,911 47,481 1 0 4,803 5,354 0 6 60 35 Switzerland 7,030 11,792 20,861 26,597 14 3,301 3,704 0 7 66 56 Syrian Arab Republic 9,502 32,890 5.348 18,407 5 4 614 1,137 2 2 -78 -79 Tajikistan 1,986 1,250 1,650 2,911 416 470 -20 57 Tanzania 9,502 14,601 10,280 15,386 2 0 553 457 -1 0 8 5 Thailand- 11,182 41,118 22,808 73.618 7 4 488 1,212 6 0 51 44 Togo - 562 1,036 715 1,530 3 8 2-84 -338 0 8 21 32 Trinidad and Tobago 13,141 17,884 3,873 8.665 3 3 3,580 6,660 2 4 -239 -106 Tunisia 6,966 7,003 3,907 7,888 3 7 - 612 825 1 6 -78 -11 Turkey 17,077 26,186 31,452 77,104 4 6 707 1,181 2 7 46 66 Turkmenistan 8,034 45,968 7,948 13,885 2,778 2,627 -1 -231 Uganda Ukraine 109,708 82,330 9-7,893 139.592 1,956 2,820 -12 41 United Arab Emirates 89,716 143,589 6,273 29,559 8 2 6,014 10,175 2 8 -1,330 -386 Unite d Kingdom 196,79-2 272,338 201,284 232,644 1 0 3,573 3,962 -08 2 -17 United States 1,553,263 1,675,770 1,811,650 2,299,669 1.5 7,973 _8,148 0 4 14 27 Uruguay 766 1,028 2,643 3,079 1 7 907 923 1.0 -71 6-7 Uzbekistan 4,615 55,066 4.821 50,151 302 2,027 4 -10 Venezuela, RB 140,578 225,470 36,148 59,256 2 5 2,395 2,452 0 1 -289 -280 Vietnam 18,364 46,299 19,573 36,965 3 2 364 471 1 2 6 -25 West Bank and Gaza Yemen, Rep 60 22,046 1,424 3,526 4.2 167 201 0.3 96 -525 Yugoslavia, Fed -Rep 10,122 13,706 1,289 .2 6' Zambia 4,179 5,916 4,719 6,244 1.3 822 619 -1 6 11 5 Zimbabwe 5,793 8,708 6,570 10,219 2 6 921 809 -0 3 12 15 Low Income 819,169 1,400,460 674,008 1,287,496 4 7 452 569 2.2 -22 -9 Middle Income 3,302,613 4,812,604 2,446,876 3,457,150 4 1 1,252 _1,318 21 -35 -39 Lower middle income 2,028,-987 3,252,169 1,856,387 2,573,688 5 0 1,156 1,206 2 9 -_9 _-26 Upper -middle inc'ome 1,273,626 1,560,436 590,489 - 883,462 2 1 1,694 1,805 0 3 _-116 -77 Low & middle Income 4,121,782 6,213,064 3,120,884 4,744,646 4 3 906 971 2 0 -32 -31 East Asia & Paciftc 842,071 1,579,933 776,249 1,549,127 3.9 578 871' 2 4 -8 -2 Europe -& Cent-ral Asia 1,241,969 1,470,085 1,332,884 1,253,443 7.6 3,348 2,653 -7 -17 Latin America & Carib 476,911 847,298 381,002 601,85 9 2.4 1,074 1,181 0 6 -25 -41 Middle East &. N Africa 981,35-0 1,268,307 138,565 398,549 4.9 798 1,368 2 2 -610 -219 South Asia 256,676 495,144 284,041 600,474 3 9 321 453 1 8 10- 18 Sub-Saharan -Africa 322,805 552,297 208,143 341,194 2.3 714 669 -0 5 -55 -62 High Income 2,790,581 3,797,081 3,809,407 5,141,500 1 8 4,623 5,430 1 1 27 26 Europe Emu 367,292 434,433 952,761 1,160,702 1 2 3,337 3,824 0.9 61 63 a A negative value indicates that a country is a net exporter 1466 El 2003 World Development Indicators Energy production and use I In developing countries growth in commercial energy Commercial energy use refers to the use of domes- * Commercial energy production refers to commer- use is closely related to growth in the modern sec- tic primary energy before transformation to other cial forms of primary energy-petroleum (crude oil, tors-industry, motorized transport, and urban end-use fuels (such as electricity and refined petro- natural gas liquids, and oil from nonconventional areas-but commercial energy use also reflects cli- leum products) It includes energy from combustible sources), natural gas, and solid fuels (coal, lignite, matic, geographic, and economic factors (such as renewables and waste-solid biomass and animal and other derived fuels)-and primary electricity, all the relative price of energy) Commercial energy use products, gas and liquid from biomass, and industri- converted into oil equivalents (see About the data) has been growing rapidly in low- and middle-income al and municipal waste Biomass is defined as any * Commercial energy use refers to apparent con- countries, but high-income countries still use more plant matter used directly as fuel or converted into sumption, which is equal to indigenous production than five times as much on a per capita basis fuel, heat, or electricity (The data series pubitshed in plus imports and stock changes, minus exports and Because commercial energy is widely traded, it is World Development Indicators 1998 and earlier edi- fuels supplied to ships and aircraft engaged in Inter- necessary to distinguish between its production and its tions did not include energy from combustible renew- national transport (see About the data) * Net energy use Net energy imports show the extent to which an ables and waste ) All forms of commercial Imports are calculated as energy use less produc- economy's use exceeds its domestic production High- energy-primary energy and primary electricity-are tion, both measured in oil equivalents A negative income countries are net energy importers, middle- converted into oil equivalents To convert nuclear value indicates that the country is a net exporter income countries have been their main suppliers electricity into oil equivalents, a notional thermal effi- Energy data are compiled by the International Energy ciency of 33 percent is assumed, for hydroelectric Agency (IEA) IEA data for countries that are not mem- power 100 percent efficiency is assumed bers of the Organisation for Economic Co-operation and Development (OECD) are based on national ener- gy data adjusted to conform to annual questionnaires completed by OECD member governments 3.7a 3.7b Commercial energy use, 2000 - Energy use per capita (thousands of kg of oil equivalent) R est ot 6 the world [ 1980 2000 _ 26% _5. - ~ ~ ~ ~~~~ ~4 Other . . hIgh-income 3 coutie 23% \ \ - Russian 2 The data on commercial energy production and Federation Indl. Japan 6Y 1 F use come from IEA electronic files The IEA's data 5 5% 0 - - are published in its annual publications, Energy High-income countries, with 15 percent of the worlds Low Lower Upper High Statistics and Balances of Non-OECD Countries, population, consume more than half its commercial income middle middle income Energy Statistics of OECD Countnes, and Energy energy m~~~~~~~~~~~~~icome Income Balances of OECD Countries Source Table 3 7 Source Table 3 7 2003 World Development Indicators I 147 Energy efficiency and emnissions GDP per unit Carbon dioxide emissions of energy use PPP $ per kg Total Per capita kg per PPP $ oil equivalent million metric tons metric tons of GDP 1980 2000 1980 1999 1980 1999 1980 1999 Afghanistan 1 7 1 0 0 1 0 0 Albania 6 7 4 8 1 5 1 8 0 5 0 2 Algeria 5 5 6 4 66 1 90.8 3 5 3 0 1 0 0 5 Angola 3 6 5.3 10 3 0 8 0 8 .0.4 Argentina 4 4 7 2 107.5 137 8 3 8 3.8 0 6 0.3 Armenia 4 5 3 1 0 8 0 4 Australia 2 0 4 3 202.8 344 4 13.8 18 2 1 5 0 8 Austria 3 4 7 5 52 4 61 4 6 9 7.6 0 7 0 3 Azerbaijan 1 9 33 6 4 2 1 8 Bangladesh 5 4 10 8 7 6 25.4 0.1 0.2 0.2 0 1 Belarus 3 0 57 6 5 7 .0 9 Belgium 2 2 4 4 131 3 104.4 13 3 10 2 1 3 0 4 Benin 1 2 2.5 0 5 1 3 0 1 0 2 0 3 -0.2 Bolivia 3 0 3 9 4 5 11.2 0 8 1 4 0 6 0 6 Bosnia and Herzegovina 5 2 4 8 ..1 2 0 2 Botswana 1 0 3.9 1 1 2 4 0 7 0.4 8razil 4 2 6 7 183 4 300.7 1 5 1 8 0 4 0 3 Bulgaria- 1 o 2 8 75 3 42 1 85 5.1 2 7 0.9 Burkina Faso 0 4 1.0 0 1 0 1 0 1 0.1 Burundi ..0 1 0 2 0.0 0 0 0 1 0 1 Cambodia 0 3 0 7 0 0 0 1 0 0 Cameroon 2 7 3 8 3 9 4 7 0 4 0.3 0 4 0 2 Canada 1 4 3 3 420 9 438 6 17 1 14 4 1 5 0 6 Central African Republic oi1 0.3 0.0 0 1 0 1 0 1 Chad .0 2 0.1 0.0 0 0 0 1 0 0 Chile 3 0 5 6 27 5 62.5 2 5 4 2 1 0 0 5 China 0 7 4.1 1,476.8 2,825 0 1 5 2 3 3 5 0 7 Hong Kong, China 6 2 10.9 16 3 41.2 3.2 6.2 0 5 0.3 Colombia 4 7 10 3 39 8 63 6 1 4 1 5 0 4 0 2 Congo, Dem Rep 3.8 2 5 3.5 2.1 0 1 0 0 0 1 0 1 Congo, Rep.- 0 8 3 2 0.4 2.4 0.2 0 8 0 6 0 9 Costa Rica 6.6 11.7 2 5 6 1 1 1 1 6 0 2 0 2 MOe d'lvo,re 2 7 3 6 4 6 12 1 0 6 0 8 0 5 0 5 Croatia 4.9 20 8 .4 8 0 6 Cuba ..30.8 25 4 3 2 2 3 Czech Republic 3 6 . 108 9 .10 6 .0 8 Denmark 3 0 7 9 62 9 49 7 12 3 9 3 1 1 0 3 Dominican Republic .4 1 7 4 6.4 23 3 1.1 2.8 -04 0 4 Ecuador 2 8 4 9 13.4 23.3 1 7 1 9 0 9 0 6 Egypt,Arab Rep 3 3 -48 45 2 123.6 1 1 2 0 0.9 0.6 El Salvador 5 0 8 1 2 1 5 8 0 5 0 9 0 2 0 2 Eritrea 0 6 -0.1 0.1 Estonta .2.9 16 2 .11 7 .1.4 Ethiop ia 1 6 2 6 1 8 5 5 0 0 0 1 0 1 0 1 Finland 1.7 3.8 56 9 58 4 11 9 11 3 1 3 0 5 France 2 8 5 4 482 7 359.7 9.0 6 1 0 9 0 3 Gabon 1 8 4.7 6 2 3.6 8.9 3 0 2 3 0 5 Gambia, The 0 2 0 3 0.2 0 2 0 2 0 1 Georgia 4-.6 4 5 5.4 1 0 0 5 Germany 2 2 6 1 . 792 2 .9 7 0 4 Ghana 31- 5 5 2.4 5 6 0 2 0 3 0 2 0 1 Gre-ece 4 7 6.3 51 7 85 9 5 4 8 2 0 7 0 5 Guatemala 4 6 7 1 4 5 9 7 0.7 0.9 03 0 2 Guinea 0 9 1.3 0.2 0.2 0 1 Guinea-Bissau 0 5 0 3 0.7 0.2 1 4 03 Haiti 4 7 7 5 08 1 4 0.1 0 2 0 1 0.1 II 2003 World Development Indicators Energy efficiency and emissions S GDP per unlt Carbon dioxide emissions of energy use PPP $ per kg Total Per capita kg per PPP $ oil equivalent million metric tons metric tons of GDP 1980 2000 1.980 1.999 1980 1.999 1.980 1999 Honduras 3 2 60 2 1 5 0 0 6 0 8 0 3 0 3 Hungary 2 0 4 9 82 5 56 9 7 7 5 6 1 5 0 5 India 22_ 5 5 347 3 1,077 0 - 0 5 1 1 0 7 0 4 Indonesia 2 0 4 2 94 6 235 6 0 6 1 2 0 8 0 4 Iran, Islamic Rep 2 7 3 2 116 1 301 4 3 0 4 8 1 1 0 9 Iraq 44 0 74 2 3 4 3 3 Ireland 2 3 7 9 25 2 40 4 7 4 .108 1 3 0 4 Israel 3 7 6 5 21 1 61 1 5 4 10 0 0 7 0 5 Italy 3 9 8 2 371 9 422 7 6 6 7 3 0 7 0 3 Jamaica 1 8 2 4 8 4 10 2 4.0 4.0 2 0 1 2 Japan 3 1 61 920 4 1,155 2 7 9 9 1 0 8 0 4 Jordan 3 1 3 6 4 7 14 6 2 2 3 1 0 9 0 8 Kazakhstan 2 2 112 8 7 4 1 5 K~enya 1 0 19- ,62 _ 8 0 4 0 3 0 6 0 3 K~orea, Oem Rep 124 9 208 7 7 3 94 Korea, Rep 2 3 3 6 125 1 393 5 3 3 8 4 1 3 0 6 Kuwait 1 4 1 8 24 7 48 0 18 0 24 9 1 5 1 4 Kyrgyz Republic 5 4 4 7 1 0 0 4 Lao PDR .0 2 0 4 0 1 0 1 .0 1 Latvia 19 8 4 6 6 6 2 8 0 4 Lebanon .3 5 6 2 16 9 2 1 4 0 1 0 Lesotho Liberia .2 0 0 4 1 1 0 1 Libya 26 9 42~8 8 8 8 3 Lithuania 3 9 . 13 2 3 8 0 5 Macedonia, FYR 11 4 5 6 1 0 Madagascar 1 6 1 9 -02 0 1 0 3 0 2 Malawi .0 7 0 8 0 1 0 1 03 0 1 Malaysia 26 43 28 0 123 7 20 5 4 0 9 0 7 Mali 04 05 0 1 00 0 1 0 1 Mauritania 06_ 3_0 04 1 2 03 06 Mauritius 06 2 5 - 06 2.1 03 0 2 Mexico 2 9 5 5 252 5 378 5 3 7 39 0,9 05 M-oldova 3.1 -6 5 ,1 5 0 8 Mongolia 68 7 5 4 1 -32 3 7 1 9 Morocco 64 95 15 9 35 8 08 1 3 05 04 Mozambique 0 7 2 5 3 2 1 3 03 0 1 -06 0 1 Myanmar 4 8 9 2 0 1 02 Namibia 12 0 0 1 0 1 00 Nepal 1 5 3 7 0.5 3 3 00 0 1 0 1 0 1 Netherlands 2 3 5 7 153 0 134 6 10 8 8 5 1 0 0 3 New Zealand 2 7 3 7 17 6 30 8 5 6 8 1 0 7 0 5 Nicaragua 4 0 4.6 2 0 3 8 0 7 0 8 0 3 0 3 Niger 0 6 1 1 0 1 0 1 0 1 0 1 Nigeria 0 8 1 2 68 1 40 4 1 0 0 3 1 7 0 4 Norway 2 3 5 1 38.7 38.7 9.5 8 7 0.9 0 3 Oman 4 5 3 0 5 9 19 9 5 3 8 5 1 3 0 7 Pakistan 2 1 4 0 31 6 98 9 0 4 0 7 0 6 0 4 Panama 4.1 6 5 3 5 8 3 1 8 2.9 0 6 0 5 Papua New Guinea 1 8 2.4 0 6 0 5 0 4 0 2 Paraguay -48 7 2 1 5 4 5 0 5 0 8 0 1 0 2 Peru 4 4 9 5 23 6 30 4 1 4 1 2 0 5 0 3 Philippines 5 3 6 8 36 5 73 2 0.8 1 0 0 3 0 3 Poland 40 456 2 314 4 12 8 8 1 0 9 Portugal 5 5 7 72 27 1 - 60.0- 2 8 6 0 0 5 0 4 Puerto Rico 14 0 10 1 4 4 2 7 0 6 0 1 2003 World Development Indicators I149 Energy efficiency and emissions GDP per Unit Carbon dioxide emissions of energy use PPP $ Pe, kg Total Per capita kg per PPP $ oil equivale.t million metric tons metric tons ol GOP ±980 2000 1980 1999 1980 1999 1980 1999 Romania 3 4 191 8 81 2 8.6 3 6 0 7 Russian Federation 1 6 1,437 3 9 8 1 6 Rvianda 0.3 0 6 0.1 0 1 0 1 0 1 Saudi Arabia 4 0 2 6 130 7 235 4 14 0 11.7 1 1 0 9 Senegal 2 2 4 5 2 8 3 7 0 5 0 4 0 7 0 3 Sierra Leone 0 6 0 5 0 2 0 1 0 3 0 3 Singapore 2 2 3 9 30 1 54 3 12 5 13 7 2 3 0 7 Stovak Republic 3 6 38 6 .7.2 0 7 Slovenia 5 0 14 4 7 3 0 5 Somalia 0 6 00- 01 0 0 South Africa 3 1 4 4 211 3 334 6 7 7 7 9 1 0 0.8 Spain 3 8 64 _ 200 0 273_7 5 3 6 8 08 04 Sri Lanka 3 1 7 8 3 4 8 6 0 2 0 5 0 2 0 2 Sudan 1 6 3.8 3 3 2 6 0 2 0 1 0 2 00C Swaziland 0 5 0 4 0 8 0 4 0 4 0 1 Sweden- 2.0 4 4 71 4 46 6 8 6 5 3 0 9 0 2 Switzerland 4 4 7 5 40 9 405 6 6.5 5 7 0.4 0 2 Syrian Arab Republic 2 6 2 9 193 53 4 22 3 4 14 1 1 Tajikistan 2.3 5 1 0 8 0.8 Tanzania 1 1 1 9 2 5 0 1 0 1 0 2 Th-ailand 2 9 5 1 40 0 199 7 0 9 3 3 0 6 0 6 Togo 4 9 4 9 06 1 3 0 2 0 3 0 2 0 2 Trinidad and Toba-go 1 2 1 3 16 7 25 1 15 4 19 4 3 6 2 4 Tunisia 3 8 7 4 9 4 17 5 ~ 15 18 06 03 Turkey 3 2 5 3 76 3 198 5 1 7 3 1 0 8 0 5 Turktmenistan 1.4 32 4 6o 4 2 1 Uganda 0 6 1 4 0 1 0 1 0 1 0 0 Ukraine 1.4 -374.3 .7 5 2 1 United ArabiEmirates A 9 -20 36_3 88 0 _ 34 8 31 3 1.2 1 6 United Kingdom 2 5 6 0 580 3 539 3 10 3 9 2 1 2 0 4 United States 1 6 4 2 4,626 8 5,495 4 20.4 19 7 1 6 0 6 Uruguay 4 8 9 4 5 8 6 5 2 0 2 0 0 5 0 2 Uzbekistan 1 2 116 6 4 8 2.2 Venezuela, RB; 1-6 23 90 1 125 8 6 0 5 3 1 6 1 0 Vietnam 4 2 16 8 46 6 0 3 0 6 0 3 We-st Bank and Gaza Yemen, Rep~ 4 0 .183 1 1 1 4 Yugoslav-ia, Fed Rep 102 0 39 5 10.4 -3 7 Zambia 0 8 -1.2 _ 35 18 0.6 0 2 0 9 0 3 Zimbabwe 1 5 3 1 9 6 17 6 1 3 1.4 1 0 0 5 LowIlncorme 2 1 4.0 774 3 2,429 2 0 5 1 0 0 6 0 5 Middle Income 2 1 4 0 4,132 9 8,484 0 2 3 3 2 1 2 0 7 Low er middle i-ncome 1J6 3 7 2,682 6 6,391 3 1 8 3 0 1 6 0 7 upper middle income 3 4 4 9 1,450 3 2.092 7 4 3 4 3 0 7 0 5 Low&-_mldd le In comle 2 1 4 0 4,907 1 10,913.2 1 5 2 2 1 0 0 6 East Asia & Pacifi-c 1.833 3 3.734 4 1 3 2 1 2 2 0 6 Europe & Central Asia-- 2 3 989 0 3,144 1 6 6 1 3 1 2 Latin Amenica &Carlib 3.6o 6 1 848 8 1,286 7 2 4 2 5 0 6 0 4 Middle East & N Africa 3 6 3.8 491 7 1,048 4 3 0 3 7 1 0 0 7 South Asia- -23 5 5 392 3 1.215 1 0 4 0 9 0 6 0 4 Sub3 -Saharan'Africa 2 0 2 9 352 0 484 6 0 9 08a 0 8 0 4 High Income 2 22 - -49 8,945 6 11,606 6 12 0 12 3 1 2 0 5 Europe EMU -28 6 2 1,565.2 2,408 4 7 5 7 9 08_ 0 4 15O0 2003 Wortd Development Indicators Energy efficiency and emissions D i The ratio of GDP to energy use provides a measure of with global warming Anthropogenmc carbon dioxide emis- calculations are based on data on fossi fuel consumption energy efficiency To produce comparable and consistent sions result primarily from fossil fuel combustion and (from the World Energy Data Set maintained by the United estimates of real GDP across countries relative to physi- cement manufacturing In combustion, different fossil Nations Statistics Divis on) and data on world cement cal inputs to GDP-that is, units of energy use-GDP is fuels release different amounts of carbon dioxide for the manufacturing (from the Cement Manufacturing Data Set converted to international doliars using purchasing same tevel of energy use Burning oil releases about 50 maintained by the U S Bureau of Mines) Emnissions of car- power parity (PPP) rates Differences in this ratio over percent more carbon dioxide than burning natural gas, bon dioxide are often calculated and reported in terms of time and across countries reflect in part structural and burning coal releases about twice as much Cement their content of elemental carbon For this tabie these val- changes in the economy, changes in the energy efficien- manufacturing releases about half a metric ton of carbon ues were converted to the actual mass of carbon dioxide cy of particular sectors, and differences in fuel mixes dioxide for each metnc ton of cement produced by multiplying the carbon mass by 3 664 (the ratio of the Carbon dioxide emissions, largely a by-product of ener- The Catbon Dioxide Information Analysis Center (CDIAC), mass of carbon to that of carbon dioxide) gy production and use (see table 3 7), account for the sponsored by the U S Department of Energy. calculates Although the estimates of global carbon dioxide emis argest share of greenhouse gases. which are associated annual anthropogenic emissions of carbon dioxide These sions are probably within 10 percent of actual emissions 3i9 eas calculated from global average fuel chemistry and use), country estimates may have larger error bounds I-AT I r *, I Trends estimated from a consistent time series tend to be Carbon dioxide emissions (billions of metric tons) more accurate than individual values Each year the CDIAC 6 recalculates the entire time series from 1950 to the pres 5s0 1980 L 1999 ent, incorporating its most recent findings and the latest corrections to its database Estimates do not include fuels 4 supplied to ships and aircraft engaged in international 3 > ,3 > transport because of the difficulty of apportioning these 12 Jii u a t fuels among the countries benefiting from that transport 01 Fi r *- S_ United States Ch na Russian Federation Japan India *GOP per unit of energy use Is the PPP GDP per hilo- Per cap ta carbon dioxide emissions (kilograms) gram of oil equivalent of commercial energy use PPP 25 GDP is gross domestic product converted to internation- al dollars using purchasing power parity rates An inter- national dollar has the same purchasing power over 15 GDP as a U S dollar has in the United States * Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement They U R t aa Ini include carbon dioxide produced dunng consumption of 0 _______-__________@_____________________________________________________ solid, liquid, and gas fuels and gas flaring United States China Russian Federation Japan India Note No data are available for the Russian Federation for 1980 Source Table 3 8 3.8h Carbon dioxide emissions per unit of GDP (kilograms per PPP $ of GDP) 20 Fq 1980 01999 15 I 0 |1 - 10 The underlying data on commercial energy pro- duction and use are from electronic files of the 0 5 International Energy Agency The data on carbon o __t_*1___d______________ dioxide emissions are from the Carbon Dioxide Low income Lower middle Upper middle High income Information Analysis Center, Environmental income income Sciences Division, Oak Ridge National Laboratory, Carbon dioxide emissions rise Initially with income but tend to decline In some countries at higher levels of income in the U.S state of Tennessee. Source Table 38 2003 World Development Indicators i 151 S S.1 Sources of electricity Electricity Access to Sources of electricitya production electricity 96 of Hydropower coal Oil Gas Nuclear power billion kwh population 96 6 96 96 9 1990 2000 2000 1980 2000 1980 2000 1.980 2000 1980 2000 1980 2000 Afghanistan 20 Albania 3 7 49 79.4 98.7 20.6 1 3 Algeria 7 1 25 4 98.0 3 6 -02 . . 12 2 3 0 84 1 96 7 Angola 0 7 1 4 12 0 88 1 63 1 . 11.9 36.9 Argentina -39.7 89 0 94 6 -381 32 4 2.1 1.8 31 6 3 5 22 0 55 1 5 9 _ 6.9 Armenia 13 0 6 0 12 0 21 2 . . 54.8 .45 2 33.2 33 7 Australia 95.2 208 1 13 6 8.1 73 3 77.2 5 4 1 3 7 3 12 6 Austria 41 6 60 3 69 1 69 6 7 0 11.1 14 0 3 3 9 2 13 0 Azerbaijan 15 0 18 7 . 7 3 8.2 . . 92 7 726 .. 19.2 Bangladesh_ 2 4 15 8 20 4 24 8 6 0 . - 26 6 7 8 48 6 86.2 Belarus 34 1 26 1 0 1 0 1 . 99 9 5.9 94 0 Belgiaum 53 1 82 7 . 0 5 0.6 29 4 19 4 34 7 1 0 11 2 19 3 23.6 58.3 Benin 0 0 0.1 22 0 .100 0 100.0 B-olivia 1_6 4.0 60 4 68 2 49 9 -- 10.3 25 20 0 46 0 Bosnia and Herzegovina 10 4 48 8 50.7 0 5 Botswana 22 0 . Brazil 139 4 349 2 94.9 92 5 87 3 2 4 2 9 3 8 4 8 . 0 7 1 7 Bulgaria 34 8 40 6 10 7 6 6 49.2 42.3 22 5 1 6 . 4.7 17 7 44.7 Burkina Faso . 13.0 .. . .. . Burundi Cambodia 15.8 Cameroon 1 5 3 5 20 0 93 9 98 9 . . 6.1 1 1 Canada 373 3 605.1 .. 67.3 59.2 16.0 19.5 3 7 2 5 2.5 5 5 10 2 12.0 Ce-ntral African Republic Chad Chile 11 8 41 3 99 0 67 0 46 2 16 1 27.0 14 7 2 9 1 3 21 9 China 300 6 1,355 6 98 6 19 4 16 4 54 6 78 3 25 8 3.4 0 2 0 5 1 2 Hong Kong, China 12 6 31.3 60.5 100 0 0 4 .. 39 1 Colombia 20 4 44 0 81 0 70 0 73 0 7 9 6 7 1 8 0 2 19 3 18 8 Congo,Dem Rep. 4 4 5.5 6 7 95 5 99 7 4 5 0.3 Congo, Rep 0 2 0 3 20 9 64 5 99 7 . 35 5 0 3 Costa Rica 2 2 6 9 95 7 95 2 82 1 . . 4 3 0.9 C6te dlvoire 17 4.8 50.0 77 3 36 6 . 22 7 11 0 52 4 Croatia 10 7 55 1 14 5 15 8 . 14 7 Cuba 99 15 0 97 0 1 0 0 6 89 7 94 0 0 3 Czech Republic 52 7 72 9 4 6 2 4 84 8 73 1 9 6 0 5 1 1 4.3 . 18 6 Denmark 26 8 36 2 0 1 0 1 81 8 46 0 18.0 12.2 . 24.3 Dominican Republic 3 3 9.5 66 8 17 1 8 0 2 6 80 5 89 0 Ecuador 3 4 10 6 80 0 25 9 71.7. 74 1 28 3 Egy pt,Arab-Rep 18 9 75 7 93 8 51_8 18 7 27 7 16 1 20.5 65 2 El Salvador 1 5 3.9 708_ 63 7 30 5 . 2 7 _485_ Eritrea 17 0.. Estonia 18 9 8 5 0 1 90 2 100.0 0 7 8.9 Ethiopia 0 7 1 7 4 7 70 2 97 5 29 8 1 4 Finland 40 7 70 0 -. 25,1 20.9 42 6 18 9 10 8 0 9 4 2 14.4 17 2 32 1 Franca 257 3 535.8 27 0 12.5 27.2 5 8 18.8 1 4 2 7 2.1 23.8 77.5 Gabon 05 1 I0 31 0 491 71 3 . . 50 9 18.1 1-07 Gambia, The Ge-orgia 14.7 7 4 43 8 79.2 . 0.4 56.2 3.4 . 17 0 Germany 466 3 567 1 4 1 3 8 62 9 52 7 5.7 0 8 14 2 9.3 11.9 29 9 Ghana 5 3 7 2 45 0 99.2 91.7 0 8 8 3 Greece 22 7 53.4 . 15 0 6 9 44 8 64 2 40 1 16 6 .. 11.1 Gua temala- -18 6 0 66 7 12 9 37.8 .. 8.3 83.0 39.9 Guinea Guinea-Bissau Haiti 0 3 0 5 34 0 70 1 51 7 . 26.1 48.3 152 I 2003 World Development Indicators Sources of electricity S.D Electricity Access to Sources of electricitya production electricity % of Hydropower Coal Oil Gas Nuclear power bilion kwh population % % % % % 1980 2000 2000 ±950 2000 1980 2000 1980 2000 1980 2000 1980 2000 Honduras 0.9 3 7 54 5 86 3 61 9 13 7 381 Hungary 23 9 35 0 0 5 0 5 50 4 27 7 13 9 12 6 35 2 18 9 40 0 India 119 3 542 3 43 0 39 0 13 7 49 1 77 4 8 2 1 0 11 4 5 2 5 3 1 Indonesia 8 4 92 6 53 4 16.0 9 8 . 311 84.0 219 34 3 Iran, Islamic Rep 22 4 1214 97 9 25 1 3 0 50 1 20 4 24 8 76 6 Iraq 11 4 33 7 95 0 6 1 18 93 9 98 2 Ireland 10 6 23 7 . 7 9 36 16.4 36 3 60 4 19 6 15 2 39 1 Israel 12 4 430 1000 00 01 690 100 0 30 9 0 0 Italy 183 5 269 9 24 7 16 4 9 9 11 3 57 0 318 5 0 37 5 1 2 Jamaica 1 7 6.6 900 7 2 1.7 760 96 7 Japan 572 5 1,081 9 15 4 8 1 9.6 23 5 46 2 14 7 14 2 22 1 14 4 29 8 Jordan 1 1 7 4 95 0 05 100 0 89 4 10 1 Kazakhstan 615 516 9 3 14 6 .. 69 9 90 7 4 9 . 10 6 Kenya 15 3 9 7 9 711 34 1 28 9 54 8 Korea, Dem Rep 35 0 316 200 67 1 67 4 316 322 12 2 0 4 Korea, Rep 37 2 292.5 5 3 14 6.7 43 2 78 7 8 4 9 6 9 3 37 3 Kuwait 9.0 32.5 100 0 . 201 75 6 79 9 24 4 Kyrgyz Republic 9 2 14 9 531 91 7 .. 41 46 9 4 1 Lao PDR Latvia 4 7 41 64 9 68 2 19 351 2 6 27 3 Lebanon 2 8 7 8 95 0 30 9 5 7 69 1 94 3 Lesotho 5 0 Liberia Libya 4 8 20 7 99 8 100 0 100 0 Lithuania 117 11 . 4 0 3 0 96 0 5.9 15 3 75 7 Macedonia, FYR Madagascar 8 0 Malawi . 5 0 Malaysia 10.0 69 2 96 9 13 9 101 2 6 84 9 8 8 1 2 78 5 Mali Mauritania Mauritius 100 0 Mexico 67 0 204 4 25 2 162 0 0 9 3 57 9 47 5 15.5 19 8 4 0 Moldova 15 4 3 3 2 6 1.8 5 0 97 4 1 0 92 3 Mongolia , 90 0 Morocco 5 2 141 711 28 9 5.1 19 5 581 516 36 4 Mozambique 0 5 7 0 7 2 65 2 99.6 17 5 17 3 0 4 0 0 Myanmar 1 5 51 5 0 53 5 36 9 2.0 313 6 1 13 2 57 0 Namibia 1 4 34 0 97.6 0 4 21 Nepal 0 2 1 7 15 4 93 5 98.4 . 6 5 16 Netherlands 64 8 89 6 0 2 13 7 28 4 38 4 3 5 39 8 57 7 6 5 4 4 New Zealand 22 6 39 0 83 6 63.1 19 2 6 0 2 7 5 23 8 Nicaragua 11 2 3 48 0 48 1 9 2 46.4 81 6 Niger Nigeria 71 15 8 40 0 39 0 36.8 0 4 451 6 3 15 5 56.9 Norway 83 8 142 4 99 8 99.5 0 0 01 0 1 00 0 1 Oman 08 91 940 215 191 785 809 Pakistan 15 0 68 1 52 9 58 2 25 2 0 2 0 4 11 39 5 40 5 32 0 00 2 9 Panama 2 0 4 7 76 1 482 67.2 50 2 313 Papua New Guinea Paraguay 0 8 53 5 74 7 80 0 99 9 . . 11 0 Peru 10 0 19.9 73 0 69 9 81.2 10 27 4 13 4 1 9 3 6 Philippines 18 0 45 3 87 4 19 6 17 2 10 36 8 67 9 20 3 0 0 Poland 1209 1432 19 15 947 961 29 13 01 07 Portugal 15.2 43 4 52.7 261 2 3 33 9 42.9 19 4 16 5 Puerto Rico 2003 World Development Indicators 1 153 Sources of electricity Electricity Access to Sources of electricity3a production electricity % of Hydropower Coal Oil Gas Nuclear power billion k Urbanization Urban population Population In Population In Access to Improved urban agglomerations largest city sanitatIon facilities of more than one million Urban Rural 96 of total % of total % of urban % of 96 of millions population population population population population 1980 2001 1980 2001 1980 2000 2015 1980 2001 1990 2000 1990 2000 Romania 10 9 12 4 49 55 9 9 10 18 16 86 10 Russian Federation 97 0 105 5 70 73 18 19 21 8 8 Rwanda 0 2 0 5 5 6 76 12 8 Sau di Arabia 6 2 18 5 66 _ 87 19 25 24 17 25 100 100 Senegal 2 0 4 7 36 48 17 22 27 48 46 86 94 38 48 Sierra Le one 0 8 1 9 24- 37 47 43 88 53 Singapore 2 4 4 1 '100 100 100 89 83 100 100 . 100 Slovak Republic 2 6 3 1 52 58 . 15 100 100 Slovenia 0 9 1 0 48 49 26 100 Somalia 1 4 2 5 22 28 27 48 South Africa 13 3 249_ 48- 58 2-7 32_ 36 13 12 93 93 80 80 Spain 27 2 32 0 73 78 20 17 17 16 13 SriLanka 3 1 4 3 22 23 16 94 97 82 93 Sudan 3 9 11 7 20 37 6 9- 11 30 24 87 87 48 48 Swaziland 0 1 0 3 18 27 28 Sweden 6 9 7 4 83 83 17 18_ 18 20 22 100 100 100 100 Switzerland 3 6 4 9 57 67 20 19 100 100 100 100 Syrian Arab Republic 4 1 8 6 47 52 28 28 31 26 27 98 81 Tajikistan 1 4 1.7 -34 28 . . 30 97 88 Tanzania 2 7 11 4 15 33 5 12 18 30 19 84 99 84 86 Thailand 80 12 3 17 20 10 12 15 59 61 95 96 75 96 Togo 06 1.6 _ 23 34 . 46 71 69 24 17 Trinidad and Tobago 0 7 _10 63 74 6 Tunisia 3 3 6 4 52 66 18 20 21 35 30 96 96 48 62 Turkey 19 5 43 8 44 -66 19 27 30 23- 21 97 97 70 70 Turkmenistan 1 3 2 4 47 45 .23 Uganda 1 1 3 3 9 15 42 39 93 77 Ukraine 30 9 33.4 62 68 14 15 17 7 7 . 100 _ 98 United Arab Emirates 0 7- ~26 71 87 34 35 United Kingdom 50 0 52 7 89 -90 25 23 23 15 15 100- 100 100 100 United States 167.6 -221.0 74 77 38 38 37 9 8 100 100 100 100 Uruguay 2 5 3 1 85 92 42 37 35 -49 43 . 95 85 Uzbekistan 6 5 --9 2 41 37 11 9 8 28 24 97 . 85 Venezuela, RB 12 0 21 5 79 -87 28_ 29 30 21 15 71 . 48 Vietnam 1-0 3- 19.5 19 25 14 13 14 33 24 52 82 23 38 West Bank and Gaza Yemen, Rep 1 6 4 5 19 25 15 31 69 89 21 21 Yugoslavia, Fed Rep 4 5 5 5 46 52 11 14 15 25 30 100 99 Zambia 2 3 4 1 40 40 9 16 22 23 41 86 99 48 64 Zimbabwe 1 6 4 6 22 36 9 14 19 39 40 70' 71 50 57 Low Income 381 0 772 5 24_ 31 .16 17 58 72 20 31 Middle Income 755 6 1,376 1 38 52 19 15 75 81 29 40 Lower middle income 515 6 987 4 32_ 46 15_ 17 20 16 13 70 80 28 39 Upper middle income 240 0 388 6 66 77 25 22 86 87 41 57 Low & middle Income 1,13 6 6 2,148.5 32 _ 42 18 16- 68 78 24 35 East Asia &Pacific 288 6 679 9 21 37 . 13 9 61 72 24 34 Europe -& Central Asia 249 2 298 1 59 _63 16 18 20 15 15 L'atin Am erica & Carib 234 1 396 9 65 76 29 32 32 27 24 85 86 41 52 Middle East & N Africa 83 7 173 3 48 58, 21 22 24 30 25- 94 7-2 South Asia 201 1 382 5 22 28 8 12 14 9 11 52 66 -11 21 Sub-Saharan Africa 80 0 217 8 21 32 . 27 26 75 76 45 45 High Income 605 2 741.9 73 78 18 17- Europe EMU 210 3 237 7 73 78 26 27 27 17 16 II 2003 World Development Indicators Urbanization No The population of a city or metropolitan area * Urban population is the midyear population of depends on the boundaries chosen For example, in 3.10a areas defined as urban in each country and reported 1990 Beijing, China, contained 2 3 million people in _ to the United Nations (see About the data) 87 square kilometers of "inner city" and 5 4 million - ' Population in urban agglomerations of more than in 158 square kilometers of 'core city " The popula- Urban population (millions) one million is the percentage of a country's popula- tion of "inner city and inner suburban districts" was 1,000 1980 i 2000 tion living in metropolitan areas that in 1990 had a 6 3 million, and that of "inner city, Inner and outer population of more than one million * Population In suburban districts, and inner and outer counties" 0 0 largest city is the percentage of a country's urban was 10 8 million (For most countries the last defini- 600 population living in that country's largest metropoli- tion is used ) tan area * Access to Improved sanitation facillties Estimates of the world's urban population would 400 refers to the percentage of the urban or rural popula- change significantly if China, India, and a few other tion with access to at least adequate excreta dispos- 200 populous nations were to change their definition of al facilities (private or shared but not public) that can urban centers According to China's State Statistical o I effectively prevent human, animal, and insect contact Bureau, by the end of 1996 urban residents Low Lower Upper High with excreta Improved facilities range from simple income middle Income accounted for about 43 percent of China's popula- but protected pit latrines to flush toilets with a sew- tion, while in 1994 only 20 percent of the population ' erage connection To be effective, facilities must be was considered urban In addition to the continuous I correctly constructed and properly maintained migration of people from rural to urban areas, one World urban population, 2001 of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as High cities in recent years Because the estimates in the icome table are based on national definitions of what con- stitutes a city or metropolitan area, cross-country middle o r - comparisons should be made with caution income mid I - To estimate urban populations, the United Nations' ratios of urban to total population were applied to the World Bank's estimates of total population (see Low-income countries, with only 31 percent of their people in urban areas, still have a larger urban population table 2 1) than high-income countries The urban population with access to improved san- itation facilities is defined as those with access to at Source Table 310 least adequate excreta disposal facilities that can effectively prevent human, animal, and insect con- tact with excreta The rural population with access is included to allow comparison of rural and urban access This definition and the definition of urban areas vary, however, so comparisons between coun- tries can be misleading The data on urban population and the population in urban agglomerations and in the largest city come from the United Nations Population Division's World Urbanization Prospects. The 2001 Revision The total population figures are World Bank estimates The data on access to san- itation in urban and rural areas are from the World Health Orgamz6otin. 2003 World Development Indicators 1 159 Urban environment City Urban Secure House Work Travel Households with Wastewater population tenure price to trips by time access to services treated Income public to work ratio trans- portation Potable Sewerage % of water connection Electncity Telephone thousands population % minutes % % % % % 2000 1998 19980 1998 1998n 1998* 1998 i998 15998 19985 Algeria Algiers 2,562b 93 2 75 80 Argentina Buenos Aires 2,996b 92 1 51 59 42 100 98 100 70 C6rdoba 1,322 b 85 0 6 8 44 32 99 40 99 80 49 Rosario 1,248b 5 7 22 98 67 93 76 1 Armenia Yerevan 1,250b 100 0 4 0 84 30 98 98 100 88 36 Bangladesh Chittagong 2,301 b 8 1 27 45 44 95 Dhaka 10,000o b 16 7 9 45 60 22 90 7 Sylhet 242 b 6 0 10 50 29 0 93 40 Tangail 152b 85 7 13 9 30 12 0 90 12 Barbados Bridgetown 99.7 4.4 .. 98 5 99 78 7 Belize Belize City 55b Bolivia Santa Cruz de la Sierra 1,065 C 87 0 29 3 29 53 33 98 59 53 Bosnia and Herzegovina Sarajevo 522c 100 12 95 90 100 Brazil Belem 1,638C Icapui 91 7 4 5 30 88 90 33 Maranguape 30 20 73 Porto Alegre 3 b 99 87 100 Recife 3,088b 12 5 46 35 89 41 100 29 33 Rio de Janeiro 10,192 b 88 80 10 Santo Andre 1,658 b 80 3 23 4 43 40 98 95 100 79 Bulgaria Bourgas b 5 1 61 32 100 93 100 93 Sofia 1,200b 100 0 13.2 79 32 95 91 100 89 94 Troyan 24b 100 0 3_7 44 22 99 82 100 45 Veliko Tarnovo 100 0 5 4 46 30 98 98 100 96 50 Burkina Faso Bobo-Dioulasso 100 0 . .. 24 29 6 Koudougou 30 26 7 Ouagadougou 1,130c 100 0 2 30 47 11 19 Burundi Bujumbura 373b 97 0 48 25 26 62 57 19 21 Cambodia Phnom Penh 1,000b 8 9 0 45 45 75 76 40 Cameroon Douala 1,148b 13 4 40 34 1 95 9 5 Yaounde 968b 42 45 34 1 95 9 24 Canada Hull 254 b 100 0 , 16 100 100 100 100 100 Central African Republic Bangui 94 0 66 60 31 18 11 0 Chad N'Djamena 998 c 35 42 0 13 6 21 Chile Gran Concepci6n . 57 35 100 91 95 69 6 Santiago de Chile 5,737 b 60 38 100 99 99 73 3 Tome 92 52 98 58 57 Valparaiso 851 b 91 8 . 55 98 92 97 63 100 VYia del Mar 851b 92 7 97 97 98 65 93 Colombia Armenia 94 1 5 0 42 60 90 50 99 97 Marinilia 170b 94 5 8 5 18 15 98 93 100 65 Medellin 2,901 b., 38 35 100 99 100 87 Congo, Rep Brazzaville 989b 87 9 55 20 56 0 52 18 Cote d'lvoire Abidjan 3,201b 14 5 45 26 15 41 5 45 Croatia Zagreb 2,497 b 96 5 7 8 56 31 98 100 94 Cuba Baracoa .. 96 2 83 3 93 32 Camaguey 84 7 2 60 72 47 97 Cienfuegos . 96 3 4 0 __ 80 100 73 100 9 2 Havana 8 5 58 83 100 85 100 14 Pinar Del Rio 96 4 . 80 97 48 100 Santa Clara 98 8 7 48 95 42 100 43 Czech Republic Brno . 50 25 100 96 100 69 100 Prague 1,193b 99 3 , 55 22 99 100 100 100 Congo, Dem Rep Kinshasa 5,398b 94,9 72 57 72 0 66 1 Dominican Republic Santiago de los Caballeros 691b 30 75 80 71 80 16e O 2003 World Development Indicators Urban environment S1 City Urban Secure House Work Travel Households with Wastewater population tenure price to trips by time access to services treated Income public to work ratio trans- portation Potable Sewerage % of water connection Electricity Telephone thousands population % minutes % % %% 2000 19988 19988 19988 19988 19988 1998a 19980 19988 19988 Ecuador Ambato 286b 90 81 91 87 Cuenca 91 0 4 6 25 97 92 97 48 82 Guayaquil 2,317 b 45 8 3 4 89 45 70 42 44 9 Manta 126b 30 70 52 98 40 Puyo 4b2 1 15 80 30 90 60 Quito 1,531lb 93 8 2 4 33 85 70 96 55 Tena -63 5 80 60 El Salvador San Salvador 1,863 b 90 5 3 5 82 80 98 70 Estonia Riik 99 5 92 90 98 55 Tallin 397 c 98 8 6 4 35 98 98 100 86 100 Gabon Libreville 52c80 30 55 0 95 45 44 Gambia, The Banjul 5ob 91 8 11 4 55 22 23 12 24 Georgia Tbilisi 1,310Oc 100 0 9 4 98 100 58 Ghana Accra 1,500b 14 0 54 21 Kumasi 780b 77 7 13 7 51 21 65 95 51 Guatemala Quezaitenango 333 b 4 3 15 60 55 80 40 Guinea Conakry 1,824 c 26 45 30 32 54 6 Indonesia Jakarta 9,489 b 95 5 14 6 50 65 99 16 Sem arang 1,076 b 80 2 34 85 Surabaya 2,373 b 97 6 3 4 18 35 41 56 89 71 Iraq -Baghdad- ,9 Italy Aversa 90 Jamaica Kingston 655c 97 88 20 Montego Bay 78 86 15 Jordan Amman 1,621 b 97 3 6 1 21 25 98 81 99 62 54 Kenya Kisumu 134b 97 3 8 5 43 24 38 31 49 65 Mombasa 47 -20 50 Nairobi 2,310 c 71 57 89 52 Korea. Rep Hanam 124b 3 7 81 68 100 100 81 Pusan 3,843 b 100 0 4 0 39 42 98 69 100 100 69 Seoul 10,389 b 98 6 5 7 71 60 100 99 100 99 Kuwait Kuwa it City l,165 c 6 5 21 10 100 98 100 98 Kyrgyz Republic Bishkek 60b 94 8 95 35 30 23 100 20 15 Lao Vientiane 562b 92 2 23 2 2 27 87 100 87 20 Latvia Riga 7751 97 4 15 6 95 93 100 70 Lebanon Sin- El Fil b8 3 50 10 -80 30 98 80 Liberia Monrovia 651 b 57 6 28 0 80 60 Libya Tripoli 1,773 b 0 8 18 20 97 90 99 6 40 Lithuania V-ilnius 578b 100.0 20 0 52 37 89 89 100 77 54 Madagascar Antananarivo lS507 c Malawi Lilongwe 765c 27 5 65 12 50 10 Malaysia Penang 7 2 55 40 99 100 98 20 Mauritania Nouakchott 8811 89 9 5 4 45 50 Mexico Ciudad Juarez 1,018 b 24 23 89 77 96 45 Moldova Chisinau 80 23 100 95 100 83 71 Mongolia Ulaanbaatar 627 b 51 6 7 8 80 30 60 60 100 90 96 Morocco Casablanca 3,292 b 30 83 93 91 Rabat -646b 40 ~ 20 93 97 52 Myanmar Yangon 3.692 b 8 3 69 45 78 81 85 17 Nicaragua Leon 98 8 15 78 84 21 Niger Niamey 73lc 87 4 30 33 0 51 4 Nigeria lbadan 1,731 c 85 -8 46 45 26 12 41 Lagos 13,427 c 93 0 48 60 41 Oman Muscat 887 b 20 80 90 89 S3 Panama Col6n 132 b 14 2 15 Paraguay Asunci6n 1,262 c 90 2 10 7 25 46 8 86 17 2003 Worid Developmenit Indicators I 161 Urban environment City Urban Secure House Work Travel Households with Wastewater population tenure price to trips by time access to services treated Income public to work ratio trans- portation Potable Sewerage % of water connection Electricity Telephone thousands population % minutes % % % % % 2000 1998' 19980 1998' 1998* 1998a 1998' 1998n 19985 1998' Peru Cajamarca 90 0 3 9 20 86 69 81 38 62 Huanuco 747b 30 0 20 57 28 80 32 Huaras 54b 6 7 15 71 Iquitos 347b 97 3 5 6 25 10 73 60 82 62 Lima 7,431b 80 6 10 4 82 75 71 99 4 Tacna 4 0 25 65 58 74 16 64 Tumbes 20 60 35 80 25 Philippines Cebu 2,189b 95 0 13 3 35 41 92 80 25 Poland Bydgoszcz 60 5 4 3 35 18 95 87 100 85 28 Gdansk 893c 4 4 56 20 99 94 100 56 100 Katowice 3,487 c 27 8 1 7 29 36 99 94 100 75 67 Poznan 65 5 5.8 51 25 95 96 100 86 78 Qatar Doha 391c Russian Federation Astrakhan 100 0 5 0 66 35 81 79 100 51 92 Belgorod 100 0 4 0 25 90 89 100 51 96 Kostroma 100 0 6 9 68 20 88 84 100 46 96 Moscow 9,321 100 0 5 1 85 62 100 100 100 100 98 Nizhny Novgorod 1,458c 1000 6 9 79 35 98 98 100 64 98 Novomoscowsk 100 0 4 2 61 25 99 93 100 62 97 Omsk 1,216 c 99 7 3 9 86 43 87 87 100 41 89 Pushkin 100 0 9 6 60 15 99 99 100 89 100 Surgut . 100 0 45 81 57 98 98 100 50 93 Veliky Novgorod 100 0 3 4 75 30 97 97 100 51 95 Rwanda Kigali 358b 114 32 45 36 20 57 6 20 Samoa Apia 34b 10 0 60 0 98 96 Singapore Singapore 3,164 b 100 0 3 1 53 30 100 100 100 100 100 Slovenia Ljubljana 273b 98 9 7 8 20 30 100 100 100 97 98 Spain Madrid 4,577 b 16 32 100 Pamplona 100 100 79 Sweden Amal 13b 29 100 100 100 . 100 Stockholm 736 b 6 0 48 28 100 100 100 100 Umea 104 b 5 3 16 100 100 100 100 Switzerland Basel 170b 12 3 100 100 100 99 100 Syrian Arab Republic Damascus 2,335b 10 3 33 40 98 71 95 10 3 Thailand Bangkok 5,647 b 77 2 8 8 28 60 99 100 100 60 Chiang Mai 499b 96 5 6 8 5 30 95 60 100 75 70 Togo Lome 663 b 64 0 40 30 70 51 18 Trinidad and Tobago Port of Spain 78 6 44 Tunisia Tunis 2,023b 50 .. 75 47 95 27 83 Turkey Ankara 2,837 b 91 3 4.5 32 97 98 100 80 Uganda Entebbe 65b 74 0 10 4 65 20 48 13 42 0 30 Jinja 92b 82 0 15 4 49 12 65 43 55 5 30 Uruguay Montevideo 1,670 b 88 0 5 6 60 45 98 79 100 75 34 West Bank and Gaza Gaza 367b 87 3 5.4 . 85 38 99 38 Yemen, Rep Aden 1,200b 78 20 96 30 Sana'a 1,200b 78 20 30 9 96 30 Yugoslavia, Fed Rep Belgrade 1,182b 96 5 13 5 72 40 95 86 100 86 20 Zimbabwe Bulawayo goo b 99.4 75 15 100 100 98 80 Chegutu 515 3 4 20 22 100 68 9 3 69 Gweru 94 0 . 15 100 100 90 61 95 Harare 1,634 b 99 9 32 45 100 100 88 42 Mutare 149b 70 20 88 88 74 4 100 a Data are preliminary b Data are for 1998 and are from the United Nations Centre for Human Settlements c Data are for 2000 and are from the United Nations Population Division's World Urbanization Prospects The 2001 Revision 162 0 2003 World Development Indicators Urban environment 3.11 1 Despite the importance of cities and urban agglom- tance of cities and is therefore biased toward smaller * Urban population refers to the population of the erations as home to almost half the world's people, cities Moreover, it is based on demand for participa- urban agglomeration, a contiguous inhabited territo- data on many aspects of urban life are sparse The tion in the Urban Indicators Programme As a result, ry without regard to administrative boundaries available data have been scattered among interna- the database excludes a large number of major cities * Secure tenure refers to the percentage of the pop- tional agencies with different mandates, and compil- The table reflects this bias as well as the criterion of ulation protected from involuntary removal from land ing comparable data has been difficult Even within data availability for the indicators shown or residence-including subtenancy, residence in cities it is difficult to assemble an integrated data The data should be used with care Because differ- social housing, and residences owned, purchased. set Urban areas are often spread across many juris- ent data collection methods and definitions may have or privately rented-except through due legal dictions with no single agency responsible for col- been used, comparisons can be misleading In addi- process * House price to Income ratio is the aver- lecting and reporting data for the entire area Adding tion, the definitions used here for urban population age house price divided by the average household to the difficulties of data collection are gaps and and access to potable water are more stringent than income * Work trips by public transportation are overlaps in the data collection and reporting respon- those used for tables 3 5 and 3 10 (see Definitions) the percentage of trips to work made by bus or sibilities of different administrative units Creating a minibus, tram, or train Buses or minibuses are road comprehensive, comparable international data set is vehicles other than cars taking passengers on a fare- further complicated by differences in the definition of paying basis Other means of transport commonly an urban area and by uneven data quality used in developing countries, such as taxi, ferry, rick- The United Nations Global Plan of Action calls for shaw, or animal, are not included * Travel time to monitoring the changing role of the world's cities and work is the average time in minutes, for all modes, human settlements The international agency with for a one-way trip to work Train and bus times the mandate to assemble information on urban include average walking and waiting times, and car areas is the United Nations Centre for Human times include parking and walking to the workplace Settlements (UNCHS, or Habitat) Its Urban * Households with access to services are the per- Indicators Programme is intended to provide data for centage of households in formal settlements with monitoring and evaluating the performance of urban access to potable water and connections to sewer- areas and for developing government policies and age, electricity, and telephone service Households strategies These data are collected through ques- with access to potable water are those having tionnaires completed by city officials in more than a access to safe or potable drinking water within 200 hundred countries meters of the dwelling Potable water is water that is The table shows selected indicators for more than free from contamination and safe to drink without 160 cities from the UNCHS data set A few more further treatment * Wastewater treated is the per- indicators are included on the World Development centage of all wastewater undergoing some form of Indicators CD-ROM These data are still preliminary treatment and are undergoing further validation The selection of cities in the UNCHS database does not reflect population weights or the economic Impor- 3.11a Share of total Share of total Country City work trips Country City work trips LLSo POR Vientiane 2 Kyrgyz Republic Bishkek 95 Spain Madrid 16 Russian Federation Moscow 85 t_Canada Hull 16 Armenia Yerevan 84 Libya Tripoli 18 Peru Lima 82 Slovenia Ljubljana 20 Gabon Libreville 80 Kuwait Kuwait City 21 Liberia Monrovia 80 I Jordan Amman _ 21 Mongolia Uabatar 80 r Mexico Ciudad Juarez 24 Moldova Chisinau 80 dGuinea Conakry 26 Bulgara Sofia 79 Malawi Lilongwe 27 Yemen, Rep Aden 78 The data are from the Global Urban Indicators database of the UNCHS Source Table 3 11 2003 World Development Indicators 1 163 4~~~ Traffic and congestion Motor vehicles Passenger Two-wheelers Road traffic Fuel prices cars Super Diesel per 1.000 per kilometer per 1,000 per 1,000 million vehicle $ $ people of road people people kilometers per liter per liter 1990 2000 ±990 2000 1990 2000 £990 2000 1990 2000 2002 2002 Afghanistan . .0 34 0 27 Albania 11 47 3 10 2 37 3 1 0 80 0 51 Algeria .. .0.22 0 10 Angola 18 . .. 14 .0 19 0 13 Argentina 181 181 27 30 _134 _140 1 43,119 27,458 0 30 0 15 Armenia 5 2 1I . . 0 42 0 29 Australia 530 11 13 450 510 18 18 138,501 0 50 0 48 Austria 421 536 3-0 22 387 495 71_ _77 0 84 0 73 Azerbaijan 52 49 7 16 36 41 5 1 0 37 0 16 Bangladesh -1 I 0 1 0 0 1 1 0 52 0 29 Bel-arus 61 135 13 20 59 145 52 10,026 4,964 0 50 0 36 Belgium -423 497 30 35 385- 448- 14 25 158,759 1 04 0 80 Benin 3 . 2 2 34 .0 54 0 41 Bolivia 41 6 8 25_ 22_ 9 3 1,139 . 0 69 0 42 Bosnia and Herzegovina 114 24 . 101 0.74 0 074 Botswana 18 68 3 11 ~ 10 29 . 1 0 41 0 38 Brazil 88 8 17 . 137 28 .. 0 55 0 31 Bulgaria 163 266 39 60 146 234 55 64 0 68 0 59 Burkina Faso 4 3 2 9 .0 83 0 62 Burundi 0 58 0 54 Cambodia 1 6 0 31 0 26 9 134 314 7,210 0 63 0 44 Cameroon 10 3 6 0 68 0 57 Canada 605 581 20 19 468 459 12 11 0 51 0 43 Central African Republic 1- 0 0 0 1 0 0 1,494 0 81 0 65 Chad 2 0 . 1 0 .0 79 0 77 Chile 81 135 13 25 52 87 2 2 . 0 58 0 39 China 5 4 11 1 7 3 26 0.42 0 37 Hong Kong, China- 66 79 253 287 42 59 4 5 8,192 10.781 1 47 0 77 Colombia 51 . 19 43 8 12 50,945 41,587 0 44 0.24 Congo, Dem. Rep 0 70 0.69 Congo, Rep 18 3 12 -069 0 48 Costa Rica 87 133 7 14 55 88 14 22 . 507,796 0 64 0 44 C6te dIlvoire 24 6 15 .0 85 0 60 Croatia . 44_ 257 .. 15 13,764 0 89 0 74 Cuba 37 32 16 6 18 16 19 16 0 50 0 27 Czech Republic 246 362 46_ 67 228 335 113 73 0 81 0 71 Denmark 368 411 27 31 320 357 9 13 36,304 45,165 1 09 0 94 Dominican Republic 75 48 . 21 . ..0 49 0 27 Ecuador 35 46 8 14 31 43 2 2 10,306 14,449 1.30 0 90 Egypt, Arab Rep 29 33 21 6 ..0.19 0 08 El Salvador 33 61 14 36 17 30 0 5 2,002 3,646 0 46 0 33 Eritrea 1 1 1I 0.36 0 25 Estonia 211 397 22 11 154 339 66 5 6,412 0 58 0 56 E-thiopia 1 1 2 3 1 1 0 0 1,642 0 52 0.32 Finland 441 462 29 31 386 403 12 35 39,750 46,010 1 12 0 80 France 494 564 32 38 405 476 55 0 422,000 519,400 1.05 0.80 Gabon- 32 4 19 .0 53 0.37 Gambia, The 135 6 0 46 0 40 Georgia 107 58 27 15 89 46 5 1 -4,620 0 48 0 41 Germany 405 53 386 516 18 56 446,000 589,500 1 03 0 82 Ghana . .0 28 0 23 Greece 248 348 22 31 171 254 120 203 77,954 0 78 0 68 Guatemala 57 .. 45 52 12 3,455 1 23 0 92 Guinea 4 1 2 ... 0 66 0 56 Guinea-Bissau 7 . 2 . 4 Haiti 0 54 0 30 16 I 2003 world Development Indicators Traffic and congestion I Motor vehicles Passenger Two-wheelers Road traffic Fuel prices cars Super Diesel per 1,000 per kilometer per 1,000 per 1,000 million vehicle $ $ people of rosad people people kilometers per liter per liter 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 2002 2002 Honduras 22 -62 9 28 52 15 3,288 0 63 0 46 Hungary 212 272 21 15- 188 238 16 14 22,898 0 94 0 85 India 4 -8 2 3 2 5 15 29 0 66 0 41 Indonesia 16 25 10 14 7 14 _34 63 0 27 0 19 Iran, Islamic Rep 34 14 25 36 0 07 0 02 I raq 14 6 1 ..0 02 0 01 Ireland 270 10 14 227 296 6 11 24,205 0 90 0 80 Israel 210 270 74 107 174 228 8 12 18,212 35,863 0 90 0 62 Italy 529 591 99 73 476 545 45 125 344,726 1 05 0 86 Jamaica 0 52 0 44 Japan 469 560 52 62 283 492 146 110 628,581 765,056 0 91 0 66 Jordan 60 26 0 1,098 0 52 0 17 Kazakhstan 76 84_ 8 12 50 65 . 10 18,248 3.215 0 35 0 29 Kenya 12 5 -10 15,170 0 70 0 56 Korea, Dem Rep 0 55 0 41 Korea, Rep 79 239 60 128 48 168 32 59 30,464 67,266 0 92 0 51 Kuwait 0 20 0 18 Kyrgyz Republic 44_ 39 10 10 44 39 .. 4 5,220 0 39 0 25 Lao PDR 9 3 6 18 0 36 0 30 Latvia 135 _ 262 6 9 106 235 76 9 3,932 0 70 0 65 Lebanon 321 336 183 300 313 13 15 0 65 0 25 Lesotho 11 4 3 0 50 0 47 Liberia 144 7 Libya 0 10 0 08 Lithuania -16-0 338 12 17 133 334 52- 6 0 69 0 59 Macedonia, FYR 132 30 121 1 3,102 0 85 0 63 Madagascar 6 2 4 41,500 1 08 0 65 Malawi 4 4 2 0 66 0 62 Malaysia 124 -200 -26 69 101 181 167 23-0 0 35 0 19 Mali -3 __ 2 . 2 0 69 0.55 Mauritania 10 3 7 0 63 0 39 Mauritius 59 98 35 49 44 73 54 96 Mexico 119 151 41 44 82 107 3 55,095 0 62 0 47 Moldova 53 70 17 24 48 54 45 538 0 45 0 31 Mongolia 21 30 1 2 6 18 -22 10 340 40 0 38 0 37 Morocco 37 52 15 21 28 41 1 1 0 87 0 55 Mozambique -4 -2 0 3 1,889 0_46 0 43 Myanmar 0 33 0 12 Namibia 71 1 2 39 1 1,896 2,706 0 45 0 43 Nepal 0 66 0 34 Netherlands 405 427 58 58 368 383 44 25 90,150 109,955 1_12 0 81 New Zealand 524 540 19 29 436 580 24 21 0 55 0 33 Nicaragua 19 10 5 8 10 12 3 5 108 523 0 58 0 53 Niger 6 4 5 5 178 240 0O77 0 55 Nigeria 30 21 14 12 5 2,927,931 2,701,208 0 20 0 19 Norway 458 505 22 25 380 412 48 56 30,148 1 23 1 18 Oman 130 9 . 83 3 0 31 0 29 Pakistan 6_ 8 4 4 4 5 8 15 18,933 218,779 0 52 0 35 Panama 75 113 18 217 60 83 2 3 0 51 0 36 Papua New Guinea 0 53 0 34 Paraguay 0 56 0 34 Peru 43 15 27 0 21 0 14 Philippines 10 31 4 11 7 10 6 14 6,189 9,548 0 35 0 27 Poland 168 286 18 33 138 259 36 21 59,608 138,100 0 83 0 68 Portugal 222 348 34 162 321 5 77 28,623 93,020 0 97 0 71 Puerto Rico 0 34 0 32 2003 World Development Indicators I 165 Traffic and congestion Motor vehicles Passenger Two-wheelers Road traffic Fuel prices cars Super Diesel per 1,000 per kilometer per 1,000 per 1,000 million vehicle $ $ people of road people people kilometers per liter per liter 1990 2000 1990 2000 ±990 2000 1990 2000 1990 2000 2002 2002 Romania 72 154 11 17 56 133 13 14 23,907 36,884 0 64 0 57 Russian Federation 87 153 14 48 65 140 . 43 60,950 0 35 0 25 Rwanda 2 ~ 1 2 1 .0 84 0 84 Saudi Arabia 165 19 98 0 .0 24 0 10 Senegal 11 6 8 8 0 0 75 0 53 Sierra Leone 10 4 2 7 2 2 0 996 529 0 51 0 50 Singapore 130 132 142 170 89 97 40 34 0 85 038 Slovak Republic 194 260 57 -33 163 229 61 8 0 0 74 0 70- Slovenia 306 4-55 42 46 289 426 8 6 5,620 9,245 0.76 0 67 Somalia- 2 1 0 1 South Africa 139 143 26 11 97 94 8 4 0.43 0 40 Spain 360 467 43 53 309 404_ 79, 90 100,981 201,896 0 83 0.72 Sri Lanka 21 36 4 7 7 12 24 44 3,468 15,630 0 54 0 31 Sudan 9 21 28 8 . 0 30 0 24 Swaziland 66 70 18 17 35 34 3 3 0 47 0 44 Sweden 464 478 29 21 426 451 11 31 61,040 _69,200 1 06 0 96 Switzerland 491 526 46 54 449 494 114 102 48,660 53,506 0,89 0 93 Syrian Arab Republic 26 30 10 11 10 9 0 53 0 18 Tajikistan 3 1 0 036_ 0_24 Tan zania 5 2 2 1 0 67 0 61 Thailand 46 36 14 86 45,769 0.36 0.32 Togo 24- 11 16 8 0 56 0.46 Trinidad and Tobago 0 40 0 21 Tunisia 48 19 40 23 54 . 1 1,092,675 0.29 -0 19 Turkey 50 85 8 14 34 63 10 15 27,041 49,846 1 02 0 78 Turkmenistan 0O02 0 01 Uganda 2 5 -4 1 ~ 2 0 3 0 83 0 70 Ukraine 63 19 63 104 . 49 59,50-0 61,200 0 47 0 34 United Arab Emirates 121 52 97 0 29 0 30 Uni-ted- Kingdom 400 424 64 62 341 389 14 3 399,000 462,400 1 18 1 20 United States 758 759 30 34 573 475 17 15 2,527,441 2,653,043 0 40 0 39 Uruguay 138 1 74 45 63 122 158 74 110 0 46 0 20 Uzbekistan .. 0 38 0 26 Venezuela, RB 563 0 05 0 05 Vietnam 45 0 34 0 27 West Bank and Gaza 0 99 0 52 Yemen, Rep 34 8 14 .8,681 11,476 0 21 0 10 Yugoslavia, Fed Rep 137 ~190 31 36 133 150 3 3 0 74 0 66 Zambia 14 . 3 8 0 72 0 60 Zimbabwe N nlll~ f~w 0 im0 Z Low Income 9 -10 6 9 0 54 0.41 Middle income 39 65 25 49 0 54 0 38 Lower middle income 19 32 .11 23 .0 51 0 37 Upper middle income 127_ 193 114 153 0S57 0 40 Low & middle Income 25 57 16 45 0S54 0 39 East Asia &Pacific 9 16 4 10 .,0 36 0 31 Europe & Central Asia 98 204 82 171 .0 64 0S56 Latin America & Carib 92 1S8 .119 .0 54 0 36 Middle East & N Africa 57 .31 .0 30 0 17 South Asia 4 8 2 5 0S54 0 34 Sub-Saharan Africa 24 14 0S59 0 48 High Income 514 586 396 443 . .0 89 0 68 Europe EMU 453 558 379 496 .1 00 0 80 .166 Li 2003 World Development Indicators Traffic and congestion 3.12 1 Traffic congestion in urban areas constrains eco- works, and central statistical offices As a result, the * Motor vehicles include cars, buses, and freight nomic productivity, damages people's health, and compiled data are of uneven quality The coverage of vehicles but not two-wheelers Population figures degrades the quality of their lives The particulate air each indicator may differ across countries because refer to the midyear population in the year for which pollution emitted by motor vehicles-the dust and of differences in definitions Comparability also is data are available Roads refer to motorways, high- soot in exhaust-is proving to be far more damaging limited when time-series data are reported ways, main or national roads, and secondary or to human health than was once believed (For infor- Moreover, the data do not capture the quality or age regional roads A motorway is a road specially mation on suspended particulates and other air pol- of vehicles or the condition or width of roads Thus designed and built for motor traffic that separates the lutants, see table 3 13 ) comparisons over time and between countries traffic flowing in opposite directions * Passenger In recent years ownership of passenger cars has should be made with caution cars refer to road motor vehicles, other than two- increased, and the expansion of economic activity has The data on fuel prices are compiled by the wheelers, intended for the carriage of passengers led to the transport by road of more goods and serv- German Agency for Technical Cooperation (GTZ) from and designed to seat no more than nine people ices over greater distances (see table 5 9) These its global network of regional offices and represen- (including the driver) * Two-wheelers refer to mope- developments have increased demand for roads and tatives as well as other sources, including the ds and motorcycles * Road traffic is the number of vehicles, adding to urban congestion, air pollution, Allgemeiner Deutscher Automobil Club (for Europe) vehicles multiplied by the average distances they trav- health hazards, traffic accidents, and injuries and a project of the Latin American Energy el * Fuel prices refer to the pump prices of the most Congestion, the most visible cost of expanding Organization (OLADE, for Latin America) Local prices widely sold grade of gasoline and of diesel fuel Prices vehicle ownership, is reflected in the indicators in the have been converted to U S dollars using the have been converted from the local currency to U S table Other relevant indicators-such as average exchange rate on the survey date as listed in the dollars (see About the data) vehicle speed in major cities or the cost of traffic international monetary table of the Financial Times congestion, which takes a heavy toll on economic For countries with multiple exchange rates, the mar- productivity-are not included here because data are ket, parallel, or black market rate was used rather incomplete or difficult to compare than the official exchange rate The data in the table except for those on fuel prices-are compiled by the International Road Federation (IRF) through questionnaires sent to national organizations The IRF uses a hierarchy of sources to gather as much information as possible The primary sources are national road associations Where such an association lacks data or does not respond, other agencies are contacted, including road directorates, ministries of transport or public 3.12a _____-- Big ..-,, - Per 1,000 people Country Passenger cars Country Passenger cars Central Afncan Republic o New Zealand 580 - Bangladesh lb Italy 545 [Tthiopia __1 Germay 516a Sierra Leone 2 Australia 510 Uganda - 2- Austria 495 Pakistan 5 Switzerland 494 iddia 5 Japan 492 China 7 France 476 j Syrian Arab Republic 9 United States 475 J Philippines 10 Canada 459 ! sri Lanka ,512 Sweden 451 - j - World (average) 141 The data on vehicles and traffic are from the IRF's electronic files and its annual World Road a one for every 4,000 peopie b One for every 2 000 people Statistics The data on fuel prices are from the Source Table 3 12 GTZ's electronic files 2003 World Development Indicators 1 167 Air pollution City City Particulate Sulfur Nitrogen _ population matter dloxide dioxide In many towns and cities exposure to air pollution is the main environmental threat to human health Long- micrograms per micrograms per mtcrograms per term exposure to high levels of soot and small parti- thousands cubic meter cubic meter cubic meter 2000 1999 199Oa98a 1990-98a cles in the air contributes to a wide range of health effects, including respiratory diseases, lung cancer, Argentina Cordoba City 1,370 52 97 and heart disease Particulate pollution, on its own or Australia Melbourne 3,293 15 30 AustraliaPMelbourne 3,293 15 5- 30__ in combination with sulfur dioxide, leads to an enor- Perth 1,245 15 5 19 Sydney 3,855 22 28 81 mous burden of Ill health Austria Vienna 1,904 39 14 42 Emissions of sulfur dioxide and nitrogen oxides lead Belgium Brussels 983 31 20 48 to the deposition of acid rain and other acidic com- Brazil Rio de Janeiro 5,902 40 129 . pounds over long distances. Acid deposition changes 530 Paulo 9,984 46 43 83 BulganaSofiauo 9,97 43 439 12 the chemical balance of soils and can lead to the Bulgaria Sofia 1,177 83 39 122 Canada Montreal 3,519 22 10 42 leaching of trace minerals and nutrients cntical to Toronto 4,535 26 17 43 trees and plants Vancouver 1,880 15 14 37 Where coal is the pnmary fuel for power plants, steel Chile Santiago 4,522 73 29 81 mills, industrial boilers, and domestic heating, the China Anshan 3,132 99 115 88 Beijing 9,302 106 90 122 resultIsusuallyhighlevelsofurbanairpollutor-esPe Changchun 3,766 88 21 64 cially particulates and sometimes sulfur dioxide-and, Chengdu 4,401 103 77 74 if the sulfur content of the coal is high, widespread acid Chongquing 3,945 147 340 70 deposition Where coal is not an important primary fuel Dalian 4,389 60 61 100 or is used by plants with effective dust control, the Guangzhu 495 74 57 136 Guiyang - 2.103 84 424 53 worst emissions of air pollutants stem from the com- Harbin 4,545 91 23 30 bustion of petroleum products Jinan 3,037 112 132 45 The data on sulfur dioxide and nitrogen dioxide con- Kunming 2,037 84 19 33 centrations are based on reports from urban monitor- Lanzhou 2,044 109 102 104 Ing sites Annual means (measured in micrograms per Liupanshui 2,330 70 102 .. Nanchang 1,594 94 69 29 cubic meter) are average concentrations observed at Pinxiang 1,754 80 75 these sites Coverage is not comprehensive because Quingdao 2,316 190 64 not all cities have monitoring systems Shanghai 10,367 87 53 73 The data on particulate matter concentrations are new Shenyang 5,881 120 99 73 estimates, for selected cities, of average annual con- Taiyuan 2,811 105 211 55 Tianjin 7,333 149 82 50 centrations In residential areas away from air pollution Urumqi 1,467 61 60 70 'hotspots," such as industnal distncts and transport Wuhan 4,842 94 40 43 corndors The data have been extracted from a com- Zhengzhou 2,214 116 63 95 plete set of estimates developed by the World Bank's Zibo 3,139 88 198 43 Development Research Group and Environment Colombia Bogota 5,442 33 .. Croatia Zagreb 908 39 31 Department i a study of annual ambient concentratons Cuba Havana 2,270 28 1 5 of particulate matter in world cities with populations Czech Republic Prague 1,211 27 14 33 exceeding 100,000 (Pandey and others 2003) Denmark Copenhagen 1,371 24 7 54 Pollutant concentrations are sensitive to local condi- Ecuador Guayaquil 2,120 26 15 tions, and even in the same city different monitoring Quito 1,598 34 22 .. Egypt, Arab Rep Cairo 7,941 178 69 . sitesmayregisterdifferentconcentratons Thusthese Finland Helsinki 1,095 22 4 35 data should be considered only a general indication of France Paris 9,851 15 14 57 air quality in each city, and cross-country comparisons Germany Berlin 3,555 25 18 26 should be made with caution The current World Health Frankfurt 668 22 11 45 Organization (WHO) air quality guidelines for annual Munich 1,275 22 8 53 GhananAccra 1,25 22 5mean concentrations are 50 micrograms per cubic Ghana Accra 1,938 31 Greece Athens 3,229 50 34 64 meter for sulfur dioxide and 40 for nitrogen dioxide Hungary Budapest 1,958 26 39 51 The WHO has set no guidelines for particulate matter Iceland Reykjavik 164 21 5 42 concentrations below which there are no appreciable health effects 16$ [ 2003 World Development Indicators Air pollution IS I City City Particulate Sulfur Nitrogen = population matter dioxide dioxide * City population is the number of residents of the city or metropolitan area as defined by national micrograms per micrograms per micrograms per authorities and reported to the United Nations thousands cubic meter cubic meter cubic meter 2000 1999 1998- 1998a * Particulate matter refers to fine suspended par- ticulates less than 10 microns in diameter that are India Ahmedabad 4,154 104 30 21 capable of penetrating deep into the respiratory tract Bangalore 5,180 56 Calcutta 13,822 153 49_ and causig signficant health damage The state of Chennai 6,002 15 17 a country's technology and pollution controls is an Delhi 10,558 187 24 41 important determinant of particulate matter concen- Hyderabad 5,448 51 12 17 trations * Sulfur dioxide is an air pollutant pro- Kanpur 2,546 136 15 14 duced when fossil fuels containing sulfur are burned Lucknow 2,093 136 26 25 Mumbai 15,797 79 33 39 It contbutes to acid rain and can damage human Nagpur 2,087 69 -6 13 health, particularly that of the young and the elderly Pune 3,128 58 * Nitrogen dioxide is a poisonous, pungent gas Indonesia Jakarta 10,845 103 formed when nitric oxide combines with hydrocar- Iran, Islamic Rep Tehran - 7,689 71 209 bons and sunlight, producing a photochemical reac- Ireland Dublin 991 23 20 Ireland Dblin 1,1 236 20 2- tion These conditions occur in both natural and Italy Milan 1,381 36 31 248 Rome 2,713 35 anthropogenic activities Nitrogen dioxide is emitted Torino 969 53 by bacteria, motor vehicles, industrial activities, Japan Osaka 2,626 39 19 63 nitrogenous fertilizers, combustion of fuels and bio- Tokyo 12,483 43 18 68 mass, and aerobic decomposition of organic matter Yokohama 3,366 32 100 13 Kenya Nairobi 2,383 49 in soils and oceans Korea, Rep Pusan 4,075 43 60 51 Seoul 11,548 45 44 60 Taegu 2,417 49 81 62 Malaysia Kuala Lumpur 1,530 24 24 Mexico Mexico City 18,017 69 74 130 Netherlands Amsterdam 1,131 37 10 58 New Zealand Auckland 989 15 3 20 Norway Oslo 805 23 8 43 Philippines Manila 10,432 60 33 - City population data are from United Nations Poland Lodz 873 45 21 43 Population Division The data on sulfur dioxide and Warsaw 1,716 49 16 32 Portugal Lisbon 3.318 30 8 52 nitrogen dioxide concentrations are from the Romania Bucharest 2,070 25 10 71 WHO's Healthy Cities Air Management Information Russian Federation Moscow 8,811 27 109 System and the World Resources Institute, which Omsk 1,206 28 20 34 relies on various national sources as well as, Singapore Singapore 3,163 41 20 30 Slovak Republic Bratislava 456 22 21 27 among others, the United Nations Environment South Africa Cape Town 2,942 15 21 72 Programme (UNEP) and WHO's Urban Air Pollution Durban 1,364 29 31 in Megacities of the World (1992), the Johannesburg 2,344 30 19 31 Organisation for Economic Co-operation and Spain Barcelona 1,645 43 11 43 Development's (OECD) OECD Environmental Data Madrid 3,068 37 24 - 66 - Compendium 1999, the U S Environmental Sweden Stockholm 916 15 3 20 Switzerland Zurich 980 24 11 39 Protection Agency's National Air Quality and Thailand Bangkok 7,296 82 11 23 Emissions Trends Report 1995, AIRS Executive Turkey Ankara 3,702 53 55 46 International database, and the United Nations Istanbul 9,286 62 120 Centre for Human Settlements' (UNCHS) Urban Ukra-ine Kiev 2,622 45 14 51 Indicators database The data on particulate mat- United Kingdom Birmingham - 2,344 17 9 45 L-ondon 7,812 23 25 77 ter concentrations are from a recent World Bank Manchester 2,325 19 26 49 study by Kiran D. Pandey, Katharine Bolt, Uwe United States Chicago 9,024 27 14 57 Deichman, Kirk Hamilton, Bart Ostro, and David Los Angeles 16,195 38 9 74 Wheeler, 'The Human Cost of Air Pollution New New York 20,951 23 6- 2 - Estimates for Developing Countries' (2003) Venezuela, RB Caracas 3,488 18 33 57 a Data are for the most recent year available 2003 World Development Indicators 1 169 i Government commitment Environ- Country Biodiverslty Participation In treaties" 1.4 mental environi- assessment, strategy mental strategy, or WlU or action profile action plan plan Completed Law A~~~~~lbania __ hna Ngr Climate Ozone CFC of the Biological Algeria Grenada Nigeria change layer control Seab diversity IA-r men-ia -Gu in-ea -Pakista n Afghanistan 2002 2002 AzerbaUan Guinea-Bissau Papua New Guinea Albania 1993 1995 1999 1999 1994 Banglade'sh Guyana -Poland_ Algeria 2001 1994 1992 1992 1996 1995 Belarus HiiRomania Angola 2000 2000 - 2000 1994 1998 ~~~~~~~~~~~~~~~ ~~~~~Benin -Honduras,- Russian Federation~ Argentina 1992 1994 1990 1990 1996 1995 Bhtnndawnd Armenia 1994 1999 1999 2002 1993 r --- Australia 1992 1994 1994 1987 1989 19'95 _1993 131'Aair'rinciSpeTm6an Austria 1994 1987 1989 1995 1994 Botswana Iran, Islamic Rep Senegal Azerbaijan 1998 1995 1996 1996 - -----2000--- - - ---- Bangladesh 1991 1989 1990 1994 1990 1990 2001 -1994 BulIgaria- ____Kazakhsten__- Seycheiies Burkina Faso Kenya Sierra Leone Belarus 2000 1986 1988 1993 Belgium --1996 1988 1988 1998 1997 -Burundi _ Kiribati Slovak Republic Benin 1993 1994 1993 1993 1997 1994 Cambodia Kyrgyz Repubiic Slovenia Bolivia -1994 1986 1988 1995 1994 1994 1995 1995 Camerbon Lao POR Solomon Islands, Bosnia end Herzegovina 2000 1992 1992 1994 2002 Cape Verde Latvia South Africa Botswana 1990 1986 1991 1994 1991 1991 1994 1996 China _ Lebanon Sri Lanka Brazil 1988 1994 -1990 1990 1994 1994 Colombia Lesotho St Kitts and Nevis Bulgaria 1994 1995 1990 1990 1996 1996 C-oirs Lthai Swzln Burkina Faso 1993 1994 1994 1989 1989 L 993 Congo, Dam Rep Macedonia, FYR Syrian Arab Rep Burundi 1994 1981 1989 1997 1997 1997 1997 Cno e aaacr Tnai Cambodia 1999 .1996 2001 2001 1995 Cameroon 1989 1989 1995 1989 1989 1994 1995 CsaRc aaiTg Canada 1990 1994 1994 1986 1988 1993 Ct ior advs T n a Central African Republic -1995 1993 1993 1995 Croatia Mali Tunisia Chad 1990 1982 1994 -1989 -1994 1994 Czech Republic Mauritania Turkey - Chile 1987 1993 1995 1990 1990 -1997 1994 Djibouti Maurirtius Uganda China 1994 1994 1994 1989 1991 1996 1993 Egypt, Arab Rep Mexico Ukran Hong Kong. China El Salvador Moldova Uruguay Colombia 1998 1990 1988 1995 1990 1993 - 1995 Eqaatorial Guinea__Mongoia Uzbektstan Congo, Dam Rep 1986 1990 1995 1994 1994 1994 1995 EiraMnsra aut Congo, Rep 1990 1997 1994 1994 1996 EsoiMrco WetBn Costa Rica 1990 1987 1992 1994 1991 1991 1994 1994 and Gaza W6e dilvoire 1994 1991 1995 1993 1993 1994 1995 EtipaMamie Venm Croatia 2001 1998 2000 1996 1991 1991 1994 1997 - - -- - Gabon Niamibia _ Yemen, Rep Cuba 1994 1992 1992 1994 1994 - _ _ - - ~~~~~~~~~Gambia, The Nepal Zambia Czech Republic 1994 1994 1993 1993 1996 1994 - - - Denmark 1994 1994 1988 1998 1994 Georgia Nicaragua- Dominican Republic 1984 1995 1999 1993 1993 1996 Ecuador 1993 1987 1995 1994 1990 1990 . 1993 Egypt, Arab Rep 1992 1992 1988 1995 1988 1988 1994 1994 Under preparatio n El Salvador 1994 1985 1988 1996 1992 1992 1994 Argentina Ecuador Tajikistan Eritrea 1995 1995 1996 Belize Korea, Rep Turkmnenistan Estonia 1998 1994 1996 1996 1994 Central Afncan Malaysia Zimbabwe Ethiopia 1994 1991 1994 -1994 1994 1994 Republic-- Finland 1995 1994 1986 1988 1996 1994 Dowinican Republic Paraguay France -1990 1994 1987 1988 1996 1994 Note Status is as of February 2003 Gabon 1990 1998 1994 1994 2000 Source World Bank Environmentally and Sociaily Gambia, The 1992 1981 1989 -1994 1990 1990 1998 1994 Sustainable Development Network Advisory Service, Georgia 1998 - . 1994 1996 1996 1996 1994 World Resources institute, and international Institute Germany -1994 1988 1988 1994 1994 for Environment and Development Ghana 1992 1985 1988 1995 1989 _1989 1994 1994 Greece 1994 1988 1988 1995 1994 Guatemala 1994 1984 1988 1996 1987 1989 1997 1995 Guinea 1994 1983 1988 1994 1992 1992 1994 1993 Guinea-Bissau 1993 - 1991 1996 _2002 2002 1994 1996 Haiti 1999 1985 1996 2000 2000 1996 1996 170 IJ 2003 World Development Indicators Government commitment 31 Environ- Country Biodiversity Participation In treaties a 3.14b mental environ- assessments, strategies mentai strategies or ________________________ or action profile action plans plans Completed Law ~~~~Anrtigua and Greece a Niuea Climate Ozone CFC of the Biological Barbudaa change layer control Sea I diversity Argentina a Grenada a Norwaya Honduras 1993 1989 1996 1993 1993 1994 1995~§ria___ utml aa Hungary 1995 1994 1988 1989 2002 1994 Austria a Guinea a Panamaa India 1993 1989 1994 1994 1991 1992 1995 1994 Aebia Honduras a Paua New Indonesia 1993 1994 1993 1994 1992 1992 1994 1994Gina Iran, Islamic Rep 1996 1990 1990 1996 Bahamas, The a Hungary a Paraguay Iraq 1994 F... ldeha- Iceand:- Perua Ireland 1994 1988 1988 1996 Barbados I India a Philippines Israel 199 6 -1992 1992 - 199-5 Ieiira Indonesia Poland Italy - 1994 1988 1988 1995 -5 1994 Benin aIreland a Portugal Jamaica 1994 1987 1995 _1993 1993 -1994 1995 1Bua0_________ Japan 1994 1988 1988 1996 1993 - - - ~~~~~~~~~~~~~- Bolivia Italy a Russian Federation Jordan 1991 1979 1994 1989 1989 -1995 1994 Jamaica a7 Kazakhstan 1995 1998 1998 1994 Kenya 1994 '1989 1992 1994 1988 1988 1994 Bugri99apn4eea Korea, Oem Rep 1995 1995 1995 1995 1Brnl odna Syhle Korea, Rep 1994 1992 1992 1996 1995 Cambodia a Kazakhstan Slovakr Republica Kuwait 1995 1992 1992 1994 2002 L±da Kiribati a Sloveniaa Kyrgyz Republic 1995 2000 2000 2000 1996 Chile a Korea, Rep a Solomon Islands Lao PDR 1995 1995 1998 1998 1998 1996 F&n anaa SuhAfrica Latvia 1995 1995 -1995_ 1996 Colombia a Lesotho a spaina Lebanon 1995 1993 1993 1995 1995 C 5o 5sads a Liechtenstein Sri Lanka-a Lesotho 1989 1982 1995 1994 1994 - 1995 ot -______ - Costa ~Rica a Lithuania a St Lucia Liberia 2002 1996 1996 2000 -__ Croatia Luvxembourg a St Vincent an~d Libya 1999 1990 1990 2001 teGeaie Lithuania 1995 1995 1995 -1996 - Cuba a Malawi a Sweden'a Macedonia, FYR 1998 1994 1994 1994 1997c Madagascar 1988 1991 1996 1996 1996 2001 1996 [yrs aasaa Sizrad Malawi - 1994 1982 1994 1991 1991~~~~~~~~~~~~~~ 199 Czech Republic Maldives a Thailanda Malai 1991 1989 1995 19941 1991994 19954Tbao Mauritai 198 1984 98 1994 1994 1994 1996 _1995 Cjiibouti a Malta a - Tunisia a Mauritaius18 8 90 8 1994 1992 1992 1996t 19936 eubi Mexico 1988 1994 19-87 1988 1994 1993 * _________ Moldova 2002 1995 1996 1996 1996 ~~~~~~~~~~Ecuador a MauritiuS a Tuvalu a Mongolia 1995 1994 1996 1996 1997 1993 rEgypt, Arab Rep Mexicoa 8 _ Uganda------- Morocco 1980 1988 1996 1995 1995 1995 El Salvador a Micronesia a Ukraine Mozambique 1994 1995 1994 1994 1997 1995 [Equatorial GieaMnco Uited ingdom! Myanmar 1982 1989 1995 1993 1993 1996 1995 Estonia a Mongolia a United States Namibia 199 2 1995 1993 1993 1994_ 1997 L ooc rga Nepal 1993 1983 1994 1994 1994 1998 1994 Fnland a Nauru Uzbekistana Netherlands 1994 1994 1988 1988 1996 -1994 LFrancean V--A-nuatu a - New Zealand 1994 -1994 1987 1988 1996 -1993 - ~~~~~~~-- Gambia, The a New Zealand a Vietnam Nicaragua 1994 1981 1996 1993 1993 2000 1996 F[:7iE - Niger 1985 1991 1995 1992 1992 1995 ~ o%g __Nicaragua mi Nigeria 1990 1992 1994 1988 1988 1994 1994 Gemna Nir Norway 1994 1994 1986 1988 1996 1993 Note Status is as of January 2003 Oman 1981 1995 1999 1999 1994 1995 a Ratification or accession signed Pakistan 1994 1994 1991 1994 1992 -1992 -1997 -1994 Source Secretariat of the United Nations Frameworki Convention on Climate Change Panama 1990 1980 1995 1989 1989 1-996 1995 Papua New Guinea 1992 -1994 1993 1994 1992 1992 1997 1993 Paraguay -1985 1994 1992 1992 1994 1994 Peru 1988 1988 1994 1989 1993 1993 Philippines 1989 1992 1989 1994 1991 1991 1994 1994 Poland 1993 1991 1994 1990 1990 1998 1996 Portugal 1995 1994 1988 1988 1997 1994 Puerto Rico 2003 World Development Indicators 1 171 Government commitment Environ- Country Biodiversity Participation In treaties 8 mental environ- assessments, strategies mental strategies or _| or action profile action plans plans Allocation of funds for Global Environment Facility Law programs, February 1995-January 2003 Climate Ozone CFC of the Biological Total allocation $7,269 million change layer control Seat' diversity By region Romania 1995 1994 1993 1993 1997 1994 Russian Federation 1999 1994 1995 1986 1988 1997 1995 & N Africa teGional, and Rwanda 1991 1987 1998 2001 2001 1996 1% I FCexecuted Saudi Arabia 1995 1993 1993 2001 Europe & poet 12% Senegal 1984 1990 1991 1995 1993 1993 1994 1995 11% Sierra Leone 1994 1995 2001 2001 1995 1995 Singapore 1993 1988 1995 1997 1989 1989 1994 1996 11% Slovak Republic 1994 1993 1993 1996 1994 Slovenia 1994 1996 1992 1992 1994 1996 Latin America Somalia 2001 2001 1994 & Caribbean South Africa 1993 1997 1990 1990 1997 2000 48% Spain 1994 1988 1989 1997 1994 Sri Lanka 1994 1983 1991 1994 1989 1989 1994 1994 By focal area Sudan 1989 1994 1993 1993 1994 1996 Ozone depletion Swaziland 1997 1992 1992 1995 3% Multiple areas Sweden 1994 1986 1988 1996 1994 waters Switzerland 1994 1987 1988 1995 5% Syrian Arab Republic 1999 1981 1996 1989 1989 1996 Tajikistan 1998 1996 1998 1997 Blodfversity Tanzania 1994 1989 1988 1996 1993 1993 1994 1996 32s Thailand 1992 1995 1989 1989 Togo 1991 1995 1991 1991 1994 1996 Trinidad and Tobago 1994 1989 1989 1994 1996 Tunisia 1994 1980 1988 1994 1989 1989 1994 1993 Turkey 1998 1982 1991 1991 1997 Source Global Environment Facility data Turkmenistan 1995 1993 1993 1996 Uganda 1994 1982 1988 1994 1988 1988 1994 1993 Ukraine 1999 1997 1986 1988 1999 1995 United Arab Emirates 1996 1989 1989 2000 United Kingdom 1995 1994 1994 1987 1988 1997 1994 United States 1995 1995 1994 1986 1988 Uruguay 1994 1989 1991 1994 1994 Uzbekistan 1994 1993 1993 1995 Venezuela 1995 1988 1989 1994 Vietnam 1993 1995 1994 1994 1994 1995 West Bank and Gaza Yemen, Rep 1996 1990 1992 1996 1996 1996 1994 1996 Yugoslavia, Fed Rep 2001 1992 1992 2001 Zambia 1994 1988 1994 1990 1990 1994 1993 Zimbabwe 1987 1982 1994 1992 1992 1994 1995 a The year shown for a country refers to the year in which a treaty entered into force in that country b Convention became effective November 16, 1994 c Ratification of the treaty II72 0 2003 World Development Indicators Government commitment S. National environmental strategies and participation To address global issues, many governments have * Environmental strategies and action plans pro- in international treaties on environmental issues pro- also signed international treaties and agreements vide a comprehensive, cross-sectoral analysis of con- vide some evidence of government commitment to launched in the wake of the 1972 United Nations servation and resource management issues to help sound environmental management But the signing Conference on Human Environment in Stockholm and integrate environmental concerns with the develop- of these treaties does not always imply ratification, the 1992 United Nations Conference on Environment ment process They include national conservation nor does it guarantee that governments will comply and Development (the Earth Summit) in Rio de Janeiro strategies, national environmental action plans, with treaty obligations * The Framework Convention on Climate Change national environmental management strategies, and In many countries efforts to halt environmental aims to stabilize atmospheric concentrations national sustainable development strategies The degradation have failed, primarily because govern- of greenhouse gases at levels that will prevent year shown for a country refers to the year in which ments have neglected to make this issue a priority, a human activities from interfering dangerously a strategy or action plan was adopted * Country reflection of competing claims on scarce resources To with the global climate environmental proflies identify how national eco- address this problem, many countries are prepanng * The Vienna Convention for the Protection of the nomic and other activities can stay within the con- national environmental strategies-some focusing nar- Ozone Layer aims to protect human health and straints imposed by the need to conserve natural rowly on environmental issues, and others integrating the environment by promoting research on the resources The year shown for a country refers to the environmental, economic, and social concerns Among effects of changes in the ozone layer and on year in which a profile was completed * Blodiversity such initiatives are conservation strategies and envi- alternative substances (such as substitutes for assessments, strategies, and action plans include ronmental action plans Some countries have also pre- chlorofluorocarbons) and technologies, moni- biodiversity profiles (see About the data) pared country environmental profiles and biodiversity toring the ozone layer, and taking measures to * Participation In treaties covers five international strategies and profiles control the activities that produce adverse treaties (see About the data) * Climate change National conservation strategies-promoted by the effects refers to the Framework Convention on Climate World Conservation Union (IUCN)-provide a com- * The Montreal Protocol for CFC Control requires Change (signed in New York in 1992) * Ozone layer prehensive, cross-sectoral analysis of conservation that countries help protect the earth from refers to the Vienna Convention for the Protection of and resource management issues to help integrate excessive ultraviolet radiation by cutting chlo- the Ozone Layer (signed in 1985) * CFC control environmental concerns with the development rofluorocarbon consumption by 20 percent refers to the Montreal Protocol for CFC Control (for- process Such strategies discuss current and future over their 1986 level by 1994 and by 50 per- mally, the Protocol on Substances That Deplete the needs, institutional capabilities, prevailing technical cent over their 1986 level by 1999, with Ozone Layer, signed in 1987) * Law of the Sea conditions, and the status of natural resources in a allowances for increases in consumption by refers to the United Nations Convention on the Law country developing countries of the Sea (signed in Montego Bay, Jamaica, in National environmental action plans, supported by * The United Nations Convention on the Law of 1982) * Biological diversity refers to the the World Bank and other development agencies, the Sea, which became effective in November Convention on Biological Diversity (signed at the describe a country's main environmental concerns, 1994, establishes a comprehensive legal Earth Summit in Rio de Janeiro in 1992) The year identify the principal causes of environmental prob- regime for seas and oceans, establishes rules shown for a country refers to the year in which a lems, and formulate policies and actions to deal with for environmental standards and enforcement treaty entered into force in that country them (table 3 14a) These plans are a continuing provisions, and develops international rules process in which governments develop comprehen- and national legislation to prevent and control sive environmental policies, recommend specific marine pollution actions, and outline the investment strategies, legis- * The Convention on Biological Diversity pro- lation, and institutional arrangements required to motes conservation of biodiversity among implement them nations through scientific and technological Country environmental profiles identify how nation- cooperation, access to financial and genetic al economic and other activities can stay within the resources, and transfer of ecologically sound The data are from the Secretariat of the United constraints imposed by the need to conserve natural technologies Nations Framework Convention on Climate resources Some profiles consider issues of equity, To help developing countries comply with their obliF Change, the Ozone Secretariat of the UNEP; the Justice, and fairness Biodiversity profiles-prepared gations under these agreements, the Global World Resources Institute, the UNEP, the U S by the World Conservation Monitoring Centre and the Environment Facility (GEF) was created to focus on National Aeronautics and Space Administration's IUCN-provide basic background on species diversity, global improvement in biodiversity, climate change, Socioeconomic Data and Applications Center, protected areas, major ecosystems and habitat international waters, and ozone layer depletion The Center for International Earth Science Information types, and legislative and administrative support In UNEP, United Nations Development Programme Network; and the World Resources Institute, an effort to establish a scientific baseline for meas- (UNDP), and the World Bank manage the GEF accord- International Institute for Environment and uring progress in biodiversity conservation, the United ing to the policies of its governing body of country rep- Development, and IUCN's 1996 World Directory Nations Environment Programme (UNEP) coordinates resentatives. The World Bank is responsible for the of Country Environmental Studies. global biodiversity assessments GEF Trust Fund and is chair of the GEF 2003 World Development Indicators I 173 Understanding savings Gross Consumption Met Education Energy Mineral Net Carbon Particulate Adjusted national of fixed national expenditure depletion depletion forest dioxide emissions net savings capital savings depletion emissions damage savings damage % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 Afghanistan Albania 13 9 9.2 4 7 2 8 1 0 0 0 0 0 0 3 0 1 6 1 Algeria 11 0 4 5 33 6 0.0 0 1 1 3 0.7 Angola 35 0 10 5 24 5 4 4 35 0 0 0 0 0 0 7 - Argentina 12 8 12 0 0 8 3 2 2.6 0 1 0 0 0 3 1 6 -0 6 Armenia 9 2 8 2 1 0 1 8 0 0 0.1 0 0 1 2 2 0 -0 5 Australia 18 9 16 1 2 8 5 4 1 6 1 6 0 0 0 6 0 1 4 3 Austria 21 2 14 5 6.7 5 0 0 1 0 0 0 0 0 2 0 2 11 2 Azerbaijan 21 5 15 0 6 5 3 0 41 7 0 0 0 0 5 3 1 0 -38 5 Bangladesh 20 8 6 1 14 7 1 7 1 6 0 0 0 8 0 4 0 3 13 3 Belarus 20 0 9 2 10 8 5 5 2 5 0 0 0 0 3 7 0 0 101I Belgium 24 9 14 3 10 6 3 1 0.0 0 0 0 0 0 3 0 2 13 2 Benin 11 6 80 3 6 2 7 0 1 0 0 1 3 0 4 0 3 4 2 Bolivia 9 4 9 3 0 1 5 5 73 0 7 0 0 1 0 0 7 -4 1 Bosnia and Herzegovina 89 0 1 0 0 0 0 0 8 0 4 Botswana 34 7 12 0 22 7 5 6 0 0 0 3 0 0 0 6 2 Brazil 17 0 10 9 6 1 4 8 2 3 1 0 0 0 0 4 0 2 7 0 Bulgaria 14 6 10 0 -46 3 1 0 3 0 4 0 0 2 4 2 1 2 5 Burkina Faso 11 5 7 2 43 24 00 00 1 3 03 0 5 46 Burundi 5 4 64 -1 0 3 1 00 0.1 11 4 02 0 1 -97 Cambodia 14 8 7 8 7 0 1 8 00 00 1 7 0 1 0 1 69 Cameroon 17 0 90 80 23 7 3 00 0 0 04 07 1 9 Canada -23 2 13 0 10 2 7 0 5 0 0 1 00 05 02 11 4 Central African Republic 17 1 7 5 9 6 1 6 0 0 0 0 0 0 0 2 0 4 10 6 Chad 4 5 7 2 -2 7 2 0 0 0 0 0 0 0 0 1 - Chile 20 3 10 0 10 3 3 4 0 3 4 8 0 0 0 6 1 0 7 0 China 40 1 9.2 30 9 2 0 2 8 0 2 0 1 2 2 1 0 26 6 Hong Kong, China 32 0 12 8 19 2 2 8 0 0 0 0 0 0 0 1 0 0 21 9 Colombia 14 8 10 3 4 5 3 1 6 6 0 1 0 0 0 5 0 1 0 3 Congo,Dem Rep. 8 4 . 0 9 2 0 0 1 0 0 0 3 0 0 Congo,Rep 44 4 12.9 31 5 6 0 54 6 0 0 0 0 0 8 1 a Costa Rica 15 1 5 8 9 3 5 1 0 0 0 0 0 4 0 3 0 3 13 4 C6te dlvoire 8 8 9 1 -0 3 4 5 0 0 0 0 0 6 0 7 0 6 2 3 Croatia 20 4 11 4 9 0 1 1 0 0 0 0 0 7 0 3 Cuba 6 1 Czech Republic 26 0 11 6 14 4 4 6 0 1 0 0 0 0 1 4 0.1 17 4 Denmark 24 6 15 3 9 3 8 2 0 5 0 0 0 0 0 2 0 1 16 7 Dominican Republic 20 6 5 4 15 2 1 7 0 0 0 1 0 0 0 8 0 2 15 8 Ecuador 23 1 10.6 12 5 3 2 19 0 0 0 0 0 0 9 0 1 -4 3 Egypt,Arab Rep 15 4 9 6 5 8 4 4 4 5 0 0 0 2 0 8 1 4 3 3 El Salvador 15 0 10 3 4 7 2 2 0 0 0 0 0 6 0 3 0 2 5 8 Eritrea 6 9 1 4 0 0 0.0 0 0 0 5 0 5 Estonia 22 8 14 4 8 4 6 3 0 6 0 0 0 0 2 4 0.2 11 5 Ethiopia 14 0 6.3 7 7 4 0 0 0 0 1 13 0 0 6 0.3 -2 3 Finland 27 0 16 4 10 6 7 1 0 0 0 0 0.0 0 3 0 1 17 3 France 21 3 12 6 8 7 5 6 0 0 0 0 0 0 0 2 0 0 14 1 Gabon 40 7 12 4 28 3 2 2 30 2 0 0 0 0 0 6 0 1 -0 4 Gambia,The 5 7 7 8 -2 1 3 6 0 0 0 0 0 5 0 5 0.7 -0 2 Georgia 6 6 16 1 -9 5 2 5 0 6 0 0 0 0 1 1 2 5 -11 2 Germany 20 6 14 9 57 4 4 0 1 0 0 00 03 01 9 6 Ghana 17 2 7 3 9.9 4 0 0 0 1.2 3 0 0 8 0 2 8 7 Greece 17 9 8 7 9.2 3 0 0 1 0 0 0 0 0 5 0 7 10 9 Guatemala 10 5 9 9 0 6 1 6 0 8 0 0 1 0 0 3 0 2 -01I Guinea 21 0 8 2 12 8 2 0 0 0 4 1 1.9 0 3 0 6 7 9 Guinea-Bissau -3 5 7 5 -11.0 00 00 00 0 8 Haiti 26 0 1 8 24 2 1 5 0 0 00 0 9 0 2 0 2 24 4 1 71 II 2003 World Development Indicators Understanding savings 31 Gross Consumption Nert Education Energy Mineral Net Carbon Particulate Adjusted national of fixed national expenditure depletion depletion forest dloxide emissions net savings capitai savings depletion emissions damage savings damage % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2001 2001 2001 2001 200.1 2001 2001 2001 2001 2001 Honduras 25 9 5 6 20 3 3 5 0 .0 0 1 0 0 0 5 0 2 23 0 Hungary 23 4 11 5 11 9 4 3 0 6 0 0 0 0 0 8 0 4 14 4 India 22 9 9 6 13 3 3 2 1 9 -04 0 0 1 7 0 7 11 8 Indonesia 23 6 5 4 18 2 0 6 12 0 1 2 0 0 1 1 0 5 4 0 Iran,Islamic Rep 35 3 10 0 25 3 3 2 31 4 0 2 0 0 1 8 0 7 -5 6 Iraq Ireland 27 4 12 3 15 1 5 5 -00 0 1 0 0 0 4 0 1 20 0 Israel 149_ 13.2 17 66 0 0 0 0 00 04 00 79 Italy 20 5 13 6 69 4 7 01 00 00 03 02 11 0 Jamaica 22 2 11 2 11 0 58 0-0 19 00 09 03 13 7 Japan 27 1 15 9 11 2 36 00 00 00 02 04 14 2 Jordan 24 4 10 5 13 9 56 00 00 00 11 07 17 7 Kazakhstan 20 1 10 2 9 9 44 30 2 00 00 4 5 04 -20 8 Kenya 10 5 80 2 5 63 00 00 0 1 05 0 2 80 Korea, Dem Rep Korea,Rep 28 8 -119 16 9 3 7 00 00 00 0 7 08 19 1 Kuwait 30 2 6 7 23 5 50 48 4 00 00 08 20 -22 7 Kyrgyz Republic 16 0 _80 8 0 5 5 1 0 0 0 0 0 2 7 0 2 9 6 Lao PDR 8 2 1 8 0 0 0 0 0 0 -02 0 2 Latvia 20 4 10 7 9 7 -62 0 0 0 0 0 0 0 7 0 3 14 9 Lebanon -4 9 10 3 -15 2 2 5 0 0 0 0 0 0 0 6 0 6 -13 9 Lesotho 20 5 6 5 14 0 6 6 0 0 0 0 2 3 0 0 0 4 17 9 Liberia 8 1 0 0 0 2 2 7 1 1 0 0 Libya Lithuania 17 0 10 1 6 9 53 04 -00 0-0 09 0 7 10 2 Macedonia, FYR 7 2 9 9 -2 7 4 4 0 0 0 0 0 0 2 2 0 3 -0 8 Madagascar 11 5 7 7 3 8 1 9 0 0 0 0 0 0 0 3 0 2 5 2 Malawi -2 0 7 0 -9 0 4 5 _ 00 0 0 1 6 0 3 0 2 -6 6 Malaysia 39 0 11 9 27 1 4 1 -112 0 0 -03 1 1 0 1 18 5 Mali 9 4 7 8 1 6 2 1 00 0 0 00 0 1 05 3 1 Mauritania 25 9 8 1 17 8 3 7 0 0 19 2 0 8 2 2 -07 a Mauritius 27 0 10 8 16 2 3 3 0 0 0 0 0 0 0 4 1_ Mexico 18 1 10 6 7 5 4 6 5 2 0 1 0 0 0 4 0 5 5 9 Moldova 12 0 7 3 4 7 9 4 0.0 0 0 0 0 3 5 0 5 10 1 Mongolia 21 7 -109 10 8 57 00 4_3 00 5 0 05 6 7 Morocco 27 7 9 6 18 1 4 9 0 0 0 3 0 2 0 7 0 2 21 6 Mozambique 29 5 7 7 21 8 38_ 0 0 0 0 0 0 0 3 0 4 24 9 Myanmar 0 9 0 0 Namibia 27 8 12 7 15 1 -85 -00 0 3 0 0 0 0 0 2 23 1 Nepal 31 7 2 3 29 4 3 2 0 0 0 0 2 6 0 4 0 1 29 5 Netherlands 27 3 14 6 12 7 4 9 0 7 0 0 0 0 0 3 0 4 16 2 New Zealand 21 4 10 7 10 7 6 9 1 6 0 0 0 0 0 4 0 0 15 6 Nicaragua 3 6 0 0 0 0 Niger 2 7 7 0 -4 3 2 3 0 0 0 0 3 7 0 4 0 4 -6 5 Nigeria 25 5 8 4 17 1 0 5 43 0 0 0 0 0 0 7 0 8 -26 9 Norway 36 8 16 2 20 6 6 8 56_ 0 0 0 0 0 1 0 1 21 6 Oman 9 8 4 1 51 8 0 0 0 0 0 7 Pakistan 19 4 7 7 11 7 2 3 5 0 0 0 0 8 1 1 1 0 6 1 Panama 24 4 7.9 16 5 4 8 0 0 0 0 0 0 0 6 0 3 20 4 Papua New Guinea 18 4 8 9 -95 11 6 111I 0 0 0 5 0 0 Paraguay 10 6 9 5 1 1 3 9 0 0 0 0 0 0 0 4 0 4 4 2 Peru 16 9 10 3 6 6 2 6 1 0 12 0 0 0 3 0 6 6 1 Philippines 24 9 8 1 16 8 2 9 0 0 0 1 0 8 0 7 0 4 17 7 Poland 18 7 11 2 7 5 7 5 0 4 0 1 0 0 1 3 0 7 12 5 Portugal 19 4 15 3 4 1 56 00 00 00 04 04 8 9 Puerto Rico 7 4 0 0 0 0 0 0 0 2 2003 Warld Development lnd,cators I175 i i 0100 Understanding savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted national of fixed national expenditure depletion depletion forest dioxide emissions net savings capital savings depletion emissions damage savings damage %of %of %of %of %of %of %of %of %of %of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 Romania 160 99 61 36 39 01 00 15 02 40 Russian Federation 32 3 10 4 219 3 6 31.0 0.3 0 0 3 4 0 6 -9 8 Rwanda 122 72 50 35 00 00 4.2 03 00 40 Saudi Arabia 280 10 0 18 0 7 2 42 5 0.0 0.0 0 8 10 -191 Senegal 145 84 61 34 0.0 00 03 06 86a Sierra Leone -9 9 6 9 -16.8 0 9 0 0 0 0 5 3 0 4 0 4 -22 0 Singapore 448 141 307 23 00 00 00 05 04 321 Slovak Republic 23 3 11 0 12 3 4 6 01 0.0 0 0 1 3 01 15 4 Slovenia 24 8 120 12.8 5 4 01 00 0.0 06 0 2 17 3 Somalia South Africa 13 9 13 3 0 6 7 5 1 3 10 0 3 2 0 0 2 3.3 Spain 228 129 99 46 00 00 00 03 04 138 SriLanka 232 50 182 29 00 00 05 03 0.3 200 Sudan 76 90 -14 09 0.0 00 00 02 06 -13 Swaziland 146 92 54 65 00 0.0 00 02 01 116 Sweden 207 141 66 83 01 01 00 02 0.0 145 Switzerland 313 14 3 17 0 4 9 0.0 0.0 0 0 0 1 0 2 216 Syrian Arab Republic 28 5 9 7 18 8 2 6 28 8 0 0 0 0 17 0 8 -9 9 Tajikistan 1 7 7 1 -5 4 2 0 0 0 0 0 3 9 0 2 Tanzania 86 76 10 24 00 0.3 00 02 02 27 Thailand 28 7 151 13 6 3 5 14 0 0 0 2 11 0 4 14.0 Togo 49 77 -28 43 00 00 44 06 03 -38 Trinidad and Tobago 27 4 12 2 15 2 3 4 23 4 0 0 0 0 21 0 0 -6 9 Tunisia 246 100 146 66 4.1 00 01 07 03 160 Turkey 167 70 97 22 04 0.1 00 08 12 94 Turkmenistan 36 3 9 5 26 8 00 0 0 51 0 3 Uganda 138 7.6 62 19 00 0.0 62 02 00 17 Ukraine 247 191 56 63 80 00 00 68 10 -39 United Arab Emirates United Kingdom 14 9 11 5 3 4 5.4 1.0 0 0 0 0 0 3 0 1 7 4 UnitedStates 175 11 9 5.6 54 11 00 00 04 03 92 Uruguay 109 11.5 -06 30 00 00 02 02 19 01 Uzbekistan 18 8 8 3 10 5 9 4 49 8 0.0 0 0 7 3 0 6 -37.8 Venezuela, RB 22 4 7 2 15.2 4 4 231 0 3 0 0 0 6 0 0 -4 4 Vietnam 326 80 246 28 70 00 09 10 0.4 181 West Bank and Gaza 8.5 0 0 0 0 0.0 Yemen, Rep 32 3 9 0 23 3 36 4 0.0 0 0 1 3 0 5 Yugoslavia, Fed Rep 8 0 9 2 -1 2 0 6 0 2 0.0 2 7 0 2 Zambia 56 82 -26 21 00 13 00 04 .. -22 Zimbabwe 68 90 -22 78 03 03 00 1.1 05 34 I- -"e s -7 -, -Ssg ii '1S-' S -6* 3 4 ' 3 : - 5 Lowincome 221 88 133 28 66 04 03 16 06 66 Middle Income 25 8 10 2 15 6 3 8 7 8 0 3 01 1 3 0 7 9 3 Lower middle income 31 2 9 8 214 3 0 81 0 2 0 1 1.9 0 8 13 3 Upper middle income 19 6 10 6 8 9 4 8 7 5 0.4 0 0 0 6 0 6 4.7 Low&middlelncome 252 99 152 37 76 0.3 01 13 07 89 East-Asia & Pacific 36 8 9 3 27 5 2 2 3 9 0 3 0 2 1 9 0 8 22 6 Europe & Central Asia 24 4 10 5 13 9 4 5 11 9 0 1 2 3 0 7 3 5 Latin Amenca & Carib 171 10 4 6 7 4 2 4.8 0 5 0 0 0 4 0 5 4 6 Middle East & N Africa 26 9 10 0 16 9 5 1 25 8 0 1 0 1 1 1 0.9 -5 9 SouthAsia 225 90 135 3.0 21 03 02 15 07 118 Sub-Saharan Africa 15 0 10 4 4 6 4 7 7 9 0 5 0.7 1 1 0 4 -1 3 High-income 23 3 13 2 101 5 0 0 8 0 0 0 3 0 3 13 7 Europe EMU 214 138 7.6 4 7 01 00 . 03 . 18 a Adjusted net savings do not include particulate emissions damage 176 0 2003 World Development indicators Understanding savings 0 Adjusted net savings measure the change in value of give rise to rents because they are not produced, in * Gross natlonal savings are calculated as the dif- a specified set of assets, excluding capital gains If contrast, for produced goods and services competl- ference between gross national income and public a country's net savings are positive and the account- tive forces will expand supply until economic profits and private consumption, plus net current transfers ing includes a sufficiently broad range of assets, are driven to zero For each type of resource and * Consumption of flxed capital represents the economic theory suggests that the present value of each country, unit resource rents are derived by tak- replacement value of capital used up in the process social welfare is increasing Conversely, persistently ing the difference between world prices and the aver- of production * Net national savings are equal to negative adjusted net savings indicate that an econ- age unit extraction or harvest costs (including a gross national savings less the value of consumption omy is on an unsustainable path normal" return on capital) Unit rents are then mul- of fixed capital * Education expenditure refers to Adjusted net savings are derived from standard tiplied by the physical quantity extracted or harvest- current operating expenditures in education, including national accounting measures of gross national sav- ed in order to arrive at a depletion figure This figure wages and salaries and excluding capital investments ings by making four adjustments First, estimates of is one of a range of depletion estimates that are pos- in buildings and equipment * Energy depletion is capital consumption of produced assets are deduct- sible, depending on the assumptions made about equal to the product of unit resource rents and the ed to obtain net national savings Second, current future quantities, prices, and costs, and there is rea- physical quantities of energy extracted It covers coal, expenditures on education are added to net national son to believe that it is at the high end of the range crude oil, and natural gas * Mineral depletion is savings (in standard national accounting these Some of the largest depletion estimates in the table equal to the product of unit resource rents and the expenditures are treated as consumption) Third, should therefore be viewed with caution physical quantities of minerals extracted It refers to estimates of the depletion of a variety of natural A positive net depletion figure for forest resources tin, gold, lead, zinc, iron, copper, nickel, silver, baux- resources are deducted to reflect the decline in implies that the harvest rate exceeds the rate of nat- ite, and phosphate * Net forest depletion is calcu- asset values associated with their extraction and ural growth, this is not the same as deforestation, lated as the product of unit resource rents and the harvest And fourth, deductions are made for dam- which represents a change in land use (see excess of roundwood harvest over natural growth age from carbon dioxide and particulate emissions Definitions for table 3 4) In principle, there should * Carbon dioxide emissions damage is estimated to (In earlier editions of the World Development be an addition to savings in countries where growth be $20 per ton of carbon (the unit damage in 1995 Indicators these adjustments were made to gross exceeds harvest, but empirical estimates suggest U S dollars) times the number of tons of carbon emit- domestic savings and adjusted net savings were that most of this net growth is in forested areas that ted * Particulate emissions damage is calculated referred to as genuine savings ) cannot be exploited economically at present as the willingness to pay to avoid mortality attributa- The exercise treats education expenditures as an Because the depletion estimates reflect only timber ble to particulate emissions * Adjusted net savings addition to savings effort But because of the wide values, they ignore all the external and nontimber are equal to net national savings plus education variability in the effectiveness of government educa- benefits associated with standing forests expenditure and minus energy depletion, mineral tion expenditures, these figures cannot be con- Pollution damage from emissions of carbon dioxide depletion, net forest depletion, and carbon dioxide strued as the value of investments in human is calculated as the marginal social cost per unit mul- and particulate emissions damage capital The accounting for human capital is also tiplied by the increase in the stock of carbon dioxide incomplete because depreciation of human capital The unit damage figure represents the present value is not estimated of global damage to economic assets and to human Gross national savings are derived from the World There are also gaps in the accounting of natural welfare over the time the unit of pollution remains in Bank's national accounts data files, described in resource depletion and pollution costs Key esti- the atmosphere the Economy section Consumption of fixed capital mates missing on the resource side include the Pollution damage from particulate emissions was is from the United Nations Statistics Division's value of fossil water extracted from aquifers, net estimated by valuing the human health effects from National Accounts Statistics Main Aggregates and depletion of fish stocks, and depletion and degrada- exposure to particulate matter less than 10 microns Detailed Tables, 1997, extrapolated to 2001. The tion of soils Important pollutants affecting human in diameter The estimates were calculated as will- education expenditure data are from the United health and economic assets are excluded because ingness to pay to avoid mortality attributable to par- Nations Statistics Division's Statistical Yearbook no internationally comparable data are widely avail- ticulate emissions (in particular, mortality relating to 1997, extrapolated to 2001 The wide range of able on damage from ground-level ozone or from sul- cardiopulmonary disease in adults, lung cancer in data sources and estimation methods used to fur oxides. For the first time, however, the table adults, and acute respiratory infections in children) arrive at resource depletion estimates are includes values for damage from particulate emis- described In a World Bank working paper, sions, based on new estimates developed by the "Estimating National Wealth" (Kunte and others World Bank (Pandey and others 2003) 1998). The unit damage figure for carbon dioxide Estimates of resource depletion are based on the emissions is from Fankhauser (1995) The esti- calculation of unit resource rents An economic rent mates of damage from particulate emissions are represents an excess return to a given factor of pro- from Pandey and others (2003) The conceptual duction-that is, in this case the returns from underpinnings of the savings measure appear in resource extraction or harvest are higher than the Hamilton and Clemens (1999) normal rate of return on capital Natural resources 2003 World Development Indicators 1 177 I I)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ l(- I} - he economy supplies the goods and services that individuals, firms, and governments demand and pay for with the income they earn by supplying labor, capital, and other resources-which in turn are used to produce goods and services. The systematic analysis of this circular flow of economic activity and its codification in a system of national accounts were among the important accomplishments of 20th-century economics. National accounting, along with the complementary analysis of the flow of exports, imports, and financing between countries through the balance of payments, allows comprehensive, consistent measurement of world economic activity such as that presented in this section. While no person on earth is completely isolated from the global economy, some economic transactions are not measured in the national accounts. Some exclusions from the national accounts are deliberate-the goods and services produced by women working at home are a famous example. But other types of production are left out also because they are unpriced, unmarketed, and therefore unrecorded. Often these are the activities of greatest importance to the poorest people. Other omissions occur because the producers or consumers have reason to hide their activities or because national statistical systems are inadequate for the task of measuring them. And measurement errors affect the reliability of all economic statistics. 179 These gaps in the statistical record, along with inevitable errors Low-income economies saw the fastest growth, almost twice the in collecting and tabulating data, limit the ability to monitor eco- rate of middle-income economies. Upper-middle-income and high- nomic activity and to shape policies based on timely and accu- income economies, affected by slowing investment and wide- rate statistics And difficult conceptual issues remain relating spread uncertainty in financial markets, had the slowest growth. to the measurement of prices and product quality across coun- Over the past decade economic growth was fastest in East tries and over time that further limit the reliability of compar- Asia and Pacific (averaging 7.5 percent a year) and South Asia isons along those dimensions (box 4a). (5.5 percent). Leading this growth were China and India, each Even so, the measurement and analysis of economic activity accounting for more than 70 percent of its region's output. Even remains a fundamental source of information about develop- in 2001 these two regions did comparatively well, with East Asia ment. The indicators in this section measure changes in the size registering 5.5 percent growth-demonstrating its recovery from and structure of the global economy and the varying effects of the financial crisis in 1998, when annual growth fell to 0.7 per- these changes on national economies. They include measures cent-and South Asia recording 4.9 percent growth. of macroeconomic performance (gross domestic product (GDP), Since 1990 growth has been slowest in the transition consumption, demand, and international trade) and of stability economies of Europe and Central Asia, which experienced (central government budgets, prices, the money supply, the bal- sharp declines in the early part of the decade and a big setback ance of payments, and external debt). Other important econom- after the Russian ruble crisis in 1998 In 2000 growth ic indicators appear throughout the book, but especially in the resumed, reaching 6.6 percent before falling to 2.3 percent in States and markets section (credit, investment, financial mar- 2001. But Kazakhstan and Turkmenistan continued to register kets, tax policies, exchange rates) and the Global links section extraordinarily high growth (13.2 percent and 20.5 percent), (trade and tariffs, foreign investment, and aid flows). buoyed by higher prices for their petroleum exports. In Sub-Saharan Africa, Latin America and the Caribbean, and sNowev ocomoc girowthI the Middle East and North Africa growth in 1990-2000 exceed- In 2001 the world economy grew by 1.1 percent, a sharp drop ed that in the previous decade but declined in 2001. The down- from the 3.9 percent growth in 2000 and well below the average turn was most severe in Latin America, where the large annual growth of 2.7 percent in the 1990s. Still, the world's economies of Argentina and Mexico shrank and Brazil grew by recorded output-and income-grew by more than $300 billion. only 1 5 percent. Reliable statistics on income, output, consumption, savings, and investment are national currencies into a a common unit of value Each has conceptual and critical for assessing the heaith of a national economy and, in aggregate, the practical difficulties. world economy Measuring income requires regular surveys of producers and households, The modern system of national accounts has its origins in the work of Richard supplemented by records of the tax system, customs service, and monetary Stone and a report prepared in the 1940s for the United Nations, Measurement and banking authorities In all economies, but particularly in developing of National Income and the Construction of Social Accounts (United Nations economies with many small, unincorporated businesses, it may be difficult to 1947). Standards for preparing national accounts have continued to evolve, and identify the population to be surveyed and to distinguish business spending most countries now use the United Nations System of National Accounts, series (investment or purchases of intermediate inputs) from household spending F, no. 2, version 3 (universally referred to as the 1968 SNA), though version 4 of (consumption) the SNA was completed in 1993 As more countries switched to the new version, Measuring real output is especially vexing As an economy grows, relative the 2001 edition of the World Development Indicators introduced the 1993 SNA prices change, as do the underlying qualities of goods. New products appear and terminology (see Pnmary data documentation) others disappear. And the value of the output of the increasingly important serv- National income may be compiled as the sum of incomes received by factors ice sector is often measured only by the cost of inputs, mainly labor. The result of production, or the sum of spending from income, or the sum of value added of all these factors? Real growth and price change are difficult to measure in each stage of production Each approach uses different data from different Comparisons across countries are complicated by multiple exchange rates, sources, but ideally each should arnve at the same total. Because these meas- some of which may be used only for official transactions, while others may not ures do not allow for the depreciation of physical capital, they are gross meas- be officially reported. Moreover, relative prices of goods and services not trad- ures When the sum is the total value of production by residents and domestic ed on the international market may vary substantially from one economy to businesses, it is gross domestic product (GDP) When it also includes net another, leading to big differences in the purchasing power of one currency com- income from abroad, it is gross national income (GNI). pared with that of another and thus to differences in welfare as measured by Defining national income is easy, but compiling consistent, timely, and accu- GNI per capita. rate national accounts is difficult and costly Three broad problems face com- Although the World Development Indicators points out the most obvious and pilers of national accounts identifying and correctly accounting for all sources serious deficiencies in international statistics, it can neither list nor correct for of income (or output) in the economy; adjusting data for price changes to allow the many sources of error and noncomparability The solution lies with the comparisons of real values over time, and, when international comparisons are national statistical offices that collect and report the data and with the inter- to be made, selecting the appropriate conversion factor to transform values in national agencies that assist their efforts and try to ensure comparability Source Adapted from World Bank. World Development Indicators 1997 i@g 0 2003 World Development Indicators Patterns of change countries. Savings rates are consistently lower in Sub-Saharan Most developing economies are following familiar patterns of Africa. And they tend to be volatile in countries dependent on growth, with agriculture giving way first to manufacturing and commodity exports. Gross domestic savings in the Middle later to services as the main source of income. But some, such East and North Africa rose from 23 percent of GDP in 1999 to as Jordan and Panama, have moved directly from agriculture to 30 percent in 2000 and 29 percent in 2001, buoyed by high- service-based economies. For most economies services have er oil prices The highest savings rate was in East Asia and been the most rapidly growing sector. In 1990-2001 the serv- Pacific, where gross domestic savings have averaged about 36 ice sector grew by 3.9 percent a year in developing and transi- percent over most of the past decade (table 4.9). tion economies and by 3 percent in high-income economies. In 1990-2001 gross capital formation increased by about Among developing regions, South Asia had the fastest growth 6.8 percent a year in East Asia and Pacific and 7.1 percent in in services in the 1990s (7 percent a year), and Europe and South Asia, but declined by 7 percent in Europe and Central Central Asia the slowest (1.8 percent) (table 4.1). Asia East Asia and Pacific continued to have the highest In developing economies services generated more than half investment rate in the world, at 31 percent of GDP in 2001 By of GDP in 2001, compared with 70 percent in high-income contrast, investment averaged only 18 percent of GDP in Sub- economies (table 4.2) But in East Asia and Pacific services pro- Saharan Africa (tables 4.9 and 4 10). duced only 36 percent of GDP in 2001, and growth in manufac- turing (10 percent a year) outpaced growth in services Fiscal affairs (6.5percent) in 1990-2001 This trend reflects the rapid growth Central governments had expenditures averaging 26 percent of of manufacturing in China (12.1 percent annually), which also GDP in 1999 while raising revenues (mainly through taxes) had rapid expansion in services (8.9 percent a year) equal to 25 percent of GDP, leaving a global fiscal deficit of about 1 percent of GDP (table 4.11). Government expenditures The contribution of trade go mostly to the purchase of goods and services (including the After expanding by 6.8 percent a year in 1990-2000, global wages and salaries of public employees) and to subsidies and trade (exports plus imports) grew by only 1 2 percent in 2001. current transfers to private and public enterprises and local High-income economies, which account for more than 75 per- governments. The rest goes to interest payments and capital cent of global trade, experienced the greatest slowdown, with expenditures In 1999 subsidies accounted for 59 percent of trade growing by only 0.3 percent in 2001. But trade by low- government spending in high-income economies and 49 per- income economies grew by 6.4 percent, compared to the 4.4 cent in Europe and Central Asia, but only 14 percent in the percent average rate in 1990-2000. Middle East and North Africa (table 4.12). Trade in services has grown rapidly, but trade in merchan- The sources of government revenue have been changing dise-primary commodities and manufactured goods-contin- Taxes on income, profits, and capital gains generated 23 per- ues to dominate. In 2001 merchandise accounted for 81 cent of current revenues in 1990, but their share fell to 18 per- percent of all exports of goods and commercial services, and cent in 1999 High-income economies depended more on manufactured goods for 78 percent of merchandise exports income taxes than did low- and middle-income economies, (tables 4.5 and 4.7). Exporters of primary nonfuel commodities which derived 35 percent of their revenue from taxes on goods saw their trade volumes increase, but a continuing decline in and services and 9 percent from taxes on trade (table 4.13) their terms of trade left them with less income (table 4.4). The Governments, because of their size, have a large effect on economies of Sub-Saharan Africa were hit particularly hard. the performance of economies. High taxes and subsidies can distort economic behavior, and large fiscal deficits make it Steady trends in consumption, Investment, and saving harder to manage the growth of the money supply and thus Most of the world's output goes to final consumption by house- increase the likelihood of inflation. As governments have adopt- holds (including individuals) and governments. The share of ed policies leading to greater fiscal stability, inflation rates and final consumption in world output has remained fairly constant interest rates have tended to decline (table 4 14). over time, averaging about 76 percent in 1990-2001 (table 4.9). The growth of per capita household consumption expen- Declining external debt diture provides an important indicator of the potential for reduc- In 2001 the external debt of low- and middle-income economies ing poverty. In 1990-2001 per capita consumption grew by 5.6 declined by $30 billion, or about 1 percent of their total stock percent a year in East Asia and Pacific but fell by an additional of debt. The decline extended to all developing regions except 0.1 percent in Sub-Saharan Africa and rose by only 0.8 percent East Asia and Pacific, where total debt increased by $7 billion in Europe and Central Asia (table 4.10). Debt stocks fell by $18 billion in Latin America and the Output that is not consumed goes to exports (less imports) Caribbean and by $8 billion in Sub-Saharan African, where and gross capital formation (investment). Investment is heavily indebted poor countries (HIPCs) have received signifi- financed out of domestic and foreign savings. In recent years cant debt relief. The debt relief appears to have boosted the the global savings rate has averaged 24 percent of total out- economic outlook for HIPCs, which had GDP growth of about put But global averages disguise large differences between 4.3 percent and GDP per capita growth of 2.1 percent in 2001 2003 World Development Indicators I 181 Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross product and services and services balance International reserves months annual annual annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2001 2002 2001 2002 2001 2002 2001 2002 2001 2002 2002 2002 Algeria 1.9 2.9 -1.1 1.8 17 2 2 2 __ 0 1 -2 1 .. 7 7 20.941 16.6 Argentina -4 5 -15.0 2.9 -6 5 -14 0 -509 -1 1 74 5 -17, 7 9 13,482 6 5 Armenia 9.6 12.9 22 9__25 0 __2 1 94 _ 4 0 _ 23 -9 5 -6 6 352 -4.1 Azerbaijan 9 9 7 9 24 4 -3 0 12 3 47 6 2.2 2 6 -09 -19 686 2 3 Bangladesh 5 3 4.4 22.8_ -8.7 23 5 -3.1 _ 1.6 _ 2 -7 -1.7 0.5 1,661 2 1 Bolivia 1 2 2.5 4 9 75 -7 4 6 6 0 7 2.0 -3 7 -4 0 858 4 5 Bosnia and Herzeovna 6 0 23 08 5.0 -0 6 5 5 4.0 2 3 -20 0 -20 5 1,540 6 1 Botswana 63 3 5 7.8 -0.9 7 8 1 7 4 4 4 5 8 4 7 9 7.648 30 8 -BrazIl 15a 1.4 12.1 -4.0 0 7 -12 5 7 4 7.5 -4.6 -.4.0 25,569 3 5 Bulgaria_ 4 0 4 0 8 5 4.0 13~.0 79 6 5 5 3 -6 6 -6 2 4,327 5 3 Cameroon 5 3 5.2 1 9 5.7 11.7 3.4 3 0 1 9 -1 7 -3 9 11 0.0 Chile 2 8 2 7 9.7 1 8 ,-1 3 -3 1 1 5 3 0 -1 9 -1 4 17,495 8 9 China 7.3 8.0 9 6 10.2 10 8 16 1 00 1 0 1 5 1 8 286.407 9.8 Colombia 1 4 1 5 4 1 , -3.3 112, 1 5 7 6 6 9 -2 2 -2 5 10,649 6 6 Congo, Oem Rep. ~-4.5 3.0 2. -31.6 10 0 -25 0 386 6 23 3 0 0 -3 7 _____ Congo,.Rep 2 9 2 5 5 0 -2 3 , 0 7 9 2 -14 5 -4 4 -4.3 173 1.0 Costa Rica 0.9 20 -6 4 -2.2 0 2 0.3 7.1 8 6 -4 4 -5 4 1.015 1 5 C6te d'Ivoire -0 9 2.9 -1 2 1 1 -0 2.6 2.1 2.3 -0 6 -2 6 Croatia 4.1 4.3 9.0 0 3 9 3 3.9 3.1 2.9 -3.0 -5 0 4.973 Dominican Republic 2 7 4 0 -7 9 6.6 -4.9 50 8.9 4 8 -4 0 -3 8 1,235 1 6 LEcuador 5.6 3.5 5 0 0.6 33.7' 22.6 25.1___ 12 8 -4.4 -5 0 1,247 1 6 Egypt, Arab Rep 2 9 2 0 8 2 ,-10 4 10 7 ,-10 8 3 8 4 5 0 0 -0 6 14,592 8 6 El Salvador__ 1 8 3 0 11.9 1.6 3.3 8.9 2.7 3 3 -1.3 -4.5 _1,890O 3.3 Estoni i 5O 4.5 -0.2 8.0, 2 1 ,9.3 5 4 4 4 ,-6 1 -5 6 , 1,217 2 4 Gabon 2 5 3 0 1 7 3.1 2.3 2.8 -11.7 2.0 10 0 4.6 Ghana 4 0 5.0 0 3 4.2 ,2 0 6 6 34 6 20 5 -5 9 -10 0 679 2 1 Guatemala 2 1 2.3 0 0 2.8 0.2 9.4 6.5 8.5 -6 0 -5.3 2,094 3 6 Honduras 2 6 2.5 4 6 4 6 3 6 2 1 9 6 9 0 -5 1 -8 4 1,132 36 India 5 4 4.4 9 0 -5.8 4.9 7.7 3.5 4.5 0.3 0.0 63,814 8.9 Indonesia "3,3 3'.7 1 9 -1 2 8 1 -83 12 6 -11 8 47 4 4 31.571 6 4 Iran, Islamic Rep 4.8 5.3' 9.7 43 4'5 3 8 8 8 14.5 4 2 2 7 8,628 3 9 JIa maicaal7 1 5 1 6 -0 2 8 1 5 5 6 9 9 0 -10 1 -7 2 1.845 4 1 Jordan 4.2 5.1 7.9 '5.2 3. 06 03 31 00 -0 3 2,220 3.7 Kazakhstan 13 2 8.0 -3 3 7 2 10 5 6 7 11 6 5 6 -5.5 -75 2,778 2 5 enya 1. . . . 12 0. 1 11.3 6 2 -2.8 -4.2 1,160 3 7 Latvia 7 6 5 0 6.5 8.2 6 1 10 6 _ 1 6 3.0 -9 7 -8 5 LLebalnon 1 1.0 -10.3 11.5 12 8 -3.6 0 0 __ 5.0 -23.8 -19.0 7,315 11.4 Lesotho 40 26 40 4 33 3 49 7 2 5 7 7 9 -11 9 -8 4 372 66 Lithuania 5 9 5.0 20.8 3.5 177 8.7 0 4 1.0 -. -6.2 Macedonia,FYR -4 1 2.5 -12.4 2 2 -13 0 3 2 2 8 2.5 -9 5 -6 5 833_ 4 8 continues on page 184 12II 2003 World Development Indicators Nominal exchange rate Real effective Money and Gross Real Interest Short- exchange rate quasi money domestic credit rate term debt local currency units annual annual % of per $ % change 1995 =100 % growth % growth %exports 2002 2001 2002 2001 2002 2001 2002 2001 2002 2001 2002 2001 ~~AIgerIa ~~~~ ~ ~ -~ 797'3 2 l059. 48.9 2 85. 6 3 0 Argentina 3 3 0 0 232 2 -19.4 2 5 29 1 21 0 54 7 Arei V84 153 8. --_4_3_ 174164 Azerbaijan 4,893 0 4 46 2 5 -10 5 14 6 -38 1 83 9 1-65 .15 0 4 1 j~~ngIadesh? 7' :7 '- 5T9 f~~~~~~~~~~7~W 1.6 ~~14 7,14 1 17 2 10 0 140 15 3 BolIvIa 75 67 98 1178 1147 22 ~~~~ ~~~~ ~~~~~~~~~~~~-83 -3 6 19 3 16 6 21 7 Botswana 5 5 30 2 -21 7 31 2 14 4 16 2 -430 109 118 0 7 ~~~.~~~~~12~~~~._-33031~~~~~~~~323 6 558 393- Bulgaria 1 9 5.6 -1~51 130 3 138 3 26 7 15 8 25 5 20 0 4 4 5 1 4 4 ~~~~~~~~~~5i. 87 0 1 15-1 16 8 8 6 9.0 17T2 16 7 351 Chile 712 4 14,6 8 6 96 6 875 0 5 101 60 12 4 0 83 0.0< ~~~~11 , 0 2 1 0~- ~ 1 290 4.2 Colombia 2,854.3 5 2 24 0 98 0 90 3 16 0 17 0 11 1 21 8 12 2 8 9 21 5 f~~Congb~~~OemRepr. ~ ~ ~ ~ ~ -' ______-- ..~~~-9 3328 Congo, Rep 625 5 5 6 -16 0 -22 8 9 8 37 4 -11 2 41 2 21 4 33 1 Cost-a Rica- 78 7 0 8 1119 109 2 *-10 1~7 9 106 3 2 5 16 0 15 C6ted'Ivoire 625 5 56 -16 0 99 9 105 2 12 0 34 2 -2 3 7 1 25 2 ti 1 25 -,21 6 25 2 63 . 27 Dominican Republic 21 2 2 8 23 6 117 0 101 4 26 9 12 9 23 6 21 1 14 1 23 0 12 4 ~~19 21723 -7 108 Ecuador 2 .0_ _ __ _ _ ___6__ _ _ __ _ _20 7 Egypt,Arab Rep 4 5 21 7 0 2 13 2 7 9 10 8 141 9 1 9 5 15 5 VEl,sdor~~½-QJ,8~~1~ ______ 210 Estonia 149 52 -15 6 23 0 11 2 24 4 27 6 2 3 3 1 20 0 Gabon~~~~~625'5 ~~~~5. -0 f5 7 93j Ghana 8,275 4 3 9 15 0 81 6 79 5 31 7 19 0 22 9 Gu emIa , ,7 8 -3t57 -24. . >8.1> 97 7 1 5 22 5 11 7 9. 3- Honduras 16 9 5 1 6 3 17 5 15 0 21 0 9 6 13 0 15 1 10 9 A 4 3~~~~1 6 8 1 1 8l4 8 48B 143 7 o, 3 7 J Indonesia 8,940 0 8 4 -14 0 12.8 5 8 6 6 6 6 5 3 12 8 33 1 fiThn,isiamic Rp. 7 7~952.0< '22 6. 3-54 2 1-181 1 293 27 7L].20 6 8 ______ Jamaica 50 8 4 1 7 4 8 6 9 6 -35 3 12 8 19 4 Jordan ~~~~~~~~~M o 1 00, 6.6 1C 9 5i4i10.6iA 7 Kazakhstan 154 6 39 2 9 40 2 30 0 17 1 29 6 7 8 2.5 7 9.7 3 2 4 7 7 6 118 20 Latvia 0 6 4 1 -6 9 19 8 17 4 34 4 49 9 9 4 4 8 80 4 "50. 0 . 75 --6 1. 6 7 44 18 Lesotho 8 6 60 2 -28 8 69 6 64 6 17 2 9 5 59 2 150 4 10 2 14 0 0 7 Lithuania 7 <- 3 00 172 . 21-4- -<<203 932 5 9 4<-4 Macedonia. FYR 4 3 73 2 734 32 1 338 16 1 4 5 continues on page 185 2003 World Development Indicators I 183 Gross domestic Exports of goods Imports of goods GDP deflaor Current account Gross product and services and services balance Intemnational reserves months annual annual annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2001 2002 2001 2002 2001 2002 2001 2002 2001 2002 2002 2002 Malawi -1.5 3 0 3.7 7 78 4.7 16.7 26.1 12 0 -30 4 '-14 0 __ Malaysia 04 4 0 -7 5 2.5 -8 6 7 2 -2.6 2 0 8.3 6.5 ,Maunitus 7.2 5.3 5.5 2.7 1 4 3 8 2.6 5 6 5.5 1.7 912 3.8 Mexico -0 3 1 5 -5 1 0 2 -2 9 0.0 5 5 5.4 -2.9 -2 7 47.232 2 8 Moldova 6.1 5.5 14.7 6.0 10 9 9.7 11.9 5.5 -6.7 -6 0 273 2.6 Morocco 6 5 4.4 1 4 -7 5 2.3 5 2 2 5 30 4.7 0.0 9,18 7 Nicaragua .. 1.5 - 6 3 _. -3.5 .. -5.8 .. -8 0 . Niger ia 3.9 18 5.6 -7 3 18 9 -17 7 60 -3-6 11 9 -10 6 Pakistan- 2 7 -4 4 11- 8 -~12.5 1.5 ---10.7 5.7 4.5 --1.9 0.3 4,818 4.1 Panama 03 10 -1.1 45 -~~ ~~~~~ ~~~~ ~~~~~9 9 2.3 1.2 0 .8 -4 9 -4 5 1,163 24 Pa~raguay 27 -5 -4_ -9.3_ -1.6 --15.0 7.0 10 2 -2.9 -0 9 758 2 9 Peru 0 2 4 6 6 9 6 0 1.6 2.5 1.3 4.0 -2.0 -1.9 7,088 Pthilippines 3.4 4 6 -3.2 3 3 0.5 4.9 6.7 4.5 6 3' 1.8 16,180 5.8 Poland 1 0 1 2 11 8 4 1 3 2 1 5 4.3 3 5 -3.0 -3.6 27,933 6.5 Romania 5 3 4 3 10 6 9 6 17.5 7.9 37.0 22.0 -6.0 -5 4 5,123 3.3 Russian Federation -5 0 4 0 2.6- 2.6 -1 6 5 -12.0O 17 9 17 0 11 2 7.0 45.810 6.1 Senegal ~~~ ~~~5 7 50 66 5.4 52 -4.5 - 2.9 27 -6 4 -5.0 660' 4.0 Slovak Republic 3 3 4 0 6.5 5.9 11 7 5 3 5 4 3.6 -8.9 South Africa 2.2 2.2 3.1 4 1 9.2 8.1 7 5 3.8 -0.1 -1.3 17,387 5.1 Sr aka -1 4 3 -65 -8.7 -10.1 21.3 13 2 8 6 -1 7 -2.2 1.214 2.0 Sudan 6.9 10.6 -9.8 .. 37.5 .. 4.8 0.9 -4.2 -14.5 323 0.9 Swaziland 1 6 5 6 -4 3 4 6 2 8 6.3 8.4 -4.0 -4 2 SY.rian Arab RePublic 2.931 . 4.5 91 6 6. 25 . 4.4 3,810 5.9 Thailand 1 8 5 0 -4 2 10 7 -8 3 10 5 2.2 1 2 5 4 6 0 38,924 5.8 Trinidad and Tobago 5 0 3.0 7.0 -52 0.0 5 0 3 3 -2.1 .. 2.7 2.385- 6.5 Tunisia 49 1 9 14 4 _-2.5- 13 6 -15 2.8 2 7 -4.3 -4 3 2,6 30 2 8 Tui!'rke'y -7 4 35 7 4 1.3 -24 8 7 3 57.2 48.6 2.3 - 0.8 '33.412 6.9 Ukraine ~~9.1 4 5 -~2.9 5 8 2 2 6 3 8.8 3.2 3.7 4.8 4,370 2 Uruguay -3 1 -10~~~~~'i 0 -8 8 -10 0 -7.7 -5.0 5.6 18.3 -2.7 0.3' 2,2'59 7'.0 Uzbekistan 4 5 3.2 -5 4 -0 5 6.4 0.5 43.1 39.1 -1.0 -0.9 1,190 42 -Venezuela RB 2____, 7 _6_1_2 2 0___ 2__ 9.4____-0.5- 6.8 -33.7- 3.1- 5.8 11,829i 5 5 Yugoslavia, Fed Rep. 5 5 4 0 9 4 18 2 30 0 32 9 91.7 25.5 -55 -8.9 2.280 3.4 4.9 4.3 29.0 _ 1.6 27 2 -3.5 24.3 16.4 . -12.. .- Zimbabwe -8 4 -5 6 -3 6 -0 8 -07 -48 70 1 107 5 --0.7 Note Data for 2002 are the latest preliminary estimates and may differ from those in earlier world Bank publications. Source World Bank staff estimates 1814 II 2003 World Development Indicators 4.h Nominal exchange rate Real effective Money and Gross Real Interest Short- exchange rate quasi money domestic credit rate term debt a local currency units annual annual % of per $ 36 change 1995 100 % growth % growth %exports 2002 2001 2002 2001 2002 2001 2002 2001 2002 2001 2002 200'1 Malawi -~~~______ 871 -16 0 29 5 116 8 112 4 14.8 15 9 74.0 33 9 23.9 45.2 9.2A Malaysia 3 8 0 0 0 0 91~ 4 9 2 5 32 2 2, 7 2 9 5 2 8 4 9 Mau ti s, 9.-09.-__ . 10,9 12.3 14.8 8.3 18 0 15 5 3~ 00 Mexico 10 3 -4 5 12 141 2.3 2 6 140 7 9 57 9 7 fMoIdova~~~~~~13.8 57 560. 73.8 8 8 96 25 8 15 0 160 2 Morocco 10.2 8.9 -12 1 103 7 101.8 14 1 6 4 -1 2 46 10 5 1 7 Niei 126 4 3 1 12 8 89.8 82 0 27.0 25 5 75 8 26 5 16 4 13 3 8 0 Pakistan ____ 58 5 4.9 -3.8 87.2 87.8 11.7 181 -0 4 1.5 .. . 14 Panama 10 00 0 0 ,. 6. 6 6 5 1 j,~~raguay 7,103.6 32.8 51.7 93.1 68~ ~~~~ ~~9 16 4 2 8 -156 -126 - 19 9 27.9 138 Peru 3 5 -2 4 2 0 . . 2 1 6 1 1.2 1 6 18 9 11 6 30 5 _____ 5.12 .8 33 -3.6 9.1 -20 1 3 5.4 33 1442 Poland 4.0 -3.8 -1 9 138 3 131.4 15 0 -1 3 11 8 13 5 6 6 12 7 omania ~ 33,500.0 21.9 6.0 108.8 110.8 46.2 36 7 26 9 36 8 .3.8 Russian Federation 31.8 7.0 5.5 105.5 107 6 361 31. 300 31 00 2 9 17 6 Senegal_ _____ _2.5. --1. 1. 1 686. Slovak Republic 40 0 2.3 -17 4 107.8 112 9 119 70 14 8 -4 3 5 5 43 19 9 SotArica 86 60 -8. 32 675 6 171 202 325 ~ 2223 Sri Lanka 96 7 12 8 3 8 14.4 8 56 1 LSuda __ _66J~ 0. . 4 30.5 22.6 32 7 . 3. Swaziland 8.6 60.2 -28 8 10 7 17 9 71 4 -185 5 4 5 9 7 7.1 ~~ -.00 0.0 . . ~O 23.5 .. 18.6 . 2 9 3 2 70.2] Thailand 43.2 22 -2.4 .2 2 0 6 -5 9 4 2 5 0 3 4 16 5 VTrinidadandTobago -, -6.3 '-0.1 '0 1 124.1 126.6 6.9 - . -1.2 . 120 1. Tunisia 1 3 60 -9a1 97 3 97.5 10 7 7 8 8 9 62 - 65 ['Turkey ______ 1.643,699.0 115.3g. 133 .. .: -86.3 . 93.7 .. .. . ~~29.23 Ukraine 5 -2 06 19 111 40 4 318 7 289 21 5 165, 3 5 27__2_____ _____-84_2 112.0 75.1 19.0 40 7 7.7 50 0 _43 6 .. 71.41 ['VenezueMRa 1.398~~~~8 ~-90- 83 3 -1731,1. 11 15 -3 13 2. 15 146 1. 12 Yugoslavia, Fed Rep 107 3 Zabia ___4.334.4- -913 2 '122 4 97.7 -13 6 34.6 -5.4 18 3 17.6 36.6158' 1 Zimbabwe- -0 . 1285 1702 839 1269 -189 155 249 Note Data for 2002 are preliminary end may not cover the entire year a More recent data on short-term debt are available on a Web site maintained by the Bank for International Settlements, the International Monetary Fund, the Organisation for Economic Co-operation and Development, and the World Bank http //wvww oecd org/dac/debt Source International Monetary Fund, intemnational rinanciaf Statistics; Worid Bank, Debtor Reporting System 2003 World Development Indicators i 18 w0UGrowth of output Gross domestic Agriculture Industry Manufacturing Services product average annval average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980_90 1.990-2001 ±.980-90 1990-2001 1980-90 1990--2001 1980-SO 1990-2001 1980-90 1990-2001 Afghantstan Albania 1 5 3 7 1.9 57 2.1 1.0 -5 0 -0 4 4 5 Algeria 2 7 - 2.0 4 1 3 7 2 6 1.9 4 1 -1 6 3 0 1-9 Angola 3 6 2 0 0 5 0 1 6 4 4 0 -11 1 0.6 1 3 -1 0 Argentina -0.7 3 6 0.7 3 2 -1 9 -. 9 0 . Armenia . -0 7 10 - -6.1 -3 2 6 Australia 3_5 3.9 3.2 2 9 3 0 3 0 1.9 2 5 3.8 4 3 Austria 2 3 2 2 1 4 3 7 1 8 2 8 2 5 2 7 28 1 Azerbaijan -0.3 .. -0 5 -4 0 -11 8 10 3 Bangladesh 4.3 4 9 2.7 3 1 .49 7 2 3 0 7 0 4 4 4 6 Belarus -0 8 -3 5 -0 7 0 4 . 0 5 Belgium 2 1 2 2 2.2 2 3 2 4 2 0 . 1 8 2 0 Benin 2 5 4 8 -51 5 7 3.4 4 4 5 1 5 9 0.7 4 2 Bolivia -0.2 3 8 . 2.8 . 3 7 3 6 4 1 Bosnia anid Herzegovina Botswana 1-1.0 5 2 2.5 -1 3 11.4 4 2 1-1.4 4 4 15.91 8.1 Brazil 2 7 2 8 - 2 8 3 3 2_0 2.4 1 6 1 5 33_ 2 9 Bulgaria 3 4 -1 2 -2 1 3 0 5 2 -4 0 4 7 -3 9 Burkina Faso 3 6 4.5 3 1 3.7 3 8 4.4 2 0 5.4 4 6 4 6 Burundi 4 4 -2 2 3 1 -1 1 4 5 -4 3 5 7 -8 0 _ 5 6 -1 5 Cambodia 5 0 1 8 10 2 8 2 6 2 Cameroon 3 4 2.1 2 2 5 5 5 9 0 0 5 0 2.0 2 1 0 5 Canada 3 2 3 1 23 1 1 2. 1 3 4 2 2 Central African Republic 1 4 2.1 1 6 3 9 1 4 1.0 5 0 0 3 1 0 -0 5 Chad 6 1 2 5 2 3 4 0 8 1 2 8 .6.7 1 7 Chile 4 2 6.3 5.9 1 9 3 5 5 7 3 4 4.1 2 9 4 9 China 10 3 10.0 5 9 4 0 11 1 13 1 10.8 _ 12 1 13 5 8.9 Hong Kong, China 6 9 3 8. Colombia 3.6 2.7 2 9 -1.8 5-0 1 4 3 5 -1 9 31_ 3-9 Congo, Dem Rep 1 6 -4 8 2.5 0 6 0 9 -7.8 1 6 1 3 -10 9 Congo, Rep 3 3 -1.4 3 4 14 _52_ 3 0 6. 12_ -0.2 Costa Rica 3 0 5 1 3 1 3 9 2.8 5 8 '30 62 3 3 4.6 CMe dIlvorre 0 7 31 03 3 2 4 4 40 3 0 2 9 -0 3 2 7 Croatia . 1 1 -1 6 -1 7 -22 2 Cuba 42 , 5.2 6.6 6 3 2.5 Czech Republic 1 2 3 5 -0 3 9 205 Denmark -20 2 4 2.6 2 7 2.0 2 2 1 3 2 2 19 2. Dom(nican Republic 3.1 0 -0 30 6 2 3 4 7 42 6 Ecuador 20 1 8 4.4 1 5 1,2 2.6 0 0 21 17 1 Egypt, Arab Rep. 5.4 4 5 2.7 3 4 3 3 4 6 6 5 7 8 4 6 El Salvador' 0 2 4 5 -1 I I1 0.1 5 1 -0.2 ~ 52 0 7 5 1 Eritrea 5 3 1. 28 8. 5 Estonia 2 2 0.2 . -2 8 -1-9 3.4 . 2 2 Eth~iopia 1 1 4 7 0 2 2.31 -04 5 4 -.09 5.-4 3.1 7 2 Finland 3 3 2.9 -0 4 1 2 3.3 4 8 3 4 6 4 3.6 2 5 France 2 4 1.9 1 3 1 9 1 4 1 5 . 2 1 3 0 2 0 Gabon 0 9 2 6 1 2 -1 0 1 5 2 4 1 8 0 6 - 01 34_ Gambia,The 3 6 3 4 0 9 5 2 4 7 2 5 7.8 1 3 2 7 3 7 Georgia 0.4 -5.6 Germany 2 3 1.-5 1.7 1 7 1 1 0.0 -0.1 3 1 2.4 Ghana 3.0 4 2 1 0 3 4 3 3 2 8 3.9 -2.2 5 7 5.5 Greece 0 9 2 4 -01I 0 7 1 3 10 . 0 9 2 8 Guatemafa 0 8 4.1 1 2 2.8- -02 4.1 0-0 2.7 0 9 4 6 Guinea 4 2 . 4 2 4 7 . 43 3 Guinea-Bissau 40 1 0 4-7 36 2,2 -31 -2 2 3 5 -0 2 Haiti -0 2 -0 4 - -0 1 -28 -1 7 1 6 -1 7 -9 3 09 02 ~ I 2003 World Development Indicators Growth of output4. Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 Honduras 2 7 3 1 2.7 1 9 3 3 3 6 3 7 4 1 2 5 3 7 Hungary 1 3 1 9 1 7 -2 2 0 2 3 8 7 9 2 1 1 4 India 5 7 5 9 3 1 ~30 6 9 6 1 7 4 6 7 6 9 7 9 Indonesia 6 1 3 8 3 6 1 9 7 3 4 8 12 8 6 3 6 5 3 6 Iran. Islamic Rep 1 7 3 6 4 5 40O 3 3 -2 8 4 5 5 1 -1 0 8 5 Iraq -6 8 Ireland 3 2 7 7 Israel 3 5 4 7 Italy 2 5 1 6 -05 1 4 1 8 1 2 2 1 1 5 3 0 1 8 Jamaica 2 0 0_2 2 8 0 6 2 4 -0 4 2 7 -1 7 1 6 0 6 Japan 4 1 1 3 1 3 -3 1 4_1 -0 2 0 7 4 2 2 3 Jordan 2 5 4 8 6 8 -2 0 1 7 4 7 0 5 5 4 2 3 5 0 Kazakhstan -2 8 . -6 5 -69_ 3 1 Kenya 4 2 2 0 3 3 1 2 3 9 1 6 4 9 2 0 4 9 3 1 Korea, Dem Rep Korea, Rep 8 9 5 7 3 0 2 0 11 4 6 3 12 1 7 6 8 4 5 6 Kuwait 1 3 3 4 14 7 1 0 2 3 2 1 Kyrgyz Republic -2 9 2 1 -8 5 -14 1 -3 9 Lao PDR 3 7 6 4 3 5 4 9 6 1 10 9 8 9 12 6 3 3 6 5 Latvia 3 5 -2 2 2 3 -5 9 4 3 -6 7 4 4 -6 2 3 3 3 1 Lebanon 5 4 1 8 -1 6 -4 3 4 1 Lesotho 4 6 4 0 2 8 1 7 3 9 7 8 8 5 6 2 5 1 3 0 Liberia -7 0 6 2 6 5 -11 2 -12 5 Libya -7 0 Lithuania -2 2 -0 3 2 8 4 4 4 3 Macedonia, FYR -0 2 -0 3 -2 3 -4 5 1 1 Madagascar 1 1 2 4 2 5 1 9 0 9 2 8 2 1 2 6 0 3 2 8 Malawi 2 5 3 6 2 0 7 2 2 9 1 8 3 6 0 4 3 3 2 3 Malaysia - 5 3 6~5 3 4 0 3 68, 8 0 9 3 8 8 4 9 6 7 Mali 0 8 4 1 3 3 2 9 4 3 7 5 6 8 2 8 1 9 3 1 Mauritania 1 8 4 2 1 7 4 8 4 9 2 3 -2 1 0 0 0O4 5 2 Mauritius 6 0 5 2 2 6 -0 2 9 2 5 5 10 4 5 3 5 1 6 3 Mexico 1 1 3 1 0 8 1 6 1 1 3 7 1 5 4 2 1 4 3 0 Moldova 2 8 -8 4 -9 5 -11 5 --34 0 3 Mongolia 5 4 1 2 1 4 3 2 6 6 -0 1 8 4 0 4 Morocco 4 2 2 5 6 7 -0 6 3 0 3 2 4 1 2 8 4 2 3 0 Mozambique -0 1 6 7 6 6 4 9 -4.5 15 2 18 0 9.1 1 9 Myanmar - 0 6 7 4 0 5 5 7 0 5 10 5 -0 2 7 9 0 8 7 2 Namibia 1 0 4 6 2 5 4 1 -0 2 2 1 3 1 2 6 2 2 4 3 Nepal 4 6 4 9 40 2 6 8 8 69 9 3 84 39 6 2 Netherlands 2 4 2 9 3 6 2 0 1 6 1 7 2 6 3 2 New Zealand 1.9 3 1 4 0 34_ 1 0 2 3 2 4 2 1 3 5 Nicaragua -1 9 2 8 -2 2 5 2 -2 3 3 2 -3 2 1 3 -1 5 1 2 Niger -0 1 2 5 1 7 3 2 -1 7 2 1 -2 7 2 7 -0 7 2 2 Nigeria 1 6 2 5 3 3 3 5 -1 1 1 0 0 7 1 2 3 7 2 9 Norway 2.8 3 5 0.1 2 4 4 0 3 9 0 2 2 3 2 9 3 4 Oman 8 4 4 3 7 9 10 3 20 6 5 9 Pakistan 6 3 3_7 4 0 4 1 7 7 4 0 8 1 3 9 6 8 4 4 Panama 0 5 3 8 2 5 2 2 -1 3 4 7 0 4 1 8 0 7 3 8 Papua New Guinea 1 9 3 6 1 8 3 5 1 9 4 6 0 1 4 4 2 0 3 0 Paraguay 2.5 2 1 3 6 2 3 0.3 ~3 1 40 0 8 3 1 1 4 Peru -0 1 4 3 3 0 5 6 0 1 5 0 -0 2 3 5 -0 4 3 7 Philippines 1 0 3 3 1 0 1 8 -0 9 3 2 0 2 3 0 2 8 4 1 Poland 4 5 -0 2 4 2 7 1 4 2 Portugal 3 2 2 7 1 5 -0 2 3 4 3 0 2 6 2 5 2 2 Puerto Rico 4 0 4 3 1 8 3 6 3 6 4 6 2003 World Development Indicators 1 187 1313L~~~~~~~L Growth of output Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth ±980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 Romanla_ 10 -0 4 Russian Federation -3 7 -4 5 -6 1 -0 3 Rwanda 2 2 0 8 0 5 3 4 2 5 -2 3 2 6 -4 8 3 6 -0.2 Saudi Ara bia 0 0 1 5 13.4 -2 3 7.5 1 3 Senegal 3 1 3 9 2.8 2 3 4 3 5.1 4 6 4 2 2 8 4 0 Sierra Leone 0 5 -4 4 3 1 -4 5 1 7 -4 6 5 0 -0 9 -3 4 Singapore 6 7 7 4 -5 3 -2 1 5 2 7 2 6 6 6 5 7 6 7 6 Slova kRepublic 2 0 2 1 1 6 1 6 2 0 -2 1 4 3 0 9 56 Slovenia 2 9 -0 1 2.9 40 39 Somal ia 2 1 3 3 .10 -1 7 0 9 South Africa 1 0 2 1 _29 0 8 0 7 1 0 1 1 -12 2 4 2 7 Spain 3 1 2 7 3 1 1 0 2 7 2 3 3 3 2 9 SriLanka 4 0 5 0 2 2 1.7 4 6 6.5 6 3 7 5 4.7 5 7 Sudan 2 3 5 6 1 8 9 0 1 6 6.8 4 8 4 6 4 5 2 7 Swaziland 6 7 3 2 2 3 1 5 12 0 3 6 15 7 2 7 4 8 3 4 Sweden 2 5 2 1 1 4 0 0 2 8 3 6 .2 4 1 8 Switze-rland 2 0 1 0 Syrian Arab Republic 1 5 4 8 -0 6 4 9 6 6 9.3 10 2 16_ 3 0 Tajikistan 2 0 -8 5 -2 8 -58 5 5 -13 2 5.6 -12.6 3.4 -1 1 Tanzaniaa 3 2 3 3 . 3 6 3 0 3.0 Tha-iland- 7 6 3.8 3 9 1 7 98_ 5 4 9 95 65 73 3 Togo 1 7 2 2 5 6 3 8 -1 1 2 7 17 3 9 -0 3 0 4 Trinridad and Tobago -0 8 3 6 -5 9 3 4 -5 5 4 2 -10 1 6 7 6 7 3 1 Tunisia 3 3 4.7 2 8 2 4 3 1 4.7 _ 3 7 5 6 3 5 5 4 Turkey 5 3 3 3 1 2 1 1 7 7 3.4 7 9 4 1 4 5 3 5 Turkmenistan -2 8 -3 2 -6 7 .-3 2 Uganda 2 9 6 8 2_1 _ 3.8 5 0 11 9 3.7 12 8 2 8 7 7 Ukraine -79 -4 9 -9 5 . -9 0 -0 9 United Arab Emirates -2 1 2 9 9.6 -4 2 3 1 3 6 Uni-ted Kingdom 3 2 2 7 2 4 -1 0 3.3 1 3 3 1 3.4 United States 3 5 3 4 3 2 3 5 3 0 3 7 4_3 3 4 3.7 Uruguay 0 5 2 8 0 1 2 0 -0 2 0 7 04 -05 1 0 4 0 Uzbekistan 04 0 9 -2 6 1 6 Venezuela, RB -11 1 5 3_1 1 4 1 7 2 6 4.4 0 8 0 5 0 5 Vietnam 4 6 7 7 2 8 4 2 4 4 11 6 1 9 11 2 7 1 7 3 West Bank and Gaza 1 2 -4 2 0 8 . 36 2 Yemen, Rep 5.8 5 6 7 5 4 1 5.0 Yugoslavi-a,- Fed Rep Zambia 1 0 0 8 3 6 3.9 1 0 -3 6 4 1 1 1 -0 2 2 8 Zimbabwe 3 6 1 8 3 1 3 7 3 2 -04 2 2.8 -0 8 3 0 2 5 My-Er" I2II .- Low Income 4 5 3 4 3 0 2 6 5 5 2.9 7 7 3 0 5 5 5 1 Middle Income 2 9 3 4 3 4 21 3 2 3 37 - 3 7 . 57 3 2 3 7 Lower middle income 4 0 3 7 3 9 21 ~ 52 4 2 63 750 4 Upper middle income 1 7 3 1 2 3 2 2 1 5 3 0 1 6 2 9 2 0 3 3 Low &middle Income 3.2 3 4 3 3 2 2 3.6 - 3.6 4 2 5 2 3 36 3 9 East Asia &Pacific 7 5 7 5 4 6 3 2 8 4 10 1 9 5 10 0 8 6 6 5 Europe & Central Asia 2 1 -1 0 -1 9 -3 0 1 8 Latin America & Carib -17_ 3 2 2 3 2.4 1.4 2 9 1 4 2 1 1 9 3 2 Middle East & N Africa. 20 30 40 30 . 42 South Asia 5 6 _ 3 1 6 8 6.0 ~ 71 6 4 65 7 0 Sub-Saharan Africa 1.6 2 6 2 2 2 8 1 2 1 7 1 7 1 6 2 4 2 8 High Income' 3 3 2 5 1.9 1 1 3 0 1 8 _ . 2 4 3 5 3 0 Europe EMU 2 4 2 0 1 3 1 6 1 6 1 1 1 2 2 9 2 3 a Data cover mainland Tanzania only 20.8 II 2003 World Development Indicators Growth of output 0 An economy's growth is measured by the change in the Obtaining a complete picture of the economy requires * Gross domestic product (GDP) at purchaser prices is volume of its output or in the real incomes of persons estimating household outputs produced for home use, the sum of gross value added by all resident producers resident in the economy The 1993 United Nations sales in informal markets, and barter exchanges, and in the economy plus any product taxes (less subsidies) System of National Accounts (1993 SNA) offers three illicit or deliberately unreported activities The consis- not included in the valuation of output It is calculated plausible indicators from which to calculate growth the tency and completeness of such estimates depend on without making deductions for depreciation of fabricat- volume of gross domestic product (GDP), real gross the skill and methods of the compiling statisticians and ed capital assets or for depletion and degradation of domestic income, and real gross national income The the resources available to them natural resources Value added is the net output of an volume of GDP is the sum of value added, measured at industry after adding up all outputs and subtracting constant prices, by households, government, and the Robasing national accounts intermediate inputs The industrial origin of value industries operating in the economy This year's edition When countnes rebase their national accounts, they added is determined by the International Standard of the World Development Indicators continues to fol- update the weights assigned to various components to Industrial Classification (ISIC) revision 3 * Agricufture low the practice of past editions, measuring the growth better reflect the current pattern of production or uses corresponds to ISIC divisions 1-5 and includes forestry of the economy by the change in GDP measured at con- of output The new base year should represent normal and fishing * Industry comprises mining, manufactur- stant prices operation of the economy-that is, it should be a year ing (also reported as a separate subgroup), construc- Each industry's contribution to the growth in the without major shocks or distortions-but the choice of tion, electricity, water, and gas (ISIC divisions 10-45) economy's output is measured by the growth in value base year is often constrained by lack of data Some * Manufacturing refers to industries belonging to divi- added by the industry In principle, value added in con- developing countries have not rebased their national sions 15-37 * Services correspond to ISIC divisions stant prices can be estimated by measuring the quan- accounts for many years Using an old base year can 50-99 This sector is derived as a residual (from GDP tity of goods and services produced in a period, be misleading because implicit price and volume less agriculture and industry) and may not properly valuing them at an agreed set of base year prices, and weights become progressively less relevant and useful reflect the sum of service output, including banking subtracting the cost of intermediate inputs, also in To obtain comparable series of constant price data, and financial services For some countries it includes constant prices This double deflation method, recom- the World Bank rescales GDP and value added by product taxes (minus subsidies) and may also include mended by the 1993 SNA and its predecessors, industrial origin to a common reference year, currently statistical discrepancies requires detailed information on the structure of 1995 This process gives rise to a discrepancy prices of inputs and outputs between the rescaled GOP and the sum of the rescaled In many industries, however, value added is extrap- components Because allocating the discrepancy olated from the base year using single volume index- would give rise to distortions in the growth rates, the es of outputs or, more rarely, inputs. Particularly in discrepancy is left unallocated As a result, the weight- the service industries, including most of government, ed average of the growth rates of the components gen- value added in constant prices is often imputed from erally will not equal the GDP growth rate. labor inputs, such as real wages or the number of Growth rates of GDP and its components are calcu- employees In the absence of well-defined measures lated using constant price data in the local currency of output, measuring the growth of services remains Regional and income group growth rates are calculated ' difficult after converting local currencies to constant price U S The national accounts data for most developing Moreover, technical progress can lead to improve- dollars using an exchange rate in the common refer- countries are collected from nationai statistical ments in production processes and in the quality of ence year The growth rates in the table are annual organizations and central banks by visiting and goods and services that, if not properly accounted for, average compound growth rates Methods of comput- resident World Bank missions The data for high- can distort measures of value added and thus of ing growth rates and the alternative conversion factor income economies come from data files of the growth When inputs are used to estimate output, as is are described in Statistical methods Organisation for Economic Co-operation and the case for nonmarket services, unmeasured techni- Development (for information on the OECD's cal progress leads to underestimates of the volume of Changes in the System of National Accounts national accounts series, see its monthly Main output Similarly, unmeasured changes in the quality of The World Development Indicators adopted the termi- Economic Indicators) The World Bank rescales goods and services produced lead to underestimates nology of the 1993 SNA in 2001 Although most coun- constant price data to a common reference year. of the value of output and value added. The result can tries continue to compile their national accounts The complete national accounts time series is be underestimates of growth and productivity improve- according to the SNA version 3 (referred to as the available on the World Development Indicators ment, and overestimates of inflation These issues are 1968 SNA), more and more are adopting the 1993 2003 CD-ROM The United Nations Statistics highly complex, and only a few high-income countries SNA Some low-income countries still use concepts Division publishes detailed national accounts for have attempted to introduce any GDP adjustments for from the even older 1953 SNA guidelines, including United Nations member countries in National these factors valuations such as factor cost, in describing major Accounts Statistics Main Aggregates and Informal economic activities pose a particular meas- economic aggregates Countries that use the 1993 Detailed Tables and publishes updates in the urement problem, especially in developing countries, SNA are identified in Pnmary data documentation Monthly Bulletin of Statistics where much economic activity may go unrecorded 2003 World Development Indicators 1 189 ___ ~Structure of output Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ millions % of GOP % of GDP % of GDP %of GOP 1990 2001 1990 2001 1990 2001 1990 2001 19g0 2001 Afghanistan Albania 2.102 4,114 36 50 48 23 42 13 16 26 Algeria 62,045 54,680 11 10 48 55 11 8 40 36 Angola 10,260 9,471 18 8 41 67 5 4 41 25 Argentina 141,352 268.638 8 5 36 27 27 17 __ 56 69 Armenia 4,124 2,118 17 28 52 34 33 22 31 38 Australia 310,202 368,726 4 4 29 26 14 13 67 70 Austria 161,692- 188,546 4 2 34 33 23 22 62 65 Azerbaijan 5,585 17 46 6 _36 Bangladesh. 30,129 46,706 29 2~3 21 25 13 15 50 52 Belarus 35,203 12,219 24 11 47 39 39 33 29 50 Belgium 197,174 229.610 2 2 33 27 20 65 71 Benin. 1,845 2,372 36 36 13 14 8 9 51 -50 Bolivia 4,868 7,969 17 16 35 29 -18 15 48 56 Bosnia and Herzego-vlna 4,769 _15 31 16 55 Botswana 3,791 5,196 5 2 57 47 5 4 39 51 Brazil 464,989 502,509 8 9 39 34 25 21 53 57 Bulgaria 20,726 13,553 17 14 49 29 18 34 57 Burkina Faso 2,765 2,486 32 38 22_ 21 16 15 45 41 Burundi 1,132 689 -56 50 19 19 13 9 25 31 Cambodia 1,115 3,404 56 37 11 22 5 33 41 Cameroon 11, 152 8,501 25 43 29 20 15 11 46 38 Canada 574,204 694,475 3 32 .. 17 65 Central African Republic 1,488 967 48 55 20 21 11 9 33 24 Chad 1,739 1,600 29 39 18 14 14 10 53 48 Chile 30,323 66,450 9 9 -41 34 20 16 50 57 China 354,644 - 1,159,031 27 15 42 51 33 35 31 34 Hong Kong, China 74,782 161,896 0 0 25 _ 14 18 -6 74 86 Colombia 40,274 82,411 17 13 38 30 21 16 45 57 Congo, Dem Rep 9,348 5,187 -30 56 28 ~ 19 11 4 42 25 Congo, Rep 2,799 2,751 13 6 41 66 8 4 46 28 Costa Rica 5,713 16,108 18 9 29 29 22 21 53 62 C6te dIlvoire 10,796 10,411 32 24 23 22 21 19 44 54 Croatia 18,156 20,260 10 9 34 33 28 23 56 58 Cuba 7 46 37 47 Czech Republic 34,880 56,784 6 4 49 41 45 55 Denmark - 133.361 _ 161,-542- 4 _ 3 27 _26 118 17 69 71 Dominican Republic 7,074 21,211 13 11 31 33 18 16 55 55 Ecuador 10,686 17,982 13 -11 38_ 33 19 18 49 56 Egypt, Arab Rep 43,130 98,476 19 17 29 33 18 19 52 50 El Salvador - 4,807 13,739 1-7 _9 26 -30 22 23 57 61 Eritrea 451 688 31 19 12 22 8 11 57 59 Estonia 6,760 -- 5,525 17 6_ 50 29 42 19 34 65 Ethiopia 6,842 6,233 49 52 13 11 8 7 38 37 Finland 136,794 120,855 7 3 34 _33 23 26 60 63 France 1,215,893 1,309,807 4 3 30 26 21 18 66 72 Gabon 5,952 4,334 7 8 43 51 6 5 50 42 Gambia, The 317 390 29 40 13 14 7 5 58 46 Georgia 12.171 3,138 32 21 33 23_ 24 35 57 Germany -1,688,568 1,846,069 2 1 38 31 28 24 60 68 Ghana 5,886 5,301 45 36 17 25 10 9 38_ 39 Greece 84,075 117,169 11 8 28 21 12 61 71 Guatemala 7.650 20,496 26 23 20 19 15 13 54 58 Guinea 2,818 - 2,989 24 24 33 38 5 4 43 38 Guinea-Bissau 244 199 61 56 19 13 8 10 21 31 Haiti 2,864 3.737 190 El 2003 World Development Indicators Structure of output4. 1 Gross domestic Agriculture Industry Manufacturing Services product value added value added value added vaiue added $ millions % of GDP % of GDP % of GDP % of GOP 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 3,049 6,386 22 14 26 32 16 20 51 55 Hungary 33,056 51,926 15 39 23 46 India 316,937 477,342 31 25 28 26 17 16 41 48 Indonesia 114,426 145,306 20 16 38 47 18 26 42 37 Iran, Islamic Rep 120.404 114,052 24 19 29 33 12 16 48 48 Iraq 48,657 Ireland 47,301 103,298 9 4 35 42 28 33 56 55 Israel 52,490 108,325 Italy 1,102,437 1,088,754 4 3 -34 29 25 21 63 68 Jamaica 4,59 2 7,784 7 6 40 3-1 19 13 52 63 Japan 3,052,058 4,141,431 2 1 39 32 27 22 58 67 Jordan 4,020 8.829 8 2 28 -25 15 ~ 15 64 73 Kazakhstan 40.304 22.389 27 -9 45 39 9 16 29 52 Kenya 8,533 11,396 29 19 19 18_ 12 13 52 63 Korea, Dem Rep Korea, Rep 252,622 422,167 9 4 43 41 29 30 48 54 Kuwait 18,428- -32,806 1 52 12- 47 Kyrgyz Republic 2,389 1,525 34 38 36 27 28 8 30 35 Lao PDR 865 1,761 61 51 15 23 10 18 24 26 Latvia 12,490 7,549 22 5 46 26 34 15 32 69 Lebanon 2,838 16,709 12 22. 10 66 Lesotho 622 797 23 16 26 42 6 14 50 42 Liberia 384 523 Libya 28,905 34,137 Lithuania 14,821 _ 11,992 277 31 35 21 23 -42 58 Macedonia, FYR 4,472 3,426 9 11 46 31 36 20 46 58 Madagascar 3 ,081 4,604 29 30 13 14 11 12 59 56 Malawi 1,881 1,749 45 34 29 18 19 13 26 48 Malaysia 44,024 88,041 15 9 42 49 24 -31 43 42 Mali 2,421 2,647 46 38 16 26 9 4 39 -36 M-auritania 1,020 1,007 30 21 29 29 10 8 42 50 Mauritius 2,383 4,500 13 6 33 31 25 23 54 62 Mexico 262,710 617,820 8 4 28 27 21 19 64 69 Moldova 10,583 1,479 51 26 31_ 24 18 18 50 Mongolia 1,049 17 30 30 17 5 52 53 Morocco- 25,821 34,219 18 16 32 31- 18 17 50 53 Mozambique 2,463 3,607 _37 ~ 2-2 18 26 10 12 44 52 Myanmar 57 57 11 10 8 7 32 33 Namibia 2,786 3,100 11- 11 31 33 -15 11 58- 56 Nepal 3,628 5,562 52 39 16 22 6 9 32 39 Netherlands 294,401 380,137 4 3_ 30 27 17 65 70 New Zealand 43,61-8 50,425 -7 28_ 19 65 Nicaragua 1.,009 31 21 .17 48 Niger 2,481 1,954 35 40 16 17 7 - 7 49, 43 Nigeria 28,472 41,373 33 30 -41_ 46- 6 4 26 25 Norway 115,453 166.145 4 2 35 43 13 61 55 Oman 10.535 19,826 3 58 4 39 Pakistan 40,010 58,668 26 25 25 23 17 16 49 52 Panama 5,313 10,171 9 7 15 16 9 7 76 77 Papua New Guinea 3,221 2,959 29 26 30 42 9 8 41 32 Paraguay 5,265 7,206 28 20 25 26 17 13 47 54 Peru 26,294 54,047 9 9 27 30 18 15 64 62 Philippines 44,331 71,438 22 15 34 3-1 25 22 44 54 Poland 58,976 176,256 -8 4 50 37 20 42 59 Portugal 71,466 109,803 9 4 32 30 22 19 60 66 Puerto Rico 30,604 67,897 1 1 42 43 40 40 57 56 2003 World Development Indicators I 191i ~(~Structure of output Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ millions % of GOP % of GOP % of GDP % of GOP 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 38.299 38,718 24 15 50 35 26 50 Russian Federation 579,068 309,951 17 7 48 37 35 56 Rwanda 2,584 1,703 33 40_ 25 22 18 10- 43 38 Saudi Arabia 104,670 186,489 6 -50 8 43 Senegal 5,699 4,645 20 18 19 27 13 18 61 55 Sierra Leone- 650 749 32 50 13 30 5 5 55 20 Singapore 36.670 85,648 0 0 34 32 27 23 65 68 Slovak Repu-blic- 15,485 20,459 7- 4 59 29 21 33 67 Slovenia 12,673 18,810 6 3 46 38 35 28 49 58 Somalia 917 . 65 5 South Africa 111.997 113,274 5 3 40 31 24 19 55 66 Spain 509,968 581,823 6 4 35 30 ..19 59 66 Sri Lanka 8,032 15,911 26 19 26 27 15 16 48 54 Sudan 13,167 12,525 39 19 10 42 Swaziland 882 1.255 13 17 42 44 35 36 45 39 Sweden 238,327 209,814 3 2 32 27 .64 71 Switzerland 228,415 247,091 Syrian Arab Republic 12,309 19,495 28 22 24 28 20 25 48 50 Tajikistan 4,339 1,056 33 29 38- 29 25 25 29 41 Tanzania a - -4,259 - 9,341 46 45 18 16 9 7 36 39 Thailand 85,345 114,681 12 10 37 40 27 32 50 49 Togo 1,628 1.259 34 39 23 21 10 10 44 39 Trinidad and Tobago 5,068 8,842 3 2 46 44 9 8 51 5-5 Tunisia 12,291 19,990 16 12 -30 29 17 18 54 60 Turkey 150.642 147,683 18 14 30 26 20 15 52 61 Turkmenistan 5,962 32 29 30 51 .38 20 Uganda- 4,304 5,675 57 36 11 21 6 10 32 43 Ukraine 91,327 37,588 -26 17 45 39 -44 23 -30 44 United Arab Emirates 34,132 - 2 - 64 .8 35 United Kingdom 989,564 1,424,094 2 1 35 27 23 19 63 72 U-nited States 5,75-0,800 10,065,265 2 2 _ 28 25 20 17 70 73 Uruguay 9,287 18,666 9 6 35 26 28 16 56 67 Uzbekistan 23,673 11,270 33 34 33 23 9 34 43 Venezuela, RB 48,593 124,948 5 5 50 50 20 20 44 45 Vietnam 6,472 32,723 -39 -24_ -23 38 12 20 39 39 West Bank and Gaza 3,972 8 27 15 66 Yemen, Rep 4,828 9,276 24 16 2-7 50 9 7 49 35 Yugoslavia, Fed Rep 10,861 15 32 ..53 Zambia 3,288 3,639 21 22 51 26 36 11 28 52 Zimbabwe 8,784 9,057 16 18 33 24 23 14 50 58 Low Income -872,667 1,082,138 29 24 30 32 16 -18 40 45 Middle Income 3,283,362 5,156.519 1-4 10 39 36 24 23 47 54 Lower middle income 1,890,857 2,739,311 20 12 39 40 26 26 41 48 Upper middle income 1,406,681 2,422,397 8 7 39 33 23 20 53 60 Low & middle Income 4,151,747 6,237,602 16 12 38 36 23 22 46 52 East Asia & Pacific 674,031 1,664,945 24 15- 39 -49 28 -32 37 36 Europe & Central Asia 1,240,117 993,753 17 10 44 34 39 55 Latin Amenca & Canb 1,134,854 1,968,782 9 8 36 32 24 20 55 60 Middle East & N Afnca 413,007 698,444 15 39 12 .47 South Asia 404,808 613,755 30 25_ __27 26_ 17 15 43 49 Sub-Saharan Africa 296,694 315,705 18_ 16 34 28 17 15 48 56 Hlighi Income 17,666,811 24,886,672 -3 2 33 29 22 20 64 70 Europe EMU 5,534,967 6,110,901 3 2 34 29 25 22 62 69 a Data cover mainland Tanzania only 192 2003 World Development Indicators Structure of output An economy's gross domestic product (GDP) represents ment of agricultural production, see About the data for * Gross domestic product (GDP) at purchaser prices is the sum of value added by all producers in that economy table 3 3 the sum of gross value added by all resident producers Value added is the value of the gross output of producers Ideally, industrial output should be measured through in the economy plus any product taxes (less subsidies) less the value of intermediate goods and services con- regular censuses and surveys of firms But in most not included in the valuation of output It is calculated sumed in production, before taking account of the con- developing countries such surveys are infrequent, so without making deductions for depreciation of fabricated sumption of fixed capital in the production process Since earlier survey results must be extrapolated using an assets or for depletion and degradation of natural 1968 the System of National Accounts has called for esti- appropriate indicator The choice of sampling unit, which resources * Value added is the net output of an indus- mates of value added to be valued at either basic prices may be the enterprise (where responses may be based try after adding up all outputs and subtracting intermedi- (excluding net taxes on products) or producer prices on financial records) or the establishment (where pro- ate inputs The industrial origin of value added is (including net taxes on products paid by the producers but duction units may be recorded separately), also affects determined by the International Standard Industrial excluding sales or value added taxes) Both valuations the quality of the data Moreover, much industrial pro- Classification (ISIC) revision 3 * Agriculture corre- exclude transport charges that are invoiced separately by duction is organized in unincorporated or owner-operated sponds to ISIC divisions 1-5 and includes forestry and the producers Some countnes, however, report such ventures that are not captured by surveys aimed at the fishing * Industry comprises mining. manufacturing data at purchaser pnces-the prices at which final sales formal sector Even in large industries, where regular sur- (also reported as a separate subgroup), construction, are made (including transport charges)which may veys are more likely, evasion of excise and other taxes electricity, water, and gas (ISIC divisions 10-45) affect estimates of the distnbution of output Total GDP and nondisclosure of income lower the estimates of * Manufacturing refers to industries belonging to divi- as shown in the table and elsewhere in this book is meas- value added Such problems become more acute as sions 15-37 * Services correspond to ISIC divisions ured at purchaser pnces Value added by industry is nor- countries move from state control of industry to private 50-99 This sector is derived as a residual (from GDP mally measured at basic prices When value added is enterprise, because new firms enter business and grow- less agriculture and industry) and may not properly measured at producer pnces, this is noted in Pnmary ing numbers of established firms fail to report In accor- reflect the sum of service output, including banking and data documentation dance with the System of National Accounts, output financial services For some countries it includes product While GDP estimates based on the production approach should include all such unreported activity as well as the taxes (minus subsidies) and may also include statistical are generally more reliable than estimates compiled from value of illegal activities and other unrecorded, informal, discrepancies the income or expenditure side, different countries use or small-scale operations Data on these activities need different definitions, methods, and reporting standards to be collected using techniques other than conventional World Bank staff review the quality of national accounts surveys of firms data and sometimes make adjustments to increase con- In industries dominated by large organizations and sistency with international guidelines Nevertheless, sig- enterprises, such as public utilities, data on output, nificant discrepancies remain between international employment, and wages are usually readily available standards and actual practice Many statistical offices, and reasonably reliable But in the service industry the especially those in developing countries, face severe limi- many self-employed workers and one-person business- tations in the resources, time, training, and budgets es are sometimes difficult to locate, and they have lit- required to produce reliable and comprehensive senes of tle incentive to respond to surveys, let alone report national accounts statistics their full earnings Compounding these problems are the many forms of economic activity that go unrecord- Data problems In measuring output ed, including the work that women and children do for Among the difficulties faced by compilers of national little or no pay For further discussion of the problems accounts is the extent of unreported economic activity of using national accounts data, see Srinivasan (1994) The national accounts data for most developing in the informal or secondary economy In developing and Heston (1994) countries are collected from national statistical countries a large share of agricultural output is either organizations and central banks by visiting and not exchanged (because it is consumed within the Dollar conversion resident World Bank missions The data for high- household) or not exchanged for money To produce national accounts aggregates that are income economies come from data files of the Agricultural production often must be estimated mdi- measured in the same standard monetary units, the Organisation for Economic Co-operation and rectly, using a combination of methods involving esti- value of output must be converted to a single com- Development (for information on the OECD's mates of inputs, yields, and area under cultivation This mon currency The World Bank conventionally uses national accounts series, see its monthly Main approach sometimes leads to crude approximations the U S dollar and applies the average official Economic Indicators). The United Nations that can differ from the true values over time and exchange rate reported by the International Monetary Statistics Division publishes detailed national across crops for reasons other than climatic conditions Fund for the year shown An alternative conversion accounts for United Nations member countries in or farming techniques Similarly, agricultural inputs that factor is applied if the official exchange rate is National Accounts Stafistics. Main Aggregates cannot easily be allocated to specific outputs are fre- judged to diverge by an exceptionally large margin and Detailed Tables and publishes updates in the quently 'netted out' using equally crude and ad hoc from the rate effectively applied to transactions in Monthly Bulletin of Statistics approximations For further discussion of the measure- foreign currencies and traded products 2003 World Development Indicators 1 193 riStructure of manufacturing Value added In Food, Textlles Machinery Chemicals Other manufacturing beverages, and clothing and transport manufacturinga and equipment tobacco $ millions % of total % of total % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan Albania 878 495 24 33 44 Algeria 6,452 3.897 13_ 17 70 Angola 513 264_ Argentina 37,868 46,877 20 10 13 12 46 Armenia 1,243 418 Australia 38,868 45,376 18 6 20 7 48 Austria 33,386 37,384 15 12 7 3 28 41 7 6 43 38 Azerbaijan 279 Bangladesh 3,839 6.922 24 22 38 33 7 16 17 10 15 19 Belarus 13,437 3,432 Belgium 39,904 17 18 7 15 24 13 7 62 37 Benin 145 198 61 17 .7 16 Bolivia 826 1,121 -28 31 5 4 1 1 3 3 63 60 Bosnia and Herzegovina 692 12 15 .. 18 7 49 Botswana 181 255_ 51 12 36 Brazil 90,052 -79,984 14 -12 .. 27 48 Bulgaria 1,985 22 9 19 5 45 Burkina Faso 423 301 69 62 2 4 2 3 0 0 27 31 Burundi 134 60 83 9 . ..2 7 Cambodia 58 178 Cameroon 1,581 937 61 47 -13 15 1 1 5 4 46 32 Canada 91,243 104,987_ 15 13 6 3 26 36 10 8 44 39 Central African Republi-c 154 81 57 6 2 6 28 Chad 239 152 42 40 .18 Chile 5,613 10.663 25 32 7 4 5 5 10 14 52 45 China 116,573 372.836 15 16 15 11 24 29 13 12 34 32 Hong Kong, China 12,625 8,953 8 8 36 22 21 29 2 3 33 37 Colombia 8,034 12,242 31 31 15 10 9 6 14 17 31 36 Cono,Dem Rep 1.029 205 Congo. Rep -234 112 Costa Rica 1,107 3,571 47 58 8 13 7 2 9 5 30 22 C6te dIlvoire 2,257 2,024 27 .. 17 10 46 Croatia 4,770 3.658 22 15 20 8 36 Cuba Czech Republic Denmark 20,757 23,156 22 21 4 8 24 25 12 7 39 40 Dominican Republic 1,270 3,300 64 67 2 3 0 0 5 6 29 24 Ecuador 2,068 2,302 22 50 10 17 5 0 8 8 56 26 Egypt, Arab Rep 7,296 17,969 19 12 15 39 9 6 14 . 43 43 El Salvador 1,044 3,029 36 45 14 34 4 1 24 7 23 12 Eritrea 35 67 Estonia 2,679 830 Ethiopia 497 409 62 21 .. 1 2 14 Finland 27,533 27,672 13 7 4 2 24 24 8 2 52 64 France 228,105 214,034 13 13 6 12 31 22 9 7 41 46 Gabon 332 205 45 2 1 7 45 Gambia, The 18 18 Georgia 2,789 Germany 456,313 402,886 Ghana 575 449 37 5 1 . 7 50 Greece 11,337 22 28 20 11 12 11 10 11 36 38 Guatemala 1,151 2,518 Guinea 126 121 Guinea-Bissau 19 21 Haiti 51 9 .40 El6 2003 World Development Indicators Structure of manufacturing 11 Q Value added In Food, Textiles Machinery Chemicals Other manutacturing beverages, and clothing and transport manufacturing' and equipment tobacco $ millions % of total % of total % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Honduras 443 1,025 45 59 10 8 3 0 5 3 36 30 Hungary 6,613 9,958 14 19 9 8 26 26 12 7 39 40 India 48,808 65,614 12 13 15 11 25 22 14 25 34 29 Indonesia 20,947 39,818 27 20 15 20 12 17 9 11 37 32 Iran, Islamic Rep 14,503 15,456 12 15 20 22 20 17 8 4 40 42 Iraq 20 38 16 22 4 11 11 4 49 25 Ireland 11,982 27,838 27 16 4 1 29 31 16 36 24 16 Israel 14 19 9 15 32 18 9 7 37 41 Italy 247,930 204,542 8 10 13 12 34 26 7 8 37 44 Jamaica 853 1,012 41 56 5 7 6 3 54 29 Japan 810,232 1,029,336 9 11 5 3 40 39 10 10 37 36 Jordan 520 1,122 28 2 7 9 4 2 15 3 47 85 Kazakhstan 2,136 3,139 Kenya 862 1,165 38 48 10 8 10 9 9 7 33 28 Korea, Dem Rep Korea, Rep 72,837 144,376 11 8 12 8 32 45 9 9 36 30 Kuwait 2,142 4 8 3 5 2 4 3 3 88 81 Kyrgyz Republic 631 105 Lao PDR 85 292 Latvia 4,150 913 Lebanon 1,560 Lesotho 33 99 Liberia Libya 24 40 2 6 0 10 7 7 67 36 Lithuania 6,218 2,127 Macedonia, FYR 1,411 621 20 26 14 9 31 Madagascar 314 430 Malawi 313 197 38 30 10 5 1 0 18 9 33 55 Malaysia 10,665 29,672 13 8 6 4 31 47 11 7 39 33 Mali 200 83 Mauritania 94 75 Mauritius 491 918 30 31 46 48 2 2 4 5 17 15 Mexico 49,992 107,166 22 25 5 4 24 28 18 15 32 28 Moldova 183 Mongolia 58 33 37 1 1 27 Morocco 4,753 5,857 22 33 17 17 8 12 12 15 41 23 Mozambique 230 432 43 2 8 1 47 Myanmar Namibia 374 350 Nepal 209 484 37 31 1 5 25 Netherlands 60,707 21 23 3 2 25 25 16 14 35 35 New Zealand 7,574 28 22 8 13 13 14 7 4 44 47 Nicaragua 170 322 Niger 163 122 37 20 29 9 34 71 Nigeria 1,562 1,635 15 46 13 4 22 Norway 13,450 17,076 18 16 2 2 25 29 9 8 46 46 Oman 396 19 8 5 7 62 Pakistan 6,184 8,637 24 16 27 33 9 6 15 6 25 38 Panama 502 713 51 45 8 7 2 2 8 4 31 42 Papua New Guinea 289 288 23 8 70 Paraguay 883 1,033 55 61 16 9 1 4 29 25 Peru 3,926 7,621 23 11 8 9 49 Philippines 11,008 16,878 39 38 11 9 13 9 12 11 26 33 Poland 28,514 21 26 9 6 26 23 7 6 37 38 Portugal 13,631 19,096 15 21 13 6 45 Puerto Rico 12,126 23,375 16 8 5 3 18 14 44 63 17 12 2003 World Development indicators 1 195 .1 W'' JJ Structure of manufacturing Value added In Food, Textiles Machinery Chemicals Other manufacturing beverages, and clothing and transport manufacturing and equipment tobacco $ millions % of total % of total % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Romania 19 18 14 4 45 Russian Federation 18 3 18 11 50 Rwanda 473 176 Saudi Arabia 7,962 Senegal 747 779 60 44 3 5 5 3 9 26 23 21 Sierra Leone 31 28 Singapore 9,937 24,896 4 3 3 1 53 62 10 15 29 20 Slovak Republic 4,197 Slovenia 4,008 4,358 12 11 15 10 16 16 9 12 48 51 Somalia 41 South Africa 24,040 21,452 14 14 8 7 18 20 9 9 50 50 Spain 103,128 18 14 8 7 25 23 10 10 39 47 Sri Lanka 1,077 2,459 51 39 24 30 4 6 4 7 17 19 Sudan 1,059 21 29 1 1 49 Swaziland 250 346 69 8 1 0 22 Sweden 10 7 2 1 32 39 9 11 47 42 Switzerland 10 9 4 3 34 27 53 60 Syrian Arab Republic 2,508 4,579 35 33 29 43 1 1 36 21 Tajikistan 1,078 237 Tanzania b 361 624 51 45 3 0 6 5 11 7 28 43 Thailand 23,217 38,650 24 26 30 17 19 10 2 8 26 40 Togo 162 118 Trinidad and Tobago 438 620 30 17 3 3 3 0 19 2 44 79 Tunisia 2,075 3,545 19 35 20 9 5 3 4 7 52 46 Turkey 26,882 26,994 16 13 15 18 16 17 10 11 43 41 Turkmenistan 838 Uganda 230 527 35 33 1 2 29 Ukraine 40,810 5,099 United Arab Emirates 2,643 3 3 3 1 90 United Kingdom 206,727 234,857 13 5 32 11 38 United States 1,040,600 1,566,600 12 5 31 12 40 Uruguay 2,597 3,490 31 43 18 9 9 4 10 8 32 36 Uzbekistan 1,136 Venezuela, RB 9,809 23,430 17 21 5 10 5 21 9 9 64 39 Vietnam 793 5,785 West Bank and Gaza 562 Yemen, Rep 449 593 Yugoslavia, Fed Rep 30 9 16 10 36 Zambia 1,048 330 44 31 11 12 7 17 9 21 29 19 Zimbabwe 1,799 1,003 28 32 19 15 9 7 6 10 38 36 Low Income 161,474 157,379 Mlddle Income 683,113 1,141,321 Lower middle income 349,698 742,365 Upper middle income 307,763 405,113 Low & middle Income 793,290 1,295,650 East Asia & Pacific 186,119 511,101 Europe & Central Asia Latin Amenca & Carib 255,228 338,774 Middle East & N Africa 75.539 South Asia 61,086 85,492 Sub-Saharan Afnca 42,925 38,043 High Income 3,706,881 4,624,819 Europe EMU 1,230,758 1,137,809 a Includes unallocated data b Data cover mainland Tanzania only 196 1 2003 World Development indicators Structure of manufacturing U The data on the distribution of manufacturing value The classification of manufactunng industries in the table ing, welding, and painting as well as advertising, added by industry are provided by the United Nations accords with the United Nations Intemational Standard accounting, and many other service activities In some Industrial Development Organization (UNIDO) UNIDO Industnal Classification (ISIC) revision 2 First published in cases the processes may be carried out by different obtains data on manufactunng value added from a van- 1948, the ISIC has its roots in the work of the League of technical units within the larger enterprise, but collect- ety of national and international sources, including the Nations Committee of Statistical Experts The committee's ing data at such a detailed level is not practical Nor United Nations Statistics Division, the World Bank, the efforts, interrupted by the Second World War, were taken would it be useful to record production data at the very Organisation for Economic Co-operation and Develop- up by the United Nations Statistical Commission, which at highest level of a large, multiplant. multiproduct firm ment, and the International Monetary Fund To improve its first session appointed a committee on industnal clas The ISIC has therefore adopted as the definition of an comparability over time and across countnes, UNIDO sification The latest revision, ISIC revision 3, was com- establishment 'an enterprise or part of an enterprise supplements these data with information from industnal pleted in 1989, and many countnes have now switched to which independently engages in one, or predominantly censuses, statistics supplied by national and interna- it But revision 2 is still widely used for compiling cross- one, kind of economic activity at or from one location tional organizations, unpublished data that it collects in country data Concordances matching ISIC categones to for which data are available " (United Nations the field, and estimates by the UNIDO Secretariat national systems of classification and to related systems 1990, p 25) By design, this definition matches the Nevertheless, coverage may be less than complete, par- such as the Standard Intemational Trade Classification reporting unit required for the production accounts of ticularly for the informal sector To the extent that direct (SITC) are readily available the United Nations System of National Accounts information on inputs and outputs is not available, esti- In establishing a classification system, compilers mates may be used that may result in errors in industry must define both the types of activities to be described totals Moreover, countries use different reference pen- and the organizational units whose activities are to be ods (calendar or fiscal year) and valuation methods reported There are many possibilities, and the choices * Value added In manufacturing is the sum of gross (basic, producer, or purchaser prices) to estimate value made affect how the resulting statistics can be inter- output less the value of intermediate inputs used in added (See also About the data for table 4 2 ) preted and how useful they are in analyzing economic production for industries classified in ISIC major divi- The data on manufacturing value added in U S dol- behavior The ISIC emphasizes commonalities in the sion 3 * Food, beverages, and tobacco comprise lars are from the World Bank's national accounts files production process and is explicitly not intended to ISIC division 31 * Textiles and clothing comprise These figures may differ from those used by UNIDO to measure outputs (for which there is a newly developed ISIC division 32 * Machinery and transport equip- calculate the shares of value added by Industry, in part Central Product Classification) Nevertheless, the ISIC ment comprise ISIC groups 382-84 * Chemicals because of differences in exchange rates Thus esti- views an activity as defined by 'a process resulting in comprise ISIC groups 351 and 352 * Other manu- mates of value added in a particular industry group cal- a homogeneous set of products" (United Nations 1990 facturlng includes wood and related products (ISIC culated by applying the shares to total manufacturing [ISIC, series M, no 4, rev 3], p 9) Firms typically use division 33), paper and related products (ISIC division value added will not match those from UNIDO sources a multitude of processes to produce a final product For 34), petroleum and related products (ISIC groups example, an automobile manufacturer engages in forg- 353-56), basic metals and mineral products (ISIC divi- 4.3a sions 36 and 37), fabricated metal products and pro- __ fessional goods (ISIC groups 381 and 385), and other Value added in manufacturing (1990 = 100) industries (ISIC group 390) When data for textiles and 300 East Asia & Pacific clothing, machinery and transport equipment, or chemicals are shown in the table as not available, they are ncluded in other manufacturing 250 200 The data on value added in manufacturing in U S South Asia dollars are from the World Bank's national /Middle East & North Africa accounts files The data used to calculate shares 150 of value added by industry are provided to the Latin/America & Caribbean World Bank in electronic files by UNIDO. The most recent published source is UNIDO's Interational Yearbook of Industnal Statistics 2002 The ISIC system is described in the United Nations' 50 International Standard Industrial Classification of 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 All Economic Activities, Third Revision (1990) The Manufacturing continues to be the dominant sector in East Asia and Pacific, growing by an average 10 percent a year discussion of the [SIC draws on Jacob Ryten's in 1990-2001 paper "Fifty Years of ISIC Historical Origins and Future Perspectives" (1998) Source World Bank data files 2003 World Development Indicators 1 197 K? -iJGrowth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 =100 1980-90 1990-2000 ±980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 1990 2000 Afghanistan -9 7 -3 9 -1 8 -0 2 -10 5 -4.7 -0 1 -1 1 99 100 Albania 1.36 18 0 Algeria 3 4 3 7 -8 1 1 6 -4 4 3 0 -2.7 1 1 128 172 Angola 6 0 0 7 2 7 3 1 6 4 4 9 0 7 4 9 136 215 Argentina 5 0 9_3 -6 8 16 8 2 1 101 -6 5 16 9 97 109 Armeniaa . -9 5 -0 3 Australiaa 6 3 7.3 6 0 9 2 _66 5 0 64 64 85 99 Austria a 6 6 10 2 5 6 8 7 4 1 Azerbaijan a -6.4 4 0 Bangladesh 0 4 15 6 -4 5 18 5 7 8 11 1 3 6 10 3 80 89 Belarus a 15 6 16 2 Belgiuma,b 4 5 62 4 0 54 7 8 63 6 4 4 5 100 Benin 3 5 6 3 -10 0 8 5 10 0 7 5 -4 9 9 5 100- 82 Bolivia 3 1 2.8 -1 2 9 0 -1 9 4 3 -0 3 9 7 115 112 Bosnia and Herzegovina Botswana 11_4 10 5 9 1 3 7 18 8 4 9 9 0 2 7 110 75 Brazil 6 3 5 1 0 7 16 7 5 1 5 9 -1.9 12 6 60 91 Bu lgari a a -12 3 2 2 -14 0 5.3 Burkina Faso -0 3 13 3 3 7 2 5 7 9 12 8 4 3 2 5 91 76 Burundi 3 5 8 6 1 0 4 0 2 5 -43 2.2 _-6 9 78 61 Cambodia Cameroon- 7 3 1 8 4 8 4 8 1 4 -0 5 0 1 1 9 90 110 Canadaa 6 4 9 1 7 4 9 0 6 8 8 3 7 9 -75 100 97 Central African Republic 0 1 19 1 4 2 6 0 3 6 28 7 9 1 9 124 52 Chad 8 7 1 9 10 8 -0 4 9 4 2 8 12 6 4 0 116 80 Chile 9 1 10 0 -3 0 10 0 8.1 8 3 2 8 9 6 84 74 China t 13 7 10 6 15 6 9 2 12 8 14.5 13.5 12 6 101 102 Hong Kong, China 10 8 8 4 9 3 9 0 16 8 8 3 15.0 8 8 101 101 Colombia 7 9 4.5 -2 1 8 5 7.7 7 3 0 0 9 7 95 116 Congo, Dem Rep 10 6 -4 2 29 6 -10.4 3 6 -4.5 19 1 -6 7 108 81 Congo,Rep 7 4 8 5 -2 2 9 8 2 1 7 9 -0 7 5 7 83 144 Costa Rica 3 7 13 9 5 2 14 8 4 6 16 9 4 4 13 9 72 96 MOe divoire 2 6 5 4 -2 1 4 4 1 7 6 0 -1 5 4 1 82 96 Croatia a 1 0 . 7 6 Cuba -11 -1 0 -0 5 3 1 -0 9 -1 5 1 5 2 3 96 92 Czech Republic a 9 9 10 3 Denmarka 4 1 5 2 3 1 6 0 8 4 3 7 6 3 4 2 100 98 Dominican Republic -0 9 3 9 0.8 13 9 -2 1 4 9 3 3 14 4 97 102 Ecuador 7 1 6 3 -1 8 5 8 -_04 6 8 -1 3 7 8 141 124 Egypt, Arab -Rep 2 1 2 7 8 1 1 7 -3 4 3 7 12_6 4 7 86 86 El Salvador -4 6 2 9 4 6 7 6 -4 6 10.1 2 4 11 1 69 83 Eritrea Estonia a 20 3 23 2 Ethiopia -0 5 8 0 3.6 2 6 -1 1 11 5 4.3 .79 90 81 Finland a 2 3 9 3 4 4 4 3 74_ 7 6 6 9 4 8 100 ill France a 3 6 6 3 3 7 5 4 7 5 4 2 6 5 3 4 103 105 Gabon 2 5 7 4 -3 5 3 7 -3 9 3 3 1 1 3 3 126 80 Gambia,The -4 1 -77 -6 0 1.1 0.0 -7 4 2 5 1 4 100 100 Georgia Germanya.c 4 5 5 9 4 9 4 3 9 2 3 9 7 1 3 4 98 107 Ghana -17 2 8 3 -20 1 10 5 -2 7 8 8 -0 4 9.6 94 94 Greece a 5 0 8 9 6 4 8 9 5 8 3 0 6 6 4 5 93 94 Guatemala -11 8 5 0 1 10 0 -2 2 10 0 0 6 11 0 98 85 Guinea 6 9 . -0 5 4 0 2.8 9 7 -0 2 135 87 Guinea-Bissau -2 0 17 7 -0 3 -3 4 4 2 13 6 5.2 _-2 6 143 90 Haiti ___-0 4 4 3 -4 6 13 3 -1 2 3 9 -2 9 14 4 116 88 t Dataortaiwn, China 16 6 3 0 17 6 4 7 14 9 7.2 12 4 8 5 102 105 18 2003 World Development Indicators Growth of merchandise trade4. Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 =100 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 1990 2000 Honduras 4 0 2 7 1 6 12 8 1 6 7 3 0 6 13 9 81 104 Hungary a 3 4 10 1 1 3 11 6 1 4 12 7 0 1 13 5 94 105 India -3 0 2 6 -2 8 4 7 - 7 3 9 5 4 2 10 1 79 93 Indonesia 8 1 82 1 8 4 0 -0 9 8 1 2 26 2 7 102 106 Iran, Islamic Rep 17 1 -0 9 -2 4 -775 -72 1 2 0 2 -6 5 170 225 Iraq 2 3 29.5 --4 5 9 8 -4.0 29 4 -2 2 10 3 132 162 Irelanda 9 3 15 2 4 8 -114 -12 8 13 5 7 0 11 0 94 99 Israel a Italy a 4 3 5 6 5 3 4 6 8 7 4 6 6 9 3 2 102 100 Jamaica 1 6 4 6 3 0 7 3 1 1 2 2 2 8 6 8 105 87 Japan a 5 1 2 3 6 6 5 2 8 9 4 1 5 1 4 6 137 104 Jordan 7 7 -53 1 1 37 6 2 66 -1 9 50 85 86 Kazakhstan a12 8 2 9 Kenya 1 7 4 1 25_ 7 6 --11 6 3 1 7 6 0 68 94 Korea, Dem Rep Korea,Rep 11 5 15 6 10 9 95 15 0 10-1 11 9 7 1 98 72 Kuwait -2.2 13 6 -6 3 65 -77 ~ 16 1 -4 1 55 95 158 Kyrgyz Republic a .. 6.5 7-8 Lao PDR a .1~10 154 6 6 12 7 Latvia a 7 2 11 6 -19 8 Lebanon -5 6 2 4 -7 5 8 6 -5 6 4 1 -5 5 8 9 105 112 Lesotho 6 2 14 2 3 5 1 0 3 7 12 5 3 5 0 6 97 77 Liberia _-3 5 74 -7 6 9 7 -3 1 46 -7 2 88 112 89 Libya --0 1 -4 1 -6 6 0 3 -7 3 -2 2 -4 4 1 8 145 200 Lithuania a 9 3 14 4 Macedonia, FYR8a 2 8 6 4 Madagascar -3 0 -5 9 -3 7 -0 9 --0.9 - -2.9 -1 7 0 2 - 92 100 Malawi 2 4 2 8 -0 1 -1 7 2 0 1 0 3 3 0 1 141 94 Malaysia - 14 6 15 2 6 0 10 4 86_ 12 2 7 7 9 5 102 90 Mali 4.4 11 3 4 6 3 2 6 0 6 9 4 3 1 7 122 88 Mauritania 3 9 6 3 -2 9 1 9 8 0 2 5 -1 8 -1 6 96 104 Mauritius --10 4 4 1 11 2 3 6 14 4 3 5 12 9 3 8 108 96 Mexico 15 3 - 15 5 0 9 - 132 59 16 1 6 4 14 2 109 107 Moldova a 9 1 11 8 Mongolia 3 1 5 0 -1 7 5 0 0 8 Morocco 5 7 7 1 3 2 7 2 -62 7 4 3 6 5.5 95 112 Mozambique -9 5 14.5 -2 7 1 7 -9.6 9 6 0 1 1 2 115 91 Myanmar -3 0 14 8 -6 5 13 7 -7 6 144 4 7 22 6 116 52 Namibia a 0 9 4 0 Nepal a 8.1 10 7 6 9 9 3 Netherlands a 4 5 7 1 4 5 6 9 4 6 5 5 4 4 5 4 102 102 New Zealand a -3 5 ~ 44_ 4 3 5 9 6.2 -38 5 4 5 6 97 101 Nicaragua -4 8 10 3 -3 5 9 2 -5 8 10 2 -3 1 11 5 119 77 Niger -5 2 3 8 -5 2 -2 7 -5 4 0 0 -3 5 0 0 136 72 Nigeria -44 3 3 -21 4 2 1 -8 4 3 2 -15 6 2 8 -162 180 Norwaya 4 1 71 3 4 7 4 - 5 3 4 9 6 2 3 8 89 66 Oman 7 1 4 3 -1 7 4 4 3.3 5 3 0 7 6 1 167 212 Pakistan -0 3 -6 3 -5 3 -5 7 8 1 4 3 - 3 0 3 3 97 95 Panama -0 6 5 9 _-67 7 8 -0 5 9 4 -3 6 8 7 69 100 Papua New Guinea 1 3 -8 1 4 9 3 1 1 3 -1 0 Paraguay 12 8 -0 2 10 4 4 0 11 6 1 7 4 2 5 5 87 84 Peru 2 7 9 3 -2 0 10 5 -1 5 8 9 1.3 10 8 93 81 Philippines -7 5 17 1 -7 8 12 4 3 9 18 8 2 9 12 0 90 III Poland a 4 8 9 3 -15 18 5 1 4 9 9 -3 2 18 3 117 108 Portugal a 11 9 15 1 5 2 10 3 5 1 Puerto Rico 2003 World Development Indicators 199 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 = 100 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 1990 2000 Romania a .. -4 0 8.5 -3 8 6 8 Russian Federation a 9 7 4.1 Rwanda 3 3 -7 0 2.4 18 -0.3 -3.5 3 3 -1 2 38 96 Saudl Arabla -6 3 1.9 -8.4 -0 8 -13 4 3 1 -6 1 0 8 168 194 Senegal 1 2 6 3 0.4 5.2 3 5 4.0 14 3 6 109 90 Sierra Leone -1 0 -31 1 -6 3 -4.5 -2 4 -29.5 -8.7 -4 2 71 72 Singapore 13 5 13 3 9.9 9 3 9.9 9.9 8.0 7.8 111 93 Slovak Republica .. 10.2 . 11 1 Slovenia a . . 8.2 .. 9 3 Somalia -1 5 -0 5 -111 2 3 -1 1 -2.4 -9.2 1.7 99 82 South Africa a,d 3 3 7.4 -0.8 7.9 0 7 2.5 -1.3 5 8 102 100 Spain a 3 0 . . 10.8 8 6 10 6 6 1 104 104 Sri Lanka -4.4 0 6 -6.8 1.9 5.4 11.3 2.2 9 6 80 107 Sudan -3.0 17.1 -7.7 11 2 -2.5 14 0 -6.4 9.9 123 141 Swaziland 8.7 3 8 4.1 2 3 4 7 5.8 -0.5 4 3 100 100 Swedena 4 4 0.7 5 0 12 8 0 6.0 6.7 4 5 101 109 Switzerland a 3 7 .. .. 95 2.6 8.8 1 9 Syrian Arab Republic 6 6 -1.2 -11 7 3.9 2 4 1 3 -8.5 4 2 131 170 Tajikistan Tanzania -1.6 5 0 -0 8 -5 3 -4 2 6.3 -0 5 0 1 110 74 Thailand 11 2 4 0 8.8 -2 5 14.0 10.5 12 7 5.0 102 86 Togo -1 2 8.7 0 6 5.8 1 1 6.6 2 0 5.5 133 104 Trinidad and Tobago -10 9 3 6 -20 4 10.2 -9 4 7.3 -12.3 11.3 117 172 Tunisia 4 9 5 3 1 7 4.2 3 5 6.0 2 7 5 2 103 100 Turkey 10 6 11 0 14.0 8 9 9.3 10.2 104 95 Turkmenistan Uganda -5 4 171 -6.1 25 4 -4 0 15 4 4 5 213 74 71 Ukraine a 7.4 . 9.1 United Arab Emirates 8 9 2 1 -1 3 9 2 -0 8 4.0 0.7 11.2 174 213 United Kingdoma 4.5 6 4 6.7 6 6 5.9 5.4 8.5 5.4 99 97 United Statesa 3 6 6 7 7.2 9 1 5.7 7.3 8 2 9 5 102 103 Uruguay 4 4 61 12 10 5 4.5 5 2 -1 2 10 1 100 86 Uzbekistan .. Venezuela, RB 3 4 5 3 -4 0 4 7 -4 4 5 5 -3 2 5 3 142 157 Vietnam West Bank and Gaza Yemen, Rep a .. . 22 7 . -1 0 Yugoslavia, Fed Rep Zambia -0 5 5.9 2.1 1.1 0 9 -2.0 0.0 -1.6 109 57 Zimbabwe 4 0 8 2 3.4 4 9 2 9 2 7 -0.5 0 0 100 94 a Data are from the International Monetary Fund's International Financial Statistics database b Includes Luxembourg c Data pnor to 1990 refer to the Federal Republic of Germany before unification d Data prior to 1998 refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa. and Swazilandl. those after January 1998 refer to South Africa only 2OO 0 2003 World Development Indicators Growth of merchandise trade 1 Data on international trade in goods are available database The United Nations Conference on Trade * Growth rates of export and import volumes are from each country's balance of payments and cus- and Development (UNCTAD) compiles a variety of average annual growth rates calculated for low- and toms records While the balance of payments focus- international trade statistics, including price and vol- middle-income economies from UNCTAD's quantum es on the financial transactions that accompany ume indexes, based on the COMTRADE data The index series and for high-income economies from trade, customs data record the direction of trade and IMF and the World Trade Organization also compile export and import data deflated by the IMF's trade the physical quantities and value of goods entering data on trade prices and volumes The growth rates price deflators * Growth rates of export and import or leaving the customs area Customs data may dif- and terms of trade for low- and middle-income values are average annual growth rates calculated fer from those recorded in the balance of payments economies shown in this table were calculated from from UNCTAD's value indexes or from current values because of differences in valuation and the time of index numbers compiled by UNCTAD Volume meas- of merchandise exports and imports * Net barter recording The 1993 System of National Accounts ures for high-income economies were derived by terms of trade are calculated as the ratio of the and the fifth edition of the International Monetary deflating the value of trade using deflators from the export price index to the corresponding import price Fund's (IMF) Balance of Payments Manual (1993) IMF's Intemational Financial Statistics In some index measured relative to the base year 1995 attempted to reconcile the definitions and reporting cases price and volume indexes from different standards for international trade statistics, but dif- sources may vary significantly as a result of differ- ferences in sources, timing, and national practices ences in estimation procedures All indexes are limit comparability Real growth rates derived from rescaled to a 1995 base year Terms of trade were trade volume indexes and terms of trade based on computed from the same Indicators unit price indexes may therefore differ from those The terms of trade measure the relative prices of a derived from national accounts aggregates country's exports and imports There are a number Trade in goods, or merchandise trade, includes all of ways to calculate terms of trade The most com- goods that add to or subtract from an economy's mon is the net barter (or commodity) terms of trade, material resources Thus the total supply of goods in constructed as the ratio of the export price index to an economy is made up of gross output plus imports the import price index When a country's net barter less exports (currency in circulation, titles of owner- terms of trade increase, its exports are becoming ship, and securities are excluded, but nonmonetary more valuable or its imports cheaper gold is included) Trade data are collected on the basis of a country's customs area, which in most cases is the same as its geographic area Goods pro- vided as part of foreign aid are included, but goods destined for extraterritorial agencies (such as embassies) are not Collecting and tabulating trade statistics is difficult Some developing countries lack the capacity to report timely data, this is a problem especially for countries that are landlocked and those whose territorial bound- aries are porous As a result, it is necessary to esti- mate their trade 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 compa- nies In some cases economic or political concerns may lead national authorities to suppress or misrepre- sent data on certain trade flows, such as oil, military l = _ equipment, or the exports of a dominant producer In The main source of trade data for developing other cases reported trade data may be distorted by countries is UNCTAD's annual Handbook of deliberate under- or overinvoicing to effect capital trans- Intemational Trade and Development Statistics fers or avoid taxes And in some regions smuggling and The IMF's International Financial Statistics black market trading result in unreported trade flows includes data on the export and import values By international agreement customs data are and deflators for high-income and selected devel- reported to the United Nations Statistics Division, oping economies which maintains the Commodity Trade (COMTRADE) 2003 World Development indicators 1 201 L~L 5~JStructure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exporft raw metals materiast $ milifons % of total % of total % of total % of total % of total 1990 2001 1990 2001 1990 2001 ±990 2001 1990 2001 1990 2001 Afghanistan 235 81 Albania 230 305 6 6 I 3 84 Algeria 12,930 20,050 0 0 0 0 96 97 0 0 3 2 Angola 3,910 6,695 0 0 93 6 . 0 Argentina 12,353 26,655 56 44 4 2 8 17 2 3 29 33 Armenia 340 14 5 11 . 22 43 Australia 39,752 63,387 22 21 10 6- 21 22 20 17 24 28 Austria 41,265 70,327 3 5 4 2 1 2_ 3 3 88 82 Azerbaijan 2,315 . . 2 1 91 1 4 Bangladesh 1,671 6,530 14 7 1 77 Belarus 7,525 8 3 . 18 1 69 Belgiuma 117,703 189,624 9 9 2 1 3 4 4 3 77 79 Benin 288 380 15 23 56 71 15 0 0 0 13 6 Bolivia 926 1, 285 19 31 8 3 25 24 44 20 5 22 Bosnia and Herzegovina 276 1,100 Botswana 1,784 2,310 Brazil 31,414 58,223 28 28 3 4 2 4_ 14 8 52 54 Bulgaria 5,030 5,105 10 3 12 13 57 Burkina Faso 152 174 Burundi 75 40, 91 8 10 Cambodia 86_ 1,552 Cameroon 2,002 1,749 20 17 14 21 50 52 7 5 9 5 Canada 127,629 259,858 9 7 9 6 10 14 9 4 59 62 Central African Republic 120 131 Chad 188 165 Chile -8,372 17,440 24 26 9 10 1 1 55 41 11 18 China t 62,091 266,155 13 _ 5 3 1 8 3 2 2 72 89 Hong Kong, China b 82,390 191,066 3 2 0 0 0 1 1 2 95 95 Colombia 6,766 12.257 33 18 4 5 37 36 0 1 25 39 Congo, Dem Rep 2,326 - 750 Congo, Rep 981 -2,080 , .-- Costa Rica -1,448 5,010 58 32 5 3 1 1 1 1 27 62 C6te dIlvoire 3,072 3,715 50 14 21 0 14 Croatia 4.597 4,659 13 10 6 4 9 10 5 3 68 73 Cuba 5,100 1,708- Czech Republic -12,170 33,405 4 2 3 .. 2 89 Denmark 36,870 51, 873 27 20 3 3 3 -6 1 1 60 65 Dominican Republic -2,170 5,333 21 0 . 0 0 78 Ecuador 2,714 4,49 5 44 42 1 6 52 40 0 0 2 12 Egypt, Arab Rep -3,477 4.128 10 10 10 -5 29 40 9 5 42 33 El Salvador 582 2.,86-5 57 35 1 1 2 6 3 3 38 55 Eritrea 15 30 Estonia 3,310 10 8 4 3 75 Ethiopia 298 420 71 19 1 10 Finland 26,571 42,929 2 2 10 6 1 3 4 3 83 86 France 216,588 321,843 16 11 2 1 2 3 -3 2 77 82 Gabon 2,204 2,626 1 12 83 2 2 Gambia, The 31 9 81 I 0 0 17 Georgia 345 Germany 421,100 570,791 5 - -4 1 1 1 1 3 2 89 86 Ghana 897 1,700 51 49 15 8 9 12 17 16 8 16 Greece 8,105 8,670 30 24 3 3 7 11 7 8 54 52 Gua-temala 1,163 2,466 67 51 6 4 2 5 0 1 24 38 Guinea 671 825 2 .. 0 1 68 28 Guinea-Bissau 19 55 Haiti ___ 10 278 14 1 0 _ __ 0 85 t Data for Taiwan, China 67,079 122,505 4 1 2 1 1 1 1 1 93 94 202J 2003 World Development Indicators Structure of merchandise exports4. 1 Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total % Of total 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 ±990 200± Honduras 831 1,318 82 64 4 4 1 0 4 4 9 27 Hungary 10,000 30,500 23 8 3 1 3 2 6 2 63 85 India 17,969 43,611 16 13 4 1 3 4 5 3 71 77 Indonesia 25,675 56,321 11 9 5 4 44 26 4 5 35 56 Iran. Islamic Rep 19,305 25,270 4 1 84 1 10 Iraq 12,380 15,905 Ireland 23,743 82, 826 22 7 2 0 1 0 1 0 70 88 Israel 12,080 29,019 8 3 3 1 1 1 2 1 87 94 Italy 170,304 -241,134 6 6 1 1 2 2 1 1 88 88 Jamaica 1,158 1,225 19 23 0 0- 1 0 10 4 69 73 Japan 287,581 403.49 6 1 1 1- 1 0 -0 1 1 96 93 Jordan 1,064 2,293 11 15 0 0 0 0 38 19 51 66 Kazakhstan 8,645 7 1 - 54 18 20 Kenya 1.-031 - 1,945 49 59 6 9 13 8 3 3 29 21 Korea, Dem Rep 1,857 661 Korea, Rep 65,016 150,439 -3 2 1 1 1 5 1 1 94 91 Kuwait 7,042 16,142 1 0 0 0 93 79 0 0 6 20 Kyrgyz Republic 475 16 6 12 6 20 Lao PDR -79 336 _ Latvia 2,000 9 _25 1 5 59 Lebanon 494 871 19 6 0 6 69 Lesotho -62 282 Liberia 330 615 Libya -13,225 11,650 0 0 -94 0 5 Lithuania 4,585 12 4 23 2 58 Macedonia, FYR 1,199 1,170 16 1 4 8 70 Madagascar 319 940 73 36 4 -6 1 2 8 4 14 50 Malawi 417 310 93 2 0 0 5 Malaysia 29,452 87,921 12 6 14 2 18 10 2 1 54 80 Mali 359 740 36 62 0 2 Mauritania- 469 280 Mauritius 1,194 1,521 32 24 1 1 1 0 0 0 66 74 Mexico 40,711 158,547 12 5 2 1 38 8 6 1 43 85 Moldova 570 63 2 0 1 34 Mongolia 661 250 4 -28 1 41 26 Morocco -4,265 7,116 26 21 3 2 4 4 15 9 52 64 Mozambique 126 703 -23 4 10 55 8 Myanmar 325_ 2,269 51 36 0 2 10 Namibia 1,085 1,500 Nepal 204 737 13 10 3 0 0 0 0 83 67 Netherlands 131,775 229,464 20 16 4_ 3 _10 8 3 2 59 70 New Zealand 9,394 13,726 47 47 18 13 4 2 6 4 23 29 Nicaragua 330 606 77 82 14 3 0 2 1 0 8 13 Niger 282 275 38 10 56 3 Nigeria 13,596 19.150 1 0 1 0 97 100 0 0 1 0 Norway 34 ,047 57,856 7 6 2 1 48 62 10 6 33 21 Oman 5,508 11,074 1 6 0 0 92 81 1 1 5 12 Pakistan 5,615 -9,242 9 11 10 2 1 2 0 0 79 85 Panama 340 911 75 77 1 1 0 7 1 2 21 13 Papua New Guinea 1,177 1,805 ~22 15 9 2 0- 29 58 51 10 2 Paraguay 959 989 52 69 38 14 0 0 0 0 10 16 Peru 3,230 7,092 21 31 3 2 -10- 7 47 37 18 22 Philippines 8,117 32,128 19 6 2 1 2 1 8 2 38 91 Poland 14,320 36,090 13 8 3 1 11 5 9 4 59 79 Portugal 16,417 23,923 7 7 6 3 3 2 3 2 80 85 Puerto Rico 2003 World Development Indicators I203 HI L!DStructure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions 96 of total % ot total 96 of total 96 of total % of total 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 4,960 11,385 1 4 3 4 18 6 4 5 73 81 Russian Federation 103,100 13 54 8 22 Rwanda 110 85 Saudi Arabia 44,417 68,200 1 1 0 0 92 90 0 0 7 9 Senegal 761 1,080 53 46 3 2 12 18 9 4 23 29 Sierra Leone 138 28 Singapore b 52,730 121,751 5 2 3 0 18 8 2 1 72 85 Slovak Republic 6,355 12,630 4 2 .. 7 3 84 Slovenia 6,681 9,251 7 4 2 1 3 1 3 4 86 90 Somalia 150 110 South Africa c 23,549 29.284 8 8 4 2 7 8 11 9 22 59 Spain 55,642 109,681 15 15 2 1 5 3 2 2 -75 78 Sri Lanka 1,912 4,817 34 21 6 2 1 0 2 0 54 77 Sudan 374 1,630 61 38 .. 0 1 Swaziland 556 810 Sweden 57,540 75,259 2 3 7 1 -3 3 3 3 83 84 Switzerland 63,784 82,064 3 3 1 1 0 0 3 4 94 92 Syrian Arab Republic 4,212 4,490 14 9 4 5 45 76 1 1 36 8 Tajikistan 650 Tanzania 331 780 70 13 0 1 15 Thailand 23,068 65,113 29 15 5 3 1 3 1 1- 63 74 Togo 268 432 23 18 21 11 0 0 45 21 9 50 Trinidad and Tobago 2,080 4,690 5 5 0 0 67 49 1 0 27 46 Tunisia 3,526 6,606 11 9 1 1 17 12 2 2 69 77 Turkey 12,959 31,197 22 13 3 1 2 1 4 2 68 82 Turkmenistan 2,620 0 10 .. 81 0 7 Uganda 152 457 69 15 6 3 7 Ukraine 16,265 United Arab Emirates 23,544 42,900 8 1 .. 5 39 46 United Kingdom 185,172 273,086 7 5 1 1 8 9 3 2 79 80 United States 393,592 730,803 11 8 4 2 3 2 3 2 74 82 Uruguay 1.693 2,060 40 45 21 11 0 2 0 1 39 42 Uzbekistan 3,450 Venezuela, RB 17,497 27,409 2 2 0 0 80 83 7 4 10 11 Vietnam 2,404 15,093 West Bank and Gaza Yemen, Rep 692 3,205 75 10 8 7 1 Yugoslavia, Fed Rep 2,929 1,903 19 17 3 6 6 0 10 16 62 59 Zambia 1,309 870 10 .. 3 1 73 13 Zimbabwe 1,726 1,770 44 47 7 13 1 1 16 11 31 28 Low Income 101,915 220,569 15 15 5 4 27 24 5 6 47 52 Middle Income 540,593 1,319,759 17 10 4 2 22 21 6 4 47 61 Lower-middle income 276,215 702,607 18 8 4 2 11 18 4 4 57 63 Upper middle income 263,168 617,152 16 12 5 2 34 24 7 4 37 58 Low & middle Income 642,652 1,540,328 17 10 5 2 22 21 6 4 47 60 East Asia & Pacific 155,919 530,693 15 8 6 2 14 7 3 2 59 80 Europe & Central Asia d 325,644 5 3 26 5 56 Latin America & Canb 143,391 344,370 26 22 4 3 24 17 12 8 34 49 Middle East & N Afnca 126,653 182,788 4 3 1 0 78 81 2 1 17 14 South Asia 27,728 65,208 16 12 5 2 2 4 4 2 -71 -78 Sub-Saharan Afnca 67,877 91,624 13 16 3 6 28 31 7 8 20 33 High income 2,799.783 4,615,416 8 6 3 2 5 4 3 2 79 82 Europe EMU 1,229,887 1,892,062 10 8 2 1 3 3 2 2 81 83 Note, Components may not sum to 100 percent because of unclassified trade a Includes Lusembourg b Includes re-exports c Oata on total merchandise exports for 1990 refer to the South Afncan Customs Union (Botswana, Lesotfho, Namibia, South Afnca, and Swaziland), those for 2001 refer to South Africa only d Oata for 2001 include the intratrade of the Baltic states and the Commonwealth of Independent States 20~1. LI 2003 World Development Indicators Structure of merchandise exports 0 Data on merchandise trade come from customs Under the special system exports comprise cate- reported here have not been fully reconciled with the reports of goods entering an economy or from gories a and c In some compilations categories b estimates of exports of goods and services from the reports of the financial transactions related to mer- and c are classified as re-exports Because of differ- national accounts (shown in table 4 9) or those from chandise trade recorded in the balance of payments ences in reporting practices, data on exports may the balance of payments (table 4 15) Because of differences in timing and definitions, not be fully comparable across economies The classification of commodity groups is based on estimates of trade flows from customs reports are The data on total exports of goods (merchandise) in the Standard International Trade Classification (SITC) likely to differ from those based on the balance of this table come from the World Trade Organization revision 1 Most countries now report using later payments Moreover, several international agencies (WTO) The WTO uses two main sources, national sta- revisions of the SITC or the Harmonized System process trade data, each making estimates to cor- tistical offices and the IMF's Intemational Financial Concordance tables are used to convert data report- rect for unreported or misreported data, and this Statistics It supplements these with the COMTRADE ed in one system of nomenclature to another The leads to other differences in the available data database and publications or databases of regional conversion process may introduce some errors of The most detailed source of data on international organizations, specialized agencies, and economic classification, but conversions from later to early sys- trade in goods is the Commodity Trade (COMTRADE) groups (such as the Commonwealth of Independent tems are generally reliable Shares may not sum to database maintained by the United Nations Statistics States, the Economic Commission for Latin America 100 percent because of unclassified trade Division In addition, the International Monetary Fund and the Canbbean, Eurostat, the Food and Agriculture (IMF) collects customs-based data on exports and Organization, the Organisation for Economic Co- l==_ imports of goods The value of exports is recorded as operation and Development, and the Organization of the cost of the goods delivered to the frontier of the Petroleum Exporting Countnes) It also consults pnvate * Merchandise exports show the f o b value of exporting country for shipment-the f o b (free on sources, such as country reports of the Economist goods provided to the rest of the world valued in U S board) value Many countries report trade data in U S Intelligence Unit and press clippings In recent years dollars * Food comprises the commodities in SITC dollars When countries report in local currency, the country Web sites and direct contacts through email sections 0 (food and live animals), 1 (beverages and United Nations Statistics Division applies the average have helped to improve the collection of up-to-date sta- tobacco), and 4 (animal and vegetable oils and fats) official exchange rate for the period shown tistics for many countries, reducing the proportion of and SITC division 22 (oil seeds, oil nuts, and oil ker- Countries may report trade according to the gener- estimated figures The WTO database now covers most nels) * Agricultural raw materials comprise SITC al or special system of trade (see Primary data doc- of the major traders in Africa, Asia, and Latin America, section 2 (crude materials except fuels) excluding umentation) Under the general system exports which together with the high-income countries account divisions 22, 27 (crude fertilizers and minerals comprise outward-moving goods that are (a) goods for nearly 90 percent of total world trade There has excluding coal, petroleum, and precious stones), and wholly or partly produced in the country, (b) foreign also been a remarkable improvement in the availability 28 (metalliferous ores and scrap) * Fuels comprise goods, neither transformed nor declared for domes- of recent, reliable, and standardized figures for coun- SITC section 3 (mineral fuels) * Ores and metals tic consumption in the country, that move outward tries in Europe and Central Asia comprise the commodities in SITC divisions 27, 28, from customs storage, and (c) goods previously The shares of exports by major commodity group and 68 (nonferrous metals) * Manufactures com- included as imports for domestic consumption but were estimated by World Bank staff from the prise the commodities in SITC sections 5 (chemi- subsequently exported without transformation COMTRADE database The values of total exports cals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manu- 4.5a factured goods), excluding division 68 Merchandise exports I$ billions) 300 I _ The WTO publishes data on world trade in its 250 _ 1990 _ 2001 Annual Report The IMF publishes estimates of 200 total exports of goods in its International Financial Statistics and Direction of Trade 150 Statistics, as does the United Nations Statistics 100 Division in its Monthly Bulletin of Statistics And _i _l the United Nations Conference on Trade and 50 3 rucuDevel opment (UNCTAD) puboishes data on the chin I 0i i l l H F s tructure of exports and Imports t hts Handbook China Mexico Russian Malaysia Saudi Thailand Brazil Indonesia India Poland of International Trade and Development Federation Arabia Staristics Tariff line records of exports and China led developing countries in merchandise exports in 2001, followed by Mexico imports are compiled in the United Nations Note No data are available for the Russian Federation for 1990 Statistics Division's COMTRADE database Source World Trade Organization data files 2003 World Development Indicators 1 205 H9 nJ Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures Imports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan 936 554 Albania 380 1,315 19 1 10 2 68 Algeria 9,780 9,700 24 28 5 3 1 1 2 1 68 67 Angola 1,578 3,351 Argentina 4,076 20,311 4 6 4 2 8 4 6 2 78 86 Armenia 870 25 .. 1 21 1 52 Australia 41,985 63,886 5 5 2 1 6 9 1 1 84 84 Austria 49,146 74,428 5 6 3 3 6 6 4 3 81 82 Azerbaijan 1,675 16 1 15 2 65 Bangladesh 3,618 8,397 19 5 16 3 56 Belarus 8,045 12 2 27 3 54 Belgium a 119,702 180,660 10 9 2 2 8 9 6 3 68 77 Benin 265 651 38 20 4 5 1 17 1 1 56 56 Bolivia 687 1,724 12 15 2 2 1 7 1 1 85 76 Bosnia and Herzegovina 360 2,790 Botswana 1,946 2,450 Brazil 22,524 58,265 9 6 3 1 27 14 5 3 56 75 Bulgaria 5,100 7,240 8 5 3 1 36 26 4 6 49 59 Burkina Faso 536 656 Burundi 231 139 _ 23 2 12 2 60 Cambodia 164 1,570 Cameroon 1,400 1,852 19 15 0 1 2 18 1 1 78 64 Canada 123,244 227,165 6 6 2 1 6 6 3 2 81 83 Central African Republic 154 130 Chad 285 632 Chile 7,742 17,243 4 7 2 1 16 17 1 1 75 73 China t 53,345 243,613 9 4 6 4 2 7 3 6 80 78 Hong Kong, China 84,725 202,008 8 4 2 1 2 2 2 2 85 91 Colombia 5,590 12,834 7 12 4 2 6 2 3 2 77 81 Congo, Dem Rep 1,739 1,024 Congo, Rep 621 940 Costa Rica 1,990 6,564 8 8 2 1 10 7 2 1 66 83 C6te d'lvoire 2,097 2,560 17 1 34 1 46 Croatia 4,500 8,044 12 9 4 2 10 13 4 2 64 74 Cuba 4,600 4,930 Czech Republic 12,880 36,490 5 2 9 3 81 Denmark 33,333 45,398 12 12 3 3 7 4 2 2 73 76 Dominican Republic 3,006 8,784 Ecuador 1,861 5,299 9 8 3 2 2 4 2 1 84 81 Egypt, Arab Rep 12,412 12,756 32 26 7 5 3 5 2 2 56 55 El Salvador 1,263 5,027 14 17 3 3 15 13 4 1 63 66 Eritrea 278 470 Estonia 4,300 11 3 7 2 78 Ethiopia 1,081 1,040 7 1 20 1 71 Finland 27,001 32,008 5 6 2 3 12 12 4 5 76 73 France 234,436 325,752 10 8 3 2 10 10 4 3 74 78 Gabon 918 940 18 1 4 1 75 Gambia, The 188 200 35 1 12 1 51 Georgia 685 Germany 355,686 492,825 10 7 3 2 8 8 4 3 72 70 Ghana 1,205 3,030 11 18 1 2 17 23 0 1 70 56 Greece 19,777 25,416 15 12 3 1 8 15 3 3 70 68 Guatemala 1,649 5,607 10 14 2 1 17 14 2 1 69 69 Guinea 723 601 23 1 19 0 56 Guinea-Bissau 86 65 Haiti 332 1,013 t Data for Taiwan, China 54,831 107,274 7 4 5 2 11 11 6 5 69 76 203 0 2003 World Development Indicators Structure ofmerchandise imports - Merchandise Food Agricultural Fuels Ores and Manufactures imports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2001 ±990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 935 2,918 10 18 1 1 16 13 1 1 71 67 Hungary 10,340 33,680- 8 3 4 1 1-4 5 4 2 70 84 India 23,580 49,618 3 5 4 3 27 37 8 5 51 48 Indonesia 21,837 30,962 5 10 5 7 9 18 4 3 77 61 Iran, Islamic Rep 20,322 17,500 16 2 4 2 76 Iraq 7,660 11,000 Ireland 20,669 50,691 11 7 2 1 6 4 2 1 76 82 Israel 16,793 35,1-23 8 5 2 1 9 10 3 2 77 81 Italy 181,968 232,910 1? 9 6 4 11 9 5 4 64 70 Jamaica 1,928 3 ,331 15 15 1 2 20 18 1 1 61 61 Japan 235,368 349,089 15 13 7 3 25 20 9 5 44 57 Jordan 2,600 4,844 26 18 2 2 18 14 1 2 51 62 Kazakhstan 6,365 9 1 12 3 75 Kenya 2,223 2,890 9 14 3 2 20 22 2 1 66 60 Korea, Dem Rep 2,930 2,400.- Korea, Rep 69,844 141,098 6 6 8_ 3 16 24 7 6 63 61 Kuwait 3,972 7,734 17 ~ 17 1 1 ~ 1 1 2 2 79 79 Kyrgyz Republic 465 14 1 20 2 64 Lao PDR 185 551 Latvia 3,505 12 2 -11 2 73 Lebanon 2,529 7,291 18 2 18 2 60 Lesotho 672 681 Liberia 220 290 Libya 5,336 - 8,700 23 -2 0 1 74 Lithuania 6,280 9 3 20 1 64 Macedonia, FYR 1,206 1,630 14 2 14 2 44 Madagascar 651 1,164 11 14 1 1 17 24 1 0 69 60 Malawi 575 550 9 1 11 1 78 Malaysia 29,258 74,079 7 5 1 1 5 5 4 3 82 83 Mal 602 657 26 1 19 1 53 Mauritania 388 335 Mauritius 1,618 1,992_ 12 16 3 2 8 11 1 1 76 69 Mexico 43,548 176,162 15 5 4 1 4 3 3 2 75 88 Moldova 895 14 2 26 1 56 Mongolia 924 461 17 1 19 0 63 Morocco 6,922 10,960 10 14 6 3 17 18 6 3 61 63 Mozambique 878 1,063 14 1 16 0 47 Myanmar - 270 2,767 13 1 5 0 81 Namibi-a 1,163 1,440 Nepal - 672 1,473 15 13 7- 4 9 16 2 3 67 49 Netherlands 126,098 207-,284 13 10 2 2- 10 11 3 2 71 74 New Zealand 9,501 13,347 7 9 1- 1- 8 8 3 2 81 80 Nicaragua ~ 638 - 1,776- 19 16 1 ~ 1 - 19 17 1 1 59 64 Niger -388 415 44 1 13 2 40 Nigeria -5.627 11,150 6 20 - 1 1 0 1 2 2 67 76 Norway 27,231~ 32,361 6 7 2 2 4 4 6 6 82 80 Oman 2,681 5,798 19 -22 1 1 4 3_ 1 3 69 68 Pakistan 7,411 10,617 17 12 4 4 21 29 4 3 54 50 Panama 1,539 2,964 12 12 1 1 16 21 1 1 70 66 Papua New Guinea 1,193 1,073 18 18 0 1 7 22 1 1 73 58 Paraguay 1,352 2,145 8 14 0 1 14 16 1 1 77 68 Peru 3,470 8,620 24 13 2 2 12 13 1 1 61 71 Philippines 13,042 31,358 10 9 2 1 15 11 3 3 53 76 Poland 11,570 50,275 8 6 3 2 22 10 4 3 63 77 Portugal 25,263 37,95-5 12 11 4 3 11 10 2 2 71 73 Puerto Rico 2003 World Development Indicators I207 /K fl Structure of merchandise imports Merchandise FoGd Agricultural Fuels Ores and Manufactures imports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2001 1990 2001. 1990 2001 1990 2001 1990 2001 1990 2001 Romania 7,600 15,550 12 8 4 1 38 13 6_ 3 39 75 Russian Federation 33,100 53,860 20 2 2 3 63 Rwanda 288 250 Saudi Arabia 24,069 31,223 15 16 1 1 0 0 3 3 81 79 Senegal 1,219 1,510 29 27 2 2 16 17 2 2 51 53 Sierra Leone 149 166 Singapore 60,774 116,000 6 4 2 0 16 13 2 2 73 81 Slovak Republic 6,670 14,765 6 2 . 15 . 3 74 Slovenia 6,142 10,144 9 6 4 3 11 8 4 5 67 77 Somalia 95 220 South Africa b 18,399 28,405 5 5 2 1 1 16 2 1 77 68 Spain 87,715 142,740 11 10 3 2 12 11 4 3 71 73 Sri Lanka 2,688 5,925 19 14 2 1 13 9 1 1 65 74 Sudan 618 1,575 13 1 20 0 66 Swaziland 663 832 Sweden 54,264 62,562 6 7 2 1 9 8 3 3 79 76 Switzerland 69,681 84,077 6 5 2 1 5 5 3 6 84 82 Syrian Arab Repulblic 2,400 4,300 31 19 2 3 3 4 1 2 62 65 Tajikistan 690 Tanzania 1,027 1,660 16_ 2 . 8 1 72 Thailand 33,045 62,058 5 5 5 3 9 12 4 3 75 76 Togo 581 620 22 23 1 1 8 16 1 2 67 58 Trinidad and Tobago 1,262 3,560 19 9 1 1 11 23 6 1 62 65 Tunisia 5,513 9,552 11 8 4 3 9 11 4 2 72 76 Turkey 22,302 40.573 -8 4 4 4 21 15 5 4 61 67 Turkmenistan 2,105 12 0 1 I 80 Uganda -288 1,594 12 2 16 2 67 Ukraine 15,775 United Arab Emirates 11,199 41,700 14 1 . 3 4_ 77 United Kingdom 222,977 331,793 10 8 3 1 6 4 4 3 75 79 United States 516,987 1,180,154 6 4 2 1 13 11 3 2 73 77 Uruguay 1,343 3,061 7 11 4 3 18 12 2 1 69 72 Uzbekistan 2,630 Venezuela, RB 7,335 18,022 11 11 4 1 3 4 4 2 77 82 Vietnam 2,752 15,550 West Bank and Gaza Yemen, Rep 1,571 2,260 27 1 40 1 31 Yugoslavia. Fed Rep 4,634 4,837 9 9 3 3 23 20 3 4 62 58 Zambia 1,220 960 8 2 9 2_ 80 Zimbabwe 1,847 1,540 4 9 3 2 16 12 2 3 73 75 Low income 105,606 202,458 7 11 3 4 17 24 4 3 64 56 Middle Income 494,625 1,265,184 10 - 8 4 2 10 9 3 3 72 76 Lower middle income 290,853 663,596 10 - 9 4 3 8 9 3 3 71 72 Upper middle income 204,672 601,580 10 - 7 2 1 11 8 4 3 73 -80 Low & middle Income 600.718 1,467,639 10 8 4 2 11 10 4 3 71 75 East Asia & Pacific 160,449 468,386 7 6 4 4 6 9 3 4 77 77 Europe & Central Asia 136,692 _325,334 10 2 9 3 71 Latin America & Carib 121,363 373,978 11 8 3 1 -13 9 3 2 69 -79 Middle East & N Africa 105.974 138,636 19 18 3 2 4 5 3 - 2 70 71 South Asia 39,124 77,158 9 7 4 3 23 35 6 4 54 48 Sub-Saharan Africa 57,164 84,141 . 11 _2 14 2 67 High Income 2,916,941 4,889,755 9 7 3 2 11 10 4 3 71 75 Europe EMU 1,253,828 1,810,883 11 8 3 2 9 9 4 3 72 73 Note Components may not sum to 100 percent because of unclassified trade a Includes Luxembourg b Date on total merchandise imports for 1990 refer to the South African Customs Union (Botswena, Lesotho, Namibia, South Africa, and Swaziland), those for 2001 refer to South Africa only c Dete for 2001 include the intratrade of the Baltic stetes end the Commonwealth of Independent States 208 H 2003 World Development Indicators Structure of merchandise imports Data on imports of goods are derived from the same (WTO) For further discussion of the WVTO's sources * Merchandise Imports show the c i f value of goods sources as data on exports In principle, world and methodology, see About the data for table 4 5 purchased from the rest of the world valued in U S exports and imports should be identical Similarly, The shares of imports by major commodity dollars * Food comprises the commodities in SITC exports from an economy should equal the sum of group were estimated by World Bank staff from the sections 0 (food and live animals), 1 (beverages and imports by the rest of the world from that economy United Nations Statistics Division's Commodity tobacco), and 4 (animal and vegetable oils and fats) But differences in timing and definitions result in dis- Trade (COMTRADE) database The values of total and SITC division 22 (oil seeds. oil nuts, and oil ker- crepancies in reported values at all levels. For fur- imports reported here have not been fully reconciled nels) * Agricultural raw materials comprise SITC ther discussion of indicators of merchandise trade, with the estimates of imports of goods and services section 2 (crude materials except fuels) excluding see About the data for tables 4 4 and 4 5 from the national accounts (shown in table 4 9) or divisions 22, 27 (crude fertilizers and minerals The value of imports is generally recorded as the those from the balance of payments (table 4 15) excluding coal, petroleum, and precious stones), and cost of the goods when purchased by the importer The classification of commodity groups is based on 28 (metalliferous ores and scrap) * Fuels comprise plus the cost of transport and insurance to the frontier the Standard International Trade Classification (SITC) SITC section 3 (mineral fuels) * Ores and metals of the importing country-the c i f (cost, insurance, revision 1 Most countries now report using later comprise the commodities in SITC divisions 27. 28, and freight) value, corresponding to the landed cost at revisions of the SITC or the Harmonized System and 68 (nonferrous metals) * Manufactures com- the point of entry of foreign goods into the country A Concordance tables are used to convert data report- prise the commodities in SITC sections 5 (chemicals), few countries, including Australia, Canada, and the ed in one system of nomenclature to another The 6 (basic manufactures), 7 (machinery and transport United States, collect import data on an f o b (free on conversion process may introduce some errors of equipment), and 8 (miscellaneous manufactured board) basis and adjust them for freight and insurance classification, but conversions from later to early sys- goods), excluding division 68 costs Many countries collect and report trade data in tems are generally reliable Shares may not sum to U S dollars When countries report in local currency, 100 percent because of unclassified trade the United Nations Statistics Division applies the aver- age official exchange rate for the period shown Countries may report trade according to the gener- al or special system of trade (see Primary data doc- umentation) Under the general system imports include goods imported for domestic consumption 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 coun- try en route to another are excluded The data on total imports of goods (merchandise) in this table come from the World Trade Organization 4.6a % of merchandise imports, 2001 The WTO publishes data on world trade in its Low and middle Income High Income Annual Report The International Monetary Fund Ores and Agricultural Ores and Agricultural (IMF) publishes estimates of total imports of metals rawmaterals metals raw materials goods in its International Financial Statistics and Food Uncassified F o o d rUncassified Direction of Trade Statistics, as does the United 8% - rade Food-trade 2% 7% 3% Nations Statistics Division in its Monthly Bulletin Fuels e !k_ Fuel s , of Statistics And the United Nations Conference 10% - -10% ' on Trade and Development (UNCTAD) publishes data on the structure of exports and imports in its Handbook of International Trade and Development Statistics Tariff line records of exports and Imports are compiled in the United Nations Developing and high-income economies have similar import structures Statistics Division's COMTRADE database Source United Nations Conference on Trade and Development 2003 World Development Indicators 1 209 nU tStructure of service exports Commercial Transport Travel Other service exports % of total % of total % of total $ millions services services services 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan Albania 32 495 20 0 3 1 11 1 90 2 68 9 6 7 Algeria 479 41 7 13 4 44 9 Angola 65 267 48 8 6 0 20 6 00 30 7 94 0 Argentina 2,264 4,152 51 1 20 5 39 9 63 0 9 1 16 5 Armenia 179 39 7 .. 36 2 24 2 Australia 9,833 15,837 35 5 26 0 43 2 48 6 21 4 25 4 Austria 22,755 32,535 6 4 15 3 59 0 31 5 34 6 53 2 Azerbaijan 256 63.2 16 6 20 2 Bangladesh 296 242 12 9 29 5 6.4 19 6 80 6 50 8 Belarus 994 63 1 . 83 28 6 Belgium a 26,646 48,970 27 5 20 8 14.0 15 5 58 5 63 7 Benin 109 126 33 4 14 3 5012 60 8 16 4 24 8 Bolivia 133 221 35 8 30.4 43.6 34 2 20.6 35 4 Bosnia and Herzegovina Botswana 183 346 20 4 27.7 64 1 67 7 15 5 4 7 Brazil 3,706 8,719 36.4 16 3 37.3 19 9 26 3 63 8 Bulgaria 837 2,377 27 5 29 3 38 2 50 4 34 2 20 3 Burkina Faso 34 . 37 1 . 34.1 . 28 9 Burundi 7 2 38.7 428 514 36 6 99 20 6 Cambodia 50 253 . 34 4 100 0 58.7 00 6 9 Cameroon 369 42.6 14.4 43.0 Canada 18,350 35,643 23 0 19 1 34.7 30 3 42 3 50 7 Central African Republic 17 . 50 9 16 0 33 1 Chad 23 . 18 4 341 47 5 Chile 1,786 3,730 40 0 44 4 29.8 22 9 30 3 32 6 China 5,748 32,901 47.1 14.1 30.2 54 1 22 7 31 8 Hong Kong, China 41,428 31 4 19.5 . 491 Colombia 1,548 2,089 313 25 4 26.2 57 9 42.5 16 8 Congo, Dem Rep Congo, Rep 65 53.9 12.9 33.1 Costa Rica 583 2,025 16 3 10 5 48 9 671 34 8 22 4 C6te d'lvoire 425 420 62 4 20.4 12.1 11 5 25 5 68 0 Croatia 4,871 12.1 .. 68 5 19 5 Cuba Czech Republic 7,034 215 441 34 4 Denmark 12,731 26,913 32.5 60 0 26.2 17 2 413 22 8 Dominican Republic 1,086 2,912 5 6 2 3 66.8 92 4 27 5 _ 5 3 Ecuador 508 848 47 6 35.1 37 0 50 7 15 4 14 2 Egypt, Arab Rep 4,812 8,815 50 1 31 1 22 9 43.1 27 1 25 8 El Salvador 301 1,052 26 2 26 8 25 2 19 1 48.6 54.1 Eritrea Estonia 200 1,634 74 7 48.3 13 7 30 9 11 6 20 7 Ethiopia 261 391 80 6 60 0 21 13 0 17 3 27 0 Finland 4,562 5,775 38 4 27 1 25 8 249 35 7 48 0 France 74,948 79,848 21 7 22 6 27 0 38.1 51 3 39 2 Gabon 214 249 33 4 60.9 1.4 60 65 2 33 2 Gambia, The 53 8 8 . 87 9 .. 3.3 Georgia 206 . 49 7 . 46.9 3 5 Germany 51,605 83,225 28 6 24.7 27 9 20 7 43 5 54.7 Ghana 79 490 49 2 20 1 5 6 68 3 45 2 116 Greece 6,514 19,384 49 _ 42.1 39 7 47 2 55 4 10 6 Guatemala 313 934 7 4 10.7 37 6 60 1 55 0 29 2 Guinea 91 72 142 43.9 32 6 01 53 3 56 0 Guinea-Bissau 4 5 4 . 0 . 94 6 Haiti 43 19 8 78.9 13 =2qD 0 2003 World Development Indicators Structure of service exports4. Commercial Transport Travel Other service exports % of total % of total % of total $ millions services services services ±990 2001 1990 2001 1990 2001 ±990 2001 Honduras 121 426 35 1 11.9 24 0 60 2 40 9 27 9 Hungary 2,677 7,627 1 6 8 5 36 8 51 4 61 6 40 1 India 4,610 20,390 b 20 8 10 6 33 8 .17 9 45 4 71 4 Indonesia 2,488 5,361 2 8 0 0 86 5 98 4 10 7 1 6 Iran, Islamic Rep 343 1,357 10 5 49 4 8 2 36 9 81 3 13 6 I raq Ireland 3.286 20.032 31 1 7 4 44 4 13 7 24 5 78 8 Israel 4,546 11,949 30 8 17 8 30 7 20 6 38 5 61 7 Italy 48.579 56,970 21 0 14 4 33 9 45 3 45 2 40 3 Jamaica 976 1,871 18 0 1-87 77 0 65 9 5 0 15 4 Japan 41,384 63,670 40 4 37 7 7 9 5 2 51 7 57 1 Jordan 1,430 1,391 26 0 18 5 35 7 50 3 38 3 31 2 Kazakhstan 1,119 55 7 35 9 8 3 Kenya 774 791 32 0 54 1 60 2 39 0 7 8 6 9 Korea, Dem Rep Korea, Rep 9,155 29,602 34 7 45 6 34 5 21 3 30 7 33 1 Kuwait 1,054 1,523 87 5 87 2 12 5 6 9 0 0 6 0 Kyrgyz Republic 72 25 9 33 8 40 3 Lao PDR 11 127 74 8 18.0 24 3 82 0 0 9 0 0 Latvia -290 1,169 94 9 -658 25 10 2 26 23 9 Lebanon Lesotho 34 35 1141 1 2 51 2 66 34 7 33 2 Liberia Libya 83 46 83 8 37 9 7 7 53 9 85 8 2 Lithuania 1,147 46 4 33 4 20 2 Macedonia, FYR 189 39 6 13 3 47 1 Madagascar 129 33_ 321 20 6 313 42 0 366 37 4 Malawi - 37 46 1 42 6 11 3 Malaysia 3,769 14,331 31 8 -192 -44 7 47 9 23 5 32 9 Mali -71 31 0 54 3 14 7 Mauritania 14 35 3 64 7 0 0 Mauritius 478 1,218 32 9 19 3 51 1 51 1 15 9 29 5 Mexico 7,222 12,547 12 4 10 2 76 5 66 9 -11 1 22 8 Moldova 164_ 47 0 28 1 24 9 Mongolia 48 82 41 8 43 1 10 4 47 8 47 8 9 1 Morocco 1,871 - 3,787 9 6 -17.4 68 4 68 2 22 0 14 4 Mozambique -103 587 61 3 25 1 0 0 23 2 38 7 51 7 Myanmar 93 401 10 3 19 8 20 9 31 0 688 49 2 Namibia 106 0 0 81 0 19 0 Nepal 168 303 3 6 15.6 65 6 47 5 30 8 36 9 Netherlands 28,478 51,973 45 4 37 8 14 6 12 9 40 0 49 3 New Zealand 2,415 4,286 43 4 26 7 42 7 54 7 13 9 18 6 Nicaragua 34 296_ 19 2 87_ 35 5 45 7 45 3 45 6 Niger 22 52 -59_5 35 3 Nigeria 965 980 39 12.0 2 5 55 93 6 82 5 Norway 12,452 17,805 68_7 -606_ 12 6 10 8 18 7 28 6 Oman 68 349 15 3 45 5 84 7 41 0 0 0 13 4 Pakistan 1,218 1,302 59 3 62 8 12 0 6 8 28 7 30 4 Panama 907 1,791 64 9 53 7 18 9 27 1 16,2 19 1 Papua New Guinea 198 285 - 11 2 7 5 12 0 1 8 - 76 8 90 7 Paraguay 404 550 18 3 15 8 21 1 14 0 60 5 70 1 Peru 714 1,378 4-34 18 7 30 4 59 3 26 2 22 1 Philippines 2,897 3,115 8 5 21 2 16 1 55 3 75 4 23 5 Poland 3,200 9,747 -57 3 27 5 11 2 47 7 31 5 24 8 Portugal 5,054 8,674 15 6 18 2 70 4 63 0 14 0 18 8 Puerto Rico 2003 World Development Indicators 1 211 (2L Structure of service exports Commercial Transport Travel Other service exports % of total % of total % of total $ millions services services services 1990 2001 1990 2001 1990 2001 1990 2001 Romania 610 1,969 50_5 40 1 17 4 18 4 32 2 41 5 Russian Federation 10,677 43.6 .. 35 1 21 3 Rwanda 31 29 561 55 3 32 8 30 6 110 14 1 Saudi Arabia 3,031 5,182 Senegail 356 351 19 1 101 42 7 49 4 381 40 4 Sierra Leone 45 9_7 .. 76 2 14 1 Singapore 12,719 26,092 17 5 18.1 36.6 19 6 459 62 3 Slovak Republic 2,218 44 9 . 19 5 35 6 Slovenia 1,219 1,956 22 6 25.6 55 0 51 2 22 4 23 3 Somalia South Africa 3,290 4,544 21 6 26 1 55 8 55 0 22 7 18 9 Spain 27,649 57,416 17 2 14 4 67 2 57 0 15 6 28 6 Sri Lanka 425 1,344 39 7 29 7 30 2 15.8 30 1 54 5 Sudan 134 14 14 1 44 4 15 7 23.0 70 2 32 6 Swaziland 102 83 24 5 16 3 29.2 34.2 46 3 49 6 Sweden 13,453 21,758 35 8 24 2 217 19 5 42 6 56 3 Switzerland 18,234 26,100 16 3 17 0 40 6 28 8 43.0 54 2 Syrian Arab Republic 740 1,481 29 7 16 6 43 3 73.1 27 0 103 TaJik!stan Tanzania 131 615 19 9 9 2 36 4 61.3 43 6 29 5 Thailand -6292 12,932 21 1 23 6 68 7 54 7 10.2 21 6 Togo 114 46 26 9 23 0 50 7 1 7.6 22 3 59.4 Trinidad and Tobago 322 50 7 .. 29 4 19 9 Tunisia 1,575 2,829 23 0 22 6 64 8 619 12 2 15 6 Turkey 7,882 15,913 11 7 17.9 40.9 50.8 47 4 31 2 Turkmenistan Uganda 174 . 3 0 90 5 6 5 Ukraine 3,897 76 0 14 7 9 3 United Arab Emirates United Kingdom 53,830 107,529 25 2 16 4 29 0 16 9 45 8 66 7 United States 132,880 259,380 28 1 17 9 37 9 34 7 34.0 47 4 Uruguay 460 1,112 36 9 30 3 518 54 9 113 14 8 Uzbekistan Venezuela, RB 1,121 1,100 40.9 31 9 44 2 62 0 14.9 6 1 Vietnam 2,810 West Bank and Gaza Yemen, Rep 82 174 27 2 123 48 8 41 7 24.0 460 Yugoslavia, Fed Rep Zambia 94 114 68 9 372 13 5 58 3 17 5 4 5 Zimbabwe 253 44 3 . 25.3 .. 30 4 Low Income 14,230 38,980 24 8 18 8 37.8 32.6 37 4 48 6 Middle Income 80,533 209,600 28 4 20.6 44.3 48 0 27 3 31 4 Lower middle income 46,342 118,290 27 5 18.9 42.7 51 6 29 8 29 5 Upper middle income 34,191 91,310 29.8 228 47.1 43 5 23 1 33 7 Low & middle Income 94,763 248,580 27 8 20.4 43.3 46 1 28 8 33.4 East Asia & Pacific 22,049 72,725 26 1 14 8 48 5 54 1 25 4 31 0 Europe & Central Asia 15,237 71,531 25 0 26.5 35 8 40 7 39 3 328 Latin America & Carib 25,940 48,279 27 7 20 2 52.0 49.9 20 3 29 9 Middle East & N Africa 15,235 23,439 33 2 24.8 40 3 531 26 5 221 South Asia 6,816 23,932 27.9 11 0 30 1 190 42 0 70 0 Sub-Saharan Africa 9,487 12,427 32 1 24 1 38 6 54 8 29 3 21 1 High Income 655,599 1,203,824 28 1 23 7 32.8 29.5 39 1 46 8 Europe EMU 300,074 484,536 24 7 225 34 5 33 7 40 8 438 a Includes Luxembourg b Data are an estimate from the World Trade Organization M 0 2003 World Development Indicators Structure of service exports 4.7' Balance of payments statistics, the main source of dimension of service trade not captured by conven- * Commercial service exports are total service information on international trade in services, have tional balance of payments statistics is establish- exports minus exports of government services not many weaknesses Some large economies-such as ment trade-sales in the host country by foreign included elsewhere International transactions in the former Soviet Union-did not report data on affiliates. By contrast, cross-border intrafirm transac- services are defined by the IMF's Balance of trade in services until recently Disaggregation of tions in merchandise may be reported as exports or Payments Manual (1993) as the economic output of important components may be limited, and it varies imports in the balance of payments intangible commodities that may be produced, trans- significantly across countries There are inconsisten- The data on exports of services in this table and ferred, and consumed at the same time Definitions cies in the methods used to report items And the on imports of services in table 4 8, unlike those in may vary among reporting economies * Transport recording of major flows as net items is common (for editions before 2000, include only commercial serv- covers all transport services (sea, air, land, internal example, insurance transactions are often recorded ices and exclude the category government services waterway, space, and pipeline) performed by resi- as premiums less claims) These factors contribute not included elsewhere The data are compiled by dents of one economy for those of another and to a downward bias in the value of the service trade the IMF based on returns from national sources involving the carriage of passengers, movement of reported in the balance of payments Data on total trade in goods and services from the goods (freight), rental of carriers with crew, and relat- Efforts are being made to improve the coverage, IMF's Balance of Payments database are shown in ed support and auxiliary services Excluded are quality, and consistency of these data. Eurostat and table 4 15 freight insurance, which is included in insurance the Organisation for Economic Co-operation and services, goods procured in ports by nonresident Development, for example, are working together to carriers and repairs of transport equipment, which improve the collection of statistics on trade in servic- are included in goods, repairs of harbors, railway es in member countries In addition, the International facilities, and airfield facilities, which are included in Monetary Fund (IMF) has implemented the new clas- construction services, and rental of carriers without sification of trade in services introduced in the fifth crew, which is included in other services * Travel edition of its Balance of Payments Manual (1993) covers goods and services acquired from an econo- Still, difficulties in capturing all the dimensions of my by travelers in that economy for their own use dur- international trade in services mean that the record is ing visits of less than one year for business or likely to remain incomplete Cross-border intrafirm personal purposes Travel services include the service transactions, which are usually not captured goods and services consumed by travelers, such as in the balance of payments, have increased in recent meals, lodging, and transport (within the economy years One example of such transactions is transna- visited), including car rental * Other commercial tional corporations' use of mainframe computers services include such activities as insurance and around the clock for data processing, exploiting time financial services, international telecommunications, zone differences between their home country and the and postal and courier services, computer data, host countries of their affiliates Another important news-related service transactions between residents and nonresidents, construction services, royalties 4.7a and license fees, miscellaneous business, profes- sional, and technical services, and personal, cultur- Commercial service exports I$ billions) al, and recreational services 35 30 U *1990 * 2001 25 20 15 10 5 ~- 0 - China India Turkey Malaysia Thailand Mexico Russian Poland Egypt. Brazil The data on exports of commercial services are Federation Arab Rep, from the IMF. The IMF publishes balance of pay- Major exporters of merchandise also tend to be major exporters of commercial services The exceptions are fuel exporters-Saudi Arabia and lndonesta ~~~~~~~~~~~~ments data in its International Financial Statistics exporters-Saudi Arabia and Indonesia and Balance of Payments Statistics Yearbook Source international Monetary Fund and World Trade Organization data files 2003 World Development Indicators 1 213 X,IStructure of service imports Commercial Transport Travel Other service Imports $ millions % of total % of total % of total 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan Albania 29 422 26 3 25 4 0 0 610 73 7 13 6 Algeria 1,155 58 1 12 9 . 29 0 Angola 1,288 2,271 38 3 13 7 3 0 6 0 58 7 80 3 Argentina 2,876 8,175 32 6 26 0 40.7 48 5 26.7 25 5 Armenia 192 62 8 20 7 16 5 Australia 13,388 16,421 33 9 35 0 31 5 35 4 34 7 29 6 Austria 14,104 31,471 8.4 10 6 54 9 28.2 36 7 61 1 Azerbaijan 650 24 1 16 7 59 2 Bangladesh 554 1,375 71 1 75 0 14.1 12.0 14 9 12 9 Belarus 591 211 44 4 34 4 Belgium a 25,924 42,856 23.3 19 5 21 1 24 7 55 6 55 8 Benin 113 186 46 9 67 4 12 8 6 6 40 3 26 0 Bolivia 291 485 61 7 58 2 20 6 17 1 17.7 24.7 Bosnia and Herzegovina Botswana 371 511 57 5 424 150 280 27 5 29 7 Brazil 6,733 15,816 44.4 27 7 22 4 20 2 33.2 52 1 Bulgaria 600 1,863 40 5 43 2 31.5 30 5 28 0 26 3 Burkina Faso 196 64 7 16.6 . 18 7 Burundi 59 34 62 6 55 7 29.0 39 7 8.4 4.5 Cambodia 64 244 24 5 63 5 14 2 75 5 22 3 Cameroon 1,018 45 3 27.5 .. 27 3 Canada 27,479 41,492 211 221 39 8 28 1 39 2 49 8 Central African Republic 166 49 7 30 6 19 6 Chad 223 . 45.1 . 312 23 7 Chile 1,982 4,673 47 4 37 0 215 12 4 311 50 5 China 4,113 39,032 78 9 29 0 114 35 6 9 7 35 4 Hong Kong, China 24,314 . 25 5 . 51 1 23 4 Colombia 1,683 3,511 34.9 39.9 27 0 33 0 38 1 27 1 Congo, Dem Rep Congo,Rep 748 18 4 . 15 2 . 66 5 Costa Rica 540 1,262 41.2 33.6 28.8 37 3 30 0 29 1 C6te d'lvoire 1,518 1,149 32 1 45 4 11 1 16 7 56 8 37 9 Croatia 1,909 22 0 31 8 46 3 Cuba Czech Republic 5,487 14 7 25 3 60 1 Denmark 10,106 23,506 38.3 52.2 36.5 23 5 25 2 24 3 Dominican Republic 435 1,260 40 0 60 6 33 1 22 8 26 9 16 6 Ecuador 755 1,390 416 39 8 23 2 24 5 35 2 35 8 Egypt, Arab Rep 3,327 6,356 44 0 32 1 3 9 17 8 52 1 50 1 El Salvador 296 1,062 45 9 36 4 20 5 18.4 33 5 45 2 Eritrea Estonia 123 972 763 50 0 154 19 6 83 30 3 Ethiopia 348 517 76 5 63 3 3 3 8 5 20 3 28 3 Finland 7,432 7,994 26 1 32 2 37 2 23 2 36 6 44 6 France 59,560 61,580 29 4 28 2 20.7 29.3 49 9 42 5 Gabon 984 854 23 2 33 7 13 9 10 7 62 9 55 6 Gambia, The 35 65 1 . 23 1 11 8 Georgia 216 412 . 510 78 Germany 79,214 137,156 21 6 18 3 42 8 33 6 35 6 48.0 Ghana 226 527 55 1 53 7 5 9 19 0 39 0 273 Greece 2,756 11,189 34 0 42 8 39 5 37 3 26 5 19 8 Guatemala 363 861 4_410 54 1 27.4 22 7 31 6 23 2 Guinea 243 220 57 5 510 12 2 8 1 303 408 Guinea-Bissau 17 54 5 19 8 . 25 6 Haiti 71 . 47 9 52.1 00 El 2003 World Development Indicators Structure of service imports E Commercial Transport Travel Other service Imports $ millions % of total % of total % of total 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 213 639 45 4 48.6 17 6 20.0 37 0 31 4 Hungary 2,264_ 5,464 8 8 10 3 25 9 23.9 65 3 65 8 India 5,4 23,419 b 57'~ - 41 1 6 6 13 1 35 9 45 8 Indonesia 5.898 15,595 47 4 24 9 14 2 21.8 38 4 53 3 Iran, Islamic Rep 3,703 1,577 - 47 3 72 4 9 2 13 0 43 5 14 6 Iraq Ireland 5,145 34,764 24 3 7 4 22 6 8 3 53 1 84 4 Israel 4,825 12.361 39 6 36 2 29 7 23 8 30 7 40 0 Italy 46,602 55,679 23 7 21 4 22 1 25 54 2 53 0 Jamaica 667 1,485 47 9 40 8 17 0 13 9 35 1 45 3 Japan 84,281 107,027 30 8 30 3 27-9 24 8 41 4 45 0 Jordan 1,118 1,519 52 0 45 9 30 1 276 17 9 285 Kazakhstan 2,785 22 5 24 1 53 4 Kenya_ 598 -- 764 66 2 48 8 64_ 18 7 27 4 32 4 Korea, Dem Rep Korea, Rep 10,050 33,128 39 8 32 4 27 5 22 9 32 7 44 7 Kuwait 2,805 4,503 31 9 34.5 65 5 63 1 2 6 2 3 Kyrgyz Republic 122 41 2 98 49 0 Lao PDR 25 5 73 0 99 0 0 0 1 0 27 0 0 0 Latvia 120 683 82 3 30 4 10 9 32 8 6 8 36 8 Lebanon Lesotho 48 38 67 9 74 5 24.7 23 8 7 3 1 7 Liberia Libya 926 824 41 9 45 7 45 7 45 3 12 4 9 0 Lithuania 669 35 4 .32.7 31 9 Macedonia, FYR 331 .42 6 11 6 45 8 Madagascar 172 51 43 5 49 8 23 4 30 6 33 0 19 6 Malawi 268 81 8 .59 12 3 Malaysia 5.394 16,539 46 9 34 7- 26 9 ~ 158 26 2 49 5 Mali 352 57 4 15 8 .26 8 Mauritania 126 76 9 .18 3 48 Mauritius ~~~~ ~~407 794 51 6 32 3 23 0 24 9 25 4 42 8 Mexico 10,063 16,520 25 0 1759345 20 2 52 7 Moldova 209 29 6 42 2 28 1 Mongolia 155 169 56.2 54 3 0 8 32 8 43 0 12 9 Morocco 940 1,705 58 3 45 9 19 9 22 8 21 9 31 3 Mozambique 206 1,439 57 7 29 2 0 0 19 7 4-23 51 1 Myanmar 72 , 361, 35 4 82 1 22 6 76 42 0 10 3 Namibia 341 46 9 17 9 35 2 Nepal 159 205 40 8 34.9 25 -38 8 30 7 26 3 Netherlands 2895 53,-313 .3 7 26 4 25 4_ 22 5 36 9 51 1 New Zealand 3,251 4,156 40 6 34 6 29 5 32 1 30 0 33 3 Nicaragua 73 336 70 7 49 8 20 1 22 6 9 3 27 6 Niger 209 68 3 10_4 21 4 Nigeria 1,901 ~ 3,311 33 6 19.8 -30-3 18 7 36 1 61 4 Norway 12,247 15.261 - 44 6 - 376_ 30 0 28 0 25 3 34 4 Oman 719 1,678 36 6 37 1 6 5 2~19 56 9 41 1 Pakistan 1.,863- 2,216 67,0 70 1 23 1 11 4 9 9 18 5 Panama 666 1,098 66 6 56 0 14 8 16 0 18 6 28 0 Papua New Guinea 393 662 35 6 26 1 12 8 5 8 51 5 68 1 Paraguay 361 ,382 61 6 561_ 19~8 23 8 18 6 20 1 Peru 1,070, 2,168 43 5 42 2 27.6 27 3 29 0 30 5 Philippines 1,721 5,088 56 9 45 7 64 24 1 36 6 30 2 Poland 2,847 8,842 52 4 17 9 14 9 39 5 32 8 42 6 Portugal 3,772 6,011 48 4 33 0 23 0 35 0 28 6 32 1 Puerto Rico 2003 World Development Indicators I215 A M[ Structure of service imports Commercial Transport Travel Other service Imports $ millions % of total % of total % of total 1990 2001 1990 2001 1990 2001 1990 2001 Romania 787 2,163 65 5 36.4 13 1 20 8 214 42 8 Russian Federation 18,651 16 0 55 5 28 5 Rwanda 96 113 69 0 719 23 7 17 8 7 3 10 3 Saudi Arabia 12,694 7,165 18 1 32 4 0 0 0 0 81 9 67.6 Senegal 368 419 601 60 4 12 4 12 9 27 5 26.8 Sierra Leone 67 29 5 32 7 37 8 Singapore 8,575 20,308 41 0 34 6 21.0 25 5 38 0 39.9 Slovak Republic 1,779 24.4 16 6 59 0 Slovenia 1,034 1,442 42 5 22 1 27 3 36 6 30 3 41 3 Somalia South Africa 3,593 5,085 40 2 44 4 31 5 37 7 28 3 17 9 Spain 15,197 33,237 308 24 6 28 0 17 9 41 2 57 4 Sri Lanka 620 1,729 64 2 48 9 11 9 14 2 23 9 36.9 Sudan 202 638 319 87 6 25 4 116 42 7 0 8 Swaziland 171 177 6 1 15 9 20.6 24 7 73 4 59 5 Sweden 16,959 22,920 23 2 15 7 37 1 30 2 39 7 54 1 Switzerland 11,093 15,159 33 7 32 9 52 9 41 9 13.4 25 2 Syrian Arab Republic 702 1,468 54 5 47.5 35 5 45 6 10 1 6 9 Tajikistan Tanzania 288 670 58 0 33.5 7 9 50.3 34 1 16 2 Thailand 6,160 14,484 58 0 47 2 23 3 20 2 18 7 32 6 Togo 217 116 56 9 72 2 18 4 1 7 24 7 26.1 Trinidad and Tobago 460 51 7 26 6 21 8 Tunisia 682 1,332 514 49 1 26 2 20 5 22 4 30 4 Turkey 2,794 6,464 32 2 313 18 6 26 9 49 2 418 Turkmenistan Uganda 195 492 58 3 32 5 0 0 0 0 41 7 67 5 Ukraine 3,167 12 9 17 9 . 69 2 United Arab Emirates United Kingdom 44,713 91,781 33.2 24 8 41 0 41 3 25 8 33 9 United States 97,950 192,690 36 3 31.8 38 9 32 5 24 8 35 7 Uruguay 363 764 48 2 47 0 30 7 33 0 211 20 0 Uzbekistan Venezuela, RB 2,390 4,442 33 5 419 42 8 40 5 23 7 17 6 Vietnam 3,382 West Bank and Gaza Yemen, Rep 639 757 27 6 44 9 9 9 92 62 5 45 9 Yugoslavia, Fed Rep Zambia 370 328 76 8 67 7 146 133 86 190 Zimbabwe 460 . 518 .. 14.4 33 8 __-mourL,W-i-i-~ l p im: LowIncome 28,314 57,743 504 350 13.9 174 35.6 476 Middle Income 93,074 227,482 41 7 32 5 21 5 30 7 36 8 368 Lower middle income 39,304 123,383 42 7 31 6 13 6 34.2 43 7 34.2 Upper middle income 53,770 104,099 40 5 33 5 31 4 270 28 1 39 6 Low & middle Income 121,387 285,225 43.7 32 9 19 7 28 7 36 6 384 East Asia & Pacific 24,308 95,651 56 0 33 1 18 2 25 9 25 8 41 0 Europe & Central Asia 9,321 61,636 24 8 201 8.6 34 6 66.6 45 3 Latin America & Carib 33,098 66,686 37 3 381 35 7 33 0 27 0 28 9 Middle East & N Africa 27,105 20,522 55 3 382 13.5 19.5 31.2 423 South Asia 9,176 29,052 60 7 44 2 112 13 5 28 2 42 2 Sub-Saharan Africa 18,380 20,160 45 8 45 0 18 0 35.4 36.1 19 6 High Income 653,171 1,142,646 30 2 26 9 34 1 31 3 35 7 41 7 Europe EMU 288,701 488,916 26 8 220 31.4 282 41 8 49.8 a Includes Luxembourg b Data are an estimate from the World Trade Organization 216 1 2003 World Development Indicators Structure of service imports 4.0 Trade in services differs from trade in goods * Commercial service Imports are total service because services are produced and consumed at the imports minus imports of government services not same time Thus services to a traveler may be con- included elsewhere International transactions in sumed in the producing country (for example, use of services are defined by the IMF's Balance of a hotel room) but are classified as imports of the Payments Manual (1993) as the economic output of traveler's country In other cases services may be intangible commodities that may be produced, trans- supplied from a remote location, for example, insur- ferred, and consumed at the same time Definitions ance services may be supplied from one location may vary among reporting economies * Transport and consumed in another For further discussion of covers all transport services (sea, air, land, internal the problems of measuring trade in services, see waterway, space, and pipeline) performed by resi- About the data for table 4 7 dents of one economy for those of another and involv- The data on exports of services in table 4 7 and on ing the carriage of passengers, movement of goods imports of services in this table, unlike those in edi- (freight), rental of carriers with crew, and related sup- tions before 2000, include only commercial services port and auxiliary services Excluded are freight insur- and exclude the category government services not ance, which is included in insurance services, goods included elsewhere The data are compiled by the procured in ports by nonresident carriers and repairs International Monetary Fund (IMF) based on returns of transport equipment, which are included in goods, from national sources repairs of harbors, railway facilities, and airfield facil- ities, which are included in construction services, and rental of carriers without crew, which is included in other services * Travel covers goods and services acquired from an economy by travelers in that econo- my for their own use during visits of less than one year for business or personal purposes Travel serv- ices include the goods and services consumed by travelers, such as meals, lodging, and transport (with- in the economy visited), including car rental * Other commercial services include such activities as insur- ance and financial services, international telecommu- nications, and postal and courier services, computer data, news-related service transactions between res- dents and nonresidents, construction services, roy- alties and license fees, miscellaneous business, professional, and technical services, and personal, cultural, and recreational services 4.8a % of commercial service imports 1990 2000 Uw Trve _ _ __g The data on imports of commercial services are from the IMF The IMF publishes balance of pay- Between 1990 and 2000 travel and other commercial services displaced transport as the most important category ments data in its International Financial Statistics of service imports for developing economies and Balance of Payments Statistics Yearbook Source International Monetary Fund and World Trade Organization data files 2003 World Development Indicators 1 217 ___ HJ U~JStructure of demand Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption final to--- t'ion and services and services expenditure consumption expenditure % of GDP % of GDP % of GDP % of GOP % of GDP % of GDP 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan Albania 61 92 19 11 29 19 15 19 23 42 21 -3 Algena 57 44 16 15 29 26 23 37 25 21 27 41 Angola 36 -54 34 .. a 12 34 39 74 21 62 30 46 Argentina -77 74 3 11 14 14 10 11 5 10 20 15 Armenia 46 91 18 11- 47 19 35 26 46 46 36 -2 Australia 59 60 19 19 22 21 17 23 17 23 22 21 Austria 55 58 19 19 25 23 40 52 38 53 26 23 AzerbaiUan 51 -65 18 10 27 21 44 42 39 38 31 25 Bangladesh- 86 79 4 5 17 23 6 15 14 22 10 16_ Belarus 47 61 24 21 27 22 46 68 44 71 29 19 Belgium 55 54 20 22 22 21 71 84 69 81 24 24 Benin 87 82 11 12 14 19 14 15 26 28 2 6 Bolivia 77 78 12 -15 13 13 23 18 24 24 11 7 Bosnia and Herzegovina 113 a 21 27 60 -13 Botswana 33 35 24 27 37 22 55 51 50 35 43 38 Brazitl 59 60 19 20 20 21 8 13 7 14 21 20 Bulgaria 60 71 18 16 26 20 33 56 37 63 22 13 Burkina Faso 77 77 15_ 14 21 25 13 10 26 26 8 10 Burundi 95 91 11 14 15 7 8 6 28 18 -5 -5 Cambodia 91 84 7 6 8 18 6 53 13 61 2 10 Cameroon 67 68 13 11 18 18 20 32 17 29 21 20 Canada 56 56 23 19 21 20 26 44 26 39 21 25 C-entral African Republic 86 77 15 11 12 14 15 12 28 15 -1 11 Chad 88 -89 10 8 16 42 13 14 28 53 2 3 Chile 62 65 10 12 25 21 35 35 31 33 28 23 China 50 46 12 14 35 38 18 26 14 23 38 40 Hong Kong, China 57 59 7 10 27 26 134 144 126 139 36 31 Colombia 66 64 9 21 19 15 21 19 15 19 24 15 Congo,.Dem Rep 79 92 12 1 9 5 30 18 29 17 9 6 Congo,-Rep. -62 28 14 11 16 27 54 84 -46 50 24 61 Costa Rica 61 70 18 14 27 18 35 43 41 45 21 16 C6te dIlvoire 72 74 17 9 7 10 32 39 27 32 11 17 Croatia 74 58 24 24 11 24 78 47 86 53 2 18 Cuba 70 -23 10 16 187 Czech Republic 49 53 23 20 25 30 45 71 43 74 28 27 Denmark 49 47 26 26 20 21 36 46 31 39 25 28 Dominican Republic 80 _76 5 9 25 23 34 24 44 32 15 15 Ecuador 69 68 9 10 17 25 33 31 27 34 23 22 Egypt, Arab Rep 73 78 11 12 29 15 20 18 33 23 16 10 El Salva-dor 89 88 10 10 14 16 19 29 31 43 1 2 Eritrea 79 40 35 21 76 -19 Estonia 62 56 16 20 30 28 60 91 54 94 22 24 Ethiopia 74_ 80 19 18 12 18 8 15 12 31 7 2 Finland 51 50 22 21 29 20 23 40 24 32 27 29 France 55 55 22 23 23 20 21 28 22 26 22 22 Gabon 50 50 13 a 22 31 46 60 -31 41 37 5Q Gambia, The 76 84 14 15 22 18 60 54 72 71 11 1 Ge orgia 65 89 10 -9 31 19 40 22 46 38 25 3 G~ermnany 55 -59 19 19 22 20 _ 29 35 25 33 26 22 Ghana 85 79 9 16 14 24 17 52 26 70 5 6 Greece 72_ 70 -15 15 23 23 18 25 28 33 13 -15 Guatemala 84 86 7 8 14 15 21 19 25 28 10 6 Guinea 73 75 9 5 18 22 31 28 31 29 18 20 Guinea-Bissau -87 99 10 12 30 22 10 41 37 74_ 3 -11 Haiti 81 86 8 7 13 31 18 13 20 33 11 10 II 2003 World Development indicators Structure of demand Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption flnal formatlon and services and services expenditure consumption expendlture % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 66 72 14 14 23 31 36 38 40 55 20 14 Hungary 61 64 11 11 25 27 31 60 29 63 28 25 India 67 66 12 13 24 23 7 14 10 15 21 21 Indonesia 59 67 9 7 31 17 25 41 24 33 32 26 Iran, Islamic Rep 62 51 11 13 29 29 22 28 24 21 27 36 Iraq Ireland 58 48 16 13 21 24 57 95 52 80 26 38 Israel 56 59 30 29 25 19 35 40 45 47 14 12 Italy 58 60 20 18 22 20 20 28 20 27 22 21 Jamaica 65 69 13 16 26 30 48 41 52 56 22 16 Japan 53 56 13 18 33 25_ 10 19 9 10 34 26 Jordan 74 76 25 23 32 26 62 44 93 69 1 1 Kazakhstan 52 60 18 16 32 26 74 46 75 49 30 23 Kenya 67 79 19_ 17 20 13 _26 26 31 35 14 4 Korea, Dem Rep Korea, Rep 53 61 10 10 38 27 29 43 30 41 37 29 Kuwait 57 48 39 26 18 9 45 55 58 37 4 26 KyrIgyz Republic 71 66 25 17 24 16- 29- 37 50 37 4 16 Lao PDR Latvia 53 -59 9 22 40- 2-8_ 48 46 49 54 39 19 Lebanon 140 94 25 is 18 19 _18 J2 100 42 -64 -12 Lesotho 132 90 20 24 52 37 17 34 121 86 -52 -15 Liberia Libya 48 46 24 21 19 13 40 -36 31 15 27 33 Lithuania 57 68 19 16 33 22 52 50 61 56 24 16 Macedonia, FYR 72 74 19 25 -19 17 -26- _40 36 56 9 1 Madagascar 86 80 8 8 17- _16 17 29 28 32 6 12 Malawi 72 83 15 18 23 11 24 26 33 38 13 -1 Malaysia 52 41 14 12 32 29 75 1-16 72 98 34 47 80 77 14 13 23 21 17 31 34 42 6 10 Mali Mauritania 69 70 26 16 20 27 -46- 38 61 5k 5 14 M-auritius 64 62 13 13 31 24 64 64 71 63 23 25 Mexico 70 70 8 12 23 21 19 28 20 30 22 18 Moldova 77 92 a 12 25 20 49 50 51 74 23 -4 Mongolia -58 67 32 19 38 30 24 64 53 80 9- 14 Morocco 65 631 15 18 25 25 26_ 30 32 36 19 19 Mozambique 101 70 12 10 16 42 8 22 36 44 -12 19 Myanmar 89 87 a a 13 13 3 0 5 0 11 13 Namibia 59 60 26 29 -28 -24 44 54 57 66 15 12 Nepal 83 75 9 10 18 24 -11- 22 21 32 15 Netherlands 50 50 24 23 23 22 54 65,_ 51 -60 27 27 New Zealand 61 60 19 18 20 20 27 37 27 35 20_ 22 Nicaragua 59 43 19 2-5 46 -2 - Niger 84 84 15 12 8 -11- 15 -17 22 25 -1 3 Nigeria 56 48 15 25 15 28 43 48 29 49 29 27 Norway 49 43 21 19 23 41 47 34 30 -30 38 Oman 27 38 13 53 31 35 Pakistan 74 75 15 10 19 16 16 1_8 23 -19 11 15 Panama 60 58 18 15 17 28_ 38 33 34 35 21 26 Papua New Guinea 59 64 25 14 24 19 4-1 47 49 43 16 22 Paraguay 77 82 6 9 23 24 33 23 39 38 17 9 Peru 74 72 8 11 16 18 16 16 14 17 18 17 Philippines 72 68 10 12 24 18 28 49 33 47 18 20 Poland 48 66 19 17 26 22 29 -29 22 33 33 18 Portugal 63 61 16 21 28 28 33 32 39 41 21 19 Puerto Rico 65 14 17 77 101 21 2003 World Development Indicators 219 Structure of demand Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption final formation and services and services expenditure consumption expenditure % of GDP % of GDP %of GDP % of GDP %of GDP %of GDP 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 66 80 13 6 30 22 17 34 26 42 21 14 Russian Federation 49 51 21 14 30 22 18 37 18 24 30 35 Rwanda 84 86 10 12 15 18 6 9 14 26 6 2 Saudi Arabia 40 37 31 27 20 19 46 42 36 24 30 36 Senegal 76 78 15 10 14 20 25 30 30 38 9 12 Sierra Leone 83 95 8 17 10 8 22 17 24 37 9 -12 Singapore 46 42 10 12 37 24 184 174 177 152 44 46 Slovak Republic 54 56 22 21 33 32 27 74 36 82 24 23 Slovenia 55 55 19 21 17 28 84 59 74 63 26 24 Somalia 113 a 16 10 38 -12 South Africa 63 63_ 20 19- 12 15 24 28 19 25 18 18 Spain 60 59 17 17 27 25 16 30 20 31 23 24 SriLanka 76 _ 75 10 10 23 22 29 37 38 44 14 15 Sudan 76 6 18 13 -16 15 Swaziland 62 74 18 20 19 19 75 69 74 81 20 6 Sweden 49 50 28 27 23 18 30 46 29 41 24 24 Switzerland 57 61 14 13 28 -22 36 45 36 41 29 26 Syrian Arab Republic 69 61 14 11 17 21 28 38 28 31 17 -29 Tajikistan 74 84 9 8 25 12 28 83 35 87 17 7 Tanzania b 81 85 18 6 26 _ 17 13 16 37 24 1 8 Thailand -57 58 9 12 41 24 34 66 42 60 34 30 Togo 71 87 14 9 27 _21 33 33 45 50 15 4 Trinidad and Tobago 59 58 12 11 13 19 45 55 29 43 29 31 Tunisia 58 61 16 16 32 28 44 48 51 52 25 23 Turkey 69 67 11 14 24 16 13 34 18 31 20 19 Turkmenistan 49 49 23 15 40 37 47 47 28 36 Uganda 92 81 8 12 13 20 7 12 19 26 1 6 Ukraine 57 55 17 23 27 20 28 -56_ 29 54 26 22 United_Arab Emirates 39 16 20 65 40 45 United Kingdom 63 -66 20 19 20 17 24 27 27 29 18 15 United States 67 69 17 14 18 -21 10 ~ 11 11 15_ 16 17 Uruguay 70 74 12 13 12 13 24 19- 18 20 18 12 Uzbekistan 61 62 25 18 32 19 _ 29 _ 28 48 28 13 20 Venezuela, RB 62 68 8 8 10 19 39 23 20 .18 29 24 Vietnam -84 65 12 6 13 3-1 36 55 45 57 3 29 West Bank and Gaza 92 32 33 14 71 -24 Yemen, Rep 74 65 17 14 15 20 14 38 20 37 9 21 Yugoslavia, Fed Rep 90 18 13 25 48 -9 Zambia 64 77 19 13 17 20 36 27 37 37 17 10 Zimbabwe 63 72 19 19 17 8 23 22 23 21 17 9 Low Income 66 68 __ 12 12 _ 24 20 18 28 21 28 21 20 Middle Income 5-9 59 15 15 25 24 21 30 19 28 26 25 Lower middle income 58 56- 14 14 29 26 21 33 22 30 29 30 Upper middle income 61 62 15 17 21 21 20 27 17 26 23 21 Low & middle Income 61 61 14 15 25 23 20 29 20 28 25 25 East Asia & Pacific 54 52 11 12 34 31 25 41 24 36 34 36 Europe & Central Asia 55 60 18 16 28 -22 23 41 24 38 26 25 Latin America & Carib 65 65 13 16 19 20 14 19 12 20 21 19 Middle East & N Africa 57 53 20 18 24 22 33 34 34 27 23 29 South Asia 70 69 12 12 23 22 9 15 13 17 19 19 Sub-Saharan Africa 66 67 18 17 15 18 27 31 26 32 16 17 Hight Income 59 61 17 17 24 22 20 25 20 25 24 22 Europe EMU 56 57 20 20 23 21 28 37 28 35 24 22 a Data on generai government final consumption expenditure are not avaiiable separateiy, they are included in househoid finai consumption expenditure, b Data cover mainiand Tanzania only 220 II 2003 World Development Indicators Structure of demand - Gross domestic product (GDP) from the expenditure that may be used by the general public, such as * Household final consumptIon expenditure is the mar- side is made up of household final consumption schools, airfields, and hospitals. These expenses ket value of all goods and services, including durable expenditure, general government final consumption were treated as consumption in the earlier version of products (such as cars, washing machines, and home expenditure, gross capital formation (private and the SNA Data on capital formation may be estimated computers), purchased by households It excludes pur- public investment in fixed assets, changes in inven- from direct surveys of enterprises and administrative chases of dwellings but includes imputed rent for owner- tories, and net acquisitions of valuables), and net records or based on the commodity flow method occupied dwellings It also includes payments and fees to governments to obtain permits and licenses The exports (exports minus imports) of goods and serv- using data from production, trade, and construction to government Indicats andensehold World Development Indicators includes in household ices Such expenditures are recorded in purchaser activities The quality of data on fixed capital forma- consumption expenditure the expenditures of nonprofit prices and include net taxes on products tion by government depends on the quality of govern- Institutions serving households, even when reported Because policymakers have tended to focus on fos- ment accounting systems (which tend to be weak in separately by the country In practice, household con- tering the growth of output, and because data on pro- developing countries) Measures of fixed capital for- sumption expenditure may include any statistical dis- duction are easier to collect than data on spending, mation by households and corporations-particularly crepancy in the use of resources relative to the supply many countnes generate their primary estimate of GDP capital outlays by small, unincorporated enterpris- of resources * General govemment flnal consumption using the production approach Moreover, many coun- es-are usually very unreliable expenditure includes all government current expendi- tries do not estimate all the separate components of Estimates of changes in inventories are rarely com- tures for purchases of goods and services (including national expenditures but instead denve some of the plete but usually include the most important activi- compensation of employees) It also includes most main aggregates indirectly using GDP (based on the ties or commodities In some countries these expenditures on national defense and security but excludes government military expenditures that poten- production approach) as the control total estimates are derived as a composite residual along tially have wider public use and are part of government Household final consumption expenditure (private with household final consumption expenditure capital formation * Gross capital formation consists of consumption in the 1968 System of National According to national accounts conventions, adjust- outlays on additions to the fixed assets of the economy, Accounts, or SNA) is often estimated as a residual, by ments should be made for appreciation of the value net changes in the level of inventories, and net acquisi- subtracting from GOP all other known expenditures of inventory holdings due to price changes, but this tions of valuables Fixed assets include land improve- The resulting aggregate may incorporate fairly large is not always done In highly inflationary economies ments (fences, ditches, drains, and so on), plant, discrepancies When household consumption is cal- this element can be substantial machinery, and equipment purchases, and the con- culated separately, the household surveys on which Data on exports and imports are compiled from struction of roads, railways, and the like, including many of the estimates are based tend to be one-year customs reports and balance of payments data schools, offices, hospitals, private residential dwellings, studies with limited coverage Thus the estimates Although the data on exports and imports from the and commercial and industrial buildings Inventories are quickly become outdated and must be supplemented payments side provide reasonably reliable records of g Y P Y unexpected fluctuations In production or sales, and by price- and quantity-based statistical estimating pro- cross-border transactions, they may not adhere 'work in progress "* E-xports and Imports of goods cedures Complicating the issue, in many developing strictly to the appropriate definitions of valuation and and services represent the value of all goods and other countries the distinction between cash outlays for timing used in the balance of payments or corre- market services provided to, or received from, the rest personal business and those for household use may spond with the change-of-ownership criterion This of the world They include the value of merchandise, be blurred The World Development Indicators issue has assumed greater significance with the freight, insurance, transport, travel, royalties, license includes in household consumption the expenditures increasing globalization of international business fees, and other services, such as communication, con- of nonprofit institutions serving households Neither customs nor balance of payments data usu- struction, financial, information, business, personal, General government final consumption expenditure ally capture the illegal transactions that occur in and government services They exclude labor and prop- (general government consumption in the 1968 SNA) many countries Goods carried by travelers across erty income (factor services in the 1968 SNA) as well includes expenditures on goods and services for indi- borders in legal but unreported shuttle trade may fur- as transfer payments * Gross domestic savings are calculated as GDP less total consumption vidual consumption as well as those on services for ther distort trade statistics collective consumption Defense expenditures, Domestic savings, a concept used by the World - I including those on capital outlays (with certain Bank, represent the difference between GDP and The national accounts indicators for most develop- exceptions), are treated as current spending total consumption Domestic savings also satisfy the ing countries are collected from national statistical Gross capital formation (gross domestic investment fundamental identity exports minus imports equal organizations and central banks by visiting and res- in the 1968 SNA) consists of outlays on additions to domestic savings minus capital formation Domestic ident World Bank missions The data for high- the economy's fixed assets plus net changes in the savings differ from savings as defined in the nation- income economies come from OECD data files (see level of inventories It is generally obtained from al accounts, this SNA concept represents the differ- the OECD's National Accounts, 1989-2000, vol- reports by industry of acquisition and distinguishes ence between disposable income and consumption umes 1 and 2) The United Nations Statistics Divi- only the broad categories of capital formation The For further discussion of the problems in compiling sion publishes detailed national accounts for United 1993 SNA recognizes a third category of capital for- national accounts, see Srinivasan (1994), Heston Nations member countries in National Accounts mation net acquisitions of valuables Included in (1994), and Ruggles (1994). For a classic analysis of Statistics Main Aggregates and Detailed Tables and gross capital formation under the 1993 SNA guide- the reliability of foreign trade and national income updates in the Monthly Bullefin of Statistics lines are capital outlays on defense establishments statistics, see Morgenstern (1963) 2003 World Development Indicators 1 221 Growth of consumption and investment Household final consumption Household final General Gross expenditure consumption government flnal capital expenditure consumption Womato per capita expenditure average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1990 2001 1980-90 1990-2001 ±980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 Afghanistan Albania 1,271 3,793 46 51 . -0 1 -0 3 20 5 Algeria 35,265 23,855 1 5 0 2 -1.4 -1 7 0 7 3 5 -1 8 -0 6 Angola 3,674 -3 6 8 4 -566 Argentina 109,038 198,798 1 9 0 6 1 9 -5 2 5 4 Armenia 2,005 1,995 0 4 -0 1 -1 4 . -12 0 Australia 182,448 233,464 2 9 3.7 1.4 2 5 3.8 _ 3 0 3 7 6.6 Austria 89,789 108,354 2 4 2 4 2 2 2 0 1 4 1 7 2.4 2 2 Azerbaujan 3,631 -15 0 Bangladesh 25,952 36,472 4 5 3 7 1.8 2 0 5 0 4 6 1.4 -9 1 Belarus 16.667 7,146 0.9 1 2 -1 1 -6 6 Belgium 109,154 124,908 2 0 1.9 1.9 1 7 1 1 1 6 2 9 1 9 Benin 1,602 1,942 1 9 4 4 -1 2 1 5 0 5 3 9 -5.3 5 9 Bolivia 3,741 6,250 1 2 3 5 -0.9 1.1 -3 8 3 4 1 0 6 0 Bosnia and Herzegovina Botswana 1,260 1,799 6 3 4 7 2 7 1 9 14 9 7 1 7 6 4 9 Brazil 275,753 302,105 1 2 5 1 -0 7 3 6 7 3 -1 1 3 3 3.5 Bulgaria 12,401 9,642 3.1 -1 7 3 2 -1.0 5 1 -7 0 2 3 0.6 Burkina Faso 2,141 1,905 2 6 3 7 0 1 1.2 6 2 -0 2 8 6 7 7 Burundi 1,070 652 3.4 -2 0 0 5 -4 1 3 2 -1.8 6.9 0 8 Cambodia 1,016 2,812 1 7 -0.7 3.1 10 5 Cameroon 7,423 5,810 3.5 3.3 0 6 0 8 6 8 1.9 -2 6 1 4 Canada 322,564 391,155 3 2 2 7 2.0 1.7 2.4_ 0 5 5 0 4 6 Central African Republic 1,274 748 1 5 -1 7 10 0 Chad 1,482 1.427 5 3 1 9 2 8 -1 1 14 5 0 0- 6.8 Chile 18,759 43,366 2.0 6 8 0 3 5.3 0 4 3 7 6 4 7 4 China 174,249 - 554,407 8 8 8 9 7 2 7 8 9 8 9 0 10.8 10 8 Hong Kong, China 42,421 94,764 6 7 3 5 5.3 1.7 5.0 3.8 4.0 4.6 Colombia 26,357 52,430 -26 2 2 0.5 0 3 4 2 9 3 1 4 1 1 Congo, Dem Rep 7,398 4,792 3 4 -5.6 0 1 --85 0.0 -17 1 -5 1 -0 2 Congo, Rep 1, 746 77 6 2 3 -0 1 -0 6 -3 1 4 3 -1.5 -11 6 1.9 Costa Rica 3.502 11,205 3 6 4 8 0 6 2.5 1.1 1.9 4.6 4 8 C6te d'ivolre 7,766 7.702 1.5 3.6 -2 1 0 5 -0 1 0 4 -10 4 6 7 Croatia 13,527 11,786 2.7 3 6 0 4 . 7 6 Cuba . 2.6 .1 9 16 9 Czech Republic 17,195 30,032 2 7 . 2 7 -1.2 5 3 Denmark 65.430 75.850 1.4 2 0 1.4_ 1 6 0 9 2 2 4 7 5 4 Dominican Republic 5,633 16,069 - 3 9 5 4 1 7 3 7 -3.2 13 8 45_ 5 7 Ecuador 7.323 12,235 1 9 1 2 _ -0 7 -0 9 -1 4 -2 0 -3 8 1 1 Egypt,Arab Rep 30,933 76,538 4 6 4 8 2 0 2 7 3 1 3 1 0 0 3 5 El Salvador 4,273 12,080 0 8 4 9 -0 2 2 8 0 1 2 7 2 2 6 1 Eritrea - -546_ I10 . -1.7 . 16 7 5 0 Estonia 4,074 3,116 0.5 1 9 4- 3 0 3 Ethiopia 5,081 5,003 0 2 2 9 -2 8 0 6 4 5 9 2 2.1 9 9 Finland - 68,939 60,487 3.9 2 1 3.4 1 8 3 2 1 0 34 2 0 France - 672,960 721.076 2.2 1 5 1 7 1 1 2 6 1 8 3.3 2.0 Gabon 2.961 3,040 1 5 1 7 -1.5 -1 0 -0 6 4 6 -5 7 3 5 Gambia,The 240 327 -2 4 4 5 -5 9 1 1 1 7 -0 5 0 0 2 2 Georgia 8,228 2,497 2 4 .. 2.7 . 6 8 . -10 3 Germany 941,915 1,090.022 2 2 1 6 2 1 1.3 1 5 1 5 2 0 1.1 Ghana 5,016 4,171 2.8 3 9 -0 8 1.6 2 4 5 9 -3.3 _ 15 Greece 60,164 78,122 2 0 2 2 1 5 1.9 1 1 1 4 -0 7 3 6 Guatemala 6,398 17,677 1 1 4 1 -1 4 1.4 2 6 5 4 -1 8 5 9 Guinea 2,068 2.236 3 5 0 9 4.6 3 1 Guinea-Bissau 212 197 0 8 2 7 -1 5 0 4 7 2 2.3 12 9 _ -9 2 Haiti 2,332 3,418 0 9 .. -4 4 -0 6 3 4 222 2003 World Development Indicators Growth of consumption and investment4.0 Household final consumption Household final General Gross expenditure consumption government final capital expenditure consumption formation per capita expenditure average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1990 2001 1980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 1980-90 1990-2001 Honduras 2,026 4,613 2 7 2 9 -0 5 0 1 3 3 3 1 2 9 6 2 Hungary 20,290_ 33,169 1 3 -0 3 1 7 0 0 1 9 0 9 -0 9 9 5 India 212,322 315,787 5 8 4 4 3 6 2 6 4 2 7 0 6 2 7 7 Indonesia 67.388 - 97,386 5 6 6 2 3 7 4 7 4 6 0 3 7 2 -2 2 Iran, Islamic Rep 74,476 58,179 2 8 3 1 -0 6 1 5 -5 0 4 9 -2 5 3 2 Iraq Ireland 27,957 45,494 2 2 5 5 1 9 4 7 -0 3 4 1 -0 4 9 6 Israel 32,112 65,189 5 4 - 6 3 3 6 3 4 0 5 2 9 2 2 4 9 Italy 634,194 655,259 2 9 1 6 2 8 1 4 2 9 0 2 2 1 1 5 Jamaica 2,980 5,348 2 8 1 9 1 7 1 1 6 2 0 7 0 0 0 3 Japan 1,617,071 2,334,025 3 6 1 5 3 0 1 2 3 4 3 0 5 5 -0 2 Jordan 2,978 - 6,582 1 9 4 4 -1 9 0 5 1 9 4 6 -1 9 1 4 Kazakhstan 14,148 13,126 -6 8 -5 9 -4 5 -12 8 Kenya 5,309 8,805 4 6 2 6 1 1 0 0 2 6 7 9 0 4 3 4 Korea, Dem Rep Korea, Rep 132,113 251,145 7 9 4 9 6 7 3 9 5 2 2 7 12 0 1 4 Kuwait 10,459 -15,661 -1 4 2 2 -4 5 Kyrgyz Republic 1,703 -1,012 -5 5 -6 4 -7 8 -3 2 Lao PDR Latvia 6,578 4,450 2 3 -1 1 1 8 0 1 5 0 3 4 3 4 -7 1 Lebanon 3.961 15,635 2 8 1 1 5 3 6 5 Lesotho 821 721 3 9 1 4 1 7 -0 5 2 8 4 8 5 0 -0 2 Liberia Libya 13,999 15,625 - -- Lithuania 8,343 7,654 5 2 5 9 0 5 6 4 Macedonia, FYR 3,021 2.535 -2 2 1 5 1 2 0 8 Madagascar 2,663 - 3666 -07 2 5 -305 0 5 07 4 9 4 7 Malawi 1,345 -1,458 1 5 4 9 -1 7 2 8 6 3 -1 9 -2 8 -8 4 Malaysia 22,806 35,707 3 3 5 0 0 4 2 5 2 7 5 3 3 1 4 0 Mali 1,943 2,031 0 6 3 7 -1 9 1 2 7 9 5 5 3 6 2 1 Mauritania 705 709 1 4 2_6 -1 1 -0 3 -3 8 1 0 6 9 9 5 Mauritius 1,519 2,816 6 2 - 5 0 5 3 3 8 33 4 8 10 3 4 5 Mexico 1.82,791 -432,958 -1_1 2 7 -1 0 ~10 2 4 18 -33 4 6 Moldova 730 1,362 9 1 9 4 -11 4 -14 1 Mongolia .-- 698 - Morocco 16,833 21,491 - 43 2 9 2 0 1 1 2 1 3 7 1 2 2 8 Moamiqe2,481 2,538 -1 4 3 5 -2 9 1 2 -2 6 2 8 3 8 15 1 Myanmar 0 6 3 9 - 1 1 Namibia 1,640 1,845 1 3 -1I0 7 8 7 5 Nepal 3,028 - 4,194 Netherlands 145,871 188.587 1 7 2 8 1 1 2 2 2 2 2 1 3 3 3 1 New Zealand 26,632 30,010 2 1 2 9 1 2 1 8 1 6 2 4 3 0 5 5 Nicaragua 592 -3 6 6 0 -6 2 3 0 3 4 -4 2 -4 8 10 0 Niger - 2,079 -1,650 0 0 1 8 -3 1 -1 7 4 4 0 8 -7 1 4 0 Nigeria 15,816 19,828 --2 6 0 2 -5 5 -2 7 -3 5 -1 8 -8 5 5 4 Norway 57,047 - 69,082 2 2 3 3 1 9 2 7 2 3 2 4 0 7 5 2 Oman 2,810 25 5 Pakistan 29,512 44,089 4 3 4 7 1 6 2.1 10 3 0 5 5 8 1 6 Panama 3,022 5,673 2 1 34 0 0 1 6 1 2 2 6 -8 9 9 5 Papua New Guinea 1,902 2,231 04 52 -2 1 2 6 -0 1 22 -0 9 1 3 Paraguay 4,063 5,916 2 4 3 5 -0 7 0 8 1 5 5 5 -0 8 -0 8 Peru 1-9,376 38,857 0 7 3 8 -1 5 1 9 -0 9 5 0 -3 8 5 8 Philippines 31,566 -50,227 -26 3 7 0 2 1 4 0 6 3 1 -2 1 2 8 Poland 2-8,281 115,720 5 0 4 9 3 0 9 6 Portugal 44,679 66,823 2 6 2 8 2 4 2 6 5 0 2 8 3 0 5 5 Puerto Rico 19,827 3 5 5 1 6 9 2003 World Development Indicators I223 Growth of consumption and investment Household flnal consumption Household final General Gross expenditure consumption government final capital expenditure consumption formation per capita expenditure average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1990 2001 1980-90 1990-2001 1980-90 1.990-2001 1980-90 1990-2001 1980-90 1990-2001 Romania 25,232 30,924 1 7 2 0 0 9 -4 7 Russian Federation 282,978 157,940 0 2 0.4 -2 4 -15 7 Rwanda 2,162 1,471 1 2 1 6 -1 8 -0 5 5 2 -0 4 4 3 2 2 Saudi Arabia 41,621 68,299 Senegal 4,353 3,620 2 1 3 9 -0 8 1 1 3 3 0 1 5 2 5 2 Sierra Leone 546 712 -2 7 -1 3 -4 7 -3 5 -4 7 1.2 44 9 2.8 Singapore 17,019 36,165 5 8 5 5 3 9 2 5 6 6 8 8 3 1 6 3 Slovak Republic 8,350 11,416 3 8 1 4 3 5 1 2 4.8 1 2 0.0 6 2 Slovenia 6,917 9,956 3 8 3 9 3 2 10 7 Somalia 1 3 7.0 -2 6 South Africa 70,283 71,899 2 4 2 6 -0 2 0 7 3 5 0 7 -5 3 2 6 Spain 306,953 341,728 2 6 2 4 2 3 1.9 4 9 2 8 5 9 3 1 SriLanka 6,143 11,864 4 0 5 7 2 9 4 5 7 3 7 5 0 6 5 7 Sudan 8,706 0 0 -0 5 -1 8 10 7 Swaziland 547 930 5 3 3 4 2 1 0.3 1 4 4 7 -0 4 2 4 Sweden 116,247 104,533 2 2 1 3 1 9 1 0 1 6 0 4 4 7 2 1 Switzerland 130,900 150,092 1 6 1 1 1 1 0 5 3 1 0 7 3 9 1 5 Syrian Arab Repubiic 8,458 11,797 3 6 2 0 0 2 -0 8 -3 6 0 5 -5 3 3 0 Tajikistan 3,202 837 4 0 -6 6 0.9 -8 1 4 1 -12 8 -6 8 -18 8 Tanzaniaa 3,526 7,967 2 2 -0 6 3 1 -0 9 Thailand 48,270 65,281 5 9 3 4 4 1 2 6 4 2 4 8 9 5 -4 3 Togo 1,158 1,096 4.7 3 7 1 3 0.9 -1.2 -2 2 2 7 0.6 Trinidad and Tobago 2,975 4,702 -1 3 1 7 -2 5 1 0 -1 7 1 5 -10 1 11 3 Tunisia 7,152 12,147 2 9 4 4 0 3 2 8 3 8 4 1 -1 8 3 8 Turkey 103,324 106,843 3 0 1 5 4 5 2 7 Turkmenistan 2,918 3 3 Uganda 4,002 3,416 2 6 7 5 0 0 4 2 2 0 6 4 8 0 9 1 Ukraine 52 131 20,835 -5 8 -5 3 -3 6 -15 9 United Arab Emirates 12,726 4 6 -3 9 -8 7 United Kingdom 619,782 937,655 4 0 3 1 3 8 2 9 0 8 1 3 6 4 4 2 United States 3,831,500 6,728,400 3 8 3_5 2 9 22 3 3 0.6 4 0 76 Uruguay 6,525 13,883 0 7 4 3 0 1 3 6 1 8 2 1 -6 6 4 7 Uzbekistan 13,321 6.947 -0 6 Venezuela, RB 30,171 85,209 1 3 0 5 -1 2 -1 6 20 0 2 -5 3 4 1 Vietnam 5,485 21,255 18 5 West Bank and Gaza 4,019 2 5 -1 7 12 6 . 3 8 Yemen, Rep 3,561 6,432 3 3 . -0 1 1 8 10 0 Yugoslavia, Fed Rep 9,793 Zambia 2,078 2,815 1 8 -2 2 -1 3 -4 6 -3 4 -7 0 -4 3 6 3 Zimbabwe 5,543 6,501 3 7 -0 2 0 0 -2 2 4 7 -2.3 3 6 -5 2 mn 0 O WO &e , M, W: v f- .- - , Low Income 572,667 727,717 4 2 3 7 1 9 1 6 3 9 2 8 4 3 1 5 Middle Income 1,890,669 3,030,497 2 8 3 7 1 1 2 5 4 9 2 0 1 6 2 1 Lower middle income 1,054,957 1,540,572 4 3 3 8 2 6 2 7 3 7 3 9 3 4 0 7 Upper middle income 844,108 1,490,652 1 1 3 7 -0 6 2 3 72 0 3 -0 1 4 7 Low & middle Income 2,459,296 3,756,453 3 1 3 7 1 1 2 1 4 8 2 2 2 0 2 0 East Asia & Pacific 363,883 854,343 6 5 6 9 4 8 5 6 6 3 6 7 8 4 6 8 Europe & Central Asia 667,012 604,551 0 9 . 0 8 -0 4 -7 0 Latin America & Carib 737,426 1,307,727 1 3 3 7 -0 6 2 0 5 6 0 4 -0 3 4 2 Middle East & N Africa 225,422 371,112 South Asia 281,101 418,649 5 4 4 4 3 1 2 4 5 2 6 1 5 5 71 Sub-Saharan Africa 191,901 203,255 1 6 2 5 -1 3 -0.1 2 7 1 4 -3 8 3 1 High Income 10,456,766 15,611,354 3 3 2 6 2 7 1 9 2 7 1 7 4 3 3 0 Europe EMU 3,116,941 3,499,161 2 3 1 8 2 1 1 5 2 3 1 6 2 7 1 9 a Data cover mainland Tanzania only 224 I0 2003 worid Development Indicators Growth of consumption and investment 0 Measures of growth in consumption and capital for- Growth rates of household final consumption expendi- * Household final consumption expenditure is the mation are subject to two kinds of inaccuracy The ture, household final consumption expenditure per market value of all goods and services, including first stems from the difficulty of measuring expendi- capita, general government final consumption expendi- durable products (such as cars, washing machines, tures at current price levels, as described in About ture, and gross capital formation are estimated using and home computers), purchased by households It the data for table 4 9 The second arises in deflating constant price data (Consumption and capital forma- excludes purchases of dwellings but includes imput- current price data to measure volume growth, where tion as shares of GDP are shown in table 4 9 ) ed rent for owner-occupied dwellings It also includes results depend on the relevance and reliability of the To obtain government consumption in constant payments and fees to governments to obtain permits price indexes and weights used Measuring price prices, countries may deflate current values by apply- and licenses The World Development Indicators changes is more difficult for investment goods than ing a wage (price) index or extrapolate from the includes in household consumption expenditure the for consumption goods because of the one-time change in government employment Neither tech- expenditures of nonprofit institutions serving house- nature of many investments and because the rate of nique captures improvements in productivity or holds, even when reported separately by the country technological progress in capital goods makes cap- changes in the quality of government services In practice, household consumption expenditure may turing change in quality difficult (An example is com- Deflators for household consumption are usually cal- include any statistical discrepancy in the use of puters-prices have fallen as quality has improved ) culated on the basis of the consumer price index resources relative to the supply of resources Several countries estimate capital formation from Many countries estimate household consumption as * General government final consumption expendi- the supply side, identifying capital goods entering an a residual that includes statistical discrepancies ture includes all government current expenditures for economy directly from detailed production and inter- associated with the estimation of other expenditure purchases of goods and services (including compen- national trade statistics This means that the price items, including changes in inventories, thus these sation of employees) It also includes most expendi- indexes used in deflating production and internation- estimates lack detailed breakdowns of household tures on national defense and security but excludes al trade, reflecting delivered or offered prices, will consumption expenditures government military expenditures that potentially determine the deflator for capital formation expendi- have wider public use and are part of government tures on the demand side capital formation * Gross capital formation con- The data in the table on household final consumption sists of outlays on additions to the fixed assets of expenditure (private consumption in the 1968 System the economy, net changes in the level of inventories, of National Accounts), in current U S dollars, are con- and net acquisitions of valuables Fixed assets verted from national currencies using official exchange include land improvements (fences, ditches, drains, rates or an alternative conversion factor as noted in and so on), plant, machinery, and equipment pur- Pnmary data documentation (For a discussion of alter- chases, and the construction of roads, railways, and native conversion factors, see Statistical methods ) the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial 4.10a buildings Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations Per capita household consumption (1995 $) in production or sales, and "work in progress 600 East Asia & Pacific 500 400 Sub-Saharan Africa The national accounts indicators for most devel- oping countries are collected from national statis- / South Asia tical organizations and central banks by visiting 300 and resident World Bank missions Data for high- income economies come from data files of the 200 Organisation for Economic Co-operation and Development (see the OECD's National Accounts, 1989-2000, volumes 1 and 2) The United 100 Nations Statistics Division publishes detailed 1980 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 national accounts for United Nations member In East Asia and Pacific per capita household consumpbon has risen more rapidly, and poverty fallen faster, than in countries in National Accounts Statistics Main South Asia, though the two regions started from similar levels in 1980 In stark contrast, in Sub-Saharan Africa per capita Aggregates and Detailed Tables and updates in household consumption started out much higher in 1980 and has since fallen below the level in East Asia Source World Bank data files the Monthly Bulletin of Statistics 2003 World Development Indicators 1 225 ____ LThLL Central government finances Current Total Overall Financing Domestic Debt revenue a expenditure budget balance from abroad flnancing and Interest (Including payments grants) Total Interest debt % Of % of current % of GDP % of GDP % of GDP % of GDP % of GDP GDP revenue 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 2000 2000 Afghanistan Albania 19 3 29 8 -8 5 2.5 .. 6 0 46 4 40 3 Algena 39 2 29 3 .. 9 9 -2 4 -7 5 561 10 3 Angola Argentina 10 4 14 2 10 6 17 0 -0 4 -2 3 0 2 2 7 0 2 -0 5 23 9 Armenia Australia 24 9 23 9 23 3 23 5 2 0 14 0 2 -0 5 -2 2 -0 9 15.4 5 3 Austria 340 373 376 404 -44 0.5 39 625 89 Azerbaijan 176 22.6 -2.5 . . 2 5 Bangladesh 9 3 12 7 -2.8 0.1 2 7 401 15 7 Belarus 309 287 373 289 -4.8 01 27 -0.5 24 05 150 29 Belgium 42 7 43 6 47 9 45.6 -5.5 -1.8 -0.3 -09 5 8 2 7 114 4 16 7 Benin Bolivia 137 175 164 238 -17 -33 07 12 10 21 604 99 Bosnia and Herzegovina Botswana 50 8 .. 33 6 112 0 0 .. -113 Brazil 22 8 24 9 34 9 26 8 -5 8 -7 8 .. 15 4 Bulgaria 471 33 7 551 35 3 -8 3 0 6 -0 8 -16 9.1 10 . 118 Burkina Faso 11 0 15 0 -1 3 Burundi 18 2 17 9 28 7 26 1 -3 3 -4 7 4 9 3 3 -1 6 1 5 183 9 13 2 Cambodia Cameroon 15 4 15 7 212 15 5 -5 9 01 5 2 0 2 12 -0 3 102 3 19.2 Canada 215 218 26.1 20 3 -4 8 13 0 2 -0 1 4 6 -12 615 14 2 Central African Republic .. Chad 67 218 -47 50 -03 Chile 206 222 204 219 08 01 09 -03 -25 01 139 20 China 6 3 7 2 10 1 10 9 -1 9 -2 9 0.8 -0.1 1 1 3 0 12.7 Hong Kong, China Colombia 12 6 126 11.6 191 3 9 -71 22 5 0 298 268 Congo, Dem Rep 101 0 0 18 8 01 -6 5 0 0 0 0 0 0 6.5 0 0 0 0 Congo,Rep 22 5 26 4 35.6 25 5 -141 12 2 0 -31 160 6 26 5 Costa Rtca 23 0 20 9 25.6 22 3 -31 -1.3 0.3 01 2 8 12 36 2 17 2 Cote d'lvoire 22 0 16 4 24.5 17 9 -2 9 -1.1 4.0 1.7 0 4 -0 6 103 5 23.8 Croatia 330 404 376 46 5 -4 6 -4.9 0.0 4 4 4 7 0 5 4 3 Cuba Czech Republic 327 . 368 . -30 . 00 . 2 9 151 3 0 Denmark 37 8 36 2 39 0 34 9 -O 7 16 . 64 5 10 8 Dominican Republic 12 0 16 9 117 16 0 0 6 1.0 0.0 -1 0 -0 6 0 0 20 7 4 5 Ecuador 18 2 14 5 3 7 Egypt, Arab Rep 23 0 . 27 8 -5 7 . -0 7 6 4 El Salvador 15 9 17 0 17 -0 2 -15 281 8 6 Eritrea Estonia 262 301 237 31.4 04 02 00 -01 -04 -01 31 06 Ethiopia 17 4 19 2 27 2 26 8 -9 8 -5 0 2 8 2 8 7 0 2 2 102 2 10 8 Finland 30 6 32 0 30.3 33 4 0 2 -0 3 0 7 -11 -0 8 1.4 611 14 3 France 39 7 418 -21 11 10 Gabon 20 6 .. 20 2 3 2 2 7 -5 8 Gambia, The 19 4 23 6 -0 8 Georgia 10 5 12 4 -16 -0 9 _ 2 5 70 8 27 1 Germany 25 6 313 26 3 32 7 -14 -09 0.5 0 6 10 -01 20 0 73 Ghana 12 5 13 2 0 2 1.3 -1.5 Greece 27 8 23 4 52 2 30 7 -22 9 -4 4 16 2 4 213 2 0 112 7 38.4 Guatemala Guinea 160 21 7 229 210 -33 -24 4.1 23 -08 02 371 Guinea-Bissau Haiti 79 105 -23 . -02 25 61 226 0 2003 World Development Indicators Central government finances Current Total Overall Financing Domestic Debt revenue a expenditure budget balance from abroad flnancing and Interest (including payments grants) Total Interest debt % of % of current % of GDP % of GDP % of GDP % of GOP % of GDP GDP revenue 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 2000 2000 Honduras Hungary 52 9 36 0 52 1 40 2 0 8 -3 5 -0 5 15 -0 3 2 0 54 9 16 6 India 126 128 163 167 -76 -52 06 00 71 52 557 370 Indonesia 18 8 18 1 18 4 205 0 4 -11 0 7 1 4 -1 1 -0 3 45 2 21 6 Iran, Islamic Rep 18 1 210 19 9 219 -1 8 -0 6 0 0 01 1 8 0 5 0 7 Iraq Ireland 33 6 37 7 -2 4 Israel 394 433 507 463 -53 09 08 -05 46 -04 978 129 Italy 38 2 413 47 4 419 -10 2 -1 6 0 0 9 9 15 5 Jamaica 25 4 34 6 232 37 3 35 -1 2 115 6 38 0 Japan 14 0 15 3 -1 5 0 0 -1 7 Jordan 26_1 25 1 35 8 312 -3 5 -2 0 3 0 0 2 0 5 1 8 93 8 13 9 Kazakhstan 11 3 14 3 -0 6 12 -0 7 215 11 9 Kenya 224 258 275 260 -38 06 13 -24 45 18 691 223 Korea, Dem Rep Korea, Rep 17 5 16 2 -0 7 -0 2 09 Kuwait 58 7 34 5 55 3 44 2 -9 7 40 Kyrgyz Republic 14 2 18 0 -2 2 114 5 10 2 Lao PDR Latvia 28 6 316 -2 7 -0 3 3 0 13 2 3 6 Lebanon 19 5 35 7 -16.2 81 8 1 135 2 74 4 Lesotho 39 0 44 1 512 49 7 -1 0 -3 6 7 9 0 7 -6 9 2 9 67 8 4 9 Liberia Libya Lithuania 319 24.6 28 9 27 6 1 4 -1 3 2 0 -0 7 23 3 7 0 Macedonia, FYR Madagascar 11 6 117 16 0 17 1 -11 -2 4 2 1 1 7 -1 2 0 5 12 1 Malawi 19 8 25 4 -1 6 Malaysia 26 4 29 3 -2 0 -0 7 2 8 Mali Mauritania Mauritius 24 3 22 2 24 3 24 0 -0 4 -1 3 -0 5 -0 4 0 9 1 7 34 8 12 0 Mexico 15 3 14 8 17 9 16 0 -2 5 -1 3 0.3 -0 9 23 2 1 23 3 14 0 Moldova 24 5 29 6 -1 3 -0 2 1 5 73 0 26 0 Mongolia 196 285 231 293 -64 -61 75 64 -11 -03 919 61 Morocco 26 4 29 6 28 8 32 5 -2 2 -2 5 39 -1 5 -1 6 40 72 7 16 5 Mozambique Myanmar 105 53 160 87 -51 -34 00 00 51 34 Namibia 26 4 32 6 28 1 36 2 -1 0 -3 6 70 Nepal 84 106 172 160 -68 -33 54 21 1 4 11 646 120 Netherlands 45 3 49 7 -4 3 -0 3 1 9 4 6 -1 8 55 3 New Zealand 42 1 30 6 43 4 30 8 4 0 -0.3 . . 32 6 7 3 Nicaragua 33 5 318 72 0 35 9 -35 6 -10 12.7 7 7 22 9 -6 7 11 5 Niger Nigeria Norway 42 4 413 413 365 0 5 -3 9 -0 6 3 8 0 0 01 20 5 3 8 Oman 389 239 395 286 -08 -48 -39 38 47 10 191 59 Pakistan 191 16 9 22 4 23 1 -5 4 -5 5 2 3 0 9 3 1 4 6 90 0 44 5 Panama 256 268 237 280 30 03 -34 17 04 -20 207 Papua New Guinea 25 2 23 0 34 7 314 -3 5 -2 8 0 4 1 7 3 0 10 63 9 19 0 Paraguay 12 3 15 6 9 4 19 4 2 9 -4 0_ -0 9 -2 1 7 2 Peru 12 5 16.5 20 6 19 3 -8 1 -1 8 5 4 11 2 7 0 7 44 9 13 2 Philippines 16 2 15 4 19 6 19 6 -3 5 -4 1 0 4 2 6 3 1 1 5 65 6 27 7 Poland 311 346 03 00 -03 396 85 Portugal 313 34 2 37 6 38 5 -4 4 -1 2 -1 3 -2 1 5 7 3 3 0 8 8 4 Puerto Rico 2003 World Development Indicators 1 227 U t1Central government finances Current Total Overall Financing Domestic Debt revenue8 expenditure budget balance from abroad financing and Interest (including payments grants) Total Interest debt % of % of current % of GDP % of GDP % of GDP % of GDP % of GDP GOP revenue 1990 2000 1990 2000 1990 2000 ±990 2000 1990 2000 2000 2000 Romania 34 4 29 6 33 8 34.2 0 9 -4 0 0.0 10 -0 9 3 0 13 7 Russian Federation . 24.6 22.9 3 9 -0 3 -3 7 62 2 14 4 Rwanda 10 8 18.9 -5 3 2 5 2 8 Saudi Arabia Senegal 181 20 6 -1 2 0 6 0 6 78 9 81 Sierra Leone 56 71 83 20 9 -2 5 -8 5 05 11 20 74 247 4 818 Singapore 26 9 26 1 21.4 18 8 10 8 10.0 -0.1 0 0 -10 7 -10 0 869 1 3 Slovak Repubiic 35 6 40 5 -3 0 31 . -01 30 2 7 7 Slovenia 39 8 38 6 38.6 40 2 0.3 -13 01 17 -0 4 -0.5 25 7 3 9 Somalia South Africa 26 3 26 7 301 291 -41 -2 2 0 0 0 3 41 19 47 0 19 6 Spain 29 3 32.6 -31 0.7 2 4 SriLanka 210 16 8 28.4 25 7 -7 8 -9 5 3 6 0.0 4 2 9 4 971 33 7 Sudan 83 8 7 -09 . 02 08 90 94 Swaziland 32 7 28 0 25 5 30 0 0 0 -0 9 -0 2 -0.6 0 2 1.5 28 6 2 0 Sweden 42 6 39 4 39 3 39 3 10 01 -0 3 -5 5 -0 7 5 4 . 114 Switzerland 20 8 25 4 23 3 26 7 -0 9 3 0 0 0 0 0 0 9 -3 0 26 7 3 6 Syrian Arab Repubiic 21 9 23 9 21.8 232 0 3 0 7 2.1 -28 Tajikistan 10 5 113 -0 2 . 0 5 -0 3 112 8 3 7 Tanzania Thailand 18 5 16 0 141 18 0 4.6 -3 0 -15 14 -31 17 22 7 7 4 Togo Trinidad and Tobago Tunisia 30 7 28 6 34.6 32 0 -5 4 -2 6 18 0 7 3 6 18 62 6 114 Turkey 13 7 28 1 17 4 39 4 -3 0 -11 4 0.0 2 8 3 0 8 6 512 58 7 Turkmenistan Uganda 113 20.4 -7 2 . 2 9 4 8 451 7 5 Ukraine 26 8 28 3 -0 6 -0 3 10 45 3 9 5 United Arab Emirates 1 6 3 5 11 5 112 0 4 -0 3 00 00 -0 4 03 00 United Kingdom 36 0 36 0 37 5 36 0 0 6 0 0 0 2 -04 -0 8 03 7 7 United States 18 9 215 22 7 19 2 -3 8 2 4 0 2 -2 2 3 6 -01 34 8 112 Uruguay 238 28 0 23 3 315 03 -3 4 14 26 -1.7 12 7 8 Uzbekistan Venezuela, RB 23 7 20 5 20 7 217 00 -17 10 -2 2 -10 3 9 12 0 Vietnam 19 9 23 4 -2 8 .. 1 4 1.4 40 West Bank and Gaza Yemen, Rep 189 239 278 267 -88 -35 32 13 56 22 98 Yugoslavia, Fed. Rep Zambia Zimbabwe 241 27 3 -5 3 0 9 . 44 Low income 15 5 15 0 18 4 18 3 -4.8 -3 6 Middle income 17 3 17 7 221 213 -2 7 -3 3 03 01 02 10 332 10 3 Lower middle income 13 7 16 6 16 3 20 7 -17 -38 0 2 15 513 116 Upper middle income 20 6 22 3 27 3 24 4 -3 5 -4 7 0 2 01 -0 4 0 8 26 7 7 8 Low & middle income 17 1 171 21 6 20.7 -30 -3 3 1 3 1 0 11.8 East Asia & Pacific 11 7 10 9 13 8 15 0 -0 9 -3.7 0 4 1 3 2 8 1 0 52.3 13 9 Europe & Central Asia 27 6 30.5 -13 . 00 . 0 7 42 4 9.5 LatinAmerica&Carib 188 200 256 219 -35 -48 03 14 -13 09 115 Middle East & N Africa 1.8 1 3 3 6 1 7 122 South Asia 13 8 13 5 17.6 17 9 -7.3 -5 4 3 0 0 5 3 6 4.9 77 3 35 4 Sub-Saharan Africa 24 0 23 5 27 6 25 9 -3 5 -1 6 High Income 23 8 26.6 -2 8 0.2 0 0 10 01 55 3 7 5 Europe EMU 33 5 37 1 -4 0 0 5 0 5 3 9 1 7 60 2 14 3 a Excluding grants 22 l 0 2003 World Development Indicators Central government finances Tables 4 11-4 13 present an overview of the size finance institutions Also missing from the data are * Current revenue includes all revenue from taxes and and role of central governments relative to national governments' contingent liabilities for unfunded pen- current nontax revenues (other than grants), such as economies The International Monetary Fund's (IMF) sion and national insurance plans fines, fees, recoveries, and income from property or Manual on Govemment Finance Statistics describes Data on government revenues and expenditures are sales * Total expenditure includes nonrepayable cur- the government as the sector of the economy collected by the IMF through questionnaires distrib- rent and capital expenditures It does not include gov- responsible for 'implementation of public policy uted to member governments and by the Organisation ernment lending or repayments to the government or through the provision of primarily nonmarket servic- for Economic Co-operation and Development Despite government acquisition of equity for public policy pur- es and the transfer of income, supported mainly by the IMF's efforts to systematize and standardize the poses * Overall budget balance is current and capital compulsory levies on other sectors" (1986, p 3) collection of public finance data, statistics on public revenue and official grants received, less total expen- The definition of government generally excludes non- finance are often incomplete, untimely, and not com- diture and lending minus repayments * Financing financial public enterprises and public financial insti- parable across countries from abroad (obtained from nonresidents) and domes- tutions (such as the central bank) Government finance statistics are reported in local tic financing (obtained from residents) refer to the Units of government meeting this definition exist at currency The indicators here are shown as percent- means by which a government provides financial many levels, from local administrative units to the ages of GDP Many countries report government resources to cover a budget deficit or allocates finan- highest level of national government Inadequate finance data according to fiscal years, see Pnmary cial resources arising from a budget surplus The data statistical coverage precludes the presentation of datadocumentationforthetimingoftheseyears For include all government liabilities-other than those for subnational data, however, making cross-country further discussion of government finance statistics, currency issues or demand, time, or savings deposits comparisons potentially misleading see About the data for tables 4 12 and 4 13 with government-or claims on others held by govern- Central government can refer to one of two ment, and changes in government holdings of cash and accounting concepts consolidated or budgetary For deposits They exclude government guarantees of the most countries central government finance data have debt of others * Debt is the entire stock of direct gov- been consolidated into one account, but for others ernment fixed term contractual obligations to others only budgetary central government accounts are outstanding on a particular date It includes domestic available Countries reporting budgetary data are debt (such as debt held by monetary authorities, noted in Pnmary data documentation Because budg- deposit money banks, nonfinancial public enterprises, etary accounts do not necessarily include all central and households) and foreign debt (such as debt to government units, the picture they provide of central international development institutions and foreign gov- government activities is usually incomplete A key ernments) It is the gross amount of government liabil- issue is the failure to include the quasi-fiscal opera- ities not reduced by the amount of government claims tions of the central bank Central bank losses arising against others Because debt is a stock rather than a from monetary operations and subsidized financing flow, it is measured as of a given date, usually the last can result in sizable quasi-fiscal deficits Such day of the fiscal year * Interest payments include deficits may also result from the operations of other interest payments on government debt-including long- financial intermediaries, such as public development term bonds, long-term loans, and other debt instru- ments-to both domestic and foreign residents The data on central government finances are from the IMF's Government Finance Statistics Yearbook, 2002 and IMF data files Each coun- try's accounts are reported using the system of common definitions and classifications in the IMF's Manual on Government Finance Statistics (1986) See these sources for complete and authoritative explanations of concepts, defini- tions, and data sources 2003 World Development Indicators 1 229 L 64 Central government expenditures Goods and Wages Interest Subsidies and Capital services and salaries a payments other current expenditure transfers % of total % of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure l990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan Albania 17 9 26 42 16 Algeria 29 20 14 30 27 Angola Argentina 30 19 23 15 8 20 57 55 5 6 Armenia Australia 27 27 2 3 8 5 56 61 9 5 Austria 25 25 10 10 9 8 57 61 9 5 Azerbaijan 31 11 2 50 17 Bangladesh 27 18 11 25 23 Belarus 37 22 2 9 2 3 46 59 16 17 Belgium 19 19 14 13 21 16 56 60 5 5 Benin Bolivia 63 39 36 24 6 7 16 37 15 17 Bosnia and Herzegovina Botswana 51 23 2 25 21 Brazil 16 22 9 12 78 14 39 62 2 2 Bulgaria 35 30 3 8 10 11 52 48 3 11 Burkina Faso 60 51 6 11 23 Burundi 34 50 22 30 5 9 10 11 51 23 Cambodia Cameroon 51 52 39 32 5 19 13 15 26 14 Canada 21 18 9 9 20 15 57 65 2 2 Central African Republic Chad 41 28 2 3 56 Chile 28 28 18 20 10 2 51 55 11 15 China Hong Kong, China Colombia 26 19 18 14 10 18 42 41 22 22 Congo, Dem Rep 73 80 23 25 7 0 4 12 16 7 Congo, Rep 56 37 49 18 22 27 20 8 2 27 Costa Rica 57 48 43 37 12 16 20 25 11 11 C6te dI'voire 69 49 38 32 1 22 30 13 0 16 Croatia 54 47 22 25 0 4 42 41 3 8 Cuba Czech Republic 13 7 3 74 10 Denmark 20 22 12 13 15 11 61 64 3 3 Dominican Republic 39 53 29 41 4 5 13 16 44 22 Ecuador 42 38 23 16 18 Egypt, Arab Rep 42 23 14 26 17 El Salvador 76 45 8 2 19 Eritrea Estonia 25 45 8 13 0 1 73 49 8 6 Ethiopia 77 52 40 18 5 8 9 31 16 19 Finland 20 18 10 7 3 14 70 63 7 5 France 26 17 5 63 6 Gabon 63 37 0 6 32 Gambia, The 41 21 16 9 34 Georgia 26 9 23 45 5 Germany 32 31 8 8 5 7 58 57 5 4 Ghana 50 32 11 20 19 Greece 31 34 21 28 20 29 41 20 8 17 Guatemala Guinea 37 29 18 19 7 21 4 8 53 36 Guinea-Bissau Haiti 65 42 5 8 22 23O 0 2003 World Development Indicators Central government expenditures 4. ' Goods and Wages Interest Subsidies and Capital services and salaries a payments other current expenditure transfers % of total % of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Honduras Hungary 27 18 6 9 6 15 64 51 4 12 India 24 23 11 10 22 28 43 41 11 8 Indonesia 23 18 16 8 13 19 21 39 43 24 Iran, Islamic Rep 53 68 40 52 0 1 22 10 25 21 Iraq Ireland 19 14 21 54 7 Israel 38 33 14 14 18 12 37 49 6 5 Italy 17 20 13 16 21 15 54 59 8 6 Jamaica 47 51 21 29 29 35 1 0 23 14 Japan 14 19 54 13 Jordan 55 66 44 48 18 11 11 7 16 15 Kazakhstan 33 8 9 49 8 Kenya 51 31 19 22 10 20 7 Korea, Dem Rep Korea, Rep 35_ 13 4 46 15 Kuwait 62 58 31 35 0 3 20 26 18 13 Kyrgyz Republic 67 26 8 13 12 Lao PDR Latvia 25 12 3 65 7 Lebanon 30 23 41 12 17 Lesotho 40 76 22 35 11 4 5 0 45 19 Liberia Libya Lithuania 12 46 6 16 6 67 41 20 7 Macedonia, FYR Madagascar 37 36 25 23 9 8 9 6 43 38 Malawi 54 23 14 8 24 Malaysia 41 26 20 16 24 Mali Mauritania Mauritius 47 45 37 34 15 11 22 29 17 14 Mexico 25 24 18 17 45 13 17 52 14 10 Moldova 20 8 22 53 6 Mongolia 30 32 7 11 1 6 56 47 13 15 Morocco 48 46 35 36 16 15 8 16 28 22 Mozambique Myanmar 29 39 Namibia 73 63 46 44 1 6 10 17 15 14 Nepal 8 Netherlands 15 9 9 70 6 New Zealand 19 52 12 15 7 64 38 2 3 Nicaragua 43 30 23 16 0 8 14 21 4 40 Niger Nigeria Norway 19 21 8 8 6 4 69 70 5 5 Oman 76 77 22 28 6 5 7 6 11 12 Pakistan 44 47 4 25 33 20 11 12 9 Panama 64 47 49 34 8 20 26 24 2 9 Papua New Guinea 61 56 34 29 11 14 18 24 11 6 Paraguay 54 51 36 45 10 6 19 23 17 20 Peru 30 39 17 18 37 11 25 36 8 14 Philippines 44 51 29 28 34 22 7 17 16 9 Poland 15 7 8 73 4 Portugal 38 41 27 32 18 7 33 38 12 13 Puerto Rico 2003 World Development Indicators 1 231 1 4 [11rV Central government expenditures Goods and Wages Interest Subsidies and Capital services and salaries a payments other current expenditure transfers % of total % of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Romania 26 36 12 16 0 12 57 42 17 11 Russian Federation 39 13 15 38 8 Rwanda 53 29 5 16 33 Saudi Arabia Senegal 45 27 7 12 34 Sierra Leone 77 60 35 46 18 28 1 6 8 11 Singapore 51 54 27 25 14 2 12 18 24 27 Slovak Republic 23 14 7 57 13 Slovenia 40 40 20 22 1 4 52 50 7 7 Somalia South Africa 53 27 23 13 14 18 23 51 10 4 Spain 19 13 9 63 9 Sri Lanka 33 41 17 21 23 22 23 16 21 21 Sudan 74 34 9 7 10 Swaziland 62 57 42 32 3 2 11 22 24 19 Sweden 15 18 6 6 11 11 72 69 2 2 Switzerland 31 28 5 4 3 3 61 64 5 5 Syrian Arab Repubic 27 36 Tajikistan 41 15 3 34 22 Tanzania Thailand 60 55 35 33 13 7 9 12 18 26 Togo Trinidad and Tobago Tunisia 34 41 28 34 10 10 35 25 22 23 Turkey 52 28 38 21 18 42 16 22 13 8 Turkmenistan Uganda 34 10 4 17 45 Ukraine . 32 12 9 53 6 United Arab Emirates 88 78 33 35 0 0 10 18 1 4 United Kingdom 30 29 13 6 9 8 52 59 10 4 United States 28 21 10 8 15 13 49 61 8 5 Uruguay 35 26 20 14 8 7 50 63 7 4 Uzbekistan Venezuela, RB 31 26 23 18 16 11 37 45 16 17 Vietnam 3 32 West Bank and Gaza Yemen,Rep 64 54 55 39 8 9 6 18 33 17 Yugoslavia, Fed Rep Zambia Zimbabwe 56 37 16 18 10 I a 2-s *5Lw- ay - - g i} - a7g - --S3 DSO G; - - k) t - @G ---i S f- - gB ==- ----$3 --- Low Income Middle Income 42 39 25 20 11 9 23 37 16 12 Lower middle income 44 41 29 26 13 11 19 23 17 16 Upper middle income 35 26 23 16 10 7 26 51 11 10 Low & middle Income 39 21 9 26 16 East Asia & Pacific 42 27 10 11 16 21 24 Europe & Central Asia 30 12 8 49 8 Latin America & Carib 35 39 23 24 10 8 25 25 11 15 Middle East & N Africa 53 50 35 35 10 11 11 14 23 19 South Asia 33 41 10 23 25 23 16 12 9 Sub-Saharan Africa 53 31 7 10 20 High Income 25 29 13 11 11 7 56 59 7 5 Europe EMU 20 25 13 13 9 14 57 58 7 5 Note Components include expenditures financed by grants in kind and other cash adjustments to total expenditure a Part of goods and services 232 0 2003 World Development Indicators Central government expenditures Government expenditures include all nonrepayable pay- government for which no data are available Defense * Total expenditure of the central government ments, whether current or capital, requited or unrequit- expenditures, usually the central government's includes both current and capital (development) expen- ed Total central government expenditure as presented responsibility, are shown in table 5 8 For more infor- ditures and excludes lending minus repayments in the International Monetary Fund's (IMF) Government mation on education expenditures, see table 2 11, * Goods and services include all government pay Finance Statistics Yearbook is a more limited measure for more on health expenditures, see table 2 15 ments in exchange for goods and services, whether in of general government consumption than that shown in The classification of expenditures by economic type the form of wages and salaries to employees or other the national accounts (see table 4 10) because it can also be problematic For example, the distinction purchases of goods and services * Wages and excludes consumption expenditures by state and local between current and capital expenditure may be arbi- salares consist of all payments in cash, but not in kind governments At the same time, the IMF's concept of trary, and subsidies to state-owned enterprises or (such as food and housing), to employees in return for central government expenditure is broader than the banks may be disguised as capital financing services rendered, before deduction of withholding national accounts definition because it includes govern- Subsidies may also be hidden in special contractual taxes and employee contributions to social security ment gross capital formation and transfer payments pricing for goods and services and pension funds * Interest payments are payments Expenditures can be measured either by function Expenditure shares may not sum to 100 percent made to domestic sectors and to nonresidents for the (health, defense, education) or by economic type because adjustments to total expenditures financed by use of borrowed money (Repayment of principal is (interest payments, wages and salaries, purchases grants in kind and other cash adjustments (which may shown as a financing item, and commission charges of goods and services) Functional data are often be positive or negative) are not shown separately are shown as purchases of services ) Interest pay- incomplete, and coverage varies by country because For further discussion of government finance sta- ments do not include payments by government as guar- functional responsibilities stretch across levels of tistics, see About the data for tables 4 11 and 4 13 antor or surety of interest on the defaulted debts of others, which are classified as government lending 4.12a * Subsidies and other current transfers include all - ,_ _ _ _ _ , _ unrequited, nonrepayable transfers on current account _ _ -__ to private and public enterprises and the cost to the Subsidies and other currrent transfers as 96 of central government expenditure public of covering the cash operating deficits on sales Developing economies to the public by departmental enterprises * Capital 80 expenditure is spending to acquire fixed capital 70 _ assets, land, intangible assets, government stocks, 60 S 1 3and nonmilitary, nonfinancial assets Also included are lh4eomoen ls 50 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~capital grants 80 40 x 30 020 1 40 \W m 60 4Y0 * '9|| | | Nigh-Income economies so 70 60 30 | | | | 1 | | | | | | | | The data on central government expenditures are from the IMF's Govemment Finance Statistics 20 Yearbook, 2002 and IMF data files Each coun- 10 try's accounts are reported using the system of ', ,~~ ,~~ ~, ~~.\ common definitions and classifications in the °'- / C+ IMF s Manual on Government Finance Statistics (1986) See these sources for complete and authoritative explanations of concepts, defini- Note Data refer to the most recent year available n 1998-2001 tions, and data sources Source International Monetary Fund, Government Finance Statistics data files 2003 World Development Indicators 1 233 d~ oCentral government revenues Taxes on Social Taxes on Taxes on Other Nontax Income, profits, security goods and International taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan Albania 7 14 40 15 1 23 Algeria 80 0 7 9 1 4 Angola Argentina 2 17 44 23 20 43 14 5 10 3 10 9 Armenia Australia 65 68 0 0 21 21 4 3 2 2 8 8 Austria 19 25 37 40 25 25 1 0 9 4 9 6 Azerbatjan -22 22 40 9 2 5 Bangladesh 11 0 40 23 1 25 Belarus 12 11 32 35 40 39 5 5 9 3 2 7 Belgium 35 37 35 33 24 25 0 0 3 3 3 2 Benin Bolivia 5 8 9 10 31 49 7 6 11 8 38 19 Bosnia and Herzegovina Botswana 39 0 2 13 0 46 Brazil 20 20 31 34 24 21 2 3 6 4 16 17 Bulgaria 30 12 23 25 18 39 2 2 1 1 27 21 Burkina Faso 23 0 30 33 7 8 Burundi 21 21 6 7 37 44 24 20 1 1 10 6 Cambodia Cameroon 18 21 6 0 21 26 14 28 4 4 28 20 Canada 51 53 16 20 17 16 3 1 0 0 13 9 Central African Republic Chad 19 0 39 24 10 8 Chile 12 18 8 6 43 46 12 6 3 3 21 20 China 31 6 0 0 18 75 14 10 0 4 37 6 Hong Kong, China Colombia 29 34 0 0 30 39 20 7 1 5 19 14 Congo, Dem Rep 27 12 1 0 18 22 46 23 1 36 7 7 Congo, Rep 26 3 0 0 16 15 21 5 2 0 35 77 Costa Rica 10 13 29 32 27 40 23 5 1 0 14 10 Cote d'lvoire 16 24 7 8 27 20 29 40 11 4 9 4 Croatia 17 9 52 32 24 46 3 6 0 1 3 5 Cuba Czech Republic 13 45 35 2 1 3 Denmark 37 35 4 4 41 45 0 0 3 4 15 12 Dominican Republic 21 18 4 4 23 25 40 43 1 2 10 8 Ecuador 62 0 22 13 1 2 Egypt, Arab Rep 19 15 14 14 11 27 El Salvador 20 15 41 7 1 17 Eritrea Estonia 27 14 28 34 41 42 1 0 1 0 2 9 Ethiopia 29 22 0 0 25 17 15 26 2 3 30 32 Finland 31 29 9 10 47 44 1 0 3 2 9 13 France 17 44 28 0 3 7 Gabon 24 1 23 18 2 32 Gambia, The 13 0 37 43 1 6 Georgia 8 21 58 7 0 5 Germany 16 15 53 48 24 20 0 0 0 0 6 16 Ghana 23 0 30 39 0. 8 Greece 22 39 29 2 43 55 0 0 8 8 8 7 Guatemala Guinea 9 10 0 1 15 5 47 77 0 4 28 4 Guinea-Bissau Haiti 234 H 2003 World Development Indicators Central government revenues 4.13 ' Taxes on Social Taxes on Taxes on Other Nontax Income, profits, security goods and International taxes revenue and capital taxes services trade galns % of total % of total % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue a990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Honduras Hungary 18 21 29 27 31 36 6 3 0 2 16 11 India 15 28 0 0 36 28 29 19 0 0 20 26 Indonesia 62 59 0 2 24 28 6 3 3 0 5 8 Iran, Islamic Rep 10 17 8 9 4 6 13 7 4 1 60 60 Iraq Ireland 37 15 . 38 0 3 7 Israel 36 40 9 14 33 30 2 1 4 3 14 13 Italy 37 36 29 30 29 24 0 0 2 3 3 7 Jamaica 36 31 0 0 30 30 12 7 9 7 13 26 Japan 69 0 17 1 7 5 Jordan 16 11 0 0 21 36 27 20 7 10 29 24 Kazakhstan 28 0 48 6 8 10 Kenya 30 31 0 0 43 37 16 14 1 0 10 18 Korea, Dem Rep Korea, Rep 34 5 35 12 5 9 Kuwait 1 1 0 6 0 0 2 3 0 0 97 90 Kyrgyz Republic 15 0 65 3 0 17 Lao PDR Latvia 12 34 . 41 1 0 11 Lebanon 11 0 20 28 13 28 Lesotho 11 18 0 0 21 12 57 48 0 0 11 22 Liberia Libya Lithuania 20 12 28 32 40 47 1 1 3 0 8 7 Macedonia, FYR Madagascar 13 15 0 0 19 28 48 52 2 2 18 3 Malawi 37 0 33 16 1 13 Malaysia 31 1 20 18 3 28 Mali Mauritania Mauritius 14 11 4 5 21 38 46 28 6 5 9 12 Mexico 31 34 13 10 56 62 6 4 2 1 11 10 Moldova 3 26 51 .. 6 0 14 Mongolia 24 12 14 18 31 37 17 7 0 1 15 25 Morocco 24 24 4 5 38 36 18 16 4 3 13 16 Mozambique Myanmar 18 20 0 0 28 33 14 4 0 0 41 44 Namibia 34 32 0 0 25 21 27 37 1 1 13 8 Nepal 11 17 0 0 36 34 31 27 5 4 17 18 Netherlands 31 35 22 0 3 9 New Zealand 53 62 0 0 27 29 2 2 3 1 15 7 Nicaragua 17 13 9 16 35 55 19 7 8 13 9 Niger Nigeria Norway 16 20 24 23 34 36 1 0 1 1 24 20 Oman 23 24 0 0 1 1 2 3 1 2 73 70 Pakistan 9 20 0 0 30 32 31 12 0 8 30 27 Panama 17 18 20 19 17 . 12 3 4 31 37 Papua New Guinea 37 54 0 0 14 11 25 27 3 4 20 5 Paraguay 9 11 0 0 21 38 20 12 24 3 25 36 Peru 5 20 7 8 50 50 17 9 19 2 7 20 Philippines 28 40 0 0 31 27 25 19 3 4 13 10 Poland 19 30 38 2 1 10 Portugal 23 27 25 25 34 36 2 0 4 2 12 10 Puerto Rico 2003 World Development Indicators 1 235 0 Collentral government revenues Taxes on Social Taxes on Taxes on Other Nontax Income, profits, security goods and International taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Romania 19 13 23 37 33 33 1 4 15 1 10 11 Russlan Federation 12 29 31 13 0 15 Rwanda 18 7 34 26 4 12 Saudi Arabia Senegal 23 0 37 32 4 4 Sierra Leone 31 26 0 0 23 22 40 49 0 0 5 4 Singapore 26 30 0 0 16 18 2 1 14 10 43 41 Slovak Republic 19 35 31 4 1 10 Slovenia 12 14 47 36 27 38 8 2 0 4 5 5 Somalia South Africa 51 53 2 2 34 34 4 3 2 3 8 5 Spain 32 38 22 2 0 5 SriLanka 11 13 0 0 46 58 29 11 5 4 10 14 Sudan 15 0 35 29 1 20 Swaziland 30 25 0 0 11 14 47 52 2 4 10 5 Sweden 18 14 31 33 29 27 1 0 9 15 13 11 Switzerland 15 16 51 47 23 25 1 1 3 4 7 7 Syrian Arab Republic 31 38 0 0 31 19 7 10 7 6 24 27 Tajikistan 3 20 55 14 1 7 Tanzania Thailand 24 30 0 3 41 43 22 11 4 1 8 11 Togo Trinidad and Tobago Tunisia 13 20 13 17 19 38 28 11 5 4 22 9 Turkey 43 29 0 0 32 41 6 1 3 7 15 21 Turkmenistan Uganda . 17 0 31 48 1 3 Ukraine 13 31 34 4 2 16 United Arab Emirates 0 0 2 1 36 51 0 0 0 0 62 48 United Kingdom 39 40 17 17 28 31 0 0 7 7 9 5 United States 52 57 35 31 3 3 2 1 1 1 8 6 Uruguay 7 15 27 29 36 34 10 3 12 8 5 12 Uzbekistan Venezuela, RB 64 27 4 4 3 25 7 7 0 4 22 34 Vietnam 27 0 34 15 6 17 West Bank and Gaza Yemen, Rep 26 18 0 0 10 9 17 10 5 2 43 61 Yugoslavia, Fed Rep Zambia Zimbabwe 45 0 26 17 1 10 | 1 - - - 2}8 2 7 tiS 6 X lE3 m o9n Go &fi - Gmi 9 ?- _g -8 _tu - - 8 Low Income Mlddle Income 21 18 4 10 25 38 14 6 3 2 16 11 Lower middle income 24 20 0 3 30 37 17 9 4 3 15 13 Upper middle income 18 17 13 29 21 39 12 3 3 1 16 10 Low & middle income 19 17 0 2 28 35 17 9 3 2 14 12 East Asia & Pacific 31 25 0 0 24 32 18 9 3 2 20 11 Europe & Central Asia 13 30 41 4 1 10 Latin America & Carib 17 18 9 10 27 41 13 7 3 3 14 18 Middle East & N Africa 21 19 2 0 17 19 15 14 5 3 28 28 South Asia 11 19 0 0 36 33 30 15 3 4 18 22 Sub-Saharan Africa 23 0 25 27 1 10 High Income 32 26 17 19 28 27 1 1 3 3 9 9 Europe EMU 31 29 35 31 28 26 0 0 3 3 7 7 Note Components may not sum to 100 percent as a result of adjustments to tax revenue 236 0 2003 World Development Indicators Central government revenues 0 The International Monetary Fund (IMF) classifies gov- and corporations) or indirect (sales and excise taxes * Taxes on Income, profits, and capital gains are ernment transactions as receipts or payments and and duties levied on goods and services) This dis- levied on the actual or presumptive net income of mdi- according to whether they are repayable or nonre- tinction may be a useful simplification, but it has no viduals, on the profits of enterprises, and on capital payable If nonrepayable, they are classified as capi- particular analytical significance except with respect gains, whether realized or not, on land, securities, or tal (meant to be used in production for more than a to the capacity to fix tax rates other assets Intragovernmental payments are elimi- year) or current and as requited (involving payment in Social security taxes do not reflect compulsory pay- nated in consolidation * Social security taxes return for a benefit or service) or unrequited ments made by employers to provident funds or other include employer and employee social security contri- Revenues include all nonrepayable receipts (other agencies with a similar purpose Similarly, expendi- butions and those of self-employed and unemployed than grants), the most important of which are taxes tures from such funds are not reflected in government people * Taxes on goods and services include gen- Grants are unrequited, nonrepayable, noncompulsory expenditure (see table 4 12) The revenue shares eral sales and turnover or value added taxes, selective receipts from other governments or from internation- shown in this table may not sum to 100 percent excises on goods, selective taxes on services, taxes al organizations Transactions are generally recorded because adjustments to tax revenues are not shown on the use of goods or property, and profits of fiscal on a cash rather than an accrual basis Measuring For further discussion of taxes and tax policies, monopolies * Taxes on Intemational trade include the accumulation of arrears on revenues or payments see About the data for table 5 6 For further discus- import duties, export duties, profits of export or on an accrual basis would typically result in a higher sion of government revenues and expenditures, see import monopolies, exchange profits, and exchange deficit Transactions within a level of government are About the data for tables 4 11 and 4 12 taxes * Other taxes include employer payroll or labor not included, but transactions between levels are taxes, taxes on property, and taxes not allocable to included In some cases the government budget may other categories They may include negative values include transfers used to finance the deficits of that are adjustments (for example, for taxes collected autonomous, extrabudgetary agencies. on behalf of state and local governments and not allo- The IMF's Manual on Government Finance cable to individual tax categories) * Nontax revenue Statistics (1986) describes taxes as compulsory, includes requited, nonrepayable receipts for public unrequited payments made to governments by indi- purposes-such as fines, administrative fees, or viduals, businesses, or institutions Taxes tradition- entrepreneurial income from government ownership of ally have been classified as either direct (those property-and voluntary, unrequited, nonrepayable levied directly on the income or profits of individuals receipts other than from government sources It does not include proceeds of grants and borrowing, funds 4.13a arising from the repayment of previous lending by gov- _ _ _ _ _ _., ernments, incurrence of liabilities, and proceeds from Direct taxes as % of current revenue, 1999-2000 the sale of capital assets 100 80 + 60 + * + ** 40 The data on central government revenues are 20 ' - ,+* * from the IMF's Govemment Finance Statistics _ 4*, * * * Yearbook, 2002 and IMF data files Each coun- try's accounts are reported using the system of 0 4 common definitions and classifications in the 100 1,000 10,000 100,000 IMF's Manual on Govemment Finance Statistics GNI per capita ($1 (1986) The iMF receives additional information 4 Low-income economies * Middle income economies t High-income economies from the Organisation for Economic Co-operation High-income countries generally rely on direct taxes (such as income and property taxes) and social security contributions, and Development on the tax revenues of some of while low- and middle-income countries tend to rely on indirect taxes on goods and services and on International trade its members See the IMF sources for complete But in all groups there are many exceptions to the rule and authoritative explanations of concepts, defi- Note Data are for the most recent year available in 1999-2000 nitions, and data sources Source International Monetary Fund Government Finance Statistics data files 2003 World Development Indicators 1 237 ~~-L?~JL Monetary indicators and prices Money and Claims on Claims on GDP Consumer Food quasi money private sector governments Implicit price price and other deflator Index Index public entities average annual average annual average annual annual % growth annual growth annual growth % growth % growth % growth of M2 as % of M2 as % of M2 1990- 1990- 1990- 1990 2001 1990 2001 1990 2001 1980-90 2001 1980-90 2001 1980-90 2001 Afghanistan- Albania 19 9 ~ 32 2 6 -04 34 4 242 31 2 Algeria 11.4 48 9 --12.2 5 5 -32 1-64 83 17 0 9 1 -15 5 9 7 16 4 Angola 160 6 - 342 42 3 59 658 8 633 2 2231_ Argentina 1,113 3 -19 4 1,444 7 -12 9 1,573 2 _15 1 391 1 4 3 390 6 7 4 486 5 6.6 Armenia 4 3 -7 8 0 1 172 0 55 8 145 8 Australia 12 8 13 2 15 3 10 8 -2 2 10 72 17 79 22 74 2 8 Austria5 a -33 1 8 3 2 22 -27 1 5 Azerbaijan -. -10 5 -1 7 . -208 96 9 134 5 135 5 Bangladesh 10 4 14 7 9 2 11 2 -0 2 5 8 95 3 9 5 1 _10 8 53 Belarus 58 9 37 4 32 7 318 1 294 7 2 4 176 5 Belgium a 4 1 1 9 -42 1 9 4 0 1 1 Benin 28 6 12 2 -1 3 -0 2 12 4 -11 5 1 7 8 2 7 9 -3 5 10 5 Bolivia 52 8 2 2 40 8 -9 4 18 0 4 7 326 9 8 0 322 5 8 1 321 8 7 6 Bosnia and Herzegovina .2 7 Botswana -14 0 31 2 12 6 7 8 -51 9 -49 5 13 6 9 0 10 0 10 0 10 1 10 0 Brazil 1,289 2 12 1 1,566 4 9 3 3,093 6 38 4 284 0 168 2 285 6 161 6 314.0 -17 6 Bulgaria -53 8 26 7 1 9 11 1 831 1 8 1.8 93.4 6 3 105.3 1 8 1011I Burkina Faso -0.5 1 6 3.6 7 9 -15_ -4 2 3 3 4 5 1 0 5.2 _ 07 5 3 Burundi 9 6 15 7 15.4 9 5 -6 9 25 2 4 4 12 6 7 1 15 9 6 1 Cambodia 20 4 2 1 -4 1 21.7 5 3 Cameroon -1 7 15 1 0 9 6 1 -3 0 1 8 5 6 4.9 8 7 5 9 . 4 1 Canada 7 8 6 5 9 2 6 3 0 6 2 1 4 6 1 5 5 3 1 7 4 6 1 6 Central African Republic -3 7 -1 1 -1 6 3 3 2 3 4 4 7 9 4 2 3.2 4 9 2 0 5 5 Chad -2 4 22 0 1 3 7 4 -17 3 14 0 1.4 6 7 0 6 7 9 -5 3 7.1 Chile 23 5 4 5 21 4 11 8 16 4 3 3 20 7 7 5 20 6 8 3 20 7 7 4 China 28 9 -15.0 26.5 7 8 1 5 4 1 5 7 6 2 7 6 8 8 11 3 Hong Kong, China 8 5 -0 3 7 9 -1 4 -1 0 1 7 7 7 3 3 4 9 6 3 4.1 Colombia 33_0 16 0 8 7 8.0 -5 1 6 9 24 8 20 0 22 7 19 5 24 6 18 2 Congo, Dem Rep 195 4 -180 429 7 62 9 846 2 57 1 813 4 Congo, Rep 18 5 -22 8 5 1 -2 6 -12_6 24 5 0 5 8 8 0 9 8 5 4 3 8 8 Costa Rica 27 5 10 4 7.3 17-3 8 2 -7 9 23 6 16 3 23 0 15 1 16 0 3 1 Cbte d'lvoire -2 6 12 0 -3 9 3 9 -3 0 -6 1 2 8 8 4 5 4 6 7 Croatia 45 7 17 9 3 5 72 2 304 1 72 1 124 6 69 5 Cuba I11 Czech Republic 11 2 -8 0 7 5 10 6 7 3 2 5 Denmark 6 5 3 6 3 0 23 8 --31 2 1 5 8 2 2 5 6 2 1 4 8 2 0 Dominican Republic 42 5 26 9 19 1 18 7 0 7 1.8 21 6 9 1 22 4 8 5 25 4 7 8 Ecuador 48 9 21 4 17.2 29 8 -27 4 -0 1 -4 9 1 7 35.8 38 7 40 7 36 5 Egypt,Arab Rep 28 7 13 2 6.3 7 8 25 3 4 4 13 7 7 8 17 4 8 1 22 0 6 9 El Salvador 32 4 1 0 8 8 -0 3 9.6 2 8 16 3 6.8 19.6 7 8 21 5 8 8 Eritrea 9 0 Estonia 76 5 23 0 27 6 12 4 -6 8 2 6 2 3 46.1 18 9 -19 2 Ethiopia_ 18 5 9.8 0.3 1 6 21 7 -5.0 4 6 6 1 4 0 4 7 3.8 -3 3 Finland a6 7 1 9 6 2 1 6 5 8 -0 5 France5 5 8 1 5 5 8 1 6 5 7 1 3 Gabon 3 3 7 5 0 7 11 6 -20 6 28 5 1 8 5 6 5 1 4 6 4 9 4 3 Gambia, The 8 4 19 4 7 8 11 2 -35 4 28 6 17 9 4 1 20 0 4 0 20 3 3 7 Georgia 18 5 .. -4.4 9 1 1.9 279 0 . 20 6 21 5 Germany a 2 4 1 8 2 2b 2.2 0 3 Ghana 13 3 -9 0 4 9 284- -08_ 65 2 42 1 26 6 _39 1 28 1 33 1 25 0 Greece a 19 3 8 5 18 7 8 3 18 0 7 1 Guatemala 25 8 18 1 15 0 8 9 0 5 -8 1 14 6 9 9 14 0 9 7 221 10 0 Guinea -17 4 12 9 13 1 2 3 2 9 8 0 . 5 1 .. 9 2 Guinea-Bissau 574 6 7.3 90 5 -11 7 460 7 -0 5 57 4 28 7 30 6 Haiti 2 5 14 1 -0 6 3 2 0 4 7 8 7 3 20 3 5 2 20 8 4 1 2 II 2003 World Development Indicators Monetary indicators and prices4. 4 Money and Claims on Claims on GDP Consumer Food quasi money private sector governments Implicit price price and other deflator Index Index public entities average annual average annual average annual annual t6 growth annual growth annual growth % growth % growth % growth of M2 as ft of M2 as % of M2 1990- 1990- 1990- 1990 2001 1990 2001 1990 2001 1980-90 2001 1980-90 2001 1980-90 2001 Honduras 21 4 17 5 13 0 8 1 -10 5 3 9 5 7 18 0 6 3 18 0 5 2 17 8 Hungary 29 2 16 6 23 0 13 0 69 7 -8 9 8 9 18 3 9 6 19 2 9 5 18 5 India 15 1 14 3 5 9 4 8 10 5 6 5 8 2 7 6 8 6 8 7 8 8 8 5 Indonesia 44 6 12 8 66 9 3 8 -8 2 3 5 8 6 15 8 8 3 13 9 8 7 17 0 Iran,Isiamic Rep 18 0 27 6 14 7 20 0 5 8 -3 1 14 4 25 7 18 2 24 7 16 2 25 5 Iraq 1o 3 Ireland a 6 6 3 7 6 8 2 4 6 0 2 7 Israel 19 4 95 18 5 11 0 4.9 -0 9 101 1 9 3 1017 8 9 102 4 8 4 Italya 10 0 3 6 91 -35 8 2 2 9 Jamaica 21 5 8 6 12 5 -40 7 -16 0 13 3 19 9 22 1 15 1 21 4 16 1 20 5 Japan 8 2 2 2 9 7 -3 2 -15 4 4 1 8 -01I 1 7 0 6 1 5 0 4 Jordan 8 3 8 1 4 7 7 1 1 0 1 8 4 3 2 9 5 7 3 3 4 7 3 7 Kazakhstan 4-0 2 57 5 -46 1 168 5 54 8 127 9 Kenya 20 1 2 5 8 0 -2 4 21 5 5~4 9 1 13 4 11 2 14 5 10 0 15 2 Korea, Dem Rep Korea,Rep 17 2 13 2 36 1 15 5 -1 2 0-5 -65 45 49 49 50 5 1 Kuwait 0 7 12 8 33 107_ -3 1 --27 -2 8 1 9 2 9 2 0 1 6 1 5 Kyrgyz Republic 11 3 1 5 -10 1 95 2 21 2 53 2 Lao PDR 7 8 13 7 3 6 12 7 7 0 30 0 37 6 28 5 29 8 Latvia 19 8 22 4 -2 6 0 0 42 0 25 0 21 1 Lebanon 55 1 7 5 27 6 0 0 18 5 10.7 15 1 19 8 Lesotho 8 4 17 2 6 8 3 4 -14 9 4 5 12 1 9 5 13 6 8 8 13 5 13 0 Liberia 19 6 12 7 16 1 7 2 29 5 206 4 2 9 53 3 Libya 19 0 4 3 2 0 0 4 15 0 14 7 1 2 Lithuania 21 4 3 2 0 4 63 3 27 0 2 7 46 6 Macedonia, FYR 32 1 -2 2 27 4 66 0 8 0 Madagascar 4 5 23 8 23 8 6 7 :-14 8 8 8 17 1 17 9 16 6 17 5 15 7 19 1 Malawi 11 1 14 8 15 5 1 4 -12 9 28 8 15_1 33 0 16 9 33 5 16 4 34 5 Malaysia 10 6 2 5 20 8 3 6 -1 2 -2 0 1 7 3 6 2 6 3 4 2 2 4_9 Mali -4 9 19.6 0 1 14 0 -13 4 6 5 4 5 6 9 48_ Mauritania 11 5 173_ 20 2 30 8 1 5 -8 8 8 4 6 2 7 1 5 9 6 3 Mauritius 21 2 10 9 10 8 7 7 0 8 6 2 9 4 6 2 6 9 6 7 7 8 5 9 Mexico 81 9 14 1 48 5 -5 6 13 6 -45 -7 15 18 2 73~8 _186 73 1 18 4 Moldova 358 0 35 8 53 3 21 8 469 1 12 5 103 1 19 3 110 5 Mongolia 31 6 27 9 40 2 21 7 38 5 -8 4 -1 6 51 4 39 0 Morocco 21 5 14 1 12 4 0 3 -4 9 1 1 7 1 2 7 7 0 3 5 6 7 3 4 Mozambique 372_ 28 2 22 0 -50 8 -5 1 70 2 38 3 29 6 28 8 Myanmar 37 7 43 9 12.8 18 7 24 2 29 4 12 2 24 6 11 5 25 0 11 9 27 5 Namibia 30 3 _45 154 14 8 -4 2 -07 14 1 85 12 6 95 139 8 6 Nepal 18 5 11 5 5 7 7 1 7 3 5 6 11 1 7 8 10 2 8.1 10 5 8 4 Netherlands a 1 5 2 1 2 0 2 4 1 3 1 3 New Zealand 12 5 6 8 4 2 9 7 -1 7 -1 1 10 5 1 6 11 0 1 8 9 8 1 5 Nicaragua 7,677 8 41 0 4,932 9 -10 2 12,679 2 30 7 422 3 45 2 535 7 35 1 69 2 25 4 Niger -4 1 31 4 -5 1 4 4 1 4 -7 3 1 9 5 8 07_ 5 7 -1 5 6 8 Nigeria 32 7 27 0 7 8 23 0 27 1 13 3 16.7 26 5 21 5 30 0 22 5 27 9 Norway 5 6 8 7 5 0 14 6 -0 6 -31 8 5 6 3 2 7 4 2 2 7 8 1 9 Oman 10 0 9 2 9 6 8 0 -10 9 9 0 -3 6 1 8 0 0 0 9 0 2 Pakistan 11 6 11 7 5 9 2 3 7 7 --27 _67 9 6 6 3 9 1 6 6 9 4 Panama 36 6 9 6 0 8 11 4 -25 7 -2 9 1 9 1 9 1 4 1 1 1 5 0 8 Papua New Guinea 4 3 1 6 -0 9 -2 1 8 8 -8 2 5 3 7 3 5 6 9 7 4 6 9 6 Paraguay 54 4 16 4 32 0 9 0 -9 2 1 6 24 4 12 0 21 9 12 5 24 9 10 9 Peru 6,384 9 2 1 2,123 7 -3 5 2,129 5 4 3 220 2 23 3 246 1 23 8 221 8 21 5 Philippines 22 4 3 6 15 6 -1 1 3 4 2 8 14 9 8 2 134 8 0 14 1 7 1 Poland 160 1 15 0 20 8 5 6 75 6 4 1 21 3 50 9 23 1 52 4 19 8 Portugal a 17 9 5 1 17 1 4 3 16 7 3 5 Puerto Rico 3 5 3 1 2 7 9 6 2003 World Development Indicators I 239 t1 tzol3 tMonetary indicators and prices Money and Claims on Claims on GDP Consumer Food quasi money private sector govemments Implicit price price and other deflator Index Index public entities average annual average annual average annual annual % growth annual growth annual growth X growth % growth % growth of M2 as % of M2 as % of M2 1990- 1990- 1990- 1990 2001 ±990 2001 1990 2001 1980-90 2001 1980-90 2001 1980-90 2001 Romania 26 4 46 2 17 6 0 0 -3 9 910 92 8 4 3 741 Russian Federation 36 1 33 4 -1 4 139 6 85 9 52 4 Rwanda 56 11 0 -100 58 268 -05 40 131 39 147 64 1441 SaudiArabia 46 51 -45 47 42 3 1 -49 37 -08 08 -02 08 Senegal -48 136 -84 43 -53 2.4 6.5 42 62 50 53 54 Sierra Leone 74 0 33 7 4 9 3 2 228 7 19 9 60 3 29 2 72 4 27.0 Singapore 200 59 137 152 -49 -47 19 09 16 1 6 10 16 Slovak Republic 11 9 -3 7 16 8 1 8 10 4 8 5 1 6 14 6 Slovenia 123 0 30 4 96.1 14 4 -10 4 3 1 18 3 22 0 129 5 23 5 Somalia . 49 7 South Africa 11 4 16 7 13 7 28 6 1 8 -1 6 15 5 9 3 14 8 8 3 15 2 9 5 Spain a 9 3 3 9 90 37 93 30 SriLanka 199 144 162 69 68 136 110 91 109 99 110 1:03 Sudan 48 8 24 7 12 6 8 6 29 4 8 6 38 4 58 2 37 6 668 Swaziland 0 6 10 7 20 5 3 6 -131 -16 7 10 7 12 3 14 6 9.3 13 3 12 2 Sweden 08 19 134 124 -121 21 73 20 70 18 82 -03 Switzerland 0 8 3 9 11 7 -1 5 10 11 3 4 1 2 2 9 15 3 1 0 7 Syrian Arab Republic 26 4 23 5 3 4 0 4 11 6 7 4 15 3 7 4 23 2 5 9 25 0 4 5 Tajikistan 35 0 223 1 -17 2 2 5 202 4 477 3 Tanzania 419 17 1 22 6 5 0 80 6 -9 9 .. 20 1 310 19 3 32 0 20 1 Thailand 267 22 300 -84 -40 11 39 39 35 46 27 54 Togo 95 -26 18 -42 69 -29 48 66 25 78 11 25 Trinidad and Tobago 62 69 27 24 -19 -60 24 54 107 57 146 127 Tunisia 76 107 59 11 6 18 -10 74 43 74 42 83 43 Turkey 53 2 86 2 42 9 12 0 2 2 96 5 45 3 74 2 44 9 77.9 18 3 318 Turkmenistan 83 3 10 8 59 0 328 0 Uganda 60 2 9 2 . 0 4 -0 9 -8 7 113 8 10 9 102 5 9 5 9 7 Ukraine 43 0 241 -01 .. 220 9 2004 2 0 120 7 United Arab Emirates -8 2 23 2 1 3 8 4 -4 8 -8 3 0 8 2 3 United Kingdom 105 86 13 1 10 7 10 08 58 28 58 28 4 5 1 8 UnitedStates 49 140 11 78 06 20 38 20 42 27 39 25 Uruguay 118_5 19 0 56 2 7 5 25 8 0 8 62 7 27 8 61 1 30.2 62.0 27 5 Uzbekistan . , . 210 7 Venezuela, RB 64 9 15 3 17 6 13 6 45 3 10 3 19.3 42 8 20 9 45 9 351 48 0 Vietnam 27 3 169 1 3 2222 138 32 West Bank and Gaza 8 4 Yemen, Rep 11 3 20 9 1 4 4 2 10 2 -1 0 0 . 21 1 32 6 Yugoslavia, Fed Rep . . . . -0 6 Zambia 47 9 13 6 22 8 3.5 195 2 -19 5 42 2 48 1 72 5 80 8 42 8 58 1 Zimbabwe 15 1 128 5 13 5 496 5 0 699 116 28 4 13 8 31 8 15 1 36 5 a As members of the European Monetary Union. these countries share a single currency, the euro b Data prior to 1990 refer to the Federal Republic of Germany before unification MO 0 2003 World Development Indicators Monetary indicators and prices O Money and the financial accounts that record the financial derivatives and the net liabilities of the * Money and quasi money comprise the sum of cur- supply of money lie at the heart of a country's finan- banking system can also be difficult rency outside banks, demand deposits other than cial system There are several commonly used defi- The quality of commercial bank reporting also may those of the central government, and the time, sav- nitions of the money supply The narrowest, Ml, be adversely affected by delays in reports from bank ings, and foreign currency deposits of resident sec- encompasses currency held by the public and branches, especially in countries where branch tors other than the central government This definition demand deposits with banks M2 includes Ml plus accounts are not computerized Thus the data in the of the money supply, often called M2, corresponds to time and savings deposits with banks that require a balance sheets of commercial banks may be based lines 34 and 35 in the International Monetary Fund s notice for withdrawal M3 includes M2 as well as var- on preliminary estimates subject to constant revi- (IMF) International Financial Statistics (IFS) The ious money market instruments, such as certificates sion This problem is likely to be even more serious change in money supply is measured as the differ- of deposit issued by banks, bank deposits denomi- for nonbank financial intermediaries ence in end-of-year totals relative to M2 in the pre- nated in foreign currency, and deposits with financial Controlling inflation is one of the primary goals of ceding year * Claims on private sector (IFS line 32d) institutions other than banks However defined, monetary policy and is intimately linked to the growth include gross credit from the financial system to indi- money is a liability of the banking system, distin- in money supply Inflation is measured by the rate of viduals, enterprises, nonfinancial public entities not guished from other bank liabilities by the special role increase in a price index, but actual price change can included under net domestic credit, and financial it plays as a medium of exchange, a unit of account, also be negative Which index is used depends on institutions not included elsewhere * Claims on gov- and a store of value which set of prices in the economy is being examined emments and other public entitles (IFS line 32an + The banking system's assets include its net for- The GDP deflator reflects changes in prices for total 32b + 32bx + 32c) usually comprise direct credit for eign assets and net domestic credit Net domestic gross domestic product. The most general measure specific purposes, such as financing the government credit includes credit to the private sector and gen- of the overall price level, it takes into account budget deficit, loans to state enterprises, advances eral government, and credit extended to the nonfi- changes in government consumption, capital forma- against future credit authorizations, and purchases of nancial public sector in the form of investments in tion (including inventory appreciation), international treasury bills and bonds, net of deposits by the pub- short- and long-term government securities and trade, and the main component, household final con- lic sector Public sector deposits with the banking sys- loans to state enterprises, liabilities to the public sumption expenditure The GDP deflator is usually tem also include sinking funds for the service of debt and private sectors in the form of deposits with the derived implicitly as the ratio of current to constant and temporary deposits of government revenues banking system are netted out Net domestic credit price GDP, resulting in a Paasche index It is defective * GDP Implicit deflator measures the average annu- also includes credit to banking and nonbank financial as a general measure of inflation for use in policy al rate of price change in the economy as a whole for institutions because of the long lags in deriving estimates and the periods shown * Consumer price Index reflects Domestic credit is the main vehicle through which because it is often only an annual measure changes in the cost to the average consumer of changes in the money supply are regulated, with cen- Consumer price indexes are more current and pro- acquiring a basket of goods and services that may be tral bank lending to the government often playing the duced more frequently They are also constructed fixed or may change at specified intervals, such as most important role The central bank can regulate explicitly, based on surveys of the cost of a defined yearly The Laspeyres formula is generally used lending to the private sector in several ways-for basket of consumer goods and services * Food price Index is a subindex of the consumer example, by adjusting the cost of the refinancing facil- Nevertheless, consumer price indexes should be price index ties it provides to banks, by changing market interest interpreted with caution The definition of a house- rates through open market operations, or by control- hold, the basket of goods chosen, and the geo- ling the availability of credit through changes in the graphic (urban or rural) and income group coverage reserve requirements imposed on banks and ceilings of consumer price surveys can all vary widely across The monetary, financial, and consumer pnce index on the credit provided by banks to the private sector countries In addition, the weights are derived from data are published by the IMF in its monthly Monetary accounts are derived from the balance household expenditure surveys, which, for budgetary Intemational Financial Statistics and annual sheets of financial institutions-the central bank, reasons, tend to be conducted infrequently in devel- International Financial Statistics Yearbook The commercial banks, and nonbank financial intermedi- oping countries, leading to poor comparability over IMF collects data on the financial systems of its aries Although these balance sheets are usually reli- time Although useful for measuring consumer price member countries The World Bank receives data able, they are subject to errors of classification, inflation within a country, consumer price indexes are from the IMF in electronic files that may contain valuation, and timing and to differences in account- of less value in making comparisons across coun- more recent revisions than the published sources ing practices For example, whether interest income tries Like consumer price indexes, food price index- The GDP deflator data are from the World Bank's is recorded on an accrual or a cash basis can make es should be interpreted with caution because of the national accounts files The food price index data a substantial difference, as can the treatment of high variability across countries in the items covered are from the United Nations Statistics Division's nonperforming assets Valuation errors typically The least-squares method is used to calculate the Statistical Yearbook and Monthly Bulletin of arise with respect to foreign exchange transactions, growth rates of the GDP implicit deflator, consumer Statistics The discussion of monetary indicators particularly in countries with flexible exchange rates price index, and food price index draws from an IMF publication by Marcello Caiola, or in those that have undergone a currency devalua- A Manual for Country Economists (1995) tion during the reporting period The valuation of 2003 World Development Indicators 1 241 mOon Balance of payments current account Goods and services fiet Income Net Current Gross curfent account Intemnational transfers balance reserves Exports Imports $ millions $ millions $ millions $ millions S millions $ millions 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan . 638 Albania 354 839 485 1,776 -2 146 15 571 -118 - -220 - 393 Algeria 13,462 21,829 10,106 11,270 -2,268 -2,877 333 1,420 2,703 19,62-5 Angola 3,992 7,011 3,385 5,908- -765 -1,491 -77 33 -236 -35-5 732 Argentina 14,800 30,920 -6,846 ~27,563 -4,400 -8 ,095 998 183 4,552 -4,554 6,222 1-4,556 Armenia 540 978 64 174 -201 1 333 Australia 49.843 79,909 53,056 78,512 -13,176 -10,293 439 21 -15,950 -8,876 19,319 18,664 Austria 63,694 99,795 61,580 99,762 -942 -2,995 -6 -1,141 1,166 -4,103 17.228 15,599 Azerba ijan 2,369 2,130 -367 . 77 -52 897 Bangladesh 1,903 7,235 4,156 10,103 '-122 _-264 - 802 2,316 -1,573 -816 - 660 1,306 Belarus 8,269 8,666 -43 154 -285 391 Belgiuma 138,605 213,811 135,098 203,106 2,316 2,9-07 -2,197 -4,220- 3,627 9,392 23,789 b 13,560 b Benin 364 554 454 736 -25 -32 97 139 _-18_ -74 69 578- Bolivia 977 1,521 1,086 1,996 -249 -210 159 393 -199 -293 511 1,146_ Bosnia and Herzegovina 1,274_ . 2,617 223 . 168 --952- Botswana 2,005 2,655 1,987 2,145 -106 -275 130 204 42 438 3,331 5,897 Brazil 35,170 67,547 28,184 72,652 -11,608 -19,745 799 1,639 -3,823 -23,211 9,200 35,867 Bulgaria 6,950 7,526 8,027 8,562 -758 -342 125 488 -1,710 --889 670 3,646 Burkina Faso 349 275 758 641 0 -24 332 53 -77 -338 305 261 Burundi 89 52 318 148 -15_ -9 174 80 -69 -24 112 18 Cambodia 314 1,634 507 1,969 -21 -43 120 273 -93 -105 587 Cameroon 2,251 2,708 1,931 2,479 -478 -494 -39 119 -196 -1 47 37 340 Canada 149,538 304,491 149,118- 268,490 -19,388 -17,778 -796 1,255 -19,764 19,479 23,530 34,253 Central African Republic 220 106 410 144 -22 -4 123 59 -89 16 123 122 Chad 271 290 488 974 -21 -8 192 31 -46_ -660_ 132 125 Chile 10,221 22,317 9,166 21,226 -1,737 -2,757 198 423 -485 -1,243 6,784 14,399 China t 57,374 299,409 46,706 271,325 1,055 -19,175 274 8,492 11,997 17,401 34, 476 220,057 Hong Kong, China 100,413 232,356 94,084 223,573 0 4,633 -1,682 6,329 11,736 24,656 111.174 Colombia 8,679 14,932 6,858 15,840 -2,305 -2,975 1,026 2,094 542 -1,788 4,869 10,244 Congo, Dem Rep 2,557 1,015 2.497 953 -770 -416 .261 Congo, Rep 1,488 2,497 1,282 1,373 -460 -733 3 -2-51 10 72 Costa Rica 1,963 6,959 2,346 7,393 -233 -415 192 148 -424 -702 525 1,330 C6te dIlvoire- 3,503 4,435 3,445 3,640 -1,091 -577 -181 -2-75 -1.214 -58 21 1,019 Croatia 9,631 10,677 . -537 966 -617 167 4,703 Cuba Czech Republic 40,495 42,049 -1,540 470 -2,624 14,464 Denmark -48.902 77,856 41,415 67,489 -5,708 -3,598 -408 -2.627 1,372 4,142 11,226 I 17,70-2 Dominican Republic 1,832 8,332 2,233 10,079 -249 -1.119 371 2,028 -280 -839 69 1,105 Ecuador 3,262 5,774 2,519 6,754 -1,210 -1.364 107 1,544 -360 -800 1,009 1,073 Egypt, Arab Rep 9,151 16,925 13,710 21,772 -912 1,072 4,836 3,742 -634 -33 3,620 13,598 El Salvador 973 3,977 1,624 5,892 -132 -266 631 2.0014 -15.2 -177 595 1,871 Eritrea 88 147 278 523 0 -4 171 173 -19 -206_ Esto-nia 664 4.981 711 5,190 -13 -281 97 151 --36 -339 198 822 Ethiopia 672 957 1,069 1,944 -67 -59 220 774 -244 -272 55 490 Finland 31,180 48,812 33,456 38,427 -3,735 -1,070 -952 -6-84 -6,962 -8.631 10,415 8,420 France _ 285,389 371,795 283,238 -351,033 -3,89 6 15,384 -8,199 -14,788 -9,944 21,359 68,291 58,637 Gabon 2,730 3,180 1,812 1 ,961_ -617 -711 -134 -73 168 435 279 1-3 Gambia, The 168 277_ 192 349 -11 -7 59 26 23 -53. -55 106 Georgia 676 1,254 125 135 _ . -269 159 Germany 474,713 657,453 423,497 619,920 20,832 -11,268 -23,745 -23,823 48,303 2,442 104,547 8-2,037 Ghana 983 2.380 1,506 -3,247 -111 -138 411 753 -223 -251 309 376 Greece 13,018 30,071 19,564 41,291 -1,709 -1,767 4.718 3,587 -3,537 -9,400 4,721 6,244 Guatemala 1,568 3,896 1,812 6,040 -96 9 2 9 23 -,3 362 2,352 Guinea 829 834 953 881 -149 -102 70 90 -203 -60 80 200 Guinea-Bissau 26 55 88 96 -22 -16 39 . -45 18 69 Haiti 303 463 655 1,230 -5 9 70 582 -288 -177 10 142 T Data for Taiwan, China 74,175 142,514 67,015 126,598 4,361 5,679 -601 -2,734 10,920 18,861 77,653 125,960 2 B 2003 World Development Indicators Balance of payments current account4.5 Goods and services Net Income Net Current Gross current account International transfers balance reserves Exports Imports $ millions $ millions $ millions $ millions $ millions $ millions 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 1,032 2,412 1,127 3,461 -237 -147 280 871 -51 -325 47 1,421 Hungary 12,035 35,778 11,017 35,633 -1,427 -1,488 787 246 379 -1,097 1,185 10,755 India 23,028 -65,200 31,485 73,700 -3,753 *-2,700 2,068 12,500 -10,142 1,300 5,637 49,051 Indonesia 29,295 62,864~ 27,5_11 .50,549 -5,190 -6,936 418 1,520 -2,988 6,899 8,657 28.104 Iran, Islamic Rep 19,741 23,716 -22,292 18,138 378 -1,144 -582 320 -2,755 4,754 Iraq Ireland -26,786 98,566 -24,576 83,221 -4,955 -16,864 2,384 477 -361 -1,043 5,362 5,636 Israel 17,312 39,669 20,228 43,505 _-1,981 -4.415 5,060~ 6,399 163 -1,852 6,598 23,379 Italy 219,971 299,978 _218,573 283,912 -14.712 -10,28-1 -3,164 -5,948 -16,479 -163 88,595 46,215 Jamaica 2,217 3,355 2,390 4,592 -430 -438 291 886 -312 -788 168 1,901 Japan 323,692 448,107 297,306 421,627 22,492 69,221 -4 ,800 -7,904 44,078 87,797 87,828 401,958 Jordan 2,511 3,776 3,56-9 6,026 - -214 187 1,045 2,059 -227 -4 1,139 3.174 Kazakhstan - 10,304 - 10,660 --1,115 -230 -1,240 2,506 Kenya 2,228 2,981 2,705 4,002 -418 -147 368 850 -527 -318 236 1,065 Korea, Dam Rep Korea, Rep 73,295 180,973 76,360 171,107 -87 -886 1.150 -363 -2,003 8,617 14,916 102,875 Kuwait 8,268 17,953 7,169 12,267 7,738 4,956 -4,951 -2,080 3,886 8,562 2,929 10.599 Kyrgyz Republic -561 566 --66 -51 -20 287 Lao PDR 102 477 21 2 560 -1 -34 56 34 -55 -82 8 151 Latvia - - 1,090 3,403 997 4,259 2 44 96 78 191 -734 1,217 Lebanon 511 1,922 2,836 7,031 -622 942 1,818 183 115 -3,984 4,210 7,564 Lesotho 100 319 754 728 433 179 286 135 65 -95 72 386 Liberia 146 181 -80 1 0 Libya -11,469 6,813 8,960 4,914 174 289 -481 -204 2,201 1,984 7,225 16,079 Lithuania 6,046 6,697 -180 2-58 -574 107 1,669 Macedonia, FYR 1,387 1,912 -41 .. 241 -324 799 Madagascar 471 152 809 175 -161 -10 234 15 -265 -17 92 398 Malawi 443 495 549 966 -80 -74 99 15 -86 -531 142 210 Malaysia 32,665 102,435 31,765 86,254 -1,872 -6,743 102 -2,152 -870 7,287 10,659 30,798 Mali 420 825 830 1,085 -37 -129 225 -221 198 349 Mauritania 471 377 520 434 -46 -16 86 137 -10 65 59 228 Mauritius - 1,722 2,837 1,916 2,675 -23 14 97 70 -119 247 761 853 Mexico 48,805 171,142 51,915 185,592 -8,316 -12,574_ 3,975 _9,341 -7.451 -17.683 10,217 44,805 Moldova 740 1,101 . 109 153 -99 229 Mongolia 493 546 1,096 723 -44 4 7 94 -640 -79 23 257 Morocco -- 6,239 11,171 7,783 12,282 -988 -833 2 ,336 3,555 -196 -1,611 2,338 8,669 Mozambique 229 2,304 996 3,905 -97 -574 448 571 -415 -1,604 233 729 Myanmar 641 2,646 1,182 3.016 -61 -57 77 209 -526 -218 410 464 Namibia 1,220 1,747 1,584 1~,985 37 ~109 354 28 50 234 Nepal 379 1,358 761 1,983 71 9 60 788 -251 172 354 1,080 Netherlands -159,304 255,875 147,652 237,984 -620 --7,522 --2,943 -6,626 8,089 3,743 34,401 16,897 New Zealand - 11,683 18,264 11,699 16.663 -1,576 -3,146 138 141 -1,453 -1,403 4,129 3,008 Nicaragua 392 934 682 1,983 -217 -249 202 740 -305 -557 166 380 Niger 533 324 728 486 -54 -11 14 4 -236 -170 226 107 Nigeria 14,550 21,201 6,909 15,418 -2,738 -2,274 85 1,292 4,988 506 4,129 10,647 Norway 47,078 77,657 38,911 49,073 -2,700 -940 -1,476 -1,684 3,992 25,960 15,788 15,815 Oman 5,577 11,423 3,342 6,988 -254 -588 -874 -1,532 1,106 2,315 1,784 2,445 Pakistan 6,217 10,284 9,351 - 12,535 -:966 -2,160 2,748 3,299 -1,352 -1,112 1,046 4,218 Panama 4,438 7,701 4,193 7,853 -255 -545 219 199 209 -500 344 1,092 Papua New Guinea 1,381 2,098 1,509 1,594 -103 -230 156 13 -76 286 427 440 Paraguay 2,514 2,989 2,169 3,364 2 2 43 166 390 -207 675 723 Peru 4,120 8,597 4,087 9,489 -1,733 -1,203 281 997 -1,419 -1,098 1,891 8,980 Philippines 11,430 34,393 13,967 33,586 -872 3,252 714 444 -2,695 4,503 2.036 15,649 Poland 19.037 51,419 15,095 58,275 -3,386 -1,390 2.511 2,889 3,067 -5,357 4,674 26,563 Portugal 21,554 34,582 27,146 44,967 -96 -3,054 5,507 3,480 -181 -9,959 20,579 15,060 Puerto Rico 2003 World Development Indicators I 243 Balance of payments current account Goods and servlces Net Income Net Current Gross current account International transfers balance reserves Exports Imports $ millions $ millions $ millions $ millions $ millions $ millions 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 6,380 13,379 9,901 16,557 161 -282 106 1,143 -3,254 -2,317 1,374 6,377 Russian Federation 112,507 73,168 -3,959 -759 34,621 36,303 Rwanda 145 143 359 434 -17 -20 145 193 -86 -118 44 212 Saudi Arabia 47,445 78,214 43,939 47,952 7,979 -520 -15,637 -15,240 -4,152 14,502 13,437 18,867 Senegal 1,453 1,373 1,840 1,745 -129 -106 153 180 -363 -297 22 447 Sierra Leone 210 91 215 284 -71 -16 7 -69 5 51 Singapore 67,489 148,646 64,953 130,048 1,006 664 -421 -1,377 3,122 17,884 27,748 75,375 Slovak Republic 15,096 16,750 -315 120 -694 4,453 Slovenia 7,900 11,302 6,930 11,420 -38 19 46 129 978 31 112 4,397 Somalia 70 322 South Africa 27,742 35,304 21,016 30,885 -4,271 -3,846 -321 -738 2,134 -166 2,583 7,627 Spain 83,595 175,336 100,870 182,577 -3,533 -9,545 2,799 1,705 -18,009 -15,082 57,238 34,235 Sri Lanka 2,293 6,187 2,965 7,130 -167 -280 541 959 -298 -265 447 1,304 Sudan 499 1,713 877 2,055 -136 -554 141 374 -372 -522 11 118 Swaziland 658 894 768 1,061 59 34 102 80 51 -53 216 272 Sweden 70,560 98,197 70,490 85,388 -4,473 -2,852 -1,936 -3,261 -6,339 6,696 20,324 15,625 Switzerland 96,928 123,552 96,389 109,531 8,746 12,677 -2,329 -4,073 6,957 22,624 61,284 51,543 Syrian Arab Republic 5,030 7,448 2,955 5,994 -401 -783 88 485 1,762 1,062 Tajikistan 185 673 238 801 0 -30 . 84 -53 -74 . 94 Tanzania 538 1,402 1,474 2,179 -185 -46 562 86 -559 -738 193 1,157 Thailand 29,229 76,226 35,870 69,239 -853 -1,361 213 601 -7,281 6,227 14,258 33,041 Togo 663 436 847 624 -32 -24 132 68 -84 -140 358 126 Trinidad and Tobago 2,289 4,827 1,427 3,788 -397 -471 -6 459 _ 513 1,924 Tunisia 5,203 9,518 6,039 10,422 -455 -940 828 935 -463 -910 867 2,050 Turkey 21,042 50,438 25,652 45,845 -2,508 -5,000 4,493 3,803 -2,625 3,396 7,626 19,911 Turkmenistan 1,238 2,777 857 2,807 0 -111 66 68 447 -74 1,513 Uganda 246 664 676 1,454 -77 -119 78 540 -429 -369 44 983 Ukraine 21,086 20,473 .._ -667 1,456 1,402 469 3,089 United Arab Emirates . 4,891 14,256 United Kingdom 239,226 385,830 264,090 418,989 -5,154 13,166 -8,794 -10,283 -38,811 -30,277 43,146 40,442 United States 535,260 998,030 616,120 1,356,320 28,560 14,370 -26,660 -49,470 -78,960 -393,390 173,094 130,077 Uruguay 2,158 3,276 1,659 3,718 -321 -114 8 43 186 -513 1,446 3,099 Uzbekistan 3,201 3,152 _ -11 -205 2 43 -236 -113 - 1,242 Venezuela, RB 18,806 28,006 9,451 22,005 -774 -1,453 -302 -617 8,279 3,931 12,733 12,264 Vietnam 17,837 17,928 -477 1,250 682 3,675 West Bank and Gaza Yemen, Rep 1,490 4,125 2,170 3,265 -372 -1,254 1,790 1,501 739 1,107 441 3,672 Yugoslavia, Fed Rep 2,762 5,160 -26 1,828 -596 Zambia 1,360 1,053 1,897 1,626 -437 -108 380 32 -594 -553 201 183 Zimbabwe 2,012 1,961 2,001 1,905 -263 -245 112 -140 295 119 I ~ ~ ± ( -4, -0 wg f 7, ,£5 B E e 9 - -e -__ Low Income 134,108 274,876 153,830 280,252 Middle Income 629,670 1,547,335 585,956 1,454,555 Lower middle income 317,372 819,932 318,412 754,424 Upper middle income 310,090 727,315 268,056 699,645 Low & middle Income 762,775 1,822,224 737,971 1,734,704 East Asia & Pacific 166,961 604,321 165,987 541,844 Europe & Centrai Asia 407,735 386,921 Latin America & Canb 169,974 404,153 147,208 431,219 Middle East & N Afnca 129,973 202,257 130,645 160,598 South Asia 34,113 90,957 49,041 106,305 Sub-Saharan Africa 81,250 113,199 74,104 107,805 High income 3,481,648 5,732,565 3,497,811 5,846,561 Europe EMU 1,518,561 2,277,557 1,482,825 2,189,310 a Includes Luxembourg b Excludes Luxembourg 244 B 2003 World Development Indicators Balance of payments current account 0 The balance of payments records an economy's residence and ownership, and the exchange rate * Exports and Imports of goods and services com- transactions with the rest of the world Balance of used to value transactions-contribute to net errors prise all transactions between residents of an econo- payments accounts are divided into two groups the and omissions In addition, smuggling and other ille- my and the rest of the world involving a change in current account, which records transactions in gal or quasi-legal transactions may be unrecorded or ownership of general merchandise, goods sent for goods, services, income, and current transfers, and misrecorded For further discussion of issues relat- processing and repairs, nonmonetary gold, and serv- the capital and financial account, which records cap- ing to the recording of data on trade in goods and ices * Net Income refers to receipts and payments ital transfers, acquisition or disposal of nonpro- services, see About the data for tables 4 4-4 8 of employee compensation for nonresident workers, duced, nonfinancial assets, and transactions in The concepts and definitions underlying the data in and investment income (receipts and payments on financial assets and liabilities The table presents the table are based on the fifth edition of the direct investment, portfolio investment, and other data from the current account with the addition of International Monetary Fund's (IMF) Balance of investments and receipts on reserve assets) Income gross international reserves Payments Manual (1993) The fifth edition redefined derived from the use of intangible assets is recorded The balance of payments is a double-entry as capital transfers some transactions previously under business services * Net current transfers are accounting system that shows all flows of goods and included in the current account, such as debt for- recorded in the balance of payments whenever an services into and out of an economy, all transfers giveness, migrants' capital transfers, and foreign aid economy provides or receives goods, services, that are the counterpart of real resources or financial to acquire capital goods Thus the current account income, or financial items without a quid pro quo All claims provided to or by the rest of the world without balance now reflects more accurately net current transfers not considered to be capital are current a quid pro quo, such as donations and grants; and transfer receipts in addition to transactions in goods, * Current account balance is the sum of net exports all changes in residents' claims on, and liabilities to, services (previously nonfactor services), and income of goods and services, net income, and net current nonresidents that arise from economic transactions (previously factor income) Many countries maintain transfers * Gross International reserves comprise All transactions are recorded twice-once as a cred- their data collection systems according to the fourth holdings of monetary gold, special drawing rights, it and once as a debit In principle the net balance edition Where necessary, the IMF converts data reserves of IMF members held by the IMF, and hold- should be zero, but in practice the accounts often do reported in such systems to conform to the fifth edi- ings of foreign exchange under the control of mone- not balance In these cases a balancing item, net tion (see Pnmary data documentation) Values are in tary authorities The gold component of these errors and omissions, is included U S dollars converted at market exchange rates reserves is valued at year-end (31 December) London Discrepancies may arise in the balance of pay- The data in this table come from the IMF's Balance prices ($385 an ounce in 1990 and $276 50 an ments because there is no single source for balance of Payments and International Financial Statistics ounce in 2001) of payments data and therefore no way to ensure databases, supplemented by estimates by World that the data are fully consistent Sources include Bank staff for countries whose national accounts are customs data, monetary accounts of the banking recorded in fiscal years (see Pnmary data documen- system, external debt records, information provided tation) and countries for which the IMF does not col- by enterprises, surveys to estimate service transac- lect balance of payments statistics In addition, tions, and foreign exchange records Differences in World Bank staff make estimates of missing data for collection methods-such as in timing, definitions of the most recent year. 4.15a Workers' remittances ($ billions) More information about the design and compila- 12 tion of the balance of payments can be found in * 1980 a 1990 U 2001 the IMF's Balance of Payments Manual, fifth edi- 10 tion (1993), Balance of Payments Textbook 8 1 1 (1996a), and Balance of Payments Compilation Guide (1995) The balance of payments data are 6 published in the IMF's Balance of Payments Statistics Yearbook and Intemational Financial 4 D 1111 _ Statistics The World Bank exchanges data with the IMF through electronic files that in most cases 2 are more timely and cover a longer period than 0 __ the published sources. The IMF's International India Mexico Morocco Egypt. Arab Rep Turkey Financial Statistics and Balance of Payments databases are available on CD-ROM. Source International Monetary Fund, Balance of Payments data files, World Bank data files. 2003 World Development Indicators 1 245 L1 0ExternaG debt Total Long-term Pubilc and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt extenmal debt IBRD loans and Total IDA credits $ millions $ millions $ millions $ millions $ millions $ millions 1990 2001 1990 2001 ±990 2001 1990 2001 1990 2001 1990 2001 Afghanistan . Albania 349 1,094 36 980 36 970 0 366 0 11 0 83 Algeria 28,149 22,503 26,688 20,786 26,688 20,786 1,208 1,328 0 0 670 1,518 Angola 8,594 9,600 7,605 7,443 7,605 7,443 0 228 0 0 0 0 Argentina 62,233 136,709 48,676 102,733 46,876 85,337 2,609 9,440 1,800 17,396 3,083 13,976 Armenia 1.001 . 786 766 435 20 173 Australia Austria Azerbaijan 1,219 821 726 235 95 295 Bangladesh 12,439 15,216 11,657 14,773 11,657 14,773 4,159 6,456 0 0 626 149 Belarus 869 . 642 641 . 91 2 81 Belgium . . Benin 1,292 1,665 1,218 1,503 1,218 1,503 326 598 0 0 18 77 Bolivia 4,275 4,682 3,864 4,095 3,687 3,116 587 1,146 177 - 979 257 207 Bosnia and Herzegovina . 2,226 2,057 - 2,045 983 12 111 Botswana 563 370 557 349 557 349 169 19 0 0 0 0 Brazil 119,964 226,362 94,427 189,748 87,756 93,467 8,427 7,963 6,671 96,280 1,821 8,337 Bulgaria 10,890 9,615 9,834 8,159 9,834 7,378 0 844 0 782 0 1,110 Burkina Faso 834 1,490 750 1,310 750 1,310 282 636 0 0 0 117 Burundi 907 1,065 851 974 851 974 398 582 0 0 43 2 Cambodia 1,845 2,704 1,683 2,401 1,683 2,401 0 238 0 0 27 80 Cameroon 6,676 8,338 5,595 7,138 5,365 6,913 889 936 230 225 121 244 Canada Central African Republic 698 822 624 757 624 757 265 372 0 0 37 31 Chad 524 1,104 464 992 464 992 186 525 0 0 31 89 Chile 19,226 38,360 14,687 35,803 10,425 5,544 1,874 734 4,263 30,259 1,156 0 China 55,301 170,110 45,515 126,190 45,515 91,706 5,881 20,203 0 34,484 469 0 Hong Kong, China . . Colombia 17,222 36,699 15,784 32,960 14,671 21,777 3,874 2,012 1,113 11,184 0 0 Congo, Dem Rep 10,259 11,392 8,994 7,584 8,994 7,584 1,161 1,232 0 0 521 377 Congo, Rep 4,947 4,496 4,200 3,631 4,200 3,631 239 203 0 0 11 39 Costa Rica 3,756 4,586 3,367 3,424 3,063 3,208 412 104 304 216 11 0 Cote d'lvoire 17,251 11,582 13,223 9,963 10,665 8,590 1,920 1,817 2,558 1,372 431 464 Croatia 10,742 10,335 6,400 .. 427 3,935 122 Cuba Czech Republic 6,383 21,691 3,983 12,735 3,983 5,915 0 205 0 6,820 0 0 Denmark Dominican Republic 4,372 5,093 3,518 3,749 3,419 3,749 258 330 99 0 72 50 Ecuador 12,107 13,910 10,029 12,220 9,865 11,149 848 908 164 1,071 265 190 Egypt, Arab Rep 33,017 29,234 28,438 25,861 27,438 25,243 2,401 1,792 1,000 618 125 0 El Salvador 2,149 4,683 1,938 3,413 1,913 3,257 164 349 26 156 0 0 Eritrea 410 398 398 158 . 0 0 Estonia 2,852 1,810 187 65 1,623 13 Ethiopia 8,630 5,697 8,479 5,532 8,479 5,532 851 2,151 0 0 6 106 Finland France Gabon 3,983 3,409 3,150 3,030 3,150 3,030 69 55 0 0 140 75 Gambia, The 369 489 308 438 308 438 102 170 0 0 45 26 Georgia 1,714 1,366 1,314 .. 396 53 287 Germany Ghana 3,881 6,759 2,816 5,921 2,783 5,666 1,423 3,178 33 255 745 284 Greece Guatemala 3,080 5,037 2,605 3,577 2,478 3.456 293 330 127 121 67 0 Guinea 2,476 3,254 2,253 2,844 2,253 2,844 420 1,003 0 0 52 123 Guinea-Bissau 692 668 630 627 630 627 146 220 0 0 5 23 Haiti 910 1,250 772 1,028 772 1,028 324 467 0 0 38 39 III 2003 World Oevelopment Indicators External debt 4.10' Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt external debt IBRD loans and Total IDA credits $ millions $ millions $ millions $ millions $ millions $ millions 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 3,718 5,051 3,487 4,501 3,420 3,995 635 1,028 66 506 32 220 Hungary 21,202 30,289 17,931 25,666 17,931 12,681 1,512 564 0 12,985 330 0 India 83,628 97,071 72,462 94,120 70,974 82,446 20.996 26,105 1,488 11,674 2,623 0 Indonesia 69,872 135,704 58,242 104,783 47,982 68,378 10,385 12,157 10,261 36,405 494 9,113 Iran, Islamic Rep 9,020 7,483 1,797 5,465 1,797 5,295 86 456 0 170 0 0 Iraq Ireland Israel Italy Jamaica 4,667 4,956 3,964 4,041 3,930 3,947 672 443 34 94 357 40 Japan Jordan 8,333 7,479 7,202 6,599 7,202 6,599 593 941 0 0 94 433 Kazakhstan 14,372 13,541 3,446 1,070 10,095 0 Kenya 7,058 5,833 5,642 5,039 4,762 4,930 2,056 2,287 880 109 482 99 Korea, Dem Rep Korea, Rep 34,968 110,109 24,168 74,994 18,768 33,742 3,337 7,900 5,400 41,252 0 0 Kuwait Kyrgyz Republic .. 1,717 1,490 1,256 389 234 179 Lao PDR 1,768 2,495 1,758 2,456 1,758 2,456 131 415 0 0 8 37 Latvia 5,710 2,651 978 243 1,673 24 Lebanon 1,779 12,450 358 9,792 358 8,956 34 259 0 836 0 0 Lesotho 396 592 378 573 378 573 112 242 0 0 15 15 Liberia 1,849 1,987 1,116 1,012 1,116 1,012 248 221 0 0 322 281 Libya Lithuania 5,248 3,539 2,359 273 1,180 151 Macedonia, FYR 1,423 1,284 . 1,136 372 149 71 Madagascar 3,704 4,160 3,335 3,793 3,335 3,793 797 1,408 0 0 144 127 Malawi 1,558 2,602 1,385 2,483 1,382 2,483 854 1,627 3 0 115 73 Malaysia 15,328 43,351 13,422 38,249 11,592 24,068 1,102 788 1,830 14,181 0 0 Mali 2,468 2,890 2,337 2,616 2,337 2,616 498 981 0 0 69 171 Mauritania 2,096 2,164 1,789 1,865 1,789 1,865 264 475 0 0 70 105 Mauritius 984 1,724 910 852 762 765 195 84 148 87 22 0 Mexico 104,442 158.290 81.809 140,290 75,974 86,199 11,030 10,883 5,835 54,091 6,551 0 Moldova 1,214 1,046 779 294 267 146 Mongolia 885 824 824 0 155 0 0 47 Morocco 24,458 16,962 23,301 16,715 23,101 14,325 3,138 2,525 200 2,390 750 0 Mozambique 4,650 4,466 4,231 3,772 4,211 2,222 268 777 19 1,550 74 196 Myanmar 4,695 5,670 4,466 5,006 4,466 5,006 716 693 0 0 0 0 Namlbia Nepal 1,640 2,700 1,572 2,643 1,572 2,643 668 1,127 0 0 44 8 Netherlands New Zealand Nicaragua 10,745 6,391 8,313 5,560 8,313 5,437 299 691 0 123 0 158 Niger 1,726 1,555 1,487 1,432 1,226 1,371 461 753 261 62 85 81 Nigeria 33,439 31,119 31,935 29,396 31,545 29,215 3,321 1,958 391 181 0 0 Norway Oman 2,736 6,025 2,400 4,759 2,400 2,691 52 1 0 2,068 0 0 Pakistan 20,663 32,019 16,643 28,899 16,506 26,801 3,922 7,041 138 2,098 836 1,807 Panama 6,507 8,245 3,856 7,727 3,856 6,332 462 282 0 1,395 272 54 Papua New Guinea 2,594 2,521 2,461 2,345 1,523 1,413 349 363 938 932 61 108 Paraguay 2,105 2,817 1,732 2,355 1,713 2,120 320 231 19 235 0 0 Peru 20,064 27,512 13,959 24,087 13,629 18,831 1,188 2,625 330 5,256 755 387 Philippines 30,580 52,356 25,241 44,355 24,040 34,190 4,044 3,454 1,201 10,165 912 1,952 Poland 49,364 62,393 39,261 55,427 39,261 24,828 55 2,211 0 30,599 509 0 Portugal Puerto Rico 2003 World Development Indicators 1 247 t1 '[1UJIExternal debt Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt external debt IBRD loans and Total IDA credits $ millions $ millions $ millions $ millions $ millions $ millions 1990 2001 1990 2001 1990 2001 1990 2001 1 990 2001 1990 2001 Romania 1,140 11,653 230 10,744 223 6,682 0 1,876 7 4,061 0 387 Russian Federation a 59,340 152,649 47,540 124,244 47,540 101,918 0 6,746 0 22,326 0 7,433 Rwanda 712 1,283 664 1,163 664 1,163 340 713 0 0 0 84 Saudi Arabia Senegal 3,736 3,461 3,000 3,012 2,940 2,961 835 1,384 60 51 314 248 Sierra Leone 1,196 1,188 940 1,014 940 1,014 92 407 0 0 108 152 Singapore Slovak Republic 2,008 11,121 1,505 8,048 1,505 5,498 0 205 0 2,550 0 0 Slovenia Somalfa -2,370 2,532 1,926 1,795 1,926 1,795 419 386 0 0 159 141 South Africa 24,050 15,695 7,941 0 7 7,754 0 0 Spain Sri Lanka 5,863 8,529 5,049 7,862 4,947 7,472 946 1,578 102 389 410 214 Sudan 14,762 15,348 9,651 8,985 9,155 8,489 1,048 1,138 496 496 956 551 Swaziland 243 308 238 236 238 236 44 13 0 0 0 0 Sweden Switzerland Synan Arab Republic 17,259 21,305 15,108 15,811 15,108 15,811 523 44 0 0 0 0 Tajikistan - -1,086 896 789 172 107 110 Tanzania 6,456 6,676 5,796 5,781 5,785 5,758 1,493 2,596 12 24 140 341 Thailand 28,095 67,384 19,771 52,480 12,460 26,411 2,530 3,084 7,311 26,069 1 1,681 Togo 1,281 1,406 1,081 1,203 1,081 1,203 398 585 0 0 87 57 Trinidad and Tobago 2,512 2,422 2,055 1,562 1,782 1,452 41 90 273 110 329 0 Tunisia 7,690 10,884 6,880 10,203 6,662 9,084 1,406 1,333 218 1,118 176 0 Turkey 49,424 115,118 39,924 84,656 38,870 56,004 6,429 4,802 1,054 28,652 0 14,117 Turkmenistan 30 0 Uganda 2,583 3,733 2,160 3,306 2,160 3,306 969 2,310 0 0 282 275 Ukraine 12,811 10,159 8,197 2,248 1,961 1,911 United Arab Emirates United Kingdom United States Uruguay 4,415 9,706 3,114 6,634 3,045 6,110 359 544 69 524 101 144 Uzbekistan 4,627 4,046 3,759 237 286 78_ Venezuela, RB 33,171 34,660 28,159 30,931 24,509 24,916 974 838 3,650 6,015 3,012 0 Vietnam 23,270 12,578 21,378 11,428 21,378 11,428 59 1,344 0 0 112 366 West Bank and Gaza Yemen, Rep 6,352 4,954 5,160 4,062 5,160 4,062 602 1,237 0 0 0 374 Yugoslavia, Fed Rep b 17,792 11,740 16,802 6,629 12,942 6,002 2,433 1,085 3,860 627 467 273 Zambia 6,916 5,671 4,554 4,513 4,552 4,394 813 1,886 2 119 949 982 Zimbabwe 3,247 3,780 2,649 3,023 2,464 2,847 449 810 185 175 7 262 | , ___ _ @ @ _ $@- 7- e - " --~ - O - - 1 Low Income 421,446 533,346 360,309 457,309 342,356 398,406 67,080 100,459 17,953 58,903 11,317 21,712 Middle Income c 1,000,132 1,799,275 794,285 1,450,505 752,125 995,967 70,258 101,295 42,160 454,538 23,334 53,569 Lower middle income 517,056 917,706 424,075 729,903 406,864 560,385 40,825 64,951 17,212 169,518 5,991 30,670 Upper middle Incomec 483,076 881,570 370,209 720,601 345,261 435,582 29,433 36,344 24,948 285,020 17,344 22,898 Low & middle Incomec 1,421,578 2,332,621 1,154,594 1,907,814 1,094,481 1,394,373 137,338 201,755 60,113 513,441 34,652 75,281 East Asia & Pacific 239,005 504,125 198,549 397,922 176,913 275,645 25,306 43,011 21,635 122,277 2,085 13,384 Europe & Central Asia 217,913 497,827 177,054 394,989 172,133 263,858 10,429 26,863 4,921 131,130 1,305 27,156 Latin America & Carib 475,374 765,395 379,681 645,027 354,630 419,010 35,877 41,739 25,051 226,017 18,298 23,901 Middle East & N Afnca 182,898 200,641 137,048 150,936 135,547 143,540 10,074 9,972 1,502 7,396 1,815 2,340 South Asia 129,481 161,657 112,573 154,354 110,845 140,192 30,717 42,743 1,727 14,161 4,537 2,178 Sub-Saharan Africa 176,906 202,976 149,689 164,587 144,413 152,128 24,935 37,426 5,276 12,459 6,612 6,323 High Income Europe EMU a Data for 1990 refer to the debt of the former Soviet Union on the assumption that 100 percent of all outstanding external debt as of December 1991 has become a liability of the Russian Federation b Data for 1990 refer to the former Socialist Federal Republic of Yugoslavia Data for 2001 are estimates and reflect borrowings by the former Socialist Federal Republic of Yugoslavia that are not yet allocated to the successor republics c Includes data for Gibraltar not included in other tables 240 0 2003 World Development Indicators External debt o I Data on the external debt of developing countries are into U S dollars to produce summary tables Stock * Total external debt is debt owed to nonresidents gathered by the World Bank through its Debtor figures (amount of debt outstanding) are converted repayable in foreign currency, goods, or services It Reporting System World Bank staff calculate the using end-of-period exchange rates, as published in is the sum of public, publicly guaranteed, and private indebtedness of these countries using loan-by-loan the IMF's International Financial Statistics (line ae) nonguaranteed long-term debt, use of IMF credit, and reports submitted by them on long-term public and Flow figures are converted at annual average short-term debt Short-term debt includes all debt publicly guaranteed borrowing, along with informa- exchange rates (line rf) Projected debt service is having an original maturity of one year or less and tion on short-term debt collected by the countries or converted using end-of-period exchange rates Debt interest in arrears on long-term debt * Long-term collected from creditors through the reporting sys- repayable in multiple currencies, goods, or services debt is debt that has an original or extended maturi- tems of the Bank for International Settlements and and debt with a provision for maintenance of the ty of more than one year It has three components the Organisation for Economic Co-operation and value of the currency of repayment are shown at public, publicly guaranteed, and private nonguaran- Development These data are supplemented by infor- book value teed debt * Public and publicly guaranteed debt mation on loans and credits from major multilateral Because flow data are converted at annual aver- comprises long-term external obligations of public banks, loan statements from official lending agen- age exchange rates and stock data at end-of-period debtors, including the national government and polit- cies in major creditor countries, and estimates by exchange rates, year-to-year changes in debt out- ical subdivisions (or an agency of either) and World Bank and International Monetary Fund (IMF) standing and disbursed are sometimes not equal to autonomous public bodies, and external obligations staff In addition, the table includes data on private net flows (disbursements less principal repayments), of private debtors that are guaranteed for repayment nonguaranteed debt for 78 countries either reported similarly, changes in debt outstanding, including by a public entity * IBRD loans and IDA credits are to the World Bank or estimated by its staff undisbursed debt, differ from commitments less extended by the World Bank The International Bank The coverage, quality, and timeliness of debt data repayments Discrepancies are particularly signifi- for Reconstruction and Development (IBRD) lends at vary across countries Coverage varies for both debt cant when exchange rates have moved sharply during market rates The International Development Associ- instrumentsandborrowers Withthewideningspectrum the year Cancellations and reschedulings of other ation (IDA) provides credits at concessional rates of debt instruments and investors and the expansion of liabilities into long-term public debt also contribute to * Private nonguaranteed external debt comprises private nonguaranteed borrowing, comprehensive cov- the differences long-term external obligations of private debtors that erage of long-term external debt becomes more com- Variations in reporting rescheduled debt also are not guaranteed for repayment by a public entity plex Reporting countries differ in their capacity to affect cross-country comparability For example, * Use of iMF credit denotes repurchase obligations monitor debt, especially pnvate nonguaranteed debt rescheduling under the auspices of the Paris Club of to the IMF for all uses of IMF resources (excluding Even data on public and publicly guaranteed debt are official creditors may be subject to lags between the those resulting from drawings on the reserve affected by coverage and accuracy in reporting-again completion of the general rescheduling agreement tranche) These obligations, shown for the end of the because of monitoring capacity and sometimes and the completion of the specific, bilateral agree- year specified, comprise purchases outstanding because of unwillingness to provide information A key ments that define the terms of the rescheduled debt. under the credit tranches (including enlarged access part often underreported is military debt Other areas of inconsistency include country treat- resources) and all special facilities (the buffer stock, Because debt data are normally reported in the ment of arrears and of nonresident national deposits compensatory financing, extended fund, and oil facil- currency of repayment, they have to be converted denominated in foreign currency ties), trust fund loans, and operations under the structural adjustment and enhanced structural 4.16a adjustment facilities Total external debt ($ bilieons) Heavily Indebted poor countries (HiPCs) Sub-Saharan Africa 250 250 200 200 The main sources of external debt information are 150 . - - - | |; | | ' v | 150 reports to the World Bank through Its Debtor Reporting System from member countries that have 100 100 received IBRD loans or IDA credits Additional infor- - 11 ~~~~~~~~~~~~~~~~~~~~mation has been drawn from the files of the Wordd 50 ~~~~~~~~~~~50 1995 1996 1997 1998 _ ___ _50 - L - L_ _ ~ Bank and the IMF Summary tables of the external 995 1996 1997 1998 1999 2000 2001 1995 1996 1997 1998 1999 2000 2001 in the World Bank's Global Development Finance and on its Global Development Finance CD-ROM. Source World Bank data files 2003 World Development Indicators 1 249 External debt management Indebtedness Present value of Total debt service Public and Short-term classification a debt publicly debt guaranteed debt service % of exports of % of exports of % of central % of goods and % of goods and government % of GNI services GNI services current revenue total debt 2001 2001 2001 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan Albania L 18 47 01 0 8 0 9 3 1 . 89.8 2.8 Algeria L 41 97 14 7 8 3 63 7 19 5 213 2 8 0 9 Angola s 119 133 4 0 23 7 7 1 26 0 .. 115 22 5 Argentina S 57 407 4 6 9.3 34 7 48.6 32 5 43.6 16 8 14 6 Armenia L 30 100 2 5 8 1 . .. 4 2 Australia Austrta Azerbaijan L 19 40 2 5 4 7 8 4 Bangladesh L 21 105 2 5 1 4 37.5 9 0 1 3 1 9 Belarus L 7 10 1 9 2 7 5 4 168 Belgium Benin S 36b 134b 2 1 2 1 9 2 b 10 ob. 4 3 5 1 Bolivia M 26b 114b 8 3 7.0 33 5b 16 1b 41 3 17 4 3 6 8 1 Bosnia and Herzegovina L 32 101 6 0 18 3 2 6 Botswana L 6 10 2 9 11 4.4 1 7 5 5 . 10 5 6 Brazil S 49 330 1 8 113 18.5 28 6 3 9 19 8 12.5 Bulgaria M 63 106 7.2 10 3 18.6 15 5 12 9 19 1 9 7 3 6 Burkina Faso S 29b,c 224 b 1c l 2 1 5 7.8b.c 11 Ob.c 9 1 10 1 4 3 Burundi S 96 1,122 3 8 34 417 36 3 1 5 8 3 Cambodfa M 70 138 2 7 0 6 1 1 . 7 3 8 3 Cameroon M 62b 181 b 4 9 4,3 14.7 b 9b 16 8 .. 14.4 11 5 Canada Central African Republic S 56_ 482 2 0 1.4 12 5 11.5 . 5 4 4.2 Chad S 39b 213b 0 7 1 5 38b 10.0 b 5.6 5 7 2 1 Chile M 59 160 9 7 10 4 18 1 5 2 25 6 8 0 17.6 6 7 China L 14 53 2 0 2 1 10 6 4 2 23 9 16 8 25 8 Hong Kong, China Colombia M 47 215 10 2 7 9 34.5 281 61 2 8 4 10 2 Congo, Dem Rep S 222 1,029 4 1 0 4 12 7 0 0 14.5 7 2 30 1 Congo, Rep S 221 170 22 9 4.8 32 2 3 3 9 2 14 9 18 4 Costa Rica L 30 62 9 2 4 4 22 0 8 2 32 8 16 9 10 0 25 3 C6te d'lvoire S 109 233 13 7 6 3 19.1 8 1 22 1 16 6 20 8 10 0 Croatia M 54 101 15 0 13.7 16 9 .. 2 7 Cuba Czech Republic L 39 50 8 7 4 4 9 7 37 6 41 3 Denmark Dominican Republic L 24 46 3 4 3 1 10 7 6 6 16 1 17 9 25 4 Ecuador S 89 200 11 4 9 6 310 22 0 45_0 15 0 10 8 Egypt, Arab Rep L 25 115 7 3 19 25 7 8 8 16.5 13 5 11 5 El Salvador L 34 76 4 4 2 9 18 2 7 4 110 9 9 8 271 Eritrea L 34 57 1 0 .. 4 5 .. 2 9 Estonia M 56 57 7 3 . 0 9 .. 2 3 36 1 Ethiopia S 470 _301b 3 5 3 0 33 7b 20.6b 13 4 1 7 1 0 Finland France Gabon S 89 102 3 3 12 1 4 8 13 6 7 6 17 4 8 9 Gambia, The S 69b 94b 12 9 2 8 21.8 b 13 8b 49 1 . 4 3 5 3 Georgia L 34 124 . 2 5 . 8.1 15 2 3 5 Germany .. - Ghana M 77b 163b 64 62 349b 89b 262 82 82 Greece Guatemala L 24 101 2 9 2 2 11 6 8.5 . 13 3 290 Guinea S 60 b 203b 6 3 3.6 19.6 b 92b 33 0 6 9 8 8 Guinea-Bissau S 231 b 747b 3 6 12 7 22 1b 070 8 2 2 7 Haiti M 22 165 1 3 0 7 .. 4.5 111 14 6 25O 0 2003 World Development Indicators External debt management ]«.1 indebtedness Present value of Total debt service Public and Short-term classification ^ debt pubtleicy debt exports of % of exports of % of central % of goods and % of goods anci government % of GNI services GNI services current revenue total debt 2001 2001 2003. t990 2001 1990 2003. 1990 2001 1990 2001 Honduras M 51 104 b 13 7 5 4 33 0 b 5 b5 4 6 5 Hungary M 56 77 13 4 27 2 33 4 8 5 21 4 13 9 15 3 India L 14 85 2 6 2 0 29_2 12 6 14 5 13 0 10 2 3 0 Indonesia -S 94 199 9 1 11 1 25 6 13 8 34 4 22 4 15 9 16 1 Iran, Islamic Rep L- 6 26 0 5 1 1 13 4- 1 0 3 80 1 27 0 Iraq Irelancl Israel Italy Jamaica M 73 119 15 9 8 8 27 0 16 8 21 6 7 4 17 7 Japan Jordan) S 78 111 16 5 7 6 22 1 14 7 52 5 26 0 12 4 6 0 Kazakhstan M 67 134 15 7 4 7 18 6 5 8 Kenya M 39 146 9 8 4 1 28 6 11 4 26 6 13 2 11 9 Korea, Dem Rep Korea, Rep ,3.3 6 2 6 3 7 1 10 5 30 9 31 9 Kuwait----- - Kyrgyz Republic. S 91 223 J2 1 . 12 0 . 2 8 Lao PDR S 77 268 1 1 2 6 8 5 9 0 0 1 0 1 Latvia M 73 147 6 8 ,2 9 4 9 53 2 Lebanon S, 76 470 2 9 8 3 3 2 40 5 79 9 21 3 Lesotho L 41 73 2.3 7 0 4 2 12 4 9 4 0 7 0.7 Liberia S 436 1,321 0 2 0 6 22 2 34 9 Libya~ Lithuania L 44 83 16.4 5 9 11 1 29 7 Macedonia, FYR L -35 78 -5 7 10 3_ . 4 8 Madagascar 5 45 b 1,317 7.5 1 5 44 4 b 3 4 b 42 9 6 1 5 7 Malawi 5 87 b 296 b 7.2 2 3 28.0 b 1 5 5b -27 2 .3 7 1 8 Malaysia M 58 44 10 3 7.8 10 6 3 6. 31 4 12 4 11 8 Mali m 56b 154 b 2 8 3.2 14 7 b 4 5 b 2 5 3 6 Mauritania S 143 b, c 359 b, c 13 6 9 1 28,8 b. C 16 5 b.c 11 3 9 0 Mauritius L 37 57 6 6 4 5 7.3 4 7 13 5 15 8 5 3 50 6 Mexico L .29 93 4 5 8 1 18 3 14.1 19 5 15 4 11 4 Moldova S 71 116 12 0 1-5.3 40 6 1 8 Mongolia M 59 103 4 4 0 3 7,9 11 6 1 6 Morocco L ~ 4-4 100 7 2 7_9 27 9 _21 9 21.3 1 7 1 5 Mozambique L 28 b 36 b 3 4 2 6 17 3 b 2 7 b 7 4 11 2 Myanmar S 150 8 8 -2 8- 2 2 4 9_ 11 7 Namibia Nepal L 27 86 1 9 1 5 14 7 6 2 18 2 13 5 1.5 1.8 Netherlands New Zealand Nicaragua S 336 b 1 6 2 3, _22 2~ 2 6 , 22 6, 10 5 Niger S 53b 282 b 4 1 1 3 6 6b 6 6 b 8 9 2 7 Nigeria 5 81 :144 13 0 6 7 22 3 11 5 4 5 5 5 Norway Oman L 50 7 8 12 0 6 8 17 4 14 7 12 3 21 0 Pakistan S 44 222 4 9 5 1 25 1 21 3 18 1 23 1 15 4 4 1 Panama S 94 99 6 8 12 2 4 1 11 2 10 4 36 6 5 6 Papua New Guinea M 78 103 17 9 9 5 18 4 7 1 33 2 2 8 2 7 Paraguay L 3I7 80 6 0 ~ 50 11'5 8 3 46 8 22 7 17 7 16_4 Peru S 53 283 1 9 4 1 7 3 20 8 4 9 20 5 26 7 11 0 Philippines M 73 132 8 1 10 3 25 6 13 3 39 5 49 4 14 5 11 6 Poland L 34 108 1 7 8 8 4 4 11 5 11 4 19 4 11 2 Portugal Puerto Rico 2003 World Development Indicators 1251L i 00 External debt management Indebtedness Present value of Total debt service Public and Short-term classification a debt publicly debt guaranteed debt service % of exports of % of exports of % of central % of goods and % of goods and government % of GNI services GNI services current revenue total debt 2001 2001 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania L 29 80 0 0 6 8 0 0 13 7 0 0 16 4 79 8 4 5 Russian Federationd M 49 123 2 0 5 8 . 12 0 12 0 19 9 13 7 Rwanda S 40b 411b 08 11 106b 76 5 4 6 6 2 8 Saudi Arabia Senegal M 53b 150b 5 9 4 7 18 3b 9 3 b 20 0 11 3 5 8 Sierra Leone S 114b 888 b 3 7 13 1 10 1 b 74 3b 45 4 12.4 1 9 Singapore Slovak Republic M 54 71 2 1 13 0 6 2 14 0 25 0 27 6 Slovenia Somalia S 1 3 . 46 12 0 23 5 South Africa L 21 62 4 0 0 0 68 8 1 34.7 Spain SriLanka L 42 93 4 9 4 4 14 8 9 2 16 8 19 5 6 9 5 3 Sudan S 130 591 0 4 0 5 4 8 3 2 28 1 37 9 Swaziland L 23 29 4 9 2 2 5 6 2.5 15 8 1 9 23 5 Sweden Switzerland Syrian Arab Republic S 111 266 9 9 1 4 20 3 2 1 21 2 12 5 25 8 Tajikistan S 83 120 7 8 6 3 25 5 7.3 Tanzaniae L 14b B1b 4 4 1 6 313b 7 3b 8 1 8 3 Thailand M 60 83 6 3 18.0 _ 11 4 7 9 20 7 24 3 29 6 19 6 Togo M 81 206 5 4 26 11 5 5 9 .. 88 10 4 Trinidad and Tobago L 31 53 9 7 2 8 15.6 3 8 5 1 35 5 Tunisia M 57 103 12 0 7 1 25 6 13 4 322 8 2 6 3 Turkey M 80 208 4 9 15 3 29 9 24 6 30 9 26 0 19 2 14.2 Turkmenistan M . . 14 4 Uganda M 21b 162b 3 4 0 9 56 9b 9 7 b49 54 4 1 Ukraine L 31 54 6 1 6 5 8 2 5 8 United Arab Emirates United Kingdom United States Uruguay S 54 241 11 0 8 1 35 2 30 3 32 0 26 3 27 2 30.2 Uzbekistan M 40 138 7 5 20 6 10 9 Venezuela, RB L 30 122 10 6 6-1 19 6 20 9 36 2 23.1 6 0 10 8 Vietnam L 33 60 2 9 3 7 6 5 17 2 7 7 6 2 West Bank and Gaza Yemen, Rep L 41 61 3 5 3 4 7 1 6 3 18 8 10 5 Yugoslavia, Fed Rep f S 108 260 1 0 . 2 0 2 9 41 2 Zambia S 115 b 365 b 6 7 3 7 14 6b 13 4b 20.4 3 1 Zimbabwe M 40 175 5 5 1 5 19 4 3 4 17.4 18 2 13 1 Low Income 4 8 4 2 23 5 11 4 11 8 10 2 Middle Income 3 9 6.9 17.0 113 18 2 16 4 Lower middle income 3 8 5 1 16 8 9 5 16 8 171 Upper middle income 4 0 8 9 17 1 13 7 19 8 15 7 Low & middle Income 4.1 64 18 2 11 4 16 3 15 0 East Asia & Pacific 4 8 47 14 5 6 1 16.1 18 4 Europe & Central Asia 2 9 9 8 18.9 11 4 18 2 15 2 Latin America & Carib 4 2 8 7 20.5 19 4 16 3 12 6 Middle East & N Africa 5 1 32 23 5 11 3 24 1 23 6 South Asia 2 9 2_3 27 6 12 9 9 6 3 2 Sub-Saharan Africa 4 5 11 6 9 0 11 6 15 8 High Income Europe EMU a S = severely indebted, M = moderately indebted, L less indebted b Data are from debt sustainability analyses undertaken as part of the Debt initiative for Heavily Indebted Poor Countries (HlPCs) Present value estimates for these countries are for public and publicly guaranteed debt only, and export figures exclude workers' remittances c Enhanced HIPC assistance will be accounted for in the World Bank s Global Development Finance 2004 d Data for 1990 are for the debt of the former Soviet Union on the assumption that 100 percent of all outstand- ing external debt as of December 1991 has become a liability of the Russian Federation e Data refer to mainland Tanzania only f Data for 1990 are for the former Socialist Federal Republic of Yugoslavia Data for 2001 are estimates and reflect borrowings by the former socialist Federal Republic of Yugoslavia that are not yet allocated to the successor republics 252 0 2003 World Development Indicators External debt management I The indicators in the table measure the relative bur- management strategies The most severely indebted * Indebtedness is assessed on a three-point scale den on developing countries of servicing external countries may be eligible for debt relief under special severely indebted (S), moderately indebted (M), and debt The present value of external debt provides a programs, such as the HIPC Debt Initiative Indebted less indebted (L) * Present value of debt is the sum measure of future debt service obligations that can countries may also apply to the Paris and London of short-term external debt plus the discounted sum be compared with the current value of such indica- Clubs for renegotiation of obligations to public and of total debt service payments due on public, publicly tors as gross national income (GNI), and exports of private creditors In 2001 countries with a present guaranteed, and private nonguaranteed long-term goods and services The table shows the present value of debt service greater than 220 percent of external debt over the life of existing loans * Total value of total debt service both as a percentage of exports or 80 percent of GNI were classified as debt service is the sum of principal repayments and GNI in 2001 and as a percentage of exports in 2001 severely indebted, countries that were not severely interest actually paid in foreign currency, goods, or The ratios compare total debt service obligations indebted but whose present value of debt service services on long-term debt, interest paid on short- with the size of the economy and its ability to obtain exceeded 132 percent of exports or 48 percent of term debt, and repayments (repurchases and foreign exchange through exports The ratios shown GNI were classified as moderately indebted, and charges) to the IMF * Public and publicly guaran- here may differ from those published elsewhere countries that did not fall into the above two groups teed debt service is the sum of principal repayments because estimates of exports and GNI have been were classified as less indebted and interest actually paid on long-term obligations of revised to incorporate data available as of February public debtors and long-term private obligations guar- 1, 2003 The ratio of total debt service to exports anteed by a public entity * Short-term debt includes reflects adjustments made to countries receiving all debt having an original maturity of one year or less debt relief under the Debt Initiative for Heavily and interest in arrears on long-term debt Indebted Poor Countries (HIPCs) The present value of external debt is calculated by discounting the debt service (interest plus amortiza- tion) due on long-term external debt over the life of existing loans Short-term debt is included at its face value The data on debt are in U S dollars converted at official exchange rates (see About the data for table 4 16) The discount rate applied to long-term debt is determined by the currency of repayment of the loan and is based on reference rates for commercial interest established by the Organisation for Economic Co-operation and Development. Loans from the International Bank for Reconstruction and Develop- ment (IBRD) and credits from the International Devel- opment Association (IDA) are discounted using an SDR (special drawing rights) reference rate, as are obligations to the International Monetary Fund (IMF). When the discount rate is greater than the interest rate of the loan, the present value is less than the nominal sum of future debt service obligations The ratios in the table are used to assess the sus- The main sources of external debt information are tainability of a country's debt service obligations, but reports to the World Bank through its Debtor there are no absolute rules that determine what val- Reporting System from member countries that ues are too high Empirical analysis of the experi- have received IBRD loans or IDA credits ence of developing countries and their debt service Additional information has been drawn from the performance has shown that debt service difficulties flies of the World Bank and the IMF The data on become increasingly likely when the ratio of the pres- GNI and exports of goods and services are from ent value of debt to exports reaches 200 percent the World Bank's national accounts files and the Still, what constitutes a sustainable debt burden IMF's Balance of Payments database Summary varies from one country to another Countries with tables of the external debt of developing countries fast-growing economies and exports are likely to be are published annually in the World Bank's Global able to sustain higher debt levels. Development Finance and on its Global The World Bank classifies countries by their level of Development Finance CD-ROM. indebtedness for the purpose of developing debt 2003 World Development Indicators 1 253 IU ~ ~~~~~~~~~~- - o I $ ' tates and markets have intertwining roles-and both are needed for a healthy economy. In countries whose public and private sectors have balanced and complementary roles, the economy has grown, poverty has declined, and the quality of life has improved. There is no "right" size for government, because each country has a unique history and culture and different starting points and objectives. More important than the size of government is its effectiveness. If public institutions function poorly and governance is weak, the private sector will be stifled, investment will be deterred, and growth and equitable develop- ment will falter. This section covers a broad range of indicators showing how effective and accountable government-combined with energetic private initiative-produces employment opportunities and services that empower the poor. Its 12 tables cover three cross-cutting development themes: private sector development, public sector policies, and infrastructure, information, and telecommunications. 255 Cieatfing the coondtNions ffor pirivate sectog deveDopmens climate will also attract foreign investors. And countries that Private firms generate jobs and bring growth to the entire receive more foreign investment-an important conduit for economy, the biggest factor in reducing poverty. But to do so new technologies, management experience, and access to they need a sound investment climate-with good macroeco- markets-enjoy faster growth and greater poverty reduction. nomic management, trade and investment policies that pro- External perceptions of the investment climate are reflect- mote openness, and good-quality infrastructure and services. ed in risk ratings. While risk ratings do not always capture the They also need a legal and regulatory system that supports actual situation or specific investment opportunities in a the day-to-day operations of firms by protecting property country, they are a reality that policymakers face. Among rights, promoting access to credit, and ensuring efficient tax, such ratings are the Euromoney creditworthiness ratings, customs, and judicial services which rank the risk of investing in an economy from 0 (high Investment in infrastructure-whether in power, transport, risk) to 100 (low risk). Countries with high risk, such as housing, telecommunications, or water and sanitation- Kenya (36) and Haiti (24), have very low foreign direct invest- enables businesses to grow. And when private firms partici- ment (0.4 percent of gross capital formation for Kenya and pate in infrastructure, bringing with them capital and know-how, 0.3 percent for Haiti). By contrast, countries with low per- they can improve access to basic infrastructure services, a key ceived risk, such as Chile (65) and the Czech Republic (66), to reducing poverty In developing countries private firms par- have much higher levels of foreign direct investment (about ticipate mainly in telecommunications and energy. From 1996 33 percent for Chile and 29 percent for the Czech Republic; to 2001 investment in telecommunications projects with pri- table 5.2). Countries with low risk ratings also have large vate participation totaled about $60 billion in Brazil and more stock markets relative to gross domestic product (GDP). than $17 billion in the Republic of Korea. Investment in energy Market capitalization is about 85 percent of GDP in Chile, projects with private participation in the 1990s increased dra- 102 percent in Australia, 135 percent in Malaysia, 137 per- matically in Brazil (from $0 6 billion in 1990-95 to $42 billion cent in Singapore, and 158 percent in Finland (table 5.4). in 1996-2001), Peru (from $1.2 billion to $2.8 billion), and in Turkey ($2.5 billion to $4.8 billion; table 5 1). Designfinig pubDlc s5ctoZ poiiDei eto enhance paivate Telecommunications has received the largest share of actiDvty investment in projects with private participation (44 percent of The public sector's main economic functions fit into three the total in 1990-2001), with water and sanitation, considered broad categories: making policy, delivering services, and pro- a "basic needs" sector, receiving only a small fraction (5 per- viding oversight and accountability. As global competition has cent) Private participation in infrastructure was initially con- increased in the past two decades, the governments of many centrated in a few countries, with the top 10 accounting for 98 developing countries have shifted their focus from trying to percent of investment in 1990, but by 2001 their share had preserve jobs in a stagnant public sector to creating jobs in a fallen to 67 percent. Private participation in infrastructure has vibrant private sector. Governments are now in the business many of the same advantages and risks as public investment of designing and implementing good policies and strong insti- financed though foreign borrowing (see tables 5.9-5.11). tutions that enhance the business and investment climate. Part of what determines the business environment in a Government functions and policies affect many areas of country is the regulation of new entry. Countries differ signif- social and economic life: health and education, natural icantly in the obstacles they impose on the entry of new resources and environmental protection, fiscal and monetary businesses. To meet government requirements for starting a stability, and flows of trade Data related to these topics are business in Mozambique, for instance, entrepreneurs must presented in the respective sections. This section provides complete 16 procedures, a process that takes an average of data on key public sector activities: tax policy, exchange 214 business days and costs the equivalent of 74 percent of rates, and defense expenditures (tables 5.6-5.8). gross national income (GNI) per capita. In Italy they must Taxes are the main source of revenue for many govern- complete 13 procedures, wait 62 business days on average, ments. They are levied mainly on income, profits, capital and pay 23 percent of GNI per capita. But Canada requires gains, goods and services, and exports and imports. (Nontax only 2 procedures, and the process takes only two days and revenue is also important in some economies; see table costs about 1 percent of GNI per capita (table 5.3). 4.13.) A comparison of taxation levels across countries pro- The case for creating a good investment climate is simple: vides an overview of the fiscal obligations and incentives fac- an economy needs a predictable environment in which people, ing the private sector. Central government tax revenues ideas, and money can work together productively and effi- (excluding state and local taxes) range from about 3 percent ciently. Countries should focus on improving the investment of GDP in Kuwait and 7 percent in Bangladesh to 35 percent climate for domestic entrepreneurs, but a better investment in Austria and 36 percent in Slovenia (table 5.6). 256 0 2003 World Development Indicators 5a early 1990s-about 1 percent of all developing countries' !-n . .. ,. m,..l, GDP. But beginning in the late 1980s countries around the i ; nl;J:T _ world had begun turning to the private sector, both to take $ billions over the operation of existing infrastructure and to finance 150 new infrastructure In 1990-2001 infrastructure projects with private participation in developing countries attracted more than $750 billion in investment (figure 5a). 120 Efficient transport is critical to the development of com- petitive economies, but measuring progress in transport is difficult. Data for most transport sectors are often not strict- 90 ly comparable across countries that do not consistently fol- low common definitions and specifications. Moreover, the data do not indicate the quality and level of service, which 60 depend on such factors as maintenance budgets, the avail- ability of trained personnel, geographic and climatic condi- tions, and incentives and competition to provide the best 30 service at the lowest cost. About 43 percent of the world's roads are paved, but the 0 share ranges from only about 16 percent in low-income 990 1991 1993 1995 1997 1999 2001 economies to 92 percent in high-income economies. Sub- Saharan Africa scores the lowest among regions, with only In 1990-2001 the private sector took over the operating or construction risk or both, for almost 2.500 infrastructure projects in developing countries Latin America and the about 13 percent of roads paved, while developing countries Caribbean led the developing regions in private participation in infrastructure, capturing in Europe and Central Asia, with 91 percent, are almost on a almost 50 percent of the total investment in 1990-2001 par with high-income economies (table 5.9). Source World Bank, Private Participation in Infrastructure (PPI) Project Database Telecommunications services are improving in quality, accessibility, and affordability around the world, thanks to competition in the marketplace accompanied by sound regu- The level and progressivity of taxes on personal and cor- lation. Globally, there are 172 fixed telephone mainlines for porate income influence incentives to work and invest. every 1,000 people, but large differences remain between Marginal tax rates on individual income range from 0 percent low-income economies (around 30 per 1,000) and high- (in countries such as Kuwait, Oman, Paraguay, the United income economies (around 600 per 1,000). And within coun- Arab Emirates, and Uruguay) to 50 percent or more (in such tries there are often stark differences in access between the countries as Austria, Belgium, the Democratic Republic of largest city and the average for the country. In Sri Lanka, for Congo, Denmark, the Islamic Republic of Iran, and Senegal). example, there are about 300 telephone mainlines for every Most marginal tax rates on corporate income are in the 1,000 residents in Colombo, while the average for the coun- 20-30 percent range (table 5.6). try is only 44 per 1,000. In many countries people are turn- ing to mobile phones. In Latin America, at 161 per 1,000 Tapping the benefits of infrastructure, information, people, mobile phones are almost as numerous as fixed line and telecommunications telephones (165 per 1,000 people; table 5.10). High-quality infrastructure and other business services help Essential to building a knowledge economy is ensuring determine the success of manufacturing and agricultural access for all to computers and the Internet The digital businesses. Investments in water, sanitation, energy, hous- divide between rich and poor economies-the gap in access ing, and transport improve health and education and help to information and communications technology-remains reduce poverty. And new information and communications wide, with high-income economies having 416 personal com- technologies offer vast opportunities for economic growth, puters per 1,000 people and low-income economies only 6 improved health, better service delivery, learning through dis- per 1,000. Even so, ownership of personal computers is tance education, and social and cultural advances. growing twice as fast in developing as in high-income Until the 1990s public sector monopolies in most develop- economies Large gaps also exist among developing regions, ing countries financed and operated the infrastructure, often with developing countries in Europe and Central Asia having with poor results. Technical inefficiencies in roads, power, about 52 personal computers per 1,000 people, but South water, and railways caused losses of $55 billion a year in the Asia only about 5 (table 5.11) 2003 World Development Indicators 1 257 OPrivate sector develpment Domestic Investment in infrastructure projects with private participation a credit to private sector Water and Telecommunications Energy Transport sanitation % of GDP $ millions $ millions $ millions $ millions ±990 2001 1990-95 1996-2001 1990-95 1996-2001 1990-95 1996-2001 1990-95 1996-2001 Afghanistan Albania 5.9 165 2 Algeria 44 4 8 0 Angola 3 5 68 0 Argentina 15 6 20 8 11,907 0 11,841 3 12,035 1 12,048 2 5,989 1 8,363 8 5,166 0 3,071 5 Armenia 40 4 8 3 442 0 50 0 Australia 64 3 898 Austria 91 6 106 9 Azerbaijan 10.8 5 0 14.0 144 6 230 0 Bangladesh 16 7 26 7 146 0 533 5 .. 1,040 2 Belarus 8 4 10 0 40 0 500 0 Belgium 37 0 77 1 Benin 20 3 111 90 4 Bolivia 24 0 54 5 38 0 692 4 252 4 518 2 163 2 682 0 Bosnia and Herzegovina Botswana 9 4 16 2 80 0 Brazil 38 9 34 7 60,485 5 614.6 42,036 2 1,230 8 18,515 7 155 3 2,750 0 Bulgaria 7 2 14 3 640 396 4 . 152 0 Burkina Faso 19 0 13 6 35 6 5 6 Burundi 13 7 22 4 0 5 15 6 Cambodia 7 0 316 115 3 123 0 120 0 65 0 Cameroon 26 7 96 266 1 70 4 308 95 0 Canada _75 9 80 8 Central African Republic 7 2 4.9 1.1 . 0 7 Chad 7 3 3 7 116 Chile 47 2 65 9 148 9 1,190 9 2,260 0 4,857 3 539 9 4,639 0 67 5 3,886 7 China 87 7 127 2 . 5,970 0 6,113 5 14,110 2 6,219 8 14,666 8 104 0 613 4 Hong Kong, China 165 1 155 9 Colombia 30 8 25 1 1,551 2 1,298 3 1,813 2 5,762 2 1,008 8 1,556 9 272 0 Congo, Dem Rep 1 8 . 228 7 Congo,Rep 15 7 5 0 4 6 104 9 325 0 Costa Rica 15 8 281 76.3 243 1 161 0 C6te d'lvoire 36 5 15 9 827 4 147 2 223 0 178 0 Croatia 41 5 1,425 5 368 5 672 2 298 7 Cuba 3710 165 0 600 0 Czech Republic 44 4 876 0 7,427 3 356 0 944 1 263 7 126.7 36 5 261 3 Denmark 52 2 141 8 Dominican Republic 27 5 38 0 10 0 274 5 372 5 1,536.3 833 9 Ecuador 13 2 32 7 512 692 8 310 0 12 5 686 8 550 0 Egypt, Arab Rep 30 6 61 6 2,550 0 1,378 0 1,057 2 El Salvador 20 1 41 6 . 701 5 106 0 879 2 Eritrea Estonia 202 27 3 2117 629 0 26 5 299 4 810 Ethiopia 19 5 27 5 Finland 86 7 57 7 France 96 1 89 8 Gabon 13 0 118 26 0 624 8 46 7 Gambia, The 11 0 14 7 Georgia 7 6 216 43 8 36 0 Germany 89 7 121 0 Ghana 4 9 14 1 25 0 436 1 60 0 10 0 Greece 36 3 63 6 Guatemala 14 2 204 200 1,443 3 134 8 1,2384 338 Guinea 3 5 3 8 45 0 75 3 36.4 Guinea-Bissau 22 0 3 0 23 2 Haiti 12 6 15 0 1.5 H 2003 World Development Indicators Private sector development 5.1 = Domestic Investment In Infrastructure projects with private participation a credit to private sector Water and Telecommunications Energy Transport sanitation % of GDP $ millions $ millions $ millions $ millions 1990 2001 1990-95 1996-2001 1990-95 1996-2001 1990-95 1996-2001 1990-95 1996-2001 Honduras 31 1 41 3 38 1 95 3 86 8 130 5 220 0 Hungary 46 6 33 8 3,510 9 5,298 9 2,156 7 1,906 1 1,004 0 135 0 2 9 167 6 India 25 2 29 1 722 9 10,511 2 2,888 5 9,582 0 126 9 1,448 8 216 0 Indonesia 46 9 20 5 3,549 0 7,780 0 3,202 5 7,347 1 1,204 9 1,728 0 3 8 882 8 Iran, Islamic Rep 32 5 33 4 5 0 23 0 Iraq Ireland 47 6 111 8 Israel 57 6 96 4 Italy 56 5 80 0 Jamaica 36 1 12 8 389 0 289 0 201 0 30 0 Japan 195 2 186 7 Jordan 72 3 75 5 43 0 732 9 182 0 55 0 Kazakhstan 15 8 30 0 1,849 5 2,125 0 40 0 Kenya 32 8 24 6 107 0 171 5 53 4 Korea, Dem Rep Korea, Rep 65 5 108 0 2,649 0 17,559 3 2,688 2 2,276 0 5,945 7 Kuwait 52 1 69 4 Kyrgyz Republic 3 8 94 0 Lao PDR 1 0 9 6 175 1 535 5 Latvia 23 2 230 0 817 8 177 1 75 0 Lebanon 79 4 90 9 100 0 273 0 . 200 0 Lesotho 15 6 13 7 33 5 Liberia 30 9 3 5 Libya 31 0 23 7 Lithuania 11 5 76 0 1,294 8 20 0 Macedonia, FYR 17 7 607 3 Madagascar 16 9 9 2 5 0 10 1 20 3 Malawi 10 9 6 8 8 0 24 5 6 0 Malaysia 69 4 149 2 2,630 0 2,603 3 6,909 5 2,121 1 4,657 6 7,603 2 3,986 7 1,105 5 Mali 12 8 17 8 0 1 697 3 Mauritania 43 5 271 99 6 Mauritius 35 6 62 7 261 0 109 3 42 6 Mexico 17 5 11 5 18,031 0 14,774.7 1 0 4,575 1 7,906 3 5,431 5 312 1 331 5 Moldova 5 9 14 8 84 6 85 3 Mongolia 19 0 12 2 8 6 20 2 Morocco 34 0 54 0 3,643 0 2,300 0 5,819.9 Mozambique 17 6 2 5 29 0 432 0 0 6 Myanmar 4 7 12 1 4 0 50 0 Namibia 19 0 47 3 18 0 4 0 5 0 Nepal 12 8 31 8 35 2 131 4 137 2 Netherlands 80 0 142 6 New Zealand 76 0 115 7 Nicaragua 112 6 9 9 54 2 347 4 104 0 Niger 12 3 4 6 18 0 Nigeria 9 4 17 8 968 7 225 0 Norway 82 2 82 8 Oman 22 9 36 9 204 5 728 3 106 1 Pakistan 27 7 28 4 602 0 173 0 3,417 3 2,519 7 299 6 118 7 Panama 46 7 126 0 1,429 2 1,064 9 409 9 806 0 25 0 Papua New Guinea 28 6 16 0 65 0 175 0 Paraguay 15 8 25 9 48 1 204 4 58 0 Peru 11 8 24 3 2,568 7 5,224 5 1,207 7 2,817 4 6 6 240 8 56 0 Philippines 22 3 40 1 1,279 0 5,528 6 6,831 3 6,943 1 300 0 1,966 8 5,846 1 Poland 3 1 25.5 479 0 10,806 5 145 0 1,503 6 3 1 705 9 22 1 Portugal 49 1 146 2 Puerto Rico 2003 World Development Indicators 1 259 71 7i 0L Private sector deveGopment Domestic Investment in Infrastructure projects with private partlclpation a credit to private sector Water and Telecommunications Energy Transport sanitation % of GDP $ millions $ millions $ millions $ millions 1990 2001 1990-95 1996-2001 1990-95 1996-2001 1990-95 1996-2001 1990-95 1996-2001 Romanla 7 8 50 2,326.3 100 0 23 4 1,025 0 Russian Federation 15 4 918 0 6,216 8 1,100 0 2,281.3 . 515 4 108 0 Rwanda 6 9 10 2 15 6 Saudi Arabia 61 0 55 0 Senegal 26 5 19 4 . 406 8 1240 3.7 Sierra Leone 3 2 2 3 20 5 Singapore 97 4 128 4 Slovak Republic 25 5 118 6 1,656 0 Slovenia 34 9 40 0 Somalia South Africa 81 0 148 5 1,072 8 8,537 7 3 0 44 3 1,874 1 212 5 Spain 80 1 105 9 . Sri Lanka 19 6 28 3 43.6 727 9 21.7 286 6 240 0 Sudan 48 31 6 0 Swaziland 20 7 13 1 12 0 Sweden 128 4 45.7 Switzerland 167 9 158 5 Syrian Arab Republic 7 5 8 1 130 0 Tajikistan 22 9 1 0 Tanzania 13 9 4 9 301 3210 60 490 0 23_0 Thailand 83 4 97 5 4,814 0 3,679 1 2,059 6 6,445.5 2,395.9 499 4 153 0 347.5 Togo 226 149 50 Trinidad and Tobago 44 7 41 8 47 0 146 7 207 0 . 120 0 Tunisia 55 1 67 9 .. 627.0 265 0 Turkey 16 7 20 6 190 3 7,875 4 2,478 0 4.807 2 724.8 942 0 Turkmenistan 2 3 Uganda 4 0 5 9 8 8 2001 Ukraine 2 6 13 2 100 6 1,299 9 160.0 United Arab Emirates 37 4 United Kingdom 115 8 138 8 United States 93 5 145 8 Uruguay 32 4 53 9 19 0 57 7 86 0 160.0 96.0 154 1 10 0 351 0 Uzbekistan 2.5 357 4 Venezuela, RB 25 4 12 0 4,603 3 5,956 7 . 133 0 100 0 268 0 40.0 Vietnam 25 390 435 5 100 85.0 2128 West Bank and Gaza 65 0 90 0 . 150 0 Yemen, Rep 61 61 25 0 .. 190 0 Yugoslavia, Fed Rep 1,929 5 Zambia 8 9 7 2 48 4 289 4 Zimbabwe 23 0 25 8 46 0 . 600.0 18 0 70 0 Low Income 26 6 24 1 26,396 0 Middle income 43 2 57 9 .. 192,659 4 132,926 4 Lower middle income 61 1 79 8 13,327 0 64,152 6 59,045 9 Upper middle income 32 5 32 9 . 128,506 8 73,880 4 Low & middle income 39 6 52 1 219,055 4 East Asia & Pacific 73 6 110 3 Europe & Central Asia 21 0 6,858 2 53,229 5 15,270.7 Latin America & Carib 28 6 24 4 39,489 4 106,928 4 79,288.0 42,147 0 Middle East & N Africa 41 2 47 1 South Asia 24 6 28 8 11,980 8 13,565.7 Sub-Saharan Africa 42 5 65 2 . 13,465 2 High Income 107 7 137 4 Europe EMU 79 5 102 8 a Data refer to total for the period shown 260 8 2003 World Development indicators Private sector development 0 = Private sector development-that is, tapping private services and reaping the benefits of greater compe- * Domestic credit to private sector refers to finan- sector initiative for socially useful purposes-is crit- tition and customer focus In 1990-2001 more than cial resources provided to the private sector-such cal for poverty reduction In parallel with public sec- 130 developing countries introduced private partici- as through loans, purchases of nonequity securities, tor efforts, private initiative, especially in competitive pation in at least one infrastructure sector, awarding and trade credits and other accounts receivable- markets, has tremendous potential to contribute to almost 2,500 proJects attracting investment commit- that establish a claim for repayment For some coun- growth Private markets serve as the engine of pro- ments of $750 billion tries these claims include credit to public ductivity growth, creating productive jobs and higher The data on investment in infrastructure projects enterprises * Investment In Infrastructure projects incomes And with government playing a comple- with private participation refer to all investment with private participation covers infrastructure proj- mentary role of regulation, funding, and provision of (public and private) in projects in which a private ects in telecommunications, energy (electricity and services, private initiative can help provide the basic company assumes operating risk during the operat- natural gas transmission and distribution), transport, services and conditions that empower the poor-by ing period or assumes development and operating and water and sanitation that have reached financial improving health, education, and infrastructure risk during the contract period Foreign state-owned closure and directly or indirectly serve the public Credit is an important link in the money transmis- companies are considered private entities for the Incinerators, movable assets, stand-alone solid sion process, it finances production, consumption, purposes of this measure The data are from the waste projects, and small projects such as windmills and capital formation, which in turn affects the level World Bank's Private Participation In Infrastructure are excluded The types of projects included are of economic activity The data on domestic credit to (PPI) Project Database, which tracks almost 2,500 operation and management contracts, operation and the private sector are taken from the banking survey projects, newly owned or managed by private management contracts with major capital expendi- of the International Monetary Fund's (IMF) companies, that reached financial closure in low- ture, greenfield projects (in which a private entity or International Financial Statistics or, when data are and middle-income economies in 1990-2001 For a public-private joint venture builds and operates a unavailable, from its monetary survey The monetary more information, go to http //www worldbank org/ new facility), and divestiture survey includes monetary authorities (the central privatesector/ppi/ppi-database htm bank) and deposit money banks In addition to these, the banking survey includes other banking institu- tions, such as finance companies, development banks, and savings and loan institutions In some cases credit to the private sector may include credit to state-owned or partially state-owned enterprises Private participation in infrastructure has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending their delivery to poor people The pri- vatization trend in infrastructure that began in the 1970s and 1980s took off in the 1990s Developing countries have been at the head of this wave, pio- neering better approaches to providing infrastructure 5.1a Water and Natural gas sanitation 5s 5% - TransPbrt The data on domestic credit are from the IMF's - 18% Intemational Financial Statistics The data on -a -_ ~ investment in infrastructure projects with private EIevt8%- _ c participation are from the World Bank's Private Participation in Infrastructure (PPI) Project Database (http //www worldbank org/privatesector/ppi/ Source World Bank, Private Participation in Infrastructure ppl_database htm) (PPI) Prolect Database 2003 World Development Indicators 1 261 i~ nvestment climate Foreign direct Entry and exit regulationsa Composite Institutional Euromoney Moody's Standard & Poor's Investment ICRG risk Investor country sovereign sovereign long-term rating b credit credit- long-term debt rating b ratingb worthiness debt ratingb ratingb % of gross Foreign Domestic Foreign Domestic capital Repatriation of currency currency currency currency formation Entry income capital December September September January January January January 1990 2001 2001 2001 2001 2002 2002 2002 2003 2003 2003 2003 Afghanistan69 6 Albania 0 0 25 9 67 3 15 9 32 1 Algeria - 0 0 8 2 63 8 31 5 40.8 Angola -27 9 34 8 . 53 8 14 0 23 6 Argentina_ 9 3 85 F F F_ 48 0 15 8 27 1 Ca Ca- SD SD Armenia 17 7_ . . 60.3 32 8 Australia 11 7 14 0 82 5 84 5 90.4 Aaa Aaa AA+ AAA Austria -16_ 13 6 .86 8 90 7 93 0 Aaa Aaa AAA AMA AzerbaiUan 19 4 67 3 26 8 42.7 Bangladesh 0 1 0 7 F F F 61.3 27 3 38 5 Belarus 3 5 . . .. 61.5 13 8 29 8 Belgium 13 5 18 2 84 3 89 5 91 0 Aal Aal AA+ AA+ Benin 23 8 28 8 20 1 281 Bolivia 45 64 1 65 8 309_ 44 0 81 Bi B+ B+ Bosnia and Herz-egovina -- 18 9 24 4 Botswana 6 8 5 0 F F F 79.3 59 0 64 7 A2 Al A A+ Brazil 1 1 21 5 F F F 62.3 39 0 42.6 8B2 82 B+ 88 Bulgaria 0 1 - 25 1 F F F 70 8 40.7 49 3 81 BI 88 BB+ Burktina Faso 0 0 4 0 58 3 18 8 30 2 Burundi 0 8 0 0 . 11 3 20 5 Cambodia 0-0 18 6 . 19 6 281 Cameroon -5 7 5 0 .62 0 19.7 29 6 Canada 6 2 20 2 84 8 89 4 91 9 Aaa Aaa AMA AMA Central African Republic 0 4 5 7 .23.0 Chad 3 2 12 0 . 14.8 23.5 Chile 8 7 32 6 F F F 768_ 66 1 64 5 Baal Al A- AA China 2 8 101 I s F F 75 0 58 9 56 4 A3 888 Hong Kong, China 54 7 84 3 67 7 81.6 A3 Aa3 A+ AA- Colombia 6 7 18 9 A_ F F 60 8 38.7 47.4 Ba2- Baa2 88 888 Congo, Dem Rep -1 7 12 1 .47 3 8 7 14 1 Coingo, Rep 1 5 7 9 60 8 10 5 26 0 Costa Rica 10 4 15 5 73 5 46.2 51 7 Bal Bal 88 88+ C6te d'lvoire 6 7 23 9 F F F 51 8 18 5 _ 30.0 Croatia 31 4 F F F 72 3 48 3 59 7_ Baa3 -Baal B-BB-- 888+ Cuba 62 5 15.7 133_ Caal- Czech Republic 0 8 28 9 F F F 76 3 64 0 65 6 Al Al A- A+- Denmarkt 4 2 21 2 87 8 90.5 96 0 Aaa Aaa AMA AMA Dominican Republic 7 5 24 2 ..67 3 38 1 47 3 Ba2 8a2 88- 88- Ecuado r 6 7 29 6 F F F 60 8 22 5 30 3 Caa2 Caal CCC+ CCC+ Egypt, Arab Rep 5 9 3 3 F F F 67 5 45 5 50 3 Bal Baal BB+ BBB El Salvador 0 3 12 2 71 8 46 0 52 1 Baa3 Baa2 88+ 88+ Eritrea 14 1 22 3 Estonia 7 3 35 2 F F F 74 8 59.5 64 0 Al Al A- A- Ethiopia 1 5 1 7 57 0 16 0 29 6 Finland 2 0 15 4 89 0 91 1 93 8 Aaa Aaa AMA AMA France 4 6 19 9 . 81 3 92 9 92 4 Aaa Aaa AMA AMA Gabon 5 7 15 1 .66 3 21 8 32 1 Gambia, The 0 0 50 8 66.3 . 31 9 Georgia 0.0 27 1 15 4 33 2 Germany -07 8 5 82 8 94 0 91 9 Aaa- Aaa AMA AMA Ghana 1 7 7 0 F F F 59 8 25 7 36 8 Gre ece 5 2 4 3 74 5 75 3 81 9 Al Al A A Guatemala 4 6 14 4 67 0 33 0 45 0 8a2 Bal 88 88+ Guinea 3 6 0 2 .63 0 15 1 28 2 Guinea-Bissau 2 7 69 7 .47 3 22 5 Haiti 0 0 0 3 . . 51 5 14 8 23 6 2132 II 2003 World Development Indicators co Investment climate5. Foreign direct Entry and exit regulations a Composite Institutionai Euromoney Moody's Standard & Poor's Investment ICRG risk Investor country sovereign sovereign long-term rating b credit credit- long-term debt rating b rating b worthiness debt rating b rating b % of gross Foreign Domestic Foreign Domestic capital Repatriation of currency currency currency currency formation Entry income capital December September September January January January January 1990 2001 2001 2001 2001 2002 2002 2002 2003 2003 2003 2003 Honduras 6 2 -100 63 5 261 39 0 B2 82 Hungary -37 17 2 F F F 78 0 66 1 69 3 Al Al A- A India 0 3 3 2 A F F 66 3 47 3 55 1 8a2 8a2 88 BB+ Indonesia 3 1 -13 2 R RS RS 58 3. 23 8 37 5 B3 83 CCC+ 8- Iran, Islamic Rep -1 1 0 1 63 3 34 1 46 4 Iraq 44 0 10.2 36_ Ireland 6 3 99 9 88 8 88 5 93 2 Aaa Aaa MAA MAA Israel 1 1 21 2 F F F 65 3 58 6 68 7 A2 A2 A- A+ Italy 2 6 -69 79 3 86 2 88 5 Aa2 Aa2 AA AA Jamaica 11 6 26 2 R F F 69 8 28 9 41 5 8a3 Baa3 8+ 88- Japan 0 2 0 6 85 3 82 7 88 7 Aal A2 AA- AA- Jordan 2 9 4 4 F F F 70 5 38 7 45 4 Ba3 8a3 88- 888- Kazakhstan 1 2 47 9 71.8 38 8 48 4 BasS Baal BB BB+i Kenya 3 4 04 R F F 57 5 22 9 36 0 Korea, Dem Rep 46 0 7 3 1 4 Korea,Rep 08 2.8 R F F 79 8 65 6 69 5 A3 A3 A- A+ Kuwait -1 4 81 0 62 9 76 2 A2 A2 A+i A+ Kyrgyz Republic 0 0 2 0 16 9 25 2 Lao PDR 6 1 15 2 24 3 Latvia I11 8 4 F F F 76 5 52 0 58 8 A2 A2 888+ A- Lebanon 1 3 8 0 F F F 55 5 26 8 39 1 B2 83 8- B- Lesotho 5 3 39 9 26 8 33 5 Liberia 45 3 9 6 14 2 Libya -70 0 3-35 24 0 Lithuania 0 0 17 3 F F F 74 0 50 8 56 0 Baal Baal BBB 888+ Macedonia, FYR 77 5 20 6 35 3 Madagascar 4 3 1 6 58 8 28 5 Malawi 5 4 30 6 53 5 19 6 28 8 Malaysia 16 4 2 2 R F D 77 5 57 7 63 0 Baal A3 888+ A+ Mali 1 0 18 4 58 3 19 1 28 3 Mauritania 3 3 11 2 24 5 Mauritius 5 6 -4 3 R F F 53 5 58 8 8aa2 A2 Mexico 4 2 19 3 F F F 70 8 59 0 60 8 Baa2 Baal 888- A- Moldova 0 0 31 5 64 0 .157 26 2 Ca Caa2 Mongolia 20 0 64_0 21 7 27 1 B B Morocco 2 5 31 6 F F F 72 8 48 2 53 6 Bal Bal 88 888 Mozambique 2 4 32 0 61 3 ~ 191 32 7 Myanmar 62 3 13 8 21 8 Namibia F F F 76 5 40 8 26 6 Nepal 0.9 1 4 24 4 28 3 Netherlands 15 4 61 8 83 5 94 6 93 6 Aaa Ass AMA AA New Zealand 204_ 32 7 80 8 81 2 86 7 Ass Asa AA+ MAA Nicaragua 0 0 53 8 17 6 30 8 82 82 Niger 20 3 5 9 57 5 13 6 30 6 Nigeria 14 0 9 7 R F F 51 0 17 6 24 5 Norway 3 7 16 2 91 3 931_ 97 7 Ass Ass AAA MAA Oman 10 3 F F F 79 8 57 8 62 7 Baa2 Baa2 888 888+ Pakistan 3 2 4 1 F F F 58 5 20 0 38 9 83 83 8 88- Panama 15 2 18.0 71 0 47 2 51 3 Bal 88 88 Papua New Guinea 19 7 46 3 63 0 30 4 37 6 81 81 8 88- Paraguay 6 4 4 6 56 5 29 7 36 8 82 81 8- B Peru 0 9 10 7 F F F 68 8 38 3 -466 Ba3 Baa3 BB- 88+ Philippines 5 0 13 9 S F F 71 0 44 9 51 0 Bal Baa3 88+ 888+ Poland 0 6 15 0 F F F 76 3 60 1 64 6 A2 A2 888+ A Portugal 13 1 19 2 78 3 84 2 84 9 Aa2 Aa2 AA MA Puerto Rico 2003 World Development Indicators I 263 In vestment climate Foreign direct Entry and exit regulations aComposite Institutional Euromoney Moody's Standard & Poor's Investment ICRG risk Investor country sovereign sovereign long-term rating b credit credit- long-term debrt rating b rating b worthiness debt rating b rating b % of gross Foreign Domestic Foreign Domestic capital Repatriation of currency currency currency currency formation Entry income capital December September September January January January January 1990 2001 2001 2001 2001 2002 2002 2002 2003 2003 2003 2003 Romania 0 0 137_ F F F 69.5 33 8 46 5 81 81 8+ 88- Russian Federation 0 0 36 F F F 70 0 39 0 45 0 Ba2 8s2 BB B8+ Rwanda 2 0 - 15 -20 1 Saudi Arabia c C -RS RS 72 5 58 0 65 8 Baa3 8a1 Senegal 7 2 135 ..65 5 27 8 38 5 8+ 8+ Sierra Leone 49 7 6 8 . 52 3 9 6 21 8 Singapore 41 5 41 4 90 0 86 1 90 2 Aaa Ass AAA AAA Slovak Republic 0 0 22 6 F F _F 75 8 51 4 58 1 A3 A3 8B8 A- Slovenia 5 0 2 7 R F RS 80 3 65 8 _75 4 Aa3 Aa3 A AA Somalia 3 9 _-445 . 14 5 South Africa 42 2 F F F 68 8 52.7 59 1 Baa2 A2 888- A- Spain 10 3 14 5 . 80 8 87 0 88 6 Ass Ass AA+ AA+ SriLanka 2 4 4 9 R RS F 63 3 33 6 40 5 Sudan 25 9 54 3 9 7 25 3 Swaziland 17-9 8 9 28 2 33 6 Sweden 3 6 -35 4 84 5 89.3 94 1 Ass Ass AA+ AAA Switzerland 9 3 16 2 91 5 96.2 98 5 Ass Ass AAA MAA Syrian-Arab Republic 3 5 5 0 69 8 23.1 36 5 Tajikistan 1 0 19 2 - 12 7 32 0 Tanzania 0.0 14 1 58 0 21 3 34 2 Thailand 6 9 13 9 R F F ~ 76 3 51 9 56 3 8aa3 Baal 888- A- Togo 4 2 2-57 . 59 3 _155 28.6 Trinidad and Tobago 17 1 49 4 R F F 73 3 53.3 58 4 Baa3 Baal 888- 88B+ Tunisia 1 9 8 3 F F F 72 0 53 7 57 2 Baa3 Baa2 888 A Turkey 1 9 13 4 F F F 59 8 33 8 43 8 81 83 8- B- Turkmenistan 6 8 19 2 33 8 82 Uganda 00 12 7 . . 62.5 20 0 38 3 Ukraine 06 10 3 F F F 67 5 25 3 34 5 82 82 8 8 United Arab Emirates -81 8 68 2 76 3 A2 United Kingdom 16 8 25.8 -82 0 94 1 92 7 Aas Ass MAA MAA United States 4 8 15 1 77 5 93 1 95 2 Aaa Aas MAA MAA Uruguay 0 0 12 7 61 5 41 9 43.1 83 83 8- 8- Uzbekistan 0 3 3 2 . .18 6 32.3 Venezuela, RB 9 1 14 7 F F F 53.8 30 6 39 9 83 Caal CCC+ Vietnam 2.0 12 9 70.3 32 3 46 1 81 88- 88 West Bank and Gaza Yemen, Rep -18 6 -10 8 .66 0 33 8 Yugoslavia, Fed Rep 0 0 50 0 16 5 29 6 Zambia 35 7 9 9 48 0 15 8 26 0 Zimbabwe -0 8 0 8 R F F 37 0 11 9 22 3 Low Income 1 6 3 9 58 4 18 0 28 4 Middle Income 2 6 13 2 70 0 39 0 46 5 Lower midd le income- 1 8 11 0 67 5 33 8 44 0 Upper middle income 4 5 _16 7 73 4 52 0 58 8 Low &middle Income 2 6 11 8 63 5 26 8 34 3 East Asia & Pacific 4.6 9.0 67 2 27 1 37 5 Europe & Central Asia 0 4 13 8 70 4 33 8 42 7 Latin America &Carib 3 8 18 9 64 7 35 5 43 5 Middle East & N Africa 4 5 69 8 36 4 45 4 South Asia - -06 3 1 62 3 25.9 387 Sub-Saharan Africa- 21 7 -58 2 19.0 284 High Income 4 4 19 2 82 5 87 0 90.4 Europe EMU 4 5 16 0 82.8 89 5 91 9 a Entry and euit regulations are classified as free (F), relatively free IR), delayed (DI, special classes of shares (5), authorized investors only IA), restricted IRS), and closed ICI For explana- tions of the terms, see About the data b This copyrighted material is reprinted with permission from the following data providers PRS Group, Inc , 6320 Fly Road, Suite 102, P0 Box 248. East Syracuse, NY 13057, Institutional Investor Inc .4.88 Madison Avenue. New York, NY 13057, Euromoney Publications PLC. Nestor House. Playhouse Yard. London EC4V 5EX, UK, Moody's Investors Service. 99 Church Street, New York, NY 10007. and Standard & Poor's Rating Services, The McGraw-Hill Companies, Inc , 1221 Avenue of the Americas, New York, NY 10020 Prior wntten consent from the original data providers cited must be obtained for third-party use of these data c Foreigners are barred from investing directly in tihe Saudi stock market, but they may invest indirectly through mutual funds 2111 H 2003 World Development Indicators Investment climate El As investment portfolios become increasingly global, prospect) (For more on the rating processes of the rat- * Foreign direct Investment is net inflows of Investment investors as well as governments seeking to attract ing agencies, see the data sources ) Risk ratings may to acquire a lasting management interest (10 percent or investment must have a good understanding of trends be highly subjective, reflecting external perceptions more of voting stock) in an enterprise operating in an in foreign direct investment and country risk This that do not always capture the actual situation in a economy other than that of the investor It is the sum of table presents data on foreign direct investment, country But these subjective perceptions are the real- equity capital, reinvestment of earnings, other long-term information on the regulation of entry to and exit from ity that policymakers face Countries not rated by cred- capital, and short-term capital as shown in the balance emerging stock markets reported by Standard & it risk rating agencies typically do not attract registered of payments Gross capital formation is the sum of gross Poor's, and country risk and creditworthiness ratings flows of private capital The risk ratings presented here fixed capital formation, changes in inventories, and from several major international rating services are included for their analytical usefulness and are not acqujisitions less disposals of valuables * Regulations The statistics on foreign direct investment are endorsed by the World Bank on entry to emerging stock markets are assessed on a based on balance of payments data reported by the The PRS Group's International Country Risk Guide scale from free to closed (see About the data) International Monetary Fund (IMF), supplemented by (ICRG) collects information on 22 components of risk, * Regulatlons on repatriation of Income (dividends, data on net foreign direct investment reported by the groups it into three major categories (political, finan- interest, and realized capital gains) and repatriation of Organisation for Economic Co-operation and cial, and economic), and converts it into a single capital from emerging stock markets are evaluated as Development and official national sources (For a numerical risk assessment ranging from 0 to 100 free, delayed, or restricted (see About the data) detailed discussion of data on foreign direct invest- Ratings below 50 indicate very high risk, and those * Composite International Country Risk Guide (ICRG) ment, see About the data for table 6 7 ) above 80 very low risk Ratings are updated monthly risk rating is an overall index, ranging from 0 to 100. Entry and exit restrictions on investments are Institutional Investor country credit ratings are based on 22 components of risk * Institutional Investor among the mechanisms by which countries attempt to based on information provided by leading internation- credit rating ranks, from 0 to 100, the chances of a reduce the risk to their economies associated with al banks Responses are weighted using a formula country's default * Euromoney country creditworthi- foreign investment Yet such restrictions may increase that gives more importance to responses from banks ness rating ranks, from 0 to 100, the risk of investing in the risk or uncertainty perceived by investors Many with greater worldwide exposure and more sophisti- an economy * Moody's sovereign foreign or domestic countries close industries considered strategic to for- cated country analysis systems Countries are rated currency long-term debt rating assesses the risk of eign or nonresident investors And national law or cor- on a scale of 0 to 100 (highest risk to lowest), and rat- lending to governments An entity's capacity to meet its porate policy may limit foreign investment in a ings are updated every six months senior financial obligations is rated from MA (offering company or in certain classes of stocks Euromoney country creditworthiness ratings are exceptional financial security) to C (usually in default, The entry and exit regulations summarized in the based on nine weighted categories (covering debt, with potential recovery values low) Modifiers 1-3 are table refer to 'new money' investment by foreign insti- economic performance, political risk, and access to applied to ratings from AA to B, with 1 indicating a high tutions, other regulations may apply to capital invested financial and capital markets) that assess country ranking in the rating category * Standard & Poor's sov- through debt conversion schemes or to capital from risk The ratings, also on a scale of 0 to 100 (highest ereign foreign or domestic currency long-term debt rat- other sources The regulations reflected here are formal risk to lowest), are based on polls of economists and Ing ranges from AAA (extremely strong capacity to meet ones But even formal regulations may have very differ- political analysts supplemented by quantitative data financial commitments) to CC (currently highly vulnera- ent effects in different countries because of differences such as debt ratios and access to capital markets ble) Ratings from AA to CCC may be modified by a plus in the bureaucratic culture, the speed with which appli- Moody's sovereign long-term debt ratings are opin- or minus sign to show relative standing in the category cations are processed, and the extent of red tape The ions of the capacity of entities to honor senior unse- An obligor rated SD (selective default) has failed to pay regulatfons on entry are evaluated using the terms free cured financial obligations and contracts one or more financial obligations when due (no significant restrictions), relatively free (some regis- denominated in foreign currency (foreign currency tration procedures required to ensure repatriation issuer ratings) or in their domestic currency (domes- rights), special classes (foreigners restricted to certain tic currency issuer ratings) The data on foreign direct investment are based on classes of stocks designated for foreign investors), Standard & Poor's ratings of sovereign long-term for- estimates compiled by the IMF in its Balance of authorized investors only (only approved foreign eign and domestic currency debt are based on current Payments Statistics Yearbook, supplemented by investors may buy stocks), and closed (closed or access information furnished by obligors or obtained by World Bank staff estimates The data on entry and severely restricted, as for nonresident nationals only) Standard & Poor's from other sources it considers reli- exit regulations are from Standard & Poor's Regulations on repatriation of income and capital are able A Standard & Poor's issuer credit rating (one form Emerging Stock Markets Factbook 2002 The coun- evaluated as free (repatriation done routinely), delayed of which is a sovereign credit rating) is a current opinion try risk and creditworthiness ratings are from the (repatriation of capital after one year), or restncted of an obligor's capacity and willingness to pay its finan- PRS Group's monthly International Country Risk (repatriation requires registration with or permission of cial obligations as they come due (its creditworthiness) Guide (http //www ICRGonline com), the monthly a government agency that may restrict the timing of This opinion does not apply to any specific financial obli- Institutional Investor, the monthly Euromoney, exchange release) gation, as it does not take into account the nature and Moody's Investors Service's Sovereign, Subnational Most risk ratings are numerical or alphabetical index- provisions of obligations, their standing in bankruptcy or and Sovereign-Guaranteed Issuers, and Standard & es, with a higher number or a letter closer to the begin- liquidation. statutory preferences, or the legality and Poor's Sovereign List in Credit Week ning of the alphabet meaning lower risk (a good enforceability of obligations 2003 World Development Indicators 1 265 1 Jt Business environment Entry regulations Contract enforcement Insolvency Costs to Minimum Costs to register capital Time enforce a Costs to resolve Time to start a business requirement Procedures to enforce contract Time to resolve insolvency Start-up up a business % of % of to enforce a contract % of insolvency % of procedures days GNI per capita GNI per capita a contract days GNI per capita days insolvency estate January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 Afghanistan Albania 11 62 63 67 37 220 73 733 38 Algeria 18 29 36 81 20 387 13 1,292 4 Angola Argentina 14 63 11 20 32 300 _ 9 730 18 Armenia 11 79 12 17 22 65 15 709 4 Australia 2 6 2 0 11 320 8 381 18 Austria 9 30 18 125 20 434 1 482 18 Azerbaijan 15 104 21 0 25 115 3 972 8 Bangladesh 7 30 78 0 15 270 48 Belarus 20 143 39 0 19 135 44 795 1 Belgium 7 34 15 69 22 365 9 323 4 Benin 9 63 168 370 44 248 31 1,150 18 Bolivia 18 77 164 0 44 464 2 736 18 Bosnia and Herzegovina 12 74 56 372 31 630 21 677 8 Botswana 8 70 16 0 20 77 21 Brazil 16 86 12 0 16 180 2 3,650 8 Bulgaria 10 30 9 154 26 410 6 1,385 18 Burkina Faso 15 39 328 627 24 376 173 Burundi Cambodia Cameroon 13 56 197 116 46 548 63 328 18 Canada 2 2 1 0 17 421 1 290 4 Central African Republic Chad Chile 10 34 14 0 21 200 15 1,454 18 China 12 55 13 4,338 20 180 32 955 18 Hong Kong, China 5 20 3 0 17 180 7 381 18 Colombia 18 60 15 0 37 527 6 1,106 1 Congo, Dem Rep .. Congo, Rep Costa Rica 11 80 21 0 21 370 23 915 18 CMte d'lvoire 10 91 136 99 18 150 83 800 18 Croatia 13 50 17 50 20 330 7 1,130 18 Cuba Czech Republic 10 89 5 53 16 270 19 3,350 38 Denmark 3 3 47 14 83 4 1,517 8 Dominican Republic 20 86 41 28 19 215 441 Ecuador 14 90 65 6 33 333 11 Egypt, Arab Rep 13 52 76 748 17 202 31 1,560 18 El Salvador Eritrea Estonia Ethiopia 8 44 429 1,752 24 895 35 Finland 7 36 1 29 19 240 16 330 1 France 10 53 3 29 21 210 4 624 Gabon Gambia, The Georgia 12 62 38 149 17 180 63 Germany 9 45 6 90 14 154 6 430 8 Ghana 10 126 98 1 21 90 24 591 18 Greece 16 45 50 131 15 315 8 795 8 Guatemala 13 41 69 37 19 1460 20 1,460 18 Guinea Guinea-Bissau Haiti 21313 e I 2003 World Development Indicators co Business environment 5.3: Entry regulations Contract enforcement Insolvency Costs to Minimum Costs to register capital Time enforce a Costs to resolve Time to start a business requirement Procedures to enforce contract Time to resolve insolvency Start-up up a business % of % of to enforce a contract % of insolvency % of procedures days GNI per capita GNI per capita a contract days GNI per capita days insolvency estate January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 Honduras 15 146 67 186 32 225 7 Hungary 5 65 65 232 17 365 2 India 10 88 51 0 22 106 222 4,115 8 Indonesia 11 168 15 326 29 225 386 Iran, Islamic Rep 9 69 11 34 23 150 6 655 8 Iraq Ireland 3 16 10 0 16 183 7 161 8 Israel 5 44 21 0 19 315 34 Italy 13 62 23 46 _ 16 645 4 226 18 Jamaica 7 37 16 0 11 202 42 400 18 Japan 11 30 12 544 16 60 1 211 4 Jordan 14 89 48 4,154 32 147 0 1,572 8 Kazakhstan 12 54 39 51 41 120 8 Kenya 11 68 44 0 25 255 26 1,667 18 Korea, Dem Rep Korea, Rep 13 36 19 429 23 75 5 535 4 Kuwait Kyrgyz Republic 9 26 13 73 44 365 255 Lao PDR 0 Latvia 7 11 17 110 19 189 8 419 4 Lebanon 6 46 116 83 27 721 54 1,460 18 Lesotho 9 92 68 20 Liberia Libya Lithuania 11 62 5 85 30 150 6 437 18 Macedonia, FYR 27 509 42 Madagascar 15 68 58 0 29 166 120 800 18 Malawi 11 56 94 0 12 108 521 Malaysia 7 56 27 0 22 270 2 Mali 13 61 230 886 27 150 7 1,278 18 Mauritania Mauritius Mexico 7 51 21 109 47 283 10 714 18 Moldova 11 41 31 106 36 210 14 545 8 Mongolia 8 31 14 2,307 26 224 2 450 8 Morocco 13 62 19 731 17 192 9 665 18 Mozambique 16 214 74 30 18 540 10 Myanmar Namibia Nepal 8 25 189 0 24 350 44 1,832 Netherlands 8 42 17 65 21 39 1 861 1 New Zealand 2 2 0 0 19 50 12 704 18 Nicaragua 12 69 309 0 17 65 18 827 8 Niger 11 27 390 755 29 365 57 18 Nigeria 9 50 92 32 25 241 4 573 18 Norway 4 24 4 32 12 87 10 336 1 Oman Pakistan 10 53 44 0 30 365 46 1,010 4 Panama 7 19 30 0 44 197 20 2,373 38 Papua New Guinea Paraguay Peru 8 114 23 0 35 441 30 763 8 Philippines 14 62 15 9 28 164 104 2,083 38 Poland 11 58 23 24 18 1000 11 558 18 Portugal 12 104 22 16 22 420 5 931 8 Puerto Rico 2003 World Development Indicators 1 267 Ousiness environment Entry regulations Contract enforcement Insolvency Costs to Minimum Costs to register capital Time enforce a Costs to resolve Time to start a business requirement Procedures to enforce contract Time to resolve insolvency Start-up up a business % of % of to enforce a contract % of insolvency % of procedures days GNI per capita GNI per capita a contract days GNI per capita days Insolvency estate January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 January 2002 Romania 9 46 36 380 28 225 13 1,171 8 Russian Federation 19 50 7 38 16 160 20 558 4 Rwanda Saudi Arabia 13 99 153 1,887 19 195 0 Senegal 9 58 116 277 30 335 49 Sierra Leone Singapore 7 8 6 0 20 47 2 258 1 Slovak Republic 11 119 13 112 26 420 13 1,734 18 Slovenia 9 62 13 64 22 1,003 4 520 1 Somalia South Africa 9 32 7 0 18 207 55 730 18 Spain 11 100 16 18 20 147 11 558 8 Sri Lanka 8 73 16 323 17 440 8 Sudan Swaziland Sweden 5 18 1 36 21 190 8 730 8 Switzerland 6 20 18 32 14 224 4 1,681 4 Syrian Arab Republic 10 42 17 6,793 36 596 31 1,513 8 Tajikistan Tanzania 13 37 229 0 14 127 4 1,095 8 Thailand 8 45 7 0 19 210 1 960 38 Togo Trinidad and Tobago Tunisia 9 47 21 33 14 7 4 906 8 Turkey 13 53 43 12 18 105 5 655 8 Turkmenistan Uganda 17 36 114 0 16 99 10 730 38 Ukraine 13 42 22 434 20 224 11 1,085 18 United Arab Emirates 10 29 24 404 27 559 11 United Kingdom 5 4 1 0 12 101 1 378 8 United States 5 4 1 0 17 270 0 1,095 4 Uruguay 10 27 49 831 38 360 14 1,460 8 Uzbekistan 9 33 17 33 34 258 2 774 4 Venezuela, RB 14 119 24 0 41 360 47 1,476 38 Vietnam 10 68 36 0 28 120 9 730 18 West Bank and Gaza Yemen, Rep 13 95 317 2,731 27 240 1 891 4 Yugoslavia, Fed Rep 16 71 20 500 40 1,028 20 2,665 38 Zambia 6 40 43 151 16 188 16 1,365 8 Zimbabwe 10 122 27 0 13 197 40 268 0 2003 World Development Indicators Business environment 5.31 This new table presents key indicators on the envi- tressed companies is the insolvency system Two * Start-up procedures include those that are always ronment for doing business The indicators, covering indicators measure the time it takes to resolve insol- required to start a business The procedures are entry regulations, contract enforcement, and access vency and the associated costs With effective insol- interactions of the company with external parties to finance, identify regulations that enhance or con- vency systems, one may expect greater access and (government agencies, lawyers, auditors, notaries. strain business investment, productivity, and growth better allocation of credit and the like), including interactions required to obtain The data are from a new World Bank database, Doing To ensure cross-country comparability, several all necessary permits and licenses and to complete Business standard characteristics of a company are defined in all inscriptions, verifications, and notifications need- A vibrant private sector is central to promoting all surveys, such as size, ownership, location, legal ed to start operation * Time to start up a business growth and expanding opportunities for poor people status, and type of activities undertaken The data refers to the time, measured in calendar days, need- But encouraging firms to invest, improve productivity, were collected through a study of laws and regula- ed to complete all the required procedures for legally and create jobs requires a legal and regulatory envi- tions in each country, surveys of regulators or private operating a business If a procedure can be speeded ronment that fosters access to credit, protection of sector professionals on each topic, and cooperative up at additional cost, the fastest procedure, inde- property rights, and efficient judicial, taxation, and arrangements with private consulting firms and busi- pendent of cost, is chosen Time spent gathering customs systems The indicators in the table point to ness and law associations information about the registration process is exclud- the administrative and regulatory reforms and insti- ed * Costs to register a business is normalized by tutions needed to create a favorable environment for presenting it as a percentage of gross national doing business income (GNI) per capita * Minimum capital require- When entrepreneurs start a business, the first ment is the amount that the entrepreneur needs to obstacles they face are the administrative and legal deposit in a bank account to obtain a company regis- procedures required to register the new firm tration number This amount is typically specified in Countries differ widely in how they regulate the entry the commercial code or the company law and is often of new businesses In some countries the process is returned to the entrepreneur only when the company straightforward and affordable But in others the pro- is dissolved * Procedures to enforce a contract are cedures are so burdensome that entrepreneurs may independent actions, with each action defined as a opt to run their business informally procedure-mandated by law or court regulation- The data on entry regulations are derived from a that demands interaction between the parties or survey of the procedures that a typical domestic between them and the judge or court officer * Time limited-liability company must complete before legal- to enforce a contract refers to the number of calen- ly starting operation The data cover the number and dar days from the moment the plaintiff files the law- duration of start-up procedures, the cost to register suit in court until the moment of final determination a business, and the minimum capital requirement and, in appropriate cases, payment * Costs to Contract enforcement is critical to enable busi- enforce a contract include filing fees, court costs, nesses to engage with new borrowers or customers and estimated attorney fees * Time to resolve insol- Without good contract enforcement, trade and credit vency refers to the number of calendar days from the will be restricted to a small community of people who moment of filing for insolvency in court until the have developed relationships through repeated deal- moment of actual resolution of distressed assets ings or through the security of assets The institution * Costs to resolve Insolvency include filing fees and that enforces contracts between debtors and credi- court costs, and attorney fees and payments to other tors, and suppliers and customers, is the court professionals (accountants, assessors), out of the The efficiency of contract enforcement is reflected insolvency estate The cost figures are averages of in three indicators the number of judicial procedures the estimates of survey respondents, who chose to resolve a dispute, the time it takes to enforce a among six options 0-2 percent, 3-5 percent, 6-10 commercial contract, and the associated costs The percent, 11-25 percent, 26-50 percent, and more data are derived from structured surveys answered than 50 percent by attorneys at private law firms The questionnaires cover the step-by-step evolution of a commercial case before local courts in the country's largest city. The continuing existence of unviable competitors is _ consistently rated by firms as one of the greatest All data are from the World Bank's Doing Business potential barriers to operation and growth The insti- project (http //rru worldbank org/DoingBusiness/) tution that deals with the exit of unviable companies and the rehabilitation of viable but financially dis- 2003 World Development Indicators 1 269 !1 UR1W 0 Stock markets Market capitalization Value traded Turnover ratio Listed domestic S&P/IFC companies Investable Index value of shares traded as % of % change in $ millions % of GDP % of GDP capitalization pnce index 1990 2002 1990 2001. 1990 2001 1990 2002 1990 2002 2001 2002 Afghanistan : Albania Algeria Angola Argentina 3,268 103,434 2 3 717 0 6 16 33 6 2 2 179 83 -31 7 -51 4 Armenia 1 4 Australia 108,879 374,269 35 1 101 5 12 9 65 3 31 6 64 3 1,089 1,334 Austria 11,476 25,204 7 1 13 4 11 5 3 8 110 3 28 8 97 114 Azerbaijan 01 . Bangladesh 321 1,193 1.1 2 5 00 16 15 64 7 134 239 -20 7a -42a Belarus Belgium 65,449 16,584 33 2 7 2 3 3 17 9 . 247 9 182 156 Benin Bolivia 1,555 19 5 00 . 0 1 29 Bosnia and Herzegovina Botswana 261 1,723 66 24.8 02 1 3 61 50 9 18 439a 31la Brazil 16,354 123,807 3 5 37 1 1 2 13 0 23 6 35 0 581 399 -22 5 -33 0 Bulgaria 733 3 7 05 :13 9 354 -7 5a 62 5a Burkina Faso Burundi Cambodia Cameroon Canada 241,920 700,751 42.1 100 9 12.4 66.5 26.7 65 9 1,144 4,004 Central African Republic Chad Chile 13,645 47,584 45.0 85 4 2 6 6 4 6 3 7 4 215 254 -8 3 -14 8 China 2,028 463,080 0.5 45 2 02 38 7 1589 85 7 14 1,235 -19 5 -1445 Hong Kong, China 83,397 506,131 111 5 312 6 46.3 121.3 43 1 38 8 284 857 Colombia 1,416 9,664 3 5 16 0 0 2 0 4 5.6 2.7 80 114 25 2a 9 7 a Congo, Dem Rep Congo, Rep Costa Rica 475 5 5 14.6 5 8 82 Cote d'lvoire 549 1,328 5 1 11.2 0 2 0 1 3 4 0 7 23 38 -2 4a 17 4a Croatia 3,976 15 2 0 6 3 8 2 66 -3 5a 44 2a Cuba Czech Republic 15,893 16 1 5 9 36 6 78 -13 7 38 9 Denmark 39,063 94,958 29 3 58 8 8 3 43 7 28 0 74 3 258 208 Dominican Republic 0 8 Ecuador 69 1,750 0 5 7 9 0 1 0 7 65 31 85 4a 23 3a Egypt, Arab Rep 1,765 26,094 4 1 24 5 0 3 4 0 16 1 573 1,148 -45 5 -5 8 El Salvador 1,522 11 1 0 2 1.5 32 Eritrea Estonia 2,430 26 7 . 4 0 . 14.9 14 -3 7a 66 3a Ethiopia Finland 22,721 190,456 16 6 157 6 2 9 148 2 . 94 0 73 152 France 314,384 1,1 74,428 25 9 89 7 9 6 82 3 91 7 578 791 Gabon Gambia, The Georgia Germany 355,073 1,071,749 210 58 1 29.7 76 9 139 3 132 4 413 988 Ghana 76 740 1 2 10 1 0 2 2 5 13 24 4 5a 27 6a Greece 15,228 86,538 18 1 73 9 4 7 319 36 3 43 2 145 338 -31 2 Guatemala 232 .. 11 0 0 31 10 Guinea Guinea-Bissau Haiti 270 1 2003 World Development Indicators Stock markets 5.41 Market capitalization Value traded Turnover ratio Listed domestic S&P/IFC companies Investable Index value of shares traded as % of % change in $ millions % of GDP % of GOP capitalization price index 1990 2002 1990 2001 1990 2001 1990 2002 1990 2002 2001 2002 Honduras 40 1 3 26 46 Hungary 505 13,110 1 5 20 0 0 3 9 3 6 3 46 5 21 48 -10 3 34 6 India 38,567 131,011 12 2 23 1 6 9 2 0 65 9 225 8 2,435 5,650 -19 9 6 8 Indonesia 8,081 29,991 7 1 15 8 3 5 171 6 75 8 42 0 125 331 -18 5 33 3 Iran, Islamic Rep 34,282 9,704 8 5 1 0 304 11 3 97 316 Iraq Ireland 75,298 72 9 21 8 29 9 68 Israel 3,324 45,371 63 53 2 10 5 27 5 95 8 517 216 615 -16 4 -26 6 Italy 148,766 527,396 13 5 48.4 3 9 50 7 26 8 104 7 220 288 Jamaica 911 5,838 19 8 59 6 0 7 1 0 3 4 1 6 44 42 4 3 a 40 Oa Japan 2,917,679 2,251,814 95 6 54 4 52 5 44 1 43 8 81 1 2,071 2,471 _33 4 b -8 7 b Jordan 2,001 7,087 498 715 101 106 200 148 105 158 314a -21a Kazakhstan 1,204 5 4 1 4 26 5 31 Kenya 453 1,423 5 3 9 2 0 1 0 4 2 2 3 8 54 57 -22 7a 42 2 a Korea, Dem Rep Korea, Rep 110,594 248,533 43 8 55 0 30 1 166 7 61 3 303 3 669 1,518 51 2 5 8 Kuwait 20,772 580 11 7 213 77 Kyrgyz Republic Lao PDR Latvia 714 9 1 2 2 24 0 62 60 1 a -14 1a Lebanon 1,401 7 3 0 3 4 7 13 -29 2 a 5 7a Lesotho Liberia Libya Lithuania 1,463 10 0 1 8 17 5 51 -23 6a 25 7a Macedonia, FYR 46 13 01 4 3 2 Madagascar Malawi 156 8 9 1 3 13 8 8 Malaysia 48,611 123,872 110 4 135 1 24 7 23 6 24 6 17 5 282 865 4 2 -2 6 Mali Mauritania 1,091 108 4 40 Mauritius 268 1,328 112 216 0 3 2 5 1 9 11 5 13 40 -21 8a 22 ga Mexico 32,725 103.137 12 5 20 5 4 6 6 5 44 0 316 199 166 12 8 -16 4 Moldova 350 23 7 14 2 60 1 22 Mongolia 35 Morocco 966 8,591 3 7 26 7 02 28 10 6 71 55 -17 3 -81 Mozambique Myanmar Namibia 21 171 07 48 03 5 2 3 13 _31 Oa 22 5a Nepal 800 14 6 0 6 110 Netherlands 119,825 458,221 40 7 120 5 13 7 271 9 29 0 225 5 260 180 New Zealand 8,835 17,779 20 3 35 3 4 4 16 7 17 3 47 4 171 145 Nicaragua Niger Nigeria 1,372 5,740 4 8 113 0 0 12 0 9 10 6 131 195 25 1a -0 3 a Norway 26,130 69,054 22 6 416 12 1 31 5 54 4 75 8 112 186 Oman 1,061 3,997 94 175 09 28 123 130 55 96 -270a 3L8a Pakistan 2,850 10,200 71 8 4 0 6 212 8 7 2519 487 712 -32 8a 112 Oa Panama 226 2,602 3 4 25 6 0 0 0 4 0 9 17 13 29 Papua New Guinea Paraguay 5 5 Peru 812 13,363 31 18 1 0 4 1 6 19 3 8 7 294 202 14 2 33 5 Philippines 5,927 39,021 13 4 29 9 2 7 4 4 13 6 14 8 153 235 -29 9 -19 7 Poland 144 28,750 02 14 7 0 0 4 2 89 7 28 7 9 216 -24 9 2 2 Portugal 9,201 46,338 12 9 42 2 2 4 24 8 16 9 58 9 181 97 Puerto Rico 2003 World Development Indicators 1 271 D1311 Stock markets Market capitalization Value traded Turnover ratio Listed domestic S&P/IFC companies Investable Index value of shares trsded as % of % change in $ millions % of GDP % of GDP capitalization price index 1990 2002 1990 2001 1990 2001 1990 2002 1990 2002 2001 2002 Romania 4,561 29 07 230 4,870 -25 3a 96 7a Russian Federation 244 124,198 0 0 24 6 7 4 30 1 13 196 52 4 34 8 Rwanda Saudi Arabia 48,213 74,855 40 8 39 3 1 9 11 9 30 4 59 68 3 7 a 3 8a Senegal Sierra Leone Singapore 34,308 117,338 93 6 137 0 55 3 74 0 54 0 150 386 Slovak Republic 1,904 2 6 4 7 179 5 354 21 3a 23 6a Slovenia 4,606 14 8 30 5 24 35 2 0 a 78 3a Somalia South Africa 137,540 184,622 122 8 78 0 7 3 61 5 78 9 732 450 -22 1 44 9 Spain 111,404 468,203 21 8 80 5 8 0 144 1 179 1 427 1,458 Sri Lanka 917 1,681 11 4 8 4 0 5 10 5 8 11 5 175 238 36 5a 28 4a Sudan Swaziland 17 127 1 9 101 .. 0 6 6 7 1 5 Sweden 97,929 232,561 411 110 8 7 4 143 7 14 9 129 7 258 285 Switzerland 160,044 521,190 70 1 210 9 29 6 121 8 57 7 182 263 Syrian Arab Republic Tajikistan Tanzania 398 4 3 01 19 4 Thailand 23,896 46,084 28 0 31 7 26 8 31 1 92 6 98 3 214 466 3 0 18 3 Togo Trinidad and Tobago 696 6,506 13 7 44 0 1 1 2 0 10 0 4 5 30 31 16a 33 2a Tunisia 533 2,131 4 3 11 5 0 2 1 6 3 3 13 7 13 47 -29 oa -2 5a Turkey 19,065 33,958 12 7 32 3 3 9 52 8 42 5 163 4 110 288 -30 2 -33 5 Turkmenistan Uganda 36 0 6 2 Ukraine 3,119 4 0 0 6 14 8 184 -36 3a 26 7a United Arab Emirates 7,881 3 4 12 United Kingdom 848,866 2,217,324 85 8 155 7 28 2 1314 33 4 84 4 1,701 1,923 -18 3c -16 5c United States 3,059,434 13,810,429 53 2 137 2 30 5 288 5 53 4 210 3 6,599 6,355 -13 od -23 4d Uruguay 153 0 8 0 0 0 5 36 15 Uzbekistan 50 0 4 0 1 5 Venezuela, RB 8,361 3,962 172 49 46 03 430 64 76 59 -201a -35la Vietnam West Bank and Gaza 723 18 2 19 10.3 24 Yemen, Rep Yugoslavia, Fed Rep 0 0 0 0 0 0 Zambia 217 6 0 13 . 22 5 9 Zimbabwe 2,395 15,632 27 3 88 0 0 6 16 9 2 9 19 2 57 76 134 3 a 97 9a | li- _ 9rg i @ s 1 3x 1i,8 ; Low Income 54,588 158,646 9 8 18 3 4 7 32 0 53.8 133 8 3,446 7,842 Middle Income 319,976 1,712,619 20 0 35 7 5 3 17 6 44 4 4,245 9,442 Lower middle income 195,766 833,032 16 6 35 2 26 6 614 2,565 5,756 Upper middle income 124,210 829,587 12 4 36 3 3 3 7 7 30 3 25 7 1,680 3,686 Low & middle Income 374,564 1,871,265 18 8 33 1 5 2 19 8 58 0 7,691 17,284 East Asia & Pacific 86,515 723,605 16 4 45 8 6 6 48 0 1181 72 5 774 2,886 Europe & Central Asia 19,065 181,064 21 19 3 12 7 54 2 110 2,759 Latin America & Carib 78,470 609,072 7 6 33 4 2 1 6 3 29 8 21 6 1,748 1,570 Middle East & N Africa 5,265 131.528 29 0 26 3 2 4 6 1 19 8 817 2,020 South Asia 42,655 117,817 10 8 19 7 5 6 3 8 54 0 180 3 3,231 7,010 Sub-Saharan Africa 142,594 108,179 52 0 47 8 32 8 23 8 1,011 1,039 High Income 9,025,095 25,690,523 51 6 103 9 31 4 165 8 59 5 138 5 17,733 26,035 Europe EMU 1,183,983 4,164,198 21 6 68 3 14 2 85 9 106 0 2,630 4,682 Note Aggregates for market capitalization are unavailable for 2002, those shown are for 2001 a Data refer to the S&P/IFC Global index b Data refer to the Nikkei 225 index c Data refer to the Fr 100 index d Data refer to the S&P 500 index 272 0 2003 World Development indicators Stock markets I= C = _ ~~~~~~~~~~~~~~~~ The development of an economy's financial markets is all size of the stock market in U S dollars and as a per- * Market capitalization (also known as market value) is closely related to its overall development Well-func- centage of GDP The number of listed domestic compa- the share price times the number of shares outstand- tioning financial systems provide good and easily nies is another measure of market size Market size is ing * Value traded refers to the total value of shares accessible information That lowers transaction costs, positively correlated with the ability to mobilize capital traded during the period * Turnover ratio is the total which in turn improves resource allocation and boosts and diversify risk value of shares traded during the period divided by the economic growth Both banking systems and stock Market liquidity, the ability to easily buy and sell secu- average market capitalization for the period Average markets enhance growth, the main factor in poverty rities, is measured by dividing the total value traded by market capitalization is calculated as the average of the reduction At low levels of economic development com- GDP This indicator complements the market capitaliza- end-of-period values for the current period and the pre- mercial banks tend to dominate the financial system, tion ratio by showing whether market size is matched vious period * Listed domestic companies are the while at higher levels domestic stock markets tend to by trading The turnover ratio-the value of shares trad- domestically incorporated companies listed on the coun- become more active and efficient relative to domestic ed as a percentage of market capitalization-is also a try's stock exchanges at the end of the year This mdi- banks The structure and development of a country's measure of liquidity as well as of transaction costs cator does not include investment companies, mutual financial system are also influenced by the legal, regu- (High turnover indicates low transaction costs ) The funds, or other collective investment vehicles latory, tax, and macroeconomic environment turnover ratio complements the ratio of value traded to * S&P/IFC Investable Index price change is the U S The stock market indicators in the table include meas- GDP, because the turnover ratio is related to the size of dollar price change in the stock markets covered by the ures of size (market capitalization, number of listed the market and the value traded ratio to the size of the S&P/IFCI country index, supplemented by the S&P/IFCG domestic companies) and liquidity (value traded as a per- economy A small, liquid market will have a high turnover country index centage of gross domestic product, turnover ratio) The ratio but a low value traded ratio Liquidity is an impor- comparability of such indicators between countries may tant attribute of stock markets because, in theory, liquid be limited by conceptual and statistical weaknesses, markets improve the allocation of capital and enhance such as inaccurate reporting and differences in account- prospects for long-term economic growth A more com- ing standards The percentage change in stock market prehensive measure of liquidity would include trading prices in U S dollars, from the Standard & Poor's costs and the time and uncertainty in finding a counter- Investable (S&P/IFCI) and Global (S&P/IFCG) country part in settling trades indexes, is an important measure of overall perform- Standard & Poor's maintains a series of indexes for ance Regulatory and institutional factors that can affect investors interested in investing in stock markets in investor confidence, such as the existence of a securi- developing countries At the core of the Standard & ties and exchange commission and the quality of laws to Poor's family of emerging market indexes, the S&P/IFCG protect investors, may influence the functioning of stock index is intended to represent the most active stocks in markets but are not included in this table the markets it covers and to be the broadest possible Stock market size can be measured in a number of indicator of market movements The S&P/IFCI index, ways, each of which may produce a different ranking which applies the same calculation methodology as the among countries Market capitalization shows the over- S&P/IFCG index, is designed to measure the returns for- eign portfolio investors might receive from investing in 5.4a emerging market stocks that are legally and practically rrT.. - - open to foreign portfolio investment Market capitalization ($ billions) Standard & Poor's Emerging Markets Data Base, the 500 source for all the data in the table, provides regular updates on 54 emerging stock markets encompassing 400 more than 2,200 stocks The S&P/IFCG index includes 300 - 34 markets and more than 1,900 stocks, and the The data on stock markets are from Standard & S&P/IFCI index covers 30 markets and close to 1,200 Poor's Emerging Stock Markets Factbook 2002, 200 stocks These indexes are widely used benchmarks for which draws on the Emerging Markets Data Base, F0 . -72 $ m minternational portfolio management See Standard & supplemented by other data from Standard & 100 - e- e 8 Poor's I2001b) for further information on the indexes Poor's The firm collects data through an annual C 0 Because markets included in Standard & Poor's survey of the world's stock exchanges, supple- China Korea, South India Russian emerging markets category vary widely in level of devel- mented by information provided by its network of Rep Africa Federation opment, it is best to look at the entire category to den- correspondents and by Reuters The GDP data Market capitalization In China fell from $524 billion in 2001 to $463 billion in 2002, yet it remains almost twice tify the most significant market trends And it is useful are from the World Bank's national accounts data that in the Republic of Korea, with the second highest to remember that stock market trends may be distort- files About the data is based on Demirguc-Kunt market capitalization ed by currency conversions, especially when a currency and Levine (1996a) and Beck and Levine (2001) Source Table 5 4 has registered a significant devaluation 2003 World Development Indicators 1 273 FinanciaG depth and effid~ency Domestic credit Uquld Quasi-liquid Ratio of bank Interest rate Risk premium provided by liabilities liabilifties liquid reserves to spread on lending banking sector bank assets Lending minus deposit rate Pnme lending percentage rate min us % of GDP % of GDP % of GOP %points Treasury bill rate 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan Albania 46 5 66 7 42 4 11 6 2 1 11 9 11 9 Algeria 74 5 39 0 73 5 58 6 24 8 29 3 1 3 10O6 3 3 3 8 Angola - 0 6 20 1 133 16 6 48 1 Argentina 32 4 37 3 11 5 27 2 7 1 21.3 7 4 9 3 11.5 Armenia 58 7 9 1 79.9 13 5 42 9 6 9 13 6 8 5 11 8 6 8 Australia 71 5 94 0 55 1 71 1 43 3 47 6 1 5 1 2 4 5 49 4-0 _33 Austria 121 4 126 3 2 1 34 Azerbaijan 65 9 5 3 38 6 12 9 13 4 6 6 4 5 10.4 11 2 3 2 Bangladesh 23.9 38 7 234 37 2 _168 27.7 12 8 10 3 4 0 7 3 Belarus 17 1 15 2 9 9 9 8 . 12 8 Belgium 73 1 120 4 .0 2 . 6 9 5 1 3 4 4 3 Bentin 22 4 4 6 26 7 31 0 5 9 7 9 29 3 19 5 9 0 Bolivia 30 7 63 0 24 5 55 9 18 0 46 9 18 8 6 0 18 0 10 2 8 6 Bosnia and- Her-zegovina Botswana -46 0 -73 5 21 9 31 7 13 6 23 9 11 0 3 4 1 8 5 6 Brazil 89 8 59 2 26.4 30 0 18 5 23 0 6 7 7 8 39 8 37 6 Bulgaria 118 5 20 4 71 9 40 9 53 6 24 4 10 2 8.2 8 9 8 2 8 6 6 5 Burkina Faso 13 7 15 3 21.3 21 6 7 5 7.2 12.7 _8.8 9.0 Burundi- 23 2 32 7 18 2 20 1 6 5 6 1 2.8 4.8 Cambodia 6 5 16 5 11 9 48 5 12 1 Cameroon 31 2 16 4 22.6 18.3 10 1 7 1 3 4 20 8 11.0 15 7 Canada 82.3 91 0 74.3 77 1 59 8 53 6 1 6 06 1 3 -20 13 2 0 Central African Republic 12 9 12 2 15 3 15 5 18_ 1 5 2.8 2 5 11 0 15 7 Chad 11 5 125 14 6 12 7 0 6 0 8 3 3 12 4 11 0 15 7 Chile 7-30 73 4 40 7 46.9 32.8 37.1 3 8 3 1 8 5 5.7 China 90 0 140 6 79 2 163 0 41 4 98.7 15 7 12 7 0 7 3 6 Hong Kong, China 156 3 142 5 181 7 237 0 166 8 220 3 0 1 0 2 3 3 2 7 2 7 3 4 Colombia 359_ 34 7 29 8 31.7 19 3 22 0 26 3 5 5 8 8 8 3 Congo, Dem Rep 25 3 12 9 2 1 49 0 Congo,Rep 29 1 13 5 22 0 12 8 6.1 1 0 2.0 21 6 11 0 15 7 Costa Rica 29 9 33 3 42 7 37 5 30 0 23 9 68 5 14 7 11 4 12 1 C6te dlvoire -44 5 21 9 28 8 24 1 10 9 6 8 2 1 5 8 9 0 Croatia . 51.9 . 62 6 48 6 11 8 499 3 6 3 C-uba Czech Republic 49 8 74 5 47 4 . 19 0 . 41 2 0 Denmark 63 0 151 6 59.0 49.7 29 4 _ 18 8 _ 1.1 0.7 6.2 4 9 Dominican Republic 31 5 41 7 28 6 39 7 13 3 28 2 31.1 24.1 15.2 8 7 Ecuador 15 0 37 1 20 4 28 9 11 3 18 2 22.6 3.3 -6.0_ 8 9 Egypt, Arab Rep 106 8 103 6 87 9 88.8 60 7 70.2 17 1 18 8 7 0 3 8 6 1 El Salvador 32 0 42 5 30 6 46 5 19 6 381 33 4 29 8 3 2 4 6 Eritrea Estonia 66 7 43 5 136 0 42 3 95 2 16 4 43 1 11 2 . 3.7 Ethi opia 67 0 58 2 42 2 -465 12 6 23 6 24 0 8 4 3 6 3 9 3_0 '78 Finland 83 1 63 7 54 4 4 1 4 1 3 8 Fr-ance 104 4 108 0 . ..1 0 6 1 4 0 0 4 2 7 Gabon 20 0 20 8 17 8 17 8 6 6 7 4 2 0 9 6 11 0 15 7 Gambia, The 3 4 25 0 20 7 38 7 8 8 20.3 8 8 12 7 15 2 11 5 Georgia 20 5 11 3 5 3 11 6 19.5 Germany 103-4 147 5 68 9 .3-2 . 4.5 6 5 3 5 6-4 Ghana 13 2 08 14 1 17 4 34 17 4 20 2 8 3 Greece 99 3 110 4 . 13 9 17 2 8 1 5 3 3.6 4 5 Guatemala 17 4 15 5 21 2 30 9 11 8 17 8 31 8 15 8 5 1 10 2 Guinea 6 0 9 0 0.8 11 8 0 8 2 2 6 2 25 7 0 2 11 9 4 7 Guinea-Bissau 77 5 13 6 68 9 48.2 4 4 0 5 10 8 32 9 13 1 Haiti 34 3 31 5 32 6 37 7 16 6 25 8 74 9 43 6 . 15 0 15 1 274 2003 World Development Indicators Financial depth and efficiency 5 5 Domestic credit Liquid Quasil4iquid Ratio of bank Interest rate Risk premium provided by liabilities liabilities liquid reserves to spread on iending banking sector bank assets Lending minus deposit rate Prime lending percentage rate minus % of GDP % of GOP % of GOP %points Treasury bill rate 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 40 9 34 7 33 6 54 8 18 8 42 3 6 6 18 2 8 3 9 3 Hungary 105 5 -500 43 8 469 190 28 2 11 0 67 4 1 29 -14 1 3 India 51 5 54 7 43 1 58 7 28 1 41 8 14 8 7 2 Indonesia 45 5 60 6 40 4 56 7 29 1 44 9 4 2 6 7 3 3 3 1 Iran, Islamic Rep 70 8 47 4 57_6 44 8 31 1 25 9 66 0 34 1 Iraq Ireland 55 2 110 8 44 5 4 8 5 0 4 7 0 4 Israel 106 2 95 2 70 2 105 4 63 6 97 1 11 9 11 9 12 0 3 9 11 4 3 5 Italy 89 4 99 8 70 5 12 0 7 3 4 6 1 7 2 5 Jamaica 32 2 23 4 47 2 46 7 35_0 31 6 37 4 22 4 6 6 11 0 4 3 3 9 Japan 259 7 317 5 182 4 199 5 155 3 143 5 1 5 2 5 3 4 1 9 Jordan 117 9 89 7 131 2 116 6 77 8 83 1 20 5 24 0 2 2 5 1 Kazakhstan 11 4 16 9 g7_ 61 Kenya 52 9 42 8 43 3 41 5 29 3 274_ 9 9 9 7 5 1 13 0 4 0 7 1 Korea, Dem Rep Korea,Rep 65 7 110 4 54 6 104 3 45 7 94 5 6 3 2 4 0 0 1 9 Kuwait 243 0 102 1 192 2 91 6 153 9 75 2 1 2 1 0 0 4 3 4 Kyrgyz Republic 9.7 11 1 3 6 7 1 24 8 18 3 Lao PDR 5 1 15 3 7 2 16 4 3 1 14 0 3 4 25 7 2 5 19 7 3 5 Latvia 31 4 33 3 -151 5 4 59 6 0 Lebanon 132 6 201 9 193 7 210 1 170 9 200 7 3 9 18 3 23 1 6 3 21 1 6 0 Lesotho 32.5 5 3 38 8 29 1 22 4 10 2 23 0 5 2 7 4 11 7 4 1 7 1 Liberia 319 5 170 3 101 9 11 2 20 8 4 5 67 3 67 8 16 2 Libya 104 1 57 8 68 1 54 7 13 7 12 6 26 4 24 3 1 5 4 0 1 5 1 5 Lithuania .. 15 8 26 5 12 4 11 5 6 6 1 9 Macedonia,FYR 19 5 25 9 21 1 6 6 9 4 Madagascar 26 2 15 9 17 8 22 3 5 3 5 0 8 5 25 9 5 3 13 3 15 0 Malawi 19 7 13 4 20 2 19 4 10 8 11 5 32 9 28 5 8 9 21 2 8 1 13 8 Malaysia 75 7 1553_ 64 4 134 2 43 0 109 2 5 9 10 6 1 3 3 3 1 1 3 9 Mali 13 7 17 1 20 5 25 3 5 5 5 7 508_ 11 5 9 0 Mauritania 54 7 0 5 28 5 15 0 7 0 4 2 6 1 3 9 5 0 Mauri-tius 48_4 79 5 67 9 85 4 52 7 72 9 8 8 5 0 5 4 11 3 Mexico 36 6 24 7 22 8 24 8 16 4 15 6 4 2 8 2 9 1 2 6 Moldova 62 8 27 5 70 3 25 5 35 4 12 4 8 3 14 4 7 8 14 5 Mongolia 73 4 12 5 56 2 28 7 _147 15 2 2 0 14 8 15 9 Morocco 60 1 85 9 61 0 86 4 18 4 21 8 11 3 8 5 0 5 8 2 Mozambique 15 6 13 1 26 5 30 7 5 2 17 3 61 5 13 7 7 7 -2 0 Myanmar 32 8 351 _27 9 33 5 7 8 13 1 271 8 221_ 2 1 5 5 Namibia -17 1 49 7 20 5 43 2 12 0 19 6 4 4 3 3 10 6 7 7 6 3 5 2 Nepal 28 9 46 2 32 2 53 2 18 5 35 6 12 7 9 7 2 5 2 9 6 5 2 7 Netherlands 103 5 155 4 0 3 84_ 1 9 New Zealand 80 6 114 9 77 0 87 1 64 0 72 3 0 8 0 5 4 4 4 5 2 2 4 3 Nicaragua 206 6 56 9 231 20 2 27 7 12 5 13 8 Niger 16 2 8 0 19 8 9 5 8 3 2 3 _42 9 13 1 9 0 Nigeria 23 7 18 0 23 6 28 6 10 3 10 8 11 6 24 0 5 5 8 2 6 9 5 9 Norway 89 5 46 3 59 9 52 7 27 0 8 6 0 5 2 2 4 6 1 5 Oman 16 6 37 0 28 9 31 5 19 3 24 3 6_9 3 5 1 4 4 7 Pakistan 50 9 45 5 39 8 49 5 10 0 21 2 8 9 12 4 Panama 52 7 114 9 41 1 92.7 33 0 79 9 3 6 4 1 Papua New Guinea 357_ 24 2 35 2 31 9 24 0 17 7 3 2 8 6 6 9 7 3 4 1 3 9 Paraguay 14 9 27 9 22 3 38 0 13 7 28 7 31 0 23 0 8 1 12 0 Peru 20 2 25 8 24 8 323 11 8 21 0 22 0 23 2 2,335 0 10 5 Philippines 26 9 63 1 37 0 62 9 28 4 52 1 20 9 7 3 4 6 3 7 0 4 2 7 Poland 195_ 377_ 34 0 47 0 17 2 33 9 20 6 6 3 462 5 6 6 -5 0 3 4 Portugal 69 4 150 1 29 0 7 8 2 8 8 3 Puerto Rico 2003 World Development Indicators I275 Li oi FinanciaG depth and efficiency Domestic credit Liquid Quasi-liquid Ratio of bank interest rate Risk premium provided by liabilities liabilitles liquid reserves to spread on lending banking sector bank assets Lending minus deposit rate Prime lending percentage rate minus % of GDP % of GDP % of GDP % points Treasury bill rate 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 79.7 12 4 60 4 23 4 32 7 18 1 1 2 44 5 Russian Federation 24 3 23 5 10.3 13 1 . 13 1 5 5 Rwanda 17 1 12 5 14 9 16 8 7 0 8.4 4.3 2.5 6.3 Saudi Arabia 58 7 66 9 47 9 47 4 219 217 5 6 4 5 Senegal 338 24 7 22 9 26 5 9 7 10 7 14.1 10.0 9 0 Sierra Leone 363 521 18 1 19 5 36 6 8 64 1 9 4 12 0 16 6 5 0 10 5 Singapore 756 102.0 123 4 117 9 100.5 94 4 3 7 2.5 2 7 4 1 3 7 4 0 Slovak Republic 61.6 68 0 45 2 9 3 4 8 Slovenia 36 8 49.5 34 2 57 3 25.8 47 1 2 7 4 3 142.0 5 2 4 2 Somalia South Africa 97 8 166.9 44.6 48 7 27 2 16 7 3 3 2 6 2 1 44 32 4 1 Spain 1069 1253 . 87 .. 54 21 1 8 12 SriLanka 43 1 47 1 34 9 47 7 22 6 39 0 9 9 8 0 -6 4 84 -1 1 1 8 Sudan 20 4 96 20 1 13.3 29 5 0 79.5 27 5 Swaziland 7 5 -6.1 28 3 216 19 8 14 5 215 5 7 5 8 7 1 34 6.1 Sweden 140 3 78 7 52.3 44 6 1.8 0.4 6 8 3 7 3 0 1 9 Switzerland 179 0 173 5 145 2 132 1 118 6 90 4 11 0.9 -0 9 2.6 -0 9 1 6 Syrian Arab Republic 56 6 28 5 54 1 713 9 8 28 3 46 0 6 2 5 0 5 0 Tajikistan 24 8 7 9 2 8 .. 13 5 -15 9 Tanzania 34 6 9 8 19 9 20 4 6 3 11 1 5 3 14 3 .. 15 5 16 1 Thailand 911 112 0 74 9 117 2 66 0 104 7 3.1 3 3 2 2 4 7 Togo 21 3 20 5 36.1 26 0 191 8 5 59 0 6 1 9 0 Trinidad and Tobago 58 5 40 3 54.6 55 4 42 7 43.1 13 5 15 6 6 9 8 0 5 4 71 Tunisia 62 5 73 9 51.5 59.5 26 7 35 1 1 6 3 8 Turkey 19 4 72 5 24.1 59.0 16 4 53 0 16 3 8 9 Turkmenistan 30 7 20 4 . 8.9 . 6 7 Uganda 1 7 8 10 2 7 6 16 5 1.4 7.4 15 2 14 1 7 4 14 2 -2 3 11 7 Ukraine 83 2 23 8 50 1 22 4 9 0 7.6 49 0 15 9 .. 213 United Arab Emirates 34 7 46 3 . 37 7 4 4 11 4 United Kingdom 1212 140 5 0.5 0 3 2 2 0 7 0 3 United States 110 9 163 9 65.5 69 2 49.4 53 3 2.3 1 1 2 5 3 5 Uruguay 46 7 57 1 58 1 59 6 515 54 1 311 14.7 76 6 37.4 Uzbekistan Venezuela, RB 37 4 15 5 38 8 18 6 29 4 8 3 219 28 7 7 7 6 9 Vietnam 4 7 39 5 22 7 51.8 93 28.6 25 3 6 6 4 1 . 3 9 West Bank and Gaza Yemen, Rep 60 6_ 2 8 55 1 36.3 10 4 18 2 1212 18 7 45 5 4 2 Yugoslavia, Fed Rep Zambia 67 8 51.6 218 210 10.6 131 33 7 16 9 9 4 22 8 9 2 20 Zimbabwe 417 52 9 418 43.6 30 3 17 1 12 2 16 5 2.9 24.1 3 3 20 4 Low Income 44 6 45 1 37.1 48 3 22 2 32 4 12.8 129 8 2 13 5 Middle Income 65 7 73 6 44 0 72 1 261 46 6 14 6 9.3 5 0 7 4 Lower middle income 71 7 96 0 61 7 100.8 36 4 63.2 18 8 8 9 6 2 8 3 Upper middle income 62 2 48 1 30 8 39.4 19 5 27.7 8 8 9 5 6 2 6 3 Low & middle Income 612 68 7 42.6 68.0 25 3 44.1 13 5 112 6 7 8 8 East Asia & Pacific 76 4 125 9 67 5 141.1 415 90 9 5.1 10.6 2 2 4 7 Europe & Central Asia 37.5 39.6 26 6 . 10 4 8 0 Latin America & Carib 59 3 38 9 25 4 29 3 17 8 20.8 22 3 15.6 8.2 10 2 Middle East & iN Africa 72 4 70 5 62.5 65 2 29 4 39 3 14 2 14.5 2 2 4 7 South Asia 48 9 52 2 410 55 8 25 2 38 6 12 7 9 7 2 5 7.3 Sub-Saharan Africa 56 8 74 2 32.4 35 2 17.2 14 5 119 11 5 8 9 14 2 Hligh Income 132 1 172 9 93 0 105 5 78 6 19 1.1 4 6 4.0 2 5 3 5 Europe EMU 99 2 124 2 . .. .. 41 6.5 4 6 2 6 3 5 27(6S H 2003 World Development Indicators Financial depth and efficiency The organization and performance of financial activities ings on assets-or the interest rate spread A narrowing * Domestic credit provided by banking sector in a country affect economic growth through their impact of the interest rate spread reduces transaction costs, includes all credit to various sectors on a gross basis, on how businesses raise and manage funds These which lowers the overall cost of investment and is there- with the exception of credit to the central government, funds come from savings savers accumulate claims on fore crucial to economic growth Interest rates reflect the which is net The banking sector includes monetary financial institutions, which pass the funds to their final responsiveness of financial institutions to competition authorities, deposit money banks, and other banking users But even if a country has savings, growth may not and pnce incentives The interest rate spread, also institutions for which data are available (including materialize-because the financial system may fail to known as the intermediation margin, is a summary meas- institutions that do not accept transferable deposits direct the savings to where they can be invested most ure of a banking system's efficiency (although if govern- but do incur such liabilities as time and savings efficiently Enabling it to do so requires established pay- ments set interest rates, the spreads become less deposits) Examples of other banking institutions ments systems, the availability of price information, a reliable measures of efficiency) The nsk premium on Include savings and mortgage loan institutions and way to manage uncertainty and control risk, and mecha- lending can be approximated by the spread between the building and loan associations * Liquid llabilltles are nisms to deal with problems of asymmetnc information lending rate to the private sector (line 60p in the also known as broad money, or M3 They include bank between parties to a financial transaction International Monetary Fund's Intemational Financial deposits of generally less than one year plus currency As an economy develops, the indirect lending by Statistics, or IFS) and the 'risk free" treasury bill interest Liquid liabilities are the sum of currency and deposits savers to investors becomes more efficient and grad- rate (IFS line 60c) A small spread indicates that the mar- in the central bank (MO), plus transferable deposits ually increases financial assets relative to gross ket considers its best corporate customers to below nsk and electronic currency (MI), plus time and savings domestic product (GDP) More specialized savings and Interest rates are expressed as annual averages deposits, foreign currency transferable deposits, cer financial institutions emerge and more financing In some countries financial markets are distorted by tificates of deposit, and securities repurchase agree- instruments become available, spreading risks and restnctions on foreign investment, selective credit con- ments (M2), plus travelers' checks, foreign currency reducing costs to liability holders Securities markets trols, and controls on deposit and lending rates Interest time deposits, commercial paper, and shares of mutu- mature, allowing savers to invest their resources rates may reflect the diversion of resources to finance al funds or market funds held by residents The ratio directly in financial assets issued by firms Financial the public sector deficit through statutory reserve of liquid liabilities to GDP indicates the relative size of systems vary widely across countries banks, nonbank requirements and direct borrowing from the banking sys- these readily available forms of money-money that financial institutions, and stock markets are larger, tem And where state-owned banks dominate the finan- the owners can use to buy goods and services without more active, and more efficient in richer countries cial sector, noncommercial considerations may unduly incurring any cost * Quasl-liquid liabilitles are the M3 The ratio of domestic credit provided by the banking influence credit allocation The indicators in the table money supply less Ml * Ratio of bank liquid reserves sector to GDP is used to measure the growth of the provide quantitative assessments of each country's to bank assets is the ratio of domestic currency hold- banking system because it reflects the extent to which financial sector, but qualitative assessments of policies, ings and deposits with the monetary authorities to savings are financial In a few countries governments laws, and regulations are needed to analyze overall claims on other governments, nonfinancial public may hold international reserves as deposits in the financial conditions Recent international financial cnses enterprises, the private sector, and other banking insti- banking system rather than in the central bank Since highlight the risks of weak financial intermediation, poor tutions * Interest rate spread is the interest rate the claims on the central government are a net item corporate governance, and deficient government poli- charged by banks on loans to prime customers minus (claims on the central government minus central gov- cies the interest rate paid by commercial or similar banks ernment deposits), this net figure may be negative, The accuracy of financial data depends on the qual- for demand, time, or savings deposits * Risk premi- resulting in a negative figure for domestic credit pro- ity of accounting systems, which are weak in some um on lending is the interest rate charged by banks on vided by the banking sector developing countries Some of the indicators in the loans to prime private sector customers minus the Liquid liabilities are a general indicator of the size of table are highly correlated, particularly the ratios of "risk free" treasury bill interest rate at which short- financial intermediaries relative to the size of the econo- domestic credit, liquid liabilities, and quasi-liquid lia- term government securities are issued or traded in the my, or an overall measure of financial sector develop- bilities to GDP, because changes in liquid and quasi- market In some countries this spread may be nega- ment Quasi-liquid liabilities are long-term deposits and liquid liabilities flow directly from changes in domestic tive, indicating that the market considers its best cor- assets-such as bonds, commercial paper, and certifi- credit Moreover, the precise definition of the financial porate clients to be lower risk than the government cates of deposit-that can be converted into currency or aggregates presented varies by country demand deposits, but at a cost The ratio of bank liquid The indicators reported here do not capture the activi- reserves to bank assets captures the banking system's ties of the informal sector, which remains an important liquidity In countries whose banking system is liquid, source of finance in developing economies Personal 111 I - adverse macroeconomic conditions should be less likely credit or credit extended through community-based pool- The data on credit, liabilities, bank reserves, to lead to banking and financial crises Data on domes- ing of assets may be the only source of credit for small and interest rates are collected from central tic credit and liquid and quasi-liquid liabilities are cited on farmers, small businesses, and home-based producers banks and finance ministries and reported in an end-of-year basis And in financially repressed economies the rationing of the print and electronic editions of the No less important than the size and structure of the formal credit forces many borrowers and lenders to turn International Monetary Fund's International financial sector is its efficiency, as indicated by the mar- to the informal market, which is very expensive, or to Financial Statistics. gin between the cost of mobilizing liabilities and the earn- self-financing and family savings 2003 World Development Indicators 1 277 |D UJJ0L-J Tax policies Tax Taxes on Domestic taxes Export Import Highest marginal revenue Income, on goods duties duties tax rate" profits, and and services capital gains % of value Individual Corporate % of % of added in industry % of % of rate on income rate GDP total taxes and services tax revenue tax revenue % over $ % 2001 1990 2001 1990 2001 1990 2001 1990 2001 2002 2002 2002 Afghanistan Albania Algeria 32 1 77 9 3.3 0.0 12 1 Angola Argentina 12 5 2 7 19 9 2 2 5 5 9 3 0 2 2 6 4 5 35 120,000 35 Armenia Australia 22 0 70 9 5 9 0.1 4 4 47 30,451 30 Austria 351 20 8 27 0 10 0 10 6 0 0 16 50 52,324 34 Azerbaijan 16 6 231 9 0 0 0 9 0 35 12,428 27 Bangladesh 7 0 14 5 5 0 0 0 30 0 Belarus 270 121 103 171 136 36 .. 04 Belgium 36 1 11.5 0 0 0 52 39,665 39 Benin Bolivia 13 9 7 9 8 7 5 6 113 00 00 11 1 64 13 25 Bosnia and Herzegovina Botswana 71 7 . 1 0 0 0 24.7 25 14,085 15 Brazil 24 5 . 7 1 0 0 2.5 28 10,575 15 Bulgaria _ 25 3 40.6 17 0 9 9 16 3 00 0.0 2 5 2 6 29 5,545 15 Burkina Faso 24 7 4 9 .. 11 331 Burundi 16 7 23 4 225 16 6 17 0 3 1 0 0 23 2 16 4 Cambodia 20 38,462 20 Cameroon 12 5 251 26 0 4 3 7 2 17 3 9 18 9 316 60 10,726 39 Canada 19 3 591 57 3 4 0 0 0 0 0 3 2 14 29 62,783 38 Central African Republic Chad 20 3 3.9 Chile 18 7 15 8 24 7 10.4 13 0 . 43 6,534 16 China 6 8 49 8 6 8 1 5 6 5 0 0 22 1 45 12,048 30 Hong Kong, China 17 13,462 16 Colombia 108 36 4 39 9 4 8 62 2 0 0 0 22 5 8.5 -35 35,073 35 Congo, Dem Rep 00 28 5 16.7 2 6 00 41 1.0 451 33 7 60 1,500 40 Congo, Rep 10 7 402 16 0 41 6 6 0 0 0.0 32 3 23 2 50 14,210 45 Costa Rica 20 3 115 15.1 8 7 10 9 8 0 0 2 18 2 4 2 25 17,464 30 Cte d'lvoire 16 9 181 210 8 9 4 9 3 7 15 3 28 4 27 6 10 3.432 35 Croatia 36 9 174 8 7 9 6 23 5 0 0 0 0 3 6 6 8 35 8,758 Cuba Czech Republic 32 3 211 113 0 0 14 32 9,134 31 Denmark 319 43 5 402 18 9 19 5 00 0 0 01 0 0 59 30 Dominican Republic 15 6 23 8 19 6 3 1 48 01 00 414 44.1 25 15,165 25 Ecuador 62 9 .. 4.5 0 3 12.1 25 49.600 25 Egypt, Arab Rep - 26 4 41 0.0 . 18 9 32 10,823 40 El Salvador 16 18 6 .. 0 8 0 0 7 3 Eritrea Estonia 27 5 275 14.7 14.8 15 3 0 0 0 0 0 8 0 2 26 678 35 Ethiopia 13 0 40.9 33.1 91 7 4 2 8 2 9 18 0 26.3 Finland 34.5 17 7 . 0.0 1 0 37 50,940 29 France .. 18 7 131 0 0 . 00 33 Gabon 35 9 5 0 2 8 . 23.4 50 31,462 35 Gambia, The 13 7 12 2 0 2 45.6 Georgia 9 7 40 8 9 .. 0 0 61 Germany . 17 5 68 00 00 49 49,736 25 Ghana 251 68 12 4 . 28 7 30 7,059 33 Greece 23 3 14 5 .. 0 0 0 1 40 21,157 35 Guatemala . 31 38,155 31 Guinea 11 2 12 6 101 3 2 0 8 517 0 2 112 42 9 Guinea-Bissau Haiti 2C74 III 2003 World Development Indicators Tax policies5. 1 Tax Taxes on Domestic taxes Export Import Highest marglnal revenue Income, on goods duties duties tax rate a profits, and and services capital gains % of value Individual Corporate % of % of added in industry % of % of rate on income rate GDP total taxes and services tan revenue tax revenue % over $ % 2001 1990 2001 1990 2001 ±990 2001 199D 2001 2002 2002 2002 Honduras 25 32,916 Hungary 32 1 21 2 23 4 22'6 1 3 0 0 5 6 3 3 40 4,373 18 India 10 0 18 6 37 4 7 4 5 6 0 1 0 1 35 8 24 1 30 3,124 36 Indonesia 13 2 65 4 48 0 5 6 6 3 0 1 0 3 6 6 4 6 35 19,231 30 Iran, Islamic Rep 8 5 24 7 41 7 1 0 16 0 0 00 186 14 4 54 128,205 25 Iraq Ireland 39 7 15 5 0 0 0 0 42 25,316 16 Israel 37 7 42 4 45 2 0 0 0 0 1 4 0 7 50 53,757 36 Italy 38 5 37 7 38 8 12 7 11 6 0 0 0 0 0 0 0 0 45 63,040 36 Jamaica 25 4 41 5 39 0 8 2 11 5 0 0 0 0 14 0 9 3 25 2.547 33 Japan 7-30 2 4 0 0 1 4 37 136,415 30 Jordan 19 0 2 2 9 16 4 6 8 10 6 0 0 0.0 34 7 20 4 Kazakhstan 9 5 28 9 6 8 0 3 5 7 30 30 Kenya 32-9 15 9 0 0 17 8 30 5,612 30 Korea, Dem Rep Korea, Rep 37 5 6 7 0.0 13 0 36 60,332 27 Kuwait 3 4 19 5 8 2 0 0 0 0 0 0 76 8 0 0 0 0 Kyrgyz Republic 11 7 1180 15 7 Lao PDR 40 7,894 Latvia 24 4 15 4 13 6 0 0 1 3 25 22 Lebanon 14 1 15 1 4 9 39 0 Lesotho 12 7 12 8 . 0 2 63 6 Liberia Libya Lithuania 22-3 -222 119 16 4 14 1 0 0 13 33 .. 5 Macedonia, FYR Madagascar 11 3 15 7 15 7 3 4 5 2 8 5 0 0 50 1 53 5 Malawi 42 5 13 9 0.0 18 7 . 38_ 948 38 Malaysia .. 42 5 6 3 .9 7- 15 1 28 65,789 28 Mali Mauritania Mauritius 17 4 15 2 14 0 7 0 9 2 4 6 0 0 45 7 29 3 25 828 25 Mexico 13 2 34 2 381 10 2 10 6 0 1 0 0 -69 4 5 40 258,269 35 Moldova 19 0 3 3 . 16 0 0 0 5 1 Mongolia 23 0 28 2 10 5 9 3 18,2 00O 0 4 19 6 9 8 Morocco 25 0 27 3 28 5 12 1 12 7 0 3 0 0 20 3 18 8 44 5,243 35 Mozambique -20 5.754 35 Myanmar 3 0 29 8 34 5 6 8 4 0 0 0 0 0 23 3 7_2 30 30 Namibia -299 ~394 35 3 8 4 8 8 3 6 26 9 36 17,241- 35 Nepal 9 5 13 0 21 9 6 6 7 1 0 4 -13 37-0 30 9 Netherlands 33 6 11 5 0 0 0 0 52 43,169 35 New Zealand 28 4 62 2 68 3 13 2 0.0 0 0 2 5 1 8 -39 24,845 33 Nicaragua .. 20 0 14 1 16 9 0 0 0.0 21 3 7 8 25 31,545 25 Niger Nigeria 25 1.553 30 Norway 33 2 21 7 24.5 17 0 17 8 0 1 0 0 0 6 0 6 28 Oman 7 2 87 6 77 1 0 3 0-0 0 0 7 8 10 3 0 12 Pakistan 12 4 12 8 29 0 8 6 8 5 0 0 0 0 44 4 15 4 35 11,111 Panama 16 8 24.4 29 4 4 8 1 3 0 0 15 8 30 200,000 30 Papua New Guinea 21 9 47 0 56 2 5 0 3 5 2 1 4 5 29 3 24 2 47 24,842 25 Paraguay 9 6 12 4 16.1 3 6 7 6 0 0 0 0 18 8 17 5 0 30 Peru 13 5 5 8 25 1 8 2 9 7 7 6 0 0 9 9 -105 27 46,619 27 Philippines 13 4 32 5 45 6 6 4 4 7 0 0 0 0 28 4 19 6 32 9,727 32 Poland 27 3 18 8 13 5 0 0 2 1 40 18,596 28 Portugal 25 7 1 3 0 00 2 6 40 46,339 30 Puerto Rico 33 50,000 20 2003 World Development Indicators I279 Tax polides Tax Taxes on Domestic taxes Export Import Highest marginal revenue income, on goods duties duties tax rater proflts, and and services capital gains % of value Individual Corporate % of % of added in industry % of % of rate on income rate GDP total taxes and services tax revenue tax revenue % over $ % 2001 1990 2001 1990 2001 1990 2001 1990 2001 2002 2002 2002 Romania 231 210 120 160 107 00 0.0 0.6 34 40 3,743 25 Russian Federation 22 5 10 8 11 4 11 2 5 1 13 6,036 35 Rwanda 20 0 . 5 5 7 4 .. 20 7 Saudi Arabia . 0 . 0 Senegal 17 0 22.8 7 2 . 50 22,469 35 Sierra Leone 68 330 269 21 28 04 00 413 49 8 Singapore 15 3 44 6 52.7 4.3 4 7 0 0 0 0 3 5 2 6 26 223,003 25 Slovak Republic 29 6 19 7 10 9 0 0 1 3 38 11,637 25 Slovenia 36 3 12 3 15.5 12 7 17.6 0 0 1 8 50 25 Somalia South Africa 26 5 55 0 57 0 10 3 10.8 0.0 0 0 3 9 2 9 42 18,534 30 Spain 34 0 . 7 5 0 0 . 17 32 60,971 35 Sri Lanka 14 6 12 0 16 9 14 7 13 4 4 2 0 0 27 4 12 7 35 4,170 35 Sudan 6 6 183 51 0 8 35 5 Swaziland 26 6 33 2 26 4 5 2 6 6 2 0 0.0 50 5 54 7 39 5,089 30 Sweden 34 9 20 6 15 5 14.5 12.2 0 0 0 0 0 6 0 0 . 28 Switzerland 23 6 17 0 17 7 0.0 0 0 6.9 1 1 Syrian Arab Republic 17 4 40 2 515 9 6 6 0 1 3 1.5 8 2 11 7 Tajikistan 10 5 3 0 9 5 0.0 171 Tanzania 30 7,074 30 Thailand 14 5 26 2 34 3 8 8 7 7 0 2 0 3 23 7 12 3 37 92,443 30 Togo Trinidad and Tobago 35 7,937 35 Tunisia 26 0 16 0 22 3 7 1 12 5 0 4 01 351 12 5 Turkey 23 8 512 42 2 5 9 15 3 0 0 0 0 7 3 11 40 65,992 30 Turkmenistan Uganda 10 7 201 . 5 3 0 0 50 3 30 2,860 30 Ukraine 219 14 3 10 7 0 0 4 4 40 3,850 30 United Arab Emirates 0 0 0 0 0 6 0 United Kingdom 34 3 43 2 418 11.3 12 8 0 0 0 0 0 0 0 0 40 43,302 30 United States 19 4 56 1 58 8 0 7 08 0 0 0 0 17 10 39 297,350 35 Uruguay 233 71 165 94 104 06 01 81 30 0 30 Uzbekistan 33 603 24 Venezuela, RB 12 3 82 2 34 0 0 8 5 8 0 0 0 0 7.1 121 34 102,406 34 Vietnam 16 8 32.7 8 9 0.0 214 32 West Bank and Gaza Yemen, Rep 9 3 44 9 45.9 2 5 2 6 0 0 0.0 29 2 25 9 Yugoslavia, Fed Rep Zambia . . 30 557 35 Zimbabwe 49 7 8 4 0.0 18 8 46 15,273 30 a These data are from PricewaterhouseCoopers's Individual Taxes Worldwide Summanes 2002-2003 and Corporate Taxes Worldwide Summanes 2002-2003, copyright 2002 by PricewaterhouseCoopers by permission of John Wiley and Sons, Inc 20O 0 2003 World Development Indlcators C= Tax policies Om Taxes are the main source of revenue for many govern- because indirect taxes on goods originating from these * Tax revenue comprises compulsory transfers to the ments The sources of the tax revenue received by gov- sectors are usually negligible What is missing here is a central government for public purposes Compulsory ernments and the relative contributions of these sources measure of the uniformity of these taxes across indus- transfers such as fines, penalties, and most social are determined by policy choices about where and how to tries and along the value added chain of production security contributions are excluded Refunds and cor- impose taxes and by changes in the structure of the econ- Without such data, no clear inferences can be drawn rections of erroneously collected tax revenue are treat- omy Tax policy may reflect concerns about distributional about how neutral a tax system is between subsectors ed as negative revenue * Taxes on Income, proflts, effects, economic efficiency (including corrections for 'Surplus' revenues raised by some governments by and capital gains are levied on wages, salaries, tips, externalities), and the practical problems of administering charging higher prices for goods produced under monop- fees. commissions, and other compensation for labor a tax system There is no ideal level of taxation But taxes oly by state-owned enterpnses are not counted as tax servces, interest, dividends, rent, and royalties, profits influence incentives and thus the behavior of economic revenues Similarly, losses from charging below-market of businesses, estates, and trusts, and capital gains actors and the economy's competitiveness prices for products are rarely identified as subsidies and losses Social security contributions based on Taxes are compulsory transfers to governments from Export and import duties are shown separately gross pay, payroll, or number of employees are not individuals, businesses, or institutions They include because the burden they impose on the economy (and included, but taxable portions of social security, pen- service fees that are clearly out of proportion to the thus growth) is likely to be large Export duties, typically sion, and other retirement account distributions are costs of providing the services but exclude fines, penal- levied on pnmary (particularly agricultural) products, often included * Domestic taxes on goods and services ties, and compulsory social security contributions take the place of direct taxes on income and profits, but include all taxes and duties levied by central govern- Taxes are considered unrequited because governments they reduce the incentive to export and encourage a shift ments on the production, extraction, sale, transfer, leas- provide nothing specifically in return for them, although to other products High import duties penalize con- ing, or delivery of goods and rendering of services, or on taxes typically are used to provide goods or services to sumers, create protective barners-which promote high- the use of goods or permission to use goods or perform individuals or communities on a collective basis er-priced output and inefficient production-and implicitly activities These include value added taxes, general The level of taxation is typically measured by tax rev- tax exports By contrast, lower trade taxes enhance open- sales taxes, single-stage and multistage taxes (where enue as a share of gross domestic product (GDP) ness-to foreign competition, knowledge, technologies, stage refers to stage of production or distribution), Comparing levels of taxation across countries provides a and resources-energizing development in many ways excise taxes, motor vehicle taxes, and taxes on the quick overview of the fiscal obligations and incentives Seeing this pattern, some of the fastest growing extraction, processing, or production of minerals or facing the private sector In this table tax data in local economies have lowered import tariffs in recent years other products * Export duties include all levies col- currencies are normalized by scaling values in the same The simple mean import tariff in India, for example, lected on goods at the point of export Rebates on units to ease cross-country comparisons The table declined from almost 80 percent in 1990 to about 30 per- exported goods that are repayments of previously paid shows only central government data, which may signifi- cent in 2001 In some countries, such as members of the general consumption taxes, excise taxes, or import cantly understate the total tax burden, particularly in European Union, most customs duties are collected by a duties are deducted from the gross amounts receivable countries where provincial and municipal governments supranational authority, these revenues are not reported from these taxes, not from amounts receivable from are large or have considerable tax authority in the individual countries' accounts export duties * Import duties comprise all levies col- Low ratios of tax revenue to GDP may reflect weak The tax revenues collected by governments are the lected on goods at the point of entry into the country administration and large-scale tax avoidance or evasion outcomes of systems that are often complex, containing The levies may be imposed for revenue or protection They may also reflect the presence of a sizable parallel many exceptions, exemptions, penalties, and other purposes and may be determined on a specific or ad val- economy with unrecorded and undisclosed incomes Tax inducements that affect the incidence of taxes and thus orem basis as long as they are restricted to imported revenue ratios tend to rise with income, with higher- influence the decisions of workers, managers, and products * Highest marginal tax rate is the highest income countries relying on taxes to finance a much entrepreneurs A potentially important influence on both rate shown on the schedule of tax rates applied to the broader range of social services and social security than domestic and international investors is a tax system's annual taxable income of individuals and corporations lower-income countries are able to provide progressivity, as reflected in the highest marginal tax Also presented are the income levels above which the As economies develop, their capacity to tax residents rate on individual and corporate income, Figures for indi- highest marginal tax rates for individuals apply directly typically expands and indirect taxes become less vidual marginal tax rates generally refer to employment important as a source of revenue Thus the share of income In some countries the highest marginal tax rate taxes on income, profits, and capital gains is one meas- is also the basic or flat rate, and other surtaxes, deduc- The definitions used here are from the International ure of an economy's (and tax system's) level of develop- tions, and the like may apply And in many countries sev- Monetary Fund's (IMF) Manual on Government ment In the early stages of development governments eral different corporate tax rates may be levied, Finance Statistics (2001) The data on tax rev- tend to rely on indirect taxes because the administrative depending on the type of business (mining, banking, enues are from print and electronic editions of the costs of collecting them are relatively low The two main insurance, agriculture, manufacturing), ownership IMF's Govemment Finance Statistics Yearbook The indirect taxes are international trade taxes (including cus- (domestic or foreign), volume of sales, or whether sur- data on individual and corporate tax rates are toms revenues) and domestic taxes on goods and serv- taxes or exemptions are included The corporate tax from PncewaterhouseCoopers's Individual Taxes ices The table shows these domestic taxes as a rates in the table are mainly general rates applied to Worldwide Summanes 2002-2003 and Corporate percentage of value added in industry and services domestic companies For more detailed information, Taxes Worldwide Summanes 2002-2003. Agnculture and mining are excluded from the denominator see the country's laws, regulations, and tax treaties 2003 World Development Indicators 1 281 5.1 Relative prices and exchange rates Exchange rate Official Purchasing Ratio of Real Interest rate arrangements8a exchange power parity (PPP) PPP effective rate conversion conversion exchange factor factor to rate official local local currency exchange currency units to rate Deposit Lending Real Classification Structure units to $ international $ 1995 = 100 % % 2001. 2001 2001 1990 2001 2001 2001 2001 2001 2001 Afghanistan IF M 3,000 00 Albania IF U 143 48 2 0 50_7 0 4 7 7 19 7 16 4 Algeria MF U 77 22 4 3 22 5 0 3 110 5 6 3 9 5 6 5 Angola MF U -22.06 00 7 6 03 47 9 96 0 -13 9 Argentina MF U 1 00 0 3 0 6 0 6 16 2 27 7 29 1 Armenia IF U 555 08 . 116 2 0 2 105.3 14 9 26 7 21 8 Australia IF1 U 1 93 1 4 14 0.8 91 1 3 3 8 1 5 6 Austria Euro- U 1 12 1 0 1 0 0 9 91.1 2 2 56 4 9 Azerbaijan MF U 4,656 58 1,061 4 0 2 8 5 19.7 16 5 Bangladesh -P U 55 81 9 6 -118 0 2 . 8 5 15.8 14 0 Belarus P M 1,390 00 222 7 0 2 34 2 47 0 -17 4 Belgi um Euro U 1 12 0 9 1.0 0 9 89 3 3 4 8 5 6 0 Benin EA/FF U 733 04 158 9 275 3 0 4 3 5 Boli via P U 6 61 1 4 -27 0 4 117 8 9 8 20 1 19 3 Bosnia and Herzegovina CB U. 0 4 Botswana P D 5 84 1 2 2 3 0 3 10 2 15 8 10 9 Brazil IF U 2 36 0 0 0 9 0 4 . 17 9 57 6 46 7 Bulgaria CB U 2 18 0 0 0 5 0 2 130 3 2 9 11 1 4 4 Burkina Faso EA/FF U 733 04 116 4 140 6 0 2 3.5 Burundi MF U 830 35 42 8 120 0 0 1 93 4 16 8 3 0 Cambodia MF D 3,916 33 58 4 585 9 0.1 4 4 16 5 19 9 C-ameroon EA/FF U 733 04 194.5 247 2 0 3 98.7 5 0 20 7 17 2 Canada IF U 1 55 1 3 1 3 08 98 7 39 5 8 4 7 Central African Republic EA/FF U 733 04 117 9 144 6 0.2 92 3 5 0 20 7 17 2 Chad EA/FF U 733 04 93 5 138.8 0.2 5.0 20 7 11 8 Chile IF U 634 94 149.3 298-.1 0 5 96 6 6 2 11 9 10 2 China P U 8 28 1.3 1 9 0 2 1105_ 2 3 5 8 5 8 Hong Kong, China CB U 7 80 6 3 7-6 -1.0 2 4 5 1 5 6 Colombia IF- U 2,299 63_ 105 1 625 8 0 3 98 0 12 4 20 7 12 2 Congo, Dem -Rep IF- U 21 82 0 0 43 4 0 4 239 8 165 0 -63 7 Congo,Rep EA/FF U 733 04 435 7 667 3 1 1 5 0 20 7 41 2 Costa Rica P U -328-.87 28 4 144 9 0 4 111 9 11 8 23 8 15 6 Met dilvoire EA/FF U 733 04 168 5 312_4 0 4 99 9 3 5 Croatia MF U 8 34 4 2 0 5 103 2 3 2 9 5 6 3 Cuba Czech Republic IF U 38 04 7 9 14-3 0 4 121 3 3 0 7 1 1.7 Denmark P U 8 32 8 1 8.7 1 0 94.9 3 3 8 2 5 3 Dominican Republic MF D 16-.95 2 2 6 0 -0.4 117-.0 15 6 24 3 14 1 Ecuador EA/cother _U- 1 00 0 4 0.4 0 4 102 3 66 15 5 -7 7 Egypt,Arab Rep -p U 3 97 0 8 1.6 0 4 9 5 13 3 9 1 El Salvador EA/other U 8 75 2 1 3 6 0 4 9 3 14 0 10 5 Eritrea -P U 1 7 Estonia G B U 17-56 0 1 7 0 0.4 4 0 7 8 2 3 Ethiopia MF U 8 46 0.6 1 0 0.1 . 7 0 10 9 19.2 Finland Euro U_ 1 12 1 0 1 1 1.0 89 1 1 9 5.8 36 France Euro U 1 12 1.0 1.0 0 9 88.4 30 7 0 5 5 Gabon EA/FF U 733 04 348 0 420 6 06 91 0 5 0 20 7 36 7 Gambia,The IF U 15-69 1 5 2 2 0 1 83 5 12 5 24.0 15 7 Georgia IF U 2 07 0 5 0 2 7 8 27 3 22 0 Germany Euro U 1 12 1 0 1 0 0.9 85 6 3 6 10 0 86 Ghana MF U 7,170 76 84 4 857 7 0.1 81 7 30 9 Greece Euro U 1 12 0 3 -07 0.6 97 2 3 3 86 4 9 -Guatemala MF U 7 86 1 2 3 1 0.4 8.8 19.0 11 7 Guinea MF U 1,950 56 223 8 392 4 0 2 8 0 19 4 9 6 Guinea-Bissau EA/FF U 733 04 10 1 123 0 0 2 3 5 Haiti IF U 24 43 1 0 5.9 0 2 13 7 28 6 10 1 282 I 2003 World Development Indicators Relative prices and exchange rates 5 7 Exchange rate Official Purchasing Ratio of Real Interest rate arrangements a exchange power parity (PPP) PPP effective rate conversion conversion exchange factor factor to rate official local local currency exchange currency units to rate Deposit Lending Reel Classifioation Structure units to $ international $ 1995 = 100 % % 2001 2001 2001 1990 2001 2001 2001 2001 2001 2001 Honduras P U 15 47 1 1 5 3 0 3 14 5 23 8 13 0 Hungary P U 286 49 22 2 118 4 0 4 118 5 9 3 12 1 2 9 India MF U 47 19 4 2 7 8 0 2 12 1 8 3 Indonesia MF U 10,260 85 643 3 2,423 7 02_ 15 5 18 5 5 3 Iran, Ialamic Rep MF D 1,753 56 182 7 1,714 0 1 0 181 1 Iraq MF U 0 31 Ireland Euro U 1 12 0 8 0 9 0 8 94 5 0 1 4 8 -0 5 Israel P U 4 21 1 6 3 6 0 8 113 8 6 2 10 0 7 8 Italy Euro U 1 12. 07 09 0 8 107 7 2 0 65 3 8 Jamaica MF U 46 00 4 2 37 1 0 8 9 6 20 6 12 8 Japan IF U 121 53 189 7 157 6 1 3 85 5 0 1 2 0 3 4 Jordan P U 0 71 0 3 0 3 0 5 5 8 10 9 10 6 Kazakhstani MF U 146 74 33 9 0 2 Kenya MF U 78 56 9 0 2-97 -04 6 6 197 7 6 Korea, Dem Rep Korea,Rep IF U 1,290 99 567 9 763 1 0 6 5 8 7 7 6 3 Kuwait P U 0 31 0 3 -03 0 9 4 5 7 9 16 7 Kyrgyz Republic MF U 48 38 5 4 0 1 12 5 37 3 27 9 Lao PDR MF D 8,954 58 173 3 1,790 3 0 2 6 5 26 2 14 8 Latvia P U 0 63 0 3 0 4 5 2 11 2 9 4 Lebanon P U 1,507 50 305 4 1,377 8 0 9 10 9 17 2 17 1 Lesotho P U 8 61 0 8 1 4 0 2 69 6 4 8 16 6 10 2 Liberia IF U 48 58 5 9 22 1 9 0 Libya p U- 0 60 3 0 7 0 Lithuania CB U 4 00 1 6 0 4 3 0 9 6 9 2 Macedonia, FYR P U 68 04 18.7 0 3 73 2 10 0 19 4 16 1 Madagascar IF U 6,588 49 511 5 2,275 3 0 3 12 0 25 3 14 9 Mala wi IF U 72 20 1 4 21 0 0 3 1-16 8 35 0 56 2 23 9 Malaysia P U 3 80 1 5 1 6 0 4 91 4 3 4 6 7 9 5 Mali EA/FF U 733 04 140,7 214 6 0 3 3 5 Mauritania MF U 255 63 30 6 472 0 2 Mauritius -MF U 29 13 6 5 105_ 0 4 9 8 21 1 18 0 Mesico IF U 9 34 1 5 6 9 0 7 4 7 13 9 7 9 Moldova IF U -12 87 2_1 0 2 107 0 20 9 28 7 15 0 Mongolia_ MF U 1,097 70 2 7 273 8 0 2 14 3 30 2 19 8 Morocco P U 11 30 _32 3 7 0 3 103 7 5 0 13 3 10 5 Mozambique IF U 20,703 64 274 3 3,622 5 0 2 15 0 22 7 10 3 Myanmar MF D 6 75 9 5 15 0 -6 2 Namibia P U 8 61 1 1 2 1 0 2 6 8 14 5 4 9 Nepal P U 74 95 6 8 13 3 0 2 4 8 7 7 4 4 Netherlands Euro U 1 12_ 09 1 0 09 927 3 1 50 0 3 New Zealand IF, U 2 38 .16 1 6 0.7 81 7 5 3 9 9 5 7 Nicaragua P U 13 37 0 0 116 5 9 0 22 8 Niger EA/FF -U 733 04 104 144 3 0 2 3 Nigeria MF M 111 23 3 7 41 6 0 4 89 8 15 3_ 23 4 16 4 Norway IF U 8 99 9 1 11 2 -12 99 6 6 7 8 9 5 2 Oman P U 0 38 0 3 0 3 0 6 4 5 9 2 -8 3 Pakistan MF U 61 93 5 8 12 8 0 2 87 2 Panama EA/oth-er U 1 00 0 6 0 6 0 6 68 11 0 9 6 Papua New Guinea IF U 3 39 0 4 0 7 0 2 86 9 8 9 16 2 7 9 Paraguay MF U 4,105 92 342 2 1,007 2 0 2 93 1 16 2 28 3 19 9 Peru IF U 3 51 0 1 1 6 -04 9 9 20 4 18 9 Philippines IF U 50 99 5 6 12 1 0 2 85 4 8 7 12 4 5 4 Poland IF U 4 09 0,3 2 0 0 5 138 3 11 8 18 4 13 5 Portugal Euro U 1 12 0 5 0 7 0 6 98 7 2 4 5 2 1.9 Puerto Rico 0 6 0 6 2003 World Development Indicators 1 283 LQI [ I Relative prices and exchange rates Exchange rate Official Purchasing Ratio of Real Interest rate arrangements exchange power parity (PPP) PPP effective rate conversion conversion exchange factor factor to rate official local local currency exchange currency units to rate Deposit Lending Real Classification Structure units to $ international $ 1995 = 100 % % % 2001 2001 2001 1990 2001 2001 2001 2001 2001 2001 Romania P U 29,060 79 7 1 8,832 0 0 3 108 8 Russian Federation MF M 29 17 8 8 0 3 105.5 4 8 17 9 0.0 Rwanda MF U 442.99 26 0 69 4 0 2 9 2 Saudi Arabia P U 3 74 2 3 2 4 0 7 111 8 3 9 Senegal EA/FF U 733 04 185 8 230 7 0 3 3 5 Sierra Leone IF D 1,986 15 29 8 615.1 0 3 116 2 7 7 24 3 17 2 Singapore MF U 1 79 1 8 1 6 0 9 96.2 1 5 5 7 7 8 Slovak Republic MF U 48 35 15 3 03 107.8 6 5 11 2 5 5 Slovenia MF U 242 75 133 8 0 6 9 8 15 1 4 7 Somalia IF D South Africa IF U 8 61 0 9 2 0 0 2 73 2 9 4 13 8 5 9 Spain Euro U 1 12 0.6 0 8 0 7 95 9 3 1 5_2 1 2 SriLanka MF U 89 38 10 2 23 5 0 3 11 0 19 4 5 5 Sudan MF U 258 70 0 6 52 0 Swaziland P U 8 61 0 9 2 3 0 3 6 2 13 3 4 5 Sweden IF U 10 33 9 6 10 1 1 0 87 6 2 2 5 8 4 7 Switzerland IF U 1 69 2 0 2 1 1 2 89 8 1 7 4 3 2 5 Synan Arab Republic P M 11 23 10 2 18 0 1 6 4 0 9 0 2 9 Tajikistan IF U 2 37 * 0.3 0 1 21 0 5 2 -16 6 Tanzania IF U 876 41 75 7 454 1 0 5 4 8 20 3 12 8 Thailand MF U 44 43 10 8 13 0 0.3 2 5 7 3 5 0 Togo EA/FF U 733 04 80 2 119 8 0 2 102 1 3 5 Trinidad and Tobago MF U 6 23 3 1 4 6 0 7 124 1 7 7 15 7 12 0 Tunisia MF U 1 44 0 4 0 5 0 3 97 3 Turkey IF U 1,225,588 00 1,638 1 464,782 5 0 4 125 3 74 7 Turkmenistan P D 5,200 00 1,321 7 0 3 Uganda IF U 1,755 66 97 1 295 0 0 2 81 7 8 5 22 7 14 2 Ukraine MF U 5 37 0 9 0 2 119 6 11 0 32.3 21 5 United Arab Emirates P U 3.67 2 9 United Kingdom IF U 0 69 0 6 0.7 1 0 129 5 5 1 2 6 United States IF U 100 10 10 10 134 5 6 9 4 5 Uruguay P U 13 32 0 6 8 8 0 7 112 0 14 3 51 7 43 6 Uzbekistan MF M 236.61 .. 79 0 0 2 Venezuela, RB IF U 723 67 24 3 648 0 0 9 173 1 15 5 22 5 14 6 Vietnam MF U 14,725 17 641 0 2,945.8 02 53 94 66 West Bank and Gaza Yemen, Rep IF U 168 67 20 3 109 4 0.7 13 0 17 5 14 9 Yugoslavia, Fed Rep MF U Zambia MF U 3,610 94 18 3 1,645 2 0 5 122 4 23 4 46 2 17 6 Zimbabwe P U 55.05 0.9 17 0 0 3 13 9 38 0 -18 9 a Exchange rate arrangements are given for the end of the year in 2001 Exchange rate classifications include independent floating (IF), managed floating (MF), pegged (P), currency board (Ce), and several exchange arrangements (EA) FF means that the currency is pegged to the French franc, and other that the currency of another country is used as legal tender Exchange rate structures include dual exchange rates (D), multiple exchange rates (M), and unitary rate (u) 284 0 2003 World Development Indicators Relative prices and exchange rates F In a market-based economy the choices households, deflator (Normalization smooths a time series by * Exchange rate arrangements describe the producers, and governments make about the alloca- removing short-term fluctuations while retaining arrangements furnished to the IMF by each member tion of resources are influenced by relative prices, changes of a large amplitude over the longer eco- country under article IV, section 2(a) of the IMF's including the real exchange rate, real wages, real nomic cycle ) For other countries the weights before Articles of Agreement Exchange rate classiflcation interest rates, and a host of other prices in the econ- 1990 take into account trade in manufactured and indicates how the exchange rate is determined in the omy Relative prices also reflect, to a large extent, the primary products in 1980-82, the weights from main market when there is more than one market choices of these agents Thus relative prices convey January 1990 onward take into account trade in floating (managed or independent), pegged (conven- vital information about the interaction of economic 1988-90, and an index of relative changes in con- tional, within horizontal bands, crawling peg, or crawl- agents in an economy and with the rest of the world sumer prices is used as the deflator An increase in ing band), currency board (implicit legislative The exchange rate is the price of one currency in the real effective exchange rate represents an appre- commitment to exchange domestic currency for a terms of another Official exchange rates and exchange ciation of the local currency Because of conceptual specified foreign currency at a fixed exchange rate). rate arrangements are established by governments and data limitations, changes in real effective and exchange arrangement (currency is pegged to the (other exchange rates fully recognized by governments exchange rates should be interpreted with caution French franc, or another country's currency is used as include market rates, which are determined largely by Many interest rates coexist in an economy, reflect- legal tender) Exchange rate structure shows whether legal market forces, and, for countries maintaining mul- ing competitive conditions, the terms governing countries have a unitary exchange rate or dual or mul- tiple exchange arrangements, principal rates, second- loans and deposits, and differences in the position tiple rates * Official exchange rate refers to the ary rates, and tertiary rates) and status of creditors and debtors In some exchange rate determined by national authorities or to The official or market exchange rate is often used economies interest rates are set by regulation or the rate determined in the legally sanctioned to compare prices in different currencies. Since administrative fiat In economies with imperfect mar- exchange market It is calculated as an annual aver- exchange rates reflect at best the relative prices of kets, or where reported nominal rates are not indica- age based on monthly averages (local currency units tradable goods, the volume of goods and services tive of effective rates, it may be difficult to obtain relative to the U S dollar) * Purchasing power parity that a U S dollar buys in the United States may not data on interest rates that reflect actual market (PPP) conversion factor is the number of units of a correspond to what a U S dollar converted to anoth- transactions Deposit and lending rates are collected country's currency required to buy the same amount er country's currency at the official exchange rate by the International Monetary Fund (IMF) as repre- of goods and services in the domestic market as a would buy in that country Since identical volumes of sentative interest rates offered by banks to resident U S dollar would buy in the United States goods and services in different countries correspond customers The terms and conditions attached to * Ratio of PPP conversion factor to official exchange to different values (and vice versa) when official these rates differ by country, however, limiting their rate is the result obtained by dividing the PPP conver- exchange rates are used, an alternative method of comparability Real interest rates are calculated by sion factor by the official exchange rate * Real effec- comparing prices across countries has been devel- adjusting nominal rates by an estimate of the infla- tive exchange rate is the nominal effective exchange oped In this method national currency estimates of tion rate in the economy A negative real interest rate rate (a measure of the value of a currency against a gross national income (GNI) are converted to a com- indicates a loss in the purchasing power of the prin- weighted average of several foreign currencies) divid- mon unit of account by using conversion factors that cipal. The real interest rates in the table are calcu- ed by a price deflator or index of costs * Deposit reflect equivalent purchasing power. Purchasing lated as ( i - P )/(1 + P ), where i Is the nominal interest rate is the rate paid by commercial or similar power parity (PPP) conversion factors are based on interest rate and P is the inflation rate (as measured banks for demand, time, or savings deposits price and expenditure surveys conducted by the by the GDP deflator) * Lending Interest rate is the rate charged by banks International Comparison Program and represent the on loans to prime customers * Real Interest rate is conversion factors applied to equalize price levels the lending interest rate adjusted for inflation as across countries See About the data for table 1 1 for measured by the GDP deflator further discussion of the PPP conversion factor The ratio of the PPP conversion factor to the offi- cial exchange rate (also referred to as the national price level) makes it possible to compare the cost of =__ the bundle of goods that make up gross domestic The information on exchange rate arrangements product (GDP) across countries These national price is from the IMF's Exchange Arrangements and levels vary systematically, rising with GNI per capita Exchange Restnctions Annual Report, 2002. The Real effective exchange rates are derived by deflat- official and real effective exchange rates and ing a trade-weighted average of the nominal deposit and lending rates are from the IMF's exchange rates that apply between trading partners Intemafional Financial Statistics PPP conversion For most high-income countries the weights are factors are from the World Bank The real interest based on trade in manufactured goods with other rates are calculated using World Bank data on the high-income countries in 1989-91, and an index of GOP deflator relative, normalized unit labor costs s used as the 2003 World Development Indicators 1 285 Defense expenditures and trade in arms Military expenditures Armed forces Arms trade personnel Exports Imports % of % of central Total % of % of % of GDP government expenditure thousands labor force total exports total imports 1992 2001 1992 2002. 1992 1999 1992 1999 1992 1999 1992 1999 Afghanistan 45 0 6 0 7 Albania 4 6 1 2 3 7 65 18 4 1 1 2 0 0 0 0 0 0 2 6 Algeria 2 2 3 5 9 5 12 0 126 120 1.6 1 2 .00 0 0 0 1 4 1 Angola 12 0 3.1 128 100 2.7 1 7 0 0 0 0 1 5 7 3 Argentina 1 4 1 4 12.0 8 1 65 73 0 5 0 5 0 0 0 0 0 3 0 4 Armenia 2 2 3 1 ..20 50 I11 2 6 0 0 0 0 0 0 1 3 Australia 2 3 1 7 8.9 _7.5_ 68 55 0 8 0 6 0.1 1.0 2 1 1 6 Austria 1 0 0 8 2.4 2 0 44 49 1.2 1 3 0.2 0 0 0 1 0.0 Azerbaijan 3 3 2 6 12.4 10 2 43 75 1 4 2 1 0 0 0 0 0 0 1 2 Bangladesh 1 1 1 3 11 2 107 110 0 2 0 2 0 0 0 0 1 1 1 0 Belarus 1 5 1 4 4 1 4 5 102 65 1 9 1 2 0 0 5 2 0 0 0 0 Belgium 1 8 1 3 3 7 32_ 79 42 1 9 1.0 0 3 0 0 0 2 0 2 Benin .. . 7 8 0.3 0.3 0 0 0 0 0-0 0 8 Bolivia 2 1 1 6 10 6 6 1 32 33 1 2 1.0 0 0 0 0 0 9 0 6 Bosnia and Herzegovina 9 5 60 30 3.2 1.7 0 0 0 0 0 0 6 2 Botswana 4 3 3 5 11 7 7 8 1 2 1 1 0 0 0 0 1 1 1 8 Brazil I11 1 5 5 2 296 300 0 4 -04 0.5 0 0 0 9 0 3 Bulgaria 2 7 2 7 6.6 7 9 99 70 2.3 1 7 3 1 5 1 0 0 0 2 Burkina Faso 2 3 1 6 14 0 9 9 0 2 0.2 0 0 0 0 1 1 0 0 Burundi 3 6 8 1 10 7 27 1 13 40 04 1 1 00 00 00 00 Cambodia 4 7 3 0 135 60 2 7 1 0 0 0 0 0 0.0 0 3 Cameroon 1 5 1 4 8 4 10.4 12 15 0O2_ 03_ 0 0 0.0 0 0 0 4 Canada 1 9 1 2 6 9 6 2 82 60 0 5 0 4 0 7 0.2 0 6 0 5 Central African Republic 1 6 4 3 0.3 0.2 0.0 0 0 0 0 0 0 Chad 2 7 1 5 -38 30 1.3 0 8 0.0 0.0 -4.1 3.2 C-hile 3 4 2 9 16 2 12 4 92 88 1 8 1.4 0 0 0 1 1 0 0 7 China 2 7 2 3 32 5 19 2 3,160 2,400 -0.5 - 0.3 1 3 0 2 1 6 0 4 Hong Kong, China Colombia 2 4 38_ 15 8 18 8 139 155 0 9 0 9 0 0 0 0 1 7 0_6 Congo, Dem Rep 2 9 1 6 45 _ 55 0.3 0 3 0 0 0 0 0 0 8 9 Congo,Rep. . . 10 -10 1 0 0_8 0 0 0 0 0 0 0 0 Costa Rica . -8 10 06_ 0 7 0 0 0 0 0 2 0 0 C6te dlvoire 1 4 0 9 4 0 3 7 15 15 0 3 0 2 0 0 0 0 0 0 0 0 Croatia 7 6 2 6 19 1 5 9 103 60 4 6 2 9 0 0 0 2 0 0 0 1 Cuba 175 50 3 5 0 9 0 0 0 0 4 5 0 0 Czech Republic 2 3 2.1 6.2 5 4 107 54 1 9 0 9 1 5 0 3 0 0 0 7 Denmark 1 9 1 6 4 8 _4.3 28 27 1.0 0 9 0 0 0.0 0 5 0 7 Dominican Republic 22 30 0.7 0 8 0 0 0 0 0 2 0 3 Ecuador 2 7 2 1 16 9 57 58 1 5 1 2 0.0 0 0 1 2 0 7 Egypt, Arab Rep -3 6 -2 6 10.5 ~102 424 430 2.2 1 8 0.7 0.0 19.2 4 4 El Salvador 2 0 0 8 31 2 49 15 2 4 0 6 _ 0 0 0 0 4 1 0 3 Eritrea 21 4 27 5 .55 -215 32 10 8 00 00 00 33 5 Estonia 0 5 0 0 2 2 5.6 3 7 0 4 0 9 0 0 0 0 1 2 0.2 Ethiopia 2 7 6 2 19 3 43 0 120 _300 0.5 1.1 0.0 00 0.0 20 5 Finland 1 9 1 2 46 44 33 35 1 3 1 3 00 0 1 2 1 1 3 France 3 4 2_5 7 6 6.4 522 421 2 1 1 6 0 9 1 0 0 2 0 3 Gabon 03 7 7 1 5 1 3 0.0 00 00 00 Gambia, The 1 0 1 0 .. 1 1 0.2 0 2 0 0 0 0 2 3 0 0 Georgia 0 7 66_ 25 14 0.9 0 5 0 0 6.2 0.0 1 0 Germany 2 1 1 5 6 3 4 7 442 331 1.1 0 8 0 3 0.3 0 6 0 3 Ghana 0 6 0 6 3 6 7 7 0.1 0 1 0 0 0 0 0 0 0 0 Greece 4 5 -46 15 5 1-56 208 204 _48_ 4 5 0 2 0 9 3 9 7 5 Guatemala 1 3 1 0 44 30 1 4 0.7 00 00 0 2 0.0 Guinea 1 9 1 7 9 0 8 5 15 -12- -05 0 3 0 0 0 0 0 0 0 0 Guinea-Bissau 03 31 i 11 7 2 3 1.3 00 00 00 0.0 Haiti . 8 0 0 3 0 0 0 0 00 00 00 II 2003 world Development Indicators Defense expenditures and trade in arms 5 D Military expenditures Armed forces Arms trade personnel Exports Imports % of % of central Total % of % of % of GDP government expenditure thousands labor force total exports total imports 1992 2001 1992 2001 1992 1999 1992 1999 1992 1999 1992 1999 Honduras 17 8 0 9 0 3 0 0 0 0 2 9 0 4 Hungary 2 4 1 8 4 3 4 3 78 51 1 6 I11 0 4 0 0 0 0 0 3 India 2 3 2 5 14 6 14 0 1,270 1,300 0 3 0 3 0 0 0 0 2 9 1 6 I-ndonesia 1 7 1 1 9 4 4 6 283 296 0 3 0 3 0 1 0 2 0 4 1 9 Iran. Islamic Rep 1 9 4-8 11 2 17 2 528 460 3 2 2 4 0 1 0 1 3 3 0 9 Iraq 407 420 8 2 6 7 0 0 0 0 0 0 0 1 Ireland 1 2 0 7 3 0 2 8 13 14 1 0 0 9 0 0 0 0 0 1 0 1 Israel 10 5 7 7 21 6 16 6 181 173 8 8 6 6 6 2 2 3 10 3 7 2 Italy 0 0 2 0 3 9 4 8 471 391 1 9 1 5 0 3 0 2 0 2 0 3 Jamaica .. 3 3 0 2 0 2 0 0 0 0 0 6 0 3 Japan 0 9 1 0 45 242 240 0 4 0 4 0 0 0 0 0 9 1 0 Jordan 8 2 8 6 27 8 26 5 100 102 9 8 7 3 0 0 0 0 1 2 1 9 Kazakhstan 1 0 1 0 6 8 15 33 0 2 0 4 0 0 0 2 0 0 4 3 Kenya 1 9 1-8 7 9 5 8 24 24 0 2 0 2 0 0 0 0 1 1 0 2 Korea, Dem Rep 1,200 1,000 11 3 8 6 13 1 22 4 7 9 2 5 Korea,Rep 3 4 2 8 20 6 16 6 750 665 3 6 2 8 0 1 0 0 1 5 1 8 Kuwait 31 8 11 3 31 5 18 8 12 21 2 1 2 7 0 2 0 0 13 8 9 5 Kyrgyz Republic 0 7 1 7 3 2 10 0 12 12 0 6 0 6 0 0 0 0 0 0 0 0 Lao PDR 2 1 37 50 1 7 2 0 0 0 0 0 3 7 0 0 Latvia 0 8 1 2 3 4 3 9 5 5 0 3 0 4 0 0 0 0 0 0 0 2 Lebanon 8 0 5 5 25 7 14 0 37~ 58 3 1 3 9 0 0 0 0 0 0 0 2 Lesotho 2 6 3 1 5 7 6 4 2 2 0 3 0 2 0 0 0 0 0 0 0 0 Liberia 10 6 2 0 2 0 0 0 0 0 0 0 0 Libya 85 85 6 6 5 8 0 1 0 8 1 7 0 2 Lithuania 0 7 1 8 3 5 6 8 10 12 0 5 0 7 0 0 0 0 0 0 0 4 Macedonia,FYR 7 0 10 16 1 1 1 7 0 0 0 0 0 0 1 1 Madagascar 1 2 1 2 66_ 7 1 21 20 04 0 3 -00 0 0 0 0 00 Malawi 1 4 0.8 10 -5 0 2 0 1 ~00 0 0 00 0 0 Malaysia 30 2 2 10 5 10 6 128 95 1 7 1 0 00 0 0 0 6 1 4 Maili 24 2 0 12 10 0 3 0 2 00 0 0 00 0 0 Mauritania 3 5 2 1 16 11 1 7 0 9 00 0 0 00 0 0 Mauritius 0 4 0 2 1 5 0 8 1 2 0 2 0 4 0 0 0 0 0 3 00 Mexico 0 5 0O5 3 3 32 175 255 0 5 0 6 0 0 0 0 0 5 0 1 Moldova 05 0 4 1 8 9 -11 0 4 0 5 0 0 2 1 0 8 0 0 Mongolia 2 5 2 3 11 6 7 5 21 20 2 1 1 7 0 0 0 0 0 0 0 0 Morocco 4 3 4 1 14 4 12 4 195 195 2 1 1 7 0 0 0 0 1 4 1 3 Mozambique 5 1 2 3 50 8 0 6 0 1 0 0 00 0 6 0 4 Myanmar 3 4 23 30 1 26 6 286 345 1 3 1 4 0 0 0 0 23 0 13 6 Namibia 4 3 2 8 10 6 9 1 8 3 1.3 0 4 0 0 0 0 0 0 1 3 Nepal 0 9 1 1 6 4 6 5 35 35 0 4 0 3 0 0 0 0 0 0 0 0 Netherlands 2 4 1 6 4 7 4 0 90 54 1 3 0 7 0 1 0 1 0 4 0 4 New Zealand 1 6 1 2 4 3 4 0 11 10 0 6 0 5 0 0 0 0 1 2 4 0 Nicaragua 2,4 1 1 7 6 3 1 15 _12 1 0 0 6 13 5 0 0 0 6 0 0 Niger 1 2 1 1 5 6 0 1 0 1 0 0 0 0 0 0 0 0 Nigeria 0 5 I11 76 77 0 2 0 2 0 0 0 0 1 9 0 0 Norway 3 0 1 8 7 0 5 9 36 33 1 7 1 4 0 1 0 0 1 7 1 4 Oman 16 2 12 2 40 9 40 7 35 38 6 7 6 1 0 0 0 0 0 3 0 6 Pakistan 6 1 4 5 27 7 -230 580 590 1 4 1 2 0 4 0 1 6 6 9 7 Panama 1 2 1 2 4 8 4 2 11 13 1 1 1 1 2 0 0 0 0 5 0 1 Papua NewGuinea 1 3 0 8 4 2 3 3 4 4_ 0 2 0 2 0 0 0 0 4 0 0 0 Paraguay 1 6 0 9 11 8 5 0 16 -17 1 0 0 8 0 0 0 0 0 7 0 6 Peru 2 2 1 7 11 9 9 2 112 115 1 4 1 2 0 0 0 0 1 4 0 4 Philippines 1 3 1 0 6 5 5 1 107 107 0 4 0 3 0 0 0 0 1 8 0 3 Poland 2 3 1 9 5 5 5 3 270 187 1 4 0 9 0 2 0 1 0 0 0 1 Portugal 2 7 2.1 6 2 5 4 80 71 1 6 1 4 0 1 0 0 0 6 0 2 Puerto Rico 2003 World Development Indicators I287 o3Defense expenditures and trade in arms Military expenditures Armed forces Arms trade personnel Exports Imports % of % of central Total % of % of % of GDP government expenditure thousands labor force total exports total imports 1992 2001 1992 2001 1992 1999 1992 1999 1992 1999 1992 1999 Romania 4 3 2 5 10 7 8 1 172 170 1 6 1 6 0 5 0 5 0 6 1 9 Russian Federation 5 5 3.8 21 1 15 4 1.900 900 2 5 1 2 5 8 4 2 0 0 1 1 Rwanda 4 4 3 9 21 6 30 40 0 8 0 9 0 0 0 0 0 0 11 9 Saudi Arabia 11 7 11 3 172 190 3 1 2 9 0 0 0 0 25 2 27 5 Senegal -1 8 1 5 6 8 18 13 0 5 0 3 0 0 0 0 1 0 0 0 Sierra Leone 2 5 3 6 17 7 6 5 8 3 0 5 0 2 0 0 0 0 6 8 12 3 Singapore 4 8 5 0 24 0 22 8 56 60 3 4 3 0 0 0 0 0 0 4 0 9 Slovak Republic 2 1 1 9 49_ 33 36 1 2 1 2 0 7 0 1 3 5 0 2 Slovenia 2 2 1 4 5 8 3 5 15 10 1.5 1 0 0 0 0 0 0 0 0 1 Somalia 0 0 0 0 0 0 0 0 South Africa 8.8 5 4 75 68 0.5 0 4 0 4 0 1 1 3 0 2 Spain 1 6 1 2 4.4 4 2 198 155 1 2 0 9 0 3 0 1 0 4 0 5 SriLanka 3 0 3 9 11.3 14 7 110 110 1 6 1 4 -00 0 0 0 3 0 7 Sudan 2 5 3 0 27 4 82 105 0 8 0 9 0 0 0 0 13 4 0.7 Swaziland 1 9 1 5 5 2 3 3 1 1 0 8 0 0 0 0 0 0 0 0 Sweden 2 6 2 0 5 6 5 4 70 52 15_ 1 1 1 5 0 8 0 3 0 3 Switzerland 1 8 1 1 7 0 4 2 31 39 0 8 1 0 1 2 0 1 0 7 1 5 Syrian Arab Republic 9 0 6 2 39 0 24 2 408 310 11 0 6 2 0 6 -00 11 2 5 5 Tajikistan 0 4 1 2 10 1 3 7 0 1 0 3 0 0 0 0 0 0 0 0 Tanzania 1 9 1 3 46 35 0 3 0.2 0.0 0 0 0 3 0 3 Thailand 2 3 1 4 15 3 7.1 283 300 0 9 0 8 0 0 0 0 1 2 0 7 Togo 2 9 8 11 0 5 0 6 0 0 0 0 0 0 0 0 Trinidad and Tobago 2 2 0 4 0 4 0 0 0 0 0 0 0 0 Tunisia 1 9 1 6 5 8 5 2 35 35 1 1 0 9 0 0 0 0 0 3 0 1 Turkey 3 7 4 9 18 8 10 0 704 789 2 7 2.6 0.1 0 3 6 6 7 9 Turkmenistan 1 8 3 8 28 15 1 7 0 7 1 4 0 0 0 0 1 0 Uganda 1 6 2 1 10 1 70 50 0 8 0 5 0.0 0 0 2 0 2 2 Ukraine 0 5 2 7 9 8 430 340 1 6 1 3 0.0 4 7 0 0 0 1 United Arab Emirates 4 5 2 5 37 4 30.1 55 65 5.2 4 7 0 0 0 0 4 2 3 8 United Kingdom 3 8 2 5 8 7 7 0 293 218 1 0 0.7 3.3 1 9 1 3 0 8 United States 4 8 3 1 21 1 16 0 1,920 1,490 1 5 1 0 5 6 4 7 0 3 0 2 Uruguay 2 1 1 3 8 0 4 2 25 24 1 8 1 6 0 0 0 0 0 5 0 3 Uzbektistan 1 5 1 1 40 60 0 5 0 6 0 0 0 4 0 0 0 0 Venezuela, RB 1 6 1 5 8 2 6 1 75 75 1 0 0.8 0 0 0.0 0 9 2 2 Vietnam 3 4 10 6 857 485 2.4 1.2 0 4 0 0 0 4 0 6 West Bank and Gaza Yemen, Rep 9 1 6 1 30 7 18 8 64 6 9 1 5 1 3 0 0 0 0 0 2 1 5 Yugoslavia, Fed Rep 4_9 137 105 2 8 2 1 0 0 0 0 Zambia 3 0 06 16 17 0 5 0 4 0 0 0 0 0 0 00 Zimbabwe 3 7 3 2 11.3 94 48 40 1 0 0 7 0 3 00 4 1 0 5 ~~ -~-"~.-.z~~- - z- -&zi ~&~W ~- 890 Low income 2 4 2 3 14.4 12 9 6,490 6,259 0 7 0 6 0 1 0 4 2 0 1 9 Middle income 3 0 2 5 15 8 11 7 11,623 9,543 1 0 0 7 0 3 0 4 3 2 1 6 Lower middle income 3 6 2 8 19 8 14 8 9.931 7,806 1 0 0 7 0 6 0.8 2 8 1 4 Upper middle income 2 4 2 2 13 1 10 2 1,692 1.737 0 9 0 8 0 1 0 0 3 6 1 9 Low &middle Income 2 9 2 5 15 6 12 6 18,113 15,802 0 9 0 7 0 3 0 4 3 0 1 7 East Asia &Pacific 2 4 2 1 23 7 16 4 6,506 5.166 0 7 0 5 0 5 0 1 1 2 0 8 Europe &Central Asia 4 4 3 0 15 2 9 6 4,303 3,192 2 1 1 3 1 4 1 8 1 4 1 8 Latin America &Carib 1 2 1 3 6 2 6 9 1,443 1,371 0.8 0.6 0 2 0 0 0 7 0 3 Middle East &N Africa 7 6 6 7 2,626 2,522 3.3 2 6 0 1 0 1 10 8 8 4 South Asia 2 7 2 6 16 8 14 7 2,152 2,153 0.4 0.4 0 1 0 0 3 3 2 4 Sub-Saharan Africa 2 3 2 0 8 4 1.083 1,398 0 5 0 5 0 2 0 0 1.3 1 4 Hilgh Income 2 9 2 3 10 8 10 4 6,420 5,396 14_ 1 1 1 4 1 1 0 7 0 6 Europe EMU 1 9 1 8 5 7 4 9 2.181 1,768 1 6 1 3 0 4 0 3 0 4 0 4 Note, Data for some countries are based on partial or uncertain data or tough estimates, see SIPRI (20021 and U S Department of State (2002) 20S 2003 World Development Indicators Defense expenditures and trade in arms I Although national defense is an important function of than is available about what is included in military budg- * Military expenditures are based on the NATO defli- government and security from external threats con- ets and off-budget military expenditure items (For nition, which includes all current and capital expendi- tributes to economic development, high levels of example, military budgets might or might not cover civil tures on the armed forces, including peacekeeping defense spending burden the economy and may defense, reserves and auxiliary forces, police and para- forces, defense ministries and other government impede growth Comparisons of defense spending military forces, dual-purpose forces such as military and agencies engaged in defense projects, paramilitary between countries should take into account the many civilian police, military grants in kind, pensions for mili- forces, if these are judged to be trained and equipped factors that influence perceptions of vulnerability and tary personnel, and social security contributions paid by for military operations, and military space activities risk, including historical and cultural traditions, the one part of government to another ) In the many cases Such expenditures include military and civil person- length of borders that need defending, the quality of where SIPRI cannot make independent estimates, it nel, including retirement pensions of military person- relations with neighbors, and the role of the armed uses the national data provided Because of the differ- nel and social services for personnel, operation and forces in the body politic ences in definitions and the difficulty in verifying the maintenance, procurement, military research and Military expenditures as a share of gross domestic accuracy and completeness of data, the data on military development, and military aid (in the military expendi- product (GDP) are a rough indicator of the portion of spending are not strictly comparable across countries tures of the donor country) Excluded are civil defense national resources used for military activities and of The data on armed forces refer to military personnel and current expenditures for previous military activi- the burden on the national economy As an 'input' on active duty, including paramilitary forces These ties, such as for veterans' benefits, demobilization, measure, military spending is not directly related to data exclude civilians in the defense establishment conversion, and destruction of weapons * Armed the "output' of military activities, capabilities, or mili- and so are not consistent with the data on military forces personnel refer to active duty military person- tary security Data on defense spending from govern- spending on personnel Moreover, because they nel, including paramilitary forces if these forces ments are often incomplete and unreliable Even in exclude personnel not on active duty, they underesti- resemble regular units in their organization, equip- countries where the parliament vigilantly reviews gov- mate the share of the labor force working for the ment, training, or mission * Arms trade comprises ernment budgets and spending, defense spending defense establishment Because governments rarely exports and imports of military equipment usually and trade in arms often do not receive close scrutiny report the size of their armed forces, such data typi- referred to as "conventional," including weapons of For a detailed critique of the quality of such data, see cally come from intelligence sources The data in the war, parts thereof, ammunition, support equipment, Ball (1984) and Happe and Wakeman-Linn (1994) table are from the U S Department of State's Bureau and other commodities designed for military use See This edition of the World Development Indicators of Verification and Compliance, which attributes its About the data for more details uses a new source of data for military expenditures, the data to unspecified U S government sources Stockholm International Peace Research Institute The Standard International Trade Classification (SIPRI) The data presented for military expenditures as does not clearly distinguish trade in military goods a percentage of GDP and central government expendi- For this and other reasons, customs-based data on ture therefore differ from those in previous editions trade in arms are of little use, so most compilers rely SIPRI's primary source of military expenditure data on trade publications, confidential government infor- is official data provided by national governments mation on third-country trade, and other sources The These data are derived from national budget docu- construction of defense production facilities and the ments, defense white papers, and other public docu- licensing fees paid for the production of arms are ments from official government agencies, including included in trade data when they are specified in mili- governments' responses to questionnaires sent by tary transfer agreements Grants in kind are usually SIPRI, the United Nations, or the Organization for included as well Definitional issues include treatment Security and Co-operation in Europe Secondary of dual-use equipment such as aircraft, use of military sources include international statistics, such as those establishments such as schools and hospitals by civil- of the North Atlantic Treaty Organization (NATO) and eans, and purchases by nongovernmental buyers the International Monetary Fund's (IMF) Government Valuation problems arise when data are reported in Finance Statistics Yearbook Other secondary sources volume terms and the purchase price must be esti- include Europa Publications' Europa World Yearbook, mated Differences between sources may reflect country reports of the Economist Intelligence Unit, reporting lags or differences in the period covered and country reports by IMF staff Still others include Most compilers revise their time-series data regularly, The data on military expenditures are from SIPRI's specialist Journals and newspapers so estimates for the same year may not be consistent Yearbook 2002 Armaments, Disarmament and Lack of sufficiently detailed data makes It difficult to between publication dates International Secunty The data on armed forces apply a common definition of military expenditure glob- The data on arms trade are from the Bureau of personnel and arms trade are from the Bureau ally, so SIPRI has adopted a definition (based on the Verification and Compliance, published in World of Verification and Compliance's World Military NATO definition) as a guideline (see Definitions) This Military Expenditures and Arms Transfers 2000 (U S Expenditures and Arms Transfers 2000 (U S definition cannot be applied for all countries, however, Department of State 2002) These data do not Department of State 2002) since that would require much more detailed information include arms supplied to subnational groups 2003 World Development Indicators 1 289 ~~u:I~J0 ~Transport infrastructure Roads Railways Ports Air Goods Traffic Employee Ratio of Total road hauled Rail lines density productivity passenger Container network Paved roads million Total Electnc traffic units traffic unitS tanffs to traffic Aircraft Passengers Air freight km % ton-km km km per km per employee freight tanffs thousand depairtures carned million ±995-- 1995- 1995- 1996- ±996- ±996- ±996- ±996- TEUs thousands thousands tont-km 20001 20001 20001 20011 20011 20011 200±1 2001± 2001 2001 2001 2001 Afghanistan 21,000 13 3 3 150 8 Albania 18,000 39 0 1,830 440 334 39 .4 146 Algeria 104,000 68 9 3,793 283 419 230 311 1 44 3,240 19 Angola 51, 429 10 4 .4 193 51 Argentina 215,471 29 4 28,291 179 318 1,209 1 28 1,058 0- 123 5,739 124 Armenia 15,918 96 3 40 842 784 465 80 0 23 3 369 8 Australia 811,603 38 _7 .3,619 7 389 33,477 1,678 Austria 200,000 100 0 16,100 5,780 3,493 4,261 482 1 14 133 6,514 356 Azerbaijan 24,981 -92 3 3,513- 8 544 66 Bangladesh 207,486 9 5 . 2.768 1,704 126 0 24 486 3 7 1,450 170 Belarus 74,385 89 0 8,982 5,512 874 7,857 630 6 222 2 Belgium 148,216 78 2 35,000 3,471 2,7_05 4,445 373 1 07 5,109 7 178 8,489 853 Benin 6.787 20 0 ..1 46 7 Bolivia 53,790 6 5 - 3.163 336 .1,381 0 31 20 1,560 -14 Bosnia and Herzegovina 21,846 52 3 5 65 1 Botswana 10,217 55 0 7 168 0 Brazil 1,724,92 9 5 5 25,652 1,220 1,805 3,970 2,616 1 654 34,286 1,467 BulIgaria 37.286 94 0 168 4,290 2,708 1,846 216 0 89 7 234 2 Burkina Faso 12,506 16 0 2 100 7 Burundi 14,480 7 1 Cambodia 12,323 16 2 412 601 228 69 0 39 Cameroon 34,300 12 5 1,006 1,333 496 0 34 5 247 42 Canada 901,903 82,500 39,400 -7,479 7,600 6 63 2,870.7 292 24,204- 1,605 Central African Republic 23,810 2 7 60 . 1 46 7 Chad 33,400 0 8 . .1 46 7 Chile 79,814 19 4 4,814 - 850 370 2,162 1,209 1 83 5,301 1,279 China 1,402,698 22 4 612,940 58,656 14,864 30,262 1,155 1 19 43,970 4 b 841 72,661 4,232 Hong Kong, China 1,831 100 0 . 88 14,050 5,051 Colombia 112,988 14 4 31 3,154 1,795 531 3 190 9,566 625 Congo, Dam Rep 157,000 .. 3.641 858 169 40 Congo, Rep 12,800 9 7 900 188 55 5 95 7 Costa Rica 35,892 22 0 3,070 424 109 .563 8 26 752 23 M6e dIlvoire 50,400 9 7 .. 639 986 540 0 67 543 8 1 46 7 Croatia 28,123 64 6 1,090 2,726 983 1,280 163 0 80 18 1,064 3 Cuba 60,858 49 0 4,667 132 468 81 12 882 54 Czech Republic 55,408 100 0 39,036 9,365 2,843 2,615 284 44 2,560 26 Denmark 71,591 100 0 11,696 2,047 625 3,648 770 - 549 1 Ill 6,382 184 Dominican Republic 12,600 49 4 466 0 Ecuador 43,197 18 9 4,176 .414 4 17 1,251 14 Egypt, Arab Rep 64,000 78 1 31,500 5,024 59 14,308 753 0.20 1,709 0 41 4,389 239 El Salvador 10,029 19 8 - 1,202- 503 367 44 2,192 47 Eritrea 4,010 21 8 Estonia 51,411 20 1 3,689 968 132 7,999 1,358 2 36 8 -278 2 Ethiopia 31,571 12 0 0 28 1,028 79 Finland 77,900 64 5 26,500 5,854 2,372 2,308 1.056 2 47 1,018 7 129 6,698 171 France 894,000 100 0 245,400 32,515 14,104 3,854 715 1 54 2,983 9 786 50.817 4,868 Gabon 8,464 9 9 - 814 2,087 894 .8 374 49 Gambia, The 2,700 35 4 Georgia 20,362 93 5 475 1,562 1,544 2,794 276 0 37 2 111 2 Germany 230,735 99 1 226,982 36,652 19,079 4,128 681 2 77 8,299 2 782 57,334 7,026 Ghana 39,409 29 6 953 1,778 376 6 301 33 Greece 117,000 91 8 17,000 2.299 830 182 1,429 7 103 7,303 99 Guatemala 14,118 34 5 528 0 3 Guinea 30,500 16 5 I , ... Guinea-Bissau 4,400 10 3 Haiti 4,160 24 3 . 290 2003 World Development indicators Transport infrastructure Roads Railways Ports Air Goods Traffic Employee Ratio of Total road hauled Rail lines density productivity passenger Container network Paved roads million Total Electnc traffic units traffic units tariffs to traffic Aircraft Passengers Air freight km % ton-krn km km per km per employee freight tanffs tfmusandt departures c-arned million 1995- 1995- 1995- 1996- 1996- 1996- 1996- 1996- TEUs thousands thousands ton km 2000- 20000 20000 20010 20010 20010 20011 20010 2001 2001 2001 2001 Honduras 13,603 20 4 406 4 Hungary 188,203 43 4 1 7,729 2,628 2,242 319 32 2,075 38 India 3,319,644 45 7 958 62,759 14,261 11,725 467 0 31 2,591 1 214 17,272 518 Indonesia 342,700 46 3 - 5,324 131 -3,974 610 0 95 3,492 2 166 10,049 415 Iran, Islamic Rep 167,157 56 3 6,688 148 3,185 758 86 9.318 77 Iraq 45,550 84 3 Ireland 92,500 94 1 5,900 1,915 37 982 171 750 3 164 16,374 147 Israel 16,281 100 0- 925 2,112 1,628 52 3,990 734 Italy 479,688 1009 - 219,800 -16,499 10,937 4,102 618 1 42 7,131 0 395 31,031 1,521 Jamaica 18,700 70 1 888 9 24 1,946 26 Japan 1,161,894 46 0 -307,149- 20,165 12,080 -13,048 1,528 12,980 6 641 107,870 7,627 Jordan 7,245 100.0 293 2,123 518_ 16 1,178 178 Kazakhstan 81,331 94 7 4,506 13,545 3,725 9,981 1,069 8 501 12 Kenya 63,942 121 I 2,634 699 184 25 1,418 93 Korea, Dem Rep 31,200 6 4 1 79 2 Korea, Rep 86,990 --74 5 -74,504 3,123 668 12,456 1,323 1 43 9,887 6 226 32,638 6.957 Kuwait 4,450 80 6 17 2,085 226 Kyrgyz Republic 18,500 91 1 --1,220- 5 192 6 Lao PDR 21,716 7 211 2 Latvia 73.202 38 6 4,789 2,331 258 5,834 917 11 305 1 Lebanon 7,300 84 9 10 816 75 Lesotho 5,940 18 3 L-iberia 10,600_ 6 2 Libya 83,200 57 2 6 583 0 Lithuania - 75,243 91 3 7,769 1,905 122 4,171 - 611 10 304 2 Macedonia, FYR 8,684 63 8 1,210 699 233 97-2 162 0 39 5 316 1 Madagascar 49,827 11 6 ..21 624 34 Malawi 28,400 18 5 710 159 176 0 25 5 113 1 Malaysia 65.877 75 8 1,622 152 1,368 370 0 87 6,224 8 176 16,311 1,533 Mali 15,100 12 1 . 734 658 322 1- 46 7 Mauritania 7,660 11 3 2 156 7 Mauritius 1,926 97 0_ 13 997 175 Mexico 329,532 32 8_ 197,958 17,697 250 2,660 3,925 1,358 2 291 20,043 269 Moldova - 12,657 87 0 952 4 120 0 Mongolia 49,250 3 5 126 1,810 - 2,963 394 18. 255 7 Moroc co 57,707 56 4 3,035 1,907 1,003 3,425 - 610 -0 86 346 7 44 3,681 63 Mozambique 30,1400 18 7 110 7 264 7 Myanmar 28,200 12 2 - 10 398 1 Namibia -66,467 13 6 2,382 474 6 212 73 Nepal 13,223 30 8 12 641 16 Netherlands 116,500 90 0 32,700- 2,802 2,061 6,631 752 2 56 6,227 3 227 20,474 4,116 New Zealand 92,053 62.8 3,913 519 938 1,120 1 46 1,144 3 266 11,095 763 Nicaragua 19,032 11 0 1 61 1 Niger 10,10-0 -79 1 46 7 Nigeria 194,394 30 9 - 3,557 287 6-5 0 10 8 529 3 Norway 91.454 76 0 12,796 307 14,559 185 Oman 32,800 30 0 .1,325 5 18 1,980 149 Pakistan 254,410 43 0 96,802 7,791 293 2,838 232 0 28 53 4,871 371 Panama 11, 400 34 6 2,170 5 25 1,115 25 Papua New Guinea 19,600 3 5 30 1,188 22 Paraguay 29,500 9 5 8 281 Peru 72,900 12 8 1,691 406 363 537 6 30 1,605 56 Philippines 201,994 21 0 491 505 112 0 09 3,091 0 44 5,652 264 Poland 364,656 68 3 72,843 22,560 11,826 3,537 415 0 79 72 2,670 69 Portugal 68,732 86 0 14,200 -2,814 904 2,066 465 804 2 96 6,651 209 Puerto Rico 14,400 100 0 1,886 0 2003 World Development Indicators I291 Lii ~~Transport infrastructure Roads Railways Ports Air Goods Traffic Employee Ratio of Total road hauled Rail lines density productivity passenger Container network Paved roads million Total Electnc traffic units traffic units tariffs to traffic Aircraft Passengers Air freight kmn % ton-km kmv km per km per employee freight tanffs thousand departures carned million 1995- 1995- 1995- 1996- 1996- 1996- 1996- 1996- TEUs thousands thousands ton-km 2O000 20001 20001 20011 2001' 20011 20018 20011 2001 2001 2001 2001 Romania 198,603 49 5 13.457 11,364 3,929 2,467 267 1 24 20 1,135 10 Russian Federation 532,393 67 4 139 86.075 40,962 15,854- 1,054 0 97 382 2 329 20,235 895 Rwanda- 12,000 8 3 Saudi Arabia 151,470 30 1 1,390 799 555 1,677 4 108 12,836 1,000~ Senegal 14,576 29 3 906 562 339 0 6 14 Sierra Leone 11,330 7 9 .. .. 0 14 6 Singapore 3,066 100 0 15,60-3 8 76 16,374 5,774 Slovak Republic 42,717 186.7 8,474 3.662 1,536 3,851 302 1 11 2 43 0 Slovenia 20,177 99 9 4,407 2,746 13 690 4 Somalia 22,100 11 8 South Africa 362,099- 20 3 . 2-2,657 10,430 5,018 2,933 1,817.8 -122 -7,948 747 Spain 663,795 99 0 98,145 13,866 7,523 2,295 842 6,153.4 518 41,470 879 Sri Lanka 96,695 30 1.447 2,271 189 0 11 1,726 6 11 1,719 218 Sudan 11,900 36 3 4,599 298 98 8 415 33 Swaziland 3,247 2 90 0 Sweden 212,402 78 4 32,000 10,068 7,405 2,492 2,144 2 34 856 6 234 13,123 264 Switzerland 71,011 22,000 285 16,915 1,642 Syrian Arab Republic 43,381 23 1 1,771 996 160 . 14 761 21 Tajikistan 27,767 5 274 3 Tanzania 88,200 -4 2 2,722 598 181 0 41 5 171 3 Thailand 64,600 97 5 4,044 -3,342 660 0.7-5 3,381.6 102- 17,662 1,669 Togo -7,520 31 6 1 46 7 Trinidad and Tobago -8,320 51 1 - 352 8 28 1,124 26 Tunisia 18,997 64 8 2,260 60 1,010 341 1 87 19 1,926 20 Turkey 385,-960 34 0 150,974 -8,671 -1,752 1,798 330 1 20 1,554 9 106 9,905 339 Turkmenistan 24,000 81 2 ..24 1,407 13 Uganda 27,000 6 7 -261 805 131 0 41 21 Ukraine 169,491 96 7 18,206 22,302 9,170 9,535 598 39 996 12 Un-ited Arab Emirates 1,088 100 0 5,082 0 51 7,676 1,631 United Kingdom 371,913 100 0 150,700- 17,067 5,225 3,500 -2,678 6,212 7 985 72,772 4,549 United States 6,304,193 58 8 1,534,430 160,000 484 13,800 13,476 9 28 28,082 5 8,535 c 619,262 c28,042 c Uruguay 8,983 90 0 3,003 127 191 10 559 13 Uzbekistan 81,600 87 3 619 4,830 304 40 2,256 104 Venezuela, RB 96,155 33 6 336 161 180 0.21 924 6 124 4,052 31 Vietnam 93,300 25 1 3,142 1,624 154_ 0 88 1,290 6 35 3,410 134 West Bank and Gaza Yemen, Rep 67,000 11 5 377 7 15 841 32 Yugoslavia, Fed Rep 49,805 62 3 -630 4,058 1,103 522 94 16 1,117 4 Zambia 66,781 1,273 144 610 0 27 5 49 -1 Zimbabwe 18,338 47 4 2,759 311 1,977 454 0 60 13 495 158 ___ r ~ ~'- ~-~- *ji~Wu I.rt Low Income 16 1_ 841 52,262 Middle Income 52 7 84,636 4,383 306,292 Lower middle income 53 0 ,. - 62,064 2,369 186,767 Upper middle income 51 1 611 0 -22,573 2,014 119,525 Low & middle Income 30 9 .93,418 5,223 358,554 East Asia & Pacific 21 2 2,293 4 382 0 61,451 1,525 128,916 Europe & Central Asia 91 3 304 0 832 49.444 Latin America & Carib 26 9 15,912 1,784 93.872 Middle East & N Africa 66 3 555 0 437 42,954 South Asia 36 9 0 24 4,804 304 26,299 Sub-Saharan Africa 12 9 342 17,069 High Income -91_8 3,64-8 3 770 0 137,253 16,272 1,262,508 Europe EMU 92 9 124,467 3,853 9 618 0 39,907 3,548 254,040 a Data are for the latest year availatile in the period shown b includes Hong Kong, China c Data cover oniy those carriers designated by the U S Department of Transportation as major and national air carriers 292 H 2003 World Development Indicators Transport infrastructure Transport infrastructure-highways, railways, ports and service, with productivity far lower in passenger service In * Total road network includes motorways, highways, main waterways, and airports and air traffic control systems- developing countries a ratio of passenger tariffs to freight or national roads, secondary or regional roads, and all and the services that flow from it are crucial to the activ- tariffs greater than 1 indicates an absence of significant other roads in a country * Paved roads are those sur- ties of households, producers, and governments Because cross-subsidies and a potential to provide higher-quality faced with crushed stone (macadam) and hydrocarbon performance indicators vary significantly by transport service This ratio, like the other railway indicators, has no binder or bituminized agents, with concrete, or with cob- mode and by focus (whether physical infrastructure or the normative value and is intended for relative analysis only blestones * Goods hauled by road are the volume of services flowing from that infrastructure), highly specialized Measures of port container traffic, much of it com- goods transported by road vehicles, measured in millions and carefully specified indicators are required The table modities of medium to high value added, give some of metric tons times kilometers traveled * Total rail lines provides selected indicators of the size, extent, and pro- indication of economic growth in a country But when refer to the length of the railway lines * Electric rail lines ductivity of roads, railways, and air transport systems and traffic is merely transshipment, much of the economic refer to the length of line with electric traction This line can of the volume of traffic in these modes as well as in ports benefit goes to the terminal operator and ancillary serv- include overhead catenary at various direct current (DC) or Data for transport sectors are not always internationally ices for ships and containers rather than to the country alternating current (AC) voltages and third-rail DC systems comparable Unlike for demographic statistics, national more broadly In transshipment centers empty contain- * Railway traffic density is the sum of passenger-kilome- income accounts, and international trade data, the collec- ers may account for as much as 40 percent of traffic ters (passengers times kilometers traveled) and ton-kilo- tion of infrastructure data has not been "internation- The air transport data represent the total (international meters (metric tons of freight times kilometers alized ' But data on roads are collected by the and domestic) scheduled traffic carried by the air carriers traveled)-together, traffic units-divided by kilometers of International Road Federation (IRF), and data on air trans- registered in a country Countries submit air transport line * Employee productivity is annual output (in traffic port by the International Civil Aviation Organization (ICAO) data to ICAD on the basis of standard instructions and units) per employee * Ratio of passenger tariffs to freight National road associations are the primary source of IRF definitions issued by ICAO In many cases, however, the tarffs is the average passenger fare (total passenger rev- data In countnes where such an association is lacking or data include estimates by ICAO for nonreporting carriers enue divided by total passenger-kilometers) divided by the does not respond, other agencies are contacted, such as Where possible, these estimates are based on previous average freight rate (total freight revenue divided by total road directorates, ministries of transport or public works, submissions supplemented by information published by ton-kilometers) A ratio less than 1 indicates a likelihood of or central statistical offices As a result, the compiled data the air carriers, such as flight schedules cross-subsidy of passengers from freight tariffs * Port are of uneven quality Even when data are available, they The data represent the air traffic carried on scheduled container traffic measures the flow of containers from are often of limited value because of incompatible defini- services, but changes in air transport regulations in land to sea transport modes, and vice versa, in twenty- tions (for example, in some countries a path used mainly Europe have made it more difficult to classify traffic as foot-equivalent units (TEUs), a standard-size container by animals may be considered a road, while in others a scheduled or nonscheduled Thus recent increases Data refer to coastal shipping as well as international jour- road must be registered with a state agency responsible shown for some European countries may be due to neys Transshipment traffic is counted as two lifts at the for its maintenance), inappropnate geographic units, lack changes in the classification of air traffic rather than intermediate port (once to off-load and again as an out- of timeliness, and variations in the nature of the terrain actual growth For countries with few air carriers or only bound lift) and includes empty units * Aircraft departures Moreover, the quality of transport service (reliability, one, the addition or discontinuation of a home-based air are domestic and international takeoffs of air carriers reg- transit time, and condition of goods delivered) is rarely carrier may cause significant changes in air traffic istered in the country * Air passengers carried include measured, though it may be as important as quantity in both domestic and international passengers of air carriers assessing an economy's transport system Serious ef- registered in the country * Air freight is the sum of the forts are needed to create international databases whose metnc tons of freight, express, and diplomatic bags carried comparability and accuracy can be gradually improved on each flight stage (the operation of an aircraft from take- New indicators for railways focus on efficiency and pro- off to its next landing), muttiplied by the stage distance, by ductivity Traffic density is an indication of the intensity of air carriers registered in the country use of a railway's largest investment-its track Traffic densities for branch tines tend to range around 500,000 G_- , traffic units per kilometer (see Definitions), while those for The data on roads are from the International mainlines range from more than 5 million traffic units per Road Federation's World Road Statistics The kilometer to 100 million (Note that kilometers of track data on railways are from a database maintained may exceed kilometers of line because of double and triple by the World Bank's Transportation, Water, and tracking, yard tracks, and the like ) Railways whose traffic Urban Development Department, Transport density averages less than 500,000 traffic units per kilo- Division Those on port container traffic are from meter need to operate at low costs and very high labor pro- Containerisation International's Containerisation ductivity to survive Labor is the most expensive factor of Intemattonal Yearbook And the data on air trans- production for a railway, and most railways have found that port are from the International Civil Aviation improving labor productivity is the most important factor in Organization's Civil Aviation Statistics of the establishing economic viability Employee productivity is World and ICAO staff estimates heavily influenced by the balance of passenger and freight 2003 World Development Indicators 1 293 LIPower and communications Electric power Telephone mainlines"a Mobile Intemnational phones' atelecommunications a Transmission and In largest Consumption distribution city Cost of Outgoing Cost of per losses per per Waiting Waiting Revenue local call per traffic call to U S capita % 1,000 1,000 list time per per line $ per 1,000 minutes per $ per kwh Of output people people thousands years employee $ 3 minutes people subscriber 3 minutes 2000 2000 2001 2001 2001 2000 2001 2001 2001 2001 2001 2001. Afghanistan 1 8 Albania 1,073 51 50 94_ 11 8 4 5 53 -853 0 02 88 361 5.92 Algeria 612 16 61 124 727 0 5 4 105 192 0 02 3 0 Angola 88 15 6 -21 - 8 5 37 1,633 0 08 6 444 3 11 Argentina 2,038 13 224 93 1 0 2 337 931 0 09 193 58 Armenia 944 25 140 212 80 8 74 119 0.11 6 62 Australia 9,006 8 519 0 0 0 0 179 1,330 0 11 576 224 0_59 Austria 6,457 7 468 0 0 0 0 217 1,512 0 13 807 274 Azerbaijan 1,852 15 111 270 55 4 1 3 99 97 0.29 80 34 6 96 Bangladesh 96 -16 4 30 199 1 3 3 29 593 0 03 4 77 2 47 Belarus 2,678 13 279 397 373 0 2 7 107 45 0.01 13 73 2 43 Belgium 7,564 4 498 180 1,269 0 13 746 344 Benin 64 72 9 23 0 4 5 62 1,044 0 09 19 294 Bolivia 387 18 62 109 0 2 171 756 0 09 90 27 Bosnia and Herzegovina 1,473 17 ill 502 2.2 248 482 0 02 57 200 Botswana 91 0 5- 0.02 165 Brazil 1,878 -18 218 311 0 5 400 822 0 04 167 21 Bulgaria 2,962 15 359 181 7 3 6 116 0 0 00 191 43 2 37 Burkina Faso 5 42 12 3 2 2 45 998 0 10 6 254 2 46 Burundi 3 ._ 7 3 3 145 Cambodia 2 19 51 705 0.03 17 320 Cameroon 183 22 7 6 2 46 0 06 20 218 Canada 15,620 8 676 0 0 238 1,044 362 347 Central African Republic 2 . >10 0 22 1,048 3 434 12.93 Chad -1 8 . 0 5 17 0 10 3 9 11 Chile 2,406 7 233 333 32.3 0 0 184 710 0 10 342 67 2 18 China 827 7 -137 584 0 0 239 110 7 Hong Kong, China 5,447 13 580 577 0 0 0 0 150 1,907 0 00 859 932 2 62 Colombia 788 24 171 327 1,174 7 2 0 227 291 0 03 76 40 Congo, Dem Rep 40 4 0 .3 Congo, Rep 86 60 7 48 Costa Rica 1,630 7 230 19 6 0 3 190 369 0 03 76 138 1 93 C6te dIlvoire 18 68 22 7 0 8 77 1,284 0 05 45 212 5.89 Croatia 2,695 19 -365 .0 9 0 08 377 129 Cuba 1,049 16 51 121 34 1,315 0 09 1 65 Czech Republic 4,807 7 375 666 30 5 0.2 157 683 0 11 675 93 0 79 Denmark 6,079 6 719 0 0 0 0 172 1,092 0 08 738 219 Dominican Republic 788 27 110 0 00 146 213 Ecuador 624 21 104 133 14 5 275 336 0_60 67 48 1 75 Egypt, Arab Rep 976 12 104 583 3 1 9 122 383 0 01 43 33 2 91 El Salvador 587 13 93 38 2 155 985 0 07 125 264 1 23 Eritrea 8 43 27 0 7 2 66 437 0 03 . 114 Estonia 3,628 15 352 422 13 8 1 4 220 621 0 09 455 153 0 70 Ethiopia 22 10 4 60 155 2 7 8 39 329 0 02 0 47 7 14 Finland 14,588 4 548 0.0 0.0 114 1,472 0 12 778 190 0 17 France 6,539 6 573 0 0 0 0 223 860 0 14 605 120 Gabon 697 10 30 >10 0 32 1,796 205 673 Gambia,The . 26 97 10 9 6 0 37 . 0 05 41 192 4,40 Georgia 1,212 15 159 233 104 8 2 2 72 114 54 74 1 88 Germany 5,963 4 634 696 0 0 0 0 216 1,083 0 09 682 168 0 33 Ghana 288 1 12 83 154 8 61 525 0,03 9 193 1 26 Greece 4,086 8 529 731 7 6 0 2 302 864 0.08 751 147 0 69 Guatemala 335 25 65 0 08 97 207 Guinea 3 1 4 0 1 32 1,119 0 09 7 180 4 61 Guinea-Bissau 10 5 1 4 4 46 271 Haiti 37 45 10 >10 0 18 .11 24H 2003 Worid Development Indicators Power and communications 51 Electric power Telephone mainlines8a Mobile international phones8 telecommunications8 Transmission and In largest Consumption distribution city Cost of Outgoing Cost of per losses per per Waiting waiting Revenue local call per traffic call to U S capita % 1,000 1.000 list time per per line $ pet 1,000 minutes per $ per kwih of output people people thousands years employee $ 3 minutes people subscriber 3 minutes 2000 2000 2001 2001 2001 2000 2001 2001 2001 2001 2001 2001 Honduras 499 19 47 7 8 50 1,115 0 07 36 144 3 72 Hungary 2,909 14 374 588 20 1 0 1 181 1,017 0 09 498 51 0 98 India 355 27 38 -136 1,648 8 0 8 91 198 0 02 6 14 3 20 Indonesia 384 11 35 261 181 -300 0 02 31 44 Iran, Islamic Rep 1,474 -16 169 381 1,155 5 1 2 229 398 0 02 32 25 7 70 Iraq 1,450 29 Ireland 5,324 9 485 116 1,536 0 14 729 786 Israel 6,188 3 476 0 3 253 1,407 0 02 808 323 Italy 4,732 7 471 0 0 0 0 358 1,288 0 11 839 169 Jamaica 2,328 9 197 209 1 _65 1 75 269 144 Japan 7,628 3 597 554 0 0 0 0 508 1,-552_ 0 07 588 36 1 67 Jordan 1,236 11 127 183 9 4 0 3 101 1,044 0 04 167 288 2 68 Kazakhstan 2,622 17 113 155 6 >10 0 60 147 36 57 Kenya 106 22 10 77 134 0 8 1 17 1,482- 0 04 19 75 5 84 Korea, Dem Rep 22 Korea. Rep 5,607 5 486 632 0 0 0 0 336 791 0 03 621 45 1 69 Kuwait 13,995 240 46 0 0 0 0 64 1,516 0 00 445 340 1 96 Kyrgyz Republic 1,606 25 78 168 37 7 -6 9 50 83 5 61 8 92 Lao PDR 10 65 5 9 1 1 39 488 0 02 5 138 6 37 Latvia 1,887 24 308 500 14 7 3 3 179 321 0 11 279 67 1 99 Lebanon 1,814 18 195 0 07 212 Lesotho 10 64 19 0 ->10 0 64 478 0 02 15 2 31 Liberia 2 >10 0 1 Libya 3,921 109 -1 2 43 9 Lithuania 1,768 12 313 427 9 1 0 9 217 230 0 11 253 30 2 13 Macedonia, FYR 263 1 2 143 406 0 01 109 122 Madagascar 4 9 0 3 0 1 23- 1,450 0 09 9 166 8 98 Malawi 5 41 20 1 9 1 9 625 0 02 5 435 0 06 Malaysia 2,628 8 196 91 0 0 7 219 958 0 02 314 146 2 37 Mali 4 24 37 1,187 0 07 4 307 12 64 Mauritania . 7 >10 0 26 1,330 0 08_ 42 476 Mauritius 257 376 9 9 1 0 165 470 0_03 _252 116 3 60 Mexico -1,655 14 137 156 0 1 139 1,055 0 16 217 148 3 04 Moldova 720 45 154 350 118 3 5 5 93 85 0 02 48 69 3 96 Mongolia 52 99 37 6 2 6 28 396 0 02 81 38 4 92 Morocco 447 6 41 5 0 0 1 74 1,189 0 08 164 1 72 Mozambique 53 10 4 21 3 3 2 39 1.319 0 07 -8 246 Myanmar 69 31 6 32 79 9 5 3 37 61 0 01 0 34 23 71 Namibia 66 157 24 0 7 70 756 0 03 56 512 4 28 Nepal 56 21 13 315 286 0 6 7 64 246 0 01 1 109 Netherlands 6,152 5 621 00 0 0 169 1,313 0 11 767 260 New Zealand 8,813 10 477 0 0 0 0 325 958 0 00 599 521 Nicaragua 267 30- 31 9 1 30 Niger 2 24 16 848 0 11 0 292 8 77 Nigeria 81 32 5 12 1 4 47 715 4 112 Norway 24,422 8 720 0 0 0 0 144 1,574 0 14 825 128 0 28 Oman 2.952 17 90 0 5 115 1,823 0 02 124 646 Pakistan 352 24 23 230.0 1 8 61 369 0 02 6 53 3 54 Panama 1,331 20 148 284 78 1,018 0 06 207 121 4 36 Papua New Guinea 12 115 0 2 36 1,221 0 06 2 402 4 31 Paraguay 838 2 51 91 0 7 25 1,068 0 09 204 110 0 82 Peru 668 11 78 1 2 271 0 07 59 58 Philippines 477 14 42 265 257 721 0 00 150 49 Poland 2,511 10 295 501 6 0 8 159 646 0 08 260 73 2 92 Portugal 3,834 8 427 0 2 236 1,170 0 11 774 125 0 87 Puerto Rico 336 261 1,480 306 2003 World Development Indicators I 295 t7~J5L ~J Power and communications Electric power Telephone mainlines" Mobile Intemnational phones" telecommunIcatIons"' Transmission and In largest Consumption distribution city Coat of outgoing Cost of per losses per per Waiting Waiting Revenue local call per traffic call to U S capita % 1.000 1,000 list time per per line $ per 1.000 minutes per $ per kwh of output people people thousands years employee $ 3 minutes people subscriber 3 minutes 2000 2000 2001 2001 2001 2000 2001 2001 2001 2001 2001 2001 Romania 1,513 13 184 576.0 3 8 103 222 0 11 172 44 1 96 Russian Federation 4.181 12 243 6,020 0 5 1 75 195 38 29 Rwanda ..3 . 4.0 61 934 0 04 8 245 Saudi Arabia 4,912 8 145 214 85 6 2 6 131 1,719 0 01 113 458 3 20 Senegal 121 17 25 71 9 8 0 8 152 852 0 11 31 294 1 81 Sierra Leone 5 >10.0 19 6 336 Singapore 6,948 4 471 471 0 0 0 0 221 1,411 0 02 724 961 0 68 Slovak Republic 4,075 - 6 288 665 7.0 0 7 106 604 0 12 397 ill 0 79 Slovenia 5,290 6 401 1.0 0 1 194 7,820 0 05 760 124 0 52 Somalia .. 4 South Africa 3,745 8 112 50 0 1 1 125 1,262_ 0.07 252 100 0 58 Spain 4,653 9 431 0 0 415 954 0 07 655 150 Sri Lanka 293 20 44 299 257 7 1 9 73 373 0 04 36 58 2 66 Sudan 66 15 14 80 444 0 4 4 150 364 0 03 3 80 39 08 Swaziland , .31 131 14 6~ 7 2 67 826 0 04 65 822 2.97 Sweden 14,471 8 739 0 0 0 0 254 1,062 0 11 790 191 0 32 Switzerland 7,294 6 746 0 0 0 0 217 1,593 012 731 481 Syrian Arab Republic 900 103 156 2,805 9 >10.0 79 253 0 01 12 96 20 04 Tajikistan -2,137 15 36 133 5 6 . 45 32 0 01 0 38 Tanzania -56 22 4 20 ~ 73 1 3 42 932_ 0 07 12 63 Thailand 1,448 8 99 452 544 2 1 6 198 579 0.07 123 52 1 49 Togo 10 35 16 8 2 9 55 908 010 20 239 7 67 Trinidad and Tobago 3,692 7 240 0 5 100 958 0 04 197 218 2 21 Tunisia 939 11 109 . 0 9 143 451 0 02 40 172 Turkey 1,468 19 285 388 198 5 0 5 270 275 0 12 302 36 3 06 Turkmenistan 1,071 14 80 61 1 8 5 52 127 2 50 Uganda 3 3 6 27 1,787 0.13 14 Ukraine 2,293 18 212 2,500 4 7 9 85 146 44 36 United Arab Emirates 10,725 9 340 348 0 3 0 0 118 1,956 0 00 616 1,326 1 73 United Kingdom 5,601 8 588. 0 0 0 0 169 1,875 0 17 770 225 United States 12,331 6 667 0 0 163 1,566 0 00 451 156 Uruguay 1,924 19 283 335 0 0 0 0 168 837 0 17 155 87 4 88 Uzbekistan 1,612 9 66 248 33 1 0 9 69 118 0 01 2 34 13 95 Venezuela, RB 2,533 24 109 . 154 2.557 010 263 104 Vietnam- 286 13 38 .. 41 414 0 02 15 18 West Bank and Gaza 78 0 7 155 0 06 91 176 Yemen, Rep 107 26 22 80 159 5 3 8 76 228 0 02 8 102 4 45 Yugoslavia, Fed Rep 229 424 143 0 1 8 174 187 114 Zambia 556 3 8 22 12 8 6 7 28 808 0 06 11 170 2 57 Zimbabwe 845 21 19 76 158 9 10 0 63 817 0 04 24 289 4 36 Low Income 352 22 30 130 8,170 7 4.4 92 241 0 05 10 112 5 27 Middle Income 1,391 11 152 406 1 0 397 0 04 129 90 2 86 Lower middle income 1,193 10 139 524 1 9 . 288 0 04 107 62 Upper middle income 2,252 14 208 289 0 5 278 859 0 09 224 108 2 46 Low & middie income 914 _13 93 270 2 0 . 370 0 04 72 102 3 54 East Asia & Pacific 760 8 110 502 1 4 . 278 0 02 97 49 4 62 Europe & Central Asia 2,753 13 235 12,154 9 1 8 131 268 0 10 140 65 2 13 Latin America & Carib 1,528 - 16 165 0 5 295 840 0 08 161 87 M-iddle East & N Africa ~1,346 12 100 5,366 8 1 2 158 585 0 02 53 102 South Asia 323 26 32 127 2,623.8 1 9 87 222 0 02 6 58 2 66 Sub-Saharan Africa 432 10 14 4 4 101 1,082 0 06 27 245 5 15 High Income 8,617 6 593 0 0 244 1,338 0 08 609 204 0 81 Europe EMU 5,757 6 540 . 14 1 0.0 255 1.108 0 11 711 169 a Data are from the international Telecommunication Union's (ITU) World Telecommunication Development Report 2002 Please cite the ITU for third-party use of these data 29(3 H 2003 World Development Indicators Power and communications El C_ ~~~~~~~~~~~~~~~~- The quality of an economy's infrastructure, including coal, oil, gas, nuclear, hydro, geothermal, wind, tide * Electric power consumption measures the produc- power and communications, is an important element and wave, and combustible renewables-where data tion of power plants and combined heat and power in investment decisions for both domestic and for- are available Neither production nor consumption plants less transmission, distribution. and transforma- eign investors Government effort alone will not suf- data capture the reliability of supplies, including tion losses and own use by heat and power plants fice to meet the need for investments in modern breakdowns, load factors, and frequency of outages * Electric power transmission and distrlbution losses infrastructure, public-private partnerships, especially Over the past decade new financing and technolo- are losses in transmission between sources of supply those involving local providers and financiers, will be gy, along with privatization and liberalization, have and points of distnbution and in distribution to con- critical in lowering costs and delivering value for spurred dramatic growth in telecommunications in sumers, including pilferage * Telephone mainlines are money In telecommunications, competition in the many countries The table presents some common telephone lines connecting a customer's equipment to marketplace, along with sound regulation, is lowering performance indicators for telecommunications, the public switched telephone network Data are pre- costs and improving the quality of and access to including measures of supply and demand, service sented for the entire country and for the largest city services around the globe quality, productivity, economic and financial perform- * Waiting list shows the number of applications for a An economy's production and consumption of elec- ance, and tariffs The quality of data varies among connection to a mainline that have been held up by a tncity is a basic indicator of its size and level of devel- reporting countries as a result of differences in reg- lack of technical capacity * Waiting time is the approx- opment Although a few countries export electric ulatory obligations for the provision of data imate number of years applicants must wait for a tele- power, most production is for domestic consumption Demand for telecommunications is often meas- phone line * Mainlines per employee are calculated Expanding the supply of electricity to meet the grow- ured by the sum of telephone mainlines and regis- by dividing the number of mainlines by the number of ing demand of increasingly urbanized and industrial- tered applicants for new connections. (A mainline is telecommunications staff (with part-time staff convert- ized economies without incurring unacceptable normally identified by a unique number that is the ed to full-time equivalents) employed by enterpnses social, economic, and environmental costs is one of one billed ) In some countries the list of registered providing public telecommunications services the great challenges facing developing countries applicants does not reflect real current pending * Revenue per line is the revenue received by firms per Data on electric power production and consump- demand, which is often hidden or suppressed, mainline for providing telecommunications services tion are collected from national energy agencies by reflecting an extremely short supply that has dis- * Cost of local call is the cost of a three-minute, peak the International Energy Agency (IEA) and adjusted by couraged potential applicants from applying for tele- rate, fixed line call within the same exchange area the IEA to meet international definitions (for data on phone service And in some countries the waiting list using the subscriber's equipment (that is, not from a electricity production, see table 3 9) Electricity con- may overstate demand because applicants have public phone) * Mobile phones refer to users of sumption is equivalent to production less power placed their names on the list several times to portable telephones subscribing to an automatic public plants' own use and transmission, distribution, and improve their chances Waiting time is calculated by mobile telephone service using cellular technology that transformation losses It includes consumption by dividing the number of applicants on the waiting list provides access to the public switched telephone net- auxiliary stations, losses in transformers that are by the average number of mainlines added each year work, per 1,000 people * Outgoing traffic is the tele- considered integral parts of those stations, and elec- over the past three years The number of mainlines phone traffic, measured in minutes per subscnber, that tricity produced by pumping installations It covers no longer reflects a telephone system's full capacity originates in the country and has a destination outside electricity generated by primary sources of energy- because mobile telephones-whose use has been the country * Cost of call to U.S. is the cost of a three- expanding rapidly in most countries, rich and poor- minute peak rate call from the country to the United 5.10a provide an alternative point of access States In addition to waiting list and waiting time, the table includes two other measures of efficiency in Per 1,000 people, 2001 telecommunications mainlines per employee and 800 revenue per mainline Caution should be used in 700 - interpreting the estimates of mainlines per employ- 600 ee because firms often subcontract part of their 500 work The cross-country comparability of revenue - 400 per mainline may also be limited because, for The data on electricity consumption and losses 300 example, some countries do not require telecom- are from the IEA's Energy Statistics and Balances 200 * m munications providers to submit financial informa- of Non-OECD Countries 1999-2000, the IEA's 100 | * g g E tion, the data usually do not include revenues from Energy Statistics of OECD Countries 1999-2000, O _ ; rg 1_ m i_ mobile phones or radio, paging, and data services, and the United Nations Statistics Division's Slovenia Mexico Gabon Morocco Philippines and there are definitional and accounting differ- Energy Statistics Yearbook The telecommu- n Mobile phone subscribers * Fixed-line subscribers ences between countries nications data are from the International Telecommunication Union's (ITU) World Telecom- Source Table 5 10 Based on International munication Development Report 2002 Telecommunication Union data 2003 World Development Indicators 1 297 The information age Daily Radios Television a Personal lntemet information and rmwspapers computers communicatiorts Monthly off-peak technology access charges a expenditures Cable Service Telephone Sets subscribers provider usage per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 In Users charge charge Secure per capita people people people people people education thousands' $ $ servers % of gdp $ 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 Afghanistan 5 114 14 0 0 Albania 35 260 123 -7 6 10 19 0 20 I Algeria 27- 244_ 114 7 1 60 27 0 17 - Mgola 11 74- 19 - 0 9 1 3 60 20 0 57 Argentina 3-7 .681 326 162 9 91 1 140,053- 3,300 78 0 47 238 4.0 310 Armenia 5 225 230 1 2 7 9 50 42 0 78 1 Australia 293 1,999, 731 72 2 515 8 706,794 7,200 13 2 60 3,422 10 7 1,939 Austria 296 753 542 147 4 335.4 149,243 2,600 23 17 21 669 7 2 1,764 Azerbaijan 27 22 321 0 6- 25 2 15 - 1 Bangladesh 53 49 17 1 9 250 17 0 33 1 Belarus 152 199 342 33 2 422 15 54 25 4 Belgium 160 793 543 370 0 232 8 193,997 3,200 23 27.52 342 8 1 1,870 Benin 5 441 44 1 7 25 129 0 93 1 Bolivia 55 676 121 9 7 20 5_ 150 5 Bosnia and H.erzegovina 152 243 III - 45 19 0 13 Botswana 27 150 30 7 3 38 7 50 15 0 14 - Brazil 43 433 349 13.8 62 9 879,575 8,000 1,028 8 3 287 Bulgaria 116 543 453 -131-.3 44 3 22,078 605 8 0 02_ - 18 3 8 65 Burkina Faso 1 433 103 0 0 1 5 19 29 0 84 Burundi 2 220 30 6 0 18 Cambodia 2 119 8 1 5 10 104 0 30 2 Cameroon 7 163 34 3 9 45 77 0 56 Canada 159 1,047 700 267 9 459.9 1,019,436 13,500 12 5,055 8 7 1,960 Central African Republic 2_ 80 6 1,9 2 166 1 40 - Ch-ad 0 236 -1 1.6 4 - Chile 98 759 286 46 0 106 5- 123-,595 3,102 141 8-1 371 China 339 312 68.6 19 0 2,092,119 33,700 7 0 14 184 5.7 53 Hong Kong, China 792 686 504 83 8 386 6 166,388 2,601 18 538 8 7 2,110 Colombia 46 549 286 13 6 42 1 118,796 1,154 0 25 71 12 0 231 Congo,De -m-Rep- 3 386 6 95 Congo,Rep _q 123 3 9 - I Costa Rica 91 816 231 170 2 384 16 0 10 56 C6te d'lvoire 16 183 60 0 0 7 2 70 183 0 25 Croatia 114 340 293 380 85.9 250 20 0 42 61 Cuba 118 185 251 19 6 120 -2 Czech Republic 254 80-3- -534 93.8 145 7 99,555 1,400 25 11 60 273 9 5 483 Denmark 283 1,400 857 200.9 540 3 154!797 2,900 21 396 9 3 2,912 Dominican Republic 27 181 186 18 8 Ecuador 96 413 225 33 8 23 3 328 11 Egypt, Arab Rep 31 -339 '217 - 15 5 48,816 600 9 0 14 11 2 5 37 El Salvador 28 478 201 49 7 21 9 50 26 0 62 7 Eritrea 464 39 0 0 1 8 15 23 0.21 Estonia 1 76 1,136 629 97 9 174 8 430 0 57 80 Ethiopia 0 189 6 00 . I 1 25 94 0 24 -3 Finian d 445 1,624 678 192 5 423 5 205,032 2,235 9 10 62 498 7 7 1,938 France 201 950 632 54 6 337 0 896,621 15,653 20 1,641 9 1 2,048 Gabon 30 501 326 11 9 17 35 1 26 1 Gambia, The 2 396 3 12 7 18 18 2 70 Georgia 5 556 474 27 25 20 0 00 - 1 -0 Germany 305 570 586 264 5 382 2 1,054,871 30,800 13 5,156 7 9 1,B80- Ghana 14 710 118 03 3 3 41 36 0 38 1 Greece 23 478_ 519 00 81 2 8-3,187 1,400 15 5 40 116 6 1 688 G-uatemala 33 79 61_, - 12 8 200 12 Guinea 52 44 0.0 40 15 58 0.86 Guinea-Bissau 5 204 36 4 Haiti 3 18 6 4.8 30 1 2993 D 2003 World Development Indicators The information age 51 Daily Radios Television aPersonal Internet Information and newspapers computers communications Monthly of f peak technology access charges' expenditures Cable Service Telephone Sets subscribers provider usage per 1.000 per 1.000 per 1,000 per 1,000 per 1,000 In Users charge charge Secure per capita people people people people people i' education thousandsav $ $ servers % of gdp $ 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 Honduras 55 413 96 7 7 12 2 40 15 0 61 4 Hungary 465 690 445 159 7 100 3 -76,731 1,480 13 13 59 127 8 9 466 India 60 120 83 38 9_ 5 8 238,667 7,000 10 0 18 122 3 9 19 Indonesia 23 159 153 0 3 11 0 58,491 4,000 12 0 20 60 2 2 17 Iran, Islamic Rep 28 281 163 69 7 .. 1,005 1 Iraq 19 222 83 Ireland 150 695 399 159 4 390 7 60,008 895 21 16 45 350 6 2 1,704 Israel 290 526 335 185 0 245 9 - 1,800 11 0 18 301 7 4 Italy 104 878 49 1 4 -194 8 852,612 16,400 23 17 62 1,041 5 7 1,117 Jamaica 62 796 194 50 0 100 49 5 Japan 578 956 731 147 4 348 8 2,172,000 55,930 17 27 67, 5,153 9 6 3,256 Jordan 75 372 111 03 32 8 212 24 0 42 2 Kazakhstan 411 241 4 1 100 1 0 02 8 Kenya 10 -221- 26 04 5 6 500 66 0 46 1 Korea, Dem Rep 208 154 59 0 0 0 Korea, Rep 393 1,034 363 182 5 256 5 610,724 24,380 8 345 7 4 676 Kuwait 374 624 482 131 9 200 32 0 00 4 Kyrgyz Republic 27 110 49 2 6 151 10 0 00 2 Lao PDR 4 148 52 0 0 3 0 10 50 0 17 Latvia 135 700 840 116 1 153 1 170 29 0 82 43 Lebanon 107 182 336 28 1 56 2 300 60 0 36 19 Lesotho 8 53 16 -5 12 0 17 Liberia 12 274 25 1 Libya 15 273 137 20 108 0 20 Lithuania 2 524 422 89 4 70 6 250 45 0 38 ~ 43 Macedonia, FYR 21_ 205 282 70 12 0 04 Madagascar 5 216 24 2 4 35 66 0 44 Malawi 3 ~499 4 0 5 1 3 20- 025 Malaysia 158 420 201 0 0 126 1 121,850 6.500 5 0 24 146 6 6 262 Mali 1 180 17 1 2 . 30 70 0 72 1 Mauritania o 149 10 3 7 29 0 76 1 Mauritius 119 379 301 109 1 158 23 0 38 12 Mexico 94 330 283 24 8 68 7 515,871 3,636 11 259 3 2 196 Moldova 13 758 296 11 3 15 9 . 60 33 0 17 3 Mongolia 30 50 72 16 5 14 6 40 52 0 17 1 Morocco 28 243 159 13.7 400 26 0 75 5 Mozambique 2 44 -5 -3 5 15 Myanmar 9 65 8 1 1 10 Namibia 19 141 38 10 6 36 4 45 3 Nepal -12 39 8 3 5 60 16 0 07 Netherlands 306 980 553 392 4 428 4 773,332 7,900 23 16 40 798 9 3 2,327 New Zealand 362 997 557 7 1 392 6 195,483 1,092 11 609 14 4 1,835 Nicaragua 30 270 69 10 8 9 6 50 30 0 54 6 Niger o 1-21 37 0 5 12 63 0 53 Nigeria -24- -200 68 0 4 6-8 11-5 44 0 57 1 Norway 569 3,324 883 185 2 508 0 - 163,399- 2,700 11 20 64 369 7 2 2,573 Oman 29 621 563 32 4 120- 19 0 79 2 Pakistan 40 105 148 4 1 500 13 0 20 6 Panama 62 300 194 37 9 90 29 Papua New Guinea 14_ 86 19 39 56 7 50 34 2 52 Paraguay 43 182 218 21 3 14 2 60 4 Peru 0 269 148 16 7 47 9 3-,000 - 35 Philippines 82 161 173 13 1 21 7 77,400 2,000 24 68 4 2 41 Poland 102 523 401 92 9 85 4 252,71 3 3,800 14 18 39 326 5 9 271 Portugal 32 304 415 108 6 117 4 48,511 2,500 19 13 00 138 6 5 735 Puerto Rico 126 761 330 600 43 1 30 63 2003 World Development Indicators I 299 The information age Daily Radios Television3a Personai intemnet Information and newspapers computers communications Monthly off-peak technology access charges 8 expenditures Cable Service Telephone Sets subscribers provider usage per 1,000 per 1,000 per 1.000 per 1,000 per 1,000 In Users charge charge Secure per capita people people people people people a education thousands8 $ $ servers % of GOP $ 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 Romania 300 358 379 120 6 35.7 36,754 1,000 15 0.37 53 2 2 43 Russian Federation 105 418 538 76 7 49.7 471,270 -4.300 15 014 _285_ 3 3 -68 Rwanda 0 76 20 38 0 36 1 Saudi Arabia- 326 326 264 62 7 . 300 31 0.13 1 Senegal 5 126 79 0 1 18 6 . 100 14 0 531 Sierra Leone 4 259 13 .. 7 .1 Singapore 298 672 300 73 1 508 3 150,702 1,500 15 0 12 _ 525 9 9 2,110 Slovak Republic 131 965 407 122 9 148 1 27,729 674 9 0 54 79 7 5 325 Slovenia 169 405 367 160.8 275 7 28,842 600 29 0 31 102 4 7 496 Somalia 1 60 14 . 1 South Africa 32 338 152 0 0 68.5 364,722 3,068 30 0 33 521 9 2 269 Spain 100 330 598 14 2 168.2 306,320 7,388 17 938 5 1 769 Sri Lanka 29 215 117 0 3 9 3 150 6 0 05 6 Sudan 26 466 386 0 0 3 6 56 3 2 33 Swaziland 26 162 128 14 12 0 24 1 Sweden 410 2,811 _965 224 5 561 2 548.698 4,600 -2 21 35 1.033 - 11 3 2,804 Switzerland 373 1,002 554 370 0 540 2 175,431 2,223 18 .30 87 1,079 10 2 3,1 Syrian Arab Republic 20 276 67 16 3 60 1 Tajikistan 20 141 326 3 Tanzania 4 406 42 0 2 3 3 300 69 0 79 Thailand 64 235 300 2 5 27 8 271,528 _3,536 9 0 75 116 3 7 76 Togo 2 265 37 21 5 150 8 0 75 Trinidad and Tobago 123 532 340 69 2 120 1 0 37 -12_ Tunisia 19 158 198 23 7 .. 400 25 0.22 4 Turkey 111 487 319 13 7 40 7 123,907 2,500 25 4 10 219 3 6 143 Turkmenistan 7 256 196 . 8 Uganda 2 127 27 02 3.1 60 30 0 82 Ukraine 175 889 456 52.3 18 3 600 7~ 0 04 44 United Arab Emirates 156 318 252 135 5 976 13 0 00 31 United Kin gdom 329 1,446 950 64 1 366 2 1,824,106 24.000 14 . 6,467 9 7 2,319 United States 213 2,117 835 256.8 625.0 16,322,694 142,823 5 3 50 78,126 7 9 2.924 Uruguay 293 603 530 125 9 110 1 400 -37 Uzbekistan 3 456 276 3 0 150 77 0 10 Venezuela, RB 206 294 185 40 2 52.8 100,663 1,265 27 92 4 0 199 Vietnam 4 109 186 . 11 7 26-,957 1,010 20 0.25 6 6 7 26 West Bank and Gaza .134 0 0 . 60 700 0 28 Yemen, Rep 15 65 283 1 9 17 45 0 09 - Yugoslavia, Fed Rep 107 297 282 23 4 . 600 0 13 7 Zambia 12 169 '113 7 0 25 19 _0 31 Zimbabwe 18 362 .12 1 .. 100 46 0 341 M m - - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Jwuyi:~~~~~~~~~s j"M *e Low Income 41- 156 91 24 0 6 1 15,932 34 0 36 279 Middle Income 350 291 56 7 35 4 96,658 19 0 37 4,7 Lower middle income 326 284 60 9 25 5 60,355 17 0 23 1,704 Upper middle income 95 457 320 40 5 77 2 36,303 23 0.44 3,271 Low & middle Income 258 191 39 5 21 6 112,591 24 0 36 5,254 East Asia & Pacific . 287 266 54.8 _ 19 1 50.9-02 22 ~0.24 -595 Europe & Central Asia 102 447' 407 61 8 52 1 18,778 15_ 0 37- 1,694 Latin America & Carib 71 4-13 274 34 8 59 3 26,282 2.199 Middle East & N Africa 33 277 171 32 0 3,356 27 0 22 79 South Asia 60 112 81 37.3 5 3 7.973 13 0 18 135 Sub-Saharan Africa 12 198 59 0 3 9 9 5,300 36 0 53 552_ High Income 285 1,268 677 178 2 416.3 388,8838 13 11 81 115.969 Europe EMU 209 810 582 130 5 286 2 91,231 23 16 42 .11,741 a Data are from the international Telecommunication Union's (ITUI World Telecommunication Development Report 2002 Please cite the ITU for third-party USe of these data 300 2003 World Development Indicators The information age E I The digital and information revolution has changed the way The data for other electronic communications and infor- * Daily newspapers refer to those published at least four the world learns, communicates, does business, and treats mation technology are from the International Telecom- times a week * Radios refer to radio receivers in use for illnesses New information and communications technolo- munication Union (ITU), the Internet Software Consortium, broadcasts to the general public * Television sets refer to gies offer vast opportunities for progress in all walks of life Netcraft, and the World Information Technology and those in use * Cable television subscribers are house- in all countries-opportunities for economic growth, Services Alliance The ITU collects data on television sets holds that subscribe to a multichannel television service improved health, better service delivery, learning through and cable television subscnbers through annual question- delivered by a flixed line connection Some countries also distance education, and social and cultural advances This naires sent to national broadcasting authorities and indus- report subscnbers to pay-television using wireless technol- table presents indicators of the penetration of the informa- try associations Some countries require that television ogy or those cabled to community antenna systems tion economy-newspapers, radios, television sets, per- sets be registered To the extent that households do not * Personal computers are self-contained computers sonal computers, and Internet use-as well as some of the register their televisions or do not register all of them, the designed to be used by a single individual * Personal comn- economics of the information age-Internet access data on licensed sets may understate the true number puters In education are those installed in primary and sec- charges, the number of secure servers, and spending on The estimates of personal computers are derived from ondary schools and universities * Internet users are information and communications technology an annual questionnaire, supplemented by other sources people with access to the worldwide network * Intemet The data on the number of daily newspapers in circu- In many countries mainframe computers are used exten- service provider charge is the cost of 30 off-peak hours of lation and radio receivers in use are from statistical sur- sively Since thousands of users can be connected to a dial-up Internet access per month It is the monthly Internet veys carried out by the United Nations Educational, single mainframe computer, the number of personal com- subscription rate plus extra charges once free hours have Scientific, and Cultural Organization (UNESCO) In some puters understates the total use of computers been used up Some countnes have peak rates that are countries definitions, classifications, and methods of The data on Internet users are based on estimates higher * Telephone usage charge refers to the amount enumeration do not entirely conform to UNESCO stan- derived from reported counts of Internet service sub- payable to the telephone company for 30 off-peak hours of dards For example, newspaper circulation data should scnbers or calculated by multiplying the number of Internet local telephone use while logged on to the Internet refer to the number of copies distributed, but in some hosts by an estimated multiplier Internet hosts are com- Excluded is the monthly telephone line tariff If a special cases the figures reported are the number of copies puters connected directly to the worldwide network, each Internet telephone tariff exists, i is used instead Some printed In addition, many countries impose radio and allowing many computer users to access the Internet This countries have peak rates that are higher * Secure servers television license fees to help pay for public broadcast- method may undercount the number of people actually are servers using encryption technology in Internet trans- ing, discouraging radio and television owners from declar- using the Internet, particularly in developing countries, actions * Iniommation and communications technology ing ownership Because of these and other data where many commercial subsonbers rent out computers expenditures include external spending on information collection problems, estimates of the number of news- connected to the Internet or pre-paid cards are used to technology (-tangible' spending on information technology papers and radios vary widely in reliability and should be access the Internet Although survey methods used to products purchased by businesses, households, govern- interpreted with caution estimate the number of Internet hosts have improved in ments, and education institutions from vendors or organF recent years, some measurement problems remain (see zations outside the purchasing entity), internal spending on 5.lla Zook 2000) For detailed analysis of Internet trends by information technology ('intangible spending on internally country, it is best to use the original source data customized software, capital depreciation, and the like), -~- _ , ,The table shows both the off-peak charge by the and spending on telecommunications and other office Internet service provider (ISP) and the telephone usage equipment Personal computers per 1,000 people charge for logging on to the Internet Some countries 60 Latin America & Caribbean have peak rates that are higher The number of secure _ 5 servers, from the Netcraft Secure Server Survey, gives The data on newspapers and radios are compiled by 50 an indication of how many companies are conducting the UNESCO Institute for Statistics The data on tele- encrypted transactions over the Internet vision sets, cable television subscnbers, personal 40 E The data on information and communications technol- computers, Internet users, and Internet access EuroPe & jrMiddle Central Asia East & ogy expenditures cover the world's 55 largest buyers of charges are from the ITU They are reported in the 30 XNorth Africa such technology among countries and regions These ITU's World Telecommunication Development Report account for 98 percent of global spending 2002 and the World Telecommunications Indicators East Asia Because of different regulatory requirements for the pro- Database (2002b) The data on secure servers are 20 & Pacific vision of data, complete measurement of the telecommunF from Netcraft (http //www netcraft com/) The data Sub-Saharan cations sector is not possible Telecommunications data on personal computers in education and on informa- 10 _ < / Afnc X are compiled through annual questionnaires sent to tele- tion and communications technology expenditures communications authorities and operating companies The are from Dlgital Planet 2002 The Global Information 0 / data are supplemented by annual reports and statistical Economy by the World Information Technology and 1990 1991 1993 1995 1997 1999 2001 yearbooks of telecommunications ministries, regulators, Services Alliance (WITSA), which uses data from the Source World Development Indicators database based operators, and industry associations In some cases esti- International Data Corporation on International Telecommunication Union data mates are derived from ITU documents or other references 2003 World Development Indicators 1 301 Science and technoDogy Scientists Technicians Scientific Expenditures lgH-technology Royalty and Patent Trademark and In and for exports license fees applications applicationts englneers R&D technical R&D filed8 fliledb In R&D journal articles % of per million per million $ manufactured Receipts Payments Non- people people % of GDP millions exports $ millions $ millions Residents residents 1990-2000- 1990-2001' 1999 1.989-2000' 2001 2001 2001 2001 2000 2000 2000 Afghanistan 0 Albania 17 2 1 1 111,609 2,166 Algeria 162 21 4 30 33,620 4,601 Angola 3 1 6 -6 Argentina 713 158 2,361 0 45 807 9 23 465 6,634 d 61,828 Arm-enia 1,313 223 142 4 4 123 58,154 3,014 Australia 3,353 792 12,525 1 51 2,671 10 298 868 10,367 70.354 71,496 Austria 2,313 979 3,580 1 80 7,471 14 138 602 3,115 197,915 20,334 Azerbaijan 2,799 160 66 0 24 9 8 0 58,076 2,140 Bangladesh 51 32 -148 0 6 32 184 Belarus 1,893 273 564 373 8 1 4 1,003 58,427 5,982 Belgium 2,953 1,157 4,896 1 96 16,183 10 887 1,246 1,835 139,931 316,709 Benin 174 53 20 . 0 Bolivia 98 72 33 0 29 37 10 2 5 Bosnia and Herzegovina 9 31 59,157 4,219 Botswana 41 0 6 0 15 Brazil 323 129 5,144 0 77 6,110 18 112 1,245 41 64,645 Bulgaria 1,316 476 801 0 57 79 2 3 12 255 60,225 9,211 Burkina Faso 16 15 23 0 19 Burundi 21 32 3 0 0 0 0 Cambodia 5 1,303 Cameroon 61 1 0 Canada 2,985 1,038 19,685 1 84 27,000 15 1,499 3,474 5,518 80,408 46,252 Central African Republic 47 27 4 Chad 2 Chile 370 380 879 0 54 108 1 5 360 241 2,879 China 545 187 11,675 1 00 49,427 20 110 1,938 25,592 96,714 6,252 Hong Kong, China 93 100 1,817 0 44 3,716 20 107 461 51 8,244 28,114 Colombia 101 207 _ 0 25 349 7 2 71 75 1,724 12,788 Congo, Dem Rep 6 Congo, Rep 33 37 13 Costa Rica 533 69 0 20 1,071 36 1 49 0 52,437 M6e dilvoire 40 15 3 0 9 Croatia 1,187 347 545 0 98 357 10 106 63 368 58,568 7,197 Cuba 480 2,160 192 0 49 0 58,418 2,222 Czech Republic 1,349 682 2,005 1 35 3,066- 10 36 93 598 62,047 19,457 Denmark 3,476 2,594 4,131 2 09 6,912 21 3,468 197,184 12,448 Dominican Republic 6 22 Ecuador 83 72 20 0 09 25 4 . 52 0 11 Egypt, Arab Rep 493 366 1,198 0 19 12 1 46 361 534 1,081 3,155 El Salvador 47 303 0 51 7 1 22 Eritrea 2 Estonia 2,128 531 261 0 76 586 19 2 11 19 60,218 5,946 Ethiopia 95 0 0 . 0 3 4 Finland -5,059 4,025 3 37 9.254 23 584 534 2,96 5 195,328 10,470 France 2,718 2,878 27,374 2 15 67,191 23 2,504 1,879 21,471 138,707 111,781 Gabon 20 Gambia, The 17 0 3 0 115,420 Georgia 2,421 97 112 0 33 235 59,133 3,149 Germany 3,161 1,345 -37,308 2 48 85,958 18 3,149 5,243 78,754 183,796 97,325 Ghana 73 1 1 0 0 115,543 Greece 1,400 554 2,241 0 67 548 8 14 225 59 140,481 10,583 Guatemala . 14 70 8 13 213 9,821 Guinea 2 0 0 0 1 Guinea-Bissau 6 . 0 1 Haiti 1 1 5 1,456 302 2003 World Development Indicators Science and technology 51 Scientists Technicians Scientific Expenditures High-technology Royalty and Patent Trademark and In and for exports license tees applications appilcations engineers R&D technical R&D fliled filed b In R&D joumnal articles % of per million per million $ manufactured Receipts Payments Non- people people % of GDP millions exports $ millions $ millions Residents residentS ±990-20001 1990-2001c 1999 1.989-2000- 2001 2001 2001 2001 2000 2000 2000 Honduras 11 4 1 0 11 8 148 5,045 Hungary 1,445 5~18 1,958 0 82 6,298 2 3 96 263 881 61,557 15,840 India 157 115 9,217 1 23 1,680 6 83 306 9 0 60,852 66.3 78 Indonesia 142 4,473 1 3 0 60,363 Iran, Islamic Rep 590 174 624 40 2 0 0 366 1 77 Iraq 21 ireland 2,184 590 1,237 1 21 35,898 -48 -346 -8,770 278 140.241 4,518 Israel 1,563 516 5,025 3 62 7,456 25 432 457 2,460 -65,398 11,730 Italy 1,128 808 - 17,149 1 04 21,486 -10~ 443 1,312 3,667 138,248 11,392 Jamaica 44 1 0 6 38_ 11 90 1,775 Japan 5,095 667 47,826 - 2 98 99,389 - 26- 10,462 11,099 388,879 97,325 145,834 Jordan 1,948 717 204 89 7 Kazakhstan 716 293 104 0 29 184 4 0 14 1,400 58,187 4.887 Kenya 252 13 4 5 62 2 115,934 1,545 Korea, Dem Rep 1 0 57,805 2,342 Korea, Rep 2,319 564 6,675 2 68 40.427 29 -688 -3,221 73,378 98,806 110,073 Kuwait 212 53 260 -0 20 -35 1 0 0 Kyrgyz Republic 581 -49 10 0 19 5 5 1- 2 80 58,116 2,430 Lao PDR 2 701 Latvia 1,078 298 153 0 40 45 3 3 7 100 112,128 6,733 Lebanon - 100 16 3 104d 3,7 Lesotho 1 .11 0 0 115,822 1,080 Liberia 1 0 58.896 1,223 Libya -361 493 19 Lithuania -2,027 631 214 -133 -5 0 10 -66 11-2,174 7,010 Macedonia, FYR 387 29 36 9 1 3 -6 71 -111,612 - 4,626 Madagascar 12 37 4 3 0 1 7 59,022 634 Malawi 36 3- 115,891 723 Malaysia -160 45 416 0 40 40,939 57 21 751 Mali 11 Mauritania 2 Mauritius 360 157 16 0 28 14 _ 1 0 1 3 12 Mexico 225 183 2,291 0 43 29,759 - 22 40 419 451 66,465 46.146 Moldova 334 1,665 92 5 3 1- 1 240 58,178 3,694 Mongolia ~ 531 116 8_ 80 0 58,983- 1,479 Morocco 386 537 11 22 256 -10-4 51,907 - 7,388 Mozambique 14 _2_ 0 0 0- 56,555 1,368 Myanmar 10 0 0 Namibia 13 Nepal 39 0 0 Netherlands 2,572 1,464 10,441 2 02 38,960 32 1,723 2,319 7,528 136,813 New Zealand - 2,197 732 2,375 1 11 466 8 61 297 2,266 65,672 24,046 Nicaragua 73 33 _8 0 15 -2 3 9 136 Niger 21 -0 8 Nigeria -397 0 I Norway 4,112 - 1,836 2,598 1 70 2,082 12 155 329 1,842 66,213 16,341 Oman 4 0 73 42 3 2,822 Pakistan 69- 12 277 24 0 2 19 8,320 Panama 124 244 37 0 35 2 1 0 31 7 153 13,223 Papua New Guinea 36 11 19 566 Paraguay 4 7 4 180 7 Peru 229 9 56 0 08 56 2 0 46 48 944 Philippines 156 22 164 21,032 70 1 158 154 3,482 10,780 Poland 1,429 463 4,523 0 70 936 3_ 48 508 2,419 62,454 28,197 Portugal 1,576 506 1,508 0 71 1,343 6 25 234 126 198,574 18,394 Puerto Rico 2003 World Development Indicators I303 Science and technology Scienvtists Technicians Scientific Expenditures High-technology Royalty and Patent Trademark and In and for exports license fees applications applications englneers R&D technical R&D filed 5 filed b In R&D journal articles % of per million per million $ manufactured Receipts Payments Non- people people % of GDP millions eaports $ millions $ millions Residents residents 1990-2000c 1990-2001c 1999 1.989-2000 c 2001. 2001 2001 2001 2000 2000 2000 Romania 913 584 785 0 37 567 6 16 60 1,019 112,360 11,326 Russian Federation 3,481 551 15,654 1 00 3,257 8 60 343 -23,658 65,771 42,806 Rwanda 6 4 0 0 0 4 129 Saudi Arabia 528 23 - 0 0 0 72 1,144- Senegal 2 3 66 0 01 -12 5 2 5_ Sierra Leone 3 ..0 116,129 -1,209 Singapore 4,140 335 1,653 1 88 62,572 60 0 62,471 145 Slovak Republic 1,844 791 871 0 69 473 4 16 58_ 247 60,264 - 11,320 Slovenia - 2,181 872 599 1 48 442 5 14 60 340 112,524 8,518 Somalia 0 South Africa 992 303 2,018 937 5 51 115 190 57,976_ Spain - 1,921 1,019 12,289 0 94 7,106 8 365 1,678 3,813 -198,626 98,739 SrI Lanka 191 46 84 0 18 109 3 0 58,929 Sudan 43 -. 0 0 5 115,855 1,243 Swaziland 6 0 46 0 58,033 1,187 Sweden 4,511 404 8,326 3 80 10,698 18 1,427 860 10,287 193,886 16,647 Switzerland 3,592 1,399 6,993 2 64 17,353 21 7,024 194,547 12,511 Syrian Arab Republic 29 24 55 0 18 2 1 249 47 0 Tajikistan 660 20 46 58,087 2,277 Tanzania 92 6 6 0 4 0 108,930 2 Thailand 74 74 470 0 10 15,286 31 9 823 1,117 4,548 27,055 Togo 102 65 11 1- 1 0 1 Trinidad and Tobago 145 258 37 0.14 _11 1- 0 58,97-4 1,196 Tunisia 336 32 237 0 45 154 3 15 6 Turkey 306 38 2,761 0 63 1,100 5 0 119 333 67,289 33,731 Turkmenistan 0 9 5 0 58,061 1,254 Uganda 24 14 59 0 75 5 22 . 0 0 115,875 Ukraine -2,118 594 2,194 0 95 5 183 5,645~ 60,272 11,310 United Arab Emirates 118 -0 56,158 United Kingdom 2,666 1,014 39,711 1 87 67,416 31 7,910 5,909 33,658 199,565 85,570 United States 4,099 163,526 2 69 178,906 32 38,660 16,360 175,582 156.191 292,464 Uruguay 219 21 144 0 26 19 2 0 7 44 572 9,741 Uzbekistan 1,754 312 236 757 59,102 3,344 Venezuela, RB 194 -32 448 0 34 95 2 0 0 56 2,292 23,703 Vietnam 274 98 35 59,741 8,123 West Bank and Gaza Yemen, Rep 10 Yugoslavia, Fed Rep 2,389 515 546 396 59,273 6,150 Zambia 26 11 1 0 0 10 959 Zimbabwe 85 0 0 115,692 14 Low Income 14,376 7 27 284 7,271 2,092,440 62,042- Middle Income 778 245 62,409 -22 1,026 8,828 61,798 2,367,259 410,686 Lower middle income 818 237 3-9,216 0 72 17 532 4,534 56,236 1,270,074 201,002 Upper middle income 453 171 23,193 100.096 22 494 4,294 5,562 1,097,185 209,684 Low & middie Income 76,785 18 1,053 9,112 69,069 4,459,699 472,728 East Asia & Pacific 545 185 13,055 1 00 31 141 3,671 2-6,898 341,636 57,298 Europe & Central 'Asia 2,074 452 34,679 0 80 16,589 8 382 1,697 39,991 1,880,499 259,416 Latin America & Carib 287 12,033 40,832 15 372 2,850 940 589,433 113,573 Middle East & N Africa 3,637 4 84 634 940 86,748 23,781 South Asia 158 -113 9,769 5 6 25 90 119,781 8,448 Sub-Saharan Africa 3,612 4 69 236 210 1,441,602 10,212 High Income 3,281 451,842 2 61 834,1 73 24 71,303 64,037 839,048 4,071,596 1,321,907 Europe EMU 2,302 1,028 122,077 2 12 285,398 19 10,381 24,286 123,862 2,007,040 415,727 Nete The original information on patent and trademark applications was provided by the World Intellectual Property Organization (WIPOI The International Bureau of WIPO assumes no liability or responsibility with respect to the transformation of these data a Other patent applications filed in 2000 include those filed under the auspices of the African Regional Industrial Property Organization (8 by residents. 58,044 by nonresidents), European Patent Office 161,837 by residents. 81,437 by nonresidents), and the Eurasian Patent Organization 1460 residents. 58,438 by nonresidents) b Other trademark applications filed in 2000 include those filed under the auspices of the Office for Harmoniration in the Internal Market 157.324) c Data are for the latest year available See Primary data documentation for the year d Total for residents and nonresidents 304~ E 2003 World Development indicators Science and technology The best opportunities to improve living standards- of regional or local importance They may also reflect * Scientists and engineers In R&D are people engaged including new ways of reducing poverty-will come from some bias toward Englishlanguage journals in professional R&D activity who have received tertiary- science and technology Science, advancing rapidly in The method used for determining a country's high- level training to work in any field of science * Technicians virtually all fields-particularly biotechnology-is playing technology exports was developed by the Organisation In R&D are people engaged in professional R&D activity a growing economic role countries able to access, gen- for Economic Co-operation and Development in collabo- who have received vocational or technical training in any erate, and apply relevant scientific knowledge will have ration with Eurostat Termed the "product approach' to branch of knowledge or technology Most such jobs a competitive edge over those that cannot And there is distinguish it from a 'sectoral approach, the method is require three years beyond the first stage of secondary education * Scientific and technical Joumai articies greater appreciation of the need for high-quality scientif- based on the calculation of R&D intensity (R&D expen- refer to scientific and engineering articles published in ic input into public policy issues such as regional and diture divided by total sales) for groups of products from the following fields physics, biology, chemistry, mathe global environmental concerns Technological innova- six countries (Germany, Italy, Japan, the Netherlands, matcs, clcal medicne, biomedical research, engineer- tion, often fueled by government-led research and devel- Sweden, and the United States) Because industrial sec- ing and technology, and earth and space sciences opment (R&D), has been the driving force for industrial tors characterized by a few high-technology products * Expenditures for R&D are current and capital expendi- growth around the world may also produce many low-technology products, the tures on creative, systematic activity that increases the Science and technology cover a range of issues too product approach is more appropriate for analyzing stock of knowledge Included are fundamental and complex and too broad to be quantified by any single set international trade than is the sectoral approach To applied research and experimental development work of indicators, but those in the table shed light on coun- construct a list of high-technology manufactured prod- leading to new devices, products, or processes * High- tries' 'technological base'-the availability of skilled ucts (services are excluded), the R&D intensity was cal- technology exports are products with high R&D intensity, human resources, the number of scientific and technical culated for products classified at the three-digit level of such as in aerospace, computers, pharmaceuticals, sci- articles published, the competitive edge countries enjoy the Standard International Trade Classification revision entific instruments, and electncal machinery * Royaity in high-technology exports, sales and purchases of tech- 3 The final list was determined at the four- and five-digit and license fees are payments and receipts between res- nology through royalties and licenses, and the number levels At these levels, since no R&D data were avail- idents and nonresidents for the authorized use of intan- of patent and trademark applications filed able, final selection was based on patent data and gible, nonproduced, nonfinancial assets and propnetary The United Nations Educational, Scientific, and expert opinion This method takes only R&D intensity nghts (such as patents, copyrights, trademarks, franchis- Cultural Organization (UNESCO) collects data on scien- into account Other characteristics of high technology licensing agreements of produced orginals of prototypes tific and technical workers and R&D expenditures from are also important, such as know-how, scientific and (such as films and manuscnpts) * Patent appications member states, mainly through questionnaires and technical personnel, and technology embodied in flled are applications filed with a national patent office for special surveys as well as from official reports and patents, considering these characteristics would result exclusive nghts to an invention-a product or process publications, supplemented by information from other in a different list (See Hatzichronoglou 1997 for further that provides a new way of doing something or offers a national and international sources UNESCO reports details ) Moreover, the R&D for high-technology exports new technical solution to a problem A patent provides either the stock of scientists, engineers, and techni- may not have occurred in the reporting country protection for the invention to the owner of the patent for cians or the number of economically active people qual- Most countries have adopted systems that protect a limited period, generally 20 years * Trademark appli- ified as such UNESCO supplements these data with patentable inventions Under most patent legislation, cations flied are applications for registration of a trade- estimates of qualified scientists and engineers by to be protected by law (patentable), an idea must be mark with a national or regional trademark office counting people who have completed education at new in the sense that it has not already been published Trademarks are distinctive signs that identify goods or ISCED (International Standard Classification of or publicly used, it must be nonobvious (involve an services as those produced or provided by a specific per- Education) levels 6 and 7, qualified technicians are inventive step) in the sense that it would not have son or enterprise A trademark provides protection to the estimated using the number of people who have com- occurred to any specialist in the industrial field had owner of the mark by ensunng the exclusive right to use pleted education at ISCED level 5 The data are nor- such a specialist been asked to find a solution to the It to Identify goods or services or to authorize another to mally calculated in terms of full-time-equivalent staff problem, and It must be capable of industrial applica- The information does not reflect the quality of training tion in the sense that it can be industrially manufac- j_ and education, which varies widely Similarly, R&D tured or used Information on patent applications filed The data on technical personnel and R&D expenditures expenditures are no guarantee of progress, govern- is shown separately for residents and nonresidents of are from UNESCO's Stabstical Yearbook The data on ments need to pay close attention to the practices that the country scientific and technical journal articles are from make them effective A trademark provides protection to its owner by the National Science Foundation's Sclence and The counts of scientific and technical journal articles ensuring the exclusive right to use it to identify goods Engineenng Indicators 2002 The information on high- include those published in a stable set of about 5,000 or services or to authorize another to use it in return technology exports is from the United Nations of the world's most influential scientific and technical for payment The period of protection varies, but a Statstics Division's Commodity Trade (COMTRADE) journals, tracked since 1985 by the Institute of trademark can be renewed indefinitely by paying addi- database. The data on royalty and license fees are from the International Monetary Fund's Balance of Scientific Information's Science Citation Index (SCI) and tional fees The trademark system helps consumers Payments Stafistics Yearbook, and the data on patents Social Science Citation Index (SSCI) (See Definitions for identify and purchase a product or service whose and trademarks are from the World Intellectual the fields covered ) The SCI and SSCI databases cover nature and quality, indicated by its unique trademark, Property Organization's Industnal Property Statistics the core set of scientific journals but may exclude some meet their needs 2003 World Development Indicators 1 305 X -i 7. lobal integration-the widening and intensifying of links between both high-income and developing economies-has accelerated, especially in the past 20 years. The reasons? Lower transport costs, lower trade barriers, faster communication of ideas, greater mobility of people, and growing capital flows. These changes have provided new opportunities for a growing number of the world's people. But while progress has been rapid, it has been uneven across countries. The challenge this poses is reflected in the Millennium Development Goals, particularly goal 8, to develop a global partnership for development. This goal includes targets for expanding market access, encouraging debt sustainability, and increasing aid and improving its targeting. It also addresses needs of countries with particular challenges, including heavily indebted poor countries and small island states. 307 Trade for development an additional 300 million people out of poverty by 2015. The The exchange of goods and services across borders is a pri- 2001 ministerial meeting of the World Trade Organization in mary indicator of a country's integration with the global econo- Doha, Qatar, articulated a commitment to lowering these barn- my. Trade spurs economic growth by encouraging specialization ers, and the new trade round agreed to at Doha is the first to in line with a country's comparative advantage while increasing put development at the top of the agenda (box 6a). potential capital inputs and consumer choice. Countries that Developing countries have a similar responsibility to lower have integrated more with the world trade system have on aver- trade barriers. While low- and middle-income countries have cut age enjoyed stronger growth: in the past decade countries that average tariffs in half in the past 20 years (from 15 percent to significantly increased their trade grew more than three times 7 percent), more needs to be done. In 2001 China's tariffs aver- as fast as those that did not. Five tables in this section exam- aged 15 percent, and India's 31 percent. Regional averages can ne the role of trade in improving prospects for growth. also be high: 20 percent for South Asia and 13 percent for Latin America. High average rates in the developing world contribute Trade flows to the fact that developing countries face tariffs twice as high on In the past decade trade between low- and middle-income coun- average as those faced by high-income countries (table 6.6). tries grew by more than 13 percent, with East Asia and Pacific, These high tariffs have a dampening effect on trade between Latin America and the Caribbean, and Sub-Saharan Africa lead- developing countries. And the practice of escalating tariffs- ing the way. Developing countries' exports to high-income coun- imposing higher tariffs on processed goods-constrains the tries also grew from 1990 to 2000-by 11 percent-while trade development of manufacturing and industry. Tariff escalation has between high-income countries grew at less than half that rate. confined many Sub-Saharan African countries to exporting A continuation of these trends would allow developing countries unprocessed goods such as cocoa, coffee, and cotton-and dis- to fully reap the benefits of global integration (table 6.2). couraged development of the labor-intensive manufacturing that The types of goods traded are changing. In 2001, 64 percent has been a key vehicle for growth in several developing countries. of OECD imports from low- and middle-income countries were manufactured goods, up from 45 percent a decade earlier (table jiFnancOsfl l gowe eaer-except foFr forelgn diect 6.3). Greater internal trade facilitation, a commitment to lower- llnvGetmems ing trade barriers, and better trade, production, and monetary Several trends have emerged in the world's financial markets in policies will all help expand trade for low- and middle-income recent years: Cross-border capital flows have shifted from pub- countries. Membership in trade blocs can often encourage coun- lic transfers to primarily private sector flows. The flow of private tries to adopt these pro-trade policies (table 6.5). lending to developing countries has declined, and capital flows For the many developing countries dependent on commodity other than foreign direct investment have become negative. And exports, global commodity prices can have a significant impact on trade receipts. The volatility of these prices means unstable eco- 01 nomic prospects-especially for some of the poorest countries. Fl4:, N While the prices of some important commodities rose in 2002- those for cocoa by 65 percent, palm oil by 36 percent, and The new trade round that emerged from the World Trade Organization nego- coconut oil by 32 percent-the prices of many commodities tiations at Doha, Qatar, in 2001 is the first to make development the pr- declined slightly (table 6.4). Diversifying exports leaves low- and mary goal And China's accession to the World Trade Organization should middle-income countries less vulnerable to these external shocks. help keep market access issues at the forefront. Although participants at Doha did not reach universal agreement on all points, the declaration Barrers to trade emerging from the negotiations gave developing countries reason to hope Tariffs and nontariff barriers hamper growth prospects for for a more welcoming trading environment and promised a focus on developing countries, and reducing trade barriers is a key tar- implementation Several key agreements at Doha relate to market access get under the Millennium Development Goal calling for a global o An agreement to substantially improve market access, to reduce all partnership for development. The poorest countries depend on forms of export subsidies (with a view to phasing them out), and to exports of agricultural goods and labor-intensive manufactures substantially reduce trade-distorting domestic support such as textiles and clothing-products highly protected in the o An agreement to support growth in service trade for developing and European Union, Japan, and the United States because of least developed countries domestic pressures. Average tariffs in high-income countries . An agreement to reduce or, as appropriate, eliminate tariffs. This are low-the weighted mean tariffs in Japan and the United includes reducing or eliminating tariff peaks, high tariffs, and tariff States are around 2 percent (table 6.6). But much higher peak escalation as well as nontariff barriers, particularly on nonagricultur- tariffs on textiles and agricultural products are common. al products of export interest to developing countries Lowering these barriers could boost annual growth in develop- Source World Trade Organization, Doha Declaration ing countries by an extra 0.5 percent over the long run-and lift 300 O 2003 world Development Indicators foreign direct investment has become the largest and most 6b resilient form of capital flow, especially for developing countries, - where it provides a stable alternative to debt financing. Foreign direct investment may also lead to many indirect benefits- In March 2002 world leaders came together at the United Nations through innovative ideas, new technologies, and improvements International Conference on Financing for Development in Monterrey, in human capital. But more capital does not automatically trans- Mexico, to discuss new strategies for attacking global poverty. As part of late into higher growth. A country also needs good government this, high-income countries made new commitments on aid that would policies and strong institutions (Stern 2002b). raise official development assistance (ODA) in real terms by about $15 The global economic slowdown has reduced financial flows in billion by 2006-and from 0 22 percent of donor countries' gross nation- the past couple of years, and political and economic instability al income (GNI) to 0.26 percent The commitments are have exacerbated problems in some regions. Capital flows in * Members of the European Union. to strive to raise ODA to at least Latin America dropped from a peak of $126 billion in 1998 to 0 33 percent of GNI by 2006, with the European Union's average ris- $72 billion in 2001, reflecting regional problems and global ing to 0 4 percent of GNI or more economic uncertainty. Private capital flows to Argentina fell * United States: to increase its core development assistance over from a peak of $21 billion in 1999 to negative flows in 2001, three years (2004-06) so as to achieve a $5 billion annual increase while its foreign direct investment flows declined from $24 bil- (almost 50 percent) over current levels by 2006 lion to $3 billion Turkey too saw private capital flows fall (from * Canada to increase its ODA budget by 8 percent annually so as to $10 billion in 1999 to less than $1 billion in 2001). But foreign double its aid by 2010 direct investment has remained strong in East Asia and Pacific * Japan to reduce its ODA budget in fiscal 2002 and 2003 as part of and in Europe and Central Asia (table 6.7). necessary fiscal consolidation Over the past two decades, as financial openness has * Norway' to increase its ODA to 1 percent of GNI by 2005 increased across the world, global flows of foreign direct . Switzerland: to increase its ODA to 0 4 percent of GNI by 2010 investment have more than doubled relative to gross domes- * Australia to increase its ODA by 3 percent in real terms in 2002-03 tic product (GDP). The flows increased in the 1990s, rising Source DevelopmentAssistanceCommittee,OECD from $324 billion in 1995 to $1.5 trillion in 2000. East Asia and Pacific experienced the largest growth, thanks mostly to China, with investment flows to the region For poor countries, especially those unable to attract signifi- rising from $1 billion in 1980 to a peak of $62 billion in cant private flows, aid is an important means of fostering 1997. Growth was also strong in Latin America and the change. According to World Bank research, an additional $10 Caribbean, where foreign direct investment flows increased billion in aid in 1998 would have enabled around 3 million more from about $6 billion in 1980 to $88 billion in 1999. The people to escape poverty (World Bank 2002b). But such out- growth was due in large part to Brazil, Argentina, Mexico, comes depend on making aid more effective, a responsibility of and Chile, which accounted for 75 percent of the flows to the both donors and recipients. Donor countries can help recipient region (table 6.7). countries build capacity to foster change. And recipient coun- tries can continue to invest in their people and build their New commitments on aid capacity in government and business. Aid has increased in dollar amount since the Second World War, but as a share of donor countries' output it has fallen sig- More people moving across borders nificantly. Between 1960 and 1990 official development assis- The movement of people is another visible and increasingly impor- tance from major aid donors declined from 0.5 percent of their tant aspect of global integration. People moving across borders gross national income (GNI) to 0.34 percent. In 2001 it had can be categorized into thiee groups-migrants (table 6.13), fallen to 0.22 percent of GNI, half its share in 1960. Only 5 of tourists (table 6.14), and refugees or displaced persons Job cre- the 22 Development Assistance Committee members gave ation in developing countries has generally failed to keep pace more than 0.7 percent of GNI in official development assis- with population growth-a situation that grows more dire as pop- tance in 2001- Denmark, Luxembourg, the Netherlands, ulations become younger. Migration and labor flows ease unem- Norway, and Sweden (tables 6.8 and 6.9). And only 43 devel- ployment pressures in the sending country and increase private oping countries received more than $50 per capita (table 6.10). financial flows through remittances. Migration to high-income At the United Nations International Conference on Financing countries has been increasing. In 2000 foreign population inflows for Development in 2002 many countries made new com- in OECD countries rose by 13 percent (table 6.13). Such increas- mitments to strengthen partnerships (box 6b). High-income es often result in higher population growth in host countries, and countries committed additional aid, and all countries that tighter controls and regulations on labor migration. In such cases attended confirmed their commitment to the goals of the illegal immigration is likely to rise as a result of the sending coun- Millennium Declaration. tries' greater dependence on remittance income. 2003 Worid Development Indicators 1 309 Gntegration with the global economy Trade In goods Change Growth Gross private Gross In trade In real capital flows foreign direct trade less Investment growth In real GDP % of % of percentage % of % of GDP goods GDP % of GOP points GOP GDP 1990 2001 1990 2001 1990-2000 1990-2001 1990 2001 1990 2001 Afghanistan Albania 29 0 39 4 34 5 53 5 6 8 18.0 11 5 0.0 5 0 Algeria -36 6 54 4 55 0 81.5 -24 6 -0 7 2 6 0 0 Angola 53 5 106 1 91 0 141 9 10 1 20 4 3 3 9 9 Argentina -116_ 17 5 270 50 0 155 5 6 4 8-2 18 4 1 3 2 2 Armenia 57 1 . 86 9 . -11 5 -107 3.3 Australia 26 3 34 5 68 9 97 5 75 7 3.7 9 3 19 5 3 7 4 8 Austria 55.9 76 8 140 5 2.03 8 58 5 4 0 9 8 36 6 1 5 5 0 Azerbaijan 71 4 107 2 7 4 .. 32 1 25.3 Bangladesh 17 6 32 0 .. 130.6 6 0 0 9 2~2 0.0 0 2 Belarus 127 4 232 1 -4 3 4.9 0 8 Belgium 120 4 161 3 321 7 491 7 46 1 2 4 185 49 4 67 96 Benin 30 0 43.5 60 8 75 1 -44 2 -1.7 10 7 14 6 3 7 00 Bolivia 33 1 37 8 .47 1 10 3 1 15.0 0.7 83 Bosnia and Herzegovina 81 6 235 9 -2 6 Botswana 98 4 91 6 . -24 2 -0 5 9.0 69 4.4 1 4 Brazil 11 6 23 2 .71 9 54 19 10 9 04 5 1 Bulgaria 48 9 91 1 70.8 186 7 54 39 2 16 6 00 52 Burkina Faso 24 9 33 4 44 4 55 3 -24 4 -3 5 1 1 0 0 Burundi 27 0 26 0 35 1 38 6 26 8 7 2 3 7 6 5 0.1 1 7 Cambodia 22 4 91 7 33 6 .10 1 3 2 6 2 1 7 3 3 C-ameroon 30 5 42 4 . 564_ 2 4 15.5 .. 1 1 Canada 437_ 70.1 11-4 5 96 9 4 3 8.1 -21 5 2 7 9 6 Central African Republic 184_ 27 0 26 4 37 3 .2 2 0 5 Chad 27 2 49 8 54 9 92 6 -29 4 -1 9 5 6 0 0 Chile 53 1 52 2 -100 5 105 1 65 5 33 ~ 15 0 24 1 2 2 9 2 China 32 5 44 0 47 4 66 3 6 2 2 5 10 4 1 2 4 9 -Hong Kong, China 223 5 242.8 78-4 6 1,268 8 209 7 4 0 97 0 . 28 8 Colombia 30 7 30 4 .80.7 3 7 3 1 14.1 -1.3 2 9 Congo. Oem Rep 43_5 342_ 74.5 45 6 60.6 5 5 Congo, Rep -- -57 2 109 8 100 152 4 -9 1 20 66 00 Costa Rica 60 2 71 9 69 0 4 1 7 0 8 5 2 9 4 2 C6te d'lvoire 47 9 60 3 86 0 131 3 25 2 -0 8 3 5 9 1 0 4 2 5 Croatia 89 3 62 7 165 6 121.6_ 4 2 32.1 . 8 2- Cuba Czech Republic 83 6 123 1 .9.2 . 2-1 8 8.8 Denmark 52 6 60 2 144 1 167 2 56 9 2 8 15 1 26 2 2.0 10.4 Do minican Republic 73 2 66 6 _163 2 52- 5 -0 4 5 0 10.5 _ 1.9 5 7- Ecuador 42 8 54 5 3 1 0 7 10 7 21 9 1 2 7 4 Egypt,Arab Rep 36 8 17 1 -72 9 32 3 -40 5 -1 2 6 8 6 7 17 0 5 El Salvador 38 4 57 4 88.5 146 7 48 5 7.4 2 0 14 7 0 8 2 0 Eritrea 65 0 72 6 117.2 167.6 . -0 2 Estonia .. 137 7 320 7 . 11 0 _ 3.7 29 8 2 0 15 4 Ethiopia 20 2 23 4 31 3 .2 6 2 0 3 2 0 0 Finland 39 2 62 0 86 5 145 6 57 5 5.2 174 62 4 3 6 14 6 France 37 1 49 4 101.6 148.9 63.3 4.2 20 6 26 2 3 39 110 4 Gabon 52 5 82 3 97 7 2 8 -1.8 18 0 24 5 8 4 14.5 Gambia, The 69 1 53 5 134 4 107 7 -34.7 -1 8 0.9 . 0.0- Georgia 32 8 69 6 16 1 . 4 9 . 4 4 Germany 46 0 57 6 106 2 160 5 40 7 3.7 9 8 30.7 1.8 5.4 Ghana- 35 7 89 2 580 C 146 0 -1 3 5.9 2.7 4 7 0 3 2.2 Greece 33 2 29 1 83.5 106 1 102 6 4.3 3 9 18 8 1 2 1.9 Guatemala 36 8 39 4 -12 9 3 4 2 9 -297 0 6 12 0 Guinea 49 5 47 7 85 5 74 1 -1 2 3 9 3.2 0 6 0 1 Guinea-Bissau 43 0 60 3 _53 3 72 9 -28 9 3 3 23 0 0.0 Haiti 17 2 34 5 168 1 8 1 1 1 0 3 3~1LO H 2003 World Development indicators Integration with the global economy 0.1 Trade In goods Change Growth Gross private Gross In trade In real capital flows foreign direct trade iess Investment growth In reai GDP % of % of percentage % of % of GDP goods GDP % of GOP points GDP GDP 1990 2001 1990 2001 1990-2000 1990-2001 1990 2001 1990 2001 Honduras 57 9 66 3 106 4 127 1 -21 6 -0 4 72 59 14 31 Hungary 61 5 123 6 102 4 81 8 85 46 23 0 00 58 India 13 1 19 5 65 1 56 08 31 00 06 Indonesia 41 5 60 1 64 4 95 5 -16 6 12 41 65 10 32 Iran,Islamic Rep 32 9 37 5 61_8 72 4 -63 3 -8 7 2 6 2 4 0 0 0 0 Iraq 41 2 Ireland 93 9 129 3 186 7 265 1 129 5 7 0 22 2 272 5 2 2 27 0 Israel 55 0 59 2 25 1 4 4 6 5 16 1 0 7 4 0 Italy 32 0 43 5 83 3 123 9 6815 3 8 10 6 16 9 1 3 3 6 Jamaica 67 2 58.5 162 2 150 5 41 9 -0 6 8 4 25 1 3 0 9 0 Japan 17 1 18 2 44 4 61 7 39 4 2 7 5 4 12 3 1 7 1 1 Jordan -91 1 80 8 205 2 224 2 -7 9 -2 8 6 3 8 0 1 7 1 2 Kazakhstan 67 0 135 5_ -3 1 25 7 12 4 Kenya 38 1 42 4 68 5 92 8 -9 1 2 2 3 6 5 4 0 7 0 0 Korea, Dem Rep Korea,Rep 53 4 69 1 102 7 152 6 121 6 7 1 5 6 11 4 0 7 1 5 Kuwait 59 8 72 8 -112 9 19 3 39 9 1 3 1 1 Kyrgyz Republic 6-16 91 2 -2 1 11 1 3 5 Lao PDR 30 5 50 4 40 2 3 7 1 4 0 7 1 4 Latvia 72 9 185 6 2 5 1 7 23 8 0 5 7 2 Lebanon 106 5 48 8 -3 1 Lesotho 118 0 120 9 -13 5 -1 6 9 4 17 9 2 7 14 7 Liberia 143 1 173 1 Libya 64 2 62 0 7 3 2 2 0 9 1 2 Lithuania .. 90 6 191 8 8 9 13 5 3 8 Macedonia, FYR 103 8 81 7 168 9 161 6 5 9 42 0 13 0 Madagascar 31 5 45 7 53 7 81 9 --24 8 25 1 8 04 0 7 0 2 Malawi 52 7 49 2 70 6 87 6 -32 8 -2 8 3 2 00 Malaysia -133 4 184 0 232 3 125 4 3 7 ~103 6 6 5 3 5 7 Mali 39 7 52 8 63 4 _753 121 4 1 2 2 0 0 2 Mauritania 84 1 61 1 134 0 103 4 -25 5 -1 8 48 8 0 7 Mauritius 118 0 78 1 219 8 174 2 0.0 8 0 19 7 1 7 1 1 Mexico - -32 1 54 2 76 9 143 8 223 6 - 9 8 9 2 7 9 1 0 4 6 Moldova 99 0 175 3 12 2 18 0 10 1 Mongolia 67 8 143 1 8 4- 6 0 Morocco 43 3 .528 86 5 113 0 304 3 1 55 10 3 06 8 5 Mozambique 40 8 49 0 68-9 89 4 2 4 -04 16 9 0 4 13 3 Myanmar 17 1 Namibia 80 7 94 8 166 1 188 8 -1 1 13 9 4_3 Nepal 24.1 39 7 3 5 3 2 0 0 0 0 Netherlands 87 6 114 9 230 9 354 0 57 8 3 8 298_ 105 5 8 3 26 0 New Zealand ~43 3 53 7 121 0 69 3 2 6 -17 8 150 11 5 8 5 Nicaragua 95 9 183 0 . 60 2 6 4 9 0 0 0 Niger 27 0 -353 -499 59 3 -46 9 -2 5 2 8 1 6 Nigeria 67 5 73 2 90 8 94 7 -39 9 2 4 5 9 13 0 2 1 29 Norway 53 1 54 3 127.8 119 5 14 8 1 7 11 9 31 4 .21 3 6 Oman 77 7 80 2 127 4 38 20 1 4 04 Pakistan 32 6 33 8 -13 1 -1 7 4 2 2 8 0 6 0 7 Panama 354_ 38 1 . -1 6 106 6 46.3 2 6 6 0 Papua New Guinea -7-36 97 3 123 9 143 1 -8 1 0 1 5 7 15 4 4 8 2 2 Paraguay 43 9 -43 5 82 8 82 7 142 3 --24 5 4 5 2 1 5 2 3 Peru 25 5 29 1 . 45 9 3 9 3 2 5 1 0 2 2 2 Philippines 47 7 88 9 84 7 142 3 3 8 4 4 42 0 1 2 2 7 Poland 43 9 49 0 75 2 111 1 9 1 11 0 10 5 0 2 4 5 Portugal 58 3 56 4 140 8 144 4 113 1 3 8 11 4 46 3 3 9 12 9 Puerto Rico 32 8 -0 4 2003 World Development Indicators I 311 LII ~~u Dntegration with the gIloball economy Trade In goods Change Growth Gross private Gross In trade In real capital flows foreign direct trade less Investment growth In real GDP % of % of percentage % of % of GDP goods GDP % of GOP points GDP GDP 1990 2001 1990 2001 1990-2000 1990-2001 1990 2001 1990 2001 Romania 32 8 69 6 45 2 126 3 8 1 2 9 9 1 0 0 3 0 Russian Federation 50.6 97 7 0 5 . 8 1 . 1 6 Rwanda 15 4 19.7 26 9 31 7 63 4 1 5 2 8 0 9 0 3 0 3 Saudi Arabia 65.4 53 3 106-7 .. 9 8 9 3 1 8 0 0 Senegal 34.7 55.8 90 0 124 5 -21 0 -1 1 4 8 8 8 1 3 4 0 Sierra Leone 44 2 25.9 -71 1 -15 4 11 0 5 0 Singapore 309 5 277.6- 891 3 54.6 60 2 20.7 22 0 Slovak Republic 110 8 133 9 192.1 302.4 9 0 29.6 . 11 8 Slovenia 102 4 103.1 196 5 209 6 0 3 3 4 15 6 0 9 3 8 Somalia 26 7 33 2 South Africa 37 5a 50.9a -752 a 22 8 3 9 2 2 22 8 0 2 10 9 Spain 28.1 43 4 70 6 119 6 161 7 6.8 11 4 28 3 34 8 7 Sri Lanka 57 3 67 5 40 1 2.6 13 1 -130 0 5 1 1 Sudan 7 5 25 6 43 8 -30 5 0 3 5 4 0 0 4.6 Swaziland 138 2 130.9 37 9 -0 5 10 7 12 9 5 0 3 6 Sweden 46 9 65.7 119 8 190 4 67 0 5 0 34 2 45 2 7 0 9 6 Switzerland 58 4 67 2 50 6 3 3 15 9 46 0 5 8 8 9 Syrian Arab Republic 53 7 45.1 102 4_ 78 0 -29 8 0.1 18.0 16 9 0.0 1 5 Tajikistan- 127 0 203 0 Tanzania 31 9 26 1 47.8 41 1 .0 7 0 2 3 3 0 0 2.1 Thailand 65 7 110 9 132 2 213 9 99.6 2 8 13 5 9 1 3 0 3 5 Togo 52 1 83.6 92 6 -138 0 -22 3 -0 8 9 6 1-74 1.1 5 7 Trinidad and Tobago 65 9 93 3 130 7 204 0 43 4 2.0 11.4 3 1 Tunisia 73 5 80 8 161 6 199 6 9 3 0 2 9 5 6 2 0 6 2 3 Turkey 23 4 48 6 44 5 101 8 7 1 4.3 15 1 0 5 2.5 Turkmenistan_ 79 3 3.6 Uganda 10 2 36 1 14 7 59 6 6 8 1 1 4 2 0 0 2 5 Ukraine 85 2 143 4 4 0 11 9 2 2 United Arab Emirates 101 8 159 6 United Kingdom 41 2 42 5 102 6 126 6 57 2 4 1 35 3 69 2 -74 12.7 United States 15 8 19 0 44 4 68.1 99.1 5 2 5 7 11 7 2 8 3 1 Uruguay 32 7 27 4 85.0 101 6 909 38 12 7 25 1 00 1 7 Uzbekistan 53 9 . 71 9 -2 8 Venezuela, RB 51 1 36 4 90 8 65 0 12 1 3 7 49 9 10 8 1 7 3 1 Vietnam 79 7 93 6 129 7 23 9 . 7 6 4 0 West Bank and Gaza .2 0 Yemen, Rep 46 9 58 9 90 0 100 7 _4 1 16 2 8 1 2.7 2 2 Yugoslavia, Fed -Rep 62 1 - Zambia 76 9 50 3 102 3 83 6 -45 1 1 8 64 7 9 3 6 2 3 8 Zimbabwe 40 7 36 5 74 5 98 9 139 6 5 3 1.7 . 0 1 1 0%~~--- . .- lII - -- Low Income 27 4 39 8 3.0 5 1 0.5 _1.7 Middle Income 35 5 50 8 74 8 93 0 6.8 12 2 1 0 4.3 Lower middle income 37 6 50 3 66 9 80 5 5 0 12 1 0 9 4 3 Upper middle income 33 4 51 3 86 4 118 6 8.6_ 12 4 1 1 -4.3 Low &mlddlelIncome 33 8 48 9 74 4 93 7 6.0 11 8 0 9 4 2 East Asi a & Pacific- 47 0 61.0 77 8 69 7 5 0 111I 1 7 4 6 Europe & Central Asia 65.9 119 4 13 2 3 9 Latin America & Carib 23 3 37 6 _66.2 110.4 79~ 12 1 0.9 4.4~ Middle East& N Africa 48 1 45 4 84 2 .785 6 2 9 7 0 8 1.3 South Asia 16 5 23 4 1.4 3 2 0 1 0.6 Sub-Saharan Africa 42 3 56 0 77.1 97 5 5 1 17 0 1.0 8 1 High Income 32.3 37.9 -82.3 _112 3 11 1 23 6 3 0 5 3 Europe EMU 44 9 56 3 112 6 141 9 14 1 49 3 2-9 14 8 a Oats refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa. and Swaziland) 322 II 2003 Wenld Developm,ent indicators Integration with the global economy D The growing integration of societies and economies has economies Comparing merchandise trade with GDP after * Trade In goods as a share of GDP is the sum of mer- helped reduce poverty in many countries Between deducting value added in services thus provides a better chandise exports and imports divided by the value of 1990 and 1999 the number of poor people in develop- measure of its relative size than does comparing it with GDP, all in current U S dollars * Trade In goods as a ing countries declined by about 125 million Although total GDP, although this neglects the growing service share of goods GDP is the sum of merchandise global integration is a powerful force in reducing pover- component of most goods output exports and imports divided by the value of GDP after ty, more needs to be done-2 billion people are in dan- Trade in services (such as transport, travel, finance, subtracting value added In services, all in current U S ger of becoming marginal to the world economy All insurance, royalties, construction, communications, and dollars * Change In trade as a share of GDP is the countries have a stake in helping developing countries cultural services) is an increasingly important element of decade-over-decade change in trade as a share of integrate with the global economy and gain better global integration The difference between the growth of GDP * Growth In real trade less growth In real GDP access to rich country markets real trade in goods and services and the growth of GDP is the difference between annual growth in trade of One indication of increasing global economic integration helps to identify economies that have integrated with the goods and services and annual growth in GDP Growth is the growing importance of trade in the world economy global economy by liberalizing trade, lowering barriers to rates are calculated using constant price series taken Another is the increased size and importance of private foreign investment, and harnessing their abundant labor from national accounts and are expressed as a per- capital flows to developing countries that have liberalized to gain a competitive advantage in labor-intensive manu- centage * Gross private capital flows are the sum of their financial markets This table presents standardized factures and services the absolute values of direct, portfolio, and other measures of the size of trade and capital flows relative to The change in trade gives an indication of the effec- investment inflows and outflows recorded in the bal- gross domestic product (GDP) The numerators are based tiveness of trade policy This indicator measures the ance of payments financial account, excluding on gross flows that capture the two-way flow of goods and effect of trade on growth using the decade-over-decade changes in the assets and liabilities of monetary capital In conventional balance of payments accounting change in a country's trade as a share of its GDP authorities and general government The indicator is exports are recorded as a credit and imports as a debit The indicators on capital flows-gross pnvate capital calculated as a ratio to GDP in U S dollars * Gross And in the financial account inward investment is a credit flows and gross foreign direct investment-are calculated foreign direct Investment is the sum of the absolute and outward investment a debit Thus net flows, the sum from detailed accounts, since higher-evel aggregates values of inflows and outflows of foreign direct invest- of credits and debits, represent a balance in which many would result in smaller totals by netting out credits and deb- ment recorded in the balance of payments financial transactions are canceled out Gross flows are a better its The comparability of the data between countries and account It includes equity capital, reinvestment of measure of integration because they show the total value over time is affected by the accuracy and completeness of earnings, other long-term capital, and short-term capi- of financial transactions dunng a given penod balance of payments records and by their level of detail tal This indicator differs from the standard measure Trade in goods (exports and imports) is shown relative Trade and capital flows are converted to U S dollars of foreign direct investment, which captures only to both total GDP and goods GDP (GDP less services at the International Monetary Fund's average official inward investment (see table 6 7) The indicator is cal- such as storage, transport, communications, retail trade, exchange rate for the year shown An alternative con- culated as a ratio to GOP in U S dollars business services, public administration, restaurants and version factor is applied if the official exchange rate hotels, and social, community, and personal services) As diverges by an exceptionally large margin from the rate a result of the growing share of services in GDP, trade as effectively applied to transactions in foreign currencies a share of total GDP appears to be declining for some and traded products 6.la % of GDP The data on merchandise trade are from the 50 World Trade Organization. The data on GDP come from the World Bank's national accounts files, 40 ., . ^ . ' i converted from national currencies to U S dollars 30 . using the official exchange rate, supplemented by 7 . . -s 9g S >i - 1 z 1* . an alternative conversion factor if the official 20 exchange rate is judged to diverge by an excep- tionally large margin from the rate effectively 0 V traded products The data on real trade and GDP -b W~~~~~~~~~~~~~~~~~~~~~~~~ple otrnatosi oreig currncies natindl IKI' X , growth come from the World Bank's national $S X'~' 6' 'k- accounts files. Gross private capital flows and for- eign direct investment were calculated using the Private capital Cows to low- and middle-Income economies continued to grow in 2001, with Latin America and the International Monetary Fund's Balance of Caribbean and Europe Central Asia capturing the largest shares Payments database Source International Monetary Fund's Balance of Payments database 2003 World Development Indicators 1 313 6 11| Direction and growth of merchandise trade High-income Importers Other All European UnIted Other All high high Union Japan States Industrial Industrial Income Income Source of exports High-income economies 30 1 3 2 11 8 6 1 51 2 7 0 58 2 industrial economies 28 4 2 0 9 6 5 7 45 7 51 50 8 European Union 22 9 0 7 3 5 2 1 291 17 30 8 Japan 1 1 2.0 0 3 3 3 1 5 49 United States 2 6 0 9 31 6 6 1.4 8 0 Other industrial economies 1 9 0 4 4 1 0.2 6 6 0 4 7 0 Other high-income economies 1 7 1 2 2 2 0.4 5 5 1 9 7 5 Low- and middle-income economies 6 3 2 0 6 2 0.7 15 2 3 4 18 6 East Asia & Pacific 14 14 17 0.2 4.8 2.3 70 Europe & Central Asia 2 5 0.1 0 2 0.1 2 9 0 2 31 Latin Amenca & Carbbean 0 7 01 3 3 0 2 4 2 0 2 4 4 Middle East & N Africa 0 9 0 3 0 4 01 17 0 5 2 2 SouthAsia 03 00 0.3 00 06 02 08 Sub-Saharan Africa 0 5 0.0 0 3 00 08 01 0 9 World 36 4 5 2 18 0 6.8 66 4 10.4 76 8 Low- and middle-Income Importers Europe Latin Middle All low East Asia & Central America East & South Sub-Saharan & middle & Pacific Asia & Caribbean N. Africa Asia Atfrca Income World Source of exports High-income economies 5.8 3.2 4 1 1 5 0 7 0 9 17 1 75 3 Industrial economies 3 1 3.1 3 8 14 0 4 0 7 12 9 63 7 European Union 0 8 2 8 0 9 0.9 0.2 0 5 6 5 37.4 Japan 12 01 0.3 01 0.1 01 17 66 United States 0 7 0 2 2 6 0 2 01 0 1 3 9 119 Other industrial economies 0 3 0 1 0 1 0 1 00 0 0 0 8 7.8 Other high-income economies 2 8 0 1 0 2 0.1 0.3 0 1 4 2 11 6 Low- and middle-income economies 1 5 1 6 1 2 0 5 0 4 0 4 6 1 24 7 EastAsia&Pacific 08 02 02 01 02 01 17 88 Europe & Central Asia 01 1 3 0 0 01 00 00 17 4 8 Latin America & Caribbean 0.1 0.1 0 9 0 1 0.0 0.0 1.3 5 7 Middle East & N Africa 0 2 01 0.0 0.1 01 0.1 0 8 3 0 SouthAsia 01 00 0 00 00 0.1 00 03 11 Sub-Saharan Africa 0.1 0 0 0 0 0 0 00 02 04 1 3 World 73 4.3 54 21 12 13 23.2 100 0 314 1 2003 World Development Indicators Direction and growth of merchandise trade 6.2 High-income Importers Other All European United Other All high high Unlon Japan States Industrial Industrial Income Income Source of exports High-income economies 3 5 2 9 6 8 4 5 4 3 5 4 4 4 Industrial economies 3 4 19 7 2 4 5 4 2 4 5 4 2 European Union 3 6 3 2 8 5 3 1 4 0 6 9 41 Japan 01 29 -05 16 26 19 United States 3 9 18 6 4 4 6 4 7 4 6 Other industrial economies 3 3 0 3 8 8 3 5 61 3 2 5 9 Other high-income economies 5 6 4 7 5 5 4 1 5 3 8 4 6 0 Low- and middle-income economies 7 0 6 7 12 3 9 6 8 9 8 5 8 9 East Asia & Pacific 12 8 9 8 15 7 13 8 12 7 81 11 0 Europe & Central Asiaa 9 8 21 121 5 2 9 6 8 6 9 5 Latin America & Caribbean 3 5 -1 0 12 6 8 9 9 8 9 4 9 7 Middle East & N Africa 2 5 3 9 4 4 2 7 31 71 3 9 South Asia 7 0 0 5 12 6 7 5 8 2 9 6 8 5 Sub-Saharan Africa 61 90 91 75 72 24 6 80 World 40 42 84 49 52 63 53 Low- and middle-income Importers Europe Latin Middle All low East Asia & Central America East & South Sub-Saharan & middle & Paclflc Asia & Carlbbean N Africa Asia Africa Income World Source of exports High-income economies 9 5 7 6 8 6 1 0 5 8 17 71 4 9 Industrial economies 8 0 7 7 8 6 0 8 3 1 17 6 4 4 6 European Union 8 4 8 7 7 6 09 3 5 18 6 1 4 4 Japan 75 -11 32 -22 -12 -27 45 25 United States 95 05 99 06 40 38 82 57 Other industrial economies 61 8 5 6 0 4 0 6 7 2 7 4 7 5 8 Other high-income economies 11 4 6 8 7 2 1 3 11 1 2 2 9 9 7 2 Low- and middle-income economies 15 8 13 7 11 0 5 7 11 1 11 4 9 6 9 0 East Asia & Pacific 17 0 71 20 8 9 2 14.4 16 4 151 117 Europe & Central Asiaa 17 -10 3 91 3 9 3 5 83 8 0 9 0 Latin America & Caribbean 12 7 8 3 10 0 51 9 9 7 7 9 2 9 6 Middle East & N Africa 18 2 3 0 0 3 4_6 7 9 14 3 7 5 4 7 South Asia 15 5 01 241 5 2 12 1 116 8 8 8 6 Sub-Saharan Africa 22 0 16 3 16 7 9 5 18_2 122 14 7 96 World 105 88 91 20 74 40 77 58 a Data are for 1993-2001 2003 World Development Indicators 1 315 Direction and growth of merchandise trade This table provides estimates of the flow of trade in The regional trade flows shown in the table were o Merchandise trade includes all trade in goods, trade goods between groups of economies. The data are calculated from current price values The growth in services is excluded o High-Income economies are from the International Monetary Fund's (IMF) rates presented are in nominal terms: that is, they those classified as such by the World Bank. Direction of Trade Statistics Yearbook and Direction include the effects of changes in both volumes and - Industrial economies are those classified as such in of Trade database, which cover 186 countries All 33 prices the IMFs Direction of Trade Statistics Yearbook. They high-income countries and 22 of the 153 developing include the countries of the European Union, Japan, countries report trade on a timely basis, covering the United States, and the other industnal economies about 80 percent of trade for recent years. listed below. - European Union compnses Austria, Trade by less timely reporters and by countries Belgium, Denmark, Finland, France, Germany, Greece, that do not report is estimated using reports of Ireland, Italy, Luxembourg, the Netherlands, Portugal, partner countries Because the largest exporting Spain, Sweden, and the United Kingdom o Other and importing countries are reliable reporters, a Industrial economies include Australia, Canada, large portion of the missing trade flows can be esti- Iceland, New Zealand, Norway, and Switzerland mated from partner reports Partner country data o Other highIncome economies include Aruba, The may introduce discrepancies due to smuggling, con- Bahamas, Bermuda, Brunei, Cyprus, Faeroe Islands, fidentiality, different exchange rates, overreporting French Polynesia, Greenland, Guam, Hong Kong of transit trade, inclusion or exclusion of freight (China), Israel, the Republic of Korea, Kuwait, Macao rates, and different points of valuation and times of (China), Netherlands Antilles, New Caledonia, Qatar, recording Singapore, Slovenia, Taiwan (China), and the United In addition, estimates of trade within the European Arab Emirates o Low- and middle-Income reglonal Union (EU) have been significantly affected by groupings are based on World Bank classifications and changes in reporting methods following the creation may differ from those used by other organizations of a customs union The new system for collecting data on trade between EU members-Intrastat, intro- duced in 1993-has less exhaustive coverage than the previous customs-based system and has result- ed in some asymmetry problems (estimated imports are about 5 percent less than exports) Despite these issues, only a small portion of world trade is estimated to be omitted from the IMFs Direction of Trade Statistics Yearbook and Direction of Trade database Most countries report their trade data in nation- al currencies, which are converted using the IMF's published period average exchange rates (series rf or rh, monthly averages of the market or official rates) for the reporting country or, if those are not available, monthly average rates in New York. Because imports are reported at c.if (cost, insur- ance, and freight) valuations, and exports at f o b (free on board) valuations, the IMF adjusts country reports of import values by dividing those values by 1 10 to estimate equivalent export values This approximation is more or less accurate, depending on the set of partners and the items traded Other factors affecting the accuracy of trade data include |1 lags in reporting, recording differences across Intercountry trade flows are published in the countries, and whether the country reports trade IMF's Direction of Trade Statistics Yearbook and according to the general or special system of Direction of Trade Statistics Quarterly, the data in trade (For further discussion of the measurement the table were calculated using the IMF's of exports and imports, see About the data for Direction of Trade database. tables 4 5 and 4 6.) 313 0 2003 World Development Indicators OECD trade with low- and fl middle-income economies U. I Highincome European Japan United States OECD countries Union 1991 20010 1991 2001' 1991 2001 1991 2001 $ billions Food 385 551 184 238 04 13 126 194 Cereals 14 6 14.1 4 2 4 6 01 10 61 61 Agricultural raw materials 9 6 15 9 31 4 9 0 7 11 3 7 5 8 Ores and nonferrous metals 9 6 16.2 2 9 6 3 0 5 2 2 4 2 3 6 Fuels 9.3 174 38 60 04 05 34 52 Crude petroleum 0 7 0 9 0 3 0 2 0 0 0 0 0 0 0 0 Petroleum products 6 7 12 8 3.2 5 2 0 2 0 5 2 4 4 3 Manufactured goods 3414 670 6 171 8 327 3 64 3 96 4 75 7 175 7 Chemical products 45 2 94 5 25 9 49 1 4 1 8 0 10 1 23 5 Mach and transport equip 198 5 391 2 94 5 181.7 42 7 64 9 47 3 108 6 Other 97 6 184 9 51.4 96 5 17 5 23 5 18 2 43 7 Miscellaneous goods 10 3 16_7 44 4 4 0 7 2 9 4 9 8 6 Total 419.9 795.8 204.3 372.7 67.0 104.5 104.4 218.3 % of total exports Food 92 69 90 64 06 13 120 89 Cereals 35 18 20 12 0.1 09 58 28 Agricultural raw materials 2 3 2 0 1 5 13 10 1 1 3 5 2 7 Ores and nonferrous metals 2 3 2 0 14 1.7 0 8 21 4 0 16 Fuels 22 22 18 16 06 05 32 24 Crude petroleum 0 2 01 0 1 00 0 0 00 00 00 Petroleum products 1 6 16 1 6 1 4 0 4 0 4 2 3 1 9 Manufactured goods 813 84 3 841 87 8 96 0 92 3 72 4 80 5 Chemical products 10 8 119 12 7 13 2 61 7 6 9 7 10 7 Mach and transport equip. 47 3 49 2 46 2 48.8 63 8 62 1 45 3 49 7 Other 23 3 23 2 251 25 9 26 1 22 5 17 4 200 Miscellaneous goods 2 4 2 1 2 2 12 10 2 8 47 40 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 2003 World Development Indicators 1 317 1X OOECD trade with low- and LL2 o middle-income economies I >r i_ - - - -f io M .- - -5 High-income European Japan United States OECD countries Union 1991 2001a 1991 2001 D 1991 2001 1991 2001 $ billions Food 69 6 95 7 37 5 44 6 111 17 3 15 4 24 9 Cereals 19 35 05 12 06 06 02 07 Agricultural raw materials 19 8 22 8 10 3 12 4 5 1 3 7 2 3 4 3 Ores and nonferrous metals 32 8 50 5 15 5 21 4 9 8 10 1 4 6 10 5 Fuels 158 5 230 4 74 0 910 310 34 2 412 76 2 Crude petroleum 1113 166 6 50 7 63 3 19 0 210 32 2 60 9 Petroleum products 25 6 31 6 11 4 13.5 4 1 2.0 8.4 13 2 Manufactured goods 205 5 745 2 91 8 268 9 20 3 80 5 75 0 334 2 Chemical products 15 8 38 7 8 7 16_6 2 1 4 0 3.1 12 7 Mach and transport equip 54 4 317 0 19 0 103 6 3 2 31 3 27 4 155 5 Other 135 3 389 5 64.1 148 7 15 0 45 2 44 5 166 0 Miscellaneous goods 71 146 40 19 05 17 26 110 Total 493.6 1,159.5 233.0 440.1 77.9 147.4 141.1 461.2 % of total Imports Food 14 1 8 3 16 1 10 1 14 3 118 10 9 5 4 Cereals 04 03 02 03 08 04 01 01 Agricultural raw materials 4 0 2 0 4 4 2 8 6 5 2 5 1 6 0 9 Ores and nonferrous metals 6 7 4 4 6 6 4 9 12 5 6 8 3 3 2 3 Fuels 32 1 19.9 318 20 7 39 8 23 2 29 2 16 5 Crude petroleum 22 6 14 4 218 14 4 24 4 14 2 22 8 13 2 Petroleum products 5 2 2 7 4 9 3 1 5 2 1 4 6 0 2 9 Manufactured goods 41 6 64 3 39 4 61 1 26 1 54 6 53 2 72.5 Chemical products 3 2 3 3 3 7 3.8 2 6 2 7 2 2 2 8 Mach and transport equip 110 27 3 8 2 23 5 4.1 213 19 4 33 7 Other 27 4 33 6 27 5 33 8 19 3 30 6 315 36 0 Miscellaneous goods 1 4 1 3 1 7 0 4 0 7 1.1 18 2 4 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 a Data for Portugal are for 2000 3 B8 3 2003 World Development Indicators OECD trade with low- and middle-income economies Developing countries are becoming increasingly impor- 42 percent in 1991 to 64 percent in 2001 At the The product groups in the table are defined in accor- tant in the global trading system Since the early 1990s same time developing countries have increased their dance with the Standard International Trade trade between high-income members of the Organisation imports of manufactured goods from high-income Classification (SITC) revision 1 food (0, 1, 22, and 4) for Economic Co-operation and Development (OECD) and countries-particularly capital-intensive goods such as and cereals (04), agricultural raw materials (2 excluding low- and middle-income economies has grown faster than machinery and transport equipment 22, 27, and 28), ores and nonferrous metals (27, 28, trade between OECD members The increased trade ben- Moreover, trade between developing countnes has and 68), fuels (3), crude petroleum (331), and petrole- efits consumers and producers But as the World Trade grown substantially over the past decade, with 40 percent um products (332), manufactured goods (5-8 exclud- Organization's (WTO) ministenal conference in Doha, of exports going to other developing countries This ing 68), chemical products (5), machinery and Qatar, in October 2001 showed, achieving a more pro- growth has resulted from many factors, including devel- transport equipment (7), and other manufactured development outcome from trade remains a major chal- oping countries' increasing share of world output and the goods (6 and 8 excluding 68), and miscellaneous lenge Meeting this challenge will require strengthening liberalization of their trade Yet trade barriers remain high goods (9) * Exports are all merchandise exports by international consultation Negotiations after the Doha (more than 70 percent of the tariff burden faced by man- high-income OECD countries to low- and middle-income meetings will be launched (or continued) on services, agn- ufactured goods from developing countnes is imposed by economies as recorded in the United Nations Statistics culture, manufactures, WTO rules, the environment, dis- other developing countries) The growing trade between Division's COMTRADE database * Imports are all mer- pute settlement, intellectual property nghts protection, developing countries strengthens the case for reducing chandise imports by high-income OECD countries from and disciplines on regional integration These negotia- these barners Despite the growth in trade between devel- low- and middle-income economies as recorded in the tions are scheduled to be concluded by 2005 oping countnes, high-income OECD countnes remain the United Nations Statistics Division s COMTRADE data- For developing countries a key issue is better access developing world's most important partners base * High-Income OECD countries in 2001 were to rich country markets What do developing countries The aggregate flows in the table were compiled from Australia, Austria, Belgium, Canada, Denmark, Finland, stand to gain" Improved access to rich country markets intercountry flows recorded in the United Nations France, Germany, Greece, Iceland, Ireland, Italy, Japan, could increase their exports by $9 billion a year in tex- Statistics Division's Commodity Trade (COMTRADE) the Republic of Korea, Luxembourg, the Netherlands, tiles alone, and another $22 3 billion in other manufac- database Partner country reports by high-income New Zealand, Norway, Portugal, Spain, Sweden, tures They would also reap large benefits from better OECD countries were used for both exports and Switzerland, the United Kingdom, and the United access to one another's markets opening their own mar- imports Exports are recorded free on board (f o b ), States * European Unlon comprises Austria, Belgium, kets would lead to gains of about $27 6 billion a year for imports include insurance and freight charges (c i f) Denmark, Finland, France, Germany, Greece, Ireland, manufactures, and $314 billion for agricultural goods Revisions have been made to the time-series data as Italy, Luxembourg, the Netherlands, Portugal, Spain, Trade flows between high-income members of the far back as 1990 Because of differences in sources of Sweden, and the United Kingdom OECD and low- and middle-income economies reflect data, timing, and treatment of missing data, the data in the changing mix of exports to and imports from devel- this table may not be fully comparable with those used oping economies While food and primary commodities to calculate the direction of trade statistics in table 6 2 have continued to fall as a share of OECD imports, the or the aggregate flows shown in tables 4 4-4 6 For fur- share of manufactures in goods imports from devel- ther discussion of merchandise trade statistics, see oping countries has grown dramatically, from about About the data for tables 4 4-4 6 and 6 2 8.3a % of total imports from low- and middle-mcome economies Manufactured goods _ Fuels _ - COMTRADE data are available in electronic form Food _ from the United Nations Statistics Division Although not as comprehensive as the underlying Ores and nonferrous metals COMTRADE records, detailed statistics on interna- tional trade are published annually in the United Agricultural raw materials Nations Conference on Trade and Development's *1991 *2001 (UNCTAD) Handbook of Intemational Trade and Miscellaneous goods Development Statistics and the United Nations Statistics Division's Intemational Trade Statistics 0 10 20 30 40 50 60 70 80 Yearbook Source Table 6 3 based on COMTRADE database 2003 World Development Indicators 1 319 LLJ0K IPrimary commodity prices 1970 1980 1990 1995 1996 1997 1998 1999 2000 2001 2002 World Bank commodity price index (1990 = 100) Non-energy commodities 156 159 100 104 103 114 99 89 89 82 86 Agriculture 163 175 100 112 113 124 108 93 90 83 90 Beverages 203 230 100 129 113 165 141 108 91 75 88 Food 166 177 100 100 111 112 105 88 87 90 94 Raw materials 130 133 100 116 114 110 88 89 94 81 86 Fertilizers 108 164 100 89 108 116 123 115 109 103 104 Metals and minerals 144 120 100 87 80 87 76 74 85 78 75 Petroleum 19 205 100 64 80 81 57 79 127 111 113 Steel products a 111 100 100 91 86 86 75 69 79 70 70 MUV G-5 index 28 79 100 117 111 103 100 99 97 96 96 Commodity prices (1990 prices) Agricultural raw materials Cotton (cents/kg) 225 260 182 182 159 169 145 118 134 110 106 Logs, Cameroon ($/cu m) a 153 319 343 290 255 275 288 271 283 277 Logs, Malaysian ($/cu m) 154 248 177 218 227 230 163 188 195 166 169 Rubber (cents/kg) 145 181 86 135 125 98 72 63 71 63 80 Sawnwood, Malaysian ($/cu m) 625 503 533 632 666 641 486 605 613 502 547 Tobacco ($/mt) 3,836 2,889 3,392 2,259 2,746 3,412 3,349 3,061 3,058 3,130 2,852 Beverages (cents/kg) Cocoa 240 330 127 122 131 156 168 114 93 111 184 Coffee, robustas 330 412 118 237 162 168 183 150 94 63 69 Coffee, Arabica 409 440 197 285 242 403 299 231 197 143 141 Tea, avg, 3 auctions 298 211 206 127 149 199 205 185 193 167 156 Energy Coal, Australian ($/mt) 50 01 39.67 33 64 34 21 33 92 29 35 26 13 26 97 33 68 28 05 Coal, U S ($/mt) 54 71 41 67 3347 3344 35 16 34 52 33.38 3397 46 75 41.49 Natural gas, Europe ($/mmbtu) 4 32 2 55 2 33 255 2 65 2 43 2 14 3.97 4 23 3 16 Natural gas, U S ($/mmbtu) 0 59 1 97 1 70 1 47 2 45 240 2 09 2 28 4 43 4 12 3 48 Petroleum ($/bbl) 4 31 46.80 22 88 14 68 18.35 18 52 13 12 18 19 29 01 25.38 25 84 Primary commodities-raw or partially processed able, the prices paid by importers are used Annual Separate indexes are compiled for petroleum and for materials that will be transformed into finished price series are generally simple averages based steel products, which are not included in the non- goods-are often the most significant exports of on higher-frequency data. The constant price energy commodity price index developing countries, and revenues obtained from series in the table is deflated using the manufac- The MUV index is a composite index of prices for them have an important effect on living standards tures unit value (MUV) index for the G-5 countries manufactured exports from the five major (G-5) Price data for primary commodities are collected (see below) industrial countries (France, Germany, Japan, the from a variety of sources, including trade journals, The commodity price indexes are calculated as United Kingdom, and the United States) to low- and international study groups, government market sur- Laspeyres index numbers, in which the fixed weights middle-income economies, valued in U.S dollars veys, newspaper and wire service reports, and are the 1987-89 export values for low- and middle- The index covers products in groups 5-8 of the commodity exchange spot and near-term forward income economies, rebased to 1990 Each index Standard International Trade Classification (SITC) prices This table is based on frequently updated represents a fixed basket of primary commodity revision 1 To construct the MUV G-5 index, unit price reports. When possible, the prices received exports. The non-energy commodity price index con- value indexes for each country are combined using by exporters are used; if export prices are unavail- tains 37 price series for 31 non-energy commodities weights determined by each country's export share 320 a 2003 World Development Indicators Primary commodity prices 0.4 1970 1980 1990 1995 1996 1997 1998 1999 2000 2001 2002 Commodity prices (continued) (1990 prices) Fertilizers ($/mt) Phosphate rock 39 59 40 30 35 40 43 44 45 44 42 TSP 152 229 132 128 158 166 174 155 142 132 138 Food Fats and oils ($/mt) Coconut oil 1,417 855 336 573 675 635 661 742 462 332 436 Groundnut oil 1,350 1,090 964 847 806 976 913 793 733 709 712 Palm oil 927 741 290 537 477 527 674 439 319 298 405 Soybeans 417 376 247 221 274 285 244 203 218 204 220 Soybean meal 367 333 200 168 240 266 171 153 194 189 182 Soybean oil 1,021 759 447 534 496 546 _ 628 430 347 369 471 Grains ($/mt) Grain sorghum 185 164 104 102 135 106 98 85 90 99 105 Maize 208 159 109 106 149 113 102 91 91 93 103 Rice 450 521 271 274 305 293 305 250 208 180 199 Wheat 196 219 136 151 187 154 127 113 117 132 154 Other food Bananas ($/mt) 590 481 541 380 422 500 491 376 436 608 548 Beef (cents/kg) 465 350 256 163 160 179 173 186 199 222 221 Oranges ($/mt) 599 496 531 454 442 443 444 434 373 621 597 Sugar, EU domestic (cents/kg) 40 62 58 59 61 61 60 60 57 55 57 Sugar, U S domestic (cents/kg) 59 84 51 43 44 47 49 47 44 49 48 Sugar, world (cents/kg) 29 80 28 25 24 24 20 14 19 20 16 Metals and minerals Aluminum ($/mt) 1,982 1,848 1,639 1,543 1,353 1,545 1,363 1,370 1,592 1,505 1,400 Copper ($/mt) 5,038 2,770 2,661 2,509 2,062 2,200 1,661 1,583 1,863 1,645 1,617 Iron ore (cents/dmtu) 35 36 32 24 27 29 31 28 30 31 30 Lead (cents/kg) 108 115 81 54 70 60 53 51 47 50 47 Nickel ($/mt) 10,148 8,274 8,864 7,031 6,741 6,694 4,648 6,050 8,876 6,196 7,021 Tin (cents/kg) 1,310 2,129 609 531 554 546 556 544 559 467 421 Zinc (cents/kg) 105 97 151 88 92 127 103 108 116 92 81 a Series not included in the non-energy index * Non-energy commodity price Index covers the 31 and zinc * Petroleum price Index refers to the Sheet") at the Global Prospects Web site non-energy primary commodities that make up the average spot price of Brent, Dubai, and West Texas (http //www worldbank org/prospects) agriculture, fertilizer, and metals and minerals index- Intermediate crude oil, equally weighted * Steel prod- es * Agriculture includes beverages, food, and agri- ucts price Index is the composite price index for eight cultural raw material * Beverages include cocoa, steel products based on quotations f o b (free on coffee, and tea * Food includes rice, wheat, maize, board) Japan excluding shipments to China and the sorghum, soybeans, soybean oil, soybean meal, palm United States, weighted by product shares of apparent _, oil, coconut oil, groundnut oil, bananas, beef, oranges, combined consumption (volume of deliveries) for Commodity price data and the G-5 MUV index are and sugar * Agricultural raw materials include cot- Germany, Japan, and the United States * MUV G-5 compiled by thie World Bank's Development ton, timber (logs and sawnwood), natural rubber, and Index is the manufactures unit value index for G-5 Prospects Group Monthly updates of commodity tobacco * Fertilizers include phosphate rock and country exports to low- and middle-income economies prices are available on the Web at triple superphosphate (TSP) * Metals and minerals * Commodity prices-for definitions and sources, see http //www worldbank org/prospects include aluminum, copper, iron ore, lead, nickel, tin, "Commodity Price Data' (also known as the "Pink 2003 World Development Indicators 1 321 1 E a Regional trade blocs $ millions 1970 1980 1990 1995 1996 1997 1998 1999 2000 2001 High-Income and low- and middle-income economies APECa 58633 357,697 901,560 1,688,707 1,755,116 1,869,192 1,734,386 1,896,217 2,262,159 2,075,735 CEFTA 1,157 7,766 4,235 12,118 12,874 13,169 14,234 13,226 15,108 16,488 European Union 76,451 456,857 981,260 1,259,699 1,273,430 1,162,419 1,226,988 1,404,833 1,418,149 1,406,859 NAFTA 22,078 102,218 226,273 394,472 437,804 496,423 521,649 581,162 676,440 639,138 Latin America and the Caribbean ACS 758 4,892 5,401 11,013 10,847 11,985 12,547 11,663 13,908 13,631 Andean Group 97 1,161 - 1,312 4,812 4,762 5,524 5,408 3,929 5,136 5,444 CACM 287 1,174 671 1,595 1,723 1,973 2,038 2,161 2,541 2,648 CARICOM 52 576 448 867 900 968 1,020 1,136 1,050 1,176 Central American Group of Four 176 692 399 1,026 1,106 1,299 1,171 1,335 1,602 1,607 Group of Three 59 706 1,046 3,460 3,131 3,944 3,921 2,912 3,731 4,177 LAIA 1,263 10,981 12,331 35,299 38,384 44,814 42,974 34,785 42,833 40,921 MERCOSUR 451 3,424 4,127 14,199 17,075 20,772 20,352 15,313 17,910 15,295 OECS 8 29 39 33 34 36 37 38 40 Africa CEMAC 22 75 139 120 164 161 153 127 103 120 CEPGL 3 2 7 8 9 6 8 9 10 11 COMESA 412 616 963 1,386 1,610 1,545 1,501 1,348 1,519 1,622 Cross-Border Initiative 209 447 613 1,002 1,191 1,144 1,156 964 1,060 987 ECCAS 162 89 163 163 212 211 198 179 200 219 ECOWAS 86 692 1,557 1,936 2,293 2,244 2,361 2,382 2,969 2,898 Indian Ocean Commission 5 8 20 61 67 70 90 86 93 103 MRU 1 7 0 1 4 7 8 8 10 11 SADC 483 617 1,630 3,373 3,963 4,471 3,865 4,224 4,380 3,626 UDEAC 22 75 139 120 163 160 152 126 102 119 UEMOA 52 460 621 560 667 707 752 805 744 761 Middle East and Asia Arab Common Market 102 661 911 1,368 1,149 1,146 978 951 1,312 1,722 ASEAN 1,456 13,350 28,648 81,911 86,925 88,773 72,352 80,418 101,848 91,675 Bangkok Agreement 132 1,464 4,476 12,066 13,092 13,640 13,175 14,910 17,235 16,719 EAEC 9,197 98,532 281,067 636,973 651,379 673,244 551,553 614,945 776,209 706,378 ECO 63 15,891 1,243 4,746 4,773 4,929 4,031 3,903 4,495 4,422 GCC 156 4,632 6,906 6,832 7,624 8,124 7,358 7,306 9,234 9,137 SAARC 93 613 863 2,024 2,144 2,004 2,834 2,615 2,798 3,094 UMA 60 109 958 1,109 1,115 924 881 919 1,076 1,140 Note: Regional bloc memberships are as follows Asia Pacific Economic Coopemtion (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam, Central European Free Trade Area (CEFTA), Bulgaria, the Czech Republic, Hungary, Poland, Romania, the Slovak RepublIc, and Slovenia, European Union (EU; formerly European Economic Community and European Community), Austria. Belgium, Denmark, Fnland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal. Spain, Sweden, and the United Kingdom, North American Free Trade Area (NAFTA), Canada, Mextco, and the United States, Association of Caribbean States (ACS), Antigua and Barbuda, the Bahamas, Barbados, Belize, Colombia, Costa Rica, Cuba, Dominica. the Dominican Republic, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, St Kitts and Nevis, St Lucia, St Vincent and the Grenadines, Sunname, Tneidad and Tobago, and Rep0blica Bolivariana de Venezuela, Andean Group, Bolivia, Colombia, Ecuador, Peru, and Repdblica Bolivariana de Venezuela, Central Amercan Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua, Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas (part of the Caribbean Community but not of the Common Market), Barbados, Belize, Dominica, Grenada, Guyana, Jamaica, Montserrat, St Kitts and Nevis, St Lucia, St Vincent and the Grenadines, Suriname, and Trinidad and Tobago, Central American Group of Four, El Salvador, Guatemala, Honduras, and Nicaragua, Group of Three, Colombia, Mexico, and Rep0blica Bolivanana de Venezuela. Latin American Integratlon Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Republica Bolivariana de Venezuela, Southem Cone Common Market (MERCOSUR), Argentina, Brazil, Paraguay, and Uruguay, Organization of Eastern Caribbean States (OECS), Antigua and Barbuda. Dominica, Grenada, Montserrat, St Kitts and Nevis, St Lucia, and St Vincent and the Grenadines, Economic and Monetary Community of Central Africa (CEMAC), Cameroon, the Central Afncan Republic, Chad, the Republic of Congo, Equatorial Guinea, Gabon, and S5o Tome and Principe, Economic Community of the Countries of the Great Lakes (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda, Common Market for Eastern and Southem Africa (COMESA), Angola, Burundi, Comoros. the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Tanzania, Zambia, 322 i 2003 World Development Indicators Regional trade blocs 6.5 % of total bloc exports 1970 1980 1990 1995 1996 1997 1998 1999 2000 2001 High-income and low- and middle-Income economies APEC 578 579 683 718 719 716 697 718 731 726 CEFrA 12 9 14 8 9 9 14 6 14 4 13 4 13 0 121 12 1 12 3 European Union 59 5 60 8 65 9 62 4 614 55 5 57 0 63 3 621 613 NAFTA 36 0 33 6 414 46 2 47 6 491 517 54 6 55 7 55 5 Latin America and the Caribbean ACS 96 87 84 85 69 69 72 58 56 59 Andean Group 18 3 8 41 12 0 9 7 10 8 12 8 8 8 8 4 10 9 CACM 26 0 24 4 15 4 21 7 22 0 18 1 16 1 12 8 13 7 15 0 CARICOM 4 2 5 3 81 121 13 0 14 4 17 3 16 9 14 6 13 3 Central American Group of Four 201 18 1 13 7 22 0 22 0 19 9 16 3 14 0 14 8 14 8 GroupofThree 11 18 20 32 24 27 26 17 17 21 LAIA 9 9 13 7 10 8 17 1 16 2 17 0 16 7 12 7 12 9 13 0 MERCOSUR 9 4 116 89 20 3 22 6 24 8 25 0 20 6 20 9 17 3 OECS 91 81 126 106 107 120 131 100 53 Africa CEMAC 48 16 23 21 23 20 23 17 10 12 CEPGL 04 01 05 05 05 04 06 07 08 08 COMESA 91 61 66 77 80 78 87 74 56 69 Cross-Border Inltative 93 88 10 3 119 124 127 13 8 121 10 7 10 3 ECCAS 96 14 14 15 16 15 18 13 11 13 ECOWAS 29 101 79 90 85 86 106 109 96 97 Indian Ocean Commission 84 39 41 60 54 39 47 4 8 43 39 MRU 02 08 00 01 03 05 05 06 06 06 SADC 8.0 20 48 87 94 10 4 104 119 117 102 COMESA 49 16 23 21 23 2.0 23 17 10 12 UDEAC 49 16 23 21 23 20 23 17 10 12 UEMOA 65 96 130 103 96 118 110 132 133 139 Middle East and Asia ArabCommonMarket 22 24 27 67 44 41 48 33 30 45 ASEAN 22 9 187 19 8 25 4 254 24 9 219 22 4 239 23 3 Bangkok Agreement 27 37 37 50 52 51 50 52 52 55 EAEC 28 9 35 6 39 7 481 49 0 48 0 42 2 44 0 46 8 46 9 ECO 15 73 2 32 79 7 1 7 5 68 57 54 54 GCC 29 30 80 68 64 65 80 67 55 57 SAARC 32 48 32 44 43 40 52 46 43 48 UMA 14 03 29 38 34 27 33 25 22 25 and Zimbabwe, Cross-Border Initiative, Burundi, Comoros. Kenya. Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe, Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, Rwanda, and Sao Tome and Principe. Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, C6te d Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo, Indian Ocean Commission, Comoros, Madagascar, Mauritius, Reunion, and Seychelles, Mano River Unlon (MRU), Guinea, Liberia, and Sierra Leone, Southern African Development Community (SADC; formerly Southern African Development Coordination Conference), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe, Central African Customs and Economic Union (UDEAC; formerly Union Douanlre et Economique de l'Afrique Centrale), Cameroon. the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon, West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso. C6te d'lvoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo, Arab Common Market, the Arab Republic of Egypt, Iraq, Jordan, Libya, Mauritania, the Syrian Arab Republic, and the Republic of Yemen, Association of South-East Asian Nations (ASEAN), Brunei, Cambodia, Indonesia, the Lao People's Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam, Bangkok Agreement, Bangladesh, India, the Republic of Korea, the Lao People's Democratic Republic, the Philippines, Sri Lanka, and Thailand, East Asian Economic Caucus (EAEC), Brunei, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, the Philippines, Singapore, Taiwan (China), and Thailand, Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan, Gulf Cooperatlon Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates, South Asian Association for Regional Cooperation (SAARC), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka, and Arab Maghreb Union (UMA), Algeria, Libya, Mauritania, Morocco, and Tunisia a No preferential trade agreement 2003 World Development Indicators 1 323 FH L2 ,Regional trade blocs % of world exports 1970 1980 1990 1995 1996 1997 1998 1999 2000 2001 High-Income and low- and middle-income economies APEC a 36 0 33 7 39 0 46 3 46 0 47 3 46.1 46 6 48 5 46 5 CEFTA 32 2.9 13 16 17 18 2.0 19 2.0 22 European Union 45 6 410 44 0 39.7 391 37.9 39.9 39 2 35 8 37 4 NAFTA 217 16.6 16 2 16 8 17 3 18 3 18 7 18 8 19 0 18 7 Latin America and the Caribbean ACS 2 8 3.1 1.9 2 6 3.0 3.1 3.2 3.5 3.9 3 7 Andean Group 1.9 1.7 09 0 8 0 9 0.9 0 8 0 8 10 0 8 CACM 0 4 0.3 0.1 0.1 0.1 0 2 0.2 0 3 0 3 0.3 CARICOM 04 06 02 01 01 0.1 0.1 01 01 01 Central American Group of Four 0 3 0.2 01 0.1 01 01 0.1 0.2 0 2 0 2 GroupofThree 18 2.1 15 21 2.5 27 28 30 33 32 LAIA 45 4.4 34 4.1 45 48 48 4.8 52 5.1 MERCOSUR 17 1.6 14 14 14 15 1.5 13 1 3 1.4 OECS 0.0 00 00 00 00 00 00 00 00 Africa CEMAC 02 03 02 01 01 01 01 01 02 02 CEPGL 0.3 0.1 00 0.0 00 00 00 00 00 0.0 COMESA 16 0.6 04 04 04 04 0.3 03 0.4 04 Cross-Border Intiative 08 0.3 02 0 2 02 02 02 0.1 02 02 ECCAS 06 0.3 03 02 03 03 02 02 0.3 03 ECOWAS 11 0.4 0.6 04 05 05 04 04 05 05 Indian Ocean Commission 01 01 01 00 00 00 0.0 00 00 00 MRU 01 0.0 01 00 00 0.0 00 00 00 0.0 SADC 22 1.6 1.0 0.8 08 0.8 07 06 06 06 UDEAC 02 03 02 01 01 01 01 01 02 02 UEMOA 03 0.3 0.1 01 01 01 01 01 01 01 Middle East and Asla Arab Common Market 16 15 10 04 05 05 04 05 07 06 ASEAN 23 3.9 43 6.4 65 64 61 63 67 64 Bangkok Agreement 18 2.2 36 4.8 48 49 49 50 5.2 49 EAEC 113 151 20 9 261 25 1 25.4 24 2 24 7 26 0 24 5 ECO 15 1.2 11 12 1.3 12 1:1 1.2 13 13 GCC 19 85 2.5 20 22 23 17 19 27 26 SAARC 11 07 08 09 0.9 0.9 1 0 1.0 10 11 UMA 15 2.3 1.0 06 0.6 0.6 05 06 08 0.7 3:24 H3 2003 World Development Indicators Regional trade blocs 00 0 Trade blocs are groups of countries that have estab- for any government. All sectors of an economy may be in later years and their membership may have lished special preferential arrangements governing affected, and some sectors may expand while others changed over time For this reason, and because sys- trade between members Although in some cases contract, so it is important to weigh the potential tems of preferences also change over time, intra- the preferences-such as lower tariff duties or costs and benefits that membership may bring trade in earlier years may not have been affected by exemptions from quantitative restrictions-may be The table shows the value of merchandise intra- the same preferences as in recent years In addition, no greater than those available to other trading part- trade for important regional trade blocs (service some countries belong to more than one trade bloc, ners, the general purpose of such arrangements is to exports are excluded) as well as the size of intra- so shares of world exports exceed 100 percent encourage exports by bloc members to one anoth- trade relative to each bloc's total exports of goods Exports of blocs include all commodity trade, which er-sometimes called intratrade and the share of the bloc's total exports in world may include items not specified in trade bloc agree- Most countries are members of a regional trade exports Although Asia Pacific Economic Cooperation ments Differences from previously published esti- bloc, and more than a third of the world's trade takes (APEC) has no preferential arrangements, it is includ- mates may be due to changes in bloc membership or place within such arrangements While trade blocs ed in the table because of the volume of trade to revisions in the underlying data vary widely in structure, they all have the same main between its members objective to reduce trade barriers among member The data on country exports are drawn from the - countries But effective integration requires more than International Monetary Fund's (IMF) Direction of reducing tariffs and quotas Economic gains from com- Trade database and should be broadly consistent * Merchandise exports within bloc are the sum of petition and scale may not be achieved unless other with those from other sources, such as the United merchandise exports by members of a trade bloc to barriers that divide markets and impede the free flow Nations Statistics Division's Commodity Trade (COM- other members of the bloc They are shown both in of goods, services, and investments are lifted For TRADE) database However, trade flows between U.S dollars and as a percentage of total merchandise example, many regional trade blocs retain contingent many developing countries, particularly in Africa, are exports by the bloc * Total merchandise exports by protections or restrictions on intrabloc trade These not well recorded Thus the value of intratrade for bloc as a share of world exports are the ratio of the include antidumping, countervailing duties, and 'emer- certain groups may be understated Data on trade bloc's total merchandise exports (within the bloc and gency protection" to address balance of payments between developing and high-income countries are to the rest of the world) to total merchandise exports problems or to protect an industry from surges in generally complete by all economies in the world imports Other barriers include differing product stan- Membership in the trade blocs shown is based on dards, discrimination in public procurement, and cum- the most recent information available, from the bersome and costly border formalities World Bank Policy Research Report Trade Blocs Membership in a regional trade bloc may reduce the (2000a) and from consultation with the World Bank's frictional costs of trade, increase the credibility of international trade unit Although bloc exports have reform initiatives, and strengthen security among been calculated back to 1970 on the basis of current partners But making it work effectively is a challenge membership, most of the blocs came into existence 0.5a Merchandise exports within bloc as % of world exports 50 AE 40 Euoen no Data on merchandise trade flows are published in 30 the IMF's Direction of Trade Statistics Yearbook EAEC and Direction of Trade Statistics Quarterly, the data in the table were calculated using the IMF's 20 NAFTA Direction of Trade database The United Nations Conference on Trade and Development (UNCTAD) publishes data on intratrade in its Handbook of 10 Bangkok Agreement LAIA ASEAN InteMational Trade and Development Statistics. _ _ _ = _ L = = The information on trade bloc membership is from O ACS Group of Three the World Bank Policy Research Report Trade 1980 1990 1995 1996 1997 1998 1999 2000 2001 Blocs (2000a) and the World Bank's international Note For the full names and memberships or the trade blocs shown, see the note to table 6 5 trade unit Source Table 6 5 2003 World Development Indicators 1 325 ____ dJ L~J Triff barriers All products Primary products Manufactured products Share of Share of Simple Standard Weighted lines with lines with Simple Weighted Simple Weighted mean deviation of mean international specific mean mean mean mean tariff tariff rates tariff peaks tariffs tanff tariff tanff tariff Year % Albania 1997 17.0 8.5 14 5 56 1 0.0 15 7 12 8 17.2 15 2 2001 11 8 6 69 11 8 38 3 0 0 11 8 10 8 11 3 12 0 Algeria 1993 22 9 16 5 15.5 46 8 0 0 15 5 8.9 22 9 18 7 2001 -22.4 14 3 15 0 50 3 0 0 15.0 11.3 22 6 16 7 Argentina 1992 12 2 - ~7 7 12 8 31 0 0.0 12 8 5.8 12 3 13.6 2001 11 6 7 2 9.2 39 1 0 0 9 2 4.8 11 7 9 7 Armenia 2001 3.3 4.7 -25 0.0 0 0 2 5 3 4 2 8 1 2 Australia 1991 131 14 3 9 5 30 5 1 2 9 5 1 6 14 2 10 5 2001 5 4 -67 3 9 6.5 1I5 3 9 0 8 5 8 4 4 Bangladesh 1989 106 2 -79 2 88 2 98.5 1 0 88 2 53 6 108 7 109 6 2000 21 6 13 6 21 0 52 9 0 0 21 0 18 6 21 5 22 3 Belarus 1996 12 4 8_7 8.9 314_ 0 0 8 9 6 5 12 9 10 5 1997 13 1 8 3 9 6 32.5 0 0 9 6 7 0 13 8 11.2 Benin 2001 14 7 6 67 -1~40 59 1 0 0 14 0 12 8 14 5 12 8 Bhutan 1996 17 7 14 1 i5 3 51 9 5 3 15 3 9 7 17 0 16_8 Bolivia 1993 9 7 1 1 9 4 0 0 0.0 9 4 10 0 9 7 9 3 -1999 9 5 1_6 9 0 00o 00 9 0 10 0 9 4 8 9 8osnia and Herzegovina 2001 7 6 4 44 6 6 0.0 0 0 6 6 5 7 7 5 6.9 Brazil 1989 - 42 2 17 2 32 0 92 4 0 2 32 0 18 6 42 4 37 1 2001 12 9 7 2 11 1 46 3 0 0 11 1 4 7 12 9 12 5 Bulgaria 2001 13 8 113 10 9 30 0 1.5 10.9 11 8 12 0 10 8 Burkina Faso 1993 25 6 10.0 19 9 73 6 0 0 1. 231 25 5 20 3 2001 12 8 7 0 10 1 45 9 0 0 10 1 14 8 12 4 8 9 Cameroon 1994 19 4 10 5 i4 0 54.7 0 0 14 0 14 9 18 6 13 6 2o6i 18 0 9 6 13 1 ~48 6 0 1 13 1 18 5 17 4 12 0 Canada 1989 8 6 7 4 61 14 6 3.4 61 26 92 66 2001 45_ -67 0O9 9 6 4 8 0 9 0.5 4.7 0 9 Central African Republic 1995- 18 3 iO 7 13 7 53 6 0 4 13 7 13 7 16 8 13.1 2001 18 4 9 9 16 0 52 4 0 2 16 0 26 9 17 5 11 9 Chad 1995 15 9 109§ 16.3 -449_ 0 0 16 3 15 9 15 5 13 4 2001 17 0 9 4 12 7 44 6 - 0.1 12 7 24.9 16 7 11 4 Chile 1992 11 0 0 5 110 00 0 0 11.0 11 0 11 0 10 9 2001 8 0 0 0 8 0 0.0 0 0 8 0 8 0 8.0 8 0 China t 1992 41 2 30 6 32 5 78 2 0 0 32 5 14 0 41 6 35.6 2001 15 3 10 0 14 3 40 5 os - 14 3 18 6 15 0 12 9 Hong Kong, China 1988 0 0 00 00o 0 0 0.0 0.0 0 0 0 0 0 0 1998 0 0 0 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0 Colombia 1991 5 7 8 2 64 -16 -00 6 4 7 5 5 5 6 1 2001 11 8 6 2 110 - 23 2 - 0.0 11 0 12.7 11 6 10 5 Congo, Rep 1994 20 9 9 3 16 4 64 0 0 0 16.4 20 5 20 2 14.6 2001 18 6 9 6 16 1 52.5 0 1 -16.1 21 9 17 8 14 4 Costa Rica 1995 10 3 8 1 - 85 29-5 0-0 8 5 -10 5 9 9 8 0 2001 6 3 7S5 4 3 05 - 00 4 3 7 8 5 6 3 8 C6te dlvoire 1993 25 4 12 1 22 1 75 6 0 0 22 1 21 6 25 0 22 5 2001 12 6 6 9 9 6 44 3 0.0 9 6 10 6 12 3 9 1 Croatia 2001 12 0 7 4 9 8 30 8 0 1 9.8 6.1 117 111I Cuba 1993 13 1 8 0 10 2 25 8 0 0 10 2 8.3 12 9 11 5 1997 11 4 6.6 8 2 9 6 0 0 8 2 5 2 11 3 9 97 Czech Republic 1996 6 9 6.2 5 8 5.4 0 0 5 8 4 1 6 6 6 2 1999 6 5 9 3 ~58 5 4 0.0 5 8 5 1 5 3 5 8 Dominican Republic 1997 15.0 9 1 16.3 33 8 0 0 16.3 10 4 14.6 17 7 2000 19.5 10 0 20 3 55.0 0.1 20 3 13 8 19 0 21 9 Ecuador 1993 87 6 0 -82 207 0 0 82 6 4 86 8 3 1999 12 9 6 3 1? 37 0 00 - 11 3 10 6 12.8 11 2 Egypt,Arab Rep 1995 25 6 33 2 167 -53 1 1 2 167i 7 6 25 6 22.2 1998 20 5 39 5 13 8 47 4 9.5 13 8 7.5 20.2 17 5 El Salvador 1995 10 3 7 8 9 3 27 8 0 0 9 3 10 2 9 8 8 7 2001 7 4 8 4 6 4 101I 0 0 6 4 -7.6 6 7 5.5 Equatorial Guinea 1998 19 9 9 7 15 3 601I 0 2 15 3 23 7 18 4 13 6 2001 18 7 9 6 13 7 53 5 00O 13 7 21 4 17.5 12 5 Ethiopia 1995 32.1 23 5 19 0 71 5 0 2 19 0 184~ 31.6 18 0 2001 17.2 12 3 11 0 45 7 0 2 11 0 11 7 16 6 10 5 European Union 1988 3.7 5 9 3.7 4 1 12 8 3 7 2 7 2 5 4 3 2001 3 9 4.9 2 6 2 6 7 4 2 6 1 7 3 2 2 9 Estonia 1995 0 1 1.0 0 4 0 1 0 0 0 4 0 0 0 1 0 5 Finland 1988 i0 3 11.0 6 2 24 4 1 0 6 2 3 0 10 4 6 9 _______________ ~~~1990 17 11 3 6 3 25 0 1 1 6 3 2 7 10 7 7 2 t Taiwan, Chtn-a 1989 1723 9 5 9 9 16 6 0 5 9 9 8 4 111 0 2001 7 6 8 0 3 5 8 8 1 6 3 5 6 2 6 6 2 9 326 H 2003 World Development Indicators Tariff barriers 0.6: All products Primary products Manufactured products Share of Share of Simple Standard Weighted line s with lines with Simple Weighted Simple Weighted mean deviation of mean international specific mean mean mean mean tariff tariff rates tariff peaks tariffs tariff tariff tariff tariff Year % Gabon 1995 20 6 9 6 16 2 61 9 ob- 16 2 20 0 19 6 15 1 2001 18 8 9 7 15 2 53 3 01i 15 2 20 2 17 9 14 0 Georgia 1999 9 9 3 2 9 9 0 0 1 0 9 9 12 0 9 5 8 3 Ghana 1993 14 4 8 5 9 5 42 1 0 0 9 5 16 2 13 7 8 7 2000 14 0 10 7 9 7 41 1 0 0 9 7 28 2 13 0 8 9 Guatemala 1995 10 0 7 5 8 6 25 7 0 0 8 6 10 2 9 5 8 0 2001 7 0 7 7 5 6 96 00 5 6 7 4 6 6 5 0 Guinea-Bissau 2001 14 0 71 14 3 56 0 0 0 14 3 19 6 13 3 10 3 Guyana -1996 22 4 12 5 17 6 47 4 39 3 17 6 15 8 21 1 16 9 2001 11 7 10 4 -99 -277 0 5 9 9 12 6 10 6 9 2 Honduras 1995 9 7 7 5 8 4 25 3 0 0 -84 12 9 9 2 7 5 2001 7 3 7 0 7 5 9 5 1 9 7 5 11 6 6 7 6 6 Hungary 1991 12 6 10 9 10 0 18 9 _ 0 0 10 0 5 5 12 3 11 7 1997 8 2 14 7 4 5 105 006 45 6 8 4 5 3 8 Iceland 1993 316 75 3'1 5-5 0 0 3 1 5 7 3 5 2 6 2001 52 ~~~~ ~~~ ~~8.1 34 3 o0 3 4 4 2 4 8 2 8 India 1990 79 0 43 6 56 2 97r1- 09 -56 2 25 4 79 9 70 8 2001 30 9 12 4 28 2 91 8 01i 28 2 28 5 30 6 29 0 Indonesia 1989 ~ 220 19 7 13 2 5605 -0 03 13 2 5 9 22 1 15 1 2000 8 4 10 8 5 4 11 2 0-00 54 2 8 8 9 6 6 Iran, Islamic Rep 2000 4 9 4 2 -31 0 600 ~ 31 0 9 50 38 Israel 1993 78 12 3 40 158i 0 0 4 0 1 9 7 9 4 4 Jamaica 1996 21 3 - 8 8 1986 45 1 41 9 19 8 14 2 20 7 20 9 2001 10 7 11 0 10'3 36 6 03j 10-3 9 5 9 7 10 1 Japan 1988 6 0 8 1 3 6 8.7 11 3 -36 4 4 4 7 2 7 2001 51 75 21 76~ 21* 21 25 39 17 Jordan 2000 22 8 16 6 18 6 63 1 0 4 18-6 16 9 22 1 19 8 - 2001 16 2 15 6 13.5 46.1 03 i 3 5 13.9 15 4 13 3 Kenya 1994 32 1 13 7 21 5 '883 0 01 21 5 17 0 31 9 23 3 - 2001 -20 2 13 6 15 5 44 3 0 1 -15 5 19 6 19.9 12 5 Korea, Rep 1988 18"8 7 9 13 8 73 0 10 3 13l8- 82 18 6 17 0 1999 87 5 9 60 4 48 - 08 - 6 0 '56 7 8 6 1 Kyrg'yz Republic 1995 0 0 - 0 0 0 060 15 9 00~ 00 00 0 0 Lao PDR 2000 9 4 7 5 14 2 11 4 2 1 14 2 14 7 8 6 12 7 Latvia .1996 4 4 -75 2 5 2 2 00 2 5 1 532 26 - ~~~~~~~~~2001 4 0 7 4 2 6 2 9 070 _ 2 6 332 2 6 Lebanon 1999 12 6 9 9 12 0 24.0 0.1 -1206 11 9 12 4 12 3 2001 - 8 3 11 2 12 0 13 0 0-7 - 12-0 .21 3 6 8 6 2 Libya 1~~~~~~~~996 - 7 4 37 1 21 3 58 8 0-4 21 727 5 Lithuania . 1995 3 9 8 6 28B 71i 0.0 286 37 2 5 1 8 1997 -39 8 0 2 4 6 5 0 0 -24 3 3 2 8 1 8 Macedonia, FYR 2001 15 9 11 6 13 8 474 060 13 8 16 4 - 14 9 12 4 Madagascar 1995 7 7 5 9 5 3 -6.0 0 0 5 3 2 9 7 7 6 3 Malawi 1994 -~31 6 14 5 23 1 8175! 00 23 1 129 31 7 2366 2001 12 6 10 5 8 2 40 1 00o 82 16 1 12 2 7 1 Mala-ysia 1988 17 0 15 1 9 9 46 4 6 7 9 9 4 6 17.3 10.8 1997 9 2 33 3 5 8 24 7 0 4 5 8 10 0 10 2 5 5 Maldives 2000 22 1 15 8 19 6 65.2 0.0 19 6 14 9 23 2 21 5 2001 _22 1 15 8 19 6 65 2 006 19 6 14 9 23 2 21 5 Mali 1995 16 4 128 9.8 431I 0 0 9 8 13 4 16 1 8 5 2001 1 6 9 9.4 46.7 0 0 9 4 1-39 12 5 8.4 Malta 1997 -87 5 6 88 6-0 00 8 8 6 2 8.9 9 3 2000 88 5 7 9 8 6 9 04 9 8 6 8 8 8 10 3 Mauritania 2001' 121 7 4 9 0 40 4 - Qo 9 0 6 8 12 1 10.5 Mauritius - -1995 36 2 28 4 20.7, 64.7i - 0 0 20 7 25 7 37 2 22 9 1998 31 2 27 8 24 5 58 0 0 0 24 5 -15 3 32 0 27_0 Mexico -1991 13 4 4 3 12 0 20 9 006 12.0 8 3 13 4 13 0 2001- 162 '93 15 4 50 8 05 15 4 199 161 14 7 Moldova 1996 6 5 9 3 2 1 20 6 1 2 - 2 1 0 8 4 7 2 7 2001 4 5 -5 5 2 6 - 0.1 -0 7 2 6 16 39 2 7 Morocco 1993 66 6 29 5 45 3 96.8 -0 1 -45 3 30 2 -67 3 55 2 - 2001 32 6 -20 5 25 4 79 1 0.0 25 4 29 0 31 1 24 6 Mozambique 1994 5 0 0 0 50 - 00 --0 0 5.0 5.0 5 0 506 2001 13 4 11 3 138 31 2 - 0 0 - 138 17 9 13 2 11 2 Nepal 1993 22 0 17 8 17 5 59 9 0 1 17 5 9 3 23 1 21 0 2000 - 14 7 13 4 -16 8 18 3 0 5 - 16 8 13 6 14 3 19 9 New Zealand 1992 -10 5 -11 0 8 5 36 2 --2.7- 85 4 0 11 0 9 4 2000 3 4 4 24 0 0 5824 0 5 36 2 8 Nicaragua 1995 7 4 7 9 - 5 0 19 5 00 6 50 7 1 7 3 4 6 2001 3 8 5 59 3 0 0 2 0 0 3 0 4 2 3 4 2 6 2003 World Development Indicators I 327 Tariff barriers All products Primary products Manufactured products Share of Share of Simple Standard Weighted lines with lines with Simple Weighted Simple Weighted mean deviation of mean International specific mean mean mean mean tariff tanff rates tariff peaks tanffs tanff tariff tariff tanff Year 9k % %% % Niger 2001 14.5 6 7 13 2 57 6 0.0 13 2 12 9 14 4 12 0 Nigeria 1988 26 0 16 7 23 8 63 0 0 3 23 8 32.4 -25 3 21 4 1995 21.8 15 7 20 0 9 7 80 5 200d 20.8 19.9 19 6 Norway 1988 1 9 5 2 0 8 5 0 8 0 0 8 0 2 2 0 0 8 2001 3 3 14 0 1 6 4 0 8 4 1 6 2.1 2 7 1 5 Oman 1992 5 5 8 2 7 6 1.5 0 0 7 6 14.2 5.1 5 4 1997 4.7 1 2 4 5 0 0 0 0 4 5 3.6 4 9 4 Pakistan 1995 50 8 21 6 46 3 92 3 3 5 463~ 240 51 5 50 8 2001 20 6 19 2 14 7 58.5 0.5 14.7 8.5 20.5 16 8 Panama 1997 15 2 13.2 10 5 36 5 0 1 10 5 9 6 14 6 110 2001 9.3 7~2 71 1 2 02 7 1 60 8.8 7_5 Papua New Guinea 1997 21.2 18 5 15 3 33 4 1.4 15.3 21.8 19.4 13 7 Paraguay 1991 15 8 11 4 12 6 42.3 0 0 12 6 - 3.6 15 7 14 5 2001 10 7 - 6.2 12 6 29 2 0 0 12 6 9.6 10 6 11 9 Peru 1993 17 4 -4.2 15 9 23 5 0 0 15 9 15.5 17 2 - 16 1 2000 13.1 2 9 -12 9 12 3 0 0 12 9 13 9 12 9 12 3 Philippines 1988 28 0 14 2 22 4 77.2 0 1 22 4 18.5 27 5 23 4 2001 7 0 7 3 4.0 6 9 0 0 4 0 59 6 5 3 4 Pc4and 1991 12 2 9.0 10.4 24.6 0 0 10 4 8 2 12 2 11 2 2000 10 0 9 8 7 3 14.3 4 5 7 3 6 2 8 3 7 7 Romania 1991 19 2 8 3 12 0 55 7 0.0 12.0 8 1 18 9 17 9 2001 18 1 15 9 13.7 45 9 0 0 13 7 11.4 15.7 14 1 Russian Federation 1993 7 8 9.9 6 3 3.3 0.0 6 3 3 9 9 3 7.4 2001 11 1 . 54 8 4 11.0 17 3 8.4 7 6 11 0 8 7 Rwanda 1993 28 5 26 9 25 7 60 2 1.2 25 7 35.8 27 5 21 9 2001 10 0 7.6 8 1 13 1 0 0 8 1 12.4 9.2 6 4 Sa-udi Arabia 1994 12.5 3 3 10 9 10 2 0 1 10 9 9 1 12 6 11.5 2000 12 3 3 1 10 5 8.2 4 0 10 5 7 9 - 12 4 1114 Senegal 2001 14 0 6 8 8 5 53 5 0 0 8 5 6 5 13 8 10 3 Singapore 1989 0 5 2 2 0 5 0.1 1 1 0 5 2 5 0 5 0 6 2001 0.0 0.0 0 0 0.0 0 2 0 0 00o 00 0.0 Slovenia 1999 11 9 6 6 11 5 21 0 3.2 11 5 7 5 11 7 12 1 2001 11 4 -70 .9.9 23 1 0 0 9 9 7 4 ii i 10 4 Solomo'n Islands 1995 37 5 48 5 34 6 52.2 1 8 3416 35 9 36 8 34 1 South Africa 1988 12 7 11_8 12 1 32 4 18 8 12 1 3 6 12.8- 12 3 2001 11 0 11 7 5 0 32.9 2 2 5 0 1 9 11 1 5 8 Sri Lanka 1990 28 3 24 5 26.9 51 6 1.4 26 9 32 3 27 7 24 2 2001 9 8 9 3 7 2 21.8 0 7 7 2 14 6 92 5.2 Sudan 1996 5 3 11 9 4 4 8 9 0 0 4 4 3.4 4.7 4.0 Switzerland- 1990 0 0 0 0 00 0 0 53 1 0 0 0.0 0 0 0 0 2001 0 0 0 0 0 0 0 0 38 9 0 0 0 0 0 0 0 0 Tanzania 1993 14 4 10 7 15.6 42 6 0 0 15 6 19 9 13 8 15 0 2000 18.2 8.4 14 5 71 2 0.0 14 5 16 1 -180 13 6 Thailand 1989 38 5 19 5 33 0 72.9 21 8 33 0 24 3 39 0 34 9 2000 17 0 14 3 9 7 47.1 1.2 9 7 7 7 15 9 101i Togo 2001 14 5 6 7 12.6 58.2 0.0 12 6 10.5 14 4 12 5 Trinidad and Tob-ago 1991 19 9 14 9 12 9 40 3 00 12 9 10 9 18 5 14 1 2001 11 2 10 6 46 36 5 0.6 46 3 2 10.2 5 8 Tunisia 1990 28 4 10 0 26 6 97 3 00 26.6 17 4 - 28.6 28 5 1998 30.6 12 6 26 3 91 9 00 26 3 18 5 30 5 27 9 Turkey 199:3 7 4 5 0 6 1 5 9 0.0 6 1 - 7.9 7.6 5 3 1999 84 14.7 54 98 07 5 4 5 5 6 3 5 3 Turkmen istan 1998 00 0.0 00 00 00 00 00 00 00 Uganda 1994 17.0 9.3 13 9 54 5 00 13 9 17 4 _ 167 -12 3 2001 82 5 7 69 00 0.0 6 9 60 80 63 Ukraine 1995 9 1 9 5 9.8 14 7 00 9.8 15 7 7 5 6 3 1997 10 5 11 0 5.3 24 1 0.0 5 3 34 8 1 7 2 United States 1989 5 6 6 8 3.8 8 1 12 4 3 8 2 0 5 9 41 2001 4 0 11 1 1.8 5 7 7.6 1 8 1 2 3 9 1 9 Urugua-y 1992 7 5 5 8 59 0.0 0 0 5 9 5 8 7 4 - 58 2001 11 0 8 0 6.6 40 7 0 0 6 6 2.1 11 1 8 4 Uzbekistan 2001 9 6 12 2 4.2 24 1 0 0 4 2 4 6 9 7 4 3 Venezuela, RB 1992 15 7 11 4 16.4 47 7 0 4 16 4 14 7 15 4 16 5 2000 12 6 5 9 13 5 25 6 0.0 13 5 13 6 12 5 13 3 Vietnam 1994 12 7 17 8 18 4 32.4 10o 18 4 46 5 11 8 12 9 2001 15 0 18 5 15.1 35 9 0.0 15 1 21.7 13 4 9.3 Zambia 1993 - 25 2 11 1 17 9 90 9 00 17.9 12 4 24 4 20 0 1997 14.7 88 13 1 31 7 0 0 13 1 13 9 14 5 12 9 Zimbabwe 1996 40 8 15 0 38 2 94 4 1 5 38 2 40 4 41 3 38 8 2001 19 0 18.6 15 6 37.5 1.7 156 20.8 17 6 15.3 II 2003 World Development Indicators Tariff barriers 0 D Poor people in developing countries work primarily in trade agreements such as the North American Free * Primary products are commodities classified In agriculture and labor-intensive manufactures, the very Trade Agreement Countries typically maintain a hier- SITC revision 2 sections 0-4 plus division 68 (non- sectors that confront the greatest trade barriers archy of trade preferences applicable to specific trad- ferrous metals) * Manufactured products are Removing barriers to merchandise trade could ing partners Where these rates were not available, commodities classified in SITC revision 2 sections increase growth by about 0 5 percent a year in these most-favored-nation rates, which are equal to or high- 5-9 excluding division 68 * Simple mean tariff is countries If trade in services (retailing, business, er than effectively applied rates, are used the unweighted average of the effectively applied financial, and telecommunications services) were Two measures of average tariffs are shown the sim- rates for all products subject to tariffs * Standard also liberalized, growth would be even higher ple and the weighted mean tariff Weighted mean tar- deviation of tariff rates measures the average dis- In general, tariffs in high-income countries on iffs are weighted by the value of the country s trade persion of tariff rates around the simple mean imports from developing countries, though low, are with each of its trading partners Simple averages are * Weighted mean tariff is the average of effective- four times those collected from other high-income often a better indicator of tariff protection than weight- ly applied rates weighted by the product import countries But protection is also an issue ed averages, which are biased downward because shares corresponding to each partner country for developing countries, which maintain high higher tariffs discourage trade and reduce the weights * International peaks are tariff rates that exceed tariffs on agricultural commodities, labor-intensive applied to these tariffs Specific duties-duties not 15 percent * Specific tariffs are tariffs that are manufactures, and other products and services In expressed as a proportion of the declared value-are set on a per unit basis or that combine ad valorem some developing regions new trade policies could not included in the table, but work is under way to esti- and per unit rates make the difference between achieving important mate ad valorem equivalents Millennium Development Goals-such as reducing Some countries set fairly uniform tariff rates poverty, lowering maternal and child mortality, and across all imports Others are more selective, set- improving educational attainment-and falling short ting high tariffs to protect favored domestic indus- by a large margin tries The standard deviation of tariffs is a measure Countries use a combination of tariff and nontariff of the dispersion of tariff rates around their mean measures to regulate their imports The most com- value Highly dispersed rates increase the costs of mon form of tariff is an ad valorem duty, based on protection substantially But these nominal tariff the value of the import, but tariffs may also be levied rates tell only part of the story The effective rate of on a specific, or per unit, basis or may combine ad protection-the degree to which the value added in valorem and specific rates Tariffs may be used to an industry is protected-may exceed the nominal raise fiscal revenues or to protect domestic indus- rate if the tariff system systematically differentiates tries from foreign competition-or both Nontariff among imports of raw materials, intermediate prod- barriers, which limit the quantity of imports of a par- ucts, and finished goods ticular good, take many forms Some common ones Two other measures of tariff coverage are shown are quotas, prohibitions, licensing schemes, export the share of tariff lines with international peaks (those restraint arrangements, and health and quarantine for which ad valorem tariff rates exceed 15 percent) measures and the share of tariff lines with specific duties (those Nontariff barriers are generally considered less not covered by ad valorem rates) Some countries-for desirable than tariffs because changes in an exporting example, Switzerland-apply only specific duties country's efficiency and costs no longer result in The indicators in the table were calculated from changes in market share in the importing country data supplied by the United Nations Conference on Further, the quotas or licenses that regulate trade Trade and Development (UNCTAD) and the World become very valuable, and resources are often wast- Trade Organization (WTO) Data are classified using ed in attempts to acquire these assets A high per- the Harmonized System of trade at the six- or eight- centage of products subject to nontariff barriers digit level Tariff line data were matched to Standard suggests a protectionist trade regime, but the fre- International Trade Classification (SITC) revision 2 quency of nontariff barriers does not measure how codes to define the commodity groups and import much they restrict trade Moreover, a wide range of weights Import weights were calculated for 1995 domestic policies and regulations (such as health reg- using the United Nations Statistics Division's All indicators in the table were calculated by World ulations) may act as nontanff barriers Because of the Commodity Trade (COMTRADE) database Data are Bank staff using the World Integrated Trade difficulty of combining nontariff barriers into an aggre- shown only for the first and last year for which com- Solution (WITS) system Tariff data were provided gate indicator, they are not included in this table plete data are available To conserve space, coun- by UNCTAD and the WTO Data on global imports The tariff rates used in calculating the indicators in tries that are members of the European Union have come from the United Nations Statistics the table are effectively applied rates, which reflect not been included Instead, data for the whole of the Division's COMTRADE database the rates actually applied to partners in preferential European Union are shown 2003 worid Development Indicators 1 329 Global financialG lw Net private Foreign direct Portfolio Investment flows Bank and capital flows Investment trade-related lending Bonds Equity $ millions $ millions $ millions $ millions $ millions 1990 2001 1990 2001 £.990 2001 1990 2001 1990 2001 Afghanistan Albania 31 203 0 207 0- 0 -0 0 31 -4 Algeria -424 243 0 1,196 -16 0 0- 0 -409 -953 Angola 235 89-7 -335 1,119 0 0 0 0 570 -222 Argentina -135 -3,897 1,836 3,214 -857 -3,815 80 -81 -1,195 -3,216 Armenia 74 70 .0 0 4 Australia 8,111 4,394 Austria 653 5,898 Azerbaijan 216 227 0 0 -11 Bangladesh 59 304 3 78 0 0 0 -4 55 230 Belarus 83 96 0 0 -13 Belgium a 8,047 73,635 Benin 62 131 62 131 0 0 0 0 0 0 Bolivia 3 637 27 662 0 0 0 0 -24 -26 Bosnia and Herzegovina 226 0 222 .0 0 4 Botswana 77 55 96 57 - 0 0 0 0 -19 -2 Brazil 666 23,336 989 22,636 129 1,704 103 2,482 -555 -3,485 Bulgaria -42 1,043 4 692 65 202 0 -9 -111 158 Burkina Faso -1 26 0 26 0 0 0 0 -1 0 Burundi -5 0 1 0 0 0 0 0 -6 0 Cambodia 0 113 0 113 0 0 0 0 0 0 Cameroon -124 -16 -113 75 0 0 0 0 -12 -91 Canada 7,581 27,438 Central African Republic 0 8 1 8 0 0 0 0 -1 0 Chad 8 80 9 80 0 0 0 0 -1 -1 Chile 2,216 5,727 661 4,476 -7 1,527 367 -219 1,194 -57 China 8,258 43,238 3,487 44,241 -48 400 151 3,015 4,668 -4,417 Hong Kong, China 22,834 Colombia 345 3,597 500 2,328 -4 1,961 0 -43 -151 -650 Congo, Dem Rep -27 32 -15 32 0 0 0 0 -12 0 Congo,Rep ~~-93 59 7 59 0 0 0 -0 -100 0 Costa Rica -22 630 163 454_ -42 208 0 0 -99 -_32 CMe dIlvoire 57 137 48 246 - -1 0 0 1 10 -110 Croatia 2,236 1,512 790 6 -72 Cuba Czech Republic 741 5,194 72 4,924 0 -263 0 616 669 -83 Denmark 1,132 7,238 Dominican Republic 129 1,729 133 1,198 0 480 0 0 -3 50 Ecuador 184 1,444 126 1,330 0 0 0 1 58 113 Egypt, Arab Rep -668 2,068 734 510 -1 1,500 0 39 -65 19 El Salvador -7 674 2 268 0 351 0 0 6 55 Eritrea 34 34 0 0 0 Estonia 624 -539 62 32 -9 Ethiopia -45 10 12 20 0 0 0 0 -57 -10 Finland 812 3,739 France 13,183 52,504 Gabon 103 170 74 200 0 0 0 0 29 -30 Gambia, The -8 36 0 36 0 0 0 0 -8 0 Georgia -173 160 -0 0 13 Germany 2,532 31,526 Ghana -5 244 15 89 0 0 0 0 -20 154 Greece 1,005 1,585 Guatemala 44 403 48 456 -11 -31 0 0 7 -22 Guinea -1 1 18 2 0 0 0 0 -19 0 Guinea-Bissau 2 30 2 30 0 0 0 0 0 0 Haiti 0 3 0 3 0 0 0 0 0 0 330 H 2003 world Development Indicators Global financial flows S 1 Net private Foreign direct Portfolio Investment flows Bank and capitai flows Investment trade-related lending Bonds Eqluity $ millions $ millions $ millions $ millions $ millions 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 75 126 44 195 0 0 0 0 32 -69 Hungary 28 3,952 311 2.440 921 94 175 134 -1,379 1,284 India 1,947 3,534 237 3,403 147 -131 105 1,739 1,458 -1,477 Indonesia 3,386 -7,312 1,093 -3,278 26 -1,376 463 164 1,804 -2,822 Iran, Islamic Rep -392 1,049 -362 33 0 0 0 0 -30 1,016 Iraq Ireland 627 9,865 Israel isi 3,224 Italy -- 6,411 14,874 Jamaica 92 1,385 138 614 0 -819- 0 0 -46 -48 Japan 1,777 6,191 Jordan 252 -114 38 100 0 -45 0 -145 214 -25 Kazakhstan .. 4,947 2,763 0 55 2,128 Kenya 122 -37 57 5 0 0 0 0 65 -43 Korea, Dem Rep Korea, Rep 1,038- 9,279 788 3,198 -151 530 518 10,165 -418 -4,614 Kuwait -39 Kyrgyz Republic . -7 3 5 -0 0 -78 Lao PDR 6_ 24 6 24 0 0 0 0 0 0 Latvia 880 177 179 6 518 Lebanon -13 2,757 7 249 0 2,500 0 0 6 7 Lesotho 17 113 17 117 0 0 0 0 0 -4 Liberia 0 13 0 13 0 0 0 0 0 0 Libya Lithu ania -521 446 179 -16 -88 Macedonia, FYR 466 443 0 0 23 Madagascar 7 9 22 11 0 0- 0 0 -15_ -2 Malawi 25 58 23 58 0 0 0 0 2 0 Malaysia 908 855 2,332 554 -1,239 -1,464 432 -650 -617 -513 Mali 5 103 6 103 0 0 0 0 -1 0 Mauritania 5 27 7 -30 -0 _ 0 0 -0 -1 -3 Mauritius 86_ -75 41 -48 0 0 0 0 45 -27 Mexico 9,600 28,079 2,549 24,731 661 -1,651 1,995 150 4,396 4,849 Moldova 70 94 -25 4 -2 Mongolia 62 63 0 0 -1 Morocco 341 2,633 165 2,658 -0 _-29 0 -8 176 12 Mozambiqiue 35 450 9 ~ 480 0 0 0 0 26 -30 Myanmar 153 1.45 161 208 0 0 0 0 -8 -63 Namibia Nepal -8 -19 6 19 -0 0 0 0 -14 0 Netherlands 10,676 51,239 New Zealand . 1,735 1,731 Nicaragua 20 13 0 132 0 0 0 0 20 -120 Niger 51 7 -41 13 0 0 0 0 10 -6 Nigeria 467 -920 588 -1,104 0 0 0 0 -121 -184 Norway 1,003 2,166 Oman -257 -867 142 42 0 0 0 -3 -400 -905 Pakistan 182 -308 245 383 0 -45 0 -130 -63 -516 Panama 130 _ 1,799 -136 513 -2 1,014 0 0 -4 273 Papua New Guinea 204 2 155 63 - 0 - - 0 0 0 49 -61 Paraguay 68 -14 77 79 0 0 0 0 -9 -93 Peru 59 -1,400 41 1,064 0 0 0 42 18 294 Philippines 779 2,076 530 1,792 395 761 139 383 -286 -859 Poland 71 9,611 89 5,713 0 667 0 -307 -18 3,537 Portugal 2,610 5,945 Puerto Rico 2003 World Development Indicators I331L Global financiaD flows Net private Foreign direct Portfolio Investment flows Bank and capital flows Investment trade-related lending Bonds Equity $ millions $ millions $ millions $ millions $ millions 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 4 2,633 0 1,157 0 375 0 8 4 1,094 Russian Federation 5,556 1,488 0 2,469 310 -679 0 543 5,246 -845 Rwanda 6 5 8 5 0 0 0 0 -2 0 Saudi Arabia Senegal 42 167 57 126 0 0 0 0 -15 41 Slerra Leone 36 4 32 4 0 0 0 0 4 0 Singapore 5,575 8,609 Slovak Republic _ 278 303 0 1,475 0 -283 0 0 278 -889 Slovenia 503 Somalia 6 0 6 0 0 0 0 0 0 0 South Africa 6,627 7,162 . 1,938 -962 -1,511 Spain 13,984 21,540 Sri Lanka 54 243 43 172 0 159 0 0 10 -88 Sudan 0 574 0 574 0 0 0 0 0 0 Swaziland 28 35 30 21 0 0 0 0 -2 14 Sweden 1,982 13,085 Switzerland 5,987 8,628 Syrian Arab Republic 63 204 72 205 0 0 0 0 -9 -1 Tajikistan 39 22 0 0 17 Tanzania 3 197 0 224 0 0 0 0 3 -28 Thailand 4,371 -3,052 2,444 3,820 -87 -1,605 440 18 1,574 -5,285 Togo 18 67 18 67 0 0 0 0 0 0 Trinidad and Tobago -68 830 109 835 -52 0 0 0 -126 -5 Tunisia -116 1,108 76 457 -60 453 5 -15 -137 213 Turkey 1,836 906 684 3,266 597 -493 89 -79 466 -1,788 Turkmenistan Uganda 16 147 0 145 0 0 0 0 16 2 Ukraine 426 792 -133 -734 502 United Arab Emirates United Kingdom 33,504 63,109 United States 48,490 130,800 Uruguay -192 796 0 318 -16 512 0 0 -176 -34 Uzbekistan 46 71 0 0 -25 Venezuela, RB -126 2,644 451 3,448 345 240 0 -74 -922 -970 Vietnam 16 710 16 1,300 0 0 0 0 0 -590 West Bank and Gaza Yemen, Rep 30 -210 -131 -205 0 0 0 0 161 -5 Yugoslavia, Fed. Rep -837 10 67 0 0 0 0 0 -904 10 Zambia 194 126 203 72 0 0 0 0 -9 54 Zimbabwe 85 -28 -12 5 -30 0 0 0 127 -33 1 k&e § - -Qe5 IM *Ft_ Low Income 7,242 2,737 2,610 8,977 142 -1,711 568 1,040 3,923 -5,569 Middle Income 36,978 165,240 21,494 161,690 889 11,791 3,979 4,918 10,617 -13,159 Lower middle income 20,486 79,848 9,157 82,262 1,140 6,513 827 2,843 9,362 -11,770 Upper middle income 16,493 85,392 12,337 79,427 -251 5,279 3,152 2,075 1,255 -1,389 Low & middle Income 44,220 167,977 24,103 170,666 1,032 10,080 4,546 5,958 14,539 -18,728 East Asia & Pacific 18,210 36,817 10,341 48,913 -952 -357 1,625 2,930 7,197 -14,668 Europe & Central Asia 7,668 36,162 1,227 30,130 1,893 671 264 258_ 4,284 5,103 Latin America & Carib 14,050 72,067 8,177 69,309 101 3,467 2,545 2,258 3,227 -2,967 Middle East & N Africa 595 7,462 2,810 5,460 -126 4,379 5 -132 -2,094 -2,245 South Asia 2,240 3,798 542 4,066 147 -18 105 1,606 1,446 -1,856 Sub-Saharan Africa 1,458 11,670 1,008 12,788 -31 1,938 2 -961 480 -2,095 High Income 178,443 575,804 Europe EMU . 60,540 272,350 . .. a Includes Luxembourg 332 0 2003 World Development Indicators Global financial flows The data on foreign direct investment are based on actions reported by market sources Transactions of * Net private capital flows consist of private debt balance of payments data reported by the public and publicly guaranteed bonds are reported and nondebt flows Private debt flows include com- International Monetary Fund (IMF), supplemented by through the Debtor Reporting System by World Bank mercial bank lending, bonds, and other private cred- data on net foreign direct investment reported by the member economies that have received either loans its, as well as foreign direct investment and portfolio Organisation for Economic Co-operation and from the International Bank for Reconstruction and equity investment * Foreign direct Investment is Development (OECD) and official national sources Development or credits from the International net inflows of investment to acquire a lasting man- The internationally accepted definition of foreign Development Association Information on private agement interest (10 percent or more of voting direct investment is provided in the fifth edition of nonguaranteed bonds is collected from market stock) in an enterprise operating in an economy the IMF's Balance of Payments Manual (1993) sources, because official national sources reporting other than that of the investor It is the sum of equi- Under this definition foreign direct investment has to the Debtor Reporting System are not asked to ty capital, reinvestment of earnings, other long-term three components equity investment, reinvested report the breakdown between private nonguaran- capital, and short-term capital, as shown in the bal- earnings, and short- and long-term intercompany teed bonds and private nonguaranteed loans ance of payments * Portfolio Investment flows are loans between parent firms and foreign affiliates But Information on transactions by nonresidents in local net and include non-debt-creating portfolio equity many countries fail to report reinvested earnings, and equity markets is gathered from national authorities, flows (the sum of country funds, depository receipts, the definition of long-term loans differs among coun- investment positions of mutual funds, and market and direct purchases of shares by foreign investors) tries Foreign direct investment, as distinguished sources and portfolio debt flows (bond issues purchased by from other kinds of international investment, is made The volume of portfolio investment reported by the foreign investors) * Bank and trade-related lending to establish a lasting interest in or effective manage- World Bank generally differs from that reported by covers commercial bank lending and other private ment control over an enterprise in another country As other sources because of differences in the sources, credits a guideline, the IMF suggests that investments in the classification of economies, and in the method should account for at least 10 percent of voting stock used to adjust and disaggregate reported informa- to be counted as foreign direct investment In prac- tion Differences in reporting arise particularly for tice, many countries set a higher threshold foreign investments in local equity markets because The OECD has also published a definition, in con- clarity, adequate disaggregation, and comprehensive sultation with the IMF, Eurostat, and the United and periodic reporting are lacking in many developing Nations Because of the multiplicity of sources and economies By contrast, capital flows through inter- differences in definitions and reporting methods, national debt and equity instruments are well record- there may be more than one estimate of foreign ed, and for these the differences in reporting lie direct investment for a country and data may not be primarily in the classification of economies, the comparable across countries exchange rates used, whether particular tranches Foreign direct investment data do not give a com- (installments) of the transactions are included, and plete picture of international investment in an econ- the treatment of certain offshore issuances omy Balance of payments data on foreign direct investment do not include capital raised locally, which has become an important source of financing for investment projects in some developing coun- tries In addition, foreign direct investment data cap- ture only cross-border investment flows involving equity participation and thus omit nonequity cross- border transactions such as intrafirm flows of goods and services For a detailed discussion of the data issues, see the World Bank's World Debt Tables 1993-94 (volume 1, chapter 3) Portfolio flow data are compiled from several mar- - = ket and official sources, including Euromoney data- The data are compiled from a variety of public and bases and publications, Micropal, Lipper Analytical private sources, including the World Bank's Services, published reports of private investment Debtor Reporting System, the IMF's International houses, central banks, national securities and Financial Statistics and Balance of Payments exchange commissions, national stock exchanges, databases, and other sources mentioned in About and the World Bank's Debtor Reporting System the data. These data are also published in the Gross statistics on international bond and equity World Bank's Global Development Finance 2003 issues are produced by aggregating individual trans- 2003 World Development Indicators 333 Net financial flows from Development Assistance Committee members Official Other Private flows Net grants Total development assistance official by NGOs net flows flows Contributions Foreign Bilateral Multilateral Private Bilateral Bilateral to mutilateral direct portfolio portfolio export Total grants loans institutions Total investment investment investment credits $ millions, 2001 Australia 873 660 212 56 43 357 -314 211 1,183 Austria 533 334 7 191 13 279 277 2 57 882 Belgium 867 507- -4 _365 7 -712 -530 _ -1,383_ - 142 141 304 Canada 1,533 1,222 -22 333 -98 --12_ -633 -601 -44 ~ 116 1,538 Denmark 1.634 1,048 -14 600 -4 998 998 17 2,645 Finland 389 229 -4 165 5 915 -624 -70- 361 9 1,3-17 France 4,198 2,9 20 -325 1,602 -39 12,168 8,049 3,838 280_ 16,327 Germany -4,990 2,858 -5 2,136 -663 737 1,798 -748 -863 551 808 5,872 Greece 202 81 1 119 202 Ireland 287 184 102 347 347 101 735 Italy 1,627 546 -104 1,185 55 -1,9 03 1,221 -3,617 494 32 -189 Japan 9,847 4,742 2,716 2.389 -854 5,380 6,473 -354 -355 -384 235 14,608 Luxembourg 141 106 35 .5 146 Netherlands 3,172 2,392 -167 948 42 -6,886 2,526 -8,462 -1,133 182 240 -3,432 New Zealand 112 85 27 16 16 11 139 Norway 1,346 938 2 406 -71 -131 . .. 60 210 1,485 Portugal 268 166 18 85 -1 1,503 1,273 . 230 5 1,775 Spain 1,737 966 184 588 146 9,640 10,160 -520 . 11.523 Sweden 1,666 1,185 20 461 1 1,394 507 888 16 3,077 Switzerland 908 643 1 263 6 -1,252 --1,107 -1 -144- 180 -158 United Kingdom 4,579 2,643 -21 1,957 23 4,669 8,164 -3,001 -493 327 9,597 United States 11,429 8,954 -670 3,145 755 21,864 24,236 -1,773 -1,729 1,130 4,569 38,618 Total 52,336 33,409 1,61.3 17,314 -549 49,117 66,602 -16,138 -4,082 2,735 7,289 108,193 Official aid Other Private flows Net grants Total official by NGGs net flows flows Contnbutions Foreign Bilateral Private Bilateral Bilateral to multilateral direct portfolio export Total grants loans institutions Total investmrent investment credits S millions, 2001 Australia 5 2 3 3 -4,110 -2,816 -1,294 -4,102 Austria- 212 161 50 2.-453 2,453 6 2,671 Belgium 88 5 84 -16 -1,252 348 -1,614 14 10 -1,170 Canada 152 152 ..-67 4,548 4,489 59 4,633 Denmark 181 101 12 68 29 565 565_ 2 777 Finland 61 31 1 28 -3 1,106 307 787 12 1,164 France 1,334 1,021 -11 323 -75 21,705 5,400 16,615 -311 22,964 Germany 687 317 -72 442 3,258 10,925 5,685 5,975 -735 90 14,960 Greece 9 7 2 9 Ireland 3 3 3 Italy 281 22 -1 260 27 -1,030 634 -1,652 -12 -721 Japan 84 138 -113 59 -651 3,168 5,671 -3,670 1,167 2,602 Luxembourg 9 3 6 9 Netherlands 214 103 -7 117 -15 3,432 4,656 -1,175 -50 3,631 New Zealand 0 Norway 32 29 2 3 542 550 -8 577 Portugal 28 1 27 13 384 374 10 425 Spain 14 16 -2 1,056 1,056 1,070 Sweden 119 113 6 -1 295 361 -66 413 Switzerland 63 53 2 7 1 5,665 5,661 4 7 5,735 United Kingdom 461 87 374 -4,737 -2,074 -2,528 -135 4 -4,272 United States 1,542 1,605 -145 83 -266 19,371 15,972 3,360 39 3,031 23,678 Total 5,574 3,967 -335 1,942 2,240 64,088 48,728 15,431 -70 3,151 75,053 Note: Data may not sum to totals because of gaps in reporting 334 1 2003 World Development Indicators Net financial flows from Development Assistance Committee members The high-income members of the Development getary expenditures reported by DAC countries and * Official development assistance comprises grants Assistance Committee (DAC) of the Organisation for flows reported by the United Nations, all United and loans (net of repayments of principal) that meet Economic Co-operation and Development (OECD) are Nations agencies revised their data to include only reg- the DAC definition of ODA and are made to countries the main source of official external finance for devel- ular budgetary expenditures since 1990 (except for and territories in part I of the DAC list of aid recipients oping countries This table shows the flow of official the World Food Programme and the United Nations * Offlcial ald comprises grants and loans (net of and private financial resources from DAC members to High Commissioner for Refugees, which revised their repayments) that meet the criteria for ODA and are official and private recipients in developing and transi- data from 1996 onward) made to countries and territories in part 11 of the DAC tion economies DAC maintains a list of countries and territories that list of aid recipients * Bilateral grants are transfers DAC exists to help its members coordinate their are aid recipients Part I of the list comprises devel- Y P Y q * Biiateral ioans are loans extended by governments development assistance and to encourage the expan- oping countries and territories considered by DAC or official agencies that have a grant element of at sion and improve the effectiveness of the aggregate members to be eligible for ODA Part II comprises or offici alculat at a ratelement of at least 25 percent (calculated at a rate of discount of 10 resources flowing to recipient economies In this economies in transition more advanced countries of capacity DAC monitors the flow of all financial Central and Eastern Europe, the countries of the for- P ) O s by multilateral ins are concession al funding received by m ultil ateral nati- resources, but its main concern is official develop- mer Soviet Union, and certain advanced developing tutions from DAC members in the form of grants or ment assistance (ODA) DAC has three criteria for countries and territories Flows to these recipients capital subscriptions . Other offlclal flows are trans- ODA It is undertaken by the official sector It pro- that meet the criteria for ODA are termed official aid actions by the official sector whose main objective is motes the economic development and welfare of The data in the table were compiled from replies by other than development or whose grant element is developing countries as a main objective And it is pro- DAC member countries to questionnaires issued by the less than 25 percent * Private flows consist of flows vided on concessional terms, with a grant element of DAC Secretariat Net flows of ODA, official aid, and at market terms financed from private sector at least 25 percent on loans (calculated at a rate of other official resources are defined as gross disburse- resources in donor countries They include changes in discount of 10 percent) ments of grants and loans minus repayments of princi- holdings of private long-term assets by residents of This definition excludes nonconcessional flows from pal on earlier loans Because the data are based on the reporting country * Foreign direct Investment is official creditors, which are classified as "other official donor country reports, they do not provide a complete investment by residents of DAC member countries to flows," and military aid, which is not recorded in DAC picture of the resources received by developing and acquire a lasting management interest (at least 10 statistics The definition includes food aid, capital proj- transition economies, for two reasons First, flows from percent of voting stock) in an enterprise operating in ects, emergency relief, technical cooperation, and DAC members are only part of the aggregate resource the recipient country The data reflect changes in the postconflict peacekeeping efforts Also included are flows to these economies Second, the data that record net worth of subsidiaries in recipient countries whose contributions to multilateral institutions, such as the contributions to multilateral institutions measure the parent company is in the DAC source country United Nations and its specialized agencies, and con- flow of resources made available to those institutions * Bilateral portfolio investment covers bank lending cessional funding to the multilateral development by DAC members, not the flow of resources from those and the purchase of bonds, shares, and real estate by residents of DAC member countries in recipient coun- banks In 1999, to avoid double counting extrabud- institutions to developing and transition economies tries * Multilateral portfolio investment records the 6.8a transactions of private banks and nonbanks in DAC member countries in the securities issued by multilat- eral institutions * Private export credits are loans Total net tows to recipient countries ($ bililons). 2001 extended to recipient countries by the private sector in 40 40 DAC member countries to promote trade, they may be * Net grants by NGOs supported by an official guarantee * Net grants by 30 * Net private flows NGOs are private grants by nongovernmental organiza- _ Total net official development assistance tions, net of subsidies from the official sector * Total O3 Other official flows net flows comprise ODA or official aid flows, other offi- 20 cial flows, private flows, and net grants by NGOs 10 **i l0 _ j - _The data on financial flows are compiled by DAC and published in its annual statistical report, o i | . { =5 i | Ll *_ _ , , Geographical Dlstribution of Financial Flows to Aid Recipients, and its annual Development Co- -10 operation Report Data are available in electronic United France Japan Spain United Germany Sweden Denmark Portugal Canada format on the OECD's International Development States Kingdom Statistics CD-ROM and to registered users at http //www oecd org/dac/htm/online htm Source Organisation for Economic Co-operation and Development data 2003 World Development Indicators 335 Aid flows from Development L05~ Assistance Committee members Net official Untied aida development assistance average annual % Per capita of change in, voume b donor country5 b of general % of bilateral $ millions ftof GNI 1995-96 to $ $ government disbursement ODA commitments 1996 2001 1996 2001 2000-2001 1.996 2001 1996 2001 1.996 2001. Australia 1,074 873 0 27 0 25 0 6 46 49 0 76 0 74 78 1 59 3 Austria 557 533 0 24 0 29 0 2 5 1 66 0 46 0 57 Belgium 913 867 0 34 0 37 3 5 67 85 0 68 0 82 89 8 Canada 1,795 1,533 0 32 0.22 -2 6 59 51 0 68 0 57 31 5 31.7 Denmark 1.772 1,634 1.04 1.03 4 4 265 306 1 72 2 00 61 3 93 3 Finland 408 389 0 33 0 32 5 0 61 75 0 59 0 72 60 2 87 5 France 7,451 4,198 0 48 0 32 -6 6 95 72 0 93 0 66 38 7 66 6 Germany 7,601 4,990 0.32 0.27 -1 2 67 62 0 67 0 59 60 0 84 6 Greece 184 202 0.15 0 17 24 3 14 19 0 33 0 40 . 17 3 Ireland 179 287 0 31 0 33 11 9 43 74 0 67 0 92 . 100 0 Italy 2,416 1,627 0 20 0 15 -2 3 34 28 0 38 0 32 7 8 Japan 9,439 9,847 0 20 0 23 3 0 73 89 0 58 0 64 98 9 81 1 Luxembourg 82 141 0 44 0 82 18 1 156 325 1 05 1 89 94 4 Netherlands 3,246 3,172 0 81 0 82 5.0 161 195 1 73 1 97 82 2 91 2 New Zealand 122 112 0 21 0 25 5 6 22 30 0 49 0 61 Norway 1,311 1,346 0 84 0 83 1 7 278 299 1 82 1 95 88 4 98 9 Portugal 218 268 0 21 0 25 6.7 18 26 0 47 0.58 100 0 57 7 Spain 1,251 1,737 0 22 0 30 7 3 25 43 0 50 0 79 0 0 68 9 Sweden 1,999 1,666 0 84 0 81 4 4 173 207 1 27 1 52 78 9 86 5 Switzerland 1,026 908 0 34 0 34 3 0 108 123 92 9 96 1 United Kingdom 3,199 4,579 0 27 0 32 5 8 58 80 0 66 0 84 86 1 93 9 United States 9,377 11,429 0 12 0 11 3 2 38 39 0 37 -0 36 28 4 Total or average 55,622 52,336 0.25 0.22 1.8 59 63 0.63 0.61 71.3 79.1 Net official aid average annual % Per capita of change in volumeb donor country5b $ millions % of GNI 1995-96 to $ $ 1996 2001 1996 2001 2000-01 1996 2001 Australia 10 5 0 00 0 00 2 8 0 0 Austria 226 -212 0 10 0 11 0 7 21 26 Belgium 70 88 0 03 0 04 7 0 5 9 Canada 181 152 0 03 0 02 -5 4 6 5 Denmark 120 181 0 07 0 11 10 3 18 34 Finland 57 61 0 05 0 05 3 6 9 12 France 711 1,334 0 05 0 10 22 4 9 23 Germany 1,329 687 0 06 0 04 --20 0 12 8 Greece 2 9 0 00 0 01 66 2 0 1 Ireland 1 0 0 00 0 00 -61 8 0 0 Italy 294 281 0 02 0 03 7 0 4 5 Japan 184 84 0 00 0 00 -35.9 1 1 Luxembourg 2 9 0 01 0.05 12.5 4 20 Netherlands 13 214 0 00 0 06 16 8 1 13 New Zealand 0 0 0 00 0 00 -1 4 0 0 Norway 50 32 0 03 0 02 -11 0 11 7 Portugal 18 28 0 02 0 03 10 8 1 3 Spain 98 14 0 02 0 00 -31 5 2 0 Sweden 178 119 0 07 0 06 -0 5 15 15 Switzerland 97 63 0 03 0 02 -3 7 10 9 United Kingdom 362 461 0 03 0 03 1 8 7 8 United States 1,694 1,542 0 02 0.02 4 6 7 5 Totai or average 5,696 5,574 0.03 0.02 0.2 6 7 a Excluding administrative costs and technical cooperation b At 2000 exchange rates and prices 3313 L 2003 World Development Indicators Aid flows from Development Assistance Committee members Effective aid supports institutional development and pol- record their concessional funding (usually grants) to mul- * Net official development assistance and net official icy reforms, which are at the heart of successful devel- tilateral agencies when they make payments, while the aid record the actual international transfer by the donor opment To be effective, especially in reducing global agencies make funds available to recipients with a time of financial resources or of goods or services valued at poverty, aid requires partnerships between recipient lag and in many cases in the form of soft loans where the cost to the donor, less any repayments of loan prin- countries, aid agencies, and donor countries It also donors' grants have been used to reduce the interest bur- cipal during the same period Data are shown at current requires improvements in economic policies and institu- den over the life of the loan prices and dollar exchange rates * Aid as a percentage tions Where traditional methods of nurturing such Aid as a share of gross national income (GNI). aid per of GNI shows the donor's contributions of ODA or official reforms have failed, aid agencies need to find alterna- capita, and ODA as a share of the general government aid as a share of its gross national income * Average tive approaches and new opportunities. disbursements of the donor are calculated by the OECD annual percentage change In volume and aid per capi- As part of its work, the Development Assistance The denominators used in calculating these ratios may ta of donor country are calculated using 2000 exchange Committee (DAC) of the Organisation for Economic Co- differ from corresponding values elsewhere in this book rates and prices * Aid as a percentage of general gov- operation and Development (OECD) assesses the aid per- because of differences in timing or definitions ernment disbursements shows the donor's contribu- formance of member countries relative to the size of their DAC members have progressively introduced the new tions of ODA as a share of public spending * Untied aid economies As measured here, aid comprises bilateral dis- United Nations System of National Accounts (adopted in is the share of ODA that is not subject to restrictions by bursements of concessional financing to recipient coun- 1993), which replaced gross national product (GNP) with donors on procurement sources tries plus the provision by donor governments of GNI Because GNI includes items not included in GNP, concessional financing to multilateral institutions Volume ratios of ODA to GNI are slightly smaller than the previ- amounts, at constant prices and exchange rates, are used ously reported ratios of ODA to GNP to measure the change in real resources provided over The proportion of untied aid is reported here because time Aid flows to part I recipients-official development tying arrangements may prevent recipients from obtaining assistance (ODA)-are tabulated separate from those to the best value for their money and so reduce the value of part 11 recipients-official aid (see About the data for table the aid received Tying arrangements require recipients to 6 8 for more information on the distinction between the purchase goods and services from the donor country or two types of aid flows) from a specified group of countries They may be justified Measures of aid flows from the perspective of donors on the grounds that they prevent a recipient from misap- differ from recipients' perceived aid receipts for two main propriating or mismanaging aid receipts, but they may reasons First, aid flows include expenditure items about also be motivated by a desire to benefit suppliers in the which recipients may have no precise information, such donor country The same volume of aid may have differ- as development-oriented research, stipends and tuition ent purchasing power depending on the relative costs of costs for aid-financed students in donor countries, or pay- suppliers in countries to which the aid is tied and the ment of experts hired by donor countries Second, donors degree to which each recipient's aid basket is untied 6.9a Net disbursements ($ millions) 1997 1998 1999 2000 2001 OECD members (non-DAC) ^___ __l Czech Republic . 16 15 16 26 Iceland 8 7 8 _ 8 _ 9 10 Korea, Rep 186 183 317 212 265 LPoland 19 20 29 3 Slovak Republic 7 6 8 ____ke 77 69 120 82 64 -7]_ Lury__ ______ _7__ __ _______ Arab countries - Kwt ___ 373 278 147 165 73 T 3 o financia flwsi Saudi Arabia 251 288 185 295 490 The data on fiancial flows are compiled by DAC ,Lted Arab Em vates 115 63 92 150 127 - and published in its annual statistical report, Other donors Geographical Distribution of Financial Flows to EstDnia _ ______________ 00 1 O _ Aid Recipients, and its annual Development Co- Israel 89 87 114 164a 768 operation Report Data are available in electronic Note China also provides aid but does not disclose the amount format on the OECD's Intemational Development a The figure for 2000 includes $66 8 milihon-and that for 2001, $50 1 million-for first-year sustenance expenses for people arriving from developing countries (many of which are experiencing civil war or severe unrest) or who have left their Statistics CD-ROM and to registered users at country for humanitarian or political reasons http //vvw oecd org/dac/htm/onimne.htm Source OECD data 2003 World Development Indicators 1 337 m L, 2W I Aid dependency Net official Aid per capita Aid dependency ratios development assistance or official aid Aid as Aid as Aid as % of % of % of central Aid as gross capital imports of government S millions $ % of GNI formation goods and services expenditure 1996 2001 1996 2001 1996 2001 1996 2001 1996 2001 1996 2001 Afghanistan 183 402 8 15 Albania 228 269 72 85 8 3 6 3 54 7 33 6 20 3 150 28 5 Algeria 304 182 11 6 0 7 0 3 2 6 1 3 2 5 12 2 2 11 Angola 473 268 40 20 8.1 3 4 18.1 8 3 7 9 3 6 Argentina 135 151 4 4 0 1 0 1 0.3 0 4 0 3 0 4 0.3 0 3 Armenia 293 212 78 56 18 3 9 7 91.8 53.8 31 8 20 9 Australia Austria Azerbaijan 96 226 12 28 3 1 4 3 10 5 19 3 5 3 8 9 18 1 Bangladesh 1,236 1,024 10 8 3 0 2 2 15 2 9 5 15 8 9 8 Belarus 77 39 8 4 05 03 22 14 10 04 16 11 Belgium Benin 288 273 51 42 13.3 116 76 3 60 1 36 1 36 0 Bolivia 832 729 110 86 11.6 9 4 69 2 70 5 42 3 313 48 9 34 2 Bosnia and Herzegovina 845 639 239 157 33 5 12 8 73 6 33 8 23 8 Botswana 75 29 48 17 1 6 0 6 6 2 2 5 2 9 1 0 4 3 Brazil 288 349 2 2 0 0 0 1 0.2 0 3 0 3 0 4 Bulgaria 182 346 22 43 1 9 2 6 22 6 12 5 2 8 3 7 3 8 7 4 Burkina Faso 420 389 41 34 16 9 15 7 61 8 61.7 55 0 57 4 Burundi 111 131 18 19 12 5 19 3 102 3 274 3 69 9 80 7 44 6 Cambodia 422 409 38 33 13 6 12 4 518 671 30.5 20 1 Cameroon 412 398 30 26 4 8 5 0 29 4 26 3 16 7 13 3 Canada Central African Republic 170 76 49 20 16 2 7.9 369 9 56 0 70 7 49 5 Chad 296 179 43 23 18 8 112 123 7 26.9 57 1 18.1 Chile 196 58 14 4 03 01 11 04 08 02 14 04 China 2,646 1,460 2 1 0 3 0 1 0 8 0 3 1 5 0 5 4 1 Hong Kong, China 13 4 2 1 0 0 0 0 0 0 0 0 0 0 0 0 Colombia 189 380 5 9 0 2 0 5 0 9 3 1 1 0 1 9 1 3 Congo, Dem Rep 166 251 4 5 3 1 5 3 10.3 95 1 9 0 18 1 24 7 Congo, Rep 429 75 160 24 26 4 3 9 62 7 10.0 15 6 3 6 56 8 10 5 Costa Rica -10 2 -3 1 -01 0.0 -0 5 01 -0 2 0 0 -0 4 0 1 C6te d'lvoire 965 187 67 11 8 6 1 9 65 6 18 2 19 3 4 3 35 6 10 6 Croatia 133 113 29 26 0 7 0 6 3 1 2 3 13 1 0 1.5 1_3 Cuba 57 51 5 5 Czech Republic 129 314 12 31 0 2 0 6 0 6 1 8 0 4 0 7 0 6 1 4 Denmark Dominican Republic 100 105 13 12 0.8 0 5 3 9 2 1 1 3 0 9 4 8 Ecuador 253 171 22 13 1 4 11 7 7 3 8 4 1 2 1 Egypt, Arab Rep 2,199 1,255 37 19 3 2 1 3 19 6 8 2 116 5 6 100 El Salvador 302 234 52 37 2 9 1 7 19 3 10 7 8 2 3 7 66 9 Eritrea 159 280 43 67 24 6 40 8 716 115 2 27 3 52 3 Estonia 59 69 42 50 1 4 1 3 4.9 4 5 1 7 1 2 4 0 4 1 Ethiopia 818 1,080 14 16 13 7 17 5 80 6 96 0 55 9 53 6 Finland France Gabon 127 9 114 7 2 6 0 2 9 4 06 4 7 0 3 Gambia, The 37 51 32 38 9.5 13.3 44 1 72 8 12 3 14 1 Georgia 310 290 57 55 10 3 9 2 933 49.2 22 1 82 7 Germany Ghana 651 652 37 33 9 6 12 7 32 2 512 25 5 19 2 Greece Guatemala 194 225 19 19 1 3 11 9 7 7 1 5 1 3 5 Guinea 299 272 44 36 7.9 9 4 44 6 413 28 4 27 4 Guinea-Bissau 181 59 164 48 71 8 32 0 290.0 135 6 172.7 52 5 Haiti 370 166 50 20 12.8 4.4 45.2 14 4 46 8 13 2 140 8 330 II 2003 World Development Indicators Aid dependency 01 Net official Aid per capita Aid dependency ratios development assistance or official aid Aid as Aid as Aid as % of % of % of central Aid as gross capital imports of government $rriflions $% of GNI formation goods anid services expenditure 1996 2001 1996 2001 1996 2001 1996 2001 1996 2001 1996 2001 Honduras 359 678 62 103_ 9 4 10 9 28 2 34 7 14 1 18 3 Hungary 204 418 20 41 0 5 0 8 1 7 30 09 1 1 1 0 tndia 1,897 1,705 2 2 0 5 04 2 2 1 6 3 2 2 2 3 3 2 0 Indonesia 1,123 1,501 6 7 0 5 1 1 1 6 611 1 7 2 5 3 4 4 3 Iran, Isilamic- Rep -169 115 3 ' 2 0.2 0O1 0 8 0 3 0 9 0 5 0 5 Iraq 34-8 12-2 16 5 Ireland Israel 2,217 172 -389 27 2 3 0 2 9 4 5 2 0 3 4 7 0 3 Italy - .. Jamaica 58 54 23 21 0 9 0 7 3 1 2.3 1 4 1 0 2 2 1 8 Japan Jordan 507 432 117 86 7 5 4 9 24 0 18 9 8 7 6 7 21 6 15 1 Kazakhstan 125 148 8 10 0 6 0 7 3 7 2 6 1 6 1_2 4 6 Kenya 597 453_ 22 15 6 6 4 0 38 4 31 1 16 1 10 8 22 3 Korea,Dem Rep 26 119 ~ 1 5 Korea,Rep -149 -111 -3 --2 0 0 00 -01I -01 -0 1 -0 1 -0 2 Kuwait 3 4 1 2 0 0 0 0 0 1 0 1 00 00 0 0 Kyrgyz Republic 231 188- 50 38 12 9 12 9 50 1 75 2 21 4 29_2 56 5 Lao PDR 332 243 69 45 17.8 14 5 61 2 62 5 42 5 40 6 Latvia 72 106 29 45 14_ 1 4 7 5 5 1 2 3 2 4 4 5 4 8 Lebanon 232 241 57 -55 1 7 1 4 6 0 7 7 2 9 4 7 Lesotho 104 54 55 26 8 2 5 5 18I,9 18 4 8 9 6 9 21 9 Liberia 173, 37 ~ 62 .11 8 3 L-ibya -8 10 2 2 - 0 2 0 1 Lithuania 91 130 25 37 1 2 1 1 4 7 5 0 1 8 1 8 4 6 4 1 Macedonia,FYR 106 248 53 121 2 4 7 3 11 9 43 3 5 7 12 4 Madagascar 357 354 26_ 22 9 3 7 8 76 7 49 6 30 5 188.8 51 4 Malawi 492 402 52 38 20 5 234, 174 9 210 2 42 8 38 3 Malaysia --457 27 -22 1 -0 5 0 0 -1 1 01 -0 5 00 -2 1 Mali 491 350 50 32 19 1 13 9 81 9 62'7 49 1 28 4 Mauritania 272 262 116 95 25 7 26 6 131 3 97 4 43 7 56 6 Mauritius 20 22 17 18 0 5 05 1 9 20 0 7 -08 2 0 2 1 Mexico 287 75 3 1 0 1 -00 04 01 0 2 0 0 -06 Moldova 36 119 8 28 2 1 7 5 8 9 40-2 2 8 9 7 7 6 35 4 Mongolia 201 212 87 88 19 4 20_6 71 8 67 5 33 5 28 7 90 8 65 9 Morocco 650 517 24 18 1 8 1 6 9 1 6 1 5 3 3 8 Mozambique 888 935 55 52 33 2 28 1 143 2 62 3 74 9 20 3 Myanmar - 43 127 1 3 18_ 4 1 0_3 Namibia 188 109 116 61 5 3 3 4 23 3 14 4 8 0 -51 14 8 Nepal 391 388 19 16 8 6 6 7 318_ 28 8 -23.8 19 1 51 0 39 4 Netherlands New Zealand Nicaragua -93-4 928 205 178 58 4 180 0 57 2 41 3 137 9 Niger . 2~55 249 27 22 13 0 12 8 132 7 111 0 51 2 47 9 Nigeria - 190 185, 2 1. 0 6 0 5 3 38 1 6 1 3 1 0 Norway Oman 62 2 28 1 0 5 1 0 ~00 1.3 0 0 Pakistan, 884 1,938, 7 14 1 4 3 4 7 3 20 7 5 1 13 1 6 2 16 2 Panama 49 28 18 10 0 6 0 3 2 0 1 0 0 5 0 3 2 2 Papua New Guinea 381 203 82 39 7 6 7.2 32 2 13 9 11 0 -27 1 Paraguay 89 61 18 11 0 9 0 9 3 9 3 6 1 7 1 7 5 9 4 8 Peru 329 451 14 17 0 6 0 9 2 6 4_5 2 6 4 0 3 3 4 6 Philippines 901 577 1-3 -7 1 0 0 8 -45 4 5 2 0 1 5 5 9 4 2 Poland 1,167 966 30 25 0 9 0 6 4 1 2 5 2 7 1 6 2 1 1 5 Portugal - Puerto Rico 2003 World Development Indicators I 339 Aid dependency Net offlcial Aid per capita Aid dependency ratios development assistance or official aid Aid as Aid as Aid as % of % of % of central Atd as gross capital imports of government $ millions $ % of GNI formation goods and services expenditure 1996 2001 1996 2001 1996 2001 1996 2001 1996 2001 1996 2001 Romania 233 648 10 29 0.7 1 7 2 7 7 6 1.8 3 7 21 5 3 Russian Federation 1,282 1,110 9 8 0.3 0 4 1 2 1I6 1 3 1 3 . 1 5 Rwanda 467 291 69 33 34 1 17 3 234 9 92 7 120.6 62 0 Saudi Arabia 23 27 1 1 0.0 0 0 0.1 01 0 0 01 Senegal 580 419 68 43 12 7 9 2 67.5 45 0 32 0 217 58 8 41 6 Sierra Leone 184 334 40 65 20.0 45 8 195.2 563.9 51 2 110 1 132 3 Singapore 15 1 4 0 0 0 0 0 00 0.0 00 0.0 0 1 0 0 Slovak Republic 98 164 18 30 05 0 8 1 3 2 5 0 7 09 11 2.1 Slovenia 82 126 41 63 04 0 7 1 9 08 1.1 11 1.7 Somalia 88 149 12 16 South Africa 364 428 9 10 0 3 0 4 1.5 2 5 1 0 1 2 0 8 1-3 Spain Sri Lanka 487 330 28 18 3 6 2 0 14.4 9 4 7 5 4 4 12 6 8 0 Sudan 220 172 8 5 3.0 1 5 12 1 7 8 14 3 6 5 Swaziland 33 29 36 27 2 3 2 3 7 8 12 5 2 4 2 6 Sweden Switzerland Syrian Arab Republic 219 153 15 9 1 6 0 8 6.6 3 7 3 1 2 1 1 6 Tajikistan 103 159 17 25 10 5 15 5 44 0 11.7 18 4 129 1 Tanzania 877 1,233 29 36 13 8 13 3 81.2 77 7 38 6 53 5 Thailand 830 281 14 5 0.5 0 3 11 1 0 0.9 0 4 2 8 1 2 Togo 157 47 39 10 10 9 3 8 57.1 17 9 18 7 6 8 Trinidad and Tobago 17 -2 13 -1 0 3 0 0 1 2 -0 1 0 6 0 0 Tunisia 124 378 14 39 0 7 2 0 2.5 6 9 1 3 3 3 1 9 Turkey 238 167 4 3 0.1 0.1 05 07 04 03 05 02 Turkmenistan 24 72 5 13 1 0 1 2 3 3 1 2 2.3 Uganda 676 783 34 34 11 3 14.1 69.6 68 9 40 5 48 3 646 Ukraine 398 519 8 11 0 9 1 4 3 9 6 8 1 8 2 4 4 7 United Arab Emirates 7 3 3 1 0 0 .. 0 1 United Kingdom United States Uruguay 35 15 11 5 02 0 1 1.1 0.6 0 8 0.3 06 03 Uzbekistan 88 153 4 6 06 1 4 22 7 0 1 8 4 5 Venezuela, RB 38 45 2 2 0 1 00 03 0.2 02 02 0.3 0 1 Vietnam 939 1,435 13 18 39 44 13.6 14 2 7 3 7 7 16 5 18 0 West Bank and Gaza 550 865 218 280 13 2 19 6 42.9 Yemen, Rep 247 426 16 24 4.8 5.0 18.6 22.5 7 0 9 1 10 8 Yugoslavia, Fed Rep a 70 1,306 7 123 12 1 89 2 1 6 25 0 Zambia 610 374 66 36 19.9 10 7 145 1 51.2 35.8 20 9 Zimbabwe 371 159 32 12 4 5 1.8 23 4 22 5 10 5 7 3 12 5 Low income 25,309 25,342 11 10 2 5 2.4 10.2 11 0 8 9 8 1 Middle income 21,799 20,284 9 8 0 5 0 4 1 7 1 6 1.6 1.2 Lower middle income 17,598 16,086 9 7 0 7 0 6 2.3 2 1 2 5 1 9 Upper middle income 3,532 3,672 7 7 0 2 0 2 0 7 0 8 0 6 0 5 Low & middle income 59,015 57,217 12 11 10 09 39 38 36 29 East Asia & Pacific 8,040 7,394 5 4 0 6 0.5 1 4 1 3 16 1 2 Europe & Central Asia 8,670 9,783 18 21 0.8 1.0 3 3 4 4 2.4 2 3 Latin America & Carib 7,446 5,992 15 11 0 4 0.3 1 9 1 6 1 9 1 2 Middle East & N Africa 5,956 4,838 22 16 1 0 0 7 5 0 3 2 3.5 2.7 South Asia 5,169 5,871 4 4 1 0 1.0 4.6 4 4 5 5 5 1 Sub-Saharan Africa 16,552 13,933 28 21 5 2 4.6 27.3 23 4 14 2 10.9 High Income 3,249 1,027 4 1 0 0 0.0 0.1 0.0 0 1 0.0 Europe EMU Note Regional aggregates include data for economies not specified elsewhere World and income group totals include aid not allocated by country or region The 2001 data exciude aid from the World Food Programme a Aid to the states of the former Socialist Federal Repubiic of Yugoslavia that is not otherwise specified is included in regional and tncome group aggregates 340 1 2003 World Development Indicators Aid dependency a. I Ratios of aid to gross national income (GNI), gross by recipients in the balance of payments, which often * Net offlcial development assistance consists of capital formation, imports, and public spending pro- excludes all or some technical assistance-particular- disbursements of loans made on concessional terms vide a measure of the recipient country's dependen- ly payments to expatriates made directly by the donor (net of repayments of principal) and grants by official cy on aid But care must be taken in drawing policy Similarly, grant commodity aid may not always be agencies of the members of DAC, by multilateral conclusions For foreign policy reasons some coun- recorded in trade data or in the balance of payments institutions, and by non-DAC countries to promote tries have traditionally received large amounts of aid Moreover, DAC statistics exclude purely military aid economic development and welfare in countries and Thus aid dependency ratios may reveal as much The nominal values used here may overstate the territories in part I of the DAC list of aid recipients It about the donors' interests as they do about the real value of aid to the recipient Changes in inter- includes loans with a grant element of at least 25 recipients' needs Ratios in Sub-Saharan Africa are national prices and in exchange rates can reduce the percent (calculated at a rate of discount of 10 per- generally much higher than those in other regions, purchasing power of aid The practice of tying aid, cent) * Net official aid refers to aid flows (net of and they increased in the 1980s These high ratios still prevalent though declining in importance, also repayments) from official donors to countries and are due only in part to aid flows Many African coun- tends to reduce its purchasing power (see About the territories in part 11 of the DAC list of aid recipients tries saw severe erosion in their terms of trade in the data for table 6 9) more advanced countries of Central and Eastern 1980s, which, along with weak policies, contributed The values for population, GNI, gross capital for- Europe, the countries of the former Soviet Union, to falling incomes, imports, and investment Thus mation, imports of goods and services, and central and certain advanced developing countries and tern- the increase in aid dependency ratios reflects events government expenditure used in computing the tories Official aid is provided under terms and con- affecting both the numerator and the denominator ratios are taken from World Bank and International ditions similar to those for ODA * Aid per capita As defined here, aid includes official development Monetary Fund databases The ratios shown may includes both ODA and official aid * Aid dependen- assistance (ODA) and official aid The data cover therefore differ somewhat from those computed and cy ratlos are calculated using values in U S dollars loans and grants from the Development Assistance published by the Organisation for Economic Co- converted at official exchange rates For definitions Committee (DAC) member countries, multilateral operation and Development (OECD) Aid not allocat- of GNI, gross capital formation, imports of goods and organizations, and non-DAC donors They do not ed by country or region-including administrative services, and central government expenditure, see reflect aid given by recipient countries to other devel- costs, research on development issues, and aid to Definitions for tables 1 1, 4 9, and 4 12 oping countries As a result, some countries that are nongovernmental organizations-is included in the net donors (such as Saudi Arabia) are shown in the world total. Thus regional and income group totals do table as aid recipients (see table 6.9a). The 2001 not sum to the world total. data exclude aid from the World Food Programme because the organization implemented an annual program budget in 2002, and the 2001 data are not yet consistent with the DAC reporting system The data in the table do not distinguish among dif- ferent types of aid (program, project, or food aid, emergency assistance, postconflict peacekeeping assistance, or technical cooperation), each of which may have a very different effect on the economy Expenditures on technical cooperation do not always directly benefit the economy to the extent that they defray costs incurred outside the country on the salaries and benefits of technical experts and the -= overhead costs of firms supplying technical services The data on financial flows are compiled by DAC In 1999, to avoid double counting extrabudgetary and published in its annual statistical report, expenditures reported by DAC countries and flows Geographical Distnbufion of Financial Flows to Aid reported by the United Nations, all United Nations Recipients, and in its annual Development Co- agencies revised their data to include only regular operation Report Data are available in electronic budgetary expenditures since 1990 (except for the format on the OECD's Intemational Development World Food Programme and the United Nations High Statistics CD-ROM and to registered users at Commissioner for Refugees, which revised their data http //www oecd org/dac/htm/online htm The from 1996 onward) These revisions have affected data on population, GNI, gross capital formation, net ODA and official aid and, as a result, aid per capi- imports of goods and services, and central gov- ta and aid dependency ratios ernment expenditure are from World Bank and Because the table relies on information from International Monetary Fund databases donors, it is not consistent with information recorded 2003 World Development Indicators 1 341 ] X ] Distribution of net and by Development (A Mssistance Committee members Total Ten major DAC donors Other DAC donors Unhted United States Japan France Germany Kingdom Netherlands Canada Sweden Denmark Norway $ millions, 2001 Afghanistan 322 9 7 7 0 6 9 6 44 1 35 4 72 0 14 2 20 6 2 4 39 7 76 5 Albania 149.8 42.3 6.7 2 0 24 6 5 3 11 5 2 5 38 3 9 3 0 44 2 Algeria 24 8 0.2 -4 0 63 5 0 6 0.2 0.5 -0 2 1 7 0 0 2.1 -39 9 Angola 179.4 34 0 20 7 5 9 9 9 7 9 20 5 2.3 13.4 3 0 17 5 44 4 Argentina 101 -0 5 16 5 5 9 9.2 0 0 0.5 0.7 0 2 0 0 0 0 -22 5 Armenia 124 2 78.0 5 2 41 16 8 24 7 9 0 4 0.9 0.3 24 5_9 Australia Austria Azerbaijan 148 4 30 9 1010 1 5 68 1.2 2.6 0 3 01 . 2.7 1 4 Bangladesh 578 4 87 1 125 6 131 30 1 124 5 43 2 30 3 28 4 418 20.6 33 7 Belarus 221 88 03 11 61 01 17 01 15 0.7 01 16 Belgium Benin 144 5 27 4 8 3 42 5 219 0 1 8.0 1.8 0 1 22 9 0 1 114 Bolivia 530.2 1191 65 9 8 4 51 7 45.6 73.3 8 8 20 2 26 4 3 2 107.6 Bosnia and Herzegovma 376 7 135 1 9.6 2 1 27.0 6.1 52.9 10.6 29.0 7 9 16.9 79 5 Botswana 24 2 -0 3 7_2 0 4 5.6 2 8 2.8 0 1 0 4 1.0 3.6 0 6 Brazil 156 8 -70 8 106 1 14 6 47 0 12 1 15 2 4 0 2 1 1 6 2.4 22 5 Bulgaria 173 4 39 8 484 11.0 37 1 5 3 4.3 04 0 2 4 6 0 1 22 4 Burkina Faso 220 9 13 1 20 4 44 0 23 6 1 7 44 4 5 9 6 0 28 8 0 3 32.7 Burundi 54 7 4 9 0 3 4.7 3 3 17 117 2 3 2 5 5 7 17 9 Cambodia 264 8 22 4 120 2 214 18 7 117 8 2 8 0 16 9 4 9 5 6 26.8 Cameroon 274 6 4 8 413 59 3 46 1 10 7 13 2 5 4 0 0 12 0 1 0 80 9 Canada Central African Republic 47 9 2 4 151 20 9 7 1 0 3 0 5 0 8 . 0 9 Chad 728 45 01 395 167 02 18 0.4 01 01 93 Chile 39 6 -18 1 21.9 8 0 18 0 4 0 0.7 2 3 0 0 0 8 2 2 China 1,075 1 4 8 686.1 42.8 163.8 50.7 26 5 24.3 10.1 10 2 9 7 46 0 Hong Kong, China 3 6 0 0 2.5 0 7 0 2 0 1 0 1 Colombia 372 3 274 7 7 1 8 2 15 0 2 6 15 5 4 8 6 2 0 0 7 4 30 8 Congo, Dem Rep 143 4 20 2 0 3 7 9 12 9 17 0 12.0 6 4 5.5 0 2 3 9 57.0 Congo,Rep 296 106 02 11.2 17 . 14 03 17 .. 03 22 Costa Rica 61 -296 -31 142 07 14 152 16 03 02 04 49 C6ted'lvoire 1585 20 43 1104 190 12 76 15 01 . 02 123 Croatia 744 285 32 14 16 25 3.0 2.8 62 181 72 Cuba 337 36 19 14 22 3.2 1.1 26 08 01 17 152 Czech Republic 29 7 0 5 1 2 5 5 9 8 1.2 31 0 3 0 2 2.1 0 0 5 7 Denmark Dominican Republic 1019 29 7 42 4 4 4 8.2 -0.9 2 4 0 8 0 1 0 3 0 4 14.1 Ecuador 147 6 55 2 16.5 5 1 13 7 1 0 13 6 6 2 0.4 0 9 2 8 32 2 Egypt, Arab Rep 1,090 3 630 1 52 7 201 5 106 3 3 6 20 3 9.6 1.9 25.2 0.9 38.4 El Salvador 231 1 50 9 58 2 11 25 8 3.1 9 5 5.3 8.3 1.2 2.6 65.2 Eritrea 1514 28 9 3.5 5 2 4.7 4 9 119 1 4 7 4 13.9 4 6 65 1 Estonia 25 6 0 5 09 09 2.4 0.1 1.8 0 2 52 7 3 2.5 38 Ethiopia .367 1 94 4 52.4 6 6 25 9 27 6 44.2 12 4 20.6 2 8 16 3 64 0 Finland France Gabon -80 23 16 -140 05 08 09 00 Gambia,The 134 16 31 05 22 38 0.7 03 04 01 02 06 Georgia 151.6 94.3 15 5 19 20 1 5 7 3.3 0.6 2.0 0 1 31 5 0 Germany Ghana 396 0 53 5 34 6 4.5 23 8 97.8 114.2 11 2 2 0 39.7 0.5 14 0 Greece Guatemala 201 2 63 6 45 7 1 6 15 5 0 6 21.2 5 0 12.4 2 0 10 0 23 7 Guinea 120 3 349 17 4 204 17.9 11 2 3 7 7 0 6 30 15 1 Guinea-Bissau 30 4 0 1 02 39 0 7 00 8.0 0.1 2 1 02 00 15 2 Haiti 136 0 811 8.6 13 7 4 7 0 2 48 13 1 0.2 0 1 2 1 7 5 I342 I 2003 World Development Indicators Distribution of net aid by Development Dii' Assistance Committee members Total Ten major DAC donors Other DAC donors United United States Japan France Germany Kingdom Netherlands Canada Sweden Denmark Norway $ mililons, 2001 Honduras 422 3 2013 76 3 41 17 3 12 101 6 0 313 3 4 218 49 6 Hungary 545 25 74 58 189 25 28 03 04 04 01 135 India 904 5 -17 3 528 9 -8 9 57 5 173 9 73 5 13 4 112 19 0 116 418 Indonesia 1,375 4 1410 8601 261 29 9 23 4 119 7 18 8 3 7 3 7 4 6 144 4 Iran, Islamic Rep 90 8 34 4 6 8 32 6 2_5 3 8 0 0 01 3 7 7 0 Iraq 1008 00 19 460 126 169 39 107 88 Ireland Israel 148 5 190 3 0 5 3 6 -47 7 0 7 01 00 0 9 Italy Jamaica -10 -17 8 -4 3 -10 -4 7 6 7 18 19 8 0 4 0 3 -2 1 Japan Jordan 3021 1559 427 16 597 85 07 17 07 30 20 255 Kazakhstan 122 7 56 0 43 9 0 7 8 2 0 9 2 2 13 0 6 01 10 7 9 Kenya 270 5 43 4 46 7 9 9 32 5 551 23 1 3 8 13 6 12 3 4 0 261 Korea, Dem Rep 523 03 03 270 1.1 01 15 34 35 152 Korea, Rep -108_6 -44 3 -79 1 10 1 3 9 0 1 0 7 Kuwait 29 01 10 18 00 00 KyrgyzRepublic 713 281 232 02 74 24 1.8 08 09 0 7 05 54 LaoPDR 1499 45 755 107 136 1 1 27 06 121 32 48 210 Latvia 496 17 12 09 46 01 21 05 127 213 07 38 Lebanon 1017 468 74 271 56 04 05 15 09 43 73 Lesotho 295 17 52 -02 44 41 07 02 32 03 99 Liberia 156 126 01 15 -65 12 23 03 11 00 07 24 Libya 43 02 11 15 01 15 Lithuania 484 -12 18 13 68 02 20 02 148 197 06 22 Macedonia, FYR 164 2 37 3 202 1 0 12 2 74 439 10 8 6 2 04 63 18 6 Madagascar 137 9 37 2 25 5 417 100 2 5 1 5 0 2 01 42 151 Malawi 195 8 30 6 18 3 0 5 19 8 66 5 131 11 0 2 3 216 96 2 7 Malaysia 249 07 131 -33 41 01 06 06 00 108 05 -22 Mali 2085 248 231 608 193 11 415 90 44 01 67 179 Mauritania 813 62 296 186 97 10 35 10 04 04 110 Mauritius 81 -04 13 32 -12 23 01 01 06 20 Mexico 407 418 -111 -27 142 13 27 15 03 -04 04 -73 Moldova 788 435 82 11 1 9 25 152 01 26 10 29 Mongolia 1411 12 7 81 5 04 24.9 21 2 5 08 2 6 1 1 5 7 6 7 Morocco 3421 -12 7 1016 174 4 29 3 0 2 10 3 2 06 -0 7 01 45 2 Mozambique 720 2 918 33 5 15 3 40 7 185 2 86 6 13 9 42 6 48 3 32 6 129 7 Myanmar 892 29 699 12 18 17 22 13 06 10 28 38 Namibia 775 139 32 3.2 184 35 50 02 86 28 37 150 Nepal 270 2 20 2 84 4 -0 6 37 9 33 2 13.4 43 13 6 261 115 26 2 Netherlands New Zealand Nicaragua 714 7 100 6 62 0 2 8 318 10 18 5 6 5 22 7 28 0 14 6 426 3 Niger 1136 80 130 370 157 05 42 23 56 26 246 Nigeria 1075 247 89 148 133 328 28 45 07 -35 11 76 Norway Oman 81 -40 116 05 01 00 00 00 Pakistan 1,110.1 775 6 2114 13.9 201 27 4 18 0 13 9 11 -0 8 5 5 24 0 Panama 171 08 35 04 17 03 06 07 17 74 Papua New Guinea 198 0 1.1 26 2 0 3 3 3 12 01 0 1 165 7 Paraguay 583 70 348 00 36 01 11 02 14 06 95 Peru 425 4 1616 156 5 3 2 242 7 5 24 5 9 5 2 7 19 2 2 318 Philippines 505_0 830 298 2 0 7 191 6 0 20 6 14 4 2 5 5 5 17 53 2 Poland 486 9 80 -3 9 182 9 39 4 45 2 7 112 7 3 5 16 2 03 120 9 Portugal Puerto Rico 2003 Wotid Development Indicators 1 343 Distribution of net aid by Development Assistance Committee members Total Ten major DAC donors Other DAC donors United United States Japan France Germany Kingdom Netherlands Canada Sweden Denmark Norway S millions, 2001 Romania 142 1 41 8 9 7 24 5 24 8 6 8 8.1 3 3 0 5 5 2 0 1 17 3 Russian Federation 906 6 659 3 4 5 14 2 64 1 38 3 9 9 18 1 32 5 14 3 18 7 32 6 Rwanda 148 9 311 1 0 6 1 14 6 36 8 19 2 6 7 8 4 2 7 2 0 20 3 Saudi Arabia 10 5 7 2 2 9 0 4 0 0 0 0 Senegal 223 7 288 22 4 102 4 16 7 1 0 12 0 8 6 0 1 14 11 29 3 Sierra Leone 166 8 26 4 00 2 0 12 0 511 38 1 2 8 3 5 0 2 93 21 5 Singapore 07 24 13 -34 02 01 01 01 SlovakRepublic 338 31 24 30 61 23 35 10 02 38 02 82 Slovenia 00 07 04 07 -67 02 0.8 10 05 00 25 Somalia 885 178 05 11 36 131 05 56 04 124 335 South Africa 313 3 85 9 134 1 3 36 9 418 34 8 7 5 26 5 15 3 17 0 33 0 Spain SriLanka 2799 -97 1847 08 311 150 158 28 183 -05 152 65 Sudan 1076 175 07 18 113 99 236 47 79 15 131 157 Swaziland 42 -01 65 _ 00 -13 -32 11 01 04 01 05 Sweden Switzerland Syrian Arab Republic 92 3 0 0 -19 5 14.6 83 3 0 1 2 8 0 1 0 1 0 9 9 8 Tajlikistan 635 404 46 00 47 09 06 08 10 02 08 93 Tanzania 943 8 25 9 260 4 13 1 48 2 290 1 751 8 4 47 3 66 6 34 9 73 7 Thailand 270 9 24 7 209 6 -10 3 7.7 -0 2 3 3 2 6 3 2 118 1 4 16 9 Togo 285 23 29 103 73 05 10 02 03 00 36 TrinidadandTobago 43 12 12 09 01 03 01 05 01 Tunisia 183 7 -18 8 88 5 87 6 8 0 0 1 0.1 0 1 0 4 0 0 17 8 Turkey -314 -595 -646 31 66.3 -02 24 00 08 00 29 175 Turkmenistan 33 1 14 1 16 4 03 09 0 1 02 00 10 00 0 1 Uganda 386 3 66 5 14 6 65 33 2 82 2 40 8 26 29 4 58 7 197 321 Ukraine 342 5 247.0 71 4 3 33 5 13 7 36 13 8 59 62 02 73 United Arab Emirates 2 5 0 1 1 9 0 5 . . 00 United Kingdom United States Uruguay 107 -17 59 14 42 01 02 01 . 00 05 Uzbekistan 106 7 50 2 30_9 3 7 13 7 0 8 11 0 3 0 0 0 8 5 0 Venezuela, RB 33 5 10 6 3 1 3 3 2 9 0 1 0 3 0 6 0 1 01 12 4 Vietnam 822 1 8 7 459 5 618 37 9 23 7 36 2 118 349 60 2 5 7 816 West Bank and Gaza 280 2 843 215 12 7 17 9 17.0 14 0 05 219 50 37 5 47 9 Yemen,Rep 998 285 49 16 273 33 287 04 06 01 01 44 Yugoslavia, Fed Rep 6311 210 2 0 1 214 78 3 17 0 516 0.3 35 0 0 9 35 8 180 5 Zambia 274 1 29 0 470 7 7 13 8 55 8 29 6 8 9 17 5 22 6 20 8 213 Zimbabwe 148 6 16 0 29 0 20 10 2 18 1 23 5 30 8 2 17 9 10 2 10 5 IzRo - ( ..- Low income 15,383 8 2,774 3 3,739 0 940 4 1,082 8 1,576 7 1,247 7 311 5 444 9 594 0 394 9 2,277 7 Middle Income 12,714 5 3,512 7 2,642 7 1,210 8 1,448 9 453 0 632 0 356 7 349 3 265 3 298 2 1,544 9 Lower middle income 10,787 8 3,388 6 2,400 5 803 2 1,185 2 341 2 553 4 189 4 295 4 151 8 244 9 1,234 1 Upper middle income 1.543 4 44 0 240 4 388 4 203 8 91.8 65 4 132 4 49 8 85 5 35 5 206 3 Low & middle Income 37,836 4 9,561 9 7,554 5 2,904 0 3,149 7 2,707 7 2,273 1 1,350.1 1,317 2 1,147 4 969 5 4,901 3 East Asia & Pacific 5,649 9 535 2 2,987 6 214 8 355 6 134 6 232 3 90.5 93 1 116 5 50 8 839 0 Europe & Central Asia 4,895 7 1,944.0 306 9 301 9 544 0 153 0 251 6 198.1 202 9 154 5 125.9 712 9 Latin America & Carib 4,465 3 1.473 3 738 2 111 9 334 3 190 1 269 6 137 8 139 3 76 0 87 1 907 8 Middle East & N Africa 2,815 1 920 6 352.4 623 8 422 9 48 8 913 29 6 37 6 33 1 65 0 190 1 South Asia 3,523 6 863 7 1,156 8 27 9 221 8 409 7 242 8 79 6 93 5 100.3 104 8 222 9 Sub-Saharan Africa 8,335 5 1,374 8 849 0 978 8 684 2 1,185 6 830 0 201 4 346 5 412 0 321 6 1,151 5 High Income 823 6 1814 -72 0 702 6 -45 6 0 7 47 6 1 4 0 6 0 0 0 3 6 7 Europe EMU Note: Regional aggregates inciude data for economies not specified elsewhere World and income group totals include aid not allocated by country or region 34 E 0 2003 World Development Indicators Distribution of net aid by Development 0.111~~0 Assistance Committee members The data in the table show net bilateral aid to low- development-oriented research, stipends and tuition * Net aid comprises net bilateral official development and middle-income economies from members of the costs for aid-financed students in donor countries, or assistance to part I recipients and net bilateral offi- Development Assistance Committee (DAC) of the payment of experts hired by donor countries cial aid to part 11 recipients (see About the data for Organisation for Economic Co-operation and Moreover, a full accounting would include donor table 6 8) * Other DAC donors are Australia, Austria, Development (OECD) The DAC compilation of the country contributions to multilateral institutions, the Belgium, Finland, Greece, Ireland, Italy, Luxembourg, data includes aid to some countries and territories flow of resources from multilateral institutions to New Zealand, Portugal, Spain, and Switzerland not shown in the table and small quantities of aid to recipient countries, and flows from countries that are unspecified economies that are recorded only at the not members of DAC regional or global level Aid to countries and territo- The expenditures that countries report as official ries not shown in the table has been assigned to development assistance (ODA) have changed For regional totals based on the World Bank's regional example, some DAC members have reported as ODA classification system Aid to unspecified economies the aid provided to refugees during the first 12 has been included in regional totals and, when pos- months of their stay within the donor's borders sible, in income group totals Aid not allocated by Some of the aid recipients shown in the table are country or region-including administrative costs, also aid donors See table 6 9a for a summary of research on development issues, and aid to non- ODA from non-DAC countries governmental organizations-is Included in the world total, thus regional and income group totals do not sum to the world total In 1999 all United Nations agencies revised their data to include only regular budgetary expenditures since 1990 (except for the World Food Programme and the United Nations High Commissioner for Refugees, which revised their data from 1996 onward) They did so to avoid double counting extra- budgetary expenditures reported by DAC countries and flows reported by the United Nations The data in the table are based on donor country reports of bilateral programs, which may differ from reports by recipient countries Recipients may lack access to information on such aid expenditures as 6.11a % of total bilateral aid, 2001 Nicaragua 3% Mozambique 3% W _ . , - ,,Vstnam3% _ g}lndi~~~~a 3% nzania 4% _P akistan 4% * Low income _ ndonesia 5% * Middle income _ _&Rssian Federation 3% I=_ Data on financial flows are compiled by DAC and published in its annual statistical report, Egypt, Arab Rep 4% Geographical Distribution of Financial Flows to Aid Recipients, and its annual Development Co- operation Report Data are available in electronic format on the OECD's International Development Note The countries shown are the top 10 recipients of bilateral aid Aid to the Russian Federation and many other middle- income economies is not classified as official development assistance The figure excludes aid to high-income countries Statistics CD-ROM and to registered users at (less than 1 percent of bilateral aid) and unallocated aid http //www oecd org/dac/htm/online htm. Source OECD data 2003 World Development Indicators I 345 9 Net financial flows from I ultilatera institutions Intemational financial Institutions United Nations Total Regional development World Bank IMF banks Conces- Non- Conces- Non- IDA IBRD sional concessional sional concessional Others UNOP UNFPA UNICEF WFP Others $ mililons, 2001 Afghanistan 3 9 0 8 9 0 6 7 13 6 27 2 Albania 343 00 41 -56 00 14 164 16 05 05 . 34 565 Algeria 0 0 -93 9 0 0 -140 6 0 0 428 -182 8 0 .7 12 09 24 49 -366 8 Angola 108 00 00 00 -01 -15 27 10 17 72 384 98 316 Argentina 00 653 3 00 9,218 3 00 1,1911 01 00 11 83 11,072 2 Armenia 55 0 -0 4 10 6 -7 2 00 -2 3 10 7 0.5 03 09 26 43 72 2 Australia Austria Azerbaijan 27 6 00 10 2 -39 3 00 -1 7 55 2.2 08 08 2.4 41 10 2 Bangladesh 217 9 -5 2 -60 1 00 122 7 02 22 9 12.2 13 7 12 8 89 14 0 3513 Belarus 0 0 -7 3 00 -29 7 0.0 -16 3 -3 0 03 01 17 -54 2 Belgium Benin 436 00 0.5 00 250 -01 135 0.9 19 18 19 32 903 Bolivia 1013 -0 2 36 00 68 4 -40 0 77 8 12 27 14 57 36 219 8 Bosnia and Herzegovina 617 -5.3 0 0 101 00 15 5 -177 4 0 2 0 1 0 4 23 4 -71 3 Botswana -0 5 -4 5 0 0 0 0 -1 6 -13 3 -4 1 0 7 1 0 0 7 2 4 -19.1 Brazil 0 0 810.2 00 6,718 3 00 728 1 -12 5 0.3 1 2 1.6 123 9 8,371 3 Bulgaria 0 0 55 8 0 0 -167 6 0 0 -9 5 -15 4 0 7 0 1 17 -134 2 Burkina Faso 690 00 145 00 138 00 185 39 14 36 11 45 1292 Burundi 22 00 -44 00 -03 -02 -0.9 45 08 25 21 72 115 Cambodia 39 6 0 0 10 6 -1 3 414 0 0 4.5 3 9 3 1 3 6 10.2 4 9 110 2 Cameroon 12 3 -8 6 20 3 -2 0 13 5 4 1 0 8 1 9 1 3 2.2 0 4 3.4 49 2 Canada Central African Republic -5 5 0 0 10 2 0 0 0 0 0.0 -11 1 2 0 8 2 0 1 6 4.2 11 6 Chad 202 91 157 00 94 00 07 2.4 09 23 35 53 660 Chile -07 -79.9 00 00 -13 117 -04 32 01 08 16 -650 China 223 7 663 1 0 0 0 0 0 0 643 7 -137 8 8.9 3 6 12 5 75 116 1,429 4 Hong Kong, China- 0°0 00 Colombia -0 7 136 1 0 0 0 0 -12 3 649 4 345 0 0 2 0 7 1 0 3 2 5 4 1,124 6 Congo,Dem.Rep. 00 00 00 00 00 00 00 37 17 180 21 428 662 Congo,Rep 324 -482 00 -12 00 -07 00 21 0.3 10 0.1 77 -67 Costa Rica -0 2 -18 6 0.0 00 -110 -34 9 43 9 0 1 0 2 0 7 . 2 3 -17 4 Cote-d'lvoire 5 0 -72 1 -66 7 0 0 0 1 -26_4 0 4 1 7 1 0 2.7 08 10 3 -144 0 Croatia 00 569 00 -308 00 56 14 02 . 101 434 Cuba . 09 0.6 0 9 20 2.3 4.6 Czech Republic 0 0 -41 2 0 0 0.0 00 0 0 177 7 0 1 1 2 137 8 Denmark Domintcan Republic -0 7 25 6 0 0 0 0 -10 2 107 1 -1 6 0.1 1 0 0 9 1.0 1.9 124 0 Ecuador -11 48 3 0 0 48 1 -16 0 68 4 -52 2 0 2 10 0 9 2 3 2 3 99.8 Egypt, Arab Rep 2 5 -55 1 0 0 0 0 17 8 -1 0 -28 1 1 7 3 2 3 4 9 1 6.7 -49 0 El Salvador -0 8 252 0.0 0 0 -17 9 128 4 0 6 -0.1 1 0 0 8 0 9 1 5 138 6 Eritrea 78 2 0 0 0 0 0.0 5 6 0 0 5.8 3.7 2 7 2 1 1.7 3 4 101 5 Estonia 0 0 -2 5 0 0 -4 9 0 0 -2 7 -3 4 0 1 0 1 0 2 -13 2 Ethiopia 433 1 0 0 32 4 0 0 24.7 -18 5 14 9 17 0 3 3 19 4 36 0 32 7 559 0 Finland France Gabon 00 -91 00 -111 00 -317 -06 01 02 08 00 3.2 -482 Gambia,The 69 00 85 0.0 31 0.0 63 19 04 09 16 17 29.7 Georgia 631 00 308 -118 00 58 -64 12 03 0.6 2.0 59 895 Germany Ghana 1589 -33 15 00 462 -85 4.1 40 28 30 15 50 2136 Greece Guatemala 00 347 00 00 -49 649 -78 0.4 06 11 27 08 898 Guinea 625 00 17 7 0 0 2 3 -0 5 -1 8 1.2 08 3 4 1.3 315 117 0 Guinea-Bissau 2 4 00 0 0 00 11 00 -1 8 1 0 0 5 1 0 0.2 1 7 5 8 Haiti 08 00 00 00 53 0.0 -01 2.5 29 22 61 14 149 3US 0 2003 World Development Indicators Net financial flows from 0.2 multilateral institutions International flnancial Institutions United Nations Total Regional development World Bank IMF banks Conces- Non- Conces- Non- IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others S millions, 2001 Honduras 94 5 -5 0 13 2 - 0 0 94 1 -14 4 10 7 0 1 1 4 1 1 0 9 1 0 196 6 Hungary 0 0 -10 3 0-0 0 0 0 0 -12 2 7 5 0 4 1 8 -12 8 India 771 1 -79.0 0 0 0 0 0 0 91 2 103 4 17 7 11 1 30 8 27 0 32 4 1,136 7 Indonesia 12 3 -280 0 0 0 -1,357 5 10 5 230 4 -21 7 3 8 6 8 5 1 0 0 16 3 -1,373 9 Iran, Islamic Rep 0 0 -25 4 0 0 0 0 0 0 0 0 00~ 12 21 18 01 17 2 -3 1 Iraq 08 03 24 01 59 94 Ireland Israel 05 05 Italy - - Jamaica 00 39 5 00 -18 4 -4 7 -9 0 -9 3 01 00 08 13 03 Japan - Jordan -2 6 106-4 00 -12 5 00 00 38 3 05 09 08 12 82 7 214 5 Kazakhstan 00 66 6 00 00 1 3 2 29 18 0 08 0 7 08 20 93 2 Kenya 80 9 -22 5 -23 7 0 0 -71 -15 1 -15 5 5 2 2 1 47 19 4 25 4 48 6 Korea, Dem Rep 0 7 0 7 1 1 06 48 7 3 Korea, Rep -3 5 -140 2 00 -5.681 2 -00 -299 0 1 -0 4 - 1 0 -5,854 1 Kuwait 0 7 0 7 Kyrgyz Republic 26 7 0 0 4 8 -6 8 58 3 -8 5 6 3 1 2 - 05 0 9 1 9 85 1 Lao PDR 269_ 0 0 -3 6 0 0 38 9 0.0 11 4 1 5 1 9 2 3 1 0 2 7 82 0 Latvia 0 0 10 7 0 0 -9 7 0 0 -14 1 21 2 0 2 0 1 -0 4 8 8 Lebanon 0 0 -23.9 0 0 0 0 - 00 0.0 53 9 0 6 11 - 08 -50 1 130 3 Lesotho 9 5 -08 4 6 0 0 3 8 -2 4 -3 5 0 5 0 3 0 7 1 2 0 8 13 5 Liberia 0 0 0 0 0 0 -0 6 0 0 0 0 0 0 2 1 0 8 1 8 12 7 8 0 12 1 Libya 5 2 5 2 Lithuania 0 0 26 7 0 0 -34 0 0 0 -6 3 21 4 0 2 0 1 0 4 8 5 Macedonia,FYR 14 9 4 5 0 0 :-76 0 0 -13 8 -07 0 6 0 8 - 6 1 4 7 Madagascar 86 8 0 0 28 1 0 0 -190 00 12 0-59 1 18 -36 -27 2 1 159 2 Malawi 98 6 -4 5 -4 0 00 88 -0 7 00 1 7 2 5 43 1 8 3 1 109 7 Malaysia 00 -22 5 00 -00 00 '-24 5 -2 3 -04 - 01 0 7 1 9 -46 3 Mali 64 6 00 84 00 58 P0 10 8 26 21 4 9 -20 --38 -103 0 Mauritania 45 4 0 .0 17 9 00 43 00 -12 4 08 1 3 1 3 22 1 4 59 9 Mauritius -- -0 6 --8 2 00 00 -0_3 74 0 -3 8 _02_- 02 06 1 0 -14 9 Mexico 00 -560 6 00 00 -0 4 300 4 00 0 5 1 6 1 4 8 3 -248 9 Moldova 14 2 -3 1 11 8 -14 3 00 3.2 -7 7 09 0 2 -0 7 1 14 7 2 Mongolia 23 5 00 -1 7 00 30 4 0 0 - 0 7 112 2 2 - 1 1 - 34 60 8 Morocco -14- _-2183 0 0 0 0 51 -5 3 99 4 11 0 8 1 8 2 2 37 -113 2 Mozambiqu~e - 48 7 0 0 10 5 0 0 48 9 -3 9 4 4 6 65 58 8 4 3 4 0 133 2 Myanmar 00 00 00 0 o op -0 00 -0- -2 15 6 1 5 - 65 - 9 2 32 6 Namibia 0 3 0 6 0 8 03 4 8 6 5 Nep al -29 6 00 -4 3 0 0 34 1 0 0 4 3 8 4 4 9 -60 6 3 11 4 94 5 Netherlands New Zealand Nicaragua -61 8 -4 6 -5 1 0 0 105 3 -8 5 0 2 1 5 2 4 1 10 8 4 1 8 155 9 Niger 61 5 0 00 10 1 0 0 8 3 0 0 -4 2. 5 0 -23 59 -30 4-41 93 0 Nigeria 1 3 -189 1 0 0 0 0 10 1 -78.0 --1 7 8 6 5 6 -22 8 -210 -199 4 Norway - Oman 0 0 -1 4 0 0 -00 0-0 0 0 285 7 0 00 0 5 - 1 2 2861 Pakistan - 530 6 -161 4 -379 297 5 432 5 9 7 8 87 5 9 3. 3 12 4 -27 24 7 1,201 8 Panama -00 -00 00 -33 3 -10 6 44 7 44 - 02 -04 0 6 -14 7 8 Papua New Guinea -3 2 40 3 0 0 70 9 -3 9 30 3 -2 4 0 3 0 5 1 1 1 - 9 135 8 Paraguay -1 5 13 0 0 0 0 0 -10_2 _327 -11 3 0 1 0 7 0 08 - 0 5 24 9 Peru 0 0 35 4 0 0 -153 4 -6 4 278 3 507 01 0 6 1 16 1 0 3 9 5 6 669 5 Philippines 1 7 -193 7 -00 -7 7 14 4 94 9 -01I 2 9 2 9 2 9 6 0 -75 8 Poland 0 0 93 4 0 0 0 0 0 0 0 0 0 0 0 3 0 1 1 3 95 1 Portugal Puerto Rico 2003 World Development Indicators I 347 Net financial flows from m Lmultilateral institutions International financial institutions UnIted Nations Total Regional development World Bank IMF banks Conces- Non- Conces- Non- IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions, 2001 Romania 0 0 314 0 0 -50 6 0 0 4 5 207 6 0.6 05 0 7 2 1 196 8 Russian Federation 0 0 23 5 0 0 -3,816 7 0 0 26 8 0 0 0.5 0 6 .. 17 3 -3,748 0 Rwanda 50 1 0 0 110 -0 9 10 2 0 0 -2 1 2 7 16 2.8 20 0 8 5 83 9 Saudi Arabia . . 0 4 .. 15 9 16 3 Senegal 109 4 -1 3 7 1 0 0 20 9 -3 6 18 3 2 3 2 2 2 5 3 0 2 8 160.5 Sterra Leone 67 2 0 0 32 0 -47 7 12 4 -0 3 -1.1 3 1 0 5 2 8 0 0 5 6 74 3 Singapore 0 2 0 2 Slovak Republic 0 0 317 0 0 0.0 0 0 -1.1 64 4 0 3 1 1 96 4 Slovenia 0 1 . 10 10 Somalia 00 00 00 00 00 00 00 3.6 02 56 07 115 208 SouthAfrica 00 40 00 00 00 00 00 15 16 20 71 161 Spain SriLanka 119 -43 -713 1316 593 02 13 32 14 08 25 90 1430 Sudan -09 -05 00 00 00 00 -02 22 25 64 67 183 278 Swaziland -03 00 00 00 16 52 80 03 02 06 14 170 Sweden Switzerland Syrian Arab Republic -1 5 -7 9 0.0 0 0 0 0 0 0 -43.6 0 9 2 5 0 9 4.6 28 5 -20 1 Tajikistan 348 00 153 -119 29 00 282 25 06 11 49 25 760 Tanzania 99 6 -3 5 48.6 0 0 9 3 0 0 4 2 5 2 4 1 5 9 25 29 9 203 3 Thailand -3 4 105 5 0 0 -1,289 0 -2 2 70.5 -15 2 1 0 0 7 12 0 0 6 5 -1,124 4 Togo 14 00 00 00 00 00 120 23 12 16 21 206 Trinidad and Tobago 0.0 1 3 0 0 0 0 2 8 -8 9 -5 4 0 0 . 0.5 -9 7 Tunisia -2 1 146 9 0 0 -31 5 0 0 47 4 120 1 0.5 0.8 0 8 2.4 285 3 Turkey -5 9 1,105 7 0 0 10,219 8 0 0 0 0 547.3 0 8 0 7 0 9 . 5 6 11,874 9 Turkmenistan 00 22 00 00 00 -30 -07 09 07 10 10 21 Uganda 2928 00 -33 00 341 -2 6 -0.4 4.3 48 54 8.6 176 3526 Ukraine 0.0 304 8 0 0 -89 7 0 0 -22.9 -15 3 1 2 0 3 4 0 182 4 United Arab Emirates . . -0 4 0 9 0 5 United Kingdom United States Uruguay 00 -7 8 0 0 0 0 -1 7 157 2 -3.8 0 3 0 1 0 9 1 0 146 1 Uzbekistan 00 312 00 -447 45 455 01 12 07 16 14 413 Venezuela, RB 0 0 -133 3 00 -198 1 0 0 119 1 102 9 0.2 0 6 0 8 0.2 2 9 -104 9 Vietnam 276 7 0 0 67 0 -5 1 138 9 0.0 14 1 7 4 3 9 4 4 10.1 7 1 514 3 West Bank and Gaza 2.4 1 2 15 11 2119 217 0 Yemen, Rep 59 8 0 0 113 0 -44 5 00 _ 0.0 -16 2 5 7 4 0 3 8 71 8 9 134 5 Yugoslavia, Fed Rep 00 0 0 0 0 127.3 0 0 0 0 5 9 1 8 1 6 0 1 02 50 8 187 5 Zambia 1216 -7.8 535 0.0 10 9 00 6 2 2 8 11 34 42 14 2 206 0 Zimbabwe -2 2 -10 9 -1 3 -7 6 00 -2 0 1 2 1 7 1 6 34 5 1 -10 9 Low income 4,458 5 -363 3 473 4 -1,378 0 1,393 5 279 2 183 1 230.1 136 4 285.4 298 0 560 7 6,258 9 Middle Income 556 8 2,862 2 -51 3 20,418 6 209 1 4,609 4 2,086 6 52 5 53 0 69 0 59 4 850 8 31,716 6 Lower middle income 557 3 2,053 8 -51 3 4,804.0 234 5 2,186 8 1,359 5 43 2 43.0 56 0 55 8 562 6 11,849 4 Upper middle income -0 5 808 3 0 0 15,614 6 -25 5 2,422.6 727.0 9 3 7 3 12 2 0 2 251 8 19,827 3 Low & middle Income 5,015 3 2,498 9 422 1 19,040 6 1,602 6 4,888 6 2,269 7 286.7 313 6 605 2 357 3 1,913 6 38,856 8 East Asia & Pacific 600 5 3089 72 3 -2,589 8 269 0 1,045 8 -152.6 51.2 30 5 46 6 29 6 91 7 -226 0 Europe & Central Asia 326 3 1,774 8 87 6 5,974 2 67 0 -3 2 897 8 211 9 8 16 0 122 144 9 9,316 4 Latin Amenca & Carib 261 3 1,013 7 6 2 15,581 5 212 9 3,797 6 1,029 6 14.4 22 3 25 4 37.3 213 8 22,178 7 Middle East& N Africa 63 1 -124 7 117 6 -231.1 24 7 83 8 3210 16 4 18.5 211 28 8 448 6 758 9 South Asia 1,566 5 -91 8 -97 7 429 1 656 9 169 8 84.0 53 1 36 5 734 556 107 1 2,986 9 Sub-Saharan Afnca 2,197 7 -382 1 236 1 -123 3 372 2 -205.4 89 8 126 5 75 6 181 2 190 6 562 4 3,130.8 High Income 0 3 0 0 00 0 0 6 7 Europe EMU Note: The aggregates for the regional development banks, United Nations, and total net financial flows include amounts for economies not specified elsewhere Because the World Food Programme implemented an annual program budget in 2002, its 2001 data are not yet consistent with the Development Assistance Committee's reporting system The World Food Programme data in the table are for 2000 and are not Incluided in the total column 348 I 2003 World Development Indicators Net financial flows from C multilateral institutions 0 This table shows concessional and nonconcessional Eligibility for IDA resources is based on gross national * Net financial flows in this table are disbursements of financial flows from the major multilateral institutions- income (GNI) per capita, countries must also meet per- public or publicly guaranteed loans and credits, less the World Bank, the International Monetary Fund (IMF), formance standards assessed by World Bank staff repayments of principal * IDA is the International regional development banks, United Nations agencies, Since July 1, 2002, the GNI per capita cutoff has been Development Association, the soft loan window of the and regional groups such as the Commission of the set at $745, measured in 2001 using the Atlas method World Bank * IBRD is the International Bank for European Communities Much of the data comes from (see Users guide) In exceptional circumstances IDA Reconstruction and Development, the founding and the World Bank's Debtor Reporting System extends eligibility temporarily to countries that are largest member of the World Bank Group * IMF is the The multilateral development banks fund their non- above the cutoff and are undertaking major adjustment International Monetary Fund Its nonconcessional lending concessional lending operations primarily by selling efforts but are not creditworthy for lending by the consists of the credit it provides to its members, mainly low-interest, highly rated bonds (the World Bank, for International Bank for Reconstruction and Development to meet their balance of payments needs It provides con- example, has a AAA rating) backed by prudent lending (IBRD) An exception has also been made for small cessional assistance through the Poverty Reduction and and financial policies and the strong financial backing island economies Lending by the International Finance Growth Facility and the IMF Trust Fund * Regional devel- of their members These funds are then on-lent at Corporation is not included in this table opment banks include the African Development Bank, in slightly higher interest rates, and with relatively long The IMF makes concessional funds available Abidjan, C6te d'lvoire, which lends to all of Africa, includ- maturities (15-20 years), to developing countries through its Poverty Reduction and Growth Facility, ing North Africa, the Asian Development Bank, in Manila, Lending terms vary with market conditions and the poli- which replaced the Enhanced Structural Adjustment Philippines, which serves countnes in South and Central cies of the banks Facility in 1999, and through the IMF Trust Fund Asia and East Asia and Pacific, the European Bank for Concessional flows from bilateral donors are defined Eligibility is based principally on a country's per capita Reconstruction and Development, in London, England, by the Development Assistance Committee (DAC) of the income and eligibility under IDA, the World Bank's con- which serves countnes in Europe and Central Asia, the Organisation for Economic Co-operation and Develop- cessional window European Development Fund, in Brussels, Belgium, which ment (OECD) as those containing a grant element of at Regional development banks also maintain conces- serves countries in Africa, the Caribbean, and the Pacific, least 25 percent The grant element of loans is evaluat- sional windows for funds Loans from the major region- and the Inter-American Development Bank, in ed assuming a nominal market interest rate of 10 per- al development banks-the African Development Bank, Washington, D C , which is the principal development cent The grant element of a loan carrying a 10 percent Asian Development Bank, and Inter-American bank of the Americas * Others is a residual category in interest rate is nil, and for a grant, which requires no Development Bank-are recorded in the table accord- the World Bank's Debtor Reporting System It includes repayment, it is 100 percent Concessional flows from ing to each institution's classification such institutions as the Caribbean Development Bank multilateral development agencies are credits provided In 1999 all United Nations agencies revised their data and the European Investment Bank * Untted Nations through their concessional lending facilities The cost of to include only regular budgetary expenditures since includes the United Nations Development Programme these loans is reduced through subsidies provided by 1990 (except for the World Food Programme and the (UNDP), United Nations Population Fund (UNFPA), United donors or drawn from other resources available to the United Nations High Commissioner for Refugees, which Nations Children's Fund (UNICEF), World Food Programme agencies Grants provided by multilateral agencies are revised their data from 1996 onward) They did so to (WFP), and other United Nations agencies, such as the not included in the net flows avoid double counting extrabudgetary expenditures United Nations High Commissioner for Refugees, United All concessional lending by the World Bank is carried reported by DAC countnes and flows reported by the Nations Relief and Works Agency for Palestine Refugees out by the International Development Association (IDA) United Nations in the Near East, and United Nations Regular Programme for Technical Assistance * Concessional flnancial flows 8.12a cover disbursements made through concessional lending __i T-1 M Ill - l7i, FTrA VT-_ T . .,, facilities * Nonconcessional flnancial flows cover all Net nonconcessional financial flows from the IMF ($ billions) other disbursements 20 15 1 The data on net financial flows from international 10 financial institutions come from the World Bank's 5 . Debtor Reporting System These data are pub- O ED | _ _ _ n . >^ lished in the World Bank's Global Development Finance 2003 and electronically as GDF Online _5 > The data on aid from United Nations agencies -10 come from the DAC annual Development Co- -15 operation Report Data are available in electronic 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 format on the OECD's Intemational Development Financial crises In Mexico (1994-95), East Asia (1997-98), and Latin America (2001) led to large disbursements of Statistics CD-ROM and to registered users at nonconcessional lending by the International Monetary Fund http //www oecd org/dac/htm/online htm Source World Bank, Debtor Reporting System 2003 World Development Indicators 1 349 X~ lForeign labor and population in Lj0L D selected OECD countries Foreign population a Foreign labor force b Inflows of foreign population % of total % of total Total Asylum seekers thousands population labor force thousands c thousands 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Austria 456 758 5 9 9 3 7 4 10 5 . 66 23 18 Belgium 905 862 9 1 8.4 7.1 8 9 50 69 13 43 Denmark 161 259 3 1 4 8 2 4 3 4 15 20 5 10 Finland 26 91 0 5 1 8 1.5 6 9 3 3 France 3,597 3,263 6.3 5.6 6 2 6 0 102d 119d 55 39 Germany 5,343 7,297 8 4 8 9 88e 842 649 193 79 Ireland 80 127 2 3 3 3 2 6 3.7 24d 0 11 Italy 781 1,388 1 4 24 1 3 3.6 272d 5 25 Japan 1,075 1,686 0 9 1.3 01 0 2 224 346 0 Luxembourg 113 165 29 4 37 3 45 2e 57 3e 9 11 0 1 Netherlands 692 668 4 6 4 2 - 3 1 e 34e 81 91 21 44 Norway 143 184 3 4 4 1 2 3 4 9 16 28 4 11 Portugal 108 208 1 1 2 1 1 0 2 0 14d 16d 0 0 Spain 279 896 0.7 2 2 0 6 1 2 . 9 8 Sweden 484 477 5.6 54 5 4 50 53 34 29 _ 16 Switzerland 1,100 1,384 16.3 19 3 18 9 18 3 101 87 36 18 United Kingdom 1,723 2,342 3 2 4 0 3 3 4 4 204 289 38 99 Foreign populatlon a Foreign labor force b Inflows of foreign populatlon % of total % of total Total Asylum seekers thousands population labor force thousands ' d thousands 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Australia 3,886 4,517 22 8 236_ 25.7 24 5 121 316 4 12 Canada 4,343 16 1 18 5 . 214 313 37 36 United States 19,767f 28,400g 7 9f 10 49 94 124 1,536 3,590 74 57 a Data are from population registers or from registers of foreigners, except for Australia, Canada, France, and the United States (censuses), Portugal and Spain (residence permits), and Ireland and the United Kingdom (labor force surveys), and refer to the population on December 31 of the year indicated b Data include the unemployed. except in Italy. Luxembourg, the Netherlands, Norway, and the United Kingdom Cross-border and seasonal workers are excluded unless otherwise noted c Inflow data are based on population registers and are not fully comparable because the cnteria governing who gets registered differ from country to country Counts for the Netherlands, Norway, and especially Germany include substantial numbers of asylum seekers d Data are based on residence permits or other sources e Includes cross-border workers f From the U S Census Bureau, 1990 Census of Population Listing g From the U S Census Bureau, Current Population Report (March 2000) 350 0 2003 World Development Indicators Foreign labor and population in 61 1 selected OECD countries The data in the table are based on national definitions placed by wars and natural disasters throughout the * Foreign (or foreign-born) population is the number and data collection practices and are not fully compa- world Systematic recording of migration flows is dif- of foreign or foreign-born residents in a country rable across countries Japan and the European merm- ficult, however, especially in poor countries and * Foreign (or foreign-born) labor force as a percent- bers of the Organisation for Economic Co-operation those affected by civil disorder age of total labor force is the share of foreign or for- and Development (OECD) have traditionally defined for- eign-born workers in a country's workforce * Inflows eigners by nationality of descent Australia, Canada, of foreign population are the gross arrivals of immi- and the United States use place of birth, which is clos- grants in the country shown The total does not er to the concept used in the United Nations' definition include asylum seekers, except as noted * Asylum of the immigrant stock Few countries. however, apply seekers are those who apply for permission to just one criterion in all circumstances For this and remain in a country for humanitarian reasons other reasons, data based on the concept of foreign nationality and data based on the concept of foreign- born cannot be completely reconciled See the notes to the table for other breaks in comparability between countries and over time Data on the size of the foreign labor force are also problematic Countries use different permit systems to gather information on immigrants Some countries issue a single permit for residence and work, while others issue separate residence and work permits Differences in immigration laws across countries, particularly with respect to immigrants' access to employment, greatly affect the recording and meas- urement of migration and reduce the international comparability of raw data The data exclude tempo- rary visitors and tourists (see table 6 14) OECD countries are not the only ones that receive substantial migration flows Migrant workers make up a significant share of the labor force in Gulf coun- tries and in southern Africa, and people are dis- 6.13a % of total labor force, 1990-2000 20 15 Austria 10 France International migration data are collected by the 5 Swe OECD through information provided by national correspondents to the Continuous Reporting Norway Kingdom System on Migration (SOPEMI) network, which provides an annual overview of trends and poli- 1990 1992 1994 1996 1998 2000 cies The data appear in the OECD's Trends in International Migration 2002. Source Organisation for Economic Co-operation and Development 2003 World Development Indicators 1 351 mTravel and tourism Intemnational tourism Intemnational tourism receipts International tourism expenditures Inbound tourists Outbound tounsts % of % of thousands thousands $ millions exports $ millions imports 1990 2001. 1.990 2000 1990 2001 1990 2001 1990 2001 1990 2001. Afghanistan 1 Albania 30 34 18 4 389 1 1 55.3 4 258 0 8 14 5 Algeria 1,137 901 3,828 903 64 102 0.5 0.5 149 193 1 5 2 0 Angola 67 67 . . 13, 18 0 3 0 2 38 127 1.1 2 2 Argentina 1,930 2,629 2,398 4,786 1,131 2,534 7 6 8 2 1,505 -3,890 22 0 14 1 Armenia 45 . . 45 10.1 .. 37 3 8 Australia -2,215 4,817 2,170 3,210 4,088 7,625 8 2 9 5 4,535 5,812 8 5 7 4 Austria 19,011 18,180 8,527 3,954 13,417 10,118- 21 1 10 1 7,748 8,886 12 6 8 9 Azerbaijan .. 766 .. 1,204 228 63 3 0 132 6 5 Bangladesh 115 207 388 1,103 11 48 0 6 0 7 78 -163 1 9 1 6 Belarus 19 0 2 133 1 6 Belgium 5,204 6,452 3,835 7,773 3,721 6.917 2 7 3 2 5-,477 9,766 4 1 4 8 Benin 110 418 28 7.7 . 12 2 6 Bolivia 254 300 242 196 91 156 9 3 10 3 130 118 12 0 5 9 Bosnia and Herzegovina 1 89 21 .. 1.7 Botswana 543 __ 995 192_ . 117 313 5.8 10 4 56 143 2 8 6 0 Brazil 1,091 4,773 1,188 2,679 1.444 3,701 4 1 5 5 1,559 3,199 5 5 4 4 Bulgaria 1,322 3.186 2,395 - 2,592 320 1,201 4.6 16.0 189 569 2 4 6 6 Burkina Faso 74 126 11 .. 3.2 . 32 4 2 Burundi 109 26 24 -16 4 4.5 17 14 5 3 9 3 Cambodia 17 466 49 50 228 15 9 15.1 19 1 0 Cameroon 89 59 53 39 24 1 4 279 14 5 Canada 15,209 19,697 20,415 1-8,368 6,339 10,774 4 2 3.5 10,931 11,624 7 3 4 3 Central African Republic 8 10 3 1 4 51 12 4 Chad 9 56 24 8 3 0 70 14 4 Chile 943 1,723 768 1,567 540 788 5 3 3.5 426 1,040 4.6 4 9 China . 33,167 2.134 10,473 2,218 17,792 3 9 5 9 470 13,114 1 0 5 2 Hong Kong, China 6,581 -13,725 2,043 4,175 5.032 8.241 5 0 3 5 12,494 5 6 Colombia 813 616 781 1,098 4-06 1,2-09 4 7 8 1 454 1,160 6 6 7 3 Congo, Dem Rep- 55 103 50 7 0 3 16 0 6 Congo,Rep 33 19 8 12 0 5 0 7 113 58 8 8 4 2 Costa-Rica 435 1,132 191 353 275 1,278 14 0 18 4 148 361 6 3 4 9 M6e dlvoire 196 2 51 57 1 5 1 3 169 226 4 9 6 2 Cro-atia 7,049 6,544 - 1,704 -3,335 . 34 6 729 606 5 7 Cuba 327 1,736 12 56 243 1,692 Czech Republic 5,194 3,510 39,977 419 2,979 7 4 455 1,388 . 3 3 Denmark 2,028 3,929 4,841 3,322 3,923 6.8 5 0 3,676 4,684 8 9 6 9 Dominican- Republic 1,305 2,778 -137 _ 364 900 2,689 49 1 32.3 144 286 6.4 2. 8 Ecuador 362 609 181 386 188 430 5 8 7.4 175 -340 6 9 5 0 Egypt, Arab Rep 2,411 4,357 2,012 2,886 1,100 -3,800 12.0 22 5 129 1,132 0 9 5 2 El Salvador 194 735 525 787 18 235 1 8 5 9 61 171 3 8 3 0 Eritrea 169 113 . . 74 50 2 Esto-nia -372 1,320 1,780 27 507 4.1 10.2 19 191- 2 7 3 7 Ethiopia 79 136 89 . 25 68 3 7 6 9 11 74 1 0 3 8 Finland 2,826 1,169 5,314 1,167 1,441 3 7 3 0 2,791 1,854 -8 3 4 8 France 52,497 76,506 19,430 16,709 -20,184 -29,97-9 7 1 8 1 12,423 17,718 4 4 5 0 Gabon 109 169 161 3 7 0 1 0 2 137 170 7 6 8 7 Gambia,The 100 96 ..26 15 5 8 4 2 Georgia 302 . 373 413 .. 62.1 . 110 . 9.3 Ge-rmany 17,045 17,861 56,261 73,400 14,288 17,225 3 0 2 6 33,771 46,222 8 0 7 5 Ghana 146 439 81 448 8 2 18 8 13 100 0_9 3 0 Greece 8,873 13,096 1,651 2,587 9,219 19 9 31 3 1,090 4,181 5 6 10 1 Guatemala 509 -835 289 391 185 493 11 8 12 7 100 182 5 5 3 3 Guinea 37 30 14 3 6 1 7 30 15 3 1 1 7 Guinea-Bissau 8 Haiti 144 140 46 54 15.2 10 9 37 5 3 352 I 2003 World Development Indicators Travel and tourism 14 International tourism International tourism receipts Intemnational tourism expenditures Inbound tourists Outbound tourists % of % of thousands thousands $ millions exports $ millions imports 1990 2001 1990 2000 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 290 518 196 235 29 262 2 8 10 6 38 157 3 4 4 5 Hungary 20,510 15,340 13,596 10,622 824 3,933 6 8 11 0 477 1,309 4 3 3 7 India 1.707 2,5_37 2,281 3,811 1,513 3,042 6 6 4- 7 393 2,567 1 2 3 4 Indonesia 2,178 5,154 688 2,105 5,411 7 2 8 6 836 3,197 3 0 5 8 Iran, Islamic Rep 154 1,402 788 1,450 61 1,122 0 3 4 7 340 1,350 1 5 8 9 Iraq 748 127 239- Ireland 3,666 6,448 1,798 3,576 1,883 3,547 7 0 3 6 1,163 2,957 4 7 3 9 Israel 1,063 1,196 883 3,203 1,396 2,166 8 1 _5 5 1.442 2,896 7 1 6 7 Italy 26,679 39,055 16,152 18,962 16,458 25,787 7 5 8 6 10,304 14,215 4 7 5 0 Jamaica 989 1,277 740 1,233 33 4 36 8 114 209 4 8 4 7 Japan 3,236 4,772 10,997 16,358 3,578 3,301 1 1 0 7 24,928 26,530 8 4 6 3 Jordan 572 1,478 1,143 1,560 512 700 20 4 18 5 336 420 9 4 7 0 Kazakhstan 1, 84~5 396 3 8 474 4 4 Kenya 814 841 210 443 ~308- 19 9 10 3 38 132 1 4 3 5 Korea, Dem Rep 115 Korea, Rep 2,959 5,147 1,561 5,508 3,559 6,283 4 9 3 5 3,166 6,887 4 1 4 0 Kuwait 15 79- 132 98 -16 0 5 1,837 2,451 25 6 21 5 Kyrgyz Republic 69 32 15 2 6 16 2 4 Lao PDR -14. 169 -3 104 2 9 21 8 1 17 0 5 2 9 Latvia 591 2,256 7 120 0 6 3 5 13 224 1 3 5 3 Lebanon 210 837 1,650 837 43 6 Lesotho 171 231 17 24 17 0 9 5 12 9 1 6 1 2 Liberia Libya 96 174 425 6 _ 28 0 1 04 424 150 4 7 3 1 Lithuania 1,271 3,482 384 6 4 218 3 3 Macedonia, FYR 562 99 45 23 1 7 Madagascar 53 170 34 40 119 8 5 10 0 40 114 4 9 7 5 Malawi 130 228 16 27 3 6 5 5 16 2 9 Malaysia 7,446 12,775 14,920 26,067 1,667 4,936 5 1 4 4 1,450 1,973 4 6 2 6 Mali 44 89 47 71 11 2 11 3 62 41 7 5 4 4 Mauritania 30 9 28 1 9 7 7 23 55 4 4 13 3 Mauritius 292 660 89 154 244 625 14 2 22 0 94 182 4 9 6 7 Mexico 17,176 19,811 7,357 11,081 5,467 -8,401 l1 2 4 9 5,519 5,702 10 6 3 1 Moldova 226 16 49 37 4 46 6 2 88 8 0 Mongolia 147_ 192 5 36 1 0 6 8 1 41 0 1 6 2 Morocco 4,024 4,223 1,202 1,612 1,259- -2,460 2-0 2 22 0 184 354 2 4 2 9 Mozambique Myanmar 21 205 9 _ 45 1 4 1 7 16 25 1 4 1 0 Namibia 213 861 85 7 0 63 4 0 Nepal 255 363 82 122 64 137 16 9 10 1 45 73 5.9 3 8 Netherlands 5,795 9,500 9,000 14,180 ~4,155 6,722 -26 2 6 -7,376 12,016 5 0 5 0 New Zealand 1,910- 717 1,185 1,030 2,252 8 8 12 3 958 1,340 8 2 8 0 Nicaragua 106 483 173 452 12 109 3 1 11 7 15 76 2 2 3 8 Niger - 21 52 18 10 17 24_ 3 2 7 5 44- 28 6 0 6 1 Nigeria 190 955 56 25 156 0 2 0 7 576 730 8 3 5 2 Norway --1,955 4,244 2,667 1,570 2,042 3 3 2 6 3,679 4,305 9 5 8 8 Oman 149 562 69 118 1 2 1 0 47 341 1 4 55 Pakistan 424 500 156 92 2.5 0.9 440 255 4 7 2 0 Panama 214 519 151 221 172 626 39 8 1 99 176 24 2 2 Papua New Guinea 41 54_ 66 106 41 1J01 30O 48_ 50 53 3 3 2 9 Paraguay 280 295 264 281 128 101- 5 1 3 6 103 91 4 7 2 7 Peru 317 1,010 329 781 217 865 5 3 10 1 295 576 7 2 6 1 Philippines 1,025 1,797 1,137 1,755 1,306 1,723 11 4 5 0 i11 1,005 0 8 2 8 Poland 11,350 15,000 22,131 55,097 358 4,815 1 9 9 4 423 3,500 2 8 6 0 Portugal 8,020 12,167 2,268 3,555 5,479 16 5 15 8 867 2,105 3 2 4 7 Puerto Rico 2,560 3,551 996 1,134 1,366 2,728 630 1,004 2003 World Development Indicators 353 0 Travel and tourism International tourism Intemational tourism receipts International tourism expenditures Inbound tourists Outbound tourists 96 of % of thousands thousands $ millions exports $ millions Imports 1990 2001 1990 2000 1990 2001 1990 2001 1990 2001 1990 2001 Romania 3,099 2,820 11,247 6,274 106 362 1 7 2 7 103 449 1 0 2 7 Russian Federation 3,009 21,169 4,150 18,371 7,510 8 9 7,434 141 Rwanda 16 10 24 6 9 219 23 20 6 4 4 7 Saudi Arabia 2,209 6,295 Senegal 246 389 167 140 115 10 5 105 54 5 7 3 0 Sierra Leone 98 24 19 8 9.1 110 4 6 1 9 25 Singapore 4,842 6,726 1,237 3,971 4,937 6,018 7.3 3 6 1,893 4,647 2 9 3 6 Slovak Republic 822 1,219 188 343 70 639 . 4 2 181 287 1 7 Slovenia 616 1,219 671 996 85 88 282 519 4 1 4 5 Somalta South Africa 1,029 5,908 616 3,363 992 2,707 3 6 7 4 1,117 2,004 5 3 6 1 Spain 34,085 49,519 10,698 4,794 18,593 32,873 22.2 18 7 4,254 5,974 4 2 3 3 Sri Lanka 298 337 297 524 132 211 5 8 3 4 74 245 2 5 3 4 Sudan 33 50 203 21 56 4 2 3 3 51 55 5 8 2 7 Swaziland 263 281 30 34 4 6 3 4 35 36 4 6 2 9 Sweden 2,894 6,232 10,500 2,906 4,162 4 1 4 2 6,286 6,803 8 9 8 0 Switzerland 13,200 10,700 9,627 12,009 7,411 7,618 7 6 6 2 5,873 6,180 6 1 5 6 Syrian Arab Republic 562 1,318 1,041 320 1,082 6 4 158 249 610 8 4 10 2 Tajikistan 4 *0 0 0 Tanzania 501 301 65 725 12 1 51 7 23 330 1 6 15 1 Thailand 5,299 10,133 883 1,909 4,326 6,731 14 8 8 8 854 2,179 2 4 3 1 Togo 103 57 58 5 8 7 1 2 40 3 4 7 0 5 Trinidad and Tobago 195 399 254 95 210 4 2 6 2 122 8 6 Tunisia 3,204 5,387 1,727 1,480 948 1,605 18 2 16 9 179 263 3 0 2 8 Turkey 4,799 10,783 2,917 4,758 3,225 8,932 15 3 17 7 520 1,738 2 0 3 8 Turkmenistan 357 Uganda 69 205 10 149 4 1 20 5 8 141 1 2 9 4 Ukraine 5,791 7,399 2,725 12 9 2,179 10 6 United Arab Emirates 973 3,907 315 1,012 United Kingdom 18,013 22,833 31,150 53,881 13,762 16,283 5 8 4 2 17,560 36,483 6 6 8 7 United States 39,362 45,491 44,623 58,386 43,007 72,295 8.0 7 2 37,349 60,117 6.1 4 4 Uruguay 1,892 .. 778 238 561 11 0 17 1 111 252 6 7 68 Uzbekistan Venezuela, RB 525 469 309 891 496 563 2 6 1 6 1,023 1,801 10 8 8 2 Vietnam 250 1,383 168 85 West Bank and Gaza 330 . 155 Yemen, Rep 52 76 20 38 1 3 0 9 64 136 2 9 4 8 Yugoslavia, Fed Rep 1,186 351 134 40 1 4 Zambia 141 457 41 85 3 0 9 7 54 2 8 Zimbabwe 605 1,868 200 331 60 125 3 0 5 9 66 110 3.3 4 8 |~ - -- , Qaff9 J3 mjXE30 1gCjg9 &Y0 4 ) ~- (r F-- J Low Income 13,437 28,833 10,970 16,709 4 9 6 5 13,100 3 8 5 1 Middle income 133,372 232,474 149,300 236,011 43,817 125,609 7 3 8 2 28,764 70,989 50 4 7 Lower middle income 64,533 125,664 43.815 63,822 22,403 71,418 80 8 9 34,596 2 7 4 6 Upper middle income 85,421 105,950 177,268 21,710 54,168 6 5 7 4 17,542 31,535 7 5 4 7 Low & middle income 151,524 264,322 294,863 51,846 142,306 6 8 8 0 35,180 84,218 4 8 4 8 East Asia & Pacific 67,164 21,567 45,404 12,218 38,207 7 3 6 7 3,946 22,600 2 3 4 7 Europe & Central Asia 64,476 98,720 176,460 9,975 40,747 7 4 11.1 21,727 2 6 4 6 Latin Amenca & Carib 33,957 49,861 17,586 28,743 15,622 32,562 8 2 7 1 13,049 21,299 9 0 5 0 Middle East & N Africa 16,544 27,419 . 3,288 6,937 South Asia 3,054 4,496 3,503 6,255 1,968 3,873 5 8 4 3 1,048 3,491 2 1 3 3 Sub-Saharan Africa 7,168 17,931 3,093 7,030 3 8 6 0 3,683 5,507 5 5 5 7 High Income 311,961 426,407 275,794 331,292 212,121 319,585 6 0 5 4 232,094 336,785 6 5 5 7 Europe EMU 184,004 253,706 100,058 149,258 6 6 6 2 88,497 127,959 6 0 5 8 354 0 2003 World Development Indicators Travel and tourism Tourism is defined as the activities of people travel- The data in the table are from the World Tourism * Internatlonal Inbound tourists are the number of ing to and staying in places outside their usual envi- Organization The data on international inbound and visitors who travel to a country other than that in ronment for no more than one consecutive year for outbound tourists refer to the number of arrivals and which they have their usual residence for a period leisure, business, and other purposes not related to departures of visitors within the reference period, not exceeding 12 months and whose main purpose an activity remunerated from within the place visited not to the number of people traveling Thus a person in visiting is other than an activity remunerated from The social and economic phenomenon of tourism who makes several trips to a country during a given within the country visited * International outbound has grown substantially over the past quarter of a period is counted each time as a new arrival tourists are the number of departures that people century International visitors include tourists (overnight visi- make from their country of usual residence to any In the past, descriptions of tourism focused on the tors), same-day visitors, cruise passengers, and other country foi any purpose other than a remuner- characteristics of visitors, such as the purpose of crew members ated activity in the country visited * International their visit and the conditions in which they traveled Regional and income group aggregates are based tourism receipts are expenditures by international and stayed Now there is a growing awareness of the on the World Bank's classification of countries and inbound visitors, including payments to national car- direct, indirect, and induced effects of tourism on differ from those shown in the World Tourism riers for international transport These receipts employment, value added, personal income, govern- Organization's Yearbook of Tourism Statistics include any other prepayment made for goods or ment income, and the like Countries not shown in the table but for which data services received in the destination country They Statistical information on tourism is based mainly are available are included in the regional and income also may include receipts from same-day visitors, on data on arrivals and overnight stays along with group totals World totals are no longer calculated by except in cases where these are important enough balance of payments information But these do not the World Tourism Organization The aggregates in to justify a separate classification Their share in completely capture the economic phenomenon of the table are calculated using the World Bank's exports is calculated as a ratio to exports of goods tourism Thus governments, businesses, and citi- weighted aggregation methodology (see Statistical and services * International tourism expenditures zens may not receive the information needed for methods) and differ from aggregates provided by the are expenditures of international outbound visitors in effective public policies and efficient business oper- World Tourism Organization other countries, including payments to foreign carri- ations Although the World Tourism Organization ers for international transport These expenditures reports that progress has been made in harmonizing may include those by residents traveling abroad as definitions and measurement units, differences in same-day visitors, except in cases where these are national practices still prevent full international com- so important as to justify a separate classification parability Credible data are needed on the scale and Their share in inports is calculated as a ratio to significance of tourism Information on the role imports of goods and services tourism plays in national economies throughout the world is particularly deficient 6.14a International arrivals (mlilions) 100 Europe & Central Asia 80 East Asia & Pacific 60 & Caribbean 40 The visitor and expenditure data are available in Middle East & North Africa the World Tourism Organization's Yearbook of Tounsm Statistics and Compendium of Tounsm 20 Sub-Saharan Africa Statistics, 2001 The data in the table were updated from electronic files provided by the South Asia World Tourism Organization The data on exports 0 1995 1996 1997 1998 1999 2000 2001 and imports are from the International Monetary Fund's International Financial Statistics and World Tourism has grown fastest in the Middle East and North Africa and in East Asia and Pacific Bank staff estimates Source World Tourism Organization data 2003 World Development Indicators 1 355 (ID The World Bank is not a primary data collection agency for most areas other than living standards surveys and debt As a major user of socioeconomic data, how- ever, the World Bank places particular emphasis on data documentation to inform users of data in economic analysis and policymaking The tables in this section provide information on the sources, treatment, and currentness of the principal demographic, economic, and environmental indicators in the World Development Indicators Differences in the methods and conventions used by the primary data collectors-usually national statistical agencies, central banks, and customs services-may give rise to significant discrepancies over time both among and within countries Delays in reporting data and the use of old surveys as the base for current estimates may severely compromise the quality of national data Although data quality is improving in some countries, many developing coun- tries lack the resources to train and maintain the skilled staff and obtain the equipment needed to measure and report demographic, economic, and environ- mental trends in an accurate and timely way The World Bank recognizes the need for reliable data to measure living standards, track and evaluate economic trends, and plan and monitor development projects Thus, working with bilateral and other multilateral agencies, it continues to fund and participate in technical assistance projects to improve statistical organization and basic data methods, collection, and dissemination. The World Bank is working at several levels to meet the challenge of improv- ing the quality of the data that it collates and disseminates At the country level the Bank is carrying out technical assistance, training, and survey activities- with a view to strengthening national capacity-in the following areas * Poverty assessments in most borrower member countries * Living standards measurement and other household and farm surveys with partner national statistical agencies * National accounts and inflation * Price and expenditure surveys for the International Comparison Program * Projects to improve statistics in the countries of the former Soviet Union * External debt management * Environmental and economic accounting 2003 World Development Indicators 357 National currency Fiscal National accounts Balance of payments Government IMF year and trade finance data end dlsserril nation Balance of stan,- Alternative PPP Payments dard Reporting Base SNA price conversion survey Manual External System Accounting period year valuation factor year in use debt of trade concept Afghanistan Afghan afghani Mar 20 FY 1975 VAB Albania Albanian lek Dec 31 CY 1990b VAP 1996 BPM5 Actual G C G Algeria Algerian dinar Dec 31 CY 1980 VAB BPM5 Actual S B Angola Angolan kwanza Dec 31 CY 1997 VAP 1991-96 8PM4 Estimate S Argentina Argentine peso Dec 31 CY 1993 VAB 1971-84 1996 BIPM5 Preliminary S C 5* Armenia Armenian dram Dec 31 CY 1996 b, VAB 1990-95 1996 BPM5 Actual S G Australia Australian dollar Jun 30 FY 1995b,c VAB 1999 BPM5 G C S* Austria Euro Dec 31 CY 1995 b VAB 1999 BPM5 S C S* Azerbaijan Azeri manat Dec. 31 CY 2000 s.c VAB 1987-95 1996 BPM5 Actual G C G Bangladesh Bangladesh take Jun 30 FY 1996b VAB 1971-2000 1996 BPM5 Actual G G Belarus Be-larussian rube] Dec 31 CY 1990 s.c VAB 1987-94 1996 BPM5 Actual G C Belgium Euro Dec 31 CY 1995 b VAB 1999 BPM5 S _ C 5* Benin CFA franc Dec 31 CY 1985 VAP 1992 1996 BPM5 Actual S G Bolivia Boliviano Dec 31 Cy 1990 b VAB 1960-85 1996 _BPM5 Actual S C G Bosnia an d Herzegovina Convertible mark Dec 31 CY 1996C VAB BPM5 Actual Botswana Botswana pula Jun 30 FY 1994 VAB 1999 1996 BPM5 Actual G B G Brazil Brazilian real Dec 31 CY 1995 VAB 1999 1996 EPM5 Preliminary S C S Bulgaria Bulgarian lev Dec 31 CY 1990~ b VAB 1978-89, 91-92 1999 BPM5 Actual G C G Burkina Faso CFA franc Dec. 31 CY 1985 VAB 1992-93 BPM4 Actual G C G Burundi Burundi franc Dec 31 CY 1980 VAB BPM5 Preliminary S C Cambodia Cambodian riel Dec 31 CY 1989 VAP BPM5 Preliminary G G Cameroon CFA franc Jun 30 FY 1990 VAB 1965-2002 1996 BPM5 Preliminary S C G Canada Canadian dollar Mar 31 CY 1995b VAB 1999 1999 BPM5 G C S* Central African Republic CFA franc Dec 31 CY 1987 VAB BPM4 Estimate S Chad CFA franc Dec 31 CY 1995 VAB BPM5 Preliminary S C G Chile- C-hilean peso Dec 31 CY 1986 VAB 1996 BPM5 Actual S C S* China Chinese yuan Dec 31 CY 1990 VAP 1987-93 1986 BPM5 Estimate S B G Hong Kong, China Hong Kong dollar Dec 31 CY 1990 VAB 1993 BPM5 G 5 Colombia Colo mbian peso Dec 31 CY 1994 VAB 1992-94 1993 BPM5 Actual S B 5* Congo, Dem Rep Congo franc Dec 31 CY 1987 VAP 1999-2000 BPM5 Actual S C Congo, Rep CFA franc Dec 31 CY 1978 VAP 1993 1996 BPM4 Estimate S C Costa Rica Costa Rican colon Dec. 31 CY 1991b VAB BPM5 Actual S C S* C6te dIlvoire CFA franc Dec. 31 CY 1986 VAP 1996 BPM5 Estimate S C G Croatia Croatian kuna Dec 31 CY 1997b VAB 1999 BPM5 Actual G C S Cuba Cuban peso Dec 31 CY 1984 VAP G Czech Republic Czech koruna Dec -31 -CY 1995 b VAB 1999 BPM5 Preliminary G C S* Denmark Danish ktrone Dec 31 CY 1995 b VAB 1999 BPM5 G C S Dominican Republic Dominican -peso Dec 31 CY 1990 VAP BPM5 Actual G C S Ecuador U S dollar Dec. 31 CY 1975 VAP 1999 1996 BPM5 Estimate S B 5 Egypt, Arab Rep. Egyptian pound Jun 30 _FY 1992 VAB 1965-91 1993 BPM5 Actual S C S El Salvador Salvadoran colone Dec 31 CY 1990 VAP 1982-90 BPM5 Actual S B S Eritrea Eritrean nakfa Dec 31 CY 1992 VAB EPM4 -Actual Estonia Estonian kroon Dec 31 CY 1995b VAB 1990-95 1999 BPM5 Actual G C S* Ethiopia Ethiopian birr Jul 7 FY 1981 VAB 1989-92. 94 BPM5 Preliminary G B G Finland Euro Dec 31 CY 1995b VAB 1999 BPM5 G C 5* France Euro Dec 31 CY 1995b, VAB 1999 BPM5 -S C 5* Gabon CFA franc Dec 31 CY 1991 VAP 1993 1996 BPM5 Actual S B G Gambia, The Gambian dalasi Jun 30 CY 1987 VAB BPM5 Actual G B G Georgia Georgian larn Dec 31 CY 1994b,c VAB 1990-94 1996 BPM5 Actual G C Germany Euro Dec 31 CY 1995 k VAB 1999 BPM5 S C S* Ghana Ghanaian cedi Dec 31 CY 1975 VAP 1973-87 BPM5 Actual G B Greece Euro Dec 31 CY 1995b ' VAB 1999 BPM4 Estimate S C S* Guatemala Guatemalan quetzal -Dec 31 CY 1958 VAP 1980 8PM5 Actual S B Guinea Guinean franc Dec 31 CY 1994 VAB 1986 1993 BPM5 Estimate S C Guinea-Bissau CFA franc Dec 31 CY 1986 VAB 1970-86 BPM5 Estimate G G Haiti Haitian gourde Sep 30 FY 1976 VAB 1991 BPM5 Preliminary G 58II 2003 world Development Indicators Latest Latest demographic, Vital Latest Latest Latest Latest Latest population househoid, or health survey registration agricultural Industriai water survey of survey of census complete census data withdrawal scientists expenditure (including data and fDr reglstration- engineers R&D based engaged censuses) In R&D Afghanistan MICS, 2000 1987 Albania 1989 MICS, 2000 Yes 195 1990 1995 Algeria 1998 MICS,_ 2000 -1973 1998 1995 Angola 1970 MICS, 2000 1964-65 1987 Argentina 2001 Yes 1988 1996 1995 2000 2000 Armenia 2001 OHS, 2000 Yes 1991 1994 2000 Australia 1996 Yes 1990 1997 1985 1998 1998 Austria 2001 Yes 1990 1998 1991 1998 2000 Azerbaijan 1999 MICS, 2000 Yes 1995 1997 1996 Bangladesh 1991 Special, 2001 1976 1997 1990 1995 Belarus 1999 -Yes 1994 1990 1997 Belgium 2001 Ye-s 1990 1997 1999 1999 Benin 1992 OHS, 2001 1992-93 1981 1994 1989 Bolivia 2001 MICS, 2000 1998 1987 2000 2000 Bosnia and Herzegovina 1991 MICS, 2000 Yes 1991 1995 Botswana 1991 - MICS, '2000 1993 1994 1992 Brazil 2000 OHS, 1996 1996 1996 1992 2000 20 Bulgaria 1992 LSMS, 1995 Yes 1998 1988 1999 1996 Burkina Faso 1996 ODHS, 1998-99 1993 1997 1992 1997 1997 Burundi 1990 MICS, 2000 1991 1987 1989 Cambodia 1998 OHS, '2000 1987 Cameroon 1987 MICS, 2000 197~2-73 1998 1987 Canada 2001, Yes 1991 1997 1991 1998 2000 Central African Republic 1988 MICS, 2000 1993 1987 1996 Chad 1993 MICS, 2000 198 Chile 1992 Yes _ 1997 1997 1987 2000 2000 China 2000 Population, 1995 1996 1998 1993 2000 2000 Hong Kong, China 2000 Yes 1998 1995 1998 Colombia 1993 OHS, 2000 1988 1997 1996 2000 2000 Congo, Dam Rep 1984 MICS, 2000 1990 1990 Congo, Rep - 1996 1986 1988 1987 2000 Costa Rica 2000 COC, 1993 Yes 1973 1997 1997 1996 1998 C6te dIlvoire 1998 MICS, -2000 1974-75 1997 1987 Croatia 2001 Yes 1992 1996 1999 1999 Cuba 1981 MICS, 2000 Yes 1989 1995 2000 2000 Czech Republic 2001 CDC, 1993 Yes 1998 1991 2000 2000 Denmark 2001 Yes 1989 1998 1990 1999 1999 Domninican Republic 1993 D HS, 2002 1971 1984 1994 Ecuador 2001 COC, 1999 1997 1998 1997 199'8 1998 Egypt, Arab Rep 1996 D HS, 2000 Yes 1989-90 1997 1996 1991 2000 El Salvador -1992 CDC, 1994 1970-71 1998 1992 2000 Eritrea 1984 ODHS, 1995 1998 Estonia 2000 Yes 1994 1995 1999 1999 Ethiopia 1994 OHS, 2000 1988-89 1998 1987 Finland 2000 Yes 1990 1998 1991 2000 2000 France 1999 Yes ~~~~~ ~~~~~ ~~~~~ ~~~~ ~~1988 1998 1999 1999 2000 Gabon 1993 D HS,2?000 -1974-75 1982 1987 Gambia, The 1993 MICS, 2000 1982 1982 Georgia 1989 MICS, 2000 Yes 1990 1999 1999 Germany Yes ~~~~~~~~~~ ~~~~~ ~~~~~~~~1993 1991 2000 2000 Ghana 2000 SPA, 2000 1984 1995 1997 Greece 2001 Yes 1993 1996 1980 1999 1999 Guatemala 1994 OHS, 1998-99 Yes 1979 1988 1992 1988 1988 Guinea 1996 OHS, 1999 1996 1987 Guinea-Bissau 1991 MICS, 2000 1988 1991 Haiti 1982 OHS, 2000 1971 1996 1991 2003 World Development Indicators 3 9 National currency Fiscal National accounts Balance of payments Government IMF year and trade finance data end dissemri- nation Balance of stan- Alternative PPP Payments dard Reporting Base SNA price conversion survey Manual External System Accounting period year valuation factor year in use debt of trade concept Honduras Honduran lempira Dec 31 CY 1978 VAB 1988-89 BPM5 Actual S Hungary - Hungarian forint Dec. 31 -CY 1994sb VAB 1999 BPM5 Actual S C S* India IninrpeMar 31 FY 1993 VAB 1971-2000 BPM5- Preliminary G C S Indonesia Indonesian rupra h Mar 31 CY 1993 VAP 1996 BPM5 Preliminary S C S* Iran. Islamic Rep Iranian rial Mar 20 FY 1982 VAB 1980-90 1996 BPM5 Actual G C Iraq Iraqi dinar Dec 31 CY 1969 VAB S Ireland Euro Dec. 31 CY 19 VAB 1999 8PM5 G C S Israel Israeli new shekel Dec 31 CY 1995b VAP 1999 BP'M5 S C S~ Italy Euro Dec. 31 CY 1995 b VAB 1999 BPM5 S C S* Jamaica Jamaica dollar Dec 31 CY 1986 VAP 1996 BPM5 Preliminary G C Japan JapnseynMar 31 CY 1995 VAB 1999 BPM5 G C S* Jo rdan Jordan dinar Dec 31 CY 1994 VAB 1996 BPM5 Actual G B G_ Kazakhstan Kazakh tenge Dec 31 CY 1993 b, VAB 1987-95 1996 BPM5 Actual G- C G Kenya Kenya shilling Jun 30 CY 1982 VAB --1996~ BPM5 Actual G B G Korea, Dem Rep Democratic Republic of Korea won Dec 31 CY - BPMS-- - Korea, Rep Korean won Dec 31 Cy 1995~ b yAP 1999 BPM5 _Actual -S C S* Kuwait Kuwaiti dinar Jun 30 CY 1984 VAP BPM5 S C G Kyrgyz Republic Kyrgyz som Dec 31 CY 1995 .CVAB 1992-96 1996 BPM5 Actual G B G Lao PDR Lao kip Dec 31 CY 1990 VAB 1960-89 1993 BPM5 Preliminary G Latvia Latvian tat Dec 31 CY 1995 b VAB 1987-95 1999 BPMS Actual S C S* Lebanon Lebanese pound Dec. 31 CY 1994 VAB 1996 BPM4 Actual G V G Lesotho Lesotho loti Mar 31 CY 1995 VAB BPM5 Actual G C Libya Libyan dinar Dec 31 CY 1975 VAB 1986 BPM5 G Liberia Liberian dollar Dec 31 CY 1971 VAB Estimate Lithuania Lithuanian litas Dec 31 CY 1995b VAB 1987-95 1999 -BPM5 Actual G C S* Macedonia, FYR Macedonian denar Dec 31 CY 1995b VA8 1999 BPM5 Actual G Madagascar Malagasy franc Dec 31 CY 1984 VAB 1996 -BPM5 Preliminary S C Malawi Malawi kwacha- Mar 31 CY 1994 VAB 1996 BPM5 Estimate G B G Malaysia Malaysian ringgit Dec 31 CY 1987 VAP 1993 BPM5 Estimate G C sn Mali CFA franc Dec 31 CY 1987 VAB 1996 BPM4 Preliminary G G Mauritania Mauritanian ouguiya Dec 31 CY 1985 VAB BPM4 Actual G Ma-uritius Mauritian rupee Jun 30 CY 1992 VAB 1996 BPM5 Actual G C G Mexico Mexican new peso Dec 31 CY 1993b VAB 1999 BPM5 Actual G c S* Moldova Moldovan leu Dec 31- CY 1996 VAB 1987-95 1996 BPM5 Actual- G C Mongolia Mongolian tugrik Dec 31 CY 1998 VAP 1996 BPM5 Actual S C G Morocco Moroccan dirham Dec 31 CY 1980 VAP 1996 BPM5 Actual S C -Mozamnbique Mozambican metical Dec 31 CY 1995 VAB 1992-95 BPM5 Estimate S Myanmar Myanmar kyat Mar 31 FY 1985 VAP 1980-82 BPM5 Estimate G C Namibia Namibia dollar Mar 31 CY 1995 VAB BPM5 Estimate B G Nepal Nepalese rupee Jul 14 FY 1985 VAB 1973-2000 1996 BPM5 Actual- S C G Netherlands Euro Dec 31 CY 1995 bSn VAB 1999 BPM5 5 C S* New Zealand New Zealand dollar Mar 31 FY 1995 VAB 1999 BPM5 G B Nicaragua Nicaraguan gold cordoba Dec 31 CY 1998 VAP 1965-93 BPM5 Actual S C Niger CFA franc Dec 31 CY 1987 VAP 1993 BPM5 Preliminary S G Nigeria Nigeria n naira - _ Dec 31 CY- 19 87 __VAB_ 1971-98 1996 BPM5 Estimate G S N-orway Norwegian krone Dec 31 CY 1995 n.yI VAB 1999 BPM5 G C S Oman Rial Omani Dec 31 CY 1978 VAP 1996 BPM5 Actual G B G Pakistan Pakistan rupee Jun 30 FY 1981 VAB 1972-2000 1996 BPM5 Pre liminary G C Panama Panamanian balboa Dec. 31 CY 1982 C VAP 1996 BPM5 Actual S C G- Papua New Guinea Papua New Guinea kina Dec 31 CY 1983 VAP 1989 BPM5 Actual G B G Paraguay Paraguayan guarani Dec 31 CY 1982 VAP 1982-88 BPM5 Actual S C G Peru Peruvian new solI Dec 31 CY 1994 VAB 1985-91 1996 BPM5 Actual S C S Philippines Philippine peso Dec 31 -CY 1985S yAP -1996 IBPM5 Actual G B ~ Poland Polish zloty Dec 31 CY l990obC VAB 1996 BPM5 Actual S C S* Portugal Euro Dec 31 CY 1995b VAB 1999 BPM5 5 C S* Puerto Rico U S dollar Jun 30 FY 1954 yAP G 313 0 I 2003 world Development indicators Latest Latest demographic, Vital Latest Latest Latest Latest Latest population household, or health survey registration agriculturai Industrial water survey of survey of census compiete census data withdrawai scientists expenditure (Including data and for registration- engineers R&D based engaged censuses) In R&D Honduras 1988 COC, 1994 1993 1997 1992 Hungary 2001 Yes 1994 1997 1991 2000 2000 India 2001 Benchmark, 1998-2002 1986 1997 1990 1996 1996 Indonesia 2000 MICS, 2000 1993 1998 1990 Iran, Islamic Rep 1991 -Demographic, 1995 1988 1996 1993 1994 1999 Iraq 1997 MICS, 2000 1981 1997 1990 1994 Ireland 1996 Yes 191 1997 1980 1999 1999 Israel 1995 Yes- 1983 1996 1997 1997 1999 Italy 2001 Yes. .. 1990- 1994 1998 1999 1999 Jamaica 2001 CDC, 1997 Yes 1979 1996 1993 Japan 2000 Yes 1990 .1998' 1992_ 2000 2000 Jordan 1994 DHS, 2002 1997 1997 1993 1998 Kazakhstan 1999 OHS, 1999 Yes 1993 1999 1997 Kenya 1999 OHS, 2003 1981 1998 1990 Korea, Dem Rep 1993 MICS, 2000 1987 2000 2000 Korea, Rep 1995 1991 1997 1994 1999 1997 Kuwait -1995 FHS, -1996 Yes 1970 1997 1994 2000 1997 Kyrgyz Republic 1999 OHS, 1997 Yes 1994 1997 1997 Lao PDR, 1995 MICS, 2000 1999 1987 Latvia 2000 Yes 1994 1998 1994 1999 1999 Lebanon 1970 MICS, 2000 1999 1996 Lesotho 1996 MICS. 2000 1989-90 1985 1987 Libya 1995 MICS, 2000 1987 1997 1999 2000 Liberia 1-987 Lithuania 2001 Yes 1994 1995 1996 Macedonia, FYR 1994 Yes 1994 -1996 1996 1999 Madagascar 1993 ODHS. 2003 1984 1988 1984 1994 Malawi 1998 EdData, 20021923 19894 Malaysia -2000 Yes 1996 1995 1998 1998 Mali 1998 OHS, 2001 -1978 1997 19 87 Mauritania 2000 PAPCHILD, 1990 1985 1985 Mau ritius 2000 CDC, 1991 Yes 1997 1992 1997 Mexico 2000 Population, 1995 1991 199'5 1998 -1999 1999 Moldova 1989 MICS, 2000 Yes 1992 1997 Mongolia 2000 MICS , 2000 1998 1993 2000 Morocco 199 4 OHS. 1995 1997 1998 1998 Mozambique 1997 Interim, 2003 1992 Myanmar 1983 IMICS, 2000 1993 1998 1987 Namibia 1991 O -HS, 2000 1995 1994 1991 Nep-al 1991 OHS, 2001 1992 1996 1994 Netherlands 2001 Yes 1989 1998 1991 1999 1999 New Zealand 2001 Yes 1990 1997 1991 1997 1997 Nicaragua 1995 OHS, 2001 193 19 99 9719 Niger 1988 MICS, 2000 1980 1998 1988 Nigeria 1991 OHS, 2003 1960 1994 1987 1987 Norway 2001 Yes 1989 1998 1985 1999 1999 Oman 1993 FHS, 1995 1979 1998 1991 2000 Pakistan 1998 RHS, 2000-0119096 19197 Panama 2000 -LSMS, 1997 1990 1998 1990 1999 1999 Papua New Guinea 2000 OHS, 1996 1987 Paraguay 1992 OHS, 1990, COO, 1998 1991 1997 1987 Peru 1993 OHS, 2000 --1994 1994 1992 1997 1999 Philippines 2000 MICS, 2000 1991 1997 1995 1992 Poland 1988 Yes 1990 1997 1991 2000 2000 Portugal 2001 Yes 1989 1997 1990 1999 2000 Puerto Rico 2000 Yes 1987 1998 2003 Worid Development Indscators I361 National currency Fscal National accounts Balance of payments Govemment IMF year and trade finance data end disseml- nation Balance of stan- Alternative PPP Payments dard Reporting Base SNA price conversion survey Manual External System Accounting period year valuation factor year in use debt of trade concept Romania Romanian leu Dec 31 CY 1993 C VAB 1987-89, 92 1999 BPM5 Actual S C G Russian Federation Russian ruble Dec 31 CY 1997 b c VAB 1987-94 1999 BPM5 Estimate G C Rwanda Rwanda franc Dec 31 CY 1995 VAP BPM5 Estimate G C Saudi Arabia Saudi Arabian riyal hijri year FY 1970 VAP 1993 BPM4 Estimate G Senegal CFA franc Dec 31 CY 1987 VAP 1996 BPM5 Estimate S B G Sierra Leone Sierra Leonean leone Jun 30 CY 1990 VAB 1971-79, 87 1996 BPM5 Actual G B Singapore Singapore dollar Mar 31 CY 1990 VAP 1996 BPM5 G C S* Slovak Republic Slovak koruna Dec 31 CY 1995b VAB 1999 BPM5 Actual G C S* Slovenia Slovenian tolar Dec 31 CY 1993b VAB 1999 BPM5 Actual S C S* Somalia Somali shilling Dec 31 CY 1985 VAB Estimate South Africa South African rand Mar 31 CY 1995 VAB BPM5 Preliminary S C S* Spain Euro Dec 31 CY 1995 b VAB 1999 BPM5 S C S* Sri Lanka Sri Lankan rupee Dec 31 CY 1996 VAB 1996 BPM5 Actual G B G Sudan Sudanese dinar Dec 31 CY 1982 VAP 1985-91 BPM5 Preliminary G B Swaziland Lilangeni Jun 30 FY 1985 VAB 1996 Actual B Sweden Swedish krona Jun 30 CY 1995c VAB 1999 BPM5 G C S* Switzerland Swiss franc Dec 31 CY 1995 VAB 1999 BPM5 Estimate S C S* Syrian Arab Republic Syrian pound Dec 31 CY 1995 VAP 1970-2000 1996 BPM5 Estimate S C Tajikistan Tajik somoni Dec 31 CY 1985b VAB 1987-95 1996 BPM5 Actual G C Tanzania Tanzania shilling Dec 31 CY 1992 VAB 1996 BPM5 Estimate S G Thailand Thai baht Sep 30 CY 1988 VAP 1996 BPM5 Preliminary G C S* Togo CFA franc Dec 31 CY 1978 VAP 1993 BPM5 Preliminary S G Trinidad and Tobago Trinidad and Tobago dollar Dec 31 CY 1985 VAP 1996 BPM5 Preliminary S C Tunisia Tunisian dinar Dec 31 CY 1990 VAP 1996 BPM5 Actual G C S* Turkey Turkish lira Dec 31 CY 1987 VAB 1999 BPM5 Actual S C S* Turkmenistan Turkmen manat Dec 31 CY 1987b VAB 1996 BPM5 Estimate G Uganda Uganda shilling Jun. 30 FY 1998 VAB 1980-99 BPM5 Actual G B G Ukraine Ukrainian hryvnia Dec 31 CY 1990 b VAB 1988-95 1999 BPM5 Actual G C S* United Arab Emirates U A E dirham Dec 31 CY 1985 VAB 1993 BPM4 G B United Kingdom Pound sterling Dec 31 CY 1995b VAB 1999 BPM5 G C S* United States U S dollar Sep 30 CY 1995' VAB 1999 BPM5 G C S* Uruguay Uruguayan peso Dec 31 CY 1983 VAP 1996 BPM5 Actual S C Uzbekistan Uzbek sum Dec. 31 CY 1997C VAB 1991-94,97-2000 1996 BPM5 Actual G Venezuela, RB Venezuelan bolivar Dec 31 CY 1984 VAB 1996 BPM5 Actual G C G Vietnam Vietnamese dong Dec. 31 CY 1994 VAP 1991 1996 BPM4 Preliminary G B West Bank and Gaza Israeli new shekel Dec 31 CY 1997 VAB 1993 Yemen, Rep Yemen rial Dec 31 CY 1990 VAP 1991-96 1996 BPM5 Preliminary G B G Yugoslavia, Fed Rep Yugoslav new dinar Dec 31 CY 1998 VAB Preliminary S Zambia Zambian kwacha Dec 31 CY 1994 VAB 1990-92 1996 BPM5 Preliminary G B G Zimbabwe Zimbabwe dollar Jun 30 CY 1990 VAB 1991, 98 1996 BPM5 Preliminary G C G Note: For an explanation of the abbreviations used in the table, see the notes a Also applies to balance of payments reporting b Country uses the 1993 System of National Accounts methodology c Original chained constant price data are rescaled 3132 H 2003 World Development Indicators Latest Latest demographic, Vital Latest Latest Latest Latest Latest population household, or health survey registration agricultural industrlal water survey of survey of census complete census data withdrawal scientists expenditure (Including data and for registration- engineers R&D based engaged censuses) In R&D Romania 1992 CDC, 1999 Yes 1997 1994 2000 2000 Russian Federation 1989 LSMS, 1992 Yes 1994-95 1998 1994 2000 2000 Rwanda 1991 SPA, 2001 1984 1986 1993 Saudi Arabia 1992 Demographic, 1999 1983 1992 Senegal 1988 MICS, 2000 1960 1997 1987 1997 1997 Sierra Leone 1985 MICS, 2000 1985 1986 1987 Singapore 2000 General household, 1995 Yes 1998 1975 1995 1995 Slovak Republic 1991 Yes 1998 1991 2000 2000 Slovenia 1991 Yes 1991 1998 1996 2000 1998 Somalia MICS, 2000 1987 South Africa 2001 DHS, 1998 1996 1990 1993 Spain 2001 Yes 1989 1998 1997 2000 2000 Sri Lanka 2001 DHS, 1993 Yes 1982 1995 1990 1996 1996 Sudan 1993 MICS, 2000 1997 1995 Swaziland MICS, 2000 Sweden 1990 Yes 1981 1997 1991 1999 1999 Switzerland 2000 Yes 1990 1998 1991 2000 2000 Syrian Arab Republic 1994 MICS, 2000 1981 1998 1995 1997 1997 Tajikistan 2000 MICS, 2000 Yes 1994 1994 1993 Tanzania 1988 DHS, 1999 1995 1997 1994 Thailand 2000 DHS, 1987 1993 1996 1990 1997 1997 Togo 1981 MICS, 2000 1996 1997 1987 1994 1995 Trinidad and Tobago 1990 MICS, 2000 Yes 1982 1997 1997 1997 1997 Tunisia 1994 MICS, 2000 1961 1998 1996 1999 2000 Turkey 1997 DHS, 1998 1991 1997 1997 1999 1999 Turkmenistan 1995 DHS, 2000 Yes 1994 Uganda 1991 HIV, 2003 1991 1997 1970 2000 1999 Ukraine 2001 MICS, 2000 Yes 1992 2000 2000 United Arab Emirates 1995 1998 1981 1995 United Kingdom 2001 Yes 1993 1998 1991 1998 1999 United States 2000 Current population, 1997 Yes 1997 1997 1990 1997 2000 Uruguay 1996 Yes 1990 1997 1965 1999 1999 Uzbekistan 1989 Special, 2002 Yes 1994 1992 Venezuela, RB 2001 MICS, 2000 Yes 1997-98 1996 1970 2000 2000 Vietnam 1999 DHS, 2002 1994 1998 1990 1995 West Bank and Gaza 1997 Demographic, 1995 1971 Yemen, Rep 1994 DHS, 1997 1982-85 1990 Yugoslavia, Fed Rep 1991 MICS, 2000 Yes 1981 1998 1995 1999 Zambia 2000 EdData, 2002 1990 1997 1994 Zimbabwe 1997 DHS, 1999 1960 1997 1987 2003 World Development Indicators 1 363 * Flscal year end is the date of the end of the fiscal for compiling and reporting data on balance of pay- or that might seek access to international capital mar- year for the central government. Fiscal years for other ments items in table 4 15 BPM4 refers to the fourth kets, to guide them in providing their economic and levels of government and the reporting years for sta- edition of the IMF's Balance of Payments Manual financial data to the public The GDDS helps guide tistical surveys may differ, but if a country is designat- (1977), and BPM5 to the fifth edition (1993) Since member countnes in disseminating comprehensive, ed as a fiscal year reporter in the following column, the 1995 the IMF has adjusted all balance of payments timely, accessible, and reliable economic, financial, date shown is the end of its national accounts report- data to BPM5 conventions, but some countries contin- and socio-demographic statistics IMF member coun- ing penod o Reporting period for national accounts ue to report using the older system o Extemal debt tries voluntarily elect to participate in either the SDDS and balance of payments data is designated as either shows the debt reporting status for 2001 data Actual or the GDDS Both the SDDS and the GDDS are expect- calendar year (CY) or fiscal year (FY) Most economies indicates that data are as reported, preliminary that ed to enhance the availability of timely and compre- report their national accounts and balance of payments data are preliminary and include an element of World hensive data and therefore contribute to the pursuit of data using calendar years, but some use fiscal years, Bank staff estimation, and estimate that data are sound macroeconomic policies, the SDDS is also which straddle two calendar years In the World World Bank staff estimates v System of trade refers expected to help improve the functioning of financial Development Indicators fiscal year data are assigned to the general trade system (G) or the special trade sys- markets o Latest population census shows the most to the calendar year that contains the larger share of tem (S) Under the general trade system both goods recent year in which a census was conducted from the fiscal year If a country's fiscal year ends before entenng directly for domestic consumption and goods which at least preliminary results have been released June 30, the data are shown in the first year of the fis- entenng customs storage are recorded, at the time of * Latest demographic, household, or health survey cal penod, if the fiscal year ends on or after June 30, their first arrival, as imports, under the special trade gives information on the surveys used in compiling the data are shown in the second year of the period system goods are recorded as imports when declared demographic and health data presented in the People Saudi Arabia follows a lunar year whose starting and for domestic consumption whether at the time of entry section CDC is U S Centers for Disease Control and ending dates change with respect to the solar year or on withdrawal from customs storage Exports under Prevention, DHS is Demographic and Health Survey, Because the International Monetary Fund (IMF) reports the general trade system comprise outward-moving EdData refers to education data collected in DHS sur- most balance of payments data on a calendar year goods (a) national goods wholly or partly produced in veys, FHS is Family Health Survey, HIV is HIV survey basis, balance of payments data for fiscal year the country, (b) foreign goods, neither transformed nor data, LSMS is Living Standards Measurement Study, reporters in the World Development Indicators are declared for domestic consumption in the country, that MICS is Multiple Indicator Cluster Survey, PAPCHILD is based on fiscal year estimates provided by World Bank move outward from customs storage, and (c) national- Pan Arab Project for Child Development, RHS is staff These estimates may differ from IMF data but ized goods that have been declared from domestic con- Reproductive Health Survey, and SPA is Service allow consistent comparisons between national sumption and move outward without having been Provision Assessments o Vital registration complete accounts and balance of payments data * Base year transformed Under the special trade system exports identifies countnes judged by the United Nations is the year used as the base period for constant price comprise categories (a) and (c) In some compilations Statistics Division to have complete registnes of vital calculations in the country's national accounts Price categones (b) and (c) are classified as re-exports (birth and death) statistics, with the statistics reported indexes derived from national accounts aggregates, Direct transit trade, consisting of goods entenng or in the United Nations Statistics Division's Population such as the GDP deflator, express the price level rela- leaving for transport purposes only, is excluded from and Vital Statistics Report Countnes with complete tive to pnces in the base year. Constant pnce data both import and export statistics See About the data vital statistics registries may have more accurate and reported in the World Development Indicators are for tables 4 5 and 4 6 for further discussion more timely demographic indicators - Latest agricul- rescaled to a common 1995 reference year See About * Govemment finance accounting concept describes tural census shows the most recent year in which an the data for table 4 1 for further discussion o SNA the accounting basis for reporting central government agncultural census was conducted and reported to the price valuation shows whether value added in the financial data For most countnes government finance Food and Agriculture Organization * Latest Industrial national accounts is reported at basic prices (VAB) or data have been consolidated (C) into one set of data refer to the most recent year for which manufac- at producers' prices (VAP) Producers' pnces include accounts captunng all the central government's fiscal tunng value added data at the three-digit level of the the value of taxes paid by producers and thus tend to activities Budgetary central government accounts (B) International Standard Industnal Classification (revi- overstate the actual value added in production See exclude central government units. See About the data sion 2 or revision 3) are available in the United Nations About the data for tables 4 1 and 4 2 for further dis- for tables 4 11, 4 12, and 4 13 for further details Industnal Development Organization (UNIDO) data- cussion of national accounts valuation * Altemative * IMF data disseminatIon standard shows the coun- base 0 Latest water withdrawal data refer to the conversion factor identifies the countries and years for tries that subscribe to the IMF's Special Data most recent year for which data have been compiled which a World Bank-estimated conversion factor has Dissemination Standard (SDDS) or the General Data from a vanety of sources See About the data for table been used in place of the official exchange rate (line rf Dissemination System (GDDS) S refers to countries 3 5 for more information * Latest surveys of sclen- in the IMF's Intemational Financial Statistics) See that subscnbe to the SDDS, St indicates subscnbers tists and engineers engaged In R&D and expenditure Statistical methods for further discussion of the use of that have posted data on the IMFs Dissemination for R&D refer to the most recent year for which data are alternative conversion factors * PPP survey year Standards Bulletin Board Web site, and G refers to available from a data collection effort by the refers to the latest available survey year for the countries that subscnbe to the GDDS (Posted data can United Nations Educational, Scientific, and Cultural International Companson Program's estimates of pur- be reached through the IMF's Dissemination Standards Organization (UNESCO) in science and technology and chasing power parities (PPPs) * Balance of Payments Bulletin Board at http-//dsbb imf org ) The SDDS was research and development (R&D) See About the data Manual In use refers to the classification system used established by the IMF for member countnes that have for table 5 12 for more information 364 | 2003 World Development Indicators ACRNYM AN ABREIATON AIDS acquired immunodeficiency syndrome ADB Asian Development Bank BOD biochemical oxygen demand AfDB African Development Bank CFC -chlo-rofluorocarbon APEC Asia Pacific Economic Cooperation C.I.1' cost, insurance, an-d freigh-t CDC Centers for Disease Control and Prevention COMTRADE United Nations Statistics Division's Commodity Trade database CO1AC - arbo-n Diox'ide Information Analys'is Center co, carbon dioxide CEC Commission of the European Communities CU. m cubic meter DACd Development As-si-stance Committee of the DECD DHS Demographic and Health Survey EBRD European Bank for Reconstruction and Development DMTUJ dry metric ton unit EDF European Development Fund DOTS directly observed treatment, short-course (strategy) -EFTA - -uropean Free Trade Area ODPT diphther-ia, pert-ussis-, and tetanus EIB European Investment Bank DRS World Bank's Debtor Reporting System - - EMU - Europ'ean Monetary Union ESAF Enharnced Structural Adjustment Facility - EU European Union f.o.b free on board Eurostat Statistical Office of the European Communities FYR former Yugoslav Republic - - FAO -Food and Agriculture Organization CDP gross domestic product -S France, Germany, Japan, United Kitigdom, and United States GEMS GIlobal Environment monitoring System- 0-7 - G-5 plus Canaaa and Italy GIs geographic information system - -0. G-7 plus Russian Federation ONI - gross national income (formerly referred to as gross national product) - EF Glob-al E-nvironm-ent Facility hta hectare IBRD International Bank for Reconstruction and Development HIPC - heavily indebted poor country - - iCAO International Civil Aviation Organization- HIV human immunod eficiency virus iciP International Comparison Programme lCD International Classihication of Diseases ICSID International Centre for Setilement of Investment Disputes ICSE - International Classification of Status in Employment IDA International Develo-pm-ent Association ICT information and communications technology IDB Inter-American Development Bank IP Internet Protocol IOC International Data Corporation ISCED International Standard Classification of Education lEA International Energy Agency iSiIC - International Standard Industrial Classificatio'n IFC International Finance Corporation liSP- Internet service provide'r ILO Int-ernational Labour Organiz ation- kg kilogram IMF International Monetary Fund km kilometer - - --IRF -International Road Federation kwh kilowatt-hour flU International Telecommunication Union UBOR - -London interbank offered -rate IUCN -World Conservation Union LSMS Living Standards Measurement Study MIGA -Multilateral Investment Guarantee Agency MO currency and coins (monetary base)_ NAFTA North American Free Trade Agreement ml narrow money (currency and demand deposits) NATO North Atlantic Treaty Organization M2 money plus quasi money NSF National Science Foundlation M3 brood mo-ney or liqui-d liab6ilities' OECD Organisation for Economic Co-operation and Development MICS Multiple Indicator Cluster Survey PAHO Pan American Health Organization mmbtu millions of British thermal units PARIS21 Partner'ship in Statistics for Development in the 21st Century Mt metric ton S&P Standard & Poor's MUV manufactures u-nit value UJIP Urban Indicators Programme NEAP national environmental action plan - IS UNESCO Institute for Statistics NGOO nongovernmental organization -UN United Nations NO, nitrogen dioxide - UNAIDS Joint United Nations Programme on HIV/AIDS ODA official development assistance UNCED United Nations Conference on Environment and Development PC - personal computer- UNCHS United Nations Centre for Human Settlements (Habitat) PMIO particulate matter smaller than 10 microns UNCTAD- United Nations Conference on Trade and Develop'ment PPI private participation in infrastructure UNDP United Nations Development Programme PPP purchasing power panity - UNECE Unite-d 'Nations Economic Commission for Europe PRGF Poverty Reduction and Growth Facility UNEP United Nations Environment Programme R&D research and development UNESCO United Nations Educational. Scientific, and Cultural Organization SDR - special drawing right UNFPA United Nations Population Fund SITC Standard International Trade Classification UiNHCR United N'ations High Commissioner for Refugees SNA System of National Accounts UNICEF United Nations Children's Fund SOPEMI Continuous Reporting System on Migration UNIDO United Nations Industrial Development Organization 802 sulfur dioxide UNRISD United Nations Research Institute for Social Development sq. km square kilometer UNSD United Nations Statistics Division STD sexually transmitted disease USAID U S Agency for International Development TB tuberculosis WCMC World Co'nservation Monitoring Centre TEU twenty-foot equivalent unit WFP World Food Pr-ogr-amme TFP total factor productivity WHO World Health Organization ton-kcm metric ton-kilometer WIPO - World Intellectual Property Organization TSP total suspended particulates WfITA - World Information Technology and Services Alliance TU traffic unit WTO - World Trade Organization WWF World Wildlife Fund 2003 World Development Indicators I 365 This section describes some of the statistical procedures used in preparing the v Aggregates of ratios are generally calculated as weighted averages of the World Development Indicators It covers the methods employed for calculating ratios (indicated by w) using the value of the denominator or, in some cases, regional and income group aggregates and for calculating growth rates, and it another indicator as a weight The aggregate ratios are based on available describes the World Bank's Atlas method for deriving the conversion factor used data, including data for economies not shown in the main tables Missing val- to estimate gross national income (GNI) (formerly referred to as GNP) and GNI ues are assumed to have the same average value as the available data No per capita in U S dollars Other statistical procedures and calculations are aggregate is calculated if missing data account for more than a third of the described in the About the data sections that follow each table value of weights in the benchmark year In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing values for miss- Aggregation rules ing data according to the above rules for computing totals Aggregates based on the World Bank's regional and income classifications of Aggregate growth rates are generally calculated as a weighted average of economies appear at the end of most tables These classifications are shown on growth rates (and indicated by a w) In a few cases growth rates may be com- the front and back cover flaps of the book Most tables also include aggregates puted from time series of group totals Growth rates are not calculated if for the member countries of the European Monetary Union (EMU) Members of more than half the observations in a period are missing For further discus- the EMU on 1 January 2001 were Austria, Belgium, Finland, France, Germany, sion of methods of computing growth rates see below Ireland, Italy, Luxembourg, the Netherlands, Portugal, and Spain Other classifi- o Aggregates denoted by an m are medians of the values shown in the table cations, such as the European Union and regional trade blocs, are documented No value is shown if more than half the observations for countries with a pop- in About the data for the tables in which they appear. ulation of more than 1 million are missing Because of missing data, aggregates for groups of economies should be Exceptions to the rules occur throughout the book Depending on the judgment treated as approximations of unknown totals or average values Regional and of World Bank analysts, the aggregates may be based on as little as 50 percent of income group aggregates are based on the largest available set of data, includ- the available data In other cases, where missing or excluded values are judged to ing values for the 148 economies shown in the main tables, other economies be small or irrelevant, aggregates are based only on the data shown in the tables shown in table 1 6, and Taiwan, China The aggregation rules are intended to yield estimates for a consistent set of economies from one period to the next and Growth rates for all indicators Small differences between sums of subgroup aggregates and Growth rates are calculated as annual averages and represented as percentages overall totals and averages may occur because of the approximations used In Except where noted, growth rates of values are computed from constant price addition, compilation errors and data reporting practices may cause discrepan- series Three principal methods are used to calculate growth rates least squares, cies in theoretically identical aggregates such as world exports and world exponential endpoint, and geometric endpoint Rates of change from one period imports to the next are calculated as proportional changes from the earlier period Five methods of aggregation are used in the World Development Indicators o 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. The and backward from 1995 Missing values in 1995 are imputed using one of least-squares growth rate, r, is estimated by fitting a linear regression trend line several proxy variables for which complete data are available in that year The to the logarithmic annual values of the variable in the relevant period The regres- imputed value is calculated so that it (or its proxy) bears the same relation- sion equation takes the form ship to the total of available data Imputed values are usually not calculated In X, = a + bt, if missing data account for more than a third of the total in the benchmark year The variables used as proxies are GNI in U S dollars, total population, which is equivalent to the logarithmic transformation of the compound growth exports and imports of goods and services in U S dollars, and value added equation, in agriculture, industry, manufacturing, and services in U S dollars X, = X. (1 + r)' o 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 = In X. and b = in the series or a proxy for the series are missing in a given year In (1 + r) are parameters to be estimated If b* is the least-squares estimate 3616 0 2003 World Development Indicators of b, the average annual growth rate, r, is obtained as [exp(b*) - 1] and is mul- The inflation rate for G-5 countries, representing international inflation, is tiplied by 100 for expression as a percentage measured by the change in the SDR deflator (Special drawing rights, or SDRs, The calculated growth rate is an average rate that is representative of the are the IMF's unit of account ) The SDR deflator is calculated as a weighted available observations over the entire period It does not necessarily match the average of the G-5 countries' GDP deflators in SDR terms, the weights being actual growth rate between any two periods the amount of each country's currency in one SDR unit Weights vary over time because both the composition of the SDR and the relative exchange Exponential growth rate. The growth rate between two points in time for cer- rates for each currency change The SDR deflator is calculated in SDR terms tain demographic indicators, notably labor force and population, is calculated first and then converted to U S dollars using the SDR to dollar Atlas conver- from the equation sion factor The Atlas conversion 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 r= In(pj/px)/n, per capita When official exchange rates are deemed to be unreliable or unrepresentative where p. and Px are the last and first observations in the period, n is the of the effective exchange rate during a period, an alternative estimate of the number of years in the period, and In is the natural logarithm operator This exchange rate is used in the Atlas formula (see below) growth rate is based on a model of continuous, exponential growth between two The following formulas describe the calculation of the Atlas conversion factor points in time It does not take into account the intermediate values of the for year t series Nor does it correspond to the annual rate of change measured at a one- year interval, which is given by (pn - Pn_,x)/p_1 x - 1 e, Pi 2 ) ( pi /P_) e _2 $ _e1 / + e, i3[i Pt2 Pt9 Pi-x Pmtx) 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 and the calculation of GNI per capita in U S dollars for year t rate, may be more realistic, most economic phenomena are measured only at intervals, in which case the compound growth model is appropriate The average Y,$ = ()Y1/N)/e,', growth rate over n periods is calculated as where e,* is the Atlas conversion factor (national currency to the U S dollar) r = exp[ln(pn /p1)/n] - 1 for year t, e, is the average annual exchange rate (national currency to the U S dollar) for year t, p, is the GDP deflator for year t, pts is the SDR deflator in U S Like the exponential growth rate, it does not take into account intermediate dollar terms for year t, y,$ is the Atlas GNI per capita in U S dollars in year t, Y, values of the series is current GNI (local currency) for year t, and N, is the midyear population for year t World Bank Atlas method In calculating GNI and GNI per capita in U S dollars for certain operational pur- Alternative conversion factors poses, the World Bank uses the Atlas conversion factor The purpose of the Atlas The World Bank systematically assesses the appropriateness of official conversion factor is to reduce the impact of exchange rate fluctuations in the exchange rates as conversion factors An alternative conversion factor is used cross-country comparison of national incomes when the official exchange rate is ludged to diverge by an exceptionally large mar- The Atlas conversion factor for any year is the average of a country's gin from the rate effectively applied to domestic transactions of foreign curren- exchange rate (or alternative conversion factor) for that year and its exchange cies and traded products This applies to only a small number of countries, as rates for the two preceding years, adjusted for the difference between the rate of shown in Primary data documentation Alternative conversion factors are used in inflation in the country and that in the G-5 countries (France, Germany, Japan, the the Atlas methodology and elsewhere in the World Development Indicators as United Kingdom, and the United States) A country's inflation rate is measured single-year conversion factors by the change in its GDP deflator 2003 World Development Indicators 1 367 This book has drawn on a wide range of World Bank reports and numerous exter- staff resources to the book, for which the team ts very grateful M H Saeed nal sources, listed in the bibliography following this section Many people inside Ordoubadi wrote the introduction to the section with valuable comments from Eric and outside the World Bank helped in writing and producing the World Development Swanson and Bruce Ross-Larson, who edited the text Other contributions were Indicators The team would like to particularly acknowledge the help and encour- made by Susmita Dasgupta, Craig Meisner, Kiran Pandey, and David Wheeler (air agement of Nicholas Stern, Senior Vice-President and Chief Economist It is also and water pollution), Juan Blazquez Ancin, Jan Boj6, Katja Erickson, Surhid Gautam, grateful to those who provided valuable comments on the entire book, especially and Kirsten Oleson (government commitment), and Katie Bolt and Kirk Hamilton Jean Baneth and Jong-goo Park This note identifies those who made specific con- (adjusted savings) Valuable comments were also provided by Jean Baneth, C tributions Numerous others, too many to acknowledge here, helped in many ways Fallert Kessides, Marianne Fay, Katie Bolt, Roberto Martin-Hurtado, Coralie Gevers, for which the team is extremely grateful Erica Soler Hampejsek, and Marcin Jan Sasin 1. World view 4. Economy was prepared by Eric Swanson and K M Vijayalakshmi Eric Swanson wrote the intro- was prepared by K M Vijayalakshmi in close collaboration with the Macroeconomic duction Mona Fetouh, Amy Heyman, Masako Hiraga, and Sulekha Patel assisted in Data Team of the World Bank's Development Data Group, led by Soong Sup Lee developing and preparing tables and figures Valuable suggestions were received from Eric Swanson and K M Vijayalakshmi wrote the introduction Contributions to the members of the World Bank's Human Development Network Yonas Biru and William section were provided by Azita Amjadi (trade) and Punam Chuhan and Ibrahim Prince provided substantial assistance with the data, preparing the estimates of gross Levent (external debt) The national accounts and balance of payments data for low- national income in purchasing power parity terms Azita Amjadi, Aki Kuwahara (UNC- and middle-income economies were gathered from the World Bank's regional staff TAD), and Jerzy Rozanski helped in preparing the market access indicators through the annual Unified Survey Maja Bresslauer, Victor Gabor, Barbro Hexeberg, Soong Sup Lee, and Naoko Watanabe worked on updating, estimating, and validat- 2. People ing the databases for national accounts The national accounts data for OECD coun- was prepared by Masako Hiraga in partnership with the World Bank's Human tries were processed by Mehdi Akhlaghi The team is grateful to Guy Karsenty and Development Network and the Development Research Group in the Development Andreas Maurer, at the World Trade Organization, and Sanja Blazevic, Arunas Economics Vice Presidency Vivienne Wang provided invaluable assistance in data Butkevicius, and Aurelie von Wartensleben, at UNCTAD, for providing data on trade and table preparation Sulekha Patel wrote the introduction, with input from Eric in goods, to Tetsuo Yamada for help in obtaining the UNIDO database, and to Jean Swanson Contributions to the section were provided by Eduard Bos and Emi Suzuki Baneth for helpful comments (demography, health, and nutrition), Raquel Artecona and Martin Rama (labor force and employment), Shaohua Chen and Martin Ravallion (poverty and income distri- 5. States and markets bution), Montserrat Pallares-Miralles (vulnerability and securty), and Barbara Bruns, was prepared by David Cieslikowski and Mona Fetouh in partnership with the World Saida Mamodova, and Lianqin Wang (education) Comments and suggestions at var- Bank's Private Sector and Infrastructure Network, its Poverty Reduction and ous stages of production also came from Jean Baneth and Enc Swanson Economic Management Network, the World Bank Institute, the International Finance Corporation, and external partners David Cieslikowski wrote the introduction to the 3. Environment section Other contributors include Ada Karna Izaguirre and Kathy Khuu (privatization was prepared by M H Saeed Ordoubadi and Mona Fetouh in partnership with the and infrastructure projects), Andrew Newby of Euromoney (credit ratings), Simeon World Bank's Environmentally and Socially Sustainable Development Network and Djankov (business environment); Isilay Cabuk and Shannon Laughlin (Standard & in collaboration with the World Bank's Development Research Group and Poor's emerging stock market indexes), Yonas Biru (purchasing power parity conver- Transportation, Water, and Urban Development Department Important contribu- sion factors), Esperanza Magpantay and Michael Minges of the International tions were made by Robin White and Christian Layke of the World Resources Telecommunication Union (communications and information), Louis Thompson (rail- Institute, Orio Tampieri of the Food and Agriculture Organization, Laura Battlebury ways), Jane Degerlund of Containerisation International (ports), Jens Johanson of the of the World Conservation Monitonng Centre, Gerhard Metchies of GTZ, and UNESCO Institute for Statistics (culture, research and development, scientists and Christine Auclair, Moses Ayiemba, Bildad Kagai, Guenter Karl, Pauline Maingi, and engineers), Anders Halvorsen of the Worid Information Technology and Services Markanley Rai of the Urban Indicators Programme, United Nations Centre for Alliance (information and communications technology); Dan Gallik of the U S Human Settlements Mehdi Akhlaghi managed the databases for this section, and Department of State (military personnel and arms exports), Petter Stalenheim of the Mona Fetouh assisted with research and data preparation The World Bank's Stockholm International Peace Research Institute (military expenditures), and Lise Environment Department and Rural Development Department devoted substantial McLeod of the World Intellectual Property Organization (patents data) 363 0 2003 World Development Indicators 6. Global links the design and planning of the World Development Indicators and the Atlas and was prepared by Mona Fetouh and Amy Heyman Substantial help came from Azita helped coordinate work with the Office of the Publisher Amjadi and Francis Ng (trade), Betty Dow (commodity prices), Aki Kuwahara of UNC- TAD and Jerzy Rozanski (tariffs), Shelly Fu, Ibrahim Levent, and Gloria Reyes (finan- Publishing and dissemination cial data), Cecile Thoreau of the OECD (migration), Yasmin Ahmad, Brian Hammond, The Office of the Publisher, under the direction of Dirk Koehler, provided valuable Aimee Nichols, Rudolphe Petras, and Simon Scott of the OECD (aid flows), and assistance throughout the production process Randi Park coordinated printing, and Antonio Massieu and Azucena Pernia of the World Tourism Organization (tourism Carlos Rossel supervised marketing and distribution Andrew Kircher of External data) Valuable comments were also provided by Barbro Hexeberg Affairs managed the communications strategy, with assistance from Lawrence Macdonald, and the regional operations group headed by Paul Mitchell helped coor- Other parts dinate the overseas release The preparation of the maps on the inside covers was coordinated by Jeff Lecksell and Greg Prakas of the World Bank's Map Design Unit The Users guide was pre- The Atlas pared by David Cieslikowski Partners was prepared by Mona Fetouh Statistical Production and design were managed by Richard Fix Content development for this methods was written by Eric Swanson Primary data documentation was coordi- year's Atlas was coordinated by a redesign team led by David Cieslikowski that nated by K M Vijayalakshmi, who served as database administrator, and Estela included Elizabeth Crayford, Richard Fix, Amy Heyman, and Eric Swanson The graph- Zamora Mehdi Akhlaghi was responsible for database updates and aggregation ic design was realized with Communications Development Incorporated and their Acronyms and abbreviations was prepared by Amy Heyman The index was collat- London partner, Grundy & Northedge Valuable input was provided by many staff of ed by Richard Fix and Gonca Okur the Development Data Group and the Office of the Publisher The preparation of data benefited from the work on corresponding sections in the World Development Data management Indicators William Prince assisted with systems support and production of tables Database management was coordinated by Mehdi Akhlaghi with cross-team partic- and graphs. Jeffrey Lecksell and Greg Prakas from the World Bank's Map Design Unit ipation of Development Data Group staff to create an integrated World Development coordinated map production Indicators database This database was used to generate the tables for the World Development Indicators and related products such as WDI Online, The World Bank World Development Indicators CD-ROM Atlas, The Llttle Data Book, and the World Development Indicators CD-ROM Programming and testing were carried out by Reza Farivari and his team Azita Amjadi, Ying Chi, Elizabeth Crayford, Ramgopal Erabelly, Nacer Megherbi, Shahin Administrative assistance and office technology support Outadi, and William Prince Masako Hiraga produced the social indicators tables Estela Zamora provided administrative assistance and assisted in updating the William Prince coordinated user interface design and overall production and pro- databases Jean-Pierre Djomalieu, Nacer Megherbi, and Shahin Outadi provided vided quality assurance information technology support. WDI Online Design, production, and editing Design, programming, and testing were carried out by Reza Farivari and his team Richard Fix coordinated all aspects of production with Communications Development Mehdi Akhlagi, Azita Amjadi, Ying Chi, Elizabeth Crayford, Shahin Outadi, and Nacer Incorporated Communications Development Incorporated provided overall design Megherbi William Prince coordinated production and provided quality assurance direction, editing, and layout Led by Meta de Coquereaumont and Bruce Ross- Cybele Bourgougnon, Hafed Al-Ghwell, and Stacey Leonard-Frank of the Office of the Larson, the editing and production team consisted of Joseph Costello, Wendy Publisher were responsible for the implementation of the WDI Online and the man- Guyette, Paul Holtz, Elizabeth McCrocklin, Alison Strong, and Elaine Wilson agement of the subscription service Communications Development's London partner, Grundy & Northedge, provided art direction and design. Staff from External Affairs oversaw publication and dis- Client feedback semination of the book The team is also grateful to the many people who took the trouble to provide com- ments on its publications Their feedback and suggestions have helped improve Client services this year's edition The Development Data Group's Client Services Team (Azita Amjadi, Elizabeth Crayford, Richard Fix, Anat Lewin, Gonca Okur, and William Prince) contributed to 2003 World Development Indicators 1 369 AbouZahr, Carla. 2000 'Maternal Mortality " OECD Observer (223) 29-30 Collier, Paul, and David Dollar. 1999 'Aid Allocation and Poverty Reduction Ahmad, Sultan. 1992 'Regression Estimates of Per Capita GDP Based on Policy Research Working Paper 2041 World Bank, Development Research Purchasing Power Parities." Policy Research Working Paper 956 World Bank, Group, Washington, D C. International Economics Department, Washington, D C - 2001 "Can the World Cut Poverty in Half? How Policy Reform and - 1994 'Improving Inter-Spatial and Inter-Temporal Comparability of Effective Aid Can Meet the International Development Goals.' Policy National Accounts - Joumal of Development Economics 44 53-75 Research Working Paper 2403 World Bank, Development Research Group, American Automobile Manufacturers Association. 1998 World Motor Vehicle Washington, D C Data Detroit, Mich Collins, Wanda W., Emile A. Frison, and Suzanne L. Sharrock. 1997 'Global Ball, Nicole. 1984 "Measuring Third World Security Expenditure A Research Programs A New Vision in Agricultural Research. Issues in Agnculture (World Note " World Development 12(2) 157-64 Bank, Consultative Group on International Agricultural Research, Washington, Barro, Robert J. 1991 "Economic Growth in a Cross-Section of Countries " D C ) 12. 1-28 Quarterly Journal of Economics 106(2) 407-44 Commission of the European Communities, International Monetary Fund, Barro, Robert J., and Jong-Wha Lee. 2000 International Data on Educational Organisatlon for Economic Co-operation and Development, United Nations, Attainment Updates and Implications NBER Working Paper 7911 and World Bank. 2002. System of Environmental and Economic Accounts Cambridge, Mass National Bureau of Economic Research SEEA 2000 New York Beck, Thorsten, and Ross Levine. 2001. "Stock Markets, Banks, and Growth, Contalnerisation International. 2003 Containensation Intemational Yearbook Correlation or Causality?' Policy Research Working Paper 2670 World Bank, 2003 London Development Research Group, Washington, D C Corrao, Mario Ann, G. Emmanuel Guindon, Namita Sharma, and Donna Behrman, Jere R., and Mark R. Rosenzweig. 1994 "Caveat Emptor Cross- Fakhrabadi Shokoohl, eds. 2000 Tobacco Control Country Profiles Atlanta Country Data on Education and the Labor Force ' Joumal of Development American Cancer Society Economics 44 147-71 Deaton, Angus. 2002 "Counting the World's Poor Problems and Possible Bhalla, Surjilt. 2002. Imagine There Is No Country Poverty, Inequality, and Solutions " World Bank Research Observer 16(2) 125-47 Growth in the Era of Globalization Washington, DC Institute for International Demirgur-Kunt, Asil, and Enrica Detraglache. 1997. "The Determinants of Economics. Banking Crises Evidence from Developed and Developing Countries Bloom, David E., and Jeffrey G. Williamson. 1998 "Demographic Transitions Working paper World Bank and International Monetary Fund, Washington, and Economic Miracles in Emerging Asia' World Bank Economic Review D.C 12(3) 419-55 Demirguq-Kunt, Ashl, and Ross Levine. 1996a "Stock Market Development Brown, Lester R., and others. 1999 Vital Signs 1999 The Environmental Trends and Financial Intermediaries- Stylized Facts " World Bank Economic Review That Are Shaping Our Future New York and London- W W Norton for 10(2). 291-321. Worldwatch Institute. . 1996b "Stock Markets, Corporate Finance, and Economic Growth An Brown, Lester R., Christopher Flavin, Hilary F. French, and others. 1998 State Overview " World Bank Economic Review 10(2)- 223-39 of the World 1998 Washington, D C Worldwatch Institute . 1999 'Bank-Based and Market-Based Financial Systems- Cross-Country Brown, Lester R., Michael Renner, Christopher Fiavin, and others. 1998 Vital Comparisons " Policy Research Working Paper 2143 World Bank, Signs 1998 Washington, D C Worldwatch Institute Development Research Group, Washington, D C. Bulatao, Rodolfo. 1998. The Value of Family Planning Programs in Developing de Onis, Mercedes, and Monika Blossner. 2000. "Prevalence and Trends of Countnes Santa Monica, Calif.. Rand Overweight among Preschool Children in Developing Countries " Amencan Caloia, Marcello. 1995 A Manual for Country Economists Training Series 1, vol Joumal of Clinical Nutntion 72: 1032-39 1 Washington, D C International Monetary Fund . Forthcoming "The WHO Global Database on Child Growth and Centro Latinoamericano de Demografia. 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Various years Taxation Eschborn, Germany Demographic Statistics Luxembourg Gupta, Sanjeev, Hamid Davoodi, and Erwin Tiongson. 2000 "Corruption and the - Various years Statistical Yearbook Luxembourg Provision of Health Care and Education Services " IMF Working Paper Evenson, Robert E., and Carl E. Pray. 1994 "Measuring Food Production (with 00/116 International Monetary Fund, Washington, D C Reference to South Asia) "Journal of Development Economics 44 173-97 Gupta, Sanjeev, Brian Hammond, and Eric Swanson. 2000 "Setting the Goals Faiz, Asif, Christopher S. Weaver, and Michael P. Walsh. 1996 Air Pollution OECD Observer (223) 15-17 from Motor Vehicles Standards and Technologies for Controlling Emissions Hamilton, Kirk, and Michael Clemens. 1999 'Genuine Savings Rates in Washington, D C . 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Washington, D C Hentige, Hemamala, Muthukumara Mani, and David Wheeler. 1998 'Industrial IMF (Intemational Monotary Fund), OECD (Organisation for Economic Co-oper- Pollution in Economic Development Kuznets Revisited n Policy Research ation and Development), United Nations, and World Bank. 2000 A Better Working Paper 1876 World Bank, Development Research Group, World for All: Progress towards the International Development Goals Washington, D C Washington, D C Hill, Kenneth, Carla AbouZahr, and Tessa Wardlaw. 2001 'Estimates of Institutional Investor. 2002. September. New York Maternal Mortality for 1995 ' Bulletin of the World Health Organization 79(3). Intemational Civil Aviation Organization. 2002 Civil Aviation Statistics of the 182-93 World, 1999-2000 Montreal Hill, Kenneth, Rohini Pande, Mary Mahe, and Gareth Jones. 1999 Trends in Intemational Road Federation. 2001. World Road Statistics 2001 Geneva Child Mortality in the Developing World 1960 to 1996 New York. 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Washington, D C Kaminsky, Graclela L., Saul Lzondo, and Carmen M. Reinhart. 1997 'Leading - 2002 Exchange Arrangements and Exchange Restrictions Annual Indicators of Currency Crises - Policy Research Working Paper 1852. 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World UNICEF (United Nations Children's Fund). 2001 Progress since the World Military Expenditures and Arms Transfers 2000 Washington, D C Summit for Children A Statistical Review New York U.S. Environmental Protection Agency. 1995 National Air Quality and Emissions - Various years The State of the World's Children New York Oxford Trends Report 1995 Washington, D C University Press Wagstaff, Adam, and Harold Alderman. 2001 "Life and Death on a Dollar a Day UNIDO (United Nations Industrial Development Organization). Various years Does It Matter Where You Live?" World Bank, Washington, D C International Yearbook of Industrial Statistics Vienna Walsh, Michael P. 1994 "Motor Vehicle Pollution Control An Increasingly Critical United Nations. 1947 Measurement of National Income and the Construction of Issue for Developing Countries " World Bank, Washington, D C Social Accounts New York Watson, Robert, John A. Dixon, Steven P. Hamburg, Anthony C. Janetos, and . 1968 A System of National Accounts Studies and Methods Series F, Richard H. Moss. 1998 Protecting Our Planet, Securing Our Future Linkages no 2, rev 3 New York among Global Environmental Issues and Human Needs A Joint publication of . 1990 International Standard Industrial Classification of All Economic the United Nations Environment Programme, U S National Aeronautics and Activities, Third Revision Statistical Papers Series M, no 4, rev 3 New York Space Administration, and World Bank, Nairobi and Washington, D C . 1992 Handbook of the International Companson Programme Studies in WCMC (World Conservation Monitoring Centre). 1992 Global Biodiversity Methods Series F, no 62 New York Status of the Earth's Living Resources London Chapman and Hall . 1993 SNA Handbook on Integrated Environmental and Economic - . 1994 Biodiversity Data Sourcebook Cambridge World Conservation Accounting Statistical Office of the United Nations Series F, no 61. New York Press - 1999 Integrated Environmental and Economic Accounting An WHO (World Health Organization). 1977 International Classification of Operational Manual Studies an Methods Series F, no 78 New York Diseases 9th rev Geneva - 2000 We the Peoples The Role of the United Nations in the 21st . 1995 Trends and Challenges in World Health Report by the Secretariat Century New York WHO Executive Board Document EB 105/4 Geneva 2003 Woed Development Indicators 1 375 1 1997 Coverage of Matemity Care Geneva - 1997e Sector Strategy Health, Nutrition, and Population Human . 2002a Global Tuberculosis Control Report 2002 Geneva Development Network, Washington, D C . 2002b The Tobacco Atlas Geneva - . 1997f World Development Report 1997. The State in a Changing World . Various years World Health Report Geneva New York Oxford University Press. - Various years World Health Statistics Annual Geneva - . 1998 1998 Catalog Operational Documents as of July 31, 1998 WHO (World Health Organization) and UNICEF (United Nations Children's Washington, D C Fund). 1992 Low Birth Weight A Tabulation of Available Information - . 1999a Fuel for Thought Environmental Strategy for the Energy Sector Geneva Environment Department, Energy, Mining, and Telecommunications . 2000 Global Water Supply and Sanitation Assessment 2000 Report Department, and International Finance Corporation, Washington, D C Geneva . 1999b Greening Industry New Roles for Communities, Markets, and WITSA (World Information Technology and Services Alliance). 2002 Digital Govemments A World Bank Policy Research Report New York Oxford Planet 2002 The Global Information Economy Based on research by University Press International Data Corporation Vienna, Va - . 1999c Health, Nutntion, and Population Indicators A Statistical Wolf, Holger C. 1997 Patterns of Intra- and Inter-State Trade NBER Working Handbook Human Development Network, Washington, D C Paper 5939 Cambridge, Mass National Bureau of Economic Research -. 1999d. Toward a Virtuous Circle A Nutrition Review of the Middle East World Bank. 1990 World Development Report 1990 Poverty. New York. Oxford and North Africa Middle East and North Africa Working Paper Series, no 17 University Press Washington, D C . 1991 World Development Report 1991 The Challenge of Development . 1999e World Development Report 1999/2000 Entenng the 21st New York Oxford University Press Century-The Changing Development Landscape New York Oxford University . 1992 World Development Report 1992 Development and the Press Environment New York Oxford University Press . 2000a Trade Blocs A World Bank Policy Research Report New York . 1993a The Environmental Data Book A Guide to Statistics on the Oxford University Press Environment and Development Washington, D C - . 2000b World Development Report 2000/2001 Attacking Poverty New . 1993b Purchasing Power Panties Companng National Incomes Using York Oxford University Press ICP Data Washington, D C - . 2002a. 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Environmentally Washington, D C Sustainable Development Studies and Monographs Series, no 12 - . 2002h World Development Report 2003 Transforming Growth- Washington, D C Neighbor, Nature, Future. Draft Washington, D C 376 1 2003 World Development Indicators - 20021 The Environment and the Millennium Development Goals Washington, D C - Forthcoming Poverty Reduction and the World Bank Operationalizing World Development Report 2000/2001 Washington, D C - Various issues Global Commodity Markets Quarterly Washington, D C - Various years Global Development Finance (formerly World Debt Tables) Washington, D C (Also available on CD-ROM ) - Various years Global Economic Prospects and the Developing Countnes Washington, D C - Various years World Development Indicators Washington, D C World Energy Council. 1995 Global Energy Perspectives to 2050 and Beyond London World Intellectual Property Organization. 2002 Industrial Property Statistics Publication A Geneva World Resources Institute, International Institute for Environment and Development, and IUCN (World Conservation Union). Various years World Directory of Country Environmental Studies Washington, D C World Resources Institute, UNEP (United Nations Environment Programme), and UNDP (United Nations Development Programme). 1994 World Resources 1994-95 A Guide to the Global Environment New York Oxford University Press World Resources Institute, UNEP (United Nations Environment Programme), UNDP (United Nations Development Programme), and World Bank. Various years World Resources A Guide to the Global Environment New York Oxford University Press World Tourism Organization. 2001a Compendium of Tounsm Statistics 2001 Madrid -. 2001b Yearbook of Tourism Statistics Vols 1 and 2 Madrid WTO (World Trade Organization). Various years Annual Report Geneva Zook, Matthew. 2000 'Internet Metrics Using Host and Domain Counts to Map the Internet " International Journal on Knowledge Infrastructure Development, Management and Regulation (University of California at Berkeley) 24(6/7) 2003 World Development Indicators I 377 References are to table numbers net official development assistance and official aid by DAC members as share of general government disbursements 6 9 Agriculture as share of GNI of donor country 1 4, 6 9 cereal average annual change in volume 6 9 area under production 3 2 by type 6 8 exports as share of total exports 6 3 from major donors, by recipient 6 11 exports, total 6 3 for basic social services as share of total ODA commitments 1 4 imports as share of total imports 6 3 per capita of donor country 6 9 imports, total 6 3 total 6 8, 6 9, 611 yield 3 3 untied aid 6 9 fertilizer commodity prices 6 4 AIDS-see HIV, prevalence of consumption 3 2 food commodity prices 6 4 Air pollution-see Pollution freshwater withdrawals for, as share of total 3 5 labor force as share of total, male and female 2 3 Air transport land aircraft departures 5 9 arable, as share of land area 31 air freight 5.9 arable, per capita 3 2 passengers carried 5 9 irrigated, as share of cropland 3 2 permanent cropland as share of land area 3 1 Anemia, pregnant women with 2 18 machinery tractors per 100 square kilometers of arable land 3.2 Asylum seekers-see Migration tractors per 1,000 agricultural workers 3 2 production indexes crop 33 food 3 3 Balance of payments livestock 3 3 current account balance 415 value added exports and imports of goods and services 4 15 annual growth of 41 gross international reserves 415 as share of GDP 4 2 net current transfers 415 per worker 3 3 net income 4.15 wage per worker 2 5 See also Exports, Imports, Investment, Private capital flows, Trade Aid Bank and trade-related lending 6.7 by recipient aid dependency ratios 6 10 Biological diversity per capita 610 assessment, date prepared, by country 314 total 610 species 3 4 net concessional flows threatened species 3.4 from international financial institutions 6 12 treaty 3.14 from United Nations agencies 6 12 378 L 2003 World Development Indicators Birds private species 3 4 annual growth of 1 4, 4 10 threatened species 3 4 as share of GDP 4 9 per capita, annual growth of 1 2, 4 10 Birth rate, crude 21 relative price level 412 total 4 10 Births attended by skilled health staff 1 2, 2 7, 2 17 See also Purchasing power parity (PPP) Birthweight, low 218 Contraceptive prevalence rate 2 17 Contract enforcement costs of 5 3 Carbon dioxide procedures for, number of 5 3 damage 315 time required for 5 3 emissions per capita 1 3, 3 8 Country risk per 1995 U S dollar of GDP 3 8 composite ICRG risk ratings 5 2 total 1 6, 3 8 Euromoney country creditworthiness ratings 5 2 Institutional Investor credit ratings 5 2 Cities Moody's sovereign long-term debt ratings 5 2 air pollution 313 Standard & Poor's sovereign long-term debt ratings 5 2 environment 3 11 population Credit, domestic in largest city 310 from banking sector 5 5 in selected cities 313 to private sector 51 telephone mainlines in largest city 5 10 See also Urban environment Current account balance 415 See also Balance of payments Commodity prices and price indexes 6 4 Communications-see Internet" users, Newspapers, Radios, 0 Telecommunications, international, Television DAC (Development Assistance Committee)-see Aid Computers Death rate, crude 2 1 in education 5 11 See also Mortality rate per 1,000 people 5 11 Debt, external Consumption debt service, total 4 17 distribution of-see Income, distribution long term 4 16 fixed capital 315 present value of 417 government, general private nonguaranteed 4 16 annual growth of 410 public and publicly guaranteed as share of GDP 4 9 debt service 417 2003 World Development Indicators 1 379 IBRD loans and IDA credits 4.16 as share of total government expenditure 2 11 IMF credit, use of 4 16 per student, as share of GDP per capita 2 10 total 4 16 per student, by level 2.11 ratings 5 2 pupil-teacher ratio, primary level 2 11 short term 4 17 teachers, primary, trained 2 11 total 4 16 unemployment by level of educational attainment 2 4 Defense Electricity armed forces personnel consumption 5 10 as share of labor force 5 8 distribution losses 5 10 total 5.8 production arms trade sources of 3 9 exports 58 total 3 9 imports 5 8 military expenditure Employment as share of central government expenditure 5 8 in agriculture, male and female 2 3 as share of GDP 5 8 in industry, male and female 2 3 in informal sector, urban Deforestation 3 4 male and female 2 9 total 2 9 Density-see Population, density in services, male and femnale 2 3 Development assistance-see Aid Endangered species-see Biological diversity, threatened species Distribution of income or consumption-see Income, distribution Energy commercial, use of annual growth of 3 7 efficiency of 3 8 Education GDP per unit of 3 8 attainment per capita 3 7 share of cohort reaching grade 5, male and female 213 total 3 7 years of schooling depletion as share of GDP 315 average 2 13 emissions-see Pollution expected 2.14 imports, net 3 7 enrollment ratio production, commercial 3 7 female to male enrollment in primary and secondary schools 2 12 See also Electricity gross, by level 2.12 Entry and exit regulations in emerging stock markets net, by level 2 12 freedom of entry 5 2 net intake rate, grade 1 2 13 repatriation primary completion rate 2 13 of capital 5 2 public spending on of income 5 2 as share of GDP 2.10 380 2003 World Development Indicators Entry regulations for business F cost to register a business as share of GNI per capita 5 3 minimum capital requirement as share of GNI per capita 5 3 Fertility rate start-up procedures, number of 5 3 adolescent 2 17 time to start up a business 5 3 total 2 7, 2 17 Environmental profile, date prepared 3 14 Financial debt and efficiency-see Liquidity, Monetary indicators Environmental strategy, year adopted 3 14 Financial flows, net from DAC members 6 8 Euromoney country creditworthiness ratings 5 2 from multilateral institutions international financial institutions 6 12 Exchange rates total 6 12 arrangements 5 7 United Nations 6 12 official, local currency units to U S dollar 5 7 official development assistance and official aid ratio of official to parallel 5 7 grants from NGOs 6 8 real effective 5 7 other official flows 6 8 See also Purchasing power parity (PPP) private 6 8 total 6 8 Exports See also Aid arms 5 8 duties on 5 6 Foreign direct investment, net-see Investment, Private capital flows, net goods and services as share of GDP 4 9 Forest total 4 15 area merchandise as share of total land area 3 4 by high-income OECD countries, by product 6 3 total 3 4 by regional trade blocs 6 5 deforestation, average annual 3 4 direction of trade 6.2 depletion of 3 15 high technology 5 12 structure of 4 5 Freshwater total 4 5 annual withdrawals of value, annual growth of 4 4, 6 2 as share of total resources 3 5 volume, annual growth of 4 4 for agriculture 35 services for domestic use 35 structure of 4 7 for industry 35 total 4 7 flows transport 4.7 internal 35 travel 4.7, 6.14 from other countries 35 See also Trade resources per capita 35 volume of 3 5 See also Water, access to improved source of 2003 World Development indicators 1 381 Fuel prices 3 12 Gross capital formation annual growth of 4 10 as share of GDP 4 9 Gender differences Gross domestic product (GDP) in education annual growth of 1 1,1 6,4 1 enrollment, primary and secondary 1 2 implicit deflator-see Prices years of schooling per capita, annual growth of 1 1, 1 6 average 2 13 total 4 2 expected 2 14 in employment 2 3 Gross domestic savings as share of GDP 4 9 in labor force participation 1 5, 2 2 in literacy Gross foreign direct investment-see Investment adult 2 14 youth 1 5, 2 14 Gross national income (GNI) in life expectancy 1 5,2 20 per capita in mortality in 2001 PPP dollars 11,16 adult 2 20 in 2001 U.S. dollars 1 1, 1 6 child 220 rank 1 1 in smoking 2 19 rank in survival to 65 2 20 in 2001 PPP dollars 1 1 women in decisionmaking positions 1 5 in 2001 U S dollars 1 1 total Gini index 2 8 in 2001 PPP dollars 11,1 6 in 2001 U.S dollars 1.1, 1.6 Government, central debt Gross national savings as share of GNI 3 15 as share of GDP 4 11 interest as share of current revenue 4.11 M interest as share of total expenditure 4 12 expenditures Health care as share of GDP 4 11 average length of hospital stay 2 15 by economic type 4 12 hospital beds per 1,000 people 2 15 military 5 8 immunization 2 16 financing inpatient admission rate 2 15 domestic 4.11 outpatient visits per capita 2 15 from abroad 4 11 pregnant women receiving prenatal care 1 5 overall deficit 4 11 physicians per 1,000 people 2 15 revenues as share of GDP 4 11 revenues, current reproductive nontax 4 13 births attended by skilled health staff 1 2, 2 7, 2 17 tax, by source 4.13, 5.6 contraceptive prevalence rate 2 17 382 0 2003 World Development Indicators fertility rate 2 adolescent 2 17 total 2 7, 2 17 Illiteracy rate -low-birthweight babies 2 18 adult, male and female 2 14 maternal mortality ratio 1 2, 2 17 gender differences in 15 women at risk of unwanted pregnancy 2 17 total, for other economies 1 6 tetanus vaccinations 2 16 youth, male and female 2 14 tuberculosis DOTS detection rate 2 16 Immunization treatment success rate 2 16 child 2 16 DPT, share of children under 12 months 2 16 Health expenditure measles, share of children under 12 months 2 16 as share of GDP 2 15 per capita 2 15 Imports private 2 10,2 15 arms 57 public 2.15 duties on 5 5 total 2 15 energy, as share of commercial energy use 3 7 goods and services Health risks as share of GDP 4 9 anemia, prevalence of 2 18 total 4 15 HIV, prevalence of 1 3,2 19 merchandise iodized salt consumption 218 by high-income OECD countries, by product 6 3 malnutrition, child 1 2,2 7,218 structure of 4 6 overweight children, prevalence of 218 total 4 6 smoking 2 19 value, annual growth of 4 4,6 2 tuberculosis, incidence of 1 3,2 19 volume, annual growth of 4 4,6 2 undernourishment, prevalence of 2 18 services structure of 4 8 Heavily indebted poor countries (HIPCs) total 4 8 completion point 14 transport 4 8 decision point 1 4 travel 4 8, 6 14 nominal debt service relief 1 4 See also Trade HIV, prevalence of 13, 2 19 Income distribution Hospital beds-see Health care Gini index 2 8 percentage shares of 1 2, 2 8 Housing, selected cities survey year 2 8 population with secure tenure 3.11 urban house price to income ratio, selected cities 3 11 price to income ratio 3 11 Indebtedness classification 4 17 2003 World Development Indicators 1 383 Industry, value added Internet annual growth of 4 1 access charges as share of GDP 4 2 by service provider 5 11 for telephone usage 5 11 Inflation-see Prices secure servers 5 11 users 5 11 Information and communications technology expenditures as share of GDP 5 11 Investment per capita 5 11 entry and exit regulations-see Entry and exit regulations in emerging stock markets Insolvency foreign direct, gross, as share of GDP 6 1 costs to resolve 5 3 foreign direct, net time to resolve 5 3 as share of gross capital formation 5 2 total 6 7 Institutional Investor credit ratings 5 2 government capital expenditure 4 12 infrastructure, private participation in Integration, global economic, indicators of 6 1 energy 5.1 telecommunications 5 1 Interest payments-see Government, central, debt transport 51 water and sanitation 51 Interest rates portfolio deposit 5 7 bonds 6 7 lending 5 7 equity 6 7 real 5 7 See also Gross capital formation risk premium on lending 5 5 spreads 5 5 Iodized salt, consumption of 218 International Bank for Reconstruction and Development (IBRD) n IBRD loans and IDA credits 4 16 net financial flows from 6 12 Labor cost, per worker in manufacturing 2.5 International Country Risk Guide (ICRG) composite risk ratings 5 3 Labor force annual growth of 2 2 International Development Association (IDA) armed forces 5 8 IBRD loans and IDA credits 4 16 children ages 10-14 in 2.9 net concessional flows from 6 12 female 2 2 foreign, in OECD countries 6 13 International Monetary Fund (IMF) in agriculture, as share of total, male and female 2.3 net financial flows from 612 in industry, as share of total, male and female 2 3 use of IMF credit 4.16 in services, as share of total, male and female 2 3 maternity leave benefits 1.5 participation gender differences in 1.5 384 I 2003 World Development Indicators of population ages 15-64 2 2 support to agriculture 14 total 2 2 tariffs on exports from low- and middle-income countries women in decisionmaking positions 1i5 agricultural products 14 See also Employment, Migration, Unemployment textiles and clothing 14 Land area Maternity leave benefits 15 arable-see Agriculture, land of selected cities 3 11 Merchandise See also Protected areas, Surface area exports agricultural raw materials 4 5 Land use, by type 31 food 4 5 fuels 45 Life expectancy at birth manufactures 45 gender differences in 15 ores and metals 4 5 total 1 6, 2 20 total 45 imports Liquidity agricultural raw materials 4 6 bank liquid reserves to bank assets 5 5 food 46 liquid liabilities 5 5 fuels 4 6 quasi-liquid liabilities 5 5 manufactures 4 6 See also Monetary indicators ores and metals 4 6 total 4 6 Literacy-see Illiteracy rate trade direction of 6 2 growth of 4 4, 6 2 Malnutrition, in children under five 1 2, 2 7, 2 18 Migration foreign labor force in OECD countries as share of total labor force 613 Mammals foreign population in OECD countries 613 species 3 4 inflows of foreign population threatened species 3 4 asylum seekers 613 total 6 13 Manufacturing labor cost per worker 25 Millennium Development Goals, indicators for structure of 43 aid value added as share of GNI of donor country 1 4, 6 9 annual growth of 41 as share of total ODA commitments 1 4 as share of GDP 42 access to Improved water source 13, 216, 3 5 per worker 2 5 access to improved sanitation facilities 1 3, 216, 310 total 4 3 births attended by skilled health staff 12, 2 7, 217 carbon dioxide emissions per capita 1 3, 3 8 Market access to high-income countries child malnutrition 1 2, 2 7, 2 18 goods admitted free of tariffs 1 4 consumption, national share of poorest quintile 1 2, 2 8 2003 World Development Indicators I 385 female to male enrollments, primary and secondary 1 2 Net adjusted savings 3 15 heavily indebted poor countries (HIPCs) completion point 1.4 Newspapers, daily 5 11 decision point 1 4 nominal debt service relief 1 4 Nutrition HIV, prevalence of, among 15- to 24-year-olds anemia, prevalence of 2.18 female 1 3, 2 19 breastfeeding 2 18 male 13, 219 iodized salt consumption 218 maternal mortality ratio 1 2, 2 17 malnutrition, child 1 2, 2 7, 2 18 net primary enrollment ratio 212 overweight children, prevalence of 218 telephone lines 1 3, 5 9 undernourishment, prevalence of 218 tuberculosis, incidence of 1 3, 2 19 vitamin A supplementation 218 under-five mortality rate 1 2, 2 20 unemployment among 15- to 24-year-olds 1.3, 2 4 Minerals, depletion of 3 15 Official aid-see Aid Monetary indicators Official development assistance-see Aid claims on governments and other public entities 4.14 claims on private sector 4.14 Official flows, other 6 8 Money and quasi money (M2), annual growth of 414 Moody's sovereign long-term debt ratings 5 2 Passenger cars per 1,000 people 3 12 Mortality rate Patent applications filed 5 12 adult, male and female 2 20 child, male and female 2 20 Pension children under five 1 2, 2 20 average, as share of per capita income 2 10 infant 2 7, 220 contributors 2 9 maternal 1 2, 2 17 public expenditure on, as share of GDP 2 10 Motor vehicles Physicians-see Health care passenger cars 3 12 per kilometer of road 3 12 Plants, higher per 1,000 people 312 species 3 4 two-wheelers 3 12 threatened species 3 4 See also Roads, Traffic Pollution M'ii] carbon dioxide damage as share of GDP 3 15 U]U carbon dioxide emissions Nationally protected areas-see Protected areas per capita 3 8 per PPP dollar of GDP 3 8 3RI3 [1 2003 World Development Indicators total 3 8 poverty gap at $1 a day 2 6 nitrogen dioxide, selected cities 3 13 poverty gap at $2 a day 2 6 organic water pollutants, emissions of survey year 2 6 by industry 3 6 per day 3 6 national poverty line per worker 3 6 population below 2 6 sulfur dioxide, selected cities 3 13 rural 2 6 suspended particulate matter, selected cities 313 survey year 2 6 urban 2 6 Population social indicators of age dependency ratio 21 body mass index, women with low 2 7 annual growth of 21 fertility rate 2 7, 217 by age group malnutrition, child 1 2, 2 7, 2 18 0-14 21 mortality rate, infant 2 7, 2 20 15-64 21 survey year 2 7 65 and above 21 density Power-see Electricity, production rural 3 1 total 11,16 Pregnancy, risk of unwanted 217 female, as share of total 15 foreign, in OECD countries 6 13 Prenatal care 15 rural annual growth of 3 1 Prices as share of total 3 1 commodity prices and price indexes 6 4 total 11,16, 21 consumer, annual growth of 414 urban food, annual growth of 414 as share of total 310 fuel 312 in largest city 310 GDP implicit deflator, annual growth of 414 in selected cities 311, 313 terms of trade 4 4 in urban agglomerations 3 10 total 310 Private capital flows See also Migration gross, as share of GDP 61 net Portfolio investment flows bank and trade-related lending 6 7 bonds 6 7 from DAC members 6 8 equity 6 7 foreign direct investment 6 7 portfolio investment 6 7 Ports, container traffic in 5 9 See also Investment Poverty Productivity international poverty line average hours worked per week 2 5 population below $1 a day 2 6 in agriCulture population below $2 a day 2 6 value added per worker 3 3 2003 World Development Indicators 1 387 wage per worker, minimum 2.5 Royalty and license fees labor cost per worker, manufacturing 2 5 payments 512 value added per worker, manufacturing 2 5 receipts 5 12 Protected areas Rural environment as share of total land area 3.4 access to improved water source 3 5 size of 3.4 access to sanitation 3 10 population Purchasing power parity (PPP) annual growth of 31 conversion factor 5 7 as share of total 31 gross national income 1.1, 1.6 density 31 Radios 5 il S&P/IFC Investable Index 5 4 Railways Sanitation lines households with sewerage connections, selected cities 3 11 electric 5 9 population with access to total 5.9 rural 310 productivity of, per employee 5 9 total 13, 216 tariffs, ratio of passenger to freight 5 9 urban 3 10 traffic density 5 9 Savings Regional development banks, net financial flows from 612 gross domestic 4 9 gross national 3 15 Relative prices (PPP)-see Purchasing power parity (PPP) net adjusted 3 15 Research and development domestic 3.15 expenditures for 512 scientists and engineers 5.12 Schooling-see Education technicians 5 12 Science and engineering Reserves, gross tnternational-see Balance of payments scientific and technical journal articles 5 12 scientists and engineers in R&D 5 12 Risk ratings-see Country risk See also Research and development Roads Services goods hauled by 5 9 exports paved, as share of total 5.9 structure of 4 7 total network 5 9 total 4 7 traffic 3.12 imports structure of 4 8 ii 2003 World Development Indicators total 4 8 on imports 5 6 value added See also Tariffs annual growth of 4 1 goods and service taxes, domestic 4 13, 5 6 as share of GDP 4 2 highest marginal tax rate Sewerage connections, selected cities 3 11 corporate 5 6 individual 56 Smoking, prevalence of, male and female 2 19 income, profit, and capital gains taxes as share of total revenue 413 Standard & Poor's sovereign long-term debt ratings 5 2 as share of total taxes 5 6 international trade taxes 413 Stock markets other taxes 4 13 listed domestic companies 5 4 social security taxes 413 market capitalization tax revenue as share of GDP 5 6 as share of GDP 5 4 total 5 4 Technology-see Computers, Exports, merchandise, high technology, Internet, S&P/IFC Investable Index 5 4 users, Research and development, Science and engineering, turnover ratio 5 4 Telecommunications, international value traded 5 4 Telecommunications, international Sulfur dioxide emissions-see Pollution cost of call to United States 5 10 outgoing traffic 5 10 Surface area 1 1, 1 6 See also Land area Telephones cost of local call 5 10 Suspended particulate matter-see Pollution mainlines per employee 5 10 per 1,000 people in largest city 5 10 Tariffs national 5 10 all products revenue per line 5.10 mean tariff 6 6 waiting list 510 standard deviation 6 6 waiting time in years 5 10 manufactured goods mobile 5 10 mean tariff 6 6 standard deviation 6 6 Television primary products cable subscribers per 1,000 people 5 11 mean tariff 6 6 sets per 1,000 people 5 11 standard deviation 6 6 See also Taxes and tax policies, duties Terms of trade, net barter 4 4 Taxes and tax policies Tetanus vaccinations, pregnant women 2 16 duties on exports 5 6 Threatened species-see Biological diversity 2003 World Development Indicators 1 389 Tourism, international Law of the Sea 3 14 expenditures 6 14 ozone layer 3 14 inbound tourists, by country 6 14 outbound tourists, by country 6 14 Tuberculosis receipts 6.14 incidence of 1 3, 2 19 treatment success rate 2 16 Trade arms 58 nn changes in, as share of GDP 61 l!J exports plus imports as share of GDP 6 1 UNDP, net concessional flows from 6 12 merchandise as share of goods GDP 6 1 Unemployment direction of, by region 6 2 incidence of long term export value 4 4, 6 2 male and female 2 4 export volume 4 4 total 2 4 import value 4 4, 6 2 rate import volume 4 4 by level of educational attainment 2 4 nominal growth of, by region 6 2 for 15- to 24-year-olds 13 OECD trade by commodity 6 3 real growth in, less growth in real GDP 6 1 UNFPA, net concessional flows from 6 12 services transport 4 7, 4 8 UNICEF, net concessional flows from 6 12 travel 4 7, 4 8 See also Balance of payments, Exports, Imports United Nations agencies, net concessional flows from 6 12 Trade blocs, regional Urban environment exports within bloc 6.5 access to sanitation 310 total exports, by bloc 6.5 population as share of total 3.10 Trademark applications filed 5 12 in largest city 3 10 in urban agglomerations of more than one million 3 10 Trade policies-see Tariffs total 3.10 selected cities Traffic area 3 11 accidents, people injured or killed by 3 2 households with road traffic 3 2 access to potable water 3.11 See also Roads regular waste collection 3 11 sewerage connections 3 11 Transport-see Air transport, Railways, Roads, Traffic, Urban environment house price to income ratio 3.11 population 3 11 Treaties, participation in travel time to work 311 biological diversity 3 14 work trips by public transportation 3 11 CFC control 3 14 See also Pollution, Population, Water, access to improved source of, climate change 3 14 Sanitation 390 0 2003 World Development Indicators V w Value added Wage as share of GDP agricultural 2 5 in agriculture 4 2 as share of total government expenditure 4 12 in industry 4 2 minimum 2 5 in manufacturing 42 in services 4 2 Waste collection, households with access to 3 11 growth of in agriculture 41 Water, access to improved source of in industry 41 population with, as share of total 13, 216 in manufacturing 41 rural 3 5 in services 41 urban 3 5 per worker urban households with 3 11 in agriculture 33 in manufacturing 2 5 WFP, net concessional flows from 6 12 total, in manufacturing 4 3 Workweek, average hours 2 5 World Bank, net financial flows from 6 12 See also International Bank for Reconstruction and Development, International Development Association 2003 World Development indicators 1 391 The world by region East Asia and Pacific Latin America and the South Asia High Income OECD American Samoa Caribbean Afghanistan Australia Cambodia Antigua and Barbuda Bangladesh Austria - China Argentina Bhutan Belgium Fiji Barbados India Canada Indonesia Belize Maldives Denmark Kiribati Bolivia Nepal Finland Korea, Dem Rep Brazil Pakistan France Lao PDR Chile Sri Lanka Germany Malaysia Colombia Greece - Marshall Islands Costa Rica Sub-Saharan Aftica Iceland Micronesia, Fed Sts Cuba Angola Ireland - Mongolia Dominica Benin Italy - Myanmar Dominican Republic Botswana Japan Palau Ecuador Burkina Faso Korea, Rep Papua New Guinea El Salvador Burundi Luxembourg - Philippines Grenada Cameroon Netherlands - Samoa Guatemala Cape Verde New Zealand Solomon Islands Guyana Central African Republic Norway Thailand Haiti Chad Portugal Timor-Leste Honduras Comoros Spain - Tonga Jamaica Congo, Dem Rep Sweden Vanuatu Mexico Congo, Rep Switzerland Vietnam Nicaragua Cote d'lvoire United Kingdom Panama Equatorial Guinea United States Europe and Central Paraguay Eritrea Asia Peru Ethiopia Other high Income Albania Puerto Rico Gabon Andorra Armenia St Kltts and Nevis Gambia, The Aruba Azerbaijan St Lucia Ghana Bahamas, The Belarus St Vincent and the Guinea Bahrain Bosnia and Herzegovina Grenadines Guinea-Bissau Bermuda Bulgaria Surlname Kenya Brunei Croatia Trinidad and Tobago Lesotho Cayman Islands Czech Republic Uruguay Libena Channel Islands Estonia Venezuela, RB Madagascar Cyprus Georgia Malawi Faeroe Islands Hungary Middle East and Mali French Polynesia Isle of Man North Africa Mauritania Greenland Kazakhstan Algeria Mauritius Guam Kyrgyz Republic Djibouti Mayotte Hong Kong, China Latvia Egypt, Arab Rep Mozambique Israel Lithuania Iran, Islamic Rep Namibia Kuwait Macedonia, FYR Iraq Niger Liechtenstein Moldova Jordan Nigeria Macao, China Poland Lebanon Rwanda Monaco Romania Libya Sao Tome and Principe Netherlands Antilles Russian Federation Malta Senegal New Caledonia Slovak Republic Morocco Seychelles Northern Mariana Tajlikistan Oman Sierra Leone Islands Turkey Saudi Arabia Somalia Qatar Turkmenistan Syrian Arab Republic South Africa San Marino Ukraire Tunisia Sudan Singapore Uzbekistan West Bank and Gaza Swaziland Slovenia Yugoslavia, Fed Rep Yemen, Rep Tanzania United Arab Emirates Togo Virgin Islands (U S Uganda Zambia Member of the Zimbabwe European Monetary Union 0 0~~~~~~~~~~ (D 0 0 Vr I ~~ -~~ 2 ~ jf>~~..I. -. ~~ 011122 ~~~~The World Bank Telephone 202 473 1000 1818 H Street N. W Fax: 202 477 6371 9 780821 35223. Washington. D C Web site. www worldbank org ISBN 082135422-1 20433 USA Email. feedback@worldbank.org 0 w o ~ORb~§ ft OM ~ fw2 &O s- ;em -- 6 -. enirnena c;r ge)o got gXobl iLe io 2 &su< ) Vt (~0 Pl Natural resoures¢ anid New opportuntites environmrental chKr ges for growth lvidencs '{jl global integration