06 WORLD 54165 DEVELOPMENT YEARS INDICATORS WORLD VIEW ECONOMY STATES & MARKETS PEOPLE ENVIRONMENT GLOBAL LINKS INCOME MAP The world by income Low-income Lower-middle-income Barbados Greenland Afghanistan Albania Belize Guam Bangladesh Algeria Botswana Hong Kong, China Benin Angola Chile Iceland Bhutan Armenia Costa Rica Ireland Burkina Faso Azerbaijan Croatia Isle of Man Burundi Belarus Czech Republic Israel Cambodia Bolivia Dominica Italy Cameroon Bosnia and Herzegovina Equatorial Guinea Japan Central African Republic Brazil Estonia Korea, Rep. Chad Bulgaria Gabon Kuwait Comoros Cape Verde Grenada Liechtenstein Congo, Dem. Rep. China Hungary Luxembourg Congo, Rep. Colombia Latvia Macao, China Côte d'Ivoire Cuba Lebanon Malta Eritrea Djibouti Libya Monaco Ethiopia Dominican Republic Lithuania Netherlands Gambia, The Ecuador Malaysia Netherlands Antilles Ghana Egypt, Arab Rep. Mauritius New Caledonia Guinea El Salvador Mayotte New Zealand Guinea-Bissau Fiji Mexico Norway Haiti Georgia Northern Mariana Islands Portugal India Guatemala Oman Puerto Rico Kenya Guyana Palau Qatar Korea, Dem. Rep. Honduras Panama San Marino Kyrgyz Republic Indonesia Poland Saudi Arabia Lao PDR Iran, Islamic Rep. Russian Federation Singapore Lesotho Iraq Seychelles Slovenia Liberia Jamaica Slovak Republic Spain Madagascar Jordan South Africa Sweden Malawi Kazakhstan St. Kitts and Nevis Switzerland Mali Kiribati St. Lucia United Arab Emirates Mauritania Macedonia, FYR St. Vincent and the United Kingdom Moldova Maldives Grenadines United States Mongolia Marshall Islands Trinidad and Tobago Virgin Islands (U.S.) Mozambique Micronesia, Fed. Sts. Turkey Myanmar Morocco Uruguay Nepal Namibia Venezuela, RB Nicaragua Paraguay Niger Peru High-income Nigeria Philippines Andorra Pakistan Romania Aruba Papua New Guinea Samoa Australia Rwanda Serbia and Montenegro Austria São Tomé and Principe Sri Lanka Bahamas, The Senegal Suriname Bahrain Sierra Leone Swaziland Belgium Solomon Islands Syrian Arab Republic Bermuda Somalia Thailand Brunei Darussalam Sudan Tonga Canada Tajikistan Tunisia Cayman Islands Tanzania Turkmenistan Channel Islands Timor-Leste Ukraine Cyprus Togo Vanuatu Denmark Uganda West Bank and Gaza Faeroe Islands Uzbekistan Finland Vietnam Upper-middle-income France Yemen, Rep. American Samoa French Polynesia Zambia Antigua and Barbuda Germany Zimbabwe Argentina Greece The world by income Low ($825 or less) Classified according to Lower middle ($826­$3,255) World Bank estimates of 2004 GNI per capita Upper middle ($3,256­$10,065) High ($10,066 or more) No data Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Grundy & Northedge, London Copyright 2006 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, D.C. 20433 USA All rights reserved Manufactured in the United States of America First printing April 2006 This volume is a product of the staff of the Development Data Group of the World Bank's Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank's Board of Execu- tive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsi- bility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth's surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemina- tion of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credits: Front cover, from top to bottom, Shaida Badiee/World Bank, Mark Edwards/Still Pictures, World Bank photo library, and Digital Vision. If you have questions or comments about this product, please contact: Development Data Center The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or www.worldbank.org/data ISBN 0-8213-6470-7 The developing world has made remarkable progress. The number of people living in extreme poverty on less than $1 a day has fallen by about 400 million in the last 25 years. Many more children, particularly girls, are completing primary school. Illiteracy rates have fallen by half in 30 years. And life expectancy is nearly 15 years longer, on aver- age, than it was 40 years ago. These often spectacular achievements have put many countries securely on track to meet the Millennium Development Goals by 2015. But many others are being left behind, and for them progress in eradicating poverty and improving living standards remains stubbornly slow. In Sub-Saharan Africa the number of people living on less than $1 a day has nearly doubled since 1981. Every day thousands of people, many of them children, still die from preventable diseases. AIDS, malaria, and simple dehydration ravage the developing world. Reaching the Millennium Development Goals is a challenge that depends on having access to the best information available. In designing policies and targeting resources, we need to know how many people are poor and where they live. We need vital information about them, such as their gender, age, and the nature of their work or, indeed, if they have work. We also need to know whether they have access to health care, schools, and safe water. And because economic growth is essential to poverty reduction, we need to know more about the economy, the business environ- ment, the expected demographic trends, the scale of environmental degradation, and the infrastructure services available, among many other statistics. Since 1978 World Development Indicators has compiled statistics to provide an annual snapshot of progress in the developing world and the challenges that remain. It is the product of intensive collaboration with numerous international organizations, government agencies, and private and nongovernmental organizations. Our collective efforts have greatly improved the coverage and reliability of statistics on poverty and development. But more is needed. Better statistics are of value to us all. They allow us to assess the scope of the problems we face and measure progress in solving them. They make politicians and policymakers more accountable. They discourage arbitrariness, corruption, and reliance on anecdotal evidence. But they are costly to produce. Improving our knowledge base will require sustained investment, backed by a sustained commitment by national governments and international agencies. To achieve the ambitious targets we have set ourselves, we must scale up our efforts to produce reliable statistics that will inform public policy, guide debate, and strengthen the effectiveness of development efforts. Paul D. Wolfowitz President The World Bank Group 2006 World Development Indicators v This book and its companion volumes, Little Data Book and The Little Green Data Book, are prepared by a team led by Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, David Cieslikowski, Mahyar Eshragh-Tabary, Richard Fix, Amy Heyman, Masako Hiraga, Raymond Muhula, M. H. Saeed Ordoubadi, Sulekha Patel, Juan Carlos Rodriguez, Changqing Sun, K. M. Vijayalakshmi, and Vivienne Wang, working closely with other teams in the Development Economics Vice Presidency's Development Data Group. The CD-ROM development team included Azita Amjadi, Ramgopal Erabelly, Saurabh Gupta, Reza Farivari, and William Prince. The work was carried out under the management of Shaida Badiee. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in five of the World Bank's thematic networks--Environmentally and Socially Sustainable Development, Human Development, Poverty Reduction and Economic Management, 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 contri- butions to the book's content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont and Bruce Ross-Larson, with the assistance of Christopher Trott. The editing and production team consisted of Jodi Baxter, Brendon Boyle, Michael Diavolikis, Timothy Walker, and Elaine Wilson. Communications Development's London partner, Grundy & Northedge, provided art direction and design. Staff from External Affairs oversaw publication and dissemination of the book. vi 2006 World Development Indicators In the 10 years that we have been producing the World Development Indicators, the world of development statistics has grown larger and deeper. It has also become better integrated. The demand for statistics to measure progress and demonstrate the effectiveness of development programs has stimulated growing interest in the production and dissemination of statistics. And not just in the traditional domains of debt, demographics, and national accounts, but in new areas such as biodiversity, information, communications, technology, and measures of government and busi- ness performance. In response World Development Indicators has continued to grow and change. In 1999 members of the statistical community, recognizing that the production of sound statistics for measuring progress is a global responsibility, established the Partnership in Statistics for Development in the Twenty-first Cen- tury (PARIS21) to strengthen statistical capacity at all levels. In 2000 the United Nations Millennium Summit called on all countries to work toward a quantified, time-bound set of development targets, which became the Millennium Development Goals. In the five years since the Millennium Summit, the idea of working toward specific goals has evolved into a general strategy of managing for development results. Countries are reporting on progress toward the Millennium Development Goals and monitoring their own results using a variety of economic and social indicators. Bilateral and multilateral development agencies are incorporating results into their own management planning and evaluation systems and using new indicators to set targets for harmonizing their joint work programs. All of these efforts depend on statistics. So, what has been done to improve the quality and availability of statistics? A lot. Supported by five donors, the Trust Fund for Statistical Capacity Building has provided $20 million in grants for 86 projects, many to create national sta- tistical development strategies. Several countries, recognizing the need for large-scale investments in their statistical systems, have taken out loans or credits to finance them. PARIS21 has conducted advocacy and training workshops around the world to strengthen national statistical systems. The International Comparison Program has more than 100 countries participating in the largest ever global collection of price data. The Health Metrics Network, sponsored by the World Health Organization and the Bill & Melinda Gates Foundation, is now under way. The United Nations Children's Fund launched a new round of data collection through its Multiple Indicators Cluster Surveys. And the program of Demographic and Health Surveys, funded largely by the United States, continues to operate in many countries. To accelerate global cooperation in statistical capacity building, the World Bank will provide $7.5 million a year toward implementing the Marrakech Action Plan for Statistics (MAPS), a grant-funded program. In its first year MAPS will fund the International Household Survey Network to harmonize, document, and provide technical support to survey programs everywhere. It is also funding work by the United Nations Statistics Division to prepare for the 2010 round of censuses; work on education by the United Nations Educational, Scientific, and Cultural Organization's Institute for Statistics; a project on migration by the International Labour Organization; and work on measuring slums by the United Nations Human Settlements Programme. And through PARIS21 it is supporting a pilot program to accelerate the production of key development indicators in low-income countries. National statistical offices and international and regional agencies now find themselves at the center of attention. The challenge is to maintain the momentum in producing more and better quality data. The fruits of today's efforts will be harvested in the years to come. When they are, you will see them here in the tables of World Development Indicators. Shaida Badiee Director Development Data Group 2006 World Development Indicators vii Foreword v Introduction 1 Acknowledgments vi Millennium Development Goals, targets, and indicators 18 Preface vii Tables Partners xii Size of the economy 20 Users guide xx Millennium Development Goals: eradicating poverty and improving lives 24 Millennium Development Goals: protecting our common environment 28 Millennium Development Goals: overcoming obstacles 32 Women in development 34 Key indicators for other economies 38 Text figures, tables, and boxes Poverty rates are falling, but progress has been uneven 2 Country by country progress on poverty 3 Malnutrition rates are predicted to fall everywhere--except in Sub-Saharan Africa 3 Malnutrition--a persistent problem 3 More children everywhere are completing primary school 4 Country by country progress toward universal primary education 5 A long march to literacy 5 Patterns of school attendance 5 More girls in school, but many countries have missed the 2005 target 6 Country by country progress toward equal enrollment 7 Degrees of difference 7 Wealth, gender, and location make a difference 7 Improving the odds for children 8 Country by country progress toward reduced child mortality 9 Prevention comes first 9 Cruel differences 9 Mothers at risk in Africa and South Asia 10 Country by country progress in providing skilled care at births 11 Decreasing risk of young motherhood 11 Poor women need reproductive health services 11 As the HIV/AIDS epidemic matures, the death toll keeps rising 12 The HIV epidemic can be reversed 13 Tuberculosis rates on the rise or falling slowly 13 Malaria is a leading killer in Africa 13 Poor children bear the burden of malaria 13 Water and sanitation--basic services needed by all 14 Country by country progress toward access to water . . . 15 . . . and to sanitation 15 Forests falling 15 Fuel for climate change--high carbon dioxide emitters 15 Many sources and many patterns 16 Official development assistance is rising, but still too little 17 Tariffs remain high on poor countries' exports 17 Debt service is falling, but more relief is needed 17 New technologies are spreading quickly 17 Developing countries produce slightly less than half the world's output 23 Location of indicators for Millennium Development Goals 1­5 27 Location of indicators for Millennium Development Goals 6­7 31 Location of indicators for Millennium Development Goal 8 33 viii 2006 World Development Indicators Introduction 41 Introduction 125 Tables Tables Population dynamics 46 Rural population and land use 130 Labor force structure 50 Agricultural inputs 134 Employment by economic activity 54 Agricultural output and productivity 138 Child labor 58 Deforestation and biodiversity 142 Unemployment 62 Freshwater 146 Wages and productivity 66 Water pollution 150 Poverty 70 Energy production and use 154 Distribution of income or consumption 76 Energy efficiency and emissions 158 Assessing vulnerability and security 80 Sources of electricity 162 Education inputs 84 Urbanization 166 Participation in education 88 Urban housing conditions 170 Education efficiency 92 Traffic and congestion 174 Education completion and outcomes 96 Air pollution 178 Health expenditure, services, and use 100 Government commitment 180 Disease prevention coverage and quality 104 Toward a broader measure of savings 184 Reproductive health 108 Text figures, tables, and boxes Nutrition 112 More than three-fourths of the 1.4 billion people living on Health risk factors and public health challenges 116 fragile lands are in Asia and Africa 126 Mortality 120 Water withdrawal is skewed toward agriculture in every Text figures, tables, and boxes developing region 126 Total fertility rates by region, 1970, 1980, and 2004 42 Many more people lack access to an improved water source Family planning and the fertility transition 42 in rural than in urban areas 127 Population growth rates by region (%) 42 Sustainable management of forests is spreading 128 Total fertility rates in selected Sub-Saharan countries, 2004 42 Use of fossil fuels continues to rise faster than that of other Desired family size in selected countries in Sub-Saharan Africa sources of energy 128 and South Asia, latest year available 43 High-income countries are the leading source of carbon Contraceptive method mix, selected countries, 2000­04 43 dioxide emissions 128 Sub-Saharan Africa's delayed demographic transition 44 Sub-Sarahan Africa has the highest death rate from Projected fertility rates in selected African regions 44 respiratory disease 129 Population projections--trends and uncertainty 45 More efficient use of traditional biomass is improving the The demographic divide: Nigeria and Japan 45 lives of women 129 Of children who work, some combine work and schooling 61 Use of renewable sources of energy is growing, but is still small 129 Regional poverty estimates 73 Ten countries with the largest forest area, 2005 133 Estimated impact of HIV/AIDS on education in three Five countries had more than half the world's forest in 2005 133 Sub-Saharan countries, 2005 87 Irrigated lands have increased in all income groups and most In Uganda most births in rural areas take place at home 103 regions, putting further pressure on water resources 137 Deaths from diarrhea can be sharply reduced with The 10 countries with the highest cereal yield in improvements in drinking water and sanitation 107 2002­04--and the 10 with the lowest 141 Agriculture uses 70 percent of freshwater globally 149 Emission of organic water pollutants declined in most countries from 1990 to 2003 153 In 2003 high-income economies, with 15 percent of world population, used 52 percent of world energy--and produced 41 percent 157 The five largest producers of carbon dioxide . . . 161 . . . differ significantly in per capita emissions 161 Electricity sources have shifted since 1990 . . . 165 . . . with a more profound shift in low-income countries 165 The urban population in developing countries has increased substantially since 1990 169 Selected housing indicators for smaller economies 173 The 15 countries with the fewest passenger cars per 1,000 people in 2003--and the 15 with the most 177 2006 World Development Indicators ix Introduction 189 Introduction 263 Tables Tables Recent economic performance 192 Private sector in the economy 266 Growth of output 194 Investment climate 270 Structure of output 198 Business environment 274 Structure of manufacturing 202 Stock markets 278 Structure of merchandise exports 206 Financial access, stability, and efficiency 282 Structure of merchandise imports 210 Tax policies 286 Structure of service exports 214 Defense expenditures and arms transfers 290 Structure of service imports 218 Transport services 294 Structure of demand 222 Power and communications 298 Growth of consumption investment, and trade 226 The information age 302 Central government finances 230 Science and technology 306 Central government expenses 234 Text figures, tables, and boxes Central government revenues 238 Africa had the lowest business environment reform intensity Monetary indicators 242 in 2004 264 Exchange rates and prices 246 Rural access index for selected low-income countries (% of Balance of payments current account 250 rural population) 265 External debt 254 Excessive paperwork adds to the time that businesses Debt ratios 258 spend complying with taxes 289 Text figures, tables, and boxes Europe and Central Asia had the highest Internet use Fast growing--and backsliding--economies in 2004 190 among developing country regions in 2004 305 Inflation, median annual growth of GDP deflator (%) 190 Real interest rates (%) 190 Accelerating regional growth 190 Raising demand for energy supplies 191 China's data revision 191 Manufacturing growth trends for selected Sub-Saharan countries 205 Developing economies' share of world merchandise exports continues to increase 209 Top 10 exporters in Sub-Saharan Africa in 2004 213 Top 10 developing country exporters of commercial services in 2004 217 The mix of commerical service imports is changing 221 Gross capital formation and government consumption are both on the rise in Sub-Saharan Africa 229 Selected developing countries with large cash deficits 233 Interest payments are a large part of government expenditure for some developing economies 237 Rich countries rely more on direct taxes 241 Top 15 countries with the largest current account surplus, and top 15 countries with the largest current account deficit in 2003 253 GDP is outpacing external debt in Sub-Saharan countries 257 The debt burden of Sub-Saharan countries has been falling since 1995 261 x 2006 World Development Indicators Introduction 311 Primary data documentation 369 Statistical methods 378 Tables Credits 380 Integration with the global economy 316 Bibliography 382 Growth of merchandise trade 320 Index of indicators 389 Direction and growth of merchandise trade 324 High-income trade with low- and middle-income economies 327 Primary commodity prices 330 Regional trade blocs 332 Tariff barriers 336 Global private financial flows 340 Net financial flows from Development Assistance Committee members 344 Aid flows from Development Assistance Committee members 346 Aid dependency 348 Distribution of net aid by Development Assistance Committee members 352 Net financial flows from multilateral institutions 356 Movement of people 360 Travel and tourism 364 Text figures, tables, and boxes Trade spurs growth and growth spurs trade 312 Foreign direct investment is the largest source of external finance for developing countries 313 Aid is the largest source of external finance for Sub-Saharan Africa 313 New promises of aid and debt relief 314 Immigrant populations are expanding in high-income economies 315 Immigrants in OECD countries are better educated 315 Trade in services is becoming increasingly important 319 Exports are growing in developing countries 323 Triangular trade in manufactures between China, selected other large East Asian economies, and the United States and Japan 326 Growing trade between developing countries 329 Regional trade agreements are proliferating 335 Which developing countries received the most net inflows of foreign direct investment in 2004? 343 Who were the largest donors in 2004? 345 Official development assistance from non-DAC donors, 2000­04 ($ millions) 347 More aid flows to developing countries 351 The flow of bilateral aid from DAC members reflects global events and priorities 355 Maintaining financial flows from the World Bank to developing countries 359 Officially recorded remittance flows are surging 363 International tourist arrivals reached an all-time high in 2004 367 2006 World Development Indicators xi Defining, gathering, and disseminating international statistics is a collective effort of many people and orga- nizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental 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 developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank's efforts, and to those of many others, to improve the quality of life of the world's people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2006. Information about the World Bank is also provided. International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and infor- mation analysis center of the U.S. Department of Energy. The CDIAC's scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere; the role of the ter- restrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases; emissions of carbon dioxide to the atmosphere; long-term climate trends; the effects of elevated carbon dioxide on vegetation; and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/. Deutsche Gesellschaft für Technische Zusammenarbeit The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GTZ's aim is to positively shape political, economic, ecologi- cal, and social development in partner countries, thereby improving people's living conditions and prospects. For more information, see www.gtz.de/. Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/. xii 2006 World Development Indicators International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is respon- sible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO's strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int/. International 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. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/. International Monetary Fund The International Monetary Fund (IMF) was established to promote international monetary cooperation, facilitate the expansion and balanced growth of international trade, promote exchange rate stability, help 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 international balance of payments of members. For more information, see www.imf.org/. International Telecommunication Union The International Telecommunication Union (ITU), a specialized agency of the United Nations, covers all aspects of telecommunication, from setting standards that facilitate seamless interworking of equip- ment and systems on a global basis to adopting operational procedures for the vast and growing array of wireless services and designing programs to improve telecommunication infrastructure in the devel- oping world. The ITU is also a catalyst for forging development partnerships between government and private industry. For more information, see www.itu.int/. National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. 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 scien- tists 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 general welfare. For more information, see www.nsf.gov/. 2006 World Development Indicators xiii Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 30 member countries shar- ing a commitment to democratic government and the market economy. With active relationships with some 70 other countries, nongovernmental organizations, and civil society, it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bod- ies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to support sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.new.oecd.org/. Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an under- standing of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI's main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/. Understanding Children's Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labor Organization, the United Nations Children's Fund (UNICEF), and the World Bank initiated the joint interagency research program "Understanding Children's Work and Its Impact" in December 2000. The Understanding Children's Work (UCW) project was located at UNICEF's Innocenti Research Centre in Flor- ence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW's online data- base contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/. United Nations The United Nations currently has 191 member states. The purposes of the United Nations, as set forth in the Charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promot- ing respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org/. xiv 2006 World Development Indicators United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/. United Nations Children's Fund The United Nations Children's Fund works with other UN bodies and with governments and nongovernmental 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. For more information, see www.unicef.org/. United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization is a specialized agency of the United Nations that promotes "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 fundamental freedoms . . . for the peoples of the world, without distinction of race, sex, language, or religion." For more information, see www.uis.unesco.org/. United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partner- ship in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/. United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, 2006 World Development Indicators xv national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/. World Bank Group The World Bank Group is the world's largest source of development assistance. Its mission is to fight poverty and improve the living standards of people in the developing world. It is a development bank, providing loans, policy advice, technical assistance, and knowledge sharing services to low- and middle-income countries to reduce poverty. The Bank promotes growth to create jobs and to empower poor people to take advantage of these opportunities. 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 more information, see www.worldbank.org/data/. World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. The WHO carries out a wide range of func- tions, including coordinating international health work; helping governments strengthen health services; providing technical assistance and emergency aid; working for the prevention and control of disease; pro- moting 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 standards for biological, pharmaceuti- cal, and similar products; and standardizing diagnostic procedures. For more information, see www.who.int/. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is an international organization dedicated to helping to ensure that the rights of creators and owners of intellectual property are protected worldwide and that inventors and authors are thus recognized and rewarded for their ingenuity. WIPO's main tasks include harmonizing national intellectual property legislation and procedures, providing services for international applications for industrial property rights, exchanging intellectual property information, providing legal and technical assistance to developing and other countries facilitating the resolution of private intellectual property disputes, and marshalling information technology as a tool for storing, accessing, and using valuable intellectual property information. A substantial part of its activities and resources is devoted to development cooperation with developing countries. For more information, see www.wipo.int/. World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.world-tourism.org/. xvi 2006 World Development Indicators World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as possible. It does this by administering trade agreements, acting as a forum for trade negotia- tions, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues--through technical assistance and training programs--and cooperating with other inter- national organizations. At the heart of the system--known as the multilateral trading system--are the WTO's agreements, negotiated and signed by a large majority of the world's trading nations and ratified by their parliaments. For more information, see www.wto.org/. Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the con- tainer industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/. International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/. International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization with a mission to encourage and promote development and maintenance of better and safer roads and road networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF has a major role to play in all aspects of road policy and development worldwide. For govern- ments and financial institutions, the IRF provides a wide base of expertise for planning road develop- ment strategy and policy. For its members, the IRF is a business network, a link to external institutions and agencies and a business card of introduction to government officials and decisionmakers. For the community of road professionals, the IRF is a source of support and information for national road associations, advocacy groups, companies, and institutions dedicated to the development of road infrastructure. For more information, see www.irfnet.org/. 2006 World Development Indicators xvii Netcraft Netcraft's work includes the provision of network security services and research data and analysis of the Internet. It is an authority on the market share of Web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For more information, see www.netcraft.com/. PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused assurance, tax, and advisory services for public and private clients in corporate accountability, risk management, structuring and mergers and acquisitions, and performance and process improvement. For more information, see www.pwcglobal.com/. Standard & Poor's Emerging Markets Data Base Standard & Poor's Emerging Markets Data Base (EMDB) is the world's leading source for information and indices on stock markets in developing countries. It currently covers 53 markets and more than 2,600 stocks. Drawing a sample of stocks in each EMDB market, Standard & Poor's calculates indices to serve as benchmarks that are consistent across national boundaries. Standard & Poor's calculates one index, the S&P/IFCG (Global) index, that reflects the perspective of local investors and those inter- ested in broad trends in emerging markets and another, the S&P/IFCI (Investable) index, that provides a broad, neutral, and historically consistent benchmark for the growing emerging market investment community. For more information, see www.standardandpoors.com/. World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world's living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world's living resources. For more information, see www.unep-wcmc.org/. World Information Technology and Services Alliance The World Information Technology and Services Alliance (WITSA) is the global voice of the information tech- nology industry. It is dedicated to advocating policies that advance the industry's growth and development; facilitating international trade and investment in information technology products and services; strengthening WITSA's national industry associations; and providing members with a broad network of contacts. WITSA also hosts the World Congress on Information Technology and other worldwide events. For more information, see www.witsa.org/. xviii 2006 World Development Indicators World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides--and helps other institutions provide--objective information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development. The institute's current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agricul- ture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/. 2006 World Development Indicators xix Tables are totals (designated by a t if the aggregates include and reliability. Statistical systems in many develop- The tables are numbered by section and display the gap-filled estimates for missing data and by an s, ing economies are still weak; statistical methods, identifying icon of the section. Tables 1.1, 1.2, 1.3, for simple totals, where they do not), median values coverage, practices, and definitions differ widely; and and 1.5 are presented by World Bank region, with the (m), weighted averages (w), or simple averages (u). cross-country and intertemporal comparisons involve economies for each region listed alphabetically within Gap filling of amounts not allocated to countries may complex technical and conceptual problems that can- the region. For other tables countries and econo- result in discrepancies between subgroup aggregates not be unequivocally resolved. Data coverage may not mies are listed alphabetically (except for Hong Kong, and overall totals. For further discussion of aggrega- be complete because of special circumstances or for China, which appears after China). Data are shown tion methods, see Statistical methods. economies experiencing problems (such as those for 152 economies with populations of more than stemming from conflicts) affecting the collection 1 million, as well as for Taiwan, China, in selected and reporting of data. For these reasons, although tables. Table 1.6 presents selected indicators for Aggregate measures for regions data are drawn from the sources thought to be most 56 other economies--small economies with popu- The aggregate measures for regions include only authoritative, they should be construed only as indi- lations between 30,000 and 1 million and smaller low- and middle-income economies (note that these cating trends and characterizing major differences economies if they are members of the International measures include developing economies with popu- among economies rather than offering precise quan- Bank for Reconstruction and Development (IBRD) or, lations of less than 1 million, including those listed titative measures of those differences. as it is commonly known, the World Bank. The term in table 1.6). Discrepancies in data presented in different edi- country, used interchangeably with economy, does The country composition of regions is based on tions of World Development Indicators reflect updates not imply political independence, but refers to any the World Bank's analytical regions and may differ by countries as well as revisions to historical series territory for which authorities report separate social from common geographic usage. For regional clas- and changes in methodology. Thus readers are or economic statistics. When available, aggregate sifications, see the map on the inside back cover and advised not to compare data series between editions measures for income and regional groups appear at the list on the back cover flap. For further discussion of World Development Indicators or between different the end of each table. of aggregation methods, see Statistical methods. World Bank publications. Consistent time-series data Indicators are shown for the most recent year for 1960­2004 are available on the World Develop- or period for which data are available and, in most ment Indicators CD-ROM and in WDI Online. tables, for an earlier year or period (usually 1990 Statistics Except where otherwise noted, growth rates are in this edition). Time-series data are available on Data are shown for economies as they were con- in real terms. (See Statistical methods for information the World Development Indicators CD-ROM and in stituted in 2004, and historical data are revised to on the methods used to calculate growth rates.) Data WDI Online. reflect current political arrangements. Exceptions are for some economic indicators for some economies Known deviations from standard definitions or noted throughout the tables. are presented in fiscal years rather than calendar breaks in comparability over time or across countries Additional information about the data is provided years; see Primary data documentation. All dollar fig- are either footnoted in the tables or noted in About in Primary data documentation. That section sum- ures are current U.S. dollars unless otherwise stated. the data. When available data are deemed to be marizes national and international efforts to improve The methods used for converting national currencies too weak to provide reliable measures of levels and basic data collection and gives information on pri- are described in Statistical methods. trends or do not adequately adhere to international mary sources, census years, fiscal years, and other standards, the data are not shown. background. Statistical methods provides technical information on some of the general calculations and Country notes formulas used throughout the book. · Unless otherwise noted, data for China do not Aggregate measures for income groups include data for Hong Kong, China; Macao, China; The aggregate measures for income groups include or Taiwan, China. 208 economies (the economies listed in the main Data consistency and reliability · Data for Indonesia include Timor-Leste through tables plus those in table 1.6) wherever data are Considerable effort has been made to standardize 1999 unless otherwise noted. available. To maintain consistency in the aggregate the data, but full comparability cannot be assured, · External debt data presented for the Russian Feder- measures over time and between tables, missing and care must be taken in interpreting the indicators. ation prior to 1992 are for the former Soviet Union. data are imputed where possible. The aggregates Many factors affect data availability, comparability, See About the data for table 4.16 for details. xx 2006 World Development Indicators Changes in the System of National Accounts at a GNI per capita of $3,255. High-income econo- World Development Indicators uses terminology in mies are those with a GNI per capita of $10,066 or line with the 1993 United Nations System of National more. The 12 participating member countries of the Accounts (SNA). For example, in the 1993 SNA gross European Monetary Union (EMU) are presented as a national income (GNI) replaces gross national product subgroup under high-income economies. (GNP). See About the data for tables 1.1 and 4.8. Most economies continue to compile their national accounts according to the 1968 SNA, but Symbols more and more are adopting the 1993 SNA. Econo- .. mies that use the 1993 SNA are identified in Primary means that data are not available or that aggregates data documentation. A few low-income economies cannot be calculated because of missing data in the still use concepts from older SNA guidelines, includ- years shown. ing valuations such as factor cost, in describing major economic aggregates. 0 or 0.0 means zero or less than half the unit shown. Classification of economies / For operational and analytical purposes the World in dates, as in 2003/04, means that the period of Bank's main criterion for classifying economies is time, usually 12 months, straddles two calendar GNI per capita. Each economy is classified as low years and refers to a crop year, a survey year, an income, middle income (subdivided into lower middle academic year, or a fiscal year. and upper middle), or high income. For income classi- fications see the map on the inside front cover and the $ list on the front cover flap. Low- and middle-income means current U.S. dollars unless otherwise noted. economies are sometimes referred to as developing economies. The use of the term is convenient; it is > not intended to imply that all economies in the group means more than. are experiencing similar development or that other economies have reached a preferred or final stage of < development. Note that classification by income does means less than. not necessarily reflect development status. Because GNI per capita changes over time, the country composition of income groups may change Data presentation conventions from one edition of World Development Indicators to · A blank means not applicable or, for an aggre- the next. Once the classification is fixed for an edi- gate, not analytically meaningful. tion, based on GNI per capita in the most recent year · A billion is 1,000 million. for which data are available (2004 in this edition), · A trillion is 1,000 billion. all historical data presented are based on the same · Figures in italics refer to years or periods other country grouping. than those specified or to growth rates calculated Low-income economies are those with a GNI per for less than the full period specified. capita of $825 or less in 2004. Middle-income econ- · Data for years that are more than three years omies are those with a GNI per capita of more than from the range shown are footnoted. $825 but less than $10,066. Lower-middle-income and upper-middle-income economies are separated The cutoff date for data is February 1, 2006. 2006 World Development Indicators xxi he Millennium Development Goals have become the principal global scorecard for develop- ment. In September 2005 the United Nations World Summit reaffirmed the principles in the 2000 Millennium Declaration and recognized the need for ambitious national development strategies backed by increased international support. Financing the needed investments. Financing the investments needed to achieve the Goals remains a challenge for the domestic resources of developing countries and the aid budgets of developed countries. Developing countries need to pursue good governance and sound macro- economic policies, and rich countries need to increase their support for developing countries able to absorb more aid. Some developed countries have adopted timetables to increase official development assistance to 0.7 percent of gross national income by 2015 and to reach at least 0.5 percent by 2010, while ensuring that at least 0.2 percent goes to the least developed coun- tries. The World Summit also called for increased debt relief or restructuring for countries with unsustainable debt burdens that are not part of the Heavily Indebted Poor Countries Initiative. The challenge of measurement. Many of these strengthened goals and targets are not eas- ily measured. Reliable, direct measures of the incidence or prevalence of many diseases are unavailable. And because models and data sources are still evolving, estimates may not be comparable over time or across countries. Gaps remain even for the well established measures of poverty, education, mortality, and health care, and major investments in statistical systems will be needed to fill them, by developing countries themselves and international agencies. Expanding targets to support the goals. The World Summit resolution draws attention to four issues that should receive greater prominence over the next five years: · Reproductive health, integrating reproductive health into strategies for achieving the goals of improving maternal health, reducing child mortality, promoting gender equality, combating HIV/AIDS, and eradicating poverty. · Combating disease, intensifying the fight against HIV/AIDS by "providing sufficient health workers, infrastructure, management systems, and supplies to achieve the health-related [goals] by 2015" and calling for renewed efforts to come "as close as possible to the goal of universal access to HIV treatment by 2010." · Employment, strengthening the focus of the goals on employment by making it "a central objective of our relevant national and international policies as well as our national develop- ment strategies. . . ." · Environment, extending the areas of concern in at least three dimensions: biodiversity, development of indigenous people, and protection from natural and human-caused hazards. The resolution calls on all states to "significantly reduce the loss of biodiversity by 2010." The next five years. When the Millennium Development Goals were promulgated in 2000, the international community reached back a decade to establish a baseline. Nothing could be done to alter the course of those preceding 10 years. In the succeeding five years the world took stock of its commitments and took the first steps to accelerate progress toward the goals. But without measures that accelerate change, many countries may fall short of the targets set for 2015. That is why the next five years are so important. By 2010 we will know whether the goals can be achieved. If by then we have not committed the necessary resources, adopted reforms, and implemented effective new programs, it will be difficult to make further course corrections. 2006 World Development Indicators 1 · Halve, between 1990 and · Halve, between 1990 and 2015, the proportion of people 2015, the proportion of people living on less than $1 a day who suffer from hunger Reducing poverty and hunger Poverty exists everywhere, but there has been But more than 600 million people will still be progress. trapped in poverty in 2015, most of them in Sub- Extreme poverty in developing countries Saharan Africa and South Asia and wherever poor fell from 28 percent in 1990 to 19 percent in health and lack of education deprive people of 2002. Over the same period the number of productive employment; environmental resourc- people in developing countries grew 20 per- es have been depleted or spoiled; and corrup- cent, to more than 5 billion, leaving 1 billion tion, conflict, and misgovernance waste public people in extreme poverty. If economic growth resources and discourage private investment. rates in developing countries are sustained, Even as the first target of the Millennium global poverty will fall to 10 percent by 2015-- Development Goals appears in sight, the ef- a striking success. forts to eliminate poverty must be renewed. Poverty rates are falling, but progress has been uneven Share of people living on less than $1 or $2 a day (%) Sub-Saharan Africa South Asia In Sub-Saharan Africa the number 50 44.0 50 of poor people has increased 38.1 40 44.6 40 41.3 31.2 by a third, but accelerating 30 30 22.3 20.7 growth in India has put South 20 20 Asia on track to meet the goal 10 10 13.8 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 East Asia & Pacific Latin America & Caribbean East Asia has experienced a 50 50 sustained period of economic 40 40 growth, led by China, while 29.6 28.4 30 30 23.4 growth and poverty reduction 14.8 17.2 20 20 11.3 have been slower in Latin 11.6 8.9 5.7 10 10 0.7 America and the Caribbean. 4.7 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Europe & Central Asia Middle East & North Africa The transition economies of Europe 50 50 and Central Asia saw poverty 40 40 rates rise in the 1990s and then 30 30 21.4 fall. There and in the Middle East 16.1 19.9 20 20 10.4 and North Africa consumption of 8.2 10 4.9 10 2.1 2.3 1.6 1.2 $2 a day may be a more realistic 0.5 0.9 0.7 0 0.3 0 limit of extreme poverty. 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Poverty rate at $1 a day Actual Projected Goal Poverty rate at $2 a day Actual Projected Source: World Bank staff estimates. 2 2006 World Development Indicators Country by country progress on poverty Malnutrition--a persistent problem Share of countries on track to achieve the poverty reduction target (%) Moderately and severely malnourished children (millions under age 5) Insufficient data Seriously off track Off track On track Reached target In 2020 the number 100 1997 2020 East Asia of malnourished 0 8 & Pacific (24 countries) children will have fallen 6 0 Europe & everywhere, except in Central Asia Sub-Saharan Africa, (27 countries) 0 4 where there are likely to Latin America 20 & Caribbean be more than in 1997. (32 countries) 0 Middle East South Sub- Southeast West Asia Latin & North Africa Asia Saharan Asia & North America (14 countries) Africa Africa Source: Tarmann 2002. South Asia Sub-Saharan Africa (8 countries) Share of children under age 5 (%) Sub-Saharan Malnutrition rates will 40 1990 Africa fall too slowly in most 2015 (48 countries) of Africa to meet the 30 100 50 0 50 100 Millennium Development Goal target, and they may 20 Source: World Bank staff estimates. rise in eastern Africa. The Millennium Development Goals are Those shown as on track could reach 10 intended to be met by all countries. This the 2015 target if they maintain their figure shows the share of countries in current progress. But those shown as off each region that are on track to achieve track or seriously off track are reducing 0 the poverty reduction target, based on poverty too slowly--or have even seen it Eastern Western Central Southern available survey estimates. Some coun- increase--to achieve the first of the Mil- Africa Africa Africa Africa tries have already achieved the target. lennium Development Goals. Source: de Onis and others 2004. Malawi Share of children under age 5, by wealth quintile (%) Malnutrition rates are predicted to fall everywhere-- except in Sub-Saharan Africa Child malnutrition 30 1992 Prevalence of moderate to severe malnutrition (% of children under age 5) remained unchanged in 2000 Malawi during the 1990s, 1990 2015 20 with improvements in South Central some groups offset by & East Asia increases in others. 10 Sub-Saharan Africa 0 Lowest Second Third Fourth Highest East quintile quintile quintile quintile quintile Asia Source: Demographic and Health Surveys. Mali West Asia & Share of children under age 5, by wealth quintile (%) North Africa In Mali average child 30 1995 malnutrition rates fell, but 2001 Caribbean most of the improvement 20 Latin was among the wealthier America part of the population. 0 10 2 0 0 3 0 4 50 10 Note: Regions differ from the World Bank's operational classification. Source: de Onis and others 2004. 0 Lowest Second Third Fourth Highest Malnutrition in children often begins at nium Development Goal target. Faster quintile quintile quintile quintile quintile birth. Malnourished children develop progress is possible. Programs to con- Source: Demographic and Health Surveys. more slowly, enter school later, and tinue breastfeeding and to improve the perform less well. The proportion of se- diets of pregnant and lactating moth- verely underweight children is falling, ers help. So do appropriate care and but fewer than 40 percent of the 77 feeding of sick children, oral rehydration countries with adequate data to monitor therapy, control of parasitic diseases, trends are on track to reach the Millen- and vitamin A supplementation. 2006 World Development Indicators 3 · Ensure that by 2015 children everywhere, boys and girls alike, will be able to complete a full course of primary schooling Educating all children Since 1990 the world has called for all chil- address parents' concern for the safety of dren to be able to complete primary school. their children. But more than 100 million primary school age Education is the foundation of all societ- children remain out of school. ies and globally competitive economies. It is To reach the target of universal primary the basis for reducing poverty and inequality, education by 2015, school systems with low improving health, enabling the use of new tech- completion rates will need to start now to train nologies, and creating and spreading knowl- teachers, build classrooms, and improve the edge. In an increasingly complex, knowledge- quality of education. Most important, they dependent world, primary education, as the will have to remove such barriers to atten- gateway to higher levels of education, must dance as fees and lack of transportation, and be the first priority. More children everywhere are completing primary school Primary completion rate, total (% of relevant age group) Sub-Saharan Africa South Asia Neither Sub-Saharan Africa nor South 100 100 Asia are on track to achieve the goal, 100 100 90 90 but in both regions some countries 80 80 73.2 82.0 have shown that it can be done. 70 61.7 70 60 60 50.8 50 50 40 40 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 East Asia & Pacific Latin America & Caribbean East Asia and Pacific and Latin 100 100 96.7 America and the Caribbean are 97.0 99.0 100 100 90 90 86.0 close to achieving universal 80 80 primary education. However, high 70 70 60 60 regional averages disguise some 50 50 countries that lag behind. 40 40 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Europe & Central Asia Middle East & North Africa Slow progress in Europe and Central 100 100 Asia reflects the dislocations of the 94.4 100 100 92.0 90 90 87.8 transition period. In the Middle East 80 80 77.5 and North Africa there has been a 70 70 60 60 decline in completion rates for boys. 50 50 40 40 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Actual Goal Source: World Bank staff estimates. 4 2006 World Development Indicators Country by country progress toward universal primary education Patterns of school attendance Share of countries on track to achieve the primary education target (%) Share of children ages 6­11 enrolled in primary school, by gender (%) Gabon, by wealth quintile, 2000 Insufficient data Seriously off track Off track On track Reached target Male Gabon has high levels Female 100 East Asia of primary school & Pacific (24 countries) attendance across 80 Europe & all income groups, Central Asia 60 (27 countries) although completion rates are still low. 40 Latin America & Caribbean (32 countries) 20 Middle East & North Africa 0 (14 countries) Poorest Second Third Fourth Richest quintile quintile quintile quintile quintile South Asia Nigeria, by wealth quintile, 2003 (8 countries) Male In Nigeria only the Female Sub-Saharan 100 wealthiest families Africa (48 countries) are able to provide 80 50 0 50 100 primary education for 60 all their children. Source: World Bank staff estimates. 40 In many developing countries children are completion rates by about 10 percentage already able to complete a full course of points to achieve the target. But those 20 primary education, but in all regions at that are seriously off track have much least a few countries remain off track farther to go. Unless they accelerate 0 and unlikely to reach the target of edu- progress, they will not reach the target Poorest Second Third Fourth Richest quintile quintile quintile quintile quintile cation for all by 2015. Countries that before 2040, depriving several more gen- are off track typically need to raise their erations of the benefits of education. Gabon, by urban and rural area, 2000 In Gabon attendance Male Female A long march to literacy rates are equally high 100 Youth literacy rate (% of youths ages 15­24) for boys and girls and in 80 urban and rural settings. 1970 1980 1990 2000­04 60 Sub-Saharan Africa 40 South & 20 West Asia 0 Arab Urban Rural states Nigeria, by urban and rural area, 2003 Latin America & Caribbean But in Nigeria rural Male Female children have fewer 100 East Asia & Pacific opportunities to 80 attend and complete Developing countries primary school. 60 Developed & transition 40 economies 0 20 40 60 80 100 20 Source: UNESCO 2005. 0 Urban Rural Literacy rates among young people Throughout the developing world liter- ages 15­24 are the only widely reported acy rates are higher among youth than Source: World Bank staff estimates based on Demographic and Helth Surveys. measure of educational outcomes. As among adults, a sign of progress. Ef- more children have entered school and forts are under way to develop better stayed in school longer, the global youth measures of literacy and more direct literacy rate has risen from 75 percent measures of the quality of educational in 1970 to 88 percent in 2000­04. outcomes. 2006 World Development Indicators 5 · Eliminate gender disparity in primary and secondary education, preferably by 2005, and at all levels by 2015. Empowering women When a country educates its girls, its mortality responsibilities for household work prevent rates usually fall, fertility rates decline, and the women from finding productive employment health and education prospects of the next or participating in public decisionmaking. generation improve. What will it take to improve girls' enroll- Unequal treatment of women--by the ments? Mainly, overcoming the social and eco- state, in the market, and by their communi- nomic obstacles that stop parents from send- ty and family--puts them at a disadvantage ing their daughters to school. For many poor throughout their lives and stifles the develop- families the economic value of girls' work at ment prospects of their societies. Illiterate and home exceeds the perceived returns to school- poorly educated mothers are less able to care ing. Improving the accessibility of schools and for their children. Low education levels and their quality and affordability is a first step. More girls in school, but many countries have missed the 2005 target Ratio of girls to boys in primary and secondary education (%) Sub-Saharan Africa South Asia The differences between boys' 120 120 100 100 and girls' schooling are greatest 100 100 in regions with the lowest primary 80 80 87.0 79.7 83.6 70.6 school completion rates and 60 60 40 40 the lowest average incomes. 20 20 0 0 1990 1995 2000 2005 1990 1995 2000 2005 East Asia & Pacific Latin America & Caribbean East Asia and Pacific has almost 120 120 100 100 achieved the 2005 target. In 100 100 a 100.9 101.5 98.1 some Latin American countries 80 89.3 80 girls' enrollments exceed boys'. 60 60 40 40 20 20 0 0 1990 1995 2000 2005 1990 1995 2000 2005 Europe & Central Asia Middle East & North Africa In Europe and Central Asia a 120 120 100 100 strong tradition of educating 100 100 97.1 96.3 girls needs to be sustained. In 80 80 90.2 81.5 the Middle East and North Africa 60 60 40 40 more girls are overcoming a 20 20 bias against educating them. 0 0 1990 1995 2000 2005 1990 1995 2000 2005 a. Based on 40 percent of the eligible population. Actual Goal Source: World Bank staff estimates. 6 2006 World Development Indicators Country by country progress toward equal enrollment Wealth, gender, and location make a difference Share of countries on track to achieve equal enrollment of Share of children 15­19 who have completed primary school, by gender (%) girls and boys in primary and secondary school (%) Benin, by wealth quintile, 2002 Insufficient data Seriously off track Off track On track Reached target School attendance 80 Male rates are low in Benin, Female 70 East Asia & Pacific except among the rich. 0 6 (24 countries) Poor children rarely 0 5 Europe & 40 Central Asia complete school, (27 countries) 30 and even among rich Latin America families girls have few 20 & Caribbean (32 countries) opportunities to complete 10 0 Middle East primary education. Poorest Second Third Fourth Richest & North Africa quintile quintile quintile quintile quintile (14 countries) Malawi, by wealth quintile, 2000 South Asia (8 countries) A recent survey in Malawi 100 Male found almost equal Female Sub-Saharan 80 Africa completion rates for (48 countries) boys and girls, although 60 50 0 50 100 children of the poorest 40 Source: World Bank staff estimates. families are still less likely to attend school. 20 The first target of the Millennium Devel- achieved the target on average, such as opment Goals to fall due calls for enroll- Europe and Central Asia and Latin Amer- 0 ing equal numbers of boys and girls in ican and the Caribbean, some countries Poorest Second Third Fourth Richest primary and secondary school by 2005, still fall short. And in South Asia and quintile quintile quintile quintile quintile an important stepping stone on the way Sub-Saharan Africa, where large num- to full gender equality at all levels of bers of children are out of school, girls Benin, by urban and rural area, 2002 education. But even in regions that have are at a severe disadvantage. In Benin efforts 60 Degrees of difference to increase girls' Urban Rural Ratio of girls' to boys' gross enrollment rates (%) 50 attendance will have to improve the accessibility 40 Primary level Secondary level of schools and overcome 30 East Asia the reluctance of rural & Pacific 20 families to send their 10 Europe & daughters to school. Central Asia 0 Male Female Latin America & Caribbean Malawi, by urban and rural area, 2000 In Malawi, where Middle East 100 & North Africa Urban completion rates have Rural risen in recent years, 8 0 South Asia rural areas still lag, 60 but boys and girls are Sub-Saharan 40 represented equally Africa among those who 20 0 20 40 60 80 100 120 complete primary school. 0 Note: A value of more than 100 means that enrollment rates of girls exceed those of boys. Male Female Source: World Bank staff estimates. In a competitive world economy both primary school or drop out of secondary boys and girls need to be educated to school faster than girls. In other regions Source: World Bank staff estimates based on Demographic and Health Surveys. higher levels. Girls are underrepresented the familiar pattern is repeated: fewer in primary education in all regions, but girls are enrolled in primary schools and in some they are overrepresented at their share falls even farther at higher the secondary level. This may happen levels. Whatever the cause, the result because boys take longer to complete is not equitable. 2006 World Development Indicators 7 · Reduce by two thirds, between 1990 and 2015, the under-five mortality rate. Saving children Rapid improvements before 1990 gave hope Only two regions, Latin America and the that mortality rates for infants and children Caribbean and Europe and Central Asia, are under five could be cut by two-thirds in the fol- close to achieving the target on average. But lowing 25 years. But progress slowed almost even there, more than half the countries are everywhere in the 1990s. off track. Progress has been particularly slow Every year almost 11 million children in in Sub-Saharan Africa, where civil disturbances developing countries still die before the age and the HIV/AIDS epidemic have driven up rates of five. Most die from causes that are read- of infant and child mortality. By the most recent ily preventable in rich countries: acute re- data available, only 35 countries are making spiratory infections, diarrhea, measles, and enough progress to reduce under-five mortality malaria. rates to one-third of their 1990 level by 2015. Improving the odds for children Under-five mortality rate (deaths per 1,000 children) Sub-Saharan Africa South Asia The gap between goal and reality 200 200 168 is greatest in Sub-Saharan Africa, 185 150 150 but millions of children are also 129 92 at risk in populous South Asia. 100 100 62 43 50 50 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 East Asia & Pacific Latin America & Caribbean The pace of mortality reduction in 200 200 East Asia and Pacific is slowing. 150 150 The regional average in Latin America and the Caribbean 100 100 59 54 disguises wide variations. 50 50 37 20 18 31 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Europe & Central Asia Middle East & North Africa In Europe and Central Asia questions 200 200 remain about the quality and 150 150 comparability of data over time. More than half the countries in 100 100 55 49 81 the Middle East and North Africa 50 50 34 16 are on track to reach the target. 27 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Actual Goal Source: World Bank staff estimates. 8 2006 World Development Indicators Country by country progress toward reduced child mortality Cruel differences Share of countries on track to achieve the child mortality target (%) Under-five mortality and immunization rates by wealth quintiles Mali Insufficient data Seriously off track Off track On track Reached target Mortality rate (per 1,000 children), 2000 East Asia Child mortality rates 0 30 & Pacific in Mali are high even (24 countries) 0 25 for comparatively Europe & 200 Central Asia wealthy families. (27 countries) 0 15 Latin America & Caribbean 100 (32 countries) 5 0 Middle East & North Africa 0 (14 countries) Lowest Second Third Fourth Highest quintile quintile quintile quintile quintile South Asia (8 countries) South Africa Mortality rate (per 1,000 children), 1998 Sub-Saharan Africa In South Africa the 300 (48 countries) disparity between rich 250 50 0 50 100 and poor is greater, but 200 the average is much Source: World Bank staff estimates. lower than in Mali. 150 A concerted effort to improve the mea- on track to achieve a two-thirds reduc- surement of infant and child mortality tion in mortality rates. Every country in 100 has filled many gaps in the international Sub-Saharan Africa is off track, and in 50 data set, revealing that many countries some countries mortality rates have in- still fall short of achieving the target, creased since 1990. The good news is 0 even where regional averages have that recent surveys have found rapidly Lowest Second Third Fourth Highest been improving. Based on estimates falling mortality rates. These could be quintile quintile quintile quintile quintile through 2004, only 35 countries are the first signs of faster progress. Mali Immunization rate, 2000 (%) Prevention comes first Mali has low 100 Measles Measles immunization rate (% of children ages 12­23 months) DPT immunization rates, 80 especially for its poorest 1990 2004 children. Diphtheria, 60 Europe & pertussis, and tetanus Central Asiaa 40 (DPT) immunization, Latin America which is harder to deliver, 20 & Caribbean lags behind measles 0 for all income groups. Lowest Second Third Fourth Highest Middle East quintile quintile quintile quintile quintile & North Africa South Africa East Asia Immunization rate, 1998 (%) & Pacific In South Africa 100 Measles Sub-Saharan immunization programs DPT Africa 80 reach most children in all South income groups, and DPT 60 Asia immunization rates are 40 0 20 40 60 80 100 almost equal to those for a. Data are for 1992 and 2004. measles immunization. 20 Source: World Health Organization and United Nations Children's Fund estimates. 0 Many causes of early childhood deaths are Measles immunization now reaches more Lowest Second Third Fourth Highest preventable through the basic elements infants, and measles deaths are falling. quintile quintile quintile quintile quintile of public health: immunization programs, Developing regions with more than 90 hand washing, access to safe water and percent immunization rates are on par sanitation facilities, and good nutrition. with the high-income economies. Source: World Bank staff estimates based on Demographic and Health Surveys. 2006 World Development Indicators 9 · Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio Caring for mothers Death in childbirth is a rare event in rich coun- More than 500,000 women die each year tries, where there are typically fewer than in childbirth, most of them in developing coun- 10 maternal deaths for every 100,000 live tries. What makes maternal mortality such a births. But in the poorest countries of Africa compelling problem is that it strikes young and Asia the ratio may be 100 times higher. women experiencing a natural function of life. And because women in poor countries have They die because they are poor. Malnourished. more children, their lifetime risk of maternal Weakened by disease. Exposed to multiple death may be more than 200 times greater pregnancies. And they die because they lack than for women in Western Europe and North access to trained health care workers and America. modern medical facilities. Mothers at risk in Africa and South Asia Left axis (line): total fertility rate (births per woman); right axis (bar): maternal mortality ratio (deaths per 100,000 live births) Sub-Saharan Africa South Asia Maternal mortality ratios are 8 921 1,000 8 1,000 still unacceptably high in many 6 750 6 750 developing countries as a result 564 of high fertility rates and a 4 500 4 500 high risk of dying each time a 2 250 2 250 woman becomes pregnant. 0 0 0 0 1990 1995 2000 2004 1990 1995 2000 2004 East Asia & Pacific Latin America & Caribbean Some developing countries 8 1,000 8 1,000 have substantially improved 6 750 6 750 maternal health through better services in hospitals and 4 500 4 500 increased numbers of trained 2 250 2 194 250 117 birth attendants and midwives. 0 0 0 0 1990 1995 2000 2004 1990 1995 2000 2004 Europe & Central Asia Middle East & North Africa Still others not only improved 8 1,000 8 1,000 maternal health, but significantly 6 750 6 750 lowered fertility rates directly through use of contraceptives and indirectly 4 500 4 500 through increased female education. 2 250 2 183 250 58 0 0 0 0 1990 1995 2000 2004 1990 1995 2000 2004 Source: World Bank staff estimates. 10 2006 World Development Indicators Country by country progress in providing skilled care at births Poor women need reproductive health services Share of countries on track to achieve adequate coverage Zimbabwe, 1999 of births by skilled health personnel (%) Total fertility rate by wealth quintile (births per woman) In Zimbabwe total fertility 5 Insufficient data Seriously off track Off track On track Reached target rates are high except East Asia 4 & Pacific for women from the (24 countries) highest income group. 3 Europe & Central Asia 2 (27 countries) Latin America 1 & Caribbean (32 countries) 0 Lowest Second Third Fourth Highest Middle East quintile quintile quintile quintile quintile & North Africa (14 countries) Zimbabwe, 1999 South Asia Contraceptive prevalence rate by wealth quintile (%) (8 countries) Wealthier men and 80 Women Sub-Saharan women are more likely Men Africa (48 countries) to use contraception. 60 50 0 50 100 40 Source: World Bank staff estimates. 20 Because few countries are able to mea- tries in each region that provide skilled sure maternal mortality over time, other health personnel for 90 percent of births indicators are often used to measure or could do so by 2015 based on current 0 progress toward this goal. Skilled health trends. Countries that are off track may Lowest Second Third Fourth Highest quintile quintile quintile quintile quintile personnel and modern medical facilities be able to achieve 75 percent coverage by are needed to deal with the complications 2015, while seriously off-track countries Zimbabwe, 1999 of childbirth that can claim mothers' lives. will not reach even that level unless they Source of contraception by wealth quintiles (%) This figure shows the proportion of coun- make rapid progress in the next decade. In Zimbabwe both poor 10 0 Decreasing risk of young motherhood Public sources and rich rely heavily Private sources Adolescent fertility rate (births per 1,000 women ages 15­19) 80 on public sources for contraception. 60 150 40 Sub-Saharan Africa 120 20 0 Lowest Second Third Fourth Highest 90 quintile quintile quintile quintile quintile South Asia Contraceptive prevalence rates Latin America & Caribbean Share of women in union (%) 60 Where contraceptive 60 Any contraceptive method Middle East & North Africa prevalence rates are Modern contraceptive method Europe & Central Asia 50 higher, men and women 30 40 High-income are more likely to be East Asia & Pacific using modern methods. 30 0 20 1997 1998 1999 2000 2001 2002 2003 2004 10 Source: World Bank staff estimates. 0 Southern Central Eastern Western Fertility rates among young women babies. They are also likely to have Africa Africa Africa Africa have been falling, but they remain high more births over their lifetime, increas- in Sub-Saharan Africa, South Asia, and ing their lifetime risk of maternal death. Source: World Bank staff estimates based on Latin America and the Caribbean. Young Education and access to reproductive Demographic and Health Surveys; UNFPA 2005. mothers run higher risks of complica- health services help to lower fertility tions in childbirth and lower birthweight rates. 2006 World Development Indicators 11 · Have halted by 2015 and begun to · Have halted by 2015 and begun to reverse the incidence of malaria reverse the spread of HIV/AIDS. and other major diseases. Combating disease Epidemic diseases exact a huge toll in human deaths. Nearly all the cases and more than 95 suffering and lost opportunities for develop- percent of the deaths occur in Sub-Saharan ment. Poverty, armed conflict, and natural Africa. Most deaths from malaria are among disasters contribute to the spread of disease children younger than five years old. and are worsened by it. Tuberculosis kills some 2 million people a In Africa the spread of HIV/AIDS has re- year, most of them 15­45 years old. The dis- versed decades of improvements in life expec- ease is spreading more rapidly because of the tancy and left millions of children orphaned. It emergence of drug-resistant strains of tuber- is draining the supply of teachers and eroding culosis; the spread of HIV/AIDS, which reduces the quality of education. resistance to tuberculosis; and the growing There are 300­500 million cases of ma- number of refugees and displaced people. laria each year, leading to more than 1 million As the HIV/AIDS epidemic matures, the death toll keeps rising Left axis: adult (ages 15­49) HIV prevalence rate (%); right axis: number of deaths due to AIDS (millions) Worldwide, 40 million adults and 8 4.0 children are living with HIV/AIDS and almost 5 million new infections Sub-Saharan Africa occurred in 2005. The adult 7 3.5 prevalence rate has stabilized in Sub-Saharan Africa and other 6 3.0 developing regions, not because the epidemic has been halted but because the death rate now 5 2.5 equals the rate of new infections. Sub-Saharan Africa 4 2.0 Although prevalence rates are lower outside of Sub-Saharan Africa, the number of people infected is 3 1.5 increasing and so is the death rate. There were almost a million 2 1.0 new cases in South and East Asia, where more than 7 million people Other developing regions are now living with HIV/AIDS. 1 0.5 Other developing regions 0 0.0 1990 1995 2000 2005 HIV prevalence Deaths due to AIDS Source: UNAIDS/WHO 2005. 12 2006 World Development Indicators The HIV epidemic can be reversed Malaria is a leading killer in Africa Model projections of HIV infections in Sub-Saharan Africa (millions) Malaria deaths (per 100,000 people) Sub-Saharan Africa Rest of the world 4 Baseline Treatment-centered response (optimal) 1970 3 Combined response (pessimistic) 2 1990 Prevention-centered response 1 Combined response (optimistic) 1997 0 2003 2005 2007 2009 2011 2013 2015 2017 2020 0 25 50 75 100 125 150 175 Source: Salomon and others 2005. What will it take to halt and reverse to alter behavior. Computer simula- Source: WHO 1999. the HIV epidemic? A combination of ef- tions of the epidemic suggest that a Malaria, once widespread, is now large- from repeated infections, leaving them fective treatment and prevention pro- combination of intensive treatment ly a disease of the tropics. It takes its unable to work for weeks at a time. grams. Antiretroviral therapy is start- and prevention programs would be greatest toll in Sub-Saharan Africa, The World Bank estimates that the ing to reach people in poor countries, most effective in reducing new infec- where more than 1 million people die disease has slowed economic growth although not yet at the levels needed, tions and averting 10 million deaths each year, most of them children un- in Africa by 1.3 percent a year (World and prevention programs have begun between now and 2020. der the age of five. Millions more suffer Bank 2001). Tuberculosis rates on the rise or falling slowly Poor children bear the burden of malaria Incidence of tuberculosis (per 100,000 people) Children under age 5 receiving antimalarial treatment (%) 1990 2004 Children in poorest quintile Children in richest quintile Sub-Saharan Africa Niger South Asia East Asia & Pacific Senegal Europe & Central Asia Latin America Chad & Caribbean Middle East & North Africa Rwanda High- income 0 50 100 150 200 250 300 350 400 0 10 20 30 40 50 60 70 Source: World Bank staff estimates. Source: World Bank 2005e. Each year there are 8 million new cases programs allow drug-resistant strains Malaria is a disease of poverty and a can save lives and reduce the burden of tuberculosis--3 million in South and to spread. And tuberculosis is often cause of poverty. Although adults may of disease, but in many countries chil- East Asia, 2 million in Sub-Saharan Af- associated with HIV infections, which experience repeated bouts of the debil- dren in the poorest families do not rica, and more than a quarter million in compromise the body's immune sys- itating disease, children are most likely receive treatment. Prevention is also countries of the former Soviet Union. tem. Positive diagnosis, effective treat- to die--more than 2,000 children die important. The use of insecticide- The disease has spread fastest in ment, and follow-up care can achieve each day because of malaria in Sub- treated bednets has been shown to poor countries with ineffective health high cure rates, but many cases go Saharan Africa. Effective treatment protect children. systems. Poorly managed tuberculosis undetected. 2006 World Development Indicators 13 · Integrate the principles of sustain- able development into country · Reduce by half the proportion · Achieve significant improvement policies and programs and reverse of people without sustainable in the lives of at least 100 million the loss of environmental resources. access to safe drinking water. slum dwellers by 2020. Using resources wisely Sustainable development can be ensured only But good intentions are not enough. Around by protecting the environment and using its re- the world land is being degraded. Forests are sources wisely. Poor people, often dependent being lost. Fisheries are being overused. Plant on environmental resources for their livelihood, and animal species are becoming extinct. And are the most affected by environmental deg- carbon dioxide emissions are driving changes radation and natural disasters (fires, storms, in global climate. earthquakes) whose effects are worsened by Rich countries are major consumers of environmental mismanagement. products and services from the environment. Most countries have adopted principles of Thus rich countries and poor countries alike sustainable development and agreed to inter- have a stake in using environmental resources national accords on protecting the environment. wisely. Water and sanitation--basic services needed by all Population without access to an improved water source or sanitation facilities (%) Sub-Saharan Africa South Asia In Sub-Saharan Africa 300 million 90 90 people lack access to improved 68 84 64 65 water sources and 450 million lack 60 60 42 adequate sanitation services. South 51 42 34 30 30 Asia has made excellent progress 30 26 16 in providing water, but progress has 15 0 0 been slower in providing sanitation. 5 0 2 2 1 0 1 2 5 0 1990 1995 2000 2005 2010 2015 East Asia & Pacific Latin America & Caribbean In East Asia rapid urbanization is 90 90 posing a challenge for the provision 70 of water and other public utilities. 60 51 60 Latin America and the Caribbean, 35 32 29 25 30 22 30 the most urban developing region, 15 16 has made slow progress in 18 11 9 0 0 providing sanitation services. 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Europe & Central Asia Middle East & North Africa Many countries in Europe 90 90 and Central Asia lack reliable benchmarks for the early 1990s. In 60 60 the Middle East and North Africa, 31 25 30 18 30 Egypt, Tunisia, and Morocco have 14 15 9 7 7 made the fastest progress. 9 5 13 12 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Without access to an improved water source Actual Goal Without access to improved sanitation facilities Actual Goal Source: World Bank staff estimates. 14 2006 World Development Indicators Country by country progress toward access to water . . . Forests falling Share of countries on track to achieve the target Forest area (thousands of square kilometers) for access to improved water source (%) Change in forest area, 1990­2005 Forest area, 1990 Insufficient data Seriously off track Off track On track Reached target Middle East 200 & North Africa East Asia & Pacific (24 countries) South 789 Asia Europe & Central Asia East Asia (27 countries) 4,581 & Pacific Latin America & Caribbean Sub-Saharan 6,913 (32 countries) Africa Middle East Europe & & North Africa 8,919 Central Asia (14 countries) High-income South Asia 9,441 economies (8 countries) Latin America Sub-Saharan 9,836 & Caribbean Africa (48 countries) ­750 ­500 ­250 0 250 100 50 0 50 100 Note: Positive values indicate an increase in forest area. Source: World Bank staff estimates. Source: FAO data and World Bank staff estimates. Lack of clean water and basic sanitation sources that are not protected from con- Since 1990 the world has lost about 1.3 ber, are important sources of livelihood is the main reason diseases transmit- tamination. Even the modest target of million square kilometers of forest-- for people in developing countries, and ted by feces are so common in develop- reducing by half the number of people almost 100,000 square kilometers each forests provide habitat for many plant ing countries. Water is a daily need that without access to an improved water year. The losses have been greatest in and animal species. To ensure sustain- must be met, but in some places people source will not be met in many countries the great tropical forests of Sub-Saha- able development, forests must be man- spend many hours to obtain water from at the current rate of progress. ran Africa and Latin America and the Ca- aged wisely to continue to benefit future ribbean. Forest products, including tim- generations. . . . and to sanitation Fuel for climate change--high carbon dioxide emitters Share of countries on track to achieve the target Emissions of carbon dioxide, 2002 (billions of metric tons) for access to improved sanitation source (%) Insufficient data Seriously off track Off track On track Reached target Rest of Rest of developing East Asia South Asia economies & Pacific 0.2 2.7 (24 countries) India Europe & 1.2 Central Asia Rest of United (27 countries) States Europe & Central Asia 5.8 Latin America & Caribbean 1.7 (32 countries) Russian Middle East Federation EU-15 & North Africa 1.4 3.2 (14 countries) China South Asia Rest of 3.5 (8 countries) East Asia Japan & Pacific 1.2 1.0 Sub-Saharan Rest of Africa high-income (48 countries) 2.5 100 50 0 50 100 Source: World Bank staff estimates. Source: CDIAC data and World Bank staff estimates. An improved sanitation system pro- substances before they are released Carbon dioxide, which is produced by economies are the largest emitters of vides disposal facilities that can into the environment. Large popula- burning fossil fuels and manufactur- carbon dioxide, and their share has in- effectively prevent human, animal, tions in Africa and Asia still lack ad- ing cement, is a greenhouse gas that creased. However, China is the world's and insect contact with excreta. It equate sanitation facilities, and few contributes to global climate change. second largest emitter, next to the does not, however, ensure treat- countries are currently on track to Emissions rose by 3 billion metric tons United States. Emissions by India are ment of effluents to remove harmful reach the target. between 1990 and 2002. High-income also increasing. 2006 World Development Indicators 15 · Develop further an open trading and landlocked and small island work for youth. financial system that is rule-based, developing states. · Provide access to affordable essential predictable, and nondiscriminatory. · Deal comprehensively with developing drugs in developing countries. · Address the special needs of the countries' debt problems to make · Make available the benefits of new least developed countries. debt sustainable in the long term. technologies--especially information · Address the special needs of · Develop decent and productive and communications technologies. Working together The eighth and final goal complements the responsibilities for achieving the Millennium others. In partnership, wealthy countries work Development Goals. The consensus calls for with developing countries to create an environ- developing countries to improve governance ment in which rapid, sustainable development and policies aimed at increasing economic is possible. Important steps toward global growth and reducing poverty and for high-in- partnership were taken at international meet- come countries to provide more and better aid ings in 2001 in Doha, which launched a new and greater access to their markets. "development round" of trade negotiations, Goal 8 also reminds us that the develop- and in 2002 at the International Conference ment challenges differ for large countries and on Financing for Development in Monterrey, small countries. And that developing countries Mexico, where high-income and develop- need access to new technologies to increase ing countries reached consensus on mutual productivity and improve people's lives. Many sources and many patterns Selected net flows, 2004 ($ billions) Sub-Saharan Africa South Asia Aid plays an important role in Multilateral Multilateral development, especially in low- finance 4.3 finance 1.8 income countries. The extremely poor Aid 26.0 Aid 6.8 countries of Sub-Saharan Africa and Private Private Asia still need substantial increases capital flows 5.9 capital flows 13.8 in aid to reach their development Trade ­8.6 Trade ­31.3 goals. Countries in all regions borrow from multilateral institutions, such Remittances 4.3 Remittances 30.3 as the World Bank, but some are ­40 ­20 0 20 40 60 ­40 ­20 0 20 40 60 repaying more than they borrow. East Asia & Pacific Latin America & Caribbean In addition to aid, developing Multilateral Multilateral countries meet part of their financing finance ­3.3 finance ­7.5 needs through private capital 6.9 Aid 6.9 Aid flows. Rapidly growing economies Private Private 9.3 need and attract large flows of capital flows 106.2 capital flows direct and portfolio investment, ­24.4 Trade 59.6 Trade which have been particularly important in East Asia and Pacific. Remittances 34.5 Remittances 39.2 ­40 ­20 0 20 40 60 ­40 ­20 0 20 40 60 Europe & Central Asia Middle East & North Africa Export demand can be an Multilateral Multilateral important source of growth, finance ­4.0 finance ­0.9 and trade surpluses can also Aid Aid 11.9 10.5 provide substantial foreign Private Private exchange earnings. Remittances capital flows 65.8 capital flows 4.1 from people living and working Trade Trade ­19.1 7.3 abroad are a growing source of income for households in Remittances 9.7 Remittances 17.2 some developing economies. ­40 ­20 0 20 40 60 -40 -20 0 20 40 60 Source: World Bank staff estimates. 16 2006 World Development Indicators Official development assistance is rising, but still too little Debt service is falling, but more relief is needed Left axis (bars): official development assistance (2003 $ billions); Ratio of external debt service to exports of goods and right axis (line): net disbursements as a share of 2003 donors' GNI (%) services including workers' remittances (%) 80 0.35 30 0.30 25 60 0.25 Middle income 0.20 20 40 0.15 Low income, 15 excluding HIPCs 0.10 20 10 0.05 Heavily indebted poor countries (HIPCs) 0 0.00 1990 1992 1994 1996 1998 2000 2002 2004 5 1990 1992 1994 1996 1998 2000 2002 2004 Source: OECD Development Assistance Committee. Source: World Bank staff estimates. Official development assistance below 0.26 percent. Since 2002 do- Low-income countries paid $26 bil- slowly, reducing debt burdens for (ODA) is the aid provided by the rich- nors have pledged to increase aid by lion in debt service on public debt in many countries. But for extremely est countries to the poorest. Through $20 billion a year in 2006 and to 2004. Middle-income countries paid poor countries debt service repre- much of the 1990s ODA levels fell provide nearly $130 billion a year by $173 billion. sents a crucial loss of potential de- while ODA as a proportion of donors' 2010. But large increases in aid have, Developing country export earnings, velopment resources. Since 1998 GNI fell even faster. Many donors so far, gone to only a few countries needed to acquire the currencies to the Heavily Indebted Poor Countries pledged to provide at least 0.7 per- such as Iraq, Afghanistan, and the pay their creditors, have been rising Initiative has provided $57 billion in cent of GNI, but the average remains Democratic Republic of Congo. while debt service has grown more debt relief. Tariffs remain high on poor countries' exports New technologies are spreading quickly Average tariffs imposed by developed countries Information and communications technology users in low- and on developing country imports (%) middle-income economies (per 1,000 people) 12 200 Clothing Mobile phones 10 Agricultural products 150 8 Textiles 6 100 Telephone mainlines 4 Internet users 50 2 Personal computers 0 0 1996 1997 1998 1999 2000 2001 2002 2003 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Note: Based on UN definitions of developed and developing countries, Source: World Bank staff estimates and data from the International Telecommunication Union. which may differ slightly from those of the World Bank. Source: International Trade Centre, World Trade Organization, and United Nations Conference on Trade and Development. New technologies bring new opportu- which reduce barriers of time, space, and nities to developing countries. Mobile culture. Developing countries also need Creating opportunities for develop- recent dropping of quotas on textiles phones help to eliminate the bottlenecks access to new medicines to reduce the ing countries to sell their products has created new opportunities for effi- of fixed, mainline phone service. Personal terrible burden of disease. Bringing these in wealthier markets is an important cient producers. But high-income coun- computers are more widely available, and and other life-saving technologies to poor complement to aid. Many high-income tries' tariffs on goods important to de- the Internet is expanding rapidly. These people will require willing cooperation be- countries allow selected exports of veloping countries, such as textiles and are examples of integrating technologies, tween the public and private sectors. poor countries to enter duty-free. The agricultural products, remain high. 2006 World Development Indicators 17 Goals, targets, and indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1 Halve, between 1990 and 2015, the proportion of 1 Proportion of population below $1 (PPP) a day a people whose income is less than $1 a day 1a Poverty headcount ratio (percentage of population below the national poverty line) 2 Poverty gap ratio [incidence x depth of poverty] 3 Share of poorest quintile in national consumption Target 2 Halve, between 1990 and 2015, the proportion of 4 Prevalence of underweight children under five years people who suf fer from hunger of age 5 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 3 Ensure that, by 2015, children everywhere, boys and 6 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 7 Proportion of pupils starting grade 1 who reach grade 5 b primar y schooling 8 Literacy rate of 15- to 24-year-olds Goal 3 Promote gender equality and empower women Target 4 Eliminate gender disparity in primary and secondary 9 Ratios of girls to boys in primary, secondary, and education, preferably by 2005, and in all levels of tertiary education education no later than 2015 10 Ratio of literate women to men ages 15­24 11 Share of women in wage employment in the nonagricultural sector 12 Proportion of seats held by women in national parliaments Goal 4 Reduce child mortality Target 5 Reduce by two-thirds, between 1990 and 2015, 13 Under-five mortality rate the under-five mor tality rate 14 Infant mor tality rate 15 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 6 Reduce by three-quarters, between 1990 and 2015, 16 Maternal mortality ratio the maternal mor tality ratio 17 Propor tion of bir ths attended by skilled health personnel Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 7 Have halted by 2015 and begun to reverse the spread 18 HIV prevalence among pregnant women ages 15­24 of HIV/AIDS 19 Condom use rate of the contraceptive prevalence rate c 19a Condom use at last high-risk sex 19b Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDS d 19c Contraceptive prevalence rate 20 Ratio of school attendance of orphans to school attendance of nonorphans ages 10­14 Target 8 Have halted by 2015 and begun to reverse the 21 Prevalence and death rates associated with malaria incidence of malaria and other major diseases 22 Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures e 23 Prevalence and death rates associated with tuberculosis 24 Proportion of tuberculosis cases detected and cured under directly observed treatment, short course (DOTS) Goal 7 Ensure environmental sustainability Target 9 Integrate the principles of sustainable development 25 Proportion of land area covered by forest into country policies and programs and reverse the 26 Ratio of area protected to maintain biological diversity to loss of environmental resources sur face area 27 Energy use (kilograms of oil equivalent) per $1 GDP (PPP) 28 Carbon dioxide emissions per capita and consumption of ozone-depleting chlorofluorocarbons (ODP tons) 29 Proportion of population using solid fuels Target 10 Halve, by 2015, the proportion of people without 30 Proportion of population with sustainable access sustainable access to safe drinking water and basic to an improved water source, urban and rural sanitation 31 Propor tion of population with access to improved sanitation, urban and rural 18 2006 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Target 11 By 2020, to have achieved a significant improvement 32 Proportion of households with access to secure tenure in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 12 Develop further an open, rule-based, predictable, Some of the indicators listed below are monitored nondiscriminatory trading and financial system separately for the least developed countries (LDCs), Africa, landlocked countries and small island developing states. Includes a commitment to good governance, development and poverty reduction--both nationally Official development assistance (ODA) and internationally 33 Net ODA, total and to the least developed countries, as a percentage of OECD/DAC donors' gross national income 34 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic Target 13 Address the special needs of the least developed education, primary health care, nutrition, safe water countries and sanitation) Includes tariff and quota free access for the least 35 Proportion of bilateral official development assistance of OECD/DAC donors that is untied developed countries' exports; enhanced programme of debt relief for heavily indebted poor countries 36 ODA received in landlocked countries as a proportion of their gross national incomes (HIPC) and cancellation of official bilateral debt; and more generous ODA for countries committed to 37 ODA received in small island developing states as proportion of their gross national incomes poverty reduction Market access 38 Proportion of total developed country imports (by value Target 14 Address the special needs of landlocked countries and excluding arms) from developing countries and from and small island developing states (through the the least developed countries, admitted free of duty Programme of Action for the Sustainable 39 Average tariffs imposed by developed countries on Development of Small Island Developing States agricultural products and textiles and clothing from and the outcome of the 22nd special session of the developing countries General Assembly) 40 Agricultural support estimate for OECD countries as a percentage of their gross domestic product 41 Proportion of ODA provided to help build trade capacity Target 15 Deal comprehensively with the debt problems of Debt sustainability developing countries through national and 42 Total number of countries that have reached their international measures in order to make debt HIPC decision points and number that have reached sustainable in the long term their HIPC completion points (cumulative) 43 Debt relief committed under HIPC Debt Initiative 44 Debt service as a percentage of exports of goods and services Target 16 In cooperation with developing countries, develop 45 Unemployment rate of 15- to 24-year-olds, male and and implement strategies for decent and productive female and total f work for youth Target 17 In cooperation with pharmaceutical companies, 46 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 18 In cooperation with the private sector, make 47 Telephone lines and cellular subscribers per 100 people available the benefits of new technologies, especially 48a Personal computers in use per 100 people infor mation and communications 48b Internet users per 100 people Note: Goals, targets, and indicators effective September 8, 2003. a. For monitoring country poverty trends, indicators based on national poverty lines should be used, where available. b. An alternative indicator under development is "primary completion rate." c. Among contraceptive methods, only condoms are effective in preventing HIV transmission. Since the condom use rate is only measured among women in union, it is supplemented by an indicator on condom use in high-risk situations (indicator 19a) and an indicator on HIV/AIDS knowledge (indicator 19b). Indicator 19c (contraceptive prevalence rate) is also useful in track- ing progress in other health, gender, and poverty goals. d. This indicator is defined as the percentage of 15- to 24-year-olds who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can transmit HIV. However, since there are currently not a sufficient number of surveys to be able to calculate the indicator as defined above, UNICEF, in collaboration with UNAIDS and WHO, produced two proxy indicators that represent two components of the actual indicator. They are the percentage of women and men ages 15­24 who know that a person can protect herself from HIV infection by "consistent use of condom," and the percentage of women and men ages 15­24 who know a healthy-looking person can transmit HIV. e. Prevention to be measured by the percentage of children under age five sleeping under insecticide-treated bednets; treatment to be measured by percentage of children under age five who are appropriately treated. f. An improved measure of the target for future years is under development by the International Labour Organization. 2006 World Development Indicators 19 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2004 2004 2004 2004b 2004 2004b 2004 2004 2004 2004 2003­04 2003­04 East Asia & Pacificc 1,870 s 16,301 s 118 w 2,647.2 t 1,416 w 9,968 t 5,332 w 9.0 w 8.1 w Cambodia 14 181 78 4.8 128 350 183 32d 2,310 d 154 7.7 5.6 China 1,296 9,598e 139 1,938.0 5 1,500 129 7,634f 5,890 f 108 10.1 9.4 Hong Kong, China 7 .. .. 183.5 28 26,660 27 217 31,560 12 8.1 6.9 Indonesia 218 1,905 120 248.0 22 1,140 137 757 3,480 140 5.1 3.7 Korea, Dem. Rep. 22 121 186 .. .. ..g .. .. .. .. .. .. Lao PDR 6 237 25 2.3 154 390 180 11 1,880 171 6.3 3.9 Malaysia 25 330 76 112.6 37 4,520 79 242 9,720 78 7.1 5.2 Mongolia 3 1,567 2 1.5 164 600 161 5 2,040 162 10.7 9.2 Myanmar 50 677 76 .. .. ..g .. .. .. .. .. .. Papua New Guinea 6 463 13 3.3 142 560 164 13d 2,280 d 155 2.5 0.4 Philippines 82 300 274 95.1 41 1,170 136 404 4,950 125 6.1 4.2 Thailand 64 513 125 158.4 31 2,490 104 505 7,930 88 6.2 5.3 Vietnam 82 332 252 44.6 58 540 168 222 2,700 149 7.7 6.6 Europe & Central Asia 472 s 24,238 s 20 w 1,557.1 t 3,295 w 3,945 t 8,350 w 7.2 w 7.1 w Albania 3 29 114 6.6 114 2,120 120 16 5,070 124 5.9 5.3 Armenia 3 30 107 3.2 143 1,060 139 13 4,160 135 7.0 7.4 Azerbaijan 8 87 101 7.8 105 940 147 32 3,810 137 10.2 9.2 Belarus 10 208 47 21.0 75 2,140 119 68 6,970 97 11.0 11.6 Bosnia and Herzegovina 4 51 76 8.0 104 2,040 122 28 7,230 95 6.2 6.4 Bulgaria 8 111 70 21.3 73 2,750 99 62 7,940 86 5.6 6.4 Croatia 4 57 79 30.3 61 6,820 69 53 11,920 69 3.8 3.8 Czech Republic 10 79 132 93.3 42 9,130 59 188 18,420 51 4.4 4.3 Estonia 1 45 32 9.5 99 7,080 67 18 13,630 62 7.8 8.2 Georgia 5 70 65 4.8 129 1,060 139 13d 2,900 d 146 6.2 7.3 Hungary 10 93 110 84.6 46 8,370 62 160 15,800 57 4.6 4.9 Kazakhstan 15 2,725 6 33.8 60 2,250 114 104 6,930 99 9.4 8.8 Kyrgyz Republic 5 200 27 2.1 155 400 178 9 1,860 172 7.1 5.9 Latvia 2 65 37 12.9 91 5,580 75 27 11,820 70 8.3 8.9 Lithuania 3 65 55 19.7 77 5,740 74 44 12,690 65 6.7 7.2 Macedonia, FYR 2 26 80 4.9 126 2,420 105 13 6,560 102 2.9 2.7 Moldova 4 34 128 2.6h 147 720 h 157 8 1,950 165 7.3 7.6 Poland 38 313 125 232.9 25 6,100 72 486 12,730 64 5.4 5.5 Romania 22 238 94 64.2 51 2,960 98 181 8,330 85 8.3 8.6 Russian Federation 144 17,098 9 488.5 16 3,400 94 1,392 9,680 79 7.1 7.7 Serbia and Montenegro 8 102 80 21.8i 72 2,680i 101 .. .. 106 8.2 8.3 Slovak Republic 5 49 112 34.9 59 6,480 71 78 14,480 59 5.5 5.4 Tajikistan 6 143 46 1.8 162 280 190 7 1,160 186 10.6 9.4 Turkey 72 784 93 269.0 20 3,750 89 554 7,720 90 8.9 7.4 Turkmenistan 5 488 10 .. .. .. .. .. .. .. .. .. Ukraine 47 604 82 60.2 53 1,270 132 300 6,330 104 12.1 13.0 Uzbekistan 26 447 62 11.9 93 450 172 49 1,860 172 7.7 6.1 Latin America & Carib. 546 s 20,418 s 27 w 1,952.1 t 3,576 w 4,183 t 7,661 w 5.9 w 4.4 w Argentina 38 2,780 14 137.3 35 3,580 93 481 12,530 66 9.0 7.9 Bolivia 9 1,099 8 8.6 101 960 145 23 2,600 151 3.6 1.6 Brazil 184 8,515 22 551.6 13 3,000 97 1,460 7,940 86 4.9 3.5 Chile 16 757 22 84.2 47 5,220 76 171 10,610 77 6.1 4.9 Colombia 45 1,139 43 90.9 43 2,020 123 312d 6,940 d 98 4.1 2.5 Costa Rica 4 51 83 19.0 79 4,470 80 39d 9,220 d 82 4.2 2.3 Cuba 11 111 102 .. .. ..j .. .. .. .. 1.1 0.8 Dominican Republic 9 49 181 18.4 81 2,100 121 60d 6,860 d 100 2.0 0.5 Ecuador 13 284 47 28.9 63 2,210 116 49 3,770 138 6.9 5.4 El Salvador 7 21 326 15.7 84 2,320 109 33d 4,890 d 126 1.5 ­0.2 Guatemala 12 109 113 26.9 65 2,190 117 52d 4,260 d 130 2.7 0.2 Haiti 8 28 305 3.3 140 400 175 .. .. 166 0.4 ­1.0 Honduras 7 112 63 7.3 109 1,040 141 19d 2,760 d 148 4.6 2.3 20 2006 World Development Indicators Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2004 2004 2004 2004b 2004 2004b 2004 2004 2004 2004 2003­04 2003­04 Jamaica 3 11 244 8.7 100 3,300k 96 10 3,950 136 0.9 0.4 Mexico 104 1,958 54 704.9 10 6,790 70 1,001 9,640 80 4.4 2.9 Nicaragua 5 130 44 4.5 132 830l 149 19 3,480 140 5.1 3.0 Panama 3 76 43 13.4 89 4,210 83 21d 6,730 d 101 6.2 4.4 Paraguay 6 407 15 6.9 113 1,140 137 29d 4,820 d 127 4.0 1.6 Peru 28 1,285 22 65.0 50 2,360 108 149 5,400 118 4.8 3.3 Trinidad and Tobago 1 5 254 11.4 95 8,730 61 15 11,430 73 6.2 5.9 Uruguay 3 176 20 13.4 88 3,900 88 31 9,030 83 11.9 11.1 Venezuela, RB 26 912 30 105.3 38 4,030 86 152 5,830 110 17.9 15.8 Middle East & N. Africa 300 s 8,984 s 34 w 592.0 t 1,972 w 1,722 t 5,734 w 5.9 w 3.8 w Algeria 32 2,382 14 73.3 49 2,270 113 204d 6,320 d 105 5.2 3.6 Egypt, Arab Rep. 73 1,001 73 90.6 45 1,250 133 305 4,200 134 4.2 2.2 Iran, Islamic Rep. 67 1,648 41 155.3 32 2,320 109 505 7,530 92 5.6 4.6 Iraq .. 438 .. .. .. ..j .. .. .. .. 46.5 .. Jordan 5 89 62 11.9 92 2,190 117 26 4,770 128 7.7 5.1 Lebanon 4 10 346 21.3 74 6,010 73 20 5,550 117 6.3 5.2 Libya 6 1,760 3 25.3 68 4,400 81 .. .. 84 4.5 2.5 Morocco 30 447 67 46.9 56 1,570 128 127 4,250 131 4.2 0.7 Oman 3 310 8 23.0 70 9,070 60 37 14,680 58 3.1 2.2 Syrian Arab Republic 19 185 101 22.8 71 1,230 134 65 3,500 139 2.0 ­0.4 Tunisia 10 164 64 26.3 66 2,650 102 74 7,430 94 5.8 4.9 West Bank and Gaza 4 .. .. 3.8 135 1,120 135 .. .. 142 ­1.7 ­5.6 Yemen, Rep. 20 528 39 11.2 96 550 167 16 810 197 2.7 ­0.5 South Asia 1,447 s 5,140 s 303 w 859.0 t 594 w 4,129 t 2,854 w 6.7 w 5.0 w Afghanistan .. 652 .. 5.5 .. ..g .. .. .. .. 7.5 .. Bangladesh 139 144 1,069 61.3 52 440 174 274 1,970 164 6.3 4.3 India 1,080 3,287 363 673.2 11 620 159 3,369d 3,120 d 144 6.9 5.4 Nepal 27 147 186 6.6 115 250 193 39 1,480 178 3.5 1.4 Pakistan 152 796 197 90.7 44 600 161 330 2,170 157 6.4 3.9 Sri Lanka 19 66 300 19.5 78 1,010 143 82 4,210 133 5.4 4.5 Sub-Saharan Africa 726 s 24,265 s 31 w 436.5 t 601 w 1,337 t 1,842 w 4.8 w 2.6 w Angola 15 1,247 12 14.4 85 930 148 30d 1,930 d 167 11.1 7.9 Benin 8 113 74 3.7 139 450 172 9 1,090 189 2.7 ­0.5 Botswana 2 582 3 7.7 106 4,360 82 17 9,580 81 4.9 5.0 Burkina Faso 13 274 47 4.4 133 350 183 15d 1,170 d 184 3.9 0.6 Burundi 7 28 284 0.7 189 90 208 5d 660 d 206 5.5 1.9 Cameroon 16 475 34 13.0 90 810 151 34 2,120 160 4.3 2.4 Central African Republic 4 623 6 1.2 169 310 187 4d 1,100 d 188 1.3 0.0 Chad 9 1,284 8 2.3 152 250 193 13 1,340 182 29.8 25.5 Congo, Dem. Rep. 56 2,345 25 6.4 116 110 206 38d 680 d 203 6.3 3.2 Congo, Rep. 4 342 11 2.9 145 760 152 3 740 201 3.6 0.6 Côte d'Ivoire 18 322 56 13.6 87 760 152 26 1,470 180 1.6 0.1 Eritrea 4 118 42 0.8 180 190 199 4d 960 d 191 1.8 ­2.5 Ethiopia 70 1,104 70 7.6 107 110 206 52d 750 d 200 13.1 10.9 Gabon 1 268 5 5.6 119 4,080 85 8 5,700 112 1.4 ­0.2 Gambia, The 1 11 148 0.4 192 280 190 3d 1,890 d 170 8.3 5.4 Ghana 22 239 95 8.3 102 380 182 48d 2,220 d 156 5.8 3.6 Guinea 9 246 37 3.8 138 410 177 20 2,160 158 2.6 0.4 Guinea-Bissau 2 36 55 0.3 203 160 201 1d 690 d 202 4.3 1.2 Kenya 33 580 59 16.1 83 480 171 38 1,130 187 4.3 2.0 Lesotho 2 30 59 1.3 166 730 156 6d 3,250 d 143 2.3 2.5 Liberia 3 111 34 0.4 195 120 205 .. .. 203 2.4 1.8 Madagascar 18 587 31 5.2 124 290 189 15 840 195 5.2 2.4 Malawi 13 118 134 2.0 156 160 201 8 630 207 6.7 4.4 Mali 13 1,240 11 4.3 134 330 184 12 950 193 2.2 ­0.8 Mauritania 3 1,026 3 1.6 163 530 169 6d 2,050 d 161 6.9 3.7 2006 World Development Indicators 21 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2004 2004 2004 2004b 2004 2004b 2004 2004 2004 2004 2003­04 2003­04 Mauritius 1 2 608 5.7 118 4,640 78 15 11,950 68 4.2 3.2 Mozambique 19 802 25 5.3 122 270 192 23d 1,170 d 184 7.2 5.1 Namibia 2 824 2 4.8 130 2,380 107 15d 7,520 d 93 6.0 4.7 Niger 13 1,267 11 2.8 146 210 196 11d 780 d 199 0.9 ­2.4 Nigeria 129 924 141 55.3 54 430 175 125d 970 d 190 6.0 3.7 Rwanda 9 26 360 1.9 158 210 196 11 1,240 183 4.0 2.5 Senegal 11 197 59 7.2 110 630 158 19d 1,660 d 176 6.2 3.7 Sierra Leone 5 72 75 1.1 175 210 196 3 550 208 7.4 3.0 Somalia 8 638 13 .. .. ..g .. .. .. .. .. .. South Africa 46 1,219 37 165.3 30 3,630 92 499d 10,960 d 75 3.7 4.4 Sudan 36 2,506 15 18.7 80 530 169 64d 1,810 d 174 6.0 4.0 Swaziland 1 17 65 1.9 160 1,660 127 6 5,650 114 2.1 0.8 Tanzania 38 945 43 11.6m 94 320 m 185 25 670 205 6.3 4.3 Togo 6 57 110 1.9 159 310 187 9d 1,510 d 177 3.0 0.4 Uganda 28 241 141 6.9 112 250 193 40 d 1,450 d 181 5.7 2.1 Zambia 11 753 15 4.6 131 400 178 10 890 194 4.6 2.9 Zimbabwe 13 391 33 8.0 103 620 159 26 2,040 162 ­4.2 ­4.7 High income 1,004 s 34,595 s 30 w 32,245.3 t 32,112 w 31,138 t 31,009 w 3.4 w 2.6 w Australia 20 7,741 3 544.3 14 27,070 25 590 29,340 22 3.0 1.8 Austria 8 84 99 263.9 21 32,280 15 260 31,800 10 2.2 1.5 Belgium 10 33 318 326.0 18 31,280 17 329 31,530 13 2.9 2.5 Canada 32 9,985 4 905.0 9 28,310 21 984 30,760 16 2.9 1.8 Denmark 5 43 127 220.2 26 40,750 6 172 31,770 11 2.4 2.1 Finland 5 338 17 171.9 29 32,880 14 156 29,800 19 3.7 3.4 France 60 552 110 1,888.4n 6 30,370 n 19 1,779 29,460 20 2.3 1.7 Germany 83 357 236 2,532.3 3 30,690 18 2,324 28,170 27 1.6 1.6 Greece 11 132 86 185.0 27 16,730 42 246 22,230 41 4.2 3.9 Ireland 4 70 59 139.6 34 34,310 12 134 32,930 8 4.9 3.0 Israel 7 22 313 118.0 36 17,360 39 162 23,770 37 4.4 2.8 Italy 58 301 196 1,513.1 7 26,280 28 1,613 28,020 28 1.2 1.4 Japan 128 378 351 4,734.3 2 37,050 9 3,809 29,810 18 2.7 2.5 Korea, Rep. 48 99 487 673.1 12 14,000 50 987 20,530 46 4.6 4.1 Kuwait 2 18 138 55.3 55 22,470 33 53d 21,610 d 43 7.2 4.5 Netherlands 16 42 481 523.1 15 32,130 16 511 31,360 15 1.4 1.1 New Zealand 4 271 15 81.2 48 19,990 37 90 22,260 40 4.4 3.1 Norway 5 324 15 237.8 24 51,810 2 178 38,680 4 2.9 2.6 Portugal 11 92 115 149.3 33 14,220 49 202 19,240 49 1.0 0.4 Puerto Rico 4 9 439 .. .. ..o .. .. .. .. .. .. Saudi Arabia 24 2,150 11 242.9 23 10,140 55 331d 13,810 d 61 5.2 2.5 Singapore 4 1 6,329 105.0 39 24,760 29 116 27,370 29 8.4 7.0 Slovenia 2 20 99 29.5 62 14,770 47 42 20,830 45 4.6 4.5 Spain 43 505 86 919.1 8 21,530 34 1,057 24,750 33 3.1 1.4 Sweden 9 450 22 322.3 19 35,840 10 269 29,880 17 3.6 3.2 Switzerland 7 41 185 366.5 17 49,600 3 264 35,660 6 2.1 1.4 United Arab Emirates 4 84 52 102.7 40 23,770 31 104 24,090 34 8.5 1.5 United Kingdom 60 244 247 2,013.4 4 33,630 13 1,882 31,430 14 3.1 2.6 United States 294 9,629 32 12,168.5 1 41,440 5 11,693 39,820 3 4.2 3.2 World 6,365 s 133,941 s 49 w 40,282.3 t 6,329 w 56,289 t 8,844 w 4.1 w 2.9 w Low income 2,343 30,276 80 1,187.7 507 5,291 2,258 6.5 4.6 Middle income 3,018 69,070 45 6,862.7 2,274 20,051 6,644 7.2 6.3 Lower middle income 2,442 39,173 63 4,116.0 1,686 14,233 5,829 7.6 6.6 Upper middle income 576 29,897 20 2,748.2 4,769 5,859 10,168 6.6 6.0 Low & middle income 5,361 99,346 55 8,050.1 1,502 25,334 4,726 7.1 5.8 High income 1,004 34,595 30 32,245.3 32,112 31,138 31,009 3.4 2.6 a. PPP is purchasing power parity. b. Calculated by the World Bank Atlas method. c. Hong Kong, China, a high-income economy, is not included in this aggregate. d. Based on regression; others are extrapolated from International Comparison Program benchmark estimates. e. Includes Hong Kong, China; Macao, China; and Taiwan, China. f. Based on a 1986 bilateral comparison of China and the United States (Rouen and Kai 1995) employing a different methodology than that used for other countries. This interim methodology will be revised in the next few years. g. Estimated to be low income. h. Excludes data for Transnistria. i. Excludes data for Kosovo. j. Estimated to be lower middle income. k. Included in the aggregates for lower-middle-income economies based on earlier data. l. Included in the aggregates for low-income economies based on earlier data. m. Data refers to mainland Tanzania only. n. Includes French Guiana, Guadeloupe, Martinique, and Réunion. o. Estimated to be high income. 22 2006 World Development Indicators Size of the economy About the data Definitions Population, land area, income, output, and growth in productivity or welfare, see About the data for tables · Population is based on the de facto definition of output are basic measures of the size of an economy. 4.1 and 4.2. population, which counts all residents regardless of They also provide a broad indication of actual and When calculating GNI in U.S. dollars from GNI legal status or citizenship--except for refugees not potential resources. Population, land area, income reported in national currencies, the World Bank follows permanently settled in the country of asylum, who (as measured by gross national income, GNI) and out- its Atlas conversion method, using a three-year average are generally considered part of the population of put (as measured by gross domestic product, GDP) of exchange rates to smooth the effects of transitory their country of origin. The values shown are midyear are therefore used throughout World Development fluctuations in exchange rates. (For further discussion estimates for 2004. See also table 2.1. · Surface Indicators to normalize other indicators. of the Atlas method, see Statistical methods.) GDP and area is a country's total area, including areas under Population estimates are generally based on GDP per capita growth rates are calculated from data in inland bodies of water and some coastal water- extrapolations from the most recent national census. constant prices and national currency units. ways. · Population density is midyear population For further discussion of the measurement of popula- Because exchange rates do not always reflect dif- divided by land area in square kilometers. · Gross tion and population growth, see About the data for ferences in price levels between countries, this table national income (GNI) is the sum of value added by table 2.1 and Statistical methods. also converts GNI and GNI per capita estimates into all resident producers plus any product taxes (less The surface area of an economy includes inland international dollars using purchasing power parity subsidies) not included in the valuation of output bodies of water and some coastal waterways. Sur- (PPP) rates. PPP rates provide a standard measure plus net receipts of primary income (compensation face area thus differs from land area, which excludes allowing comparison of real levels of expenditure of employees and property income) from abroad. bodies of water, and from gross area, which may between countries, just as conventional price indexes Data are in current U.S. dollars converted using the include offshore territorial waters. Land area is par- allow comparison of real values over time. The PPP World Bank Atlas method (see Statistical methods). ticularly important for understanding an economy's conversion factors used here are derived from price · GNI per capita is gross national income divided by agricultural capacity and the environmental effects surveys covering 118 countries conducted by the midyear population. GNI per capita in U.S. dollars is of human activity. (For measures of land area and International Comparison Program. For Organisation converted using the World Bank Atlas method. · PPP data on rural population density, land use, and agri- for Economic Co-operation and Development (OECD) GNI is gross national income converted to interna- cultural productivity, see tables 3.1­3.3.) Innova- countries data come from the most recent round of tional dollars using purchasing power parity rates. An tions in satellite mapping and computer databases surveys, completed in 2002; the rest are from either international dollar has the same purchasing power have resulted in more precise measurements of land the 1996 or the 1993 survey or earlier round and over GNI as a U.S. dollar has in the United States. and water areas. extrapolated to the 1996 benchmark. Estimates for · Gross domestic product (GDP) is the sum of value GNI measures the total domestic and foreign value countries not included in the surveys are derived added by all resident producers plus any product added claimed by residents. GNI comprises GDP from statistical models using available data. taxes (less subsidies) not included in the valuation plus net receipts of primary income (compensation All economies shown in World Development Indica- of output. Growth is calculated from constant price of employees and property income) from nonresident tors are ranked by size, including those that appear in GDP data in local currency. · GDP per capita is gross sources. The World Bank uses GNI per capita in U.S. table 1.6. The ranks are shown only in table 1.1. No domestic product divided by midyear population. dollars to classify countries for analytical purposes rank is shown for economies for which numerical esti- and to determine borrowing eligibility. For definitions mates of GNI per capita are not published. Economies of the income groups in World Development Indica- with missing data are included in the ranking at their tors, see Users guide. For discussion of the useful- approximate level, so that the relative order of other ness of national income and output as measures of economies remains consistent. Developing countries produce slightly less than half the world's output Data sources Share of PPP GNI, 2004 East Asia & Pacific 18% Population estimates are prepared by World Bank staff from a variety of sources (see Data sources for table 2.1). Data on surface and land area are Latin America & Caribbean 7% from the Food and Agriculture Organization (see Data sources for table 3.1). GNI, GNI per capita, High-income 56% South Asia 7% GDP growth, and GDP per capita growth are esti- mated by World Bank staff based on national Europe & Central Asia 7% accounts data collected by World Bank staff dur- Middle East & North Africa 3% ing economic missions or reported by national Sub-Saharan Africa 2% statistical offices to other international organiza- When measured by purchasing power parities (PPPs), which take into account national differences in the cost of tions such as the OECD. Purchasing power parity living, developing countries produce a large part of the world's output. Much of this is in the form of nontradable conversion factors are estimates by World Bank goods and services, which are undervalued at market exchange rates. For this reason PPPs are used in international comparisons of well-being such as $1 and $2 a day measures of absolute poverty. staff based on data collected by the International Source: World Bank staff estimates. Comparison Program. 2006 World Development Indicators 23 Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of poorest Maternal quintile in mortality national Prevalence of child ratio consumption malnutrition Ratio of female to male Modeled or income Underweight Primary completion enrollments in primary estimates Births attended by % % of children ratea and secondary schoola Under-five mortality rate per 100,000 skilled health staff under age 5 % % per 1,000 live births live births % of total 1992­ 2004b,c 1989­94b 2000­04b 1991 2004 1991 2004 1990 2004 2000 1989­94b 2000­04b East Asia & Pacificd 19 w 12 w 97 w 99 w 89 w 98 w 59 w 37 w 117 w .. w 86 w Cambodia 6.9 .. 45 .. 82 73 85 115 141 450 .. 32 China 4.7 17 8 103 99 87 98 49 31 56 .. 96 Hong Kong, China 5.3 .. .. 102 111 107 104 .. .. .. .. .. Indonesia 8.4 .. 28 91 101 93 98 91 38 230 37 72 Korea, Dem. Rep. .. .. 24 .. .. .. .. 55 55 67 .. 97 Lao PDR 8.1 40 40 43 74 75 84 163 83 650 .. 19 Malaysia 4.4 22 11 90 95 101 105 22 12 41 .. 97 Mongolia 5.6 12 13 .. 95 109 108 108 52 110 .. 99 Myanmar .. 31 32 .. 72 96 99 130 106 360 .. 57 Papua New Guinea 4.5 .. .. 50 55 80 87 101 93 300 .. 41 Philippines 5.4 30 28 86 98 100 102 62 34 200 53 60 Thailand 6.3 19 .. .. .. 95 98 37 21 44 .. 99 Vietnam 7.5 45 28 .. 101 .. 94 53 23 130 .. 90 Europe & Central Asia .. w .. w 92 w 94 w 97 w 96 w 49 w 34 w 58 w .. w 94 w Albania 9.1 .. 14 .. 99 96 97 45 19 55 .. 98 Armenia 8.5 .. 3 90 107 .. 103 60 32 55 .. 97 Azerbaijan 12.2 .. 7 .. 96 100 97 105 90 94 .. 84 Belarus 8.5 .. .. 95 101 .. 100 17 11 35 .. 100 Bosnia and Herzegovina 9.5 .. 4 .. .. .. .. 22 15 31 97 100 Bulgaria 8.7 .. .. 90 97 99 97 19 15 32 .. 99 Croatia 8.3 1 .. 85 91 106 98 12 7 8 .. 100 Czech Republic 10.3 1 .. .. 102 101 103 13 4 9 .. 100 Estonia 6.7 .. .. 93 103 68 73 16 8 63 .. 100 Georgia 5.6 .. .. .. 86 99 99 47 45 32 .. .. Hungary 9.5 .. .. 82 96 100 100 17 8 16 .. 100 Kazakhstan 7.4 .. .. .. 110 102 98 63 73 210 .. .. Kyrgyz Republic 8.9 .. 7 .. 93 .. 101 80 68 110 .. 99 Latvia 6.6 .. .. .. 98 100 99 18 12 42 .. .. Lithuania 6.8 .. .. 89 105 .. 98 13 8 13 .. 100 Macedonia, FYR 6.1 .. .. 98 97 99 99 38 14 23 .. 99 Moldova 7.8 .. .. .. 83 105 102 40 28 36 .. .. Poland 7.5 .. .. 96 100 101 97 18 8 13 .. 100 Romania 8.1 6 3 96 90 99 100 31 20 49 99 99 Russian Federation 6.1 4 6 93 .. 104 99 29 21 67 .. 99 Serbia and Montenegro .. .. 2 71 96 103 101 28 15 11 .. 93 Slovak Republic 8.8 .. .. 96 101 .. 100 14 9 3 .. 99 Tajikistan 7.9 .. .. .. 92 .. 88 119 93 100 .. 71 Turkey 5.3 10 4 90 .. 81 85 82 32 70 76 83 Turkmenistan 6.1 .. 12 .. .. .. .. 97 103 31 .. 97 Ukraine 9.2 .. 1 92 91 .. 99 26 18 35 .. 100 Uzbekistan 9.2 .. 8 .. 98 94 98 79 69 24 .. 96 Latin America & Carib. .. w .. w 86 w 97 w .. w 102 w 54 w 31 w 194 w 77 w 87 w Argentina 3.2e 2 .. .. 102 .. 103 29 18 82 96 99 Bolivia 1.5 15 8 71 100 .. 98 125 69 420 47 67 Brazil 2.6 7 .. 93 111 .. 103 60 34 260 72 96 Chile 3.3 1 1 .. 97 100 99 21 8 31 100 100 Colombia 2.5 10 7 71 94 .. .. 36 21 130 82 86 Costa Rica 3.9 2 .. 74 92 65 68 18 13 43 98 98 Cuba .. .. 4 96 93 98 101 13 7 33 100 100 Dominican Republic 3.9 10 5 61 91 .. 100 65 32 150 93 98 Ecuador 3.3 .. 12 91 101 81 94 57 26 130 .. .. El Salvador 2.7 11 10 41 84 .. 73 60 28 150 51 92 Guatemala 2.9 .. 23 .. 70 46 72 82 45 240 .. 41 Haiti 2.4 27 17 27 .. 108 .. 150 117 680 23 24 Honduras 3.4 18 17 65 79 103 95 59 41 110 45 56 24 2006 World Development Indicators Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of poorest Maternal quintile in mortality national Prevalence of child ratio consumption malnutrition Ratio of female to male Modeled or income Underweight Primary completion enrollments in primary estimates Births attended by % % of children ratea and secondary schoola Under-five mortality rate per 100,000 skilled health staff under age 5 % % per 1,000 live births live births % of total 1992­ 2004b,c 1989­94b 2000­04b 1991 2004 1991 2004 1990 2004 2000 1989­94b 2000­04b Jamaica 6.7 5 4 90 84 102 101 20 20 87 79 97 Mexico 4.3 17 .. 86 97 98 102 46 28 83 .. 95 Nicaragua 5.6 11 10 41 73 109 103 68 38 230 .. 67 Panama 2.5 6 .. 86 97 .. 101 34 24 160 86 93 Paraguay 2.2 4 5 65 89 99 98 41 24 170 67 77 Peru 3.2 11 7 .. 96 96 97 80 29 410 .. 59 Trinidad and Tobago 5.5 .. 6 100 94 101 101 33 20 160 .. 96 Uruguay 5.0e 4 .. 95 94 .. 105 25 17 27 .. .. Venezuela, RB 4.7 5 4 81 89 105 103 27 19 96 .. 94 Middle East & N. Africa .. w .. w 78 w 88 w 81 w 90 w 81 w 55 w 183 w 42 w 72 w Algeria 7.0 9 10 79 94 83 99 69 40 140 77 96 Egypt, Arab Rep. 8.6 10 9 .. 93 102 98 104 36 84 41 69 Iran, Islamic Rep. 5.1 .. .. 91 95 85 100 72 38 76 .. 90 Iraq .. 12 16 59 74 78 78 50 .. 250 54 72 Jordan 6.7 6 4 101 97 101 101 40 27 41 87 100 Lebanon .. .. .. .. 94 .. 102 37 31 150 .. .. Libya .. .. .. .. .. .. 103 41 20 97 .. .. Morocco 6.5 10 10 46 67 70 88 89 43 220 31 63 Oman .. 24 .. 74 91 89 98 32 13 87 .. 95 Syrian Arab Republic .. 12 7 89 107 85 94 44 16 160 77 .. Tunisia 6.0 .. 4 74 94 86 102 52 25 120 .. 90 West Bank and Gaza .. .. .. .. 98 .. 103 .. .. .. .. 97 Yemen, Rep. 7.4 39 46 .. 62 .. 63 142 111 570 16 27 South Asia 53 w .. w 73 w 82 w 71 w 87 w 129 w 92 w 564 w .. w 36 w Afghanistan .. .. 39 25 .. 54 34 260 .. 1,900 .. 14 Bangladesh 9.0 68 48 49 73 .. 106 149 77 380 10 13 India 8.9 53 .. .. 84 70 88 123 85 540 34 43 Nepal 6.0 .. 48 51 71 59 90 145 76 740 7 15 Pakistan 9.3 40 38 .. .. .. 73 130 101 500 19 23 Sri Lanka 8.3 38 30 94 .. 102 102 32 14 92 94 96 Sub-Saharan Africa .. w .. w 51 w 62 w 80 w 84 w 185 w 168 w 921 w 41 w 42 w Angola .. 20 31 35 .. .. .. 260 260 1,700 .. 45 Benin 7.4 .. 23 21 49 49 71 185 152 850 .. 66 Botswana 2.2 .. 13 79 92 109 102 58 116 100 .. 94 Burkina Faso 6.9 33 38 21 29 62 76 210 192 1,000 42 38 Burundi 5.1 .. 45 46 33 82 82 190 190 1,000 .. 25 Cameroon 5.6 15 18 56 72 83 87 139 149 730 58 62 Central African Republic 2.0 .. 24 27 .. 60 .. 168 193 1,100 .. 44 Chad .. .. 37 18 29 41 58 203 200 1,100 .. 14 Congo, Dem. Rep. .. .. 31 46 .. 85 87 205 205 990 .. 61 Congo, Rep. .. .. .. 54 66 101 101 110 108 510 .. .. Côte d'Ivoire 5.2 24 17 43 43 102 101 157 194 690 45 68 Eritrea .. 41 40 19 44 104 100 147 82 630 .. 28 Ethiopia 9.1 48 47 21 51 109 106 204 166 850 .. 6 Gabon .. .. 12 58 66 64 85 92 91 420 .. 86 Gambia, The 4.8 .. 17 44 .. 98 99 154 122 540 44 55 Ghana 5.6 27 22 63 65 99 101 122 112 540 44 47 Guinea 6.4 27 33 17 48 .. 65 240 155 740 31 56 Guinea-Bissau 5.2 .. 25 .. 27 95 .. 253 203 1,100 .. 35 Kenya 6.0 23 20 .. 89 94 94 97 120 1,000 45 42 Lesotho 1.5 21 18 58 71 124 104 104 112 550 50 60 Liberia .. .. 27 .. .. .. .. 235 235 760 .. 51 Madagascar 4.9 45 42 33 45 98 .. 168 123 550 57 51 Malawi 4.9 28 22 31 58 81 98 241 175 1,800 55 61 Mali 4.6 .. 33 11 44 59 74 250 219 1,200 .. 41 Mauritania 6.2 48 32 33 43 67 96 133 125 1,000 40 57 2006 World Development Indicators 25 Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of poorest Maternal quintile in mortality national Prevalence of child ratio consumption malnutrition Ratio of female to male Modeled or income Underweight Primary completion enrollments in primary estimates Births attended by % % of children ratea and secondary schoola Under-five mortality rate per 100,000 skilled health staff under age 5 % % per 1,000 live births live births % of total 1992­ 2004b,c 1989­94b 2000­04b 1991 2004 1991 2004 1990 2004 2000 1989­94b 2000­04b Mauritius .. .. .. 102 100 102 103 23 15 24 97 99 Mozambique 6.5 .. 24 26 29 72 82 235 152 1,000 .. 48 Namibia 1.4 26 24 78 81 108 105 86 63 300 68 76 Niger 2.6 43 40 17 25 57 71 320 259 1,600, 15 16 Nigeria 5.0 39 29 .. 76 79 84 230 197 800 31 35 Rwanda .. 29 24 47 37 96 100 173 203 1,400 26 31 Senegal 6.4 22 23 39 45 69 90 148 137 690 47 58 Sierra Leone .. 29 27 .. .. 67 71 302 283 2,000 .. 42 Somalia .. .. 26 .. .. .. .. 225 225 1,100 .. 25 South Africa 3.5 .. .. 75 96 104 101 60 67 230 .. .. Sudan .. 34 41 40 49 78 88 120 91 590 86 87 Swaziland 2.7 .. 10 62 61 98 96 110 156 370 56 74 Tanzania 7.3 29 .. 61 57 97 .. 161 126 1,500 44 46 Togo .. .. .. 35 66 59 73 152 140 570 .. 61 Uganda 5.9 23 23 .. 57 82 97 160 138 880 38 39 Zambia 6.1 25 23 .. 66 .. 93 180 182 750 51 43 Zimbabwe 4.6 16 .. 91 80 92 96 80 129 1,100 69 .. High income .. w .. w .. w .. w 100 w 101 w 11 w 7w 14 w .. w .. w Australia 5.9 .. .. .. 100 101 98 10 6 8 100 .. Austria 8.6 .. .. .. .. 95 96 10 5 4 100 .. Belgium 8.5 .. .. 79 .. 101 106 10 5 10 .. .. Canada 7.2 .. .. .. .. 99 100 8 6 6 .. 98 Denmark 8.3 .. .. 98 103 .. 105 9 5 5 .. .. Finland 9.6 .. .. 97 102 102 100 7 4 6 100 100 France 7.2 .. .. 104 .. .. .. 9 5 17 99 .. Germany 8.5 .. .. 100 97 79 91 9 5 8 .. .. Greece 6.7 .. .. 99 .. .. 91 11 5 9 .. .. Ireland 7.4 .. .. .. 101 104 103 9 6 5 .. 100 Israel 5.7 .. .. .. 101 105 99 12 6 17 .. .. Italy 6.5 .. .. 104 103 100 99 9 5 5 .. .. Japan 10.6 .. .. 101 .. 101 100 6 4 10 100 .. Korea, Rep. 7.9 .. .. 98 105 99 100 9 6 20 98 .. Kuwait .. .. .. 57 91 97 104 16 12 5 .. .. Netherlands 7.6 .. .. .. 100 97 98 9 6 16 .. .. New Zealand 6.4 .. .. 100 .. 100 107 11 7 7 95 .. Norway 9.6 .. .. 100 103 102 101 9 4 16 .. .. Portugal 5.8 .. .. 95 .. 103 102 14 5 5 98 100 Puerto Rico .. .. .. .. .. .. .. .. .. 25 .. .. Saudi Arabia .. 15 .. 56 62 84 92 44 27 23 .. .. Singapore 5.0 .. 3 .. .. 95 .. 8 3 30 .. .. Slovenia 9.1 .. .. 95 102 .. 99 10 4 17 100 100 Spain 7.0 .. .. .. .. 104 102 9 5 4 .. .. Sweden 9.1 .. .. 96 .. 102 111 7 4 2 .. .. Switzerland 7.6 .. .. 53 96 97 96 9 5 7 .. .. United Arab Emirates .. .. .. 95 75 104 102 14 8 54 .. .. United Kingdom 6.1 .. .. .. .. 98 116 10 6 13 .. .. United States 5.4 1 2 .. .. 100 100 11 8 17 99 .. World .. w .. w .. w .. w 86 w 93 w 95 w 79 w 410 w .. w 60 w Low income .. .. 66 74 74 86 147 122 682 .. 40 Middle income .. 11 92 97 91 98 57 37 142 .. 87 Lower middle income .. 11 93 98 89 98 61 40 153 .. 86 Upper middle income .. .. 88 96 98 98 42 28 92 .. 95 Low & middle income .. .. 81 86 84 92 103 86 450 .. 60 High income .. .. .. .. 100 101 11 7 14 .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Data are for the most recent year available. c. See table 2.8 for survey year and whether share is based on income or consumption expenditure. d. Hong Kong, China, is classified as a high-income economy and is not included in the East Asia and Pacific aggregate. e. Urban data. 26 2006 World Development Indicators Millennium Development Goals: eradicating poverty and improving lives About the data This table and the following two present indicators Progress toward achieving universal primary education indicators for the first five goals. For more informa- for 17 of the 18 targets specified by the Millennium is measured by primary school completion rates. Before tion about data collection methods and limitations, Development Goals. Each of the eight goals com- World Development Indicators 2003, progress was mea- see About the data for the tables listed there. For prises one or more targets, and each target has sured by net enrollment ratios. But official enrollments information about the indicators for goals 6, 7, and associated with it several indicators for monitoring sometimes differ significantly from actual attendance, 8, see About the data for tables 1.3 and 1.4. progress toward the target. Most of the targets are and even school systems with high average enrollment set as a value of a specific indicator to be attained ratios may have poor completion rates. Estimates of pri- Definitions by a certain date. In some cases the target value is mary school completion rates were calculated by World · Share of poorest quintile in national consumption or set relative to a level in 1990. In others it is set at Bank staff using data provided by the United Nations income is the share of consumption or, in some cases, an absolute level. Some of the targets for goals 7 Educational, Scientific, and Cultural Organization Insti- income that accrues to the poorest 20 percent of the and 8 have not yet been quantified tute of Statistics and national sources. population. · Prevalence of child malnutrition is the The indicators in this table relate to goals 1­5. Eliminating gender disparities in education would percentage of children under age five whose weight Goal 1 has two targets between 1990 and 2015: help to increase the status and capabilities of for age is more than two standard deviations below to reduce by half the proportion of people whose women. The ratio of girls' to boys' enrollments in the median for the international reference population income is less than $1 a day and to reduce by half primary and secondary school provides an imperfect ages 0­59 months. The reference population, adopted the proportion of people who suffer from hunger. measure of the relative accessibility of schooling for by the World Health Organization in 1983, is based on Estimates of poverty rates can be found in table 2.7. girls. With a target date of 2005, this is the first of children from the United States, who are assumed to The indicator shown here, the share of the poorest the goals to fall due. be well nourished. · Primary completion rate is the quintile in national consumption, is a distributional The targets for reducing under-five and maternal percentage of students completing the last year of measure. Countries with more unequal distributions mortality are among the most challenging. Although primary school. It is calculated as the total number of of consumption (or income) will have a higher rate of estimates of under-five mortality rates are available students in the last grade of primary school, minus the poverty for a given average income. No single indica- at regular intervals for most countries, maternal number of repeaters in that grade, divided by the total tor captures the concept of suffering from hunger. mortality is difficult to measure, in part because it number of children of official graduation age. · Ratio of Child malnutrition is a symptom of inadequate food is relatively rare. female to male enrollments in primary and secondary supply, lack of essential nutrients, illnesses that Most of the 48 indicators relating to the Millennium school is the ratio of female to male gross enrollment deplete these nutrients, and undernourished moth- Development Goals can be found in World Develop- rate in primary and secondary school. · Under-five ers who give birth to underweight children. ment Indicators. Table 1.2a shows where to find the mortality rate is the probability that a newborn baby will die before reaching age five, if subject to current age- specific mortality rates. The probability is expressed Location of indicators for Millennium Development Goals 1­5 as a rate per 1,000. · Maternal mortality ratio is the number of women who die from pregnancy-related Goal 1. Eradicate extreme poverty and hunger Table 1. Proportion of population below $1 a day 2.7 causes during pregnancy and childbirth, per 100,000 2. Poverty gap ratio 2.7 live births. The data shown here have been collected 3. Share of poorest quintile in national consumption 1.2, 2.8 in various years and adjusted to a common 2000 base 4. Prevalence of underweight in children under age five 1.2, 2.17 year. The values are modeled estimates (see About 5. Proportion of population below minimum level of dietary energy consumption 2.17 the data for table 2.16). · Births attended by skilled Goal 2. Achieve universal primary education health staff are the percentage of deliveries attended 6. Net enrollment ratio 2.11 by personnel trained to give the necessary supervision, 7. Proportion of pupils starting grade 1 who reach grade 5 2.12 care, and advice to women during pregnancy, labor, and 8. Literacy rate of 15- to 24-year-olds 2.13 Goal 3. Promote gender equality and empower women the postpartum period; to conduct deliveries on their 9. Ratio of girls to boys in primary, secondary, and tertiary education 1.2* own; and to care for newborns. 10. Ratio of literate females to males among 15- to 24-year-olds 2.13* 11. Share of women in wage employment in the nonagricultural sector 1.5, 2.2* Data sources 12. Proportion of seats held by women in national parliament 1.5 The indicators here and throughout this book have Goal 4. Reduce child mortality been compiled by World Bank staff from primary 13. Under-five mortality rate 1.2, 2.19 and secondary sources. Efforts have been made to 14. Infant mortality rate 2.19 harmonize these data series with those published 15. Proportion of one-year-old children immunized against measles 2.15 Goal 4. Improve maternal health on the United Nations Millennium Development 16. Maternal mortality ratio 1.2, 2.16 Goals Web site (www.un.org/millenniumgoals), but 17. Proportion of births attended by skilled health personnel 1.2, 2.16 some differences in timing, sources, and defini- tions remain. * Table shows information on related indicators. 2006 World Development Indicators 27 Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2003 2004 1990 2002 1990 2002 1990 2002 2004 2004 East Asia & Pacificb 0.2 w 138 w 1.9 w 2.4 w 71 w 78 w 30 w 49 w .. w 435 w Cambodia 2.6 510 0.0 0.0 .. 34 .. 16 .. 40 China 0.1 101 2.1 2.7 70 77 23 44 .. 499 Hong Kong, China 0.1 75 4.6 5.2 .. .. .. .. 15 1,733 Indonesia 0.1 245 0.9 1.4 71 78 46 52 .. 184 Korea, Dem. Rep. .. 178 12.4 6.5 100 100 .. 59 .. 41 Lao PDR 0.1 156 0.1 0.2 .. 43 .. 24 .. 48 Malaysia 0.4 103 3.1 6.3 .. 95 96 .. .. 766 Mongolia <0.1 192 4.7 3.4 62 62 .. 59 20 184 Myanmar 1.2 171 0.1 0.2 48 80 21 73 .. 10 Papua New Guinea 0.6 233 0.6 0.4 39 39 45 45 .. 14 Philippines <0.1 293 0.7 0.9 87 85 54 73 26 446 Thailand 1.5 142 1.8 3.7 81 85 80 99 5 537 Vietnam 0.4 176 0.3 0.8 72 73 22 41 5 184 Europe & Central Asia 0.7 w 83 w 10.2 w 6.7 w .. w 91 w 86 w 82 w .. w 536 w Albania .. 22 2.2 0.8 97 97 .. 89 36 438 Armenia 0.1 78 1.1 1.0 .. 92 .. 84 .. 260 Azerbaijan <0.1 75 6.4 3.4 66 77 .. 55 .. 333 Belarus .. 60 9.3 6.0 100 100 .. .. .. 424 Bosnia and Herzegovina <0.1 53 1.2 4.7 98 98 .. 93 .. 507 Bulgaria 0.1 36 8.6 5.3 100 100 100 100 28 966 Croatia <0.1 41 3.8 4.7 .. .. .. .. 37 996 Czech Republic 0.1 11 13.1 11.2 .. .. .. .. 20 1,392 Estonia 1.1 46 16.2 11.7 .. .. .. .. 21 1,260 Georgia 0.1 82 2.8 0.7 .. 76 .. 83 25 337 Hungary 0.1 26 5.8 5.6 99 99 .. 95 16 1,217 Kazakhstan 0.2 151 15.4 9.9 86 86 72 72 15 351 Kyrgyz Republic 0.1 122 2.4 1.0 .. 76 .. 60 20 106 Latvia 0.6 68 4.8 2.7 .. .. .. .. 19 937 Lithuania 0.1 63 5.8 3.6 .. .. .. .. 25 1,235 Macedonia, FYR <0.1 30 5.5 5.1 .. .. .. .. 66 642 Moldova 0.2 138 4.8 1.6 .. 92 .. 68 15 391 Poland 0.1 29 9.1 7.7 .. .. .. .. 41 777 Romania <0.1 146 6.7 4.0 .. 57 .. 51 19 673 Russian Federation 1.1 115 13.3 9.8 94 96 87 87 .. 508 Serbia and Montenegro 0.2 33 .. 3.7 93 93 87 87 .. 910 Slovak Republic <0.1 19 8.1 6.8 100 100 100 100 33 1,027 Tajikistan <0.1 177 3.7 0.7 .. 58 .. 53 .. 46 Turkey .. 28 2.6 3.0 81 93 84 83 20 751 Turkmenistan <0.1 65 7.2 9.1 .. 71 .. 62 .. 82 Ukraine 1.4 101 11.5 6.4 .. 98 99 99 17 545 Uzbekistan 0.1 117 5.3 4.8 89 89 58 57 .. 79 Latin America & Carib. 0.7 w 64 w 2.4 w 2.4 w 82 w 89 w 68 w 75 w 14 w 499 w Argentina 0.7 43 3.4 3.5 94 .. 82 .. 34 579 Bolivia 0.1 217 0.8 1.2 72 85 33 45 .. 269 Brazil 0.7 60 1.4 1.8 83 89 70 75 18 587 Chile 0.3 16 2.7 3.6 90 95 85 92 19 799 Colombia 0.7 50 1.6 1.3 92 92 82 86 .. 427 Costa Rica 0.6 14 0.9 1.4 .. 97 .. 92 15 533 Cuba 0.1 10 3.0 2.1 .. 91 98 98 .. 75 Dominican Republic 1.0c 91 1.3 2.5 86 93 48 57 .. 396 Ecuador 0.3 131 1.6 2.0 69 86 56 72 22 472 El Salvador 0.7 54 0.5 1.0 67 82 51 63 11 402 Guatemala 1.1 77 0.6 0.9 77 95 50 61 .. 350 Haiti 5.6 306 0.1 0.2 53 71 15 34 .. 64 Honduras 1.8 77 0.5 0.9 83 90 49 68 8 153 28 2006 World Development Indicators Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2003 2004 1990 2002 1990 2002 1990 2002 2004 2004 Jamaica 1.2 7 3.3 4.1 92 93 75 80 26 1,021 Mexico 0.3 32 4.5 3.8 80 91 66 77 6 545 Nicaragua 0.2 63 0.7 0.7 69 81 47 66 13 177 Panama 0.9 45 1.3 2.0 .. 91 .. 72 29 388 Paraguay 0.5 71 0.5 0.7 62 83 58 78 14 349 Peru 0.5 178 1.0 1.0 74 81 52 62 19 223 Trinidad and Tobago 3.2 9 13.9 31.8 92 91 100 100 21 745 Uruguay 0.3 28 1.3 1.2 .. 98 .. 94 38 465 Venezuela, RB 0.7 42 5.9 4.3 .. 83 .. 68 28 450 Middle East & N. Africa 0.1 w 54 w 2.5 w 3.2 w 87 w 88 w 69 w 75 w .. w 219 w Algeria 0.1 54 3.0 2.9 95 87 88 92 .. 215 Egypt, Arab Rep. <0.1 27 1.4 2.1 94 98 54 68 28 235 Iran, Islamic Rep. 0.1 27 4.0 5.5 91 93 83 84 .. 270 Iraq <0.1 132 2.6 .. 83 81 81 80 .. 57 Jordan <0.1 5 3.2 3.2 98 91 .. 93 .. 407 Lebanon 0.1 11 3.3 4.7 100 100 .. 98 .. 429 Libya 0.3 20 8.7 9.1 71 72 97 97 .. 156 Morocco 0.1 110 1.0 1.5 75 80 57 61 17 357 Oman 0.1 11 6.0 12.1 77 79 83 89 .. 413 Syrian Arab Republic <0.1 41 2.8 2.8 79 79 76 77 26 269 Tunisia <0.1 22 1.6 2.3 77 82 75 80 .. 480 West Bank and Gaza .. 23 .. .. .. 94 .. 76 43 380 Yemen, Rep. 0.1 89 0.8 0.7 69 69 21 30 .. 92 South Asia 0.8 w 177 w 0.7 w 1.0 w 70 w 84 w 16 w 35 w .. w 76 w Afghanistan .. 333 0.2 .. .. 13 .. 8 .. 23 Bangladesh .. 229 0.1 0.3 71 75 23 48 .. 37 India 0.9 168 0.8 1.2 68 86 12 30 .. 85 Nepal 0.5 184 0.0 0.2 69 84 12 27 .. 22 Pakistan 0.1 181 0.6 0.7 83 90 38 54 13 63 Sri Lanka <0.1 60 0.2 0.5 68 78 70 91 27 165 Sub-Saharan Africa 7.2 w 363 w 0.8 w 0.7 w 49 w 58 w 32 w 36 w .. w 65 w Angola 3.9 259 0.4 0.5 32 50 30 30 .. 29 Benin 1.9 87 0.1 0.3 60 68 11 32 .. 38 Botswana 37.3 670 1.5 2.3 93 95 38 41 40 396 Burkina Faso 1.8 d 191 0.1 0.1 39 51 13 12 .. 37 Burundi 6.0 343 0.0 0.0 69 79 44 36 .. 12 Cameroon 5.5e 179 0.1 0.2 50 63 21 48 .. 74 Central African Republic 13.5 322 0.1 0.1 48 75 23 27 .. 18 Chad 4.8 279 0.0 0.0 20 34 6 8 .. 14 Congo, Dem. Rep. 4.2 366 0.1 0.0 43 46 18 29 .. 11 Congo, Rep. 4.9 377 0.5 0.6 .. 46 .. 9 .. 102 Côte d'Ivoire 7.0 393 0.4 0.4 69 84 31 40 .. 86 Eritrea 2.7 271 0.0 0.2 40 57 8 9 .. 14 Ethiopia 4.4 353 0.1 0.1 25 22 4 6 .. 8 Gabon 8.1 280 6.3 2.6 .. 87 .. 36 .. 388 Gambia, The 1.2 233 0.2 0.2 .. 82 .. 53 .. 99 Ghana 2.2d 206 0.2 0.4 54 79 43 58 .. 93 Guinea 3.2 240 0.2 0.1 42 51 17 13 .. 15 Guinea-Bissau .. 199 0.2 0.2 .. 59 .. 34 .. 8 Kenya 6.7d 619 0.2 0.2 45 62 42 48 .. 85 Lesotho 28.9 696 .. .. .. 76 37 37 .. 109 Liberia 5.9 310 0.2 0.1 56 62 38 26 .. 3 Madagascar 1.7 218 0.1 0.1 40 45 12 33 .. 19 Malawi 14.2 413 0.1 0.1 41 67 36 46 .. 25 Mali 1.9 281 0.0 0.0 34 48 36 45 .. 36 Mauritania 0.6 287 1.3 1.1 41 56 28 42 .. 135 2006 World Development Indicators 29 Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2003 2004 1990 2002 1990 2002 1990 2002 2004 2004 Mauritius .. 64 1.4 2.6 100 100 99 99 .. 700 Mozambique 12.2 460 0.1 0.1 .. 42 .. 27 .. 27 Namibia 21.3 717 0.0 1.1 58 80 24 30 45 206 Niger 1.2 157 0.1 0.1 40 46 7 12 .. 13 Nigeria 5.4 290 0.5 0.4 49 60 39 38 .. 79 Rwanda 5.1 371 0.1 0.1 58 73 37 41 .. 18 Senegal 0.8 245 0.4 0.4 66 72 35 52 .. 72 Sierra Leone .. 443 0.1 0.1 .. 57 .. 39 .. 19 Somalia .. 411 0.0 .. .. 29 .. 25 .. 88 South Africa 15.6c 718 8.1 7.6 83 87 63 67 60 473 Sudan 2.3 220 0.2 0.3 64 69 33 34 .. 58 Swaziland 38.8 1,226 0.6 0.9 .. 52 .. 52 .. 119 Tanzania 7.0 e 347 0.1 0.1 38 73 47 46 .. 32 Togo 4.1 355 0.2 0.3 49 51 37 34 .. 48 Uganda 4.1 402 0.0 0.1 44 56 43 41 .. 44 Zambia 15.6f 680 0.3 0.2 50 55 41 45 .. 29 Zimbabwe 24.6 674 1.6 1.0 77 83 49 57 25 55 High income 0.4 w 17 w 11.8 w 12.8 w .. w 99 w .. w .. w 13 w 1,306 w Australia 0.1 6 16.0 18.1 100 100 100 100 12 1,359 Austria 0.3 14 7.5 7.9 100 100 100 100 10 1,438 Belgium 0.2 13 10.1 8.9 .. .. .. .. 18 1,333 Canada 0.3 5 15.0 16.5 100 100 100 100 13 1,053 Denmark 0.2 8 9.7 8.8 100 100 .. .. 8 1,599 Finland 0.1 9 10.3 12.0 100 100 100 100 21 1,407 France 0.4 12 6.4 6.2 .. .. .. .. 23 1,299 Germany 0.1 8 12.3 10.3 100 100 .. .. 12 1,525 Greece 0.2 19 7.1 8.5 .. .. .. .. 27 1,465 Ireland 0.1 11 8.7 11.0 .. .. .. .. 8 1,425 Israel 0.1 9 7.1 10.6 100 100 .. .. 22 1,499 Italy 0.5 7 6.9 7.5 .. .. .. .. 24 1,541 Japan <0.1 30 8.7 9.4 100 100 100 100 10 1,176 Korea, Rep. <0.1 90 5.6 9.4 .. 92 .. .. 10 1,303 Kuwait .. 26 21.3 25.6 .. .. .. .. .. 1,015 Netherlands 0.2 8 9.3 9.3 100 100 100 100 8 1,393 New Zealand 0.1 11 6.8 8.6 97 .. .. .. 9 1,189 Norway 0.1 5 11.1 13.9 100 100 .. .. 12 1,396 Portugal 0.4 42 4.3 6.0 .. .. .. .. 15 1,384 Puerto Rico .. 5 3.3 3.5 .. .. .. .. 24 974 Saudi Arabia .. 40 11.0 15.0 90 .. .. .. .. 537 Singapore 0.2 40 14.8 13.7 .. .. .. .. 8 1,350 Slovenia <0.1 15 6.2 7.7 .. .. .. .. 15 1,278 Spain 0.7 25 5.5 7.4 .. .. .. .. 22 1,321 Sweden 0.1 4 5.8 5.8 100 100 100 100 17 1,750 Switzerland 0.4 7 6.4 5.6 100 100 100 100 8 1,560 United Arab Emirates .. 17 34.2 25.0 .. .. 100 100 .. 1,128 United Kingdom 0.2 12 9.9 9.2 .. .. .. .. 11 1,584 United States 0.6 5 19.3 20.2 100 100 100 100 12 1,223 World 1.1 w 139 w 4.0 w 3.9 w 75 w 82 w 43 w 54 w .. w 476 w Low income 2.1 224 0.8 0.8 64 75 20 36 .. 76 Middle income 0.7 114 3.5 3.3 77 83 48 61 .. 486 Lower middle income 0.3 114 2.4 2.6 75 81 42 57 .. 438 Upper middle income 2.6 112 8.1 6.2 88 93 80 81 .. 564 Low & middle income 1.2 162 2.4 2.2 71 79 37 50 .. 312 High income 0.4 17 11.8 12.8 .. 99 .. .. 13 1,306 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. Hong Kong, China, is classified as a high-income economy and is not included in the East Asia and Pacific aggregate. c. Survey data, 2002. d. Survey data, 2003. e. Survey data, 2004. f. Survey data, 2001/02. 30 2006 World Development Indicators Millennium Development Goals: protecting our common environment About the data The Millennium Development Goals address issues from sentinel sites or through targeted surveys. In among the telecommunications technologies that of common concern to all nations. Diseases and older, generalized epidemics antenatal clinics are a key are changing the way the global economy works. For environmental degradation do not respect national site for monitoring HIV and other sexually transmitted more information on goal 8, see table 1.4. boundaries. Epidemic diseases, wherever they per- diseases. Recently, household surveys have been used sist, pose a threat to people everywhere. And damage to track the disease. The table shows the estimated Definitions to the environment in one location may affect the prevalence among adults ages 15­49. Prevalence well-being of plants, animals, and humans far away. rates in the older population can be affected by life- · HIV prevalence is the percentage of people ages The indicators in the table relate to goals 6 and 7 prolonging treatment. The incidence of tuberculosis is 15­49 who are infected with HIV. · Incidence of tuber- and the targets of goal 8 that address youth employ- based on data on case notifications and estimates of culosis is the estimated number of new tuberculosis ment and access to new technologies. For the other the proportion of cases detected in the population. cases (pulmonary, smear positive, extrapulmonary). targets of goal 8, see table 1.4. Carbon dioxide emissions are the primary source · Carbon dioxide emissions are those stemming from Measuring the prevalence or incidence of a disease of greenhouse gases, which are believed to contrib- the burning of fossil fuels and the manufacture of can be difficult. Much of the developing world lacks ute to global warming. cement. They include carbon dioxide produced during reporting systems for monitoring diseases. Estimates Access to reliable supplies of safe drinking water consumption of solid, liquid, and gas fuels and gas are often derived from surveys and reports from sen- and sanitary disposal of excreta are two of the most flaring. · Access to an improved water source refers tinel sites that must be extrapolated to the general important means of improving human health and to the percentage of the population with reasonable population. Tracking diseases such as HIV/AIDS, protecting the environment. There is no widespread access to an adequate amount of water from an which has a long latency between contraction of the program for testing the quality of water. The indicator improved source, such as a household connection, virus and the appearance of symptoms, or malaria, shown here measures the proportion of households public standpipe, borehole, protected well or spring, or which has periods of dormancy, can be particularly with access to an improved source, such as piped rainwater collection. Unimproved sources include ven- difficult. For some of the most serious illnesses inter- water or protected wells. Improved sanitation facili- dors, tanker trucks, and unprotected wells and springs. national organizations have formed coalitions such ties prevent human, animal, and insect contact with Reasonable access is defined as the availability of at as the Joint United Nations Programme on HIV/AIDS excreta but do not include treatment to render sew- least 20 liters a person a day from a source within and the Roll Back Malaria campaign to gather infor- age outflows innocuous. 1 kilometer of the dwelling. · Access to improved mation and coordinate global efforts to treat victims The eighth goal--to develop a global partnership sanitation facilities refers to the percentage of the and prevent the spread of disease. for development--takes note of the need for decent population with access to at least adequate excreta The models and data used to estimate HIV preva- and productive work for youth. Labor market informa- disposal facilities (private or shared but not public) lence depend on the nature of the epidemic in each tion, such as unemployment rates, is still generally that can effectively prevent human, animal, and insect country. In early stages infections are usually concen- unavailable for most low- and middle-income econo- contact with excreta. Improved facilities range from trated in high risk groups for which data are collected mies. Fixed telephone lines and mobile phones are simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained. · Youth unemployment refers to the share of the labor force Location of indicators for Millennium Development Goals 6­7 ages 15­24 without work but available for and seeking Goal 6. Combat HIV/AIDS, malaria, and other diseases Table employment. Definitions of labor force and unemploy- 18. HIV prevalence among pregnant women ages 15­24 1.3*, 2.18* ment differ by country. · Fixed-line and mobile phone 19. Condom use rate of the contraceptive prevalence rate -- subscribers are telephone mainlines connecting a 19a. Condom use at last high-risk sex -- customer's equipment to the public switched telephone 19b. Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDS -- network, and users of portable telephones subscribing 19c. Contraceptive prevalence rate 2.16 to an automatic public mobile telephone service using 20. Ratio of school attendance of orphans to school attendance of nonorphans ages cellular technology that provides access to the public 10­14 -- switched telephone network. 21. Prevalence and death rates associated with malaria -- 22. Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures 2.15* 23. Prevalence and death rates associated with tuberculosis 1.3*, 2.18* 24. Proportion of tuberculosis cases detected and cured under DOTS 2.15 Goal 7. Ensure environmental sustainability 25. Proportion of land area covered by forest 3.4 26. Ratio of area protected to maintain biological diversity to surface area 3.4 Data sources 27. Energy use (kilograms of oil equivalent) per $1 of GDP (PPP) 3.8 The indicators here and throughout this book have 28. Carbon dioxide emissions per capita and consumption of ozone-depleting chloro- been compiled by World Bank staff from primary fluorocarbons 3.8* and secondary sources. Efforts have been made to 29. Proportion of population using solid fuels 3.7* harmonize these data series with those published 30. Proportion of population with sustainable access to an improved water source, on the United Nations Millennium Development urban and rural 2.15, 3.5 Goals Web site (www.un.org/millenniumgoals), but 31. Proportion of population with access to improved sanitation, urban and rural 2.15, 3.10 some differences in timing, sources, and defini- 32. Proportion of population with access to secure tenure 3.11 tions remain. -- No data are available in the World Development Indicators database. * Table shows information on related indicators. 2006 World Development Indicators 31 Millennium Development Goals: overcoming obstacles Development Assistance Committee members Official development Least developed countries' access Support to assistances (ODA) to high-income markets agriculture by donor For basic Average tariff on exports of Net social servicesa Goods least developed countries % of % of total (excluding arms) donor sector-allocable admitted free of tariffs Agricultural products Textiles Clothing GNI ODA % % % % % of GDP 2004 2003­04 1997 2004 1997 2004 1997 2004 1997 2004 2004 b Australia 0.25 15.8 96.6 97.3 0.2 0.4 10.0 0.9 28.3 0.0 0.3 Canada 0.27 29.0 65.9 98.6 0.5 0.2 11.4 0.3 21.8 1.4 0.7 European Union 97.3 95.9 3.4 2.8 0.0 0.2 0.0 1.0 1.2 Austria 0.23 12.6 Belgium 0.41 14.7 Denmark 0.85 23.6 Finland 0.35 15.3 France 0.41 10.0 Germany 0.28 12.7 Greece 0.23 20.6 Ireland 0.39 28.9 Italy 0.15 18.4 Luxembourg 0.83 20.7 Netherlands 0.73 18.1 Portugal 0.63 2.8 Spain 0.24 13.8 Sweden 0.78 16.0 United Kingdom 0.36 31.8 Japan 0.19 5.4 67.9 37.9 7.4 6.6 3.9 1.7 0.5 0.1 1.3 New Zealand 0.23 19.1 0.4 Norway 0.87 18.0 1.3 Switzerland 0.41 8.4 72.8 99.4 7.2 6.7 0.0 0.0 0.0 0.0 1.7 United States 0.17 19.1 22.5 67.0 4.9 3.5 6.9 5.7 14.6 12.3 0.9 Heavily indebted poor countries (HIPCs) HIPC HIPC Estimated HIPC HIPC Estimated decision completion total nominal decision completion total nominal pointc pointd debt service pointc pointd debt service relief e relief e $ millions $ millions Benin Jul. 2000 Mar. 2003 460 Madagascar Dec. 2000 Oct. 2004 1,900 Bolivia Feb. 2000 Jun. 2001 2,060 Malawi Dec. 2000 Floating 1,000 Burkina Faso Jul. 2000 Apr. 2002 930 Mali Sep. 2000 Mar. 2003 895 Cameroon Aug. 2005 Floating 1,472 Mauritania Feb. 2000 Jun. 2002 1,100 Burundi Oct. 2000 Floating 2,800 Mozambique Apr. 2000 Sep. 2001 4,300 Chad May 2001 Floating 260 Nicaragua Dec. 2000 Jan. 2004 4,500 Congo, Dem. Republic Jul. 2003 Floating 10,389 Niger Dec. 2000 Apr. 2004 1,190 Côte d'Ivoire Mar. 1998 .. 800 Rwanda Dec. 2000 Apr. 2005 1,400 Ethiopia Nov. 2001 Apr. 2004 3,275 São Tomé & Principe Dec. 2000 Floating 200 Gambia, The Dec. 2000 Floating 90 Senegal Jun. 2000 Apr. 2004 850 Ghana Feb. 2002 Jul. 2004 3,500 Sierra Leone Mar. 2002 Floating 950 Guinea Dec. 2000 Floating 800 Tanzania Apr. 2000 Nov. 2001 3,000 Guinea-Bissau Dec. 2000 Floating 790 Uganda Feb. 2000 May 2000 1,950 Guyana Nov. 2000 Dec. 2003 1,353 Zambia Dec. 2000 Apr. 2005 3,900 Honduras Jul. 2000 Apr. 2005 1,053 a. Includes basic health, education, nutrition, and water and sanitation services. b. Preliminary. c. Except for Côte d'Ivoire the date refers to the Enhanced Heavily Indebted Poor Countries (HIPC) Initiative. The following countries also reached their decision point under the original HIPC framework: Bolivia in September 1997, Burkina Faso in September 1997, Côte d'Ivoire in March 1998, Guyana in December 1997, Mali in September 1998, Mozambique in April 1998, and Uganda in April 1997. d. The date refers to the Enhanced HIPC Initiative. The following countries also reached completion points under the original framework: Bolivia in September 1998, Burkina Faso in July 2000, Guyana in May 1999, Mali in September 2000, Mozambique in July 1999, and Uganda in April 1998. e. Includes estimated total nominal debt service relief under original and enhanced HIPC, as well as a topping-up of HIPC debt relief at completion point for Burkina Faso, Ethiopia, and Niger. 32 2006 World Development Indicators Millennium Development Goals: overcoming obstacles About the data Definitions Achieving the Millennium Development Goals will to exports of countries designated least developed · Net official development assistance (ODA) com- require an open, rule-based global economy in which countries by the United Nations. Agricultural com- prises grants and loans (net of repayments of prin- all countries, rich and poor, participate. Many poor modities, textiles, and clothing are three of the most cipal) that meet the DAC definition of ODA and are countries, lacking the resources to finance their devel- important categories of goods exported by develop- made to countries and territories on part I of the opment, burdened by unsustainable levels of debt, ing economies. Although average tariffs have been DAC list of recipient countries. · ODA for basic and unable to compete in the global marketplace, falling, averages may disguise high tariffs targeted social services is aid reported by DAC donors for need assistance from rich countries. For goal 8-- at specific goods (see table 6.7 for estimates of basic health, education, nutrition, and water and develop a global partnership for development--many the share of tariff lines with "international peaks" sanitation services. · Goods admitted free of tar- of the indicators therefore monitor the actions of in each country's tariff schedule). The averages in iffs refer to the value of exports of goods (exclud- members of the Development Assistance Committee the table include ad valorem duties and ad valorem ing arms) from least developed countries admitted (DAC) of the Organisation for Economic Co-operation equivalents of non-ad valorem duties. Subsidies to without tariff, as a share of total exports from least and Development (OECD). agricultural producers and exporters in OECD coun- developed countries. · Average tariff is the simple Official development assistance (ODA) has risen tries are another form of barrier to developing econo- mean tariff, the unweighted average of the effec- in recent years as a share of donor countries' gross mies' exports. The table shows the value of total tively applied rates for all products subject to tariffs. national income (GNI), but the poorest countries will support to agriculture as a share of the economy's · Agricultural products comprise plant and animal need additional assistance to achieve the Millennium gross domestic product (GDP). Agricultural subsi- products, including tree crops but excluding timber Development Goals. Official aid rose to a record high dies in OECD economies are estimated at $378 and fish products. · Textiles and clothing include of $79 billion in 2004, and donor countries have billion in 2004. natural and synthetic fibers and fabrics and articles pledged to increase ODA by $20 billion by 2006 and The Debt Initiative for Heavily Indebted Poor Coun- of clothing made from them. · Support to agriculture to a total of more than $100 billion by 2010. However, tries (HIPCs) is the first comprehensive approach is the annual monetary value of all gross transfers this would still fall short of levels considered neces- to reducing the external debt of the world's poor- from taxpayers and consumers arising from policy sary to achieve the Millennium Development Goals. est, most heavily indebted countries. It represents measures that support agriculture, net of the asso- One of the most important actions that high-income an important step forward in placing debt relief ciated budgetary receipts, regardless of their objec- economies can take to help is to reduce barriers to within an overall framework of poverty reduction. tives and impacts on farm production and income, the exports of low- and middle-income economies. A major review in 1999 led to an enhancement or consumption of farm products. · HIPC decision The European Union has launched a program to of the original framework. Through the HIPC Initia- point is the date at which a heavily indebted poor eliminate tariffs on developing country exports of tive nominal debt service relief of more than $56 country with an established track record of good per- "everything but arms," and the United States offers billion has been approved for 28 countries, reduc- formance under adjustment programs supported by special concessions to exports from Sub-Saharan ing the net present value of their external debt by the International Monetary Fund and the World Bank Africa. However, there are still many restrictions built approximately two-thirds. Of these countries, 19 commits to undertake additional reforms and to into these programs. have reached the completion point and have been develop and implement a poverty reduction strategy. The average tariffs in the table reflect the tariff granted unconditional debt service relief of more · HIPC completion point is the date at which the schedules applied by high-income OECD members than $37 billion. country successfully completes the key structural reforms agreed on at the decision point, including developing and implementing its poverty reduction Location of indicators for Millennium Development Goal 8 strategy. The country then receives the bulk of debt Goal 8. Develop a global partnership for development Table relief under the HIPC Initiative without further policy 33. Net ODA as a percentage of DAC donors' gross national income 6.9 conditions. · Estimated total nominal debt service 34. Proportion of ODA for basic social services 1.4 relief is the amount of debt service relief, calculated 35. Proportion of ODA that is untied 6.9 at the decision point, that will allow the country to 36. Proportion of ODA received in landlocked countries as a percentage of GNI -- achieve debt sustainability at the completion point. 37. Proportion of ODA received in small island developing states as a percentage of GNI -- 38. Proportion of total developed country imports (by value, excluding arms) from developing countries admitted free of duty 1.4 39. Average tariffs imposed by developed countries on agricultural products and textiles and clothing from developing countries 1.4, 6.7* 40. Agricultural support estimate for OECD countries as a percentage of GDP 1.4 Data sources 41. Proportion of ODA provided to help build trade capacity -- The indicators here, and where they appear 42. Number of countries reaching HIPC decision and completion points 1.4 throughout the rest of the book, have been com- 43. Debt relief committed under new HIPC initiative 1.4 piled by World Bank staff from primary and sec- 44. Debt services as a percentage of exports of goods and services 4.17 ondary sources. The World Trade Organization, in 45. Unemployment rate of 15- to 24-year-olds 1.3, 2.9 collaboration with the UN Conference on Trade and 46. Proportion of population with access to affordable, essential drugs on a sustainable basis -- Development and the International Trade Centre, 47. Telephone lines and cellular subscribers per 100 people 1.3, 5.10 provided the estimates of goods admitted free 48a. Personal computers in use per 100 people 5.11 of tariffs and average tariffs. Subsidies to agri- 48b. Internet users per 100 people 5.11 culture are compiled by the OECD. Data on the HIPC Initiative are from the August 2005 "HIPC -- No data are available in the World Development Indicators database. * Table shows information on related indicators. Status Report." 2006 World Development Indicators 33 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2004 2004 2004 2000­04a 1995­2004a 2003 2000­04a 2000­04a 1990 2006 East Asia & Pacificb 49.1 w 68 w 72 w 39.7 w .. w .. w 19 w 17 w Cambodia 51.7 53 60 38 8 52.6 31.6 53.3 .. 10 China 48.6 70 73 89 .. 39.5 .. .. 21 20 Hong Kong, China 52.7 79 85 .. .. 46.9 0.2 1.4 .. .. Indonesia 50.1 66 69 92 10 30.8 .. .. 12 11 Korea, Dem. Rep. 50.0 61 67 .. .. .. .. .. 21 20 Lao PDR 50.0 54 57 27 .. .. .. .. 6 23 Malaysia 49.2 71 76 74 .. 38.0 2.2 9.6 5 9 Mongolia 49.9 62 68 94 .. 49.4 18.4 31.7 25 7 Myanmar 50.3 58 64 76 .. .. .. .. .. .. Papua New Guinea 48.4 55 57 .. .. 35.4 .. .. 0 1 Philippines 49.7 69 73 88 8 41.1 .. .. 9 16 Thailand 50.8 67 74 92 .. 46.9 16.0 35.2 3 11 Vietnam 50.1 68 73 86 3 51.8 21.9 50.3 18 27 Europe & Central Asia 52.1 w 64 w 73 w 47.3 w 2.9 w 7.3 w .. w 13 w Albania 50.4 71 77 91 .. 40.3 .. .. 29 7 Armenia 53.3 68 75 92 6 47.0 1.1 0.8 36 5 Azerbaijan 51.4 70 75 66 .. 48.5 .. .. .. 12 Belarus 53.2 63 74 .. .. 55.9 .. .. .. 29 Bosnia and Herzegovina 51.4 72 77 99 .. .. .. .. .. 17 Bulgaria 51.5 69 76 .. .. 52.2 1.3 2.6 21 22 Croatia 51.9 72 79 .. .. 46.3 1.8 6.3 .. 22 Czech Republic 51.3 73 79 .. .. 45.8 0.3 1.2 .. 17 Estonia 54.0 66 77 .. .. 51.5 0.3 0.4 .. 19 Georgia 52.7 67 74 .. .. 45.2 19.9 38.8 .. 9 Hungary 52.4 69 77 .. .. 47.1 0.4 0.7 21 9 Kazakhstan 52.0 60 71 .. 7 48.7 0.8 1.2 .. 10 Kyrgyz Republic 50.8 64 72 .. 9 44.0 6.5 15.9 .. 0 Latvia 54.2 66 78 .. .. 53.4 3.5 3.9 .. 21 Lithuania 53.3 66 78 .. .. 50.0 2.8 4.3 .. 22 Macedonia, FYR 50.1 71 76 81 .. 42.2 7.0 18.1 .. 19 Moldova 52.2 65 72 .. .. 54.6 1.3 3.4 .. 22 Poland 51.5 70 79 .. .. 47.7 4.0 7.2 14 20 Romania 51.2 68 75 .. .. 45.3 7.8 23.4 34 11 Russian Federation 53.6 59 72 .. .. 50.1 0.1 0.1 .. 10 Serbia and Montenegro 50.3 71 76 .. .. 44.9 .. .. .. 8 Slovak Republic 51.5 70 78 .. .. 52.1 .. .. .. 17 Tajikistan 50.3 61 67 71 .. 52.3 .. .. .. 18 Turkey 49.6 69 71 81 10 20.6 8.2 49.0 1 4 Turkmenistan 50.7 59 67 98 4 .. .. .. 26 16 Ukraine 54.1 63 74 .. .. 53.6 1.1 2.0 .. 5 Uzbekistan 50.3 64 70 97 10 41.5 .. .. .. 18 Latin America & Carib. 50.6 w 69 w 75 w 43.7 w .. w .. w 8w 20 w Argentina 51.1 71 78 98 .. 47.6 0.8 1.6 6 35 Bolivia 50.2 62 67 79 16 36.5 5.2 11.1 9 17 Brazil 50.7 67 75 .. 18 46.9 .. .. 5 9 Chile 50.5 75 81 .. .. 37.3 1.4 3.3 .. 15 Colombia 50.6 70 76 91 19 48.8 4.3 8.5 5 12 Costa Rica 49.2 76 81 .. .. 39.5 2.1 3.6 11 35 Cuba 50.0 75 79 100 .. 37.7 .. .. 34 36 Dominican Republic 49.5 64 71 99 23 34.9 .. .. 8 17 Ecuador 49.8 72 78 .. .. 41.1 3.0 8.6 5 16 El Salvador 50.9 68 74 86 .. 31.1 8.5 9.0 12 11 Guatemala 51.2 64 71 84 22 38.7 21.3 24.5 7 8 Haiti 50.8 51 53 79 18 .. .. .. .. 4 Honduras 49.6 66 70 83 .. 50.5 12.9 11.0 10 23 34 2006 World Development Indicators Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2004 2004 2004 2000­04a 1995­2004a 2003 2000­04a 2000­04a 1990 2006 Jamaica 50.6 69 73 .. .. 48.0 0.7 2.0 5 12 Mexico 51.1 73 78 .. .. 37.4 5.5 11.3 12 24 Nicaragua 50.0 68 73 86 25 .. .. .. 15 21 Panama 49.5 73 78 .. .. 44.0 3.1 4.0 8 17 Paraguay 49.6 69 74 94 .. 42.0 .. .. 6 10 Peru 49.7 68 73 84 13 37.2 2.0 6.2 6 18 Trinidad and Tobago 50.6 67 73 92 .. 41.3 0.5 1.9 17 19 Uruguay 51.5 72 79 .. .. 46.3 0.9 2.0 6 11 Venezuela, RB 49.7 71 77 94 .. 41.5 1.8 3.3 10 17 Middle East & N. Africa 49.5 w 68 w 71 w .. w .. w .. w 4w 8w Algeria 49.6 70 73 81 .. 15.5 .. .. 2 6 Egypt, Arab Rep. 49.8 68 72 69 9 21.6 8.4 19.5 4 2 Iran, Islamic Rep. 49.3 69 72 .. .. .. .. .. 2 4 Iraq .. .. .. 77 .. .. .. .. 11 26 Jordan 48.0 70 73 99 4 24.9 .. .. 0 6 Lebanon 51.0 70 75 .. .. .. .. .. 0 5 Libya 48.4 72 77 .. .. .. .. .. .. 5 Morocco 50.3 68 72 68 7 26.2 21.6 52.5 0 11 Oman 43.4 73 76 100 .. 25.6 .. .. .. 2 Syrian Arab Republic 49.7 72 75 71 .. 18.2 .. .. 9 12 Tunisia 49.6 71 75 92 .. 25.3 .. .. 4 23 West Bank and Gaza 49.1 71 75 .. .. .. 7.0 32.5 .. .. Yemen, Rep. 49.3 60 63 41 16 6.1 .. .. 4 0c South Asia 48.7 w 63 w 64 w 18.1 w .. w .. w 6w 14 w Afghanistan .. .. .. 16 .. .. .. .. 4 .. Bangladesh 48.9 63 64 49 33 d 24.2 10.1 73.2 10 15 India 48.7 63 64 .. 21 17.5 .. .. 5 8 Nepal 50.4 62 63 28 21 .. .. .. 6 0 Pakistan 48.5 64 66 43 .. 8.7 16.4 46.9 10 21 Sri Lanka 49.2 72 77 95 .. 43.2 4.2 20.9 5 5 Sub-Saharan Africa 50.1 w 46 w 47 w .. w .. w .. w .. w 16 w Angola 50.7 40 43 66 .. .. .. .. 15 15 Benin 49.7 54 55 81 22 .. .. .. 3 7 Botswana 50.9 36 35 97 .. 47.0 1.4 1.2 5 11 Burkina Faso 49.8 47 49 73 23 15.2 .. .. .. 12 Burundi 51.3 43 45 78 .. .. .. .. .. 31 Cameroon 50.3 45 47 83 28 .. 9.5 27.2 14 9 Central African Republic 51.3 39 40 62 36 .. .. .. 4 11 Chad 50.5 43 45 42 39 .. .. .. .. 7 Congo, Dem. Rep. 50.4 43 45 68 .. .. .. .. 5 12 Congo, Rep. 50.4 51 54 .. .. .. .. .. 14 9 Côte d'Ivoire 49.1 45 47 88 31 20.2 .. .. 6 9 Eritrea 51.0 53 56 70 14 35.0 .. .. .. 22 Ethiopia 50.3 42 43 27 16 .. .. .. .. 21 Gabon 50.2 54 55 94 33 .. .. .. 13 9 Gambia, The 50.4 55 58 91 .. .. .. .. 8 13 Ghana 49.4 57 58 92 14 .. .. .. .. 11 Guinea 48.8 54 54 84 37 .. .. .. .. 19 Guinea-Bissau 50.6 44 46 62 .. .. .. .. 20 14 Kenya 50.0 49 47 88 23 38.5 .. .. 1 7 Lesotho 53.5 35 37 85 .. .. .. .. .. 12 Liberia 50.1 42 43 85 .. .. .. .. .. 13 Madagascar 50.3 54 57 80 34 .. 29.7 51.9 7 7 Malawi 50.4 40 40 94 33 12.5 .. .. 10 14 Mali 50.2 48 49 57 40 .. .. .. .. 10 Mauritania 50.6 52 55 64 16 .. .. .. .. .. 2006 World Development Indicators 35 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2004 2004 2004 2000­04a 1995­2004a 2003 2000­04a 2000­04a 1990 2006 Mauritius 50.3 69 76 .. .. 38.5 .. .. 7 17 Mozambique 51.7 41 42 85 41 .. .. .. 16 35 Namibia 50.4 47 48 91 18 50.8 12.8 22.0 7 27 Niger 48.9 45 45 41 43 .. .. .. 5 12 Nigeria 49.4 43 44 58 25 .. .. .. .. 6 Rwanda 51.6 42 46 92 7 .. .. .. 17 49 Senegal 50.8 55 57 79 22 .. .. .. 13 19 Sierra Leone 50.7 40 43 68 .. .. .. .. .. 15 Somalia 50.4 46 48 .. .. .. .. .. 4 8 South Africa 50.9 44 45 .. 16 .. 0.5 1.1 3 33 Sudan 49.7 55 58 60 .. 18.9 .. .. .. 15 Swaziland 51.9 43 42 90 .. 31.3 .. .. 4 11 Tanzania 50.3 46 47 94 25 .. 3.0 4.6 .. 30 Togo 50.6 53 57 85 19 .. .. .. 5 7 Uganda 50.0 48 49 92 31 .. 10.3 40.5 12 24 Zambia 50.0 39 38 93 32 .. .. .. 7 13 Zimbabwe 50.5 38 37 .. 21 21.8 10.4 13.6 11 16 High income 50.7 w 76 w 82 w 46.0 w .. w 3.1 w 8w 22 w Australia 50.6 77 83 .. .. 48.9 0.3 0.4 6 25 Austria 51.1 76 82 .. .. 44.5 1.4 3.0 12 34 Belgium 50.9 76 82 .. .. 44.4 .. .. 9 35 Canada 50.4 77 83 .. .. 49.2 0.2 0.3 13 21 Denmark 50.5 75 80 .. .. 48.3 0.3 1.1 31 37 Finland 51.1 75 82 .. .. 50.6 0.4 0.4 32 38 France 51.3 77 84 .. .. 47.0 .. .. 7 12 Germany 51.2 76 81 .. .. 46.4 0.5 1.9 .. 32 Greece 50.6 77 81 .. .. 41.1 3.9 14.2 7 13 Ireland 50.3 76 81 .. .. 47.4 0.8 1.3 8 13 Israel 50.5 77 81 .. .. 48.9 0.2 0.7 7 15 Italy 51.5 77 83 .. .. 41.2 3.1 5.8 13 12 Japan 51.1 78 85 .. .. 40.8 1.6 9.2 1 9 Korea, Rep. 49.8 74 81 .. .. 41.2 1.3 16.7 2 13 Kuwait 39.8 75 79 .. .. 24.1 .. .. .. 2 Netherlands 50.4 76 81 .. .. 45.7 0.2 1.1 21 37 New Zealand 50.9 77 81 .. .. 51.3 0.5 0.9 14 32 Norway 50.4 78 82 .. .. 49.1 0.3 0.4 36 38 Portugal 51.7 74 81 .. .. 46.9 1.0 2.3 8 21 Puerto Rico 52.0 73 82 .. .. 40.1 0.1 1.1 .. .. Saudi Arabia 46.0 70 74 .. .. 14.5 .. .. .. 0 Singapore 49.7 77 81 .. .. 47.8 0.3 1.3 5 16 Slovenia 51.2 73 80 .. .. 47.4 3.1 5.6 .. 12 Spain 50.9 77 84 .. .. 40.7 0.9 2.7 15 36 Sweden 50.4 78 83 .. .. 50.9 .. .. 38 45 Switzerland 51.5 79 84 .. .. 46.9 1.6 3.0 14 25 United Arab Emirates 32.0 77 81 .. .. 14.4 .. .. 0 0 United Kingdom 51.2 76 81 .. .. 49.9 0.6 0.7 6 20 United States 50.8 75 80 .. .. 48.8 .. 0.1 7 15 World 49.7 w 65 w 69 w 38.1 w .. w .. w 11 w 17 w Low income 49.3 58 60 23.3 .. .. 7 16 Middle income 49.8 68 73 40.5 .. .. 15 15 Lower middle income 49.4 68 73 39.9 .. .. 16 15 Upper middle income 51.4 66 73 44.1 2.8 6.7 .. 15 Low & middle income 49.6 63 67 36.1 .. .. 12 15 High income 50.7 76 82 46.0 .. 3.1 8 22 a. Data are for the most recent year available. b. Hong Kong, China, is classified as a high-income economy and is not included in the East Asia and Pacific aggregate. c. Less than 0.5. d. Refers to women ages 15­49. 36 2006 World Development Indicators Women in development About the data Definitions Despite much progress in recent decades, gender Women's wage work is important for economic · Female population is the percentage of the popu- inequalities remain pervasive in many dimensions of growth and the well-being of families. But restricted lation that is female. · Life expectancy at birth is life--worldwide. But while disparities exist through- access to education and vocational training, heavy the number of years a newborn infant would live if out the world, they are most prevalent in poor devel- workloads at home and in nonpaid domestic and prevailing patterns of mortality at the time of its birth oping countries. Gender inequalities in the alloca- market activities, and labor market discrimination were to stay the same throughout its life. · Pregnant tion of such resources as education, health care, often limit women's participation in paid economic women receiving prenatal care are the percentage nutrition, and political voice matter because of the activities, lower their productivity, and reduce their of women attended at least once during pregnancy strong association with well-being, productivity, and wages. When women are in salaried employment, by skilled health personnel for reasons related to economic growth. This pattern of inequality begins they tend to be concentrated in the nonagricultural pregnancy. · Teenage mothers are the percentage at an early age, with boys routinely receiving a larger sector. However, in many developing countries of women ages 15­19 who already have children share of education and health spending than do girls, women are a large part of agricultural employment, or are currently pregnant. · Women in nonagricul- for example. often as unpaid family workers. Among people who tural sector refers to women wage employees in the Because of biological differences girls are expected are unsalaried, women are more likely than men to nonagricultural sector as a percentage of total non- to experience lower infant and child mortality rates be unpaid family workers, while men are more likely agricultural employment. · Unpaid family workers and to have a longer life expectancy than boys. This than women to be self-employed or employers. There are those who work without pay in a market-oriented biological advantage, however, may be overshad- are several reasons for this. establishment or activity operated by a related per- owed by gender inequalities in nutrition and medical Few women have access to credit markets, capital, son living in the same household. · Women in parlia- interventions, and by inadequate care during preg- land, training, and education, which may be required ments are the percentage of parliamentary seats in a nancy and delivery, so that female rates of illness to start up a business. Cultural norms may prevent single or lower chamber occupied by women. and death sometimes exceed male rates, particularly women from working on their own or from super- during early childhood and the reproductive years. In vising other workers. Also, women may face time high-income countries women tend to outlive men by constraints due to their traditional family respon- four to eight years on average, while in low-income sibilities. Because of biases and misclassification countries the difference is narrower--about two to substantial numbers of employed women may be three years. The difference in child mortality rates underestimated or reported as unpaid family workers (table 2.19) is another good indicator of female even when they work in association or equally with social disadvantage because nutrition and medi- their husbands in the family enterprise. cal interventions are particularly important for the Women are vastly underrepresented in decision- 1­5 age group. Female child mortality rates that are making positions in government, although there is as high as or higher than male child mortality rates some evidence of recent improvement. Gender parity might be indicative of discrimination against girls. in parliamentary representation is still far from being Having a child during the teenage years limits girls' realized. In 2005 women represented 16 percent of opportunities for better education, jobs, and income parliamentarians worldwide, compared with 9 per- and increases the likelihood of divorce and separa- cent in 1987. Without representation at this level, it tion. Pregnancy is more likely to be unintended dur- is difficult for women to influence policy. ing the teenage years, and births are more likely to For information on other aspects of gender, see be premature and are associated with greater risks tables 1.2 (Millennium Development Goals: eradicat- of complications during delivery and of death. In ing poverty and improving lives), 2.3 (employment many countries maternal mortality (tables 1.2 and by economic activity), 2.4 (child labor), 2.5 (unem- 2.16) is a leading cause of death among women of ployment), 2.12 (education efficiency), 2.13 (educa- Data sources reproductive age. Most maternal deaths result from tion completion and outcomes), 2.16 (reproductive Data on female population and life expectancy preventable causes--hemorrhage, infection, and health), 2.18 (health risk factors and future chal- are from the World Bank's population database. complications from unsafe abortions. Prenatal care lenges), and 2.19 (mortality). Data on pregnant women receiving prenatal is essential for recognizing, diagnosing, and promptly care are from United Nations Children's Fund's treating complications that arise during pregnancy. State of the World's Children 2006. Data on teen- In high-income countries most women have access age mothers are from Demographic and Health to health care during pregnancy, but in developing Surveys by Macro International. Data on labor countries an estimated 8 million women suffer preg- force and employment are from the ILO's Key nancy-related complications every year, and over half Indicators of the Labour Market, fourth edition. a million die (WHO 2004). This is reflected in the Data on women in parliaments are from the Inter- differences in maternal mortality ratios between Parliamentary Union. high- and low-income countries. 2006 World Development Indicators 37 Key indicators for other economies Population Surface Population Gross national Gross domestic Life Adult Carbon area density income product expectancy literacy dioxide at birth rate emissions PPPa thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2004 2004 2004 2004b 2004 2004 2004 2003­04 2003­04 2004 2004 2002 American Samoa 57 0.2 285 .. ..c .. .. .. .. .. .. 286 Andorra 66 0.5 140 .. ..d .. .. .. .. .. .. .. Antigua and Barbuda 80 0.4 182 759 9,480 889 11,100 4.1 2.9 75 .. 370 Aruba 99 0.2 521 .. ..d .. .. .. .. .. .. 1,982 Bahamas, The 319 13.9 32 4,684 15,100 5,071 16,350 0.7 ­0.7 70 .. 2,081 Bahrain 716 0.7 1,008 10,288 14,370 14,080 19,670 5.4 3.9 75 .. 21,292 Barbados 269 0.4 625 2,831e 10,530e .. .. .. .. 75 100 1,220 Belize 283 23.0 12 1,115 3,940 1,851 6,550 4.2 0.9 72 .. 788 Bermuda 64 0.1 1,280 .. ..d .. .. .. .. .. .. 498 Bhutan 896 47.0 19 677 760 .. .. 4.9 2.3 64 .. 399 Brunei Darussalam 366 5.8 69 .. ..d .. .. .. .. 77 .. 6,174 Cape Verde 495 4.0 123 852 1,720 2,803f 5,660 f 5.5 3.1 70 76 147 Cayman Islands 44 0.3 169 .. ..d .. .. .. .. .. .. 289 Channel Islands 149 0.2 745 .. ..d .. .. .. .. 79 .. .. Comoros 588 2.2 264 328 560 1,135f 1,930 f 1.9 ­0.2 63 56 84 Cyprus 826 9.3 89 13,633 16,510 18,360 f 22,230 f 3.7 2.5 79 .. 6,661 Djibouti 779 23.2 34 739 950 1,675f 2,150 f 3.0 1.1 53 .. 359 Dominica 71 0.8 95 262 3,670 378 5,290 2.0 1.6 77 .. 121 Equatorial Guinea 492 28.1 18 .. ..c 3,731f 7,580 f 10.0 7.5 43 .. 169 Faeroe Islands 48 1.4 34 .. ..d .. .. .. .. .. .. 652 Fiji 841 18.3 46 2,286 2,720 4,835 5,750 4.1 3.2 68 .. 1,352 French Polynesia 253 4.0 69 .. ..d .. .. .. .. 74 .. 700 Greenland 57 410.5 0 .. ..d .. .. .. .. 69 .. 564 Grenada 106 0.3 311 397 3,750 746 7,050 ­2.8 ­3.8 73 .. 231 Guam 167 0.6 303 .. ..d .. .. .. .. 75 .. 4,089 Guyana 750 215.0 4 765 1,020 3,181f 4,240 f 1.6 1.4 64 .. 1,608 Iceland 292 103.0 3 11,077 37,920 9,455 32,370 5.2 4.3 80 .. 2,213 Isle of Man 77 0.6 135 2,138 23,750 .. .. 6.3 .. .. .. .. About the data Definitions This table shows data for 55 economies--small · Population is based on the de facto definition of of output plus net receipts of primary income (com- economies with populations between 30,000 and population, which counts all residents regardless of pensation of employees and property income) from 1 million and smaller economies if they are members legal status or citizenship--except for refugees not abroad. Data are in current U.S. dollars converted of the World Bank. Where data on gross national permanently settled in the country of asylum, who using the World Bank Atlas method (see Statisti- income (GNI) per capita are not available, the esti- are generally considered part of the population of cal methods). · GNI per capita is gross national mated range is given. For more information on the their country of origin. The values shown are mid- income divided by midyear population. GNI per cap- calculation of GNI (gross national product, or GNP, in year estimates for 2004. See also table 2.1. · Sur- ita in U.S. dollars is converted using the World Bank the System of National Accounts 1968) and purchas- face area is a country's total area, including areas Atlas method. · PPP GNI is gross national income ing power parity (PPP) conversion factors, see About under inland bodies of water and some coastal converted to international dollars using purchasing the data for table 1.1. Since 2000 this table has waterways. · Population density is midyear popu- power parity rates. An international dollar has the excluded France's overseas departments--French lation divided by land area in square kilometers. same purchasing power over GNI as a U.S. dollar Guiana, Guadeloupe, Martinique, and Réunion--for · Gross national income (GNI) is the sum of value has in the United States. · Gross domestic product which GNI and other economic measures are now added by all resident producers plus any product (GDP) is the sum of value added by all resident included in the French national accounts. taxes (less subsidies) not included in the valuation producers plus any product taxes (less subsidies) 38 2006 World Development Indicators Key indicators for other economies Population Surface Population Gross national Gross domestic Life Adult Carbon area density income product expectancy literacy dioxide at birth rate emissions PPPa thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2004 2004 2004 2004b 2004 2004 2004 2003­04 2003­04 2004 2004 2002 Kiribati 98 0.7 134 95 970 .. .. 1.8 0.3 63 .. 29 Liechtenstein 34 0.2 213 .. ..d .. .. .. .. .. .. .. Luxembourg 453 2.6 174 25,559 56,380 27,928 61,610 4.5 3.8 78 .. 9,427 Macao, China 457 .. .. .. ..d .. .. 10.1 9.3 80 .. 1,806 Maldives 321 0.3 1,071 773 2,410 .. .. 10.8 8.1 67 .. 1,030 Malta 401 0.3 1,254 4,834 12,050 7,460 18,590 0.4 ­0.2 79 .. 2,953 Marshall Islands 61 0.2 340 142 2,320 .. .. 1.5 ­2.2 .. .. .. Mayotte 172 0.4 430 .. ..c .. .. .. .. .. .. .. Micronesia, Fed. Sts. 110 0.7 157 252 2,300 .. .. ­3.8 ­4.6 68 .. .. Monaco 33 0.0 16,923 .. ..d .. .. .. .. .. .. .. Netherlands Antilles 181 0.8 226 .. ..d .. .. .. .. 76 97 4,928 New Caledonia 230 18.6 13 .. ..d .. .. .. .. 75 .. 1,821 Northern Mariana Islands 77 0.5 161 .. ..c .. .. .. .. .. .. .. Palau 20 0.5 43 137 6,870 .. .. 2.0 0.5 .. .. 234 Qatar 777 11.0 71 .. ..d .. .. .. .. 74 89 36,391 Samoa 184 2.8 65 338 1,840 1,031f 5,610 f 3.1 2.3 70 99 143 São Tomé and Principe 153 1.0 159 60 390 .. .. 4.5 2.1 63 .. 92 Seychelles 84 0.5 182 685 8,190 1,328 f 15,880 f ­2.0 ­3.0 73 92 535 Solomon Islands 466 28.9 17 263 560 838 1,800 5.5 2.8 63 .. 172 San Marino 28 0.1 463 653 ..d .. .. 2.3 .. .. .. .. St. Kitts and Nevis 47 0.4 131 326 6,980 510 10,910 2.1 2.1 71 .. 114 St. Lucia 164 0.6 268 684 4,180 915 5,590 3.5 1.6 73 .. 377 St. Vincent & Grenadines 118 0.4 304 403 3,400 714 6,030 6.0 5.4 71 .. 183 Suriname 446 163.3 3 997 2,230 .. .. 4.6 3.9 69 .. 2,250 Timor-Leste 887 14.9 60 506 570 .. .. 1.8 ­5.0 56 .. .. Tonga 102 0.8 142 190 1,860 801f 7,850 f 4.3 3.9 72 .. 106 Vanuatu 207 12.2 17 287 1,390 612 f 2,950 f 3.0 1.0 69 .. 84 Virgin Islands (U.S.) 113 0.4 323 .. ..d .. .. .. .. 79 .. 10,241 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be upper middle income ($3,256­$10,065). d. Estimated to be high income ($10,066 or more). e. Refers to GDP and GDP per capita at factor cost. f. The estimate is based on regression; others are extrapolated from the latest International Comparison Program benchmark estimates. not included in the valuation of output. Growth is calculated from constant price GDP data in local currency. · Life expectancy at birth is the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to Data sources stay the same throughout its life. · Adult illiteracy rate is the percentage of adults ages 15 and older The indicators here and throughout the rest of the who cannot, with understanding, read and write a book have been compiled by World Bank Group short, simple statement about their everyday life. staff from primary and secondary sources. More · Carbon dioxide emissions are those stemming information about the indicators and their sources from the burning of fossil fuels and the manufacture can be found in the About the data, Definitions, of cement. They include carbon dioxide produced and Data sources entries that accompany each during consumption of solid, liquid, and gas fuels table in subsequent sections. and gas flaring. 2006 World Development Indicators 39 he world is in the middle of a major demographic transition. Its population continues to grow every year, but the pace of growth has slowed as fertility rates decline. As population growth slows, the age structure of the population is changing, with the share of the young declining and that of the elderly growing. This changing age structure has important implications for economic and social policies and hence for sustainable development. But different countries and regions are at varying stages of this transition, depending on their fertility, mortality, and migration trends, creating a "demographic divide" between countries (Kent and Haub 2005). In much of the industrial world increasing life expectancy and aging populations have coincided with income growth, healthier lifestyles, and fertility rates that are below population replacement levels. For these countries there will be little change in future population size in the absence of inmigration. In fact, large increases in inmigration or in the retirement age would be needed to stabilize the labor force and maintain current labor force to population ratios. In developing countries fertility rates have also declined but remain much higher than in industrial countries, and fertility rates vary considerably across regions: high in Sub-Saharan Africa and the Middle East, but low in East Asia. Except in the transition economies of Eastern Europe, where fertility rates are near or below replacement levels, the population in developing countries will continue to grow well into the twenty-first century, and outmigration will only modestly reduce the population growth rate. Technology, consumption patterns, unequal distribution of wealth, and the choices people and governments make all affect demographic trends. These, in turn, affect social and economic outcomes, and, consequently, what place these countries will take on the world stage in the future. Sub-Saharan African countries are trailing most others in their progress through the demographic transition. And if economic growth continues to lag behind population growth, as was the case in the early 1990s, it will exacerbate poverty in the region. Rapid population growth in Sub-Saharan Africa The challenges facing Sub-Saharan Africa as it strives to meet its development objectives are more daunting than those facing other regions. Its efforts to alleviate poverty, empower women, reduce child mortality, and improve maternal health have been undercut by the AIDS epidemic, by conflict, and by human displacement in the wake of natural disasters. In the past three decades its population has grown faster than that of any other region, doubling between 1975 and 2000 and now growing at 2.5 percent a year. Roughly 47 percent of the Sub-Saharan population is between the ages of 5 and 24, indicating that the population will continue to increase well into the twenty-first century. This large cohort will require substantial increases in future spending on health, education, and care for dependents. 2006 World Development Indicators 41 Has success bred complacency? ductive health (table 2a). Globally, contraceptive prevalence Too little is being said about the challenge of continuing rapid increased from 54 percent in 1990 to 59 percent in 1995 population growth to African development. One possible rea- and to more than 60 percent in 2003 (box 2b). son for this may be that the success of fertility reductions The slowdown in population growth (table 2c) can be traced in other regions and in some African countries has left the to these fertility declines. In Europe and Central Asia women impression that the population problem has been solved now have on average only 1.6 births--too few to replace (Cleland and Sinding 2005). Fertility rates have declined today's population. At the other extreme is Sub-Saharan dramatically in the past 25 years where governments have Africa, with average fertility remaining very high. increased investments in education and in women's repro- Even in Sub-Saharan Africa regional figures mask huge differences across countries (table 2d). In South Africa, Botswana, Zimbabwe, and Lesotho fertility continues to Total fertility rates by region, 1970, 1980, and 2004 decline as a result of successful family planning programs. Region 1970 1980 2004 Of women ages 15­49, 54 percent were using contracep- East Asia & Pacific 5.4 3.0 2.1 tion in Zimbabwe and 48 percent in Botswana, compared Europe & Central Asia 2.5 2.2 1.6 with 14 percent in Niger and 8 percent in Chad in the past Latin America & Caribbean 5.3 4.2 2.4 decade. Even in countries with high fertility, the rates vary Middle East & North Africa 6.7 6.2 3.1 South Asia 6.0 5.2 3.1 by socioeconomic status. In Benin the fertility rate was 7.3 Sub-Saharan Africa 6.8 6.7 5.4 births for women in the lowest asset quintile and 3.8 for High-income 2.5 1.9 1.7 women in the richest quintile. World 4.8 3.7 2.6 Source: World Bank database. Why is fertility still high? Sub-Saharan Africa is becoming fragmented in its fertility declines. There are several reasons for this. The logistical and cultural challenge of delivering family planning programs, Family planning and the fertility transition the often poor quality of health services, ignorance about reproductive health issues, differences in economic status, The use of family planning among married women worldwide rose from and continuing gender inequality all contribute to high fertility 10 percent in 1960 to more than 60 percent in 2003. Due in part to mod- rates. Desired family size, though decreasing slowly over past ern contraception, the decline in fertility and the shift to smaller families decades, remains high--as high as eight children in some occurred faster in developing countries--in only a few decades--than had occurred in industrial countries, where the transition to low fertility began in the 1830s. Crude birth rates were about 37 per 1,000 people in pre-Revolutionary France and 42­43 in the 1850s in the United States, Population growth rates by region (%) before gradually commencing a decline to their current levels of 8 per Region 1950­80 1980­90 1990­2004 1,000 people. East Asia & Pacific 2.0 1.6 1.2 What contributed to smaller families? Organized family planning Europe & Central Asia 1.3 0.9 0.1 programs bringing contraceptive supplies and services to the people, Latin America & Caribbean 2.6 2.0 1.6 along with information campaigns promoting smaller, healthier families. Middle East & North Africa 2.6 3.0 2.1 Studies in the 1990s showed that these programs were responsible South Asia 2.2 2.2 1.8 for about half the fertility decline of developing countries since the Sub-Saharan Africa 2.6 2.9 2.5 1960s. Even couples in remote rural communities in Bangladesh and High-income 1.1 0.7 0.8 Vietnam gained access to modern contraceptives through nationwide World 1.9 1.7 1.4 family planning programs. Contraceptive prevalence is a key determinant of declining fertility. Source: World Bank database. Based on the current use of family planning services, contraceptive rates are not expected to increase rapidly because of Africa's widespread poverty, high rates of illiteracy, largely rural populations, and strong Total fertility rates in selected Sub-Saharan countries, 2004 traditional preferences for large families. However, there is an emerg- Country Fertility rate Country Fertility rate ing preference for spacing and limiting births among married women Niger 7.7 Lesotho 3.5 of reproductive age in African countries, ranging from 10 percent to Uganda 7.1 Zimbabwe 3.4 35 percent. The increased availability of contraception has reduced Guinea-Bissau 7.1 Botswana 3.1 the gap between the number of women who want to limit births and Mali 6.9 South Africa 2.7 those who can in most countries. But in some countries unmet need Burundi 6.8 Mauritius 2.0 remains high. Source: World Bank database. 42 2006 World Development Indicators African countries (table 2e). By contrast, the desired family bers of old and very young and a relatively small working-age size in South Asia is typically fewer than three children. population. But recent data indicate that prevalence among High desired family sizes are associated with high infant pregnant women attending antenatal clinics in Zimbabwe is mortality rates. But when birth rates began to drop in Ban- declining in all age groups. In South Africa, with the largest gladesh and Nepal in the 1980s their infant mortality rates number of infected people, rates of HIV infection among were higher than those in many western and central African pregnant women ages 15­24 have stabilized since 2000. countries (Cleland and Sinding 2005). HIV prevalence among pregnant women has declined coun- Another reason for high fertility rates is that contraceptive trywide in Kenya and Uganda (UNAIDS and WHO 2005). But prevalence rates remain low. For 9 of 20 African countries in western and central Africa there is no consistent evidence that conducted Demographic and Health Surveys between of declining prevalence among pregnant women in recent 1999 and 2005, contraceptive use, including traditional years. And overall in Sub-Saharan Africa the prevalence of methods, was less than 10 percent for women ages 15­49. HIV infections in people ages 15­49 has remained at about Compare that with other regions, where on average 40 per- 7 percent since 2000. So while life expectancy has fallen cent of women were using a method of contraception. In in some cases, fertility remains stubbornly high for many addition to contraceptive use, the method of contraception Sub-Saharan African countries, and high fertility remains the is also important for sustained fertility declines. In countries dominant influence on current and future population growth with low contraceptive prevalence, fewer women use modern and size. methods, further diluting the effect of low contraceptive use In many West African countries, where HIV prevalence on fertility (table 2f). Of 17 African countries that conducted has remained lower than in other regions in Africa, more Demographic and Health Surveys between 2000 and 2004, women die from unsafe abortions than as a result of AIDS in 8 of them use of modern methods was estimated at less (Population Action International 2006). If African nations can than 10 percent. expand the capacity and quality of family planning sevices, Finally, HIV/AIDS has affected fertility and mortality trends that will bring about much needed declines in fertility rates in Sub-Saharan Africa. AIDS-related deaths among working- while strengthening the status of women. Until this happens, age adults in the seven worst AIDS-affected countries will continuing high fertility rates and rapid population growth produce an age structure not seen before, with large num- may prove a more serious obstacle to poverty reduction than will AIDS. What will high fertility mean for Desired family size in selected countries in Sub-Saharan Africa and Sub-Saharan Africa's future population? South Asia, latest year available The population of Sub-Saharan Africa has grown from Desired Desired number of number of 225 million in 1960 to 733 million in 2004. The World Sub-Saharan Africa children South Asia children Bank projects a doubling of the population to 1.4 bil- Cameroon (2004) 5.7 Bangladesh (1999/2000) 2.5 lion by 2050, increasing the region's share of the world Chad (1996/97) 8.3 India (1998/99) 2.6 population from 13 percent today to 20 percent. Fertility Eritrea (2002) 5.8 Nepal (2001) 2.6 Niger (1998) 8.2 rates will remain over 3.5 births per woman until 2025, producing a youthful age structure, with a large proportion Source: Demographic and Health Surveys. of children under 15 years old. Comparisons with South Asia, another region with high fertility, show that the fertil- ity transition in Sub-Saharan Africa lags one generation behind (figure 2g). Contraceptive method mix, selected countries, 2000­04 Very rapid population growth is expected to continue in Contraceptive use several African countries, with the population likely to triple in Country Any method Any modern Burkina Faso, Burundi, Chad, Democratic Republic of Congo, Kenya 39.3 31.5 Republic of Congo, Guinea-Bissau, Liberia, Mali, Niger, and Madagascar 27.1 18.3 Uganda (United Nations 2005). Among the nine countries Benin 18.6 7.2 expected by the United Nations to account for half the world's Burkina Faso 13.8 8.8 projected population increase between 2005 and 2050, four Nigeria 12.6 8.2 are in Sub-Saharan Africa: Democratic Republic of Congo, Bangladesh 58.5 47.6 Ethiopia, Nigeria, and Uganda. Haiti 28.1 22.8 Although fertility rates have started to decline in many Cambodia 23.8 18.8 Sub-Saharan countries, the rates of decline are expected to be more modest and to be achieved over a longer period Source: Demographic and Health Surveys. of time. And they will occur at different paces. For several 2006 World Development Indicators 43 Sub-Saharan Africa's delayed demographic transition Share of deaths in each age group (%) Male Female Sub-Saharan Africa South Asia 75+ 75+ 70­74 70­74 65­69 65­69 60­64 60­64 55­59 55­59 50­54 2000 50­54 45­49 45­49 40­44 40­44 35­39 35­39 30­34 30­34 25­29 25­29 20­24 20­24 15­19 15­19 10­14 10­14 5­9 5­9 0­4 0­4 10 5 0 0 5 10 10 5 0 0 5 10 75+ 75+ 70­74 70­74 65­69 65­69 60­64 60­64 55­59 55­59 50­54 2020 50­54 45­49 45­49 40­44 40­44 35­39 35­39 30­34 30-34 25­29 25-29 20­24 20­24 15­19 15­19 10­14 10­14 5­9 5­9 0­4 0­4 10 5 0 0 5 10 10 5 0 0 5 10 75+ 75+ 70­74 70­74 65­69 65­69 60­64 60­64 55­59 55­59 50­54 2040 50­54 45­49 45­49 40­44 40­44 35­39 35­39 30­34 30­34 25­29 25­29 20­24 20­24 15­19 15­19 10­14 10­14 5­9 5­9 0­4 0­4 10 5 0 0 5 10 10 5 0 0 5 10 Source: World Bank staff estimates. Projected fertility rates in selected African regions Republic of Korea, and Thailand. Other countries with mod- 2005­ 2010­ 2015­ 2020­ 2025­ 2030­ 2035­ erate growth rates--such as Bangladesh, Brazil, India, and Region 10 15 20 25 30 35 40 Indonesia, which have had impressive fertility declines--still Western Africa 5.4 4.8 4.4 3.9 3.5 3.2 3.0 Central Africa 6.1 5.8 5.4 5.0 4.5 4.1 3.6 have considerable momentum for future growth due to a Southern Africa 2.7 2.5 2.4 2.3 2.2 2.1 2.0 young age structure. Each demographic situation is associated with its own Source: United Nations 2005. social, economic, environmental, and political challenge (box 2i). What is of concern about the demographic divide is not the differences in population growth rates, but the dis- decades fertility declines in western and central Africa are parities in living standards, personal well-being, and future expected to lag behind those that have already taken place prospects associated with these trends. in southern Africa (table 2h). People in Japan and Nigeria, with populations of similar size in 2004 but at opposing ends of the divide, have starkly differ- What does high fertility mean for ent lives today--and they face very different futures (table 2j). Africa's development? In Japan the elderly dependency ratio is expected to increase As average population growth slowed globally over the past dramatically, straining government budgets because of higher half century, the range of national and regional demographic spending on pensions, health care, and long-term residential experiences widened. Growth rates remained high in many care. Econometric models suggest that the projected decline African countries such as Burkina Faso and Chad, while they in the working-age population could result in lower savings plummeted in countries in other regions, including Italy, the and investment rates and slower GDP growth (IMF 2004). 44 2006 World Development Indicators By contrast, in Nigeria, a microcosm of Sub-Saharan Africa, per skills and find productive employment. Its inability to deal capita growth could be boosted by the increase in the working- with a higher burden of infectious diseases, lower education age population. With 36 percent of its population under age levels, and limited investment in health infrastructure could 15 in 2025, Nigeria has a considerable momentum for future result in very different economic outcomes. Without invest- growth well into the twenty-first century. This growth depends, ments in physical stocks and human capital, Nigeria's popu- however, on the country pursuing sound economic and social lation growth will exert an unsustainable demand for public- policies to enable the large wave of potential workers to acquire sector-provided health, education, and other services. Population projections--trends and uncertainty The demographic divide: Nigeria and Japan Nigeria Japan Future trends in population size, age structure, births, deaths, and other 2004 2025 2004 2025 demographic variables are of interest to policymakers, government plan- Population (millions) 137 205 128 120 ners, and industry strategists. The reason: population forecasts can Total fertility rate per woman 5.6 3.3 1.3 1.8 Population ages 0­14 (percent) 45.1 36.2 14.8 12.5 imply a wide range of consequences for society and its environment. Population ages 65 and older (percent) 2.7 3.4 16.5 28.1 Country projections became more accurate over the 1950s and 1960s, Life expectancy at birth (years) 45 52 82 84 as demographic data improved, but since then there have been few Infant mortality rate (per 1,000 live births) 98 72 3 3 significant improvements. Adults with HIV/AIDS (percent ages 15­49) 5.4 .. 0.1 .. Fertility, mortality, and migration are the components of population Health expenditure per capita 60 .. 2,476 .. GNI per capita 430 .. 37,060 .. growth. While broad trends can be discerned and projected into the future with reasonable confidence, substantial uncertainty is attached Source: World Bank database. to the specific trend for any country or region. Uncertainty arises in part because the present demographic situation in any country is not known perfectly. But the main cause of uncertainty is that future trends in fer- tility, mortality, and migration are subject to unpredictable influences. Future economic development; societal, cultural, epidemiological, and environmental changes; or progress in science and technology cannot be predicted. Uncertainty also arises from the fact that humans can influ- ence the future through deliberate policy intervention, such as investing heavily in family planning and reproductive services. Some demographers argue that population forecasts should not go beyond a horizon of 30­35 years, due to the rapid increase in uncer- tainty beyond this point. Others note, however, that if the forecast carries an appropriate indication of the range of uncertainty, users can decide when the informational content of the forecast ceases to be useful. Source: NRC 2000. 2006 World Development Indicators 45 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2004 2020 1990­2004 2004­20 2004 2004 2004 2004 2004 2004 2004 Afghanistan 14.6 .. .. .. .. .. .. .. .. .. .. .. Albania 3.3 3.1 3.4 ­0.4 0.6 27.6 64.3 8.1 0.4 0.1 6 17 Algeria 25.3 32.4 40.6 1.8 1.4 30.4 65.1 4.5 0.5 0.1 5 21 Angola 10.5 15.5 23.8 2.8 2.7 46.6 51.0 2.5 0.9 0.0a 22 48 Argentina 32.6 38.4 44.5 1.2 0.9 26.7 63.1 10.1 0.4 0.2 8 18 Armenia 3.5 3.0 3.0 ­1.1 ­0.2 21.7 66.4 11.9 0.3 0.2 9 12 Australia 17.1 20.1 23.3 1.2 0.9 20.0 67.5 12.6 0.3 0.2 7 13 Austria 7.7 8.2 8.3 0.4 0.1 15.8 67.8 16.4 0.2 0.2 9 10 Azerbaijan 7.2 8.3 9.4 1.1 0.8 26.8 66.2 6.9 0.4 0.1 6 16 Bangladesh 104.0 139.2 181.2 2.1 1.6 35.9 60.5 3.6 0.6 0.1 8 27 Belarus 10.2 9.8 8.9 ­0.3 ­0.6 15.8 69.7 14.6 0.2 0.2 15 9 Belgium 10.0 10.4 10.6 0.3 0.1 16.9 65.6 17.5 0.3 0.3 10 11 Benin 5.2 8.2 12.7 3.3 2.8 44.5 52.8 2.7 0.8 0.1 12 41 Bolivia 6.7 9.0 11.6 2.1 1.6 38.5 57.0 4.5 0.7 0.1 8 29 Bosnia and Herzegovina 4.3 3.9 3.8 ­0.7 ­0.1 16.9 69.6 13.5 0.2 0.2 9 9 Botswana 1.4 1.8 1.7 1.5 ­0.4 37.9 58.9 3.2 0.6 0.1 26 26 Brazil 149.4 183.9 219.2 1.5 1.1 28.1 65.9 6.0 0.4 0.1 7 20 Bulgaria 8.7 7.8 6.9 ­0.8 ­0.8 14.1 69.2 16.8 0.2 0.2 14 9 Burkina Faso 8.5 12.8 20.3 2.9 2.9 47.4 49.8 2.8 1.0 0.1 17 47 Burundi 5.7 7.3 12.3 1.8 3.3 45.5 51.7 2.8 0.9 0.1 18 45 Cambodia 9.7 13.8 18.6 2.5 1.9 37.7 59.0 3.4 0.6 0.1 11 30 Cameroon 11.7 16.0 20.4 2.3 1.5 41.6 54.7 3.7 0.8 0.1 17 35 Canada 27.8 32.0 36.4 1.0 0.8 17.9 69.1 13.0 0.3 0.2 7 10 Central African Republic 3.0 4.0 5.0 2.0 1.4 43.1 52.9 4.0 0.8 0.1 22 37 Chad 6.1 9.4 14.9 3.2 2.8 47.2 49.7 3.1 0.9 0.1 20 48 Chile 13.2 16.1 18.6 1.4 0.9 25.5 66.6 7.9 0.4 0.1 5 16 China 1,135.2 1,296.2 1,423.9 0.9 0.6 22.0 70.5 7.5 0.3 0.1 6 12 Hong Kong, China 5.7 6.9 8.1 1.3 1.0 14.8 73.4 11.8 0.2 0.2 5 7 Colombia 35.0 44.9 55.0 1.8 1.3 31.4 63.6 5.0 0.5 0.1 5 21 Congo, Dem. Rep. 37.8 55.9 90.0 2.8 3.0 47.2 50.1 2.7 0.9 0.1 20 50 Congo, Rep. 2.5 3.9 6.4 3.2 3.1 47.0 50.1 2.9 0.9 0.1 13 44 Costa Rica 3.1 4.3 5.3 2.3 1.3 29.0 65.3 5.7 0.4 0.1 4 17 Côte d'Ivoire 12.7 17.9 23.3 2.5 1.7 42.1 54.6 3.2 0.8 0.1 17 37 Croatia 4.8 4.4 4.4 ­0.5 ­0.1 15.8 67.2 17.0 0.2 0.3 11 9 Cuba 10.5 11.2 11.4 0.5 0.1 19.5 70.0 10.5 0.3 0.2 7 11 Czech Republic 10.4 10.2 9.9 ­0.1 ­0.2 15.0 71.0 14.1 0.2 0.2 11 10 Denmark 5.1 5.4 5.6 0.4 0.2 18.8 66.3 14.9 0.3 0.2 10 12 Dominican Republic 7.1 8.8 10.7 1.5 1.2 33.1 62.8 4.1 0.5 0.1 6 24 Ecuador 10.3 13.0 16.0 1.7 1.3 32.8 61.5 5.7 0.5 0.1 5 23 Egypt, Arab Rep. 55.7 72.6 94.8 1.9 1.7 33.9 61.4 4.7 0.6 0.1 6 26 El Salvador 5.1 6.8 8.5 2.0 1.5 34.3 60.4 5.3 0.6 0.1 6 24 Eritrea 3.0 4.2 6.6 2.4 2.8 44.8 52.9 2.3 0.8 0.0a 11 39 Estonia 1.6 1.3 1.3 ­1.1 ­0.4 15.6 68.1 16.3 0.2 0.2 13 10 Ethiopia 51.2 70.0 107.7 2.2 2.7 44.8 52.3 2.9 0.9 0.1 19 40 Finland 5.0 5.2 5.4 0.3 0.2 17.5 66.8 15.7 0.3 0.2 9 11 France 56.7 60.4 63.0 0.4 0.3 18.2 65.2 16.6 0.3 0.3 8 13 Gabon 1.0 1.4 1.7 2.5 1.4 40.5 55.1 4.4 0.7 0.1 13 30 Gambia, The 0.9 1.5 2.1 3.3 2.1 40.3 56.0 3.7 0.7 0.1 12 35 Georgia 5.5 4.5 4.1 ­1.4 ­0.7 19.5 66.5 14.1 0.3 0.2 11 11 Germany 79.4 82.5 82.3 0.3 0.0a 14.6 67.2 18.3 0.2 0.3 10 9 Ghana 15.5 21.7 28.8 2.4 1.8 39.5 56.9 3.6 0.7 0.1 11 31 Greece 10.2 11.1 11.2 0.6 0.1 14.4 67.6 18.0 0.2 0.3 9 9 Guatemala 8.9 12.3 17.5 2.3 2.2 43.5 52.3 4.3 0.8 0.1 6 35 Guinea 6.2 9.2 13.4 2.8 2.3 43.8 52.7 3.5 0.8 0.1 13 41 Guinea-Bissau 1.0 1.5 2.5 3.0 3.0 47.4 49.5 3.1 1.0 0.1 20 50 Haiti 6.9 8.4 10.3 1.4 1.3 38.0 58.1 4.0 0.7 0.1 13 30 46 2006 World Development Indicators Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2004 2020 1990­2004 2004­20 2004 2004 2004 2004 2004 2004 2004 Honduras 4.9 7.0 9.5 2.6 1.9 39.7 56.5 3.8 0.7 0.1 6 29 Hungary 10.4 10.1 9.6 ­0.2 ­0.3 16.0 68.9 15.1 0.2 0.2 13 9 India 849.5 1,079.7 1,332.0 1.7 1.3 32.5 62.3 5.2 0.5 0.1 8 24 Indonesia 178.2 217.6 255.9 1.4 1.0 28.6 66.0 5.4 0.4 0.1 7 20 Iran, Islamic Rep. 54.4 67.0 85.0 1.5 1.5 29.8 65.7 4.5 0.5 0.1 5 19 Iraq 18.5 .. .. .. .. .. .. .. .. .. .. .. Ireland 3.5 4.1 4.9 1.1 1.2 20.3 68.7 10.9 0.3 0.2 7 16 Israel 4.7 6.8 8.3 2.7 1.2 27.9 62.0 10.1 0.4 0.2 6 21 Italy 56.7 57.6 57.1 0.1 0.0a 14.1 66.3 19.7 0.2 0.3 9 10 Jamaica 2.4 2.6 2.8 0.7 0.3 31.7 60.8 7.6 0.5 0.1 6 18 Japan 123.5 127.8 126.7 0.2 ­0.1 14.1 66.7 19.2 0.2 0.3 9 9 Jordan 3.2 5.4 7.6 3.9 2.1 37.6 59.3 3.1 0.6 0.1 4 27 Kazakhstan 16.3 15.0 14.9 ­0.6 0.0a 23.9 67.8 8.3 0.4 0.1 10 15 Kenya 23.4 33.5 49.6 2.5 2.5 42.9 54.2 2.8 0.8 0.1 15 39 Korea, Dem. Rep. 19.7 22.4 23.7 0.9 0.4 25.4 68.0 6.5 0.4 0.1 11 16 Korea, Rep. 42.9 48.1 49.4 0.8 0.2 19.1 71.9 9.0 0.3 0.1 5 9 Kuwait 2.1 2.5 3.7 1.0 2.5 24.5 73.8 1.7 0.3 0.0a 2 19 Kyrgyz Republic 4.4 5.1 6.1 1.0 1.1 32.1 61.8 6.1 0.5 0.1 7 22 Lao PDR 4.1 5.8 8.0 2.4 2.0 41.2 55.1 3.6 0.7 0.1 12 35 Latvia 2.7 2.3 2.1 ­1.0 ­0.5 15.2 68.1 16.6 0.2 0.2 14 9 Lebanon 2.7 3.5 4.1 1.8 1.0 29.1 63.6 7.3 0.5 0.1 7 19 Lesotho 1.6 1.8 1.7 0.9 ­0.3 39.0 55.8 5.2 0.7 0.1 25 28 Liberia 2.1 3.2 5.0 3.0 2.8 47.0 50.8 2.2 0.9 0.0a 21 50 Libya 4.3 5.7 7.5 2.0 1.7 30.4 65.7 4.0 0.5 0.1 4 23 Lithuania 3.7 3.4 3.2 ­0.5 ­0.4 17.4 67.4 15.2 0.3 0.2 12 9 Macedonia, FYR 1.9 2.0 2.1 0.4 0.1 20.1 69.0 10.9 0.3 0.2 9 12 Madagascar 12.0 18.1 26.6 2.9 2.4 44.2 52.7 3.1 0.8 0.1 12 39 Malawi 9.5 12.6 17.8 2.1 2.2 47.3 49.7 3.0 1.0 0.1 21 43 Malaysia 17.8 24.9 31.5 2.4 1.5 32.8 62.8 4.5 0.5 0.1 5 22 Mali 8.9 13.1 20.9 2.8 2.9 48.3 49.0 2.7 1.0 0.1 17 49 Mauritania 2.0 3.0 4.5 2.7 2.5 43.1 53.5 3.4 0.8 0.1 14 41 Mauritius 1.1 1.2 1.4 1.1 0.7 24.9 68.6 6.5 0.4 0.1 7 16 Mexico 83.2 103.8 124.7 1.6 1.1 31.6 63.2 5.2 0.5 0.1 5 19 Moldova 4.4 4.2 4.1 ­0.2 ­0.2 19.1 70.9 10.0 0.3 0.1 12 10 Mongolia 2.1 2.5 3.1 1.3 1.4 31.3 65.0 3.8 0.5 0.1 6 22 Morocco 23.9 29.8 38.3 1.6 1.6 31.5 63.8 4.8 0.5 0.1 6 23 Mozambique 13.4 19.4 25.5 2.6 1.7 44.1 52.6 3.3 0.8 0.1 20 39 Myanmar 40.8 50.0 57.1 1.5 0.8 30.1 65.0 4.9 0.5 0.1 10 20 Namibia 1.4 2.0 2.4 2.6 1.1 42.1 54.4 3.4 0.8 0.1 6 23 Nepal 19.1 26.6 35.7 2.4 1.8 39.5 56.9 3.6 0.7 0.1 8 29 Netherlands 15.0 16.3 17.0 0.6 0.3 18.3 67.7 14.0 0.3 0.2 8 12 New Zealand 3.4 4.1 4.4 1.2 0.5 21.7 66.1 12.2 0.3 0.2 7 14 Nicaragua 4.0 5.4 7.2 2.2 1.8 39.5 57.2 3.3 0.7 0.1 5 28 Niger 8.5 13.5 22.6 3.3 3.2 49.0 49.0 2.0 1.0 0.0a 21 54 Nigeria 90.6 128.7 175.8 2.5 1.9 44.5 52.5 3.0 0.8 0.1 19 41 Norway 4.2 4.6 5.0 0.6 0.5 19.7 65.3 15.0 0.3 0.2 9 12 Oman 1.8 2.5 3.5 2.3 2.0 34.9 62.7 2.5 0.6 0.0a 3 25 Pakistan 108.0 152.1 211.7 2.4 2.1 38.9 57.3 3.8 0.7 0.1 7 27 Panama 2.4 3.2 4.0 2.0 1.5 30.6 63.5 5.9 0.5 0.1 5 22 Papua New Guinea 4.1 5.8 7.6 2.4 1.7 40.7 56.9 2.4 0.7 0.0a 10 30 Paraguay 4.2 6.0 8.3 2.5 2.0 38.0 58.3 3.7 0.7 0.1 5 29 Peru 21.8 27.6 34.2 1.7 1.4 32.7 62.1 5.2 0.5 0.1 6 23 Philippines 61.1 81.6 103.3 2.1 1.5 35.7 60.5 3.8 0.6 0.1 5 25 Poland 38.1 38.2 37.7 0.0a ­0.1 16.8 70.3 12.8 0.2 0.2 10 9 Portugal 9.9 10.5 10.9 0.4 0.2 15.9 67.2 16.9 0.2 0.3 10 10 Puerto Rico 3.5 3.9 4.2 0.7 0.5 22.5 65.6 11.9 0.3 0.2 8 14 2006 World Development Indicators 47 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2004 2020 1990­2004 2004­20 2004 2004 2004 2004 2004 2004 2004 Romania 23.2 21.7 20.4 ­0.5 ­0.4 15.9 69.5 14.6 0.2 0.2 12 10 Russian Federation 148.3 143.8 133.1 ­0.2 ­0.5 15.7 70.7 13.6 0.2 0.2 16 11 Rwanda 7.1 8.9 12.4 1.6 2.1 44.1 53.5 2.4 0.8 0.0a 18 41 Saudi Arabia 16.4 24.0 34.0 2.7 2.2 37.8 59.4 2.9 0.6 0.0a 4 27 Senegal 8.0 11.4 16.0 2.5 2.1 43.0 53.9 3.1 0.8 0.1 11 36 Serbia and Montenegro 10.5b 8.1 10.3 0.1c 1.5 18.6 67.4 14.0 0.3 0.2 14 11 Sierra Leone 4.1 5.3 7.7 1.9 2.3 42.8 53.9 3.3 0.8 0.1 23 46 Singapore 3.0 4.2 5.0 2.4 1.0 20.2 71.6 8.2 0.3 0.1 4 10 Slovak Republic 5.3 5.4 5.4 0.1 0.0a 17.2 71.1 11.7 0.2 0.2 10 10 Slovenia 2.0 2.0 1.9 0.0a ­0.3 14.2 70.4 15.4 0.2 0.2 9 9 Somalia 6.7 8.0 12.3 1.3 2.7 44.1 53.3 2.6 0.8 0.0a 17 45 South Africa 35.2 45.5 48.1 1.8 0.3 32.8 63.1 4.1 0.5 0.1 22 24 Spain 38.8 42.7 44.4 0.7 0.2 14.3 69.2 16.5 0.2 0.2 9 11 Sri Lanka 17.0 19.4 22.9 0.9 1.0 24.5 68.4 7.1 0.4 0.1 6 19 Sudan 26.1 35.5 47.5 2.2 1.8 39.5 56.9 3.6 0.7 0.1 11 32 Swaziland 0.8 1.1 1.0 2.7 ­0.8 41.6 55.0 3.4 0.8 0.1 20 34 Sweden 8.6 9.0 9.5 0.4 0.3 17.7 65.1 17.1 0.3 0.3 10 11 Switzerland 6.7 7.4 7.4 0.7 0.0a 16.8 67.6 15.7 0.2 0.2 8 10 Syrian Arab Republic 12.8 18.6 26.0 2.6 2.1 37.4 59.5 3.1 0.6 0.1 3 28 Tajikistan 5.3 6.4 8.2 1.4 1.5 39.7 56.5 3.8 0.7 0.1 7 29 Tanzania 26.2 37.6 49.3 2.6 1.7 42.9 53.9 3.2 0.8 0.1 17 37 Thailand 54.6 63.7 71.0 1.1 0.7 24.1 69.0 6.9 0.3 0.1 7 16 Togo 4.0 6.0 8.7 3.0 2.4 43.7 53.2 3.1 0.8 0.1 12 38 Trinidad and Tobago 1.2 1.3 1.3 0.5 0.2 22.0 70.7 7.2 0.3 0.1 8 14 Tunisia 8.2 9.9 11.6 1.4 1.0 26.7 67.1 6.2 0.4 0.1 6 17 Turkey 56.2 71.7 86.8 1.7 1.2 29.5 65.1 5.4 0.5 0.1 6 19 Turkmenistan 3.7 4.8 5.8 1.9 1.2 32.7 62.7 4.7 0.5 0.1 8 22 Uganda 17.8 27.8 50.6 3.2 3.7 50.4 47.1 2.5 1.1 0.1 15 50 Ukraine 51.9 47.5 39.6 ­0.6 ­1.1 15.4 68.8 15.8 0.2 0.2 16 9 United Arab Emirates 1.8 4.3 6.1 6.4 2.2 22.4 76.5 1.1 0.3 0.0a 1 16 United Kingdom 57.6 59.9 62.5 0.3 0.3 18.2 65.9 15.9 0.3 0.2 10 12 United States 249.6 293.7 338.4 1.2 0.9 20.9 66.8 12.3 0.3 0.2 8 14 Uruguay 3.1 3.4 3.8 0.7 0.6 24.4 62.4 13.2 0.4 0.2 9 15 Uzbekistan 20.5 26.2 32.5 1.7 1.3 34.0 61.3 4.7 0.6 0.1 7 21 Venezuela, RB 19.8 26.1 33.5 2.0 1.5 31.7 63.4 4.9 0.5 0.1 5 22 Vietnam 66.2 82.2 99.9 1.5 1.2 30.3 64.2 5.5 0.5 0.1 6 18 West Bank and Gaza 2.0 3.5 5.7 4.1 3.0 45.7 51.1 3.1 0.9 0.1 4 35 Yemen, Rep. 12.1 20.3 32.7 3.7 3.0 46.7 51.0 2.3 0.9 0.0a 8 40 Zambia 8.4 11.5 15.1 2.3 1.7 46.0 51.0 3.0 0.9 0.1 22 41 Zimbabwe 10.6 12.9 14.1 1.4 0.6 40.5 55.9 3.6 0.7 0.1 23 30 World 5,256.3 s 6,365.0 s 7,573.5 s 1.4 w 1.1 w 28.5 w 64.2 w 7.3 w 0.4 w 0.1 w 9w 20 w Low income 1,763.4 2,343.0 3,084.4 2.0 1.7 36.8 58.9 4.3 0.6 0.1 11 29 Middle income 2,589.4 3,017.8 3,427.1 1.1 0.8 25.4 67.4 7.2 0.4 0.1 7 16 Lower middle income 2,082.8 2,441.6 2,796.9 1.1 0.8 25.5 67.6 6.8 0.4 0.1 7 16 Upper middle income 506.6 576.2 630.2 0.9 0.6 24.9 66.3 8.8 0.4 0.1 10 16 Low & middle income 4,352.8 5,360.8 6,511.5 1.5 1.2 30.4 63.7 5.9 0.5 0.1 9 22 East Asia & Pacific 1,596.1 1,869.5 2,107.6 1.1 0.7 24.5 68.7 6.8 0.4 0.1 7 15 Europe & Central Asia 466.1 472.5 476.9 0.1 0.1 20.2 68.2 11.6 0.3 0.2 12 13 Latin America & Carib. 437.6 545.9 660.3 1.6 1.2 30.4 63.6 5.9 0.5 0.1 6 21 Middle East & N. Africa 225.5 300.3 399.1 2.0 1.8 34.0 61.8 4.2 0.6 0.1 6 25 South Asia 1,113.1 1,446.8 1,834.9 1.9 1.5 33.8 61.4 4.8 0.6 0.1 8 25 Sub-Saharan Africa 514.4 725.8 1,032.7 2.5 2.2 43.7 53.2 3.1 0.8 0.1 18 40 High income 903.5 1,004.2 1,062.0 0.8 0.4 18.4 67.0 14.6 0.3 0.2 8 12 Europe EMU 293.3 309.3 315.7 0.4 0.1 15.6 66.9 17.5 0.2 0.3 9 10 a. Less than 0.05. b. Includes population of Kosovo until 1999. c. Data are for 1990­99. 48 2006 World Development Indicators Population dynamics About the data Definitions Population estimates are usually based on national Dependency ratios take into account variations in · Total population of an economy includes all resi- population censuses, but the frequency and quality the different age groups: the proportions of children, dents regardless of legal status or citizenship-- of these vary by country. Most countries conduct a elderly people, and working-age people in the popula- except for refugees not permanently settled in the complete enumeration no more than once a decade. tion. Separate calculations of young-age and old-age country of asylum, who are generally considered part Estimates for the years before and after the cen- dependency suggest the burden of dependency that of the population of their country of origin. The values suses are interpolations or extrapolations based the working-age population must bear in relation to shown are midyear estimates for 1990 and 2004 on demographic models. Errors and undercounting children and the elderly. But dependency ratios show and projections for 2020. · Average annual popula- occur even in high-income countries; in developing only the age composition of a population, not eco- tion growth rate is the exponential change for the countries such errors may be substantial because nomic dependency. Some children and elderly people period indicated. See Statistical methods for more of limits in the transport, communications, and are part of the labor force, and many working-age information. · Population age composition refers other resources required to conduct and analyze a people are not. to the percentage of the total population that is in full census. The vital rates shown in the table are based on specific age groups. · Dependency ratio is the ratio The quality and reliability of official demographic data derived from birth and death registration sys- of dependents--people younger than 15 or older data are also affected by the public trust in the gov- tems, censuses, and sample surveys conducted by than 64--to the working-age population--those ernment, the government's commitment to full and national statistical offices and other organizations, ages 15­64. · Crude death rate and crude birth accurate enumeration, the confidentiality and protec- or on demographic analysis. The estimates for 2004 rate are the number of deaths and the number of live tion against misuse accorded to census data, and for many countries are national projections based births occurring during the year, per 1,000 popula- the independence of census agencies from undue on extrapolations of levels and trends measured in tion, estimated at midyear. Subtracting the crude political influence. Moreover, the international com- earlier years. death rate from the crude birth rate provides the rate parability of population indicators is limited by dif- Vital registers are the preferred source of these of natural increase, which is equal to the population ferences in the concepts, definitions, data collec- data, but in many developing countries systems for growth rate in the absence of migration. tion procedures, and estimation methods used by registering births and deaths do not exist or are national statistical agencies and other organizations incomplete because of deficiencies in the coverage that collect population data. of events or of geographic areas. Many developing Of the 152 economies listed in the table, 119 countries carry out special household surveys that (about 78 percent) conducted a census between estimate vital rates by asking respondents about 1995 and 2005. The currentness of a census, along births and deaths in the recent past. Estimates with the availability of complementary data from sur- derived in this way are subject to sampling errors veys or registration systems, is one of many objective as well as errors due to inaccurate recall by the ways to judge the quality of demographic data. In respondents. some European countries registration systems offer The United Nations Statistics Division monitors complete information on population in the absence the completeness of vital registration systems. The of a census. See Primary data documentation for share of countries with at least 90 percent complete the most recent census or survey year and for the vital registration increased from 45 percent in 1988 completeness of registration. to 54 percent in 2004. Still, some of the most popu- Current population estimates for developing coun- lous developing countries--China, India, Indonesia, tries that lack recent census-based data, and pre- Brazil, Pakistan, Bangladesh, Nigeria--do not have and post-census estimates for countries with census complete vital registration systems. Fewer than 30 Data sources data, are provided by the United Nations Population percent of births and deaths and fewer than 40 per- Division, national statistical offices, and other agen- cent of infant deaths worldwide are thought to be The World Bank's population estimates are com- cies. The standard estimation method requires fer- registered and reported. piled and produced by its Human Development tility, mortality, and net migration data, which are International migration is the only other factor Network and Development Data Group in consulta- often collected from sample surveys, some of which besides birth and death rates that directly deter- tion with its operational staff and country offices. may be small or limited in coverage. The population mines a country's population growth. From 1990 Important inputs to the World Bank's demographic estimates are the product of demographic modeling to 2000 the number of migrants in high-income work come from the United Nations Population and so are susceptible to biases and errors because countries increased by 23 million. About 175 mil- Division's World Population Prospects: The 2004 of shortcomings in the model as well as in the data. lion people currently live outside their home country, Revision; census reports and other statistical Population projections are made using the cohort accounting for about 3 percent of the world's popula- publications from national statistical offices; component method. tion. Estimating international migration is difficult. household surveys conducted by national agen- The growth rate of the total population conceals At any time many people are located outside their cies, Macro International, and the U.S. Centers for the fact that different age groups may grow at very home country as tourists, workers, or refugees or Disease Control and Prevention; Eurostat, Demo- different rates. In many developing countries the pop- for other reasons. Standards relating to the dura- graphic Statistics (various years); Centro Latino- ulation under age 15 was previously growing rapidly tion and purpose of international moves that qualify americano de Demografía, Boletín Demográfico but is now starting to shrink. Previously high fertility as migration vary, and accurate estimates require (various years); and U.S. Bureau of the Census, rates and declining mortality rates are now reflected information on flows into and out of countries that International Database. in the larger share of the working-age population. is difficult to collect. 2006 World Development Indicators 49 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2004 1990 2004 1990 2004 1990­2004 1990 2004 Afghanistan 88.7 .. 38.2 .. 5.0 .. .. 28.4 .. Albania 86.3 76.3 63.3 55.1 1.6 1.4 ­1.0 40.2 42.1 Algeria 81.0 83.3 23.7 37.0 7.2 12.9 4.2 22.6 30.2 Angola 90.9 92.2 76.0 75.7 4.5 6.8 2.9 46.4 45.8 Argentina 84.7 82.4 43.5 59.9 13.0 17.9 2.3 34.4 42.4 Armenia 89.7 66.4 76.7 55.6 1.9 1.3 ­3.0 47.7 48.9 Australia 84.4 81.0 61.5 67.0 8.4 10.2 1.3 41.3 45.3 Austria 80.1 77.6 55.3 63.3 3.5 3.9 0.8 40.8 44.4 Azerbaijan 80.6 78.0 68.5 65.5 3.3 4.0 1.4 47.4 47.4 Bangladesh 89.8 88.1 64.5 55.4 46.9 62.4 2.0 40.2 37.0 Belarus 82.2 72.5 72.4 66.5 5.3 4.8 ­0.7 48.6 49.2 Belgium 71.3 72.7 46.2 56.9 3.9 4.5 0.9 39.0 43.2 Benin 90.0 86.7 59.2 55.0 2.0 3.2 3.3 40.8 38.5 Bolivia 80.9 84.0 49.9 63.9 2.5 4.0 3.4 39.2 43.5 Bosnia and Herzegovina 82.4 78.3 66.1 69.4 2.3 2.0 ­0.7 44.7 47.8 Botswana 76.0 68.6 58.9 47.2 0.5 0.6 1.3 45.2 41.9 Brazil 88.8 84.1 47.6 60.6 62.4 89.9 2.6 35.0 42.5 Bulgaria 77.8 63.0 72.3 53.3 4.4 3.1 ­2.5 48.0 46.2 Burkina Faso 92.1 90.4 79.3 79.5 3.8 5.6 2.8 46.3 46.6 Burundi 90.7 93.0 91.8 92.8 2.8 3.7 1.9 52.6 52.2 Cambodia 86.7 81.3 81.0 78.0 4.4 6.6 2.9 52.6 51.5 Cameroon 83.5 81.6 58.2 54.1 4.4 6.2 2.4 41.5 39.8 Canada 84.9 82.5 68.3 72.4 14.7 17.4 1.2 44.0 46.2 Central African Republic 89.4 89.4 71.7 70.9 1.4 1.8 2.1 47.0 46.2 Chad 79.0 77.4 64.7 66.0 2.3 3.6 3.0 46.0 46.8 Chile 80.9 76.7 35.2 40.6 5.0 6.4 1.8 30.5 34.6 China 88.9 88.0 79.1 76.2 650.1 768.0 1.2 44.8 44.6 Hong Kong, China 85.5 81.4 53.0 61.2 2.9 3.6 1.7 36.3 45.8 Colombia 85.0 85.3 48.5 65.0 14.1 21.8 3.1 36.9 44.0 Congo, Dem. Rep. 91.2 91.1 62.6 63.1 15.0 22.3 2.8 41.6 41.2 Congo, Rep. 86.3 86.6 57.7 56.2 1.0 1.5 3.0 41.5 40.3 Costa Rica 87.6 84.9 35.3 47.2 1.2 1.9 3.5 27.6 34.5 Côte d'Ivoire 90.3 89.1 44.5 40.2 4.6 6.7 2.6 30.2 29.3 Croatia 76.9 71.4 55.0 57.4 2.2 2.0 ­0.9 42.1 44.8 Cuba 79.5 82.4 43.5 50.5 4.5 5.3 1.2 34.8 37.3 Czech Republic 82.2 77.7 74.1 63.7 5.5 5.2 ­0.4 47.4 44.9 Denmark 87.1 82.9 77.6 74.3 2.9 2.8 ­0.2 46.1 46.6 Dominican Republic 85.6 84.1 37.8 47.5 2.6 3.8 2.5 29.6 35.3 Ecuador 85.9 85.2 33.6 62.7 3.7 6.2 3.7 27.8 42.0 Egypt, Arab Rep. 76.7 76.6 27.6 21.6 16.6 22.3 2.1 26.3 21.8 El Salvador 81.9 79.5 53.5 49.7 2.0 2.7 2.3 41.2 39.6 Eritrea 92.6 90.7 63.1 59.9 1.2 1.7 2.4 42.4 41.1 Estonia 83.0 73.7 76.0 64.3 0.9 0.7 ­1.8 49.9 49.3 Ethiopia 92.3 90.9 74.5 73.5 22.6 30.9 2.2 44.9 44.9 Finland 79.0 76.8 72.2 72.7 2.6 2.6 0.2 47.2 47.8 France 75.0 73.8 57.0 62.4 24.8 26.9 0.6 43.3 45.9 Gabon 85.5 84.0 65.5 64.2 0.4 0.6 2.7 43.9 43.4 Gambia, The 86.2 86.7 63.3 60.4 0.4 0.6 3.5 43.4 41.7 Georgia 78.2 76.2 79.1 53.7 2.9 2.3 ­1.7 52.3 43.9 Germany 81.4 79.2 56.8 66.5 38.3 40.8 0.5 40.4 44.9 Ghana 80.5 75.8 77.5 72.0 6.7 9.6 2.5 48.9 48.1 Greece 76.7 78.6 43.1 54.8 4.2 5.1 1.4 36.2 40.5 Guatemala 90.7 84.8 30.2 35.1 2.9 4.0 2.3 24.7 31.1 Guinea 90.8 88.9 82.8 82.7 3.0 4.3 2.6 46.2 46.5 Guinea-Bissau 91.4 93.0 60.5 62.9 0.4 0.6 2.9 40.3 40.9 Haiti 82.7 83.1 59.1 57.4 2.6 3.6 2.2 43.3 41.7 50 2006 World Development Indicators Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2004 1990 2004 1990 2004 1990­2004 1990 2004 Honduras 89.0 90.5 34.6 54.6 1.6 3.0 4.4 27.7 36.9 Hungary 74.4 66.9 57.3 53.4 4.5 4.2 ­0.5 44.5 45.0 India 86.6 84.4 40.3 36.1 335.1 427.2 1.7 29.9 28.3 Indonesia 82.9 87.0 52.1 52.8 75.3 105.1 2.4 38.4 37.8 Iran, Islamic Rep. 82.3 75.4 22.5 39.1 15.6 26.2 3.7 20.2 33.0 Iraq 77.8 .. 16.4 .. 4.7 .. .. 16.8 .. Ireland 77.9 80.1 42.3 60.7 1.3 2.0 2.9 34.3 42.5 Israel 68.1 65.9 46.8 58.1 1.6 2.7 3.4 40.5 46.8 Italy 76.7 74.5 44.6 49.3 23.9 24.0 0.0 37.1 39.6 Jamaica 83.0 78.4 71.3 60.0 1.1 1.2 0.3 46.8 43.7 Japan 83.1 85.0 57.1 60.4 63.9 67.0 0.3 40.6 41.0 Jordan 71.3 79.5 18.6 28.3 0.8 1.8 6.3 18.8 24.1 Kazakhstan 81.6 80.1 68.0 73.1 7.7 8.0 0.2 46.3 49.4 Kenya 90.6 89.6 76.2 71.4 9.8 15.1 3.1 46.0 44.0 Korea, Dem. Rep. 84.0 80.7 56.4 50.0 9.7 10.6 0.6 39.3 38.6 Korea, Rep. 75.3 77.3 49.7 54.0 19.1 24.1 1.7 39.3 40.7 Kuwait 83.1 86.5 35.6 49.3 0.9 1.3 3.2 21.8 24.8 Kyrgyz Republic 78.0 77.6 65.0 60.0 1.8 2.2 1.4 46.2 44.3 Lao PDR 81.6 82.3 56.3 56.4 1.5 2.3 2.8 41.3 40.7 Latvia 83.4 71.9 75.0 62.8 1.5 1.1 ­2.0 49.5 48.6 Lebanon 81.5 83.7 34.4 35.0 0.9 1.4 2.7 31.8 30.0 Lesotho 86.8 74.3 59.4 49.2 0.6 0.6 0.4 46.5 44.7 Liberia 85.2 84.0 55.9 55.7 0.8 1.2 2.9 39.4 39.9 Libya 81.4 82.3 19.9 32.3 1.3 2.2 4.2 17.3 26.3 Lithuania 81.7 72.8 70.4 65.9 1.9 1.6 ­1.1 48.1 49.1 Macedonia, FYR 77.5 73.2 52.8 47.8 0.9 0.9 0.1 40.0 39.1 Madagascar 83.6 86.3 79.5 79.8 5.4 8.3 3.1 49.2 48.4 Malawi 91.7 90.0 86.2 86.0 4.5 5.8 1.9 50.3 49.8 Malaysia 82.7 83.8 45.3 47.6 7.1 10.7 2.9 34.8 35.6 Mali 90.7 85.9 75.1 74.7 3.8 5.3 2.5 46.0 47.3 Mauritania 87.6 85.0 57.8 56.5 0.8 1.2 2.7 40.7 40.4 Mauritius 86.6 84.4 45.2 46.4 0.5 0.6 1.4 33.9 35.3 Mexico 85.4 83.8 36.2 42.2 29.5 42.4 2.6 30.6 34.7 Moldova 81.5 75.8 70.4 65.4 2.1 2.1 0.0 48.6 47.8 Mongolia 83.7 83.2 59.3 56.3 0.8 1.2 2.4 41.0 40.2 Morocco 83.9 83.8 25.6 28.5 7.5 10.9 2.7 23.7 25.4 Mozambique 88.0 83.1 88.1 85.1 6.3 9.1 2.6 54.0 53.5 Myanmar 89.2 87.7 71.2 70.0 20.0 26.9 2.1 44.6 44.9 Namibia 67.1 65.0 50.6 48.8 0.4 0.6 2.6 44.1 43.6 Nepal 82.5 80.6 50.4 52.3 7.1 10.2 2.6 37.9 40.3 Netherlands 80.0 84.5 53.1 69.0 6.9 8.6 1.5 39.1 44.0 New Zealand 83.0 83.4 63.2 70.5 1.7 2.1 1.7 43.1 46.3 Nicaragua 87.0 87.2 36.8 36.7 1.3 2.0 3.1 30.1 29.7 Niger 94.7 95.5 72.4 72.9 3.6 5.7 3.4 42.6 41.9 Nigeria 86.9 85.9 49.0 46.8 32.7 46.7 2.5 36.2 34.8 Norway 82.5 83.7 69.9 77.2 2.2 2.5 0.9 44.7 47.2 Oman 83.8 82.8 15.7 22.7 0.6 0.9 3.6 11.1 15.6 Pakistan 88.1 85.7 28.8 33.0 35.2 54.5 3.1 23.3 26.5 Panama 82.7 83.4 41.6 53.8 0.9 1.4 3.1 32.5 38.3 Papua New Guinea 75.9 75.1 72.3 72.7 1.8 2.5 2.5 46.4 47.6 Paraguay 85.7 86.9 54.4 67.7 1.6 2.8 3.7 38.3 43.1 Peru 82.0 83.5 48.6 60.3 8.5 13.0 3.0 37.0 41.6 Philippines 83.7 84.6 48.7 55.5 23.4 35.9 3.1 36.6 39.4 Poland 79.2 69.2 65.1 57.8 18.6 17.3 ­0.5 45.8 45.7 Portugal 82.6 79.6 59.2 67.1 4.8 5.5 1.0 42.7 46.2 Puerto Rico 67.4 67.8 35.0 44.0 1.2 1.4 1.6 35.8 41.2 2006 World Development Indicators 51 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2004 1990 2004 1990 2004 1990­2004 1990 2004 Romania 77.2 68.8 61.1 55.6 11.0 10.4 ­0.4 44.3 46.1 Russian Federation 81.6 75.2 71.7 66.9 77.2 73.1 ­0.4 48.3 49.0 Rwanda 88.3 85.1 87.4 82.4 3.1 4.1 2.0 51.0 51.4 Saudi Arabia 81.3 80.5 15.6 18.2 5.1 7.7 2.9 11.4 14.8 Senegal 87.8 83.9 63.4 58.7 3.1 4.5 2.6 43.4 42.4 Serbia and Montenegro 77.0 75.9 54.9 53.3 4.9a 3.9 0.0 b 41.7 41.7 Sierra Leone 90.2 94.4 55.6 58.3 1.7 2.3 2.1 38.5 38.4 Singapore 83.9 83.2 54.2 56.7 1.6 2.2 2.3 38.8 39.8 Slovak Republic 82.5 76.5 70.6 62.4 2.6 2.7 0.1 46.3 45.2 Slovenia 76.9 75.2 63.3 66.1 1.0 1.0 0.3 45.5 46.2 Somalia 95.8 95.1 63.1 61.0 2.8 3.4 1.3 39.9 39.3 South Africa 81.6 82.1 57.4 49.7 14.4 19.1 2.0 41.6 38.4 Spain 80.3 80.5 41.9 56.3 16.0 20.3 1.7 34.3 40.8 Sri Lanka 82.9 81.8 48.2 38.6 7.3 8.3 0.9 34.8 30.5 Sudan 78.9 72.6 27.8 24.2 7.8 10.3 2.0 26.0 24.8 Swaziland 79.6 74.9 39.6 33.2 0.2 0.3 2.7 38.0 33.1 Sweden 86.0 79.2 81.9 75.1 4.7 4.7 ­0.1 47.7 47.4 Switzerland 90.2 88.0 62.8 74.7 3.7 4.2 0.9 40.4 46.3 Syrian Arab Republic 83.7 88.8 29.7 39.3 3.7 7.3 4.9 26.2 30.4 Tajikistan 77.6 66.1 56.2 49.7 1.9 2.1 0.8 42.2 43.6 Tanzania 92.1 90.8 90.2 88.4 12.8 18.9 2.8 50.2 49.5 Thailand 90.6 84.6 79.2 70.7 30.4 35.3 1.1 46.6 46.0 Togo 90.8 90.4 55.2 51.9 1.5 2.4 3.1 38.5 37.0 Trinidad and Tobago 79.7 82.2 45.9 51.2 0.5 0.6 2.0 36.1 38.9 Tunisia 79.2 78.3 22.1 30.4 2.4 3.7 3.0 21.5 27.1 Turkey 84.5 80.3 36.2 29.0 21.0 26.5 1.7 29.4 26.4 Turkmenistan 80.0 76.5 69.1 65.0 1.5 2.1 2.4 46.9 46.6 Uganda 92.4 87.8 82.0 81.2 7.8 11.5 2.8 47.5 48.2 Ukraine 79.7 72.3 70.7 63.1 26.3 22.4 ­1.1 49.2 49.1 United Arab Emirates 92.4 92.1 25.9 38.2 0.9 2.6 7.5 9.8 13.2 United Kingdom 87.9 82.2 67.2 69.2 29.4 30.4 0.2 44.0 45.9 United States 85.1 81.7 67.5 70.1 129.3 153.7 1.2 44.4 46.2 Uruguay 85.9 86.1 54.3 65.7 1.4 1.7 1.6 39.9 43.9 Uzbekistan 78.5 75.6 64.4 60.2 8.2 11.1 2.2 45.4 44.5 Venezuela, RB 82.4 85.5 39.8 60.1 7.3 12.4 3.8 31.8 40.3 Vietnam 85.5 82.6 79.4 77.6 31.3 43.1 2.3 48.3 48.5 West Bank and Gaza 67.0 69.0 9.5 10.9 0.4 0.7 4.5 11.9 13.1 Yemen, Rep. 76.1 77.4 28.6 30.4 3.0 5.7 4.7 27.3 27.7 Zambia 90.4 91.5 67.8 68.3 3.5 4.9 2.4 43.2 42.4 Zimbabwe 81.0 85.1 69.9 64.8 4.3 5.7 2.1 47.2 44.2 World 85.5 w 83.9 w 58.9 w 57.8 w 2,390.7 t 2,981.1 t 1.6 w 39.9 w 40.0 w Low income 87.0 85.0 50.7 47.8 709.0 956.4 2.1 35.7 35.0 Middle income 85.8 84.3 64.0 62.7 1,254.1 1,536.1 1.4 41.8 42.1 Lower middle income 86.6 85.5 66.3 65.2 1,033.5 1,280.1 1.5 42.0 42.4 Upper middle income 82.5 78.9 54.7 52.4 220.6 256.0 1.1 40.5 40.5 Low & middle income 86.3 84.6 59.0 56.7 1,963.1 2,492.5 1.7 39.6 39.3 East Asia & Pacific 87.8 87.2 74.3 71.6 856.8 1,049.4 1.4 44.1 43.8 Europe & Central Asia 80.8 74.6 65.1 58.1 223.8 218.5 ­0.2 45.6 44.9 Latin America & Carib. 85.9 83.8 43.8 55.3 171.0 248.8 2.7 34.0 40.2 Middle East & N. Africa 79.9 79.1 24.5 30.5 64.9 104.6 3.4 22.9 27.2 South Asia 86.9 84.9 41.7 38.0 437.0 572.7 1.9 30.6 29.3 Sub-Saharan Africa 87.8 86.4 65.1 62.7 209.7 298.5 2.5 43.0 42.2 High income 82.1 80.6 58.6 63.5 427.6 488.5 1.0 41.3 43.6 Europe EMU 78.5 77.5 51.6 60.4 130.5 144.5 0.7 39.5 43.4 a. Includes population of Kosovo until 1999. b. Data are for 1990­99. 52 2006 World Development Indicators Labor force structure About the data Definitions The labor force is the supply of labor available for oping countries, where the household is often the · Labor force participation rate is the proportion the production of goods and services in an economy. basic unit of production and all members contribute of the population ages 15­64 that is economically It includes people who are currently employed and to output, but some at low intensity or irregular inter- active: all people who supply labor for the produc- people who are unemployed but seeking work as well vals, the estimated labor force may be significantly tion of goods and services during a specified period. as first-time job-seekers. Not everyone who works smaller than the numbers actually working. · Total labor force comprises people ages 15 and is included, however. Unpaid workers, family work- The labor force estimates in the table were cal- older who meet the ILO definition of the economically ers, and students are among those usually omitted, culated by World Bank staff by applying labor force active population. It includes both the employed and and in some countries members of the military are participation rates from the ILO database to popu- the unemployed. While national practices vary in the not counted. The size of the labor force tends to lation estimates to create a series consistent with treatment of such groups as the armed forces and vary during the year as seasonal workers enter and these population estimates. This procedure some- seasonal or part-time workers, the labor force gener- leave it. times results in estimates of labor force size that ally includes the armed forces, the unemployed, and Data on the labor force are compiled by the Inter- differ slightly from those in the ILO's Yearbook of first-time job-seekers, but excludes homemakers and national Labour Organization (ILO) from labor force Labour Statistics. The labor force estimates in this other unpaid caregivers and workers in the informal surveys, censuses, establishment censuses and sur- year's World Development Indicators are for the pop- sector. · Average annual growth rate of the labor veys, and various types of administrative records ulation ages 15 and older. In previous editions the force is calculated using the exponential endpoint such as employment exchange registers and unem- labor force included children under age 15. For this method (see Statistical methods for more informa- ployment insurance schemes. For some countries reason, labor force estimates are not comparable tion). · Females as a percentage of the labor force a combination of these sources is used. While the across editions. The labor force participation rate of show the extent to which women are active in the resulting statistics may provide rough estimates of the population ages 15­64 provides an indication of labor force. the labor force, they are not comparable across coun- the relative size of the supply of labor. But in many tries because of the noncomparability of the original developing countries children under age 15 work data and the different ways the original sources may full or part time. And in some high-income countries be combined. many workers postpone retirement past age 65. As For international comparisons the most compre- a result, labor force participation rates calculated in hensive source is labor force surveys, which can be this way may systematically over- or under-estimate designed to cover all noninstitutionalized civilians, actual rates. all branches and sectors of the economy, and all In general, estimates of women in the labor force categories of workers, including people who hold mul- are lower than those of men and are not compa- tiple jobs. Despite the ILO's efforts to encourage the rable internationally, reflecting the fact that for use of international standards, labor force data are women demographic, social, legal, and cultural not fully comparable because of differences among trends and norms determine whether their activities countries, and sometimes within countries, in both are regarded as economic. In many countries large concepts and methodologies. Most important to numbers of women work on farms or in other fam- data comparability is the nature of the data source. ily enterprises without pay, while others work in or Labor force data obtained from population censuses near their homes, mixing work and family activities are often based on a limited number of questions on during the day. Countries differ in the criteria used the economic characteristics of individuals, with little to determine the extent to which such workers are scope to probe. The resulting data are often contrary to be counted as part of the labor force. In most to labor force survey data and often vary consider- economies the gap between male and female labor ably from economy to economy, depending on the force participation rates has been narrowing since scope and coverage of the census. Establishment 1980. This stems from both falling rates for men and censuses and surveys on the other hand provide rising rates for women. The largest gap between men Data sources data only on the employed population, leaving out and women in labor force participation is observed in The labor force participation rates are from the unemployed workers, workers in small establish- the Middle East and North Africa, where low partici- ILO database Estimates and Projections of the ments, and workers in the informal sector (ILO, Key pation of women in the work force also brings down Economically Active Population, 1980­2020, Indicators of the Labour Market 2001­2002). the overall labor force participation rate. fifth edition. The ILO publishes estimates of the The reference period of the census or survey is economically active population in its Yearbook of another important source of differences: in some Labour Statistics. Labor force numbers were cal- countries data refer to people's status on the day culated by World Bank staff, applying labor force of the census or survey or during a specific period participation rates from the ILO database to popu- before the inquiry date, while in others the data are lation estimates. recorded without reference to any period. In devel- 2006 World Development Indicators 53 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b, c 2c 0 b, c 1b, c 40 c 28 c 18 c 9c 59 c 70 c 81c 90 c Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 6 5c 4 3c 32 30 c 12 10 c 62 65c 85 87c Austria 6 5 8 6 47 43 20 13 46 51 72 81 Azerbaijan .. 41 .. 39 .. 15 .. 8 .. 44 .. 54 Bangladesh 54 53 85 77 16 8 9 9 26 30 2 12 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 3 2 2 1 38 35 13 12 57 63 84 87 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 3c 6 1c 3 42c 39 17c 14 55c 55 82c 82 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 17 .. 6 .. 31 .. 17 .. 51 .. 76 Brazil 31c 23 c 25c 16c 27c 28 c 10 c 13c 43 c 49 c 65c 71c Bulgaria .. 12 .. 8 .. 37 .. 29 .. 51 .. 64 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 72 .. 75 .. 7 .. 10 .. 20 .. 15 Cameroon 53 .. 68 .. 14 .. 4 .. 26 .. 23 .. Canada 6c 4c 3c 2c 31c 32c 11c 11c 63 c 64 c 86c 87c Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 18 6 5 32 29 15 12 45 53 79 83 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China 1 0b 0b 0b 37 25 27 8 63 75 73 92 Colombia 2c 31c 1c 8c 35c 21c 25c 17c 63 c 49 c 74 c 75c Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 21 5 4 27 26 25 14 41 52 69 81 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 16 .. 18 .. 39 .. 19 .. 45 .. 64 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 9 6 7 3 55 50 33 27 36 45 61 70 Denmark 7 5c 3 2c 37 34 c 16 12c 56 61c 81 86c Dominican Republic 26 23 3 2 23 24 21 15 52 53 76 83 Ecuador 10 c 11c 2c 5c 29 c 28 c 17c 14 c 62c 61c 81c 81c Egypt, Arab Rep. 35 28 52 28 25 23 10 10 41 50 37 62 El Salvador 48 29 15 4 23 27 23 22 29 45 63 74 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23 9 13 4 42 42 30 23 36 50 57 73 Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 12 7 6 3 39 39 15 13 49 54 78 84 France .. .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 53 .. 57 .. 12 .. 4 .. 35 .. 39 Germany 4 3 4 2 51 44 24 17 45 53 72 81 Ghana 66 60 59 50 10 14 10 15 23 27 32 36 Greece 20 c 15c 26c 18 c 32c 30 c 17c 11c 48 c 56c 56c 71c Guatemala .. 50 .. 18 .. 18 .. 23 .. 27 .. 56 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 54 2006 World Development Indicators Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a Honduras 53c 52c 6c 9c 18 c 19 c 25c 26c 29 c 29 c 69 c 66c Hungary 15 8 8 3 42 42 29 24 44 50 64 74 India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 55 43 56 45 10 13 12 14 35 45 32 42 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 19 10 3 2 33 39 18 13 48 51 78 85 Israel 5 3 2 1 38 33 15 11 57 64 83 88 Italy 8 6 9 4 38 40 22 20 54 55 70 76 Jamaica 36 28 16 8 25 26 12 6 39 46 72 84 Japan 6 5 7 5 40 36 27 19 54 59 65 75 Jordan .. .. .. .. .. .. .. .. .. .. .. .. Kazakhstan .. 36 .. 34 .. 23 .. 10 .. 40 .. 56 Kenya 19 c .. 20 c .. 23 c .. 9c .. 58 c .. 71c .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 12c 8c 17c 10 c 41c 34 c 28 c 18 c 47c 58 c 55c 72c Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 43 .. 43 .. 19 .. 10 .. 38 .. 47 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 25 17 14 10 37 35 26 18 38 47 59 71 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 21 .. 15 .. 35 .. 22 .. 44 .. 64 Macedonia, FYR .. 22 .. 22 .. 36 .. 31 .. 42 .. 47 Madagascar .. 77 .. 79 .. 7 .. 6 .. 16 .. 15 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 16 20 11 31 35 32 27 46 49 48 62 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 17 11 11 6 32 36 64 40 48 53 24 54 Mexico 33 22 10 5 25 28 19 20 41 50 71 75 Moldova .. 44 .. 42 .. 20 .. 12 .. 35 .. 46 Mongolia .. 44 .. 40 .. 17 .. 14 .. 39 .. 46 Morocco .. 39 .. 57 .. 21 .. 19 .. 40 .. 25 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 33 52 29 21 17 8 7 34 49 40 63 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 5 4 3 2 33 29 10 9 60 64 82 87 New Zealand 13c 10 c 8c 6c 31c 32c 13c 11c 56c 58 c 80 c 83 c Nicaragua .. 43 .. 10 .. 19 .. 17 .. 32 .. 52 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 8 6 3 2 35 35 10 9 57 60 86 89 Oman .. 7 .. 5 .. 11 .. 14 .. 82 .. 80 Pakistan 45 38 69 65 20 22 15 16 35 40 16 20 Panama 35 25 3 4 20 22 11 9 45 53 85 88 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 3c 39 0 b, c 20 33 c 19 19 c 10 64 c 42 80 c 70 Peru 1c 1c 0 b, c 0c 30 c 28 c 13c 11c 69 c 71c 87c 89 c Philippines 53 45 32 25 17 18 14 12 29 37 55 63 Poland .. 19 .. 18 .. 38 .. 17 .. 43 .. 65 Portugal 11c 12 13c 14 40 c 43 24 c 20 49 c 45 63 c 66 Puerto Rico 5 3 0b 0b 27 26 19 12 67 71 80 88 2006 World Development Indicators 55 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a Romania 29 34 38 37 44 34 30 25 28 32 33 38 Russian Federation .. 12 .. 8 .. 39 .. 23 .. 48 .. 70 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5 .. 1 .. 24 .. 1 .. 71 .. 98 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 0b 0b 0b 36 29 32 18 63 70 68 82 Slovak Republic .. 8c .. 4c .. 49 c .. 26c .. 43 c .. 71c Slovenia .. 8 .. 8 .. 46 .. 26 .. 45 .. 65 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 13 .. 7 .. 33 .. 14 .. 54 .. 79 Spain 11 7 8 4 41 42 16 14 48 52 76 82 Sri Lanka .. 32c .. 40 c .. 40 c .. 35c .. 29 c .. 25c Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5 3 2 1 40 35 12 10 55 62 86 89 Switzerland 5 5 4 3 39 33 15 12 57 62 81 85 Syrian Arab Republic .. 24 .. 58 .. 31 .. 7 .. 45 .. 35 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 78 c 80 c 90 c 84 c 7c 4c 1c 1c 15c 16c 8c 15c Thailand 59 47 62 43 17 21 13 19 24 33 25 39 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 10 6 2 34 37 14 14 51 53 80 84 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 24 72 59 26 26 11 13 41 49 17 28 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 91 60 91 77 4 11 6 5 5 29 3 18 Ukraine .. 21 .. 17 .. 38 .. 21 .. 41 .. 62 United Arab Emirates .. 9 .. 0b .. 36 .. 14 .. 55 .. 86 United Kingdom 3 2 1 1 41 35 16 10 55 64 82 89 United States 4c 4c 1c 1c 33 c 31c 14 c 11c 62c 65c 85c 88 c Uruguay 7c 7c 1c 2c 36c 29 c 21c 12c 57c 65c 78 c 86c Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 16 2 2 32 25 16 11 52 59 82 86 Vietnam .. 58 .. 62 .. 20 .. 13 .. 23 .. 25 West Bank and Gaza .. 12 .. 34 .. 30 .. 8 .. 57 .. 57 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 16 .. 11 .. 33 .. 19 .. 51 .. 71 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 20 .. 19 .. 35 .. 20 .. 45 .. 61 Latin America & Carib. 20 21 14 9 30 27 14 14 50 52 72 76 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 4 3 38 35 19 14 56 60 77 83 Europe EMU 7 5 7 4 43 42 21 17 49 53 72 80 Note: Data across sectors may not sum to 100 percent because of workers not classified by sectors. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. 56 2006 World Development Indicators Employment by economic activity About the data Definitions The International Labour Organization (ILO) classi- patterns. Most economies report economic activity · Agriculture corresponds to division 1 (ISIC revi- fies economic activity using the International Stan- according to the ISIC revision 2, although a group of sion 2) or tabulation categories A and B (ISIC revi- dard Industrial Classification (ISIC) of All Economic economies moved to ISIC revision 3. The use of one sion 3) and includes hunting, forestry, and fishing. Activities, revision 2 (1968) and revision 3 (1990). classification or another should not have a significant · Industry corresponds to divisions 2­5 (ISIC revi- Because this classification is based on where work impact on the information for the three broad sectors sion 2) or tabulation categories C­F (ISIC revision is performed (industry) rather than on what type of presented in this table. 3) and includes mining and quarrying (including oil work is performed (occupation), all of an enterprise's The distribution of economic wealth in the world production), manufacturing, construction, and public employees are classified under the same industry, remains strongly correlated with employment by utilities (electricity, gas, and water). · Services corre- regardless of their trade or occupation. The catego- economic activity. The wealthier economies are spond to divisions 6­9 (ISIC revision 2) or tabulation ries should add up to 100 percent. Where they do those with the largest share of total employment in categories G­P (ISIC revision 3) and include whole- not, the differences arise because of workers who services, whereas the poorer economies are largely sale and retail trade and restaurants and hotels; cannot be classified by economic activity. agriculture based. transport, storage, and communications; financing, Data on employment are drawn from labor force The distribution of economic activity by gender insurance, real estate, and business services; and surveys, household surveys, establishment cen- reveals some clear patterns. Industry accounts community, social, and personal services. suses and surveys, administrative records of social for a larger share of male employment than female insurance schemes, and official national estimates. employment worldwide, whereas a higher proportion The concept of employment generally refers to peo- of women work in the services sector. Employment ple above a certain age who worked, or who held in agriculture is also male-dominated, although not a job, during a reference period. Employment data as much as industry. Segregating one sex in a nar- include both full-time and part-time workers. row range of occupations significantly reduces eco- There are many differences in how countries define nomic efficiency by reducing labor market flexibility and measure employment status, particularly for and thus the economy's ability to adapt to change. students, part-time workers, members of the armed This segregation is particularly harmful for women, forces, and household or contributing family work- who have a much narrower range of labor market ers. Where the armed forces are included, they are choices and lower levels of pay than men. But it is allocated to the service sector, causing that sector also detrimental to men when job losses are concen- to be somewhat overstated relative to the service trated in industries dominated by men and job growth sector in economies where they are excluded. Where is centered in service occupations, where women data are obtained from establishment surveys, they often dominate, as has been the recent experience cover only employees; thus self-employed and con- in many countries. tributing family workers are excluded. In such cases There are several explanations for the rising impor- the employment share of the agricultural sector is tance of service jobs for women. Many service jobs-- severely underreported. Moreover, the age group and such as nursing and social and clerical work--are area covered could differ by country or change over considered "feminine" because of a perceived simi- time within a country. For detailed information on larity to women's traditional roles. Women often do breaks in series, consult the original source. not receive the training needed to take advantage of Countries also take different approaches to the changing employment opportunities. And the greater treatment of unemployed people. In most countries availability of part-time work in service industries unemployed people with previous job experience are may lure more women, although it is unclear whether classified according to their last job. But in some this is a cause or an effect. countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribu- tion of employment by economic activity may not be fully comparable across countries. The ILO's Yearbook of Labour Statistics and its data- base Key Indicators of the Labour Market report data by major divisions of the ISIC revision 2 or revision 3. In this table the reported divisions or categories Data sources are aggregated into three broad groups: agriculture, Data on employment are from the ILO database Key industry, and services. Such broad classification may Indicators of the Labour Market, fourth edition. obscure fundamental shifts within countries' industrial 2006 World Development Indicators 57 Child labor Economically active children Employment by economic activitya % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Survey Work and year Total Male Female Work only study Male Female Male Female Male Female Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2000 36.6 41.1 31.8 43.1 56.9 .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. Angola 1995 5.2 4.9 5.6 77.6 22.4 5.7 7.9 6.2 8.4 82.4 79.9 Argentina 1997 20.7 25.4 16.0 8.6 91.4 .. .. .. .. .. .. Armenia .. .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2000 9.7 12.0 7.3 4.2 95.8 .. .. .. .. .. .. Bangladesh 2003 17.5 20.9 13.9 63.3 36.7 61.4 64.0 11.6 15.5 25.2 18.3 Belarus .. .. .. .. .. .. .. .. .. .. .. Belgium .. .. .. .. .. .. .. .. .. .. .. Benin .. .. .. .. .. .. .. .. .. .. .. Bolivia 2000 19.2 20.4 18.0 19.7 80.3 77.8 72.9 4.3 3.5 15.0 23.6 Bosnia and Herzegovina 2000 20.2 22.8 17.6 4.0 96.0 .. .. .. .. .. .. Botswana .. .. .. .. .. .. .. .. .. .. .. Brazil 2003 7.1 9.5 4.6 5.8 94.2 64.3 49.8 6.5 9.1 26.8 40.9 Bulgaria .. .. .. .. .. .. .. .. .. .. .. Burkina Fasob 1998 66.5 65.4 67.7 95.9 4.1 98.0 98.2 0.6 0.5 1.3 1.2 Burundi 2000 37.0 38.4 35.7 48.3 51.7 .. .. .. .. .. .. Cambodia 2001 52.3 52.4 52.1 16.5 83.5 78.5 73.6 4.7 5.4 15.7 20.4 Cameroonb 2001 15.9 14.5 17.4 52.5 47.5 90.4 86.3 1.9 2.3 5.1 8.8 Canada .. .. .. .. .. .. .. .. .. .. .. Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. .. .. Chad 2000 69.9 73.5 66.5 44.6 55.4 .. .. .. .. .. .. Chile 2003 8.8 10.5 6.9 4.0 96.0 31.5 11.9 7.6 5.8 58.5 80.6 China .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 2001 12.2 16.6 7.7 23.0 77.0 .. .. .. .. .. .. Congo, Dem. Rep. 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2002 6.7 9.7 3.5 20.8 79.2 56.5 55.2 8.7 2.7 28.0 42.1 Côte d'Ivoire 2000 40.7 40.9 40.5 46.4 53.6 .. .. .. .. .. .. Croatia .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2000 12.5 16.7 8.1 7.2 92.8 .. .. .. .. .. .. Ecuador 2001 17.9 22.1 13.6 25.0 75.0 65.1 69.2 10.7 8.6 21.2 22.1 Egypt, Arab Rep. 1998 6.4 4.0 8.9 60.9 39.1 .. .. .. .. .. .. El Salvador 2003 12.7 17.1 8.1 19.5 80.5 66.4 17.6 10.8 16.1 21.2 66.3 Eritrea .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2001 57.1 67.9 45.9 63.5 36.5 96.5 88.7 0.5 2.8 2.5 6.2 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2000 25.3 25.4 25.3 41.6 58.4 .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. Germany .. .. .. .. .. .. .. .. .. .. .. Ghana 2000 28.5 28.5 28.4 36.4 63.6 81.0 59.1 4.5 7.6 13.8 32.0 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala 2000 20.1 25.9 13.9 38.5 61.5 74.5 39.8 5.9 20.1 14.7 40.0 Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. .. Guinea-Bissau 2000 67.5 67.4 67.5 63.7 36.3 .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. 58 2006 World Development Indicators Child labor Economically active children Employment by economic activitya % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Survey Work and year Total Male Female Work only study Male Female Male Female Male Female Honduras 2002 11.4 16.5 6.1 41.9 58.1 73.6 19.8 5.9 24.4 18.6 55.7 Hungary .. .. .. .. .. .. .. .. .. .. .. India 2000 5.2 5.3 5.1 89.8 10.2 70.5 76.6 10.0 15.4 15.9 6.5 Indonesia .. .. .. .. .. .. .. .. .. .. .. Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. Iraq 2000 13.7 17.4 9.7 51.7 48.3 .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica .. .. .. .. .. .. .. .. .. .. .. Japan .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 1996 29.7 30.3 29.1 4.4 95.6 .. .. .. .. .. .. Kenya 1999 6.7 6.9 6.4 44.8 55.2 87.3 74.4 2.5 0.3 8.8 25.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 1998 8.6 9.7 7.6 7.0 93.0 93.0 96.3 0.0 0.0 7.0 2.7 Lao PDR .. .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. .. Lesotho 2000 30.8 34.2 27.5 17.6 82.4 .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. Madagascar 2001 25.6 26.1 25.1 85.1 14.9 94.1 93.9 0.6 1.4 2.0 2.9 Malawi 2000 10.6 9.4 11.6 17.1 82.9 .. .. .. .. .. .. Malaysia .. .. .. .. .. .. .. .. .. .. .. Mali 2001 25.3 32.3 18.6 68.7 31.3 .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. Mexicoc 1996 14.7 20.0 9.5 45.6 54.4 61.3 38.3 11.4 12.9 22.6 48.2 Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. .. .. Mongolia 2000 22.0 23.5 20.6 28.2 71.8 .. .. .. .. .. .. Morocco 1998-99 13.2 13.5 12.8 93.2 6.8 60.8 60.3 8.1 8.5 13.5 6.4 Mozambique .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 91.7 0.4 0.4 8.1 8.0 Nepal 1999 47.2 42.2 52.4 35.6 64.4 89.0 86.1 1.2 1.5 9.7 12.3 Netherlands .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2001 12.1 17.5 6.5 33.3 66.7 73.2 32.0 3.0 10.2 23.3 57.8 Niger .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. Pakistan .. .. .. .. .. .. .. .. .. .. .. Panama 2000 4.0 6.4 1.4 37.5 62.5 71.1 38.4 1.4 8.0 27.2 49.5 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. Paraguay 1999 8.1 11.7 4.4 24.2 75.7 61.2 30.9 3.8 4.6 33.1 64.5 Peru 1994 17.7 20.4 15.2 7.3 92.7 78.9 76.3 3.6 3.4 17.5 20.3 Philippines 2001 13.3 16.3 10.0 14.8 85.2 72.6 53.6 3.6 5.3 22.1 41.0 Poland .. .. .. .. .. .. .. .. .. .. .. Portugal 2001 3.6 4.6 2.6 3.6 96.4 52.7 40.7 11.4 10.7 25.6 47.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 59 Child labor Economically active children Employment by economic activitya % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Survey Work and year Total Male Female Work only study Male Female Male Female Male Female Romania .. .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. .. Rwanda 2000 33.1 36.1 30.3 27.5 72.5 .. .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. Senegal 2000 35.4 43.2 27.7 56.2 43.8 .. .. .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 2000 74.0 24.7 72.7 53.8 46.2 .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. .. .. .. Sri Lanka .. .. .. .. .. .. .. .. .. .. .. Sudan 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. .. .. Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. Tanzania 2001 40.4 41.5 39.2 40.0 60.0 83.5 73.1 0.1 0.2 16.3 26.7 Thailand .. .. .. .. .. .. .. .. .. .. .. Togo 2000 72.5 73.4 71.6 28.4 71.6 .. .. .. .. .. .. Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. Turkey 1999 4.5 5.2 3.8 66.8 33.2 52.7 83.4 19.9 10.2 10.2 1.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 2002­03 13.1 15.0 11.3 18.3 81.7 94.3 92.3 1.5 1.3 3.2 6.0 Ukraine .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. .. Uzbekistan 2000 18.1 22.0 14.0 4.1 95.9 .. .. .. .. .. .. Venezuela, RB 2003 9.1 11.4 6.6 17.6 82.4 35.2 9.2 7.3 9.5 53.9 81.0 Vietnam .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1999 13.1 12.4 14.0 64.3 35.7 87.2 96.6 1.2 0.8 10.8 1.8 Zambia 1999 14.4 15.0 13.9 72.8 27.2 92.7 88.1 0.3 0.8 6.6 11.0 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. a. Shares by major industrial category do not sum to 100 percent because of a residual category not included in the table. b. Data are for children ages 10­14. c. Data are for children ages 12­14. 60 2006 World Development Indicators Child labor About the data Definitions The data in the table refer to children's economic activ- who is not a member of the household, whether a · Economically active children refer to children ity, a broader concept than child labor. According to a child is involved in any type of family work (on the involved in economic activity for at least one hour gradually emerging consensus, child labor is a subset farm or in a business), and the like. The ages used in in the reference week of the survey. · Work only of children's economic activity or children's work that country surveys to define child labor range from 5 to refers to children involved in economic activity and is injurious and therefore targeted for elimination. 14 years old. The data in the table have been recalcu- not attending school. · Work and study refer to chil- In line with the international definition of employ- lated to present statistics for children ages 7­14. dren attending school in combination with economic ment, the threshold for classifying a child as eco- Although efforts are made to harmonize the defini- activity. · Employment by economic activity refers nomically active is spending one hour on economic tion of employment and the questions on employment to the distribution of economically active children by activity during the reference week. Economic activity used in survey questionnaires, some differences the major industrial categories (ISIC revision 2 or is as defined by the 1993 United Nations System of remain among the survey instruments used to col- revision 3). · Agriculture corresponds to division 1 National Accounts (revision 3) and corresponds to the lect the information on working children. Differences (ISIC revision 2) or categories A and B (ISIC revision international definition of employment adopted by the exist not only among different household surveys in 3) and includes agriculture and hunting, forestry and Thirteenth International Conference of Labor Statisti- the same country, but also within the same type of logging, and fishing. · Manufacturing corresponds cians in 1982. Economic activity covers all market survey carried out in different countries. to division 3 (ISIC revision 2) or category D (ISIC production and certain types of nonmarket produc- Because of the differences in the underlying sur- revision 3). · Services correspond to divisions 6­9 tion, including production of goods for own use. It vey instruments and in survey dates, estimates of (ISIC revision 2) or categories G­P (ISIC revision 3) excludes household chores performed by children in the economically active child population are not fully and include wholesale and retail trade, hotels and their own household. But some forms of economic comparable across countries. Caution should be restaurants, transport, financial intermediation, real activity are not captured by household surveys and exercised in drawing conclusions concerning relative estate, public administration, education, health and so are not reflected in the estimates. These include levels of child economic activity across countries or social work, other community services, and private unconditional forms of child labor, such as child com- regions based on the published estimates. household activity. mercial sexual exploitation and child slavery, which The table aggregates the distribution of working require different data collection methodologies. children by the industrial categories of the Inter- The data used to develop the indicators are from national Standard Industrial Classification (ISIC): household surveys conducted by the International agriculture, industry, and services. The residual Labor Organization (ILO), the United Nations Children's category, which includes mining and quarrying; elec- Fund (UNICEF), the World Bank, and national statistical tricity, gas, and water; construction; extraterritorial offices. These surveys yield a variety of data in educa- organization; and other inadequately defined activi- tion, employment, health, expenditure, and consump- ties, is not presented in the table, and so the broad tion that relate to child work. But they do not provide groups do not add up to 100 percent. The use of information on unconditional forms of children's work. either ISIC revision 2 or revision 3 is strictly related Household survey data generally include informa- to the codification applied by each country in describ- tion on work type--for example, whether a child is ing the economic activity. The use of two different working for pay in cash or in kind or is involved in classifications does not affect the definition of the unpaid work, whether a child is working for someone groups presented in the table. Data sources Estimates are produced by the Understanding Of children who work, some combine work and schooling Children's Work project based on household Share of children (%) Work only Work and study survey datasets made available by the ILO's Ethiopia, 2001 India, 2000 Philippines, 2001 International Programme on the Elimination of 100 100 100 Child Labour under its Statistical Monitoring Pro- gramme on Child Labour, UNICEF under its Mul- 80 80 80 tiple Indicator Cluster Survey program, the World 60 60 60 Bank under its Living Standards Measurement 40 40 40 Study program, and national statistical offices. Information on how the data were collected and 20 20 20 some indication of their reliability can be found 0 0 0 at www.ilo.org/public/english/standards/ipec/ Girls Boys Girls Boys Girls Boys simpoc/, www.childinfo.org, and www.worldbank. A little light work that does not interfere with education is not necessarily bad, but long working hours are likely to have org/lsms. Detailed country statistics can be found serious health and developmental consequences for children. Studies suggest more children who work are working long hours. at www.ucw-project.org. Source: Understanding Children's Work project. 2006 World Development Indicators 61 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 13.1 .. 18.3 .. 15.2 .. .. .. 56.4 38.4 3.4 Algeria 24.2 26.6 20.3 31.4 23.0 27.3 .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.4b 16.3 b 7.0 b 14.7b 6.7b 15.6b .. .. .. 42.8 b 38.5b 17.7b Armenia .. .. .. .. .. .. 72.2b 70.8 b 71.6b 5.2 81.5 13.3 Australia 11.3 5.3 b 9.5 5.5b 10.5 5.4b 27.1b 17.0 b 22.5b 48.3 32.7 19.0 Austria 3.5 4.5 3.8 5.4 3.6 4.9 25.0 23.9 24.5 37.3 55.7 7.0 Azerbaijan .. .. .. .. .. .. .. .. .. 4.6 31.4 64.1 Bangladesh .. 3.2 .. 3.3 .. 3.3 .. .. .. 54.3 22.7 8.4 Belarus .. .. .. .. .. .. .. .. .. 10.2 40.6 49.1 Belgium 4.8 6.6 9.5 8.3 6.7 7.4 44.8 48.2 46.3 43.7 38.1 18.2 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 5.5b 4.3 5.6b 6.9 5.5b 5.5 .. .. .. 60.2b 32.5b 4.4b Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 11.7 15.7 17.3 22.3 13.9 18.6 .. .. .. 63.8 23.8 .. Brazil 5.4b 7.8 b 7.9 b 12.3 b 6.4b 9.7b .. .. .. .. .. .. Bulgaria .. 14.1 .. 13.2 .. 13.7 .. .. .. 37.8 50.9 11.4 Burkina Faso .. .. .. .. .. .. .. .. .. 46.8 19.3 5.6 Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 1.4 .. 2.0 .. 1.8 .. .. .. .. .. .. Cameroon .. 8.2 .. 6.7 .. 7.5 .. .. .. .. .. .. Canada 12.1b 7.5b 10.2b 6.8 b 11.2b 7.2b 11.4b 8.4b 10.1b 29.0 b 30.8 b 40.2b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 3.9 6.9 5.3 8.3 4.4 7.4 .. .. .. 18.5 59.0 21.8 China .. .. .. .. 2.3 b 4.0 b .. .. .. .. .. .. Hong Kong, China 2.0 b 9.3 b 1.9 b 6.2b 2.0 b 7.9 b .. .. .. 48.6 39.4 10.1 Colombia 6.7 11.0 13.0 18.5 9.4 14.2 .. .. .. 26.9 52.9 16.5 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 3.4 5.8 5.4 8.2 4.0 6.7 8.9 13.3 10.9 62.2 24.1 9.9 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 13.1 .. 15.7 .. 14.3 .. .. 56.4 21.5 68.4 9.8 Cuba .. .. .. .. 4.6 3.3 .. .. .. .. .. .. Czech Republic .. 7.0 .. 9.9 .. 8.3 47.4 51.9 49.9 24.6 71.8 3.5 Denmark 8.3 5.0 9.9 5.4 9.0 5.2 21.8 17.9 19.9 25.9 46.6 25.5 Dominican Republic 11.7 9.4 34.9 26.0 20.3 15.6 2.2 1.3 1.6 .. .. .. Ecuador 6.0 b 9.0 b 13.2b 15.0 b 8.9 b 11.4b .. .. .. 28.8 47.7 21.9 Egypt, Arab Rep. 6.4 6.3 17.0 23.9 9.0 11.0 .. .. .. .. .. .. El Salvador 8.4b 9.3 7.2b 3.5 7.9 b 6.9 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.9 10.2 3.5 9.9 3.7 10.0 .. .. .. 20.9 62.1 16.8 Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 13.6 8.8 9.7 9.0 11.7 8.9 27.7 21.4 24.7 35.8 46.3 17.5 France 7.9 b 9.0 b 12.7b 11.1b 10.0 b 9.9 b 43.1b 42.8 b 42.9 b 40.6 39.9 17.7 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 11.5 .. 11.5 .. 11.5 .. .. .. 5.8 57.6 36.5 Germany 5.3 10.2 8.4 9.3 6.6 9.8 48.3 52.3 50.0 27.1 60.5 12.4 Ghana .. 7.5 .. 8.7 .. 8.2 .. .. .. .. .. .. Greece 4.9 6.4 12.9 15.9 7.8 10.2 49.2 61.0 56.5 34.2 50.0 15.1 Guatemala 2.6b 2.2 4.6b 3.7 3.2b 2.8 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 11.2 .. 13.6 .. 12.2 .. .. .. .. .. .. .. 62 2006 World Development Indicators Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Honduras 3.3 b .. 3.0 b .. 3.2b 5.1b .. .. .. .. .. .. Hungary 11.0 6.1 8.7 6.1 9.9 6.1 42.2 42.2 42.2 33.5 61.2 5.4 India .. 4.4b .. 4.1b .. 4.3 b .. .. .. 27.0 41.1 31.9 Indonesia 3.5 8.1 4.5 12.9 3.9 9.9 .. .. .. 26.3 52.8 6.7 Iran, Islamic Rep. .. 10.1 .. 20.4 .. 11.6 .. .. .. 38.3 37.1 19.3 Iraq .. 30.2 .. 16.0 .. 28.1 .. .. .. .. .. .. Ireland 15.2 4.9 15.2 3.7 15.2 4.4 40.9 26.0 35.4 48.2 24.9 24.0 Israel 9.2 10.2 13.9 11.3 11.2 10.7 .. .. .. 20.2 48.8 27.0 Italy 8.1 6.4 17.3 10.5 11.6 8.0 57.5 58.9 58.2 49.4 41.4 7.5 Jamaica 9.4 8.1 22.2 15.7 15.4 11.4 24.4 36.2 31.7 13.0 5.4 6.1 Japan 2.1b 4.9 b 2.2b 4.4b 2.2b 4.7b 38.9 b 24.6b 33.5b 70.8 0.0 29.2 Jordan .. 11.8 .. 20.7 .. 13.2 .. .. .. .. .. .. Kazakhstan .. 7.2 .. 10.4 .. 8.8 .. .. .. 7.9 53.2 38.9 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.8 3.7 2.1 3.1 2.5 3.5 0.7 0.3 0.6 17.0 53.4 29.6 Kuwait .. .. .. .. .. .. .. .. .. 27.5 39.9 6.1 Kyrgyz Republic .. 9.4 .. 10.5 .. 9.9 47.8 54.3 50.9 7.7 77.7 18.5 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 10.7 .. 10.5 .. 10.6 .. .. .. 22.4 68.5 8.8 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 12.7 .. 12.2 .. 12.4 .. .. 57.8 15.0 68.5 16.5 Macedonia, FYR .. 37.0 .. 36.3 .. 36.7 .. .. .. .. .. .. Madagascar .. 3.5 .. 5.6 .. 4.5 .. .. .. 42.7 18.8 6.1 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia .. .. .. .. 3.7 3.5 .. .. .. 32.0 48.8 15.6 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 3.2 9.0 3.6 12.6 3.3 10.2 .. .. .. 71.5b 28.2b .. Mexico 2.7 2.9 4.0 3.4 3.1 3.0 1.1 0.8 1.0 13.7 30.1 46.4 Moldova .. 9.6 .. 6.4 .. 7.9 .. .. .. .. .. .. Mongolia .. 14.3 .. 14.1 .. 14.2 .. .. .. 35.0 45.8 18.4 Morocco 13.0 b 10.6 25.3 b 11.4 16.0 b 10.8 .. .. .. 50.9b 20.6b 19.3 b Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 20.0 26.8 19.0 35.9 19.0 31.1 .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 4.3 4.1 7.3 4.4 5.5 4.3 30.1 28.1 29.2 46.3 35.1 17.4 New Zealand 11.0 b 3.5b 9.6b 4.4b 10.4b 3.9 b 15.5b 11.0 b 13.3 b 1.0 48.8 16.0 Nicaragua 11.3 7.6 19.4 8.0 14.4 7.8 .. .. .. 50.8 b 24.8 b 19.7b Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 6.6 4.8 5.1 3.8 5.9 4.4 7.1 5.4 6.4 21.7 54.7 21.7 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 4.3 6.2 14.2 16.4 5.8 7.8 .. .. .. 14.7 12.3 24.1 Panama 10.8 10.5 22.3 18.8 14.7 13.6 24.0 35.7 29.3 35.9 37.3 26.0 Papua New Guinea 9.0 4.3 5.9 1.3 7.7 2.8 .. .. .. .. .. .. Paraguay 6.3 b 6.7 3.8b 8.9 5.2b 7.6 .. .. .. .. .. .. Peru 7.5b 9.0 b 12.5b 11.9 b 9.4b 10.3 b .. .. .. 9.4b 61.4b 28.6b Philippines 7.9 9.4 9.9 10.3 8.6 9.8 .. .. .. .. .. .. Poland 12.2 18.2 14.7 19.9 13.3 19.0 48.6 50.8 49.7 18.0 75.4 6.7 Portugal 3.5b 5.8 5.0 b 7.6 4.1b 6.7 31.2 32.7 32.0 70.7 14.6 8.8 Puerto Rico 19.1b 12.8b 13.3 b 10.9 b 16.9 b 12.0 b .. .. .. .. .. .. 2006 World Development Indicators 63 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Romania .. 7.5 .. 6.4 .. 7.0 .. .. .. 26.0 66.9 5.4 Russian Federation 5.2 9.9 5.2 8.8 5.2 8.6 .. .. .. .. .. .. Rwanda 0.6 .. 0.2 .. 0.3 .. .. .. .. 60.7 24.1 5.9 Saudi Arabia .. 4.2 .. 11.5 .. 5.2 .. .. .. 38.3 34.7 20.1 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. 14.4b .. 16.4b .. 15.2b .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7 5.5 2.6 5.3 2.7 5.4 .. .. .. 22.4 25.0 38.8 Slovak Republic .. 17.3 .. 19.1 .. 18.1 60.2 62.1 61.1 24.1 71.7 4.3 Slovenia .. 6.2 .. 6.6 .. 6.6 .. .. .. 26.2 63.9 8.2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 25.5b .. 31.7b .. 28.4b .. .. .. 50.2 41.0 5.1 Spain 13.9 8.2 25.8 15.0 18.1 11.0 34.3 43.9 39.8 56.0 20.4 22.7 Sri Lanka 10.1b 6.2b 19.9 b 14.7b 13.3 b 9.0 b .. .. .. 47.2 0.0 52.8 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 6.8 6.9 4.6 6.2 5.7 6.5 19.6 15.3 17.8 23.2 58.1 17.5 Switzerland 2.3 3.9 3.5 4.8 2.8 4.3 21.6 32.6 27.0 28.7 54.5 16.9 Syrian Arab Republic .. 8.3 .. 24.1 .. 11.7 .. .. .. 75.2 10.3 9.8 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 2.7b 4.4 4.2b 5.8 3.5b 5.1 .. .. .. .. .. .. Thailand 1.3 1.6 1.5 1.4 1.4 1.5 .. .. .. 40.0 47.2 0.2 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 17.0 7.8 23.9 14.5 19.6 10.4 20.3 34.7 27.6 55.5 40.5 1.8 Tunisia .. .. .. .. .. 14.3 .. .. .. 43.4 37.4 10.0 Turkey 8.8 10.5 7.8 9.7 8.5 10.3 22.1 30.9 24.4 53.5 29.2 12.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 2.5 .. 3.9 .. 3.2 .. .. .. .. .. .. Ukraine .. 8.9 .. 8.3 .. 8.6 .. .. .. 13.5 54.3 32.2 United Arab Emirates .. 2.2 .. 2.6 .. 2.3 .. .. .. .. .. .. United Kingdom 11.5b 5.0 b 7.3 b 4.2b 9.7b 4.6b 26.5 17.1 23.0 30.3 44.4 14.6 United States 7.9 b 5.6b 7.0 b 5.4b 7.5b 5.5b 12.5b 11.0 b 11.8 b 18.4 34.3 47.3 Uruguay 6.8b 13.5b 11.8 b 20.8 b 9.0 b 16.8 b .. .. .. 54.8 b 31.3 b 13.9 b Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 8.1 14.4 6.8 20.3 b 7.7 16.8 b .. .. .. .. .. .. Vietnam .. 1.9 .. 2.4 .. 2.1 .. .. .. .. .. .. West Bank and Gaza .. 26.9 .. 18.6 .. 25.6 .. .. .. 57.5 14.5 17.6 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 16.3 .. 22.4 .. 18.9 .. .. .. .. .. .. .. Zimbabwe .. 10.4 .. 6.1 .. 8.2 .. .. .. .. .. .. World .. w .. w .. w .. w .. w 6.5 w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. 4.1 6.8 .. .. .. .. .. .. Lower middle income .. .. .. .. 3.6 5.9 .. .. .. .. .. .. Upper middle income 6.2 11.0 6.8 13.4 6.3 12.0 .. .. .. 38.2 47.3 11.5 Low & middle income .. .. .. .. .. 5.6 .. .. .. .. .. .. East Asia & Pacific .. .. .. .. 2.5 4.4 .. .. .. .. .. .. Europe & Central Asia .. 11.1 .. 10.7 .. 10.6 .. .. .. .. .. .. Latin America & Carib. 5.5 8.0 8.5 11.8 6.7 9.5 .. .. .. .. .. .. Middle East & N. Africa .. 12.7 .. 21.9 .. 13.6 .. .. .. .. .. .. South Asia .. 4.4 .. 5.0 .. 4.5 .. .. .. 30.0 33.8 27.4 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.0 6.2 7.9 6.6 7.4 6.4 27.3 23.9 26.0 34.8 34.4 29.7 Europe EMU 7.5 8.2 12.6 10.6 9.5 9.2 44.2 46.4 45.5 40.4 42.3 16.4 a. Data are for the most recent year available. b. Limited coverage. 64 2006 World Development Indicators Unemployment About the data Unemployment and total employment in an economy more comparable internationally. But the age group educational attainment of workers and unemploy- are the broadest indicators of economic activity as and area covered could differ by country or change ment and may be used to draw inferences about reflected by the labor market. The International over time within a country. For detailed information changes in employment demand. Information on Labour Organization (ILO) defines the unemployed as on breaks in series, consult the original source. education attainment is the best available indicator members of the economically active population who In contrast, the quality and completeness of data of skill levels of the labor force. are without work but available for and seeking work, from employment offices and social insurance pro- Besides the limitations to comparability raised including people who have lost their jobs and those grams vary widely. Where employment offices work for measuring unemployment, the different ways of who have voluntarily left work. Some unemployment closely with social insurance schemes and registration classifying the level of education across countries is unavoidable in all economies. At any time some with such offices is a prerequisite for receipt of unem- may also cause inconsistency. The level of educa- workers are temporarily unemployed--between jobs ployment benefits, the two sets of unemployment tion is supposed to be classified according to Inter- as employers look for the right workers and workers estimates tend to be comparable. Where registration national Standard Classification of Education 1997 search for better jobs. Such unemployment, often is voluntary and where employment offices function (ISCED97). For more information on ISCED97, see called frictional unemployment, results from the nor- only in more populous areas, employment office statis- About the data for table 2.10. mal operation of labor markets. tics do not give a reliable indication of unemployment. Changes in unemployment over time may reflect Most commonly excluded from both these sources Definitions changes in the demand for and supply of labor, but are discouraged workers who have given up their job they may also reflect changes in reporting practices. search because they believe that no employment · Unemployment refers to the share of the labor Ironically, low unemployment rates can often disguise opportunities exist or do not register as unemployed force without work but available for and seeking substantial poverty in a country, while high unemploy- after their benefits have been exhausted. Thus mea- employment. Definitions of labor force and unemploy- ment rates can occur in countries with a high level of sured unemployment may be higher in countries that ment differ by country (see About the data). · Long- economic development and low incidence of poverty. offer more or longer unemployment benefits. term unemployment refers to the number of people In countries without unemployment or welfare ben- Women tend to be excluded from the unemploy- with continuous periods of unemployment extending efits, people eke out a living in the informal sector. ment count for various reasons. Women suffer more for a year or longer, expressed as a percentage of In countries with well-developed safety nets, workers from discrimination and from structural, social, and the total unemployed. · Unemployment by educa- can afford to wait for suitable or desirable jobs. But cultural barriers that impede them from actively seek- tional attainment shows the unemployed by level of high and sustained unemployment indicates serious ing work. Also, women are often responsible for the educational attainment, as a percentage of the total inefficiencies in the allocation of resources. care of children and the elderly or for other household unemployed. The levels of educational attainment The ILO definition of unemployment notwithstand- affairs. They may not be available for work during accord with the International Standard Classification ing, reference periods, the criteria for those consid- the short reference period, as they need to make of Education 1997 of the United Nations Educational, ered to be seeking work, and the treatment of people arrangements before starting work. Furthermore, Cultural, and Scientific Organization. temporarily laid off and those seeking work for the women are considered to be employed when they are first time vary across countries. In many developing working part-time or in temporary jobs in the informal countries it is especially difficult to measure employ- sector, despite the instability of these jobs or their ment and unemployment in agriculture. The timing of active searching for more secure employment. a survey, for example, can maximize the effects of Long-term unemployment is measured by the seasonal unemployment in agriculture. And informal length of time that an unemployed person has been sector employment is difficult to quantify where infor- without work and looking for a job. The underlying mal activities are not registered and tracked. assumption is that shorter periods of joblessness Data on unemployment are drawn from labor force are of less concern, especially when the unemployed sample surveys and general household sample sur- are covered by unemployment benefits or similar veys, censuses, and other administrative records forms of welfare support. The length of time that a such as social insurance statistics, employment person has been unemployed is difficult to measure, office statistics, and official estimates, which are because the ability to recall that time diminishes as usually based on information drawn from one or more the period of joblessness extends. Women's long- of the above sources. Labor force surveys generally term unemployment is likely to be lower in countries yield the most comprehensive data because they where women constitute a large share of the unpaid include groups not covered in other unemployment family workforce. Women in such countries have statistics, particularly people seeking work for the more access than men to nonmarket work and are Data sources first time. These surveys generally use a definition more likely to drop out of the labor force and not be Data on unemployment are from the ILO data- of unemployment that follows the international rec- counted as unemployed. base Key Indicators of the Labour Market, fourth ommendations more closely than that used by other Unemployment by level of educational attainment edition. sources and therefore generate statistics that are provides insights into the relationship between the 2006 World Development Indicators 65 Wages and productivity Hours worked Minimum wage Agricultural wage Labor cost per worker Value added per worker in manufacturing in manufacturing average per week $ per year $ per year $ per year $ per year 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. 1,340 .. .. 6,242 2,638 11,306 .. Angola .. .. .. .. .. .. .. .. .. .. Argentina 41 40 .. 2,400 .. .. 6,768 7,338 33,694 37,480 Armenia .. .. .. .. .. .. .. .. .. .. Australia 37 39 .. 12,712 11,212 15,124 14,749 26,087 27,801 57,857 Austria 33 32 .. b .. .. 11,949 28,342 20,956 53,061 Azerbaijan .. .. .. .. .. .. .. .. .. .. Bangladesh .. 52 .. 492 192 360 556 671 1,820 1,711 Belarus .. .. .. .. 1,641 410 2,233 754 .. .. Belgium .. 38 7,661 15,882 6,399 .. 12,805 24,132 25,579 58,678 Benin .. .. .. .. .. .. .. .. .. .. Bolivia .. 46 .. 529 .. .. 4,432 2,343 21,519 26,282 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 45 .. 894 961 650 1,223 3,250 2,884 7,791 .. Brazil .. .. 1,690 1,308 .. .. 10,080 14,134 43,232 61,595 Bulgaria .. .. .. 573 .. 1,372 2,485 1,179 .. .. Burkina Faso .. .. 695 585 .. .. 3,282 .. 15,886 .. Burundi .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. Canada 38 38 4,974 7,897 20,429 30,625 17,710 28,424 36,903 60,712 Central African Republic .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile 43 45 663 1,781 .. .. 6,234 5,822 32,805 32,977 China .. .. .. .. 349 325 472 729 3,061 2,885 Hong Kong, China 48 46 .. .. .. .. 4,127 10,353 7,886 32,611 Colombia .. .. .. 1,128 .. .. 2,988 2,507 15,096 17,061 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. Costa Rica .. 47 1,042 1,638 982 1,697 2,433 2,829 7,185 7,184 Côte d'Ivoire .. .. 1,246 871 .. .. 5,132 9,995 16,158 .. Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 43 43 .. 942 2,277 3,090 2,306 3,815 5,782 5,094 Denmark .. 37 9,170 19,933 .. .. 16,169 29,235 27,919 49,273 Dominican Republic 44 44 .. 1,439 .. .. 2,191 1,806 8,603 .. Ecuador .. .. 1,637 492 .. .. 5,065 3,738 12,197 9,747 Egypt, Arab Rep. 58 .. 343 415 .. .. 2,210 1,863 3,691 5,976 El Salvador .. .. .. 790 .. .. 3,654 .. 14,423 .. Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia .. .. .. .. .. .. .. 1,596 .. 7,094 Finland .. 38 .. b .. .. 11,522 26,615 25,945 55,037 France 40 39 6,053 12,072 .. .. 18,488 .. 26,751 61,019 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. Germany 41 40 .. b .. .. 15,708 33,226 34,945 79,616 Ghana .. .. .. .. 1,470 .. 2,306 .. 12,130 .. Greece .. 41 .. 6,057 .. .. 6,461 12,296 14,561 30,429 Guatemala .. .. .. 459 .. .. 2,605 1,802 11,144 9,235 Guinea 40 .. .. .. .. .. .. .. .. .. Guinea-Bissau 48 .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 66 2006 World Development Indicators Wages and productivity Hours worked Minimum wage Agricultural wage Labor cost per worker Value added per worker in manufacturing in manufacturing average per week $ per year $ per year $ per year $ per year 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a Honduras .. 44 .. .. 1,623 .. 2,949 2,658 7,458 7,427 Hungary 35 33 1,186 1,132 1,186 2,676 1,410 3,755 4,307 10,918 India 46 .. .. 408 205 245 1,035 1,192 2,108 3,118 Indonesia 40 43 .. 241 .. .. 898 3,054 3,807 5,139 Iran, Islamic Rep. .. .. .. .. .. .. 9,737 30,562 17,679 89,787 Iraq .. .. .. .. .. .. 4,624 13,288 13,599 34,316 Ireland 41 41 5,556 12,087 .. .. 10,190 22,681 26,510 86,036 Israel 36 36 .. 5,861 4,582 7,906 13,541 21,150 23,459 35,526 Italy .. 32 .. b .. .. 9,955 34,859 24,580 50,760 Jamaica .. 39 782 692 .. .. 5,218 3,655 12,056 11,091 Japan 47 47 3,920 12,265 .. .. 12,306 31,687 34,456 92,582 Jordan .. 50 b b .. .. 4,643 2,082 16,337 11,906 Kazakhstan .. .. .. .. .. .. .. .. .. .. Kenya 41 39 .. 551 508 568 1,043 810 2,345 1,489 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 52 48 .. 3,903 .. .. 3,153 10,743 11,617 40,916 Kuwait .. .. .. 8,244 .. .. 10,281 .. 30,341 .. Kyrgyz Republic .. .. .. 65 1,695 168 2,287 687 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. 366 .. .. Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho .. 45 .. .. .. .. 1,442 .. 6,047 .. Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. 8,648 .. 21,119 .. Lithuania .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar .. 40 .. .. .. .. 1,575 .. 3,542 .. Malawi .. .. .. .. .. .. .. .. .. .. Malaysia .. .. .. b 1,435 .. 2,519 3,429 8,454 12,661 Mali .. .. 321 459 .. .. 2,983 .. 10,477 .. Mauritania .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. 1,465 1,973 2,969 4,217 Mexico 43 45 1,343 768 1,031 908 3,772 7,607 17,448 25,931 Moldova .. .. .. .. .. .. .. .. .. .. Mongolia .. .. .. .. .. .. .. .. .. .. Morocco .. .. .. 1,672 .. .. 2,583 3,391 6,328 9,089 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. .. .. .. .. Nepal .. .. .. .. .. .. 371 .. 1,523 .. Netherlands 40 40 9,074 15,170 .. .. 18,891 34,326 27,491 56,801 New Zealand 39 39 3,309 9,091 .. .. 10,605 18,419 16,835 32,723 Nicaragua .. 44 .. .. .. .. .. .. .. .. Niger 40 .. .. .. .. .. 4,074 .. 22,477 .. Nigeria .. .. .. 300 .. .. 4,812 .. 20,000 .. Norway 35 35 .. b .. .. 14,935 38,415 24,905 51,510 Oman .. .. .. .. .. .. .. 3,099 .. 61,422 Pakistan 48 .. .. 600 427 416 1,264 .. 6,214 .. Panama .. .. .. .. .. .. 4,768 6,351 15,327 17,320 Papua New Guinea 44 .. .. .. .. .. 4,825 .. 13,563 .. Paraguay 36 39 .. .. 1,606 1,210 2,509 3,241 .. 14,873 Peru 48 .. .. .. .. 944 2,988 .. 15,962 .. Philippines 47 43 915 1,472 382 .. 1,240 2,450 5,266 10,781 Poland 36 33 320 1,584 1,726 1,301 1,682 1,714 6,242 7,637 Portugal 39 40 1,606 4,086 .. .. 3,115 6,237 7,161 17,273 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 67 Wages and productivity Hours worked Minimum wage Agricultural wage Labor cost per worker Value added per worker in manufacturing in manufacturing average per week $ per year $ per year $ per year $ per year 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a Romania 34 34 .. 531 1,669 1,864 1,757 1,190 .. 3,482 Russian Federation .. .. 863 297 2,417 659 2,524 1,528 .. .. Rwanda .. .. .. .. .. .. 1,871 .. 9,835 .. Saudi Arabia .. .. .. .. .. .. 9,814 .. .. .. Senegal .. .. 993 848 .. .. 2,828 7,754 6,415 .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 44 .. .. .. .. .. 1,624 .. 7,807 .. Singapore 46 47 .. .. .. 4,856 5,576 21,317 16,442 40,674 Slovak Republic 43 40 .. .. 2,277 1,885 2,306 1,876 5,782 5,094 Slovenia .. .. .. .. .. .. .. 9,632 .. 12,536 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 42 41 .. b 888 .. 6,261 8,475 12,705 16,612 Spain 38 37 3,058 5,778 .. .. 8,276 19,329 18,936 47,016 Sri Lanka 50 53 .. .. 198 264 447 604 2,057 3,405 Sudan .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. Sweden 36 37 .. .. 9,576 27,098 13,038 26,601 32,308 56,675 Switzerland 44 42 .. b .. .. .. .. 61,848 Syrian Arab Republic .. .. .. .. .. .. 2,844 4,338 9,607 9,918 Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania .. .. .. .. .. .. 1,123 .. 3,339 .. Thailand 50 47 749 1,159 .. .. 2,305 3,868 11,072 19,946 Togo .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. 40 .. 2,974 .. .. .. .. 14,008 .. Tunisia .. .. 1,381 1,525 668 968 3,344 3,599 7,111 .. Turkey .. 48 594 1,254 1,015 2,896 3,582 7,958 13,994 32,961 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 43 .. .. .. .. .. 253 .. .. .. Ukraine .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. 6,968 .. 20,344 .. United Kingdom 42 40 .. b .. .. 11,406 23,843 24,716 55,060 United States 40 41 6,006 8,056 .. .. 19,103 28,907 47,276 81,353 Uruguay 48 42 1,262 1,027 1,289 .. 4,128 3,738 13,722 16,028 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 41 .. 1,869 1,463 .. .. 11,188 4,667 37,063 24,867 Vietnam .. 47 .. 134 .. 442 .. 711 .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. .. .. 4,492 1,291 17,935 5,782 Zambia .. 45 .. .. .. .. 3,183 4,292 11,753 16,615 Zimbabwe .. .. .. .. 1,065 .. 4,097 3,422 9,625 11,944 Note: Data are period averages. a. Figures in italics refer to 1990­94. b. Country has sectoral minimum wage but no minimum wage policy. 68 2006 World Development Indicators Wages and productivity About the data Definitions Much of the data on labor markets are collected In many developing countries agricultural workers · Hours worked are average hours per week actu- through national reporting systems that depend on are hired on a casual or daily basis and lack any ally worked, hours paid for, or statutory hours of plant-level surveys. Even when these data are com- social security benefits. International comparisons of work in a normal workweek for all workers (male and piled and reported by international agencies such as agricultural wages should be subject to more caution female) in nonagricultural activities or, if unavailable, the International Labour Organization or the United than those of wages in other activities. The nature in manufacturing. · Minimum wage corresponds to Nations Industrial Development Organization, differ- of the work carried out by different categories of the most general regime for nonagricultural activi- ences in definitions, coverage, and units of account agricultural workers and the length of the workday ties. When rates vary across sectors, only that for limit their comparability across countries. The indica- and workweek vary considerably from one country to manufacturing (or commerce, if the manufacturing tors in this table are the result of a research project another. Seasonal fluctuations in agricultural wages wage is unavailable) is reported. · Agricultural wage at the World Bank that has compiled results from are more important in some countries than in others. is the daily wage in agriculture. To ensure comparabil- more than 300 national and international sources And the methods followed in different countries for ity with the other wage series, full employment over to provide a set of uniform and representative labor estimating the monetary value of payments in kind the year is assumed, although many wage earners market indicators. Nevertheless, many differences are not uniform. in agriculture are employed seasonally. · Labor cost in reporting practices persist, some of which are Labor cost per worker in manufacturing is some- per worker in manufacturing is the total payroll of described below. The purpose of the table is to times used as a measure of international com- manufacturing establishments divided by the number explore the relationship between labor markets and petitiveness. The indicator reported in the table of people employed or engaged in those establish- economic growth in the long run, not to follow labor is the ratio of total compensation to the number ments. · Value added per worker in manufactur- market developments in the short run. of workers in the manufacturing sector. Com- ing is the value added of manufacturing establish- Analyses of labor force participation, employment, pensation includes direct wages, salaries, other ments divided by the number of people employed or and underemployment often rely on the number of remuneration paid directly by employers, and all engaged in those establishments. hours worked per week, which is the time spent at contributions by employers to social security pro- the workplace working, preparing for work, or waiting grams on behalf of their employees. But there are for work to be supplied or for a machine to be fixed. It unavoidable differences in concepts and reference also includes the time spent at the workplace when periods and in reporting practices. Remuneration no work is being performed but for which payment for time not worked, bonuses and gratuities, and is made under a guaranteed work contract and time housing and family allowances should be consid- spent on short periods of rest. Hours paid for but ered part of the compensation costs, along with not spent at the workplace--such as paid annual severance and termination pay. These indirect and sick leave, paid holidays, paid meal breaks, and labor costs can vary substantially from country to time spent commuting--are not included. When this country, depending on labor laws and collective information is not available, the number of hours bargaining agreements. paid for--the hours actually worked plus the hours International competitiveness also depends on paid for but not spent in the workplace--is reported. productivity, which is often measured by value added Data on hours worked are influenced by differences per worker in manufacturing. The indicator reported in methods of compilation and coverage and by in the table is the ratio of total value added in manu- national practices relating to number of days worked facturing to the number of employees engaged in and overtime, making comparisons across countries that sector. Total value added is estimated as the difficult. difference between the value of industrial output and Wages refer to remuneration in cash and in kind the value of materials and supplies for production paid to employees at regular intervals. They exclude (including fuel and purchased electricity) and cost employer contributions to social security and pen- of industrial services received. sion schemes as well as other benefits received by Observations on labor costs and value added per employees under these schemes. In some countries worker are from plant surveys covering relatively large the national minimum wage represents a "floor," establishments, usually employing 10 or more work- with higher minimum wages for particular occupa- ers and mostly in the formal sector. In high-income tions and skills sets through collective bargaining. In countries the coverage of these surveys tends to be those countries the agreements reached by employ- quite good. In developing countries there is often a ers associations and trade unions are extended by substantial bias toward very large establishments Data sources the government to all firms in the sector or at least to in the formal sector. As a result, the data may not Data on wages and productivity are drawn from Mar- large firms. Changes in the national minimum wage be strictly comparable across countries. The data tin Rama and Raquel Artecona's "Database of Labor are generally associated with parallel changes in the are converted into U.S. dollars using the average Market Indicators across Countries" (2002). minimum wages set through collective bargaining. exchange rate for each year. 2006 World Development Indicators 69 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 2002 29.6 19.8 25.4 .. .. .. 2002a <2 <0.5 11.8 2.0 Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995a <2 <0.5 15.1 3.8 Angola .. .. .. .. .. .. .. .. .. .. Argentina 1995 .. 28.4 1998 .. 29.9 .. 2003 b 7.0 2.0 23.0 8.4 Armenia 1998­99 50.8 58.3 55.1 2001 48.7 51.9 50.9 2003a <2 <0.5 31.1 7.1 Australia .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. Azerbaijan 1995 .. .. 68.1 2001 42.0 55.0 49.0 2002a <2 <0.5 <2 <0.5 Bangladesh 1995­96 55.2 29.4 51.0 2000 53.0 36.6 49.8 2000a 36.0 8.1 82.8 36.3 Belarus 2000 .. .. 41.9 .. .. .. 2002a <2 <0.5 <2 <0.5 Belgium .. .. .. .. .. .. .. .. .. .. Benin 1995 25.2 28.5 26.5 1999 33.0 23.3 29.0 2003a 30.9 8.2 73.7 31.7 Bolivia 1997 77.3 53.8 63.2 1999 81.7 50.6 62.7 2002b 23.2 13.6 42.2 23.2 Bosnia and Herzegovina 2001­02 19.9 13.8 19.5 .. .. .. .. .. .. .. Botswana .. .. .. .. .. .. 1993a 23.5 7.7 50.1 22.8 Brazil 1996 54.0 15.4 23.9 1998 51.4 14.7 22.0 2003 b 7.5 3.4 21.2 8.5 Bulgaria 1997 .. .. 36.0 2001 .. .. 12.8 2003a <2 <0.5 6.1 1.5 Burkina Faso 1998 61.1 22.4 54.6 2003 52.4 19.2 46.4 2003a 27.2 7.3 71.8 30.4 Burundi 1990 36.0 43.0 36.4 .. .. .. 1998a 54.6 22.7 87.6 48.9 Cambodia 1997 40.1 21.1 36.1 1999 40.1 13.9 35.9 1997a 34.1 9.7 77.7 34.5 Cameroon 1996 59.6 41.4 53.3 2001 49.9 22.1 40.2 2001a 17.1 4.1 50.6 19.3 Canada .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. 1993a 66.6 38.1 84.0 58.4 Chad 1995­96 67.0 63.0 64.0 .. .. .. .. .. .. .. Chile 1996 .. .. 19.9 1998 .. .. 17.0 2000 b <2 <0.5 9.6 2.5 China 1996 7.9 <2 6.0 1998 4.6 <2 4.6 2001a 16.6 3.9 46.7 18.4 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 1995 79.0 48.0 60.0 1999 79.0 55.0 64.0 2003 b 7.0 3.1 17.8 7.7 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. Costa Rica 1992 25.5 19.2 22.0 .. .. .. 2001b 2.2 0.8 7.5 2.8 Côte d'Ivoire .. .. .. .. .. .. 2002a 14.8 4.1 48.8 18.4 Croatia .. .. .. .. .. .. 2001a <2 <0.5 <2 <0.5 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. 1996b <2 <0.5 <2 <0.5 Denmark .. .. .. .. .. .. .. .. .. .. Dominican Republic 1992 49.0 19.3 33.9 1998 42.1 20.5 28.6 2003b 2.5 0.8 11.0 3.6 Ecuador 1995 56.0 19.0 34.0 1998 69.0 30.0 46.0 1998b 15.8 6.3 37.2 15.8 Egypt, Arab Rep. 1995­96 23.3 22.5 22.9 1999­2000 .. .. 16.7 1999­2000a 3.1 <0.5 43.9 11.3 El Salvador 1992 55.7 43.1 48.3 .. .. .. 2002b 19.0 9.3 40.6 17.7 Eritrea 1993­94 .. .. 53.0 .. .. .. .. .. .. .. Estonia 1995 14.7 6.8 8.9 .. .. .. 2003a <2 <0.5 7.5 1.9 Ethiopia 1995­96 47.0 33.3 45.5 1999­2000 45.0 37.0 44.2 1999­2000a 23.0 4.8 77.8 29.6 Finland .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The 1992 .. .. 64.0 1998 61.0 48.0 57.6 1998a 59.3 28.8 82.9 51.1 Georgia 2002 55.4 48.5 52.1 2003 52.7 56.2 54.5 2003a 6.5 2.1 25.3 8.6 Germany .. .. .. .. .. .. .. .. .. .. Ghana 1992 .. .. 50.0 1998­99 49.9 18.6 39.5 1998­99a 44.8 17.3 78.5 40.8 Greece .. .. .. .. .. .. .. .. .. .. Guatemala 1989 71.9 33.7 57.9 2000 74.5 27.1 56.2 2002b 13.5 5.5 31.9 13.8 Guinea 1994 .. .. 40.0 .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 1987 .. .. 65.0 1995 66.0 .. .. 2001b 53.9 26.6 78.0 47.4 70 2006 World Development Indicators Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Honduras 1997 58.0 35.0 47.0 1999 58.0 37.0 48.0 1999 b 20.7 7.5 44.0 20.2 Hungary 1993 .. .. 14.5 1997 .. .. 17.3 2002a <2 <0.5 <2 <0.5 India 1993­94 37.3 32.4 36.0 1999­2000 30.2 24.7 28.6 1999­2000a 34.7 8.2 52.4 15.7 Indonesia 1996 .. .. 15.7 1999 34.4 16.1 27.1 2002a 7.5 0.9 52.4 15.7 Iran, Islamic Rep. .. .. .. .. .. .. 1998a <2 <0.5 7.3 1.5 Iraq .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. Jamaica 1995 37.0 18.7 27.5 2000 25.1 12.8 18.7 2000a <2 <0.5 13.3 2.7 Japan .. .. .. .. .. .. .. .. .. .. Jordan 1991 .. .. 15.0 1997 .. .. 11.7 2002­03a <2 <0.5 7.0 1.5 Kazakhstan 1996 39.0 30.0 34.6 .. .. .. 2003a <2 <.5 16.0 3.8 Kenya 1994 47.0 29.0 40.0 1997 53.0 49.0 52.0 1997a 22.8 5.9 58.3 23.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. 1998b <2 <0.5 <2 <0.5 Kuwait .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2000 56.4 43.9 52.0 2001 51.0 41.2 47.6 2003a <2 <0.5 21.4 4.4 Lao PDR 1993 48.7 33.1 45.0 1997­98 41.0 26.9 38.6 2002a 27.0 6.1 74.1 30.2 Latvia .. .. .. .. .. .. 2003a <2 <0.5 4.7 1.2 Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. 1995a 36.4 19.0 56.1 33.1 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. 2003a <2 <0.5 7.8 1.8 Macedonia, FYR .. .. .. .. .. .. 2003a <2 <0.5 <2 <0.5 Madagascar 1997 76.0 63.2 73.3 1999 76.7 52.1 71.3 2001a 61.0 27.9 85.1 51.8 Malawi 1990­91 .. .. 54.0 1997­98 66.5 54.9 65.3 1997­98a 41.7 14.8 76.1 38.3 Malaysia 1989 .. .. 15.5 .. .. .. 1997b <2 <0.5 9.3 2.0 Mali 1998 75.9 30.1 63.8 .. .. .. 1994a 72.3 37.4 90.6 60.5 Mauritania 1996 65.5 30.1 50.0 2000 61.2 25.4 46.3 2000a 25.9 7.6 63.1 26.8 Mauritius .. .. .. .. .. .. .. .. .. .. Mexico 1996 52.4 26.5 37.1 2002 34.8 11.4 20.3 2002a 4.5 1.2 20.4 6.5 Moldova 2001 64.1 58.0 62.4 2002 67.2 42.6 48.5 2001a 22.0 5.8 63.7 25.1 Mongolia 1995 33.1 38.5 36.3 1998 32.6 39.4 35.6 1998a 27.0 8.1 74.9 30.6 Morocco 1990­91 18.0 7.6 13.1 1998­99 27.2 12.0 19.0 1999a <2 <0.5 14.3 3.1 Mozambique 1996­97 71.3 62.0 69.4 .. .. .. 1996a 37.9 12.0 78.4 36.8 Myanmar .. .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. 1993 b 34.9 14.0 55.8 30.4 Nepal 1995­96 43.3 21.6 41.8 2003­04 34.6 9.6 30.9 2003­04a 24.1 5.4 68.5 26.8 Netherlands .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. Nicaragua 1993 76.1 31.9 50.3 1998 68.5 30.5 47.9 2001a 45.1 16.7 79.9 41.2 Niger 1989­93 66.0 52.0 63.0 .. .. .. 1995a 60.6 34.0 85.8 54.6 Nigeria 1985 49.5 31.7 43.0 1992­93 36.4 30.4 34.1 2003a 70.8 34.5 92.4 59.5 Norway .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. Pakistan 1993 33.4 17.2 28.6 1998­99 35.9 24.2 32.6 2002a 17.0 3.1 73.6 26.1 Panama 1997 64.9 15.3 37.3 .. .. .. 2002b 6.5 2.3 17.1 6.9 Papua New Guinea 1996 41.3 16.1 37.5 .. .. .. .. .. .. .. Paraguay 1991 28.5 19.7 21.8 .. .. .. 2002b 16.4 7.4 33.2 16.2 Peru 1994 67.0 46.1 53.5 1997 64.7 40.4 49.0 2002b 12.5 4.4 31.8 13.4 Philippines 1994 53.1 28.0 40.6 1997 50.7 21.5 36.8 2000a 15.5 3.0 47.5 17.8 Poland 1993 .. .. 23.8 .. .. .. 2002a <2 <0.5 <2 <0.5 Portugal .. .. .. .. .. .. 1994b <2 <0.5 <2 <0.5 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 71 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Romania 1994 27.9 20.4 21.5 .. .. .. 2003a <2 0.5 12.9 3.0 Russian Federation 1994 .. .. 30.9 .. .. .. 2002a <2 <0.5 12.1 3.1 Rwanda 1993 .. .. 51.2 1999­2000 65.7 14.3 60.3 1999­2000a 51.7 20.0 83.7 45.5 Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegal 1992 40.4 23.7 33.4 .. .. .. 1995a 22.3 5.7 63.0 25.2 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 1989 .. .. 82.8 2003­04 79.0 56.4 70.2 1989a 57.0 39.5 74.5 51.8 Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. 1996b <2 <0.5 2.9 0.8 Slovenia .. .. .. .. .. .. 1998a <2 <0.5 <2 <0.5 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. 2000a 10.7 1.7 34.1 12.6 Spain .. .. .. .. .. .. .. .. .. .. Sri Lanka 1990­91 22.0 15.0 20.0 1995­96 27.0 15.0 25.0 2002a 5.6 0.8 41.6 11.9 Sudan .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. 2003a 7.4 1.3 42.8 13.0 Tanzania 1991 40.8 31.2 38.6 2000­01 38.7 29.5 35.7 2000­01a 57.8 20.7 89.9 49.3 Thailand 1990 .. .. 18.0 1992 15.5 10.2 13.1 2002a <2 <0.5 25.2 6.2 Togo 1987­89 .. .. 32.3 .. .. .. .. .. .. .. Trinidad and Tobago 1992 20.0 24.0 21.0 .. .. .. 1992b 12.4 3.5 39.0 14.6 Tunisia 1990 13.1 3.5 7.4 1995 13.9 3.6 7.6 2000a <2 <0.5 6.6 1.3 Turkey 1994 .. .. 28.3 2002 34.5 22.0 27.0 2003a 3.4 0.8 18.7 5.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 1999­2000 37.4 9.6 33.8 2002­03 41.7 12.2 37.7 .. .. .. .. Ukraine 2000 34.9 31.5 2003 28.4 .. 19.5 2003 b <2 <0.5 4.9 0.9 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. Uruguay 1994 .. 20.2 .. 1998 .. 24.7 .. 2003 b <2 <0.5 5.7 1.6 Uzbekistan 2000 30.5 22.5 27.5 .. .. .. .. .. .. .. Venezuela, RB 1989 .. .. 31.3 .. .. .. 2000 b 8.3 2.8 27.6 10.2 Vietnam 1998 45.5 9.2 37.4 2002 35.6 6.6 28.9 .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1998 45.0 30.8 41.8 .. .. .. 1998a 15.7 4.5 45.2 15.0 Zambia 1996 82.8 46.0 69.2 1998 83.1 56.0 72.9 2002­03a 75.8 36.4 94.1 62.2 Zimbabwe 1990­91 35.8 3.4 25.8 1995­96 48.0 7.9 34.9 1995­96a 56.1 24.2 83.0 48.2 a. Expenditure base. b. Income base. 72 2006 World Development Indicators Poverty Regional poverty estimates Region 1981 1984 1987 1990 1993 1996 1999 2002a People living on less than $1 a day (millions) East Asia & Pacific 796 562 426 472 415 287 282 214 China 634 425 308 375 334 212 223 180 Europe & Central Asia 3 2 2 2 17 20 30 10 Latin America & Caribbean 36 46 45 49 52 52 54 47 Middle East & North Africa 9 8 7 6 4 5 8 5 South Asia 475 460 473 462 476 461 429 437 Sub-Saharan Africa 164 198 219 227 242 271 294 303 Total 1,482 1,277 1,171 1,218 1,208 1,097 1,096 1,015 Excluding China 848 852 863 844 873 886 873 835 Share of people living on less than $1 a day (%) East Asia & Pacific 57.7 38.9 28.0 29.6 24.9 16.6 15.7 11.6 China 63.8 41.0 28.5 33.0 28.4 17.4 17.8 14.0 Europe & Central Asia 0.7 0.5 0.4 0.5 3.7 4.3 6.3 2.1 Latin America & Caribbean 9.7 11.8 10.9 11.3 11.3 10.7 10.5 8.9 Middle East & North Africa 5.1 3.8 3.2 2.3 1.6 2.0 2.6 1.6 South Asia 51.5 46.8 45.0 41.3 40.1 36.6 32.2 31.2 Sub-Saharan Africa 41.6 46.3 46.8 44.6 44.0 45.6 45.7 44.0 Total 40.4 32.8 28.4 27.9 26.3 22.8 21.8 19.4 Excluding China 31.7 29.8 28.4 26.1 25.6 24.6 23.1 21.1 People living on less than $2 a day (millions) East Asia & Pacific 1,170 1,109 1,028 1,116 1,079 922 900 748 China 876 814 731 825 803 650 627 533 Europe & Central Asia 20 18 15 23 81 98 113 76 Latin America & Caribbean 99 119 115 125 136 117 127 123 Middle East & North Africa 52 50 53 51 52 61 70 61 South Asia 821 859 911 958 1,005 1,029 1,039 1,091 Sub-Saharan Africa 288 326 355 382 410 447 489 516 Total 2,450 2,480 2,478 2,654 2,764 2,674 2,739 2,614 Excluding China 1,574 1,666 1,747 1,829 1,961 2,024 2,111 2,082 Share of people living on less than $2 a day (%) East Asia & Pacific 84.8 76.6 67.7 69.9 64.8 53.3 50.3 40.7 China 88.1 78.5 67.4 72.6 68.1 53.4 50.1 41.6 Europe & Central Asia 4.7 4.1 3.3 4.9 17.2 20.7 23.8 16.1 Latin America & Caribbean 26.9 30.4 27.8 28.4 29.5 24.1 25.1 23.4 Middle East & North Africa 28.9 25.2 24.2 21.4 20.2 22.3 24.3 19.8 South Asia 89.1 87.2 86.7 85.5 84.5 81.7 78.1 77.8 Sub-Saharan Africa 73.3 76.1 76.1 75.0 74.6 75.1 76.1 74.9 Total 66.7 63.7 60.1 60.8 60.2 55.5 54.4 50.0 Excluding China 58.8 58.4 57.5 56.6 57.4 56.3 55.8 52.7 Note: Estimates are computed based on population data from World Development Indicators 2005. a. Preliminary estimates not strictly comparable with earlier estimates. See About the data for more information. 2006 World Development Indicators 73 Poverty About the data The World Bank produced its first global poverty because of differences in timing or the quality and when making comparisons over time. The commonly estimates for developing countries for World Devel- training of survey enumerators. used $1 a day standard, measured in 1985 interna- opment Report 1990 using household survey data Comparisons of countries at different levels of tional prices and adjusted to local currency using for 22 countries (Ravallion, Datt, and van de Walle development also pose a potential problem because purchasing power parities (PPPs), was chosen for 1991). Incorporating survey data collected during the of differences in the relative importance of consump- the World Bank's World Development Report 1990: last 15 years, the database has expanded consid- tion of nonmarket goods. The local market value of Poverty because it is typical of the poverty lines in erably and now includes 440 surveys representing all consumption in kind (including own production, low-income countries. PPP exchange rates, such as almost 100 developing countries. Some 1.1 million particularly important in underdeveloped rural econo- those from the Penn World Tables or the World Bank, randomly sampled households were interviewed in mies) should be included in total consumption expen- are used because they take into account the local these surveys, representing 93 percent of the popu- diture. Similarly, imputed profit from the production of prices of goods and services not traded internation- lation of developing countries. The surveys asked nonmarket goods should be included in income. This ally. But PPP rates were designed for comparing detailed questions on sources of income and how it is not always done, though such omissions were a far aggregates from national accounts, not for making was spent and on other household characteristics bigger problem in surveys before the 1980s. Most international poverty comparisons. As a result, there such as the number of people sharing that income. survey data now include valuations for consumption is no certainty that an international poverty line mea- Most interviewas were conducted by staff of govern- or income from own production. Nonetheless, valu- sures the same degree of need or deprivation across ment statistics offices. Along with improvements in ation methods vary. For example, some surveys use countries. data coverage and quality, the underlying methodol- the price in the nearest market, while others use the Early editions of World Development Indicators ogy has also improved, resulting in better and more average farmgate selling price. used PPPs from the Penn World Tables. Recent edi- comprehensive estimates. Whenever possible, the table uses consumption tions use 1993 consumption PPP estimates pro- data for deciding who is poor and income surveys only duced by the World Bank. Recalculated in 1993 PPP Data availability when consumption data are unavailable. In recent terms, the original international poverty line of $1 a Since 1979 there has been considerable expansion editions there has been a change in how income day in 1985 PPP terms is now about $1.08 a day. in the number of countries that field such surveys, surveys are used. In the past, average household Any revisions in the PPP of a country to incorporate the frequency of the surveys, and the quality of their income was adjusted to accord with consumption better price indexes can produce dramatically differ- data. The number of data sets rose dramatically from and income data from national accounts. But in test- ent poverty lines in local currency. a mere 13 between 1979 and 1981 to 100 between ing this approach using data for some 20 countries Issues also arise when comparing poverty mea- 1997 and 1999. The drop to 41 available surveys for which income and consumption expenditure data sures within countries. For example, the cost of living after 1999 reflects the lag between the time data were both available from the same surveys, income is typically higher in urban than in rural areas. One are collected and the time they become available was found to yield a higher mean than consumption reason is that food staples tend to be more expen- for analysis, not a reduction in data collection. Data but also higher inequality. When poverty measures sive in urban areas. So the urban monetary poverty coverage is improving in all regions, but Sub-Saharan based on consumption and income were compared, line should be higher than the rural poverty line. But it Africa continues to lag, with only 28 of 48 countries these two effects roughly cancelled each other out: is not always clear that the difference between urban having at least one data set available. A complete statistically, there was no significant difference. and rural poverty lines found in practice reflects only overview of data availability by year and country So recent editions use income data to estimate differences in the cost of living. In some countries can be obtained at http://iresearch.worldbank.org/ poverty directly, without adjusting average income the urban poverty line in common use has a higher povcalnet/. measures. real value--meaning that it allows the purchase of more commodities for consumption--than does Data quality International poverty lines the rural poverty line. Sometimes the difference The problems of estimating poverty and comparing International comparisons of poverty estimates has been so large as to imply that the incidence of poverty rates do not end with data availability. Sev- entail both conceptual and practical problems. poverty is greater in urban than in rural areas, even eral other issues, some related to data quality, also Countries have different definitions of poverty, and though the reverse is found when adjustments are arise in measuring household living standards from consistent comparisons across countries can be made only for differences in the cost of living. As with survey data. One relates to the choice of income difficult. Local poverty lines tend to have higher international comparisons, when the real value of the or consumption as a welfare indicator. Income is purchasing power in rich countries, where more gen- poverty line varies it is not clear how meaningful such generally more difficult to measure accurately, and erous standards are used, than in poor countries. urban-rural comparisons are. consumption comes closer to the notion of stan- Is it reasonable to treat two people with the same By combining all this information, a team in the dard of living. And income can vary over time even standard of living--in terms of their command over World Bank's Development Research Group cal- if the standard of living does not. But consumption commodities--differently because one happens to culates the number of people living below various data are not always available. Another issue is that live in a better-off country? international poverty lines, as well as other poverty household surveys can differ widely, for example, in Poverty measures based on an international and inequality measures that are published in World the number of consumer goods they identify. And poverty line attempt to hold the real value of the Development Indicators. The database is updated even similar surveys may not be strictly comparable poverty line constant across countries, as is done annually as new survey data become available, and 74 2006 World Development Indicators Poverty Definitions a major reassessment of progress against poverty Note on the 2002 estimates · Survey year is the year in which the underlying data is made about every three years. The 2002 estimates are adapted from Global Eco- were collected. · Rural poverty rate is the percent- nomic Prospects 2006 (page 9, table 1.3). Note age of the rural population living below the national Do it yourself: PovcalNet that a typesetting error occurred in the printed edi- rural poverty line. · Urban poverty rate is the per- Recently, this research team developed PovcalNet, tion of Global Economic Prospects 2006; the 2002 centage of the urban population living below the an interactive Web-based computational tool that poverty rate estimates reported in table 2.7a are national urban poverty line. · National poverty rate allows users to replicate the calculations by the the correct estimates. is the percentage of the population living below the World Bank's researchers in estimating the extent national poverty line. National estimates are based of absolute poverty in the world. PovcalNet is self- on population-weighted subgroup estimates from contained and powered by reliable built-in software household surveys. · Population below $1 a day that performs the relevant calculations from a pri- and population below $2 a day are the percentages mary database. The underlying software can also of the population living on less than $1.08 a day and be downloaded from the site and used with distri- $2.15 a day at 1993 international prices. As a result butional data of various formats. The PovcalNet of revisions in PPP exchange rates, poverty rates for primary database consists of distributional data individual countries cannot be compared with poverty calculated directly from household survey data. rates reported in earlier editions. · Poverty gap is Detailed information for each of these is also avail- the mean shortfall from the poverty line (counting able from the site. the nonpoor as having zero shortfall), expressed as a Estimation from distributional data requires an percentage of the poverty line. This measure reflects interpolation method. The method chosen was Lorenz the depth of poverty as well as its incidence. curves with flexible functional forms, which have proved reliable in past work. The Lorenz curve can be graphed as the cumulative percentages of total consumption or income against the cumulative num- ber of people, starting with the poorest individual. The empirical Lorenz curves estimated by PovcalNet are weighted by household size, so they are based on percentiles of population, not households. PovcalNet also allows users to calculate poverty measures under different assumptions. For example, instead of $1 a day, users can specify a different poverty line, say $1.50 or $3. Users can also spec- ify different PPP rates and aggregate the estimates using alternative country groupings (for example, UN country groupings or groupings based on average Data sources incomes) or a selected set of individual countries. The poverty measures are prepared by the World PovcalNet is available online at http://iresearch. Bank's Development Research Group. The national worldbank.org/povcalnet/. poverty lines are based on the World Bank's country poverty assessments. The international poverty lines are based on nationally represen- tative primary household surveys conducted by national statistical offices or by private agencies under the supervision of government or interna- tional agencies and obtained from government statistical offices and World Bank Group country departments. The World Bank Group has prepared an annual review of its poverty work since 1993. For details on data sources and methods used in deriving the World Bank's latest estimates, see Chen and Ravallion (2004), "How Have the World's Poorest Fared Since the Early 1980s?" 2006 World Development Indicators 75 Distribution of income or consumption Gini Percentage share of index income or consumption Survey year Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Afghanistan .. .. .. .. .. .. .. .. Albania 2002a 28.2 3.8 9.1 13.5 17.3 22.8 37.4 22.4 Algeria 1995a 35.3 2.8 7.0 11.6 16.1 22.7 42.6 26.8 Angola .. .. .. .. .. .. .. .. Argentina b 2003 c 52.8 1.1 3.2 7.0 12.1 20.7 56.8 39.6 Armenia 2003a 33.8 3.6 8.5 12.3 15.7 20.6 42.8 29.0 Australia 1994 c 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 2000 c 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0 Azerbaijan 2002a 19.0 5.4 12.2 15.8 18.7 22.2 31.1 18.0 Bangladesh 2000a 31.8 3.9 9.0 12.5 15.9 21.2 41.3 26.7 Belarus 2002a 29.7 3.4 8.5 13.2 17.3 22.7 38.3 23.5 Belgium 2000 c 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Benin 2003a 36.5 3.1 7.4 11.3 15.4 21.5 44.5 29.0 Bolivia 2002c 60.1 0.3 1.5 5.9 10.9 18.7 63.0 47.2 Bosnia and Herzegovina 2001a 26.2 3.9 9.5 14.2 17.9 22.6 35.8 21.4 Botswana 1993a 63.0 0.7 2.2 4.9 8.2 14.4 70.3 56.6 Brazil 2003c 58.0 0.8 2.6 6.2 10.7 18.4 62.1 45.8 Bulgaria 2003a 29.2 3.4 8.7 13.7 17.2 22.1 38.3 23.9 Burkina Faso 2003a 39.5 2.8 6.9 10.9 14.5 20.5 47.2 32.2 Burundi 1998a 42.4 1.7 5.1 10.3 15.1 21.5 48.0 32.8 Cambodia 1997a 40.4 2.9 6.9 10.7 14.7 20.1 47.6 33.8 Cameroon 2001a 44.6 2.3 5.6 9.3 13.7 20.4 50.9 35.4 Canada 2000 c 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Central African Republic 1993a 61.3 0.7 2.0 4.9 9.6 18.5 65.0 47.7 Chad .. .. .. .. .. .. .. .. Chile 2000 c 57.1 1.2 3.3 6.6 10.5 17.4 62.2 47.0 China 2001a 44.7 1.8 4.7 9.0 14.2 22.1 50.0 33.1 Hong Kong, China 1996c 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2003 c 58.6 0.7 2.5 6.2 10.6 18.0 62.7 46.9 Congo, Dem. Rep. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. Costa Rica 2001c 49.9 1.3 3.9 8.1 12.8 20.4 54.8 38.4 Côte d'Ivoire 2002a 44.6 2.0 5.2 9.1 13.7 21.3 50.7 34.0 Croatia 2001a 29.0 3.4 8.3 12.8 16.8 22.6 39.6 24.5 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996 c 25.4 4.3 10.3 14.5 17.7 21.7 35.9 22.4 Denmark 1997c 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 2003c 51.7 1.4 3.9 7.8 12.1 19.4 56.8 41.3 Ecuador 1998a 43.7 0.9 3.3 7.5 11.7 19.4 58.0 41.6 Egypt, Arab Rep. 1999­2000a 34.4 3.7 8.6 12.1 15.4 20.4 43.6 29.5 El Salvador 2002c 52.4 0.7 2.7 7.5 12.8 21.2 55.9 38.8 Eritrea .. .. .. .. .. .. .. .. Estonia 2003a 35.8 2.5 6.7 11.8 16.3 22.4 42.8 27.6 Ethiopia 1999­00a 30.0 3.9 9.1 13.2 16.8 21.5 39.4 25.5 Finland 2000 c 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995c 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon .. .. .. .. .. .. .. .. Gambia, The 1998a 50.2 1.8 4.8 8.7 12.8 20.3 53.4 37.0 Georgia 2003a 40.4 2.0 5.6 10.5 15.3 22.3 46.4 30.3 Germany 2000 c 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 1998­99a 40.8 2.1 5.6 10.1 14.9 22.9 46.6 30.0 Greece 2000 c 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2002c 55.1 0.9 2.9 7.0 11.6 19.0 59.5 43.4 Guinea 1994a 40.3 2.6 6.4 10.4 14.8 21.2 47.2 32.0 Guinea-Bissau 1993a 47.0 2.1 5.2 8.8 13.1 19.4 53.4 39.3 Haiti 2001c 59.2 0.7 2.4 6.2 10.4 17.7 63.4 47.7 76 2006 World Development Indicators Distribution of income or consumption Gini Percentage share of index income or consumption Survey year Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Honduras 2003 c 53.8 1.2 3.4 7.1 11.6 19.6 58.3 42.2 Hungary 2002a 26.9 4.0 9.5 13.9 17.6 22.4 36.5 22.2 India 1999­2000a 32.5 3.9 8.9 12.3 16.0 21.2 43.3 28.5 Indonesia 2002a 34.3 3.6 8.4 11.9 15.4 21.0 43.3 28.5 Iran, Islamic Rep. 1998a 43.0 2.0 5.1 9.4 14.1 21.5 49.9 33.7 Iraq .. .. .. .. .. .. .. .. Ireland 2000 c 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2 Israel 2001c 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8 Italy 2000 c 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2000a 37.9 2.7 6.7 10.7 15.0 21.7 46.0 30.3 Japan 1993 c 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2002­03a 38.8 2.7 6.7 10.8 14.9 21.3 46.3 30.6 Kazakhstan 2003a 33.9 3.0 7.4 11.9 16.4 22.8 41.5 25.9 Kenya 1997a 42.5 2.5 6.0 9.8 14.3 20.8 49.1 33.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998c 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2003a 30.3 3.8 8.9 12.8 16.4 22.5 39.4 24.3 Lao PDR 2002a 34.6 3.4 8.1 11.9 15.6 21.1 43.3 28.5 Latvia 2003a 37.7 2.5 6.6 11.2 15.5 22.0 44.7 29.1 Lebanon .. .. .. .. .. .. .. .. Lesotho 1995a 63.2 0.5 1.5 4.3 8.9 18.8 66.5 48.3 Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 2003a 36.0 2.7 6.8 11.6 16.0 22.3 43.2 27.7 Macedonia, FYR 2003a 39.0 2.4 6.1 10.8 15.5 22.2 45.5 29.6 Madagascar 2001a 47.5 1.9 4.9 8.5 12.7 20.4 53.5 36.6 Malawi 1997a 50.3 1.9 4.9 8.5 12.3 18.3 56.1 42.2 Malaysia 1997c 49.2 1.7 4.4 8.1 12.9 20.3 54.3 38.4 Mali 1994a 50.5 1.8 4.6 8.0 11.9 19.3 56.2 40.4 Mauritania 2000a 39.0 2.5 6.2 10.6 15.2 22.3 45.7 29.5 Mauritius .. .. .. .. .. .. .. .. Mexico 2002a 49.5 1.6 4.3 8.3 12.6 19.7 55.1 39.4 Moldova 2003a 33.2 3.2 7.8 12.2 16.5 22.1 41.4 26.4 Mongolia 1998a 30.3 2.1 5.6 10.0 13.8 19.4 51.2 37.0 Morocco 1998­99a 39.5 2.6 6.5 10.6 14.8 21.3 46.6 30.9 Mozambique 1996­97a 39.6 2.5 6.5 10.8 15.1 21.1 46.5 31.7 Myanmar .. .. .. .. .. .. .. .. Namibia 1993c 74.3 0.5 1.4 3.0 5.4 11.5 78.7 64.5 Nepal 2003­04a 47.2 2.6 6.0 9.0 12.4 18.0 54.6 40.6 Netherlands 1999c 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9 New Zealand 1997c 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2001a 43.1 2.2 5.6 9.8 14.2 21.1 49.3 33.8 Niger 1995a 50.5 0.8 2.6 7.1 13.9 23.1 53.3 35.4 Nigeria 2003a 43.7 1.9 5.0 9.6 14.5 21.7 49.2 33.2 Norway 2000 c 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2002a 30.6 4.0 9.3 13.0 16.3 21.1 40.3 26.3 Panama 2002c 56.4 0.8 2.5 6.4 11.2 19.6 60.3 43.6 Papua New Guinea 1996a 50.9 1.7 4.5 7.9 11.9 19.2 56.5 40.5 Paraguay 2002c 57.8 0.6 2.2 6.3 11.3 18.8 61.3 45.4 Peru 2002c 54.6 1.1 3.2 7.1 11.8 19.3 58.7 43.2 Philippines 2000a 46.1 2.2 5.4 8.8 13.1 20.5 52.3 36.3 Poland 2002a 34.5 3.1 7.5 11.9 16.1 22.2 42.2 27.0 Portugal 1997c 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. 2006 World Development Indicators 77 Distribution of income or consumption Gini Percentage share of index income or consumption Survey year Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Romania 2003a 31.0 3.3 8.1 12.9 17.1 22.7 39.2 24.4 Russian Federation 2002a 39.9 2.4 6.1 10.5 14.9 21.8 46.6 30.6 Rwanda 1983­85a 28.9 4.2 9.7 13.2 16.5 21.6 39.1 24.2 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 1995a 41.3 2.6 6.4 10.3 14.5 20.6 48.2 33.5 Serbia and Montenegro .. .. .. .. .. .. .. .. Sierra Leone 1989a 62.9 0.5 1.1 2.0 9.8 23.7 63.4 43.6 Singapore 1998c 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996c 25.8 3.1 8.8 14.9 18.7 22.8 34.8 20.9 Slovenia 1998­99c 28.4 3.6 9.1 14.2 18.1 22.9 35.7 21.4 Somalia .. .. .. .. .. .. .. .. South Africa 2000a 57.8 1.4 3.5 6.3 10.0 18.0 62.2 44.7 Spain 2000 c 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 1999­2000a 33.2 3.4 8.3 12.5 16.0 21.0 42.2 27.8 Sudan .. .. .. .. .. .. .. .. Swaziland 1994 c 60.9 1.0 2.7 5.8 10.0 17.1 64.4 50.2 Sweden 2000 c 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 2000 c 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9 Syrian Arab Republic .. .. .. .. .. .. .. .. Tajikistan 2003a 32.6 3.3 7.9 12.3 16.5 22.4 40.8 25.6 Tanzania 2000­01a 34.6 2.9 7.3 12.0 16.1 22.3 42.4 26.9 Thailand 2002a 42.0 2.7 6.3 9.9 14.0 20.8 49.0 33.4 Togo .. .. .. .. .. .. .. .. Trinidad and Tobago 1992c 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 2000a 39.8 2.3 6.0 10.3 14.8 21.7 47.3 31.5 Turkey 2003a 43.6 2.0 5.3 9.7 14.2 21.0 49.7 34.1 Turkmenistan 1998a 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7 Uganda 1999a 43.0 2.3 5.9 10.0 14.0 20.3 49.7 34.9 Ukraine 2003a 28.1 3.9 9.2 13.6 17.3 22.4 37.5 23.0 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 1999 c 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000 c 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguay b 2003 c 44.9 1.9 5.0 9.1 14.0 21.5 50.5 34.0 Uzbekistan 2000a 26.8 3.6 9.2 14.1 17.9 22.6 36.3 22.0 Venezuela, RB 2000 c 44.1 1.6 4.7 9.4 14.5 22.1 49.3 32.8 Vietnam 2002a 37.0 3.2 7.5 11.2 14.8 21.1 45.4 29.9 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 1998a 33.4 3.0 7.4 12.2 16.7 22.5 41.2 25.9 Zambia 2002­03a 42.1 2.4 6.1 10.2 14.2 20.7 48.8 33.7 Zimbabwe 1995a 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. b. Urban data. c. Refers to income shares by percentiles of population, ranked by per capita income. 78 2006 World Development Indicators Distribution of income or consumption About the data Definitions Inequality in the distribution of income is reflected World Bank staff have made an effort to ensure · Survey year is the year in which the underlying data in the percentage shares of income or consumption that the data are as comparable as possible. Wher- were collected. · Gini index measures the extent to accruing to portions of the population ranked by ever possible, consumption has been used rather which the distribution of income (or consumption income or consumption levels. The portions ranked than income. Income distribution and Gini indexes expenditure) among individuals or households within lowest by personal income receive the smallest shares for high-income countries are calculated directly from an economy deviates from a perfectly equal distribu- of total income. The Gini index provides a convenient the Luxembourg Income Study database, using an tion. A Lorenz curve plots the cumulative percent- summary measure of the degree of inequality. estimation method consistent with that applied for ages of total income received against the cumula- Data on the distribution of income or consump- developing countries. tive number of recipients, starting with the poorest tion come from nationally representative household individual. The Gini index measures the area between surveys. Where the original data from the house- the Lorenz curve and a hypothetical line of absolute hold survey were available, they have been used to equality, expressed as a percentage of the maximum directly calculate the income or consumption shares area under the line. Thus a Gini index of 0 represents by quintile. Otherwise, shares have been estimated perfect equality, while an index of 100 implies per- from the best available grouped data. fect inequality. · Percentage share of income or con- For most countries the income distribution indica- sumption is the share of total income or consumption tors are based on the same data used to derive the that accrues to subgroups of population indicated by $1 and $2 a day poverty estimates in table 2.7. This deciles or quintiles. Percentage shares by quintile table contains additional countries for which poverty may not sum to 100 because of rounding. estimates are not provided in table 2.7, either because no reasonable purchasing power parity estimates are available or because the international poverty lines are not relevant for high-income economies. The distribution data have been adjusted for household size, providing a more consistent mea- sure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavail- able. For further details on the estimation method for low- and middle-income economies, see Raval- lion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achiev- ing strict comparability is still impossible (see About the data for table 2.7). Two sources of noncomparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or con- sumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the Data sources definitions of income used differ more often among surveys. Consumption is usually a much better wel- Data on distribution are compiled by the World fare indicator, particularly in developing countries. Bank's Development Research Group using pri- Second, households differ in size (number of mem- mary household survey data obtained from govern- bers) and in the extent of income sharing among ment statistical agencies and World Bank country members. And individuals differ in age and consump- departments. Data for high-income economies are tion needs. Differences among countries in these from the Luxembourg Income Study database. respects may bias comparisons of distribution. 2006 World Development Indicators 79 Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female pension Male Female labor force labor force % of % of % of per 1995­ 1995­ ages 15­24 ages 15­24 total labor % of capita 2003 a 2003a 2000­04a 2000­04a 1990­2004a Year force Year GDP Year income Afghanistan .. .. .. .. .. .. .. .. Albania .. .. 42 27 .. 2004 40.7 2004 4.6 1995 36.4 Algeria .. .. .. .. .. 2002 38.9 1997 2.1 1991 75.0 Angola .. .. .. .. .. .. .. .. Argentina .. .. 34 34 .. 2004 34.9 1994 6.2 .. Armenia .. .. .. .. 29 1995 66.6 2004 3.4 1996 18.7 Australia .. .. 12 11 .. 2003 92.6 1997 5.9 1989 37.3 Austria .. .. 9 10 .. 2004 86.8 1995 14.9 1993 69.3 Azerbaijan .. .. .. .. .. 1996 52.0 2003 3.0 1996 51.4 Bangladesh .. .. 11 10 10 1993 3.5 1992 0.0 .. Belarus .. .. .. .. .. 1992 97.0 2004 10.6 1995 31.2 Belgium .. .. 16 20 .. 1995 86.2 1997 12.9 .. Benin 50 41 .. .. 21 1996 4.8 2003 1.3 1993 189.7 Bolivia .. .. 7 10 20 2002 10.8 2000 4.5 .. Bosnia and Herzegovina .. .. .. .. .. 2004 37.7 2003 7.4 .. Botswana .. .. 34 46 .. .. 1996 2.7 .. Brazil 27 27 15 22 20 2004 56.4 1997 9.8 .. Bulgaria .. .. 31 25 .. 1994 64.0 2005 8.9 1995 39.3 Burkina Faso .. .. .. .. 9 1993 3.1 1992 0.3 1992 207.3 Burundi .. .. .. .. .. 1993 3.3 1991 0.2 1991 57.4 Cambodia .. .. .. .. 25 .. .. .. Cameroon .. .. .. .. 24 1993 13.7 2004 0.1 .. Canada .. .. 15 12 .. 2003 68.3 1997 5.4 1994 54.3 Central African Republic .. .. .. .. 21 .. 1990 0.3 .. Chad .. .. .. .. 22 1990 1.1 1997 0.1 .. Chile .. .. 17 23 .. 2003 56.2 2001 2.9 1993 56.1 China .. .. .. .. .. 1994 17.6 1996 2.7 .. Hong Kong, China .. .. 19 11 .. .. .. .. Colombia .. .. .. .. 28 1999 20.7 1994 1.1 1989 72.2 Congo, Dem. Rep. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. 1992 5.8 1992 0.9 .. Costa Rica .. .. 13 18 .. 2004 62.5 1997 4.2 1993 76.1 Côte d'Ivoire .. .. .. .. 14 1997 9.3 1997 0.3 .. Croatia .. .. 35 40 .. 2004 100.0 2005 12.3 .. Cuba .. .. .. .. .. .. 1992 12.6 .. Czech Republic .. .. 21 20 .. 1995 85.0 2003 8.5 1996 37.0 Denmark .. .. 9 7 .. 2003 91.4 1997 8.8 1994 46.7 Dominican Republic .. .. 16 34 28 2001 26.8 2000 0.8 2000 42.0 Ecuador .. .. 19 25 .. 2004 30.9 2002 1.4 2002 55.3 Egypt, Arab Rep. .. .. 19 51 12 2004 65.0 1994 2.5 1994 45.0 El Salvador .. .. 14 8 .. 2003 25.1 1997 1.3 .. Eritrea .. .. .. .. 47 .. 2001 0.3 .. Estonia .. .. 17 26 .. 1995 76.0 2003 6.1 1995 56.7 Ethiopia 39 65 .. .. 24 .. 2003 0.4 .. .. Finland .. .. 22 19 .. 2003 91.2 1997 12.1 1994 57.4 France .. .. 22 24 .. 2003 90.1 1997 13.4 .. Gabon .. .. .. .. 26 1995 15.0 .. .. Gambia, The .. .. .. .. .. .. .. .. Georgia 21 7 20 32 .. 2004 25.9 2004 3.0 1996 12.6 Germany .. .. 13 10 .. 2003 87.9 1997 12.1 1995 62.8 Ghana .. .. 13 19 34 2003 7.4 2004 0.6 .. Greece .. .. 19 36 .. 2002 81.9 1993 11.9 1990 85.6 Guatemala .. .. .. .. 20 2000 16.4 1995 0.7 1995 27.6 Guinea .. .. .. .. 13 1993 1.5 .. .. Guinea-Bissau .. .. .. .. .. .. .. .. Haiti .. .. .. .. 43 .. .. .. 80 2006 World Development Indicators Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female pension Male Female labor force labor force % of % of % of per 1995­ 1995­ ages 15­24 ages 15­24 total labor % of capita 2003 a 2003a 2000­04a 2000­04a 1990­2004a Year force Year GDP Year income Honduras .. .. 6 12 .. 1999 20.6 1994 0.6 .. Hungary .. .. 16 14 .. 1996 77.0 2001 11.0 1996 33.6 India 54 41 10 10 10 1992 10.6 .. .. Indonesia .. .. .. .. 12 1995 8.0 .. .. Iran, Islamic Rep. .. .. .. .. .. 2000 35.1 2001 1.5 .. Iraq .. .. .. .. .. 2004 18.4 .. .. Ireland .. .. 9 7 .. 2002 100.0 1997 4.6 1993 77.9 Israel .. .. 22 22 .. 1992 82.0 1996 5.9 1992 48.1 Italy .. .. 21 27 .. 2003 86.0 1997 17.6 .. Jamaica .. .. 22 32 .. 1999 44.4 1996 .. 1989 25.9 Japan .. .. 11 8 .. 2003 92.8 1997 6.9 1989 33.9 Jordan .. .. .. .. 12 2001 36.0 2003 1.9 1995 144.0 Kazakhstan .. .. 13 16 33 2004 35.4 2004 4.9 2001 23.0 Kenya .. .. .. .. 32 1995 18.0 2003 6.4 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. .. .. 12 9 .. 2003 88.7 1997 1.3 .. Kuwait .. .. .. .. .. .. 1990 3.5 .. Kyrgyz Republic 52 48 15 16 33 1997 44.0 1997 6.4 2001 45.0 Lao PDR .. .. .. .. .. .. .. .. Latvia .. .. 17 20 .. 1995 60.5 2002 8.2 1994 47.6 Lebanon .. .. .. .. .. 2003 26.1 .. .. Lesotho .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. 2003 72.8 .. .. Lithuania 50 27 23 28 .. 2004 70.7 2003 6.2 1995 21.3 Macedonia, FYR .. .. 65 67 .. 1995 49.0 1998 8.7 1996 91.6 Madagascar .. .. .. .. 22 1993 5.4 1990 0.2 .. Malawi .. .. .. .. 27 .. .. .. Malaysia .. .. 8 8 .. 1993 48.7 2004 0.7 .. Mali .. .. .. .. 11 1990 2.5 1991 0.4 .. Mauritania .. .. .. .. 29 1995 5.0 1996 0.2 .. Mauritius .. .. .. .. .. 1995 60.0 2002 4.5 .. Mexico 18 22 6 8 .. 2002 25.1 2001 7.8 .. Moldova .. .. 17 13 .. .. 2003 8.0 .. Mongolia .. .. 20 21 .. .. 2004 8.3 .. Morocco .. .. 17 16 17 2003 19.2 2002 2.5 1994 118.0 Mozambique .. .. .. .. 26 1995 2.0 2004 1.4 .. Myanmar .. .. .. .. .. .. .. .. Namibia .. .. 40 49 42 .. .. .. Nepal 60 76 .. .. 16 .. .. .. Netherlands .. .. 8 8 .. 2002 100.0 1997 11.1 1989 48.5 New Zealand .. .. 9 10 .. 2003 95.7 1997 6.5 .. Nicaragua .. .. 11 16 31 2001 14.9 1996 2.5 .. Niger .. .. .. .. 13 1992 1.3 1992 0.1 .. Nigeria .. .. .. .. 17 1993 1.3 2002 1.5 1991 40.5 Norway .. .. 13 11 .. 2003 95.3 1997 8.2 1994 49.9 Oman .. .. .. .. .. .. .. .. Pakistan 64 61 12 21 7 1993 3.5 2004 0.9 .. Panama .. .. 24 38 .. 1998 51.6 1996 4.3 .. Papua New Guinea .. .. .. .. .. .. .. .. Paraguay .. .. 12 17 17 2004 14.3 2001 0.7b .. Peru .. .. 18 21 20 2003 20.8 2000 2.6 .. Philippines 16 19 24 31 15 1996 28.3 1993 1.0 .. Poland .. .. 39 43 .. 1996 68.0 2003 13.9 1995 61.2 Portugal .. .. 14 18 .. 2003 94.7 1997 10.0 1989 44.6 Puerto Rico .. .. 24 25 .. .. .. .. 2006 World Development Indicators 81 Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female pension Male Female labor force labor force % of % of % of per 1995­ 1995­ ages 15­24 ages 15­24 total labor % of capita 2003 a 2003a 2000­04a 2000­04a 1990­2004 a Year force Year GDP Year income Romania .. .. 18 19 .. 1994 55.0 2002 7.1 1994 34.1 Russian Federation 10 9 .. .. .. .. 2004 5.8 1995 18.3 Rwanda .. .. .. .. 36 1993 9.3 .. .. Saudi Arabia .. .. .. .. .. .. .. .. Senegal .. .. .. .. 18 2003 4.1 1998 1.5 1997 85.0 b Serbia and Montenegro .. .. .. .. .. .. 2004 10.3 .. Sierra Leone .. .. .. .. .. 2004 3.5 .. .. Singapore .. .. 6 10 .. 1995 73.0 1999 0.5 .. Slovak Republic .. .. 34 31 .. 2000 70.9 2003 7.4 1994 44.5 Slovenia .. .. 13 15 .. 1995 86.0 2003 10.1 1996 49.3 Somalia .. .. .. .. .. .. .. .. South Africa 16 28 56 65 42 .. .. .. Spain .. .. 19 26 .. 2003 91.2 1997 10.9 1995 54.1 Sri Lanka .. .. 22 36 .. 1992 28.8 2005 1.8 .. Sudan .. .. .. .. .. 1995 12.1 .. .. Swaziland .. .. .. .. .. .. .. .. Sweden .. .. 18 16 .. 2003 87.1 1997 11.1 1994 78.0 Switzerland .. .. 8 7 .. 2003 100.0 1997 13.4 1993 44.4 Syrian Arab Republic .. .. 21 39 .. .. 1991 0.5 .. Tajikistan .. .. .. .. .. .. 1996 3.0 .. Tanzania 60 85 .. .. 23 1996 2.0 .. .. Thailand .. .. 5 4 .. 1999 18.0 .. .. Togo .. .. .. .. 24 1997 15.9 1997 0.6 1993 178.8 Trinidad and Tobago .. .. 17 26 .. .. 1996 0.6 .. .. Tunisia .. .. .. .. .. 2003 48.2 2000 4.2 1991 89.5 Turkey 10 6 20 19 10 2002 33.2 2002 7.1 1993 56.0 Turkmenistan .. .. .. .. 27 .. 1996 2.3 .. Uganda .. .. .. .. 28 1994 8.2 2003 0.3 .. Ukraine 5 5 16 17 .. 2005 67.5 2005 15.4 1995 30.9 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom .. .. 12 10 .. 2003 96.2 1997 10.3 .. United States .. .. 13 11 .. 2003 92.2 1997 7.5 1989 33.0 Uruguay .. .. 34 44 .. 2004 37.1 1996 15.0 1996 64.1 Uzbekistan .. .. .. .. 22 .. 2004 0.1 1995 45.8 Venezuela, RB .. .. 24 35 .. 2004 29.3 2001 2.7 .. Vietnam .. .. 4 5 26 1998 8.4 1998 1.6 .. West Bank and Gaza .. .. 43 37 .. 2000 18.6 .. .. Yemen, Rep. .. .. .. .. 9 1999 13.5 1994 0.1 .. Zambia .. .. .. .. 23 1994 10.2 1993 0.1 .. Zimbabwe .. .. 28 21 34 1995 12.0 .. .. World .. w .. w Low income .. .. Middle income .. .. Lower middle income .. .. Upper middle income 19 20 Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia .. .. Latin America & Carib. 12 17 Middle East & N. Africa .. .. South Asia 10 13 Sub-Saharan Africa .. .. High income 14 13 Europe EMU 18 20 a. Data are for the most recent year available. b. Refers to system covering private sector workers. 82 2006 World Development Indicators Assessing vulnerability and security About the data Definitions As traditionally defined and measured, poverty is the year as a result of different school opening and · Urban informal sector employment is broadly char- a static concept, and vulnerability a dynamic one. closing dates. The youth unemployment rate shares acterized as employment in urban areas in units that Vulnerability reflects a household's resilience in similar limitations on comparability as the general produce goods or services on a small scale with the the face of shocks and the likelihood that a shock unemployment rate. For further information, see About primary objective of generating employment and will lead to a decline in well-being. Thus, it depends the data for table 2.5. income for those concerned. These units typically primarily on the household's asset endowment and The data on female-headed households are operate at a low level of organization, with little or insurance mechanisms. Because poor people have from recent Demographic and Health Surveys. no division between labor and capital as factors of fewer assets and less diversified sources of income The definition and concept of the female-headed production. Labor relations are based on casual than the better-off, fluctuations in income affect household differ greatly across economies, making employment, kinship, or social relationships rather them more. cross-country comparison difficult. In some cases than contractual arrangements. · Youth unemploy- Enhancing security for poor people means reduc- it is assumed that a woman cannot be the head of ment refers to the share of the labor force ages ing their vulnerability to such risks as ill health, pro- any household in which an adult male is present, 15­24 without work but available for and seeking viding them the means to manage risk themselves, because of sex-biased stereotype. Users need to employment. Definitions of labor force and unem- and strengthening market or public institutions for be cautious when interpreting the data. ployment may differ by country (see About the data). managing risk. The tools include microfinance pro- The data on pension contributors come from · Female-headed households refer to the percent- grams, old age assistance and pensions, and public national sources, the International Labour Organiza- age of households with a female head. · Pension provision of education and basic health care (see tion (ILO), and International Monetary Fund country contributors refer to the share of the labor force tables 2.10 and 2.14). reports. Coverage by pension schemes may be broad covered by a pension scheme. · Public expenditure Poor households face many risks, and vulnerability or even universal where eligibility is determined by on pensions includes all government expenditures on is thus multidimensional. The indicators in the table citizenship, residency, or income status. In contribu- cash transfers to the elderly, the disabled, and survi- focus on individual risks--informal sector employ- tion-related schemes, however, eligibility is usually vors and the administrative costs of these programs. ment, youth unemployment, female-headed house- restricted to individuals who have made contribu- · Average pension is estimated by dividing total pen- holds, income insecurity in old age, and the extent tions for a minimum number of years. Definitional sion expenditure by the number of pensioners. to which publicly provided services may be capable issues--relating to the labor force, for example-- of mitigating some of these risks. Poor people face may arise in comparing coverage by contribution- labor market risks, often having to take up precari- related schemes over time and across countries ous, low-quality jobs in the informal sector and to (for country-specific information, see Palacios and increase their household's labor market participation Pallares-Miralles 2000). The share of the labor force by sending their children to work. Income security is covered by a pension scheme may be overstated in a prime concern for the elderly. countries that do not attempt to count informal sec- For informal sector employment, the data are from tor workers as part of the labor force. labor force and special informal sector surveys, Public interventions and institutions can provide various household surveys, surveys of household services directly to poor people, although whether industries or economic activities, surveys of small these work well for the poor is debated. State action and micro enterprises, and official estimates. The is often ineffective, in part because governments international comparability of the data is affected can influence only a few of the many sources of well- by differences among countries in definitions and being and in part because of difficulties in delivering coverage and in the treatment of domestic workers good and services. The effectiveness of public provi- and those who have a secondary job in the informal sion is further constrained by the fiscal resources Data sources sector. The data in the table are based on national at governments' disposal and the fact that state Data on urban informal sector employment and definitions of urban areas established by countries. institutions may not be responsive to the needs of youth unemployment are from the ILO database Key For details on these definitions, see Data sources. poor people. Indicators of the Labour Market, fourth edition. Data Youth unemployment is an important policy issue for The data on public pension spending are from on female-headed household are from Demographic many economies. Experiencing unemployment may per- national sources and cover all government expendi- and Health Surveys by Macro International. Data on manently impair a young person's productive potential tures, including the administrative costs of pension pension contributors and pension spending are from and future employment opportunities. The table pres- programs. They cover noncontributory pensions or Robert Palacios and Montserrat Pallares-Miralles's ents unemployment among youth ages 15­24, but the social assistance targeted to the elderly and dis- "International Patterns of Pension Provision" (2000) lower age limit for young people in a country could be abled and spending by social insurance schemes for and updates. Further updates, notes, and sources determined by the minimum age for leaving school, so which contributions had previously been made. The will be available under "Knowledge and information" age groups could differ across countries. Also, since pattern of spending in a country is correlated with on the World Bank's Web site on pensions (www. this age group is likely to include school leavers, the its demographic structure--spending increases as worldbank.org/pensions). level of youth unemployment varies considerably over the population ages. 2006 World Development Indicators 83 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primarya Secondary Tertiary % of GDP expenditure % of total teacher 1991 2004b 1999 2004b 1999 2004b 2004b 2004b 2004b 2004b Afghanistan .. .. .. .. .. .. .. .. .. 65 Albania .. 7.7 .. 11.9 .. 36.3 2.8 .. .. 21 Algeria .. 11.3 .. 17.1 .. .. .. .. 98.3 27 Angola .. .. .. .. .. .. .. .. .. .. Argentina .. 10.9 16.4 14.9 17.7 13.1 4.0 13.8 .. 17 Armenia .. 8.9 12.4 11.1 29.1 38.3 3.2 .. 66.7 22 Australia .. 16.4 14.9 14.6 26.3 22.6 4.9 .. .. .. Austria 18.6 23.9 30.7 28.2 53.0 47.0 5.7 .. .. 13 Azerbaijan .. 7.6 17.2 13.4 19.3 12.8 3.3 19.2 99.8 14 Bangladesh .. 7.2 12.7 13.7 47.2 33.8 2.2 15.5 51.2 54 Belarus .. 13.7 .. 22.9 .. 27.6 5.8 13.0 98.5 15 Belgium 16.3 19.0 24.4 25.2 46.0 38.6 6.3 .. .. 12 Benin .. 12.2 26.1 22.1 202.9 .. 3.3 .. 72.2 52 Bolivia .. 16.4 11.7 13.0 44.1 35.9 6.4 18.1 .. 24 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. .. 5.6 .. 92.6 .. .. .. 89.7 26 Brazil .. .. 10.5 .. 63.2 .. .. .. .. 24 Bulgaria 22.4 16.2 18.8 19.0 18.0 18.7 3.6 .. .. 17 Burkina Faso .. .. .. .. .. .. .. .. 89.5 49 Burundi 13.4 19.9 .. 75.8 1,190.1 442.1 5.2 13.0 .. 51 Cambodia .. 6.5 6.8 .. 46.8 .. 2.0 .. 96.5 55 Cameroon .. .. 18.6 .. 71.3 75.7 3.8 17.2 68.5 53 Canada .. .. .. .. 49.3 .. .. .. .. 17 Central African Republic 11.9 .. .. .. .. .. .. .. .. .. Chad 8.0 .. 33.6 .. .. .. .. .. .. 69 Chile .. 15.3 14.9 16.3 19.4 15.3 4.1 19.1 .. 34 China .. .. 12.7 .. 99.2 .. .. .. 96.8 21 Hong Kong, China .. 16.0 18.2 22.0 62.9 67.9 4.7 23.3 91.8 19 Colombia .. 16.7 17.0 16.0 39.9 26.3 4.9 11.7 .. 28 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. 7.9 .. 18.3 404.9 245.9 3.2 .. 62.2 83 Costa Rica 7.8 17.1 23.2 19.7 55.0 36.3 4.9 18.5 97.4 22 Côte d'Ivoire .. .. 54.5 .. 212.8 .. .. .. 100.0 42 Croatia .. 24.0 .. 23.5 41.3 34.5 4.5 10.0 100.0 18 Cuba 21.4 .. 41.3 .. 86.4 .. .. 19.4 100.0 10 Czech Republic .. 12.0 22.1 23.0 34.4 31.8 4.4 .. .. 17 Denmark .. 24.9 38.3 36.1 66.2 74.6 8.5 .. .. .. Dominican Republic .. 5.0 2.5 1.3 .. .. 1.1 6.3 79.4 21 Ecuador .. .. 9.6 .. .. .. .. .. 70.9 23 Egypt, Arab Rep. .. .. .. .. .. .. .. .. .. 22 El Salvador .. 9.4 7.9 9.0 9.3 11.1 2.8 20.0 .. .. Eritrea .. 9.8 37.2 17.4 428.9 855.5 3.8 .. 83.1 47 Estonia .. 19.8 28.0 25.5 32.7 24.9 5.7 .. .. 14 Ethiopia 32.0 .. .. .. .. .. 6.1 20.4 .. 65 Finland 21.9 18.3 26.4 27.4 41.3 38.1 6.4 .. .. 16 France 12.0 17.8 29.0 28.6 30.2 29.3 5.6 .. .. 19 Gabon .. .. .. .. .. .. .. .. 100.0 36 Gambia, The 13.7 7.1 .. 8.7 .. 229.7 1.9 8.9 .. .. Georgia .. .. .. .. .. .. 2.9 13.1 97.4 22 Germany .. 16.7 20.8 22.6 41.1 43.0 4.8 .. .. 14 Ghana .. .. .. .. .. .. .. .. 60.7 32 Greece 7.6 15.6 17.4 .. 29.5 26.8 4.0 .. .. 12 Guatemala .. 4.7 4.2 3.7 .. .. .. .. .. 31 Guinea .. .. .. .. .. .. .. .. .. 45 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 9.1 .. .. .. .. .. .. .. .. .. 84 2006 World Development Indicators Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primarya Secondary Tertiary % of GDP expenditure % of total teacher 1991 2004b 1999 2004b 1999 2004b 2004b 2004b 2004b 2004b Honduras .. .. .. .. .. .. .. .. 87.2 34 Hungary 21.1 20.8 19.1 21.4 34.3 36.1 5.5 .. .. 10 India .. .. 21.6 .. 75.7 .. .. .. .. 41 Indonesia .. 2.9 8.7 5.6 21.9 15.6 1.1 9.0 .. 20 Iran, Islamic Rep. .. 10.5 10.9 11.5 38.4 26.5 4.8 17.7 100.0 20 Iraq .. .. .. .. .. .. .. .. 100.0 21 Ireland 11.6 12.4 16.9 18.1 28.8 26.6 4.3 .. .. 19 Israel 11.9 23.0 22.7 23.5 32.1 26.6 7.5 13.7 .. 15 Italy 15.3 25.4 26.8 28.1 25.6 27.4 4.7 .. .. 11 Jamaica 9.9 15.5 25.6 24.8 85.9 44.6 5.3 9.5 .. 30 Japan .. 22.2 20.5 21.7 14.9 17.1 3.6 .. .. 20 Jordan .. 15.2 16.4 18.0 .. .. .. .. .. 20 Kazakhstan .. 10.1 .. 7.9 .. 6.2 2.4 .. .. 18 Kenya 13.4 25.2 18.4 25.1 255.5 409.2 7.0 29.2 98.8 40 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 11.8 16.3 15.6 23.7 8.3 5.0 4.2 15.5 .. 30 Kuwait 34.8 25.9 .. 28.3 .. 178.1 8.2 17.4 100.0 13 Kyrgyz Republic .. 7.7 11.9 14.5 27.7 21.2 4.6 23.0 57.9 24 Lao PDR .. 6.7 4.3 8.9 66.9 82.4 2.3 11.0 79.4 31 Latvia .. 22.4 23.5 25.9 23.0 19.0 5.8 .. .. 14 Lebanon .. .. 6.2 .. 12.9 14.7 2.6 12.7 13.5 14 Lesotho .. 20.8 69.1 48.6 1,249.9 603.3 9.0 .. 66.8 44 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. 23.8 .. .. .. .. .. Lithuania .. .. .. .. 34.4 32.9 5.9 .. .. 16 Macedonia, FYR .. 23.6 .. 7.7 .. 23.7 3.5 .. .. 20 Madagascar .. .. 39.9 .. 180.9 184.2 3.3 18.2 .. 52 Malawi 7.4 14.4 .. 30.3 .. .. 6.0 .. .. .. Malaysia 10.1 20.2 22.3 28.3 83.3 102.4 8.1 20.3 .. 19 Mali .. .. 61.6 .. 265.0 .. .. .. .. 52 Mauritania .. .. 45.4 .. 99.9 .. 3.4 .. 100.0 45 Mauritius 10.3 13.6 15.6 20.3 41.2 45.3 4.7 15.7 100.0 22 Mexico 4.9 14.4 14.5 16.2 48.8 49.8 5.3 .. .. 27 Moldova .. 17.1 21.2 26.2 19.0 20.7 4.9 21.4 .. 19 Mongolia .. 15.7 19.5 14.6 36.4 25.0 7.5 19.1 .. 35 Morocco 15.8 19.3 51.4 46.9 109.6 87.2 6.3 27.8 .. 28 Mozambique .. .. .. .. .. .. .. .. .. 65 Myanmar .. .. 7.1 .. 29.0 .. .. .. 65.0 33 Namibia .. 21.3 37.4 25.5 161.7 112.6 7.2 .. 49.5 28 Nepal .. 12.7 13.5 10.7 144.9 72.7 3.4 14.9 30.5 40 Netherlands 12.6 18.0 21.8 22.9 44.2 39.8 5.1 .. .. .. New Zealand 17.2 18.7 24.3 22.2 41.6 35.6 6.7 15.1 .. 18 Nicaragua .. 9.1 .. 10.7 .. .. 3.1 15.0 74.6 35 Niger .. 19.0 88.4 64.3 344.8 345.1 2.3 .. 75.6 44 Nigeria .. .. .. .. .. .. .. .. 50.7 36 Norway 32.8 20.5 27.0 30.7 46.3 48.5 7.6 .. .. 10 Oman 11.7 13.1 22.2 20.5 47.5 53.8 4.6 26.1 99.8 19 Pakistan .. .. .. .. .. .. 2.0 .. .. 47 Panama 11.3 9.9 19.1 12.6 33.6 27.0 3.9 8.9 74.3 24 Papua New Guinea .. .. .. .. .. .. .. .. .. 35 Paraguay .. 12.3 17.3 13.7 55.2 28.2 4.4 11.4 .. 27 Peru .. 6.4 9.2 8.7 21.3 14.0 3.0 17.1 .. 25 Philippines .. 11.1 10.7 9.2 15.0 14.5 3.2 17.8 .. 35 Poland .. 23.5 11.6 20.8 21.5 22.1 5.6 12.8 .. 13 Portugal 17.2 24.0 29.1 31.6 29.7 26.0 5.8 .. .. 11 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 85 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primarya Secondary Tertiary % of GDP expenditure % of total teacher 1991 2004b 1999 2004b 1999 2004b 2004b 2004b 2004b 2004b Romania .. 9.9 15.9 15.1 32.4 26.5 3.5 .. .. 17 Russian Federation .. .. .. .. .. .. 3.8 10.7 99.0 17 Rwanda .. .. 28.4 .. .. .. .. .. 81.7 62 Saudi Arabia .. .. 31.4 .. .. .. .. .. .. 12 Senegal 19.4 .. .. .. .. .. 4.0 .. 50.9 43 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. 11.3 18.5 18.8 33.0 31.1 4.3 .. .. 18 Slovenia 16.8 .. .. .. 28.7 26.3 6.0 .. .. 13 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. 13.7 21.5 20.3 65.2 47.1 5.4 18.1 78.7 34 Spain 11.7 19.2 25.3 24.7 20.4 23.1 4.5 .. .. 14 Sri Lanka .. .. .. .. .. .. .. .. .. 23 Sudan .. .. .. .. .. .. .. .. .. 29 Swaziland 7.6 11.0 26.7 47.4 397.3 260.5 6.2 .. 90.6 31 Sweden 46.5 24.4 26.4 26.7 53.3 50.6 7.7 .. .. 11 Switzerland 36.5 24.3 27.7 29.2 54.5 59.9 5.8 .. .. .. Syrian Arab Republic .. 14.5 22.1 26.8 .. .. .. .. .. 18 Tajikistan .. 6.7 .. 9.2 .. 8.8 2.8 16.9 84.1 22 Tanzania .. .. .. .. .. .. .. .. 100.0 58 Thailand 11.6 13.8 11.5 13.0 35.5 22.7 4.2 27.5 .. 21 Togo .. 6.7 30.9 .. 317.9 .. 2.6 13.6 45.0 44 Trinidad and Tobago .. 16.0 12.2 .. 147.6 .. 4.3 .. 81.0 18 Tunisia .. 15.5 26.7 23.6 78.7 62.8 6.4 .. .. 22 Turkey 10.9 13.9 14.4 9.4 46.0 50.3 3.6 .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 11.6 .. 34.9 .. 194.1 5.2 18.3 80.4 50 Ukraine .. 10.4 11.2 15.7 36.4 27.1 4.6 18.3 99.7 19 United Arab Emirates .. 7.7 11.6 13.3 .. 2.2 1.6 22.5 60.9 15 United Kingdom 14.9 16.4 15.7 15.5 26.2 28.9 5.3 11.5 .. 17 United States .. 21.8 22.7 25.2 27.2 26.2 5.7 .. .. 15 Uruguay 7.8 7.9 11.4 9.0 19.3 19.0 2.6 9.6 .. 21 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB .. .. .. .. .. .. .. .. .. 20 Vietnam .. .. .. .. .. .. 4.4 17.1 87.0 23 West Bank and Gaza .. .. .. .. .. .. .. .. .. 27 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. Zambia .. 9.3 19.9 11.9 168.2 .. 2.8 14.8 100.0 49 Zimbabwe 21.3 .. 20.0 .. 200.4 .. .. .. .. 39 World .. m 15.2 m 20.0 m 18.9 m 38.5 m 34.5 m 4.4 m .. m .. m 29 m Low income .. .. .. .. .. .. .. .. .. 43 Middle income .. 13.1 16.3 16.3 37.8 32.5 4.4 .. .. 22 Lower middle income .. 11.3 14.1 13.6 .. 29.9 3.5 .. .. 22 Upper middle income .. 14.4 17.8 19.6 33.9 32.7 4.5 .. .. 22 Low & middle income .. 12.4 .. .. .. .. 4.1 .. .. 31 East Asia & Pacific .. 9.5 8.4 .. 34.3 .. 3.2 .. 95.4 22 Europe & Central Asia .. 12.0 16.4 16.4 31.1 25.7 4.1 .. .. 17 Latin America & Carib. .. 12.3 14.9 14.3 37.5 29.0 4.3 15.3 .. 25 Middle East & N. Africa .. 14.5 .. 20.5 .. .. .. .. .. 24 South Asia .. .. 13.5 .. 86.7 .. 2.4 .. .. 42 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. 49 High income 16.3 19.1 24.3 24.7 32.1 28.9 5.6 .. .. 16 Europe EMU 14.0 18.3 25.3 26.3 30.2 29.3 5.1 .. .. 14 a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Provisional data. 86 2006 World Development Indicators Education inputs About the data Data on education are compiled by the United national practices vary with respect to whether par- a variety of reporting errors (for further discussion of Nations Educational, Scientific, and Cultural Organi- ents or schools pay for books, uniforms, and other enrollment data, see About the data for table 2.11). zation (UNESCO) Institute for Statistics from official supplies. For greater detail, see the country- and While the pupil-teacher ratio is often used to compare responses to surveys and from reports provided by indicator-specific notes in the source. the quality of schooling across countries, it is often education authorities in each country. Such data are The share of public expenditure devoted to educa- weakly related to the value added of schooling sys- used for monitoring, policymaking, and resource allo- tion allows an assessment of the priority a government tems (Behrman and Rosenzweig 1994). cation. For a variety of reasons, however, education assigns to education relative to other public invest- In 1998 UNESCO introduced the new International statistics generally fail to provide a complete and ments. It also reflects a government's commitment Standard Classification of Education (ISCED) 1997. accurate picture of a country's education system. to investing in human capital development. However, Thus the time-series data for the years through 1997 Statistics often lag by two to three years, though an returns on investment to education, especially primary are not consistent with those for 1998 and later. Any effort is being made to shorten the delay. Moreover, and lower secondary education, cannot be understood time-series analysis should therefore be undertaken coverage and data collection methods vary across simply by comparing current education indicators with with extreme caution. countries and over time within countries, so compari- national income. It takes a long time before currently And in 2006 the UNESCO Institute for Statistics sons should be interpreted with caution. enrolled children can productively contribute to the changed its convention for citing the reference year The data on education spending in the table refer national economy (Hanushek 2002). of education data and indicators to the calendar year solely to public spending--government spending on The share of trained teachers in primary educa- in which the academic or financial year ends. Data public education plus subsidies for private education. tion measures the quality of the teaching staff. It that used to be listed for 2003/04, for example, is The data generally exclude foreign aid for education. does not take account of competencies acquired by now listed for 2004. This change was implemented They may also exclude spending by religious schools, teachers through their professional experience or to present the most recent data available and to which play a significant role in many developing coun- self-instruction or of such factors as work experi- align the data reporting with that of other interna- tries. Data for some countries and for some years ence, teaching methods and materials, or classroom tional organizations (in particular the Organisation refer to spending by the ministry of education only conditions, which may affect the quality of teaching. for Economic Co-operation and Development and (excluding education expenditures by other minis- Since the training teachers receive varies greatly Eurostat). tries and departments and local authorities). (pre-service or in-service), care should be taken in Many developing countries have sought to supple- comparing across countries. Definitions ment public funds for education. Some countries The primary pupil-teacher ratio reflects the average · Public expenditure per student is public current have adopted tuition fees to recover part of the cost numbers of pupils per teacher. It is different from the spending on education divided by the number of stu- of providing education services or to encourage devel- average class size because of the different practices dents by level, as a percentage of gross domestic opment of private schools. Charging fees raises dif- countries employ, such as part-time teaching, school product (GDP) per capita. · Public expenditure on ficult questions relating to equity, efficiency, access, shifts, and multigrade classes. The comparability of education is current and capital public expenditure and taxation, however, and some governments have pupil-teacher ratios across countries is affected by on education, as a percentage of GDP and as a per- used scholarships, vouchers, and other methods of the definition of teachers and by differences in class centage of total government expenditure. · Trained public finance to counter criticism. For most coun- size by grade and in the number of hours taught, as teachers in primary education are the percentage of tries, the data reflect only public spending. Data for well as the different practices mentioned above. More- primary school teachers who have received the mini- a few countries include private spending, although over, the underlying enrollment levels are subject to mum organized teacher training (pre-service or in-ser- vice) required for teaching in their country. · Primary pupil-teacher ratio is the number of pupils enrolled in primary school divided by the number of primary school Estimated impact of HIV/AIDS on education in three Sub-Saharan countries, 2005 teachers (regardless of their teaching assignment). Kenya Tanzania Zambia Low Medium High Low Medium High Low Medium High Teacher deaths due to AIDS 700 1,620 3,020 605 1,290 2,010 580 1,030 1,500 Share of teacher attrition (%) 7.5 18 29.6 9.3 19.9 31 23.3 40.4 48.8 Teacher-years of absenteeism due to AIDS 690 1,590 2,930 610 1,290 2,200 605 1,090 1,580 Share of total teacher-years (%) 0.4 0.8 1.8 0.5 1.1 2.3 1.3 2.2 3.9 Data sources In the best-case scenario (low estimate) Kenya, Tanzania, and Zambia will lose 600­700 teacher-years through absentee- Data on education inputs are from the UNESCO ism caused by HIV/AIDS. In the worst-case scenario (high estimate) they will lose 1,200­3,000 teacher-years. Institute for Statistics, which compiles international Note: The teacher infection rate is half the general population infection rate for the low estimate, the same as the general data on education in cooperation with national com- population infection rate for the medium estimate, and twice the general population infection rate for the high estimate. All missions and national statistical services. Data for estimates are modeled estimates. latest years are provisional, as of January 2006. Source: Desai and Jukes 2005 cited in UNESCO 2006 (p.88). 2006 World Development Indicators 87 Participation in education Gross enrollment Net enrollment Children out of ratio ratio school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primarya Secondarya Male Female 2004b 2004b 2004b 2004b 1991 2004b 1991 2004b 2004 2004 Afghanistan 1 90 13 1 .. .. .. .. .. .. Albania 49 104 78 16 95 96 .. 74 5 6 Algeria 5 112 81 20 89 97 53 66 0 41 Angola .. .. .. 1 50 .. .. .. .. .. Argentina 62 118 99 61 .. .. .. 81 3 11 Armenia 31 101 91 26 .. 97 .. 89 3 0 Australia 100 102 154 74 99 95 79 85 51 44 Austria 88 105 100 49 88 .. .. 89 .. .. Azerbaijan 28 97 83 15 89 84 .. 77 50 51 Bangladesh 12 106 51 7 .. .. .. 48 .. .. Belarus 100 101 93 61 86 95 .. 87 13 19 Belgium 116 105 160 61 96 100 87 97 4 4 Benin 4 99 26 .. 41 83 .. .. .. .. Bolivia 48 113 89 41 .. 95 .. 74 25 18 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 104 74 6 83 82 35 60 29 22 Brazil 55 145 110 20 85 97 17 75 .. .. Bulgaria 77 105 99 41 86 94 63 88 6 7 Burkina Faso 1 53 12 1 29 40 .. 10 590 681 Burundi 1 80 12 2 53 57 .. 8 240 278 Cambodia 9 137 26 3 69 98 .. 25 .. .. Cameroon 20 114 44 5 74 .. .. .. .. .. Canada 65 101 105 57 98 .. 89 .. .. .. Central African Republic 3 64 12 .. 52 .. .. .. .. .. Chad .. 71 15 .. 35 57 .. 11 243 413 Chile 50 99 88 43 89 86 55 78 119 124 China 36 115 70 15 97 .. .. .. .. .. Hong Kong, China 70 108 85 32 .. 97 .. 78 1 12 Colombia 38 111 75 27 69 83 34 55 379 334 Congo, Dem. Rep. 1 .. .. .. 54 .. .. .. .. .. Congo, Rep. 6 89 32 4 79 .. .. .. .. .. Costa Rica 64 112 68 19 87 92 38 50 22 18 Côte d'Ivoire 3 72 25 .. 45 56 .. 20 519 705 Croatia 47 94 88 39 79 87 63 85 7 7 Cuba 116 100 93 54 93 96 70 87 9 20 Czech Republic 104 102 97 37 87 87 .. 90 38 36 Denmark 90 103 127 67 98 100 87 95 0c 0 Dominican Republic 32 112 68 33 57 86 .. 49 77 63 Ecuador 77 117 61 .. 98 99 .. 52 11 0 Egypt, Arab Rep. 14 100 87 29 84 94 .. 79 78 218 El Salvador 50 113 60 18 .. 91 .. 48 35 32 Eritrea 7 66 28 1 16 48 .. 19 135 156 Estonia 109 100 96 64 99 95 .. 88 1 1 Ethiopia 2 77 28 2 22 46 .. 25 .. .. Finland 57 102 127 87 98 100 93 94 1 1 France 113 105 110 55 100 100 .. 95 7 3 Gabon 14 130 50 .. 85 .. .. .. .. .. Gambia, The 18 .. .. 1 48 .. .. 33 .. .. Georgia 49 95 82 41 97 93 .. 69 14 14 Germany 99 99 100 50 84 .. .. .. .. .. Ghana 46 81 42 3 54 58 .. 36 624 597 Greece 67 100 96 72 95 98 83 84 2 2 Guatemala 28 113 49 10 .. 93 .. 34 32 80 Guinea 6 79 26 2 27 64 .. 21 228 291 Guinea-Bissau .. .. .. .. 38 .. .. .. .. .. Haiti .. .. .. .. 22 .. .. .. .. .. 88 2006 World Development Indicators Participation in education Gross enrollment Net enrollment Children out of ratio ratio school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primarya Secondarya Male Female 2004b 2004b 2004b 2004b 1991 2004b 1991 2004b 2004 2004 Honduras 33 118 .. 16 89 91 21 .. 57 45 Hungary 79 99 103 52 91 89 75 92 9 8 India 34 107 52 11 .. 87 .. .. .. .. Indonesia 22 116 62 16 97 96 39 55 0 215 Iran, Islamic Rep. 37 103 82 22 92 89 .. 78 400 402 Iraq 6 98 45 15 94 88 .. 38 .. .. Ireland .. 106 109 55 90 96 80 85 9 8 Israel 110 112 93 57 92 99 .. 89 7 6 Italy 101 101 99 59 100 99 .. 91 4 9 Jamaica 81 93 84 19 96 88 64 75 20 18 Japan 84 100 102 52 100 100 97 100 6 0 Jordan 30 100 88 35 94 93 .. 82 18 11 Kazakhstan 31 109 98 48 89 98 .. 92 4 6 Kenya 53 111 48 .. .. 76 .. .. 618 607 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 87 105 91 89 100 100 86 88 0c 9 Kuwait 71 96 90 22 49 86 .. 78 9 6 Kyrgyz Republic 12 98 88 40 92 90 .. .. 10 9 Lao PDR 8 116 46 6 63 84 .. 37 50 68 Latvia 75 95 95 71 92 87 .. 87 6 6 Lebanon 74 107 89 48 73 93 .. .. 10 10 Lesotho 31 131 36 3 71 86 15 23 27 18 Liberia .. .. .. .. .. .. .. .. .. .. Libya 8 112 104 56 96 .. .. .. .. .. Lithuania 62 100 103 69 .. 92 .. 94 5 4 Macedonia, FYR 31 98 84 27 94 92 .. 81 2 1 Madagascar 10 134 .. 3 64 89 .. .. 136 136 Malawi .. 125 29 0c 48 95 .. 25 71 19 Malaysia 99 93 70 29 .. 93 .. 70 113 107 Mali 3 64 22 2 21 46 5 .. 557 615 Mauritania 2 94 20 3 35 74 .. 14 58 60 Mauritius 95 103 80 17 91 95 .. 75 4 2 Mexico 81 109 79 22 98 100 44 62 25 8 Moldova 50 85 74 32 89 78 .. 69 23 22 Mongolia 33 102 93 39 90 84 .. 82 13 11 Morocco 53 106 47 11 56 87 .. 35 204 302 Mozambique .. 95 11 1 43 71 .. 4 475 614 Myanmar .. 93 38 11 98 85 .. 34 408 374 Namibia 29 101 58 6 .. 74 .. 37 59 47 Nepal 17 114 43 6 .. .. .. .. .. .. Netherlands 87 108 122 58 95 99 84 89 0c 8 New Zealand 90 102 119 72 98 100 85 92 1 1 Nicaragua 35 112 64 18 73 88 .. 41 25 23 Niger 1 45 8 1 22 39 5 7 609 717 Nigeria 15 99 35 10 .. 88 .. 28 .. .. Norway 82 99 114 80 100 99 88 95 1 1 Oman 6 87 86 13 69 78 .. 75 38 33 Pakistan 45 82 27 3 33 66 .. .. 2,294 3,834 Panama 55 112 70 46 .. 100 .. 64 2 3 Papua New Guinea 59 75 26 .. .. .. .. .. .. .. Paraguay 30 110 65 26 94 89 26 51 46 42 Peru 58 118 90 .. .. 100 .. 69 3 0 Philippines 39 113 84 29 96 94 .. 59 385 269 Poland 51 100 105 59 97 98 76 91 33 25 Portugal 75 118 109 56 98 .. .. 82 2 4 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 89 Participation in education Gross enrollment Net enrollment Children out of ratio ratio school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primarya Secondarya Male Female 2004b 2004b 2004b 2004b 1991 2004b 1991 2004b 2004 2004 Romania 76 100 85 36 81 90 .. 81 33 34 Russian Federation 67 118 93 65 99 .. .. .. .. .. Rwanda 3 119 14 3 66 73 7 .. 205 185 Saudi Arabia 5 67 68 28 59 53 31 52 824 806 Senegal 6 76 19 5 43 66 .. 15 296 320 Serbia and Montenegro .. .. .. .. 69 .. 62 .. .. .. Sierra Leone .. .. .. 2 43 .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic 88 100 92 34 .. 85 .. 88 21 19 Slovenia 68 111 112 70 96 96 .. 95 1 1 Somalia .. .. .. .. 9 .. .. .. .. .. South Africa 33 105 90 15 90 89 45 .. 287 200 Spain 109 107 117 64 100 100 .. 95 2 10 Sri Lanka .. 102 81 .. .. 99 .. .. 9 13 Sudan 23 60 33 .. 40 .. .. .. .. .. Swaziland .. 101 42 4 75 77 30 29 24 23 Sweden 80 109 137 82 100 100 85 98 1 3 Switzerland 93 103 93 45 84 94 80 83 5 3 Syrian Arab Republic 10 123 63 .. 91 98 43 58 0 32 Tajikistan 9 100 82 16 77 98 .. 79 3 17 Tanzania 25 101 .. 1 49 86 .. .. 465 518 Thailand 92 99 77 41 76 87 .. .. 365 433 Togo 2 101 39 .. 64 79 15 .. .. .. Trinidad and Tobago 86 102 84 12 91 92 .. 72 2 2 Tunisia 22 111 77 26 94 97 .. 64 10 9 Turkey 7 95 85 28 89 89 42 .. 332 548 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 3 125 19 3 .. 98 .. 15 .. .. Ukraine 82 95 93 66 80 86 .. 84 162 155 United Arab Emirates 64 84 66 22 99 71 60 62 41 42 United Kingdom 77 101 170 63 98 100 81 95 0c 1 United States 60 100 95 83 97 94 85 89 740 584 Uruguay 64 109 106 38 91 90 .. 73 16 15 Uzbekistan 28 100 95 15 78 .. .. .. .. .. Venezuela, RB 55 105 72 39 87 92 18 61 109 89 Vietnam 47 98 73 10 90 93 .. .. .. .. West Bank and Gaza 30 93 94 38 .. 86 .. 89 22 19 Yemen, Rep. 1 87 48 9 51 75 .. .. .. .. Zambia .. 99 26 .. .. 80 .. 24 221 214 Zimbabwe 43 96 36 4 .. 82 .. 34 224 206 World 37 w 104 w 66 w 24 w 84 w .. w .. w .. w 53,784 s 61,590 s Low income 27 100 46 9 .. 79 .. .. .. .. Middle income 38 111 75 24 92 .. .. .. .. .. Lower middle income 35 112 72 20 92 .. .. .. .. .. Upper middle income 53 106 87 40 94 .. .. .. .. .. Low & middle income 32 105 61 17 82 .. .. .. 52,451d 60,508d East Asia & Pacific 36 113 69 17 96 .. .. .. 5,158d 4,870 d Europe & Central Asia 45 102 92 47 90 .. .. .. 1,439 d 1,669 d Latin America & Carib. 57 121 87 26 85 96 30 65 1,789d 1,497d Middle East & N. Africa 23 104 67 23 84 89 .. .. 2,585d 3,807d South Asia 33 103 49 10 .. 88 .. .. 18,742d 23,552d Sub-Saharan Africa 16 93 30 5 47 64 .. .. 22,738d 25,112d High income 76 100 105 67 95 95 85 90 1,333d 1,083d Europe EMU 101 104 108 57 95 99 .. 92 .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Provisional data. c. Less than 0.5. d. Data are for 2001/02. 90 2006 World Development Indicators Participation in education About the data Definitions School enrollment data are reported to the United ing laws or regulations. Errors are also introduced · Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organi- when parents round up children's ages. While census ment, regardless of age, to the population of the zation (UNESCO) Institute for Statistics by national data are often adjusted for age bias, adjustments age group that officially corresponds to the level of education authorities and statistical offices. Enroll- are rarely made for inadequate vital registration education shown. · Preprimary education refers to ment ratios help to monitor two important issues systems. Compounding these problems, pre- and the initial stage of organized instruction, designed for universal primary education: whether a country post-census estimates of school-age children are primarily to introduce very young children to a school- is on track to achieve the Millennium Development interpolations or projections based on models that type environment. · Primary education provides Goal of universal primary completion by 2015, which may miss important demographic events (see the children with basic reading, writing, and mathemat- implies achieving a net primary enrollment ratio of discussion of demographic data in About the data ics skills along with an elementary understanding 100 percent, and whether an education system has for table 2.1). of such subjects as history, geography, natural sci- sufficient capacity to meet the needs of universal In using enrollment data, it is also important to ence, social science, art, and music. · Secondary primary education, as indicated in part by its gross consider repetition rates. These rates are quite education completes the provision of basic educa- enrollment ratios. high in some developing countries, leading to a sub- tion that began at the primary level and aims at lay- Enrollment ratios, while a useful measure of partici- stantial number of overage children enrolled in each ing the foundations for lifelong learning and human pation in education, also have some limitations. They grade and raising the gross enrollment ratio. development by offering more subject- or skill-ori- are based on data collected during annual school Thus gross enrollment ratios indicate the capacity ented instruction using more specialized teachers. surveys, which are typically conducted at the begin- of each level of the education system, but a high ratio · Tertiary education, whether or not leading to an ning of the school year. They do not reflect actual does not necessarily mean a successful education advanced research qualification, normally requires, rates of attendance or dropouts during the school system. The net enrollment ratio excludes overage as a minimum condition of admission, the success- year. And school administrators may report exagger- students in an attempt to capture more accurately ful completion of education at the secondary level. ated enrollments, especially if there is a financial the system's coverage and internal efficiency. It does · Net enrollment ratio is the ratio of children of offi- incentive to do so. Often the number of teachers not solve the problem completely, however, because cial school age based on the International Standard paid by the government is related to the number of some children fall outside the official school age Classification of Education 1997 who are enrolled in pupils enrolled. because of late or early entry rather than because school to the population of the corresponding official Also as international indicators, the gross and of grade repetition. The difference between gross school age. · Children out of school are the number net primary enrollment ratios have an inherent and net enrollment ratios shows the incidence of of primary-school-age children not enrolled in primary weakness: the length of primary education differs overage and underage enrollments. or secondary school. significantly across countries, although the Interna- Out of school children are children in the primary tional Standard Classification of Education tries to school age group who are not enrolled in primary or in minimize the difference. A relatively short duration secondary education. The data are calculated by the for primary education tends to increase the ratio, UNESCO Institute for Statistics using administrative whereas a relatively long duration tends to decrease data. Children out of school include dropouts and it (in part because there are more dropouts among children who never enrolled as well as children of older children). primary age enrolled in pre-primary education. The Overage or underage enrollments frequently occur, large number of children out of school creates pres- particularly when parents prefer, for cultural or eco- sure for the education system to enroll children and nomic reasons, to have children start school at other to provide classrooms, teachers, and educational than the official age. Children's age at enrollment materials, a task made difficult in many developing may be inaccurately estimated or misstated, espe- countries by limited education budgets. cially in communities where registration of births In 2006 the UNESCO Institiute for Statistics changed is not strictly enforced. Parents who want to enroll its convention for citing the reference year. For more an underage child in primary school may do so by information, see About the data for table 2.10. overstating the child's age. And in some education systems ages for children repeating a grade may be underreported. Other problems affecting cross-country compari- Data sources sons of enrollment data stem from errors in esti- Data on gross and net enrollment ratios and out mates of school-age populations. Age-gender struc- of school children are from the UNESCO Institute tures from censuses or vital registration systems, for Statistics. Data on gross and net enrollment the primary sources of data on school-age popula- ratios for latest years are provisional, as of tions, are commonly subject to underenumeration January 2006. (especially of young children) aimed at circumvent- 2006 World Development Indicators 91 Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5 primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Malea Femalea Male Female Male Female 2004b 2004b 1991 2003b 1991 2003b 2004b 2004b 2004b 2004b Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 103 102 .. .. .. .. 3 2 98 100 Algeria 103 100 95 95 94 97 14 9 76 83 Angola .. .. .. .. .. .. .. .. .. .. Argentina 112 112 .. 91 .. 93 7 5 .. .. Armenia 96 101 .. .. .. .. 0c 0c 99 97 Australia 106 104 98 98 99 100 0 0 100 100 Austria .. .. .. .. .. .. .. .. .. .. Azerbaijan 96 93 .. .. .. .. 0c 0c 99 99 Bangladesh 134 128 .. 51 .. 54 7 7 92 99 Belarus 103 102 .. .. .. .. 0c 0c 100 97 Belgium 101 101 90 .. 92 .. .. .. .. .. Benin 112 94 54 70 56 69 23 23 51 51 Bolivia 119 120 .. 87 .. 86 2 1 92 91 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 110 107 81 87 87 92 6 4 97 97 Brazil 127 117 .. .. .. .. .. .. .. .. Bulgaria 106 106 91 .. 90 .. 3 2 96 95 Burkina Faso 76 66 71 74 68 78 13 13 42 37 Burundi 95 86 65 64 58 62 28 31 35 33 Cambodia 154 143 .. 58 .. 61 12 9 85 80 Cameroon 115 100 .. 64 .. 63 26 25 47 49 Canada .. .. 95 .. 98 .. .. .. .. .. Central African Republic 75 52 24 .. 22 .. .. .. .. .. Chad 98 70 56 51 41 39 24 25 60 46 Chile 98 96 94 100 91 98 2 1 95 98 China 100 99 58 100 78 98 0c 0c 92 92 Hong Kong, China 101 95 .. 100 .. 100 1 1 100 100 Colombia 126 120 .. 75 .. 80 5 4 100 100 Congo, Dem. Rep. .. .. 58 .. 50 .. .. .. .. .. Congo, Rep. 66 63 56 65 65 67 25 24 78 78 Costa Rica 108 107 83 92 85 93 8 6 93 90 Côte d'Ivoire 75 68 75 .. 70 .. 17 18 .. .. Croatia 99 97 .. .. .. .. 0c 0c 100 100 Cuba 105 103 .. 98 .. 97 1 0 98 99 Czech Republic 94 93 .. 97 .. 98 1 1 99 99 Denmark 98 98 94 100 94 99 .. .. 100 99 Dominican Republic 118 104 .. 52 .. 70 9 6 92 84 Ecuador 136 134 .. 75 .. 77 2 2 76 71 Egypt, Arab Rep. 100 98 .. 96 .. 100 6 3 83 86 El Salvador 134 130 56 67 60 70 8 6 94 94 Eritrea 63 52 .. 86 .. 73 21 22 85 76 Estonia 97 98 .. 98 .. 99 3 1 93 98 Ethiopia 106 93 16 59 23 54 12 11 90 88 Finland 100 98 100 100 100 100 1 0c 100 100 France .. .. 69 .. 95 .. .. .. .. .. Gabon 94 94 .. 68 .. 71 35 34 .. .. Gambia, The 95 101 .. .. .. .. .. .. .. .. Georgia 104 102 .. .. .. .. 0c 0c 98 99 Germany 101 100 .. .. .. .. 2 2 .. .. Ghana 87 89 81 62 79 65 6 6 95 100 Greece .. .. 100 .. 100 .. .. .. .. .. Guatemala 129 125 .. 79 .. 76 14 13 97 95 Guinea 87 79 64 87 48 76 10 11 49 45 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 92 2006 World Development Indicators Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5 primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Malea Femalea Male Female Male Female 2004b 2004b 1991 2003b 1991 2003b 2004b 2004b 2004b 2004b Honduras 129 127 .. 63 .. 69 9 7 .. .. Hungary 99 98 77 .. 98 .. 3 2 99 99 India 135 129 .. 60 .. 64 4 4 85 89 Indonesia 120 120 34 88 78 90 4 4 80 83 Iran, Islamic Rep. 102 118 91 94 89 94 3 2 97 95 Iraq 110 103 .. .. .. .. 9 7 .. .. Ireland 104 104 99 98 100 100 1 1 .. .. Israel 97 98 .. 100 .. 99 2 1 72 73 Italy 98 97 .. 96 .. 97 0c 0c 100 100 Jamaica 90 88 .. 88 .. 93 4 3 .. .. Japan .. .. 100 .. 100 .. .. .. .. .. Jordan 99 100 .. 97 .. 98 0c 0c 97 98 Kazakhstan 106 105 .. .. .. .. 0c 0c 100 100 Kenya 122 119 75 78 78 73 11 10 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 103 103 99 100 100 100 0 0 99 99 Kuwait 96 97 .. .. .. .. 3 2 95 95 Kyrgyz Republic 99 97 .. .. .. .. 0c 0c 98 100 Lao PDR 123 114 .. 62 .. 63 21 18 80 76 Latvia 92 90 .. .. .. .. 2 1 99 99 Lebanon 100 99 .. 95 .. 100 12 9 83 89 Lesotho 144 131 58 58 73 69 21 16 64 62 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania 97 97 .. .. .. .. 1 0c 99 99 Macedonia, FYR 98 96 .. .. .. .. 0c 0c 98 97 Madagascar 168 164 22 56 21 58 31 29 56 55 Malawi 164 178 71 50 57 38 18 18 .. .. Malaysia 93 92 97 87 97 87 .. .. .. .. Mali 69 58 71 78 67 70 19 19 62 57 Mauritania 106 105 76 81 75 83 14 15 47 44 Mauritius 95 95 97 98 98 100 6 4 .. .. Mexico 108 107 35 92 71 94 6 4 94 92 Moldova 89 88 .. .. .. .. 0c 0c 97 99 Mongolia 113 115 .. .. .. .. 1 1 99 99 Morocco 100 96 75 82 76 80 16 11 78 81 Mozambique 138 129 36 53 32 45 21 21 .. .. Myanmar 124 125 .. 64 .. 66 1 1 74 66 Namibia 99 99 60 87 65 90 15 12 87 88 Nepal 115 105 51 63 51 67 22 22 80 76 Netherlands 98 97 .. 100 .. 100 .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. Nicaragua 144 135 11 52 37 61 12 9 .. .. Niger 68 51 61 75 65 72 5 5 43 41 Nigeria 120 103 .. 33 .. 38 3 3 .. .. Norway 99 99 99 100 100 99 0 0 .. .. Oman 74 75 97 97 96 98 1 1 99 99 Pakistan 126 95 .. .. .. .. .. .. .. .. Panama 121 118 .. 82 .. 87 6 5 63 65 Papua New Guinea 101 90 70 72 68 66 0 0 77 77 Paraguay 109 106 73 68 75 71 9 6 .. .. Peru 113 114 .. 85 .. 83 10 10 .. .. Philippines 140 130 .. 72 .. 80 3 1 98 97 Poland 97 98 89 .. 96 .. .. .. .. .. Portugal .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 93 Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5 primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Malea Femalea Male Female Male Female 2004b 2004b 1991 2003b 1991 2003b 2004b 2004b 2004b 2004b Romania 107 106 .. .. .. .. 3 2 98 98 Russian Federation .. .. .. .. .. .. .. .. .. .. Rwanda 183 183 61 43 59 49 19 19 .. .. Saudi Arabia 66 66 82 94 84 93 5 3 100 93 Senegal 89 91 .. 79 .. 77 13 13 49 45 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic 96 94 .. .. .. .. 3 2 98 98 Slovenia 122 120 .. .. .. .. 1 0c 100 99 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 118 112 .. 82 .. 87 6 4 94 96 Spain .. .. .. .. .. .. .. .. .. .. Sri Lanka .. .. 92 .. 93 .. .. .. 96 98 Sudan 73 62 90 92 99 92 1 4 88 92 Swaziland 110 104 74 74 80 80 19 14 76 78 Sweden 97 97 100 .. 100 .. .. .. .. .. Switzerland 89 92 .. .. .. .. 2 2 100 100 Syrian Arab Republic 122 118 97 93 95 92 8 7 93 95 Tajikistan 98 94 .. .. .. .. 0c 0c 98 97 Tanzania 118 114 81 86 82 90 5 5 34 33 Thailand .. .. .. .. .. .. .. .. .. .. Togo 90 82 52 79 42 73 24 24 67 61 Trinidad and Tobago 97 96 .. 66 .. 76 6 4 96 99 Tunisia 98 100 94 96 77 97 11 7 86 90 Turkey .. .. 98 .. 97 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 164 163 .. 63 .. 64 14 14 .. .. Ukraine 105 105 .. .. .. .. 0c 0c .. .. United Arab Emirates 89 88 80 94 80 95 3 2 96 96 United Kingdom .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. Uruguay 109 107 96 91 98 95 10 7 .. .. Uzbekistan 102 102 .. .. .. .. 0 0 100 99 Venezuela, RB 103 100 82 89 90 94 9 6 97 100 Vietnam 101 95 .. 90 .. 88 3 2 99 100 West Bank and Gaza 85 84 .. .. .. .. 0 0 100 100 Yemen, Rep. 122 97 .. 78 .. 67 5 4 .. .. Zambia 110 110 .. .. .. .. 7 7 .. .. Zimbabwe 122 118 72 68 81 71 .. .. 69 70 World 116 w 110 w .. w .. w .. w .. w 4w 4w .. w .. w Low income 125 115 .. 63 .. 65 6 6 82 83 Middle income 107 106 61 93 81 93 3 2 91 91 Lower middle income 107 106 59 94 80 93 3 2 90 91 Upper middle income .. .. .. .. .. .. .. .. .. .. Low & middle income 117 111 .. .. .. .. 4 4 87 87 East Asia & Pacific 107 106 55 94 78 93 1 1 90 90 Europe & Central Asia .. .. .. .. .. .. .. .. .. .. Latin America & Carib. 120 115 .. .. .. .. .. .. .. .. Middle East & N. Africa 106 105 .. 92 .. 92 8 5 87 89 South Asia 132 122 .. 59 .. 63 4 4 86 90 Sub-Saharan Africa 113 104 .. .. .. .. 11 11 .. .. High income .. .. .. .. .. .. .. .. .. .. Europe EMU .. .. .. .. .. .. .. .. .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Provisional data. c. Less than 0.5. 94 2006 World Development Indicators Education efficiency About the data Definitions Indicators of students' progress through school are Measuring actual learning outcomes requires set- · Gross intake rate in grade 1 is the number of estimated by the United Nations Educational, Scien- ting curriculum standards and measuring students' new entrants in the first grade of primary education tific, and Cultural Organization (UNESCO) Institute learning progress against those standards through regardless of age, expressed as a percentage of the for Statistics and the World Bank. These indicators standardized assessments or tests. population of the official primary school entrance measure an education system's success in extend- The data on repeaters are often used to indicate age. · Share of cohort reaching grade 5 is the per- ing coverage to all students, maintaining the flow of the internal efficiency of the education system. centage of children enrolled in the first grade of pri- students from one grade to the next, and imparting Repeaters not only increase the cost of education mary school who eventually reach grade 5. The esti- a particular level of education. for the family and for the school system, but also use mate is based on the reconstructed cohort method Gross intake rate indicates the general level of limited school resources. Countries have different (see About the data). · Repeaters in primary school access to primary education. It also indicates the policies on repetition and promotion; in some cases are the number of students enrolled in the same capacity of the education system to provide access the number of repeaters is controlled because of grade as in the previous year, as a percentage of all to primary education. Low gross intake rates in grade limited capacity. Care should be taken in interpreting students enrolled in primary school. · Transition to 1 reflect the fact that many children do not enter this indicator. secondary education refers to the number of new primary school even though school attendance, at The transition rate from primary school to second- entrants to the first grade of secondary school in least through the primary level, is mandatory in all ary school conveys the degree of access or transition a given year, as a percentage of the number of stu- countries. Because the gross intake rate includes all between the two levels of education. A low transi- dents enrolled in the final grade of primary school in new entrants regardless of age, it can be more than tion rate can signal problems such as an inadequate the previous year. 100 percent. Once enrolled, students drop out for a promotion and examination system or insufficient variety of reasons, including low quality of schooling, capacity in secondary schools. The quality of data on discouragement over poor performance, and the direct the transition rate is affected when new entrants and and indirect costs of schooling. Students' progress to repeaters are not correctly distinguished in the first higher grades may also be limited by the availability of grade of secondary school. Students who interrupt teachers, classrooms, and educational materials. their studies for one or more years after complet- The share of cohort reaching grade 5 (cohort ing primary school could also affect the quality of survival rate) is estimated as the proportion of an the data. entering cohort of grade 1 students that eventually In 2006 the UNESCO Institiute for Statistics changed reaches grade 5. It measures the holding power and its convention for citing the reference year. For more internal efficiency of an education system. Cohort information, see About the data for table 2.10. survival rates approaching 100 percent indicate a high level of retention and a low level of dropout. Cohort survival rates are typically estimated from data on enrollment and repetition by grade for two consecutive years, in a procedure called the recon- structed cohort method. This method makes three simplifying assumptions: dropouts never return to school; promotion, repetition, and dropout rates remain constant over the entire period in which the cohort is enrolled in school; and the same rates apply to all pupils enrolled in a given grade, regardless of whether they previously repeated a grade (Fredrick- sen 1993). Given these assumptions, cross-country comparisons should be made with caution, because other flows--caused by new entrants, reentrants, grade skipping, migration, or school transfers during the school year--are not considered. The UNESCO Institute for Statistics measures the share of cohort reaching grade 5 because research suggests that five to six years of schooling is a critical Data sources threshold for the achievement of sustainable basic Data on education efficiency are from the UNESCO literacy and numeracy skills. But the indicator only Institute for Statistics. Data for latest years are indirectly reflects the quality of schooling, and a high provisional, as of January 2006. rate does not guarantee these learning outcomes. 2006 World Development Indicators 95 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2004 1991 2004 1991 2004 1990 2002 1990 2002 2002 2002 Afghanistan 25 .. 37 .. 13 .. .. .. .. .. .. .. Albania .. 99 .. 99 .. 99 97 99 b 92 99 b 99 b 98 b Algeria 79 94 86 94 73 94 86 94 c 68 86c 79 c 60 c Angola 35 .. .. .. .. .. .. 83 c .. 63 c 82c 54 c Argentina .. 102 .. 100 .. 105 98 99 b 98 99 b 97b 97b Armenia 90 107 .. 106 .. 108 100 100 b 99 100 b 100 b 99 b Australia .. 100 .. 100 .. 100 .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan .. 96 .. 97 .. 95 .. .. .. .. .. .. Bangladesh 49 73 .. 70 .. 75 51 58 33 41 50 31 Belarus 95 101 95 103 96 99 100 .. 100 .. .. .. Belgium 79 .. 76 .. 82 .. .. .. .. .. .. .. Benin 21 49 28 59 13 38 57 58 b 25 33 b 46b 23 b Bolivia 71 100 78 102 64 98 96 99 b 89 96 b 93 b 80 b Bosnia and Herzegovina .. .. .. .. .. .. .. 100 c .. 100 c 98 c 91c Botswana 79 92 71 89 87 94 79 85 87 93 76 82 Brazil 93 111 .. 110 .. 111 91 96 c 93 98 c 88 c 89 c Bulgaria 90 97 89 98 92 96 100 98 b 99 98 b 99 b 98 b Burkina Faso 21 29 26 34 16 25 .. .. .. .. .. .. Burundi 46 33 49 39 43 27 58 76 c 45 69 c 67c 52c Cambodia .. 82 .. 85 .. 78 81 88 c 66 79 c 85 c 64 c Cameroon 56 72 60 77 52 66 86 .. 76 .. 77c 60 c Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 27 .. 35 .. 18 .. 66 70 c 39 47c 65 c 33 c Chad 18 29 30 41 7 18 58 55 c 38 23 c 41c 13 c Chile .. 97 .. 98 .. 97 98 99 b 98 99 b 96b 96b China 103 99 .. 99 .. 100 97 99 b 93 99 b 95 b 87b Hong Kong, China 102 111 .. 113 .. 108 .. .. .. .. .. .. Colombia 71 94 60 92 82 96 94 97c 96 98 c 94 c 95 c Congo, Dem. Rep. 46 .. 58 .. 34 .. 80 77c 58 61c 80 c 52c Congo, Rep. 54 66 59 70 49 63 95 98 90 97 89 77 Costa Rica 74 92 77 91 81 94 97 98 98 99 96 96 Côte d'Ivoire 43 43 55 52 32 34 65 70 c 40 51c 60 c 38 c Croatia 85 91 .. 92 .. 91 100 100 b 100 100 b 99 b 97b Cuba 96 93 .. 93 .. 92 99 100 b 99 100 b 100 b 100 b Czech Republic .. 102 .. 103 .. 101 .. .. .. .. .. .. Denmark 98 103 98 103 98 104 .. .. .. .. .. .. Dominican Republic 61 91 .. 88 .. 93 87 93 b 88 95b 87b 87b Ecuador 91 101 91 100 92 101 96 96 b 95 96 b 92b 90 b Egypt, Arab Rep. .. 93 .. 95 .. 91 71 .. 51 .. .. .. El Salvador 41 84 38 84 43 85 85 90 83 88 82 77 Eritrea 19 44 22 53 17 36 73 .. 49 .. .. .. Estonia 93 103 93 105 94 101 100 100 b 100 100 b 100 b 100 b Ethiopia 21 51 26 58 16 43 52 63 34 52 49 34 Finland 97 102 98 102 97 102 .. .. .. .. .. .. France 104 .. .. .. .. .. .. .. .. .. .. .. Gabon 58 66 55 65 61 68 .. .. .. .. .. .. Gambia, The 44 .. 55 .. 33 .. 50 .. 34 .. .. .. Georgia .. 86 .. 84 .. 87 .. .. .. .. .. .. Germany 100 97 99 97 100 97 .. .. .. .. .. .. Ghana 63 65 70 65 55 67 88 .. 75 .. 63 b 46 b Greece 99 .. 99 .. 98 .. 99 99 c 100 100 c 94 c 88 c Guatemala .. 70 .. 75 .. 65 80 86b 66 78 b 75b 63 b Guinea 17 48 24 58 9 39 62 .. 26 .. .. .. Guinea-Bissau .. 27 .. 35 .. 19 .. .. .. .. .. .. Haiti 27 .. 29 .. 26 .. 56 66 54 67 54 50 96 2006 World Development Indicators Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2004 1991 2004 1991 2004 1990 2002 1990 2002 2002 2002 Honduras 65 79 67 77 62 82 78 87b 81 91b 80 b 80 b Hungary 82 96 88 97 90 96 100 99 b 100 100 b 99 b 99 b India .. 84 .. 88 .. 79 73 84 b 54 68 b 73 b 48 b Indonesia 91 101 .. 101 .. 101 97 99 93 98 92 83 Iran, Islamic Rep. 91 95 97 92 85 97 92 .. 81 .. 84c 70 c Iraq 59 74 64 85 53 63 56 .. 25 .. .. .. Ireland .. 101 .. 101 .. 100 .. .. .. .. .. .. Israel .. 101 .. 101 .. 101 99 100 c 98 99 c 98 c 96 c Italy 104 103 104 103 104 102 .. .. .. .. .. .. Jamaica 90 84 86 84 94 89 87 91 95 98 84 91 Japan 101 .. 101 .. 102 .. .. .. .. .. .. .. Jordan 101 97 101 97 101 96 98 99 95 99 95 85 Kazakhstan .. 110 .. 110 .. 109 100 .. 100 .. .. .. Kenya .. 89 .. 90 .. 89 93 80 87 81 78 70 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 98 105 98 104 98 106 .. .. .. .. .. .. Kuwait 57 91 58 91 56 92 88 92 87 94 85 81 Kyrgyz Republic .. 93 .. 93 .. 93 .. .. .. .. .. .. Lao PDR 43 74 48 78 38 70 79 83 c 61 75 c 77c 61c Latvia .. 98 .. 99 .. 97 .. 100 b .. 100 b 100 b 100 b Lebanon .. 94 .. 92 .. 96 95 .. 89 .. .. .. Lesotho 58 71 41 60 75 82 77 .. 97 .. 74 c 90 c Liberia .. .. .. .. .. .. 75 86 39 55 72 39 Libya .. .. .. .. .. .. 99 100 83 94 92 71 Lithuania 89 105 .. 105 .. 105 100 100 b 100 100 b 100 b 100 b Macedonia, FYR 98 97 .. 97 .. 97 .. 99 b .. 98 b 98 b 94b Madagascar 33 45 33 45 34 46 78 72c 67 68 c 76 c 65 c Malawi 31 58 35 60 28 57 76 .. 51 .. .. .. Malaysia 90 95 90 95 90 95 95 97b 94 97b 92b 85 b Mali 11 44 13 58 9 30 .. .. .. .. .. .. Mauritania 33 43 40 45 26 41 56 68 b 36 55 b 60 b 31b Mauritius 102 100 103 98 102 102 91 94 b 91 95 b 88 b 81b Mexico 86 97 89 97 90 98 96 98 c 94 97c 92c 89 c Moldova .. 83 .. 82 .. 83 100 98 b 100 99 b 97b 95 b Mongolia .. 95 .. 95 .. 96 .. 97b .. 98 b 98 b 98 b Morocco 46 67 54 71 37 63 68 77 42 61 63 38 Mozambique 26 29 32 35 21 23 66 77 32 49 62 31 Myanmar .. 72 .. 72 .. 73 90 96 c 86 93 c 94 c 86 c Namibia 78 81 70 76 86 85 86 87b 89 92b 81b 81b Nepal 51 71 70 76 41 65 67 81b 27 60 b 63 b 35 b Netherlands .. 100 .. 101 .. 99 .. .. .. .. .. .. New Zealand 100 .. 100 .. 99 .. .. .. .. .. .. .. Nicaragua 41 73 43 70 59 77 68 84 c 69 89 c 77c 77c Niger 17 25 21 30 12 20 25 26 b 9 14 b 20 b 9b Nigeria .. 76 .. 83 .. 69 81 91 66 87 74 59 Norway 100 103 100 102 100 103 .. .. .. .. .. .. Oman 74 91 78 93 70 90 95 100 75 97 82 65 Pakistan .. .. .. .. .. .. 63 75 c 31 54 c 62c 35 c Panama 86 97 86 96 86 97 96 97b 95 96 b 93 b 91b Papua New Guinea 50 55 56 59 44 51 74 69 b 62 64 b 63 b 51b Paraguay 65 89 64 88 65 90 96 96 c 95 96 c 93 c 90 c Peru .. 96 .. 97 .. 95 97 98 c 92 96 c 93 c 82c Philippines 86 98 84 94 84 102 97 94 b 97 96 b 93 b 93 b Poland 96 100 .. .. .. .. .. .. .. .. .. .. Portugal 95 .. 94 .. 95 .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 97 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2004 1991 2004 1991 2004 1990 2002 1990 2002 2002 2002 Romania 96 90 96 90 96 89 99 98 b 99 98 b 98 b 96b Russian Federation 93 .. 92 .. 93 .. 100 100 b 100 100 b 100 b 99 b Rwanda 47 37 47 38 46 37 78 77c 67 76 c 70 c 59 c Saudi Arabia 56 62 60 62 51 61 91 98 c 79 94 c 87c 69 c Senegal 39 45 47 49 30 42 50 58 c 30 41c 51c 29 c Serbia and Montenegro 71 96 .. 97 .. 96 .. 99 b .. 99 b 99 b 94b Sierra Leone .. .. .. .. .. .. .. 47c .. 30 c 40 c 21c Singapore .. .. .. .. .. .. 99 99 b 99 100 b 97b 89 b Slovak Republic 96 101 95 102 96 100 .. 100 b .. 100 b 100 b 100 b Slovenia 95 102 .. 103 .. 102 100 100 100 100 100 100 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 75 96 71 94 80 98 89 .. 88 .. .. .. Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 94 .. 94 .. 94 .. 96 95 b 94 96 b 92b 89 b Sudan 40 49 45 53 36 44 76 82c 54 69 c 69 c 50 c Swaziland 62 61 59 58 65 64 85 87c 85 89 c 80 c 78 c Sweden 96 .. 96 .. 96 .. .. .. .. .. .. .. Switzerland 53 96 53 95 54 97 .. .. .. .. .. .. Syrian Arab Republic 89 107 94 109 84 104 92 97c 67 93 c 91c 74 c Tajikistan .. 92 .. 94 .. 90 100 100 b 100 100 b 100 b 99 b Tanzania 61 57 60 57 62 56 89 81b 77 76b 78 b 62b Thailand .. .. .. .. .. .. .. 98 b .. 98 b 95 b 91b Togo 35 66 48 78 22 55 79 83 c 48 63 c 68 c 38 c Trinidad and Tobago 100 94 97 93 102 95 100 100 100 100 99 98 Tunisia 74 94 79 94 69 94 93 96 b 75 92b 83 b 65 b Turkey 90 .. 93 .. 86 .. 97 98 c 88 95 c 96 c 81c Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 57 .. 61 .. 53 80 86 60 74 79 59 Ukraine 92 91 98 .. 97 .. 100 100 b 100 100 b 100 b 99 b United Arab Emirates 95 75 95 77 94 74 82 88 89 95 76 81 United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 95 94 92 92 97 97 98 99 99 99 97 98 Uzbekistan .. 98 .. 98 .. 98 100 100 100 100 100 99 Venezuela, RB 81 89 76 87 86 92 95 96 b 97 98 b 93 b 93 b Vietnam .. 101 .. 104 .. 98 94 .. 94 .. .. .. West Bank and Gaza .. 98 .. 98 .. 99 .. 99 c .. 99 c 96c 87c Yemen, Rep. .. 62 .. 78 .. 46 74 84 25 51 69 29 Zambia .. 66 .. 71 .. 61 86 .. 76 .. .. .. Zimbabwe 91 80 94 82 89 79 97 99 91 96 94 86 World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income 66 74 75 78 59 70 73 81 55 67 73 50 Middle income 92 97 93 97 92 96 95 98 91 97 94 87 Lower middle income 93 98 94 98 92 96 95 98 90 97 93 86 Upper middle income 88 96 88 95 90 96 97 98 95 97 95 94 Low & middle income 81 86 86 88 78 84 86 90 77 83 86 74 East Asia & Pacific 97 99 98 99 96 97 97 98 93 97 95 87 Europe & Central Asia 92 94 92 95 93 94 99 99 97 98 99 99 Latin America & Carib. 86 97 85 96 89 97 93 96 93 97 91 90 Middle East & N. Africa 78 88 83 89 71 86 80 .. 59 .. .. .. South Asia 73 82 87 85 65 78 70 82 50 65 72 46 Sub-Saharan Africa 51 62 55 66 47 56 76 .. 61 .. .. .. High income .. .. .. .. .. .. .. .. .. .. .. .. Europe EMU .. .. .. .. .. .. .. .. .. .. .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Based on census data. c. Based on survey data. 98 2006 World Development Indicators Education completion and outcomes About the data Definitions Many governments collect and publish statistics that lished standards. In many countries national learning · Primary completion rate is the percentage of stu- indicate how their education systems are working and assessments are enabling ministries of education to dents completing the last year of primary school. It developing--statistics on enrollment and on such monitor progress in these outcomes. Internationally, is calculated by taking the total number of students efficiency indicators as repetition rates, pupil-teacher the UNESCO Institute for Statistics has established in the last grade of primary school, minus the num- ratios, and cohort progression through school. literacy as an outcome indicator based on an inter- ber of repeaters in that grade, divided by the total The World Bank and the United Nations Educa- nationally agreed definition. number of children of official graduation age. · Youth tional, Scientific, and Cultural Organization (UNESCO) The literacy rate is defined as the percentage of literacy rate is the percentage of people ages 15­24 Institute for Statistics worked jointly to develop the people who can, with understanding, both read and who can, with understanding, both read and write a primary completion rate indicator. Increasingly used write a short, simple statement about their every- short, simple statement about their everyday life. as a core indicator of an education system's perfor- day life. In practice, literacy is difficult to measure. · Adult literacy rate is the literacy rate among peo- mance, it reflects both the coverage of the education To estimate literacy using such a definition requires ple ages 15 and older. system and the educational attainment of students. census or survey measurements under controlled The indicator is vital as a key measure of educational conditions. Many countries estimate the number of outcome at the primary level and of progress on the literate people from self-reported data. Some use Millennium Development Goals and the Education for educational attainment data as a proxy but apply All initiative. However, because curricula and stan- different lengths of school attendance or levels of dards for school completion vary across countries, a completion. Because definition and methodologies high rate of primary completion does not necessarily of data collection differ across countries, data need mean high levels of student learning. to be used with caution. The primary completion rate reflects the primary The reported literacy data are compiled by the cycle as defined by the International Standard Clas- UNESCO Institute for Statistics based on national sification of Education (ISCED), ranging from three censuses and household surveys during 1995­2004. or four years of primary education (in a very small When countries did not report data, the estimates number of countries) to five or six years (in most generated in July 2002 by UNESCO Institute for Sta- countries) and seven (in a small number of coun- tistics are used. The data for 1990 are also from the tries). For the countries that changed the primary model estimation. The estimation methodology can cycle, the most recent ISCED primary cycle is applied be reviewed at www.uis.unesco.org. consistently to the whole series. Literacy statistics for most countries cover the pop- The data in the table are for the proxy primary com- ulation ages 15 and older, by five-year age groups, pletion rate, calculated by subtracting the number of but some include younger ages or are confined to age students who repeat the final primary grade from the ranges that tend to inflate literacy rates. As an alter- number of students in that grade and dividing the native, the UNESCO Institute for Statistics has pro- result by the number of children of official gradua- posed the narrower age range of 15­24, which bet- tion age in the population. Data limitations preclude ter captures the ability of participants in the formal adjusting this number for students who drop out dur- education system. The youth literacy rate reported ing the final year of primary school. Thus proxy rates in the table measures the accumulated outcomes of should be taken as an upper-bound estimate of the primary education over the previous 10 years or so by actual primary completion rate. indicating the proportion of people who have passed The numerator may include late entrants and over- through the primary education system and acquired age children who have repeated one or more grades basic literacy and numeracy skills. of primary school but are now graduating as well as children who entered school early. The denominator is the number of children of official graduation age, which could cause the primary completion rate to exceed 100 percent. There are other data limitations that contribute to completion rates exceeding 100 Data sources percent, such as the use of estimates for the popula- Data on the primary completion rate for 1991 and tion, the conduct of school and population surveys 2004 are primarily from the UNESCO Institiute for at different times of year, and other discrepancies Statistics. The data for the latest years are provi- in the numbers used in the calculation. sional, as of January 2006. Data on literacy rates Basic student outcomes include achievements are from the UNESCO Institiute for Statistics. in reading and mathematics judged against estab- 2006 World Development Indicators 99 Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index physicians, Out of nurses, and pocket External midwives Total Public % of resources Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2003 2003 2003 2003 2003 2003 1990 1997­2004a 2000­03a 1990 2000­03a Afghanistan 6.5 2.6 39.5 76.5 45.6 11 0.1 0.2 0.4 0.2 0.4 Albania 6.5 2.7 41.7 99.8 3.4 118 1.4 1.3 5.4 4.0 3.1 Algeria 4.1 3.3 80.8 95.3 0.0 89 0.9 1.1 .. 2.5 .. Angola 2.8 2.4 84.2 100.0 6.7 26 0.0 b 0.1 .. 1.3 .. Argentina 8.9 4.3 48.6 55.6 0.2 305 2.7 3.0 .. 4.6 4.1 Armenia 6.0 1.2 20.2 80.6 17.2 55 3.9 3.6 8.8 9.1 4.4 Australia 9.5 6.4 67.5 67.8 0.0 2,519 2.3 2.5 10.8 9.2 7.4 Austria 7.5 5.1 67.6 59.2 0.0 2,358 2.2 3.4 9.3 10.2 8.3 Azerbaijan 3.6 0.9 23.8 96.8 1.9 32 3.9 3.5 12.0 10.1 8.3 Bangladesh 3.4 1.1 31.3 85.8 12.4 14 0.2 0.3 0.5 0.3 .. Belarus 6.4 4.9 75.9 80.5 0.1 116 3.6 4.6 17.5 13.2 11.3 Belgium 9.4 6.3 67.2 66.6 0.0 2,796 3.3 3.9 15.6 8.0 6.9 Benin 4.4 1.9 43.1 90.3 11.5 20 0.1 0.0 b .. 0.8 .. Bolivia 6.7 4.3 64.0 79.3 7.3 61 0.4 1.2 1.1 1.3 1.0 Bosnia and Herzegovina 9.5 4.8 50.7 100.0 1.5 168 1.6 1.3 5.7 4.5 3.1 Botswana 5.6 3.3 58.2 28.8 2.9 232 0.2 0.4 .. 1.6 .. Brazil 7.6 3.4 45.3 64.2 0.3 212 1.4 2.1 2.6 3.3 2.7 Bulgaria 7.5 4.1 54.5 98.4 1.0 191 3.2 3.6 8.3 9.8 6.3 Burkina Faso 5.6 2.6 46.8 98.1 7.4 19 0.0 b 0.1 0.3 0.3 .. Burundi 3.1 0.7 23.3 100.0 14.1 3 0.1 0.0 b 0.3 0.7 .. Cambodia 10.9 2.1 19.3 86.2 18.5 33 0.1 0.2 1.0 2.1 0.5 Cameroon 4.2 1.2 28.9 98.3 3.2 37 0.1 0.2 .. 2.6 .. Canada 9.9 6.9 69.9 49.6 0.0 2,669 2.1 2.1 12.2 6.0 3.7 Central African Republic 4.0 1.5 38.6 95.3 2.9 12 0.0b 0.1 .. 0.9 .. Chad 6.5 2.6 39.9 96.3 11.8 16 0.0 b 0.0 b 0.2 0.7 .. Chile 6.1 3.0 48.8 46.2 0.0 282 1.1 1.1 1.7 3.2 2.6 China 5.6 2.0 36.2 87.6 0.1 61 1.5 1.6 2.7 2.6 2.5 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 7.6 6.4 84.1 47.2 0.0 138 1.1 1.4 1.9 1.4 1.1 Congo, Dem. Rep. 4.0 0.7 18.3 100.0 15.1 4 0.1 0.1 .. 1.4 .. Congo, Rep. 2.0 1.3 64.2 100.0 2.2 19 0.3 0.2 .. 3.3 .. Costa Rica 7.3 5.8 78.8 88.7 2.7 305 1.3 1.3 2.4 2.5 1.4 Côte d'Ivoire 3.6 1.0 27.6 90.5 3.4 28 0.1 0.1 .. 0.8 .. Croatia 7.8 6.5 83.6 100.0 0.6 494 2.1 2.4 7.7 7.4 5.6 Cuba 7.3 6.3 86.8 75.2 0.2 211 3.6 5.9 13.4 5.4 4.9 Czech Republic 7.5 6.8 90.0 83.9 0.0 667 2.8 3.5 13.4 11.3 8.8 Denmark 9.0 7.5 83.0 92.5 0.0 3,534 3.1 2.9 13.6 5.6 4.0 Dominican Republic 7.0 2.3 33.2 70.8 1.5 132 1.5 1.9 3.7 1.9 2.1 Ecuador 5.1 2.0 38.6 88.1 0.9 109 1.5 1.5 3.1 1.6 1.5 Egypt, Arab Rep. 5.9 2.2 37.0 99.0 0.8 64 0.8 0.5 4.9 2.1 2.2 El Salvador 8.1 3.7 46.1 93.5 1.0 183 0.8 1.2 2.0 1.5 .. Eritrea 4.4 2.0 45.5 100.0 19.6 8 .. 0.1 .. .. .. Estonia 5.3 4.1 77.1 88.3 0.1 366 3.5 3.2 9.8 11.6 6.0 Ethiopia 5.9 3.4 58.4 78.7 26.0 5 0.0 b 0.0 b 0.2 0.2 .. Finland 7.4 5.7 76.5 81.2 0.0 2,307 2.4 2.6 25.6 12.5 7.2 France 10.1 7.7 76.3 42.2 0.0 2,981 2.6 3.4 10.2 9.7 7.7 Gabon 4.4 2.9 66.6 100.0 0.7 196 0.5 0.3 .. 3.2 .. Gambia, The 8.1 3.2 40.0 67.0 21.8 21 .. 0.1b .. 0.6 .. Georgia 4.0 1.0 23.9 98.2 5.3 35 4.9 4.1 7.9 9.8 4.2 Germany 11.1 8.7 78.2 47.9 0.0 3,204 3.1 3.4 13.2 10.4 8.9 Ghana 4.5 1.4 31.8 100.0 15.8 16 0.0 b 0.2 0.9 1.5 .. Greece 9.9 5.1 51.3 95.4 .. 1,556 3.4 4.4 7.5 5.1 4.7 Guatemala 5.4 2.1 39.7 91.9 3.8 112 0.8 0.9 .. 1.1 0.5 Guinea 5.4 0.9 16.6 99.4 7.3 22 0.1 0.1 0.6 0.6 .. Guinea-Bissau 5.6 2.6 45.8 80.2 26.8 9 .. 0.1 .. 1.5 .. Haiti 7.5 2.9 38.1 69.5 12.4 26 0.1 0.2 .. 0.8 0.8 100 2006 World Development Indicators Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index physicians, Out of nurses, and pocket External midwives Total Public % of resources Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2003 2003 2003 2003 2003 2003 1990 1997­2004a 2000­03a 1990 2000­03a Honduras 7.1 4.0 56.5 85.8 9.3 72 0.7 0.6 .. 1.0 1.0 Hungary 8.4 6.1 72.4 88.9 0.4 684 2.9 3.2 11.9 .. 7.8 India 4.8 1.2 24.8 97.0 1.6 27 0.5 0.6 .. 0.8 0.9 Indonesia 3.1 1.1 35.9 74.3 1.4 30 0.1 0.1 0.7 0.7 .. Iran, Islamic Rep. 6.5 3.1 47.3 94.8 0.1 131 0.3 0.4 .. 1.4 1.6 Iraq 2.7 1.4 51.8 100.0 3.8 23 0.6 0.7 3.6 1.7 1.3 Ireland 7.3 5.8 78.9 61.9 0.0 2,860 1.6 2.8 19.0 6.1 4.3 Israel 8.9 6.1 68.2 89.1 3.4 1,514 3.2 3.8 10.3 6.2 6.1 Italy 8.4 6.3 75.1 83.3 0.0 2,139 4.7 4.2 10.5 7.2 4.4 Jamaica 5.3 2.7 50.6 64.7 1.2 164 0.6 0.8 2.5 2.2 1.4 Japan 7.9 6.4 81.0 90.1 0.0 2,662 1.7 2.0 10.4 .. 14.3 Jordan 9.4 4.2 45.2 74.0 4.2 177 1.3 2.0 4.8 1.8 1.7 Kazakhstan 3.5 2.0 57.3 100.0 1.2 73 4.0 3.5 9.5 13.7 7.7 Kenya 4.3 1.7 38.7 82.6 15.3 20 0.0 b 0.1 .. 1.6 .. Korea, Dem. Rep. 5.8 5.3 91.2 100.0 19.1 1 .. 3.3 .. .. .. Korea, Rep. 5.6 2.8 49.4 82.8 0.0 705 0.8 1.6 5.4 3.1 7.1 Kuwait 3.5 2.7 77.5 91.2 0.0 580 0.2 1.5 5.4 3 2.2 Kyrgyz Republic 5.0 2.2 43.6 .. .. .. 3.4 2.5 10.1 12.0 5.3 Lao PDR 3.2 1.2 38.5 75.5 30.0 11 0.2 .. .. 2.6 1.2 Latvia 6.4 3.3 51.3 94.3 0.4 301 4.1 3.0 8.2 14.1 7.8 Lebanon 10.2 3.0 29.3 79.4 0.1 573 1.3 3.3 4.4 1.7 3.0 Lesotho 5.2 4.1 79.7 18.2 8.2 31 0.0 b 0.0 b .. .. .. Liberia 4.7 2.7 56.7 98.5 32.3 6 .. 0.0 b .. .. .. Libya 4.1 2.6 62.9 100.0 0.0 171 1.1 1.3 .. 4.2 3.9 Lithuania 6.6 5.0 76.0 96.6 1.3 351 4.0 4.0 12.4 12.5 8.7 Macedonia, FYR 7.1 6.0 84.5 100.0 1.7 161 2.2 2.2 8.1 5.9 4.8 Madagascar 2.7 1.7 63.4 91.7 22.0 8 0.1 0.3 0.4 0.9 0.4 Malawi 9.3 3.3 35.2 42.7 25.1 13 0.0 b 0.0 b 0.3 1.6 .. Malaysia 3.8 2.2 58.2 73.8 0.1 163 0.4 0.7 2.4 2.1 1.9 Mali 4.8 2.8 57.4 89.3 13.7 16 0.1 0.1 0.2 .. .. Mauritania 4.2 3.2 76.8 100.0 4.7 17 0.1 0.1 .. 0.7 .. Mauritius 3.7 2.2 60.8 100.0 1.0 172 0.8 1.1 .. 2.9 .. Mexico 6.2 2.9 46.4 94.2 0.4 372 1.1 1.5 3.9 1.0 1.0 Moldova 7.2 3.9 54.5 96.1 2.5 34 3.6 2.6 9.2 13.1 6.7 Mongolia 6.7 4.3 63.8 91.1 3.2 33 2.5 2.6 6.0 11.5 .. Morocco 5.1 1.7 33.1 76.1 1.0 72 0.2 0.5 1.5 1.3 0.8 Mozambique 4.7 2.9 61.7 38.8 40.8 12 0.0 b 0.0 b 0.3 0.9 .. Myanmar 2.8 0.5 19.4 99.7 2.2 394 0.1 0.4 0.8 0.6 0.6 Namibia 6.7 4.7 70.0 19.2 5.3 145 0.2 0.3 .. .. .. Nepal 5.3 1.5 27.8 92.2 9.9 12 0.1 0.2 0.3 0.2 .. Netherlands 9.8 6.1 62.4 20.8 0.0 3,088 2.5 3.1 16.7 5.8 4.7 New Zealand 8.1 6.3 78.3 72.1 0.0 1,618 1.9 2.2 10.9 8.5 6.1 Nicaragua 7.7 3.7 48.4 95.7 11.2 60 0.7 0.4 1.8 1.8 0.9 Niger 4.7 2.5 53.0 89.2 32.8 9 0.0 b 0.0 b 0.3 .. .. Nigeria 5.0 1.3 25.5 91.2 5.3 22 0.2 0.3 1.5 1.7 .. Norway 10.3 8.6 83.7 95.4 0.0 4,976 2.6 3.1 24.9 4.6 3.8 Oman 3.2 2.7 83.0 56.1 0.0 278 0.6 1.3 4.2 2.1 2.0 Pakistan 2.4 0.7 27.7 98.0 2.5 13 0.5 0.7 1.1 0.6 0.7 Panama 7.6 5.0 66.4 82.2 0.2 315 1.6 1.5 3.2 2.5 2.5 Papua New Guinea 3.4 3.0 88.9 87.2 28.3 23 0.1 0.1 0.6 4.0 .. Paraguay 7.3 2.3 31.5 74.6 1.8 75 0.6 1.1 1.4 0.9 1.2 Peru 4.4 2.1 48.3 79.0 3.2 98 1.1 1.2 .. 1.4 1.4 Philippines 3.2 1.4 43.7 78.2 3.8 31 0.1 1.2 7.4 1.4 1.0 Poland 6.5 4.5 69.9 87.8 0.0 354 2.1 2.5 7.7 5.7 5.6 Portugal 9.6 6.7 69.7 95.7 0.0 1,348 2.8 3.3 7.0 4.1 3.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 101 Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index physicians, Out of nurses, and pocket External midwives Total Public % of resources Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2003 2003 2003 2003 2003 2003 1990 1997­2004a 2000­03a 1990 2000­03a Romania 6.1 3.8 62.9 90.4 3.8 159 1.8 1.9 6.2 8.9 6.6 Russian Federation 5.6 3.3 59.0 71.1 0.2 167 4.1 4.3 12.5 13.1 10.5 Rwanda 3.7 1.6 43.5 41.7 54.5 7 0.0 b 0.0 b 0.2 1.7 .. Saudi Arabia 3.3 2.5 75.4 31.0 .. 348 1.4 1.4 4.4 2.5 2.2 Senegal 5.1 2.1 41.8 95.3 15.4 29 0.1 0.1 .. 0.7 .. Serbia and Montenegro 9.6 7.2 75.5 85.3 0.5 181 2.0 2.1 .. 5.9 6.0 Sierra Leone 3.5 2.0 58.3 100.0 15.5 7 .. 0.0 b .. .. .. Singapore 4.5 1.6 36.1 97.1 0.0 964 1.3 1.4 5.6 3.6 2.9 Slovak Republic 5.9 5.2 88.3 100.0 0.0 360 2.9 3.1 10.6 7.4 7.2 Slovenia 8.8 6.7 76.3 41.1 0.1 1,218 2.0 2.3 9.4 6.0 5.0 Somalia 2.6 1.2 44.6 100.0 9.3 8 .. 0.0 b .. 0.8 .. South Africa 8.4 3.2 38.6 17.1 0.5 295 0.6 0.8 4.6 .. .. Spain 7.7 5.5 71.3 82.0 0.0 1,541 2.3 3.2 6.8 4.6 3.8 Sri Lanka 3.5 1.6 45.0 88.9 2.3 31 0.1 0.5 1.2 2.7 .. Sudan 4.3 1.9 43.2 96.3 2.2 21 .. 0.2 1.0 1.1 0.7 Swaziland 5.8 3.3 57.3 42.4 5.5 107 0.1 0.2 3.4 .. .. Sweden 9.4 8.0 85.2 92.1 0.0 3,149 2.9 3.3 13.5 12.4 3.6 Switzerland 11.5 6.7 58.5 76.0 0.0 5,035 3.0 3.6 12.1 19.9 6.0 Syrian Arab Republic 5.1 2.5 48.2 100.0 0.2 59 0.8 1.4 3.3 1.1 1.5 Tajikistan 4.4 0.9 20.8 100.0 14.9 11 2.6 2.0 7.2 10.7 6.1 Tanzania 4.3 2.4 55.4 81.1 21.9 12 .. 0.0 b 0.4 1.0 .. Thailand 3.3 2.0 61.6 74.8 0.3 76 0.2 0.4 .. 1.6 .. Togo 5.6 1.4 24.8 88.0 2.3 16 0.1 0.0 b 0.3 1.5 .. Trinidad and Tobago 3.9 1.5 37.8 88.6 1.4 316 0.7 0.8 .. 4.0 3.4 Tunisia 5.6 2.8 50.0 .. .. 126 0.5 1.3 .. 1.9 1.7 Turkey 7.6 5.4 71.6 69.9 0.0 257 0.9 1.4 4.2 2.4 2.6 Turkmenistan 3.9 2.6 67.4 100.0 0.4 89 3.6 4.2 .. 11.5 .. Uganda 7.3 2.2 30.4 52.8 28.5 18 0.0 b 0.1 0.1 0.9 .. Ukraine 5.7 3.8 65.9 78.6 0.1 60 4.3 3.0 11.2 13.0 8.8 United Arab Emirates 3.3 2.5 74.7 70.4 0.0 661 0.8 2.0 6.2 2.6 2.2 United Kingdom 8.0 6.9 85.7 76.7 0.0 2,428 1.4 2.2 .. 5.9 4.2 United States 15.2 6.8 44.6 24.3 0.0 5,711 2.4 2.3 13.2 4.9 3.3 Uruguay 9.8 2.7 27.2 25.0 0.4 323 3.7 3.7 4.5 4.5 1.9 Uzbekistan 5.5 2.4 43.0 95.5 3.0 21 3.4 2.7 13.7 12.5 5.5 Venezuela, RB 4.5 2.0 44.3 95.5 0.1 146 1.6 1.9 2.6 2.7 0.8 Vietnam 5.4 1.5 27.8 74.2 2.6 26 0.4 0.5 1.3 3.8 2.4 West Bank and Gaza .. .. .. .. .. .. .. 0.8 .. .. .. Yemen, Rep. 5.5 2.2 40.9 95.5 8.8 32 0.0 b 0.3 0.7 0.8 0.6 Zambia 5.4 2.8 51.4 68.2 44.7 21 0.1 0.1 .. .. .. Zimbabwe 7.9 2.8 35.9 56.7 6.8 40 0.1 0.2 0.6 0.5 .. World 10.2 w 5.9 w 60.4 w 43.4 w 0.1 w 588 w 1.6 w 1.5 w .. w 3.7 w .. w Low income 4.6 1.3 29.1 95.1 4.4 30 0.5 0.4 .. .. .. Middle income 6.0 3.0 49.5 77.5 0.5 116 1.6 1.7 .. 3.5 .. Lower middle income 5.6 2.5 43.7 81.4 0.6 77 1.4 1.5 .. 2.9 .. Upper middle income 6.5 3.7 57.6 72.0 0.3 280 2.4 2.4 7.6 6.6 5.6 Low & middle income 5.8 2.8 46.4 81.6 1.2 79 1.3 1.1 .. 3.1 .. East Asia & Pacific 5.0 1.9 39.0 88.3 0.8 64 1.2 1.3 3.0 2.3 2.4 Europe & Central Asia 6.5 4.5 67.3 79.9 0.5 194 3.2 3.0 10.3 10.2 7.6 Latin America & Carib. 6.8 3.3 48.2 75.3 0.6 222 1.4 1.9 .. 2.5 .. Middle East & N. Africa 5.6 2.7 50.9 89.2 0.8 92 .. 1.2 .. 1.8 .. South Asia 4.4 1.1 26.3 96.2 2.7 24 0.5 0.5 .. 0.7 0.9 Sub-Saharan Africa 6.1 2.4 41.2 47.4 5.5 36 .. 0.1 .. 1.2 .. High income 11.2 6.7 63.9 37.0 0.0 b 3,449 2.3 3.7 .. 6.2 6.4 Europe EMU 9.6 7.1 74.1 57.6 0.0 2,552 3.1 3.9 12.2 8.1 6.6 a. Data are for the most recent year available. b. Less than 0.05. 102 2006 World Development Indicators Health expenditure, services, and use About the data Definitions National health accounts track financial flows in the offices. The data are scrutinized using such additional · Total health expenditure is the sum of public and health sector, including public and private expendi- recources as national and international employment private health expenditure. It covers the provision of tures, by source of funding. In contrast with high- surveys, records from professional associations, health services (preventive and curative), family plan- income countries, few developing countries have and other publications. Significant inconsistenties ning activities, nutrition activities, and emergency aid health accounts that are methodologically consistent are returned to national authorities for validation and designated for health but does not include provision with national accounting approaches. The difficulties resubmission. of water and sanitation. · Public health expendi- in creating national health accounts go beyond data The health worker density index indicates the over- ture consists of recurrent and capital spending from collection. To establish a national health accounting all level of health workers (physicians, nurses, and government (central and local) budgets, external system, a country needs to define the boundaries of midwives) in the country. Dentists and pharmacists borrowings and grants (including donations from the health care system and to define a taxonomy of are not included. Comparability of the index across international agencies and nongovernmental organi- health care delivery institutions. The accounting sys- countries is affected by differences in the definition zations), and social (or compulsory) health insurance tem should be comprehensive and standardized, pro- of health workers. Many countries continue to use funds. · Out of pocket health expenditure is any viding not only accurate measures of financial flows national definitions and classifications for data col- direct outlay by households, including gratuities and but also information on the equity and efficiency of lection, and some countries provide information only in-kind payments, to health practitioners and suppli- health financing to inform health policy. for public sector workers. ers of pharmaceuticals, therapeutic appliances, and The absence of consistent national health account- other goods and services whose primary intent is to ing systems in most developing countries makes contribute to the restoration or enhancement of the cross-country comparisons of health spending dif- health status of individuals or population groups. It ficult. Compiling estimates of public health expen- is a part of private health expenditure. · External ditures is complicated in countries where state or resources for health are funds or services in kind provincial and local governments are involved in that are provided by entities not part of the country financing and delivering health care, because the in question. The resources may come from interna- data on public spending often are not aggregated. tional organizations, other countries through bilat- There are a number of potential data sources related eral arrangements, or foreign nongovernmental orga- to external resources for health, including govern- nizations. These resources are part of total health ment expenditure accounts, government records expenditure. · Health expenditure per capita is total on external assistance, routine surveys of external health expenditure divided by number of people in the financing assistance, and special surveys. Survey country. · Physicians are graduates of any faculty or data are the major source of information about out school of medicine who are working in the country in of pocket expenditure on health. The data in the table any medical field (practice, teaching, or research). are the product of an effort by the World Health Orga- · Health worker density index reflects a combined nization (WHO), the Organisation for Economic Co- density of physicians, nurses, and midwives per operation and Development (OECD), and the World 1,000 people. · Hospital beds include inpatient beds Bank to collect all available information on health available in public, private, general, and specialized expenditures from national and local government hospitals and rehabilitation centers. In most cases budgets, national accounts, household surveys, beds for both acute and chronic care are included. insurance publications, international donors, and existing tabulations. Data sources Indicators on health services (physicians, health worker density, and hospital beds per 1,000 people) Data on health expenditure come mostly from the come from a variety of sources (see Data sources). WHO's World Health Report 2006 and from the In Uganda most births in rural areas take Data are lacking for many countries, and for oth- OECD for its member countries, supplemented by place at home ers comparability is limited by differences in defini- World Bank poverty assessments and country and Location of delivery, 2000 (% of births) Urban tions. In estimates of health personnel, for example, sector studies. Data are also drawn from World Rural some countries incorrectly include retired physicians 80 Bank public expenditure reviews, the International (because deletions to physician rosters are made Monetary Fund's Government Finance Statistics only periodically) or those working outside the health 60 database, and other studies. Data on out of pocket sector. There is no universally accepted definition of expenditure in developing countries are drawn hospital beds. Moreover, figures on physicians and largely from household surveys conducted by gov- 40 hospital beds are indicators of availability, not of ernments or by statistical or international organiza- quality or use. They do not show how well trained the tions. Data on physicians are from the WHO's World physicians are or how well equipped the hospitals 20 Health Report 2006 and Global Atlas of the Health or medical centers are. And physicians and hospital Workforce database, OECD, and TransMONEE, beds tend to be concentrated in urban areas, so 0 supplemented by country data. Data for the health these indicators give only a partial view of health In a public In a private At home worker density index are from the Joint Learning health facility health facility services available to the entire population. Initiative's Human Resources for Health. Data on The WHO receives data on health professionals Rural areas lack accessibile medical facilities, as shown by hospital beds are from the WHO's World Health the low share of births in public or private health facilities Statistics 2005, OECD's Health Data 2005, and from ministries of health through its six regional offices, often in cooperation with national statistical Source: Demographic and Health Survey. TransMONEE, supplemented by country data. 2006 World Development Indicators 103 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS detection an improved improved immunization with acute diarrhea who sleeping with fever treatment rate water source sanitation rate respiratory received oral under receiving success facilities infection rehydration treated antimalarial rate taken to and continued bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of % of % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DPT with ARI with diarrhea under age 5 with fever cases cases 1990 2002 1990 2002 2004 2004 2000­04 c 1998­2004c 2000­04 c 2000­04 c 2003 2004 Afghanistan .. 13 .. 8 61 66 28 48 .. .. 86 19 Albania 97 97 .. 89 96 97 83 51 .. .. 91 34 Algeria 95 87 88 92 81 86 52 .. .. .. 90 105 Angola 32 50 30 30 64 59 58 32 2.3 63.0 68 94 Argentina 94 .. 82 .. 95 90 .. .. .. .. 66 65 Armenia .. 92 .. 84 92 91 25 48 .. .. 77 44 Australia 100 100 100 100 93 92 .. .. .. .. 82 33 Austria 100 100 100 100 74 83 .. .. .. .. 68 42 Azerbaijan 66 77 .. 55 98 96 36 40 1.4 1.0 70 47 Bangladesh 71 75 23 48 77 85 20 35 .. .. 85 44 Belarus 100 100 .. .. 99 99 .. .. .. .. 73 42 Belgium .. .. .. .. 82 95 .. .. .. .. 73 65 Benin 60 68 11 32 85 83 29 42 7.4 60.0 81 82 Bolivia 72 85 33 45 64 81 52 54 .. .. 81 71 Bosnia and Herzegovina 98 98 .. 93 88 84 80 23 .. .. 94 96 Botswana 93 95 38 41 90 97 14 7 .. .. 77 67 Brazil 83 89 70 75 99 96 .. .. .. .. 83 47 Bulgaria 100 100 100 100 81 90 .. .. .. .. 91 104 Burkina Faso 39 51 13 12 78 88 36 .. 2.0 50.0 66 18 Burundi 69 79 44 36 75 74 40 16 1.3 31.0 79 29 Cambodia .. 34 .. 16 80 85 35 59 .. .. 93 61 Cameroon 50 63 21 48 64 73 40 33 1.3 66.1 70 91 Canada 100 100 100 100 95 91 .. .. .. .. 35 58 Central African Republic 48 75 23 27 35 40 32 47 1.5 69.0 59 4 Chad 20 34 6 8 56 50 12 50 0.6 32.0 78 16 Chile 90 95 85 92 95 94 .. .. .. .. 85 114 China 70 77 23 44 84 91 .. .. .. .. 94 63 Hong Kong, China .. .. .. .. .. .. .. .. .. .. 78 55 Colombia 92 92 82 86 92 89 51 44 0.7 .. 83 17 Congo, Dem. Rep. 43 46 18 29 64 64 36 17 0.7 45.0 83 70 Congo, Rep. .. 46 .. 9 65 67 38 .. .. .. 69 65 Costa Rica .. 97 .. 92 88 90 .. .. .. .. 94 153 Côte d'Ivoire 69 84 31 40 49 50 38 34 4.0 57.5 72 38 Croatia .. .. .. .. 96 96 .. .. .. .. .. .. Cuba .. 91 98 98 99 88 .. .. .. .. 93 90 Czech Republic .. .. .. .. 97 98 .. .. .. .. 79 61 Denmark 100 100 .. .. 96 95 .. .. .. .. 84 78 Dominican Republic 86 93 48 57 79 71 61 53 .. .. 81 71 Ecuador 69 86 56 72 99 90 .. .. .. .. 84 42 Egypt, Arab Rep. 94 98 54 68 97 97 70 29 .. .. 80 61 El Salvador 67 82 51 63 93 90 62 .. .. .. 88 57 Eritrea 40 57 8 9 84 83 44 54 4.2 4.0 85 14 Estonia .. .. .. .. 96 94 .. .. .. .. 70 74 Ethiopia 25 22 4 6 71 80 16 38 .. 3.0 70 36 Finland 100 100 100 100 97 98 .. .. .. .. .. .. France .. .. .. .. 86 97 .. .. .. .. .. .. Gabon .. 87 .. 36 55 38 48 44 .. .. 34 81 Gambia, The .. 82 .. 53 90 92 75 38 14.7 55.0 75 66 Georgia .. 76 .. 83 86 78 99 .. .. .. 66 79 Germany 100 100 .. .. 92 97 .. .. .. .. 71 51 Ghana 54 79 43 58 83 80 44 40 4.0 63.0 66 37 Greece .. .. .. .. 88 88 .. .. .. .. .. .. Guatemala 77 95 50 61 75 84 64 22 .. .. 91 55 Guinea 42 51 17 13 73 69 33 44 4.0 56.0 75 52 Guinea-Bissau .. 59 .. 34 80 80 64 23 7.4 58.0 80 75 Haiti 53 71 15 34 54 43 26 41 .. 12.0 78 49 104 2006 World Development Indicators Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS detection an improved improved immunization with acute diarrhea who sleeping with fever treatment rate water source sanitation rate respiratory received oral under receiving success facilities infection rehydration treated antimalarial rate taken to and continued bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of % of % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DPT with ARI with diarrhea under age 5 with fever cases cases 1990 2002 1990 2002 2004 2004 2000­04 c 1998­2004c 2000­04 c 2000­04 c 2003 2004 Honduras 83 90 49 68 92 89 .. .. .. .. 87 83 Hungary 99 99 .. 95 99 99 .. .. .. .. 48 47 India 68 86 12 30 56 64 .. 22 .. .. 86 57 Indonesia 71 78 46 52 72 70 61 61 0.1 1.0 87 53 Iran, Islamic Rep. 91 93 83 84 96 99 93 .. .. .. 84 58 Iraq 83 81 81 80 90 81 76 .. .. .. 85 20 Ireland .. .. .. .. 81 89 .. .. .. .. .. .. Israel 100 100 .. .. 96 96 .. .. .. .. 80 34 Italy .. .. .. .. 84 96 .. .. .. .. 95 58 Jamaica 92 93 75 80 80 77 39 21 .. .. 53 79 Japan 100 100 100 100 99 99 .. .. .. .. 76 45 Jordan 98 91 .. 93 99 95 72 44 .. .. 87 79 Kazakhstan 86 86 72 72 99 82 .. 22 .. .. 75 79 Kenya 45 62 42 48 73 73 46 33 5.0 27.0 80 46 Korea, Dem. Rep. 100 100 .. 59 95 72 93 .. .. .. 88 103 Korea, Rep. .. 92 .. .. 99 88 .. .. .. .. 82 21 Kuwait .. .. .. .. 97 98 .. .. .. .. 62 83 Kyrgyz Republic .. 76 .. 60 99 99 .. .. .. .. 84 62 Lao PDR .. 43 .. 24 36 45 36 37 18.0 9.0 79 55 Latvia .. .. .. .. 99 98 .. .. .. .. 74 83 Lebanon 100 100 .. 98 96 92 74 .. .. .. 92 82 Lesotho .. 76 37 37 70 78 .. 29 .. .. 70 86 Liberia 56 62 38 26 42 31 70 .. .. .. 73 58 Libya 71 72 97 97 99 97 .. .. .. .. 62 169 Lithuania .. .. .. .. 98 94 .. .. .. .. 74 89 Macedonia, FYR .. .. .. .. 96 94 .. .. .. .. 84 73 Madagascar 40 45 12 33 59 61 48 47 0.2 34.0 71 74 Malawi 41 67 36 46 80 89 27 51 2.9 27.0 73 40 Malaysia .. 95 96 .. 95 99 .. .. .. .. 72 69 Mali 34 48 36 45 75 76 43 45 8.4 38.0 65 19 Mauritania 41 56 28 42 64 70 39 28 .. 33.4 58 43 Mauritius 100 100 99 99 98 98 .. .. .. .. 87 33 Mexico 80 91 66 77 96 98 .. .. .. .. 83 71 Moldova .. 92 .. 68 96 98 78 52 .. .. 65 59 Mongolia 62 62 .. 59 96 99 78 66 .. .. 87 80 Morocco 75 80 57 61 95 97 35 50 .. .. 86 80 Mozambique .. 42 .. 27 77 72 51 .. .. .. 76 46 Myanmar 48 80 21 73 78 82 66 48 .. .. 81 83 Namibia 58 80 24 30 70 81 53 39 3.4 14.0 63 88 Nepal 69 84 12 27 73 80 24 43 .. .. 87 67 Netherlands 100 100 100 100 96 98 .. .. .. .. 86 61 New Zealand 97 .. .. .. 85 90 .. .. .. .. 36 59 Nicaragua 69 81 47 66 84 79 57 49 .. 2.0 84 87 Niger 40 46 7 12 74 62 27 43 5.8 48.0 70 46 Nigeria 49 60 39 38 35 25 31 28 1.0 34.0 59 21 Norway 100 100 .. .. 88 91 .. .. .. .. 97 46 Oman 77 79 83 89 98 99 .. .. .. .. 90 123 Pakistan 83 90 38 54 67 65 .. .. .. .. 75 27 Panama .. 91 .. 72 99 99 .. .. .. .. 74 133 Papua New Guinea 39 39 45 45 44 46 .. .. .. .. 58 19 Paraguay 62 83 58 78 89 76 .. .. .. .. 85 21 Peru 74 81 52 62 89 87 58 46 .. .. 89 83 Philippines 87 85 54 73 80 79 55 76 .. .. 88 73 Poland .. .. .. .. 97 99 .. .. .. .. 78 56 Portugal .. .. .. .. 95 95 .. .. .. .. 84 78 Puerto Rico .. .. .. .. .. .. .. .. .. .. 66 76 2006 World Development Indicators 105 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS detection an improved improved immunization with acute diarrhea who sleeping with fever treatment rate water source sanitation rate respiratory received oral under receiving success facilities infection rehydration treated antimalarial rate taken to and continued bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of % of % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DPT with ARI with diarrhea under age 5 with fever cases cases 1990 2002 1990 2002 2004 2004 2000­04 c 1998­2004c 2000­04 c 2000­04 c 2003 2004 Romania .. 57 .. 51 97 97 .. .. 31.0 .. 80 41 Russian Federation 94 96 87 87 98 97 .. .. .. .. 61 13 Rwanda 58 73 37 41 84 89 20 16 5.0 13.0 67 29 Saudi Arabia 90 .. .. .. 97 96 .. .. .. .. 79 40 Senegal 66 72 35 52 57 87 27 33 1.7 36.0 70 52 Serbia and Montenegro 93 93 87 87 96 97 97 .. .. .. 89 32 Sierra Leone .. 57 .. 39 64 61 50 39 1.5 61.0 83 36 Singapore .. .. .. .. 94 94 .. .. .. .. 77 67 Slovak Republic 100 100 100 100 98 99 .. .. .. .. 87 34 Slovenia .. .. .. .. 94 92 .. .. .. .. 85 66 Somalia .. 29 .. 25 40 30 .. .. .. .. 90 44 South Africa 83 87 63 67 81 93 .. 37 .. .. 67 83 Spain .. .. .. .. 97 96 .. .. .. .. .. .. Sri Lanka 68 78 70 91 96 97 .. .. .. .. 81 70 Sudan 64 69 33 34 59 55 57 38 0.4 50.0 82 35 Swaziland .. 52 .. 52 70 83 60 24 0.1 26.0 42 38 Sweden 100 100 100 100 94 99 .. .. .. .. 83 69 Switzerland 100 100 100 100 82 95 .. .. .. .. .. .. Syrian Arab Republic 79 79 76 77 98 99 66 .. .. .. 88 46 Tajikistan .. 58 .. 53 89 82 51 29 1.9 69.0 86 12 Tanzania 38 73 47 46 94 95 .. 38 10.0 58.0 81 47 Thailand 81 85 80 99 96 98 .. .. .. .. 73 71 Togo 49 51 37 34 70 71 30 25 2.0 60.0 63 17 Trinidad and Tobago 92 91 100 100 95 94 74 31 .. .. .. .. Tunisia 77 82 75 80 95 97 43 .. .. .. 91 95 Turkey 81 93 84 83 81 85 41 19 .. .. 93 3 Turkmenistan .. 71 .. 62 97 97 51 .. .. .. 82 38 Uganda 44 56 43 41 91 87 67 29 0.2 .. 68 43 Ukraine .. 98 99 99 99 99 .. .. .. .. .. .. United Arab Emirates .. .. 100 100 94 94 .. .. .. .. 64 17 United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States 100 100 100 100 93 96 .. .. .. .. 70 85 Uruguay .. 98 .. 94 95 95 .. .. .. .. 86 86 Uzbekistan 89 89 58 57 98 99 57 33 .. .. 81 28 Venezuela, RB .. 83 .. 68 80 86 72 51 .. .. 82 77 Vietnam 72 80 32 41 97 96 71 39 15.8 7.0 92 89 West Bank and Gaza .. 94 .. 76 .. .. 65 .. .. .. 80 1 Yemen, Rep. 69 69 21 30 76 78 47 .. .. .. 82 40 Zambia 50 55 41 45 84 80 69 48 6.5 52.0 75 54 Zimbabwe 77 83 49 57 80 85 .. 80 .. .. 66 42 World 75 w 82 w 43 w 54 w 76 w 79 w .. w 55 w Low income 64 75 20 36 64 67 24 47 Middle income 77 83 48 61 87 89 .. 68 Lower middle income 75 81 42 57 86 88 .. 65 Upper middle income 88 93 80 81 91 93 37 74 Low & middle income 71 79 37 50 74 77 .. 55 East Asia & Pacific 71 78 30 49 82 87 .. 77 Europe & Central Asia .. 91 86 82 93 93 22 51 Latin America & Carib. 82 89 68 75 92 91 .. 62 Middle East & N. Africa 87 88 69 75 92 93 .. 50 South Asia 70 84 16 35 61 67 23 42 Sub-Saharan Africa 49 58 32 36 64 64 .. 51 High income .. 99 .. .. 93 96 .. 57 Europe EMU .. .. .. .. 89 96 .. .. a. For malaria prevention only. b. Refers to children who were immunized before 12 months or, in some cases, at any time before the survey (12­23 months). c. Data are for the most recent year available. 106 2006 World Development Indicators Disease prevention coverage and quality About the data Definitions The indicators in the table are based on data provided to Malaria is endemic to the poorest countries in the world, · Access to an improved water source refers to the per- the World Health Organization (WHO) by member states mainly in tropical and subtropical regions of Africa, Asia, centage of the population with reasonable access to an as part of their efforts to monitor and evaluate progress and the Americas. An estimated 300­500 million clinical adequate amount of water from an improved source, such in implementing national health strategies. Because reli- malaria cases and more than 1 million malaria deaths as a household connection, public standpipe, borehole, able, observation-based statistical data for these indica- occur each year--the vast majority in Sub-Saharan Africa protected well or spring, or rainwater collection. Unim- tors do not exist in some developing countries, some of and among children under age five. Insecticide-treated proved sources include vendors, tanker trucks, and unpro- the data are estimated. bednets, if properly used and maintained, are one of the tected wells and springs. Reasonable access is defined People's health is influenced by the environment in most important malaria-preventive strategies to limit as the availability of at least 20 liters a person a day from which they live. Lack of clean water and basic sanita- human-mosquito contact. Studies have emphasized that a source within 1 kilometer of the dwelling. · Access to tion is the main reason diseases transmitted by feces mortality rates could be reduced by about 25­30 percent improved sanitation facilities refers to the percentage of are so common in developing countries. The data on if every child under age five in malaria-risk areas such as the population with at least adequate access to excreta access to an improved water source measure the share Africa slept under a treated bednet every night. disposal facilities that can effectively prevent human, ani- of the population with ready access to water for domes- Prompt and effective treatment of malaria is a critical mal, and insect contact with excreta. Improved facilities tic purposes. The data are based on surveys and esti- elemet of malaria control. It is vital that sufferers, espe- range from simple but protected pit latrines to flush toi- mates provided by governments to the Joint Monitoring cially children under age five, start treatment within 24 lets with a sewerage connection. To be effective, facilities Programme of the WHO and United Nations Children's hours of the onset of symptoms, to prevent progression-- must be correctly constructed and properly maintained. Fund (UNICEF). The coverage rates for water and sanita- often rapid--to severe malaria and death. · Child immunization rate is the percentage of children tion are based on information from service users on Data on the success rate of tuberculosis treatment ages 12­23 months who received vaccinations before the facilities their households actually use rather than are provided for countries that have implemented the 12 months or at any time before the survey for four on information from service providers, who may include recommended control strategy: directly observed treat- diseases--measles and diphtheria, pertussis (whoop- nonfunctioning systems. Access to drinking water from ment, short course (DOTS). Countries that have not ing cough), and tetanus (DPT). A child is considered an improved source does not ensure that the water is adopted DOTS or have only recently done so are omit- adequately immunized against measles after receiving safe or adequate, as these characteristics are not tested ted because of lack of data or poor comparability or one dose of vaccine and against DPT after receiving three at the time of the surveys. reliability of reported results. The treatment success rate doses. · Children with acute respiratory infection taken Governments in developing countries usually finance for tuberculosis provides a useful indicator of the quality to a health provider refer to the percentage of children immunization against measles and diphtheria, pertussis of health services. A low rate or no success suggests under age five with acute respiratory infection in the two (whooping cough), and tetanus (DPT) as part of the basic that infectious patients may not be receiving adequate weeks prior to the survey who were taken to an appropri- public health package. In many developing countries, treatment. An essential complement to the tuberculosis ate health provider, including hospital, health center, dis- lack of precise information on the size of the cohort treatment success rate is the DOTS detection rate, which pensary, village health worker, clinic, and private physician of one-year-old children makes immunization coverage indicates whether there is adequate coverage by the rec- · Children with diarrhea who received oral rehydration difficult to estimate from program statistics. The data ommended case detection and treatment strategy. A and continued feeding refer to the percentage of children shown here are based on an assessment of national country with a high treatment success rate may still face under age five with diarrhea in the two weeks prior to immunization coverage rates by the WHO and UNICEF. big challenges if its DOTS detection rate remains low. the survey who received either oral rehydration therapy The assessment considered both administrative data or increased fluids, with continued feeding. · Children from service providers and household survey data on sleeping under treated bednets refer to the percentage children's immunization histories. Based on the data of children under age five who slept under an insecticide- available, consideration of potential biases, and con- treated bednet to prevent malaria. · Children with fever tributions of local experts, the most likely true level of Deaths from diarrhea can be sharply reduced receiving antimalarial drugs refer to the percentage of immunization coverage was determined for each year. with improvements in drinking water and children under age five who were ill with fever in the last Acute respiratory infection continues to be a lead- sanitation two weeks and received any appropriate (locally defined) ing cause of death among young children, killing about Percent change, 2005 antimalarial drugs. · Tuberculosis treatment success 2 million children under age five in developing countries 50 rate is the percentage of new, registered smear-positive in 2000. An estimated 60 percent of these deaths can (infectious) cases that were cured or in which a full course be prevented by the selective use of antibiotics by appro- of treatment was completed. · DOTS detection rate is priate health care providers. Data are drawn mostly from the percentage of estimated new infectious tuberculosis 40 household health surveys in which mothers report on cases detected under the directly observed treatment, number of episodes and treatment for acute respira- short course case detection and treatment strategy. tory infection. 30 Data sources Since 1990 diarrhea-related deaths among children Data on water and sanitation are from the WHO and have declined tremendously. Most diarrhea-related UNICEF's Meeting the MDG Drinking Water and Sanita- deaths are due to dehydration, and many of these deaths 20 tion Target (www.unicef.org/wes/mdgreport). Data on can be prevented with the use of oral rehydration salts immunization are from WHO and UNICEF estimates of at home. However, recommendations for the use of oral national immunization coverage. Data on children with rehydration therapy have changed over time based on acute respiratory infection, children with diarrhea, scientific progress, so it is difficult to accurately com- 10 children sleeping under treated bednets, and children pare use rates among countries. Until the current recom- receiving antimalarial drugs are from UNICEF's State mended method for home management of diarrhea is of the World's Children 2006, Childinfo, and Demo- adopted and applied in all countries, the data should be 0 Hygiene Household Sanitation Drinking graphic and Health Surveys by Macro International. used with caution. Also, the prevalence of diarrhea may water water treatment Data on tuberculosis are from the WHO's Global Tuber- vary by season. Since country surveys are administered at Source: WHO/UNICEF 2005. culosis Control Report 2006. different times, data comparability is further affected. 2006 World Development Indicators 107 Reproductive health Total fertility Adolescent Unmet need for Contraceptive Tetanus Births attended by Maternal mortality rate fertility rate contraception prevalence vaccinations skilled health staff ratio rate births per % of married per 100,000 live births 1,000 women women ages % of women % of pregnant National Modeled births per woman ages 15­19 15­49 ages 15­49 women % of total estimates estimates 1990 2004 2004 1995­2004a 1996­2004a 2004 1990­92a 2000­04a 1990­2004a 2000 Afghanistan 8.0 .. .. .. 10 35 .. 14 1,600 1,900 Albania 2.9 2.2 16 .. 75 .. .. 98 23 55 Algeria 4.6 2.5 8 .. 57 .. 77 96 120 140 Angola 7.1 6.6 141 .. 6 75 .. 45 .. 1,700 Argentina 3.0 2.3 59 .. .. .. 96 99 44 82 Armenia 2.5 1.3 30 12 61 .. .. 97 9 55 Australia 1.9 1.8 15 .. .. .. 100 .. .. 8 Austria 1.5 1.4 13 .. 51 .. .. .. .. 4 Azerbaijan 2.7 2.0 31 .. 55 .. .. 84 25 94 Bangladesh 4.3 3.0 123 11 59 45 .. 13 380 380 Belarus 1.9 1.2 26 .. .. .. .. 100 18 35 Belgium 1.6 1.6 8 .. .. .. .. .. .. 10 Benin 6.7 5.7 130 27 19 69 .. 66 500 850 Bolivia 4.9 3.7 82 23 58 .. .. 67 230 420 Bosnia and Herzegovina 1.7 1.3 23 .. 48 .. 97 100 .. 31 Botswana 4.4 3.1 76 .. 48 .. .. 94 330 100 Brazil 2.8 2.3 89 7 77 .. 72 96 64 260 Bulgaria 1.8 1.3 44 .. 42 .. .. 99 15 32 Burkina Faso 7.3 6.5 159 29 14 65 .. 38 480 1,000 Burundi 6.8 6.8 50 .. 16 45 .. 25 .. 1,000 Cambodia 5.5 4.0 48 30 24 51 .. 32 440 450 Cameroon 5.9 4.8 114 20 26 60 58 62 .. 730 Canada 1.8 1.5 14 .. .. .. .. 98 .. 6 Central African Republic 5.6 4.8 126 16 28 42 .. 44 1,100 1,100 Chad 6.7 6.4 192 10 3 42 .. 14 830 1,100 Chile 2.6 2.0 61 .. .. .. .. 100 17 31 China 2.1 1.8 5 .. 87 .. .. 96 51 56 Hong Kong, China 1.3 0.9 5 .. .. .. .. .. .. .. Colombia 3.1 2.4 77 6 77 86 82 91 78 130 Congo, Dem. Rep. 6.7 6.7 227 .. 31 58 .. 61 1,300 990 Congo, Rep. 6.3 6.3 145 .. .. 65 .. .. .. 510 Costa Rica 3.2 2.0 75 .. 80 .. 98 98 33 43 Côte d'Ivoire 6.5 4.8 123 28 15 75 .. 68 600 690 Croatia 1.6 1.4 15 .. .. .. .. 100 .. 8 Cuba 1.7 1.5 50 .. 73 .. .. 100 34 33 Czech Republic 1.9 1.2 12 .. 72 .. .. 100 .. 9 Denmark 1.7 1.8 7 .. .. .. .. .. 10 5 Dominican Republic 3.3 2.8 91 11 70 .. 93 98 180 150 Ecuador 3.6 2.7 84 .. 66 .. .. .. 80 130 Egypt, Arab Rep. 4.3 3.2 43 11 60 71 41 69 84 84 El Salvador 3.7 2.8 85 .. 67 .. .. 92 170 150 Eritrea 6.2 5.3 93 27 8 62 .. 28 .. 630 Estonia 2.0 1.4 23 .. .. .. .. 100 46 63 Ethiopia 6.9 5.4 90 35 8 45 .. 6 870 850 Finland 1.8 1.8 10 .. .. .. .. 100 6 6 France 1.8 1.9 9 .. .. .. .. .. 10 17 Gabon 5.3 3.8 106 28 33 45 .. 86 520 420 Gambia, The 5.8 4.5 119 .. 18 .. 44 55 730 540 Georgia 2.1 1.4 33 .. 41 .. .. .. 52 32 Germany 1.5 1.4 10 .. .. .. .. .. 8 8 Ghana 5.7 4.2 64 34 25 70 .. 47 .. 540 Greece 1.4 1.3 9 .. .. .. .. .. 1 9 Guatemala 5.6 4.4 112 23 43 .. .. 41 150 240 Guinea 6.5 5.8 191 24 7 77 31 56 530 740 Guinea-Bissau 7.1 7.1 194 .. 8 56 .. 35 910 1,100 Haiti 5.2 3.8 62 40 27 52 .. 24 520 680 108 2006 World Development Indicators Reproductive health Total fertility Adolescent Unmet need for Contraceptive Tetanus Births attended by Maternal mortality rate fertility rate contraception prevalence vaccinations skilled health staff ratio rate births per % of married per 100,000 live births 1,000 women women ages % of women % of pregnant National Modeled births per woman ages 15­19 15­49 ages 15­49 women % of total estimates estimates 1990 2004 2004 1995­2004a 1996­2004a 2004 1990­92a 2000­04a 1990­2004a 2000 Honduras 5.1 3.6 99 .. 62 .. 45 56 110 110 Hungary 1.8 1.3 21 .. .. .. .. 100 .. 16 India 3.8 2.9 73 16 47 80 .. 43 540 540 Indonesia 3.1 2.3 54 9 57 54 32 72 310 230 Iran, Islamic Rep. 4.8 2.1 20 .. 74 .. .. 90 37 76 Iraq 5.9 4.6 40 .. 44 70 .. 72 290 250 Ireland 2.1 2.0 14 .. .. .. .. 100 6 5 Israel 2.8 2.9 15 .. .. .. .. .. 5 17 Italy 1.3 1.3 7 .. 60 .. .. .. 7 5 Jamaica 2.9 2.4 79 .. 65 .. .. 97 110 87 Japan 1.5 1.3 4 .. .. .. 100 .. 8 10 Jordan 5.4 3.4 26 11 56 .. 87 100 41 41 Kazakhstan 2.7 1.8 29 9 66 .. .. .. 50 210 Kenya 5.8 5.0 96 25 39 70 .. 42 410 1,000 Korea, Dem. Rep. 2.4 2.0 2 .. .. .. .. 97 110 67 Korea, Rep. 1.6 1.2 3 .. 81 .. 98 .. 20 20 Kuwait 3.4 2.5 24 .. 50 .. .. .. 5 5 Kyrgyz Republic 3.7 2.5 33 12 60 .. .. 99 .. 110 Lao PDR 6.0 4.6 89 .. 32 30 .. 19 .. 650 Latvia 2.0 1.2 17 .. .. .. .. .. 25 42 Lebanon 3.1 2.3 26 .. 63 .. .. .. .. 150 Lesotho 4.8 3.5 37 .. 30 .. .. 60 .. 550 Liberia 6.9 6.8 224 .. 10 35 .. 51 .. 760 Libya 4.7 2.9 7 .. .. .. .. .. .. 97 Lithuania 2.0 1.3 21 .. .. .. .. 100 13 13 Macedonia, FYR 2.1 1.7 23 .. .. .. .. 99 7 23 Madagascar 6.2 5.1 124 24 27 55 57 51 470 550 Malawi 7.0 5.9 158 30 31 70 55 61 1,100 1,800 Malaysia 3.8 2.8 18 .. .. .. .. 97 30 41 Mali 7.4 6.8 201 26 8 50 .. 41 580 1,200 Mauritania 6.1 5.7 99 32 8 33 40 57 750 1,000 Mauritius 2.3 2.0 32 .. 76 .. .. 98 22 24 Mexico 3.3 2.2 67 .. 73 .. .. 95 65 83 Moldova 2.4 1.4 31 .. 62 .. .. .. 44 36 Mongolia 4.0 2.4 53 .. 69 .. .. 99 99 110 Morocco 4.0 2.5 42 10 63 .. 31 63 230 220 Mozambique 6.2 5.4 102 18 17 60 .. 48 410 1,000 Myanmar 4.0 2.3 19 .. 34 85 .. 57 230 360 Namibia 5.9 3.8 53 22 44 67 68 76 270 300 Nepal 5.1 3.5 114 28 38 42 7 15 540 740 Netherlands 1.6 1.7 5 .. 75 .. .. .. 7 16 New Zealand 2.2 2.0 24 .. .. .. .. .. 15 7 Nicaragua 4.8 3.2 120 15 69 .. .. 67 83 230 Niger 8.2 7.7 260 17 14 43 15 16 590 1,600 Nigeria 6.7 5.6 142 17 13 51 31 35 .. 800 Norway 1.9 1.8 10 .. .. .. .. .. 6 16 Oman 6.5 3.6 46 .. 32 .. .. 95 23 87 Pakistan 5.8 4.3 69 .. 28 45 19 23 530 500 Panama 3.0 2.6 86 .. .. .. .. 93 70 160 Papua New Guinea 5.1 3.9 60 .. 26 10 .. 41 .. 300 Paraguay 4.7 3.7 65 .. 57 .. 67 77 180 170 Peru 3.9 2.8 53 10 69 .. .. 59 190 410 Philippines 4.3 3.1 36 17 49 70 .. 60 170 200 Poland 2.0 1.2 15 .. .. .. .. 100 4 13 Portugal 1.4 1.4 19 .. .. .. .. 100 8 5 Puerto Rico 2.2 1.9 56 .. .. .. .. .. .. 25 2006 World Development Indicators 109 Reproductive health Total fertility Adolescent Unmet need for Contraceptive Tetanus Births attended by Maternal mortality rate fertility rate contraception prevalence vaccinations skilled health staff ratio rate births per % of married per 100,000 live births 1,000 women women ages % of women % of pregnant National Modeled births per woman ages 15­19 15­49 ages 15­49 women % of total estimates estimates 1990 2004 2004 1995­2004a 1996­2004a 2004 1990­92a 2000­04a 1990­2004a 2000 Romania 1.8 1.3 35 .. 64 .. .. 99 31 49 Russian Federation 1.9 1.3 29 .. .. .. .. 99 .. 67 Rwanda 7.4 5.5 47 36 13 76 26 31 1,100 1,400 Saudi Arabia 5.9 4.0 33 .. 21 .. .. .. .. 23 Senegal 6.4 4.8 82 35 11 85 .. 58 560 690 Serbia and Montenegro 2.1 1.7 23 .. 58 .. .. 93 7 11 Sierra Leone 6.5 6.5 179 .. 4 76 .. 42 1,800 2,000 Singapore 1.9 1.2 5 .. .. .. .. .. 6 30 Slovak Republic 2.1 1.3 21 .. .. .. .. 99 16 3 Slovenia 1.5 1.2 6 .. .. .. 100 100 17 17 Somalia 6.8 6.3 69 .. .. 60 .. 25 .. 1,100 South Africa 3.3 2.7 67 15 56 61 .. .. 150 230 Spain 1.3 1.3 9 .. .. .. .. .. 6 4 Sri Lanka 2.5 1.9 19 .. 70 .. .. 96 92 92 Sudan 5.6 4.2 52 .. 7 37 69 87 .. 590 Swaziland 5.3 4.0 37 .. 48 .. .. 74 230 370 Sweden 2.1 1.8 7 .. .. .. .. .. 5 2 Switzerland 1.6 1.4 5 .. .. .. .. .. 5 7 Syrian Arab Republic 5.2 3.3 34 .. 48 .. .. .. 65 160 Tajikistan 5.1 3.6 30 .. 34 .. .. 71 45 100 Tanzania 6.1 4.8 110 22 25 90 44 46 580 1,500 Thailand 2.2 1.9 48 .. 72 .. .. 99 24 44 Togo 6.4 5.1 98 32 26 61 .. 61 480 570 Trinidad and Tobago 2.4 1.6 36 .. 38 .. .. 96 45 160 Tunisia 3.5 2.0 7 .. 66 .. .. 90 69 120 Turkey 3.0 2.2 41 10 71 41 .. 83 .. 70 Turkmenistan 4.2 2.7 16 10 62 .. .. 97 14 31 Uganda 7.2 7.1 208 35 23 53 .. 39 510 880 Ukraine 1.8 1.2 29 .. 89 .. .. 100 13 35 United Arab Emirates 4.3 2.2 20 .. .. .. .. .. 3 54 United Kingdom 1.8 1.7 26 .. .. .. .. .. 7 13 United States 2.1 2.0 50 .. 64 .. .. .. 8 17 Uruguay 2.5 2.1 69 .. .. .. .. .. 26 27 Uzbekistan 4.1 2.4 36 14 68 .. .. 96 34 24 Venezuela, RB 3.4 2.7 91 .. 77 .. .. 94 68 96 Vietnam 3.6 1.8 20 5 79 85 .. 90 170 130 West Bank and Gaza 6.3 4.9 .. .. 42 .. .. 97 .. .. Yemen, Rep. 7.5 5.9 93 39 23 21 16 27 370 570 Zambia 6.5 5.5 128 27 34 83 51 43 730 750 Zimbabwe 5.2 3.4 92 13 54 70 .. .. 700 1,100 World 3.1 w 2.6 w 58 w 60 w .. w 60 w 410 w Low income 4.7 3.7 95 40 .. 40 682 Middle income 2.6 2.1 32 76 .. 87 142 Lower middle income 2.6 2.1 28 76 .. 86 153 Upper middle income 2.7 2.0 47 69 .. 95 92 Low & middle income 3.4 2.8 62 60 .. 60 450 East Asia & Pacific 2.5 2.0 16 78 .. 86 117 Europe & Central Asia 2.3 1.6 30 69 .. 94 58 Latin America & Carib. 3.2 2.5 78 72 77 87 194 Middle East & N. Africa 4.8 3.0 33 59 42 72 183 South Asia 4.1 3.1 80 46 .. 36 564 Sub-Saharan Africa 6.2 5.3 135 22 .. 42 921 High income 1.8 1.7 25 64 41 .. 14 Europe EMU 1.5 1.5 9 .. .. .. 10 a. Data are for the most recent year available. 110 2006 World Development Indicators Reproductive health About the data Definitions Reproductive health is a state of physical and mental and one booster shot during each subsequent preg- · Total fertility rate is the number of children that well-being in relation to the reproductive system and nancy, with five doses considered adequate for lifetime would be born to a woman if she were to live to the its functions and processes. Means of achieving repro- protection. Information on tetanus shots during preg- end of her childbearing years and bear children in ductive health include education and services during nancy is collected through surveys in which pregnant accordance with current age-specific fertility rates. pregnancy and childbirth, provision of safe and effec- respondents are asked to show antenatal cards on · Adolescent fertility rate is the number of births tive contraception, and prevention and treatment of which tetanus shots have been recorded. Because per 1,000 women ages 15­19. · Unmet need for sexually transmitted diseases. Complications of preg- not all women have antenatal cards, respondents are contraception is the percentage of fertile, mar- nancy and childbirth are the leading cause of death also asked about their receipt of these injections. Poor ried women of reproductive age who do not want to and disability among women of reproductive age in recall may result in a downward bias in estimates of become pregnant and are not using contraception. developing countries. Reproductive health services will the share of births protected. But in settings where · Contraceptive prevalence rate is the percentage need to expand rapidly over the next two decades, receiving injections is common, respondents may erro- of women who are practicing, or whose sexual part- when the number of women and men of reproductive neously report having received tetanus shots. ners are practicing, any form of contraception. It is age is projected to increase by about 500 million. The share of births attended by skilled health staff usually measured for married women ages 15­49 Total and adolescent fertility rates are based on is an indicator of a health system's ability to provide only. · Tetanus vaccinations refer to the percent- data on registered live births from vital registration adequate care for pregnant women. Good antena- age of pregnant women who receive two tetanus tox- systems or, in the absence of such systems, from tal and postnatal care improve maternal health and oid injections during their first pregnancy and one censuses or sample surveys. As long as the surveys reduce maternal and infant mortality. But data may booster shot during each subsequent pregnancy, are fairly recent, the estimated rates are generally not reflect such improvements because health infor- with five doses considered adequate for a lifetime. considered reliable measures of fertility in the recent mation systems are often weak, maternal deaths are · Births attended by skilled health staff are the per- past. Where no empirical information on age-specific underreported, and rates of maternal mortality are centage of deliveries attended by personnel trained fertility rates is available, a model is used to estimate difficult to measure. to give the necessary supervision, care, and advice the share of births to adolescents. For countries with- Maternal mortality ratios are generally of unknown to women during pregnancy, labor, and the postpar- out vital registration systems, fertility rates are gener- reliability, as are many other cause-specific mortality tum period; to conduct deliveries on their own; and to ally based on extrapolations from trends observed in indicators. Household surveys such as the Demo- care for newborns. · Maternal mortality ratio is the censuses or surveys from earlier years. graphic and Health Surveys attempt to measure number of women who die from pregnancy-related An increasing number of couples in the develop- maternal mortality by asking respondents about sur- causes during pregnancy and childbirth, per 100,000 ing world want to limit or postpone childbearing vivorship of sisters. The main disadvantage of this live births. but are not using effective contraceptive methods. method is that the estimates of maternal mortality These couples have an unmet need for contracep- that it produces pertain to 12 years or so before the tion, shown in the table as the percentage of mar- survey, making them unsuitable for monitoring recent ried women of reproductive age who do not want changes or observing the impact of interventions. Data sources to become pregnant but are not using contracep- In addition, measurement of maternal mortality is Data on fertility rates are compiled and estimated tion (Bulatao 1998). Information on this indicator is subject to many types of errors. Even in high-income by the World Bank's Development Data Group. collected through surveys and excludes women not countries with vital registration systems, misclassi- Important inputs come from the following sources: exposed to the risk of unintended pregnancy because fication of maternal deaths has been found to lead the United Nations Population Division's World of menopause, infertility, or postpartum anovulation. to serious underestimation. Population Prospects: The 2004 Revision; cen- Common reasons for not using contraception are The maternal mortality ratios shown in the table as sus reports and other statistical publications from lack of knowledge about contraceptive methods and national estimates are based on national surveys, national statistical offices; and household surveys concerns about possible health side-effects. vital registration, or surveillance or are derived from such as Demographic and Health Surveys. Data Contraceptive prevalence reflects all methods-- community and hospital records. Those shown as on women with unmet need for contraception and ineffective traditional methods as well as highly modeled estimates are based on an exercise by the contraceptive prevalence rates are from house- effective modern methods. Contraceptive prevalence World Health Organization (WHO), United Nations hold surveys, including Demographic and Health rates are obtained mainly from household surveys, Children's Fund (UNICEF), and United Nations Popu- Surveys by Macro International and Multiple Indi- including Demographic and Health Surveys, Multiple lation Fund (UNFPA). For countries with national data cator Cluster Surveys by UNICEF. Data on tetanus Indicator Cluster Surveys, and contraceptive preva- reported maternal mortality was adjusted by a factor vaccinations and births attended by skilled health lence surveys (see Primary data documentation for of under- or over-estimation and misclassification. staff and national estimates of maternal mortality the most recent survey year). Unmarried women are For countries with no national data maternal mor- ratios are from UNICEF's State of the World's Chil- often excluded from such surveys, which may bias tality was estimated with a regression model using dren 2006 and Childinfo, and Demographic and the estimates. information on fertility, birth attendants, and GDP. Health Surveys by Macro International. Modeled Neonatal tetanus is an important cause of infant Neither set of ratios can be assumed to provide an estimates for maternal mortality ratios are from mortality in some developing countries. It can be accurate estimate of maternal mortality for any of Carla AbouZahr and Tessa Wardlaw's "Maternal prevented through immunization of the mother during the countries in the table. Mortality in 2000: Estimates Developed by WHO, pregnancy. Recommended doses for full protection are UNICEF, and UNFPA" (2003). generally two tetanus shots during the first pregnancy 2006 World Development Indicators 111 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2001­03 1995­2004a 1995­2004 a 1995­2004 a 1998­2004 a 1998­2004 a 1998­2004 a 2003 Afghanistan .. .. 39.3 47.6 4.0 .. .. 28 86 Albania 5b 6 14.0 35.1 22.4 3 6 62 .. Algeria 5 5 10.4 19.1 10.1 7 13 69 .. Angola 58 38 30.5 45.2 0.5 12 11 35 68 Argentina <3 <3 5.4 12.4 9.2 8 .. .. .. Armenia 52b 29 2.6 12.9 10.4 7 30 84 .. Australia .. .. 0.0 0.0 5.2 7 .. .. .. Austria .. .. .. .. .. 7 .. .. .. Azerbaijan 34b 10 6.8 13.3 2.6 11 7 26 .. Bangladesh 35 30 47.5 43.0 0.8 36 36 70 87 Belarus <3b 3 .. .. .. 5 .. 55 .. Belgium .. .. .. .. .. .. .. .. .. Benin 20 14 22.9 30.7 1.8 16 38 72 98 Bolivia 28 23 7.6 26.7 5.6 7 54 90 38 Bosnia and Herzegovina 9b 9 4.1 9.7 13.2 4 6 77 .. Botswana 23 30 12.5 23.1 6.9 10 34 66 85 Brazil 12 8 5.7 10.5 4.9 .. .. 88 .. Bulgaria 8b 9 .. .. .. 10 .. 98 .. Burkina Faso 21 17 37.7 38.7 2.9 19 19 45 95 Burundi 48 67 45.1 56.8 0.7 16 62 96 95 Cambodia 43 33 45.2 44.6 2.0 11 12 14 47 Cameroon 33 25 18.1 31.7 5.2 11 21 61 86 Canada .. .. .. .. .. 6 .. .. .. Central African Republic 50 45 24.3 28.4 0.8 14 17 86 84 Chad 58 33 36.7 40.9 1.5 31 2 58 85 Chile 8 4 0.7 1.4 8.1 5 63 100 .. China 16 12 7.8 14.2 2.6 4 51 93 .. Hong Kong, China .. .. .. .. .. 5 .. .. .. Colombia 17 14 7.0 12.0 3.7 6 26 43 .. Congo, Dem. Rep. 31 72 31.0 38.1 3.9 12 24 72 80 Congo, Rep. 54 34 13.0 .. .. .. 4c .. 89 Costa Rica 6 4 5.1 6.1 6.2 7 .. .. .. Côte d'Ivoire 18 14 17.2 25.1 2.5 17 5 31 97 Croatia 16b 7 0.6 0.8 5.9 6 .. 90 .. Cuba 8 <3 3.9 4.6 .. 6 41 88 .. Czech Republic <3 b <3 .. .. .. 7 .. .. .. Denmark .. .. .. .. .. 5 .. .. .. Dominican Republic 27 27 5.3 8.9 6.5 11 10 18 40 Ecuador 8 5 11.6 26.4 .. 16 35 99 50 Egypt, Arab Rep. 4 3 8.6 15.6 6.7 12 30 56 .. El Salvador 12 11 10.3 18.9 3.6 7 24 .. .. Eritrea .. 73 39.6 37.6 0.7 .. 52 68 52 Estonia 9b 3 .. .. .. 4 .. .. .. Ethiopia .. 46 47.2 51.5 1.2 15 55 28 65 Finland .. .. .. .. .. 4 .. .. .. France .. .. .. .. .. 7 .. .. .. Gabon 10 5 11.9 20.7 3.7 14 6 36 30 Gambia, The 22 27 17.2 19.2 1.5 17 26 8 91 Georgia 44b 13 3.1 11.7 12.7 7 18 c 68 .. Germany .. .. .. .. .. 7 .. .. .. Ghana 37 12 22.1 29.9 2.9 16 53 28 78 Greece .. .. .. .. .. 8 .. .. .. Guatemala 16 23 22.7 49.3 5.4 12 51 67 33 Guinea 39 24 32.7 26.1 2.7 16 23 68 98 Guinea-Bissau 24 37 25.0 30.5 3.3 22 37 2 80 Haiti 65 47 17.2 22.7 2.0 21 24 11 25 112 2006 World Development Indicators Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2001­03 1995­2004a 1995­2004 a 1995­2004 a 1998­2004 a 1998­2004 a 1998­2004 a 2003 Honduras 23 22 16.6 29.2 2.2 14 35 80 35 Hungary <3b <3 .. .. .. 9 .. .. .. India 25 20 46.7 44.9 2.2 30 37c 50 45d Indonesia 9 6 28.2 42.2 4.0 9 40 73 62 Iran, Islamic Rep. 4 4 10.9 15.4 4.3 .. 44 94 .. Iraq .. .. 15.9 22.1 3.0 15 12 40 .. Ireland .. .. .. .. .. 6 .. .. .. Israel .. .. .. .. .. 8 .. .. .. Italy .. .. .. .. .. 6 .. .. .. Jamaica 14 10 3.6 4.4 3.8 10 .. 100 .. Japan .. .. .. .. .. 8 .. .. .. Jordan 4 7 4.4 8.5 3.5 .. 27 88 .. Kazakhstan <3 b 8 4.2 9.7 3.0 8 36 83 .. Kenya 39 31 19.9 30.3 3.7 10 13 91 33 Korea, Dem. Rep. 18 35 23.9 38.6 0.6 7 65 40 95 Korea, Rep. <3 <3 .. .. .. 4 .. .. .. Kuwait 24 5 1.7 3.2 5.7 7 ..c .. .. Kyrgyz Republic 21b 4 6.7 24.8 6.3 .. .. 42 .. Lao PDR 29 21 40.4 42.4 1.2 14 23 75 64 Latvia 3b 3 .. .. .. 5 .. .. .. Lebanon <3 3 3.0 12.2 .. 6 27c 87 .. Lesotho 17 12 18.0 46.1 12.1 14 15 69 75 Liberia 34 49 26.5 39.5 2.3 .. 35 .. 40 Libya <3 <3 4.7 15.1 .. .. .. .. .. Lithuania 4b <3 .. .. .. 4 .. .. .. Macedonia, FYR 15b 7 5.9 6.9 4.9 6 99 80 95 Madagascar 35 38 41.9 47.7 2.0 17 67 75 91 Malawi 50 34 21.9 49.0 4.3 16 44 49 92 Malaysia 3 3 10.6 15.6 3.3 9 ..c .. .. Mali 29 28 33.2 38.2 1.5 23 25 74 61 Mauritania 15 10 31.8 34.5 .. .. 20 2 89 Mauritius 6 6 14.9 9.7 4.0 14 21c .. .. Mexico 5 5 7.5 17.7 5.3 8 .. 91 .. Moldova 5b 11 3.2 .. .. 5 .. 33 .. Mongolia 34 28 12.7 24.6 4.8 7 51 75 87 Morocco 6 6 10.2 18.1 9.2 .. 31 41 .. Mozambique 66 45 23.7 41.0 3.0 15 30 54 50 Myanmar 10 5 31.8 32.2 1.6 15 15c 60 87 Namibia 34 23 24.0 23.6 2.2 14 19 63 93 Nepal 20 17 48.3 50.5 0.2 21 68 63 96 Netherlands .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. 6 .. 83 .. Nicaragua 30 27 9.6 20.2 4.7 12 31 97 91 Niger 41 32 40.1 39.7 0.8 13 1 15 95 Nigeria 13 9 28.7 38.3 3.6 14 17 97 27 Norway .. .. .. .. .. 5 .. .. .. Oman .. .. 17.8 10.4 1.0 8 .. 61 97 Pakistan 24 23 37.8 36.8 2.1 .. .. 17 95 Panama 21 25 8.1 18.2 4.2 10 .. 95 .. Papua New Guinea 15 13 .. .. .. .. .. .. 1 Paraguay 18 15 4.6 .. .. .. 22 88 .. Peru 42 12 7.1 25.4 7.6 .. 67 .. 6 Philippines 26 19 27.6 32.1 1.0 20 34 56 76 Poland <3 b <3 .. .. .. 6 .. .. .. Portugal .. .. .. .. .. 8 .. .. .. Puerto Rico .. .. .. .. .. 14 .. .. .. 2006 World Development Indicators 113 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2001­03 1995­2004a 1995­2004 a 1995­2004 a 1998­2004 a 1998­2004 a 1998­2004 a 2003 Romania <3b <3 3.2 10.1 5.5 9 .. 53 .. Russian Federation 4b 3 5.5 10.6 .. 6 .. 35 .. Rwanda 43 36 24.3 42.6 4.0 9 84 90 86 Saudi Arabia 4 4 14.3 .. .. .. ..c .. .. Senegal 23 23 22.7 25.4 2.2 18 24 c 16 83 Serbia and Montenegro 5b 10 1.9 5.1 12.9 4 11c 73 .. Sierra Leone 46 50 27.2 33.8 .. 23 4 23 84 Singapore .. .. 3.4 2.2 2.2 8 .. .. .. Slovak Republic 4b 6 .. .. .. 7 .. .. .. Slovenia 3b 3 .. .. .. 6 .. .. .. Somalia .. .. 25.8 23.3 .. .. 9 .. 60 South Africa .. .. 11.5 24.9 6.2 15 7 62 .. Spain .. .. .. .. .. .. .. .. .. Sri Lanka 28 22 29.7 13.9 .. 22 84 88 .. Sudan 31 27 40.7 43.3 3.4 31 16 1 34 Swaziland 14 19 10.3 30.2 .. 9 24 59 80 Sweden .. .. .. .. .. 4 .. .. .. Switzerland .. .. .. .. .. 6 .. .. .. Syrian Arab Republic 5 4 6.9 18.8 .. 6 81c 79 .. Tajikistan 22b 61 .. 36.2 .. 15 50 28 .. Tanzania 37 44 29.4 43.8 1.7 13 41 67 91 Thailand 30 21 17.6 13.4 2.8 9 .. 63 .. Togo 33 25 25.1 21.7 1.5 18 18 67 84 Trinidad and Tobago 13 11 5.9 3.6 .. 23 2 1 .. Tunisia <3 <3 4.0 12.3 4.5 7 47 97 .. Turkey <3 3 3.9 16.0 2.2 16 21 64 .. Turkmenistan 12b 8 12.0 22.3 .. 6 13 100 .. Uganda 24 19 22.9 39.1 2.6 12 63 95 46 Ukraine <3 b 3 1.0 2.7 20.1 5 22 32 .. United Arab Emirates 4 <3 7.0 .. .. .. .. .. .. United Kingdom .. .. .. .. .. 8 .. .. .. United States .. .. 1.6 1.1 5.6 8 .. .. .. Uruguay 7 3 4.5 .. .. 8 .. .. .. Uzbekistan 8b 26 7.9 21.1 14.4 7 19 19 93 Venezuela, RB 11 18 4.4 12.8 3.2 9 7c 90 .. Vietnam 31 17 28.4 36.5 2.7 9 15 83 99d West Bank and Gaza .. 16 4.1 7.3 2.3 .. .. .. .. Yemen, Rep. 34 37 45.6 51.7 1.9 .. 12 30 36 Zambia 48 47 23.0 46.8 3.0 12 40 77 73 Zimbabwe 45 45 13.0 26.5 7.0 11 33 93 46 World 20 w 16 w .. w .. w .. w .. w .. w .. w Low income 27 24 43.4 43.1 22 33 .. 61 Middle income 14 10 11.1 26.9 .. .. .. .. Lower middle income 15 11 11.2 15.1 .. 43 70 .. Upper middle income .. 4 .. .. 10 18 .. .. Low & middle income 20 16 .. .. .. 36 62 .. East Asia & Pacific 17 12 11.5 17.1 11 .. .. .. Europe & Central Asia 6b 6 .. .. .. 22 .. .. Latin America & Carib. 13 10 9.1 19.1 11 .. .. .. Middle East & N. Africa 6 7 14.7 20.0 .. 31 67 .. South Asia 26 21 48.5 46.1 .. 38 .. 58 Sub-Saharan Africa 31 32 .. .. 25 28 56 62 High income .. .. .. .. .. 12 .. .. Europe EMU .. .. .. .. .. .. .. .. a. Data are for the most recent year available. b. Data are for 1993­95. c. Refers to exclusive breastfeeding for less than four months. d. Country's vitamin A supplementation programs do not target children all the way up to 59 months of age. 114 2006 World Development Indicators Nutrition About the data Definitions Data on undernourishment are produced by the Food diseases. Estimates of low-birthweight infants are · Prevalence of undernourishment is the percentage and Agriculture Organization (FAO) of the United drawn mostly from hospital records and house- of the population that is undernourished. · Preva- Nations based on the calories available from local hold surveys. Many births in developing countries lence of child malnutrition is the percentage of food production, trade, and stocks; the number of take place at home, and these births are seldom children under age five whose weight for age (under- calories needed by different age and gender groups; recorded. A hospital birth may indicate higher weight) or height for age (stunting) is more than two the proportion of the population represented by each income and therefore better nutrition, or it could standard deviations below the median for the inter- age group; and a coefficient of distribution to take indicate a higher-risk birth, possibly skewing the national reference population ages 0­59 months. account of inequality in access to food (FAO 2000). data on birthweights downward. The data should For children up to two years old height is measured From a policy and program standpoint, however, this therefore be treated with caution. by recumbent length. For older children height is measure has its limits. First, food insecurity exists It is estimated that improved breastfeeding prac- measured by stature while standing. The reference even where food availability is not a problem because tice can save some 1.5 million children a year. Breast population, adopted by the WHO in 1983, is based of inadequate access of poor households to food. milk alone contains all the nutrients, antibodies, on children from the United States, who are assumed Second, food insecurity is an individual or household hormones, and antioxidants an infant needs to to be well nourished. · Prevalence of overweight phenomenon, and the average food available to each thrive. It protects babies from diarrhea and acute children is the percentage of children under age five person, even corrected for possible effects of low respiratory infections, stimulates their immune sys- whose weight for height is more than two standard income, is not a good predictor of food insecurity tems and response to vaccination, and according deviations above the median for the international among the population. And third, nutrition security to some studies confers cognitive benefits as well. reference population of the corresponding age, is determined not only by food security but also by The data on breastfeeding are derived from national established by the U.S. National Center for Health the quality of care of mothers and children and the surveys. Statistics and the WHO. · Low-birthweight babies quality of the household's health environment (Smith Iodine deficiency is the single most important are the percentage of newborns weighing less than and Haddad 2000). cause of preventable mental retardation, and it 2,500 grams, with the measurement taken within the Estimates of child malnutrition, based on weight contributes significantly to the risk of stillbirth and first hours of life, before significant postnatal weight for age (underweight) and height for age (stunting), miscarriage. Iodized salt is the best source of iodine, loss has occurred. · Exclusive breastfeeding refers are from national survey data. The proportion of chil- and a global campaign to iodize edible salt is sig- to the percentage of children less than six months dren who are underweight is the most common indi- nificantly reducing the risks (UNICEF, State of the old who are fed breast milk alone (no other liquids). cator of malnutrition. Being underweight, even mildly, World's Children 1999). · Consumption of iodized salt refers to the percent- increases the risk of death and inhibits cognitive Vitamin A is essential for the functioning of the age of households that use edible salt fortified with development in children. Moreover, it perpetuates immune system. A child deficient in vitamin A faces iodine. · Vitamin A supplementation refers to the the problem from one generation to the next, as mal- a 25 percent greater risk of dying from a range of percentage of children ages 6­59 months old who nourished women are more likely to have low-birth- childhood ailments such as measles, malaria, and received at least one high-dose vitamin A capsule in weight babies. Height for age reflects linear growth diarrhea. Improving the vitamin A status of pregnant the previous six months. achieved pre- and post-natally, and a deficit indicates women helps reduce anemia, improves their resis- long-term, cumulative effects of inadequacies of tance to infection, and may reduce their risk of dying health, diet, or care. It is often argued that stunting during pregnancy and childbirth. Giving vitamin A to is a proxy for multifaceted deprivation and is a better new mothers who are breastfeeding helps to protect indicator of long-term changes in malnutrition. their children during the first months of life. Food for- Estimates of children who are overweight are also tification with vitamin A is being introduced in many from national survey data. Overweight in children has developing countries. become a growing concern in developing countries. Researchers show an association between obesity in childhood and a high prevalence of diabetes, respira- tory disease, high blood pressure, and psychosocial Data sources and orthopedic disorders (de Onis and Blossner Data on undernourishment are from www.fao.org/ 2000). The survey data were analyzed in a standard- faostat/foodsecurity/index_en.htm. Data on mal- ized way by the World Health Organization (WHO) to nutrition and overweight are from WHO's Global allow comparisons across countries. Database on Child Growth and Malnutrition. Data Low birthweight, which is associated with mater- on low-birthweight babies, breastfeeding, iodized nal malnutrition, raises the risk of infant mortality salt consumption, and vitamin A supplementation and stunts growth in infancy and childhood. There are from the WHO's World Health Report 2004 and is also emerging evidence that low-birthweight the United Nations Children's Fund's State of the babies are more prone to noncommunicable dis- World's Children 2006. eases such as diabetes and cardiovascular heart 2006 World Development Indicators 115 Health risk factors and public health challenges Prevalence of Incidence of Prevalence Mortality Prevalence smoking tuberculosis of diabetes caused by of HIV road traffic injury Total Female % of adults per 100,000 % of population per 100,000 % of population % of population Male Female people ages 20­79 people ages 15­49 with HIV 2000­05 a 2000­05 a 2004 2003 1998­2003a 2001 2003 2001 2003 Afghanistan .. .. 333 8.2 .. .. .. .. .. Albania 60 18 22 3.8 11.1 .. .. .. .. Algeria 32 0b 54 4.1 .. <0.1 0.1 11.8 15.6 Angola .. .. 259 2.7 .. 3.7 3.9 55.0 59.1 Argentina 32 25 43 5.4 .. 0.7 0.7 19.2 20.0 Armenia 62 2 78 8.1 5.6 0.1 0.1 35.0 36.0 Australia 19 16 6 6.2 8.2 0.1 0.1 6.7 7.1 Austria .. .. 14 9.6 11.5 0.2 0.3 22.2 22.0 Azerbaijan .. 1 75 6.9 6.9 .. <0.1 .. .. Bangladesh 55 27 229 3.9 .. .. .. .. .. Belarus 53 7 60 6.9 14.3 .. .. .. .. Belgium 30 25 13 4.2 13.1 0.2 0.2 35.8 35.0 Benin .. .. 87 2.1 .. 1.9 1.9 57.6 56.5 Bolivia .. .. 217 4.8 .. 0.1 0.1 27.5 27.1 Bosnia and Herzegovina 49 30 53 9.6 .. .. <0.1 .. .. Botswana .. .. 670 3.6 .. 38.0 37.3 57.6 57.6 Brazil 22 14 60 5.2 .. 0.6 0.7 37.1 36.9 Bulgaria 44 23 36 10.0 10.2 .. 0.1 .. .. Burkina Faso .. .. 191 2.7 .. 4.2 1.8 c 56.0 55.6 Burundi .. .. 343 1.3 .. 6.2 6.0 54.5 59.1 Cambodia .. .. 510 2.0 .. 2.7 2.6 30.0 30.0 Cameroon .. .. 179 0.8 .. 7.0 5.5d 56.0 55.8 Canada 22 17 5 9.0 8.7 0.3 0.3 25.0 23.6 Central African Republic .. .. 322 2.3 .. 13.5 13.5 56.5 54.2 Chad .. .. 279 2.7 .. 4.9 4.8 57.1 55.6 Chile 48 37 16 5.6 10.7 0.3 0.3 32.0 33.5 China 67 4 101 2.7 19.0 0.1 0.1 20.0 22.9 Hong Kong, China 22 4 75 8.8 .. 0.1 0.1 30.8 34.6 Colombia .. .. 50 4.3 24.2 0.5 0.7 33.3 34.4 Congo, Dem. Rep. .. .. 366 2.5 .. 4.2 4.2 56.8 57.0 Congo, Rep. .. .. 377 2.6 .. 5.3 4.9 56.3 56.3 Costa Rica 29 10 14 6.9 20.1 0.6 0.6 31.8 33.3 Côte d'Ivoire .. .. 393 2.3 .. 6.7 7.0 56.3 56.6 Croatia 34 27 41 5.8 11.4 .. <0.1 .. .. Cuba .. .. 10 13.2 13.9 0.1 0.1 31.3 33.3 Czech Republic 31 20 11 9.5 14.2 <0.1 0.1 35.7 32.0 Denmark 31 25 8 6.9 8.0 0.2 0.2 17.4 18.0 Dominican Republic 16 11 91 10.0 41.1 1.8 1.0e 26.4 27.1 Ecuador .. .. 131 4.8 16.9 0.3 0.3 32.6 34.0 Egypt, Arab Rep. 40 18 27 9.8 7.5 <0.1 <0.1 10.9 13.3 El Salvador 42 15 54 6.2 41.7 0.6 0.7 32.1 34.3 Eritrea .. .. 271 1.9 .. 2.8 2.7 56.4 56.4 Estonia 45 18 46 9.7 14.8 0.7 1.1 32.0 33.8 Ethiopia 6 0b 353 1.9 .. 4.1 4.4 55.8 55.0 Finland 26 19 9 7.2 7.3 0.1 0.1 .. .. France 30 21 12 6.2 10.2 0.4 0.4 27.3 26.7 Gabon .. .. 280 2.9 .. 6.9 8.1 56.8 57.8 Gambia, The .. .. 233 2.2 .. 1.2 1.2 55.6 57.1 Georgia 53 6 82 9.0 6.2 <0.1 0.1 .. 33.3 Germany 37 28 8 10.2 8.0 0.1 0.1 19.8 22.1 Ghana 7 1 206 3.3 .. 3.1 2.2c 54.8 56.3 Greece 47 29 19 6.1 19.3 0.2 0.2 20.5 20.0 Guatemala 21 2 77 5.5 .. 1.1 1.1 41.5 41.9 Guinea .. .. 240 2.0 .. 2.8 3.2 59.0 55.4 Guinea-Bissau .. .. 199 2.0 .. .. .. .. .. Haiti 15 6 306 5.7 .. 5.5 5.6 58.3 57.7 116 2006 World Development Indicators Health risk factors and public health challenges Prevalence of Incidence of Prevalence Mortality Prevalence smoking tuberculosis of diabetes caused by of HIV road traffic injury Total Female % of adults per 100,000 % of population per 100,000 % of population % of population Male Female people ages 20­79 people ages 15­49 with HIV 2000­05 a 2000­05 a 2004 2003 1998­2003a 2001 2003 2001 2003 Honduras .. .. 77 5.7 .. 1.6 1.8 56.3 55.9 Hungary 41 28 26 9.7 13.1 .. 0.1 .. .. India 47 17 168 5.9 .. 0.8 0.9 39.5 38.0 Indonesia 58 3 245 1.9 .. 0.1 0.1 12.1 13.6 Iran, Islamic Rep. 22 2 27 3.6 .. 0.1 0.1 10.6 12.3 Iraq .. .. 132 7.7 8.4 .. <0.1 .. .. Ireland 28 26 11 3.4 10.1 0.1 0.1 31.8 30.8 Israel 32 18 9 7.1 5.9 .. 0.1 .. .. Italy 31 17 7 6.6 10.5 0.5 0.5 32.3 32.1 Jamaica .. .. 7 7.2 .. 0.8 1.2 51.4 47.6 Japan 47 15 30 6.9 7.0 <0.1 <0.1 22.5 24.2 Jordan 51 8 5 7.0 .. <0.1 <0.1 .. .. Kazakhstan 65 9 151 5.5 .. 0.1 0.2 34.0 33.5 Kenya 21 1 619 2.5 .. 8.0 6.7c 62.5 65.5 Korea, Dem. Rep. .. .. 178 5.2 .. .. .. .. .. Korea, Rep. .. .. 90 6.4 15.1 <0.1 <0.1 10.7 10.8 Kuwait .. .. 26 12.8 23.7 .. .. .. .. Kyrgyz Republic 51 5 122 4.3 12.9 <0.1 0.1 .. .. Lao PDR 59 13 156 1.1 .. <0.1 0.1 .. .. Latvia 51 19 68 9.9 22.7 0.5 0.6 32.2 33.3 Lebanon 42 31 11 6.4 .. 0.1 0.1 .. .. Lesotho .. .. 696 3.1 .. 29.6 28.9 56.7 56.7 Liberia .. .. 310 2.0 .. 5.1 5.9 56.3 56.3 Libya .. .. 20 3.7 .. .. 0.3 .. .. Lithuania 44 13 63 9.4 19.3 0.1 0.1 .. .. Macedonia, FYR .. .. 30 4.9 5.1 <0.1 <0.1 .. .. Madagascar .. .. 218 2.5 .. 1.3 1.7 56.1 58.5 Malawi 21 5 413 1.7 .. 14.3 14.2 57.1 56.8 Malaysia 43 2 103 9.4 .. 0.4 0.4 15.4 16.7 Mali .. .. 281 2.0 .. 1.8f 1.9 54.2 59.2 Mauritania .. .. 287 3.5 .. 0.5 0.6 55.9 57.3 Mauritius 32 1 64 10.7 14.7 .. .. .. .. Mexico 13 5 32 7.4 11.8 0.3 0.3 32.7 33.1 Moldova 34 2 138 9.3 14.1 .. 0.2 .. .. Mongolia 68 26 192 1.4 .. <0.1 <0.1 .. .. Morocco 29 0b 110 4.2 .. .. 0.1 .. .. Mozambique .. .. 460 3.1 .. 12.1 12.2 58.2 55.8 Myanmar 36 12 171 1.1 .. 1.0 1.2 28.9 30.3 Namibia 23 10 717 3.1 .. 21.3 21.3 52.6 55.0 Nepal 49 24 184 4.1 .. 0.4 0.5 20.7 26.7 Netherlands 36 28 8 3.7 6.4 0.2 0.2 19.4 20.0 New Zealand 24 22 11 7.6 11.5 0.1 0.1 .. .. Nicaragua .. 5 63 6.1 20.1 0.2 0.2 32.7 33.9 Niger .. .. 157 3.1 .. 1.1 1.2 56.9 56.3 Nigeria .. 1 290 2.2 .. 5.5 5.4 58.1 57.6 Norway 27 25 5 6.7 6.1 0.1 0.1 .. .. Oman .. .. 11 11.4 .. 0.1 0.1 .. .. Pakistan .. .. 181 8.5 .. 0.1 0.1 6.9 12.2 Panama .. .. 45 7.3 16.4 0.7 0.9 37.3 41.3 Papua New Guinea .. .. 233 1.9 .. 0.4 0.6 29.0 30.0 Paraguay 23 7 71 3.9 .. 0.4 0.5 27.0 26.0 Peru .. .. 178 5.1 17.6 0.4 0.5 31.4 33.8 Philippines 41 8 293 2.4 .. <0.1 <0.1 20.9 22.5 Poland 40 25 29 9.0 14.8 .. 0.1 .. .. Portugal .. .. 42 7.8 14.8 0.4 0.4 20.0 19.5 Puerto Rico 17 10 5 13.2 .. .. .. .. .. 2006 World Development Indicators 117 Health risk factors and public health challenges Prevalence of Incidence of Prevalence Mortality Prevalence smoking tuberculosis of diabetes caused by of HIV road traffic injury Total Female % of adults per 100,000 % of population per 100,000 % of population % of population Male Female people ages 20­79 people ages 15­49 with HIV 2000­05 a 2000­05 a 2004 2003 1998­2003a 2001 2003 2001 2003 Romania 32 10 146 9.3 16.8 .. <0.1 .. .. Russian Federation 60 16 115 9.2 19.4 0.7 1.1 32.1 33.7 Rwanda .. .. 371 1.1 .. 5.1 5.1 54.5 56.5 Saudi Arabia 19 8 40 9.4 .. .. .. .. .. Senegal .. .. 245 2.3 .. 0.8 0.8 55.3 56.1 Serbia and Montenegro 48 34 33 5.6 .. 0.2 0.2 20.0 20.0 Sierra Leone .. .. 443 2.2 .. .. .. .. .. Singapore 24 4 40 12.3 5.2 0.2 0.2 23.5 24.4 Slovak Republic .. .. 19 8.7 11.3 .. <0.1 .. .. Slovenia 28 20 15 9.6 12.1 <0.1 <0.1 .. .. Somalia .. .. 411 2.3 .. .. .. .. .. South Africa 23 8 718 3.4 .. 20.9 15.6e 56.3 56.9 Spain 39 25 25 9.9 12.8 0.6 0.7 20.0 20.8 Sri Lanka 23 2 60 2.1 .. <0.1 <0.1 .. 17.1 Sudan .. .. 220 3.2 .. 1.9 2.3 56.7 57.9 Swaziland 11 3 1,226 3.0 .. 38.2 38.8 57.9 55.0 Sweden 17 18 4 7.3 5.9 0.1 0.1 27.3 25.7 Switzerland 27 23 7 9.5 7.5 0.4 0.4 30.0 30.0 Syrian Arab Republic .. .. 41 6.2 .. .. <0.1 .. .. Tajikistan .. .. 177 3.7 5.6 .. <0.1 .. .. Tanzania .. .. 347 2.3 .. 9.0 7.0 d 58.6 56.0 Thailand 49 3 142 2.1 .. 1.7 1.5 32.3 35.7 Togo .. .. 355 2.1 .. 4.3 4.1 56.4 56.3 Trinidad and Tobago .. .. 9 7.9 .. 3.0 3.2 50.0 50.0 Tunisia 50 2 22 4.6 .. <0.1 <0.1 .. .. Turkey 49 18 28 7.0 .. .. .. .. .. Turkmenistan .. .. 65 4.0 10.3 .. <0.1 .. .. Uganda 25 3 402 1.5 .. 5.1 4.1 59.6 60.0 Ukraine 53 11 101 9.7 10.8 1.2 1.4 32.0 33.3 United Arab Emirates 17 1 17 20.1 .. .. .. .. .. United Kingdom 27 25 12 3.9 6.1 0.2 0.2 28.2 29.8 United States 24 19 5 8.0 14.7 0.6 0.6 20.2 25.5 Uruguay 35 24 28 6.8 10.0 0.3 0.3 32.7 32.8 Uzbekistan 24 1 117 4.0 9.8 <0.1 0.1 33.3 33.6 Venezuela, RB .. .. 42 5.2 23.1 0.6 0.7 32.4 32.0 Vietnam 35 2 176 1.0 .. 0.3 0.4 27.3 32.5 West Bank and Gaza .. .. 23 .. .. .. .. .. .. Yemen, Rep. .. .. 89 7.7 .. .. 0.1 .. .. Zambia 16 1 680 3.0 .. 16.7 15.6g 56.3 56.6 Zimbabwe 20 2 674 2.6 .. 24.9 24.6 56.3 58.1 World .. w .. w 139 w 5.1 w .. w 1.1 w 1.1 w 29.4 w 30.8 w Low income .. 15 224 4.7 .. 2.1 2.1 41.2 41.1 Middle income .. .. 114 4.4 .. 0.7 0.7 23.7 25.7 Lower middle income .. .. 114 3.6 .. 0.3 0.3 21.9 24.1 Upper middle income .. .. 112 7.6 .. 2.7 2.6 33.3 34.2 Low & middle income .. .. 162 4.5 .. 1.2 1.2 30.8 32.0 East Asia & Pacific 67 4 138 2.6 19.0 0.2 0.2 20.2 22.9 Europe & Central Asia .. .. 83 8.3 .. .. 0.7 .. .. Latin America & Carib. .. .. 64 5.9 .. 0.6 0.7 34.0 34.5 Middle East & N. Africa .. .. 54 6.2 .. .. 0.1 .. .. South Asia 47 18 177 5.9 .. 0.7 0.8 35.5 34.5 Sub-Saharan Africa .. .. 363 2.4 .. 7.3 7.2 57.1 57.3 High income .. .. 17 7.6 10.9 0.3 0.4 22.0 24.2 Europe EMU .. .. 13 7.8 9.9 0.3 0.3 24.5 25.0 a. Data are for the most recent year available. b. Less than 0.5. c. Survey data, 2003. d. Survey data, 2004. e. Survey data, 2002. f. Survey data, 2001. g. Survey data, 2001/02. 118 2006 World Development Indicators Health risk factors and public health challenges About the data Definitions The limited availability of data on health status is a is considerable difference in completeness of the · Prevalence of smoking is the percentage of men major constraint in assessing the health situation in vital registry data. In some countries the vital registry and women who smoke cigarettes. The age range developing countries. Surveillance data are lacking system covers only part of the country. In some, not varies, but in most countries is 18 and older or 15 for many major public health concerns. Estimates all deaths are registered. In addition, mortality from and older. · Incidence of tuberculosis is the esti- of prevalence and incidence are available for some different causes is difficult to measure. For countries mated number of new tuberculosis cases (pulmo- diseases but are often unreliable and incomplete. with incomplete vital registry systems, the WHO has nary, smear positive, extrapulmonary). · Incidence National health authorities differ widely in their used demographic techniques to estimate deaths. of tuberculosis is the estimated number of new pul- capacity and willingness to collect or report infor- Adult HIV prevalence rates reflect the rate of HIV monary, smear positive, and extrapulmonary tuber- mation. To compensate for the paucity of data and infection in each country's population. Low national culosis cases. · Prevalence of diabetes refers to the ensure reasonable reliability and international com- prevalence rates can be very misleading, however. percentage of people ages 20­79 who have type 1 or parability, the World Health Organization (WHO) pre- They often disguise serious epidemics that are ini- type 2 diabetes. · Mortality caused by road traffic pares estimates in accordance with epidemiological tially concentrated in certain localities or among spe- injury refers to the number of deaths per 100,000 models and statistical standards. cific population groups and threaten to spill over into people caused by road traffic injury. · Prevalence Smoking is the most common form of tobacco use the wider population. In many parts of the developing of HIV is the percentage of people who are infected in many countries, and the prevalence of smoking is world most new infections occur in young adults, with with HIV. therefore a good measure of the extent of the tobacco young women especially vulnerable. epidemic (Corrao and others 2000). While the preva- Estimates from recent Demographic and Health lence of smoking has been declining in some high- Surveys that have collected data on HIV/AIDS differ income countries, it has been increasing in many from those of the Joint United Nations Programme on developing countries. Tobacco use causes heart and HIV/AIDS (UNAIDS) and the WHO, which are based on other vascular diseases and cancers of the lung and surveillance systems that focus on pregnant women other organs. Given the long delay between starting who attend sentinel antenatal clinics. There are rea- to smoke and the onset of disease, the health impact sons to be cautious about comparing the two sets of smoking in developing countries will increase rap- of estimates. Demographic and Health Survey is a idly in the next few decades. Because the data pres- household survey that uses a representative sample ent a one-time estimate, with no information on the from the whole population, whereas surveillance intensity or duration of smoking, and because the data from antenatal clinics is limited to pregnant definition of adult varies across countries, the data women. Representative household surveys also fre- should be interpreted with caution. quently provide better coverage of rural populations. Tuberculosis is one of the main causes of death However, the fact that some respondents refuse to from a single infectious agent among adults in devel- participate or are absent from the household adds oping countries. In high-income countries tubercu- considerable uncertainty to survey-based HIV esti- losis has reemerged largely as a result of cases mates, because the possible association of absence among immigrants. The estimates of tuberculosis or refusal with higher HIV prevalence is unknown. incidence in the table are based on a new approach UNAIDS and WHO estimates are generally based on in which reported cases are adjusted using the ratio surveillance systems that focus on pregnant women of case notifications to the estimated share of cases who attend sentinel antenatal clinics. UNAIDS and detected by panels of 80 epidemiologists convened the WHO use a methodology to estimate HIV preva- by the WHO. lence for the adult population (ages 15­49) that Data sources Diabetes, an important cause of ill health and a risk assumes that prevalence among pregnant women is Data on smoking are from the American Cancer factor for other diseases in developed countries, is a good approximation of prevalence among men and Society's Tobacco Atlas, 2nd edition. Data on spreading rapidly in developing countries. While dia- women. However, this assumption might not apply to tuberculosis are from the WHO's Global Tuber- betes is most common among the elderly, prevalence all countries or over time. There are also other poten- culosis Control Report 2006. Data on diabetes rates are rising among younger and productive popula- tial biases associated with the use of antenatal clinic are from the International Diabetes Federation's tions in developing countries. Economic development data, such as differences among women who attend e-Atlas. Data on mortality caused by road traffic has led to the greater adoption of Western lifestyles antenatal clinics and those who do not. injury are from the WHO and the World Bank's and diet in developing countries, resulting in a substan- World Report on Road Traffic Injury Prevention tial increase in diabetes. Without effective prevention and the Organisation for Economic Co-operation and control programs, diabetes will likely continue to and Development. Data on HIV are from UNAIDS increase. Data are based on sample surveys. and the WHO's 2004 Report on the Global AIDS Data for mortality caused by road traffic injury are Epidemic. collected by the WHO based on vital registries. There 2006 World Development Indicators 119 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2004 1990 2004 1990 2004 1997­2004a 1997­2004a 2002­04a 2002­04a 2003 2003 Afghanistan 45 .. 168 .. 260 .. .. .. .. .. .. .. Albania 72 74 37 17 45 19 .. .. 99 56 77 85 Algeria 67 71 54 35 69 40 .. .. 138 121 74 79 Angola 40 41 154 154 260 260 .. .. 512 462 34 39 Argentina 72 75 26 16 29 18 .. .. 180 92 75 87 Armenia 68 71 52 29 60 32 5 3 209 95 71 84 Australia 77 80 8 5 10 6 .. .. 89 50 85 92 Austria 76 79 8 5 10 5 .. .. 120 59 83 92 Azerbaijan 71 72 84 75 105 90 .. .. 230 107 59 72 Bangladesh 55 63 100 56 149 77 24 29 252 220 59 62 Belarus 71 68 13 9 17 11 .. .. 366 131 55 81 Belgium 76 79 8 4 10 5 .. .. 125 67 82 91 Benin 53 55 111 90 185 152 72 79 325 292 43 50 Bolivia 59 65 89 54 125 69 25 29 262 199 61 69 Bosnia and Herzegovina 72 74 18 13 22 15 .. .. 159 82 75 86 Botswana 64 35 45 84 58 116 .. .. 823 793 13 18 Brazil 66 71 50 32 60 34 .. .. 268 139 62 79 Bulgaria 72 72 15 12 19 15 .. .. 216 91 69 84 Burkina Faso 48 48 113 97 210 192 110 113 427 400 28 32 Burundi 44 44 114 114 190 190 .. .. 534 513 25 28 Cambodia 54 57 80 97 115 141 34 30 391 214 42 49 Cameroon 52 46 85 87 139 149 73 72 513 493 35 40 Canada 77 80 7 5 8 6 .. .. 97 60 84 92 Central African Republic 48 39 102 115 168 193 .. .. 658 649 24 29 Chad 46 44 117 117 203 200 106 99 500 471 39 44 Chile 74 78 17 8 21 8 .. .. 136 68 79 89 China 69 71 38 26 49 31 .. .. 145 91 73 79 Hong Kong, China 77 82 .. .. .. .. .. .. 83 36 85 92 Colombia 68 73 30 18 36 21 4 3 191 108 71 84 Congo, Dem. Rep. 46 44 129 129 205 205 .. .. 497 471 32 36 Congo, Rep. 54 52 83 81 110 108 .. .. 469 444 36 45 Costa Rica 77 79 16 11 18 13 .. .. 121 68 82 90 Côte d'Ivoire 52 46 103 117 157 194 83 58 475 457 31 34 Croatia 72 75 11 6 12 7 .. .. 173 70 71 87 Cuba 75 77 11 6 13 7 .. .. 132 86 81 88 Czech Republic 71 76 11 4 13 4 .. .. 167 74 76 88 Denmark 75 77 8 4 9 5 .. .. 121 74 80 88 Dominican Republic 66 68 50 27 65 32 9 9 280 151 63 76 Ecuador 69 75 43 23 57 26 .. .. 188 109 70 81 Egypt, Arab Rep. 63 70 76 26 104 36 15 16 181 113 70 76 El Salvador 66 71 47 24 60 28 .. .. 227 142 69 81 Eritrea 48 54 88 52 147 82 55 50 482 405 37 43 Estonia 69 72 12 6 16 8 .. .. 310 101 60 85 Ethiopia 45 42 131 110 204 166 83 86 453 420 26 31 Finland 75 79 6 3 7 4 .. .. 133 61 80 91 France 77 80 7 4 9 5 .. .. 137 61 83 92 Gabon 60 54 60 60 92 91 31 33 418 397 46 51 Gambia, The 50 56 103 89 154 122 .. .. 335 290 41 47 Georgia 70 71 43 41 47 45 .. .. 219 84 72 87 Germany 75 78 7 4 9 5 .. .. 119 61 82 91 Ghana 56 57 75 68 122 112 44 52 351 334 48 52 Greece 77 79 10 4 11 5 .. .. 115 51 83 91 Guatemala 62 68 60 33 82 45 15 18 304 175 59 72 Guinea 47 54 145 101 240 155 101 98 316 285 32 33 Guinea-Bissau 42 45 153 126 253 203 .. .. 464 411 34 39 Haiti 49 52 102 74 150 117 52 54 477 460 38 48 120 2006 World Development Indicators Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2004 1990 2004 1990 2004 1997­2004a 1997­2004a 2002­04a 2002­04a 2003 2003 Honduras 65 68 44 31 59 41 .. .. 259 214 59 73 Hungary 69 73 15 7 17 8 .. .. 253 108 67 85 India 59 63 80 62 123 85 25 37 241 161 62 65 Indonesia 62 67 60 30 91 38 13 11 221 167 64 72 Iran, Islamic Rep. 65 71 54 32 72 38 .. .. 166 114 72 76 Iraq 62 .. 40 .. 50 .. .. .. .. .. .. .. Ireland 75 78 8 5 9 6 .. .. 100 59 80 89 Israel 76 79 10 5 12 6 .. .. 91 49 84 90 Italy 77 80 8 4 9 5 .. .. 96 49 82 91 Jamaica 71 71 17 17 20 20 .. .. 241 194 81 87 Japan 79 82 5 3 6 4 .. .. 95 45 86 94 Jordan 68 72 33 23 40 27 5 5 175 134 75 81 Kazakhstan 68 65 53 63 63 73 11 6 351 158 48 71 Kenya 58 48 64 79 97 120 42 39 522 587 28 32 Korea, Dem. Rep. 65 64 42 42 55 55 .. .. 320 219 55 63 Korea, Rep. 71 77 8 5 9 6 .. .. 152 59 73 87 Kuwait 75 77 14 10 16 12 .. .. 91 60 82 88 Kyrgyz Republic 68 68 68 58 80 68 10 11 273 129 57 76 Lao PDR 50 55 120 65 163 83 .. .. 330 280 45 51 Latvia 69 71 14 10 18 12 .. .. 300 116 61 85 Lebanon 69 72 32 27 37 31 .. .. 160 107 71 79 Lesotho 57 36 74 80 104 112 .. .. 837 758 15 19 Liberia 43 42 157 157 235 235 .. .. 528 477 33 37 Libya 68 74 35 18 41 20 .. .. 146 100 73 83 Lithuania 71 72 10 8 13 8 .. .. 303 106 67 87 Macedonia, FYR 72 74 33 13 38 14 .. .. 145 84 75 85 Madagascar 51 56 103 76 168 123 45 45 335 286 49 55 Malawi 46 40 146 110 241 175 101 102 651 652 19 23 Malaysia 70 73 16 10 22 12 .. .. 164 94 72 83 Mali 46 48 140 121 250 219 132 125 366 329 25 29 Mauritania 49 53 85 78 133 125 38 38 353 295 43 49 Mauritius 69 73 20 14 23 15 .. .. 218 115 71 85 Mexico 71 75 37 23 46 28 .. .. 164 91 75 86 Moldova 68 68 30 23 40 28 .. .. 302 154 59 76 Mongolia 62 65 78 41 108 52 .. .. 252 178 66 72 Morocco 64 70 69 38 89 43 9 11 170 117 68 76 Mozambique 43 42 158 104 235 152 61 64 591 558 25 30 Myanmar 56 61 91 76 130 106 .. .. 305 208 47 58 Namibia 62 47 60 47 86 63 22 20 600 560 21 25 Nepal 55 62 100 59 145 76 28 40 261 237 58 57 Netherlands 77 79 7 5 9 6 .. .. 94 66 84 90 New Zealand 75 79 8 5 11 7 .. .. 99 65 83 90 Nicaragua 64 70 52 31 68 38 10 9 230 155 67 77 Niger 40 45 191 152 320 259 184 202 368 337 30 37 Nigeria 46 44 120 101 230 197 120 123 504 494 32 36 Norway 77 80 7 4 9 4 .. .. 99 59 84 91 Oman 70 75 25 10 32 13 .. .. 121 91 79 85 Pakistan 59 65 100 80 130 101 .. .. 191 162 65 71 Panama 72 75 27 19 34 24 .. .. 156 87 79 86 Papua New Guinea 52 56 74 68 101 93 .. .. 406 367 49 53 Paraguay 68 71 33 21 41 24 .. .. 165 111 70 80 Peru 66 70 60 24 80 29 19 17 193 127 69 79 Philippines 66 71 41 26 62 34 14 9 179 126 70 78 Poland 71 74 19 7 18 8 .. .. 201 78 72 87 Portugal 74 77 11 4 14 5 .. .. 130 60 78 89 Puerto Rico 75 77 .. .. .. .. .. .. 198 73 77 91 2006 World Development Indicators 121 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2004 1990 2004 1990 2004 1997­2004a 1997­2004a 2002­04a 2002­04a 2003 2003 Romania 70 71 27 17 31 20 .. .. 234 101 65 81 Russian Federation 69 65 23 17 29 21 .. .. 450 166 49 77 Rwanda 31 44 103 118 173 203 105 97 522 462 23 25 Saudi Arabia 68 72 35 21 44 27 .. .. 157 110 76 83 Senegal 53 56 90 78 148 137 76 74 319 271 38 47 Serbia and Montenegro 72 73 24 13 28 15 .. .. 172 94 73 83 Sierra Leone 39 41 175 165 302 283 .. .. 442 387 25 29 Singapore 74 79 7 3 8 3 .. .. 90 53 83 90 Slovak Republic 71 74 12 6 14 9 .. .. 209 79 70 86 Slovenia 73 77 8 4 10 4 .. .. 151 66 77 89 Somalia 42 47 133 133 225 225 .. .. 409 357 38 45 South Africa 62 45 45 54 60 67 18 13 615 542 26 33 Spain 77 80 8 3 9 5 .. .. 117 49 83 93 Sri Lanka 71 74 26 12 32 14 .. .. 139 82 77 85 Sudan 53 57 74 63 120 91 .. .. 335 288 53 58 Swaziland 57 42 78 108 110 156 .. .. 862 837 25 29 Sweden 78 80 6 3 7 4 .. .. 82 52 86 92 Switzerland 77 81 7 5 9 5 .. .. 87 47 85 93 Syrian Arab Republic 68 74 35 15 44 16 .. .. 138 97 69 79 Tajikistan 63 64 92 75 119 93 .. .. 223 149 62 75 Tanzania 53 46 102 78 161 126 61 58 517 516 27 30 Thailand 68 71 31 18 37 21 .. .. 258 129 67 78 Togo 57 55 88 78 152 140 73 65 388 316 38 43 Trinidad and Tobago 71 70 28 18 33 20 .. .. 268 175 74 82 Tunisia 70 73 41 21 52 25 .. .. 141 82 76 83 Turkey 66 70 67 28 82 32 10 13 193 121 69 79 Turkmenistan 63 63 80 80 97 103 19 17 311 161 57 73 Uganda 46 49 93 80 160 138 78 70 527 528 25 28 Ukraine 70 68 19 14 26 18 .. .. 421 161 57 81 United Arab Emirates 73 79 12 7 14 8 .. .. 83 55 80 86 United Kingdom 76 79 8 5 10 6 .. .. 101 63 82 90 United States 75 77 9 7 11 8 .. .. 144 84 81 90 Uruguay 73 75 20 15 25 17 .. .. 169 87 74 88 Uzbekistan 69 67 65 57 79 69 .. .. 252 149 63 77 Venezuela, RB 71 74 24 16 27 19 .. .. 192 98 75 86 Vietnam 65 70 38 17 53 23 10 7 182 130 68 78 West Bank and Gaza 69 73 .. .. .. .. .. .. 146 109 74 84 Yemen, Rep. 55 61 98 82 142 111 33 36 282 237 50 53 Zambia 46 38 101 102 180 182 89 74 690 728 16 21 Zimbabwe 59 37 53 79 80 129 35 31 780 796 18 20 World 65 w 67 w 64 w 54 w 95 w 79 w 222 w 153 w 66 w 73 w Low income 56 59 94 79 147 122 300 246 54 58 Middle income 68 70 43 30 57 37 202 121 68 78 Lower middle income 67 70 45 32 61 40 182 114 70 78 Upper middle income 69 69 34 23 42 28 289 152 63 78 Low & middle income 63 65 69 59 103 86 241 171 62 70 East Asia & Pacific 67 70 43 29 59 37 169 111 70 77 Europe & Central Asia 69 69 40 28 49 34 316 134 60 80 Latin America & Carib. 68 72 43 27 54 31 219 124 69 82 Middle East & N. Africa 64 69 60 44 81 55 179 128 69 75 South Asia 59 63 86 66 129 92 237 169 61 65 Sub-Saharan Africa 49 46 111 100 185 168 489 467 32 36 High income 76 79 9 6 11 7 122 65 82 91 Europe EMU 76 79 8 4 9 5 117 57 82 91 a. Data are for the most recent year available. 122 2006 World Development Indicators Mortality About the data Definitions Mortality rates for different age groups (infants, chil- interventions are more important in this age group. · Life expectancy at birth is the number of years dren, and adults) and overall indicators of mortality Where female child mortality is higher, as in some a newborn infant would live if prevailing patterns of (life expectancy at birth or survival to a given age) countries in South Asia, girls probably have unequal mortality at the time of its birth were to stay the are important indicators of health status in a country. access to resources. same throughout its life. · Infant mortality rate is Because data on the incidence and prevalence of Adult mortality rates have increased in many coun- the number of infants dying before reaching one year diseases (morbidity data) are frequently unavailable, tries in Sub-Saharan Africa and Europe and Central of age, per 1,000 live births in a given year. · Under- mortality rates are often used to identify vulnerable Asia. In Sub-Saharan Africa the increase stems from five mortality rate is the probability that a newborn populations. And they are among the indicators most AIDS-related mortality and affects both men and baby will die before reaching age five, if subject to frequently used to compare levels of socioeconomic women. In Europe and Central Asia the causes are current age-specific mortality rates. The probability development across countries. more diverse and affect men more. They include a is expressed as a rate per 1,000. · Child mortality The main sources of mortality data are vital reg- high prevalence of smoking, a high-fat diet, exces- rate is the probability of dying between the ages of istration systems and direct or indirect estimates sive alcohol use, and stressful conditions related to one and five, if subject to current age-specific mortal- based on sample surveys or censuses. A "complete" the economic transition. ity rates. The probability is expressed as a rate per vital registration system--one covering at least 90 The percentage of a cohort surviving to age 65 1,000. · Adult mortality rate is the probability of percent of vital events in the population--is the best reflects both child and adult mortality rates. Like dying between the ages of 15 and 60--that is, the source of age-specific mortality data. But such sys- life expectancy, it is a synthetic measure based on probability of a 15-year-old dying before reaching age tems are fairly uncommon in developing countries. current age-specific mortality rates and used in the 60--if subject to current age-specific mortality rates Thus estimates must be obtained from sample sur- construction of life tables. It shows that even in between those ages. · Survival to age 65 refers to veys or derived by applying indirect estimation tech- countries where mortality is high, a certain share of the percentage of a cohort of newborn infants that niques to registration, census, or survey data. Survey the current birth cohort will live well beyond the life would survive to age 65, if subject to current age- data are subject to recall error, and surveys esti- expectancy at birth, while in low-mortality countries specific mortality rates. mating infant deaths require large samples because close to 90 percent will reach at least age 65. households in which a birth or an infant death has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on estimated actuarial "life" tables that may be inap- propriate for the population concerned. Because life expectancy at birth is constructed using infant mortality data and model life tables, similar reliability issues arise for this indicator. Life expectancy at birth and age-specific mortality rates are generally estimates based on vital registra- tion or the most recent census or survey available (see Primary data documentation). Extrapolations Data sources based on outdated surveys may not be reliable for Data on infant and under-five mortality are the monitoring changes in health status or for compara- harmonized estimates of the World Health Orga- tive analytical work. nization, UNICEF, and the World Bank, based To produce harmonized estimates of infant and mainly on household surveys, censuses, and vital under-five mortality rates that make use of all avail- registration, supplemented by the World Bank's able information in a transparent way, the United estimates based on household surveys and vital Nations Children's Fund (UNICEF) and the World Bank registration. Other estimates are compiled and developed and adopted a methodology that fits a produced by the World Bank's Human Develop- regression line to the relationship between mortal- ment Network and Development Data Group in ity rates and their reference dates using weighted consultation with its operational staff and coun- least squares. (For further discussion of methodol- try offices. Important inputs to the World Bank's ogy for childhood mortality estimates, see Hill and demographic work come from the United Nations others 1999.) Population Division's World Population Prospects: Infant and child mortality rates are higher for boys The 2004 Revision, census reports and other than for girls in countries in which parental gender statistical publications from national statistical preferences are insignificant. Child mortality cap- offices, and Demographic and Health Surveys by tures the effect of gender discrimination better than Macro International. does infant mortality, as malnutrition and medical 2006 World Development Indicators 123 evelopment and economic growth have improved the quality of life for many people, but the gains have been uneven and economic growth has often had negative environmental consequences, with profound impact on poor people. Using the environment wisely is crucial for reducing poverty. Many poor people depend on the environment for their livelihoods. Because poor people control far fewer natural and produced resources, environmental degradation affects them disproportionately. The indicators in this section measure environmental resources and the goods and services produced from them--helping to establish the link between growth and environmental change and pointing the way toward sustainable development. Environmental changes and their impact Income derived from the environment is a major source of livelihood for many people, par- ticularly for the rural population--a majority of the people who live on less than $1 a day. Despite rapid urbanization in most regions, almost half the world's population still lives in rural areas. In South Asia more than 70 percent of people live in rural areas, and in Sub- Saharan Africa more than 60 percent do. An estimated 75 percent of poor people live in rural areas. The sustainability and proper management of natural resources are crucial for maintaining rural livelihoods and safety nets in difficult times. Without proper management of natural resources and environmentally sustainable development, it would be difficult to reverse environmental losses--one of the main Millennium Development Goals. At the same time, the environment is a source of vulnerability. Increasing use of fossil energy--mainly by industrial economies--and the resulting climate change add to poor people's vulnerability. The adverse impact of environmental change will be most striking in developing countries--and particularly among the poor--because of their high dependence on natural resources, their limited capacity to adapt to a changing climate, and their limited resources to remedy the impact of such changes or to implement mitigating policies. Low-income families and regions are more vulnerable not only to human-induced environ- mental hazards but also to natural disasters and environmental risks such as the impact of global climate changes. Water scarcity is already a major problem for the world's poor, and changes in rainfall and temperature associated with climate change will likely make this scarcity worse. Crop yields are expected to decline in most tropical and subtropical regions as rainfall and temperature patterns change with a changing climate (IPCC 2001b, p. 84). The Food and Agriculture Organization estimates that land suitable for rainfed agriculture may shrink by 11 percent in developing countries by 2080 due to climate change (FAO 2005, p. 2). There is also some evidence that disease vectors such as malaria-bearing mosquitoes will spread more widely (IPCC 2001a, p. 455). Global warming may bring an increase in severe weather events like cyclones and torrential rains. The inadequate construction and exposed locations of poor people's dwellings often make poor people the most likely victims of such disasters. Hence mitigating the consequences of environmental changes that affect their livelihoods must be an integral part of poverty reduction efforts. 2006 World Development Indicators 125 The following discussion highlights selected issues related Climate change makes the situation even worse, and the to the indicators in the tables in this section, issues with region appears to be the most vulnerable to the consequences profound impact on the livelihoods of the populace, particu- of global warming. In the past 30 years Africa has experi- larly poor people: enced at least one major drought each decade. Changes in · Agriculture and land use. rainfall--there are already indications of significant changes · Water quality and availability. in the last decades--could also have serious consequences · Shrinking forests. for parts of Africa that depend on hydroelectricity. · Mix of energy use. Climate variability and associated floods and droughts increase the risk of crop failure, reducing food security Natural resources--a major source of livelihoods and increasing the incidence of malnutrition and disease. In many developing countries agriculture is still a major source In Ethiopia, for example, the 1984 drought affected 8.7 of employment and income. While globally 44 percent of the million people: 1 million people died and millions more active workforce is engaged in agriculture, the importance of suffered from malnutrition and famine (UNEP 2002, this sector as a source of employment varies by region and p. 218). Nearly 1.5 million livestock also died (FAO 2000). The income. About 60 percent of the active workforce is employed 1991­92 drought in southern Africa reduced the cereal harvest in agriculture, fisheries, and livestock in the Sub-Saharan by more than half and exposed more than 17 million people to Africa and Asia and Pacific regions, but only 19 percent in the risk of starvation. More than 100,000 people died in the Latin America, where the urban population share is as high Sahelian drought of the 1970s and 1980s (UNEP 2002, p. 219). as in high-income Europe. In high-income countries only 7 Crop failure and livestock losses increase the dependence on percent of the workforce is engaged in these activities. imports and foreign aid, reducing economic performance and These variations are even more profound across coun- the ability to cope with future environmental disasters. tries: 2 percent in the United Kingdom and United States, 59 percent in India, 67 percent in China, and 93 percent in Water is life, but water is getting dirtier and scarcer Nepal. Water scarcity is a major reason for the low levels of food pro- Population growth in developing countries will put further duction in most parts of Africa. Average per capita renewable pressure on agriculture as rising demand for food requires water resources in Africa are below the world average, and more land and more forests to be turned to agricultural use. the distribution of surface water and ground water is uneven Greater numbers of poor people will be forced to live and (figure 3b). By 2020 an estimated two-thirds of the world's work on marginal and fragile lands. In 2002 almost 1.4 billion people were living on fragile lands--more than three-fourths in Africa and Asia (figure 3a). This has an important impact on food production and food security in these regions-- Water withdrawal is skewed toward agriculture in every developing particularly in Sub-Saharan Africa, where food production region barely keeps up with population growth. Annual water withdrawal per capita, 2002 (cubic meters) 1,000 Domestic Industry Agriculture More than three-fourths of the 1.4 billion people living on fragile 800 lands are in Asia and Africa Rural population living on fragile lands as a share of world total East Asia Europe & & Pacific Central Asia 600 4% 5% High-income 7% 400 Sub-Saharan Latin America Africa & Caribbean 8% 33% 200 South Asia 19% 0 Middle East & North Africa East Asia Europe Latin Middle East South Sub-Saharan High- 24% & Pacific & Central America & & North Asia Africa income Asia Caribbean Africa Source: World Bank 2003. Source: Tables 2.1 and 3.5. 126 2006 World Development Indicators people will be living in water-stressed countries (CSD 1997 as Forests are still shrinking--but the cited in UNEP 2002, p. 150). By then, water use is expected to rate of net loss is slowing have increased 40 percent, and 17 percent more water will be In developing regions population growth, increasing demand required for food production to meet the needs of growing popu- for food, particularly meat and dairy products, and declin- lations (World Water Council 2000; UNEP 2002, p. 151). ing growth in agricultural productivity are maintaining the Population growth, expansion of irrigated agriculture, and pressure for deforestation. Total forest area in 2005 was industrial development are all behind the growing demand for just under 4 billion hectares, covering 30 percent of total water. Globally, agriculture accounts for 70 percent of fresh- land area, for an average of 0.62 hectare per capita. But water withdrawal. Most is used for irrigation, which provides forest area is unevenly distributed. For example, 64 coun- about 40 percent of world food production (CSD 1997 as cited tries with a combined population of 2 billion have less in UNEP 2002). In Africa agriculture uses more than 85 percent than 0.1 hectare of forest per capita. The 10 most forest- of total water withdrawal, and population growth and demand rich countries account for two-thirds of total forest area, for food are continuing to put more pressure on water availabil- while 7 countries or territories have no forest at all, and ity. Without efficient and comprehensive water resources man- an additional 57 have forest on less than 10 percent of agement that considers all aspects of water use, the projected their land area. water scarcity will have an even more profound impact. Deforestation, mainly for conversion to agricultural Water quality can often be as severe a problem as water land, continues--about 13 million hectares a year. At availability, but it receives less attention, particularly in devel- the same time, forest planting, landscape restoration, oping regions. For many of the world's poorest populations, one and natural expansion of forests have reduced the net of the greatest environmental threats to health remains the use loss of forest area. The net change in forest area during of untreated water. While the share of people with access to 2000­05 is estimated at a loss of 7.3 million hectares a an improved water source increased from 75 percent in 1990 year (an area about the size of Panama or Sierra Leone), to 82 percent in 2002, 1.1 billion people still lack access to an improvement from 8.9 million hectares a year dur- safe drinking water (figure 3c) and 2.8 billion lack access to ing 1990­2000. Africa and Latin America continued to improved sanitation (table 3.10). Most of them are in Africa have the largest net loss of forests, while forest area in and Asia. Lack of access to safe water and sanitation results Europe continued to expand, although at a slower rate. in hundreds of millions of cases of water-related diseases and Asia, which had a net loss in the 1990s, reported a net more than 5 million deaths every year (UNEP 2002, p. 153). gain of forests in 2000­05, due primarily to large-scale reforestation reported by China. Forests contribute directly and indirectly to the livelihoods of many people. Recognizing that, countries in most regions Many more people lack access to an improved water source in rural understand the need for more efficient forest management than in urban areas (box 3d). This effort has been very slow in developing regions, Population without access to improved water, 2002 (millions) however, particularly in Asia and Sub-Saharan Africa. 350 Urban Rural Energy--the mix affects the impact 300 Economic growth and energy use move together. Energy, especially electricity, is important in raising people's stan- 250 dard of living. High-income countries use more than five times as much energy per capita as developing countries 200 do, and with only 15 percent of the world's population they use more than half of its energy. Despite high and increasing energy costs, and the Kyoto Protocol, which 150 calls for reduction in carbon dioxide emissions, fossil fuels are still the main source of energy--and their use has 100 been rising faster than that of any other source of energy (figure 3e). 50 How energy is generated largely determines the result- ing environmental damages. Generating energy from fos- 0 sil fuels produces emissions of carbon dioxide, the main East Asia Europe Latin Middle East South Sub-Saharan High- greenhouse gas contributing to global warming and cli- & Pacific & Central America & & North Asia Africa income Asia Caribbean Africa mate change. Human-induced carbon dioxide emissions result primarily from fossil fuel combustion and cement Source: WHO and World Bank database. manufacturing, with high-income countries contributing half 2006 World Development Indicators 127 (figure 3f). Burning coal releases twice as much carbon death rate from respiratory disease followed by North Africa dioxide as burning an equivalent amount of natural gas. and Asia (figure 3g). Sub-Saharan Africa uses coal as its main source of electric- In South Africa children living in homes with wood stoves ity generation (more than two-thirds). So do East Asia and are almost five times more likely than others to develop respi- the Pacific and South Asia. Even though the low-income ratory infections severe enough to require hospitalization. In countries contribute less than 8 percent of global carbon dioxide emissions, they are affected by the consequences of climate change. Furthermore, there are local impacts Use of fossil fuels continues to rise faster than that of other sources from the type of energy use as well. of energy Most low-income countries depend on biomass energy for Energy use, 1990, 2003, and 2030 (billions of metric tons of oil equivalent) cooking and heating, a health hazard to billions of people. 6 1990 More than 3 million deaths a year are caused by air pollution, 2003 2030 mostly due to particulate pollution. Many of these deaths 5 are among children in developing countries, who die of acute respiratory infections due to indoor air pollution resulting from burning fuelwood, crop residues, or animal dung for 4 cooking and heating. Sub-Saharan Africa has the highest 3 Sustainable management of forests is spreading 2 Regulatory pressure, social activism, and consumer preferences have encouraged producers and marketers to provide a range of sustainably produced forest products, including timber, coffee, and fruit. Some 1 products are certified as having been produced in an environmentally and socially responsible manner. About 2 percent of forests worldwide are now certified as managed for sustainable yield and for providing 0 wildlife habitat, watershed protection, biodiversity, and other ecological Oil Coal Gas Other services. While the market share for certified products is small, it is Source: International Energy Agency. growing rapidly, although developed country regions are far ahead of developing regions in product certification (see figure). Developing regions lag far behind developed regions in certifying High-income countries are the leading source of carbon dioxide forest area emissions Certified forest area, 2000 (millions of hectares) Carbon dioxide emissions per capita, 1990 and 2002 (metric tons) 50 15 1990 2002 40 12 30 9 20 6 10 3 0 0 Asia Oceania Sub-Saharan Latin America North Europe East Asia Europe Latin Middle East South Sub-Saharan High- Africa & Caribbean America & Pacific & Central America & & North Asia Africa income Asia Caribbean Africa Source: FAO 2001. Source: Table 3.8. 128 2006 World Development Indicators Tanzania children under age 5 who die of acute respiratory modern renewable energy sources, up from 3.2 percent in infection are three times more likely to have been sleeping in 1971. Hydropower is the largest renewable energy source, a room with an open cookstove than are healthy children. In but large-scale hydropower can have major adverse environ- The Gambia children carried on their mothers' backs as they mental and social impacts. Modern biomass and geother- cook over smoky stoves contract pneumonia at a rate 2.5 mal energy are the other major renewable sources and have times higher than unexposed children (WRI 2005). Efforts substantial growth potential. Wind and solar energy, while to reduce indoor air pollution focus on improved cookstoves growing rapidly, provide only about 0.02 percent each of the (box 3h). global energy supply. The use of cleaner energy sources is another path toward sustainable energy use. Use of renewable energy is grow- ing, but it is still a very small share of the total (figure 3i). About 4.5 percent of global energy production comes from Sub-Sarahan Africa has the highest death rate from respiratory disease Deaths per 100,000 people, 2002 200 150 100 50 Use of renewable sources of energy is growing, but is still small 0 Sources of energy, 2003 (%) Sub-Saharan North Africa Developing Latin OECD Eastern Europe 15 Africa & West Asia Asia America & former Soviet Union Renewable sources All sources Source: World Health Organization. a Others Nuclear 12 7% Combustible renewables More efficient use of traditional biomass is improving the lives of women 9 Renewables For most poor households in rural Africa and Asia improved biomass 13% Oil 34% cookstoves are the most feasible option for reducing death and dis- ease from traditional biomass cooking. They also conserve biomass Gas resources and reduce the time and energy needed for collecting fuel 6 21% and cooking, thus freeing women's time for other productive activi- Coal ties. The Upesi stove developed in Kenya, for example, with a clay 24% liner in a mud and stone hearth, uses 40 percent less fuel than the 3 traditional three-stone stove and emits 60 percent less smoke. For higher income rural households, expanding the distribution networks Hydropower for canisters of liquefied petroleum gas can improve the welfare of 0 women and children. a. Includes wind, solar, and geothermal sources. Source: United Nations 2002. Source: International Energy Agency. 2006 World Development Indicators 129 Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares % of total % growth sq. km Forest area Permanent cropland Arable land per capita 1990 2004 1990­2004 2004 1990 2005 1990 2003 1990 2003 1989­91 2001­03 Afghanistan 82 .. .. 652 2.0 1.3 0.2 0.2 12.1 12.1 0.54 0.31 Albania 64 56 ­1.4 27 28.8 29.0 4.6 4.4 21.1 21.1 0.18 0.19 Algeria 49 41 0.5 2,382 0.8 1.0 0.2 0.3 3.0 3.2 0.28 0.24 Angola 74 64 1.7 1,247 48.9 47.4 0.4 0.2 2.3 2.7 0.28 0.21 Argentina 13 10 ­1.0 2,737 12.9 12.1 0.4 0.4 9.7 10.2 0.81 0.74 Armenia 33 36 ­0.6 28 12.3 10.0 2.7 2.1 17.7 17.7 .. 0.16 Australia 15 8 ­3.5 7,682 21.9 21.3 0.0 0.0 6.2 6.2 2.76 2.49 Austria 34 34 0.4 82 45.8 46.8 1.0 0.9 17.3 16.9 0.19 0.17 Azerbaijan 46 50 1.6 83 11.3 11.3 3.5 2.7 18.1 21.6 .. 0.22 Bangladesh 80 75 1.6 130 6.8 6.7 2.3 3.4 70.2 61.3 0.09 0.06 Belarus 34 29 ­1.5 207 35.6 38.1 0.9 0.6 29.3 26.8 .. 0.57 Belgium 4 3 ­1.6 33 20.6 20.3 0.5a 0.6 23.3a 26.6 .. .. Benin 66 55 2.0 111 30.0 21.3 1.0 2.4 14.6 24.0 0.31 0.33 Bolivia 44 36 0.7 1,084 57.9 54.2 0.1 0.2 1.9 2.8 0.31 0.35 Bosnia and Herzegovina 61 55 ­1.4 51 .. 42.7 2.9 1.9 16.6 19.6 .. 0.26 Botswana 58 48 0.2 567 24.2 21.1 0.0 0.0 0.7 0.7 0.29 0.21 Brazil 25 16 ­1.6 8,459 61.5 56.5 0.8 0.9 6.0 7.0 0.34 0.33 Bulgaria 34 30 ­1.7 111 30.1 32.8 2.7 1.9 34.9 30.0 0.44 0.43 Burkina Faso 86 82 2.5 274 26.2 24.8 0.2 0.2 12.9 17.7 0.41 0.39 Burundi 94 90 1.5 26 11.3 5.9 14.0 14.2 36.2 38.6 0.16 0.14 Cambodia 87 81 1.9 177 73.3 59.2 0.6 0.6 20.9 21.0 0.38 0.28 Cameroon 60 48 0.7 465 52.7 45.7 2.6 2.6 12.8 12.8 0.51 0.39 Canada 23 19 ­0.4 9,094 34.1 34.1 0.7 0.7 5.0 5.0 1.64 1.46 Central African Republic 63 57 1.3 623 37.3 36.5 0.1 0.2 3.1 3.1 0.64 0.50 Chad 79 75 2.8 1,259 10.4 9.5 0.0 0.0 2.6 2.9 0.54 0.41 Chile 17 13 ­0.5 749 20.4 21.5 0.3 0.4 3.7 2.7 0.22 0.13 Chinab 73 60 ­0.4 9,327 16.9 21.2 0.8 1.3 13.3 15.3 0.11 0.11 Hong Kong, China 0 0 .. .. .. .. .. .. .. .. .. .. Colombia 31 23 ­0.4 1,039 59.2 58.5 1.6 1.5 3.2 2.2 0.09 0.05 Congo, Dem. Rep. 72 68 2.4 2,267 62.0 58.9 0.5 0.5 2.9 3.0 0.18 0.13 Congo, Rep. 52 46 2.4 342 66.6 65.8 0.1 0.2 1.4 1.5 0.20 0.13 Costa Rica 46 39 1.1 51 50.2 46.8 4.9 5.9 5.1 4.4 0.08 0.05 Côte d'Ivoire 60 55 1.8 318 32.1 32.7 11.0 11.3 7.6 10.4 0.19 0.19 Croatia 46 41 ­1.4 56 .. 38.2 2.0 2.2 21.7 26.1 .. 0.33 Cuba 26 24 ­0.2 110 18.7 24.7 7.4 6.6 27.6 27.9 0.29 0.28 Czech Republic 25 26 0.1 77 .. 34.3 .. 3.1 .. 39.6 .. 0.30 Denmark 15 15 0.1 42 10.5 11.8 0.2 0.2 60.4 53.4 0.50 0.42 Dominican Republic 45 40 0.8 48 28.4 28.4 9.3 10.3 21.7 22.7 0.15 0.13 Ecuador 45 38 0.5 277 49.9 39.2 4.8 4.9 5.8 5.9 0.16 0.13 Egypt, Arab Rep. 57 58 2.1 995 0.0 0.1 0.4 0.5 2.3 2.9 0.04 0.04 El Salvador 51 40 0.3 21 18.1 14.4 12.6 12.1 26.5 31.9 0.11 0.10 Eritrea 84 80 2.0 101 .. 15.4 .. 0.0 .. 5.6 .. 0.15 Estonia 29 30 ­0.7 42 51.0 53.9 0.3 0.4 26.3 12.9 .. 0.45 Ethiopia 87 84 2.0 1,000 .. 13.0 .. 0.7 .. 11.1 .. 0.16 Finland 39 39 0.4 305 72.9 73.9 0.0 0.0 7.5 7.3 0.46 0.42 France 26 24 ­0.3 550 26.4 28.3 2.2 2.0 32.7 33.5 0.32 0.31 Gabon 32 16 ­2.6 258 85.1 84.5 0.6 0.7 1.1 1.3 0.31 0.25 Gambia, The 75 74 3.1 10 44.2 47.1 0.5 0.5 18.2 31.5 0.20 0.23 Georgia 45 48 ­0.8 69 39.7 39.7 4.8 3.8 11.4 11.5 .. 0.17 Germany 15 12 ­1.4 349 30.8 31.7 1.3 0.6 34.3 33.9 0.15 0.14 Ghana 64 54 1.3 228 32.7 24.3 6.6 9.7 11.9 18.4 0.17 0.20 Greece 41 39 0.2 129 25.6 29.1 8.3 8.8 22.5 20.9 0.28 0.25 Guatemala 59 53 1.6 108 43.8 36.3 4.5 5.6 12.0 13.3 0.15 0.12 Guinea 75 64 1.7 246 30.2 27.4 2.0 2.7 3.0 4.5 0.12 0.12 Guinea-Bissau 76 65 1.9 28 78.8 73.7 4.2 8.9 10.7 10.7 0.30 0.21 Haiti 71 62 0.5 28 4.2 3.8 11.6 11.6 28.3 28.3 0.11 0.10 130 2006 World Development Indicators Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares % of total % growth sq. km Forest area Permanent cropland Arable land per capita 1990 2004 1990­2004 2004 1990 2005 1990 2003 1990 2003 1989­91 2001­03 Honduras 60 54 1.9 112 66.0 41.5 3.2 3.2 13.1 9.6 0.30 0.16 Hungary 38 34 ­0.9 92 19.5 21.5 2.5 2.1 54.7 50.1 0.49 0.45 India 74 71 1.4 2,973 21.5 22.8 2.2 3.1 54.8 54.0 0.19 0.15 Indonesia 69 53 ­0.5 1,812 64.4 48.9 6.5 7.4 11.2 11.6 0.11 0.10 Iran, Islamic Rep. 44 33 ­0.6 1,636 6.8 6.8 0.8 1.3 9.3 9.9 0.29 0.23 Iraq 30 .. .. 437 1.8 1.9 0.7 0.6 12.1 13.2 0.29 0.22 Ireland 43 40 0.5 69 6.4 9.7 0.0 0.0 15.1 17.2 0.30 0.29 Israel 10 8 1.6 22 7.1 7.9 4.1 4.0 15.8 15.8 0.07 0.05 Italy 33 33 ­0.1 294 28.5 33.9 10.1 9.3 30.6 27.1 0.16 0.14 Jamaica 49 48 0.6 11 31.9 31.3 9.2 10.2 11.0 16.1 0.05 0.07 Japan 37 34 ­0.3 365 68.4 68.2 1.3 0.9 13.1 12.1 0.04 0.03 Jordan 28 21 1.8 88 0.9 0.9 1.0 1.2 3.3 3.3 0.09 0.05 Kazakhstan 43 44 ­0.4 2,700 1.3 1.2 0.1 0.1 13.0 8.4 .. 1.51 Kenya 75 60 0.9 569 6.5 6.2 0.9 1.0 7.4 8.2 0.18 0.14 Korea, Dem. Rep. 42 39 0.4 120 68.1 51.4 1.5 1.7 19.0 22.4 0.12 0.12 Korea, Rep. 26 19 ­1.3 99 64.5 63.5 1.6 2.0 19.8 16.7 0.05 0.03 Kuwait 5 4 ­1.3 18 0.2 0.3 0.1 0.2 0.2 0.8 0.00 0.01 Kyrgyz Republic 62 66 1.4 192 4.4 4.5 0.3 0.3 7.0 6.8 .. 0.26 Lao PDR 85 79 1.9 231 75.0 69.9 0.3 0.4 3.5 4.1 0.19 0.17 Latvia 30 34 ­0.1 62 44.7 47.4 0.4 0.5 27.2 29.4 .. 0.78 Lebanon 17 12 ­0.4 10 11.8 13.3 11.9 14.0 17.9 16.6 0.07 0.05 Lesotho 83 82 0.8 30 0.2 0.3 0.1 0.1 10.4 10.9 0.20 0.18 Liberia 58 53 2.3 96 42.1 32.8 2.2 2.3 4.2 4.0 0.19 0.12 Libya 20 13 ­0.8 1,760 0.1 0.1 0.2 0.2 1.0 1.0 0.42 0.33 Lithuania 32 33 ­0.3 63 31.0 33.5 0.9 0.9 47.8 46.7 .. 0.84 Macedonia, FYR 42 40 0.1 25 .. 35.6 2.2 1.8 23.8 22.3 .. 0.28 Madagascar 76 73 2.6 582 23.5 22.1 1.0 1.0 4.7 5.1 0.23 0.17 Malawi 88 83 1.6 94 41.4 36.2 1.2 1.5 19.3 26.0 0.19 0.19 Malaysia 50 36 ­0.1 329 68.1 63.6 16.0 17.6 5.2 5.5 0.10 0.08 Mali 76 67 1.9 1,220 11.5 10.3 0.0 0.0 1.7 3.8 0.23 0.38 Mauritania 56 37 ­0.2 1,025 0.4 0.3 0.0 0.0 0.4 0.5 0.20 0.17 Mauritius 60 56 0.7 2 19.2 18.2 3.0 3.0 49.3 49.3 0.09 0.08 Mexico 28 24 0.7 1,909 36.2 33.7 1.0 1.3 12.6 13.0 0.29 0.25 Moldova 53 54 ­0.2 33 9.7 10.0 14.2 9.1 52.8 56.1 .. 0.43 Mongolia 43 43 1.3 1,567 7.3 6.5 0.0 0.0 0.9 0.8 0.65 0.49 Morocco 52 42 0.1 446 9.6 9.8 1.7 2.0 19.5 19.0 0.36 0.30 Mozambique 79 63 1.1 784 25.5 24.6 0.3 0.3 4.4 5.6 0.26 0.22 Myanmar 75 70 1.0 658 59.6 49.0 0.8 1.4 14.6 15.4 0.23 0.20 Namibia 73 67 1.9 823 10.6 9.3 0.0 0.0 0.8 1.0 0.47 0.42 Nepal 91 85 1.8 143 33.7 25.4 0.5 0.9 16.0 16.5 0.12 0.09 Netherlands 40 34 ­0.6 34 10.2 10.8 0.9 0.9 25.9 26.9 0.06 0.06 New Zealand 15 14 0.6 268 28.8 31.0 5.1 7.0 9.4 5.6 0.73 0.38 Nicaragua 47 42 1.4 121 53.9 42.7 1.6 1.9 10.7 15.9 0.33 0.37 Niger 84 77 2.7 1,267 1.5 1.0 0.0 0.0 8.7 11.4 1.28 1.15 Nigeria 65 53 1.0 911 18.9 12.2 2.8 3.2 32.4 33.5 0.33 0.24 Norway 28 20 ­1.7 306 29.8 30.7 .. .. 2.8 2.9 0.21 0.19 Oman 38 22 ­1.6 310 0.0 0.0 0.2 0.1 0.1 0.1 0.02 0.02 Pakistan 69 66 2.0 771 3.3 2.5 0.6 0.9 26.6 25.2 0.19 0.14 Panama 46 43 1.4 74 58.8 57.7 2.1 2.0 6.7 7.4 0.21 0.18 Papua New Guinea 87 87 2.4 453 69.6 65.0 1.3 1.4 0.4 0.5 0.05 0.04 Paraguay 51 42 1.1 397 53.3 46.5 0.2 0.2 5.3 7.7 0.50 0.53 Peru 31 26 0.4 1,280 54.8 53.7 0.3 0.5 2.7 2.9 0.16 0.14 Philippines 51 38 0.0 298 35.5 24.0 14.8 16.8 18.4 19.1 0.09 0.07 Poland 39 38 ­0.2 306 29.2 30.0 1.1 1.0 47.3 41.1 0.38 0.35 Portugal 53 45 ­0.8 92 33.9 41.3 8.5 7.9 25.6 17.4 0.24 0.16 Puerto Rico 28 3 ­15.0 9 45.6 46.0 5.6 5.6 7.3 3.7 0.02 0.01 2006 World Development Indicators 131 Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares % of total % growth sq. km Forest area Permanent cropland Arable land per capita 1990 2004 1990­2004 2004 1990 2005 1990 2003 1990 2003 1989­91 2001­03 Romania 47 45 ­0.7 230 27.8 27.7 2.6 2.0 41.2 40.9 0.41 0.43 Russian Federation 27 27 ­0.2 16,381 49.4 49.4 0.1 0.1 8.1 7.5 .. 0.85 Rwanda 95 80 0.4 25 12.9 19.5 12.4 10.9 35.7 48.6 0.12 0.13 Saudi Arabia 22 12 ­1.6 2,150 1.3 1.3 0.0 0.1 1.6 1.7 0.21 0.16 Senegal 60 50 1.2 193 48.6 45.1 0.1 0.2 12.1 12.8 0.29 0.23 Serbia and Montenegro 49 48 ­2.0 102 .. 26.4 3.5 3.2 36.5 33.2 .. 0.42 Sierra Leone 70 60 0.9 72 42.5 38.5 0.8 1.1 6.8 8.0 0.12 0.11 Singapore 0 0 .. 1 3.0 3.0 1.5 1.5 1.5 1.5 0.00 0.00 Slovak Republic 44 42 ­0.1 48 .. .. .. .. .. .. .. .. Slovenia 49 49 0.0 20 .. 62.8 1.8 1.4 9.9 8.6 .. 0.09 Somalia 71 65 0.6 627 13.2 11.4 0.0 0.0 1.6 1.7 0.15 0.14 South Africa 51 43 0.5 1,214 7.6 7.6 0.7 0.8 11.1 12.2 0.38 0.33 Spain 25 23 0.3 499 27.0 35.9 9.7 10.0 30.7 27.5 0.40 0.33 Sri Lanka 79 79 1.0 65 36.4 29.9 15.9 15.5 13.5 14.2 0.05 0.05 Sudan 73 60 0.8 2,376 32.2 28.4 0.1 0.2 5.5 7.2 0.50 0.48 Swaziland 77 76 2.6 17 27.4 31.5 0.7 0.8 10.5 10.4 0.24 0.16 Sweden 17 17 0.2 410 66.7 67.1 0.0 0.0 6.9 6.5 0.33 0.30 Switzerland 32 32 0.9 40 28.9 30.5 0.5 0.6 9.8 10.2 0.06 0.06 Syrian Arab Republic 51 50 2.5 184 2.0 2.5 4.0 4.5 26.6 25.0 0.38 0.26 Tajikistan 68 75 2.1 140 2.9 2.9 0.9 0.9 6.1 6.6 .. 0.15 Tanzania 78 64 1.1 884 46.9 39.9 1.0 1.2 4.0 4.5 0.13 0.11 Thailand 71 68 0.8 511 31.3 28.4 6.1 7.0 34.2 27.7 0.32 0.24 Togo 72 64 2.2 54 12.6 7.1 1.7 2.2 38.6 46.2 0.53 0.44 Trinidad and Tobago 31 24 ­1.3 5 45.8 44.1 9.0 9.2 14.4 14.6 0.06 0.06 Tunisia 42 36 0.3 155 4.1 6.8 12.5 13.8 18.7 18.0 0.36 0.28 Turkey 41 33 0.3 770 12.6 13.2 3.9 3.5 32.0 30.4 0.44 0.34 Turkmenistan 55 54 1.8 470 8.8 8.8 0.1 0.1 2.9 4.7 .. 0.42 Uganda 89 88 3.1 197 25.0 18.4 9.4 10.9 25.4 26.4 0.28 0.20 Ukraine 33 33 ­0.8 579 16.0 16.5 1.9 1.6 57.6 56.1 .. 0.67 United Arab Emirates 17 15 5.2 84 2.9 3.7 0.2 2.3 0.4 0.8 0.02 0.02 United Kingdom 11 11 0.0 242 10.8 11.8 0.3 0.2 27.4 23.4 0.12 0.10 United States 25 20 ­0.5 9,159 32.6 33.1 0.2 0.2 20.3 18.9 0.74 0.60 Uruguay 11 7 ­2.3 175 5.2 8.6 0.3 0.2 7.2 7.8 0.41 0.40 Uzbekistan 60 63 2.2 425 7.2 7.8 0.9 0.8 10.5 11.1 .. 0.18 Venezuela, RB 16 12 0.0 882 59.0 54.1 0.9 0.9 3.2 3.0 0.14 0.10 Vietnam 80 74 1.0 325 28.8 39.7 3.2 7.1 16.4 20.5 0.08 0.08 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 79 74 3.3 528 1.0 1.0 0.2 0.3 2.9 2.9 0.12 0.08 Zambia 61 64 2.6 743 66.1 57.1 0.0 0.0 7.1 7.1 0.63 0.47 Zimbabwe 71 65 0.8 387 57.5 45.3 0.3 0.3 7.5 8.3 0.27 0.25 World 57 w 51 w 0.6 w 129,663 s 31.6 w 30.5 w 0.9 w 1.1 w 10.8 w 10.8 w 0.24 w 0.23 w Low income 74 69 1.6 29,192 27.8 24.8 1.0 1.2 12.9 13.5 0.21 0.17 Middle income 56 47 ­0.2 67,543 34.5 33.6 1.0 1.2 9.6 9.7 0.18 0.22 Lower middle income 62 51 ­0.2 38,470 32.1 30.7 1.3 1.6 10.0 10.5 0.15 0.17 Upper middle income 31 28 0.1 28,983 37.7 37.3 0.6 0.6 9.0 8.7 0.37 0.45 Low & middle income 63 57 0.7 96,645 32.5 30.9 1.0 1.2 10.5 10.8 0.20 0.20 East Asia & Pacific 71 59 ­0.2 15,885 28.8 28.4 2.2 2.8 12.1 13.3 0.12 0.12 Europe & Central Asia 37 36 0.0 23,371 38.2 38.3 0.4 0.4 12.3 11.4 .. 0.57 Latin America & Carib. 29 23 ­0.1 20,057 49.0 45.6 0.9 1.0 6.6 7.1 0.30 0.27 Middle East & N. Africa 48 44 1.3 8,955 2.2 2.4 0.7 0.9 5.6 5.8 0.23 0.18 South Asia 75 72 1.6 4,781 16.5 16.8 1.8 2.4 42.6 41.7 0.18 0.14 Sub-Saharan Africa 72 64 1.6 23,596 30.0 26.5 0.8 0.9 6.3 7.5 0.31 0.25 High income 25 22 ­0.4 33,018 28.9 29.3 0.7 0.7 11.4 10.8 0.43 0.37 Europe EMU 26 24 ­0.3 2,435 33.2 37.0 4.7 4.5 27.1 25.8 0.23 0.21 a. Includes Luxembourg. b. Includes Taiwan, China. 132 2006 World Development Indicators Rural population and land use About the data Definitions Indicators of rural development are sparse, as few · Rural population is calculated as the difference indicators are disaggregated between rural and between the total population and the urban popula- urban areas (for some that are, see tables 2.7 and tion (see Definitions for tables 2.1 and 3.10). · Land 3.10). This table shows indicators of rural population area is a country's total area, excluding area under and land use. Rural population is approximated as inland water bodies, national claims to the continen- the midyear nonurban population. tal shelf, and exclusive economic zones. In most The data in the table show that land use patterns cases the definition of inland water bodies includes are changing. They also indicate major differences in major rivers and lakes. (See table 1.1 for the total resource endowments and uses among countries. surface area of countries.) · Land use is broken into True comparability of the data is limited, however, three categories. · Forest area is land under natural by variations in definitions, statistical methods, and or planted stands of trees, whether productive or not. the quality of data collection. Countries use differ- · Permanent cropland is land cultivated with crops ent definitions of rural population and land use, for that occupy the land for long periods and need not example. The Food and Agriculture Organization be replanted after each harvest, such as cocoa, cof- (FAO), the primary compiler of these data, occasion- fee, and rubber. This category includes land under ally adjusts its definitions of land use categories and flowering shrubs, fruit trees, nut trees, and vines, but sometimes revises earlier data. Because the data excludes land under trees grown for wood or timber. reflect changes in reporting procedures as well as · Arable land includes land defined by the FAO as actual changes in land use, apparent trends should land under temporary crops (double-cropped areas be interpreted with caution. are counted once), temporary meadows for mowing Satellite images show land use that differs from or for pasture, land under market or kitchen gardens, that given by ground-based measures in both area and land temporarily fallow. Land abandoned as a under cultivation and type of land use. Moreover, result of shifting cultivation is excluded. land use data in countries such as India are based on reporting systems that were designed for the col- lection of tax revenue. Because taxes on land are no longer a major source of government revenue, the quality and coverage of land use data (except for cropland) have declined. Data on forest area may be particularly unreliable because of differences in definitions and irregular surveys (see About the data for table 3.4). Five countries had more than half the world's forest in 2005 Forest area as share of world total 3,950 million hectares Russian Ten countries with the largest forest area, Federation Data sources 2005 20% Data on urban population shares used to estimate Country Million hectares Russian Federation 809 Others rural population come from the United Nations Brazil 478 47% Brazil Population Division's World Urbanization Pros- 12% Canada 310 pects: The 2005 Revision. The total population United States 303 figures are World Bank estimates. Data on land China 197 Canada 8% area and land use are from the FAO's electronic Australia 164 Congo, Dem. Rep. 134 files. The FAO gathers these data from national Indonesia 88 agencies through annual questionnaires and by Peru 69 China 5% United States analyzing the results of national agricultural cen- India 68 8% suses. Data on forest area are from the FAO's Source: Food and Agricultural Organization's Global Forest Source: Food and Agricultural Organization's Global Global Forest Resources Assessment. Resources Assessment. Forest Resources Assessment. 2006 World Development Indicators 133 Agricultural inputs Agricultural Irrigated Land under Fertilizer Agricultural Agricultural land land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1989­91 2001­03 1989­91 2001­03 1989­91 2003­05 1989­91 2000­02 1989­91 2001­03 1989­91 2001­03 Afghanistan 58.3 58.3 33.8 33.8 .. .. 63 19 .. .. 1 1 Albania 40.9 41.4 58.9 49.6 295 144 1,378 420 .. .. 195 141 Algeria 16.2 16.8 5.8 6.9 2,807 2,784 157 130 .. 21.1 125 129 Angola 46.1 46.1 2.3 2.3 883 1,372 46 2 .. .. 35 33 Argentina 46.6 47.0 5.7 5.4 8,557 9,633 61 295 0.3 1.0 103 108 Armenia .. 49.5 .. 51.1 168 194 .. 157 20.5 .. .. 288 Australia 60.5 58.2 4.0 5.1 12,823 18,360 272 465 5.4 4.4 67 64 Austria 42.7 41.2 0.3 0.3 940 783 2,001 1,533 7.7 5.7 2,378 2,368 Azerbaijan .. 56.8 .. 72.5 627 794 .. 63 31.4 40.1 .. 169 Bangladesh 76.5 69.5 30.5 54.3 11,083 11,624 1,049 1,738 65.7 .. 6 7 Belarus .. 43.1 .. 2.3 2,578 2,066 .. 1,325 21.6 .. .. 111 Belgium 44.0a 46.3 2.3a 4.5 367a 314 4,969a 3,322a 2.7 1.7 1,530a 1,202a Benin 20.4 30.4 0.6 0.4 658 948 54 154 .. .. 1 1 Bolivia 32.8 34.2 5.4 4.1 620 748 37 37 1.7 .. 25 20 Bosnia and Herzegovina .. 41.7 .. 0.3 .. 340 .. 356 .. .. .. 289 Botswana 45.9 45.8 0.3 0.3 203 137 22 122 .. 12.3 135 159 Brazil 28.6 31.2 4.6 4.4 20,101 19,772 653 1,201 23.0 20.2 142 137 Bulgaria 55.7 48.7 30.1 16.5 2,152 1,760 1,698 500 18.9 18.0 135 95 Burkina Faso 35.0 39.2 0.5 0.5 2,743 3,249 59 30 .. .. 2 4 Burundi 82.8 90.8 1.2 1.6 218 211 32 33 .. .. 2 2 Cambodia 30.1 30.1 6.3 7.1 1,860 2,247 9 0 .. 70.2 3 7 Cameroon 19.7 19.7 0.3 0.4 755 863 38 75 60.6 .. 1 1 Canada 7.5 7.4 1.4 1.5 21,446 17,276 470 549 4.3 2.8 165 160 Central African Republic 8.0 8.3 0.0 0.1 110 187 5 3 .. .. 0 0 Chad 38.4 38.6 0.5 0.8 1,170 1,887 20 49 .. .. 1 0 Chile 21.3 20.4 51.8 82.4 778 687 1,082 2,386 19.3 13.6 129 272 China 57.0 59.5 36.0 35.4 93,047 79,896 2,222 2,578 55.8 44.7 67 65 Hong Kong, China .. .. .. .. .. .. .. .. 0.9 0.2 .. .. Colombia 43.4 44.2 13.1 22.6 1,655 1,195 1,770 2,605 1.3 21.6 98 89 Congo, Dem. Rep. 10.1 10.1 0.1 0.1 1,840 2,049 11 7 .. .. 4 4 Congo, Rep. 30.8 30.9 0.2 0.4 15 11 24 67 .. .. 14 14 Costa Rica 55.6 56.1 15.1 20.6 89 58 4,256 6,455 25.9 15.5 248 311 Côte d'Ivoire 59.4 62.4 1.1 1.1 1,401 1,638 152 256 .. .. 15 12 Croatia .. 56.2 .. 0.4 .. 701 .. 1,303 .. 15.8 .. 25 Cuba 61.5 60.6 23.2 22.5 233 326 1,771 476 24.9 25.5 256 249 Czech Republic .. 55.3 .. 0.7 .. 1,557 .. 1,186 11.4 4.7 .. 305 Denmark 65.5 62.9 16.9 19.6 1,564 1,493 2,436 1,393 5.6 3.2 639 540 Dominican Republic 74.0 76.4 15.1 17.2 135 150 859 848 20.3 15.4 22 17 Ecuador 28.3 29.2 27.9 29.0 828 844 465 1,531 7.1 8.5 65 91 Egypt, Arab Rep. 2.6 3.4 100.0 100.0 2,280 2,846 4,181 4,478 37.6 28.0 250 309 El Salvador 70.1 82.2 4.9 5.0 428 330 1,392 1,054 9.3 19.9 62 52 Eritrea .. 74.6 .. 3.7 .. 371 .. 119 .. .. .. 8 Estonia .. 19.0 .. 0.6 454 267 .. 432 20.5 6.7 .. 889 Ethiopia .. 31.3 .. 2.6 .. 7,233 .. 145 .. .. .. 3 Finland 7.9 7.3 2.8 2.9 1,144 1,168 1,904 1,353 8.9 5.3 1,059 882 France 55.6 53.9 10.3 13.3 9,244 9,158 3,217 2,221 6.4 .. 799 685 Gabon 20.0 20.0 1.0 1.4 14 20 33 9 .. .. 50 46 Gambia, The 63.8 77.9 0.7 0.6 92 182 64 26 .. .. 2 1 Georgia .. 43.2 .. 44.1 249 332 .. 412 .. 53.8 .. 254 Germany 50.8 48.7 4.0 4.0 6,864 6,983 3,070 2,245 4.1 2.5 1,314 801 Ghana 55.4 64.4 0.7 0.5 1,066 1,376 36 60 .. .. 15 9 Greece 71.4 65.6 30.3 37.4 1,473 1,278 2,307 1,580 23.8 15.9 752 939 Guatemala 39.5 42.7 6.6 6.4 726 666 999 1,427 49.9 38.7 32 30 Guinea 48.7 50.4 7.2 5.6 603 778 18 32 .. .. 5 5 Guinea-Bissau 53.2 57.9 4.1 4.6 106 134 22 80 .. .. 1 1 Haiti 58.0 57.7 7.6 8.4 408 458 32 181 .. .. 3 2 134 2006 World Development Indicators Agricultural inputs Agricultural Irrigated Land under Fertilizer Agricultural Agricultural land land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1989­91 2001­03 1989­91 2001­03 1989­91 2003­05 1989­91 2000­02 1989­91 2001­03 1989­91 2001­03 Honduras 29.9 26.2 3.8 5.6 475 391 191 1,193 44.1 35.1 31 49 Hungary 70.1 63.7 3.8 4.8 2,827 2,934 1,459 992 .. 6.0 126 246 India 60.9 60.8 27.6 32.7 102,279 97,569 739 1,044 68.7 .. 61 141 Indonesia 24.1 24.9 14.2 13.3 13,442 15,140 1,227 1,321 55.7 44.8 15 45 Iran, Islamic Rep. 37.3 37.5 41.1 44.1 9,503 9,013 760 921 26.0 .. 136 168 Iraq 21.9 22.9 57.3 58.6 .. .. 350 968 .. .. 72 80 Ireland 76.2 63.6 .. .. 306 292 6,609 5,308 14.6 6.8 1,624 1,360 Israel 26.6 26.2 47.0 45.4 111 84 2,877 2,598 4.1 1.9 795 718 Italy 56.4 52.2 21.9 24.9 4,481 4,124 2,135 1,819 8.9 5.1 1,593 2,031 Jamaica 44.0 47.4 11.3 8.8 2 1 2,079 1,258 .. 19.9 252 177 Japan 15.6 14.2 54.3 54.7 2,469 2,001 3,865 3,066 7.2 4.7 4,306 4,588 Jordan 13.3 12.9 16.5 18.8 101 53 663 977 .. .. 213 212 Kazakhstan .. 76.9 .. 15.8 22,152 13,794 .. 23 22.5 35.4 .. 22 Kenya 45.7 46.5 1.2 1.8 1,776 2,101 258 320 19.0 .. 24 28 Korea, Dem. Rep. 20.9 24.2 57.5 50.9 1,604 1,296 3,577 1,018 .. .. 299 241 Korea, Rep. 22.1 19.5 46.8 47.2 1,427 1,093 4,888 4,317 17.4 9.4 215 1,239 Kuwait 7.9 8.6 60.0 77.0 1 2 417 711 .. .. 222 69 Kyrgyz Republic .. 56.0 .. 78.3 578 568 .. 213 33.8 48.4 .. 171 Lao PDR 7.2 8.1 15.7 17.6 643 819 18 95 .. .. 11 12 Latvia 40.8 39.9 1.1 1.1 699 451 995 300 17.6 14.8 .. 305 Lebanon 31.0 32.2 28.2 33.2 41 61 1,510 2,838 .. .. 175 488 Lesotho 76.3 76.9 0.6 0.9 199 247 161 309 .. .. 59 61 Liberia 27.1 27.0 0.4 0.5 179 120 25 0 .. .. 8 9 Libya 8.8 8.8 20.2 21.9 418 341 445 349 .. .. 179 219 Lithuania .. 55.6 .. 0.2 1,134 880 .. 579 .. 17.7 .. 349 Macedonia, FYR .. 48.8 .. 9.0 .. 195 .. 502 .. 23.0 .. 954 Madagascar 47.0 47.4 30.1 30.6 1,308 1,423 32 31 .. 78.0 11 12 Malawi 40.1 45.8 1.0 2.3 1,415 1,694 317 400 .. .. 8 6 Malaysia 22.0 24.0 5.0 4.8 696 699 5,386 6,548 27.4 14.8 153 241 Mali 26.3 28.4 3.7 5.0 2,340 3,275 78 89 .. .. 10 6 Mauritania 38.7 38.8 12.2 9.8 156 121 95 40 .. .. 8 8 Mauritius 55.7 55.7 16.0 20.1 1 0 2,867 3,035 15.8 10.0 36 37 Mexico 54.2 56.2 21.6 23.2 10,014 10,946 716 727 24.2 17.0 124 131 Moldova .. 77.1 .. 14.0 676 882 .. 86 .. 47.9 .. 223 Mongolia 80.3 83.3 5.7 7.0 647 185 116 31 33.8 45.0 79 42 Morocco 67.8 68.0 13.5 15.5 5,545 5,578 369 440 .. 44.2 45 58 Mozambique 60.8 61.8 2.8 2.7 1,561 2,112 10 53 .. .. 17 14 Myanmar 15.8 16.8 10.2 17.9 5,154 6,827 84 146 67.5 .. 13 10 Namibia 47.0 47.2 0.6 1.0 214 244 0 4 48.2 .. 46 39 Nepal 29.0 29.5 41.6 47.1 3,013 3,333 320 333 82.8 .. 22 24 Netherlands 59.0 57.2 61.0 59.9 192 222 6,506 4,286 4.6 3.0 2,074 1,641 New Zealand 65.3 64.3 7.3 8.5 161 121 1,525 5,704 10.6 8.7 301 507 Nicaragua 52.0 57.5 4.0 2.8 305 502 281 177 38.7 37.0 20 15 Niger 25.9 30.4 0.6 0.5 6,232 7,138 2 3 .. .. 0 0 Nigeria 79.2 78.8 0.7 0.8 15,596 21,508 135 66 .. .. 8 10 Norway 3.2 3.4 .. .. 357 326 2,388 2,100 6.3 3.9 1,741 1,498 Oman 3.5 3.5 72.6 88.4 2 2 2,185 2,491 .. .. 41 50 Pakistan 33.7 34.4 76.8 83.9 11,794 12,507 921 1,377 49.9 45.3 127 154 Panama 28.5 30.0 4.8 6.2 179 208 656 545 28.2 17.7 103 148 Papua New Guinea 2.0 2.3 .. .. 2 3 671 556 .. .. 60 53 Paraguay 58.6 62.5 3.0 2.2 447 782 92 319 1.7 32.2 72 55 Peru 16.3 16.6 30.5 27.9 802 1,118 320 759 1.1 0.8 37 36 Philippines 37.3 40.9 15.7 14.5 7,113 6,562 955 1,313 45.2 37.4 19 20 Poland 61.7 56.1 0.7 0.7 8,541 8,276 1,383 1,140 25.3 18.9 815 1,025 Portugal 43.3 41.5 20.2 27.5 832 465 1,152 1,234 18.1 12.6 563 1,029 Puerto Rico 49.0 25.5 34.5 48.2 1 0 .. .. 3.7 2.0 .. .. 2006 World Development Indicators 135 Agricultural inputs Agricultural Irrigated Land under Fertilizer Agricultural Agricultural land land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1989­91 2001­03 1989­91 2001­03 1989­91 2003­05 1989­91 2000­02 1989­91 2001­03 1989­91 2001­03 Romania 64.4 64.4 31.1 31.1 5,927 5,655 1,077 355 28.9 38.1 147 178 Russian Federation .. 13.2 .. 3.7 59,541 39,476 .. 121 14.1 10.5 .. 52 Rwanda 75.8 74.8 0.3 0.7 250 330 20 48 90.1 .. 1 1 Saudi Arabia 56.7 80.9 45.6 42.7 1,009 646 1,461 1,067 .. 5.4 19 28 Senegal 41.9 42.4 3.6 4.6 1,211 1,165 58 140 .. .. 2 3 Serbia and Montenegro .. 54.8 .. 0.8 .. 2,047 .. 732 .. .. .. 1,023 Sierra Leone 38.3 39.1 5.2 5.0 462 253 24 5 .. .. 6 2 Singapore 3.0 3.0 .. .. .. .. 54,333 25,920 0.4 0.3 620 650 Slovak Republic .. .. .. .. .. .. .. .. .. 6.0 .. .. Slovenia .. 25.2 .. 1.5 .. 99 .. 4,239 .. 9.3 .. 6,314 Somalia 70.2 70.3 19.2 18.7 .. .. 18 5 .. .. 21 16 South Africa 79.7 82.0 8.4 9.5 6,192 4,469 573 558 .. 10.7 108 46 Spain 61.1 60.2 16.8 20.3 7,756 6,570 1,282 1,605 11.5 6.0 481 689 Sri Lanka 36.2 36.4 27.5 34.4 810 873 2,127 2,862 44.6 34.7 75 113 Sudan 51.9 56.4 13.7 11.0 5,376 9,365 48 40 .. .. 7 7 Swaziland 74.3 80.9 22.9 26.0 80 64 697 371 .. .. 233 222 Sweden 8.3 7.7 4.0 4.3 1,235 1,099 1,148 1,010 3.4 2.1 604 616 Switzerland 50.5 38.1 6.1 5.8 210 164 4,297 2,272 4.3 4.1 2,874 2,644 Syrian Arab Republic 73.5 74.8 12.9 24.1 3,969 3,194 581 718 .. 30.3 129 225 Tajikistan .. 30.4 .. 68.2 266 391 .. 175 63.4 .. .. 233 Tanzania 53.7 54.4 3.3 3.5 2,990 3,243 378 31 84.2 82.1 19 19 Thailand 41.9 38.4 20.6 26.6 10,991 11,152 537 1,039 63.2 45.7 33 144 Togo 58.4 66.7 0.3 0.3 625 767 57 74 .. .. 0 0 Trinidad and Tobago 25.5 25.9 3.3 3.3 6 2 833 631 12.6 7.4 355 360 Tunisia 56.9 62.3 6.7 8.0 1,375 1,377 328 372 .. .. 86 126 Turkey 51.8 51.0 14.5 19.5 13,679 13,809 736 768 47.4 35.5 278 410 Turkmenistan .. 69.7 .. 89.1 331 948 .. 543 42.4 .. .. 256 Uganda 60.6 62.7 0.1 0.1 1,078 1,532 1 14 80.1 69.1 9 9 Ukraine .. 71.4 .. 6.8 12,542 13,089 .. 154 .. 19.5 .. 124 United Arab Emirates 3.3 6.8 128.1 29.2 1 0 4,247 5,149 .. .. 55 55 United Kingdom 75.3 70.1 2.4 3.0 3,677 3,039 3,553 3,141 2.2 1.3 762 877 United States 46.6 44.7 11.1 12.7 63,775 57,028 1,006 1,101 2.9 2.5 248 273 Uruguay 84.7 85.5 9.6 14.3 508 567 586 849 .. 4.3 262 241 Uzbekistan .. 64.2 .. 87.4 1,225 1,753 .. 1,614 40.6 .. .. 373 Venezuela, RB 24.8 24.5 13.1 16.9 819 1,040 1,579 1,132 13.1 10.0 169 189 Vietnam 20.7 29.3 44.8 33.8 6,549 8,378 1,183 3,172 74.7 61.9 51 245 West Bank and Gaza .. .. .. .. .. .. .. .. .. 14.2 .. .. Yemen, Rep. 33.4 33.5 22.4 31.4 781 638 134 95 .. .. 40 43 Zambia 47.4 47.5 0.6 2.9 929 861 128 84 .. .. 11 11 Zimbabwe 52.1 53.1 3.5 5.2 1,606 1,610 562 443 .. .. 60 75 World 38.4 w 38.4 w 16.8 w 18.0 w 690,883 s 669,691 s 992 w 986 w 41.7 w .. w 187 w 194 w Low income 43.4 44.6 21.8 23.8 205,135 230,127 529 663 65.9 .. 47 83 Middle income 36.2 35.6 17.9 17.7 341,823 305,289 1,124 1,057 46.8 35.8 123 130 Lower middle income 40.6 43.1 22.7 23.1 226,040 208,050 1,324 1,431 50.5 40.3 90 105 Upper middle income 30.8 25.7 9.04 8.6 115,782 97,239 788 471 20.8 14.2 187 171 Low & middle income 38.4 38.3 19.3 20.0 546,957 535,416 887 910 50.6 .. 93 112 East Asia & Pacific 48.4 50.6 .. .. 141,839 133,283 1,767 2,148 56.0 45.1 56 73 Europe & Central Asia 38.5 28.6 11.6 11.1 137,259 113,303 1,098 345 23.8 20.2 231 184 Latin America & Carib. 34.6 36.1 11.1 11.4 48,262 50,630 602 895 17.9 17.2 121 123 Middle East & N. Africa 22.5 23.0 28.9 32.9 26,822 25,887 651 841 .. .. 113 144 South Asia 54.8 54.7 33.0 39.0 129,072 125,982 745 1,066 66.6 .. 62 130 Sub-Saharan Africa 42.9 44.0 3.6 3.6 63,703 86,332 142 123 .. .. 20 13 High income 38.3 38.5 10.5 11.9 143,926 134,275 1,248 1,208 6.0 3.9 420 436 Europe EMU 50.6 48.4 14.4 16.9 33,599 31,385 2,524 2,040 8.7 4.8 997 980 a. Includes Luxembourg. 136 2006 World Development Indicators Agricultural inputs About the data Definitions Agricultural activities provide developing countries Fertilizer consumption measures the quantity of · Agricultural land refers to the share of land area with food and revenue, but they also can degrade plant nutrients. Consumption is calculated as pro- that is arable, under permanent crops, and under natural resources. Poor farming practices can duction plus imports minus exports. Because some permanent pastures. Arable land includes land cause soil erosion and loss of soil fertility. Efforts chemical compounds used for fertilizers have other defined by the FAO as land under temporary crops to increase productivity through the use of chemi- industrial applications, the consumption data may (double-cropped areas are counted once), tempo- cal fertilizers, pesticides, and intensive irrigation overstate the quantity available for crops. rary meadows for mowing or for pasture, land under have environmental costs and health impacts. To smooth annual fluctuations in agricultural activ- market or kitchen gardens, and land temporarily fal- Excessive use of chemical fertilizers can alter the ity, the indicators in the table have been averaged low. Land abandoned as a result of shifting culti- chemistry of soil. Pesticide poisoning is common in over three years. vation is excluded. Land under permanent crops is developing countries. And salinization of irrigated land cultivated with crops that occupy the land for land diminishes soil fertility. Thus inappropriate long periods and need not be replanted after each use of inputs for agricultural production has far- harvest, such as cocoa, coffee, and rubber. This reaching effects. category includes land under flowering shrubs, fruit This table provides indicators of major inputs to trees, nut trees, and vines, but excludes land under agricultural production: land, fertilizer, labor, and trees grown for wood or timber. Permanent pasture is agricultural machinery. There is no single correct mix land used for five or more years for forage, including of inputs: appropriate levels and application rates natural and cultivated crops.· Irrigated land refers to vary by country and over time, depending on the type areas purposely provided with water, including land of crops, the climate and soils, and the production irrigated by controlled flooding. · Cropland refers to process used. arable land and permanent cropland (see table 3.1). The data shown here and in table 3.3 are col- · Land under cereal production refers to harvested lected by the Food and Agriculture Organization areas, although some countries report only sown (FAO) through annual questionnaires. The FAO tries or cultivated area. · Fertilizer consumption is the to impose standard definitions and reporting meth- quantity of plant nutrients used per unit of arable ods, but exact consistency across countries and over land. Fertilizer products cover nitrogenous, potash, time is not possible. Data on agricultural employ- and phosphate fertilizers (including ground rock ment, in particular, should be used with caution. In phosphate). Traditional nutrients--animal and plant many countries much agricultural employment is manures--are not included. The time reference for informal and unrecorded, including substantial work fertilizer consumption is the crop year (July through performed by women and children. June). · Agricultural employment refers to employ- ment in agriculture, forestry, hunting, and fishing (see table 2.3 for more detail). · Agricultural machinery Irrigated lands have increased in all income groups and most regions, putting further pressure refers to wheel and crawler tractors (excluding gar- on water resources den tractors) in use in agriculture at the end of the Irrigated lands (% of cropland) 1989­91 2001­03 calendar year specified or during the first quarter of 30 the following year. 25 20 15 10 5 0 World Low- Lower- Upper- High- income middle-income middle-income income 50 40 30 20 Data sources 10 Data on agricultural inputs are from electronic files 0 Europe & Latin America Middle East & South Sub-Saharan that the FAO makes available to the World Bank Central Asia & Caribbean North Africa Asia Africa and that may contain more recent information than Note: Data are not available for East Asia and Pacific. those published in the FAO's Production Yearbook. Source: Table 3.2. 2006 World Development Indicators 137 Agricultural output and productivity Crop production Food production Livestock production Cereal Agricultural index index index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1992­94 2002­04 1992­94 2002­04 1992­94 2002­04 1993­95 2003­05 1992­94 2002­04 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 85.1 100.6 81.1 105.9 78.7 108.9 2,662 3,491 916 1,469 Algeria 85.3 122.9 84.5 113.2 85.4 103.3 774 1,468 1,743 1,983 Angola 67.9 119.4 71.3 113.0 79.4 100.0 323 547 99 168 Argentina 68.8 106.4 76.0 101.4 92.5 92.0 2,821 3,771 7,335 9,311 Armenia 106.2 119.2 103.5 121.8 96.9 123.2 1,646 2,122 1,464 2,717 Australia 60.2 81.6 73.0 91.7 85.4 96.9 1,706 1,960 20,693 27,058 Austria 89.5 99.1 90.9 99.0 96.3 99.6 5,338 5,738 12,882 21,083 Azerbaijan 115.6 122.7 90.8 118.6 88.1 113.6 1,586 2,633 922 1,061 Bangladesh 74.8 104.7 74.0 104.6 80.2 103.2 2,572 3,533 251 309 Belarus 115.1 124.7 127.7 107.2 135.3 99.7 2,377 2,850 1,964 2,612 Belgium 87.8a 106.0 98.4a 101.3 98.7a 99.7 6,726a 8,710 .. .. Benin 66.0 125.4 67.9 126.3 92.5 109.2 988 1,147 391 599 Bolivia 68.2 116.4 72.6 111.6 77.5 107.9 1,513 1,857 678 749 Bosnia and Herzegovina 97.6 101.1 108.7 96.0 109.4 86.6 3,569 3,393 2,951 5,709 Botswana 95.8 111.5 111.9 104.8 115.4 103.4 325 514 532 409 Brazil 82.1 119.6 76.9 118.0 73.4 116.8 2,384 3,149 1,839 3,111 Bulgaria 116.1 110.9 113.7 101.7 117.7 96.2 2,794 3,157 2,153 6,639 Burkina Faso 76.6 126.6 79.1 116.2 75.6 108.1 858 959 157 166 Burundi 108.0 107.0 108.1 106.3 137.2 100.2 1,329 1,336 115 103 Cambodia 64.1 105.8 66.1 106.3 75.8 103.5 1,520 2,062 276 289 Cameroon 76.2 103.0 78.8 104.2 85.7 103.1 1,034 1,720 720 1,177 Canada 89.1 93.8 84.1 94.8 80.4 103.6 2,647 2,962 29,378 38,509 Central African Republic 76.7 97.7 74.6 106.9 73.7 113.5 902 1,046 292 415 Chad 68.8 110.9 73.9 110.2 84.1 105.4 611 711 189 199 Chile 85.7 110.5 81.5 108.6 76.0 107.0 4,403 5,621 4,235 3,222 China 75.0 110.6 68.5 113.2 61.2 116.1 4,581 5,057 273 373 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 97.5 107.4 85.7 106.8 82.2 107.1 2,552 3,567 3,207 2,751 Congo, Dem. Rep. 127.8 97.2 124.8 97.5 103.2 99.2 778 767 227 197 Congo, Rep. 83.2 105.1 82.2 107.0 79.6 114.5 770 806 295 337 Costa Rica 77.1 99.6 77.6 101.0 82.8 101.4 3,671 4,001 3,364 4,285 Côte d'Ivoire 73.4 96.2 75.2 100.0 78.1 110.9 946 1,266 608 757 Croatia 83.8 97.2 96.7 98.9 116.4 108.2 4,255 4,179 5,189 9,237 Cuba 82.0 112.6 83.2 108.1 98.9 92.7 1,697 3,076 .. .. Czech Republic 97.1 94.8 111.6 96.6 114.9 95.8 4,099 4,816 3,531 4,543 Denmark 94.4 97.7 96.8 100.6 93.9 102.8 5,833 6,080 22,271 37,443 Dominican Republic 114.7 110.0 104.8 106.1 86.1 103.7 3,739 4,177 2,482 4,169 Ecuador 90.7 95.9 80.4 106.5 70.9 115.3 1,983 2,485 1,027 1,478 Egypt, Arab Rep. 74.3 104.2 72.5 107.7 71.0 115.3 5,920 7,528 1,575 2,007 El Salvador 102.7 90.6 87.9 102.9 77.7 108.5 1,883 2,462 1,639 1,607 Eritrea 91.9 67.7 83.5 83.2 76.4 97.1 487 296 91 56 Estonia 115.3 89.9 161.6 100.4 166.6 101.7 1,815 2,274 2,693 3,199 Ethiopia 69.7 106.7 71.5 110.6 78.8 117.7 1,106 1,261 123 118 Finland 91.9 102.4 99.2 103.3 101.3 104.3 3,534 3,284 17,815 31,339 France 92.2 98.8 96.0 99.5 97.4 100.4 6,504 6,876 24,724 40,521 Gabon 86.7 101.9 88.6 101.6 88.5 100.5 1,848 1,641 1,535 1,747 Gambia, The 55.5 65.2 59.7 68.7 102.1 102.6 1,129 1,155 204 204 Georgia 119.4 91.9 98.6 100.9 74.5 110.3 1,978 2,152 2,127 1,442 Germany 83.8 95.1 91.8 97.5 96.8 101.0 5,882 6,497 13,908 23,616 Ghana 69.1 117.0 70.2 116.9 90.9 108.7 1,341 1,437 301 341 Greece 93.9 90.4 97.0 92.2 101.0 98.2 3,717 3,699 8,315 9,303 Guatemala 79.8 102.6 78.7 105.5 80.2 100.6 1,873 1,747 2,178 2,275 Guinea 80.5 107.5 80.0 110.7 68.5 111.8 1,178 1,476 175 229 Guinea-Bissau 73.1 104.9 75.9 105.1 86.1 106.6 1,410 1,149 211 224 Haiti 101.9 98.8 95.7 101.9 76.7 111.6 930 824 682 427 138 2006 World Development Indicators Agricultural output and productivity Crop production Food production Livestock production Cereal Agricultural index index index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1992­94 2002­04 1992­94 2002­04 1992­94 2002­04 1993­95 2003­05 1992­94 2002­04 Honduras 96.6 118.9 90.6 111.9 77.5 105.8 1,393 1,095 992 1,163 Hungary 89.1 99.7 93.6 100.9 101.9 101.9 3,706 4,718 2,825 3,986 India 84.5 100.0 80.8 102.5 74.5 110.5 2,104 2,391 362 391 Indonesia 88.0 112.7 90.3 113.1 99.4 127.3 3,875 4,278 498 564 Iran, Islamic Rep. 83.0 118.1 82.1 113.3 77.6 103.3 1,782 2,411 2,042 2,438 Iraq .. .. .. .. .. .. .. .. .. 2,271 Ireland 89.8 100.3 95.1 96.4 94.8 96.1 6,183 7,390 .. .. Israel 91.5 103.3 81.8 106.5 79.5 113.1 2,678 3,725 .. .. Italy 98.8 92.6 98.4 94.3 95.3 99.4 4,732 5,057 13,672 21,553 Jamaica 102.2 96.7 94.9 97.9 81.9 102.8 1,447 1,162 2,162 1,916 Japan 108.8 95.0 106.9 97.4 107.0 100.2 5,627 5,807 19,958 26,557 Jordan 106.8 136.6 97.1 124.1 92.8 94.1 1,363 1,354 1,810 1,192 Kazakhstan 121.7 108.4 140.6 106.4 178.3 111.6 803 994 1,585 1,420 Kenya 88.8 103.2 86.4 106.4 82.5 110.4 1,711 1,409 301 317 Korea, Dem. Rep. 124.4 108.4 118.5 109.1 113.7 114.2 4,455 3,408 .. .. Korea, Rep. 89.1 91.3 84.2 92.8 80.3 100.4 5,780 6,233 6,257 9,996 Kuwait 39.1 110.6 35.6 122.0 39.2 115.7 5,998 1,975 .. 13,898 Kyrgyz Republic 60.0 102.9 71.0 101.0 101.4 98.4 1,968 2,984 625 942 Lao PDR 63.4 115.3 63.4 115.9 72.1 107.5 2,447 3,180 376 461 Latvia 127.4 119.4 186.1 111.0 219.0 101.1 1,778 2,225 1,566 2,415 Lebanon 116.9 94.1 107.4 100.4 68.1 120.4 2,264 2,377 15,726 25,189 Lesotho 74.1 100.8 94.6 100.4 125.8 100.0 855 906 447 495 Liberia 56.9 97.7 72.9 96.2 89.9 107.8 1,106 889 .. .. Libya 81.9 96.9 79.4 101.6 76.7 101.0 683 626 .. .. Lithuania 88.8 113.1 136.5 111.0 154.3 107.8 1,907 3,183 .. 4,280 Macedonia, FYR 90.9 93.3 94.8 96.2 105.8 103.3 2,571 3,053 2,105 3,034 Madagascar 95.4 103.5 93.4 101.8 95.2 97.1 1,939 2,321 183 174 Malawi 57.7 84.3 49.3 87.1 85.2 101.8 1,233 1,150 73 131 Malaysia 78.1 114.0 78.6 113.7 98.8 115.1 3,051 3,293 3,918 4,690 Mali 75.6 107.4 81.8 105.8 85.2 112.9 797 872 205 229 Mauritania 84.4 97.2 81.8 107.6 80.9 109.3 763 1,075 283 282 Mauritius 107.3 101.6 100.4 104.9 82.4 116.8 3,942 3,436 4,034 4,996 Mexico 83.4 103.8 81.6 105.5 78.3 107.8 2,559 2,872 2,295 2,727 Moldova 147.6 112.2 151.4 112.6 170.3 103.2 3,001 2,572 902 732 Mongolia 173.0 107.3 83.3 96.4 81.4 95.9 780 808 697 661 Morocco 95.2 133.4 91.1 122.6 80.7 102.0 864 1,282 1,275 1,582 Mozambique 60.5 106.1 65.5 103.0 91.5 100.9 579 921 98 142 Myanmar 71.7 114.7 71.9 115.2 68.2 115.1 2,895 3,420 .. .. Namibia 68.6 111.4 104.8 109.8 112.0 109.3 299 441 845 1,097 Nepal 75.2 111.2 77.2 109.4 82.4 107.3 1,841 2,284 191 208 Netherlands 98.3 97.9 107.4 94.8 106.4 92.6 7,644 8,036 27,857 39,358 New Zealand 86.4 101.9 84.3 112.1 85.8 112.1 5,457 7,360 20,319 27,660 Nicaragua 73.7 115.3 69.7 119.4 65.3 119.9 1,731 1,778 1,221 1,916 Niger 73.4 119.5 77.9 116.3 85.9 104.7 311 394 165 172 Nigeria 79.4 103.4 79.6 103.7 82.8 106.6 1,165 1,057 610 863 Norway 113.4 103.4 101.3 98.6 98.1 97.3 3,768 4,121 23,252 32,779 Oman 69.2 87.3 67.0 89.9 75.3 94.0 2,185 2,332 1,000 1,128 Pakistan 80.3 102.5 77.3 106.0 75.6 109.1 1,946 2,438 603 688 Panama 110.2 104.2 96.7 101.8 80.2 101.1 1,863 1,958 2,450 3,570 Papua New Guinea 83.8 101.6 84.5 105.9 85.3 110.1 2,865 3,539 451 482 Paraguay 81.3 120.7 80.1 110.3 92.5 98.2 2,074 2,245 2,165 2,453 Peru 57.4 108.1 61.3 110.7 70.0 114.1 2,745 3,399 1,169 1,764 Philippines 87.3 109.6 81.5 112.2 67.4 120.7 2,263 2,946 907 1,001 Poland 101.3 91.6 99.2 103.6 99.4 105.0 2,781 3,191 1,026 1,408 Portugal 88.1 98.6 90.4 99.1 88.9 98.2 2,142 2,683 4,414 5,735 Puerto Rico 160.5 114.6 121.6 97.8 113.2 94.1 1,548 1,731 .. .. 2006 World Development Indicators 139 Agricultural output and productivity Crop production Food production Livestock production Cereal Agricultural index index index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1992­94 2002­04 1992­94 2002­04 1992­94 2002­04 1993­95 2003­05 1992­94 2002­04 Romania 94.6 112.2 101.1 110.7 112.7 107.6 2,760 3,255 2,312 3,519 Russian Federation 113.9 116.0 122.0 110.2 142.8 103.2 1,439 1,839 1,746 2,297 Rwanda 86.5 117.6 85.0 117.2 74.3 107.3 1,208 989 183 229 Saudi Arabia 126.0 114.8 100.6 116.0 70.4 104.9 4,264 4,430 8,905 14,284 Senegal 72.3 68.3 74.1 74.9 83.9 98.2 820 1,013 236 235 Serbia and Montenegro 99.8 110.0 106.1 106.0 101.7 94.9 3,385 4,056 .. 1,446 Sierra Leone 123.0 113.5 114.7 112.3 88.6 105.2 1,181 1,223 .. .. Singapore 116.1 100.0 240.4 69.3 235.0 74.2 .. .. 28,729 32,267 Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia 101.1 110.2 86.4 105.8 83.1 103.6 4,433 5,247 12,494 32,311 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 80.9 102.4 85.2 105.7 93.8 108.2 2,052 2,907 1,764 2,414 Spain 81.6 106.1 83.6 105.3 81.0 107.2 2,267 3,040 12,611 19,132 Sri Lanka 89.4 98.8 94.4 100.0 107.5 109.9 2,993 3,428 713 743 Sudan 80.8 110.8 77.6 107.6 75.4 106.3 479 481 364 688 Swaziland 94.5 100.1 106.1 105.3 140.4 111.9 1,607 1,114 1,040 1,161 Sweden 94.0 102.1 96.3 100.0 97.2 97.7 4,336 4,835 21,654 31,716 Switzerland 112.2 95.3 104.3 100.1 103.3 101.9 6,220 6,150 21,565 22,190 Syrian Arab Republic 83.9 117.1 81.0 122.2 69.6 115.6 1,558 1,786 2,356 2,977 Tajikistan 119.0 132.9 128.5 132.6 176.0 139.2 994 2,252 367 401 Tanzania 87.9 103.6 87.0 105.0 87.7 109.4 1,292 1,469 242 287 Thailand 85.6 106.1 88.6 106.0 98.9 105.5 2,383 2,725 481 599 Togo 81.4 110.3 81.6 104.2 83.6 106.7 816 1,040 360 409 Trinidad and Tobago 116.8 91.9 91.7 122.1 76.1 142.6 3,533 2,722 1,748 2,368 Tunisia 91.5 104.2 84.9 103.0 66.7 99.9 1,069 1,539 2,365 2,415 Turkey 88.1 104.0 90.2 103.2 94.6 101.6 2,068 2,399 1,772 1,793 Turkmenistan 115.6 116.5 68.4 125.2 71.4 121.7 2,215 3,011 1,179 .. Uganda 81.4 106.6 82.5 107.7 86.1 112.9 1,536 1,667 192 231 Ukraine 127.5 114.0 134.8 108.1 155.5 108.1 2,881 2,436 1,235 1,442 United Arab Emirates 31.8 56.0 34.1 62.2 65.5 116.9 1,485 3,119 11,659 34,739 United Kingdom 102.3 100.3 106.3 98.9 106.1 98.5 6,618 7,097 23,089 26,897 United States 92.2 101.5 88.7 102.7 87.1 102.6 4,836 6,444 22,868 36,863 Uruguay 78.7 112.7 82.2 104.3 88.3 98.3 2,880 4,279 6,213 7,102 Uzbekistan 105.1 109.0 92.6 107.9 103.1 104.7 1,730 3,461 1,263 1,567 Venezuela, RB 79.9 96.0 77.3 98.9 79.2 100.4 2,949 3,329 4,781 5,899 Vietnam 67.0 116.6 70.1 118.3 64.7 124.9 3,463 4,651 225 294 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 83.0 100.1 79.1 107.4 73.7 115.5 1,102 772 383 511 Zambia 88.8 102.4 92.5 104.1 87.3 99.2 1,684 1,584 160 206 Zimbabwe 72.0 69.3 75.2 85.7 86.4 100.1 1,154 676 238 242 World 85.0 w 105.7 w 84.8 w 106.2 w 85.7 w 107.0 w 2,789 w 3,254 w 770 w 864 w Low income 82.4 103.5 80.6 105.2 77.8 109.6 1,820 2,083 336 378 Middle income 83.4 110.2 82.6 110.5 83.4 111.0 2,815 3,318 576 719 Lower middle income 81.4 111.7 78.0 112.5 74.2 114.1 3,217 3,644 456 588 Upper middle income 90.8 104.9 97.3 104.3 107.5 102.7 2,028 2,619 2,227 2,650 Low & middle income 83.1 108.1 82.0 109.0 82.0 110.6 2,420 2,787 477 568 East Asia & Pacific 77.0 110.8 72.1 112.4 63.8 116.6 4,031 4,466 .. .. Europe & Central Asia 105.9 107.1 114.7 106.1 128.8 104.1 1,902 2,331 1,602 1,914 Latin America & Carib. 80.7 111.5 79.1 110.4 78.7 108.9 2,493 3,159 2,234 2,812 Middle East & N. Africa 84.1 113.7 80.9 112.5 74.8 107.7 1,877 2,439 1,589 1,975 South Asia 83.8 101.0 80.3 103.5 75.0 109.8 2,128 2,505 364 401 Sub-Saharan Africa 80.3 103.9 81.7 105.1 85.9 107.1 1,034 1,087 294 341 High income 91.0 98.2 91.1 99.9 91.7 101.2 4,297 5,115 .. .. Europe EMU 90.6 97.8 93.3 98.8 96.2 99.7 4,855 5,427 14,186 21,695 a. Includes Luxembourg. 140 2006 World Development Indicators Agricultural output and productivity About the data Definitions The agricultural production indexes in the table are the following year. Cereal crops harvested for hay or · Crop production index shows agricultural pro- prepared by the Food and Agriculture Organization harvested green for food, feed, or silage, and those duction for each period relative to the base period (FAO). The FAO obtains data from official and semiof- used for grazing, are generally excluded. But millet 1999­2001. It includes all crops except fodder ficial reports of crop yields, area under production, and sorghum, which are grown as feed for livestock crops. The regional and income group aggregates and livestock numbers. If data are not available, the and poultry in Europe and North America, are used for the FAO's production indexes are calculated from FAO makes estimates. The indexes are calculated as food in Africa, Asia, and countries of the former the underlying values in international dollars, normal- using the Laspeyres formula: production quantities Soviet Union. So some cereal crops are excluded ized to the base period 1999­2001. The data in of each commodity are weighted by average inter- from the data for some countries and included else- this table are three-year averages. · Food produc- national commodity prices in the base period and where, depending on their use. tion index covers food crops that are considered summed for each year. Because the FAO's indexes Agricultural productivity is measured by value edible and that contain nutrients. Coffee and tea are based on the concept of agriculture as a single added per unit of input. (For further discussion of the are excluded because, although edible, they have enterprise, estimates of the amounts retained for calculation of value added in national accounts, see no nutritive value. · Livestock production index seed and feed are subtracted from the production About the data for tables 4.1 and 4.2.) Agricultural includes meat and milk from all sources, dairy prod- data to avoid double counting. The resulting aggre- value added includes that from forestry and fishing. ucts such as cheese, and eggs, honey, raw silk, wool, gate represents production available for any use Thus interpretations of land productivity should be and hides and skins. · Cereal yield, measured in except as seed and feed. The FAO's indexes may made with caution. To smooth annual fluctuations in kilograms per hectare of harvested land, includes differ from other sources because of differences in agricultural activity, the indicators in the table have wheat, rice, maize, barley, oats, rye, millet, sorghum, coverage, weights, concepts, time periods, calcula- been averaged over three years. buckwheat, and mixed grains. Production data on tion methods, and use of international prices. cereals refer to crops harvested for dry grain only. To ease cross-country comparisons, the FAO uses Cereal crops harvested for hay or harvested green international commodity prices to value production. for food, feed, or silage, and those used for grazing, These prices, expressed in international dollars are excluded. · Agricultural productivity refers to (equivalent in purchasing power to the U.S. dollar), are the ratio of agricultural value added, measured in derived using a Geary-Khamis formula applied to agri- constant 2000 U.S. dollars, to the number of work- cultural outputs (see Inter-Secretariat Working Group ers in agriculture. on National Accounts 1993, sections 16.93­96). This method assigns a single price to each commodity so that, for example, one metric ton of wheat has the same price regardless of where it was produced. The use of international prices eliminates fluctuations in the value of output due to transitory movements of nominal exchange rates unrelated to the purchasing power of the domestic currency. Data on cereal yield may be affected by a variety of reporting and timing differences. The FAO allo- cates production data to the calendar year in which the bulk of the harvest took place. But most of a crop harvested near the end of a year will be used in The 10 countries with the highest cereal yield in 2003­05--and the 10 with the lowest Kilograms Kilograms Country per hectare Country per hectare Data sources Belgium 8,710 Eritrea 296 Netherlands 8,036 Niger 394 The agricultural production indexes are prepared Egypt, Arab Rep. 7,528 Namibia 441 by the FAO and published annually in its Produc- Ireland 7,390 Sudan 481 tion Yearbook. The FAO makes these data and the New Zealand 7,360 Botswana 514 data on cereal yield and agricultural employment United Kingdom 7,097 Angola 547 France 6,876 Libya 626 available to the World Bank in electronic files that Germany 6,497 Zimbabwe 676 may contain more recent information than the pub- United States 6,444 Chad 711 lished versions. For sources of data on agricultural Korea, Rep. 6,233 Congo, Dem. Rep. 767 value added, see Data sources for table 4.2. Source: Table 3.3. 2006 World Development Indicators 141 Deforestation and biodiversity Average annual Mammals Birds Higher GEF Nationally Marine protected deforestationa plantsb benefits protected areas areas index for biodiversity thousand % of % of sq. km % Total known Threatened Total known Threatened Total known Threatened thousand total land thousand surface 1990­ 1990­ species species species species species species sq. km area sq. km area 2005 2005 2004 2004 2004 2004 2004 2004 2005 2004c 2004 c 2004c 2004 c Afghanistan 295 2.3 144 12 434 17 4,000 1 24.1 2.0 0.3 .. .. Albania ­3 0.0 73 1 303 9 3,031 0 1.3 1.0 3.8 0.3 1.0 Algeria ­325 ­1.8 100 12 372 11 3,164 2 19.9 119.1 5.0 0.9 0.0 Angola 1,248 0.2 296 11 930 20 5,185 26 63.4 82.3 6.6 29.1 2.3 Argentina 1,494 0.4 375 32 1,038 55 9,372 42 122.9 180.6 6.6 7.8 0.3 Armenia 42 1.2 78 9 302 12 3,553 1 1.7 2.1 7.6 .. .. Australia 2,817 0.2 376 63 851 60 15,638 56 635.5 1,029.4 13.4 680.8 8.8 Austria ­57 ­0.2 101 5 412 8 3,100 3 2.3 27.3 33.1 .. .. Azerbaijan 0 0.0 82 11 364 11 4,300 0 5.7 5.3 6.4 1.2 1.4 Bangladesh 7 0.1 131 22 604 23 5,000 12 10.5 1.0 0.8 0.3 0.2 Belarus ­345 ­0.5 71 6 226 4 2,100 0 0.1 13.1 6.3 .. .. Belgiumd 7 0.1 92 9 427 10 1,550 0 0.1 .. .. 0.0 0.0 Benin 647 2.0 159 6 485 2 2,500 14 1.6 12.6 11.4 .. .. Bolivia 2,703 0.4 361 26 1,414 30 17,367 70 91.9 145.3 13.4 .. .. Bosnia and Herzegovina 17 0.1 78 8 312 8 .. 1 2.5 0.3 0.5 .. .. Botswana 1,183 0.9 169 6 570 9 2,151 0 9.9 104.9 18.5 .. .. Brazil 28,219 0.5 578 74 1,712 120 56,215 381 663.7 566.6 6.7 47.5 0.6 Bulgaria ­199 ­0.6 106 12 379 11 3,572 0 6.1 5.0 4.5 0.0 0.0 Burkina Faso 240 0.3 129 6 452 2 1,100 2 1.9 31.5 11.5 .. .. Burundi 91 3.2 116 7 597 9 2,500 2 3.3 1.5 5.7 .. .. Cambodia 1,666 1.3 127 23 521 24 .. 31 25.8 32.7 18.5 1.9 1.1 Cameroon 2,200 0.9 322 42 936 18 8,260 334 88.4 20.9 4.5 3.9 0.8 Canada 0 0.0 211 16 472 19 3,270 1 147.3 1,023.5 11.3 362.7 3.6 Central African Republic 299 0.1 187 11 663 3 3,602 15 11.0 54.2 8.7 .. .. Chad 793 0.6 104 12 531 5 1,600 2 14.1 114.6 9.1 .. .. Chile ­572 ­0.4 159 22 445 32 5,284 40 107.3 141.5 18.9 114.5 15.1 China ­26,766 ­1.7 502 80 1,221 82 32,200 443 430.4 727.5 7.8 16.0 0.2 Hong Kong, China .. .. 57 1 306 20 .. 6 .. .. .. 0.3 26.5 Colombia 474 0.1 467 39 1,821 86 51,220 222 380.0 106.0 10.2 8.1 0.7 Congo, Dem. Rep. 4,614 0.3 430 29 1,148 30 11,007 65 113.0 113.4 5.0 .. .. Congo, Rep. 170 0.1 166 14 597 4 6,000 35 22.8 22.2 6.5 .. .. Costa Rica 115 0.5 232 13 838 18 12,119 110 73.6 11.7 23.0 4.8 9.4 Côte d'Ivoire ­122 ­0.1 229 23 702 11 3,660 105 25.7 19.1 6.0 0.3 0.1 Croatia ­13 ­0.1 96 7 365 9 4,288 0 3.6 4.2 7.5 2.5 4.4 Cuba ­437 ­2.1 65 11 358 18 6,522 163 89.8 75.9 69.1 31.7 28.6 Czech Republic ­12 ­0.1 88 6 386 9 1,900 4 0.9 12.4 16.1 .. .. Denmark ­37 ­0.8 81 4 427 10 1,450 3 1.1 14.4 34.0 5.1 11.8 Dominican Republic 0 0.0 36 5 224 16 5,657 30 45.0 25.1 51.9 8.6 17.6 Ecuador 1,976 1.4 341 34 1,515 69 19,362 1 199.4 50.7 18.3 141.0 49.7 Egypt, Arab Rep. ­15 ­3.5 118 6 481 17 2,076 2 21.5 96.6 9.7 76.7 7.7 El Salvador 51 1.4 137 2 434 3 2,911 25 5.5 0.1 0.4 0.1 0.4 Eritrea 45 0.3 70 9 537 7 .. 3 6.0 4.3 4.3 .. .. Estonia ­81 ­0.4 67 4 267 3 1,630 0 0.3 5.0 11.8 .. .. Ethiopia 1,409 0.9 288 35 839 20 6,603 22 56.7 169.0 16.9 .. .. Finland ­204 ­0.1 80 3 421 10 1,102 1 1.2 28.3 9.3 1.1 0.3 France ­677 ­0.5 148 16 517 15 4,630 2 26.1 73.2 13.3 0.5 0.1 Gabon 101 0.1 166 11 632 5 6,651 107 22.8 1.8 0.7 1.0 0.4 Gambia, The ­19 ­0.4 133 3 535 2 974 4 0.7 0.2 2.3 0.2 2.0 Georgia 0 0.0 98 11 268 8 4,350 0 4.6 1.6 2.3 0.0 0.1 Germany ­223 ­0.2 126 9 487 14 2,682 12 4.4 113.8 32.6 9.1 2.6 Ghana 1,287 1.7 249 15 729 8 3,725 117 13.0 12.7 5.6 .. .. Greece ­302 ­0.9 118 11 412 14 4,992 2 20.0 4.6 3.6 2.5 1.9 Guatemala 540 1.1 193 7 684 10 8,681 85 58.9 21.7 20.0 0.1 0.1 Guinea 456 0.6 215 18 640 10 3,000 22 17.0 1.7 0.7 .. .. Guinea-Bissau 96 0.4 101 5 459 1 1,000 4 4.6 .. .. .. .. Haiti 7 0.6 41 4 271 15 5,242 28 38.4 0.1 0.4 .. .. 142 2006 World Development Indicators Deforestation and biodiversity Average annual Mammals Birds Higher GEF Nationally Marine protected deforestationa plantsb benefits protected areas areas index for biodiversity thousand % of % of sq. km % Total known Threatened Total known Threatened Total known Threatened thousand total land thousand surface 1990­ 1990­ species species species species species species sq. km area sq. km area 2005 2005 2004 2004 2004 2004 2004 2004 2005 2004c 2004 c 2004c 2004 c Honduras 1,825 2.5 201 10 699 6 5,680 111 52.7 7.2 6.4 1.9 1.7 Hungary ­117 ­0.7 88 7 367 9 2,214 1 1.2 6.5 7.0 .. .. India ­2,508 ­0.4 422 85 1,180 79 18,664 246 291.3 154.6 5.2 16.1 0.5 Indonesia 18,715 1.6 667 146 1,604 121 29,375 383 597.0 373.2 20.6 130.1 6.8 Iran, Islamic Rep. 0 0.0 158 21 498 18 8,000 1 52.2 78.5 4.8 6.2 0.4 Iraq ­12 ­0.2 102 9 396 18 .. 0 11.2 0.0 0.0 .. .. Ireland ­152 ­3.5 63 4 408 8 950 1 5.0 1.2 1.7 0.0 0.0 Israel ­11 ­0.7 115 13 534 18 2,317 0 5.7 3.3 15.0 0.1 0.6 Italy ­1,064 ­1.3 132 12 478 15 5,599 3 28.9 23.2 7.9 1.5 0.5 Jamaica 4 0.1 35 5 298 12 3,308 208 32.8 .. .. 8.2 74.5 Japan 55 0.0 171 37 592 53 5,565 12 274.6 24.8 6.8 10.6 2.8 Jordan 0 0.0 93 7 397 14 2,100 0 2.3 3.0 3.4 0.0 0.1 Kazakhstan 57 0.2 145 15 497 23 6,000 1 36.1 72.9 2.7 0.5 0.0 Kenya 124 0.3 407 33 1,103 28 6,506 103 65.9 45.5 8.0 3.1 0.5 Korea, Dem. Rep. 1,343 1.6 105 12 369 22 2,898 3 4.7 3.1 2.6 .. .. Korea, Rep. 71 0.1 89 12 423 34 2,898 0 12.2 6.8 6.9 3.5 3.5 Kuwait ­2 ­6.7 23 1 358 12 234 0 0.9 0.3 1.5 0.3 1.5 Kyrgyz Republic ­22 ­0.3 58 6 207 4 4,500 1 7.8 28.9 15.0 .. .. Lao PDR 781 0.5 215 30 704 21 8,286 19 35.7 6.9 3.0 .. .. Latvia ­111 ­0.4 68 4 325 8 1,153 0 0.3 8.3 13.4 0.2 0.2 Lebanon ­10 ­0.8 70 5 377 10 3,000 0 1.2 0.1 0.5 0.0 0.1 Lesotho ­2 ­4.0 59 3 311 7 1,591 1 2.0 0.1 0.2 .. .. Liberia 603 1.5 183 20 576 11 2,200 46 19.5 1.6 1.7 0.6 0.5 Libya 0 0.0 87 5 326 7 1,825 1 11.5 1.8 0.1 0.5 0.0 Lithuania ­103 ­0.5 71 5 227 4 1,796 0 0.2 6.7 10.7 0.5 0.8 Macedonia, FYR 0 0.0 89 9 291 9 3,500 0 1.5 1.8 7.1 .. .. Madagascar 569 0.4 165 49 262 34 9,505 276 208.7 25.0 4.3 0.2 0.0 Malawi 329 0.9 207 7 658 13 3,765 14 26.1 10.5 11.2 .. .. Malaysia 991 0.4 337 50 746 40 15,500 683 98.5 18.7 5.7 5.0 1.5 Mali 1,000 0.7 134 12 624 5 1,741 6 10.3 45.2 3.7 .. .. Mauritania 99 2.4 94 7 521 5 1,100 0 9.5 17.4 1.7 15.0 1.5 Mauritius 1 0.3 14 3 137 13 750 87 27.9 .. .. 0.1 4.4 Mexico 3,185 0.5 544 72 1,026 57 26,071 261 503.1 194.7 10.2 82.1 4.2 Moldova ­7 ­0.2 50 4 203 8 1,752 0 0.1 0.5 1.4 .. .. Mongolia 827 0.7 140 13 387 22 2,823 0 29.5 180.2 11.5 .. .. Morocco ­50 ­0.1 129 12 430 13 3,675 2 26.5 3.1 0.7 0.5 0.1 Mozambique 500 0.3 228 12 685 23 5,692 46 54.4 65.9 8.4 22.5 2.8 Myanmar 4,665 1.2 288 39 1,047 41 7,000 38 70.5 2.0 0.3 0.2 0.0 Namibia 734 0.8 192 10 619 18 3,174 24 39.1 112.0 13.6 74.0 9.0 Nepal 787 1.6 203 29 .. 31 6,973 7 14.9 12.7 8.9 .. .. Netherlands ­13 ­0.4 95 9 444 11 1,221 0 0.6 4.8 14.2 0.8 1.9 New Zealand ­393 ­0.5 73 8 351 74 2,382 21 147.8 79.3 29.6 22.7 8.4 Nicaragua 899 1.4 181 6 632 8 7,590 39 23.7 21.6 17.8 1.3 1.0 Niger 453 2.3 123 10 493 2 1,460 2 6.0 97.5 7.7 .. .. Nigeria 4,097 2.4 290 25 899 9 4,715 170 43.6 30.1 3.3 .. .. Norway ­171 ­0.2 83 9 442 6 1,715 2 10.7 20.9 6.8 1.3 0.4 Oman 0 0.0 74 12 483 14 1,204 6 29.3 43.3 14.0 29.6 9.6 Pakistan 417 1.7 195 17 625 30 4,950 2 33.6 37.8 4.9 2.2 0.3 Panama 55 0.1 241 17 904 20 9,915 195 78.0 16.2 21.7 10.0 13.3 Papua New Guinea 1,391 0.4 260 58 720 33 11,544 142 183.7 10.4 2.3 3.5 0.8 Paraguay 1,788 0.9 168 11 696 27 7,851 10 22.2 13.9 3.5 .. .. Peru 943 0.1 441 46 1,781 94 17,144 274 241.0 78.1 6.1 3.4 0.3 Philippines 2,275 2.2 222 50 590 70 8,931 212 224.0 17.0 5.7 16.6 5.5 Poland ­207 ­0.2 110 12 424 12 2,450 4 3.8 37.8 12.3 0.7 0.2 Portugal ­456 ­1.5 105 15 501 15 5,050 15 25.1 6.0 6.6 2.0 2.2 Puerto Rico ­3 ­0.1 38 2 310 12 2,493 52 25.1 .. .. 1.7 19.1 2006 World Development Indicators 143 Deforestation and biodiversity Average annual Mammals Birds Higher GEF Nationally Marine protected deforestationa plantsb benefits protected areas areas index for biodiversity thousand % of % of sq. km % Total known Threatened Total known Threatened Total known Threatened thousand total land thousand surface 1990­ 1990­ species species species species species species sq. km area sq. km area 2005 2005 2004 2004 2004 2004 2004 2004 2005 2004c 2004 c 2004c 2004 c Romania 1 0.0 101 15 365 13 3,400 1 .. 10.8 4.7 6.1 2.6 Russian Federation 107 0.0 296 43 645 47 11,400 7 246.4 1,317.3 8.0 301.8 1.8 Rwanda ­108 ­3.4 206 13 665 9 2,288 3 7.0 1.5 6.2 .. .. Saudi Arabia 0 0.0 94 9 433 17 2,028 3 22.8 823.3 38.3 5.2 0.2 Senegal 450 0.5 191 11 612 5 2,086 7 8.5 22.3 11.6 0.9 0.4 Serbia and Montenegro ­90 ­0.4 96 10 381 10 4,082 1 .. .. .. 0.1 0.1 Sierra Leone 193 0.6 197 12 626 10 2,090 47 10.1 1.5 2.1 .. .. Singapore 0 0.0 73 3 400 10 2,282 54 1.0 0.0 4.5 0.0 0.2 Slovak Republic ­5 0.0 87 7 332 11 3,124 2 0.8 .. .. .. .. Slovenia ­51 ­0.4 87 7 350 7 3,200 0 1.1 1.2 6.0 0.0 0.0 Somalia 767 0.9 182 15 642 13 3,028 17 44.2 5.0 0.8 3.3 0.5 South Africa 0 0.0 320 29 829 36 23,420 75 156.1 67.2 5.5 3.4 0.3 Spain ­2,957 ­2.2 132 20 515 20 5,050 14 44.0 42.5 8.5 1.8 0.4 Sri Lanka 278 1.2 123 21 381 16 3,314 280 43.9 8.7 13.5 2.3 3.5 Sudan 5,890 0.8 302 16 952 10 3,137 17 36.4 123.6 5.2 0.3 0.0 Swaziland ­46 ­1.0 124 6 490 6 2,715 11 0.9 .. .. .. .. Sweden ­107 0.0 85 5 457 9 1,750 3 2.2 37.5 9.1 4.3 1.0 Switzerland ­44 ­0.4 93 4 382 8 3,030 2 1.7 11.9 29.7 .. .. Syrian Arab Republic ­59 ­1.6 82 3 350 11 3,000 0 6.2 .. .. .. .. Tajikistan ­1 0.0 76 7 351 9 5,000 2 4.9 5.9 4.2 .. .. Tanzania 4,123 1.0 375 34 1,056 37 10,008 239 100.4 263.3 29.8 2.3 0.2 Thailand 963 0.6 300 36 971 42 11,625 84 53.0 71.0 13.9 5.8 1.1 Togo 199 2.9 175 7 565 2 3,085 10 2.5 4.3 7.9 .. .. Trinidad and Tobago 6 0.3 116 1 435 2 2,259 1 16.0 0.3 6.0 0.1 1.3 Tunisia ­275 ­4.3 78 10 360 9 2,196 0 3.5 0.5 0.3 0.2 0.1 Turkey ­330 ­0.3 145 15 436 14 8,650 3 39.6 12.3 1.6 4.5 0.6 Turkmenistan 0 0.0 103 12 318 13 .. 0 13.0 19.7 4.2 .. .. Uganda 865 1.8 360 29 1,015 15 4,900 38 22.1 48.5 24.6 .. .. Ukraine ­201 ­0.2 120 14 325 13 5,100 1 2.8 22.6 3.9 3.1 0.5 United Arab Emirates ­45 ­1.8 30 5 268 11 .. 0 1.4 0.0 0.0 .. .. United Kingdom ­156 ­0.6 103 10 557 10 1,623 13 14.2 50.3 20.8 22.5 9.2 United States ­2,961 ­0.1 468 40 888 71 19,473 240 599.1 2,372.2 25.9 909.5 9.5 Uruguay ­401 ­4.4 118 6 414 24 2,278 1 9.5 0.5 0.3 0.1 0.0 Uzbekistan ­167 ­0.6 91 7 343 16 4,800 1 7.9 8.3 2.0 .. .. Venezuela, RB 2,875 0.6 353 26 1,392 25 21,073 67 178.2 562.8 63.8 21.3 2.3 Vietnam ­2,379 ­2.5 279 41 837 41 10,500 145 77.4 12.0 3.7 0.7 0.2 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 0 0.0 74 6 385 14 1,650 159 22.3 .. .. .. .. Zambia 4,448 0.9 255 11 770 12 4,747 8 33.4 237.1 31.9 .. .. Zimbabwe 3,129 1.4 222 8 661 10 4,440 17 13.7 46.8 12.1 .. .. World 83,484 s 0.1 w .. .. .. .. .. .. .. 13,740.8 s 10.7 w 4,348.9 s 3.0 w Low income 48,343 0.5 .. .. .. .. .. .. .. 2,203.2 7.7 79.0 .. Middle income 42,278 0.1 .. .. .. .. .. .. .. 5,703.8 8.5 1,228.0 1.9 Lower middle income 33,970 0.2 .. .. .. .. .. .. .. 2,947.4 7.7 633.5 1.7 Upper middle income 8,308 0.1 .. .. .. .. .. .. .. 2,756.4 .. 594.5 2.1 Low & middle income 90,621 0.2 .. .. .. .. .. .. .. 7,907.0 8.3 1,307.0 1.6 East Asia & Pacific 4,939 ­0.2 .. .. .. .. .. .. .. 1,454.8 .. 192.1 1.3 Europe & Central Asia ­1,789 0.0 .. .. .. .. .. .. .. 1,610.2 6.9 321.6 1.4 Latin America & Carib. 45,753 0.4 .. .. .. .. .. .. .. 2,228.7 11.1 495.8 2.7 Middle East & N. Africa ­747 ­0.5 .. .. .. .. .. .. .. 345.9 4.2 114.7 1.5 South Asia ­831 ­0.2 .. .. .. .. .. .. .. 228.6 4.8 20.9 0.5 Sub-Saharan Africa 43,296 0.6 .. .. .. .. .. .. .. 2,038.8 8.7 162.0 .. High income ­7,137 ­0.1 .. .. .. .. .. .. .. 5,833.9 .. 3,041.9 6.1 Europe EMU ­6,101 ­0.8 .. .. .. .. .. .. .. 324.9 13.5 19.5 0.8 a. Negative numbers indicate an increase in forest area. b. Flowering plants only. c. Data may refer to earlier years. They are the most recent reported by the World Conservation Monitoring Centre in 2004. d. Includes Luxembourg. 144 2006 World Development Indicators Deforestation and biodiversity About the data Definitions Biological diversity is defined in terms of the variabil- Research Group and in close collaboration with sci- · Average annual deforestation refers to the perma- ity in genes, species, and ecosystems. Faced with entific nongovernmental organizations, the Global nent conversion of natural forest area to other uses, mounting threats to biodiversity, the international Environment Facility (GEF) developed the GEF ben- including shifting cultivation, permanent agriculture, community has increasingly focused on conserving efits index for biodiversity, a comprehensive indicator ranching, settlements, and infrastructure develop- this diversity. Deforestation is a major cause of loss of of national biodiversity status, to guide its biodiversity ment. Deforested areas do not include areas logged biodiversity, and habitat conservation is vital for stem- priorities. This indicator incorporates information on but intended for regeneration or areas degraded by ming this loss. Conservation efforts traditionally have individual species range maps available from the IUCN fuelwood gathering, acid precipitation, or forest fires. focused on protecting areas of high biodiversity. for virtually all mammals (4,612), amphibians (5,327), Negative numbers indicate an increase in forest The estimates of forest area are from the Food and endangered birds (1,098); country-level data from area. · Mammals exclude whales and porpoises. and Agriculture Organization's (FAO) Global Forest the World Resources Institute (WRI) for reptiles and · Birds are listed for countries included within their Resources Assessment, which provides information vascular plants; country-level data from FishBase for breeding or wintering ranges. · Higher plants refer to on forest cover in 2005 and estimates of forest cover 27,669 fish species; and the ecological characteristics native vascular plant species. · Threatened species in 1990 and 2000. The current survey is the latest of 867 terrestial ecoregions of the world from World are the number of species classified by the IUCN global forest assessment and uses a uniform global Wildlife Federation (WWF) International. For each as endangered, vulnerable, rare, indeterminate, out definition of forest. No breakdown of forest cover country the biodiversity indicator incorporates the best of danger, or insufficiently known. · GEF benefits between natural forest and plantation is shown in the available and comparable information in four relevant index for biodiversity is a composite index of rela- table because of space limitations. (This breakdown dimensions: represented species, threatened species, tive biodiversity potential for each country based on is provided by the FAO only for developing countries.) represented ecoregions, and threatened ecoregions. the species represented in each country, their threat For this reason the deforestation data in the table To combine these dimensions into one measure, the status, and the diversity of habitat types in each may underestimate the rate at which natural forest indicator uses dimensional weights that reflect the country. · Nationally protected areas are totally or is disappearing in some countries. consensus of conservation scientists in the GEF, partially protected areas of at least 1,000 hectares Measures of species richness are among the most IUCN, WWF International, and other nongovernmental that are designated as scientific reserves with lim- straightforward ways to indicate the importance of an organizations. Each unit of the index represents 0.01 ited public access, national parks, natural monu- area for biodiversity. The number of threatened spe- percent of the total global biodiversity value, ments, nature reserves or wildlife sanctuaries, and cies is also an important measure of the immediate The table shows information on protected areas, protected landscapes. Marine areas, unclassified need for conservation efforts in a geographic area. numbers of certain species, and numbers of those areas, and litoral (intertidal) areas are not included. Global analyses of the status of threatened species species under threat. The World Conservation The data also do not include sites protected under have been carried out for few groups of organisms. Monitoring Centre (WCMC) compiles these data local or provincial law. Total land area is used to Only for mammals, birds, and amphibians has the from a variety of sources. Because of differences calculate the percentage of total area protected (see status of virtually all known species been assessed. in definitions and reporting practices, cross-country table 3.1). · Marine protected areas are areas of Threatened species are defined according to the World comparability is limited. Compounding these prob- intertidal or subtidal terrain--and overlying water Conservation Union's (IUCN) classification categories: lems, available data cover different periods. and associated flora and fauna and historical and endangered (in danger of extinction and unlikely to sur- Nationally protected areas are areas of at least cultural features--which have been reserved by law vive if causal factors continue operating), vulnerable 1,000 hectares that fall into one of five management or other effective means to protect part or all of the (likely to move into the endangered category in the categories defined by the WCMC: enclosed environment. near future if causal factors continue operating), rare · Scientific reserves and strict nature reserves (not endangered or vulnerable but at risk), indetermi- with limited public access. nate (known to be endangered, vulnerable, or rare but · National parks of national or international not enough information is available to say which), out significance and not materially affected by of danger (formerly included in one of the above cat- human activity. egories but now considered relatively secure because · Natural monuments and natural landscapes with appropriate conservation measures are in effect), unique aspects. and insufficiently known (suspected but not definitely · Managed nature reserves and wildlife sanctu- known to belong to one of the above categories). aries. While the number of birds and mammals is fairly · Protected landscapes (which may include cul- well known, it is difficult to make an accurate count of tural landscapes) and areas managed mainly plants. The number of plant species is highly debated. for the sustainable use of natural systems to The IUCN's 2004 IUCN Red List of Threatened Plants ensure long-term protection and maintenance provides the most comprehensive list of threatened of biological diversity. Data sources species on a global scale, the result of more than 20 Designating land as a protected area does not nec- Data on deforestation are from the FAO's Global years' work by botanists from around the world. Only 5 essarily mean that protection is in force. For small Forest Resources Assessment 2005. Data on spe- percent of plant species have been evaluated, and 70 countries that may only have protected areas smaller cies are from the WCMC's electronic files and the percent of these are threatened with extinction. Plant than 1,000 hectares, this size limit in the definition IUCN's 2002 IUCN Red List of Threatened Animals species data should be interpreted with caution since will result in an underestimate of the extent and num- and 1997 IUCN Red List of Threatened Plants. The they are not necessarily comparable across countries ber of protected areas. GEF benefits index for biodiversity is from Kiran because of differences in taxonomic concepts and The dataset on protected areas is tentative and is Dev Pandey, Piet Buys, Ken Chomitz, and David coverage. However, they do identify countries that are being revised. Due to variations in consistency and meth- Wheeler's, "Biodiversity Conservation Indicators: major sources of global biodiversity and that show odology of collection, the quality of the data are highly New Tools for Priority Setting at the Global Environ- national commitments to habitat protection. variable across countries. Some countries update their ment Facility" (2006). Data on protected areas are Setting priorities for conserving biodiversity requires information more frequently than others, some may have from the United Nations Environment Programme a broader set of information than species richness. more accurate data on extent of coverage, and many and WCMC. With the support of the World Bank's Development underreport the number or extent of protected areas. 2006 World Development Indicators 145 Freshwater Renewable internal Annual freshwater Water freshwater resourcesa withdrawalsb productivity GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per cu. m billion cu. m cu. m cu. m resources agriculture industry domestic Total Agriculture Industry 2004 2004 1987­2002 1987­2002 1987­2002 1987­2002 1987­2002 1987­2004 1987­2004 1987­2004 Afghanistan 55 .. 23.3 42.3 98 0 2 .. .. .. Albania 27 8,645 1.7 6.4 62 11 27 2.4 1.0 3.7 Algeria 11 348 6.1 54.0 65 13 22 9.4 1.3 38.4 Angola 148 9,555 0.4 0.2 60 17 23 30.8 3.3 130.7 Argentina 276 7,193 29.2 10.6 74 10 17 8.3 0.6 21.6 Armenia 9 2,998 3.0 32.5 66 4 30 0.8 0.3 6.1 Australia 492 24,464 23.9 4.9 75 10 15 17.4 0.6 41.2 Austria 55 6,729 2.1 3.8 1 64 35 93.6 183.0 40.8 Azerbaijan 8 977 17.3 212.6 68 28 5 0.4 0.1 0.6 Bangladesh 105 754 79.4 75.6 96 1 3 0.6 0.2 25.3 Belarus 37 3,786 2.8 7.5 30 47 23 5.0 1.9 3.7 Belgium 12 1,152 .. .. .. .. .. .. .. .. Benin 10 1,260 0.1 1.3 45 23 32 19.3 15.4 12.1 Bolivia 304 33,692 1.4 0.5 81 7 13 6.1 1.0 22.5 Bosnia and Herzegovina 36 9,080 .. .. .. .. .. .. .. .. Botswana 2 1,357 0.2 8.1 41 18 41 29.9 1.8 77.0 Brazil 5,418 29,460 59.3 1.1 62 18 20 10.5 1.0 12.6 Bulgaria 21 2,706 10.5 50.0 19 78 3 1.3 0.8 0.4 Burkina Faso 13 975 0.8 6.4 86 1 13 3.6 1.3 76.7 Burundi 10 1,382 0.3 2.9 77 6 17 2.5 1.5 9.5 Cambodia 121 8,738 4.1 3.4 98 1 2 1.0 0.3 52.1 Cameroon 273 17,022 1.0 0.4 74 8 18 9.8 5.8 24.8 Canada 2,850 89,134 46.0 1.6 12 69 20 16.4 2.5 7.1 Central African Republic 141 35,374 0.0 0.0 4 16 80 38.4 517.4 46.8 Chad 15 1,588 0.2 1.5 83 0 17 7.3 2.9 .. Chile 884 54,826 12.6 1.4 64 25 11 6.4 0.4 9.1 China 2,812 2,170 630.3 22.4 68 26 7 2.2 0.4 4.0 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 2,112 47,022 10.7 0.5 46 4 50 8.1 2.0 56.0 Congo, Dem. Rep. 900 16,114 0.4 0.0 31 17 53 12.1 23.9 15.4 Congo, Rep. 222 57,173 0.1 0.0 9 22 70 76.0 47.0 233.6 Costa Rica 112 26,428 2.7 2.4 53 17 30 6.2 0.9 9.9 Côte d'Ivoire 77 4,299 0.9 1.2 65 12 24 11.0 3.9 19.2 Croatia 38 8,487 .. .. .. .. .. .. .. .. Cuba 38 3,390 8.2 21.5 69 12 19 .. .. .. Czech Republic 13 1,287 2.6 19.6 2 57 41 22.5 32.1 13.6 Denmark 6 1,110 1.3 21.2 43 25 32 126.8 6.8 112.4 Dominican Republic 21 2,395 3.4 16.1 66 2 32 6.3 1.1 113.3 Ecuador 432 33,129 17.0 3.9 82 5 13 1.0 0.1 6.5 Egypt, Arab Rep. 2 25 68.3 3,794.4 86 6 8 1.6 0.3 8.2 El Salvador 18 2,625 1.3 7.2 59 16 25 10.7 1.6 21.1 Eritrea 3 662 0.3 10.7 97 0 3 2.3 0.3 .. Estonia 13 9,423 0.2 1.2 5 38 57 39.5 31.3 26.5 Ethiopia 122 1,744 5.6 4.6 94 0 6 1.3 0.6 29.2 Finland 107 20,466 2.5 2.3 3 84 14 50.0 60.5 17.6 France 179 2,956 40.0 22.4 10 75 16 34.3 8.8 9.4 Gabon 164 120,382 0.1 0.1 42 8 50 42.1 7.0 274.9 Gambia, The 3 2,030 0.0 1.0 65 12 23 14.1 5.2 15.7 Georgia 58 12,866 3.6 6.2 59 21 20 0.9 0.3 1.0 Germany 107 1,297 47.1 44.0 20 68 12 40.9 2.3 15.9 Ghana 30 1,399 1.0 3.2 66 10 24 5.5 3.0 14.8 Greece 58 5,246 7.8 13.4 80 3 16 15.6 1.1 97.2 Guatemala 109 8,882 2.0 1.8 80 13 7 10.0 2.8 14.5 Guinea 226 24,561 1.5 0.7 90 2 8 2.2 0.6 39.5 Guinea-Bissau 16 10,392 0.2 1.1 82 5 13 1.1 0.8 3.9 Haiti 13 1,548 1.0 7.6 94 1 5 3.7 1.0 56.7 146 2006 World Development Indicators Freshwater Renewable internal Annual freshwater Water freshwater resourcesa withdrawalsb productivity GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per cu. m billion cu. m cu. m cu. m resources agriculture industry domestic Total Agriculture Industry 2004 2004 1987­2002 1987­2002 1987­2002 1987­2002 1987­2002 1987­2004 1987­2004 1987­2004 Honduras 96 13,610 0.9 0.9 80 12 8 7.3 1.3 16.8 Hungary 6 594 7.6 127.3 32 59 9 6.6 0.8 3.1 India 1,261 1,167 645.8 51.2 87 6 8 0.8 0.2 3.5 Indonesia 2,838 13,043 82.8 2.9 91 1 8 2.2 0.4 145.0 Iran, Islamic Rep. 129 1,918 72.9 56.7 91 2 7 1.6 0.2 26.2 Iraq 35 .. 42.7 121.3 92 5 3 0.5 0.0 10.0 Ireland 49 12,045 1.1 2.3 0 77 23 94.5 .. .. Israel 1 110 2.1 273.3 62 7 31 55.5 .. .. Italy 183 3,170 44.4 24.3 45 37 18 24.7 1.3 17.2 Jamaica 9 3,556 0.4 4.4 49 17 34 20.1 2.5 34.2 Japan 430 3,366 88.4 20.6 63 18 20 53.6 1.2 91.9 Jordan 1 125 1.0 148.5 75 4 21 9.3 0.3 58.0 Kazakhstan 75 5,030 35.0 46.4 82 17 2 0.7 0.1 1.5 Kenya 21 619 1.6 7.6 64 6 30 8.4 3.8 20.0 Korea, Dem. Rep. 67 2,993 9.0 13.5 55 25 20 .. .. .. Korea, Rep. 65 1,349 18.6 28.7 48 16 36 30.6 2.4 66.7 Kuwait 0 0 0.4 .. 52 2 46 84.0 0.8 2,070.0 Kyrgyz Republic 46 9,121 10.1 21.7 94 3 3 0.1 0.1 1.2 Lao PDR 190 32,878 3.0 1.6 90 6 4 0.6 0.4 2.8 Latvia 17 7,238 0.3 1.8 13 33 53 29.6 8.6 19.0 Lebanon 5 1,356 1.4 28.8 67 1 33 12.9 1.1 368.3 Lesotho 5 2,909 0.1 1.0 20 40 40 18.4 13.9 17.8 Liberia 200 61,717 0.1 0.1 55 18 27 5.4 .. .. Libya 1 105 4.3 711.3 83 3 14 8.7 .. .. Lithuania 16 4,529 0.3 1.7 7 15 78 47.9 41.4 91.3 Macedonia, FYR 5 2,659 .. .. .. .. .. .. .. .. Madagascar 337 18,606 15.0 4.4 96 2 3 0.2 0.1 1.9 Malawi 16 1,280 1.0 6.3 80 5 15 1.7 0.7 5.0 Malaysia 580 23,298 9.0 1.6 62 21 17 10.5 1.5 24.2 Mali 60 4,572 6.6 10.9 90 1 9 0.4 0.2 11.8 Mauritania 0 134 1.7 425.0 88 3 9 0.7 0.1 7.9 Mauritius 3 2,229 0.6 22.2 .. .. 30 8.1 .. .. Mexico 409 3,940 78.2 19.1 77 6 17 7.5 0.4 33.2 Moldova 1 237 2.3 231.0 33 58 10 0.6 0.5 0.2 Mongolia 35 13,839 0.4 1.3 52 27 21 2.3 0.9 2.1 Morocco 29 972 12.6 43.4 87 3 10 2.9 0.6 31.9 Mozambique 100 5,164 0.6 0.6 87 2 11 7.3 2.0 120.3 Myanmar 881 17,611 33.2 3.8 98 1 1 .. .. .. Namibia 6 3,066 0.3 4.9 71 5 24 12.4 1.6 69.7 Nepal 198 7,454 10.2 5.1 97 1 3 0.6 0.2 19.0 Netherlands 11 676 7.9 72.2 34 60 6 47.6 3.3 18.5 New Zealand 327 80,522 2.1 0.6 42 10 48 26.7 5.2 66.0 Nicaragua 190 35,293 1.3 0.7 83 2 15 3.1 0.7 34.4 Niger 4 259 2.2 62.3 95 1 4 0.9 0.4 33.7 Nigeria 221 1,717 8.0 3.6 69 10 21 5.5 2.3 20.7 Norway 382 83,205 2.2 0.6 11 67 23 79.2 14.5 43.6 Oman 1 389 1.4 138.1 90 2 7 16.0 0.3 388.6 Pakistan 52 345 169.4 323.3 96 2 2 0.5 0.1 4.7 Panama 147 46,426 0.8 0.6 28 5 67 14.6 3.8 46.7 Papua New Guinea 801 138,775 0.1 0.0 .. .. .. .. .. .. Paraguay 94 15,622 0.5 0.5 71 8 20 15.8 4.9 46.5 Peru 1,616 58,631 20.1 1.2 82 10 8 2.8 0.3 7.7 Philippines 479 5,869 28.5 6.0 74 9 17 2.8 0.6 9.2 Poland 54 1,404 16.2 30.2 8 79 13 10.5 4.2 3.7 Portugal 38 3,618 11.3 29.6 78 12 10 9.7 0.4 20.2 Puerto Rico 7 1,823 .. .. .. .. .. .. .. .. 2006 World Development Indicators 147 Freshwater Renewable internal Annual freshwater Water freshwater resourcesa withdrawalsb productivity GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per cu. m billion cu. m cu. m cu. m resources agriculture industry domestic Total Agriculture Industry 2004 2004 1987­2002 1987­2002 1987­2002 1987­2002 1987­2002 1987­2004 1987­2004 1987­2004 Romania 42 1,951 23.2 54.8 57 34 9 1.8 0.4 1.7 Russian Federation 4,313 29,981 76.7 1.8 18 64 19 3.7 1.3 2.0 Rwanda 10 1,070 0.2 1.6 68 8 24 14.1 9.1 35.9 Saudi Arabia 2 100 17.3 721.7 89 1 10 11.0 0.6 482.0 Senegal 26 2,266 2.2 8.6 93 3 4 2.1 0.3 17.8 Serbia and Montenegro 44 5,401 .. .. .. .. .. .. .. .. Sierra Leone 160 29,982 0.4 0.2 92 3 5 1.9 .. .. Singapore 1 142 .. .. .. .. .. .. .. .. Slovak Republic 13 2,341 .. .. .. .. .. .. .. .. Slovenia 19 9,349 .. .. .. .. .. .. .. .. Somalia 6 753 3.3 54.8 100 0 0 .. .. .. South Africa 45 984 12.5 27.9 63 6 31 11.3 0.5 53.0 Spain 111 2,605 35.6 32.0 68 19 13 17.3 0.9 24.9 Sri Lanka 50 2,575 12.6 25.2 95 3 2 1.3 0.2 12.7 Sudan 30 845 37.3 124.4 97 1 3 0.4 0.1 11.2 Swaziland 3 2,357 1.0 39.5 97 1 2 1.4 0.1 37.6 Sweden 171 19,017 3.0 1.7 9 54 37 83.4 16.7 39.4 Switzerland 40 5,467 2.6 6.4 2 74 24 97.1 67.5 36.5 Syrian Arab Republic 7 377 20.0 285.0 95 2 3 1.0 0.3 13.9 Tajikistan 66 10,311 12.0 18.0 92 5 4 0.1 0.0 0.8 Tanzania 84 2,232 5.2 6.2 89 1 10 2.0 0.9 61.7 Thailand 210 3,297 87.1 41.5 95 3 3 1.5 0.1 26.2 Togo 12 1,920 0.2 1.5 45 2 53 8.2 6.5 63.8 Trinidad and Tobago 4 2,951 0.3 8.1 7 26 68 28.1 6.4 55.5 Tunisia 4 422 2.6 62.9 82 4 14 7.9 1.0 55.2 Turkey 227 3,165 37.5 16.5 74 11 15 5.3 1.0 10.4 Turkmenistan 1 285 24.7 1,812.5 98 1 2 .. .. .. Uganda 39 1,402 0.3 0.8 40 17 43 22.4 18.3 25.2 Ukraine 53 1,119 37.5 70.7 53 35 12 1.0 0.3 0.9 United Arab Emirates 0 35 2.3 1,533.3 68 9 23 34.5 1.5 197.8 United Kingdom 145 2,422 9.5 6.6 3 75 22 157.0 49.0 49.2 United States 2,800 9,535 479.3 17.1 41 46 13 20.9 0.5 9.6 Uruguay 59 17,154 3.2 5.3 96 1 3 5.6 0.4 115.2 Uzbekistan 16 623 58.3 357.0 93 2 5 0.3 0.1 2.5 Venezuela, RB 722 27,652 8.4 1.2 47 7 46 13.2 1.2 83.8 Vietnam 367 4,461 71.4 19.5 68 24 8 0.5 0.2 0.8 West Bank and Gaza 0 13 .. .. .. .. .. .. .. .. Yemen, Rep. 4 202 6.6 161.7 95 1 4 1.5 0.2 116.0 Zambia 80 6,987 1.7 2.2 76 8 17 2.0 0.5 6.7 Zimbabwe 12 948 4.2 34.3 79 7 14 1.6 0.3 4.3 World 43,507 s 6,872 w 3,807.4 s 9.0 w 70 w 20 w 10 w 8.6 w 1.0 w 18.7 w Low income 8,095 3,456 1,245.4 15.5 88 5 6 0.8 0.3 7.0 Middle income 25,971 8,611 1,662.3 6.4 71 19 10 3.3 0.6 19.0 Lower middle income 17,807 7,295 1,355.8 7.7 75 17 8 2.5 0.4 17.9 Upper middle income 8,164 14,190 306.4 3.8 53 28 19 7.2 1.4 23.7 Low & middle income 34,066 6,358 2,907.7 8.6 78 13 8 2.3 0.5 14.0 East Asia & Pacific 9,454 5,062 958.9 10.2 74 20 7 2.1 0.5 19.2 Europe & Central Asia 5,255 11,123 383.2 7.5 59 31 10 2.7 1.0 2.9 Latin America & Carib. 13,429 24,619 265.3 2.0 71 10 19 7.6 0.7 26.3 Middle East & N. Africa 228 761 239.8 105.0 89 4 7 2.0 0.3 24.7 South Asia 1,816 1,255 941.1 51.8 90 4 6 0.7 0.2 5.9 Sub-Saharan Africa 3,884 5,353 119.3 3.1 87 3 10 3.1 1.0 21.8 High income 9,441 9,703 899.7 10.4 43 43 15 28.2 2.7 33.6 Europe EMU 910 2,942 199.7 22.3 38 48 15 30.3 5.7 20.3 a. River flows from other countries are not included because of data unreliability. b. Data are for the most recent year available for 1987­2004 (see Primary data documentation). 148 2006 World Development Indicators Freshwater About the data Definitions The data on freshwater resources are based on esti- · Renewable internal freshwater resources flows Agriculture uses 70 percent of freshwater mates of runoff into rivers and recharge of groundwa- globally refer to internal renewable resources (internal river ter. These estimates are based on different sources flows and groundwater from rainfall) in the country. Share of annual freshwater withdrawals, most recent and refer to different years, so cross-country compari- year available · Renewable internal freshwater resources per cap- sons should be made with caution. Because the data ita are calculated using the World Bank's population World are collected intermittently, they may hide significant estimates (see table 2.1). · Annual freshwater with- variations in total renewable water resources from Domestic drawals refer to total water withdrawals, not counting 10% one year to the next. The data also fail to distinguish evaporation losses from storage basins. Withdrawals between seasonal and geographic variations in water also include water from desalination plants in coun- Industry availability within countries. Data for small countries 20% tries where they are a significant source. Withdraw- and countries in arid and semiarid zones are less als can exceed 100 percent of internal renewable Agriculture reliable than those for larger countries and countries 70% resources because river flows from other countries with greater rainfall. are not included, because extraction from nonrenew- Caution is also needed in comparing data on able aquifers or desalination plants is considerable, annual freshwater withdrawals, which are subject or because there is significant water reuse. Withdraw- to variations in collection and estimation methods. als for agriculture and industry are total withdrawals Low-income In addition, inflows and outflows are estimated at Domestic 6% for irrigation and livestock production and for direct Industry 5% different times and at different levels of quality and industrial use (including withdrawals for cooling ther- precision, requiring caution in interpreting the data, moelectric plants). Withdrawals for domestic uses particularly for water-short countries, notably in the include drinking water, municipal use or supply, and Middle East. use for public services, commercial establishments, Water productivity is an indication only of the effi- and homes. · Water productivity is calculated as Agriculture ciency by which each country uses its water resources. 88% GDP in constant prices divided by annual total water Given the different economic structure of each coun- withdrawal. Sectoral water productivity is calculated try, these indicators should be used with proper as annual value added in agriculture or industry caution, taking into account the countries' sectoral divided by water withdrawal in each sector. activities and natural resource endowments. Middle-income Domestic 10% Industry 19% Agriculture 71% High-income Domestic 15% Data sources Agriculture 43% Data on freshwater resources and withdrawals are compiled by the World Resources Institute from Industry 43% various sources and published in World Resources 2005 (produced in collaboration with the United Nations Environment Programme, United Nations Development Programme, and World Bank). These Note: Components may not sum to 100 percent because of rounding. data are supplemented by the Food and Agricul- ture Organization's AQUASTAT data. Source: Table 3.5. 2006 World Development Indicators 149 Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2003a 1990 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a Afghanistan 5,864 236 0.16 0.21 .. 37.7 17.5 31.1 0.4 13.2 .. .. Albania 34,785 .. 0.14 .. .. .. .. .. .. .. .. .. Algeria 106,977 .. 0.25 .. .. .. .. .. .. .. .. .. Angola 4,544 .. 0.19 .. .. .. .. .. .. .. .. .. Argentina 186,686 149,455 0.20 0.23 4.9 7.2 4.2 71.1 0.0 6.6 1.5 4.3 Armenia 37,900 7,104 0.11 0.28 .. .. 0.0 77.6 .. 22.4 .. .. Australia 186,110 111,658 0.18 0.18 .. .. 5.6 77.1 0.2 5.1 5.3 6.5 Austria 94,121 93,463 0.15 0.14 14.6 17.8 10.9 35.6 0.4 4.5 5.3 10.8 Azerbaijan 53,251 17,511 0.15 0.17 17.6 5.5 15.2 41.6 0.3 12.3 1.1 6.2 Bangladesh 171,087 .. 0.17 .. .. .. .. .. .. .. .. .. Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 117,978 102,460 0.16 0.17 13.7 18.0 12.0 40.4 0.2 6.0 2.0 7.5 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 8,404 12,759 0.24 0.25 0.9 20.5 6.6 61.4 0.3 7.1 2.4 0.5 Bosnia and Herzegovina 50,741 .. 0.14 .. .. .. .. .. .. .. .. .. Botswana 4,509 5,204 0.19 0.18 2.0 7.2 5.1 55.7 0.3 25.8 1.5 2.2 Brazil 780,395 .. 0.19 .. .. .. .. .. .. .. .. .. Bulgaria 149,381 97,137 0.11 0.17 8.5 9.9 7.0 45.1 0.2 21.7 2.1 5.3 Burkina Faso .. 2,598 .. 0.22 3.5 1.1 5.4 73.8 0.1 4.1 10.1 1.7 Burundi 1,570 .. 0.24 .. .. .. .. .. .. .. .. .. Cambodia 11,823 .. 0.14 .. .. .. .. .. .. .. .. .. Cameroon 13,989 10,714 0.28 0.20 3.1 6.3 28.3 52.7 0.0 3.6 5.6 0.2 Canada 321,471 313,431 0.17 0.16 9.7 22.8 8.3 38.5 0.1 6.1 5.2 9.1 Central African Republic 998 .. 0.18 .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 66,783 72,850 0.22 0.24 6.9 11.3 8.9 62.7 0.1 5.0 2.6 2.3 China 7,038,131 6,088,663 0.14 0.14 20.4 10.9 14.8 28.1 0.5 15.5 0.9 8.8 Hong Kong, China 86,124 38,698 0.12 0.21 1.4 41.6 3.4 34.5 - 14.4 0.2 4.3 Colombia 93,253 93,879 0.19 0.21 3.1 16.2 9.7 53.2 0.2 14.2 1.0 2.2 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 2,456 .. 0.32 .. .. .. .. .. .. .. .. .. Costa Rica 27,249 31,236 0.20 0.22 1.6 10.0 8.2 65.7 0.1 10.2 1.3 2.7 Côte d'Ivoire 7,874 .. 0.22 .. .. .. .. .. .. .. .. .. Croatia 80,034 42,734 0.15 0.17 6.7 15.3 7.6 47.5 0.2 12.8 3.6 6.1 Cuba 172,973 .. 0.25 .. .. .. .. .. .. .. .. .. Czech Republic 205,102 158,462 0.13 0.14 15.6 7.0 7.9 43.6 0.3 10.4 3.9 11.2 Denmark 91,871 83,591 0.18 0.17 4.4 29.1 7.9 44.2 0.2 2.2 3.5 8.3 Dominican Republic 47,900 .. 0.36 .. .. .. .. .. .. .. .. .. Ecuador 25,567 41,171 0.23 0.28 2.2 10.1 5.6 73.7 0.1 5.6 1.4 1.0 Egypt, Arab Rep. 211,531 186,059 0.20 0.20 10.8 8.2 9.0 50.7 0.3 17.7 0.6 2.5 El Salvador 7,663 22,760 0.22 0.18 2.1 10.2 8.1 43.5 0.1 34.1 0.5 1.2 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 18,593 22,085 0.23 0.23 2.3 11.0 5.5 61.0 0.3 17.3 2.0 0.4 Finland 79,514 68,819 0.18 0.16 9.0 39.6 6.6 27.7 0.2 2.6 4.0 10.1 France 653,455 281,747 0.15 0.10 14.7 31.0 23.0 .. 0.3 9.0 2.8 19.1 Gabon 2,018 .. 0.25 .. .. .. .. .. .. .. .. .. Gambia, The 832 .. 0.34 .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 835,019 1,020,145 0.12 0.14 9.0 20.9 11.3 38.7 0.2 2.8 2.5 14.5 Ghana 13,667 .. 0.17 .. .. .. .. .. .. .. .. .. Greece 63,479 57,178 0.18 0.20 6.3 11.8 9.1 54.0 0.2 13.2 1.5 3.7 Guatemala 16,070 19,253 0.27 0.28 4.9 7.2 6.1 72.8 0.1 6.9 0.8 0.9 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 5,427 .. 0.20 .. .. .. .. .. .. .. .. .. 150 2006 World Development Indicators Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2003a 1990 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a Honduras 17,824 .. 0.23 .. .. .. .. .. .. .. .. .. Hungary 178,002 155,386 0.16 0.16 7.9 11.9 8.4 47.1 0.2 13.8 2.3 8.2 India 1,410,617 1,515,683 0.20 0.20 12.4 7.2 9.4 53.9 0.2 12.7 0.4 3.6 Indonesia 495,594 720,326 0.19 0.18 2.6 8.1 9.3 51.5 21.0 5.1 2.2 Iran, Islamic Rep. 102,689 141,982 0.16 0.16 17.2 7.1 10.8 43.8 0.6 12.5 0.8 7.0 Iraq 20,352 .. 0.16 .. .. .. .. .. .. .. .. .. Ireland 34,610 49,144 0.18 0.15 1.3 14.2 11.4 56.4 0.2 3.1 1.6 11.7 Israel 46,359 51,740 0.16 0.16 3.4 21.5 10.6 45.6 0.1 6.6 1.5 10.5 Italy 358,084 504,492 0.13 0.12 9.4 16.8 10.8 29.9 0.3 15.9 3.7 13.1 Jamaica 18,736 .. 0.29 .. .. .. .. .. .. .. .. .. Japan 1,556,648 1,279,287 0.14 0.15 7.0 21.9 9.0 43.2 0.2 5.0 1.6 12.0 Jordan 8,325 18,516 0.19 0.19 4.8 17.2 12.0 52.6 0.5 9.7 0.5 2.5 Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya 42,588 54,246 0.23 0.25 .. 11.8 5.7 69.0 0.1 9.9 1.8 1.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 369,193 309,517 0.12 0.12 11.4 18.2 12.9 26.0 0.2 14.4 1.4 15.4 Kuwait 9,052 11,897 0.16 0.17 2.1 16.6 11.1 50.2 0.4 11.6 2.8 5.0 Kyrgyz Republic 30,885 20,801 0.12 0.21 8.5 5.9 3.3 65.6 0.3 11.6 1.0 3.6 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 39,887 29,166 0.12 0.19 4.1 14.6 3.2 55.4 0.1 9.8 9.4 3.2 Lebanon .. 14,899 .. 0.19 0.9 15.6 4.0 60.7 0.5 10.2 4.6 3.3 Lesotho 2,958 3,123 0.16 0.16 1.2 4.0 0.7 39.7 0.1 51.3 0.6 2.2 Liberia 615 .. 0.30 .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 53,818 37,477 0.13 0.18 1.0 10.3 5.1 54.8 0.2 18.3 6.1 4.0 Macedonia, FYR 32,419 .. 0.18 .. .. .. .. .. .. .. .. .. Madagascar 11,043 .. 0.27 .. .. .. .. .. .. .. .. .. Malawi 10,024 11,805 0.29 0.29 - 16.0 3.7 70.0 - 7.8 1.7 0.5 Malaysia 104,728 170,662 0.13 0.12 7.9 14.4 15.1 34.4 0.2 8.0 7.1 12.8 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 17,813 17,700 0.16 0.15 0.9 6.6 2.6 32.8 0.1 55.4 0.6 0.9 Mexico 174,266 296,093 0.18 0.20 7.8 12.5 10.4 55.6 0.2 7.5 0.9 4.9 Moldova 55,887 21,409 0.15 0.45 .. 2.0 .. 98.0 .. .. .. .. Mongolia 10,160 .. 0.18 .. .. .. .. .. .. .. .. .. Morocco 41,710 69,060 0.14 0.16 2.2 8.6 6.4 42.1 0.3 36.3 1.2 2.7 Mozambique 20,414 10,230 0.27 0.31 1.1 7.1 2.7 81.2 0.1 5.8 1.4 0.3 Myanmar 7,663 6,159 0.17 0.18 56.5 4.6 13.2 14.9 0.4 2.9 1.7 5.6 Namibia 7,350 .. 0.35 .. .. .. .. .. .. .. .. .. Nepal 20,894 26,908 0.13 0.16 3.5 9.7 5.9 55.1 1.4 21.7 1.7 0.8 Netherlands 136,686 124,182 0.18 0.18 7.3 26.7 11.3 43.0 0.2 2.3 1.2 7.8 New Zealand 50,243 46,099 0.22 0.22 3.2 21.7 5.2 57.3 0.1 4.6 3.6 4.1 Nicaragua 10,465 .. 0.27 .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 52,350 .. 0.23 .. .. .. .. .. .. .. .. .. Norway 54,996 51,693 0.20 0.19 9.0 31.3 4.7 42.8 0.1 1.4 3.1 7.4 Oman 360 5,739 0.11 0.17 7.3 13.4 10.2 54.0 0.9 8.4 2.4 3.2 Pakistan 104,095 .. 0.18 .. .. .. .. .. .. .. .. .. Panama 9,700 11,692 0.26 0.32 1.5 13.2 4.6 76.6 0.2 3.2 0.4 .. Papua New Guinea 5,729 .. 0.25 .. .. .. .. .. .. .. .. .. Paraguay 3,250 .. 0.28 .. .. .. .. .. .. .. .. .. Peru 56,144 .. 0.20 .. .. .. .. .. .. .. .. .. Philippines 228,301 .. 0.21 .. .. .. .. .. .. .. .. .. Poland 428,894 416,934 0.14 0.16 12.8 11.4 6.3 45.4 0.4 12.7 3.4 7.4 Portugal 147,873 133,570 0.15 0.14 3.9 15.8 4.9 36.2 0.4 26.5 5.5 6.7 Puerto Rico 19,026 15,367 0.15 0.14 1.9 14.9 21.9 34.4 0.2 15.5 1.4 9.7 2006 World Development Indicators 151 Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2003a 1990 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a Romania 413,864 38,395 0.12 0.07 .. 17.6 .. 5.1 .. 28.7 12.5 36.0 Russian Federation 1,911,348 1,518,704 0.13 0.16 18.1 7.7 8.6 48.0 0.4 5.9 2.5 8.6 Rwanda 1,624 .. 0.25 .. .. .. .. .. .. .. .. .. Saudi Arabia 18,476 .. 0.15 .. .. .. .. .. .. .. .. .. Senegal 10,309 6,603 0.32 0.30 5.8 8.4 10.7 70.1 0.1 4.2 0.4 - Serbia and Montenegro 137,795 98,696 0.15 0.16 9.9 11.8 8.2 47.4 0.3 12.7 2.2 7.3 Sierra Leone 4,170 .. 0.32 .. .. .. .. .. .. .. .. .. Singapore 32,364 33,644 0.09 0.09 1.3 25.9 16.2 22.8 0.1 4.1 1.8 27.7 Slovak Republic 77,174 45,011 0.13 0.14 4.2 15.0 8.4 44.2 0.4 15.0 1.7 11.0 Slovenia 55,640 38,390 0.16 0.16 33.7 14.7 8.3 23.7 0.2 10.8 2.0 6.4 Somalia 6,177 .. 0.38 .. .. .. .. .. .. .. .. .. South Africa 261,618 221,256 0.17 0.18 15.1 18.0 10.5 36.0 0.1 10.9 3.9 5.3 Spain 320,262 374,589 0.17 0.15 6.7 19.8 8.9 42.5 0.3 9.3 4.0 8.3 Sri Lanka 53,024 88,943 0.19 0.18 0.5 7.0 6.4 52.3 0.2 31.2 1.1 1.1 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 6,586 .. 0.33 .. .. .. .. .. .. .. .. .. Sweden 109,582 103,913 0.15 0.14 11.3 35.0 7.8 26.6 0.1 1.3 3.0 14.8 Switzerland 146,038 .. 0.16 .. .. .. .. .. .. .. .. .. Syrian Arab Republic 21,702 15,115 0.22 0.20 4.1 1.5 3.9 69.8 0.9 19.4 0.2 - Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 31,125 35,155 0.24 0.25 1.5 9.4 2.7 69.3 0.1 14.0 1.5 1.3 Thailand 291,552 .. 0.17 .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 9,951 7,945 0.26 0.23 6.5 18.8 11.9 55.3 0.2 3.8 2.0 1.3 Tunisia 44,551 54,191 0.18 0.14 2.8 6.5 6.7 35.6 0.4 41.9 1.9 4.1 Turkey 177,264 188,199 0.18 0.16 11.5 7.3 7.7 42.6 0.3 24.4 1.4 4.6 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 16,728 .. 0.30 .. .. .. .. .. .. .. .. .. Ukraine 692,373 445,758 0.14 0.18 28.1 4.2 7.0 46.8 0.4 5.4 1.1 6.8 United Arab Emirates 5,638 .. 0.14 .. .. .. .. .. .. .. .. .. United Kingdom 739,562 615,410 0.15 0.15 6.7 28.5 11.4 33.8 0.2 5.5 2.5 11.3 United States 2,565,226 1,897,480 0.15 0.13 9.8 10.9 13.8 40.3 0.2 6.3 4.1 14.5 Uruguay 38,661 16,362 0.23 0.28 1.2 7.2 6.7 75.9 0.1 8.2 0.6 .. Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 96,495 94,175 0.21 0.21 13.7 10.4 10.2 53.1 0.3 7.5 1.5 3.1 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,873 13,082 0.27 0.22 - 5.1 7.6 76.0 0.5 7.6 1.3 1.7 Zambia 15,880 .. 0.23 .. .. .. .. .. .. .. .. .. Zimbabwe 37,149 .. 0.20 .. .. .. .. .. .. .. .. .. Note: Industry shares may not sum to 100 percent because data may be for different years. a. Data refer to any year from 1993 to 2003. 152 2006 World Development Indicators Water pollution About the data Definitions Emissions of organic pollutants from industrial activi- Data on water pollution are more readily available · Emissions of organic water pollutants are mea- ties are a major cause of degradation of water qual- than other emissions data because most industrial sured in terms of biochemical oxygen demand, which ity. Water quality and pollution levels are generally pollution control programs start by regulating emis- refers to the amount of oxygen that bacteria in water measured in terms of concentration or load--the sions of organic water pollutants. Such data are fairly will consume in breaking down waste. This is a stan- rate of occurrence of a substance in an aqueous reliable because sampling techniques for measur- dard water treatment test for the presence of organic solution. Polluting substances include organic mat- ing water pollution are more widely understood and pollutants. Emissions per worker are total emissions ter, metals, minerals, sediment, bacteria, and toxic much less expensive than those for air pollution. divided by the number of industrial workers. · Indus- chemicals. This table focuses on organic water pol- Hettige, Mani, and Wheeler (1998) used plant- and try shares of emissions of organic water pollutants lution resulting from industrial activities. Because sector-level information on emissions and employ- refer to emissions from manufacturing activities as water pollution tends to be sensitive to local condi- ment from 13 national environmental protection defined by two-digit divisions of the International tions, the national-level data in the table may not agencies and sector-level information on output Standard Industrial Classification (ISIC) revision 2: reflect the quality of water in specific locations. and employment from the United Nations Industrial primary metals (ISIC division 37); paper and pulp The data in the table come from an international Development Organization (UNIDO). Their economet- (34); chemicals (35); food and beverages (31); stone, study of industrial emissions that may be the first ric analysis found that the ratio of BOD to employ- ceramics, and glass (36); textiles (32); wood (33); to include data from developing countries (Hettige, ment in each industrial sector is about the same and other (38 and 39). Mani, and Wheeler 1998). These data were updated across countries. This finding allowed the authors through 2003 by the World Bank's Development to estimate BOD loads across countries and over Research Group. Unlike estimates from earlier stud- time. The estimated BOD intensities per unit of ies based on engineering or economic models, these employment were multiplied by sectoral employ- estimates are based on actual measurements of ment numbers from UNIDO's industry database for plant-level water pollution. The focus is on organic 1980­98. The estimates of sectoral emissions were water pollution caused by organic waste, measured in then totaled to get daily emissions of organic water terms of biochemical oxygen demand (BOD), because pollutants in kilograms per day for each country and the data for this indicator are the most plentiful and year. The data in the table were derived by updating the most reliable for cross-country comparisons of these estimates through 2003. emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD. Emission of organic water pollutants declined in most countries from 1990 to 2003 Tons per day 8,000 1990 2003 7,000 6,000 5,000 Data sources 4,000 Data on water pollution come from a 1998 study 3,000 by Hemamala Hettige, Muthukumara Mani, and David Wheeler, "Industrial Pollution in Economic 2,000 Development: Kuznets Revisited" (available at www.worldbank.org/nipr). The data were updated 1,000 through 2003 by the World Bank's Development Research Group using the same methodology as 0 China United States Russian India Japan Germany Brazil the initial study. Sectoral employment numbers Federation Source: Table 3.6. are from UNIDO's industry database. 2006 World Development Indicators 153 Energy production and use Total energy Energy Net energy production use importsa Total Combustible Per capita thousand thousand renewables average average metric tons of metric tons of and waste annual kilograms of oil annual % of oil equivalent oil equivalent % of total % growth equivalent % growth energy use 1990 2003 1990 2003 1990 2003 1990­2003 1990 2003 1990­2003 1990 2003 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2,449 899 2,662 2,084 13.6 6.8 ­1.9 809 674 ­1.4 8 57 Algeria 104,439 163,271 23,858 32,998 0.1 0.2 2.5 943 1,036 0.7 ­338 ­395 Angola 28,652 50,730 6,280 9,115 68.8 66.4 2.9 596 606 0.1 ­356 ­457 Argentina 48,456 84,318 46,110 59,851 3.7 5.3 2.0 1,415 1,575 0.8 ­5 ­41 Armenia 263 692 4,298 2,004 0.0 0.1 .. 1,246 660 .. 94 65 Australia 157,712 253,534 87,536 112,645 4.5 4.4 1.9 5,130 5,668 0.8 ­80 ­125 Austria 8,104 10,025 25,026 33,183 9.9 11.1 2.2 3,246 4,086 1.8 68 70 Azerbaijan 18,150 19,826 16,675 12,290 0.0 0.0 .. 2,259 1,493 .. ­9 ­61 Bangladesh 10,758 17,532 12,826 21,682 53.5 36.9 4.0 123 159 1.9 16 19 Belarus 4,103 3,497 39,703 25,797 1.5 4.2 .. 3,886 2,613 .. 90 86 Belgium 12,481 13,381 49,109 59,157 1.4 2.0 1.4 4,927 5,701 1.1 75 77 Benin 1,774 1,584 1,678 2,310 93.2 68.6 2.5 324 292 ­0.8 ­6 31 Bolivia 4,923 7,728 2,774 4,451 27.2 16.2 3.6 416 504 1.5 ­77 ­74 Bosnia and Herzegovina 3,642 3,109 4,474 4,453 3.6 4.2 .. 1,130 1,137 .. 19 30 Botswana .. .. .. .. .. .. .. .. .. .. .. .. Brazil 97,554 171,139 133,469 193,245 30.8 25.9 2.9 893 1,065 1.4 27 11 Bulgaria 9,613 10,062 28,820 19,510 0.6 3.6 ­3.0 3,306 2,494 ­2.2 67 48 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 12,090 12,135 5,032 6,754 75.9 78.8 2.3 432 429 ­0.1 ­140 ­80 Canada 273,695 385,291 209,104 260,641 3.9 4.5 1.7 7,524 8,240 0.7 ­31 ­48 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 7,638 8,336 14,064 26,268 19.0 15.4 4.8 1,067 1,647 3.3 46 68 China 902,689 1,380,786 879,923 1,409,377 22.8 15.5 3.6 775 1,094 2.7 ­3 2 Hong Kong, China 43 48 10,662 16,515 0.5 0.3 3.4 1,869 2,428 2.0 100 100 Colombia 48,479 74,363 25,048 28,371 23.2 17.4 1.0 716 642 ­0.9 ­94 ­162 Congo, Dem. Rep. 12,019 16,547 11,903 15,884 84.0 93.5 2.2 315 293 ­0.6 ­1 ­4 Congo, Rep. 9,005 12,112 1,056 1,028 69.4 62.1 ­0.2 425 273 ­3.4 ­753 ­1,078 Costa Rica 1,032 1,626 2,025 3,675 36.6 8.2 4.6 658 880 2.2 49 56 Côte d'Ivoire 3,382 6,690 4,408 6,577 72.1 65.7 3.1 348 374 0.5 23 ­2 Croatia 4,346 3,745 6,714 8,779 3.8 4.3 .. 1,502 1,976 .. 35 57 Cuba 6,271 6,661 16,535 11,216 33.7 22.3 ­3.0 1,569 1,000 ­3.5 62 41 Czech Republic 38,474 33,002 47,379 44,117 1.2 2.7 ­0.6 4,572 4,324 ­0.4 19 25 Denmark 9,996 28,498 17,847 20,755 6.4 10.7 1.2 3,472 3,853 0.8 44 ­37 Dominican Republic 1,031 1,546 4,139 7,971 24.2 18.1 5.0 584 923 3.5 75 81 Ecuador 16,474 23,617 6,128 9,105 13.5 7.1 3.1 597 708 1.3 ­169 ­159 Egypt, Arab Rep. 54,869 60,998 31,895 52,356 3.3 2.6 3.8 573 735 1.9 ­72 ­17 El Salvador 1,722 2,390 2,535 4,487 48.2 32.1 4.4 496 675 2.4 32 47 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 4,118 3,661 6,271 4,915 2.9 10.6 .. 4,091 3,631 .. 34 26 Ethiopia 14,158 18,903 15,151 20,509 92.9 91.2 2.3 296 299 0.1 7 8 Finland 12,081 15,976 29,171 37,554 15.6 19.5 1.9 5,851 7,204 1.6 59 57 France 111,445 136,003 227,282 271,287 4.9 4.4 1.4 4,006 4,519 0.9 51 50 Gabon 14,630 12,418 1,243 1,685 59.8 58.8 2.3 1,298 1,256 ­0.3 ­1,077 ­637 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 1,470 1,376 8,757 2,727 7.7 23.7 .. 1,642 597 .. 83 50 Germany 186,159 134,520 356,221 347,118 1.4 2.8 ­0.2 4,485 4,205 ­0.5 48 61 Ghana 4,392 5,991 5,337 8,493 73.1 66.6 3.6 345 400 1.2 18 29 Greece 9,200 9,915 22,181 29,887 4.0 3.3 2.3 2,183 2,709 1.7 59 67 Guatemala 3,390 5,469 4,478 7,293 67.9 53.3 3.8 504 608 1.5 24 25 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1,253 1,673 1,585 2,237 76.5 73.8 2.7 231 270 1.2 21 25 154 2006 World Development Indicators Energy production and use Total energy Energy Net energy production use importsa Total Combustible Per capita thousand thousand renewables average average metric tons of metric tons of and waste annual kilograms of oil annual % of oil equivalent oil equivalent % of total % growth equivalent % growth energy use 1990 2003 1990 2003 1990 2003 1990­2003 1990 2003 1990­2003 1990 2003 Honduras 1,694 1,659 2,416 3,597 62.0 40.9 3.1 496 522 0.4 30 54 Hungary 14,325 10,411 28,553 26,341 1.3 3.1 ­0.6 2,755 2,600 ­0.4 50 60 India 334,056 453,147 365,377 553,390 48.1 38.2 3.2 430 520 1.5 9 18 Indonesia 162,556 249,955 96,085 161,553 38.8 26.8 4.0 539 753 2.6 ­69 ­55 Iran, Islamic Rep. 179,738 265,400 68,775 136,443 1.0 0.6 5.3 1,264 2,055 3.7 ­161 ­95 Iraq 104,933 68,448 19,060 25,750 0.1 0.1 2.3 1,029 .. .. ­451 ­166 Ireland 3,467 1,896 10,424 15,092 1.0 1.1 2.9 2,973 3,777 1.8 67 87 Israel 433 751 12,112 20,638 0.0 0.0 4.1 2,599 3,086 1.3 96 96 Italy 25,312 27,660 148,031 181,026 0.6 1.7 1.6 2,610 3,140 1.4 83 85 Jamaica 485 468 2,943 4,059 16.2 11.3 2.5 1,231 1,543 1.7 84 88 Japan 75,745 84,643 445,336 517,103 1.3 1.3 1.2 3,605 4,053 0.9 83 84 Jordan 162 285 3,498 5,450 0.1 0.1 3.4 1,103 1,027 ­0.6 95 95 Kazakhstan 89,007 105,522 79,661 49,829 0.1 0.2 .. 4,846 3,342 .. ­12 ­112 Kenya 10,272 13,492 12,479 16,170 78.4 77.5 2.0 533 494 ­0.6 18 17 Korea, Dem. Rep. 28,725 18,760 32,874 19,944 2.9 5.1 ­3.8 1,670 896 ­4.8 13 6 Korea, Rep. 21,908 36,920 92,650 205,300 0.3 0.4 6.1 2,161 4,291 5.3 76 82 Kuwait 50,401 120,722 8,110 22,924 0.1 .. 8.0 3,816 9,566 7.1 ­521 ­427 Kyrgyz Republic 1,818 1,366 5,066 2,661 0.1 0.2 .. 1,114 528 .. 64 49 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 794 1,977 4,258 4,375 13.2 28.9 .. 1,618 1,881 .. 81 55 Lebanon 143 251 2,309 5,956 4.5 2.1 7.3 842 1,700 5.4 94 96 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 73,173 77,498 11,541 17,963 1.1 0.8 3.4 2,663 3,191 1.4 ­534 ­331 Lithuania 4,298 5,216 11,017 8,930 2.6 7.6 .. 2,978 2,585 .. 61 42 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 48,753 83,843 22,637 56,655 9.4 4.6 7.1 1,269 2,318 4.6 ­115 ­48 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 194,783 242,511 124,341 159,935 5.9 5.1 1.9 1,494 1,564 0.4 ­57 ­52 Moldova 58 61 6,884 3,267 0.5 1.8 .. 1,575 772 .. 99 98 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 773 637 6,725 10,891 4.7 4.1 3.7 281 378 2.3 89 94 Mozambique 6,846 7,990 7,203 8,198 94.4 86.1 1.0 536 430 ­1.7 5 3 Myanmar 10,651 18,345 10,683 13,673 84.4 73.5 1.9 262 276 0.4 0 ­34 Namibia 218 308 652 1,262 16.0 14.5 .. 449 635 .. 67 76 Nepal 5,501 7,795 5,806 8,751 93.4 86.8 3.2 304 336 0.8 5 11 Netherlands 60,447 58,465 66,623 80,829 1.3 2.4 1.5 4,456 4,982 0.9 9 28 New Zealand 12,019 13,171 13,769 17,372 4.0 4.8 1.8 3,993 4,333 0.6 13 24 Nicaragua 1,495 1,805 2,118 3,099 53.3 49.9 2.9 535 588 0.7 29 42 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 150,453 214,580 70,905 97,789 79.8 79.4 2.5 783 777 ­0.1 ­112 ­119 Norway 120,304 233,205 21,492 23,347 4.8 6.6 0.6 5,067 5,100 0.1 ­460 ­899 Oman 38,313 59,824 4,562 12,492 .. .. 7.8 2,475 4,975 5.4 ­740 ­379 Pakistan 34,360 55,494 43,424 69,309 43.2 37.3 3.6 402 467 1.2 21 20 Panama 612 689 1,490 2,607 28.3 17.1 4.3 618 836 2.3 59 74 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 4,578 6,623 3,083 3,989 72.3 54.4 2.0 731 679 ­0.6 ­48 ­66 Peru 10,596 9,444 9,952 12,003 26.9 18.7 1.4 457 442 ­0.3 ­6 21 Philippines 13,701 22,503 26,159 42,124 29.2 24.5 3.7 428 525 1.6 48 47 Poland 99,228 79,969 99,847 93,666 2.2 5.6 ­0.5 2,619 2,452 ­0.5 1 15 Portugal 3,393 4,340 17,746 25,778 14.0 11.0 2.9 1,793 2,469 2.5 81 83 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 155 Energy production and use Total energy Energy Net energy production use importsa Total Combustible Per capita thousand thousand renewables average average metric tons of metric tons of and waste annual kilograms of oil annual % of oil equivalent oil equivalent % of total % growth equivalent % growth energy use 1990 2003 1990 2003 1990 2003 1990­2003 1990 2003 1990­2003 1990 2003 Romania 40,834 28,927 62,403 39,009 1.0 7.5 ­3.6 2,689 1,794 ­3.1 35 26 Russian Federation 1,118,707 1,106,924 774,823 639,717 1.6 1.0 .. 5,211 4,424 .. ­44 ­73 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 376,342 533,664 64,976 130,783 0.0 0.0 5.4 3,967 5,607 2.7 ­479 ­308 Senegal 1,362 1,744 2,238 3,193 60.6 53.0 2.7 281 287 0.2 39 45 Serbia and Montenegro 11,835 11,474 15,002 16,235 5.0 4.9 .. 1,435 1,991 .. 21 29 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore .. 140 13,357 22,427 .. .. 4.0 4,384 5,359 1.5 .. 99 Slovak Republic 5,281 6,401 21,434 18,521 0.8 1.9 ­1.1 4,057 3,443 ­1.3 75 65 Slovenia 2,765 3,285 5,008 7,021 1.9 6.7 .. 2,508 3,518 .. 57 53 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 114,534 154,480 91,229 118,566 11.4 11.1 2.0 2,592 2,587 0.0 ­26 ­30 Spain 34,513 32,995 91,073 136,102 4.5 3.5 3.1 2,345 3,240 2.5 62 76 Sri Lanka 4,191 4,294 5,516 8,110 71.0 49.5 3.0 324 421 2.0 24 47 Sudan 8,775 26,974 10,642 16,615 81.7 80.6 3.4 408 477 1.2 18 ­62 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 29,754 31,664 47,566 51,532 11.6 17.1 0.6 5,557 5,754 0.3 37 39 Switzerland 9,830 11,999 25,105 27,075 4.1 6.2 0.6 3,740 3,689 ­0.1 61 56 Syrian Arab Republic 22,319 33,989 11,677 17,882 0.0 0.0 3.3 909 986 0.6 ­91 ­90 Tajikistan 1,553 1,450 9,087 3,187 .. .. .. 1,647 501 .. 83 55 Tanzania 9,063 16,027 9,808 17,154 91.0 92.0 4.3 374 465 1.7 8 7 Thailand 26,496 48,255 43,860 88,762 33.4 16.5 5.4 803 1,406 4.3 40 46 Togo 1,203 1,873 1,447 2,598 82.6 71.3 4.5 365 445 1.5 17 28 Trinidad and Tobago 12,612 28,842 6,037 11,096 0.8 0.2 4.7 4,968 8,553 4.2 ­109 ­160 Tunisia 6,127 6,452 5,536 8,240 18.7 12.7 3.1 679 837 1.6 ­11 22 Turkey 25,857 23,635 53,005 78,954 13.6 7.3 3.1 944 1,117 1.3 51 70 Turkmenistan 48,822 58,551 11,314 17,203 .. .. .. 2,912 3,662 .. ­332 ­240 Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 110,170 75,537 218,376 132,555 0.1 0.2 .. 4,187 2,772 .. 50 43 United Arab Emirates 109,446 159,162 19,618 39,226 0.2 0.0 5.3 11,065 9,707 ­1.0 ­458 ­306 United Kingdom 207,007 246,083 212,176 231,954 0.3 1.2 0.7 3,686 3,893 0.4 2 ­6 United States 1,650,464 1,631,383 1,927,628 2,280,791 3.2 3.0 1.3 7,722 7,843 0.1 14 28 Uruguay 1,149 1,161 2,251 2,519 24.3 17.0 0.9 725 738 0.1 49 54 Uzbekistan 40,461 55,735 44,994 52,254 .. .. .. 2,092 2,023 .. 10 ­7 Venezuela, RB 148,854 179,622 43,918 54,227 1.2 1.0 1.6 2,224 2,112 ­0.4 ­239 ­231 Vietnam 24,711 54,528 24,324 44,260 77.7 53.0 4.6 367 544 3.0 ­2 ­23 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9,384 21,895 2,543 5,697 3.0 1.4 6.2 210 289 2.5 ­269 ­284 Zambia 4,923 6,352 5,470 6,688 73.4 80.8 1.6 653 592 ­0.8 10 5 Zimbabwe 8,550 8,532 9,384 9,668 50.4 60.5 0.2 888 752 ­1.3 9 12 World 8,804,865 t 10,672,009 t 8,615,951 t 10,543,712 t 10.8 w 10.4 w 1.6 w 1,685 w 1,734 w 0.2 w ­2 w ­1 w Low income 816,190 1,148,130 793,735 1,107,697 54.8 48.9 2.6 464 501 0.6 ­3 ­4 Middle income 4,378,339 5,234,462 3,499,414 4,114,859 11.6 10.8 1.3 1,349 1,373 0.1 ­25 ­27 Lower middle income 2,198,061 3,004,295 1,987,670 2,639,085 17.4 14.6 2.2 953 1,090 1.0 ­11 ­14 Upper middle income 2,181,284 2,230,191 1,511,828 1,474,925 3.8 3.9 ­0.2 2,980 2,574 ­1.1 ­44 ­51 Low & middle income 5,190,966 6,370,660 4,284,003 5,204,041 18.9 18.2 1.5 1,008 1,014 0.0 ­21 ­22 East Asia & Pacific 1,229,840 1,894,782 1,147,328 1,853,770 25.6 17.7 3.7 722 1,007 2.6 ­7 ­2 Europe & Central Asia 1,890,157 1,659,437 1,736,190 1,318,474 1.9 2.4 ­2.1 3,726 2,794 ­2.2 ­9 ­26 Latin America & Carib. 617,444 865,000 459,196 617,665 18.1 15.0 2.3 1,050 1,148 0.7 ­34 ­40 Middle East & N. Africa 601,905 768,565 194,412 336,326 1.8 1.3 4.2 861 1,144 2.2 ­210 ­129 South Asia 392,146 542,802 436,601 666,820 48.7 38.8 3.3 394 474 1.4 10 19 Sub-Saharan Africa 489,789 693,529 321,812 435,623 56.7 57.4 2.3 693 681 ­0.1 ­52 ­59 High income 3,654,490 4,351,438 4,363,308 5,377,597 2.9 3.0 1.6 4,842 5,410 0.9 16 19 Europe EMU 466,633 445,236 1,046,458 1,221,275 3.1 3.9 1.2 3,568 3,964 0.8 55 64 a. A negative value indicates that a country is a net exporter. 156 2006 World Development Indicators Energy production and use About the data Definitions In developing countries growth in energy use is closely oil equivalents. To convert nuclear electricity into · Total energy production refers to forms of primary related to growth in the modern sectors--industry, oil equivalents, a notional thermal efficiency of 33 energy--petroleum (crude oil, natural gas liquids, motorized transport, and urban areas--but energy percent is assumed; for hydroelectric power 100 and oil from nonconventional sources), natural gas, use also reflects climatic, geographic, and economic percent efficiency is assumed. solid fuels (coal, lignite, and other derived fuels), factors (such as the relative price of energy). Energy The IEA makes these estimates in consultation and combustible renewables and waste--and pri- use has been growing rapidly in low- and middle- with national statistical offices, oil companies, elec- mary electricity, all converted into oil equivalents income countries, but high-income countries still tricity utilities, and national energy experts. The IEA (see About the data). · Energy use refers to use use more than five times as much energy on a per occasionally revises its time series to reflect politi- of primary energy before transformation to other capita basis. cal changes. Since 1990, for example, it has con- end-use fuels, which is equal to indigenous produc- Energy data are compiled by the International structed energy statistics for countries of the former tion plus imports and stock changes, minus exports Energy Agency (IEA). IEA data for countries that are Soviet Union. In addition, energy statistics for other and fuels supplied to ships and aircraft engaged in not members of the Organisation for Economic Co- countries have undergone continuous changes in international transport (see About the data). · Com- operation and Development (OECD) are based on coverage or methodology as more detailed energy bustible renewables and waste comprise solid bio- national energy data adjusted to conform to annual accounts have become available in recent years. mass, liquid biomass, biogas, industrial waste, and questionnaires completed by OECD member govern- Breaks in series are therefore unavoidable. municipal waste, measured as a percentage of total ments. energy use. · Net energy imports are estimated as Total energy use refers to the use of primary energy energy use less production, both measured in oil before transformation to other end-use fuels (such equivalents. as electricity and refined petroleum products). It includes energy from combustible renewables and waste--solid biomass and animal products, gas and liquid from biomass, and industrial and munici- pal waste. Biomass is defined as any plant matter used directly as fuel or converted into fuel, heat, or electricity. (The data series published in World Development Indicators 1998 and earlier editions did not include energy from combustible renewables and waste.) Data for combustible renewables and waste are often based on small surveys or other incomplete information. Thus the data give only a broad impres- sion of developments and are not strictly compa- rable between countries. The IEA reports (see Data sources) include country notes that explain some of these differences. All forms of energy--primary energy and primary electricity--are converted into In 2003 high-income economies, with 15 percent of world population, used 52 percent of world energy--and produced 41 percent Share of energy use Share of energy production East Asia & Pacific East Asia & Pacific 18% 18% Europe & High- Data sources High- Central Asia income Europe & income 13% 41% Central Asia 51% 16% Data on energy production and use come from Latin America & Caribbean 6% IEA electronic files. The IEA's data are published Latin America Middle East & Caribbean 8% in its annual publications, Energy Statistics and & North Africa Sub-Saharan Africa Sub-Saharan Africa 4% Middle East & North Africa 3% 6% Balances of Non-OECD Countries, Energy Statis- South Asia 6% South Asia 7% 5% tics of OECD Countries, and Energy Balances of OECD Countries. Source: Table 3.7. 2006 World Development Indicators 157 Energy efficiency and emissions GDP per unit of Carbon dioxide Methane Nitrous oxide energy use emissions emissions emissions million metric tons thousand 2000 PPP $ kilograms per of carbon metric tons of per kilogram Total Per capita 2000 PPP $ dioxide % carbon dioxide % of oil equivalent million metric tons metric tons of GDP equivalent change equivalent change 1990 2003 1990 2002 1990 2002 1990 2002 2000 1990­2000 2000 1990­2000 Afghanistan .. .. 2.6 0.6 0.2 .. .. .. 13.2 53.5 7,482 33.8 Albania 3.8 6.4 7.3 2.6 2.2 0.8 0.7 0.2 0.5 ­37.5 52 ­5.5 Algeria 5.7 5.6 77.0 92.0 3.0 2.9 0.6 0.5 28.5 40.4 9,196 14.2 Angola 3.7 3.1 4.7 7.7 0.4 0.5 0.2 0.3 15.8 16.2 6,135 20.0 Argentina 6.4 7.2 109.7 133.1 3.4 3.5 0.4 0.3 86.7 7.4 63,384 12.0 Armenia 1.6 5.2 3.7 2.9 1.1 1.0 0.5 0.3 2.8 ­20.0 291 ­41.1 Australia 4.0 4.8 272.2 355.8 16.0 18.1 0.8 0.7 113.2 0.5 26,974 33.7 Austria 7.1 7.2 57.7 63.6 7.5 7.9 0.3 0.3 9.7 ­16.4 2,802 9.8 Azerbaijan .. 2.3 47.1 28.0 6.4 3.4 .. 1.1 11.9 ­23.7 782 ­42.2 Bangladesh 9.8 10.4 15.4 34.5 0.2 0.3 0.1 0.2 47.6 8.9 44,800 37.0 Belarus 1.2 2.2 94.6 59.9 9.3 6.0 2.0 1.1 21.6 ­10.7 8,318 ­34.3 Belgium 4.7 4.9 100.6 91.5 10.1 8.9 0.4 0.3 11.7 ­4.1 13,282 0.9 Benin 2.6 3.5 0.7 1.9 0.1 0.3 0.2 0.3 3.3 22.2 2,704 27.4 Bolivia 5.1 4.9 5.5 10.1 0.8 1.2 0.4 0.5 21.3 11.5 5,824 ­0.9 Bosnia and Herzegovina .. 5.3 4.7 18.6 1.2 4.7 .. 0.8 1.4 ­30.0 556 ­51.1 Botswana .. .. 2.2 4.1 1.5 2.3 0.3 0.3 7.0 12.9 4,842 9.8 Brazil 7.3 6.9 202.6 313.2 1.4 1.8 0.2 0.2 297.2 9.1 207,696 10.9 Bulgaria 2.1 2.8 75.3 41.9 8.6 5.3 1.2 0.8 10.0 ­63.0 18,483 ­22.4 Burkina Faso .. .. 1.0 1.1 0.1 0.1 0.1 0.1 8.8 20.6 11,733 23.1 Burundi .. .. 0.2 0.3 0.0 0.0 0.0 0.1 1.8 20.0 1,212 9.1 Cambodia .. .. 0.5 0.6 0.1 0.0 .. 0.0 68.0 10.4 105 36.4 Cameroon 4.7 4.6 1.6 3.5 0.1 0.2 0.1 0.1 11.8 12.4 9,821 18.5 Canada 3.0 3.4 415.8 516.3 15.0 16.5 0.7 0.6 123.4 57.6 57,464 9.2 Central African Republic .. .. 0.2 0.3 0.1 0.1 0.1 0.1 6.6 15.8 5,055 18.1 Chad .. .. 0.1 0.1 0.0 0.0 0.0 0.0 9.6 15.7 8,699 21.6 Chile 5.5 5.9 35.3 57.2 2.7 3.6 0.5 0.4 14.5 15.1 7,474 35.6 China 2.1 4.5 2,398.9 3,507.4 2.1 2.7 1.3 0.6 802.9 18.1 644,725 23.8 Hong Kong, China 10.6 10.9 26.2 35.4 4.6 5.2 0.2 0.2 .. .. .. .. Colombia 8.4 10.1 56.8 57.3 1.6 1.3 0.3 0.2 55.5 11.7 41,220 48.0 Congo, Dem. Rep. 5.0 2.1 4.0 1.8 0.1 0.0 0.1 0.1 32.9 5.8 17,186 ­0.3 Congo, Rep. 2.3 3.3 1.2 2.3 0.5 0.6 0.5 0.7 3.2 18.5 1,031 25.7 Costa Rica 9.7 9.9 2.9 5.8 1.0 1.4 0.2 0.2 3.6 ­2.7 3,555 ­9.9 Côte d'Ivoire 5.2 3.8 5.4 6.4 0.4 0.4 0.2 0.3 6.5 20.4 2,892 17.7 Croatia 5.0 5.6 16.8 21.1 3.8 4.8 0.5 0.5 3.8 ­5.0 3,363 ­12.1 Cuba .. .. 32.1 23.6 3.0 2.1 .. .. 9.1 ­8.1 9,288 ­33.3 Czech Republic 3.1 3.9 135.4 114.4 13.1 11.2 1.0 0.7 10.8 ­34.9 8,186 ­48.1 Denmark 6.9 7.5 49.8 47.5 9.7 8.9 0.4 0.3 6.0 ­3.2 9,331 ­15.3 Dominican Republic 7.1 7.4 9.6 21.5 1.4 2.5 0.3 0.4 5.9 11.3 4,287 3.6 Ecuador 5.9 4.9 16.6 24.8 1.6 2.0 0.5 0.6 16.2 18.3 2,878 ­2.7 Egypt, Arab Rep. 5.1 5.2 75.4 143.5 1.4 2.1 0.5 0.6 34.3 40.6 15,965 39.4 El Salvador 7.3 6.9 2.6 6.2 0.5 1.0 0.1 0.2 3.2 18.5 2,208 6.7 Eritrea .. .. .. 0.7 .. 0.2 .. 0.2 0.0 .. .. .. Estonia 1.6 3.4 24.9 15.9 16.2 11.7 2.4 1.0 2.4 ­44.2 444 ­57.8 Ethiopia 2.1 2.1 3.0 6.2 0.1 0.1 0.1 0.1 47.5 20.3 12,170 65.9 Finland 3.8 3.7 51.2 62.6 10.3 12.0 0.5 0.5 4.3 ­33.9 7,302 ­14.9 France 5.5 5.9 362.4 367.7 6.4 6.2 0.3 0.2 59.3 ­11.0 72,265 ­16.5 Gabon 4.8 4.9 6.0 3.5 6.3 2.6 1.0 0.4 3.8 22.6 1,847 ­0.4 Gambia, The .. .. 0.2 0.3 0.2 0.2 0.1 0.1 0.7 16.7 506 3.1 Georgia 1.2 4.1 15.1 3.3 2.8 0.7 1.4 0.3 4.4 ­18.5 1,129 ­44.4 Germany 4.7 6.1 980.6 850.0 12.3 10.3 0.6 0.4 62.7 ­44.2 60,468 ­32.1 Ghana 4.6 5.0 3.8 7.5 0.2 0.4 0.2 0.2 7.1 34.0 7,431 63.8 Greece 6.7 7.3 72.2 94.0 7.1 8.5 0.5 0.5 10.9 23.9 11,198 2.4 Guatemala 6.7 6.5 5.1 10.3 0.6 0.9 0.2 0.2 6.2 5.1 5,165 8.1 Guinea .. .. 1.0 1.3 0.2 0.2 0.1 0.1 5.7 18.8 2,409 29.4 Guinea-Bissau .. .. 0.2 0.3 0.2 0.2 0.2 0.3 0.9 0.0 776 24.4 Haiti 10.4 6.4 1.0 1.8 0.1 0.2 0.1 0.1 3.4 17.2 2,632 6.7 158 2006 World Development Indicators Energy efficiency and emissions GDP per unit of Carbon dioxide Methane Nitrous oxide energy use emissions emissions emissions million metric tons thousand 2000 PPP $ kilograms per of carbon metric tons of per kilogram Total Per capita 2000 PPP $ dioxide % carbon dioxide % of oil equivalent million metric tons metric tons of GDP equivalent change equivalent change 1990 2003 1990 2002 1990 2002 1990 2002 2000 1990­2000 2000 1990­2000 Honduras 5.0 4.9 2.6 5.9 0.5 0.9 0.2 0.4 4.9 ­2.0 3,540 ­0.4 Hungary 4.2 5.6 60.1 56.6 5.8 5.6 0.5 0.4 11.3 ­24.7 12,896 136.1 India 4.0 5.3 677.9 1,218.9 0.8 1.2 0.5 0.5 445.3 16.3 398,980 26.4 Indonesia 4.2 4.3 165.7 306.0 0.9 1.4 0.4 0.5 169.2 13.7 38,747 10.1 Iran, Islamic Rep. 3.6 3.2 218.3 359.6 4.0 5.5 0.9 0.9 96.9 67.4 43,768 14.9 Iraq .. .. 48.5 79.3 2.6 .. .. .. 14.4 6.7 6,461 1.4 Ireland 5.2 9.3 30.6 43.1 8.7 11.0 0.6 0.3 12.9 ­0.8 9,787 5.9 Israel 7.0 7.1 33.1 69.5 7.1 10.6 0.4 0.5 11.4 32.6 1,672 20.1 Italy 8.4 8.2 389.6 432.3 6.9 7.5 0.3 0.3 37.0 ­7.0 43,522 5.7 Jamaica 3.0 2.5 8.0 10.8 3.3 4.1 0.9 1.1 1.3 8.3 1,251 2.8 Japan 6.5 6.5 1,070.7 1,201.6 8.7 9.4 0.4 0.4 21.8 ­17.1 36,982 ­6.3 Jordan 3.5 4.0 10.2 16.7 3.2 3.2 0.8 0.8 7.9 9.7 232 91.7 Kazakhstan 1.0 1.9 252.7 147.7 15.4 9.9 3.2 1.8 27.3 ­44.9 7,830 ­65.0 Kenya 2.2 2.1 5.8 7.2 0.3 0.2 0.2 0.2 21.5 10.8 22,588 3.5 Korea, Dem. Rep. .. .. 244.6 143.0 12.4 6.5 .. .. 33.5 3.4 6,535 ­39.9 Korea, Rep. 4.5 4.2 241.2 445.5 5.6 9.4 0.6 0.5 25.0 ­1.6 16,094 47.7 Kuwait 1.2 1.8 45.2 59.8 21.3 25.6 1.5 1.6 9.9 65.0 159 140.9 Kyrgyz Republic 1.7 3.2 11.0 5.0 2.4 1.0 1.3 0.6 2.2 ­24.1 82 12.3 Lao PDR .. .. 0.2 1.3 0.1 0.2 0.1 0.1 6.2 8.8 52 26.8 Latvia 3.5 5.3 12.7 6.3 4.8 2.7 0.9 0.3 2.6 ­39.5 1,245 ­66.0 Lebanon 2.7 3.0 9.1 16.4 3.3 4.7 1.4 1.0 1.3 85.7 1,149 54.6 Lesotho .. .. .. .. .. .. .. .. 1.2 20.0 1,519 4.5 Liberia .. .. 0.5 0.5 0.2 0.1 .. .. 1.2 ­7.7 840 8.8 Libya .. .. 37.8 50.3 8.7 9.1 .. .. 9.6 9.1 2,534 ­11.4 Lithuania 2.9 4.3 21.4 12.6 5.8 3.6 0.7 0.4 5.9 ­42.2 3,516 167.6 Macedonia, FYR .. .. 10.6 10.2 5.5 5.1 0.9 0.9 1.3 0.0 1,063 15.0 Madagascar .. .. 0.9 2.3 0.1 0.1 0.1 0.2 18.9 14.6 11,600 11.7 Malawi .. .. 0.6 0.8 0.1 0.1 0.1 0.1 3.6 16.1 2,277 13.2 Malaysia 4.4 3.9 55.3 151.4 3.1 6.3 0.6 0.7 30.4 42.7 13,304 14.7 Mali .. .. 0.4 0.6 0.1 0.0 0.1 0.1 12.0 9.1 13,764 24.2 Mauritania .. .. 2.6 3.1 1.3 1.1 0.9 0.7 4.4 12.8 6,427 13.1 Mauritius .. .. 1.5 3.1 1.4 2.6 0.2 0.3 0.3 50.0 856 17.4 Mexico 5.1 5.6 375.2 383.1 4.5 3.8 0.6 0.4 111.7 ­0.5 10,027 10.6 Moldova 1.4 1.9 20.9 6.7 4.8 1.6 2.2 1.1 2.6 ­40.9 1,576 ­59.5 Mongolia .. .. 10.0 8.3 4.7 3.4 3.1 2.1 8.2 17.1 12,072 37.2 Morocco 11.9 10.2 23.5 43.6 1.0 1.5 0.3 0.4 10.0 9.9 15,673 5.6 Mozambique 1.3 2.5 1.0 1.5 0.1 0.1 0.1 0.1 11.1 18.1 3,234 9.8 Myanmar .. .. 4.3 7.6 0.1 0.2 .. .. 61.1 23.9 12,470 32.2 Namibia 12.3 9.9 0.0 2.2 0.0 1.1 0.0 0.2 4.5 4.7 4,170 ­1.5 Nepal 3.4 4.0 0.6 3.8 0.0 0.2 0.0 0.1 16.4 15.5 11,301 15.5 Netherlands 5.2 5.8 139.6 150.6 9.3 9.3 0.4 0.3 21.6 ­22.9 17,242 3.0 New Zealand 4.1 4.8 23.6 33.9 6.9 8.6 0.4 0.4 36.2 ­5.0 12,411 5.4 Nicaragua 5.3 5.5 2.7 3.9 0.7 0.8 0.2 0.2 5.3 12.8 4,048 7.8 Niger .. .. 1.1 1.2 0.1 0.1 0.2 0.1 6.5 25.0 4,999 28.2 Nigeria 1.1 1.3 45.3 52.0 0.5 0.4 0.6 0.5 72.5 41.9 41,556 18.7 Norway 5.1 6.8 46.9 63.1 11.1 13.9 0.4 0.4 7.1 6.0 5,123 0.1 Oman 4.3 2.8 11.2 30.1 6.1 12.1 0.6 0.9 3.7 85.0 1,033 19.3 Pakistan 3.9 4.2 68.0 108.5 0.6 0.8 0.4 0.4 94.7 25.1 84,591 34.0 Panama 7.4 7.6 3.1 6.2 1.3 2.0 0.3 0.4 3.3 10.0 2,694 7.0 Papua New Guinea .. .. 2.4 2.5 0.6 0.5 0.3 0.2 3.9 39.3 2,349 18.0 Paraguay 6.5 6.4 2.3 4.1 0.5 0.7 0.1 0.2 12.3 5.1 10,157 1.8 Peru 8.4 11.3 21.7 25.5 1.0 1.0 0.3 0.2 19.6 15.3 21,919 80.2 Philippines 9.1 7.8 43.9 73.7 0.7 0.9 0.2 0.2 34.2 6.9 20,795 33.4 Poland 3.0 4.6 347.6 295.9 9.1 7.7 1.2 0.7 47.2 ­21.7 23,921 ­22.3 Portugal 7.9 7.2 42.3 62.2 4.3 6.0 0.3 0.3 14.3 2.9 8,073 3.4 Puerto Rico .. .. 11.8 13.6 3.3 3.5 0.2 0.1 .. .. .. .. 2006 World Development Indicators 159 Energy efficiency and emissions GDP per unit of Carbon dioxide Methane Nitrous oxide energy use emissions emissions emissions million metric tons thousand 2000 PPP $ kilograms per of carbon metric tons of per kilogram Total Per capita 2000 PPP $ dioxide % carbon dioxide % of oil equivalent million metric tons metric tons of GDP equivalent change equivalent change 1990 2003 1990 2002 1990 2002 1990 2002 2000 1990­2000 2000 1990­2000 Romania 2.5 4.0 155.1 86.6 6.7 4.0 1.0 0.6 36.1 ­16.6 7,160 ­66.1 Russian Federation 1.6 1.9 1,984.0 1,430.6 13.3 9.9 1.6 1.3 298.7 ­46.0 51,508 ­37.1 Rwanda .. .. 0.5 0.6 0.1 0.1 0.1 0.1 2.2 ­15.4 1,170 ­14.2 Saudi Arabia 2.9 2.2 179.9 340.0 11.0 15.0 0.9 1.3 54.4 56.8 8,666 15.2 Senegal 5.0 5.2 3.1 4.2 0.4 0.4 0.3 0.3 8.4 25.4 6,598 38.1 Serbia and Montenegro .. .. .. .. .. .. .. .. 9.5 ­26.4 6,089 ­34.7 Sierra Leone .. .. 0.3 0.6 0.1 0.1 0.1 0.3 2.6 8.3 941 30.2 Singapore 3.3 4.5 45.1 57.3 14.8 13.7 1.0 0.6 1.2 71.4 897 460.6 Slovak Republic 2.7 3.7 43.1 36.8 8.1 6.8 0.9 0.6 4.2 ­31.2 3,172 ­48.9 Slovenia 4.9 5.2 12.3 15.3 6.2 7.7 0.5 0.4 2.5 ­7.4 1,978 22.6 Somalia .. .. 0.0 .. 0.0 .. .. .. .. .. .. .. South Africa 3.8 3.9 285.5 344.8 8.1 7.6 0.8 0.8 37.4 6.6 25,752 1.4 Spain 7.4 7.0 211.8 304.1 5.5 7.4 0.3 0.3 39.6 22.6 30,094 15.2 Sri Lanka 7.3 8.8 3.8 10.3 0.2 0.5 0.1 0.2 13.3 29.1 2,884 19.8 Sudan 2.7 3.7 5.4 8.8 0.2 0.3 0.2 0.2 46.6 17.1 47,090 19.5 Swaziland .. .. 0.4 1.0 0.6 0.9 0.1 0.2 1.1 10.0 1,216 11.9 Sweden 4.0 4.6 49.5 51.8 5.8 5.8 0.3 0.2 7.1 ­10.1 7,096 ­3.6 Switzerland 8.2 8.1 42.7 40.8 6.4 5.6 0.2 0.2 5.0 ­10.7 3,720 2.6 Syrian Arab Republic 2.9 3.4 35.8 49.0 2.8 2.8 1.1 0.8 9.7 67.2 9,359 19.7 Tajikistan 0.9 2.1 20.6 4.7 3.7 0.8 2.6 0.8 1.4 7.7 54 14.9 Tanzania 1.4 1.3 2.3 3.6 0.1 0.1 0.2 0.2 31.7 17.8 27,110 16.3 Thailand 5.7 5.0 95.7 231.6 1.8 3.7 0.4 0.6 75.9 4.3 13,083 6.9 Togo 4.3 3.2 0.8 1.7 0.2 0.3 0.1 0.2 2.1 16.7 2,294 15.1 Trinidad and Tobago 1.4 1.2 16.9 41.2 13.9 31.8 2.0 3.4 3.1 24.0 287 ­15.8 Tunisia 6.7 8.1 13.3 22.0 1.6 2.3 0.4 0.4 4.8 29.7 5,176 14.4 Turkey 5.8 6.0 143.8 207.7 2.6 3.0 0.5 0.5 97.4 21.1 40,615 ­11.7 Turkmenistan 1.6 1.3 28.0 42.1 7.2 9.1 1.5 2.1 27.1 17.3 573 ­13.8 Uganda .. .. 0.8 1.7 0.1 0.1 0.1 0.1 12.4 25.3 12,891 26.9 Ukraine 1.7 1.9 600.0 306.3 11.5 6.4 1.6 1.3 153.5 ­22.4 19,874 ­42.8 United Arab Emirates 2.2 2.3 60.7 94.0 34.2 25.0 1.4 1.2 35.2 70.9 136 32.0 United Kingdom 5.9 7.1 569.3 542.7 9.9 9.2 0.5 0.3 51.1 ­33.3 43,775 ­35.4 United States 3.7 4.5 4,817.5 5,834.5 19.3 20.2 0.7 0.6 613.4 ­4.8 429,959 8.2 Uruguay 9.9 10.5 3.9 4.1 1.3 1.2 0.2 0.2 18.3 19.6 673 29.7 Uzbekistan 0.7 0.8 113.4 122.1 5.3 4.8 3.4 3.0 46.2 15.2 13,478 19.1 Venezuela, RB 2.6 2.3 117.3 108.0 5.9 4.3 1.0 0.8 95.1 24.3 6,870 ­5.4 Vietnam 3.3 4.4 21.4 66.2 0.3 0.8 0.3 0.4 68.1 15.0 12,873 52.2 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 3.0 2.8 9.6 13.0 0.8 0.7 1.2 0.8 8.7 89.1 5,591 9.5 Zambia 1.5 1.4 2.4 2.0 0.3 0.2 0.3 0.2 11.2 14.3 5,502 12.8 Zimbabwe 3.1 2.6 16.7 12.4 1.6 1.0 0.6 0.4 11.0 1.9 8,576 ­5.0 World 3.9 w 4.7 w 21,254.1 t 24,355.2 t 4.0 w 3.9 w 0.6 w 0.5 t 5,835.0 t ­5.5 w 3,454,338 t 3.0 w Low income 3.5 4.2 1,368.2 1,898.2 0.8 0.8 0.4 0.4 1,367.4 18.3 928,766 26.5 Middle income 3.0 4.2 9,255.9 9,795.3 3.5 3.3 0.9 0.6 3,007.8 8.3 1,528,288 9.7 Lower middle income 3.1 4.6 5,086.8 6,250.2 2.5 2.6 0.8 0.6 2,092.1 15.0 1,231,441 16.3 Upper middle income 2.8 3.5 4,176.7 3,547.9 8.1 6.2 1.0 0.7 916.0 ­0.9 297,173 0.8 Low & middle income 3.1 4.2 10,622.4 11,693.5 2.4 2.2 0.8 0.6 4,375.1 9.7 2,456,507 12.1 East Asia & Pacific 2.6 4.6 3,047.9 4,506.5 1.9 2.5 1.0 0.6 1,365.1 17.3 779,925 21.9 Europe & Central Asia 2.1 2.7 4,827.8 3,118.8 10.2 6.7 1.3 1.0 844.1 ­20.3 236,256 ­19.4 Latin America & Carib. 6.0 6.2 1,038.5 1,264.8 2.4 2.4 0.4 0.3 800.5 7.7 419,712 13.8 Middle East & N. Africa 4.6 4.2 576.5 926.3 2.6 3.2 0.7 0.7 233.0 42.2 118,262 19.0 South Asia 4.2 5.3 768.6 1,378.1 0.7 1.0 0.4 0.4 631.7 17.2 550,313 27.9 Sub-Saharan Africa 2.8 2.8 418.3 511.2 0.8 0.7 0.5 0.4 501.5 15.3 353,802 11.8 High income 4.7 5.2 10,654.1 12,685.3 11.8 12.8 0.5 0.5 1,432.7 ­9.0 960,933 0.9 Europe EMU 5.8 6.4 2,448.7 2,531.1 8.4 8.3 0.4 0.3 284.5 ­17.6 276,251 ­10.8 160 2006 World Development Indicators Energy efficiency and emissions About the data Definitions The ratio of GDP to energy use provides a measure effective at trapping heat in the earth's atmosphere · GDP per unit of energy use is the PPP GDP per of energy efficiency. To produce comparable and as a kilogram of carbon dioxide within a time horizon of kilogram of oil equivalent of energy use. PPP GDP consistent estimates of real GDP across countries 100 years. The global warming potential of a kilogram is gross domestic product converted to 2000 con- relative to physical inputs to GDP--that is, units of of nitrous oxide is nearly 300 times that of a kilogram stant international dollars using purchasing power energy use--GDP is converted to 2000 constant of carbon dioxide within the same time horizon. parity rates. An international dollar has the same international dollars using purchasing power parity The Carbon Dioxide Information Analysis Cen- purchasing power over GDP as a U.S. dollar has in (PPP) rates. Differences in this ratio over time and ter (CDIAC), sponsored by the U.S. Department of the United States. · Carbon dioxide emissions are across countries reflect in part structural changes Energy, calculates annual anthropogenic emissions those stemming from the burning of fossil fuels and in the economy, changes in the energy efficiency of of carbon dioxide. These calculations are based on the manufacture of cement. They include carbon particular sectors, and differences in fuel mixes. data on fossil fuel consumption (from the World dioxide produced during consumption of solid, liquid, Because commercial energy is widely traded, it is Energy Data Set maintained by the United Nations and gas fuels and gas flaring. · Methane emissions necessary to distinguish between its production and Statistics Division) and data on world cement manu- are those stemming from human activities such as its use. Net energy imports show the extent to which an facturing (from the Cement Manufacturing Data Set agriculture and from industrial methane production. economy's use exceeds its domestic production. High- maintained by the U.S. Bureau of Mines). Emissions · Nitrous oxide emissions are those stemming from income countries are net energy importers; middle- of carbon dioxide are often calculated and reported agriculture, biomass burning, industrial activities, income countries have been their main suppliers. in terms of their content of elemental carbon. For and livestock management. Carbon dioxide emissions, largely a by-product of this table these values were converted to the actual energy production and use (see table 3.7), account mass of carbon dioxide by multiplying the carbon for the largest share of greenhouse gases, which are mass by 3.664 (the ratio of the mass of carbon to associated with global warming. Anthropogenic car- that of carbon dioxide). Although the estimates of bon dioxide emissions result primarily from fossil fuel global carbon dioxide emissions are probably within combustion and cement manufacturing. In combus- 10 percent of actual emissions (as calculated from tion, different fossil fuels release different amounts global average fuel chemistry and use), country of carbon dioxide for the same level of energy use. estimates may have larger error bounds. Trends Burning oil releases about 50 percent more carbon estimated from a consistent time series tend to be dioxide than burning natural gas, and burning coal more accurate than individual values. Each year the releases about twice as much. Cement manufactur- CDIAC recalculates the entire time series from 1950 ing releases about half a metric ton of carbon dioxide to the present, incorporating its most recent findings for each metric ton of cement produced. and the latest corrections to its database. Estimates Methane emissions, largely the result of agricultural do not include fuels supplied to ships and aircraft activities and industrial production of methane, are engaged in international transport because of the expressed in carbon dioxide equivalents using the difficulty of apportioning these fuels among the coun- global warming potential, which allows the different tries benefiting from that transport. gases to be compared on the basis of their effective contributions. A kilogram of methane is 23 times as The five largest producers of carbon dioxide . . . . . . differ significantly in per capita emissions Carbon dioxide emissions (billions of metric tons) Per capita emissions of carbon dioxide (metric tons) 6 1990 2002 25 1990 2002 5 20 4 15 Data sources 3 The underlying data on energy use are from elec- 10 2 tronic files of the International Energy Agency. Data 5 on carbon dioxide emissions are from the CDIAC, 1 Environmental Sciences Division, Oak Ridge 0 0 National Laboratory, in the U.S. state of Tennes- United China Russian India Japan United China Russian India Japan States Federation States Federation see. Data on methane and nitrous oxide emissions are compiled by the World Resources Institute. Source: Table 3.8. Source: Table 3.8. 2006 World Development Indicators 161 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Hydropower Coal Oil Gas Nuclear power 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.2 5.2 89.1 98.8 .. .. 10.9 1.2 .. .. .. .. Algeria 16.1 29.6 0.8 0.9 .. .. 5.4 2.3 93.7 96.8 .. .. Angola 0.8 2.0 86.2 62.2 .. .. 13.8 37.8 .. .. .. .. Argentina 51.0 92.1 35.6 36.8 1.3 1.0 9.7 1.1 39.0 51.7 14.3 8.2 Armenia 10.0 5.5 33.8 36.0 .. .. 43.3 .. 22.9 27.6 .. 36.3 Australia 154.3 227.9 9.2 7.0 77.1 77.2 2.7 1.0 10.6 13.8 .. .. Austria 49.3 61.2 63.9 59.4 14.2 15.4 3.8 2.9 15.7 18.3 .. .. Azerbaijan 21.8 21.3 8.9 11.6 .. .. 91.1 35.7 0.5 52.7 .. .. Bangladesh 7.7 19.7 11.4 5.7 .. .. 4.3 6.7 84.3 87.5 .. .. Belarus 41.6 26.6 0.0 0.1 .. 0.0 52.1 4.4 47.9 95.5 .. .. Belgium 70.3 83.6 0.4 0.3 28.2 13.9 1.9 1.2 7.7 25.9 60.8 56.7 Benin 0.0 0.1 2.6 .. .. 100.0 97.4 .. .. .. .. Bolivia 2.1 4.3 55.3 60.3 .. .. 5.3 19.1 37.6 18.5 .. .. Bosnia and Herzegovina 6.5 11.2 52.2 48.0 47.8 50.9 .. 1.1 .. .. .. .. Botswana .. .. .. .. .. .. .. .. .. .. .. .. Brazil 222.8 364.9 92.8 83.8 2.1 2.4 2.5 3.0 0.0 3.6 1.0 3.7 Bulgaria 42.1 42.3 4.5 7.0 50.3 46.1 2.9 1.9 7.6 4.2 34.8 40.9 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 2.7 3.7 98.5 95.8 .. .. 1.5 4.2 .. .. .. .. Canada 481.9 586.9 61.6 57.5 17.1 19.3 3.4 3.0 2.0 5.8 15.1 12.8 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 18.4 48.8 55.1 46.3 34.4 13.5 7.6 1.3 1.3 35.4 .. .. China 621.2 1,907.4 20.4 14.9 71.2 79.4 7.9 3.0 0.5 0.3 0.2 2.3 Hong Kong, China 28.9 35.5 .. .. 98.3 77.7 1.7 0.5 .. 21.8 .. .. Colombia 36.2 47.1 76.0 76.8 9.8 8.1 1.0 0.3 12.4 13.6 .. .. Congo, Dem. Rep. 5.6 6.3 99.6 99.7 .. .. 0.4 0.3 .. .. .. .. Congo, Rep. 0.5 0.3 99.4 99.7 .. .. 0.6 0.3 .. .. .. .. Costa Rica 3.5 7.6 97.5 78.3 .. .. 2.5 1.8 .. .. .. .. Côte d'Ivoire 2.0 5.1 66.7 36.0 .. .. 33.3 0.1 .. 63.9 .. .. Croatia 8.9 12.6 48.8 38.7 5.0 19.1 35.8 25.0 15.4 17.2 .. .. Cuba 15.0 15.9 0.6 0.6 .. .. 91.5 94.3 0.2 0.0 .. .. Czech Republic 62.6 82.8 2.3 1.7 71.8 62.3 4.8 0.4 1.0 3.7 20.1 31.2 Denmark 26.0 46.3 0.1 0.0 90.3 54.7 3.7 5.1 2.7 21.2 .. .. Dominican Republic 3.7 13.5 9.4 8.9 1.2 21.1 88.6 69.5 .. 0.1 .. .. Ecuador 6.3 11.5 78.5 62.2 .. .. 21.5 29.7 .. 8.1 .. .. Egypt, Arab Rep. 42.3 91.9 23.5 14.1 .. .. 36.9 5.7 39.6 79.9 .. .. El Salvador 2.2 4.1 73.5 35.8 .. .. 6.9 40.0 .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 13.1 10.2 0.0 0.1 90.0 92.2 4.5 0.4 5.5 6.9 .. .. Ethiopia 1.2 2.3 88.4 99.3 .. .. 11.6 0.7 .. .. .. .. Finland 54.4 84.2 20.0 11.4 33.0 31.8 3.1 1.1 8.6 16.6 35.3 27.0 France 417.8 561.7 12.9 10.5 8.5 5.3 2.1 1.5 0.7 3.1 75.2 78.5 Gabon 1.0 1.5 72.1 59.8 .. .. 11.2 22.8 16.4 16.9 .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 12.4 7.1 58.3 91.7 .. .. 5.0 0.4 36.6 7.9 .. .. Germany 547.7 594.3 3.2 3.2 58.8 52.9 1.9 0.8 7.4 9.8 27.8 27.8 Ghana 5.7 5.9 100.0 65.8 .. .. 0.3 34.2 .. .. .. .. Greece 34.8 57.9 5.1 8.2 72.4 60.7 22.3 15.1 0.3 13.8 .. .. Guatemala 2.3 6.6 76.0 37.8 .. 14.5 9.0 34.8 .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.6 0.5 76.5 47.7 .. .. 20.6 52.3 .. .. .. .. 162 2006 World Development Indicators Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Hydropower Coal Oil Gas Nuclear power 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 Honduras 2.3 4.5 98.3 48.0 .. .. 1.7 51.7 .. .. .. .. Hungary 28.4 34.1 0.6 0.5 30.5 27.1 4.8 4.8 15.7 34.8 48.3 32.3 India 289.4 633.3 24.8 11.9 65.3 68.3 4.3 4.6 3.4 11.5 2.1 2.8 Indonesia 33.3 112.9 20.2 8.0 31.5 41.1 42.7 24.9 2.3 20.3 .. .. Iran, Islamic Rep. 59.1 152.6 10.3 7.3 .. .. 37.3 16.0 52.5 76.7 .. .. Iraq 24.0 28.3 10.8 1.5 .. .. 89.2 98.5 .. .. .. .. Ireland 14.2 24.9 4.9 2.4 57.4 33.1 10.0 9.9 27.7 52.5 .. .. Israel 20.9 47.0 0.0 0.1 50.1 77.0 49.9 22.9 .. 0.1 .. .. Italy 213.1 283.4 14.8 11.9 16.8 15.6 48.2 26.8 18.6 41.4 0.1 .. Jamaica 2.5 7.1 3.6 1.6 .. .. 92.4 96.9 .. .. .. .. Japan 834.5 1,037.7 10.7 9.1 14.4 28.2 29.6 13.2 19.5 24.3 24.2 23.1 Jordan 3.6 8.5 0.3 0.5 .. .. 87.8 90.7 11.9 8.8 .. .. Kazakhstan 91.6 63.8 8.3 13.5 72.3 69.9 8.8 6.0 10.6 10.6 .. .. Kenya 3.0 4.9 81.6 67.0 .. .. 7.6 16.8 .. .. .. .. Korea, Dem. Rep. 27.7 21.0 56.3 55.7 40.1 39.4 3.6 4.9 .. .. .. .. Korea, Rep. 105.4 344.9 6.0 1.4 16.8 38.9 17.9 9.2 9.1 12.3 50.2 37.6 Kuwait 18.5 39.8 .. .. .. .. 54.3 80.0 45.7 20.0 .. .. Kyrgyz Republic 13.2 14.0 77.4 92.7 9.1 3.6 .. .. 13.6 3.6 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 4.2 4.0 65.8 57.0 2.0 0.6 7.9 2.1 26.3 38.5 .. .. Lebanon 1.5 10.5 33.3 12.9 .. .. 66.7 87.1 .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 10.2 18.9 .. .. .. .. 100.0 79.6 .. 20.4 .. .. Lithuania 20.7 18.8 2.5 1.7 .. .. 12.4 1.7 6.7 13.4 78.2 82.2 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23.0 78.4 17.3 7.3 12.3 14.4 50.0 4.3 20.4 74.0 .. .. Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 122.7 218.7 19.1 9.1 6.3 14.3 57.3 32.4 10.6 35.4 2.4 4.8 Moldova 12.5 3.4 2.3 1.9 34.4 5.4 26.4 0.5 36.9 92.2 .. .. Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 9.6 18.1 12.7 8.0 23.0 67.7 64.4 23.2 .. .. .. .. Mozambique 0.5 10.6 62.6 99.7 13.9 .. 23.6 0.3 0.2 0.1 .. .. Myanmar 2.5 6.2 48.1 36.2 1.6 .. 10.9 6.8 39.3 57.0 .. .. Namibia 1.4 1.5 95.2 96.9 1.5 0.4 3.3 2.7 .. .. .. .. Nepal 0.9 2.3 99.9 99.8 .. .. 0.1 0.2 .. .. .. .. Netherlands 71.9 96.8 0.1 0.1 38.2 28.4 4.3 3.0 50.9 58.8 4.9 4.2 New Zealand 32.3 41.1 72.3 57.5 1.5 8.1 0.0 0.0 17.6 24.4 .. .. Nicaragua 1.4 2.7 28.8 11.0 .. .. 39.8 75.2 .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 13.5 20.2 32.6 38.8 0.1 .. 13.7 22.7 53.7 38.5 .. .. Norway 121.6 106.7 99.6 98.9 0.2 0.1 0.0 0.0 0.0 0.3 .. .. Oman 4.5 10.7 .. .. .. .. 18.4 18.0 81.6 82.0 .. .. Pakistan 37.7 80.8 44.9 33.3 0.1 0.2 20.6 15.7 33.6 48.5 0.8 2.2 Panama 2.7 5.6 83.2 50.6 .. .. 14.7 49.0 .. .. .. .. Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 27.2 51.8 99.9 100.0 .. .. 0.0 .. .. .. .. .. Peru 13.8 22.9 75.8 80.8 .. 3.3 21.5 9.7 1.7 5.2 .. .. Philippines 25.2 52.9 24.0 14.9 7.7 27.5 46.7 14.2 .. 24.9 .. .. Poland 134.4 150.0 1.1 1.1 97.5 95.1 1.2 1.6 0.1 1.6 .. .. Portugal 28.4 46.5 32.3 33.8 32.1 31.2 33.1 13.2 .. 16.6 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 163 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Hydropower Coal Oil Gas Nuclear power 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 Romania 64.3 55.1 17.7 24.0 28.8 42.9 18.4 6.6 35.1 17.6 .. 8.9 Russian Federation 1,116.7 914.3 17.0 17.0 15.3 18.8 9.9 3.0 45.7 44.5 11.9 16.4 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 69.2 153.0 .. .. .. .. 56.5 53.6 43.5 46.4 .. .. Senegal 0.9 2.1 .. 15.7 .. .. 98.0 74.6 2.0 1.6 .. .. Serbia and Montenegro 36.5 35.4 31.1 27.9 65.6 69.9 1.7 0.8 1.6 1.5 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 15.7 35.3 .. .. .. .. 100.0 34.6 0.0 60.8 .. .. Slovak Republic 23.4 31.0 8.0 11.2 32.2 20.6 3.4 2.3 4.9 7.7 51.4 57.7 Slovenia 12.1 14.0 28.2 22.5 36.2 36.4 2.5 0.4 0.2 2.6 32.9 37.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 165.4 229.1 0.6 0.8 94.3 93.5 0.0 .. .. 0.0 5.1 5.5 Spain 151.2 257.9 16.8 15.9 40.1 29.5 5.7 9.3 1.0 15.3 35.9 24.0 Sri Lanka 3.2 7.6 99.8 43.5 .. .. 0.2 56.5 .. .. .. .. Sudan 1.5 3.4 63.2 34.7 .. .. 36.8 65.3 .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 146.0 135.6 49.7 39.3 1.2 3.1 0.8 2.9 0.3 0.4 46.7 49.7 Switzerland 54.7 64.9 54.5 53.6 0.1 .. 0.5 0.1 0.6 1.4 43.2 42.3 Syrian Arab Republic 11.6 29.5 23.5 9.5 .. .. 56.0 41.7 20.5 48.9 .. .. Tajikistan 18.6 16.5 94.7 97.7 .. .. .. .. 5.3 2.3 .. .. Tanzania 1.6 2.7 95.1 93.0 .. 2.7 4.9 4.3 .. .. .. .. Thailand 44.2 117.0 11.3 6.2 25.0 15.8 23.5 2.7 40.2 73.0 .. .. Togo 0.2 0.3 60.1 82.8 .. .. 39.9 17.2 .. .. .. .. Trinidad and Tobago 3.6 6.4 .. .. .. .. 0.1 0.1 99.0 99.7 .. .. Tunisia 5.8 12.4 0.8 1.3 .. .. 35.5 8.7 63.7 89.7 .. .. Turkey 57.5 140.6 40.2 25.1 35.1 22.9 6.9 6.5 17.7 45.2 .. .. Turkmenistan 14.6 10.8 0.0 0.0 .. .. .. .. 100.0 100.0 .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 279.6 180.2 3.2 5.1 18.1 18.3 9.1 0.5 40.4 30.9 29.2 45.2 United Arab Emirates 17.1 49.5 .. .. 4.0 0.6 96.0 99.4 .. .. United Kingdom 317.8 395.9 1.6 0.8 65.0 35.4 10.9 1.8 1.6 37.5 20.7 22.4 United States 3,202.8 4,054.4 8.5 6.9 53.1 51.4 4.1 3.4 11.9 16.5 19.1 19.4 Uruguay 7.4 8.6 94.2 99.4 .. .. 5.1 0.2 .. 0.0 .. .. Uzbekistan 56.4 49.4 12.3 12.8 4.9 2.9 6.9 11.4 75.9 72.9 .. .. Venezuela, RB 59.3 91.8 62.3 66.0 .. .. 11.5 16.4 26.2 17.6 .. .. Vietnam 8.7 40.9 61.8 46.4 23.1 17.7 15.0 6.5 0.1 29.4 .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.7 4.1 .. .. .. .. 100.0 100.0 .. .. .. .. Zambia 8.0 9.6 99.2 99.5 0.5 0.2 0.3 0.4 .. .. .. .. Zimbabwe 9.4 8.8 46.7 60.9 53.3 38.9 .. 0.2 .. .. .. .. World 11,697.4 16,618.4 18.1 15.8 38.0 40.1 11.2 6.8 13.9 19.4 17.2 15.9 Low income 535.1 981.1 34.4 23.8 40.9 46.3 7.3 7.2 14.5 20.0 1.2 2.0 Middle income 3,753.1 5,818.3 21.5 20.8 35.2 42.4 15.0 7.3 19.8 21.5 7.6 7.1 Lower middle income 1,817.8 3,591.1 27.4 23.3 37.4 49.4 16.8 7.2 14.0 14.4 5.1 4.5 Upper middle income 1,935.3 2,227.2 16.1 16.6 33.2 31.0 13.4 7.4 25.3 32.9 10.0 11.3 Low & middle income 4,288.2 6,799.4 23.1 21.2 35.9 42.9 14.0 7.3 19.1 21.3 6.8 6.4 East Asia & Pacific 785.8 2,336.8 21.7 14.8 61.3 69.4 12.7 4.4 3.5 8.6 0.2 1.9 Europe & Central Asia 2,143.0 1,946.4 12.9 15.7 31.9 29.8 12.6 3.6 29.2 34.0 12.3 16.8 Latin America & Carib. 607.0 1,037.0 63.7 56.4 3.8 5.4 19.0 14.2 9.5 18.1 2.1 3.0 Middle East & N. Africa 190.0 415.3 12.2 7.4 1.2 3.0 48.2 27.4 38.4 62.2 .. .. South Asia 338.8 743.7 27.6 14.7 55.8 58.2 6.1 6.4 8.6 17.4 1.9 2.6 Sub-Saharan Africa 223.5 320.3 18.5 20.1 72.1 68.0 2.2 4.0 3.3 3.5 3.8 4.0 High income 7,409.2 9,818.9 15.2 12.1 39.2 38.2 9.6 6.5 10.8 18.1 23.2 22.4 Europe EMU 1,653.6 2,155.1 11.0 10.2 34.4 27.7 9.5 6.4 8.7 17.0 35.5 34.4 a. Shares may not sum to 100 percent because some sources of generated electricity are not shown. 164 2006 World Development Indicators Sources of electricity About the data Definitions Use of energy is important in improving people's aggregates from which key data are missing, and · Electricity production is measured at the termi- standard of living. But electricity generation also adjustments are made to compensate for differ- nals of all alternator sets in a station. In addition to can damage the environment. Whether such damage ences in definitions. hydropower, coal, oil, gas, and nuclear power genera- occurs depends largely on how electricity is gener- The IEA makes these estimates in consultation tion, it covers generation by geothermal, solar, wind, ated. For example, burning coal releases twice as with national statistical offices, oil companies, elec- and tide and wave energy as well as that from com- much carbon dioxide--a major contributor to global tricity utilities, and national energy experts. The IEA bustible renewables and waste. Production includes warming--as does burning an equivalent amount occasionally revises its time series to reflect politi- the output of electricity plants designed to produce of natural gas (see About the data for table 3.8). cal changes. Since 1990, for example, it has con- electricity only, as well as that of combined heat and Nuclear energy does not generate carbon dioxide structed energy statistics for countries of the former power plants. · Sources of electricity refer to the emissions, but it produces other dangerous waste Soviet Union. In addition, energy statistics for other inputs used to generate electricity: hydropower, coal, products. The table provides information on electric- countries have undergone continuous changes in oil, gas, and nuclear power. · Hydropower refers to ity production by source. Shares may not sum to coverage or methodology as more detailed energy electricity produced by hydroelectric power plants. 100 percent because some sources of generated accounts have become available in recent years. · Oil refers to crude oil and petroleum products. electricity (such as wind, solar, and geothermal) are Breaks in series are therefore unavoidable. · Gas refers to natural gas but not to natural gas liq- not shown. uids. · Nuclear power refers to electricity produced The International Energy Agency (IEA) compiles by nuclear power plants. data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Devel- opment (OECD) are based on national energy data adjusted to conform to annual questionnaires com- pleted by OECD member governments. In addition, estimates are sometimes made to complete major Electricity sources have shifted since 1990 . . . 1990 2003 Other 2% Other 2% Oil 7% Oil 11% Gas Coal Gas Coal 14% 38% 19% 40% Nuclear Nuclear 17% 16% Hydropower Hydropower 18% 16% Source: Table 3.9. . . . with a more profound shift in low-income countries 1990 2003 Other 2% Other 0.5% Nuclear 1% Nuclear 2% Oil Oil 7% 7% Gas Data sources 15% Coal Gas Coal 41% 20% 46% Data on electricity production are from the IEA's electronic files and its annual publications Energy Hydropower Hydropower 34% 24% Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. Source: Table 3.9. 2006 World Development Indicators 165 Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2004 1990 2004 1990­2004 1990 2005 1990 2005 1990 2002 1990 2002 Afghanistan 2.7 .. 18 .. .. 11 .. 59 .. .. .. 5 .. Albania 1.2 1.4 36 44 1.1 .. .. .. .. 99 99 .. 81 Algeria 13.0 19.2 51 59 2.8 8 10 15 17 99 99 76 82 Angola 2.8 5.6 26 36 5.1 15 18 58 48 62 56 19 16 Argentina 28.3 34.7 87 90 1.4 41 42 39 38 87 .. 47 .. Armenia 2.4 1.9 67 64 ­1.4 33 35 49 55 96 96 .. 61 Australia 14.5 18.6 85 92 1.8 60 61 25 23 100 100 100 100 Austria 5.1 5.4 66 66 0.4 27 27 41 41 100 100 100 100 Azerbaijan 3.8 4.2 54 50 0.5 24 22 45 44 .. 73 .. 36 Bangladesh 20.6 34.3 20 25 3.7 9 13 32 35 71 75 11 39 Belarus 6.7 7.0 66 71 0.3 16 17 24 24 .. .. .. .. Belgium 9.6 10.1 96 97 0.4 .. .. 10 10 .. .. .. .. Benin 1.8 3.7 34 45 5.2 .. .. .. .. 31 58 1 12 Bolivia 3.7 5.8 56 64 3.1 25 31 29 26 49 58 13 23 Bosnia and Herzegovina 1.7 1.8 39 45 0.3 .. .. .. .. 99 99 .. 88 Botswana 0.6 0.9 42 52 3.0 .. .. .. .. 61 57 21 25 Brazil 111.6 153.8 75 84 2.3 33 36 13 12 82 83 37 35 Bulgaria 5.8 5.4 66 70 ­0.4 14 13 21 19 100 100 100 100 Burkina Faso 1.2 2.3 14 18 5.0 .. .. 51 35 47 45 8 5 Burundi 0.4 0.7 6 10 5.3 .. .. .. .. 42 47 44 35 Cambodia 1.2 2.6 13 19 5.5 6 8 .. .. .. 53 .. 8 Cameroon 4.7 8.4 40 52 4.1 16 23 21 23 43 63 7 33 Canada 21.3 25.8 77 81 1.4 34 37 18 19 100 100 99 99 Central African Republic 1.1 1.7 37 43 3.1 .. .. .. .. 32 47 18 12 Chad 1.3 2.4 21 25 4.5 .. .. .. .. 27 30 1 0 Chile 11.0 14.1 83 87 1.8 35 35 42 39 91 96 52 64 China 311.0 513.0 27 40 3.6 14 15 4 2 64 69 7 29 Hong Kong, China 5.7 6.9 100 100 1.4 100 100 100 100 .. .. .. .. Colombia 24.0 34.5 69 77 2.6 28 34 21 22 95 96 52 54 Congo, Dem. Rep. 10.6 18.0 28 32 3.8 9 10 32 30 56 43 3 23 Congo, Rep. 1.2 2.1 48 54 4.0 .. .. 59 53 .. 14 2 2 Costa Rica 1.6 2.6 54 61 3.3 .. .. 45 43 .. 89 97 97 Côte d'Ivoire 5.0 8.1 40 45 3.4 17 19 42 42 52 61 16 23 Croatia 2.6 2.6 54 59 0.2 .. .. .. .. .. .. .. .. Cuba 7.8 8.5 74 76 0.7 20 19 27 26 99 99 95 95 Czech Republic 7.8 7.6 75 74 ­0.2 12 11 16 15 .. .. .. .. Denmark 4.4 4.6 85 85 0.4 26 20 31 24 .. .. .. .. Dominican Republic 3.9 5.2 55 60 2.1 21 22 39 36 60 67 33 43 Ecuador 5.7 8.1 55 62 2.6 26 29 28 29 73 80 36 59 Egypt, Arab Rep. 24.2 30.7 43 42 1.7 22 20 37 36 70 84 42 56 El Salvador 2.5 4.0 49 60 3.4 19 21 39 36 70 78 33 40 Eritrea 0.5 0.9 16 20 4.2 .. .. .. .. 46 34 0 3 Estonia 1.1 0.9 71 70 ­1.2 .. .. .. .. .. 93 .. .. Ethiopia 6.5 11.1 13 16 3.8 3 4 28 25 14 19 2 4 Finland 3.1 3.2 61 61 0.3 17 21 28 35 100 100 100 100 France 42.0 46.2 74 76 0.7 23 23 22 21 .. .. .. .. Gabon 0.7 1.2 68 84 4.1 .. .. .. .. .. 37 .. 30 Gambia, The 0.2 0.4 25 26 3.6 .. .. .. .. .. 72 .. 46 Georgia 3.0 2.3 55 52 ­1.8 22 23 41 45 96 96 .. 69 Germany 67.8 72.9 85 88 0.5 40 42 9 9 .. .. .. .. Ghana 5.6 9.9 36 46 4.0 8 9 21 19 54 74 37 46 Greece 6.0 6.8 59 61 0.9 30 29 51 48 .. .. .. .. Guatemala 3.7 5.7 41 47 3.2 .. .. 22 17 71 72 35 52 Guinea 1.6 3.3 25 36 5.2 14 16 56 43 27 25 13 6 Guinea-Bissau 0.2 0.5 24 35 5.7 .. .. .. .. .. 57 .. 23 Haiti 2.0 3.2 29 38 3.3 17 25 56 63 27 52 11 23 166 2006 World Development Indicators Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2004 1990 2004 1990­2004 1990 2005 1990 2005 1990 2002 1990 2002 Honduras 2.0 3.2 40 46 3.6 .. .. 36 32 77 89 31 52 Hungary 6.4 6.6 62 66 0.2 19 17 31 25 100 100 .. 85 India 217.0 308.0 26 29 2.5 9 11 6 6 43 58 1 18 Indonesia 54.5 101.6 31 47 4.4 9 11 14 12 66 71 38 38 Iran, Islamic Rep. 30.6 45.1 56 67 2.8 22 22 21 16 86 86 78 78 Iraq 12.9 .. 70 .. .. 29 .. 32 .. 95 .. 48 .. Ireland 2.0 2.4 57 60 1.5 .. .. 46 41 .. .. .. .. Israel 4.2 6.2 90 92 2.8 48 58 43 48 100 100 .. .. Italy 37.8 38.8 67 67 0.2 23 20 12 10 .. .. .. .. Jamaica 1.2 1.4 51 52 0.8 .. .. .. .. 85 90 64 68 Japan 77.9 83.8 63 66 0.5 42 44 42 42 100 100 100 100 Jordan 2.3 4.3 72 79 4.5 27 23 37 29 97 94 .. 85 Kazakhstan 9.3 8.4 57 56 ­0.8 7 7 12 13 87 87 52 52 Kenya 5.8 13.6 25 40 6.1 6 8 24 20 49 56 40 43 Korea, Dem. Rep. 11.5 13.8 58 61 1.3 16 20 22 24 .. 58 .. 60 Korea, Rep. 31.7 38.7 74 81 1.4 49 46 33 25 .. .. .. .. Kuwait 2.0 2.4 95 96 1.1 48 .. 51 .. .. .. .. .. Kyrgyz Republic 1.7 1.7 38 34 0.2 .. .. 38 48 .. 75 .. 51 Lao PDR 0.6 1.2 15 21 4.7 .. .. .. .. .. 61 .. 14 Latvia 1.9 1.5 70 66 ­1.5 .. .. 48 47 .. .. .. .. Lebanon 2.3 3.1 83 88 2.2 42 52 51 60 100 100 .. 87 Lesotho 0.3 0.3 17 18 1.2 .. .. .. .. 61 61 32 32 Liberia 0.9 1.5 42 47 3.8 .. .. .. .. 59 49 24 7 Libya 3.5 5.0 80 87 2.6 35 36 43 41 97 97 96 96 Lithuania 2.5 2.3 68 67 ­0.6 .. .. .. .. .. .. .. .. Macedonia, FYR 1.1 1.2 58 60 0.7 .. .. .. .. .. .. .. .. Madagascar 2.8 4.9 24 27 3.8 8 10 33 36 25 49 8 27 Malawi 1.1 2.1 12 17 4.6 .. .. .. .. 52 66 34 42 Malaysia 8.9 16.0 50 64 4.2 6 5 13 8 94 .. 98 98 Mali 2.1 4.3 24 33 5.1 8 10 35 30 50 59 32 38 Mauritania 0.9 1.9 44 63 5.3 .. .. .. .. 31 64 26 9 Mauritius 0.4 0.5 41 44 1.6 .. .. .. .. 100 100 99 99 Mexico 60.3 78.6 72 76 1.9 32 34 25 24 84 90 20 39 Moldova 2.0 1.9 47 46 ­0.3 .. .. .. .. .. 86 .. 52 Mongolia 1.2 1.4 57 57 1.3 .. .. 48 58 .. 75 .. 37 Morocco 11.6 17.3 48 58 2.9 21 25 23 21 87 83 28 31 Mozambique 2.8 7.1 21 37 6.6 6 7 27 18 .. 51 14 14 Myanmar 10.1 15.0 25 30 2.8 7 8 29 26 39 96 15 63 Namibia 0.4 0.7 27 33 4.1 .. .. .. .. 68 66 8 14 Nepal 1.7 4.1 9 15 6.2 .. .. .. .. 62 68 7 20 Netherlands 9.0 10.8 60 66 1.3 14 14 12 11 100 100 100 100 New Zealand 2.9 3.5 85 86 1.3 25 28 30 33 .. .. 88 .. Nicaragua 2.1 3.1 53 58 2.8 19 21 35 36 64 78 27 51 Niger 1.4 3.1 16 23 5.8 .. .. 33 31 35 43 2 4 Nigeria 31.7 61.1 35 47 4.7 11 13 15 18 50 48 33 30 Norway 3.1 3.7 72 80 1.3 .. .. 22 22 .. .. .. .. Oman 1.1 2.0 62 78 3.9 .. .. .. .. 97 97 61 61 Pakistan 33.0 52.4 31 34 3.3 16 18 22 22 81 92 19 35 Panama 1.3 1.8 54 57 2.4 .. .. 65 51 .. 89 .. 51 Papua New Guinea 0.5 0.8 13 13 2.5 .. .. .. .. 67 67 41 41 Paraguay 2.1 3.5 49 58 3.8 22 28 45 49 71 94 46 58 Peru 15.0 20.5 69 74 2.2 27 29 39 39 68 72 15 33 Philippines 29.8 50.4 49 62 3.8 14 14 27 21 63 81 46 61 Poland 23.2 23.7 61 62 0.2 14 13 15 12 .. .. .. .. Portugal 4.6 5.8 47 55 1.6 30 31 40 34 .. .. .. .. Puerto Rico 2.6 3.8 72 97 2.8 44 60 60 62 .. .. .. .. 2006 World Development Indicators 167 Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2004 1990 2004 1990­2004 1990 2005 1990 2005 1990 2002 1990 2002 Romania 12.4 11.9 53 55 ­0.3 9 8 17 15 .. 86 .. 10 Russian Federation 108.8 105.4 73 73 ­0.2 18 20 8 10 93 93 70 70 Rwanda 0.4 1.8 5 20 11.1 .. .. .. .. 49 56 36 38 Saudi Arabia 12.8 21.1 78 88 3.6 30 44 18 25 100 100 .. .. Senegal 3.2 5.7 40 50 4.2 18 20 46 39 52 70 23 34 Serbia and Montenegro 5.4 4.3 51 52 ­1.6 11 14 21 26 97 97 77 77 Sierra Leone 1.2 2.1 30 40 3.9 .. .. 47 45 .. 53 .. 30 Singapore 3.0 4.2 100 100 2.4 99 100 99 100 100 100 .. .. Slovak Republic 3.0 3.1 56 58 0.3 .. .. .. .. 100 100 100 100 Slovenia 1.0 1.0 51 51 0.0 .. .. .. .. .. .. .. .. Somalia 2.0 2.8 29 35 2.6 11 15 39 43 .. 47 .. 14 South Africa 17.2 26.1 49 57 3.0 23 30 11 13 85 86 42 44 Spain 29.3 32.7 75 77 0.8 23 22 16 15 .. .. .. .. Sri Lanka 3.6 4.1 21 21 0.9 .. .. .. .. 89 98 64 89 Sudan 6.9 14.2 27 40 5.1 9 12 34 30 53 50 26 24 Swaziland 0.2 0.3 23 24 2.9 .. .. .. .. .. 78 .. 44 Sweden 7.1 7.5 83 83 0.4 17 19 21 23 100 100 100 100 Switzerland 4.6 5.0 68 68 0.6 .. .. 18 20 100 100 100 100 Syrian Arab Republic 6.3 9.3 49 50 2.8 26 25 25 26 97 97 56 56 Tajikistan 1.7 1.6 32 25 ­0.4 .. .. .. .. .. 71 .. 47 Tanzania 5.7 13.7 22 36 6.3 5 7 23 19 51 54 45 41 Thailand 16.1 20.5 29 32 1.7 11 10 37 32 95 97 74 100 Togo 1.1 2.1 29 36 4.6 .. .. .. .. 71 71 24 15 Trinidad and Tobago 0.8 1.0 69 76 1.2 .. .. .. .. 100 100 100 100 Tunisia 4.7 6.4 58 64 2.1 19 21 33 32 95 90 47 62 Turkey 33.2 47.9 59 67 2.6 22 25 20 20 96 94 67 62 Turkmenistan 1.7 2.2 45 46 2.0 .. .. .. .. .. 77 .. 50 Uganda 2.0 3.4 11 12 3.9 4 5 38 38 54 53 41 39 Ukraine 34.6 31.9 67 67 ­0.6 14 15 7 8 100 100 97 97 United Arab Emirates 1.5 3.7 83 85 6.6 .. .. 32 26 100 100 100 100 United Kingdom 51.1 53.4 89 89 0.3 24 22 15 14 .. .. .. .. United States 188.0 236.2 75 80 1.6 40 42 9 8 100 100 100 100 Uruguay 2.8 3.2 89 93 1.0 41 39 46 42 95 95 .. 85 Uzbekistan 8.2 9.6 40 37 1.1 10 8 25 22 73 73 48 48 Venezuela, RB 16.6 23.0 84 88 2.3 31 34 17 14 .. 71 .. 48 Vietnam 13.4 21.6 20 26 3.4 13 13 30 23 46 84 16 26 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2.6 5.3 21 26 5.1 6 8 26 29 59 76 11 14 Zambia 3.3 4.2 39 36 1.6 12 12 30 34 64 68 26 32 Zimbabwe 3.1 4.6 29 35 2.9 10 12 34 33 69 69 40 51 World 2,259.9 s 3,091.5 s 43 w 49 w 2.2 w .. w .. w 17 w 16 w 76 w 79 w 22 w 35 w Low income 454.7 717.1 26 31 3.3 10 12 17 17 49 61 10 24 Middle income 1,146.2 1,604.3 44 53 2.4 .. .. 16 14 79 81 25 41 Lower middle income 797.7 1,187.9 38 49 2.8 16 17 14 12 75 78 22 39 Upper middle income 348.5 416.4 69 72 1.3 .. .. 20 20 91 91 58 61 Low & middle income 1,600.9 2,321.4 37 43 2.7 .. .. 16 15 71 74 18 32 East Asia & Pacific 459.5 759.0 29 41 3.6 .. .. 10 7 65 72 15 35 Europe & Central Asia 293.2 300.4 63 64 0.2 16 18 14 15 94 93 72 63 Latin America & Carib. 310.9 420.8 71 77 2.2 31 34 24 22 83 84 35 44 Middle East & N. Africa 115.5 167.2 52 56 2.6 21 21 28 25 86 89 51 56 South Asia 278.6 409.9 25 28 2.8 10 12 10 11 50 64 5 23 Sub-Saharan Africa 143.2 264.3 28 36 4.4 .. .. 26 25 53 55 24 26 High income 659.0 770.1 75 78 1.1 .. .. 20 19 .. .. .. .. Europe EMU 216.5 235.5 74 76 0.6 28 27 17 16 .. .. .. .. 168 2006 World Development Indicators Urbanization About the data Definitions The population of a city or metropolitan area is included to allow comparison of rural and urban · Urban population is the midyear population of depends on the boundaries chosen. For example, in access. This definition and the definition of urban areas defined as urban in each country and reported 1990 Beijing, China, contained 2.3 million people in areas vary, however, so comparisons between coun- to the United Nations (see About the data). · Popula- 87 square kilometers of "inner city" and 5.4 million tries can be misleading. tion in urban agglomerations of more than 1 million in 158 square kilometers of "core city." The popu- is the percentage of a country's population living in lation of "inner city and inner suburban districts" metropolitan areas that in 2000 had a population was 6.3 million, and that of "inner city, inner and of more than 1 million. · Population in largest city outer suburban districts, and inner and outer coun- is the percentage of a country's urban population ties" was 10.8 million. (For most countries the last living in that country's largest metropolitan area. definition is used.) · Access to improved sanitation facilities refers to Estimates of the world's urban population would the percentage of the urban or rural population with change significantly if China, India, and a few other access to at least adequate excreta disposal facili- populous nations were to change their definition of ties (private or shared but not public) that can effec- urban centers. According to China's State Statis- tively prevent human, animal, and insect contact with tical Bureau, by the end of 1996 urban residents excreta. Improved facilities range from simple but accounted for about 43 percent of China's popula- protected pit latrines to flush toilets with a sewerage tion, while in 1994 only 20 percent of the population connection. To be effective, facilities must be cor- was considered urban. In addition to the continuous rectly constructed and properly maintained. migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years. Because the estimates in the table are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population (see table 2.1). The urban population with access to improved sani- tation facilities is defined as people with access to at least adequate excreta disposal facilities that can effectively prevent human, animal, and insect con- tact with excreta. The rural population with access The urban population in developing countries has increased substantially since 1990 Urban population (millions) 1990 2004 800 700 600 500 Data sources 400 300 Data on urban population and the population in 200 urban agglomerations and in the largest city are from the United Nations Population Division's World 100 0 Urbanization Prospects: The 2005 Revision. The East Asia Europe & Latin America Middle East & South Sub-Saharan High- total population figures are World Bank estimates. & Pacific Central Asia & Caribbean North Africa Asia Africa income Data on access to sanitation in urban and rural Source: Table 3.10. areas are from the World Health Organization. 2006 World Development Indicators 169 Urban housing conditions Household Overcrowding Durable Home Multiunit Vacancy size dwelling ownership dwellings rate units People living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings Census people % of total % of total % of total % of total % of total year National Urban National Urban National Urban National Urban National Urban National Urban Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. .. Albania 2001 4.2 3.9 .. .. .. .. 65b 30 b .. .. 12 13 Algeria 1998 4.9 .. .. .. .. .. 67 .. .. .. 19 .. Angola .. .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2001 3.6 .. 19 .. 97 .. .. .. 4 .. 16b .. Armenia 2001 4.1 4.0 4 6 93 93 95 90 1 1 .. .. Australia 2001 3.8 .. 1 .. .. .. .. .. .. .. .. .. Austria 1991 2.6 .. 2 .. .. .. .. .. 50 .. 13 .. Azerbaijan 1999 4.7 4.4 .. .. .. .. 74 62 4 5 .. .. Bangladesh 2001 4.8 4.8 .. .. 21b 42b 88 b 61b .. .. .. .. Belarus 1999 .. .. .. .. .. .. .. .. .. .. .. .. Belgium 2001 2.6 .. 0b .. .. .. 67 .. 32b .. .. .. Benin 1992 5.9 .. .. .. 26 .. 59 .. .. .. .. .. Bolivia 2001 4.2 4.3 40 .. 43 58 70 59 3b 5b 6 4 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. .. Botswana 2001 4.2 3.9 27 47 88 90 b 61 47 1 .. .. .. Brazil 2000 3.8 3.7 .. .. .. .. 74 75 .. .. .. .. Bulgaria 2001 2.7 2.7 .. .. 79 89 98 98 .. .. 23 17 Burkina Faso 1996 6.2 5.8 30 53 .. .. .. .. .. .. .. .. Burundi 1990 4.7 .. .. .. .. .. .. .. .. .. .. .. Cambodia 1998 5.2 .. .. .. .. .. .. .. .. .. .. .. Cameroon 1987 5.2 5.1 67 77 77 .. 73 48 27 42 .. .. Canada 2001 2.6 .. .. .. .. .. 64 .. 32 .. 8 .. Central African Republic 2003 5.2 5.8 32 36b 78 92 85 74 .. .. .. .. Chad 1993 5.1 5.1 .. .. .. .. .. .. .. .. .. .. Chile 2002 3.4 3.5 .. .. 91 92 66 65 13 15 11 10 China 2000 3.4 3.2 .. .. 82 .. 88 74 .. .. 1 .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. .. Colombia 1993 4.8 .. 27b .. 83 b .. 68 b .. 13 .. 10 b .. Congo, Dem. Rep. 1984 5.4 .. 55 .. .. .. .. .. .. .. .. .. Congo, Rep. 1984 10.5 .. .. .. .. .. 76 .. .. .. .. .. Costa Rica 2000 4.0 .. 22 .. 88 .. 72 .. 2 3 9 6 Côte d'Ivoire 1998 5.4 .. .. .. .. .. .. .. .. .. .. .. Croatia 2001 3.0 .. .. .. .. .. .. .. .. .. 12 .. Cuba 1981 4.2 4.2 .. .. .. .. .. .. 15 21 0 0 Czech Republic 2001 2.4 .. .. .. .. .. 52 .. 49 .. 12 .. Denmark 2001 2.2 .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2002 3.9 .. .. .. 97 .. .. .. 8 .. 11 .. Ecuador 2001 3.5 3.7 30 .. 81 88 68 b 58 b 9 14 12 7 Egypt, Arab Rep. 1996 4.7 .. .. .. .. .. .. .. 75 .. .. .. El Salvador 1992 .. .. 63 .. 67 83 70 68 3 6 11 11 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. .. Estonia 2000 2.4 2.3 3 .. .. .. .. .. 72 .. 13 .. Ethiopia 1994 4.8 4.7 .. .. .. 23 .. 54 .. .. .. .. Finland 2000 2.2 .. .. .. .. .. 64 .. 44 .. .. .. France 1999 2.5 .. .. .. .. .. 55 .. .. .. 7 .. Gabon 2003 5.2 .. .. .. .. .. .. .. .. .. .. .. Gambia, The 1993 8.9 .. .. .. 18 .. 68 .. .. .. .. .. Georgia 2002 3.5 3.5 .. .. .. .. .. .. .. .. .. .. Germany 2001 2.3 .. .. .. .. .. 43 .. .. .. 7 .. Ghana 2000 5.1 5.1 .. .. 45 .. 57 .. 53 .. 5 .. Greece 2001 3.0 .. 1 .. .. .. .. .. .. .. .. .. Guatemala 2002 4.4 4.7 .. .. 67 80 81 74 2 4 13 11 Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1982 4.2 .. 26 .. .. .. 92 68 .. .. 9 19 170 2006 World Development Indicators Urban housing conditions Household Overcrowding Durable Home Multiunit Vacancy size dwelling ownership dwellings rate units People living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings Census people % of total % of total % of total % of total % of total year National Urban National Urban National Urban National Urban National Urban National Urban Honduras 2001 4.4 .. .. .. 69 85 .. .. .. .. 14 .. Hungary 1990 2.7 .. .. .. .. .. .. .. .. .. 4 .. India 2001 5.3 5.3 77 71 83 81 87 67 .. .. 6 9 Indonesia 2000 4.0 .. .. .. .. .. .. .. .. .. .. .. Iran, Islamic Rep. 1996 4.8 4.6 33 b 26b 72 76 73 67 .. .. .. .. Iraq 1997 7.7 7.2 .. .. 88 96 70 66 4 5 13 15 Ireland 2002 3.0 .. .. .. .. .. .. .. 8b .. .. .. Israel 1995 3.5 .. .. .. .. .. .. .. .. .. .. .. Italy 2001 2.8 .. .. .. .. .. .. .. .. .. 21 .. Jamaica 2001 3.5 .. .. .. 98 b .. 58 b .. 2b .. .. .. Japan 2000 2.7 .. .. .. .. .. 61 .. 37 .. .. .. Jordan 1994 6.2 6.0 1 .. 97 97 69 64 57 67 .. .. Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. .. Kenya 1999 4.6 3.4 .. .. 35 72 72 25 .. .. 39 17 Korea, Dem. Rep. 2000 3.8 .. 23 .. .. .. 50 .. 15 .. .. .. Korea, Rep. 1993 4.4 .. .. .. .. .. .. .. .. .. .. .. Kuwait 1995 6.4 .. .. .. .. .. .. .. 9b .. 11 .. Kyrgyz Republic 1999 4.4 3.6 .. .. .. .. .. .. .. .. .. .. Lao PDR 1995 6.1 6.1 .. .. 49 77 96 86 .. .. .. .. Latvia 2000 3.0 2.6 4 .. 88 .. 58 .. 74 .. 0 .. Lebanon .. .. .. .. .. .. .. .. .. .. .. .. .. Lesotho 2001 5.0 .. 10 b .. .. .. 84 .. 0 .. .. .. Liberia 1974 4.8 .. 31 .. 20 .. 1 .. .. .. .. .. Libya .. 6.4 .. .. .. .. .. .. .. .. .. 7 .. Lithuania 2001 2.6 .. 7 .. .. .. .. .. .. .. .. .. Macedonia, FYR 2002 3.6 3.6b 8b .. 95b 95b 48 b .. .. .. 7b 3b Madagascar 1993 4.9 4.8 64 57 .. .. 81 59 .. .. .. .. Malawi 1998 4.4 4.4 30 .. 48 84 86 47 .. .. .. .. Malaysia 2000 4.5 4.4 .. .. .. .. .. .. 10 b 16b .. .. Mali 1998 5.6 .. .. .. .. .. .. .. .. .. .. .. Mauritania 1988 .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 2000 3.9 3.8 6 7 91 94 87 81 .. .. 7 6 Mexico 2000 4.4 .. 27b .. 87 .. 78 .. 6 .. .. .. Moldova 2003 .. .. .. .. .. .. .. .. .. .. .. .. Mongolia 2000 4.4 4.5 .. .. .. .. .. .. 48 56 .. .. Morocco 1982 5.9 5.3 .. .. .. .. .. .. .. .. .. .. Mozambique 1997 4.4 4.9 37 28 7 20 92 83 1 1 0 .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2001 5.3 .. .. .. .. .. .. .. .. .. .. .. Nepal 2001 5.4 4.9 .. .. .. .. 88 .. .. .. 0 .. Netherlands .. .. .. .. .. .. .. .. .. .. .. .. .. New Zealand 2001 2.8 .. 1b .. .. .. 65 .. 17 .. 10 .. Nicaragua 1995 5.3 .. .. .. 79 87 84 86 0 0 8 .. Niger 2001 6.4 6.0 .. .. .. .. 77 40 .. .. .. .. Nigeria 1991 5.0 4.7 .. .. .. .. .. .. .. .. .. .. Norway 1980 2.7 .. 1 .. .. .. 67 .. 38 .. .. .. Oman 2003 7.1 .. .. .. .. .. .. .. .. .. .. .. Pakistan 1998 6.8 6.8 .. .. 58 86 81 .. .. .. .. .. Panama 2000 4.1 .. 28 b .. 88 98 b 80 66b 10 b 10 b 14 .. Papua New Guinea 1990 4.5b 6.5 .. .. .. .. .. 44 .. 8 .. .. Paraguay 2002 4.6 4.5 38 b ..b 95b 98 b 79 75 1b 2b 6b 6b Peru 1993 .. .. .. .. 49 64 .. .. .. .. 7 3 Philippines 1990 5.3 5.3 .. .. 62 .. 83 76 6 11 4 4 Poland 1988 3.2 .. .. .. .. .. .. .. .. .. 1 .. Portugal 2001 2.8 .. .. .. .. .. 76 .. 86 .. .. .. Puerto Rico 1990 3.3 .. .. .. .. .. 72 .. .. .. 11 .. 2006 World Development Indicators 171 Urban housing conditions Household Overcrowding Durable Home Multiunit Vacancy size dwelling ownership dwellings rate units People living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings Census people % of total % of total % of total % of total % of total year National Urban National Urban National Urban National Urban National Urban National Urban Romania 1992 3.1 3.1 .. .. 58 .. 87 77 39 71 6 4 Russian Federation 2002 2.8 2.7 7 5 .. .. .. .. 73 86 .. .. Rwanda 1991 4.7 .. .. .. 79 78 92 73 19 25 .. .. Saudi Arabia 1992 6.1 .. .. .. 92 .. 42 .. .. .. .. .. Senegal .. .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro 2001 2.9 2.2 .. .. .. .. .. .. .. .. .. .. Sierra Leone 1985 6.8 .. .. .. 34 .. 68 .. .. .. .. .. Singapore 2000 4.4 .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. .. Slovenia 1991 3.1 .. .. .. .. .. 69 .. 37 .. 9 .. Somalia 1975 .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2001 4.0 .. .. .. .. .. .. .. 7 .. .. .. Spain 1991 3.3 .. 0 .. .. .. 78 .. .. .. .. .. Sri Lanka 2001 3.8 .. .. .. 93b 92b 70 b 58 b 1 14b 13 1b Sudan 1993 5.8 6.0 .. .. .. .. 86b 58 b 0b 1b .. .. Swaziland 1997 5.4 3.7 .. .. .. .. .. .. .. .. .. .. Sweden 1990 2.0 .. .. .. .. .. .. .. 54 .. 1 .. Switzerland 1990 2.4 2.1 .. .. .. .. 31 24 28 32 11 7 Syrian Arab Republic 1981 6.3 6.0 .. .. .. .. .. .. .. .. .. .. Tajikistan 2000 .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 2002 4.9 4.5b 33 b 7b .. .. 82b 43 b .. .. .. .. Thailand 2000 3.8 .. .. .. 93 93 81 62 3 .. 3 .. Togo .. .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 2000 3.7 .. 9b .. 98 b .. 74b .. 17b .. .. .. Tunisia 1994 8.0 .. .. .. 99 .. 71 89 b 6 10 b 15 12b Turkey 1990 5.0 .. .. .. .. .. 70 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. .. Uganda 1991 4.9 4.0 b .. .. 21b .. 80 b 24b 0b 2b .. .. Ukraine 2003 .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 2001 .. 2.4 .. .. .. .. .. 69 .. 19 .. .. United States 2000 2.7 .. .. .. .. .. 66 .. .. .. 9 7 Uruguay 1996 3.3 3.4b 22b .. .. .. 57b 57b .. .. 13b 13 b Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 2001 4.4 .. .. .. .. .. 78 .. 14 .. 16 .. Vietnam 1999 4.6 4.5 .. .. 77 89 95 86 .. .. .. .. West Bank and Gaza 1997 7.1 .. .. .. .. .. 78 .. 45 .. .. .. Yemen, Rep. 1994 6.7 6.8 54b 6b .. .. 88 b 68 b 3b 11b .. .. Zambia 2000 5.3 5.9 .. .. .. .. 94 30 .. .. .. .. Zimbabwe 1992 4.8 4.2 .. .. .. .. 94 30 6 .. .. .. a. More than two people per room. b. Data are from previous census. 172 2006 World Development Indicators Urban housing conditions About the data Definitions Urbanization can yield important social benefits, There is a strong demand for quantitative indi- · Household size refers to the average number of improving access to public services and the job cators that can measure housing conditions on a people within a household. It is calculated by dividing market. At the same time it also leads to signifi- regular basis to monitor progress. However, data total population by the number of households in the cant demands for services. Inadequate living quar- deficiencies and lack of rigorous quantitative analy- country and in urban areas. · Overcrowding refers to ters and demand for housing and shelter are major sis hamper informed decisionmaking on desirable the number of households living in dwellings with two concerns for policymakers. The unmet demand for policies to improve housing conditions. The data or more people per room as a percentage of the total affordable housing, along with urban poverty, has led in the table are from housing and population cen- number of households in the country and in urban to the emergence of slums in many poor countries. suses, collected using similar definitions. The table areas. · Durable dwelling units refer to the num- Improving the shelter situation requires a better will incorporate household survey data in future edi- ber of housing units in structures made of durable understanding of the mechanisms governing hous- tions. The table focuses attention on urban areas, building materials (concrete, stone, cement, brick, ing markets and the processes governing housing where housing conditions are typically most severe. asbestos, zinc, and stucco) expected to maintain availability. That requires good data and adequate Not all the compiled indicators are presented in the their stability for 20 years or longer under local condi- policy-oriented analysis so that housing policy can be table because of space limitations. Additional indica- tions with normal maintenance and repair, taking into formulated in a global comparative perspective and tors for more countries will be available in the online account location and environmental hazards such as drawn from the lessons learned in other countries. version of World Development Indicators and on the floods, mudslides, and earthquakes as a percentage Housing policies and outcomes affect such broad companion CD-ROM. of total dwellings. · Home ownership refers to the socioeconomic conditions as the infant mortality number of privately owned dwellings as a percentage rate, performance in school, household saving, pro- of total dwellings or the number of households that ductivity levels, capital formation, and government own housing units as a percentage of total house- budget deficits. A good understanding of housing holds. This category includes privately owned and conditions thus requires an extensive set of indica- owner-occupied units, depending on the definition tors within a reasonable framework. used in the census data. State- and community- owned units, rented, squatted, and rent-free units are not included. · Multiunit dwellings refer to the Selected housing indicators for smaller economies number of multiunit dwellings, such as apartments, Household Overcrowding Durable Home Multiunit Vacancy flats, condominiums, barracks, boarding houses, size dwelling ownership dwellings rate orphanages, retirement houses, hostels, hotels, and units collective dwellings, as a percentage of total occu- People living Buildings Privately in overcrowded with durable owned Unoccupied pied dwellings. · Vacancy rate refers to the percent- Census number of dwellings a structure dwellings dwellings age of completed dwelling units that are currently year people % of total % of total % of total % of total % of total unoccupied. It includes all vacant units, whether on Antigua and Barbuda 2001 3.0 .. 99 b 65b 3b 22 Bahamas, The 1990 3.8 12 99 55 13 14 the market or not (such as second homes). Bahrain 2001 5.9 .. 94b 51 28 6 Barbados 1990 3.5 3 100 76 9 9 Belize 2000 4.6 .. 93 63 4 .. Cape Verde 1990 5.1 28 78 72 2 .. Cayman Islands 1999 3.1 .. 100 53 38 19 Equatorial Guinea 1993 7.5 14 56b 75 14 .. Fiji 1996 5.4 .. 60 65 7 .. Guam 2000 4.0 2b 93 48 29 19 Isle of Man 2001 2.4 0 .. 68 16 .. Maldives 2000 6.6 .. 93 .. 1 15 Marshall Islands 1999 7.8 .. 95 72 12 8 Netherlands Antilles 2001 2.9 24b 99 60 16 12 New Caledonia 1989 4.1 .. 77 53 9 13 Northern Mariana Islands 1995 4.9 9b 99 33 27 17 Palau 2000 5.7 8 76 79 11 3 Seychelles 1997 4.2 15b 97 78 .. 0 Solomon Islands 1999 6.3 51 23 85 1 .. St. Vincent & Grenadines 1991 3.9 .. 98 71 7 .. Turks and Caicos 1990 3.3 4 96 66 11 .. Virgin Islands (U.K.) 1991 3.0 2 99 40 46 .. Data sources Western Samoa 1991 7.3 .. 42 90 47 30 Data on urban housing conditions are from a. More than two people per room. b. Data are from previous census. national population and housing censuses. Source: National population and housing censuses. 2006 World Development Indicators 173 Traffic and congestion Motor Passenger Two- Road Particulate matter vehicles cars wheelers traffic concentrations Urban-population- weighted PM10 per 1,000 per kilometer per 1,000 per 1,000 million vehicle micrograms per people of road people people kilometers cubic meter 1990 2003a 1990 2003a 1990 2003a 1990 2003a 1990 2003a 1990 2002 Afghanistan 3 .. 3 .. 2 .. .. .. .. .. 34 27 Albania 11 70 3 12 2 47 3 1 .. .. 93 58 Algeria 55 .. 15 .. 26 .. .. .. .. .. 123 65 Angola 19 .. 3 .. 14 .. .. .. .. .. 142 113 Argentina 181 181 27 37 134 140 1 .. 43,119 27,458 105 78 Armenia 5 .. 2 .. 1 .. 2 .. .. 316 .. 84 Australia 530 .. 11 .. 450 .. 18 18 138,501 .. 22 18 Austria 421 545 30 33 387 501 71 73 .. 49,800 39 37 Azerbaijan 52 57 7 17 36 45 5 1 .. 5,263 .. 64 Bangladesh 1 1 0 1 0 0 1 1 .. .. 229 157 Belarus 61 168 13 18 59 168 45 51 10,026 5,650 7 9 Belgium 423 527 30 37 385 470 14 31 150,750 156,633 31 28 Benin 3 .. 2 .. 2 .. 34 .. .. .. 75 51 Bolivia 41 10 6 1 25 3 9 0 1,139 .. 119 92 Bosnia and Herzegovina 114 .. 24 .. 101 .. .. .. .. .. 41 22 Botswana 18 92 3 6 10 38 1 1 .. .. 40 25 Brazil 88 170 8 17 84 137 .. 28 .. .. 41 35 Bulgaria 163 335 39 26 146 295 55 28 .. 285 109 69 Burkina Faso 4 .. 3 .. 2 .. 9 .. .. .. 140 97 Burundi .. .. .. .. .. .. .. .. .. .. 168 99 Cambodia .. 30 0 31 .. 25 9 127 314 7,210 83 51 Cameroon 10 .. 3 .. 6 .. .. .. .. .. 118 86 Canada 605 577 20 34 468 561 12 11 .. .. 25 21 Central African Republic 1 .. 0 .. 1 .. 0 .. 1,494 .. 29 24 Chad 2 .. 0 .. 1 .. 0 .. .. .. 103 73 Chile 81 136 13 26 52 89 2 2 .. .. 85 56 China 5 15 4 11 1 10 3 46 .. 659,390 113b 80 b Hong Kong, China 66 79 253 287 42 59 4 5 8,192 10,781 .. 38 Colombia 39 51 13 19 21 43 8 12 50,945 41,587 37 24 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 70 57 Congo, Rep. 18 .. 3 .. 12 .. .. .. .. .. 126 74 Costa Rica 87 185 7 21 55 103 14 29 .. .. 50 40 Côte d'Ivoire 24 .. 6 .. 15 .. .. .. .. .. 94 38 Croatia .. 324 .. 50 .. 291 .. 22 .. 18,104 52 35 Cuba 37 .. 16 .. 18 .. 19 .. .. .. 43 25 Czech Republic 246 391 46 31 228 358 113 75 37,350 .. 70 25 Denmark 368 424 27 32 320 360 9 15 36,304 46,520 29 22 Dominican Republic 75 .. 48 .. 21 .. .. .. .. .. 44 36 Ecuador 35 53 8 16 31 47 2 2 10,306 19,604 37 28 Egypt, Arab Rep. 29 .. 33 .. 21 .. 6 .. .. .. 227 136 El Salvador 33 .. 14 .. 17 .. 0 .. 2,002 .. 46 40 Eritrea 1 .. 1 .. 1 .. .. .. .. .. 178 109 Estonia 211 386 22 9 154 321 66 6 5,455 6,843 18 17 Ethiopia 1 2 2 4 1 1 0 0 .. 1,495 175 88 Finland 441 450 29 30 386 433 12 47 39,750 49,790 24 22 France 494 596 32 40 405 495 55 .. 422,000 548,900 18 15 Gabon 32 .. 4 .. 19 .. .. .. .. .. 21 13 Gambia, The 13 8 5 3 6 6 .. .. .. .. 189 138 Georgia 107 63 27 16 89 50 5 0 4,620 .. 204 46 Germany 405 578 53 206 386 545 18 45 446,000 639,100 28 22 Ghana 8 .. 4 .. 5 .. .. .. .. 15,320 39 42 Greece 248 435 22 34 171 331 120 229 .. 79,377 70 48 Guatemala 21 57 16 45 11 52 10 12 3,243 4,547 64 76 Guinea 4 .. 1 .. 2 .. .. .. .. .. 87 63 Guinea-Bissau 7 .. 2 .. 4 .. .. .. .. .. 114 84 Haiti 8 .. 14 .. 5 .. .. .. .. .. 69 47 174 2006 World Development Indicators Traffic and congestion Motor Passenger Two- Road Particulate matter vehicles cars wheelers traffic concentrations Urban-population- weighted PM10 per 1,000 per kilometer per 1,000 per 1,000 million vehicle micrograms per people of road people people kilometers cubic meter 1990 2003a 1990 2003a 1990 2003a 1990 2003a 1990 2003a 1990 2002 Honduras 22 61 10 28 5 52 .. 14 3,288 .. 44 46 Hungary 212 313 21 19 188 274 16 10 22,898 23,260 35 22 India 4 9 2 3 2 6 15 35 .. .. 109 84 Indonesia 16 .. 10 .. 7 .. 34 59 .. .. 137 114 Iran, Islamic Rep. 34 .. 14 .. 25 .. 36 .. .. .. 85 68 Iraq 14 .. 6 .. 1 .. .. .. .. .. 150 167 Ireland 270 447 10 11 227 382 6 9 24,205 33,915 25 20 Israel 210 284 74 110 174 231 8 11 18,212 38,273 70 53 Italy 529 610 99 73 476 545 45 125 344,726 65,983 43 33 Jamaica 52 .. 7 .. 43 .. .. .. .. .. 57 43 Japan 469 582 52 63 283 433 146 105 628,581 790,829 42 33 Jordan 60 99 26 71 44 67 0 0 1,098 526,677 124 69 Kazakhstan 76 96 8 6 50 77 .. 5 18,248 4,087 12 27 Kenya 12 11 5 5 10 8 1 1 5,170 .. 66 38 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 203 88 Korea, Rep. 79 304 60 150 48 215 32 36 30,464 .. 83 43 Kuwait 474 332 165 .. 368 326 .. .. .. .. 77 129 Kyrgyz Republic 44 38 10 10 44 38 16 2 5,220 1,992 .. 36 Lao PDR 9 .. 3 .. 6 .. 18 .. .. .. 35 25 Latvia 135 329 6 11 106 280 76 10 3,932 .. 30 17 Lebanon 321 .. 183 .. 300 .. 13 .. .. .. 41 43 Lesotho 11 .. 4 .. 3 .. .. .. 445 .. 176 94 Liberia 14 .. 4 .. 7 .. .. .. .. .. 57 39 Libya 165 .. 10 .. 96 .. 0 .. .. .. 109 121 Lithuania 160 397 12 17 133 364 52 6 .. 2,709 28 22 Macedonia, FYR 132 .. 30 .. 121 .. 1 .. 3,102 .. 36 29 Madagascar 6 .. 2 .. 4 .. .. .. 41,500 .. 101 51 Malawi 4 .. 4 .. 2 .. .. .. .. .. 169 88 Malaysia 124 254 26 75 101 222 167 249 .. .. 36 28 Mali 3 .. 2 .. 2 .. .. .. .. .. 144 102 Mauritania 10 .. 3 .. 7 .. .. .. .. .. 55 42 Mauritius 59 119 35 72 44 88 54 103 .. 78 85 47 Mexico 119 201 41 59 82 133 3 4 55,095 .. 70 43 Moldova 53 78 17 26 48 60 45 3 891 678 .. 41 Mongolia 21 41 1 2 6 26 22 10 340 2,321 22 16 Morocco 37 45 15 23 28 45 1 1 .. 14,242 33 27 Mozambique 4 .. 2 .. 3 .. .. .. 1,889 .. 108 44 Myanmar 2 .. 3 .. 1 .. .. .. .. .. 130 75 Namibia 71 82 1 4 39 42 1 2 1,896 .. 74 50 Nepal .. .. .. .. .. .. .. .. .. .. 67 43 Netherlands 405 427 58 58 368 383 44 25 90,150 109,955 47 40 New Zealand 524 730 19 31 436 613 24 21 .. .. 16 16 Nicaragua 19 39 5 11 10 16 3 8 108 412 49 32 Niger 6 .. 4 .. 5 .. .. .. 178 .. 112 86 Nigeria 30 .. 21 .. 12 .. 5 .. .. .. 172 95 Norway 458 527 22 26 380 424 48 64 28,136 35,047 26 18 Oman 130 .. 9 .. 83 .. 3 .. .. .. 163 124 Pakistan 6 8 4 5 4 7 8 11 25,317 205,385 226 165 Panama 75 107 18 27 60 76 2 .. .. .. 58 58 Papua New Guinea 27 .. 6 .. 7 .. .. .. .. .. 12 11 Paraguay 27 88 4 15 16 52 .. .. .. .. 109 103 Peru 128 46 43 16 62 30 .. 9 .. 23,360 98 68 Philippines 10 34 4 13 7 9 6 18 6,189 9,548 55 34 Poland 168 354 18 33 138 294 36 22 59,608 138,100 59 39 Portugal 222 463 34 278 162 429 5 56 28,623 47,943 53 31 Puerto Rico 295 .. 79 .. 242 .. .. .. .. 741,445 70 62 2006 World Development Indicators 175 Traffic and congestion Motor Passenger Two- Road Particulate matter vehicles cars wheelers traffic concentrations Urban-population- weighted PM10 per 1,000 per kilometer per 1,000 per 1,000 million vehicle micrograms per people of road people people kilometers cubic meter 1990 2003a 1990 2003a 1990 2003a 1990 2003a 1990 2003a 1990 2002 Romania 72 168 11 19 56 144 13 12 23,907 35,675 36 20 Russian Federation 87 174 14 48 65 140 .. 43 .. 56,455 13 25 Rwanda 2 .. 1 .. 1 .. .. .. .. .. 162 100 Saudi Arabia 165 .. 19 .. 98 .. 0 .. 94,141 .. 105 91 Senegal 11 14 6 9 8 11 0 0 .. 4,013 99 93 Serbia and Montenegro 137 .. 31 .. 133 .. 3 .. .. .. 26 17 Sierra Leone 10 4 4 2 7 2 2 0 996 .. 101 69 Singapore 130 135 142 111 89 100 40 32 .. 16,133 107 48 Slovak Republic 194 286 57 36 163 252 61 9 8,127 10,992 40 20 Slovenia 306 490 42 25 289 446 8 21 5,620 10,261 .. 33 Somalia 2 .. 1 .. 1 .. .. .. .. .. 81 35 South Africa 139 144 26 24 97 92 8 4 .. .. 34 24 Spain 360 558 43 34 309 455 79 37 100,981 224,370 42 40 Sri Lanka 21 34 4 .. 7 13 24 49 3,468 15,630 97 93 Sudan 9 .. 22 .. 8 .. .. .. .. .. 291 219 Swaziland 66 83 18 24 35 40 3 3 .. .. 117 71 Sweden 464 504 29 11 426 455 11 24 61,040 58,992 15 14 Switzerland 491 553 46 57 449 511 114 103 48,660 59,052 37 27 Syrian Arab Republic 26 36 10 7 10 12 .. 6 .. .. 151 89 Tajikistan 3 .. 1 .. 0 .. .. .. .. 1,092 .. 57 Tanzania 5 .. 2 .. 1 .. .. .. .. .. 57 38 Thailand 46 .. 36 .. 14 .. 86 .. 45,769 .. 87 77 Togo 24 .. 11 .. 16 .. 8 .. .. .. 50 45 Trinidad and Tobago 117 .. 19 .. 98 .. .. .. .. .. 124 22 Tunisia 48 88 19 43 23 60 .. 1 .. 19,231 71 46 Turkey 50 90 8 101 34 66 10 15 27,041 52,344 76 56 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. 73 Uganda 2 5 .. 4 1 2 0 3 .. .. 60 33 Ukraine 63 137 20 39 63 114 59 28 59,500 13,755 45 29 United Arab Emirates 121 .. 52 .. 97 .. .. .. .. .. 233 109 United Kingdom 400 442 64 42 341 439 14 19 399,000 484,722 25 17 United States 758 808 30 36 573 482 17 17 2,527,441 4,208,594 30 24 Uruguay 138 .. 45 .. 122 .. 74 .. .. .. 235 154 Uzbekistan .. .. .. .. .. .. .. .. .. .. 95 81 Venezuela, RB 93 .. 25 .. 73 .. .. .. 563 .. 22 12 Vietnam .. .. .. .. .. .. 45 .. .. .. 127 66 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 34 .. 8 .. 14 .. .. .. 8,681 .. .. 82 Zambia 14 .. 3 .. 8 .. .. .. .. .. 133 71 Zimbabwe 32 50 4 7 29 44 33 4 .. .. 60 43 World 118 w 141 w .. .. 91 w 100 w .. .. .. .. 77 w 60 w Low income 5 8 .. .. 3 6 .. .. .. .. 129 89 Middle income 37 69 .. .. 24 51 .. .. .. .. 79 62 Lower middle income 22 39 .. .. 10 29 .. .. .. .. 92 70 Upper middle income 121 187 .. .. 91 143 .. .. .. .. 50 40 Low & middle income 25 47 .. .. 16 35 .. .. .. .. 93 70 East Asia & Pacific 9 20 .. .. 4 14 .. .. .. .. 112 80 Europe & Central Asia 97 170 .. .. 79 142 .. .. .. .. 39 35 Latin America & Carib. 100 153 .. .. 72 108 .. .. .. .. 60 43 Middle East & N. Africa 36 .. .. .. 24 .. .. .. .. .. 126 89 South Asia 4 10 .. .. 2 6 .. .. .. .. 131 99 Sub-Saharan Africa 21 .. .. .. 15 .. .. .. .. .. 114 73 High income 499 623 .. .. 390 433 .. .. .. .. 37 29 Europe EMU 429 570 .. .. 379 502 .. .. .. .. 33 27 a. Data are for 2003 or most recent year available. b. Includes data for Hong Kong, China; Macao, China; and Taiwan, China. 176 2006 World Development Indicators Traffic and congestion About the data Definitions Traffic congestion in urban areas constrains eco- may differ across countries because of differences in · Motor vehicles include cars, buses, and freight nomic productivity, damages people's health, and definitions. Comparability also is limited when time- vehicles but not two-wheelers. Population figures degrades the quality of their lives. The particulate series data are reported. Moreover, the data do not refer to the midyear population in the year for which air pollution emitted by motor vehicles--the dust and capture the quality or age of vehicles or the condition data are available. Roads refer to motorways, soot in exhaust--is proving to be far more damaging or width of roads. Thus comparisons over time and highways, main or national roads, and secondary to human health than was once believed. (For infor- between countries should be made with caution. or regional roads. A motorway is a road specially mation on particulate matter and other air pollutants, Estimates of particulate matter concentrations designed and built for motor traffic that separates see table 3.13.) weighted by urban population represent the aver- the traffic flowing in opposite directions. · Passen- In recent years ownership of passenger cars has age annual exposure level of the average urban ger cars refer to road motor vehicles, other than increased, and the expansion of economic activity resident to outdoor particulate matter under 10 two-wheelers, intended for the carriage of pas- has led to the transport by road of more goods and microns (PM10). Data for countries and aggregates sengers and designed to seat no more than nine services over greater distances (see table 5.8). These for regions and income groups are urban-population- people (including the driver). · Two-wheelers refer to developments have increased demand for roads and weighted PM10 levels in residential areas of cities mopeds and motorcycles. · Road traffic is the num- vehicles, adding to urban congestion, air pollution, with more than 100,000 residents; they are available ber of vehicles multiplied by the average distances health hazards, traffic accidents, and injuries. at www.worldbank.org/research. Data for selected they travel. · Particulate matter concentrations Congestion, the most visible cost of expanding cities are in table 3.13. refer to fine suspended particulates less than 10 vehicle ownership, is reflected in the indicators in Significant uncertainties exist around these esti- microns in diameter that are capable of penetrating the table. Other relevant indicators--such as aver- mates, and caution should be used in interpreting deep into the respiratory tract and causing significant age vehicle speed in major cities or the cost of traffic them. But they do allow for cross-country compari- health damage. The state of a country's technology congestion, which takes a heavy toll on economic sons of the relative risk of particulate matter pollution and pollution controls is an important determinant productivity--are not included because data are that urban residents face. Major sources of urban of particulate matter concentrations. incomplete or difficult to compare. outdoor particulate matter pollution are emissions The data in the table--except those on fuel from traffic and industrial sources, but nonanthro- prices--are compiled by the International Road Fed- pogenic sources such as dust storms may also be a eration (IRF) through questionnaires sent to national significant contributor for some cities. Estimates of organizations. The IRF uses a hierarchy of sources to economic damages from death and illness due to par- gather as much information as possible. The primary ticulate matter pollution are shown in table 3.15. 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 works, and central statistical offices. As a result, the compiled data are of uneven quality. The coverage of each indicator The 15 countries with the fewest passenger cars per 1,000 people in 2003--and the 15 with the most Country Number of cars Country Number of cars Bangladesh 0.5 New Zealand 613 Ethiopia 1 Canada 561 Sierra Leone 2 Germany 545 Uganda 2 Italy 545 Bolivia 3 Switzerland 511 Data sources Gambia, The 6 Austria 501 Data on vehicles and traffic are from the IRF's India 6 France 495 electronic files and its annual World Road Statis- Pakistan 7 United States 482 Kenya 8 Belgium 470 tics. Data on particulate matter concentrations Afghanistan 9 Spain 455 are from Kiran Dev Pandey, David Wheeler, Bart Philippines 9 Sweden 455 Ostro, Uwe Deichmann, Kirk Hamilton, and Katie China 10 Slovenia 446 Bolt's "Ambient Particulate Matter Concentrations Senegal 11 United Kingdom 439 in Residential and Pollution Hotspot Areas of World Syrian Arab Republic 12 Finland 433 Sri Lanka 13 Japan 433 Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS)" (2006). Source: Table 3.12. 2006 World Development Indicators 177 Air pollution City Particulate Sulfur Nitrogen About the data population matter dioxide dioxide In many towns and cities exposure to air pollution is the main environmental threat to human health. Long-term exposure to high levels of soot and small micrograms per micrograms per micrograms per thousands cubic meter cubic meter cubic meter particles in the air contributes to a wide range of City 2005 2002 1995­2001 a 1995­2001 a health effects, including respiratory diseases, lung Argentina Cordoba City 1,592 58 .. 97 cancer, and heart disease. Particulate pollution, on Australia Melbourne 3,663 13 .. 30 its own or in combination with sulfur dioxide, leads Perth 1,484 13 5 19 to an enormous burden of ill health. Sydney 4,388 22 28 81 Austria Vienna 2,190 44 14 42 Emissions of sulfur dioxide and nitrogen oxides Belgium Brussels 1,027 30 20 48 lead to the deposition of acid rain and other acidic Brazil Rio de Janeiro 11,469 42 129 .. compounds over long distances. Acid deposition Sao Paulo 18,333 49 43 83 changes the chemical balance of soils and can lead Bulgaria Sofia 1,045 76 39 122 to the leaching of trace minerals and nutrients criti- Canada Montreal 3,511 20 10 42 Toronto 5,060 24 17 43 cal to trees and plants. Vancouver 2,125 14 14 37 Where coal is the primary fuel for power plants, steel Chile Santiago 5,623 62 29 81 mills, industrial boilers, and domestic heating, the result China Anshan 1,459 92 115 88 is usually high levels of urban air pollution--especially Beijing 10,849 99 90 122 particulates and sometimes sulfur dioxide--and, if the Changchun 3,092 82 21 64 Chengdu 3,478 95 77 74 sulfur content of the coal is high, widespread acid depo- Chongquing 4,975 137 340 70 sition. Where coal is not an important primary fuel or Dalian 2,709 55 61 100 is used in plants with effective dust control, the worst Guangzhu 976 70 57 136 emissions of air pollutants stem from the combustion Guiyang 2,467 78 424 53 of petroleum products. Harbin 2,898 85 23 30 Jinan 2,654 104 132 45 The data on sulfur dioxide and nitrogen dioxide con- Kunming 1,748 78 19 33 centrations are based on reports from urban monitor- Lanzhou 1,788 101 102 104 ing sites. Annual means (measured in micrograms per Liupanshui 2,118 65 102 .. cubic meter) are average concentrations observed at Nanchang 1,742 87 69 29 these sites. Coverage is not comprehensive because Pinxiang 1,562 74 75 .. Quingdao 2,431 68 190 64 not all cities have monitoring systems. Shanghai 12,665 81 53 73 The data on concentrations of particulate matter Shenyang 4,916 112 99 73 are estimates, for selected cities, of average annual Taiyuan 2,516 98 211 55 concentrations in residential areas away from air pol- Tianjin 9,346 139 82 50 lution "hotspots," such as industrial districts and Urumqi 1,467b 57 60 70 Wuhan 6,003 88 40 43 transport corridors. The data have been extracted Zhengzhou 2,250 108 63 95 from a complete set of estimates developed by the Zibo 2,775 82 198 43 World Bank's Development Research Group and Envi- Colombia Bogota 5,442b 32 .. .. ronment Department in a study of annual ambient Croatia Zagreb 908 b 37 31 .. concentrations of particulate matter in world cities Cuba Havana 2,192 28 1 5 Czech Republic Prague 1,164 25 14 33 with populations exceeding 100,000 (Pandey and Denmark Copenhagen 1,091 23 7 54 others 2006). Ecuador Guayaquil 2,387 25 15 .. Pollutant concentrations are sensitive to local con- Quito 1,514 33 22 .. ditions, and even in the same city different monitor- Egypt, Arab Rep. Cairo 11,146 159 69 .. ing sites may register different concentrations. Thus Finland Helsinki 1,103 23 4 35 France Paris 9,854 12 14 57 these data should be considered only a general indica- Germany Berlin 3,328 25 18 26 tion of air quality in each city, and cross-country com- Frankfurt 668 b 22 11 45 parisons should be made with caution. The current Munich 2,318 22 8 53 World Health Organization (WHO) air quality guidelines Ghana Accra 1,970 40 .. .. for annual mean concentrations are 50 micrograms Greece Athens 3,238 51 34 64 Hungary Budapest 1,670 23 39 51 per cubic meter for sulfur dioxide and 40 micrograms Iceland Reykjavik 164b 20 5 42 for nitrogen dioxide. The WHO has set no guidelines India Ahmedabad 5,171 98 30 21 for particulate matter concentrations below which Bangalore 6,532 53 .. .. there are no appreciable health effects. 178 2006 World Development Indicators Air pollution City Particulate Sulfur Nitrogen Definitions population matter dioxide dioxide · City population is the number of residents of the city or metropolitan area as defined by national authorities and reported to the United Nations. micrograms per micrograms per micrograms per thousands cubic meter cubic meter cubic meter · Particulate matter refers to fine suspended par- City 2005 2002 1995­2001 a 1995­2001 a ticulates less than 10 microns in diameter that are India Calcutta 14,299 145 49 34 capable of penetrating deep into the respiratory tract Chennai 6,915 44 15 17 and causing significant health damage. The state Delhi 15,334 177 24 41 of a country's technology and pollution controls Hyderabad 6,145 48 12 17 Kanpur 3,040 128 15 14 is an important determinant of particulate matter Lucknow 2,589 129 26 25 concentrations. · Sulfur dioxide is an air pollutant Mumbai 18,336 74 33 39 produced when fossil fuels containing sulfur are Nagpur 2,359 65 6 13 burned. It contributes to acid rain and can damage Pune 4,485 55 .. .. human health, particularly that of the young and the Indonesia Jakarta 13,194 115 .. .. Iran, Islamic Rep. Tehran 7,352 68 209 .. elderly. · Nitrogen dioxide is a poisonous, pungent Ireland Dublin 1,033 21 20 .. gas formed when nitric oxide combines with hydro- Italy Milan 4,007 36 31 248 carbons and sunlight, producing a photochemical Rome 2,628 35 .. .. reaction. These conditions occur in both natural and Torino 969 b 53 .. .. anthropogenic activities. Nitrogen dioxide is emit- Japan Osaka 2,626b 37 19 63 Tokyo 35,327 42 18 68 ted by bacteria, motor vehicles, industrial activities, Yokohama 3,366 32 100 13 nitrogenous fertilizers, combustion of fuels and bio- Kenya Nairobi 2,818 42 .. .. mass, and aerobic decomposition of organic matter Korea, Rep Pusan 3,527 44 60 51 in soils and oceans. Seoul 9,592 46 44 60 Taegu 2,510 50 81 62 Malaysia Kuala Lumpur 1,392 28 24 .. Mexico Mexico City 19,013 55 74 130 Netherlands Amsterdam 1,157 40 10 58 New Zealand Auckland 1,152 15 3 20 Norway Oslo 808 19 8 43 Philippines Manila 10,432b 42 33 .. Data sources Poland Lodz 943 39 21 43 Warsaw 2,204 43 16 32 Data on city population are from the United Portugal Lisbon 1,977 28 8 52 Nations Population Division. Data on particulate Romania Bucharest 1,764 22 10 71 matter concentrations are from a recent World Russian Federation Moscow 10,672 25 109 .. Bank study by Kiran D. Pandey, David Wheeler, Omsk 1,132 27 20 34 Singapore Singapore 4,372 48 20 30 Bart Ostro, Uwe Deichman, Kirk Hamilton, and Slovak Republic Bratislava 456b 19 21 27 Kathrine Bolt, "Ambient Particulate Matter Con- South Africa Capetown 3,103 15 21 72 centration in Residential and Pollution Hotspot Durban 2,643 29 31 .. Areas of World Cities: New Estimates Based on the Johannesburg 3,288 30 19 31 Global Model of Ambient Particulates (GMAPS)" Spain Barcelona 4,424 43 11 43 Madrid 5,145 37 24 66 (2006). Data on sulfur dioxide and nitrogen diox- Sweden Stockholm 1,729 13 3 20 ide concentrations are from the WHO's Healthy Switzerland Zurich 984 26 11 39 Cities Air Management Information System and Thailand Bangkok 6,604 83 11 23 the World Resources Institute, which relies on vari- Turkey Ankara 3,593 54 55 46 ous national sources as well as, among others, Istanbul 9,760 64 120 .. Ukraine Kiev 2,623 38 14 51 the Organisation for Economic Co-operation and United Kingdom Birmingham 2,215 26 9 45 Development's OECD Environmental Data Com- London 7,615 23 25 77 pendium 1999, the U.S. Environmental Protec- Manchester 2,193 17 26 49 tion Agency's National Air Quality and Emissions United States Chicago 8,711 26 14 57 Trends Report 1995, the Aerometric Information Los Angeles 12,146 36 9 74 New York 18,498 22 26 79 Retrieval System Executive International data- Venezuela, RB Caracas 3,276 17 33 57 base, and the United Nations Centre for Human Settlements' Urban Indicators database. a. Data are for the most recent year available. b. Data are for 2000. 2006 World Development Indicators 179 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of the Biological Kyoto Stockholm changeb layer control Sea c diversity b Protocol CITES CCD Convention Afghanistan .. .. 2002 2004f 2004f .. 2002 .. 1985f 1995f .. Albania 1993 .. 1995 1999 f 1999 f 2003f 1994f 2005f 2003f 2000 f 2004 Algeria 2001 .. 1994 1992 f 1992 f 1996 1995 2005f 1983f 1996 .. Angola .. .. 2000 2000 f 2000 f 1994 1998 .. .. 1997 .. Argentina 1992 .. 1994 1990 1990 1995 1994 2001 1981 1997 2005 Armenia .. .. 1994 1999 f 1999 f 2002 f 1993d 2003f .. 1997 2003 Australia 1992 1994 1994 1987f 1989 1994 1993 .. 1976 2000 2004 Austria .. .. 1994 1987 1989 1995 1994 2002 1982f 1997f 2002 Azerbaijan 1998 .. 1995 1996f 1996f .. 2000 e 2000 f 1998f 1998f 2004f Bangladesh 1991 1990 1994 1990 f 1990 f 2001 1994 2001f 1981 1996 .. Belarus .. .. 2000 1986d 1988d .. 1993 .. 1995f 2001f 2004f Belgium .. .. 1996 1988 1988 1998 1996 2002 1983 1997f .. Benin 1993 .. 1994 1993f 1993f 1997 1994 2002 f 1984f 1996 2004 Bolivia 1994 1988 1995 1994f 1994f 1995 1994 1999 1979 1996 2003 Bosnia and Herzegovina .. .. 2000 1992g 1992g 1994g 2002 f .. 2002 2002 f .. Botswana 1990 1991 1994 1991f 1991f 1994 1995 2003f 1977f 1996 2002 f Brazil .. 1988 1994 1990 f 1990 f 1994 1994 2002 1975 1997 2004 Bulgaria .. 1994 1995 1990 f 1990 f 1996 1996 2002 1991f 2001f 2004 Burkina Faso 1993 .. 1994 1989 1989 2005 1993 2005f 1989 f 1996 2004 Burundi 1994 1989 1997 1997f 1997f .. 1997 2001f 1988f 1997 2005 Cambodia 1999 .. 1996 2001f 2001f .. 1995f 2002 f 1997 1997 .. Cameroon .. 1989 1995 1989 f 1989 f 1994 1994 2002 f 1981f 1997 .. Canada 1990 1994 1994 1986 1988 2003 1992 2002 1975 1995 2001 Central African Republic .. .. 1995 1993f 1993f .. 1995 .. 1980 f 1996 .. Chad 1990 .. 1994 1989 f 1994 .. 1994 .. 1989 f 1996 2004 Chile .. 1993 1995 1990 1990 1997 1994 2002 1975 1997 2005 China 1994 1994 1994 1989 f 1991f 1996 1993 2002e 1981f 1997 2004 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 1998 1988 1995 1990 f 1993f .. 1994 2001f 1981 1999 .. Congo, Dem. Rep. .. 1990 1995 1994f 1994f 1995 1996 2005f 1976f 1997 2005f Congo, Rep. .. 1990 1997 1994f 1994f .. 1994 .. 1983f 1999 .. Costa Rica 1990 1992 1994 1991f 1991f 1994 1994 2002 1975 1998 .. Côte d'Ivoire 1994 1991 1995 1993f 1993f 1994 1994 .. 1994f 1997 2004 Croatia 2001 2000 1996 1991d 1991d 1994g 1996 .. 2000 f 2000 d .. Cuba .. .. 1994 1992 f 1992 f 1994 1994 2002 1990 f 1997 .. Czech Republic 1994 .. 1994 1993 d 1993d 1996 1993e 2001e 1993 g 2000 f 2002 Denmark 1994 .. 1994 1988 1988 2004 1993 2002 1977 1995f 2003 Dominican Republic .. 1995 1999 1993f 1993f .. 1996 2002 f 1986f 1997f .. Ecuador 1993 1995 1994 1990 f 1990 f .. 1993 2000 1975 1995 2004 Egypt, Arab Rep. 1992 1988 1995 1988 1988 1994 1994 2005f 1978 1995 2003 El Salvador 1994 1988 1996 1992 1992 .. 1994 1998 1987f 1997f .. Eritrea 1995 .. 1995 2005f 2005f .. 1996f 2005f 1994f 1996 2005f Estonia 1998 .. 1994 1996f 1996f 2005f 1994 2002 1992 f .. .. Ethiopia 1994 1991 1994 1994f 1994f .. 1994 2005f 1989 f 1997 2003 Finland 1995 .. 1994 1986 1988 1996 1994 d 2002 1976f 1995d 2002d France 1990 .. 1994 1987e 1988e 1996 1994 2002e 1978 1997 2004 e Gabon .. 1990 1998 1994f 1994f 1998 1997 .. 1989 f 1996f .. Gambia, The 1992 1989 1994 1990 f 1990 f 1994 1994 2001f 1977f 1996 .. Georgia 1998 .. 1994 1996f 1996f 1996f 1994f 1999 f 1996f 1999 .. Germany .. .. 1994 1988 1988 1994f 1993 2002 1976 1996 2002 Ghana 1992 1988 1995 1989 f 1989 1994 1994 2003f 1975 1996 2003 Greece .. .. 1994 1988 1988 1995 1994 2002 1992 f 1997 .. Guatemala 1994 1988 1996 1987f 1989 f 1997 1995 1999 1979 1998f .. Guinea 1994 1988 1994 1992 f 1992 f 1994 1993 2000 f 1981f 1997 .. Guinea-Bissau 1993 1991 1996 2002 f 2002 f 1994 1995 .. 1990 f 1995 .. Haiti 1999 .. 1996 2000 f 2000 f 1996 1996 2005f .. 1996 .. 180 2006 World Development Indicators Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of the Biological Kyoto Stockholm changeb layer control Sea c diversity b Protocol CITES CCD Convention Honduras 1993 .. 1996 1993f 1993f 1994 1995 2000 1985f 1997 2005 Hungary 1995 .. 1994 1988f 1989 f 2002 1994 2002 f 1985f 1999 f .. India 1993 1994 1994 1991f 1992 f 1995 1994 2002 f 1976 1996 .. Indonesia 1993 1993 1994 1992 f 1992 1994 1994 2004 1978f 1998 .. Iran, Islamic Rep. .. .. 1996 1990 f 1990 f .. 1996 2005f 1976 1997 .. Iraq .. .. .. .. .. 1994 .. .. .. .. .. Ireland .. .. 1994 1988 f 1988 1996 1996 2002 2002 1997 .. Israel .. .. 1996 1992 f 1992 .. 1995 2004 1979 1996 .. Italy .. .. 1994 1988 1988 1995 1994 2002 1979 1997 .. Jamaica 1994 .. 1995 1993f 1993f 1994 1995 1999 f 1997f 1997f .. Japan .. .. 1994 1988f 1988 1996 1993d 2002d 1980 1998d 2002 f Jordan 1991 .. 1994 1989 f 1989 f 1995f 1993 2003f 1978f 1996 2004 Kazakhstan .. .. 1995 1998f 1998f .. 1994 .. 2000 f 1997 .. Kenya 1994 1992 1994 1988f 1988 1994 1994 2005f 1978 1997 2004 Korea, Dem. Rep. .. .. 1995 1995f 1995f .. 1994 e 2005f .. 2003f 2002 f Korea, Rep. .. .. 1994 1992 1992 1996 1994 2002 1993f 1999 .. Kuwait .. .. 1995 1992 f 1992 f 1994 2002 2005f 2002 1997 .. Kyrgyz Republic 1995 .. 2000 2000 f 2000 f .. 1996e 2003f .. 1997f .. Lao PDR 1995 .. 1995 1998f 1998f 1998 1996e 2003f 2004f 1996d .. Latvia .. .. 1995 1995f 1995f 2004f 1995 2002 1997f 2002 f 2004 Lebanon .. .. 1995 1993f 1993f 1995 1994 .. .. 1996 2003 Lesotho 1989 .. 1995 1994f 1994f .. 1995 2000 f 2003 1995 2002 Liberia .. .. 2003 1996f 1996f .. 2000 2002 f 1981f 1998f 2002 f Libya .. .. 1999 1990 f 1990 f .. 2001 .. 2003f 1996 2005f Lithuania .. .. 1995 1995f 1995f 2003f 1996 2003 2001f 2003f .. Macedonia, FYR .. .. 1998 1994g 1994g 1994g 1997f 2004f 2000 f 2002 f 2004 Madagascar 1988 1991 1999 1996f 1996f 2001 1996 2003f 1975 1997 .. Malawi 1994 .. 1994 1991f 1991f .. 1994 2001f 1982 f 1996 .. Malaysia 1991 1988 1994 1989 f 1989 f 1996 1994 2002 1977f 1997 .. Mali .. 1989 1995 1994f 1994f 1994 1995 2002 1994f 1995 2003 Mauritania 1988 .. 1994 1994f 1994f 1996 1996 2005f 1998f 1996 2005 Mauritius 1990 .. 1994 1992 f 1992 f 1994 1992 2001f 1975 1996 2004 Mexico .. 1988 1994 1987 1988 1994 1993 2000 1991f 1995 2003 Moldova 2002 .. 1995 1996f 1996f .. 1995 2003f 2001f 1999 f 2004 Mongolia 1995 .. 1994 1996f 1996f 1996 1993 1999 f 1996f 1996 2004 Morocco .. 1988 1996 1995 1995 .. 1995 2002 f 1975 1996 2004 Mozambique 1994 .. 1995 1994f 1994f 1997 1995 2005f 1981f 1997 2005 Myanmar .. 1989 1995 1993f 1993f 1996 1995 2003f 1997f 1997f 2004f Namibia 1992 .. 1995 1993f 1993f 1994 1997 2003f 1990 f 1997 2005f Nepal 1993 .. 1994 1994f 1994f 1998 1993 2005f 1975f 1996 .. Netherlands 1994 .. 1994 1988f 1988d 1996 1994 d 2002 f 1984 1995d 2002d New Zealand 1994 .. 1994 1987 1988 1996 1993 2002 1989 f 2000 f 2004 Nicaragua 1994 .. 1996 1993f 1993f 2000 1995 1999 1977f 1998 .. Niger .. 1991 1995 1992 f 1992 f .. 1995 2004 1975 1996 .. Nigeria 1990 1992 1994 1988f 1988f 1994 1994 2004f 1974 1997 2004 Norway .. 1994 1994 1986 1988 1996 1993 2002 1976 1996 2002 Oman .. .. 1995 1999 f 1999 f 1994 1995 2005f .. 1996f 2005 Pakistan 1994 1991 1994 1992 f 1992 f 1997 1994 2005f 1976f 1997 .. Panama 1990 .. 1995 1989 f 1989 1996 1995 1999 1978 1996 2003 Papua New Guinea 1992 1993 1994 1992 f 1992 f 1997 1993 2002 1975f 2000 f 2003 Paraguay .. .. 1994 1992 f 1992 f 1994 1994 1999 1976 1997 2004 Peru .. 1988 1994 1989 1993f .. 1993 2002 1975 1995 2005 Philippines 1989 1989 1994 1991f 1991 1994 1993 2003 1981 2000 2004 Poland 1993 1991 1994 1990 f 1990 f 1998 1996 2002 1989 2001f .. Portugal 1995 .. 1994 1988f 1988 1997 1993 2002e 1980 1996 2004 d Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 181 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of the Biological Kyoto Stockholm changeb layer control Sea c diversity b Protocol CITES CCD Convention Romania 1995 .. 1994 1993f 1993f 1996 1994 2001 1994f 1998f 2004 Russian Federation 1999 1994 1995 1986d 1988d 1997 1995 2004 1992 2003f .. Rwanda 1991 .. 1998 2001f 2001f .. 1996 2004f 1980 f 1998 2002 f Saudi Arabia .. .. 1995 1993f 1993f 1996 2001e 2005f 1996f 1997f .. Senegal 1984 1991 1995 1993f 1993 1994 1994 2001f 1977f 1995 2003 Serbia and Montenegro .. .. 2001 2001 g 2001 g 2001 g 2002 .. 2002 .. 2002 Sierra Leone 1994 .. 1995 2001f 2001f 1994 1994 e .. 1994f 1997 2003f Singapore 1993 1995 1997 1989 f 1989 f 1994 1995 .. 1986f 1999 f 2005 Slovak Republic .. .. 1994 1993 g 1993 g 1996 1994 e 2002 1993 2001f 2002 Slovenia 1994 .. 1996 1992g 1992g 1995g 1996 2002 2000 f 2001f 2004 Somalia .. .. .. 2001f 2001f 1994 .. .. 1985f 2002 f .. South Africa 1993 .. 1997 1990 f 1990 f 1997 1995 2002 f 1975 1997 2002 Spain .. .. 1994 1988f 1988 1997 1995 2002 1986f 1996 2004 Sri Lanka 1994 1991 1994 1989 f 1989 f 1994 1994 2002 f 1979 f 1998f .. Sudan .. .. 1994 1993f 1993f 1994 1995 2004f 1982 1995 .. Swaziland .. .. 1997 1992 f 1992 f .. 1994 .. 1997f 1996 .. Sweden .. .. 1994 1986 1988 1996 1993 2002 1974 1995 2002 Switzerland .. .. 1994 1987 1988 .. 1994 2003 1974 1996 2003 Syrian Arab Republic 1999 .. 1996 1989 f 1989 f .. 1996 .. 2003f 1997 2005 Tajikistan .. .. 1998 1996f 1998f .. 1997e .. .. 1997f .. Tanzania 1994 1988 1996 1993f 1993f 1994 1996 2002 f 1979 1997 2004 Thailand .. .. 1995 1989 f 1989 .. 2004 2002 1983 2001f 2005 Togo 1991 .. 1995 1991f 1991 1994 1995d 2004f 1978 1995d 2004 Trinidad and Tobago .. .. 1994 1989 f 1989 f 1994 1996 1999 1984f 2000 f 2002 f Tunisia 1994 1988 1994 1989 f 1989 f 1994 1993 2003f 1974 1995 2004 Turkey 1998 .. 2004 1991f 1991f .. 1997 .. 1996f 1998 .. Turkmenistan .. .. 1995 1993f 1993f .. 1996e 1999 .. 1996 .. Uganda 1994 1988 1994 1988 f 1988 1994 1993 2002 f 1991f 1997 2004f Ukraine 1999 .. 1997 1986d 1988d 1999 1995 2004 1999 f 2002 f .. United Arab Emirates .. .. 1996 1989 f 1989 f .. 2000 2005f 1990 f 1998f 2002 United Kingdom 1995 1994 1994 1987 1988 1997f 1994 2002 1976 1996 2005 United States 1995 1995 1994 1986 1988 .. .. .. 1974 2000 .. Uruguay .. .. 1994 1989 f 1991f 1994 1993 2001 1975 1999 f 2004 Uzbekistan .. .. 1994 1993f 1993f .. 1995e 1999 1997f 1995 .. Venezuela, RB .. .. 1995 1988f 1989 .. 1994 .. 1977 1998f 2005 Vietnam .. 1993 1995 1994f 1994f 1994 1994 2002 1994f 1998f 2002 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1996 1992 1996 1996f 1996f 1994 1996 2004f 1997f 1997f 2004 Zambia 1994 .. 1994 1990 f 1990 f 1994 1993 .. 1980 f 1996 .. Zimbabwe 1987 .. 1994 1992 f 1992 f 1994 1994 .. 1981f 1997 .. a. Ratification of the treaty. b. Years shown refer to the year the treaty entered into force in that country. c. Convention became effective November 16, 1994. d. Acceptance. e. Approval. f. Accession. g. Succession. 182 2006 World Development Indicators Government commitment About the data Definitions National environmental strategies and participation de Janeiro, which produced Agenda 21--an array of · Environmental strategies and action plans provide in international treaties on environmental issues pro- actions to address environmental challenges: a comprehensive, cross-sectoral analysis of conser- vide some evidence of government commitment to · The Framework Convention on Climate Change vation and resource management issues to help inte- sound environmental management. But the signing aims to stabilize atmospheric concentrations grate environmental concerns with the development of these treaties does not always imply ratification, of greenhouse gases at levels that will prevent process. They include national conservation strate- nor does it guarantee that governments will comply human activities from interfering dangerously gies, national environmental action plans, national with treaty obligations. with the global climate. environmental management strategies, and national In many countries efforts to halt environmental · The Vienna Convention for the Protection of the sustainable development strategies. The year shown degradation have failed, primarily because govern- Ozone Layer aims to protect human health and for a country refers to the year in which a strategy ments have neglected to make this issue a pri- the environment by promoting research on the or action plan was adopted. · Biodiversity assess- ority, a reflection of competing claims on scarce effects of changes in the ozone layer and on ments, strategies, and action plans include biodiver- resources. To address this problem, many countries alternative substances (such as substitutes for sity profiles (see About the data). · Participation in are preparing national environmental strategies-- chlorofluorocarbons) and technologies, moni- treaties covers nine international treaties (see About some focusing narrowly on environmental issues, toring the ozone layer, and taking measures the data). · Climate change refers to the Framework and others integrating environmental, economic, to control the activities that produce adverse Convention on Climate Change (signed in New York and social concerns. Among such initiatives are effects. in 1992). · Ozone layer refers to the Vienna Conven- conservation strategies and environmental action · The Montreal Protocol for Chlorofluorocarbon tion for the Protection of the Ozone Layer (signed plans. Some countries have also prepared country Control requires that countries help protect in 1985). · CFC control refers to the Montreal Pro- environmental profiles and biodiversity strategies the earth from excessive ultraviolet radiation tocol for Chlorofluorocarbon Control (formally, the and profiles. by cutting chlorofluorocarbon consumption by Protocol on Substances That Deplete the Ozone National conservation strategies--promoted by 20 percent over their 1986 level by 1994 and Layer, signed in 1987). · Law of the Sea refers to the World Conservation Union (IUCN)--provide a by 50 percent over their 1986 level by 1999, the United Nations Convention on the Law of the Sea comprehensive, cross-sectoral analysis of conser- with allowances for increases in consumption (signed in Montego Bay, Jamaica, in 1982). · Bio- vation and resource management issues to help inte- by developing countries. logical diversity refers to the Convention on Biologi- grate environmental concerns with the development · The United Nations Convention on the Law of the cal Diversity (signed at the Earth Summit in Rio de process. Such strategies discuss current and future Sea, which became effective in November 1994, Janeiro in 1992). · Kyoto Protocol refers to the pro- needs, institutional capabilities, prevailing technical establishes a comprehensive legal regime for tocol on climate change adopted at the third confer- conditions, and the status of natural resources in seas and oceans, establishes rules for environ- ence of the parties to the United Nations Framework a country. mental standards and enforcement provisions, Convention on Climate Change, held in Kyoto, Japan, National environmental action plans, supported and develops international rules and national leg- in December 1997. · CITES refers to the Conven- by the World Bank and other development agencies, islation to prevent and control marine pollution. tion on International Trade in Endangered Species of describe a country's main environmental concerns, · The Convention on Biological Diversity promotes Wild Fauna and Flora, an agreement between govern- identify the principal causes of environmental prob- conservation of biodiversity through scientific ments to ensure that the survival of wild animals and lems, and formulate policies and actions to deal and technological cooperation among countries, plants is not threatened by uncontrolled exploitation. with them. These plans are a continuing process in access to financial and genetic resources, and · CCD refers to the United Nations Convention to which governments develop comprehensive envi- transfer of ecologically sound technologies. Combat Desertification, an international convention ronmental policies, recommend specific actions, But 10 years after Rio the World Summit on Sus- dedicated to addressing the problems of land deg- and outline the investment strategies, legislation, tainable Development in Johannesburg recognized radation in the world's drylands. Adopted in Paris on and institutional arrangements required to imple- that many of the proposed actions have yet to mate- June 17, 1994, it entered into force on December 26, ment them. rialize. To help developing countries comply with their 1996. · Stockholm Convention is an international Biodiversity profiles--prepared by the World Con- obligations under these agreements, the Global Envi- legally binding instrument designed to protect human servation Monitoring Centre and the IUCN--provide ronment Facility (GEF) was created to focus on global health and the environment from persistent organic basic background on species diversity, protected improvement in biodiversity, climate change, interna- pollutants. It was adopted on May 22, 2001, and areas, major ecosystems and habitat types, and tional waters, and ozone layer depletion. The UNEP, entered into force May 17, 2004. legislative and administrative support. In an effort United Nations Development Programme (UNDP), to establish a scientific baseline for measuring prog- and the World Bank manage the GEF according to ress in biodiversity conservation, the United Nations the policies of its governing body of country repre- Data sources Environment Programme (UNEP) coordinates global sentatives. The World Bank is responsible for the Data on environmental strategies and participa- biodiversity assessments. GEF Trust Fund and is chair of the GEF. tion in international environmental treaties are To address global issues, many governments have from the Secretariat of the United Nations Frame- also signed international treaties and agreements work Convention on Climate Change, the Ozone launched in the wake of the 1972 United Nations Secretariat of the UNEP, the World Resources Conference on Human Environment in Stockholm Institute, the UNEP, the Center for International and the 1992 United Nations Conference on Envi- Earth Science Information Network, and the ronment and Development (the Earth Summit) in Rio United Nations Treaty Series. 2006 World Development Indicators 183 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed savings expenditure depletion depletion forest dioxide emission net capital depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Afghanistan .. 7.7 .. .. 0.0 .. 1.3 0.1 0.1 .. Albania 17.1 10.8 6.3 2.8 1.2 0.0 0.0 0.2 0.3 7.4 Algeria 47.6 11.6 36.0 4.5 35.2 0.0 0.1 0.8 0.7 3.6 Angola 18.4 11.5 6.9 3.1 45.0 0.0 0.0 0.3 1.1 ­36.3 Argentina 22.2 12.4 9.8 4.3 8.2 0.3 0.0 0.6 2.3 2.7 Armenia 13.7 9.8 3.9 3.0 0.0 0.7 0.0 0.9 3.0 2.4 Australia 19.5 15.0 4.5 4.8 1.5 1.3 0.0 0.4 0.1 6.0 Austria 24.3 14.4 9.9 5.6 0.1 0.0 0.0 0.1 0.5 14.7 Azerbaijan 26.3 10.8 15.5 3.3 54.6 0.0 0.0 3.4 0.7 ­40.0 Bangladesh 29.0 8.2 20.8 1.9 2.4 0.0 0.7 0.4 0.5 18.7 Belarus 23.6 11.0 12.6 5.4 2.1 0.0 0.0 2.1 .. 13.8a Belgium 23.2 15.6 7.5 3.0 0.0 0.0 0.0 0.2 0.3 10.1 Benin 12.5 9.0 3.5 2.4 0.1 0.0 0.0 0.3 0.5 5.1 Bolivia 18.2 10.3 7.9 6.3 15.4 1.0 0.0 0.7 1.0 ­3.9 Bosnia and Herzegovina 3.9 10.6 ­6.7 .. 0.1 0.0 .. 1.6 0.2 .. Botswana 40.0 12.5 27.5 5.6 0.0 2.0 0.0 0.4 .. 30.8a Brazil 24.0 11.8 12.1 4.1 3.7 1.1 0.0 0.4 0.4 10.7 Bulgaria 16.3 11.6 4.7 3.5 0.2 0.7 0.0 1.4 2.4 3.5 Burkina Faso .. 8.6 .. 2.4 0.0 0.0 1.2 0.2 0.5 .. Burundi 15.5 6.8 8.7 3.7 0.0 0.0 14.6 0.3 0.3 ­2.9 Cambodia 19.6 8.9 10.8 1.8 0.0 0.0 0.8 0.1 0.1 11.6 Cameroon 15.4 10.0 5.5 3.5 10.8 0.0 0.0 0.2 0.9 ­3.0 Canada 20.7b 14.7 6.0 5.2 5.1 0.3 0.0 0.4 0.3 5.2 Central African Republic 14.1 8.3 5.8 1.6 0.0 0.0 0.0 0.1 0.1 7.2 Chad 10.0 13.7 ­3.8 1.4 79.1 0.0 0.0 0.0 0.7 ­82.2 Chile 22.7 12.5 10.2 3.9 0.2 10.8 0.0 0.5 1.2 1.5 China 42.3 10.4 31.9 2.0 3.0 0.2 0.0 1.4 1.5 27.8 Hong Kong, China 31.7 13.8 17.9 3.7 0.0 0.0 0.0 0.2 .. 21.5a Colombia 17.6 11.4 6.2 5.0 7.2 0.8 0.0 0.4 0.1 2.6 Congo, Dem. Rep. 7.6 7.3 0.3 0.9 2.8 0.9 0.0 0.3 0.6 ­3.3 Congo, Rep. 36.0 13.3 22.7 3.8 54.1 0.0 0.0 0.2 .. ­27.8a Costa Rica 17.8 6.1 11.7 4.2 0.0 0.0 0.3 0.2 0.5 14.9 Côte d'Ivoire 15.5 10.2 5.4 4.6 2.9 0.0 0.6 0.3 0.3 5.7 Croatia 24.6 12.9 11.8 4.1 1.0 0.0 0.2 0.5 0.3 13.8 Cuba .. .. .. 8.1 .. .. .. .. 0.2 .. Czech Republic 23.6 13.7 9.9 4.2 0.1 0.0 0.0 0.8 0.1 13.0 Denmark 22.7 15.4 7.3 8.1 1.3 0.0 0.0 0.1 0.1 13.9 Dominican Republic 29.3 11.8 17.5 2.2 0.0 2.2 0.0 0.9 0.3 16.3 Ecuador 28.4 11.6 16.8 1.4 19.0 0.1 0.0 0.6 0.2 ­1.6 Egypt, Arab Rep. 21.1 10.0 11.1 4.4 10.6 0.1 0.3 1.2 1.7 1.6 El Salvador 9.4 11.3 ­2.0 2.8 0.0 0.0 0.6 0.3 0.3 ­0.2 Eritrea ­8.9 8.0 ­16.9 1.6 0.0 0.0 1.2 0.5 0.6 ­17.6 Estonia 19.9 13.5 6.4 5.1 0.6 0.0 1.0 1.2 0.1 8.6 Ethiopia 13.5 6.9 6.6 3.0 0.0 0.0 11.9 0.5 0.3 ­3.2 Finland 23.7 16.2 7.5 5.9 0.0 0.0 0.0 0.2 0.1 13.1 France 19.0 12.6 6.4 5.2 0.1 0.0 0.0 0.1 0.0 11.2 Gabon 34.6 14.0 20.6 3.3 25.5 0.0 0.0 0.4 .. ­2.0 Gambia, The 19.1 8.6 10.5 2.6 0.0 0.0 0.6 0.5 1.1 10.8 Georgia 18.0 9.9 8.1 4.3 0.6 0.0 0.0 0.7 1.1 10.0 Germany 20.7 14.9 5.7 4.5 0.1 0.0 0.0 0.2 0.1 9.7 Ghana 28.1 8.7 19.3 2.8 0.1 0.2 2.3 0.5 0.3 18.8 Greece 17.7 8.7 9.0 3.1 0.0 0.0 0.0 0.3 1.0 10.8 Guatemala 13.4 11.1 2.4 1.6 1.2 0.0 0.9 0.3 0.4 1.2 Guinea 8.0 8.8 ­0.8 2.0 0.0 1.9 1.8 0.2 0.8 ­3.5 Guinea-Bissau 8.8 7.9 0.9 .. 0.0 0.0 0.0 0.6 1.0 .. Haiti 20.1 8.5 11.6 1.5 0.0 0.0 1.0 0.3 0.3 11.5 184 2006 World Development Indicators Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed savings expenditure depletion depletion forest dioxide emission net capital depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Honduras .. 10.3 .. 3.5 0.0 0.2 0.0 0.6 0.3 .. Hungary 15.4 13.8 1.7 5.2 0.5 0.0 0.0 0.4 0.3 5.7 India 23.0 9.3 13.7 4.0 2.5 0.4 0.7 1.3 0.8 12.0 Indonesia 24.6 10.4 14.2 1.1 9.4 1.6 0.0 0.7 0.9 2.6 Iran, Islamic Rep. 39.7 11.0 28.7 4.4 36.0 0.2 0.0 1.7 0.9 ­5.6 Iraq .. .. .. .. .. .. .. .. 2.7 .. Ireland 29.9 b 11.0 18.9 4.8 0.0 0.0 0.0 0.2 0.1 23.3 Israel 10.0 b 14.0 ­4.0 7.3 0.0 0.0 0.0 0.4 1.2 1.6 Italy 19.5 13.9 5.6 4.5 0.1 0.0 0.0 0.2 0.3 9.5 Jamaica 26.6 12.1 14.5 5.0 0.0 1.3 0.0 0.9 0.3 17.0 Japan 26.3 14.4 12.0 3.1 0.0 0.0 0.0 0.2 0.6 14.4 Jordan 21.0 10.7 10.3 5.6 0.3 0.1 0.0 1.0 0.9 13.5 Kazakhstan 27.0 12.0 15.0 4.4 39.9 1.6 0.0 3.0 0.5 ­25.5 Kenya 13.7 9.0 4.7 6.6 0.0 0.0 0.2 0.4 0.2 10.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. 2.0 .. Korea, Rep. 34.3 13.3 20.9 3.8 0.0 0.0 0.0 0.5 1.3 22.9 Kuwait 47.2b 12.6 34.7 5.0 46.8 0.0 0.0 0.7 2.7 ­10.5 Kyrgyz Republic 9.8 9.1 0.7 4.4 1.0 0.0 0.0 1.6 0.2 2.2 Lao PDR 10.7 9.1 1.7 1.3 0.0 0.0 0.0 0.4 0.1 2.5 Latvia .. .. .. .. .. .. .. .. .. .. Lebanon 2.4 12.9 ­10.5 2.6 0.0 0.0 0.0 0.6 0.9 ­9.3 Lesotho .. .. .. .. .. .. .. .. .. .. Liberia 35.8 9.2 26.7 .. 0.0 0.0 6.3 0.5 0.2 .. Libya .. 12.6 .. .. 60.7 0.0 0.0 1.2 .. .. Lithuania 15.3 12.8 2.6 5.7 0.5 0.0 0.3 0.5 0.4 6.6 Macedonia, FYR 15.2 11.2 3.9 4.9 0.0 0.0 0.2 1.4 0.3 6.9 Madagascar 17.6 8.1 9.5 2.1 0.0 0.0 0.0 0.4 0.3 10.8 Malawi ­7.9 7.5 ­15.4 5.0 0.0 0.0 2.1 0.3 0.3 ­13.1 Malaysia 37.3 12.6 24.6 5.1 14.1 0.0 0.0 0.9 0.1 14.5 Mali 11.3 8.9 2.3 2.7 0.0 0.0 0.0 0.1 0.6 4.4 Mauritania ­6.3 8.5 ­14.9 3.2 0.0 10.9 0.7 1.5 0.6 ­25.3 Mauritius 23.8 12.0 11.8 3.3 0.0 0.0 0.0 0.4 .. 14.8a Mexico 21.1 12.6 8.6 5.3 7.4 0.1 0.0 0.4 0.6 5.3 Moldova 18.8 8.2 10.6 6.9 0.0 0.0 0.0 1.8 0.7 14.9 Mongolia 40.8 9.3 31.6 8.1 0.9 8.4 0.0 3.8 0.0 26.6 Morocco 28.0 10.7 17.3 6.1 0.0 0.3 0.0 0.6 0.3 22.3 Mozambique 6.6 8.8 ­2.2 1.8 0.0 0.0 0.0 0.2 0.4 ­1.0 Myanmar .. .. .. 0.8 .. .. .. .. 0.6 .. Namibia 39.2 11.1 28.1 7.3 0.0 0.6 0.0 0.3 0.2 34.3 Nepal 26.9 8.1 18.8 2.6 0.0 0.0 2.8 0.4 0.1 18.2 Netherlands 23.6 16.0 7.6 4.9 1.0 0.0 0.0 0.2 0.7 10.6 New Zealand 22.6b 14.8 7.8 7.1 0.8 0.1 0.0 0.3 0.0 13.7 Nicaragua 10.8 10.1 0.7 2.9 0.0 0.0 1.0 0.6 0.1 1.8 Niger 8.0 7.9 0.1 2.3 0.0 0.0 3.0 0.3 0.5 ­1.4 Nigeria 32.6 10.9 21.7 0.9 49.1 0.0 0.1 0.6 1.0 ­28.2 Norway 32.5 13.7 18.9 7.0 10.9 0.0 0.0 0.1 0.1 14.8 Oman 29.4b 13.4 16.0 4.2 58.8 0.0 0.0 0.8 1.1 ­40.6 Pakistan 23.6 8.2 15.4 2.3 5.3 0.0 0.5 0.8 1.4 9.7 Panama 13.8 12.8 1.1 4.4 0.0 0.0 0.0 0.4 0.5 4.6 Papua New Guinea .. 10.4 .. .. 10.7 25.1 0.0 0.5 .. .. Paraguay 22.9 10.1 12.8 4.2 0.0 0.0 0.0 0.4 0.7 16.0 Peru 19.2 11.7 7.6 2.9 1.5 2.1 0.0 0.3 1.0 5.6 Philippines 34.1 9.2 24.8 2.8 0.3 0.4 0.2 0.6 0.3 25.9 Poland 18.9 12.6 6.3 5.3 0.5 0.3 0.1 0.9 0.7 9.1 Portugal 15.6 18.2 ­2.6 5.7 0.0 0.0 0.0 0.2 0.5 2.3 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 185 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed savings expenditure depletion depletion forest dioxide emission net capital depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Romania 18.2 11.7 6.5 3.2 3.3 0.0 0.0 0.9 0.2 5.3 Russian Federation 32.1 7.1 25.0 3.5 29.7 0.6 0.0 2.0 0.6 ­4.4 Rwanda 17.7 7.9 9.8 3.5 0.0 0.0 3.7 0.2 0.2 9.1 Saudi Arabia 46.6b 13.0 33.6 7.2 50.1 0.0 0.0 0.9 1.0 ­11.1 Senegal 17.2 9.6 7.5 3.4 0.0 0.0 0.2 0.4 1.4 8.9 Serbia and Montenegro 4.6 11.4 ­6.8 .. 0.9 0.0 .. 0.3 0.1 .. Sierra Leone 11.4 7.9 3.4 1.0 0.0 0.0 4.3 0.4 0.6 ­1.0 Singapore 46.6b 15.0 31.7 2.7 0.0 0.0 0.0 0.4 0.9 33.0 Slovak Republic 23.4 21.8 1.6 4.2 0.1 0.0 0.4 0.7 0.1 4.6 Slovenia 25.9 13.6 12.3 5.6 0.0 0.0 0.2 0.3 0.2 17.2 Somalia .. .. .. .. .. .. .. .. 0.2 .. South Africa 14.7 12.1 2.6 5.3 0.0 0.6 0.3 1.2 0.3 5.6 Spain 23.4 14.5 8.9 4.1 0.0 0.0 0.0 0.2 0.6 12.2 Sri Lanka 19.4 10.3 9.1 2.6 0.0 0.0 0.4 0.4 0.5 10.4 Sudan 15.8 9.9 5.9 0.9 15.1 0.0 0.0 0.3 0.8 ­9.4 Swaziland 16.8 11.0 5.8 5.5 0.0 0.0 0.0 0.3 0.2 10.8 Sweden 23.6 12.1 11.6 8.0 0.0 0.1 0.0 0.1 0.0 19.4 Switzerland .. 13.9 .. 5.0 0.0 0.0 0.0 0.1 0.3 .. Syrian Arab Republic 20.7 10.5 10.1 2.6 38.6 0.1 0.0 1.5 1.0 ­28.4 Tajikistan 5.7 8.7 ­3.0 2.8 0.4 0.0 0.0 2.0 0.2 ­2.8 Tanzania 8.5 8.2 0.3 2.4 0.0 0.1 0.0 0.2 0.3 2.0 Thailand 31.8 11.3 20.5 4.9 2.4 0.0 0.2 1.0 0.6 21.2 Togo 8.6 8.6 0.0 2.6 0.0 0.1 3.0 0.6 0.3 ­1.3 Trinidad and Tobago 28.5 12.3 16.2 4.0 46.2 0.0 0.0 2.3 0.0 ­28.3 Tunisia 23.5 11.8 11.7 5.9 4.2 0.2 0.1 0.6 0.4 12.1 Turkey 20.0 11.8 8.2 3.5 0.2 0.0 0.0 0.5 1.4 9.4 Turkmenistan 33.6b 10.4 23.3 .. .. 0.0 .. 4.0 0.6 .. Uganda 9.9 8.2 1.7 1.9 0.0 0.0 6.4 0.2 0.0 ­2.9 Ukraine 29.9 10.4 19.6 4.4 5.7 0.0 0.0 4.5 0.9 12.8 United Arab Emirates 39.2b 14.1 25.2 .. 29.2 0.0 .. 0.7 2.5 .. United Kingdom 14.5 10.3 4.1 5.3 1.1 0.0 0.0 0.2 0.1 8.1 United States 13.4 12.2 1.2 4.8 1.3 0.0 0.0 0.3 0.4 4.0 Uruguay 12.0 12.2 ­0.2 2.6 0.0 0.0 0.3 0.3 2.7 ­0.8 Uzbekistan 30.2 8.9 21.3 9.4 59.3 0.0 0.0 7.6 0.7 ­37.0 Venezuela, RB 35.2 12.2 23.0 4.4 34.7 0.4 0.0 0.9 0.0 ­8.6 Vietnam 32.7 9.2 23.6 2.8 9.5 0.0 0.6 1.1 0.5 14.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 12.9 10.0 2.9 .. 44.2 0.0 0.0 0.7 0.5 .. Zambia 13.3 9.4 3.9 2.5 0.0 3.7 0.0 0.3 1.0 1.4 Zimbabwe 3.2 8.6 ­5.5 6.9 0.6 1.3 0.0 1.6 0.4 ­2.5 World 20.8 w 12.7 w 8.1 w 4.4 w 2.8 w 0.1 w 0.0 w 0.4 w 0.5 w 8.7 w Low income 22.7 9.2 13.5 3.4 6.7 0.4 0.7 1.1 0.8 7.3 Middle income 28.3 11.1 17.2 3.6 8.4 0.5 0.0 1.0 0.9 9.8 Lower middle income 32.1 10.8 21.3 2.9 6.5 0.5 0.0 1.1 1.0 15.1 Upper middle income 23.1 11.5 11.6 4.5 11.2 0.6 0.0 0.9 0.7 2.6 Low & middle income 27.5 10.8 16.6 3.5 8.2 0.5 0.1 1.0 0.9 9.4 East Asia & Pacific 39.1 10.5 28.6 2.3 4.1 0.4 0.0 1.2 1.2 23.9 Europe & Central Asia 23.4 10.7 12.7 4.1 12.0 0.3 0.0 1.4 0.7 2.3 Latin America & Carib. 22.7 12.1 10.6 4.4 7.2 1.1 0.0 0.5 0.6 5.6 Middle East & N. Africa 30.0 11.2 18.8 4.5 27.3 0.1 0.1 1.2 0.9 ­6.2 South Asia 23.6 9.1 14.4 3.6 2.7 0.3 0.7 1.2 0.8 12.4 Sub-Saharan Africa 17.1 10.9 6.2 3.9 9.8 0.4 0.6 0.7 0.5 ­2.0 High income 19.4 13.2 6.2 4.6 1.4 0.0 0.0 0.3 0.4 8.7 Europe EMU 20.8 14.2 6.6 4.6 0.1 0.0 0.0 0.2 0.3 10.6 a. Adjusted net savings do not include particulate emission damage. b. World Bank staff estimate. 186 2006 World Development Indicators Toward a broader measure of savings About the data Definitions Adjusted net savings measure the change in value of extraction or harvest are higher than the normal rate · Gross savings are the difference between gross a specified set of assets, excluding capital gains. If of return on capital. Natural resources give rise to national income and public and private consumption, a country's net savings are positive and the account- rents because they are not produced; in contrast, for plus net current transfers. · Consumption of fixed ing includes a sufficiently broad range of assets, produced goods and services competitive forces will capital represents the replacement value of capital economic theory suggests that the present value expand supply until economic profits are driven to used up in the process of production. · Net savings of social welfare is increasing. Conversely, persis- zero. For each type of resource and each country, unit are gross savings minus the value of consumption tently negative adjusted net savings indicate that an resource rents are derived by taking the difference of fixed capital. · Education expenditure refers to economy is on an unsustainable path. between world prices and the average unit extrac- public current operating expenditures in education, The table provides a test to check the extent tion or harvest costs (including a "normal" return on including wages and salaries and excluding capital to which today's rents from a number of natural capital). Unit rents are then multiplied by the physical investments in buildings and equipment. · Energy resources and changes in human capital are bal- quantity extracted or harvested in order to arrive at a depletion is the product of unit resource rents and anced by net savings, that is, this generation's depletion figure. This figure is one of a range of deple- the physical quantities of energy extracted. It covers bequest to future generations. tion estimates that are possible, depending on the coal, crude oil, and natural gas. · Mineral depletion Adjusted net savings are derived from standard assumptions made about future quantities, prices, is the product of unit resource rents and the physical national accounting measures of gross savings by and costs, and there is reason to believe that it is at quantities of minerals extracted. It refers to tin, gold, making four adjustments. First, estimates of capital the high end of the range. World prices are used in lead, zinc, iron, copper, nickel, silver, bauxite, and consumption of produced assets are deducted to order to reflect the social opportunity cost of deplet- phosphate. · Net forest depletion is the product of obtain net savings. Second, current public expen- ing minerals and energy. Researchers should keep unit resource rents and the excess of roundwood ditures on education are added to net savings (in this in mind when using the depletion estimates. In harvest over natural growth. · Carbon dioxide dam- standard national accounting these expenditures general, the data on energy and mineral depletion age is estimated to be $20 per ton of carbon (the are treated as consumption). Third, estimates of should not be considered a substitute for energy and unit damage in 1995 U.S. dollars) times the number the depletion of a variety of natural resources are mineral gross domestic product. of tons of carbon emitted. · Particulate emission deducted to reflect the decline in asset values asso- A positive net depletion figure for forest resources damage is the willingness to pay to avoid mortality ciated with their extraction and harvest. And fourth, implies that the harvest rate exceeds the rate of and morbidity attributable to particulate emissions. deductions are made for damages from carbon diox- natural growth; this is not the same as deforesta- · Adjusted net savings are net savings plus educa- ide and particulate emissions. tion, which represents a change in land use (see tion expenditure and minus energy depletion, mineral The exercise treats public education expenditures Definitions for table 3.4). In principle, there should depletion, net forest depletion, and carbon dioxide as an addition to savings effort. The adjustment be an addition to savings in countries where growth and particulate emissions damage. made to savings goes in the right direction. How- exceeds harvest, but empirical estimates suggest ever, because of the wide variability in the effective- that most of this net growth is in forested areas ness of government education expenditures, these that cannot be exploited economically at present. Data sources figures cannot be construed as the value of invest- Because the depletion estimates reflect only timber Gross savings are derived from the World Bank's ments in human capital. The reader should bear in values, they ignore all the external and nontimber national accounts data files, described in the Econ- mind that current expenditure of $1 on education benefits associated with standing forests. omy section. Consumption of fixed capital is from does not necessarily yield $1 of human capital. The Pollution damage from emissions of carbon dioxide the United Nations Statistics Division's National calculation should also consider private education is calculated as the marginal social cost per unit mul- Accounts Statistics: Main Aggregates and Detailed expenditure, but data are not available for a large tiplied by the increase in the stock of carbon dioxide. Tables, 1997, extrapolated to 2004. Data on edu- number of countries. The unit damage figure represents the present value cation expenditure are from the United Nations While extensive, the accounting of natural resource of global damage to economic assets and to human Statistics Division's Statistical Yearbook 1997 and depletion and pollution costs still has some gaps. welfare over the time the unit of pollution remains from the United Nations Educational, Scientific, and Key estimates missing on the resource side include in the atmosphere. Cultural Organization Institute for Statistics online the value of fossil water extracted from aquifers, net Pollution damage from particulate emissions is database. Missing data are estimated. The wide depletion of fish stocks, and depletion and degrada- estimated by valuing the human health effects from range of data sources and estimation methods tion of soils. Important pollutants affecting human exposure to particulate matter pollution in urban used to arrive at resource depletion estimates are health and economic assets are excluded because areas. The estimates are calculated as willingness described in a World Bank working paper, "Estimating no internationally comparable data are widely avail- to pay to avoid mortality and morbidity from cardiopul- National Wealth" (Kunte and others 1998). The unit able on damage from ground-level ozone or from monary disease and lung cancer in adults and acute damage figure for carbon dioxide emissions is from sulfur oxides. respiratory infections in children that is attributable Fankhauser (1995). The estimates of damage from Estimates of resource depletion are based on to particulate emissions. particulate emissions are from Pandey and others the calculation of unit resource rents. An economic For a detailed methodological note, see www. (2006). The conceptual underpinnings of the savings rent represents an excess return to a given factor of worldbank.org/data. measure appear in Hamilton and Clemens (1999). production--in this case the returns from resource 2006 World Development Indicators 187 he world economy continued to recover in 2004 from the slowdown of 2000­01. Gross domestic product (GDP) rose 4.1 percent, more than a full percentage point higher than in 2003 and the fastest rate of growth of global output in 15 years. High-income economies grew at an average annual rate of 3.4 percent, while developing countries averaged a remark- able 7.1 percent, the highest rate of growth since 1970. The recovery has been widespread throughout the developing world. East Asia and Pacific grew fastest--9 percent over 2003. But all regions grew at nearly 6 percent or higher, except Sub-Saharan Africa, which grew at 4.8 percent. Fourteen countries registered growth rates of 10 percent or higher, and only four countries experienced negative growth (figure 4a). Many of the fastest growing economies are oil and gas producers and exporters, which benefited from the run-up in energy prices. Iraq's GDP increased more than 40 percent after four years of falling output, and Chad's grew by 30 percent. Factors contributing to growth in 2004 The high growth throughout the developing world in 2004 was due in part to increased prices of primary commodities and supportive monetary conditions. Increases in the prices of oil, met- als and minerals, and agricultural commodities boosted growth in a wide range of commodity producers. Oil prices rose 30 percent in 2004 even as production increased. While higher oil revenues were responsible for strong economic performance by oil producers, the impact on oil-importing countries was cushioned by increased volumes and prices of other primary com- modities. For example, Brazil, an oil-importing country, achieved a growth rate of 4.9 percent in 2004. Argentina, Brazil, and South Africa saw their barter terms of trade improve by 10­20 percent over 2000 (table 6.2). Meanwhile, global short-term interest rates declined sharply as major central banks reduced policy rates to support economic expansion. Inflation rates remained low, however, because of improved fiscal and monetary discipline. The median inflation rate was below 10 percent in all regions, well below the average of about 15 percent or higher in 1990 in three regions (table 4b). The number of countries with double-digit inflation was 38, the same as in 2003 despite the oil price hikes. The combination of reduced global interest rates and stable or falling inflation led to substantial declines in real interest rates (table 4c). Long-term growth trends Although growth was higher in most regions in 2000­04 than in the preceding decade, the continuing recovery in Sub-Saharan Africa remains one of the most remarkable stories of the past five years (figure 4d). By 2004 the region had experienced five years of continu- ous positive growth in per capita incomes, after two decades of decline (except for a slight increase in 1997). Increasing prices of primary commodities, particularly oil, but also impor- tant agricultural crops, get much of the credit. Oil and natural gas producers achieved very rapid growth, including Chad and Equatorial Guinea, where GDP rose more than 10 percent, and Nigeria, where GDP increased 6 percent. Countries left out of the commodity boom such 2006 World Development Indicators 189 Fast growing--and backsliding--economies in 2004 Accelerating regional growth GDP growth (%) Average annual growth of GDP (%) 50 10 1980­90 1990­2000 2000­03 2004 40 8 30 6 20 4 10 2 0 ba . 0 q ue d Et B pia Ur ne An y Be la Ma us Mo ves Ta lia Az stan an lG a Se inea ne Gra s , F da e Zim Sts ua lle bw ria in ­2 Ira ez Cha R go o lar i aij sia na ­10 ra to Ch hio ldi la, he ug ng . i u jik erb ed Uk yc East Asia Europe & Latin Middle East South Sub-Saharan High- n Ve ua & Pacific Central Asia America & & North Asia Africa income cro Eq Mi Caribbean Africa Source: World Bank data files. Source: World Bank data files. as Central African Republic, Côte d'Ivoire, Eritrea, and Niger regional gross savings rates, at 16 percent of GDP, and the have done less well, with growth below 2 percent. Average highest government consumption rate, at 17 percent. And rates of investment have also risen: from 17 percent of GDP despite a few years of economic growth, Sub-Saharan Africa in 2000 to 19 percent in 2004, reversing the falling trend of still has the highest poverty rate in the world. A large pro- the 1980s and 1990s. portion of the population in more than half the countries in But even at this broad regional level Sub-Saharan Africa's Sub-Saharan Africa is still in need of food aid according to macroeconomic indicators remain weak, with the lowest the World Food Programme. Strong growth of 5 percent in Europe and Central Asia in 2000­04 was also assisted by higher oil prices. GDP in Inflation, median annual growth of GDP deflator (%) the Middle East and North Africa rose 3.8 percent over the Region 1990 1995 2000 2003 2004 period, about the same as in the 1980s and 1990s, again East Asia 5.8 7.9 2.5 3.9 5.7 driven by increasing oil prices. But East Asia and Pacific, Europe & Central Asia 14.8 46.4 8.5 4.7 6.1 which has grown at about 8 percent a year during the past 20 Latin America & Caribbean 21.2 11.0 5.2 6.5 7.6 years, continues to be the top performer. The region's excep- Middle East & North Africa 17.0 9.4 9.8 5.9 9.6 tional performance was due largely to rapid growth in China. South Asia 8.5 9.1 4.6 4.5 5.0 Sub-Saharan Africa 9.7 10.7 6.1 6.5 6.0 Similarly strong performance by India enabled South Asia to grow at nearly 6 percent over the same period. Despite rapid Source: World Bank data files and table 4.14. growth in 2004, Latin America and the Caribbean is the only region that failed to improve on growth rates of the 1990s because of low or negative growth in 2001 and 2002. Real interest rates (%) In achieving consistently high rates of growth over a long Country 2000 2001 2002 2003 2004 period, India and China have become more important in the Brazil 44.7 46.7 47.8 45.3 43.2 world economy, as both consumers and producers. Growth China 3.7 3.7 4.7 2.6 ­1.2 has brought increasing demand for energy inputs, and grow- India 8.2 8.4 7.4 8.0 5.4 ing imports of fuel have been blamed for rising fuel prices. Japan 3.6 3.3 3.2 3.2 4.0 But too much may be made of this. While China and India are Mexico 4.3 6.5 1.2 ­1.5 1.1 Russian Federation ­9.6 1.2 0.2 ­0.9 ­5.6 now among the top 10 fuel importers and account for a large United States 6.9 4.4 3.0 2.2 1.7 share of the increased demand for oil, they remain relatively Note: Real interest rates are computed as the difference between the prime rate charged small consumers compared with the major industrial coun- by banks and the rate of inflation measured by the growth of the GDP deflator. tries. China and India accounted for only about 11 percent of Source: World Bank data files and table 4.13. the global increase in fuel imports between 2000 and 2004, 190 2006 World Development Indicators Raising demand for energy supplies Fuel imports as share of Increase in Share of global Fuel imports merchandise imports fuel imports increase in fuel ($ billions) (%) (%) imports (%) Economy 1995 2000 2004 1995 2000 2004 2000­04 2000­04 Brazil 7 9 12 12.1 15.1 18.8 40 1 China 5 21 48 3.9 9.2 8.5 131 7 India 8 19 34 23.8 36.7 34.6 78 4 Japan 54 77 99 16.1 20.4 21.7 28 6 United States 63 140 216 8.2 11.1 14.2 55 20 European Union 136 219 347 6.5 8.8 9.4 59 33 World 386 690 1,075 7.4 10.4 11.5 56 100 Source: World Bank data files. whereas the United States alone accounted for 20 percent consumption expenditure in dollar terms has been dropped (table 4e). And in both China and India the share of fuel in from table 4.8. However, these data are still available on the merchandise imports has declined slightly since 2000. World Development Indicators CD-ROM and World Develop- ment Indicators Online database. In table 4.8 gross sav- Changes from the last edition ings, which has been changed to conform to the System of Interest and exchange rate indicators (table 5.7), which used National Accounts definition, now includes net income as to appear in section 5, States and markets, have moved well as transfers. In table 4.14 the food price index data, to the Economy section, resulting in an additional table on which were inconsistent with the consumer price index data monetary indicators (table 4.14), while the table showing from the International Monetary Fund's International Finan- growth in merchandise trade and terms of trade (table 4.4) cial Statistics, were replaced by wholesale price index data has moved to section 6, Global links (table 6.2). Economy from the International Financial Statistics. Total debt service now shows the growth of exports and imports of goods and ratios replace public and publicly guaranteed debt service services from the national accounts data. Household final ratios in table 4.17. China's data revision Recently, the national accounts of China have been revised by the National Bureau of Statistics (NBS), incorporating new information from the 2005 National Economic Census. The earlier economic census was taken in 1993. As the information from the 2005 census has been incorporated, the revised national accounts have for the first time been able to capture the growing private sector, including the services industry. The revised accounts show not only that the size of the economy is larger, but also that it is growing at a slightly higher rate then previously shown. The NBS has not only revised the estimates for 2004, but has also revised time series back to 1993. So far, however, revised data are available only for production. The old data are retained here for the expenditure accounts, and the differences are shown as a statistical discrepancy. As a result of this large statistical discrepancy final household consumption is larger than it will be when the final set of data is released. While the constant price series for the years before 1993 were scaled upward using the previous growth rates to yield a consistent series for calculating long-term growth trends, the current price series contains a break in the series in 1993, as current price data beyond 1993 are unadjusted. As a result of the revision, Chinese GDP for 2004 is about $1.93 trillion, some 17 percent higher than earlier published estimates. In real terms the economy grew at 10.1 percent, slightly higher than the previously published growth data. By the revised value-added estimates the service sector accounts for 41 percent of the economy, up from earlier estimates of 37 percent, and the industrial sector has declined from 49 percent to 46 per- cent, and agriculture from 14 percent to 13 percent. By the old data China's manufacturing sector was the fastest growing sector, contrary to trends in emerging economies like India, where the service sector has been growing faster. Now though still lower, the service sector is growing at almost the same rate as the manufacturing sector, nearly 10 percent in 2004. China was ranked sixth in the global economy based on gross national income (GNI) in the last two editions of the World Development Indicators. The revised GNI estimates move China ahead of France to become the fifth largest economy in 2004 and, according to projections, will move it ahead of the United Kingdom next year to become the fourth largest. While still a lower-middle-income country, China has a more important role in the global economy than many of the largest industrial countries. For example, China is the fourth largest receiver of foreign direct investment, its reserves are second only to those of Japan, and its merchandise exports in dollar terms exceed all countries except Germany and the United States. 2006 World Development Indicators 191 Recent economic performance Gross domestic Exports of goods Imports of goods GDP deflator Current account Total reservesa product and services and services balance months average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2004 2005 2004 2005 2004 2005 2004 2005 2004 2005 2005 2005 Algeria 5.2 5.3 3.4 6.0 13.3 17.8 10.2 14.9 .. 18.3 56,461 24.9 Argentina 9.0 7.5 8.2 15.1 39.8 18.6 9.2 8.2 2.2 ­0.1 .. .. Armenia 7.0 13.9 14.8 22.0 8.5 17.2 0.6 3.1 ­5.3 ­3.6 755 4.2 Azerbaijan 10.2 24.3 10.7 76.1 23.3 18.2 6.4 12.0 ­30.4 ­5.2 1,178 3.3 Bangladesh 6.3 5.4 12.5 11.9 10.6 10.7 4.2 5.0 ­0.5 ­0.5 2,944 2.5 Bolivia 3.6 3.9 16.1 5.5 5.4 12.5 8.5 5.3 3.3 2.0 1,798 6.8 Bosnia and Herzegovina 6.2 5.3 8.9 11.5 2.8 5.2 2.9 2.5 ­22.5 ­23.9 2,668 4.0 Botswana 4.9 3.8 ­0.3 19.5 2.7 12.0 4.8 9.3 .. 8.9 5,865 19.3 Brazil 4.9 2.5 18.0 21.5 14.3 11.2 8.2 6.0 1.9 2.0 62,779 6.0 Bulgaria 5.6 5.6 13.1 4.7 14.1 11.4 4.2 3.6 ­8.5 ­14.9 8,701 4.6 Cameroon 4.3 3.9 1.7 3.1 4.1 4.8 0.4 ­0.6 .. ­2.4 124 0.3 Chile 6.1 5.2 22.0 5.1 ­2.3 5.1 6.6 2.4 1.5 0.5 15,047 5.5 China 10.1 9.9 28.4 24.4 22.5 17.7 6.9 5.9 3.6 5.6 818,900 13.0 Colombia 4.1 4.0 10.2 15.5 16.7 16.7 7.1 5.1 ­1.0 ­1.1 14,956 6.7 Congo, Dem. Rep. 6.3 7.0 .. .. .. .. 5.9 22.1 .. ­4.2 360 1.2 Congo, Rep. 3.6 9.2 8.1 14.9 62.1 16.4 6.9 7.2 .. 4.9 75 0.2 Costa Rica 4.2 3.5 7.1 6.8 7.8 13.1 11.6 10.8 ­4.5 ­4.7 2,080 2.2 Côte d'Ivoire 1.6 ­0.3 15.7 ­1.5 12.6 4.4 0.8 3.9 2.0 3.0 330 3.4 Croatia 3.8 3.7 5.4 6.1 3.5 3.5 3.3 3.4 ­4.8 ­3.8 9,082 5.1 Ecuador 7.0 3.3 15.1 4.2 9.8 5.3 4.1 2.8 ­0.5 0.3 2,147 2.9 Egypt, Arab Rep. 4.2 4.9 27.6 25.6 22.0 30.7 11.5 5.4 5.0 3.3 19,302 8.0 El Salvador 1.5 2.5 6.6 3.3 3.4 3.3 4.3 4.0 ­3.9 ­3.6 2,139 3.1 Estonia 7.8 5.6 16.0 5.7 14.6 2.0 3.1 ­1.5 ­12.7 ­6.5 1,780 2.4 Gabon 1.4 2.2 3.3 ­5.8 3.8 1.8 7.0 8.9 .. 7.0 .. .. Ghana 5.8 5.8 3.5 4.0 4.5 7.3 14.1 14.8 ­2.7 ­1.3 1,747 3.9 Guatemala 2.7 3.2 12.4 5.8 15.8 5.3 8.2 6.0 ­4.3 ­4.1 3,375 4.0 India 6.9 8.5 7.9 26.6 47.1 34.6 5.3 4.2 .. ­3.7 142,489 7.2 Indonesia 5.1 5.9 8.5 14.5 25.0 15.2 7.1 7.5 1.2 1.0 47,088 5.1 Iran, Islamic Rep. 5.6 5.9 12.2 27.5 5.0 12.8 16.6 17.6 .. 6.4 47,410 9.4 Jamaica 0.9 1.8 .. .. .. .. 12.6 16.0 ­5.7 ­7.8 1,975 5.8 Jordan 7.7 7.5 13.0 8.7 27.4 8.1 5.2 4.0 ­0.2 ­10.4 5,396 5.8 Kazakhstan 9.4 9.4 10.5 11.5 14.5 13.9 9.9 16.1 1.3 5.3 7,070 3.4 Kenya 4.3 5.0 19.8 22.2 15.3 40.6 6.9 5.7 ­2.4 ­9.3 2,327 2.3 Latvia 8.3 6.0 9.3 7.5 15.6 4.5 7.2 0.3 ­13.0 ­8.5 .. .. Lebanon 6.3 1.0 23.4 ­4.0 7.1 0.0 2.9 1.0 .. ­18.1 10,556 12.3 Lesotho 2.3 0.0 0.7 5.5 1.5 3.7 1.7 2.1 ­5.8 ­5.5 447 3.8 192 2006 World Development Indicators Gross domestic Exports of goods Imports of goods GDP deflator Current account Total reservesa product and services and services balance months average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2004 2005 2004 2005 2004 2005 2004 2005 2004 2005 2005 2005 Lithuania 6.7 6.0 11.7 8.6 12.1 7.7 3.3 ­0.7 ­7.8 ­4.4 3,041 2.6 Macedonia, FYR 2.9 3.6 11.7 11.0 10.6 ­0.8 1.5 3.4 ­7.8 ­0.9 1,328 4.3 Malawi 6.7 2.6 ­4.6 8.9 ­5.3 12.7 11.6 15.3 .. ­14.3 159 1.8 Malaysia 7.1 5.0 16.3 8.1 20.7 6.4 6.2 1.5 .. 13.8 80,739 7.1 Mauritius 4.2 4.0 ­1.7 1.7 2.0 0.0 6.0 5.4 ­1.8 ­0.4 1,485 4.7 Mexico 4.4 3.0 11.5 9.1 10.2 7.3 6.1 5.4 ­1.1 ­1.1 70,158 3.3 Moldova 7.3 7.0 8.3 15.3 0.4 27.0 8.0 7.0 ­2.7 ­5.5 597 2.7 Morocco 4.2 1.5 4.7 3.4 8.8 6.0 1.5 2.0 1.9 0.1 18,671 9.2 Nicaragua 5.2 4.0 15.8 9.9 9.4 12.1 10.2 12.0 ­17.0 ­18.0 782 2.7 Nigeria 6.0 4.0 3.1 ­3.7 2.3 26.1 19.9 26.6 17.0 9.8 26,400 8.4 Pakistan 6.4 7.8 ­1.5 16.9 ­8.6 38.5 7.8 9.3 ­0.8 ­3.2 10,143 3.0 Panama 6.2 6.0 6.7 18.1 8.1 15.5 0.5 1.8 ­8.2 ­7.9 677 0.7 Paraguay 4.0 3.2 4.1 0.1 5.5 6.0 9.2 10.0 0.3 ­0.3 981 3.9 Peru 4.8 6.7 14.7 14.2 10.4 9.9 5.7 3.5 0.0 1.3 14,097 14.0 Philippines 6.1 5.1 14.1 2.3 5.9 1.8 6.1 6.0 2.5 1.6 18,400 3.8 Poland 5.4 3.2 10.2 11.3 8.7 5.3 2.9 1.7 ­4.3 ­1.6 42,571 5.2 Romania 8.3 4.5 15.9 5.4 18.3 15.7 15.8 12.1 ­7.6 ­8.9 21,395 5.7 Russian Federation 7.1 6.4 12.3 2.5 23.5 19.2 18.1 19.7 10.3 11.3 182,200 17.5 Senegal 6.2 5.7 4.8 5.8 6.0 5.2 1.9 2.5 .. ­7.6 1,092 3.5 Serbia and Montenegro 8.2 4.8 38.2 22.0 38.1 4.0 8.9 17.3 .. ­9.0 5,900 6.2 Slovak Republic 5.5 6.0 11.5 9.0 12.7 8.7 4.6 1.6 .. ­6.3 15,400 5.3 South Africa 3.7 5.0 2.9 11.3 12.9 9.5 5.9 4.3 ­3.3 ­4.2 658 3.5 Sri Lanka 5.4 5.3 7.8 7.5 9.3 8.7 9.4 9.7 ­3.2 ­5.6 2,571 2.9 Swaziland 2.1 1.8 1.1 ­3.4 1.4 1.7 5.3 4.5 4.8 ­1.6 230 1.1 Syrian Arab Republic 2.0 3.8 13.3 ­9.2 27.0 6.7 10.5 9.6 0.9 1.2 4,790 5.8 Thailand 6.2 4.5 9.6 4.6 13.5 8.5 3.3 7.5 4.1 ­2.1 52,066 5.3 Trinidad and Tobago 6.2 6.7 14.3 9.1 25.4 17.2 12.5 122.4 .. 18.8 3,893 7.0 Tunisia 5.8 5.0 5.2 3.4 3.7 4.7 3.0 2.5 ­2.0 ­2.6 .. .. Turkey 8.9 6.0 12.5 5.0 24.7 10.6 9.9 7.7 ­5.1 ­6.2 52,433 5.4 Uganda 5.7 5.6 6.2 5.7 3.7 9.5 6.0 9.0 ­2.9 ­1.2 1,326 6.0 Ukraine 12.1 2.4 13.8 4.0 8.6 ­9.0 15.1 15.0 10.5 2.7 19,395 5.5 Uruguay 11.9 6.0 22.9 16.2 29.7 21.0 7.4 5.0 ­0.8 ­0.8 2,837 6.8 Uzbekistan 7.7 7.0 21.8 11.5 19.3 7.3 15.1 15.9 .. 9.5 2,889 7.9 Venezuela, RB 17.9 9.0 11.8 9.0 60.0 18.3 31.2 16.2 12.6 18.1 35,667 12.3 Zambia 4.7 5.1 12.6 16.3 10.3 18.1 20.2 18.9 .. ­11.9 303 1.4 Note: Data for 2005 are the latest preliminary estimates and may differ from those in earlier World Bank publications. a. International reserves including gold valued at London gold price. Source: World Bank staff estimates. 2006 World Development Indicators 193 Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Afghanistana .. 16.8 .. .. .. .. .. .. .. .. Albania 3.5 5.4 4.3 5.1 ­0.5 4.8 .. .. 7.0 7.7 Algeria 1.9 4.8 3.6 7.5 1.8 3.7 ­2.1 ­0.2 1.9 5.5 Angolaa 1.6 8.1 ­1.4 13.7 4.4 8.6 ­0.3 11.3 ­2.3 4.3 Argentina 4.3 ­0.1 3.5 1.2 3.8 1.3 2.7 1.7 4.5 ­1.2 Armenia ­1.9 11.3 0.4 5.0 ­7.9 12.6 ­4.3 8.4 ­5.8 15.8 Australia 3.9 3.5 3.6 ­2.8 2.9 3.7 2.2 2.4 4.3 3.7 Austria 2.4 1.2 1.6 0.6 2.7 1.8 2.7 0.8 2.3 0.9 Azerbaijan ­6.3 10.6 ­2.1 6.7 ­0.8 12.8 ­12.0 9.5 ­2.3 8.2 Bangladesha 4.8 5.2 2.9 2.4 7.3 7.1 7.2 6.4 4.5 5.5 Belarus ­1.7 6.8 ­4.0 6.1 ­1.8 9.7 ­0.7 10.2 ­0.4 4.7 Belgium 2.1 1.4 2.8 1.3 1.7 0.3 2.8 0.3 1.9 1.8 Benina 4.8 4.5 5.8 5.7 4.1 6.5 5.8 5.9 4.2 2.7 Boliviaa 4.0 2.6 2.9 3.3 4.1 2.3 3.8 2.8 4.3 2.2 Bosnia and Herzegovina .. 4.9 .. 0.0 .. 3.2 .. 4.1 .. 5.0 Botswana 4.9 5.5 ­1.2 1.5 3.6 5.2 4.3 1.6 7.8 4.5 Brazil 2.9 2.0 3.3 5.4 2.6 2.1 1.5 3.1 3.0 ­1.1 Bulgaria ­1.8 4.8 3.0 1.8 ­5.0 5.3 .. 8.2 ­5.2 4.9 Burkina Faso 4.0 5.2 4.2 5.1 2.3 2.7 1.6 2.2 4.5 11.9 Burundi ­2.6 2.7 ­1.6 1.9 ­5.6 .. ­8.0 .. ­2.0 .. Cambodiaa 7.1 6.3 3.9 2.8 14.3 14.2 18.6 14.6 7.1 3.9 Cameroon 1.7 4.5 5.5 6.0 ­0.9 6.9 1.2 8.6 0.2 2.3 Canada 3.1 2.6 1.1 ­1.5 3.2 0.5 4.5 ­0.6 3.0 3.5 Central African Republic 2.0 ­2.0 3.8 3.0 0.7 4.6 ­0.2 4.0 ­0.3 ­16.0 Chad 1.9 14.1 4.4 .. 2.2 .. .. .. 1.0 6.2 Chilea 6.6 3.7 ­2.7 ­0.5 7.1 7.6 6.7 2.8 6.8 1.7 Chinaa, b 10.6 9.4 4.1 3.4 13.7 10.6 .. .. 10.2 9.8 Hong Kong, China 4.1 3.2 .. ­1.0 .. ­3.6 .. ­6.9 .. 4.3 Colombiaa 2.8 2.9 ­2.6 1.2 1.5 3.9 ­2.5 2.7 4.5 2.7 Congo, Dem. Rep. ­4.9 3.6 1.2 .. ­9.0 .. ­13.4 .. ­11.5 .. Congo, Rep.a 1.2 3.1 1.0 5.5 3.2 1.4 ­3.0 12.7 ­0.6 4.2 Costa Rica 5.3 3.9 4.1 1.6 6.2 2.7 6.7 2.1 4.6 5.1 Côte d'Ivoirea 3.3 ­0.7 3.3 0.5 5.7 ­3.5 4.9 ­4.0 2.4 ­0.2 Croatia 0.6 4.5 ­3.0 0.2 ­2.5 5.5 ­3.3 3.5 2.2 5.2 Cubaa 4.2 .. 5.2 .. 6.6 .. 6.3 .. 2.5 .. Czech Republic 1.1 2.8 2.4 1.2 ­0.2 4.1 3.8 6.0 1.7 2.0 Denmark 2.5 1.1 2.9 0.2 2.4 ­0.8 2.1 0.1 2.5 1.7 Dominican Republica 6.1 2.4 3.8 6.9 7.1 ­1.1 4.9 0.0 6.0 2.5 Ecuadora 1.9 4.2 ­1.7 2.9 2.7 5.5 1.5 2.2 2.4 3.6 Egypt, Arab Rep. 4.7 3.4 3.1 3.7 5.1 2.3 6.4 2.9 4.1 4.1 El Salvadora 4.8 1.9 1.2 0.2 5.2 2.5 5.2 2.5 4.0 1.9 Eritrea 5.7 3.3 1.5 ­0.5 15.0 4.1 10.6 6.6 5.7 2.0 Estonia 0.2 7.0 ­3.4 ­2.0 ­3.3 10.5 5.9 11.6 3.1 5.9 Ethiopia 4.2 3.6 1.9 0.9 3.7 4.4 3.7 2.5 6.5 4.3 Finland 2.6 2.3 1.8 ­0.6 3.9 1.8 5.8 0.9 2.2 2.5 France 2.0 1.5 2.0 ­0.6 1.0 1.0 .. .. 2.4 1.6 Gabona 2.8 1.6 ­1.4 5.1 2.5 2.7 0.6 .. 3.9 ­0.1 Gambia, The 3.0 3.8 3.3 ­0.2 1.0 7.3 0.9 4.2 3.7 5.9 Georgia ­7.2 7.2 ­11.0 2.6 ­8.1 10.5 ­7.0 5.9 ­0.3 8.2 Germany 1.8 0.6 ­0.2 ­0.1 ­0.1 ­0.1 ­0.1 ­0.4 2.9 1.3 Ghanaa 4.3 4.9 3.4 5.0 2.6 4.1 ­3.2 2.0 5.7 5.0 Greece 2.2 4.2 0.5 ­0.3 1.0 4.0 2.0 2.7 2.6 4.9 Guatemalaa 4.2 2.3 2.8 2.1 4.3 1.4 2.8 1.1 4.7 2.7 Guinea 4.4 2.9 4.6 4.5 4.7 3.2 4.1 2.0 3.6 1.8 Guinea-Bissau 1.2 ­1.2 3.9 3.3 ­3.1 14.1 ­2.0 14.6 ­0.6 4.9 Haitia ­1.5 ­0.4 ­7.4 ­1.2 3.2 0.5 ­8.4 ­2.2 ­1.2 ­0.3 194 2006 World Development Indicators Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Honduras 3.2 3.3 2.2 4.4 3.6 2.2 4.0 3.8 3.8 3.6 Hungary 1.6 4.0 ­2.4 5.5 3.5 3.3 7.9 4.5 1.2 3.9 India 6.0 6.2 3.0 2.0 6.3 6.2 7.0 6.5 8.0 8.2 Indonesiaa 4.2 4.6 2.1 3.9 5.3 3.8 6.7 5.1 4.0 5.7 Iran, Islamic Rep. 3.5 6.0 3.5 5.2 ­3.3 8.3 4.9 10.7 8.9 4.9 Iraq .. ­11.4 .. ­3.6 .. ­17.0 .. ­12.8 .. 5.9 Ireland 7.5 5.1 .. .. .. .. .. .. .. .. Israel 5.3 1.0 .. .. .. .. .. .. .. .. Italy 1.6 0.8 1.6 ­0.8 1.1 0.2 1.5 ­0.9 1.7 1.2 Jamaicaa 0.9 1.5 ­0.3 ­1.4 ­1.0 1.8 ­2.3 0.2 2.3 1.6 Japan 1.3 0.9 ­3.1 ­2.2 ­0.1 ­0.1 0.8 0.7 2.2 0.6 Jordan 5.0 5.5 ­3.0 11.6 5.2 9.3 5.6 11.4 5.0 4.3 Kazakhstan ­4.1 10.3 ­8.0 4.9 ­9.3 11.5 2.7 9.2 ­1.5 10.2 Kenya 2.2 2.7 1.9 1.9 1.2 3.5 1.3 2.5 3.2 3.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 5.8 4.7 1.6 ­1.0 6.0 6.2 7.3 6.6 5.6 4.0 Kuwaita 4.7 4.7 1.0 15.1 0.3 1.9 ­0.1 2.5 2.7 4.5 Kyrgyz Republic ­4.1 4.5 1.5 4.1 ­10.3 2.4 ­7.5 3.6 ­4.9 6.6 Lao PDR 6.5 6.0 4.8 3.3 11.1 10.8 11.7 10.9 6.6 6.4 Latvia ­1.6 7.4 ­5.7 2.7 ­8.7 8.6 ­7.8 8.1 2.6 7.4 Lebanon 5.8 4.4 2.1 2.7 ­3.8 4.2 ­5.2 4.4 .. 3.5 Lesotho 3.9 3.1 2.0 ­1.8 5.1 4.7 6.6 3.9 4.4 3.2 Liberia 4.1 ­8.7 .. .. .. .. .. .. .. .. Libya .. 5.5 .. .. .. .. .. .. .. .. Lithuania ­2.7 7.5 ­0.8 2.7 3.3 10.5 5.7 9.6 5.5 6.4 Macedonia, FYR ­0.8 0.7 0.2 ­0.6 ­2.9 0.4 ­5.4 ­1.3 1.0 1.3 Madagascar 2.0 0.9 1.8 1.3 2.4 ­0.2 2.0 1.4 2.4 0.3 Malawi 3.7 2.9 8.6 1.8 2.0 0.1 0.5 ­0.8 1.6 2.5 Malaysiaa 7.0 4.4 0.3 3.4 8.6 4.2 9.5 4.4 7.3 4.7 Mali 4.1 6.3 2.6 5.1 6.4 5.9 ­1.4 5.3 3.0 5.9 Mauritania 4.6 4.7 4.4 ­0.3 3.5 4.3 ­1.9 ­6.3 5.5 6.7 Mauritius 5.2 4.4 ­0.5 2.8 5.4 2.9 5.3 2.0 6.4 6.1 Mexicoa 3.1 1.5 1.5 2.6 3.8 0.0 4.4 ­0.6 2.9 2.1 Moldova ­9.6 7.0 ­11.2 1.3 ­13.6 9.5 ­7.1 8.8 0.7 6.0 Mongoliaa 3.5 5.2 3.7 ­3.3 2.3 8.4 ­9.7 14.3 0.5 6.9 Moroccoa 2.3 4.7 ­0.8 12.6 3.2 3.6 2.7 3.5 2.8 3.5 Mozambique 6.4 8.8 4.8 8.9 12.8 11.8 18.6 15.2 4.8 7.5 Myanmara 7.0 .. 5.7 .. 10.5 .. 7.9 .. 7.2 .. Namibia 4.0 4.7 3.8 1.2 2.4 7.3 2.6 6.7 4.5 4.5 Nepal 4.9 2.5 2.4 3.3 7.2 0.7 8.9 ­1.4 6.2 2.3 Netherlands 2.9 0.5 2.0 0.1 1.5 ­0.6 2.3 ­1.4 3.3 1.0 New Zealand 3.2 4.0 2.9 2.5 2.5 3.6 2.2 3.1 3.4 4.2 Nicaraguaa 3.7 2.5 4.9 2.5 3.9 3.2 3.5 3.8 3.0 3.3 Nigera 2.4 4.1 3.0 6.4 2.0 3.1 2.6 3.9 1.9 4.3 Nigeria 2.5 5.4 3.4 5.3 1.0 5.1 1.1 8.8 3.1 6.1 Norway 4.0 1.6 2.6 0.4 3.8 ­0.2 1.6 .. 4.0 2.6 Omana 4.5 3.0 5.0 2.2 3.9 ­0.5 6.0 9.3 5.0 5.9 Pakistan 3.8 4.1 4.4 1.3 4.1 5.3 3.8 8.0 4.4 4.8 Panama 4.7 3.3 3.1 4.2 6.0 1.5 2.7 ­2.1 4.5 3.6 Papua New Guinea 4.3 0.6 4.1 2.2 5.6 ­3.6 5.5 ­1.1 1.5 1.4 Paraguay 2.2 1.4 2.5 6.1 3.2 ­1.9 0.7 ­0.4 1.6 0.1 Perua 4.6 3.7 5.5 2.6 5.0 5.0 3.8 2.6 4.2 2.8 Philippinesa 3.4 3.9 1.7 2.4 3.5 2.4 3.0 3.9 4.0 5.8 Poland 4.6 2.8 0.9 4.7 7.3 2.3 10.0 5.2 4.6 2.9 Portugal 2.7 0.3 0.0 0.9 3.0 ­1.2 2.4 ­0.1 2.2 1.3 Puerto Ricoa 4.2 .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 195 Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Romania ­0.6 5.9 ­1.9 8.9 ­1.2 5.9 .. .. 0.9 5.6 Russian Federation ­4.7 6.1 ­4.9 5.4 ­7.1 6.2 .. .. ­1.7 6.0 Rwanda ­0.3 5.2 2.6 4.7 ­3.7 6.1 ­6.0 5.8 ­1.2 5.4 Saudi Arabiaa 2.1 3.4 1.6 1.1 2.2 3.6 5.6 5.5 2.2 3.6 Senegala 3.2 4.4 2.9 0.0 4.1 6.8 3.1 5.9 3.0 5.1 Serbia and Montenegro 1.5 4.7 .. ­5.5 .. 1.2 .. .. .. 7.2 Sierra Leonea ­6.1 7.2 ­13.0 .. ­4.5 .. 6.1 .. ­2.9 .. Singaporea 7.7 2.9 ­2.6 ­1.1 8.8 1.2 7.9 3.2 7.5 3.7 Slovak Republica 1.9 4.6 2.7 3.6 2.4 5.2 6.6 5.7 5.7 4.4 Slovenia 2.7 3.2 ­0.5 ­1.2 1.6 3.9 1.4 4.7 3.2 3.3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.1 3.2 1.0 ­0.4 1.1 2.0 1.6 1.7 2.7 4.1 Spain 2.6 3.0 1.2 ­0.4 2.1 2.8 .. 1.0 2.8 3.1 Sri Lanka 5.3 3.7 1.8 0.4 7.0 2.5 8.1 2.0 5.7 5.6 Sudan 5.4 6.0 9.2 .. 5.8 .. 4.4 .. 2.7 .. Swaziland 3.3 2.3 1.2 ­0.3 3.8 2.0 2.9 1.8 3.6 3.5 Sweden 2.2 2.0 ­0.7 2.5 4.2 3.1 8.5 .. 1.8 1.4 Switzerland 1.0 0.6 ­2.0 .. 0.4 .. 1.2 .. 1.2 .. Syrian Arab Republica 5.0 3.5 5.8 3.3 8.7 ­4.7 .. .. 2.0 9.0 Tajikistan ­10.4 10.0 ­6.8 12.3 ­10.8 10.9 ­10.0 11.0 ­4.0 5.6 Tanzaniac 2.9 6.8 3.2 4.9 3.1 8.9 2.7 7.6 2.7 5.9 Thailanda 4.2 5.4 1.0 3.2 5.7 6.9 6.9 7.2 3.8 4.3 Togo 3.5 2.6 4.0 2.7 1.8 8.2 1.8 7.6 3.9 ­0.2 Trinidad and Tobagoa 3.2 7.2 2.7 ­6.8 3.5 12.0 4.9 6.0 2.9 5.1 Tunisia 4.7 4.3 2.3 3.7 4.6 3.0 5.5 3.0 5.3 5.1 Turkey 3.8 4.2 1.4 0.6 4.1 3.4 4.9 5.2 4.0 4.4 Turkmenistan ­4.8 .. ­5.7 .. ­3.4 .. .. .. ­5.4 .. Uganda 7.1 5.8 3.7 3.9 12.2 7.0 14.1 5.0 8.2 7.2 Ukraine ­9.3 8.6 ­5.6 3.0 ­12.9 10.8 ­11.2 14.0 ­8.1 8.8 United Arab Emirates 4.8 7.9 13.2 1.7 3.0 5.5 11.9 6.5 7.2 8.2 United Kingdom 2.7 2.3 ­0.2 1.2 1.5 ­0.1 .. .. 3.5 2.9 United States 3.5 2.5 3.7 ­0.7 3.7 0.0 .. 0.6 3.4 2.5 Uruguay 3.4 ­1.2 2.8 6.0 1.1 ­2.1 ­0.1 ­0.8 4.6 ­2.2 Uzbekistan ­0.2 4.8 0.5 6.7 ­3.4 3.6 0.7 2.0 0.4 4.3 Venezuela, RBa 1.6 ­1.2 1.3 ­0.4 1.2 ­2.8 4.5 ­2.1 ­0.1 1.4 Vietnama 7.9 7.2 4.3 3.6 11.9 10.1 11.2 11.2 7.5 6.6 West Bank and Gaza 3.4 ­13.3 ­3.4 ­10.7 ­0.6 ­22.0 4.1 ­16.2 4.7 ­9.7 Yemen, Rep.a 6.0 3.6 5.6 5.3 7.5 2.8 3.7 2.5 5.4 3.1 Zambiaa 0.5 4.4 4.2 1.3 ­4.2 8.9 0.8 5.9 2.5 4.0 Zimbabwe 2.1 ­5.9 4.3 ­9.0 0.4 ­10.1 0.4 ­11.1 2.9 ­7.5 World 2.9 w 2.5 w 1.8 w 2.1 w 2.4 w 1.4 w .. w 1.0 w 3.1 w 2.3 w Low income 4.7 5.5 3.1 2.7 4.9 6.0 5.8 6.5 5.9 6.7 Middle income 3.8 4.7 2.0 3.4 4.3 5.6 .. .. 3.9 4.1 Lower middle income 5.2 6.0 2.6 3.8 6.4 7.3 .. .. 5.1 5.4 Upper middle income 2.1 2.7 0.3 2.2 1.5 2.5 4.5 2.1 2.8 2.7 Low & middle income 3.9 4.8 2.3 3.2 4.3 5.6 .. .. 4.2 4.4 East Asia & Pacific 8.5 8.1 3.4 3.4 11.0 9.1 .. .. 8.0 8.4 Europe & Central Asia ­0.8 5.0 ­1.7 3.3 ­3.0 5.3 .. .. 0.8 4.8 Latin America & Carib. 3.3 1.6 1.8 3.1 3.2 1.4 2.9 1.1 3.3 0.7 Middle East & N. Africa 3.9 3.8 2.9 5.1 2.1 1.9 3.7 5.2 4.7 4.5 South Asia 5.6 5.8 3.1 1.9 6.2 6.1 6.6 6.5 7.1 7.5 Sub-Saharan Africa 2.5 3.9 3.3 3.6 1.9 4.0 1.9 2.3 2.7 3.9 High income 2.7 2.0 1.0 ­1.3 1.9 0.3 .. 0.7 3.0 2.0 Europe EMU 2.1 1.3 1.3 ­0.4 1.0 0.6 1.8 ­0.2 2.5 1.6 a. Components are at producer prices. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are linked to the revised data on the basis of earlier growth rates. c. Data cover mainland Tanzania only. 196 2006 World Development Indicators Growth of output About the data Definitions An economy's growth is measured by the change and illicit or deliberately unreported activities. The · Gross domestic product (GDP) at purchaser prices in the volume of its output or in the real incomes consistency and completeness of such estimates is the sum of gross value added by all resident pro- of its residents. The 1993 United Nations System depend on the skill and methods of the compiling ducers in the economy plus any product taxes (less of National Accounts (1993 SNA) offers three plau- statisticians and the resources available to them. subsidies) not included in the valuation of output. It sible indicators for calculating growth: the volume of is calculated without deducting for depreciation of gross domestic product (GDP), real gross domestic Rebasing national accounts fabricated capital assets or for depletion and degra- income, and real gross national income. The vol- When countries rebase their national accounts, they dation of natural resources. Value added is the net ume of GDP is the sum of value added, measured update the weights assigned to various components to output of an industry after adding up all outputs and at constant prices, by households, government, and better reflect the current pattern of production or uses subtracting intermediate inputs. The industrial origin industries operating in the economy. This year's edi- of output. The new base year should represent normal of value added is determined by the International tion of World Development Indicators continues to operation of the economy--that is, it should be a year Standard Industrial Classification (ISIC) revision 3. measure growth of the economy by the change in without major shocks or distortions. Some developing · Agriculture corresponds to ISIC divisions 1­5 and GDP measured at constant prices. countries have not rebased their national accounts for includes forestry and fishing. · Industry covers min- Each industry's contribution to growth in the econ- many years. Using an old base year can be misleading ing, manufacturing (also reported separately), con- omy's output is measured by growth in the industry's because implicit price and volume weights become struction, electricity, water, and gas (ISIC divisions value added. In principle, value added in constant progressively less relevant and useful. 10­45). · Manufacturing corresponds to industries prices can be estimated by measuring the quantity To obtain comparable series of constant price belonging to ISIC divisions 15­37. · Services cor- of goods and services produced in a period, valu- data, the World Bank rescales GDP and value added respond to ISIC divisions 50­99. This sector is ing them at an agreed set of base year prices, and by industrial origin to a common reference year. This derived as a residual (from GDP less agriculture and subtracting the cost of intermediate inputs, also in year's World Development Indicators continues to industry) and may not properly reflect the sum of constant prices. This double-deflation method, rec- use 2000 as the reference year. Because rescaling services output, including banking and financial ser- ommended by the 1993 SNA and its predecessors, changes the implicit weights used in forming regional vices. For some countries it includes product taxes requires detailed information on the structure of and income group aggregates, aggregate growth (minus subsidies) and may also include statistical prices of inputs and outputs. rates in this year's World Development Indicators are discrepancies. In many industries, however, value added is not comparable with those from earlier publications extrapolated from the base year using single vol- with different base years. ume indexes of outputs or, more rarely, inputs. Par- Rescaling may result in a discrepancy between ticularly in the services industries, including most of the rescaled GDP and the sum of the rescaled com- government, value added in constant prices is often ponents. Because allocating the discrepancy would imputed from labor inputs, such as real wages or cause distortions in the growth rates, the discrep- number of employees. In the absence of well-defined ancy is left unallocated. As a result, the weighted measures of output, measuring the growth of ser- average of the growth rates of the components gen- vices remains difficult. erally will not equal the GDP growth rate. Moreover, technical progress can lead to improve- Growth rates of GDP and its components are calcu- ments in production processes and in the quality of lated using constant price data in the local currency. Data sources goods and services that, if not properly accounted Regional and income group growth rates are calcu- National accounts data for most developing for, can distort measures of value added and thus lated after converting local currencies to constant countries are collected from national statistical of growth. When inputs are used to estimate output, price U.S. dollars using an exchange rate in the com- organizations and central banks by visiting and as is the case for nonmarket services, unmeasured mon reference year. The growth rates in the table are resident World Bank missions. Data for high- technical progress leads to underestimates of the vol- average annual compound growth rates. Methods of income economies come from data files of the ume of output. Similarly, unmeasured improvements computing growth rates and the alternative conver- Organisation for Economic Co-operation and in the quality of goods and services produced lead sion factor are described in Statistical methods. Development (for information on the OECD's to underestimates of the value of output and value national accounts series, see its Annual National added. The result can be underestimates of growth Changes in the System of National Accounts Accounts for OECD Member Countries: Data from and productivity improvement and overestimates of World Development Indicators adopted the termi- 1970 Onwards). The World Bank rescales constant inflation. These issues are highly complex, and only nology of the 1993 SNA in 2001. Although many price data to a common reference year. The com- a few high-income countries have attempted to intro- countries continue to compile their national accounts plete national accounts time series is available on duce any GDP adjustments for these factors. according to the SNA version 3 (referred to as the the World Development Indicators 2006 CD-ROM. Informal economic activities pose a particular mea- 1968 SNA), more and more are adopting the 1993 The United Nations Statistics Division publishes surement problem, especially in developing countries, SNA. Some low-income countries still use concepts detailed national accounts for UN member coun- where much economic activity may go unrecorded. from the even older 1953 SNA guidelines, including tries in National Accounts Statistics: Main Aggre- Obtaining a complete picture of the economy requires valuations such as factor cost, in describing major gates and Detailed Tables and publishes updates estimating household outputs produced for home economic aggregates. Countries that use the 1993 in the Monthly Bulletin of Statistics. use, sales in informal markets, barter exchanges, SNA are identified in Primary data documentation. 2006 World Development Indicators 197 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistana .. 5,761 .. .. .. .. .. .. .. .. Albania 2,102 7,590 36 25 48 19 .. 11 16 56 Algeria 62,045 84,649 11 10 48 57 11 7 41 34 Angolaa 10,260 19,493 18 9 41 58 5 4 41 33 Argentina 141,352 153,014 8 10 36 36 27 24 56 54 Armenia 2,257 3,079 17 23 52 37 33 23 31 40 Australia 310,518 637,327 4 3 29 26 14 12 67 71 Austria 164,984 292,328 4 2 32 31 21 20 64 67 Azerbaijan 8,858 8,523 29 12 33 55 19 9 38 32 Bangladesha 30,129 56,585 30 21 22 27 13 16 48 52 Belarus 17,370 22,889 24 11 47 40 39 32 29 50 Belgium 197,176 352,312 2 1 33 25 .. 18 65 73 Benina 1,845 4,075 36 37 13 15 8 9 51 48 Boliviaa 4,868 8,773 17 16 35 31 19 14 49 54 Bosnia and Herzegovina .. 8,533 .. 12 .. 28 .. 13 .. 61 Botswana 3,792 8,974 5 3 61 51 5 5 34 47 Brazil 461,952 603,973 8 10 39 40 25 11 53 50 Bulgaria 20,731 24,131 17 11 49 31 .. 19 34 58 Burkina Faso 3,120 4,824 28 31 20 20 15 14 52 49 Burundi 1,132 657 56 51 19 20 13 .. 25 29 Cambodiaa 1,115 4,884 .. 33 .. 29 .. 22 .. 38 Cameroon 11,152 14,391 25 44 30 16 15 8 46 40 Canada 574,192 977,968 3 .. 32 .. 17 .. 65 .. Central African Republic 1,488 1,307 48 56 20 22 11 .. 33 23 Chad 1,739 4,221 29 64 18 8 14 6 53 29 Chilea 30,323 94,105 9 4 42 45 20 19 50 52 Chinaa, b 354,644 1,931,710 27 13 42 46 33 .. 31 41 Hong Kong, China 75,433 163,005 0 0 25 11 18 4 74 89 Colombiaa 40,274 97,718 17 12 38 31 21 14 45 58 Congo, Dem. Rep. 9,350 6,628 31 58 29 19 11 4 40 22 Congo, Rep.a 2,799 4,343 13 6 41 57 8 6 47 37 Costa Rica 5,713 18,496 18 9 29 29 22 21 53 63 Côte d'Ivoirea 10,796 15,475 33 22 23 21 21 17 44 58 Croatia 24,778 34,311 10 8 34 30 28 19 56 62 Cubaa .. .. .. .. .. .. .. .. .. .. Czech Republic 34,880 107,015 6 3 49 38 .. 26 45 59 Denmark 133,360 241,437 5 2 27 25 18 16 69 73 Dominican Republica 7,074 18,673 13 11 31 26 18 13 55 63 Ecuadora 10,356 30,282 13 7 38 31 19 10 49 62 Egypt, Arab Rep. 43,130 78,796 19 15 29 37 18 18 52 48 El Salvadora 4,801 15,824 17 10 27 31 22 24 55 60 Eritrea 477 925 31 15 12 24 8 11 57 61 Estonia 5,010 11,239 17 4 50 29 42 18 34 67 Ethiopia 8,609 8,003 49 47 13 10 8 .. 38 44 Finland 136,962 185,923 7 3 34 31 23 23 59 66 France 1,239,256 2,046,646 4 3 27 22 .. 14 70 76 Gabona 5,952 7,229 7 8 43 61 6 5 50 31 Gambia, The 317 415 29 32 13 14 7 5 58 54 Georgia 7,738 5,202 32 18 34 25 24 19 35 57 Germany 1,707,383 2,740,551 2 1 38 29 28 23 61 70 Ghanaa 5,886 8,869 45 38 17 25 10 9 38 37 Greece 84,073 205,215 11 7 28 23 .. 12 61 70 Guatemalaa 7,650 27,451 26 23 20 19 15 13 54 58 Guinea 2,818 3,870 24 25 33 37 5 4 43 38 Guinea-Bissau 244 280 61 63 19 12 8 9 21 25 Haitia 2,864 3,530 .. 27 .. 17 .. 9 .. 55 198 2006 World Development Indicators Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 3,049 7,371 22 14 26 31 16 20 51 56 Hungary 33,056 100,685 15 3 39 31 23 23 46 66 India 316,937 691,163 31 21 28 27 17 16 41 52 Indonesiaa 114,426 257,641 19 15 39 44 21 28 42 41 Iran, Islamic Rep. 120,404 163,445 24 11 29 42 12 12 48 48 Iraq 48,422 12,602 .. 9 .. 70 .. 2 .. 21 Ireland 47,299 181,623 9 3 35 41 28 31 56 56 Israel 52,490 116,879 .. .. .. .. .. .. .. .. Italy 1,102,380 1,677,834 4 3 34 28 25 20 63 70 Jamaicaa 4,592 8,865 8 6 46 33 21 14 59 62 Japan 3,039,693 4,622,771 3 1 39 31 27 21 58 68 Jordan 4,020 11,515 8 3 28 29 15 19 64 68 Kazakhstan 26,933 40,743 27 8 45 40 9 16 29 52 Kenya 8,591 16,088 30 27 19 17 12 11 51 56 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 263,776 679,674 9 4 42 41 27 29 50 56 Kuwaita 18,428 55,718 1 1 52 59 12 3 47 41 Kyrgyz Republic 2,674 2,205 34 37 36 21 28 14 30 42 Lao PDR 866 2,452 61 47 15 28 10 20 24 26 Latvia 7,447 13,571 22 4 46 23 35 13 32 73 Lebanon 2,838 21,768 .. 7 .. 21 .. 13 .. 72 Lesotho 615 1,312 24 18 33 41 14 19 44 42 Liberia 384 492 54 43 17 6 .. 6 29 51 Libya 28,905 29,119 .. .. .. .. .. .. .. .. Lithuania 10,506 22,263 27 6 31 34 21 21 42 60 Macedonia, FYR 4,478 5,355 9 13 45 28 36 16 47 59 Madagascar 3,081 4,364 29 29 13 16 11 14 59 55 Malawi 1,881 1,879 45 39 29 17 20 11 26 44 Malaysiaa 44,024 118,318 15 10 42 50 24 31 43 40 Mali 2,421 4,863 46 36 16 26 9 3 39 39 Mauritania 1,020 1,534 30 18 29 34 10 10 42 48 Mauritius 2,383 6,034 13 6 33 30 25 21 54 64 Mexicoa 262,710 676,497 8 4 28 26 21 18 64 70 Moldova 3,593 2,595 36 21 37 24 .. 17 27 55 Mongoliaa .. 1,612 17 21 30 30 .. 5 52 49 Moroccoa 25,784 50,031 18 16 32 30 18 17 50 54 Mozambique 2,463 6,086 37 22 18 31 10 13 45 47 Myanmara .. .. 57 .. 11 .. 8 .. 32 .. Namibia 2,350 5,712 12 10 38 32 14 14 50 58 Nepal 3,628 6,707 52 40 16 23 6 9 32 37 Netherlands 294,761 578,979 5 2 31 26 19 15 65 72 New Zealand 43,618 98,944 7 .. 28 .. 19 .. 65 .. Nicaraguaa 1,009 4,555 37 19 25 31 20 20 56 50 Nigera 2,481 3,081 35 40 16 17 7 7 49 43 Nigeria 28,472 72,053 33 17 41 57 6 4 26 27 Norway 116,108 250,052 4 2 36 39 13 11 61 59 Omana 11,685 24,284 3 2 54 56 3 8 43 42 Pakistan 40,010 96,115 26 22 25 25 17 18 49 53 Panama 5,313 13,733 10 8 15 18 10 8 75 74 Papua New Guinea 3,221 3,909 29 29 30 42 9 9 41 29 Paraguay 5,265 7,343 28 27 25 24 17 14 47 49 Perua 26,294 68,637 9 10 27 30 18 16 64 60 Philippinesa 44,312 84,567 22 14 35 32 25 24 44 54 Poland 58,976 242,293 8 3 50 33 .. 20 42 64 Portugal 71,462 167,716 9 4 32 27 22 17 60 70 Puerto Ricoa 30,604 .. 1 .. 42 .. 40 .. 57 .. 2006 World Development Indicators 199 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 38,299 73,167 24 14 50 37 34 31 26 49 Russian Federation 516,814 581,447 17 5 48 35 .. .. 35 60 Rwanda 2,584 1,845 33 41 25 22 18 10 43 38 Saudi Arabiaa 116,778 250,557 6 4 49 59 9 10 46 37 Senegala 5,699 7,775 20 17 19 20 13 12 61 63 Serbia and Montenegro .. 23,997 .. 19 .. 36 .. 22 .. 45 Sierra Leonea 650 1,075 47 .. 19 .. 5 .. 34 .. Singaporea 36,901 106,818 .. 0 .. 35 .. 29 .. 65 Slovak Republica 15,485 41,094 7 4 59 30 .. 19 34 67 Slovenia 17,413 32,182 6 3 42 37 34 27 52 61 Somalia 917 .. 66 .. .. .. 5 .. .. .. South Africa 112,014 212,777 5 3 40 32 24 20 55 65 Spain 526,471 1,039,927 7 4 34 29 .. 16 59 67 Sri Lanka 8,032 20,055 26 18 26 27 15 15 48 55 Sudan 13,167 21,098 .. 39 .. 25 .. 6 .. 36 Swaziland 882 2,396 13 13 42 47 35 39 45 40 Sweden 240,153 346,412 3 2 32 29 .. 21 64 69 Switzerland 235,808 357,542 3 1 33 29 22 20 64 70 Syrian Arab Republica 12,309 24,022 28 23 24 27 20 .. 48 50 Tajikistan 2,629 2,073 33 24 38 31 25 22 29 45 Tanzaniac 4,259 10,851 46 45 18 17 9 7 36 39 Thailanda 85,345 161,688 13 10 37 44 27 35 50 46 Togo 1,628 2,061 34 41 23 23 10 9 44 36 Trinidad and Tobagoa 5,068 12,544 3 1 45 47 13 7 52 52 Tunisia 12,291 28,185 16 13 30 28 17 18 55 60 Turkey 150,642 302,786 18 13 30 22 20 14 52 65 Turkmenistan 3,232 6,167 32 21 30 45 .. 21 38 34 Uganda 4,304 6,822 57 32 11 21 6 9 32 47 Ukraine 81,456 64,828 26 12 45 37 39 23 30 51 United Arab Emirates 33,653 104,204 2 3 64 55 8 13 35 42 United Kingdom 989,524 2,124,385 2 1 35 26 23 .. 63 73 United States 5,757,200 11,711,834 2 1 28 22 19 15 70 77 Uruguay 9,287 13,215 9 11 33 29 27 21 58 60 Uzbekistan 13,361 11,960 33 31 33 25 22 10 34 44 Venezuela, RBa 47,027 110,104 6 5 61 52 15 18 34 44 Vietnama 6,472 45,210 39 22 23 40 12 20 39 38 West Bank and Gaza .. 3,454 .. 6 .. 12 .. 10 .. 82 Yemen, Rep.a 4,828 12,834 24 14 27 38 9 5 49 49 Zambiaa 3,288 5,402 21 21 51 27 36 12 28 52 Zimbabwe 8,784 4,696 17 18 33 23 23 14 50 60 World 21,735,592 t 41,290,409 t 6w 4w 33 w 28 w 22 w 18 w 61 w 68 w Low income 609,821 1,239,169 32 23 26 28 15 15 42 49 Middle income 3,238,587 7,156,777 16 10 39 37 25 18 46 53 Lower middle income 1,656,377 4,165,291 19 12 39 41 27 .. 42 46 Upper middle income 1,582,075 2,991,524 10 6 39 32 22 20 51 62 Low & middle income 3,849,026 8,395,211 18 12 37 36 23 18 45 52 East Asia & Pacific 665,783 2,650,867 25 13 40 45 30 .. 35 42 Europe & Central Asia 1,107,862 1,769,739 16 8 43 32 .. 19 41 60 Latin America & Carib. 1,101,298 2,021,995 9 9 36 34 22 16 55 58 Middle East & N. Africa .. 547,496 19 12 33 39 14 14 49 49 South Asia 401,923 880,212 31 21 27 27 17 16 43 52 Sub-Saharan Africa 298,442 523,310 20 16 34 32 17 15 47 52 High income 17,887,372 32,900,093 3 2 33 26 22 18 65 72 Europe EMU 5,583,289 9,500,919 4 2 33 27 .. 19 63 71 a. Components are at producer prices. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are not comparable with the later data. c. Data cover mainland Tanzania only. 200 2006 World Development Indicators Structure of output About the data Definitions An economy's gross domestic product (GDP) repre- Ideally, industrial output should be measured · Gross domestic product (GDP) at purchaser prices sents the sum of value added by all producers in that through regular censuses and surveys of firms. But is the sum of gross value added by all resident pro- economy. Value added is the value of the gross output in most developing countries such surveys are infre- ducers in the economy plus any product taxes (less of producers less the value of intermediate goods quent, so earlier survey results must be extrapo- subsidies) not included in the valuation of output. It and services consumed in production, before taking lated using an appropriate indicator. The choice of is calculated without deducting for depreciation of account of the consumption of fixed capital in the sampling unit, which may be the enterprise (where fabricated assets or for depletion and degradation of production process. The United Nations System of responses may be based on financial records) or natural resources. Value added is the net output of National Accounts calls for estimates of value added the establishment (where production units may be an industry after adding up all outputs and subtract- to be valued at either basic prices (excluding net taxes recorded separately), also affects the quality of the ing intermediate inputs. The industrial origin of value on products) or producer prices (including net taxes data. Moreover, much industrial production is orga- added is determined by the International Standard on products paid by producers but excluding sales or nized in unincorporated or owner-operated ventures Industrial Classification (ISIC) revision 3. · Agricul- value added taxes). Both valuations exclude transport that are not captured by surveys aimed at the formal ture corresponds to ISIC divisions 1­5 and includes charges that are invoiced separately by producers. sector. Even in large industries, where regular surveys forestry and fishing. · Industry covers mining, manu- Total GDP shown in the table and elsewhere in this are more likely, evasion of excise and other taxes and facturing (also reported separately), construction, book is measured at purchaser prices. Value added by nondisclosure of income lower the estimates of value electricity, water, and gas (ISIC divisions 10­45). industry is normally measured at basic prices. When added. Such problems become more acute as coun- · Manufacturing corresponds to industries belong- value added is measured at producer prices, this is tries move from state control of industry to private ing to ISIC divisions 15­37. · Services correspond noted in Primary data documentation. enterprise, because new firms enter business and to ISIC divisions 50­99. This sector is derived as a While GDP estimates based on the production growing numbers of established firms fail to report. residual (from GDP less agriculture and industry) and approach are generally more reliable than estimates In accordance with the System of National Accounts, may not properly reflect the sum of services output, compiled from the income or expenditure side, dif- output should include all such unreported activity as including banking and financial services. For some ferent countries use different definitions, methods, well as the value of illegal activities and other unre- countries it includes product taxes (minus subsidies) and reporting standards. World Bank staff review corded, informal, or small-scale operations. Data on and may also include statistical discrepancies. the quality of national accounts data and sometimes these activities need to be collected using techniques make adjustments to improve consistency with other than conventional surveys of firms. international guidelines. Nevertheless, significant In industries dominated by large organizations discrepancies remain between international stan- and enterprises, such as public utilities, data on dards and actual practice. Many statistical offices, output, employment, and wages are usually read- especially those in developing countries, face severe ily available and reasonably reliable. But in the limitations in the resources, time, training, and bud- services industry the many self-employed workers gets required to produce reliable and comprehensive and one-person businesses are sometimes difficult series of national accounts statistics. to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Data problems in measuring output Compounding these problems are the many forms Among the difficulties faced by compilers of national of economic activity that go unrecorded, including Data sources accounts is the extent of unreported economic activ- the work that women and children do for little or no National accounts data for most developing coun- ity in the informal or secondary economy. In develop- pay. For further discussion of the problems of using tries are collected from national statistical organi- ing countries a large share of agricultural output is national accounts data, see Srinivasan (1994) and zations and central banks by visiting and resident either not exchanged (because it is consumed within Heston (1994). World Bank missions. Data for high-income econ- the household) or not exchanged for money. omies come from data files of the Organisation Agricultural production often must be estimated Dollar conversion for Economic Co-operation and Development (for indirectly, using a combination of methods involv- To produce national accounts aggregates that are information on the OECD's national accounts ing estimates of inputs, yields, and area under cul- measured in the same standard monetary units, series, see its Annual National Accounts for OECD tivation. This approach sometimes leads to crude the value of output must be converted to a single Member Countries: Data from 1970 Onwards). The approximations that can differ from the true values common currency. The World Bank conventionally complete national accounts time series is avail- over time and across crops for reasons other than uses the U.S. dollar and applies the average official able on the World Development Indicators 2006 climatic conditions or farming techniques. Similarly, exchange rate reported by the International Monetary CD-ROM. The United Nations Statistics Division agricultural inputs that cannot easily be allocated to Fund for the year shown. An alternative conversion publishes detailed national accounts for UN mem- specific outputs are frequently "netted out" using factor is applied if the official exchange rate is judged ber countries in National Accounts Statistics: Main equally crude and ad hoc approximations. For further to diverge by an exceptionally large margin from the Aggregates and Detailed Tables and publishes discussion of the measurement of agricultural pro- rate effectively applied to transactions in foreign cur- updates in the Monthly Bulletin of Statistics. duction, see About the data for table 3.3. rencies and traded products. 2006 World Development Indicators 201 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 393 24 .. 33 .. .. .. .. .. 44 .. Algeria 6,452 4,109 13 .. 17 .. .. .. .. .. 70 .. Angola 513 404 .. .. .. .. .. .. .. .. .. .. Argentina 37,868 20,763 20 .. 10 .. 13 .. 12 .. 46 .. Armenia 681 483 .. .. .. .. .. .. .. .. .. .. Australia 38,871 44,802 18 15 6 12 20 25 7 7 49 42 Austria 31,439 37,308 15 20 7 17 28 18 8 9 43 37 Azerbaijan 1,561 463 .. .. .. .. .. .. .. .. .. .. Bangladesh 3,839 7,278 24 .. 38 .. 7 .. 17 .. 15 .. Belarus 6,630 3,790 .. .. .. .. .. .. .. .. .. .. Belgium .. 41,434 17 18 7 15 .. 24 13 7 62 37 Benin 145 244 .. .. .. .. .. .. .. .. .. .. Bolivia 826 1,039 28 .. 5 .. 1 .. 3 .. 63 .. Bosnia and Herzegovina .. 559 12 .. 15 .. 18 .. 7 .. 49 .. Botswana 181 233 51 19 12 4 .. .. .. .. 36 77 Brazil 89,966 53,032 14 .. 12 .. 27 .. .. .. 48 .. Bulgaria .. 2,391 22 .. 9 .. 19 .. 5 .. 45 .. Burkina Faso 460 394 1 1 8 17 1 3 .. .. 90 80 Burundi 134 .. 83 .. 9 .. .. .. 2 .. 7 .. Cambodia 58 755 .. .. .. .. .. .. .. .. .. .. Cameroon 1,581 845 61 .. ­13 .. 1 .. 5 .. 46 .. Canada 91,671 118,620 15 17 6 9 26 21 10 7 44 46 Central African Republic 154 81 58 .. 6 .. 2 .. 6 .. 28 .. Chad 239 263 .. .. .. .. .. .. .. .. .. .. Chile 5,613 12,348 25 25 8 18 5 12 10 8 52 37 Chinab 116,573 .. 15 15 15 12 24 32 13 12 34 28 Hong Kong, China 12,639 7,033 8 11 36 20 21 25 2 3 33 41 Colombia 8,034 10,783 31 21 15 4 9 4 14 .. 31 71 Congo, Dem. Rep. 1,029 220 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 234 158 58 .. 4 .. 3 .. .. .. 35 .. Costa Rica 1,107 3,277 47 45 8 5 7 6 9 11 30 33 Côte d'Ivoire 2,257 1,949 38 26 7 13 8 9 .. .. 47 52 Croatia 6,475 3,771 22 .. 15 .. 20 .. 8 .. 36 .. Cuba .. .. 67 .. 5 .. 1 .. .. .. 27 .. Czech Republic .. 17,407 .. .. .. .. .. .. .. .. .. .. Denmark 20,757 23,862 22 21 4 7 24 25 12 8 39 38 Dominican Republic 1,270 3,393 .. .. .. .. .. .. .. .. .. .. Ecuador 1,988 2,663 22 11 10 3 5 1 8 1 56 85 Egypt, Arab Rep. 7,296 16,250 19 .. 16 .. 9 .. 14 .. 43 .. El Salvador 1,043 3,318 36 44 14 27 4 3 24 9 23 17 Eritrea 35 66 53 55 18 11 2 2 18 6 9 26 Estonia 1,985 1,085 .. .. .. .. .. .. .. .. .. .. Ethiopia 624 .. .. .. 13 .. 2 .. 2 .. 83 .. Finland 27,531 27,212 13 17 4 12 24 19 8 7 52 45 France .. 192,279 13 13 6 12 31 22 9 7 41 45 Gabon 332 234 45 28 2 4 1 3 7 2 45 63 Gambia, The 18 19 .. .. .. .. .. .. .. .. .. .. Georgia 1,773 599 .. .. .. .. .. .. .. .. .. .. Germany 456,405 410,644 .. 8 .. 2 .. 41 .. 10 .. 38 Ghana 575 556 .. 38 .. 11 .. 4 .. 8 .. 39 Greece .. 13,845 22 24 20 21 12 13 10 8 36 35 Guatemala 1,151 2,985 38 41 11 10 4 4 18 14 29 31 Guinea 126 128 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 19 20 .. .. .. .. .. .. .. .. .. .. Haiti .. 268 51 .. 9 .. .. .. .. .. 40 .. 202 2006 World Development Indicators Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 443 1,172 45 33 10 3 3 0 6 .. 36 64 Hungary 6,613 12,449 14 12 9 13 26 10 12 7 39 58 India 48,808 72,681 12 2 15 27 26 17 14 5 34 49 Indonesia 23,643 59,471 28 23 15 17 12 22 9 10 37 28 Iran, Islamic Rep. 14,503 13,938 12 36 20 30 20 9 8 2 40 23 Iraq .. 319 20 51 16 53 4 25 11 7 49 -35 Ireland 11,982 34,732 27 35 4 20 29 11 17 5 24 29 Israel .. .. 14 20 9 15 32 17 9 6 37 42 Italy 247,917 216,177 8 9 13 13 35 27 7 8 37 44 Jamaica 853 1,071 41 29 5 3 .. 2 .. 4 54 63 Japan 810,232 811,829 9 10 5 0 40 9 10 11 37 70 Jordan 520 1,393 28 27 7 9 4 5 15 17 47 42 Kazakhstan 1,941 3,566 .. .. .. .. .. .. .. .. .. .. Kenya 864 1,299 39 37 10 19 10 7 9 10 33 28 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 64,605 129,449 11 27 12 20 32 6 9 10 36 37 Kuwait 2,142 1,031 4 10 3 5 2 5 3 3 88 76 Kyrgyz Republic 706 210 .. .. .. .. .. .. .. .. .. .. Lao PDR 85 344 .. .. .. .. .. .. .. .. .. .. Latvia 2,474 1,128 .. 27 .. 11 .. 10 .. 4 .. 49 Lebanon .. 2,114 .. .. .. .. .. .. .. .. .. .. Lesotho 71 128 .. .. .. .. .. .. .. .. .. .. Liberia .. 27 .. .. .. .. .. .. .. .. .. .. Libya .. .. .. 49 .. 3 .. 2 .. 10 .. 36 Lithuania 2,164 2,398 .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 1,411 589 20 .. 26 .. 14 .. 9 .. 31 .. Madagascar 314 518 39 11 36 35 3 6 8 1 14 48 Malawi 313 197 38 62 10 12 1 1 18 8 33 17 Malaysia 10,665 29,095 13 9 7 4 31 41 11 8 39 38 Mali 200 98 .. .. .. .. .. .. .. .. .. .. Mauritania 94 112 .. .. .. .. .. .. .. .. .. .. Mauritius 491 922 30 .. 46 .. 2 .. 4 .. 17 .. Mexico 49,992 110,667 22 25 5 4 24 27 18 15 32 28 Moldova .. 247 .. 59 .. 10 .. 6 .. .. .. 25 Mongolia .. 70 33 .. 37 .. 1 .. 1 .. 27 .. Morocco 4,753 6,067 22 33 17 18 8 8 12 13 41 28 Mozambique 230 493 12 49 2 15 7 5 .. 3 79 29 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 292 314 .. .. .. .. .. .. .. .. .. .. Nepal 209 432 37 45 31 19 1 2 5 10 26 23 Netherlands 52,330 56,954 21 19 3 14 25 33 17 12 35 23 New Zealand 7,574 8,037 28 3 8 12 13 10 7 5 44 71 Nicaragua 170 721 56 53 10 8 0 0 6 10 28 29 Niger 163 143 37 .. 29 .. .. .. .. .. 34 .. Nigeria 1,562 2,075 15 .. 46 .. 13 .. 4 .. 22 .. Norway 13,450 18,563 18 1 2 10 25 21 9 3 46 65 Oman 343 1,564 .. 9 .. 2 .. 3 .. 5 .. 82 Pakistan 6,184 10,440 24 23 28 2 9 5 15 11 25 59 Panama 502 974 51 52 8 7 2 3 8 4 31 35 Papua New Guinea 289 241 12 21 .. 0 .. 3 .. 4 88 73 Paraguay 883 775 56 45 16 18 .. 1 .. 5 29 32 Peru 3,926 8,149 23 .. 11 .. 8 .. 9 .. 49 .. Philippines 11,003 17,735 39 38 11 10 13 8 12 12 26 33 Poland .. 29,220 21 20 9 19 26 23 7 6 37 32 Portugal 13,630 18,319 15 19 21 23 13 10 6 9 45 40 Puerto Rico 12,126 27,099 16 8 5 2 18 18 44 61 17 12 2006 World Development Indicators 203 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 9,152 16,141 19 .. 18 .. 14 .. 4 .. 45 .. Russian Federation .. .. .. 19 .. 2 .. 24 .. 5 .. 50 Rwanda 473 194 .. .. .. 2 10 .. .. 2 90 97 Saudi Arabia 10,049 19,460 7 .. 1 .. 4 .. 39 .. 50 100 Senegal 747 626 60 .. 3 .. 5 .. 9 .. 23 .. Serbia and Montenegro .. 2,596 .. 36 .. 6 .. 14 .. 11 .. 33 Sierra Leone 28 21 .. .. .. .. .. .. .. .. .. .. Singapore .. 22,942 4 2 3 1 53 52 10 22 29 23 Slovak Republic .. 4,811 .. .. .. .. .. .. .. .. .. .. Slovenia 5,200 5,170 12 10 15 9 16 17 9 13 48 50 Somalia 41 .. 86 50 3 3 .. .. .. .. 12 47 South Africa 24,043 19,885 15 16 8 13 18 15 9 9 50 48 Spain .. 108,351 18 17 8 22 25 18 10 11 39 33 Sri Lanka 1,077 2,320 51 39 24 31 4 6 4 4 17 21 Sudan .. 930 16 .. 4 .. 0 .. 21 .. 59 .. Swaziland 250 283 69 .. 8 .. 1 .. 0 .. 22 .. Sweden .. 43,749 10 11 2 8 33 30 9 2 47 49 Switzerland 49,484 53,226 10 .. 4 .. 34 .. .. .. 53 .. Syrian Arab Republic 2,508 .. 35 35 29 43 .. 2 .. 1 36 19 Tajikistan 653 415 .. .. .. .. .. .. .. .. .. .. Tanzaniac 361 660 51 42 3 27 7 3 11 3 29 26 Thailand 23,217 42,739 24 23 30 14 19 4 2 25 26 34 Togo 162 134 60 10 7 13 .. .. .. .. 33 77 Trinidad and Tobago 681 692 31 .. 3 .. 3 .. 19 .. 44 .. Tunisia 2,075 3,910 19 35 20 11 5 5 4 20 52 30 Turkey 26,882 21,912 16 31 15 23 16 8 10 6 43 32 Turkmenistan .. 647 .. .. .. .. .. .. .. .. .. .. Uganda 230 535 61 19 14 1 3 2 6 1 16 79 Ukraine 31,517 7,582 .. .. .. .. .. .. .. .. .. .. United Arab Emirates 2,643 10,268 .. 5 .. 2 .. 6 .. 4 .. 84 United Kingdom 206,719 220,429 13 12 5 11 32 32 11 10 38 36 United States 1,040,600 1,463,300 12 12 5 8 31 30 12 10 40 39 Uruguay 2,597 2,145 31 .. 18 .. 9 .. 10 .. 32 .. Uzbekistan .. 782 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 6,921 15,270 17 28 5 5 5 4 9 .. 64 64 Vietnam 793 7,218 .. 30 .. 21 .. 15 .. 6 .. 28 West Bank and Gaza .. 341 .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 449 507 .. 50 .. 5 .. 0 .. .. .. 44 Zambia 1,048 385 44 .. 12 .. 7 .. 9 .. 29 .. Zimbabwe 1,799 3,692 28 28 19 18 10 3 6 5 38 48 World 4,528,705 t 5,446,980 t Low income 83,631 123,365 Middle income .. .. Lower middle income .. .. Upper middle income 265,244 386,586 Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia .. .. Latin America & Carib. 204,350 258,603 Middle East & N. Africa .. 56,686 South Asia 60,476 93,715 Sub-Saharan Africa 43,316 40,599 High income 3,627,337 4,286,603 Europe EMU 838,928 1,159,441 a. Includes unallocated data. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are not comparable with the later data. c. Data cover mainland Tanzania only. 204 2006 World Development Indicators Structure of manufacturing About the data Definitions The data on the distribution of manufacturing value manufacturing value added will not match those · Manufacturing value added is the sum of gross added by industry are provided by the United Nations from UNIDO sources. The classification of manu- output less the value of intermediate inputs used Industrial Development Organization (UNIDO). UNIDO facturing industries in the table accords with the in production for industries classified in ISIC major obtains data on manufacturing value added from a United Nations International Standard Industrial division 3. · Food, beverages, and tobacco corre- variety of national and international sources, includ- Classification (ISIC) revision 2. First published spond to ISIC division 31. · Textiles and clothing ing the United Nations Statistics Division, the World in 1948, the ISIC has its roots in the work of the correspond to ISIC division 32. · Machinery and Bank, the Organisation for Economic Co-operation League of Nations Committee of Statistical Experts. transport equipment correspond to ISIC groups and Development, and the International Monetary The committee's efforts, interrupted by the Second 382­84. · Chemicals correspond to ISIC groups Fund. To improve comparability over time and across World War, were taken up by the United Nations 351 and 352. · Other manufacturing covers wood countries, UNIDO supplements these data with infor- Statistical Commission, which at its first session and related products (ISIC division 33), paper and mation from industrial censuses, statistics supplied appointed a committee on industrial classification. related products (ISIC division 34), petroleum and by national and international organizations, unpub- The latest revision, ISIC revision 3, was completed related products (ISIC groups 353­56), basic met- lished data that it collects in the field, and estimates in 1989, and many countries have now switched to als and mineral products (ISIC divisions 36 and 37), by the UNIDO Secretariat. Nevertheless, coverage it. But revision 2 is still widely used for compiling fabricated metal products and professional goods may be less than complete, particularly for the infor- cross-country data. Concordances matching ISIC cat- (ISIC groups 381 and 385), and other industries mal sector. To the extent that direct information on egories to national systems of classification and to (ISIC group 390). When data for textiles and clothing, inputs and outputs is not available, estimates may related systems such as the Standard International machinery and transport equipment, or chemicals be used, which may result in errors in industry totals. Trade Classification are readily available. are shown in the table as not available, they are Moreover, countries use different reference periods In establishing a classification system, compil- included in "other manufacturing." (calendar or fiscal year) and valuation methods (basic ers must define both the types of activities to be or producer prices) to estimate value added. (See described and the organizational units whose activi- also About the data for table 4.2.) ties are to be reported. There are many possibili- The data on manufacturing value added in U.S. ties, and the choices made affect how the resulting dollars are from the World Bank's national accounts statistics can be interpreted and how useful they files. These figures may differ from those used by are in analyzing economic behavior. The ISIC empha- UNIDO to calculate the shares of value added by sizes commonalities in the production process and industry, in part because of differences in exchange is explicitly not intended to measure outputs (for rates. Thus estimates of value added in a particular which there is a newly developed Central Product industry calculated by applying the shares to total Classification). Nevertheless, the ISIC views an activ- ity as defined by "a process resulting in a homoge- neous set of products" (United Nations 1990 [ISIC, Manufacturing growth trends for selected series M, no. 4, rev. 3], p. 9). Sub-Saharan countries Firms typically use a multitude of processes to Value added in manufacturing (1995 = 100) produce a final product. For example, an automo- bile manufacturer engages in forging, welding, and 500 painting as well as advertising, accounting, and many Mozambique other service activities. In some cases the processes 400 may be carried out by different technical units within the larger enterprise, but collecting data at such a detailed level is not practical, nor would it be useful 300 to record production data at the very highest level of Data sources a large, multiplant, multiproduct firm. The ISIC has therefore adopted as the definition of an establish- Data on value added in manufacturing in U.S. dol- Cameroon lars are from the World Bank's national accounts 200 ment "an enterprise or part of an enterprise which Tanzania independently engages in one, or predominantly one, files. Data used to calculate shares of value South Africa kind of economic activity at or from one location . . . added by industry are provided to the World Bank 100 Kenya for which data are available . . ." (United Nations in electronic files by UNIDO. The most recent pub- 1990, p. 25). By design, this definition matches the lished source is UNIDO's International Yearbook reporting unit required for the production accounts of Industrial Statistics 2005. The ISIC system 0 is described in the United Nations' International 1995 2000 2004 of the UN System of National Accounts. Standard Industrial Classification of All Economic Mozambique had impressive growth in the manufacturing Activities, Third Revision (1990). The discussion sector with over 15 percent growth between 1995 and 2004. By contrast, South Africa--with the largest manufacturing of the ISIC draws on Jacob Ryten's paper "Fifty sector in the region--had modest growth of slightly more Years of ISIC: Historical Origins and Future Per- than 1.5 percent over the same period. spectives" (1998). Source: World Bank data files. 2006 World Development Indicators 205 Structure of merchandise exports Merchandise Food Agricultural raw Fuels Ores and Manufactures exports materials metals $ millions % of total % of total % of total % of total % of total 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan 235 420 .. .. .. .. .. .. .. .. .. .. Albania 230 596 .. 6 .. 4 .. 3 .. 6 .. 82 Algeria 12,930 32,298 0 0 0 0 96 97 0 0 3 2 Angola 3,910 13,850 0 .. 0 .. 93 .. 6 .. 0 .. Argentina 12,353 34,453 56 48 4 2 8 16 2 4 29 29 Armenia .. 705 .. 12 .. 2 .. 3 .. 21 .. 62 Australia 39,752 86,423 22 19 10 4 20 19 20 16 26 25 Austria 41,265 117,417 3 6 4 3 1 3 3 3 88 84 Azerbaijan .. 3,615 .. 4 .. 1 .. 82 .. 1 .. 10 Bangladesh 1,671 8,150 14 8 7 2 1 0 0 0 77 90 Belarus .. 13,752 .. 8 .. 3 .. 27 .. 1 .. 60 Belgium 117,703a 306,509 9a 9 2a 1 3a 6 4a 3 77a 81 Benin 288 672 15 41 56 49 15 0 0 0 13 9 Bolivia 926 2,129 19 27 8 2 25 38 44 19 5 14 Bosnia and Herzegovina 276 1,789 .. .. .. .. .. .. .. .. .. .. Botswana 1,784 3,467 .. .. .. .. .. .. .. .. .. .. Brazil 31,414 96,475 28 28 3 4 2 5 14 9 52 54 Bulgaria 5,030 9,918 .. 10 .. 2 .. 8 .. 12 .. 62 Burkina Faso 152 445 .. 16 .. 72 .. 3 .. 1 .. 8 Burundi 75 47 .. 92 .. 1 .. 0 .. 2 .. 5 Cambodia 86 2,798 .. 1 .. 2 .. 0 .. 0 .. 97 Cameroon 2,002 2,700 20 19 14 24 50 47 7 5 9 5 Canada 127,629 316,547 9 7 9 5 10 17 9 5 59 60 Central African Republic 120 120 31 2 24 25 0 0 1 36 44 37 Chad 188 2,200 .. .. .. .. .. .. .. .. .. .. Chile 8,372 32,025 24 21 9 8 1 3 55 54 11 13 China 62,091 593,329 13 4 3 1 8 2 2 2 72 91 Hong Kong, Chinab 82,390 265,670 4 1 1 1 1 0 1 1 92 96 Colombia 6,766 16,224 33 17 4 5 37 38 0 1 25 38 Congo, Dem. Rep. 2,326 1,413 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 981 3,900 .. .. .. .. .. .. .. .. .. .. Costa Rica 1,448 6,297 58 33 5 3 1 0 1 1 27 63 Côte d'Ivoire 3,072 6,475 .. 56 .. 9 .. 13 .. 0 .. 20 Croatia 4,597 8,022 13 9 6 4 9 11 5 3 68 72 Cuba 5,100 2,192 .. .. .. .. .. .. .. .. .. .. Czech Republic 12,170 68,657 .. 3 .. 2 .. 3 .. 2 .. 90 Denmark 36,870 76,821 27 19 3 3 3 8 1 1 60 66 Dominican Republic 2,170 5,750 21 .. 0 .. 0 .. 0 .. 78 .. Ecuador 2,714 7,634 44 31 1 5 52 54 0 0 2 9 Egypt, Arab Rep. 3,477 7,682 10 10 10 7 29 43 9 4 42 31 El Salvador 582 3,295 57 32 1 1 2 4 3 3 38 60 Eritrea 16 35 .. .. .. .. .. .. .. .. .. .. Estonia .. 5,945 .. 7 .. 8 .. 4 .. 3 .. 77 Ethiopia 298 639 .. 62 .. 26 .. 0 .. 1 .. 11 Finland 26,571 61,334 2 2 10 6 1 4 4 3 83 83 France 216,588 448,714 16 11 2 1 2 3 3 2 77 83 Gabon 2,204 3,490 .. 1 .. 10 .. 76 .. 6 .. 7 Gambia, The 31 22 .. 63 .. 7 .. 1 .. 2 .. 27 Georgia .. 649 .. 32 .. 2 .. 4 .. 25 .. 37 Germany 421,100 912,261 5 4 1 1 1 2 3 2 89 84 Ghana 897 2,580 51 72 15 10 9 0 17 4 8 14 Greece 8,105 15,198 30 20 3 3 7 7 7 8 54 59 Guatemala 1,163 2,938 67 45 6 4 2 8 0 0 24 42 Guinea 671 700 .. 2 .. 1 .. 0 .. 72 .. 25 Guinea-Bissau 19 81 .. .. .. .. .. .. .. .. .. .. Haiti 160 391 14 .. 1 .. 0 .. 0 .. 85 .. Data for Taiwan, China 67,245 182,424 4 1 2 1 1 3 1 2 93 93 206 2006 World Development Indicators Structure of merchandise exports Merchandise Food Agricultural raw Fuels Ores and Manufactures exports materials metals $ millions % of total % of total % of total % of total % of total 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 831 1,537 82 63 4 4 1 0 4 6 9 27 Hungary 10,000 54,857 23 7 3 1 3 2 6 2 63 88 India 17,969 75,595 16 10 4 1 3 9 5 7 70 73 Indonesia 25,675 72,330 11 14 5 6 44 18 4 7 35 56 Iran, Islamic Rep. 19,305 44,446 .. 4 .. 0 .. 85 .. 1 .. 9 Iraq 12,380 17,810 .. .. .. .. .. .. .. .. .. .. Ireland 23,743 104,281 22 8 2 0 1 0 1 1 70 86 Israel 12,080 38,520 8 3 3 1 1 0 2 1 87 94 Italy 170,304 349,153 6 7 1 1 2 2 1 1 88 88 Jamaica 1,158 1,390 19 23 0 0 1 3 9 10 70 65 Japan 287,581 565,807 1 0 1 0 0 0 1 2 96 93 Jordan 1,064 3,887 10 14 1 0 0 1 33 12 56 72 Kazakhstan .. 20,093 .. 4 .. 1 .. 65 .. 14 .. 16 Kenya 1,031 2,693 49 40 6 12 13 23 3 4 30 21 Korea, Dem. Rep. 1,857 1,380 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 65,016 253,845 3 1 1 1 1 4 1 2 94 92 Kuwait 7,042 28,729 1 .. 0 .. 93 .. 0 .. 6 .. Kyrgyz Republic .. 719 .. 18 .. 12 .. 19 .. 7 .. 43 Lao PDR 79 361 .. .. .. .. .. .. .. .. .. .. Latvia .. 3,951 .. 9 .. 19 .. 5 .. 3 .. 61 Lebanon 494 1,747 .. 19 .. 2 .. 0 .. 10 .. 68 Lesotho 62 726 .. .. .. .. .. .. .. .. .. .. Liberia 868 235 .. .. .. .. .. .. .. .. .. .. Libya 13,225 20,844 1 .. 0 .. 95 .. .. .. 4 .. Lithuania .. 9,269 24 11 6 4 8 25 1 2 59 58 Macedonia, FYR 1,199 1,661 .. 15 .. 1 .. 5 .. 2 .. 77 Madagascar 319 990 73 61 4 6 1 4 8 5 14 22 Malawi 417 441 91 78 2 5 0 0 0 0 7 16 Malaysia 29,452 126,503 12 8 14 2 18 12 2 1 54 76 Mali 359 1,123 36 .. 62 .. .. .. 0 .. 2 .. Mauritania 469 410 .. .. .. .. .. .. .. .. .. .. Mauritius 1,194 2,004 32 27 1 0 1 0 0 0 66 71 Mexico 40,711 189,083 12 5 2 1 38 12 6 2 43 80 Moldova .. 986 .. 53 .. 7 .. 2 .. 2 .. 36 Mongolia 661 880 .. 3 .. 13 .. 3 .. 43 .. 38 Morocco 4,265 9,739 26 19 3 2 4 2 15 8 52 69 Mozambique 126 1,504 .. 19 .. 6 .. 16 .. 55 .. 3 Myanmar 325 2,850 51 .. 36 .. 0 .. 2 .. 11 .. Namibia 1,085 1,833 .. 48 .. 1 .. 1 .. 7 .. 41 Nepal 204 756 13 21 3 1 .. 0 0 4 83 74 Netherlands 131,775 358,187 20 15 4 3 10 9 3 3 59 70 New Zealand 9,394 20,373 45 49 18 11 4 1 5 4 26 31 Nicaragua 330 756 77 85 14 2 0 1 1 1 8 11 Niger 282 370 .. 30 .. 4 .. 2 .. 55 .. 8 Nigeria 13,596 23,657 1 0 1 0 97 98 0 0 1 2 Norway 34,047 81,752 7 6 2 1 48 64 10 7 32 19 Oman 5,508 13,342 1 4 0 0 92 83 1 1 5 12 Pakistan 5,615 13,379 9 10 10 2 1 3 0 0 79 85 Panama 340 944 75 84 1 1 0 1 1 4 21 10 Papua New Guinea 1,177 2,532 22 21 9 3 0 22 58 49 10 6 Paraguay 959 1,626 52 75 38 12 0 0 0 1 10 13 Peru 3,230 12,547 21 24 3 2 10 6 47 47 18 20 Philippines 8,117 39,689 19 6 2 1 2 1 8 2 38 55 Poland 14,320 74,854 11 8 2 1 10 5 8 4 54 81 Portugal 16,417 35,767 7 8 6 2 3 3 3 2 80 85 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 207 Structure of merchandise exports Merchandise Food Agricultural raw Fuels Ores and Manufactures exports materials metals $ millions % of total % of total % of total % of total % of total 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 4,960 23,485 1 3 3 3 18 7 4 5 73 82 Russian Federation .. 183,452 .. 1 .. 3 .. 50 .. 8 .. 21 Rwanda 110 99 .. 52 .. 7 .. 7 .. 23 .. 10 Saudi Arabia 44,417 126,230 1 1 0 0 90 86 1 0 8 12 Senegal 761 1,529 53 35 3 3 12 19 9 4 23 39 Serbia and Montenegro 2,929 3,979 19 23 3 4 6 3 10 12 62 57 Sierra Leone 138 139 .. 92 .. 1 .. .. .. 0 .. 7 Singaporeb 52,730 179,547 5 2 3 0 18 9 2 1 72 84 Slovak Republic 6,355 27,548 .. 4 .. 1 .. 7 .. 3 .. 86 Slovenia 6,681 15,831 7 3 2 1 3 2 3 4 86 90 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 23,549 46,029 8c 9 4c 2 7c 9 10 c 22 29 c 58 Spain 55,642 178,607 15 14 2 1 4 4 2 2 75 77 Sri Lanka 1,912 5,757 34 21 6 2 1 0 2 3 54 74 Sudan 374 3,778 60 10 38 6 .. 81 0 0 2 2 Swaziland 556 1,900 .. 15 .. 8 .. 1 .. 0 .. 76 Sweden 57,540 122,537 2 3 7 4 3 4 3 3 83 81 Switzerland 63,784 118,527 3 3 1 0 0 0 3 3 94 93 Syrian Arab Republic 4,212 4,930 14 15 4 4 45 68 1 1 36 11 Tajikistan .. 915 .. .. .. .. .. .. .. .. .. .. Tanzania 331 1,338 .. 53 .. 13 .. 2 .. 12 .. 20 Thailand 23,068 97,414 29 14 5 5 1 2 1 1 63 75 Togo 268 771 23 24 21 16 0 0 45 13 9 47 Trinidad and Tobago 1,960 6,349 5 4 0 0 67 60 1 0 27 35 Tunisia 3,526 9,685 11 11 1 1 17 10 2 1 69 78 Turkey 12,959 63,121 22 9 3 1 2 2 4 2 68 85 Turkmenistan .. 3,870 .. .. .. .. .. .. .. .. .. .. Uganda 152 635 .. 64 .. 15 .. 5 .. 0 .. 15 Ukraine .. 32,672 .. 13 .. 2 .. 9 .. 8 .. 67 United Arab Emirates 23,544 82,750 2 .. 0 .. 7 .. 78 .. 12 .. United Kingdom 185,172 346,863 7 6 1 1 8 9 3 3 79 76 United States 393,592 818,775 11 7 4 2 3 2 3 2 75 82 Uruguay 1,693 2,950 40 55 21 8 0 4 0 1 39 32 Uzbekistan .. 4,280 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17,497 34,210 2 1 0 0 80 85 7 3 10 12 Vietnam 2,404 25,625 .. 23 .. 2 .. 21 .. 1 .. 53 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 692 4,150 8 4 1 0 74 92 1 0 15 3 Zambia 1,309 1,576 .. 16 .. 10 .. 2 .. 62 .. 10 Zimbabwe 1,726 1,520 44 31 7 16 1 2 16 23 31 28 World 3,475,089 t 9,145,027 t 10 w 7w 3w 2w 9w 8w 3w 3w 73 w 77 w Low income 71,042 212,988 15 15 4 3 27 28 .. 3 49 50 Middle income 549,007 2,259,406 17 9 4 2 22 17 5 5 50 64 Lower middle income 274,053 1,229,532 17 10 4 2 16 13 5 4 55 68 Upper middle income 276,680 1,029,873 17 8 6 2 27 21 8 5 42 60 Low & middle income 621,233 2,472,407 17 9 4 2 20 17 6 5 51 64 East Asia & Pacific 155,928 966,841 15 6 6 2 13 6 3 2 60 80 Europe & Central Asia d .. 623,360 .. 5 .. 2 .. 24 .. 5 .. 57 Latin America & Carib. 143,296 463,326 21 16 3 2 30 19 10 7 36 56 Middle East & N. Africa 81,103 170,601 .. 6 .. 1 .. 70 .. 2 .. 20 South Asia 27,754 104,394 16 11 5 1 2 6 4 5 71 76 Sub-Saharan Africa 68,368 143,866 .. 16 .. 5 .. 38 .. 10 .. 31 High income 2,849,973 6,672,648 8 6 3 2 6 5 3 3 77 81 Europe EMU 1,234,747 2,903,656 11 8 2 1 3 4 3 2 80 82 Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Includes re-exports. c. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 208 2006 World Development Indicators Structure of merchandise exports About the data Definitions Data on merchandise trade are from customs reports in reporting practices, data on exports may not be · Merchandise exports are the f.o.b. value of goods of goods movement into or out of an economy or from fully comparable across economies. provided to the rest of the world, valued in U.S. dol- reports of the financial transactions related to merchan- The data on total exports of goods (merchandise) lars. · Food corresponds to the commodities in dise trade recorded in the balance of payments. Because in this table come from the World Trade Organization SITC sections 0 (food and live animals), 1 (bever- of differences in timing and definitions, estimates of (WTO). The WTO uses two main sources, national statis- ages and tobacco), and 4 (animal and vegetable oils trade flows from customs reports are likely to differ tical offices and the IMF's International Financial Statis- and fats) and SITC division 22 (oil seeds, oil nuts, from those based on the balance of payments. More- tics. It supplements these with the Comtrade database and oil kernels). · Agricultural raw materials cor- over, several international agencies process trade data, and publications or databases of regional organizations, respond to SITC section 2 (crude materials except each correcting unreported or misreported data, and this specialized agencies, economic groups, and private fuels) excluding divisions 22, 27 (crude fertilizers leads to other differences in the available data. sources (such as Eurostat, the Food and Agriculture and minerals excluding coal, petroleum, and pre- The most detailed source of data on international Organization, and country reports of the Economist Intel- cious stones), and 28 (metalliferous ores and scrap). trade in goods is the Commodity Trade (Comtrade) ligence Unit). In recent years country Web sites and · Fuels correspond to SITC section 3 (mineral fuels). database maintained by the United Nations Statistics direct contacts through email have helped to improve · Ores and metals correspond to the commodities Division. In addition, the International Monetary Fund the collection of up-to-date statistics for many coun- in SITC divisions 27, 28, and 68 (nonferrous met- (IMF) collects customs-based data on exports and tries, reducing the proportion of estimated figures. The als). · Manufactures correspond to the commodities imports of goods. The value of exports is recorded WTO database now covers most of the major traders in in SITC sections 5 (chemicals), 6 (basic manufac- as the cost of the goods delivered to the frontier of Africa, Asia, and Latin America, which together with the tures), 7 (machinery and transport equipment), and the exporting country for shipment--the free on board high-income countries account for nearly 95 percent 8 (miscellaneous manufactured goods), excluding (f.o.b.) value. Many countries report trade data in U.S. of total world trade. There has also been a remarkable division 68. dollars. When countries report in local currency, the improvement in the availability of reliable figures for United Nations Statistics Division applies the average countries in Europe and Central Asia. offcial exchange rate for the period shown. The shares of exports by major commodity group are Countries may report trade according to the gen- from Comtrade. The values of total exports reported eral or special system of trade (see Primary data here have not been fully reconciled with the estimates documentation). Under the general system exports of exports of goods and services from the national comprise outward-moving goods that are (a) goods accounts or those from the balance of payments. wholly or partly produced in the country; (b) foreign The classification of commodity groups is based goods, neither transformed nor declared for domes- on the Standard International Trade Classification tic consumption in the country, that move outward (SITC) revision 1. Most countries now report using from customs storage; and (c) goods previously later revisions of the SITC or the Harmonized Sys- included as imports for domestic consumption but tem. Concordance tables are used to convert data subsequently exported without transformation. reported in one system of nomenclature to another. Under the special system exports comprise catego- The conversion process may introduce some errors ries a and c. In some compilations categories b and c of classification, but conversions from later to earlier are classified as re-exports. Because of differences systems are generally reliable. Data sources Developing economies' share of world merchandise exports continues to increase Share of world merchandise exports The WTO publishes data on world trade in its Annual Report. The IMF publishes estimates of 1990 2004 East Asia & Pacific 11% total exports of goods in its International Finan- East Asia & Pacific 4% Europe & Central Asia 7% cial Statistics and Direction of Trade Statistics, Europe & Central Asia 4% Latin America & Caribbean 4% as does the United Nations Statistics Division in Middle East & North Africa 2% Latin America & Caribbean 5% Sub-Saharan Africa 2% its Monthly Bulletin of Statistics. And the United South Asia 1% Middle East & North Africa 2% Sub-Saharan Africa 2% Nations Conference on Trade and Development South Asia 1% High-income publishes data on the structure of exports and 83% High-income 72% imports in its Handbook of International Trade and Development Statistics. Tariff line records of exports and imports are compiled in the Developing economies' share of world merchandise exports increased by 11 percentage points from 1990 to 2004. United Nations Statistics Division's Comtrade East Asia and Pacific was the biggest gainer, capturing an additional 7 percentage points. database. Source: World Trade Organization data files. 2006 World Development Indicators 209 Structure of merchandise imports Merchandise Food Agricultural raw Fuels Ores and Manufactures imports materials metals $ millions % of total % of total % of total % of total % of total 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan 936 2,300 .. .. .. .. .. .. .. .. .. .. Albania 380 2,268 .. 19 .. 1 .. 8 .. 2 .. 70 Algeria 9,780 18,199 24 22 5 2 1 1 2 1 68 74 Angola 1,578 6,500 .. .. .. .. .. .. .. .. .. .. Argentina 4,076 22,320 4 2 4 1 8 3 6 2 78 91 Armenia .. 1,318 .. 22 .. 1 .. 17 .. 1 .. 59 Australia 41,985 109,376 5 5 2 1 6 9 1 1 84 82 Austria 49,146 117,765 5 6 3 2 6 10 4 4 81 78 Azerbaijan .. 3,516 .. 12 .. 1 .. 11 .. 2 .. 74 Bangladesh 3,618 12,026 19 19 5 9 16 8 3 2 56 62 Belarus .. 16,346 .. 10 .. 2 .. 28 .. 4 .. 55 Belgium 119,702a 285,450 10a 8 2a 1 8a 9 6a 4 68a 77 Benin 265 865 38 24 4 5 1 17 1 1 56 53 Bolivia 687 1,842 12 12 2 2 1 7 1 1 85 79 Bosnia and Herzegovina 360 5,907 .. .. .. .. .. .. .. .. .. .. Botswana 1,946 3,340 .. .. .. .. .. .. .. .. .. .. Brazil 22,524 65,921 9 5 3 2 27 19 5 4 56 70 Bulgaria 5,100 14,424 8 5 3 1 36 4 4 6 49 69 Burkina Faso 536 1,155 .. 12 .. 1 .. 24 .. 1 .. 62 Burundi 231 176 .. 9 .. 1 .. 16 .. 1 .. 72 Cambodia 164 3,170 .. 8 .. 2 .. 10 .. 0 .. 79 Cameroon 1,400 2,100 19 18 0 2 2 18 1 1 78 61 Canada 123,244 279,779 6 6 2 1 6 7 3 3 81 81 Central African Republic 154 150 19 23 1 5 7 11 2 4 71 56 Chad 285 770 .. .. .. .. .. .. .. .. .. .. Chile 7,742 24,871 4 7 2 1 16 21 1 2 75 68 China 53,345 561,230 9 4 6 4 2 8 3 7 80 77 Hong Kong, China 84,725 272,893 8 3 2 1 2 2 2 2 85 92 Colombia 5,590 16,746 7 11 4 2 6 2 3 2 77 82 Congo, Dem. Rep. 1,739 1,873 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 621 1,720 .. .. .. .. .. .. .. .. .. .. Costa Rica 1,990 8,268 8 9 2 1 10 9 2 2 66 79 Côte d'Ivoire 2,097 3,783 .. 22 .. 1 .. 17 .. 1 .. 48 Croatia 4,500 16,583 12 8 4 1 10 12 4 2 70 76 Cuba 4,600 5,286 12 .. 3 .. 32 .. 1 .. 46 .. Czech Republic 12,880 69,510 .. 5 .. 2 .. 7 .. 4 .. 83 Denmark 33,333 68,191 12 12 3 2 7 5 2 2 73 77 Dominican Republic 3,006 7,845 .. .. .. .. .. .. .. .. .. .. Ecuador 1,861 7,861 9 9 3 1 2 7 2 1 84 81 Egypt, Arab Rep. 12,412 12,831 32 22 7 5 3 8 2 4 56 50 El Salvador 1,263 6,269 14 18 3 2 15 14 4 1 63 65 Eritrea 351 650 .. .. .. .. .. .. .. .. .. .. Estonia .. 8,728 .. 9 .. 4 .. 7 .. 2 .. 79 Ethiopia 1,081 3,080 .. 21 .. 1 .. 12 .. 2 .. 64 Finland 27,001 50,824 5 6 2 3 12 12 4 7 76 70 France 234,436 465,454 10 8 3 2 10 11 4 3 74 77 Gabon 918 1,280 .. 24 .. 1 .. 3 .. 1 .. 70 Gambia, The 188 200 .. 38 .. 2 .. 11 .. 1 .. 49 Georgia .. 1,847 .. 21 .. 0 .. 17 .. 1 .. 59 Germany 355,686 716,926 10 7 3 1 8 9 4 3 72 69 Ghana 1,205 4,320 11 21 1 1 17 2 0 2 70 74 Greece 19,777 52,577 15 11 3 1 8 13 3 3 70 72 Guatemala 1,649 7,808 10 12 2 1 17 14 2 1 69 71 Guinea 723 690 .. 23 .. 1 .. 22 .. 1 .. 53 Guinea-Bissau 86 86 .. .. .. .. .. .. .. .. .. .. Haiti 332 1,306 .. .. .. .. .. .. .. .. .. .. Data for Taiwan, China 54,782 168,444 7 4 5 2 11 13 6 6 69 76 210 2006 World Development Indicators Structure of merchandise imports Merchandise Food Agricultural raw Fuels Ores and Manufactures imports materials metals $ millions % of total % of total % of total % of total % of total 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 935 3,916 10 16 1 1 16 16 1 1 71 66 Hungary 10,340 59,332 8 4 4 1 14 7 4 3 70 84 India 23,580 97,339 3 4 4 3 27 35 8 5 51 53 Indonesia 21,837 54,895 5 10 5 5 9 20 4 4 77 61 Iran, Islamic Rep. 20,322 34,705 .. 11 .. 2 .. 6 .. 2 .. 79 Iraq 7,660 21,302 .. .. .. .. .. .. .. .. .. .. Ireland 20,669 60,651 11 8 2 1 6 5 2 1 76 78 Israel 16,793 42,864 8 6 2 1 9 11 3 2 77 80 Italy 181,968 351,034 12 9 6 3 11 10 5 4 64 70 Jamaica 1,928 3,772 15 15 1 1 20 18 1 1 61 63 Japan 235,368 454,543 15 12 7 2 24 22 9 6 44 57 Jordan 2,600 8,189 26 17 1 1 18 19 1 2 52 58 Kazakhstan .. 12,781 .. 7 .. 1 .. 13 .. 2 .. 77 Kenya 2,223 4,553 9 10 3 2 20 24 2 2 66 61 Korea, Dem. Rep. 2,930 2,540 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 69,844 224,463 6 5 8 2 16 22 7 7 63 63 Kuwait 3,972 12,005 17 .. 1 .. 1 .. 2 .. 79 .. Kyrgyz Republic .. 941 .. 13 .. 2 .. 27 .. 3 .. 54 Lao PDR 185 506 .. .. .. .. .. .. .. .. .. .. Latvia .. 7,005 .. 11 .. 3 .. 12 .. 2 .. 70 Lebanon 2,529 9,397 .. 18 .. 2 .. 16 .. 2 .. 62 Lesotho 672 1,400 .. .. .. .. .. .. .. .. .. .. Liberia 570 900 .. .. .. .. .. .. .. .. .. .. Libya 5,336 5,650 24 17 2 1 0 1 1 1 73 81 Lithuania .. 12,283 12 8 5 2 44 19 2 2 35 68 Macedonia, FYR 1,206 2,875 .. 14 .. 1 .. 13 .. 2 .. 58 Madagascar 651 1,230 11 14 1 0 17 23 1 0 69 62 Malawi 575 792 9 13 1 1 11 3 1 1 78 82 Malaysia 29,258 105,287 7 6 1 1 5 6 4 4 82 81 Mali 602 1,320 26 .. 1 .. 19 .. 1 .. 53 .. Mauritania 388 400 .. .. .. .. .. .. .. .. .. .. Mauritius 1,618 2,778 12 18 3 2 8 13 1 1 76 66 Mexico 43,548 206,423 15 6 4 1 4 4 3 3 64 85 Moldova .. 1,774 .. 12 .. 6 .. 21 .. 1 .. 61 Mongolia 924 990 .. 14 .. 1 .. 20 .. 1 .. 65 Morocco 6,922 17,625 10 11 6 3 17 17 6 3 61 67 Mozambique 878 1,970 .. 11 .. 1 .. 12 .. 0 .. 43 Myanmar 270 2,220 13 .. 1 .. 5 .. 0 .. 81 .. Namibia 1,163 2,435 .. 15 .. 1 .. 10 .. 4 .. 69 Nepal 672 1,870 15 17 7 5 9 16 2 4 67 59 Netherlands 126,098 319,330 13 10 2 2 10 12 3 3 71 73 New Zealand 9,501 23,201 7 8 1 1 8 6 3 2 81 82 Nicaragua 638 2,212 19 17 1 0 19 19 1 0 59 64 Niger 388 560 .. 34 .. 4 .. 17 .. 1 .. 44 Nigeria 5,627 11,096 6 15 1 1 0 16 2 2 67 66 Norway 27,231 48,082 6 7 2 2 4 4 6 6 82 80 Oman 2,681 8,865 19 14 1 0 4 2 1 4 69 76 Pakistan 7,411 17,949 17 11 4 6 21 22 4 3 54 58 Panama 1,539 3,530 12 14 1 1 16 12 1 1 70 72 Papua New Guinea 1,193 1,680 18 16 0 1 7 13 1 0 73 69 Paraguay 1,352 2,652 8 9 0 1 14 16 1 1 77 74 Peru 2,634 10,101 24 13 2 2 12 19 1 1 61 66 Philippines 13,042 42,345 10 6 2 1 15 11 3 2 53 79 Poland 11,570 89,174 12 6 4 2 13 9 5 3 58 80 Portugal 25,263 54,914 12 12 4 2 11 11 2 2 71 72 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 211 Structure of merchandise imports Merchandise Food Agricultural raw Fuels Ores and Manufactures imports materials metals $ millions % of total % of total % of total % of total % of total 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 7,600 32,664 12 6 4 1 38 12 6 3 39 78 Russian Federation .. 96,307 .. 17 .. 1 .. 3 .. 3 .. 69 Rwanda 288 285 .. 12 .. 4 .. 16 .. 2 .. 67 Saudi Arabia 24,069 44,576 15 16 1 1 0 0 3 3 81 79 Senegal 1,219 2,710 29 28 2 2 16 18 2 2 51 49 Serbia and Montenegro 4,634 11,752 9 11 3 2 23 17 3 3 62 67 Sierra Leone 149 286 .. 23 .. 8 .. 40 .. 1 .. 29 Singapore 60,774 163,854 6 3 2 0 16 15 2 1 73 80 Slovak Republic 6,670 29,471 .. 5 .. 1 .. 13 .. 3 .. 78 Slovenia 6,142 17,197 9 6 4 3 11 8 4 5 67 78 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 18,399 57,100 8b 5 2b 1 1b 14 1b 2 75 b 69 Spain 87,715 249,308 11 10 3 1 12 11 4 3 71 74 Sri Lanka 2,688 7,973 19 12 2 1 13 15 1 3 65 69 Sudan 618 4,075 13 16 1 1 20 3 0 1 66 78 Swaziland 663 2,000 .. 18 .. 2 .. 13 .. 1 .. 64 Sweden 54,264 99,324 6 8 2 2 9 10 3 3 79 75 Switzerland 69,681 111,603 6 6 2 1 5 5 3 4 84 84 Syrian Arab Republic 2,400 6,287 31 17 2 4 3 7 1 3 62 64 Tajikistan .. 1,375 .. .. .. .. .. .. .. .. .. .. Tanzania 1,027 2,490 .. 15 .. 2 .. 16 .. 1 .. 66 Thailand 33,045 95,353 5 5 5 3 9 12 4 3 75 76 Togo 581 1,050 22 18 1 1 8 23 1 2 67 56 Trinidad and Tobago 1,109 4,894 19 9 1 1 11 27 6 3 62 59 Tunisia 5,513 12,738 11 9 4 3 9 10 4 3 72 76 Turkey 22,302 97,540 8 3 4 3 21 15 5 6 61 72 Turkmenistan .. 3,320 .. .. .. .. .. .. .. .. .. .. Uganda 288 1,491 .. 17 .. 2 .. 10 .. 2 .. 70 Ukraine .. 28,996 .. 6 .. 1 .. 39 .. 3 .. 48 United Arab Emirates 11,199 47,640 17 .. 0 .. 6 .. 4 .. 72 .. United Kingdom 222,977 463,467 10 9 3 1 6 6 4 2 75 77 United States 516,987 1,525,516 6 4 2 1 13 14 3 2 73 75 Uruguay 1,343 3,114 7 9 4 4 18 24 2 2 69 62 Uzbekistan .. 3,392 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7,335 14,995 11 15 4 2 3 1 4 2 77 80 Vietnam 2,752 31,091 .. 6 .. 3 .. 11 .. 3 .. 77 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,571 4,190 27 28 1 1 40 13 1 1 31 56 Zambia 1,220 2,143 .. 7 .. 1 .. 11 .. 3 .. 79 Zimbabwe 1,847 2,550 4 19 3 2 16 14 2 10 73 54 World 3,550,315 t 9,376,651 t 9w 7w 3w 2w 11 w 11 w 4w 3w 71 w 74 w Low income 81,953 252,827 .. 11 .. 3 .. 22 .. 3 .. 61 Middle income 509,902 2,161,147 10 7 4 2 9 9 3 4 70 77 Lower middle income 277,830 1,187,304 10 7 5 3 9 12 3 5 71 73 Upper middle income 228,653 973,843 11 7 3 2 8 7 4 3 70 79 Low & middle income 593,527 2,413,971 10 7 4 2 10 11 4 4 69 75 East Asia & Pacific 160,502 903,670 8 5 5 3 5 9 3 6 77 77 Europe & Central Asia c 164,871 631,428 .. 7 .. 2 .. 10 .. 4 .. 75 Latin America & Carib. 120,374 436,972 12 7 3 2 10 8 3 3 66 80 Middle East & N. Africa 79,941 160,252 .. 17 .. 3 .. 8 .. 2 .. 68 South Asia 39,124 140,502 8 8 4 4 24 28 6 4 54 56 Sub-Saharan Africa 57,641 141,150 .. 12 .. 1 .. 14 .. 2 .. 67 High income 2,943,378 6,962,657 9 7 3 2 11 12 4 3 71 74 Europe EMU 1,255,515 2,744,049 11 8 3 2 9 10 4 3 71 73 Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 212 2006 World Development Indicators Structure of merchandise imports About the data Definitions Data on imports of goods are derived from the same The data on total imports of goods (merchandise) · Merchandise imports are the c.i.f. value of goods sources as data on exports. In principle, world in this table come from the World Trade Organization purchased from the rest of the world valued in U.S. exports and imports should be identical. Similarly, (WTO). For further discussion of the WTO's sources dollars. · Food corresponds to the commodities in exports from an economy should equal the sum of and methodology, see About the data for table 4.4. SITC sections 0 (food and live animals), 1 (bever- imports by the rest of the world from that economy. The shares of imports by major commodity group are ages and tobacco), and 4 (animal and vegetable oils But differences in timing and definitions result in dis- from the United Nations Statistics Division's Commod- and fats) and SITC division 22 (oil seeds, oil nuts, crepancies in reported values at all levels. For further ity Trade (Comtrade) database. The values of total and oil kernels). · Agricultural raw materials cor- discussion of indicators of merchandise trade, see imports reported here have not been fully reconciled respond to SITC section 2 (crude materials except About the data for tables 4.4 and 6.2. with the estimates of imports of goods and services fuels) excluding divisions 22, 27 (crude fertilizers The value of imports is generally recorded as the from the national accounts (shown in table 4.8) or and minerals excluding coal, petroleum, and pre- cost of the goods when purchased by the importer those from the balance of payments (table 4.15). cious stones), and 28 (metalliferous ores and scrap). plus the cost of transport and insurance to the fron- The classification of commodity groups is based · Fuels correspond to SITC section 3 (mineral fuels). tier of the importing country--the cost, insurance, on the Standard International Trade Classification · Ores and metals correspond to the commodities and freight (c.i.f.) value, corresponding to the landed (SITC) revision 1. Most countries now report using in SITC divisions 27, 28, and 68 (nonferrous met- cost at the point of entry of foreign goods into the later revisions of the SITC or the Harmonized Sys- als). · Manufactures correspond to the commodities country. A few countries, including Australia, Canada, tem. Concordance tables are used to convert data in SITC sections 5 (chemicals), 6 (basic manufac- and the United States, collect import data on a free reported in one system of nomenclature to another. tures), 7 (machinery and transport equipment), and on board (f.o.b.) basis and adjust them for freight and The conversion process may introduce some errors 8 (miscellaneous manufactured goods), excluding insurance costs. Many countries collect and report of classification, but conversions from later to earlier division 68. trade data in U.S. dollars. When countries report in systems are generally reliable. Shares may not sum local currency, the United Nations Statistics Division to 100 percent because of unclassified trade. applies the average official exchange rate for the period shown. Countries may report trade according to the gen- eral or special system of trade (see Primary data documentation). 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 (includ- ing transformation and repair) and withdrawals for domestic consumption from bonded warehouses and free trade zones. Goods transported through a country en route to another are excluded. Top 10 exporters in Sub-Saharan Africa in 2004 Data sources Merchandise exports ($ billions) 50 The WTO publishes data on world trade in its 1990 Annual Report. The International Monetary Fund 40 2004 publishes estimates of total imports of goods in its International Financial Statistics and Direction 30 of Trade Statistics, as does the United Nations 20 Statistics Division in its Monthly Bulletin of Statis- tics. And the United Nations Conference on Trade 10 and Development publishes data on the structure of exports and imports in its Handbook of Inter- 0 South Nigeria Angola Côte Equatorial Congo, Sudan Gabon Botswana Cameroon national Trade and Development Statistics. Tariff Africa d'Ivoire Guinea Rep. line records of exports and imports are compiled Sub-Saharan economies accounted for about 6 percent of developing economy exports and 1.6 percent of world exports. in the United Nations Statistics Division's Com- Note: No data are available for Equatorial Guinea for 1990. trade database. Source: World Trade Organization data files. 2006 World Development Indicators 213 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan 1 .. .. .. .. .. .. .. .. .. Albania 32 695 20.0 9.9 11.1 75.2 2.2 3.4 66.7 11.5 Algeria 479 .. 41.7 .. 13.4 .. 5.9 .. 39.0 .. Angola 65 323 48.8 5.5 20.6 20.4 4.6 .. 26.1 74.1 Argentina 2,264 5,065 51.1 22.2 39.9 50.6 0.0 0.0 9.0 27.1 Armenia 17 238 .. 30.9 .. 35.9 .. 5.0 .. 28.3 Australia 9,833 24,774 35.5 23.6 43.2 51.3 4.2 5.0 17.2 20.1 Austria 22,755 48,297 6.4 19.4 59.0 31.7 2.9 6.1 31.7 42.9 Azerbaijan .. 453 .. 45.4 .. 14.4 .. 1.7 .. 38.6 Bangladesh 296 420 13.0 18.4 6.4 15.9 0.1 4.5 80.6 61.1 Belarus 185 1,729 54.1 58.9 13.3 16.6 1.0 0.2 31.6 24.3 Belgium 26,646a 50,459 27.5a 25.5 14.0a 18.2 18.2a 7.6 40.3a 48.7 Benin 109 163 33.4 8.8 50.2 65.4 6.9 0.5 9.5 25.3 Bolivia 133 384 35.8 30.0 43.6 46.0 10.0 12.4 10.6 11.6 Bosnia and Herzegovina .. 825 .. 3.7 .. 59.2 .. 1.7 .. 35.4 Botswana 183 647 20.4 10.7 64.1 70.6 8.2 5.5 7.3 13.2 Brazil 3,706 11,615 36.4 21.2 37.3 27.7 3.1 4.5 23.2 46.5 Bulgaria 837 4,083 27.5 29.1 38.2 52.4 3.1 1.6 31.2 16.9 Burkina Faso 34 32 37.1 14.6 34.1 61.6 .. 0.4 28.9 23.4 Burundi 7 2 38.7 31.6 51.4 32.2 1.6 0.6 8.3 35.6 Cambodia 50 759 .. 13.7 .. 79.5 .. .. .. 6.9 Cameroon 369 .. 42.6 .. 14.4 .. 9.4 .. 33.6 .. Canada 18,350 46,370 23.0 18.3 34.7 27.6 .. 9.7 42.3 44.4 Central African Republic 17 .. 50.9 .. 16.0 .. 18.8 .. 14.3 .. Chad 23 .. 18.4 .. 34.1 .. 0.2 .. 47.3 .. Chile 1,786 5,872 40.0 56.2 29.8 18.6 4.9 3.1 25.3 22.1 China 5,748 62,056 47.1 19.5 30.2 41.5 4.0 0.8 18.7 38.3 Hong Kong, China .. 54,175 .. 29.8 .. 15.4 .. 8.9 .. 46.0 Colombia 1,548 2,179 31.3 31.2 26.2 47.3 17.1 1.4 25.5 20.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 65 79 53.9 3.6 12.9 25.1 .. 0.7 33.2 70.6 Costa Rica 583 2,206 16.3 11.1 48.9 66.1 .. 0.4 34.8 22.3 Côte d'Ivoire 425 631 62.4 22.2 12.1 12.0 8.3 .. 17.2 65.8 Croatia 2,216 9,619 29.2 10.2 59.1 72.5 1.4 0.6 10.3 16.7 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 4,679 9,656 26.5 29.2 33.3 43.3 9.6 4.4 30.6 23.1 Denmark 12,731 36,304 32.5 47.1 26.2 15.6 2.3 .. 39.0 37.4 Dominican Republic 1,086 3,463 5.6 2.9 66.9 91.8 0.2 0.1 27.3 5.2 Ecuador 508 847 47.6 40.2 37.0 43.4 9.3 0.3 6.1 16.1 Egypt, Arab Rep. 4,812 14,046 50.1 28.6 22.9 43.6 1.0 0.8 26.1 27.0 El Salvador 301 921 26.2 37.2 25.3 36.6 7.5 4.6 41.1 21.6 Eritrea 73 .. 85.7 .. 1.0 .. .. .. 13.3 .. Estonia 200 2,786 74.7 43.2 13.7 31.5 0.1 1.4 11.5 24.0 Ethiopia 261 799 80.7 46.3 2.1 21.7 0.7 0.5 16.6 31.6 Finland 4,562 9,792 38.4 24.5 25.8 21.1 0.1 1.8 35.6 52.6 France 74,948 109,518 21.7 23.4 27.1 37.2 14.8 2.7 36.4 36.8 Gabon 214 167 33.4 50.7 1.4 8.7 5.8 30.8 59.4 9.8 Gambia, The 53 .. 8.8 .. 87.9 .. 0.1 .. 3.3 .. Georgia .. 492 .. 50.8 .. 35.9 .. 5.6 .. 7.8 Germany 50,561 133,856 29.2 24.8 28.3 20.6 1.0 6.8 41.5 47.8 Ghana 79 684 49.2 20.0 5.6 68.2 2.7 1.1 42.6 10.7 Greece 6,514 32,986 4.9 50.1 39.7 38.6 0.1 1.1 55.2 10.3 Guatemala 313 1,063 7.4 8.2 37.6 73.0 2.0 6.4 53.1 12.4 Guinea 91 31 14.2 21.8 32.6 0.1 0.1 0.4 53.2 77.8 Guinea-Bissau 4 5 5.4 15.5 .. 37.0 .. 15.0 94.6 32.5 Haiti 43 116 19.8 .. 78.9 80.2 1.3 .. .. 19.8 214 2006 World Development Indicators Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 121 593 35.1 12.1 24.0 66.8 12.9 3.1 28.0 18.0 Hungary 2,677 10,255 1.6 13.0 36.8 39.3 0.2 2.8 61.4 44.9 India 4,610 39,638 b 20.8 13.3 33.8 16.8 2.7 3.5 42.7 66.4 Indonesia 2,488 17,331 2.8 13.2 86.5 27.7 0.0 1.8 10.7 57.4 Iran, Islamic Rep. 343 .. 10.5 .. 8.2 .. 6.4 .. 74.9 .. Iraq .. .. .. .. .. .. .. .. .. .. Ireland 3,286 52,158 31.1 4.5 44.4 8.2 0.0 29.1 24.5 58.2 Israel 4,546 14,830 30.8 21.5 30.7 16.1 -0.3 0.1 38.8 62.3 Italy 48,579 82,484 21.0 16.9 33.9 42.9 5.5 3.2 39.6 37.0 Jamaica 975 2,262 18.0 22.0 77.0 63.6 1.4 1.6 3.6 12.8 Japan 41,384 94,933 40.4 33.9 7.9 11.9 -0.4 5.8 52.1 48.5 Jordan 1,430 2,036 26.0 20.9 35.8 65.3 .. .. 38.3 13.8 Kazakhstan .. 1,807 .. 46.3 .. 39.1 .. 1.2 .. 13.4 Kenya 774 1,150 32.1 48.9 60.2 43.0 0.7 1.2 7.1 6.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 9,155 40,047 34.7 56.0 34.5 14.3 0.1 2.9 30.7 26.8 Kuwait 1,054 2,067 87.5 84.7 12.5 8.7 .. 3.8 .. 2.8 Kyrgyz Republic 9 193 25.1 26.4 3.8 39.3 .. 1.1 71.1 33.3 Lao PDR 11 127 74.8 18.0 24.3 82.0 0.9 .. .. .. Latvia 290 1,752 94.9 56.6 2.5 15.2 .. 7.2 2.6 21.0 Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho 34 56 14.1 1.1 51.2 60.4 .. 4.7 34.7 33.8 Liberia 32 .. 84.6 .. 15.4 .. .. .. .. .. Libya 83 351 83.8 18.0 7.7 62.1 .. 17.1 8.5 2.9 Lithuania 198 2,431 83.6 55.7 10.9 31.9 .. 0.5 5.5 11.9 Macedonia, FYR .. 384 .. 29.3 .. 18.7 .. 2.0 .. 50.0 Madagascar 129 202 32.1 28.5 31.3 37.5 0.3 1.7 36.4 32.3 Malawi 37 49 46.1 32.7 42.6 67.3 0.1 .. 11.2 0.0 Malaysia 3,769 13,459 31.8 20.6 44.7 43.8 0.1 2.5 23.5 33.1 Mali 71 208 31.0 20.5 54.3 61.6 4.9 1.5 9.8 16.4 Mauritania 14 .. 35.3 .. 64.7 .. .. .. .. .. Mauritius 478 1,449 33.0 25.7 51.1 59.1 0.1 1.7 15.8 13.5 Mexico 7,222 13,931 12.4 9.8 76.5 77.2 4.6 6.2 6.5 6.8 Moldova .. 325 .. 43.9 .. 29.2 .. 1.2 .. 25.7 Mongolia 48 329 41.8 32.7 10.4 56.2 4.6 1.2 43.2 9.9 Morocco 1,871 6,304 9.6 16.3 68.4 62.2 0.8 1.5 21.2 20.0 Mozambique 103 246 61.3 32.5 .. 38.7 .. 0.8 38.7 28.1 Myanmar 94 232 10.3 36.8 20.9 36.2 0.5 .. 68.3 27.0 Namibia 106 463 .. 7.1 81.0 87.5 5.9 .. 13.1 5.5 Nepal 166 356 3.6 9.1 65.6 64.7 .. 0.1 30.8 26.1 Netherlands 28,478 71,784 45.4 27.0 14.6 14.4 0.8 1.7 39.2 56.9 New Zealand 2,415 7,753 43.4 19.1 42.7 65.4 -0.3 0.7 14.2 14.8 Nicaragua 34 254 19.2 14.4 35.5 73.9 0.0 1.0 45.4 10.7 Niger 22 57 5.2 8.9 59.5 48.5 13.5 1.3 21.8 41.3 Nigeria 965 3,336 3.9 20.2 2.5 0.6 0.3 0.2 93.3 79.0 Norway 12,452 25,893 68.7 57.5 12.6 11.3 0.4 4.0 18.3 27.2 Oman 68 830 15.3 34.7 84.7 62.4 .. 0.4 .. 2.5 Pakistan 1,218 1,697 59.3 54.2 12.0 10.5 1.4 3.5 27.3 31.9 Panama 907 2,690 64.9 57.0 18.9 24.2 3.8 9.7 12.4 9.1 Papua New Guinea 198 285 11.2 7.5 12.0 1.8 0.5 1.8 76.3 88.9 Paraguay 404 556 18.3 15.6 21.1 12.1 .. 5.9 60.5 66.4 Peru 714 1,795 43.4 21.2 30.4 60.1 11.2 4.6 15.0 14.2 Philippines 2,897 4,101 8.5 27.3 16.1 49.1 0.5 1.4 74.9 22.2 Poland 3,200 13,437 57.3 31.3 11.2 43.4 4.0 1.7 27.6 23.6 Portugal 5,054 14,596 15.6 19.6 70.4 53.5 0.7 2.4 13.3 24.5 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 215 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 610 3,590 50.5 43.4 17.4 14.0 5.6 2.8 26.6 39.8 Russian Federation .. 20,164 .. 38.6 .. 25.9 .. 2.5 .. 32.9 Rwanda 31 72 56.1 29.8 32.8 60.4 1.0 .. 10.0 9.9 Saudi Arabia 3,027 5,852 .. .. .. .. .. .. .. .. Senegal 356 488 19.2 15.7 42.8 42.7 0.5 2.0 37.6 39.7 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 45 61 9.7 2.3 76.2 94.8 .. 0.8 14.1 2.1 Singapore 12,719 41,077 17.5 35.6 36.6 12.4 0.7 9.5 45.3 42.5 Slovak Republic 1,939 3,270 23.7 43.2 19.8 26.4 .. 2.3 56.5 28.1 Slovenia 1,219 3,449 22.6 29.1 55.0 47.1 1.2 0.8 21.2 22.9 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 3,291 8,066 21.6 17.1 55.8 70.3 10.8 4.5 11.9 8.1 Spain 27,649 84,105 17.2 16.6 67.2 53.6 4.3 3.1 11.3 26.7 Sri Lanka 425 1,506 39.7 41.4 30.2 34.1 4.2 3.3 25.9 21.2 Sudan 134 35 14.1 27.4 15.7 60.7 0.5 2.5 69.7 9.5 Swaziland 102 485 24.5 5.5 29.2 19.6 .. 60.3 46.3 14.7 Sweden 13,453 38,320 35.8 20.8 21.7 16.1 9.1 5.2 33.5 57.9 Switzerland 18,325 41,544 16.3 10.1 40.4 25.0 23.7 33.1 19.6 31.8 Syrian Arab Republic 740 2,222 29.8 9.5 43.3 81.0 .. 1.2 27.0 8.3 Tajikistan .. 81 .. 64.8 .. 1.5 .. 5.4 .. 28.4 Tanzania 131 845 19.9 9.1 36.4 70.4 0.5 6.9 43.1 13.7 Thailand 6,292 18,932 21.1 23.0 68.7 53.1 0.2 0.7 10.0 23.3 Togo 114 72 26.9 29.8 50.8 20.5 13.7 1.2 8.6 48.6 Trinidad and Tobago 322 672 50.7 36.7 29.4 37.0 .. 16.1 19.9 10.2 Tunisia 1,575 3,520 23.0 26.0 64.8 56.0 1.5 2.4 10.7 15.6 Turkey 7,882 23,806 11.7 13.7 40.9 66.7 .. 2.4 47.4 17.2 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 436 .. 11.1 .. 61.1 .. 10.8 .. 17.0 Ukraine .. 6,041 .. 66.9 .. 18.9 .. 0.7 .. 13.5 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 53,830 179,649 25.2 16.7 29.0 15.7 16.4 22.5 29.4 45.1 United States 132,880 321,837 28.1 17.3 37.9 29.2 3.5 8.7 30.5 44.8 Uruguay 460 959 36.9 34.9 51.8 51.5 1.0 6.8 10.3 6.9 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 1,121 1,008 40.9 35.1 44.3 47.3 0.2 0.2 14.7 17.4 Vietnam .. 2,948 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 82 292 27.2 15.8 48.8 47.6 .. .. 24.0 36.7 Zambia 94 .. 68.9 .. 13.6 .. 4.1 .. 13.4 .. Zimbabwe 253 .. 44.3 .. 25.3 .. 1.2 .. 29.2 .. World 815,710 t 2,190,577 t 28.5 w 24.3 w 34.6 w 28.5 w 4.7 w 6.7 w 38.5 w 41.7 w Low income 13,854 67,030 24.7 19.5 23.1 19.7 2.0 3.0 50.4 58.1 Middle income 96,848 358,642 29.6 23.3 45.0 46.8 3.1 2.7 22.3 27.3 Lower middle income 46,348 197,288 32.3 22.8 39.3 40.8 3.3 1.6 25.2 34.9 Upper middle income 51,220 161,008 26.4 23.8 52.1 53.6 2.7 3.9 18.8 18.7 Low & middle income 110,619 423,636 29.2 23.5 43.0 45.6 3.0 2.6 24.9 28.4 East Asia & Pacific 22,615 129,117 32.3 19.9 43.5 42.2 2.0 1.0 22.2 37.0 Europe & Central Asia .. 121,538 .. 34.1 .. 35.5 .. 2.4 .. 28.1 Latin America & Carib. 26,062 61,844 25.8 19.4 56.2 58.4 4.1 4.3 13.9 17.9 Middle East & N. Africa .. .. 31.1 .. 29.3 .. 3.4 .. 36.4 .. South Asia 6,847 44,325 26.8 22.1 28.2 17.4 2.3 3.7 42.7 56.8 Sub-Saharan Africa 9,561 24,238 25.7 18.1 31.6 40.2 5.4 2.9 37.9 39.2 High income 701,271 1,767,896 28.3 24.5 32.1 24.0 5.2 7.8 42.2 45.2 Europe EMU 311,131 723,009 27.1 22.5 30.3 27.7 5.9 5.6 36.7 45.1 a. Includes Luxembourg. b. World Trade Organization estimate. 216 2006 World Development Indicators Structure of service exports About the data Definitions Balance of payments statistics, the main source of by conventional balance of payments statistics is · Commercial service exports are total service information on international trade in services, have establishment trade--sales in the host country by exports minus exports of government services not many weaknesses. Some large economies--such foreign affiliates. By contrast, cross-border intrafirm included elsewhere. International transactions in ser- as the former Soviet Union--did not report data on transactions in merchandise may be reported as vices are defined by the IMF's Balance of Payments trade in services until recently. Disaggregation of exports or imports in the balance of payments. Manual (1993) as the economic output of intangible important components may be limited, and it varies The data on exports of services in this table and on commodities that may be produced, transferred, and significantly across countries. There are inconsisten- imports of services in table 4.7, unlike those in edi- consumed at the same time. Definitions may vary cies in the methods used to report items. And the tions before 2000, include only commercial services among reporting economies. · Transport covers all recording of major flows as net items is common (for and exclude the category "government services not transport services (sea, air, land, internal waterway, example, insurance transactions are often recorded included elsewhere." The data are compiled by the IMF space, and pipeline) performed by residents of one as premiums less claims). These factors contribute based on returns from national sources. Data on total economy for those of another and involving the car- to a downward bias in the value of the service trade trade in goods and services from the IMF's Balance of riage of passengers, movement of goods (freight), reported in the balance of payments. Payments database are shown in table 4.15. rental of carriers with crew, and related support and Efforts are being made to improve the coverage, auxiliary services. Excluded are freight insurance, quality, and consistency of these data. Eurostat and which is included in insurance services; goods pro- the Organisation for Economic Co-operation and Devel- cured in ports by nonresident carriers and repairs of opment, for example, are working together to improve transport equipment, which are included in goods; the collection of statistics on trade in services in mem- repairs of harbors, railway facilities, and airfield facil- ber countries. In addition, the International Monetary ities, which are included in construction services; Fund (IMF) has implemented the new classification of and rental of carriers without crew, which is included trade in services introduced in the fifth edition of its in other services. · Travel covers goods and ser- Balance of Payments Manual (1993). vices acquired from an economy by travelers in that Still, difficulties in capturing all the dimensions of economy for their own use during visits of less than international trade in services mean that the record one year for business or personal purposes. Travel is likely to remain incomplete. Cross-border intrafirm services include the goods and services consumed service transactions, which are usually not captured by travelers, such as meals, lodging, and transport in the balance of payments, have increased in recent (within the economy visited), including car rental. years. One example of such transactions is trans- · Insurance and financial services cover freight national corporations' use of mainframe computers insurance on goods exported and other direct insur- around the clock for data processing, exploiting ance such as life insurance, financial intermediation time zone differences between their home country services such as commissions, foreign exchange and the host countries of their affiliates. Another transactions, and brokerage services; and auxil- important dimension of service trade not captured iary services such as financial market operational and regulatory services. · Computer, information, communications, and other commercial services Top 10 developing country exporters of commercial services in 2004 include such activities as international telecommu- Commercial services exports ($ billions) nications and postal and courier services; computer 80 data; news-related service transactions between 1990 residents and nonresidents; construction services; 2004 60 royalties and license fees; miscellaneous business, professional, and technical services; and personal, 40 cultural, and recreational services. 20 0 China India Turkey Russian Thailand Egypt Mexico Malaysiab Poland Brazil Data sources Federationa Data on exports of commercial services are from The top 10 developing country exporters accounted for about 55 percent of developing country commercial service exports and 11 percent of world commercial service exports. the IMF. The IMF publishes balance of payments a. Data are for 1994 and 2004. data in its International Financial Statistics and b. Data are for 1990 and 2003. Balance of Payments Statistics Yearbook. Source: International Monetary Fund data files and staff estimates. 2006 World Development Indicators 217 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan 97 .. 85.9 .. .. .. 9.5 .. 4.6 .. Albania 29 734 26.3 21.3 .. 66.6 2.9 3.6 70.8 8.5 Algeria 1,155 .. 58.1 .. 12.9 .. 9.8 .. 19.2 .. Angola 1,288 4,803 38.3 18.3 3.0 0.8 2.6 4.9 56.1 76.1 Argentina 2,876 6,596 32.6 24.8 40.7 44.9 .. 3.8 26.7 26.5 Armenia 40 305 89.2 58.7 0.9 21.2 9.9 6.7 0.0 13.4 Australia 13,388 25,613 33.9 36.3 31.5 36.7 4.8 4.2 29.8 22.8 Austria 14,104 46,195 8.4 13.7 54.9 24.4 4.6 6.1 32.1 55.8 Azerbaijan .. 2,702 .. 10.7 .. 4.7 .. 1.4 .. 83.3 Bangladesh 554 1,835 71.1 77.3 14.1 8.8 6.6 7.8 8.3 6.1 Belarus 125 1,009 34.0 24.8 44.6 51.9 12.3 1.7 9.2 21.5 Belgium 25,924a 48,234 23.3a 22.9 21.1a 28.9 14.8a 7.9 40.8a 40.3 Benin 113 244 46.9 70.0 12.8 8.7 5.7 13.4 34.6 7.9 Bolivia 291 578 61.7 34.8 20.6 25.6 10.0 20.5 7.6 19.2 Bosnia and Herzegovina .. 438 .. 45.2 .. 28.4 .. 14.0 .. 12.4 Botswana 371 652 57.5 38.1 15.0 35.2 5.5 4.2 22.0 22.5 Brazil 6,733 16,111 44.4 27.6 22.4 17.8 2.7 7.1 30.5 47.4 Bulgaria 600 3,225 40.5 47.1 31.5 29.6 4.5 3.7 23.5 19.7 Burkina Faso 196 135 64.7 65.1 16.6 16.1 5.1 14.7 13.6 4.2 Burundi 59 38 62.6 52.6 29.0 38.3 6.3 4.1 2.2 5.0 Cambodia 64 462 24.5 62.8 .. 10.3 .. 5.0 75.5 22.0 Cameroon 1,018 .. 45.3 .. 27.5 .. 7.2 .. 20.1 .. Canada 27,479 56,571 21.1 21.6 39.8 28.3 .. 12.2 39.2 38.0 Central African Republic 166 .. 49.7 .. 30.6 .. 8.9 .. 10.7 .. Chad 223 .. 45.1 .. 31.2 .. 4.4 .. 19.2 .. Chile 1,982 6,401 47.4 50.3 21.5 13.9 3.3 10.9 27.9 24.8 China 4,113 71,602 78.9 34.3 11.4 26.7 2.3 8.8 7.4 30.2 Hong Kong, China .. 30,016 .. 25.9 .. 44.0 .. 5.8 .. 24.3 Colombia 1,683 3,987 34.9 40.5 27.0 32.4 13.7 8.6 24.4 18.6 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 748 537 18.4 12.6 15.2 9.8 1.6 8.1 64.9 69.6 Costa Rica 540 1,293 41.2 39.8 28.8 31.4 6.0 6.3 24.0 22.6 Côte d'Ivoire 1,518 1,880 32.1 48.5 11.1 19.1 4.7 .. 52.0 32.4 Croatia 1,088 3,583 30.5 19.0 34.4 23.4 3.7 3.6 31.4 54.1 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 3,701 9,130 19.8 16.5 14.2 25.0 14.0 12.5 52.0 46.0 Denmark 10,106 33,401 38.3 43.4 36.5 21.8 1.6 .. 23.6 34.8 Dominican Republic 435 1,159 40.0 55.9 33.1 26.7 4.1 9.6 22.9 7.8 Ecuador 755 1,735 41.6 47.9 23.2 22.5 8.1 3.8 27.2 25.7 Egypt, Arab Rep. 3,327 7,470 44.0 40.0 3.9 16.8 4.6 8.2 47.5 35.0 El Salvador 296 1,056 45.9 45.8 20.6 22.7 12.1 12.9 21.5 18.6 Eritrea 1 .. .. .. .. .. .. .. .. .. Estonia 123 1,707 76.3 42.4 15.4 23.4 0.3 1.4 8.0 32.7 Ethiopia 348 938 76.5 62.4 3.3 6.4 3.4 5.1 16.9 26.1 Finland 7,432 12,129 26.1 27.9 37.2 23.3 1.8 1.6 34.8 47.3 France 59,560 96,452 29.4 27.2 20.7 29.6 19.2 4.9 30.7 38.3 Gabon 984 821 23.2 32.3 13.9 23.6 5.3 5.7 57.6 38.4 Gambia, The 35 .. 65.1 .. 23.1 .. 9.0 .. 2.8 .. Georgia .. 437 .. 46.8 .. 33.7 .. 9.9 .. 9.6 Germany 83,338 191,706 20.6 21.1 46.9 36.8 1.0 4.7 31.6 37.4 Ghana 226 881 55.1 45.6 5.9 21.2 11.2 5.3 27.8 27.9 Greece 2,756 13,560 34.0 52.5 39.5 21.2 5.4 5.0 21.0 21.3 Guatemala 363 1,247 41.0 50.7 27.4 31.3 3.4 12.2 28.2 5.8 Guinea 243 195 57.5 47.3 12.2 12.8 5.5 12.7 24.9 27.2 Guinea-Bissau 17 36 54.5 59.4 19.8 37.1 5.6 1.0 20.0 2.6 Haiti 71 244 47.9 97.5 52.1 .. .. .. .. 2.5 218 2006 World Development Indicators Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 213 736 45.4 49.0 17.6 28.5 15.0 .. 22.0 22.5 Hungary 2,264 10,239 8.8 15.3 25.9 27.8 1.0 5.8 64.4 51.1 India 5,943 40,950 b 57.5 36.7 6.6 13.8 5.8 6.5 30.1 43.1 Indonesia 5,898 28,265 47.4 19.5 14.2 12.4 4.0 3.4 34.5 64.7 Iran, Islamic Rep. 3,703 .. 47.3 .. 9.2 .. 10.8 .. 32.8 .. Iraq .. .. .. .. .. .. .. .. .. .. Ireland 5,145 64,461 24.3 3.5 22.6 8.0 1.9 16.1 51.2 72.4 Israel 4,825 12,342 39.6 35.2 29.7 22.7 4.4 3.5 26.3 38.7 Italy 46,602 80,412 23.7 24.4 22.1 25.4 10.4 4.7 43.9 45.5 Jamaica 667 1,677 47.9 38.6 17.0 17.1 6.7 9.3 28.4 35.0 Japan 84,281 134,013 30.8 31.9 27.9 28.5 2.1 4.6 39.3 35.0 Jordan 1,118 1,972 52.0 56.1 30.1 26.6 5.2 8.3 12.7 9.1 Kazakhstan .. 4,933 .. 17.7 .. 15.4 .. 2.6 .. 64.4 Kenya 598 675 66.2 49.1 6.4 15.9 8.9 10.5 18.5 24.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 10,050 49,641 39.8 36.1 27.5 24.2 0.3 1.2 32.4 38.6 Kuwait 2,805 6,135 31.9 36.1 65.5 60.2 1.2 1.6 1.4 2.2 Kyrgyz Republic 51 226 74.1 39.2 0.8 22.2 7.6 14.3 17.6 24.3 Lao PDR 25 5 73.0 99.0 .. 1.0 6.4 .. 20.6 0.0 Latvia 120 1,164 82.3 35.5 10.9 32.4 4.8 5.8 2.1 26.3 Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho 48 86 67.9 64.6 24.7 35.3 5.6 .. 1.7 0.1 Liberia 74 .. 60.8 .. 33.7 .. 5.6 .. .. .. Libya 926 1,603 41.9 40.2 45.8 37.6 4.1 5.5 8.3 16.7 Lithuania 177 1,578 90.7 42.1 6.9 40.3 .. 1.7 2.4 16.0 Macedonia, FYR .. 440 .. 42.5 .. 12.4 .. 3.9 .. 41.3 Madagascar 172 405 43.5 58.4 23.4 15.8 3.5 3.6 29.5 22.2 Malawi 268 222 81.8 50.1 5.9 35.2 8.7 0.0 3.7 14.7 Malaysia 5,394 17,323 46.9 36.1 26.9 16.4 .. 3.5 26.2 44.0 Mali 352 478 57.4 65.3 15.8 10.0 1.9 5.7 24.9 19.0 Mauritania 126 .. 76.9 .. 18.3 .. 3.1 .. 1.7 .. Mauritius 407 1,005 51.6 46.7 23.0 25.4 5.5 6.1 19.9 21.9 Mexico 10,063 19,250 25.0 11.1 54.9 36.2 6.2 42.0 14.0 10.8 Moldova .. 352 .. 33.0 .. 38.2 .. 1.3 .. 27.6 Mongolia 155 496 56.2 40.2 0.8 38.8 6.3 8.2 36.8 12.8 Morocco 940 2,805 58.3 49.0 19.9 20.5 6.0 2.9 15.9 27.6 Mozambique 206 511 57.7 37.3 .. 26.2 4.3 1.6 38.1 34.9 Myanmar 73 444 35.4 51.2 22.6 6.5 2.5 .. 39.5 42.4 Namibia 341 376 46.9 36.1 17.9 23.3 6.8 5.5 28.5 35.1 Nepal 159 364 40.8 36.1 28.5 42.3 3.2 4.5 27.5 17.1 Netherlands 28,995 68,564 37.7 20.0 25.4 23.8 1.0 2.4 35.9 53.8 New Zealand 3,251 6,806 40.6 36.3 29.5 34.6 2.5 3.8 27.5 25.3 Nicaragua 73 375 70.7 56.5 20.1 22.8 7.9 3.6 1.4 17.2 Niger 209 175 68.3 76.7 10.4 12.6 4.3 2.1 17.1 8.5 Nigeria 1,901 4,969 33.6 29.5 30.3 23.4 3.1 .. 32.9 47.2 Norway 12,247 23,988 44.6 36.2 30.0 35.0 1.7 3.8 23.7 25.1 Oman 719 2,740 36.6 37.1 6.5 22.5 4.1 9.4 52.8 31.1 Pakistan 1,863 5,089 67.0 40.4 23.1 24.9 1.4 3.3 8.6 31.5 Panama 666 1,402 66.6 55.2 14.8 17.0 10.2 13.1 8.4 14.6 Papua New Guinea 393 662 35.6 26.1 12.8 5.8 4.0 7.3 47.6 60.8 Paraguay 361 323 61.6 52.8 19.8 22.1 11.4 16.8 7.3 8.3 Peru 1,070 2,628 43.5 41.7 27.6 23.6 10.9 8.8 18.0 25.9 Philippines 1,721 5,081 56.9 48.1 6.5 25.9 3.4 5.5 33.2 20.5 Poland 2,847 12,272 52.4 24.0 14.9 31.3 1.0 5.1 31.8 39.6 Portugal 3,772 9,464 48.5 30.5 23.0 29.2 5.1 4.3 23.5 36.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 219 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 787 3,829 65.5 39.2 13.1 14.1 7.3 5.9 14.1 40.9 Russian Federation .. 32,766 .. 11.9 .. 48.0 .. 5.7 .. 34.5 Rwanda 94 136 69.0 64.5 23.7 23.2 .. .. 7.3 12.3 Saudi Arabia 12,677 11,057 18.1 30.1 .. .. 2.2 3.3 79.7 66.6 Senegal 368 567 60.1 56.8 12.4 9.8 8.8 11.1 18.7 22.3 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 67 84 29.5 34.4 32.7 35.3 4.8 6.1 33.0 24.2 Singapore 8,575 40,470 41.0 38.2 21.0 19.2 9.1 7.0 29.0 35.6 Slovak Republic 1,666 3,012 17.3 29.8 13.1 19.0 .. 8.7 69.6 42.4 Slovenia 1,034 2,581 42.5 23.4 27.3 33.8 2.5 2.2 27.8 40.7 Somalia 122 .. 38.2 .. .. .. 4.2 .. 57.6 .. South Africa 3,594 9,079 40.2 50.6 31.5 29.5 11.6 7.6 16.7 12.3 Spain 15,197 57,016 30.9 27.3 28.0 21.3 6.3 5.5 34.9 45.9 Sri Lanka 620 1,872 64.2 60.6 11.9 15.8 6.8 5.9 17.1 17.7 Sudan 202 1,023 31.9 82.2 25.4 17.2 4.9 0.2 37.8 0.5 Swaziland 171 517 6.1 14.0 20.6 10.0 .. 49.0 73.4 27.0 Sweden 16,959 32,908 23.2 15.2 37.1 30.8 7.9 2.8 31.7 51.2 Switzerland 11,093 23,653 33.7 22.0 53.0 37.1 1.4 4.9 12.0 36.0 Syrian Arab Republic 702 1,813 54.5 55.2 35.5 35.9 4.4 3.5 5.7 5.4 Tajikistan .. 205 .. 77.4 .. 1.7 .. 8.1 .. 12.8 Tanzania 288 963 58.0 24.9 7.9 43.8 6.2 8.8 27.9 22.5 Thailand 6,160 22,948 58.1 47.3 23.3 19.7 5.5 5.6 13.3 27.4 Togo 217 204 56.9 72.8 18.4 3.5 9.1 11.8 15.6 11.9 Trinidad and Tobago 460 335 51.7 48.2 26.6 32.0 9.9 0.0 11.9 19.8 Tunisia 682 1,869 51.4 52.9 26.2 18.2 7.4 7.7 15.0 21.2 Turkey 2,794 10,299 32.2 42.1 18.6 24.5 .. 11.8 49.2 21.6 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 195 679 58.3 38.6 .. 17.9 6.5 9.7 35.2 33.8 Ukraine .. 4,695 .. 34.7 .. 21.2 .. 11.5 .. 32.6 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 44,713 140,060 33.2 24.3 41.0 40.3 2.4 5.5 23.4 29.9 United States 97,950 263,598 36.3 29.5 38.9 26.4 4.5 13.2 20.4 30.9 Uruguay 363 649 48.2 45.2 30.7 29.8 1.5 6.4 19.6 18.5 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 2,390 4,271 33.5 42.1 42.8 25.2 4.3 6.9 19.4 25.9 Vietnam .. 3,698 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 639 1,004 27.6 49.4 9.9 12.5 5.4 8.7 57.1 29.4 Zambia 370 .. 76.8 .. 14.6 .. 5.3 .. 3.3 .. Zimbabwe 460 .. 51.8 .. 14.4 .. 3.4 .. 30.4 .. World 833,781 t 2,117,904 t 34.7 w 27.9 w 32.5 w 28.4 w 5.0 w 8.5 w 32.4 w 35.8 w Low income 23,328 84,535 55.6 44.3 13.4 18.0 5.2 6.0 26.2 32.3 Middle income 104,787 387,261 48.7 30.8 25.3 27.7 4.1 13.2 22.1 28.4 Lower middle income 50,060 221,076 61.7 37.4 15.8 23.2 4.3 7.4 18.2 32.0 Upper middle income 56,000 166,327 34.4 23.7 35.6 32.6 3.8 19.4 26.4 24.4 Low & middle income 128,523 468,869 49.3 31.7 24.1 27.3 4.2 12.8 22.5 28.3 East Asia & Pacific 25,043 160,657 65.5 36.8 15.8 23.9 2.6 7.4 16.2 32.0 Europe & Central Asia 37,010 113,048 30.9 26.6 19.2 30.0 6.2 7.5 44.2 35.9 Latin America & Carib. 33,523 74,822 34.0 24.5 40.8 30.4 5.9 24.7 19.6 20.5 Middle East & N. Africa 18,678 .. 49.2 .. 16.3 .. 6.9 .. 27.7 .. South Asia 9,262 50,739 60.5 44.9 10.7 16.3 5.3 6.3 23.5 32.5 Sub-Saharan Africa 18,321 38,142 45.1 44.7 22.6 24.3 7.6 6.5 25.5 24.7 High income 700,780 1,652,657 30.8 27.0 34.8 28.7 5.2 7.4 34.8 37.6 Europe EMU 300,933 708,916 26.3 23.3 31.1 28.5 7.7 5.2 35.0 43.9 a. Includes Luxembourg. b. World Trade Organization estimate. 220 2006 World Development Indicators Structure of service imports About the data Definitions Trade in services differs from trade in goods because · Commercial service imports are total service services are produced and consumed at the same imports minus imports of government services not time. Thus services to a traveler may be consumed included elsewhere. International transactions in ser- in the producing country (for example, use of a hotel vices are defined by the IMF's Balance of Payments room) but are classified as imports of the traveler's Manual (1993) as the economic output of intangible country. In other cases services may be supplied commodities that may be produced, transferred, and from a remote location; for example, insurance consumed at the same time. Definitions may vary services may be supplied from one location and among reporting economies. · Transport covers all consumed in another. For further discussion of the transport services (sea, air, land, internal waterway, problems of measuring trade in services, see About space, and pipeline) performed by residents of one the data for table 4.6. economy for those of another and involving the car- The data on exports of services in table 4.6 and on riage of passengers, movement of goods (freight), imports of services in this table, unlike those in edi- rental of carriers with crew, and related support and tions before 2000, include only commercial services auxiliary services. Excluded are freight insurance, and exclude the category "government services not which is included in insurance services; goods pro- included elsewhere." The data are compiled by the cured in ports by nonresident carriers and repairs of International Monetary Fund (IMF) based on returns 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 ser- vices acquired from an economy by travelers in that economy for their own use during visits of less than one year for business or personal purposes. Travel services include the goods and services consumed by travelers, such as meals, lodging, and transport (within the economy visited), including car rental. · Insurance and financial services cover freight insurance on goods imported and other direct insur- ance such as life insurance, financial intermediation services such as commissions, foreign exchange transactions, and brokerage services; and auxil- iary services such as financial market operational and regulatory services. · Computer, information, communications, and other commercial services include such activities as international telecommu- nications, and postal and courier services; computer The mix of commerical service imports is changing data; news-related service transactions between Commercial service imports by developing economies (% of total) residents and nonresidents; construction services; royalties and license fees; miscellaneous business, 1990 2004 professional, and technical services; and personal, cultural, and recreational services. Other 22% Other 28% Transport Insurance 32% and finance Transport 4% 50% Travel Insurance Data sources Travel 24% and finance 27% 13% Data on imports of commercial services are from the IMF. The IMF publishes balance of payments Between 1990 and 2004 travel, insurance and finance, and other services displaced transport as the most data in its International Financial Statistics and important service imports for developing economies. Balance of Payments Statistics Yearbook. Source: International Monetary Fund data files. 2006 World Development Indicators 221 Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 61 89 19 9 29 24 15 21 23 43 21 17 Algeria 57 39 16 15 29 33 23 40 25 26 26 46 Angola 36 73 35 ..a 12 12 39 71 21 55 9 16 Argentina 77 63 3 11 14 19 10 25 5 18 13 21 Armenia 46 82 18 11 47 20 35 39 46 53 .. 14 Australia 59 60 19 18 22 25 17 18 17 21 18 19 Austria 57 56 19 18 24 22 38 51 37 46 24 24 Azerbaijan 51 58 18 12 27 55 44 50 39 74 .. 24 Bangladesh 86 76 4 6 17 24 6 16 14 21 14 31 Belarus 47 57 24 20 27 28 46 69 44 74 29 24 Belgium 55 54 20 23 22 20 71 84 69 81 24 24 Benin 87 77 11 14 14 20 14 15 26 26 10 13 Bolivia 77 68 12 15 13 12 23 31 24 26 10 17 Bosnia and Herzegovina .. 84 .. 25 .. 21 .. 26 .. 55 .. 4 Botswana 33 28 24 34 37 31 55 40 50 32 43 39 Brazil 59 55 19 19 20 21 8 18 7 13 19 23 Bulgaria 60 68 18 19 26 24 33 58 37 69 16 16 Burkina Faso 82 82 13 13 18 19 11 9 24 23 13 6 Burundi 95 98 11 8 15 11 8 9 28 25 .. 15 Cambodia 91 80 7 5 8 26 6 65 13 76 6 19 Cameroon 67 72 13 12 18 17 20 26 17 26 16 15 Canada 56 56 23 20 21 20 26 38 26 34 18 23 Central African Republic 86 75 15 12 12 18 15 12 28 16 0 14 Chad 98 55 10 5 7 25 14 52 28 36 ­3 6 Chile 62 58 10 12 25 23 35 36 31 30 23 22 Chinab 50 49 c 12 10 35 39 19 34 16 31 39 42 Hong Kong, China 58 59 7 10 28 22 132 193 124 184 34 32 Colombia 66 62 9 20 19 19 21 21 15 22 22 17 Congo, Dem. Rep. 79 91 12 5 9 13 30 19 29 22 1 7 Congo, Rep. 62 33 14 16 16 24 54 85 46 57 7 27 Costa Rica 61 66 18 15 27 22 35 47 41 49 17 17 Côte d'Ivoire 72 71 17 8 7 11 32 48 27 38 ­4 15 Croatia 75 58 24 20 10 30 78 48 86 56 ­16 24 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 49 50 23 23 25 28 45 72 43 72 28 22 Denmark 49 48 26 27 20 20 36 44 31 38 22 23 Dominican Republic 80 73 4 5 25 21 34 50 44 49 22 27 Ecuador 68 65 11 9 21 28 33 27 32 29 11 27 Egypt, Arab Rep. 73 71 11 12 29 17 20 29 33 29 21 21 El Salvador 89 91 10 11 14 16 19 27 31 44 4 9 Eritrea 104 97 22 54 8 22 11 13 45 86 10 ­9 Estonia 51 58 16 19 27 31 60 78 54 86 40 19 Ethiopia 74 83 19 17 12 21 8 19 12 40 10 13 Finland 51 53 22 22 29 19 23 37 24 32 24 24 France 57 56 22 24 22 20 21 26 23 26 20 19 Gabon 50 52 13 7 22 25 46 61 31 40 24 30 Gambia, The 76 75 14 11 22 24 60 42 72 52 5 18 Georgia 65 72 10 15 31 29 40 31 46 48 .. 18 Germany 57 59 20 19 24 17 25 38 25 33 23 21 Ghana 85 76 9 16 14 28 17 35 26 54 7 28 Greece 72 66 15 17 23 26 18 21 28 30 22 18 Guatemala 84 92 7 4 14 18 21 18 25 32 10 13 Guinea 73 86 9 6 18 11 31 21 31 23 11 8 Guinea-Bissau 87 88 10 14 30 12 10 35 37 49 15 9 Haiti 81 95 8 4 13 27 18 12 20 39 6 21 222 2006 World Development Indicators Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 67 74 14 14 23 29 36 37 40 54 14 21 Hungary 61 69 11 10 25 24 31 64 29 68 26 15 India 66 68 12 11 24 24 7 19 9 23 22 23 Indonesia 59 65 9 8 31 23 25 31 24 27 28 24 Iran, Islamic Rep. 62 49 11 12 29 37 22 32 24 30 27 40 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 58 45 16 15 21 25 57 80 52 65 23 23 Israel 56 59 30 29 25 17 35 44 45 49 22 .. Italy 58 60 20 19 22 20 20 27 20 26 20 19 Jamaica 65 72 13 14 26 31 48 41 52 58 19 25 Japan 53 57 13 18 33 24 10 12 10 10 34 27 Jordan 74 93 25 16 32 24 62 48 93 80 22 21 Kazakhstan 52 55 18 12 32 24 74 55 75 46 .. 25 Kenya 63 70 19 17 24 18 26 26 31 32 19 14 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 52 52 12 14 38 30 28 44 29 40 37 34 Kuwait 57 38 39 21 18 14 45 60 58 33 .. .. Kyrgyz Republic 71 79 25 17 24 14 29 43 50 53 4 9 Lao PDR .. .. .. .. .. .. 12 29 25 42 .. .. Latvia 53 63 9 20 40 33 48 44 49 60 56 18 Lebanon 140 82 25 17 18 21 18 21 100 41 22 2 Lesotho 138 94 14 22 53 42 17 48 122 105 60 28 Liberia .. 91 .. 10 .. 12 .. 35 .. 48 .. 29 Libya 48 58 24 17 19 14 40 47 31 36 .. .. Lithuania 57 67 19 16 33 24 52 54 61 61 .. 15 Macedonia, FYR 72 79 19 20 19 22 26 40 36 61 10 15 Madagascar 86 80 8 9 17 28 17 32 28 48 9 17 Malawi 72 95 15 17 23 11 24 27 33 49 14 ­8 Malaysia 52 43 14 13 32 23 75 121 72 100 30 35 Mali 80 78 14 10 23 20 17 28 34 36 15 11 Mauritania 69 104 26 15 20 22 46 29 61 70 18 ­7 Mauritius 64 62 13 14 31 24 64 56 71 56 26 24 Mexico 70 69 8 12 23 21 19 30 20 32 20 21 Moldova 76 90 ..a 15 25 25 48 51 51 82 58 21 Mongolia 57 57 34 19 38 37 24 75 53 87 7 41 Morocco 65 60 16 21 25 25 27 33 33 39 25 28 Mozambique 92 78 14 10 22 20 8 30 36 38 2 6 Myanmar 89 .. ..a .. 13 .. 3 .. 5 .. 12 .. Namibia 51 49 31 25 34 26 52 46 67 45 35 40 Nepal 84 76 9 11 18 26 11 18 22 31 10 27 Netherlands 50 49 24 25 23 21 55 65 51 60 25 23 New Zealand 61 60 19 18 20 23 27 29 27 29 16 18 Nicaragua 59 89 44 10 19 28 25 26 46 54 ­4 10 Niger 84 82 15 12 8 16 15 16 22 26 ­2 8 Nigeria 56 38 15 22 15 22 43 55 29 37 19 27 Norway 49 45 21 22 23 19 40 44 34 30 25 33 Oman 46 45 22 23 12 18 47 57 28 43 .. .. Pakistan 74 73 15 8 19 17 16 16 23 15 22 23 Panama 57 68 18 13 17 21 87 63 79 65 24 13 Papua New Guinea 59 .. 25 .. 24 .. 41 71 49 60 9 .. Paraguay 77 73 6 7 23 22 33 36 40 37 20 23 Peru 74 69 8 10 17 19 16 21 14 18 16 18 Philippines 72 72 10 10 24 17 28 52 33 51 20 37 Poland 48 64 19 18 26 20 29 39 22 41 16 19 Portugal 63 63 16 21 28 24 33 31 40 38 28 15 Puerto Rico 65 .. 15 .. 17 .. 72 .. 70 .. .. .. 2006 World Development Indicators 223 Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 66 70 13 15 30 25 17 37 26 47 22 18 Russian Federation 49 50 21 17 30 21 18 35 18 22 36 31 Rwanda 84 84 10 13 15 21 6 10 14 27 11 17 Saudi Arabia 47 30 29 23 15 19 41 53 32 25 18 29 Senegal 76 77 15 13 14 23 25 28 30 41 6 17 Serbia and Montenegro .. 93 .. 18 .. 17 .. 24 .. 52 .. 5 Sierra Leone 84 87 8 14 10 16 22 23 24 39 3 11 Singapore 47 41 10 11 36 18 .. .. .. .. 45 .. Slovak Republic 54 56 22 20 33 26 27 77 36 80 .. 23 Slovenia 53 54 17 20 17 27 91 60 79 61 24 26 Somalia 112 .. ..a .. 16 .. 10 .. 38 .. 17 .. South Africa 57 63 20 20 18 18 24 27 19 27 20 14 Spain 60 58 16 18 27 28 16 26 20 29 24 23 Sri Lanka 77 76 10 8 23 25 29 36 38 46 17 19 Sudan .. 71 .. 12 .. 20 .. 18 .. 21 .. 15 Swaziland 73 65 18 25 19 18 75 84 87 92 27 17 Sweden 49 48 27 28 24 16 30 46 29 38 22 24 Switzerland 57 61 11 12 31 20 36 44 34 37 34 29 Syrian Arab Republic 69 64 14 14 17 21 28 35 28 34 15 20 Tajikistan 74 101 9 9 25 9 28 46 35 65 24 6 Tanzaniad 81 78 18 13 26 19 13 19 38 29 8 9 Thailand 57 57 9 11 41 27 34 71 42 66 33 31 Togo 71 86 14 10 27 18 34 34 45 47 20 9 Trinidad and Tobago 59 58 12 9 13 20 45 60 29 48 21 27 Tunisia 58 63 16 16 33 25 44 45 51 48 27 22 Turkey 69 67 11 13 24 26 13 29 18 35 24 20 Turkmenistan 49 52 23 14 40 26 .. 66 .. 57 .. 34 Uganda 92 77 8 15 13 23 7 14 19 28 1 10 Ukraine 57 55 17 18 28 19 28 61 29 54 36 30 United Arab Emirates 38 48 16 13 21 22 66 82 41 65 .. .. United Kingdom 63 65 20 21 20 17 24 25 27 28 15 15 United States 67 71 17 16 18 18 10 10 11 14 15 13 Uruguay 70 74 12 11 12 13 24 30 18 28 14 12 Uzbekistan 61 56 25 17 32 20 29 40 48 33 3 30 Venezuela, RB 62 50 8 13 10 22 40 36 20 20 27 34 Vietnam 84 65 12 6 13 36 36 66 45 74 ­2 32 West Bank and Gaza .. 84 .. 53 .. 3 .. 10 .. 49 .. ­13 Yemen, Rep. 74 78 18 13 15 17 14 25 20 34 28 12 Zambia 64 69 19 13 17 26 36 20 37 27 7 13 Zimbabwe 63 74 19 21 17 13 23 36 23 44 16 3 World 60 w 62 w 17 w 17 w 23 w 21 w 19 w 24 w 19 w 24 w 22 w 20 w Low income 70 69 13 11 21 23 13 24 17 27 18 22 Middle income 60 58 13 14 26 26 22 35 21 33 26 28 Lower middle income 58 56 14 13 29 29 19 33 19 31 28 32 Upper middle income 63 61 13 14 23 22 25 38 23 35 21 22 Low & middle income 61 60 13 13 25 25 21 33 20 32 25 27 East Asia & Pacific 53 52 12 10 34 34 24 43 23 40 35 39 Europe & Central Asia 56 60 17 16 27 23 24 42 24 42 25 23 Latin America & Carib. 67 62 12 14 19 21 17 26 15 23 20 22 Middle East & N. Africa 66 59 15 14 26 26 26 34 34 34 24 30 South Asia 69 69 11 11 23 23 9 19 12 22 21 24 Sub-Saharan Africa 64 65 18 18 18 19 27 32 26 33 16 16 High income 60 63 18 18 23 20 19 23 19 23 22 19 Europe EMU 57 57 20 20 24 20 27 37 28 34 22 21 a. Data on general government final consumption expenditure are not available separately; they are included in household final consumption expenditure. b. China has revised its national accounts data from 1993 onwards, but revised expenditure data are not available. The data shown here are based on earlier series. c. Includes the difference between the old and the new GDP series. d. Data cover mainland Tanzania only. 224 2006 World Development Indicators Structure of demand About the data Definitions Gross domestic product (GDP) from the expenditure The quality of data on fixed capital formation by · Household final consumption expenditure is the side is made up of household final consumption government depends on the quality of government market value of all goods and services, including expenditure, general government final consumption accounting systems (which tend to be weak in devel- durable products (such as cars, washing machines, expenditure, gross capital formation (private and oping countries). Measures of fixed capital formation and home computers), purchased by households. It public investment in fixed assets, changes in inven- by households and corporations--particularly capital excludes purchases of dwellings but includes imputed tories, and net acquisitions of valuables), and net outlays by small, unincorporated enterprises--are rent for owner-occupied dwellings. It also includes exports (exports minus imports) of goods and ser- usually unreliable. payments and fees to governments to obtain permits vices. Such expenditures are recorded in purchaser Estimates of changes in inventories are rarely and licenses. World Development Indicators includes prices and include net taxes on products. complete but usually include the most important in household consumption expenditure the expendi- Because policymakers have tended to focus on activities or commodities. In some countries these tures of nonprofit institutions serving households, even fostering the growth of output, and because data on estimates are derived as a composite residual along when reported separately by the country. In practice, production are easier to collect than data on spending, with household final consumption expenditure. household consumption expenditure may include any many countries generate their primary estimate of GDP According to national accounts conventions, adjust- statistical discrepancy in the use of resources relative using the production approach. Moreover, many coun- ments should be made for appreciation of the value to the supply of resources. · General government final tries do not estimate all the separate components of of inventory holdings due to price changes, but this consumption expenditure includes all government cur- national expenditures but instead derive some of the is not always done. In highly inflationary economies rent expenditures for purchases of goods and services main aggregates indirectly using GDP (based on the this element can be substantial. (including compensation of employees). It also includes production approach) as the control total. Data on exports and imports are compiled from most expenditures on national defense and security Household final consumption expenditure (pri- customs reports and balance of payments data. but excludes government military expenditures that vate consumption in the 1968 System of National Although the data from the payments side provide potentially have wider public use and are part of gov- Accounts, or SNA) is often estimated as a residual, by reasonably reliable records of cross-border transac- ernment capital formation. · Gross capital formation subtracting from GDP all other known expenditures. tions, they may not adhere strictly to the appropriate consists of outlays on additions to the fixed assets of The resulting aggregate may incorporate fairly large definitions of valuation and timing used in the bal- the economy, net changes in the level of inventories, discrepancies. When household consumption is cal- ance of payments or correspond to the change-of- and net acquisitions of valuables. Fixed assets include culated separately, many of the estimates are based ownership criterion. This issue has assumed greater land improvements (fences, ditches, drains, and so on); on household surveys, which tend to be one-year stud- significance with the increasing globalization of inter- plant, machinery, and equipment purchases; and the ies with limited coverage. Thus the estimates quickly national business. Neither customs nor balance of construction of roads, railways, and the like, including become outdated and must be supplemented by esti- payments data usually capture the illegal transac- schools, offices, hospitals, private residential dwell- mates using price- and quantity-based statistical pro- tions that occur in many countries. Goods carried ings, and commercial and industrial buildings. Invento- cedures. Complicating the issue, in many developing by travelers across borders in legal but unreported ries are stocks of goods held by firms to meet tempo- countries the distinction between cash outlays for shuttle trade may further distort trade statistics. rary or unexpected fluctuations in production or sales, personal business and those for household use may Gross savings represent the difference between and "work in progress." · Exports and imports of be blurred. World Development Indicators includes in disposable income and consumption and replace goods and services are the value of all goods and other household consumption the expenditures of nonprofit gross domestic savings, a concept used by the World market services provided to, or received from, the rest institutions serving households. Bank and included in previous editions of World of the world. They include the value of merchandise, General government final consumption expenditure Development Indicators. The change was made to freight, insurance, transport, travel, royalties, license (general government consumption in the 1968 SNA) conform to the SNA concepts and definitions. For fur- fees, and other services, such as communication, con- includes expenditures on goods and services for ther discussion of the problems in compiling national struction, financial, information, business, personal, individual consumption as well as those on services accounts, see Srinivasan (1994), Heston (1994), and government services. They exclude compensation for collective consumption. Defense expenditures, and Ruggles (1994). For an analysis of the reliability of employees and investment income (factor services including those on capital outlays (with certain excep- of foreign trade and national income statistics, see in the 1968 SNA) as well as transfer payments. · Gross tions), are treated as current spending. Morgenstern (1963). savings are calculated as gross national income less Gross capital formation (gross domestic investment total consumption, plus net transfers. in the 1968 SNA) consists of outlays on additions to the economy's fixed assets plus net changes in Data sources the level of inventories. It is generally obtained from reports by industry of acquisition and distinguishes National accounts indicators for most developing only the broad categories of capital formation. The countries are collected from national statistical 1993 SNA recognizes a third category of capital forma- organizations and central banks by visiting and tion: net acquisitions of valuables. Included in gross resident World Bank missions. Data for high- capital formation under the 1993 SNA guidelines are income economies come from Organisation for capital outlays on defense establishments that may Economic Co-operation and Development data be used by the general public, such as schools, air- files (see the OECD's Annual National Accounts fields, and hospitals, and intangibles such as com- for OECD Member Countries: Data from 1970 puter software and mineral exploration outlays. Data Onwards). The United Nations Statistics Division on capital formation may be estimated from direct publishes detailed national accounts for UN mem- surveys of enterprises and administrative records ber countries in National Accounts Statistics: Main or based on the commodity flow method using data Aggregates and Detailed Tables and updates in the from production, trade, and construction activities. Monthly Bulletin of Statistics. 2006 World Development Indicators 225 Growth of consumption investment, and trade Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure Per capita average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth Exports Imports 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 4.3 2.5 5.2 2.1 2.4 0.7 25.8 3.3 17.9 16.9 15.8 16.8 Algeria ­0.1 6.4 ­1.9 4.8 3.6 3.1 ­0.9 11.9 3.3 4.1 ­1.1 9.0 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2.7 ­1.7 1.5 ­2.6 2.2 ­1.0 7.4 ­1.4 8.7 4.9 15.6 ­7.3 Armenia ­0.5 9.0 1.0 9.5 ­1.7 6.6 26.2 13.3 ­18.4 26.7 ­12.5 15.7 Australia 3.6 4.2 2.8 2.9 2.8 2.9 6.3 11.0 7.2 ­0.2 8.1 9.4 Austria 1.9 0.7 1.5 0.2 2.5 0.4 2.1 0.4 5.5 4.7 5.0 3.6 Azerbaijan 1.5 12.1 0.4 11.2 ­1.7 6.1 42.9 48.5 6.8 11.1 15.5 33.0 Bangladesh 2.6 4.8 0.4 2.8 4.7 12.6 9.2 7.8 13.1 6.7 9.7 2.8 Belarus ­0.5 11.5 ­0.3 12.0 ­1.9 0.4 ­7.5 12.5 ­4.8 10.1 ­8.7 12.8 Belgium 1.9 1.3 1.6 0.9 1.5 2.6 2.3 0.3 4.7 2.4 4.5 2.4 Benin 2.6 13.5 ­0.7 10.0 4.4 9.5 12.2 6.4 1.8 4.4 2.1 3.7 Bolivia 3.6 2.0 1.4 0.0 3.6 2.5 8.5 ­4.8 4.5 9.8 6.0 3.9 Bosnia and Herzegovina .. .. .. .. .. .. .. 4.7 .. 5.3 .. 2.3 Botswana 2.7 3.7 0.6 3.5 7.1 8.7 2.5 16.4 4.7 ­5.8 3.8 ­0.4 Brazila 4.8 ­0.2 3.3 ­1.6 ­0.4 4.3 3.4 ­0.2 6.1 12.4 11.1 ­1.6 Bulgaria ­3.7 5.0 ­3.0 5.9 ­8.5 3.7 ­4.9 12.5 3.9 9.1 2.7 11.7 Burkina Faso 4.2 3.6 1.3 0.3 ­0.5 2.6 7.0 7.7 0.0 7.5 1.4 13.2 Burundi ­2.3 .. ­3.5 .. ­2.1 .. 0.4 .. 7.9 10.6 1.1 .. Cambodiaa 6.0 2.8 3.4 0.7 7.2 4.4 10.9 23.4 21.7 13.9 14.8 12.7 Cameroon 3.1 6.1 0.7 4.1 0.7 6.5 0.4 7.8 3.2 1.5 5.1 5.0 Canada 2.6 3.1 1.6 2.1 0.3 3.4 4.5 3.6 8.7 ­1.1 7.2 0.2 Central African Republica .. .. .. .. .. .. .. .. .. .. .. .. Chada 0.6 16.9 ­2.4 12.8 ­2.8 5.8 4.4 12.8 1.2 39.2 ­1.1 27.4 Chile 6.4 1.2 4.7 0.1 9.6 2.1 4.7 6.4 9.4 4.7 10.4 ­1.8 Chinab 9.0 7.0 7.8 6.3 8.8 7.2 11.2 15.0 13.0 24.2 14.3 22.2 Hong Kong, China 3.9 1.1 2.1 0.3 3.3 2.6 5.8 0.1 8.1 9.2 8.4 8.0 Colombia 2.2 3.0 0.3 1.3 10.5 1.1 2.0 12.6 5.3 1.6 9.0 7.8 Congo, Dem. Rep.a ­4.5 .. ­7.1 .. ­17.4 .. ­0.7 .. ­0.5 .. ­2.4 .. Congo, Rep.a ­1.7 15.4 ­4.8 11.9 ­2.0 12.7 0.4 21.3 5.1 3.8 2.9 24.9 Costa Ricaa 5.0 2.8 2.5 0.8 1.9 1.9 4.7 9.1 11.0 4.0 9.0 4.2 Côte d'Ivoire 4.0 ­2.2 1.1 ­3.8 0.8 3.7 8.6 ­4.6 1.5 3.5 6.9 6.3 Croatia 2.6 5.1 3.5 4.8 1.4 ­2.0 5.4 15.5 5.9 6.1 4.6 8.6 Cuba .. .. .. .. .. .. .. .. 7.1 .. 7.9 .. Czech Republic 2.9 3.2 2.9 3.3 ­0.6 2.8 4.7 4.2 8.8 9.9 12.1 10.0 Denmark 2.2 1.3 1.8 1.0 2.3 1.6 5.6 2.2 4.4 2.6 6.0 3.9 Dominican Republica 3.4 ­95.7 1.8 ­95.7 15.9 ­14.9 10.5 ­10.2 13.2 5.7 11.4 1.2 Ecuadora 2.1 4.3 0.3 2.8 ­1.5 1.9 ­0.7 14.5 5.3 3.8 2.8 10.6 Egypt, Arab Rep. 4.5 2.8 2.5 0.8 2.1 3.3 5.9 ­0.2 3.4 6.7 2.7 3.8 El Salvador 5.3 2.1 3.1 0.2 2.8 2.0 7.1 1.0 13.4 4.2 11.6 3.5 Eritrea ­5.0 1.8 ­6.7 ­2.5 22.6 5.0 19.1 ­13.5 ­2.5 ­7.4 7.5 ­3.7 Estonia 0.6 7.7 2.2 8.1 4.9 5.3 0.2 12.1 11.2 5.0 12.0 7.6 Ethiopia 5.5 4.3 3.2 2.1 9.7 ­1.3 6.4 12.1 7.1 16.7 5.8 13.2 Finland 1.7 2.8 1.4 2.5 0.8 2.4 1.3 0.5 9.9 2.5 6.2 2.4 France 1.6 2.0 1.3 1.4 1.8 2.4 .. .. 7.5 1.0 5.7 2.5 Gabona 1.2 5.1 ­1.7 3.3 5.4 1.6 3.8 1.0 1.5 2.9 0.7 3.0 Gambia, The 3.5 1.8 0.0 ­1.1 ­2.2 4.2 1.9 2.3 ­0.2 3.2 ­0.8 4.5 Georgia 6.1 6.4 7.6 7.6 12.0 0.3 ­12.5 17.1 12.2 3.8 11.2 4.6 Germany 1.9 0.3 1.6 0.2 1.8 0.4 1.1 ­2.8 6.0 4.9 5.8 2.5 Ghana 0.2 9.2 ­2.3 6.9 4.8 4.2 1.3 7.6 10.1 1.0 10.4 3.3 Greece 2.2 3.4 1.4 3.1 2.1 2.4 4.1 7.8 7.6 ­0.4 7.4 1.1 Guatemalaa 4.2 4.2 1.9 1.7 5.1 ­2.0 6.2 2.3 6.2 0.8 9.2 6.6 Guinea 3.6 5.0 0.5 2.7 5.0 6.7 2.8 ­7.7 4.6 1.0 1.3 1.5 Guinea-Bissau 2.6 7.1 ­0.4 4.0 1.9 ­3.9 ­6.5 ­8.6 15.4 3.8 ­0.5 ­6.3 Haiti .. .. .. .. .. .. 8.6 1.5 ­10.3 7.7 ­5.2 10.6 226 2006 World Development Indicators Growth of consumption, investment, and trade Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure Per capita average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth Exports Imports 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Hondurasa 3.0 3.4 0.1 1.0 2.0 4.1 7.0 2.0 1.6 1.6 3.8 2.4 Hungary ­0.2 6.8 0.1 6.7 0.9 3.7 10.0 ­1.4 9.8 8.0 11.4 8.7 India 4.9 6.2 3.0 4.6 6.6 2.6 7.0 7.7 12.3 12.0 14.4 15.8 Indonesia 6.6 4.0 5.0 2.6 0.1 8.7 ­0.7 5.2 5.9 3.8 5.7 4.9 Iran, Islamic Rep. 2.9 5.5 1.3 4.1 5.2 3.4 3.1 11.8 ­1.3 4.4 ­11.6 11.7 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 5.3 3.3 4.5 1.6 4.1 6.1 10.4 2.9 15.5 4.0 14.3 2.2 Israel 4.5 2.1 1.9 0.2 3.0 1.0 1.5 ­6.9 10.6 2.2 7.4 0.2 Italy 1.6 0.9 1.4 1.0 0.1 2.2 ­0.3 16.9 6.1 ­0.6 4.7 0.8 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 1.5 0.6 1.2 0.4 3.1 2.3 ­0.2 ­2.6 4.2 3.6 4.1 1.5 Jordan 5.9 6.0 1.9 3.2 1.7 3.7 1.1 8.8 2.4 9.1 2.0 8.8 Kazakhstana ­8.1 9.4 ­7.0 9.2 ­7.1 7.5 ­18.3 14.2 ­2.6 9.8 ­11.2 2.8 Kenya 3.6 1.9 0.8 ­0.3 6.9 1.8 6.1 1.5 1.1 9.5 9.4 4.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 4.9 2.8 3.9 2.2 4.7 4.5 3.4 3.4 16.0 11.8 10.0 9.3 Kuwait 5.0 6.1 1.2 3.1 ­2.9 7.1 ­4.4 5.5 ­1.6 0.2 0.8 8.3 Kyrgyz Republic ­6.5 10.9 ­7.4 9.9 ­8.8 0.8 ­3.9 ­10.6 ­1.6 6.2 ­8.2 8.9 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia ­3.9 8.0 ­2.7 8.7 1.8 2.2 ­3.9 18.3 4.2 6.4 7.6 11.2 Lebanon 4.6 3.4 2.3 2.3 1.2 ­1.6 8.2 8.4 7.2 16.2 3.1 5.7 Lesotho 0.1 1.4 ­1.1 1.2 6.2 6.7 1.5 ­7.3 11.1 12.4 0.9 4.8 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuaniaa 5.2 6.6 6.0 7.1 1.9 4.3 11.1 14.3 4.9 14.1 7.5 13.8 Macedonia, FYR 2.2 3.4 1.7 3.1 ­0.4 1.3 3.1 3.6 4.2 ­2.3 7.4 0.1 Madagascar 2.3 2.0 ­0.7 ­0.9 0.0 3.8 3.4 11.2 3.9 ­5.8 4.3 8.4 Malawi 5.4 5.2 3.5 2.8 ­4.4 7.8 ­8.4 ­4.8 4.0 0.3 ­1.1 5.6 Malaysia 5.3 5.9 2.6 3.8 4.8 11.2 5.3 1.4 12.0 4.5 10.3 5.2 Mali 3.0 1.9 0.2 ­1.1 3.2 25.2 0.4 4.7 10.0 8.2 3.5 5.0 Mauritania 3.6 .. 0.9 .. 0.2 .. 9.3 .. ­1.5 .. ­0.6 .. Mauritius 5.1 3.9 3.9 2.9 4.8 4.6 4.7 3.6 5.4 6.6 5.2 3.6 Mexico 2.4 2.7 0.7 1.3 1.8 ­0.5 4.7 ­2.1 14.6 2.7 12.3 2.3 Moldovaa 9.9 9.3 10.2 9.7 ­12.4 10.4 ­15.5 7.6 0.7 16.3 5.6 16.0 Mongoliaa .. .. .. .. .. .. .. .. 30.9 8.4 29.3 7.8 Morocco 1.6 3.8 0.1 2.1 3.8 4.4 2.9 6.6 5.4 4.4 4.4 3.8 Mozambiquea 4.8 6.5 1.8 4.3 3.1 9.3 15.5 4.8 11.0 23.3 6.3 10.8 Myanmar 3.9 .. .. .. .. .. 15.3 .. 10.0 .. 5.8 .. Namibia 4.8 ­0.1 1.7 ­1.5 3.3 1.3 6.9 12.3 3.8 8.7 5.4 2.3 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 2.8 0.4 2.2 ­0.1 2.0 2.5 3.2 ­1.6 6.8 2.2 6.6 2.4 New Zealand 3.2 4.4 2.0 3.0 2.5 3.4 5.8 8.9 5.1 4.0 6.3 7.5 Nicaraguaa 6.1 4.1 3.8 2.0 ­1.5 ­0.5 11.3 ­3.7 9.3 5.7 12.2 3.2 Niger 1.8 .. .. .. 0.8 .. 4.0 .. 3.1 .. ­2.1 .. Nigeria 0.2 5.7 .. .. ­1.8 2.2 5.4 12.4 4.4 4.7 4.5 9.5 Norway 3.5 3.0 2.9 2.4 2.8 3.1 6.0 ­1.2 5.6 1.5 5.8 2.8 Oman 5.4 1.3 2.4 0.4 2.4 6.1 4.0 17.0 6.2 7.0 5.9 12.8 Pakistan 4.9 2.3 2.3 ­0.2 0.7 5.7 1.8 1.9 1.7 13.1 2.5 2.8 Panamaa 6.4 3.7 4.2 1.9 1.7 6.4 10.4 1.1 ­0.4 0.3 1.2 0.4 Papua New Guinea 5.6 .. .. .. 2.7 .. 0.5 .. 4.3 .. 2.8 .. Paraguay 3.7 1.5 1.0 ­0.9 6.4 ­4.1 0.2 ­2.5 ­1.0 5.7 3.2 4.9 Perua 4.0 3.3 2.2 1.8 5.2 1.7 7.5 2.2 8.5 8.3 9.0 4.5 Philippines 3.7 4.7 1.5 2.8 3.8 ­1.5 4.1 ­0.6 7.8 4.3 7.8 6.2 Polanda 5.1 3.0 5.0 3.3 3.5 1.1 10.6 ­1.6 11.3 8.3 16.7 4.1 Portugal 2.8 1.0 2.5 0.3 2.8 1.7 5.4 ­3.9 5.6 3.3 7.3 1.5 Puerto Rico .. .. .. .. .. .. .. .. 1.6 .. 4.5 .. 2006 World Development Indicators 227 Growth of consumption, investment, and trade Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure Per capita average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth Exports Imports 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Romaniaa 1.4 7.5 1.7 8.4 0.8 2.8 ­5.1 10.4 8.1 12.5 6.0 14.8 Russian Federation ­0.9 8.7 ­0.8 9.1 ­2.2 1.7 ­19.1 8.9 0.8 10.1 ­6.1 18.1 Rwandaa 1.2 3.4 ­0.1 0.9 ­1.7 13.1 1.4 0.4 ­3.8 5.8 5.0 ­0.6 Saudi Arabia .. 1.9 .. ­0.9 .. 0.6 .. 6.2 .. 2.2 .. 2.2 Senegal 2.4 5.1 ­0.2 2.6 2.1 4.9 7.9 9.0 6.3 4.6 3.5 8.1 Serbia and Montenegro .. 9.5 .. 9.4 .. 6.1 .. 15.3 .. 14.5 .. 23.1 Sierra Leone 1.2 13.5 0.3 8.8 10.4 .. ­5.6 .. ­11.2 .. ­0.2 .. Singapore 5.7 4.0 2.6 2.7 9.1 1.2 7.7 ­10.9 .. .. .. .. Slovak Republic 4.7 3.1 4.5 3.1 2.9 3.5 7.9 3.6 9.0 11.7 11.7 10.4 Slovenia 3.8 2.1 3.8 2.0 2.1 2.4 11.3 6.2 2.2 6.7 4.8 6.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2.9 3.9 0.7 3.0 0.3 5.3 5.0 7.1 5.6 0.8 7.1 6.6 Spain 2.2 3.1 1.8 1.7 2.8 4.6 2.8 4.5 10.6 2.9 9.0 5.4 Sri Lankaa 5.7 .. .. .. 7.5 .. 6.9 4.6 7.5 3.9 8.6 6.1 Sudan 6.2 .. 3.7 .. 0.2 .. 11.3 19.5 14.2 7.4 8.8 4.5 Swazilanda 3.8 2.1 0.6 0.3 5.5 ­2.2 2.7 4.2 3.8 2.0 4.5 0.8 Sweden 1.3 1.3 1.0 1.0 0.6 1.2 1.8 ­1.0 8.6 4.0 6.2 1.7 Switzerland 1.1 0.9 0.5 0.1 0.8 3.0 1.4 ­1.9 4.0 0.0 4.2 0.2 Syrian Arab Republic 2.9 4.6 0.1 2.0 1.9 7.0 3.2 12.0 12.0 ­0.5 4.4 7.8 Tajikistan ­4.2 .. ­5.5 .. ­19.2 .. ­17.5 26.9 ­1.4 12.8 ­3.9 1.7 Tanzaniac 2.1 1.8 ­0.8 ­0.2 3.4 19.3 ­1.6 9.6 7.1 4.2 0.3 6.1 Thailand 3.7 5.6 2.5 4.6 5.1 2.3 ­4.0 9.1 9.5 6.6 4.6 8.0 Togo 5.0 0.6 1.8 ­2.1 0.0 0.2 ­0.1 5.8 1.2 6.1 1.1 3.2 Trinidad and Tobago 0.7 9.5 0.1 9.1 0.3 5.4 12.5 2.7 6.9 4.3 9.9 7.1 Tunisia 4.3 4.9 2.6 3.9 4.0 4.5 3.5 3.8 5.2 1.4 3.8 2.1 Turkey 3.5 2.6 1.7 1.0 4.9 ­0.8 5.0 9.1 11.7 12.1 11.0 10.9 Turkmenistan .. .. .. .. .. .. 2.2 .. ­6.1 10.9 0.6 10.4 Uganda 6.2 5.4 3.0 1.9 7.1 6.8 8.9 6.2 14.7 9.9 10.1 5.4 Ukraine ­6.9 11.1 ­6.4 12.0 ­4.1 5.2 ­18.5 7.1 ­3.6 9.1 ­6.6 8.0 United Arab Emirates 7.1 12.9 0.7 5.1 6.9 0.8 5.5 5.5 5.5 12.2 6.4 13.6 United Kingdom 2.9 2.9 2.6 2.9 1.1 3.5 4.6 2.3 6.6 1.5 6.8 3.8 United States 3.6 3.0 2.4 1.9 0.7 3.7 7.4 ­1.3 7.3 ­2.0 9.8 1.8 Uruguaya 5.0 ­3.2 4.2 ­3.9 2.3 ­4.1 6.3 ­4.2 6.0 0.1 9.9 ­5.5 Uzbekistan .. .. .. .. .. .. ­2.6 4.3 2.4 3.3 ­1.2 3.2 Venezuela, RB 0.6 0.6 ­1.5 ­1.2 3.7 5.0 11.0 ­9.1 1.0 ­2.8 8.2 ­3.2 Vietnam 5.4 7.0 3.8 5.8 3.2 6.6 19.8 11.6 24.1 15.3 28.2 19.4 West Bank and Gaza 2.9 ­10.4 ­1.3 ­14.1 16.1 ­1.7 ­1.7 ­53.9 1.0 ­19.5 2.4 ­13.5 Yemen, Rep. 3.4 8.2 ­0.7 4.9 1.3 5.3 10.9 6.7 16.5 ­4.8 8.0 4.5 Zambia ­3.6 1.5 ­5.9 ­0.3 ­8.1 6.9 5.4 6.1 2.8 13.1 1.5 6.4 Zimbabwe 0.0 ­3.3 ­1.7 ­3.9 ­2.2 ­7.9 ­2.5 ­8.3 10.5 ­5.7 9.4 ­4.2 World 2.9 w 2.4 w 1.5 w 1.1 w 1.7 w 2.9 w 3.2 w 1.2 w 7.0 w 5.0 w 6.9 w 3.3 w Low income 4.2 5.4 2.0 3.4 4.2 3.6 6.1 7.3 8.2 9.6 8.9 11.9 Middle income 3.8 3.6 2.6 2.7 2.6 3.5 2.5 7.4 7.2 10.1 6.3 9.3 Lower middle income 4.9 4.2 3.6 3.2 3.3 4.9 4.6 10.4 6.9 14.0 5.2 12.4 Upper middle income 2.6 2.9 1.6 2.1 1.7 1.6 ­0.4 2.0 7.6 5.9 7.4 6.0 Low & middle income 3.8 3.9 2.2 2.5 2.7 3.5 2.8 7.4 7.3 10.1 6.5 9.6 East Asia & Pacific 7.4 6.3 6.1 5.4 7.1 6.8 7.9 13.0 11.0 15.3 10.4 15.0 Europe & Central Asia 1.0 5.6 0.9 5.5 0.1 1.7 ­7.3 6.6 3.5 9.8 1.9 10.9 Latin America & Carib. 3.4 0.9 1.8 ­0.6 1.6 2.0 5.0 ­0.6 8.5 4.4 10.6 1.1 Middle East & N. Africa 3.2 4.1 1.1 2.2 3.5 3.5 3.0 8.3 3.4 4.5 ­1.6 6.7 South Asia 4.7 5.5 2.6 3.7 5.8 3.2 6.5 7.1 10.0 11.2 11.2 12.6 Sub-Saharan Africa 2.7 3.9 0.2 1.6 0.5 5.2 4.0 7.0 4.8 3.3 5.5 6.8 High income 2.8 2.2 2.0 1.5 1.6 2.8 3.4 ­0.1 6.9 1.8 7.0 2.3 Europe EMU 1.9 1.3 1.6 0.8 1.6 2.0 1.4 3.7 6.9 2.7 6.1 2.5 a. Household final consumption expenditure includes statistical discrepancy. b. China has revised its national accounts data from 1993 onwards, but revised expenditure data are not available. The data shown here are based on earlier series. c. Data cover mainland Tanzania only. 228 2006 World Development Indicators Growth of consumption, investment, and trade About the data Definitions Measures of growth in consumption and capital forma- improvements in productivity or changes in the quality · Household final consumption expenditure is the tion are subject to two kinds of inaccuracy. The first of government services. Deflators for household con- market value of all goods and services, including stems from the difficulty of measuring expenditures sumption are usually calculated on the basis of the durable products (such as cars, washing machines, at current price levels, as described in About the data consumer price index. Many countries estimate house- and home computers), purchased by households. It for table 4.8. The second arises in deflating current hold consumption as a residual that includes statistical excludes purchases of dwellings but includes imputed price data to measure volume growth, where results discrepancies associated with the estimation of other rent for owner-occupied dwellings. It also includes depend on the relevance and reliability of the price expenditure items, including changes in inventories; payments and fees to governments to obtain permits indexes and weights used. Measuring price changes is thus these estimates lack detailed breakdowns of and licenses. World Development Indicators includes more difficult for investment goods than for consump- household consumption expenditures. in household consumption expenditure the expendi- tion goods because of the one-time nature of many tures of nonprofit institutions serving households, even investments and because the rate of technological when reported separately by the country. In practice, progress in capital goods makes capturing change household consumption expenditure may include any in quality difficult. (An example is computers--prices statistical discrepancy in the use of resources rela- have fallen as quality has improved.) Several countries tive to the supply of resources. · General government estimate capital formation from the supply side, identi- final consumption expenditure includes all government fying capital goods entering an economy directly from current expenditures for purchases of goods and ser- detailed production and international trade statistics. vices (including compensation of employees). It also This means that the price indexes used in deflating includes most expenditures on national defense and production and international trade, reflecting delivered security but excludes government military expendi- or offered prices, will determine the deflator for capital tures that potentially have wider public use and are formation expenditures on the demand side. part of government capital formation. · Gross capital Growth rates of household final consumption expen- formation consists of outlays on additions to the fixed diture, household final consumption expenditure per assets of the economy, net changes in the level of capita, general government final consumption expendi- inventories, and net acquisitions of valuables. Fixed ture, gross capital formation, and exports and imports assets include land improvements (fences, ditches, of goods and services are estimated using constant drains, and so on); plant, machinery, and equipment price data. (Consumption, capital formation, and purchases; and the construction of roads, railways, and exports and imports of goods and servces as shares the like, including schools, offices, hospitals, private of GDP are shown in table 4.8.) residential dwellings, and commercial and industrial To obtain government consumption in constant buildings. Inventories are stocks of goods held by firms prices, countries may deflate current values by applying to meet temporary or unexpected fluctuations in pro- a wage (price) index or extrapolate from the change in duction or sales, and "work in progress." · Exports government employment. Neither technique captures and imports of goods and services represent the value of all goods and other market services provided to, or received from, the rest of the world. They include the value of merchandise, freight, insurance, transport, Gross capital formation and government consumption are both on the rise in Sub-Saharan travel, royalties, license fees, and other services, such Africa as communication, construction, financial, information, 2000 $ billions business, personal, and government services. They 80 exclude compensation of employees and investment Gross capital formation income (factor services in the 1968 SNA) as well as transfer payments. 60 Data sources National accounts indicators for most developing Government consumption countries are collected from national statistical 40 organizations and central banks by visiting and res- ident World Bank missions. Data for high-income economies come from data files of the Organisation 20 for Economic Co-operation and Development (see the OECD's Annual National Accounts for OECD Member Countries: Data from 1970 Onwards). The United Nations Statistics Division publishes detailed 0 national accounts for UN member countries in 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 National Accounts Statistics: Main Aggregates and Gross capital formation has been increasing in Sub-Saharan Africa since the mid-1990s, after a decline in the previous Detailed Tables and updates in the Monthly Bulletin decade. of Statistics. Source: World Bank data files. 2006 World Development Indicators 229 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest payments % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 2004 2004 Afghanistan .. 4.6 .. 13.2 .. ­0.7 .. 0.3 .. 3.4 10.6 0.2 Albaniab 21.2 .. 25.6 .. ­8.9 .. 7.4 .. 2.1 .. .. .. Algeriab 30.2 36.0 24.2 24.6 ­1.3 1.2 ­7.4 1.8 8.6 ­1.7 48.1 8.6 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 18.1 .. 18.3 .. ­0.5 .. 1.4 .. 1.5 .. 26.5 Armeniab .. 19.9 .. 18.6 .. ­0.9 .. 0.3 .. 2.0 35.5 2.7 Australia .. 26.4 .. 25.5 .. 0.8 1.7 .. 0.7 .. 22.4 4.0 Austria 37.4 38.2 40.6 40.1 ­2.9 ­1.9 .. 2.2 28.0 .. 65.3 7.9 Azerbaijanb 18.0 .. 19.8 .. ­3.1 .. .. .. .. .. .. .. Bangladeshb .. 10.0 .. 8.8 .. ­0.7 .. 2.3 .. 0.9 36.2 16.4 Belarusb 30.0 30.6 28.7 28.5 ­2.7 ­0.2 2.2 0.7 0.4 0.5 12.3 1.5 Belgium 43.5 43.7 45.0 43.9 ­1.2 ­0.3 .. ­4.6 .. 1.8 139.8 11.4 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia .. 20.2 .. 27.2 .. ­5.4 .. 3.6 .. 3.6 93.8 10.4 Bosnia and Herzegovina .. 41.2 .. 38.6 .. 1.8 .. 0.1 .. 1.0 .. 1.5 Botswanab 40.5 .. 30.3 .. 4.9 .. 0.2 .. ­0.4 .. .. .. Brazilb 26.9 .. 32.9 .. ­2.7 .. .. .. .. .. .. .. Bulgaria b 35.5 38.2 39.4 35.3 ­5.1 1.6 7.4 0.9 ­0.8 ­3.0 .. 4.6 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundib 19.3 .. 23.6 .. ­4.7 .. 3.0 .. 4.0 .. .. .. Cambodia .. 10.7 .. 9.1 .. ­2.2 .. ­0.7 .. 2.6 .. 1.9 Cameroon 11.3 .. 13.1 .. ­2.9 .. ­0.4 .. 3.5 .. .. .. Canadab 20.6 19.9 24.6 18.3 ­4.4 1.4 5.0 ­1.0 0.0 0.3 48.7 7.9 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 22.3 .. 18.4 .. 2.2 .. ­1.0 .. 0.2 15.7 4.4 China 5.4 8.8 .. 10.4 .. ­2.4 1.6 .. .. .. .. 7.6 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 17.1 .. 22.9 .. ­8.0 .. 7.5 .. ­1.7 57.7 27.4 Congo, Dem. Rep.b 5.3 7.9 8.2 7.8 0.0 ­0.1 0.0 .. 0.2 .. .. .. Congo, Rep.b .. 30.9 .. 19.9 .. 6.4 .. .. .. .. 0.2 18.1 Costa Ricab 20.3 22.4 21.3 22.7 ­2.1 ­1.3 .. .. ­0.8 1.4 38.3 18.3 Côte d'Ivoire 20.1 17.1 .. 17.5 .. 0.1 ­1.2 2.4 3.8 6.8 104.3 15.5 Croatiab 43.1 41.9 42.5 42.0 ­1.3 ­4.0 ­2.7 2.0 0.8 2.0 .. 5.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 32.4 .. 36.1 .. ­3.3 .. 0.6 .. 3.0 21.4 3.1 Denmark 39.4 36.5 38.5 35.2 1.5 1.8 .. ­2.5 .. .. 42.8 8.4 Dominican Republicb 16.0 16.3 10.2 13.2 0.8 1.4 0.0 1.0 ­1.0 2.3 .. 9.4 Ecuador 14.1 .. 12.0 .. .. .. .. .. .. .. .. .. Egypt, Arab Rep.b 34.8 .. 28.1 .. 3.4 .. .. .. .. .. .. .. El Salvador .. 15.5 .. 17.0 .. ­3.3 .. 2.0 .. 0.6 49.0 14.4 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estoniab 32.8 28.0 32.0 26.7 0.6 0.9 ­0.2 0.0 0.9 ­0.1 2.5 0.6 Ethiopiab .. 18.5 .. 26.7 .. ­9.8 .. 1.2 .. 9.2 .. 7.6 Finland 40.2 39.1 38.9 36.9 1.9 2.5 0.3 ­0.6 ­1.3 3.8 45.9 4.3 France 43.3 43.3 46.5 47.1 ­2.9 ­3.5 .. 2.0 .. 1.6 70.7 5.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgiab 12.2 15.8 15.4 14.4 ­4.3 0.5 2.2 0.1 2.4 0.3 43.2 8.2 Germany 29.9 28.6 38.6 31.3 ­8.3 ­2.4 ­0.6 .. 3.2 .. .. 6.1 Ghana 17.0 23.8 .. 20.9 .. ­2.9 .. .. .. 3.3 .. 14.4 Greece 45.4 .. 45.6 .. ­2.6 .. .. .. .. .. .. .. Guatemalab 8.4 10.6 7.6 11.1 ­0.5 ­0.9 .. 0.8 0.4 1.7 19.0 10.9 Guinea 13.4 .. 10.5 .. ­3.2 .. ­0.1 .. 4.2 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 230 2006 World Development Indicators Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest payments % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 2004 2004 Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary .. 37.1 .. 41.6 .. ­6.2 .. 0.3 .. 5.4 58.2 10.9 Indiab 12.3 12.6 14.5 15.9 ­2.2 ­3.6 5.2 3.6 0.0 0.3 65.8 31.9 Indonesiab 17.7 18.3 9.7 16.8 3.0 ­1.1 ­0.6 0.0 ­0.4 ­0.4 28.7 14.8 Iran, Islamic Rep.b 23.0 28.9 15.1 20.2 1.1 3.6 .. 1.2 0.1 ­1.8 .. 0.8 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Irelandb 25.4 .. 28.6 .. ­2.0 .. .. .. .. .. .. .. Israel .. 41.6 .. 48.8 .. ­4.8 4.9 .. 0.1 .. 98.1 13.3 Italy 38.9 37.7 41.7 40.0 ­2.9 ­2.3 .. .. .. .. .. 13.8 Jamaicab .. 32.0 33.3 41.1 .. ­9.7 .. .. .. .. 145.0 59.2 Japan 20.6 .. .. .. .. .. 1.5 .. .. .. .. .. Jordanb 28.2 25.6 26.1 31.9 0.9 ­1.4 ­2.5 3.0 6.1 ­3.0 88.2 5.8 Kazakhstanb 14.0 16.0 18.7 14.9 ­1.8 0.2 0.8 1.2 2.8 ­0.9 13.2 3.6 Kenyab 26.0 18.2 25.5 20.6 ­1.1 ­2.4 4.3 4.5 0.0 ­4.1 .. 18.0 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.b 17.8 22.8 14.3 18.6 2.4 2.9 ­0.3 ­2.3 ­0.1 ­0.1 .. 5.1 Kuwaitb 36.8 54.4 46.4 43.3 ­13.6 6.5 .. .. .. .. .. 0.3 Kyrgyz Republicb 16.7 16.1 25.6 15.8 ­10.8 ­0.8 .. .. .. .. 99.3 8.4 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latviab 25.8 25.9 28.3 28.1 ­2.7 ­1.0 2.4 0.4 1.5 1.7 13.8 2.4 Lebanon .. 20.6 .. 30.8 .. ­13.4 .. 5.2 .. 6.9 .. 80.0 Lesothob 49.8 49.7 34.4 38.0 5.1 5.3 0.0 .. 6.2 .. .. 3.8 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 28.1 .. 28.8 .. ­1.6 .. ­0.3 .. 0.6 23.6 3.4 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 60.4 .. 63.0 .. ­22.5 .. ­3.6 .. 31.8 483.8 14.5 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiab 24.4 23.7 17.2 20.1 2.4 ­4.3 .. .. ­0.8 .. .. 10.5 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusb 21.6 21.8 19.9 21.8 ­1.3 ­3.2 3.1 ­1.6 ­0.6 ­0.3 42.7 13.9 Mexicob 15.3 .. 15.0 .. ­0.6 .. .. .. 5.5 .. .. .. Moldovab 28.4 28.8 38.4 27.1 ­6.3 0.4 3.0 2.4 2.7 ­1.9 52.0 8.2 Mongolia .. 37.9 .. 30.8 .. ­0.5 .. 11.3 .. ­6.8 119.8 3.1 Moroccob 27.6 .. 28.6 .. ­4.5 .. 5.6 .. ­0.7 .. .. .. Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.4 4.7 .. .. .. .. .. .. .. .. .. .. Namibiab 31.7 28.1 35.7 31.1 ­5.0 ­6.8 .. ­20.0 .. ­0.1 .. 9.1 Nepal 10.5 12.2 .. .. .. .. 0.6 0.1 2.5 1.4 66.7 11.7 Netherlands .. 41.1 .. 42.6 .. ­1.7 .. 0.9 3.3 .. 54.3 5.5 New Zealand .. 35.8 .. 31.5 .. 3.7 .. ­0.4 .. ­0.3 45.4 4.7 Nicaraguab 15.0 21.3 16.3 19.7 0.6 ­1.0 .. .. 3.4 .. .. 8.3 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 49.3 .. 37.2 .. 11.8 .. 2.3 .. 6.9 37.7 2.1 Omanb 27.8 27.0 32.4 26.9 ­8.9 ­2.8 ­0.1 3.0 0.0 ­2.1 19.9 4.5 Pakistanb 17.2 13.8 19.1 14.7 ­5.3 ­2.0 .. .. .. .. .. 39.9 Panamab 26.1 25.6 22.0 23.2 1.5 0.9 .. .. .. .. .. 19.3 Papua New Guineab 23.9 23.8 25.8 23.4 ­0.5 ­2.4 1.5 5.2 ­0.7 ­2.3 73.9 19.9 Paraguay b 15.3 16.9 13.0 12.9 0.1 1.4 0.0 ­1.2 ­0.8 ­0.2 .. 6.2 Perub 17.1 16.7 17.1 16.9 ­1.3 ­1.2 0.2 1.1 3.9 3.6 .. 11.9 Philippinesb 17.7 14.8 15.9 .. ­0.8 .. ­0.5 4.0 ­0.7 1.8 70.1 39.7 Polandb .. 35.0 .. 39.3 .. ­3.4 .. 3.7 .. 0.4 43.2 7.7 Portugal 37.1 37.8 39.7 41.9 ­3.1 ­2.3 ­3.7 1.2 4.3 3.2 .. 7.5 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 231 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest payments % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 2004 2004 Romaniab .. 25.8 .. 25.9 .. ­2.0 .. 0.4 .. 1.7 .. 8.4 Russian Federation .. 27.3 .. 21.9 .. 5.4 .. ­0.1 .. ­1.3 41.4 4.0 Rwanda 10.6 .. 15.0 .. ­5.6 .. 2.9 .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegalb 16.6 18.0 .. 15.6 .. ­2.2 .. 1.4 .. 1.6 73.6 4.6 Serbia and Montenegrob .. 35.8 .. 39.9 .. ­3.0 .. .. .. .. .. 2.6 Sierra Leone 9.4 .. .. .. .. .. 0.3 .. .. .. .. .. Singaporeb 26.8 20.2 12.5 15.5 19.9 4.2 10.3 9.3 0.0 .. 109.6 0.8 Slovak Republic .. 35.2 .. 36.8 .. ­3.3 .. 2.9 .. ­0.2 46.5 7.0 Sloveniab 37.2 40.7 35.7 41.2 ­0.2 ­1.3 ­0.4 2.3 0.3 ­0.8 .. 3.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa b .. 27.8 .. 29.4 .. ­1.9 .. 2.9 .. 0.4 36.9 12.7 Spain 29.2 26.0 33.0 29.7 ­2.6 0.6 3.4 0.1 .. .. .. 6.6 Sri Lanka b 20.4 16.4 26.0 22.9 ­7.6 ­7.6 5.2 7.0 3.2 0.1 105.5 43.6 Sudanb 7.0 .. 6.6 .. ­0.4 .. 0.3 .. .. .. .. .. Swazilandb .. 25.5 .. 23.2 .. ­2.3 .. .. .. .. .. 4.7 Sweden 40.7 38.0 39.3 37.5 2.2 0.3 .. 1.6 .. 0.5 62.4 5.3 Switzerlandb 22.7 19.4 25.8 19.1 ­0.6 0.6 ­0.5 ­0.6 .. .. 28.5 4.5 Syrian Arab Republicb 22.9 .. .. .. .. .. .. .. .. .. .. .. Tajikistanb 9.3 13.5 11.4 13.8 ­3.3 ­6.6 0.1 ­0.2 2.3 0.2 79.8 5.1 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 19.6 .. 17.1 .. 0.6 .. 4.0 .. ­0.3 26.1 6.7 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobagob 27.2 .. 25.3 .. ­0.1 .. 2.8 .. 2.6 .. .. .. Tunisiab 30.1 29.7 28.4 28.4 ­2.5 ­2.4 0.9 0.9 2.9 0.6 59.6 9.4 Turkey b 17.9 .. 21.0 .. ­4.1 .. 5.5 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda b 10.6 12.1 .. 22.8 .. ­3.8 .. 0.5 .. 4.2 .. 6.5 Ukraineb .. 30.7 .. 33.0 .. ­3.2 .. 2.9 .. 0.2 .. 2.8 United Arab Emiratesb 10.1 .. 9.3 .. 0.5 .. .. .. .. .. .. .. United Kingdom 37.3 36.6 37.2 39.9 0.3 ­3.2 ­0.3 3.6 0.0 0.0 .. 5.4 United States .. 17.2 .. 20.9 .. ­3.8 .. 0.1 .. 3.0 38.1 11.0 Uruguay b 27.6 26.5 27.1 27.5 ­1.2 ­2.5 7.9 ­0.5 1.1 1.8 88.5 18.5 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBb 16.9 24.0 18.5 25.2 ­2.3 ­4.1 1.1 6.3 0.1 0.2 .. 19.5 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.b 17.3 .. 19.1 .. ­3.9 .. .. .. .. .. .. .. Zambiab 20.0 .. 21.4 .. ­3.1 .. 28.0 .. 16.2 .. .. .. Zimbabweb 26.7 .. 32.1 .. ­5.4 .. ­1.4 .. 1.6 .. .. .. World .. w 24.6 w .. w 27.3 w .. w ­2.7 w .. m .. m .. m .. m .. m 7.9 m Low income 13.5 13.0 15.5 15.5 ­2.6 ­3.2 .. .. .. .. .. .. Middle income 17.3 .. .. .. .. .. .. 1.1 .. 0.8 .. 9.1 Lower middle income 16.7 .. .. .. .. .. .. 0.9 .. 1.1 .. 8.5 Upper middle income .. .. .. .. .. .. .. 2.9 .. 0.6 .. 10.5 Low & middle income 16.7 .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 8.4 11.5 .. 12.0 .. ­2.1 .. .. .. .. .. 7.6 Europe & Central Asia .. 31.0 .. 31.1 .. ­1.2 .. 0.9 .. 0.4 .. 3.5 Latin America & Carib. 20.9 .. 23.0 .. ­0.4 .. .. 1.0 .. 2.3 .. 11.9 Middle East & N. Africa 28.3 .. 23.5 .. 0.0 .. .. .. .. .. .. .. South Asia 13.2 12.4 15.4 15.1 ­2.7 ­3.1 3.8 1.3 1.1 1.1 65.8 16.4 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income .. 26.0 .. 28.9 .. ­2.8 .. 1.2 .. .. .. 6.0 Europe EMU 36.3 35.7 38.8 38.6 ­2.3 ­2.3 .. 1.1 .. .. .. 6.4 a. Excluding grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework. 232 2006 World Development Indicators Central government finances About the data Definitions Tables 4.10­4.12 present an overview of the size definition of government excludes public corporations · Revenue is cash receipts from taxes, social con- and role of central governments relative to national and quasi corporations (such as the central bank). tributions, and other revenues such as fines, fees, economies. The data in these tables are based on Units of government meeting this definition exist rent, and income from property or sales. Grants are the concepts and recommendations of the second at many levels, from local administrative units to the also considered as revenue but are excluded here. edition of the International Monetary Fund's (IMF) highest level of national government, but inadequate · Expense is cash payments for operating activities Government Finance Statistics Manual 2001. Before statistical coverage precludes the presentation of of the government in providing goods and services. It 2005, World Development Indicators reported data subnational data. Although data for general govern- includes compensation of employees (such as wages derived on the basis of the 1986 manual. The 2001 ment are available for a few countries under the 2001 and salaries), interest and subsidies, grants, social manual, which is harmonized with the 1993 System of manual, only data for the central government are benefits, and other expenses such as rent and divi- National Accounts, recommends an accrual account- shown to minimize disparities. However, cross-country dends. · Cash surplus or deficit is revenue (includ- ing method instead of the cash-based method of the comparisons are potentially misleading due to differ- ing grants) minus expense, minus net acquisition of 1986 manual. The new manual focuses on all eco- ent accounting concepts of central government. nonfinancial assets. In the earlier version nonfinancial nomic events affecting assets, liabilities, revenues, Central government can refer to one of two account- assets were included under revenue and expenditure and expenses, instead of only those represented by ing concepts: consolidated or budgetary. For most in gross terms. This cash surplus or deficit is closest cash transactions. The new manual takes all stocks countries central government finance data have been to the earlier overall budget balance (still missing is into account, so that the stock data at the end of consolidated into one account, but for others only lending minus repayments, which are brought in below an accounting period is equal to the stock data at budgetary central government accounts are avail- as a financing item under net acquisition of financial the beginning of the period plus the flows during able. Countries reporting budgetary data are noted assets). · Net incurrence of government liabilities the period. The 1986 manual considered only the in Primary data documentation. Because budgetary includes domestic financing (obtained from residents) debt stock data. Further, the new manual does not accounts do not necessarily include all central gov- and foreign financing (obtained from nonresidents), or distinguish between current and capital revenue ernment units (such as extrabudgetary accounts and the means by which a government provides financial or expenditures, unlike the 1986 manual. The new social security funds), the picture they provide of resources to cover a budget deficit or allocates financial manual also introduces the concepts of nonfinancial central government activities is usually incomplete. resources arising from a budget surplus. The net incur- and financial assets. Most countries still follow the Data on government revenues and expenditures rence of liabilities should be offset by the net acqui- previous manual, however. The IMF has reclassified are collected by the IMF through questionnaires dis- sition of financial assets (a third financing item). The historical Government Finance Statistics Yearbook tributed to member governments and by the Organ- difference between the cash surplus or deficit and the data to conform to the format of the 2001 manual. isation for Economic Co-operation and Development. three financing items is the net change in the stock of Because of differences in reporting, the reclassified Despite the IMF's efforts to systematize and stan- cash. · Total debt is the entire stock of direct govern- data understate both revenue and expense. dardize the collection of public finance data, statis- ment fixed-term contractual obligations to others out- Government Finance Statistics Manual 2001 tics on public finance are often incomplete, untimely, standing on a particular date. It includes domestic and describes the economic functions of a government as and not comparable across countries. foreign liabilities such as currency and money deposits, the provision of goods and services to the community Government finance statistics are reported in local securities other than shares, and loans. It is the gross on a nonmarket basis for collective or individual con- currency. The indicators here are shown as percent- amount of government liabilities reduced by the amount sumption, and the redistribution of income and wealth ages of GDP. Many countries report government of equity and financial derivatives held by the govern- through transfer payments. The activities of govern- finance data by fiscal year; see Primary data documen- ment. Because debt is a stock rather than a flow, it is ment are financed mainly by taxation and other trans- tation for information on fiscal year end by country. measured as of a given date, usually the last day of fers of income, though other forms of financing such as the fiscal year. · Interest payments include interest borrowing for temporary periods can also be used. The payments on government debt--including long-term bonds, long-term loans, and other debt instruments --to domestic and foreign residents. Selected developing countries with large cash deficits Central government cash deficit as a share of GDP (%) 0 -5 Malaysia Venezuela, RB Croatia Uganda Maldives India Bolivia Hungary Namibia Tajikistan Colombia Sri Lanka -10 Ethiopia Jamaica Data sources Lebanon -15 Data on central government finances are from the -20 IMF's Government Finance Statistics Yearbook, 2005 and IMF data files. Each country's accounts Madagascar -25 are reported using the system of common defi- -30 nitions and classifications in the IMF's Govern- Sixteen developing economies had a cash deficit of about 4 percent of GDP. ment Finance Statistics Manual 2001. See these sources for complete and authoritative explana- Note: Data for 2004 refer to the most recent year for which dara are available in 2002­04. tions of concepts, definitions, and data sources. Source: International Monetary Fund, Government Finance Statistics data files. 2006 World Development Indicators 233 Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Afghanistan .. 51 .. 42 .. 0 .. 5 .. 2 Albaniaa 18 .. 14 .. 9 .. 59 .. 0 .. Algeriaa 6 6 39 32 13 12 34 50 8 .. Angola .. .. .. .. .. .. .. .. .. .. Argentina .. 5 .. 12 .. 26 .. 50 .. 7 Armeniaa .. 42 .. 17 .. 3 .. 32 .. 6 Australia .. 10 .. 10 .. 4 .. 70 .. 5 Austria 7 5 13 13 8 8 65 70 7 5 Azerbaijana 49 .. 10 .. 0 .. 41 .. 0 .. Bangladesha .. 17 .. 25 .. 20 .. 29 .. 9 Belarusa 39 19 5 13 1 2 55 64 0 3 Belgium 2 3 7 7 15 11 71 75 4 4 Benin .. .. .. .. .. .. .. .. .. .. Bolivia .. 17 .. 24 .. 9 .. 44 .. 5 Bosnia and Herzegovina .. 24 .. 28 .. 2 .. 42 .. 5 Botswanaa 32 .. 30 .. 2 .. 36 .. 2 .. Brazila 12 .. 9 .. 14 .. 66 .. .. .. Bulgariaa 18 22 7 11 37 5 38 59 2 3 Burkina Faso .. .. .. .. .. .. .. .. .. .. Burundia 20 .. 30 .. 6 .. 14 .. 10 .. Cambodia .. 32 .. 36 .. 3 .. 18 .. 12 Cameroon 13 .. 47 .. 24 .. 13 .. .. .. Canadaa 8 8 10 11 18 9 64 66 .. 6 Central African Republic .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile .. 9 .. 22 .. 5 .. 55 .. 8 China .. .. .. .. .. 7 .. 62 .. 0 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia .. 8 .. 14 .. 20 .. 25 .. 32 Congo, Dem. Rep.a 37 36 58 21 1 .. 2 43 .. .. Congo, Rep.a .. 29 .. 37 .. 29 .. 5 .. 0 Costa Ricaa 12 12 38 43 20 18 26 26 4 2 Côte d'Ivoire .. 30 .. 39 .. 16 .. 16 .. 1 Croatiaa 35 7 27 27 3 5 32 55 3 6 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. 6 .. 8 .. 3 .. 74 .. 9 Denmark 8 10 13 13 13 9 62 64 4 4 Dominican Republica 16 16 41 45 9 12 19 15 6 12 Ecuador 6 .. 49 .. 26 .. .. .. .. .. Egypt, Arab Rep.a 18 .. 22 .. 26 .. 6 .. .. .. El Salvador .. 13 .. 42 .. 13 .. 5 .. 27 Eritrea .. .. .. .. .. .. .. .. .. .. Estoniaa 33 32 14 10 1 1 46 58 0 0 Ethiopiaa .. 24 .. 14 .. 7 .. 42 .. 14 Finland 10 10 10 10 9 5 65 68 7 7 France 7 7 23 22 6 5 58 58 5 7 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. Georgiaa 52 22 11 16 10 10 26 50 .. 1 Germany 4 4 5 5 6 6 66 81 20 4 Ghana .. .. .. 45 .. 21 .. 5 .. .. Greece 10 .. 24 .. 20 .. 40 .. 6 .. Guatemalaa 15 12 50 28 12 11 18 21 6 29 Guinea 31 .. 47 .. 13 .. 10 .. 0 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 234 2006 World Development Indicators Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Honduras .. .. .. .. .. .. .. .. .. .. Hungary .. 8 .. 14 .. 10 .. 60 .. 8 Indiaa 14 15 10 10 27 26 33 .. 0 .. Indonesiaa 21 8 20 13 16 16 41 63 2 0 Iran, Islamic Rep.a 21 12 56 45 0 1 .. 34 .. 7 Iraq .. .. .. .. .. .. .. .. .. .. Irelanda 5 .. 13 .. 15 .. 63 .. 4 .. Israel .. 26 .. 24 .. 12 .. 29 .. 8 Italy 4 4 15 16 19 13 57 61 5 5 Jamaicaa 22 13 24 32 32 46 1 2 21 8 Japan .. .. .. .. .. .. .. .. .. .. Jordana 7 6 67 58 11 6 12 18 4 12 Kazakhstana .. 25 .. 9 3 4 58 54 .. 8 Kenyaa 17 32 32 47 30 17 .. 1 1 3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 16 12 15 11 3 6 63 56 3 15 Kuwaita 33 27 31 34 5 0 24 27 7 13 Kyrgyz Republica 32 34 36 41 5 9 27 17 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. Latviaa 20 12 20 16 3 2 56 44 0 25 Lebanon .. 3 .. 30 .. 54 .. 12 .. 2 Lesothoa 32 31 45 38 5 5 8 26 3 .. Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. 12 .. 18 .. 3 .. 58 .. 8 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar .. 14 .. 39 .. 23 .. 11 .. 13 Malawi .. .. .. .. .. .. .. .. .. .. Malaysiaa 23 26 34 30 17 12 27 31 1 1 Mali .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 12 45 39 12 14 28 32 2 3 Mexicoa 9 .. 19 .. 19 .. .. .. .. .. Moldovaa 10 18 8 15 11 9 71 49 1 9 Mongolia .. 36 .. 30 .. 4 .. 31 .. 0 Moroccoa 17 .. 39 .. 21 .. 19 .. 4 .. Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. Namibiaa 28 28 53 49 1 8 .. 14 4 2 Nepal .. .. .. .. .. .. .. .. .. .. Netherlands .. 7 .. 8 .. 5 .. 76 .. 3 New Zealand .. 26 .. 25 .. 5 .. 36 .. 6 Nicaraguaa 16 15 23 30 15 10 34 42 13 2 Niger .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. Norway .. 12 .. 16 .. 3 .. 64 .. 5 Omana 55 54 30 32 7 5 8 10 0 .. Pakistana .. 31 .. 5 28 39 2 25 .. .. Panamaa 16 16 45 37 8 21 30 25 1 .. Papua New Guineaa 19 35 36 28 20 21 26 16 1 .. Paraguaya 12 8 51 52 5 8 31 28 0 3 Perua 21 21 18 21 19 12 33 44 8 2 Philippinesa 15 .. 34 .. 33 .. 15 .. .. .. Polanda .. 13 .. 12 .. 7 .. 60 .. 7 Portugal 7 7 30 32 10 7 41 45 11 9 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 235 Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Romaniaa .. 22 .. 16 .. 8 .. 43 .. 12 Russian Federation .. 23 .. 18 .. 5 .. 46 .. 8 Rwanda 52 .. 36 .. 12 .. 5 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegala .. 25 .. 34 .. 6 .. 34 .. .. Serbia and Montenegroa .. 10 .. 14 .. 2 .. 68 .. 6 Sierra Leone .. .. .. .. .. .. .. .. .. .. Singaporea 38 35 39 31 8 1 15 33 .. .. Slovak Republic .. 12 .. 13 .. 7 .. 63 .. 5 Sloveniaa 19 16 21 19 3 4 55 59 3 2 Somalia .. .. .. .. .. .. .. .. .. .. South Africaa .. 12 .. 14 .. 12 .. 56 .. 6 Spain 5 6 18 17 11 7 59 66 7 4 Sri Lankaa 23 14 20 25 22 32 24 22 10 7 Sudana 44 .. 38 .. 8 .. 10 .. .. .. Swazilanda .. 4 .. 44 .. 5 .. 22 .. 25 Sweden 11 12 9 11 13 5 62 65 5 7 Switzerlanda 24 9 6 7 4 5 66 74 0 5 Syrian Arab Republica .. .. .. .. .. .. .. .. .. .. Tajikistana 47 29 8 9 12 5 33 27 .. 30 Tanzania .. .. .. .. .. .. .. .. .. .. Thailand .. 21 .. 32 .. 8 .. 33 .. 7 Togo .. .. .. .. .. .. .. .. .. .. Trinidad and Tobagoa 20 .. 36 .. 20 .. 24 .. 1 .. Tunisiaa 7 7 37 40 13 10 36 .. 7 .. Turkeya 8 .. 32 .. 13 .. 31 .. 4 .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Ugandaa .. 36 .. 11 .. 6 .. 47 .. .. Ukrainea .. 11 .. 16 .. 3 .. 62 .. 8 United Arab Emiratesa 50 .. 37 .. .. .. .. .. .. .. United Kingdom 22 19 7 13 9 5 53 53 9 10 United States .. 16 .. 13 .. 9 .. 60 .. 2 Uruguaya 13 14 17 22 6 18 64 46 0 .. Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 6 8 22 19 27 19 61 53 2 2 Vietnam .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep.a 8 .. 67 .. 16 .. 8 .. 0 .. Zambiaa 32 .. 35 .. 16 .. 19 .. 0 .. Zimbabwea 16 .. 34 .. 31 .. 19 .. .. .. World .. m .. m .. m .. m .. m .. m .. m .. m .. m .. m Low income .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. Upper middle income .. 13 .. 20 .. 13 .. 50 .. .. Low & middle income .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 20 .. 16 .. 4 .. 52 .. 8 Latin America & Carib. 14 13 36 29 15 14 .. 26 .. .. Middle East & N. Africa 13 .. 39 .. 13 .. 12 .. .. .. South Asia 32 38 23 26 22 12 15 8 .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. High income .. 10 .. 16 .. .. .. 61 .. .. Europe EMU 7 6 15 13 9 6 59 68 6 5 Note: Components may not sum to 100 percent because of missing data. a. Data were reported on a cash basis and have been adjusted to the accrual framework. 236 2006 World Development Indicators Central government expenses About the data Definitions The term expense replaced expenditure in this table to households are shown as subsidies and other · Goods and services include all government pay- in the 2005 edition of World Development Indicators transfers, and other expenses. The economic clas- ments in exchange for goods and services used for in accordance with use in the International Monetary sification can be problematic. For example, the dis- the production of market and nonmarket goods and Fund's (IMF) Government Finance Statistics Manual tinction between current and capital expense may services. Own-account capital formation is excluded. 2001. Government expenses include all nonrepay- be arbitrary, and subsidies to public corporations or · Compensation of employees consists of all pay- able payments, whether current or capital, requited banks may be disguised as capital financing. Subsi- ments in cash, as well as in kind (such as food and or unrequited. Total central government expense as dies may also be hidden in special contractual pric- housing), to employees in return for services ren- presented in the IMF's Government Finance Statis- ing for goods and services. For further discussion of dered, and government contributions to social insur- tics Yearbook is comparable to the concept used in government finance statistics, see About the data for ance schemes such as social security and pensions the 1993 System of National Accounts. tables 4.10 and 4.12. that provide benefits to employees. · Interest pay- Expenses can be measured either by function ments are payments made to nonresidents, to resi- (health, defense, education) or by economic type dents, and to other general government units for the (interest payments, wages and salaries, purchases use of borrowed money. (Repayment of principal is of goods and services). Functional data are often shown as a financing item, and commission charges incomplete, and coverage varies by country because are shown as purchases of services.) · Subsidies functional responsibilities stretch across levels of and other transfers include all unrequited, nonrepay- government for which no data are available. Defense able transfers on current account to private and expenses, usually the central government's respon- public enterprises; grants to foreign governments, sibility, are shown in table 5.7. For more information international organizations, and other government on education expenses, see table 2.10; for more on units; and social security, social assistance benefits, health expenses, see table 2.14. and employer social benefits in cash and in kind. The classification of expenses by economic type in · Other expense is spending on dividends, rent, and this table shows whether the government produces other miscellaneous expenses, including provision goods and services and distributes them, purchases for consumption of fixed capital. the goods and services from a third party and dis- tributes them, or transfers cash to households to make the purchases directly. When the government produces and provides goods and services, the cost is reflected in compensation of employees, use of goods and services, and consumption of fixed capi- tal. Purchases from a third party and cash transfers Interest payments are a large part of government expenditure for some developing economies Central government interest payments as share of total expense (%) 60 1995 50 2004 40 30 20 Data sources 10 Data on central government expenses are from 0 the IMF's Government Finance Statistics Year- vis n ca an ka p. a a a ea r a ca book, 2005 and IMF data files. Each country's no in di an bi Re ai an st in Ne nt In m as ba Gh m Gu ki iL lo ge o, ag Ja Pa d Le Co ng Sr an Ar accounts are reported using the system of com- w ad Ne Co M s itt a .K pu mon definitions and classifications in the IMF's Pa St Government Finance Statistics Manual 2001. Interest payments accounted for over 20 percent of total expense in 2004 for 12 countries. See these sources for complete and authorita- Note: Data for 2004 refer to the most recent year for which data are available in 2002­04. For Lebanon and Madagascar data for 1995 refer to 2000. And no data for 1995 are available for Republic of Congo, Argentina, tive explanations of concepts, definitions, and Ghana, St. Kitts and Nevis, and Colombia. data sources. Source: International Monetary Fund, Government Finance Statistics data files. 2006 World Development Indicators 237 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Afghanistan .. 3 .. 3 .. 18 .. 1 .. 1 .. 75 Albaniaa 8 .. 39 .. 14 .. 1 .. 15 .. 22 .. Algeriaa 65 66 10 9 18 13 1 1 .. .. 5 11 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 19 .. 29 .. 16 .. 14 .. 17 .. 5 Armeniaa .. 15 .. 31 .. 3 .. 25 .. 14 .. 12 Australia .. 62 .. 25 .. 2 .. 2 .. .. .. 9 Austria 24 25 24 25 0 0 4 4 42 40 6 6 Azerbaijana 31 .. 34 .. 33 .. 2 .. .. .. 0 .. Bangladesha .. 12 .. 29 .. 33 .. 4 .. .. .. 22 Belarusa 16 8 33 36 6 7 11 10 31 35 3 4 Belgium 37 35 23 23 .. .. 2 1 35 34 3 7 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia .. 7 .. 38 .. 3 .. 13 .. 8 .. 31 Bosnia and Herzegovina .. 2 .. 36 .. 10 .. 6 .. 34 .. 12 Botswanaa 21 .. 4 .. 15 .. 0 .. .. .. 59 .. Brazila 14 .. 24 .. 2 .. 4 .. 31 .. 26 .. Bulgariaa 17 15 28 40 8 2 3 0 21 27 23 16 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundia 14 .. 30 .. 20 .. 1 .. 5 .. 30 .. Cambodia .. 6 .. 38 .. 24 .. 0 .. .. .. 33 Cameroon 24 .. 23 .. 22 .. 4 .. 4 .. 21 .. Canadaa 50 52 17 18 2 1 .. .. 22 23 10 6 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 18 .. 45 .. 2 .. 6 .. 6 .. 22 China 9 21 61 79 7 -8 0 1 .. .. 22 7 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 35 .. 35 .. 5 .. 5 .. 0 .. 20 Congo, Dem. Rep.a 21 25 12 24 21 27 5 1 1 .. 41 23 Congo, Rep.a .. 4 .. 16 .. 7 .. 1 .. 4 .. 69 Costa Ricaa 11 15 32 38 15 5 1 2 28 32 12 8 Côte d'Ivoire 15 8 14 18 58 46 3 13 5 8 5 7 Croatiaa 11 7 42 47 9 2 1 1 33 34 4 9 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 20 .. 27 .. 0 .. 1 .. 45 .. 6 Denmark 34 38 40 44 .. .. 7 2 5 4 14 12 Dominican Republica 16 24 34 41 36 21 1 2 4 3 9 9 Ecuador 50 .. 26 .. 11 .. 1 .. .. .. 12 .. Egypt, Arab Rep.a 17 .. 13 .. 10 .. 10 .. 10 .. 41 .. El Salvador .. 21 .. 41 .. 7 .. 1 .. 14 .. 16 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estoniaa 19 13 39 41 0 0 0 .. 31 35 10 11 Ethiopiaa .. 15 .. 12 .. 27 .. 0 .. 5 .. 41 Finland 21 21 34 35 0 0 2 2 32 30 12 11 France 23 23 26 24 0 0 3 4 40 41 7 7 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgiaa 7 2 48 47 10 8 .. 0 13 23 22 21 Germany 16 16 20 22 .. .. 0 .. 58 59 6 3 Ghana 15 22 31 22 24 29 .. 2 .. .. 9 26 Greece 20 .. 30 .. 0 .. 3 .. 30 .. 16 .. Guatemalaa 19 25 46 58 23 10 3 1 2 2 6 4 Guinea 11 .. 20 .. 36 .. 0 .. .. .. 32 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 238 2006 World Development Indicators Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary .. 19 .. 36 .. 2 .. 2 .. 33 .. 8 Indiaa 23 35 28 31 24 14 0 0 0 0 25 19 Indonesiaa 46 28 33 32 4 3 1 4 6 3 9 30 Iran, Islamic Rep.a 12 9 5 2 9 8 1 1 6 13 66 66 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Irelanda 38 .. 35 .. .. .. 3 .. 14 .. 10 .. Israel .. 29 .. 29 .. 1 .. 6 .. 17 .. 18 Italy 33 31 23 24 .. 0 6 6 33 34 5 5 Jamaicaa .. 30 .. 34 .. 9 .. 7 .. 7 .. 0 Japan 35 .. 14 .. 1 .. 5 .. 26 .. 18 .. Jordana 10 8 23 32 22 11 9 10 .. 1 36 38 Kazakhstana 11 40 28 40 3 5 5 0 48 .. 6 15 Kenyaa 34 34 36 50 15 3 1 0 0 0 14 12 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 31 25 32 31 7 4 10 7 8 15 12 18 Kuwaita 1 0 0 .. 2 2 0 0 .. .. 97 97 Kyrgyz Republica 26 16 56 55 5 2 1 .. .. .. 11 26 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latviaa 7 12 41 36 3 1 0 0 35 31 13 20 Lebanon .. 10 .. 46 .. 8 .. 12 .. 1 .. 24 Lesothoa 15 20 12 17 49 45 1 0 .. .. 24 17 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 22 .. 36 .. 0 .. 0 .. 32 .. 9 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 6 .. 16 .. 27 .. 4 .. .. .. 46 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiaa 37 47 26 21 12 6 5 0 1 .. 19 26 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 13 25 43 34 20 6 5 6 4 16 16 Mexicoa 27 .. 54 .. 4 .. 2 .. 14 .. 16 .. Moldovaa 6 3 38 48 5 5 1 0 38 27 2 17 Mongolia .. 16 .. 35 .. 6 .. 0 .. 16 .. 27 Moroccoa 20 .. 40 .. 15 .. 3 .. 9 .. 13 .. Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 20 16 26 22 12 2 .. .. .. .. 42 60 Namibiaa 27 38 32 20 28 32 2 2 .. 1 11 8 Nepal 10 11 33 30 26 22 4 5 .. .. 27 33 Netherlands .. 23 .. 29 .. 1 .. 3 .. 37 .. 8 New Zealand .. 53 .. 29 .. 3 .. 0 .. 0 .. 15 Nicaraguaa 8 18 46 41 6 4 0 0 10 16 29 21 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 31 .. 26 .. 0 .. 1 .. 20 .. 22 Omana 21 21 1 1 3 3 2 2 .. .. 74 73 Pakistana 18 20 27 33 24 11 7 9 .. .. 24 27 Panamaa 20 15 17 9 11 9 3 4 16 20 34 44 Papua New Guineaa 40 50 8 13 27 26 2 3 0 0 23 8 Paraguaya 15 12 36 39 18 12 4 3 6 6 22 30 Perua 15 24 49 53 10 7 8 4 11 9 10 12 Philippinesa 33 40 26 25 29 18 4 3 .. .. 8 15 Polanda .. 14 .. 34 .. 1 .. 0 .. 40 .. 11 Portugal 23 23 32 32 0 0 2 2 29 31 14 12 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 239 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Romaniaa .. 9 .. 33 .. 3 .. 1 .. 42 .. 13 Russian Federation .. 4 .. 26 .. 19 .. 0 .. 27 .. 24 Rwanda 11 .. 25 .. 23 .. 3 .. 2 .. 36 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegala 17 20 19 30 36 33 2 4 .. .. 26 13 Serbia and Montenegroa .. 13 .. 39 .. 7 .. 4 .. 29 .. 9 Sierra Leone 15 .. 34 .. 39 .. 0 .. .. .. 12 .. Singaporea 26 28 20 24 1 0 15 11 .. .. 38 38 Slovak Republic .. 17 .. 29 .. 1 .. 0 .. 40 .. 13 Sloveniaa 13 15 33 33 9 1 0 4 42 38 3 10 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africaa .. 51 .. 35 .. 3 .. 4 .. 2 .. 5 Spain 26 23 23 15 0 0 0 0 40 41 10 22 Sri Lankaa 12 14 49 56 17 12 4 1 1 1 18 16 Sudana 17 .. 41 .. 27 .. 1 .. .. .. 14 .. Swazilanda .. 29 .. 15 .. 50 .. 0 .. .. .. 6 Sweden 15 5 26 34 0 .. 12 12 35 38 13 10 Switzerlanda 11 16 21 30 1 1 2 3 49 39 17 11 Syrian Arab Republica 23 .. 37 .. 13 .. 8 .. 0 .. 19 .. Tajikistana 6 3 63 54 12 11 0 1 13 12 5 18 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 32 .. 40 .. 8 .. 1 .. 5 .. 14 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobagoa 50 .. 26 .. 6 .. 1 .. 2 .. 15 .. Tunisiaa 16 23 20 35 28 7 4 4 15 18 17 12 Turkeya 31 .. 39 .. 4 .. 3 .. .. .. 23 .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandaa 10 12 45 24 7 16 2 0 .. .. 37 48 Ukrainea .. 15 .. 21 .. 5 .. 0 .. 38 .. 20 United Arab Emiratesa .. .. 15 .. .. .. .. .. 1 .. 84 .. United Kingdom 39 36 31 32 .. .. 6 6 19 22 5 4 United States .. 51 .. 4 .. 1 .. 1 .. 40 .. 3 Uruguaya 10 10 32 49 4 5 10 6 31 19 8 12 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 38 13 33 23 9 4 0 8 4 2 19 50 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.a 17 .. 10 .. 18 .. 3 .. .. .. 51 .. Zambiaa 27 .. 22 .. 36 .. 0 .. 0 .. 15 .. Zimbabwea 36 .. 22 .. 17 .. 3 .. 2 .. 19 .. World .. m .. m .. m .. m .. m .. m .. m .. m .. m .. m .. m .. m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 15 .. 34 .. 4 .. 2 .. .. .. 12 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 10 .. 36 .. 5 .. 0 .. 33 .. 15 Latin America & Carib. 16 .. 29 .. 12 7 3 .. .. 10 14 .. Middle East & N. Africa 17 .. 13 .. 15 .. 3 .. .. .. 36 .. South Asia 11 12 28 29 24 18 2 1 .. .. 26 33 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income .. .. .. 28 .. .. .. .. .. .. .. 11 Europe EMU 23 24 24 24 0 0 3 3 35 37 7 7 Note: Components may not sum to 100 percent because of missing data or adjustment to tax revenue. a. Data were reported on a cash basis and have been adjusted to the accrual framework. 240 2006 World Development Indicators Central government revenues About the data Definitions The International Monetary Fund (IMF) classifies Social security taxes do not reflect compulsory · Taxes on income, profits, and capital gains are government revenues as taxes, grants, and property payments made by employers to provident funds or levied on the actual or presumptive net income income. Taxes are classified by the base on which other agencies with a like purpose. Similarly, expen- of individuals, on the profits of corporations and the tax is levied, grants by the source, and property ditures from such funds are not reflected in govern- enterprises, and on capital gains, whether real- income by type (for example, interest, dividends, ment expenses (see table 4.11). For further discus- ized or not, on land, securities, and other assets. or rent). The most important source of revenue is sion of taxes and tax policies, see About the data Intragovernmental payments are eliminated in con- taxes. Grants are unrequited, nonrepayable, non- for table 5.6. For further discussion of government solidation. · Taxes on goods and services include compulsory receipts from other government units revenues and expenditures, see About the data for general sales and turnover or value added taxes, and foreign governments or from international orga- tables 4.10 and 4.11. selective excises on goods, selective taxes on ser- nizations. Transactions are generally recorded on an vices, taxes on the use of goods or property, taxes accrual basis. on extraction and production of minerals, and prof- The IMF's Government Finance Statistics Manual its of fiscal monopolies. · Taxes on international 2001 describes taxes as compulsory, unrequited trade include import duties, export duties, profits payments made to governments by individuals, busi- of export or import monopolies, exchange profits, nesses, or institutions. Taxes are classified in six and exchange taxes. · Other taxes include employer major groups by the base on which the tax is levied: payroll or labor taxes, taxes on property, and taxes income, profits, and capital gains; payroll and work- not allocable to other categories, such as penalties force; property; goods and services; international for late payment or nonpayment of taxes. · Social trade and transactions; and other taxes. However, contributions include social security contributions by the distinctions are not always clear. Taxes levied employees, employers, and self-employed individu- on the income and profits of individuals and corpora- als, and other contributions whose source cannot tions are classified as direct taxes, and taxes and be determined. They also include actual or imputed duties levied on goods and services are classified contributions to social insurance schemes operated as indirect taxes. This distinction may be a useful by governments. · Grants and other revenue include simplification, but it has no particular analytical sig- grants from other foreign governments, international nificance except with respect to the capacity to fix tax organizations, and other government units; interest; rates. Direct taxes tend to be progressive, whereas dividends; rent; requited, nonrepayable receipts indirect taxes are proportional. for public purposes (such as fines, administrative fees, and entrepreneurial income from government ownership of property); and voluntary, unrequited, Rich countries rely more on direct taxes nonrepayable receipts other than grants. Taxes on income and capital gains as a share of revenue, 2002­04 (%) 70 60 50 40 Data sources 30 Data on central government revenues are from the IMF's Government Finance Statistics Yearbook, 20 2005 and IMF data files. Each country's accounts are reported using the system of common defini- 10 tions and classifications in the IMF's Government 0 Finance Statistics Manual 2001. The IMF receives 1,000 10,000 100,000 additional information from the Organisation for 100 GNI per capita ($) Economic Co-operation and Development on the Low-income economies Middle-income economies High-income economies tax revenues of some of its members. See the IMF High-income economies prefer to tax income and property. Low-income economies tend to rely on indirect taxes on sources for complete and authoritative explana- international trade and goods and services. But in all groups there are exceptions. tions of concepts, definitions, and data sources. Source: International Monetary Fund, Government Finance Statistics data files and World Bank data files. 2006 World Development Indicators 241 Monetary indicators Money and Claims on Claims on Interest rate quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 13.1 .. 4.2 .. 2.0 18.5 6.6 20.6 11.8 ­65.5 5.7 Algeria 11.4 11.3 12.2 2.6 3.2 ­11.5 8.0 2.5 .. 8.0 .. ­2.0 Angola .. 37.0 .. 19.9 .. ­15.0 .. 15.4 .. 82.3 .. 28.3 Argentina 1,113.3 21.4 1,444.7 5.4 1,573.2 5.8 1,517.9 2.6 .. 6.8 .. ­2.2 Armenia 1,076.8 22.3 92.0 16.9 534.3 ­1.1 .. 4.9 .. 18.6 .. 17.9 Australia 12.8 11.7 13.8 14.8 ­2.2 0.5 13.5 3.6 17.9 8.9 13.9 5.2 Austriaa .. .. .. .. .. .. 3.4 .. .. .. .. .. Azerbaijan 825.8 46.1 134.1 28.2 574.2 ­1.2 .. 9.2 .. 15.7 .. 8.8 Bangladesh 10.4 16.3 9.2 11.5 ­0.1 3.8 12.0 7.1 16.0 14.8 9.1 10.1 Belarus .. 45.6 .. 42.6 .. 3.5 65.1 12.7 71.6 16.9 ­85.1 ­4.0 Belgiuma .. .. .. .. .. .. 6.1 1.6 13.0 6.7 9.9 4.3 Benin 28.6 ­9.3 ­1.3 3.3 12.4 ­0.4 7.0 3.5 16.0 .. 14.2 .. Bolivia 52.8 2.2 40.8 ­3.3 17.5 0.8 23.8 7.4 41.8 14.5 22.0 5.5 Bosnia and Herzegovina .. 22.4 .. 13.7 .. 0.3 .. 3.7 .. 10.3 .. 7.2 Botswana ­14.0 16.0 12.6 10.8 ­51.9 9.2 6.1 9.9 7.9 15.8 1.5 10.4 Brazil 1,306.0 19.4 1,841.5 13.4 3,178.2 ­1.0 9,394.3 15.4 .. 54.9 .. 43.2 Bulgaria 51.7 24.0 37.5 27.5 43.1 ­6.6 39.5 3.0 52.5 8.8 ­53.3 4.4 Burkina Faso ­0.5 ­8.3 3.6 6.4 ­1.5 ­4.6 7.0 3.5 16.0 .. 14.4 .. Burundi 9.6 17.8 15.4 1.5 ­6.9 34.2 4.0 .. 12.3 18.3 6.0 11.2 Cambodia .. 30.4 .. 14.5 .. ­2.4 .. 1.8 .. 17.6 .. 11.7 Cameroon ­1.7 6.4 0.9 1.1 ­3.0 ­1.5 7.5 5.0 18.5 18.0 16.6 17.5 Canada 7.8 10.2 9.2 13.1 0.5 0.8 9.9 0.8 14.1 4.0 10.5 0.9 Central African Republic ­3.7 14.2 ­1.6 9.0 2.3 4.7 7.5 5.0 18.5 18.0 15.9 20.2 Chad ­2.4 3.5 1.3 3.3 ­17.3 ­0.8 7.5 5.0 18.5 18.0 9.7 4.1 Chile 24.2 8.3 21.7 23.7 16.3 ­0.4 40.4 1.9 48.9 5.1 22.8 ­1.4 China 28.9 14.8 26.5 8.6 1.5 1.6 8.6 2.3 9.4 5.6 3.5 ­1.2 Hong Kong, China 8.5 7.3 7.9 2.2 ­1.0 0.7 6.7 0.0 10.0 5.0 0.5 8.0 Colombia 33.0 18.2 8.7 7.7 ­7.5 5.1 36.4 7.8 45.2 15.1 15.2 7.5 Congo, Dem. Rep. 195.4 72.6 18.0 16.1 429.7 ­15.9 .. .. .. 66.8 .. 31.5 Congo, Rep. 18.5 17.4 5.1 ­0.7 ­12.6 3.9 7.5 5.0 18.5 18.0 19.7 10.4 Costa Rica 27.5 33.8 7.3 15.5 8.2 9.8 21.2 9.5 32.6 23.4 11.8 10.6 Côte d'Ivoire ­2.6 9.6 ­3.9 4.9 ­3.0 ­3.9 7.0 3.5 16.0 .. 21.5 .. Croatia .. 8.2 .. 11.1 .. 0.3 658.5 1.9 1,157.8 11.7 81.0 8.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 4.4 .. 5.3 .. ­5.0 7.0 1.3 14.1 6.0 ­3.6 2.9 Denmark 6.5 11.0 3.0 26.5 ­3.1 ­5.4 7.9 2.4 14.1 7.1 10.1 5.4 Dominican Republic 42.5 9.0 19.1 ­1.2 1.1 21.2 20.0 21.1 35.3 32.6 ­14.5 ­12.3 Ecuador 50.3 24.2 9.3 23.4 ­26.5 ­11.7 43.5 4.1 37.5 9.6 29.9 5.3 Egypt, Arab Rep. 28.7 14.4 6.3 2.1 25.3 8.7 12.0 7.7 19.0 13.4 0.5 1.7 El Salvador ­17.5 1.6 ­24.2 4.5 10.2 0.7 18.0 .. 21.2 .. 15.7 .. Eritrea .. 11.6 .. 3.7 .. 11.3 .. .. .. .. .. .. Estonia 76.5 15.8 27.6 36.2 ­7.4 ­2.0 .. 2.2 30.5 5.7 ­86.6 2.5 Ethiopia 19.9 19.3 0.3 4.5 21.8 10.9 3.6 3.4 6.0 7.0 2.5 ­2.3 Finlanda .. .. .. .. .. .. 7.5 1.0 11.6 3.7 4.9 2.9 Francea .. .. .. .. .. .. 4.5 2.3 10.6 6.6 8.2 4.9 Gabon 3.3 11.4 0.7 ­8.5 ­20.6 ­14.5 7.5 5.0 18.5 18.0 2.7 10.3 Gambia, The 8.4 18.3 7.8 ­3.9 ­35.4 ­13.2 11.3 22.0 26.5 36.5 13.0 18.6 Georgia .. 42.4 .. 20.3 .. ­4.5 .. 7.2 .. 31.2 .. 19.7 Germanya .. .. .. .. .. .. 7.1 2.7 11.6 9.7 8.1 8.1 Ghana 13.3 27.4 4.9 13.0 9.9 23.2 21.3 13.6 25.6 .. ­5.9 .. Greecea .. .. .. .. .. .. 19.5 2.3 27.6 6.8 5.7 3.2 Guatemala 25.8 9.4 15.0 8.8 0.5 ­5.2 18.2 4.2 23.3 13.8 ­12.3 5.2 Guinea ­17.4 36.5 13.1 1.8 2.9 19.2 21.0 8.9 21.2 .. ­2.2 .. Guinea-Bissau 574.6 42.8 90.5 ­1.3 460.7 ­17.7 32.7 3.5 45.8 .. 11.9 .. Haiti 2.5 5.2 ­0.6 1.2 0.4 0.2 .. 10.8 .. 34.1 .. 2.9 242 2006 World Development Indicators Monetary indicators Money and Claims on Claims on Interest rate quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 21.4 20.7 13.0 11.0 ­10.9 ­3.2 8.8 11.1 17.1 19.9 ­3.4 11.3 Hungary 29.2 8.9 23.0 16.7 69.4 ­2.9 24.7 9.1 28.8 12.8 2.5 7.9 India 15.1 16.7 5.9 15.3 10.5 0.8 .. .. 16.5 10.9 5.4 5.4 Indonesia 44.6 8.2 66.9 11.7 ­6.7 0.7 17.5 6.4 20.8 14.1 12.2 6.6 Iran, Islamic Rep. 18.0 23.0 14.7 27.6 5.8 ­4.3 .. 11.7 .. 16.7 .. 0.1 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Irelanda .. .. .. .. .. .. 6.3 0.0 11.3 2.6 12.1 ­0.9 Israel 19.4 3.6 18.5 3.9 4.9 ­3.9 14.4 3.6 26.4 7.4 9.1 7.6 Italya .. .. .. .. .. .. 6.8 0.9 14.1 5.0 5.4 2.0 Jamaica 21.5 14.0 12.5 7.3 ­16.0 ­18.7 23.9 8.0 30.5 18.1 4.3 4.9 Japan 8.2 1.6 9.7 ­1.9 1.5 0.5 3.6 0.1 7.0 1.8 4.4 4.0 Jordan 8.3 10.5 4.7 9.4 1.0 2.9 8.2 2.5 10.3 8.3 ­1.0 2.9 Kazakhstan .. 69.3 .. 58.9 .. ­17.1 .. .. .. .. .. .. Kenya 20.1 13.7 8.0 14.5 21.5 ­1.2 13.7 2.4 18.8 12.5 7.3 5.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 17.2 ­0.6 36.1 1.6 ­1.2 1.4 10.0 3.9 10.0 5.9 ­0.5 3.2 Kuwait 4.8 12.1 0.4 13.9 ­1.6 ­9.7 7.4 2.7 8.4 5.6 10.3 ­14.2 Kyrgyz Republic .. 32.1 .. 18.1 .. ­30.6 .. 6.7 .. 29.3 .. 23.4 Lao PDR 7.8 21.6 3.6 4.9 7.0 ­0.2 30.0 7.9 26.0 29.3 11.4 17.2 Latvia .. 26.7 .. 47.3 .. ­2.9 34.8 3.3 86.4 7.4 21.3 0.3 Lebanon 55.1 10.1 27.6 1.7 18.5 5.8 16.9 7.4 39.9 10.8 21.2 7.7 Lesotho 8.4 3.3 6.8 1.9 ­14.9 ­23.3 13.0 4.2 20.4 12.4 10.8 10.5 Liberia 21.1 45.7 19.0 15.8 31.8 204.7 6.8 3.8 13.8 18.1 10.2 15.7 Libya 19.0 17.9 2.0 ­0.1 15.0 ­115.9 5.5 2.1 7.0 6.1 0.4 ­11.5 Lithuania .. 24.1 .. 26.0 .. 1.7 88.3 1.2 91.8 5.7 ­52.8 2.4 Macedonia, FYR .. 16.1 .. 14.3 .. ­0.3 .. 6.5 .. 12.4 .. 10.8 Madagascar 4.5 25.2 23.8 13.9 ­14.8 ­13.2 20.5 15.2 25.8 25.5 12.9 9.8 Malawi 11.1 29.7 15.5 11.5 ­12.9 10.2 12.1 13.7 21.0 36.8 9.3 22.7 Malaysia 10.6 19.3 20.8 22.5 ­1.2 ­4.5 5.7 3.0 8.8 6.0 4.8 ­0.2 Mali ­4.9 ­2.6 0.1 4.1 ­13.4 1.7 7.0 3.5 16.0 .. 10.6 .. Mauritania 11.5 10.5 20.2 18.7 1.5 ­15.8 5.0 8.0 10.0 21.0 7.2 10.8 Mauritius 21.2 13.2 10.8 7.6 0.8 8.9 12.6 8.1 18.0 21.0 6.6 14.2 Mexico 83.8 10.7 48.4 3.8 7.3 3.3 30.4 2.7 17.7 7.2 7.5 1.1 Moldova 358.0 39.8 53.3 13.6 447.0 9.8 .. 15.1 .. 20.9 .. 12.0 Mongolia 31.6 20.5 40.2 24.2 29.8 ­8.5 300.0 14.2 300.0 25.4 10.4 6.1 Morocco 21.5 7.8 12.4 4.4 ­4.9 ­1.6 8.5 3.6 9.0 11.5 2.1 9.9 Mozambique 37.2 5.8 22.0 1.6 ­6.8 ­13.7 .. 9.9 .. 22.1 .. 8.4 Myanmar 37.7 32.4 12.8 5.6 23.9 29.3 5.9 9.5 8.0 15.0 ­8.9 ­6.2 Namibia 30.3 20.7 15.4 30.3 ­4.7 9.5 12.8 6.4 23.4 11.4 17.9 8.2 Nepal 18.5 12.6 5.7 .. 7.3 2.4 11.9 2.7 14.4 8.5 3.2 3.8 Netherlandsa .. .. .. .. .. .. 3.3 2.3 11.8 2.8 9.3 1.5 New Zealand 12.5 5.6 4.2 14.6 ­1.6 0.5 11.7 5.8 16.0 10.4 13.3 6.4 Nicaragua 7,677.8 17.2 4,932.9 15.2 .. ­6.0 9.5 4.7 22.0 13.5 ­97.6 3.0 Niger ­4.1 19.7 ­5.1 9.5 1.4 10.7 7.0 3.5 16.0 .. 17.9 .. Nigeria 32.7 14.0 7.8 15.7 26.3 ­38.1 19.8 13.7 25.3 19.2 16.9 ­0.6 Norway 5.6 3.4 5.0 10.4 ­0.1 ­5.4 9.7 1.5 14.2 4.0 9.9 ­0.8 Oman 10.0 4.0 9.6 7.0 ­10.9 ­4.2 8.3 2.3 9.7 7.6 ­12.1 ­1.4 Pakistan 11.6 20.5 5.0 18.6 7.6 4.1 .. .. .. .. .. .. Panama 36.6 8.4 0.8 6.6 ­25.7 4.8 8.4 2.2 12.0 8.8 11.4 8.3 Papua New Guinea 4.3 12.4 ­1.1 ­2.0 8.6 3.9 8.7 1.7 15.5 13.3 10.9 12.5 Paraguay 54.4 14.2 32.0 6.6 ­9.2 ­1.4 22.9 5.1 31.0 33.5 ­3.9 22.3 Peru 6,384.9 3.1 2,123.7 ­0.2 2,127.1 ­4.8 2,439.6 3.0 4,774.5 14.5 ­29.7 8.3 Philippines 22.4 9.9 15.6 5.0 3.4 5.9 19.5 6.2 24.1 10.1 9.9 3.7 Poland 160.1 6.9 158.7 2.5 ­20.6 ­1.2 41.7 3.8 504.2 7.6 ­0.4 4.5 Portugala .. .. .. .. .. .. 14.0 .. 21.8 .. 7.6 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 243 Monetary indicators Money and Claims on Claims on Interest rate quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 26.4 39.9 .. 12.8 51.2 ­10.5 .. .. .. .. .. .. Russian Federation .. 33.7 .. 33.7 .. ­16.9 .. 3.8 .. 11.4 .. ­5.6 Rwanda 5.6 15.4 ­10.0 8.4 26.8 13.3 6.9 8.1 13.2 .. ­0.3 .. Saudi Arabia 4.6 17.3 ­4.5 20.6 4.2 ­10.3 8.0 1.7 .. .. .. .. Senegal ­4.8 12.2 ­8.4 5.5 ­5.3 ­3.1 7.0 3.5 16.0 .. 14.6 .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 74.0 20.1 4.9 9.2 228.7 ­10.5 40.5 10.1 52.5 22.1 ­10.6 5.3 Singapore 20.0 6.2 13.7 4.0 ­4.9 ­2.1 4.7 0.4 7.4 5.3 2.8 1.7 Slovak Republic .. 6.8 .. 3.2 .. 3.2 8.0 4.1 14.4 9.1 ­11.0 4.3 Slovenia 123.0 7.6 96.1 15.4 ­10.4 2.7 682.5 3.8 824.6 8.7 374.3 5.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 11.4 13.7 13.7 13.7 1.8 ­0.2 18.9 6.6 21.0 11.3 4.7 5.1 Spaina .. .. .. .. .. .. 10.7 2.5 16.0 4.3 8.1 ­0.1 Sri Lanka 19.9 19.6 16.2 15.8 4.4 6.3 19.4 5.1 13.0 9.5 ­5.9 0.1 Sudan 48.8 30.8 12.6 19.3 29.4 ­9.5 .. .. .. .. .. .. Swaziland 0.6 10.4 20.5 23.0 ­13.1 4.8 8.7 4.6 14.5 11.3 ­0.4 5.7 Sweden 0.8 2.4 13.6 13.4 ­12.2 16.0 9.9 1.0 16.7 4.0 7.3 3.2 Switzerland 0.8 2.9 11.7 4.7 1.0 0.0 8.3 0.4 7.4 3.2 2.9 2.7 Syrian Arab Republic 26.1 7.8 3.4 3.0 11.4 2.5 4.0 4.0 9.0 9.0 ­8.7 10.6 Tajikistan .. 9.8 .. 102.8 .. ­13.8 .. 9.7 .. 20.3 .. 2.9 Tanzania 41.9 19.2 22.6 10.2 80.6 ­2.5 17.0 4.2 31.0 13.9 8.6 9.5 Thailand 26.7 5.1 30.0 4.4 ­4.0 0.6 12.3 1.0 14.4 5.5 8.2 2.1 Togo 9.5 18.1 1.8 2.8 6.9 ­3.7 7.0 3.5 16.0 .. 12.6 .. Trinidad and Tobago 6.2 14.1 2.7 14.5 ­1.9 ­19.8 6.0 2.8 12.9 9.3 ­2.3 ­2.8 Tunisia 7.6 11.3 5.9 10.9 1.8 2.3 7.4 .. 4.8 .. ­3.7 .. Turkey 53.2 22.1 42.9 18.5 0.1 6.0 47.5 24.3 .. .. .. .. Turkmenistan .. 23.8 .. 3.4 .. ­10.3 .. .. .. .. .. .. Uganda 60.2 11.1 23.3 3.5 0.8 ­4.6 31.3 7.7 38.7 20.6 ­4.0 13.8 Ukraine 1,809.2 32.8 78.3 22.4 1,554.7 ­2.0 148.6 7.8 184.3 17.4 ­91.7 2.0 United Arab Emirates ­8.2 23.8 1.3 19.7 ­4.8 ­0.3 .. 3.6 .. 8.1 .. 18.4 United Kingdom 10.5 10.3 13.1 14.1 1.9 0.7 12.5 .. 14.8 4.4 6.7 2.2 United States 4.9 3.0 ­0.4 8.9 1.4 ­0.5 .. .. 10.0 4.3 5.9 1.7 Uruguay 118.5 ­1.7 56.2 ­11.9 25.5 1.5 147.5 6.2 163.8 23.7 27.5 15.1 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 64.9 46.9 17.6 37.6 45.3 ­8.2 27.8 12.6 35.5 18.5 ­4.4 ­9.7 Vietnam 12.3 31.1 19.6 32.6 23.7 ­6.5 22.0 6.6 32.2 9.5 12.6 2.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 11.3 14.6 1.4 5.9 10.2 ­2.4 .. 13.0 .. 18.5 .. 3.6 Zambia 47.9 32.1 22.8 16.4 195.2 13.0 25.7 11.5 35.1 30.7 ­34.5 8.7 Zimbabwe 15.1 229.3 13.5 103.2 5.0 180.7 8.8 103.2 11.7 278.9 ­2.6 ­15.8 a. As members of the European Monetary Union, these countries share a single currency, the euro. 244 2006 World Development Indicators Monetary indicators About the data Definitions Money and the financial accounts that record the reporting period. The valuation of financial derivatives · Money and quasi money comprise the sum of supply of money lie at the heart of a country's and the net liabilities of the banking system can also currency outside banks, demand deposits other financial system. There are several commonly used be difficult. The quality of commercial bank reporting than those of the central government, and the time, definitions of the money supply. The narrowest, also may be adversely affected by delays in reports savings, and foreign currency deposits of resident M1, encompasses currency held by the public and from bank branches, especially in countries where sectors other than the central government. This demand deposits with banks. M2 includes M1 plus branch accounts are not computerized. Thus the data definition of the money supply, often called M2, time and savings deposits with banks that require a in the balance sheets of commercial banks may be corresponds to lines 34 and 35 in the IMF's Inter- notice for withdrawal. M3 includes M2 as well as vari- based on preliminary estimates subject to constant national Financial Statistics (IFS). The change in ous money market instruments, such as certificates revision. This problem is likely to be even more serious money supply is measured as the difference in end- of deposit issued by banks, bank deposits denomi- for nonbank financial intermediaries. of-year totals relative to M2 in the preceding year. nated in foreign currency, and deposits with finan- Many interest rates coexist in an economy, · Claims on private sector (IFS line 32d) include cial institutions other than banks. However defined, reflecting competitive conditions, the terms govern- gross credit from the financial system to individuals, money is a liability of the banking system, distin- ing loans and deposits, and differences in the posi- enterprises, nonfinancial public entities not included guished from other bank liabilities by the special role tion and status of creditors and debtors. In some under net domestic credit, and financial institutions it plays as a medium of exchange, a unit of account, economies interest rates are set by regulation or not included elsewhere. · Claims on governments and a store of value. administrative fiat. In economies with imperfect and other public entities (IFS line 32an + 32b + 32bx The banking system's assets include its net for- markets, or where reported nominal rates are not + 32c) usually comprise direct credit for specific eign assets and net domestic credit. Net domestic indicative of effective rates, it may be difficult to purposes, such as financing the government budget credit includes credit extended to the private sector obtain data on interest rates that reflect actual deficit; loans to state enterprises; advances against and general government and credit extended to the market transactions. Deposit and lending rates are future credit authorizations; and purchases of trea- nonfinancial public sector in the form of investments collected by the International Monetary Fund (IMF) sury bills and bonds, net of deposits by the public in short- and long-term government securities and as representative interest rates offered by banks sector. Public sector deposits with the banking sys- loans to state enterprises; liabilities to the public to resident customers. The terms and conditions tem also include sinking funds for the service of debt and private sectors in the form of deposits with the attached to these rates differ by country, however, and temporary deposits of government revenues. banking system are netted out. Net domestic credit limiting their comparability. Real interest rates are · Deposit interest rate is the rate paid by commer- also includes credit to banking and nonbank financial calculated by adjusting nominal rates by an estimate cial or similar banks for demand, time, or savings institutions. of the inflation rate in the economy. A negative real deposits. · Lending interest rate is the rate charged Domestic credit is the main vehicle through which interest rate indicates a loss in the purchasing power by banks on loans to prime customers. · Real inter- changes in the money supply are regulated, with cen- of the principal. The real interest rates in the table est rate is the lending interest rate adjusted for tral bank lending to the government often playing the are calculated as (i ­ P) / (1 + P), where i is the inflation as measured by the GDP deflator. most important role. The central bank can regulate nominal lending interest rate and P is the inflation lending to the private sector in several ways--for rate (as measured by the GDP deflator). example, by adjusting the cost of the refinancing facilities it provides to banks, by changing market interest rates through open market operations, or by Data sources controlling the availability of credit through changes in the reserve requirements imposed on banks and Monetary and financial data are published by the ceilings on the credit provided by banks to the pri- IMF in its monthly International Financial Statis- vate sector. tics and annual International Financial Statistics Monetary accounts are derived from the balance Yearbook. The IMF collects data on the financial sheets of financial institutions--the central bank, systems of its member countries. The World Bank commercial banks, and nonbank financial interme- receives data from the IMF in electronic files that diaries. Although these balance sheets are usually may contain more recent revisions than the pub- reliable, they are subject to errors of classification, lished sources. The discussion of monetary indi- valuation, and timing and to differences in account- cators draws from an IMF publication by Marcello ing practices. For example, whether interest income Caiola, A Manual for Country Economists (1995). is recorded on an accrual or a cash basis can make a Also see the IMF's Monetary and Financial Statis- substantial difference, as can the treatment of non- tics Manual (2000) for guidelines for the presen- performing assets. Valuation errors typically arise with tation of monetary and financial statistics. World respect to foreign exchange transactions, particularly Bank data on the GDP deflator are used to derive in countries with flexible exchange rates or in those real interest rates. that have undergone a currency devaluation during the 2006 World Development Indicators 245 Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion official rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2004 2005 1990 2004 2004 2004 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Afghanistan 47.85 541.20 .. .. .. .. .. 8.5 .. .. .. .. Albania 102.78 94.58a 2.0 49.5 0.5 .. 23.9 4.2 17.3 3.5 .. .. Algeria 72.06 73.28 5.0 29.0 0.4 85.9 14.1 6.0 11.6 2.8 0.5 0.4 Angola 83.54 85.64a 0.0 52.1 0.6 .. 459.4 94.1 446.2 97.7 .. .. Argentina 2.92 2.90 0.3 0.9 0.3 .. 5.2 13.3 7.1 12.0 7.0 26.9 Armenia 533.45 457.69 0.0 147.1 0.3 80.7 102.5 3.0 31.4 2.4 3.1 1.0 Australia 1.36 1.31 1.4 1.4 1.0 121.9 2.0 3.1 2.4 3.1 1.3 1.6 Austriab 0.81 0.80 0.9 0.9 1.1 105.6 1.5 1.6 2.0 1.9 0.7 1.6 Azerbaijan 4,913.48 4,727.10 .. 1,211.5 0.2 .. 100.6 4.5 76.8 3.1 .. .. Bangladesh 59.51 64.33 9.5 12.7 0.2 .. 3.7 4.2 4.9 3.7 .. .. Belarus 2,160.26 2,153.82 0.0 728.2 0.3 .. 224.9 41.6 163.7 36.3 168.6 41.7 Belgiumb 0.81 0.80 0.9 0.9 1.1 106.5 1.8 1.9 1.9 1.9 1.6 0.9 Benin 528.29 527.47 159.8 277.2 0.5 .. 6.7 3.2 6.0 2.2 .. .. Bolivia 7.94 8.07 1.3 2.9 0.4 80.1 7.0 4.8 6.6 2.5 .. .. Bosnia and Herzegovina 1.58 1.57 .. 0.5 0.3 .. 3.3 3.6 .. .. .. .. Botswana 4.69 5.11 1.2 2.5 0.5 .. 8.4 6.2 9.4 7.9 .. .. Brazil 2.93 2.43 0.0 1.2 0.4 .. 102.7 10.6 98.3 9.6 105.4 18.3 Bulgaria 1.58 1.57 0.0 0.6 0.4 121.6 67.5 4.0 75.1 5.1 60.7 3.8 Burkina Faso 528.29 527.47 135.5 169.1 0.3 .. 4.5 3.0 4.3 2.2 .. .. Burundi 1,100.91 1,106.17a 49.4 145.9 0.1 63.2 11.7 6.6 13.9 5.7 .. .. Cambodia 4,016.25 4,092.50 .. 576.9 0.1 .. 2.9 2.1 4.0 2.0 .. .. Cameroon 528.29 527.47 171.0 221.6 0.4 112.7 4.1 2.3 5.5 .. .. .. Canada 1.30 1.21 1.3 1.3 1.0 111.1 1.7 2.1 1.9 2.4 2.2 0.5 Central African Republic 528.29 527.47 135.9 158.3 0.3 111.7 3.7 2.2 4.1 2.3 6.0 5.3 Chad 528.29 527.47 107.3 113.3 0.2 .. 6.5 5.3 5.6 2.3 .. .. Chile 609.37 560.09 148.7 312.9 0.5 83.0 7.0 5.0 6.7 2.5 6.7 6.1 China 8.28 8.19 1.3 c 1.9 c 0.2c 95.0 5.5 2.7 5.5 1.0 .. .. Hong Kong, China 7.79 7.78 6.4 6.0 0.8 .. 1.1 ­3.9 3.0 ­2.1 0.0 ­0.8 Colombia 2,628.61 2,320.75 117.9 793.5 0.3 88.4 16.9 7.1 16.1 6.8 13.9 7.2 Congo, Dem. Rep. 401.04 441.74a 0.0 66.6 0.2 32.0 523.0 55.8 496.4 53.0 .. .. Congo, Rep. 528.29 527.47 385.8 606.3 1.1 .. 7.5 ­2.8 6.7 1.6 .. .. Costa Rica 437.91 477.79 32.8 202.3 0.5 91.0 14.4 9.2 13.7 10.3 12.6 10.5 Côte d'Ivoire 528.29 527.47 167.1 320.7 0.6 117.6 6.7 2.9 5.6 3.1 .. .. Croatia 6.04 5.95 0.0 3.8 0.6 104.7 46.4 3.3 19.7 2.3 44.3 1.4 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 25.70 23.96 7.4 13.9 0.5 115.8 9.1 3.2 5.6 2.1 5.8 1.4 Denmark 5.99 6.00 8.3 8.4 1.4 109.5 2.0 1.9 2.2 2.1 1.3 0.9 Dominican Republic 42.12 30.41 2.5 12.1 0.3 79.3 10.2 20.6 9.8 20.7 .. .. Ecuador 1.00 1.00 0.4 0.6 0.6 146.4 4.0 11.9 36.0 13.6 38.5 5.9 Egypt, Arab Rep. 6.20 5.78 0.8 1.6 0.3 .. 6.7 4.9 6.8 4.8 5.6 9.7 El Salvador 8.75 8.75 2.4 4.0 0.5 .. 4.9 2.7 6.2 2.8 .. 1.8 Eritrea 13.79 13.79a 1.0 2.7 0.2 .. 10.8 15.8 .. .. .. .. Estonia 12.60 12.58 0.1 7.3 0.6 .. 31.5 3.7 13.3 3.2 4.9 1.6 Ethiopia 8.64 8.64a 0.7 1.2 0.1 .. 5.1 1.9 4.0 4.4 .. .. Finlandb 0.81 0.80 1.0 1.0 1.2 106.1 2.0 1.1 1.6 1.3 1.0 ­0.1 Franceb 0.81 0.80 1.0 0.9 1.1 107.8 1.3 1.8 1.6 2.0 .. 0.9 Gabon 528.29 527.47 339.4 413.9 0.8 108.2 4.9 1.2 3.3 1.2 .. .. Gambia, The 30.03 29.81a 1.8 4.3 0.1 51.1 6.9 18.9 4.8 10.9 .. .. Georgia 1.92 1.81 0.0 0.7 0.4 .. 156.4 5.8 13.9 5.2 .. .. Germany b 0.81 0.80 1.0 0.9 1.2 108.1 1.3 1.0 1.7 1.5 0.8 1.3 Ghana 9,004.63 9,072.54 95.6 1,592.3 0.2 100.4 26.0 25.0 26.3 21.3 .. .. Greeceb 0.81 0.80 0.3 0.7 0.8 111.3 7.1 3.6 6.8 3.4 3.4 2.8 Guatemala 7.95 7.63 1.4 4.1 0.5 .. 9.1 7.2 8.8 7.1 .. .. Guinea 2,225.03 2,550.00a 223.9 493.6 0.2 .. 5.8 8.7 .. .. .. .. Guinea-Bissau 528.29 527.47 11.0 133.9 0.3 .. 20.5 0.0 22.1 0.7 .. .. Haiti 38.35 40.45 1.1 9.7 0.3 .. 19.0 15.1 19.7 21.6 .. .. 246 2006 World Development Indicators Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion official rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2004 2005 1990 2004 2004 2004 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Honduras 18.21 18.83 1.3 6.7 0.4 .. 15.4 7.3 15.7 8.2 .. .. Hungary 202.75 199.58 20.9 120.6 0.6 131.4 15.4 7.4 15.9 6.2 12.7 1.9 India 45.32 44.10 4.8 9.2 0.2 .. 6.6 3.9 7.5 3.9 6.3 4.7 Indonesia 8,938.85 9,704.74 639.3 2,953.7 0.3 .. 16.1 7.9 13.5 9.1 15.2 5.9 Iran, Islamic Rep. 8,613.99 8,963.96 179.5 2,775.3 0.3 122.1 24.3 18.9 22.0 14.4 22.1 9.3 Iraq 0.31 .. .. .. .. .. .. 0.3 .. .. .. .. Irelandb 0.81 0.80 0.8 0.9 1.1 118.5 3.8 3.6 2.8 3.9 1.4 ­0.9 Israel 4.48 4.49 1.5 3.2 0.7 77.2 7.5 1.7 7.1 2.0 6.4 3.5 Italy b 0.81 0.80 0.7 0.8 1.0 111.0 3.3 2.9 3.2 2.5 2.5 1.3 Jamaica 61.20 62.28 4.9 46.8 0.8 .. 18.1 10.6 17.3 9.3 .. .. Japan 108.19 110.22 186.2 132.5 1.2 81.6 ­0.5 ­1.5 0.3 ­0.5 ­1.0 ­1.1 Jordan 0.71 0.71 0.3 0.3 0.5 .. 2.5 2.0 2.8 2.1 .. .. Kazakhstan 136.04 132.88 0.0 49.4 0.4 .. 104.4 9.2 33.6 6.7 12.6 6.1 Kenya 79.17 75.55 9.0 35.8 0.5 .. 12.2 4.0 12.0 6.9 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 1,145.32 1,024.12 543.9 793.8 0.7 .. 4.5 2.9 4.4 3.4 2.9 1.7 Kuwait 0.30 0.29 0.4 0.3 1.0 .. 3.0 4.8 1.8 1.1 1.2 2.0 Kyrgyz Republic 42.65 41.01 0.0 9.6 0.2 .. 63.7 4.2 14.7 3.7 25.0 6.9 Lao PDR 10,585.54 10,655.17 174.2 2,296.5 0.2 .. 27.9 11.1 29.0 11.5 .. .. Latvia 0.54 0.56 0.0 0.3 0.5 .. 27.9 3.9 17.0 3.2 6.9 3.3 Lebanon 1,507.50 1,507.50 305.4 1,354.5 0.9 .. 11.1 2.7 .. .. .. .. Lesotho 6.46 6.36 1.0 1.9 0.3 88.7 9.0 6.3 8.7 11.1 .. .. Liberia 54.91 57.10 .. .. .. .. 49.7 12.9 .. .. .. .. Libya 1.31 1.31 .. .. .. .. .. 16.3 1.9 ­5.9 .. .. Lithuania 2.78 2.77 0.0 1.4 0.5 .. 40.1 0.4 16.7 0.2 12.9 ­0.5 Macedonia, FYR 49.41 49.28 0.0 19.6 0.4 98.5 42.9 2.4 6.3 2.0 5.5 0.5 Madagascar 1,868.86 2,003.03 102.7 553.1 0.3 .. 15.3 9.6 15.1 8.3 .. .. Malawi 108.90 108.94a 1.4 27.9 0.3 74.6 29.9 15.0 29.7 14.0 .. .. Malaysia 3.80 3.79 1.5 1.8 0.5 91.9 3.4 2.8 3.0 1.4 2.9 3.8 Mali 528.29 527.47 140.7 209.0 0.4 .. 5.7 4.7 4.0 1.5 .. .. Mauritania 265.23a .. 36.0 55.2 0.2 .. 6.9 7.0 5.6 5.7 .. .. Mauritius 27.50 29.50 6.5 11.3 0.4 .. 5.8 5.3 6.3 5.1 .. .. Mexico 11.29 10.90 1.4 7.5 0.7 .. 15.8 7.0 15.7 5.1 15.0 6.6 Moldova 12.33 12.60 0.0 4.4 0.4 97.6 70.0 11.4 17.0 9.5 .. .. Mongolia 1,185.28 1,211.77a 2.3 352.7 0.3 .. 34.6 10.8 26.3 4.7 .. .. Morocco 8.87 8.87 3.2 3.5 0.4 92.9 2.1 0.8 2.9 1.5 2.2 ­0.6 Mozambique 22,581.34 23,060.98 316.9 5,312.1 0.2 .. 23.7 13.5 23.4 13.4 .. .. Myanmar 5.75 5.76 .. .. .. .. 24.6 .. 25.7 31.8 .. .. Namibia 6.46 6.36 1.0 2.7 0.4 .. 9.8 6.5 .. .. .. .. Nepal 73.67 71.37 6.8 13.2 0.2 .. 6.6 3.8 7.0 3.7 .. .. Netherlandsb 0.81 0.80 0.9 0.9 1.1 111.9 2.4 3.1 2.6 2.7 1.6 2.0 New Zealand 1.51 1.42 1.6 1.6 1.1 128.5 1.9 2.4 1.9 2.3 1.8 1.3 Nicaragua 15.94 16.73 0.0 3.7 0.2 82.6 26.5 6.5 20.4 5.9 .. .. Niger 528.29 527.47 121.2 160.7 0.3 .. 4.9 1.9 4.6 1.1 .. .. Nigeria 132.89 132.36a 3.7 61.6 0.5 107.2 23.2 15.3 24.5 14.9 .. .. Norway 6.74 6.44 8.2 9.6 1.4 106.0 3.2 1.4 2.2 1.8 1.3 0.6 Oman 0.39 0.38 0.3 0.2 0.6 .. 1.2 1.8 0.1 ­0.5 .. .. Pakistan 58.26 59.51 6.2 16.5 0.3 91.3 10.2 5.1 7.7 4.0 8.4 5.4 Panama 1.00 1.00 0.6 0.6 0.6 .. 2.6 0.9 1.1 0.9 0.8 ­0.2 Papua New Guinea 3.22 3.14a 0.5 0.9 0.3 99.2 7.5 7.4 10.3 10.1 .. .. Paraguay 5,974.58 6,177.96 400.1 1,513.4 0.3 73.6 11.3 12.4 11.5 9.7 12.2 15.9 Peru 3.41 3.30 0.1 1.5 0.4 .. 16.3 2.3 16.6 1.9 14.6 1.5 Philippines 56.04 55.09 5.6 12.8 0.2 79.6 7.5 4.5 6.7 4.5 7.9 8.1 Poland 3.66 3.24 0.2 1.8 0.5 99.8 16.6 2.0 17.5 2.6 13.6 2.9 Portugalb 0.81 0.80 0.4 0.7 0.8 109.9 4.6 3.5 3.9 3.4 .. 1.5 Puerto Rico .. .. 0.7 .. .. .. 3.1 .. .. .. .. .. 2006 World Development Indicators 247 Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion official rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2004 2005 1990 2004 2004 2004 1990­2000 2000­04 1990­2000 2000­04 1990­2000 2000­04 Romania 32,636.57 29,136.53 6.6 13,095.7 0.4 109.9 71.8 23.3 72.3 20.4 69.5 24.0 Russian Federation 28.81 28.28 0.0 11.9 0.4 136.5 94.7 15.8 59.4 15.3 37.9 ­13.3 Rwanda 574.62 555.94 31.9 95.0 0.2 .. 10.3 5.0 11.7 5.7 .. .. Saudi Arabia 3.75 3.75 2.8 2.9 0.8 82.3 2.2 4.0 0.5 0.1 0.9 0.8 Senegal 528.29 527.47 185.1 222.0 0.4 .. 4.0 1.9 3.9 1.4 .. .. Serbia and Montenegro .. .. .. .. .. .. 51.3 29.3 .. .. .. .. Sierra Leone 2,701.30 2,889.59 29.5 653.4 0.2 74.4 25.8 13.1 20.9 4.4 .. .. Singapore 1.69 1.66 1.8 1.5 0.9 92.8 0.6 0.5 1.3 0.6 0.0 0.8 Slovak Republic 32.26 31.02 5.9 16.9 0.5 97.4 8.9 4.3 8.1 6.5 7.8 5.1 Slovenia 192.38 192.71 16.0 149.2 0.8 .. 19.2 6.4 9.7 6.3 7.5 4.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 6.46 6.36 1.0 2.7 0.4 107.8 9.0 7.1 7.7 5.9 7.3 6.4 Spainb 0.81 0.80 0.6 0.8 0.9 111.6 3.8 4.2 3.4 3.2 2.2 1.7 Sri Lanka 101.19 100.50 10.2 25.0 0.2 .. 8.9 8.5 9.5 9.1 7.6 8.9 Sudan 257.91 243.61 0.7 78.4 0.3 .. 43.8 8.3 46.1 8.0 .. .. Swaziland 6.46 6.36 0.8 2.8 0.4 .. 11.9 10.5 9.2 8.7 .. .. Sweden 7.35 7.47 9.1 9.6 1.3 100.7 1.9 1.8 1.7 1.8 2.2 1.0 Switzerland 1.24 1.25 2.0 1.8 1.5 105.4 0.9 1.0 1.3 0.7 ­0.3 0.2 Syrian Arab Republic 11.23 11.23 10.2 16.5 1.5 .. 6.6 3.2 4.9 .. 3.2 .. Tajikistan 2.97 3.12 0.0 0.8 0.3 .. 136.2 23.9 .. .. .. .. Tanzania 1,089.34 1,128.93 75.7 488.8 0.4 .. 16.5 5.9 14.9 2.4 .. .. Thailand 40.22 40.22 10.8 12.9 0.3 .. 3.2 1.8 3.9 1.6 3.6 3.5 Togo 528.29 527.47 93.8 127.5 0.2 112.8 5.0 0.7 6.1 1.5 .. .. Trinidad and Tobago 6.30 6.28 3.1 5.0 0.8 101.5 5.2 3.6 5.1 4.2 2.3 1.3 Tunisia 1.25 1.30 0.4 0.5 0.4 90.3 3.8 2.4 3.8 2.7 3.1 2.6 Turkey 1.43d 1.34 d 1,560.9 778,515.9 0.5 .. 65.0 31.9 68.4 32.6 .. .. Turkmenistan .. .. 0.0 1,931.0 .. .. 329.1 .. .. .. .. .. Uganda 1,810.31 1,780.67 109.1 327.9 0.2 82.7 8.3 3.8 7.4 3.3 .. .. Ukraine 5.32 5.12 0.0 1.1 0.2 81.7 133.5 8.9 73.3 5.9 75.5 8.9 United Arab Emirates 3.67 3.67 3.4 .. .. .. 2.7 2.7 .. .. .. .. United Kingdom 0.55 0.55 0.5 0.6 1.2 100.8 2.7 2.8 2.7 2.3 1.7 0.9 United States 1.00 1.00 1.0 1.0 1.0 92.6 1.9 2.1 2.6 2.3 1.4 2.3 Uruguay 28.70 24.48 0.6 11.8 0.4 59.6 22.4 13.4 23.9 12.6 22.1 24.8 Uzbekistan .. .. 0.0 251.3 .. .. 144.3 33.1 .. .. .. .. Venezuela, RB 1,891.33 2,089.75 24.4 1,323.1 0.7 66.2 38.3 27.8 39.3 22.7 37.6 35.2 Vietnam 15,509.58 15,776.00a 641.1 3,209.5 0.2 .. 10.9 5.1 3.0 3.5 .. .. West Bank and Gaza .. .. .. .. .. .. 8.9 10.9 .. .. .. .. Yemen, Rep. 184.78 185.58a 20.3 135.1 0.7 .. 18.1 8.0 20.8 11.7 .. .. Zambia 4,778.88 4,463.50 18.6 2,653.0 0.6 109.5 39.4 20.9 42.4 21.0 .. .. Zimbabwe 5,068.66 22,363.64 0.9 .. .. .. 53.7 207.2 36.1 .. 25.9 .. Note: Inconsistencies in the growth rates of the GDP deflator and the consumer and wholesale price indexes are due mainly to uneven coverage of the time period. a. Latest quarterly or monthly data available. b. As members of the European Monetary Union, these countries share a single currency, the euro. c. Based on a 1986 bilateral comparison of China and the United States (Rouen and Kai 1995), employing a different methodology than that used for other countries. This interim methodology will be revised in the next rfew years. d. New liras per dollar. 248 2006 World Development Indicators Exchange rates and prices About the data Definitions In a market-based economy the choices that house- effective exchange rate index and a cost indicator of · Official exchange rate is the exchange rate deter- holds, producers, and governments make about the relative normalized unit labor costs in manufacturing. mined by national authorities or the rate determined allocation of resources are influenced by relative For selected other countries the nominal effective in the legally sanctioned exchange market. It is calcu- prices, including the real exchange rate, real wages, exchange rate index is based on each country's trade lated as an annual average based on monthly aver- real interest rates, and a host of other prices in the in both manufactured goods and primary products ages (local currency units relative to the U.S. dollar). economy. Relative prices also reflect, to a large with its partner or competitor countries. For these · Purchasing power parity (PPP) conversion factor extent, the choices of these agents. Thus relative countries the real effective exchange rate index is is the number of units of a country's currency required prices convey vital information about the interaction derived from the nominal index adjusted for relative to buy the same amount of goods and services in the of economic agents in an economy and with the rest changes in consumer prices. An increase in the real domestic market as a U.S. dollar would buy in the of the world. effective exchange rate represents an appreciation United States. · Ratio of PPP conversion factor to The exchange rate is the price of one currency of the local currency. Because of conceptual and official exchange rate is the result obtained by divid- in terms of another. Official exchange rates and data limitations, changes in real effective exchange ing the PPP conversion factor by the official exchange exchange rate arrangements are established by gov- rates should be interpreted with caution. rate. · Real effective exchange rate is the nominal ernments. (Other exchange rates fully recognized by Controlling inflation is one of the primary goals of effective exchange rate (a measure of the value of governments include market rates, which are deter- monetary policy and is intimately linked to the growth a currency against a weighted average of several mined largely by legal market forces, and for coun- in money supply. Inflation is measured by the rate of foreign currencies) divided by a price deflator or index tries maintaining multiple exchange arrangements, increase in a price index, but actual price change can of costs. · GDP implicit deflator measures the aver- principal rates, secondary rates, and tertiary rates.) be negative. Which index is used depends on which age annual rate of price change in the economy as Also see Statistical methods for information on alter- set of prices in the economy is being examined. The a whole for the periods shown. · Consumer price native conversion factors used in the World Bank GDP deflator reflects changes in prices for total gross index reflects changes in the cost to the average con- Atlas method of calculating gross national income domestic product. The most general measure of the sumer of acquiring a basket of goods and services (GNI) per capita in U.S. dollars. overall price level, it takes into account changes in that may be fixed or may change at specified inter- The official or market exchange rate is often used government consumption, capital formation (includ- vals, such as yearly. The Laspeyres formula is gener- to compare prices in different currencies. Since ing inventory appreciation), international trade, and ally used. · Wholesale price index refers to a mix of exchange rates reflect at best the relative prices of the main component, household final consumption agricultural and industrial goods at various stages of tradable goods, the volume of goods and services expenditure. The GDP deflator is usually derived production and distribution, including import duties. that a U.S. dollar buys in the United States may not implicitly as the ratio of current to constant price The Laspeyres formula is generally used. correspond to what a U.S. dollar converted to another GDP, resulting in a Paasche index. It is defective as a country's currency at the official exchange rate would general measure of inflation for use in policy because buy in that country. Since identical volumes of goods of the long lags in deriving estimates and because it and services in different countries correspond to dif- is often only an annual measure. ferent values (and vice versa) when offcial exchange Consumer price indexes are produced more fre- rates are used, an alternative method of comparing quently and so are more current. They are also con- prices across countries has been developed. In this structed explicitly, based on surveys of the cost of method national currency estimates of GNI are con- a defined basket of consumer goods and services. verted to a common unit of account by using conver- Nevertheless, consumer price indexes should be sion factors that reflect equivalent purchasing power. interpreted with caution. The definition of a house- Purchasing power parity (PPP) conversion factors are hold, the basket of goods chosen, and the geo- based on price and expenditure surveys conducted graphic (urban or rural) and income group coverage by the International Comparison Program and rep- of consumer price surveys can all vary widely across resent the conversion factors applied to equalize countries. In addition, the weights are derived from price levels across countries. See About the data household expenditure surveys, which, for budget- for table 1.1 for further discussion of the PPP con- ary reasons, tend to be conducted infrequently in version factor. developing countries, leading to poor comparability The ratio of the PPP conversion factor to the official over time. Although useful for measuring consumer exchange rate (also referred to as the national price price inflation within a country, consumer price level) makes it possible to compare the cost of the indexes are of less value in making comparisons bundle of goods that make up gross domestic prod- across countries. Food price indexes, like consumer uct (GDP) across countries. These national price lev- price indexes, should be interpreted with caution els vary systematically, rising with GNI per capita. because of the high variability across countries in Real effective exchange rates represent a nominal the items covered. effective exchange rate index adjusted for relative Wholesale price indexes are based on the prices movements in national price or cost indicators of the of commodities that have some significance in the home country, selected countries, and the euro area. output or consumption of the country at the first A nominal effective exchange rate index represents commercial transaction. The prices are farm gate the ratio (expressed on the base 2000 = 100) of an prices for agricultural commodities and ex-factory Data sources index of a currency's period-average exchange rate to prices for industrial goods. Preference should be a weighted geometric average of exchange rates for given to indexes that provide the broadest coverage Data on official and real effective exchange rates currencies of selected countries and the euro area. of the economy. and consumer and wholesale price indexes are For most high-income countries, weights are derived The least-squares method is used to calculate the from the International Monetary Fund's International from trade in manufactured goods among industrial growth rates of the GDP implicit deflator, consumer Financial Statistics. PPP conversion factors and GDP countries. The data are compiled from the nominal price index, and wholesale price index. deflators are from the World Bank's data files. 2006 World Development Indicators 249 Balance of payments current account Goods and Net income Net current Current account Total services transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan 261 .. 727 .. 12 .. 311 .. ­143 .. 638 .. Albania 354 1,167 485 2,586 ­2 170 15 842 ­118 ­407 .. 1,388 Algeria 13,462 .. 10,106 .. ­2,268 .. 333 .. 1,420 .. 2,703 45,692 Angola 3,992 13,798 3,385 10,635 ­765 ­2,484 ­77 7 ­236 686 .. 1,374 Argentina 14,800 39,702 6,846 28,152 ­4,400 ­8,884 998 688 4,552 3,353 6,222 19,660 Armenia .. 985 .. 1,514 .. 37 .. 330 .. ­162 1 576 Australia 49,843 112,514 53,056 131,417 ­13,176 ­20,487 439 ­269 ­15,950 ­39,658 19,319 36,926 Austria 63,694 161,062 61,580 155,304 ­942 ­2,237 ­6 ­2,756 1,166 765 17,228 12,188 Azerbaijan .. 4,235 .. 6,312 .. ­701 .. 188 .. ­2,589 .. 1,090 Bangladesh 2,064 9,234 3,960 13,089 ­116 ­371 1,613 3,948 ­398 ­279 660 3,222 Belarus .. 15,666 .. 17,019 .. 26 .. 285 .. ­1,043 .. 837 Belgium 138,605b 297,953 135,098 b 284,718 2,316b 5,631 ­2,197b ­6,952 3,627b 11,914 23,789 13,991 Benin 364 713 454 1073 ­25 ­38 97 66 ­18 ­331 69 640 Bolivia 977 2,546 1,086 2,319 ­249 ­385 159 444 ­199 285 511 1,271 Bosnia and Herzegovina .. 2,914 .. 7,111 .. 446 .. 1,833 .. ­1,918 .. 2,408 Botswana 2,005 3,689 1,987 2,780 ­106 ­716 69 290 ­19 483 3,331 5,661 Brazil 35,170 109,059 28,184 80,069 ­11,608 ­20,520 799 3,268 ­3,823 11,738 9,200 52,935 Bulgaria 6,950 13,975 8,027 16,465 ­758 ­658 125 1,094 ­1,710 ­2,053 670 9,337 Burkina Faso 349 .. 758 .. 0 .. 332 .. ­77 .. 305 669 Burundi 89 43 318 175 ­15 ­17 174 124 ­69 ­25 112 66 Cambodia 314 3,243 507 3,663 ­21 ­239 120 442 ­93 ­217 .. 1,118 Cameroon 2,508 .. 2,475 .. ­558 .. ­26 .. ­551 .. 37 842 Canada 149,538 377,646 149,118 336,733 ­19,388 ­19,167 ­796 254 ­19,764 22,000 23,530 34,476 Central African Republic 220 .. 410 .. ­22 .. 123 .. ­89 .. 123 153 Chad 271 .. 488 .. ­21 .. 192 .. ­46 .. 132 227 Chile 10,221 37,981 9,166 29,542 ­1,737 ­8,101 198 1,051 ­485 1,390 6,784 15,997 China 57,374 655,827 46,706 606,543 1,055 ­3,523 274 22,898 11,997 68,659 34,476 622,949 Hong Kong, China .. 314,438 .. 299,591 .. 3,491 .. ­1,981 .. 16,357 24,656 123,569 Colombia 8,679 19,496 6,858 19,929 ­2,305 ­4,183 1,026 3,650 542 ­967 4,869 13,537 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 261 .. Congo, Rep. 1,488 1,546 1,282 995 ­460 ­546 3 ­8 ­251 ­3 10 124 Costa Rica 1,963 8,610 2,346 9,140 ­233 ­517 192 216 ­424 ­831 525 1,919 Côte d'Ivoire 3,503 7,650 3,445 6,181 ­1,091 ­705 ­181 ­462 ­1,214 303 21 1,694 Croatia .. 17,828 .. 20,180 .. ­772 .. 1,483 .. ­1,641 167 8,758 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 76,569 .. 76,966 .. ­5,433 .. 235 .. ­5,595 .. 28,451 Denmark 48,902 111,355 41,415 98,925 ­5,708 ­2,330 ­408 ­4,159 1,372 5,941 11,226 40,021 Dominican Republic 1,832 9,283 2,233 9,049 ­249 ­1,332 371 2,498 ­280 1,399 69 806 Ecuador 3,262 8,734 2,519 9,306 ­1,210 ­1,479 107 1,894 ­360 ­157 1,009 1,440 Egypt, Arab Rep. 9,895 26,516 14,091 26,915 ­1,022 ­246 7,545 4,567 2,327 3,922 3,620 15,339 El Salvador 973 4,301 1,624 7,029 ­132 ­459 631 2,576 ­152 ­612 595 1,938 Eritrea .. .. .. .. .. .. .. .. .. .. .. 35 Estonia 664 8,794 711 9,674 ­13 ­718 97 165 36 ­1,432 198 1,792 Ethiopia 597 1,684 1,271 3,778 ­69 ­29 449 1,372 ­294 ­751 55 1,497 Finland 31,180 71,099 33,456 60,636 ­3,735 238 ­952 ­1,003 ­6,962 9,698 10,415 13,010 France 285,389 531,488 283,238 526,635 ­3,896 8,540 ­8,199 ­21,775 ­9,944 ­8,382 68,291 77,353 Gabon 2,730 3,351 1,812 1,882 ­617 ­713 ­134 ­181 168 575 279 449 Gambia, The 168 .. 192 .. ­11 .. 59 .. 23 .. 55 84 Georgia .. 1,631 .. 2,491 .. 97 .. 414 .. ­349 .. 383 Germany 473,670 1,051,303 427,621 912,587 20,593 283 ­21,954 ­35,229 44,688 103,770 104,547 97,170 Ghana 983 3,487 1,506 5,356 ­111 ­198 411 1,831 ­223 ­236 309 1,750 Greece 13,018 48,824 19,564 61,380 ­1,709 ­5,097 4,718 4,504 ­3,537 ­13,148 4,721 2,708 Guatemala 1,568 4,608 1,812 8,483 ­196 ­319 227 3,006 ­213 ­1,188 362 3,522 Guinea 829 811 953 964 ­149 ­27 70 18 ­203 ­162 145 263 Guinea-Bissau 26 71 88 102 ­22 ­9 39 39 ­45 0 18 73 Haiti 318 469 515 1,375 ­18 ­14 193 907 ­22 ­13 10 115 Data for Taiwan, China 74,172 198,943 67,015 187,757 4,362 11,245 ­596 ­3,826 10,923 18,606 77,653 247,699 250 2006 World Development Indicators Balance of payments current account Goods and Net income Net current Current account Total services transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 1,032 3,066 1,127 4,430 ­237 ­279 280 1,230 ­51 ­413 47 1,980 Hungary 12,035 66,351 11,017 69,425 ­1,427 ­6,086 787 317 379 ­8,842 1,185 15,951 India 22,911 82,735 29,527 93,918 ­3,257 ­4,451 2,837 22,488 ­7,036 6,853 5,637 131,631 Indonesia 29,295 89,789 27,511 79,116 ­5,190 ­8,704 418 1,139 ­2,988 3,108 8,657 36,311 Iran, Islamic Rep. 19,741 .. 22,292 .. 378 .. 2,500 .. 327 .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 26,786 152,172 24,576 124,724 ­4,955 ­29,269 2,384 397 ­361 ­1,423 5,362 2,908 Israel 17,312 51,445 20,228 52,040 ­1,981 ­4,162 5,060 6,230 163 1,474 6,598 27,094 Italy 219,971 435,871 218,573 423,241 ­14,712 ­18,204 ­3,164 ­9,563 ­16,479 ­15,137 88,595 62,386 Jamaica 2,217 3,899 2,390 5,272 ­430 ­583 291 1,446 ­312 ­509 168 1,846 Japan 323,692 636,610 297,306 542,380 22,492 85,703 ­4,800 ­7,875 44,078 172,059 87,828 844,667 Jordan 2,511 5,983 3,569 9,407 ­214 190 1,045 3,216 ­227 ­18 1,139 5,447 Kazakhstan .. 22,602 .. 18,800 .. ­2,784 .. ­488 .. 530 .. 9,277 Kenya 2,228 4,202 2,705 5,115 ­418 ­114 368 649 ­527 ­378 236 1,520 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 73,297 299,174 76,373 269,782 ­88 725 1,150 ­2,504 ­2,014 27,613 14,916 199,195 Kuwait 8,268 33,543 7,169 18,510 7,738 6,400 ­4,951 ­2,548 3,886 18,884 2,929 9,354 Kyrgyz Republic .. 942 .. 1,135 .. ­90 .. 209 .. ­75 .. 565 Lao PDR 102 .. 212 .. ­1 .. 56 .. ­55 .. 8 275 Latvia 1,090 6,001 997 8,180 2 ­272 96 685 191 ­1,766 .. 2,021 Lebanon .. .. .. .. .. .. .. .. .. .. 4,210 15,774 Lesotho 100 771 754 1,398 433 303 286 248 65 ­76 72 503 Liberia .. .. .. .. .. .. .. .. .. .. 1 19 Libya 11,468 17,862 8,960 10,532 174 ­1,301 ­481 ­2,324 2,201 3,705 7,225 27,714 Lithuania .. 11,751 .. 13,321 .. ­612 .. 458 .. ­1,725 107 3,594 Macedonia, FYR .. 2,080 .. 3,247 .. ­39 .. 791 .. ­415 .. 991 Madagascar 471 1,126 809 1,654 ­161 ­79 234 299 ­265 ­309 92 504 Malawi 443 472 549 795 ­80 ­38 99 161 ­86 ­201 142 139 Malaysia 32,665 118,577 31,765 96,820 ­1,872 ­5,928 102 ­2,447 ­870 13,381 10,659 66,897 Mali 420 1,152 830 1,471 ­37 ­160 225 207 ­221 ­271 198 861 Mauritania 471 .. 520 .. ­46 .. 86 .. ­10 .. 59 420 Mauritius 1,722 3,460 1,916 3,603 ­23 ­14 97 49 ­119 ­107 761 1,633 Mexico 48,805 202,003 51,915 216,589 ­8,316 ­9,812 3,975 17,044 ­7,451 ­7,354 10,217 64,202 Moldova .. 1,331 .. 2,122 .. 357 .. 365 .. ­69 2 470 Mongolia 493 1,211 1,096 1,405 ­44 ­11 7 269 ­640 63 23 250 Morocco 6,239 16,632 7,783 19,860 ­988 ­671 2,336 4,868 ­196 970 2,338 16,647 Mozambique 229 1,759 996 2,381 ­97 ­300 448 314 ­415 ­607 232 1,159 Myanmar 319 3,181 603 2,458 ­192 ­745 39 134 ­436 112 410 773 Namibia 1,220 2,310 1,584 2,495 37 151 354 669 28 634 50 345 Nepal 422 1,224 834 2,186 14 ­15 109 1,173 ­289 197 354 1,529 Netherlands 159,304 388,899 147,652 341,622 ­620 15,088 ­2,943 ­7,952 8,089 54,414 34,401 21,050 New Zealand 11,683 28,305 11,699 28,791 ­1,576 ­5,793 138 79 ­1,453 ­6,199 4,129 5,294 Nicaragua 392 1,653 682 2,851 ­217 ­192 202 619 ­305 ­772 166 668 Niger 533 415 728 681 ­54 ­26 14 73 ­236 ­219 226 258 Nigeria 14,550 26,993 6,909 16,064 ­2,738 ­916 85 2,252 4,988 12,264 4,129 17,257 Norway 47,078 109,104 38,910 73,557 ­2,700 1,544 ­1,476 ­2,645 3,992 34,445 15,788 44,308 Oman 5,577 14,175 3,342 10,613 ­254 ­1,293 ­874 ­1,826 1,106 443 1,784 3,598 Pakistan 6,835 16,079 10,205 22,057 ­1,084 ­2,362 2,794 7,532 ­1,661 ­808 1,046 10,718 Panama 4,438 8,859 4,193 9,172 ­255 ­1,042 219 228 209 ­1,127 344 631 Papua New Guinea 1,381 .. 1,509 .. ­103 .. 156 .. ­76 .. 427 660 Paraguay 2,514 3,397 2,169 3,540 2 ­31 43 194 390 20 675 1,168 Peru 4,120 14,530 4,087 12,581 ­1,733 ­3,421 281 1,461 ­1,419 ­11 1,891 12,665 Philippines 11,430 42,829 13,967 50,492 ­872 147 714 9,596 ­2,695 2,080 2,036 16,234 Poland 19,037 95,333 15,095 99,935 ­3,386 ­11,399 2,511 5,644 3,067 ­10,357 4,674 36,773 Portugal 21,554 51,899 27,146 65,411 ­96 ­3,095 5,507 3,449 ­181 ­13,158 20,579 11,684 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 251 Balance of payments current account Goods and Net income Net current Current account Total services transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 6,380 27,099 9,901 34,029 161 ­1,766 106 3,107 ­3,254 ­5,589 1,374 16,095 Russian Federation .. 203,741 .. 130,144 .. ­13,000 .. ­677 .. 59,920 .. 126,258 Rwanda 143 201 354 493 ­16 ­27 143 314 ­85 ­6 44 315 Saudi Arabia 47,381 131,849 43,880 66,746 7,968 478 ­15,616 ­13,655 ­4,147 51,926 13,437 29,304 Senegal 1,453 1,826 1,840 2,657 ­129 ­136 153 530 ­363 ­437 22 1,386 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 210 215 215 342 ­71 ­67 7 119 ­69 ­74 5 125 Singapore 67,489 238,522 64,953 206,796 1,006 ­2,686 ­421 ­1,144 3,122 27,897 27,748 112,232 Slovak Republic .. 25,241 .. 25,649 .. ­119 .. 245 .. ­282 .. 14,912 Slovenia 7,900 19,519 6,930 19,927 ­38 ­300 46 38 978 ­670 112 8,900 Somalia 68 .. 468 .. ­84 .. 328 .. ­157 .. 23 .. South Africa 27,160 56,734 21,017 57,888 ­4,271 ­4,343 ­321 ­1,485 1,552 ­6,982 2,583 14,886 Spain 83,595 269,030 100,870 307,365 ­3,533 ­16,985 2,799 ­60 ­18,009 ­55,380 57,238 19,759 Sri Lanka 2,293 7,284 2,965 9,108 ­167 ­204 541 1,380 ­298 ­648 447 2,205 Sudan 499 3,822 877 4,651 ­136 ­1,113 141 1,123 ­372 ­818 11 1,626 Swaziland 658 2,438 768 2,448 59 32 102 92 51 114 216 324 Sweden 70,560 163,934 70,490 134,855 ­4,473 3,224 ­1,936 ­4,818 ­6,339 27,485 20,324 24,740 Switzerland 97,033 181,568 96,389 146,291 7,878 30,974 ­2,398 ­6,005 6,124 60,246 61,284 74,568 Syrian Arab Republic 5,030 8,175 2,955 7,915 ­401 ­729 88 679 1,762 210 535 .. Tajikistan .. 1,220 .. 1,445 .. ­58 .. 226 .. ­57 .. 172 Tanzania 538 2,179 1,474 3,196 ­185 ­39 562 618 ­559 ­437 193 2,296 Thailand 29,229 114,019 35,870 107,512 ­853 ­2,022 213 2,148 ­7,281 6,632 14,258 49,847 Togo 663 693 847 959 ­32 ­23 132 128 ­84 ­162 358 360 Trinidad and Tobago 2,289 5,890 1,427 4,283 ­397 ­681 ­6 59 459 985 513 3,195 Tunisia 5,203 13,308 6,039 14,099 ­455 ­1,298 828 1,534 ­463 ­555 867 4,031 Turkey 21,042 91,048 25,524 102,199 ­2,508 ­5,519 4,365 1,127 ­2,625 ­15,543 7,626 37,304 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 178 1,153 686 2,154 ­48 ­172 293 974 ­263 ­200 44 1,308 Ukraine .. 39,719 .. 34,846 .. ­645 .. 2,576 .. 6,804 469 9,526 United Arab Emirates .. .. .. .. .. .. .. .. .. .. 4,891 18,530 United Kingdom 239,226 533,167 264,090 604,562 ­5,154 48,582 ­8,794 ­19,697 ­38,811 ­42,511 43,146 49,740 United States 535,260 1,151,448 616,120 1,769,031 28,560 30,440 ­26,660 ­80,931 ­78,960 ­668,074 173,094 190,465 Uruguay 2,158 4,008 1,659 3,673 ­321 ­527 8 89 186 ­103 1,446 2,512 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 18,806 39,846 9,451 22,042 ­774 ­3,885 ­302 ­89 8,279 13,830 12,733 23,408 Vietnam .. 19,654 .. 21,458 .. ­721 .. 1,921 .. ­604 .. 7,041 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,490 5,045 2,170 4,918 ­372 ­1,346 1,790 1,444 739 225 441 5,687 Zambia 1,360 .. 1,897 .. ­437 .. 380 .. ­594 .. 201 337 Zimbabwe 2,012 .. 2,001 .. ­263 .. 112 .. ­140 .. 295 132 World 4,323,913 t 11,238,500 t 4,305,881 t 11,096,960 t Low income 84,525 224,181 104,291 247,585 Middle income 629,219 2,682,381 584,802 2,466,936 Lower middle income 302,952 1,477,304 303,741 1,362,939 Upper middle income 330,225 1,212,867 280,527 1,108,946 Low & middle income 714,917 2,954,236 689,885 2,775,051 East Asia & Pacific 167,280 1,105,736 166,172 1,026,871 Europe & Central Asia c .. 764,285 .. 729,833 Latin America & Carib. 170,341 541,417 147,342 502,855 Middle East & N. Africa .. .. 105,814 .. South Asia 34,864 114,362 48,099 131,775 Sub-Saharan Africa 78,044 166,824 72,993 166,946 High income 3,593,864 8,314,172 3,592,267 8,352,618 Europe EMU 1,523,198 3,491,397 1,484,793 3,286,395 a. International reserves including gold valued at London gold price. b. Includes Luxembourg. 252 2006 World Development Indicators Balance of payments current account About the data Definitions The balance of payments records an economy's trans- of residence and ownership, and the exchange rate · Exports and imports of goods and services com- actions with the rest of the world. Balance of payments used to value transactions--contribute to net errors prise all transactions between residents of an econ- accounts are divided into two groups: the current and omissions. In addition, smuggling and other ille- omy and the rest of the world involving a change in account, which records transactions in goods, ser- gal or quasi-legal transactions may be unrecorded or ownership of general merchandise, goods sent for vices, income, and current transfers, and the capital misrecorded. For further discussion of issues relat- processing and repairs, nonmonetary gold, and ser- and financial account, which records capital transfers, ing to the recording of data on trade in goods and vices. · Net income refers to receipts and payments acquisition or disposal of nonproduced, nonfinancial services, see About the data for tables 4.4­4.7. of employee compensation for nonresident workers, assets, and transactions in financial assets and liabili- The concepts and definitions underlying the data in and investment income (receipts and payments on ties. The table presents data from the current account the table are based on the fifth edition of the Inter- direct investment, portfolio investment, and other with the addition of gross international reserves. national Monetary Fund's (IMF) Balance of Payments investments and receipts on reserve assets). Income The balance of payments is a double-entry account- Manual (1993). The fifth edition redefined as capi- derived from the use of intangible assets is recorded ing system that shows all flows of goods and services tal transfers some transactions previously included under business services. · Net current transfers into and out of an economy; all transfers that are the in the current account, such as debt forgiveness, are recorded in the balance of payments whenever counterpart of real resources or financial claims pro- migrants' capital transfers, and foreign aid to acquire an economy provides or receives goods, services, vided to or by the rest of the world without a quid pro capital goods. Thus the current account balance now income, or financial items without a quid pro quo. All quo, such as donations and grants; and all changes reflects more accurately net current transfer receipts transfers not considered to be capital are current. in residents' claims on and liabilities to nonresidents in addition to transactions in goods, services (previ- · Current account balance is the sum of net exports that arise from economic transactions. All transac- ously nonfactor services), and income (previously of goods and services, net income, and net current tions are recorded twice--once as a credit and once factor income). Many countries maintain their data transfers. · Total reserves comprise holdings of as a debit. In principle the net balance should be collection systems according to the fourth edition. monetary gold, special drawing rights, reserves of zero, but in practice the accounts often do not bal- Where necessary, the IMF converts data reported IMF members held by the IMF, and holdings of foreign ance. In these cases a balancing item, net errors and in such systems to conform to the fifth edition (see exchange under the control of monetary authorities. omissions, is included. Primary data documentation). Values are in U.S. dol- The gold component of these reserves is valued at Discrepancies may arise in the balance of pay- lars converted at market exchange rates. year-end (31 December) London prices ($385.00 an ments because there is no single source for balance The data in this table come from the IMF's Balance ounce in 1990, and $438.00 an ounce in 2004). of payments data and therefore no way to ensure of Payments and International Financial Statistics that the data are fully consistent. Sources include databases, supplemented by estimates by World customs data, monetary accounts of the banking Bank staff for countries for which the IMF does not system, external debt records, information provided collect balance of payments statistics. In addition, by enterprises, surveys to estimate service transac- World Bank staff make estimates of missing data for tions, and foreign exchange records. Differences in the current year for major countries to obtain mean- collection methods--such as in timing, definitions ingful aggregates. Top 15 countries with the largest current account surplus, and top 15 countries with the largest current account deficit in 2003 Data sources Data on the balance of payments are published in Country $ billions % of GDP Country $ billions % of GDP the IMF's Balance of Payments Statistics Yearbook Japan 136.2 3.2 United States ­519.7 ­4.7 Germany 51.3 2.1 Spain ­31.7 ­3.6 and International Financial Statistics. The World China 45.9 2.8 Austria ­30.7 ­5.8 Bank exchanges data with the IMF through elec- Switzerland 44.7 13.9 United Kingdom ­27.5 ­1.5 tronic files that in most cases are more timely Russian Federation 35.4 8.2 Italy ­19.4 ­1.3 and cover a longer period than the published Netherlands 29.7 5.8 Greece ­12.6 ­7.2 sources. More information about the design and Taiwan, China 29.3 10.2 Portugal ­8.7 ­5.9 Norway 28.3 12.8 Mexico ­8.6 ­1.3 compilation of the balance of payments can be Saudi Arabia 28.1 13.1 Turkey ­8.0 ­3.3 found in the IMF's Balance of Payments Manual, Singapore 27.0 29.2 Hungary ­7.2 ­8.7 fifth edition (1993), Balance of Payments Textbook Sweden 22.8 7.6 Czech Republic ­5.8 ­6.4 (1996a), and Balance of Payments Compilation Hong Kong, China 16.5 10.6 Poland ­4.6 ­2.2 Guide (1995). The IMF's International Financial Malaysia 13.4 12.9 New Zeland ­3.4 ­4.2 Canada 13.4 1.6 Romania ­3.3 ­5.8 Statistics and Balance of Payments databases Korea, Rep. 11.9 2.0 South Africa ­2.6 ­1.6 are available on CD-ROM. Source: International Monetary Fund, Balance of Payments data files. 2006 World Development Indicators 253 External debt Total external Long-term Public and publicly Private Use of IMF debt debt guaranteed debt nonguaranteed credit external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 1,549 .. 1,451 .. 1,404 .. 677 .. 46 .. 97 Algeria 28,149 21,987 26,688 20,913 26,688 20,249 1,208 909 0 664 670 643 Angola 8,592 9,521 7,604 8,631 7,604 8,631 0 318 0 0 0 0 Argentina 62,233 169,247 48,676 127,661 46,876 103,850 2,609 7,447 1,800 23,811 3,083 14,091 Armenia .. 1,224 .. 984 .. 961 .. 780 .. 24 .. 218 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan .. 1,986 .. 1,640 .. 1,409 .. 493 .. 230 .. 208 Bangladesh 12,439 20,344 11,658 19,171 11,658 19,171 4,159 8,895 0 0 626 231 Belarus .. 3,717 .. 772 .. 744 .. 73 .. 29 .. 9 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 1,292 1,916 1,218 1,827 1,218 1,827 326 791 0 0 18 65 Bolivia 4,275 6,096 3,864 5,663 3,687 4,645 587 1,750 177 1,019 257 307 Bosnia and Herzegovina .. 3,202 .. 2,734 .. 2,644 .. 1,454 .. 90 .. 109 Botswana 553 524 547 488 547 488 169 11 0 0 0 0 Brazil 119,964 222,026 94,427 171,729 87,756 97,865 8,427 8,668 6,671 73,864 1,821 25,029 Bulgaria .. 15,661 .. 11,241 .. 7,434 .. 1,498 .. 3,807 .. 1,183 Burkina Faso 834 1,967 750 1,823 750 1,823 282 1,027 0 0 0 115 Burundi 907 1,385 851 1,325 851 1,325 398 793 0 0 43 41 Cambodia 1,845 3,377 1,683 3,016 1,683 3,016 0 467 0 0 27 97 Cameroon 6,657 9,496 5,577 8,557 5,347 7,924 871 1,200 230 632 121 333 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 698 1,078 624 926 624 926 265 455 0 0 37 44 Chad 529 1,701 469 1,582 469 1,582 186 910 0 0 31 96 Chile 19,226 44,058 14,687 36,351 10,425 9,426 1,874 445 4,263 26,925 1,156 0 China 55,301 248,934 45,515 131,342 45,515 90,815 5,881 21,705 0 40,527 469 0 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 17,222 37,732 15,784 32,391 14,671 23,372 3,874 3,494 1,113 9,019 0 0 Congo, Dem. Rep. 10,259 11,841 8,994 10,532 8,994 10,532 1,161 1,993 0 0 521 818 Congo, Rep. 4,947 5,829 4,200 5,051 4,200 5,051 239 269 0 0 11 29 Costa Rica 3,756 5,700 3,367 4,013 3,063 3,859 412 72 304 154 11 0 Côte d'Ivoire 17,251 11,739 13,223 10,837 10,665 9,828 1,920 2,383 2,558 1,010 431 311 Croatia .. 31,548 .. 29,338 .. 11,668 .. 856 .. 17,670 .. 0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 45,561 .. 28,470 .. 12,020 .. 52 .. 16,450 .. 0 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 4,372 6,965 3,518 5,815 3,419 5,815 258 390 99 0 72 204 Ecuador 12,107 16,868 10,029 15,062 9,865 10,629 848 851 164 4,433 265 290 Egypt, Arab Rep. 33,017 30,291 28,439 27,353 27,439 27,353 2,401 1,968 1,000 0 125 0 El Salvador 2,149 7,250 1,938 5,470 1,913 5,384 164 361 26 86 0 0 Eritrea .. 681 .. 666 .. 666 .. 352 .. 0 .. 0 Estonia .. 10,008 .. 7,009 .. 562 .. 49 .. 6,447 .. 0 Ethiopia 8,630 6,574 8,479 6,351 8,479 6,351 851 3,488 0 0 6 183 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 3,983 4,150 3,150 3,800 3,150 3,800 69 38 0 0 140 100 Gambia, The 369 674 308 622 308 622 102 247 0 0 45 25 Georgia .. 2,082 .. 1,710 .. 1,611 .. 678 .. 99 .. 266 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 3,734 7,035 2,670 5,861 2,637 5,861 1,423 4,312 33 0 745 469 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 2,849 5,532 2,368 3,916 2,241 3,796 293 478 127 120 67 0 Guinea 2,476 3,538 2,253 3,188 2,253 3,188 420 1,287 0 0 52 122 Guinea-Bissau 692 765 630 738 630 738 146 301 0 0 5 16 Haiti 910 1,225 772 1,186 772 1,186 324 504 0 0 38 11 254 2006 World Development Indicators External debt Total external Long-term Public and publicly Private Use of IMF debt debt guaranteed debt nonguaranteed credit external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 3,718 6,332 3,487 5,692 3,420 5,124 635 1,380 66 568 32 195 Hungary 21,202 63,159 17,931 50,829 17,931 20,725 1,512 220 0 30,103 330 0 India 83,628 122,723 72,462 115,199 70,974 88,699 20,996 28,507 1,488 26,499 2,623 0 Indonesia 69,872 140,649 58,242 106,463 47,982 72,917 10,385 9,939 10,261 33,546 494 9,686 Iran, Islamic Rep. 9,020 13,622 1,797 10,103 1,797 9,985 86 316 0 118 0 0 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 4,752 6,399 4,049 5,269 4,015 5,171 672 439 34 98 357 1 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 8,333 8,175 7,202 7,234 7,202 7,234 593 1,018 0 0 94 338 Kazakhstan .. 32,310 .. 28,738 .. 3,209 .. 1,275 .. 25,528 .. 0 Kenya 7,055 6,826 5,639 5,988 4,759 5,978 2,056 2,883 880 10 482 103 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 2,100 .. 1,885 .. 1,740 .. 579 .. 145 .. 207 Lao PDR 1,768 2,056 1,758 2,013 1,758 2,013 131 616 0 0 8 38 Latvia .. 12,661 .. 5,008 .. 1,587 .. 187 .. 3,421 .. 0 Lebanon 1,779 22,177 358 18,206 358 17,460 34 387 0 746 0 0 Lesotho 396 764 378 726 378 726 112 284 0 0 15 38 Liberia 1,849 2,706 1,116 1,168 1,116 1,168 248 263 0 0 322 347 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 9,475 .. 5,683 .. 2,507 .. 197 .. 3,176 .. 26 Macedonia, FYR .. 2,044 .. 1,901 .. 1,537 .. 605 .. 364 .. 63 Madagascar 3,689 3,462 3,320 3,232 3,320 3,232 797 2,269 0 0 144 226 Malawi 1,558 3,418 1,385 3,297 1,382 3,297 854 2,076 3 0 115 93 Malaysia 15,328 52,145 13,422 40,713 11,592 25,560 1,102 638 1,830 15,153 0 0 Mali 2,468 3,316 2,337 3,132 2,337 3,132 498 1,441 0 0 69 145 Mauritania 2,113 2,297 1,806 2,046 1,806 2,046 264 694 0 0 70 90 Mauritius 984 2,294 910 941 762 859 195 88 148 82 22 0 Mexico 104,442 138,689 81,809 129,600 75,974 77,193 11,030 9,564 5,835 52,407 6,551 0 Moldova .. 1,868 .. 1,168 .. 754 .. 386 .. 415 .. 126 Mongolia .. 1,517 .. 1,306 .. 1,306 .. 287 .. 0 .. 44 Morocco 25,004 17,672 23,847 17,461 23,647 14,863 3,138 2,545 200 2,598 750 0 Mozambique 4,650 4,651 4,231 4,108 4,211 3,157 268 1,475 19 951 74 197 Myanmar 4,695 7,239 4,466 5,647 4,466 5,647 716 774 0 0 0 0 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 1,640 3,354 1,572 3,332 1,572 3,332 668 1,491 0 0 44 22 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 10,745 5,145 8,313 4,403 8,313 4,125 299 1,167 0 278 0 248 Niger 1,726 1,950 1,487 1,811 1,226 1,772 461 1,106 261 39 85 135 Nigeria 33,439 35,890 31,935 31,304 31,545 31,304 3,321 1,994 391 0 0 0 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 2,736 3,872 2,400 2,565 2,400 1,209 52 0 0 1,356 0 0 Pakistan 20,663 35,687 16,643 32,566 16,506 31,029 3,922 9,278 138 1,537 836 1,876 Panama 6,493 9,469 3,842 9,047 3,842 7,305 462 246 0 1,742 272 36 Papua New Guinea 2,594 2,149 2,461 1,976 1,523 1,445 349 344 938 531 61 64 Paraguay 2,105 3,433 1,732 2,765 1,713 2,453 320 268 19 312 0 0 Peru 20,044 31,296 13,959 28,679 13,629 23,500 1,188 2,834 330 5,179 755 104 Philippines 30,580 60,550 25,241 54,748 24,040 35,564 4,044 3,531 1,201 19,184 912 756 Poland 49,364 99,190 39,261 82,343 39,261 36,595 55 1,912 0 45,748 509 0 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 255 External debt Total external Long-term Public and publicly Private Use of IMF debt debt guaranteed debt nonguaranteed credit external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 1,140 30,034 230 24,756 223 13,667 0 2,522 7 11,089 0 443 Russian Federation .. 197,335 .. 158,624 .. 99,646 .. 5,743 .. 58,978 .. 3,562 Rwanda 712 1,656 664 1,545 664 1,545 340 1,020 0 0 0 92 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 3,739 3,938 3,003 3,698 2,943 3,553 835 2,040 60 145 314 204 Serbia and Montenegro .. 15,882 .. 13,052 .. 9,508 .. 3,302 .. 3,545 .. 964 Sierra Leone 1,196 1,723 940 1,520 940 1,520 92 591 0 0 108 196 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. 22,068 .. 11,603 .. 5,163 .. 401 .. 6,440 .. 0 Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 2,370 2,849 1,926 1,949 1,926 1,949 419 444 0 0 159 174 South Africa .. 28,500 .. 20,591 .. 9,793 0 23 .. 10,798 0 0 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 5,863 10,887 5,049 10,061 4,947 9,765 946 2,168 102 296 410 294 Sudan 14,762 19,332 9,651 12,220 9,155 11,724 1,048 1,300 496 496 956 593 Swaziland 298 470 294 456 294 456 44 27 0 0 0 0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 17,259 21,521 15,108 15,742 15,108 15,742 523 24 0 0 0 0 Tajikistan .. 896 .. 773 .. 744 .. 296 .. 29 .. 122 Tanzania 6,454 7,799 5,794 6,237 5,782 6,225 1,493 3,916 12 12 140 423 Thailand 28,095 51,307 19,771 39,819 12,460 15,323 2,530 559 7,311 24,496 1 0 Togo 1,281 1,812 1,081 1,597 1,081 1,597 398 716 0 0 87 27 Trinidad and Tobago 2,512 2,926 2,055 1,531 1,782 1,421 41 70 273 110 329 0 Tunisia 7,688 18,700 6,878 16,243 6,660 14,574 1,406 1,854 218 1,669 176 0 Turkey 49,424 161,595 39,924 108,188 38,870 68,212 6,429 6,230 1,054 39,976 0 21,507 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2,583 4,822 2,160 4,498 2,160 4,498 969 3,303 0 0 282 192 Ukraine .. 21,652 .. 18,279 .. 10,729 .. 2,168 .. 7,551 .. 1,605 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 4,415 12,376 3,114 7,712 3,045 7,251 359 785 69 462 101 2,684 Uzbekistan .. 5,007 .. 4,810 .. 4,302 .. 317 .. 508 .. 19 Venezuela, RB 33,171 35,570 28,159 31,218 24,509 25,852 974 294 3,650 5,366 3,012 0 Vietnam 23,270 17,825 21,378 15,412 21,378 15,412 59 3,039 0 0 112 277 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,352 5,488 5,160 4,799 5,160 4,799 602 1,701 0 0 0 376 Zambia 6,905 7,279 4,543 6,257 4,541 5,871 813 2,637 2 386 949 890 Zimbabwe 3,279 4,797 2,681 3,604 2,496 3,558 449 983 185 46 7 293 World .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s Low income 332,618 426,945 285,169 379,869 277,476 346,191 56,639 110,191 7,692 33,678 10,818 10,888 Middle income 1,004,055 2,328,780 813,715 1,785,050 761,412 1,147,246 80,680 120,261 52,304 637,804 23,834 85,156 Lower middle income 553,760 1,140,272 456,404 859,746 423,325 589,496 53,183 84,079 33,079 270,250 8,305 43,131 Upper middle income 450,295 1,188,508 357,311 925,304 338,086 557,750 27,497 36,182 19,225 367,554 15,529 42,024 Low & middle income 1,336,673 2,755,725 1,098,884 2,164,919 1,038,888 1,493,438 137,319 230,452 59,996 671,482 34,652 96,044 East Asia & Pacific 234,078 588,888 194,619 403,085 172,984 269,630 25,307 42,041 21,635 133,455 2,085 10,964 Europe & Central Asia 217,224 794,943 176,378 603,687 171,457 321,739 10,429 32,978 4,921 281,947 1,305 30,735 Latin America & Carib. 444,629 778,970 352,716 639,331 327,698 433,342 35,877 41,926 25,018 205,989 18,298 43,307 Middle East & N. Africa 139,541 163,935 118,031 141,014 116,613 133,862 10,074 10,849 1,418 7,152 1,815 1,378 South Asia 124,396 193,933 107,527 181,227 105,800 152,895 30,717 50,465 1,727 28,332 4,537 2,423 Sub-Saharan Africa 176,805 235,056 149,612 196,576 144,336 181,970 24,916 52,193 5,276 14,606 6,612 7,238 High income Europe EMU 256 2006 World Development Indicators External debt About the data Definitions Data on the external debt of developing countries using end-of-period exchange rates, as published · Total external debt is debt owed to nonresidents are gathered by the World Bank through its Debtor in the IMF's International Financial Statistics (line repayable in foreign currency, goods, or services. It Reporting System. World Bank staff calculate the ae). Flow figures are converted at annual average is the sum of public, publicly guaranteed, and private indebtedness of these countries using loan-by-loan exchange rates (line rf). Projected debt service is nonguaranteed long-term debt, use of IMF credit, and reports submitted by them on long-term public and converted using end-of-period exchange rates. Debt short-term debt. Short-term debt includes all debt publicly guaranteed borrowing, along with informa- repayable in multiple currencies, goods, or services having an original maturity of one year or less and tion on short-term debt collected by the countries and debt with a provision for maintenance of the interest in arrears on long-term debt. · Long-term or collected from creditors through the reporting value of the currency of repayment are shown at debt is debt that has an original or extended maturity systems of the Bank for International Settlements book value. of more than one year. It has three components: and the Organisation for Economic Co-operation and Because flow data are converted at annual aver- public, publicly guaranteed, and private nonguaran- Development. These data are supplemented by infor- age exchange rates and stock data at end-of-period teed debt. · Public and publicly guaranteed debt mation on loans and credits from major multilateral exchange rates, year-to-year changes in debt out- comprises the long-term external obligations of pub- banks, loan statements from official lending agen- standing and disbursed are sometimes not equal lic debtors, including the national government and cies in major creditor countries, and estimates by to net flows (disbursements less principal repay- political subdivisions (or an agency of either) and World Bank and International Monetary Fund (IMF) ments); similarly, changes in debt outstanding, autonomous public bodies, and the external obli- staff. In addition, the table includes data on private including undisbursed debt, differ from commitments gations of private debtors that are guaranteed for nonguaranteed debt for 80 countries either reported less repayments. Discrepancies are particularly repayment by a public entity. · IBRD loans and IDA to the World Bank or estimated by its staff. significant when exchange rates have moved sharply credits are extended by the World Bank. The Inter- The coverage, quality, and timeliness of debt data during the year. Cancellations and reschedulings of national Bank for Reconstruction and Development vary across countries. Coverage varies for both other liabilities into long-term public debt also con- (IBRD) lends at market rates. The International Devel- debt instruments and borrowers. With the widening tribute to the differences. opment Association (IDA) provides credits at con- spectrum of debt instruments and investors and Variations in reporting rescheduled debt also cessional rates. · Private nonguaranteed external the expansion of private nonguaranteed borrowing, affect cross-country comparability. For example, debt consists of the long-term external obligations comprehensive coverage of long-term external debt rescheduling under the auspices of the Paris Club of of private debtors that are not guaranteed for repay- becomes more complex. Reporting countries differ official creditors may be subject to lags between the ment by a public entity. · Use of IMF credit denotes in their capacity to monitor debt, especially private completion of the general rescheduling agreement repurchase obligations to the IMF for all uses of IMF nonguaranteed debt. Even data on public and pub- and the completion of the specific bilateral agree- resources (excluding those resulting from drawings licly guaranteed debt are affected by coverage and ments that define the terms of the rescheduled debt. on the reserve tranche). These obligations, shown for accuracy in reporting--again because of monitoring Other areas of inconsistency include country treat- the end of the year specified, comprise purchases capacity and sometimes because of an unwilling- ment of arrears and of nonresident national deposits outstanding under the credit tranches (including ness to provide information. A key part often under- denominated in foreign currency. enlarged access resources) and all special facilities reported is military debt. (the buffer stock, compensatory financing, extended Because debt data are normally reported in the fund, and oil facilities), trust fund loans, and opera- currency of repayment, they have to be converted tions under the structural adjustment and enhanced into U.S. dollars to produce summary tables. Stock structural adjustment facilities. figures (amount of debt outstanding) are converted GDP is outpacing external debt in Sub-Saharan countries $ billions External debt Gross domestic product 600 Data sources 500 The main sources of external debt information are reports to the World Bank through its Debtor 400 Reporting System from member countries that 300 have received IBRD loans or IDA credits. Additional 200 information is from the files of the World Bank and the IMF. Summary tables of the external debt of 100 developing countries are published annually in the 0 World Bank's Global Development Finance and on 1990 1995 2000 2001 2002 2003 2004 its Global Development Finance CD-ROM. Source: World Bank data files. 2006 World Development Indicators 257 Debt ratios Present value Total debt Multilateral Short-term of debt service debt service debt % of exports of goods, sevices, % of exports of goods, % of public and publicly % of exports of goods, % of GNI and income % of GNI sevices, and income guaranteed debt % of total debt sevices, and income 2004 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 17 51 .. 1.0 .. .. .. 49.2 .. 0.1 .. .. Algeria 32 80 14.7 7.1 63.4 .. 5.0 28.1 2.8 2.0 5.7 .. Angola 68 82 4.0 11.9 8.1 14.8 2.2 0.6 11.5 9.4 24.7 6.4 Argentina 159 510 4.6 8.6 37.0 28.5 16.2 95.7 16.8 16.3 62.9 63.3 Armenia 50 130 .. 3.4 .. 8.1 .. 41.7 .. 1.8 .. 1.7 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 23 45 .. 3.0 .. 5.3 .. 31.5 .. 7.0 .. 3.1 Bangladesh 26 124 2.4 1.1 25.8 5.2 22.8 65.0 1.3 4.6 5.4 7.3 Belarus 20 30 .. 1.4 .. 2.1 .. 16.9 .. 79.0 .. 18.6 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 24a 113a 2.1 1.6 8.2 .. 95.7 57.4 4.3 1.3 11.9 .. Bolivia 38a 136a 8.3 6.1 38.6 18.6 67.6 94.6 3.6 2.1 15.5 4.6 Bosnia and Herzegovina 34 63 .. 2.0 .. 3.7 .. 66.5 .. 11.2 .. 7.5 Botswana 6 12 2.9 0.6 4.4 .. 61.3 69.6 1.1 6.8 0.2 .. Brazil 47 258 1.8 9.2 22.2 46.8 43.5 21.4 19.8 11.4 64.4 22.0 Bulgaria 83 143 .. 10.4 .. 17.1 .. 17.7 .. 20.7 .. 22.6 Burkina Faso 23a 203a 1.1 1.2 6.8 .. 73.0 62.5 10.1 1.5 16.6 .. Burundi 15 203 3.8 13.7 43.4 .. 51.1 90.3 1.5 1.4 13.7 .. Cambodia 68 99 2.7 0.6 .. 0.8 0.0 71.2 7.3 7.8 .. 7.7 Cameroon 20a 72a 4.9 4.6 20.5 .. 43.5 22.7 14.4 6.4 37.9 .. Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 75 599 2.0 1.4 13.2 .. 50.0 13.3 5.4 10.0 17.1 .. Chad 33a 79a 0.7 1.7 4.4 .. 72.3 76.3 5.6 1.4 10.9 .. Chile 57 141 9.7 10.4 25.9 24.2 35.7 27.8 17.6 17.5 31.6 19.5 China 15 46 2.0 1.2 11.7 3.5 7.6 28.7 16.9 47.2 15.4 17.3 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 49 204 10.2 8.2 40.9 33.0 32.2 38.7 8.4 14.2 15.1 22.9 Congo, Dem. Rep. 36 131 4.1 1.9 .. .. 49.6 50.9 7.3 4.1 .. .. Congo, Rep. 331 356 22.9 10.7 35.4 .. 12.7 47.5 14.9 12.9 49.0 .. Costa Rica 36 70 9.2 3.8 23.9 7.3 36.1 38.2 10.1 29.6 18.0 17.9 Côte d'Ivoire 90 170 13.7 3.7 35.4 6.9 77.5 32.1 20.9 5.0 101.0 7.5 Croatia 110 194 .. 15.8 .. 27.2 .. 9.1 .. 7.0 .. 11.3 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 51 71 .. 8.2 .. 10.5 .. 7.1 .. 37.5 .. 21.6 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 39 61 3.4 4.4 10.4 6.4 50.3 25.8 17.9 13.6 35.0 8.0 Ecuador 70 205 11.9 13.0 32.5 36.0 34.8 31.8 15.0 9.0 54.4 14.6 Egypt, Arab Rep. 32 108 7.3 3.0 20.4 7.6 18.7 23.0 13.5 9.7 29.6 9.7 El Salvador 54 123 4.4 4.0 15.3 8.8 60.2 53.4 9.8 24.6 15.5 25.5 Eritrea 53 154 .. 2.1 .. .. .. 50.3 .. 2.1 .. .. Estonia 111 132 .. 13.8 .. 15.7 .. 15.7 .. 30.0 .. 32.5 Ethiopia 30a 144a 2.8 1.2 39.0 5.3 14.6 77.7 1.7 0.6 24.0 2.1 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 75 117 3.3 3.6 6.4 .. 32.6 47.4 17.4 6.0 25.2 .. Gambia, The 108a 231a 12.9 8.6 22.2 .. 25.4 65.6 4.3 4.0 9.3 .. Georgia 37 100 .. 4.1 .. 11.2 .. 21.0 .. 5.1 .. 5.5 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 32a 76a 6.3 2.7 38.1 6.6 31.2 35.7 8.6 10.0 33.5 19.5 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 23 88 3.1 2.0 13.6 7.4 33.4 54.6 14.5 29.2 24.4 22.0 Guinea 45a 186a 6.3 4.5 20.0 19.9 22.1 66.4 7.0 6.5 20.5 26.5 Guinea-Bissau 326a 779a 3.6 16.7 31.0 .. 70.2 12.4 8.2 1.5 208.5 .. Haiti 29 76 1.3 3.7 11.0 .. 69.2 92.9 11.1 2.3 31.0 .. 258 2006 World Development Indicators Debt ratios Present value Total debt Multilateral Short-term of debt service debt service debt % of exports of goods, sevices, % of exports of goods, % of public and publicly % of exports of goods, % of GNI and income % of GNI sevices, and income guaranteed debt % of total debt sevices, and income 2004 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 38 68 13.7 4.7 35.3 7.8 90.7 70.9 5.4 7.0 18.1 10.5 Hungary 76 108 13.4 18.1 34.3 25.2 8.0 4.6 13.9 19.5 23.9 18.1 India 18 95 2.6 2.8 31.9 .. 22.5 9.0 10.2 6.1 33.3 .. Indonesia 61 175 9.1 8.2 33.3 22.1 22.5 26.7 15.9 17.4 37.3 26.4 Iran, Islamic Rep. 9 31 0.5 1.2 3.2 .. 30.5 5.1 80.1 25.8 35.8 .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 89 141 15.9 9.9 26.9 14.8 38.6 23.0 7.3 17.6 14.1 20.0 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 72 101 16.5 6.0 20.4 8.2 26.8 49.3 12.4 7.4 33.7 7.1 Kazakhstan 101 182 .. 23.1 .. 38.0 .. 32.9 .. 11.1 .. 15.5 Kenya 34 136 9.6 2.3 35.4 8.6 44.7 41.8 13.2 10.8 41.8 17.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 82 173 .. 7.6 .. 14.2 .. 90.9 .. 0.4 .. 0.7 Lao PDR 76 276 1.1 2.3 8.7 .. 53.6 74.3 0.1 0.2 2.1 .. Latvia 110 239 .. 10.0 .. 21.2 .. 92.5 .. 60.5 .. 117.7 Lebanon 121 488 2.9 21.0 .. .. 27.8 2.7 79.9 17.9 .. .. Lesotho 44 64 2.3 3.2 4.2 4.5 44.7 44.5 0.7 0.0 0.5 0.0 Liberia 760 2,133 .. 0.2 .. .. 100.0 .. 22.2 44.0 .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 53 96 .. 8.2 .. 14.3 .. 22.7 .. 39.8 .. 30.7 Macedonia, FYR 39 94 .. 4.6 .. 10.5 .. 40.9 .. 4.0 .. 3.5 Madagascar 38a 170a 7.5 1.9 45.5 .. 23.7 67.4 6.1 0.1 46.0 .. Malawi 60a 186a 7.2 3.3 29.3 .. 38.2 79.1 3.8 0.8 12.9 .. Malaysia 53 42 10.3 8.2 12.6 .. 9.9 3.6 12.4 21.9 5.5 .. Mali 33a 98a 2.8 2.2 12.3 .. 54.3 63.8 2.5 1.2 11.3 .. Mauritania 57a 186a 13.5 3.5 29.9 .. 73.8 61.3 11.2 7.0 48.7 .. Mauritius 43 69 6.6 4.3 8.8 7.4 51.6 22.4 5.3 59.0 2.9 38.5 Mexico 24 77 4.5 7.7 20.7 22.9 26.0 14.6 15.4 6.6 29.5 4.1 Moldovaa 75 108 .. 8.5 .. 12.1 .. 40.7 .. 30.7 .. 28.1 Mongolia 86 108 .. 2.6 .. 2.9 .. 40.5 .. 11.0 .. 11.7 Morocco 39 91 7.2 6.1 21.6 14.0 39.8 40.4 1.6 1.2 4.9 1.0 Mozambique 17a 54a 3.4 1.4 26.2 4.5 30.6 54.6 7.4 7.4 115.2 18.8 Myanmar .. 176 .. .. 18.4 3.8 43.6 2.4 4.9 22.0 69.8 48.2 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 37 119 1.9 1.7 15.7 5.5 36.8 72.6 1.5 0.0 5.4 0.0 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 35 78 1.6 2.9 3.9 5.8 21.1 55.5 22.6 9.6 602.0 22.7 Niger 25a 156a 4.1 1.7 17.4 .. 71.3 83.8 8.9 0.2 27.1 .. Nigeria 71 140 13.0 4.0 22.6 8.2 15.5 20.2 4.5 12.8 10.2 15.6 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 18 29 6.5 4.3 12.3 6.9 5.1 38.8 12.3 33.8 5.6 9.1 Pakistan 35 156 4.6 4.6 21.3 21.2 40.3 65.2 15.4 3.5 35.6 6.2 Panama 94 129 6.8 11.0 6.2 14.3 90.6 16.7 36.6 4.1 42.5 3.9 Papua New Guinea 66 80 17.9 13.6 37.2 .. 23.0 40.3 2.8 5.1 4.8 .. Paraguay 52 104 6.0 6.8 12.4 13.5 35.9 55.1 17.7 19.5 14.2 18.0 Peru 57 265 1.9 4.2 10.8 17.1 28.8 34.8 26.6 8.0 121.1 15.7 Philippines 73 124 8.2 12.8 27.0 20.9 28.7 12.4 14.5 8.3 33.3 9.1 Poland 45 121 1.7 14.5 4.9 34.6 9.2 24.6 19.4 17.0 48.9 16.9 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 259 Debt ratios Present value Total debt Multilateral Short-term of debt service debt service debt % of exports of goods, sevices, % of exports of goods, % of public and publicly % of exports of goods, % of GNI and income % of GNI sevices, and income guaranteed debt % of total debt sevices, and income 2004 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 51 136 0.1 6.6 0.3 17.2 .. 28.5 79.8 16.1 13.9 17.6 Russian Federation 46 120 .. 3.7 .. 9.8 .. 8.5 .. 17.8 .. 16.3 Rwanda 15a 150a 0.8 1.3 14.2 11.2 60.7 55.0 6.6 1.1 31.8 8.5 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 22a 61a 5.9 4.4 20.0 .. 39.8 36.2 11.3 0.9 25.9 .. Serbia and Montenegro 77 209 .. 4.1 .. .. .. 81.0 .. 11.8 .. .. Sierra Leone 37a 188a 3.7 2.5 10.1 10.9 26.1 31.4 12.4 0.4 70.6 3.0 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 67 86 .. 12.4 .. .. .. 26.8 .. 47.4 .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. 1.3 .. .. .. 100.0 .. 12.0 25.5 .. .. South Africa 17 54 .. 1.8 .. 6.4 .. 1.5 .. 27.8 .. 13.2 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 50 111 4.9 4.0 13.8 8.5 13.8 22.2 6.9 4.9 14.5 5.9 Sudan 151 625 0.4 1.6 8.7 6.0 100.0 18.3 28.2 33.7 724.8 124.3 Swaziland 27 25 4.9 1.8 5.7 1.7 72.9 50.1 1.5 3.0 0.6 0.5 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 101 249 10.0 1.4 21.8 3.5 3.5 33.5 12.5 26.9 39.4 62.5 Tajikistan 41 55 .. 5.1 .. 6.8 .. 11.0 .. 0.0 .. 0.0 Tanzania 22a, b 115a, b 4.4 1.1 32.9 5.3 52.7 80.1 8.1 14.6 95.5 50.4 Thailand 35 50 6.3 7.8 16.9 10.6 22.1 42.0 29.6 22.4 26.6 9.8 Togo 83 191 5.4 1.0 11.9 .. 40.8 45.7 8.8 10.4 15.6 .. Trinidad and Tobago 31 53 9.6 3.4 19.3 .. 4.7 31.6 5.1 47.7 5.5 .. Tunisia 79 147 12.1 7.6 24.5 13.7 26.0 41.8 8.3 13.1 10.8 16.5 Turkey 69 221 4.9 11.3 29.4 35.9 23.3 7.8 19.2 19.7 37.7 33.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 33a 162a 3.4 1.5 81.4 6.9 37.4 87.0 5.4 2.8 78.7 8.9 Ukraine 42 71 .. 6.7 .. 10.7 .. 22.7 .. 8.2 .. 4.4 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 108 351 11.0 12.2 40.8 34.9 16.2 37.3 27.2 16.0 49.7 44.8 Uzbekistan 45 123 .. 7.1 .. .. .. 13.3 .. 3.6 .. .. Venezuela, RB 45 125 10.8 6.2 23.3 16.0 1.6 16.9 6.0 12.2 9.3 10.5 Vietnam 39 65 2.9 1.8 .. .. 3.4 10.4 7.7 12.0 .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 37 66 3.5 1.9 5.6 3.5 51.0 58.4 18.8 5.7 39.4 4.9 Zambia 36 112 6.7 8.3 14.7 .. 41.0 48.2 20.5 1.8 103.8 .. Zimbabwe 33 264 5.5 2.0 23.2 .. 24.0 32.9 18.0 18.8 29.0 .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. Low income 3.9 3.0 22.4 10.1 27.7 24.8 11.0 8.5 39.9 17.7 Middle income 4.7 6.0 19.4 15.0 18.1 20.9 16.6 19.7 29.0 17.3 Lower middle income 4.1 4.5 24.3 13.1 22.3 26.9 16.1 20.8 30.1 16.5 Upper middle income .. 8.1 15.6 17.0 13.5 14.9 17.2 18.6 27.5 18.3 Low & middle income 4.5 5.6 19.8 14.5 19.4 21.4 15.2 18.0 30.5 17.3 East Asia & Pacific 4.8 3.0 17.5 6.8 17.7 23.0 16.0 29.7 20.5 16.8 Europe & Central Asia .. 8.9 .. 19.6 10.0 14.3 18.2 20.2 36.0 19.8 Latin America & Carib. 4.2 8.1 23.8 26.4 27.6 24.4 16.6 12.4 39.7 16.2 Middle East & N. Africa 6.3 4.4 21.4 10.6 13.1 24.3 14.1 13.1 22.5 12.9 South Asia 2.9 2.9 27.6 12.4 25.3 21.2 9.9 5.3 30.1 6.1 Sub-Saharan Africa .. 2.9 13.5 7.9 30.0 23.0 11.6 13.3 .. 18.0 High income Europe EMU a. Data are from debt sustainability analyses undertaken as part of the Heavily Indebted Poor Countries Initiative. Present value estimates for these countries are for public and publicly guaranteed debt only. b. Data refer to mainland Tanzania only. 260 2006 World Development Indicators Debt ratios About the data Definitions The indicators in the table measure the relative bur- rights reference rate, as are obligations to the Inter- · Present value of debt is the sum of short-term den on developing countries of servicing external national Monetary Fund (IMF). When the discount external debt plus the discounted sum of total debt debt. The present value of external debt provides a rate is greater than the interest rate of the loan, service payments due on public, publicly guaranteed, measure of future debt service obligations that can the present value is less than the nominal sum of and private nonguaranteed long-term external debt be compared with the current value of such indica- future debt service obligations. over the life of existing loans. · Exports of goods, tors as gross national income (GNI) and exports of The ratios in the table are used to assess the services, and income refer to international trans- goods and services. The table shows the present sustainability of a country's debt service obliga- actions involving a change in ownership of general value of total debt service both as a percentage tions, but there are no absolute rules to determine merchandise, goods sent for processing and repairs, of GNI in 2004 and as a percentage of exports in what values are too high. Empirical analysis of the nonmonetary gold, services, receipts of employee 2004. The ratios compare total debt service obliga- experience of developing countries and their debt compensation for nonresident workers, and invest- tions with the size of the economy and its ability service performance has shown that debt service ment income. · Total debt service is the sum of to obtain foreign exchange through exports. The difficulties become increasingly likely when the ratio principal repayments and interest actually paid on ratios shown here may differ from those published of the present value of debt to exports reaches 200 total long-term debt (public and publicly guaranteed elsewhere because estimates of exports and GNI percent. Still, what constitutes a sustainable debt and private nonguaranteed), use of IMF credit, and have been revised to incorporate data available as of burden varies from one country to another. Countries interest on short-term debt. · Multilateral debt ser- February 1, 2006. Exports refer to exports of goods, with fast-growing economies and exports are likely vice is the repayment of principal and interest to services, and income. Workers' remittances are not to be able to sustain higher debt levels. the World Bank, regional development banks, and included here, though they are included with income The most indebted low-income countries may be other multilateral and intergovernmental agencies. receipts in other World Bank publications such as eligible for debt relief under special programs, such · Short-term debt includes all debt having an original Global Development Finance. as the Heavily Indebted Poor Countries Debt Initia- maturity of one year or less and interest in arrears The present value of external debt is calculated tive. Indebted countries may also apply to the Paris on long-term debt. by discounting the debt service (interest plus amor- and London Clubs for renegotiation of obligations tization) due on long-term external debt over the to public and private creditors. The World Bank no life of existing loans. Short-term debt is included longer classifies countries by their level of indebted- at its face value. The data on debt are in U.S. dol- ness for the purposes of developing debt manage- lars converted at official exchange rates (see About ment startegies. 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 refer- ence rates for commercial interest established by the Organisation for Economic Co-operation and Development. Loans from the International Bank for Reconstruction and Development (IBRD) and credits from the International Development Asso- ciation (IDA) are discounted using a special drawing Data sources The debt burden of Sub-Saharan countries has been falling since 1995 Percent Total debt service Total debt service The main sources of external debt information (% of exportsa) (% of GNI) 20 are reports to the World Bank through its Debtor Reporting System from member countries that 15 have received IBRD loans or IDA credits. Additional information is from the files of the World Bank 10 and the IMF. Data on GNI and exports of goods and services are from the World Bank's national 5 accounts files and the IMF's Balance of Payments database. Summary tables of the external debt of 0 developing countries are published annually in the 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 World Bank's Global Development Finance and on a. Includes goods, services, income, and workers' remittances. its Global Development Finance CD-ROM. Source: World Bank data files. 2006 World Development Indicators 261 ffective governments improve people's standard of living by ensuring access to essen- tial services, such as health, education, water and sanitation, electricity, and trans- port, and the opportunity to live and work in peace and security. Countries confront different development challenges and face unique constraints. The main elements to get right are economic management, regulation, and taxation; efficient financial and labor markets; public safety and security; and the building and maintenance of infrastructure. Together, they pro- vide an environment of incentives and opportunities in which firms and individuals can invest and work productively. This section brings together indicators that measure the actions of governments and the responses of markets through three cross-cutting development themes: managing the public sector, developing the private sector, and providing infrastructure. Without good governance, other reforms have limited impact In successful, high-growth economies such as Botswana, China, India, the Republic of Korea, Mauritius, and Singapore, the state has played an important role in attracting investment; improving productivity, technology, and competitiveness; and promoting property rights, con- tract enforcement, and economic and political stability. Institutions differ in each of these countries, as do the choices of legal regimes, balance between regulation and competition, size of the public sector, and flexibility of fiscal and monetary policies. Solutions that work in one place may not work in another. And while an accountable and capable state with strong institutions has come to be recognized as fundamental to economic and social development, it is still difficult to quantify what is meant by good governance or to measure the quality of institutions. More research is needed to understand the role of institutions and how to improve them in countries with weak institutions. Many African leaders recognize that building strong institutions and improving public sector management are needed to encourage investment and economic growth and that poverty reduction is impossible without that. Improving public revenue and expenditure management is on the agenda of several African countries and is also a priority of the New Partnership for Africa's Development. Many countries are using public expenditure tracking surveys to identify shortcomings in the delivery of public services, including Cameron, Ghana, Madagascar, Mozambique, Rwanda, Senegal, Tanzania, Uganda, and Zambia. Expenditure transactions can be complex, with leak- ages and diversions common in a range of processes, including procurement. Such surveys map and track specific expenditure flows from allocation through intended use. In Uganda a survey conducted in 1996­99 increased intended resources arriving at a school from an average of 13 percent to 78 percent (World Bank 2005c). Although such surveys are not a substitute for broad strengthening of public sector financial management, they can help in understanding weaknesses in public financial management capacity and accountability mechanisms at various levels. 2005 World Development Indicators 263 Improving the investment climate for increased private sector investment, growth, and poverty Africa had the lowest business environment reform intensity in 2004 reduction Average number of reforms Better policies, institutions, physical infrastructure, and 2.5 human resources are needed to attract domestic and foreign investors and to improve the efficiency of firms. But the goal is an investment climate that benefits society, not just firms. 2.0 What can governments do to attract the investment needed for its citizens? They can create stability. Combat corruption by public officials, firms, and other interest groups. Foster 1.5 trust and legitimacy through participatory policymaking and transparency. And develop policies that address current eco- nomic and business conditions. Investment climate surveys 1.0 draw data directly from firms and cover both objective and subjective indicators. Investment climate indicators cover eight factors that influence investment decisions, from policy 0.5 uncertainty and corruption to reliability of electricity and the availability of skilled labor and labor regulations (table 5.2). In investment climate surveys senior managers ranked 0.0 policy uncertainty as the main business constraint. These Sub-Saharan Middle Latin East Asia South High-income Eastern surveys tell us that, compared with other developing country Africa East & America & & Pacific Asia OECD Europe & regions, Sub-Saharan Africa is a high-cost, high-risk place North Africa Caribbean Central Asia Source: Doing Business database. to do business, resulting in less investment, less employ- ment, lower incomes, less growth and competitiveness, and higher poverty. Overall, doing business in Africa costs But in some African countries, like Rwanda, reforms are about 20­40 percent more than in other developing country paying off. In 2001 Rwanda introduced new company and regions. Costs are higher because of burdensome regula- labor laws. Land titling reforms followed in 2002. In 2004 tions, difficulty securing property rights, ineffective courts, Rwanda was among the top reformers: it streamlined cus- weak infrastructure, and uncompetitive services. Because 80 toms procedures, improved credit registries, and simplified percent of investment is from domestic sources, institutions judicial procedures. Since initiating reform, Rwanda has had and policies in Africa need to focus on the domestic invest- economic growth averaging 3.6 percent a year--among the ment climate, especially for agriculture and in rural areas. In highest for non-oil-producing states in Africa. Uganda has 14 African countries with investment climate surveys, the high also benefited from an improved investment climate, posting cost of financing for firms is the number one complaint. GDP growth of about 7 percent a year during 1993­2002 and New studies of business regulations and their enforcement reducing poverty measured by a national poverty line from 55 have been conducted in 155 countries jointly by the Interna- percent in 1993 to 37.7 percent in 2000. Other African coun- tional Finance Corporation and World Bank through the Doing tries that have made progress in business reform include Business survey program. The Doing Business findings are Mauritius, Namibia, Nigeria, and South Africa. based on responses to objective questions using standardized surveys of experts, usually lawyers and accountants. These sur- Infrastructure for development veys complement the investment climate surveys by comparing Infrastructure services affect people in many ways--what the ease of doing business in 10 areas ranging from starting a they consume and produce; how they heat and light their business and dealing with licenses to hiring and firing, protect- homes; how they travel to work, to school, or to visit friends ing investors, trading across borders, enforcing contracts, and and family; and how they communicate, share information, closing a business. Data on most of these dimensions of doing and learn at home, school, and work. And the profitability business are presented in tables 5.3 and 5.6 . and competitiveness of businesses depend on the cost and Doing Business surveys have been conducted in 33 Afri- availability of infrastructure services such as the power and can countries. One conclusion of the Doing Business 2006 fuel used to operate machines or the transportation services report: more reform is needed in Africa (figure 5a). Entrepre- needed to deliver raw materials to factories and finished neurs face greater regulatory obstacles in Africa than in any products to market. other region. Of the 16 countries surveyed in West Africa just Physical isolation is a strong contributor to poverty. Popu- 2 carried out business regulation reforms. In the region as lations without reliable access to social and economic ser- a whole for every three countries that improved regulation, vices are poorer than those with reliable access. Problems one made it more burdensome. of access are particularly severe in rural areas far from roads 264 2006 World Development Indicators used regularly for motorized transport services. An estimated Latin America and the Caribbean (54 percent), South Asia (58 900 million rural dwellers in developing countries, most of percent), Europe and Central Asia (75 percent), and East Asia them poor, are without reliable access. (94 percent). In many developing countries increasing agricultural pro- Improvements in roads and transport services generally ductivity is central to rural development and poverty reduc- have significant positive effects on school attendance. In tion strategies. Improved rural transport makes it easier for Morocco in the early 1990s a paved road in the community farmers to obtain inputs and advice at reasonable cost and more than doubled girls' school attendance rates from 21 to sell their products at good prices. Farmers with difficult percent to 48 percent and raised boys' attendance rates access to local markets earn less for their products than from 58 percent to 76 percent, according to survey findings. farmers with easier access, and increases in output are In health, transport services play several important roles: associated with agricultural areas with improved roads. ensuring adequate and reliable availability of food, providing An indicator has been developed to measure rural transport medical supplies, transporting health personnel to facilities, access based on the proportion of the rural populations that and the most difficult role, bringing people to medical sta- lives within 2 kilometers of an all-season road (a road that can tions, whether for urgent care or regular treatment. be used all year by the prevailing means of rural transport, often Information and communication technology has the poten- a pick-up or other truck without four-wheel drive). Predictable tial for reducing poverty and fostering growth in developing interruptions of short duration during inclement weather (for countries. Mobile phones provide market information for example, heavy rainfall) are accepted, particularly on low-volume farmers and businesspeople, the Internet delivers infor- roads. The rural access index, calculated from representative mation to schools and hospitals, and computers improve household surveys, is shown for selected International Develop- public and private services and increase productivity and ment Association (IDA)­eligible countries, in table 5b. participation. Firms that use information and communica- Values of the rural access index were also calculated on the tion technology grow faster, invest more, and are more pro- basis of rural population, road length, and arable land area ductive and profitable than those that do not. A survey of for more that 30 other countries (mainly non-IDA recipients) firms in developing countries found that sales growth is 3.4 for which there are no suitable household survey results. The percentage points higher and value added per employee is values for 64 countries (representing 85 percent of the world's $3,400 higher in firms that use email to do business with rural population) show that 57 percent of rural inhabitants in clients and suppliers. And by making information accessible IDA countries enjoy adequate access compared with 87 per- to more people, information and communication technol- cent in non-IDA countries. Among developing country regions, ogy enhances social inclusion and promotes more effective, Sub-Saharan Africa had the lowest level of rural access (30 accountable governments. percent), followed by Middle East and North Africa (34 percent), Africa lags behind other regions in most infrastructure indicators. The high cost and poor quality of infrastructure services--transport, energy, water and sanitation, and infor- mation and communication technology--have limited growth Rural access index for selected low-income countries (% of rural potential. For Africa to reach the Millennium Development population) Goal of halving poverty by 2015, average growth rates need Country Index Country Index Albania 31 Kenya 44 to reach 7 percent a year. That will require annual investment Azerbaijan 67 Lao PDRa 59 of $20 billion in infrastructure, about twice as much as Africa Bangladesh 37 Madagascar 25 has historically invested. About 40 percent of that needs to Benin 32 Malawi 38 go into roads and 20 percent each into energy and water Burkina Faso 25 Mongolia 36 (World Bank 2005c). Burundi 19 Nicaragua 28 Cambodiaa 87 Niger 37 Financing these infrastructure needs in Africa will require a Cameroon 20 Nigeria 47 concerted effort from all funding sources, public and private. Chad 5 Pakistan 77 Led by the African Union and the New Partnership for Africa's Congo, Dem. Rep 26 Papua New Guinea 68 Development, and including the African Development Bank Ethiopia 17 Tajikistan 74 and the World Bank, the Africa Infrastructure Consortium is Gambia, Theb 77 Tanzania 38 Ghana 34 Uzbekistan 57 working to mobilize and allocate infrastructure resources to Guineac 22 Vietnam 73 support country and regional projects. The goal is to improve India 60 Yemen, Rep. 21 infrastructure, whether services are delivered by public or pri- Indonesia 94 vate providers or jointly. Reformers need to consider multiple Note: Based on surveys between 1997 and 2003. factors--the strength of institutions, regulatory rules, fiscal a. Nonstandard measurement process resulting in a higher index value. b. Survey conducted during 1994­96. health, investor interest, the competitiveness of markets, c. Survey conducted in 2004. and other specific characteristics that influence the perfor- Source: World Bank Transport Technical Paper based on household surveys. mance of public and private operators. 2006 World Development Indicators 265 Private sector in the economy Investment in infrastructure projects Domestic Micro, small, and with private participationa credit to medium-size private sector enterprisesb $ millions Water and number Employment Telecommunications Energy Transport sanitation % of GDP of firms % of total 1995­99 2000­04 1995­99 2000­04 1995­99 2000­04 1995­99 2000­04 1990 2004 2000­04 2000­04 Afghanistan .. 204.0 .. .. .. .. .. .. .. .. .. .. Albania .. 443.2 0.0 8.0 .. .. .. 0.0 .. 9.9 35,694 56.7 Algeria .. 2,052.5 .. .. .. .. .. .. 44.4 11.0 580,000 .. Angola .. 278.7 .. 45.0 .. 55.0 .. .. .. 5.4 .. .. Argentina 10,498.6 5,130.2 13,093.6 3,389.5 8,848.1 200.2 3,473.6 688.9 15.6 10.5 .. .. Armenia 442.0 94.1 0.0 47.0 .. 50.0 .. 0.0 .. 7.8 34,000 31.3 Australia .. .. .. .. .. .. .. .. 61.6 102.4 1,269,000 52.3 Austria .. .. .. .. .. .. .. .. 89.7 105.6 252,399 65.3 Azerbaijan 122.0 232.6 .. 375.2 .. .. .. 0.0 .. 9.1 21,178 5.0 Bangladesh 438.1 651.3 554.9 501.5 0.0 0.0 .. .. 16.7 30.1 177,000 80.0 Belarus 20.0 534.3 500.0 .. .. .. .. .. .. 14.0 237,467 9.7 Belgium .. .. .. .. .. .. .. .. 37.0 73.1 437,000 69.3 Benin .. 106.9 .. .. .. .. .. .. 20.3 14.5 .. .. Bolivia 528.0 471.5 2,777.3 679.8 168.7 16.6 682.0 .. 24.0 42.2 .. .. Bosnia and Herzegovina 0.0 0.0 .. .. .. .. .. .. .. 43.8 75,000 62.6 Botswana 97.0 85.0 .. .. .. .. .. .. 9.4 19.0 16,466 .. Brazil 45,052.4 36,039.1 34,196.8 24,638.9 17,460.5 3,082.6 2,137.0 1,587.6 38.9 35.1 4,667,609 56.5 Bulgaria 202.5 1,418.6 .. 1,246.0 .. .. .. 152.0 .. 37.1 224,211 64.7 Burkina Faso .. 41.9 5.6 .. .. .. .. .. 16.8 14.9 .. .. Burundi .. 53.6 .. .. .. .. .. .. 8.6 22.6 .. .. Cambodia 102.4 79.3 143.0 38.1 120.0 125.3 .. .. .. 9.3 .. .. Cameroon 12.7 365.4 .. 91.9 95.0 0.0 .. .. 26.7 9.9 .. .. Canada .. .. .. .. .. .. .. .. 75.9 86.0 2,374,247 65.0 Central African Republic 1.1 .. .. .. .. .. .. .. 7.2 7.2 .. .. Chad 2.0 11.0 .. 0.0 .. .. .. .. 7.3 3.3 .. .. Chile 3,489.0 3,134.6 6,808.6 1,224.2 3,104.1 4,499.0 3,111.2 1,563.0 47.2 63.1 700,000 95.0 China 5,970.0 8,495.0 16,916.2 5,359.1 10,802.8 5,201.1 719.8 2,332.8 87.7 120.1 25,110,000 78.0 Hong Kong, China .. .. .. .. .. .. .. .. 163.7 150.2 284,000 60.0 Colombia 1,384.3 715.5 6,964.8 107.6 995.5 1,160.5 233.0 237.3 30.8 22.8 967,315 49.0 Congo, Dem. Rep. 68.0 431.4 .. .. 0.0 .. .. .. 1.8 1.5 .. .. Congo, Rep. 12.2 61.8 325.0 .. .. .. .. .. 15.7 3.2 .. .. Costa Rica .. .. 301.2 80.0 .. 161.0 .. .. 15.8 32.3 40,921 54.3 Côte d'Ivoire 752.3 114.9 260.6 .. 178.0 140.0 .. .. 36.5 14.4 .. .. Croatia 978.0 926.5 368.5 7.1 672.2 405.0 .. 298.7 .. 57.5 96,146 40.0 Cuba .. 60.0 165.0 .. .. 0.0 .. 600.0 .. .. .. .. Czech Republic 6,178.5 4,751.6 944.1 3,865.3 283.7 106.7 44.9 318.3 .. 33.4 2,350,584 62.2 Denmark .. .. .. .. .. .. .. .. 52.2 160.3 205,000 78.4 Dominican Republic 163.0 306.8 979.0 1,264.1 .. 898.9 .. .. 27.5 27.9 .. .. Ecuador 696.4 197.0 30.0 302.0 686.8 20.0 .. 550.0 13.6 22.5 .. .. Egypt, Arab Rep. 1,914.5 2,049.9 700.0 678.0 123.9 735.3 .. .. 30.6 54.5 2,500,000 .. El Salvador 610.5 668.1 900.2 85.0 .. .. .. .. 19.1 40.5 465,969 .. Eritrea .. 40.0 .. .. .. .. .. .. .. 32.8 .. .. Estonia 628.2 244.7 26.5 .. 1.0 298.4 .. 81.0 .. 42.4 32,801 55.0 Ethiopia .. .. .. 300.0 .. .. .. .. 19.5 24.3 .. .. Finland .. .. .. .. .. .. .. .. 86.6 68.7 221,000 59.2 France .. .. .. .. .. .. .. .. 94.3 90.8 2,971,178 62.7 Gabon 8.4 26.6 624.8 .. 46.7 85.6 .. .. 13.0 8.6 .. .. Gambia, The .. 6.6 .. .. .. .. .. .. 11.0 11.7 .. .. Georgia 61.0 142.8 159.0 13.0 .. .. .. .. .. 9.7 25,593 50.6 Germany .. .. .. .. .. .. .. .. 88.7 112.3 3,008,000 70.4 Ghana 491.1 101.3 383.8 184.0 .. 10.0 .. .. 4.9 13.1 25,679 66.0 Greece .. .. .. .. .. .. .. .. 36.3 78.6 771,000 74.0 Guatemala 1,366.3 440.2 1,223.2 110.0 33.8 .. .. .. 14.2 19.8 .. .. Guinea 120.3 18.0 36.4 .. .. .. 0.0 .. .. 3.6 .. .. Guinea-Bissau .. 21.3 .. .. .. .. .. .. 22.0 1.5 .. .. Haiti 1.5 18.0 4.7 .. .. .. .. .. 12.6 15.6 .. .. 266 2006 World Development Indicators Private sector in the economy Investment in infrastructure projects Domestic Micro, small, and with private participationa credit to medium-size private sector enterprisesb $ millions Water and number Employment Telecommunications Energy Transport sanitation % of GDP of firms % of total 1995­99 2000­04 1995­99 2000­04 1995­99 2000­04 1995­99 2000­04 1990 2004 2000­04 2000­04 Honduras 51.3 94.3 112.1 358.8 10.5 120.0 .. 220.0 31.1 42.0 257,422 .. Hungary 6,430.2 4,632.7 3,812.1 260.6 135.0 .. 178.5 0.0 46.6 46.5 855,058 55.8 India 7,456.8 14,321.9 7,165.6 7,559.8 1,272.8 1,854.3 .. 223.2 25.2 37.1 .. .. Indonesia 9,103.9 4,989.6 9,942.1 315.6 2,223.1 590.3 882.8 36.7 46.9 23.6 41,362,315 .. Iran, Islamic Rep. 28.0 345.0 .. 650.0 .. .. .. .. 32.5 38.3 .. .. Iraq .. 420.0 .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. 47.6 136.9 97,000 72.1 Israel .. .. .. .. .. .. .. .. 57.6 92.2 391,106 44.0 Italy .. .. .. .. .. .. .. .. 56.5 87.8 4,486,000 73.0 Jamaica 235.5 700.3 43.0 201.0 0.0 565.0 .. .. 36.1 18.1 .. .. Japan .. .. .. .. .. .. .. .. 175.7 99.5 5,712,191 88.0 Jordan 39.9 1,351.0 .. .. 182.0 0.0 0.0 169.0 72.3 72.1 139,844 54.5 Kazakhstan 1,633.5 669.2 1,825.0 300.0 .. .. .. 40.0 .. 28.3 .. .. Kenya 193.0 787.0 189.0 .. 53.4 .. .. .. 32.7 26.8 22,014 74.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. 62.8 98.2 2,948,171 86.7 Kuwait .. .. .. .. .. .. .. .. 0.0 71.6 .. .. Kyrgyz Republic 100.0 9.1 .. .. .. .. .. .. .. 7.1 22,670 62.2 Lao PDR 157.1 77.7 535.5 .. .. 0.0 .. .. 1.0 6.5 .. .. Latvia 600.9 609.4 106.0 71.1 75.0 .. .. .. .. 44.8 32,571 36.6 Lebanon 485.7 138.1 .. .. .. 153.0 .. 0.0 79.4 75.9 .. .. Lesotho 15.7 85.4 .. 0.0 .. .. .. .. 15.8 6.5 .. .. Liberia .. 50.0 .. .. .. .. .. .. 0.0 6.1 .. .. Libya .. .. .. .. .. .. .. .. 31.0 16.9 .. .. Lithuania 832.7 933.0 10.0 399.3 .. .. .. .. .. 25.9 55,825 70.6 Macedonia, FYR .. 706.6 .. .. .. .. .. .. .. 23.2 27,938 .. Madagascar 10.1 12.6 .. .. .. 20.3 .. .. 16.9 10.0 .. .. Malawi 23.1 11.3 .. .. 6.0 .. .. .. 10.9 8.4 747,396 38.0 Malaysia 4,187.6 2,253.0 1,610.2 5,048.1 8,200.1 3,347.3 1,084.4 48.1 69.4 130.1 204,669 .. Mali .. 82.6 .. 747.0 .. .. .. .. 12.8 20.1 .. .. Mauritania .. 119.7 .. .. .. .. .. .. 43.5 25.9 .. .. Mauritius .. 406.0 109.3 .. 42.6 .. .. .. 35.6 59.5 75,267 .. Mexico 10,757.5 14,743.6 2,095.8 6,494.3 4,988.5 1,047.3 276.5 520.7 17.5 16.7 2,891,300 71.9 Moldova 84.6 .. 60.0 25.3 .. .. .. .. .. 21.3 20,518 8.2 Mongolia 21.9 21.6 .. .. .. .. .. .. .. 32.0 .. .. Morocco 1,240.0 5,233.0 5,819.9 1,049.0 .. .. 1,000.0 .. 34.1 56.7 450,000 .. Mozambique 29.0 109.0 .. 1,200.0 441.0 797.1 0.6 .. 17.6 2.1 .. .. Myanmar 4.0 .. 394.0 .. 50.0 .. .. .. 4.7 12.1 .. .. Namibia 53.2 35.2 4.0 1.0 .. 450.0 .. 0.0 22.6 50.4 .. .. Nepal .. 20.0 98.2 39.0 .. .. .. .. 12.8 .. .. .. Netherlands .. .. .. .. .. .. .. .. 79.9 166.3 570,000 58.5 New Zealand .. .. .. .. .. .. .. .. 76.0 121.1 323,998 29.2 Nicaragua 24.5 240.3 232.4 115.0 .. 104.0 .. .. 112.6 26.8 .. .. Niger .. 99.9 .. .. .. .. .. 4.9 12.3 6.2 .. .. Nigeria 69.0 4,639.7 .. 709.0 .. 22.8 .. .. 9.4 15.6 .. .. Norway .. .. .. .. .. .. .. .. 81.7 9.9 288,368 56.9 Oman .. .. 183.0 1,364.3 77.5 473.8 .. .. 20.6 34.9 .. .. Pakistan 75.5 1,877.7 4,298.3 .. 421.3 47.0 .. .. 24.2 29.3 .. .. Panama 1,429.2 10.7 669.2 395.7 994.6 51.4 25.0 .. 46.7 91.2 .. .. Papua New Guinea .. .. 65.0 .. .. .. 175.0 .. 28.6 11.0 .. .. Paraguay 199.3 77.6 .. .. 58.0 .. .. .. 15.8 15.4 .. .. Peru 4,774.5 1,948.4 3,004.9 2,092.6 86.3 239.5 .. 56.0 11.8 18.6 509,424 .. Philippines 5,154.6 3,719.0 6,998.0 2,793.7 1,364.0 1,060.5 5,847.7 0.0 22.3 34.8 806,866 70.4 Poland 4,751.1 15,673.7 628.1 2,277.5 169.4 657.5 6.1 21.8 21.1 27.7 1,654,822 68.0 Portugal .. .. .. .. .. .. .. .. 49.1 150.3 693,000 81.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2,069 43.6 2006 World Development Indicators 267 Private sector in the economy Investment in infrastructure projects Domestic Micro, small, and with private participationa credit to medium-size private sector enterprisesb $ millions Water and number Employment Telecommunications Energy Transport sanitation % of GDP of firms % of total 1995­99 2000­04 1995­99 2000­04 1995­99 2000­04 1995­99 2000­04 1990 2004 2000­04 2000­04 Romania 2,072.8 2,355.9 100.0 .. 23.4 .. .. 1,134.0 .. 10.0 347,064 .. Russian Federation 4,665.6 13,404.3 2,281.3 14.0 406.0 109.4 108.0 480.5 .. 24.5 8,441,000 49.0 Rwanda 8.0 39.3 .. 0.0 .. .. .. .. 6.9 10.7 .. .. Saudi Arabia .. 8,537.0 .. .. 55.0 190.0 .. 52.0 54.7 55.9 .. .. Senegal 273.9 342.6 124.0 .. .. .. 6.3 0.0 26.5 21.2 .. .. Serbia and Montenegro 1,590.0 830.6 .. .. .. .. .. 0.0 .. .. 68,220 70.4 Sierra Leone 7.0 48.5 .. .. .. .. .. .. 3.2 4.7 .. .. Singapore .. .. .. .. .. .. .. .. 96.8 106.9 134,098 51.2 Slovak Republic 488.5 2,359.8 .. 3,323.6 .. .. 0.0 .. .. 31.2 70,553 66.0 Slovenia .. .. .. .. .. .. .. .. .. 46.3 93,392 64.1 Somalia 0.0 3.0 .. .. .. .. .. .. .. .. .. .. South Africa 5,978.3 9,144.0 3.0 1,244.3 1,386.4 504.7 209.3 3.2 81.0 141.3 .. .. Spain .. .. .. .. .. .. .. .. 77.7 125.4 3,058,631 .. Sri Lanka 601.9 524.3 176.3 132.0 240.0 .. .. .. 19.6 31.5 131,387 27.6 Sudan 6.0 991.1 .. .. .. .. .. .. 4.8 7.7 .. .. Swaziland 21.2 24.7 .. .. .. .. .. .. 20.7 19.5 .. .. Sweden .. .. .. .. .. .. .. .. 127.4 105.9 868,497 39.3 Switzerland .. .. .. .. .. .. .. .. 162.6 161.2 343,000 75.3 Syrian Arab Republic .. 191.0 .. .. .. .. .. .. 7.5 10.2 .. .. Tajikistan 1.2 8.5 .. 16.0 .. .. .. .. .. 17.4 92,964 25.0 Tanzania 100.2 391.1 150.0 340.0 16.5 6.5 .. 4.8 13.9 9.0 2,700,000 .. Thailand 4,190.5 2,788.0 6,550.4 3,950.4 1,791.4 939.0 239.4 261.1 83.4 97.4 842,360 18.0 Togo 5.0 0.0 0.0 67.7 0.0 .. .. .. 22.6 16.0 .. .. Trinidad and Tobago 146.7 .. 207.0 .. .. .. 0.0 120.0 40.0 27.8 .. .. Tunisia .. 610.0 265.0 .. .. .. .. .. 55.1 65.2 .. .. Turkey 3,269.7 5,372.8 2,992.2 4,835.0 610.0 359.8 942.0 .. 16.7 20.5 210,134 64.3 Turkmenistan .. .. .. .. .. .. .. .. .. 1.9 .. .. Uganda 119.3 242.6 .. 18.1 .. .. .. 0.0 0.0 6.8 160,453 .. Ukraine 1,094.6 1,473.5 .. 160.0 .. .. .. .. .. 25.0 283,398 20.2 United Arab Emirates .. .. .. .. .. .. .. .. 38.0 53.5 .. .. United Kingdom .. .. .. .. .. .. .. .. 115.8 156.3 4,352,275 39.5 United States .. .. .. .. .. .. .. .. 144.0 249.2 5,680,914 50.1 Uruguay 63.7 105.8 86.0 330.0 20.0 280.2 .. 351.0 32.4 30.4 .. .. Uzbekistan 503.8 189.7 .. .. .. .. .. .. .. .. 237,500 57.0 Venezuela, RB 4,877.9 2,639.5 103.0 30.0 268.0 34.0 29.0 15.0 26.2 11.2 11,314 .. Vietnam 248.0 380.0 435.5 2,192.0 85.0 30.0 38.8 174.0 .. 58.9 59,831 85.7 West Bank and Gaza 265.0 256.7 .. 150.0 .. .. 9.5 .. .. .. .. .. Yemen, Rep. .. 358.0 .. .. 190.0 .. .. .. 6.1 7.7 .. .. Zambia 32.8 81.4 277.0 12.4 .. .. .. .. 8.9 8.0 .. .. Zimbabwe 46.0 59.0 600.0 .. 85.0 .. .. .. 23.0 22.3 .. .. World .. s .. s .. s .. s .. s .. s .. s .. s 108.1 w 138.1 w 25,102,874 s .. w Low income 11,627.2 28,029.4 16,349.4 14,156.8 3,014.0 3,157.8 220.7 406.9 22.1 31.2 224,693 .. Middle income 162,329.7 171,333.8 137,624.9 81,914.1 66,579.6 28,069.2 21,239.7 11,956.8 43.1 59.7 7,160,980 .. Lower middle income 91,141.3 84,325.1 100,450.1 47,260.2 36,250.7 15,234.3 11,751.2 7,376.5 51.3 75.9 100,593 71.0 Upper middle income 71,188.4 87,008.7 37,174.8 34,653.9 30,328.9 12,834.9 9,488.5 4,580.3 32.4 37.7 7,060,387 .. Low & middle income 173,956.9 199,363.2 153,974.3 96,070.9 69,593.6 31,227.0 21,460.4 12,363.7 39.2 55.6 7,385,673 .. East Asia & Pacific 29,304.5 23,042.7 43,589.9 19,697.0 24,636.4 11,293.5 8,987.9 2,852.7 73.7 105.2 43,073,436 75.2 Europe & Central Asia 36,751.4 58,017.2 13,812.8 17,244.0 2,375.7 1,986.8 1,279.5 2,526.3 .. 26.6 3,528,759 .. Latin America & Carib. 86,705.0 68,076.9 74,159.1 41,898.5 37,723.4 12,517.8 9,967.3 6,579.6 28.6 25.7 3,632,221 .. Middle East & N. Africa 3,973.1 13,005.2 6,967.9 3,891.3 573.4 1,412.1 1,009.5 169.0 34.5 39.8 139,844 .. South Asia 8,604.5 17,612.5 12,293.3 8,232.3 1,934.1 1,901.3 .. 223.2 24.2 35.7 177,000 .. Sub-Saharan Africa 8,618.4 19,608.7 3,151.3 5,107.8 2,350.6 2,115.5 216.2 12.9 42.4 67.0 47,693 .. High income .. 8,537.0 .. .. 55.0 190.0 .. 52.0 119.8 159.1 17,717,201 60.4 Europe EMU .. .. .. .. .. .. .. .. 78.6 106.0 6,306,542 68.6 a. Data refer to total for the period shown. b. Data are for the most recent year available. 268 2006 World Development Indicators Private sector in the economy About the data Definitions Private sector development and investment--that by the World Bank's Development Data Group. For · Investment in infrastructure projects with private is, tapping private sector initiative and investment more information, see http://ppi.worldbank.org/. participation refers to infrastructure projects in tele- for socially useful purposes--are critical for pov- Credit is an important link in the money transmis- communications, energy (electricity and natural gas erty reduction. In parallel with public sector efforts, sion process; it finances production, consumption, transmission and distribution), transport, and water private investment, especially in competitive mar- and capital formation, which in turn affect the level and sanitation that have reached financial closure and kets, has tremendous potential to contribute to of economic activity. The data on domestic credit to directly or indirectly serve the public. Incinerators, growth. Private markets are the engine of produc- the private sector are taken from the banking survey movable assets, stand-alone solid waste projects, tivity growth, creating productive jobs and higher of the International Monetary Fund's (IMF) Interna- and small projects such as windmills are excluded. incomes. And with government playing a complemen- tional Financial Statistics or, when data are unavail- Included are operation and management contracts, tary role of regulation, funding, and service provi- able, from its monetary survey. The monetary survey operation and management contracts with major sion, private initiative and investment can help pro- includes monetary authorities (the central bank), capital expenditure, greenfield projects (in which a pri- vide the basic services and conditions that empower deposit money banks, and other banking institutions, vate entity or a public-private joint venture builds and poor people--by improving health, education, and such as finance companies, development banks, and operates a new facility), and divestitures. · Domestic infrastructure. savings and loan institutions. In some cases credit to credit to private sector refers to financial resources Private participation in infrastructure has made the private sector may include credit to state-owned provided to the private sector--such as through important contributions to easing fiscal constraints, or partially state-owned enterprises. loans, purchases of nonequity securities, and trade improving the efficiency of infrastructure services, Formal and informal micro, small, and medium-size credits and other accounts receivable--that estab- and extending their delivery to poor people. The enterprises employ more than half of the working lish a claim for repayment. For some countries these privatization trend in infrastructure that began in the population in many market economies and account claims include credit to public enterprises. · Micro, 1970s and 1980s took off in the 1990s, peaking in for about 90 percent of all firms. And they contrib- small, and medium-size enterprises are business 1997. Developing countries have been at the head of ute significantly to innovation. If small businesses that may be defined by the number of employees. this wave, pioneering better approaches to providing are allowed to compete on an equal playing field, There is no international standard definition of firm infrastructure services and reaping the benefits of the good ones can become larger, workers can earn size; however, many institutions that collect informa- greater competition and customer focus. Between higher wages, and productivity will increase. A good tion use the following size categories: micro enter- 1990 and 2004 more than 2,900 projects in more investment climate--one that provides opportunities prises have 0­9 employees, small enterprises have than 130 developing countries introduced private and incentives for firms, reduces legal and regulatory 10­49 employees, and medium-size enterprises participation in at least one infrastructure sector, costs, lowers the costs of financial institutions in have 50­249 employees. with $866 billion in investments. providing financial services, and facilitates the trans- In 2004, 132 new infrastructure projects with pri- fer of technology and knowledge and the upgrading vate participation valued at $23 billion were imple- of capabilities in small and medium-size firms--is mented. In addition, $41 billion in investment proj- important for economic progress, better jobs, and a ects reached financial closure between 1990 and more inclusive society. 2003. Telecommunications attracted $45 billion in Data on the activities of micro, small, and medium- investment in 2004, mostly in standalone mobile size enterprises are collected by governments, inter- operations. Except for water and sanitation, with national organizations, foundations, and small busi- $1.9 billion in investment in 2004 (up from about ness organizations. These data have been collated $1.4 billion in 2003), investment in other infrastruc- by the International Finance Corporation (IFC) and ture sectors declined in 2004. are available in the Micro, Small, and Medium Enter- The data on investment in infrastructure projects prises: A Collection of Published Data database. This with private participation refer to all investment (pub- IFC initiative is a work in progress, improved and lic and private) in projects in which a private company updated as new data become available. Because assumes operating risk during the operating period the concepts and definitions of micro, small, and Data sources or assumes development and operating risk during medium-size enterprises vary by source, using these Data on investment in infrastructure projects with the contract period. Foreign state-owned companies data for precise country rankings may be inappro- private participation are from the World Bank's are considered private entities for the purposes of priate. See www.ifc.org/ifcext/sme.nsf/Content/ PPI Project Database (http://ppi.worldbank. this measure. The data are from the World Bank's Resources for additional information on sources org). Data on domestic credit are from the IMF's Private Participation in Infrastructure (PPI) Project and precise firm size. International Financial Statistics. Data on micro, Database, which tracks more than 3,200 projects, small, and medium-size enterprises are from the newly owned or managed by private companies, that International Finance Corporation's micro, small, reached financial closure in low- and middle-income and medium-size enterprises database (www.ifc. economies in 1990­2004. Aggregates for geo- org/ifcext/sme.nsf/Content/Resources). graphic regions and income groups are calculated 2006 World Development Indicators 269 Investment climate Policy Corruption Courts Crime Regulation and tax administration Finance Electricity Labor uncertainty Lack confidence courts Time dealing Average uphold Tax rates with officials time to Major Major Major property Major as major % of clear Major Major Major constraint Survey constraint constraint constraint rights constraint constraint management customs constraint constraint % year % % % % % % time days % % Skills Regulation Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2005 19.1 31.8 23.9 43.6 8.6 40.9 12.2 2.1 19.5 34.7 10.4 2.5 Algeria 2003 .. 35.2 .. 27.3 .. 44.8 .. 21.6 51.3 11.5 25.5 12.9 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. .. .. .. .. .. .. .. .. .. .. .. Armenia 2005 12.2 20.1 12.4 47.5 2.3 38.4 10.4 4.4 20.8 3.2 2.3 2.9 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2005 2.9 21.3 4.4 30.8 2.4 22.9 16.6 1.6 7.0 4.9 1.8 1.5 Bangladesh 2000 45.4 57.9 .. 83.0 39.4 35.8 4.6 11.5 45.7 73.2 19.8 10.8 Belarus 2005 23.4 6.6 3.0 33.4 2.9 20.4 8.3 4.4 22.5 0.9 6.6 3.4 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 2004 .. .. .. .. .. .. .. 9.3 .. .. .. .. Bosnia and Herzegovina 2005 35.1 24.7 21.5 41.6 19.9 15.6 9.5 2.6 25.8 8.2 3.6 3.2 Botswana .. .. .. .. .. .. .. .. .. .. .. .. Brazil 2003 75.9 67.2 32.8 39.6 52.2 84.5 9.4 13.8 71.7 20.3 39.6 56.9 Bulgaria 2005 27.6 19.0 17.2 56.7 11.5 20.4 7.4 2.9 22.0 6.4 10.4 7.8 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia 2003 40.1 55.9 31.4 61.0 41.7 18.6 14.6 .. 9.9 12.7 6.6 5.9 Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. .. .. .. .. .. .. .. .. .. .. .. China 2002 32.9 27.3 .. 17.5 20.0 36.8 12.6 7.9 22.3 29.7 30.7 20.7 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. .. .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica .. .. .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia 2005 17.9 18.5 29.3 26.0 3.9 12.0 7.5 3.7 12.7 2.1 7.2 3.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 2005 22.0 20.5 25.2 53.1 15.8 59.1 7.1 5.1 17.4 15.5 12.5 15.6 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic .. .. .. .. .. .. .. .. .. .. .. .. Ecuador 2003 60.7 49.2 34.1 70.8 27.8 38.1 17.7 16.4 42.2 28.3 22.3 14.1 Egypt, Arab Rep. 2004 65.8 51.3 27.4 .. .. 81.8 .. 9.9 39.0 26.5 29.8 28.1 El Salvador 2004 28.4 35.1 16.4 46.6 49.0 22.6 9.3 6.2 29.6 21.5 20.0 3.9 Eritrea 2002 31.5 2.7 .. .. 1.3 31.1 5.9 9.1 53.7 38.2 41.0 5.2 Estonia 2005 5.3 4.3 2.0 29.6 1.9 3.0 5.7 1.9 6.1 3.3 7.1 18.8 Ethiopia 2002 39.3 39.0 .. .. 9.5 73.6 5.7 13.5 40.2 42.5 17.9 4.6 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 2005 45.2 20.1 13.5 29.0 24.5 35.7 12.2 2.8 25.4 33.5 14.1 7.6 Germany 2005 5.8 3.9 2.3 10.3 1.9 29.6 3.5 5.7 16.7 1.0 6.9 9.6 Ghana .. .. .. .. .. .. .. .. .. .. .. .. Greece 2005 9.3 10.0 4.7 18.2 5.2 27.5 8.4 5.9 16.3 4.6 8.6 7.7 Guatemala 2003 66.4 80.9 31.2 71.3 80.4 56.5 17.4 9.3 38.7 26.6 31.4 16.7 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 270 2006 World Development Indicators Investment climate Policy Corruption Courts Crime Regulation and tax administration Finance Electricity Labor uncertainty Lack confidence courts Time dealing Average uphold Tax rates with officials time to Major Major Major property Major as major % of clear Major Major Major constraint Survey constraint constraint constraint rights constraint constraint management customs constraint constraint % year % % % % % % time days % % Skills Regulation Honduras 2003 47.0 62.8 21.8 56.1 60.9 35.6 14.2 5.1 55.4 36.4 26.4 14.2 Hungary 2005 26.3 9.4 7.4 49.8 5.6 50.6 8.3 7.7 27.9 2.1 12.9 10.3 India 2003 20.9 37.4 .. 29.4 15.6 27.9 15.3 6.7 19.2 28.9 12.5 16.7 Indonesia 2004 48.2 41.5 24.7 40.8 22.0 29.5 14.6 5.8 23.0 22.3 18.9 25.9 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 2005 5.6 3.0 2.8 28.3 4.8 17.4 5.8 3.3 9.0 6.4 15.6 9.6 Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 2005 9.2 12.7 8.2 42.7 5.3 15.6 10.7 6.0 14.9 2.7 8.6 2.5 Kenya 2003 51.5 73.8 .. 51.3 69.8 68.2 13.8 8.9 58.3 48.1 27.6 22.5 Korea, Dem. Rep. 2005 40.9 8.5 3.6 37.2 3.5 15.1 1.3 8.0 12.5 8.3 6.8 4.1 Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2005 33.2 32.8 17.1 50.8 19.4 31.3 16.1 5.4 23.1 4.0 18.9 2.5 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 2005 22.3 9.6 5.8 51.3 3.1 29.4 8.0 3.3 6.5 4.5 17.8 3.5 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 2005 23.2 14.0 15.3 49.7 9.5 40.9 8.3 2.0 10.3 3.9 15.3 8.9 Macedonia, FYR 2005 27.9 34.7 31.0 55.4 12.8 20.7 15.6 3.1 31.6 12.0 6.1 9.2 Madagascar 2005 41.5 46.6 34.8 44.6 37.7 44.9 25.4 7.0 62.9 41.3 30.5 14.8 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 2005 22.4 14.5 .. 19.1 11.4 21.7 10.2 3.7 17.8 14.8 25.0 14.5 Mali 2004 21.9 48.7 16.9 33.1 22.1 36.6 10.8 10.0 57.0 24.2 20.8 3.9 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico .. .. .. .. .. .. .. .. .. .. .. .. Moldova 2005 31.6 17.6 22.1 64.2 10.1 37.8 10.9 2.8 31.9 2.9 12.0 8.2 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 2004 .. 16.9 29.1 23.5 7.6 62.6 10.5 3.0 78.5 8.9 21.1 16.2 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 2000 .. .. .. .. .. .. .. .. .. 41.7 .. .. Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2003 58.2 65.7 33.3 60.4 39.2 34.7 17.3 5.8 57.6 34.7 17.0 6.9 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 2001 .. .. .. .. 36.3 .. .. 17.8 .. 97.4 .. .. Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 2004 20.7 11.9 14.9 12.9 8.6 20.7 .. 8.0 29.4 10.1 34.6 34.8 Pakistan 2002 40.1 40.4 .. 62.6 21.5 45.6 10.6 17.1 40.1 39.2 12.8 15.0 Panama .. .. .. .. .. .. .. .. .. .. .. .. Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay .. .. .. .. .. .. .. .. .. .. .. .. Peru 2002 71.1 59.6 .. 34.7 51.6 .. .. 7.9 55.8 11.1 12.5 .. Philippines 2003 29.5 35.2 .. 33.8 26.5 30.4 11.0 9.1 18.2 33.4 11.9 24.7 Poland 2005 42.7 18.2 21.0 47.4 15.0 57.7 7.2 3.8 39.6 4.1 15.3 17.9 Portugal 2005 22.2 15.4 17.8 47.7 15.7 20.5 5.8 6.0 18.3 7.8 12.4 18.1 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 271 Investment climate Policy Corruption Courts Crime Regulation and tax administration Finance Electricity Labor uncertainty Lack confidence courts Time dealing Average uphold Tax rates with officials time to Major Major Major property Major as major % of clear Major Major Major constraint Survey constraint constraint constraint rights constraint constraint management customs constraint constraint % year % % % % % % time days % % Skills Regulation Romania 2005 33.9 30.1 19.7 44.3 15.3 34.1 3.6 3.0 22.6 8.1 14.2 16.4 Russian Federation 2005 26.2 16.5 9.5 63.9 9.3 21.8 14.8 9.5 15.7 5.1 13.1 3.1 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2004 31.3 39.9 13.3 40.5 15.4 50.8 13.8 7.0 60.3 30.7 18.5 16.3 Serbia and Montenegro 2005 61.2 25.5 30.0 43.1 13.5 29.5 12.4 5.7 43.9 4.7 10.7 13.4 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 2005 13.0 10.6 13.1 44.4 5.1 8.3 9.3 3.9 7.9 2.7 8.2 4.6 Slovenia 2005 11.5 3.7 8.1 34.4 0.9 12.7 6.4 3.2 9.5 2.7 5.4 4.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2004 17.9 16.1 8.8 20.8 29.0 18.6 10.7 6.5 14.5 9.0 35.5 32.9 Spain 2005 10.3 7.8 7.9 16.6 9.8 18.8 5.1 5.5 13.3 8.3 13.8 11.8 Sri Lanka 2003 34.0 16.9 .. 31.2 14.0 19.1 4.7 4.1 20.4 41.3 21.3 25.6 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2004 27.0 57.6 .. .. .. 62.5 14.5 15.8 24.8 57.5 36.3 33.8 Tajikistan 2005 5.6 15.7 4.9 35.9 4.1 22.2 8.1 5.9 7.2 10.1 4.6 1.5 Tanzania 2003 31.5 51.1 20.0 55.1 25.5 73.4 16.2 17.5 53.0 58.9 25.0 12.1 Thailand 2004 29.1 18.3 .. 25.8 10.3 24.4 2.9 4.6 15.2 25.6 30.0 11.4 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 2005 31.5 17.0 12.4 28.5 14.7 37.8 11.7 6.4 17.5 9.2 9.8 12.2 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2003 27.6 38.2 .. 30.1 26.8 48.3 5.0 .. 52.8 44.5 30.8 10.8 Ukraine 2005 31.3 22.6 15.2 48.2 12.3 45.7 13.7 6.8 29.9 4.9 19.8 6.5 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. .. .. Uzbekistan 2005 11.5 8.9 6.6 41.7 8.9 18.3 7.7 8.7 12.5 7.2 4.6 3.0 Venezuela, RB .. .. .. .. .. .. .. .. .. .. .. .. Vietnam 2005 14.7 12.8 5.5 23.1 4.0 13.8 6.9 4.5 30.3 15.7 22.3 10.9 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2005 31.1 62.8 32.0 58.4 28.7 71.9 21.3 13.3 31.8 47.6 23.6 12.8 Zambia 2003 57.0 46.4 38.6 36.0 48.8 57.5 14.1 4.8 67.7 39.6 35.7 16.9 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. Note: Data are based on enterprise surveys conducted by the World Bank and its partners during 2001­05. While averages are reported, there are significant variations across firms. Surveys of Eastern Europe and Central Asia were conducted under the joint World Bank­European Bank for Reconstruction and Development Business Environment and Enterprise Performance Surveys Initiative. 272 2006 World Development Indicators Investment climate About the data Definitions The table includes recently available data from World several ways. It can distort policymaking, undermine · Policy uncertainty measures the share of senior Bank­sponsored firm-level Investment Climate Sur- government credibility, tax entrepreneurial activities, managers who ranked economic and regulatory pol- veys covering more than 50,000 firms in 63 develop- and divert resources from public coffers. Better courts icy uncertainty as a major or very severe constraint. ing countries for 2001­05. The data provide fresh reduce the risks firms face, so that firms are willing · Corruption measures the share of senior manag- insights into how investment climates vary around to invest more. And the importance of courts grows ers who ranked corruption as a major or very severe the world. In addition to these surveys, data from as the number of large and complex long-term trans- constraint. · Courts measure the share of senior the Doing Business project, which benchmarks regu- actions increases. Robbery, fraud, and other crimes managers who ranked courts and dispute resolution latory regimes in 155 countries, are presented in against property and against the person undermine systems as a major or very severe constraint. · Lack table 5.3. the investment climate. Crime retards entrepreneurial confidence courts uphold property rights measures A good investment climate requires government activity. In Latin America, more than 50 percent of the share of managers who do not agree with the policies that provide an environment for firms and surveyed firms judged crime to be a serious obstacle statement: "I am confident that the judicial system entrepreneurs to invest productively, create jobs, to doing business. will enforce my contractual and property rights in and contribute to growth and poverty reduction. The Most countries have room to improve regulation business disputes." · Crime measures the share of goal is to create a better investment climate that and taxation without compromising broader social senior managers who ranked crime, theft, and disor- benefits society as a whole, not just firms. interests. The investment climate is harmed when der as a major or very severe constraint. · Tax rates Improving government policies and behaviors is key governments impose unnecessary costs, by increas- as major constraint measure the share of senior to shaping the investment climate because they are ing uncertainty and risk and by erecting unjustified managers who ranked tax rates as a major or very influential in driving growth and poverty reduction. barriers to competition. Improvements in the tax severe constraint. · Time dealing with officials is Governments face four primary challenges in improv- system may include broadening the tax base, sim- the percentage of management time in a given week ing the investment climate and getting the balance plifying tax structures, increasing the autonomy of spent on requirements imposed by government regu- right between society's interests and firms' incen- tax agencies, and improving compliance through lations (taxes, customs, labor regulations, licensing tives to invest. One is restraining corruption by pub- computerization. When financial markets work well, and registration). · Average time to clear customs lic officials, firms, and other interest groups. Two is they connect firms to lenders and investors, which is the number of days to clear an imported good establishing credibility by maintaining economic and allows firms to seize business opportunities and grow through customs. · Finance measures shares of political stability and restraining arbitrary behavior by their businesses. But too often government distor- senior managers who ranked access to finance or the key agencies of the state. Three is fostering pub- tions introduced by state ownership or directed credit cost of finance as a major or very severe constraint. lic trust and legitimacy through open and participa- undermine financial sector development, productiv- · Electricity measures the share of senior manag- tory policymaking, transparency, and equity. Four is ity, and economic growth. Firms that have access to ers who ranked electricity as a major or severe con- ensuring that government policies realistically reflect modern infrastructure--telecommunications, reliable straint. · Labor skills measure the share of senior current conditions and continue to adapt to changing electricity supplies, and efficient transportation--are managers who ranked skills of available workers as economic and business conditions. more productive, and improvements in infrastructure a major or severe constraint. · Labor regulations Firms evaluating alternative investment options, services also benefit households. Ill-considered labor measure the share of senior managers who ranked governments interested in improving their investment regulations can discourage firms from creating more labor regulations as a major or severe constraint. climates, and economists seeking to understand jobs, and while some employees may benefit, the the role of different factors in explaining economic unemployed, the low skilled, and those working in performance have all grappled with defining and the informal economy will not. measuring the investment climate. The World Bank, The Investment Climate Surveys follow a stratified working with client governments and others, recently random sampling methodology, drawing from regis- pioneered new measures of the investment climate. tered establishments with at least 10 employees. The Investment Climate Surveys measure specific Samples are stratified on sectors and size of firms. constraints facing firms and relate them to measures Sectors are selected based on their contribution to of firm performance, growth, and investment. GDP and for comparability with sectors in other coun- The indicators included in the table cover eight tries. Because the distribution of establishments in dimensions of the investment climate: policy uncer- most countries is overwhelmingly populated by small tainty, corruption, courts, crime, regulation and tax and medium-size enterprises, surveys generally administration, finance, infrastructure, and labor. oversample large establishments. Sample sizes for Firms in developing countries rate policy uncertainty recent surveys range from 250 to 1,800 businesses as their dominant concern among investment climate and average 550 establishments. Data sources constraints. It measures the credibility of governments For more information on the investment climate, Data on the investment climate are from the World and their policies and the ability to deliver what is see http://econ.worldbank.org/wdr/wdr2005 and Bank's Investment Climate Surveys (http://iresearch. promised. Corruption--the exploitation of public office http://iresearch.worldbank.org/ics. worldbank.org/ics). for private gain--can harm the investment climate in 2006 World Development Indicators 273 Business environment Starting a Registering Dealing with Hiring Enforcing Protecting Closing a business property licenses and firing contracts investors business workers Rigidity of Disclosure employment index Cost Number of index 0 (less Time to Time % of per Time procedures Time 0 (less rigid) Time disclosure) resolve Number of required capita Number of required to build a required to 100 (more Number of required to 10 (more insolvency procedures days income procedures days warehouse days rigid) procedures days disclosure) years January January January January January January January January January January January January 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan 1 7 52.8 11 252 .. .. 39 .. 400 0 .. Albania 11 41 31.1 7 47 22 344 48 39 390 0 4.0 Algeria 14 26 25.3 16 52 25 244 51 49 407 8 3.5 Angola 14 146 642.8 7 334 15 326 64 47 1,011 5 6.2 Argentina 15 32 13.4 5 44 23 288 48 33 520 7 2.8 Armenia 10 25 6.1 4 6 20 176 49 24 185 .. 1.9 Australia 2 2 1.9 5 5 16 121 17 11 157 8 1.0 Austria 9 29 5.7 3 32 14 195 44 20 374 2 1.1 Azerbaijan 14 115 12.5 7 61 28 212 38 25 267 0 2.7 Bangladesh 8 35 81.4 11 363 13 185 24 29 365 6 4.0 Belarus 16 79 22.9 7 231 18 354 27 28 225 1 5.8 Belgium 4 34 11.1 7 132 15 184 20 27 112 8 0.9 Benin 8 32 190.8 3 50 22 335 53 49 570 5 3.1 Bolivia 15 50 154.8 7 92 13 187 40 47 591 1 1.8 Bosnia and Herzegovina 12 54 40.9 7 331 17 476 42 36 330 3 3.3 Botswana 11 108 10.9 6 69 42 160 30 26 154 8 2.2 Brazil 17 152 10.1 15 47 19 460 56 24 546 5 10.0 Bulgaria 11 32 9.6 9 19 24 212 44 34 440 8 3.3 Burkina Faso 12 45 149.9 8 107 46 241 84 41 446 6 4.0 Burundi 11 43 200.7 5 94 18 302 69 47 433 .. 4.0 Cambodia 10 86 276.1 7 56 28 247 59 31 401 5 .. Cameroon 12 37 172.8 5 93 15 444 56 58 585 8 3.2 Canada 2 3 0.9 6 10 15 87 14 17 346 8 0.8 Central African Republic 10 14 211.6 3 69 21 237 76 45 660 .. 4.8 Chad 19 75 360.8 6 44 16 199 72 52 526 3 10.0 Chile 9 27 10.3 6 31 12 191 24 28 305 8 5.6 China 13 48 13.6 3 32 30 363 30 25 241 10 2.4 Hong Kong, China 5 11 3.4 5 83 22 230 0 16 211 10 1.1 Colombia 12 43 25.3 7 23 12 150 57 37 363 7 3.0 Congo, Dem. Rep. 13 155 503.3 8 106 16 306 90 51 909 3 5.2 Congo, Rep. 8 67 288.8 6 103 15 174 80 47 560 4 3.0 Costa Rica 11 77 23.8 6 21 19 120 39 34 550 2 3.5 Côte d'Ivoire 11 45 134.0 7 369 22 569 45 25 525 6 2.2 Croatia 12 49 13.4 5 956 28 278 57 22 415 2 3.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 10 40 9.5 4 123 31 245 24 21 290 2 9.2 Denmark 3 5 0.0 6 42 7 70 20 15 83 7 3.3 Dominican Republic 10 75 30.9 7 107 12 150 44 29 580 3 3.5 Ecuador 14 69 38.1 10 21 19 149 58 41 388 1 4.3 Egypt, Arab Rep. 10 34 104.9 7 193 30 263 53 55 410 5 4.2 El Salvador 12 40 118.0 5 52 22 144 41 41 275 6 4.0 Eritrea 13 91 128.6 6 91 19 187 27 27 385 4 1.7 Estonia 6 35 6.2 4 65 12 116 51 25 150 8 3.0 Ethiopia 7 32 65.1 15 56 12 133 41 30 420 1 2.4 Finland 3 14 1.2 3 14 17 56 48 27 228 6 0.9 France 7 8 1.2 9 183 10 185 66 21 75 10 1.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 8 21 13.7 6 9 29 282 43 18 375 4 3.3 Germany 9 24 4.7 4 41 11 165 55 26 175 5 1.2 Ghana 12 81 78.6 7 382 16 127 34 23 200 7 1.9 Greece 15 38 24.6 12 23 17 176 66 14 151 1 2.0 Guatemala 15 39 58.4 5 69 22 294 40 37 1,459 1 4.0 Guinea 13 49 178.8 6 104 29 278 48 44 306 5 3.8 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 12 203 153.1 5 683 12 186 24 35 368 4 5.7 274 2006 World Development Indicators Business environment Starting a Registering Dealing with Hiring Enforcing Protecting Closing a business property licenses and firing contracts investors business workers Rigidity of Disclosure employment index Cost Number of index 0 (less Time to Time % of per Time procedures Time 0 (less rigid) Time disclosure) resolve Number of required capita Number of required to build a required to 100 (more Number of required to 10 (more insolvency procedures days income procedures days warehouse days rigid) procedures days disclosure) years January January January January January January January January January January January January 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Honduras 13 62 64.1 7 36 14 199 34 36 545 1 3.8 Hungary 6 38 22.4 4 78 25 213 37 21 365 1 2.0 India 11 71 61.7 6 67 20 270 62 40 425 7 10.0 Indonesia 12 151 101.7 7 42 19 224 57 34 570 8 5.5 Iran, Islamic Rep. 8 47 6.3 9 36 21 668 49 23 545 3 4.5 Iraq 11 77 37.4 5 8 14 210 69 65 320 4 .. Ireland 4 24 5.3 5 38 10 181 33 16 217 9 0.4 Israel 5 34 5.3 7 144 21 219 33 27 585 8 4.0 Italy 9 13 15.7 8 27 17 284 57 18 1,390 7 1.2 Jamaica 6 9 8.3 5 54 13 242 10 18 202 3 1.1 Japan 11 31 10.7 6 14 11 87 19 16 60 6 0.6 Jordan 11 36 45.9 8 22 17 122 34 43 342 5 4.3 Kazakhstan 7 24 8.6 8 52 32 258 23 47 380 7 3.3 Kenya 13 54 48.2 8 73 11 170 28 25 360 4 4.5 Korea, Dem. Rep. 12 22 15.2 7 11 14 60 45 29 75 7 1.5 Korea, Rep. 12 .. 15.2 .. .. .. .. .. .. .. .. .. Kuwait 13 35 2.2 8 75 26 149 20 52 390 5 4.2 Kyrgyz Republic 8 21 10.4 7 10 16 152 38 46 492 8 3.5 Lao PDR 9 198 15.1 9 135 24 208 50 53 443 4 5.0 Latvia 7 18 4.2 9 54 21 160 59 20 186 5 1.1 Lebanon 6 46 110.6 8 25 16 275 24 39 721 8 4.0 Lesotho 9 92 56.1 6 101 12 254 42 49 285 2 2.6 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 8 26 3.3 3 3 14 151 44 17 154 5 1.2 Macedonia, FYR 13 48 11.3 6 74 18 214 54 27 509 5 3.7 Madagascar 11 38 54.3 8 134 19 356 59 29 280 5 .. Malawi 10 35 139.6 6 118 23 205 21 16 277 4 2.6 Malaysia 9 30 20.9 4 143 25 226 10 31 300 10 2.2 Mali 13 42 190.7 5 44 17 260 66 28 340 6 3.6 Mauritania 11 82 143.6 4 49 19 152 73 28 410 .. 8.0 Mauritius 6 46 8.8 5 210 21 132 37 17 367 6 2.0 Mexico 9 58 15.6 5 74 12 222 51 37 421 6 1.8 Moldova 10 30 17.1 6 48 20 122 68 37 340 7 2.8 Mongolia 8 20 6.2 5 11 18 96 34 26 314 .. 4.0 Morocco 5 11 12.0 3 82 21 217 60 17 240 6 1.8 Mozambique 14 153 95.0 8 42 14 212 61 38 580 .. 5.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 10 95 18.8 9 28 11 169 27 31 270 8 1.0 Nepal 7 21 69.9 2 2 12 147 44 28 350 4 5.0 Netherlands 7 11 13.0 2 2 18 184 49 22 48 4 1.7 New Zealand 2 12 0.2 2 2 7 65 7 19 50 10 2.0 Nicaragua 8 42 139.1 7 65 12 192 47 20 155 4 2.2 Niger 13 35 465.4 5 49 27 165 90 33 330 6 5.0 Nigeria 9 43 73.8 21 274 16 465 38 23 730 6 1.5 Norway 4 13 2.7 1 1 13 97 38 14 87 7 0.9 Oman 9 34 4.8 4 16 16 271 35 41 455 8 7.0 Pakistan 11 24 18.6 5 49 12 218 46 46 395 6 2.8 Panama 7 19 24.8 7 44 22 128 63 45 355 3 2.0 Papua New Guinea 8 56 30.2 4 72 20 218 21 22 440 5 2.8 Paraguay 17 74 147.8 7 48 15 273 59 46 285 6 3.9 Peru 10 102 38.0 5 33 19 201 48 35 381 7 3.1 Philippines 11 48 20.3 8 33 23 197 45 25 360 1 5.7 Poland 10 31 22.2 6 197 25 322 37 41 980 7 1.4 Portugal 11 54 13.4 5 83 20 327 58 24 320 7 2.0 Puerto Rico 7 7 1.0 8 15 20 137 35 43 270 .. 3.8 2006 World Development Indicators 275 Business environment Starting a Registering Dealing with Hiring Enforcing Protecting Closing a business property licenses and firing contracts investors business workers Rigidity of Disclosure employment index Cost Number of index 0 (less Time to Time % of per Time procedures Time 0 (less rigid) Time disclosure) resolve Number of required capita Number of required to build a required to 100 (more Number of required to 10 (more insolvency procedures days income procedures days warehouse days rigid) procedures days disclosure) years January January January January January January January January January January January January 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania 5 11 5.3 8 170 15 291 59 43 335 8 4.6 Russian Federation 8 33 5.0 6 52 22 528 30 29 330 7 3.8 Rwanda 9 21 280.2 5 371 17 252 59 27 310 .. .. Saudi Arabia 13 64 68.5 4 4 18 131 13 44 360 8 2.8 Senegal 9 57 108.7 6 114 18 185 64 33 485 7 3.0 Serbia and Montenegro 10 15 6.0 6 111 21 212 28 33 635 7 2.7 Sierra Leone 9 26 835.4 8 58 48 236 80 58 305 3 2.6 Singapore 6 6 1.1 3 9 11 129 0 23 69 10 0.8 Slovak Republic 9 25 5.1 3 17 13 272 39 27 565 2 4.8 Slovenia 9 60 10.1 6 391 14 207 64 25 913 3 3.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 9 38 8.6 6 23 18 176 52 26 277 8 2.0 Spain 10 47 16.5 3 25 12 277 66 23 169 4 1.0 Sri Lanka 8 50 10.4 8 63 18 167 40 17 440 4 2.2 Sudan 10 38 68.1 .. .. .. .. 43 67 915 .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 3 16 0.7 1 2 8 116 43 23 208 2 2.0 Switzerland 6 20 8.7 4 16 15 152 17 22 170 1 3.0 Syrian Arab Republic 12 47 34.5 4 34 20 134 40 47 672 5 4.1 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 13 35 161.3 12 61 26 313 69 21 242 3 3.0 Thailand 8 33 6.1 2 2 9 147 18 26 390 10 2.7 Togo 13 53 218.3 6 212 14 273 79 37 535 4 3.0 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 9 14 10.0 5 57 21 154 54 14 27 0 1.3 Turkey 8 9 27.7 8 9 32 232 55 22 330 8 5.9 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 17 36 117.8 8 48 19 155 13 15 209 7 2.2 Ukraine 15 34 10.6 10 93 18 265 61 28 269 1 2.9 United Arab Emirates 12 54 44.3 3 9 21 125 33 53 614 4 5.1 United Kingdom 6 18 0.7 2 21 19 115 14 14 288 10 1.0 United States 5 5 0.5 4 12 19 70 3 17 250 7 2.0 Uruguay 11 45 43.9 8 66 17 146 31 39 620 3 2.1 Uzbekistan 9 35 15.5 12 97 .. .. 34 35 368 4 4.0 Venezuela, RB 13 116 15.7 7 33 13 276 38 41 445 3 4.0 Vietnam 11 50 50.6 5 67 14 143 51 37 343 4 5.0 West Bank and Gaza 11 106 275.4 7 58 18 144 38 26 465 .. .. Yemen, Rep. 12 63 240.2 6 21 13 131 37 37 360 6 3.0 Zambia 6 35 18.1 6 70 16 165 10 16 274 10 3.1 Zimbabwe 10 96 1,442.5 4 30 21 481 24 33 350 8 2.2 World 10 u 48 u 77.3 u 6u 86 u 18 u 209 u 41 u 32 u 394 u 5u 3.2 u Low income 10 60 167.6 7 114 19 231 50 36 421 5 3.7 Middle income 10 49 42.3 6 82 19 216 38 32 424 5 3.5 Lower middle income 10 52 54.2 6 74 18 216 39 32 433 4 3.6 Upper middle income 9 42 18.4 6 99 20 216 38 30 405 5 3.5 Low & middle income 10 53 94.8 6 95 19 222 43 34 423 5 3.6 East Asia & Pacific 9 56 47.2 5 64 17 147 28 31 426 5 3.7 Europe & Central Asia 10 36 13.6 6 117 22 254 44 30 372 5 3.5 Latin America & Carib. 12 66 58.8 7 79 16 210 41 35 470 4 3.5 Middle East & N. Africa 10 45 75.6 7 50 19 236 45 38 414 5 3.8 South Asia 8 35 39.7 7 124 16 195 39 30 386 5 5.1 Sub-Saharan Africa 11 64 215.3 7 118 20 251 53 36 439 5 3.3 High income 7 24 9.4 5 47 16 157 34 24 282 6 1.9 Europe EMU 8 27 10.2 6 55 15 201 51 22 296 6 1.3 276 2006 World Development Indicators Business environment About the data Definitions The table presents key indicators on the environment firing index. All subindexes have several components · Number of procedures for starting a business for doing business. The indicators identify regula- and take values between 0 and 100, with higher val- is the number of procedures required to start a tions that enhance or constrain business invest- ues indicating more rigid regulation. business, including interactions required to obtain ment, productivity, and growth. The data are from Contract enforcement is critical to enable busi- necessary permits and licenses and to complete all the World Bank's Doing Business database. nesses to engage with new borrowers or customers. inscriptions, verifications, and notifications to start A vibrant private sector is central to promoting growth Without good contract enforcement trade and credit operations. Data are for businesses with specific and expanding opportunities for poor people. But will be restricted to a small community of people characteristics of ownership, size, and type of pro- encouraging firms to invest, improve productivity, and who have developed relationships through repeated duction. · Time required for starting a business is create jobs requires a legal and regulatory environment dealings or the security of assets. The institution the number of calendar days needed to complete that fosters access to credit, protects property rights, that enforces contracts between debtors and credi- the required procedures for legally operating a busi- and supports efficient judicial, taxation, and customs tors, and suppliers and customers, is the court. The ness. If a procedure can be speeded up at additional systems. The indicators in the table point to the admin- efficiency of contract enforcement is reflected in two cost, the fastest procedure, independent of cost, is istrative and regulatory reforms and institutions needed indicators: number of judicial procedures to resolve a chosen. · Cost for starting a business is normalized to create a favorable environment for doing business. dispute and time to enforce a commercial contract. by presenting it as a percentage of gross national When entrepreneurs start a business, the first What companies disclose to the public has a large income (GNI) per capita. · Number of procedures obstacles they face are the administrative and legal impact on investor protection. Both investors and to register property is the number of procedures procedures required to register the new firm. Coun- entrepreneurs benefit greatly from such legal protec- required for a business to secure rights to property. tries differ widely in how they regulate the entry of new tion. The disclosure index is based on measures that · Time required for registering property is the num- businesses. In some countries the process is straight- cover ownership disclosure, measures that reduce ber of calendar days needed for a business to secure forward and affordable. But in others the procedures expropriation, and disclosures to help investors. rights to property. · Number of procedures to build are so burdensome that entrepreneurs may opt to run Unviable businesses prevent assets and human a warehouse is the number of interactions of a com- their business informally. The data on starting a busi- capital from being allocated to more productive uses pany's employees or managers with external parties, ness cover the number of start-up procedures and the in new companies or in viable companies that are including government agency staff, public inspectors, time required and cost to complete them. financially distressed. The time to close a business notaries, land registry and cadastre staff, and tech- Property registries were first developed to help (resolve an insolvency) captures the average time to nical experts apart from architects and engineers. raise tax revenue, but they have benefited entrepre- complete a procedure, as estimated by insolvency · Time required to build a warehouse is the number neurs as well. Securing rights to land and buildings, lawyers. Information is collected on the sequence of calendar days needed to complete the required a major source of wealth in most countries, strength- of bankruptcy procedures and on whether any proce- procedures for building a warehouse. If a procedure ens incentives to invest and facilitates trade. More dures can be carried out simultaneously. Delays due can be speeded up at additional cost, the fastest complex procedures to register property are associ- to legal derailment tactics that parties to the insol- procedure, independent of cost, is chosen. · Rigid- ated with less perceived security of property rights, vency may use, in particular extension of response ity of employment index measures the regulation more informality, and more corruption. The data periods or appeals, are taken into account. of employment, specifically the hiring and firing of cover the number procedures required and time To ensure cross-country comparability, several workers and the rigidity of working hours. This index required to secure rights to property. standard characteristics of a company are defined is the average of three subindexes: a difficulty of Lack of access to credit is one of the biggest bar- in all surveys, such as size, ownership, location, legal hiring index, a rigidity of hours index, and a diffi- riers entrepreneurs face in starting and operating a status, and type of activities. The data were collected culty of firing index. The index ranges from 0 to 100, business. Indicators covering financial access and through a study of laws and regulations in each coun- with higher values indicating more rigid regulations. financial information are presented in table 5.5. try, surveys of regulators or private sector profes- · Number of procedures for enforcing contracts is There are many types of business licenses required, sionals on each topic, and cooperative arrangements the number of independent actions, mandated by law and striking the right balance between the ease of with private consulting firms and business and law or court regulation, that demand interaction between doing business and consumer safety requires continu- associations. These standard characteristics include the parties to a contract or between them and the ous reform. Since construction is a large sector in limited liability company; operates in the country's judge or court officer. · Time required for enforcing most economies, the procedures required for a busi- most populous city; 100 percent domestically owned contracts is the number of calendar days from the ness in the construction industry to build a standard- and has five owners, none of whom is a legal entity; filing of the lawsuit in court to the final determination ized warehouse are recorded. These include obtaining start-up capital of 10 times income per capita at the and, in appropriate cases, payment. · Disclosure all necessary licenses and permits, completing all end of 2004, paid in cash; performs general indus- index measures the degree to which investors are required notifications and inspections, and submitting trial or commercial activities, such as production or protected through disclosure of ownership and finan- the relevant documents to the authorities. The data sale of products or services to the public; does not cial information. The index ranges from 0 to 7, with cover the number of procedures and time needed by perform foreign trade activities or handle products higher values indicating more disclosure. · Time to the construction firm to complete all procedures. subject to a special tax regime; does not use heavily resolve insolvency is the number of years from the Every economy has a complex system of laws and polluting production processes; leases the commer- time of filing for insolvency in court until resolution institutions to protect the interests of workers and cial plant and offices and is not a proprietor of real of distressed assets. guarantee a minimum standard of living for its popula- estate; does not qualify for investment incentives or tion. The rigidity of employment index focuses on the any special benefits; up to 50 employees within one Data sources regulation of employment, specifically the hiring and month of commencement of operations, all of them Data on the business environment are from the firing of workers and the rigidity of working hours. This nationals; turnover at least 100 times income per World Bank's Doing Business project (http://rru. index is the average of three subindexes: a difficulty of capita; and company deed 10 pages long. worldbank.org/DoingBusiness/). hiring index, a rigidity of hours index, and a difficulty of 2006 World Development Indicators 277 Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of shares traded Value traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2005 2000 2004 2000 2004 2000 2005 2000 2005 2004 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 166,068 61,478 58.4 30.3 2.1 5.0 4.8 29.7 127 101 24.6a 45.4a Armenia .. 18 .. 0.6 .. 0.1 4.6 3.9 .. 194 .. .. Australia 372,794 776,403 96.2 121.8 58.4 80.7 56.5 75.5 1,330 1,515 .. .. Austria 29,935 85,815 15.4 29.4 4.8 8.2 29.8 34.0 97 99 .. .. Azerbaijan 4 .. 0.1 .. .. .. .. .. 2 .. .. .. Bangladesh 1,186 3,035 2.6 5.9 1.7 1.6 74.4 32.3 221 262 104.3 b ­27.7b Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 182,481 768,377 79.9 218.1 16.6 20.0 20.7 14.9 174 170 .. .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 1,742 1,988 20.7 22.7 0.0 0.1 1.0 0.2 26 34 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 978 2,437 18.6 28.4 0.9 0.6 4.8 2.0 16 18 21.1b ­3.2b Brazil 226,152 474,647 37.6 54.7 16.8 15.5 43.5 37.2 459 381 33.7a 47.6a Bulgaria 617 5,086 4.9 11.6 0.5 2.1 9.2 35.2 503 331 82.7b 16.4b Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 841,385 1,177,518 117.8 120.4 88.8 66.9 77.3 63.1 1,418 3,597 .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 60,401 136,446 79.7 124.4 8.0 12.3 9.4 15.5 258 245 18.3a 14.7a China 580,991 780,763 48.5 33.1 60.2 38.7 158.3 82.6 1,086 1,387 ­2.1a 13.3a Hong Kong, China 623,398 861,463 377.0 528.5 228.5 269.3 61.3 55.7 779 1,086 .. .. Colombia 9,560 46,016 11.4 25.8 0.5 1.5 3.8 17.8 126 114 115.4b 108.1b Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2,924 1,920 18.3 10.4 .. 0.7 12.0 .. 21 23 .. .. Côte d'Ivoire 1,185 2,327 11.4 13.5 0.3 0.3 2.6 1.5 41 39 41.1b 16.9 b Croatia 2,742 12,918 14.9 31.9 1.0 1.4 7.4 6.6 64 145 ­7.7b 7.4b Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 11,002 38,345 19.7 28.8 11.8 16.5 60.3 120.7 131 36 76.3a 43.5a Denmark 107,666 151,342 68.0 62.7 57.9 40.4 86.0 71.4 225 178 .. .. Dominican Republic 141 .. 0.8 .. .. .. .. .. 6 .. .. .. Ecuador 704 3,214 4.4 8.5 0.1 0.3 5.5 5.0 30 32 46.7b 26.0 b Egypt, Arab Rep. 28,741 79,672 28.1 48.9 10.9 7.1 34.7 42.4 1,076 744 126.4a 158.0a El Salvador 2,041 2,643 15.5 16.7 0.2 3.1 2.7 0.3 40 32 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 1,846 3,495 33.7 55.2 6.0 7.4 18.9 51.5 23 15 70.5b 22.8 b Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 293,635 183,765 244.9 98.8 172.3 118.4 64.3 124.3 154 134 .. .. France 1,446,634 1,857,235 108.9 90.7 81.6 64.1 74.1 81.7 808 701 .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 24 186 0.8 3.6 .. 0.5 .. 0.5 269 277 .. .. Germany 1,270,243 1,194,517 66.8 43.6 56.3 51.3 79.1 123.7 1,022 660 .. .. Ghana 502 1,661 10.1 29.8 0.2 0.7 1.5 3.2 22 30 32.7b ­33.9 b Greece 110,839 125,242 98.9 61.0 84.8 21.2 63.7 37.5 329 340 .. .. Guatemala 240 .. 1.2 .. 0.0 .. 2.9 .. 44 5 .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 278 2006 World Development Indicators Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of shares traded Value traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2005 2000 2004 2000 2004 2000 2005 2000 2005 2004 2005 Honduras 458 .. 8.7 .. .. .. .. .. 46 .. .. .. Hungary 12,021 32,576 25.8 28.5 26.0 12.9 90.7 79.2 60 44 93.7a 16.1a India 148,064 553,074 32.4 56.1 111.5 54.8 133.6 93.6 5,937 4,763 20.1a 33.6a Indonesia 26,834 81,428 16.3 28.4 8.7 10.7 32.9 54.8 290 335 39.3a 9.1a Iran, Islamic Rep. 7,350 46,995 7.3 28.8 4.9 8.1 12.4 21.7 304 411 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 81,882 114,085 86.2 62.8 15.2 24.4 19.2 44.5 76 53 .. .. Israel 64,081 120,114 55.5 81.7 20.3 39.6 36.3 55.4 654 572 13.4a 24.1a Italy 768,364 789,563 71.5 47.1 72.4 47.9 104.0 114.5 291 269 .. .. Jamaica 3,582 13,028 44.6 162.6 0.9 5.4 2.5 3.1 46 39 107.4b ­14.1b Japan 3,157,222 3,678,262 66.5 79.6 56.8 74.2 69.9 103.5 2,561 3,220 12.5c 21.7c Jordan 4,943 37,639 58.4 159.6 4.9 46.3 7.7 85.0 163 201 55.0 b 117.8 b Kazakhstan 1,342 3,941 7.3 9.7 0.5 2.4 1.2 22.0 23 54 .. .. Kenya 1,283 6,384 10.1 24.2 0.4 2.0 3.6 9.7 57 47 ­15.0 b 60.0 b Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 171,587 718,180 33.5 63.1 208.7 94.0 233.2 210.8 1,308 1,620 25.7a 58.8a Kuwait 20,772 .. 56.3 .. 11.4 .. 21.3 .. 77 .. .. .. Kyrgyz Republic 4 34 0.3 1.5 1.7 3.0 .. 205.3 80 6 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 563 2,527 7.3 12.2 3.0 0.8 48.6 4.6 64 45 49.8 b 32.8 b Lebanon 1,583 4,929 9.5 10.7 0.7 0.9 6.7 25.5 12 11 53.5 b 111.8b Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 1,588 8,183 13.9 29.0 1.8 2.1 14.8 10.2 54 43 56.2b 6.2b Macedonia, FYR 7 413 0.2 7.7 0.1 0.5 348.3 8.1 1 68 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi 126 .. 7.2 .. 0.5 .. .. .. 7 8 .. .. Malaysia 116,935 180,346 129.5 160.6 64.8 50.6 44.6 26.9 795 1,020 12.7a ­2.9a Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 1,331 2,617 30.1 39.4 1.7 1.6 5.0 6.1 40 42 17.8 b 10.5b Mexico 125,204 239,128 21.5 25.4 7.8 6.3 32.3 25.7 179 151 47.9a 43.9a Moldova 392 574 30.4 22.1 1.9 2.1 97.9 7.7 36 23 .. .. Mongolia 37 25 3.9 1.5 .. 0.0 7.3 2.2 410 395 .. .. Morocco 10,899 27,220 32.7 50.1 3.3 3.4 9.2 16.4 53 56 18.3a 8.4a Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 311 415 9.1 7.7 0.6 0.3 4.5 1.6 13 13 36.7b ­1.1b Nepal 790 576 14.4 8.6 0.6 0.4 6.9 .. 110 114 .. .. Netherlands 640,456 622,284 172.8 107.5 182.7 104.4 101.4 104.1 234 177 .. .. New Zealand 18,866 43,731 36.2 44.2 20.7 15.6 45.9 40.1 142 158 .. .. Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 4,237 19,356 10.1 20.1 0.6 2.3 7.3 11.5 195 214 23.9 b 20.7b Norway 65,034 141,430 39.0 56.6 36.0 54.2 93.4 114.7 191 148 .. .. Oman 3,463 15,269 17.4 26.0 2.8 7.4 14.2 29.8 131 96 25.2b 38.0 b Pakistan 6,581 45,937 9.0 30.2 45.0 76.9 475.5 375.7 762 661 20.7b 58.5b Panama 2,794 3,401 24.0 24.8 1.3 0.4 1.5 1.5 29 22 .. .. Papua New Guinea .. 2,942 .. 75.3 .. 0.1 .. .. .. 9 .. .. Paraguay 423 212 5.5 2.9 .. 0.0 3.5 .. 55 52 .. .. Peru 10,562 35,995 19.9 29.3 2.9 1.6 12.6 7.1 230 196 ­0.7a 29.8a Philippines 25,957 40,153 34.2 34.2 10.8 4.3 15.8 20.4 228 235 25.0a 21.3a Poland 31,279 93,873 18.8 29.3 8.8 6.8 49.9 37.3 225 248 59.3a 20.8a Portugal 60,681 73,404 57.0 43.8 51.1 20.6 85.5 52.5 109 56 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 279 Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of shares traded Value traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2005 2000 2004 2000 2004 2000 2005 2000 2005 2004 2005 Romania 1,069 20,588 2.9 16.1 0.6 1.3 23.1 28.8 5,555 3,747 99.3 b 58.7b Russian Federation 38,922 548,579 15.0 46.1 7.8 22.5 36.9 39.0 249 296 12.8a 64.9a Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 67,171 646,104 35.6 122.2 9.2 188.8 27.1 231.7 75 77 83.6b 111.0 b Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. 3,281 .. 13.7 0.3 1.8 0.0 122.3 6 404 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 152,827 171,555 167.1 160.6 100.0 76.1 52.1 51.2 418 489 .. .. Slovak Republic 1,217 4,393 6.0 10.7 4.4 1.6 129.8 1.6 493 209 41.0b 16.6b Slovenia 2,547 7,899 13.4 30.1 .. 3.6 20.7 9.1 38 116 128.5b ­6.9 b Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 204,952 565,408 154.2 214.1 58.3 76.5 33.9 41.6 616 388 50.1a 24.8a Spain 504,219 940,673 86.8 90.5 169.8 114.9 210.7 143.3 1,019 3,272 .. .. Sri Lanka 1,074 5,720 6.6 18.2 0.9 2.9 11.0 23.7 239 239 ­59.2b 29.3 b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 73 225 5.3 9.4 0.0 0.0 0.2 0.0 6 6 .. .. Sweden 328,339 376,781 137.1 108.8 162.8 119.1 111.2 123.7 292 256 .. .. Switzerland 792,316 825,849 322.0 231.0 247.6 203.4 82.0 93.7 252 282 .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 233 670 2.6 6.2 0.4 0.2 3.4 .. 4 6 .. .. Thailand 29,489 123,539 24.0 71.4 19.0 67.5 53.2 75.2 381 468 ­6.4a 3.8a Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 4,330 16,972 53.1 135.9 1.7 4.2 3.1 4.0 27 37 36.8b ­1.3 b Tunisia 2,828 2,876 14.5 9.4 3.2 0.8 23.3 16.8 44 46 4.2b 11.1b Turkey 69,659 161,537 35.0 32.5 89.9 48.7 206.2 153.9 315 302 32.9a 49.5a Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 96 .. 1.4 .. 0.0 .. .. .. 5 .. .. Ukraine 1,881 24,976 6.0 18.2 0.9 0.3 19.6 3.6 139 221 170.3 b 52.8 b United Arab Emirates 5,727 30,363 8.1 34.3 0.2 4.3 .. .. 54 30 .. .. United Kingdom 2,576,992 2,815,928 179.2 132.6 127.6 174.5 66.6 140.5 1,904 2,486 15.3d 4.4 d United States 15,104,037 16,323,726 154.7 139.4 326.3 165.3 200.8 126.5 7,524 5,231 9.0 e 3.0 e Uruguay 161 330 0.8 2.5 0.0 0.0 0.9 0.4 16 10 .. .. Uzbekistan 32 4 0.2 0.0 0.1 0.0 .. 108.7 5 145 .. .. Venezuela, RB 8,128 5,017 6.9 5.6 0.6 0.4 8.9 4.6 85 50 ­50.4b ­22.0 b Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 765 1,097 16.5 18.8 4.1 1.7 20.9 1.7 24 27 .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 236 430 7.3 8.0 0.2 0.1 4.7 .. 9 11 .. .. Zimbabwe 2,432 2,402 32.9 41.3 3.8 2.9 10.8 6.4 69 79 ­26.7b 36.6b World 32,187,883 s 38,904,431 s 103.1 w 96.3 w 153.6 w 97.0 w 122.3 w 53.7 w 47,884 s 48,874 s .. .. Low income 167,320 450,544 24.2 44.5 78.9 45.0 152.7 107.6 7,965 6,756 .. .. Middle income 1,851,805 2,982,006 37.5 43.7 26.9 23.9 71.5 41.6 15,497 14,117 .. .. Lower middle income 980,024 1,426,779 35.5 36.7 32.3 26.2 91.3 37.1 11,450 10,584 .. .. Upper middle income 871,781 1,555,228 40.0 52.8 20.1 20.9 47.4 44.3 4,047 3,533 .. .. Low & middle income 2,019,125 3,432,550 35.9 43.8 33.3 26.7 81.4 53.7 23,462 20,873 .. .. East Asia & Pacific 780,487 1,050,879 47.2 41.0 50.0 37.0 125.2 50.0 3,190 3,794 .. .. Europe & Central Asia 176,208 561,440 19.3 32.8 25.6 19.4 82.1 59.0 8,295 7,023 .. .. Latin America & Carib. 626,283 767,136 32.6 39.6 8.6 8.3 26.9 26.1 1,806 1,525 .. .. Middle East & N. Africa 60,573 141,343 19.8 37.1 6.3 7.4 18.7 16.5 1,807 1,627 .. .. South Asia 157,695 424,403 26.4 48.7 90.9 52.2 168.0 120.6 7,269 6,000 .. .. Sub-Saharan Africa 217,880 487,349 91.2 129.6 32.8 43.9 22.5 27.6 1,095 904 .. .. High income 30,168,757 35,471,881 117.8 108.9 179.9 113.9 130.9 114.0 24,422 28,001 .. .. Europe EMU 5,423,385 6,805,103 88.5 71.6 81.8 60.6 90.8 102.0 4,367 5,973 .. .. Note: Because aggregates for market capitalization are unavailable for 2005, those shown refer to 2004. a. Data refer to the S&P/IFC Investable index. b. Data refer to the S&P/IFC Global index c. Data refer to the Nikkei 225 index. d. Data refer to the FT 100 index. e. Data refer to the S&P 500 index. 280 2006 World Development Indicators Stock markets About the data Definitions The development of an economy's financial markets size is positively correlated with the ability to mobilize · Market capitalization (also known as market is closely related to its overall development. Well capital and diversify risk. value) is the share price times the number of shares functioning financial systems provide good and eas- Market liquidity, the ability to easily buy and sell outstanding. · Market liquidity is the total value ily accessible information. That lowers transaction securities, is measured by dividing the total value traded divided by GDP. Value traded is the total costs, which in turn improves resource allocation traded by GDP. The turnover ratio--the value of value of shares traded during the period. This indi- and boosts economic growth. Both banking systems shares traded as a percentage of market capital- cator complements the market capitalization ratio by and stock markets enhance growth, the main fac- ization--is also a measure of liquidity as well as showing whether market size is matched by trading. tor in poverty reduction. At low levels of economic of transaction costs. (High turnover indicates low · Turnover ratio is the total value of shares traded development commercial banks tend to dominate transaction costs.) The turnover ratio complements during the period divided by the average market capi- the financial system, while at higher levels domes- the ratio of value traded to GDP, because the turn- talization for the period. Average market capitaliza- tic stock markets tend to become more active and over ratio is related to the size of the market and tion is calculated as the average of the end-of-period efficient relative to domestic banks. the value traded ratio to the size of the economy. A values for the current period and the previous period. Open economies with sound macroeconomic poli- small, liquid market will have a high turnover ratio · Listed domestic companies are the domestically cies, good legal systems, and shareholder protection but a low value traded ratio. Liquidity is an impor- incorporated companies listed on the country's stock attract capital and therefore have larger financial mar- tant attribute of stock markets because, in theory, exchanges at the end of the year. This indicator does kets. Recent research on stock market development liquid markets improve the allocation of capital and not include investment companies, mutual funds, or shows that new communications technology and enhance prospects for long-term economic growth. other collective investment vehicles. · S&P/EMDB increased financial integration have resulted in more A more comprehensive measure of liquidity would indexes measure the U.S. dollar price change in the cross-border capital flows, a stronger presence of include trading costs and the time and uncertainty stock markets covered by the S&P/IFCI and S&P/ financial firms around the world, and the migration of in finding a counterpart in settling trades. IFCG country indexes. stock exchange activities to international exchanges. Standard & Poor's maintains a series of indexes for Many firms in emerging markets now cross-list on investors interested in investing in stock markets in international exchanges, which provides them with developing countries. At the core of the S&P/EMDB lower cost capital and more liquidity-traded shares. indexes, the Global (S&P/IFCG) index is intended to However, this also means that exchanges in emerg- represent the most active stocks in the markets it cov- ing markets may not have enough financial activity ers and to be the broadest possible indicator of market to sustain them, putting pressure on them to rethink movements. The Investable (S&P/IFCI) index, which their operations. applies the same calculation methodology as the The stock market indicators in the table include S&P/IFCG index, is designed to measure the returns measures of size (market capitalization, number of that foreign portfolio investors might receive from listed domestic companies) and liquidity (value traded investing in emerging market stocks that are legally as a percentage of gross domestic product, value of and practically open to foreign portfolio investment. shares traded as a percentage of market capitaliza- The S&P/EMDB, the source for all the data in the tion). The comparability of such indicators between table, provides regular updates on 53 emerging stock countries may be limited by conceptual and statistical markets encompassing more than 2,613 stocks. The weaknesses, such as inaccurate reporting and dif- S&P/IFCG index includes 32 markets and 2,125 ferences in accounting standards. The percentage stocks, and the S&P/IFCI index covers 22 markets change in stock market prices in U.S. dollars, from and 1,377 stocks. In addition 251 companies from the Standard & Poor's Emerging Markets Data Base 21 "frontier" markets are covered. These indexes are (S&P/EMDB) indexes is an important measure of widely used benchmarks for international portfolio overall performance. Regulatory and institutional fac- management. See Standard & Poor's (2001b) for tors that can affect investor confidence, such as entry further information on the indexes. Data sources and exit restrictions, the existence of a securities and Because markets included in Standard & Poor's Data on stock markets are from Standard & Poor's exchange commission, and the quality of laws to pro- emerging markets category vary widely in level of Global Stock Markets Factbook 2005, which draws tect investors, may influence the functioning of stock development, it is best to look at the entire category on the Emerging Markets Data Base, supple- markets but are not included in the table. to identify the most significant market trends. And it mented by other data from Standard & Poor's. Stock market size can be measured in a number is useful to remember that stock market trends may The firm collects data through an annual survey of ways, and each may produce a different ranking be distorted by currency conversions, especially when of the world's stock exchanges, supplemented by of countries. Market capitalization shows the over- a currency has registered a significant devaluation. information provided by its network of correspon- all size of the stock market in U.S. dollars and as a About the data is based on Demirgüç-Kunt and dents and by Reuters. Data on GDP are from the percentage of GDP. The number of listed domestic Levine (1996a), Beck and Levine (2001), and Claes- World Bank's national accounts data files. companies is another measure of market size. Market sens, Klingebiel, and Schmukler (2002). 2006 World Development Indicators 281 Financial access, stability, and efficiency Bank Bank Financial Banking Bank Bank non- Deposit Domestic Interest Risk premium branches deposit information system capital to performing insurance credit rate on lending accounts infrastruture ownership asset ratio loans to total coverage provided by spread index gross loans banking sector % of total banking assets Lending Prime lending 0 (less Held by rate minus rate minus per per developed) foreign- Held by deposit rate treasury bill rate 100,000 1,000 to 10 (more owned government- % of GDP percentage percentage people people developed) banks owned banks % % per capita % of GDP points points 2001­04a 2001­04a 2005 2001 2001 2004 2004 2003 2004 2004 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2.1 161 .. 46.0 54.0 .. .. 3.0 45.7 5.2 5.0 Algeria .. .. .. 3.9 95.7 .. .. 3.7 24.8 5.5 7.9 Angola .. .. .. .. .. 11.3 13.3 .. 4.5 66.9 .. Argentina 10.0 369 7.5 31.8 31.9 .. 18.6 3.1 45.5 4.2 .. Armenia 7.6 111 4.5 59.0 0.0 17.8 7.2 .. 7.2 13.7 13.4 Australia 29.9 .. .. 17.0 0.0 5.9 0.3 .. 109.0 5.2 .. Austria 53.9 3,120 .. .. 0.0 6.0 2.2 0.7 121.9 .. .. Azerbaijan 4.1 .. .. 4.6 58.3 11.9 9.5 .. 11.2 6.5 11.1 Bangladesh 4.5 229 .. .. .. 2.7 17.6 4.6 41.1 7.6 .. Belarus 4.8 .. .. 26.0 74.0 20.0 4.6 0.6 21.2 4.2 .. Belgium 53.2 3,080 .. .. 0.0 3.2 2.2 0.8 104.9 5.2 4.7 Benin .. .. 3.0 91.0 0.0 .. .. .. 9.9 .. .. Bolivia 1.5 41 5.5 36.3 0.0 11.5 14.0 .. 52.5 7.1 7.1 Bosnia and Herzegovina 3.9 429 .. 73.0 10.0 13.2 3.5 1.7 43.5 6.6 .. Botswana 3.8 .. .. 100.0 0.0 9.7 2.8 .. ­3.0 5.9 .. Brazil 14.6 631 4.0 29.9 32.0 16.0 3.9 2.3 98.8 39.5 37.8 Bulgaria 13.9 1,351 7.0 74.6 17.6 11.0 7.1 3.4 36.2 5.8 6.1 Burkina Faso .. .. .. 56.0 0.0 .. .. .. 13.5 .. .. Burundi .. .. .. .. .. .. .. .. 38.4 .. .. Cambodia .. .. 1.0 .. .. .. .. .. 8.2 15.8 .. Cameroon .. .. 3.0 .. .. .. .. .. 15.2 13.0 .. Canada 45.6 .. .. 4.8 0.0 4.4 0.7 1.6 97.0 3.2 1.8 Central African Republic .. .. .. .. .. .. .. .. 16.7 13.0 .. Chad .. .. .. .. .. .. .. .. 7.9 13.0 .. Chile 9.4 1,045 6.5 46.8 13.3 7.0 1.2 0.7 70.2 3.2 .. China 1.3 .. 5.5 .. .. 3.9 15.6 .. 142.6 3.3 .. Hong Kong, China .. .. .. .. 0.0 12.3 2.2 .. 149.3 5.0 4.9 Colombia 8.7 612 7.0 21.5 18.3 12.1 3.3 4.0 41.2 7.3 .. Congo, Dem. Rep. .. .. .. .. .. .. .. .. 1.2 .. .. Congo, Rep. .. .. 1.0 .. .. .. .. .. 12.0 13.0 .. Costa Rica 9.6 .. 6.5 23.3 62.2 11.9 2.0 .. 42.3 13.9 .. Côte d'Ivoire .. .. .. 84.2 10.6 .. .. .. 18.6 .. .. Croatia 23.4 .. .. 89.3 5.0 8.5 4.5 2.3 68.2 9.9 .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic 11.2 1,923 3.5 90.0 3.8 5.6 4.1 3.4 45.8 4.7 3.5 Denmark 37.6 2,706 .. 0.0 0.0 .. .. 1.2 167.0 4.7 .. Dominican Republic 6.0 720 .. .. .. 7.4 7.3 0.2 37.3 11.5 .. Ecuador 9.3 420 .. 7.0 14.0 9.9 6.4 .. 20.1 5.6 .. Egypt, Arab Rep. 3.6 .. 3.5 13.3 64.7 5.1 24.2 .. 106.3 5.7 3.5 El Salvador 4.6 457 5.5 12.3 4.2 8.0 12.0 3.0 49.2 .. .. Eritrea .. .. .. .. .. .. .. .. 141.9 .. .. Estonia 15.2 .. .. 98.9 0.0 9.8 0.3 1.2 62.9 3.5 .. Ethiopia 0.4 .. .. .. .. .. .. .. 61.1 3.6 6.4 Finland 19.1 .. .. 6.2 0.0 8.2 0.4 0.9 70.7 2.7 .. France 43.2 1,801 .. .. 0.0 6.5 4.8 2.7 106.4 4.4 4.3 Gabon .. .. 1.0 .. .. .. 15.8 .. 12.7 13.0 .. Gambia, The .. .. .. 95.8 .. .. .. .. 19.9 14.5 .. Georgia 3.1 .. 5.5 .. .. .. .. .. 18.4 24.0 12.1 Germany 49.4 .. .. 4.3 42.2 4.4 5.3 0.8 138.0 7.0 6.7 Ghana 1.6 .. .. 53.5 12.1 12.4 16.1 .. 30.5 .. .. Greece 30.8 2,418 .. 10.8 22.8 7.9 7.1 1.4 106.0 4.3 4.4 Guatemala 10.1 404 7.0 9.0 3.0 .. .. 1.3 16.1 9.6 .. Guinea .. .. .. 90.0 0.0 .. .. .. 15.5 .. .. Guinea-Bissau .. .. .. 100.0 0.0 .. .. .. 8.1 .. .. Haiti .. .. .. .. .. .. .. .. 31.7 23.3 21.8 282 2006 World Development Indicators Financial access, stability, and efficiency Bank Bank Financial Banking Bank Bank non- Deposit Domestic Interest Risk premium branches deposit information system capital to performing insurance credit rate on lending accounts infrastruture ownership asset ratio loans to total coverage provided by spread index gross loans banking sector % of total banking assets Lending Prime lending 0 (less Held by rate minus rate minus per per developed) foreign- Held by deposit rate treasury bill rate 100,000 1,000 to 10 (more owned government- % of GDP percentage percentage people people developed) banks owned banks % % per capita % of GDP points points 2001­04a 2001­04a 2005 2001 2001 2004 2004 2003 2004 2004 2004 Honduras 0.7 287 5.5 18.5 0.0 8.4 6.4 9.5 45.1 8.8 .. Hungary 28.3 .. 5.5 88.8 9.0 8.9 2.7 1.6 59.0 3.7 1.5 India 6.3 .. 5.5 7.3 75.3 5.9 6.6 3.9 60.1 .. .. Indonesia 8.4 .. 6.5 .. .. 9.3 13.4 .. 48.8 7.7 .. Iran, Islamic Rep. 8.4 2,249 5.0 .. .. .. .. .. 46.2 5.0 .. Iraq .. .. .. .. .. .. .. .. .. .. .. Ireland 23.4 .. .. .. .. 4.9 0.8 0.6 137.0 2.6 .. Israel 14.7 .. .. 1.2 46.1 7.1 10.5 .. 83.3 3.8 2.7 Italy 52.1 976 .. 5.7 10.0 6.9 6.5 4.6 106.5 4.1 2.8 Jamaica .. .. .. .. .. .. 3.0 1.7 28.4 10.2 2.7 Japan 10.0 .. .. 6.7 0.0 4.2 2.9 2.5 154.9 1.7 .. Jordan 10.0 465 .. 64.3 0.0 6.4 19.9 7.6 91.5 5.8 .. Kazakhstan 2.5 .. 5.5 17.9 0.5 8.0 29.9 1.3 18.6 .. .. Kenya 1.4 70 3.5 39.3 1.1 11.4 22.9 3.1 39.7 10.1 9.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 13.4 .. 7.5 29.5 40.0 4.8 1.9 3.3 100.8 2.0 .. Kuwait 8.3 .. .. 0.0 0.0 11.0 5.4 .. 85.3 3.0 .. Kyrgyz Republic 3.1 .. .. 24.7 16.0 .. .. .. 8.4 22.6 24.3 Lao PDR .. .. 2.5 .. .. .. .. .. 9.5 21.4 8.9 Latvia .. .. 4.5 65.2 3.2 8.2 1.1 1.3 54.7 4.2 2.1 Lebanon 18.0 383 6.0 15.9 2.0 5.7 10.1 0.8 179.0 3.4 5.6 Lesotho .. .. .. 100.0 0.0 .. .. .. ­1.3 8.1 3.8 Liberia .. .. .. .. .. .. .. .. 219.7 14.3 .. Libya .. .. .. .. .. .. .. .. ­0.5 4.0 0.6 Lithuania 3.4 1,166 6.0 78.2 12.2 9.5 2.3 2.8 30.0 4.5 3.2 Macedonia, FYR .. .. .. 51.1 1.3 .. .. 9.9 22.1 5.9 .. Madagascar 0.7 14 4.0 67.8 0.0 6.2 11.4 .. 15.0 10.3 12.6 Malawi .. .. .. .. .. .. .. .. 22.8 23.1 8.3 Malaysia 9.8 1,250 6.5 19.0 0.0 8.1 11.8 .. 138.7 3.0 3.7 Mali .. .. .. 67.0 21.8 .. .. .. 17.7 .. .. Mauritania .. .. .. .. .. .. .. .. ­5.9 13.0 .. Mauritius 11.9 1,586 .. 24.5 0.0 .. .. .. 84.6 12.9 .. Mexico 7.6 310 8.0 82.7 0.0 11.5 2.5 489.1 38.4 4.5 0.4 Moldova .. .. .. 36.7 13.6 20.2 6.5 .. 32.0 5.8 9.0 Mongolia .. .. .. .. .. .. .. .. 34.8 11.2 .. Morocco 6.6 .. 3.0 20.8 35.0 7.6 19.4 .. 82.6 7.9 .. Mozambique .. .. 5.0 .. .. 6.5 6.4 .. 5.4 12.2 9.7 Myanmar .. .. .. .. .. .. .. .. .. 5.5 .. Namibia 4.5 423 .. 70.0 0.0 .. .. .. 53.6 5.0 3.6 Nepal 1.7 .. 3.5 .. .. .. .. .. .. 5.8 6.1 Netherlands 34.2 .. .. 2.2 3.9 3.9 1.8 0.7 178.8 0.4 .. New Zealand 28.0 .. .. 99.1 0.0 .. .. .. 121.5 4.6 4.5 Nicaragua 2.8 96 4.0 .. .. .. 9.3 27.5 85.2 8.8 .. Niger .. .. 3.5 73.4 0.0 .. .. .. 11.4 .. .. Nigeria 1.6 .. 1.0 .. 4.7 9.9 21.6 1.0 13.2 5.5 4.8 Norway 22.9 1,611 .. 19.2 0.0 6.1 1.0 5.8 11.1 2.6 .. Oman .. .. .. 11.9 0.0 .. .. 6.5 34.9 5.3 .. Pakistan 4.7 192 5.0 20.1 53.8 6.2 9.0 .. 41.8 .. .. Panama 12.9 .. 8.5 59.3 11.8 13.2 2.6 .. 90.8 6.6 .. Papua New Guinea 1.6 120 .. .. .. .. .. .. 21.9 11.5 4.4 Paraguay .. .. .. 83.5 9.2 10.5 10.8 9.7 18.3 28.4 .. Peru 4.2 316 7.5 42.5 0.0 9.8 9.5 8.8 17.4 11.5 .. Philippines 7.8 302 6.0 15.0 11.2 12.8 24.7 1.9 59.8 3.9 2.8 Poland 8.2 .. 7.5 68.7 23.5 8.2 15.5 5.0 34.6 3.8 .. Portugal 51.6 .. .. 17.7 22.8 6.1 2.2 1.9 153.9 .. .. Puerto Rico .. .. .. 30.4 0.7 .. .. .. .. .. .. 2006 World Development Indicators 283 Financial access, stability, and efficiency Bank Bank Financial Banking Bank Bank non- Deposit Domestic Interest Risk premium branches deposit information system capital to performing insurance credit rate on lending accounts infrastruture ownership asset ratio loans to total coverage provided by spread index gross loans banking sector % of total banking assets Lending Prime lending 0 (less Held by rate minus rate minus per per developed) foreign- Held by deposit rate treasury bill rate 100,000 1,000 to 10 (more owned government- % of GDP percentage percentage people people developed) banks owned banks % % per capita % of GDP points points 2001­04a 2001­04a 2005 2001 2001 2004 2004 2003 2004 2004 2004 Romania 13.8 1,208 8.0 47.3 41.8 8.5 8.1 1.4 15.3 .. .. Russian Federation 2.2 1,892 2.0 8.8 35.5 14.0 3.8 1.1 25.9 7.6 7.6 Rwanda .. .. 2.0 0.0 6.6 .. .. .. 13.5 .. .. Saudi Arabia 5.4 214 .. 20.7 21.4 8.0 3.1 .. 64.2 .. .. Senegal .. .. 3.5 78.7 0.0 8.4 14.2 .. 21.8 .. .. Serbia and Montenegro .. .. .. 13.2 3.8 .. 22.8 0.0 .. .. .. Sierra Leone .. .. .. .. .. 11.6 14.8 .. 30.3 11.9 ­4.1 Singapore 9.1 1,671 .. .. 0.0 9.7 2.9 .. 80.2 4.9 4.3 Slovak Republic 10.3 .. 3.5 85.5 4.4 7.2 5.4 4.2 44.0 4.9 .. Slovenia 2.2 .. .. 20.6 12.2 7.5 5.7 1.8 55.7 4.8 4.5 Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 6.0 .. 6.0 7.7 0.0 7.0 1.8 .. 86.7 4.7 3.8 Spain 95.9 2,076 .. 8.5 0.0 8.5 0.8 1.1 140.6 1.8 1.0 Sri Lanka 6.9 .. 4.0 .. .. .. .. 1.1 44.6 4.4 1.8 Sudan .. .. .. 4.0 12.0 .. .. .. 11.5 .. .. Swaziland .. .. .. 85.8 14.2 .. .. .. 15.6 6.7 3.4 Sweden 21.8 .. .. .. 0.0 6.3 0.9 0.9 113.1 3.0 1.7 Switzerland 38.0 1,986 .. 10.7 14.1 5.0 1.6 0.5 176.1 2.8 2.8 Syrian Arab Republic .. .. .. .. .. .. .. .. 30.3 5.0 .. Tajikistan .. .. .. 50.0 4.6 .. .. .. 16.5 10.6 .. Tanzania 0.6 .. 2.0 .. .. .. .. 0.9 9.2 9.7 5.6 Thailand 7.2 1,423 5.5 6.8 30.6 8.7 11.9 .. 105.3 4.5 .. Togo .. .. .. 17.5 51.0 .. .. .. 16.7 .. .. Trinidad and Tobago 9.2 1,073 .. 2.4 14.5 .. .. 1.0 18.8 6.5 4.5 Tunisia .. .. 3.5 15.7 42.7 .. 23.7 .. 70.9 .. .. Turkey 8.5 1,114 5.0 3.5 31.8 14.3 6.0 12.6 54.9 .. .. Turkmenistan .. .. .. 0.0 96.0 .. .. .. .. .. .. Uganda 0.5 47 .. .. .. 10.1 2.2 6.5 11.0 12.9 11.6 Ukraine .. .. 3.5 10.5 12.0 13.1 30.0 0.3 30.8 9.6 .. United Arab Emirates .. .. .. 27.0 35.0 12.1 12.5 .. 50.6 .. .. United Kingdom 18.3 .. .. 46.0 0.0 6.8 2.2 1.9 159.1 .. 0.0 United States 30.9 .. .. 19.0 0.0 10.3 0.8 2.7 215.5 .. 3.0 Uruguay 6.4 .. 5.5 43.3 42.5 .. 3.6 .. 53.3 17.5 8.9 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 4.4 487 4.5 43.2 6.9 12.5 2.8 1.9 11.0 5.9 .. Vietnam .. .. 5.5 .. .. .. .. 4.0 58.4 2.9 3.7 West Bank and Gaza 3.3 254 .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. 3.5 .. .. .. .. .. 5.2 5.5 4.7 Zambia 1.5 .. 1.5 .. .. .. 7.6 .. 35.2 19.2 18.1 Zimbabwe 3.3 174 .. 28.0 6.1 10.7 4.7 .. 49.8 175.7 153.2 World 9.8 w .. w .. m 25.4 m 5.5 m 8.5 m 6.4 m .. m 143.7 w 6.5 m .. m Low income 4.9 .. .. .. .. .. .. .. 47.7 11.9 .. Middle income 5.0 .. .. 30.9 10.6 9.7 7.1 2.3 78.5 6.5 .. Lower middle income 4.4 .. .. 21.2 13.0 10.2 10.8 2.3 101.3 6.7 .. Upper middle income 7.1 1,096 .. 45.0 5.9 8.9 3.2 2.1 47.4 5.8 5.3 Low & middle income 4.9 .. .. 40.9 9.1 .. 7.6 .. 74.1 7.4 .. East Asia & Pacific 2.9 .. .. .. .. .. .. .. 125.9 6.3 .. Europe & Central Asia 6.6 .. 5.3 50.0 12.0 9.8 5.7 1.7 36.8 5.8 .. Latin America & Carib. 9.9 500 .. 31.8 11.8 11.0 5.2 3.0 56.6 7.6 5.0 Middle East & N. Africa .. .. .. 15.7 35.0 .. .. .. 59.4 5.5 .. South Asia 5.9 .. 4.5 .. .. 5.9 9.0 3.9 56.4 6.5 .. Sub-Saharan Africa .. .. .. 68.9 0.0 .. .. .. 47.1 12.5 .. High income 33.0 .. .. 10.8 0.0 6.4 2.2 1.4 161.3 4.1 .. Europe EMU 53.1 .. .. 6.2 2.0 6.1 2.0 0.9 124.8 4.0 3.6 a. Data are for the most recent year available in the period shown. 284 2006 World Development Indicators Financial access, stability, and efficiency About the data Definitions This year the table includes new indicators from the costs on the economy. The ratio of a bank's capital to · Bank branches are deposit money bank branches. World Bank and the International Monetary Fund (IMF) its total assets measures the extent to which a bank · Bank deposit accounts are deposit accounts, covering financial access, stability, and efficiency. can deal with unexpected losses. Aggregating the including checking, savings, and time deposit Financial sector development has positive impacts ratios across banks provides a measure of the solvency accounts for businesses, individuals, and others. on economic growth and poverty. The size of the sec- and resiliency of a country's banking system. · Financial information infrastructure index is tor determines the amount of resources mobilized for The share of bank nonperforming loans to total based on 10 measures, 6 covering the scope, qual- investment. Access to finance can expand opportuni- gross loans is a measure of bank health and effi- ity, and availability of credit reporting data (in private ties for all--not just the rich and well connected--with ciency. It helps to identify problems with asset quality and public registries) and the existence of a basic higher levels of access and use of banking services in the loan portfolio. A high ratio may signal deteriora- legal framework for credit reporting, and 4 covering associated with lower financing obstacles for people tion in the quality of the credit portfolio. International the availability of public registry data for collateral and businesses. A stable financial system that pro- guidelines recommend that loans be classified as (fixed and moveable) and corporate registries and motes efficient savings and investment is also crucial nonperforming when payments of principal and inter- court records. · Banking system ownership refers for a thriving democracy and market economy. The est are past due by 90 days or more or when future to the shares of assets of foreign-owned banks and banking system is the largest sector in the financial payments are not expected to be received in full. See government-owned banks as percentages of total system in most countries, so most of the indicators the IMF's Global Financial Stability Report for more banking system assets in a country. · Bank capi- in the table cover the banking system. detailed background information. tal to asset ratio is the ratio of bank capital and There are several aspects of access to financial Deposit insurance is a tool used by governments to reserves to total assets. Capital and reserves include services: availability, cost, and quality of services. promote financial stability and to protect small deposi- funds contributed by owners, retained earnings, gen- Two measures of access and use are presented in tors from losses due to bank failures. Almost all coun- eral and special reserves, provisions, and valuation the table: number of bank branches and bank deposit tries have financial safety nets that include explicit or adjustments. Capital includes tier 1 capital (paid- accounts. The number of bank branches measures the implicit deposit insurance, bank regulation and supervi- up shares and common stock), which is a common physical outreach of the banking sector to a country's sion, central bank lender of last resort facilities, and feature in all countries' banking systems, and total population. As a measure of access to bank outlets, bank insolvency resolution procedures. But deposit regulatory capital, which includes several specified this indicator has limitations: it assumes a uniform dis- insurance can lead banks to take too much risk. Coun- types of subordinated debt instruments that need tribution of bank outlets across a country's population. tries with excessive explicit deposit insurance are more not be repaid if the funds are required to maintain But in many countries bank branches are concentrated likely to experience a financial crisis and to have poorer minimum capital levels (these comprise tier 2 and in urban areas, with accessibility limited for people financial intermediation. Deposit insurance coverage tier 3 capital). Total assets include all nonfinancial who live in rural areas. The number of bank deposit as a percentage of GDP is presented in the table. and financial assets. · Bank nonperforming loans accounts is an indicator of actual use of banking ser- Domestic credit provided by the banking sector as to total gross loans are the value of nonperforming vices. An individual can have more than one account, a share of GDP is a measure of banking sector depth loans divided by the total value of the loan portfolio so it is still an imperfect measure. Further analysis and and financial sector development in terms of size. In (including nonperforming loans before the deduction detailed explanation of the data can be found in Beck, a few countries governments may hold international of specific loan-loss provisions). The loan amount Demirgüç-Kunt, and Martinez Peria (2005). reserves as deposits in the banking system rather than recorded as nonperforming should be the gross The development and growth of credit markets in the central bank. Since the claims on the central value of the loan as recorded on the balance sheet, depend on access to timely, reliable, and accurate government are a net item (claims on the central gov- not just the amount that is overdue. · Deposit insur- data on borrowers' credit experiences. For secured ernment minus central government deposits), this net ance coverage is the value of deposits per depositor transactions, such as mortgages or vehicle loans, figure may be negative, resulting in a negative figure of protected by a formal deposit insurance scheme as having rapid access to information in property reg- domestic credit provided by the banking sector. a percentage of GDP per capita. · Domestic credit istries is also vital, and for small business loans, The interest rate spread--the margin between provided by the banking sector includes all credit corporate registry data are needed. the cost of mobilizing liabilities and the earnings on to various sectors on a gross basis, except credit to The financial information infrastructure index is assets--is a measure of the efficiency by which the the central government, which is net. The banking based on both the quality and availability of data in financial sector intermediates funds. A narrow inter- sector includes monetary authorities, deposit money credit reports, public registries, corporate registries, est rate spread means low transaction costs, which banks, and other banking institutions for which data and court records. Basic consumer protections are lowers the overall cost of funds for investment, cru- are available (including institutions that do not accept also included in the index. The index ranges from 0 cial to economic growth. The risk premium on lending transferable deposits but do incur such liabilities as (less developed financial information infrastructure) is the spread between the lending rate to the private time and savings deposits). · Interest rate spread is to 10 (more developed financial information infra- sector and the "risk-free" government rate. A small the interest rate charged by banks on loans to prime structure). Data are from the World Bank's Financial spread indicates that the market considers its best customers minus the interest rate paid by commercial Sector Operations and Policy Department. corporate customers to be low risk. Interest rate or similar banks for demand, time, or savings depos- The type of bank ownership--foreign, government, spreads are expressed as annual averages. In some its. · Risk premium on lending is the interest rate or domestic private--has important implications for countries this spread may be negative, indicating charged by banks on loans to prime private sector the efficiency and stability of financial intermedia- that the market considers its best corporate clients customers minus the "risk free" treasury bill interest tion. Studies show that banking systems with more to be lower risk than the government. rate at which short-term government securities are foreign-owned (more than half of assets owned by issued or traded in the market. foreigners) and domestic private banks tend to be Data sources more efficient and resilient to crises than banking sys- tems with mostly government-owned (more than half Data on bank branches, deposit accounts, finan- of assets owned by the government) banks. These cial information infrastructure, bank ownership, and data were collected through a survey of bank regula- deposit insurance coverage are collected from sur- tors conducted by Barth, Caprio, and Levine (2006). veys of banking and regulatory institutions by the The frequency and magnitude of financial crises over World Bank's Research Department and Financial the past two decades have made it clear how impor- Sector and Operations Policy Department. Data tant it is to monitor the strength of financial systems. on bank capital and nonperforming loans are from Robust financial systems help increase economic activ- the IMF's Global Financial Stability Report. Data on ity and welfare, but unstable financial systems can dis- credit and interest rates are from the IMF's Interna- rupt financial activity and impose huge and widespread tional Financial Statistics. 2006 World Development Indicators 285 Tax policies Tax revenue collected Tax payments Highest marginal by central government by businesses tax ratea Time to prepare Number and pay taxes Total tax payable Individual of payments hours % of gross profit On income Corporate % of GDP January January January % over $ % 2000 2004 2005 2005 2005 2004 2004 2004 Afghanistan .. 3.5 2 80 21.4 .. .. .. Albaniab 13.6 .. 53 240 71.6 .. .. .. Algeriab 37.9 32.0 63 504 58.5 .. .. .. Angola .. .. 30 656 32.5 .. .. .. Argentina 9.8 14.2 35 580 97.9 35 41,667 35 Armeniab .. 15.3 50 1,120 53.8 .. .. .. Australia 22.8 24.1 12 107 37.0 47 46,538 30 Austria 19.3 20.5 20 272 50.8 50 64,052 34 Azerbaijanb 12.7 .. 35 756 41.4 35 7,307 24 Bangladeshb 7.6 8.1 17 640 50.4 .. .. .. Belarusb 16.6 18.6 113 1,188 121.8 .. .. .. Belgium 27.8 25.8 10 160 44.6 50 30,210 33 Benin .. .. 75 270 53.1 .. .. .. Bolivia 13.2 15.0 41 1,080 64.0 13 .. 25 Bosnia and Herzegovina .. 22.4 73 100 19.7 .. .. .. Botswanab .. .. 24 140 52.9 25 20,950 15 Brazilb 12.2 .. 23 2,600 147.9 28 8,843 15 Bulgaria b 18.3 22.3 27 616 38.6 29 4,550 20 Burkina Faso .. .. 40 270 48.3 .. .. .. Burundib 15.4 .. 41 140 173.5 .. .. .. Cambodia 8.6 8.6 27 97 31.1 20 36,356 20 Cameroonb .. .. 51 1,300 47.6 60 10,726 39 Canadab 15.3 14.2 10 119 32.5 29 809,718 21 Central African Republic .. .. 66 504 60.9 .. .. .. Chad .. .. 65 122 51.3 .. .. .. Chile 16.6 15.9 8 432 46.7 40 6,127 17 Chinab 6.8 8.5 34 584 46.9 45 12,082 30 Hong Kong, China .. .. 1 80 14.3 17 13,462 18 Colombia 13.3 13.8 54 432 75.1 35 29,426 37 Congo, Dem. Rep.b 3.5 6.3 34 312 134.7 50 6,056 40 Congo, Rep.b 9.2 8.5 94 576 66.9 .. .. .. Costa Ricab 12.1 13.4 41 402 54.3 30 16,860 30 Côte d'Ivoireb 14.6 14.9 71 270 46.9 10 3,837 35 Croatiab 26.2 24.1 39 232 47.1 45 35,171 .. Cuba .. .. .. .. .. .. .. .. Czech Republic 15.7 16.1 14 930 40.1 32 12,910 28 Denmark 31.3 31.1 18 135 63.4 59 51,162 30 Dominican Republic 15.7b 15.1c 85 124 57.2 25 23,734 25 Ecuador b .. .. 33 600 33.9 25 57,600 25 Egypt, Arab Rep.b .. .. 39 504 32.1 32 10,823 40 El Salvador 10.7 11.0 65 224 32.2 .. .. .. Eritrea .. .. 18 216 66.3 .. .. .. Estoniab 16.0 .. 11 104 39.5 26 1,354 35 Ethiopiab 13.2 13.2 20 52 43.6 .. .. .. Finland 25.0 23.0 19 .. 52.1 34 68,517 29 France 23.6 22.4 29 72 42.8 48 60,673 33 Gabon .. .. .. .. .. 50 .. 35 Gambia, Theb .. .. .. .. .. .. .. .. Georgiab 7.7 9.8 49 448 49.7 .. .. .. Germany 11.9 10.9 32 105 50.3 45 65,224 25 Ghanab 17.2 22.4 35 304 45.3 30 5,647 33 Greece 26.0 .. 32 204 47.9 40 29,464 35 Guatemalab 10.1 10.1 50 260 53.4 31 35,853 31 Guineab .. .. 55 416 51.2 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. Haiti .. .. 53 .. 31.7 .. .. .. 286 2006 World Development Indicators Tax policies Tax revenue collected Tax payments Highest marginal by central government by businesses tax ratea Time to prepare Number and pay taxes Total tax payable Individual of payments hours % of gross profit On income Corporate % of GDP January January January % over $ % 2000 2004 2005 2005 2005 2004 2004 2004 Honduras .. .. 48 424 43.2 25 27,778 25 Hungary 22.7 22.1 24 304 56.8 38 7,214 16 Indiab 9.0 10.2 59 264 43.2 30 3,283 36 Indonesiab 11.3 12.3 52 560 38.8 35 22,371 30 Iran, Islamic Rep.b 6.4 6.0 28 292 14.6 35 125,345 25 Iraq .. .. 13 48 5.6 .. .. .. Irelandb .. .. 8 76 45.3 42 35,443 13 Israel 31.0 28.8 33 210 57.5 49 90,040 36 Italy 23.6 22.9 20 360 59.8 45 88,608 33 Jamaicab 24.7 24.8 72 414 49.4 25 1,993 33 Japanb .. .. 26 315 34.6 37 167,395 30 Jordanb 19.0 20.8 10 101 39.8 .. .. .. Kazakhstanb 10.2 14.7 34 156 41.6 20 47,552 30 Kenyab 18.8 17.2 17 372 68.2 30 5,841 30 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep.b 16.1 .. 26 290 29.6 36 66,644 27 Kuwaitb 1.3 1.5 14 .. 8.2 0 .. 0 Kyrgyz Republicb 11.7 .. 95 204 59.4 .. .. .. Lao PDR .. .. 31 180 24.7 40 7,894 .. Latviab 14.4 13.8 39 320 38.7 25 .. 15 Lebanon 12.4 15.5 33 208 30.4 .. .. .. Lesothob 32.4 43.5 19 564 37.7 .. .. .. Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 14.6 17.2 13 162 41.6 .. .. 15 Macedonia, FYR .. .. 54 96 40.1 .. .. .. Madagascar 56.6 54.4 29 400 58.9 .. .. .. Malawi .. .. 33 782 56.5 .. .. .. Malaysiab 14.3 17.6 28 .. 11.6 28 65,789 28 Mali .. .. 60 270 44.0 .. .. .. Mauritania .. .. 61 696 75.8 .. .. .. Mauritiusb 18.4 17.8 7 158 38.2 25 951 25 Mexico 11.7 .. 49 536 31.3 33 9,555 33 Moldovab 14.7 16.4 44 250 44.7 .. .. .. Mongolia .. 22.6 43 .. 45.3 .. .. .. Morocco 23.5 b 21.9d 28 690 54.8 44 5,243 35 Mozambique .. .. 35 230 50.9 32 42,314 32 Myanmar b 3.0 .. .. .. .. .. .. .. Namibiab 30.0 25.9 23 50 43.9 35 29,851 35 Nepalb 8.7 9.8 23 408 31.8 .. .. .. Netherlands 23.1 22.8 22 700 53.3 52 63,777 35 New Zealand 29.8 30.3 8 70 44.2 39 39,242 33 Nicaraguab 13.8 15.5 64 240 54.3 25 31,545 25 Niger .. .. 44 270 49.4 .. .. .. Nigeria .. .. 36 1,120 27.1 25 1,553 30 Norway 27.7 29.0 3 87 60.1 .. .. 28 Omanb 7.2 .. 13 52 5.2 0 .. 12 Pakistanb 10.2 10.5 32 560 57.4 35 11,746 41 Panamab 10.2 .. 45 424 32.9 30 200,000 30 Papua New Guineab 19.4 22.3 43 198 36.7 47 24,842 25 Paraguay b 9.9 11.2 33 328 37.9 0 .. 30 Perub 12.3 13.3 53 424 50.7 30 49,899 30 Philippinesb 13.7 12.6 62 94 46.4 32 8,995 32 Polandb 16.4 17.3 43 175 55.6 40 19,211 19 Portugal 22.7 22.6 7 328 45.4 40 67,139 25 Puerto Rico .. .. 41 140 17.8 33 50,000 20 2006 World Development Indicators 287 Tax policies Tax revenue collected Tax payments Highest marginal by central government by businesses tax ratea Time to prepare Number and pay taxes Total tax payable Individual of payments hours % of gross profit On income Corporate % of GDP January January January % over $ % 2000 2004 2005 2005 2005 2004 2004 2004 Romaniab 11.7 11.7 62 188 51.1 40 4,617 25 Russian Federation 13.7 13.5 27 256 40.8 13 .. 24 Rwandab .. .. 42 168 53.9 .. .. .. Saudi Arabia .. .. 13 70 1.4 0 .. 0 Senegalb 17.3 .. 59 696 45.0 0 22,469 35 Serbia and Montenegrob 23.0 23.0 41 168 46.3 .. .. .. Sierra Leoneb 6.8 .. 20 399 163.9 .. .. .. Singaporeb 15.6 12.5 16 30 19.5 22 188,191 20 Slovak Republic .. 16.7 31 344 39.5 38 14,087 25 Sloveniab 21.4 21.5 29 272 47.3 50 .. 25 Somalia .. .. .. .. .. .. .. .. South Africa b 24.0 26.0 32 350 43.8 40 38,060 30 Spain 15.9 11.8 7 56 48.4 29 56,962 35 Sri Lanka b 14.5 14.0 42 .. 49.4 30 8,083 30 Sudanb 6.3 .. .. .. .. .. .. .. Swaziland 26.7 24.9 .. .. .. 33 5,496 30 Sweden 19.9 19.7 5 122 52.6 25 59,756 28 Switzerlandb 11.3 10.0 25 63 22.0 .. .. 9 Syrian Arab Republicb 17.4 .. 22 336 20.8 .. .. .. Tajikistanb 7.7 9.8 .. .. .. .. .. .. Tanzania .. .. 48 248 51.3 30 6,090 30 Thailand .. 15.9 44 52 29.2 37 101,420 30 Togo .. .. 51 270 50.9 .. .. .. Trinidad and Tobagob .. .. .. .. .. 30 7,937 30 Tunisiab 21.3 20.7 31 112 52.7 .. .. .. Turkey b 20.2 .. 18 254 51.1 40 100,298 30 Turkmenistan .. .. .. .. .. .. .. .. Uganda b 10.9 11.9 31 237 42.9 30 2,523 30 Ukraineb 14.1 13.3 84 2,185 51.0 13 3,826 25 United Arab Emiratesb 1.7 .. 15 12 8.9 0 .. 0 United Kingdom 29.1 27.4 22 .. 52.9 40 51,358 30 United States 12.7 9.8 9 325 21.5 35 319,100 35 Uruguay 16.7 18.5 54 300 80.2 0 .. 35 Uzbekistan .. .. 118 152 75.6 30 666 18 Venezuela, RBb 13.3 11.5 68 864 48.9 34 60,324 34 Vietnamb .. .. 44 1,050 31.5 .. .. 28 West Bank and Gaza .. .. 49 .. 42.0 .. .. .. Yemen, Rep.b 9.4 .. 32 248 128.8 .. .. .. Zambiab 18.4 .. 36 132 38.6 30 368 35 Zimbabweb .. .. 59 216 48.6 45 26,249 30 World 15.7 w 14.5 w 35 u 354 u 46.5 u Low income 10.0 10.5 44 385 54.4 Middle income 12.3 12.4 38 406 44.5 Lower middle income 9.6 10.1 42 444 44.5 Upper middle income .. .. 30 333 44.4 Low & middle income 11.9 12.0 40 398 48.5 East Asia & Pacific 7.7 10.0 31 270 33.1 Europe & Central Asia 15.6 15.8 48 438 50.3 Latin America & Carib. 11.8 .. 49 549 54.5 Middle East & N. Africa .. .. 30 281 40.4 South Asia 9.3 10.1 26 332 35.3 Sub-Saharan Africa .. .. 41 394 58.1 High income 16.5 14.6 18 181 38.8 Europe EMU 19.2 17.9 19 233 49.2 a. These data are from PricewaterhouseCoopers' Individual Taxes: Worldwide Summaries 2004­2005 and Corporate Taxes: Worldwide Summaries 2004­2005, copyright 2004 by PricewaterhouseCoopers and used by permission of John Wiley and Sons, Inc. b. Data on central government tax revenue were reported on a cash basis and have been adjusted to the accrual framework of the Government Finance Statistics Manual 2001. c. World Bank staff estimate. d. International Monetary Fund staff estimate. 288 2006 World Development Indicators Tax policies About the data Definitions The table includes new information on taxes that The new indicators covering taxes paid by busi- · Tax revenue collected by central government businesses must pay and measures of the admin- nesses go beyond the usual measures of tax rates, refers to compulsory transfers to the central gov- istrative burden in paying taxes. Data are from the which capture only part of the taxpayer burden. In some ernment for public purposes. Certain compulsory World Bank's Doing Business 2006. countries tax systems are so complex that businesses transfers such as fines, penalties, and most social Taxes are the main source of revenue for many gov- must make more than 100 payments and spend up to security contributions are excluded. Refunds and ernments. The sources of tax revenue and their rela- 2,600 hours a year to prepare and pay taxes. corrections of erroneously collected tax revenue are tive contributions are determined by government policy Taxes are measured at all levels of government and treated as negative revenue. The analytic framework choices about where and how to impose taxes and by include corporate income tax, personal income tax of the International Monetary Fund's (IMF) Govern- changes in the structure of the economy. Tax policy withheld by businesses, value added or sales taxes, ment Finance Statistics Manual 2001 (GFSM 2001) may reflect concerns about distributional effects, eco- property transfer taxes, financial transactions taxes, is based on accrual accounting and balance sheets. nomic efficiency (including corrections for externali- dividend taxes, waste collection taxes, and vehicle For countries still reporting government finance data ties), and the practical problems of administering a tax and road taxes. To make the data comparable across on a cash basis, the IMF adjusts reported data to system. There is no ideal level of taxation. But taxes countries, several assumptions are made about the GFSM 2001 accrual framework. These countries influence incentives and thus the behavior of economic the business. The main assumptions are that they are footnoted in the table. · Tax payments by busi- actors and the economy's competitiveness. are limited liability companies, they operate in the nesses are the total number of taxes paid by busi- Taxes are compulsory transfers to governments from country's most populous city, they are domestically nesses, including electronic filing. The tax is counted individuals, businesses, or institutions. Certain compul- owned, they perform general industrial or commercial as paid once a year even if payments are more fre- sory transfers, such as fines, penalties, and most social activities, and they have a certain level of start-up quent. · Time to prepare and pay taxes is the time, security contributions are excluded from tax revenue. capital, employees, and turnover. For details about in hours per year, it takes to prepare, file, and pay (or The level of taxation is typically measured by tax the assumptions, see Doing Business 2006. withhold) three major types of taxes: the corporate revenue as a share of gross domestic product (GDP). A potentially important influence on both domestic and income tax, the value added or sales tax, and labor Comparing levels of taxation across countries pro- international investors is a tax system's progressivity, taxes, including payroll taxes and social security con- vides a quick overview of the fiscal obligations and as reflected in the highest marginal tax rate levied at tributions. · Total tax payable is the total amount incentives facing the private sector. The table shows the national level on individual and corporate income. of taxes payable by the business (except for labor only central government data, which may significantly Figures for individual marginal tax rates generally refer taxes) after accounting for deductions and exemp- understate the total tax burden, particularly in coun- to employment income. In some countries the highest tions as a percentage of gross profit. For further tries where provincial and municipal governments are marginal tax rate is also the basic or flat rate, and other details on the method used for assessing the total large or have considerable tax authority. surtaxes, deductions, and the like may apply. And in tax payable, see Doing Business 2006. · Highest Low ratios of tax revenue to GDP may reflect weak many countries several different corporate tax rates may marginal tax rate is the highest rate shown on the administration and large-scale tax avoidance or eva- be levied, depending on the type of business (mining, national level schedule of tax rates applied to the sion. Low ratios may also reflect a sizable parallel banking, insurance, agriculture, manufacturing), owner- annual taxable income of individuals and corpora- economy with unrecorded and undisclosed incomes. ship (domestic or foreign), volume of sales, or whether tions. Also presented are the income levels for indi- Tax revenue ratios tend to rise with income, with surtaxes or exemptions are included. The corporate tax viduals above which the highest marginal tax rates higher income countries relying on taxes to finance rates in the table are mainly general rates applied to levied at the national level apply. a much broader range of social services and social domestic companies. For more detailed information, see security than lower income countries are able to. the country's laws, regulations, and tax treaties. Excessive paperwork adds to the time that businesses spend complying with taxes Time to prepare and pay taxes, 2005 (hours per year) 600 500 Data sources 400 Data on central government tax revenues are from 300 print and electronic editions of the IMF's Govern- ment Finance Statistics Yearbook. Data on taxes 200 paid by businesses are from Doing Business 2006. 100 Data on individual and corporate tax rates are 0 from PricewaterhouseCoopers's Individual Taxes: OECD: Middle East East Asia South Sub-Saharan Europe & Latin America Worldwide Summaries 2004­2005 and Corporate high-income & North Africa & Pacific Asia Africa Central Asia & Caribbean Taxes: Worldwide Summaries 2004­2005. Source: Table 5.6. 2006 World Development Indicators 289 Defense expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Afghanistan .. .. .. .. 383 27 5.5 0.3 0 .. 0 0 Albania 2.1 1.2 8.2 .. 87 22 6.0 1.6 .. .. 21 6 Algeria 3.0 3.3 12.2 15.2 163 318 1.8 2.5 .. .. 342 282 Angola 18.1 9.1 .. .. 122 118 2.3 1.7 0 0 1 5 Argentina 1.6 1.0 .. 5.6 99 102 0.7 0.6 3 0 67 129 Armenia 4.1 2.9 .. 15.5 61 49 4.2 3.8 .. .. 49 68 Australia 2.0 1.8 .. 7.2 57 52 0.6 0.5 20 52 147 334 Austria 0.9 0.7 .. 1.9 56 39 1.4 1.0 0 1 23 46 Azerbaijan 2.3 1.8 11.7 .. 127 81 3.8 2.0 .. .. 0 0 Bangladesh 1.4 1.2 .. 13.2 171 251 0.3 0.4 .. .. 121 26 Belarus 1.6 1.2 5.5 4.1 106 182 2.1 3.8 8 50 0 0 Belgium 1.6 1.4 .. 2.9 47 36 1.1 0.8 297 0 16 12 Benin .. .. .. .. 7 6 0.3 0.2 .. .. 0 0 Bolivia 1.9 1.6 .. 5.9 64 68 2.2 1.7 .. .. 0 1 Bosnia and Herzegovina .. 2.4 .. 6.2 92 24 5.2 1.2 0 0 0 0 Botswana 3.5 3.6 11.5 .. 9 10 1.4 1.6 .. .. 7 10 Brazil 2.1 1.4 4.8 .. 681 687 0.9 0.8 40 100 226 38 Bulgaria 2.6 2.4 6.6 6.9 136 85 3.5 2.7 2 0 0 12 Burkina Faso 1.5 1.4 .. .. 10 10 0.2 0.2 .. .. 0 .. Burundi 4.2 5.8 17.8 .. 15 81 0.5 2.2 .. .. 0 0 Cambodia 5.4 2.2 .. 24.1 309 192 6.2 2.9 0 0 0 0 Cameroon 1.4 1.5 11.7 .. 24 23 0.5 0.4 .. .. 0 0 Canada 1.6 1.2 6.3 6.5 76 71 0.5 0.4 378 543 146 340 Central African Republic 1.2 1.1 .. .. 5 2 0.3 0.1 .. .. 0 .. Chad 1.4 1.1 .. .. 35 34 1.3 1.0 0 .. 1 0 Chile 3.3 3.9 .. 21.0 130 116 2.3 1.8 0 0 461 43 China 1.7a 1.9a ..a 19.3a 4,130 3,755 0.6 0.5 897 125 419 2,238 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2.6 4.3 .. 18.9 233 336 1.4 1.5 .. .. 37 17 Congo, Dem. Rep. 1.5 .. 13.5 .. 65 64 0.4 0.3 .. .. 0 0 Congo, Rep. .. 1.4 .. 6.9 17 12 1.4 0.8 .. .. 0 0 Costa Rica .. .. .. .. 16 0 1.2 0.0 .. .. 0 0 Côte d'Ivoire 0.8 1.6 .. 8.9 15 18 0.3 0.3 .. .. 2 14 Croatia 9.4 1.7 22.2 4.1 150 30 7.2 1.5 0 0 22 8 Cuba .. .. .. .. 124 75 2.5 1.4 .. .. 0 0 Czech Republic 1.7 1.8 .. 5.1 92 27 1.8 0.5 156 0 0 18 Denmark 1.7 1.5 .. 4.2 33 21 1.2 0.7 0 6 127 194 Dominican Republic .. .. .. .. 40 39 1.3 1.0 .. .. 0 21 Ecuador 2.4 1.9 20.1 .. 57 46 1.3 0.8 .. .. 10 22 Egypt, Arab Rep. 3.5 2.8 12.5 .. 610 798 3.5 3.6 16 0 1,696 398 El Salvador 1.0 0.7 .. 3.9 39 15 1.8 0.6 0 .. 3 0 Eritrea 20.8 19.4 .. .. 55 201 4.4 11.7 0 0 3 382 Estonia 1.0 1.8 3.0 .. 6 6 0.8 0.9 0 0 18 5 Ethiopia 2.2 4.3 .. 21.8 120 182 0.5 0.6 0 .. 0 162 Finland 1.5 1.2 .. 3.3 35 31 1.4 1.2 20 17 159 57 France 3.0 2.5 .. 5.4 502 358 2.0 1.3 681 2,122 43 89 Gabon .. .. .. .. 10 6 2.0 1.0 .. .. 0 0 Gambia, The 0.8 0.4 .. .. 1 1 0.2 0.1 .. .. 0 0 Georgia 2.2 1.4 8.2 9.4 14 22 0.5 1.0 0 20 0 0 Germany 1.6 1.4 8.3 4.4 365 284 0.9 0.7 1,435 1,091 218 190 Ghana 0.8 0.8 .. 3.8 13 7 0.2 0.1 .. .. 0 27 Greece 4.3 4.1 .. .. 202 167 4.5 3.3 0 0 865 1,434 Guatemala 1.0 0.4 13.1 3.6 57 48 1.8 1.2 .. .. 3 0 Guinea 1.4 2.9 .. .. 19 11 0.5 0.3 .. .. 0 0 Guinea-Bissau 0.9 .. .. .. 9 9 1.9 1.5 .. .. 0 .. Haiti .. .. .. .. 7 0 0.2 0.0 .. .. .. .. 290 2006 World Development Indicators Defense expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Honduras .. 0.7 .. .. 24 20 1.2 0.7 .. .. 0 .. Hungary 1.6 1.7 .. 4.4 73 46 1.8 1.1 6 0 21 15 India 2.2 2.3 15.1 14.8 2,150 2,617 0.6 0.6 2 22 945 2,375 Indonesia 1.6 1.4 16.2 8.3 461 582 0.5 0.6 30 50 334 85 Iran, Islamic Rep. 2.3 3.4 15.2 17.0 763 460 4.4 1.8 1 1 306 283 Iraq .. .. .. .. 407 179 7.0 2.2 0 .. 0 82 Ireland 1.0 0.6 2.8 .. 13 10 0.9 0.5 0 .. 0 25 Israel 9.0 9.3 .. 19.1 178 176 8.5 6.6 110 283 244 724 Italy 1.8 1.9 .. 4.7 585 445 2.5 1.9 305 261 270 317 Jamaica .. .. .. .. 4 2 0.3 0.2 .. .. 0 0 Japan 0.9 1.0 .. .. 252 251 0.4 0.4 16 0 757 195 Jordan 12.4 7.6 47.5 24.0 129 110 10.2 6.1 0 72 19 132 Kazakhstan 1.1 1.0 5.7 7.0 75 99 1.0 1.2 24 5 99 27 Kenya 1.6 1.6 6.5 7.9 29 29 0.2 0.2 .. .. 0 0 Korea, Dem. Rep. .. .. .. .. 1,243 1,295 12.4 12.2 52 0 72 5 Korea, Rep. 2.8 2.5 19.4 .. 641 696 3.0 2.9 25 50 1,638 737 Kuwait 13.6 7.5 29.3 20.9 22 21 2.5 1.6 0 0 630 0 Kyrgyz Republic 1.6 2.9 6.1 .. 7 17 0.4 0.8 61 0 0 5 Lao PDR 2.9 .. .. .. 137 129 7.7 5.6 .. .. 0 0 Latvia 0.9 1.7 3.1 6.0 11 5 0.9 0.5 0 0 12 14 Lebanon 6.7 3.8 .. 12.8 63 85 5.5 6.2 0 0 34 0 Lesotho 3.7 2.6 10.7 6.8 2 2 0.3 0.3 .. .. 0 1 Liberia 31.2 7.5 .. .. 21 0 2.7 0.0 .. .. 0 0 Libya 4.1 1.9 .. .. 81 76 5.2 3.4 0 0 0 74 Lithuania 0.4 1.7 .. 5.8 9 28 0.5 1.7 0 0 4 31 Macedonia, FYR 3.0 2.5 .. .. 18 17 2.2 2.0 0 29 0 0 Madagascar 4.3 .. .. .. 29 21 0.5 0.3 .. .. 0 0 Malawi 0.8 .. .. .. 10 6 0.2 0.1 0 0 0 0 Malaysia 2.8 2.3 16.0 13.8 140 130 1.7 1.2 0 0 900 277 Mali 2.2 1.9 .. .. 15 11 0.4 0.2 .. .. 0 0 Mauritania 2.6 1.2 .. .. 21 20 2.3 1.7 .. .. 1 0 Mauritius 0.4 0.2 1.8 0.9 2 0 0.4 0.0 .. .. 0 0 Mexico 0.6 0.4 3.8 .. 189 203 0.5 0.5 .. .. 43 265 Moldova 0.9 0.4 2.4 1.3 15 9 0.8 0.4 0 0 6 0 Mongolia 1.7 2.1 .. .. 31 15 3.3 1.3 .. .. 0 0 Morocco 4.6 4.5 16.1 .. 238 250 2.7 2.3 .. .. 30 0 Mozambique 1.5 1.2 .. .. 12 11 0.2 0.1 .. .. 0 0 Myanmar 3.7 .. .. .. 371 482 1.7 1.8 .. .. 216 65 Namibia 2.0 2.4 .. 8.5 8 15 1.5 2.3 .. .. 2 53 Nepal 0.9 1.7 .. .. 63 131 0.8 1.3 .. .. 1 32 Netherlands 1.9 1.6 .. 3.8 78 53 1.0 0.6 350 211 33 183 New Zealand 1.4 1.0 .. 3.2 10 8 0.6 0.4 0 1 4 42 Nicaragua 1.1 0.7 6.8 3.5 12 14 0.8 0.7 5 0 0 0 Niger 1.0 0.9 .. .. 11 10 0.3 0.2 .. .. 0 0 Nigeria 0.7 0.8 .. .. 89 160 0.2 0.3 0 .. 2 10 Norway 2.4 1.9 .. 5.0 31 25 1.4 1.0 22 51 83 1 Oman 14.6 10.4 45.2 .. 48 45 6.2 4.8 0 0 157 123 Pakistan 6.0 4.1 31.4 28.1 846 921 2.2 1.7 0 10 .. .. Panama 1.2 .. 5.6 .. 12 0 1.1 0.0 .. .. 0 0 Papua New Guinea 1.0 0.6 3.9 2.1 4 3 0.2 0.1 .. .. 0 0 Paraguay 1.1 0.7 10.0 5.4 28 24 1.4 0.9 .. .. 0 4 Peru 1.9 1.2 10.9 7.1 178 157 1.8 1.2 0 5 32 14 Philippines 1.4 0.9 8.5 .. 149 146 0.5 0.4 .. .. 32 59 Poland 2.0 1.9 .. 5.0 302 162 1.7 0.9 187 86 125 256 Portugal 2.5 2.1 .. 5.1 104 91 2.1 1.7 0 .. 18 59 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 291 Defense expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Romania 2.8 2.2 .. 8.9 297 176 2.4 1.7 6 0 0 276 Russian Federation 4.4 3.9 .. 17.9 1,800 1,452 2.5 2.0 3,181 6,197 40 0 Rwanda 4.4 2.1 .. .. 47 53 2.0 1.3 .. .. 0 0 Saudi Arabia 9.3 7.7 .. .. 178 214 3.0 2.8 0 0 974 838 Senegal 1.8 1.4 .. .. 17 18 0.5 0.4 .. .. 2 0 Serbia and Montenegro 5.3 3.4 .. 11.0 165 110 3.5 2.8 0 0 18 0 Sierra Leone 2.9 1.6 .. .. 7 13 0.4 0.6 .. .. 15 0 Singapore 4.4 4.7 35.1 30.5 66 165 3.7 7.6 0 70 225 456 Slovak Republic 3.2 1.7 .. 5.2 51 20 2.1 0.8 114 0 220 0 Slovenia 1.7 1.6 4.7 3.9 13 10 1.3 1.0 .. .. 18 14 Somalia .. .. .. .. 225 0 8.3 0.0 .. .. 0 0 South Africa 2.2 1.5 .. 5.0 277 55 1.7 0.3 16 35 38 8 Spain 1.4 1.0 .. 3.7 282 220 1.7 1.1 65 75 348 261 Sri Lanka 5.3 2.8 20.3 13.6 236 239 3.3 2.9 .. .. 49 6 Sudan 1.9 2.2 .. .. 134 121 1.6 1.2 .. .. 3 270 Swaziland 2.4 .. .. .. 3 .. 1.1 .. .. .. 0 0 Sweden 2.3 1.7 .. 4.7 100 28 2.2 0.6 185 260 70 13 Switzerland 1.3 1.0 5.2 5.4 31 4 0.8 0.1 77 154 93 125 Syrian Arab Republic 7.1 7.0 .. .. 531 415 11.2 5.7 0 0 43 0 Tajikistan 1.0 2.2 .. 15.8 18 12 0.9 0.6 .. .. 0 0 Tanzania 1.5 3.0 .. .. 36 28 0.2 0.1 .. .. 0 0 Thailand 2.3 1.2 .. 6.7 421 419 1.3 1.2 0 5 562 105 Togo 2.4 1.5 .. .. 8 9 0.4 0.4 .. .. 3 0 Trinidad and Tobago .. .. .. .. 7 2 1.3 0.3 .. .. 0 0 Tunisia 1.9 1.5 6.7 5.4 59 47 2.1 1.3 .. .. 59 0 Turkey 3.9 3.9 18.6 .. 690 616 3.0 2.3 0 18 1,288 418 Turkmenistan 2.3 .. .. .. 11 26 0.7 1.2 .. .. 0 20 Uganda 2.2 2.5 .. 11.1 52 55 0.6 0.5 .. .. 39 19 Ukraine 2.8 2.6 .. 7.9 519 271 2.0 1.2 218 452 0 29 United Arab Emirates 5.2 2.8 49.2 .. 71 50 5.5 1.9 27 3 432 1,246 United Kingdom 3.0 2.6 .. 6.5 233 205 0.8 0.7 1,109 985 635 171 United States 3.8 4.0 .. 19.0 1,636 1,473 1.2 1.0 9,690 5,453 389 533 Uruguay 1.7 1.4 6.3 5.0 27 25 1.8 1.4 0 0 7 0 Uzbekistan 1.1 0.5 .. .. 42 75 0.5 0.7 0 170 0 0 Venezuela, RB 1.6 1.2 8.7 5.4 80 82 0.9 0.7 0 1 0 12 Vietnam 2.6 .. .. .. 622 5,564 1.8 12.9 .. .. 270 247 West Bank and Gaza .. .. .. .. .. 0 .. .. .. .. 1 0 Yemen, Rep. 6.4 6.6 33.4 .. 70 136 1.8 2.4 .. .. 120 309 Zambia 2.2 .. .. .. 23 16 0.6 0.3 0 0 0 0 Zimbabwe 3.6 3.4 11.2 .. 68 50 1.4 0.9 .. .. 0 0 World 2.4 w 2.5 w .. w 10.9 w 30,182 s 32,645 s 1.2 w 1.1 w 19,837 s 19,152 s 19,528 s 18,814 s Low income 2.4 2.3 17.0 15.6 7,768 13,185 1.0 1.4 120 202 1,822 3,949 Middle income 2.2 1.9 .. 13.6 16,059 13,882 1.2 0.9 4,905 7,251 7,868 5,989 Lower middle income 2.1 2.0 .. 16.4 11,683 10,550 1.0 0.8 1,242 914 4,404 4,283 Upper middle income 2.3 1.8 .. .. 4,376 3,332 1.9 1.3 3,663 6,337 3,464 1,706 Low & middle income 2.2 2.0 .. 13.7 23,826 27,067 1.1 1.1 5,025 7,453 9,690 9,938 East Asia & Pacific 1.8 1.8 .. 17.3 8,021 12,716 0.9 1.2 979 180 2,813 3,081 Europe & Central Asia 2.8 2.3 .. 11.0 4,971 3,669 2.3 1.7 3,963 7,027 1,943 1,208 Latin America & Carib. 1.8 1.3 5.3 .. 2,112 2,066 1.1 0.8 48 106 889 566 Middle East & N. Africa 4.1 3.7 17.6 .. 3,172 2,930 4.2 2.8 17 73 2,810 1,683 South Asia 2.7 2.5 17.8 15.9 3,852 4,186 0.8 0.7 2 32 1,116 2,439 Sub-Saharan Africa 2.3 1.9 .. .. 1,698 1,500 0.7 0.5 16 35 119 961 High income 2.5 2.6 .. 10.5 6,356 5,578 1.3 1.1 14,812 11,699 9,838 8,876 Europe EMU 2.0 1.7 .. 4.4 2,270 1,736 1.7 1.2 3,153 3,778 1,993 2,673 Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2005). a. Estimate differs from official statistics of the government of China, which has published the following estimates: military expenditure as 1.1 percent of GDP in 1995 and 1.6 percent in 2003 and 9.3 percent of central government expenditure in 1995 and 7.7 percent in 2003 (see National Bureau of Statistics of China, www.stats.gov.cn). 292 2006 World Development Indicators Defense expenditures and arms transfers About the data Definitions Although national defense is an important function 2005­2006. These data refer to military personnel · Military expenditures data from SIPRI are derived of government and security from external threats on active duty, including paramilitary forces. Reserve from the NATO definition, which includes all current contributes to economic development, high lev- forces, which are units that are not fully staffed or and capital expenditures on the armed forces, includ- els of defense spending burden the economy and operational in peace time, are not included. These ing peacekeeping forces; defense ministries and other may impede growth. Data on military expenditures data also exclude civilians in the defense establish- government agencies engaged in defense projects; as a share of gross domestic product (GDP) are a ment and so are not consistent with the data on mili- paramilitary forces, if these are judged to be trained rough indicator of the portion of national resources tary spending on personnel. Moreover, because data and equipped for military operations; and military used for military activities and of the burden on the exclude personnel not on active duty, they underes- space activities. Such expenditures include military national economy. Comparisons of defense spend- timate the share of the labor force working for the and civil personnel, including retirement pensions of ing between countries should take into account the defense establishment. Because governments rarely military personnel and social services for personnel; many factors that influence perceptions of vulner- report the size of their armed forces, such data typi- operation and maintenance; procurement; military ability and risk, including historical and cultural tradi- cally come from intelligence sources. research and development; and military aid (in the tions, the length of borders that need defending, the The data on arms transfers are from SIPRI's Arms military expenditures of the donor country). Excluded quality of relations with neighbors, and the role of Transfers Project, which reports on international are civil defense and current expenditures for previ- the armed forces in the body politic. As an "input" flows of conventional weapons. Data are collected ous military activities, such as for veterans' benefits, measure, military spending is not directly related to from open sources, and since publicly available infor- demobilization, conversion, and destruction of weap- the "output" of military activities, capabilities, or mation is inadequate for tracking all weapons and ons. This definition cannot be applied for all countries, military security. other military equipment, SIPRI covers only what it however, since that would require much more detailed Data on defense spending reported by governments terms major conventional weapons. information than is available about what is included in are not compiled using standard definitions. They are SIPRI's data on arms transfers cover sales of military budgets and off-budget military expenditure often incomplete and unreliable. Even in countries weapons, manufacturing licenses, aid, and gifts; items. (For example, military budgets might or might where the parliament vigilantly reviews budgets and therefore the term arms transfers rather than arms not cover civil defense, reserves and auxiliary forces, spending, defense spending and arms transfers rarely trade is used. The transferred weapons must be police and paramilitary forces, dual-purpose forces receive close scrutiny and full, public disclosure (see transferred voluntarily by the supplier, must have such as military and civilian police, military grants Ball 1984 and Happe and Wakeman-Linn 1994). The a military purpose, and must be destined for the in kind, pensions for military personnel, and social data on military expenditures as a share of GDP and armed forces, paramilitary forces, or intelligence security contributions paid by one part of government a share of central government expenditure are esti- agencies of another country. SIPRI data also cover to another.) · Armed forces personnel are active duty mated by the Stockholm International Peace Research weapons supplied to or from rebel forces in an armed military personnel, including paramilitary forces if the Institute (SIPRI). Central government expenditures are conflict as well as arms deliveries for which neither training, organization, equipment, and control suggest from the International Monetary Fund (IMF). Therefore the supplier nor the recipient can be identified with they may be used to support or replace regular military the data shown in the table may differ from compa- an acceptable degree of certainty; these data are forces. · Arms transfers cover the supply of military rable data published by national governments. available in SIPRI's database. weapons through sales, aid, gifts, and those made SIPRI's primary source of military expenditure data SIPRI's estimates of arms transfers, presented in through manufacturing licenses. Data cover major is offcial data provided by national governments. 1990 constant price U.S. dollars, are designed as conventional weapons such as aircraft, armored These data are derived from national budget docu- a trend-measuring device in which similar weapons vehicles, artillery, radar systems, missiles, and ships ments, defense white papers, and other public docu- have similar values, reflecting both the value and designed for military use. Excluded are transfers of ments from offcial government agencies, including quality of weapons transferred. The trends presented other military equipment such as small arms and light governments' responses to questionnaires sent by in the tables are based on actual deliveries only. weapons, trucks, small artillery, ammunition, support SIPRI, the United Nations, or the Organization for SIPRI cautions that these estimated values do not equipment, technology transfers, and other services. Security and Co-operation in Europe. Secondary reflect financial value (payments for weapons trans- See About the data for more detail. sources include international statistics, such as those ferred) for three reasons: reliable data on the value of the North Atlantic Treaty Organization (NATO) and of the transfer are not available; even when the value the IMF's Government Finance Statistics Yearbook. of a transfer is known, it usually includes more than Other secondary sources include country reports of the actual conventional weapons such as spares, the Economist Intelligence Unit, country reports by support systems, and training; and even when the IMF staff, and specialist journals and newspapers. value of the transfer is known, details of the financial Lack of sufficiently detailed data makes it diffcult to arrangements such as credit and loan conditions and apply a common definition of military expenditure glob- discounts are usually not known. ally, so SIPRI has adopted a definition (derived from Given these measurement issues, SIPRI's method the NATO definition) as a guideline (see Definitions). of estimating the transfer of military resources This definition cannot be applied for all countries, includes an evaluation of the technical parameters however, since that would require much more detailed of the weapons. Weapons for which a price is not information than is available about what is included in known are compared with the same weapons for military budgets and off-budget military expenditure which actual acquisition prices are available ("core Data sources items. In the many cases where SIPRI cannot make weapons") or for the closest match. These weapons independent estimates, it uses the national data pro- are assigned a value in an index that reflects their mil- Data on military expenditures and arms transfers vided. Because of the differences in definitions and itary resource value in relation to the core weapons. are from SIPRI's Yearbook 2005: Armaments, the difficulty in verifying the accuracy and complete- These matches are based on such characteristics Disarmament and International Security. Data on ness of data, the data on military spending are not as size, performance, and type of electronics, and armed forces personnel are from the International strictly comparable across countries. adjustments are made for second-hand weapons. Institute for Strategic Studies' The Military Bal- The data on armed forces are from the International More information on SIPRI's arms transfers project ance 2005­2006. Institute for Strategic Studies' The Military Balance is available at www.sipri.org/contents/armstrad/. 2006 World Development Indicators 293 Transport services Roads Railways Ports Air Passengers Registered Passengers Goods carried Goods Port container carrier Total road Paved carried million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 1999­2003a 1999­2003a 1999­2003a 1999­2003a 2000­04a 2000­04a 2000­04a 2004 2004 2004 2004 Afghanistan 34,789 23.7 .. .. .. .. .. .. 3 150 8 Albania 18,000 39.0 197 .. 447 89 32 .. 5 189 0 Algeria 104,000 68.9 .. .. 3,572 950 1,945 311.1 49 3,236 21 Angola 51,429 10.4 166,045 4,709 2,761 .. .. .. 5 223 64 Argentina 215,471 29.4 .. .. 35,754 .. .. 1,251.9 100 6,851 115 Armenia 7,633 .. 1,867 280 711 48 452 .. 6 510 7 Australia 811,601 .. .. .. 9,474 1,347 41,314 5,129.8 325 41,597 1,898 Austria 133,718 100.0 82,330 26,411 5,801 8,375 19,047 .. 137 7,619 502 Azerbaijan 27,016 47.0 9,862 53,738 2,122 584 6,980 .. 11 1,007 34 Bangladesh 239,226 9.5 .. .. 2,745 .. .. 625.2 7 1,647 180 Belarus 93,055 100.0 10,739 12,710 5,498 13,893 40,331 .. 6 274 1 Belgium 149,757 78.2 118,340 32,450 3,536 8,676 8,725 7,292.9 154 3,265 713 Benin 6,787 20.0 .. .. 438 66 86 .. 1 46 7 Bolivia 60,762 7.1 .. .. 3,698 .. .. .. 29 1,853 24 Bosnia and Herzegovina 21,846 52.3 .. 332 1,032 53 293 .. 5 73 0 Botswana 25,233 35.1 .. .. 888 171 842 .. 8 214 0 Brazil 1,724,929 5.5 .. .. 30,403 .. .. 5,058.6 486 35,264 1,499 Bulgaria 102,016 92.0 8,596 .. 4,259 2,628 5,212 .. 8 476 3 Burkina Faso 12,506 16.0 .. .. 622 .. .. .. 1 62 0 Burundi 14,480 7.1 .. .. .. .. .. .. .. .. .. Cambodia 12,323 16.2 201 308 650 45 92 .. 4 163 4 Cameroon 80,932 6.7 .. .. 974 308 1,115 .. 10 358 23 Canada 1,408,900 .. .. 184,774 49,422 3,122 323,600 3,926.1 989 40,701 1,657 Central African Republic 23,810 .. .. .. .. .. .. .. 1 46 7 Chad 33,400 0.8 .. .. .. .. .. .. 1 46 7 Chile 79,604 20.2 .. .. 2,035 820 1,935 1,473.5 86 5,464 1,094 China 1,809,829 79.5 769,560 709,950 61,015 551,196 1,828,548 74,540.1b 1,216 119,789 8,188 Hong Kong, China 1,831 100.0 .. .. .. .. .. .. 111 17,893 6,932 Colombia 112,988 14.4 .. .. 3,154 .. .. 1,073.1 156 8,965 1,079 Congo, Dem. Rep. 157,000 .. .. .. 4,499 140 491 .. 5 95 7 Congo, Rep. 12,800 9.7 .. .. 1,026 76 307 .. 5 52 0 Costa Rica 35,889 22.5 .. .. 848 .. .. 734.1 34 884 10 Côte d'Ivoire 50,400 9.7 .. .. 639 148 129 670.0 1 46 7 Croatia 28,588 84.6 3,716 8,241 2,726 1,213 2,733 .. 20 1,336 2 Cuba 60,856 49.0 .. .. 4,382 .. .. .. 11 773 33 Czech Republic 127,672 100.0 90,055 475 9,511 6,553 16,214 .. 66 4,219 41 Denmark 71,847 100.0 61,258 17,766 2,141 5,390 1,888 997.5 100 6,429 175 Dominican Republic 12,600 49.4 .. .. 1,743 .. .. 537.3 .. .. .. Ecuador 43,197 16.9 10,276 5,170 966 .. .. 564.1 12 478 1 Egypt, Arab Rep. 64,000 78.1 .. .. 5,150 40,837 4,188 1,422.2 42 4,584 248 El Salvador 10,029 19.8 .. .. 283 .. .. .. 26 2,535 25 Eritrea 4,010 21.8 .. .. 306 .. .. .. .. .. .. Estonia 56,849 23.4 2,299 6,364 959 192 9,567 .. 8 510 1 Ethiopia 33,856 12.9 219,113 2,456 .. .. .. .. 30 1,403 117 Finland 78,216 61.0 67,300 27,800 5,741 3,352 10,105 1,308.1 112 7,201 325 France 891,290 100.0 744,900 266,500 29,246 74,014 45,121 3,947.0 685 48,583 5,584 Gabon 32,333 3.7 .. .. 650 92 1,949 .. 8 433 62 Gambia, The 3,742 19.3 16 .. .. .. .. .. .. .. .. Georgia 20,247 39.4 4,987 22,500 1,565 401 5,065 .. 4 203 3 Germany 231,581 100.0 1,062,700 227,197 34,729 70,286 77,640 12,457.7 942 82,156 8,064 Ghana 47,787 17.9 .. .. 977 85 242 .. 1 96 7 Greece 116,470 91.8 5,889 18,360 2,449 1,668 588 1,877.7 138 9,277 58 Guatemala 14,095 34.5 .. .. 886 .. .. 817.3 .. .. .. Guinea 44,348 9.8 .. .. 837 .. .. .. .. .. .. Guinea-Bissau 4,400 10.3 .. .. .. .. .. .. .. .. .. Haiti 4,160 24.3 .. .. .. .. .. .. .. .. .. 294 2006 World Development Indicators Transport services Roads Railways Ports Air Passengers Registered Passengers Goods carried Goods Port container carrier Total road Paved carried million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 1999­2003a 1999­2003a 1999­2003a 1999­2003a 2000­04a 2000­04a 2000­04a 2004 2004 2004 2004 Honduras 13,600 20.4 .. .. 699 .. .. 555.5 .. .. .. Hungary 159,568 43.9 13,300 12,505 8,000 7,380 8,713 .. 47 2,546 24 India 3,851,440 62.6 .. .. 63,221 541,208 381,241 4,266.9 302 23,797 689 Indonesia 368,360 58.0 .. .. .. .. .. 5,566.6 319 26,785 434 Iran, Islamic Rep. 178,152 66.3 .. .. 6,405 10,012 18,182 1,220.7 104 12,234 98 Iraq 45,550 84.3 .. .. 2,339 570 1,682 .. .. .. .. Ireland 95,736 100.0 39,440 6,500 1,919 1,582 399 924.9 262 34,783 124 Israel 17,237 100.0 .. .. 493 1,423 1,173 1,607.9 34 4,954 1,355 Italy 479,688 100.0 759,200 184,756 16,235 46,768 21,581 8,473.2 384 35,932 1,393 Jamaica 18,700 70.1 .. .. 272 .. .. 1,360.6 23 2,008 38 Japan 1,177,278 77.7 955,412 312,028 20,060 242,300 22,200 15,937.5 646 103,116 8,938 Jordan 7,364 100.0 .. .. 291 .. 522 .. 18 1,660 254 Kazakhstan 258,029 95.9 55,676 382 13,770 11,816 163,420 .. 12 843 13 Kenya 63,942 12.1 .. 22 1,917 226 1,399 .. 26 2,005 193 Korea, Dem. Rep. 31,200 6.4 .. .. 5,214 .. .. .. 2 95 2 Korea, Rep. 97,252 76.8 9,404 565 3,129 28,641 10,641 14,299.4 233 33,390 7,969 Kuwait 4,450 80.6 .. .. .. .. .. .. 19 2,317 224 Kyrgyz Republic 18,500 91.1 5,274 797 424 50 561 .. 6 246 5 Lao PDR 32,620 14.1 1,290 121 .. .. .. .. 9 276 2 Latvia 69,919 100.0 2,550 2,324 2,270 810 16,877 .. 16 594 1 Lebanon 7,300 84.9 .. .. 401 .. .. 299.4 12 1,087 85 Lesotho 5,940 18.3 .. .. .. .. .. .. .. .. .. Liberia 10,600 6.2 .. .. 490 .. .. .. .. .. .. Libya 83,200 57.2 .. .. 2,757 .. .. .. 8 850 0 Lithuania 78,893 27.4 20,982 11,462 1,782 443 11,637 .. 11 448 1 Macedonia, FYR 8,684 63.8 .. .. 699 94 426 .. 2 211 0 Madagascar 49,827 11.6 .. .. 883 10 12 .. 18 514 13 Malawi 28,400 18.5 .. .. 710 25 88 .. 6 114 1 Malaysia 71,814 77.9 .. .. 1,667 1,931 1,224 11,264.4 171 19,268 2,599 Mali 15,100 12.1 .. .. 733 196 189 .. 1 46 7 Mauritania 7,660 11.3 .. .. 717 .. .. .. 2 128 0 Mauritius 2,015 100.0 .. .. .. .. .. 381.5 15 1,089 220 Mexico 349,038 33.5 399,000 195,200 26,656 .. .. 1,905.9 333 21,240 403 Moldova 12,730 86.2 1,640 1,577 1,120 355 2,715 .. 5 201 1 Mongolia 49,250 3.5 761 1,889 1,810 1,073 6,452 .. 7 318 6 Morocco 57,694 56.4 .. 18 1,907 2,614 5,535 560.7 42 3,004 62 Mozambique 30,400 18.7 .. .. 2,072 137 808 .. 9 299 5 Myanmar 27,966 78.0 2,028 9,493 .. .. .. .. 25 1,408 3 Namibia 42,237 12.8 47 591 .. .. .. .. 7 281 56 Nepal 15,905 53.9 .. .. 59 .. .. .. 6 449 7 Netherlands 116,500 90.0 193,900 481 2,811 14,097 4,026 8,482.4 250 25,304 4,773 New Zealand 92,662 63.8 .. .. 3,898 .. 3,853 1,614.9 197 11,305 749 Nicaragua 18,658 11.4 .. .. 6 .. .. .. 1 61 1 Niger 10,100 7.9 .. .. .. .. .. .. 1 46 7 Nigeria 194,394 30.9 .. .. 3,505 973 39 512.6 10 682 10 Norway 91,916 77.5 56,573 13,614 4,077 2,477 2,668 .. 260 13,230 177 Oman 32,800 30.0 .. .. .. .. .. 2,515.5 32 3,267 235 Pakistan 254,410 60.0 209,959 .. 7,791 23,911 5,004 1,101.5 50 5,097 402 Panama 11,643 34.6 .. .. 355 .. .. 2,428.8 27 1,501 34 Papua New Guinea 19,600 3.5 .. .. .. .. .. .. 19 759 23 Paraguay 29,500 50.8 .. .. 441 .. .. .. 9 373 0 Peru 78,672 13.1 .. 72 2,123 .. .. 695.6 43 2,666 200 Philippines 200,037 9.9 .. .. .. .. .. 3,673.3 57 7,406 301 Poland 423,997 69.7 29,996 85,989 19,576 18,626 47,847 428.4 78 3,493 77 Portugal 72,600 86.0 98,328 20,470 2,849 3,415 2,675 865.7 127 9,052 237 Puerto Rico 24,023 94.0 .. 10 96 .. .. 1,671.3 .. .. .. 2006 World Development Indicators 295 Transport services Roads Railways Ports Air Passengers Registered Passengers Goods carried Goods Port container carrier Total road Paved carried million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 1999­2003a 1999­2003a 1999­2003a 1999­2003a 2000­04a 2000­04a 2000­04a 2004 2004 2004 2004 Romania 198,817 50.4 5,283 25,350 10,844 8,633 14,262 .. 30 1,338 5 Russian Federation 537,289 67.4 164 5,702 85,542 157,100 1,664,300 1,368.0 399 25,949 1,416 Rwanda 12,000 8.3 .. .. .. .. .. .. .. .. .. Saudi Arabia 152,044 29.9 .. .. 1,390 364 1,173 3,185.7 113 14,943 957 Senegal 13,576 29.3 .. .. 906 138 371 .. 6 421 0 Serbia and Montenegro 45,290 62.0 .. .. 3,809 .. .. .. 25 1,414 6 Sierra Leone 11,300 8.0 .. .. .. .. .. .. 0 16 8 Singapore 3,165 100.0 .. .. .. .. .. 21,311.0 75 17,718 7,193 Slovak Republic 42,993 87.3 32,981 16,859 3,660 2,227 9,675 .. 16 825 0 Slovenia 38,400 100.0 1,065 6,305 1,229 764 3,462 .. 17 765 3 Somalia 22,100 11.8 .. .. .. .. .. .. .. .. .. South Africa 362,099 20.3 .. 434 20,047 10,001 106,549 2,675.3 134 9,876 930 Spain 666,292 99.0 397,117 132,868 14,395 20,237 14,117 7,809.6 550 45,529 1,043 Sri Lanka 97,286 81.0 21,067 .. .. .. .. 2,220.6 16 2,416 300 Sudan 11,900 36.3 .. .. 5,478 32 889 .. 8 476 41 Swaziland 3,594 .. .. .. 301 .. .. .. 2 90 0 Sweden 424,981 31.1 105,834 37,048 9,895 5,544 13,122 933.8 192 11,539 257 Switzerland 71,220 .. 94,622 26,100 3,378 12,869 9,313 .. 144 9,279 1,090 Syrian Arab Republic 91,795 20.1 589 .. 2,798 635 1,924 .. 16 1,141 20 Tajikistan 27,767 .. .. .. 617 41 1,087 .. 8 498 6 Tanzania 78,891 8.6 .. .. 2,600 c 471c 1,351c .. 6 248 2 Thailand 57,403 98.5 .. .. 4,044 10,092 3,422 4,855.8 129 20,625 1,869 Togo 7,520 31.6 .. .. 568 .. .. .. 1 46 7 Trinidad and Tobago 8,320 51.1 .. .. .. .. .. 440.4 17 1,132 42 Tunisia 18,997 65.4 .. 16,611 1,909 1,242 2,173 .. 21 1,940 20 Turkey 354,421 41.6 163,327 152,163 8,697 5,237 9,332 2,942.4 110 12,516 369 Turkmenistan 24,000 81.2 .. .. 2,523 1,118 6,437 .. 29 1,779 17 Uganda 70,746 23.0 .. .. 259 .. 218 .. 0 46 27 Ukraine 169,739 97.0 40,131 24,387 22,011 51,726 233,961 .. 34 1,924 23 United Arab Emirates 1,088 100.0 .. .. .. .. .. 8,661.6 87 14,314 3,734 United Kingdom 619,398 100.0 666,000 159,000 16,514 42,626 20,700 7,480.9 970 86,055 5,698 United States 6,378,154 58.8 .. 1,599,754 141,961 .. 2,200,123d 35,612.7 9,566e 678,111e 37,450 e Uruguay 8,983 90.0 .. .. 2,993 .. .. 301.6 8 564 0 Uzbekistan 81,600 87.3 .. .. 4,126 2,163 18,428 .. 23 1,588 83 Venezuela, RB 96,155 33.6 .. .. 433 .. 32 920.9 129 4,592 2 Vietnam 215,628 .. 18,116 4,772 2,600 4,376 2,682 2,138.8 51 5,050 217 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 67,000 11.5 .. .. .. .. .. 377.4 16 995 60 Zambia 91,440 22.0 .. .. 1,273 c 186c 554 c .. 5 49 0 Zimbabwe 97,267 19.0 .. .. .. .. .. .. 4 238 17 World 36.3 m .. m .. m .. s .. m .. m 326,382.9 s 23,754 s 1,886,515 s 140,242 s Low income 13.3 .. .. .. .. .. 8,435.5 696 49,908 2,157 Middle income 50.8 .. .. .. 1,230 .. 134,631.2 5,021 404,182 22,844 Lower middle income 50.1 .. .. .. .. .. 104,722.1 3,087 272,310 15,055 Upper middle income 51.1 .. .. .. 2,079 9,621 29,909.1 1,934 131,872 7,788 Low & middle income 29.5 .. .. .. .. .. 143,321.1 5,718 454,090 25,001 East Asia & Pacific 32.3 1,659 .. .. .. 2,662 102,039.0 2,079 203,261 13,730 Europe & Central Asia 74.0 9,603 11,400 216,994 2,227 9,675 .. 985 65,135 2,139 Latin America & Carib. 26.8 .. .. .. .. .. 19,377.2 1,561 98,165 4,629 Middle East & N. Africa 66.4 .. .. .. 1,265 .. .. 358 33,999 1,103 South Asia 53.9 .. .. .. .. .. 7,589.0 389 33,527 1,579 Sub-Saharan Africa 12.5 .. .. .. .. .. .. 347 20,003 1,821 High income 91.8 .. .. .. 8,375 10,105 183,061.8 18,037 1,432,425 115,241 Europe EMU 99.5 108,334 26,460 120,432 8,676 10,105 44,966.0 3,783 309,557 27,487 a. Data are for the latest year available in the period shown. b. Includes Hong Kong, China. c. Excludes Tazara railway. d. Refers to Class 1 railways only. e. Data cover only those carriers designated by the U.S. Department of Transportation as major and national air carriers. 296 2006 World Development Indicators Transport services About the data Definitions Transport infrastructure--highways, railways, ports either air transport for passengers and freight or · Total road network covers motorways, highways, and waterways, and airports and air traffic control sea transport for freight tend to be more competi- main or national roads, secondary or regional roads, systems--and the services that flow from it are cru- tive. The railways indicators in the table focus on and all other roads in a country. · Paved roads are cial to the activities of households, producers, and scale and output measures: total route-kilometers, roads surfaced with crushed stone (macadam) and governments. Because performance indicators vary passenger-kilometers, and goods (freight) hauled in hydrocarbon binder or bituminized agents, with con- significantly by transport mode and focus (whether ton-kilometers. crete, or with cobblestones. · Passengers carried physical infrastructure or the services flowing from Measures of port container traffic, much of it by road are the number of passengers transported that infrastructure), highly specialized and carefully commodities of medium to high value added, give by road times kilometers traveled. · Goods hauled specified indicators are required. The table provides some indication of economic growth in a country. by road are the volume of goods transported by road selected indicators of the size, extent, and produc- But when traffic is merely transshipment, much of vehicles, measured in millions of metric tons times tivity of roads, railways, and air transport systems the economic benefit goes to the terminal operator kilometers traveled. · Rail lines are the length of rail- and of the volume of traffic in these modes as well and ancillary services for ships and containers rather way route available for train service, irrespective of as in ports. than to the country more broadly. In transshipment the number of parallel tracks. · Passengers carried Data for transport sectors are not always inter- centers empty containers may account for as much by railway are the number of passengers transported nationally comparable. Unlike for demographic sta- as 40 percent of traffic. by rail times kilometers traveled. · Goods hauled tistics, national income accounts, and international The air transport data represent the total (inter- by railway are the volume of goods transported by trade data, the collection of infrastructure data has national and domestic) scheduled traffic carried by railway, measured in metric tons times kilometers not been "internationalized." But data on roads are the air carriers registered in a country. Countries traveled. · Port container traffic measures the flow collected by the International Road Federation (IRF), submit air transport data to ICAO on the basis of of containers from land to sea transport modes and and data on air transport by the International Civil standard instructions and definitions issued by ICAO. vice versa in twenty-foot-equivalent units (TEUs), a Aviation Organization (ICAO). In many cases, however, the data include estimates standard-size container. Data cover coastal shipping National road associations are the primary source by ICAO for nonreporting carriers. Where possible, as well as international journeys. Transshipment traf- of IRF data. In countries where such an association these estimates are based on previous submissions fic is counted as two lifts at the intermediate port is lacking or does not respond, other agencies are supplemented by information published by the air (once to off-load and again as an outbound lift) and contacted, such as road directorates, ministries carriers, such as flight schedules. includes empty units. · Registered carrier depar- of transport or public works, or central statistical The data cover the air traffic carried on scheduled tures worldwide are domestic takeoffs and take- offices. As a result, due to differing definitions and services, but changes in air transport regulations offs abroad of air carriers registered in the country. data collections methods and quality, the compiled in Europe have made it more difficult to classify · Air passengers carried include both domestic and data are of uneven quality. Moreover, the quality of traffic as scheduled or nonscheduled. Thus recent international passengers of air carriers registered transport service (reliability, transit time, and condi- increases shown for some European countries may in the country. · Air freight is the volume of freight, tion of goods delivered) is rarely measured, though be due to changes in the classification of air traffic express, and diplomatic bags carried on each flight it may be as important as quantity in assessing an rather than actual growth. For countries with few air stage (operation of an aircraft from takeoff to its next economy's transport system. Several new initiatives carriers or only one, the addition or discontinuation landing), measured in metric tons times kilometers are under way to improve data availability and con- of a home-based air carrier may cause significant traveled. sistency. The IRF is collaborating with national and changes in air traffic. international development agencies to improve the quality and coverage of road statistics. To improve measures of progress and performance, the World Bank is also working on better measures of access, affordability, efficiency, quality, and fiscal and insti- tutional aspects of infrastructure. Data sources Unlike the road sector, where numerous qualified Data on roads are from the IRF's World Road Statis- motor vehicle operators can operate anywhere on tics, supplemented by World Bank staff estimates. the road network, railways are a restricted transport Data on railways are from a database maintained system with vehicles confined to a fixed guideway. by the World Bank's Transport and Urban Devel- Considering their cost and service characteristics, opment Department, Transport Division. Data on railways generally are best suited to carry--and can port container traffic are from Containerisation effectively compete for--bulk commodities and con- International's Containerisation International Year- tainerized freight for distances of 500­5,000 kilo- book. And the data on air transport are from the meters, and passengers for distances of 50­1,000 ICAO's Civil Aviation Statistics of the World and kilometers. Below these limits road transport tends ICAO staff estimates. to be more competitive, while above these limits 2006 World Development Indicators 297 Power and communications Electric power Telephones Access Quality Affordability and efficiency Transmission Population $ per month and covered International Price Cost of Total tele- Total tele- Consumption distribution per 1,000 people by mobile voice traffic Faults basket for call to U.S. communications phone sub- per capita losses Fixed Mobile telephonya minutes per per 100 residential Price basket $ per revenuea scribers per kWh % of output mainlinesa subscribersa % persona mainlinesa fixed linea for mobileb 3 minutesa % of GDP employeea 2003 2003 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Afghanistan .. .. 2 21 .. .. .. .. .. .. .. .. Albania 1,311 40 82 356 90 442 57.2 5.6 24.3 1.34 5.6 319 Algeria 796 14 71 145 84 .. 6.0 5.1 10.2 2.08 2.5 .. Angola 113 14 6 61 .. 7 .. 11.9 11.2 3.23 2.0 96 Argentina 2,185 15 227 352 95 40 .. 6.6 8.3 .. 2.4 625 Armenia 1,312 23 192 67 81 67 52.9 3.1 12.2 2.42 3.2 146 Australia 10,713 7 541 818 97 214 8.0 29.4 17.8 0.68 3.5 374 Austria 8,104 5 460 978 98 293 5.4 30.6 26.4 0.71 2.5 .. Azerbaijan 2,355 16 118 215 96 24 45.2 1.6 10.5 4.18 1.7 225 Bangladesh 128 12 6 31 50 5 .. 7.2 3.7 1.21 1.3 .. Belarus 3,039 13 311 113 87 57 24.8 2.0 7.5 2.25 3.0 159 Belgium 8,412 4 456 876 99 316 5.6 34.6 24.9 0.75 2.6 759 Benin 61 .. 9 30 23 8 6.2 12.2 15.5 4.80 2.6 .. Bolivia 422 13 69 200 60 24 .. 7.9 6.3 1.89 3.8 .. Bosnia and Herzegovina 2,096 17 239 268 90 89 .. 5.1 9.1 3.62 4.4 238 Botswana .. .. 77 319 85 62 .. 11.3 11.1 2.88 3.0 341 Brazil 1,883 17 230 357 68 12 1.6 7.4 18.9 0.71 4.0 .. Bulgaria 3,965 14 357 609 98 36 2.6 8.5 17.3 0.57 5.9 199 Burkina Faso .. .. 6 31 60 6 51.8 11.9 15.4 1.14 2.0 138 Burundi .. .. 3 9 82 2 .. 4.5 11.6 2.45 2.1 98 Cambodia .. .. 3 37 87 2 .. 9.3 4.0 2.94 2.8 .. Cameroon 178 24 6 96 70 .. .. 6.7 16.6 .. 3.3 .. Canada 17,290 6 634 469 93 439 .. 16.1 6.7 .. 2.7 301 Central African Republic .. .. 3 15 .. 2 56.0 32.8 12.7 1.99 1.0 54 Chad .. .. 1 13 8 2 60.8 12.8 27.7 9.11 4.0 .. Chile 2,880 6 206 593 99 57 25.0 16.4 17.0 2.18 3.8 567 China 1,379 6 241 258 73 6 .. 3.6 3.7 2.90 3.2 656 Hong Kong, China 5,653 13 549 1,184 100 895 1.3 15.1 3.4 2.62 3.9 539 Colombia 834 19 195 232 74 44 33.0 5.8 9.1 .. 4.9 .. Congo, Dem. Rep. 87 3 0 18 55 .. .. .. 10.4 .. 4.6 .. Congo, Rep. 122 .. 4 99 65 .. .. .. 17.5 5.39 .. .. Costa Rica 1,666 7 316 217 .. 82 4.0 5.9 4.2 1.93 2.5 316 Côte d'Ivoire 174 14 14 86 55 15 81.0 28.2 23.9 2.25 3.7 .. Croatia 3,156 20 425 575 98 170 12.0 14.7 14.4 .. 5.4 389 Cuba 1,200 15 68 7 50 28 9.5 12.4 20.0 7.35 2.6 .. Czech Republic 6,070 6 338 1,054 99 163 6.8 16.7 15.1 1.06 3.7 512 Denmark 6,602 5 643 956 99 244 9.0 25.7 19.9 0.89 2.6 437 Dominican Republic 1,060 32 107 289 88 232 .. 16.9 7.0 0.22 7.5 .. Ecuador 677 34 124 348 88 51 35.3 9.0 10.6 1.75 2.1 .. Egypt, Arab Rep. 1,127 12 130 105 91 23 0.1 3.8 4.1 1.45 3.4 274 El Salvador 584 13 131 271 86 322 35.2 12.9 13.5 2.40 36.0 .. Eritrea .. .. 9 5 0 9 51.1 4.9 .. 3.55 2.8 56 Estonia 5,224 12 329 931 99 128 16.3 14.2 11.4 0.90 6.3 .. Ethiopia 28 10 6 2 .. 1 100.0 2.9 3.4 7.05 1.8 65 Finland 16,427 4 453 954 99 178 .. 24.7 13.6 1.80 3.1 329 France .. 6 561 738 99 210 .. 25.7 29.7 0.84 2.2 .. Gabon 922 18 28 359 24 54 50.2 27.8 16.8 5.68 2.6 .. Gambia, The .. .. 27 118 60 .. .. 3.9 .. 1.81 10.3 124 Georgia 1,507 16 151 186 79 57 17.2 4.6 6.5 0.68 4.0 191 Germany 6,896 5 661 864 99 191 .. 17.5 30.6 0.43 2.9 526 Ghana 248 12 14 78 28 15 67.4 3.6 11.1 0.39 5.2 137 Greece 5,041 9 466 999 99 164 13.6 14.3 19.3 1.09 4.0 .. Guatemala 396 21 92 258 78 114 .. 10.5 4.3 1.21 2.8 .. Guinea .. .. 3 12 .. 7 1.6 9.4 .. 4.61 1.1 150 Guinea-Bissau .. .. 7 1 .. 9 70.5 .. .. .. .. .. Haiti 31 52 17 48 .. .. .. .. .. .. .. .. 298 2006 World Development Indicators Power and communications Electric power Telephones Access Quality Affordability and efficiency Transmission Population $ per month and covered International Price Cost of Total tele- Total tele- Consumption distribution per 1,000 people by mobile voice traffic Faults basket for call to U.S. communications phone sub- per capita losses Fixed Mobile telephonya minutes per per 100 residential Price basket $ per revenuea scribers per kWh % of output mainlinesa subscribersa % persona mainlinesa fixed linea for mobileb 3 minutesa % of GDP employeea 2003 2003 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Honduras 556 23 53 100 49 82 3.6 5.9 6.9 2.52 5.8 183 Hungary 3,637 12 354 863 99 49 8.7 20.3 13.3 1.01 5.6 508 India 435 27 41 44 41 3 126.0 3.2 3.2 1.19 1.9 .. Indonesia 440 16 46 138 85 5 20.0 6.2 4.6 2.79 2.3 665 Iran, Islamic Rep. 1,916 17 219 64 .. 8 .. 2.8 2.9 0.55 1.1 304 Iraq 977 6 37 20 .. .. .. .. .. .. .. .. Ireland 6,098 8 496 929 99 983 6.0 39.7 19.1 0.71 2.7 401 Israel 6,599 3 441 1,057 97 306 .. 14.9 9.3 0.59 4.5 825 Italy 5,620 7 451 1,090 100 236 .. 23.8 14.0 0.79 2.6 .. Jamaica 2,481 9 189 832 95 233 39.7 10.3 8.1 0.87 5.5 345 Japan 7,818 5 460 716 99 36 .. 26.0 29.1 1.66 3.0 .. Jordan 1,453 9 113 293 99 104 12.6 10.0 9.4 1.44 8.2 303 Kazakhstan 3,510 16 167 184 94 26 .. 3.8 4.7 .. 3.0 108 Kenya 125 19 9 76 .. 5 149.1 12.5 14.0 3.00 4.8 80 Korea, Dem. Rep. 795 16 44 0 .. .. .. .. .. .. .. .. Korea, Rep. 7,018 3 542 761 99 116 1.0 7.3 2.1 0.76 4.2 .. Kuwait 14,808 11 202 813 99 .. 4.0 10.3 7.4 1.50 2.4 234 Kyrgyz Republic 1,647 29 79 59 .. 14 .. 1.4 10.8 8.92 3.8 71 Lao PDR .. .. 13 35 7 3 .. 2.3 2.2 1.11 1.5 85 Latvia 2,456 23 273 664 97 66 20.3 15.6 14.9 1.63 6.4 415 Lebanon 2,558 15 178 251 .. .. .. 20.4 20.1 2.19 4.9 304 Lesotho .. .. 21 88 80 .. 75.0 18.6 14.3 3.28 2.5 .. Liberia .. .. 2 15 16 .. .. .. .. .. 3.5 .. Libya 2,415 28 133 23 .. .. .. .. .. .. .. .. Lithuania 3,055 7 239 996 100 34 16.3 14.6 6.9 2.31 3.5 .. Macedonia, FYR .. .. 259 383 99 127 .. 5.3 .. .. 6.7 .. Madagascar .. .. 3 18 30 1 59.6 7.5 4.0 0.59 12.5 93 Malawi .. .. 7 18 70 5 .. 4.5 20.0 3.56 2.0 49 Malaysia 3,061 5 179 587 96 85 40.0 8.7 5.6 0.71 4.4 728 Mali .. .. 6 30 15 6 177.6 8.5 13.5 12.28 3.0 71 Mauritania .. .. 13 175 .. 20 .. 12.3 .. .. 7.5 .. Mauritius .. .. 287 413 99 92 41.5 7.4 4.8 1.67 3.8 373 Mexico 1,801 15 174 370 86 82 1.7 15.5 11.4 3.04 2.7 505 Moldova 1,166 57 205 187 92 61 5.2 1.8 27.6 2.21 7.5 169 Mongolia .. .. 53 124 64 4 20.6 2.5 9.6 4.92 3.5 82 Morocco 577 16 44 313 95 55 25.0 18.4 16.0 1.41 4.9 .. Mozambique 339 10 4 36 .. 32 66.0 16.5 10.9 1.17 2.8 158 Myanmar 101 20 8 2 .. 1 155.0 2.9 .. 0.36 0.2 49 Namibia 1,277 18 64 142 88 57 40.4 15.8 14.7 4.28 4.4 181 Nepal 68 19 15 7 .. 6 78.0 3.1 2.8 2.04 1.7 88 Netherlands 6,748 4 483 910 100 311 .. 31.7 24.5 0.32 3.3 593 New Zealand 8,896 13 443 745 97 347 30.7 18.1 19.8 1.30 3.3 .. Nicaragua 361 29 40 137 48 44 4.6 14.3 16.0 3.20 2.5 196 Niger .. .. 2 11 13 .. 104.6 9.1 19.3 8.77 0.9 .. Nigeria 107 33 8 71 58 2 20.6 13.7 11.2 1.49 4.4 192 Norway 23,169 9 487 910 99 236 .. 29.9 6.4 0.31 1.8 412 Oman 3,505 18 95 318 .. 108 .. 12.9 5.1 1.87 2.6 319 Pakistan 408 25 30 33 45 11 .. 6.1 2.9 1.03 2.1 97 Panama 1,401 18 118 270 87 55 13.9 10.9 18.1 3.64 4.7 188 Papua New Guinea .. .. 11 3 .. 8 .. 6.6 8.4 4.32 2.3 .. Paraguay 801 4 48 294 60 20 3.4 14.0 7.3 0.90 4.0 .. Peru 759 10 74 148 75 68 .. 19.4 21.9 1.80 3.0 .. Philippines 574 13 42 404 80 29 .. 12.2 4.0 1.20 3.7 .. Poland 3,329 10 322 605 98 61 17.2 17.3 7.7 0.99 3.5 603 Portugal 4,383 8 404 981 99 194 10.1 25.8 31.7 1.04 5.4 922 Puerto Rico .. .. 285 689 .. .. .. .. .. .. 3.8 .. 2006 World Development Indicators 299 Power and communications Electric power Telephones Access Quality Affordability and efficiency Transmission Population $ per month and covered International Price Cost of Total tele- Total tele- Consumption distribution per 1,000 people by mobile voice traffic Faults basket for call to U.S. communications phone sub- per capita losses Fixed Mobile telephonya minutes per per 100 residential Price basket $ per revenuea scribers per kWh % of output mainlinesa subscribersa % persona mainlinesa fixed linea for mobileb 3 minutesa % of GDP employeea 2003 2003 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Romania 2,221 9 202 471 97 49 8.9 9.6 8.8 0.82 3.8 252 Russian Federation 5,480 12 256 517 78 15 .. 7.8 6.3 2.03 3.2 193 Rwanda .. .. 3 16 65 .. .. 7.9 24.8 2.43 4.2 .. Saudi Arabia 6,259 5 154 383 92 120 1.7 11.7 9.6 2.40 3.2 .. Senegal 167 13 21 90 85 21 17.3 14.5 13.5 1.02 7.1 .. Serbia and Montenegro 3975 16 330 581 95 112 .. 2.3 6.4 2.08 3.1 374 Sierra Leone .. .. 5 22 35 .. .. 3.0 13.6 .. 2.8 .. Singapore 7,977 6 440 910 100 728 99.2 6.7 5.7 0.69 3.4 403 Slovak Republic 5,010 6 232 794 99 66 10.0 11.4 10.3 1.06 4.0 508 Slovenia 6,817 4 407 871 99 .. 23.4 12.6 11.7 0.65 2.8 556 Somalia .. .. 25 63 .. .. .. .. .. .. .. .. South Africa 4399 10 103 413 96 39 48.2 21.6 13.5 0.79 5.8 .. Spain 5,701 9 416 905 99 117 14.2 20.3 21.5 0.60 4.4 868 Sri Lanka 325 18 51 114 40 20 6.8 7.3 3.7 2.11 2.2 166 Sudan 81 16 29 30 60 11 .. 4.4 3.2 39.18 2.4 225 Swaziland .. .. 45 109 90 51 70.0 8.3 16.6 2.97 3.7 209 Sweden 15,403 8 767 1,034 99 .. .. 27.1 15.8 0.41 1.9 780 Switzerland 8,191 6 710 849 99 665 .. 29.6 33.0 0.29 3.6 516 Syrian Arab Republic 1,243 24 143 126 99 37 50.0 3.0 48.2 4.81 2.1 .. Tajikistan 2,206 15 39 7 .. 10 144.0 1.0 12.3 6.96 2.1 57 Tanzania 54 27 4 44 25 1 24.0 11.6 11.1 3.17 2.4 262 Thailand 1,752 7 107 430 92 12 2.5 8.3 6.8 0.67 3.6 .. Togo 94 30 10 38 80 13 6.2 10.4 13.4 3.98 3.4 248 Trinidad and Tobago 4,721 5 247 498 .. 334 .. 7.0 7.8 0.95 3.7 .. Tunisia 1,118 11 121 359 95 61 30.0 4.7 6.8 2.28 4.0 224 Turkey 1,656 17 267 484 68 32 30.4 10.3 6.4 2.09 3.0 664 Turkmenistan 1,750 14 80 2 .. 6 86.4 1.5 .. .. 0.8 59 Uganda .. .. 3 42 70 2 .. 16.6 7.9 3.51 4.6 .. Ukraine 2,998 18 256 289 75 36 .. 2.5 10.3 1.65 6.1 142 United Arab Emirates 10,992 10 275 853 99 .. 0.3 5.0 3.5 1.73 2.7 500 United Kingdom 6,209 8 563 1,021 99 262 11.0 29.5 19.1 0.77 3.9 358 United States 13,078 7 606 617 95 201 12.5 25.0 10.8 .. 2.5 344 Uruguay 1,781 21 291 174 99 60 .. 9.0 7.4 0.52 2.9 .. Uzbekistan 1,741 9 66 21 75 6 87.4 1.4 4.6 13.95 2.1 87 Venezuela, RB 2,664 26 128 322 90 23 2.0 16.2 14.5 0.84 3.0 .. Vietnam 429 14 122 60 67 8 .. 4.3 6.9 1.95 3.5 73 West Bank and Gaza .. .. 102 278 95 34 94.0 9.4 .. 1.03 0.6 .. Yemen, Rep. 158 24 39 53 68 12 .. 3.0 5.9 2.39 1.5 176 Zambia 576 3 8 26 51 7 124.9 5.4 13.1 6.45 1.9 .. Zimbabwe 819 16 25 31 .. 19 63.0 2.0 17.8 4.36 1.6 138 World 2,456 w 9w 195 w 281 w 69 w 30 w 23.6 m 9.7 m 11.1 m 1.20 m 3.0 w 232 m Low income 358 24 34 42 43 5 .. 6.6 11.6 5.58 3.4 93 Middle income 1,720 11 192 293 77 22 22.8 7.7 9.1 1.45 3.0 278 Lower middle income 1,329 10 189 249 76 16 25.0 5.5 8.9 1.62 2.4 191 Upper middle income 3,378 13 221 483 84 46 19.8 13.9 11.1 1.06 3.0 402 Low & middle income 1,159 13 125 187 64 12 35.3 7.3 10.5 1.65 3.0 166 East Asia & Pacific 1,184 7 191 244 73 8 .. 4.5 5.1 1.20 2.6 .. Europe & Central Asia 3,531 13 242 463 82 35 19.9 3.5 10.3 1.61 3.0 150 Latin America & Carib. 1,615 16 180 318 76 44 4.7 9.0 9.1 1.96 3.9 .. Middle East & N. Africa 1,212 16 91 128 .. .. .. 4.9 8.1 1.66 1.2 .. South Asia 394 26 35 41 43 4 88.1 3.2 3.2 1.21 1.9 97 Sub-Saharan Africa 513 12 16 78 .. 8 61.6 8.5 13.5 2.43 3.6 138 High income 9,503 6 535 771 98 149 7.0 25.8 17.8 0.76 3.7 472 Europe EMU 6,506 6 525 904 99 199 8.0 31.2 24.5 0.75 3.0 487 a. Data are from the International Telecommunication Union's World Telecommunication Development Report database, and World Bank estimates. b. World Bank estimates. 300 2006 World Development Indicators Power and communications About the data Definitions The quality of an economy's infrastructure, includ- available for most countries. This gives a general idea · Electric power consumption measures the produc- ing power and communications, is an important ele- of access, but a more precise measure is the pen- tion of power plants and combined heat and power ment in investment decisions for both domestic and etration rate--the share of households with access plants less transmission, distribution, and trans- foreign investors. Government effort alone is not to telecommunications. Also important are data on formation losses and own use by heat and power enough to meet the need for investments in modern actual use of the telecommunications equipment. Ide- plants plus imports less exports. · Electric power infrastructure; public-private partnerships, especially ally, statistics on telecommunications (and other infor- transmission and distribution losses are losses in those involving local providers and financiers, are mation and communications technologies) should be transmission between sources of supply and points critical for lowering costs and delivering value for compiled for all three measures: subscription and pos- of distribution and in distribution to consumers, money. In telecommunications, competition in the session, access, and use. The quality of data varies including pilferage. · Fixed telephone mainlines marketplace, along with sound regulation, is lower- among reporting countries as a result of differences are telephone lines connecting a subscriber to the ing costs and improving the quality of and access to in regulations covering the provision of data. telephone exchange equipment. · Mobile telephone services around the globe. Globally there have been huge improvements subscribers are subscribers to a public mobile tele- An economy's production and consumption of in access to telecommunications, driven mainly phone service using cellular technology. · Popula- electricity is a basic indicator of its size and level of by mobile telephony. By 2002 access to mobiles tion covered by mobile telephony is the percentage development. Although a few countries export elec- outpaced access to fixed-line telephones in devel- of people within range of a mobile cellular signal tric power, most production is for domestic consump- oping countries, and rural areas are catching up regardless of whether they are subscribers. · Inter- tion. Expanding the supply of electricity to meet the with urban areas (although gaps are still large). national voice traffic is the sum of international growing demand of increasingly urbanized and indus- By 2002 there were over a billion mobile subscrib- incoming and outgoing telephone traffic (in minutes) trialized economies without incurring unacceptable ers and an estimated 4.7 billion people, or about divided by total population. · Telephone mainline social, economic, and environmental costs is one of 77 percent of the world's population, were covered faults are the number of reported faults for the year the great challenges facing developing countries. by a mobile cellular signal. divided by the number of telephone mainlines and Data on electric power production and consumption Telephone mainline faults are a measure of tele- multiplied by 100. · Price basket for residential are collected from national energy agencies by the communications quality. The definition varies among fixed line is calculated as one-fifth of the installa- International Energy Agency (IEA) and adjusted by the countries: some operators define faults as includ- tion charge, the monthly subscription charge, and IEA to meet international definitions (for data on elec- ing malfunctioning customer equipment while others the cost of local calls (15 peak and 15 off-peak calls tricity production, see table 3.9). Electricity consump- include only technical faults. of three minutes each). · Price basket for mobile tion is equivalent to production less power plants' own Although access is the key to delivering telecom- is calculated as the pre-paid price for 25 calls per use and transmission, distribution, and transformation munications services to people, if that service is not month spread over the same mobile network, other losses less exports plus imports. It includes consump- affordable to most people, then goals of universal mobile networks, and mobile to fixed calls and during tion by auxiliary stations, losses in transformers that usage will not be met. Three indicators of telecom- peak, off-peak, and weekend times. It also includes are considered integral parts of those stations, and munications affordability are presented in the table 30 text messages per month. · Cost of call to U.S. electricity produced by pumping installations. Where (price basket for fixed-line telephone service, price is the cost of a three-minute, peak rate, fixed-line call data are available, it covers electricity generated by basket for mobile service, and the cost of a local from the country to the United States. · Total tele- primary sources of energy--coal, oil, gas, nuclear, call). Telecommunications efficiency is measured by communications revenue is the revenue from the hydro, geo-thermal, wind, tide and wave, and combus- total telecommunications revenue as percent of GDP provision of telecommunications services such as tible renewables. Neither production nor consumption and by total telephone subscribers per employee. fixed-line, mobile, and data divided by GDP. · Total data capture the reliability of supplies, including break- telephone subscribers per employee are telephone downs, load factors, and frequency of outages. subscribers (fixed-line plus mobile) divided by total Over the past decade new financing and technol- telecommunications employees. ogy, along with privatization and liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of Data sources mobile telephony and the global expansion of the Data on electricity consumption and losses are Internet, information and communication technolo- from the IEA's Energy Statistics and Balances of gies are increasingly recognized as essential tools of Non-OECD Countries 2002­2003, the IEA's Energy development, contributing to global integration and Statistics of OECD Countries 2002­2003, and the enhancing public sector effectiveness, efficiency, United Nations Statistics Division's Energy Statis- and transparency. The table presents telecommu- tics Yearbook. Data on telecommunications are nications indicators covering access, quality, and from the International Telecommunication Union's affordability and efficiency. World Telecommunication Development Report Operators are the main source of telecommunica- database and World Bank estimates. tions data, so information on subscribers is widely 2006 World Development Indicators 301 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona technology expenditures Access Quality Application Affordability Schools Broadband Secure per 1,000 people connected to subscribers International Internet servers Price basket per 1,000 Personal Internet the Internet per 1,000 Internet bandwidth per million people for Internet Per capita people % computersa usersa % peoplea bits per capitaa November $ per monthb % of GDP $ 2000 2004 2004 2004 2004 2004 2004 2005 2003 2004 2004 Afghanistan .. .. .. 1 .. 0.0 1 0 .. .. .. Albania .. 90 12 24 .. 0.0 4 0 28.6 .. .. Algeria 27 98 9 26 53 1.1 5 0 17.8 .. .. Angola 11 9 3 11 .. 0.0 0 0 78.8 .. .. Argentina 40 97 96 133 .. 13.5 319 11 13.3 5.6 224 Armenia .. 91 66 50 .. 0.0 12 1 44.8 .. .. Australia 161 96 682 646 97 77.0 1,097 500 18.1 5.4 1,714 Austria 309 97 418 477 94 101.3 6,682 232 32.9 5.1 1,816 Azerbaijan 10 99 18 49 .. 0.0 0 0 108.3 .. .. Bangladesh .. 29 12 2 .. 0.0 0 0 20.0 2.9 12 Belarus .. 91 .. 163 .. 0.0 36 1 12.8 .. .. Belgium 153 97 348 403 93 155.4 11,279 118 28.6 5.3 1,783 Benin 5 20 4 12 .. 0.0 6 0 46.4 .. .. Bolivia 99 .. 36 39 .. 0.0 44 2 22.3 5.6 55 Bosnia and Herzegovina .. 87 45 58 .. 0.1 77 3 7.3 .. .. Botswana 25 15 45 34 .. 0.0 23 1 27.0 .. .. Brazil 46 90 105 120 50 12.4 149 14 28.0 6.3 208 Bulgaria 173 97 59 283 60 5.6 80 9 12.4 3.8 117 Burkina Faso 1 7 2 4 .. 0.0 4 0 45.4 .. .. Burundi 2 14 5 3 .. 0.0 1 0 80.9 .. .. Cambodia .. .. 3 3 .. 0.0 1 0 57.4 .. .. Cameroon 6 18 10 10 .. 0.0 3 0 51.7 5.1 45 Canada 168 99 700 626 98 164.3 6,803 570 12.7 5.4 1,641 Central African Republic 2 2 3 2 .. 0.0 0 .. 175.0 .. .. Chad 0 2 2 6 .. 0.0 0 .. 68.9 .. .. Chile .. 95 133 267 62 29.7 788 21 21.8 5.8 340 China 59 91 41 73 .. 16.5 57 0 10.1 4.4 66 Hong Kong, China 218 99 608 506 100 215.7 4,793 159 3.8 8.7 2,065 Colombia 26 92 67 80 50 2.8 124 4 18.6 8.3 180 Congo, Dem. Rep. 3 2 .. 1 .. 0.0 0 0 74.0 .. .. Congo, Rep. 6 6 4 9 .. 0.0 0 1 121.2 .. .. Costa Rica 70 91 238 235 15 0.1 .. 62 25.8 7.8 337 Côte d'Ivoire 16 35 15 17 .. 0.0 2 0 67.2 .. .. Croatia 134 93 190 293 .. 5.0 317 40 17.1 .. .. Cuba 54 .. 27 13 .. 0.0 8 0 57.8 .. .. Czech Republic .. .. 240 470 90 16.5 2,450 42 20.8 6.0 632 Denmark 283 98 656 696 100 168.6 34,870 411 17.6 5.6 2,487 Dominican Republic 28 88 0 91 .. 3.8 .. 6 33.0 .. .. Ecuador 98 89 56 48 .. 0.0 38 4 31.8 3.6 83 Egypt, Arab Rep. 31 95 32 54 66 0.4 19 1 5.5 1.4 15 El Salvador 29 .. 44 87 .. 2.8 62 5 48.1 .. .. Eritrea .. 14 4 12 .. 0.0 2 .. 26.8 .. .. Estonia 192 93 921 497 75 102.8 3,410 102 13.6 .. .. Ethiopia 0 2 3 2 1 0.0 0 0 27.4 .. .. Finland 445 91 481 629 99 149.2 4,326 308 22.5 6.6 2,344 France 142 95 487 414 97 108.1 3,312 79 14.1 5.6 1,899 Gabon 29 54 29 29 .. 0.0 33 6 121.9 .. .. Gambia, The 2 12 16 33 .. 0.0 1 .. 27.1 .. .. Georgia 5 76 42 39 .. 0.3 .. 4 26.2 .. .. Germany 291 94 561 500 99 83.7 6,860 274 14.1 5.5 1,822 Ghana 14 21 5 17 1 0.0 1 0 43.8 .. .. Greece .. 98 89 177 59 4.7 589 31 37.6 4.2 774 Guatemala .. .. 19 61 .. 0.0 57 6 31.2 .. .. Guinea .. 9 5 5 .. 0.0 0 .. 63.3 .. .. Guinea-Bissau 5 26 .. 17 .. 0.0 .. .. 105.1 .. .. Haiti .. 26 .. 59 .. 0.0 .. 1 130.0 .. .. 302 2006 World Development Indicators The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona technology expenditures Access Quality Application Affordability Schools Broadband Secure per 1,000 people connected to subscribers International Internet servers Price basket per 1,000 Personal Internet the Internet per 1,000 Internet bandwidth per million people for Internet Per capita people % computersa usersa % peoplea bits per capitaa November $ per monthb % of GDP $ 2000 2004 2004 2004 2004 2004 2004 2005 2003 2004 2004 Honduras .. 58 16 32 .. 0.0 3 4 40.6 4.7 49 Hungary 162 92 146 267 85 36.2 989 30 10.2 5.9 588 India 60 37 12 32 .. 0.6 11 1 8.7 3.8 24 Indonesia 23 66 14 67 .. 0.3 10 0 22.3 3.1 37 Iran, Islamic Rep. .. 77 110 8 .. 0.2 15 0 5.9 2.2 54 Iraq .. .. 8 1 .. 0.0 .. .. .. .. .. Ireland 148 95 494 265 99 33.9 6,044 355 28.3 3.7 1,653 Israel .. 93 741 471 95 135.3 2,501 163 29.8 7.8 1,349 Italy 109 .. 315 501 88 81.7 2,078 45 16.5 4.0 1,171 Jamaica .. 70 63 403 10 9.6 .. 14 43.5 11.8 395 Japan 566 99 542 587 99 145.8 1,038 257 21.1 7.6 2,732 Jordan 74 97 55 110 18 0.9 57 4 26.3 8.4 178 Kazakhstan .. 95 .. 27 .. 0.0 3 1 34.5 .. .. Kenya 8 19 13 45 .. 0.0 1 0 45.7 2.9 14 Korea, Dem. Rep. .. .. .. .. .. 0.0 .. .. .. .. .. Korea, Rep. .. 93 545 657 100 247.9 1,485 20 9.7 6.5 924 Kuwait .. 95 183 244 .. 5.4 117 21 24.7 1.5 338 Kyrgyz Republic .. .. 17 52 .. 0.0 4 1 15.0 .. .. Lao PDR .. .. 4 4 .. 0.0 1 0 31.9 .. .. Latvia 138 85 217 350 97 16.9 972 38 58.1 .. .. Lebanon 63 93 113 169 20 10.1 56 10 36.9 .. .. Lesotho 9 17 .. 24 .. 0.0 1 .. 43.4 .. .. Liberia 14 .. .. .. .. 0.0 0 .. .. .. .. Libya 14 .. 24 36 .. 0.0 1 0 18.9 .. .. Lithuania 31 97 155 282 56 37.6 194 22 34.1 .. .. Macedonia, FYR 54 .. 69 78 .. 1.5 25 0 18.9 .. .. Madagascar 5 8 5 5 .. 0.0 2 0 67.3 .. .. Malawi 2 2 2 4 1 0.0 0 0 62.0 .. .. Malaysia 95 98 197 397 .. 10.1 128 15 8.4 6.7 316 Mali 1 15 3 4 .. 0.0 1 0 58.0 .. .. Mauritania .. 21 14 5 .. 0.0 3 0 38.6 .. .. Mauritius 116 94 279 146 19 2.0 146 18 15.0 .. .. Mexico 94 92 108 135 60 3.1 108 8 22.6 3.0 196 Moldova 153 75 27 96 50 0.7 43 4 19.0 .. .. Mongolia 18 29 124 80 19 0.2 9 3 17.8 .. .. Morocco 29 76 21 117 .. 2.1 26 1 25.3 5.5 93 Mozambique 3 6 6 7 0 0.0 1 0 50.8 .. .. Myanmar 9 3 6 1 .. 0.0 1 0 42.5 .. .. Namibia 17 39 109 37 4 0.0 4 7 33.4 .. .. Nepal .. .. 4 7 .. 0.0 1 0 13.5 .. .. Netherlands 279 99 682 614 92 189.4 20,549 327 24.1 6.2 2,214 New Zealand 202 98 474 788 99 18.0 1,127 493 12.9 9.3 2,257 Nicaragua .. .. 37 23 .. 0.4 186 2 51.0 .. .. Niger 0 5 1 2 .. 0.0 0 0 96.8 .. .. Nigeria 25 26 7 14 .. 0.0 1 0 85.5 .. .. Norway 569 100 573 390 99 87.1 9,370 309 26.3 5.0 2,716 Oman .. 79 47 97 .. 0.0 15 4 23.6 .. .. Pakistan 39 39 5 13 .. 0.0 5 0 15.6 7.1 45 Panama .. 77 41 94 .. 5.8 292 56 36.0 9.3 400 Papua New Guinea .. .. 64 29 .. 0.0 1 1 20.0 .. .. Paraguay .. .. 59 25 .. 0.1 26 1 36.3 .. .. Peru 23 .. 98 117 .. 7.6 205 5 32.8 6.7 166 Philippines .. 76 45 54 .. 0.3 39 3 17.0 6.4 67 Poland 102 92 193 236 90 32.7 560 22 15.7 4.3 270 Portugal 102 99 133 281 92 81.7 833 57 20.6 4.3 679 Puerto Rico .. 97 .. 221 .. 5.9 .. 31 .. .. .. 2006 World Development Indicators 303 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona technology expenditures Access Quality Application Affordability Schools Broadband Secure per 1,000 people connected to subscribers International Internet servers Price basket per 1,000 Personal Internet the Internet per 1,000 Internet bandwidth per million people for Internet Per capita people % computersa usersa % peoplea bits per capitaa November $ per monthb % of GDP $ 2000 2004 2004 2004 2004 2004 2004 2005 2003 2004 2004 Romania .. .. 113 208 57 0.7 186 5 26.4 2.6 88 Russian Federation .. 98 132 111 65 0.9 100 2 10.0 3.3 135 Rwanda 0 2 .. 4 .. 0.0 1 .. 66.8 .. .. Saudi Arabia .. 99 354 66 .. 0.3 31 3 34.7 2.2 235 Senegal .. 29 21 42 .. 0.2 27 0 40.6 7.5 51 Serbia and Montenegro .. 92 48 147 70 0.0 87 2 13.2 .. .. Sierra Leone .. 7 .. 2 .. 0.0 0 .. 12.0 .. .. Singapore 273 98 763 571 100 120.8 5,826 270 11.0 9.9 2,498 Slovak Republic 131 100 296 423 65 11.6 2,295 18 20.7 5.0 386 Slovenia 168 98 353 476 99 59.1 1,085 79 25.4 .. .. Somalia .. 8 6 25 .. 0.0 0 .. .. .. .. South Africa 25 54 82 78 27 1.3 19 21 33.3 7.3 343 Spain 98 99 257 336 94 80.9 2,822 82 20.7 3.5 843 Sri Lanka 29 32 27 14 .. 0.1 17 2 15.1 5.9 61 Sudan .. 49 17 32 .. 0.1 6 .. 160.6 .. .. Swaziland .. 18 32 32 .. 0.0 1 2 20.6 .. .. Sweden 410 94 763 756 99 152.6 17,531 331 22.4 6.7 2,570 Switzerland 372 99 826 474 .. 173.5 9,671 473 22.4 7.0 3,370 Syrian Arab Republic .. 80 32 43 .. 0.0 1 .. 55.2 .. .. Tajikistan .. .. .. 1 .. 0.0 0 .. 54.3 .. .. Tanzania .. 14 7 9 .. 0.0 0 0 117.0 .. .. Thailand 197 92 58 109 37 0.2 47 5 7.0 3.6 91 Togo 2 51 29 37 .. 0.0 2 0 30.4 .. .. Trinidad and Tobago .. 88 105 123 15 0.1 138 21 13.4 .. .. Tunisia 19 90 48 84 25 0.7 44 1 17.3 5.3 149 Turkey .. .. 52 142 40 0.8 124 17 19.8 6.9 293 Turkmenistan 7 94 .. 8 .. 0.0 0 .. 20.2 .. .. Uganda 3 6 4 7 1 0.0 2 0 96.8 .. .. Ukraine 175 97 28 79 .. 0.0 17 1 16.7 6.1 83 United Arab Emirates .. 86 116 321 .. 13.0 351 49 13.1 .. .. United Kingdom 326 99 599 628 99 102.5 13,055 466 23.9 6.9 2,450 United States 196 97 749 630 99 129.0 3,305 783 14.9 9.0 3,595 Uruguay .. .. 125 198 50 3.2 291 26 26.5 6.7 259 Uzbekistan .. .. .. 34 .. 0.1 1 0 20.2 .. .. Venezuela, RB .. 90 82 89 .. 8.0 51 5 19.5 4.5 189 Vietnam 6 83 13 71 .. 0.6 23 0 19.9 .. .. West Bank and Gaza .. 94 48 46 .. 0.0 23 1 25.4 .. .. Yemen, Rep. .. 43 15 9 .. 0.0 .. 0 30.8 .. .. Zambia 22 26 10 20 .. 0.0 1 0 32.6 .. .. Zimbabwe .. 26 77 63 .. 0.4 151 0 23.3 16.0 58 World 90 w 84 m 130 w 139 w .. m 32.0 w 816 w 65 w 25.8 m 6.6 w 508 w Low income 44 16 11 24 .. 0.1 10 0 45.5 4.2 .. Middle income 55 89 61 90 .. 12.9 91 4 22.3 4.7 111 Lower middle income 61 89 46 74 .. 12.5 60 2 25.4 4.7 79 Upper middle income .. 92 122 159 62 3.7 218 13 20.8 4.7 241 Low & middle income 49 54 41 62 .. 7.7 59 3 27.4 4.6 82 East Asia & Pacific 60 80 38 74 .. 13.4 48 1 19.9 4.4 67 Europe & Central Asia .. 92 110 138 .. 2.4 210 9 19.8 4.7 202 Latin America & Carib. 61 88 92 115 .. 5.2 159 11 31.5 5.1 200 Middle East & N. Africa .. 88 49 42 .. 0.2 9 1 24.4 .. .. South Asia 59 32 12 26 .. 0.6 10 1 15.1 4.2 26 Sub-Saharan Africa 12 15 15 19 .. 0.1 6 2 51.2 .. .. High income 262 98 574 545 98 125.9 4,545 384 20.9 7.1 2,329 Europe EMU 188 97 421 443 94 93.7 5,788 149 22.5 5.0 1,530 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database, and World Bank estimates. b. Data are from the ITU's World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. 304 2006 World Development Indicators The information age About the data Definitions The digital and information revolution has changed computers are used extensively. Since thousands · Daily newspapers refer to those published at least the way the world learns, communicates, does busi- of users can be connected to a single mainframe four times a week and calculated as average circula- ness, and treats illnesses. New information and com- computer, the number of personal computers under- tion (or copies printed) per 1,000 people. · House- munications technologies offer vast opportunities for states the total use of computers. holds with television are the share of households progress in all walks of life in all countries--opportu- The data on Internet users and related Internet with a television set. Some countries report only the nities for economic growth, improved health, better indicators are based on nationally reported data. number of households with a color television set, service delivery, learning through distance education, Some countries derive these data from Internet and therefore the true number may be higher than and social and cultural advances. The table presents surveys, but since survey questions and definitions reported. · Personal computers are self-contained indicators of the penetration of the information econ- differ across countries, the estimates may not be computers designed for use by a single individual. omy (newspapers, televisions, personal computers, strictly comparable. For example, questions on the · Internet users are people with access to the world- and Internet use), quality (broadband subscribers, age of Internet users and frequency of use vary by wide network. · Schools connected to the Internet international Internet bandwidth, and secure Internet country. Countries that do not have surveys generally are the share of primary and secondary schools in the servers), and some of the economics of the informa- derive their estimates from reported Internet service country that have access to the Internet. · Broadband tion age (Internet access charges and spending on provider (ISP) subscriber counts, calculated by multi- subscribers are the total number of broadband subs- information and communications technology). plying the number of subscribers by a selected multi- cibers with a digital subscriber line, cable modem, or The data on the number of daily newspapers in circu- plier. This method may undercount the actual number other high-speed technologies. Reporting countries lation are from surveys by the United Nations Educa- of people using the Internet, particularly in develop- may have different definitions of broadband, so data tional, Scientific, and Cultural Organization (UNESCO) ing countries, where many commercial subscribers are not strictly comparable across countries. · Inter- Institute for Statistics. In some countries definitions, rent out computers connected to the Internet or pre- national Internet bandwidth is the contracted capac- classifications, and methods of enumeration do not paid cards are used to access the Internet. ity of international connections between countries entirely conform to UNESCO standards. For example, The number of secure Internet servers, from the for transmitting Internet traffic. · Secure Internet newspaper circulation data should refer to the number Netcraft Secure Server Survey, gives an indication servers are servers using encryption technology in of copies distributed, but in some cases the figures of how many companies are conducting encrypted Internet transactions. · Information and communi- reported are the number of copies printed. transactions over the Internet. cations technology expenditures include computer The data for other electronic communications and The data on information and communications tech- hardware (computers, storage devices, printers, and information technology are from the International nology expenditures cover the world's 70 largest buy- other peripherals); computer software (operating sys- Telecommunication Union (ITU), the Internet Soft- ers of such technology among countries and regions. tems, programming tools, utilities, applications, and ware Consortium, Netcraft, the World Information Ensuring universal access to information and com- internal software development); computer services Technology and Services Alliance (WITSA), Global munication technology is a goal of many countries, (information technology consulting, computer and Insights, and World Bank staff estimates. Estimates but not all countries regularly track accessibility. network systems integration, Web hosting, data pro- of households with television are derived from house- There is no common set of information and com- cessing services, and other services); and commu- hold surveys; data presented in the table are from munications technology indicators and definitions, nications services (voice and data communications the ITU and World Bank staff estimates. and data are often drawn from administrative records services) and wired and wireless communications The estimates of personal computers are derived rather than from specific surveys. Access needs to equipment. from an annual ITU questionnaire, supplemented be accurately measured in three major areas: indi- by other sources. In many countries mainframe vidual, household, and community access. Europe and Central Asia had the highest Internet use among developing country regions in 2004 Data sources Internet users per 1,000 people Data on newspapers are compiled by the UNESCO 600 Institute for Statistics. Data on televisions, per- sonal computers, Internet users, price basket 500 for Internet, broadband subscribers, and interna- 400 tional Internet bandwidth are from the ITU and are reported in the ITU's World Telecommunication 300 Development Report database and World Bank 200 estimates. Data on schools connected to the Internet are World Bank staff estimates. Data on 100 secure Internet servers are from Netcraft (www. 0 netcraft.com/). Data on information and commu- Sub-Saharan South Middle East East Asia Latin America Europe & High- Africa Asia & North Africa & Pacific & Caribbean Central Asia income nications technology expenditures are from Digital Planet 2004: The Global Information Economy by Source: Table 5.10 based on International Telecommunication Union and World Bank data. WITSA, and Global Insight, Inc. 2006 World Development Indicators 305 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda filedb journal articles % of manu- per million per million factured Receipts Payments Non- Non- people people % of GDP $ millions exports $ millions $ millions Residents residents Residents residents 1996­2004c 1996­2004c 2001 1996­2003c 2004 2004 2004 2004 2002 2002 2002 2002 Afghanistan .. .. 0 .. .. .. .. .. .. .. .. .. Albania .. .. 17 .. 5 1 5 8 0 89,821 0 1,758 Algeria .. .. 225 .. 7 1 .. .. 42 88,839 1,313 3,088 Angola .. .. 3 .. .. .. 227 2 .. .. .. .. Argentina 720 316 2,930 0.41 749 8 58 483 0 6,634 30,839 12,007 Armenia 1,537 103 152 0.25 4 1 .. .. 204 89,361 388 2,084 Australia 3,670 .. 14,788 1.63 3,128 14 472 1,437 10,823 96,434 26,831 17,113 Austria 2,968 1,254 4,526 2.22 10,597 12 170 1,241 3,313 250,719 7,272 9,996 Azerbaijan 1,236 195 68 0.30 8 2 .. 0 0 89,337 144 2,051 Bangladesh .. .. 177 .. 3 0 0 5 .. .. .. .. Belarus 1,871 207 528 0.62 215 3 2 10 908 89,686 1,730 4,548 Belgium 3,478 1,473 5,984 2.33 19,583 8 .. .. 2,122 161,472 21,010 d 10,695d Benin .. .. 20 .. 1 2 0 2 .. .. .. .. Bolivia 120 6 33 0.28 28 9 2 10 .. .. .. .. Bosnia and Herzegovina .. .. 9 .. .. .. .. .. 0 89,872 0 3,283 Botswana .. .. 41 .. .. .. 3 12 0 10 .. .. Brazil 344 332 7,205 0.98 5,929 12 114 1,197 6,521 95,225 81,036 13,218 Bulgaria 1,263 477 784 0.50 247 4 7 30 306 158,051 4,043 5,576 Burkina Faso 17 16 23 0.17 3 10 .. .. .. .. .. .. Burundi .. .. 3 .. 0 6 0 0 .. .. 20 132 Cambodia .. .. 5 .. 4 0 .. 6 .. .. 333 1,305 Cameroon .. .. 75 .. 2 1 .. .. .. .. .. .. Canada 3,597 .. 22,626 1.94 25,625 14 3,019 5,528 5,934 102,418 17,068 19,664 Central African Republic 47 27 4 .. 0 0 .. .. .. .. .. .. Chad .. .. 2 .. .. .. .. .. .. .. .. .. Chile 444 303 1,203 0.61 195 5 48 283 241 2,879 .. .. China 663 .. 20,978 1.31 161,603 30 236 4,497 40,346 140,910 321,034 57,597 Hong Kong, China 1,564 225 1,817 0.60 80,109 32 341 864 112 9,018 5,903 14,543 Colombia 109 77 324 0.17 347 6 7 82 52 87,859 7,265 7,096 Congo, Dem. Rep. .. .. 6 .. .. .. .. .. .. .. .. .. Congo, Rep. 30 32 13 .. .. .. .. .. .. .. .. .. Costa Rica 368 .. 92 0.39 1,374 37 1 51 0 89,225 .. .. Côte d'Ivoire .. .. 40 .. 93 8 0 0 .. .. .. .. Croatia 1,296 455 710 1.14 759 13 41 146 444 89,877 843 5,600 Cuba 537 2,447 299 0.65 .. .. .. .. 13 89,468 0 1,551 Czech Republic 1,594 879 2,622 1.27 7,662 13 57 172 608 158,592 8,114 9,756 Denmark 5,016 2,713 4,988 2.53 9,686 20 .. .. 3,875 250,103 3,914 6,744 Dominican Republic .. .. 6 .. .. .. 0 30 .. .. .. .. Ecuador 50 73 20 0.07 49 7 0 43 13 85,290 4,219 4,634 Egypt, Arab Rep. .. .. 1,548 0.19 15 1 100 108 627 798 0 2,496 El Salvador 47 .. 0 0.08 37 4 0 18 .. .. .. .. Eritrea .. .. 2 .. .. .. .. .. .. .. .. .. Estonia 2,523 427 339 0.83 587 14 4 18 33 157,901 1,017 5,213 Ethiopia .. .. 93 .. 0 0 0 0 3 4 .. .. Finland 7,992 3,472 5,098 3.49 10,625 21 850 805 2,941 248,668 2,830 6,095 France 3,213 .. 31,317 2.19 64,871 19 5,070 3,142 21,959 160,056 58,035 12,774 Gabon .. .. 20 .. 28 15 .. .. .. .. .. .. Gambia, The .. .. 17 .. 0 3 .. .. 0 177,146 .. .. Georgia 2,600 270 110 0.29 89 38 8 6 202 89,881 202 2,438 Germany 3,261 1,089 43,623 2.50 131,838 17 5,103 5,759 80,661 230,066 53,817 12,827 Ghana .. .. 90 .. 8 4 0 0 0 177,371 .. .. Greece 1,413 895 3,329 0.65 1,031 11 32 466 614 162,387 5,290 6,075 Guatemala .. .. 14 .. 88 7 0 0 5 0 3,048 5,040 Guinea 251 91 2 .. 0 0 0 0 .. .. .. .. Guinea-Bissau .. .. 6 .. .. .. .. 0 .. .. .. .. Haiti .. .. 1 .. .. .. 0 0 .. .. .. .. 306 2006 World Development Indicators Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda filedb journal articles % of manu- per million per million factured Receipts Payments Non- Non- people people % of GDP $ millions exports $ millions $ millions Residents residents Residents residents 1996­2004c 1996­2004c 2001 1996­2003c 2004 2004 2004 2004 2002 2002 2002 2002 Honduras 78 253 11 0.05 6 2 0 22 7 161 .. .. Hungary 1,472 457 2,479 0.95 14,158 29 551 949 962 91,497 4,316 9,546 India 119 102 11,076 0.85 2,840 5 25 421 220 91,704 .. .. Indonesia .. .. 207 .. 5,809 16 221 990 0 90,922 .. .. Iran, Islamic Rep. 467 376 995 .. 51 2 .. .. 691 0 9,858 1,224 Iraq .. .. 21 .. .. .. .. .. .. .. .. .. Ireland 2,674 621 1,665 1.11 30,239 34 221 18,444 1,255 162,170 1,167 4,577 Israel 1,613 532 6,487 4.93 6,861 19 493 464 2,323 94,961 2,842 4,827 Italy 1,213 1,347 22,313 1.16 23,504 8 770 1,751 4,086 159,865 0 9,385 Jamaica .. .. 44 0.07 3 0 10 9 15 54 663 1,433 Japan 5,287 528 57,420 3.15 124,045 24 15,701 13,644 371,495 115,411 100,645 16,827 Jordan 1,927 709 240 .. 147 5 .. .. .. .. .. .. Kazakhstan 629 92 116 0.22 72 2 0 26 2 89,421 1,809 2,902 Kenya .. .. 230 .. 18 3 17 50 0 177,559 0 1,166 Korea, Dem. Rep. .. .. 1 .. .. .. .. .. 0 88,052 0 1,913 Korea, Rep. 3,187 .. 11,037 2.64 75,742 33 1,790 4,450 76,860 126,836 90,014 17,862 Kuwait 69 172 257 0.20 .. .. 0 0 .. .. .. .. Kyrgyz Republic 406 51 10 0.20 2 2 5 4 123 89,357 67 1,850 Lao PDR .. .. 2 .. .. .. .. .. .. .. 25 656 Latvia 1,434 318 157 0.38 130 5 8 14 8 140,637 1,262 5,699 Lebanon .. .. 202 .. 17 2 .. .. 0 104 .. .. Lesotho 42 26 1 0.01 .. .. 17 0 0 177,309 0 774 Liberia .. .. 1 .. .. .. .. .. 0 89,507 0 760 Libya 361 493 19 .. .. .. 0 0 .. .. .. .. Lithuania 2,136 427 272 0.69 250 5 1 18 91 140,674 1,540 5,602 Macedonia, FYR .. .. 74 0.26 16 1 3 9 42 140,588 411 3,541 Madagascar 15 45 .. 0.12 1 1 1 13 4 89,526 162 293 Malawi .. .. 36 .. 2 2 0 0 0 177,315 138 440 Malaysia 299 58 494 0.69 52,868 55 20 782 .. .. .. .. Mali .. .. 11 .. .. .. 0 1 .. .. .. .. Mauritania .. .. 2 .. .. .. .. .. .. .. .. .. Mauritius 201 126 16 0.35 61 4 0 4 .. .. .. .. Mexico 268 96 3,209 0.42 31,832 21 92 805 627 94,116 40,141 18,509 Moldova 172 201 77 0.81 13 4 2 3 240 89,396 1,391 2,690 Mongolia 681 69 8 0.28 0 0 .. .. 121 89,864 255 3,260 Morocco 782 .. 469 0.62 696 10 16 37 0 89,300 0 2,849 Mozambique .. .. 14 .. 2 9 1 3 0 176,319 0 931 Myanmar .. .. 10 .. .. .. 0 0 .. .. .. .. Namibia .. .. 13 .. 15 3 0 3 .. .. .. .. Nepal 59 137 39 0.66 1 0 .. .. .. .. .. .. Netherlands 2,482 1,725 12,602 1.80 55,211 29 4,205 3,339 7,496 158,485 .. .. New Zealand 3,405 .. 2,903 1.17 858 14 98 485 2,137 91,240 8,818 11,276 Nicaragua 44 39 8 0.05 5 6 0 0 .. .. .. .. Niger .. .. 21 .. 1 3 .. 0 .. .. .. .. Nigeria .. .. 332 .. 9 2 .. 64 .. .. .. .. Norway 4,587 1,754 3,252 1.75 2,759 18 242 485 504 90,712 0 6,981 Oman .. .. 96 .. 22 1 .. .. 0 75,825 .. .. Pakistan 86 13 282 0.22 150 1 10 95 58 0 5,342 1,560 Panama 97 387 37 0.34 2 2 0 49 7 153 .. .. Papua New Guinea .. .. 36 .. 47 39 .. .. .. .. .. .. Paraguay 79 113 4 0.10 14 7 194 7 .. .. .. .. Peru 226 .. 93 0.10 43 2 2 68 .. .. 6,940 6,983 Philippines .. .. 158 .. 13,913 64 12 270 0 81,697 .. .. Poland 1,581 282 5,686 0.56 1,932 3 27 880 2,324 92,176 12,355 11,607 Portugal 1,949 307 2,142 0.93 2,639 9 40 337 185 251,752 6,929 7,829 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 307 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda filedb journal articles per million per million % of % of manu- people people GDP factured Receipts Payments Non- Non- $ millions exports $ millions $ millions Residents residents Residents residents 1996­2002c 1996­2002c 2001 1996­2002c 2003 2003 2002 2002 2002 2002 2002 2002 Romania 976 249 997 0.40 653 3 8 108 1,486 141,294 6,026 6,485 Russian Federation 3,319 557 15,846 1.28 3,432 9 227 1,094 24,049 96,315 29,279 14,215 Rwanda .. .. 4 .. 1 25 0 0 .. .. .. .. Saudi Arabia .. .. 580 .. 122 2 0 0 61 552 .. .. Senegal .. .. 62 .. 33 6 0 1 .. .. .. .. Serbia and Montenegro 1,031 440 547 .. .. .. .. .. 507 90,893 0 4,758 Sierra Leone .. .. 3 .. 1 31 1 0 0 177,366 0 787 Singapore 4,745 381 2,603 2.15 87,742 59 224 5,647 511 93,748 3,344 20,282 Slovak Republic 1,984 460 955 0.59 1,217 5 50 91 276 157,652 2,350 7,742 Slovenia 2,543 1,600 876 1.53 794 6 12 123 332 136,912 1,086 6,612 Somalia .. .. 0 .. .. .. .. .. .. .. .. .. South Africa 307 73 2,327 0.76 1,300 6 48 381 184 90,471 .. .. Spain 2,195 861 15,570 1.11 9,932 7 486 3,032 4,330 251,260 66,471 12,460 Sri Lanka 181 44 76 0.18 60 1 .. .. 0 89,759 .. .. Sudan 263 131 43 0.34 0 0 .. .. 2 177,336 0 795 Swaziland .. .. 6 .. 4 1 0 96 0 88,379 0 828 Sweden 5,416 .. 10,314 3.98 17,022 17 3,459 1,420 9,443 246,886 0 5,976 Switzerland 3,601 2,319 8,107 2.57 24,121 22 .. .. 7,977 246,451 0 10,592 Syrian Arab Republic 29 24 55 .. 6 1 .. 10 0 30 0 0 Tajikistan .. .. 20 .. .. .. 1 0 40 89,352 0 1,522 Tanzania .. .. 87 .. 3 2 0 1 0 176,850 0 16 Thailand 286 115 727 0.24 18,203 30 14 1,584 1,117 4,548 .. .. Togo .. .. 11 .. 0 0 0 1 .. .. .. .. Trinidad and Tobago 399 889 37 0.12 22 1 .. .. 2 89,901 340 1,317 Tunisia 1,013 34 344 0.63 370 5 18 8 0 72,604 .. .. Turkey 341 37 4,098 0.66 1,064 2 0 362 550 250,492 28,209 7,611 Turkmenistan .. .. 0 .. .. .. .. .. 0 89,333 0 1,648 Uganda 24 14 91 0.81 12 13 6 6 0 177,305 0 14 Ukraine 1,774 463 2,256 1.16 572 5 40 268 37 90,563 0 5,285 United Arab Emirates .. .. 159 .. .. .. .. .. 0 89,666 .. .. United Kingdom 2,706 .. 47,660 1.89 64,295 24 12,019 8,368 33,671 251,239 51,399 17,135 United States 4,484 .. 200,870 2.60 216,016 32 52,643 23,901 198,339 183,398 181,693 30,944 Uruguay 366 50 155 0.26 22 2 0 10 44 572 5,863 9,514 Uzbekistan .. .. 204 .. .. .. .. .. 717 89,902 756 2,166 Venezuela, RB 236 .. 535 0.28 118 3 0 219 56 2,292 .. .. Vietnam .. .. 158 .. 594 6 .. .. 2 90,135 0 1,929 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. 10 .. 17 13 .. 9 .. .. .. .. Zambia 51 16 26 0.01 1 1 .. .. 0 157,720 0 554 Zimbabwe .. .. 113 .. 5 1 .. .. 0 177,483 1 17 World .. w .. w 648,500 s 2.36 w 1,269,586 s 20 w 109,808 s120,273 s 936,630 s 12,882,065 s1,316,564 s 604,897 s Low income .. .. 13,147 0.73 .. 4 59 248 1,469 3,003,874 8,489 26,165 Middle income 851 .. 83,927 0.87 266,410 20 2,447 15,526 81,493 4,789,712 589,487 258,839 Lower middle income 609 .. 39,520 1.02 .. 23 1,282 9,566 51,330 2,439,396 423,019 134,156 Upper middle income 1,411 308 44,407 0.68 119,785 17 1,165 5,961 30,163 2,350,316 166,468 124,683 Low & middle income .. .. 97,074 0.85 201,022 18 2,506 15,774 82,962 7,793,586 597,976 285,004 East Asia & Pacific 663 .. 22,722 1.31 .. 34 484 7,347 40,469 581,580 321,648 66,765 Europe & Central Asia 1,907 379 39,077 0.98 32,514 9 992 4,118 34,159 3,071,921 106,252 137,176 Latin America & Carib. .. .. 16,045 0.57 40,852 13 563 3,425 7,255 1,166,254 163,101 62,928 Middle East & N. Africa .. .. 4,119 .. 1,152 3 134 172 669 327,396 1,313 8,433 South Asia 119 102 11,611 0.75 .. 4 17 100 220 181,463 5,342 2,242 Sub-Saharan Africa .. .. 3,500 .. .. 4 317 612 190 2,464,972 320 7,460 High income 3,558 .. 551,426 2.54 1,170,986 20 107,302 104,498 853,668 5,088,479 718,588 319,893 Europe EMU 2,607 1,230 148,169 2.20 361,128 16 17,110 38,459 129,155 2,448,271 222,821 92,713 Note: The original information on patent and trademark applications was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. a. Excludes applications filed under the auspices of the African Regional Industrial Property Organization (3 by residents, 88,378 by nonresidents), European Patent Office (67,677 by residents, 97,737 by nonresidents), and the Eurasian Patent Organization (549 by residents, 88,857 by nonresidents). b. Excludes applications filed under the auspices of the Office for Harmonization in the Internal Market (29,345 by residents, 15,669 by nonresidents). c. Data are for the most recent year available. d. Includes Luxembourg and the Netherlands. 308 2006 World Development Indicators Science and technology About the data Definitions During the last century technological innovation in The method for determining a country's high- · Researchers in R&D are professionals engaged in public health, nutrition, and agriculture has led to technology exports was developed by the Organ- the conception or creation of new knowledge, products, improvements in human welfare--child mortality isation for Economic Co-operation and Develop- processes, methods, or systems and in the manage- rates have been reduced, and life expectancy has ment in collaboration with Eurostat. The product ment of the projects concerned. Postgraduate PhD stu- increased in all regions of the world. Knowledge is approach method is based on calculation of R&D dents (ISCED97 level 6) engaged in R&D are included. a key factor in economic development, and soci- intensity (R&D expenditure divided by total sales) · Technicians in R&D and equivalent staff are people eties that are able to produce, select, adapt, and for groups of products from six countries (Germany, whose main tasks require technical knowledge and commercialize knowledge have better chances of Italy, Japan, the Netherlands, Sweden, and the experience in engineering, physical and life sciences achieving sustained growth and improved quality of United States). Because industrial sectors char- (technicians), or social sciences and humanities life. Science, advancing rapidly in virtually all fields-- acterized by a few high-technology products may (equivalent staff). They participate in R&D by perform- particularly in biotechnology--is playing a growing also produce many low-technology products, the ing scientific and technical tasks involving the applica- economic role: countries able to access, generate, product approach is more appropriate for analyzing tion of concepts and operational methods, normally and apply scientific knowledge will have a competi- international trade than is a sectoral approach. To under the supervision of researchers. · Scientific tive edge over those that cannot. And there is greater construct a list of high-technology manufactured and technical journal articles refer to published appreciation of the need for high-quality scientific products (services are excluded), R&D intensity scientific and engineering articles in physics, biology, input into public policy, such as regional and global was calculated for products classified at the chemistry, mathematics, clinical medicine, biomedi- environmental concerns. three-digit level of the Standard International cal research, engineering and technology, and earth Science and technology cover a range of issues too Trade Classification revision 3. The final list was and space sciences. · Expenditures for R&D are cur- complex and too broad to be quantified by any single determined at the four-and five-digit levels. At these rent and capital expenditures on creative work under- set of indicators, but those in the table shed light levels final selection was based on patent data taken systematically to increase knowledge, including on countries' "technological base"--the availability and expert opinion, since no R&D data were avail- knowledge of humanity, culture, and society, and the of skilled human resources, the number of scientific able. This method takes only R&D intensity into use of knowledge for new applications. R&D covers and technical articles published, the competitive account. Other characteristics of high technology basic research, applied research, and experimental edge countries enjoy in high-technology exports, are also important, such as know-how, scientific development. · High-technology exports are products sales and purchases of technology through royal- and technical personnel, and technology embodied with high R&D intensity, as in aerospace, computers, ties and licenses, and the number of patent and in patents; considering these characteristics would pharmaceuticals, and scientific instruments. · Royalty trademark applications filed. result in a different list. (See Hatzichronoglou 1997 and license fees are payments and receipts between The United Nations Educational, Scientific, and for further details.) Moreover, the R&D for high- residents and nonresidents for the authorized use Cultural Organization (UNESCO) Institute for Statis- technology exports may not have occurred in the of intangible, nonproduced, nonfinancial assets and tics collects data on researchers, technicians, and reporting country. proprietary rights (patents, copyrights, trademarks, research and development (R&D) expenditure from Most countries have adopted systems that pro- franchises, industrial processes) and for the use, countries and territories around the world through tect patentable inventions. Most patent legislation through licensing agreements, of produced originals questionnaires and special surveys, supplemented requires that an idea, to be protected by law (patent- of prototypes (films, manuscripts). · Patent applica- by information from other international sources. Data able), be new in the sense that it has not already tions filed are applications filed with a national patent for researchers and technicians are normally calcu- been published or publicly used; nonobvious (involve office for exclusive rights to an invention--a product or lated in full-time equivalents. an inventive step) in the sense that it would not have process that provides a new way of doing something R&D expenditures are all expenditures for R&D per- occurred to any specialist in the industrial field had or a new technical solution to a problem. A patent formed within a country, including both capital expen- such a specialist been asked to find a solution to protects the invention for the patent owner for a set ditures and current costs (annual wages, salaries, the problem; and capable of industrial application period, generally 20 years. · Trademark applications and associated costs of researchers, technicians, in the sense that it can be industrially manufactured filed are applications to register a trademark with a and supporting staff and noncapital purchases of or used. Information on patent applications filed is national or regional trademark office. Trademarks are materials, supplies, and R&D equipment such as shown separately for residents and nonresidents. distinctive signs identifying goods or services as pro- utilities, books, journals, reference materials, sub- A trademark provides protection to its owner by duced or provided by a specific person or enterprise. scriptions to libraries and scientific societies, and ensuring exclusive right to use it to identify goods or Trademarks protect owners of the mark by ensuring materials for laboratories). services or to authorize another to use it in return exclusive right to use it to identify goods or services The information does not reflect the quality of train- for payment. The period of protection varies, but a or to authorize its use in return for payment. ing and education, which varies widely. Similarly, R&D trademark can be renewed indefinitely by paying addi- expenditures are no guarantee of progress; govern- tional fees. The trademark system helps consumers Data sources ments need to pay close attention to the practices identify and purchase a product or service whose that make R&D expenditures effective. nature and quality, indicated by its unique trademark, Data on researchers, technicians, and expenditures Scientific and technical journal article counts are meet their needs. in R&D are from the UNESCO Institute for Statis- from a set of journals classified and covered by the tics. Data on journal articles are from the National Institute for Scientific Information's Science Citation Science Foundation's Science and Engineering Index (SCI) and the Social Sciences Citation Index Indicators 2004. Data on high-technology exports (SSCI). Article counts are based on fractional assign- are from the United Nations Statistics Division's ments; for example, an article with two authors from Commodity Trade (Comtrade) database. Data on different countries is counted as half an article for royalty and license fees are from the International each country (see Definitions for the fields covered). Monetary Fund's Balance of Payments Statistics The SCI and SSCI databases cover the core set of Yearbook. Data on patents and trademarks are scientific journals but may exclude some of regional from the World Intellectual Property Organization's or local importance. They may also reflect some bias Industrial Property Statistics database. toward English-language journals. 2006 World Development Indicators 309 n an era of uncertain alliances and global fears, it is striking that the world economy con- tinues to become more integrated. Celebrated by some, deplored by others, globalization has been loudly debated over the past decade. But globalization is not a single process. It proceeds as people and institutions seek profits and competitive advantage through expand- ing trade in goods and services and cross-border flows of financial resources and people. It has been propelled by cheaper and faster transportation, more innovative information technology, fewer or lower trade barriers, and better economic management. As the world becomes more integrated, decisions made in Washington, London, or Tokyo along with decisions made in New Delhi and Lagos and deals brokered in the virtual world of electronic communications can all have an impact on the lives and prospects of the world's people. But not all people have shared in the benefits of an expanding global economy. Compared with other developing country regions, Sub-Saharan Africa lags in this integra- tion process and has not yet been able to take full advantage of opportunities brought by globalization. Expanding trade In an integrated world, trade spurs growth and growth spurs trade (figure 6a). The five fastest growing economies in the world from 1990 to 2004 measured by GDP per capita--Albania, Vietnam, Ireland, China, and Bosnia and Herzegovina--all experienced double-digit annual growth in trade. Rapid expansion of China's trade has not only been a driving force of China's continuous high growth, but has also helped its trading partners in East Asia and Pacific to integrate faster into the global manufacturing sector. The global economy has become more open. In 1990 the total value of trade was less than 40 percent of global GDP; by 2004 the world economy had grown 50 percent and two-way trade exceeded 55 percent of global GDP. Trade in goods makes up 81 percent of trade and has been increasing 7 percent a year on average between 1990 and 2004. Although the nominal value of global trade in services nearly tripled over the same period and its share of GDP rose from less than 8 percent to more than 10 percent, its share of global trade has remained largely unchanged (table 6.1). In a world economy where services account for 70 percent of output, there appear to be unrealized opportunities for further trade. During the last decade developing countries have become more important players in world trade. By 2004 low- and middle-income economies accounted for 28.5 percent of world trade, up from 22.3 percent in 1999. Between 1990 and 2004 their trade grew 11.5 percent a year compared with 7.2 percent for high-income economies. The middle-income economies were by far the fastest growing traders. Still, high-income economies, which account for more than 70 percent of world trade and almost 80 percent of global output (measured at market exchange rates), remain the most important markets (table 6.3). 2006 World Development Indicators 311 for 88 percent of merchandise exports from Sub-Saharan Trade spurs growth and growth spurs trade Africa to high-income countries. By 2004 that share had Annual growth of real GDP per capita and real trade by size of economy, 1990­2004 fallen to 73 percent. GDP (%) The decline of primary products in overall exports is 20 more pronounced if petroleum exports and South Africa are excluded, dropping from 67 percent to 46 percent 15 Bosnia and Herzegovina over the same period. Some Sub-Saharan countries have 10 made significant progress in specific product sectors: China United Ireland cut flowers from Kenya; music from Mali; clothing and Kingdom Albania 5 Vietnam textiles from Mauritius, Lesotho, and Madagascar; and Iran outsourced services from Ghana are making headway into 0 United Japan States world markets. Ukraine Within Sub-Saharan Africa intraregional trade is also lim- ­5 ited. A recent study found that only 15 percent of merchan- ­10 dise exports go to other countries in the region, and only ­10 ­5 0 5 10 15 20 25 30 10 percent of merchandise imports originate in the region Trade (%) Source: World Bank staff estimates. (Newfarmer 2006). Because of gaps in the statistical report- ing, it is hard to know whether intraregional trade is accu- rately estimated. The United Nations Statistics Division's Many obstacles to trade remain, especially for low-income Comtrade database shows no entries for three-quarters economies. Trade-supporting infrastructure is essential and of all possible two-way trade flows between Sub-Saharan is improving due to more efficient communication and trans- economies in 2004. portation technology. But landlocked economies and those Although regional agreements have proliferated, signifi- lacking suitable seaports remain at a disadvantage. Poor cant barriers to trade remain because of imperfect imple- roads and high inland transportation costs have kept many mentation of agreements, high border and behind-the-border people from trading with the outside world. costs, absence of common standards, restrictive rules of Other obstacles include an unfriendly business environ- origin even within customs unions, and inconsistent (and ment and inadequate policies and institutions. For exam- inconsistently applied) tax policies. All of these issues will ple, in Sub-Saharan Africa it takes twice as long to comply have to be dealt with before Africa can achieve effective with the procedures required to export or import goods as regional integration. it does in East Asia and Pacific and four times as long as in high-income countries (World Bank 2006a; see also table Expanding flows of financial resource 5.3). Lack of access to capital and a small entrepreneurial Global capital markets are expanding rapidly. One measure class willing or able to take risks also impede the growth of financial market integration is the size of gross private of trade. capital flows recorded in the balance of payments (table But the greatest barriers are those erected by high-income 6.1). Between 1990 and 2004 flows to and from high-income economies. Even in an era of falling tariffs (table 6.7), devel- economies tripled as a share of their GDP. Expansion has oping countries have a hard time reaching high-value mar- been slower for most developing countries. Gross capital kets. Tariff escalation is one of the rich countries' protection- flows as a share of GDP have more than doubled but remain ist strategies. EU tariffs are almost zero for cocoa beans but less than half those in high-income countries. rise to about 10 percent for semiprocessed cocoa and to For developing countries foreign direct investment (FDI) is about 30 percent for chocolate. So tariff escalation penalizes the largest source of external funding, but portfolio equity producers when they add value. and bond investments continue to expand following the Subsidies paid by Organisation for Economic Co-operation turnaround in 2002 (figure 6b). Although low- and middle- and Development (OECD) governments to their agricultural income economies still receive only one-third of global FDI, produces (see table 1.4) are another source of formidable the absolute level has increased nearly tenfold between disadvantage for developing economies. Cotton subsidies, 1990 and 2004, growing much faster than in high-income particularly in the United States, lower world prices and cost economies. But their net inflow as a share of GDP has still West African economies an estimated $250 million a year. not fully recovered to the peak achieved before the East The economies of Sub-Saharan Africa are far from reach- Asian financial crisis. ing their potential for trade. Their share in global trade There are large differences in FDI inflows among developing is low--about 1.5 percent in 2004--and has changed economies. Middle-income economies received more than little since 1999. However, the region's export structure 90 percent of net FDI inflows in 2004, and if measured as is improving slowly. In 1984 primary products accounted a share of GDP, these inflows are twice those to low-income 312 2006 World Development Indicators Foreign direct investment is the largest source of external finance Aid is the largest source of external finance for Sub-Saharan for developing countries Africa Resource flows to developing countries, 1990­2004 (% of GDP) Resource flows to Sub-Saharan Africa, 1990­2004 (% of GDP) 3.5 8 Foreign direct investment 7 3.0 6 Aid 2.5 5 2.0 Remittances 4 Foreign direct investment 1.5 3 Aid 2 1.0 Remittances 1 Bonds 0.5 Bonds 0 Equity Equity 0.0 ­1 1990 1992 1994 1996 1998 2000 2002 2004 1990 1992 1994 1996 1998 2000 2002 2004 Source: World Bank staff estimates Source: World Bank staff estimates. countries. The increase in FDI inflows has been greatest in expected to rise to about $97 billion in 2006 and to reach Europe and Central Asia, followed by Latin America and the $128 billion in 2010 (box 6d). Caribbean and East Asia and Pacific. Although Sub-Saharan Africa has larger inflows than South Asia and the Middle Movement of people East and North Africa, FDI in Sub-Saharan Africa is domi- The movement of people across national borders is another nated by extractive industries, including oil and minerals. mark of integration. International tourist arrivals worldwide for Four countries--Angola, Chad, Nigeria, and Sudan--together 2005 exceeded 800 million--an all-time high--and these tour- received nearly half of Sub-Saharan Africa's FDI inflows in ists also spent considerable amounts of money on their trips. 2004 (table 6.8). Receipts from international tourists were 6.5 percent of exports Countries that have difficulty tapping financial markets in 2004 for middle-income countries and as high as 17 percent must rely largely on aid flows to fund development programs. for the Middle East and North Africa (table 6.15). In 2004 developing countries received official development Meanwhile, an estimated 190 million people (3 percent assistance and official aid totaling $85.5 billion, up from of the world's population) are living in countries in which $76.7 billion in 2003 and $56.6 billion in 2000. Aid to they were not born. International migration has enormous Afghanistan (up from $141 million in 2000 to $2.19 billion economic, social, and cultural implications in both origin and in 2004) and Iraq (up from $116 million in 2000 to $4.66 destination countries. billion in 2004) accounted for a large part of the overall Demographic trends in both developed and develop- increase in aid (table 6.11). ing countries point to significant potential gains from Even so, a substantial increase in aid flows and private migration. In many developed countries the population capital flows will be required to help developing countries is aging fast, while in many developing countries the achieve the Millennium Development Goals. For example, population is young and growing rapidly. This imbalance aid is the largest source of external finance for the countries is likely to create strong demand in developed country of Sub-Saharan Africa, but as a share of GDP aid declined labor markets for developing country workers, especially from more than 6 percent at the beginning of the 1990s to to provide services that can be supplied only locally. 4 percent at the end of the decade (figure 6c). It has since The share of immigrants in the total population of high- increased to 5 percent. income countries increased to 11 percent in 2005, up If measured as a share of donors' gross national income, from 7 percent two decades ago, while the shares for aid also declined sharply in the 1990s and rebounded some- low- and middle-income countries have remained about what after 2000. Only five rich countries have fulfilled the the same (figure 6e). UN official development assistance target of 0.7 of GNI: Den- The large wage gap between developed and developing mark, Luxembourg, the Netherlands, Norway, and Sweden. countries, especially for unskilled and semiskilled labor, If donor countries follow through on their promises at the indicates that migration from developing countries to devel- United Nations International Conference on Financing for oped countries can generate significant welfare gains. The Development, in Monterey, Mexico, in 2002 and at the more flow of formal remittances from migrants back to their coun- recent Group of Eight summit at Gleneagles, Scotland, aid is try of origin has been increasing rapidly and has become 2006 World Development Indicators 313 New promises of aid and debt relief At the Group of Eight (G-8) summit at Gleneagles, Scotland, in 2005 commitments were made to relieve poor countries of their debts, increase aid and make it more effective, remove trade barriers, improve governance, and build stronger development partnerships. Specific commitments included agreements to relieve 100 percent of the multilateral debts owed to the International Development Association, the African Development Bank, and the International Monetary Fund by all countries that have reached the completion point under the Heavily Indebted Poor Countries Debt Initiative. Also notable were EU commitments to spend at least 0.56 percent of gross national income on aid by 2010 and G-8 commitments to double aid to Africa. The Organisation for Economic Co-operation and Development estimates that if all 2005 commitments to increase aid are met, official develop- ment assistance from Development Assistance Committee countries alone will rise by $50 billion in real terms between 2004 and 2010, to nearly $130 billion. Aid commitments after the G-8 meeting at Gleneagles, Scotland 2004 2010 projection Real change in ODA compared with 2004 Net ODA ODA/GNI Net ODA ODA/GNI Amount Country ($ millions) (%) ($ millions) (%) ($ millions) Percent Denmark 2,037 0.85 2,185 0.80 148 7 France 8,473 0.41 14,110 0.61 5,638 67 Germany 7,534 0.28 15,509 0.51 7,975 106 Italy 2,462 0.15 9,262 0.51 6,801 276 Luxembourg 236 0.83 328 1.00 93 39 Netherlands 4,204 0.73 5,070 0.80 867 21 Spain 2,437 0.24 6,925 0.59 4,488 184 Sweden 2,722 0.78 4,025 1.00 1,303 48 United Kingdom 7,883 0.36 14,600 0.59 6,717 85 Other EU membersa 4,899 0.36 9,206 0.60 4,306 88 EU members, total 42,886 0.35 81,221 0.59 38,335 89 Canada 2,599 0.27 3,648 0.33 1,049 40 Japan 8,906 0.19 11,906 0.22 3,000 34 Norway 2,199 0.87 2,876 1.00 677 31 United States 19,705 0.17 24,000 0.18 4,295 22 Other DAC membersb 3,218 0.30 4,477 0.37 1,260 39 DAC members, total 79,512 0.26 128,128 0.36 48,616 61 a. Austria, Belgium, Finland, Greece, Ireland, and Portugal. b. Australia, New Zealand, and Switzerland. Source: OECD Journal on Development 2006. the largest source of foreign capital for many developing levels of human capital and skilled workers. The flight of countries. human capital, or "brain drain," may increase the con- Remittance flows have more than tripled since 1990, centration of poverty and reduce the beneficial impact of reaching $227.6 billion in 2004, with $161 billion going to globalization. A recent study estimates that highly skilled developing countries (table 6.14). Already twice the size of immigrants represented 34.6 percent of the OECD immi- foreign aid, remittances are expected to continue growing. gration stock in 2000, while only 11.3 percent of the world Empirical studies have found that remittances typically boost labor force had a tertiary education (Özden and Schiff income levels, especially for the poor, and may encourage 2006). The most affected areas are Sub-Saharan Africa investment in physical and human capital and help to buffer and small island economies in the Caribbean. Although the impact of negative shocks. Sub-Saharan Africa's emigration rate is not particularly Among these international migrants are millions of highly high, 13 percent of those who do migrate have a tertiary educated people who have moved to developed countries education. In Jamaica four of five trained doctors were from developing countries that already suffer from low employed outside the country. 314 2006 World Development Indicators Immigrant populations are expanding in high-income economies Immigrants in OECD countries are better educated Immigrants as a share of total population (%) Working-age immigrants by level of education, 1990 and 2000 (millions) 12 60 1985 Tertiary education 1995 Secondary education 10 2005 50 Primary education 8 40 6 30 4 20 2 10 0 Low- Middle- High- 0 income income income 1990 2000 Source: World Bank staff estimates based on United Nations Population Fund data. Source: Özden and Schiff 2005. 2006 World Development Indicators 315 Integration with the global economy Merchandise Trade Growth in real Gross private Foreign direct trade in services trade less growth capital flows investment in real GDP % of GDP % of GDP % of GDP percentage points % of GDP Net inflows Net outflows 1990 2004 1990 2004 1990­2004 1990 2004 1990 2004 1990 2004 Afghanistan .. 47.2 .. .. .. .. .. .. .. .. .. Albania 29.0 37.7 2.9 26.6 12.8 18.0 6.2 0.0 5.6 0.0 0.0 Algeria 36.6 59.7 2.9 .. 0.1 2.6 .. 0.1 1.0 0.0 .. Angola 53.5 104.4 18.7 26.3 .. 10.1 25.7 ­3.3 7.4 0.0 0.2 Argentina 11.6 37.1 3.9 7.8 4.1 8.2 15.6 1.3 2.7 0.0 0.2 Armenia .. 65.7 .. 18.3 ­7.6 .. 13.9 0.2 7.1 .. 0.1 Australia 26.3 30.7 7.7 8.1 3.0 9.3 32.0 2.6 6.7 0.3 2.8 Austria 54.8 80.5 22.7 32.6 3.3 9.6 41.9 0.4 1.4 1.0 2.5 Azerbaijan .. 83.7 .. 37.8 14.4 .. 100.4 0.0 41.7 .. 14.1 Bangladesh 17.6 35.7 3.6 5.3 4.3 0.9 1.9 0.0 0.8 0.0 0.0 Belarus .. 131.5 .. 12.2 ­1.9 .. 7.5 0.0 0.7 .. 0.0 Belgium 120.4 168.0 26.4a 40.9a 2.2 81.5a 222.2a 3.9a 30.9a 3.0a 26.4a Benin 30.0 37.7 13.9 12.0 ­2.3 10.7 5.7 3.4 1.5 0.0 0.1 Bolivia 33.1 45.3 9.4 11.3 1.5 3.1 5.0 0.6 1.3 0.0 0.0 Bosnia and Herzegovina .. 90.2 .. 15.0 ­3.8 .. 21.8 .. 7.2 .. 0.0 Botswana 98.4 75.9 15.4 17.0 ­2.3 9.0 20.6 2.5 0.5 0.2 2.7 Brazil 11.7 26.9 2.4 4.9 4.6 1.9 8.8 0.2 3.0 0.1 1.6 Bulgaria 48.9 100.9 6.9 30.6 5.8 39.2 29.6 0.0 8.3 0.0 ­0.9 Burkina Faso 22.0 33.2 9.1 .. ­1.5 1.0 .. 0.0 0.7 0.0 .. Burundi 27.0 34.0 12.9 8.5 6.8 3.7 6.2 0.1 0.5 0.0 0.0 Cambodia 22.4 122.2 5.7 25.3 9.8 3.2 8.1 0.0 2.7 .. 0.2 Cameroon 30.5 33.4 12.8 .. 2.0 15.5 .. ­1.0 0.0 0.1 .. Canada 43.7 61.0 8.3 10.7 3.3 8.1 14.0 1.3 0.6 0.9 4.8 Central African Republic 18.4 20.7 16.0 .. .. 2.2 .. 0.1 ­1.0 0.3 .. Chad 27.2 70.4 15.5 .. 4.5 5.6 .. 0.5 11.3 0.0 .. Chile 53.1 60.5 12.9 13.3 1.6 15.0 21.5 2.2 8.1 0.0 1.0 China 32.5 59.8 2.9 7.0 5.7 2.5 10.0 1.0 2.8 0.2 0.1 Hong Kong, China 221.5 330.4 .. 51.6 3.6 .. 145.5 .. 20.9 .. 24.4 Colombia 30.7 33.7 8.3 6.4 2.7 3.1 10.9 1.2 3.1 0.0 0.1 Congo, Dem. Rep. 43.5 49.6 .. .. 5.9 .. .. ­0.2 0.0 .. .. Congo, Rep. 57.2 129.4 31.0 17.7 3.4 6.6 11.7 0.8 0.0 0.0 0.0 Costa Rica 60.2 78.7 20.3 19.2 3.1 7.0 12.3 2.9 3.4 0.0 0.3 Côte d'Ivoire 47.9 66.3 20.5 17.8 0.7 3.5 5.2 0.4 1.1 0.0 0.0 Croatia 88.8 71.7 .. 38.6 4.0 .. 20.8 0.0 3.6 .. 1.0 Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic 83.6 129.1 .. 17.7 8.7 .. 19.6 0.2 4.2 .. 0.5 Denmark 52.6 60.1 17.3 28.9 3.4 15.1 38.0 0.8 ­3.6 1.1 ­4.1 Dominican Republic 73.2 72.8 21.7 25.4 3.4 5.0 13.5 1.9 3.5 0.0 0.0 Ecuador 44.2 51.2 13.0 9.0 1.9 11.0 13.1 1.2 3.8 0.0 0.0 Egypt, Arab Rep. 36.8 26.0 22.6 28.2 ­1.7 6.8 13.3 1.7 1.6 0.0 0.2 El Salvador 38.4 60.4 13.4 13.0 6.2 2.0 12.5 0.0 2.9 0.0 0.0 Eritrea 77.0 74.1 .. .. ­1.9 53.0 .. 0.0 3.2 .. .. Estonia .. 130.6 9.1 40.6 7.5 3.9 51.9 0.0 9.3 0.0 2.4 Ethiopia 16.0 46.5 7.7 24.6 3.6 1.6 4.0 0.1 6.8 0.0 .. Finland 39.1 60.3 9.0 11.9 4.4 17.4 42.1 0.6 1.7 2.0 ­0.8 France 36.4 44.7 11.1 10.2 4.2 20.2 26.1 1.1 1.2 2.8 2.3 Gabon 52.5 66.0 21.0 16.7 ­1.4 18.0 18.7 1.2 4.5 0.5 0.3 Gambia, The 69.1 53.5 34.5 .. ­3.3 0.9 .. 4.5 14.5 0.0 .. Georgia .. 48.0 .. 19.6 10.2 .. 12.4 0.0 9.6 .. 0.2 Germany 45.5 59.4 8.7 12.2 4.3 9.6 27.4 0.2 ­1.3 1.4 ­0.3 Ghana 35.7 77.8 6.6 19.9 3.1 2.9 6.8 0.3 1.6 0.0 0.0 Greece 33.2 33.0 11.4 23.0 3.7 3.9 32.3 1.2 0.7 0.0 0.3 Guatemala 36.8 39.1 9.7 9.0 2.9 2.9 11.6 0.6 0.6 0.0 0.0 Guinea 49.5 35.9 18.6 9.3 ­1.2 3.9 1.6 0.6 2.6 .. 0.0 Guinea-Bissau 43.0 59.6 11.0 17.7 4.0 23.0 14.4 0.8 1.8 0.0 0.2 Haiti 17.2 48.1 4.3 13.4 ­1.2 1.1 3.9 0.3 0.2 ­0.3 0.0 316 2006 World Development Indicators Integration with the global economy Merchandise Trade Growth in real Gross private Foreign direct trade in services trade less growth capital flows investment in real GDP % of GDP % of GDP % of GDP percentage points % of GDP Net inflows Net outflows 1990 2004 1990 2004 1990­2004 1990 2004 1990 2004 1990 2004 Honduras 57.9 74.0 11.7 19.1 ­0.4 7.2 8.0 1.4 4.0 0.0 0.0 Hungary 61.5 113.4 16.0 20.5 8.5 4.6 24.7 1.9 4.6 0.0 1.1 India 13.1 25.0 3.4 8.2 6.8 0.8 5.9 0.1 0.8 0.0 0.2 Indonesia 41.5 49.4 7.5 17.9 0.5 4.1 4.6 1.0 0.4 0.0 0.0 Iran, Islamic Rep. 32.9 48.4 3.7 .. ­7.3 2.6 .. ­0.3 0.3 0.0 .. Iraq 55.4 155.9 .. .. .. .. .. .. .. .. .. Ireland 93.9 90.8 18.2 64.4 5.7 22.2 314.1 1.3 6.1 0.8 8.7 Israel 55.0 69.6 18.1 23.5 1.1 6.5 18.7 0.3 1.4 0.4 2.7 Italy 32.0 41.7 8.8 9.9 2.7 10.6 10.4 0.6 1.0 0.7 1.1 Jamaica 67.2 58.2 37.5 45.4 .. 8.4 45.5 3.0 6.8 0.0 0.7 Japan 17.2 22.1 4.1 5.0 2.7 5.4 14.4 0.1 0.2 1.7 0.7 Jordan 91.1 104.9 67.5 36.9 ­1.6 6.3 18.1 0.9 5.4 ­0.8 0.0 Kazakhstan .. 80.7 .. 17.1 ­2.6 .. 41.8 0.0 10.1 .. ­3.1 Kenya 37.9 45.0 21.4 14.1 2.7 3.5 7.2 0.7 0.3 0.0 0.0 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 51.1 70.4 7.5 13.5 6.3 5.3 8.5 0.3 1.2 0.4 0.7 Kuwait 59.8 73.1 25.2 19.6 ­3.2 19.3 35.8 0.0 0.0 1.3 3.4 Kyrgyz Republic .. 75.3 .. 19.9 ­1.5 .. 17.2 0.0 3.5 .. 2.0 Lao PDR 30.5 35.4 5.8 .. .. 3.7 .. 0.7 0.7 0.0 .. Latvia .. 80.7 9.2 21.8 5.1 2.3 43.1 0.0 5.1 0.0 0.8 Lebanon 106.5 51.2 .. .. ­0.7 .. .. 0.2 1.3 .. .. Lesotho 119.3 162.0 19.8 12.2 ­0.5 9.6 14.2 2.8 9.4 0.0 0.0 Liberia 374.1 230.6 .. .. .. .. .. 58.6 4.1 .. .. Libya 64.2 91.0 5.2 7.6 .. 7.3 24.5 .. .. 0.4 ­0.7 Lithuania .. 96.8 .. 18.3 8.2 .. 19.4 0.0 3.5 .. 1.2 Macedonia, FYR 103.8 84.7 .. 16.2 4.6 .. 11.6 0.0 2.9 .. 0.0 Madagascar 31.5 50.9 12.8 14.9 2.1 1.8 1.1 0.7 1.0 0.0 0.0 Malawi 52.7 65.6 16.2 14.0 ­2.1 3.2 3.1 1.2 0.9 0.0 .. Malaysia 133.4 195.9 21.2 29.9 2.9 10.3 22.6 5.3 3.9 0.0 1.3 Mali 39.7 50.2 19.0 16.3 2.0 2.0 8.4 0.2 3.7 0.0 0.0 Mauritania 84.1 52.8 16.0 .. ­5.7 48.8 .. 0.7 19.6 0.0 .. Mauritius 118.0 79.2 38.0 41.1 0.1 8.0 6.5 1.7 0.2 0.0 0.5 Mexico 32.1 58.5 7.0 5.0 8.1 9.2 6.9 1.0 2.6 0.0 0.5 Moldova .. 106.4 .. 27.4 11.4 .. 16.9 0.0 3.1 .. 0.1 Mongolia .. 116.0 .. 52.2 15.5 .. 26.1 .. 5.8 .. 0.0 Morocco 43.4 54.7 13.4 20.3 1.9 5.5 7.6 0.6 1.5 0.0 0.0 Mozambique 40.8 57.1 12.5 12.9 2.4 0.4 7.9 0.4 4.0 0.0 0.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 95.6 74.7 20.7 15.2 0.2 16.5 23.5 .. .. 0.1 ­0.4 Nepal 24.1 39.2 10.2 12.4 .. 3.5 6.8 0.2 0.0 0.0 .. Netherlands 87.5 117.0 20.0 24.7 3.4 29.8 66.6 3.6 0.1 4.7 3.0 New Zealand 43.3 44.0 13.3 14.9 2.1 17.8 15.4 4.0 2.3 3.7 ­0.8 Nicaragua 95.9 65.2 17.0 15.1 5.5 9.0 6.1 0.1 5.5 0.0 0.0 Niger 27.0 30.2 10.9 9.4 .. 2.8 2.3 1.7 0.0 0.0 0.0 Nigeria 67.5 48.2 10.3 11.5 1.5 5.9 11.0 2.1 2.6 0.0 .. Norway 52.8 51.9 21.6 20.1 1.3 11.9 31.8 0.9 0.2 1.3 0.8 Oman 70.1 91.4 6.7 14.7 2.5 3.5 8.6 1.2 ­0.1 0.0 0.0 Pakistan 32.6 32.6 8.8 8.4 ­0.8 4.2 3.5 0.6 1.2 0.0 0.1 Panama 35.4 32.6 33.5 30.4 ­3.7 106.6 39.0 2.6 7.4 0.0 0.0 Papua New Guinea 73.6 107.8 18.9 .. .. 5.7 .. 4.8 0.7 0.0 .. Paraguay 43.9 58.3 16.2 12.6 ­3.3 5.4 3.4 1.5 1.3 0.0 0.1 Peru 22.3 33.0 7.5 6.8 3.2 3.2 6.8 0.2 2.6 0.0 0.0 Philippines 47.8 97.0 11.3 11.2 2.6 4.4 13.7 1.2 0.6 0.0 0.5 Poland 43.9 67.7 10.3 10.7 7.5 11.0 18.1 0.2 5.2 0.0 0.3 Portugal 58.3 54.1 12.7 14.6 3.2 11.4 37.6 3.7 0.5 0.2 3.6 Puerto Rico .. .. .. .. ­0.5 .. .. .. .. .. .. 2006 World Development Indicators 317 Integration with the global economy Merchandise Trade Growth in real Gross private Foreign direct trade in services trade less growth capital flows investment in real GDP % of GDP % of GDP % of GDP percentage points % of GDP Net inflows Net outflows 1990 2004 1990 2004 1990­2004 1990 2004 1990 2004 1990 2004 Romania 32.8 76.7 3.6 10.2 8.5 2.9 14.2 0.0 7.4 0.0 0.1 Russian Federation .. 48.1 .. 9.3 3.3 .. 16.0 0.0 2.1 .. 1.8 Rwanda 15.4 20.8 6.6 18.3 ­0.6 2.8 1.0 0.3 0.4 0.0 0.0 Saudi Arabia 58.6 68.2 21.8 12.6 .. 8.8 20.0 .. .. 0.0 0.0 Senegal 34.7 54.5 20.9 15.5 2.1 4.8 4.9 1.0 0.9 ­0.2 0.0 Serbia and Montenegro .. 65.6 .. .. .. .. .. .. 4.0 .. .. Sierra Leone 44.2 39.5 20.9 14.0 .. 11.0 6.8 4.9 2.4 0.0 0.0 Singapore 307.6 321.5 58.1 76.6 .. 54.2 116.9 15.1 15.0 5.5 9.9 Slovak Republic 110.8 138.8 .. 19.4 6.8 .. 15.5 0.0 2.7 .. 0.1 Slovenia 102.4 102.6 18.0 18.8 1.9 3.4 22.4 0.9 2.6 0.0 1.7 Somalia .. .. 11.2 .. .. 21.3 .. 0.6 .. 0.0 .. South Africa 37.4 48.5 6.4 8.3 2.6 2.2 8.6 ­0.1 0.3 0.0 0.7 Spain 27.2 41.1 8.4 13.7 5.7 11.0 30.8 2.7 1.6 0.7 4.8 Sri Lanka 57.3 68.5 13.4 17.1 2.5 13.1 5.2 0.5 1.2 0.0 0.0 Sudan 7.5 37.2 3.0 5.3 4.8 0.3 10.4 ­0.2 7.2 0.0 0.0 Swaziland 138.2 162.8 32.4 43.1 1.1 10.7 7.0 3.4 2.9 0.9 0.1 Sweden 46.6 64.0 12.8 20.7 4.2 33.9 44.8 0.8 ­0.2 6.1 4.4 Switzerland 56.6 64.4 12.8 18.8 2.9 28.1 54.8 2.4 ­0.2 2.3 7.3 Syrian Arab Republic 53.7 46.7 14.3 19.1 2.8 18.0 1.6 0.6 1.1 0.0 0.0 Tajikistan .. 110.5 .. 16.2 4.5 .. 16.1 0.0 13.1 .. 0.0 Tanzania 31.9 35.3 9.8 17.6 ­1.0 0.2 3.5 0.0 2.3 0.0 0.0 Thailand 65.7 119.2 14.9 26.1 2.9 13.5 7.9 2.9 0.9 0.2 0.1 Togo 52.1 88.4 24.1 17.0 ­1.3 9.6 14.8 1.1 2.9 0.0 ­0.4 Trinidad and Tobago 60.6 89.6 15.9 10.1 3.7 11.4 25.2 2.2 8.0 0.0 ­2.1 Tunisia 73.5 79.6 20.6 19.9 ­0.1 9.5 6.6 0.6 2.1 0.0 0.0 Turkey 23.4 53.1 7.4 11.7 7.0 4.3 12.8 0.5 0.9 0.0 0.3 Turkmenistan .. 116.6 .. .. 8.5 .. .. .. .. .. .. Uganda 10.2 31.2 4.5 16.8 2.8 1.1 4.8 ­0.1 3.3 0.0 0.0 Ukraine .. 95.1 .. 17.6 3.6 .. 34.2 0.0 2.6 .. 0.0 United Arab Emirates 103.2 125.1 .. .. 1.9 .. .. .. .. .. .. United Kingdom 41.2 38.1 10.6 15.4 3.4 35.3 91.7 3.4 3.4 2.0 3.8 United States 15.8 20.0 4.6 5.4 4.0 5.6 20.0 0.8 0.9 0.6 2.2 Uruguay 32.7 45.9 9.2 12.6 2.1 12.7 22.0 0.4 2.4 0.0 0.1 Uzbekistan .. 64.1 .. .. ­1.2 .. .. 0.0 1.2 .. .. Venezuela, RB 52.8 44.7 7.9 5.3 0.6 51.6 16.2 1.0 1.4 0.8 ­0.3 Vietnam 79.7 125.4 .. 19.0 14.6 .. 5.8 2.8 3.6 .. 0.0 West Bank and Gaza .. .. .. .. ­3.1 .. .. .. .. .. .. Yemen, Rep. 46.9 65.0 16.3 11.1 1.7 16.2 1.6 ­2.7 1.1 .. 0.0 Zambia 76.9 68.8 15.0 .. 1.4 64.7 .. 6.2 6.2 0.0 .. Zimbabwe 40.7 86.7 8.6 .. 5.7 1.7 .. ­0.1 1.3 0.0 .. World 32.4 w 44.9 w 7.8 w 10.5 w 10.3 w 28.4 w 1.0 w 1.6 w 1.2 w 2.1 w Low income 24.1 37.8 6.5 9.4 2.7 6.9 0.4 1.4 0.0 0.2 Middle income 34.4 61.5 7.1 10.2 6.6 12.2 0.8 2.8 0.1 0.5 Lower middle income 31.5 57.5 6.2 10.3 4.3 10.7 0.7 2.7 0.1 0.3 Upper middle income 38.3 67.0 8.1 10.2 8.0 14.2 1.0 2.8 0.3 0.7 Low & middle income 32.5 58.1 7.0 10.3 5.9 11.9 0.7 2.6 0.1 0.5 East Asia & Pacific 47.0 71.1 7.3 9.6 5.0 9.4 1.6 2.5 0.2 0.1 Europe & Central Asia 49.7 70.9 7.1 13.4 5.3 18.8 0.3 3.5 0.0 0.9 Latin America & Carib. 23.3 44.6 5.8 6.9 8.0 10.4 0.8 3.0 0.1 0.7 Middle East & N. Africa 42.9 55.1 9.1 .. 4.9 .. 0.3 1.1 0.0 .. South Asia 16.5 27.9 4.2 8.2 1.4 5.4 0.1 0.8 0.0 0.2 Sub-Saharan Africa 42.4 54.7 11.0 12.1 5.1 9.5 0.4 2.2 0.0 0.3 High income 32.3 41.5 8.0 10.5 11.0 32.0 1.0 1.3 1.4 2.4 Europe EMU 44.4 59.4 11.1 14.8 13.4 41.3 1.1 1.3 1.7 2.6 a. Includes Luxembourg. 318 2006 World Development Indicators Integration with the global economy About the data Definitions The growing integration of societies and economies This year the table includes net inflows and outflows · Merchandise trade is the sum of merchandise has helped reduce poverty in many countries. One of foreign direct investment based on balance of pay- exports and imports divided by the value of GDP, all indication of increasing global economic integration ments data reported by the International Monetary in current U.S. dollars. · Trade in services is the sum is the growing importance of trade in the world econ- Fund (IMF), supplemented by staff estimates using of services exports and imports divided by the value omy. Another is the increasing size and importance data reported by the United Nations Conference on of GDP, all in current U.S. dollars. · Growth in real of private capital flows to developing countries that Trade and Development and official national sources. trade less growth in real GDP is the difference have liberalized their financial markets. The internationally accepted definition of foreign between annual growth in trade of goods and ser- The table presents standardized measures of direct investment is provided in the fifth edition of vices and annual growth in GDP. Growth rates are the size of trade and capital flows relative to gross the IMF's Balance of Payments Manual (1993). For calculated using constant price series taken from domestic product (GDP). The numerators on trade a more detailed explanation of foreign direct invest- national accounts and are expressed as a percent- and private capital flows are based on gross flows ment, see About the data for table 6.8. age. · Gross private capital flows are the sum of the that capture the two-way flow of goods, services, Foreign direct investment may be understated in absolute values of direct, portfolio, and other invest- and capital. In conventional balance of payments many developing countries. Some countries fail to ment inflows and outflows recorded in the balance of accounting exports are recorded as a credit and report reinvested earnings, and the definition of long- payments financial account, excluding changes in the imports as a debit. And in the financial account term loans differs among countries. Underreporting assets and liabilities of monetary authorities and inward investment is a credit and outward investment of FDI outflows is more pervasive, particularly when general government. The indicator is calculated as a a debit. Thus net flows, the sum of credits and deb- investors are attempting to avoid controls on capital ratio to GDP in U.S. dollars. · Foreign direct invest- its, represent a balance in which many transactions and foreign exchange or high taxes on investment ment net inflows are the net inflows of investment to are canceled out. Gross flows are a better measure income. Some countries do not identify FDI outflows acquire a lasting management interest in an enter- of integration because they show the total value of in their balance of payments statistics. However, the prise operating in an economy other than that of the financial transactions during a given period. quality and coverage of the data are improving as investor. It is the sum of equity capital, reinvestment Merchandise trade and trade in services (exports a result of continuous efforts by international and of earnings, and other short- and long-term capital, and imports) are shown relative to total GDP. Merchan- national statistics agencies. as shown in the balance of payments. This series dise trade is an important part of global trade. Trade in Trade and capital flows are converted to U.S. dol- shows net inflows in the reporting economy and is services (such as transport, travel, finance, insurance, lars at the IMF's average official exchange rate for divided by the value of GDP. · Foreign direct invest- royalties, construction, communications, and cultural the year shown. An alternative conversion factor is ment net outflows are the net outflows of investment services) is an increasingly important element of applied if the official exchange rate diverges by an from the reporting economy to the rest of the world. global integration. The difference between the growth exceptionally large margin from the rate effectively of real trade in goods and services and the growth of applied to transactions in foreign currencies and GDP helps to identify economies that have integrated traded products. with the global economy by liberalizing trade, lowering barriers to foreign investment, and harnessing their abundant labor to gain a competitive advantage in labor-intensive manufactures and services. Data sources Data on merchandise trade are from the World Trade Organization. Data on GDP are from the World Bank's national accounts files, converted Trade in services is becoming increasingly important from national currencies to U.S. dollars using the Share of GDP (%) 1990 2004 official exchange rate, supplemented by an alter- 70 Ireland native conversion factor if the official exchange 60 rate is judged to diverge by an exceptionally 50 large margin from the rate effectively applied 40 to transactions in foreign currencies and traded 30 Bulgaria products. Data on trade in services are from the Cambodia 20 Honduras Ghana IMF's Balance of Payments database. Data on real India trade and GDP growth are from the World Bank's 10 national accounts files. Gross private capital 0 East Asia Europe & Latin America South Sub-Saharan High- flows and foreign direct investment are reported & Pacific Central Asia & Caribbean Asia Africa income in the World Bank Debtor Reporting System and Although services trade makes up only 20 percent of global trade, it is rising fast in some regions. Some countries are calculated using mainly the IMF's Balance of have benefited considerably from the fast-growing services trade. Payments database. Source: International Monetary Fund Balance of Payments database. 2006 World Development Indicators 319 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1980­90 1990­2004 1980­90 1990­2004 1980­90 1990­2004 1980­90 1990­2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. Albaniaa .. .. .. .. .. 14.6 .. 16.6 .. .. Algeria 4.7 2.7 ­8.0 1.7 ­3.1 5.8 ­2.7 1.2 74 126 Angola 9.0 5.8 ­1.9 8.2 6.4 9.6 0.7 9.3 94 121 Argentina 2.1 6.9 ­9.6 6.4 2.1 7.8 ­6.5 5.3 64 110 Armeniaa .. .. .. .. .. 1.3 .. 3.6 .. .. Australiaa 6.3 5.5 6.0 7.6 6.6 4.8 6.4 6.0 116 116 Austriaa 6.6 .. 5.7 .. 10.2 6.3 8.7 4.6 .. .. Azerbaijana .. .. .. .. .. 8.6 .. 8.2 .. .. Bangladesh 7.1 10.5 1.8 4.1 7.8 12.4 3.6 8.2 117 88 Belarusa .. .. .. .. .. 13.2 .. 13.2 .. .. Belgiuma .. 5.8 .. 5.4 .. 6.2 .. 6.4 106 99 Benin 11.8 3.0 ­9.9 5.9 18.8 3.6 ­4.9 6.3 107 93 Bolivia 3.1 4.9 ­1.3 5.2 ­1.9 5.6 ­0.3 5.8 102 108 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 14.8 3.8 11.5 2.3 18.8 3.4 11.1 1.3 98 92 Brazil 6.3 6.7 0.7 10.6 5.1 6.6 ­1.9 7.6 66 110 Bulgariaa .. .. .. .. ­12.3 4.8 ­14.0 8.2 .. .. Burkina Faso ­0.3 12.1 3.8 5.0 7.9 10.5 4.3 5.2 119 97 Burundi 3.5 8.0 1.0 6.3 2.5 ­6.3 2.2 ­3.9 128 84 Cambodia .. .. .. .. .. .. .. .. .. .. Cameroon 7.0 3.3 4.8 7.2 1.4 2.7 0.1 4.6 81 112 Canadaa 6.4 6.9 7.4 7.2 6.8 6.7 7.9 6.1 97 107 Central African Republic 0.0 14.7 4.2 1.6 3.5 1.7 7.9 ­1.3 238 99 Chad 8.6 7.7 11.0 13.0 9.4 7.8 12.6 13.6 112 101 Chile 9.2 9.4 ­3.0 6.8 8.1 8.2 2.8 7.1 114 115 China 13.6 15.6 11.9 15.1 12.8 15.5 13.5 15.4 102 92 Hong Kong, China 15.4 7.4 13.7 7.5 16.8 6.5 15.0 6.7 100 99 Colombia 7.9 4.4 ­2.1 6.2 7.7 5.9 0.0 6.6 81 93 Congo, Dem. Rep. 9.7 4.4 12.2 11.0 2.7 ­3.3 3.1 1.5 86 94 Congo, Rep. 7.4 4.5 3.3 6.7 2.1 8.6 5.3 6.1 63 121 Costa Rica 3.8 10.1 5.2 11.8 4.6 11.3 4.4 11.0 75 102 Côte d'Ivoire 2.6 4.6 ­2.1 0.7 1.7 6.0 ­1.5 3.0 143 121 Croatiaa .. .. .. .. .. 3.5 .. 9.1 .. .. Cuba .. .. .. .. .. .. .. .. .. .. Czech Republica .. .. .. .. .. 12.5 .. 11.7 .. .. Denmarka 4.1 5.1 3.1 5.1 9.0 4.1 6.8 4.2 102 102 Dominican Republic ­0.9 3.5 2.9 8.3 ­2.1 4.2 5.4 8.6 96 95 Ecuador 7.1 4.7 ­1.8 7.6 ­0.4 6.2 ­1.3 8.8 114 108 Egypt, Arab Rep. 13.4 3.4 8.1 ­0.5 7.3 4.2 12.6 2.0 101 107 El Salvador ­4.6 3.2 4.6 6.6 ­4.6 6.9 2.4 9.4 84 91 Eritrea .. ­2.8 .. 8.1 .. ­4.2 .. 7.2 99 93 Estoniaa .. .. .. .. .. 16.0 .. 16.7 .. .. Ethiopia ­1.0 9.7 4.0 9.6 ­1.1 7.6 4.3 10.2 121 91 Finlanda 2.3 9.3 4.4 4.3 7.4 6.5 6.9 5.3 111 90 Francea 3.6 3.6 3.7 3.6 7.5 4.2 6.5 4.1 103 111 Gabon 2.5 5.8 ­3.5 1.3 ­3.9 2.5 1.1 1.1 157 125 Gambia, The 2.2 ­9.3 ­6.0 ­0.8 6.6 ­9.1 2.5 ­1.3 100 115 Georgia .. .. .. .. .. 12.5 .. 9.6 .. .. Germanya, b 4.5 5.9 4.9 4.3 9.2 5.0 7.1 4.0 110 107 Ghana ­17.2 4.7 ­19.3 6.5 ­2.7 6.6 0.6 6.6 100 123 Greecea 5.0 9.1 6.4 9.2 5.8 2.8 6.6 5.6 99 103 Guatemala ­1.1 6.8 0.1 9.8 ­2.2 6.7 0.6 10.7 115 93 Guinea .. 3.2 .. 2.5 4.0 0.2 9.7 ­0.5 122 106 Guinea-Bissau ­2.0 14.0 ­0.3 ­4.1 4.2 11.7 5.2 ­3.1 146 94 Haiti ­0.4 11.6 ­4.6 10.8 ­1.2 11.2 ­2.9 12.4 132 87 Data for Taiwan, China 26.1 2.8 30.3 3.3 14.9 5.8 12.4 6.4 97 92 320 2006 World Development Indicators Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1980­90 1990­2004 1980­90 1990­2004 1980­90 1990­2004 1980­90 1990­2004 1990 2004 Honduras 4.1 2.7 1.6 10.5 1.6 4.9 0.6 11.6 78 90 Hungarya 3.4 11.6 1.3 11.9 1.6 13.8 0.1 13.8 111 99 India 4.2 11.5 4.7 10.8 7.3 9.7 4.2 10.3 86 76 Indonesia 7.6 5.9 0.3 3.3 ­1.3 6.5 2.6 3.2 95 104 Iran, Islamic Rep. .. .. .. .. 7.2 4.8 0.2 0.8 .. .. Iraq .. .. .. .. 2.0 23.6 ­2.8 1.5 .. .. Irelanda 9.3 12.8 4.8 9.0 12.8 12.0 7.0 8.9 106 97 Israela 6.9 8.6 5.8 6.6 8.3 9.3 5.9 6.4 89 95 Italya 4.3 3.2 5.3 3.6 8.7 4.5 6.9 4.4 94 103 Jamaica ­1.0 1.4 .. .. 1.1 0.8 2.8 5.8 .. .. Japana 5.0 2.6 6.6 4.8 8.9 3.3 5.1 4.2 105 92 Jordan 7.7 7.3 1.2 4.3 6.2 8.4 ­1.9 6.4 94 87 Kazakhstana .. .. .. .. .. 14.9 .. 9.9 .. .. Kenya 1.7 5.0 2.5 6.1 ­1.1 5.5 1.7 5.5 70 91 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 12.4 14.9 11.8 8.9 15.0 8.8 11.9 6.9 133 85 Kuwait .. .. .. .. ­7.7 13.2 ­4.1 5.6 .. .. Kyrgyz Republica .. .. .. .. .. 4.8 .. 4.8 .. .. Lao PDRa .. .. .. .. 11.0 9.2 6.6 6.0 .. .. Latviaa .. 7.5 .. .. .. 11.7 .. 16.5 .. .. Lebanon .. .. .. .. ­5.2 8.2 ­5.5 8.5 .. .. Lesotho 7.2 16.0 3.9 2.8 3.7 14.7 3.5 1.6 100 91 Liberia .. .. .. .. .. .. .. .. .. .. Libya 2.8 ­3.3 ­1.8 ­1.3 ­7.4 ­1.7 ­4.4 ­1.9 .. .. Lithuaniaa .. .. .. .. .. 13.8 .. 15.0 .. .. Macedonia, FYRa .. .. .. .. .. 2.4 .. 5.5 .. .. Madagascar ­2.5 4.6 ­6.2 4.3 ­1.2 7.9 ­4.3 5.8 81 82 Malawi 2.4 3.0 ­0.1 ­0.3 2.0 0.8 3.3 1.0 148 82 Malaysia 4.8 11.4 8.5 8.5 8.6 9.3 7.7 7.2 103 99 Mali 4.4 10.9 3.0 6.2 6.0 8.6 2.7 5.7 135 113 Mauritania 3.9 1.2 ­3.1 3.9 8.0 ­3.0 ­2.1 0.3 97 95 Mauritius 11.5 2.3 11.7 2.3 14.4 3.4 12.9 3.2 93 89 Mexico 15.3 12.4 0.9 11.1 5.9 12.9 6.4 11.6 102 98 Moldovaa .. .. .. .. .. 2.7 .. 6.0 .. .. Mongolia .. .. .. .. 5.0 3.3 5.5 4.9 .. .. Morocco 5.7 6.6 3.2 7.6 6.2 6.5 3.6 6.0 85 98 Mozambique ­9.5 21.5 ­2.7 2.3 ­9.6 16.3 0.1 3.1 175 94 Myanmar ­8.4 18.5 ­18.1 8.6 ­7.6 16.7 ­4.7 14.8 252 102 Namibia .. 1.5 .. 4.7 .. 0.3 .. 1.7 93 97 Nepala .. .. .. .. 8.1 8.7 6.9 6.8 .. .. Netherlandsa 4.4 6.0 4.3 5.5 4.6 5.6 4.4 5.2 101 96 New Zealanda 3.5 4.3 4.4 5.9 6.2 4.2 5.4 5.5 105 110 Nicaragua ­4.8 8.6 ­3.5 7.2 ­5.8 7.7 ­3.1 9.3 155 91 Niger ­5.2 1.4 ­5.2 ­0.9 ­5.4 0.8 ­3.5 1.4 165 131 Nigeria ­4.4 1.9 ­20.8 5.2 ­8.4 5.7 ­15.0 5.8 89 122 Norwaya 4.2 5.3 3.5 6.5 5.3 6.2 6.2 3.7 67 100 Oman 11.2 2.4 .. .. 3.3 7.4 0.7 6.5 .. 136 Pakistan 8.0 4.7 2.7 2.2 8.1 4.9 3.0 3.7 109 85 Panama ­0.5 5.1 ­6.7 4.2 ­0.5 7.0 ­3.6 5.3 69 94 Papua New Guinea ­0.6 ­6.8 .. .. 4.9 1.8 0.7 ­0.7 .. 113 Paraguay 12.8 1.9 10.4 1.6 11.6 3.6 4.2 2.7 103 112 Peru 2.7 10.0 ­2.0 5.4 ­1.5 8.9 1.3 5.9 114 109 Philippines 19.5 13.8 21.0 9.9 3.9 13.6 2.9 8.8 87 84 Polanda 4.8 10.9 1.5 14.7 1.4 11.8 ­3.2 15.0 92 107 Portugala 11.9 0.1 15.1 ­0.2 15.1 4.8 10.3 4.3 103 102 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 321 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1980­90 1990­2004 1980­90 1990­2004 1980­90 1990­2004 1980­90 1990­2004 1990 2004 Romaniaa .. .. .. .. ­4.0 11.0 ­3.8 10.3 .. .. Russian Federationa .. .. .. .. .. 9.8 .. 5.0 .. .. Rwanda 2.6 ­2.6 1.8 ­0.2 ­0.9 ­0.5 2.7 ­0.9 40 89 Saudi Arabia ­8.3 1.1 .. .. ­12.7 5.6 ­6.1 2.3 .. 135 Senegal 1.2 10.5 0.4 6.1 3.5 4.7 1.4 5.8 172 96 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. 78 Singapore 12.2 9.7 8.6 5.9 9.9 7.5 8.0 5.5 116 89 Slovak Republica .. .. .. .. .. 13.0 .. 12.8 .. .. Sloveniaa .. .. .. .. .. 7.9 .. 8.3 .. .. Somalia .. .. .. .. .. .. .. .. .. .. South Africa 1.6 4.7 ­0.9 7.1 0.7 3.5 ­1.3 5.6 104 120 Spain 2.5 9.7 8.8 8.9 10.8 8.1 10.6 7.2 100 102 Sri Lanka 4.6 5.9 2.1 6.4 5.4 7.7 2.7 6.6 82 104 Sudan .. .. .. .. .. .. .. .. .. 121 Swaziland 7.6 4.0 2.4 2.0 4.7 4.6 ­0.5 3.1 100 94 Swedena 4.4 7.6 5.0 5.4 8.0 5.0 6.7 4.2 108 92 Switzerlanda 3.7 .. 4.3 .. 9.5 3.6 8.8 2.8 .. .. Syrian Arab Republic 19.6 3.4 .. .. 2.4 4.5 ­8.4 3.1 .. 113 Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania .. 8.6 .. 2.0 ­5.1 8.7 ­0.5 2.7 107 100 Thailand 13.8 8.9 11.1 2.8 14.0 8.8 12.7 5.2 119 92 Togo ­1.2 12.6 0.7 0.3 1.1 7.1 2.0 6.3 133 30 Trinidad and Tobago 0.1 .. ­2.4 .. ­9.4 9.2 ­12.3 11.1 .. .. Tunisia 3.0 6.6 1.6 5.6 3.5 6.5 2.7 5.5 109 99 Turkey 19.4 11.6 15.6 9.6 14.0 10.2 9.3 9.4 109 102 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda ­13.5 14.4 ­6.8 13.2 ­4.0 9.8 4.5 12.7 146 88 Ukrainea .. .. .. .. .. 9.5 .. 7.9 .. .. United Arab Emirates .. .. .. .. ­0.8 8.1 0.7 10.7 .. .. United Kingdoma .. .. .. .. 5.9 4.3 8.5 5.1 101 105 United Statesa 3.6 4.8 7.2 8.0 5.7 5.1 8.2 8.3 101 101 Uruguay 4.4 3.4 ­0.5 3.4 4.5 2.9 ­1.2 3.5 116 108 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 3.4 2.4 ­4.1 1.6 ­4.4 5.7 ­3.2 2.5 90 108 Vietnam .. .. .. .. .. 15.8 .. 13.7 .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. ­7.2 6.3 ­3.2 17.0 ­5.0 2.9 .. 117 Zambia ­0.5 6.5 2.0 6.6 0.9 ­0.1 0.0 4.5 207 119 Zimbabwe 3.6 7.7 3.4 7.2 2.5 2.6 ­0.5 1.9 98 104 a. Data are from the International Monetary Fund's International Financial Statistics database. b. Data prior to 1990 refer to the Federal Republic of Germany before unification. 322 2006 World Development Indicators Growth of merchandise trade About the data Data on international trade in goods are available for countries that are landlocked and those whose 2000 base year. Terms of trade were computed from from each country's balance of payments and cus- territorial boundaries are porous. As a result, it is the same indicators. toms records. While the balance of payments focuses necessary to estimate their trade from the data The terms of trade measures the relative prices of on the financial transactions that accompany trade, reported by their partners. (For further discussion a country's exports and imports. There are a number customs data record the direction of trade and the of the use of partner country reports, see About the of ways to calculate terms of trade. The most com- physical quantities and value of goods entering or data for table 6.3.) Countries that belong to common mon is the net barter (or commodity) terms of trade leaving the customs area. Customs data may dif- customs unions may need to collect data through index, constructed as the ratio of the export price fer from data recorded in the balance of payments direct inquiry of companies. In some cases economic index to the import price index. When a country's because of differences in valuation and the time of or political concerns may lead national authorities net barter terms of trade index increases, its exports recording. The 1993 System of National Accounts to suppress or misrepresent data on certain trade become more valuable or its imports cheaper. and the fifth edition of the International Monetary flows, such as oil, military equipment, or the exports Fund's (IMF) Balance of Payments Manual (1993) of a dominant producer. In other cases reported trade Definitions attempted to reconcile the definitions and reporting data may be distorted by deliberate under- or over- · Export and import volumes are average annual standards for international trade statistics, but dif- invoicing to effect capital transfers or avoid taxes. growth rates calculated for low- and middle-income ferences in sources, timing, and national practices And in some regions smuggling and black market economies from UNCTAD's quantum index series limit comparability. Real growth rates derived from trading result in unreported trade flows. and for high-income economies from export and trade volume indexes and terms of trade based on By international agreement customs data are import data deflated by the IMF's trade price defla- unit price indexes may therefore differ from those reported to the United Nations Statistics Division, tors. · Export and import values are average annual derived from national accounts aggregates. which maintains the Commodity Trade (Comtrade) growth rates calculated from UNCTAD's value indexes Trade in goods, or merchandise trade, includes all database. The United Nations Conference on Trade or from current values of merchandise exports and goods that add to or subtract from an economy's and Development (UNCTAD) compiles a variety of imports. · Net barter terms of trade index is cal- material resources. Thus the total supply of goods in international trade statistics, including price and culated as the ratio of the export price index to the an economy is made up of gross output plus imports volume indexes, based on the Comtrade data. The corresponding import price index measured relative less exports (currency in circulation, titles of owner- IMF and the World Trade Organization also compile to the base year 2000. ship, and securities are excluded, but nonmonetary data on trade prices and volumes. The growth rates gold is included). Trade data are collected on the and terms of trade for low- and middle-income econo- basis of a country's customs area, which in most mies shown in the table were calculated from index cases is the same as its geographic area. Goods numbers compiled by UNCTAD. Volume measures provided as part of foreign aid are included, but for high-income economies were derived by deflat- goods destined for extraterritorial agencies (such ing the value of trade using deflators from the IMF's as embassies) are not. International Financial Statistics. In some cases Collecting and tabulating trade statistics are dif- price and volume indexes from different sources ficult. Some developing countries lack the capacity may vary significantly as a result of differences in to report timely data. This is a problem especially estimation procedures. All indexes are rescaled to a Exports are growing in developing countries Average annual growth in export volume (%) 30 1980­90 1990­2000 25 2000­04 20 15 10 5 Data sources 0 The main source of trade data for developing coun- ­5 Nigeria Ethiopia Burkina Senegal South Lesotho Sub-Saharan tries is UNCTAD's annual Handbook of Statistics. The Faso Africa Africa IMF's International Financial Statistics includes data Sub-Saharan countries have reversed the decline of exports in goods since 1990 and experienced on the export and import values and deflators for encouraging growth over the last five years. high-income and selected developing economies. Source: United Nations Conference on Trade and Development. 2006 World Development Indicators 323 Direction and growth of merchandise trade Direction of trade High-income importers % of world trade, 2004 European United Other high- All high- Union Japan States income income Source of exports High-income economies 30.7 2.7 9.9 12.5 55.8 European Union 24.3 0.6 3.3 3.8 32.1 Japan 1.0 .. 1.5 2.0 4.5 United States 1.9 0.6 .. 3.7 6.3 Other high-income economies 3.4 1.5 5.2 2.9 13.0 Low- and middle-income economies 7.7 1.9 6.2 5.3 21.0 East Asia & Pacific 1.8 1.5 2.2 3.9 9.3 China 1.1 0.8 1.4 2.2 5.6 Europe & Central Asia 3.6 0.1 0.3 0.4 4.3 Russian Federation 0.8 0.0 0.1 0.2 1.1 Latin America & Caribbean 0.7 0.1 2.9 0.3 4.0 Brazil 0.2 0.0 0.2 0.1 0.6 Middle East & N. Africa 0.9 0.1 0.2 0.2 1.5 Algeria 0.2 0.0 0.1 0.0 0.3 South Asia 0.3 0.0 0.2 0.3 0.8 India 0.2 0.0 0.1 0.2 0.6 Sub-Saharan Africa 0.4 0.1 0.4 0.1 1.0 South Africa 0.2 0.0 0.1 0.1 0.3 World 38.4 4.6 16.1 17.8 76.8 Low- and middle-income importers % of world trade, 2004 Europe Latin Middle All low- East Asia & Central America East & South Sub-Saharan & middle- & Pacific Asia & Caribbean N. Africa Asia Africa income World Source of exports High-income economies 6.8 4.2 1.6 1.3 0.9 0.9 15.7 71.5 European Union 1.1 3.6 0.5 0.8 0.3 0.5 6.9 39.0 Japan 1.5 0.1 0.2 0.1 0.1 0.1 1.9 6.4 United States 0.7 0.1 0.7 0.1 0.1 0.1 1.8 8.1 Other high-income economies 3.5 0.3 0.3 0.3 0.4 0.2 5.1 18.1 Low- and middle-income economies 2.2 2.4 1.1 0.6 0.5 0.5 7.5 28.5 East Asia & Pacific 1.3 0.4 0.2 0.2 0.3 0.2 2.5 11.8 China 0.4 0.3 0.1 0.1 0.1 0.1 1.1 6.7 Europe & Central Asia 0.2 1.8 0.0 0.2 0.1 0.0 2.4 6.7 Russian Federation 0.1 0.6 0.0 0.1 0.0 0.0 0.8 1.9 Latin America & Caribbean 0.2 0.1 0.8 0.1 0.0 0.0 1.2 5.2 Brazil 0.1 0.0 0.2 0.0 0.0 0.0 0.4 1.0 Middle East & N. Africa 0.2 0.1 0.0 0.1 0.0 0.0 0.5 2.0 Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.4 South Asia 0.1 0.0 0.0 0.0 0.1 0.1 0.3 1.2 India 0.1 0.0 0.0 0.0 0.1 0.0 0.3 0.9 Sub-Saharan Africa 0.2 0.0 0.1 0.0 0.0 0.2 0.5 1.5 South Africa 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.5 World 9.0 6.7 2.7 1.9 1.4 1.5 23.2 100.0 324 2006 World Development Indicators Direction and growth of merchandise trade Nominal growth of trade High-income importers annual % growth, 1994­2004 European United Other high- All high- Union Japan States income income Source of exports High-income economies 7.8 3.8 6.3 5.6 6.8 European Union 8.5 4.9 9.4 6.4 8.3 Japan 3.3 .. 0.8 3.9 2.6 United States 4.5 0.2 .. 4.5 4.0 Other high-income economies 6.9 5.5 6.7 7.8 6.8 Low- and middle-income economies 11.9 8.2 11.9 10.1 11.0 East Asia & Pacific 13.2 9.5 13.1 10.1 11.2 China 20.5 13.1 19.3 14.8 16.5 Europe & Central Asia 15.4 5.9 11.9 12.1 14.6 Russian Federation 11.6 4.4 8.3 9.4 10.6 Latin America & Caribbean 5.8 2.6 11.6 9.7 9.9 Brazil 5.9 0.7 8.5 9.1 6.9 Middle East & N. Africa 10.3 7.1 14.6 9.9 10.5 Algeria 11.0 3.0 17.7 26.4 13.3 South Asia 8.4 ­1.5 9.5 11.5 9.0 India 9.2 ­0.1 10.7 13.7 10.5 Sub-Saharan Africa 4.5 4.9 9.3 4.8 6.0 South Africa 3.5 2.7 2.0 3.7 3.2 World 8.5 5.4 8.1 6.7 7.8 Low- and middle-income importers annual % growth, 1994­2004 Europe Latin Middle All low- East Asia & Central America East & South Sub-Saharan & middle- & Pacific Asia & Caribbean N. Africa Asia Africa income World Source of exports High-income economies 9.2 13.5 3.0 7.6 9.0 7.0 9.0 7.2 European Union 9.0 14.4 3.3 7.7 9.7 7.0 10.4 8.6 Japan 7.8 14.2 0.4 2.9 3.0 0.6 6.6 3.7 United States 8.4 4.4 3.8 1.7 9.5 6.8 5.7 4.3 Other high-income economies 10.0 11.1 2.4 11.7 9.6 9.8 9.6 7.5 Low- and middle-income economies 16.9 12.1 9.0 14.3 13.5 12.1 13.0 11.5 East Asia & Pacific 16.6 19.4 15.2 15.8 16.5 19.7 17.0 12.2 China 19.9 23.0 19.2 21.8 20.3 23.9 21.1 17.1 Europe & Central Asia 11.7 11.0 9.2 14.5 15.0 15.3 11.4 13.3 Russian Federation 12.5 8.2 5.7 19.9 20.2 16.9 9.6 10.2 Latin America & Caribbean 18.4 16.6 7.3 13.3 8.8 11.2 9.5 9.8 Brazil 14.3 17.8 6.9 14.4 1.5 11.4 9.5 7.9 Middle East & N. Africa 25.6 14.0 14.2 14.1 4.5 20.3 16.2 11.7 Algeria 48.9 21.7 23.6 9.5 ­14.6 10.5 20.1 14.0 South Asia 18.6 9.0 16.9 15.2 15.7 17.4 15.8 10.6 India 20.6 7.7 20.3 19.7 15.6 18.2 17.1 12.2 Sub-Saharan Africa 18.8 10.2 16.8 5.6 3.7 6.2 10.3 7.2 South Africa 2.0 2.8 2.9 1.8 3.2 1.6 1.9 2.8 World 10.6 13.0 5.1 9.4 10.4 8.6 10.1 8.3 2006 World Development Indicators 325 Direction and growth of merchandise trade About the data Definitions The table provides estimates of the flow of trade in only a small portion of world trade is estimated to be · Merchandise trade includes all trade in goods; goods between groups of economies. The data are omitted from the IMF's Direction of Trade Statistics trade in services is excluded. · High-income econo- from the International Monetary Fund's (IMF) Direc- Yearbook and Direction of Trade database. mies are those classified as such by the World Bank tion of Trade database. All developed and 23 develop- Most countries report their trade data in national cur- (see inside front cover). · European Union is defined ing countries report trade on a timely basis, covering rencies, which are converted into U.S. dollars using as all high-income EU members: Austria, Belgium, about 80 percent of trade for recent years. Trade by the IMF's published period average exchange rates Cyprus, Denmark, Finland, France, Germany, Greece, less timely reporters and by countries that do not (series rf or rh, monthly averages of the market or offi- Ireland, Italy, Luxembourg, Malta, the Netherlands, report is estimated using reports of trading partner cial rates) for the reporting country or, if those are not Portugal, Slovenia, Spain, Sweden, and the United countries. Because the largest exporting and import- available, monthly average rates in New York. Because Kingdom. · Other high-income economies include ing countries are reliable reporters, a large portion imports are reported at cost, insurance, and freight all high-income economies (OECD and non-OECD) of the missing trade flows can be estimated from (c.i.f.) valuations, and exports at free on board (f.o.b.) except the European Union, Japan, and the United partner reports. Partner country data may introduce valuations, the IMF adjusts country reports of import States. · Low- and middle-income regional group- discrepancies due to smuggling, confidentiality, dif- values by dividing them by 1.10 to estimate equivalent ings are based on World Bank classifications and ferent exchange rates, overreporting of transit trade, export values. This approximation is more or less accu- may differ from those used by other organizations. inclusion or exclusion of freight rates, and different rate, depending on the set of partners and the items points of valuation and times of recording. traded. Other factors affecting the accuracy of trade In addition, estimates of trade within the European data include lags in reporting, recording differences Union (EU) have been significantly affected by changes across countries, and whether the country reports trade in reporting methods following the creation of a cus- according to the general or special system of trade. (For toms union. The current system for collecting data on further discussion of the measurement of exports and trade between EU members--Intrastat, introduced in imports, see About the data for tables 4.4 and 4.5.) 1993--has less exhaustive coverage than the previ- The regional trade flows shown in the table were ous customs-based system and has resulted in some calculated from current price values. The growth rates problems of asymmetry (estimated imports are about are presented in nominal terms; that is, they include 5 percent less than exports). Despite these issues, the effects of changes in both volumes and prices. Triangular trade in manufactures between China, selected other large East Asian economies, and the United States and Japan $ billions 300 U.S. and Japanese imports from China 250 200 U.S. and Japanese imports from selected 150 other large East Asian economies 100 Chinese imports from selected 50 other large East Asian economies 0 1990 1992 1994 1996 1998 2000 2002 2004 Data sources The strong rise in manufactured exports to China and Hong Kong, China, from selected large East Asian trading partners Intercountry trade flows are published in the IMF's since the early 1990s has been accompanied by an almost equally strong rise in exports from China and Hong Kong, Direction of Trade Statistics Yearbook and Direc- China, to the United States and Japan. tion of Trade Statistics Quarterly; the data in the Note: Selected other large East Asian economies are Indonesia; Republic of Korea; Malaysia; Philippines; Taiwan, China; and Thailand. table were calculated using the IMF's Direction of Source: United Nations Statistic Division, Comtrade database. Trade database. 326 2005 World Development Indicators High-income trade with low- and middle-income economies Exports to low-income economies High-income countries European Union Japan United States 1994 2004 1994 2004 1994 2004 1994 2004 Total ($ billions) 56.6 118.9 27.0 52.3 8.1 11.1 6.2 15.8 % of total exports Food 9.0 6.8 9.4 7.4 0.7 0.5 22.8 13.3 Cereals 3.3 2.1 2.8 1.7 0.2 0.2 17.0 8.2 Agricultural raw materials 2.3 2.0 1.5 1.6 1.6 1.5 4.4 4.8 Ores and nonferrous metals 2.1 2.7 1.6 2.4 0.6 1.1 2.5 1.7 Fuels 4.7 5.8 2.5 3.5 0.9 0.8 0.8 1.8 Crude petroleum 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 3.9 4.1 2.4 3.4 0.8 0.4 0.8 1.0 Manufactured goods 78.8 79.1 81.2 82.9 95.3 92.3 65.8 74.5 Chemical products 12.0 11.7 12.6 12.7 5.9 6.5 12.8 12.2 Iron and steel 3.6 3.1 4.3 3.0 5.9 9.9 1.0 1.3 Machinery and transport equipment 45.0 41.5 41.0 40.6 71.6 61.6 41.9 46.2 Furniture 0.2 0.3 0.3 0.4 0.1 0.2 0.1 0.2 Textiles 6.2 5.5 2.8 2.6 4.0 4.6 4.7 5.6 Footwear 0.2 0.1 0.2 0.1 0.0 0.0 0.1 0.1 Other 11.6 16.9 19.9 23.5 7.7 9.4 5.3 9.0 Miscellaneous goods 3.0 3.5 3.8 2.2 0.8 3.9 3.7 3.9 Imports from low-income economies Total ($ billions) 63.1 151.8 32.2 61.9 7.3 11.2 15.5 53.6 % of total imports Food 19.7 12.4 22.7 16.9 26.9 19.0 9.4 6.8 Cereals 0.7 0.4 0.3 0.4 0.5 0.1 0.2 0.2 Agricultural raw materials 7.0 2.6 8.6 4.6 10.5 2.0 1.5 0.8 Ores and nonferrous metals 5.7 3.6 5.0 5.1 15.6 8.7 2.2 0.5 Fuels 22.9 25.2 17.1 11.9 13.7 31.0 36.3 37.7 Crude petroleum 21.4 21.4 16.6 9.0 10.3 27.5 33.5 34.6 Petroleum products 1.4 2.6 0.3 0.9 2.8 1.5 2.7 2.5 Manufactured goods 44.2 55.7 46.0 61.0 32.7 38.8 50.1 53.5 Chemical products 2.1 3.3 2.1 3.5 1.0 2.9 1.8 2.4 Iron and steel 1.0 1.9 0.4 2.1 2.0 1.6 1.2 1.9 Machinery and transport equipment 2.6 5.1 2.7 5.9 0.3 9.9 1.7 3.2 Furniture 0.2 1.4 0.2 1.5 0.4 1.7 0.1 1.4 Textiles 26.1 26.6 27.0 29.9 16.9 10.9 27.8 30.6 Footwear 1.1 3.5 1.6 6.4 0.2 2.3 0.6 1.2 Other 11.1 14.0 11.9 11.6 11.9 9.6 16.8 12.8 Miscellaneous goods 0.5 0.5 0.7 0.4 0.6 0.5 0.5 0.7 Simple applied tariff rates on imports from low-income economies (%) Food 2.8 2.5 2.9 3.6 6.7 5.6 1.2 3.2 Cereals 5.3 2.3 13.0 1.5 13.0 11.3 1.0 1.0 Agricultural raw materials 0.3 0.6 0.2 0.2 0.3 0.4 0.2 0.3 Ores and nonferrous metals 0.4 0.5 0.2 0.5 0.2 0.1 0.2 0.2 Fuels 0.9 0.6 0.2 0.1 1.0 0.3 2.6 0.6 Crude petroleum 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 1.5 0.9 0.3 0.1 1.6 0.6 2.6 1.1 Manufactured goods 3.9 3.1 1.2 0.9 2.5 2.7 5.4 4.6 Chemical products 1.3 1.3 0.5 0.5 0.9 0.4 2.0 1.7 Iron and steel 2.4 1.1 0.8 1.1 0.3 0.2 4.7 0.3 Machinery and transport equipment 0.8 0.8 0.2 0.2 0.0 0.0 0.6 0.3 Furniture 2.3 2.3 0.0 0.0 0.0 0.0 0.8 1.0 Textiles 8.9 6.9 4.1 3.0 4.9 5.8 11.6 10.3 Footwear 9.0 7.0 1.0 3.4 13.0 11.1 14.5 9.2 Other 5.0 4.0 1.8 1.3 2.9 3.6 6.6 5.8 Miscellaneous goods 0.6 0.2 0.5 0.5 0.0 0.0 1.7 0.1 Average 3.9 2.9 1.4 1.1 2.9 2.8 4.6 4.2 2006 World Development Indicators 327 High-income trade with low- and middle-income economies Exports to middle-income economies High-income countries European Union Japan United States 1994 2004 1994 2004 1994 2004 1994 2004 Total ($ billions) 565.7 1,309.6 207.5 544.8 85.2 161.9 134.7 252.8 % of total exports Food 7.2 4.8 9.0 5.0 0.4 0.3 9.7 8.7 Cereals 1.7 1.0 1.4 0.7 0.1 0.0 3.4 2.8 Agricultural raw materials 2.1 1.9 1.3 1.4 0.9 0.9 3.2 3.5 Ores and nonferrous metals 1.7 2.5 1.5 1.9 1.1 2.3 1.6 2.4 Fuels 2.5 2.4 1.7 1.5 0.8 0.7 2.1 2.9 Crude petroleum 0.2 0.1 0.2 0.0 0.0 0.0 0.0 0.0 Petroleum products 2.0 1.9 1.3 1.3 0.7 0.6 1.5 2.4 Manufactured goods 83.4 85.6 82.5 87.9 95.9 92.2 79.3 79.3 Chemical products 10.2 12.3 11.7 13.3 6.0 8.8 10.4 12.2 Iron and steel 3.0 3.0 2.9 3.2 6.5 6.3 1.0 1.0 Machinery and transport equipment 48.3 50.7 46.2 49.4 68.2 62.1 49.1 48.4 Furniture 0.6 0.5 0.9 0.8 0.1 0.2 0.7 0.5 Textiles 7.4 5.6 6.2 5.4 3.4 2.8 5.7 5.6 Footwear 0.3 0.2 0.5 0.3 0.0 0.0 0.1 0.0 Other 13.6 13.4 14.3 15.5 11.7 12.0 12.2 11.6 Miscellaneous goods 3.1 2.7 4.0 2.3 0.9 3.6 4.0 3.2 Imports from middle-income economies Total ($ billions) 622.1 1,833.7 213.2 674.0 80.5 186.4 187.9 606.6 % of total imports Food 12.7 6.7 16.1 8.3 19.5 9.8 8.7 4.9 Cereals 0.6 0.3 0.2 0.3 2.2 0.3 0.2 0.1 Agricultural raw materials 3.4 1.6 4.4 2.1 6.5 2.4 1.6 1.1 Ores and nonferrous metals 5.4 4.0 7.1 4.7 9.9 7.9 3.4 2.1 Fuels 14.5 15.6 19.2 18.6 20.6 15.8 12.4 15.8 Crude petroleum 9.5 10.1 12.1 12.2 11.8 7.2 9.1 12.0 Petroleum products 2.5 2.7 3.5 3.3 1.3 1.5 2.9 2.7 Manufactured goods 62.1 70.7 50.2 65.0 42.6 62.9 71.7 73.9 Chemical products 3.4 3.2 4.6 3.4 2.6 3.2 2.6 2.6 Iron and steel 2.4 2.6 2.4 3.0 1.8 1.4 2.3 2.3 Machinery and transport equipment 20.3 33.4 11.9 29.4 8.7 27.8 29.5 35.9 Furniture 1.4 2.4 1.5 2.3 1.6 1.7 1.6 3.4 Textiles 15.3 10.3 15.8 10.5 15.0 12.0 13.1 9.2 Footwear 3.2 1.7 1.8 1.3 1.4 1.2 4.6 2.4 Other 16.0 17.0 12.1 15.2 11.5 15.7 18.1 18.0 Miscellaneous goods 1.9 1.4 3.1 1.3 1.0 1.2 2.2 2.2 Simple applied tariff rates on imports from middle-income economies (%) Food 5.6 3.7 9.4 3.6 9.4 7.9 1.6 2.7 Cereals 6.5 3.1 18.2 1.5 15.6 13.7 1.3 0.9 Agricultural raw materials 0.7 0.6 0.9 0.2 0.3 0.4 0.5 0.3 Ores and nonferrous metals 1.1 0.5 1.4 0.5 0.3 0.1 0.8 0.5 Fuels 1.0 0.7 1.0 0.1 0.6 0.6 1.1 1.4 Crude petroleum 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 1.6 1.0 1.5 0.1 1.4 1.0 1.7 2.2 Manufactured goods 5.0 2.7 3.6 0.9 1.8 2.2 4.6 2.8 Chemical products 2.8 1.1 3.1 0.5 0.8 0.4 2.3 1.0 Iron and steel 3.2 1.0 3.2 1.1 0.8 0.3 4.1 0.2 Machinery and transport equipment 2.2 1.1 1.6 0.2 0.0 0.0 1.2 0.3 Furniture 3.7 2.8 1.5 0.0 0.0 0.0 0.8 0.3 Textiles 11.4 7.1 8.5 2.9 5.0 6.8 11.5 8.7 Footwear 12.1 7.6 4.6 3.4 16.1 18.7 14.0 8.9 Other 6.3 3.7 4.5 1.3 2.5 3.3 5.9 4.0 Miscellaneous goods 1.5 0.5 1.9 0.5 0.0 0.0 1.3 0.3 Average 4.8 2.7 3.9 1.1 2.6 2.7 4.0 2.7 Note: Data for 1993 were used in the computation when data for 1994 were not available, and data for 2003 when data for 2004 were not available. 328 2006 World Development Indicators High-income trade with low- and middle-income economies About the data Definitions Developing countries are becoming increasingly countries has grown. Moreover, trade between devel- The product groups in the table are defined in accor- important in the global trading system. Since the oping countries has grown substantially over the past dance with the SITC revision 1: food (0, 1, 22, and early 1990s trade between high-income countries decade. This growth has resulted from many factors, 4) and cereals (04); agricultural raw materials and low- and middle-income economies has grown including developing countries' increasing share of (2 excluding 22, 27, and 28); ores and nonferrous met- faster than trade among high-income economies. world output and the liberalization of their trade. als (27, 28, and 68); fuels (3), crude petroleum (331), The increased trade benefits consumers and produc- Yet trade barriers remain high. The table includes and petroleum products (332); manufactured goods ers. But as the World Trade Organization's (WTO) information about tariff rates by selected product (5­8 excluding 68), chemical products (5), iron and Ministerial Conferences in Doha, Qatar, in October groups. Applied tariff rates are the tariffs in effect steel (67), machinery and transport equipment (7), 2001, Cancun, Mexico, in September 2003, and for partners in preferential trade agreements such furniture (82), textiles (65 and 84), footwear (85), Hong Kong, China, in December 2005 showed, as the North American Free Trade Agreement. When and other manufactured goods (6 and 8 excluding 65, achieving a more pro-development outcome from these are unavailable, most favored nation rates are 67, 68, 82, 84, and 85); and miscellaneous goods trade remains a challenge. Meeting it will require used. The difference between most favored nation (9). · Exports are all merchandise exports by high- strengthening international consultation. Nego- and applied rates can be substantial. Simple aver- income OECD countries to low-income and middle- tiations after the Doha meetings were launched on ages of applied rates are shown because they are income economies as recorded in the United Nations services, agriculture, manufactures, WTO rules, the generally a better indicator of tariff protection. Statistics Division's Comtrade database. · Imports environment, dispute settlement, intellectual prop- The data are from the United Nations Conference are all merchandise imports by high-income countries erty rights protection, and disciplines on regional on Trade and Development (UNCTAD). Partner country from low-income and middle-income economies as integration. At the most recent negotiations in Hong reports by high-income countries were used for both recorded in the United Nations Statistics Division's Kong, China, trade ministers agreed to eliminate exports and imports. Exports are recorded free on Comtrade database. · High-, middle-, and low- subsidies of agricultural exports by 2013; to abolish board (f.o.b.); imports include insurance and freight income economies are those classified as such by cotton export subsidies in 2006 and grant unlimited charges (c.i.f.). Because of differences in sources the World Bank (see inside front cover). · European export access to selected cotton-growing countries of data, timing, and treatment of missing data, the Union is defined as all high-income EU members: in Sub-Saharan Africa; to cut more domestic farm numbers in the table may not be fully comparable Austria, Belgium, Cyprus, Denmark, Finland, France, supports in the European Union, Japan, and the with those used to calculate the direction of trade Germany, Greece, Ireland, Italy, Luxembourg, Malta, United States; and to offer more aid to developing statistics in table 6.3 or the aggregate flows in tables the Netherlands, Portugal, Slovenia, Spain, Sweden, countries to help them compete in global trade. 4.4, 4.5, and 6.2. Data are classified using the Har- and the United Kingdom. Trade flows between high-income countries and monized System of trade at the six- or eight-digit low- and middle-income economies reflect the chang- level. Tariff line data were matched to Standard Inter- ing mix of exports to and imports from developing national Trade Classification (SITC) revision 1 codes economies. While food and primary commodities to define commodity groups. For further discussion have continued to fall as a share of high-income of merchandise trade statistics, see About the data countries' imports, the share of manufactures in for tables 4.4, 4.5, 6.2, and 6.3, and for information goods imports from both low- and middle-income about tariff barriers, see table 6.7. Growing trade between developing countries Trade among developing countries as a share of total developing country trade, 1975­2004 (%) 50 Agricultural materials 40 Fuels, ores, 30 and metals Total merchandise trade 20 Manufactures 10 Data sources Trade values are from United Nations Statistics 0 Division's Comtrade database. Tariff data are 1975 1980 1985 1990 1995 2000 2004 from UNCTAD's Trade Analysis and Information System database and are calculated by World As developing countries' share in global trade has steadily increased over the past three decades, the share of trade among developing countries has greatly increased, especially since 1990. Bank staff using the World Integrated Trade Solu- Source: United Nations Statistics Division, Comtrade database. tion system. 2006 World Development Indicators 329 Primary commodity prices 1970 1980 1990 1995 2000 2001 2002 2003 2004 2005 World Bank commodity price index (1990 = 100) Nonenergy commodities 156 159 100 104 89 84 89 91 100 111 Agriculture 163 175 100 112 90 84 93 95 98 103 Beverages 203 230 100 129 91 76 91 87 88 107 Food 166 177 100 100 87 91 97 96 103 100 Raw materials 130 133 100 116 93 81 89 98 99 104 Fertilizers 108 164 100 88 109 105 108 106 118 123 Metals and minerals 144 120 100 87 85 80 78 82 105 130 Petroleum 19 204 100 64 127 113 117 126 154 213 Steel productsa 111 100 100 91 79 71 73 79 114 126 MUV G-5 index 28 79 100 117 97 94 93 100 107 110 Commodity prices (1990 prices) Agricultural raw materials Cotton (cents/kg) 225 260 182 182 134 112 109 140 128 111 Logs, Cameroon ($/cu. m)a 153 319 344 290 283 282 .. .. .. .. Logs, Malaysian ($/cu. m) 154 248 177 218 195 169 175 187 184 185 Rubber (cents/kg) 145 181 86 135 69 61 82 108 122 137 Sawnwood, Malaysian ($/cu. m) 625 503 533 632 612 510 565 550 543 601 Tobacco ($/mt) 3,836 2,887 3,392 2,258 3,063 3,185 2,947 2,643 2,561 2,524 Beverages (cents/kg) Cocoa 240 330 127 122 93 113 191 175 145 140 Coffee, robustas 330 411 118 237 94 64 71 81 74 102 Coffee, Arabica 409 440 197 285 198 146 146 141 166 231 Tea, avg., 3 auctions 298 211 206 127 193 169 162 151 157 150 Energy Coal, Australian ($/mt) .. 50 40 34 27 34 29 28 51 45 Coal, U.S. ($/mt) .. 55 42 33 34 48 43 .. .. .. Natural gas, Europe ($/mmbtu) .. 4 3 2 4 4 3 4 4 6 Natural gas, U.S. ($/mmbtu) 1 2 2 1 4 4 4 5 6 8 Petroleum ($/bbl) 4 47 23 15 29 26 27 29 35 49 About the data Primary commodities--raw or partially processed the prices paid by importers are used. Annual price indexes are compiled for petroleum and steel prod- materials that will be transformed into finished series are generally simple averages based on higher ucts, which are not included in the nonenergy com- goods--are often the most significant exports of frequency data. The constant price series in the modity price index. developing countries, and revenues obtained from table is deflated using the manufactures unit value The MUV index is a composite index of prices them have an important effect on living standards. (MUV) index for the Group of Five (G-5) countries for manufactured exports from the five major Price data for primary commodities are collected from (see below). (G-5) industrial countries (France, Germany, Japan, a variety of sources, including trade journals, inter- The commodity price indexes are calculated as the United Kingdom, and the United States) to low- national study groups, government market surveys, Laspeyres index numbers, in which the fixed weights and middle-income economies, valued in U.S. dol- newspaper and wire service reports, and commodity are the 1987­89 export values for low- and middle- lars. The index covers products in groups 5­8 of the exchange spot and near-term forward prices. income economies, rebased to 1990. Each index rep- Standard International Trade Classification revision The table is based on frequently updated price resents a fixed basket of primary commodity exports. 1. To construct the MUV G-5 index, unit value indexes reports. When possible, the prices received by The nonenergy commodity price index contains 37 for each country are combined using weights deter- exporters are used; if export prices are unavailable, price series for 31 nonenergy commodities. Separate mined by each country's export share. 330 2006 World Development Indicators Primary commodity prices 1970 1980 1990 1995 2000 2001 2002 2003 2004 2005 Commodity prices (continued) (1990 prices) Fertilizers ($/mt) Phosphate rock 39 59 41 30 45 44 43 38 38 38 TSP 152 229 132 128 142 135 143 149 174 184 Food Fats and oils ($/mt) Coconut oil 1,417 855 337 572 463 337 452 467 617 562 Groundnut oil 1,350 1,090 964 846 734 721 738 1,242 1,085 968 Palm oil 927 740 290 536 319 303 419 443 440 385 Soybeans 417 376 247 221 218 208 228 264 286 250 Soybean meal 367 332 200 168 195 192 188 211 225 195 Soybean oil 1,021 758 447 534 348 375 488 553 576 497 Grains ($/mt) Sorghum 185 164 104 102 91 101 109 106 103 88 Maize 208 159 109 105 91 95 107 105 104 90 Rice 450 521 271 274 208 183 206 197 222 261 Wheat 196 219 136 151 117 134 159 146 147 139 Other food Bananas ($/mt) 590 481 541 380 436 618 568 374 490 550 Beef (cents/kg) 465 350 256 163 199 226 226 198 235 239 Oranges ($/mt) 599 496 531 454 374 631 606 680 803 800 Sugar, EU domestic (cents/kg) 40 62 58 59 57 56 59 60 63 61 Sugar, U.S. domestic (cents/kg) 59 84 51 43 44 50 50 47 42 43 Sugar, world (cents/kg) 29 80 28 25 19 20 16 16 15 20 Metals and minerals Aluminum ($/mt) 1,982 1,847 1,639 1,542 1,594 1,531 1,449 1,430 1,603 1,732 Copper ($/mt) 5,038 2,768 2,662 2,508 1,866 1,673 1,674 1,777 2,678 3,357 Iron ore (cents/dmtu) 35 36 33 24 30 32 31 32 35 59 Lead (cents/kg) 108 115 81 54 47 50 49 51 83 89 Nickel ($/mt) 10,148 8,270 8,864 7,028 8,888 6,303 7,271 9,617 12,915 13,453 Tin (cents/kg) 1,310 2,128 609 531 559 475 436 489 795 673 Zinc (cents/kg) 105 97 151 88 116 94 84 83 98 126 a. Series not included in the nonenergy index. Definitions · Nonenergy commodity price index covers the iron ore, lead, nickel, tin, and zinc. · Petroleum price (also known as the "Pink Sheet") at the Global Pros- 31 nonenergy primary commodities that make up index refers to the average spot price of Brent, Dubai, pects Web site (www.worldbank.org/prospects). the agriculture, fertilizer, and metals and minerals and West Texas Intermediate crude oils, equally indexes. · Agriculture includes beverages, food, weighted. · Steel products price index is the com- and agricultural raw materials. · Beverages include posite price index for eight steel products based on cocoa, coffee, and tea. · Food includes rice, wheat, quotations free on board (f.o.b.) Japan excluding ship- maize, sorghum, soybeans, soybean oil, soybean ments to China and the United States, weighted by Data sources meal, palm oil, coconut oil, groundnut oil, bananas, product shares of apparent combined consumption Data on commodity prices and the MUV G-5 index beef, oranges, and sugar. · Agricultural raw mate- (volume of deliveries) for Germany, Japan, and the are compiled by the World Bank's Development rials include cotton, timber (logs and sawnwood), United States. · MUV G-5 index is the manufactures Prospects Group. Monthly updates of commodity natural rubber, and tobacco. · Fertilizers include unit value index for G-5 country exports to low- and prices are available on the Web at www.worldbank. phosphate rock and triple superphosphate (TSP). middle-income economies. · Commodity prices--for org/prospects. · Metals and minerals include aluminum, copper, definitions and sources, see "Commodity Price Data" 2006 World Development Indicators 331 Regional trade blocs Merchandise exports within bloc $ millions Year of creation 1990 1995 1998 1999 2000 2001 2002 2003 2004 High-income and low- and middle-income economies APECa 1989 901,560 1,688,708 1,734,386 1,896,213 2,261,791 2,070,973 2,168,705 2,420,758 2,903,670 CEFTA 1992 4,235 12,118 14,234 13,226 15,123 17,054 19,180 25,309 37,340 CIS 1991 .. 29,943 27,037 20,842 27,043 22,264 28,029 36,466 40,340 EMFTA 1995 1,057,338 1,366,726 1,341,891 1,522,340 1,548,718 1,526,481 1,620,324 1,952,123 2,315,677 European Union 1957 981,260 1,259,699 1,223,801 1,396,574 1,409,464 1,398,298 1,480,493 1,782,423 2,089,442 FTAA 1994 300,700 525,346 682,067 734,848 857,300 812,144 787,492 830,495 969,106 NAFTA 1994 226,273 394,472 521,649 581,161 676,141 639,419 626,020 651,060 737,591 Africa CEMAC 1994 139 120 153 127 97 118 136 148 176 CEPGL 1976 7 8 8 9 10 11 13 15 19 COMESA 1994 963 1,386 1,501 1,348 1,536 1,496 1,786 2,189 2,848 Cross Border Initiative 1992 613 1,002 1,156 964 1,058 849 1,169 1,370 1,700 EAC 1996 230 530 555 438 485 453 479 573 753 ECCAS 1983 163 163 198 179 191 203 199 198 238 ECOWAS 1975 1,557 1,936 2,350 2,364 2,835 2,371 3,229 3,147 3,973 Indian Ocean Commission 1984 73 127 95 91 106 134 105 179 155 MRU 1973 0 1 2 4 5 4 5 5 6 SADC 1992 1,630 3,373 3,865 4,224 4,282 3,771 4,316 5,377 6,384 UDEAC 1964 139 120 152 126 96 117 134 146 174 UEMOA 1994 621 560 752 805 741 775 857 1,078 1,283 Latin America and the Caribbean ACS 1994 5,398 11,049 12,505 11,199 16,326 15,543 15,464 16,090 21,839 Andean Group 1969 1,312 4,812 5,408 3,929 5,310 5,623 5,070 5,203 7,094 CACM 1961 667 1,594 2,010 2,175 2,657 2,535 2,574 2,734 3,554 CARICOM 1973 448 867 1,020 1,136 1,050 1,420 1,184 1,410 1,797 Central American Group of Four 1993 399 1,026 1,230 1,369 1,838 1,712 1,737 1,843 2,297 Group of Three 1995 1,046 3,460 3,921 2,912 3,721 4,178 3,839 3,367 5,664 LAIA (ALADI) 1980 12,331 35,299 42,959 34,785 42,911 40,795 36,060 40,250 55,639 MERCOSUR 1991 4,127 14,199 20,352 15,313 17,829 15,156 10,228 12,732 17,470 OECS 1981 29 39 36 37 38 37 40 48 60 Middle East and Asia Arab Common Market 1964 911 1,368 978 951 1,312 1,728 1,998 1,797 6,297 ASEAN 1967 27,365 79,544 69,809 77,889 98,060 86,331 91,765 101,140 122,369 Bangkok Agreement 1975 4,476 12,066 12,851 14,463 16,844 16,733 17,957 21,809 24,925 EAEC 1990 281,067 634,606 549,010 612,415 772,423 698,552 779,390 940,963 1,177,295 ECO 1985 1,243 4,746 4,031 3,903 4,518 4,498 5,016 7,539 9,371 GAFTA 1997 13,313 13,129 13,548 13,752 16,238 17,528 19,195 21,511 36,027 GCC 1981 6,906 6,832 7,358 7,306 7,958 8,103 8,899 9,580 12,532 SAARC 1985 863 2,024 2,466 2,180 2,593 2,827 3,402 4,873 5,706 UMA 1989 958 1,109 881 919 1,094 1,137 1,202 1,338 1,372 Note: Regional bloc memberships are as follows: Asia Pacific Economic Cooperation (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; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, the Kyrgyz Republic, Moldova, the Russian Federation, Tajikistan, Ukraine, and Uzbekistan; Euro-Mediterranean Free Trade Area (EMFTA), European Union, Algeria, Cyprus, Egypt, Israel, Jordan, Lebanon, Malta, Morocco, Syrian Arab Republic, Tunisia, Turkey, and West Bank and Gaza; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom; Free Trade Areas of the Americas (FTAA), Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Republica Bolivariana de Venezuela, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago, the United States, and Uruguay; North American Free Trade Area (NAFTA), Canada, Mexico, and the United States; Economic and Monetary Community of Central Africa (CEMAC), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé 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 Southern 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, and Zimbabwe; Cross Border Initiative, Burundi, Comoros, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe; East African Community (EAC), Kenya, Tanzania, and Uganda; 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 São Tomé and Principe; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d'Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Mano River Union (MRU), Guinea, Liberia, and Sierra Leone; Southern African Development Community (SADC; formerly Southern African Development Coordination Conference), Angola, Botswana, the Democratic Republic of Congo, a. No preferential trade agreement. 332 2006 World Development Indicators Regional trade blocs Merchandise exports within bloc % of total bloc exports Year of creation 1990 1995 1998 1999 2000 2001 2002 2003 2004 High-income and low- and middle-income economies APECa 1989 68.3 71.8 69.7 71.8 73.1 72.6 73.4 72.6 72.0 CEFTA 1992 9.9 14.6 13.0 12.1 12.2 12.4 12.2 12.5 13.9 CIS 1991 .. 27.6 26.6 20.7 19.2 18.2 18.8 19.6 16.7 EMFTA 1995 68.5 65.3 60.0 65.8 64.6 63.3 63.3 63.9 63.6 European Union 1957 65.9 62.4 56.8 62.9 61.6 60.8 60.6 61.2 60.7 FTAA 1994 46.6 52.5 58.1 59.7 60.7 60.6 60.8 60.1 60.1 NAFTA 1994 41.4 46.2 51.7 54.6 55.7 55.5 56.6 56.1 55.9 Africa CEMAC 1994 2.3 2.1 2.3 1.7 1.1 1.4 1.5 1.4 1.3 CEPGL 1976 0.5 0.5 0.6 0.8 0.8 0.8 0.9 1.3 1.2 COMESA 1994 6.6 7.7 8.7 7.4 5.7 6.4 6.4 6.6 6.7 Cross Border Initiative 1992 10.3 11.9 13.9 12.1 10.6 9.0 12.3 11.4 13.2 EAC 1996 13.4 17.4 19.0 14.4 16.1 13.7 13.3 14.0 14.6 ECCAS 1983 1.4 1.5 1.8 1.3 1.1 1.3 1.1 1.0 0.9 ECOWAS 1975 7.9 9.0 10.7 10.4 7.9 8.5 10.9 8.6 8.5 Indian Ocean Commission 1984 4.1 6.0 4.7 4.8 4.4 5.6 4.3 6.1 4.3 MRU 1973 0.0 0.1 0.1 0.4 0.4 0.3 0.2 0.3 0.3 SADC 1992 4.8 8.7 10.4 11.9 9.3 8.6 9.5 9.8 9.5 UDEAC 1964 2.3 2.1 2.3 1.7 1.0 1.4 1.4 1.4 1.2 UEMOA 1994 13.0 10.3 11.0 13.1 13.1 12.7 12.2 13.3 13.9 Latin America and the Caribbean ACS 1994 8.4 8.5 7.2 5.6 6.7 6.8 6.7 6.6 7.6 Andean Group 1969 4.1 12.0 12.8 8.8 8.8 10.6 9.5 8.6 8.7 CACM 1961 15.3 21.8 15.8 13.6 17.3 17.9 17.5 16.8 20.0 CARICOM 1973 8.1 12.1 17.3 16.9 14.7 16.5 13.8 12.4 12.5 Central American Group of Four 1993 13.7 22.2 17.1 14.6 19.2 18.7 18.4 18.2 20.0 Group of Three 1995 2.0 3.2 2.6 1.7 1.7 2.1 1.9 1.6 2.3 LAIA (ALADI) 1980 10.8 17.1 16.7 12.7 12.8 12.8 11.2 11.4 12.6 MERCOSUR 1991 8.9 20.3 25.0 20.6 20.0 17.1 11.5 11.9 12.6 OECS 1981 8.1 12.6 12.0 13.1 10.0 6.0 4.0 7.6 11.6 Middle East and Asia Arab Common Market 1964 2.7 6.7 4.8 3.3 2.9 4.4 5.1 4.1 7.9 ASEAN 1967 19.0 24.6 21.2 21.7 23.0 22.4 22.7 22.2 22.2 Bangkok Agreement 1975 3.7 5.0 5.0 5.1 5.1 5.5 5.5 5.7 5.2 EAEC 1990 39.7 47.9 42.0 43.8 46.6 46.6 48.2 49.4 49.8 ECO 1985 3.2 7.9 6.8 5.8 5.6 5.5 5.9 6.7 6.3 GAFTA 1997 10.3 9.9 11.0 8.9 7.2 8.4 9.3 8.5 10.2 GCC 1981 8.0 6.8 8.0 6.7 4.8 5.2 5.9 5.1 5.0 SAARC 1985 3.2 4.4 4.8 4.0 4.1 4.3 4.8 5.7 5.6 UMA 1989 2.9 3.8 3.3 2.5 2.3 2.6 2.8 2.4 1.9 Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Central African Customs and Economic Union (UDEAC; formerly Union Douanière 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, Côte d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo; 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, Suriname, Trinidad and Tobago, and República Bolivariana de Venezuela; Andean Group, Bolivia, Colombia, Ecuador, Peru, and República Bolivariana de Venezuela; Central American 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 República Bolivariana de Venezuela; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and República Bolivariana de Venezuela; Southern 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; 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 Asia Economic Caucus (EAEC, formerly East Asia Economic Group), 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 Cooperation 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. 2006 World Development Indicators 333 Regional trade blocs Total merchandise exports by bloc % of world exports Year of creation 1990 1995 1998 1999 2000 2001 2002 2003 2004 High-income and low- and middle-income economies APECa 1989 39.0 46.3 46.1 46.6 48.4 46.5 46.0 44.5 44.3 CEFTA 1992 1.3 1.6 2.0 1.9 1.9 2.2 2.4 2.7 2.9 CIS 1991 .. 2.1 1.9 1.8 2.2 2.0 2.3 2.5 2.7 EMFTA 1995 45.6 41.2 41.4 40.8 37.5 39.2 39.9 40.7 40.0 European Union 1957 44.0 39.7 39.9 39.2 35.8 37.5 38.0 38.8 37.9 FTAA 1994 19.1 19.7 21.8 21.7 22.1 21.8 20.2 18.4 17.7 NAFTA 1994 16.2 16.8 18.7 18.8 19.0 18.7 17.2 15.5 14.5 Africa CEMAC 1994 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 CEPGL 1976 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 COMESA 1994 0.4 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.5 Cross Border Initiative 1992 0.2 0.2 0.2 0.1 0.2 0.2 0.1 0.2 0.1 EAC 1996 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 ECCAS 1983 0.3 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 ECOWAS 1975 0.6 0.4 0.4 0.4 0.6 0.5 0.5 0.5 0.5 Indian Ocean Commission 1984 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MRU 1973 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SADC 1992 1.0 0.8 0.7 0.6 0.7 0.7 0.7 0.7 0.7 UDEAC 1964 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 UEMOA 1994 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Latin America and the Caribbean ACS 1994 1.9 2.6 3.2 3.5 3.8 3.7 3.6 3.3 3.2 Andean Group 1969 0.9 0.8 0.8 0.8 1.0 0.9 0.8 0.8 0.9 CACM 1961 0.1 0.1 0.2 0.3 0.2 0.2 0.2 0.2 0.2 CARICOM 1973 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 Central American Group of Four 1993 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 Group of Three 1995 1.5 2.1 2.8 3.0 3.3 3.2 3.1 2.8 2.7 LAIA (ALADI) 1980 3.4 4.1 4.8 4.8 5.3 5.2 5.0 4.7 4.8 MERCOSUR 1991 1.4 1.4 1.5 1.3 1.4 1.4 1.4 1.4 1.5 OECS 1981 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Middle East and Asia Arab Common Market 1964 1.0 0.4 0.4 0.5 0.7 0.6 0.6 0.6 0.9 ASEAN 1967 4.3 6.4 6.1 6.3 6.7 6.3 6.3 6.1 6.1 Bangkok Agreement 1975 3.6 4.8 4.8 5.0 5.2 4.9 5.1 5.1 5.3 EAEC 1990 20.9 26.1 24.2 24.7 26.0 24.4 25.2 25.4 26.0 ECO 1985 1.1 1.2 1.1 1.2 1.3 1.3 1.3 1.5 1.6 GAFTA 1997 3.8 2.6 2.3 2.7 3.5 3.4 3.2 3.4 3.9 GCC 1981 2.5 2.0 1.7 1.9 2.6 2.5 2.3 2.5 2.7 SAARC 1985 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.1 1.1 UMA 1989 1.0 0.6 0.5 0.6 0.7 0.7 0.7 0.7 0.8 a. No preferential trade agreement. 334 2006 World Development Indicators Regional trade blocs About the data Trade blocs are groups of countries that have estab- The table shows the value of merchandise intratrade world exports exceed 100 percent. Exports of blocs lished special preferential arrangements governing for important regional trade blocs (service exports are include all commodity trade, which may include items trade between members. Although in some cases excluded) as well as the size of intratrade relative to not specified in trade bloc agreements. Differences the preferences--such as lower tariff duties or each bloc's total exports of goods and the share of the from previously published estimates may be due to exemptions from quantitative restrictions--may be bloc's total exports in world exports. Although the Asia changes in bloc membership or to revisions in the no greater than those available to other trading part- Pacific Economic Cooperation (APEC) has no preferen- underlying data. ners, such arrangements are intended to encourage tial arrangements, it is included in the table because exports by bloc members to one another--sometimes of the volume of trade between its members. Definitions called intratrade. The data on country exports are drawn from the · Merchandise exports within bloc are the sum of Most countries are members of a regional trade International Monetary Fund's (IMF) Direction of merchandise exports by members of a trade bloc bloc, and more than a third of the world's trade takes Trade database and should be broadly consistent to other members of the bloc. They are shown both place within such arrangements. While trade blocs with those from other sources, such as the United in U.S. dollars and as a percentage of total mer- vary widely in structure, they all have the same main Nations Statistics Division's Commodity Trade (Com- chandise exports by the bloc. · Total merchandise objective: to reduce trade barriers between member trade) database. However, trade flows between many exports by bloc as a share of world exports are the countries. But effective integration requires more than developing countries, particularly in Sub-Saharan ratio of the bloc's total merchandise exports (within reducing tariffs and quotas. Economic gains from Africa, are not well recorded. Thus the value of intra- the bloc and to the rest of the world) to total mer- competition and scale may not be achieved unless trade for certain groups may be understated. Data on chandise exports by all economies in the world. other barriers that divide markets and impede the free trade between developing and high-income countries flow of goods, services, and investments are lifted. are generally complete. For example, many regional trade blocs retain con- Membership in the trade blocs shown is based on tingent protections or restrictions on intrabloc trade. the most recent information available, from the World These include antidumping, countervailing duties, and Bank Policy Research Report Trade Blocs (2000a), "emergency protection" to address balance of pay- from the World Bank's Global Economic Prospects ments problems or to protect an industry from surges 2005, and from consultation with the World Bank's in imports. Other barriers include differing product international trade unit. This year, the date of standards, discrimination in public procurement, and each trade bloc's creation has also been included. cumbersome and costly border formalities. Although bloc exports have been calculated back to Membership in a regional trade bloc may reduce 1990 on the basis of current membership, several the frictional costs of trade, increase the credibility of the blocs came into existence in later years and of reform initiatives, and strengthen security among their membership may have changed over time. For partners. But making it work effectively is challenging this reason, and because systems of preferences for any government. All sectors of an economy may also change over time, intratrade in earlier years be affected, and some sectors may expand while oth- may not have been affected by the same prefer- ers contract, so it is important to weigh the potential ences as in recent years. In addition, some countries costs and benefits that membership may bring. belong to more than one trade bloc, so shares of Regional trade agreements are proliferating Annual number Total number 30 300 25 250 20 200 Data sources Data on merchandise trade flows are published 15 150 in the IMF's Direction of Trade Statistics Yearbook and Direction of Trade Statistics Quarterly; the 10 100 data in the table were calculated using the IMF's Direction of Trade database. The United Nations Conference on Trade and Development (UNCTAD) 5 50 publishes data on intratrade in its Handbook of International Trade and Development Statistics. 0 0 The information on trade bloc membership is 1958 1964 1971 1975 1980 1984 1987 1990 1993 1996 1999 2002 2004 from the World Bank Policy Research Report Trade There are more than 250 regional trade agreements in force---six times as many as two decades ago. About a third of Blocs (2000a), the World Bank's Global Economic global trade takes place between countries that have some form of reciprocal regional trade agreement. Prospects 2005, and the World Bank's interna- Source: World Bank 2005. Global Economic Prospects 2005. tional trade unit. 2006 World Development Indicators 335 Tariff barriers All Primary Manufactured products products products % Share of Share of Most Simple Simple Weighted lines with lines with % % recent Binding mean mean mean international specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Albania 2002 100.0 7.0 8.3 8.4 0.0 0.0 9.2 7.7 8.2 8.7 Algeria 2003 .. .. 17.9 12.0 39.4 0.0 18.1 10.5 17.8 12.5 Angola 2002 .. .. 8.1 8.5 15.2 1.6 11.6 14.7 7.5 5.9 Argentina 2004a 100.0 31.9 11.4 9.3 35.5 0.0 8.4 2.4 11.7 9.9 Armenia 2001 100.0 8.5 3.2 2.2 0.0 0.0 6.5 3.3 2.6 1.3 Australia 2005a 97.1 10.0 4.2 3.1 5.6 0.2 1.5 0.7 4.6 3.6 Azerbaijan 2002 .. .. 9.7 6.2 0.1 6.6 11.7 4.8 9.4 7.0 Bangladesh 2004 14.9 162.4 16.4 15.4 43.8 0.0 16.2 12.7 16.4 17.1 Belarus 2002 0.0 .. 11.2 8.9 16.1 2.2 11.1 7.1 11.3 10.4 Belize 2003 .. .. 12.9 11.4 38.9 0.4 20.1 13.3 11.7 10.8 Benin 2004 39.1 28.6 14.0 12.7 54.1 0.0 15.5 12.9 13.7 12.5 Bolivia 2004a 100.0 40.0 7.5 5.4 0.0 0.0 7.6 5.0 7.5 5.6 Bosnia and Herzegovina 2001a .. .. 5.1 4.9 0.0 0.0 3.7 5.3 5.3 4.7 Botswana 2001a 89.0 17.3 5.0 1.0 16.0 1.4 2.1 0.3 5.4 1.1 Brazil 2004a 100.0 31.4 13.1 7.6 37.0 0.0 9.0 1.8 13.5 9.9 Brunei 2004a 95.3 24.3 3.0 4.3 22.3 1.3 0.1 0.1 3.5 4.9 Bulgaria 2004a 100.0 24.7 10.2 9.6 24.5 2.5 15.4 10.7 9.6 9.3 Burkina Faso 2004 39.3 41.9 13.2 11.4 49.5 0.0 13.7 11.3 13.1 11.5 Burundi 2002a 20.9 67.6 20.0 14.7 32.6 0.6 22.4 10.6 19.6 16.9 Cambodia 2003a .. .. 15.6 16.4 24.8 0.0 16.8 15.6 15.4 16.6 Cameroon 2002 31.0 79.9 18.1 15.0 49.5 .. 21.1 16.5 17.7 14.4 Canada 2005a 99.7 5.1 3.7 0.9 6.0 3.6 1.8 0.3 4.0 1.0 Chile 2004a 100.0 25.1 4.9 3.8 0.0 0.0 4.4 2.4 4.9 4.4 China 2004 100.0 10.0 9.6 6.0 14.9 0.0 9.6 6.2 9.5 5.8 Colombia 2004a 100.0 42.8 11.3 9.3 18.4 0.0 11.1 10.2 11.3 9.0 Congo, Dem. Rep. 2003 .. .. 13.0 12.8 42.5 0.5 14.8 12.1 12.7 13.2 Costa Rica 2004a 100.0 42.9 5.7 3.8 0.9 0.0 8.3 5.8 5.4 3.4 Côte d'Ivoire 2004 33.2 11.2 12.7 10.7 44.9 0.0 14.7 11.5 12.3 10.1 Croatia 2004 100.0 5.9 4.1 3.2 5.7 3.9 6.5 3.6 3.7 3.1 Cuba 2004 31.0 21.3 10.7 9.9 11.6 .. 11.1 8.8 10.6 10.4 Czech Republic 2003 100.0 5.0 5.1 4.4 4.8 0.0 5.7 4.1 4.9 4.3 Djibouti 2002 100.0 40.9 30.9 26.8 92.3 2.3 21.9 19.7 32.6 32.3 Dominican Republic 2004 100.0 34.9 10.3 8.1 32.5 0.2 13.5 7.1 9.8 8.7 Ecuador 2004a 99.9 21.8 11.5 9.0 20.5 0.0 10.7 6.6 11.5 9.5 Egypt, Arab Rep. 2002 99.0 37.2 18.9 13.9 46.2 6.8 18.1 7.9 19.0 16.9 El Salvador 2004a 100.0 36.6 5.4 4.3 8.0 0.0 6.4 3.8 5.3 4.5 Estonia 2003 100.0 8.7 1.0 0.9 5.4 0.0 8.2 4.0 0.0 0.0 Ethiopia 2002 .. .. 19.4 13.5 52.0 0.2 22.0 6.7 19.1 15.7 European Union 2005a 100.0 4.2 1.8 1.7 1.8 10.3 2.6 0.9 1.7 2.1 Fiji 2004 51.4 40.1 .. .. .. .. .. .. .. .. Gabon 2002 100.0 21.4 18.6 14.7 52.3 .. 23.2 19.7 17.9 13.5 Gambia, The 2002 13.6 100.6 .. .. .. .. .. .. .. .. Georgia 2004 100.0 7.2 7.3 9.1 5.3 1.4 12.0 13.2 6.5 6.3 Ghana 2004 14.3 92.1 13.1 11.0 45.0 0.2 17.6 17.1 12.3 8.8 Guatemala 2004a 34.9 36.5 5.1 4.9 1.4 0.0 7.3 5.1 4.8 4.8 Guinea 2004 39.0 20.1 .. .. .. .. .. .. .. .. Guinea-Bissau 2004 .. .. 13.9 13.6 56.0 0.0 16.6 14.5 13.4 12.9 Guyana 2003 100.0 56.7 12.0 11.5 36.2 0.4 20.2 14.1 10.8 9.6 Honduras 2004a 100.0 32.5 5.2 5.4 1.1 0.0 7.2 8.0 4.9 4.0 Hungary 2002 96.2 9.8 8.9 7.9 10.9 0.0 17.9 6.7 7.7 8.0 Iceland 2003a 95.0 13.5 5.1 3.0 5.7 1.3 6.2 3.8 4.9 2.7 India 2004a 73.8 49.6 28.1 28.0 92.1 0.0 29.0 36.9 27.8 25.3 Indonesia 2004a 96.6 37.5 6.4 5.5 8.7 0.3 7.7 3.2 6.2 6.2 Iran, Islamic Rep. 2004 .. .. 17.5 14.8 41.6 0.5 14.3 13.6 17.6 15.0 Israel 2005a 76.2 20.5 4.5 2.4 1.1 3.7 5.3 3.0 4.4 2.2 Jamaica 2003 100.0 49.8 9.4 9.8 36.5 0.3 16.0 11.0 8.4 9.3 Japan 2004a 99.6 3.0 2.9 2.4 8.1 2.8 5.3 3.9 2.4 1.6 Jordan 2003 100.0 16.3 14.4 11.4 40.8 0.2 20.0 11.9 13.6 11.0 Kenya 2004 14.0 95.1 16.2 10.3 39.1 0.1 19.5 10.1 15.8 10.2 336 2006 World Development Indicators Tariff barriers All Primary Manufactured products products products % Share of Share of Most Simple Simple Weighted lines with lines with % % recent Binding mean mean mean international specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Korea, Rep. 2002 94.4 15.8 9.3 10.0 5.2 0.4 19.5 19.0 7.7 5.0 Kuwait 2002 .. .. 3.5 3.9 0.1 1.4 1.5 3.7 4.0 4.0 Kyrgyz Republic 2003 99.9 7.4 4.1 4.3 0.1 2.3 6.6 6.1 3.6 2.9 Lao PDR 2004a .. .. 8.5 12.1 8.4 0.1 13.6 13.8 7.8 11.4 Latvia 2001 100.0 12.8 3.2 2.6 2.9 0.0 8.0 5.5 2.5 1.5 Lebanon 2002a .. .. 7.2 6.3 12.5 0.4 13.6 6.1 6.3 6.4 Lesotho 2001a .. .. 10.8 17.8 42.1 2.9 16.0 9.2 10.5 17.8 Libya 2002 .. .. 20.2 25.2 46.6 2.1 19.2 15.1 20.1 28.5 Lithuania 2003a 100.0 9.2 1.2 0.6 3.0 0.0 3.3 1.2 0.9 0.4 Macedonia, FYR 2004 .. .. 10.2 7.6 26.0 3.4 12.2 8.4 9.9 7.2 Madagascar 2001 29.7 27.4 5.2 3.6 4.4 0.0 5.5 1.7 5.1 4.6 Malawi 2001a 30.2 75.0 12.9 10.2 40.4 0.0 12.6 9.0 12.9 10.7 Malaysia 2003a 83.7 14.5 7.4 4.1 21.2 0.9 4.6 2.1 7.8 4.5 Maldives 2003 97.1 37.1 21.3 20.7 71.4 0.0 17.8 18.5 22.2 21.9 Mali 2004 40.7 28.8 12.8 10.7 45.9 0.0 15.4 11.5 12.4 10.4 Mauritania 2001 39.4 19.6 12.8 9.3 51.5 0.0 12.6 7.9 12.8 10.0 Mauritius 2002 18.0 94.0 23.5 13.0 40.0 0.1 19.6 9.9 23.8 14.4 Mexico 2004a 100.0 35.0 14.6 3.7 38.5 0.0 13.0 2.6 14.7 3.8 Moldova 2001 .. .. 4.6 2.8 0.1 0.9 8.3 2.6 4.0 2.9 Mongolia 2004 100.0 17.5 .. .. .. .. .. .. .. .. Morocco 2003 100.0 41.3 28.3 24.9 75.1 0.0 33.5 25.4 27.8 24.6 Mozambique 2003a .. .. 12.7 9.9 36.8 0.0 16.0 9.9 12.1 9.9 Myanmar 2004 16.5 83.2 4.4 4.1 3.3 0.0 7.2 4.6 4.1 3.9 Namibia 2001a 88.9 17.3 4.5 0.5 13.8 2.4 3.5 0.4 4.6 0.6 Nepal 2004 .. .. 14.8 14.3 21.5 0.6 13.9 9.3 14.7 16.3 New Zealand 2004a 100.0 10.3 3.7 3.0 7.0 5.6 1.7 0.6 4.0 3.5 Nicaragua 2004a 100.0 41.7 4.9 3.8 0.9 0.0 7.0 3.9 4.7 3.6 Niger 2004 96.8 44.3 12.8 13.8 48.3 0.0 15.6 15.9 12.4 13.0 Nigeria 2002 19.3 118.0 24.8 18.5 51.8 1.0 36.8 26.7 23.2 15.7 Norway 2003a 100.0 3.0 0.5 0.4 0.5 5.5 1.5 1.4 0.3 0.2 Oman 2002 100.0 13.8 8.0 13.6 0.6 2.6 9.5 31.6 7.6 6.5 Pakistan 2004 44.8 52.2 16.1 13.2 50.3 0.1 14.7 9.4 16.2 15.7 Panama 2001 .. .. 7.9 6.9 1.2 0.2 11.3 5.9 7.5 7.4 Papua New Guinea 2005 100.0 31.8 5.7 2.2 24.5 0.5 14.2 3.2 4.7 1.7 Paraguay 2004a 100.0 33.6 8.5 6.3 23.7 0.0 6.2 1.9 8.7 7.9 Peru 2004a 100.0 30.1 9.6 8.7 10.4 0.0 10.7 9.7 9.4 8.2 Philippines 2003 67.0 25.7 4.4 2.6 1.6 0.0 5.7 5.0 4.2 2.0 Poland 2003a 96.2 11.9 4.3 2.2 8.8 5.0 18.1 6.7 2.4 1.2 Qatar 2002 100.0 16.0 4.1 4.3 0.2 0.0 4.5 5.7 4.0 4.0 Romania 2004a 100.0 40.4 13.3 12.0 37.7 0.0 17.1 10.3 12.8 12.4 Russian Federation 2002 0.0 .. 10.3 8.7 8.3 19.1 9.7 8.2 10.4 8.9 Rwanda 2003a 100.0 89.1 8.3 6.6 10.7 0.0 11.8 6.4 7.9 6.6 Saudi Arabia 2004 .. .. 6.6 7.3 10.9 0.3 6.0 10.5 6.7 6.6 Senegal 2004 100.0 30.0 13.4 9.2 50.4 0.0 14.7 8.1 13.2 10.5 Serbia and Montenegro 2002 .. .. 10.0 7.9 21.7 0.0 13.3 7.6 9.5 7.9 Seychelles 2001 .. .. 27.2 23.4 57.6 0.3 38.8 46.6 25.4 18.5 Sierra Leone 2004 100.0 47.4 .. .. .. .. .. .. .. .. Singapore 2003 69.3 6.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Slovak Republic 2002 100.0 5.0 22.1 21.2 51.0 0.0 19.5 12.8 22.4 23.6 Slovenia 2003a 100.0 23.7 3.9 1.6 10.4 1.4 6.1 3.3 3.5 1.1 South Africa 2001a 88.9 17.3 8.5 5.6 30.9 2.0 6.7 3.9 8.6 5.8 Sri Lanka 2004a 36.5 29.7 9.8 6.7 23.2 0.8 14.4 8.0 9.2 6.0 Sudan 2002 .. .. 21.1 19.6 43.8 0.0 28.2 24.0 20.5 18.9 Suriname 2000 .. .. 15.3 12.5 7.2 68.3 23.9 12.7 12.3 12.1 Swaziland 2001a 88.9 17.3 1.8 0.6 7.0 0.8 0.9 0.1 2.0 0.9 Sweden 1989 .. .. 5.4 4.3 3.6 2.3 1.4 1.0 6.0 5.0 Switzerlandb 2001a 99.8 1.7 3.2 1.5 .. 37.7 15.0 9.5 1.1 0.2 Syrian Arab Republic 2002 .. .. 14.6 15.5 23.2 0.1 14.2 11.7 14.5 16.6 Tanzania 2003a 13.4 120.0 14.1 8.2 37.4 0.0 15.2 7.4 14.0 8.6 2006 World Development Indicators 337 Tariff barriers All Primary Manufactured products products products % Share of Share of Most Simple Simple Weighted lines with lines with % % recent Binding mean mean mean international specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Thailand 2003a 74.8 25.8 13.3 8.3 46.4 0.7 15.4 4.4 12.9 9.3 Togo 2004 13.2 80.0 14.4 10.8 55.7 0.0 15.5 10.1 14.1 11.3 Trinidad and Tobago 2003 100.0 55.8 9.7 5.5 36.2 0.0 15.5 4.8 8.8 5.9 Tunisia 2004 57.8 57.7 25.3 22.3 65.6 0.0 36.8 18.4 24.2 23.5 Turkey 2003a 50.0 28.7 2.6 2.0 4.6 1.2 11.7 3.5 1.7 1.5 Turkmenistan 2002 .. .. 5.1 2.9 13.7 3.3 15.6 13.2 3.6 1.1 Uganda 2004a 14.9 73.5 6.7 5.4 0.0 0.0 9.5 6.4 6.4 5.0 Ukraine 2002 .. .. 7.6 3.9 11.2 10.5 6.8 1.5 7.6 6.4 United States 2005a 100.0 3.6 3.0 1.7 3.8 6.6 2.5 1.0 3.1 1.8 Uruguay 2004a 100.0 31.7 10.8 4.2 33.5 0.0 6.9 1.4 11.1 5.8 Uzbekistan 2001 .. .. 10.4 5.9 26.7 0.0 10.4 4.6 10.5 6.2 Venezuela, RB 2004a 99.9 36.8 12.2 11.9 20.6 0.0 12.2 11.8 12.1 11.8 Vietnam 2004a .. .. 13.6 14.5 33.6 0.2 17.8 17.7 12.8 13.3 Yemen, Rep. 2000 .. .. 12.7 11.8 10.9 0.0 13.5 10.8 12.6 12.4 Zambia 2005a 15.9 105.6 13.2 9.6 29.5 0.0 13.4 11.4 13.1 9.0 Zimbabwe 2003 20.8 91.3 15.9 18.7 36.3 5.7 19.2 27.2 15.4 15.7 a. Rates are either partially or fully recorded applied rates. All other simple and weighted tariff rates are most favored nation rates. b. Data for Switzerland include all specific rates converted to their ad valorem equivalents. 338 2006 World Development Indicators Tariff barriers About the data Poor people in developing countries work primar- Free Trade Agreement. The difference between and Development (UNCTAD) and the World Trade ily in agriculture and labor-intensive manufactures, most favored nation and applied rates can be sub- Organization (WTO). Data are classified using the sectors that confront the greatest trade barriers. stantial. As more countries report their free trade Harmonized System of trade at the six- or eight-digit Removing barriers to merchandise trade could agreements, suspensions of tariffs, or other spe- level. Tariff line data were matched to Standard increase growth by about 0.8 percent a year in these cial preferences, World Development Indicators will International Trade Classification (SITC) revision countries--even more if trade in services (retailing, include their effectively applied rates. All estimates 2 codes to define commodity groups and import business, financial, and telecommunications ser- are calculated using the most up-to-date information, weights. Import weights were calculated using the vices) were also liberalized. which is not necessarily updated every year. As a United Nations Statistics Division's Commodity In general, tariffs in high-income countries on result, data for the same year may differ from data Trade (Comtrade) database. Data are shown only for imports from developing countries, though low, are in last year's publication. the last year for which complete data are available. twice the size of those collected from other high- Three measures of average tariffs are shown: sim- To conserve space, data for the European Union are income countries. But protection is also an issue ple bound rates and the simple and the weighted shown instead of data for individual members. for developing countries, which maintain high tariffs mean tariffs. The most favored nation or applied on agricultural commodities, labor-intensive manu- rates are calculated using all traded items, while Definitions factures, and other products and services. In some bound rates are based on all products in a country's · Binding coverage is the percentage of product developing regions new trade policies could make tariff schedule. Weighted mean tariffs are weighted lines with an agreed bound rate. · Simple mean the difference between achieving important Millen- by the value of the country's trade with each trading bound rate is the unweighted average of all the lines nium Development Goals--reducing poverty, lower- partner. Simple averages are often a better indicator in the tariff schedule in which bound rates have been ing maternal and child mortality rates, improving of tariff protection than weighted averages, which are set. · Simple mean tariff is the unweighted average educational attainment--and falling far short. biased downward because higher tariffs discourage of effectively applied rates or most favored nation Countries use a combination of tariff and nontariff trade and reduce the weights applied to these tariffs. rates for all products subject to tariffs calculated measures to regulate imports. The most common Bound rates have resulted from trade negotiations for all traded goods. · Weighted mean tariff is the form of tariff is an ad valorem duty, based on the that are incorporated into a country's schedule of average of effectively applied rates or most favored value of the import, but tariffs may also be levied concessions and are thus enforceable. If a contract- nation rates weighted by the product import shares on a specific, or per unit, basis or may combine ad ing party raises a tariff to a higher level than its corresponding to each partner country. · Share of valorem and specific rates. Tariffs may be used to bound rate, beneficiaries of the earlier binding have lines with international peaks is the share of lines raise fiscal revenues or to protect domestic indus- a right to receive compensation, usually as reduced in the tariff schedule with tariff rates that exceed 15 tries from foreign competition--or both. Nontariff tariffs on other products they export to the country. percent. · Share of lines with specific rates is the barriers, which limit the quantity of imports of a par- If the beneficiaries are not compensated, they may share of lines in the tariff schedule that are set on ticular good, include quotas, prohibitions, licensing retaliate by raising their own tariffs against an equiv- a per unit basis or that combine ad valorem and per schemes, export restraint arrangements, and health alent value of the original country's exports. unit rates. · Primary products are commodities clas- and quarantine measures. Some countries set fairly uniform tariff rates across sified in SITC revision 2 sections 0­4 plus division Nontariff barriers are generally considered less all imports. Others are more selective, setting high tar- 68 (nonferrous metals). · Manufactured products desirable than tariffs because changes in an export- iffs to protect favored domestic industries. The share are commodities classified in SITC revision 2 sec- ing country's efficiency and costs no longer result in of tariff lines with international peaks (those for which tions 5­8 excluding division 68. changes in market share in the importing country. ad valorem tariff rates exceed 15 percent) provides an Further, the quotas or licenses that regulate trade indication of how selectively tariffs are applied. The become very valuable, and resources are often wasted effective rate of protection--the degree to which the in attempts to acquire these assets. A high percent- value added in an industry is protected--may exceed age of products subject to nontariff barriers suggests the nominal rate if the tariff system systematically a protectionist trade regime, but the frequency of differentiates among imports of raw materials, inter- nontariff barriers does not measure how much they mediate products, and finished goods. restrict trade. Moreover, a wide range of domestic The share of tariff lines with specific duties shows policies and regulations (such as health regulations) the extent to which countries utilize tariffs based on may act as nontariff barriers. Based on the difficulty of physical quantities or other, non ad valorem mea- combining nontariff barriers into an aggregate indica- sures. Some countries--for example, Switzerland-- Data sources tor, they are not included in this table. apply only specific duties. Specific duties are not All indicators in the table were calculated by World The tariff rates used in calculating the indicators in included in the table, except for Switzerland. Work is Bank staff using the World Integrated Trade Solution the table are most favored nation rates unless they under way to complete the estimations for ad valorem system. Data on tariffs were provided by UNCTAD and are specified as applied rates. Effectively applied equivalents of specific duties for all countries. the WTO. Data on global imports are from the United rates are those in effect for partners in preferen- The indicators were calculated from data sup- Nations Statistics Division's Comtrade database. tial trade agreements such as the North American plied by the United Nations Conference on Trade 2006 World Development Indicators 339 Global private financial flows Foreign direct Portfolio Bank and trade- investment investment flows related lending $ millions $ millions Bonds Equity $ millions 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. Albania 0 426 .. 0 0 0 .. 30 Algeria 40 882 ­16 0 0 0 ­409 ­479 Angola ­335 1,444 0 0 0 0 570 1,288 Argentina 1,836 4,084 ­857 ­671 0 ­86 ­1,195 ­823 Armenia 4 219 .. 0 0 1 .. 0 Australia 8,111 42,469 .. .. .. .. .. .. Austria 653 4,022 .. .. .. .. .. .. Azerbaijan .. 3,556 .. 0 0 0 .. 117 Bangladesh 3 449 0 0 0 4 55 ­16 Belarus .. 169 .. 0 0 1 .. ­62 Belgium 8,047a 118,758a .. .. .. .. .. .. Benin 62 60 0 0 0 1 0 0 Bolivia 27 116 0 0 0 0 ­24 3 Bosnia and Herzegovina .. 613 .. 0 0 0 .. 39 Botswana 96 47 0 0 0 10 ­19 ­1 Brazil 989 18,166 129 ­4,436 103 2,081 ­555 ­178 Bulgaria 4 2,005 .. ­548 0 0 .. 1,618 Burkina Faso 0 35 0 0 0 0 ­1 0 Burundi 1 3 0 0 0 0 ­6 ­5 Cambodia .. 131 0 0 0 0 0 0 Cameroon ­113 0 0 0 0 0 ­12 24 Canada 7,581 6,284 .. .. .. .. .. .. Central African Republic 1 ­13 0 0 0 0 ­1 ­4 Chad 9 478 0 0 0 0 ­1 0 Chile 661 7,603 ­7 1,451 367 8 1,194 ­1,093 China 3,487 54,936 ­48 3,690 0 10,923 4,668 4,280 Hong Kong, China .. 34,034 .. .. .. .. .. .. Colombia 500 3,052 ­4 553 0 130 ­151 ­1,844 Congo, Dem. Rep. ­14 0 0 0 0 0 ­12 ­4 Congo, Rep. 23 0 0 0 0 0 ­100 0 Costa Rica 163 620 ­42 49 0 0 ­99 ­21 Côte d'Ivoire 48 175 ­1 0 0 ­1 10 ­134 Croatia .. 1,243 .. 910 0 177 .. 2,808 Cuba .. .. .. .. .. .. .. .. Czech Republic 72 4,454 .. 2,696 0 738 .. ­658 Denmark 1,132 ­8,804 .. .. .. .. .. .. Dominican Republic 133 645 0 ­20 0 0 ­3 440 Ecuador 126 1,160 0 0 0 1 58 598 Egypt, Arab Rep. 734 1,253 ­1 ­100 0 26 ­65 ­128 El Salvador 2 466 0 294 0 0 6 ­35 Eritrea .. 30 .. 0 0 0 .. 0 Estonia .. 1,049 .. 857 0 176 .. 1,570 Ethiopia 12 545 0 0 0 0 ­57 71 Finland 812 3,075 .. .. .. .. .. .. France 13,183 24,521 .. .. .. .. .. .. Gabon 73 323 0 0 0 0 29 ­23 Gambia, The 14 60 0 0 0 0 ­8 0 Georgia .. 499 .. 0 0 0 .. 63 Germany 3,005 ­34,903 .. .. .. .. .. .. Ghana 15 139 0 0 0 0 ­23 31 Greece 1,005 1,355 .. .. .. .. .. .. Guatemala 48 155 ­11 380 0 0 1 ­16 Guinea 18 100 0 0 0 0 ­19 0 Guinea-Bissau 2 5 0 0 0 0 0 0 Haiti 8 7 0 0 0 0 0 0 340 2006 World Development Indicators Global private financial flows Foreign direct Portfolio Bank and trade- investment investment flows related lending $ millions $ millions Bonds Equity $ millions 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 44 293 0 0 0 0 32 161 Hungary 623 4,608 921 2,875 0 1,491 ­1,379 7,978 India 237 5,335 147 3,722 0 8,835 1,459 ­40 Indonesia 1,093 1,023 26 1,520 0 2,129 1,804 ­2,467 Iran, Islamic Rep. ­362 500 0 0 0 0 ­30 652 Iraq .. .. .. .. .. .. .. .. Ireland 627 11,040 .. .. .. .. .. .. Israel 151 1,664 .. .. .. .. .. .. Italy 6,411 16,772 .. .. .. .. .. .. Jamaica 138 602 0 641 0 0 ­46 56 Japan 1,777 7,805 .. .. .. .. .. .. Jordan 38 620 0 ­11 0 ­120 214 ­5 Kazakhstan .. 4,104 .. 3,075 0 ­14 .. 5,102 Kenya 57 46 0 0 0 3 65 ­111 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 788 8,189 .. .. .. .. .. .. Kuwait 0 ­20 .. .. .. .. .. .. Kyrgyz Republic .. 77 .. 0 0 0 .. ­54 Lao PDR 6 17 0 0 0 0 0 0 Latvia .. 699 .. 503 0 23 .. 1,129 Lebanon 6 288 0 2,632 0 0 6 ­48 Lesotho 17 123 0 0 0 0 0 ­9 Liberia 225 20 0 0 0 0 0 0 Libya .. .. .. .. .. .. .. .. Lithuania .. 773 .. 696 0 8 .. 598 Macedonia, FYR .. 157 .. 0 0 15 .. 27 Madagascar 22 45 0 0 0 0 ­15 ­2 Malawi 23 16 0 0 1 0 2 ­2 Malaysia 2,332 4,624 ­1,239 2,063 0 4,400 ­617 ­2,039 Mali 6 180 0 0 0 1 ­1 1 Mauritania 7 300 0 0 0 0 ­1 0 Mauritius 41 14 0 0 0 19 45 ­40 Mexico 2,549 17,377 661 ­1,904 1,995 ­2,522 4,396 294 Moldova .. 81 .. ­2 0 ­2 .. ­28 Mongolia 0 93 .. 0 0 0 .. 0 Morocco 165 769 0 ­40 0 572 318 ­532 Mozambique 9 245 0 0 0 0 26 ­23 Myanmar 163 214 0 0 0 0 ­8 ­32 Namibia .. .. .. .. .. .. .. .. Nepal 6 0 0 0 0 0 ­14 0 Netherlands 10,676 377 .. .. .. .. .. .. New Zealand 1,735 2,271 .. .. .. .. .. .. Nicaragua 1 250 0 0 0 0 20 26 Niger 41 0 0 0 0 0 10 ­7 Nigeria 588 1,875 0 0 0 0 ­121 ­145 Norway 1,003 502 .. .. .. .. .. .. Oman 142 ­17 0 550 0 147 ­400 ­578 Pakistan 245 1,118 0 283 0 50 ­63 ­132 Panama 136 1,012 ­2 769 ­1 0 ­4 11 Papua New Guinea 155 25 0 0 0 0 49 ­214 Paraguay 77 92 0 0 0 0 ­9 ­129 Peru 41 1,816 0 1,242 0 ­47 18 34 Philippines 530 469 395 1,823 0 418 ­286 ­252 Poland 89 12,613 0 3,370 0 1,913 ­18 ­100 Portugal 2,610 825 .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. 2006 World Development Indicators 341 Global private financial flows Foreign direct Portfolio Bank and trade- investment investment flows related lending $ millions $ millions Bonds Equity $ millions 1990 2004 1990 2004 1990 2004 1990 2004 Romania 0 5,440 0 ­187 0 111 4 4,152 Russian Federation .. 12,479 .. 7,904 0 528 .. 2,805 Rwanda 8 8 0 0 0 0 ­2 0 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 57 70 0 0 1 4 ­15 92 Serbia and Montenegro .. 966 .. 0 0 0 .. 1,191 Sierra Leone 32 26 0 0 0 0 4 0 Singapore 5,575 16,032 .. .. .. .. .. .. Slovak Republic .. 1,122 .. 622 0 60 .. 404 Slovenia .. 827 .. .. .. .. .. .. Somalia 6 9 0 0 0 0 0 0 South Africa ­76 585 .. 1,249 389 6,661 .. ­668 Spain 13,984 16,594 .. .. .. .. .. .. Sri Lanka 43 233 0 100 0 ­100 10 ­57 Sudan ­31 1,511 0 0 0 0 0 54 Swaziland 30 68 0 0 ­2 0 ­2 16 Sweden 1,982 ­588 .. .. .. .. .. .. Switzerland 5,545 ­797 .. .. .. .. .. .. Syrian Arab Republic 71 275 0 0 0 0 ­9 ­4 Tajikistan .. 272 .. 0 0 0 .. ­24 Tanzania 0 249 0 0 0 0 5 ­4 Thailand 2,444 1,412 ­87 597 440 ­295 1,574 117 Togo 18 60 0 0 4 3 0 0 Trinidad and Tobago 109 1,001 ­52 ­150 0 0 ­126 0 Tunisia 76 593 ­60 282 5 24 ­137 141 Turkey 684 2,733 597 2,109 89 1,427 466 5,826 Turkmenistan .. .. .. .. .. .. .. .. Uganda ­6 222 0 0 0 1 16 7 Ukraine .. 1,715 .. 856 0 ­2,204 .. 4,637 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 33,504 72,561 .. .. .. .. .. .. United States 48,490 106,831 .. .. .. .. .. .. Uruguay 42 311 ­16 ­186 0 0 ­176 ­123 Uzbekistan .. 140 .. 0 0 0 .. ­160 Venezuela, RB 451 1,518 345 872 0 ­170 ­922 ­82 Vietnam 180 1,610 0 ­13 0 0 0 23 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. ­131 144 0 0 0 0 161 0 Zambia 203 334 0 ­1 0 0 ­9 ­22 Zimbabwe ­12 60 ­30 0 0 0 127 7 World 201,413 s 624,797 s .. s .. s .. s .. s .. s .. s Low income 2,233 17,031 116 3,990 7 8,899 1,532 ­844 Middle income 20,523 194,354 966 39,007 3,383 28,660 13,935 35,627 Lower middle income 10,307 111,023 388 9,711 545 13,653 6,535 18,478 Upper middle income 10,216 83,331 577 29,297 2,838 15,007 7,400 17,149 Low & middle income 22,756 211,385 1,081 42,997 3,390 37,559 15,467 34,784 East Asia & Pacific 10,505 64,563 ­952 9,679 440 17,575 7,180 ­594 Europe & Central Asia 1,476 62,211 1,893 25,738 89 4,450 4,281 38,901 Latin America & Carib. 8,244 60,843 101 ­1,087 2,464 ­606 2,430 ­2,767 Middle East & N. Africa 780 5,340 ­76 3,313 5 649 ­350 ­982 South Asia 541 7,151 147 4,105 1 8,789 1,446 ­224 Sub-Saharan Africa 1,209 11,276 ­31 1,249 393 6,701 479 450 High income 178,657 413,412 .. .. .. .. .. .. Europe EMU 61,012 122,354 .. .. .. .. .. .. a. Includes Luxembourg. 342 2006 World Development Indicators Global private financial flows About the data The data on foreign direct investment are based on important source of financing for investment projects in in the classification of economies, and in the method balance of payments data reported by the Interna- some developing countries. In addition, foreign direct used to adjust and disaggregate reported informa- tional Monetary Fund (IMF), supplemented by staff investment data capture only cross-border investment tion. Differences in reporting arise particularly for estimates using data reported by the United Nations flows involving equity participation and thus omit non- foreign investments in local equity markets because Conference on Trade and Development and official equity crossborder transactions such as intrafirm flows clarity, adequate disaggregation, and comprehensive national sources. of goods and services. For a detailed discussion of the and periodic reporting are lacking in many develop- The internationally accepted definition of foreign data issues, see the World Bank's World Debt Tables ing economies. By contrast, capital flows through direct investment is provided in the fifth edition of 1993­94 (volume 1, chapter 3). international debt and equity instruments are well the IMF's Balance of Payments Manual (1993). Under Portfolio flow data are compiled from several market recorded, and for these the differences in reporting this definition foreign direct investment has three and official sources, including Euromoney databases lie primarily in the classification of economies, the components: equity investment, reinvested earn- and publications; Micropal; Lipper Analytical Services; exchange rates used, whether particular installments ings, and short- and long-term intercompany loans published reports of private investment houses, cen- of the transactions are included, and the treatment between parent firms and foreign affiliates. But many tral banks, national securities and exchange commis- of certain offshore issuances. countries fail to report reinvested earnings, and the sions, and national stock exchanges; and the World Net private capital flows--calculated as the sum of definition of long-term loans differs among countries. Bank's Debtor Reporting System. foreign direct investment, portfolio investment flows, Foreign direct investment, as distinguished from Gross statistics on international bond and equity and bank and trade-related lending--are no longer other kinds of international investment, is made to issues are produced by aggregating individual trans- included in the table because they are likely to be establish a lasting interest in or effective manage- actions reported by market sources. Transactions of overestimated. The source of overestimation is the ment control over an enterprise in another country. public and publicly guaranteed bonds are reported possible double counting of intercompany lending, As a guideline, the IMF suggests that investments through the Debtor Reporting System by World Bank which is a debt liability but may also be included in should account for at least 10 percent of voting stock member economies that have received either loans foreign direct investment flows. There is currently to be counted as foreign direct investment. In prac- from the International Bank for Reconstruction and no practical way to know when double counting has tice, many countries set a higher threshold. Development or credits from the International Devel- occured and therefore to adjust for it. The Organisation for Economic Co-operation and opment Association. Information on private nonguar- Development has also published a definition, in anteed bonds is collected from market sources, Definitions consultation with the IMF, Eurostat, and the United because official national sources reporting to the · Foreign direct investment is net inflows of invest- Nations. Because of the multiplicity of sources and Debtor Reporting System are not asked to report the ment to acquire a lasting management interest differences in definitions and reporting methods, breakdown between private nonguaranteed bonds in an enterprise operating in an economy other than there may be more than one estimate of foreign and private nonguaranteed loans. Information on that of the investor. It is the sum of equity capital, direct investment for a country and data may not be transactions by nonresidents in local equity markets reinvestment of earnings, other long-term capital, comparable across countries. is gathered from national authorities, investment and short-term capital, as shown in the balance Foreign direct investment data do not give a complete positions of mutual funds, and market sources. of payments. · Portfolio investment flows are net picture of international investment in an economy. Bal- The volume of portfolio investment reported by the and include portfolio debt flows (public and publicly ance of payments data on foreign direct investment do World Bank generally differs from that reported by guaranteed and private nonguaranteed bond issues not include capital raised locally, which has become an other sources because of differences in the sources, purchased by foreign investors) and non-debt-creat- ing portfolio equity flows (the sum of country funds, depository receipts, and direct purchases of shares Which developing countries received the most net inflows of foreign direct investment in 2004? by foreign investors). · Bank and trade-related 1995 2004 lending covers commercial bank lending (public and publicly guaranteed and private nonguaranteed) and other private credits. China China Others 26% Others 34% 42% 37% Data sources Mexico 8% Romania 0.4% Data are compiled from a variety of public and Russian Mexico 9% Romania 3% Brazil 9% private sources, including the World Bank's Debtor Federation 2% Russian Brazil 5% Federation 6% Poland 6% Reporting System, the IMF's International Finan- India 2% Poland 4% Chile 3% India 3% Chile 4% cial Statistics and Balance of Payments data- bases, and other sources mentioned in About the Economic integration in the past decade has boosted foreign direct investment (FDI) inflows to developing countries-- particularly those with improved investment climates. FDI is also increasingly concentrated. The top eight FDI-receiving data. These data are also published in the World developing countries account for 63 percent of net FDI inflow in 2004, up from 58 percent in 1995. Bank's Global Development Finance 2006. Source: World Bank Debtor Reporting System. 2006 World Development Indicators 343 Net financial flows from Development Assistance Committee members Net flows to part I countries Official Other Private Net Total net development assistance official flows grants by flows flows NGOs Contributions Foreign Bilateral Multilateral Private Bilateral Bilateral to multilateral direct portfolio portfolio export Total grants loans institutions Total investment investment investment credits 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 $ millions Australia 1,460 1,191 .. 270 35 482 506 ­24 .. .. 489 2,466 Austria 678 380 ­28 325 ­229 815 924 .. .. ­109 89 1,352 Belgium 1,463 953 ­50 561 ­93 ­735 ­169 .. .. ­566 181 816 Canada 2,599 2,022 ­31 608 ­794 3,542 3,613 ­71 .. 0 639 5,986 Denmark 2,037 1,192 11 835 21 518 518 .. .. .. 58 2,634 Finland 655 353 9 293 .. .. .. .. .. .. .. .. France 8,473 6,067 ­500 2,906 ­216 4,342 1,534 2,831 .. ­23 .. 12,599 Germany 7,534 4,513 ­690 3,712 ­1,051 4,199 3,613 ­278 ­85 949 1,148 11,830 Greece 465 304 .. 161 4 ­14 ­14 .. .. .. 17 472 Ireland 607 410 .. 198 .. 3,010 .. 3,010 .. .. 234 3,851 Italy 2,462 855 ­151 1,757 507 221 808 ­2,269 .. 1,682 49 3,239 Japan 8,906 7,131 ­1,213 2,988 ­2,372 4,392 9,171 ­3,426 ­3,020 1,667 425 11,351 Luxembourg 236 171 .. 64 .. .. .. .. .. .. 6 242 Netherlands 4,204 3,217 ­547 1,534 151 9,339 1,986 3,086 559 3,708 412 14,106 New Zealand 212 159 .. 53 5 25 25 .. .. .. 29 271 Norway 2,199 1,496 41 662 0 586 635 .. .. ­49 .. 2,785 Portugal 1,031 179 694 158 ­692 335 187 .. .. 148 3 676 Spain 2,437 1,227 173 1,037 25 10,300 10,503 .. .. ­203 .. 12,762 Sweden 2,722 2,066 10 646 ­64 266 594 .. .. ­328 31 2,954 Switzerland 1,545 1,173 14 359 .. ­2,810 ­2,082 .. ­966 238 316 ­949 United Kingdom 7,883 5,239 100 2,544 ­155 18,805 13,335 5,826 .. ­356 390 26,922 United States 19,705 17,027 ­777 3,455 ­679 6,465 20,355 ­12,343 ­1,255 ­293 6,792 32,283 Total 79,512 57,322 ­2,937 25,126 ­5,599 64,082 66,041 ­3,658 ­4,766 6,465 11,307 148,646 Net flows to part II countries Official Other Private Net Total net aid official flows grants by flows flows NGOs Contributions Foreign Bilateral Private Bilateral Bilateral to multilateral direct portfolio export Total grants loans institutions Total investment investment credits 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 $ millions Australia 10 5 .. 6 23 ­1,478 ­1,324 ­154 .. .. ­1,445 Austria 260 158 0 101 ­2 3,702 3,778 0 ­76 12 3,973 Belgium 190 8 .. 182 ­44 6,636 6,657 0 ­21 .. 6,782 Canada 93 93 0 .. ­71 3,403 3,301 150 ­48 .. 3,425 Denmark 140 64 ­21 97 5 767 767 .. .. 5 918 Finland 92 45 .. 47 .. .. .. .. .. .. 92 France 2,358 1,532 32 795 ­97 .. 6,038 4,078 1,938 22 8,299 Germany 1,435 549 ­74 959 ­1,076 .. 7,600 2,825 4,564 211 7,958 Greece 130 51 .. 80 11 2 93 93 .. .. 237 Ireland 3 3 .. .. .. .. .. .. .. .. 3 Italy 664 14 .. 650 ­59 .. 170 494 ­1,758 1,434 775 Japan 121 129 ­68 60 ­90 .. 5,671 5,344 1,081 ­754 5,702 Luxembourg 15 3 .. 13 .. .. .. .. .. .. 15 Netherlands 222 64 ­12 169 .. .. 17,745 8,513 7,398 1,834 17,967 New Zealand 1 1 .. 0 .. .. .. .. .. .. 1 Norway 45 45 .. .. 0 .. ­1 .. .. ­1 44 Portugal 62 1 .. 61 ­5 .. ­82 ­89 .. 7 ­24 Spain 15 15 .. .. .. .. 2,169 2,169 .. .. 2,184 Sweden 123 123 .. .. ­13 .. 862 724 .. 138 972 Switzerland 100 85 4 12 1 13 8,262 8,312 0 ­50 8,375 United Kingdom 834 70 0 764 .. 4 20,667 4,284 16,648 ­266 21,505 United States 1,605 1,702 ­167 70 ­278 3,577 9,124 18,713 ­9,663 74 14,027 Total 8,519 4,759 ­305 4,065 ­1,694 3,613 91,347 68,639 20,204 2,504 101,785 Note: Data may not sum to totals because of gaps in reporting. A substantial part of the increase in private flows to part II countries is due to the transfer of countries from part I to part II of the Development Assistance Committee list of aid recipients. 344 2006 World Development Indicators Net financial flows from Development Assistance Committee members About the data Definitions The high-income members of the Development countries and flows reported by the United Nations, · Official development assistance comprises grants Assistance Committee (DAC) of the Organisation for all UN agencies revised their data to include only reg- and loans (net of repayments of principal) that meet Economic Co-operation and Development (OECD) are ular budgetary expenditures since 1990 (except for the DAC definition of ODA and are made to countries the main source of official external finance for devel- the World Food Programme and the United Nations and territories in part I of the DAC list of aid recipi- oping countries. The table shows the flow of official High Commissioner for Refugees, which revised their ents. · Official aid comprises grants and loans (net and private financial resources from DAC members data from 1996 onward). of repayments) that meet the criteria for ODA and to official and private recipients in developing and DAC maintains a list of countries and territories are made to countries and territories in part II of transition economies. that are aid recipients. Part I of the list comprises the DAC list of aid recipients. · Bilateral grants are DAC exists to help its members coordinate their developing countries and territories considered by transfers of money or in kind for which no repayment development assistance and to encourage the DAC members to be eligible for ODA. Part II com- is required. · Bilateral loans are loans extended by expansion and improve the effectiveness of the prises economies in transition: more advanced coun- governments or official agencies that have a grant aggregate resources flowing to recipient econo- tries of Central and Eastern Europe, the countries element of at least 25 percent (calculated at a rate of mies. In this capacity DAC monitors the flow of all of the former Soviet Union, and certain advanced discount of 10 percent). · Contributions to multilat- financial resources, but its main concern is official developing countries and territories. Flows to these eral institutions are concessional funding received development assistance (ODA). DAC has three cri- recipients that meet the criteria for ODA are termed by multilateral institutions from DAC members in teria for ODA: It is undertaken by the official sector. official aid. the form of grants or capital subscriptions. · Other It promotes the economic development and welfare The table was compiled from replies by DAC mem- official flows are transactions by the official sector of developing countries as a main objective. And it ber countries to questionnaires issued by the DAC whose main objective is other than development or is provided on concessional terms, with a grant ele- Secretariat. Net flows of ODA, official aid, and other whose grant element is less than 25 percent. · Pri- ment of at least 25 percent on loans (calculated at official resources are defined as gross disburse- vate flows consist of flows at market terms financed a rate of discount of 10 percent). ments of grants and loans minus repayments of prin- from private sector resources in donor countries. This definition excludes nonconcessional flows cipal on earlier loans. Because the table is based on They include changes in holdings of private long-term from official creditors, which are classified as "other donor country reports, it does not provide a complete assets by residents of the reporting country. · For- official flows," and military aid, which is not recorded picture of the resources received by developing and eign direct investment is investment by residents of in DAC statistics. The definition includes food aid, transition economies, for two reasons. First, flows DAC member countries to acquire a lasting manage- capital projects, emergency relief, technical coop- from DAC members are only part of the aggregate ment interest (at least 10 percent of voting stock) eration, and postconflict peacekeeping efforts. Also resource flows to these economies. Second, the data in an enterprise operating in the recipient country. included are contributions to multilateral institutions, that record contributions to multilateral institutions The data reflect changes in the net worth of subsid- such as the United Nations and its specialized agen- measure the flow of resources made available to iaries in recipient countries whose parent company cies, and concessional funding to the multilateral those institutions by DAC members, not the flow of is in the DAC source country. · Bilateral portfolio development banks. In 1999, to avoid double count- resources from those institutions to developing and investment covers bank lending and the purchase of ing extrabudgetary expenditures reported by DAC transition economies. bonds, shares, and real estate by residents of DAC member countries in recipient countries. · Multilat- eral portfolio investment records the transactions of private banks and nonbanks in DAC member coun- Who were the largest donors in 2004? tries in the securities issued by multilateral institu- Official development assistance as a share of total tions. · Private export credits are loans extended to recipient countries by the private sector in DAC member countries to promote trade; they may be supported by an official guarantee. · Net grants by Others 16% United NGOs are private grants by nongovernmental orga- States Spain 3% 26% nizations, net of subsidies from the official sector. Italy 3% · Total net flows comprise ODA or official aid flows, other official flows, private flows, and net grants by Canada 3% nongovernmental organizations. Sweden 3% Japan Data sources 11% Netherlands 5% Data on financial flows are compiled by DAC and Germany published in its annual statistical report, Geo- United France 9% Kingdom 11% graphical Distribution of Financial Flows to Aid 10% Recipients, and its annual Development Coopera- tion Report. Data are available electronically on The United States is the largest donor, contributing a quarter of total official development assistance in 2004. the OECD's International Development Statistics The next four largest donors---Japan, France, the United Kingdom, and Germany---contributed 41 percent combined. CD-ROM and to registered users at www.oecd. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. org/dataoecd/50/17/5037721.htm. 2006 World Development Indicators 345 Aid flows from Development Assistance Committee members Net flows to part I countries Net official development assistance Untied aida average annual % change in Per capita of volumeb donor country b % of general % of bilateral $ millions % of GNI 1998­99 to $ government disbursement ODA commitments 1999 2004 1999 2004 2003­04 1999 2004 1999 2004 1999 2004 Australia 982 1,460 0.26 0.25 1.7 59 62 0.70 0.67 86.7 77.1 Austria 492 678 0.24 0.23 1.0 69 74 0.43 0.46 39.8 52.2 Belgium 760 1,463 0.30 0.41 11.4 84 125 0.60 0.84 39.0 92.7 Canada 1,706 2,599 0.28 0.27 1.7 65 73 0.61 0.66 29.6 56.7 Denmark 1,733 2,037 1.01 0.85 ­2.0 378 336 1.78 1.50 70.8 88.8 Finland 416 655 0.33 0.35 5.0 91 113 0.63 0.70 84.7 .. France 5,639 8,473 0.38 0.41 3.3 109 122 0.73 0.77 70.6 94.2 Germany 5,515 7,534 0.26 0.28 2.5 74 82 0.54 0.59 84.7 92.2 Greece 194 465 0.15 0.23 12.2 21 37 0.32 0.44 3.3 23.0 Ireland 245 607 0.31 0.39 13.5 82 133 0.75 0.98 .. 100.0 Italy 1,806 2,462 0.15 0.15 ­0.6 37 38 0.31 0.30 22.6 .. Japan 12,163 8,906 0.27 0.19 ­4.7 88 67 0.72 0.51 96.4 94.4 Luxembourg 119 236 0.66 0.83 8.8 320 466 1.43 1.61 96.1 .. Netherlands 3,134 4,204 0.79 0.73 0.7 244 233 1.68 1.49 94.1 86.8 New Zealand 134 212 0.27 0.23 1.7 42 44 0.63 0.64 .. 81.2 Norway 1,370 2,199 0.88 0.87 2.5 399 430 1.80 1.89 99.1 100.0 Portugal 276 1,031 0.26 0.63 13.8 33 89 0.53 1.27 73.7 99.2 Spain 1,363 2,437 0.23 0.24 4.0 43 50 0.56 0.60 .. 67.7 Sweden 1,630 2,722 0.70 0.78 7.0 202 272 1.08 1.38 91.5 87.5 Switzerland 984 1,545 0.35 0.41 4.7 160 192 1.07 1.20 96.8 96.8 United Kingdom 3,426 7,883 0.24 0.36 10.2 65 115 0.59 0.84 91.8 100.0 United States 9,145 19,705 0.10 0.17 12.7 36 66 0.29 0.47 .. .. Total or average 53,233 79,512 0.22 0.26 4.3 69 84 0.56 0.63 85.8 90.6 Net flows to part II countries Net official aid average annual % change in Per capita of volumeb donor country b $ millions % of GNI 1998­99 to $ 1999 2004 1999 2004 2003­04 1999 2004 Australia 3 10 0.00 0.00 33.3 0 0 Austria 184 260 0.09 0.09 2.8 26 28 Belgium 82 190 0.03 0.05 14.6 9 16 Canada 165 93 0.03 0.01 ­13.2 6 3 Denmark 128 140 0.07 0.06 3.0 28 23 Finland 74 92 0.06 0.05 ­1.0 16 16 France 745 2,358 0.05 0.11 19.0 14 34 Germany 729 1,434 0.03 0.05 10.6 10 16 Greece 11 131 0.01 0.06 45.2 1 10 Ireland .. 3 .. 0.00 .. 0 1 Italy 92 664 0.01 0.04 22.9 2 10 Japan 67 121 0.00 0.00 ­187.9 0 1 Luxembourg 3 15 0.01 0.05 27.1 7 30 Netherlands 22 222 0.01 0.04 22.6 2 12 New Zealand 0 1 0.00 0.00 36.1 0 0 Norway 28 45 0.02 0.02 ­3.0 8 9 Portugal 28 62 0.03 0.04 11.9 3 5 Spain 13 15 0.00 0.00 ­3.6 0 0 Sweden 99 123 0.04 0.04 1.5 12 12 Switzerland 70 100 0.03 0.03 0.2 11 12 United Kingdom 407 834 0.03 0.04 8.8 8 12 United States 3,521 1,605 0.04 0.01 ­14.9 14 5 Total or average 6,468 8,519 0.03 0.03 1.6 8 9 a. Excludes administrative costs and technical cooperation. b. At 2003 exchange rates and prices. 346 2006 World Development Indicators Aid flows from Development Assistance Committee members About the data Effective aid supports institutional development and for aid-financed students in donor countries, or pay- priating or mismanaging aid receipts, but they may policy reforms, which are at the heart of successful ment of experts hired by donor countries. Second, also be motivated by a desire to benefit suppliers development. To be effective, especially in reducing donors record their concessional funding (usually in the donor country. The same volume of aid may global poverty, aid requires partnerships among recip- grants) to multilateral agencies when they make have different purchasing power depending on the ient countries, aid agencies, and donor countries. It payments, while the agencies make funds available relative costs of suppliers in countries to which the also requires improvements in economic policies and to recipients with a time lag and in many cases in aid is tied and the degree to which each recipient's institutions. Where traditional methods of nurturing the form of soft loans where donors' grants have aid basket is untied. such reforms have failed, aid agencies need to find been used to reduce the interest burden over the alternative approaches and new opportunities. life of the loan. Definitions As part of its work, the Development Assistance Aid as a share of gross national income (GNI), aid · Net official development assistance (ODA) and net Committee (DAC) of the Organisation for Economic per capita, and ODA as a share of the general gov- official aid record the actual international transfer Co-operation and Development (OECD) assesses ernment disbursements of the donor are calculated by the donor of financial resources or of goods or the aid performance of member countries relative by the OECD. The denominators used in calculating services valued at the cost to the donor, less any to the size of their economies. As measured here, aid these ratios may differ from corresponding values repayments of loan principal during the same period. comprises bilateral disbursements of concessional elsewhere in this book because of differences in tim- Data are shown at current prices and dollar exchange financing to recipient countries plus the provision by ing or definitions. rates. · Aid as a percentage of GNI shows the donor's donor governments of concessional financing to mul- DAC members have progressively introduced the contributions of ODA or official aid as a share of its tilateral institutions. Volume amounts, at constant new United Nations System of National Accounts gross national income. · Average annual percent- prices and exchange rates, are used to measure (adopted in 1993), which replaced gross national age change in volume and aid per capita of donor the change in real resources provided over time. Aid product (GNP) with GNI. Because GNI includes country are calculated using 2002 exchange rates flows to part I recipients--official development assis- items not included in GNP, ratios of ODA to GNI are and prices. · Aid as a percentage of general govern- tance (ODA)--are tabulated separately from those slightly smaller than the previously reported ratios ment disbursements shows the donor's contributions to part II recipients--official aid (see About the data of ODA to GNP. of ODA as a share of public spending. · Untied aid is for table 6.9 for more information on the distinction The proportion of untied aid is reported here the share of ODA that is not subject to restrictions by between the two types of aid flows). because tying arrangements may prevent recipi- donors on procurement sources. Measures of aid flows from the perspective of ents from obtaining the best value for their money donors differ from aid receipts from the perspective and so reduce the value of the aid received. Tying of recipients for two main reasons. First, aid flows arrangements require recipients to purchase goods include expenditure items about which recipients and services from the donor country or from a speci- may have no precise information, such as develop- fied group of countries. They may be justified on the ment-oriented research, stipends and tuition costs grounds that they prevent a recipient from misappro- Official development assistance from non-DAC donors, 2000­04 ($ millions) Donor 2000 2001 2002 2003 2004 OECD members (non-DAC) Czech Republic 16 26 45 91 108 Hungary .. .. .. 21 55 Iceland 9 10 13 18 21 Korea, Rep. 212 265 279 366 423 Poland 29 36 14 27 118 Slovak Republic 6 8 7 15 28 Turkey 82 64 73 67 339 Arab countries Kuwait 165 73 20 133 209 Saudi Arabia 295 490 2,478 2,391 1,734 Data sources United Arab Emirates 150 127 156 188 181 Data on financial flows are compiled by DAC and Other donors Israela 164 76 114 92 66 published in its annual statistical report, Geo- Other donorsb 1 2 3 4 22 graphical Distribution of Financial Flows to Aid Total 1,128 1,178 3,201 3,416 3,726 Recipients, and its annual Development Coopera- Note: China also provides aid, but does not disclose the amount. tion Report. Data are available electronically on a. Includes $66.8 million in 2000, $50.1 million in 2001, $87.8 million in 2002, $68.8 million in 2003, and $47.9 million in 2004 for first-year sustenance expenses for people arriving from developing countries (many of which are experiencing civil war the OECD's International Development Statistics or severe unrest) or people who have left their country for humanitarian or political reasons. CD-ROM and to registered users at www.oecd. b. Includes Estonia, Latvia, and Lithuania. org/dataoecd/50/17/5037721.htm. Source: Organisation for Economic Co-operation and Development. 2006 World Development Indicators 347 Aid dependency Net official Aid per Aid dependency development capita ratios assistance or official aid Aid as % Aid as % of of central Aid as Aid as % of gross imports of goods government $ millions $ % of GNI capital formation and services expenditure 1999 2004 1999 2004 1999 2004 1999 2004 1999 2004 1999 2004 Afghanistan 143 2,190 6 .. .. 38.0 .. .. .. .. .. 288.8 Albania 488 362 159 116 13.8 4.7 70.7 20.2 43.9 .. .. .. Algeria 138 313 5 10 0.3 0.4 1.0 1.1 .. .. 1.2 .. Angola 388 1,144 29 74 8.2 6.6 23.2 49.1 5.5 8.7 .. .. Argentina 100 91 3 2 0.0 0.1 0.2 0.3 0.2 0.2 .. 0.3 Armenia 209 254 68 84 11.0 8.1 61.8 40.5 21.9 14.2 .. 41.9 Australia Austria Azerbaijan 169 176 21 21 3.7 2.2 14.0 3.8 8.6 2.5 19.5 .. Bangladesh 1,215 1,404 10 10 2.6 2.4 11.9 10.3 13.2 10.4 .. 28.7 Belarus 39 46 4 5 0.3 0.2 1.4 0.7 0.6 0.3 1.2 0.7 Belgium Benin 211 378 30 46 8.9 9.3 50.5 45.6 23.7 .. .. .. Bolivia 569 767 70 85 7.0 9.1 36.6 70.7 24.3 27.4 .. 32.1 Bosnia and Herzegovina 1,040 671 277 172 21.0 7.7 79.3 37.8 23.2 9.3 .. 20.4 Botswana 61 39 35 22 1.3 0.5 4.3 1.4 2.0 .. .. .. Brazil 187 285 1 2 0.0 0.0 0.2 0.2 0.2 0.3 .. .. Bulgaria 271 622 33 80 2.1 2.6 11.7 11.0 3.9 3.6 6.7 7.3 Burkina Faso 398 610 36 48 14.2 12.7 59.6 66.2 .. .. .. .. Burundi 74 351 12 48 10.6 54.6 114.1 501.4 55.5 .. 44.5 .. Cambodia 277 478 22 35 8.1 10.3 45.2 38.0 11.0 12.1 .. 107.3 Cameroon 435 762 30 47 5.0 5.4 25.3 31.8 .. .. .. .. Canada Central African Republic 118 105 32 26 11.4 7.9 78.0 45.6 .. .. .. .. Chad 188 319 24 34 12.4 11.8 121.6 30.6 .. .. .. .. Chile 70 49 5 3 0.1 0.1 0.5 0.2 0.3 0.1 .. 0.3 China 2,394 1,661 2 1 0.2 0.1 0.6 0.2 1.1 0.3 .. .. Hong Kong, China 4 7 1 1 0.0 0.0 0.0 0.0 0.0 0.0 .. .. Colombia 302 509 7 11 0.4 0.5 2.7 2.8 1.9 2.1 .. 2.3 Congo, Dem. Rep. 132 1,815 3 32 3.1 28.6 91.0 213.9 .. .. 10.2 .. Congo, Rep. 142 116 43 30 8.6 3.5 21.7 11.0 7.2 .. .. .. Costa Rica -8 13 -2 3 -0.1 0.1 -0.3 0.3 -0.1 0.1 -0.3 0.3 Côte d'Ivoire 448 154 27 9 3.8 1.0 27.2 9.2 8.7 2.2 .. .. Croatia 48 121 11 27 0.2 0.4 1.1 1.2 0.5 0.6 0.5 0.8 Cuba 59 90 5 8 .. .. .. .. .. .. .. .. Czech Republic 325 280 32 27 0.6 0.3 2.0 0.9 0.9 0.3 .. 0.7 Denmark Dominican Republic 195 87 24 10 1.2 0.5 4.6 2.3 1.9 0.8 8.3 .. Ecuador 149 160 12 12 1.0 0.6 6.1 1.9 2.7 1.5 .. .. Egypt, Arab Rep. 1,582 1,458 24 20 1.7 1.9 8.1 11.1 7.0 5.3 .. .. El Salvador 184 211 30 31 1.5 1.4 9.0 8.6 3.6 2.8 .. 68.7 Eritrea 149 260 43 61 20.3 28.5 60.0 129.0 24.1 .. .. .. Estonia 84 136 61 101 1.5 1.3 6.0 3.9 1.9 1.3 4.9 .. Ethiopia 643 1,823 10 26 10.0 23.0 58.4 107.7 33.8 47.5 .. .. Finland France Gabon 48 38 38 28 1.2 0.6 3.9 2.1 2.0 .. .. .. Gambia, The 34 63 26 43 8.2 16.0 43.6 63.3 .. .. .. .. Georgia 245 315 51 70 8.3 6.0 39.6 20.7 21.2 11.9 60.6 42.2 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 609 1,358 31 63 8.1 15.4 37.9 54.9 15.0 24.3 .. 73.2 Greece Guatemala 293 218 27 18 1.6 0.8 9.2 4.5 5.5 2.4 15.2 7.2 Guinea 238 279 29 30 7.0 7.3 31.1 68.6 22.6 27.9 .. .. Guinea-Bissau 52 76 39 50 24.9 28.3 139.0 219.4 .. .. .. .. Haiti 263 243 34 29 6.4 6.7 23.2 .. 20.8 .. .. .. 348 2006 World Development Indicators Aid dependency Net official Aid per Aid dependency development capita ratios assistance or official aid Aid as % Aid as % of of central Aid as Aid as % of gross imports of goods government $ millions $ % of GNI capital formation and services expenditure 1999 2004 1999 2004 1999 2004 1999 2004 1999 2004 1999 2004 Honduras 818 642 131 91 15.6 9.1 41.8 .. 25.2 13.5 .. .. Hungary 249 303 25 30 0.6 0.3 1.8 1.3 0.7 0.4 .. .. India 1,491 691 1 1 0.3 0.1 1.4 0.4 2.2 .. 2.2 0.6 Indonesia 2,125 84 10 0 1.6 0.0 13.3 0.1 3.9 0.1 9.3 0.2 Iran, Islamic Rep. 162 189 3 3 0.2 0.1 0.5 0.3 1.0 .. 0.4 0.6 Iraq 76 4,658 3 .. .. .. .. .. .. .. .. .. Ireland Israel 906 479 148 70 0.9 0.4 3.8 2.4 1.9 0.8 .. 0.8 Italy Jamaica -22 75 -8 29 -0.3 0.9 -1.2 2.7 -0.5 1.2 -0.8 .. Japan Jordan 432 581 91 107 5.4 5.0 22.7 21.1 7.9 6.0 19.8 15.9 Kazakhstan 175 265 12 18 1.1 0.7 5.8 2.7 2.4 1.2 7.3 4.4 Kenya 310 635 10 19 2.4 4.0 15.5 21.6 8.9 12.0 13.4 .. Korea, Dem. Rep. 201 196 9 9 .. .. .. .. .. .. .. .. Korea, Rep. -55 -68 -1 -1 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 .. Kuwait 7 3 3 1 0.0 0.0 0.2 0.0 0.1 0.0 0.1 .. Kyrgyz Republic 283 258 58 51 24.1 12.2 125.8 84.9 35.8 20.9 127.8 .. Lao PDR 295 270 57 47 21.0 11.4 .. .. 46.9 .. .. .. Latvia 100 165 42 71 1.4 1.2 5.9 3.7 2.6 1.8 4.3 4.3 Lebanon 194 265 58 75 1.1 1.3 5.1 5.7 .. .. .. .. Lesotho 31 102 18 57 2.7 6.3 7.0 18.8 3.4 6.9 .. 20.5 Liberia 94 210 32 65 28.1 53.4 .. 346.8 .. .. .. .. Libya 7 18 1 3 .. 0.1 0.2 .. 0.1 0.1 .. .. Lithuania 134 252 38 73 1.3 1.2 5.5 4.8 2.3 1.8 .. 3.9 Macedonia, FYR 277 248 138 122 7.6 4.7 38.3 21.5 14.0 7.4 .. .. Madagascar 359 1,236 23 68 9.8 28.8 64.7 103.2 28.4 .. .. 44.9 Malawi 447 476 40 38 25.8 25.9 171.7 229.4 55.1 .. .. .. Malaysia 144 290 6 12 0.2 0.3 0.8 1.1 0.2 .. 1.0 .. Mali 355 567 31 43 14.0 12.2 65.1 59.2 32.9 .. .. .. Mauritania 219 180 85 60 20.1 11.1 120.2 54.5 .. .. .. .. Mauritius 42 38 36 31 1.0 0.6 3.8 2.6 1.4 1.0 4.4 2.9 Mexico 37 121 0 1 0.0 0.0 0.0 0.1 0.0 0.1 0.1 .. Moldova 107 118 25 28 9.0 4.0 40.0 18.0 12.1 5.2 31.5 16.8 Mongolia 222 262 93 104 24.9 16.4 66.4 44.3 33.5 18.3 .. .. Morocco 679 706 24 23 2.0 1.4 8.3 5.6 5.2 3.4 6.8 .. Mozambique 805 1,228 46 63 21.3 21.4 55.1 100.6 47.1 44.6 .. .. Myanmar 81 121 2 2 .. .. .. .. 3.2 3.7 .. .. Namibia 179 179 97 89 5.3 3.1 22.7 12.3 9.2 6.6 15.8 .. Nepal 351 427 15 16 7.0 6.4 34.0 24.2 20.2 18.9 .. .. Netherlands New Zealand Nicaragua 673 1,232 138 229 19.0 28.3 46.9 95.3 28.3 40.4 93.8 137.0 Niger 187 536 16 40 9.4 17.5 90.7 109.3 36.8 .. .. .. Nigeria 152 573 1 4 0.5 1.0 1.9 3.6 1.0 3.3 .. .. Norway Oman 40 55 17 22 0.3 0.2 1.7 1.3 0.6 0.5 0.9 .. Pakistan 733 1,421 5 9 1.2 1.5 7.5 8.5 5.4 5.8 6.5 10.2 Panama 15 38 5 12 0.1 0.3 0.5 1.3 0.1 0.3 0.6 .. Papua New Guinea 216 266 42 46 6.6 7.6 38.6 .. 10.3 .. 21.4 .. Paraguay 78 0 15 0 1.0 0.0 4.4 0.0 2.3 0.0 6.1 0.0 Peru 451 487 18 18 0.9 0.7 4.1 3.8 4.2 3.0 5.1 4.2 Philippines 690 463 9 6 0.9 0.5 4.8 3.1 1.7 0.9 .. .. Poland 1,186 1,525 31 40 0.7 0.6 2.9 3.1 2.2 1.3 .. .. Portugal Puerto Rico 2006 World Development Indicators 349 Aid dependency Net official Aid per Aid dependency development capita ratios assistance or official aid Aid as % Aid as % of of central Aid as Aid as % of gross imports of goods government $ millions $ % of GNI capital formation and services expenditure 1999 2004 1999 2004 1999 2004 1999 2004 1999 2004 1999 2004 Romania 387 916 17 42 1.1 1.3 6.8 5.0 3.2 2.5 .. .. Russian Federation 1,946 1,313 13 9 1.0 0.2 6.7 1.1 3.0 0.9 .. 1.0 Rwanda 373 468 50 53 19.4 25.8 112.2 121.8 81.4 88.8 .. .. Saudi Arabia 29 32 1 1 0.0 0.0 0.1 0.1 0.1 0.0 .. .. Senegal 535 1,052 53 92 11.5 13.9 60.9 57.9 26.7 .. 91.0 .. Serbia and Montenegro 676 1,170 64 144 6.6 4.9 56.9 29.0 .. .. .. .. Sierra Leone 74 360 17 67 11.5 34.3 204.7 211.3 36.5 87.1 .. .. Singapore -1 9 0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 Slovak Republic 319 235 59 44 1.6 0.6 5.7 2.2 2.3 .. .. .. Slovenia 31 62 16 31 0.1 0.2 0.5 0.7 0.3 0.3 0.4 0.5 Somalia 115 191 17 24 .. .. .. .. .. .. .. .. South Africa 541 617 13 14 0.4 0.3 2.5 1.6 1.5 0.9 .. 1.0 Spain Sri Lanka 263 519 14 27 1.7 2.7 6.2 10.4 3.7 5.5 7.9 .. Sudan 243 882 8 25 2.6 4.5 13.9 20.9 14.7 15.3 30.0 .. Swaziland 29 117 28 104 2.0 4.9 11.2 26.6 2.2 4.5 8.1 .. Sweden Switzerland Syrian Arab Republic 228 110 14 6 1.5 0.5 6.8 2.2 3.7 1.2 .. .. Tajikistan 123 241 20 37 11.9 12.1 65.0 124.7 .. 16.0 112.7 84.4 Tanzania 990 1,746 29 46 11.6 16.2 73.8 83.8 42.0 52.7 .. .. Thailand 1,014 -2 17 0 0.9 0.0 4.0 0.0 1.6 0.0 .. 0.0 Togo 71 61 14 10 4.7 3.0 33.9 16.5 10.2 .. .. .. Trinidad and Tobago 26 -1 20 -1 0.4 0.0 1.8 0.0 0.8 .. .. .. Tunisia 253 328 27 33 1.3 1.2 4.6 4.7 2.5 2.1 4.4 4.1 Turkey 11 257 0 4 0.0 0.1 0.0 0.3 0.0 0.2 .. .. Turkmenistan 24 37 5 8 1.0 0.6 2.5 2.4 .. .. .. .. Uganda 590 1,159 25 42 9.9 17.3 50.3 75.5 37.6 49.1 70.6 .. Ukraine 569 360 11 8 1.9 0.6 10.3 2.9 3.5 1.0 7.2 1.7 United Arab Emirates 4 6 1 1 0.0 0.0 0.0 0.0 .. .. 0.1 .. United Kingdom United States Uruguay 22 22 7 6 0.1 0.2 0.7 1.3 0.5 0.5 0.4 0.6 Uzbekistan 155 246 6 9 0.9 2.1 5.2 10.2 .. .. .. .. Venezuela, RB 44 49 2 2 0.0 0.0 0.2 0.2 0.2 0.2 0.2 .. Vietnam 1,429 1,830 18 22 5.0 4.1 18.0 11.4 10.1 .. .. .. West Bank and Gaza 516 1,136 182 324 10.2 .. 35.1 .. .. .. .. .. Yemen, Rep. 458 252 26 12 6.6 2.1 25.6 11.5 12.8 4.0 27.7 .. Zambia 624 1,081 59 94 21.0 21.2 113.4 77.1 46.1 .. 114.2 .. Zimbabwe 245 186 20 14 4.3 4.0 28.5 31.0 .. .. .. .. World 60,715 s 87,307 s 10 w 14 w 0.2 w 0.2 w 0.9 w .. w 0.7 w 0.7 w .. w .. w Low income 19,714 33,954 9 14 2.4 2.8 10.9 11.9 10.3 .. .. .. Middle income 27,137 31,603 9 10 0.6 0.4 2.4 1.6 1.9 1.2 .. .. Lower middle income 19,713 23,430 8 10 0.7 0.6 2.8 1.8 2.8 1.6 .. .. Upper middle income 6,158 6,766 11 12 0.3 0.2 1.4 1.0 0.8 0.5 .. .. Low & middle income 58,885 85,456 12 16 1.1 1.0 4.5 3.8 3.6 2.8 .. .. East Asia & Pacific 9,890 6,916 6 4 0.6 0.3 2.1 0.7 2.1 0.6 .. .. Europe & Central Asia 11,478 11,869 24 25 1.3 0.7 6.1 2.9 3.2 1.5 .. .. Latin America & Carib. 5,937 6,869 12 13 0.3 0.4 1.6 1.6 1.3 1.2 .. .. Middle East & N. Africa 5,149 10,517 19 35 1.1 1.7 4.7 6.4 4.0 .. .. .. South Asia 4,293 6,758 3 5 0.7 0.8 3.2 3.3 4.2 .. .. .. Sub-Saharan Africa 13,263 26,004 21 36 4.2 5.3 22.2 26.1 11.5 13.9 .. .. High income 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. 350 2006 World Development Indicators Aid dependency About the data Definitions Ratios of aid to gross national income (GNI), gross reported by the United Nations, all UN agencies · Net official development assistance consists of capital formation, imports, and government spending revised their data since 1990 to include only regular disbursements of loans made on concessional terms provide a measure of the recipient country's depen- budgetary expenditures (except for the World Food (net of repayments of principal) and grants by offi- dency on aid. But care must be taken in drawing Programme and the United Nations High Commis- cial agencies of the members of DAC, by multilateral policy conclusions. For foreign policy reasons, some sioner for Refugees, which revised their data from institutions, and by non-DAC countries to promote countries have traditionally received large amounts 1996 onward). These revisions have affected net economic development and welfare in countries and of aid. Thus aid dependency ratios may reveal as ODA and official aid and, as a result, aid per capita territories in part I of the DAC list of aid recipients. much about a donor's interest as they do about a and aid dependency ratios. It includes loans with a grant element of at least recipient's needs. Ratios in Sub-Saharan Africa are Because the table relies on information from 25 percent (calculated at a rate of discount of 10 generally much higher than those in other regions, donors, it is not necessarily consistent with infor- percent). · Net official aid refers to aid flows (net and they increased in the 1980s. These high ratios mation recorded by recipients in the balance of pay- of repayments) from official donors to countries and are due only in part to aid flows. Many African coun- ments, which often excludes all or some technical territories in part II of the DAC list of aid recipients: tries saw severe erosion in their terms of trade in the assistance--particularly payments to expatriates more advanced countries of Central and Eastern 1980s, which, along with weak policies, contributed made directly by the donor. Similarly, grant commod- Europe, the countries of the former Soviet Union, to falling incomes, imports, and investment. Thus ity aid may not always be recorded in trade data or in and certain advanced developing countries and the increase in aid dependency ratios reflects events the balance of payments. Moreover, DAC statistics territories. Official aid is provided under terms and affecting both the numerator and the denominator. exclude purely military aid. conditions similar to those for ODA. · Aid per capita As defined here, aid includes official development The nominal values used here may overstate the includes both ODA and official aid. · Aid dependency assistance (ODA) and official aid (see About the data for real value of aid to the recipient. Changes in interna- ratios are calculated using values in U.S. dollars con- table 6.9). The data cover loans and grants from Devel- tional prices and in exchange rates can reduce the verted at official exchange rates. For definitions of opment Assistance Committee (DAC) member coun- purchasing power of aid. The practice of tying aid, GNI, gross capital formation, imports of goods and tries, multilateral organizations, and non-DAC donors. still prevalent though declining in importance, also services, and central government expenditure, see They do not reflect aid given by recipient countries to tends to reduce its purchasing power (see About the Definitions for tables 1.1, 4.8, and 4.11. other developing countries. As a result, some countries data for table 6.10). that are net donors (such as Saudi Arabia) are shown in The values for population, GNI, gross capital for- the table as aid recipients (see table 6.10a). mation, imports of goods and services, and central The table does not distinguish among different government expenditure used in computing the ratios types of aid (program, project, or food aid; emer- are taken from World Bank and International Mon- gency assistance; postconflict peacekeeping assis- etary Fund (IMF) databases. The aggregates also tance; or technical cooperation), each of which may refer to World Bank definitions. Therefore the ratios have very different effects on the economy. Expendi- shown may differ somewhat from those computed tures on technical cooperation do not always directly and published by the Organisation for Economic Co- benefit the economy to the extent that they defray operation and Development (OECD). Aid not allocated costs incurred outside the country on the salaries by country or region--including administrative costs, and benefits of technical experts and the overhead research on development issues, and aid to nongov- costs of firms supplying technical services. ernmental organizations--is included in the world In 1999, to avoid double counting extrabudgetary total. Thus regional and income group totals do not expenditures reported by DAC countries and flows sum to the world total. More aid flows to developing countries Aid per capita ($) 1999 2004 40 Data sources 30 Data on financial flows are compiled by DAC and published in its annual statistical report, Geo- 20 graphical Distribution of Financial Flows to Aid Recipients, and in its annual Development Coop- 10 eration Report. Data are available in electronic format on the OECD's International Development Statistics CD-ROM and to registered users at 0 East Asia Europe & Latin America Middle East South Sub-Saharan www.oecd.org/dataoecd/50/17/5037721.htm. & Pacific Central Asia & Caribbean & North Africa Asia Africa Data on population, GNI, gross capital formation, imports of goods and services, and central govern- Between 1999 and 2004 the flow of aid to Sub-Saharan Africa and Middle East and North Africa increased while the flow of aid to other regions changed little. ment expenditure are from World Bank and IMF Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. databases. 2006 World Development Indicators 351 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United United DAC donors $ millions States France Japan Kingdom Germany Netherlands Sweden Canada Norway Spain $ millions 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Afghanistan 1,697.5 778.3 15.0 172.5 224.0 75.1 90.3 55.7 56.2 67.7 16.6 146.5 Albania 228.4 40.3 6.6 9.8 4.0 23.6 11.0 5.7 1.1 8.0 1.6 117.1 Algeria 234.6 3.8 172.9 ­1.0 0.0 ­2.6 0.1 2.0 1.1 1.6 12.7 44.6 Angola 1,015.0 121.3 21.9 25.5 15.1 13.0 19.6 17.3 4.8 24.8 9.3 743.2 Argentina 78.5 1.8 13.6 9.3 .. 10.9 1.0 0.3 2.3 0.0 33.4 6.4 Armenia 133.9 73.1 4.0 4.7 6.6 18.7 9.2 2.3 0.6 3.3 1.4 10.4 Australia Austria Azerbaijan 91.9 47.6 2.4 9.6 0.2 17.0 3.5 0.5 0.9 5.7 0.1 5.1 Bangladesh 632.9 62.9 0.9 38.2 252.7 25.2 65.2 26.6 48.9 23.9 0.1 88.7 Belarus 31.4 2.0 4.4 0.2 0.2 12.6 1.5 6.0 0.2 .. 0.0 4.8 Belgium Benin 210.3 27.9 62.9 11.2 .. 24.5 10.4 0.1 6.4 0.2 0.3 67.1 Bolivia 557.4 137.6 17.8 50.8 50.8 75.3 48.1 29.0 11.9 3.4 54.5 78.8 Bosnia and Herzegovina 301.0 61.9 3.4 22.2 11.0 29.9 24.8 34.1 5.9 17.0 24.7 66.6 Botswana 32.6 21.1 1.9 ­1.4 0.5 3.8 1.2 0.4 1.2 1.6 0.0 2.8 Brazil 147.3 ­57.5 31.1 41.7 11.1 51.9 16.3 2.5 9.1 3.1 9.9 28.7 Bulgaria 246.0 38.9 24.7 28.7 3.0 106.5 3.5 0.8 1.7 1.2 0.5 37.1 Burkina Faso 331.3 17.6 83.6 8.5 6.5 38.5 55.0 12.2 16.2 0.2 2.3 91.3 Burundi 184.3 43.8 34.8 0.4 9.5 10.4 23.3 6.6 4.3 11.9 0.8 39.1 Cambodia 297.8 48.1 25.6 86.4 17.6 22.5 8.7 22.6 8.5 3.3 0.1 54.9 Cameroon 572.0 17.2 129.1 16.9 30.0 205.7 11.6 14.3 43.2 1.7 ­5.6 108.5 Canada Central African Republic 54.8 12.0 36.6 0.1 .. 2.4 0.6 0.7 0.4 0.5 0.1 2.0 Chad 162.2 47.4 46.3 0.7 8.1 31.0 4.4 1.5 1.7 .. 3.1 18.7 Chile 25.9 ­1.7 15.1 ­34.6 1.1 27.4 1.4 0.8 3.5 3.9 3.4 6.2 China 1,585.4 21.5 102.8 964.7 72.2 260.5 25.6 18.2 34.9 14.8 12.7 58.2 Hong Kong, China 6.3 0.2 2.1 2.2 .. .. 0.0 .. .. .. 0.0 2.3 Colombia 470.0 375.6 5.6 ­8.4 2.0 8.8 26.0 14.1 9.2 8.5 9.6 19.7 Congo, Dem. Rep. 1,164.4 189.6 134.7 48.5 301.0 59.3 58.8 23.3 20.3 18.0 6.1 305.5 Congo, Rep. 47.5 0.4 36.1 0.3 4.9 0.5 1.3 3.6 0.4 1.7 ­0.3 ­0.8 Costa Rica 11.2 ­15.1 5.8 ­5.2 ­15.9 8.9 12.2 1.2 2.9 4.6 9.9 2.5 Côte d'Ivoire 196.0 31.8 62.2 1.9 5.9 14.5 1.9 1.6 4.7 3.0 3.9 65.2 Croatia 87.3 45.9 3.8 0.7 2.3 2.4 2.3 6.9 0.8 14.9 0.6 7.3 Cuba 69.3 10.6 4.3 3.4 3.3 2.9 1.4 2.7 8.2 4.5 16.6 12.1 Czech Republic 42.8 0.7 11.4 1.7 .. 18.2 2.2 0.1 0.5 0.1 0.5 8.2 Denmark Dominican Republic 84.4 ­4.1 6.6 15.3 0.2 10.5 1.9 0.1 2.8 0.6 45.1 5.9 Ecuador 159.8 74.5 1.5 ­2.7 ­14.8 15.6 12.9 1.3 7.8 2.4 31.5 30.6 Egypt, Arab Rep. 1,177.1 704.5 109.2 64.9 76.8 107.3 9.8 1.7 10.6 0.7 19.8 72.6 El Salvador 201.9 114.8 3.6 2.3 0.2 12.7 6.5 7.7 4.8 1.1 27.5 21.4 Eritrea 178.3 95.0 0.8 1.6 5.0 4.4 12.5 3.2 5.2 18.6 0.2 32.3 Estonia 27.4 2.9 2.3 0.8 .. 4.5 0.6 4.6 0.4 0.4 0.1 11.5 Ethiopia 1,026.2 402.3 26.3 33.3 147.1 126.1 57.5 50.8 59.5 34.0 0.8 89.1 Finland France Gabon 23.5 2.9 13.7 2.7 .. 1.0 0.8 .. 1.7 0.1 0.0 1.2 Gambia, The 11.7 3.2 0.2 2.7 0.5 1.9 0.5 0.6 0.9 0.3 .. 1.6 Georgia 210.3 92.3 3.9 10.6 3.1 58.4 7.5 4.1 2.5 4.8 0.1 23.5 Germany Ghana 896.9 80.4 74.5 115.4 263.5 65.6 152.6 0.5 48.5 1.7 19.1 75.6 Greece Guatemala 203.4 53.5 2.4 25.4 ­0.3 23.3 20.9 16.0 7.6 13.0 22.4 19.7 Guinea 178.4 47.7 72.3 16.5 3.2 20.2 3.2 1.4 8.0 1.6 .. 4.8 Guinea-Bissau 28.6 0.1 5.4 0.0 .. 0.8 3.3 2.4 0.7 0.1 1.5 14.9 Haiti 208.7 91.2 25.0 5.9 3.5 7.5 7.1 2.2 37.4 7.0 4.5 18.0 352 2006 World Development Indicators Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United United DAC donors $ millions States France Japan Kingdom Germany Netherlands Sweden Canada Norway Spain $ millions 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Honduras 328.5 112.1 21.3 31.3 1.2 26.1 16.1 27.7 9.4 1.8 54.0 28.0 Hungary 61.0 1.5 12.8 4.7 .. 22.9 1.8 1.1 0.8 0.1 4.4 11.4 India 20.9 50.5 ­13.0 ­82.1 370.2 ­10.7 ­315.8 11.6 33.6 13.3 9.5 ­45.6 Indonesia -145.6 68.9 ­28.2 ­318.5 8.5 ­8.6 ­0.5 9.1 9.3 7.4 10.9 96.9 Iran, Islamic Rep. 140.2 4.8 15.7 19.8 4.8 41.2 11.1 2.6 0.4 11.5 3.7 25.2 Iraq 4,392.1 3,022.0 4.2 662.1 275.1 10.2 140.8 21.3 72.0 18.5 59.4 107.0 Ireland Israel 477.9 501.1 9.1 0.7 .. ­36.6 1.0 .. 0.1 .. 0.9 2.1 Italy Jamaica 7.8 11.6 ­1.5 ­15.8 7.5 ­10.4 7.7 0.2 9.2 0.3 0.4 ­0.9 Japan Jordan 432.9 374.0 6.3 ­5.3 4.7 21.0 0.6 0.8 7.0 1.3 3.3 19.9 Kazakhstan 203.3 56.4 2.8 130.8 1.8 ­0.6 3.3 1.2 0.5 1.5 4.5 1.7 Kenya 469.5 140.9 32.2 70.9 45.8 41.7 24.4 29.9 18.1 7.9 2.4 55.9 Korea, Dem. Rep. 137.1 55.7 ­0.5 .. 37.4 7.5 0.5 5.4 2.4 5.6 0.1 23.6 Korea, Rep. -68.7 ­44.4 18.2 ­58.9 .. 11.5 0.1 .. 3.2 .. .. 2.2 Kuwait 2.2 .. 1.7 0.3 .. 0.1 0.0 .. .. .. 0.0 0.7 Kyrgyz Republic 108.8 39.9 0.9 26.7 6.3 13.7 3.1 2.5 0.3 3.1 0.1 12.8 Lao PDR 177.6 3.5 19.7 71.7 2.2 15.9 2.4 22.2 2.5 2.5 .. 35.6 Latvia 29.1 2.8 2.0 0.7 .. 6.0 0.6 5.7 0.2 0.3 0.0 11.4 Lebanon 128.2 28.8 58.6 8.1 0.3 12.0 0.2 0.7 2.7 6.4 3.3 7.6 Lesotho 35.1 4.0 ­0.8 1.2 7.2 5.3 0.1 0.0 0.5 1.3 .. 16.8 Liberia 161.9 102.5 0.8 .. 16.5 ­3.1 8.6 12.5 1.0 11.6 .. 11.9 Libya 9.6 0.0 3.0 0.3 .. 2.8 0.1 .. .. .. .. 3.9 Lithuania 32.1 1.6 3.7 3.2 .. 9.8 1.2 7.0 0.3 0.6 0.1 5.3 Macedonia, FYR 161.4 53.1 4.3 4.2 3.0 18.2 28.5 9.3 0.7 12.6 2.3 25.7 Madagascar 684.8 40.7 484.5 28.0 27.5 7.6 0.9 0.2 17.6 8.5 8.0 62.0 Malawi 308.4 56.8 1.3 19.0 119.5 24.6 15.8 15.9 16.0 27.2 ­0.4 13.3 Malaysia 286.8 1.1 ­2.4 256.5 0.5 7.3 0.2 .. 0.3 0.9 0.9 22.1 Mali 327.5 45.5 81.5 13.7 0.4 26.4 64.1 14.9 44.1 8.0 1.3 28.2 Mauritania 82.6 11.0 29.2 11.1 0.5 11.3 0.8 0.4 2.1 0.7 11.5 4.5 Mauritius 14.7 0.3 12.3 1.5 0.2 ­1.3 0.0 0.0 0.3 0.0 .. 2.0 Mexico 78.9 42.8 18.7 13.0 0.2 23.3 0.2 0.2 5.1 0.4 ­28.3 3.9 Moldova 76.9 32.8 4.2 3.3 4.9 6.3 5.6 7.2 0.6 1.3 0.0 11.3 Mongolia 154.7 25.9 5.3 65.6 7.4 26.5 9.5 2.4 1.3 1.2 2.8 7.4 Morocco 393.5 ­10.3 218.1 66.3 0.1 34.5 6.5 0.7 4.6 0.8 51.0 21.7 Mozambique 728.1 110.0 14.6 19.4 65.9 38.7 54.7 67.9 27.3 61.1 32.5 236.6 Myanmar 81.4 5.7 2.0 26.8 12.0 4.7 2.8 3.7 0.6 7.1 .. 16.5 Namibia 129.7 34.3 4.3 1.2 2.7 33.2 4.2 9.8 0.7 2.7 11.1 26.0 Nepal 318.5 35.4 ­2.2 56.4 65.8 52.6 14.1 1.1 7.7 23.0 0.1 65.2 Netherlands New Zealand Nicaragua 858.0 69.7 65.3 29.9 13.4 278.0 40.8 41.1 9.0 12.6 207.7 91.2 Niger 305.7 19.3 195.8 14.1 8.4 16.7 5.5 0.1 7.6 1.9 1.0 35.8 Nigeria 314.2 120.2 7.4 8.7 126.1 13.7 3.8 1.2 15.2 5.5 0.6 12.6 Norway Oman 2.0 ­5.0 1.0 5.3 .. 0.2 .. .. .. .. .. 1.0 Pakistan 382.7 76.9 5.1 134.1 90.8 20.4 7.9 2.1 15.5 8.1 0.1 22.2 Panama 25.3 9.3 0.5 6.2 0.0 1.3 0.3 .. 0.7 .. 6.6 0.8 Papua New Guinea 249.9 0.1 0.2 ­6.1 .. 1.9 0.8 0.1 0.6 0.4 0.1 252.3 Paraguay 5.4 14.8 ­0.4 ­3.3 ­0.5 ­19.1 1.9 1.7 2.1 0.6 6.4 1.8 Peru 460.2 177.9 12.8 89.8 5.3 40.7 18.1 4.6 14.2 1.3 56.2 40.0 Philippines 433.4 79.5 ­6.9 211.4 0.4 39.1 16.9 6.3 12.4 1.9 14.1 59.0 Poland 413.0 ­0.8 196.5 ­4.0 .. 72.9 1.0 1.3 49.6 0.4 2.1 94.6 Portugal Puerto Rico 2006 World Development Indicators 353 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United United DAC donors $ millions States France Japan Kingdom Germany Netherlands Sweden Canada Norway Spain $ millions 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Romania 209.3 38.0 42.1 34.2 8.5 51.4 2.3 1.0 1.9 0.8 1.5 28.0 Russian Federation 1,075.2 716.4 31.9 4.8 37.9 98.3 12.8 69.7 13.2 29.6 3.3 57.9 Rwanda 217.2 50.3 7.3 0.9 58.2 16.6 25.5 8.5 6.1 6.2 0.4 37.8 Saudi Arabia 8.5 0.1 6.8 ­0.2 .. 1.7 0.1 .. 0.0 .. .. 0.6 Senegal 755.4 49.8 509.8 50.4 9.1 33.1 16.9 8.9 24.6 1.0 18.3 34.0 Serbia and Montenegro 585.3 181.2 9.0 11.4 9.6 115.7 22.8 39.4 10.4 37.0 18.5 130.7 Sierra Leone 163.1 30.1 3.5 0.2 60.9 11.7 9.5 3.2 3.2 5.2 1.6 34.6 Singapore 9.1 .. 3.6 2.7 0.1 1.9 .. .. 0.0 .. 0.1 1.3 Slovak Republic 58.9 1.1 6.7 22.8 0.0 11.9 2.5 0.1 1.2 0.1 0.3 12.7 Slovenia 4.3 0.9 1.5 0.1 0.1 ­2.1 0.3 0.1 0.1 .. 0.2 3.7 Somalia 139.3 31.9 0.4 .. 11.8 2.5 18.9 13.7 1.8 33.7 .. 25.1 South Africa 460.4 94.7 2.5 18.8 87.1 56.5 55.7 25.6 12.0 15.9 0.5 91.7 Spain Sri Lanka 337.8 ­3.4 4.7 179.5 16.8 28.6 13.9 23.0 5.5 30.3 0.6 38.9 Sudan 744.8 377.6 11.6 1.6 116.6 48.3 0.3 26.5 26.3 57.2 7.9 71.5 Swaziland 104.5 1.2 0.1 4.9 1.4 ­3.1 97.6 0.2 0.6 0.4 .. 1.8 Sweden Switzerland Syrian Arab Republic 14.9 0.0 23.7 ­26.5 0.1 ­0.2 4.8 0.1 1.6 1.3 0.7 9.8 Tajikistan 92.5 47.5 0.3 6.6 1.5 5.4 1.2 3.1 6.9 1.5 .. 19.0 Tanzania 1,029.5 89.5 120.0 52.5 215.6 56.4 117.6 83.6 32.7 59.6 5.8 196.7 Thailand -24.9 10.1 ­0.5 ­55.6 ­19.7 ­1.5 4.0 6.4 4.0 2.2 0.7 25.6 Togo 52.3 3.6 26.5 0.8 0.3 9.7 1.2 0.2 6.0 0.3 0.7 3.8 Trinidad and Tobago 7.2 1.8 1.7 1.9 0.4 0.4 0.1 .. 1.0 .. 0.1 0.6 Tunisia 230.8 ­15.7 141.4 59.7 .. 12.3 ­2.4 0.6 0.5 0.1 9.7 25.2 Turkey -45.4 ­29.7 10.7 ­25.9 ­3.7 ­74.6 2.3 1.8 ­2.4 1.6 49.5 25.6 Turkmenistan 11.3 6.5 0.8 2.2 0.1 1.2 0.0 .. 0.1 0.1 .. 0.8 Uganda 682.6 207.7 6.2 11.8 107.6 41.8 70.9 42.7 10.2 41.7 3.3 139.3 Ukraine 233.4 102.8 11.3 2.1 11.1 50.8 5.8 9.6 19.3 0.2 0.2 20.6 United Arab Emirates 5.2 0.3 3.6 0.2 .. 1.0 .. .. .. .. 0.0 0.5 United Kingdom United States Uruguay 9.4 ­1.6 3.3 2.0 .. 0.3 0.0 0.2 1.4 .. 2.7 1.6 Uzbekistan 206.1 61.2 3.1 99.8 1.5 20.3 0.7 0.4 0.8 1.0 1.3 16.8 Venezuela, RB 28.3 9.0 6.6 4.6 0.4 2.1 0.1 0.0 0.9 0.3 2.8 2.1 Vietnam 1,181.5 30.5 106.8 615.3 67.7 74.8 52.3 26.8 25.3 12.1 6.3 164.2 West Bank and Gaza 605.1 273.9 25.2 9.0 29.5 31.2 20.9 39.4 22.4 53.8 23.8 76.6 Yemen, Rep. 152.6 43.3 3.7 18.2 12.7 35.8 29.8 0.3 0.6 0.4 2.6 5.8 Zambia 745.3 81.8 103.8 14.3 282.6 36.2 53.6 26.2 25.0 37.4 0.9 84.2 Zimbabwe 165.4 30.4 3.1 3.6 49.7 15.7 12.4 12.9 11.9 8.1 0.2 18.0 World 58,838.7 s 17,785.0 s 7,130.4 s 5,977.9 s 5,408.9 s 4,298.3 s 2,722.8 s 2,198.7 s 2,084.1 s 1,581.5 s 1,415.0 s 8,236.1 s Low income 20,730.2 4,206.9 2,730.1 1,973.9 3,307.0 1,748.2 943.1 736.0 780.2 682.2 380.9 3,273.6 Middle income 21,485.4 8,147.2 2,055.1 2,768.1 785.4 1,764.1 839.2 532.3 467.9 430.8 776.2 2,977.5 Lower middle income 16,988.3 6,666.7 1,151.0 2,431.0 637.1 1,375.7 698.4 381.2 343.8 307.0 641.9 2,385.8 Upper middle income 3,359.0 948.1 659.5 333.8 147.3 329.3 101.0 127.6 106.5 82.2 114.6 434.1 Low & middle income 57,254.0 17,285.8 5,988.5 6,030.1 5,408.7 4,311.0 2,732.8 2,198.6 2,080.7 1,581.1 1,413.6 8,210.6 East Asia & Pacific 5,271.0 541.5 395.0 1,980.3 202.3 456.6 141.6 138.4 110.0 68.0 49.9 1,203.2 Europe & Central Asia 5,230.6 1,758.6 483.9 417.4 123.8 730.4 188.4 238.7 119.3 167.0 117.6 902.2 Latin America & Carib. 5,141.7 1,810.3 343.3 309.3 149.8 662.8 289.0 188.5 211.5 90.5 631.5 476.2 Middle East & N. Africa 8,073.3 4,437.9 862.3 888.7 407.6 313.9 225.3 78.0 126.2 98.2 208.6 434.9 South Asia 3,452.1 1,000.5 10.6 514.4 1,020.7 192.2 ­112.1 121.0 168.5 167.5 26.9 346.4 Sub-Saharan Africa 17,125.5 3,497.9 3,011.6 641.3 2,329.4 1,246.5 1,210.8 671.2 614.5 623.5 180.6 3,124.9 High income 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. 354 2006 World Development Indicators Distribution of net aid by Development Assistance Committee members About the data Definitions The table shows net bilateral aid to low- and middle- avoid double counting extrabudgetary expenditures · Net aid comprises net bilateral official development income economies from members of the Develop- reported by DAC countries and flows reported by the assistance to part I recipients and net bilateral official ment Assistance Committee (DAC) of the Organi- United Nations. aid to part II recipients (see About the data for table sation for Economic Co-operation and Development The table is based on donor country reports of 6.9). · Other DAC donors are Australia, Austria, Bel- (OECD). The DAC compilation of the data includes bilateral programs, which may differ from reports by gium, Denmark, Finland, Greece, Ireland, Luxembourg, aid to some countries and territories not shown in recipient countries. Recipients may lack access to New Zealand, Norway, Portugal, and Switzerland. the table and aid to unspecified economies that is information on such aid expenditures as develop- recorded only at the regional or global level. Aid to ment-oriented research, stipends and tuition costs countries and territories not shown in the table has for aid-financed students in donor countries, and been assigned to regional totals based on the World payment of experts hired by donor countries. More- Bank's regional classification system. Aid to unspeci- over, a full accounting would include donor country fied economies has been included in regional totals contributions to multilateral institutions, the flow and, when possible, in income group totals. Aid not of resources from multilateral institutions to recipi- allocated by country or region--including adminis- ent countries, and flows from countries that are not trative costs, research on development issues, and members of DAC. aid to nongovernmental organizations--is included The expenditures that countries report as official in the world total. Thus regional and income group development assistance (ODA) have changed. For totals do not sum to the world total. example, some DAC members have reported as In 1999 all UN agencies revised their data since ODA the aid provided to refugees during the first 12 1990 to include only regular budgetary expenditures months of their stay within the donor's borders. (except for the World Food Programme and the United Some of the aid recipients shown in the table are Nations High Commissioner for Refugees, which also aid donors. See table 6.10a for a summary of revised their data from 1996 onward). They did so to ODA from non-DAC countries. The flow of bilateral aid from DAC members reflects global events and priorities Total bilateral aid, 2004 United States France Senegal 7% Madagascar 7% Iraq Morocco 3% 17% Afghanistan 4% Poland 3% Russian Federation 4% Niger 3% Others Egypt 4% Others 68% Israel 3% 77% Japan United Kingdom India 7% Congo, Dem. Rep. 6% China Zambia 5% 16% Iraq Iraq 5% 11% Ghana 5% Others Others 55% Vietnam 10% 72% Malaysia 4% Philippines 4% Germany Netherlands Nicaragua 6% Ghana 6% China 6% Iraq 5% Cameroon 5% Tanzania 4% Data sources Ethiopia 3% Swaziland 4% Afghanistan 3% Data on financial flows are compiled by DAC and Serbia and Montenegro 3% published in its annual statistical report, Geo- Others Others graphical Distribution of Financial Flows to Aid 77% 78% Recipients, and its annual Development Coopera- tion Report. Data are available electronically on the OECD's International Development Statistics This figure shows the distribution of aid from the top six donors to their top five recipients in 2004. CD-ROM and to registered users at www.oecd. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. org/dataoecd/50/17/5037721.htm. 2006 World Development Indicators 355 Net financial flows from multilateral institutions International financial institutions United Nations Total $ millions Regional development IMF banks World Bank Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Afghanistan .. .. .. .. .. .. .. 5.8 3.3 11.1 0.2 53.7 74.1 Albania 64.3 0.0 2.4 0.0 0.0 11.2 22.3 2.0 0.3 0.7 0.4 1.5 105.2 Algeria 0.0 ­149.2 0.0 ­371.6 0.0 ­679.6 ­53.0 1.1 .. 1.2 2.8 2.3 ­1,246.0 Angola 12.9 0.0 0.0 0.0 0.6 ­1.6 4.3 3.7 2.1 5.2 14.4 8.7 50.2 Argentina 0.0 ­61.0 0.0 ­2,035.0 0.0 ­145.5 0.0 0.0 0.4 0.4 .. 6.3 ­2,234.4 Armenia 77.8 ­0.6 1.9 ­8.3 0.0 ­7.8 ­4.0 1.0 0.6 0.7 0.3 2.5 64.0 Australia Austria Azerbaijan 49.2 0.0 ­21.7 ­38.0 0.0 ­8.5 7.7 2.4 0.6 1.1 1.4 2.4 ­3.4 Bangladesh 474.3 ­7.3 147.4 0.0 ­20.8 71.8 13.1 19.6 6.8 11.0 9.9 7.7 733.3 Belarus 0.0 ­13.5 0.0 ­17.3 0.0 ­13.2 0.0 0.6 0.3 0.6 .. 1.7 ­41.0 Belgium Benin 36.5 0.0 ­5.6 0.0 33.7 ­0.4 ­12.5 2.3 2.3 2.1 2.3 2.9 63.7 Bolivia 116.6 0.2 ­21.8 55.6 80.1 ­69.3 103.2 1.4 2.6 2.1 2.2 2.3 275.1 Bosnia and Herzegovina 208.5 ­24.4 0.0 ­29.8 0.0 4.8 37.9 0.8 0.2 0.8 .. 11.9 210.7 Botswana ­0.5 ­1.7 0.0 0.0 1.0 ­9.3 ­10.2 0.5 1.2 0.6 .. 4.2 ­14.3 Brazil 0.0 ­116.3 0.0 ­4,356.8 0.0 ­1,468.2 ­4.5 1.1 0.9 1.7 .. 132.4 ­5,809.7 Bulgaria 0.0 123.7 0.0 ­55.1 0.0 ­10.9 80.6 0.6 0.3 .. .. 1.9 141.0 Burkina Faso 128.6 0.0 2.2 0.0 37.6 ­0.2 14.6 7.0 2.7 5.3 4.5 3.4 205.7 Burundi 29.3 0.0 39.1 ­28.5 ­13.3 ­7.0 ­0.6 8.9 1.6 3.4 3.8 9.6 46.3 Cambodia 46.3 0.0 ­10.4 0.0 53.5 0.0 9.5 6.3 2.8 4.1 3.1 2.3 117.5 Cameroon 96.9 ­29.7 ­22.9 0.0 48.5 ­26.1 2.6 3.6 .. 2.7 1.4 5.2 82.1 Canada Central African Republic 0.0 0.0 ­2.4 8.3 0.0 0.0 ­1.6 4.2 1.8 2.7 3.8 5.3 21.9 Chad 69.9 5.9 ­12.9 0.0 7.5 0.0 12.7 5.7 2.3 4.8 4.7 6.5 107.1 Chile ­0.7 18.8 0.0 0.0 ­1.8 ­49.8 ­1.5 0.5 0.2 0.5 .. 1.8 ­32.0 China ­116.7 306.3 0.0 0.0 0.0 200.3 ­201.5 9.0 4.8 12.3 6.3 7.3 228.2 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. 0.0 0.0 Colombia ­0.7 200.0 0.0 0.0 ­17.3 ­71.0 115.3 1.4 1.9 1.0 0.0 3.8 234.3 Congo, Dem. Rep. 166.6 0.0 79.1 0.0 44.4 0.0 ­10.9 11.7 4.8 18.7 3.4 15.3 333.1 Congo, Rep. 23.6 ­3.2 7.5 ­7.8 ­0.7 ­74.7 ­2.3 1.1 0.9 1.0 1.9 8.0 ­44.7 Costa Rica ­0.2 ­8.7 0.0 0.0 ­11.8 1.7 39.1 0.5 0.5 0.6 .. 2.0 23.8 Côte d'Ivoire 33.1 ­38.4 ­126.7 0.0 1.1 ­0.4 ­12.2 4.9 1.4 3.5 ­0.4 12.0 ­122.1 Croatia 0.0 36.0 0.0 0.0 0.0 43.6 19.0 0.4 .. 0.2 .. 5.3 104.5 Cuba .. .. .. .. .. .. .. 0.8 0.9 0.8 3.0 1.7 7.1 Czech Republic 0.0 ­18.9 0.0 0.0 0.0 ­13.4 87.1 .. .. .. .. 2.4 57.2 Denmark Dominican Republic ­0.7 29.3 0.0 64.9 ­20.3 208.7 2.6 0.6 0.8 0.9 0.2 4.9 291.8 Ecuador ­1.1 ­52.9 0.0 ­112.0 ­27.0 ­90.5 46.6 0.9 1.1 1.3 0.1 3.2 ­230.3 Egypt, Arab Rep. 36.2 ­51.8 0.0 0.0 15.4 0.3 ­18.3 1.2 2.4 2.7 3.9 6.5 ­1.4 El Salvador ­0.8 ­24.3 0.0 0.0 ­23.5 ­20.6 ­34.1 0.5 1.0 0.6 1.1 1.3 ­98.8 Eritrea 35.1 0.0 0.0 0.0 19.1 0.0 ­1.4 2.4 1.9 1.6 2.5 5.3 66.4 Estonia 0.0 ­4.1 0.0 0.0 0.0 0.0 ­3.4 .. 0.0 .. .. 0.3 ­7.1 Ethiopia 189.2 0.0 21.9 0.0 68.3 ­4.4 11.1 11.0 5.3 18.6 9.8 15.9 346.7 Finland France Gabon 0.0 ­11.7 0.0 37.0 ­0.2 52.6 5.0 0.5 0.2 0.6 .. 5.4 89.5 Gambia, The 19.4 0.0 ­11.2 0.0 6.7 0.0 7.6 2.1 0.7 0.9 0.9 2.5 29.7 Georgia 64.4 0.0 ­20.4 ­13.7 0.0 ­6.3 ­5.6 1.8 0.5 0.7 0.0 4.2 25.7 Germany Ghana 218.3 ­1.6 15.5 0.0 47.9 6.6 32.9 4.1 4.2 3.9 0.8 6.7 339.2 Greece Guatemala 0.0 50.0 0.0 0.0 ­18.4 61.1 10.3 0.8 4.4 1.1 0.4 1.2 111.0 Guinea 31.2 0.0 ­19.1 0.0 1.3 ­40.6 ­24.2 1.1 2.0 2.8 2.5 13.5 ­29.5 Guinea-Bissau 24.9 0.0 ­3.1 ­2.2 4.1 0.0 ­0.7 2.6 1.0 1.3 2.2 2.0 32.1 Haiti ­40.5 0.0 ­4.4 ­2.7 ­11.5 0.0 ­1.4 3.8 2.2 4.2 6.9 1.7 ­41.8 356 2006 World Development Indicators Net financial flows from multilateral institutions International financial institutions United Nations Total $ millions Regional development IMF banks World Bank Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Honduras 113.4 ­15.1 21.0 0.0 114.5 ­21.6 12.7 1.0 3.2 1.4 2.1 2.2 234.8 Hungary 0.0 ­39.5 0.0 0.0 0.0 ­3.1 478.7 .. .. .. .. 2.7 438.7 India 422.3 616.2 0.0 0.0 0.0 423.4 ­24.0 20.3 11.9 28.9 8.5 11.1 1,518.6 Indonesia 94.8 ­825.6 0.0 ­1,004.9 34.8 5.1 ­36.3 7.8 5.3 5.5 0.6 6.9 ­1,705.9 Iran, Islamic Rep. 0.0 ­34.3 0.0 0.0 0.0 0.0 2.0 1.2 2.2 1.8 0.1 19.7 ­7.3 Iraq .. .. .. .. .. .. .. 3.9 3.8 1.3 3.0 1.5 13.5 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. 0.2 0.2 Italy Jamaica 0.0 ­40.7 0.0 ­8.0 ­5.3 1.9 33.7 0.5 .. 0.9 .. 1.3 ­15.6 Japan .. .. .. .. .. .. .. .. .. .. .. .. .. Jordan ­2.6 ­56.7 0.0 ­98.0 0.0 0.0 ­38.0 0.7 0.6 0.6 1.7 92.4 ­99.3 Kazakhstan 0.0 ­27.4 0.0 0.0 0.0 0.7 ­17.9 0.7 0.6 1.0 .. 2.0 ­40.3 Kenya 26.7 ­4.8 ­14.1 0.0 16.6 ­1.9 ­7.5 5.0 2.7 5.3 7.4 18.4 53.8 Korea, Dem. Rep. .. .. .. .. .. .. .. 0.9 1.0 1.1 7.5 2.6 13.0 Korea, Rep. .. .. .. .. .. .. .. 0.1 .. .. .. 0.9 1.0 Kuwait .. .. .. .. .. .. .. .. .. .. .. 0.4 0.4 Kyrgyz Republic 22.7 0.0 ­3.9 0.0 54.4 ­8.0 1.5 2.1 0.7 1.1 .. 1.4 72.0 Lao PDR 29.2 0.0 ­7.8 0.0 35.0 0.0 ­2.7 3.3 1.2 1.8 2.0 2.5 64.4 Latvia 0.0 ­8.0 0.0 ­5.7 0.0 ­2.5 ­238.1 0.4 0.1 .. .. 0.5 ­253.3 Lebanon 0.0 14.2 0.0 0.0 0.0 0.0 ­1.8 0.7 0.8 0.6 .. 62.1 76.6 Lesotho 10.1 ­2.4 9.8 0.0 9.0 ­1.7 ­0.3 1.3 0.4 1.5 6.3 1.8 35.7 Liberia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.3 0.9 3.2 6.6 2.1 18.1 Libya .. .. .. .. .. .. .. .. .. .. .. 6.6 6.6 Lithuania 0.0 ­92.8 0.0 ­19.8 0.0 ­0.6 ­0.9 0.3 0.1 .. .. 0.4 ­113.4 Macedonia, FYR 13.6 32.2 ­8.1 ­0.4 0.0 4.1 27.7 1.0 .. 0.7 .. 3.0 73.8 Madagascar 216.7 0.0 45.5 0.0 46.8 ­2.4 19.5 3.8 2.0 5.0 2.4 2.0 341.2 Malawi 50.9 ­0.5 ­9.5 0.0 23.4 ­0.4 ­6.2 5.6 4.0 4.9 4.9 3.7 80.8 Malaysia 0.0 ­69.8 0.0 0.0 0.0 ­43.1 49.5 0.2 0.4 0.5 .. 1.1 ­61.2 Mali 69.0 0.0 ­16.1 0.0 38.2 0.0 14.2 8.3 2.3 6.0 3.0 3.3 128.1 Mauritania 36.8 0.0 ­9.4 0.0 10.3 11.1 25.4 2.2 2.0 1.3 1.7 3.1 84.6 Mauritius ­0.6 ­9.0 0.0 0.0 ­0.1 ­9.4 ­2.6 0.1 0.1 .. .. 1.0 ­20.6 Mexico 0.0 ­1,153.3 0.0 0.0 0.0 174.6 0.0 0.8 2.1 0.8 .. 9.0 ­966.0 Moldova 17.2 ­10.4 0.0 ­21.6 0.0 ­8.0 ­13.1 1.5 0.2 0.7 .. 1.6 ­32.0 Mongolia 47.2 0.0 ­7.2 0.0 36.1 0.0 4.6 1.5 1.1 1.0 .. 2.3 86.5 Morocco ­1.4 ­338.2 0.0 0.0 3.9 120.0 59.9 0.8 2.6 1.8 0.2 3.6 ­146.8 Mozambique 185.7 0.0 ­6.7 0.0 86.0 ­0.8 21.9 8.5 9.0 8.5 5.2 6.8 324.0 Myanmar 0.0 0.0 0.0 0.0 0.0 0.0 ­1.7 7.5 4.0 7.0 1.3 5.1 23.2 Namibia .. .. .. .. .. .. .. 0.8 1.1 1.1 2.5 6.5 12.0 Nepal 45.3 0.0 9.8 0.0 ­9.1 0.0 ­2.0 7.4 5.8 5.4 9.6 5.9 78.1 Netherlands New Zealand Nicaragua 126.0 0.0 32.7 0.0 139.7 ­1.5 9.7 2.9 3.4 0.8 4.5 1.9 320.0 Niger 63.2 0.0 5.8 0.0 27.6 12.5 2.2 5.9 3.1 6.9 5.6 2.6 135.3 Nigeria 137.2 ­216.4 0.0 0.0 ­1.2 ­60.9 0.0 3.5 6.4 24.5 .. 10.9 ­96.0 Norway Oman 0.0 0.0 0.0 0.0 0.0 0.0 ­135.2 .. .. .. .. 1.1 ­134.1 Pakistan 676.1 ­303.1 146.9 ­460.5 70.8 ­950.5 92.0 7.1 5.0 12.6 8.6 28.6 ­666.4 Panama 0.0 ­25.4 0.0 ­9.9 ­8.9 ­27.9 ­6.0 0.6 0.6 0.6 .. 15.1 ­61.3 Papua New Guinea ­3.6 ­13.0 0.0 ­59.9 2.7 ­3.6 ­3.6 2.2 0.8 1.3 .. 2.3 ­74.2 Paraguay ­1.5 ­16.7 0.0 0.0 ­15.1 8.4 ­3.1 0.4 1.1 0.8 .. 0.6 ­25.2 Peru 0.0 45.4 0.0 ­39.6 ­10.1 245.4 237.4 0.8 9.4 1.2 1.2 6.6 497.7 Philippines ­6.9 ­226.4 0.0 ­472.5 ­15.7 ­133.9 21.9 2.2 4.5 2.3 .. 5.0 ­819.5 Poland 0.0 ­676.5 0.0 0.0 0.0 0.0 0.0 0.5 0.1 .. .. 1.2 ­674.7 Portugal Puerto Rico 2006 World Development Indicators 357 Net financial flows from multilateral institutions International financial institutions United Nations Total $ millions Regional development IMF banks World Bank Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 Romania 0.0 142.0 0.0 ­170.9 44.3 62.0 161.7 0.7 0.5 0.8 .. 2.0 243.1 Russian Federation 0.0 ­608.3 0.0 ­1,656.0 0.0 143.8 0.0 0.8 0.7 1.3 1.6 9.3 ­2,106.9 Rwanda 82.2 0.0 1.1 0.0 15.9 0.0 ­1.9 5.1 2.1 3.6 7.0 6.9 121.9 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. 14.6 14.6 Senegal 158.0 0.0 ­29.4 0.0 51.0 ­1.7 79.8 4.4 2.2 2.5 2.5 4.2 273.4 Serbia and Montenegro 161.9 0.0 0.0 0.0 0.0 0.0 0.0 0.6 .. 0.7 0.0 22.9 186.0 Sierra Leone 30.6 0.0 40.2 0.0 31.0 0.0 12.8 5.9 1.8 2.5 4.8 13.2 142.9 Singapore .. .. .. .. .. .. .. .. .. .. .. 0.1 0.1 Slovak Republic 0.0 74.4 0.0 0.0 0.0 ­1.6 ­152.4 0.5 .. .. .. 1.7 ­77.4 Slovenia .. .. .. .. .. .. .. .. .. .. .. 0.9 0.9 Somalia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 0.4 4.8 1.7 3.9 15.7 South Africa 0.0 7.0 0.0 0.0 0.0 30.5 0.0 3.1 0.4 1.1 .. 5.7 47.8 Spain Sri Lanka 29.9 ­2.0 ­8.3 ­103.4 75.2 17.2 33.5 2.5 1.0 1.0 3.4 4.2 54.1 Sudan ­1.8 0.0 0.0 ­31.3 0.0 ­3.1 45.2 10.7 4.0 7.0 10.8 15.7 57.2 Swaziland ­0.3 8.8 0.0 0.0 ­0.8 9.7 7.9 0.4 0.6 1.2 0.7 .. 28.1 Sweden Switzerland Syrian Arab Republic ­1.5 ­4.5 0.0 0.0 0.0 0.0 ­47.2 1.4 2.4 1.0 1.7 35.1 ­11.7 Tajikistan 54.6 0.0 17.1 0.0 19.1 0.0 13.2 3.6 0.6 1.3 1.7 1.9 113.1 Tanzania 329.9 ­2.9 ­16.2 0.0 58.6 0.0 6.3 7.2 5.9 10.0 3.5 3.1 405.5 Thailand ­3.4 ­1,615.0 0.0 0.0 ­3.0 ­76.7 ­15.0 1.4 0.9 0.9 .. 6.4 ­1,703.5 Togo 0.0 0.0 ­16.1 0.0 0.0 9.9 7.1 2.1 1.3 1.6 .. 1.9 7.8 Trinidad and Tobago 0.0 ­15.2 0.0 0.0 ­0.1 ­16.1 ­6.1 0.5 .. .. .. 1.9 ­35.2 Tunisia ­2.1 ­88.7 0.0 0.0 0.0 72.2 223.0 0.5 0.8 0.8 .. 2.2 208.7 Turkey ­5.9 919.5 0.0 ­3,504.1 0.0 0.0 285.1 0.1 1.1 1.4 .. 6.9 ­2,296.0 Turkmenistan .. .. .. .. .. .. .. 0.7 0.5 1.1 .. 1.3 3.5 Uganda 120.6 0.0 ­26.2 0.0 52.9 0.0 ­0.6 5.4 5.3 7.8 12.5 12.3 190.0 Ukraine 0.0 ­132.9 0.0 ­299.1 0.0 11.7 ­62.6 2.2 0.5 1.1 .. 3.6 ­475.5 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. 0.3 0.3 United Kingdom United States Uruguay 0.0 63.6 0.0 151.9 ­2.4 ­57.2 ­0.5 0.2 0.3 0.5 .. 0.9 157.4 Uzbekistan 4.5 4.5 0.0 ­24.6 2.6 85.6 5.0 2.3 0.7 1.9 .. 1.9 84.3 Venezuela, RB 0.0 ­191.4 0.0 0.0 0.0 ­189.7 100.1 0.5 1.0 0.7 .. 6.0 ­272.9 Vietnam 435.7 0.0 ­71.6 ­1.5 145.6 ­2.0 4.4 6.4 7.7 4.4 .. 4.8 533.8 West Bank and Gaza .. .. .. .. .. .. .. 4.0 1.9 1.6 2.7 273.9 284.1 Yemen, Rep. 66.2 0.0 ­31.4 ­9.9 0.0 0.0 11.7 5.5 4.1 3.6 7.5 5.7 63.0 Zambia 140.7 ­3.3 ­4.5 0.0 9.2 ­15.2 28.3 4.3 1.5 4.0 7.7 13.7 186.4 Zimbabwe 0.0 ­1.7 ­15.1 ­5.8 ­1.1 0.0 5.6 3.0 1.4 2.5 ­2.4 4.9 ­8.8 World .. s .. s .. s .. s .. s .. s .. s 388.9 s 314.3 s 655.1 s 269.6 s 1,534.2 s 3,162.1 s Low income 5,195.7 ­11.8 117.5 ­648.2 1,309.1 ­594.7 389.3 289.2 159.4 293.4 209.3 400.3 7,108.4 Middle income 938.1 ­4,782.7 ­45.4 ­14,114.9 234.3 ­1,686.7 1,427.3 86.1 86.8 83.2 59.9 907.2 ­16,806.9 Lower middle income 938.7 ­2,921.1 ­50.4 ­7,073.3 260.0 ­1,565.4 857.3 72.5 70.4 71.1 58.3 719.4 ­8,562.4 Upper middle income ­0.6 ­1,861.6 5.0 ­7,041.6 ­25.7 ­121.3 569.9 12.7 11.7 11.6 1.6 165.0 ­8,273.4 Low & middle income 6,133.9 ­4,794.5 72.1 ­14,763.1 1,543.4 ­2,281.4 1,816.5 380.5 314.3 655.1 269.6 1,513.6 ­9,139.9 East Asia & Pacific 525.6 ­2,445.8 ­97.0 ­1,538.8 287.9 ­47.3 ­172.7 52.0 39.2 46.1 21.0 58.1 ­3,271.8 Europe & Central Asia 732.8 ­323.6 ­32.6 ­5,858.5 120.5 353.2 832.4 28.2 9.3 19.2 5.4 98.2 ­4,015.6 Latin America & Carib. 323.6 ­1,314.4 35.4 ­6,291.7 202.0 ­1,532.8 746.7 22.2 43.4 26.7 21.8 236.3 ­7,480.7 Middle East & N. Africa 103.5 ­709.2 ­31.4 ­479.6 21.8 ­487.0 19.8 21.5 24.8 18.2 25.0 552.8 ­919.7 South Asia 1,661.6 303.8 295.7 ­564.0 122.7 ­438.1 114.0 65.0 35.2 71.9 42.8 114.2 1,824.8 Sub-Saharan Africa 2,786.7 ­305.4 ­98.0 ­30.5 788.4 ­129.5 276.6 186.4 106.2 200.2 153.6 351.7 4,286.3 High income Europe EMU Note: The aggregates for the United Nations and total net financial flows include amounts for economies not specified elsewhere. 358 2006 World Development Indicators Net financial flows from multilateral institutions About the data Definitions The table shows concessional and nonconcessional (IDA). Eligibility for IDA resources is based on gross · Net financial flows are disbursements of public financial flows from the major multilateral institutions-- national income (GNI) per capita; countries must also or publicly guaranteed loans and credits, less repay- the World Bank, the International Monetary Fund meet performance standards assessed by World ments of principal. · IDA is the International Develop- (IMF), regional development banks, UN agencies, Bank staff. Since July 1, 2005, the GNI per capita ment Association, the concessional loan window of and regional groups such as the Commission of the cutoff has been set at $825, measured in 2003 using the World Bank. · IBRD is the International Bank for European Communities. Much of the data comes the World Bank Atlas method (see Users guide). In Reconstruction and Development, the founding and from the World Bank's Debtor Reporting System. exceptional circumstances IDA extends eligibility tem- largest member of the World Bank Group. · IMF is The multilateral development banks fund their porarily to countries that are above the cutoff and the International Monetary Fund. Its nonconcessional nonconcessional lending operations primarily by are undertaking major adjustment efforts but are not lending consists of the credit it provides to its mem- selling low-interest, highly rated bonds (the World creditworthy for lending by the International Bank for bers, mainly to meet their balance of payments needs. Bank, for example, has a AAA rating) backed by pru- Reconstruction and Development (IBRD). An excep- It provides concessional assistance through the Pov- dent lending and financial policies and the strong tion has also been made for small island economies. erty Reduction and Growth Facility and the IMF Trust financial support of their members. These funds are Lending by the International Finance Corporation is Fund. · Regional development banks include the Afri- then on-lent at slightly higher interest rates and with not included in this table. can Development Bank, in Tunis, Tunisia, which lends relatively long maturities (15­20 years) to developing The IMF makes concessional funds available to all of Africa, including North Africa; the Asian Devel- countries. Lending terms vary with market conditions through its Poverty Reduction and Growth Facility, opment Bank, in Manila, Philippines, which serves and the policies of the banks. which replaced the Enhanced Structural Adjustment countries in South and Central Asia and East Asia Concessional flows from bilateral donors are Facility in 1999, and through the IMF Trust Fund. Eli- and Pacific; the European Bank for Reconstruction defined by the Development Assistance Committee gibility is based principally on a country's per capita and Development, in London, United Kingdom, which (DAC) of the Organisation for Economic Co-operation income and eligibility under IDA, the World Bank's serves countries in Europe and Central Asia; the Euro- and Development (OECD) as financial flows contain- concessional window. pean Development Fund, in Brussels, Belgium, which ing a grant element of at least 25 percent. The grant Regional development banks also maintain con- serves countries in Africa, the Caribbean, and the element of loans is evaluated assuming a nominal cessional windows for funds. Loans from the major Pacific; and the Inter-American Development Bank, in market interest rate of 10 percent. The grant ele- regional development banks--the African Develop- Washington, D.C., which is the principal development ment is nil for a loan carrying a 10 percent interest ment Bank, Asian Development Bank, and Inter- bank of the Americas. Concessional financial flows rate, and it is 100 percent for a grant, which requires American Development Bank--are recorded in the cover disbursements made through concessional no repayment. Concessional flows from multilateral table according to each institution's classification. lending facilities. Nonconcessional financial flows development agencies are credits provided through In 1999 all UN agencies revised their data since cover all other disbursements. · Others is a residual their concessional lending facilities. The cost of 1990 to include only regular budgetary expenditures category in the World Bank's Debtor Reporting Sys- these loans is reduced through subsidies provided (except for the World Food Programme and the United tem. It includes such institutions as the Caribbean by donors or drawn from other resources available to Nations High Commissioner for Refugees, which Development Bank and the European Investment the agencies. Grants provided by multilateral agen- revised their data from 1996 onward). They did so to Bank. · United Nations includes the United Nations cies are not included in the net flows. avoid double counting extrabudgetary expenditures Development Programme (UNDP), United Nations All concessional lending by the World Bank is car- reported by DAC countries and flows reported by the Population Fund (UNFPA), United Nations Children's ried out by the International Development Association United Nations. Fund (UNICEF), World Food Programme (WFP), and other UN agencies, such as the United Nations High Commissioner for Refugees, United Nations Relief and Works Agency for Palestine Refugees in the Near Maintaining financial flows from the World Bank to developing countries East, and United Nations Regular Programme for Tech- $ billions nical Assistance. 15 IBRD lending 10 IDA disbursement IDA net disbursement 5 0 Data sources Data on net financial flows from international finan- cial institutions are from the World Bank's Debtor -5 Reporting System. These data are published in the IBRD net lending World Bank's Global Development Finance 2006 and electronically as GDF Online. Data on aid from ­10 UN agencies are from the DAC annual Development 1970 1975 1980 1985 1990 1995 2000 2005 Cooperation Report. Data are available in electronic As the World Bank's nonconcessional lending portfolio matures, repayment of principal has begun to balance out new format on the OECD's International Development disbursements. IDA, as the World Bank's concessional financing arm, has maintained a steady flow of new funds to the Statistics CD-ROM and to registered users at www. world's poorest countries. oecd.org/dataoecd/50/17/5037721.htm. Source: World Bank Debtor Reporting System. 2006 World Development Indicators 359 Movement of people Net migration Migration stock Refugees Workers' remittances and compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 1995­2000 1990 2005 1995 2004 1995 2004 1990 2004 1990 2004 Afghanistan 3,313 ­397 29 43 2,679.1 2,085.5 19.6 0.0 .. .. .. .. Albania ­423 ­267 66 83 5.8 10.5 4.7 0.1 0 889 .. 4 Algeria ­58 ­185 274 242 1.5 10.7 192.5 169.0 352 2,460 31 .. Angola 143 ­120 34 56 246.7 228.8 10.9 14.0 .. .. 150 296 Argentina 50 ­100 1,650 1,500 0.3 0.8 10.3 2.9 15 288 21 151 Armenia ­500 ­225 659 235 201.4 13.4 219.0 235.2 .. 336 .. 127 Australia 390 510 3,984 4,097 0.0 0.0 62.1 63.5 2,370 2,744 674 1,955 Austria 262 45 473 1,234 0.1 0.1 34.4 17.8 635 2,475 320 2,013 Azerbaijan ­116 ­128 361 182 200.5 250.6 233.7 8.6 .. 228 .. 200 Bangladesh ­260 ­300 882 1,032 57.0 5.7 51.1 20.4 779 3,584 .. 8 Belarus 15 14 1,271 1,191 0.1 8.2 29.0 0.7 .. 244 .. 80 Belgium 85 99 899 719 0.0 0.0 31.7 13.5 3,583 6,840 2,310 2,623 Benin 105 ­29 76 175 0.1 0.3 23.8 4.8 101 55 21 6 Bolivia ­100 ­100 60 116 0.2 0.3 0.7 0.5 5 158 8 43 Bosnia and Herzegovina ­1,000 350 56 41 769.8 229.3 40.0 22.2 .. 1,824 .. 26 Botswana ­7 ­7 27 80 0.0 0.0 0.3 2.8 86 39 119 206 Brazil ­184 ­130 804 641 0.1 0.4 2.1 3.3 573 3,575 12 401 Bulgaria ­309 ­50 22 104 4.2 2.6 1.3 4.7 .. 103 .. 11 Burkina Faso ­128 ­121 345 773 0.1 0.9 29.8 0.5 140 50 81 44 Burundi ­250 ­400 333 100 350.6 485.8 173.0 48.8 .. .. 6 4 Cambodia 194 100 38 304 61.2 18.1 0.0 0.4 9 177 14 139 Cameroon ­5 0 171 137 2.0 7.6 45.8 58.9 23 11 111 .. Canada 643 733 4,319 6,106 0.0 0.1 152.1 141.4 .. .. .. .. Central African Republic 37 11 63 76 0.2 31.1 33.9 25.0 .. .. 36 .. Chad 20 99 74 437 59.7 52.7 0.1 259.9 1 .. 39 .. Chile 90 60 108 231 14.3 1.2 0.3 0.6 1 13 7 3 China ­1,281 ­1,950 380 596 104.7 134.7 288.3 299.4 210 21,283 5 2,067 Hong Kong, China 300 300 2,218 2,999 0.2 0.0 1.5 1.9 .. 240 .. 319 Colombia ­200 ­200 102 123 1.9 47.4 0.2 0.1 495 3,190 44 50 Congo, Dem. Rep. 1,208 ­1,410 919 539 89.7 462.2 1,433.8 199.3 .. .. .. .. Congo, Rep. 14 42 130 288 0.2 28.2 19.4 68.5 4 1 55 24 Costa Rica 62 128 418 441 0.2 0.1 24.2 10.4 12 320 .. 192 Côte d'Ivoire 200 150 1,953 2,371 0.2 23.7 297.9 72.1 44 148 471 635 Croatia 153 ­150 475 661 245.6 215.5 198.6 3.7 .. 1,222 .. 69 Cuba ­100 ­100 100 74 24.9 15.7 1.8 0.8 .. .. .. .. Czech Republic 38 52 424 453 2.0 4.5 2.7 1.1 .. 454 .. 1,337 Denmark 58 84 220 389 0.0 0.0 64.8 65.3 464 1,075 160 1,226 Dominican Republic ­220 ­180 103 156 0.0 0.1 1.0 .. 315 2,471 .. 24 Ecuador ­50 ­300 78 114 0.2 0.7 0.2 8.5 51 1,610 2 7 Egypt, Arab Rep. ­600 ­500 176 166 0.9 5.4 5.4 90.3 4,284 3,341 27 13 El Salvador ­57 ­38 47 24 23.5 4.5 0.2 0.2 366 2,564 3 33 Eritrea ­359 ­9 12 15 286.7 131.1 1.1 4.2 .. .. .. .. Estonia ­117 ­46 382 202 0.4 1.0 .. 0.0 .. 164 .. 27 Ethiopia 888 ­77 1,155 555 101.0 63.1 393.5 116.0 5 133 1 9 Finland 43 20 61 156 0.0 0.0 10.2 11.3 63 577 16 164 France 424 219 5,907 6,471 0.0 0.1 155.2 139.9 4,035 12,663 6,949 4,882 Gabon 20 14 128 245 0.0 0.0 0.8 13.8 .. 6 147 115 Gambia, The 45 45 118 232 0.2 0.8 6.6 7.3 10 8 .. .. Georgia ­560 ­350 338 191 0.3 6.6 0.1 2.6 .. 303 .. 26 Germany 2,688 1,134 5,936 10,144 0.4 0.7 1,267.9 876.6 4,876 6,497 6,856 10,442 Ghana 40 ­51 717 1,669 13.6 14.8 83.2 42.1 6 82 4 6 Greece 470 300 412 974 0.2 0.3 4.4 2.5 1,817 1,242 122 497 Guatemala ­360 ­390 264 53 42.9 4.4 1.5 0.7 119 2,591 14 36 Guinea 350 ­227 402 406 0.4 3.9 672.3 139.3 27 42 20 48 Guinea-Bissau 20 ­11 14 19 0.8 1.0 15.4 7.5 1 23 12 7 Haiti ­105 ­105 19 30 13.9 9.2 .. .. 61 876 63 .. 360 2006 World Development Indicators Movement of people Net migration Migration stock Refugees Workers' remittances and compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 1995­2000 1990 2005 1995 2004 1995 2004 1990 2004 1990 2004 Honduras ­40 ­20 270 26 1.2 0.6 0.1 0.0 63 1,142 .. 1 Hungary 101 100 348 316 2.4 3.4 11.4 7.7 .. 307 .. 128 India ­1,407 ­1,400 7,493 5,700 5.0 13.3 227.5 162.7 2,384 21,727 106 1,008 Indonesia ­725 ­900 466 160 9.8 27.9 0.0 0.2 166 1,866 .. 913 Iran, Islamic Rep. ­1,512 ­456 3,809 1,959 112.4 115.1 2,072.0 1,046.0 1,200 1,032 .. .. Iraq 170 139 84 28 718.7 311.9 116.7 46.1 .. .. .. .. Ireland ­1 89 230 585 0.0 0.0 0.4 7.2 286 358 165 856 Israel 484 276 1,633 2,661 0.9 0.6 .. 0.6 812 398 850 2,116 Italy 573 600 1,346 2,519 0.1 0.2 74.3 15.7 5,075 2,172 3,764 4,745 Jamaica ­100 ­100 21 18 0.0 0.3 0.0 .. 229 1,623 27 425 Japan 248 280 877 2,048 0.0 0.0 5.4 2.0 508 931 290 1,411 Jordana 495 35 1,146 2,225 0.5 1.2 0.7 1.1 499 2,287 71 272 Kazakhstan ­1,509 ­1,320 3,619 2,502 0.1 6.1 15.6 15.8 .. 167 .. 1,353 Kenya 222 ­21 146 345 9.3 3.8 234.7 239.8 139 494 7 34 Korea, Dem. Rep. 0 0 34 37 0.0 0.3 .. .. .. .. .. .. Korea, Rep. ­115 ­80 572 551 0.0 0.2 0.0 0.0 1,037 832 364 2,545 Kuwait ­626 347 1,551 1,669 0.8 0.6 3.3 1.5 .. .. 770 2,402 Kyrgyz Republic ­273 ­27 623 288 0.0 3.3 13.4 3.8 .. 189 .. 73 Lao PDR ­10 ­7 23 25 58.2 16.1 .. .. 11 1 .. 1 Latvia ­174 ­56 805 449 0.2 3.2 .. 0.0 .. 230 .. 13 Lebanona 178 ­30 520 657 13.5 19.9 1.9 1.8 1,818 2,700 .. .. Lesotho ­84 ­36 7 6 0.0 0.0 0.1 .. 428 355 .. 29 Liberia ­283 555 81 50 744.6 335.5 120.1 15.2 .. .. .. .. Libya 10 10 457 618 0.6 1.6 4.0 12.2 .. 10 446 790 Lithuania ­100 ­109 349 165 0.1 1.5 0.0 0.5 .. 325 .. 28 Macedonia, FYR ­27 ­5 95 121 12.9 5.1 9.0 1.0 .. 213 .. 16 Madagascar ­6 ­3 58 63 0.1 0.1 0.1 .. 8 16 18 7 Malawi ­835 ­50 1,157 279 0.0 0.1 1.0 3.7 .. 1 .. 1 Malaysia 230 390 1,014 1,639 0.1 0.2 5.3 24.9 325 987 230 3,464 Mali ­260 ­284 60 46 77.2 0.5 17.9 11.3 107 154 45 58 Mauritania ­15 10 94 66 84.3 31.1 34.4 0.5 14 2 31 .. Mauritius ­7 ­2 9 21 0.0 0.1 .. .. .. 215 1 11 Mexico ­1,800 ­2,000 702 644 0.4 1.7 38.7 4.3 3,098 18,143 .. .. Moldova ­121 ­70 579 440 0.5 11.9 .. 0.1 .. 703 .. 52 Mongolia ­60 ­90 7 9 0.0 0.3 .. .. .. 203 .. 49 Morocco ­300 ­300 85 132 0.3 1.3 0.1 2.1 2,006 4,221 16 42 Mozambique 650 75 122 406 125.6 0.1 0.1 0.6 70 58 25 20 Myanmar ­126 60 101 117 152.3 161.0 .. .. 6 118 .. 25 Namibia 3 20 119 143 0.0 1.3 1.7 14.8 13 15 30 18 Nepal ­101 ­99 413 819 0.0 1.2 124.8 124.9 0 823 .. 64 Netherlands 190 161 1,192 1,638 0.1 0.3 80.0 126.8 709 2,164 1,393 5,153 New Zealand 79 20 529 642 .. 0.0 3.8 5.4 762 1,132 367 911 Nicaragua ­110 ­155 41 28 23.9 1.8 0.6 0.3 0 519 .. .. Niger 5 ­6 115 124 10.3 0.7 27.6 0.3 14 26 66 9 Nigeria ­96 ­95 447 971 1.9 23.9 8.1 8.4 10 2,273 9 21 Norway 42 67 185 344 0.0 0.0 47.6 44.0 158 392 295 916 Oman 25 ­40 452 628 0.0 0.0 .. 0.0 39 40 856 1,826 Pakistan ­2,611 ­41 6,556 3,254 5.3 25.9 1,202.5 960.6 2,006 3,945 1 11 Panama 8 11 62 102 0.2 0.0 0.9 1.6 110 127 22 87 Papua New Guinea 0 0 33 25 2.0 0.0 9.6 7.6 5 6 43 17 Paraguay ­25 ­25 183 168 0.1 0.0 0.1 0.0 34 260 .. .. Peru ­450 ­350 56 42 5.9 4.8 0.6 0.8 87 1,123 75 123 Philippines ­900 ­900 164 374 0.5 0.4 0.8 0.1 1,465 11,634 5 16 Poland ­77 ­71 1,127 703 19.7 10.7 0.6 2.5 .. 2,710 .. 460 Portugal ­7 175 436 764 0.0 0.1 0.2 0.4 4,479 3,212 77 1,024 Puerto Rico ­4 ­1 322 418 0.0 .. .. .. .. .. .. .. 2006 World Development Indicators 361 Movement of people Net migration Migration stock Refugees Workers' remittances and compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 1995­2000 1990 2005 1995 2004 1995 2004 1990 2004 1990 2004 Romania ­529 ­350 143 133 17.0 5.9 0.2 1.6 .. 132 .. 8 Russian Federation 1,858 2,300 11,525 12,080 207.0 108.0 246.7 1.9 .. 2,668 .. 5,534 Rwanda ­1,714 1,977 73 121 1,819.4 63.8 7.8 50.2 3 10 21 31 Saudi Arabia ­325 75 4,743 6,361 0.3 0.2 13.2 240.6 .. .. 11,221 13,555 Senegal ­100 ­100 293 326 17.6 8.3 66.8 20.8 142 511 79 57 Serbia and Montenegro 200 ­100 130 512 86.1 237.0 650.7 276.7 .. 4,129 .. .. Sierra Leone ­380 ­110 112 119 379.5 41.8 4.7 65.4 .. 25 .. 3 Singapore 250 368 727 1,843 0.0 0.0 0.1 0.0 .. .. .. .. Slovak Republic 9 9 41 124 0.0 0.7 2.3 0.4 .. 425 .. 15 Slovenia 38 8 178 167 12.9 0.6 22.3 0.3 38 290 2 60 Somalia ­1,083 ­214 633 282 638.7 389.3 0.6 0.4 .. .. .. .. South Africa 1,125 364 1,225 1,106 0.5 0.3 101.4 27.7 136 521 1,199 935 Spain 500 676 766 4,790 0.0 0.1 5.9 5.6 2,186 6,900 254 5,411 Sri Lanka ­182 ­160 461 368 107.6 114.0 0.0 0.1 401 1,590 .. 236 Sudan ­158 ­207 1,273 639 445.3 730.7 674.1 141.6 62 1,403 2 2 Swaziland ­38 ­12 73 45 0.0 0.0 0.7 0.7 113 89 4 131 Sweden 151 60 781 1,117 0.0 0.0 199.2 73.4 153 643 654 672 Switzerland 80 80 1,376 1,660 0.0 0.0 82.9 47.7 924 1,760 8,168 12,796 Syrian Arab Republica ­30 ­30 711 985 8.0 21.4 36.2 15.6 385 855 .. 42 Tajikistan ­313 ­345 426 306 59.0 56.8 0.6 3.3 .. 252 .. 119 Tanzania 591 ­206 574 792 0.1 0.7 829.7 602.1 .. 11 .. 33 Thailand ­88 ­88 391 1,050 0.2 0.3 106.6 121.1 973 1,622 199 .. Togo ­122 128 163 183 93.2 10.8 10.9 11.3 27 149 13 28 Trinidad and Tobago ­24 ­20 51 38 0.0 0.0 .. .. 3 87 22 .. Tunisia ­22 ­20 38 38 0.3 2.6 0.2 0.1 551 1,432 13 19 Turkey 71 135 1,150 1,328 44.9 174.6 12.8 3.0 3,246 804 .. .. Turkmenistan 50 ­50 307 224 0.1 0.8 23.3 13.3 .. .. .. .. Uganda 135 ­66 550 518 24.2 32.0 229.4 250.5 .. 306 .. 231 Ukraine 598 ­700 7,097 6,833 1.7 89.6 5.2 2.5 .. 411 .. 20 United Arab Emirates 340 567 1,330 3,212 0.0 0.0 0.4 0.1 .. .. .. .. United Kingdom 381 574 3,753 5,408 0.1 0.2 90.9 289.1 2,099 6,350 2,034 2,957 United States 5,200 6,200 23,251 38,355 0.3 0.4 623.3 420.9 1,170 3,038 11,850 38,751 Uruguay ­20 ­16 98 84 0.3 0.1 0.1 0.1 .. 57 .. 1 Uzbekistan ­340 ­400 1,653 1,268 0.1 7.3 2.6 44.5 .. .. .. .. Venezuela, RB 40 40 1,024 1,010 0.5 0.6 1.6 0.2 1 20 701 214 Vietnam ­270 ­200 28 21 543.5 349.8 34.4 2.4 .. 3,200 .. .. West Bank and Gazaa ­5 11 911 1,680 72.8 350.6 .. .. .. 692 .. .. Yemen, Rep. 650 ­50 107 265 0.4 1.6 53.5 66.4 1,498 1,283 106 108 Zambia ­7 86 280 275 0.0 0.1 130.0 173.9 .. .. 17 24 Zimbabwe ­182 ­125 804 511 0.0 9.6 0.5 6.9 1 .. 16 .. World ..b ..b 154,688 s 190,206 s12,518.5 s 8,650.0 s14,896.1 s 9,236.8 s 68,584 s 227,579 s 66,295 s 154,070 s Low income ­3,592 ­4,422 32,672 28,018 9,143.2 5,854.5 7,369.9 4,054.4 8,115 43,967 1,471 3,049 Middle income ­9,367 ­9,689 50,374 49,923 3,358.7 2,795.0 4,487.2 2,563.5 23,036 117,127 4,610 22,929 Lower middle income ­11,096 ­10,646 25,684 24,234 2,804.9 2,260.7 4,060.2 2,438.6 13,919 84,060 793 7,284 Upper middle income 1,729 957 24,690 25,689 553.8 534.3 427.0 124.9 9,117 33,067 3,817 15,645 Low & middle income ­12,958 ­14,111 83,047 77,942 12,501.9 8,649.4 11,857.0 6,617.8 31,151 161,094 6,081 25,978 East Asia & Pacific ­3,072 ­3,859 2,748 4,432 932.9 708.3 447.0 456.1 3,263 41,250 527 6,770 Europe & Central Asia ­3,398 ­1,858 34,071 31,137 1,881.8 1,454.9 1,436.9 657.3 3,246 19,431 .. 9,725 Latin America & Carib. ­3,776 ­4,156 6,355 5,795 155.7 88.9 93.9 36.2 5,776 41,051 1,002 1,895 Middle East & N. Africa ­1,030 ­1,396 8,828 9,642 948.0 835.5 2,510.4 1,468.7 11,432 20,353 1,566 3,112 South Asia ­1,368 ­2,401 15,845 11,229 2,958.7 2,349.9 1,625.5 1,268.8 5,572 31,671 115 1,388 Sub-Saharan Africa ­314 ­439 15,200 15,706 5,624.8 3,211.9 5,743.4 2,730.8 1,862 7,339 2,871 3,089 High income 12,929 14,104 71,641 112,264 16.6 0.6 3,039.1 2,618.9 37,433 66,485 60,214 128,092 Europe EMU 5,247 3,538 17,772 30,167 1.0 1.8 1,665.3 1,218.9 27,744 46,269 22,226 43,819 a. Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East are not included in statistics from the United Nations Office of the High Commissioner for Refugees. b. World totals computed by the United Nations sum to zero, but because the aggregates shown here refer to World Bank definitions, regional and income group totals do not equal zero. 362 2006 World Development Indicators Movement of people About the data Movement of people, most often through migration, international migrant stock on July 1 of the refer- migrants who have lived in the host country for less is a significant part of integration. Migrants contrib- ence years. For countries with only one observation, than a year. Migrants' transfers are defined as the ute to the economies of both their host country and estimates for the reference years were derived using net worth of migrants who are expected to remain their country of origin. Yet reliable statistics on migra- rates of change in the migrant stock in the years pre- in the host country for more than one year that is tion are difficult to collect and are often incomplete, ceding or following the single observation available. transferred from one country to another at the time making international comparisons a challenge. A model was used to estimate migration for countries of migration. The United Nations Population Division provides that had no data. The distinction between these three items is not data on net migration and migration stock. To derive Registration, together with other sources--including always consistent in the data reported by countries to estimates of net migration, the organization takes estimates and surveys--are the main sources of refu- the IMF. In some cases, countries compile data on the into account the past migration history of a country gee data. Yet there are difficulties in collecting accu- basis of the citizenship of migrant workers rather than or area, the migration policy of a country, and the rate statistics. Although refugees are often registered their residency status. Some countries also report influx of refugees in recent periods. The data to cal- individually, the accuracy of registrations varies greatly. remittances entirely as workers' remittances or com- culate these official estimates come from a variety of Many refugees may not be aware of the need to register pensation of employees. Following the fifth edition of sources, including border statistics, administrative or may choose not to do so. And administrative records the Balance of Payments Manual in 1993, migrants' records, surveys, and censuses. When no official tend to overestimate the number of refugees because transfers are considered a capital transaction but estimates can be made due to insufficient data, net it is easier to register than to de-register. Palestinian in previous editions they were regarded as current migration is derived through the balance equation, refugees under the mandate of the United Nations transfers. For these reasons the figures presented in which is the difference between overall population Relief and Works Agency for Palestinian Refugees in the table take all three items into account. growth and the natural increase during the 1990­ the Near East are not included in the statistics of the 2000 intercensal period. United Nations Office of the High Commissioner for Definitions The data used to estimate the international migrant Refugees (UNHCR). stock at a particular point in time are obtained mainly Workers' remittances and compensation of employ- · Net migration is the net average annual number from population censuses. The estimates are derived ees are World Bank staff estimates based on data of migrants during the period, that is, the annual from the data on foreign-born population--those who from the International Monetary Fund's (IMF) Bal- number of immigrants less the annual number of emi- have residence in one country but who were born ance of Payments Yearbook. The IMF data are supple- grants, including both citizens and noncitizens. Data in another country. When data on the foreign-born mented by World Bank staff estimates for missing are five-year estimates. · Migration stock is the population are not available, data on foreign popula- data for countries where workers' remittances are number of people born in a country other than that tion are used as estimates. important. The data reported here are the sum of in which they live. It includes refugees. · Refugees After the breakup of the Soviet Union in 1991, three items defined in the IMF Balance of Payments are people who are recognized as refugees under the people living in one of the newly independent coun- Manual (fifth edition). These are workers' remit- 1951 Convention Relating to the Status of Refugees tries who were born in another of the countries were tances, compensation of employees, and migrants' or its 1967 Protocol, the 1969 Organization of Afri- classified as international migrants. Estimates of transfers. Workers' remittances are classified as cur- can Unity Convention Governing the Specific Aspects migration stock in the newly independent states rent private transfers from migrant workers who are of Refugee Problems in Africa, people recognized from 1990 on are based on the 1989 census of the residents of the host country to recipients in their as refugees in accordance with the UNHCR statute, Soviet Union. country of origin. They include only transfers made people granted a refugee-like humanitarian status, For countries with information on the international by workers who have been living in the host country and people provided with temporary protection. Asy- migration stock for at least two points in time, inter- for more than a year, irrespective of their immigration lum seekers are people who have applied for asylum polation or extrapolation was used to estimate the status. Compensation of employees is the income of or refugee status and who have not yet received a decision or who are otherwise registered as asylum seekers. · Country of origin generally refers to the nationality or country of citizenship of a claimant. Officially recorded remittance flows are surging · Country of asylum is the country where an asylum Remittances as a share of GDP (%) 1995 claim was filed. · Workers' remittances and com- 2004 pensation of employees, received and paid comprise 4 current transfers by migrant workers and wages and salaries earned by nonresident workers. 3 Data sources 2 Data on net migration come from the United Nations Population Division's World Population Prospects: The 2004 Revision. Data on migration 1 stock come from the United Nations Population Division's Trends in Total Migrant Stock: The 2005 0 East Asia Europe & Latin America Middle East & South Sub-Saharan Revision. Data on refugees are from the United & Pacific Central Asia & Caribbean North Africa Asia Africa Nations Office of the High Commissioner for Refu- gees' Statistical Yearbook 2004. Data on remit- Officially recorded remittances have increased dramatically in all regions. As a share of GDP, remittances have more than doubled in East Asia and Pacific and Latin America and the Caribbean. tances are World Bank staff estimates based on Source: International Monetary Fund Balance of Payments database. IMF balance of payments data. 2006 World Development Indicators 363 Travel and tourism International tourists Tourism expenditure in the country Tourism expenditure in other countries thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Afghanistan .. .. .. .. .. .. .. .. 1 .. .. .. Albania 40 42 12 .. 70 756 23.2 46.0 19 668 2.3 19.6 Algeria 520 1,234 1,090 1,417 32 112 .. .. 186 255 .. .. Angola 9 194 3 .. 27 82 0.7 0.6 113 86 3.2 0.8 Argentina 2,289 3,353 3,815 3,385 2,550 2,990 10.2 7.5 4,013 3,561 15.4 12.6 Armenia 12 263 .. 169 14 103 4.7 10.5 12 102 1.7 6.7 Australia 3,726 5,215 2,519 3,388 11,658 17,946 16.8 15.9 7,074 13,004 9.5 9.9 Austria 17,173 19,373 3,713 6,798 14,529 18,401 16.2 11.4 11,686 12,811 12.7 8.2 Azerbaijan 93 1,349 432 1,473 88 79 11.2 1.9 165 140 12.8 2.2 Bangladesh 156 271 830 1,414 .. 59 .. 0.7 251 389 3.4 3.5 Belarus 161 67 626 514 28 379 0.5 2.4 101 574 1.8 3.4 Belgium 5,560 6,690 5,645 7,268 .. 10,044 .. .. .. 15,295 .. .. Benin 138 72 418 .. 79 108 12.1 15.1 48 53 5.4 4.9 Bolivia 284 405 249 281 92 265 7.5 10.4 72 219 4.6 9.4 Bosnia and Herzegovina 115 165 .. .. .. 514 .. 17.6 .. 169 .. 2.4 Botswana 521 975 369 .. 176 459 7.3 12.4 153 235 7.5 8.5 Brazil 1,991 4,794 2,600 2,293 1,085 3,389 2.1 3.1 3,982 3,752 6.3 4.7 Bulgaria 3,466 4,630 3,524 3,882 662 2,718 9.8 19.4 312 1,356 4.8 8.2 Burkina Faso 124 163 .. .. .. .. .. .. .. .. .. .. Burundi 34 .. 36 .. 2 1 1.9 2.8 .. .. .. .. Cambodia 220 1,055 31 239 71 674 7.3 20.8 22 80 1.6 2.2 Cameroon 100 226 .. .. 75 .. 3.7 .. 140 .. 8.7 .. Canada 16,932 19,095 18,206 19,595 9,176 14,925 4.2 4.0 12,658 19,730 6.3 5.9 Central African Republic 26 .. .. .. 4 3 .. .. 43 29 .. .. Chad 19 21 .. .. 43 25 .. .. 38 80 .. .. Chile 1,540 1,785 1,070 2,343 1,186 1,554 6.1 4.1 934 1,196 5.1 4.0 China 20,034 41,761 4,520 20,222 12,626 27,755 6.1 4.2 9,220 21,360 5.6 3.5 Hong Kong, China 10,200 21,811 3,023 5,003 .. 11,815 .. 3.8 .. .. .. .. Colombia 1,433 791 1,057 1,405 887 1,340 7.2 6.9 1,162 1,644 7.3 8.2 Congo, Dem. Rep. 35 35 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 37 22 .. .. 15 26 1.1 1.0 69 85 5.1 5.3 Costa Rica 785 1,453 273 425 763 1,585 17.1 18.4 336 481 7.1 5.3 Côte d'Ivoire 188 .. .. .. 103 76 2.4 1.2 312 551 8.2 11.0 Croatia 1,485 7,912 .. .. .. 7,191 .. 40.3 .. 872 .. 4.3 Cuba 742 2,017 72 115 963 1,915 .. .. .. .. .. .. Czech Republic 3,381 6,061 44,873 36,650 .. 4,956 .. 6.5 .. 2,659 .. 3.5 Denmark 2,124 3,358 5,035 4,630 .. .. .. .. .. .. .. .. Dominican Republic 1,776 3,450 168 368 .. .. .. .. 267 448 4.4 5.0 Ecuador 440 793 271 605 315 369 6.1 4.2 331 577 5.8 6.2 Egypt, Arab Rep. 2,871 5,746 2,683 3,644 2,954 6,328 22.3 23.9 1,371 1,543 8.0 5.7 El Salvador 235 966 348 1,218 152 632 7.5 14.7 99 321 2.7 4.6 Eritrea 315 87 .. .. 58 73 .. .. .. .. .. .. Estonia 530 1,750 1,764 2,075 452 1,102 17.6 12.5 121 481 4.2 5.0 Ethiopia 103 180 120 .. 177 457 23.1 27.1 30 61 2.1 1.6 Finland 1,779 2,047 5,147 5,798 2,384 2,867 5.0 4.0 2,853 3,597 7.6 5.9 France 60,033 75,121 18,686 21,131 31,295 .. 8.6 .. 20,699 .. 6.2 .. Gabon 125 222 .. 236 94 84 3.2 2.5 183 239 10.3 12.7 Gambia, The 45 73 .. .. 67 .. 30.5 .. 16 .. 6.9 .. Georgia 85 368 228 317 75 209 13.1 12.8 171 196 12.1 7.9 Germany 14,847 18,399 55,800 74,600 24,052 35,589 4.0 3.4 66,981 78,553 11.3 8.6 Ghana 286 483 .. .. 30 495 1.9 14.2 74 270 3.5 5.0 Greece 10,130 13,969 1,738 .. 4,182 12,809 26.9 26.2 1,495 2,880 6.0 4.7 Guatemala 563 1,182 333 854 216 806 7.7 17.5 167 456 4.5 5.4 Guinea 12 45 .. .. 1 32 0.1 4.3 29 29 2.9 3.0 Guinea-Bissau .. .. .. .. .. 3 .. 3.7 6 21 6.7 20.7 Haiti 145 .. .. .. .. .. .. .. .. .. .. .. 364 2006 World Development Indicators Travel and tourism International tourists Tourism expenditure in the country Tourism expenditure in other countries thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Honduras 271 672 149 295 85 403 5.2 13.1 99 269 5.3 6.1 Hungary 2,878 3,270 13,083 17,558 2,938 4,084 14.9 6.2 1,501 2,908 7.5 4.2 India 2,124 2,726 3,056 5,351 .. 4,128 .. 5.0 .. 4,758 .. 5.1 Indonesia 4,324 5,321 1,782 .. .. 5,226 .. 5.8 .. 4,570 .. 5.8 Iran, Islamic Rep. 489 1,659 1,000 2,921 205 1,324 1.1 .. 247 4,353 1.6 .. Iraq 61 .. 199 .. .. .. .. .. .. .. .. .. Ireland 4,818 6,982 2,547 5,409 2,698 5,962 5.5 3.9 .. 5,287 .. 4.2 Israel 2,215 1,506 2,259 3,614 3,491 2,819 12.7 5.5 2,626 3,663 7.4 7.0 Italy 31,052 37,071 18,173 23,349 30,426 37,872 10.3 8.7 17,219 24,062 6.9 5.7 Jamaica 1,147 1,415 .. .. 1,199 1,733 35.3 44.5 173 318 4.6 6.0 Japan 3,345 6,138 15,298 16,831 4,894 14,343 1.0 2.3 46,966 48,175 11.2 8.9 Jordan 1,074 2,013 1,128 1,533 973 1,621 28.0 27.1 719 585 14.7 6.2 Kazakhstan .. 3,073 523 3,915 155 793 2.6 3.5 296 917 4.9 4.9 Kenya 896 927 .. .. 590 808 20.0 19.2 183 .. 5.2 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 3,753 5,818 3,819 8,826 6,670 7,870 4.5 2.6 6,947 13,103 4.5 4.9 Kuwait 72 91 878 1,928 309 414 2.2 1.2 2,513 4,140 19.9 22.4 Kyrgyz Republic 36 248 42 45 .. 97 .. 10.3 7 63 0.7 5.5 Lao PDR 60 236 .. .. 52 .. 12.8 .. 34 .. 4.5 .. Latvia 539 1,079 1,812 2,457 37 343 1.8 5.7 62 429 2.8 5.2 Lebanon 450 1,278 .. .. 710 5,931 .. .. .. 3,719 .. .. Lesotho 87 .. .. .. 29 .. 14.6 .. 17 37 1.6 2.6 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 56 142 484 .. 4 261 0.1 1.5 98 789 1.7 7.5 Lithuania 650 1,491 1,925 3,504 102 874 3.2 7.4 107 646 2.7 4.8 Macedonia, FYR 147 165 .. .. 35 77 2.7 3.7 30 84 1.7 2.6 Madagascar 75 139 39 .. 106 118 14.2 10.5 79 67 8.0 4.1 Malawi 192 421 .. .. 22 43 4.7 9.5 53 48 8.0 10.8 Malaysia 7,469 15,703 20,642 30,761 5,044 6,799 6.1 5.7 2,722 3,401 3.1 3.5 Mali 42 70 .. .. 26 136 4.9 11.8 74 94 7.5 6.4 Mauritania .. .. .. .. .. .. .. .. 30 .. 5.9 .. Mauritius 422 719 107 180 616 1,156 26.2 33.4 184 277 7.5 7.7 Mexico 20,241 20,618 8,450 12,494 6,847 11,566 7.7 5.7 3,587 8,034 4.4 3.7 Moldova 32 24 71 68 71 119 8.0 8.9 73 157 7.3 7.4 Mongolia 108 301 .. .. 33 205 6.5 16.9 22 207 4.2 14.7 Morocco 2,602 4,552 1,317 1,694 1,469 4,541 16.2 27.3 356 913 3.2 4.6 Mozambique .. 441 .. .. .. 96 .. 5.5 .. 140 .. 5.9 Myanmar 117 242 .. .. 169 98 12.9 3.1 38 32 1.5 1.3 Namibia 399 695 .. .. .. 426 .. 18.4 .. .. .. .. Nepal 363 385 100 286 232 260 22.5 21.2 167 205 10.3 9.4 Netherlands 6,574 9,646 12,313 16,463 10,611 11,745 4.4 4.4 13,151 14,201 6.1 5.7 New Zealand 1,409 2,334 920 1,733 .. .. .. .. .. .. .. .. Nicaragua 281 615 255 701 51 191 7.7 11.6 56 158 4.9 5.5 Niger 35 55 10 .. 26 29 7.1 7.0 26 39 5.7 5.7 Nigeria 656 887 .. .. 54 263 0.4 1.5 939 .. 7.3 .. Norway 2,880 3,600 590 2,588 2,730 3,400 4.9 3.1 4,481 8,788 9.6 11.9 Oman 279 630 .. 2,060 .. 708 .. 5.0 .. 795 .. 7.5 Pakistan 378 648 .. .. 582 763 5.7 4.7 654 1,590 4.6 7.2 Panama 345 621 185 256 372 903 4.9 10.2 181 344 2.3 3.8 Papua New Guinea 42 59 51 92 .. .. .. .. .. .. .. .. Paraguay 438 309 427 170 162 84 3.4 2.5 173 121 3.3 3.4 Peru 444 1,203 508 1,281 521 1,169 7.9 8.0 428 821 4.5 6.5 Philippines 1,760 2,291 1,615 1,803 1,141 2,412 4.3 5.6 551 1,558 1.7 3.1 Poland 19,215 14,290 36,387 27,226 6,927 6,499 19.4 6.8 5,865 4,157 17.3 4.2 Portugal 9,511 11,617 .. .. 5,646 8,922 17.5 17.2 2,540 3,359 6.4 5.1 Puerto Rico 3,131 3,541 1,237 1,361 1,828 3,024 .. .. 1,155 1,584 .. .. 2006 World Development Indicators 365 Travel and tourism International tourists Tourism expenditure in the country Tourism expenditure in other countries thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Romania 2,757 .. 5,737 6,972 689 607 7.3 2.2 749 672 6.6 2.0 Russian Federation 10,290 22,051 21,329 24,410 .. 6,958 .. 3.4 .. 16,527 .. 12.7 Rwanda .. .. .. .. 4 .. 5.4 .. 13 .. 3.5 .. Saudi Arabia 3,325 8,579 .. 3,811 .. 6,540 .. 5.0 .. 4,262 .. 6.4 Senegal 280 363 .. .. 168 269 11.2 14.7 154 129 8.5 4.9 Serbia and Montenegro 228 580 .. .. .. .. .. .. .. .. .. .. Sierra Leone 38 44 6 28 .. .. .. .. 51 30 19.4 8.9 Singapore 6,422 5,705 2,867 4,221 .. .. .. .. .. .. .. .. Slovak Republic 903 1,401 218 457 630 932 5.7 3.5 338 903 3.2 2.6 Slovenia 732 1,499 .. 2,800 1,128 1,726 10.9 8.8 606 940 5.6 4.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 4,488 6,678 2,520 3,794 2,655 6,729 7.7 11.9 2,414 3,661 7.2 6.3 Spain 34,920 53,599 3,648 5,121 27,510 51,125 20.5 19.0 5,768 13,337 4.3 4.3 Sri Lanka 403 566 504 680 367 808 7.9 11.1 279 499 4.7 5.5 Sudan 29 51 195 .. .. .. .. .. .. .. .. .. Swaziland 300 459 .. .. 54 16 5.3 0.9 45 34 3.5 3.0 Sweden 2,310 7,627 10,127 13,977 4,390 6,548 4.6 4.9 6,816 9,375 8.4 8.4 Switzerland 6,946 6,530 11,148 11,427 11,354 12,208 9.2 6.7 9,478 10,599 8.7 7.2 Syrian Arab Republic 815 3,032 1,746 3,997 .. 1,888 .. 23.1 .. 698 .. 8.8 Tajikistan .. .. .. .. .. 9 .. 0.7 .. .. .. .. Tanzania 285 566 157 .. 344 610 28.4 28.0 424 446 21.6 14.0 Thailand 6,952 11,737 1,820 2,709 9,257 13,054 13.2 11.4 4,791 5,343 5.8 5.0 Togo 53 61 .. .. .. 26 .. 3.8 41 37 6.1 3.9 Trinidad and Tobago 260 443 261 .. 232 437 8.3 7.4 91 143 4.3 3.3 Tunisia 4,120 5,998 1,778 2,274 1,838 2,432 23.0 18.3 294 427 3.3 3.0 Turkey 7,083 16,826 3,981 7,299 .. .. .. .. .. .. .. .. Turkmenistan 218 .. 21 .. 13 .. 0.7 .. 74 .. 4.1 .. Uganda 160 512 148 231 .. 306 .. 26.5 .. .. .. .. Ukraine 3,716 12,514 6,552 14,795 448 1,512 2.2 3.8 405 1,193 1.9 3.4 United Arab Emirates 2,315 5,871 .. .. 632 1,594 .. .. .. 4,475 .. .. United Kingdom 23,537 27,755 41,345 64,194 27,624 37,193 8.6 7.0 30,749 68,778 9.4 11.4 United States 43,490 46,085 51,285 61,776 93,700 112,780 11.8 9.8 60,924 93,217 6.8 5.3 Uruguay 2,022 1,756 562 495 725 579 20.7 14.4 332 281 9.3 7.7 Uzbekistan 92 231 246 400 15 48 .. .. .. .. .. .. Venezuela, RB 700 492 534 816 995 531 4.8 1.3 1,852 1,603 11.0 7.3 Vietnam 1,351 2,928 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 40 .. .. 104 4 .. .. .. .. .. .. Yemen, Rep. 61 155 .. .. .. .. .. .. .. 183 .. 3.7 Zambia 163 578 .. .. 47 149 6.1 .. 83 .. 6.2 .. Zimbabwe 1,363 .. 256 .. 145 194 .. .. 106 .. .. .. World 538,382 t 777,534 t 643,624 t 813,857 t 497,366 t 743,043 t 8.0 w 6.7 w 481,338 t 704,549 t 7.9 w 6.7 w Low income 11,676 18,248 .. .. .. 11,002 .. 6.3 .. 13,586 .. 5.5 Middle income 160,566 274,130 229,999 288,152 109,238 173,162 8.2 6.5 72,853 107,645 5.6 5.2 Lower middle income 69,138 138,963 48,397 90,985 50,953 92,627 7.6 6.4 35,108 56,308 5.4 4.2 Upper middle income 91,253 136,098 170,126 183,796 49,746 79,894 9.0 6.6 33,865 51,483 6.9 6.7 Low & middle income 175,026 259,534 276,829 350,077 118,632 189,112 8.1 6.6 83,495 123,650 5.7 5.2 East Asia & Pacific 44,247 84,175 36,006 71,020 34,630 60,901 7.1 5.4 20,630 35,398 4.9 3.9 Europe & Central Asia 59,537 106,564 152,343 164,140 .. .. .. 6.3 .. .. .. 6.9 Latin America & Carib. 39,852 51,220 21,948 27,174 20,622 34,115 7.1 5.8 18,987 25,624 6.5 5.3 Middle East & N. Africa 13,594 25,048 13,353 20,255 11,217 19,489 12.3 17.3 5,190 13,083 4.3 5.9 South Asia 3,819 4,979 5,151 8,690 .. 6,343 .. 7.4 .. 7,017 .. 6.9 Sub-Saharan Africa 12,536 18,873 .. .. 6,345 12,459 6.9 12.5 7,078 9,642 7.0 7.5 High income 357,681 469,854 314,894 429,211 393,324 554,322 7.9 6.7 408,872 581,301 8.2 7.2 Europe EMU 197,165 255,184 137,608 177,310 175,494 262,294 8.2 7.8 175,352 228,047 8.2 7.0 366 2006 World Development Indicators Travel and tourism About the data Definitions Tourism is defined as the activities of people trav- The data in the table are from the World Tourism · International inbound tourists (overnight visitors) eling to and staying in places outside their usual Organization, a specialized agency of the United are the number of tourists who travel to a country environment for no more than one year for leisure, Nations. The data on international inbound and out- other than that in which they have their usual resi- business, and other purposes not related to an activ- bound tourists refer to the number of arrivals and dence, but outside their usual environment, for a ity remunerated from within the place visited. The departures of visitors within the reference period, period not exceeding 12 months and whose main social and economic phenomenon of tourism has not to the number of people traveling. Thus a per- purpose in visiting is other than an activity remuner- grown substantially over the past quarter century. son who makes several trips to a country during a ated from within the country visited. · International Past descriptions of tourism focused on the char- given period is counted each time as a new arrival. outbound tourists are the number of departures that acteristics of visitors, such as the purpose of their International visitors include tourists (overnight people make from their country of usual residence visit and the conditions in which they traveled and visitors), same-day visitors, cruise passengers, and to any other country for any purpose other than a stayed. Now, there is a growing awareness of the crew members. remunerated activity in the country visited. · Tour- direct, indirect, and induced effects of tourism on The World Tourism Organization is improving its ism expenditure in the country is expenditures by employment, value added, personal income, govern- coverage of tourism expenditure data. It is now international inbound visitors, including payments to ment income, and the like. using balance of payments data from the Interna- national carriers for international transport. These Statistical information on tourism is based mainly tional Monetary Fund (IMF), supplemented by data receipts include any other prepayment made for on data on arrivals and overnight stays along with received from individual countries. The new data, goods or services received in the destination coun- balance of payments information. But these data shown in the table, now include travel and passenger try. They also may include receipts from same-day do not completely capture the economic phenom- transport items as defined in the IMF's Balance of visitors, except in cases where these are important enon of tourism or give governments, businesses, Payments Manual. enough to justify separate classification. Their share and citizens the information needed for effective Aggregates are based on the World Bank's clas- in exports is calculated as a ratio to exports of goods public policies and efficient business operations. sification of countries and differ from those in the and services (for definition of exports of goods and Credible data are needed on the scale and signifi- World Tourism Organization's publications. Coun- services see Definitions for table 4.8). · Tourism cance of tourism. Information on the role tourism tries not shown in the table but for which data are expenditure in other countries is expenditures of plays in national economies throughout the world available are included in the regional and income international outbound visitors in other countries, is particularly deficient. Although the World Tourism group totals. The aggregates in the table are calcu- including payments to foreign carriers for interna- Organization reports that progress has been made lated using the World Bank's weighted aggregation tional transport. These expenditures may include in harmonizing definitions and measurement units, methodology (see Statistical methods) and differ those by residents traveling abroad as same-day differences in national practices still prevent full from aggregates provided by the World Tourism visitors, except in cases where these are important international comparability. Organization. enough to justify separate classification. Their share in imports is calculated as a ratio to imports of goods and services (for definition of imports of goods and services see Definitions for table 4.8). International tourist arrivals reached an all-time high in 2004 International tourist arrivals (millions) 900 800 700 Personal 600 500 Business 400 Leisure Data sources 300 Data on visitors and tourism expenditure are avail- 200 able in the World Tourism Organization's Yearbook 100 of Tourism Statistics and Compendium of Tourism Not specified Statistics 2006. Data in the table are updated 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 from electronic files provided by the World Tour- ism Organization. Data on exports and imports are Since 1990 international tourist arrivals have been increasing steadily at 4 percent a year. In 2004 about half of international tourist arrivals were for leisure, recreation, and holidays; 16 percent for business; and another 24 percent from the IMF's International Financial Statistics for other personal motives such as visiting friends and relatives, religious purposes, and health treatment. and World Bank staff estimates. Source: World Tourism Organization. 2006 World Development Indicators 367 The World Bank is not a primary data collection agency for most areas other than business and investment climate surveys, living standards surveys, and external debt. As a major user of socioeconomic data, however, the World Bank places particular emphasis on data documentation to inform users of data in economic analysis and policymaking. 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 within countries and across them. Delays in reporting data and the use of old surveys as the base for current estimates may severely compromise the quality of national data. The tables in this section provide information on sources, methods, and reporting standards of the principal demographic, economic, and environmen- tal indicators in World Development Indicators. Additional documentation is available from the World Bank's Country Statistical Information Database at www.worldbank.org/data. The demand for good quality statistical data is increasing. Timely and reliable statistics are key to the broad development strategy often referred to as "manag- ing for results." Monitoring and reporting on publicly agreed indicators is central to implementing poverty reduction strategies and lies at the heart of the Millen- nium Development Goals and the new Results Measurement System adopted for the 14th replenishment of the International Development Association. In October 2002 an information paper prepared for the World Bank's Board of Executive Directors, "Building Statistical Capacity to Monitor Development Progress," requested a briefing on the state of national statistics and statistical capacity. This briefing highlighted the increasing demand for better data, the need for action--particularly through a strategic approach to statistical capac- ity building at the country level--and the value of engagement in this area by the World Bank. A global action plan to improve national and international statistics was agreed on during the Second Roundtable on Measuring for Results in February 2004 in Marrakech, Morocco. The plan, now referred to as the Marrakech Action Plan for Statistics, or MAPS, has been widely endorsed and forms the overarching framework for statistical capacity building. 2006 World Development Indicators 369 National currency Fiscal National Balance of payments Government IMF year accounts and trade finance data end dissem- ination stan- Balance of dard PPP Payments Reporting Base SNA price Alternative conversion survey Manual External System Accounting period year valuation factora year in use debt of trade concept Afghanistan Afghan afghani Mar. 20 FY 2002 VAB B Albania Albanian lek Dec. 31 CY 1996b,c VAB 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 1991­96 BPM4 Preliminary S G Argentina Argentine peso Dec. 31 CY 1993 VAB 1971­84 1996 BPM5 Actual S C S Armenia Armenian dram Dec. 31 CY 1996b,c VAB 1990­95 2000 BPM5 Actual S C S Australia Australian dollar Jun. 30 FY 2000b,c VAB 2002 BPM5 G C S Austria Euro Dec. 31 CY 2000b VAB 2002 BPM5 S C S Azerbaijan Azeri manat Dec. 31 CY 2003b,c VAB 1992­95 2000 BPM5 Actual G C G Bangladesh Bangladesh taka Jun. 30 FY 1996b VAB 1960­2004 1996 BPM5 Actual G C G Belarus Belarusian rubel Dec. 31 CY 2000b VAB 1990­95 2000 BPM5 Actual G C S Belgium Euro Dec. 31 CY 2000b VAB 2002 BPM5 S C S Benin CFA franc Dec. 31 CY 1985 VAP 1992 1996 BPM5 Estimate S G Bolivia Boliviano Dec. 31 CY 1990b VAB 1960­85 1996 BPM5 Actual S C G Bosnia and Herzegovina Konvertible mark Dec. 31 CY 1996c VAB BPM5 Actual C Botswana Botswana pula Jun. 30 FY 1994b VAB 1996 BPM5 Actual G B G Brazil Brazilian real Dec. 31 CY 1995 VAB 1996 BPM5 Actual S C S Bulgaria Bulgarian lev Dec. 31 CY 2002b,c VAB 1978­89, 1991­92 2002 BPM5 Actual G C S Burkina Faso CFA franc Dec. 31 CY 1990 VAP 1992­93 BPM4 Actual G C G Burundi Burundi franc Dec. 31 CY 1980 VAB BPM5 Actual S C Cambodia Cambodian riel Dec. 31 CY 2000 VAB BPM5 Actual G C G Cameroon CFA franc Dec. 31 CY 1980 VAB 1965­2001 1996 BPM5 Preliminary S B G Canada Canadian dollar Mar. 31 CY 2000b VAB 2002 BPM5 G C S Central African Republic CFA franc Dec. 31 CY 1987 VAB BPM4 Preliminary S G Chad CFA franc Dec. 31 CY 1995b VAB BPM5 Preliminary S C G Chile Chilean peso Dec. 31 CY 1986 VAB 1996 BPM5 Actual S C S China Chinese yuan Dec. 31 CY 1990 VAP 1978­93 1986 BPM5 Preliminary S B G Hong Kong, China Hong Kong dollar Dec. 31 CY 2000 VAB 1996 BPM5 G C S Colombia Colombian peso Dec. 31 CY 1994 VAB 1992­94 1993 BPM5 Actual S B S Congo, Dem. Rep. Congo franc Dec. 31 CY 1987 VAB 1999­2001 BPM5 Preliminary S C G Congo, Rep. CFA franc Dec. 31 CY 1978 VAP 1996 BPM5 Preliminary S C G Costa Rica Costa Rican colon Dec. 31 CY 1991b VAB BPM5 Actual S C S Côte d'Ivoire CFA franc Dec. 31 CY 1996 VAP 1996 BPM5 Estimate S C G Croatia Croatian kuna Dec. 31 CY 1997b VAB 2002 BPM5 Actual G C S Cuba Cuban peso Dec. 31 CY 1984 VAP G S Czech Republic Czech koruna Dec. 31 CY 1995b VAB 2002 BPM5 Preliminary G C S Denmark Danish krone Dec. 31 CY 2000b VAB 2002 BPM5 G C S Dominican Republic Dominican peso Dec. 31 CY 1990 VAP BPM5 Estimate G C G Ecuador U.S. dollar Dec. 31 CY 2000 VAP 1996 BPM5 Preliminary S B S Egypt, Arab Rep. Egyptian pound Jun. 30 FY 1992 VAB 1965­91 1996 BPM5 Actual S C S El Salvador Salvadoran colon Dec. 31 CY 1990 VAP 1982­90 BPM5 Actual S C S Eritrea Eritrean nakfa Dec. 31 CY 1992 VAB BPM4 Actual Estonia Estonian kroon Dec. 31 CY 2000b VAB 1991­95 2002 BPM5 Actual G C S Ethiopia Ethiopian birr Jul. 7 FY 1981b VAB 1965­2004 BPM5 Actual G C G Finland Euro Dec. 31 CY 2000b VAB 2002 BPM5 G C S France Euro Dec. 31 CY 2000b,c VAB 2002 BPM5 S C S Gabon CFA franc Dec. 31 CY 1991 VAP 1993 1996 BPM5 Estimate S B G Gambia, The Gambian dalasi Jun. 30 CY 1987 VAB BPM5 Actual G B G Georgia Georgian lari Dec. 31 CY 1994b,c VAB 1990­95 2000 BPM5 Actual G C Germany Euro Dec. 31 CY 2000b VAB 2002 BPM5 S C S Ghana Ghanaian cedi Dec. 31 CY 1975 VAP 1973­87 BPM5 Actual G B Greece Euro Dec. 31 CY 2000b,c VAB 2002 BPM5 S C S Guatemala Guatemalan quetzal Dec. 31 CY 1958 VAP 1980 BPM5 Actual S B G Guinea Guinean franc Dec. 31 CY 1994 VAB 1996 BPM5 Estimate S B G Guinea-Bissau CFA franc Dec. 31 CY 1986 VAB 1970­86 BPM5 Preliminary G G Haiti Haitian gourde Sep. 30 FY 1976 VAB 1991 BPM5 Actual G 370 2006 World Development Indicators Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade freshwater census household survey and expenditure data complete census data data withdrawal (including data registration- based censuses) Afghanistan 1979 MICS, 2003 1977 1987 Albania 2001 MICS, 2000 LSMS, 2002 Yes 1995 1990 2004 1995 Algeria 1998 MICS, 2000 HLSS, 1995 1973 2003 2004 1995 Angola 1970 MICS, 2000 1964­65 1991 1987 Argentina 2001 EPH, 2001 Yes 1988 1999 2004 1995 Armenia 2001 DHS, 2005 ILCS, 2003 Yes 2004 1994 Australia 2001 SIHC, 1994 Yes 1990 2003 2004 1985 Austria 2001 Microcensus 2000 Yes 1990 2003 2004 1991 Azerbaijan 1999 MICS, 2000 HBS, 2003 Yes 2004 1995 Bangladesh 2001 DHS, 2004 HES.2000 1976 2003 2004 1990 Belarus 1999 IES, 2002 Yes 1994 2004 1990 Belgium 2001 ECHP, 2000 Yes 1990 2003 2004 .. Benin 2002 DHS, 2001 1992­93 1999 2002 1994 Bolivia 2001 DHS, 2003 MECOVI, 2002 2000 2004 1987 Bosnia and Herzegovina 1991 MICS, 2000 LSMS, 2001 Yes 1991 2004 1995 Botswana 2001 MICS, 2000 HIES, 1993­94 1993 2002 2001 1992 Brazil 2000 DHS, 1996 PNAD, 2002 1996 1995 2004 1992 Bulgaria 2001 HBS, 2003 Yes 2002 2004 1988 Burkina Faso 1996 DHS, 2003 EVCBM, 2003 1993 2003 2004 1992 Burundi 1990 MICS, 2000 Priority survey, 1998 1991 2004 1987 Cambodia 1998 DHS, 2005 SES, 1997 2004 1987 Cameroon 1987 DHS, 2004 Priority survey, 2001 1972­73 1999 2004 1987 Canada 2001 SLID, 2000 Yes 1991 2003 2004 1991 Central African Republic 1988 MICS, 2000 EPI, 1993 1993 2003 1987 Chad 1993 DHS, 2004 1975 1995 1987 Chile 2002 CASEN, 2000 Yes 1997 2003 2004 1987 China 2000 Population, 1995 HHS (rural/urban), 2001 1996 2001 2004 1993 Hong Kong, China 2001 Yes 2002 Colombia 1993 DHS, 2005 ECV, 2003 1988 2003 2004 1996 Congo, Dem. Rep. 1984 DHS, 2006 1990 1986 1990 Congo, Rep. 1996 DHS, 2005 1986 1988 1995 1987 Costa Rica 2000 CDC, 1993 EHPM, 2001 Yes 1973 2003 2004 1997 Côte d'Ivoire 1998 AIS, 2005 LSMS, 2002 1974­75 2003 2003 1987 Croatia 2001 HBS, 2001 Yes 1992 2004 1996 Cuba 2002 MICS, 2000 Yes 1989 2001 1995 Czech Republic 2001 CDC, 1993 Microcensus 1996/97 Yes .. 1998 2004 1991 Denmark 2001 Income Tax Register 1997 Yes 1989 2003 2004 1990 Dominican Republic 2002 DHS, 2002 ENFT, 2003 1971 2003 2001 1994 Ecuador 2001 CDC, 1999 LSMS, 1998 1997 2003 2004 1997 Egypt, Arab Rep. 1996 DHS, 2005 HECS, 2000 Yes 1989­90 2002 2004 1996 El Salvador 1992 CDC, 1994 EHPM, 2002 Yes 1970­71 2003 2004 1992 Eritrea 1984 DHS, 2002 2001 2003 Estonia 2000 HBS, 2003 Yes 1994 2001 2004 1995 Ethiopia 1994 DHS, 2005 ICES, 2000 1988­89 2002 2003 1987 Finland 2000 IDS, 2000 Yes 1990 2003 2004 1991 France 1999 HBS, 1994/95 Yes 1988 2003 2004 1999 Gabon 1993 DHS, 2000 1974­75 2003 2004 1987 Gambia, The 2003 MICS, 2000 HHS, 1998 1982 2003 1982 Georgia 2002 MICS, 2000 SGH, 2003 Yes 2004 1990 Germany 1995 GSOEP, 2000 Yes 1993 2000 2004 1991 Ghana 2000 SPA, 2002, DHS, 2003 LSMS, 1998/99 1984 2003 2004 1997 Greece 2001 ECHP, 2000 Yes 1993 2003 2004 1980 Guatemala 2002 DHS, 1998­99 ENEI­2, 2002 Yes 1979 2003 2004 1992 Guinea 1996 DHS, 2005 LSMS, 1994 1996 2002 1987 Guinea-Bissau 1991 MICS, 2000 IES, 1993 1988 1995 1991 Haiti 2003 DHS, 2005 ECVH, 2001 1971 1996 1997 1991 2006 World Development Indicators 371 National currency Fiscal National Balance of payments Government IMF year accounts and trade finance data end dissem- ination stan- Balance of dard PPP Payments Reporting Base SNA price Alternative conversion survey Manual External System Accounting period year valuation factora year in use debt of trade concept Honduras Honduran lempira Dec. 31 CY 1978 VAB 1988­89 BPM5 Actual S G Hungary Hungarian forint Dec. 31 CY 2000b VAB 2002 BPM5 Actual S C S India Indian rupee Mar. 31 FY 1993 VAB 1960­2004 BPM5 Actual G C S Indonesia Indonesian rupiah Mar. 31 CY 1993 VAP 1996 BPM5 Preliminary S C S Iran, Islamic Rep. Iranian rial Mar. 20 FY 1982 VAB 1980­90 1996 BPM5 Actual G C Iraq Iraqi dinar Dec. 31 CY 1997 VAB S Ireland Euro Dec. 31 CY 2000b VAB 2000 BPM5 G C S Israel Israeli new shekel Dec. 31 CY 2000b VAP 2002 BPM5 S C S Italy Euro Dec. 31 CY 2000b VAB 2002 BPM5 S C S Jamaica Jamaica dollar Dec. 31 CY 1996 VAP 1996 BPM5 Actual G C G Japan Japanese yen Mar. 31 CY 2000 VAB 2002 BPM5 G C S Jordan Jordan dinar Dec. 31 CY 1994 VAB 1996 BPM5 Actual G B G Kazakhstan Kazakh tenge Dec. 31 CY 1995b,c VAB 1987­95 2000 BPM5 Actual G C S Kenya Kenya shilling Jun. 30 CY 2001 VAB 1996 BPM5 Actual G B G Korea, Dem. Rep. Democratic Republic Dec. 31 CY .. .. BPM5 of Korea won Korea, Rep. Korean won Dec. 31 CY 2000b VAP 2002 BPM5 S C S Kuwait Kuwaiti dinar Jun. 30 CY 1995 VAP BPM5 S C G Kyrgyz Republic Kyrgyz som Dec. 31 CY 1995b,c VAB 1990­95 2000 BPM5 Actual G B S Lao PDR Lao kip Dec. 31 CY 1990 VAB 1993 BPM5 Preliminary G Latvia Latvian lat Dec. 31 CY 2000b VAB 1991­95 2002 BPM5 Actual S C S Lebanon Lebanese pound Dec. 31 CY 2002 VAB BPM4 Actual G B G Lesotho Lesotho loti Mar. 31 CY 1995b VAB BPM5 Actual G C G Libya Libyan dinar Dec. 31 CY 1975 VAB 1986 BPM5 G Liberia Liberian dollar Dec. 31 CY 1992 VAB Estimate G Lithuania Lithuanian litas Dec. 31 CY 2000b VAB 1990­95 2002 BPM5 Actual G C S Macedonia, FYR Macedonian denar Dec. 31 CY 1995b VAB 2002 BPM5 Actual G G Madagascar Malagasy ariary Dec. 31 CY 1984 VAB 1996 BPM5 Actual S C G Malawi Malawi kwacha Mar. 31 CY 1994 VAB 1996 BPM5 Actual G B G Malaysia Malaysian ringgit Dec. 31 CY 1987 VAP 1993 BPM5 Preliminary G C S Mali CFA franc Dec. 31 CY 1987 VAB 1996 BPM4 Actual G G Mauritania Mauritanian ouguiya Dec. 31 CY 1985 VAB BPM4 Actual G G Mauritius Mauritian rupee Jun. 30 FY 1998 VAB 1996 BPM5 Actual G C G Mexico Mexican new peso Dec. 31 CY 1993b VAB 2002 BPM5 Actual G C S Moldova Moldovan leu Dec. 31 CY 1996b,c VAB 1987­95 2000 BPM5 Actual G C G Mongolia Mongolian tugrik Dec. 31 CY 1995 VAP 2000 BPM5 Actual S C G Morocco Moroccan dirham Dec. 31 CY 1980 VAP 1996 BPM5 Actual S C S Mozambique Mozambican metical Dec. 31 CY 1995 VAB 1992­95 BPM5 Preliminary S G Myanmar Myanmar kyat Mar. 31 FY 1985 VAP BPM5 Estimate G C Namibia Namibia dollar Mar. 31 CY 1995b VAB BPM5 B G Nepal Nepalese rupee Jul. 14 FY 1995 VAB 1966­2004 1996 BPM5 Actual S C G Netherlands Euro Dec. 31 CY 2000b,c VAB 2002 BPM5 S C S New Zealand New Zealand dollar Mar. 31 FY 2000 VAB 2002 BPM5 G C Nicaragua Nicaraguan gold cordoba Dec. 31 CY 1998 VAP 1965­93 BPM5 Actual S C G Niger CFA franc Dec. 31 CY 1987 VAP 1993 BPM5 Preliminary S G Nigeria Nigerian naira Dec. 31 CY 1987 VAB 1971­98 1996 BPM5 Preliminary G G Norway Norwegian krone Dec. 31 CY 2000b,c VAB 2002 BPM5 G C S Oman Rial Omani Dec. 31 CY 1988 VAP 1996 BPM5 Actual G B G Pakistan Pakistan rupee Jun. 30 FY 2000 VAB 1972­2004 1996 BPM5 Preliminary G C G Panama Panamanian balboa Dec. 31 CY 1996c VAP 1996 BPM5 Actual S C G Papua New Guinea Papua New Guinea kina Dec. 31 CY 1983 VAP 1989 BPM5 Actual G B Paraguay Paraguayan guarani Dec. 31 CY 1982 VAP 1982­88 BPM5 Actual S B G Peru Peruvian new sol Dec. 31 CY 1994 VAB 1985­91 1996 BPM5 Actual S C S Philippines Philippine peso Dec. 31 CY 1985 VAP 1996 BPM5 Actual G B S Poland Polish zloty Dec. 31 CY 2002b,c VAB 2000 BPM5 Actual S C S Portugal Euro Dec. 31 CY 2000b VAB 2002 BPM5 S C S Puerto Rico U.S. dollar Jun. 30 FY 1954 VAP G 372 2006 World Development Indicators Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade freshwater census household survey and expenditure data complete census data data withdrawal (including data registration- based censuses) Honduras 2001 DHS, 2005 EPHPM, 2003 1993 2003 2003 1992 Hungary 2001 FBS, 2002 Yes 1994 2003 2004 1991 India 2001 DHS, 2005 LSMS, 1999/2000 1986 2003 2004 1990 Indonesia 2000 DHS, 2002, Special, 2002 SUSENAS, 2002 1993 2002 2004 1990 Iran, Islamic Rep. 1996 Demographic, 1995 SECH, 1998 Yes 1988 2003 2003 1993 Iraq 1997 MICS, 2000 1981 2003 1976 1990 Ireland 2002 ECHP, 2000 Yes 1991 2003 2004 1980 Israel 1995 HES, 2001 Yes 1983 2003 2004 1997 Italy 2001 SHIW, 2000 Yes 1990 2000 2004 1998 Jamaica 2001 CDC, 1997, MICS, 2000 LSMS, 2000 Yes 1979 2003 2002 1993 Japan 2000 Yes 1990 2003 2004 1992 Jordan 1994 DHS, 2002 HIES, 1997 1997 2003 2004 1993 Kazakhstan 1999 DHS, 1999 HBS, 2003 Yes 2004 1993 Kenya 1999 DHS, 2004 WMS II, 1997 1981 2003 2004 1990 Korea, Dem. Rep. 1993 MICS, 2000 1987 Korea, Rep. 2000 NSFIE, 1998/99 Yes 1991 2003 2004 1994 Kuwait 1995 FHS, 1996 Yes 1970 2001 2001 1994 Kyrgyz Republic 1999 DHS, 1997 HBS, 2003 Yes 2004 1994 Lao PDR 1995 MICS, 2000 ECS I, 2000 1999 1974 1987 Latvia 2000 HBS, 2003 Yes 1994 2002 2004 1994 Lebanon 1970 MICS, 2000 1999 2003 1996 Lesotho 2001 MICS, 2004 HBS, 1995 1989­90 1985 2002 1987 Libya 1995 MICS, 2000 1987 2003 2004 1999 Liberia 1984 1984 1987 Lithuania 2001 HBS, 2003 Yes 1994 2004 1995 Macedonia, FYR 2002 HBS, 2003 Yes 1994 1996 2004 1996 Madagascar 1993 DHS, 2003/04 Priority survey, 2001 1984 2003 2004 1984 Malawi 1998 DHS, 2004 HHS, 1997/98 1992­93 2003 2004 1994 Malaysia 2000 HIBAS, 1997 Yes 2001 2004 1995 Mali 1998 DHS, 2006 EMCES, 1994 1978 2001 1987 Mauritania 2000 Special, 2003 LSMS, 2000 1985 1978 1996 1985 Mauritius 2000 CDC, 1991 Yes 1998 2004 .. Mexico 2000 Population, 1995 ENIGH, 2002 Yes 1991 2000 2004 1998 Moldova 1989 DHS, 2005 HBS, 2003 Yes 2002 2004 1992 Mongolia 2000 MICS, 2000 LSMS/Integrated Survey, 1998 Yes 1995 2003 1993 Morocco 1994 DHS, 2003/04 LSMS, 1998/99 1997 2001 2004 1998 Mozambique 1997 Interim, 2003 NHS, 1996/97 2003 2002 1992 Myanmar 1983 MICS, 2000 1993 2002 1992 1987 Namibia 2001 DHS, 2000 NHIES, 1993 1995 1994 2003 1991 Nepal 2001 DHS, 2006 LSMS, 2003/04 1992 2002 2003 1994 Netherlands 2002 ECHP, 1999 Yes 1989 2003 2004 1991 New Zealand 2001 Yes 1990 2003 2004 1991 Nicaragua 1995 DHS, 2001 LSMS, 2001 1963 2003 2004 1998 Niger 2001 DHS, 2006 1980 1998 2003 1988 Nigeria 1991 DHS, 2003 LSMS, 2003 1960 2003 2003 1987 Norway 2001 IF 2000 Yes 1989 2003 2004 1985 Oman 2003 FHS, 1995 1979 2003 2004 1991 Pakistan 1998 RHS, 2000­01 PIHS, 2002 1990 2003 2004 1991 Panama 2000 LSMS, 1997 EH, 2002 1990 2003 2004 1990 Papua New Guinea 2000 DHS, 1996 HGS, 1996 2003 2003 1987 Paraguay 2002 CDC, 1998 EIH, 2002 1991 2003 2004 1987 Peru 1993 DHS, 2000 ENAHO, 2002 1994 1996 2004 1992 Philippines 2000 DHS, 2003 FIES, 2000 Yes 1991 2003 2004 1995 Poland 2002 HBS, 2002 Yes 1990 2003 2004 1991 Portugal 2001 Yes 1989 2003 2004 1990 Puerto Rico 2000 Yes 1987 2001 2006 World Development Indicators 373 National currency Fiscal National Balance of payments Government IMF year accounts and trade finance data end dissem- ination stan- Balance of dard PPP Payments Reporting Base SNA price Alternative conversion survey Manual External System Accounting period year valuation factora year in use debt of trade concept Romania Romanian leu Dec. 31 CY 1999b,c VAB 1987­89, 1992 2002 BPM5 Actual S C S Russian Federation Russian ruble Dec. 31 CY 2000b,c VAB 1987­95 2000 BPM5 Preliminary G C S Rwanda Rwanda franc Dec. 31 CY 1995 VAP BPM5 Estimate G C G Saudi Arabia Saudi Arabian riyal Dec. 31 CY 1999 VAP BPM4 G Senegal CFA franc Dec. 31 CY 1987b VAP 1996 BPM5 Preliminary S B G Serbia and Montenegro Yugoslav new dinar Dec. 31 CY 1998 VAB Actual C Sierra Leone Sierra Leonean leone Jun. 30 CY 1990b VAB 1971­79, 1987 1996 BPM5 Actual G B G Singapore Singapore dollar Mar. 31 CY 1995 VAB 1996 BPM5 G B S Slovak Republic Slovak koruna Dec. 31 CY 1995b VAP 2002 BPM5 Actual G C S Slovenia Slovenian tolar Dec. 31 CY 2000b VAB 2002 BPM5 S C S Somalia Somali shilling Dec. 31 CY 1985 VAB 1977­90 Estimate South Africa South African rand Mar. 31 CY 2000b VAB BPM5 Preliminary S C S Spain Euro Dec. 31 CY 2000b VAB 2002 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 VAB 1970­95 BPM5 Actual G B G Swaziland Lilangeni Mar. 31 CY 1985 VAB Preliminary B G Sweden Swedish krona Jun. 30 CY 2000c VAB 2002 BPM5 G C S Switzerland Swiss franc Dec. 31 CY 2000 VAB 2002 BPM5 S C S Syrian Arab Republic Syrian pound Dec. 31 CY 2000 VAP 1970­2004 1996 BPM5 Estimate S C Tajikistan Tajik somoni Dec. 31 CY 1997b,c VAB 1990­95 2000 BPM5 Preliminary G C G Tanzania Tanzania shilling Dec. 31 CY 1992 VAB 1996 BPM5 Preliminary 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 Actual S G Trinidad and Tobago Trinidad and Dec. 31 CY 2000 VAP 1996 BPM5 Actual S C G Tobago dollar Tunisia Tunisian dinar Dec. 31 CY 1990 VAP 1996 BPM5 Actual G C S Turkey Turkish lira Dec. 31 CY 1987 VAB 2000 BPM5 Actual S B S Turkmenistan Turkmen manat Dec. 31 CY 1987b,c VAB 1987­95, 1997­2004 2000 BPM5 G Uganda Uganda shilling Jun. 30 FY 1998 VAB 1980­99 BPM5 Actual G B G Ukraine Ukrainian hryvnia Dec. 31 CY 2003b,c VAB 1990­95 2000 BPM5 Actual G C S United Arab Emirates U.A.E. dirham Dec. 31 CY 1995 VAB 1993 BPM4 G C United Kingdom Pound sterling Dec. 31 CY 2000b VAB 2002 BPM5 G C S United States U.S. dollar Sep. 30 CY 2000c VAB 2002 BPM5 G C S Uruguay Uruguayan peso Dec. 31 CY 1983 VAP 1996 BPM5 Actual S C S Uzbekistan Uzbek sum Dec. 31 CY 1997c VAB 1990­95 2000 BPM5 Actual G Venezuela, R.B. 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 C G West Bank and Gaza Israeli new shekel Dec. 31 CY 1998 VAB 1993 Yemen, Rep. Yemen rial Dec. 31 CY 1990 VAP 1991­96 1996 BPM5 Actual G B G Zambia Zambian kwacha Dec. 31 CY 1994 VAB 1990­92 1996 BPM5 Estimate G B G Zimbabwe Zimbabwe dollar Jun. 30 CY 1990 VAB 1991, 1998 1996 BPM5 Actual G C G Note: For explanation of the abbreviations used in the table see notes following the table. a. World Bank estimates including adjustments for fiscal year reporting. b. Country uses the 1993 System of National Accounts methodology. c. Original chained constant price data are rescaled. 374 2006 World Development Indicators Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade freshwater census household survey and expenditure data complete census data data withdrawal (including data registration- based censuses) Romania 2002 CDC, 1999 LSMS, 2003 Yes 2000 2004 1994 Russian Federation 2002 LSMS, 1992 LMS, Round 9, 2002 Yes 1994­95 2002 2004 1994 Rwanda 2002 DHS, 20055 LSMS, 1999/2000 1984 1986 2003 1993 Saudi Arabia 2004 Demographic, 1999 1983 1989 2003 1992 Senegal 2002 DHS, 2005 ESASM, 1995 1960 2002 2004 1987 Serbia and Montenegro 2002 MICS, 2000 Yes 2002 1990 Sierra Leone 2004 MICS, 2000 SHEHEA, 1989­90 1985 1993 2002 1987 Singapore 2000 General household, 1995 Yes 2003 2004 1975 Slovak Republic 2001 Microcensus, 1996 Yes 1999 2004 1991 Slovenia 2002 HBS, 1998 Yes 1991 2002 2004 1996 Somalia 1987 MICS, 2000 2003 1982 1987 South Africa 2001 DHS, 2004 IES, 2000 2003 2004 1990 Spain 2001 ECHP, 2000 Yes 1989 2003 2004 1997 Sri Lanka 2001 DHS, 1993 HIES, 2002 Yes 1982 2000 2004 1990 Sudan 1993 MICS, 2000 2003 2003 1995 Swaziland 1997 MICS, 2000 SHIES, 1994/95 2003 2002 .. Sweden 1990 DHS, 2006 HINK, 2000 Yes 1981 2003 2004 1991 Switzerland 2000 EVE, 2000 Yes 1990 1997 2004 1991 Syrian Arab Republic 1994 MICS, 2000 1981 2003 2004 1995 Tajikistan 2000 MICS, 2000 LSMS, 2003 Yes 1994 2000 1994 Tanzania 2002 SPA, 2006 HIES, 2000/01 1995 2003 2004 1994 Thailand 2000 DHS, 1987 SES, 2002 1993 2002 2003 1990 Togo 1981 MICS, 2000 1996 2003 2004 1987 Trinidad and Tobago 2000 MICS, 2000 LSMS, 1992 Yes 1982 2000 2003 1997 Tunisia 1994 MICS, 2000 1961 2003 2004 1996 Turkey 2000 DHS, 1998 LSMS, 2002 1991 2003 2004 1997 Turkmenistan 1995 DHS, 2000 LSMS, 1998 Yes 2000 1994 Uganda 2002 AIS, 2004 NIHS III, 2002 1991 2003 2004 1970 Ukraine 2001 MICS, 2000 HBS, 2003 Yes 2002 1992 United Arab Emirates 1995 1998 2001 2001 1995 United Kingdom 2001 FRS, 1999 Yes 1993 2001 2004 1991 United States 2000 Current population, 1997 CPS, 2000 Yes 1997 2003 2004 1990 Uruguay 1996 ECH, 2003 Yes 1990 2000 2004 1965 Uzbekistan 1989 Special, 2002 FBS, 2000 Yes 1994 Venezuela, R.B. 2001 MICS, 2000 EHM, 2000 Yes 1997­98 2003 2004 1970 Vietnam 1999 AIS, 2005 LSMS, 2002 1994 2000 2003 1990 West Bank and Gaza 1997 Demographic, 1995 1971 Yemen, Rep. 1994 DHS, 1997 HBS, 1998 1982­85 2001 2004 1990 Zambia 2000 SPA, 2005 LCMS II, 1998 1990 2003 2004 1994 Zimbabwe 2002 DHS, 2005/06 LCMS III, 2002/03 1960 2003 2004 1987 2006 World Development Indicators 375 Primary data documentation notes · Fiscal year end is the date of the end of the fis- · Purchasing power parity (PPP) survey year refers Data Dissemination Standard (SDDS) or General cal year for the central government. Fiscal years for to the latest available survey year for the Interna- Data Dissemination System (GDDS). S refers to other levels of government and the reporting years tional Comparison Program's estimates of purchasing countries that subscribe to the SDDS and have for statistical surveys may differ, but if a country is power parities (PPPs). For a more detailed descrip- posted data on the Dissemination Standards designated as a fiscal year reporter in the following tion of PPP see About the data for table 1.1. Bulletin Board web site (posted data are at column, the date shown is the end of its national · Balance of Payments Manual in use refers to the http://dsbb.imf.org). G refers to countries that accounts reporting period. classification system used for compiling and report- subscribe to the GDDS. The SDDS was estab- · Reporting period for national accounts and bal- ing data on balance of payments items in table 4.15. lished by the IMF for member countries that have ance of payments data is designated as either BPM4 refers to the fourth edition of the IMF's Bal- or that might seek access to international capital calendar year basis (CY) or fiscal year basis (FY). ance of Payments Manual (1977), and BPM5 to the markets to guide them in providing their economic Most economies report their national accounts and fifth edition (1993). and financial data to the public. The GDDS helps balance of payments data using calendar years, but · External debt shows debt reporting status for 2004 countries disseminate comprehensive, timely, some use fiscal years that straddle two calendar data. Actual indicates that data are as reported, accessible, and reliable economic, financial, and years. In World Development Indicators fiscal year preliminary indicates that data are preliminary and sociodemographic statistics. IMF member coun- data are assigned to the calendar year that contains include an element of staff estimation, and estimate tries voluntarily elect to participate in either the the larger share of the fiscal year. If a country's fis- indicates that data are World Bank staff estimates. SDDS or the GDDS. Both the GDDS and the SDDS cal year ends before June 30, the data are shown · System of trade refers to the United Nations gen- are expected to enhance the availability of timely in the first year of the fiscal period; if the fiscal year eral trade system (G) or the special trade system and comprehensive data and therefore contribute ends on or after June 30, the data are shown in (S). For imports under the general trade system to the pursuit of sound macroeconomic policies. the second year of the period. Balance of payments both goods entering directly for domestic consump- The SDDS is also expected to improve the function- data are shown by calendar year and so are not tion and goods entered into customs storage are ing of financial markets. comparable to the national accounts data of the recorded as imports at the time of arrival; under the · Latest population census shows the most recent countries that report their national accounts on a special trade system goods are recorded as imports year in which a census was conducted and in which fiscal year basis. when they are declared for domestic consumption at least preliminary results have been released. · Base year is the year used as the base period for whether at the time of entry or on withdrawal from · Latest demographic, education, or health house- constant price calculations in the country's national customs storage. Exports under the general sys- hold survey gives information on the household sur- accounts. Price indexes derived from national tem comprise outward-moving goods: (a) national veys used in compiling the demographic, education, accounts aggregates, such as the gross domes- goods wholly or partly produced in the country; (b) and health data in section 2. CDC is Centers for Dis- tic product (GDP) deflator, express the price level foreign goods, neither transformed nor declared for ease Control and Prevention, DHS is Demographic relative to prices in the base year. Constant price domestic consumption in the country, that move and Health Survey, FHS is Family Health Survey, data reported in World Development Indicators are outward from customs storage; and (c) nationalized MICS is the Multiple Indicator Cluster Survey, and rescaled to a common 2000 reference year. See goods that have been declared from domestic con- RHS is Reproductive Health Survey. About the data for table 4.1 for further discussion sumption and move outward without having been · Source of most recent income and expenditure of rescaling. transformed. Under the special system of trade, data shows household surveys that collect income · System of National Accounts (SNA) price valu- exports comprise categories (a) and (c). In some and expenditure data. HBS is Household Budget Sur- ation shows whether value added in the national compilations categories (b) and (c) are classified as vey; ICES is Income, Consumption, and Expenditure accounts is reported at basic prices (VAB) or at pro- re-exports. Direct transit trade, consisting of goods Survey; IES is Income and Expenditure Survey; LSMS ducer prices (VAP). Producer prices include the value entering or leaving for transport purposes only, is is Living Standards Measurement Study; and SES is of taxes paid by producers and thus tend to overstate excluded from both import and export statistics. Socio-Economic Survey. the actual value added in production. See About the See About the data for tables 4.4, 4.5, and 6.2 for · Vital registration complete identifies countries data for tables 4.1 and 4.2 for further discussion of further discussion. judged to have complete registries of vital (birth and national accounts valuation. · Government finance accounting concept death) statistics by the United Nations Department · Alternative conversion factor identifies the coun- describes the accounting basis for reporting central of Economic and Social Information and Policy Analy- tries and years for which a World Bank­estimated government financial data. For most countries gov- sis, Statistical Division, and reported in Population conversion factor has been used in place of the offi- ernment finance data have been consolidated (C) and Vital Statistics Reports. Countries with complete cial exchange rate (line rf in the International Mon- into one set of accounts capturing all the central vital statistics registries may have more accurate etary Fund's [IMF] International Financial Statistics). government's fiscal activities. Budgetary central and more timely demographic indicators than other Estimates also include adjustments to correspond government accounts (B) exclude some central gov- countries. to the fiscal years in which national accounts data ernment units. See About the data for tables 4.10, · Latest agricultural census shows the most recent have been reported. See Statistical methods for fur- 4.11, and 4.12 for further details. year in which an agricultural census was conducted ther discussion of the use of alternative conversion · IMF data dissemination standard shows the and reported to the Food and Agriculture Organiza- factors. countries that subscribe to the IMF's Special tion of the United Nations. 376 2006 World Development Indicators Primary data documentation notes · Latest industrial data refer to the most recent year for which manufacturing value added data at the three-digit level of the International Standard Indus- trial Classification (ISIC, revision 2 or revision 3) are available in the United Nations Industrial Develop- ment Organization database. · Latest trade data show the most recent year for which structure of merchandise trade data from the United Nations Statistical Division's Commodity Trade (Comtrade) database are available. · Latest freshwater withdrawal data refer to the most recent year for which data on freshwater withdrawals have been compiled from a variety of sources. See About the data for table 3.5 for more information. 2006 World Development Indicators 377 This section describes some of the statistical procedures used in preparing the indicator as a weight) and denoted by a u when calculated as unweighted World Development Indicators. It covers the methods employed for calculating averages. The aggregate ratios are based on available data, including data regional and income group aggregates and for calculating growth rates, and it for economies not shown in the main tables. Missing values are assumed describes the World Bank Atlas method for deriving the conversion factor used to have the same average value as the available data. No aggregate is cal- to estimate gross national income (GNI) and GNI per capita in U.S. dollars. Other culated if missing data account for more than a third of the value of weights statistical procedures and calculations are described in the About the data sec- in the benchmark year. In a few cases the aggregate ratio may be computed tions following each table. as the ratio of group totals after imputing values for missing data according to the above rules for computing totals. Aggregation rules · Aggregate growth rates are denoted by a w when calculated as a weighted Aggregates based on the World Bank's regional and income classifications of econo- average of growth rates. In a few cases growth rates may be computed from mies appear at the end of most tables. The countries included in these classifica- time series of group totals. Growth rates are not calculated if more than half tions are shown on the flaps on the front and back covers of the book. Most tables the observations in a period are missing. For further discussion of methods also include aggregates for the member countries of the European Monetary Union of computing growth rates see below. (EMU). Members of the EMU on 1 January 2004 were Austria, Belgium, Finland, · Aggregates denoted by an m are medians of the values shown in the table. France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, and No value is shown if more than half the observations for countries with a Spain. Other classifications, such as the European Union and regional trade blocs, population of more than 1 million are missing. are documented in About the data for the tables in which they appear. Exceptions to the rules occur throughout the book. Depending on the judg- Because of missing data, aggregates for groups of economies should be ment of World Bank analysts, the aggregates may be based on as little as 50 treated as approximations of unknown totals or average values. Regional and percent of the available data. In other cases, where missing or excluded values income group aggregates are based on the largest available set of data, including are judged to be small or irrelevant, aggregates are based only on the data values for the 152 economies shown in the main tables, other economies shown shown in the tables. in table 1.6, and Taiwan, China. The aggregation rules are intended to yield esti- mates for a consistent set of economies from one period to the next and for all Growth rates indicators. Small differences between sums of subgroup aggregates and overall Growth rates are calculated as annual averages and represented as percentages. totals and averages may occur because of the approximations used. In addition, Except where noted, growth rates of values are computed from constant price compilation errors and data reporting practices may cause discrepancies in theo- series. Three principal methods are used to calculate growth rates: least squares, retically identical aggregates such as world exports and world imports. exponential endpoint, and geometric endpoint. Rates of change from one period Five methods of aggregation are used in World Development Indicators: to the next are calculated as proportional changes from the earlier period. · For group and world totals denoted in the tables by a t, missing data are imputed based on the relationship of the sum of available data to the total Least-squares growth rate. Least-squares growth rates are used wherever in the year of the previous estimate. The imputation process works forward there is a sufficiently long time series to permit a reliable calculation. No growth and backward from 2000. Missing values in 2000 are imputed using one of rate is calculated if more than half the observations in a period are missing. several proxy variables for which complete data are available in that year. The The least-squares growth rate, r, is estimated by fitting a linear regression trend imputed value is calculated so that it (or its proxy) bears the same relation- line to the logarithmic annual values of the variable in the relevant period. The ship to the total of available data. Imputed values are usually not calculated regression equation takes the form 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, ln Xt = a + bt exports and imports of goods and services in U.S. dollars, and value added in agriculture, industry, manufacturing, and services in U.S. dollars. which is equivalent to the logarithmic transformation of the compound growth · Aggregates marked by an s are sums of available data. Missing values are equation, not imputed. Sums are not computed if more than a third of the observations Xt = Xo (1 + r )t . in the series or a proxy for the series are missing in a given year. · Aggregates of ratios are denoted by a w when calculated as weighted averages In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are of the ratios (using the value of the denominator or, in some cases, another parameters to be estimated. If b* is the least-squares estimate of b, then the 378 2006 World Development Indicators average annual growth rate, r, is obtained as [exp(b*) ­ 1] and is multiplied by The inflation rate for Japan, the United Kingdom, the United States, and the 100 for expression as a percentage. The calculated growth rate is an average rate Euro Zone, representing international inflation, is measured by the change in the that is representative of the available observations over the entire period. It does SDR deflator. (Special drawing rights, or SDRs, are the International Monetary not necessarily match the actual growth rate between any two periods. Fund's unit of account.) The SDR deflator is calculated as a weighted average of these countries' GDP deflators in SDR terms, the weights being the amount of Exponential growth rate. The growth rate between two points in time for cer- each country's currency in one SDR unit. Weights vary over time because both tain demographic indicators, notably labor force and population, is calculated the composition of the SDR and the relative exchange rates for each currency from the equation change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conver- r = ln(pn/p 0)/n sion factor is then applied to a country's GNI. The resulting GNI in U.S. dollars is divided by the midyear population to derive GNI per capita. where pn and p 0 are the last and first observations in the period, n is the number When official exchange rates are deemed to be unreliable or unrepresenta- of years in the period, and ln is the natural logarithm operator. This growth rate is tive of the effective exchange rate during a period, an alternative estimate of the based on a model of continuous, exponential growth between two points in time. exchange rate is used in the Atlas formula (see below). It does not take into account the intermediate values of the series. Nor does it The following formulas describe the calculation of the Atlas conversion fac- correspond to the annual rate of change measured at a one-year interval, which tor for year t: is given by (pn ­ pn­1)/pn­1. Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at and the calculation of GNI per capita in U.S. dollars for year t: intervals, in which case the compound growth model is appropriate. The average growth rate over n periods is calculated as Yt$ = (Yt/Nt)/et* r = exp[ln(pn/p 0)/n] ­ 1. where et* is the Atlas conversion factor (national currency to the U.S. dollar) for year t, et is the average annual exchange rate (national currency to the U.S. dollar) Like the exponential growth rate, it does not take into account intermediate for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar values of the series. terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current GNI (local currency) for year t, and Nt is the midyear population for year t. World Bank Atlas method In calculating GNI and GNI per capita in U.S. dollars for certain operational Alternative conversion factors purposes, the World Bank uses the Atlas conversion factor. The purpose of the The World Bank systematically assesses the appropriateness of official exchange Atlas conversion factor is to reduce the impact of exchange rate fluctuations in rates as conversion factors. An alternative conversion factor is used when the the cross-country comparison of national incomes. official exchange rate is judged to diverge by an exceptionally large margin from The Atlas conversion factor for any year is the average of a country's exchange the rate effectively applied to domestic transactions of foreign currencies and rate (or alternative conversion factor) for that year and its exchange rates for the traded products. This applies to only a small number of countries, as shown two preceding years, adjusted for the difference between the rate of inflation in the in Primary data documentation. Alternative conversion factors are used in the country and that in Japan, the United Kingdom, the United States, and the Euro Atlas methodology and elsewhere in World Development Indicators as single-year Zone. A country's inflation rate is measured by the change in its GDP deflator. conversion factors. 2006 World Development Indicators 379 This book draws on a wide range of World Bank reports and numerous external of the World Resources Institute; Laura Battlebury of the World Conservation sources, listed in the bibliography following this section. Many people inside and Monitoring Centre; and Gerhard Metchies of GTZ. Mehdi Akhlaghi made important outside the World Bank helped in writing and producing World Development Indica- contributions to the statistical methods and management of the databases for tors. The team would like to particularly acknowledge the help and encouragement of the section. The World Bank's Environment Department and Rural Development François Bourguignon, Senior Vice President and Chief Economist of the World Bank, Department devoted substantial staff resources to the book, for which the team and Shaida Badiee, Director, Development Data Group. The team is also grateful is very grateful. M. H. Saeed Ordoubadi wrote the introduction with valuable to those who provided valuable comments on the entire book. This note identifies comments from Sarwar Lateef, Eric Swanson, and Bruce Ross-Larson and Meta many of those who made specific contributions. Numerous others, too many to de Coquereaumont, who also edited the text. Other contributions were made acknowledge here, helped in many ways for which the team is extremely grateful. by Kiran Pandey (biodiversity); Susmita Dasgupta, Craig Meisner, Kiran Pandey, and David Wheeler (air and water pollution); Mahyar Eshragh-Tabary, Augusto 1. World view Clavijo, Maria Emilia Ferire, Christine Kessides, Solly Angel, and Micah Perlin The introduction to section 1 was prepared by Eric Swanson and Sulekha Patel (urban housing conditions); and Kirk Hamilton, Giovanni Ruta, Beat Hintermann, with help from K. M. Vijayalakshmi and M. H. Saeed Ordoubadi. Stimulating dis- and Suzette Galitano (adjusted savings). Valuable comments and contributions cussions with members of the UN Interagency and Expert Group on the MDGs were also provided by Kyran O'Sullivan and Anton Dobronogov. are gratefully acknowledged. K. M. Vijayalakshmi prepared table 1.1. Yonas Biru prepared the estimates of gross national income in purchasing power parity terms. 4. Economy Tables 1.2, 1.3, and 1.5 were prepared by Masako Hiraga. The team is grateful to Section 4 was prepared by K. M. Vijayalakshmi in close collaboration with the Rudy Petras and Yasmin Ahmad at the Organisation for Economic Co-operation and Macroeconomic Data Team of the World Bank's Development Data Group, led by Development (OECD) for data and advice on official development assistance flows Soong Sup Lee. K. M. Vijayalakshmi and Eric Swanson wrote the introduction with and agricultural support estimates; Peter Ghys and Elizabeth Zaniewski at the Joint valuable suggestions from Barbro Hexeberg, Sarwar Lateef, and W. Bill Shaw. Con- United Nations Programme on HIV/AIDS for historical estimates of HIV/AIDS; and tributions to the section were provided by Azita Amjadi (trade) and Ibrahim Levent Joshua Solomon and his colleagues for projections of HIV prevalence rates. (external debt). The national accounts data for low- and middle-income economies were gathered by the World Bank's regional staff through the annual Unified Sur- 2. People vey. Maja Bresslauer, Victor Gabor, Augusto Clavijo, and Soong Sup Lee worked Section 2 was prepared by Masako Hiraga in partnership with the World Bank's on updating, estimating, and validating the databases for national accounts. The Human Development Network and the Development Research Group in the team is grateful to the World Trade Organization, the United Nations Industrial Development Economics Vice Presidency. Mehdi Akhlaghi provided invaluable Development Organization, and the OECD for access to the databases. assistance in data and table preparation, and Vivienne Wang prepared the demo- graphic estimates and projections. Sulekha Patel wrote the introduction with 5. States and markets input from John May and Sarwar Lateef. The poverty estimates were prepared by Section 5 was prepared by David Cieslikowski in partnership with the World Shaohua Chen of the World Bank's Poverty Monitoring Group with help from Prem Bank's Private Sector Department, the Infrastructure Network, its Poverty Reduc- Sangraula and Johan Mistiaen. The table on child labor was prepared by Furio tion and Economic Management Network, the International Finance Corporation, Rosati of the Understanding Children's Work project. Contributions were provided and external partners. Raymond Muhula and Juan Carlos Rodriguez provided by Eduard Bos and Emi Suzuki (population, health, and nutrition); Montserrat invaluable assistance in data and table preparation. David Cieslikowski wrote Pallares-Miralles (vulnerability and security); Raymond Muhula and Lianqin Wang the introduction to the section with valuable comments from Sarwar Lateef, (education); and Lucia Fort and Juan Carlos Guzman Roa (gender). Comments and Eric Swanson, and Peter Roberts. Other contributors include Ada Karina Iza- suggestions at various stages of production came from Eric Swanson. guirre and William Butterfield (privatization and infrastructure projects); Marta Kozak (micro, small, and medium-size enterprises); Mary Hallward-Driemeier 3. Environment (investment climate); Simeon Djankov and Caralee McLeish (business environ- Section 3 was prepared by M. H. Saeed Ordoubadi and Mayhar Eshragh-Tabary ment); Alka Banerjee and Isilay Cabuk (Standard & Poor's global stock market in partnership with the World Bank's Environmentally and Socially Sustainable indexes); Stijn Claessens, Asli Demirgüç-Kunt, Margaret Miller, and Himmat Kalsi Development Network and in collaboration with the World Bank's Development (financial); Peter Roberts, Tsukasa Hattori, and Henrich Bofinger (transport); Research Group and Transportation, Water, and Urban Development Depart- Jane Degerlund of Containerisation International (ports); Esperanza Magpantay ment. Important contributions were made by Edward Gillin and Carola Fabi of and Vanessa Grey of the International Telecommunication Union, and Christine the Food and Agriculture Organization; Ricardo Quercioli of the International Zhen-Wei Qiang (communications and information); Ernesto Fernandez Polcuch Energy Agency; Amay Cassara, Christian Layke, Daniel Prager, and Robin White of the United Nations Educational, Scientific, and Cultural Organization Institute 380 2006 World Development Indicators for Statistics (research and development, researchers, and technicians); Anders by Meta de Coquereaumont and Bruce Ross-Larson with the assistance of Halvorsen of the World Information Technology and Services Alliance (information Christopher Trott. The editing and production team consisted of Jodi Baxter, and communication technology expenditures); Terrence Taylor of the International Brendon Boyle, Michael Diavolikis, Timothy Walker, and Elaine Wilson. Com- Institute for Strategic Studies (military personnel); and Bjorn Hagelin and Petter munications Development's London partner, Grundy & Northedge, provided Stålenheim of the Stockholm International Peace Research Institute (military art direction and design. Staff from External Affairs oversaw printing and dis- expenditures and arms transfers). semination of the book. 6. Global links Client services Section 6 was prepared by Amy Heyman, Changqing Sun, and Eric Swanson The Development Data Group's Client Services Team (Azita Amjadi, Richard Fix, in partnership with the World Bank's Development Research Group (trade), Gonca Okur, Priya Pandya, and William Prince) contributed to the design and the Prospects Group (commodity prices), and external partners. Many thanks planning of World Development Indicators and helped coordinate work with the to Sarwar Lateef, Neil Fantom, Ibrahim Levent, Azita Amjadi, Jean-Jacques Office of the Publisher. Dethier, and Andrew Burns for initial comments and feedback about pos- sible revisions to the section. Substantial input for the data came from Azita Administrative assistance and office technology support Amjadi, Jerzy Rozanski (tariffs), Gloria Moreno, Nevin Fahmy, and Ibrahim Estela Zamora and Awatif Abuzeid provided administrative assistance. Jean- Levent (financial data). Other contributors include Francis Ng and Dominique Pierre Djomalieu, Gytis Kanchas, Nacer Megherbi, and Shahin Outadi provided van der Mensbrugghe (trade); Dilek Aykut (foreign direct investment flows); information technology support. Betty Dow (commodity prices); Bela Hovy and Christian Oxenball of the United Nations Office of the High Commissioner for Refugees; Marta Roig, Hania Zlot- Publishing and dissemination nik, and Francois Pelletier of the United Nations Population Division (migration); The Office of the Publisher, under the direction of Dirk Koehler, provided valu- Brian Hammond and Yasmin Ahmad of the OECD (aid); Antonio Massieu and able assistance throughout the production process. Brenda Mejia and Stephen Teresa Ciller of the World Tourism Organization (tourism); Henri Laurencin and McGroarty coordinated printing and supervised marketing and distribution. Chris David Cristallo of the United Nations Conference on Trade and Development Neal of the Development Economics Vice President's managed the communica- (trade), and K. M. Vijayalatshmi (remittances). Mehdi Akhlaghi and Will Prince tions strategy. provided valuable technical assistance. World Development Indicators CD-ROM Other parts Programming and testing were carried out by Reza Farivari and his team: Azita Preparation of the maps on the inside covers was coordinated by Jeff Lecksell Amjadi, Ying Chi, Ramgopal Erabelly, Nacer Megherbi, Gonca Okur, Shahin Outadi, of the World Bank's Map Design Unit. Users guide was prepared by David Cies- and William Prince. Masako Hiraga produced the social indicators tables. William likowski. Statistical methods was written by Eric Swanson. Primary data documen- Prince coordinated user interface design and overall production and provided tation was coordinated by K. M. Vijayalakshmi. Awatif Abuzeid assisted in updat- quality assurance. Photo credits: Curt Carnemark, Francis Dobbs, Julio Etchart, ing the Primary data documentation table. Partners and Index of indicators were Tran Thi Hoa, Edwin Hu man, Anvar Ilyasov, Michael Mertaugh, Shehzad Noorani, prepared by Richard Fix with assistance from Gonca Okur and Priya Pandya. Tomas Sennett, and Ray Witlin (World Bank). The interactive text was produced by Intermax, Inc. Database management Database management was coordinated by Mehdi Akhlaghi with cross-team WDI Online participation, including Deepa Ramachandran Pai for systems development, to Design, programming, and testing were carried out by Reza Farivari and his create an integrated World Development Indicators database. This database team: Mehdi Akhlaghi, Azita Amjadi, Saurabh Gupta, Gonca Okur, and Shahin was used to generate the tables for World Development Indicators and related Outadi. William Prince coordinated production and provided quality assurance. products such as WDI Online, Little Data Book, The Little Green Data Book, and Valentina Kalk and Triinu Tombak of the Office of the Publisher were responsible the World Development Indicators CD-ROM. for implementation of WDI Online and management of the subscription service. Design, production, and editing Client feedback Richard Fix, with the assistance of Gonca Okur, coordinated all stages of produc- The team is grateful to the many people who have taken the time to provide com- tion with Communications Development Incorporated. Communications Devel- ment on its publications. Their feedback and suggestions have helped improve opment Incorporated provided overall design direction, editing, and layout, led this year's edition. 2006 World Development Indicators 381 AbouZahr, Carla, and Tessa Wardlaw. 2004. Maternal Mortality in 2000: Estimates Brown, Lester R., Christopher Flavin, Hilary F. French, and others. 1998. State Developed by WHO, UNICEF, and UNFPA. Geneva: World Health Organization. of the World 1998: A Worldwatch Institute Report on Progress toward a Sus- African Union and UNECA (United Nations Economic Commission for Africa). tainable Society. New York: W.W. Norton. 2005. "Transport and the Millennium Development Goals in Africa." 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Geneva. 388 2006 World Development Indicators References are to table numbers. aid dependency ratios 6.11 per capita 6.11 Agriculture total 6.11 cereal net concessional flows area under production 3.2 from international financial institutions 6.13 exports, as share of total exports 6.4 from UN agencies 6.13 exports, total 6.4 net official development assistance and official aid by DAC members imports, as share of total imports 6.4 as share of general government disbursements 6.10 imports, total 6.4 as share of GNI of donor country 1.4, 6.10 yield 3.3 average annual change in volume 6.10 employment, as share of total 3.2 by type 6.9 fertilizer for basic social services, as share of sector-allocable ODA commodity prices 6.5 commitments 1.4 consumption, per hectare of arable land 3.2 from major donors, by recipient 6.12 food per capita of donor country 6.10 commodity prices 6.5 total 6.9, 6.10, 6.12 exports, as share of total exports 4.4, 6.4 untied aid 6.10 exports, total 6.4 imports, as share of total imports 4.5, 6.4 AIDS--see HIV, prevalence imports, total 6.4 freshwater withdrawals for, as share of total 3.5 Air pollution--see Pollution labor force, as share of total, male and female 2.3 land Air transport agricultural, as share of land area 3.2 air freight 5.8 arable, as share of land area 3.1 passengers carried 5.8 arable, per capita 3.1 registered carrier departures 5.8 area under cereal production 3.2 irrigated, as share of cropland 3.2 Asylum seekers--see Migration permanent cropland, as share of land area 3.1 machinery tractors per 100 square kilometers of arable land 3.2 production indexes Balance of payments crop 3.3 current account balance 4.15 food 3.3 exports and imports of goods and services 4.15 livestock 3.3 net current transfers 4.15 value added net income 4.15 annual growth 4.1 total 4.15 as share of GDP 4.2 See also Exports; Imports; Investment; Private capital flows; Trade per worker 3.3 water productivity Bank and trade-related lending 6.8 in agriculture 3.5 Biological diversity Aid assessment, date prepared, by country 3.14 by recipient benefits index 3.4 2006 World Development Indicators 389 treaty 3.14 by economic activity 2.4 male and female 2.4 Birds study and work 2.4 species 3.4 total 2.4 threatened species 3.4 work only 2.4 Birth rate, crude 2.1 Cities air pollution 3.13 Births attended by skilled health staff 1.2 population in largest city 3.10 Birthweight, low 2.17 in selected cities 3.13 in urban agglomerations of more than one million 3.10 Breastfeeding, exclusive 2.17 urban population 3.10 See also Urban environment Business environment closing a business Closing a business--see Business environment time to resolve insolvency 5.3 dealing with licenses Commodity prices and price indexes 6.5 number of procedures to build a warehouse 5.3 time required to build a warehouse 5.3 Communications--see Internet, users; Newspapers; Telephones; Television enforcing contracts procedures to enforce a contract 5.3 Compensation of government employees 4.11 time to enforce a contract 5.3 hiring and firing workers Computers per 1,000 people 5.10 rigidity of employment index 5.3 protecting investors disclosure, index 5.3 Consumption registering property distribution--see Income, distribution number of procedures 5.3 fixed capital 3.15 time to register 5.3 government, general starting a business annual growth 4.9 cost to start a business 5.3 as share of GDP 4.8 number of start-up procedures 5.3 household time to start a business 5.3 annual growth 4.9 as share of GDP 4.8 per capita, annual growth 1.2, 4.9 See also Purchasing power parity (PPP) Carbon dioxide damage 3.15 Corruption, major constraint, in investment climate 5.2 emissions per capita 1.3, 3.8 Contraceptive prevalence rate 2.16 per 2000 PPP dollar of GDP 3.8 total 1.6, 3.8 Contract enforcement number of procedures 5.3 Child labor time required for 5.3 390 2006 World Development Indicators Courts as share of central government expenditure 5.7 lack confidence in courts to uphold property rights 5.2 as share of GDP 5.7 major constraint, in investment climate 5.2 Deforestation 3.4 Credit provided by banking sector 5.5 Density--see Population, density to private sector 5.1 Dependency ratio--See Population Crime, major constraint, in investment climate 5.2 Development assistance--see Aid Current account balance 4.15 See also Balance of payments Disease--see Health risks Customs, average days to clear 5.2 Distribution of income or consumption--see Income, distribution DAC (Development Assistance Committee)--see Aid Education attainment Death rate, crude 2.1 share of cohort reaching grade 5, male and female 2.12 See also Mortality rate enrollment ratio female to male enrollment in primary and secondary schools 1.2 Debt, external gross, by level 2.11 debt service net, by level 2.11 multilateral 4.17 gross intake rate, grade 1 2.12 total 4.17 out of school children, male and female 2.11 IMF credit, use of 4.16 primary completion rate 1.2 long-term 4.16 male and female 2.13 present value 4.17 public expenditure on private nonguaranteed 4.16 as share of GDP 2.10 public and publicly guaranteed as share of total government expenditure 2.10 IBRD loans and IDA credits 4.16 per student, as share of GDP per capita, by level 2.10 Total 4.16 pupil-teacher ratio, primary level 2.10 short-term 4.17 repeaters, primary level 2.12 total 4.16 teachers, primary, trained 2.10 transition to secondary school2.12 Defense unemployment by level of educational attainment 2.5 armed forces personnel as share of labor force 5.7 Electricity total 5.7 consumption 5.9 arms transfers major constraint, in investment climate 5.2 exports 5.7 production imports 5.7 sources 3.9 military expenditure total 3.9 2006 World Development Indicators 391 transmissions and distribution losses 5.9 average annual growth 4.9 total 4.15 Employment high-technology in agriculture, as share of total employment 3.2 share of manufactured exports 5.11 in agriculture, male and female 2.3 total 5.11 in industry, male and female 2.3 merchandise in informal sector, urban annual growth 6.3 male and female 2.9 by high-income OECD countries, by product 6.4 in services, male and female 2.3 by regional trade blocs 6.6 laws index, rigidity 5.3 direction of trade 6.3 structure 4.4 Endangered species--see Biological diversity; Birds; Mammals; Plants total 4.4 value, average annual growth 6.2 Energy volume, average annual growth 6.2 depletion, as share of GNI 3.15 services emissions--see Pollution structure 4.6 imports, net 3.7 total 4.6 production 3.7 transport 4.6 use travel 4.6, 6.15 annual growth 3.7 See also Trade efficiency, GDP per unit 3.8 per capita average annual growth 3.7 total 3.7 Female-headed households 2.9 total 3.7 See also Electricity Fertility rate adolescent 2.16 Enforcing contracts--see Business environment total 2.16 Entry regulations for business--see Business environment Finance, major constraint, in investment climate 5.2 Environmental strategy, year adopted 3.14 Financial access, stability, and efficiency bank branches 5.5 Exchange rates bank deposit accounts 5.5 official, local currency units to U.S. dollar 4.14 banking system ownership, foreign 5.5 ratio of PPP conversion factor to official exchange rate 4.14 banking system ownership, government 5.5 real effective 4.14 bank capital to asset ratio 5.5 See also Purchasing power parity (PPP) bank nonperforming loans 5.5 deposit insurance coverage 5.5 Exports financial information infrastructure 5.5 arms 5.7 goods and services Financial flows, net as share of GDP 4.8 from DAC members 6.9 392 2006 World Development Indicators from multilateral institutions youth 2.13 international financial institutions 6.13 in mortality total 6.13 adult 2.19 United Nations 6.13 child 2.19 official development assistance and official aid in smoking 2.18 grants from NGOs 6.9 in survival to age 65 2.19 other official flows 6.9 in youth employment 2.9 private 6.9 unpaid family workers 1.5 total 6.9 women in parliaments 1.5 See also Aid women in nonagricultural sector 1.5 Food--see Agriculture, production indexes; Commodity prices and price indexes Gini index 2.8 Foreign direct investment, net--see Investment; Private capital flows Government, central debt Forest as share of GDP 4.10 area, as share of total land area 3.1 interest, as share of revenue 4.10 deforestation, average annual 3.4 interest, as share of total expenses 4.11 depletion of 3.15 expense as share of GDP 4.10 Freshwater by economic type 4.11 annual withdrawals military 5.7 as share of total resources 3.5 net incurrence of liabilities, as share of GDP for agriculture 3.5 cash deficit 4.10 for domestic use 3.5 cash surplus 4.10 for industry 3.5 domestic 4.10 flows foreign 4.10 internal 3.5 revenue 4.10 resources per capita 3.5 revenues, current volume 3.5 grants and other 4.12 See also Water, access to improved source of social contributions 4.12 tax, as share of GDP 5.6 tax, by source 4.12 Gender differences Gross capital formation in education annual growth 4.9 enrollment, primary and secondary 1.2 as share of GDP 4.8 in employment 2.3 in HIV prevalence 2.18 Gross domestic product (GDP) in labor force participation 2.2 annual growth 1.1, 1.6, 4.1 in life expectancy at birth 1.5 implicit deflator--see Prices in literacy per capita, annual growth 1.1, 1.6 adult 2.13 total 4.2 2006 World Development Indicators 393 Gross foreign direct investment--see Investment DOTS detection rate 2.15 incidence 1.3, 2.18 Gross national income (GNI) treatment success rate 2.15 per capita PPP dollars 1.1, 1.6 Health expenditure rank 1.1 as share of GDP 2.14 U.S. dollars 1.1, 1.6 external resources 2.14 rank out of pocket 2.14 PPP dollars 1.1 per capita 2.14 U.S. dollars 1.1 public 2.14 total total 2.14 PPP dollars 1.1, 1.6 U.S. dollars 1.1, 1.6 Health risks child malnutrition, prevalence 1.2, 2.17 Gross national savings, as share of GNI 3.15 diabetes, prevalence 2.18 HIV, prevalence 1.3, 2.18 Gross savings, as share of GDP 4.8 overweight children, prevalence 2.17 road traffic injury 2.18 smoking 2.18 tuberculosis, incidence 1.3, 2.18 Health care undernourishment, prevalence 2.17 children sleeping under treated bednets 2.15 children with ARI taken to health provider 2.15 Heavily indebted poor countries (HIPCs) children with diarrhea who received oral rehydration and continued feeding completion point 1.4 2.15 decision point 1.4 children with fever receiving antimalarial drugs 2.15 nominal debt service relief 1.4 health worker density 2.14 hospital beds per 1,000 people 2.14 Hiring and firing workers immunization 2.15 rigidity of employment index 5.3 physicians per 1,000 people 2.14 pregnant women receiving prenatal care 1.5 HIV, prevalence 1.3, 2.18 pregnant women receiving tetanus vaccinations 2.16 female 2.18 reproductive births attended by skilled health staff 1.2, 2.16 Hospital beds--see Health care contraceptive prevalence rate 2.16 fertility rate Housing conditions, national and urban adolescent 2.16 durable dwelling units 3.11 total 2.16 home ownership 3.11 low-birthweight babies 2.17 household size 3.11 maternal mortality ratio 1.2, 2.16 multiunit dwellings 3.11 tetanus vaccinations, share of pregnant women receiving 2.16 overcrowding 3.11 unmet need for contraception 2.16 vacancy rate 3.11 tuberculosis 394 2006 World Development Indicators per capita 5.10 Immunization rate child Integration, global economic, indicators 6.1 DPT, share of children ages 12­23 months 2.15 measles, share of children ages 12­23 months 2.15 Interest payments--see Government, central, debt tetanus, share of pregnant women receiving 2.16 Interest rates Imports deposit 4.13 arms 5.7 lending 4.13 energy, as share of total energy use 3.7 real 4.13 goods and services risk premium on lending 5.5 as share of GDP 4.8 spread 5.5 average annual growth 4.9 total 4.15 International Bank for Reconstruction and Development (IBRD) merchandise IBRD loans and IDA credits 4.16 annual growth 6.3 net financial flows from 6.13 by high-income OECD countries, by product 6.4 direction of trade 6.3 International Development Association (IDA) structure 4.5 IBRD loans and IDA credits 4.16 total 4.5 net concessional flows from 6.13 value, average annual growth 6.2 volume, average annual growth 6.2 International Monetary Fund (IMF) services net financial flows from 6.13 structure 4.7 use of IMF credit 4.16 total 4.7 transport 4.7 Internet travel 4.7, 6.15 broadband subscribers 5.10 See also Trade price basket 5.10 secure servers 5.10 Income users 5.10 distribution international bandwidth 5.10 Gini index 2.8 schools connected 5.10 percentage of 1.2, 2.8 Investment Industry climate 5.2 annual growth 4.1 foreign direct, net inflows as share of GDP 4.2 as share of GDP 6.1 labor force, as share of total, male and female 2.3 total 6.8 foreign direct, net outflows Inflation--see Prices as share of GDP 6.1 infrastructure, private participation in Information and communications technology expenditures energy 5.1 as share of GDP 5.10 telecommunications 5.1 2006 World Development Indicators 395 transport 5.1 water and sanitation 5.1 Malnutrition, in children under age 5 1.2, 2.17 portfolio bonds 6.8 Malaria equity 6.8 children sleeping under treated bednets 2.15 See also Gross capital formation; Private capital flows children with fever receiving antimalarial drugs 2.15 Iodized salt, consumption of 2.17 Mammals species 3.4 threatened species 3.4 Labor force Management time dealing with officials 5.2 annual growth 2.2 armed forces 5.7 Manufacturing child labor 2.4 structure 4.3 female 2.2 value added in agriculture, as share of total, male and female 2.3 annual growth 4.1 in industry, as share of total, male and female 2.3 as share of GDP 4.2 in services, as share of total, male and female 2.3 total 4.3 participation of population ages 15­64 2.2 regulation, major constraint, in investment climate 5.2 Market access to high-income countries skills, major constraint, in investment climate 5.2 goods admitted free of tariffs 1.4 total 2.2 support to agriculture 1.4 See also Employment; Migration; Unemployment tariffs on exports from low- and middle-income countries agricultural products 1.4 Land area textiles and clothing 1.4 arable--see Agriculture, land, land use See also Protected areas; Surface area Merchandise exports Land use agricultural raw materials 4.4 arable land, as share of total land 3.1 food 4.4 area under cereal production 3.2 fuels 4.4 by type 3.1 manufactures 4.4 forest area, as share of total land 3.1 ores and metals 4.4 irrigated land 3.2 total 4.4 permanent cropland, as share of total land 3.1 value, average annual growth 6.2 total area 3.1 volume, average annual growth 6.2 imports Life expectancy at birth agricultural raw materials 4.5 male and female 1.5 food 4.5 total 1.6, 2.19 fuels 4.5 manufactures 4.5 Literacy ores and metals 4.5 adult, male and female 1.6, 2.13 total 4.5 youth, male and female 1.6, 2.13 value, average annual growth 6.2 396 2006 World Development Indicators volume, average annual growth 6.2 undernourishment, prevalence 2.17 trade youth unemployment 1.3, 2.9 as share of GDP 6.1 direction 6.3 Minerals, depletion of 3.15 growth 6.3 Monetary indicators Methane claims on governments and other public entities 4.13 emissions claims on private sector 4.13 percentage change 3.8 total 3.8 Money and quasi money, annual growth 4.13 Micro, small, and medium-size enterprises Mortality rate employment, percent of total 5.1 adult, male and female 2.19 number 5.1 caused by road traffic injury 2.18 child, male and female 2.19 Migration children under age 5 1.2, 2.19 net 6.14 infant 2.19 stock 6.14 maternal 1.2, 2.16 See also Refugees; Remittances Motor vehicles Military--see Defense passenger cars 3.12 per kilometer of road 3.12 Millennium Development Goals, indicators for per 1,000 people 3.12 aid two-wheelers 3.12 as share of GNI of donor country 1.4, 6.10 See also Roads; Traffic as share of total ODA commitments 1.4 access to improved water source 1.3, 2.15, 3.5 access to improved sanitation facilities 1.3, 2.15, 3.10 births attended by skilled health staff 1.2, 2.16 Nationally protected areas--see Protected areas carbon dioxide emissions per capita 1.3, 3.8 children sleeping under treated bednets 2.15 Net national savings 3.15 consumption, national share of poorest quintile 1.2, 2.8 female to male enrollments, primary and secondary 1.2 Newspapers, daily 5.10 heavily indebted poor countries (HIPCs) completion point 1.4 Nitrous oxide decision point 1.4 emissions nominal debt service relief 1.4 percentage change 3.8 malnutrition, prevalence 1.2, 2.17 total 3.8 maternal mortality ratio 1.2, 2.16 primary enrollment ratio, net 2.11 Nutrition poverty gap 2.7 breastfeeding 2.17 poverty, population below a $1 a day 2.7 iodized salt consumption 2.17 telephone lines, fixed-line and mobile 1.3, 5.9 malnutrition, child 1.2, 2.17 tuberculosis, incidence1.3, 2.18 overweight children, prevalence 2.17 under-five mortality rate 1.2, 2.19 undernourishment, prevalence 2.17 2006 World Development Indicators 397 vitamin A supplementation 2.17 nitrous oxide emissions percentage change 3.8 total 3.8 Official aid--see Aid organic water pollutants, emissions by industry 3.6 Official development assistance--see Aid per day 3.6 per worker 3.6 Official flows, other 6.9 particulate matter, selected cities 3.13 sulfur dioxide, selected cities 3.13 suspended particulate matter, selected cities 3.13 urban-population-weighted PM10 3.12 Passenger cars per 1,000 people 3.12 Policy uncertainty, major constraint, in investment climate 5.2 Particulate matter emission damage 3.15 Population selected cities 3.13 age dependency ratio 2.1 urban-population-weighted PM10 3.12 annual growth 2.1 by age group Patent applications filed 5.11 0­14 2.1 15­64 2.1 Pension 65 and older 2.1 average, as share of per capita income 2.9 density 1.1, 1.6 contributors, as share of labor force 2.9 female, as share of total 1.5 public expenditure on rural as share of GDP 2.9 annual growth 3.1 as share of total 3.1 Physicians--see Health care total 1.1, 1.6, 2.1 urban Plants, higher as share of total 3.10 species 3.4 average annual growth 3.10 threatened species 3.4 in largest city 3.10 in selected cities 3.13 Pollution in urban agglomerations 3.10 carbon dioxide damage, as share of GNI 3.15 total 3.10 carbon dioxide emissions See also Migration per capita 3.8 per 2000 PPP dollar of GDP 3.8 Portfolio investment flows total 3.8 bonds 6.8 methane equity 6.8 emissions percentage change 3.8 Ports, container traffic in 5.8 total 3.8 nitrogen dioxide, selected cities 3.13 Poverty 398 2006 World Development Indicators international poverty line total 3.4 population below $1 a day 2.7 population below $2 a day 2.7 Protecting investors disclosure, index 5.3 poverty gap at $1 a day 2.7 poverty gap at $2 a day 2.7 Purchasing power parity (PPP) national poverty line conversion factor 4.14 population below 2.7 gross national income 1.1, 1.6 national 2.7 rural 2.7 survey year 2.7 urban 2.7 Railways goods hauled by 5.8 Power--see Electricity, production lines, total 5.8 passengers carried 5.8 Prenatal care, pregnant women receiving 1.5 Regulation and tax administration Prices average days to clear customs 5.2 commodity prices and price indexes 6.5 management time dealing with officials 5.2 consumer, annual growth 4.14 tax rates, major constraint, in investment climate 5.2 GDP implicit deflator, annual growth 4.14 terms of trade 6.2 Refugees wholesale, annual growth 4.14 country of asylum 6.14 country of origin 6.14 Private capital flows bank and trade-related lending 6.8 Regional development banks, net financial flows from 6.13 foreign direct investment, net inflows 6.8 from DAC members 6.9 Registering property gross, as share of GDP 6.1 number of procedures 5.3 portfolio investment 6.8 time to register 5.3 See also Investment Relative prices (PPP)--see Purchasing power parity (PPP) Productivity in agriculture Remittances value added per worker 3.3 workers' remittances and compensation of employees, paid 6.14 water productivity, agriculture 3.5 workers' remittances and compensation of employees, received 6.14 water productivity, industry 3.5 water productivity, total 3.5 Research and development expenditures 5.11 Protected areas researchers 5.11 marine technicians 5.11 as share of total surface area 3.4 total 3.4 Reserves, gross international--see Balance of payments national as share of total land area 3.4 Roads 2006 World Development Indicators 399 goods hauled by 5.8 structure 4.7 passengers carried 5.8 total 4.7 paved, as share of total 5.8 labor force by economic activity, as share of total, male and female 2.3 total network 5.8 trade, as share of GDP 6.1 traffic 3.12 value added annual growth 4.1 Royalty and license fees as share of GDP 4.2 payments 5.11 receipts 5.11 Smoking, prevalence, male and female 2.18 Rural environment Starting a business--see Business environment access to improved sanitation facilities 3.10 population Stock markets annual growth 3.1 listed domestic companies 5.4 as share of total 3.1 market capitalization as share of GDP 5.4 total 5.4 market liquidity 5.4 S&P/EMDB Indices 5.4 S&P/ EMDB Indices 5.4 turnover ratio 5.4 Sanitation access to improved facilities, population with Sulfur dioxide emissions--see Pollution rural 3.10 total 1.3, 2.15 Surface area 1.1, 1.6 urban 3.10 See also Land area Savings Survival to age 65 gross domestic, as share of GDP 4.8 male and female 2.19 gross national, as share of GNI 3.15 net Suspended particulate matter--see Pollution adjusted 3.15 national 3.15 Schooling--see Education Tariffs all products Science and technology simple mean tariff 6.7 scientific and technical journal articles 5.11 weighted mean tariff 6.7 See also Research and development manufactured products simple mean tariff 6.7 Services weighted mean tariff 6.7 exports primary products structure 4.6 simple mean tariff 6.7 total 4.6 weighted mean tariff 6.7 imports See also Taxes and tax policies, duties 400 2006 World Development Indicators Taxes and tax policies inbound tourists, by country 6.15 business taxes outbound tourists, by country 6.15 number of payments 5.6 receipts 6.15 time to prepare and pay 5.6 total tax payable, share of gross profit 5.6 Trade goods and services taxes, domestic 4.12 arms 5.7 highest marginal tax rate merchandise corporate 5.6 as share of GDP 6.1 individual 5.6 direction of, by region 6.3 income, profit, and capital gains taxes nominal growth, by region 6.3 as share of revenue 4.12 OECD trade by commodity 6.4 international trade taxes 4.12 real growth in, less growth in real GDP 6.1 other taxes 4.12 services rates, major constraint, in investment climate 5.2 as share of GDP 6.1 social contributions 4.12 computer, information, communications and other 4.6, 4.7 tax revenue, as share of GDP 5.6 insurance and financial 4.6, 4.7 transport 4.6, 4.7 Technology--see Computers; Exports, high-technology; Internet; Research and travel 4.6, 4.7 development; Science and technology See also Balance of payments; Exports; Imports; Terms of trade Telephones Trade blocs, regional cost of call to U.S. 5.9 exports within bloc 6.6 international voice traffic 5.9 total exports, by bloc 6.6 mainlines faults per 100 5.9 Trademark applications filed 5.11 per 1,000 people 5.10 price basket 5.9 Trade policies--see Tariffs mobile per 1,000 people 1.3, 5.9 Traffic population covered 5.9 road traffic 3.12 price basket 5.9 road traffic injury and mortality 2.18 total revenue 5.9 See also Roads total subscribers per employee 5.9 Transport--see Air transport; Railways; Roads; Traffic; Urban environment Television, households with 5.11 Treaties, participation in Terms of trade, net barter 6.2 biological diversity 3.14 CFC control 3.14 Tetanus vaccinations, share of pregnant women receiving 2.16 climate change 3.14 Convention on International Trade on Endangered Species (CITES) 3.14 Threatened species--see Biological diversity Convention to Combat Desertification (CCD) 3.14 Kyoto Protocol 3.14 Tourism, international Law of the Sea 3.14 expenditures 6.15 ozone layer 3.14 2006 World Development Indicators 401 Stockholm Convention on Persistent Organic Pollutants 3.14 as share of GDP in agriculture 4.2 Tuberculosis, incidence 1.3, 2.18 in industry 4.2 in manufacturing 4.2 in services 4.2 growth UN agencies, net concessional flows from 6.13 in agriculture 4.1 in industry 4.1 UNDP, net concessional flows from 6.13 in manufacturing 4.1 in services 4.1 Unemployment per worker incidence of long-term in agriculture 3.3 total, male and female 2.5 total, in manufacturing 4.3 by level of educational attainment primary, secondary, tertiary 2.5 total, male and female 2.5 youth 1.3 Wage and productivity male and female 2.9 agricultural wage 2.6 average hours worked 2.6 UNFPA, net concessional flows from 6.13 labor cost per worker in manufacturing 2.6 minimum wage 2.6 UNICEF, net concessional flows from 6.13 value added per worker in manufacturing 2.6 Urban environment Water access to sanitation 3.10 access to improved source of, population with 1.3, 2.15 employment, informal sector 2.8 pollution--see Pollution, organic water pollutants population productivity as share of total 3.10 in agriculture 3.5 average annual growth 3.10 in industry 3.5 in largest city 3.10 total 3.5 in urban agglomerations 3.10 total 3.10 WFP, net concessional flows from 6.13 selected cities nitrogen dioxide 3.13 Women in development particulate matter 3.13 teenage mothers 1.5 population 3.13 women in nonagricultural sector 1.5 sulfur dioxide 3.13 women in parliaments 1.5 See also Pollution; Population; Water, access to improved source of; Sanitation World Bank, net financial flows from 6.13 See also International Bank for Reconstruction and Development; International Development Association Value added 402 2006 World Development Indicators The world by region Classified according to Low- and middle-income economies World Bank analytical East Asia and Pacific Middle East and North Africa High-income economies grouping Europe and Central Asia South Asia OECD Latin America and the Caribbean Sub-Saharan Africa Other No data REGION MAP The world by region East Asia and Pacific Argentina Sub-Saharan Africa Greece* American Samoa Barbados Angola Iceland Cambodia Belize Benin Ireland* China Bolivia Botswana Italy* Fiji Brazil Burkina Faso Japan Indonesia Chile Burundi Korea, Rep. Kiribati Colombia Cameroon Luxembourg* Korea, Dem. Rep. Costa Rica Cape Verde Netherlands* Lao PDR Cuba Central African Republic New Zealand Malaysia Dominica Chad Norway Marshall Islands Dominican Republic Comoros Portugal* Micronesia, Fed. Sts. Ecuador Congo, Dem. Rep. Spain* Mongolia El Salvador Congo, Rep. Sweden Myanmar Grenada Côte d'Ivoire Switzerland Northern Mariana Islands Guatemala Equatorial Guinea United Kingdom Palau Guyana Eritrea United States Papua New Guinea Haiti Ethiopia Philippines Honduras Gabon Other high income Samoa Jamaica Gambia, The Andorra Solomon Islands Mexico Ghana Aruba Thailand Nicaragua Guinea Bahamas, The Timor-Leste Panama Guinea-Bissau Bahrain Tonga Paraguay Kenya Bermuda Vanuatu Peru Lesotho Brunei Darussalam Vietnam St. Kitts and Nevis Liberia Cayman Islands St. Lucia Madagascar Channel Islands Europe and Central St. Vincent and the Malawi Cyprus Asia Grenadines Mali Faeroe Islands Albania Suriname Mauritania French Polynesia Armenia Trinidad and Tobago Mauritius Greenland Azerbaijan Uruguay Mayotte Guam Belarus Venezuela, RB Mozambique Hong Kong, China Bosnia and Herzegovina Namibia Isle of Man Bulgaria Middle East and North Niger Israel Croatia Africa Nigeria Kuwait Czech Republic Algeria Rwanda Liechtenstein Estonia Djibouti São Tomé and Principe Macao, China Georgia Egypt, Arab Rep. Senegal Malta Hungary Iran, Islamic Rep. Seychelles Monaco Kazakhstan Iraq Sierra Leone Netherlands Antilles Kyrgyz Republic Jordan Somalia New Caledonia Latvia Lebanon South Africa Puerto Rico Lithuania Libya Sudan Qatar Macedonia, FYR Morocco Swaziland San Marino Moldova Oman Tanzania Saudi Arabia Poland Syrian Arab Republic Togo Singapore Romania Tunisia Uganda Slovenia Russian Federation West Bank and Gaza Zambia United Arab Emirates Serbia and Montenegro Yemen, Rep. Zimbabwe Virgin Islands (U.S.) Slovak Republic Tajikistan South Asia High-income OECD Turkey Afghanistan Australia Turkmenistan Bangladesh Austria* Ukraine Bhutan Belgium* Uzbekistan India Canada Maldives Denmark Latin America and the Nepal Finland* Caribbean Pakistan France* *Member of the European Antigua and Barbuda Sri Lanka Germany* Monetary Union The World Bank 1818 H Street N.W. Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: www.worldbank.org ISBN 0-8213-6470-7 Email: feedback@worldbank.org World Development Indicators · Includes more than 800 indicators for 152 economies · Provides definitions, sources, and other information about the data · Organizes the data into six thematic areas WORLD VIEW Progress toward the Millennium Development Goals PEOPLE Gender, health, and employment ENVIRONMENT Natural resources and environmental changes ECONOMY New oppor tunities for growth STATES & MARKETS Elements of a good investment climate GLOBAL LINKS Evidence on globalization