World W ,Development' Indicators 24886 April 2002 w .'~~~~~~' LW~~~~~~~~~~~~~~~~~~~~~~~~~~' * .1 * * ;f S%* Th GOW L7 Wxo    Qin   - * _ D.  fl   ffl1?  _ rn P nifguo iflirn !ri1o K  Netheland Santedvdn ~~ r ^ ; - Z - ....~~~~- ren35Um -_ ... ., - s Xr a JR) X,, ". . rl Mg>thI.tai', . . t anqa~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~n Fed. Re ___,!_ Ass J3rroira R apabre]5,lic03 ?- *5 RB de ... Guy~5 SmanshSapahIeC 0 ' ,V nnaue ->tunm E C_____ bAbsI arra/hn Eoust0rilg [FfFidlPlF.SI}~c5rad5 -\ -, -M. e |'i,.-,C't, ~ ~ ~ ~ ~ ~ ~ .5 dOC Voannc .a.a , P3ay. j / - W R'WbSt F { j . 6 t A ;* ,Potmd~~~~~~~~~~~~~~~~~~~~FY The world by Income Low ($755 or less) 0 Lower middle ($756-2,995) 0 Classified according to World Bank estimates of Upper middle ($2,996-9,265) 0 1999 GNI per capita High ($9,266 or more) * No data Q No t fl CO eusslaRFederation Denr C J-an**D- z - W Ukrab'ne;' J). , . 'Ma!R o n naaakhstan DD,~~~~~~~~~~~A le; Uibeitc,X . RI Gh n Rep+ IPq of ,ran J Nep im. naiO ge6a Pakibann e lntmlBRioOnn Annboatio Nig na * Ab- Rep of .Chad,. . E-vlc i-D6\8rabd. , M o Cu -azai dT. 2 ~ ~ ~ d g A_oa , 1 - -DiojAi$ ,ei Ukaifn | St - . ,g, , C,6Ethio la,Zeabnr Romania p3 c RR C.C A-g.ia ~ ~ ~ __5 s The World Bank World w F: & Development I A , - Indicators Copyright 2002 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW. Washington, DC 20433, USA All rights reserved Manufactured in the United States of America First printing April 2002 This volume is a product of the staff of the Development Data Group of the World Bank's Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank's Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no respon- sibility whatsoever for any consequence of their use. The boundaries. colors, denominations, and other informa- tion 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 projec- tion for maps, which represents both area and shape reasonably well for most of the earth's surface. Neverthe- less, 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 dissemi- nation of its work and will normally give permission promptly and, when reproduction is for noncommercial pur- poses, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copy- right Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, Massachusetts 01923, USA. Photo credits: Curt Carnemark/World Bank. 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, DC 20433, USA Hotline: 800 590 1906 or 202 473 7824: fax 202 522 1498 Email: info@worldbank.org Web site: www.worldbank.org or www.worldbank.org/data ISBN 0-8213-5088-9 The World Bank World (W,U2il t Development A Indicators Foreworld Eradicating world poverty is the greatest challenge of our age, and the greatest weapon we have to fight poverty is knowledge. Knowledge of policies that work to increase economic growth, of how to protect people from disease and protect the environment from degradation, to train young minds and equip them for productive work, and knowledge of where we stand now and how far we have to go to achieve our goal of a world free from poverty. The World Development Indicators gives us access to this last kind of knowledge to helps us assess our past efforts and measure the challenge ahead. However much the facts and figures tell us about the condition of the world, it is too easy to think that a wall separates the rich world and the poor world. Belief in that separation allowed us to view as normal a world where fewer than 15 percent of us-in rich countries-dominate the world's wealth and take 80 percent of its dollar income. And for too long it has allowed us to view as normal a world where a woman dies in childbirth every minute, and where violence, disenfran- chisement, and inequality are seen as problems of poor, weak countries and not our own. In September 2000, during the Millennium Summit held at the United Nations, more than 140 world leaders agreed to launch a campaign to attack poverty on a number of fronts. Together, we agreed to support the Millennium Declaration-to reduce poverty and hunger, disease and early death, inequality and inequity-and to work together in partnership to make this happen by 2015. A year later the shattering events of September 11 toppled the imaginary wall that divided the rich world from the poor world and made it clear that there are not two worlds. The process of globalization and growing interdependence has been at work for thousands of years and today we are linked by communication, trade, investment, travel and migration, by environmental degradation, crime, disease, financial crisis, and terror. It is time to recognize that in this unified world poverty is our collective enemy. We must fight it because it is morally repugnant, and because its existence is like a cancer-weakening the whole of the body not just the parts directly affected. We have made important progress in the past and we will make progress in the futule. Consider these facts: * Over the past 40 years life expectancy at birth in developing countries has increased by 20 years-about as much as was achieved in all of human history before the middle of the twentieth century. * Over the past 30 years adult illiteracy in the developing world has been cut nearly in half, from 47 percent to 25 percent. * Over the past 20 years the number of people living on less than $1 a day has fallen by 200 million, after rising steadily for 200 years. * Over the past 10 years average incomes in developing countries have risen by 20 percent. These advances have come not by chance. They have come by action of developing countries themselves in partnership with the richer world and with the international institutions, with civil society, and the private sector. Now, it is more important than ever to continue that partnership, based on shared respect, shared interests, shared experience, and to act on our knowledge to create a better world for all. /ames D. Wolfensohn President The World Bank Group Acknowledgements This book and its companion volumes, the World Bank Atlas and The Little Data Book, were prepared by a team coordinated by Sulekha Patel. The team consisted of Mehdi Akhlaghi, David Cieslikowski, Mona Fetouh, Richard Fix, Masako Hiraga, M. H. Saeed Ordoubadi, Eric Swanson, K. M. Vijayalakshmi, Vivienne Wang, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency's Development Data Group. The CD- ROM development team included Azita Amjadi, Elizabeth Crayford, Reza Farivari, and William Prince. The work was carried out under the management of Shaida Badiee. The choice of indicators and textual content was shaped through close consultation with vi and substantial contributions from staff in the World Bank's four thematic networks-Envi- ronmentally and Socially Sustainable Development; Private Sector Development. and Infra- (J) structure; Human Development; and Poverty Reduction and Economic Management-and X staff of the International Finance Corporation. Most important, we received substantial help, guidance, and data from our external partners. For individual acknowledgments of contribu- tions to the book's content, please see the Credits section. For a listing of our key partners, E see the Partners section. 0. We are grateful to Graphic Visions Associates, Mike James, Communication Develop- ment Incorporated, and Grundy and Northedge for their contributions to the editing, design and layout of this book. Staff from External Affairs oversaw publication and dissemination of the book. N, 0 Preface This is the 25'h edition of the World Development Indicators, the 6th in its new format. We offer it now as we did 25 years ago, in the belief that reliable quantitative evidence is essential for understanding economic and social development-evidence to set policies, monitor progress, and evaluate results. The World Development Indicators begins with a report on the Millennium Development Goals, which set specific, measurable targets for development in the early 215' century. These goals, agreed to by all member states of the United Nations, represent an enormous challenge to the international community to work together to ensure that all the people of the world will share the benefits of social, economic, and technical progress. They focus our vii efforts on improving people's lives: reducing poverty, educating children, combating illness and disease. To measure progress and ensure that everyone benefits, we must rigorously measure results. And for that we need good statistics. Most of the statistics in the World Development Indicators are the product of national statistical agencies. In poor countries these agencies are often underfunded and their C- 0 work underused. They need training, equipment, and a clear mandate from their govern- C ments to produce better, more reliable, more timely statistics. But the work does not o stop there, for the international community also plays a role, by establishing standards, sharing knowledge, and coordinating the collection and dissemination of international statistics. The World Bank supports national and international efforts to improve statistics. We are working closely with our development partners through the Partnership in Statistics for the 21s' Century-PARIS21. The goals are to raise awareness of the need for and value of good statistics and to strengthen international coordination and governance. We have establishied a trust fund to support statistical capacity-building in countries preparing poverty reduction strategies, drawing on the generous support of several donors. We are working through the International Comparison Programme to improve the measurement of living standards around the world. And we are participating in the International Monetary Fund's General Data Dis- semination System initiative to help interested countries document their current statistical practices and develop plans to improve them. As users of the full array of development statistics we all benefit from the work of national data providers. And we all benefit when the international community is better informed of the challenges and successes of development. That is why we report on the Millennium Development Goals and why we invest in better statistics and disseminate them widely. But in the end, it is the citizens of developing countries who will benefit most when their governments, working in partnership with the World Bank and other development agencies, make better decisions based on good evidence. Through the World Development Indicators we will continue to bring you the latest avail- able information in the most useful and timely ways. We encourage you to send us your comments and suggestions, so that by working together we can improve the quality of the data we publish and our understanding of the world they describe. Shaida Badiee Director Development Data Group Contents <'~-' 1 World View Front matter Introduction 3 Foreword v 1.1 Size of the economy 18 Acknowledgments vi 1.2 Millennium Development Goals: Preface vii eradicating poverty and improving lives 22 Partners xii 1.3 Millennium Development Goals: Users guide xxiv protecting our common environment 26 1.4 Millennium Development Goals: overcoming obstacles 30 1.5 Women in development 32 1.6 Key indicators for other economies 36 Text tables 1.2a Location of indicators for goals 1-5 25 1.3a Location of indicators for goals 6 and 7 29 Viii 1.4a Location of indicators for goal 8 31 ° Figures ° 1.5 Women judges in selected countries 35 0 0 a) N 0 2 2 People 3 Environment Introduction 39 Introduction 127 1 oplaio 48amc 3.1 Rral environment and land use 134 2.2 Labor force structure 52 3.2 Agricultural inputs13 2.3 Employment by economic activity 56 3.3 Agricultural output and productivity 142 2.4 Unemployment 60 3.4 Deforestation and biodiversity 146 2.5 Wages and productivity 64 3.5 Freshwater 150 2.6 Poverty 68 3.6 Water pollution 154 2.7 Social indicators of poverty 72 3.7 Energy production and use 158 2.8 Distribution of income or consumption 74 3.8 Energy efficiency and emissions 162 2.9 Assessing vulnerability 78 3.9 Sources of electricity 166 2.10 Enacn scrt 82 3.10 Urbanization 170 2.11 Education inputs 86 3.11 Urban environment 174 2.12 Participation in education 90 3.12 Traffic and congestion 178_ 2.13 Education efficiency 94 3.13 Air pollution 182 ----- -- -- - -- - -- -- -- - - -- --- -- -- - - - -- -- -- -- --- - -- ------ -- --- -- -- - - -- --- -- - ---- -- --- - -- ---- --- --I-- ---- - - ----. .--i x 2.14 Education outcomes 98 3.14 Government commitment 184 2.15 Health expenditure, services, and use 102 3.15 Understanding savings 188- I -- -- - - - --- - - -- - - --- -- -- - -- - - -- - - - - - --- - -- -- -- - - -- ---- -- -- - - - - -- - - - - - - - - -- -- -- 2.16 Disease prevention: coverage and quality 106e 2.17 Reproductive health 110 Figures 2.18 Nutrition 114 3.2 The land under cereal production is increasing in0 2.19 Health: risk factors and future challenges 118 low-income economies 141 ---- - - -- -- - -- -- - -- -- - -- - - --- - - -- -- -- ------ -- - - - ----- -- - - - -- - -- - - - - - - - - -- - - - -- ----- - --- -- - -- - - - -- 2.20 Mortality 122 3.3 Food production has outpaced population growth in C --- - -- -- -- --- -- ---- -- - - --- - --- -- - _1 -- -- --- -- - -- - - --- - - - -- - - --- - -- ---- -- --- - C low- and middle-income economies 1415 o ---- --- - - - - - --- - - - - - -- - - --- --- -- ---- -- -- - --- -- - -- -- - --- -- - - - -- - --- --- --- - - - -- - - - - - - --- - - - - - -- - - - -- - --- - - - - - --- --- - - - -- -- --- -- .. -- --m Figures 3.5a Freshwater resources per capita varied significantly 3_ - - - -- - - -- -- -- -- - - -- -- -- -- - - - - - -- -- - - - -- --- -- -- -- -- -- - - - -- -- -- - -- --- -- -- -- --- -- -- - - - - -- - -- - - -- -- -- -- -- -- - -- -- -- - -- -- C 2.2 Labor force participation rate 55across regions in 2000 153 2.3 Labor market segregation can be harmful 59 3.5b Agriculture uses most water in low- and middle-income economies 153 E 1_ ---- -------- ,-------------- ------------------------------------------- --- -- --.--------- - -- -------- ---- ------ ---------- _ __-------0 2.4 Youth unemployment is rising in many countries 63 3.6a Emissions of organic water pollutants 157 --- - ---- --- ------- --- - - - - --- - --- -- ---- - - - -- --- -------- - ----- -- - - --- ---- - ---- - --- - ------ ------- - -- -------- --- - --- - ---- - - - - ---- 2.7 Children fully immunized, by quintile 73 3.6b Contributions to global emissions of water pollutants, 1998 157 E 2.10 Out-o-f-po ck-et-h-e-alth e-x-p-en-d,itu-r-e-s c-a-ni-m p o v-e-r-i s-h -p-e- o-pl e 8 3.7 While the world's use of coal is decreasing, its use of 2.14 Reading and mathematical literacy among 15-year-oIds, 2000 101 other fossil fuels continues to increase 161 3.8a Per capita emissions of carbon dioxide rises with income 165 Texdt ta'bles 3.8b High-income economies accounted for only 15 percent of the world's 2.11a Why the break in data? Comparing ISCED76 to ISCED97 8 population in 1998 - but half its carbon dioxide emissions 1-65 2.15a How important are the different elements of client responsiveness 105 3.9a There was a significant shift in the sources 2.19a Bednets save lives 121 of electricity from 1980 to 1999 169 2.20a Differences in life expectancy shrink at older ages 125 3.9b High-income economies - with 15 percent of the world's population - generate eight times as much electricity as low-income economies 169 3.10 The 10 cities expected to be the most_populous in 2015 173 3.2 World production of automobiles and bicycles has increased significantly since 1950 181 3.14 Global focus on biodiversity and climate change i 3.15 Adjusted net saving is far lower in low-income economies 191 Text tables 3.1a The 10 economies with the highest rural population density in 1999 - and the 10 with the lowest 137 3.11a House prices vary widely relative to household income 177 3.14a Status of national environmental action plans 18 4 3.14b States that have signed the Convention on Climate Change 185 4 Economy - 5 States and Markets Introduction 193 Introduction 273 4.1 Growth of output 204 5.1 Private sector development 280 4.2 Structure of output 208 5.2 Investment climate 284 4.3 Structure of manufacturing 212 5.3 Stock markets 288 4.4 Growth of merchandise trade 216 5.4 Financial depth and efficiency 292 4.5 Structure of merchandise exports 220 5.5 Tax policies 296 4.6 Structure of merchandise imports 224 5.6 Relative prices and exchange rates 300 4.7 Structure of service exports 228 5.7 Defense expenditures and trade in arms 304 4.8 Structure of service imports 232 5.8 Transport infrastructure 308 4.9 Structure of demand 236 5.9 Power and communications 312 4.10 Growth of consumption and investment 240 5.10 The information age 316 4.11 Central government finances 244 5.11 Science and technology 320 4.12 Central government expenditures 248 4.13 Central government revenues 252 Figures X 4.14 Monetary indicators and prices 256 5.3 The developing countries of Europe and Central Asia have seen a 4.15 Balance of payments current account 260 dramatic increase in the number of listed companies 291 n 4.16 External debt 264 5.8 Air carriers registered in East Asia and Pacific more than doubled (° 4.17 External debt management 268 the number of passengers they carried in the 1990s 311 5.9 In many countries telephone access is far better in S Figures the largest city than the average for that country 315 c) 4.3 Between 1990 and 2000 manufacturing value added E more than doubled in East Asia and Pacific 215 o 4.5 Top developing economy exporters tend to be important exporters 223 a) >, 4.6 Structure of imports of developing and high-income economies look similar 227 4.7 Export shares of other commercial services have grown in developing economies 231 o 4.8 The changing structure of commercial service imports 234 CN 4.10 More spending 243 4.11 Some developing countries are spending a large proportion of their current revenue on interest payments 247 4.12 Some economies spend more than half of central government expenditures on subsidies and other current transfers 251 4.13 Many developing countries rely heavily on taxes from international trade 255 4.15 Suddenly positive 263 4.17 Short term debt falls back into line 271 Text tables 4a Recent economic performance 200 4b Key macroeconomic indicators 201 V 6 Global Links Introduction 325 B ac k -m a -tteor ------- --------------------- --- --- --- - -- 6. 1 Integration with the global economy 332 Statistical methods39 6.2 Direction and growth of merchandise trade 336 Primary data documentation 381 6.3 OECD trade with low- and middle-income economies 339 Acronyms and abbreviations 389 6.4 Primary commodity prices 342 Credits 390 6.5 Regional trade blocs 344 Bibliography 392 6.6 Tariff barriers 348 Index of indicators 397 6.7 Global financial flows 352 6.8 Net financial flows from Development Assistance Committee members 356 6.9 Aid flows from Development Assistance Committee members 358 6.10 Aid dependency 360 6.11 Distribution of net aid b y- D e v- el o'-p--me n_t A_s,_s I s t-a-n c-e Committee members 364 6.12 Net financial flows from multilateral institutions 368 - .1._ ._1 ----------------- ---- - ----- ------------------- x~~~~~~~~~~~~~~~~~~~~~~~~~~~X 6.13 Foreign labor and population in OECD countries 372 6.14 Travel and tourism 374 6.1 Gross private capital flows to the top 10 developing economy recipients,0 2000 or latest year available 335 6.2 About 20 percent of high-income , ----- ------- -- ------------------- ------ CD~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~C economies' imports came from developing economies in 2000 338 6.3 High-income economies' imports from developing countries 3 are mainly manufactured goods 341 (D 65 Eprt-s w_it_hi-n,s sm- all -r e-g-i o-n al _bl o- c-s i-s -o-ft-en m u- c"h h ig-h-e r --------- --~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 than their share of exports to the rest of the world... 347 - - -------------- - ------- ----- ---------------------~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 6.9 ODA levels have dropped in some DAC countries and risen in others, 1995-2000 359 6.11 Bilateral aid flows from selected DAC members to largest country recipients 367 6.13 Foreign population in selected OECD countries, 1985-99 373 6.14 Top 10 Country recipients of inbound tourists, 1990 and 2000 377 6.8a Official development assistance from selected non-DAC donors 357 Partners Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in the World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agen- cies that develop the nomenclature, classifications, and standards fundamental to an inter- national 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 meth- xii ods 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 U') the quality of public and private decision-making. The organizations listed here have made the World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to C: the World Bank's efforts, and to those of many others, to improve the quality of life of the world's E people. We acknowledge our debt and gratitude to all who have helped to build a base of compre- <^, hensive, quantitative information about the world and its people. For your easy reference we have included URLs (Web addresses) for organizations that maintain Web sites. The addresses shown were active on 1 March 2002. Information about the 0 World Bank is also provided. N International and government agencies Bureau of Verification and Compilance, U.S. Department of State The Bureau of Verification and Compliance, U.S. Department of State, is responsible for interna- tional agreements on conventional, chemical, and biological weapons and on strategic forces; treaty verification and compliance; and support to ongoing negotiations, policymaking, and inter- agency implementation efforts. For information contact the Public Affairs Officer, Bureau of Verification and Compliance, U.S. Department of State, 2201 C Street NW, Washington, DC 20520, USA; telephone: 202 647 6946; Web site: www.state.gov/www/global/arms/bureauvc.html. Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global change data and information analysis center of the U.S. Department of Energy. The CDIAC's scope includes po- tentially anything that would 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 terrestrial 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 information contact the CDIAC, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831-6335, USA; telephone: 865 574 0390; fax: 865 574 2232; email: cdiac@ornl.gov; Web site: cdiac.esd.ornl.gov . Food and Agriculture Organization The Food and Agriculture Organization (FAO), a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to in- crease agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; of- fers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. Statistical publications of the FAO include the Production Yearbook, Trade Yearbook, and Fertilizer Yearbook. The FAO makes much of its data available on diskette through its Agrostat ii PC system. FAO publications can be ordered from national sales agents or directly from the FAO Sales o and Marketing Group, Viale delle Terme di Caracalla, 00100 Rome, Italy; telephone: 39 06 57051; fax: 39 06 5705/3152; email: Publications- sales@fao.org ; Web site: www.fao.org. 0. International Civil Aviation Organization International Health Conference, convened in New York by the Economic and Social Council. The o i7ff:2- objective of the WHO, a specialized agency of the United Nations, is the attainment by all people 97, [/Jjyof the highest possible level of health. The WHO carries out a wide range of functions, including coordinating international health work; helping governments strengthen health services; providing technical assistance and emergency aid; working for the prevention and control of disease; promoting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promoting and coordinating bio- medical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for biological, pharmaceu- tical, and similar products: and standardizing diagnostic procedures. The WHO publishes the World Health Statistics Annual and many other technical and statis- tical publications. For publications contact Distribution and Sales, Division of Publishing, Language, and Li- brary Services, World Health Organization Headquarters, CH-1211 Geneva 27, Switzerland; tele- phone: 41 22 791 2476 or 2477; fax: 41 22 791 4857; email: publications@who.ch; Web site: www.who.ch. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations based in Geneva, Switzerland. The objectives of WIPO are to promote the protection of intellec- tual property throughout the world through cooperation among states and, where appropriate, in collaboration with other international organizations and to ensure administrative cooperation among the intellectual property unions-that is, the "unions" created by the Paris and Berne Conventions and several subtreaties concluded by members of the Paris Union. WIPO is respon- sible for administering various multilateral treaties dealing with the legal and administrative aspects of intellectual property. A substantial part of its activities and resources is devoted to development cooperation with developing countries. For information contact the World Intellectual Property Organization, 34. chemin des Colombettes, Geneva, Switzerland; mailing address: PO Box 18, CH-1211 Geneva 20, Switzer- land; telephone: 41 22 338 9111; fax: 41 22 733 5428; telex: 412912 ompi ch; email: publications.mail@wipo.int; Web site: www.wipo.int. World Tourism Organization The World Tourism Organization is an intergovernmental body charged 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. The organization began as the International Union of Official Tourist Publicity Organizations, set up in 1925 in The Hague. Renamed the World Tourism Organization, it held its first general assembly in Madrid in May 1975. Its membership includes 132 countries and territories and more than 350 affiliate members representing local governments, tourism associations, and private companies, including airlines, hotel groups, and tour operators. The World Tourism Organization publishes the Yearbook of Tourism Statistics, Compen- xix dium of Tourism Statistics, and Travel and Tourism Barometer (triannual). For information contact the World Tourism Organization, Capitan Haya, 42, 28020 Madrid, o Spain; telephone: 34 91 567 81 00; fax: 34 91 567 82 18; email: omt@world-tourism.org; Web M site: www.world-tourism.org. 0. CD World Trade Organization ,, 10022, USA; telephone: 212 224 3300; email: info@iimagazine.com; Web site: CD www.iimagazine.com. 0 N _ __ _ _ __ International Road Federation N The International Road Federation (IRF) is a not-for-profit, nonpolitical service organization. Its purpose is to encourage better road and transport systems worldwide and to help apply technol- ogy and management practices that will maximize economic and social returns from national S L) road investments. The IRF has led global road infrastructure developments and is the international point of affiliation for about 600 member companies, associations, and governments. The IRF's mission is to promote road development as a key factor in social and economic growth, to provide governments and financial institutions with professional ideas and expertise, to facilitate business exchange among members, to establish links between members and exter- nal institutions and agencies, to support national road federations, and to give information to professional groups. The IRF publishes World Road Statistics. Contact the Geneva office at 2 chemin de Blandonnet, CH-1214 Vernier, Geneva, Switzer- land; telephone: 41 22 306 0260; fax: 41 22 306 0270; or the Washington, DC, office at 1010 Massachusetts Avenue NW, Suite 410, Washington, DC 20001, USA; telephone: 202 371 5544; fax: 202 371 5565; email: info@irfnet.com; Web site: www.irfnet.org. Monetary Research Institute The Monetary Research Institute (MRI) was founded in 1990 to collect information about the current means of payment in the world. Its flagship publication, the quarterly MRI Bankers' Guide to Foreign Currency, is designed for use by banks, foreign exchange bureaus, libraries, universi- ties, coin dealers, travel agents, and those relying on international trade. It features information on and images of all currencies and banknotes in circulation, information on travelers checks, and currency histories, news, and approaching expiration dates. It also lists tourist and parallel exchange rates for every country. The MRI maintains relationships with all currency issuing au- thorities. For information contact the Monetary Research Institute, 1014 Wirt Road, Suite 200, Hous- ton, TX 77055, USA; telephone: 713 827 1796; fax: 713 827 8665; email: info@mriguide.com; Web site: www.mriguide.com. Moody's Investors Service Moody's Investors Service is a global credit analysis and financial opinion firm. It provides the international investment community with globally consistent credit ratings on debt and other Moodys Investors Service securities issued by North American state and regional government entities, by corporations worldwide, and by some sovereign issuers. It also publishes extensive financial data in both print and electronic form. Its clients include investment banks, brokerage firms, insurance com- panies, public utilities, research libraries, manufacturers, and government agencies and depart- ments. Moody's publishes Sovereign, Subnational and Sovereign-Guaranteed Issuers. For informa- xxi tion contact Moody's Investors Service, 99 Church Street, New York, NY 10007, USA; tele- phone: 212 553 1658; fax: 212 553 0882; Web site: www.moodys.com. o Netcraft 0. Netcraft is an Internet consultancy based in Bath, England. Most of its work relates to the 0 development of Internet services for its clients or for itself acting as principal. CD For information visit its Web site: www.netcraft.com. PricewaterhouseCoopers , Drawing on the talents of 150,000 people in more than 150 countries, PricewaterhouseCoopers provides a full range of business advisory services to leading global, national, and local compa- nies and public institutions. Its service offerings have been organized into six lines of service, each staffed with highly qualified, experienced professionals and leaders. These services are audit, assurance, and business advisory services; business process outsourcing; financial advi- sory services; global human resource solutions; management consulting services; and global tax services. PricewaterhouseCoopers publishes Corporate Taxes: Worldwide Summaries and Individual Taxes: Worldwide Summaries. For information contact PricewaterhouseCoopers, 1301 Avenue of the Americas, New York, NY 10019, USA; telephone: 212 596 8000: fax: 212 259 1301; Web site: www.pwcglobal.com. The PRS Group PRS Group is a global leader in political and economic risk forecasting and market analysis and has served international companies large and small for about 20 years. The data it contributed M,.%@ r to this year's World Development Indicators come from the International Country Risk Guide I i PRS Ci monthly publication that monitors and rates political, financial, and economic risk in 140 coun- tries. The guide's data series and commitment to independent and unbiased analysis make it the standard for any organization practicing effective risk management. For information contact the PRS Group, 6320 Fly Road, Suite 102, P0 Box 248, East Syra- cuse, NY 13057-0248, USA; telephone: 315 431 0511; fax: 315 431 0200; email: custserv@PRSgroup.com; Web site: www.prsgroup.com. Standard & Poor's Equity Indexes and Rating Services Standard & Poor's, a division of the McGraw-Hill Companies, has provided independent and objective financial information, analysis, and research for nearly 140 years. The S&P 500 index, one of its most popular products, is calculated and maintained by Standard & Poor's Index Services, a leading provider of equity indexes. Standard & Poor's indexes are used by investors around the world for measuring investment performance and as the basis for a wide range of financial instruments. Standard & Poor's Sovereign Ratings provides issuer and local and foreign currency debt xxii ratings for sovereign governments and for sovereign-supported and supranational issuers world- wide. Standard & Poor's Rating Services monitors the credit quality of $1.5 trillion worth of 0 bonds and other financial instruments and offers investors global coverage of debt issuers. 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N 0 0 0. 3) 0D User's guide Q 4.5 Str,cture of merchandise exports 4.5 xxiv to *0 CD C~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~7 5) ~ ~ ~ ~ ~ ~ ~~Tbe E~~~~~~~~~~~~~~~~h alsaenmee yscinad0Idctr 0 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~ipa heietfigio ftescin niatr r hw o h otrcn ero 0 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~onre n cnme r itdpro o hc aaaeaalbead nms to ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~apaeialy(xetfrHn og hn, tbe,fo nerirya rpro uuly19 ii) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ wihaper fe hn) aaaesoni hsedto) iesre aaaeaalbeo 0~~~~~~~~~~~~~~~fr12eooiswt oultoso oe teWrdDvlpetIdctr DRM V~~~~~~~~~~~~~~~~hn1mlin swl sfrTia,Cia C~~~~~~~~~~~~~~~i eetdtbe.Slcedidctr o 5 Ageaemaue o noegop ofthe Itabesren umbered byn so econti u ioand dat InIareaalbe.Ntohtrntisdiin si adipa theve idpent Ifying o, a icon io the setin.In icaors aesowne forbte m oest rno t yearuor Chountries tande economies aere lisnted ue puaeriodufrewic dartaniare, availableund,nwin h most winechappearsy aftherchina). , datoaes shownipl ino tiscluedition) time-seriesnata a reont avalabes o forit152 inenomesnwit poultionfes tof more ntheiorald Dnoevel dopm ent Indcaorsmi measures trintselecte tables Selecrtied indcaortsefor55 0 rgate meFace orasuresn corsistncymeinouhe sothelr economies-sallsteconomies wvithb The aggregate measures fov r tinme groupswee populgation betwueen 30,000om and 1rmilion,l incluesmiin 207 teonom es impthedweoomeposlisted.i andup sallear ecnmisi theyedo eare memblers The mainregtabes plus thotas(esignatabed 1.6 whereve of te Inernaionl Bak fo Reonstuctindtae agregavasiable.Noe that finle ethis atediins inr and Devlopment(IBRD)or, asit iswheprevihyou none, tabed1.6 doles not incud commony know, theWorld ank-ar Franeigte oversgeas de)Gpartmeints-frec Gmuina,no show in able1.6.The erm y,uedGadeloupted.t Maunrtiiue. mand resuntion-whischrepace politcal ndepedenc, butrefes tonybntioena incgome aggeaend othreooiverltoasurs. terrtoryfor hichautorites rportsepaate orfuFrance. Tomintasion onsistencytion rnthe d socil o ecnomc sttisics Whn avilale, aggeg Satesiclmeasures.oe iean ewe Q45 4 5 ~~~:~~~~.w '~~~~~~~'_ _: ;x ___- _ †,_ , _, a, S 1 t_ _ _ }_.D . .D_.____, ____,,,_.- . __________~ (D 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 3 CD 0.Aggregate measures for region. 0 Footnotes The aggregate measures for regions include only Known deviations from standard definitions low- and middle-income economies (note that or breaks in comparability over time or across these measures include developing economies countries are either footnoted in the tables or with populations of less than 1 million, including noted in About the data. When available data those listed in table 1.6). are deemed to be too weak to provide reliable measures of levels and trends or do not The country composition of regions is based on adequately adhere to international standards, the World Bank's analytical regions and may the data are not shown. differ from common geographic usage. For regional classifications see the map on the inside back cover and the list on the back cover flap. For further discussion of aggregation methods see Statistical methods. Statistics Data are shown for economies as they were China Rep6bilca Bolivariana de Venezuela constituted in 2000. and historical data are revised On 1 July 1997 China resumed its exercise of In December 1999 the official name of Venezuela to reflect current political arrangements. sovereignty over Hong Kong, and on 20 December was changed to Republica Bolivariana de Venezuela Exceptions are noted throughout the tables. 1999 it resumed its exercise of sovereignty over (Venezuela, RB, in the table listings). Macao. Unless otherwise noted, data for China do Additional information about the data is provided in not include data for Hong Kong, China: Taiwan, Republic of Yemen Primary data documentation. That section China; or Macao. China. Data for the Republic of Yemen refer to that country summarizes national and international efforts to from 1990 onward: data for previous years refer to improve basic data collection and gives information Democratic Repubiic of the Congo aggregated data for the former People's Democratic on primary sources. census years. fiscal years. and Data for the Democratic Republic of the Congo Republic of Yemen and the former Yemen Arab xxvi other background. Statistical methods provides (Congo, Dem. Rep., in the table listings) refer to the Republic unless otherwise noted. technical information on some of the general former Zaire. The Republic of Congo is referred to u) calculations and formulas used throughout as Congo. Rep., in the table listings. Former Socialist Federal Republic of Yugoslavia 0 , the book. Available data are shown for the individual countries c Czech Repubiic and Slovak Republic formed from the former Socialist Federal Republic -8 Discrepancies in data presented in different Data are shown whenever possible for the individual of Yugoslavia-Bosnia and Herzegovina, Croatia, c editions of the World Development Indicators countries formed from the former Czechoslovakia- the former Yugoslav Republic of Macedonia, E, reflect updates by countries as well as revisions the Czech Republic and the Slovak Republic. Slovenia, and the Federal Republic of Yugoslavia. S) to historical series and changes in methodology. v Thus readers are advised not to compare data East Timor Changes In the System of Natlonal Accounts o This edition of the World Development Indicarors series between editions of the World Development On 25 October 1999 the United Nations Transi- o Indicators or between different World Bank tional Administration in East Timor (UNTAET) uses terminology in line with the 1993 System of publications. Consistent time-series data for assumed responsibility for the administration of National Accounts (SNA). For example, in the 1993 o 1960-2000 are available on the World Develop- East Timor. Data for Indonesia include East Timor SNA gross nationa/ income replaces gross nationa/ (N ment Indicators CD-ROM. through 1999 unless otherwise noted, product. See About the data for tables 1.1 and 4.9. Most countries continue to compile their national Except where noted, growth rates are in real terms. Erftrea accountsiaccordingt th le th mor and (See Statistical methods for information on the Data are shown for Eritrea whenever possible, but accounts according to the 1968 SNA, but more and methods used to calculate growth rates.) Data for in most cases before 1992 Eritrea is included in more are adoptmg the 1993 SNA. Countries that m ~~~~~~~~~~~~~~~~~~~~~~~~~~use the 1993 SNA are identified in Primary data some economic indicators for some economies are the data for Ethiopia. usenthei199 A ae identife intrims dta documentation. A few low-pncome countries still use presented in fiscal years rather than calendar concepts from older SNA guidelines. including years; see Primary data documentation. All dollar Jordan valuats such as fA cost.linescribing figures are current U.S. dollars unless otherwise Data for Jordan refer to the East Bank only unless valuations such as factor cost, in describing major stated. The methods used for converting national otherwise noted. economic aggregates. currencies are described in Statistical methods. Germany Data for Germany refer to the unified Germany unless otherwise noted. Union of Soviet Socialist Republics In 1991 the Union of Soviet Socialist Republics came to an end. Available data are shown for the individual countries now existing on its former territory (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, the Russian Federation, Tajikistan, Turkmenistan. Ukraine, and Uzbekistan). Classification of economies Symbols Data presentation conventions For operational and analytical purposes the World .. * A blank means not applicable or, for an aggregate, Bank's main criterion for classifying economies means that data are not available or that not analytically meaningful. is gross national income (GNI) per capita. Every aggregates cannot be calculated because of * A billion is 1,000 million. economy is classified as low income, middle missing data in the years shown. * A trillion is 1,000 billion. income (subdivided into lower middle and upper * Figures in italics refer to years or periods other than middle), or high income. For income classifications 0 or 0.0 those specified. see the map on the inside front cover and the list means zero or less than half the unit shown. * Data for years that are more than three years from on the front cover flap. Note that classification by the range shown are footnoted. income does not necessarily reflect development / status. Because GNI per capita changes over time, in dates, as in 1990/91, means that the period of The cutoff date for data is 1 February 2002. the country composition of income groups may time, usually 12 months, straddles two calendar xxvii change from one edition of the World Development years and refers to a crop year, a survey year, or a Indicators to the next. Once the classification is fiscal year. N fixed for an edition, based on GNI per capita in the most $ recent year for which data are available (2000 means current U.S. dollars unless otherwise noted. in this edition), all historical data presented are based on the same country grouping. > CD (D means more than. o Low-income economies are those with a GNI per 3 capita of $755 or less in 2000. Middle-income < economies are those with a GNI per capita of more means less than. than $755 but less than $9,266. Lower-middle- income and upper-middle-income economies are separated at a GNI per capita of $2,995. High- income economies are those with a GNI per capita of $9,266 or more. The 11 participating member countries of the European Monetary Union (EMU) are presented as a subgroup under high-income economies. Recent revisions of 2000 GNI per capita for Antigua and Barbuda, from $9,190 to $9,440, would place this country in a higher ircome category; revisions to data for Belize from $2,940 to $3,110, would place this country in a higher income category; revisions to data for Papua New Guinea, from 760 to $700, would place this country in a lower income category; and, revisions to Turkmenistan from $840 to $750, would place this country in a lower income category. However, since the official analytical classifications are fixed during the World Bank's fiscal year (ending on 30 June), these countries remain in the income categories in which they were classified before these revisions: Antigua and Barbuda in the upper-middle-income category, and Belize, Papua New Guinea, and Turkmenistan in the lower-middle-income category. - -; t=- -I. I [lA ,@ _ _ _ _ _ _ _ _ _ A ~ Il h \ ( G ~ ~ O ) A A ,-1-.l-.\JLW-- -./LiXJn Millennium Development . ,Eradicate extreme poverty Development and e extremenpover tY 5 Improve maternal health Goals I ~and hunger 2 Achieve universal primary Combat HIV/AIDS, malaria, 2 education 6 and other diseases Promote gender equality and . Ensure environmental empower women sustalnability 3 4 A Reduce child mortality 8 i Develop a global partnership 0 for development 1" 0 a 3 CD z ET 27 0~ At the Millennium Summit in September 2000 the states of the United Nations reaffirmed their commitment to working toward a world in which sustaining development and eliminating poverty would have the highest priority. The Millennium Development Goals grew out of the agreements and resolutions of world conferences organized by the United Nations in the past decade. The goals have been commonly accepted as a framework for measuring development progress. The goals focus the efforts of the world community on achieving significant, measurable improvements in people's lives. They establish yardsticks for measuring results, notjust for develop- ing countries but for rich countries that help to fund development programs and for the multilateral institutions that help countries implement them. The first seven goals are mutually reinforcing and are directed at reducing poverty in all its forms. The last goal-global partnership for development-is about the means to achieve the first seven. Many of the poorest countries will need additional assistance and must look to the rich countries to provide it. Countries that are poor and heavily indebted will need further help in reducing their debt burdens. And all countries will benefit if trade barriers are lowered, allowing a freer exchange of goods and services. For the poorest countries many of the goals seem far out of reach. Even in better-off countries there may be regions or groups that lag behind. So countries need to set their own goals and work to ensure that poor people are included in the benefits of development. How many countries are likely to reach the Millennium Development Goals? Percent LI Likely Possible a Unlikely I Very unlikely QI No data East Asia and Pacific 23 countries Europe and Central Asia 28 countries 100 100 4 50 50 0 a)0 C~~~~~~~~~~~~~~~C 0~ 0 0~~~~~~~~~05 0~~~~~~~~~~ CN~~~~~~L 50 50 0 0i South Asia 8 countries Sub-Saharan Africa 48 countries 100Ag-C: 100 a> L E O~L Source: World Bank data. Are we reaching the goals? The eight Millennium Development Goals comprise 18 targets and 48 indicators. Where possible, the targets are given as quantified, time-bound values for specific indicators. Data for the indicators come from official statistics and surveys conducted by countries and international agen- cies. Most of the data are included in this volume, but. missing data and the lack of reliable statistics limit the ability to monitor progress. How many countries are likely to reach the Millennium Development Goals? Much depends on whether the progress in the past decade can be sustained-or accelerated in countries falling behind. The charts show the prospects for low- and middle-income countries of r eaching six of the targets of the Millennium Development Goals. Prospects for each country have been assessed based on their its of progress over the past 5 decade and, in some cases, on its level of attainment. For two indicators lacking time-series data- maternal mortality and HIV prevalence-prospects have been assessed based on level alone. The assessments were made uising data available inJanuary 2002 and may be revised in the future. These assessments are based on past performance and existing data. They are not a finial ver- dict, but they are a warning. Too many countries are falling short of the goals or lack the data to < 0 monitor progress. Now is the time to take actions to accelerate progress, not 5 or 10 years fi-om 3 (D now. E. or United Nations Millennium Declaration, September 2000 Regional . Countries in medium gray The indicators and Target: Achieve equality in enroll- made still slower progress. They ment ratios by 2005. assessm ents are unlikely' to reach the goals. To their targets . Child mortality Indicator: reach them, they will need to make Under-five child mortality. progress at unprecedented rates. Target: Reduce by two-thirds Countries in dark blue made * For countries in black, condi- * Child malnutrition Indicator: between 1990 and 2015. progress in the 1990s fast enough tions have worsened since 1990, Prevalence of malnutrition among * Maternal mortality Indicator: to attain the target value in the or they currently have very high children under age five, measured Maternal deaths per 100,000 live specified time period (by 2005 for maternal mortality and HIV/AIDS by weight for age (wasting). births. gender equality and by 2015 for all prevalence. They are "very unlikely" Target: Reduce by half between Target: Reduce by three-quarters others). They are "likely" to achieve to reach the goals. 1990 and 2015. between 1990 and 2015. the goals. * Countries In light gray lack * Primary school completion * HIV/AIDS prevalence Indica- * Countries in light blue made adequate data to measure Indicator: Percentage of children of tor: Prevalence of HIV/AIDS among progress, but too slowly to reach progress. Improvements in the sta- appropriate age completing last young women (ages 15-24). the goals in the time specified. tistical systems of many countries grade of official primary school. Target: Have halted by 2015 and Continuing at the same rate, they are needed to provide a complete Target: Achieve 100 percent com- begun to reverse the spread of will need as much as twice the time and accurate picture of their pletion by 2015. HIV/AIDS. as the "likely" countries to reach progress. * Gender equality in school Idi- the goals. Rated "possible," they cator: Ratio of girls to boys need to accelerate progress. enrolled in primary and secondary school. Poverty With sustained growth, many regions will achieve the goal Population living below $1 and $2 a day _$1 a day poverty rate _ $2 a day poverty rate _OAverage path to $1 a day target East Asia and Pacific Europe and Central Asia 60 60 60 CD 40 40 .2 Below $1 a day c.,CD 20 v...20 Below $2 a day V ~~~~~~~~~~20 Tag; 20 *B-elw -$ day ' E 0 Projected * e *--O__ o 1990 1999 2015 1990 1999 2015 D Latin America and Caribbean Middle East and North Africa 0 ~~~~~~~~~~~60 60 C4 ~ ~ ~ ~ 6 Below $2 a day 6 40 B $ a d 40 Below $2 a day 20 Below $1 a day 0 20 B w a ~~~~...Below $1 a day 1990 1999 2015 1990 1999 2015 South Asia Sub-Saharan Africa 60 60 Below 60 a day Below $1 a day 40Below $1 a day40 *-- 40 40 20 - . 20 0 _ _ _ _ _ _ _ 0 _ _ _ _ _ _ _ 1990 1999 2015 1990 1999 2015 Source: world Bank staff estimates. Eradicate extreme were 125 million fewer people liv- poverty. In other regions the num- economies to 14.5 percent. ing in extreme poverty, continuing ber of poor people has increased, Recent projections by the World poverty ... a downward trend that began in even as the proportion in extreme Bank show that it is possible to the early 1980s. But much of the poverty has fallen. achieve that goal in most regions progress has been in Asia. where if growth in per capita income During the 1990s GDP per capita sustained growth in China lifted The Millennium Development accelerates to an average of 3.6 in developing countries grew by nearly 150 million people out of Goals call for reducing the propor- percent a year. This would be 1.6 percent a year, and the pro- poverty after 1990. Faster growth tion of people living on less than nearly twice the rate achieved portion of people living on less in parts of South Asia has also $1 a day to half the 1990 level by over the past decade, but such than $1 a day fell from 29 percent led to modest declines in the 2015-from 29 percent of all peo- growth is possible. to 23 percent. By 1999 there number of people living in extreme ple in low- and middle-income ... and hunger Malnutrition rates among children under five in the developing world Malnutrition falls as average Income rises fell from 46.5 percent in 1970 to As average incomes grow, extreme 27 percent in 2000. Even so, 150 poverty declines and children Under-five malnutrition rate, most recent year, and million children in low- and middle- become better nourished. Very few GNI per capita, 2000 income economies are still malnour- upper-middle-income countries (D 60 ished, and at current rates of upper-middle-income countries 60 * Bangladesh improvement 140 million children report significant levels of under- we - 50 wilebmunereigtRne220 weight children. But the data are X 50 Yemen, Rep. incomplete, and more systematic r9 Chad m r40 Ca The number of undernourished peo- monitoring is needed. 0. o 30 . Uganda ple in the developing world fell from M. U zbekistan 840 million in 1990 to about 777 Most regions of the world have Uzeisa made dramatic progress in reduc- CD 20 Mexico million in 1997-99 and is expected ing the proportion of underweight 10 Bolivia* to decrease by 200 million more by ingcthildren. Buoportiog s hs uderweit I 2015. But greater reductions will children. But progress has been m be needed to reach the World Food 7 slowing, leaving the prospect of 0 Summit goal of cutting the number reaching the targets of the Millen- 0 2,000 4,000 6,000 8,000 undernous people nuhalf 0 , , , , ~~~~~~~~~~~of undernourished people in half ° nium Development Goals in doubt. Dollars by 2015. N Source: UNICEF and World Bank staff estimates. o 0 CD (0 '0 :3 Improving but Raising incomes and reducing poverty is part of the answer. But persistent Wihncutis antiinas olw noeeven poor countries need not suffer Wlthin cunttles malnutrtion alo follow Incomehigh rates of child malnutrition. Under-five malnutrition rate by quintile in selected countries They can make big improvements Malnutrition in children is caused through such low-cost measures as by consuming too little food energy * Poorest fifth *Richest fifth nutrition education and food sup- to meet the body's needs. Adding -, 7 plementation and fortification. to the problem are diets that lack D7 Other things that help include essential nutrients, illnesses that , D improving the status and education deplete those nutrients, and under- 50 * of women, increasing government nourished mothers who give birth commitment to health and to underweight children. 25 nutrition, and developing an effec- *l * * * ~~~~~~~~~~~~tive health infrastructure. Just as poor countries tend to *- * have high rates of malnutrition, O - *0- * the poorest segment of the popu- v lbt 6 lation within a country is the most 2° c ;O ci <\g Fq° t9 S * 6~~4 Improving but Raising incomes and reducing l malnourished. Even in countries en pr c i nd n with relatively low average rates of a. Children under four yeara oldo malnutrition, poor people suffer ource:ulmographicoandmHealthSurvyadata. disproportionately. Achieve universal and 74 percent living in South Asia and Sub-Saharan Africa. The Millen- primary education nium Development Goals set a Slow prog,ress toward education for all more realistic but still difficult dead- Average primary school completion rate line of 2015 when all children every Education is a powerful instrument where should be able to complete a for reducing poverty and inequality, * 1990 * Most recent year full course of primary schooling. improving health and social well- n 100 being, and laying the basis for sus- o Recent work at the World Bank tained economic growth. It is (2002) has produced new essential for building democratic 60 estimates of primary completion societies and dynamic, globally 40 rates. These show small improve- competitive economies. ments everywhere, but progress 20 * | * | * | * | * | * | overall has been too slow to reach The 1990 Conference on Education O the goal by 2015. for All, held in Jomtien, Thailand. 8 pledged to achieve universal What can be done? Lower costs to primary education by 2000. But in 4I < students and their families. o 1999 there were still 120 million * G >, Improve the quality of schools. And J primary-school-age children not in increase the efficiency of the cc school, 53 percent of them girls Source: Word Bank staff estimates. school system. E 0- 0 C) D -o 0 Reading, writing, Some 79 developing countries have already built sufficient schools and and retention Finish what's started places to educate 100 percent of their primary-school-age children. Primary school enrollment and completion rates, Only 27 of those countries retain To reach the goal, schools must most recent year 100 percent of children in school first enroll all school-age children * Proportion of children enrolled in primary school through primary graduation. and then keep them in school for U Proportion of children completing primary school the full course of the primary stage. Since 1990, 17 middle-income and In many places schools fail to do , 100 21 low-income countries have seen both. As a result, there can be v 80 completion rates stagnate or large gaps between reported enroll- 60 decline. Afghanistan fell from an ment, attendance, and completion already low 22 percent in 1990 to rates. Disparities arise for many 40 an estimated 8 percent. A number reasons. Children may start school 20 * of middle-income Gulf states, Latin late or they may repeat grades, 0American countries such as putting them off track. Frequently 0cO. -- *- A" Trinidad and Tobago and Republica children drop out of school because 53 (p ' so° Bolivariana de Venezuela, and low- of their own or a family member's ' ' , o & \N income countries such as illness or because their families Cameroon, Kenya, Madagascar, and need their labor. If they return, they Zambia have also lost ground. re-enroll in the same grade the fol- Source: World Bank staff estimates. lowing year. But many never finish. Promote gender Girls reach adulthood with lower literacy rates than boys (except in equality and Starting life In second place Latin America and the Caribbean). empower____________________________women____________________ Informal training, such as adult lit- empower women Youth literacy rate (ages 15-24), 2000 eracy classes, can make up some of the difference. But many girls, * Male * Female who begin with fewer opportunities In most low-income countries girls -M 100i than boys, are at a permanent dis- are less likely to attend school than z * advantage. CD boys. And even when girls start 90 school at the same rate as boys, 80 E* they are more likely to drop out- 70 often because parents think boys' 70 I i i. schooling is more important or 60 because girls' work at home seems 50 more valuable than schooling. Con- cerns about the safety of girls or 4g kfi ,i 9 traditional biases against educating 'z°9 0 -& 9N them can mean that they never G v start school or do not continue '' M Co~ ~ ~~ ~ ~ ~~~~~~~~ beyond the primary stage. source: UNEsco and worl Bank sta es t mu es. 0 BEduainy omnd andhgiving eqaiy,btitieotteonyoe Educating women and giving them ~~~~~~~~~~~~~~labor market opportunities, and the equal rights is important for Ratio of female to male, global averageablttopriptenpuicifad many reasons: *Primary and secondary enrollment development decision-making. *It increases their productivity, ao oc atcpto raiin oupu ad r(Jcig pvety Parliamentary representation Recognizing that empowering 100 ~~~~~~~~~~~~~~women extends beyond the class- Itprmte gneieuait iti room and the household, the Millen- 0~~~~~~~~~~~~~~~~~~~~~~~~ households and removes constraints m. _ onwoe'sdeiioi-ain-tu 80 nium Development Goals include three additional indicators of gender reducing fertility rates and improving 60eqaiyiltrcyaesthpoor nmaternal health. Itinress hicle'schncs40 tion of women working outside agri- of surviving to become healthier ~~~~~~~~~~~~culture, and the proportion of seats an bttredcaedbcaseed-20 women hold in national parliaments. catd wmendoa bDttrjb crin 0These indicators suggest that even cated women do a better job caring 0 ~~~~~~~~~~~~~~~after reaching the goal of full partici- for~~~ ~ chlrn 190 99 20 pation in primary and secondary Surce: World Bank staff estimates. education, the world will still fall Equal access to education is an K shr_fgne qaiy important step toward greater gender Reduce child Rapid improvements before 1990 gave hope that mortality rates of mortality Still far to go children under five could be cut by two-thirds in the following 25 years. Under-five mortality rate But progress slowed almost every- Deaths of infants and children where in the 1990s, and in parts of dropped rapidly over the past 25 _Progress to date _O Path to goal Africa infant and child mortality years. The number of deaths of chil- *-Projected progress at current rate rates increased. dren under five fell from 15 million in r 200 1980 to about 11 million in 1990, a Sub-Saharan Afric At the end of the 20th century only penod when the number of children LO 1 36 developing countries were mak- fl 150 being born was still rising. This was ing fast enough progress to reduce success borne on many wings- 9 4 o wznRcome , i under-five child mortality to a third of vaccination programs, the spread of 8 100i its 1990 level by 2015. Most of oral rehydration therapy, wider avail- those are middle-income countries, ability of antibiotics to treat pneumo- 50 Middle income although a few poor countries- 10 nia, andl better economic and social notably Bangladesh and Indonesia- conditions all contributed. and some of the poorest countries o2 of the former Soviet Union are on co 1990 1995 2000 2005 2010 2015 track to achieve the goal. Source: World Bank data. C 0 N > ii) 0 CN! I I S~~- i rt ik Addressing the One-third of child deaths occur in the neonatal period. They are causes Causes of child mortality caused by poor maternal health _____ _____ _____ ____ _____ _____ ____ _____ _____ ____ and lack of care during pregnancy Deaths among children under five, global, 1999 and delivery. For 70 percent of children who die before their fifth birthday the cause To ensure continuing is a disease or combination of dis- 29% Other 20% Acute improvements, disease-specific vac- eases and malnutrition that would be respiratory cination and treatment programs readily preventable in a high-income 6nec%o must be sustained while new country: acute respiratory infections, Dathsstaeisdrssumtndso diarrhea, measles, and malaria. asocated unevd populations. In all coun- w ~~~~~2% Diarrhea tries the poorest people are least In some parts of the world vaccina- alulikely to receive health services and tion coverage has begun to decline. 5% Measle so have the highest mortality rates. In 1999, 55 countries had not Addressing the underlying causes attained 80 percent coverage of 22% Perinatal 8%Mlra of poverty will improve health, and measles vaccinations among causes 4% HlV/AIDS better health will reduce poverty. children under one year: another 48 reported no data. source: WHO. Improve maternal Many women deliver their children alone or with traditional birth atten- health dants who lack the skills to deal Skilled health personnel reduce maternal deaths with comlationhe skills th __ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~with complications. Skilled birth Births attended by skilled health personnel, 1999, and attendants help to recognize and In 1995 more than 500,000 maternal mortality ratio, 1995 prevent medical crises. They also women died from complications of provide mothers with basic informa- pregnancy and childbirth, most of X Bulgaria tion about care for themselves and them in developing countries, CD their children before and after giv- where these complications are the r 80 * ing birth. Lack of current data on (la leading cause of death among o *maternal deaths limits monitoring women of reproductive age. More 60 of trends over time. than half of all maternal deaths I* occur in Africa. In many African n 40 1 M CD countries one mother dies for every : 0 100 live births. In Rwanda there c, 0 * Niger were more than 2 deaths for every aBangladesh 100 live births. Compare that with 500 1,000 Greece, which reported only 0 0 100 1,0 2 maternal deaths per 100,000 Maternal deaths per 100,000 live births 0 live births. Source: WHO and UNICEF. 0 CD (CD 0 0 Oc Preventing * Prevent complications during pregnancy and childbirth. maternal deaths M h Inadequate nutrition, unsafe sex. and poor health care during preg- Share of births attended by skilled health personnel nancy increase the risk of health Women die in childbirth for many problems during pregnancy and reasons, most of them preventable * Latin America and Caribbean :7 Sub-Saharan Africa childbirth. Yet in some countries or treatable using cost-effective * Middle East and North Africa U Asia fewer than 25 percent of pregnant interventions: -u 100 women visit a clinic for care. * Prevent deaths when complica- Reduce the number of pregnan- = 80 tions arise. Complications during cies. Early childbearing and closely pregnancy and delivery must be spaced pregnancies increase the 60 quickly diagnosed and treated in risks for mothers and children. And 40 suitable health care facilities. But in some countries unsafe abortions providing prompt emergency ser- add to the toll. Although many per- 20 vices is beyond the capacity of sonal and cultural factors affect the 0 many countries' health systems. desired family size, access to fam- ily planning services helps women 1989 1994 1999 make decisions about whether and Source: WHO. when to have children. Combat HIV/AIDS, HIV/AIDS is the leading cause of death in Sub-Saharan Africa and malaria, and other the fourth largest killer worldwide. diseases HIV continues to spread Among those lost are teachers, dlSeaSeS , ~~~~~~~~~~~~~~~~~~~health care workers, and farmers, Newly infected adults and children, 2001 3,400 fohealthce wosrkers andf ' p forcing the closure of schools and i 1,000 clinics and threatening food secu- With an estimated 40 million peo- 0 800 rity. Deaths of parents have left ple living with HIV/AIDS and 20 mil- °, 800 more than 13 million HIV/AIDS lion deaths since the disease was 600 orphans-a figure expected to first identified, AIDS poses an more than double by 2010. unprecedented public health, eco- 4000250 270 nomic, and social chal enge. By 200 190 2 2 infecting young people dispropor- 76 80 tionately-half of all new HIV infec- O * * I I I tions are among 15- to 24-year- . , ,, * * olds-and by killing so many adults \', S , ,W t0 C in their prime, the epidemic k fl 0°, CG z pr, seriously undermines development. 't c Note: Regions may differ from World Bana difiniions. Source: UNAIDS 2001. E ro a 0 fL 0.4 Epidemi c the health effects for those who become infected. proportions Tuberculosis-treatable, but cases still rising Tuberculosis is the main cause of Incidence of tuberculosis, 1999 death from a single infectious agent Malaria is endemic in more than among adults in developing coun- 100 countries and territories and 0 400 tries. Over the past decade the inci- 0) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~0 3 affects an estimated 300 million CD D dence of tuberculosis has grown people each year. Although the D 300 rapidly in Europe and Central Asia, mosquitoes that spread the o 9 Africa, and parts of South and East 0~~~~~~~~~~~~~~~ 9 disease have been eradicated in ° 200 42Asia. On present trends, there will 0~~~~~~~~~~~~~~~~ 4 0 ~ ~ ~ ~ ~~~~~ some countries where malaria was $ * be 10.2 million new cases in notEwidespread, this has not been o ts h 75 66o possible in wet, tropical climates. rCaD 1 | | | * :L6 have more cases than other pi Te bt c e s t regions. The directly observed Estimates based on malaria cases treatment, short-course (DOTS) reported to the WHO show that c ' 'sG °c ,N the under-five mortality rate . Infant mortality rate * Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Reduce by three-quarters, between 1990 and 2015. * Maternal mortality ratio O the maternal mortality ratio . Proportion of births attended by skilled health personnel 0 Goal 6 Combat HIV/AIDS, malaria, and other diseases Have halted by 2015 and begun to reverse the spread . HIV prevalence among 15- to 24-year-old pregnant women of HIV/AIDS . Contraceptive prevalence rateb * Number of children orphaned by HIV/AIDS Have halted by 2015 and begun to reverse the * Prevalence and death rates associated with malaria incidence of malaria and other major diseases . Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures * Prevalence and death rates associated with tuberculosis * Proportion of tuberculosis cases detected and cured under directly observed treatment, short-course (DOTS) Goal 7 Ensure environmental sustainability Integrate the principles of sustainable development . Change in land area covered by forest into country policies and programs and reverse the . Land area protected to maintain biological diversity loss of environmental resources . GDP per unit of energy use * Carbon dioxide emissions (per capita) Halve, by 2015, the proportion of people without . Proportion of population with sustainable access to an sustainable access to safe drinking water improved water source Have achieved, by 2020, a significant improvement . Proportion of population with access to improved sanitation in the lives of at least 100 million slum dwellers . Proportion of population with access to secure tenure [Urban-rural disaggregation of several of the above indicators may be relevant for monitoring improvement in the lives of slum dwellers] Goals and targets Indicators' Goal 8 Develop a global partnership for development Develop further an open, rule-based, predictable, Some of the indicators listed below will be monitored separately for the nondiscriminatory trading and financial system least developed countries (LDCs), Africa, landlocked countries, and (includes a commitment to good governance, small island developing states. development, and poverty reduction-both nationally and internationally) Official development assistance (ODA) e Net ODA as a percentage of DAC donors' GNI * Proportion of ODA for basic social services (basic education, pri- Address the special needs of the least developed mary health care, nutrition, safe water, and sanitation) countries (includes tariff and quota-free access for * Proportion of ODA that is untied their exports; enhanced program of debt relief for * Proportion of ODA for the environment in small island developing heavily indebted poor countries and cancellation of states official bilateral debt; and more generous ODA for * Proportion of ODA for the transport sector in landlocked countries 17 countries committed to poverty reduction) Market access 0 * Proportion of exports (by value, excluding arms) admitted free of Address the special needs of landlocked countries duties and quotas 0 and small island developing states (through Barbados . Average tariffs and quotas on agricultural products and textiles and Program and 22nd General Assembly provisions) clothing < * Domestic and export agricultural subsidies in OECD countries o * Proportion of ODA provided to help build trade capacity 3 r. Deal comprehensively with the debt problems of developing countries through national and international Debt sustalnability measures in order to make debt sustainable in the 7 Proportion of official bilateral HIPC debt canceled 0 long term * Debt service as a percentage of exports of goods and services 0 . Proportion of ODA provided as debt relief . Number of countries reaching HIPC decision and completion points In cooperation with developing countries, develop and - Unemployment rate of 15- to 24-year-olds implement strategies for decent and productive work for youth In cooperation with pharmaceutical companies, * Proportion of population with access to affordable, essential provide access to affordable, essential drugs in drugs on a sustainable basis developing countries In cooperation with the private sector, make available * Telephone lines per 1,000 people the benefits of new technologies, especially information * Personal computers per 1,000 people and communications a. Some indicators, particularly for goals 7 and 8, remain under discussion. Additions or revisions to the list may be made in the future. b. Only one form of contraception-condoms-is effective in reducing the spread of HIV. A.- 1.1 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross area density Income Income per capita Income domestic product Per Per thousand people capita capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2000 2000 2000 2000" 2000 2000" 2000 2000 2000 2000 1999-2000 1999-2000 Afghanistan 27 ~ 652 41 d.0. Albania 3 29 124 3.8 126 1,120 130 12 3,600 130 7.8 6.9 Algeria 30 2,382 13 47.9 49 1,580 117 153 5,040 107 2.4 0.9 Angola 13 1,247 11 3.8 125 290 178 15 1,180 181 2.1 -0.8 Argentina 37 2,780 14 276.2 16 7,460 58 446 12,050 58 -0.5 -1.7 Armenia 4 30 135 2.0 146 520 155 10 2,580 147 6.0 5.9 Australia 19 7,741 2 388.3 15 20,240 27 479 24,970 19 1.9 0.8 Austria 8 84 98 204.5 21 25,220 14 214 26,330 14 3.0 2.7 Azerbaijan 8 87 93 4.9 115 600 148 22 2,740 142 11.1 10.2 18 Bangladesh 131 144 1,007 47.9 50 370 167 209 1,590 165 5.9 4.1 Belarus 10 208 48 28.7 60 2,870 94 76 7,550 82 5.8 6.1 o Belgium 10 30 331 251.6 18 24,540 20 282 27,470 9 4.0 3.8 m Benin 6 113 57 2.3 142 370 167 6 980 186 5.8 3.1 Bolivia 8 1,099 8 8.2 95 990 133 20 2,360 151 2.4 0.0 Bosnia and Herzegovina 4 51 78 4.9 112 1,230 126 ... . 5.9 3.1 E) Botswatna 2 582 3 5.3 109 3,300 85 11 7,170 84 3.4 2.5 o Brazil 170 8,547 20 610.1 9 3,580 82 1,243 7,300 83 4.5 3.2 Bulgaria 8 ill 74 12.4 80 1,520 119 45 5,560 100 5.8 6.3 Burkina Faso 11 274 41 2.4 141 210 193 11 970" 187 2.2 -0.4 ~0 Cambodia 12 181 68 3.1 135 260 186 17 1,440 173 5.0 2.7 o Cameroon 15 475 32 8.6 90 580 151 24 1,590 165 4.2 2.0 N Canada 31 9.971 3 649.8 8 21,130 26 836" 27,170" 11 4.5 3.6 Central African Republic 4 623 6 1.0 166 280 183 4 1.160" 182 2.5 1.1 Chad 8 1.284 6 1.5 153 200 195 7 870 190 0.6 -2.1 Chile 15 757 20 69.8 43 4,590 73 138 9,100 73 5.4 4.0 China 1,262 9.598 135 1,062.9 7 840 141 4.951 3,920 124 7.9 7.2 Hong Kong, China 7 . .. 176.2 23 25.920 13 174 25,590 17 10.5 9.2 Colombia 42 1,139 41 85.3 40 2,020 102 256 6,060 94 2.8 1.0 Congo, Dem. Rep. 51 2.345 22 . .. Congo, Rep. 3 342 9 1.7 151 570 153 2 570 205 7.9 4.9 Costa Rica 4 51 75 14.5 77 3,810 78 30 7,980 80 1.7 -0.5 C6te dIlvoire 16 322 50 9.6 85 600 148 24 1,500 170 -2.3 -4.9 Croatia 4 57 78 20.2 62 4,620 72 35 7.960 81 3.7 3.6 Cuba 11 111 102 .. ,,. e,,, Czech Republic 10 79 133 53.9 45 5,250 68 142 13,780 55 2.9 3.0 Denmark 5 43 126 172.2 24 32,280 8 145 27,250 10 2.9 2.6 Dominican Republic 8 49 173 17.8 70 2,130 97 48 5,710 97 7.8 6.0 Ecuador 13 284 46 15.3 75 1,210 127 37 2,910 140 2.3 0.4 Egypt. Arab Rep. 64 1.001 64 95.4 38 1,490 120 235 3,670 128 5.1 3.1 El Salvador 6 21 303 12.6 79 2,000 103 28 4,410 117 2.0 0.0 Eritrea 4 118 41 0.7 178 170 200 4 960 188 -8.2 -10.6 Estonia 1 45 32 4.9 113 3,580 82 13 9,340 71 6.4 7.8 Ethiopia 64 1,104 64 6.7 99 100 206 43 660 202 5.4 3.0 Finland 5 338 17 130.1 28 25,130 16 127 24,570 23 5.7 5.5 France 59 552 107 1,438.3 5 24,'090h 23 1,438 24,420 24 3.1 2.6 Gabon 1 268 5 3.9 122 3,190 88 7 5,360 103 2.0 -0.6 Gambia. The 1 11 130 0.4 191 340 173 2" 1,620"- 164 5.6 2.3 Georgia 5 70 72 3.2 134 630 146 13 2.680 144 1.9 1.9 Germany 82 357 230 2,063.7 3 25,120 17 2,047 24,920 20 3.0 2.9 Ghana 19 239 85 6.6 102 340 173 37" 1,910"1 159 3.7 1.3 Greece 11 132 82 126.3 30 11.960 47 178 16,860 48 4.3 4.1 Guatemala 11 109 105 19.2 67 1,680 ill 43 3,770 126 3.3 0.6 Guinea 7 246 30 3.3 132 450 159 14 1,930 158 2.0 -0.3 Guinea-Bissau 1 36 43 0.2 201 180 197 1 710 200 7.5 5.2 Haiti 8 28 289 4.1 121 510 156 12" 1,470" 172 1.1 -0.9 Hon duras 6 112 57 5.5 108 860 138 15 2,400 150 4.8 2.2 1.1 0 Population Surface Population Gross national Gross national PPP gross national Gross area density Income Income per capita Income domestic product Per Per thousand people capita capita millions sqt. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growtir 2000 2000 2000 20001 2000 2000' 2000 2000 2000 2000 1999-2000 1999-2000 Hungary 10 93 109 47.2 -51 4,710 71 --120 11,990 59 5.2 5.6 India 1,016 3,287 342 454.8 12 450 159 2,375 2,340 153 3.9 2.0 Indonesi'a 210 1,905 116 119.9 32 570 153 596 2,830 141 4.8 3.1 Iran, Islamic Rep. 64 1,633 39 106.7 34 1,680 111 376 5,910 95 5.4 3.9 Iraq 23 438 53 .. ... -. - ---- Ireland 4 70 55 86.0 39 22,660 24 97 25,520 18 11.5 10.3 Israel 6 21 302 104.1 35 16,710 36 121 19,330 37 6.0 3.8 Italy 58 301 196 1,163.2 6 20,160 30 1,354 23,470 28 2.9 2.8 Jamaica 3 11 243 6.9 98 2,610 96 9 3,440 135 0.8 -0.9 Japan 127 378 348 4,519.1 2 35,620 5 3,436 27,080 12 2.4 2.2 1 Jordan 5 89 55 8.4 93 1,710 110 19 3,950 123 3.9 0.8 Kazakhstan 15 2,725 6 18.8 68 1,260 125 82 5,490 101 9.6 10.0 Kenya 30 580 53 10.6 82 350 172 30 1,010 185 -0.2 -2.5 Korea, Dem. Rep. 22 121 185 .d . Korea, Rep. 47 99 479 421.1 13 8,910 54 818 17,300 46 8.8 7.8 Kuwait 2 18 111 35.8 53 18,030 31 37 18,690 39 1.7 -1.4 C Kyrgyz Republic 5 200 26 1.3 158 270 184 13 2,540 149 5.0 3.9 i 3. Latvia 2 65 38 6.9 97 2,920 93 17 7.070 85 6.6 8.3 ( Lebanon 4 10 423 17.4 71 4,010 77 20 4,550 113 0.0 -1.3 5 Lesotho 2 30 67 1.2 163 580 151 5- 2,590 146 3.8 2.5 R Liberia 3 Ill 32_ ..d. . .. Libya 5 1,760 3 . Lithuani'a 4 65 57 10.8 81 2,930 92 26 6,980 87 3.9 4.0 Macedonia, FYR 2 26 80 3.7 128 1,820 108 10 5,020 108 4.3 3.6 Madagascar 16 587 27 3.9 124 250 188 13 820 191 4.8 1.6 Malawi 10 118 110 1.7 150 170 200 6 600 203 1.7 -0.4 Malaysia 23 330 71 78.7 42 3.380 84 194 8,330 77 8.3 5.7 Mali 11 1,240 9 2.5 138 240 190 8 780 195 4.5 2.1 Mauritania 3 1,026 3 1.0 170 370 167 4 1,630 163 5.2 1.7 Mauritius 1 2 584 4.4 119 3,750 80 12 9,940 70 8.0 6.9 Mexico 98 1.958 51 497.0 11 5,070 69 861 8,790 76 6.9 5.3 Moldova 4 34 130 1.4 157 400 162 10 2,230 154 1.9 2.1 Mongolia 2 1,567 2 0.9 172 390 164 4 1,760 161 1.1 0.3 Morocco 29 447 64 33.9 55 1,180 128 99 3,450 134 0.9 -0.8 Mozambique 18 802 23 3.7 127 210 193 14 800, 193 1.6 .0.7 Myanmar 48 677 73 -------- ---- ---- Namibia 2 824 2 3.6 130 2,030 101 11 e 6,410 89 3.9 1.6 Nepal 23 147 161 5.6 107 240 _190 32 1,370 176 6.5 3.9 Netherlands 16 42 470 397.5 14 24,970 18 412 25.850 15 3.5 2.8 New Zealand 4 271 14 49.8 48 12,990 45 71 18.530 41 2.5 2.0 Nicaragua 5 130 42 2.1 145 400 162 11 2,080 156 4.3 1.6 Niger 11 1,267 9 1.9 148 180 197 8- 740 199 0.1 .3.2 Nigeri'a 127 924 139 32.7 56 260 186 102 800 193 3.8 1.3 Norway 4 324 --15 155.1 26 34,530 6 133 29.630 6 2.3 1.6 Oman 2 212 11 . . . . . Pakistan 138 796 179 61 0 44 440 161 257 1,860 160_ 4.4 1.9 Panama 3 76 38 9.3 87 3,260 86 16 5,6801 98 2.7 1.0 Papua New Guinea 5 463 11 3.6 129 700 J 144 11ll 2,180 155 0.3 -2.1I Paraguay 5 407 14 7.9 96 1.440 122 24 e 4,450 e 115 -0.3 -2.8 Peru 26 1,285 20 53.4 46 2,080 100 120 4,660 111 3.1 1.4 Philippines 76 300 253 78.8 41 1,040 131 319 4,220 120 4.0 2.1 Poland 39 323 127 161.8 25 4.190 75 348 9,000 74 4.0 4.0 Portugal 10 92 109 111.3 33 11.120 49 170 16,990 47 3.3 3.1 Puerto Rilco 4 9 442 . ....... ... Romania 22 238 97 37.4 52 1,670 113 143 6,360 90 1.6 1.7 Russian Federation 146 17,075 9 241.0 19 1.660 114 1,165 8,010 79 8.3 8.9 I ~1.1 Population Surfaceo Population Gross natlonal Gross national PPP gross national Gross area density Income Income per capita Income domestic product Per Per thousand people capita capita millions sq. km per sq km $ billions Reek $ Rack $ billions $ Reek % growth % growth 2000 2000 2000 2000k 2000 2000r 2000 2000 2000 2000 1999-2000 1999-2000 Rwanda 9 26 345 2.0 147 230 192 8 930 189 5.6 3.1 Saudi Arabia 21 2,150 10 149.9 27 7,230 61 236 11,390 60 4.5 1.8 Senegal 10 197 49 4.7 116 490 157 14 1,480 171 5.6 2.9 Sierra Leone 5 72 70 0.6 180 130 204 2 480 207 7.0 4.9 Singapore 4 1 6,587 99.4 37 24,740 19 100 24,910 21 9.9 8.1 Slovak Republic 5 49 112 20.0 66 3,700 81 60 11,040 62 2.2 2.1 Slovenia 2 20 99 20.0 65 10,050 50 34 17.310 45 4.6 4.5 Somalia 9 638 14 .. ... . ... South Africa 43 1,221 35 129.2 29 3,020 91 392 9,160 72 3.1 1.4 20 Spain 39 506 79 595.3 10 15,080 38 760 19,260 38 4.1 3.9 Sri Lanka 19 66 300 16.4 73 850 140 67 3.460 133 6.0 4.3 U) Sudan 31 2.506 13 9.6 84 310 175 47 1,520 169 8.3 6.4 co Swaziland 1 17 61 1.5 156 1,390 123 5 4,600 112 2.6 0.0 Sweden 9 450 22 240.7 20 27,140 11 213 23.970 26 3.6 3.4 Switzerland 7 41 182 273.8 17 38.140 3 219 30.450 5 3.0 2.4 E) Syrian Arab Republic 16 185 88 15.1 76 940 135 54 3,340 136 2.5 0.0 o Tajikistan 6 143 44 1.1 165 180 197 7 1,090 183 8.3 8.1 > Tanzania 34 945 38 9.0 88 270 184 18 520 206 5.1 2.7 0) o Thailand 61 513 119 121.6 31 2.000 103 384 6,320 92 4.3 3.5 o Togo 5 57 83 1.3 159 290 178 6 1,410 175 -0.7 -3.7 3 ~Trinidad and Tobago 1 5 254 6.4 104 4,930 70 11 8,220 78 4.8 4.1 o Tunisia 10 164 62 20.1 63 2,100 99 58 6,070 93 4.7 3.5 0 CN Turkey 65 775 85 202.1 22 3,100 90 459 7.030 86 7.2 5.6 Turkmenistan 5 488 11 3.9 123 750 143 20 3.800 125 17.6 15.3 Uganda 22 241 113 6.7 100 300 176 27 1,210 e 178 3.5 0.8 Ukraine 50 604 85 34.6 54 700 144 183 3,700 127 5.8 6.7 United Arab Emirates 3 84 35 . ... . ... United Kingdom 60 243 248 1,459.5 4 24,430 21 1,407 23,550 27 3.1 2.7 United States 282 9,629 31 9,601.5 1 34,100 7 9.601 34,100 3 4.2 3.0 Uruguay 3 176 19 20.0 64 6,000 66 30 8,880 75 -1.3 -2.0 Uzbekistan 25 447 60 8.8 89 360 171 58 2,360 151 4.0 2.5 Venezuela, RB 24 912 27 104.1 36 4,310 74 139 5,740 96 3.2 1.2 Vietnam 79 332 241 30.4 59 390 164 157 2,000 157 5.5 4.1 West Bank and Gaza 3 . .. 4.9 114 1.660 114 ... . -6.4 -10.3 Yemen, Rep. 18 528 33 6.6 103 370 167 14 770 197 5.1 2.4 Yugoslavia, Fed. Rep. 11 102 108 10.0 83 940 135 ... . 5.0 4.9 Zambia 10 753 14 3.0 137 300 176 8 750 198 3.5 1.3 Zimbabwe 13 391 33 5.9 106 460 158 32 2,550 148 -4.9 -6.7 .1 . :s. . p --~~~~~~~~~~~~~~~~~~~1: - pi p*K Low Income 2,460 33,740 76 997 410 4,809 1,980 4.2 2.2 Middle Income 2,695 67,751 40 5,319 1,970 15,196 5,680 5.6 4.6 Lower middle income 2,048 44.421 47 2,324 1,130 9,359 4,600 6.3 5.4 Upper middle income 647 23,330 28 3.001 4,640 5,915 9,210 5.1 3.7 Low & middle Income 5,154 101,491 52 6,315 1.230 19,980 3,910 5.4 3.9 East Asia & Pacific 1,855 16,385 116 1,962 1,060 7,609 4,130 7.4 6.4 Europe & Central Asia 474 24.217 20 953 2.010 3.140 6,670 6.3 6.2 Latin America & Carib. 516 20,459 26 1,895 3,670 3.624 7.080 3.8 2.3 Middle East & N. Africa 295 11,023 27 618 2,090 1,545 5,270 4.0 2.0 South Asia 1,355 5,140 283 595 440 2,984 2,240 4.2 2.3 Suba-Saharan Africa 659 24,267 28 310 470 1,044 1,600 3.1 0.6 High Income 903 32,315 29 24,994 27,680 24.793 27,770 3.5 2.8 Europe EMU 304 2,569 120 6,604 21,730 7,117 23,600 3.4 3.1 a. PPP is porches ng power parity: see Definitions. b. Caiculated using the World Bank Atias method. C. Estimate does not account for recent refugee flews, d. Estimated to be low income ($755 or less). a. The estimate is based on regression: others are extrapolated from the latest international Comparison Programme benchmark estimates. f. Includes Taiwan, Chine; Macso, Chine: end Hong Kong. Chine. g. Estimated to be lower middle income l$756-2,995). h. GNI and GNI per capita estimates inclode the French oversees cepartments of French Guiana, Guadeloupe, Martiniqoe, and Reunion. I. Estimated to be upper middle income ($2,996-9,265). j. Included under lower-middle income economies in calculating the aggregates based on earlier date. k. Data refer to mainland Tanzania only. I. Estimated to be high income ($9,266 or more). 1.1 S About the data Definitions Population, land area, income, and output are parison of real values over time. The PPP con- * Population is based on the de facto definition basic measures of the size of an economy. They version factors used here are derived from price of population, which counts all residents also provide a broad indication of actual and surveys covering 118 countries conducted by the regardless of legal status or citizenship- potential resources. Therefore, population, land International Comparison Programme (ICP). For except for refugees not permanently settled in area, income-as measured by gross national 62 countries data come fromn the most recent the country of asylum, who are generally income (GNI)-and output-as measured by round of surveys, completed in 1996; the rest considered part of the population of their gross domestic product (GDP)-are used are from the 1993 round and have been extrapo- country of origin. The values shown are midyear throughout the World Development Indicatorsto lated to the 1996 benchmark. Estimates for estimates for 2000. See also table 2.1. normalize other indicators. countries not included in the surveys are derived * Surface area is a country's total area, Population estimates are generally based on from statistical models using available data. All including areas under inland bodies of water extrapolations from the most recent national economies shown in the World Development and some coastal waterways. * Population census. For further discussion of the Indicators are ranked by size, including those density is midyear population divided by land measurement of population and population that appear in table 1.6. Ranks are shown only area in square kilometers. * Gross national growth see About the data for table 2.1 and in table 1.1. (The World Bank Atlas includes a Income (GNI) is the sum of value added by all Statistical methods. table comparing the GNI per capita rankings resident producers plus any product taxes (less 21 The surface area of a country or economy in- based on the Atlas method with those based on subsidies) not included in the valuation of cludes inland bodies of water and some coastal the PPP method for all economies with available output plus net receipts of primary income waterways. Surface area thus differs from land data.) No rank is shown for economies for which (compensation of employees and property area, which excludes bodies of water, and from numerical estimates of GNI per capita are not income) from abroad. Data are in current U.S. gross area, which may include offshore territo- published. Economies with missing data are in- dollars converted using the World Bank Atlas rial waters. Land area is particularly important cluded in the ranking process at their approxi- method (see Statistical methods). * GNI per D for understanding the agricultural capacity of an mate level, so that the relative order of other capita is gross national income divided by -a economy and the effects of human activity on economies remains consistent. Where available, midyear population. GNI per capita in U.S. 3 the environment. (For measures of land area and rankings for small economies are shown in the dollars is converted using the World Bank Atlas data on rural population density, land use, and World Bank Atlas. In 2000 Luxembourg and method. * PPP GNI is gross national income agricultural productivity see tables 3.1-3.3.) Liechtenstein were judged to have the highest converted to international dollars using Recent innovations in satellite mapping tech- GNI per capita in the world, purchasing power parity rates. An international niques and computer clatabases have resulted Growth in GDP and growth in GDP per capita dollar has the same purchasing power over GNI in more precise measurements of land and wa- are based on GDP measured in constant prices. as a U.S. dollar has in the United States. ter areas. Growth in GDP is considered a broad measure * Gross domestic product (GDP) is the sum of GNI (gross national product, or GNP, in the of the growth of an economy, as GDP in con- value added by all resident producers plus any 1968 SNA terminology) measures the total do- stant prices can be estimated by measuring the product taxes (less subsidies) not included in mestic and foreign value added claimed by resi- total quantity of goods and services produced the valuation of output. * GDP per capita is dents. GNI comprises GDP plus net receipts of in a period, valuing them at an agreed set of gross domestic product divided by midyear primary income (compensation of employees and base year prices, and subtracting the cost of population. Growth is calculated from constant property income) from nonresident sources. intermediate inputs, also in constant prices. For price GDP data in local currency. The World Bank uses GNI per capita in U.S. further discussion of the measurement of eco- dollars to classify countries for analytical nomic growth see About the data for table 4.1. D purposes and to determine borrowing eligibility. Data sources See the Users guide for definitions of the income Population estimates are prepared by World groups used in the World Development Bank staff from a variety of sources (see Data Indicators. For further discussion of the sourcesfortable 2.1). The data on surface and usefulness of national income as a measure of land area are from the Food and Agriculture productivity or welfare see About the data for Organization (see Data sources for table 3.1). tables 4.1 and 4.2. GNI, GNI per capita, GDP growth, and GDP per l When calculating GNI in U.S. dollars from GNI capita growth are estimated by World Bank staff reported in national currencies, the World Bank based on national accounts data collected by follows its Atlas conversion method. This in- Bank staff during economic missions or volves using a three-year average of exchange reported by national statistical offices to other rates to smooth the effects of transitory ex- international organizations such as the change rate fluctuations. (For further discussion Organisation for Economic Co-operation and of the Atlas method see Statistical methods.) Development. Purchasing power parity Note that growth rates are calculated from data conversion factors are estimates by World Bank i in constant prices and national currency units, staff based on data collected by the not from the Atlas estimates. International Comparison Programme. Because exchange rates do not always reflect _n international differences in relative prices, this table also shows GNI and GNI per capita esti- mates converted into international dollars us- ing purchasing power parity (PPP) rates. PPP rates provide a standard measure allowing com- parison of real price levels between countries, just as conventional price indexes allow com- Millennium Development Goals: 1.2 eradicating poverty and improving lives Eradicate extreme poverty Achieve universal Promote gender Reduce chld Improve maternal health and hunger primary education equality mortality Maternal Share Of poorest Child malnutrition Ratio of female to morta lity ratio Births atteoded quintile in weight for age Net primary male enrollments Under-five per 100.,000 by skilled national income % of enrollment in primary and mortality rate live births hrealthr staff or consumption ch Idren ratio secondary school' per 1,000 modeled % ~~~under 5 % live births estimates % of total 1986-2000, 1990 2000 1.990 2.998 1990 1,998 2.99 2000 1995 11990) 1999 Afghanistan 49 . .. 50 .. 257 279 ,. 9 Albania ... 8 . .. 90 .. 42 .31 Algeria 7.0 9 13 93 94 80 91 55 39 150 77 Angola .. 20 41 .. 57 .. 81 .. 208 1,300 1 7 Argentina . .. 5 .. 107 .. 100 28 22 85 Armenia 5.5 .. 3 . .. .. 24 17 29 .. 96 Australia 5.9 .. 0 99 .. 96 .. 10 7 6 100 Austria 6.9 . .. 90a 88 90 92 9 6 11 Azerbaijan 6.9 . 1 7 .. 96 94 95 .. 21 37 .. 99 22 Bangladiesh 8.7 66 61 64 104 72 95 136 83 600 7 14 Belarus 11.4 .. . . . , 96 16 14 33 Belgium 8.3 . .. 97 .. 97 99 9 7 8 m Benin . .. 29 49 . .. 61 185 143 880 38 60 Bolivia 4.0 11 8 91 97 89 .. 120 79 550 43 59 Bosnia and Herzegovina .. . . . . . . 21 18 15 E) Botswana . ., 1 7 93 81 107 102 62 99 480 79 oL Brazil 2.2 7 6 86 98 .. 100 58 39 260 .. 88 > Bulgaria 10.1 . .. 86 93 94 93 19 16 23 .. 99 o Burkina Faso 4.6 .. 34 27 34 61 66 229 206 1,400 30 27 0 Burundi 5.1 . .. 52 38 82 81 180 176 1.900 20 Cambodia 6.9 .. 47 .. 14 .. 79 119 120 590 47 31 C4d o Cameroon 4.6 15 22 . .. 82 81 141 155 720 58 55 0 Canada 7.5 . .. 97 96 94 95 8 7 6 Central African Republic 2.0 .. 23 53 53 61 . .. 152 1.200 66 Chad . .. 39 .. 55 .. 53 209 188 1.500 15 11 Chile 3.3 .. 1 88 88 98 95 20 12 33 .. 100 China 5.9 17 10 97 91 81 89 47 39 60 Hong Kong, China ... . .. .... ..... 100 Colombia 3.0 10 8 69 87 104 101 40 23 120 94 Congo. Dam. Rep. . .. 34 54 32 69 80 155 163 940 Congo, Rep. .. . . . . 88 . .. 106 1.100 Costa Rica 4.5 3 5 86 .. 96 .. 16 13 35 97 C6te dIlvoire 7.1 .. 24 47 59 .. 69 150 180 1,200 50 47 Croatia 8.8 .. 1 79 .. 97 97 13 9 18 Cuba .. . . 92 97 101 97 13 9 24 Czech Republic 10.3 1 . .. 90 94 97 12 7 14 Denmark 9.6 . .. 98 101 96 98 9 6 15 Dominican Republic 5.1 10 6 .. 87 .. 103 59 47 110 92 96 Ecuador 5.4 .. . . 97 97 98 51 34 210 56 Egypt, Arab Rep. 9.8 10 4 .. 92 78 88 85 52 170 37 56 El Salvador 3.3 15 12 75 81 100 95 54 35 180 90 90 Eritrea . . 44 24 34 82 78 140 103 1,100 Estonia 7.0 . .. 94 96 99 96 17 11 80 Ethiopia 7.1 48 47 .. 35 68 61 211 179 1.800 8 Finland 10.0 . .. 99 99 105 100 7 5 6 France 7.2 . .. 101 100 98 95 10 6 20 Gabon . .. . .. . .. 95 94 89 620 79 Gambia, The 4.0 .. 26 51 61 64 80 127 ., 1,100 44 Georgia 6.1 .. 3 . .. 94 95 .. 21 22 Germany 8.2 . .. 84 87 94 .. 9 6 12 Ghana 5.6 30 25 . .. . .. 119 112 590 55 44 Greece 7.5 . .. 94 95 93 95 11 8 2 Guatemala 3.8 .. 24 .. 3 . .. 68 49 270 30 Guinea 6.4 .. 23 .. 46 43 56 215 161 1,200 31 35 Guinea-Bissau 2.1 . .. . .. . .. 246 211 910 Haiti .. 27 28 22 80 . .. 131 i11 1.100 78 Honduras 2.2 18 25 89 .. 103 .. 65 44 220 47 55 Eradicate exctreme poverty Achieve universal Promote gender Reduce child Improve maternai health and hunger primary education equality mortality Maternal Share of poorest Child malnutrition Rat,o of female to mortality ratio Births attended quintile in weight for age Net primary male enrollments Under-five per 100,000 by ski, eo national income % of enrollment in primary and mortality rate live births health staff or consumption children ratio' secondary school' per 1,000 modeled % ~~~under 5 % live births estimates % of total 1986-2000, 1990 2000 ±990 1998 1990 1998 199 2000 1995 199 199 Hungary 10.0 2 .. 91 82 96 96 17 11 23 India 8.1 64 47 ..68 75 112 88 440 44 Indonesia 9.0 34 97 91 .. 83 51 470 47 43 Iran, Islamic Rep. ... 11 99 80 .. 72 41 130 78 Iraq .. 12_ - 79 80 . 75 75 50 121 370 50 Ireland 6.7 . .. 91 104 99 97 9 7 9 Israel 6.1 .. . . 95 99 94 12 7 8 Italy 8.7 .. . . 101 95 94 10 7 11 Jamaica 6.7 5 4 96 92 97 99 32 24 120 92 95 Japan 10.6 .. 100 102 96 96 6 5 12 100 ..23 Jordan 7.6 6 5 66 64 93 96 34 30 41 87 9 7 Kazakhstan 6.7 4 97 34 28 80 .. 98 0 Kenya 5.6 22 . ..96 97 120 1,300 50 44 Korea, Dem. Rep. 32 . .35 90 35. . Korea, Rep. 7.5 .. 104 .. 93 . 10 20 95 .. a Kuwait 2 45 .. 97 97 16 13 25 .. 98 Kyrgyz Republic 7.6 .. 11 85 100 98 41 35 80 .. 98 CD Lao PDR 7.6 40 61 76 75 79 1 70 .. 650.. . 3e Latvia 7.6 83 94 96 98 18 17 70.. . Leba non 3 78 .. 100 4 0 30 130 95 95 E Leso'tho 2.8 16 16 73 60 124 112 148 143 530 50 .. 0, Liberia . i 41 .. 71 . 185 .... . Libya 5 96 .. 100 42 32 120 76 94 Lithuani'a 7.8 94 93 96 14 11 27 Macedonia, FYR 6 94 96 94 93 33 17 17 88 Madagascar 6.4 41 40 63 96 170 144 580 57 47 Malawi 28 30 50 79 . 234 193 580 50 Malaysia 4.4 25 20 98 98 99 21 11 39 Mali 4.6 2 7 21 42 57 66 268 218 630 .. 24 Mauritania 6.4 48 23 60 67 90 .. 164 870 40 58 Mauritius ... 15 95 93 98 98 25 20 45 92 Mexi-co 3.5 17 8 100 102 96 97 46 36 65 Moldova 5.6 . .. . .. 103 .. 25 22 65 Mongolia 7.3 12 13 .. 85 107 .. 102 71 65 100 Morocco 6.5 10 58 79 67 78 83 60 390 31 Mozambique 6.5 . - 6 47 -41 73 72 238 200 980 .. 44 Myanmar .. 32 28 . .. 95 97 130 126 170 94 57 Namibia 26 .. 89 86 111 103 84 112 370 68 - Nepal 7.6 47 . .. 53 69 138 105 830 8 10 Netherlands 7.3 95 100 93 92 8 7 10 100 New Zealand 101 96 11 7 15 Nicaragua 2.3 12 72 63 41 250 .. 65 Niger 2.6 43 40 25 26 54 64 335 248 920 15 18 Nigeria 4.4 35 27 . .. 76 .. 136 153 1,100 31 Norway 9.7 .. 100 102 97 96 9 5 9 Oman 24 23 70 66 86 94 30 22 120 87 Pakistan 9.5 40 38 47 138 110 200 40 Panama 3.6 6 8 91 96 .. 24 100 Papuas New Guinea 4.5 85 77 79 108 75 390 40 53 Paraguay 1.9 4 93 92 95 96 37 28 170 71 71 Peru 4.4 11 8 103 93 94 75 41 240 78 56 Philippines 5.4 34 32 97 62 39 240 .. 56 Poland 7.8 ..97 96 22 11 12 Portugal .... 7.3 ..102 _108 99 97 15 8 12 98 100 Puerto Ri'co . - ..30.. - Romani'a 8.0 6 77 94 95 96 36 23 60 Russian Federation 4.4 .. 3 .. . . 74 21 19 75 .. 99 Eradicate extreme poverty Achieve universai Promote gender Reduce chld Improve maternal health and hunger primary education equality mortality Maternal Share of poorest Child malnutrition Ratio of female to mortality ratio Births attenoed quintile in weight for age Net primary male enrollments Under-fioe per 100,000 by skilled national income hO of enrollment in primary and mortality rate live births health staff or corsumption children ratio' secondary schoolF per 1,000 modeled hO under 5 hO line births estimates % of total 1986&2000' 1990 2000 1990 1.998 1.990 1998 1990 2000 1.999 1.990 1.999 Rwanda 9.7 29 27 66 91 98 100 .. 203 2,300 26 Saudi Arabia ... . 59 59 82 89 45 23 23 88 91 Senegal 6.4 22 13 48 59 69 78 148 129 1.200 42 47 Sierra Leone 1.1 29 .. . . 67 .. 323 267 2.100 Singapore ... . . . 89 .. 8 6 9 ., 100 Slonak Republic 11.9 . .. . .. 98 97 14 10 14 Slonenia 9.1 .. . . 94 97 97 10 7 17 Somalia ... 26 . .. .. 215 195 South Africa 2.9 .. 9 103 .. 103 102 73 79 340 .. 84 24 Spain 7.5 . .. 103 105 99 98 9 6 8 SriLantka 8.0 .. 33 .. 102 99 99 23 18 60 85 95 an Sudan . 34 .. 46 75 86 125 .. 1,500 69 to Smaziland 2.7 .. 88 77 .. 96 115 119 .. 55 Sweden 9.6 .. 100 103 97 110 7 4 8 11 Switzerland 6.9 . . 84 94 92 91 8 6 8 QE Syrian Arab Republic . 13 98 93 82 88 59 29 200 64 Co Tajikistan 8.0 .. -18 .. . . . . 30 120 > Tanzania 6.8 29 29 51 48 97 . 1 78 149 1.100 44 35 5, 0 Thailand 6.4 .. . . 77 94 96 41 33 44 71 o Togo .. 25 25 75 88 59 67 142 142 980 32 51 Trinidad and Tobago 5.5 . .. 91 93 98 100 24 19 65 .. 99 Tunisia 5.7 10 4 94 98 82 93 52 30 70 80 82 ~" Turkey 5.8 .. 8 89 100 77 .. 67 43 55 77 81 Turkmenistan 6.1 .. . . . . . . 43 65 Uganda 7.1 23 26 .. . . 88 165 161 1.100 38 Ukraine 8.8 . .. . .. 106 .. 16 45 United Arab Emirates 7 94 83 96 96 .. 10 30 96 United Kingdom 6.1 . .. 97 102 97 103 9 7 10 100 United States 5.2 .. 1 96 95 95 83 10 9 12 99 Uruguay 5.4 6 4 91 92 .. 108 24 17 50 Uzbekistan 4.0 .. 19 .. . . . . 27 60 .. 98 Venezuela. RB 3.0 8 4 88 .. 101 .. 27 24 43 97 Vietiraml 8.0 45 37 .. 97 .. 88 54 34 95 95 77 West Bank and Gaza ... 15 . ... .. 53 26 Yemen. Rep. 7.4 30 46 .. 61 .. 47 130 95 850 16 22 Yugoslavia. Fed. Rep. ... 2 69 .. 96 96 26 15 15 .. 93 Zambia 3.3 25 24 .. 73 .. 89 194 186 870 41 47 Zimbabwe 4.7 12 13 . .. 96 .. 77 116 610 62 84 Low Income . .. . .. 79 123 115 43 Middie Income 13 95 92 84 90 49 39 Lower middle income 18 11 96 91 82 88 50 41 Upper middle income .. 91 97 93 99 48 35 Low & middle income .. . . 82 86 88 84 East Asia & Pacific 19 13 98 91 84 89 55 45 Europe & Central Asia . .. . .. 90 88 34 25 Latin America & Carib. .. 9 89 97 .. 99 49 37 Middle East & N. Africa .. 15 .. 83 79 84 72 54 South Asia 64 49 .. . . 78 121 96 39 Sub-Saharan Africa . .. . .. 79 80 .. 162 High Income .. 98 .. 96 92 9 7 Europe EMU .. 93 .. 97 96 10 6 a Data are for the most recent year availab e. See table 2.8 for survey year and whether share is based on income or consamption expenditure. b. Net enrollment ratios exceeding 100 percent indicate discrepancies between estimates of toe scnool-age population and reported enrollment data. c. Break is series between 1997 sod 1998 is due to change from ISCED76 to ISCED97. 6* MMAIT 1.2 About the data Definitions This table and the following two provide Progress toward achieving universal * Share of the poorest quintile In national In- indicators for 17 of the 18 targets specified by primary education has commonly been come or consumptlon is the share of consump- the Millennium Development Goals (MDGs). measured by net enrollment ratios. However, tion or, in some cases, income that accrues Each of the eight goals comprises one or more there are sometimes large differences to the poorest 20 percent of the population. targets and each target has associated with it between official enrollments and actual * Child malnutrition is the percentage of chil- several indicators by which progress toward the attendance, and even school systems with dren under five whose weight for age is less target can be monitored. Most of the targets high average enrollment ratios may have poor than minus two standard deviations from the are set as a value of a specific indicator to be completion rates. median for the international reference popula- attained by a certain date. In some cases the Eliminating gender disparities in education tion ages 0-59 months. The reference popula- target value is set relative to a level in 1990. In would help to increase the status and capabili- tion, adopted by the World Health Organization others it is set at an absolute level. Some of ties of women. The ratio of girls' to boys' enroll- in 1983, is based on children from the United the targets for goals 7 and 8 have not yet been ment provides an imperfect rneasure of the rela- States, who are assumed to be well nourished. quantified. tive accessibility of schooling for girls. With a * Net primary enrollment ratio is the ratio of The indicators in the table are taken from target date of 2005, this is the first of the tar- the number of children of official school age goals 1-5. Goal 1 has two targets between gets to fall due. (as defined by the education system) enrolled 25 1990 and 2015: to reduce by half the The targets for reducing under-five and in school to the number of children of official £ proportion of people whose income is less maternal mortality are among the most school age in the population. * Ratiooffemale 0 than $1 a day and to reduce by half the challenging of the Millennium Development to male enrollments In primary and secondary proportion of people who suffer from hunger. Goals. Although estimates of under-five school is the ratio of the number of female stu- 0 Estimatesofpovertyratescan befound intable mortality rates are available at regular dents enrolled in primary and secondary school C0 2.6. The indicator shown here, the share of the intervals for most countries, maternal to the number of male students. Under-five m poorest quintile in national income or mortality is difficult to measure, in part mortality rate is the probability that a newbom (D consumption, is a distributional measure. because it is a relatively rare event. baby will die before reaching age five, if sub- 3 Countries with less equal income distributions In addition to the indicators shown in these ject to current age-specific mortality rates. The C will have a higher rate of poverty for a given tables, most of the 48 indicators included in probability is expressed as a rate per 1,000. average income. There is no single indicator that the Millennium Development Goals can be found * Maternal mortality ratio is the number of a captures the concept of suffering from hunger. elsewhere in the World Development Indicators. women who die durng pregnancy and childbirth, Child malnutrition is a symptom of inadequate Table 1.2a provides an index for locating the per 100,000 live births. The data shown here food supply, lack of essential nutrients, illnesses indicators for the first five goals in other tables. have been collected in various years and ad- thatdepletethesenutrients,andundernourished More information about data collection justed to a common 1995 base year. * Births mothers who give birth to underweight children. methodologies and limitations can be found in attended by skilled health staff are the per- Table 1.2a About the data for those tables. centage of deliveries attended by personnel trained to give the necessary supervision, care, Location of indicators for goals 1- 5 and advice to women during pregnancy, labor, Goal 1. Eradicate extreme poverty and hunger and the postpartum period, to conduct deliver- 1. Proportion of population below $1 a day (table 2.6) 2. Poverty gap ratio (table 2.6) 3. Share of poorest quintile in national consumption (table 2.8) Data sources 4. Prevalence of underweight in children (under five years of age) (table 2.18) The indicators here, and where they appear throughout the rest of the book, have been 5. Proportion of population below minimum level of dietary energy consumption (table 2.18) compiled by World Bank staff from primary i Goal 2. Achieve universal primary education and secondary sources. More information can 6. Net enrollment ratio in primary education (table 2.12) be found in About the data, Definitions, and 7. Proportion of pupils starting grade 1 who reach grade 5 (table 2.13) Data sources entries that accompany each table in subsequent sections. More informa- I 8. Literacy rate of 15- to 24-year-olds (table 2.14) tion about the Millennium Development GoEIls Goal 3. Promote gender equality and empower women and related indicators can be found at 9. Ratio of girls to boys in primary, secondary and tertiary education (tables 1.2 and 2.12) www.developmentgoals.org. 10. Ratio of literate females to males, among 15- to 24-year-olds (tables 1.5 and 2.14) 11. Share of women in wage employment in the nonagricultural sector (table 2.3) 12. Proportion of seats held by women in national parliament (See women in decision- making positions in table 1.5.) Goal 4. Reduce child mortality 13. Under-five mortality rate (table 2.20) 14. Infant mortality rate (table 2.20) 15. Proportion of one year-old children immunized against measles (table 2.16) Goal 5. Improve maternal health 16. Maternal mortality ratio (table 2.17) 17. Proportion of births attended by skilled health personnel (table 2.17) Millennium Development Goals: 1.3 protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustalnablity partnership for development Access to Incidence of co, Access to an) improved HlV prevalence tuberculosis emissions improved water sani tation Telephone' ma e female per 100,000 per capita source facilities Unemployment lines per % ages 15-24 % ages 15-24 people metric tons % of population % of population % ages 15-24 1,000 people 1999, 1999, 1999 1990 1995 1990 2000 1990 2000 1999 2000 Afghanistan ... 325 0.1 0.0 .. 13 .. 12 I. Albania 29 2.2 0.5 ......39 Algeria 45 3.2 3.6 94 .. 73 ..57 Angola 1.3 2.7 271 0.5 0.5 .. 38 .. 44 .5 Argentina 0.9 0.3 55 3.4 3.8 .. 79 .. 85 . 213 Armenia ... 58 1.0 0.9 ... .. .152 Australia 0.1 0.OC 8 15.6 17.7 100 100 100 100 14 525 Austria 0.2 0.1 16 7.4 7.9 100 100 100 100 6 467 Azerbaijan . 62 6.4 4.9 ... .. .104 26 Bangladesh 0.0 0.0 c 241 0.1 0.2 91 97 97 53 ..4 Belarus 0.4 0.2 80 9.3 6.0 100 .. . . 269 Ut~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- ---- o Belgium 0.1 0.1 15 10.1 9.9 ... . 23 498 co Benin 0.9 2.2 266 0.1 0.1 .. 63 20 23 ..8 Bolivia 0.1 0.0' 238 0.8 1.5 74 79 55 66 .. 61 Bosnia and Herzegovina ... 87 .. 1.2 ... .. .103 E Botswana 15.8 34.3 702 1.7 2.4 95 .. 61 ... 93 o Brazil 0.7 0.3 70 1.4 1.8 82 87 72 77 18 182 a, Bulgaria ... 46 8.6 5.7 ......33 350 Burkina Faso 2.3 5.8 319 0.1 0.1 53 .. 24 29 ..4 o Burundi 5.7 11.6 382 0.0 0.0 65 .. 89 . ..3 Cambodia 2.4 3.5 560 0.0 0.1 .. 30 . 18 ..2 o Cameroon 3.8 7.8 335 0.1 0.1 52 62 87 92 ..6 0D C4 Canada 0.3 0.1 7 15.4 15.4 100 100 100 100 14 677 Central African Republic 6.9 14.1 415 0.1 0 .1 59 60 30 31 ..3 Chad 1.9 3.0 270 0.0 0.0 .. 27 18 29 .. Chile 0.3 0.1 26 2.7 4.1 90 94 97 97 21 221 China 0.1 0.0' 103 2.1 2.5 71 75 29 38 3 112 Hong Kong, China 0.1 0.0' 91 4.6 5.4 . ... .. 10 583 Col ombia 0.4 0.1 51 1.6 1.7 87 91 82 85 24 169 Congo. Dem. Rep. 2.5 5.1 301 0.1 0.1 .. 45 . 20 ..0 Congo, Rep. 3.2 6.5 318 0.9 0.6 .. 51 . .7 Costa Rica 0.6 0.3 17 1.0 1.4 .. 98 .. 96 12 249 Cote dIlvoire 3.8 9.5 375 1.0 0.9 65 77 49 ... 18 Croatia 0.0' 0.0' 61 3.5 4.5 .. 95 . 100 30 365 Cuba 0.1 0.0' 15 3.0 2.2 .. 95 . 95 .. 44 Czech Republic 0.1 0.0' 19 13.1 11.5 ...... 17 378 Denmark 0.2 0.1 12 9.9 10.1 .. 100 . .. 10 720 Dominican Republic 2.6 2.8 135 1.3 2.5 78 79 60 71 ,. 105 Ecuad or 0.4 0.1 172_ _1.6_ _2.2 ..71 .. 59 24 100 Egypt, Arab Rep. . .. 39 1.4 1.7 94 95 87 94 .. 86 El Sa vador 0.7 0.3 67 0.5 1.0 .. 74 .. 83 13 100 Eritrea ... 272 ... .46 .13 ..8 Estonia ... 61 15.9 12.1 ...... 16 363 Ethiopia 7.5 11.9 373 0.1 0.0 22 24 13 15 ..4 Finland 0.0' 0.0' 12 10.6 10.3 100 100 100 100 22 550 France 0.3 0.2 16 6.3 6.3 ...... 27 579 Gabon 2.3 4.7 289 7.1 2.4 .. 70 . 21 ., 32 Gambia. The 0.9 2.2 260 0.2 0.2 .. 62 . 37 .. 26 Georgia ... 72 2.8 1.0 ..76 .. 99 ..139 Germany 0.1 0.0' 13 11.1 10.1 . .. .. 9 611 Ghana 1.4 3.4 281 0.2 0.2 56 64 60 63 .. 12 Greece 0.1 0.1 22 7.1 8.1 ... . 30 532 Guatemala 1.2 0.9 85 0.6 0.9 78 92 77 85 .. 57 Guinea 0.6 1.4 255 0.2 0.2 4 5 48 5 5 5 8 ..8 Guinea-Bissau 1.0 2.5 267 0.8 0.8 .. 49 . 4 7 ..9 Haiti 4.9 2.9 361 0.2 0.2 4 6 4 6 2 5 2 8 ..9 Honduras 1.4 1.7 92 0.5 0.8 84 90 .. 77 6 46 Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustalnablllty partnership for development AcCess to Incidence of co, Access to en improved HIV prevalence tuberculosis emissions improved water sanitation Telephone, male female per 100.000 per capita source facilities Unempioyment linns per % ages 15-24 %ages 15-24 people metric tons % of population % of population % ages 15-24 1,000 peopie 1999k 1999, ±999 ±990 1999 ±990 2000 1990 2000 1999 2000 Hungary 0.1 0.0 40 5.6 5.8 99 99 99 99 12 372 India 0.4 0.6 185 0.8 1.1 78 88 21 31 32 Indonesia 0.0 c 0.0 282 0.9 1.1 69 76 54 66 ..31 Iran, Islamic Rep. 54 39 4.7 86 95 81 81 149 Iraq __156 2.7 3.7 85 79 29 Ireland 0.1 0.0 15 8 5 10.3 ......9 420 Israel 01 08 74 .1 .. 1 7 482 Italy 0.3 0.2 9 7.0 7.2 ... 33 474 Jamaica 0.6 0.4 8 3.3 4.3 .. 71 .. 84 34199 Japan QQC 0.0 29 8.7 9.0 9 586 2 Jordan 11 3.2 3.0 97 96 98 99 93 Kazakhstan 0.1 ..- 130 15.6 8.2 9 1 99 113 Kenya 6.4 13.0 417 0.2 0.3 40 49 84 86 10 Korea, Dem. Rep.. 176 12.3 10.3 ...460 Korea, Rep. 00'I 0.0 69 5.6 7.8 .. 92 63 14 464 c Kuwait ..31 19.9 26.3 244 Kyrgyz Republic . 130 2.5 1.3 77 . . 100 77 (D -- -- - - - - - -- -- ------ -- - - - - -- --- - - -- --0 Lao PDR 0.0' 0.1 171 0.1 0.1 .. 90 .. 46 8 ' Latvia 0.2 0.1 105 4.8 3.2 . . -23 303 C Lebanon- .. -24 2.5 3.9 .. 100 .. 99 195 ------------------ ~ ~ ~ ~ a Lesotho - 12.1 26.4 542 ..91 92 10 2 -- 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~) Liberia ..271 0.2 0.1 ... .. 2 E Libya . 24 8.8 7.2 71 7297 97 108 Lithuania . 99 5.7 4.2 ... .25 321 Macedonia, FYR 50 5.5 6.1 99 99 255 Madagascar 0.0 0.1 236 0.1 0.1 44 47 36 42 3 Malawi 7.0 15.3 443 0.1 0.1 49 57 73 77 4 Malaysia 0.6 01 11 3.0 5.4 ...199 Mali 1.3 2.1 261 0.0 0.0 55 65 70 69 3 Mauritania 0.4 0.6 241 1.3 1.2 37 37 30 33 7 Mauritius 0.0' 0.0' 68 1.1 1.5 100 100 100 99 .. 235 Mesico 0.4 0.1 39 3.7 3.9 83 86 69 73 3 125 Moldova 0.3 0.1 130 4.8 2.2 100 ... 133 Mongolia ... 205 4.7 3.3 .. 60 30 ..56 Morocco ..119 1.0 1.2 75 82 62 7 5 35 50 Mozambique 6.7 14.7 407 0.1 0.1 .. 60 43 4 Myanmar 1.0 1.7 169 0.1 0.2 64 68 45 46 ..6 Namibia 9.1 19.8 490 .. 0.0 72 77 33 41 ..63 Nepal 0.1 0.2 209 0.0 0.1 66 81 21 27 12 Netherlands 0.2 0.1 10 10.0 10.4 100 100 100 100 7 618 New Zealand 0.1 0.0 6 6.9 7.9 ...... 14 500 Nicaragua 0.2 0.1 88 0.7 0.7 70 79 76 84 ..31 Niger 0.9- -1.5 252 0.1 0.1 53 59 15 20 ..2 Nigeria 2.5 5.1 301 0.9 0.6 49 57 60 63 ..4 Norway 0.1 0.0' 5 7.5 7.6 100 100 .. 10 532 Oman .. 10 7.1 8.8 37 39 84 92 ..89 Pakistan 0.1 0.0' 177 0.6 0.7 84 88 34 61 10 22 Panama 1.6 1.4 54 1.3 2.1 87 94 29 151 Papua New Guinea 0.1 0.2 250 0.6 0.5 42 42 82 82 13 Paraguay 0.1 00' 68 0.5 0.9 63 79 89 95 50 Peru 0.4 0.2 228 1.0 1.1 72 77 64 76 ..64 Philippines 0.0' 0.1 314 0.7 1.0 87 87 74 83 19 40 Poland 39 9.1 8.3 30 282 Portugal 0.6 0.2 53 4.3 5.5 9 430 Puerto Rico 9 3.3 4.6 ... 23 332 Romania 00' 00' 130 6.7 4.1 .. 58 53 20 175 Russian Federation 0.3 0.1 123 13.3 9.8 99 .. 27 218 - Wi) Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustalnablity partnership for development Access to Incidence of co, Access to an improved HIV prevalence tuberculosis emissions improved water sanitation Telephone' male female per 100,000 per capita source facilities Unemployment lines per % ages 15-24 % ages 15-24 people metric tons % of population % of population % ages 15-24 1,000 people 1999k 1999, 1999 1990 1998 1990 2000 1990 2000 1999 2000 Rwanda 5.2 10.6 381 0.1 0.1 .. 41 ..8 - 2 Saudi Arabia ... 45 11.3 14.4 .. 95 .. 100 .. 137 Senegal 0.7 1.6 258 0.4 0.4 72 78 57 70 ..22 Sierra Leone 1.2 2.9 274 0.1 0.1 .. 28 . 28 ..4 Singapore 0.2 0.2 48 13.8 21.0 100 100 100 100 7 484 Slovak Republic 0.0 0.0 28 8.1 7.1 -. 100 .. 100 32 314 Slovenia 0.0 0.0 27 6.1 7.4 100 100 .. 18 386 Somalia ... 365 0.0 0.0 ... .. .2 South Africa 11.3 24.8 495 8.3 8.3 .. 86 .. 86 58 114 28 Spain 0.5 0.2 59 5.5 6.3 ...... 29 421 Sri Lanka 0.00 0.1 59 0.2 0.4 66 83 82 83 28 41 Sudan . . 195 0.1 0.1 67 75 58 62 ..12 Co Swaziland .. 564 0.6 0.4 ... .. .32 'O Sweden 0.1 0.0 4 5.7 5.5 100 100 100 100 14 682 Switzerland 0.4 0.3 9 6.4 5.9 100 100 100 100 6 727 E Syrian Arab Republic ... 85 3.0 3.3 .. 80 .. 90 .. 103 o Tajikistan ... 105 3.7 0.8 ... .. .36 > Tanzania 4.0 8.1 340 0.1 0.1 50 54 88 90 ..5 a) Thailand 1.2 2.3 141 1.7 3.2 71 80 86 96 7 92 o Togo 2.2 5.5 313 0.2 0.2 51 54 37 34 ..9 3 ~Trinidad and Tobago 0.8 0.6 12 13.9 17.4 .. 86 .. 88 25 231 o Tunisia ... 37 1.6 2.4 80 ..76 ...90 a) CN Turkey ... 38 2.6 3.2 80 83 87 91 15 280 Turkmenistan ... 90 6.9 5.7 .. 58 -. 100 ..82 Uganda 3.8 7.8 343 0.0 0.1 44 50 84 75 ..3 Ukraine 1.3 0.8 73 11.5 7.0 ....,. 23 199 United Arab Emirates ... 21 33.0 32.4 ... .. . 391 United Kingdom 0.1 0.0 0 12 9.9 9.2 100 100 100 100 12 589 United States 0.5 0.2 6 19.3 19.8 100 100 100 100 10 700 Uruguay 0.4 0.2 29 1.3 1.8 .. 98 .. 95 24 278 Uzbekistan ... 97 5.3 4.5 .. 85 .. 100 ..67 Venezuela, RB 0.7 0.1 42 5.8 6.7 .. 84 .. 74 26 108 Vietnam 0.3 0.1 189 0.3 0.6 48 56 73 73 ..32 West Bank and Gaza .., 28 . .. .. Yemen, Rep. ... 108 0.7 0.9 66 69 39 45 ..19 Yugoslavia, Fed. Rep. ... 47 12.4 ......... 226 Zambia 8.2 17.8 495 0.3 0.2 52 64 63 78 ..8 Zimbabwe 11.3 24.5 562 1.6 1.2 77 85 64 68 ..18 Low Income 1.1 2.0 229 0.7 1.0 70 76 40 45 23 Middle Income 0.5 0.6 104 2.7 3.5 75 81 47 59 139 Lower middle income 0.2 0.2 110 2.2 3.1 74 80 41 52 116 Upper middle income 1.5 2.2 84 4.1 4.9 .. 87 .. 81 213 Low & middle income 0.8 1.3 163 1.8 2.3 73 79 44 52 84 East Asia & Pacific 0.2 0.2 142 2.0 2.4 70 75 38 47 101 Europe & Central Asia 0.4 .. 85 9.2 6.8 .. 90 ...222 Latin America & Carib. 0.7 0.3 75 2.2 2.6 81 85 72 78 148 Middle East & N. Africa ... 66 3.3 3.9 84 89 78 83 92 South Asia 0.3 0.5 191 0.7 0.9 80 87 31 37 27 Sub-Saharan Africa 4.5 9.2 339 0.9 0.8 49 55 55 55 14 High Income 0.3 0.1 16 12.1 12.6 ......604 Europe EMU 0.3 0.2 20 6.9 8.0 ......534 a. Data are from Internat[onal Telecommunicat ens Union's (tTUI World Telecomn,un:cation Devetopment Report 2001. Please cite the ITU for third party use of these data. b. Average of high and low estimutes. c. Less than 0.05. 1.3 10 About the data Definitions The Millennium Development Goals address is- people provides an indicator of the spread of * HIV Prevalence refers to the percentage of sues of common concern to people of all na- the epidemic. Prevalence rates in the older popu- people ages 15-24 who are infected with HIV. tions. Diseases and environmental degradation lation can be affected by life-prolonging treat- * Incidence of tuberculosis is the estimated do not respect national boundaries. Wherever ment. The indicator shown here is the esti- number of new tuberculosis cases (pulmonary, epidemic diseases persist, they pose a threat mated prevalence among women, ages 15- smear positive, extrapulmonary). * Carbon di- to people everywhere. And damage done to the 24. oxide emissions are those stemming from the environment in one location may affect the The incidence of tuberculosis is based on data burning of fossil fuels and the manufacture of wellbeing of plants. animals, and human beings on case notifications and estimates of the pro- cement. They include carbon dioxide produced in distant locations. portion of cases detected in the population. during consumption of solid, liquid, and gas The indicators in the table are taken from Carbon dioxide emissions are the primary fuels and gas flaring. * Access to an improvecl goals 6 and 7 and the targets of goal 8 that source of greenhouse gases, which are believed water source refers to the share of the popula- address youth employment and access to new to contribute to global warming. tion with reasonable access to an adequate technologies. For the other targets of goal 8 Access to reliable supplies of safe drinking amount of water from an improved source, such see table 1.4. water and sanitary disposal of excrement are as a household connection, public standpipe, Measuring the prevalence or incidence of a two of the most important means of improving borehole, protected well or spring, or rainwa- 29 disease can be difficult. Much of the developing human health and protecting the environment. ter collection. Unimproved sources include ven- world lacks reporting systems needed for moni- There is no widespread program for testing the dors, tanker trucks, and unprotected wells and 0 toring the course of a disease. Estimates are quality of water. The indicator shown here mea- springs. Reasonable access is defined as theE often derived from surveys and reports from sures the proportion of households with access availability of at least 20 liters a person a day o sentinel sites that must be extrapolated to the to an improved source, such as piped water or from a source within one kilometer of the dwell G general population. Tracking diseases such as protected wells. Improved sanitation services ing. * Access to Improved sanitation facilities CD HIV/AIDS, which has a long latency between prevent human, animal, and insect contact with refers to the percentage of the population with D 0 contracting the disease and the appearance of excreta. but do not include treatment to render at least adequate excreta disposal facilities 3 outward symptoms, or malaria, which has peri- sewage outflows innocuous. (private or shared, but not public) that can ef- CD ods of dormancy, can be particularly difficult. The eighth goal-to develop a global partner- fectively prevent human, animal, and insect S CL For some of the most serious illnesses interna- ship for development-takes note of the need contact with excreta. Improved facilities range tional organizations have formed coalitions such for decent and productive work for youth. Labor from simple but protected pit latrines to flush as UNAIDS and the Roll Back Malaria campaign market information, such as unemployment toilets with a sewerage connection. To be ef- 0 to gather information and coordinate global ef- rates, is still not generally available for most fective, facilities must be correctly constructed forts to treat victims and prevent the diseases low- and middle-income economies. Telephone and properly maintained. * Unemployment re- from spreading. lines are one element of the new telecommu- ferstothe share of the laborforce without work Antenatal care clinics are a key site for moni- nications technologies that are changing the but available for and seeking employment. toringsexuallytransmitteddiseasessuchasHIV way the global economy works. Definitions of labor force and unemployment and syphilis. The prevalence of HIV in young differ by country. * Telephone lines are tele- phone mainlines connecting a customer's Table 1.3a equipment to the public switched telephone network. Location of indicators for goals 6 and 7 Goal 6. Combat HIV/AIDS, malaria, and other diseases 18. HIV prevalence among 15-to-24-year-old pregnant women (tables 1.3 and 2.19) Data sources 19. Contraceptive prevalence rate (table 2.17) Data on HIV/AIDS and the incidence of tuberculosis come from UNAIDS and the WHO's 20. Number of children orphaned by HIV/AIDS (no data currently available) AIDS Epidemic Update (2000,) and the WHO's 21. Prevalence and death rates associated with malaria (no data currently available) World Health Report 2000 and Global 22. Proportion of population in malaria-risk areas using effective malaria Tuberculosis Control Report 1999. The data prevention and treatment measures (no data currently available) on C02emissions are from the Carbon Dioxide 23. Incidence of tuberculosis (per 100,000 people) (table 2.19) Information Analysis Center, Environmental 24. Proportion of tuberculosis cases detected and cured under directly observed Sciences Division, Oak Ridge National Laboratory, in the U.S. state of Tennessee. treatment, short course (table 2.16) Data on access to water and sanitation come from the WHO and UNICEF's Global Water| Goal 7. Ensure environmental sustalnabilitySupyadantioAsemnt20 Supply and Sanitation Assessment 2000 25. Change in land area covered by forest (table 3.4) I Report. Unemployment data are from the 26. Land area protected to maintain biological diversity (table 3.4) Intemational Labour Organization, database Key 27. GDP per unit of energy use (table 3.8) Indicators of the Labour Market (2001-02 28. Carbon dioxide emissions per capita (table 3.8) issue). Data on telephone lines are from the International Telecommunication Union's (ITU)I 29. Proportion of population with sustainable access to an World Telecommunication Development improved water source (tables 2.16 and 3.5) Report 2001. 30. Proportion of population with access to improved sanitation (table 2.16) _ _ 31. Proportion of population with access to secure tenure (table 3.11) Aofthl.Millennium Development Goals: 0 ~~~1.4 overcoming obstacles Official aid by donor Market access to high-income countries Support to Debt agriculture sustainability Net official CDA provoded Goods Tariffs on exports of low- and middle-income economies development for (excluding arms) Proportion of CPA assistance basic social admitted free of tariffs Agricultural Textiles and provided by (ODA) services' products clothing donors as debt relief Simp e Simple Total support % of total mean mean as snare of % of donor CDA tariff tariff GDP - GNIl commitments % %%%%%% 2000 2000 1990 2000 1.990 2000 1.990 2000 2000 2000 Australia 0.27 14 38.8 42.7 1.9 1.6 29.3 14.6 0.3 1.3 Canada 0.25 6 27.8 65.2 3.6 2.7 20.0 11.5 0.5 5.0 European Union 48.2 72.9 11.1 4.9 6.3 4.3 1.52 Austria 0.23 8 ...13.2 Belgium 0.36 12 ... .C Denniark 1.06 6 ... .. .1.6 Finland 0.31 7 . . . 30 France 0.32 .... .....12.1 U) Germany 0.27 14 ... .. .4.7 Greece 0.20... . Ireland 0.30 35 ... .. .1.5 Italy 0.13 7 ... .. .. .17.3 (D Luxeimbourg 0.71. 27 . . . .. aL Netherlands 0.84 17 ... .5.3 a) Portugal 0.26 5 ... .9.6 Spain 0.22 12 ..1.4 Sweden 0.80 is . 2.1 United Kingdom 0.32 24 ..3.4 o Japan 0.28 3 42.2 57.2 9.4 9.1 5.0 4.1 1.4 3.4 New Zealand 0.25 9 54.4 52.4 5.7 1.7 18.4 8.2 0.3 1.4 Norway 0.80 10 87.1 71.7 0.5 15.2 14.0 11.6 1.4 2.2 Switzerland 0.34 13 2.6 61.8 ...2.0 2.3 United States 0.10 20 20.3 56.2 3.7 4.4 11~8 10.2 0.9 1.3 Highly Indebted poor countries (HIPC) HIPC decision HIPC completion Estimated total HIPC decision HIPC completion Estimated total point' point' nominal debt point ~ point' nominal debt service relief service relief date date $ millions date date $millions Benin Jul CC floating 460 Malawi Dec CC floating 1.000 Bolivia Feb 00 Jun 01 2,060 Mali Sep CC floating 870 Burkina Faso Jul CC floating 700 Mauritania Feb 00 floating 1.100 Cameroon Oct CC floating 2,0CC Mozambique Apr 00 Sep Cl 4,300 Chad May 01 floating 260 Nicaragua Dec CC floating 4.500 C6te dIlvoire Mar 98 ..800 Niger Dec CC floating 9CC Ethiopia Nov 01 floating 1,93C Rmanda Dec CC floating 8CC Gambia Dec CC floating 9C S5o Toni & Principe Dec CC floating 200 Ghana" Feb C2 floating 3,7CC Senegal Jun CC floating 850 Guinea Dec 00 floating 8CC Sierra Leone' .. 900 Guinea-Bissau Dec 00 floating 790 Tanzania Apr CC Nov Cl 3,000 Guyana Nov CC floating 1.030 Uganda Feb CC May 00 1,950 Honiduras Jul CC floating 9CC Zambia Dec 00 floating 3,820 Madagascar Dec 00 floating 1.500 a. IccL Jdes basic heath, enucation, nutrit ion, and water and sanitation services. b. Except for Cbte dilvoire, Ghrana and Sierra Leone. data refer to tne enhanced framework date; the following countries also reached decision points under the original framework on these dates: Bolivia. Sept. 1997: Burkina Faso, Sept. 1997; Guanaa, Dec. 1997: Mali, Sept. 1998: Mozambique, April 1998: Uganda, April 1997. c. Except for Cdte dIlvoire, Ghana and Sierra Leone, date refer to the enhanced tramework date: the following countries also reached completion ponots under the original framnework on these dates: Bolly a, Sept. 1998: Burkina Faso, July 2000; Guyana, may 1999: Mali, Sept. 2000: Mozambique, July 1999: Uganda, April 1998. d. Figures are based on preliminary assessments at the time of the issuance of the preliminary HIPC document and are subject to change. 1.4Q About the data Definitions Achieving the Millennium Development Goals important categories of goods exported by de- * Net Official development assistance comn- (MDGs) will require an open, rule-based, global veloping economies. Although average tariffs prises grants and loans that meet the DAC economy in which all countries, rich and poor, have been falling, averages may disguise high definition of ODA and are made to developing participate. Many poor countries, lacking the tariffs targeted at specific goods. (See table 6.6 countries and territories in Part 1 of the DAC resources needed to finance their own develop- for an estimate of the number of "international list of recipient countries. * ODA provided for ment, burdened by unsustainable levels of debt, peaks" in each country's tariff schedule.) Only basic social services is aid reported by DAC and unable to compete in the global market- ad valorem duties are included in the averages. donors for basic health, education, nutrition, place, need assistance from rich countries. No data are shown for Switzerland, which ap- and water and sanitation services. * Goods Therefore, many of the indicators for goal 8 plies specific duties almost exclusively. Compa- admitted free of tariffs is the value of exports monitor the actions of members of the Develop- rable data on nontariff barriers are not currently of goods (excluding arms) received from devel- ment Assistance Committee (DAC) of the available. oping countries and admitted without tariff as Organisation for Economic Co-operation and Subsidies to agricultural producers and ex- a share of total exports from developing coun- Development (OECD). porters in OECD countries are another form of tries. * Agricultural products comprise plant Official development assistance (ODA) has barrier to developing economies' exports. The and animal products, including tree crops but been decreasing in recent years, both in real table shows the value of total support to the excluding timber and fish products. * Textiles 31 value and as a share of the gross national in- agricultural sector as a share of the economy's and clothing include natural and man-made fi- > come of donor countries. The poorest countries GDP. In 2000 the total value of all subsidies in bers and fabrics and articles of clothing made 0 will need additional assistance to achieve the high-income OECD economies was $277 billion. from them. * Simple mean tariff is the Millennium Development Goals. Recent estimates The heavily indebted poor country (HIPC) debt unweighted average of the effectively applied o suggest that $40-60 billion more a year, if initiative is the first comprehensive approach to rates for all products subject to tariffs. * Sup- provided to countries with good policies, would reducing the external debt of the world's poor- port to agriculture is the value of subsidies to (D allow most of them to achieve the goals. est, most heavily indebted countries. It repre- the agricultural sector. * Proportion of ODA C 0 One of the most important things that high- sents an important step forward in placing debt provided as debt relief is the share of aid from 3 income economies can do to help is to reduce relief within an overall framework of poverty re- DAC donors going to debt relief. * HIPC decl- CD barriers to the exports of low- and middle-income duction. While the HIPC initiative yielded signifi- sion point is the date at which a heavily in- economies. The European Union has announced cant early progress, multilateral organizations, debted poor country with an established track n a program to eliminate tariffs on developing coun- bilateral creditors, HIPC governments, and civil record of good performance under adjustment 0 try exports of "everything but arms." The data in society have engaged in an intensive dialogue programs supported by the International the table reflect the tariff schedules applied by about the strengths and weaknesses of the pro- Monetary Fund and the World Bank, commits high-income OECD members to low- and middle- gram. A major review in 1999 resulted in an to undertake additional reforms and to de- income economies. Agricultural commodities enhancement of the original framework. velop and implement a poverty reduction and clothing and textiles are two of the most strategy. * HIPC completion point is the date at which the country successfully completes the key structural reforms agreed at the de- Table 1.4a cision point, including the development and Location of indicators for goal 8 implementation of its poverty reduction strat- egy. The country then receives the bulk of Goal 8. Develop a global partnership for development debt relief under the HIPC initiative without 32. Net ODA as a percentage of DAC donors' gross national income (table 6.9) any further policy conditions. 33. Proportion of ODA for basic social services (table 1.4)t 34. Proportion of ODA that is untied (table 6.9) Data sources 35. Proportion of ODA for the environment in small island developing states (no data D currently available) ~~~~~~~~~~~~Data on official development assistance areI currently available) compiled by the DAC and published in the DAC 36. Proportion of ODA for the transport sector in landlocked countries (no data chairman's annual report, Development Co- currently available) operation. Data on tariffs and trade flows are 37. Proportion of exports (by value, excluding arms) admitted free of duties and quotas calculated by World Bank staff usingthe World (table 1.4) Integrated Trade Solution system. Data on 38. Average tariffs and quotas on agricultural products and textiles and clothing supports to agriculture were provided by the (See related indicators in table 6.6) OECD. Information on the HIPC program is I 39. Domestic and export agricultural subsidies in OECD countries (table 1.4) available from the World Bank's HIPC Web 40. Proportion of ODA provided to help build trade capacity (no data currently available) 41. Proportion of official bilateral HIPC debt canceled (no data currently available) 42. Debt service as a percentage of exports of goods and services (table 4.17) 43. Proportion of ODA provided as debt relief (table 1.4) 44. Number of countries reaching HIPC decision and completion points (table 1.4) 45. Unemployment rate of 15-to-24-year-olds (See table 2.4 for related indicators) 46. Proportion of population with access to affordable, essential drugs on a sustainable basis (no data currently available) 47. Telephone lines per 1,000 people (tables 1.3 and 5.9) 48. Personal computers per 1,000 people (table 5.10) * ~~1.5 Women in development Female Life expectancy Pregnant Literacy Labor force Maternity Women in population at birth women gender gender parity leave decision-making receiving parity index benefits positions prenatal Index care % of wages paid in Male Ferna e covered % of total years years i % ages 15-24 period at ministerial level 2000 2000__ 2000 I 1996 -2000 1990 2000 1998 1994 1999 Afghanistan 48.4 43 43 ...0.5 0.6 Albania 48.9 72 76 .. 1.0 0.7 0.7 ..0 11 Algeria 49.3 69 73 58 0.9 0.3 0.4 100 4 0 Angola 50.5 45 48 25 ..0.9 0.9 100 7 14 Argentina 51.0 70 77 .. 1.0 0.4 0.5 100 0 8 Armenia 51.6 71 77 95 1.0 0.9 0.9 ..3 0 Australia 50.2 76 82 ...0.7 0.8 0 13 14 Austria 51.2 75 81 0.7 0.7 100 16 20 Azerbaijan 50.8 68 75 95 ..0.8 0.8 ..5 10 32 Bangladesh 48.4 61 62 23 0.7 0.7 0.7 100 8 5 Belarus 53.4 62 74 .. 1.0 1.0 1.0 100 3 3 CO Belgium 51.0 75 81 ...0.7 0.7 82 11 3 Benin 50.7 51 55 60 0.5 0.9 0.9 100 10 13 n Bolivia 50.2 61 64 52 1.0 0.6 0.6 70 b0 6 Bosnia and Herzegovina 50.5 71 76 ...0.6 0.6 ..0 6 (-' Botswana 51.0 39 39 92 1.1 0.9 0.8 25 6 14 E CL Brazil 50.6 64 72 74 1.0 0.5 0.6 100 5 4 a, > Bulgaria 51.4 68 75 .. 1.0 0.9 0.9 100 0 CD O Burkina Faso 51.7 44 45 59 0.5 0.9 0.9 100 7 10 Z Buruncli 51.4 41 43 88 0.9 1.0 0.9 50 7 8 0 ?: Cambodia 51.2 52 55 52 0.9 1.2 1.1 50 0 N Cameroon 50.2 49 51 73 1.0 0.6 0.6 100 3 6 CS Canada 50.5 76 82 ...0.8 0.8 55 14 Central African Republic 51.3 43 44 67 0.8 ...50 5 4 Chad 50.5 47 50 30 0.8 0.8 0.8 50 5 0 Chile 50.5 73 79 91 1.0 0.4 0.5 100 13 13 China 48.6 69 72 79 1.0 0.8 0.8 100 6 Hong Kong. China 49.1 77 82 100 1.0 0.6 0.6 Colombia 50.6 68 75 83 1.0 0.6 0.6 100 11 18 Congo, Dem. Rep. 50.4 45 46 66 0.8 0.8 0.8 67 6 Congo, Rep. 51.0 49 53 55 1.0 0.8 0.8 100 6 6 Costa Rica 49.3 75 80 95 1.0 0.4 0.5 100 10 15 CSte dIlvoire 48.8 45 46 83 0.8 0.5 0.5 100 8 3 Croatia 51.6 69 78 .. 1.0 0.7 0.8 ..4 12 Cuba 49.9 75 78 100 1.0 0.6 0.7 100 0 5 Czech Republic 51.4 72 78 ...0.9 0.9 ..0 17 Denmark 50.5 74 79 ...0.9 0.9 100 29 41 Dominican Republic 49.2 65 70 97 1.0 0.4 0.4 100 4 10 Ecuador 49.8 68 71 75 1.0 0.3 0.4 100 6 20 Egypt, Arab Rep. 49.4 66 69 53 0.8 0.4 0.4 100 4 6 El Salvador 50.9 67 73 69 1.0 0.5 0.6 75 10 6 Eritrea 50.3 51 53 19 0.8 0.9 0.9 ..7 5 Estonia 53.4 65 76 ...1.0 1.0 ..15 12 Ethiopia 50.3 41 43 20 0.8 0.7 0.7 100 10 5 Finland 51.2 74 81 0.9 0.9 80 39 29 France 51.3 75 83 0.8 0.8 100 7 12 Gabon 80.5 51 54 86 ..0.8 0.8 100 7 3 Gambia. The 50.5 52 55 91 0.7 0.8 0.8 100 0 29 Georgia 52.3 69 77 95 ..0.9 0.9 ..0 4 Germany 51.0 74 81 ...0.7 0.7 100 16 8 Ghana 50.2 56 58 86 0.9 1.0 1.0 50 11 9 Greece 50.7 75 81 .. 1.0 0.5 0.6 75 4 5 Guatemala 49.6 62 68 53 0.9 0.3 0.4 100 19 0 Guinea 49.7 46 47 59 ..0.9 0.9 100 9 8 Guinea-Bissau 50.7 43 46 50 0.6 0.7 0.7 100 4 18 Haiti S1.0 51 56 68 1.0 0.8 0.8 100 13 0 Honduras 49.7 63 69 73 1.0 0.4 0.5 100 11 11 I.5Q Female Life expectancy Pregnant Literacy Labor force Maternity Women In population at birth women gender gender parity leave decision-making receiving parity Index benefits positions prenatal Index care % of wages paid in Male Female covered % of total years years % ages 15-24 period at ministerial level 2000 2000 2000 1996 2000 1990 2000 1998 1994 1998 Hungary 52.3 67 76 .. 1.0 0.8 _0.8 100 0 5 India 48.4 62 63 62 0.8 0.5 0.5 100 3 Indonesia 49.8 64 68 82 1.0 0.6 0.7 100 6 3 Iran, Islamic Rep. 48.8 68 7621.0 0.3 0.4 _67 0 0 iraq 49.2 60 62 59 0.9 0.2 0.2 100 0 0 Ireland 50.3 74 79 0.5 0.5 70 d 16 21 Israel 50.7 76 80 90 1.0 0.6 0.7 75 4 0 Italy 51.5 76 82 . 1.0 0.6 0.6 80 12 13 Jamaica 50.7 73 77 98 1.1 0.9 0.9 100 5 12 Japan 51.1 78 84 ..0.7 0.7 60 6 0 3 Jordan 48.0 70 73 80 1.0 0.2 0.3 100 3 2 Kazakhstan 51.5 60 71 92 0.9 0.9 6. 6 5 Kenya 50.2 47 47 95 1.0 0.8 0.9 100 0 0 Korea. Dem. Rep. 49.8 59 62 100 -0.8 0.8 ..0 Korea, Rep. __49.7 70 77 96 1.0 0.6 0.7 100 4..o - - - - - - - - - - - - - - - - - . . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . . . Kuwait 41.8 75 79 99 :1.0 0.3 0.5 100 0 0 C Kyrgyz Republic 51.0 63 _72 90 0.9 0.9 . 0 4 CD Lao PDR 50.1 53 55 25 01.7 . . .100 0 0 ' Latvia 53.9 65 76 .. 1.0 1.0 1.0 ..0 7 C Lebanon 51.1 69 72 85 1.0 0.4 0.4 100 0 0 E 0. Lesotho 50.4 44 491 1.2 0.6 0.6 0 6 6 Liberia 49.7 46 48 0 0.6 0.6 0.7 0 0 a Libya 48.2 69 73 100 0.9 0.2 0.3 50 0 7 ( Lithuania 52.8 68 78 .. 1.0 0.9 0.9 ..0 6 Macedonia, FYR 50.0 71 75 0.7 0.7 ..8 9 Madagascar 50.3 53 56 78 0.9 0.8 0.8 100 ~ 0 19 Malawi 50.3 39 39 90 0.8 1.0 0.9 ..9 4 Malaysia 49.3 70 7 5 90 1.0 0.6 0.6 100 7 16 Mali 50.5 41 44 25 0.8 0.9 0.9 100 10 21 Mauritania 50.4 50 53 49 0.7 0.8 0.8 100 0 4 Mauritius 50.2 68 76 99 1.0 0.4 0.5 100 3 Mexico 50.5 70 76 71 1.0 0.4 0.5 100 5 5 Moldova 52.2 64 72 1.0 0.9 0.9 ..0 0 Mongolia 49.9 65 69 90 ..0.9 0.9 ..0 0 Morocco 49.9 66 69 45 0.8 0.5 0.5 100 0 0 Mozambique 50.6 41 44 54 0.6 0.9 0.9 100 4 0 Myanmar 50.3 54 59 80 1.0 0.8 0867 0 0 Namibia 50.6 47 47 88 1.0 0.7 0.7 ..10 8 Nepal 48.7 59 59 15 0.6 0.7 0.7 100 0 3 Netherlands 50.4 75 81 ...0.6 0.7 100 31 28 New Zealand 50.7 76 81 ...0.8 0.8 0 8 8 Nicaragua_____50.2 67 71 71 1.0 0.5 0.6 60 10 5 Niger 49.6 44 4830 0.4 0.8 0.8 50 5 10 Nigeria 49.6 46 48 60 0.9 0.5 0.6 50 3 6 Norway 50.5 76 81 ...0.8 0.9 100 35 20 Oman 46.9 72 75 98 1.0 0.1 0.2 0 0 Pakistan -- -- ---- - - 48.6 - --62-- --- -64 27 0.6 0.3 0.4 100 4 7 Panama 49.5 72 77 72 1.0 0.5 0.5 100 13 6 Papua New Guiinea 47.9 58 5970 0.9 0.7 070 0 0 Paraguay 49.6 68 73 83 _1.0 0.......4... OA___ ___0.4_ 50 0 7 Peru 50.4 67 72 64 1.0 0.4 0.5 100 6 10 Philippines 49.6 67 71 83 1.0 0.6 0.6 100 8 10 Poland 51.4 69 78 .. 1.0 0.8 0.9 100 17 12 Portugal 51.9 72... .....79 .. 1.0 0.7 0.8 100 10 10 Puerto Rico 51.9 72 81 99 1.0 0.5 0 6 .. Romania 51.1 66 74 1.0 0.8 0.8 50-94 0 8 Russian Federation 53.2 59 72 .. 1.0 0.9 1.0 100 0 8 *1.51. Female Life expectancy Pregnant Literacy Labor force Maternity Women In population at birth women gender gender parity leave decision-making receiving parity Index benefits positions prenatal Index care % of wages paidi in Male Female covered % of total years years % ages 15-24 period at ministerial level 2000 2000 2000 1998 2000 1990 2000 1998 1994 1998 Rwanda 50.5 39 40 94 1.0 1.0 1.0 67 9 5 Saudi Arabia 46.6 7 1 74 87 1.0 0.1 0.2 50-100 0 0 Senegal 50.1 51 54_ 74 0.7 0.7 0.7 100 7 7 Sierra Leone SO.8 38 41 30 ..0.6 0.6 ..0 10 Singapore 49.6 76 80 100 1.0 0.6 0.6 100 0 0 Slova k Republic 51.4 69 77 0.9 0.9 5 19 Sloveni'a 51.4 72 79 1 0 0.9 0.9 5 0 Somalia 50.4 47 50 0 ..0.8 0.8 0 0 0 South Africa 50.8 47 49 89 1.0 0.6 0.6 45 6 34 Spai'n 51.1 75 82 .. 1.0 0.5 0.6 100 14 18 Sri Lanka 48.6 7 1 7 6 100 1.0 0.5 0.6 100 3 13 Sudan 49.7 55 58 54 0.9 0.4 0.4 100 0 0 CC Swaziland 50.7 4 5 46 0 1.0 0.6 0.6 0 0 0 Sweden 50.5 77 8 2 ...0.9 0.9 75 30 43 Switzerland 50.5 77 83 ...0.6 0.7 100 17 17 E Syrian Arab Republic 49.3 67 72 33 0.8 0.3 0.4 100 7 8 o Tajikistan 50.2 66 72 90 1.0 0.7 0.8 ..3 6 >v Tanzania 50.4 44 45 92 0.9 1.0 1.0 100 13 13 0 Thailand 50.5 67 7 1 77 1.0 0.9 0.9 100 0 4 aO o Togo 50.3 48 50 43 0,7 0.7 0.7 100 5 9 Trinidad and Tobago 50.3 70 75 98 1.0 0.5 0.5 60-100 19 14 o Tunisia 49.5 70 74 71 0.9 0.4 0.5 67 4 3 Turkey 49.5 67 72 62 1.0 0.5 0.6 67 5 5 Turkmenistan 50.5 63 70 90 ..0.8 0.8 ..3 4 Uganda 50.1 42 42 87 0.8 0.9 0.9 1001 10 13 Ukraine 53.6 63 74 .. 1.0 1.0 1.0 100 0 5 United Arab Emirates 33.9 74 77 95 1.1 0.1 0.2 100 0 0 United Kingdom 50.8 75 80 ...0.7 0.8 90 9 24 United States 50.7 74 80 ...0.8 0.9 0 14 26 Uruguay 51.5 71 78 80 1.0 0.6 0.7 100 0 7 Uzbekistan 50.3 67 73 90 1.0 0.8 0.9 ..3 3 Venezuela. RB 49.7 71 76 74 1.0 0.5 0.5 100 11 3 Vietnam 50.2 67 72 78 1.0 1.0 1.0 100 5 0 West Bank and Gaza 49.3 70 74 . .. Yemen, Rep. 50.2 56 57 26 0.6 0.4 0.4 100 0 0 Yugoslavia. Fed. Rep. 50.3 70 75 ...0.7 0.8 5.. Zambia 49.8 38 38 92 0.9 0.8 0.8 100 5 3 Zimbabwe 50.0 40 40 93 1.0 0.8 0.8 60-75 3 12 Low Income 49 .3 58 60 62 0.8 0.6 0.6 4 Middie Income 49.5 67 72 77 1.0 0.7 075 Lower middle income 49.2 67 72 76 1.0 0.8 0.8 5 Upper middle income 50.2 67 73 80 1.0 0.5 0.6 6 6 Low & middie Income 49.4 63 66 70 0.9 0.7 0.7 5 East Asia & Pacific 49.0 67 71 80 1.0 0.8 0.8 5 Europe & Central Asia 51.8 64 74 .. 1.0 0.8 0.9 3 7 Latin America & Carib. 50.4 67 74 75 1.0 0.5 0.5 6 7 Middle East & N. Africa 48.6 66 69 58 0.9 0.3 0.4 2 2 South Asia 48.5 62 63 55 0.8 0.5 0.5 4 Sub-Saharan Africa 50.1 46 47 65 0.9 0.7 0.7 6 7 High Income 49.5 75 81 .0.7 0.8 12 16 Europe EMU 51.2 75 81 ... .7 0.7 14 13 a. For 30 days. 75 percent thereafter. b. Benefit is 70 percent of wages above the minimam wage, 100 percent of national minimum wage. c. For 15 weeks, d. Up to a ceiling. e. Fore6 weeks. f. For 54 days. g. For S weeks. h. For 9 weeks. i. Benefit is 100 percent for the first 45 days, then 50 percent for 15 days. j. For 1 month. k. For 6 weeks; flat rate thereafter. 1.5Q About the data Definitions Despite considerable progress in recent do not include contractual benefits negotiated * Female population is the percentage of the decades, gender inequalities remain pervasive through labor union contracts. The benefits population that is female. * Life expectancy in many dimensions of life-worldwide. Butwhile generally apply only in the formal sector, leaving at birth is the number of years a newbom infant disparities exist throughout the world, they are out the vast majority of working women in would live if prevailing patterns of mortality at most prevalent in poordevelopingcountries.The developing countries. As a result, while the the time of its birth were to stay the sarne differences in outcomes between men and situation in the United States is much better throughout its life. * Pregnant women receing women-and between boys and girls-are a than the data indicate, the situation in Thailand prenatal care are the percentage of women consequence of differences in the opportunities is likely to be much worse. attended at least once during pregnancy by and resources available to them. Inequalities in Women are vastly underrepresented in skilled health personnel for reasons related to the allocation of resources such as education, decision-making positions in government, pregnancy. * Literacy gender parity index is health care, and nutrition matter because of the although there is some evidence of recent the ratio of the female literacy rate to the male strong association of these resources with well- improvement. While 6 percent of the world's rate, for the age group 15-24. * Labor force being, productivity, and growth. This pattern of cabinet ministers were women in 1994, 8 gender parity index is the ratio of the per- inequality begins at an early age, with boys percent were in 1998. Without representation centage of women who are economically active routinely receiving a larger share of education at this level, it is difficult for women to influence to the percentage of men who are. According 35 and health spending than girls do, for example. policy. to the International Labour Organization (tLO) Life expectancy has increased for both men For information on other aspects of gender, definition, the economically active population 0 0 and women in all regions, but female morbidity see tables 1.2 (Millennium Development Goals: is all those who supply labor for the production M and mortality rates sometimes exceed male eradicating poverty and improving lives), 2.3 of goods and services during a specified period. E rates, particularly during early childhood and the (employment by economic activity), 2.4 It includes both the employed and the reproductive years. In high-income countries (unemployment), 2.13 (education efficiency), unemployed. While national practices vary in r, women tend to outlive men by four to eight years 2.14 (education outcomes), 2.17 (reproductive the treatment of such groups as the armed CD on average, while in low-income countries the health), 2.19 (health: risk factors and future forces and seasonal or part-time workers, in 3 differenceisnarrower-abouttwotothreeyears. challenges), and 2.20 (mortality). general the labor force includes the armed CD The female disadvantage is best reflected in forces, the unemployed, and first-time job , differences in child mortality rates (see table Figure 1.5 seekers, but excludes homemakers and other 2. 2.20). Child mortality captures the effect of unpaid caregivers and workers in the informal preferences for boys because adequate nutrition Women judges in selected countries sector. * Maternity leave benefrts refer to the 0 and medical interventions are particularly Womenasapercentageofjudges compensation provided to women during important for the age group 1-5. Because of Sweden maternity leave, as a share of their full wages. the natural female biological advantage, when Tu* Women In decision-making positions are female child mortality is as high as or higher Turkey those in ministerial or equivalent positions in United States_ than male child mortality, there is good reason the government. to believe that girls are discriminated against. tay Female disadvantage in mortality is carried Austria into adolescence and the reproductive years. Spal= Data sources Serious health risks for adolescents arise when czeCh Rep The data are from the World Bank's population they become sexually active. And while in high- Uknane database; electronic databases of the Unitea income countries women have universal access 0 20 40 60 80 Nations Educational, Scientific, and Cultural to health care during pregnancy, in developing So,ce: UNECE 2000 Organization (UNESCO); the ILO database countries it is estimated that 35 percent of Women have begun to make Estimates and Projections of the Economically pregnant women-some 45 million each year- polea governenta nd civic apects of lfe that Active Population, 1950-2010; and the United receive no care at all (United Nations 2000b). give them decision-making power and Influence and Nations' World's Women: Trends and Statistics i Prenatal care is essential for recognizing, place them on a more equal footing with men. 2000. However, they still have a long way to go to achieve diagnosing, and promptly treating complications their share of positions where they can make a that arise during pregnancy. difference. Judgeships are the only positlons of power Girls in many developing countries are allowed and Influence In which women have reached parity In less education by their families than boys are- a number of countries. a disparity reflected in lower female primary enrollment (see table 1.2) and higher female illiteracy. As a result, women have fewer employment opportunities, especially in the formal sector. A labor force gender parity index of less than 1.0 shows that women's labor force participation in the formal sector is lower than men's. (A ratio of 1.0 indicates gender equality). Women who work outside the home continue to bear a disproportionate share of the responsibility for housework and child rearing. They also face discriminatory practices in the workplace, especially relating to equal pay and maternity benefits. The maternity benefits data in the table relate only to legislated benefits and * ~1.6 Key indicators for other economies Population Surface Population Gross national Income Gross domestic Life Adult Carbon area density product expectancy Illiteracy dioxide at rate emissions birth PPP, Per Per Per % of thousand people capita capita capita people 15 thousand thousands sq. km per sq. km $ millions $ $ millions $ % growth % growth years and above metric toes 2000 2000 2000 20001 2000k 2000 2000 1999-2000 1999-2000 2000 2000 1998 American Samoa 65 0.2 327 ... .. .. 282 Andorra 67 0.5 149 ,0.. 80 Antigua and Barbuda 68 0.4 155 642 9,440 680 10.000 3.7 2.8 75 ..337 Aruba 101 0.2 532 0. 1,883 Bahamas. The 303 13.9 30 4,533 14,960 4,969 16,400 4.5 2.9 69 5 1.792 Bahrain 691 0.7 1.001 .. ... . .. 73 12 18,688 Barbados 267 0.4 621 2,469 9,250' 4.010 15,020 4.1 3.8 75 .. 1.569 Belize 240 23.0 11 746 3.110 1,258 5,240 10.2 6.6 74 7 399 Bermuda 63 0.1 1,260 d. .....462 36 Bhutan 805 47.0 17 479 590 1,161 1.440 7.0 3.9 62 ..386 Brunei 338 5.8 64 ... ... . .. 76 8 5.488 cv Cape Verde 441 4.0 109 588 1,330 2,100 h 4,760 6.8 3.6 69 26 121 c Cayman Islands 35 0.3 135 .0. .....289 ~6 channel Islands 149 0.2 768 0. 79 - Comoros 558 2.2 250 212 380 887 " 1,590 -1.1 -3.6 61 44 70 a) Cyprus 757 9.3 82 9.361 12.370 15,734 20,7801 4.8 4.4 78 3 5,918 D jibouti 632 23.2 27 553 880 ... 0.7 -1.3 46 35 366 33 Dominica 7 0. 97 ... ... 0.5 . 76 ..84 o)a Equatorial Guinea 457 28.1 16 363 800 2.560 5.600 16.9 13.8 51 17 253 Faeroe Islands 45 1.4 32 ..0. . . .641 0 3: Fij i 812 18.3 44 1,480 1.820 3,636 4,480 -8.0 -9.2 69 7 721 French Polynesia 235 4.0 64 4,064 17,290 5,486 23.340 4.0 2.4 73 ..561 C' Greenland 56 341.7 0 0 .. .528 Grenada 98 0.3 288 370 3,770 682 6.960 6.5 5.4 72 ..183 Guam 155 0.6 281 0 ~. . 78 .. 4,111 Guyana 761 215.0 4 652 860 2.795 3,670 -0.7 -1.3 63 2 1,649 Iceland 281 103.0 3 8,540 30,390 8.069 28.7 10 5.0 3.7 80 .. 2.083 Isle of Man 75 0.6 131 ... ... ... Ktiribati 91 0.7 124 86 950 ... -1.8 .4.2 62 ..22 Liechtenstein 32 0.2 200 0 . .d. Luxembourg 438 2.6 169 18,439 42,060 19,934 45.470 8.5 6.9 77 .. 7,678 Macao. China 438 . .. 6.385 14.580 7,967 18.190 4.6 3.7 79 6 1,630 Maldives 276 0.3 920 541 1,960 1,171 4,240 4.8 2.3 68 3 330 Malta 390 0.3 1,219 3,559 9,120' 6,448 16,530 4.7 4.2 78 8 1.803 Marshall Islancds 52 0.2 286 102 1.970 ..0.5 .. 65 Mayotte 145 0.4 388 ... ... . Micronesia, Fed. Sts. 118 0.7 168 250 2,110 ..3.0 1.2 68 Monaco 32 0.0 16.410 d~, . . Netherlands Antilles 215 0.8 269 d0.. . 76 3 7,753 New Caledonia 213 18.6 12 3,203 15,060 4.641 21,820 2.1 0.3 73 .. 1,746 Northern Mariana Islands 72 0.5 151 .. . . . . Palau 19 0.5 41 ... ... 5.4 . 70 ..242 Qatar 585 11.0 53 ... ... . .. 75 19 46,772 Samoa 170 2.8 60 246 1,450 859 5.050 7.0 6.4 69 20 132 S5c, TomLd and Principe 148 1.0 154 43 290 ... 2.9 0.7 65 ..77 Seychelles 81 0.5 181 573 7,050 -.. 1.2 -0.3 72 ..198 Solomon Islands 447 28.9 16 278 620 766 1,710 -14.0 -16.9 69 ..161 San Marino 27 0.1 450 a d.. . 80 St. Kitts and Nevis 41 0.4 114 269 6,570 449 10,960 2.6 2.3 71 ..103 St. Lucia 156 0.6 256 642 4,120 842 5,400 2.0 0.5 71 ..198 St. Vincent and the Grenadines 115 0.4 295 313 2,720 599 5,210 2.3 1.4 73 ..161 Suriname 417 163.3 3 788 1,890 1,450 3,480 -7.3 -7.9 70 .. 2,139 Tonga 100 0.8 139 166 1.660 ... 6.2 5.5 71 ..117 Vanuatu 197 12.2 16 226 1.150 583 2,960 2.2 0.1 68 ..62 Virgin Islands (U.S.) 121 0.3 356 , a ,, ... 78 .. 11,706 a. PPP is purchasing power parity; see Def.n,tions. b. Calculated uning the World sans Atlas method. c. Estimated to be upper m ddle incume i$2.996-9,265). d. Estimated to he high sncums $9,266 or more). a. Included under upper middle income economies in calculating the aggregates based on earlier data. f. Included under high inicome economies in calcu ating the aggregates bsaed on earlier data. g. Included under lower middle income economies in calculating the aggregates basedi on earlier data. h. The estimate is based on regression; others are extrapolated from the [atest International Comparison Programme benchmnark estimates. i. Raters to GOP and GOP per capita. 1.6 S About the data Definitions This table shows data for 55 economies-small * Population is based on the de facto defini- economies with populations between 30,000 tion of population, which counts all residents and 1 million and smaller economies if they are regardless of legal status or citizenship-ex- members of the World Bank. Where data on cept for refugees not permanently settled in gross national income (GNI) per capita are not the country of asylum, who are generally con- available, the estimated range is given. For more sidered part of the population of their country information on the calculation of GNI (gross of origin. The values shown are midyear esti- national product, or GNP, in the 1968 System mates for 2000. See also table 2.1. * Sur- of National Accounts), see About the data for face area is a country's total area, including table 1.1. As in last year's edition, this table areas under inland bodies of water and some excludes France's overseas departments- coastal waterways. * Population density is French Guiana, Guadeloupe, Martinique, and midyear population divided by land area in Reunion-for which GNI and other economic square kilometers. * Gross national income measures are now included in the French (GNI) is the sum of value added by all resident national accounts. producers plus any product taxes (less subsi- 37 dies) not included in the valuation of output M plus net receipts of primary income (compen- g sation of employees and property income) from abroad. Data are in current U.S. dollars con- S verted using the World Bank Atlas method (see E Statistical methods). * GNI per capita is gross CD national income divided by midyear population. O GNI per capita in U.S. dollars is converted LIs- 3 ing the World Bank Atlas method. * PPP GNI is C gross national income converted to interna- tional dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. * Gross domestic prod- uct (GDP) is the sum of value added by all resi- dent producers plus any product taxes (less subsidies) not included in the valuation of out- put. Growth is calculated from constant price GDP data in local currency. * Life expectancy at birth is the number of years a newborn in- fant would live if prevailing patterns of mortal- ity at the time of its birth were to stay the same throughout its life. * Adult illiteracy rate is the percentage of adults ages 15 and above who cannot, with understanding, read and write a short, simple statement about their everyday life. * Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include car- bon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. Data sources The indicators here and throughout the rest of the book have been compiled by World Bank staff from primary and secondary sources., More information about the indicators and their sources can be found in the About x the data, Definitions, and Data sources entries that accompany each table in subsequent X sections. /10 ½ ®LPZLLo /D - X Nutrition Reduced capacity Higher Impaired mental Increased to care for baby mortality rate development risk of throughout the adult chronic disease life cycle / Elderly U%Q Baby -f- Untimely malnourishedl beght .f weaning Inadequate M / r--- Frequent food, health, Inadequate (E infections and care catch-up growth i- / -- Inadequate Inadequate V food, health, Woman ,jfS fetal nutrition and care malnourished Pregn a ncy J[-T Adol escent (27 Child low weight gain r stunted stunted Inadequate Inadequate Higher maternal food, health, Reduced food, health, Reduced mortality and care mental capacity and care mental capacity 0 0 Source: UN ACC/SCN 2000. CD rD *0 :3 CD Hunger and malnutrition still pose a major challenge to many developing countries. In Child malnutrition is highest among the poor countries already saddled with poverty, malnu- trition starts a vicious cycle of ill health, lower Under-five child malnutrition rate by quintile learning capacity, and poor physical growth. U Poorest fifth * Richest fifth Because that undermines a country's social and R 60 CD 50 economic development, investing in better X53 nutrition is essential. 40 30 Reflecting this development priority, the 20 Millennium Development Goals adopted a tar- 10 liii' get to halve, between 1990 and 2015, the 0 L proportion of people in developing countries "' a , who suffer from hunger. Two indicators were 0 ' 6 S99 C identified to track progress: the prevalence of ource: DemographlcandHealthSurveydata underwveight in children under age five and the proportion of undernourished people. Malnutrition rates are falling-except in Africa Trends in child malnutrition rate in developing countries by region, 1980-2000 The prevalence of child malnutrition in the developing world fell from r 1980 El 1985 El 1990 El 1995 El 2000 46.5 percent in 1970 to 27 percent , 50 in 2000. Even so, 150 million _ children under five are still malnour- R 40 ished. The situation is bleakest in Africa, where both the number and 30 the proportion of malnourished chil- _ dren have been rising. At current 20 lI rates of improvement, now slowing. rll halving child malnutrition by 2015 10 n-l 40 is unlikely. In 2020, 140 million 0 IIILiM OOLiIFiL 40 children under five in developing O _ Lati A r A developing o2 countries will still be underweight, Africa Asia Latin America All developing co:. and Caribbean countries Q or about 50 million short of the Note: UN regions. goal (Smith and Haddad 2000). source. UN ACC/SCN 2000. aE C 0 0 Oveme5gM a[md ~~~~~~~~~~~~~~~~~~~~~~~in the bottom quarter of weight-for- height standards and the bottom undevw(95ght Overweightt and underweight coexist in some countries fifth of height. In addition, weight gains during pregnancy are usually Underweight and overweight preschool children, latest half or less of those recommended Some wealthier developing available year (percent) (McGuire 19961. countries are also starting to haveAretnThnubroudroriedpo worrisome rates of overweight chil-AretnThnubrfudroriedp- dren. These countries are undergo- Uruguay F =pIe in the developing world is ing a rapid nutrition transition, often South Africa espected to decline, from 777 mil- to diets high in saturated fats, Jamaica F=lion in 1997-99 to 576 million in sugar, and refined foods (UN Egypt, Arab Rep. 2015, halving the proportion of the ACC/SCN 20001. In these countries ,population that was undernourished obesity coexists with undernutrition Malawi L .~in 1990-92 and thus meeting the ide Onis and Blossner 20001. Algeria Millennium Development Goal. But Uzbekistan ~ 1 Zthe number of people undernour- Data on nutritional status during -30 -20 -10 0 10 20 30 ished in 2015 will still be around the life cycle are slowly becomning Unewih vregt70 percent of the 840 million peo- available, mainly for women. The Unewih vregtpie undernourished in 1990-92, far limited data suggest that women in Source, die Onis anti Blossner 2000 anci WHO child growth and short of the World Food Summit developing countries fall on average goal of a reduction by half in the number of undernourished people. Undernourishment and food Insecurity The chronic undernourishment measure, based on average insecurity can be a seasonal phenomenon even when there caloric consumption (also called food inadequacy or food is aggregate food security. insecurity), developed by the Food and Agriculture * In addition to being influenced by access to food, Organization (FAO 2000), has the value of focusing world nutrition security is also determined by the quality of care attention on food insecurity and food-insecure people. It for mothers and children and the quality of the household's also focuses the attention of national governments and health environment. international development agencies on a numerical goal * Food-insecure households often have well-nourished and the political will to attain it, as part of the Millennium children, which shows that some households have adaptive Development Goals. behaviors that contribute to better nutrition. * The estimation method has problems because However, the measure, derived largely from food supply distribution of consumption among households is often not data and an estimate of the distribution of food directly measured, and food availability at the national level consumption across households, has its limitations: is subject to many unmeasured errors. * Food insecurity is an individual, household, or national These limitations become harder to ignore with the phenomenon. And the average amount of food available to increasing numbers of nationally representative household 41 each person in the population, even if corrected for the food consumption and expenditure surveys that are now possible effects of low income, is not a good predictor of available. food insecurity in the population. Furthermore, food Source: Adapted from Smth 1998. I I . C(Dt CD ~0 Co n CD =3 0 Stunting-a strong indicator of Stunting In children under five is a robust Indicator of poverty poverty Estimated number of stunted children under five, 1980-2000 * 1980 * 1985 U 1990 U 1995 * 2000 Malnutrition affects the poor more K 250 than the rich because factors asso- ciated with income poverty-such as ' 200 female illiteracy, food insecurity, and a poor health environment-also 150 cause malnutrition. Malnutrition is thus a cause and a consequence of 100 poverty. Tracking trends in nutritional 50 I status is therefore useful in tracking m * UE the overall effectiveness of poverty o * * _ reduction strategies. Stunting in chil- Africa Asia Latin America All developing dren under five is the most appropri- and Caribbean countries ate indicator for populationwide Note: UN regions. monitoring. Stunting is an inexpen- Source: UN ACC/SCN 2000. sive and robust indicator when mea- sured in a representative sample. Higher mortality deaths. And addressing vitamin A deficiency in areas where it is com- mon can rcsult in a 23 percent Survival prospects are poor for underweight children can resultlit amon cnt Nearly a third of poor health ~~~~~~~~~~~~~~reduction in mortality among chil- Nearly a third of poor health outcomes are associated with mal- Regression of malnutrition on under-five mortality, dren between ages two and six. nutrition. More than half of child latest available data deaths-mostly from diarrheal dis- c 60 eases and respiratory infections- 0 .- are associated with low weight for - p* * age. In India underweight children r . had two to four times the mortality 4 ** _ rate of normal weight children r 30 (McGuire 1996). Mortality is also ( * * associated with essential micronu- E 20 * U trient deficiencies. Severe y anemic 0 42 women are at considerably greater 10 risk of death during childbirth, 0 since anemia lowers the tolerance 0 50 100 150 200 250 300 to blood loss and the resistance to Under-five mortality rate (per 1,000), 2000 infection. Anemia may account for source: WHO child growth and mainutition database and World Bank data. C almost 20 percent of maternal 0 tem are substantial. Poor nutritional Malnutrition Is by far the greatest health hazard status is by far the largest single risk factor for disease in the WHO's Morbdit inicaorsareals liked Burden of disease due to selected risk factors, 1995 calculations of the total burden of with malnutrition. Chronic noncom-..dies,langt .Lblonay municable diseases, such as dia- malnutrition dsae edn o11blindy betes and cardiovascular disease, Water/sanitation of illness a year worldwide. are associated with inadequate Unsafe sex Woe_npo evlpn on diets for mothers and low Alcohol _Wmni ordvlpn on birthweights for infants. Malnour- Indoor air pollution tre arfcedibymanuropotionandtely t ished children have less resistance Tobacco afetdb_nlutiinadhat to infection. Malnutrition has been Occupation riiosks cauircle The intergeneratonalow associated with a 10 45 percent Hypertension victwious circle.the isnhidener aofngw increase in the Incidence of diar- Physical inactivity birtwoeniwht inants ishihortan arnder- rhea and a 30 55 percent Illicit drugs womrsed. whow-bishrthwih and fander increase in its duration. Similarly, Outdoor air pollution nr ourishied. Lowbirheighuted infat twotoamiu t-eiimes nt sucepiblden are 0 5 10 15 20 stunted girls grow up to be short two to four times as susceptible to ~Percentage of global disability- women. respiratory disease and twice as adjusted life years susceptible to diarrhea. Surce: WHO 1995. Less education Chronic malnutrition and bouts of and l ns ahunger in children affect school and learning stunted children enrollment, attendance, and cogni- tive development. In Brazil a 12 per Female secondary enrollment and child stunting, cent reduction in malnutrition Childhood malnutrition is often latest available data resulted in a 4 percent improvement caused by improper feeding and ? 80 in passing rates for first and second caring practices, making the knowl- rD 70 grades (McGuire 1996). A study of 9- edge and values of caregivers very i 60 to 11-year-olds in Indonesia found important. Women with at least a o \ h that the achievement scores of ane- secondary education tend to have w mic children improved by more than fewer children. They also have the = 40 n j 10 percent after 12 weeks of iron knowledge and skills to provide * * supplementation (Soemantri, Pollitt, them with better nutritional care. C D and Kim 1985). Nutrition affects Women's education levels there- 28 20 * school performance indirectly as fore influence nutritional status, 1 i0 * * l; E well. Stunted children tend to enroll and nutritional status affects chil- 0 * * * later in school than better-nourished 43 dren's educational attainment. 0 25 50 75 100 children. In Ghana a 10 percent Female gross secondary enrollment (percent) increase in stunting caused a o a Source: WHO child growth and malnutrition database and UNESCO Institute 3.5 percent increase in the age of N oStatistics data. first enrollment in school (UN ACC/SCN 2000). oL 0 CD 0. CD ibb 0-~~~~~~~ Lower productivity Adult nutrition affects body mass. In India a 30 percent reduction in Malnutrition has economic costs as well lean body mass was associated with 20 percent lower wages The economic livelihood of popula- tions depends on the health and Estimated economic costs of anemia, selected countries (McGuire 1996). Deficiencies in nutrition of adults. This reflects the (through effects on cognitive ability and productivity) vitamin A, iron, and iodine can also cause prolonged impairment, legacy of malnutrition in childhood -X 2.0 . . . Cs * reducing productivity and gross as well as whether adults have suf- e r CD domestic product. ficient food intake to sustain both d prdut a, 1.5 normal body weight and the physi- Cs cal activity needed for the tasks of o) 1.0 daily life. Child malnutrition mani- fests itself in reduced schooling and shorter stature, both linked to 0.5 iIiiIi.i lower wages in rural and urban set- tings (Thomas and Strauss 1997). 0 Source: Measham 2001. Causes of poor nutrition Manuriio Immediate causes affecting the individual t ~ ~ Iaeut fE o inak / Household foSocial and care environment *Public health i G Hou~~seholdr oody Direct caring behaviors *Health environment Asecurity f * Women's roles, status, and rights * Access to 4.Acst So *Social organization and networks health care 44 Basic causes Local priorities Z2 CoI o Formal and Informal infrastructure '0 c Political ideology and policies E o , 0 a) Resources human, structural, financial Source: Young 2001. N 0 0 CN4 _~~ ~~~~~~~~~~~~~ - M_U Slow progress Second, because malnutrition is ment does respond, it usually tries Third, even when confronting malnu- not highly visible, its severity and to increase agricultural output or trition is a priority, lack of against effects may be ignored. Even coun- undertake expensive, ineffective government capacity results in inap- malnutrition tries with national nutrition plans food giveaways. This does not nec- propriate policies and programs, may not have a clearly articulated essarily mean additional food con- such as untargeted and unafford- _______________________________ strategy for addressing malnutrition sumption or increased income for able food subsidies, with implemen- because politicians and decision- the malnourished. Seldom is there tation depending on institutions that There are three main reasons for the makers fail to see the urgency and a well-defined strategy for translat- are already overburdened. Good generally slow progress in tackling significance of the problem. And ing the demand for food into ways nutrition programs need not be malnutrition (Measham 2001). First, unlike education or health, malnu- of increasing the nutritional well- expensive, but they require skilled malnutrition is a complex intersec- trition does not have a constituency being of those in need. administrators and appropriate toral problem. It encompasses bio- to demand policies and programs design. As nongovernmental organi- logical and socioeconomic causes at to address it. Poor people often say zations conduct more nutrition pro both micro and macro levels. It that food is their first priority, but grams, government resources therefore rarely has an institutional they lack the political power to get become less of a constraint. home, such as a single ministry. government to respond. If govern- Factors Variables Food product on Food availability | Food imports Food storage Poverty Access to food * Market integra on 45 Access to markets 0 Food consumption - Food use practices c b t ,2 t ~~~~~~~~~~~* Food intake C Nutritional status r Anth opometry * Micronutrient deficiency E. 0 Food security and hunger and malnutrition, and the Policymakers often assume that To monitor food security, most coun- World Food Summit in 1996 set the interventions at any point in the tries collect information from a vari- food policy less ambitious goal of reducing the chain will have a direct effect in ety of sources, including national number of undernourished people reducing undernutrition or food population censuses, agricultural by half no later than 2015. insecurity. But links are more com- surveys, agroecological zoning, mar Food security and food policy are plex than they appear. For example, ket monitoring, health center important in dealing with the under- Food security is determined by four a school feeding scheme may have records, livelihood monitoring, vul- lying and basic causes of malnutri- sets of factors: little impact on nutritional status if nerability mapping, and income, tion. The need for adequate * Food availability. parents reallocate household consumption, and expenditure sur- information on food security at * Access to food. resources away from providing food veys. Often not all these data global, national, and subnational * Food consumption. to the child. sources are fully exploited because levels received attention when inter- * Nutritional status. data collection and reporting tend to national targets for the eradication be divided among ministries, and as of hunger and malnutrition were a result databases and information adopted. The World Food Confer- are not always coordinated. ence in 1974 called for eradicating Source: Adapted from Devereux 2001. The way forward_ Income growth should therefore be What reduces malnutrition? part of a balanced strategy for _____ _____ _____ ____ _____ _____ ____ _____ _____ ____ addressing nutritional problem s. As Sustained income growth can do much to reduce malnutritio n the Estimated contributions, 1970-95 income rises, so does investment in other factors that influence nutri- next two decades. But economic * Food availability Q Women's education tion, notably education and health. growth by itself is unlikely to * Health environment * Women's status achieve the Millennium Development Goal for malnutrition (Alderman and others 2001). Although economic growth can fos- IWA: ter improvements in nutrition, many other factors influence the process. The most important appears to be women's education, fol owed by V:W 46 food availability (or income), the - government's commitment to En health at local and national levels, and women's status (Smith and Haddad 2000). Scurce Smith and Haddad 2000. E 0~ 0 C) 0 0 CN Some impressive The World Bank estimates that sus- tained elimination of micronutrient returns Returns to nutrition programs vary widely deficiencies could alone contribute as much as 5 percent of GDP annu- Returns to nutrition programs (in wages) ally to an affected country-for an But given the difficulty many coun- investment of less than 0.3 percent -o 100 o D MGie19) h tries face in achieving sustained c of GDP (McGuire 1996). The economic growth, especially those in C 80 returns per dollar invested in higher Sub-Saharan Africa, nutrition educa- 60 lifelong wages and lower disability tion, supplementation, fortification, 6 are impressive. and supply and price mechanisms 40 should be considered at both 20 national and community levels. Note: Estimated returns in doilar terms (In ifeiong wages) per s1 spent on programs. Source: Measham 2001. Nutrition needs But high proportions of Asian and s African mothers are malnourished, are still great Malnutrition will remain high in South Asia and Africa and the numbers are expected to grow. In developing countries some Regional distribution of malnourished children, 2020 30 million children are born each Over the past two decades year with their growth already progress has been dramatic in U South Asia IN Near East and North Africa retarded. More than 150 million some areas of nutrition, especially * Sub-Saharan Africa * Latin America and Caribbean preschool children are still under- in reducing micronutrient deficien- U East Asia weight, many with anemia and vita- cies. The proportion of stunted and 2% - 0 1% min A deficiency. And more children underweight preschool children has 7 and adults are becoming declined in all regions except parts overweight or obese. of Sub-Saharan Africa. Seurce Smith and Haddad 2000. 0~ Making malnutrition Tracking malnutrition visible Indicators that focus attention on nutritional status and behavior can be identified at household, community, and national levels. Because malnutrition is not very Household visible, it is often overlooked until * Growth promotion * Access to health care it becomes severe. Making it visi- * Breastfeeding practices * Household food security ble is central to an effective strat- egy. Countries need to identify Community * Well-functioning food markets * Availability of health care appropriate indicators of nutritional * Access to clean water and sanitation * Nutrition education status and trends-and strengthen.... . their statistical systems for collect- National ing, analyzing, publishing, and * Trends in child growth * Food prices and price variability across time using data. * Women's health and regions * Girls' education * Wage and employment rates, especially among the * Trends in childhood infections rural poor * Immunization trends * Income of the poor 2.1 Population dynamics Total Average annual Population age Dependency Crude death Crude birth population population composition ratios rate rate growth rate dependents as proportion of Ages Ages Ages working 0414 15-64 65+ age population per 1,000 per 1,000 m onss % % oung old people people 1980 2000 2015 1980-2000 2000-2015 2000 2000 2000 2000 2000 2000 2000 Afgnanistan 16.0 26.61 37.8 2.5 2.4 43.5 53.7 2.8 0.8 0.1 22 48 Albania 2.7 3.4 4.0 1.2 1.0 30.0 64.2 5.9 0.5 0.1 6 17 Algeria 18.7 30.4 39.1 2.4 1.7 34.8 61.0 4.1 0.6 0.1 5 25 Angola 7.1 13.1 19.6 3.1 2.7 48.2 49.0 2.8 1.0 0.1 19 48 Argentina 28.1 37.0 42.8 1.4 1.0 27.7 62.6 9.7 0.4 0.2 8 19 Armenia 3.1 3.8 4.0 1.0 0.4 23.7 67.6 8.6 0.4 0.1 6 11 Australia 14.7 19.2 21.5 1.3 0.8 20.5 67.2 12.3 0.3 0.2 7 13 Austria 7.6 8.1 8.0 0.4 -0.1 16.6 67.8 15.6 0.3 0.2 10 10 Azerbaijan 6.2 8.0 9.2 1.3 0.9 29.0 64.2 6.8 0. 0.1 6 15 48 Bangladesh 85.4 131.1 167.7 2.1 1.6 38.7 58.2 3.1 0.7 0.1 9 28 Belarus 9.6 10.0 9.4 0.2 -0.4 18.7 68.0 13.3 0.3 0.2 14 9 o Belgium 9.8 10.3 10.3 0.2 0.0 17.3 65.7 17.0 0.3 0.3 10 11 Benin 3.5 6.3 9.0 3.0 2.4 46.4 50.9 2.7 1.0 0.1 13 39 Bolivia 5.4 8.3 10.9 2.2 1.8 39.6 56.4 4.0 0.7 0.1 9 31 Bosnia and Herzegovina 4.1 4.0 4.4 -0.1 0.6 18.9 71.2 9.9 0.3 0.1 8 12 a) Botswana 0.9 1.6 1.7 2.8 0.6 42.1 55.1 2.8 0.8 0.1 20 32 0= o) Brazil 121.6 170.4 201.3 1.7 1.1 28.8 66.1 5.1 0.4 0.1 7 20 > Bulgaria 8.9 8.2 7.4 -0.4 -0.6 15.7 68.1 16.1 0.2 0.2 14 9 Burkina Faso 7.0 11.3 15.6 2.4 2.2 48.7 48.1 3.2 1.0 0.1 19 44 Burundi 4.1 6.8 8.8 2.5 1.7 47.6 49.6 2.9 1.0 0.1 20 40 Cambodia 6.8 12.0 15.2 2.8 1.6 43.9 53.3 2.8 0.8 0.1 12 30 (.4 o Cameroon 8.7 14.9 19.4 2.7 1.8 43.1 53.2 3.7 0.8 0.1 14 37 0 Canada 24.6 30.8 33.6 1.1 0.6 19.1 68.3 12.6 0.3 0.2 8 11 Central African Republic 2.3 3.7 4.6 2.4 1.5 43.0 53.0 4.0 0.8 0.1 20 36 Chad 4.5 7.7 11.8 2.7 2.9 46.5 50.4 3.1 0.9 0.1 16 45 Chile 11.1 15.2 17.7 1.6 1.0 28.5 64.4 7.2 0.4 0.1 6 17 China 981.2 1.262,5 1,392.6 1.3 0.7 24.8 68.3 6.9 0.4 0.1 7 15 Hong Kong, China 5.0 6.8 7.5 1.5 0.6 16.3 73.1 10.6 0.2 0.2 5 8 Colombia 28.4 42.3 51.6 2.0 1.3 32.8 62.5 4.7 0.5 0.1 6 23 Congo, Dem. Rep. 26.9 50.9 75.6 3.2 2.6 48.8 48.4 2.9 1.0 0.1 17 46 Congo, Rep. 1.7 3.0 4.6 3.0 2.8 46.3 50.4 3.3 0.9 0.1 14 43 Costa R ca 2.3 3.8 4.7 2.6 1.5 32.4 62.5 5.1 0.5 0.1 4 20 CMe dIlvoire 8.2 16.0 20.5 3.3 1.7 42.1 54.8 3.1 0.8 0.1 17 37 Croatia 4.6 4.4 4.2 -0.2 -0.3 18.0 67.8 14.1 0.3 0.2 12 10 Cuba 9.7 11.2 11.7 0.7 0.3 21.2 69.2 9.6 0.3 0.1 7 13 Czech Republic 10.2 10.3 9.9 0.0 -0.2 16.4 69.7 13.8 0.2 0.2 11 9 Denmark ~ 5.1 5.3 5.4 0.2 0.1 18.3 66.7 15.0 0.3 0.2 11 12 Dominican Republic 5.7 8.4 10.1 1.9 1.3 33.5 62.2 4.3 0.5 0.1 6 23 Ecuador 8.0 12.6 15.8 2.3 1.5 33.8 61.5 4.7 0.6 0.1 6 24 Egypt, Arab Rep. 40.9 64.0 80.7 2.2 1.6 35.4 60.5 4.1 0.6 0.1 6 25 El Salvador 4.6 6.3 8.0 1.6 1.6 35.6 59.4 5.0 0.6 0.1 6 26 Eritrea 2.4 4.1 5.9 2.7 2.4 43.9 53.2 2.9 0.8 0.1 13 39 Estonia 1.5 1.4 1.3 -0.4 -0.5 17.7 67.9 14.4 0.3 0.2 13 9 Ethiopia 37.7 64.3 88.1 2.7 2.1 45.2 51.9 3.0 0.9 0.1 20 44 Finland 4.8 5.2 5.3 0.4 0.1 18.0 67.0 14.9 0.3 0.2 10 11 France 53.9 58.9 61.6 0.4 0.3 18.7 65.3 16.0 0.3 0.2 9 13 Gabon 0.7 1.2 1.7 2.9 2.2 40.2 54.0 5.8 0.7 0.1 16 36 Gambia. The 0.6 1.3 1.8 3.5 2.1 40.3 56.6 3.1 0.7 0.1 13 39 Georgia 5.1 5.0 4.8 0.0 -0.3 20.5 66.6 12.9 0.3 0.2 9 9 Germany 78.3 82.2 80.0 0.2 -0.2 15.5 68.1 16.4 0.2 0.2 11 9 Ghana 10.7 19.3 24.7 2.9 1.6 40.9 55.8 3.2 0.7 0.1 11 30 Greece 9.6 10.6 10.3 0.5 -0.2 15.1 67.4 17.6 0.2 0.3 11 12 Guatemala 6.8 11.4 16.3 2.6 2.4 43.6 52.8 3.5 0.8 0.1 7 33 Guinea 4.5 7.4 9.8 2.5 1.9 44.1 53.2 2.8 0.8 0.1 17 39 Guinea-Bissau 0.8 1.2 1.7 2.3 2.2 43.5 52.9 3.6 0.8 0.1 20 42 Haiti 5.4 8.0 10.3 2.0 1.7 40.6 55.7 3.7 0.7 0.1 13 32 Honduras 3.6 6.4 8.5 2.9 1.9 41.8 54.8 3.4 0.8 0.1 6 31 2.1 I0 Total Average annual Population age Dependency Crude death Crude birth populatlon population composition ratios rate rate growth rate dependents as proportion of Ages Ages Ages working 0-14 15-64 65+ age population per 1,000 per 1.000 millions % % young old people people 1980 2000 2015 1980-2000 2000-2015 2000 2000 2000 2000 2000 2000 2000 Hungary 10.7 10.0 9.4 -0.3 -0.4 16.9 68.4 14.6 0.3 0.2 14 10 India 687.3 1,015.9 1,227.9 2.0 1.3 33.5 61.5 5.0 0.5 0.1 9 25 Indonesia 148.3 210.4 250.5 1.7 1.2 30.8 64.4 4.8 0.5 0.1 7 22 Iran, Islamic Rep. 39.1 63.7 80.4 2.4 1.6 37.4 59.2 3.4 0.6 0.1 6 22 Iraq 13.0 23.3 31.2 2.9 2.0 41.6 55.5 2.9 0.8 0.1 9 31 Ireland 3.4 3.8 4.3 0 .5 0.8 21.6 67.1 11.3 0.3 0.2 8 14 Israel 3.9 6.2 7.9 2.4 1.6 28.3 61.9 9.9 0.5 0.2 6 21 Italy 56.4 57 7 54.8 0.1 .0.3 14.3 67.6 18.1 0.2 0.3 10 9 Jamaica 2.1 2.6 3.1 1.1 1.0 31.5 61.3 7.2 . 0.1 6 2 Japan 116.8 126.9_ 124.6 ....0.4 __-0.1 14.7 68.1 17.2 0.2 0.3 8 9 49 Jordan 2.2 4.9 6.8 4.0 2.2 40.0 57.2 2.8 0.7 0.1 4 29 Kazakhstan 14.9 14.9 15.3 0.0 0.2 27.0 66.2 6.9 0.4 0.1 10 15 0 Kenya 16.6 30.1 37.5 3.0 1.5 43.5 53.7 2.8 0.8 0.1 14 35 Korea, Dem. Rep. 17.2 22.3 24.2 1.3 0.6 26.5 67.6 5.9 0.4 0.1 11 18 Korea, Rep. 38.1 47.3 50.3 1.1 0.4 20.8 72.1 7.1 0.3 0.1 6 1 Kuwait 1.4 2.0 2.7 1.8 2.1 31.3 66.5 2.2 0.5 0.0 2 20 C Kyrgyz Republic 3.6 4.9 5.8 1.5 1.1 33.9 60.0 6.0 0.6 0.1 7 21 a Lao PDR 3.2 5.3 7.3 2.5 2.2 42.7 53.8 3.5 0.8 0.1 13 37 0 Latvia 2.5 2.4 2.1 .0.4 -0.7 17.4 67.8 14.8 0.3 0.2 14 9 ( Lebanon 3.0 4.3 5.2 1.8 1.2 31 .1 62.8 6.1 0.5 0.1 6 20 S --------- -- - - - -~ ~~~ ~~~ ~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Lesotho 1.4 2.0 2.3 2.0 0.8 39.3 56.6 4.2 0.7 0.1 17 33 - - ------- - 0)0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Liberia 1.9 3.1 4.5 2.6 2.5 42.7 54.5 2.9 0.8 0.1 1744E Libya 3.0 53 70 2.8 1.9 33.9 62.7 3.4 0.5 0.1 5 27 ( Lithuania 3.4 3.7 3.6 0.4 -0.2 19.5 67.2 13.4 0.3 0.2 11 9 Macedonia, FYR 1.9 2.0 2.2 0.4 0.4 22.6 67.4 10.0 0.3 0.2 8 13 Madagascar 8.9 15.5 22.5 2.8 2.5 44.7 52.3 3.0 0.9 0.1 12 40 Malawi 6.2 10.3 13.6 2.6 1.8 46.3 50.7 2.9 0 9 0.1 24 46 Malaysia 13.8 23.3 29.3 2.6 1.5 34.1 61.8 4.1 0.6 0.1 4 25 Mali 6.6 10.8 15.0 2.5 2.2 46.1 49.9 4.0 0.9 0.1 20 46 Mauritania 1.6 2.7 3.9 2.7 2.5 44.1 52.7 3.2 0.8 0.1 15 42 Mauritius 1.0 1.2 1.4 1.0 0.9 25.6 68.2 6.2 0 4 0.1 7 17 Mexico 67.6 98.0 121.1 1.9 1.4 33.1 62.1 4.7 0.5 0.1 5 25 Moldova 4.0 4.3 4.2 0.3 -01 23.1 67.6 9.3 0.3 0.1 11 10 Mongolia 1.7 2.4 2.9 1.8 1.3 35.2 61.0 3s 8 06 0.1 6 22 Morocco 19.4 28.7 35.4 2.0 1.4 34.7 61.2 4.1 0 6 0.1 6 24 Mozambique 12.1 17.7 22.7 1.9 1.7 43.9 52.8 3.2 0 8 0.1 20 40 Myanmar 33.7 47.7 55.8 1.7 1.0 33.1 62.3 4.6 0~5 0.1 12 25 Namibia 1.0 1.8 2.1 2.9 1.2 43.7 52.5 3.8 0.8 0.1 17 36 Nepal 14.6 23.0 31.1 2.3 2.0 41.0 55.2 3.7 0.7 0.1 10 33 Netherlands 14.2 15.9 16.9 0.6 0.4 18.3 68.1 13.6 0.3 0.2 9 13 New Zealand 3.1 3.8 4.1 1.0 0.5 23.0 65.4 11.7 0.4 0.2 7 15 Nicaragua 2.9 5.1 7.0 2.8 2.1 42.6 54.3 3.0 0.8 0.1 5 30 Niger 5.6 10.8 18 33 2.9 49.9 48.1 2.0 1.0 0.0 19 51 Nigeria 71.1 126.9 169.4 2.9 1.9 45.1 51.9 3.0 0.9 0.1 16 40 Norway 4.1 4.5 4.8 0.5 0.4 19.8 64.9 15.4 0.3 0.2 10 13 Oman 1.1 2.4 3.3 3.9 2.2 44.1 53.4 2.5 0.8 0.1 3 28 Pakistan 82.7 138.1 192.8 2.6 2 2 41.8 __54.5 3.7 0.8 0.1 8 34 Panama 2.0 2.9 3.5 1.9 1.3 31.3 63.2 5.5 0.5 0.1 5 21 Papua New Guinea 3.1 5.1 6.9 2.5 2.0 40.1 57.5 2.4 0.7 0.0 9 32 Paraguay 3.1 5.5 7.5 2.8 2.1 39.5 57.0 3.5 0.7 0.1 5 30 Peru 17.3 25.7 31.4 2.0 1.3 33.4 61.8 4.8 0.5 0.1 7 23 Philippines 48.0 75.6 97.3 2.3 1.7 37.5 58.9 3.5 0.6 0.1 5 27 Poland 35.6 38.7 38.8 0.4 0.0 19.2 68.7 12.1 0.3 0.2 10 10 Portugal 9.8 10.0 9.9 0.1 -0. 1 16.7 67.7 15.6 0.3 0.2 11 12 Puerto Rico 3.2 3.9 4.4 1.0 0,7 23.8 65.7 10 5 0.4 0.2 8 15 Romania 22.2 22.4 21.4 0.1 -0.3 18.3 68.4 13.3 0.3 0.2 11 10 Russian Federation 139.0 145.6 134.5 0.2 -0.5 18.0 69.6 12.5 0.3 0.2 15 9 D ~2.1 Total Average annual Population age Dependency Crude death Crude birth population population composition ratios rate rate growth rate dependents as proportion of Ages Ages Ages working 0-14 15-64 65.- age population per 1.000 per 1.000 millions % % young old people people 1980 2000 2015 1980-2000 2000-2015 2000 2000 2000 2000 2000 2000 2000 Rwanda 5.2 8.5 11.1 2.5 1.8 44.3 53.1 2.6 0.9 0.1 22 44 Saudi Arabia 9.4 20.7 32.1 4.0 2.9 42.9 54.1 3.0 0.8 0.1 4 33 Senegal 5.5 9.5 13.0 2.7 2.1 44.3 53.2 2.5 0.8 0.1 13 37 Sierra Leone 3.2 5.0 6.9 2.2 2.1 44.2 52.8 2.9 0.8 0.1 23 44 Singapore 2.4 4.0 4.9 2.5 1.3 21.9 70.9 7.2 0.3 0.1 4 12 Slovak Republic 5.0 5.4 5.4 0.4 0.0 19.5 69.1 11.4 0.3 0.2 10 10 Slovenia 1.9 2.0 1.9 0.2 -0.2 15.9 70.2 13.9 0.2 0.2 10 9 Somalia 6.5 8.8 14.2 1.5 3.2 48.0 49.6 2.4 1.0 0.1 17 51 South Africa 27.6 42.8 45.8 2.2 0.5 34.0 62.4 3.6 0.6 0.1 16 26 50 Spain 37.4 39.5 38.8 0.3 -0.1 14.7 68.3 17.0 0.2 0.3 9 10 Sri Lanka 14.7 19.4 23.0 1.4 1.1 26.3 67.4 6.3 0.4 0.1 6 18 Sudan 19.3 31.1 41.8 2.4 2.0 40.1 56.4 3.4 0.7 0.1 11 34 tO Swaziland 0.6 1.0 1.3 3.1 1.3 41.6 55.0 3.5 0.8 0.1 15 36 Sweden 8.3 8.9 8.8 0.3 .0. 1 18.2 64.4 17.4 0.3 0.3 11 10 Switzerland 6.3 7.2 7.1 0.6 0.0 16.7 67.3 16.0 0.3 0.2 9 10 E Syrian Arab Republic 8.7 16.2 22.1 3.1 2.1 40.8 56.0 3.1 0.7 0.1 5 29 o Tajikistan 4.0 6.2 7.7 2.2 1.5 39.4 56.0 4.6 0.7 0.1 5 19 > Tanzania 18.6 33.7 43.9 3.0 1.8 45.0 52.6 2.4 0.9 0.1 17 39 a) ~0 Thailand 46.7 60.7 68.7 1.3 0.8 26.7 68.1 5.2 0.4 0.1 7 17 o Togo 2.5 4.5 6.0 2.9 1.9 44.3 52.6 3.1 0.8 0.1 15 37 ? ~Trinidad and Tobago 1.1 1.3 1.5 0.9 0.8 25.0 68.4 6.7 0.4 0.1 7 15 N- o Tunisia 6.4 9.6 11.6 2.0 1.3 29.7 64.4 5.9 0.5 0.1 6 17 0 Turkey 44.5 65.3 77.8 1.9 1.2 30.0 64.2 5.8 0.5 0.1 6 20 Turkmenistan 2.9 5.2 6.4 3.0 1.3 37.6 58.1 4.3 0.7 0.1 7 21 Uganda 12.8 22.2 31.6 2.8 2.4 49.2 48.3 2.5 1.0 0.1 19 45 Ukraine 50.0 49.5 44.9 .0.1 -0.6 17.8 68.3 13.8 0.3 0.2 15 9 United Arab Emirates 1.0 2.9 3.8 5.1 1.8 26.0 71.3 2.7 0.4 0.0 3 17 United Kingdom 56.3 59.7 59.7 0.3 0.0 19.0 65.3 15.8 0.3 0.2 11 11 United States 227.2 281.6 317.8 1.1 0.8 21.7 66.0 12.3 0.3 0.2 9 15 Uruguay 2.9 3.3 3.7 0.7 0.6 24.8 62.3 12.9 0.4 0.2 10 16 Uzbekistan 16.0 24.8 30.1 2.2 1.3 36.3 59.1 4.7 0.6 0.1 6 22 Venezuela, RB 15.1 24.2 30.3 2.4 1.5 34.0 61.5 4.4 0.6 0.1 4 22 Vietnam 53.7 78.5 94.4 1.9 1.2 33.4 61.3 5.3 0.5 0.1 6 19 West Bank and Gaza .. 3.0 5.0 .. 3.5 .. . . . . 4 40 Yemen, Rep. 8.5 17.5 27.0 3.6 2.9 50.1 47.6 2.3 1.1 0.1 11 40 Yugoslavia, Fed. Rep. 9.8 10.6 10.7 0.4 0.1 20.0 66.9 13.1 0.3 0.2 11 12 Zambia 5.7 10.1 12.2 2.8 1.3 46.5 50.5 2.9 0.9 0.1 21 40 Zimbabwe 7.1 12.6 14.0 2.9 0.7 45.2 51.6 3.2 0.9 0.1 18 30 Low Income 1,609.5 2,459.8 3,090.3 2.1 1,5 36.9 58.7 4.4 0.6 0.1 11 29 Middle Income 2,030.0 2,694.6 3,063.4 1.4 0.9 27.4 66.0 6.6 0.4 0.1 8 18 Lower middle income 1,563.7 2,047.6 2,306.4 1.3 0.8 26.9 66.4 6.8 0.4 0.1 8 17 Upper middle income 466.3 647.0 757.1 1.6 1.0 29.1 64.6 6.2 0.5 0.1 7 20 Low & middle Income 3,639.5 5,154.4 6,153.7 1.7 1.2 31.9 62.5 5.6 0.5 0.1 9 23 East Asia & Pacific 1,396.9 1,855.2 2,097.8 1.4 0.8 26.9 66.8 6.2 0.4 0.1 7 17 Europe & Central Asia 425.8 474.3 478.8 0.5 0.1 22.0 67.1 10.8 0.3 0.2 11 12 Latin America & Carib. 359.6 515.7 625.4 1.8 1.3 31.5 63.0 5.4 0.5 0.1 6 22 Middle East & N. Africa 174.0 295.2 388.7 2.6 1.8 37.8 58.6 3.6 0.6 0.1 6 26 South Asia 901.4 1,355.1 1,681.9 2.0 1.4 35.1 60.3 4.6 0.6 0.1 9 27 Sub-Saharan Africa 381.7 658.9 881.1 2.7 1.9 44.4 52.6 3.0 0.8 0.1 17 39 High Income 789.8 902.9 947.5 0.7 0.3 18.5 66.9 14.7 0.3 0.2 9 12 Europe EMU 286.7 304.0 302.3 0.3 0.0 16.2 67.3 16.4 0.2 0.2 10 11 a. Estimate does not account for recent refugee flows. About the data Definitions Population estimates are usually based on na- Separate calculations of young-age dependency * Total population of an economy includes all tional population censuses, but the frequency and old-age dependency reflect the burden of residents who are present regardless of legal and quality of these vary by country. Most coun- dependency that the working-age population status or citizenship- except for refugees not tries conduct a complete enumeration no more must bear in relation to the proportion of chil- permanently settled in the country of asylum, than once a decade. Pre- and postcensus esti- dren and the aged in the population. Age de- who are generally considered part of the mates are interpolations or extrapolations based pendency ratios are a measure of the age com- population of their country of origin. The on demographic models. Errors and position, not of economic dependency. It should indicators shown are midyear estimates for undercounting occur even in high-income coun- be noted that some people in the dependent 1980 and 2000 and projections for 2015. tries; in developing countries such errors may age range are part of the labor force, and many * Average annual populatlon growth rate is the be substantial because of limits in the trans- persons in the working age range are not in the exponential change for the period indicated. port, communications, and other resources re- labor force. See Statistical methods for more information. quired to conduct a full census. The quality and The vital rates shown in the table are based * Population age composition represents the reliability of official demographic data are also on data derived from birth and death registra- percentage of the total population that is in affected by the public trust in the government, tion systems, censuses, and sample surveys specific age groups. * Dependency ratios are the government's commitment to full and accu- conducted by national statistical offices, United the ratios of dependents-people younger than 51 rate enumeration, the confidentiality and protec- Nations agencies, and other organizations. The 15 and older than 64-to the working-age N) tion against misuse accorded to census data, estimates for 2000 for many countries are based population-those between ages 15-64. 0 and the independence of census agencies from on extrapolations of levels and trends measured * Crude death rate and crude birth rate are undue political influence. Moreover, the inter- in earlier years. the number of deaths and the number of live E national comparability of population indicators Vital registers are the preferred source of births occurring during the year, per 1,000 E is limited by differences in the concepts, defini- these data, but in many developing countries population estimated at midyear. Subtracting tions, data collection procedures, and estima- systems for registering births and deaths do not the crude death rate from the crude birth rate tion methods used by national statistical agen- exist or are incomplete because of deficiencies provides the rate of natural increase, which is 3 cies and other organizations that collect popu- in geographic coverage or coverage of events. equal to the population growth rate in the iD lation data. Many developing countries carry out specialized absence of migration. Of the 152 economies listed in the table, 118 household surveys that estimate vital rates by . _ (about 78 percent) conducted a census between asking respondents about births and deaths in 1990 and 2001. The currentness of a census, the recent past. Estimates derived in this way Data sources , u. along with the availability of complementary data are subject to sampling errors as well as errors The World Bank's population estimates are from surveys or registration systems, is one of due to inaccurate recall by the respondents. produced by its Human Development Network | many objective ways to judge the quality of de- The United Nations Statistics Division moni- and Development Data Group in consultation mographic data. In some European countries tors the completeness of vital registration sys- with its operational staff and country offices. registration systems offer complete information tems. The share of countries with at least 90 Important inputs to the World Bank's on population in the absence of a census. See percent complete vital registration increased demographic work come from the following | Primary data documentation for the most recent from 45 percent in 1988 to 53 percent in 1999. sources: census reports and other statistical census or survey year and for registration Still, some of the most populous developing publications from national statistical offices; completeness. countries-China, India, Indonesia, Brazil, Paki- Demographic and Health Surveys conducted by Current population estimates for developing stan, Bangladesh, Nigeria-do not have com- national agencies, Macro International, and the countries that lack recent census-based data, plete vital registration systems. Fewer than 30 U.S. Centers for Disease Control and and pre- and postcensus estimates for countries percent of births and fewer than 40 percent of Prevention; United Nations Statistics Division,i with census data, are provided by national sta- deaths worldwide are thought to be registered Population and Vital Statistics Report tistical offices, the United Nations Population and reported. (quarterly); United Nations Population Division, Division, and other agencies. The standard esti- International migration is the only other factor World Population Prospects: The 2000 I mation method requires fertility, mortality, and besides birth and death rates that directly Revision; Eurostat, Demographic Statistics net migration data, which are often collected determines a country's population growth. In the (various years); Centro Latinoamericano de from sample surveys, some of which may be high-income countries about 40 percent of Demografia, Boletin Demografico (various small or limited in coverage. The population annual population growth in 1990-95 was due years); and U.S. Bureau of the Census, estimates are the product of demographic mod- to migration, while in the developing countries International Database. eling and so are susceptible to biases and er- migration reduced population growth by about 3 _____ __ rors because of shortcomings in the model as percent. Estimating international migration is well as in the data. Population projections are difficult. At any time many people are located made using the cohort component method. outside their home country as tourists, workers, The growth rate of the total population con- or refugees or for other reasons. Standards cealsthefactthatdifferentagegroups maygrow relating to the duration and purpose of at very different rates. In many developing coun- international moves that qualify as migration tries the population under 15 was earlier grow- vary, and accurate estimates require information ing rapidly, but is now starting to shrink. Previ- on flows into and out of countries that is difficult ously high fertility rates and declining mortality to collect. rates are now reflected in the larger share of the working-age population. The variations in the proportions of children, aged persons, and persons of working age are taken into account in the dependency ratio. (0 ~~2.2 Labor force structure Population ages Labor force 15-64 Total Average annual Fe male m illions millions growth rate % 8of labor force 1980 2000 1980 2000 2010 1980-2000 2000-2010 1980 2000 Afghanistan 8.5 14.2 6.8 11.2 13.8 2.5 2.1 34.8 35.5 Albania 1.6 2.2 1.2 1.7 2.0 1.7 1.5 38.8 41.3 Algeria 9.3 18.6 4.8 10.2 14.6 3.7 3.5 21.4 27.6 Angola 3.7 6.4 3.5 6.0 8.1 2,7 3.0 47.0 46.3 Argentina 17.2 23.2 10.7 15.0 18.5 1.7 2.1 27.6 33.2 Armenia 2.0 2.6 1.4 1.9 2.2 1.4 1.3 47.9 48.6 Australia 9.6 12.9 6.7 9.8 10.6 1.9 0.8 36.8 43.7 Austria 4.8 5.5 3.4 3.8 3.8 0.6 0.0 40.5 40.3 Azerbaijan 3.7 5.2 2.7 3.6 4.3 1.4 1.9 47.5 44.6 52 Bangladesh 44.8 76.2 40.3 69.2 86.7 2.7 2.3 42.3 42.4 Belarus 6.4 6.8 5.1 5.3 5.3 0.2 0.1 49.9 49.0 (e o Belgium 6.5 6.7 3.9 4.3 4.2 0.4 -0.2 33.9 40.9 m Benin 1.8 3.2 1.7 2.8 3.7 2.7 2.8 47.0 48.3 Bolivia 2.9 4.7 2.0 3.4 4.4 2.6 2.5 33.3 37.8 Bosnia and Herzegovina 2.7 2.8 1.6 1.9 2.0 0.7 0.9 32.8 38.1 E Botswana 0.4 0.9 0.4 0.7 0.8 2.9 0.9 50.1 45.3 0L o Brazil 70.3 112.6 47,7 79.7 90.0 2.6 1.2 28.4 35.5 >, Bulgaria 5.8 5.6 4.6 4.2 3.9 -0.5 -0.7 45.3 48.2 Burkina Faso 3.4 5.4 3.8 5.6 6.7 1.9 1.9 47.6 46.5 '0 Burundi 2.1 3.4 2.3 3.7 4.6 2.5 2.2 50.2 48.7 Cambodia 3.9 6.4 3.7 6.3 7.9 2.7 2.3 55.4 51.7 o Cameroon 4.5 7.9 3.6 6.1 7.5 2.5 2.1 36.8 38.0 0 CNi Canada 16.7 21.0 12.2 16.5 17.5 1.5 0.6 39.5 45.8 Central African Republic 1.3 2.0 1.2 1.6 2.1 1.9 1.5 Chad 2.3 3.9 2.2 3.7 5.0 2.6 3.0 43.4 44.7 Chile 6.8 9.8 3.8 6.2 7.5 2.4 1.9 26.3 33.6 China 586.3 862.2 538.7 756.8 818.3 1.7 0.8 43.2 45.2 Hong Kong, China 3.4 5.0 2.5 3.6 3.9 1.9 0.8 34.3 37.1 Colombia 15.8 26.4 9.4 18.5 23.0 3.4 2.2 26.2 38.7 Congo, Dem. Rep. 13.8 24.6 12.0 21.0 28.2 2.8 3.0 44.5 43.4 Congo, Rep. 0.9 1.5 0.7 1.2 1.7 2.9 3.0 42.4 43.5 Costa Rica 1.3 2.4 0.8 1.5 1.9 3.3 2.1 20.8 31.1 C6te dIlvoire 4.2 8.8 3.3 6.4 8.0 3.3 2.3 32.2 33.4 Croatia 3.1 3.0 2.2 2.1 2.0 -0.2 -0.2 40.2 44.2 Cuba 5.9 7.7 3.7 5.5 5.9 2.0 0.7 31.4 39.5 Czech Republic 6.5 7.2 5.3 5.8 5.5 0.4 -0.4 47.1 47.3 Denmark 3.3 3.6 2.7 2.9 2.8 0.4 -0.5 44.0 46.4 Dominican Republic 3.1 5.2 2.1 3.7 4.6 2.8 2.2 24.7 30.8 Ecuador 4.2 7.8 2.5 4.9 6.5 3.3 2.7 20.1 28.0 Egypt , Arab Rep. 23.1 38.7 14.3 24.4 32.2 2.7 2.8 26.5 30.4 El Salvador 2.4 3.7 1.6 2.7 3.6 2.8 2.9 26.5 36.5 Eritree 1.3 2.2 1.2 2.1 2.7 2.6 2.7 47.4 47.4 Estonia 1.0 0.9 0.8 0.8 0.7 -0.4 -0.2 50.6 49.0 Ethiopia 19.9 33.4 16.9 27.6 34.6 2.4 2.3 42.3 40.9 Finland 3.2 3.5 2.4 2.6 2.5 0.4 -0.5 46.5 48.1 France 34.4 38.5 23.8 26.7 27.6 0.6 0.3 40.1 45.1 Gabon 0.4 0.7 0.4 0.6 0,7 2.2 2.0 45.0 44.7 Gambia, The 0.3 0.7 0.3 0.7 0.8 3.5 2.4 44.8 45.1 Georgia 3.3 3.3 2.6 2.5 2.5 -0.2 0.1 49.3 46.8 Germany 51.6 55.9 37.5 40.9 40.8 0.4 0.0 40.1 42.3 Ghana 5.5 10.8 5.1 9.2 11.2 2.9 2.0 51.0 50.5 Greece 6.2 7.1 3.8 4.6 4.6 1.0 0.1 27.9 37.8 Guatemala 3.5 6.0 2.3 4.2 6.0 2.9 3.5 22.4 28.9 Guinea 2.3 3.9 2.3 3.5 4.3 2.1 2.0 47.1 47.2 Guinea-Bissau 0.4 0.6 0.4 0.6 0.7 1.9 2.3 39.9 40.5 Haiti 2.9 4.4 2.5 3.5 4.2 1.6 1.8 44.6 42.9 Honduras 1.8 3.5 1.2 2.4 3.4 3.5 3.3 25.2 31.8 2.2 Population ages Labor force 15-64 Total Average annual Female mrIllions millions growth rate % % of labor force 1980 2000 1980 2000 201 LO ±980-2000 2000-2010 i980 2000 Hungary 6.9 6.9 5.1 4.8 4.6 -0.3 -0.5 43.3 44.7 India 394.5 625.2 299.5 450.8 543.6 2.0 1.9 33.7 32.3 Indonesi'a 83.2 135.6 58.6 101.8 124.5 2.8 2.0 35.2 40.8 Iran, Islamic Rep. 20.5 37.7 11.7 19.7 27.7 2.6 3.4 20.4 27.1 Iraq 6.7 12.9 3.5 6.5 8.6 3.0 2.8 17.3 19.7 Ireland 2.0 2.5 1.3 1.6 1.8 1.2 1.3 28.1 34.5 Israel 2.3 3.9 1.5 2.7 3.5 3.1 2.5 33.7 41.2 Italy 36.4 39.0 22.6 25.7 24.7 0.7 -0.4 32.9 38.5 Jamaica 1.1 1.6 1.0 1.4 1.6 1.8 1.5 46.3 46.2 Japan 78.7 86.4 57.2 68.3 66.1 0.9 -0.3 37.9 41.4 53 Jordan 1.0 28 8 . .5 1.5 2.0 5.2 3.4 14.7 24.6 Kazakhstan 9.1 9.8 7.0 7.3 7.7 0.2 0.6 47.6 47.11` Kenya 7.8 16.2 7.8 15.5 19.0 342.0 46.0 46.1 Korea, Dem. Rep. 10.5 15.1 7.5 11.7 12.3 2.2 0.5 44.8 43.3 Korea, Rep. 23.7 34.1 15.5 24.2 26.6 2.2 0.9 38.7 41.4 Ca. Kuwait 0.8 1.3 0.5 0.8 1.2 2.4 4.0 13.1 31.3 -- ---------- - - ------- <~~~~~~~~~~~~~~~~~~~ Kyrgyz Republic 2.1 2.9 152.1 2.6 _1.6 2.1 47.5 47.3 C Lao PDR 1.8 2.8 1.7 2.5 3.3 2.1 2.6 . Latvi'a 1.7 1.6 1.4 1.3 1.3 -0.5 -0.4 50.8 50.5 Lebanon 1.6 2.7 0.8 1.5 2.0 2.9 2.6 22.6 29.6 Lesotho 0.7 1.2 0.6 0.8 0 9- 1.9 1237.9 36.9 - ----------- - --~~~~~~~~~~~~~~~~~~~~~~~~~~~~a Liberia 1.0 1.7 0.8 1 3 1.6 2.3 2.1 38.4 39.6 Libya 1.6 3.3 0.9 1.5 1.9 2.4 2.4 18.6 23.1 c Lithuania 2.2 2.5 1.8 1:9 2.0 0.3 0.2 49.7 48.0 Macedonia, FYR 1.2 1.4 0.8 1.0 1.0 0.8 0.6 36.1 41.7 Madagascar 4.6 8.1 4.3 7.3 9.7 2.6 2.9 45.2 44.7 Malawi 3.1 5.2 3.1 5.0 6.0 2.3 1.9 50.'6 48.6 Malaysia 7.8 14.4 5.3 9.6 12.7 3.0 2.8 33.7 37.9 Mali 3.3 5.4 3.4 5.3 6.6 2.2 2.3 46.7 46.2 Mauritani'a 0.8 1.4 0.7 1.2 1.6 2.5 2.7 45.0 43.6 Mauritius 0.6 0.8 0.3 0.5 0.6 2.0 1.1 25.7 32.6 Mexico 34.5 60.9 22.0 40.4 50.9 3.0 2.3 26.9 33.2 Moldova 2.6 2.9 2.1 2.2 2.2 0.1 0.2 50.3 48.6 Mongolia 0.9 1.5 0.8 1.2 1.5 2.2 2.1 45.7 47.0 Morocco 10.2 17.6 7.0 11.5 14.7 2.5 2.5 33.'5 34.7 Mozambique 6.4 9 3 6.7 9.2 11.1 1.6 1.9 49.0 48.4 Myanmar 18.6 29.7 17.1 25.4 29.3 2.0 1.5 43.7 43.4 Namibia 0.5 0.9 0.4 0.7 0.8 2.6 1.4 40.1 40.9 Nepal 8.1 127 7.1 10.7 13.6 2.1 2.4 38.8 40.5 Netherlands 9.4 10.8 5.6 7.4 7.6 1.4 0.2 31.5 40.6 New Zealand 2.0 2.5 1.3 1.9 2.0 1.9 0.6 34.3 45.0 Nicaragua 1.5 2.8 1.0 212.9 3.6 3.4 27.6 35.9 Niger 2.7 5.2 2.8 5.1 7.0 3.0 3.2 44.6 44.3 Nigeria 37.0 65 9 29.5 50.3 63.2 2.7 2.3 36.2 36.5 Norw~ay 2.6 2.9 1.9 2.3 2.4 0.9 0.3 40.'5 46.4 Oman 0.6 1.3 0.3 0.6 0.8 3.3 2.7 6.2 17.1 Pakistan 45.4 75.3 29.3 51.7 71.4 2.8 3.2 22.7 28.6 Panama 1.1 1.8 0.7 1.2 1.5 2.8 2.0 29.9 35.3 Papua New Guinea 1.7 2.9 152.5 3.2 2.5 2.3 41.7 42.2 Paraguay 1.7 3.1 1.1 2.1 2.8 3.0 3.0 26.7 30.0 Peru 9.4 15.9 5.4 9.7 12.6 2.9 2.6 23.9 31.3 Philippines 25.8 44.5 18.7 31.9 41.0 2.7 2.5 35.0 37.8 Poland 23.3 26.6 18.5 19.9 20.3 0.4 0.2 45.3 46.4 Portugal 6.2 6.8 4.6 5.1 5.0 0.5 -0.1 38.7 44.0 Puerto Rico 1.9 2.6 1.0 1.5 1.7 1.9 1.2 31.8 37.2 Romania 14.0 15.4 10.9 10.7 10.6 -01 .0. 1 45.8 44.5 Russian Federation 94.7 101.2 76.0 77.7 77.0 0.1 -0.1 49.4 49.2 2.2 Population ages Labor force 15-64 Total Average annual Female millions millions growth rate % % of labor force 1980 2000 1980 2000 2010 1980-2000 2000-2010 1980 2000 Rwancda 2.5 4.5 2.6 4.6 5.8 2.8 2.2 49.1 48.8 Saudi Arabia 5.0 11.2 2.8 6.8 9.6 4.5 3.4 7.6 16.1 Senegal 2.9 5.1 2.5 4.3 5.4 2.6 2.3 42.2 42.6 Sierra Leone 1.7 2.7 1.2 1.9 2.4 2.0 2.4 35.5 36.8 Singapore 1.6 2.8 1.1 2.0 2.2 2.9 1.1 34.6 39.1 Slovak Republic 3.2 3.7 2.5 3.0 3.0 0.9 0.2 45.3 47.8 Slovenia 1.2 1.4 1.0 1.0 1.0 0.3 -0.3 45.8 46.5 Somalia 3.3 4.4 3.0 3.8 5.2 1.2 3.3 43.4 43.4 South Africa 15.2 26.7 10.3 17.0 18.4 2.5 0.8 35.1 37.8 54 Spain 23.5 27.0 14.0 17.4 17.6 1.1 0.1 28.3 37.2 Sri Lanka 8.9 13.1 5.4 8.5 10.1 2.2 1.7 26.9 36.6 Sudan 10.2 17.6 7.1 12.4 16.2 2.8 2.6 26.9 29.5 m Swaziland 0.3 0.6 0.2 0.4 0.5 3.2 2.0 33.5 37.7 Sweden 5.3 5.7 4.2 4.8 4.6 0.7 .0.3 43.8 48.0 Switzerland 4.2 4.8 3.1 3.9 3.9 1.2 0.0 36.7 40.5 E) Syrian Arab Republic 4.2 9.1 2.5 5.2 7.5 3.7 3.8 23.5 27.0 ci0 Tajikistan 2.1 3.5 1.5 2.4 3.3 2.3 3.0 46.9 44.9 > Tanzania 9.3 17.7 9.5 17.3 21.1 3.0 2.0 49.8 49.1 a) 0 Thailand 26.9 41.4 24.4 36.8 40.8 2.1 1.0 47.4 46.3 ~0 3: Trinidad and Tobago 0.7 0.9 0.4 0.6 0.7 1.6 1.6 31.4 34.3 N o Tunisia 3.5 6.2 2.2 3.8 4.8 2.7 2.4 28.9 31.7 0 Turkey 24.9 41.9 18.7 31.3 37.1 2.6 1.7 35.5 37.6 Turkmenistan 1.6 3.0 1.2 2.3 2.9 3.2 2.4 47.0 45.9 Uganda 6.4 10.7 6.6 10.9 14.0 2.5 2.5 47.9 47.6 Ukraine 33.4 33.8 26.4 25.1 24.4 -0.3 -0.3 50.2 48.9 United Arab Emirates 0.7 2.1 0.6 1.4 1.7 4.7 1.9 5.1 14.8 United Kingdom 36.1 39.0 26.9 29.9 29.7 0.5 -0. 1 38.9 44.1 United States 150.6 185.8 110.1 144.7 158.0 1.4 0.9 41.0 46.0 Uruguay 1.8 2.1 1.2 1.5 1.7 1.4 0.9 30.8 41.8 Uzbekistan 8.6 14.6 6.5 10.5 13.3 2.4 2.4 48.0 46.9 Venezue a. RB 8.5 14.9 5.2 9.9 12.8 3.3 2.6 26.7 34.8 Vietnam 28.6 48.1 25.6 40.4 48.0 2.3 1.7 48.1 48.9 West Bank and Gaza... .. . Yemen, Rep. 4.0 8.3 2.5 5.5 7.7 4.0 3.3 32.5 28.1 Yugoslavia, Fed. Rep. 6.5 7.1 4.5 5.1 5.2 0.6 0.3 38.7 42.9 Zambia 2.9 5.1 2.4 4.3 5.1 2.9 1.8 45.4 44.8 Zimbabwe 3.5 6.5 3.2 5.8 6.6 3.0 1.2 44.4 44.5 Low Income 894.7 1,443.2 708.7 1,115.1 1,367.6 2.3 2.0 37.8 37.8 Middle Income 1,200.1 1,774.6 969.3 1,388.8 1,558.3 1.8 1.2 40.2 42.1 Lower middle income 929.9 1,356.8 785.4 1,100.4 1,223.6 1.7 1.1 41.9 43.4 Upper middle income 270.2 417.8 183.9 288.4 334.7 2.2 1.5 33.0 36.7 Low & middle Income 2,094.8 3,217.8 1,678.0 2,503.9 2,926.0 2.0 1.6 39.2 40.2 East Asia & Pacific 820.4 1,239.7 719.3 1,051.7 1,170.0 1.9 1.1 42.5 44.4 Europe & Central Asia 274.2 318.4 214.1 238.1 249.0 0.5 0.4 46.7 46.3 Latin America & Carib. 201.0 324.9 129.8 222.1 269.1 2.7 1.9 27.8 34.8 Middle East & N. Africa 91.6 171.2 54.1 99.0 134.5 3.0 3.1 23.8 27.7 South Asia 510.7 817.4 388.7 602.6 739.9 2.2 2.1 33.8 33.4 SutD-Saharan Africa 197.0 346.3 172.0 290.5 363.5 2.6 2.2 42.0 42.0 High Income 506.2 588.6 358.1 439.4 454.3 1.0 0.3 38.4 43.2 Europe EMU 185.1 204.6 123.4 141.0 141.2 0.7 0.0 36.4 41.3 2.2 About the data Definitions The labor force is the supply of labor available mixing work and family activities during the day. * Population ages 15-64 is the number of for the production of goods and services in an Countries differ in the criteria used to determine people who could potentially be economically economy. It includes people who are currently the extent to which such workers are to be active. * Total labor force comprises people employed and people who are unemployed but counted as part of the labor force. who meetthe ILO definition ofthe economically seeking work as well as first-time job-seekers. active population: all people who supply labor Not everyone who works is included, however. Figure 2.2 forthe production of goods and services during Unpaid workers, family workers, and students a specified period. It includes both the are among those usually omitted, and in some Labor force participation rate, employed and the unemployed. While national countries members of the military are not ages 25-54,1990 and 2000 practices vary in the treatment of such groups counted. The size of the labor force tends to 120 as the armed forces and seasonal or part-time vary during the year as seasonal workers enter 100 workers, the labor force generally includes the and leave it. armed forces, the unemployed, and first-time Data on the labor force are compiled by the . 80 job-seekers, but excludes homemakers and International Labour Organization (ILO) from other unpaid caregivers and workers in the censuses or labor force surveys. For international 60 l informal sector. * Average annual growth rate 55 comparisons the most comprehensive source o 40 l l _ l of the labor force is calculated using the is labor force surveys. Despite the ILO's efforts f l l exponential endpoint method (see Statistical 0 to encourage the use of international standards, 20 i methods for more information). * Females as labor force data are not fully comparable _ . a percentage of the labor force show the extent E because of differences among countries, anda France Norway Philipp.nes- Brazil' to which women are active in the labor force.a sometimes within countries, in their scope and o l990male lN2000male ,D . coverage. In some countries data on the labor 0 1990 female I 2000 female Da s force refer to people above a specific age, while in others there is no specific age provision. The a. Data refer to 1999 rather than 2000. b. Data refer to i The population estimates are from the World CD reference period of the census or survey is 1998 rather than 2000. Bank's population database. The economic another important source of differences: in some Sou,ce: ILO. Key Indrcators orfthe Laou,r Market database 'Iactivity rates are from the LO database countries data refer to people's status on the I Estimates and Projections of the Economicelly E day of the census or survey or during a specific The analysls of labor force partlcipatlon by sex shows Active Population, 1950-2010. The ILO (7 that for economies for which Information Is available, I period before the inquiry date, while in others women re less likely than men to participate In the j publishes estimates of the economically active the data are recorded without reference to any labor force. This reflects whether their work Is re- l population in its Yearbook of Labour Statistics. period. In developing countries, where the gardedaseconomicasItdoesthecompetingdemands of household work and childbearing and childcare. household is often the basic unit of production For the majority of economies, the gap between and all members contribute to output, but some male and female labor force participation has been at low intensity or irregular intervals, the failing. This results from both the reduced rates for estimated labor force may be significantly menandtherisingratesforwomen. smaller than the numbers actually working (ILO, Yearbook of Labour Statistics 1997). The labor force estimates in the table were calculated by World Bank staff by applying eco- nomic activity rates from the ILO database to World Bank population estimates to create a series consistent with these population esti- mates. This procedure sometimes results in estimates of labor force size that differ slightly from those in the ILO's Yearbook of Labour Sta- tistics. The population ages 15-64 is often used to provide a rough estimate of the potential la- bor force. But in many developing countries chil- dren under 15 work full or part time. And in some high-income countries many workers postpone retirement past age 65. As a result, labor force participation rates calculated in this way may systematically over- or underestimate actual rates. In general, estimates of women in the labor force are lower than those of men and are not comparable internationally, reflecting the fact that for women, demographic, social, legal, and cultural trends and norms determine whether their activities are regarded as economic. In many countries large numbers of women work on farms or in other family enterprises without pay, while others work in or near their homes, (D ~2.3 Employment by economic activity Agriculture Industry Services Mae Femnale Male Female Male Female % Of male % of female % of male % of female % of male % of female labor force labor force labor force labor force labor force labor force 1980-82- 1998-2000' 1980-82' 1998-2000' 1980-82' 1998-2000- 1980-82- 1998-20D00 1980-821 1998-2000, 1B04821 1998-20001 Afghanistan 66 .. 86 .. 9 . 12 . 26 2 Algeria 27 .. 69 . 33 ..6 . 40 .. 25 Angola 67 87 .. 13 ..1 . 20 .. 11 Argentina 1 0' . 34 .. 10 . 65 .. 89 Armenia . . . .. Australia 8 6 4 4 39 30 16 10 53 64 79 86 Austria .. 6 .. 7 . 43 14 .. 52 79 Azerbaijan 56 Bangladesh Belarus -- . . . on Belglum 3 .. 2 37 13 .. 60 .. 86 m Benin 66 .. 69 10 ..4 24 .. 27 Bolivia 52 58' 28 2' 21 40' 19 16' 27 58 c 53 82c Bosnia and Herzegovina 26 .. 38 45 24 30 .. 39 E Botswana 6 ..3 .. 41 ..8 .. 53 .. 89 E o Brazil 34 26 20 19 30 27 13 10 36 47 67 71 a) 0 Burkina Faso 92 .. 93 .. 3 ..2 .5 ..5 o Burundi . .. .. .. Cambodia . .. .. .. .. o Cameroon 65 .. 87 .. 11 ..2 .. 24 .. 11 0 (N Canada 7 5 3 2 37 32 16 11 56 63 81 87 Central African Republic 79 .. 90 . 5 ..1 . 15 ..9 Chad 82 .. 95 .. 6 ..0' . . 4 Chile 22 19 3 5 27 31 16 14 51 49 81 82 Hong Kong. China 2 0 1 0' 47 28 56 12 52 71 43 88 Colombia 2 2 1 1 39 27 26 20 59 71 74 79 Congo. Dem. Rep. 62 84 .. 18 ..4 . 20 .. 12 Congo.Rep. 42 .. 81 .. 20 ..2 .. 38 .. 17 Costa Rica 34 27 6 5 25 26 20 17 40 46 74 77 Cote dlvoire 60 .. 75 .. 10 ..5 .. 30 .. 20 Croatia .. 15 . 13 .. 34 . 21 .. 51 . 66 Cuba 30 .. 10 . 32 .. 22 . 39 .. 68 Czech Republic 13 6 11 4 57 49 39 28 30 48 50 69 Denmark 11 5 4 2 41 37 16 15 48 58 80 83 Dominican Republic.. . . ..... .- Ecuador .. 11 .. 2 . 26 .. 14 . 63 .. 84 Egypt, Arab Rep. 45 29 10 35 21 25 13 9 33 46 69 56 El Salvador 51 37 10 6 21 24 21 25 28 38 69 69 Eritrea 79 . 88 .. 7 ..2 . 14 .. 11 Estonia .. 11 .. 7 . 40 .. 23 .. 49 . 70 Finland 15 8 12 4 44 40 23 14 41 52 65 82 France 3 2 1 1 50 35 25 13 48 63 75 86 Gabon 59 . 74 .. 18 ..6 . 24 .. 21 Gambia.The 78 .. 93 .. 10 ..3 .. 13 ..5 Georgia... ........ Germany .. 3 . 2 .. 46 .. 19 so50. 79 Greece 26 16 42 20 34 29 18 12 40 54 40 67 Guinea 86 . 97 .. 2 ..1 . 12 ..3 Guinea-Bissau 81 .. 98 .. 3 ... 17 ..3 Haiti 81 . 53 .. 8 ..8 . 11 .. 39 Honduras .. 50 .. 9 . 21 .. 25 . 30 .. 67 2.3 Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female labor force labor force labor force labor force labor force labor force 1980-82, 1998-2000' 3980-21 i998-2000, 1980821 1998-20001 1980-821 1998-20001 1980-S21 1998-2000' 1980-21 i998-21000 Hungary 24 9 19 4 45 42 36 25 31 48 45 71 Indonesi'a 57 54 -.13 13 29 .. 33 Iran, Islamic Rep. Ira q 21 62 .... 24 11 55 28 Ireland .. 12 2 38 15 50 .. 83 Isra-el 8 3 4 1 39 35 16 13 52 61 79 86 Italy 13 6 ~~~ ~~~~~~~ ~~~~~~~16 5 - 3 39 28 21 44 55 56 74 Jamaica 47 30 23 10 20 26 8 9 33 45 69 81 Japan 9 5 13_ 6 40 38 28.22 51 57 58 73 57 Jordan Kazakhstan ..0 .. .. .. - ---------- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Kenya 23 20 25 16 24 23 9 10 53 57 65 75 Korea, Dem. Rep. 39 .. 52 .. 37 .. 20 .. 24 .. 28 Korea, Rep. 31 10 39 13 32 34 24 19 37 56 37 68 Kuwait 2 .. 36 ..3 .62 .. 97 Kyrgyz Republic .. 52 53 14 .. 8 .. 34 .. 38 CD 0 Lao PDR 77 82 7 .. 4 16 .. 13 'a Latvia .. 17 14 35 .. 18 49 69 69 Lebanon 13 . 20 29 .. 21 .. 58 - . 59 -. Lesotho 26 .. 64 .. 50.5 . 2. 1. Liberia 69 .. 89 .. 9 ..1 .22 .. 10 Libya 16 63 .. 29 3 .. 55 .. 34 . Lithuania .. 24 ... 33 ... 43 Macedonia, FYR Madagascar 73 77 93 76 9 6 2 4 19 16 5 20 Malawi Malaysia 34 21 44 13 26 33 20 29 40 46 36 58 Mali 86 92 2 .. 1 12 7 Mauritania 65 79 11 .. 2 25 19 Mauritius 29 30 .. 19 .. 40 . 47 .. 31 Mexico 27 9 .. 27 .. 21 .. 45 - 69 Moldova .. .. .. .. ..~~~~~~~~. ........ . Mongolia . *. .. Morocco .. 6 6 .. 32 40 .. 63 .. 54 Mozambique 72 .. 97 .. 14 .. 1 . 14 2 Myanmar Namibia 52 42 22 .. 10 . 27 .. 47 Nepal.. . Netherlands 7 4 3 2 39 31 13 9 54 63 84 84 New Zealand .. 11 .. 6 32 12 .. 56 .. 81 Nicaragua Niger 7 6 69 29 25 .. 66 Nigeria Norway 10 6 6 2 41 33 13 9 49 61 81 88 Oman 52 24 21 33 27 43 Pakistan Panama 37 2 5 6 2 21 22 12 10 39 52 81 88 Papua New Guinea 76 92 8 .. 2 . 16 6 Paraguay 2 0 b 35 -. 13 .. 63 86 Peru -. 8 a. 25 .. 11 . 67 .. 86 Philippines 60 47 37 27 16 18 15 13 25 36 48 61 Poland .. 19 19 41 .. 21 -. 39 .. 60 Portugal_ 22 11 35 14 44 44 25 -24 34 45 40 62 Puerto Rico 8 3 Q b 0 b 27 28 24 14 65 69 75 85 Romania 22 39 39 45 52 33 34 22 26 29 27 33 Russian Federation 19 15 13 8 50 36 37 - 23 --31 49 50 69 (9 ~2.3 Agriculture Industry Services male Female Male Female Male Female % of male % of female % of male % of female % of male % of female labor force labor force labor force labor force labor force labor force 1980-821 1998-2000' 1980-21 1998-20001 1980-82V 1998-2000' 1980-82' 199S-2000, 1980-82, 1998-20001 198O-821 1998-20001 Rwanda 88 98 .. 5. 1 .7 ..1 Saudi Arabia 45 .. 25 .. 17 ..5 .. 39 70 Senegal 74 . 90 .. 9 .2 .. 17 8 Sierra Leone 63 .. 82 .. 20 4 .. 17 14 Singapore 2 0~ 1 0~ 33 33 40 23 65 67 59 77 Slovak Republic .. 10o 5 .. 49 .. 26 . 42 .. 69 Slovenia .. 11 . 11 .. 46 .. 28 . 42 .. 61 Somalia 69 .. 90 12 ..2 .. 19 ..8 South Africa.. . 58 Spain 20 9 18 5 42 40 21 14 39 51 60 81 SriLanka 44 38 51 49 19 23 18 22 30 37 28 27 (n Sudan 66 .. 88 9 ..4 24 ..8 co Swaziland 40 .. 38 .. 29 .. 14 . 30 48 Sweden 8 4 3 1 45 38 16 12 47 59 81 87 Switzerlarrd 8 5 5 4 47 36 23 13 46 59 72 83 E Syrian Arab Republic.. ... ....... 0). o Tajikistan.. ... ....... a) > Tanzania.. ....... Thailand 68 50 74 47 13 20 8 17 20 31 18 36 *0 ?: Trinidad and Tobago 11 11 9 3 44 37 21 13 45 52 70 83 O Tunisia 33 .. 53 .. 30 .. 32 .. 37 .. 16 (N Turkey 4 34 9 72 36 25 31 10 60 41 60 18 Turkmenistan - .. .. .. .. Uganda . .. .. .. .. United Arab Emirates 5 ... . 40 ..7 . 55 .. 93 United Kingdom 4 2 1 1 48 36 23 12 49 61 76 87 United States 5 4 2 1 39 32 19 12 56 64 80 86 Uruguay . 6 .. 1 .. 34 . 14 . 61 .. 85 Uzbekistan... ...... ... Venezuela,RB 20 .. 2 . 31 .. 18 . 49 .. 79 West Bank and Gaza 22 .. 25 .. 43 . 25 . 36 .. 50 Yemen, Rep. 60 .. 98 .. 19 ..1 .. 21 ..1 Yugoslavia, Fed. Rep. . .. .. .. .. Zambia 69 .. 85 . 13 ..3 . 19 .. 13 Zimbabwe 29 .. 50 . 31 ..8 . 40 .. 42 Low Income Middie Income . .. .. .. Lower middle income.. .. ...... ... Upper middle income .. 22 . 21 . 31 .. 16 . 48 .. 64 Low & middle Income . . . .. .. East Asia & Pacific . .. .. .. Europe&GCentral Asia .. 21 .. 21 .. 35 . 21 . 44 .. 58 Latin America & Carib. .. 20 .. 11 . 28 .. 14 .. 52 . 75 Middle East & N. Africa .. ... .......... Sub-Saharan Africa . ... ...... .... High Income 7 4 6 2 42 36 22 15 51 60 72 82 Europe EMU .. 4 .. 2 . 41 .. 17 .. 55. 80 a. Data are for fhe most recent year available. o. Less than 0.5. c. Break in series between 1980 and 1990. 2.3 ( About the data Definitions The International Labour Organization (ILO) clas- account for much of the increase in women's * Agriculture includes hunting, forestry, and sifies economic activity on the basis of the In- labor force participation in North Africa, fishing, corresponding to division 1 (ISIC ternational Standard Industrial Classification Latin America and the Caribbean, and high-in- revision 2) or tabulation categories A and B (ISIC) of All Economic Activities. Because this come economies. Worldwide, women are (ISIC revision 3). * Industry includes mining classification is based on where work is per- underrepresented in industry. and quarrying (including oil production), formed (industry) rather than on what type of Segregating one sex in a narrow range of oc- manufacturing, construction, electricity, gas, work is performed (occupation), all of an cupations significantly reduces economic effi- and water, corresponding to divisions 2-5 (ISIC enterprise's employees are classified under the ciency by reducing labor market flexibility and revision 2) or tabulation categories C-F ([SIC same industry, regardless of their trade or oc- thus the economy's ability to adapt to change. revision 3). * Services include wholesale and cupation. The categories should add up to 100 This segregation is particularly harmful for retail trade and restaurants and hotels; percent. Where they do not, the differences arise women, who have a much narrower range of la- transport, storage, and communications; because of people who are not classifiable by bor market choices and lower levels of pay than financing, insurance, real estate, and business economic activity. men. But it is also detrimental to men when job services; and community, social, and personal Data on employment are drawn from labor losses are concentrated in industries dominated services-corresponding to divisions 6-9 (ISIC force surveys, establishment censuses and sur- by men and job growth is centered in service revision 2) or tabulation categories G-P (ISIC 59 veys, administrative records of social insurance occupations, where women often dominate, revision 3). 1) schemes, and official national estimates. The as has been the recent experience in many _ concept of employment generally refers to people countries. D s above a certain age who worked, or who held a There are several explariations for the rising Data sources job, duringa reference period. Employmentdata importance of service jobs for women. Many The employment data are from the ILO a. include both full-time and part-time workers. service jobs- such as nursing and social and database Key Indicators of the Labour Market * There are, however, many differences in how clerical work-are considered "feminine" be- 1(2001-02 issue). C countries define and measure employment sta- cause of a perceived similarity to women's tra- -3 tus, particularly for part-time workers, students, ditional roles. Women often do not receive the CD members of the armed forces, and household training needed to take advantage of changing or contributing family workers. When the armed employment opportunities. And the greater avail- 2, forces are included, they are allocated to the ability of part-time work in service industries may 0 service sector, causing that sector to be some- lure more women, although it is not clear whether what overstated in comparison with economies this is a cause or an effect. where they are excluded. Where data are ob- tained from establishment surveys, they cover only employees; thus self-employed and contrib- Figure 2.3 uting family workers are excluded. In such cases the employment share of the agricultural sector Labor market segregation can be harmful is severely underreported. Countries also take 25 very different approaches to the treatment of unemployed people. In most countries unem- 20 ployed people with previous job experience are classified according to their lastjob. But in some -. countries the unemployed and people seeking la I j , their first job are not classifiable by economic if I . ' activity. Because of these differences, the size 5 s and distribution of employment by economic o __ _ _ _ _.. activity may not be fully comparable across coun- 0 5 10 15 20 25 tries (ILO, Yearbook of Labour Statistics 1996, Male workers I% employed In sector) p. 64). 0 Education * Construction The ILO's Yearbook of Labour Statistics and Soune: ILO. Key Indicator of the Labour Market database Key Indicators of the Labour Market database (200142). report data by major divisions of the ISIC revi- Labor market segregation Is a consequence of men's sion 2 or ISIC revision 3. In this table the re- and women's tendency to be employed In different ported divisions or categories are aggregated occupatlons. The Interest In studying occupaetiona segregation ranges from concerns to Identify whether into three broad groups: agriculture, industry, market forces or policies produced the exisng occu- and services. An increasing number of countries pational structure, to the practical Issues of advanc- report economic activity according to the ISIC. Ing the equalty of women and men In employment Where data are supplied according to national classifications, however, industry definitions and descriptions may differ. In addition, classifica- tion into broad groups may obscure fundamen- tal differences in countries' industrial patterns. The distribution of economic activity by gen- der reveals some interesting patterns. Agricul- ture accounts for the largest share of female employment in much of Africa and Asia. Services D ~2.4 1Unemployment Unemployment Long term Unemployment by level unemployment of educational attaInment Male Female Total % of male % of female % of total % of total unemployment % of total unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 198G-82' 1998-2000' 1980-82' 1.998-2000' 1980-82' 1.998-2000' 1998-2000' 1.998-2000' 1998-2000' 1997-99' 1.997-99' 1997-99' Afghanistan... ........ Albania .. 15.8 .. 20.9 5.6 18.0... Algeria . . . ... Argentina .. 11.9 .. 14.3 2.3 12.8.... Armenia .. 4.9 .. 15.0 .. 9.3 . .. AuStralia 5.0 7.2 7.4 6.7 5.9 6.4 30.6 24.0 27.9 53.3 32.1 11.8 Austria 1.6 4.7 2.3 4.8 1.9 4.7 28.1 36.1 31.7 35.2 60.3 4.6 Azerbaijan .. 1.0 .. 1.4 .. 1.2 ... . 6.7 30.8 62.5 60 Bangladesh . . . .. .. .. Belarus ... .. . 2.0 ... . 7.8 15.5 76.7 o Belgium 5.5 5.8 15.0 8.7 9.1 7.0 60.1 60.9 60.5 53.1 33.4 13.6 Bosnia and Herzegovina . . . .. .. .. E Botswana . . . .. .. o Brazil 2.8 7.2 2.8 11.6 2.8 9.6 . >) Bulgaria .. 16.7 .. 15.9 .. 16.3 58.6 58.7 58.7 7.4 85.3 7.3 o Burkina Paso . . . .... .. Cambodia . . . .. .. .. N o Cameroon... ...,...... cl Canada 7.0 6.9 8.2 6.7 7.5 6.8 11.7 9.5 10.7 25.9 31.2 35.6 Central African Republic . . . .. .. .. Chile 10.6 7.0 10.0 7.6 10.4 9.9 ... . 28.5 56.2 14.6 China ... ... 4.9 3.1 . .. Hong Kong, China 3.9 5.1 3.4 4.0 3.8 5.0 . .. Colombia 7.5 17.2 11.5 23.3 9.1 20.1 ,.. . 21.3 57.8 19.1 Congo, Dem. Rep. . . . .. .. .. Congo, Rep.,.. .. . .. .. Costa Rica 5.3 4.9 7.8 8.2 5.9 6.0 ... . 75.1 12.7 8.1 Ci5te dIlvoire . . . .. .. .. Croatia 3.4 12.8 8.2 14.5 5.3 16.1 56.3 53.6 60.7 19.5 69.1 11.4 Czech Republic .. 7.3 .. 10.6 .. 8.8 47.5 49.8 48.8 24.2 72.1 3.7 Denmark 6.5 4.5 7.6 5.9 7.0 5.4 20.9 20.1 20.5 34,6 47.7 16.7 Dominican Republic ... .. .. .. . 50.4 31.1 9.6 Ecuador .. 8.4 .. 16.0 .. 11.5 . .. Egypt. Arab Rep. 3.9 5.1 19.2 19.9 5.2 8.2 . El Salvador .. 8.2 .. 6.0 12.9 7.3 ... . 57.1 23.4 7.5 Estonia .. 13.0 .. 10.2 .. 14.8 45.4 49,1 47.0 22.5 54.4 23.1 Ethiopia 3.6 .. 9.5 .. 5.2 ...... 26.9 61.3 8.1 Finland 4.6 9.7 4.7 10.7 4.7 9.8 30.1 25.2 27.6 41.1 49.8 9.1 France 4.1 8.5 9.1 11.9 6.1 10.0 41.1 43.6 42.5 Gabon . Gambia. The . Georgia .. 15.3 .. 12.2 .. 13.8 ... . 3.9 32.4 60.8 Germany .. 7.6 .. 8.6 .. 8.1 49.9 54.0 51.7 28.9 57.5 13.6 Greece 3.3 7.0 5.7 16.5 2.4 10.8 44.7 61.5 54.9 36.9 40.5 21.9 Guinea-Bissau... ............ Honduras 8.6 3.7 6.0 3.8 7.3 3.7 ... . 63.2 22.4 5.8 2.4@ Unemployment Long term Unemployment by level unemployment of educational attaInment Male Female Total % of male % of female % of total ft of total unemployment % of total unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1980-821 1998-20001 198G-821 1998-2000, 1980-82V 1998-20001 1998-2000, 1998-2000, 1998-20001 1.997-99' 1997-99' 1997-991 Hungary .. 7.5 .. 6.3 .. 6.5 45.0 43.2 44.3 35.2 61.6 3.2 Indonesia . ... .. 6.1 ... . 38.3 47.9 9.2 Iran, Islamic Rep. Ireland 11.4 4.8 8.2 4.6 10.5 4.7 44.9 23.4 36.5 60.7 20.8 16.1 Israel 4.1 8.5 6.0 8.1 4.8 8.3 .... 23.9 42.2 33.1 Italy 4.8 8.7 13.2 15.7 7.6 10.8 62.1 60.7 61.4 52.3 39.0 6.9 Jamaica 16.3 10.0 39.6 22.5 27.3 15.7 18.0 29.6 25.6 Japan 2.0 5.0 2.0 4.5 2.0 4.8 30.7 17.1 25.5 23.3 51.2 25.6 61 Jordan .. 11.8 20.7 .. 13.2 . .. Kazakhstan ... . 13.7 ... . 7.2 52.5 40.3NJ Kenya . . . ... .. Korea, Dem. Rep. . . . ... .. Korea, Rep. 6.2 7.1 3.5 5.1 5.2 4.1 3.1 0.7 2.3 16.4 52.7 20.0 Kyrgyz Republic ... .. .. .. .-- 33.4 55.7 10.9a Lao PDR Latvia 15.5 13.3 .. 8.4 50.5 52.8 51.5 _ 20.8 68.1 8.5 C Lebanon ... . Lesotho . . -. . .. L ib e r ia -..-- - - --- - - - - - - - - - - - - - - -- - - -------- Libya Lithuania 17.3 13.3 .. 11.1 23.4 19.2 21.6 15.4 56.2 28.5 Macedonia, FYR 15.6 32.5 32.8 37.5 22.0 34.5 -- --.--- . ------- Madagascar... ...... Malawi Malaysia 3.0 Mali Mauritania Mauritius ... 33.2 66.1 Mexico .. 1.8 .. 2.6 .. 2.0 0.4 1.5 0.8 15.5 36.0 37.7 Mongolia .. 5.2 .. 6.3 .. 5.7 ... . 47.9 24.1 17.3 Morocco .. 20.3 .. 27.6 .. 22.0 . .. Mozambique . . . .. .. .. Myanmar.. ....... Namibia.. ....... Nepal 1.5 .. 0.7.. 11. Netherlands 4.3 2.7 5.2 4.9 4.6 3.6 47.7 40.4 43.5 30.4 33.0 14.3 New Zealand .. 6.1 .. 5.8 .. 6.0 20.7 12.6 17.1 0.5 38.5 22.6 Nicaragua .. 8.8 14.5 .. 13.3 ... . 54.9 24.7 14.9 Norway 1.2 3.7 2.1 3.2 1.7 3.4 6.7 2.9 5.0 25.3 54.7 17.3 Oman... ... Pakistan 3.0 4.2 7.5 14.9 3.6 5.9 Panama 6.3 8.9 13.3 16.9 8.4 11.8 . . . Papua Nem Gui'nea... Paraguay 3.8 .. 4.8 .. 4.1 Peru .. 7.5 . 8.6 8.0 ... . 13.1 52.6 33.3 Philippines 3.2 10.3 7.5 9.9 4.8 10.1...... Poland .. 15.2 .. 18.5 16.7 34.2 41.4 37.9 33.1 64.8 2.0 Portugal 3.3 2.9 12.2 4.8- 6.7 3.8 _39.5__ _42.9 41.2 73.9 14.9 5.8 Puerto RICO 19.5 11.9 12.3 7.8 17.1 10.1...... Romania .. 7.4 .. 6.2 .. 10.8 41.0 48.4 44.0 21.7 70.6 6.4 Russian Federation .. 13.6 .. 13.1 .. 11.4 ... 11.9 16.8 41.6 41.6 k ~2.4 Unemployment Long term Unemployment by level unemployment of educational attainment Male Fema e Total % of male % of femnale % of total % of tota. seemployment ft of total unemployment labor force labor force abor force Mae Female Total Pr mrary Secondary Tertiary i980-82V 1998-2000' 198o-s2' 1998-2000, 1980-82' 1998&2000' 1998-2000, 1998-2000' 1998-2000' 1997-99V 1997-99' 1997-99' Rwanda . . . .. .. .. Saudi Arabia . . . .. .. .. Sierra Leone . . . .. .. .. Singapore 2.9 4.5 3.4 4.6 3.0 4.4 ... . 26.8 27.4 28.6 Slovak Republic .. 15.9 .. 16.4 .. 18.9 43.2 49.7 46.1 .. 75.6 3.0 Slovenia .. 7.5 .. 7.4 .. 7.5 44.3 36.8 40.7 28.2 64.8 7.0 South Africa .. 19.8 .. 27.8 .. 23.3 . .. 62 Spain 10.4 9.7 12.8 20.5 11.1 14.1 39.5 52.4 46.8 52.3 19.1 21.5 Sri Lanka .. 5.9 .. 11.0 .. 7.7 ... . 49.8 .. 50.2 di Swaziland . . . .. .. .. C) 11 Switzerland 0.2 2.3 0.3 3.1 0.2 2.7 27.5 29.1 28.3- E Syrian Arab Republic 3.8 .. 3.8 .. 3.9... C. o Tajikistan ... .. .. . 10.6 83.2 6.3 > Tanzania . . . .. .. .. o Thailand 1.0 3.0 0.7 3.0 0.8 2.4 ... . 71.7 12.3 12.9 *0 3: Trinidad and Tobago 8.0 10.9 14.0 16.8 10.0 13.1 19.9 42.3 31.0 38.2 60.7 0.8 04 o Tunisia ,.. ..........,. 33.7 4.1 Turkey 9.0 7.6 23.0 6.6 10.9 8.3 29.6 44.1 33.7 Turkmenistan . . . .. .. Uganda . . . .. .. Ukraine .. 12.2 .. 11.5 .. 11.9 .. 9.4 27.2 63.4 United Arab Emirates . . .. .. .. Unitod Kingdom 8.3 6.7 4.8 5.1 6.8 5.3 34.8 21.6 29.8 9.3 43,4 12.1 United Status 6.9 3.7 7.4 4.6 7.1 4.1 6.7 5.3 6.0 22.2 35.6 42.1 Uruguay .. 8.7 .. 14.6 .. 11.3 . .. Uzbekistan... .......... Venezuela, RB ... . 5.9 14.9 . .. West Bank and Gaza ... . 14.1 . .. Yemen, Rep. . .. .. .. Yugoslavia. Fed. Rep. Zambia 32.7 .. 59.0 .. 42.2... Zimbabwe .. 7.3 .. 4.6 .. 6.0 Low Income . . . .. .. .. Middle Income ... ... 4.8 4.9 . Lower middle income ... ... 4.9 4.3 . Upper middle income .. 7.0 .. 8.9 .. 9.0 Low & middle Income . . . . . .. East Asia & Pacific ... ... 4.7 3.7 . Europe & Central Asia .. 11.3 .. 11.1 .. 11.1 27.1 17.6 47.3 34.8 Latin Amierica & Carib. .. 7.2 .. 10.6 . 9.2 . Middle East & N. Africa . . . South Asia Sub-Saharan Africa High Income 5.5 5.4 7.0 6.7 6.0 6.2 28.4 25.6 27.3 27.3 41.2 27.4 Europe EMU 5.5 7.9 10.8 11.6 7.1 9.8 48.5 50.9 49.8 42.3 42.9 12.9 a. Oats are for the most recast year avaiiable. 2.4 If . About the data Definitions Unemployment and total employment in a coun- fices is a prerequisite for receipt of unemploy- * Unemployment refers to the share of the la- try are the broadest indicators of economic ac- ment benefits, the two sets of unemployment bor force without work but available for and tivity as reflected by the labor market, The Inter- estimates tend to be comparable. Where regis- seeking employment. Definitions of labor force national Labour Organization (ILO) defines the tration is voluntary, and where employment of- and unemployment differ by country (see About unemployed as members of the economically fices function only in more populous areas, the data). * Long-term unemployment refers active population who are without work but avail- employment office statistics do not give a reli- to the number of people with continuous peri- able for and seeking work, including people who able indication of unemployment. Most com- ods of unemployment extending for a year or have lost their jobs and those who have volun- monly excluded from both these sources are longer, expressed as a percentage of the total tarily left work. Some unemployment is unavoid- discouraged workers who have given up their unemployed. * Unemployment by level of edu- able in all economies. At any time some work- job search because they believe that no employ- catlonal attainment shows the unemployed by ers are temporarily unemployed-between jobs ment opportunities exist or do not register as level of educational attainment, as a percent- as employers look for the right workers and work- unemployed after their benefits have been ex- age of the total unemployed. The levels of edu- ers search for betterjobs. Such unemployment, hausted. Thus measured unemployment may be cational attainment accord with the United often called frictional unemployment, results higher in economies that offer more or longer Nations Educational, Cultural, and Scientific from the normal operation of labor markets. unemployment benefits. Organization's (UNESCO) International Stan- 63 Changes in unemployment over time may re- Long-term unemployment is measured in dard Classification of Education. N flect changes in the demand for and supply of terms of duration, that is, the length of time that _ labor, but they may also reflect changes in re- an unemployed person has been without work porting practices. Ironically, low unemployment and looking for a job. The underlying assump- Data sources rates can often disguise substantial poverty in tion is that shorter periods of joblessness are The unemployment data are from the ILO . . a country, while high unemployment rates can of lessconcern, especiallywhenthe unemployed database Key Indicators of the Labour Market (D occur in countries with a high level of economic are covered by unemployment benefits or simi- l (2001-02 issue). ~0 development and low incidence of poverty. In lar forms of welfare support. The length of time L countries without unemployment or welfare ben- a person has been unemployed is difficult to (D efits, people eke out a living in the informal sec- measure, because the ability to recall the length tor. In countries with well-developed safety nets, of that time diminishes as the period of jobless- a workers can afford to wait for suitable or desir- ness extends. Women's long-term unemploy- able jobs. But high and sustained unemployment ment is likely to be lower in countries where indicates serious inefficiencies in the allocation women constitute a large share of the unpaid of resources. family workforce. Such women have more ac- The ILO definition of unemployment notwith- cess than men to nonmarket work and are more standing, reference periods, the criteria for those likely to drop out of the labor force and not be considered to be seeking work, and the treat- counted as unemployed. ment of people temporarily laid off and those No data are given in the table for economies seeking work for the first time vary across coun- for which unemployment data are not consis- tries. In many developing countries it is espe- tently available or are deemed unreliable. cially difficult to measure employment and un- employment in agriculture. The timing of a sur- vey, for example, can maximize the effects of Figure 2.4 seasonal unemployment in agriculture. And in- formal sector employment is difficult to quantify Youth unemployment is rising in many where informal activities are not registered countries and tracked. so - unemployed, ages 15-24 Data on unemployment are drawn from labor force sample surveys and general household 40 l 19s0 sample surveys, social insurance statistics, em- U 1999 ployment office statistics, and official estimates, 30 which are usually based on information drawn from one or more of the above sources. Labor 20 force surveys generally yield the most compre- hensive data because they include groups-par- 10 ticularly people seeking work for the first time- not covered in other unemployment statistics. Turkey Colombia Pakistan Philippines Puerto These surveys generally use a definition of un- RICO employment that follows the international recom- So.xe: ILO. Key Andc.dorS of the Labou, Market daabaose mendations more closely than that used by other (2001.02). sources and therefore generate statistics that are more comparable internationally. the laborforce ages 15-24 who are unemployed. Youth In contrast, the quality and completeness of unemployment Is generally viewed as an Important data obtained from employment offices and so- pollcy Issue for many economies. Low unemployment among youth does not necessarily Imply a high level cial insurance programs vary widely. Where em- of school enrollment, It could Indicate the difficulties ployment offices work closely with social insur- young people have In finding a Job. ance schemes, and registration with such of- __ ~ .5 Wages adproductivity Average hours Minimum wage Agricultural wage Labor cost Value added worked per week per worker per worker In manufacturing In manufacturing $ per year $ per year $ per year $ per year 1980-84 1995-99, 1980-84 1998-99, 1980-84 i995-99' 1 1980-84 1998-99, 1980-84 1899-99, 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 Australia 37 39 .. 12. 712 11.212 15.124 14.749 26,087 27,801 57,857 Austria 33 32 ..11,949 28,342 20,956 53.061 Azerbaijan......... 64 Bangladesh .. 52 .. 492 192 360 556 671 1,820 1. 711 Belarus ...... 1,641 410 2,233 754 o Belgium .. 38 7.661 15.882 6.399 .. 12,805 24 .132 25,579 58.678 V Bolivia .. 46 .. 529 ... 4.432 2,343 21.519 26,282 Bosnia and Herzegovina . .. .. .. E Botswana 45 ..894 961 650 1,223 3.250 2,884 7,791 oL Brazil 1,690 1,308 ... 10,080 14,134 43.232 61,595 a,- > Bulgaria .. 573 .. 1.372 2.485 1,1 79 a) Burkina Faso . 695 585 ... 3,282 .. 15.886 ~0 Cambodia . .. .. .. o Cameroon . .. .. .. 0 N 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 1 7.061 Congo, Dem. Rep. . .. Congo, Rep. . .. .. .. Costa Rica .. 47 1.042 1.638 982 1,697 2,433 2,829 7.185 7,184 C6te dIlvoire ... 1,246 871 ... 5,132 9.995 16,158 Croatia... ..... 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 49.2 73 Dominican Republic 44 44 .. 1.439 ... 2.191 1.806 8.603 Ecuador ... 1.637 492 ... 5.065 3.738 12,197 9.74 7 Egypt, Arab Rep. 56 . 343 415 ... 2.210 1.863 3,691 5.976 El Salvador ... .790 ... 3.654 .. 14.423 Ethiopia ... .. .. . 1.596 .. 7,094 Finland .. 38 ... 11,522 26,615 25,945 55.037 France 40 39 6,053 12.0 72 ... 18.488 .. 26,751 61 .019 Gabon . .. .. .. Gambia. The . .. .. .. Georgia . .. .. .. Germany 41 40 ... . 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 . .. .. .. Honduras .. 44 ... 1.623 .. 2,949 2.658 7,458 7.427 2.50 Average hours Minimum wage Agricultural wage Labor cost Value added worked per week per worker per worker In manufacturing In manufacturing $ per year $ per year $ per year $ per year 198G-84 1995-991 1980-84 1995-99, 1980-84 1995-99' 11980-84 1995-991 1198G-84 1995-99, 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 .24 - ..898 3,054 3,807 5. 13S9 Iran, Islamic Rep. ... .9,737 30,562 17,679 89,787 I raq ... 4,624 13.286 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 39 782 625,218 3,655 12,056 11,091 Japan 47 47 3,920 12,265 . 12,306 31,687 34,456 92,582 6 Jordan .. 50 b 4.643 2,082 16,337 11,906 Kazakhstan 1........ 0 Kenya 41 30 551 508 568 1,043 810 2.345 1,489 Korea, Dem. Rep. - --. Korea, Rep. 52 48 3,903 .. 3,153 10,743 11,617 40,916 o. - -- ------- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Kuwait .. 8,244 .. 10,281 .. 30,341 CDs Kyrgyz Republic 6.58 1,695 168 2,287 687 ..*.C Lao PDR ... . .. 3 Latvia ... .356 CD Lebanon ET... Lesotho .. 45 .. 1,442 .. 6,047 .. 0 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 4,21 7 Mexico 43 45 1,343 768 1,031 98 3,772 7,607 17,448 25.931 Moldova Mongolia Morocco .. 1,672 ..2,583 3,391 6,328 9,089 Mozambique . .. .. Myanmar . .. .. Namibi:a -.... -----. Nepal ... .. .371 .. 1.523 Netherlands 40 40 9,074 15,170 .. 18,891 34,326 27,491 56,801 New Zealand 39 30 3,309 9,091 . 10,605 18,419 16,835 32,723 Nicaragua 44 . ... Niger 40 .... 4,074 .. 22,477 Nigeria 33--0-- ---- 4,812 -..---- 20.000 Norway 35 35 b . 14,935 30,415 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 4,2 . 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 35 SO 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 Romania 34 34 531 1,689 1,864 1,757 1,190 .. 3,482 Russian Federation ..86 297 2,417 659 2,524 1,528 / 2.5 Average hours Minimum wage Agricultural wage Labor coat Value added worked per week per worker per worker In manufacturing In manufacturing $ per year $ per year $ per year $ per year i980-a4 1995-99, 1980-84 1995-99- 1980-84 1985-99, 1980-84 1995-99, 1980-84 1995-99, Rwanda ....... . 1,871 9.835 Saudi Arabia ... ... . 9,814 Senegal ..993 848 ... 2,828 7,754 6,415 Sierra Leone 44 ... . 1,624 .. 7,807 Singapore 48 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 8. .. 6,261 8,475 12,705 16,612 66 Spain 38 37 3,058 5,778 ... 8,276 19,329 18.936 47,016 Sri Lanka 60 53 ... 199 264 447 604 2,057 3,405 (n Sudan 05 Swaziland '~Sweden 36 37 ... 9,576 27.098 13,038 26,601 32,308 56.675 C: (5 Switzerland 44 42 ... .. . 61,848 E) Syrian Arab Republic .... ... 2,844 4.338 9,607 9,918 o Tajikistan......... > Tanzania ... .. . 1,123 .. 3,339 a) Thailand 50 47 749 1,159 .. 2,305 3,868 11,072 19.946 ~0 Trinidad and Tobago 40 .. 2,974 ...... 14,008 o Tunisia 1,381 1,525 668 98 3,344 3.599 7,111 CN 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 ... . 11,406 23,843 24,716 55,060 United States 40 41 6,006 8,056 ... 19,103 28,90qO7 47,276 81,353 Uruguay 48 42 1,262 1,027 1,289 .. 4.128 3,738 13.722 16,028 Uzbekistan . .. .. .. Venezuela 41 .. 1,869 1,463 ... 11,168 4.667 37,063 24,867 Vietnam .47 ..134 ..442 ..71.1 West Bank and Gaza......... Yemen, Rep. ... .. . 4,492 1.291 17,935 5,782 Yugoslavia, FR (Serb./Mont.) . .. .. Zambia ..45 ...... 3,183 4,292 11,753 16,615 Zimbabwe .... . 1.065 4,097 3.422 9,625 11,944 a. Figures in italics refer to 1990-94. b. Country has sectoral minimum wage but no minimum wage policy. 2.5 About the data Definitions Much of the available data on labor markets are the length of the workday and workweek vary * Average hours worked per week refer to all collected through national reporting systems that considerably from one country to another. Sea- workers (male and female) in nonagricultural depend on plant-level surveys. Even when these sonal fluctuations in agricultural wages are more activities or, if unavailable, in manufacturing. data are compiled and reported by international important in some countries than in others. And The data correspond to hours actually worked, agencies such as the International Labour the methods followed in different countries for to hours paid for, or to statutory hours of work Organization or the United Nations Industrial estimating the monetary value of payments in in a normal workweek. * Minimum wage cor- Development Organization, differences in defi- kind are not uniform. responds to the most general regime for nona- nitions, coverage, and units of account limit their Labor cost per worker in manufacturing is gricultural activities. When rates vary across comparability across countries. The indicators sometimes used as a measure of international sectors, only that for manufacturing (or com- in this table are the result of a research project competitiveness. The indicator reported in the merce, if the manufacturing wage is unavail- at the World Bank that has compiled results from table is the ratio of total compensation to the able) is reported. * Agricultural wage is based more than 300 national and international number of workers in the manufacturing sector. on daily wages in agriculture. * Labor cost per sources in an effort to provide a set of uniform Compensation includes direct wages, salaries, worker in manufacturing is obtained by divid- and representative labor market indicators. and other remuneration paid directly by employ- ing the total payroll by the number of employ- Nevertheless, many differences in reporting prac- ers plus all contributions by employers to social ees, orthe number of people engaged, in manu- 67 tices persist, some of which are described security programs on behalf of their employees. facturing establishments. * Value added per M below. But there are unavoidable differences in con- worker In manufacturing is obtained by divid- 0 Analyses of labor force participation, employ- cepts and reference periods and in reporting ing the value added of manufacturing estab- ment, and underemployment often rely on the practices. Remuneration for time not worked, lishments by the number of employees, or the o number of hours of work per week. The indica- bonuses and gratuities, and housing and family number of people engaged, in those establish- a tor reported in the table is the time spent at the allowances should be considered part of the ments. C workplace working, preparing for work, or wait- compensation costs, along with severance and CD_ ing for work to be supplied or for a machine to termination pay. These indirect labor costs can 1 1 be fixed. It also includes the time spent at the vary substantially from country to country, de- Data sources 3CD workplace when no work is being performed but pending on the labor laws and collective bar- The data in the table are drawn from Martin . for which payment is made under a guaranteed gaining agreements in force. Rama and Raquel Artecona's 'Database of j work contract, or time spent on short periods of International competitiveness also depends Labor Market Indicators across Countries, 9 rest. Hours paid for but not spent at the place on productivity, which is often measured by value (2001). of work-such as paid annual and sick leave, added per worker in manufacturing. The indica- i _ paid holidays, paid meal breaks, and time spent tor reported in the table is the ratio of total value in commuting between home and workplace- added in manufacturing to the number of em- are not included. When this information is not ployees engaged in that sector. Total value available, the table reports the number of hours added is estimated as the difference between paid for, comprising the hours actually worked the value of industrial output and the value of plus the hours paid for but not spent in the work- materials and supplies for production (including place. Data on hours worked are influenced by fuel and purchased electricity) and cost of in- differences in methods of compilation and cov- dustrial services received. erage as well as by national practices relating Observations on labor costs and value added to the number of days worked and overtime, per worker are from plant-level surveys covering making comparisons across countries difficult. relatively large establishments, usually employ- Wages refer to remuneration in cash and in ing 10 or more workers and mostly in the formal kind paid to employees at regular intervals. They sector. In high-income countries the coverage exclude employers' contributions to social se- of these surveys tends to be quite good. In de- curity and pension schemes as well as other veloping countries there is often a substantial benefits received by employees under these bias toward very large establishments in the for- schemes. In some countries the national mini- mal sector. As a result, the data may not be mum wage represents a 'floor,"with higher mini- strictly comparable across countries. The data mum wages for particular occupations and skills are converted into U.S. dollars using the aver- set through collective bargaining. In those coun- age exchange rate for each year. tries the agreements reached by employers as- The data in the table are period averages and sociations and trade unions are extended by the refer to workers of both sexes. government to all firms in the sector, or at least to large firms. Changes in the national minimum wage are generally associated with parallel changes in the minimum wages set through col- lective bargaining. In many developing countries agricultural work- ers are hired on a casual or daily basis and lack any social security benefits. International com- parisons of agricultural wages are subject to greater reservations than those of wages in other activities. The nature of the work carred out by different categories of agricultural workers and (D ~~~2.6 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 Nationaul Survey Rural Urban National survey $1 a day $1 a day $2 a duy $2 a day year % % % year % % year % % % Afghanistan.. . .. . .. ..* Albania 1994 28.9 . .. 1996 .. 15.0 Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995 <2 <0.5 15.1 3.6 Angola.. ....... Argentina 1991 . .. 25.5 1993 ... 17.6 . Armenia . .. .... 1996 7.8 1.7 34.0 11.3 Australia.. . ..... . Austria.. ..... ... Azerbaijan 1995 . .. 68.1 ... . 1995 <2 <0.5 9.6 2.3 68 Bangladesh 1991-92 46.0 23.3 42.7 1995-96 39.8 14.3 35.6 1996 29.1 5.9 77.8 31.8 Belarus 2000 . .. 41.9 ... . 1998 <2 <0.5 <2 <0.5 rn Benin 1995 . .. 33.0... ... Bolivia 1993 .. 29.3 .. 1995 79.1 . .. 1999 14.4 5.4 34.3 14.9 Bosnia and Herzegovina . . . .. .. a) Botswana . .. .... .. 1985-86 33.3 12.5 61.4 30.7 o Brazil 1990 32.6 13.1 17.4 ... . 1998 11.6 3.9 26.5 11.6 > Bulgaria . . .... .. 1997 <2 <0.5 21.9 4.2 Burkina Faso . . .... .. 1994 61.2 25.5 85.8 50.9 Burundi 1990 . .. 36,2... ... 0 Cambodia 1993-94 43.1 24.8 39.0 1997 40.1 21.1 36.1 . C'd o Cameroon 1984 32.4 44.4 40.0 .. . . 1996 33.4 11.8 64.4 31.2 0 CN Canada . . . .. .. Central African Republic . .. .... .. 1993 66.6 38.1 84.0 58.4 Chad 1995-96 67.0 63.0 64.0... ... Chile 1996 . .. 24.6 1998 ... 21.2 1998 <2 <0.5 8.7 2.3 China 1996 7.9 <2 6.0 1998 4.6 <2 4.6 1999 18.8 4.4 52.6 20.9 Hong Kong, China . . . .. .. Colombia 1991 29.0 7.8 16.9 1992 31.2 8.0 17.7 1998 19,7 10.8 36.0 19.4 Congo. Dem. Rep. . . . .. .. Congo, Rep. . . . .. .. Costa Rica 1992 25.5 19.2 22.0 ... . 1998 12.6 6.2 26.0 12.8 CMe dIlvoire 1993 . .. 32.3 1995 ... 36.8 1995 12.3 2.4 49.4 16.8 Croatia . .. .... .. 1998 <2 <0.5 <2 <0.5 Czech Republic . .. .... .. 1996 <2 <0.5 <2 <0.5 Denmark . . . .. .. Dominican Republic 1989 27.4 23.3 24.5 1992 29.8 10.9 20.6 1996 3.2 0.7 16.0 5.0 Ecuador 1994 47.0 25.0 35.0 ... . 1995 20.2 5.8 52.3 21.2 Egypt, Arab Rep. 1995-96 23.3 22.5 22.9 ... . 1995 3.1 <0.5 52.7 13.9 El Salvador 1992 55.7 43.1 48.3 ... . 1998 21.0 7.8 44.5 20.6 Eritrea 1993-94 . .. 53.0 . . . . . Estonia 1995 14.7 6.8 8.9 ... . 1998 <2 <0.5 5.2 0.8 Ethiopia . .. .... .. 1995 31.3 8.0 76.4 32.9 France.. ...... .... Gambia. The 1992 . .. 64.0 ... . 1998 59.3 28.8 82.9 51.1 Georgia 1997 9.9 12.1 11.1 ... . 1996 <2 <0.5 <2 <0.5 Germany.. . ..... .... Ghana 1992 34.3 26.7 31.4 ... . 1999 44.8 17.3 78.5 40.8 Greece . . . .. .. .. Guatemala 1989 71.9 33.7 57.9 ... . 1998 10.0 2.2 33.8 11.8 Guinea 1994 . .. 40.0... .. . Guinea-Bissau 1991 . .. 48.7... ... . Haiti 1987 . .. 65.0 1995 66.0 .. .. Honduras 1992 46.0 56.0 50.0 1993 51.0 57.0 53.0 1998 24.3 11.9 45.1 23.5 2.6 0- 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 dap $1 a day $2 a day $2 a day pear % % % year % p ear % % Hungary 1989 . .. 1.6 1993 ... 8.6 1998 <2 <0.5 7.3 1.7 India 1992 43.5 33.7 40.9 1994 36.7 30.5 35.0 1997 44.2 12.0 86.2 41.4 Indonesia 1996 15.7 1999 ... 27.1 1999 7.7 1.0 55.3 16.5 Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica 1992 . .. 33.9 2000 18.7 1996 3.2 0.7 25.2 6.9 Japan . . .. .. .. 6 Jordan 1991 . .. 15.0 1997 ... 11.7 1997 <2 <0.5 7.4 1. 4 -- -------- - ---- -- ---- - -- - ---- ------- - -- ------- ........ ....~ ~ ~ ~ ~ ~ ~ ~ ~~ ~~ ~~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~I" Kazakhstan 1996 39.0 30.0 34.6 1996 <2 <0.5 15.3 3.9 N - - - - - -..-- ..- .. -.- ..... _ _ .-_ _ ' . .0 Kenya ___1992 46.4 29.3 42.0 1994 26.5 9.0 62.3 27.5 Korea, Dem. Rep. Korea, Rep. . .. .... .. 1993 <2 <0.5 <2 <0 5 CL Kyrgyz Republic 1993 48.1 28.7 40.0 1997 64.5 28.5 51.0 ..D. Lao PDR 1993 53.0 24 0 46.1 ...1997 26.3 6.3 73.2 29.60 Latvia - .....-... ..1998 <2 <0.5 8.3 2.0 Lebanon . .. -. . . ----------- ----- --- ----- - ------ ET~~~~~~~~~~~~~~~~~~~~0 Lesotho 1993 53.9 27.8 49.2 ...1993 43.1 20.3 65.7 38.1 2 Liberia E.. . . .. Libya CO ( Lithuania ...1996 <2 <0.5 7.8 2.0C Macedonia, FYR . . . .. Madagascar 1993-94 77.0 47.0 70.0 .. 1999 49.1 18.3 83.3 44.0 Malawi 1990-91 . .. 54.0 Malaysia 1989 . .. 15.5 Mali . . .194 78 37.4 906 60.5 Mauritania 1989-90 . .. 57.0 199 28.6 9.1 68.7 29.6 Mauritius 1992 10.6 - - ----- Z Mexico 1988 10.1 1998 15.9 5.2 37.7 16.0 Moldova 1997 26.7 23.3 .. 1997 11.3 3.0 38.4 14.0 Mongolia 1995 33.1 38.5 36.3 ... . 195 13.9 3. 1 50.0 17.5 Morocco 1990-91 18.0 7.6 13.1 1998-99 27.2 12.0 19.0 1990-91 <2 <0.5 7.5 1.3 Mozambique .. . .. .1996 37.9 12.0 78.4 36.8 Myanmnar.. . ...... Nam bi --.------- ------ -. ---- 1993 34.9 14.0 5.5.8 30.4 Nepal 1995-96 44.0 23.0____42.0 . .... ..... ........ .. .. ... 1995 37.7 9.7 82.5 37.5 Netherlands New Zealand Nicaragua 1993 76.1 31.9 50.3 Niger 1989-93 66.0 52.0 63.0 ... . 1995 61.4 33.9 85.3 54.8 Nigeria 1985 49.5 31.7 43.0 1992-93 36.4 30.4 34.1 1997 70.2 34.9 90.8 59.0 Norway Oman Pakistan 1991 36.9 28.0 34.0 1996 31 0 6.2 84.7 35.0 Panama 1997 64.9 15.3 37.3 ...1998 14.0 5.9 29.0 13.8 Papua New Guinea... ... Paraguay 1991 28.5 19.7 21.8 ...1998 19.5 9.8 49.3 26.3 Peru 1994 67.0 46.1 - 53.5 1997 64.7 40.4 49 0 1996 15.5 5.4 41.4 17.1 Philippines 1994 53.1 28.0 40.6 1997 50.7 21.5 36.8 Poland 1993 23.8 ...1998 <2 <0.5 <2 <0.5 Portugal ...1994 <2 <0.5 <2 <0.5 Puerto Rico Romania 1994 27.9 20.4 21.5 1994__ 2.8 0.8 27.5 6.9 Russian Federation 1994 . .. 30.9 1998 7 1 1.4 25.1 8.7 2.6 National poverty line International poverty line Population below the Population below the Populiation Poverty Population Poverty poverly line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban Nat onal I Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % year % % % Rwanda 1993 . .. 51.2 .. . .1983-85 35.7 7.7 84.6 36.7 Saudi Arabia..... . Senegal 1992 40.4 .. 33.4 .. . . 1995 26.3 7.0 67.8 28.2 Sierra Leone 1989 76.0 53.0 68.0 .. . . 1989 57.0 39.5 74.5 51.8 Singapore.. .... *. Slovak Republic . .. .... .. 1992 <2 <0.5 <2 <0.5 Slovenia , . ... 1998 <2 <0.5 <2 <0. 5 Somalia . . . .. South Africa . .. . .. 1993 11.5 1.8 35.8 13.4 70 Spain . . . .. .. Sri Lanka 1990-91 . .. 20.0 1995-96 . .. 25.0 1995 6.6 1.0 45.4 13.5 Sudan . . . .. .. to Swaziland 1995 . .. 40.0... ... Sweden . . . .. Switzerland . . . .. a) Syrian Arab Republic . . . .. a) Tajikistan . . . .. .. > Tanzania 1991 . .. 51.1 1993 49.7 24.4 41.6 1993 19.9 4.8 59.7 23.0 a) o Thailand 1990 . .. 18.0 1992 15.5 10.2 13.1 1998 <2 <0.5 28.2 7.1 ~0 3: Trinidad and Tobago 1992 20.0 24.0 21.0 .. . . 1992 12.4 3.5 39.0 14.6 O Tunisia 1985 29.2 12.0 19.9 1990 21.6 8.9 14.1 1995 <2 <0.5 10.0 2.3 0 Turkey .. . .1994 2.4 0.5 18.0 5.0 Turkmenistan .. . .1998 12.1 2.6 44.0 15.4 Uganda 1993 . .. 55.0 Ukraine 1995 . .. 31.7 1999 2.9 0.6 31.0 8.0 United Arab Emnirates . . . .. United Kingdom United States Uruguay 1989 <2 <0.5 6.6 1.9 Uzbekistan . ..1993 3.3 0.5 26.5 7.3 Venezuela, RB 1989 -. . 31.3 1998 23.0 10.8 47.0 23.0 Vietnam 1993 57.2 25.9 50.9 . West Bank and Gaza.. . .... ... Yemen, Rep. 1992 19.2 18.6 19.1 1998 15.7 4.5 45.2 1S.0 Yugoslavia. FR (Serb./Mont.) . . . .. Zambia 1991 88.0 46.0 68.0 1993 86.0 1998 63.7 32.7 87.4 55.4 Zimbabwe 1990-91 31.0 10.0 25.5 ,1990.91 36.0 9.6 64.2 29.4 2.6 About the data Definitions International comparisons of poverty data en- for differences in the cost of living. As with inter- * Survey year is the year in which the underly- tail both conceptual and practical problems. Dif- national comparisons, when the real value of ing data were collected. * Rural poverty rate ferent countries have different definitions of the poverty line varies, it is not clear how mean- is the percentage of the rural population living poverty, and consistent comparisons between ingful such urban-rural comparisons are. below the national rural poverty line. * Urban countries can be difficult. Local poverty lines tend The problems of making poverty comparisons poverty rate is the percentage of the urban to have higher purchasing power in rich coun- do not end there. More issues arise in measur- population living below the national urban tries, where more generous standards are used ing household living standards. The choice be- poverty line. * National poverty rate is the per than in poor countries. Is it reasonable to treat tween income and consumption as a welfare centage of the population living below the two people with the same standard of living-in indicator is one issue. Income is generally more national poverty line. National estimates are terms of their command over commodities-dif- difficult to measure accurately, and consump- based on population-weighted subgroup esti-- ferently because one happens to live in a bet- tion accords better with the idea of the stan- mates from household surveys. * Population ter-off country? Can we hold the real value of dard of living than does income, which can vary below $1 a day and population below $2 a day the poverty line constant across countries, just over time even if the standard of living does not. are the percentages of the population living on as we do when making comparisons over time? But consumption data are not always available, less than $1.08 a day and $2.15 a day at 1993 Poverty measures based on an intemational and when they are not there is little choice but international prices (equivalent to $1 and $2 71 poverty line attempt to do this. The commonly to use income. There are still other problems. in 1985 prices, adjusted for purchasing power used $1 a day standard, measured in 1985 Household survey questionnaires can differ parity). Poverty rates are comparable across 0 intemational prices and adjusted to local currency widely, for example, in the number of distinct countries, but as a result of revisions in PPP using purchasing power parities (PPPs), was categories of consumer goods they identify. exchange rates, they cannot be compared with E chosen for the World Bank's World Development Survey quality varies, and even similar surveys poverty rates reported in previous editions for CL Report 1990: Poverty because it is typical of may not be strictly comparable. individual countries. * Poverty gap is the mean C the poverty lines in low-income countries. PPP Comparisons across countries at different shortfall from the poverty line (counting the CD 0 exchange rates, such as those from the Penn levels of development also pose a potential prob- nonpoor as having zero shortfall), expressed B World Tables or the World Bank, are used be- lem, because of differences in the relative im- as a percentage of the poverty line. This C cause they take into account the local prices of portance of consumption of nonmarket goods. measure reflects the depth of poverty as well goods and services not traded internationally. The local market value of all consumption in kind as its incidence. But PPP rates were designed not for making (including consumption from own production, international poverty comparisons but for com- particularly important in underdeveloped rural paring aggregates from national accounts. As a economies) should be included in the measure Data sources result, there is no certainty that an intemational of total consumption expenditure. Similarly, the The poverty measures are prepared by the poverty line measures the same degree of need imputed profit from production of nonmarket World Bank's Development Research Group. or deprivation across countries. goods should be included in income. This is not The national poverty lines are based on the Past editions of the World Development Indi- always done, though such omissions were a far I Bank's country poverty assessments. The cators used PPPs from the Penn World Tables. bigger problem in surveys before the 1980s. 1 international poverty lines are based on Because the Penn World Tables updated to 1993 Most survey data now include valuations for i nationally representative primary household are not yet available, this year's edition (like last consumption or income from own production. surveys conducted by national statistical offices year's) uses 1993 consumption PPP estimates Nonetheless, valuation methods vary. For ex- I or by private agencies under the supervision produced by the World Bank. The international ample, some surveys use the price in the near- i of government or international agencies and poverty line, set at $1 a day in 1985 PPP terms, est market, while others use the average farm obtained from government statistical offices has been recalculated in 1993 PPP terms at gate selling price. and World Bank country departments. The about $1.08 a day. Any revisions in the PPP of a Whenever possible, consumption has been World Bank has prepared an annual review of country to incorporate better price indexes can used as the welfare indicator for deciding who is poverty work in the Bank since 1993. Poverty produce dramatically different poverty lines in poor. When only household income was avail- Reduction and the World Bank: Operationalizing local currency. able, average income has been adjusted to ac- the World Develoment Report 2000/01 is Problems also exist in comparing poverty cord with either a survey-based estimate of forthcoming. measures within countries. For example, the cost mean consumption (when available) or an esti- of living is typically higher in urban than in rural mate based on consumption data from national areas. (Food staples, for example, tend to be accounts. This procedure adjusts only the mean, more expensive in urban areas.) So the urban however; nothing can be done to correct for the monetary poverty line should be higher than the difference in Lorenz (income distribution) curves rural poverty line. But it is not always clear that between consumption and income. the difference between urban and rural poverty Empirical Lorenz curves were weighted by lines found in practice properly reflects the dif- household size, so they are based on percen- ference in the cost of living. In some countries tiles of population, not households. In all cases the urban poverty line in common use has a the measures of poverty have been calculated higher real value-meaning that it allows the from primary data sources (tabulations or house- purchase of more comrnodities for consump- hold data) rather than existing estimates. Esti- tion-than does the rural poverty line. Some- mation from tabulations requires an interpola- times the difference has been so large as to tion method; the method chosen was Lorenz imply that the incidence of poverty is greater in curves with flexible functional forms, which have urban than in rural areas, even though the re- proved reliable in past work. verse is found when adjustments are made only CO ~2.7 Social indicators of poverty Survey year Infant Delivery attendance Prevalence of Low mother's Total mortality rate by a medically child malnutrition body-mass fertility rate trained person Index % of births in the per 1.000 five years prior to % of children line births the Survey under flve % of women births per woman Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest qulntlie qulntle quintle quintle qulntile quintle quintle qulntile quintlie quintle Bangladesh 1996-97 96 57 2 30 60 28 64.4 32.6 3.8 2.2 Benin 1996 119 63 34 98 37 19 21.0 7.0 7.3 3.8 Bolivia 1998 107 26 20 98 17 3 0.5 2.2 7.4 2.1 Brazil 1996 83 29 72 99 12 3 8.8 5.4 4.8 1.7 Burkina Faso 1992-93 114 80) 26 86 36 22 15.7 10.2 7.5 4.6 Cameroon 1991 104 51 32 95 25 6 ... 6.2 4.8 Central African Republic 1994-95 132 54 14 82 37 20 16.3 11.2 5.1 4.9 Chad 1996-97 80 89 3 47 50 29 27.5 21,0 7.1 6.2 Colombia 1995 4 1 16 6 1 98 15 3 5.9 1.2 5.2 1.7 72 Comoros 1996 87 65 26 85 36 18 7.4 8.6 6.4 3.0 Cote dIlvoire 1994 117 63 17 84 31 13 11.0 5.7 6.4 3.7 Dominican Republic 1996 67 23 89 98 13 1 8.9 3.0 5.1 2.1 o Egypt, Arab Rep. 1995-96 110 32 21 86 17 8 2.9 0.4 4.4 2.7 Ghana 1993 78 46 25 8 5 33 13 11.3 7.2 6.7 3.4 c: Guatermala 1995 57 35 9 92 35 7 4.2 2.0 8.0 2.4 E Haiti 1994-95 94 74 24 78 39 10 24.9 9.3 7.0 2.3 o lIndia 1992-93 109 44 12 79 60 34 4.1 2.1 a) o Kazakhstan 1995 35 29 99 100 11 3 7.9 3.8 3.2 1.3 o Kenya 1998 103 50 23 80 32 10 17.6 8.0 6.6 3.0 Kyrgyz Republic 1997 83 46 96 100 13 8 5.6 3.7 4.6 2.0 a Madagascar 1997 119 58 30 89 45 32 24.3 15.1 8.1 3.4 0 Malawi 1992 141 106 45 78 34 17 14.1 6.0 7.2 6.1 Mali 1995-96 151 93 11 81 47 28 15.9 12.2 6.9 5.1 Morocco 1993 80 35 5 78 17 2 6.2 1.8 6.7 2.3 Mozambique 1997 188 9 5 18 82 37 14 17.2 4.2 5.2 4.4 Namibia 1992 64 57 51 91 36 13 19.3 5.3 6.9 3.6 Nepal 1996 96 64 3 34 53 28 25.7 21.4 6.2 2.9 Nicaragua 1997-98 51 26 33 92 18 4 4.0 4.1 6.6 1.9 Niger 1998 131 86 4 63 52 37 26.7 12.8 8.4 5.7 Nigeria 1990 102 69 12 70 40 22 ... 6.6 4.7 Pakistant 1990-91 89 63 5 55 54 26 5.1 4.0 Paraguay 1990-91 43 16 41 98 6 1 ... 7.9 2.7 Peru 1996 78 20 14 97 17 1 1.3 1.1 6.6 1.7 Philippines 1998 49 21 21 92 ...... 6.5 2.1 Senegal 1997 85 45 20 86 ...... 7.4 3.6 Tantzani'a 1996 87 65 27 81 40 18 12.2 7.1 7.8 3.9 Togo 1998 84 66 25 91 32 12 13.3 7.9 7.3 2.9 Turkey 1993 100 25 43 99 22 3 2.7 3.2 3.7 1.5 Uganda 1995 109 63 23 70 31 16 12.7 5.8 7.5 5.4 Uzbekistan 1996 50 47 92 100 25 12 11.4 5.7 4.4 2.1 Vietnam 1997 43 17 49 99 ...... 3.1 1.6 Yemen, Rep. 1997 109 60 7 50 20 6 39.0 13.1 7.3 4.7 Zambia 1996 124 70 19 91 32 13 10.2 7.9 7.4 4.4 Zimbabwe 1994 52 42 55 93 19 9 5.7 1.2 6.2 2.8 2.7 About the data Definitions The data in the table describe the health status Figure 2.7 * Survey year is the year in which the underly- of individuals in different socioeconomic groups ing data were collected. * Infant mortality rate within countries. The data are from Demographic Children fully immunized, by quintile, is the number of infants dying before reaching and Health Surveys conducted by Macro Inter- various years one year of age, per 1,000 live births. The es- national with the support of the U.S. Agency for timates are based on births in the 10 years International Development. These large-scale 100 Poorestquintile preceding the survey and may therefore differ household sample surveys, conducted periodi- 0 Richestquintile from the estimates in table 2.20. * Delivery cally in about 50 developing countries, collect 80 attendance by a medically trained person re- information on a large number of health, nutri- s fers to births attended by a doctor, nurse, or E 60 tion, and population measures as well as on nurse-midwife. * Prevalence of child malnutri- respondents' social, demographic, and eco- N 4 tion is the percentage of children whose weight nomic characteristics using a standard set of & 4 is more than two standard deviations belowv questionnaires. at the median reference standard for their age In the table socioeconomic status is defined as established by the U.S. National Center for in terms of household assets, including owner- Health Statistics, the U.S. Centers for Disease 73 0 ship of consumer items, characteristics of the ,e v1' e' Control and Prevention, and the World Health household's dwelling, and other characteristics ,7P 0 sf s Organization. The data are based on a sample ° related to wealth. Each household asset for of children who survived to age three, four, or which information was collected was assigned 70 five years, depending on the country. * Low E a weight generated through principal component 60 0 Male mother's body mass index refers to the pera analysis. The resulting scores were standard- Female centage of women whose body mass index cD ized and then used to create break points defin- s (BMI) is less than 18.5, a cutoff point indicat- ( 0 0 ing wealth quintiles, expressed as quintiles of ,, 40 ing acute malnutrition. The BMI is the weight individuals. in kilograms divided by the square of the height 30 The choice of the asset index for defining , in meters. * Total fertility rate is the number socioeconomic status was based on pragmatic , 20 of children that would be born to a woman it , rather than conceptual considerations: Demo- I0 she were to live to the end of her childbearing graphic and Health Surveys do not provide in- years and bear children in accordance with come or consumption data but do have detailed 0 Poorest Second Third Fourth Richest current age-specific fertility rates. The esti- information on household ownership of con- mates are based on births during the three sumer goods and access to a variety of goods Source Demographic and Health Saney data years preceding the survey and may therefore and services. Like income or consumption, the Governments In developing countrles usually finance differ from those in table 2.17. asset index defines disparities in primarily eco- Immunization against childhood diseases as part nomic terms. It therefore excludes other possi- of the basic health package. The large discrepancies between poor and rich quintiles Indicate the lack of I Data sources bilities of disparities among groups, such as accessto basic health care amongthe poor. And while those based on gender, education, ethnic back- thedifferencesinimmunizationratesforboysandgirls Data are from an analysis of Demographic and i ground, or other facets of social exclusion. To acrossquintilesinindiapolntstofemaledisadvantage, Health Surveys by the World Bank and Macro the data underscore that poverty has a larger Impact i that extent the index provides only a partial view on access to health care than does gender. International. Country reports are available at of the multidimensional concepts of poverty, www.worldbank.org/poverty/health/data/ inequality, and inequity. index.htm. The analysis has been carried out for 45 coun- - tries, with the results issued in country reports. The table shows the estimates for the poorest and richest quintiles only; the full set of esti- mates for more than 20 indicators is available in the country reports (see Data sources). (D 2.8 Distribution of income or consumption Survey Gini Index Percentage share of Income or consumption year Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Afghanistan .. .. .. .. Albania Algeria 1995 d 35.3 2.8 7.0 11.6 16.1 22.7 42.6 26.8 Angola Argentina Armenia 1996 s. 44.4 2.3 5.5 9.4 13.9 20.6 50.6 35.2 Australia 1994 c.d 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 1995 c. 31.0 2.5 6.9 13.2 18.1 23.9 38.0 22.5 Azerbaijan 1995 cd 36.0 2.8 6.9 11.5 16.1 22.3 43.3 27.8 74 Bangladesh 1995-96 33.6 3.9 8.7 12.0 15.7 20.8 42.8 28.6 Belarus 1998 a 21.7 5.1 11.4 15.2 18.2 21.9 33.3 20.0 O Belgium 1996 28.7 3.2 8.3 13.9 18.0 22.6 37.3 23.0 ic Benin D Bolivia 1999a 44.7 1.3 4.0 9.2 14.8 22.9 49.1 32.0 Bosnia and Herzegovina .. .. .. .. E' Botswana E oL Brazil 1998 60.7 0.7 2.2 5.4 10.1 18.3 64.1 48.0 > Bulgaria 1997 26.4 4.5 10.1 13.9 17.4 21.9 36.8 22.8 D Burkina Faso 1998 cc 55.1 2.0 4.6 7.2 10.8 17.1 60.4 46.8 Burundi 1998 cc 42.5 1.8 5.1 10.3 15.1 21.5 48.0 32.9 B Cambodia 1997 db 40.4 2.9 6.9 10.7 14.7 20.1 47.6 33.8 o Cameroon 1996 a. 47.7 1.9 4.6 8.3 13.1 20.9 53.1 36.6 0 C'J Canada 1994 d 31.5 2.8 7.5 12.9 17.2 23.0 39.3 23.8 Central African Republic 1993 b 61.3 0.7 2.0 4.9 9.6 18.5 65.0 47.7 Chad Chile 1998 'd 56.7 1.3 3.3 6.5 10.9 18.4 61.0 45.6 China 1998 c,d 40.3 2.4 5.9 10.2 15.1 22.2 46.6 30.4 Hong Kong, China 1996 Cu 52.2 1.8 4.4 8.0 12.2 18.3 57.1 43.5 Colombia 1996 c 57.1 1.1 3.0 6.6 11.1 18.4 60.9 46.1 Congo, Dem. Rep. Congo, Rep. .. .. .. .. .. Costa Rica 1997 c 45.9 1.7 4.5 8.9 14.1 21.6 51.0 34.6 Cote d'lvoire 1995 36.7 3.1 7.1 11.2 15.6 21.9 44.3 28.8 Croatia 1998 c.c 29.0 3.7 8.8 13.3 17.4 22.6 38.0 23.3 Cuba Czech Republic 1996 c'd 25.4 4.3 10.3 14.5 17.7 21.7 35.9 22.4 Denmark 1992 ' 24.7 3.6 9.6 14.9 18.3 22.7 34.5 20.5 Dominican Republic 1998 c.c 47.4 2.1 5.1 8.6 13.0 20.0 53.3 37.9 Ecuador 1995 ab 43.7 2.2 5.4 9.4 14.2 21.3 49.7 33.8 Egypt, Arab Rep. 1995 a,S 28.9 4.4 9.8 13.2 16.6 21.4 39.0 25.0 El Salvador 1998 52.2 1.2 3.3 7.3 12.4 20.7 56.4 39.5 Eritrea .. .. .. .. .. Estonia 1998 Cc 37.6 3.0 7.0 11.0 15.3 21.6 45.1 29.8 Ethiopia 1995 40.0 3.0 7.1 10.9 14.5 19.8 47.7 33.7 Finland 1991 cc 25.6 4.2 10.0 14.2 17.6 22.3 35.8 21.6 France 1995 .cd 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon Gambia, The 1998 a 50.2 1.6 4.0 7.6 12.4 20.8 55.3 38.2 Georgia 1996 cd 37.1 2.3 6.1 11.4 16.3 22.7 43.6 27.9 Germany 1994 c.u 30.0 3.3 8.2 13.2 17.5 22.7 38.5 23.7 Ghana 1999 c.c 40.7 2.2 5.6 10.0 15.1 22.6 46.7 30.1 Greece 1993 c.d 32.7 3.0 7.5 12.4 16.9 22.8 40.3 25.3 Guatemala 1998 c.d 55.8 1.6 3.8 6.8 10.9 17.9 60.6 46.0 Guinea 1994 a. 40.3 2.6 6.4 10.4 14.8 21.2 47.2 32.0 Guinea-Bissau 1991 c 56.2 0.5 2.1 6.5 12.0 20.6 58.9 42.4 Guyana 1993 ". 40.2 2.4 6.3 10.7 15.0 21.2 46.9 32.0 Haiti Honduras 1998 S.d 56.3 0.6 2.2 6.4 11.8 20.3 59.4 42.7 2.80 Survey Gini Index Percentage share of Income or consumption year Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Hungary 1998 24.4 4,1 10.0 14,7 18.3 22.7 34.4 20.5 Indi 1997' 37.8 3.5 8.1 11.6 15.0 19.3 46.1 33.5 Indonesia 1999 ~ 31.7 4.0 9.0 12.5 16.1 21.3 41.1 26.7 Iran, Islamic Rep.. .. Iraq - ----- -- --- Ireland 1987 C 35.9 2.5 6.7 11.6 16.4 22.4 42.9 27.4 Israel 1997 38.1 2.4 6.1 10.7 15.9 23.0 44.2 28.3 Italy 1995C 1 __27.3 _35 8.7 14.0 18.1 22.9 36.3 21.8 Jamaica 2000 SCb 37.9 2.7 6.7 10.7 15.0 21.8 46.0 30.3 Japan 1993 294.8 10.6 14.2 17,6 22.0 35.7 21.7 Jordan 1997 36A4 _33__ 7 6__ 11.4__ 15.5 21.1 44.4 29.8 __ Kazakhstan 1996 35.4 2.7 6.7 11.5 16.4 23.1 42.3 26.3 Kenya 1997 44.9 2 4 5.6 9.3 13.6 20.3 51.2 36.1 Korea, Dem. Rep.. ... - - -- --- --- --- - - - - - - . ..... --- ----- -- - - --- ------- -- -- - --0 Korea, Rep. 1993 31.6 2.9 7.5 12.9 17.4 22.9 39.3 24.3 R Kyrgyz Republic 1999 34.6 3.2 7.6 11.7 16.1 22.1 42.5 27.2 C Lao POR 1997 303.2 7.6 11.4 15.3 20.8 45.0 -. 30.6 Latvi:a-- - 1998C 32.4 2.9 7.6 12.9 17.1 22.1 40.3 25.9 C Lebanon- Lesotho 1986-87 56.0 0.9 2.8 6.5 11.2 19.4 60.1 43.4 0. Liberia 0 Libya . Lithuania 1996 ~ 32.4 3.1 7.8 12.6 16.8 22.4 40.3 25.6 Luxembourg 1994C 26.9 4.0 9.4 13.8 17.7 22.6 36.5 22.0 Macedonia, FYR Madagascar 1999 38.1 2.6 6.4 10.7 15.6 22.5 44.9 28.6 Malawi Malaysia 1997 C 49.2 1.7 4.4 8.1 12.9 20.3 54.3 38.4 Mali 1994 50.5 1.8 4.6 8.0 11.9 19.3 56.2 40.4 Mauritania 1995 S 37.3 2.5 6.4 11.2 16.0 22.4 44.1 28.4 Mauritius Mexico 1998 53.1 1.3 3.5 7.3 12.1 19.7 57.4 41.7 Mold-ova 1997 40.6 2.2 5.6 10.2 15.2 22.2 46.8 30.7 Mon golIi a 1995 33.2 --2.9 7.3 12.2 16.6 23.0 40.9 24.5 Morocco 1998-99 395 2.6 6.5 10.6 14.8 21.3 46.6 30.9 Mozambique 1996-971-1 39.6 2.5 6.5 10.8 15.1 21.1 46.5 31.7 Myanmar...... Namibia...... Nepal 1995-96 ~ 36.7 3.2 7.6 11.5 15.1 21.0 44.8 29.8 Netherlands 1994 CC 32.6 2.8 7.3 12.7 17.2 22.8 40.1 25.1 New Zealand Nicaragua 1998 60.3 0.7 2.3 5.9 10.4 17.9 63.6 48.8 Niger 1995_ SC 50.5 0.8 2.6 7.1 13.9 23.1 53.3 35.4 Nigeria 1996-97 S 50.6 1.6 4.4 8.2 12.5 19.3 55.7 40.8 Norway 1995 25.8 4.1 9.7 14.3 17.9 22.2 35.8 21.8 Oman . .. Pakistan 1996-97 . 31.2 4.1 9.5 12.9 16.0 20.5 41.1 27.6 Panama 1997 C 48.5 1.2 3.6 8.1 13.6 21.9 52.8 35.7 Papua New Guinea 1966C 50.9 1.7 4.5 7.9 11.9 19.2 56.5 40.5 Paraguay 1998 1. 57.7 0.5 1.9 6.0 11.4 20.1 60.7 43.8 Peru 1996 C 46.2 1.6 4.4 9.1 14.1 21.3 51.2 35.4 Philippi-nes 1997 46.2 2.3 5.4 8.8 13.2 20.3 52.3 36.6 Poland 1998C 31.6 3.2 7.8 12.8 17.1 22.6 39.7 24.7 Portugal 1994-95CC, 35.6 3.1 .... 7.3 11.6 15.9 21.8 43.4 28.4 Puerto Rico.. .... Romania 1998 S.C 31.1 3.2 8.0 13.1 17.2 22.3 39.5 25.0 Russian Federation 1998 .. 48.7 1.7 4.4 8.6 13.3 20.1 53.7 38.7 CD 2.8 Survey Gini Index Percentage share of Income or consumption year Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Rwanda 1983-85 28.9 4.2 9.7 13.2 16.5 21.6 39.1 24.2 Saudi Arabia .. .. .. .. Senegal 1995 41.3 2.6 6.4 10.3 14.5 20.6 48.2 33.5 Sierra Leone 1989 8 62.9 0.5 1.1 2.0 9.8 23.7 63.4 43.6 Singapore .. .. .. .. .. Slovak Republic 1992 19.5 5.1 11.9 15.8 18.8 22.2 31.4 18.2 Slovenia 1998' 28.4 3.9 9.1 13.4 17.3 22.5 37.7 23.0 Somalia .. .. .. .. .. South Africa 1993-94 a 59.3 1.1 2.9 5.5 9.2 17.7 64.8 45.9 76 Spain 1990 e 32.5 2.8 7.5 12.6 17.0 22.6 40.3 25.2 - Sri Lanka 1995 ' 34.4 3.5 8.0 11.8 15.8 21.5 42.8 28.0 St. Lucia 1995 42.6 2.0 5.2 9.9 14.8 21.8 48.3 32.5 ,,o Sudan .. .. .. .. .. G Swaziland 1994 60.9 1.0 2.7 5.8 10.0 17.1 64.4 50.2 Sweden 1992 ' 25.0 3.7 9.6 14.5 18.1 23.2 34.5 20.1 a) Switzerland 1992 G 33.1 2.6 6.9 12.7 17.3 22.9 40.3 25.2 o Syrian Arab Republic .. .. .. > Tajikistan 1998 34.7 3.2 8.0 12.9 17.0 22.1 40.0 25.2 a) O Tanzania 1993 b 38.2 2.8 6.8 11.0 15.1 21.6 45.5 30.1 D'O Thailand 1998 a 41.4 2.8 6.4 9.8 14.2 21.2 48.4 32.4 0 B Togo : Trinidad and Tobago 1992 ' 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 c° ~ Tunisia 1995 41.7 2.3 5.7 9.9 14.7 21.8 47.9 31.8 Turkey 1994 tr 41.5 2.3 5.8 10.2 14.8 21.6 47.7 32.3 Turkmenistan 1998 c 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7 Uganda 1996 a 37.4 3.0 7.1 11.1 15.4 21.5 44.9 29.8 Ukraine 1999 ab 29.0 3.7 8.8 13.3 17.4 22.7 37.8 23.2 United Arab Emirates .. .. .. .. .. United Kingdom 1995 36.8 2.3 6.1 11.6 16.4 22.7 43.2 27.7 United States 1997 40.8 1.8 5.2 10.5 15.6 22.4 46.4 30.5 Uruguay 1989 ca 42.3 2.1 5.4 10.0 14.8 21.5 48.3 32.7 Uzbekistan 1998 C4 44.7 1.2 4.0 9.5 15.0 22.4 49.1 32.8 Venezuela, RB 1998 Cb 49.5 0.8 3.0 8.2 13.8 21.8 53.2 36.5 Vietnam 1998 ab 36.1 3.6 8.0 11.4 15.2 20.9 44.5 29.9 West Bank and Gaza .. .. .. .. .. Yemen, Rep. 1998 33.4 3.0 7.4 12.2 16.7 22.5 41.2 25.9 Yugoslavia, FR (Serb./Mont.) .. .. .. .. .. .. Zambia 1998 a.b 52.6 1.1 3.3 7.6 12.5 20.0 56.6 41.0 Zimbabwe 1995 db 50.1 2.0 4.7 8.0 12.3 19.4 55.7 40.4 a. Refers to expenditure shares by percentiles of population. b. Ranked by per capita expenditure. c. Refers to income shares by percentiles of population. d. Ranked by per capita income. About the data Definitions Inequality in the distribution of income is re- tries are calculated directly from the Luxem- * Surveyyear is the year in which the underlying flected in the percentage shares of either in- bourg Income Study database, using an esti- data were collected. * Gini Index measures come or consumption accruing to segments of mation method consistent with that applied for the extent to which the distribution of income the population ranked by income or consump- developing countries. (or, in some cases, consumption expenditure) tion levels. The segments ranked lowest by per- among individuals or households within an sonal income receive the smallest share of to- economy deviates from a perfectly equal tal income. The Gini index provides a conve- distribution. A Lorenz curve plots the cumulative nient summary measure of the degree of in- percentages of total income received against equality. the cumulative number of recipients, starting Data on personal or household income or with the poorest individual or household. The consumption come from nationally representa- Gini index measures the area between the tive household surveys. The data in the table Lorenz curve and a hypothetical line of absolute refer to different years between 1985 and equality, expressed as a percentage of the 2000. Footnotes to the survey year indicate maximum area under the line. Thus a Gini index whether the rankings are based on per capita of zero represents perfect equality, while an 77 income or consumption. Each distribution is index of 100 implies perfect inequality. based on percentiles of population-rather than * Percentage share of Income or consumption g of households-with households ranked by in- is the share that accrues to subgroups of come or expenditure per person. population indicated by deciles or quintiles. R Where the original data from the household Percentage shares by quintile may not sum to survey were available, they have been used to 100 because of rounding. (D directly calculate the income (or consumption) oD shares by quintile. Otherwise, shares have been I Data sources i ~~~~~~~~~~~~~~~CD estimated from the best available grouped data. Data sources The distribution indicators have been ad- The data on distribution are compiled by the I justed for household size, providing a more World Bank's Development Research Group I , consistent measure of per capita income or using primary household survey data obtained consumption. No adjustment has been made from government statistical agencies and World for spatial differences in cost of living within Bank country departments. The data for high- countries, because the data needed for such income economies are from the Luxembourg calculations are generally unavailable. For fur- Income Study database. ther details on the estimation method for low- and middle-income economies see Ravallion and Chen (1996). Because the underlying household surveys differ in method and in the type of data col- lected, the distribution indicators are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achieving strict comparability is still impossible (see About the data for table 2.6). Two sources of noncomparability should be noted. First, the surveys can differ in many re- spects, including whether they use income or consumption expenditLire as the living standard indicator. The distribution of income is typically more unequal than the distribution of consump- tion. In addition, the definitions of income used usually differ among surveys. Consumption is usually a much better welfare indicator, par- ticularly in developing countries. Second, house- holds differ in size (number of members) and in the extent of income sharing among mem- bers. And individuals differ in age and consump- tion needs. Differences among countries in these respects may bias comparisons of dis- tribution. World Bank staff have made an effort to en- sure that the data are as comparable as pos- sible. Whenever possible, consumption has been used rather than income. The income dis- tribution and Gini indexes for high-income coun- 00 2.9 Assessing vulnerability Urban Informal sector Children 10-14 Pension contributors Private employment In the labor force health expenditure % of urban employment % of Male Female Total % of age group % of working age % of 1995-99, 1995-99, 1995-99, ±980 2000 Year l abor force population Year total Afghanistan ... .28 24..- Albania .. . .4 0 1995 32.0 31.0 1999 25.9 Algeria .. . .7 0 1997 31.0 23.0 1998 27.8 Angola ... .30 26 Argentina 48 36 43 8 2 1995 53.0 39.0 1999 71.9 Armenia .. . .0 0 1995 66.6 49.4 1995 60.3 Australia ... .0 0 ... 1998 30.0 Austria .. . .0 0 1993 95.8 76.6 1999 27.9 Azerbaijan .. . .0 0 1996 52.0 46.0 1997 32.5 78 Bangladesh .. . . 35 28 1993 3.5 2.6 1998 52.5 Belarus .. . .0 0 1992 97.0 94.0 1998 18.1 o Belgium .. . .0 0 1995 86.2 65.9 1999 28.7 m Benin .. . . 30 26 1996 4.8 .. 1998 50.6 0O Bolivia ... 53 19 11 1999 14.8 13.3 1998 36.6 C Bosnia arid Herzegovina ... .1 0 E Botswana 12 28 19 26 14 .. 1998 38.3 a. o Brazil 43 31 38 19 14 1996 36.0 31.0 1998 55.9 a) 0 Burundi .. . . 50 49 1993 3.3 3.0 1999 5.7 Cambodia .. . . 27 24 ... 1998 91.6 o Cameroon .. . . 34 23 1993 13.7 11.5 1997 79.9 0 Canada ..0 0 1992 91.9 80.2 1998 29.9 Central African Republic ... .. . 1998 33.0 Chad .. . . 42 37 1990 1.1 1.0 1998 21.4 Chile 33 32 32 0 0 1995 70.0 43.0 1998 53.5 China .. . . 30 8 1994 17.6 17.4 1999 59.2 Hong Kong, China .. . .6 0 ... 1995 55.0 Colombia 49 44 47 12 6 1999 35.0 29.3 1998 44.8 Congo, Dem. Rep. .. . . 33 29 Congo, Rep. .. . . 27 25 1992 5.8 5.6 1998 65.8 Costa Rica 43 36 40 10 4 1998 50.6 38.5 1998 22.6 C6te dIlvoire 37 73 53 28 19 1997 9.3 9.1 1998 67.6 Croatia 6 7 6 0 0 1997 66.0 57.0 1997 16.4 Cubea.... 0 0 ... 1994 9.4 Czech Republic .. . .0 0 1995 85.0 67.2 1999 8.5 Denmark .. . .0 0 1993 89.6 88.0 1999 17.8 Dominican Republic .. . . 25 13 1999 14.4 12.4 1998 61.3 Ecuador 54 55 53 9 4 1999 43.1 33.8 1998 54.1 Egypt, Arab Rep. .. . . 18 9 1994 50.0 34.2 1997 52.6 El Salvador .. . . 17 14 1996 26.2 25.0 1998 64.2 Eritreea.... 44 38 ... 1994 45.1 Estonia .. . .0 0 1995 76.0 67.0 1999 16.6 Ethiopia 19 53 33 46 41 ... 1998 58.4 Finland .. . .0 0 1993 90.3 83.6 1999 24.3 France .. . .0 0 1993 88.4 74.6 1999 21.9 Gabon .. . . 29 14 1991 7.3 7.0 1998 33.3 Gambia, The .. . . 44 34 . 1998 50.1 Georgia .. . .0 0 1996 77.0 72.0 1999 73.0 Germany .. . .0 0 1995 94.2 82.3 1999 24.7 Ghana ... 79 16 12 1993 7.2 9.0 1998 61.4 Greece .. . .5 0 1996 88.0 73.0 1998 43.7 Guatemala .. 19 14 1999 22.8 19.3 1998 52.5 Guinea .. . . 41 31 1993 1.5 1.8 1998 39.6 Guinea-Bissau ... .43 37... Haiti ... .33 23 ... 1998 66.0 Honduras 53 58 55 14 7 1999 20.6 17.7 1998 54.4 2.9 . i Urban Informal sector Children 10-14 Pension contributors Private employment In the iabor force health expenditure % of urban employment % of Male Female Total % of age group % of working age % of 1995-99' 1995-991 1995-99' 1980 2000 1 Year labor force population Year total Hungary ... .0 0 1996 77.0 65.0 1998 23.5 India ... .21 12 1992 10.6 7.9 1997 85.0 Indonesia 19 23 21 13 8 1995 8.0 7.0 1998 53.7 Iran, Islamic Rep. 3 90 18 14 3 1994 29.8 .. 1998 59.3 Iraq . .11 2 ... 1998 32.1 Ireland . .1 0 1992 79.3 64.7 1998 23.2 Israel ....0 0 1992 82.0 63.0 1998 37.4 Italy ....2 0 1997 87.0 68.0 1999 32.0 Jamai'ca 26 21 24 0 0 1999 44.4 45.8 1998 44.6 Japan . ..0 0 1994 97.5 92.3 1998 21.5 79 Jordan ...4 0 1995 40.0 25.0 1998 47.0 Kazakhstan . ..0 0 1997 51.0 44.0 1999 51.9 Kenya .58 45 39 1995 18.0 *24.0 1998 69.8 Korea, Dem. Rep. . .3 0 . .. Korea, Rep. ... .0 0 1996 58.0 43.0 1999 56.1 C Kuwait ...0 0 ... 1998 12.1 ( Kyrgyz Republic -..---------- 0- 0 1997 44.0 42.0 1999 50.5 C Lao PDR ....31 25 ... 1998 51.6 ' Latvia . . 17 0 0 1995 60.5 52.3 1998 38.3 C Lebanon ....5 0 ... 1998 80.1 E Lesotho ... .28 21 . Liberia . ..26 15 .. Libya ... .9 0 . .~ f Lithuania 12 5 9 0 0 ... 1998 24.2 Macedonia, FYR . .1 0 1995 49.0 47.0 1998 15.4 Madagascar . . 58 40 34 1993 5.4 4.8 1998 46.7 Malawi . .45 31 ... 1998 56.0 Malaysia . .8 2 1993 48.7 37.8 1998 42.3 Mali 71 61 51 1990 2.5 2.0 1998 51.4 Mauritania . .30 22 ... 1998 71.1 Mauritius ..5 2 ... 1998 46.4 Mexico 38 30 35 9 5 1997 30.0 31.0 1998 52.0 Moldova ...3 0 ... 1998 32.7 Mongolia . .4 1 ... 1992 8.0 Morocco . . 21 1 1994 20.9 17.8 1998 72.7 Mozambique . .39 32 ... 1998 19.0 Myanmar 53 57 54 28 23 1998 87.0 Namibia ..34 17 .. 1998 47.8 Nepal ..56 42 .. 1998 76.5 Netherlands .0 0 1993 91.7 75.4 1999 31.5 New Zealand .0 0 ... 1999 22.5 Nicaragua .19 12 1999 14.3 13.3 1998 32.1 Niger 48 44 1992 1.3 1.5 1998 53.1 Nigeria 29 24 1993 1 .3 1.3 1998 70.1 Norway 0 0 1993 94.0 85.8 1999 24.2 Oman . .6 0 ... 1998 17.1 Pakistan . .23 15 1993 3.5 2.1 1998 76.4 Panama 36 28 32 6_ 3 1998 51.6 40.7 1998 32.3 Papua New Guinea . .28 17 ... 1998 21.6 Paraguay .58 15 6 1997 31.0 29.0 1998 68.0 Peru 45 53 48 4 2 1997 _20.0 16.0 1998 61.0 Philippines 16 19 17 14 5 1996 28.3 13.6 1999 57.1 Poland 14 11 13 0 0 1996 68.0 64.0 1999 24.9 Portugal ... .8 1 196 84.3 80.0 1998 33.1 Puerto Rico ... .0 0... Romania .. . .0 0 1994 55.0 48.0 1998 32.6 Russian Federation .0 0 ... 1997 27.8 (CD 2.9 Urban Informal sector Children 10.14 Pension contributors Private employment In the labor force health expenditure % of urban emrploymnent % of Male Female Total % of age groap % of work ng age % of 1.595-995 1995-99, 1995-99* 1980 2000 Year abof force popu ation Year total Rwanda .. . . 43 41 1993 9.3 13.3 1998 51.8 Saudi Arabia ... .5 0 ... 1997 20.0 Senegal .. . . 43 27 1998 4.3 4.7 1998 41.6 Sierra Leone ... .19 14 ... 1998 83.4 Singapore .. . .2 0 1995 73.0 56.0 1998 64.0 Slovak Republic 25 11 19 0 0 1996 73.0 72.0 1998 21.2 Slovenia ....0 0 1995 86.0 68.7 1998 12.0 Somalia ... .38 31 South Africa 11 26 17 1 0 . . 1998 53.4 80 Spain .. . .0 0 1994 85.3 61.4 1998 23.1 Sri Lanka ... .4 2 1992 28.8 20.8 1999 51.0 (n Sudan ... .33 27 1996 3.9 .. 1997 79.1 at Swaziland ..17 12 ... 1998 28.0 Sweden ..0 0 1994 91 1 88.9 1998 16.2 Switzerland ..0 0 1994 98.1 96.8 1998 26.4 E) Syrian Arab Republic ... .14 2 ... 1998 65.0 o Tajikistan ... .0 0 ... 1998 14.5 > Tanzania 60 85 67 43 37 1996 2.0 2.0 1998 58.0 a) o Thailand 75 79 77 25 12 1999 18.0 17.0 1998 68.7 ~0 ?: Trinidad and Tobago ... .1 0 ... 1998 42.4 N Tunisia ... .6 0 1991 39.4 27.2 1998 56.5 Turkey ... .21 8 1990 34.6 .. 1998 28.1 Turkmenistan ... .0 0 ..1998 20.8 Uganda .. . . 49 44 1994 8.2 1998 68.8 Ukraine 5 5 5 0 0 1995 69.8 66.1 1999 33.3 United Arab Emirates ... .0 0 ..1998 90.3 United Kingdom ... .0 0 1994 89.7 84.5 1999 16.7 United States ... .0 0 1993 94.0 91.9 1999 55.5 Uruguay 39 41 36 4 1 1995 82.0 78.0 1998 79.4 Uzbekistan ... .0 0 ... 1998 15.6 Venezuela, RB 47 46 47 4 0 1999 23.6 18.2 1998 38.1 Vietnam .. . . 22 5 1998 8.4 10.0 1998 83.5 West Bank and Gaza . .. .. Yemen, Rep. ... .26 19 .. 1997 57.1 Yugoslavia, FR (Serb./Mont.) ... .0 0 Zambia .. . . 19 16 1994 10.2 7.9 1998 48.3 Zimbabwe ... .37 27 ... 1999 50.1 Low income 25 18 71.4 Middle Income 21 6 52.7 Lower ntiddle income 24 6 54.6 Upper miiddle income 10 6 46.8 Low & middle income 23 12 61.3 East As a & Pacific 26 8 60.3 Europe & Central Asia 3 1 28.1 Latin America & Carib. 13 8 54.2 Middle East & N. Africa 14 4 50.6 South Asia 23 15 80.2 Sub-Saharan Africa 35 29 60.6 High Income 0 0 35.1 Europe EMU 1 0 26.3 a. Data are for tbs most recent year available. 2.9 About the data Definitions As traditionally defined and measured, poverty zation, and International Monetary Fund country * Urban Informal sector employment is broadly is a static concept, and vulnerability a dynamic reports. Coverage by pension schemes may be characterized as employment in units in urban one. Vulnerability reflects a household's resil- broad or even universal where eligibility is de- areas that produce goods or services on a small ience in the face of shocks and the likelihood termined by citizenship, residency, or income scale with the primary objective of generating that a shock will lead to a decline in well-being. status. In contribution-related schemes, how- employment and income for those concerned. It is therefore primarily a function of a house- ever, eligibility is usually restricted to individu- These units typically operate at a low level of hold's asset endowment and insurance mecha- als who have made contributions for a minimum organization, with little or no division between nisms. Because poor people have fewer assets number of years. Definitional issues-relating labor and capital as factors of production. Labor and less diversified sources of income than to the labor force, for example-may arise in relations are based on casual employment, the better-off, fluctuations in income affect comparing coverage by contribution-related kinship, or social relationships rather than them more. schemes over time and across countries (for contractual arrangements. * Children 10-14 Poor households face many risks, and vul- country-specific information see Palacios and In the labor force refer to the share of that age nerability is thus multidimensional. The indica- Pallares-Miralles 2000). Coverage may be over- group active in the labor force. * Pension tors in the table focus on individual risks-infor- stated in countries that do not attempt to count contributors refer to the share of the labor force mal sector employment, child labor, income in- informal sector workers as part of the labor force. or working-age population (here defined as ages 81 security in old age-and the extent to which Total expenditure on health in a country can 20-59) covered by a pension scheme. * Private N) publicly provided services may be capable of be divided into two main categories by source health expenditure includes direct household g mitigating some of these risks. Poor people face of funding: public and private. Public health ex- (out-of-pocket) spending, private insurance, labor market risks, often having to take up pre- penditure consists of spending by central and spending by non-profit institutions serving o carious, low-quality jobs in the informal sector local govemments, including social health insur- households (other than social insurance), and c and to increase their household's labor market ance funds. Private health expenditure includes direct service payments by private C participation through their children. Income se- private insurance, direct out-of-pocket payments corporations. curity is a prime concern for the elderly. And by households, and spending by non-profit 3 affordable access to health care is a primary institutions serving households, and private . (D concern for all poor people, for whom illness corporations. In countries where the propor- j Data sources and injury have both direct and opportunity costs. tion of out-of-pocket private expenditure is large, The data on urban informal sector employment a Forinformal sectoremploymentthemostcom- lower-income households may be particularly are from the International Labour Organization I E mon sources of data are labor force and special vulnerable to the impoverishing effects of nec- i (ILO) database Key Indicators of the Labour j informal sector surveys, based on a mixed essary health care. I Market (2001-02 issue). The child labor force household and enterprise survey approach or participation rates are from the ILO database | an economic or establishment census approach. Estimates and Projections of the Economically Other sources include multipurpose household Active Population, 1950-2010. The data onVi surveys, household income and expenditure pension contributors are drawn from Robert surveys, surveys of household industries or eco- Palacios and Montserrat Pallares-Miralles's 'In- nomic activities, small and micro enterprise ternational Patterns of Pension Provision', surveys, and official estimates. The international (2000). For updates and further notes and comparability of the data is affected by differ- sources go to the World Bank's Web site on | ences among countries in definitions and cover- pensions (www.worldbank.org/pensions). The age and in the treatment of domestic workers data on private health expenditure for develop- and those who have a secondary job in the in- ing countries are largely from the World Health formal sector. The data in the table are based Organization's World Health Report 2000 andJ on national definitions of urban areas estab- World Health Report 2001, from household sur i lished by countries. For details on country defi- veys and from World Bank poverty assessments' nitions see the notes in the data source. and sector studies. The data on private healthi Reliable estimates of child labor are hard to expenditure for member countries of the obtain. In many countries child labor is officially j Organisation for Economic Co-operation an(j presumed not to exist and so is not included in Development (OECD) are from the OECD. surveys or in official data. Underreporting also occurs because data exclude children engaged in agricultural or household activities with their families. Most child workers are in Asia. But the share of children working is highest in Africa, where, on average, one in three children ages 10-14 is engaged in some form of economic activity, mostly in agriculture (Fallon and Tzannatos 1998). Available statistics suggest that more boys than girls work. But the number of girls working is often underestimated because surveys exclude those working as unregistered domestic help or doing full-time household work to enable their parents to work outside the home. Data on pension contributors come from na- tional sources, the International Labour Organi- (~~D 2.10 Enhancing security Public expenditure Public expenditure Public expenditure on pensions on health on education' Average Per student pension % of % of GDP % of % of per % of GDP per capita Year GDP Year capita income Year GDP 1998 1998 Afghanistan... Albania 1995 5.1 ..1999 2.0 Algeria 1997 2.1 1991 75.0 1998 2.6 6.0 22.2 Angola ......2.6 19.1 Argentina 1994 6.2 ..1999 2.4 ..14.7 Armenia 1996 3.1 1996 18.7 1999 4.0 2.0 Australia 1997 5.9 1989 37.3 1998 6.0 4.8 Austria 1997 14.4 1993 69.3 1999 5.9 6.3 36.5 Azerbaijan 1996 2.5 1996 51.4 1999 1.0 3.4 15.1 82 Bangladesh 1992 0.0 ..1998 1.7 Belarus 1997 7.7 1995 31.2 1998 4.6 5.6 o Belgium 1997 12.9 ..1999 6.3 iv Benin 1993 0.4 1993 189.7 1998 1.6 2.6 13.8 -o Bolivia 1995 2.5 ..1998 4.1 Bosnia and Herzegovina ...1999 8.0 E Botswana ...1998 2.5 9.1 30.1 o) Brazil 1996 4.9 ..1999 2.9 4.6 16.1 >, Bulgaria 1996 7.3 1995 39.3 1999 3.9 3.4 Burkina Paso 1992 0.3 1992 207.3 1999 1.5 3.0 Burundi 1991 0.2 1991 57.4 1998 0.6 3.9 39.9 Cambodia ...1998 0.6 5.5 26.0 o Cameroon 1993 0.4 ..1998 1.0 2.6 13.7 0 04 Canada 1997 5.4 1994 54.3 1999 6.6 5.6 27.6 Central African Republic 1990 0.3 ..1998 2.0 1.9 Chad 1997 0.1 ..1998 2.3 1.7 Chile 1993 5.8 1993 56.1 1998 2.7 3.7 15.5 China 1996 2.7 ..1999 2.1 Hong Kong, China ...1996 2.1 Colombia 1994 1.1 1989 72.2 1998 5.2 Congo. Dem. Rep.... Congo, Rep. 1992 0.9 ..1998 2.0 4.7 Costa Rica 1996 3.8 1993 76.1 1998 5.2 6.0 C6te dIlvoire 1997 0.3 ..1998 1.2 4.2 24.3 Croatia 1997 11.6 ..1999 9.5 Cuba 1992 12.6 ..1994 8.3 Czech Republic 1999 9.8 1996 37.0 1999 6.6 4.2 23.8 Denmark 1997 8.8 1994 46.7 1999 6.9 8.2 44.3 Dominican Republic ...1998 1.9 Ecuador 1997 1.0 ..1998 1.7 Egypt, Arab Rep. 1994 2.5 1994 45.0 1997 1.8 El Salvador 1996 1.3 ..1998 2.6 Eritrea .. 1997 2.9 5.0 51.3 Estonia 1995 7.0 1995 56.7 1999 5.1 6.8 32.8 Ethiopia 1993 0.9 ..1999 1.3 4.3 41.6 Finland 1997 12.1 1994 57.4 1999 5.2 France 1997 13.4 ..1999 7.3 5.9 28.9 Gabon ...1998 2.1 3.3 10.8 Gambia, The ...1999 2.3 4.8 Georgia 2000 2.7 1996 12.6 1999 0.8 Germany 1997 12.1 1995 62.8 1999 7.9 4.6 27.2 Ghana 1993 0.1 ..1999 1.7 4.0 Greece 1993 11.9 1990 85.6 1998 4.7 Guatemala 1995 0.7 1995 27.6 1998 2.1 2.0 Guinea ...1998 2.3 1.8 Guinea-Bissau ..1994 1.1 . Haiti ...1998 1.4 Honduras 1994 0.6 ..1998 3.9 4.0 2.10 Public expenditure Public expenditure Public expenditure on pensions on health on education' Average Per student pension %of %of GDP % of % of per % of GDP per capita Year GDP Year capita income Year GOP 1998 1998 Hungary ___1996 9.7 1996 33.6 1998 5.2 4.6 25.8 India ...1997 0.8 Indonesia ...1999 0.8 1.4 Iran, Islamic Rep. 1994 1.5 ..1998 1.7 4.6 Iraq .. 1998 3.8 Ireland 1997 4.6 1993 77.9 1998 5.2 4.5 17.4 Israel 1996 5.9 1992 48.1 1998 6.0 7.7 29.7 Italy 1997 17.6 1999 5.6 4.7 29.8 Jamaica 1996 0.3 1989 25.9 1998 3.1 6.3 28.7 Japan 1997 6.9 --- --- -----1-989 339__ 1998 5.7 3.5 21.3 8 Jordan 1995 4.2 1995 144.0 1998 36 6 Kazakhstan 1997 5 0 1996 18.8 1999 2.7 .. ------ --- ----------- ------ ----- ------ ... ... Kenya 1993 0.5 1998 2.4 6.6 28.2 Korea, Oem. Rep. - - --- -- -- ---- -- - - - - -- - - - - - ----- -- -- - -- -- - -- -- ----- -~ Korea, Rep. 1997 1.3 ..1999 2,4 4.1 .. o --- ---- - --- - -- ---- - - --- --- - ---- -- ------ - Kuwait 1990 3.5 1997 2.9 6.5 29.9 Kyrgyz Republic 1997 6.4__ 1994 35.0 1999 2.2 5.4 21.1 I ----. -. ~~~~~~~~~~~~~~~~0 Lao PDR ..1998 1.2 2.4 11.2 ' Latvia -1995 10.2 1994 47.6 1999 4.0 6.8 35.0 C Lebanon ..1998 2.2 2.1 9.8 - - -- - - - - - - - - - -- --- - -- - -- - -- - --- -- - - - - -- - - - - - - - - - - - - -- -- - - - - - -- - - - --- - - - - - - --- - -- - - -- - --- Lesotho ..1995 3.4 13.0 57.9 Liberya .. Libya Lithuania 1998 7.3 1995 21.3 1998 4.8 6.4 32.2 Macedonia, FYR 1998 8.7 1996 91.6 1998 5.3 Madagascar 1990 0.2 1998 1.1 1.9 11.4 Malawi 1998 2,8 4.6 Malaysia 1999 6.5 1998 1.4 Mali 1991 0.4 1998 2.1 3.0 25.8 Mauritania 1992 0.2 1998 1.4 4.3 25.6 Mauritius 1999 4.4 ..1998 1.8 4.0 19.5 Mexico 1997 461998 2.6 ..15.9 Moldova 1967.5 1999 2.9 Mongolia 1995 4.7 Morocco 1994 1.8 1994 118.0 1998 1 2 Mozambique 1996 0.0 ..1998 2.8 2.9 22.6 Myan-mar ..1998 -02- Namibia ..1999 3.3 8.1 26.8 Nepal ..1998 1.3 2.5 11.0 Netherlands 1997 11.1 1989 48.5 1999 6.0 4.9 24.8 New Zealand 1997 6.5 1999 6.3 7.2 Nicaragua 1996 4.3 ..1998 8 5 4.2 Niger _____1992 0.1 1998 1.2 2.7 Nigeria _1991 0.1 1991 40.5 1998 0.8 Norway 1997 8.2_ 1994 49.9 1999 7.0 7.7 34.8 Oman 1998 2.9 3.9 Pakistan 1993 0.9 1999 0.7 Panama 1996 4.3 1998 4.9 Papua New Guinea 1998 2.5 Paraguay ..1998 1.7 4.5 Pe ru 1996 1.2 1998 2.4 3.2 11.0 Philippines 193 0 1999 1 6 3.2 Poland 1997 15.5 1995 61.2 1999 4 7 5.4 Portugal ___1997 10.0 1989 44.6 1998 5 1 5.7 27.9 Pulerto Rico.. .... ---- Romania 1996 5.1 1994 34.1 1999 3.8 4.4 Russian- Federation 1996 5.7 1995 18.3 1997 4.6 D 2.10 Public expenditure Public expenditure Public expenditure on pensions on health on education' Average Per student pensive of ft of GDP ft of ft of per ft of GDP per capita Year GDP Year capita income Year GDP 1998 1998 Rwanda ...1998 2.0 Saudi Arabia ...1997 6.4 Senegal 1998 1.5 ..1998 2.6 3.5 24.1 Sierra Leone ...1998 0.9 1.0 Singapore 1996 1.4 ..1998 1.2 Slovak Republic 1994 9.1 1994 44.5 1998 5.7 4.3 20.7 Slovenia 1996 13.6 1996 49.3 1998 6.7 5.8 29.3 Somalia... South Africa ...1998 3.3 6.1 19.6 84 Spain 1997 10.9 1995 54.1 1998 5.4 4.5 23.3 Sri Lanka 1996 2.4 ..1999 1.7 6 Sudan ...1997 0.7 3.7 30.1 co Swaziland ...1998 2.5 6.1 21.9 02 'O Sweden 1997 11.1 1994 78.0 1998 6.6 8.0 34.2 C Switzerland 1997 13.4 1993 44.4 1998 7.6 5.5 31.7 0) E Syrian Arab Republic 1991 0.5 -. 1998 0.9 a o Tajikistan 1996 3.0 ..1998 5.2 0) > Tanzania ...1998 1.3 2.1 a) o Thailand ...1998 1.9 4.7 20.0 0 3: Trinidad and Tobago 1996 0.6 ..1998 2.5 o Tunisia 1991 2.6 1991 89.5 1998 2.2 7.6 26.5 Turkey 1997 4.5 1993 112.7 1999 3.3 Turkmenistan 1996 2.3 ..1998 4.1 Uganda 1997 0.8 ..1998 1.9 1.6 4.6 Ukraine 1996 8.6 1995 30.9 1999 2.9 4.4 25.6 United Arab Emirates ...1998 0.8 1.9 10.7 United Kingdom 1997 10.3 1999 5.8 4.7 18.8 United States 1997 7.5 1989 33.0 1999 5.7 5.0 22.5 Uruguay 1996 15.0 1996 64.1 1998 1.9 2.5 11.4 Uzbekistan 1995 5.3 1995 45.8 1998 3.4 Venezuela. RB 1990 0.5 ..1998 2.6 Vietnam 1998 1.6 ..1998 0.8 West Bank and Gaza ...1996 4.9 Yemen, Rep. 1994 0.1 ..1997 2.4 6.7 31.5 Yugoslavia. FR (Serb./Mont.) ... .4.2 31.5 Zambia 1993 0.1 1998 3.6 2.3 12.0 Zimbabwe 1999 3.0 10.8 Low Income 0.9 3.4 24.1 Middle Income 2.9 4.5 Lower middle income 2.7 Upper middle income 3.2 4.2 17.8 Low & middle Income 2.5 4.1 22.2 East Asia & Pacific 1.8 Europe & Central Asia 4.4 4.4 25.7 Latin America & Carib. 2.8 Middle East & N. Africa 2.9 South Asia 0.9 Sub-Saharan Africa 2.0 3.6 23.4 High Income 6.0 5.6 28.4 Europe EMU 6.7 4.8 27.6 a. Break in series between 1997 avd 1998 dye to change from ISCED76 to ISCED97. b. Data refer mo 1999. 2.10 About the data Definitions Enhancing security for poor people means re- difficult. Compiling estimates of public health * Public expenditure on pensions includes all ducing their vulnerability to such risks as ill expenditures is complicated in countries where government expenditures on cash transfers to health, providing them the means to manage state or provincial and local governments are the elderly, the disabled, and survivors and the risk themselves, and strengthening market or involved in health care financing and delivery administrative costs of these programs. public institutions for managing risk. The tools because the data on public spending often are * Average pension is estimated by dividing include microfinance programs, old age assis- not aggregated. The data in the table are the total pension expenditure by the number of tance and pensions, and public provision of ba- product of an effort to collect all available infor- pensioners. * Public expenditure on health sic health care and education. mation on health expenditures from national and consists of recurrent and capital spending frorn Public interventions and institutions can pro- local government budgets, national accounts, government (central and local) budgets and vide services directly to poor people, although household surveys, insurance publications, in- social (or compulsory) health insurance funds. whether these work well for the poor is debated. ternational donors, and existing tabulations. * Public expenditure on educatlon consists of State action is often ineffective, in part because The data on education spending in the table public spending on public education plus governments can influence only a few of the refer solely to public spending-government subsidies to private education at the primary, many sources of well-being and in part because spending on public education plus subsidies for secondary, and tertiary levels. of difficulties in delivering goods and services. private education. The data generally exclude 85 The effectiveness of public provision is further foreign aid for education. They may also exclude constrained by the fiscal resources at govern- spending by religious schools, which play a sig- Data sources . ments' disposal and the fact that state institu- nificant role in many developing countries. Data The data on pension spending are drawn from K tions may not be responsive to the needs of for some countries and for some years refer to Robert Palacios and Montserrat Pallares- . Eo poor people. spending by the ministry of education only (ex- Miralles's "International Patterns of Pension l Data on public pension spending are from cluding education expenditures by other minis- Provision" (2000). For updates and further (D national sources and cover all government ex- tries and departments, local authorities, and so notes and sources go to the World Bank's Web I C penditures, including the administrative costs on). The share of gross domestic product (GDP) site on pensions (www.worldbank.org/ of pension programs. They cover noncontribu- devoted to education can be interpreted as re- pensions). The estimates of health expenditure ( tory pensions or social assistance targeted to flecting a country's effort in education. It often come from the World Health Organization's World the elderly and disabled and spending by social bears a weak relationship to measures of out- Health Report 2000 and World Health Report t insurance schemes for which contributions had put of the education system, as reflected in 2001, from the Organisation for Economic previously been made. The pattern of spending educational attainment. The pattern in this rela- Co-operation and Development for its member in a country is correlated with its demographic tionship suggests wide variations across coun- countries, from National Health Accounts of a structure-spending increases as the popula- tries in the efficiency with which the country, from the web site The European tion ages. government's resources are translated into edu- Observatory on Health Care Systems I The lack of consistent national health account- cation outcomes. (www.observatory.dk), supplemented by World ing systems in most developing countries makes Bank country and sector studies, including the cross-country comparisons of health spending Human Development Network's Sector Strategy:. Health, Nutrition, and Population (World Bank Figure 2.10 1997a). Data are also drawn from World Bank public expenditure reviews, the International Monetary Fund's Govemment Finance Statistics Out-of-pocket health expenditures can Impoverish people database, and other studies, The data on Egypt. A,ab Rep _ .. i- "w"- - W a l education expenditure are from the UNESCO Bhutan Institute for Statistics. Indonesia _ _ | _ Bosa --. I Rica c oa a ~ | I j Japan No-iay i l Kingdorn 0 20 40 60 80 100 Percentage of totrl health expenditure CD Out or pocket E Pubhc Source WHO, World Health Repon 2000. Out-of-pocket payments are generally regressive because they have the potential not only to Impoverish people but also to deter the poor from obtaining care. Exempting the poor from user fees at public facilities, or Imposing a sliding scale, based on socloeconomic characteristics, are attempts to reduce the risks assoclated with out-of- pocket payments. However, such schemes require relatively high administrative costs to distinguish users, and may end up affecting only a small amount of total risk-related payments. 2.11 Euaininputs Expenditure per student Expenditure Primary teachers Primary on teachers with required pupil- compensation academic teacher qualifications ratio' Primary Secondary Tertiary of rota S of S of S%of current education S of pupils per GDP per capita GDP per capita GDP per capita expenditure total teacher 1980 1997 1980 1997 1980 1997 1960 1997 1992-98' 1998 Afghanistan 10.8 .. 46.7 ..46.8 ..18 Albania....... Algeria 8.7 .. 23.2 ... . 63.6 74.3 93 2B Angola . .. Argentina .. 9.0 11.0 16.2 29.8 . . 841 2 Armnenia ... ... 26.3 ..93 Australia .. 14.0 42.5 15.8 48.8 27.9 .541"d Austria 15.4 21.4 19.6 24.4 36.7 34.8 53.1 61.7 ..13 Azerbaijan .. 21.6 ... . 17.3 .. 193 19 86 Bangladesh ... 9.3 .. 33.9 .. 33.5 68 5 59 - Belarus . 45.8 .. 28.6 . 1 7.7 ...1121 o Belgium .. 8.5 32.4 13.5 50.3 17.6 73.0 73.6 Benin .. 11.6 ... . 244.2 ...1093 53 'O Bolivia .. 10.9 ... . 53.3 ...64 1 Bosnia and H-erzegovina ............84 ED Botswana ......... 54.9 2B o Brazil .. 11.0 8.. .... 3 33 > Bulgaria 17.2 29.6 ... 50.5 16.7 ..99 is Burkina Faso ... 102.9 .. 2,938.5 .. 61.0 67.8 193 49 Burundi ......... 74.3 ...46 Cambodia .........91 48 o Cameroon ......65.4 ..91 52 04 Canada ...... 37.7 .. 52.2 ...1.8 Central African Republic ... 23.9 .. 938.8 .. ...99 Chad .. 6.3 .. 24.0 .. 234.5 .. 64.4 68 5 Chile 9.2 10.5 15.7 11.4 107.8 19.9 76.8 ..93 27 China 3.8 6.5 12.4 11.5 246.2 65.3 ...95 21. Hong Kong, China .. 7.8 8.2 12.6 ... 72.9 Colombia 5.2 .. 7.7 10.3 43.6 30.1 93.4 82.0 90 23 Congo. Dem. Rep. ... .. .. .. .26 Congo, Rep. .. 107 15.4 5.7 334.4 .. 70.8 .193 61 Costa Rica ... 24.5 1 7.9 72.4 .. 50.2 .. 9 CMe dilvoire ...... 357.4 ......43 Croatia ... .. ... .94 Cuba .. 16.3 .. 34.0 . 98.2 38.8 ..10(1 13 Czech Republic .. 13.0 .. 20.8 .. 33.7 .. 44.4 ..18 Denmark .. 24.1 11.0 34.2 48.7 49.2 49.3 43.1 .1.0 Dominican Republic ... 5.8 4.7 .. 9.3 62.2 ...37 Ecuador ... 12.5 15.0 23.0 34.4 77.4 8 3 27 Egypt, Arab Rep. ...... 54.1 ... .193 23 El Salvador .. 7.0 13.9 5.5 138.4 7.7 Eritrea .. 11.1 .. 11.9 ,.. .. .47 Estonia ... . 45.2 .. 37.9 16 Ethiopia .. 26.5 .. 71.2 .. 862.6 68.4 Finland .. 21.9 21.2 26.2 35.9 43.5 50.5 47.7 ..17 France 11.7 15.8 19,7 26.4 28.6 27.6 68.1 ...19 Gabon ............56 44 Gambia, The 18.4 13.5 43.2 29.0 ......193) 33 Georgia ... .. .94 17 Germany ... .. . 37.0 ..17 Ghana ... 10.3 ... . 60.0 Greece ... . 15.0 .. 22.1 84.8 ...14 Guatemala .. 6.1 .. 5.1 . 30.7 .. 62.8 ..38 Guinea ... . 27.9 .. 421.9 8..93 47 Guinea-Bissau 19.0 .. 63.5 ... . 73.5 Haiti ... 12.8 .. 128.6 .. 66.9 83 31 Honduras 13.8 .. 73.2 59.4 71.1 67.8 193 2.11 ~~A; Expenditure per student Expenditure Primary teachers Primary on teachers' with required pupil- compensation academic teacher quaifications ratio' Primary Secondary Tertiary % of total % of % of % of current education % of popls per GDP per capita GDP per capita GDP per capita expenditure total teacher 1980 1997 1980 ±997 1980 1997 1980 1997 1992-985 1998 Hungary 13.7 .17.9 25.5 17.6 83.8 30.4 45.2 11 India .. 8.4 15.1 16.4 83.3 92.5 ..8B 72 Indonesia ... ... 12.3 ...94 Iran, Islamic Rep. 22.6 8.0 36.4 10.8 ..7.4 ..47.4 36 Iraq ... 6.5 .. 87.5 .... .22 Ireland 10.7 11.6 22.5 18.2 55.6 30.1 67.6 73.6" 1W0 22 Israel 15.6 .. 41.7 .. 71.6 .. 51.2 ...13 Italy .. 21.7 .. 27.7 .. 20.6 ..67.3" .. L Jamaica 12.7 11.8 ... 185.5 .. 65.6 64.1 101)1 31 Japan 14.6 .. 16.4 .. 20.7 49.8 87 Jordan ...61.7 75.8 70.5 70.4 47 __ Kazakhstan ... . 21.3 9..N.) 0 Kenya ... 35.2 899.2 ......2B Korea, Dem. Rep. .........10D Korea, Rep. . 1 7.4 9.1 11.9 15.7 5.5 69.2 .160 . Kuwait .. 23.6 .. 6.6 43.8 102.6 46.5 . 1W . Kyrgyz Republic ... . 39.7 .. 48.2 ...95 24 ( Lao PODR. 6.5 .. 13.9 .. 61.0 ..67.1 87 31..O Latvia ... 16.1 51.3 13.6 33.1 ..40.5 87) 15 ( Lebanon ... .. . 23.1 . .14 Lesotho 12.7 18.1 107.3 70.4 1,500.8 1,022.3 60.9 57.6 79 25 Liberia ......... 99 3 Lithuania .... 27.8 .. 41.9 . .17 Macedonia, FYR ... . 24.2 .. 61.5 ...100 22 Madagascar ...397.9 .. 81.8 ...47 Malawi 7.0 8.2 89.2 25.4 1,685.7 1,492.0 43.4 Malaysi'a .. 10.7 20.5 17.2 140.9 53.6 57.5 58.6 ..22 Mali 29.6 13.3 87.3 28.5 .. 369.4 51.0 6.. 2 Mauritania 28.8 10.1 167.6 56.1 .. 191.2 ... .47 Mauritius .. 9.7 20.2 15.3 337.1 140.6 31.4 ..10 26 Mexico 4.2 .. 10.0 .. 25.5 ... .84 27 Moldova .. 60.6 . Mongolia 95.5 45.9 ...97 32 Morocco .53.6 43.1 150.3 67.5 ..78.0 ..28 Mozambique ... .. .61 Myanmar ... .. .31 Namibia 34.7 .. 103.4A.. 25 32 Nepal .. 9.3 12.1 274.9 110.7 59.2 ..99 39 Netherlands 13.2 14.1 22.3 20.6 70.1 45.8 73.5 New Zealand 14.7 16.6 13.4 22.1 58.5 42.4 82.7 Nicaragua .. 12.6 .. 6.4 ... 66.7 6. 3 Niger ..81.0 ... .41 N igeria - . -.---- ------.-------- - 91. Norway . 27.6 14.5 18.7 37.1 45.1 Oman 8.9 .. 16.4 .. 30.1 . .20 Pakistan 17.1 ... ..99 32 Panama ... 10.2 11.2 26.5 39.2 65.3 ..10 Papua New Guinea .......1(X) 36 Paraguay . 10.9 .. 12.0 .. 90.6 ...59 20 Peru 6.9 4.8 8.0 7.3 4.7 16.4 59.4 40.1 74 25 Philippines .. 9.3 4.2 9.8 13.7 14.8 ...1W0 Poland .. 16.7 .. 15.9 .. 25.4 Portugal .. 18.7 19.2 20.8 34.4 23. 7 .98 Puerto Rico ...... Romania . 19.9 .. 87 .. 31.3 23 19 Russian Federation --------- 2.11 Expenditure per student Expenditure Primary teachers Primary on teachers' with required pupil- compensation academic teacher qualifications ratio' Primary Secondary Tertiary % of total % of % of % of current education % of pupils per GDP per capita GDP per capita GDP per capita expenditure total teacher 1980 1997 1980 1997 1980 1997 1980 1997 1992-98' 1998 Rwanda 11.1 . 112.4 902.7 .. 74.8 ..47 54 Saudi Arabia .. 109.5 58.1 ..109 12 Senegal 68.5 63.8 432.5 . .99 49 Sierra Leone Singapore 12.4 .. 41.5 34.1 47.52o Slovak Republic .. 21.8 .. 9.7 .. 29.3 37.9 79 19 Slovenia 20.6 .. 24.6 .. 37.9 ..62.2 ..14 Somalia South Africa ... ...64.51 37 88 Spain .. 16.4 .. 21.1 .. 16.8 ... .15 Sri Lanka ... .. . 84.2 ...109 Sudan .. 45.6 601.0 38.0 ... .. .26 tO Swaziland .. 8.6 35.3 23.0 139.5 229.8 86.3 ..109 33 Sweden 41.7 26.2 14.0 31.4 33.9 66.6 46.4 ...12 Switzerland .. 20.1 31.0 30.3 60.8 47.4 61.0 59.9 ..13 a) Syrian Arab Republic ... 15.1 14.6 74.7 .. 57.8 ...23 a, Tajikistan......... > Tanzania ......... ...38 o Thailand 8.8 11.9 9.8 10.5 59.7 25.4 80.3 56.8u 84 21 0 3: Trinidad and Tobago .. 4.8 12.4 .. 56.4 .. 73.2 ..109 21. o Tunisia . .. 36.4 20.8 188.1 75.0 81.3 77.0 ..24 0 (N Turkey ... 8.7 . 96.3 ..O. 10 Turkmenistan . ., .. Uganda ... .. .. .. .60 Ukraine 2.1 .. 1.2 .. 2.0 22.4 . United Arab Emirates ... .. .. .30.2 ..16 United Kingdom . 1 7.2 22.2 20.1 80.1 39.9 52.1 41.0 ..19 United Statens.. 17.3 .. 47.8 ......15 Uruguay 8.9 .. 13.6 9.3 27.0 21.3 56.9 41.5 109 21. Uzbekistan . .. .. .. Venezuela. RB 5.8 2.1 .. 4.7 71.4 .. 68.8 Vietnam .. 7.3 86.1 66.0 77 30 West Bank and Gaza Yemnen, Rep. ......74 32 Yugoslavia, Fed. Rep. .. 71.1 ... .17 Zambia 9.8 4.7 56.4 ... . 52.6 ..71 45 Zimbabwe 19.5 19.3 103.8 34.6 326.8 .. 75.2 91.1 109 Low Income . .. 66.7 67.5 88 42 Middie Income . ..66.5 40.8 65.3 58.6 91 22 Lower middle income .. .. 38.1 65.6 64.1 91 22 Upper middle incomre . ..71.4 .. 61.4 47.8 87 28 Low & middle Income . ... .. 65.5 64.4 89 38 East Asia & Pacific .. 8.3 ... . 42.4 69.2 62.3 94 23 Europe & Central Asia ... .. . 31.3 45.2 40.5 Latin AmTerica & Carib. ... 12.4 8.4 56.4 .. 66.7 57.0 84 28 Middle East & N. Africa ...... 87.5 .. 67.1 74.3 76 24 South Asia 16.1 .. 83.3 84.2 46.4 ..87 66 Sub-Saharan Africa ...... 65.4 67.8 High income .. 18.7 19.6 20.4 45.8 36.9 52.6 57.3 ..17 Europe EMU ......... 67.9 67.4 ..16 a. Data are for the most recent pear available. b. Break in series between 1997 and 1998 due to change from ISCED76 to ISCED57. c. Not including tertiary educateon d. Not including preprimary educat on. e. Flemish Community only. f. Ministry of Education only. g. Not noluding expenditure on universities h Data refer to expenditure on public institutions only. i. Net including expenditure on independent private institutions. 2.11 0i) About the data Definitions Data on education are compiled by the United country and may not relate specifically to teach- * Expenditure per student is the public current Nations Educational, Scientific, and Cultural ing. Since the indicator is based on minimum spending on education divided by the total Organization (UNESCO) from official responses national qualifications, which may vary greatly, number of students by level, as a percentage to surveys and from reports provided by educa- care should be taken in comparing across coun- of gross domestic product (GDP) per capita. tion authorities in each country. Such data are tries. * Expenditure on teachers' compensation is used for monitoring, policymaking, and resource The comparability of pupil-teacher ratios the public expenditure on teachers' gross allocation. For a variety of reasons, however, across countries is affected by the definition of salaries and other benefits as a percentage of education statistics generally fail to provide a teachers and by differences in class size by the total public current spending on education. complete and accurate picture of a country's grade and in the number of hours taught. More- * Primary teachers with required academic education system. Statistics often have two to over, the underlying enrollment levels are sub- qualifications referto the percentage of primary three years' time lag, but an effort is being made ject to a variety of reporting errors (for further school teachers with at least the minimum to shorten the delay. Coverage and data collec- discussion of enrollment data see About the data academic qualifications required by national tionmethodsvaryacrosscountriesandovertime for table 2.12). While the pupil-teacher ratio is public authorities for teaching in primary within countries and should be interpreted with often used to compare the quality of schooling education. * Primary pupil-teacher ratio is the caution. (For further discussion of the reliability across countries, it is often weakly related to number of pupils enrolled in primary school 89 of education data see Behrman and Rosenzweig the value added of schooling systems (Behrman divided by the number of primary school 1 1994.) and Rosenzweig 1994). teachers (regardless of their teaching g The data on education spending in the table The International Standard Classification of assignment). refer solely to public spending-government Education 1976 (ISCED76) was used for two . _ _ o spending on public education plus subsidies for decades as an instrument to assemble, compile t s private education. The data generally exclude and present education statistcs. In 1998 ISCED97 Data sources -i D foreign aid for education. They may also exclude was introduced and UNESCO's data collection International data on education are compiled 0 spending by religious schools, which play a sig- program and country reporting of education sta- by the UNESCO Institute for Statistics in 3 nificant role in many developing countries. Data tistics were adjusted to this new classification. cooperation with national commissions and CD for some countries and for some years refer to The adjustments were made to facilitate the in- national statistical services. Data on qualified spending by the ministry of education only (ex- ternational compilation and comparison of edu- teachers come from UNESCO's special data °- cluding education expenditures by other minis- cational statistics as well as to take into ac- Lcollection for the Education for All initiative. I tries and departments and local authorities), count new types of learning opportunities and ________ Many developing countries have sought to activities available for both children and adults. supplement public funds for education. Some Thus the time series data up to 1997 are not countries have adopted tuition fees to recover consistent with data for 1998 and after. Any time part of the cost of providing education services series analysis should therefore be made with or to encourage development of private schools. extreme caution. Charging fees raises difficult questions relating ISCED97 introduced a new level 4, to equity, efficiency, access, and taxation, how- "postsecondary nontertiary education". The ever, and some governments have used schol- students who fall into this category are not arships, vouchers, and other methods of public counted as either secondary or tertiary even finance to counter this criticism. Data for a few though they are in the education system. countries include private spending, although national practices vary with respect to whether Table 2.11a parents or schools pay for books, uniforms, and other supplies. For greater detail see the coun- Why the break in data? Comparing ISCED76 with ISCED97. try- and indicator-specific notes in the source. Well-trained and motivated teachers are a ISCED76 ISCED97 critical input to education, but they come at a 0 Education preceding the first level 0 Pre-primary education cost: teachers' compensation (gross salaries 1 Education at the first level 1 Primary education or first stage of basic education and other benefits) typically accounts for two- 2 Education at the second level, first stage 2 Lower secondary or second stage of basic thirds of education spending. Teachers are de- 3 Education at the second level, second stage education (2A, 2B and 2C) fined here as including both full- and part-time 5 Education at the third level, first stage, of the 3 Upper secondary education (3A, 3B, 3C) teaching staff. Teachers assigned to nonteach- type that leads to an award not equivalent to 4 Postsecondary non-tertiary education (4A, 48) ing duties are excluded, but country reporting a first university degree 5 First stage of tertiary education not leading 6 Education at the third level, first stage, of the type directly to an advanced research qualification varies. Comparisons should thus be made with that leads to a first university degree or equivalent (5A, 58) caution. 7 Education at the third level, second stage of the 6 Second stage of tertiary education leading The share of teachers with required academic type that leads to a post-graduate university to an advanced research qualification qualifications measures the quality of the teach- degree or equivalent ing staff available in primary schools. It does 9 Education not definable by level not take account of coinpetencies acquired by ISCED97 provides an Improved set of definitions and criteria aiming to ensure International comparability In the teachers through their professional experience classification of educational programs by level and field of education. It Includes seven levels of education while the earlier version had eight levels. Other differences are that a new level 4 'post-secondary non-tertiary education' has or self-instruction, or of such factors as work been Introduced while level 9 has been deleted. experience, teaching methods and materials, or classroom conditions, all of which may affect the quality of teaching. The qualifications are specified by the national authorities of each ~t) 2.12 Participation in education Gross enrollment Net enrollment ratio' ratlo' Preprimary Primary Secondary Tertiary Primary Secondary % of relevant % of relevant % of relevant %of relevant % of relevant % of relevant age group age group age group age group age group age group 1998 ±.980 ±998 ±980 1998 ±980 i998 ±.980 1998 1980 1998 Afghanistan .. 34 10 . .29 Albania .. 113 67 5 Algeria 2 94 109 33 66 6 15 81 94 31 58 Angola .. 175 91 21 16 00 1 57 Argentina 57 106 120 56 89 22 47 .. 107 74 Armenia .. ........ Australia . 112 .. 71 ..25 . 102 .. 70 Austria 80 99 100 93 96 22 50 87 88 Azerbaijan 19 115 103 95 84 24 22 96 82 90 Bangladesh 31 61 122 18 47 3 5 104 Belarus .. 104 98 39 o Belgium .. 104 91 26 .. 97 Benin 5 67 84 16 21 1 3 ...16 Bolivia .. 87 .. 37 ..15 79 97 16 Bosnia and Herzegovina.... .... Botswana .. 91 105 19 77 1 4 76 81 14 57 o) Brazil 55 98 154 33 83 11 14 80 98 14 > Bulgaria 63 98 101 84 87 16 43 96 93 73 81 Burkina Faso 2 17 42 3 10 0 ~ 15 34 9 *0 ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... .... Burundi 1 26 51 3 7 CC 1 20 38 Cambodia 6 139 119 .. 22 00 1 . 104 .. 20 o Cameroon 12 98 90 18 20 2 5 ... 15 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ,:----... CN Canada 66 99 97 88. 105 57 58 ..96 .. 94 Central African Republic .. 71 57 14 ..1 2 56 53 Chad ... 67 .. 11 .. .55 ..7 Chile 74 109 106 53 85 12 34 ..88 .. 70 China 39 113 107 4 6 6 2 2 6 9 1 . 5 0 Hong Kong, China .. 107 .. 64 .10 95 .. 61 Colombia 35 112 112 39 53 9 ... 87 Congo, Dem. Rep. .. 92 46 24 18 1 1 ..32 12 Congo, Rep. 2 141 57 74 5 .. 96 Costa Rica .. 105 .. 47 .. 21 .89 39 COte dIlvoire 3 75 78 19 23 3 7 . 59 Croatia ... . 77 ..19 Cuba 96 106 100 81 79 17 19 95 97 75 Czech Republic 90 95 104 99 82 17 26 .. 90 .. 79 Denmark 93 95 103 105 126 28 55 95 101 88 89 Dominican Republic 34 118 133 42 66 ..87 .. 53 Ecuador 63 117 113 53 56 35 ... 97 .. 46 Egypt, Arab Rep. 10 73 100 50 81 16 39 .. 92 El Salvador 40 75 ill 24 50 9 18 ..81 .. 37 Eritrea 5 .. 53 . 24 I.1. 34 ..19 Estonia 90 103 101 127 104 25 47 .. 96 .. 77 Ethiopia 2 37 63 9 17 00 1 ..35 16 Finland 48 96 99 100 121 32 83 .. 99 .. 95 France 83 ill 105 85 111 25 51 100 100 79 94 Gabon ... 154 .. 55 ..8.. Gambia, The 26 53 81 11 31 ... 50 61 .. 23 Georgia 28 93 95 109 79 30 34 ... 78 Germany 94 .. 105 .. 98 .. 46 .87 88 Ghana .. 79 . 41 ..2 Greece 70 103 97 81 96 17 50 96 95 86 Guatenmala 47 71 102 19 33 8 59 83 13 Guinea .. 36 59 17 15 5 46 .. 13 Guinea-Bissau .. 68 ..6 .47 ..3 Haiti 63 77 152 14 ..1 ..38 80 . Honduras .. 98 .. 30- . 7 .13 .78 - --- 2.12 0 Gross enrollment Net enrollment ratio' ratio., Preprimary Primary Secondary Tertiary Primary Secondary % of relevant % of relevant % of relevant % of relevant % of relevant % of relevant age group age group age group age group age group age group 1L998 1980 1999 1980 1.998 1980 1998 1980 1.998 1980 1998 Hungary 106 96 103 _70 98_ 14 34 95 82 85 India 29 83 100 30 49 5 ...... 39 Indonesia .. 107 .. 29 .4 ..88 Iran, Islamic Rep. .. 87 ..... 42 Iraq 11 -11-3 88 ..... 57 20 _9_ 13 99 80 47 31 Ireland 3 100 141 90 109 18 45 90 104 78 77 Israel 77 95 107 73 89 29 49 .. 95 .. 85 Italy __95 100 1-02 72 ..... 95 27 47 .. 101 ..88 Jamaica 83 103 98 67 90 7 9 96 92 64 79 Japan 83 101 102 93 102 -31 44 101 102 93 ..91 Jordan 20 82 69 59 66 13 .. 73 64 53 60 Kazakhstan 14 84 97 93 87 34 23 74 .. .. 7 Kenya 39 115 92 20 31 1 1 91 . . . Korea, Dem. Rep. ... .... . Korea, Rep. .. 110 78 ..15 .. 104 ..70 .. o Kuwait .. 102 80 - .11 - .85 CD... K-yrgyz Republic --.14 116 104 110_ 86 16 30 85 CD -- ----- ---- ~~~~~~~~0 Lao PDR 7 113 111 21 33 0' 3 76 .. 27 VD 2 Latvia 54 102 103 99 87 24 51 94 83 83 Lebanon 64 Ill 110 59 89 30 38 .. 78 .. 76 Lesotho 20 103 102 18 32 1 2 67 60 13 14 9 --- --- ------~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Liberia 48 48 83 22 24 7 ..41 E. . Lib-ya .4 125 153___ _76 77 8 57 ...62 71 Lithuania 50 79 101 114 90 35 41 .. 94 .. 85 Macedonia, FYR 27 100 103 61 83 28 22 .. 96 .. 79 Madagascar 130 93 16 3 2 .. 63 .. 13 Malawi 60 5 .0' 0 43 ...7 Malaysia 55 93 99 48 98 4 98 .. 93 Mali: 2 26 53 8 14 1 2 20 42 Mauritania 37 83 11 18 .. 6 - 60 Mauritius 100 93 108 50 71 1 7 79 93 .. 63 Mexico 76 120 114 49 71 14 18 .. 102 .. 56 Moldova .. 83 78 ..30... Mongolia 24 107 -94 - ----92 - - ----- -2-2 ----- 25 ----- -85 . 53 Morocco 69 83 97 26 40 6 9 62 79 20 Mozambique 99 71 5 9 0' 1 36 41 *. 7 Myanmar 3 91 114 22 36 5 Namibia --- --- 126 59 7 86 31 Nepal 86 114 22 48 3 3 Netherlands 98 100 108 93 125 29 49 93 100 81 93 New Zealand 111 83 27 81 Nicaragua 94141 12 ---- 70 ..22 Niger 1 25 31 5 7 0' 21 26 4 6 Nigeria .. 109 18 ..3 Norway 77 99 102 94 121 25 65 98 102 84 96 Oman 10 51 75 12 67 0' . 43 66 10 58 Pakistan 8 40 86 14 37 . Panama 106 .. 61 .. 21 . 89 .. 46 Papua New Guinea 20 59 85 12 22 2 2 85 22 Paraguay 77 106 115 ......27 51 9 89 92 42 Peru 60 114 126 59 81 17 29 86 103 .. 61 Philippines 112 64 .. 24 28 94 .. 45 Poland 100 77 .. 18 98 .. 70 Portugal 67 123 124 37 113 d 11 45 98 108 .. 88 Puerto Rico ... 42 .. Romania 132 104 103 94 80 12 94 .. 76 Russian Federation .. 102 96 46... D 2.12 Gross enrollment Net enrollment ratio' ratio.,' Preprimary Pr mary Secondary Tertiary Primary Secondary % of relevant % of relevant % of relevant % of relevant % of relevant % of relevant age group age group age group age group age group age group 1998 1980 1998 1980 1998 1980 1998 1980 1998 1980 1998 Rwanda .. 63 114 3 9 CC 1 59 91 Saudi Arabia 5 61 71 29 66 7 19 49 59 21 Senegal 3 46 70 11 20 3 4 37 59 Sierra Leone .. 52 .. 14 ..1 Singapore .. 108 92 60 67 8 .. 99 Slovak Republic 80 .. 101 .. 86 .. 27 Slovenia 72 98 98 .. 99 20 53 .. 94 .. 89 Somnalia .. 21 ..9 ... 16 .. South Africa 26 90 127 .. 104 17 . 92 Spain 75 109 108 87 113 23 56 102 105 74 92 Sri Lanka 103 ill 55 71 3 ... 102 m) Sudan 24 50 56 16 29 2 7 .. 46 tO Swaziland 103 117 38 56 4 5 80 77 .. 35 'O Sweden 77 97 Ill 88 161C 31 63 .. 103 .. 100 C: Switzerland 89 84 102 94 94 18 35 79 94 78 83 E) Syrian Arab Republic 9 100 104 46 42 17 6 89 93 39 38 o Tajikistan ... .. .24... > Tanzania .. 93 65 3 .. 0 1 68 48 ..4 Thailand 92 99 94 29 88 15 30 .. 77 .. 55 o Togo 3 118 124 33 33 2 4 .. 88 .. 23 Trinidad and Tobago 12 99 102 69 80 4 6 90 93 .. 72 o Tunisia 14 102 119 27 73 5 17 82 98 23 55 0 Turkey 7 96 .. 35 70 5 14 .. 100 Turkmenistan ... ., .22... Uganda .. 50 154 5 16 1 2 ... . 9 Ukraine .. 102 . 94 ..42... United Arab Emirates 73 89 94 52 78 3 13 74 83 .. 70 United Kingdom 78 103 102 83 156 19 58 97 102 79 94 United States 59 99 102 91 97 56 77 .. 95 .. 90 Uruguay 56 107 113 62 88 17 35 .. 92 .. 66 Uzbekistan . 81 .. 105 ..28... Venezuela, RB .. 93 .. 21 .. 21 .. 82 14 14 Vietnam 39 109 110 42 61 2 11 95 97 .. 49 West Bank and Gaza . .. .. .. Yement,Rep. 1 -. 78 . 45 .. 10 . 61 .. 35 Yugoslavia, Fed. Rep. ........... Zambia 3 90 86 16 27 1 3 77 73 .. 22 Zimbabwe .. 85 .8 ..1... Low income 25 83 96 29 42 6 ... Middle Income 41 106 ill 52 67 10 12 .. 92 Lower mniddle income 39 107 106 52 63 9 10 .. 91 .. 51 Upper middle income 48 102 129 50 81 13 19 .. 97 Low & middle Income 34 96 104 41 56 8 ... East Asia & Pacific 40 ill 107 44 62 4 8 .. 91 .. 51 Europe & Central Asia .. 99 . 86 ..31 . Latin America & Carib. 60 105 130 42 75 14 20 .. 97 Middle East & N. Africa 17 87 97 42 60 11 22 .. 83 South Asia 27 77 101 27 48 5 .... .. 39 Sub-Saharan Africa .. 80 78 15 .. 1 4 . High Income .. 102 .. 87 ..36 . . Europe EMU .. 106 .. 81 .. 24 a. Break in series between 1997 and 1998 due to change from ISCED76 to ISCED97. b. Net enrollment ration exceeding 100 percent indicate discrepancies between estiwates of the school-age population ano reported enrollment data. c Less teen 0.5. d. Includes training for the unemployed. 2.12 q About the data Definitions School enrollment data are reported to the adjusted for age bias, adjustments are rarely * Gross enrollment ratio is the ratio of total United Nations Educational, Scientific, and Cul- made for inadequate vital registration systems. enrollment, regardless of age, to the population tural Organization (UNESCO) by national educa- Compounding these problems, pre-and post-cen- of the age group that officially corresponds to tion authorities. Enrollment ratios help to moni- sus estimates of school-age children are inter- the level of education shown. * Net enrollment tor two important issues for universal primary polations or projections based on models that ratio is the ratio of the number of children of education: an international development goal may miss important demographic events (see official school age (as defined by the national that implies achieving a net primary enrollment the discussion of demographic data in About the education system) who are enrolled in school ratio of 100 percent; and gross enrollment ra- data for table 2.1). to the population of the corresponding official tios that help to assess whether an education In using enrollment data, it is also important school age. Based on the International system has sufficient capacity to meet the needs to consider repetition rates, which are quite high Standard Classification of Education 1976 of universal primary education. Net enrollment in some developing countries, leading to a sub- (ISCED76) and 1997 (ISCED97), * Preprimary ratios also show the proportion of children of stantial number of overage children enrolled in education refers to the initial stage of organized primary school age who are enrolled in school each grade and raising the gross enrollment ra- instruction, designed primarily to introduce very and consequently also the proportion who are tio. A common error that may also distort enroll- young children to a school-type environment. not in formal education. ment ratios is the lack of distinction between * Primary education provides children with 93 Enrollment ratios, while a useful measure of new entrants and repeaters, which, other things basic reading, writing, and mathematics skills participation in education, also have significant equal, leads to underreporting of repeaters and alongwith an elementary understandingof such limitations. They are based on data collected overestimation of dropouts. Thus gross enroll- subjects as history, geography, natural science, m during annual school surveys, which are typically ment ratios provide an indication of the capac- social science, art, and music. * Secondary E conducted at the beginning of the school year. ity of each level of the education system, but a education completes the provision of basic: E They do not reflect actual rates of attendance high ratio does not necessarily indicate a suc- education that began at the primary level, and o or dropouts during the school year. And school cessful education system. The net enrollment aims at laying the foundations for lifelong CD administrators may report exaggerated enroll- ratio excludes overage students in an attempt learning and human development, by offering 3 ments, especially if there is a financial incen- to capture more accurately the system's cover- more subject- or skill-oriented instruction using D tive to do so. Often the number of teachers paid age and internal efficiency. It cloes not solve the more specialized teachers. * Tertiary 0. by the government is related to the number of problem completely, however, because some education, whether or not leading to an pupils enrolled. Behrman and Rosenzweig children fall outside the official school age be- advanced research qualification, normally (1994), comparing official school enrollment cause of late or early entry rather than because requires, as a minimum condition of admission, data for Malaysia in 1988 with gross school at- of grade repetition. The difference between gross the successful completion of education at the tendance rates from a household survey, found and net enrollment ratios shows the incidence seconcary level. that the official statistics systematically over- of overage and underage enrollments. __ stated enrollment. In 1998, ISCED97 was introduced and Overage or underage enrollments frequently UNESCO's data collection program and country Data sources occur, particularly when parents prefer, for cul- reporting of education statistics were adjusted The data are from the UNESCO Institute for tural or economic reasons, to have children start to this new classification. This was to facilitate Statistics. school at other than the official age. Children's the international compilation and comparison of i..- . - age at enrollment may be inaccurately estimated educational statistics, as well as to take into ac- or misstated, especially in communities where count new types of learning opportunities and registration of births is not strictly enforced. activities available for both children and adults. Parents who want to enroll their underage chil- Thus the time series data up to 1997 are not dren in primary school may do so by overstating consistent with data for 1998 and after. Any time the age of the children. And in some education series analysis should therefore be made with systems ages for children repeating a grade may extreme caution. be deliberately or inadvertently underreported. ISCED97 introduced a new level 4 labeled As an international indicator, the gross primary 'post-secondary non-tertiary education". The enrollment ratio has been used to indicate broad students who fall into this category are not levels of participation as well as school capacity. counted as either secondary or tertiary although It has an inherent weakness: the length of they are in the education system. primary education differs significantly across The year shown in the table usually indicates countries. A short duration tends to increase the the beginning of the school year but in most of ratio and a long duration to decrease it (in part the countries school year ends the following year. because there are more dropouts among older children). Other problems affecting cross-country com- parisons of enrollment data stem from errors in estimates of school-age populations. Age-gen- der structures from censuses or vital registra- tion systems, the primary sources of data on school-age populations, are commonly subject to underenumeration (especially of young chil- dren) aimed at circumventing laws or regulations; errors are also introduced when parents round up children's ages. While census data are often 0 2.13 Education efficiency Net Intake rate In Percentage of cohort Primary Avearge years of grade 1 reaching grade 5 completion rate schooling % of all children % of school-age % of grade one students who complete population who reach grace 5 primary school Male Female Male Female Total Male Female Total Male Female 1998 1998 1980 1997 1980 1997 1992-2000- 1992-20001 1992-2000' 2000 2000 2000 Afghanistan . .. 62 .. 61 ..8 15 0 1.7 2.6 0.8 Albania 97 103 .. 81 .. 83 89 84 95 Algeria 78 75 90 93 85 95 91 93 88 5.4 6.2 4.5 Angola 27 22 . .. .. .. Argentina 107 105 .. 70 .. 70 96 97 98 8.8 8.8 8.9 Armenia . .. ...... 82... Australia .. . .. .. .. . 10.9 11.2 10.7 Austria .. . .. .8.4 9.2 7.6 Azerbaijan 12 13 . .. . .. 101 103 100 94 Bangladesh 95 91 18 .. 26 .. 70 68 72 2.6 3.3 1.8 Belarus . .. . .... 93 95 92 on Belgium . .. 75 .. 77 ......9.3 9.6 9.1 t~ Benin . .. 59 64 62 57 39 52 25 2.3 3.3 1.4 Bolivia . . ...... 77 80 75 5.6 6.1 5.1 Bosnia and Herzegovina ...88... a) Botswana 20 23 80 87 84 93 102 96 107 6.3 6.2 6.3 a) > Bulgaria . .. ...... 92 92 92 o Burkina Faso 22 15 76 74 74 77 25 29 20 ~0 Cambodia 80 77 .. 51 .. 46 60 68 51 o Cameroon . .. 70 .. 69 .. 43 ... 3.5 4.2 2.9 CN Canada .. . .. .. .. . 11.6 11.7 11.6 Central African Republic . .. 63 .. 50 . 19 ...2.5 3.4 1.7 Chad 27 19 .. 62 .. 53 19 26 10 Chile 37 38 .. 100 .. 100 92 92 92 7.5 7.6 7.5 Chinsa. .. . 93 .. 94 108 ill 106 6.4 7.6 5.1 Hong Kong. China 98 .. 99 ...... 9.4 9.9 8.9 Colombia 56 55 .. 70 .. 76 85 84 87 5.3 4.9 5.7 Congo, Dem. Rep. 20 22 56 .. 59 . 40 . .. 3.0 4.1 2.0 Congo, Rep. 11 10 81 40 83 78 44 45 43 5.1 5.8 4.6 Costa Rice 58 60 77 86 82 89 89 91 87 6.0 6.1 6.0 Cote dIlvoire 34 27 .. 77 .. 71 40 50 31 Croatia . ... .. 79 80 79 Cuba 90 90 . .. .. Czech Republic .. . .. . 109 110 107 Denmark 99 100 99 99 .. . . 9.7 9.8 9.5 Dominican Republic 59 60 . .. . .. 82 78 86 4.9 4.9 5.0 Ecuador 82 83 .. 84 .. 86 96 96 96 6.4 6.4 6.4 Egypt. Arab Rep. . .. 92 .. 88 .. 99 104 92 5.5 6.5 4.5 El Salvador 54 55 17 76 16 77 76 77 75 5.2 5.2 5.1 Eritrea 18 16 .. 73 .. 67 35 43 28 Estonia .. . . 96 .. 97 88 89 86 Ethiopia 25 20 50 51 51 50 24 31 18 Finland .. . . 100 .. 100 ... . 10.0 10.2 9.8 France .. . .. .. .. .7.9 8.1 7.6 Gaboni 62 63 57 58 56 61 80 79 80 Gambia, The 10 10 74 78 71 83 70 80 60 2.3 3.0 1.6 Georgia . .. ...... 90... Germany .. . . . .. .. . 10.2 10.5 9.9 Ghana ...... 64 ...3.9 5.7 2.2 Greece 99 .. 98 ...... 8.7 9.8 7.6 Guatemala 59 56 .. 52 .. 47 56 63 50 3.5 3.8 3.1 Guinea 23 20 . . .. 34 49 19 . Guinea-Bissau . . 25 .. 17 .. 31 ...0.8 0.9 0.7 Haiti 37 48 20 .. 21 ...... 2.8 3.5 2.1 Honduras 46 47 . .. . .. 67 64 71 4.8 5.6 4.0 2.13 i Net Intake rate In Percentage of cohort Primary Avearge years of grade 1 reaching grade 5 completion rate schooling % of all children % of school-age % of grade one students who com plete population who reach grade 5 primary school Male Female Male Female Total Male Female Total Male Female 1998 1998 1980 1997 1980 1997 1992-2000, 1992-2000- 1992-2000- 2000 2000 2000 Hungary . .. 96 .. 97 .. 102 ... 9.1 9.6 8.7 India .. . . 76 88 63 5.1 6.3 3.7 Indonesi'a .. . . 88 89 91 90 92 5.0 5.5 4.5 Iran, Islamic Rep. . .. ... 92 95 89 5.3 6.1 4.5 Iraq 76 71 .... .. 55 59 51 4.0 4.6 3.3 Ireland ... .. .. .9.4 9.3 9.4 Israel ... .. .. .9.6 9.8 9.4 Italy ..99 98 99 99 ... .7.2 7.6 6.8 Jamaica . . .... .. 89 85 93 5.3 4.9 5.6 Japan ..100 .. 100 ...... 9.5 9.9 9.1 95 Jordan 46 47 100 98 ...... 6.9 7.7 6.0 Kazakhstan . ., ... 100 99 101 . .. Kenya . .. 60 62 .. 58 58 57 4.2 4.7 3.7 Korea, Dem. Rep... . ......... .. Korea, Rep. .. 94 98 94 99 96 95 98 10.8 11.7 10.0 c Kuwait .. . .. . 70 69 71 7.1 7.2 6.9 Kyrgyz Republic . .. ...... 100 CD... 0 Lao PDR 52 50 .. 57 .. 54 64 70 59 .. . 3 Lebanon 14 14 ... 70 . .. .. Lesotho 16 15 50 55 68 71 69 55 83 4.2 3.6 4.8 2~ 0, Liberia 48 31 ... .. .2.5 3.3 1.5 9S Lithuani'a .. . .. . 95 97 94 Macedoni'a, FYR .. . . 95 .. 95 91 94 87 Madagascar 56 46 49 .. 33 26 26 27 Malawi . .. 48 36 40 32 50 61 40 3.2 3.6 2.8 Malaysia 95 94 97 .. 97 .. 90 89 90 6.8 7.4 6.2 Mali .. . . 92 . 70 23 33 14 0.9 1.2 0.6 Mauritania .. . . 61 .. 68 46 52 39 Mauritius 27 27 .. 98 .. 99 ill. . 6.0 6.5 5.6 Mexico 92 93 .. 85 .. 86 89 87 86 7.2 7.6 6.9 Moldova . .. ... 81 82 81 Mongolia . ... 82 77 88 Morocco 59 55 79 76 78 74 55 63 47 Mozambiu 13 12 .. 52 39 3 6 4 3 29 1.1 1.4 0.8 Myanmar .. . .. ... ..2.8 3.0 2.5 Namibia 63 67 .. 76 .. 82 90 86 94 Nepal O.. . .. 57 70 42 2.4 3.4 1.5 Netherlands 94 98 ...9.4 9.6 9.1 New Zealand .. 93 97 94 97 11.7 12.0 11.5 Nicaragua .. . . 43 52 65 61 70 4.6 4.5 4.6 Niger 32 21 74 72 72 73 20 25 iS i.o 1.4 0.7 Nigeria . .. ... 67 75 59 Norway . .. 100 100 100 100 .. . . 11.8 12.2 11.6 Oman 57 56 .. 96 .. 96 76 76 76 Pakistan 1 4 ... .. .. .3.9 5.1 2.5 Panama 83 69 74 .. 79 ...... 8.6 8.6 8.5 Papua New Guinea 108 97 .. 59 .. 60 59 64 53 2.9 3.3 2.4 Paraguay 70 72 58 77 58 80 86 85 87 6.2 6.3 6.1 Peru 97 96 78 .. 74 .. 90 90 89 7.6 8.0 7.1 Philippines . .. ...... 92 ...8.2 8.2 8.2 Poland . .. ... 96 ...9.8 10.0 9.7 Portugal .. . .. . .. .. .9 6.1 5.7 Puerto Rico.. . ............ Romania . .. ...... 98 . .. Russian Federation . .. . ..... 90 91 90 . ~j)2.13 Net Intake rate In Percentage of cohort Pri Mary Avearge years of grade 1 reaching grade 5 completion rate schooling % of all children % of school-age % of grade one students who cornplete population whto reach grade 5 primary school Male Female Male Female Total Male Female Total Male Female 1998 1998 1980 1997 1980 1997 1992-2000' 1992-2000' 1992-2000. 2000 2000 2000 Rwanda . .. 69 .. 74 ...... 2.6 3.0 2.2 Saudi Arabia 49 33 82 87 86 92 69 68 69 Senegal 78 .. 89 89 82 85 41 48 34 2.6 3.1 2.0 Sierra Leone ... .. .2.4 3.1 1.7 Singapore ... .. .7.0 7.5 6.6 Slovak Republic . ... . 97 96 97 9.3 Slovenia . . 92 90 94 7.1 Somalia South Africa 36 34 98 95 100 6.1 5.7 6.6 96 Spain . 95 .. 94 . ... 7.3 7.4 7.1 Sri Lanka .. 83 .. 84 100 98 102 6.9 7.2 6.6 U) Sudan 68 75 71 73 35 38 33 2.1 2.7 1.6 co Swaziland 41 43 77 73 81 79 81 78 85 6.0 5.8 6.2 m Sweden . .. 98 97 98 97 ..11.4 11.4 11.4 Switzerland . .. 75 .. 74 ...... 10.5 11.1 9.9 a) Syrian Arab Republic 62 60 93 93 68 94 90 95 86 5.8 6.8 4.8 o0 Tajikistan . ........ 95... > Tanzania 11 13 89 78 90 84 59 58 60 2.7 3.1 2.3 o Thailanid . .. ...... 84 ...6.5 7.0 6.0 o Togo 43 38 59 79 44 60 63 66 41 3.3 4.6 2.1 Trinidad and Tobago 86 94 85 98 87 97 81 79 84 7.8 7.5 8.0 o Tunisia 79 80 89 90 84 92 91 93 90 5.0 5.8 4.2 0 Turkey . .. ...... 92 95 89 5.3 6.2 4.3 Turkmenistan . . . .. .. .. Uganda . .. .... .. 61 74 49 3.5 4.3 2.7 Ukraine . .. ...... 55 55 55 United Arab Emirates 53 53 100 83 100 84 80 76 86 United Kingdom .. . .. .9.4 9.5 9.4 United States .. . . . .. .. . 12.0 12.1 12.0 Uruguay 49 49 .. 96 .. 99 98 95 101 7.6 7.2 7.9 Uzbekistan . ..... 100... Venezuela, RB . ..86 .. 92 78 77 79 6.6 6.5 6.8 Vietnam 78 83 . .. .. .. West Bank and Gaza . . . .. .. .. Yemen, Rep. 32 21 . .. .. Yagoslavia, Fed. Rep. . ..... 96... Zambia 40 42 88 82 .. 80 . .. 5.5 6.0 5.0 Zimbabwe . ..78 .. 79 113 116 ill 5.4 6.0 4.7 Low Income . ... .. 69 77 61 4.4 5.4 3.3 Middle income .. . .. .. .6.4 7.3 5.5 Lower middle income .. . . 91 .. 92 101 104 99 6.3 7.3 5.2 Upper middle income 74 70 .. . .. .. .6.9 7.3 6.5 Low & middle Income . . .. . .. 84 90 80 5.6 6.5 4.6 East Asia & Pacific .. 92 .. 93 103 107 102 6.3 7.3 5.2 Europe & Central Asia . . . .. .. .. Latin America & Carib. 77 74 . . 6.0 6.3 5.8 Middle East & N. Africa . ... 84 88 80 5.3 6.1 4,4 South Asia .... .. 74 84 63 4.7 5.8 3.4 Sub-Sahiaran Africa 53 59 48 High Income . . 10.0 10.2 9.8 Europe EMU .. . . 8.4 8.6 8.1 a. Data are for the west recast year asaiiable. 2.13 v(J About the data Definitions Indicators of students' progress through school, ratios. It is also the most direct measure of na- * Net Intake rate In grade I is the number of estimated by the United Nations Educational, tional progress toward the Millennium Devel- new entrants in the first grade of primary edu- Scientific, and Cultural Organization (UNESCO) opment Goal of universal primary education. cation who are of official primary school en- and the World Bank, measure an education The primary completion rate reflects the pri- trance age, expressed as a percentage of the system's success in extending coverage to all mary cycle as nationally defined, ranging from a population of the corresponding age. students, maintaining the flow of students from very small number of countries with 3 or 4 years * Percentage of cohort reaching grade 5 is one grade to the next, and, ultimately, impart- of primary education, to a majority of countries the share of children enrolled in the first grade ing a particular level of education. with 5 or 6 years, and a relatively small number of primary school who eventually reach grade Low net intake rates in grade 1 reflect the of countries with 7 or 8 years. For any given 5. The estimate is based on the reconstructed fact that many children do not enter primary country it is therefore consistent with the gross cohort method (see About the data). * Primary school at the official age, even though school and net enrollment ratios. The numerator may completion rate is the total number of students attendance, at least through the primary level, include overage children who have repeated one successfully completing (or graduating frorn) is mandatory in all countries. Once enrolled, stu- or more grades of primary school but are now the last year of primary school in a given year, dents drop out for a variety of reasons, includ- graduating successfully. For countries where divided by the total number of children ing the low quality of schooling, discouragement the number of primary graduates is not reported, of official graduation age in the population. 97 over poor performance, and the direct and indi- a proxy primary completion rate is calculated: * Average years of schooling are the years of rect costs of schooling. Students' progress to the total number of students in the final year of formal schooling received, on average, by higher grades may also be limited by the avail- primary school, minus the number of students adults ages 15 and over. Because of data ability of teachers, classrooms, and educational who repeat the grade in a typical year, divided limitations it is not possible to adjust this materials. by the total number of children of official gradu- number for students who drop out during the The cohort survival rate is estimated as the ation age in the population. final year of school. Thus, proxy rates should proportion of an entering cohort of grade 1 stu- Average years of schooling measure the be taken as an upper-bound estimate of the dents that eventually reaches grade 5. It mea- educational attainment of the population ages likely actual primary completion rate. sures the holding power and internal efficiency 15 and over, which provides another indication __C__ _D of an education system. Cohort survival rates of the human capital stock of the country. How- approaching 100 percent indicate a high level ever, the data do not directly measure the hu- Data sources D 0) of retention and a low level of dropout. man skills obtained in schools and, specifically, Data on the net intake rate come from, Cohort survival rates are typically estimated do not take account of differences in the quality UNESCO's special data collection for the i from data on enrollment and repetition by grade of schooling across countries. Average years of Education for All initiative. The data on the for two consecutive years, in a procedure called schooling are computed using a perpetual in- cohort reaching grade 5 are from the UNESCO the reconstructed cohort method. This method ventory method. For further details, see Barro Institute for Statistics. The data on the primary makes three simplifying assumptions: dropouts and Lee (2000). completion rate are compiled by staff in the never return to school; promotion, repetition, and education group of the World Bank's Humanr dropout rates remain constant over the entire Development Network. Data on average years period in which the cohort is enrolled in school; of schooling are from Robert Barro and Jong- and the same rates apply to all pupils enrolled I Wha Lee's Intemational Data on Educational in a given grade, regardless of whether they pre- Attainment Updates and Implicafions, (2000). viously repeated a grade (Fredricksen 1993). L Given these assumptions, cross-country compari- sons should be made with caution, because other flows-caused by new entrants, reen- trants, grade skipping, migration, or school trans- fers during the school year-are not considered. UNESCO measures cohort survival to grade 5 because research suggests that five to six years of schooling is a critical threshold for the achievement of sustainable basic literacy and numeracy skills. However, it should be noted that the cohort survival rate does not guarantee these learning outcomes, and only indirectly re- flects the quality of schooling. Measuring actual learning outcomes requires setting curriculum standards and measuring students' learning progress against those standards through stan- dardized assessments, or tests. The primary completion rate is being used in- creasingly by the World Bank as a core indicator of education system performance. Because it measures both education system coverage and student attainment, the primary completion rate is a more accurate indicator of human capital formation and school system quality and effi- ciency than are either gross or net enrollment D ~2.14 Education outcomes Adutt Illiteracy rate Youth Illliteracy rate Expected years of schooling Male Female Male Female % ages 15 and over % ages 15 and over % ages 15-24 % ages 15-24 Males Females 1990 2000 1990 2000 1990 2000 1990 2000 19,90 ±998 1990 1.995 Afghanistan . . ..... Albania 13 8 33 23 3 1 8 4 . Algeria 36 24 59 43 14 6 32 16 11 11 9 11 Angola ... .. .. .6 ..5 Argentina 4 3 4 3 2 2 2 1 14 .. 15 Armenia 1 1 4 2 0~ 0~ 1 Oa0 Australia ..13 .. 13 Austria .. 15 14 Azerbaijan ... .. .. . 11 ..11 98 Bangladesh 54 48 77 70 45 39 68 60 6 8 4 8 Belarus 0 0 1 1 0~ 0 ~ 0 0~ a) o Belgium ... . ... ... 14 ..14 Benin 62 48 85 76 43 29 75 64 .. 8 .. 5 Bolivia 13 8 30 21 4 2 11 6 .. 13 .. 12 Bosnia and Herzegovina ... .. ...... E Botswana 34 25 30 20 21 15 13 8 10 12 11 12 o Brazil 18 15 20 15 12 9 9 6 .. 13 .. 13 >, Bulgaria 2 1 4 2 0~ 0 ~ 1 0 12 .. 12 Burkina Faso 75 66 92 86 64 54 86 77 3 4 2 3 Burundi 51 44 73 60 42 34 55 38 6 4 4 3 Cambodia 22 20 52 43 19 17 34 25 .. 9 .. 7 o Cameroon 28 18 47 31 10 6 16 7 .. 13 .. 11 O (N Canada ... .... ..... 17 15 17 15 Central African Republic 53 40 79 65 34 24 61 41 ..6 ..3 Chad 63 48 81 66 42 27 62 40 .. 7 .. 3 Chile 5 4 6 4 2 1 2 1 .. 13 .. 13 China 14 8 33 24 3 1 8 4 9 .. 9 Hong Kong, China 5 3 16 10 2 1 1 Colombia 11 8 12 8 6 4 4 2 .. 11 .. 11 Congo. Dem. Rep. 39 27 66 50 20 12 42 25 .. 5 .. 4 Congo, Rep. 23 13 42 26 5 2 10 3 ..7 ..5 Costa Rica 6 4 6 4 3 2 2 1 .. 11 .. 11 C6te dlvoire 57 46 77 61 40 30 59 40 .. 8 .. Croatia 1 1 5 3 0~ 0 ~ 0 ~ 0 Cuba 5 3 5 3 1 0~ 1 0~ 12 11 13 12 Czech Republic . . . .. .. .. Denmark ... .... ... 14 .. 14 Dominican Republic 20 16 21 16 13 10 12 8 .. 11 .. 12 Ecuador 10 7 15 10 4 2 5 3 .. 11 .. 11 Egypt, Arab Rep. 40 33 66 56 29 24 49 37 .. 12 .. 11 El Salvador 24 18 31 24 15 11 17 13 .. 11 .. 10 Eritrea 42 33 65 55 27 20 51 40 ..5 ..4 Ethiopia 62 53 80 69 48 39 66 52 .. 5 .. 3 France ... .... ..... 14 ..15 Gambia,The 68 56 80 71 49 34 66 51 .. 8 .. 6 Georgia ... .. .. .. .5 ..5 Germany ... .... .... 15 .. 14 Ghana 30 20 53 37 12 6 25 12 .. 3 .. 2 Greece 2 1 8 4 1 0 ~ 0 ~ 0. 13 .. 13 Guatemala 31 24 47 39 20 14 34 27 .. 10 .. 8 Guinea ... .. . . .. .6 ..3 Guinea-Bissau 57 46 87 77 37 27 74 57 .. 8 .. 5 Haiti 57 48 63 52 44 36 46 35 .. 12 .. 12 Honduras 31 25 32 25 22 18 21 15 .. 8 .. 9 2.14 Adult Illiteracy rate Youth Illitteracy rate Expected years of schooling Male Female Male Female % ages 15 and over % ages 15 and over % ages 15-24 % ages 15-24 Males Females 1990 2000 1990 2000 1990 2000 1990 2000 1990 1998 1990 1998- Hungary 1 1 1 1 00 00 Q0 QO 11 1 India 38 32 64 55 27 20 46 35 .. 9 8 Indonesia 13 8 27 18 3 2 7 3 10 9 Iran, Islamic Rep. 28 17 46 31 8 4 19 8 Iraq 43 34 67 54 29 22 48 33 9 7 Ireland ... .12 13 Israel 5 3 13 - 8 1 0 O 2 10 . 14 .. 15 Italy 2 1 3 2 00 00 00 00- Jamaica 22 17 14 9 13 9 5 - 3-11 11 11 11 Japan ... ...... 14 .14 9 Jordan 10 5 29 16 2 1 4 10 9 9 9 9 Kazakhstan ... .. .1 . 10 N Kenya 19 11 39 24 7 4 13 6 8 8 Korea, Dem. Rep.. .. . --. - - --------- - - ---------------~~~ Korea,Rep. 2 1 7 4 00 0 00 00 14 - 13 Kuwait 21 16 27 20 12 8 13 7 7 9 7 10 Kyrgyz Republic ... . . .11 - 10 (D 0 Lao PDR 47 36 80 67 28 17 62 42 9 9 6 7 - 3 Latvia 00 O 00 0 00 0 0 0 00 Lebanon 12 - 8 27 20 5 3 11 7 .. 13 .. 14 - - - -- - - -- -- -- - -- - -- -- - -- - - - - - -- - - - - - - -- - - -- - - - -- -- - - - - - -- -- - - - - - --- - - - - - - -- - - - Lesotho 35 -28 -11 6 23 17 -3 1 9 - 9 11 10 C Liberia 45 30 77 62 25 15 60 46 .. 6 . . 4 Libya 17 9 49 32 1 00. 17 7 .. 13 .. 13 Lithuania 00 00 1 1 00 00 Q 00 Macedonia, FYR Madagascar 34 26 50 40 22 16 33 23 6 6 Malawi- - 31 26 64 53 24 19 49 39 10 .. 10 Malaysia 13 9 26 17 5 3 6 2 10 .. 11 Mali 67 51 81 -66 46 28 63 40 3 - 5 1 3 Mauritania 54 49 76 70 44 43 64 59 7 .. 6 M a u ri-tihus---- --------- 15 - ---- --1-2 ------- 25 ------1-9 - ----------9 79 6 12 ..12 Mexico 9 7 15 10 4 3 6 3 .. 12 -- 11 Moldova 1 0 4 2 00 00 00 00 Mongolia 1 1 2 1 1 1 1 00 * 7 9 Morocco 47 38 75 64 32 24 58 42 .. 10 .. 8 Mozambique 51 40 82 71 34 25 68 54 4 5 3 4 Myanmar 13 11 26 19 10 9 14 9 .. 7 -- 8 Namibia 23 - -7 28 19 14 10 11 7 .. 13 . - 13 Nepal 52 40 86 76 33 23 73 57 .. 10 -- 7 Nletherlandls -. . .. 15 -- 15 New Zealand .. . .. - 14 10 15 11 Nicaragua 37 34 37 33 32 29 31 28 10 -- 10 Niger 82 76 95 92 75 68 91 86 3 -. 2 Nigeria 40 28 62 44 19 10 34 16 7 -- Norway ..- .- . . . 14 14 Oman 33 20 62 38 5 00. 25 4 10 9 9 8 Pakistan 51 43 80 72 37 29 69 58 5 -- 3 Panama 10 7 12 9 4 3 5 4 .. 12 12 Papua New Guinea __36 29 52 43 26 20 38 29 -. 9 -- 8 Paraguay 8 6 12 8 4 3 5 3 9 10 8 11 Peru - ----- 8 5--- ----- 21 15- --- - -3--2 S513 --11 Philippines - 7 55 8- - - -3 2 3 1 . 1 - 2 Poland QO 00 00 Q0 00. 0 0 O 00 12 -- 12 - Portugal 9 5 16 10 1 Q0 00 QO 13 -- 14 Puerto Rico 8 6 9 6 5 3 3 2 - - Romania 1 1 4 3 1 Q0 1 Q0 11 Russian Federation 00 00 1 1- 00 00 00- . - 2.14 Adult Illiteracy rate Youth Illiteracy rate Expected years of schooling Male Female Male Female %X ages 15 and over % ages 15 and over % ages 15-24 % ages 15-24 Males Females 1990 2000 1990 2000 1990 2000 1990 2000 1990 1998 1990 1999 Rwanda 37 26 56 40 22 15 33 19 .. 8 .. 8 Saudi Arabia 24 17 50 33 9 5 21 10 9 9 7 9 Senegal 62 53 81 72 50 40 70 58 .. 6 .. 5 Sierra Leone.. .... Singapore 6 4 17 12 1 0. 1 Slovak Republic . . . Slovenia 0 . 0 . 0 . 0 0~ 0 . 0. South Africa 18 14 20 15 11 9 12 9 13 14 13 14 ioo Spi 2 1 5 3 0 . 0 . 0 . 0 Sri Lanka 7 6 15 11 4 3 6 3 .. 11 .. 11 Sudan 40 31 68 54 24 17 46 29 .. 5 .. c~Swaziland 26 19 30 21 15 10 15 9 11 11 10 10 Sweden ... .. . 13 13 C: Switzerland .. . . .. 4 13 E) Syrian Arab Republic 18 12 52 40 8 5 33 21 11 9 9 9 o) Tajikistan 1 0~ 3 1 0. 0. 0 ~ 0~ . a), > Tanzania 24 16 49 33 11 7 23 12 .. 5 5 a) o Thailand 5 3 11 6 1 1 2 2 .. 10 .. 11 o Togo 39 28 71 58 21 13 52 36 11 12 6 8 Trinidad and Tobago 6 4 11 8 3 2 4 3 11 12 11 12 N O Tunisia 28 19 53 39 7 3 25 11 11 13 10 12 N Turkey 11 7 33 23 3 1 12 6 .. 10 .. 9 Turkmenistan .. ..... - Uganda 31 22 57 43 20 14 40 28 .. 11 .. 10 Ukraine D . 0'. 1 1 0 . 0' 0 . 0~ United Arab Emirates 29 25 29 21 18 13 11 6 10 11 11 11 United Kingdom . . .... ..... 14 .. 14 United States *,. .. . . .. 15 16 16 15 Uruguay 4 3 3 2 1 1 1 0 . 11 .. 14 Uzbekistan 1 0~ 2 1 o' 0 ' oa o0 . Venezuiela, RB 10 7 12 8 5 3 3 1 .. 10 .. 11 Vietnanm 6 4 13 9 5 3 5 3 .. 10 .. 10 West Bank and Gaza . . . .. .. .. Yemen, Rep. 45 32 87 75 26 17 75 54 .. 11 .. 5 Yugoslavia, Fed. Rep. ... .... ..... Zambia 21 15 41 29 14 9 24 15 8 .. 7 Zimbabwe 13 7 25 15 3 1 9 4 Low Income 35 28 56 47 24 18 40 31 Middle Income 13 9 26 19 5 4 10 6 Lower middle income 14 9 29 21 5 3 10 7 Upper middle income 11 8 16 12 6 4 7 4 Low & middle Income 22 18 39 31 13 11 23 19 East Asia &Pacific 13 8 29 21 3 2 8 4 Europe &Central Asia 2 2 6 5 1 1 3 2 Latin America & Carib. 14 11 17 13 8 6 8 6 Middle East & N. Africa 34 25 59 46 18 12 37 24 South Asia 40 34 66 57 29 23 50 40 Sub-Saharan Africa 40 30 60 47 25 17 40 27 . . High Income ... . .. . ... 15 .. 16 Europe EMU . . . . . 15 .. 15 a. Less than 0.5. 2.14 ')) About the data Definitions Many governments collect and publish statistics the current enrollment ratio for that age, it does * Adult Illiteracy rate is the percentage of that indicate how their education systems are not account for changes and trends in future people ages 15 and over who cannot, with working and developing-statistics on enrollment ratios. The expected number of years understanding, read and write a short, simple enrollment and on such efficiency indicators as and the expected number of grades completed statement about their everyday life. * Youth pupil-teacher ratios, repetition rates, and cohort are not necessarily consistent, because the first illiteracy rate is the illiteracy rate among people progression through school. But until recently, includes years spent in repetition. Comparability ages 15-24. * Expected years of schooling despite an obvious interest in what education across countries and over time may be affected are the average number of years of formal achieves, few systems in high-income or by differences in the length of the school year schoolingthat children are expected to receive, developing countries had systematically or changes in policies on automatic promotions including university education and years spent collected information oin outcomes of education. and grade repetition. in repetition. They are the sum of the underlying Basic student outcomes include achieve- age-specific enrollment ratios for primary, ments in reading and mathematics judged Figure 2.14 secondary, and tertiary education. against established standards. In many coun-- tries national learning assessments are enabling Reading and mathematical literacy among Data sources 101 ministries of education to monitor progress in 15-year-olds, 2000 these outcomes. Internationally, the United Na- sso = 5 The data on illiteracy are based on the UNESCO i tions Educational, Scientific, and Cultural Orga- Institute for Statistics estimates and projec- 0 nization (UNESCO) has established literacy as 0 Math tions assessed in 2000 and 2002. The data an outcome indicator based on an internation- U Reading on expected years of schooling are from the o ally agreed definition. The rate of illiteracy " 450 - - UNESCO Institute for Statistics. . is defined as the percentage of people who X : *_CD cannot, with understanding, read and write a _D 0 short, simple statement about their everyday life. 350 3 In practice, illiteracy is difficult to measure. To (D estimate illiteracy using such a definition requires census or survey measurements under e e a controlled conditions. Many countries estimate .0e .? 0t°° o the number of illiterate people from self-reported data, or by taking people with no schooling sou.e Programme (or International SWdent Assessment sunvev as illiterate. Literacy statistics for most countries cover The absence of regular and reliable measures of Literacy ~~~~~~~~~education outcomes across countries, especialiy the population ages 15 and above, by five-year measuresofskills,remainsthemostsigniflantgapIn age groups, but some include younger ages or educatlon Indicators. The Programme forrinternational Student Assessment (PISA) was carried out by OECD are confined to age ranges that tend to inflate andpartlclpatingcountriestomeasuresklilsforilfe- literacy rates. As an alternative, UNESCO has reading ilteracy, mathematicai literacy, and scientific proposed the narrower age range of 15-24, literacy-among 15-year-old students. Thirty two countries, Including eight developing countries, which better captures the ability of participants conducted the first PISA survey In 2000. The PISA in the formal education system. The youth scale for each literacy area was devised so that across illiteracy rate reported in the table measures the OECD countries the average score Is 500 points. accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of people who have passed through the primary education system (or never entered it) without acquiring basic literacy and numeracy skills. Reasons for this may include difficulties in attending school or dropping out before reaching grade 5 (see About the data for table 2.13) and thereby failing to achieve basic learning competencies. The indicator expected years of schooling is an estimate of the total years of schooling that an average child at the age of school entry will receive, including years spent on repetition, given the current patterns of enrollment across cycles of education. It may also be interpreted as an indicator of the total education resources, measured in school years, that a child will ac- quire over his or her "lifetime" in school-or as an indicator of an education system's overall level of development. Because the calculation of this indicator assumes that the probability of a child's being enrolled in school at any future age is equal to ) 2.15 Health expenditure, services, and use Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita Public Private Total per 1,000 per 1,000 % of % of GDP % of GDP % of GDP $ people people populat 0n days 195-991 155-599 1995-99' 1995-99, S980 I.990.99, 1980 1990-99, 1,99099. j*99099* 19980-599 Afghanistan ... .. . 0.1 ..0.2 Albania 2.0 0.9 3.3 36 .. 1.3 ..3.2 . 13 2 Algeria 2.6 1.0 3.6 68 .. 1.0 ..2.1 Angola ... .. . 0.1 ..1.3 Argentina 2.4 6.1 8.4 654 .. 2.7 ..3.3 Armenia 4.0 4.2 7.8 27 3.5 3.2 8.4 0.7 8 15 2 Australia 6.0 2.6 8.6 1,714 1.8 2.5 ..8.5 16 16 6 Austria 5.9 2.3 8.2 2,121 .. 3.0 11.2 8.7 29 9 7 Azerbaijan 1.0 0.6 1.8 9 3.4 3.6 9.7 9.7 6 18 1 102 Bangladesh 1.7 1.9 3.6 12 0.1 0.2 0.2 0.3 Belarus 4.6 1.0 5.6 85 3.4 4.4 12.5 12.2 26 18 1 1 o Belgium 6.3 2.5 8.8 2.137 2.5 3.8 ..7.3 20 11 8 Benin 1.6 1.6 3.3 12 0.1 0.1 1.5 0.2 Bolivia 4.1 2.4 6.5 69 .. 1.3 ..1.7 CM C5 Bosnia and Herzegovina 8.0 .... .. 1.4 .1.8 .. 15 g) Botswana 2.5 1.5 4.0 127 0.1 0.2 2.4 1.6 0. o Brazil 2.9 3.6 6.5 308 .. 1.3 ..3.1 0 ..2 > Bulgaria 3.9 0.2 4.1 62 2.5 3.5 11.1 8.6 18 12 5 Burkina Faso 1.5 2.8 4.1 9 0.0 0.0 ..1.4 2 3 0 Burundi 0.6 3.0 3.7 5 .. 0.1 ..0.7 Cambodia 0.6 6.3 6.9 17 .. 0.3 ..2.1 o Cameroon 1.0 4.0 5.0 31 .. 0.1 ..2.6 R Canada 6.6 2.7 9.3 1,939 .. 2.1 ..4.1 10 8 7 Central African Republic 2.0 1.0 3.0 9 0.0 0.0 1.6 0.9 Chad 2.3 0.6 2.9 7 .. 0.0 . 0.7 Chile 2.7 3.1 5.9 269 .. 1.1 3.4 2.7 China 2.1 3.0 5.1 40 0.9 1.7 2.0 2.4 4 12 Hong Kong, China 2.1 2.8 5.0 1.134 0.8 1.3 4.0 ..2 I. Colombia 5.2 4.2 9.4 227 .. 1.2 1.6 1.5 Congo, Dem. Rep. ... .. . 0.1 ..1.4 Congo, Rep. 2.0 3.8 5.6 40 .. 0.3 ..3.4 Costa Rica 5.2 1.5 6.7 257 .. 0.9 3.3 1.7 9 6 1 Cbte dIlvoire 1.2 2.5 3.7 28 .. 0.1 ..0.6 Croatia 9.5 2.0 9.6 440 .. 2.3 ..5.9 12 Cuba ... .. .5.3 .. .1 Czech Republic 6.6 0.6 7.2 380 .. 3.0 ..8.7 20 11 12 Denmark 6.9 1.5 8.4 2,785 .. 3.4 ..4.5 20 7 6 Dominican Republic 1.9 3.0 4.8 95 .. 2.2 .,1.5 Ecuador 1.7 2.0 3.6 59 .. 1.7 1.9 1.6 Egypt. Arab Rep. 1.8 2.0 3.8 48 1.1 1.6 2.0 2.1 3 6 4 El Salvador 2.6 4.6 7.2 143 0.3 1.1 ..1.6 Eritrea 2.9 .... ..0.0... Estonia 5.1 1.3 6.6' 243 4.2 3.0 12.4 7.4 18 9 5 Ethiopia 1.3 2.4 4.1 4 0.0 0 0.0 0.O3 0.2 Finland 5.2 1.7 6.8 1,704 1.9 3.1 15.5 7.5 27 11 4 France 7.3 2.0 9.3 2,288 .. 3.0 ,,8.5 23 11 7 Gabon 2.1 1.0 3.1 122 .. 0.2 ..3.2 Gambia, The 2.3 1.9 3.7 13 .. 0.0' a 0.6 Georgia 0.8 2.0 2.8 16 4.8 4.4 10.7 4.8 5 11 1 Germany 7.9 2.6 10.5 2.697 2.2 3.5 ..9.3 21 12 7 Ghana 1.7 2.9 4.7 19 .. 0.1 ..1.5 Greece 4.7 3.6 8.4 965 2.4 4.1 6.2 5.0 15 8 Guatemala 2.1 2.3 4.3 78 .. 0.9 ..1.0 Guinea 2.3 1.5 3.8 19 .. 0.1 ..0.6 Guinea-Bissau ... ... 0.1 0.2 1.9 1.5 . Haiti 1.4 2.8 4.2 21 .. 0.2 0.7 0.7 . Honduras 3.9 4.7 8.6 74 .. 0.8 1.3 1.1 . 2.15 ' Health expenditure Health Physicians Hospitai beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita Public Private Total per 1,000 per 1,000 % of % of GOP ft of GDP ft of GDP $ people people population days 1995-991 1995-991 1995.991 i995-99, I±98s 1990-99, 2.90 iaoaa1990-9SW1 -9 1.990.99W 1990-9W1 Hungary 5.2 1.6 6.8 318 2.5 3.2 9.1 8.3 24 10 15 India 0.8 4.2 5.4 20 0.4 0.4 0.8 0.8 Indonesia 0.8 0.9 1.6 8 .. 0.2 ..0.7 Iran, Islamic Rep. 1.7 2.5 4.2 128 0.9 1.5 1.6 Iraq 3.8 1.8 5.6 .. 0.6 0.5 1.9 1.4 Ireland 5.2 1.6 6.8 1,569 1.3 2.3 9.7 3.7 14 8 Israel 6.0 3.6 9.5 1,607 .. 3.9 5.1 6.0 Italy 5.6 2.6 8.2 1,676 .. 5.9 ..5.5 18 8 5 Jamaica 3.0 2.5 5.5 157 .. 1.4 ..2.1 Japan 5.7 1.6 7.2 2,243 .. 1.9 11.3 16.4 10 40 16 103 Jordan 3.6 3.8 8.0 139 0.8 1.7 1.3 1.8 11 4 3 Kazakhstan 2.7 2.9 5.5 62 3.2 3.5 13.2 8.5 15 16 00N Kenya 2.4 5.5 7.8 31 .. 0.1 ..1.6 . . Korea, Dem. Rep. - . ... .. 3.0..... , Korea, Rep. 2.4 3.0 5.4 470 0.6 1.3 1.7 5.5 6 12 10 Kuwait 2.9 0.4 3.3 551 1.7 1.9 4.1 2.8 . .. Kyrgyz Republic 2.2 2.2 4.4 11 2.9 3.0 12.0 9.5 21 15 1 8. Lao PDR 1.2 1.3 2.5 6 .. 0.2 ..2.6 .. Latvia 4.0 2.6 6.7 166 4.1 2.8 13.7 10.3 21 14 4 Lebanon 2.2 9.7 12.1 469 .. 2.1 ..2.7 17 4 . Lesotho 3.4 2.2 ... . 0.1 ... . .. Liberia ... .. .0.0 .. Libya ...... 1.3 1.3 ..4.3 .. . Lithuania 4.7 1.5 6.3 183 3.9 4.0 12.1 9.2 24 11 7 Macedonia. FYR 5.3 1.0 4.9 90 .. 2.2 ..4.7 9 13 3 Madagascar 1.1 1.0 2.1 5 .. 0.1 ..0.9 Malawi' 2.8 3.5 6.3 11 .. 0.0 ..1.3 ...2 Malaysia 1.4 1.0 2.5 81 0.3 0.7 ..2.0 Mali 2.1 2.2 4.3 11 0.0 ~ 0.1 ..0.2 1 7 Mauritania 1.4 3.4 4.8 19 .. 0.1 ..0.7 Mauritius 1.8 1.6 3.4 120 0.5 0.9 3.1 3.1 0 ..4 Mexico 2.6 2.8 5.3 236 .. 1.7 ..1.1 6 4 2 Moldova 2.9 2.1 6.4 25 3.1 3.5 12.0 12.1 19 18 8 Mongolia 4.7 .. .. 2.4 11.2 11.5 Morocco 1.2 3.2 4.4 49 .. 0.5 ..1.0 3 7 Mozambique 2.8 0.7 3.5 8 00 I 1.1 0.9 Myanmar 0.2 1.6 1.8 97 0.3 0.9 0.6 Namibia 3.3 3.3 7.0 142 0.3 Nepal 1.3 4.2 5.4 11 0.0d 00 d 0.2 0.2 Netherlands 6.0 2.8 8.7 2,173 . 3.1 12.5 11.3 11 34 6 New Zealand 6.3 1.8 8.1 1,163 1.6 2.3 ..6.2 13 9 Nicaragua 8.5 4.0 12.5 54 0.4 0.9 ..1.5 Niger 1.2 1.4 2.6 5 0.0 ..0.1 28 5 Nigeria 0.8 2.0 2.8 30 0.1 0.2 0.9 1.7 Norway 7.0 2.2 9.2 3,182 1.9 2.8 15 0 14.4 16 9 4 Oman 2.9 0.6 3.5 0.5 1.3 1.6 2.2 9 4 4 Pakistan 0.7 3.1 4.0 18 0.3 0.6 0.6 0.7 3 Panama 4.9 2.3 7.3 246 .. 1.7 2.2 Papua New Guinea 2.5 0.7 3.2 25 0.1 0.1 5.5 4.0 Paraguay 1.7 3.6 5.2 86 . 111.3 Peru 2.4 3.8 6.2 141 0.7 0.9 ..1.5 1 6 2 Philippines 1.6 2.1 3.6 37 0.1 1.2 1.7 1.1 . Poland 4.7 1.5 6.2 248 1.8 2.3 5.6 5,1 15 9 5 Portugal 5.1 2.5 7.7 859 . 3.2 ..4.0 12 9 3 Puerto Rico ... 1.7 .3.3 Romania 3.8 1.5 4.6 86 1.5 1.8 8.8 7 6 18 10 4 Russian Federation 4.6 1.2 4.6 133 4.0 4.2 13.0 12.1 22 17 8 D 2.15 Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission iength visits per capita rate of stay per capita Public Private Total per 1.000 per 1.000 % of % of GDP % of GDP % of GOP $ people people popu ation days 1.55-991 1995-99 1995-995, 1995-99- I9SO 1590-S99 1950 190-599 1590-991 1990-995 15990-99' Rwanda 2.0 2.1 4.1 10 0.0 ~ 0.0 ' 15 1.7 Saudi Arabia 6.4 1.6 8.0 611 1.7 2.3 11 4 1 Senegal 2.6 1.9 4.5 23 0.1 0.4 22 10 1 Sierra Leone 0.9 4.4 5.3 8 0.1 0.1 1.2 Singapore 1.1 2.1 3.2 678 0.9 1.6 4.0 3.6 12 Slovak Republic 5.7 1.5 6.5 285 .. 3.5 ..7.1 20 9 4 Slovenia 6.7 0.9 7.6 746 -. 2.3 7.0 5.7 16 11 Somalia ... ... 0.0 0.0 ..0.8 South Africa 3.3 3.8 7.2 230 .. 0.6... 104 Spain 5.4 1.6 7.0 1,043 .. 3.1 ..3.9 11 10 - Sri Lanka 1.7 1.8 3.5 29 0.1 0.4 2.9 2.7 Sudan 0.7 2.6 3.3 119 0.1 0.1 0.9 1.1 ( ~ Swaziland 2.5 1.0 3.5 46 .. 0.2 .. Swedeni 6.6 1.3 7.9 2.145 2.2 3.1 14.8 3,7 17 7 3 Switzerland 7.6 2.8 10.4 3,857 .. 3.4 .. 18.1 17 14 11 E Syrian Arab Republic 0.9 1.6 2.5 116 0.4 1.3 1.1 1.4 o Tajikistan 5.2 0.9 6.1 13 2.4 2.0 10.0 8.8 16 15 a) > Tanzania 1.3 1.8 3.0 8 .. 0.0 1.4 0.9 0 Thailand 1.9 4.1 6.0 112 0.1 0.4 1.5 2.0...1 T ogo 1.3 1.3 2.6 9 0.1 0.1 ..1.5 Trinidad and Tobago 2.5 1.8 4.3 204 0.7 0.8 ..5.1 o Tunisia 2.2 2.9 5.1 108 0.3 0.7 2,1 1.7 8 0 Turkey 3.3 1.4 4.8 153 0.6 1.2 2.2 2.6 7 6 2 Turkmenistan 4.1 1.1 5.2 30 2.9 3.0 10.6 11.5 17 15- Uganda 1.9 4.1 5.9 18 .. 0.0 ..0.9 Ukraine 2.9 1.5 4.4 28 3.7 3.0 12.5 11.8 20 17 10 United Arab Emirates 0.8 7.6 8.4 1.428 1.1 1.8 2.8 2.6 11 5 United Kingdomn 5.8 1.2 6.9 1,675 .. 1.8 9.3 4.1 15 10 6 United States 5.7 7.1 12.9 4,271 1.8 2.7 5.9 3.6 13 7 6 Uruguay 1.9 7.3 9.1 621 .. 3.7 ..4.4 Uzbekistan 3.4 0.6 4.1 25 2.9 3.1 11.5 8.3 19 14 Venezuela. RB 2.6 1.6 4.2 171 0.8 2.4 0.3 1.5 Vietnam 0.8 4.0 4.8 17 0.2 0.5 3.5 1.7 8 7 3 West Bank and Gaza 4.9 3.7 8.6 82 .. 0.5 ..1.2 9 34 Yemen, Rep. 2.4 3.2 5.6 18 .. 0.2 ..0.6 Yugoslavia, Fed. Rep. ... .. . 2.0 ..5.3 8 12 2 Zambia 3.6 3.4 6.9 23 0.1 0.1 Zimbabwe 3.0 4.0 8.1 C 36 0.2 0.1 3.0 0.5 Low Income 0.9 2.7 3.8 21 0.5 0.5 1.7 1.3 13 11 4 Middle income 2.9 2.9 5.7 119 1.2 1.7 3.4 3.4 6 12 4 Lower middle income 2.7 2.6 5.0 62 1.2 1.7 3.4 3.5 6 13 5 Upper middle income 3.2 3.1 6.2 303 .. 1.6 ..3.2 6 7 4 Low & middle income 2.5 2.9 5.3 74 0.9 1.1 2.7 2.5 7 12 4 East Asia & Pacific 1.8 2.7 4.5 51 0.8 1.3 2.0 2.S 4 13 4 Europe & Central Asia 4.4 1.4 5.2 126 3.0 3.1 10.4 8.8 17 14 6 Latin America & Carib. 2.8 3.7 6.5 264 .. 1.6 ..2.2 2 5 2 Middle East & N. Africa 2.9 2.2 5.1 125 .. 1.0 ..1.7 5 6 3 South Asia 0.9 3.8 5.1 19 0.3 0.4 0.7 0.7 ...3 Sub-Saharan Africa 2.0 2.8 4.9 41 .. 0.1 ..1.1 12 6 1 High Income 6.0 4.0 10.1 2,733 .. 2.9 ..7.2 15 14 8 Europe EMU 6.7 2.4 9.1 2,029 .. 3.8 ..7.4 19 12 6 a. Data are for the weost recent year available. b. Oats way not suw to total because of roassing ard because of differences in the year for which the miost recent data are available. C. A country has one more category, external resources, in addit or to public and private. d. L.ess than 0.05. 2.15 About the data Definitions National health accounts track financial flows average length of stay, and outpatient visits) * Public health expenditure consists of in the health sector, including both public and come from a variety of sources (see Data recurrent and capital spending from private expenditures by sources of funding. In sources). Data are lacking for many countries, government (central and local) budgets and contrast with high-income countries, few and for others comparability is limited by social (or compulsory) health insurance funds. developing countries have health accounts that differences in definitions. In estimates of health * Private health expenditure includes direct are methodologically consistent with national personnel, for example, some countries household (out-of-pocket) spending, private accounting approaches. The difficulties in incorrectly include retired physicians (because insurance, spending by non-profit institutions creatingnationalhealthaccountsgobeyonddata deletions are made only periodically) or those serving households (other than social collection. To establish a national health working outside the health sector. There is no insurance) and direct service payments by accounting system. a country needs to define universally accepted definition of hospital beds. private corporations. * Total health expenditure the boundaries of the health care system and a Moreover, figures on physicians and hospital is the sum of public and private health taxonomy of health care delivery institutions. The beds are indicators of availability, not of quality expenditure, plus, for some countries, external accounting system should be comprehensive or use. They do not show how well trained the sources (mainly foreign assistance). It covers and standardized, providing not only accurate physicians are or how well equipped the the provision of health services (preventive and measurements of financial flows, but also hospitals or medical centers are. And physicians curative), family planning activities, nutrition 105 information on the equity and efficiency of health and hospital beds tend to be concentrated in activities, and emergency aid designated for financing to inform health policy. urban areas, so these indicators give only a health but does not include provision of water g The absence of consistent national health partial view of health services available to the and sanitation. * Physicians are defined Eas accounting systems in most developing entire population. graduates of any faculty or school of medicine c countries makes cross-country comparisons of The average length of stay in hospitals is an who are working in the country in any medical health spending difficult. Records of private out- indicator of the efficiency of resource use. Longer field (practice, teaching, research). * Hospital of-pocket expenditures are often lacking. And stays may reflect a waste of resources if patients beds include inpatient beds available in public, 0 compiling estimates of public health are kept in hospitals beyond the time medically private, general, and specialized hospitals and expenditures is complicated in countries where required, inflating demand for hospital beds and rehabilitation centers. In most cases beds for state or provincial and local governments are increasing hospital costs. Aside from differences both acute and chronic care are included. involved in health care financing and delivery in cases and financing methods, cross-country * Inpatient admission rate is the percentage because the data on public spending often are variations in average length of stay may result of the population admitted to hospitals during not aggregated. The data in the table are the from differences in the role of hospitals. Many a year. * Average length of stay is the average product of an effort by the World Health developing countries do not have separate duration of inpatient hospital admissions. Organization (WHO), the Organisation for extended care facilities, so hospitals become * Outpatient visits per capita are the number Economic Co-operation and Development the source of both long-terrn and acute care. of visits to health care facilities per capita, (OECD), and the World Bank to collect all Other factors may also explain the variations. including repeat visits. available information on health expenditures Data for some countries may not include all - from national and local government budgets, public and private hospitals. Admission rates D national accounts, household surveys, insurance may be overstated in soime countries if publications, international donors, and existing outpatient surgeries are counted as hospital | The estimates of health expenditure come tabulations. admissions. And in many countries outpatient from the WHO's World Health Report 2000 Health service indicators (physicians and visits, especially emergency visits, may result and World Health Report 2001, from the OECD hospital beds per 1,000 people) and health care in double counting if a patient receives treatment for its member countries, from national healih 1 utilization indicators (inpatient admission rates, in more than one department. accounts of a country, from the Web site The European Observatory on Health Care Systems (www.observatory.dk), supplemented by World Table 2.15a1 Bank country and sector studies, and poverty How Important are the different elements of client responsiveness? assessments, including the Human Development Network's Sector Strategy:, Respect for persons Ctient orentation jHealth, Nutrition, and Population (World Bank | Respect for dignity Prompt attention Confidespectfdiaity Quarom attenities |1997). Data are also drawn from World Bank Confidentiality Quality of amenities 'public expenditure reviews, the International Autonomy Access to social support ne!tworks publicrexpendiu revew,tenternanal eMonetary Fund's Government Finance Choice of providers Statistics database, and other studies. The Source. WHO, World Health RIPraon 2000. data on private expenditure in developing Use of health services depends not only on easy access, but on responsiveness to clients by health providers. In a countries are largely drawn from household survey of 35 countries the poor were Identified as the maln disadvantaged group. They were considered to be surveys conducted by a government, cr | treated with less respect fortheirdignity,to have less cholce of providers, and to be offered poorer quality amenitles statistical or international organizations. The l than the nonpoor. Rural populations were regarded as being treated worse than urban dwellers, suffering especially l from less prompt attention. In several countries women, children or adolescents, and Indigenous or tribal groups data on physicians, hospital beds, and received worse treatment than the rest of the population. utilization of health services are from the WHO, Land OECD, supplemented by country data. D ~2.16 Disease prevention: coverage and quality Access to an Access to Teta nus Child Immunization Tuberculosis DOTS improved Improved vaccinations rate treatment detection water source sanitation faciiities success rate rate % of % of children % of % of pregnant under 12 months % of % of population population women measles DPT cases canes 1990 2000 1990 2000 1996-2000. 1995-99- 1995-99, 1995-99- 1995-99, Afghanistan .. 13 12 40 35 33 5 Albania 65 85 97 Algeria 94 73 52 83 83 Angola 38 44 24 46 22 83 62 Argentina .. 79 85 99 83 53 18 Armenia 91 91 61 42 Australia 109 10(X 10X) 1(X) 89 83 75 2 Austria 1(X) 113) 1(X) 1( 92 99 Azerbaijan 99 99 86 9 106 Bangladesh 91 97 37 53 64 71 72 89 25 Belarus 109 9.83 99 o Belgium ...83 8 Benin 63 2) 23 50 79 79 77 31 Bolivia 74 79 53 83 27 79 78 62 77 Bosnia and Herzegovina 83 99 83 52 8 Botswana 93 61 54 83 92 47 63 a) > Bulgaria9 9 o Burkina Faso 53 ..24 29 313 53 42 59 9 0 Burundi 65 ..89. 9 75 74 74 28 3: Cambodia 39 .. I 31 53 49 95 57 o Cameroon 52 62 87 92 49 62 48 75 10 0 CN Canada 113) 119 1139 10X) 96 97 Central African Republic 59 89 30 31 6 39 33 Chad 27 18 29 24 39 21, 64 33 Chile 92) 94 97 97 .. 8 94 83 83 China 71 75 29 39 13 92) 90 97 32 Hong Kong, China .. . ..83 53 Colombia 87 91 82 83 75 74 74 39 Congo, Dem, Rep. .. 45 29 10 . .70 53 Congo,Rep. 51 ..30 23 2) Costa Rica 9.83 96 83 83 . 30 C6te dlvoire 65 77 49 49 62 62 62 44 Croatia 9 8 . 1(3) 92 93 Cuba 83 .. 8 9.8 94 94 95 Czech Republic 958 98 65 51 Denmark 113 00.. 92 99 Dominican Republic 78 79 8c) 71 83 93 73 ..7 Ecuador 71 .59 . 99 89) 26 Egypt, Arab Rep. 94 95 87 94 36 93 94 87 25 El Salvador 74 ..83 ..86 83 77 53 Eritrea 46 ..13 34 83 93 73 12 Estonia .. . .. .92 95 Ethiopia 22 24 13 15 17 27 21. 74 22 Finland 103) 1139 1139 1139 96 99 France ......84 83..B Gabon 70 ..21 54 53 37 Gambia,The .. 62 ..37 83 . Georgia .. 76 99 89 90 78 46 Germany .. .75 83 Ghana 56 64 8) 63 51 73 72 59 23 Greece ..... 83 83 Guatemala 78 92 77 83 39 83 78 79 54 Guinea 45 48 53 58 61 52 46 73 43 Guinea-Bissau 49 ..47 46 70 39 Haiti 46 46 25 2) 52 83 43 79 24 Honduras 84 9 . 77 9.83 93 93 15 2.16 Access to an Access to Tetanus Child Immunization Tuberculosis DOTS Improved Improved vaccinations rate treatment detection water source sanitation facilities success rate rate % of % of children % of % of pregnan't under 12 months % of % of population population women measles DPT cases cases 1990 2000 1 1990 2000 199e-20001 1995-99, 1995-99, 1995-99, 1995-991 Hungary 99 99 99 99 ..99 99 80 36 India 78 88 21 31 67 50 55 84 6 Indonesia 69 76 54 66 54 71 72 58 19 Iran, Islamic Rep. 86 95 81 81 75 ...83 31 Iraq -.------85 79 56 63 76 83 5 Ireland ...77 86 Israel ...94 96 ..83 Italy ...70 95 72 54 Jamaica 71 ..84 96 84 89 105 Japan -.----.----94 71 -_107 Jordan 97 96 98 99 15 94 97 92 33 Kazakhstan 91 99 99 98 79 73 Kenya 40 49 84 86 51 79 ..-79 77 53 Korea, Dem. Rep. 5 34 37 91 2 Korea, Rep. .. 92 ..63 85 74 . .a Kuwait ...8 96 94..- t Kyrgyz Republic 77 100 97 98 82 60 CD - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - Lao PDR 90 ..46 32 71 56 'a- . Latvia ... .97 95 71 52 CD Lebanon .. 100 99 ..88 94 73 72 Lesotho .. 91 92 17 77 85 . Liberia ... Libya 71 72 97 97 ..68 134 Lithuania ..97 93 79 2 Macedonia, FYR 99 99 . Madagascar 44 47 36 42 35 55 55 Malawi 49 57 73 77 81 83 84 69 42 Malaysia 71 88 93 Mali 55 65 70_ 69 32 57 52 70 19 Mauritania 37 37 30 33 63 62 40 ..50 Mauritius 1-00 100 100 99 78 79 85 91 34 Mexico 83 86 69 73 ..95 96 78 38 Moldova .. 100 Mongolia 60 30 93 94 84 63 Morocco 75 82 62 75 33 90 91 82 90 Mozambique .. 60_ . ...... .....43 29 57 61 Myanmar 64 68 45 46 78 85 83 82 33 Namibia 72 77 33 41 70 66 72 60 105 Nepal 66 81 21 27 33 73 76 89 44 Netherlands 100 100 100 100 ..96 97 65 40 New Zealand E. 83 88 Nicaragua 70 79 76 84 42 99 83 82 80 Niger 53 59 15 20 41 36 28 Nigeria 49 57 60 63 44 41 26 73 11 Norway 100 100 ..93 95 69 20 Oman 37 39 84 92 96 99 99 86 106 Pakistan 84 88 34 61 58 54 56 66 2 Panama .. 87 94 90 92 51 9 Papua New Guinea 42 42 82 82 11 58 56 72 5 Paraguay 63 79 89 95 92 66 Peru 72 77 64 76 59 93 93 92 95 Philippi-nes 87 87 74 83 35 79 79 84 20 Poland 97 98 75 3 Portugal 96 97 74 77 Puerto Rico Romania 58 53 98 97 85 4 Russian Federation .. 99 97 95 68 2 (D 2.16 Access to an Access to Tetanus Child Immunization Tuberculosis DOTS Improved Improved vaccinations rate treatment detection water source sanitation faciiities success rate rate % of % of chitldrern % of % of pregnant under 12 months % of % of population population women measles DPT cases canes 1990 2000 1990 2000 1991-200' 1995-99, 1995-599 199s-99' 1995-99' Rwanda .. 41 8 43 87 48 72 37 Saudi Arabia 95 4 .. 00 66 94 96 57 2 Senegal 72 78 57 70 64 81) 60 48 48 Sierra Leone 28 ..28 42 62 46 Singapore 109 lix) lix 1(X0. 973 94 Slovak Republic .. 1(X 1(X) 99 09 48 36 Slovenia 109 109 9. 8 92 78 09 Somalia 263 1S 4882 South Africa 86 48 26 82 76 74 68 108 Spain .... 93 94 Sri Lanka 09 83 82 83 78 95 99 76 76 Sudan 67 75 58 62 56 53 50 6593 m Swaziland 82 099- Sweden lix) 109 109 0 10. 49 99 C Switzerland 109 109 109 109) 79 81 94 2 Syrian Arab Republic . 580 09 53 97 94 48 17 o Tajikistan ......79 81 > Tanzania 58 54 48 90 61 ...76 51 a) Thailand 71 89 86 98 81 98 97 68 40 0 Trinidad and Tobago . 486 488 91 09c 65 123 o Tunisia 58 . 76 580 84 96 91 79 Turkey 58 83 87 91 30 81) 79 Turkmenistan .. 58 109 . 97 98 Uganda 44 58 84 75 38 53 56 62 59 Ukraine .. .. .87 99 099 United Arab Emirates 95. .4 94 United Kingdom 109 109) 109 109 91 093 United Statues 109 109 10109O.. 92 98 72 09c Uruguay 9.48..4 ..093 93 84 91 Uzbekistan 485. 109 9. 4 99 78 2 Venezuiela, RB 84 ..74 8. 2 77 81 82 Vietnam 48 56 73 73 56 93 93 93 58 West Bank and Gaza . .....31. Yemen, Rep. 66 49 39 45 9 74 72 Yugoslavia, Fed. Rep. ........ Zambia 52 64 63 78 35 909 84 Zimbabwe 77 48 64 68 58 79 81 70 56 Low Income 70 76 36 45 57 57 Middle Income 75 81 47 59 909 89 Lower middle income 74 58 41 52 09 89 Upper middle income ,. 7 ..81 92 48 Low &middle Income 73 79 42 52 71 70 East Asia & Pacific 70 75 38 47 48 48 Europe & Central Asia . 9 . 93 93 Latin America & Carib. 81 48 72 78 93 87 Middle East & N. Africa 84 89 78 83 48 48 SoutthAsia 58 87 25 37 53 57 Sub-Saharan Africa 49 56 56 56 53 46 High Income . .... 89 92 Europe EMU 8. . .. 2 93 a. Oats are for the wont recent year available. 2.16 K** ' * .- About the data Definitions The indicators in the table are based on data formation on the size of the cohort of children * Access to an improved water source refers provided to the World Health Organization (WHO) under one year of age makes immunization cov- to the percentage of the population with rea- by member states as part of their efforts to erage difficult to estimate. The data shown here sonable access to an adequate amount of monitor and evaluate progress in implementing are based on an assessment of national immu- water from an improved source, such as a national health strategies. Because reliable, nization coverage rates carried out in 2000-01 household connection, public standpipe, bore- observation-based statistical data for these by the WHO and UNICEF. The assessment con- hole, protected well or spring, and rainwater indicators do not exist in some developing sidered both administrative data from service collection. Unimproved sources include ven- countries, the data are at times estimated. providers and household survey data on dors, tanker trucks, and unprotected wells and People's health is influenced by the children's immunization histories. Based on the springs. Reasonable access is defined as the environment in which they live. Lack of clean data available, consideration of potential availability of at least 20 liters a person a day water and basic sanitation is the main reason biases, and contributions of local experts, the from a source within one kilometer of the dwell- diseases transmitted by feces are so common most likely true level of immunization coverage ing. * Access to improved sanitation facilities in developing countries. Drinking water was determined for each year. refers to the percentage of the population with contaminated by feces deposited near homes Data on the success rate of tuberculosis treat- at least adequate excreta disposal facilities and an inadequate water supply cause diseases ment are provided for countries that have imple- (private or shared, but not public) that can ef- 109 accounting for 10 percent of the disease burden mented the recommended control strategy: di- fectively prevent human, animal, and insect M in developing countries (World Bank 1993c). rectly observed treatment, short course (DOTS). contact with excreta. Improved facilities range g The data on access to an improved water source Countries that have not adopted DOTS or have from simple but protected pit latrines to flush measure the share of the population with ready only recently done so are omitted because of toilets with a sewerage connection. To be ef- a access to water for domestic purposes. The data lack of data or poor comparability or reliability fective, facilities must be correctly constructed E 0 are based on surveys and estimates provided of reported results. The treatment success rate and properly maintained. * Tetanus vaccina- (D by governments to the WHO-UNICEF Joint for tuberculosis provides a useful indicator of tions refer to the percentage of pregnant CD Monitoring Programme. The coverage rates for the quality of health services. A low rate or no women who receive two tetanus toxoid injec- 3 water and sanitation are based on information success suggests that infectious patients may tions during their first pregnancy and one C from service users on the facilities their not be receiving adequate treatment. An essen- booster shot during each subsequent preg- S households actually use, rather than on tial complement to the tuberculosis treatment nancy. * Child Immunization rate is the per- information from service providers, who may success rate is the DOTS detection rate, which centage of children under one year of age re- include nonfunctioning systems. Access to indicates whether there is adequate coverage ceiving vaccination coverage for four diseases- drinking water from an improved source does by the recommended case detection and treat- measles and diphtheria, pertussis (whooping not ensure that the water is adequate or safe, ment strategy. A country with a high treatment cough), and tetanus (DPT). A child is consid- as these characteristics are not tested at the success rate may still face big challenges if its ered adequately immunized against measles time of the surveys. DOTS detection rate remains low. after receiving one dose of vaccine, and against Neonatal tetanus is an important cause of DPT after receiving three doses. infant mortality in some developing countries. It * Tuberculosis treatment success rate refers can be prevented through immunization of the to the percentage of new, registered smear- mother during pregnancy. Recommended doses positive (infectious) cases that were cured or for full protection are generally two tetanus shots in which a full course of treatment was com- during the first pregnancy and one booster shot pleted. * DOTS detection rate is the percent- during each subsequent pregnancy, with five age of estimated new infectious tuberculosis doses considered adequate for lifetime cases detected under the directly observed protection. Information on tetanus shots during treatment, short-course (DOTS) case detection pregnancy is collected through surveys in which and treatment strategy. pregnant respondents are asked to show antenatal cards on which tetanus shots have been recorded. Because not all women have I Data sources antenatal cards, respondents are also asked The table was produced using information abouttheir receipt of these injections. Poor recall provided to the WHO by countries, the WHO's may result in a downward bias in estimates of EPI Information System, and its Global the share of births protected. But in settings r Tuberculosis Control Report 2001; the United where receiving injections is common, Nations Children's Fund's (UNICEF) State of) respondents may erroneously report having the World's Children 2001: and the WHO and received tetanus toxoid. UNICEF's Global Water Supply and Sanitation i Governments in developing countries usually Assessment 2000 Report. finance immunization against measles and diph- L -- ----- theria, pertussis (whooping cough), and tetanus (DPT) as part of the basic public health pack- age. According to the World Bank's World De- velopment Report 1993: Investing in Health, these diseases accounted for about 10 percent of the disease burden among children under five in 1990, compared with an expected 23 per- cent at 1970 levels of vaccination. In many de- veloping countries, however, lack of precise in- ((jo)) 2.17 Reproductive health Total fertility Adolescent Women at risk Contraceptive Births attended Maternal mortality rate fertility of unintended prevalence by skilled ratio rate pregnancy rate health staff birthS % of per 1,000 married % of per 100,000 live births births women women women National Modelled per woman ages 15-19 ages 15-49 ages 15-49 % of total estimates estimates 1980 2000 2000 1990-2000' 1990-2000' 1982 i5996-99' 1990-95, 1995 Afghanistan 7.0 6.7 153 . .. Albania 3.6 2.1 16 ...99 ...31 Algeria 6.7 3.2 24 ..51 15 ..220 150 Angola 6.9 6.6 219 ...34 ...1,300 Argentina 3.3 2.5 61 ......36 85 Armenia 2.3 1.3 44 ... .96 35 29 Australia 1.9 1.8 18 ...99 ...6 Austria 1.6 1.3 21 ... .. .11 Azerbaijan 3.2 2.0 32 ... .99 43 37 iio Bangladesh 6.1 3.1 142 15 54 2 14 440 600 Belarus 2.0 1.3 28 ..28 33 o Belgium 1.7 1.6 11 ...8 m Benin 7.0 5.5 123 21 16 60 500 880 C Bolivia 5.5 3.9 80 26 49 59 390 550 Bosnia and Herzegovina 2.1 1.6 23 . ...10 15 E Botswana 6.1 4.0 78 ...61 .. 330 480 o) Brazil 3.9 2.2 70 7 77 98 88 160 260 > Bulgaria 2.0 1.3 49 ... .99 15 23 Burkina Faso 7.5 6.5 144 26 12 12 27 .. 1,400 Burundi 6.8 6.0 55 ..,12 ...1,900 3: Cambodia 5.7 4.0 60 ..24 ..31 470 590 C04 o Cameroon 6.4 4.8 142 13 19 10 55 430 720 0 Canada 1.7 1.5 20 ... .. .6 Central African Republic 5.8 4.7 140 16 15 ... 1,100 1,200 Chad 6.9 6.4 194 9 4 24 11 830 1,500 Chile 2.8 2.2 49 ...95 100 20 33 China 2.5 1.9 17 ..83 ...55 60 Hong Kong, China 2.0 1.0 7 ...100 Colombia 3.9 2.6 80 8 77 ... 80 120 Congo. Dem. Rep. 6.6 6.1 215 ... .. .940 Congo. Rep. 6.3 6.0 141 ...1,100 Costa Rica 3.6 2.5 85 ......29 35 C6te dIlvoire 7.4 4.8 130 43 15 13 47 600 1.200 Croatia 1.9 1.4 19 ..69 ..6 18 Cuba 2.0 1.6 65 ......27 24 Czech Republic 2.1 1.2 23 ..69 .9 14 Denmark 1.5 1.7 9 ......10 15 Dominican Republic 4.2 2.7 90 13 64 ..96 230 110 Ecuador 5.0 3.0 72 ..66 62 .. 160 210 Egypt. Arab Rep. 5.1 3.3 53 11 56 ..56 170 170 El Salvador 4.9 3.1 10 8 60 35 90 120 180 Eritrea 7.5 5.4 119 28 8 ... 1,000 1,100 Estonia 2.0 1.2 25 ......50 80 Ethiopia 6.6 5.6 152 36 8 58 .. 870 1,800 Finland 1.6 1.7 11 ......6 6 France 1.9 1.9 9 ..71 ...10 20 Gabon 4.5 4.2 172 28 33 ... 520 620 Gambia, The 6.5 5.0 139 ...41 ...1,100 Georgia 2.3 1.1 47 21 41 ...70 22 Germany 1.4 1.4 13 ......8 12 Ghana 6.5 4.2 90 23 22 47 44 210 590 Greece 2.2 1.3 18 ...99 ..1 2 Guatemala 6.3 4.6 117 23 38 40 .. 190 270 Guinea 6.1 5.2 168 24 6 ..35 670 1,200 Guinea-Bissau 6.0 5.8 190 ......910 910 Haiti 5.9 4.3 80 40 28 34 .. 525 1,100 Honduras 6.5 3.9 102 11 50 50 55 110 220 2.17 (j Total fertility Adolescent Women at risk Contraceptive Births attended Maternal mortality rate fertilifty of unintended prevalence by skilled ratio rate pregnancy rate health staff births % of per 1,000 married % of per 100,000 live births births women women women Nat oval Modelled per woman ages 15-19 ages 15-49 ages 15-49 % of total estimates estimates 1980 2000 2000 19D0-2000- 1990-20001 1982 1996-991 1 1990-98, 1995 Hungary 1.9 1.3 28 ..73 ...15 23 India 5.0 3.1 104 16 52 23 .. 410 440 Indonesi'a 4.3 2.5 60 11 57 31 43 450 470 Iran, Islamic Rep. 6.7 2.6 45 ..73 ...37 130 Iraq 6.4 4.3 38 ..... 370 Ireland 3.2 1.9 14 ..60 ...6 9 Israel 3.2 2.8 19 ...99 ..5 8 Italy 1.6 1.2 8 ...100 ..7 11 Jamaica 3.7 2.5 84 15 65 89 95 120 120 Japan 1.8 1.4 4 ...100 ..8 12 Jordan 6.8 3.7 33 14 50 75 97 41 41 Kazakhstan 2.9 2.0 40 11 66 ..98 70 80 0 Kenya 7.8 4.4 ill 24 39 ..44 590 1,300 Korea, Dem. Rep. 2.8 2.1 2 ...100 ..110 35 Korea, Rep. 2.6 1.4 4 ...70 ..20 20 c Kuwait 5.3 2.7 34 ... .98 5 25 Kyrgyz Republic 4.1 2.6 40 12 60 ..98 65 80 C ---- ------ ~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Lao PDR 6.7 5.0 91 ..25 ...650 650 Latvia 1.9 1.2 32 ......45 70 C Lebanon 4.0 2.3 30 ..61 ..95 100 130 Lesotho 5.5 4.4 86 ..23 28 ...530 0) Libya 7.3 3.5 35 ..45 76 94 75 120C' Lithuania 2.0 1.3 36 ......18 27 Macedonia, FYR 2.5 1.8 26 ......3 17 Madagascar 6.6 5.4 180 26 19 62 47 490 580 Malawi 7.6 6.3 136 30 31 59 .. 1,120 5801 Malaysi'a 4.2 3.0 25 ...82 ..39 39 Mali 7.1 6.3 180 26 7 14 24 580 630 Mauritania 6.4 5.7 147 ...23 58 550 8701 Mauritius 2.7 2.0 37 ..75 84 ..50 45 Mexico 4.7 2.6 64 ..65 ...55 65 Moldova 2.4 1.4 57 ..74 ...42 65 Mongolia 5.3 2.6 58 10 60 100 .. 150 65 Morocco 5.4 2.9 50 16 59 24 .. 230 390 Mozambique 6.5 5.1 172 7 6 28 44 1,100 9801 Myanmar 4.9 3.0 29 -.. 97 57 230 17(1 Namibia 5.9 5.0 105 22 29 ...230 370 Nepal 6.1 4.3 120 28 29 10- 10 ..83(0 Netherlands 1.6 1.7 4 ..75 100 ..7 1(1 New Zealand 2.0 2.0 30 ...99 ..15 15 Nicaragu a 6.3 3.5 135_ 15 60 ..65 150 25(1 Niger 8.0 7.2 215 17 8 20 18 590 92(1 Nigeria 6.9 5.3 128 22 15 ...700 1,10(1 Norway 1.7 1.9 12 ...100 ..6 9 Oman 9.9 4.3 80 ,.24 60 ..19 12(1 Pakistan 7.0 4.7 64 32 28 ... .200 Panama 3.7 2.5 75 -.. 83 ..70 10(1 Papua New Guinea 5.8 4.4 77 29 26 34 53 370 39(1 Paraguay 5.2 4.0 75 17 57 22 71 190 170 Peru 4.5 2.8 66 10 69 44 56 265 240) Philippines 4.8 3.4 33 26 47 57 56 170 240 Poland 2.3 1.4 21 -.. .. 8 12 Portugal 2.2 1.5 22 ... .100 8 12 Puerto Rico 2.6 1.9 73 78 ... .30 Romania 2.4 1.3 36 48 99 .. 41 60 Russian Federation 1.9 1.2 46 ..34 ..99 50 75 2.17 Total fertility Adolescent Women at risk Contraceptive Births attencded Maternal mortality rate fertility of unintended prevalence by skil led ratio rate pregnancy rate health staf birthsn % of per 1.000 married % of per 100,000 line births births women women women National Modelled per woman agen 15.19 ages 15-49 ages 15.49 % of totalI estinitaes estimates 1980 2000 2000 1990-2000' 1990-20001 1982 1.996-99, 1990-955 1995 Rwanda 8.3 5.9 56 37 2 1 20 ... 2.300 Saudi Arabia 7.3 5.5 105 ..21 74 91 ..23 Senegal 6.8 5.1 103 33 11 ..47 560 1,200 Sierra Leone 6.5 5.8 212 ...25 ...2,100 Singapore 1.7 1.5 9 ... 100 100 6 9 Slovak Republic 2.3 1.3 26 .... 9 14 Slovenia 2.1 1.2 10 ......11 17 Somalia 7.3 7.1 210 ...2 South Africa 4.6 2.9 70 ..62 .84 ..340 112 Spain 2.2 1.2 9 ...96 ..6 8 Sri Lanka 3.5 2.1 20 ...87 95 60 60 Sudan 6.1 4.6 62 25 10 20 -. 500 1,500 m Swaziland 6.2 4.4 121 ...50 'O Sweden 1.7 1.6 11 ......8 Smitzerland 1.5 1.5 5 ......5 8 z Syrian Arab Republic 7.4 3.6 44 ..45 43 .. 110 200 o Tajikistan 5.6 3.1 35 ......65 120 > Tanzania 6.7 5.3 125 13 25 74 35 530 1,100 Thailand 3.5 1.9 65 ..72 52 ..44 44 o Togo 6.8 5.0 89 ..24 ..51 480 980 3: Trinidad and Tobago 3.3 1.8 40 ...90 99 ..65 o Tunisia 5.2 2.1 13 ..60 50 82 70 70 (N Turkey 4.3 2.4 60 11 64 76 81 130 55 Turkmenistan 4.9 2.3 20 ......65 65 Uganda 7.2 6.2 204 29 15 ... 510 1,100 Ukraine 2.0 1.2 43 ..68 ...27 45 Lnited Arab Emirates 5.4 3.2 73 ...96 ..3 30 United Kingdom 1.9 1.7 28 98 ..7 10 United States 1.8 2.1 48 ..64 99 99 8 12 Uruguay 2.7 2.2 70 ......26 50 Uzbekistan 4.8 2.6 56 14 56 ..98 21 60 Vonozuela, RB 4.2 2.8 98 ...82 ..60 43 Vietnam 5.0 2.2 31 ..75 100 77 160 95 West Bank and Gaza .. 5.7 90 ..42 . Yemen, Rep. 7.9 6.2 105 39 21 22 350 850 Yugoslavia. Fed. Rep. 2.3 1.7 32 ...93 10 15 Zambia 7.0 5.3 156 27 26 ..47 650 870 Zimbabwe 6.4 3.8 112 15 54 69 84 695 610 Low income 5.3 3.6 104 Middle Income 3.2 2.2 39 Lomer middle income 3.0 2.1 32 80 Upper middle income 3.7 2.3 59 Low & middle Income 4.1 2.8 74 East Asia & Pacific 3.0 2.1 28 83 Europe & Central Asia 2.5 1.6 43 Latin America & Carib. 4.1 2.6 72 Middle East & N. Africa 6.2 3.4 51 South Asia 5.3 3.3 105 52 Sub-Saharan Africa 6.6 5.2 138 High Income 1.8 1.7 25 Europe EMU 1.8 1.5 11 a. Oats are for most recent year available. 2.17 About the data Definitions Reproductive health is a state of physical and systems are often weak, maternal deaths are * Total fertility rate is the number of children mental well-being in relation to the reproductive underreported, and rates of maternal mortality that would be born to a woman if she were to system and its functions and processes. Means are difficult to measure. live to the end of her childbearing years and of achieving reproductive health include Maternal mortality ratios are generally of bear children in accordance with current age- education and services during pregnancy and unknown reliability, as are many other cause- specific fertility rates. * Adolescentfertillty rate childbirth, provision of safe and effective specific mortality indicators. Household surveys is the number of births per 1,000 women ages contraception, and prevention and treatment of such as the Demographic and Health Surveys 15-19. * Women at risk of unintended preg- sexually transmitted diseases. Health conditions attempt to measure maternal mortality by asking nancy are fertile, married women of reproduc- related to sex and reproduction have been respondents about survivorship of sisters. The tive age who do not want to become pregnant estimated to account for 25 percent of the global main disadvantage of this method is that the and are not using contraception. * Contracep- disease burden in women (Murray and Lopez estimates of maternal mortality that it produces tive prevalence rate is the percentage of 1998). Reproductive health services will need pertain to 12 years or so before the survey, women who are practicing, or whose sexual to expand rapidly over the next two decades, making them unsuitable for monitoring recent partners are practicing, any form of contracep- when the number of women and men of changesorobservingtheimpactofinterventions. tion. Itis usually measured for married women reproductive age is projected to increase by more In addition, measurement of maternal mortality ages 15-49 only. * Births attended by skilled 113 than 300 million. is subject to many types of errors. Even in high- health staff are the percentage of deliveries Total and adolescent fertility rates are based income countries with vital registration systems, attended by personnel trained to give the nec- on data on registered live births from vital misclassification of maternal deaths has been essary supervision, care, and advice to women registration systems or, in the absence of such found to lead to serious underestimation. during pregnancy, labor, and the postpartum E systems, from censuses or sample surveys. As The maternal mortality ratios shown in the period, to conduct deliveries on their own, and a long as the surveys are fairly recent, the table as reported are estimates based on to care for newborns. * Maternal mortality ra- X estimated rates are generally considered reliable national surveys, vital registration, or tio is the number of women who die during preg- CD measures of fertility in the recent past. In cases surveillance or are derived from community and nancy and childbirth, per 100,000 live births. 3 where no empirical information on age-specific hospital records. Those shown as modeled are _C fertility rates is available, a model is used to based on an exercise carried out by the World - estimate the share of births to adolescents. For Health Organization (WHO) and United Nations Data sources | countries without vital registration systems, Children's Fund (UNICEF). In this exercise The data on reproductive health come from o fertility rates for 2000 are generally based on maternal mortality was estimated with a Demographic and Health Surveys, the WHO's extrapolations from trends observed in censuses regression model using information on fertility, Coverage of Matemity Care (1997) and other or surveys from earlier years. birth attendants, and HIV prevalence. Neither WHO sources, UNICEF, and national statistical | An increasing number of couples in the de- set of ratios can be assumed to provide an offices. Modelled estimates for maternal veloping world want to limit or postpone child- accurate estimate of maternal mortality in any mortality ratios are from Kenneth Hill, Carla bearing but are not using effective contracep- of the countries in the table. AbouZhar and Tessa Wordlaw's "Estimates tive methods. These couples face the risk of of Maternal Mortality for 1995," (2001). unintended pregnancy, shown in the table as the percentage of married women of reproduc- tive age who do not want to become pregnant but are not using contraception (Bulatao 1998). Information on this indicator is collected through surveys and excludes women not exposed to the risk of pregnancy because of postpartum ano- vulation, menopause, or infertility. Common reasons for not using contraception are lack of knowledge about contraceptive methods and concerns about their possible health side- effects. Contraceptive prevalence reflects all methods-ineffective traditional methods as well as highly effective modern methods. Contraceptive prevalence rates are obtained mainly from Demographic and Health Surveys and contraceptive prevalence surveys (see Primary data documentation for the most recent survey year). Unmarried women are often excluded from such surveys, which may bias the estimates. The share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women. Good antenatal and postnatal care improves maternal health and reduces maternal and infant mortality. But data may not reflect such improvements because health information 2.18 1Nutrition Prevalence Prevalence Prevalence Prevalence Low- Breast Consump- Vitamin of of child of of anemia birthweight feeding tion of A undernourishment malnutrition overweight babies Iodized supplemern- salt tatlon Weight for age Height for age % of %of % of % of exclusive % Of % of children children children pregnant breastfeeding % of children population under 5 under 5 under 5 women % of births less they 4 months households 6-59 months 1990-92 1996-98 1L993-20001 1993-20001 Year % 1986-99, 1993-991 Year % 1992-9W1 1998-2000 Afghanistan 63 70 49 48 1997 4 78 Afghanistan 63 70 49 48 1997 4 78 Albania 14 3 8 15 8 Algeria 5 5 13 18 1995 9 42 92 Angola 51 43 41 53 29 - . 10 94 Argentina 5 12 1994 7 26 7 90 Armenia 21 3 12 1998 6 - . 70 Australia -.0 0 1995-96 5 7 Austria 6 - 114 Azerbaijan -- 32 17 20 1996 4 -. 6 - . Bangladesh 35 38 61 55 1996-97 1 53 50 1996-97 26 55 79 Belarus -.- -6 - . 37 - r~0 Belgium .- .- - . ~5 Benin 21 14 29 25 1996 1 41 9 1996 2 79 100 Bolivia 25 23 8 27 1998 7 54 9 1998 32 91 85 a) Bosnia and Herzegovina 10 - -- E Ca Bot swana 20 27 17 29 - .1988 8 27 C0 > Brazil 13 10 6 11 1996 5 33 8 1996 20 95 20 ~j Bulgaria 13 - - - 7 - - 0 Burkina Faso 32 32 34 37 1992-93 2 24 1998-99 6 23 99 0 0 Cameroon 29 19 22 29 1991 3 44 -- 1998 5 83 100 Canada - - --- 1970-72 5 --6 - -- - Central African Republic 46 41 23 28 1995 1 67 -- 1994-95 0 87 100 Chad 58 38 39 40 - -- 37 -- 1996-97 1 55 92 Chile 8 4 1 2 1996 7 13 5 - -- 100 - China 17 11 10 14 1992 4 52 - -- 91 - Hong Kong, China -- - -- - - - - - -- Colombia 17 13 8 15 1995 3 24 17 1995 4 92 - Congo, Dem. Rep. 37 61 34 45 - 20 -- 90 78 Congo, Rep. 34 32 --- - - -- - - -74 Costa Rica 6 6 5 6 1996 6 27 6 . -- 97 - C4e dIlvoire 15 14 24 24 1994 2 34 1994 2 -- Croatia -- 12 1 1 1995-96 6 - - -- 90 - Cuba 4 19 - - -- 47 8 - - 45 - Czech Republic -- -- ---- 1991 4 23 6 - ---- Denm ark-- ---- - ---- ---- Dominican Republic 29 28 6 11 1996 3 -- 14 1996 8 13 53 Ecuador 8 5 - . - 17 17 1987 20 99 42 Egypt, Arab Rep. 5 4 4 19 1995-96 9 24 -- 1995 25 84 - El Salvador 12 11 12 23 1993 2 14 11 - -- 91 - Eritrea -- 65 44 38 - - - - 1995 41 80 94 Estonia -- 6 -- - - --- .- - Ethiopia -- 49 47 51 - -- 42 9 --0 86 Finland - -- -- -- - - -- France - .- -6 - ----- Gabon 11 8 - -- - - - - .- Gambia,The 18 16 26 30 - -. 80 -- 9 - Georgia -- 23 3 12 - - .- -- - Germany - -- . --- .- -- Ghana 29 10 25 26 1993-94 2 64 8 1998 18 28 91 Greece - - 1995 4 -.- -- - Guatemala 14 24 24 46 - - 45 8 1998-99 27 49 Guinea 37 29 23 26 -- - - 13 1999 10 37 100 Guinea-Bissau - --- - -- 74 - -- 77 Haiti 64 62 28 32 1994-95 3 64 15 1994-95 1 10-- Honduras 23 22 25 39 1996 1 14 9 - -- 80 53 2.18 1 Prevalence Prevalence Prevalence Prevalence Low- Breast Consump- Vitamin of of child of of anemia blrthwelght feeding tion of A undernourishment mainutrtiton overweight babies Iodized supplemen- salt tation Weight for age Height for age % of % of % of % of exclusive % of %of children children childreni pregnant breastfeeding % of children population under 5 under 5 under 5 women % of births lens then 4 months households 6-59 monthn 1990-92 1999-98 1993-20001 1993-20001 Year % 1985-99' 1 993-99- Year % 1992-985 1998-2000 Hungary . .. ... 1980-88 2 ..8 India 26 21 47 46 1992-93 2 88 34 1999 28 70 15 Indonesi'a 10 6 34 42 1995 4 64 15 1997 20 64 64 Iran, Islamic Rep. 6 6 11 15 1995 3 17 10 . . 94 Iraq 9 17 ..18 24 . .. 10 Italy . ..... 1975-77 - 4... Jamaica 12 10 4 7 2000 5 40 11 . 100 Japan 1978-81 2 ..8 . . . 1 Jordan 4 5 5 8 1990 6 50 2 1997 4 95 Kazakhstan .. 5 4 10 1995 4 27 9 1995 4 53 .. Kenya 47 43 22 33 1993 4 35 .. 1998 3 100 80 2 Korea, Dem. Rep. 19 57 32 15 . .. 71 5 100 : Korea, Rep. .. . . . . Kuwait 22 4 2 3 1996-97 6 40 7 ----- - ----- (~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~D Kyrgyz Republic .. 17 11 25 .. . .6 1997 8 27 <.. ....... ------- ~~~~~~~~~~~~~~~0 Lao PDR 31 29 40 47 62 60 . . 95 80 ' Latvia .. 443 . - Lebanon -.3 12 49 19 . . 92 . Lesotho 31 29 16 44 . .. 7 . . 73..0 Liberia 49 46 ...- .. 78_ 1986 7 ..93 Libya 5 15 ... . 90 . Lithuani'a -.4 Macedonia, FYR 7 6 7 8 . Madagascar 33 40 40 48 1992 1 -. 15 1997 17 73 94 Malawi 47 32 30 48 1992 7 55 1992 5 58 Malaysia 3 20 56 8 Mali 24 32 27 49 1995-96 1 58 1996 3 9 100 Mauritania 15 13 23 44 24 9 3 833 Mauritius 6 6 15 10 1995 4 29 . . 0 Mexico 5 5 8 18 1988 4 41 9 1987 22 97 Moldova .. 11 -.. 20 5 . Mongolia 34 45_ 13 25 1997 4 45 11 . .. 68 837 Morocco 5 5 ..1992 7 45 4 - i992 30 ..0 Mozambique 67 58 26 36 ..58 .. 1997 13 62 100 Myanmar 10 7 28 42 58 ... . 65 42 Namibia 27 31 ..1992 3 16 1992 4 59 83 Nepal 21 28 47 54 1996 1 65 23 1996 52 55 85 Netherlands New Zealand - . - 6 Nicaragua 29 -31 12 25 1993 3 36 8 1997-98 8 86 633 Niger 42 46 40 40 1992 1 41 .. 1998 0 64 100 Nigeria 16 8 27 46 1993 3 55 1990 2 98 :23 Norway 5 .. - Oman 23 23 1994-95 1 54 8 .. 61 Pakistan 26 20 38 36 1990-91 3 37 25 1990-91 20 19 38 Panama 19 16 8 18 1980 4 8 - . 95 Papua New Guinea 26 29 1982-83 2 16 16 Paraguay 18 13 1990 4 44 9 1990 4 83 - Peru 40 18 8 26 1996 7 53 6 1996 34 93 5 Philippi'nes 24 21 32 32 1993 1 48 11 1998 22 15 78 Poland .. . .8 - - Portugal . . .-7 . - Puerto Rico 1991 2 .. 14.. - Romania 3 --. - 31 10 . - Russian Federation .. 6 3 13 - .. 30 -.. . 30 O 2.18 Prevalence Prevalence Prevalence Prevalence Low- Breast Consump- Vitamin of of child of of anemia blrthweight feeding tion of A undernourishment malnutrition overweight babies lodlzed supplemen- salt tation weight for age Ho ght for age % of % of % of % of exclusive % of % of children cfhildren children pregnant breastfeeding % of children population under 5 under 5 under 5 women % of births less then 4 months houiseholds 6-59 months 1990-92 1996-98 1993-2000, 1993-2000, Year % 1985-995 1993-99, Year % i 992-981 1998-2000 Rwanda 37 39 27 42 1992 2 . .. 1992 76 95 93 Saudi Arabia 3 3 ... . . .5 Senegal 211 23 13 23 1992-93 3 26 1997 3 9 87 Sierra Leone 45 43 ... . 31 ..75 8) Singapore .. . . 1970-77 1 ..7 Slovak Republic .. 4 Slovenia 3 ... 5 . Somalia 67 75 26 23 . . 78 ..63 South Africa ..9 23 1994-95 7 37 .6-2 116 Spain - .. Sri Lanka 28 25 33 20 1987 0 39 18 1987 4 47 aT Sudan 32) 18 34 34 .. 36 15 1990 10 0 79 0 Sweden . . . .- - Switzerland .. . . . .5 a) Syrian Arab Republic -.13 21 .. . .7 - 40 E a. Tajikistan 32 *. 23 0 33 Tanzania 31 41 29 44 1996 3 598 . 1998 7 74 21 Thailand 31 21 18 13 1987 1 57 7 1987 4 50 Togo 29 18 25 22 1988 3 48 .. 1998 2 73 10X) Trinidad and Tobago 12 13 . 1987 3 53 14 1987 7 C1 Tunisia . . 4 8 1988 4 33 16 1988 13 98 0 Turkey . .. 8 16 1993 3 74 .. 1998 2 18 Turkmenistan .. 10 ... . - .. .0 Uganda 23 30 23 38 1995 3 33 . 1995 35 69 79 Ukraine .. 5 .. . .8 . ..4 United Arab Emirates . .. 7 .....- United Kingdom ... 1973-79 3 ..6 United States . . 1 2 1988-94 5 ..7 Uruguay 7 4 4 10 1992-93 6 20 8 Uzbekistan . 1-1 19 31 1998 14 - . 1996 0 17 Venezuela,RB LI. 16 4 13 1997 3 29 12 . .. 92) Vietnam 28 22 37 39 1998 1 .. 11 1997 1 89 55 West Bank and Gaza . -. 15 ... . . 6 Yemen, Rep. 37 35 46 529 1998 4 26 3 1997 7 33 10X) Yugoslavia, Fed. Rep. .. 3 2 7 1996 5 . . .. 6-3 Zambia 40 45 24 42 1996-97 3 34 10 1998 4 90) 75 Zimbabwe 41 37 13 27 1994 4 .. 11 1994 1 98 ji~~~= E~~~~- E!ŽT c63 Q ~~Oa . Low Income 27 24 . .98 61 50 Middle Income 15 II 13 ..44 ..98 Lower middle income 17 li 11 17 46 ..87 Upper middle income 9 8 . ..40 6. 7 Low& middle Income 21 18 .. ..98 . 74 East Asia & Pacific 17 12 13 18 54 ..25 Europe & Central Asia .. 8 ...40 *.89 Latin America & Carib. 14 12 9 19 34 10 53 Middle East &N. Africa 7 8 15 ..3 as 8 South Asia 27 24 49 47 78 34 60 34 Sub-Saharan Africa 32 33 ...-46 69..8 High income . . . Europe EMU - .. a Data are for the most recent year available. 2.18 1 L L About the data Definitions Data on undernourishment are produced by the Low birthweight, which is associated with * Prevalence of undernourishment refers to the Food and Agriculture Organization (FAO) based maternal malnutrition, raises the risk of infant percentage of the population that is undernour- on the calories available from local food produc- mortality and stunts growth in infancy and child- ished. * Prevalence of child malnutrition is the tion, trade, and stocks; the number of calories hood. Estimates of low-birthweight infants are percentage of children under five whose weight needed by different age and gender groups; the drawn mostly from hospital records. But many for age and height for age are less than minus proportion of the population represented by each births in developing countries take place at two standard deviations from the median for age group: and a coefficient of distribution to home, and these births are seldom recorded. A the intemational reference population ages O-59 take account of inequality in access to food (FAO, hospital birth may indicate higher income and months. For children up to two years of age, 2000). From a policy and program standpoint, therefore better nutrition, or it could indicate a height is measured by recumbent length. For however, this measure has its limits. First, food higher-risk birth, possibly skewing the data on older children, height is measured by stature insecurity exists even where food availability is birthweights downward. The data should there- while standing. The reference population, not a problem because of inadequate access of fore be treated with caution. adopted by the WHO in 1983, is based on chil- poor households to food. Second, food insecu- It is estimated that breastfeeding can save dren from the United States, who are assumed rity is an individual or household phenomenon, some 1.5 million children a year. Breast milk to be well nourished. * Prevalence of overweight and the average food available to each person, alone contains all the nutrients, antibodies, hor- is the percentage of children under five whose 117 even corrected for possible effects of low in- mones, and antioxidants an infant needs to weight for height is greater than two standard come, is not a good predictor of food insecurity thrive. It protects babies from diarrhea and deviations from the National Center for Health 0 among the population. And third, nutrition se- acute respiratory infections, stimulates their Statistics and WHO international reference me- curity is determined not only by food security, immune systems and response to vaccination, dian value, as recommended by a WHO Expert o but also by the quality of care of mothers and and, according to some studies, confers cogni- Committee. * Prevalence of anemia, or iron children and the quality of the household's tive benefits as well. The data are derived from deficiency, refers to the percentage of pregnant health environment (Smith and Haddad 2000). national surveys. women with hemoglobin levels less than 11 0 Estimates of child malnutrition, based on both Iodine deficiency is the single most important grams per deciliter. * Low-birthweight babies weight for age (underweight) and height for age cause of preventable mental retardation, and it are newborns weighing less than 2,500 grams, D (stunting), are from national survey data. The contributes significantly to the risk of stillbirth with the measurement taken within the first proportion of children underweight is the most and miscarriage. Iodized salt is the best source hours of life, before significant postnatal weight common indicator of malnutrition. Being under- of iodine, and a global campaign to iodize ed- loss has occurred. * Exclusive breastfeeding is weight, even mildly, increases the risk of death ible salt is significantly reducing the risks the proportion of children less than 4-6 months and inhibits cognitive development in children. (UNICEF, The State of the World's Children old who are fed breast milk alone (no other liq- Moreover, it perpetuates the problem from one 1999). uids). * Consumption of Iodized salt refers to generation to the next. as malnourished women Vitamin A is essential for the functioning of the percentage of households that use edible are more likely to have low-birthweight babies. the immune system. A child deficient in vitamin salt fortified with iodine. * Vitamin A supple- Height for age reflects linear growth achieved A faces a 25 percent greater risk of dying from mentation is the percentage of children ages 6-59 pre- and postnatally, and a deficit indicates long- a range of childhood ailments such as measles, months who received at least one high dose term, cumulative effects of inadequacies of malaria, or diarrhea. Improving the vitamin A vitamin A capsule in the previous six months. health, diet, or care. It is often argued that stunt- status of pregnant women may reduce their ing is a proxy for multifaceted deprivation. risk of dying during pregnancy and childbirth, Estimates of children overweight are also from improves their resistance to infection, and helps Data sources national survey data. Overweight in children has reduce anemia. Giving vitamin A to new moth- Data are drawn from a variety of sources, become a matter of growing concern in develop- ers who are breastfeeding helps to protect their including FAO's The State of Food Insecurity in i ing countries. Researchers show an associa- children during the first months of life. Food the World 2000; the United Nations tion between obesity in childhood and high fortification with vitamin A is also being intro- Administrative Committee on Coordination, i prevalences of high blood pressure, diabetes, duced in many developing countries. Subcommittee on Nutrition's Update on the respiratory disease and psychosocial and ortho- Nutrition Situation; the WHO's World Health pedic disorders (de Onis and Blossner, 2000). Report 2000; and UNICEF's State of the The survey data were analyzed in a standard- World's Children 2001. ized way by the World Health Organization (WHO) to allow comparisons across countries. Adequate quantities of micronutrients (vita- mins and minerals) are essential for healthy growth and development. Studies indicate that more people are deficient in iron (anemic) than any other micronutrient, and most are women of reproductive age. Anemia during pregnancy can harm both the mother and the fetus, caus- ing loss of the baby, premature birth, or low birthweight. Estimates of the prevalence of ane- mia among pregnant women are generally drawn from clinical data, which suffer from two weak- nesses: the sample is based on those who seek care and is therefore not random, and private clinics or hospitals may not be part of the re- porting network. ~--L),' 2.19 Health: risk factors and future challenges Prevalence Incidence of Prevalence of HIV of smoking tuberculosis Young people male female % of adults per 100.000 % of % age % age Year Males Females people adults 15-24 15-24 1999 1999 1999' 1999* Afghanistan ...325 <0.01 Albania 1996 44 6 29 <0.01 Algeria 1998 44 7 45 0.07 Angola ...271 2.78 1.25 2.72 Argentina 2015) 47 34 56 0.69 0.86 0.29 Armenia ...58 0.01 Australia 1995 27 23 8 0.15 0.14 0.02 Austria 1997 31 19 16 0.23 0.19 0.10 Azerbaijan 1999 30 1 62 <0.01 118 Bangladesh 1998 40 10 241 0.02 0.01 0.01 Belarus 1999 55 5 8) 0.28 0.40 0.19 (A o Belgium 1999 31 26 15 0.15 0.11 0.11 ' ~ Benin ...266 2.45 0.89 2.24 VO Bolivia 1998 43 18 238 0.10 0.13 0.03 Em Bosnia and Herzegovina 87 0.04 Botswana 702 35.80 15.84 34.31 a, Brazil 1995 38 29 70 0.57 0.70 0.28 > Bulgaria 1998 49 24 46 0.01 o Burkina Faso ...319 6.44 2.31 5.79 Burundi 382 11.32 5.89 11.60 Cambodia 1994 65 560 4.04 2.36 3.51 o Cameroon 335 7.73 3.82 7.78 0 Canada 1999 27 23 7 0.30 0.29 0.07 Central African Republic ...415 13.84 6.91 14.07 Chad ...270 2.69 1.92 3.03 Chile 1997 26 1826 0.19 0.29 0.08 China 1996 63 4 103 0.07 0.12 0.02 Hong Kong, China ...91 0.06 0.10 0.05 Colombia 1997 24 21 51 0.31 0.44 0.10 Congo, Dem. Rep. ...311 5.07 2.49 5.07 Congo, Rep. 318 6.43 3.17 6.46 Costa Rica 1995 29 7 17 0.54 0.65 0.28 Cote dIlvoire 375 10.76 3.78 9.51 Croatia 61 0.02 0.02 0.01 Cuba 1995 48 26 15 0.03 0.06 0.02 Czech Republic 1998 28 12 19 0.04 0.06 0.03 Denmark 1998 32 31 12 0.17 0.16 0.08 Dominican Republic 1993 24 17 135 2.60 2.58 2.78 Ecuador 1991 46 17 172 0.29 0.37 0.06 Egypt, Arab Rep. 1997 43 5 39 0.02 El Salvador 1999 38 12 67 0.60 0.60 0,27 Eritrea ...272 2.87 Estonia 1996 48 22 61 0.04 Ethiopia ...373 10.63 7.50 11.86 Finland 19099 27 2) 12 0.05 0.03 0.02 France 1997 39 27 16 0.44 0.33 0.23 Gabon 2B9 4.16 2.32 4.72 Gambia, The ...260 1.95 0.86 2.17 Georgia 1999 60 15 72 <0.01 Germany 1997 43 30 13 0.10 0.09 0.04 Ghana ...281 3.60 1.36 3.42 Greece 1994 46 28 22 0.16 0.12 0.05 Guatemala 1989 38 18 86 1.38 1.16 0.92 Guinea 1998 60 44 255 1.54 0.57 1.43 Guinea-Bissau ...267 2.50 0.99 2.48 Haiti 1990 11 9 381 5.17 4.86 2.91 Honduras 1988 36 1192 1.92 1.40 1866 2.190 Prevalence Incidence of Prevalence of HIV of smoking tuberculosis Young people male female % of adults per 100,000 % of % age % age Year Males Females people adults 15-24 15-24 1999 1999 1999' 1999' Hungary 1999 44 27 40 0.05 0.08 0.02 India ..185 0.70_ 0.36 0.61 Indonesia 1995 89 3 282 0.05_ _0.03__ 0.03 Iran, Islamic Rep. 1998 25 5 54 <0.01- I raq 1990 40 5 156 <0.01 Ireland 1998 32 31 15 0.10 0.06 0.05 Israel 1999 33 28 8 0.08 0.06 0.06 Italy 1998 32 17 9 0.35 0.29 0.24 Jamaica ..8 0.71. 0.59 0.40 Japan 199 8 53 13 29 0.02 0.03 0.01 119 Jordan 1996 44 5 11 0.02 __ Kazakhstan ... 3 .0 .7. Kenya 1995 67 32 _417 13.95_ 6.39 13.02 t Korea, Dem. Rep. 176 <0.01. .. Korea, Rep. ..98 0.01 0.02 0.06 o. ----- --- -- - - ----- -- - ---- - - - -- - - -- - - -- -- ---- - -- -- Kuwait 1998 34 2 31 0.12 ..D. Kyrgyz Republic 1998 60 16 13) <0.01.C Lao PDR ..171 0.05 0.04 0.05 1 Latvia 1998 53 18 106 0.11. 0.18 0.06 Lebanon ..24 0.09 S ------ -------------- - -- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 Lesotho 1992 39 1 542 23.57 12.05 26.40 2 Liberia 271 2.80 E Libya ..24 0.05 Lithuania 1997 41 9 99 0.02 Macedonia, FYR 50 <0.01. Madagascar 236 0.14 0.40.13 Malawi 1996 20 9 443 15.96 7.04 15.26 Malaysia 1996 49 4 111 0.42 0.57 0.09 Mali ..261 2.03 1.31 2.07 Mauritania ..241 0.52 0.37 0.59 Mauritius 1998 42 3 68 0.06 0.04 0.04 Mexico 1998 51 18 39 0.29 0.40 0.06 Moldova 1998 44 3 13) 0.20 0.28 0.11 Mongolia 1999 56 19 205 <0.01 Morooco 1999 3) 1 1-19 0.03 Mozambique ..407 13.22 6.73 14.74 Myanmar 1993 74 46 189 1.99 1.04 1.72 Namibia 1994 65 35 490 19.54 9.14 19.80 Nepal 1998 ------ 2) 15 209 _0.29 0.14 0.20 Netherlands 1998 37 30 10 0.19 0.18 0.06 New Zealand 1998 26 24 6 0.06 0.05 0.02 Nicaragua --- ------------ 88 0.20 0.22 0.06 Niger ------252 1.35 0.95 1.50 Nigeria 301 5.06 2.52 5.12 Norway 1998 34 32 .5 0.07 0.06 0.03 Oman 1995 13 0 10 0.11 Pakistan 1994 36 9 177 0.10 0.06 0.04 Panama 1993 56 20 54 1.54 1.65 1.36 Papua New Guinea -- ------------------------250 0.22 0.'08 0.25 Paraguay 1990 24 6 685 0.11 0.13 0.04 Peru 1998 42 16 228 0.35 0.39 0.17 Philippines 1999 75 18 314 0.07 0.03 0.06 Poland 1998 39 19 39 0.06 Portugal 1996 30 7 53 0.74 0.57 0.25 Puerto Rico ...9 Romania 1994 43 15 13) 0.02 0.02 0.02 Russian Federation 1996 63 14 123 0.18 0.25 0.12 (D 2.19 Prevalence Incidence of Prevalence of HIV of smoking tuberculosis Young people male female % of adults per 100,000 % of % age % age Year Males Females people adults 15-24 15-24 1999 1999 1999- 1999* Rwanda 1994 7 4 381 11.21 5.22 10.63 Saudi Arabia 1994 40 8 45 0.01 Senegal .. 258 1.77 0.71 1.60 Sierra Leone .. .. 274 2.99 1.16 2.92 Singapore 1998 27 3 48 0.19 0.22 0.16 Slovak Republic 1996 55 30 28 <0.01 0.02 0.01 Slovenia 1999 30 2D 27 0.02 0.03 0.01 Somalia .. .. 365 South Africa 1998 42 11 495 19.94 11.34 24.82 120 Spain 1997 42 25 59 0.58 0.48 0.22 Sri Lanka 1998 41 .. 59 0.07 0.04 0.05 j"D) Sudan 1999 24 2 195 0.99 " Swaziland 1994 25 2 564 25.25 z Sweden 1998 17 22 4 0.08 0.06 0.04 c: Switzerland 1997 38 27 9 0.46 0.37 0.33 E Syrian Arab Republic 2000 53 9 85 0.01 oL Tajikistan .. .. 105 <0.01 > Tanzania 1995 50 12 340 8.10 3.96 8.06 a), o Thailand 1999 39 2 141 2.15 1.18 2.32 o Togo .. .. 313 5.98 2.20 5.53 Trinidad and Tobago .. .. 12 1.05 0.84 0.59 (N 8 Tunisia 1996 61 4 37 0.04 0 N Turkey 1997 51 49 38 0.01 Turkmenistan 1990 27 1 90 <0.01 Uganda 1995 52 17 343 8.30 3.84 7.82 Ukraine 2000 58 14 73 0.96 1.29 0.79 United Arab Emirates 1995 24 1 21 0.18 United Kingdom 1997 29 28 12 0.11 0.09 0.05 United States 1997 28 22 6 0.61 0.50 0.23 Uruguay 1995 32 14 29 0.33 0.41 0.21 Uzbekistan 1991 40 1 97 <0.01 Venezuela. RB 1992 42 39 42 0.49 0.65 0.15 Vietnam 1995 73 4 189 0.24 0.27 0.09 West Bank and Gaza . .. 28 Yemen, Rep. 1997 60 29 108 0.01 Yugoslavia, FR (Serb./Mont.) . .. 47 0.10 Zambia 1996 35 10 495 19.95 8.20 17.77 Zimbabwe 1993 34 1 562 25.06 11.31 24.50 Low Income 43 9 229 2.01 1.13 2.00 Middle income 55 11 104 0.53 0.49 0.59 Lower middle income 58 7 110 0.18 0.21 0.16 Upper middle income 44 26 84 1.84 1.47 2.23 Low & middle Income 50 10 163 1.19 0.79 1.25 East Asia & Pacific 64 6 142 0.22 0.19 0.16 Europe & Central Asia 51 20 85 0.18 0.39 Latin America & Carib. 37 25 75 0.58 0.67 0.30 Middle East & N. Africa 40 7 66 0.03 South Asia 40 8 191 0.56 0.29 0.48 Sub-Saharan Africa .. .. 339 8.38 4.54 9.20 High Income 35 22 16 0.33 0.28 0.14 Europe EMU 38 25 20 0.31 0.25 0.15 a. Average of high and low est mates. 2.19 About the data Definitions The limited availability of data on health status Table 2.19a * Prevalence of smoking is the percentage of is a major constraint in assessing the health men and women who smoke cigarettes. The situation in developing countries. Surveillance Bednets save lives age range varies among countries, but in most data are lacking for a number of major public Percentage of children under five who sleep under a is 18 and above or 15 and above. health concerns. Estimates of prevalence and treated bednet * Incidence of tuberculosis is the estimated incidence are available for some diseases but S5o Tome and Principe 53 number of new tuberculosis cases (pulmonary, are often unreliable and incomplete. National Malawi 38 smear positive, extrapulmonary). * Prevalence health authorities differ widely in their capacity Niger 35 of HIV refers to the percentage of people who and willingness to collect or report information. vietnam 32 are infected with HIV. To compensate for the paucity of data and Tajikistan 32 _ ensure reasonable reliability and international cameroon 12 comparability, the World Health Organization Senegal t| Data sources (WHO) prepares estimates in accordance with Azeaijan 1The data are drawn from a variety of sources, epidemiological models and statistical Sierra Leone 10 including the WHO's World Health Report standards. Tanzania 10 2000 and Global Tuberculosis Control Re- 121 Smoking is the most common form of to- Chad 2 port 1999; the NATIONS database (http:// bacco use in many countries, and the prevalence Madagascar 1 apps.nccd.cdc.gov/nations/) and UNAIDS 0 of smoking is therefore a good measure of the Lao. PDR 0 and the WHO's AIDS Epidemic Update (2000). to extent of the tobacco epidemic (Corrao and oth- S0nv UNICEF M,Ie Indicat Cluter smvvnv, iw.chilnlnfo.rgl. L o ers 2000). While the prevalence of smoking has Malaria Is endemic in the poorest countries In the E been declining in some high-income countries, world, causing 300-500 mlilon clinical cases and more , than one million deaths per year. More than 90 tries will increase rapidly in the next few decades. Roll Back Malaria is a partnership, founded by the Because the data present a one-time estimate, WHO, UNICEF, the United Nations Development with no information on intensity of smoking or Programme, and the World Bank In 1998 with the objective of halving the malaria burden world-wide duration, they should be interpreted with cau- by the year 2010. This goal can be achieved only If tion. The data in the table are based on surveys a number of strategies that have proven effective, and other studies compiled in Tobacco Control sustainable, and cost-effe I the wideipread ul e of Country Profiles (Corrao and others 2000), is- Insecticide-treated bednetsto limit human-mosquito sued for the 2000 World Conference on Tobacco contact. In areas of Sub-Saharan Africa with high lees of malaria transmission, regular use of an or Health. :n isect cide-treated bednet can reduce mortality In Tuberculosis is the main cause of death from children under five by as much as 30 percent. a single infectious agent among adults in developing countries. In high-income countries tuberculosis has reemerged largely as a result of cases among immigrants. The estimates of tuberculosis incidence in the table are based on a new approach in which reported cases are adjusted using the ratio of case notifications to the estimated share of cases detected by panels of 80 epidemiologists convened by the WHO. Adult HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates, however, can be very misleading. They often disguise serous epidem- ics that are initially concentrated in certain lo- calities or among specific population groups and that threaten to spill over into the wider popula- tion. In many parts of the developing world the majority of new infections occur in young adults, with young women especially vulnerable. About one-third of those currently living with HIV/AIDS are in the age group 15-24. The estimates of HIV prevalence are based on extrapolations from data collected through surveys and surveillance of small, nonrepresentative groups. 1) 2.20 Mortality Life expectancy Infant mortality Unde-fitve Child mortality Adult mortality Survival at birth rate mortality rate rate rate to age 65 per 1.000 Male Femaie Male Female he of cohort years Jive births per 1,000 per 1,000 per 1,000 per 1,000 per 4,000 Male Female 1980 2000 1980 2000 1980 2000 198&2000. 198a20DD0 2000O 2000 , 2000 2000 Afghanistan 40 43 177 163 280 279 394 353 31 31 Alban)ia 639 74 47 20 57 .. 15 15 171 86 76 84 Algeria 59 71 98 33 139 39 - . 149 127 73 79 Angola 41 47 154 128 261 208 442 391 34 38 Argentina 70 74 35 17 38 22 178 89 74 86 Armenia 73 74 26 15 .. 17 171 76 74 86 Australia 74 79 11 5 13 7 .. 102 54 83 91 Austria 73 78 14 5 17 6 126 60 81 90 Azerbaijjan 69 72 30 13 ,. 21 . 207 103 68 83 122 Bangraoesh 49 61 132 60 211 83 28 38 278 272 57 59 Belarus 71 68 16 11 .. 14 361 128 53 80 o Belgium 73 78 12 5 15 7 129 66 81 90 Benin 48 53 116 87 214 143 89 90 373 322 42 48 0: Bolivia 52 63 118 57 170 79 26 26 258 214 58 66 Bosnia and Herzegovina 70 73 31 13 .. 18 . . 165 90 73 84 8 Botswana 58 39 71 58 94 99 18 16 792 747 .13 17 o Brazil 63 68 71 32 .. 39 8 9 252 137 61 78 ~, Bsgaria 71 72 20 13 25 16 . 227 106 67 82 0 Burkina Faso 44 44 134 104 .. 206 131 128 557 524 27 31 Burundi 47 42 122 102 193 176 101 114 620 582 25 28 Cambodia 39 54 183 88 3 30' 120 34 30 381 322 4 1 4 7 o Camerooni 50 50 103 7 6 173 155 69 7 5 490 433 34 39 (N Canada 75 7 9 10) 5 13 7 . .. 105 60 83 91 Central African Republic 46 43 117 - 96 .. 152 63 64 612 561 24 28 Chad 42 48 123 101 235 188 106 99 433 383 37 42 Chile 69 76 32 10 35 12 3 2 153 83 77 87 China 67 70 42 32 65 39 10 11 161 115 71 77 Hong Kong, Chiina 74 80 11 3 . .. . 102 52 84 91 Colombia 66 72 41 20 58 23 4 3 203 114 70 82 Congo, Dem, Rep. 49 46 112 85 210 163 . .. 514 481 30 33 Congo, Rep. 50 51 88 68 125 106 . .. 476 403 35 42 Costa Pica 73 77 19 10 29 13 . .. 120 72 81 89 OSte d'lvo,re 49 46 108 111 170 180 71 58 535 506 30 33 Croatia 70O 73 21 8 23 9 . . 154 117 69 86 Cuba 74 76 20 6 22 9 . ., 121 76 80 87 Czech Repubiic 70 75 16 4 19 7 . .. 168 78 74 86 Denmark 74 76 8 4 10 6 . .. 132 83 79 87 Dominican Repuiblic 63 67 76 39 92 47 13 13 233 148 61 73 Ecuador 63 70 74 28 101 34 12 9 185 123 70 75 Egypt. Arab Rep. 56 67 121 42 175 52 15 16 189 153 67 73 El Salvador 57 70 84 29 120 3.5 17 20 243 141 67 80 Eritrea 44 52 . 60 .. 103 89 78 466 417 37 41 Estonia 69 71 17 8 25 11 . . 294 104 58 83 Ethiopia 42 42 155 98 213 179 83 86 575 530 25 29 Finland 73 77 8 4 9 5 ..137 60 79 90 France 74 79 10 4 14 6 . . 138 59 81 91 Gabon 48 53 104 58 .. 89 32 33 391 348 44 49 Gamnbia. The 40 53 159 73 216 .. 83 79 436 388 40 46 Georgia 71 73 25 17 .. 21 . .. 211 82 70 85 Germany 73 77 12 4 16 6 . .. 127 61 79 89 Ghana 53 57 94 58 157 112 53 51 334 294 46 49 Greece 74 78 18 5 23 8 . . 114 51 81 89 Guate mala 57 65 84 39 .. 49 15 18 288 185 58 70 Guinea 40 46 151 95 .. 161 101 98 448 443 31 32 Guinea-Bissau 39 45 169 126 290 211 .473 420 33 37 Haiti 51 53 124 73 200 il1 52 54 459 355 38 46 Honduras 60 66 70 35 103 44 . .. 245 152 58 70 2.20 01 Ufa expectancy Infant mortality Under-five Child mortality Adult mortality Survival at birth rate mortality rate rate rate to age 65 per 1,000 Male Female Male Female % of cohort years live births per 1,000 per 1,000 per 1,000 per 1,000 per 1.000 Male Female 1980 2000 1980 2000 1980 2000 1988-2000, 1988-20001 2000 2000 2000 2000 Hungary 70 71 23 9 26 11 . .. 272 118 65 8:3 India 54 63 115 69 177 88 25 37 222 209 60 6:3 Indonesia 55 66 90 41 125 51 19 20 232 180 62 70 Iran, Islamic Rep. 58 69 98 33 126 41 . . 166 148 71 74 Iraq 62 61 80 93 95 121 . .. 225 185 62 643 Ireland 73 76 11 6 14 7 . .. 112 67 78 87 Israel 73 78 16 6 19 7 . .. 104 62 83 89 Italy 7 79 15 5 177. . 113 52 80 91 Jamaica 71 75 33 20 39 24 . . 127 85 79 863 Ja pan 76 81 8 4 10 5 . .96 44 85 93 123 Jordan .. 72 41 25 30 7 5 153 116 73 79 Kazakhstan 67 65 33 21 .. 28 11 6 378 166 49 7:3 ... .... .... ... C)~~~~~~~~~~~~~~~~~ Kenya 55 47 75 78 115 120 36 38 600 558 28 3:2 Korea, Dem. Rep. 67 61 32 54 43 90 . 315 233 53 60 Korea, Rep. 67 73 26 8 27 10 . . 186 81 71 85 o. Kuwait 71 77 27 9 35 13 . .. 117 70 81 87 C Kyrgyz Republic 65 67 43 23 .. 35 10 11 297 136 57 77 CD 0 Lao PDR 45 54 127 92 200 ... . 374 313 43 48 ' Latvia 69 70 20 10 26 17 . .. 296 121 59 83 ( Lebanon 65 70 48 26 30 . . 171 127 70 77 S Lesotho 53 44 119 91 168 143 .557 523 25 28 0) Liberia --51 47 - 153 i11 235 185 . . 431 395 34 38 Libya 60 71 53 26 80 32 6 5 181 135 71 80 Lithuania 71 73 20 9 24 11 .248 86 65 863 Macedonia, FYR .. 73 54 14 69 17 .159 100 74 83 Madagascar 51 _55 119 88 216 144 75 68 324 283 48 53 Malawi 44 39 169 103 265 193 101 102 593 574 19 22 Malaysia 67 73 30 8 42 11 4 4 186 110 71 81 Mall 42 42 184 120 218 136 138 496 441 25 28 Mauritania 47 52 120 101 188 164 .. . 360 307 44 49 Mauritius 66 72 32 16 40 20 .. . 199 102 69 83 Mexico 67 73 51 29 74 36 15 17 155 94 74 84t Moldova 66 68 35 18 22 . .. 306 172 58 74 Mongolia 58 67 82 56 71 27 22 196 168 68 73 Morocco 58 67 99 47 152 60 21 19 195 142 66 74 Mozambique 44 42 145 129 .. 200 84 82 591 527 24 29 Myanmar 51 56 113 89 134 126 . .. 357 262 44 55 Namibia 53 47 90 62 114 112 30 34 588 542 21 24 Nepal 48___ 59 ....132_ 74_ 180 105 . . 260 265 57 54 Netherlands 76 78 9 5 11 7 .100 65 81 89 New Zealand 73 78 13 6 16 7 . .. 119 69 82 89 Nicaragua 59 69 84 33 143 41 12 11 200 136 67 76 Niger 42 46 135 114 317 248 184 202 476 389 30 36 Nigeria 46 47 99 84 196 153 66 69 468 418 32 35 Norway 76 79 8 4 11 5 . .. 107 61 82 90 Oman 60 74 41 17 95 22 . .. 136 101 77 82. Pakistan 55 63 127 83 157 110 22 37 194 164 63 68 Panama 70 75 32 20 36 24 . . 133 81 77 85 Papua New Guinea 51 59 78 56 75 28 21 360 329 49 5 2 Paraguay 67 70 50 23 61 28 10 12 184 119 68 79 Peru 60 69 81 32 126 41 19 20 193 132 68 77 Philippines 61 69 65 31 81 39 21 19 190 142 68 76 Poland 70 73 26 9 11 . .. 221 86 70 86 Portugal 71 76 24 6 31 8 . .. 153 69 76 88 Puerto Rico 74 76 19 10 .... .. 151 57 75 90) Romania 69 70 29 19 36 23 7 5 250 117 63 79 Russian Federation 67 65 22 16 .. 19 3 2 416 148 47 75 D 2.20 Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival at birth rate mortality rate rate rate to age 65 per 1,000 Male Female Male Female % of cohort years live birth5 per 1.000 per 1,000 per 1,000 per 1.000 per 1.000 Male Female 1980 2000 iaao 2000 1980 2000 1988-2000. 1988-20D00'i 2000 2000 2000 2000 Rwanda 46 40 128 123 .. 203 87 73 614 581 22 24 Saudi Arabia 61 73 65 18 85 23 . .. 155 120 75 81 Senegal 45 52 117 60 .. 129 76 74 401 303 32 40 Sierra Leone 35 39 190 154 336 267 . .. 527 477 26 30 Singapore 71 78 12 3 13 6 122 68 82 88 Slovak Republic 70 73 21 8 23 10 212 85 69 85 Slovenia 70 75 15 5 18 7 . .. 165 73 75 88 Somalia 43 48 145 117 246 195 . .. 397 340 38 44 South Africa 57 48 67 63 91 79 549 487 26 32 124 Spain 76 78 12 4 16 6 125 52 80 91 Sri Lanka 68 73 34 15 48 18 10 9 161 92 76 83 Sudan 48 56 117 81 145 .. 62 63 330 289 51 55 Swaziland 52 46 100 89 151 119 567 526 25 29 Sweden 76 80 7 3 8 4 91 56 84 91 -' Switzerland 76 80 9 4 11 6 .. .. lO 58 84 92 CL o Tajikistan 66 69 58 21 .. 30 . . 236 142 63 75 > Tanzania s0 44 108 93 176 149 61 58 562 521 26 30 a) 0 Thailand 64 69 49 28 58 33 11 11 229 144 66 75 o Togo 49 49 100 75 188 142 75 90 473 431 37 41 Trinidad and Tobago 68 73 35 16 40 19 4 3 181 133 72 80 o Tunisia 62 72 69 26 100 30 19 19 166 121 74 81 0 N Turkey 61 70 109 34 133 43 12 14 188 125 68 78 Turkmenistan 64 66 54 27 .. 43 . .. 282 157 58 73 Uganda 48 42 116 83 180 161 82 72 604 590 24 27 Ukraine 69 68 17 13 .. 16 - .. 335 132 55 79 United Arab Emirates 68 75 55 7 .. 10 . .. 127 91 79 84 United Kingdom 74 77 12 6 14 7 . . 113 66 80 88 United States 74 77 13 7 15 9 . . 138 81 80 90 Uruguay 70 74 37 14 42 17 . . 166 74 73 87 Uzbekistan 67 70 47 22 .. 27 15 9 226 127 65 78 Venezuela. RB 68 73 36 19 42 24 . . 176 100 74 84 Vietnam 60 69 57 27 105 34 . . 206 141 66 76 West Bank and Gaza .. 72 .. 22 .. 26 10 7 160 103 73 82 Yemen. Rep. 49 56 141 76 198 95 33 36 311 288 49 51 Yugoslavia. Fed. Rep. 70 72 33 13 .. 15 . .. 174 105 72 81 Zambia 50 38 90 115 149 186 96 93 655 634 16 20 Zimbabwe 55 40 80 69 108 116 35 31 630 594 18 19 Low income 53 59 112 76 176 115 . .. 294 261 64 69 Middle Income 66 70 55 31 79 39 . . 199 127 63 80 Lower middle income 66 69 54 33 81 41 10 11 192 125 61 78 Upper middle income 65 70 57 28 .. 35 . .. 224 136 68 82 Low & middle Income 60 64 87 58 136 84 . .. 242 187 64 73 East Asia & Pacific 64 69 56 35 82 45 10 11 183 132 69 76 Europe & Central Asia 68 69 41 20 .. 25 . .. 298 127 59 80 Latin America & Carib. 65 70 61 29 .. 37 . .. 208 121 67 81 Middle East & N. Africa 58 68 98 43 136 54 . .. 183 151 68 73 South Asia 54 62 119 73 179 96 25 37 227 212 82 65 Sub-Saharan Africa 48 47 116 91 187 162 5. . 04 459 40 46 High Income 74 78 12 6 15 7 . .. 122 64 81 90 Europe EMU 74 78 13 5 16 6 . .. 125 68 80 90 a. Data are for the most recent year available. 2.20 1I(i) About the data Definitions Mortality rates for different age groups-infants, Central Asia. In Sub-Saharan Africa the increase * Life expectancy at birth is the number of children, or adults-and overall indicators of stems from AIDS-related mortality and affects years a newborn infant would live if prevailing mortality-life expectancy at birth or survival to both men and women. In Europe and Central patterns of mortality at the time of its birth a given age-are important indicators of health Asia the causes are more diverse and affect men were to stay the same throughout its life. status in a country. Because data on the inci- more. They include a high prevalence of smoking, * Infant mortality rate is the number of infants dence and prevalence of diseases (morbidity a high-fat diet, excessive alcohol use, and dying before reaching the age of one year, per data) frequently are unavailable, mortality rates stressful conditions related to the economic 1,000 live births in a given year. * Under-five are often used to identify vulnerable populations. transition. mortality rate is the probability that a new- And they are among the indicators most fre- The percentage of a cohort surviving to age born baby will die before reaching age five, if quently used to compare levels of socioeconomic 65 reflects both child and adult mortality rates. subject to current age-specific mortality rates. development across countries. Like life expectancy, it is a) synthetic measure * Child mortality rate is the probability of dy- The main sources of mortality data are vital based on current age-specific mortality rates and ing between the ages of one and five, if subject registration systems and direct or indirect used in the construction of life tables. It shows to current age-specific mortality rates. estimates based on sample surveys or that even in countries where mortality is high, a * Adult mortality rate is the probability of dy- censuses. A "complete" vital registration certain share of the current birth cohort will live ing between the ages of 15 and 60-that is, 125 system-one covering at least 90 percent of vital well beyond the life expectancy at birth, while in the probability of a 15-year-old dying before MJ events in the population-is the best source of low-mortality countries close to 90 percent will reaching age 60, if subject to current age- g age-specific mortality data. But such systems reach at least age 65. specific mortality rates between ages 15 and 9 are fairly uncommon in developing countries. 60. * Survival to age 65 refers to the percentage E Thus estimates must be obtained from sample Table 2.20a of a cohort of newborn infants that would E surveys or derived by applying indirect estimation survive to age 65, if subject to current age- CD techniques to registration, census, or survey Differences In life expectancy shrink at specific mortality rates. CD data. Survey data are subject to recall error, and older ages r_--- surveys estimating infant deaths require large Additional years of life expectancy at age 60, selected CD samples because households in which a birth countries ' Data sources or an infant death has occurred during a given 2000 2020 The data are from the United Nations Statistics year cannot ordinarily be preselected for (estimate) (projection) Division's Population and Vital Statistics sampling. Indirect estimates rely on estimated Brazil :17.1 18.6 Report; publications and other releases from U) actuarial ("life") tables that may be inappropriate China 17.9 19.5 country statistical offices; Demographic and for the population concerned. Because life India 15.6 16.8 Health Surveys from national sources and expectancy at birth is constructed using infant Nigeria 15.1 15.8 Macro International; and the United Nations mortality data and model life tables, similar Russian Federation 15.7 17 Children's Fund's (UNICEF) State ofthe Wodld's reliability issues arise for this indicator. Turkey 17.8 19.4 Children 2000. Life expectancy at birth and age-specific S-_e;Word Baw xtiff,est mortality rates for 2000 are generally estimates based on vital registration or the most recent Changes In life expectancy at birth are strongly Influenced by trends in Infant and child mortality. The census or survey available (see Primaty data rapid Improvements In life expectancy in the second documentation). Extrapolations based on half of the 20th century were the result of declining outdated surveys may not be reliable for childhoodmortality.Improvementsinmortalityatthe oldest ages add fewer years of life to overall life monitoring changes in health status or for eXpectancy, and differences among countries In life comparative analytical work. expectancy at older ages are therefore considerably Specific problems arise in calculating infant smaller than at birth. Nevertheless, mortality at older ages has also decilned, and Is expected to continue to mortality rates in developing countries, where do so In the next decades. This trend, together with routine data collection in the health system often the Increasing number of people who are enterlngthe omits many infant deaths. In countries where older ages, will result In a rapidly growing elderly civil registration of deaths is incomplete, many population. infants dying during the first weeks of life may not even have been registered as having been born. Rates based on civil registration in these countries, or on hospital data covering mainly urban areas, are therefore biased because they reflect the more privileged population. Infant and child mortality rates are higher for boys than for girls in countries in which parental gender preferences are absent. Child mortality captures the effect of gender discrimination better than does infant mortality, as malnutrition and medical interventions are more important in this age group. Where female child mortality is higher, as in some countries in South Asia, it is likely that girls have unequal access to resources. Adult mortality rates have increased in many countries in Sub-Saharan Africa and Europe and II 444 - . . . 4  4. 4 444 44 1< 4 - A Urn N '4- I.. - / 444 A 4,. 4. Environment and the rural poor { 1 - - Reducing rural poverty Fostering Improving Sustaining broad-based social natural 127 rural growth well-being resources 00 0~ CD *0 CD 0~ Q) 0 Poverty is overwhelmingly rural, with some 70 percent of the poorest people in developing coun- tries living in rural areas. Although the number and proportion of poor people in cities are expected to grow rapidly in the next decades, the majority of the poor will continue to live in the counltryside. So reducing poverty and ending hunger require more attentioni to the rural economy and to rural development. But there's a problem: most countries-in their development strategies and in their allocations of resources-favor cities. Rural people, especially women and ethnic minorities, have little political clout, so they cannot influence public policy to attract more public investment to rural areas. Reducing rural poverty requires dealing with the entire rural space-with all of rural society and with both farm and nonfarm aspects of the economy. What will contribute most to faster growth in rural economies and to more poverty reduction? Three things: fostering broad-based rural growth, improving social well-being (in part by managing risk and reducing vulnerability), and sustaining natural resources. Each country's priorities will depend on its level of development-and its success on a policy and institutional environment conducive to rural development. Ag uCa Dft D3 Y503d About 900 million of the world's poor people live In rural areas. ~~o'tntw~~o~g b~~ Farmers in the world's poorest countries are still po epelv nrrlaes glromni2g, buDt 3)w- untouched by yield increases most of them farmers, many of Oncom e countuv5es them untouched by the yield Cereal yields by income level, 1970-2000 advances in industrial countries. 381gagg'0g Yet for many poorer developing El Low income Ol Lower middle income countries agriculture is the main El Upper middle income C] High income source of economic growth, and It took more than 1,000 years for 7, 5,000 agricultural growth is the the United Kingdom to increase 4.000 cornerstone of poverty reduction. wheat yields from 0.5 to 2 tons a 3 hectare (in the 1 950s) but only 40 0 3,000 Increasing the productivity of agri- hectare (in triple yields buto 6only a c u lture is thus essential for these years to triple yields to 6tons a hectare. What made such a ' 2,000 countries. A 10 percent increase in dramatic breakthrough possible? crop yields can reduce the propor- Massive public investment in agri- 1,000 tion of people living on less than $1 128 cultural research-research that 0 a oay by between 6 and 12 percent has allowed most industrial and (Thirtle and others 2000). Imagine °n manydevelopingcountriestosus- what a tripling of yields might do. CD tain food surpluses. .ouce Word Bank and FAG C, 0 0 Ca ax [Fead piroducUon ~~~~~~~~~~~~~~~~~~~Because of such productivity gains (and the food aid from industrial cu~~paces Food production outpaces population growth countries that subsidize po ue ol bu agriculture), food prices have been Growth in global food production and population falling. Even so, more than 150 mil- maonoufthm(DM ~~1970-2000 lion children under five are malnour- pem5sft Population Food production ~~~~ished-because of low incomnes LI Population El Food production ~~and poor food distribution. 250 (0 The rise in food production has out-20 paced population growth in all 0 regions except Africa. And this has been achieved with only small 5 increases in cropland. For example, Asia doubled cereal production after 1970 with only 4 percent 100 more croplanco (Hazell 20011. gQ1 Alb R 2 Nw anS rAO. Agriculture is The new activities off the farm pro- - vide work in the slack periods of r o ~~~~~Nonfarm economic activities are Importantviewrintesakpiosf not enough onfarm the agricultural cycle. Studies of .________________________________ _ African farm households suggest Nonfarm rural employment by gender, selected countries that 15-65 percent of farmers also As economies develop, activities work off the farm and that 15-40 off the farm become much more * Male * Female percent of family labor hours go to important, providing jobs and gD 100 such income-generating activities. reducing poverty. Workers follow a (D 80 And these are underestimates. diverse array of opportunities, g Much nonfarm activity in developing often sending much of their va 60 countries, especially that of women, income back home. The new activi- 40 is not taken into account. Activities ties, generally linked to agriculture ri such as clothing production, food and infrastructure, contribute 20 * 1 processing, and education for the 30-50 percent of total income in 0 I t household are not included in fig- ruralareas. LS ures on incomegeneration.12 129 Sorce: Lanjouw and Lanjouw 2001. 0 0 5. CD :3 r ,, IF .73 I . - -IdIIKq6 I III Rapid urban Rapid urbanization has strengthened the links between growth affects the Urban populations are growing faster rural and urban economies, blurring rural space the distinction between them, in Urban population as share of total, by region part because rural workers now take advantage of the new opportu- * 1960 * 1980 * 2000 nities in small towns and cities. In the next 30 years almost all , 80 population growth will be concen- But t has also increased air and CD trated in urban areas. The pace 60 water pollution and traffic conges- will be fastest in developing coun- tion. Such environmental problems tries, where the urban population 40 stretch beyond urban boundaries, is forecast to increase from 1.94 affecting rural people as well. billion to 3.88 billion. The number 20 Industrial effluents in rivers can poi- of people in African cities will jump l t l son agriculture downstream. And in from 297 million to 766 million, or 0 I I some parts of the world urban more than the total population South Sub- East Middle Europe Latin sprawl is encroaching on prime agri- today. In Asia the urban population Asia Saharan Asia East and and America cultural land. will almost double from 1.35 bil- Africa and North Central and lion to 2.61 billion. Pacific Africa Asia Caribbean Source: World Bank and UN. Rural Dependence on the weather makes the rural poor more vulnerable to infrastructure Access to electricity Is much higher In urban than Ineonomi shok Nor are th rural areas economic shocks. Nor are they is lagging spared a country's financial shocks, Share of households with access to electricity, which often hurt them as much as selected countries, latest available data urban dwellers, sometimes even Rural residents are often more Urban Rural more. Better social and physical Rural residents are often more _ Urban U Rural infrastructure can do much to help deprived of health and education _V 100 reduce their vulnerability, to man- than they are of income, since , 800 age their risks, and to improve their their access to those services is * well-being. often limited and the services 60 available are lower in quality than 40 those in urban areas. They are l f - also deprived of physical 20 l infrastructure, again of low quality 0 130 if it is available. This "urban bias' imposes substantial costs on ° almost all rural economic activity. 9 , .G 4y- Source; Komives, Whittington, and Wu 2000. E C1 0 P (.4 0 0 CN Limited a frequent cause of death among children in rural areas. Also infrastructure And access to In-house water supply Is even higher contributing to illness for the rural hurts rural ~~~~~~~~~~~~~~~~~~~~~poor is their lack of access to hurts rural ~ Share of households with access to in-house water, appropriate sanitation. Globally, the well-being selected countries, latest available data number of people with access to UUrban URural improved sanitation increased from 2.9 billion in 1990 to 3.7 billion in DC- 50 2000. But 2.4 billion people still The availability of transport, energy. a cD 40 lack access. Most-2 billion of water supply, sanitation, and com- ~ munication services in rural areas 30thmlvinralres remains limited. Access to electric- ity, in-house water supply, and tele- 20 phones is on average two to five 10 times higher in urban areas than in m rural (Komives, Whittington, and Wu0 2000). That is bad for markets. 's- ~ ' ' r '' . which thrive on good transport and Bulgaria 39 30 -1.6 59 111 34.6 38.9 3.2 1.9 62.2 59.2 Burkina Faso 92 82 1.8 285 274 10.0 12.4 0.1 0.2 89.8 87.4 Burundi 96 91 2.2 792 26 35.8 30.0 10.1 12.9 54.0 57.2 i Cambodia 88 84 2.6 268 177 11.3 21.0 0.4 0.6 88.3 78.4 o Cameroon 69 51 1.2 127 465 12.7 12.8 2.2 2.6 85.1 84.6 Canada 24 23 0.8 15 9,221 4.9 4.9 0.01 0.0 95.0 95.0 Central African Republic 65 59 1.9 112 623 3.0 3.1 0.1 0.1 96.9 96.8 Chad 81 76 2.4 163 1.259 2.5 2.8 0.0 0.0 97.5 97.2 Chile 19 15 0.5 118 749 5.1 2.6 0.3 0.4 94.6 96.9 China' 80 68 0.4 691 9.327 10.4 13.3 0.4 1.2 89.3 85.5 Hong K(ong, China 9 0 ..0 1 7.0 5.1 1.0 1.0 92.0 93.9 Colombia 36 25 0.2 508 1,039 3.6 2.0 1.4 2.2 95.0 95.8 Congo, Dem. Rep. 71 70 3.1 518 2,267 2.9 3.0 0.4 0.5 96.6 96.5 Congo. Rep. 59 38 0.7 642 342 0.4 0.5 0.1 0.1 99.5 99.4 Costa Rica 57 48 1.7 806 51 5.5 4.4 4.4 5.5 90.1 90.1 C6te dIlvoire 65 54 2.4 286 318 6.1 9.3 7.2 13.8 86.6 76.9 Croatia 50 42 -1,1 128 56 .. 26.1 .. 2.3 .. 71.6 Cuba 32 25 -0.6 76 110 23.9 33.1 6.4 7.6 69.7 59.3 Czech Republic 25 25 0.0 84 77 .. 40.1 .. 3.1 .. 56.9 Denmark 16 15 -0.2 35 42 62.3 54.1 0.3 0.2 37.4 45.7 Dominican Republic 50 35 0.2 274 48 22.1 22.1 7.2 10.3 70.6 67.5 Ecuador 53 38 0.6 302 277 5.6 5.7 3.3 5.2 91.1 89.2 Egypt. Arab Rep. 56 55 2.1 1,217 995 2.3 2.8 0.2 0.5 97.5 96.7 El Salvador 58 53 1,1 590 21 26.9 27.0 11.7 12.1 61.4 60.9 Eritrea 87 81 2.4 654 101 .. 4.9 .. 0.0 . 95.0 Estonia 30 31 -0.2 39 42 .. 26.5 .. 0.4 .. 73.1 Ethiopia 90 82 2.3 520 1,000 .. 10.0 .. 0.7 .. 89.3 Finland 40 33 -0.6 79 305 7.8 7.1 0.0 0.0 92.2 92.9 France 27 24 0.0 78 SSO 31.8 33.4 2.5 2.1 65.7 64.5 Gabon 50 19 -2.1 73 258 1.1 1.3 0.6 0.7 98.2 98.1 Gambia, The 80 68 2.7 442 10 15.5 19.5 0.4 0.5 84.1 80.0 Georgia 48 39 -1.1 251 70 .. 11.4 .. 3.8 .. 84.7 Germany 17 13 -1.4 88 357 33.7 33.1 1.4 0.6 64.9 66.2 Ghana 69 62 2.4 325 228 8.4 15.8 7.5 7.5 84.2 76.7 Greece 42 40 0.2 153 129 22.5 21.4 7.9 8.6 69.6 70.0 Guatemala 63 60 2.3 488 108 11.7 12.5 4.4 5.0 83.9 82.4 Gui'nea 81 67 1.6 556 246 2.9 3.6 1.8 2.4 95.4 94.0 Guinea-Bissau 83 76 1.8 300 28 9.1 10.7 1.1 1.8 89.9 87.6 Haiti 76 64 1.1 905 28 19.8 20.3 12.5 12.7 67.7 67.0 Honduras 65 53 1.9 229 112 13.9 13.1 1.8 3.2 84.3 83.7 3.I1 Rural population Rural Land area Land use population density average people per Permanent annual % sq. km thousand Arable land cropland Other % of total growth of arabie land sq. km % of land area % of land area % of land area 1980 2000 1980-2000 I 1999 S1999 1980 1999 1980 1999 1980 1999 Hungary 43 36 -1.2 76 92 54.4 52.1 3.3 2.4 42.2 45.4 India 77 72 1.6 444 2,973 54.8 54.4 1.8 2.7 43.4 42.9 Indonesia 78 59 0.4 694 1,812 9.9 9.9 4.4 7.2 85.6 82.9 Iran, Islamic Rep. 50 38 1.1 141 1,622 8.0 10.7 0.5 1.2 91.5 88:1 I raq 35 2 3 0.9 104 437 12.0 11.9 0.4 0.8 87.6 87.3 Ireland 45 41 0.1 144 69 16.1 15.6 0.0 0.0 83.9 84.3 Israel 11 9 1.1 155 21 15.8 17.0 4.3 4.3 80.0 78.7 Italy 33 33 0.0 223 294 32.2 29.1 10.0 9.8 57.7 61.2 Jamaica 53 44 0.1 661 11 12.5 16.1 9.7 9.2 77.8 74.7 Japan 24 21 -0.2 600 365 13.3 12.4 1.6 1.0 85.1 86.7 135 Jordan 40 26 1.8 512 89 3.4 2.7 0.4 1.6 96.2 95.6 Kazakhstan 46 44 -0.3 22 2,700 .. 11.1 .. 0.1 .. 88.8 Kenya 84 67 1.8 499 569 6.7 7.0 0.8 0.9 92.5 92.1 Korea, Dem. Rep. 43 40 0.9 522 120 13.4 14.1 2.4 2.5 84.2 83.4 Korea, Rep. 43 18 _3.3 520 99 20.9 17.2 1.4 2.0 77.8 80.8 Kuwait 10 2 .5.2 808 18 0.1 0.3 0.0 0.1 99.9 99.16 Kyrgyz Republic 62 67 1.9 236 192 .. 7.1 .. 0.3 . 92.5 CD 0 Lao PDR 87 77 1.9 454 231 3.4 3.8 0.1 0.3 96.5 95.9 ' 3 Latvia 32 31 -0.5 40 62 .. 29.8 .. 0.5 .. 69.7 ( Lebanon 26 10 -2.9 255 10 20.5 17.6 8.9 12.5 70.6 69.9 E Lesotho 87 72 1.1 450 30 9.6 10.7 . -:-- . ........ .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~5 Liberia 65 55 1.7 892 96 1.3 2.0 2.5 2.1 96.1 96.1) Libya 31 12 -1.8 37 1,760 1.0 1.0 0.2 0.2 98.8 98.8 Lithuania 39 32 -0.6 40 65 .. 45.3 .. 0.9 .. 53.8 Macedonia, FYR 47 38 -0.6 132 25 .. 23.1 .. 1.9 .. 75.10 Madagascar 82 71 2.1 417 582 4.3 4.4 0.9 0.9 94.8 94.7 Malawi 91 85 2.2 458 94 16.1 19.9 0.9 1.3 83.0 78.7 Malaysia 58 43 1.1 541 329 3.0 5.5 11.6 17.6 85.4 76.9 Mali 82 70 1.7 162 1.220 1.6 3.8 0.0 0.0 98.3 96.2 Mauritania 73 42 0.0 230 1.025 0.2 0.5 0.0 0.0 99.8 99.5 Mauritius 58 59 1.1 691 2 49.3 49.3 3.4 3.0 47.3 47.8 Mexico 34 26 0.5 100 1,909 12.1 13.0 0.8 1.3 87.1 85.7 Moldova 60 54 -0.2 128 33 .. 55.0 . 11.3 .. 33.7 Mongolia 48 41 1.1 75 1.567 0.8 0.8 0.0 0.0 99.2 99.2 Morocco 59 44 0.5 148 446 16.9 19.0 1.1 2.1 82.0 78.8 Mozambique 87 60 0.0 339 784 3.7 4.0 0.3 0.3 96.0 95.7 Myanmar 76 72 1.5 359 658 14.6 14.5 0.7 0.9 84,8 84.63 Namibia 77 69 2.3 146 823 0.8 1.0 0.0 0.0 99.2 99.1) Nepal 94 88 2.0 686 143 16.0 20.3 0.2 0.5 83.8 79.2 Netherlands 12 11 0.1 185 34 23.3 27.0 0.9 1.0 75.7 72.1) New Zealand 17 13 -0.1 33 268 9.3 5.8 3.7 6.4 86.9 87.8 Nicaragua 47 35 1.4 72 121 9.5 20.2 1.5 2.4 89.1 77.41 Niger 87 79 2.8 168 1.267 2.8 3.9 0.0 0.0 97.2 96.1 Nigeria 73 56 1.6 250 911 30.6 31.0 2.8 2.8 66.6 66.3 Norway 30 25 -0.5 126 307 2.7 2.9... Oman 69 16 -3.4 2,595 212 0.1 0.1 0.1 0.3 99.8 99.13 Pakistan 72 63 1.9 403 771 25.9 27.5 0.4 0.8 73.7 71.13 Panama 50 42 1.1 240 74 5.8 6.7 1.6 2.1 92.5 91.2 Papua New Guinea 87 83 2.3 .. 453 0.0 0.1 1.1 1.3 98.9 98.5 Paraguay 58 44 1.4 109 397 4.1 5.5 0.3 0.2 95.6 94.2 Peru 35 27 0.6 188 1.280 2.5 2.9 0.3 0.4 97.2 96.7 Philippines 63 41 0.2 566 298 17.5 18.6 14.8 15.1 67.7 66.3 Poland 42 34 -0.6 96 304 48.0 46.2 1.1 1.1 50.9 52.7 Portugal 71 36 -3.3 189 92 26.5 21.5 7.8 8.1 65.7 70.4 Puerto Rico 33 25 -0.4 2,798 9 8.3 3.9 7.3 5.2 84.3 90.9 Romania 51 44 -0.7 106 230 42.7 40.5 2.9 2.2 54.4 57.3 Russian Federation 30 27 -0.3 31 16.889 .. 7.4 -. 0.1 . 92.5 / 3.1 Rural population Rural Land area Land use population density average people per Permanent annual % sq. km thousand Arable land cropland Other % of tots,I growth of arable land sq. km % of land area % of land area % of land ares 1980 2000 1980-2000 1999 1999 1980 1999 1980 1999 1980 1999 Rwanda 95 94 2.4 901 25 30.8 35.1 10.3 10.1 58.9 54.8 Saudi Arabia 34 14 -0.4 84 2,150 0.9 1.7 0.0 0.1 99.1 98.2 Senegal 64 53 1.7 222 193 12.2 11.6 0.0 0.2 87.8 88.2 Sierra Leone 76 63 1.3 653 72 6.3 6.8 0.7 0.8 93.0 92.5 Singapore 0 0 ..0 1 3.3 1.6 9.8 0.0 86.9 98.4 Slovak Republic 48 43 -0.2 158 48 .. 30.4 .. 2.8 .. 66.8 Slovenia 52 50 0.0 577 20 . 85 .. 1.5 .. 90.0 Somalia 78 73 1.2 592 627 1.6 1.7 0.0 0.0 98.4 98.3 South Africa 52 45 1.5 129 1,221 10.2 12.1 0.7 0.8 89.1 87.1 136 Spain 27 22 -0.7 65 499 31.1 27.4 9.9 9.7 59.0 62.9 - Sri Lanka 78 76 1.2 1,660 65 13.2 13.6 15.9 15.8 70.9 70.6 Sudan 80 64 1.3 119 2,376 5.2 7.0 0.0 0.1 94.8 92.9 rz Swaziland 82 74 2.5 448 17 10.8 9.8 0.2 0.7 89.0 89.5 Sweden 17 17 0.3 54 412 7.2 6.7 . Switzerland 43 32 -0.8 556 40 9.9 10.5 0.5 0.6 89.6 88.9 E Syrian Arab Republic 53 46 2.3 154 184 28.5 25.6 2.5 4.4 69.1 70.1 o Tajikistan 66 73 2.7 611 141 .. 5.2 . 0.9 .. 93.9 a) > Tanzania 85 72 2.1 640 884 3.5 4.2 1.0 1.0 95.5 94.7 Thailand 83 78 1.0 323 511 32.3 28.8 3.5 6.5 64.2 64.8 ~0 3: Trinidad and Tobago 37 26 -0.8 455 5 13.6 14.6 9.0 9.2 77.4 76.2 a Tunisia 49 35 0.3 117 155 20.5 18.3 9.7 14.5 69.7 67.2 0 (N Turkey 56 25 -2.2 69 770 32.9 31.4 4.1 3.3 63.0 65.3 Turkmenistan 53 55 3.2 173 470 .. 3.5 .. 0.1 . 96.4 Uganda 91 86 2.4 368 197 20.7 25.7 8.1 8.9 71.2 65.4 Ukraine 38 32 -1.0 49 579 .. 56.4 .. 1.6 .. 42.0 United Arab Emirates 29 14 1.6 498 84 0.2 1.0 0.1 0.6 99.7 98.4 United Kingdom 11 11 0.0 106 241 28.7 24.6 0.3 0.2 71.0 75.2 United States 26 23 0.4 36 9.159 20.6 19.3 0.2 0.2 79.2 80.5 Uruguay 15 9 -2.0 23 175 8.0 7.2 0.3 0.3 91.7 92.5 Uzbekistan 59 63 2.5 342 414 .. 10.8 .. 0.9 .. 88.3 Venezuela, RB 21 13 -0.1 116 882 3.2 3.0 0.9 1.0 95.9 96.0 Vietnam 81 76 1.6 1.031 325 18.2 17.7 1.9 4.9 79.8 77.4 West Bank and Gaza............ Yemen. Rep. 81 75 3.2 833 528 2.6 2.9 0.2 0.2 97.2 96.8 Yugoslavia, Fed. Rep. 54 48 -0.2 ... 28.0 .. 2.9 .. 69.1 Zambia 60 56 2.4 105 743 6.9 7.1 0.0 0.0 93.1 92.9 Zimbabwe 78 65 1.9 252 387 6.5 8.3 0.3 0.3 93.3 91.3 Low Income 76 68 1.6 510 32,536 11.8 13.2 1.0 1.4 87.1 85.4 Middle Income 62 50 0.3 589 66,644 7.9 8.8 1.0 1.0 91.0 90.2 Lower middle income 69 58 0.5 642 43.596 8.8 9.2 1.0 0.9 90.2 89.9 Upper middle income 38 24 -0.6 184 23,048 7.0 8.0 1.1 1.3 91.9 90.7 Low & middle Income 68 59 1.0 545 99,180 9.5 10.2 1.0 1.2 89.5 88.6 East Asia & Pacific 78 65 0.5 694 15.969 10.1 11.8 1.5 2.6 88.4 85.5 Europe & Central Asia 41 35 -0.4 125 23,771 37.1 11.7 3.1 0.4 59.8 87.9 Latin America & Carib. 35 25 0.0 252 20,062 5.8 6.6 1.1 1.3 93.1 92.1 Middle East & Nv. Africa 52 41 1.4 543 10,995 4.5 5.1 0.4 0.8 95.1 94.1 South Asia 78 72 1.6 542 4,781 42.5 42.4 1.S 2.1 56.1 55.4 Sub-Saharan Africa 77 66 1.9 377 23.603 5.5 6.5 0.7 0.9 93.8 92.6 High Income 25 21 -0. 1 180 30.920 12.0 11.6 0,S 0.5 87.5 87.9 Europe EMU 27 23 -0.5 140 2.537 26.2 25.1 4.6 4.4 69.2 70.5 a. Estimate dues not account for recent refugee flows. b. Includes Luxemhourg. c. Includes Taiwan. Ch na. 3.1 1o About the data Definitions Indicators of rural development are sparse, as this year's edition of the World Development * Rural population is calculated as the differ- few indicators are disaggregated between rural Indicators, like the previous three, breaks down ence between the total population and the ur- and urban areas (for some that are, see tables the category cropland, used in earlier editions, ban population (see Definitions for tables 2.1 2.6, 3.5, and 3.10). This table shows indicators into arable land and permanent cropland. Be- and 3.10). * Rural population density is the of rural population and land use. Rural population cause the data reflect changes in data report- rural population divided by the arable land area. is approximated as the midyear nonurban ing procedures as well as actual changes in land * Land area is a country's total area, exclud- population. use, apparent trends should be interpreted with ing area under inland water bodies, national The data in the table show that land use pat- caution. claims to continental shelf, and exclusive eco- terns are changing. They also indicate major dif- Satellite images show land use that differs nomic zones. In most cases the definition of ferences in resource endowments and uses from that given by ground-based measures in inland water bodies includes major rivers and among countries. True comparability of the data both area under cultivation and type of land use. lakes. (See table 1.1 for the total surface area is limited, however, by variations in definitions, Furthermore, land use data in countries such of countries.) * Land use is broken into three statistical methods, and the quality of data col- as India are based on reporting systems that categories. * Arable land includes land defined lection. Countries use different definitions of were geared to the collection of tax revenue. by the FAO as land under temporary crops rural population and land use, for example. The Because taxes on land are no longer a major (double-cropped areas are counted once), tem- 137 Food and Agriculture Organization (FAO), the pri- source of government revenue, the quality and porary meadows for mowing or for pasture, land N mary compiler of these data, occasionally ad- coverage of land use data (except for cropland) under market or kitchen gardens, and land tem- 0 justs its definitions of land use categories and have declined. Data on forest area, aggregated porarily fallow. Land abandoned as a result of sometimes revises earlier data. (In 1985, for in the category other, may be particularly shifting cultivation is excluded. * Permanent E example, the FAO began to exclude from crop- unreliable because of differences in definitions cropland is land cultivated with crops that oc- E 0 land, land used for shifting cultivation but cur- and irregular surveys (see About the data for cupy the land for long periods and need nol be C rently lying fallow.) And following FAO practice, table 3.4). replanted after each harvest, such as cocoa, CD coffee, and rubber. This category includes land 3 Table 3.1a under flowering shrubs, fruit trees, nut trees, CD and vines, but excludes land under trees grown The 10 economies with the highest rural population density In 1999 - and the 10 with the for wood or timber O land iles fr- lowest frwo rtme.-Ohrln nldsfr est and woodland as well as logged-over areas People per sq. km of arable land to be forested in the near future. Also included n Rural Rural are uncultivated land, grassland not used for population density populationdensity pasture, wetlands, wastelands, and built-up Puerto Rico 2,798 United States 36 areas-residential, recreational, and indus- Oman 2,595 Belgium 35 trial lands and areas covered by roads and other fabricated infrastructure. Sri Lanka 1,660 Denmark 35 Egypt. Arab Rep. 1,217 New Zealand 33 - Bangladesh 1,209 Russian Federation 31 | Data sources Vietnam 1,031 Uruguay 23 The data on urban population shares used to Haiti 905 Kazakhstan 22 estimate rural population come from the United Rwanda 901 Argentina 16 Nations Population Division's World Urbaniza- Liberia 892 Canada 15 tion Prospects: The 1999 Revision. The total Yemen, Rep. 833 Australia 6 population figures are World Bank estimates. The data on land area and land use are from So,re- Tabil 3.1. the FAO's electronic files and are published in its Production Yearbook. The FAO gathers these data from national agencies through annual questionnaires and by analyzing the results of national agricultural censuses. 3.2 Agricultural inputs Arable land Irrigated land Land under Fertilizer Agricultural machinery cereal consumption production Tractors Tractors hundreds of grams per 1,000 per 100 hiectares % of thousard per hectare agricultural sq. km. of per capita cropland hectares of arable land workers arable land 1979-81 1997-99 1979-81 1997-99 1979-81 1999-2001 1979-81 1997-99 1979-81 1997-99 1979-81 1997-99 Afghanistan 0.50 0.32 31.1 29.6 3.037 2,345 62 7 0 0 1 1 Albania 0.22 0.17 53.0 48.6 367 213 1,556 228 15 11 173 140 Algeria 0.37 0.26 3.4 6.8 2,968 1,903 277 152 27 38 68 121 Angola 0.41 0.24 2.2 2.1 705 888 49 10 4 3 35 34 Argentina 0.89 0.69 5.7 5.7 11,154 10,803 46 322 132 191 73 112 Armenia .. 0.13 .. 51.3 .. 182 .. 168 . 73 .. 354 Australia 2.97 2.69 3.5 4.6 15,986 16,347 269 446 751 707 75 63 Austria 0.20 0.17 0.2 0.3 1,062 839 2,615 1,774 945 1,672 2,084 2,522 Azerbaijan .. 0.21 .. 74.1 . 6.15 .. 168 35 .. 194 138 Bangladesh 0.10 0.06 17.1 46.1 10.823 11,568 459 1,491 0 0 5 7 Belarus 0.61 .. 1.8 .. 2,406 .. 1.417 .. 11 . 140 Ln Belgium, 0.08 0.08 1.7 4.6 426 334 5,323 3,766 917 1,222 1,416 1,312 Cs Benin 0.43 0.29 0.3 0.6 525 841 11 262 0 0 1 1 Bolivia 0.35 0.24 6.6 5.9 559 780 23 34 4 4 21 29 Bosnia and Herzegovina .. 0.13 .. 0.4 .. 401 .. 653 .. 263 580 E Botswana 0.44 0.22 0.5 0.3 153 128 32 123 9 19 54 175 oL Brazil 0.32 0.32 3.3 4.4 20,612 17,807 915 1,099 31 59 139 151 > Bulgaria 0.43 0.52 28.3 17.7 2.110 1,905 2,334 381 88 73 161 56 a) Burkina Faso 0.39 0.32 0.4 0.7 2,026 2,957 26 141 0 0 0 6 ~0 3: Cambodia 0.29 0.32 5.8 7.1 1,241 2.037 45 27 0 0 6 4 o Cameroon 0.68 0.42 0.2 0.5 1,021 844 56 72 0 0 1 1 CN Canada 1.86 1.51 1.3 1.6 19,561 17,454 416 582 824 1,717 144 156 Central African Republic 0.81 0.54 . . 194 153 5 3 0 0 0 0 Chad 0.70 0.48 0.4 0.6 907 2.000 6 40 0 0 1 0 Chile 0.34 0.13 31.1 78.4 820 580 338 2,323 43 56 90) 272 China 0.10 0.10 45.1 39.0 94,647 87,077 1,494 2,911 2 1 76 80 Hong Kong, China 0.00 0.00 37.5 33.3 0 0 ...0 0 10 8 Colombia 0.13 0.05 7.7 20.4 1,361 1,075 812 2,848 8 6 77 103 Congo. Dem. Rep. 0.25 0.14 0.1 0.1 1,115 2,100 12 2 0 0 3 4 Congo, Rep. 0.08 0065 0.6 0.5 19 3 27 270 2 1 49 41 Costa Rica 0.12 0.06 12.1 20.9 136 86 2,650 8,323 22 21. 210 31-1 Cote dIlvoire 0.24 0.19 1.0 1.0 1,908 1.621 261 306 1 1 16 13 Croatia .. 0.32 .. 0.2 .. 684 .. 1,558 .. 13 ..19 Cuba 0.27 0.33 22.9 19.5 224 202 2,024 510 78 97 259 215 Czech Republic .. 0.30 .. 0.7 .. 1.646 .. 951 .. 171 .. 274 Denmark 0.52 0.44 14.5 19.6 1,818 1,515 2,453 1,763 973 1,119 708 570 Dominican Republic 0.19 0.13 11.7 17.2 149 150 572 954 3 4 20 22 Ecuador 0.20 0.13 24.8 28.8 419 904 471 1,024 6 7 40 57 Egypt, Arab Rep. 0.06 0.05 100.0 100.0 2,007 2,715 2.864 4,043 4 10 158 303 El Salvador 0.12 0.09 4.3 4.8 422 405 1,376 1,570 5 4 61 61 Eritrea 0.12 .. 4.8 .. 374 .. 168 ..0 ..12 Estonia .. 0.80 .. 0.4 .. 337 2680. 538 .. 453 Ethiopia .. 0.16 .. 1.8 .. 7,020 .. 156 . 0 ..3 Finland 0.50 0.42 2.5 3.0 1,190 1.180 2,022 1.441 721 1,242 892 89 France 0.32 0.31 4.6 10.3 9,804 9,032 3,268 2.649 737 1.303 836 694 Gabon 0.42 0.28 2.4 3.0 6 17 20 6 5 7 43 46 Gambia, The 0.26 0.16 0.6 1.0 54 141 136 82 0 0 3 2 Georgia .. 0.15 .. 44.2 .. 375 . 4667 .. 21 .. 138 Germany 0.15 0.14 3.7 4.0 7,692 6,951 4,249 2.485 624 959 1.340 90 Ghana 0.18 0.20 0.2 0.2 902 1,305 104 45 1 1 19 10 Greece 0.30 0.26 24.2 37.3 1,600 1,266 1,927 1.741 120 299 485 875 Guatemala 0.19 0.13 5.0 6.8 716 687 726 1,570 3 2 32 32 Guinea 0.16 0.12 7.9 6.4 708 744 16 31 0 0 2 6 Guinea-Bissau 0.34 0.26 6.0 4.9 142 132 24 17 0 0 1 1 Haiti 0.10 0.07 7.9 8.2 416 457 62 192 0 0 3 3 Honduras 0.44 0.25 4.1 4.1 421 465 163 983 5 7 21 34 3.20 Arabie land Irrigated land Land under Fertilizer Agricultural machinery cereal consumiption production Tractors Tractors hundreds of grams per 1.000 per 100 hectares % of thousand per hectare agricultural sq. km. of per capita cropland hectares of arable land workers arable land 1979-e1 1997-99 1979-81 1997-99 1979-81 1999-2001 1979-el 1997-99 ±979-81. 1997-99 1979-81 1997-99 Hungary 0.47 0.48 3.6 4.2 2,878 2,671 2,906 832 59 168 11 c192 India 0.24 0.17 22.8 33.6 104,349 100,602 345 1,058 2 6 24 9-2 Indonesia 0.12 0.09 16.2 15.5 11,825 15,149 645 1,415 0 1 5 39 Iran, Islamic Rep. 0.36 0.27 35.5 39.8 8,062 7,424 43) 647 17 41 57 149 Iraq 0.40 0.23 32.1 63.6 2,159 2,712 172 735 23 75 44 015 Ireland 0.33 0.29 -.425 279 5,373 6,391 606 1,048 1,289 1,638 Israel 0.08 0.06 49.3 45.3 129 74 2,384 3,474 2964 327 809 606e Italy 0.17 0.15 19.3 24.1 5.062 4,192 2,295 2,151 370 1.115 1,117 1,966 Jamaica 0.06 0.07 10.1 9.1 4 2 1,231 1,339 9 11 206 177 Japan 0.04 0.04 56.0 54.6 2,724 2,048 4,131 3,207 209 690 2,723 4,675 139 Jordan 0.14 0.05 .11.0 195 158 42 404 963 48 29 153 196O Kazakhstan . 1.99 7 6 11,991 12 . 54 2. 3 Kenya 0.23 0.14 09 1.5 1,692 1.828 160 346 1 1. 17 36 Korea, Dem. Rep- 0.09 0.08 58.9 73.0 1,625 1.258 4,688 1,032 13 2) 275 441 Korea, Rep 0.05 0.04 59.6 60.7 1,689 1.174 3,920 5.323 1 60 14 9033C Kuwait 0.00 0.90 83.3 90.5 0 1 4.500 1,833 3 11 220 1,37 C Kyrgyz Republic .. 0.28 .. 75.0 .. 648 .. 218 .. 46 .. 181 CD 0 Lao PDR 0.24 0.17 13.3 17.8 751 742 33 79 0 1 7 .12 Latvia .. 0.75 .. 1.1 421 .. 252 .. 328 301 C1 Lebanon 0.07 0.04 28.3 38.6 34 39 1,683 3,384 28 120 141 312 Lesotho 0.22 0.16 ..203 170 150 171 6 6 47 82 Liberia 0.07 0.06 0.5 0.7 203 158 3633. 0 0 24 17 Libya 0.58 0.37 10.7 21.2 538 327 357 302 101 3)3 134 181 Lithuania 0.79 0.3 .. 975 .. 521 -. 381 .. 32 Macedonia, FYR . 0.29 8.6 220 .. 729 . 416 .. 913 Madagascar 0.28 0.18 21.5 35.1 1,309 1,374 31 29 1 1 11 :14 Malawi 0.25 0.19 1.1 1.4 1,155 1,541 203 271 0 0 8 8 Malaysi'a 0.07 0.06 6.7 4.8 729 714 ...4 24 77 238 Mali 0.31 0.45 4.5 3.0 1,346 2.397 61 84 0 1 5 6 Mauritania 0.14 0.20 22.8 9.8 125 249 57 12 1 1 13 8 Mauritius 0.10 0.09 15.0 18.2 0 0 2,547 3,319 4 6 33 :37 Mexico 0.34 0.26 20.3 23.8 9,356 10,952 570 706 16 20 54 69 Moldova .. 0.42 .. 14.1 .. 765 . 279 8203. 24.5 Mongolia 0.71 0.56 3.0 6.4 559 226 83 33 32 21 82 53 Morocco 0.39 0.32 15.0 13.1 4,414 4,904 268 36 7 10 34 419 Mozambique 0.24 0.18 2.1 3.2 1,077 1,731 107 24 1 1 20 .18 Myanmar 0.28 0.21 10.4 16.7 5,133 6,817 ill 173 1 1 9 1.0 Namibia 0.68 0.49 0.6 0.9 195 323 0 2 10 11 39 .39 Nepal 0.16 0.13 22.5 38.2 2,251 3,305 95 324 0 0 10 16 Netherlands 0.06 0.06 58.5 6025 213 8,620 5,374 561 596 2.238 1,7:12 New Zealand 0680 0.41 5.2 8.7 193 132 1,965 4.241 619 437 367 489 Nicaragua 0.39 0.51 60O_ 3.2 266 387 382 172 6 7 19 11 Niger 0.62 0.49 0.7 1.3 3,872 7,455 10 3 0 0 0 0 Nigeria 0.39 0.23 0.7 0.8 6,048 18,765 59 61 1 2 3 11. Norway 0.20 0.20 311 337 3,146 2,252 824 1.266 1.603 1.537 Oman 0.01 0.01 92.7 80.5 2 2 840 4,356 1 1 76 94 Pakistan 0.24 0.16 72.7 81.7 10.693 12,364 525 1,261 5 12 50 150 Panama 0.22 0.18 5.0 5.3 166 165 692 731 27 20 1.22 100O Papua New Guinea 0.01 0.01 ........ 2 3 3,827 1,700 1 1 699 193 Paraguay 0.52 0.42 3.4 2.9 307 548 44 297 14 24 45 75 Peru 0.19 0.15 32.3 28.6 732 1,189 381 602 5 5 37 36 Philippines 0.11 0.08 12.8 15.5 6,790 6,611 636 1,315 1 1 20 21 Poland 0.41 0.33 0.7 0.7 7,875 8,569 2,393 1,135 112 291 425 932 Portugal 0.25 0.19 20.1 24.6 1,099 584 1,113 1,297 72 233 351 840 Puerto Rico 0.02 0.01 27.2 49.6 1 0 ... .,. Romania 0.44 0.41 21.9 29.2 6,340 5,687 1,4.48 325 39 92 150 177 Russian Federation .. 0.86 . 3.7 40,539 . 1-10 .. 97 .. 67 TT 3.2 Arable land Irrigated land Land under Fertilizer Agricuftural machinery cereal consumption production Tractors Tractors hundreds of grams per 1.000 per 100 hiectares %of thousand per hectare agricultura sq. km. of per capita cropland hectares of arable land workers arable land 1979-81 1997-99 1979-81 1997-99 1979-8i 1999-2001 1979-81 1997-99 1979-81 1997-99 ±979-81 1997-99 Rwanda 0.15 0.10 0.4 0.4 239 233 3 4 0 0 1 1 Saudi Arabia 0.20 0.18 28.9 42.8 388 625 228 925 2 12 10 26 Senegal 0.42 0.25 2.6 3.1 1,216 1,360 104 1-16 0 0 2 2 Sierra Leone 0.14 0.10 4.1 5.4 4.34 235 58 23 0 0 6 2 Singapore 0.00 0.00 . ... .. ...3 22 220 650 Slovak Republic .. 0.27 .. 10.9 ,. . . 716 -. 91 .. 169 Slovenia .. 0.09 .. 1.0 .. 97 .. 4,442 .. 4.231 .. 6,090 Somalia 0.15 0.13 13.3 18.8 638 464 9 5 1 1 17 18 South Africa 0.45 0.36 8.4 8.5 6,760 4,735 874 527 94 53 140 59 140 Spain 0.42 0.35 14.8 19.5 7,391 6.598 1,012 1,626 200 618 335 621 Sri Lanka 0.06 0.05 28.3 33.7 864 9D7 1.800 2,677 4 2 141 84 St Sudan 0.64 0.56 14.4 11.5 4,447 7,068 51 41 2 2 8 6 (0 Smaziland 0.30 0.17 34.0 38.3 70 61 1.050 327 29 25 173 174 'O Sweden 0.36 0.31 - 1,505 1.191 1,654 1.021 715 1.064 623 620 C 1 Switzerland 0.06 0.06 6.2 5.7 172 185 4,623 2,882 494 648 2,428 2.692 E) Syrian Arab Republic 0.60 0.31 9.6 21.6 2.642 2.977 250 754 29 67 54 195 o Tajikistan .. 0.12 .. 82.4 .. 391 .. 657 .. 37 .. 404 a) Tanzania 0.16 0.12 3.1 3.3 2,834 3.544 1-10 81 1 1 35 20 Thailand 0.35 0.25 16.4 26.0 10,625 11,684 177 1,102 1 10 11 147 ~0 Trinidad and Tobago 0.06 0.06 1.7 2.5 4 4 1,064 1,036 50 53 337 360 o Tunisia 0.51 0.31 4.9 7.5 1,416 1,368 212 377 3) 38 79 123 C) Turkey 0.57 0.40 9.6 15.8 13,499 13.204 529 831 36 62 169 358 Turkmenistan .. 0.33 .. . . 732 .. 651 .. 89 307 Uganda 0.32 0,24 0.1 0.1 752 1,366 1 6 0 1 6 9 Ukraine .. 0.65 .. 7.2 .. 12.616 .. 151 .. 94 . 1-14 United Arab Emirates 0.01 0.03 .. 57.4 0 1 2,250 4.153 6 4 106 34 United Kingdom 0.12 0.10 2.0 1.7 3,930 3,140 3.191 3.453 726 914 744 810 United States 0.83 0.64 10.8 12.5 72,639 58.055 1.092 1.127 1.230 1,5416 253 271 Uruguay 0.48 0.36 5.4 13.8 614 554 564 1.041 171 173 236 262 Uzbeki stan . 0.19 .. 88.3 .. 1,413 .. 1,912 .. 59 380 Venezuela. RB 0.19 0.11 10.0 16.3 814 68 711 934 50 60 133 186 Vietnam 0.11 0.07 25.6 41.3 5,962 8,299 302 3,179 1 5 38 218 West Bank and Gaza.. . .. .... ... Yemen,Rep. 0.16 0.09 19.9 20.0 065 639 93 183 3 2 33 37 Yugoslavia, Fed. Rep. 0.73 .. 1.9 .. 4.310 2.048 1,261 .. 140 .. 616 Zambia 0.89 0.54 0.4 0.9 595 811 148 93 3 2 9 11 Zimbabwe 0.35 0.27 3.1 3.5 1,633 1,787 610 552 7 7 66 72 Low Income 0.22 0.18 19.9 25.8 199,694 257,986 290 669 2 5 20 70 Middie Income 0.18 0.22 23.4 20.3 233,883 279,983 985 1,111 8 11 103 126 Lower middle income 0.13 0.20 33.6 23.8 155,654 203,551 1.060 1,181 5 7 83 96 Upper middle income 0.34 0.29 10.4 12.8 78,229 76,432 888 959 39 82 137 206 Low &middle Income 0.20 0.20 21.7 22.6 433.577 537,969 644 924 5 8 62 102 East Asia & Pacific 0.12 0.10 36.9 38.1 141,593 141.801 1,154 2.407 2 2 55 74 Europe & Central Asia 0.16 0.59 10.6 10.4 37.380 110.208 1,445 337 .. 100 223 166 Latin America & Carib. 0.32 0.27 11.8 13.9 49.846 49,106 586 854 25 36 95 118 Middle East & N. Africa 0.29 0.20 25.8 36.4 25,653 25,677 421 715 12 24 61 122 South Asia 0.23 0.16 28.7 40.9 132,128 131,199 360 1,051 2 5 25 91 Sub-Saharan Africa 0.32 0.24 4.0 4.2 46.978 79,978 158 134 3 1 23 16 High Income 0.46 0.40 9.8 11.6 155.024 132,111 1.314 1,265 519 942 387 428 Europe EMU 0.23 0.21 13,4 18.3 35,999 31.478 2,704 2.306 452 868 896 950 a. Includes Luxembourg. 3.22 About the data Definitions Agricultural activities provide developing coun- Figure 3.2 * Arable land includes land defined by the FAO tries with food and revenue, but they also can as land under temporary crops (double-cropped degrade natural resources. Poor farming prac- The land under cereal production is areas are counted once), temporary meadows tices can cause soil erosion and loss of fertility. Increasing in low-income economies for mowing or for pasture, land under market Efforts to increase productivity through the use 300 Thousandsofrhectares or kitchen gardens, and land temporarily fal- of chemical fertilizers, pesticides, and intensive low. Land abandoned as a result of shifting irrigation have environmental costs and health 250 01979-81 cultivation is excluded. * Irrigated land relers impacts. Excessive use of chemical fertilizers 0 1997-99 to areas purposely provided with water, includ- can alter the chemistry of soil. Pesticide poi- 200 - ing land irrigated by controlled flooding. Crop- soning is common in developing countries. And R land refers to arable land and land used for salinization of irrigated land diminishes soil fer- s permanent crops (see table 3.1). * Land un- tility. Thus inappropriate use of inputs for ag- 100 der cereal production refers to harvested ar- ricultural production has far-reaching effects. eas, although some countries report only sown This table provides indicators of major inputs 50 or cultivated area. * Fertilizer consumption to agricultural production: land, fertilizers, and measures the quantity of plant nutrients used 141 agricultural machinery. There is no single cor- Low Lower Upper H,gh per unit of arable land. Fertilizer products cover rect mix of inputs: appropriate levels and appli- income middle middle income nitrogenous, potash, and phosphate fertilizers 8 income income0 cation rates vary by country and over time, de- (including ground rock phosphate). The time pending on the type of crops, the climate and reference for fertilizer consumption is the crop o soils, and the production process used. iaus her ue ofhar machiner. year (July through June). * Agricultural machin- lags far behind other economies'. The data shown here and in table 3.3 are Agricultural machinery per 100 sq. km. of arableland ery refers to wheel and crawler tractors (ex- an collected by the Food and Agriculture Organiza- 450 cluding garden tractors) in use in agriculture D tion (FAO) through annual questionnaires. The 400 at the end of the calendar year specifiecl or 3 C1 1979-81 C FAO tries to impose standard definitions and 350 01997-99 during the first quarter of the following year. reporting methods, but exact consistency 300 i . across countries and over time is not possible. 2 D s Data on agricultural employment in particular 200 should be used with caution. In many countries The data are from electronic files that the F-AO 150 much agricultural employment is informal and _ makes available to the World Bank. Data on unrecorded, including substantial work per- 100 arable land, irrigated land, and land under formed by women and children. 50 cereal production are published in the FAO's Fertilizer consumption measures the quantity L Production Yearbook. Low Lower Upper Highr of plant nutrients in the form of nitrogen, potas- income middie middle income- mncome income sium, and phosphorous compounds available for Source Tabie 3.2 direct application. Consumption is calculated as production plus imports minus exports. Tradi- tional nutrients-animal and plant manures- are not included. Because some chemical com- pounds used for fertilizers have other industrial applications, the corisumption data may over- state the quantity available for crops. To smooth annual fluctuations in agricultural activity, the indicators in the table have been averaged over three years. Q ~3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production production production yield productivity Index Index Index Agriculture value added kilograms per worker 1989-91 = 100 1989-91 =100 1989-91 =100 per hectare 1995 $ 1979-81 1998-2000 1979-81 1998-2000 1979-81 1998-2000 1979-81 1998-2000 1979-81 1998-2000 Afghanistan ..... .. 1.337 1,145 - Albania ..,.... 2,500 2.664 1,184 1,978 Algeria 77.5 126.2 67.6 130.9 55.0 124.5 656 846 1,357 1,962 Angola 101.9 148.5 90.0 144.1 83.8 135.6 526 646 ..121 Argentina 83.5 160.0 91.7 137.5 101.1 106.7 2.184 3.448 7,148 10.243 Armenia .. 97.9 .. 75.5 .. 60.5 .. 1.532 .. 5.477 Australia 79.9 165.4 91.3 140.8 85.6 112.2 1.321 2,034 20,354 33,765 Austria 92.8 102.3 92.2 105.3 94.5 105.6 4,131 5,646 11,197 28.523 Azerbaijan .. 47.4 .. 65.8 .. 75.5 .. 2,056 ..847 142 Bangladesh 80.0 117.4 79.2 119.8 81.3 137.7 1,938 2.927 217 296 Belarus .. 87.0 .. 61.9 .. 60.2 .. 1,942 .. 3,832 o Belgium' 84.9 141.4 88.5 114.5 88.8 112.7 4,861 7.594 21,868 55,874 0 Beamn 53.8 175.3 63.1 151.3 69.0 119.7 698 1,056 311 586 'O Bolivia 71.2 151.6 70.9 137.4 75.5 129.0 1.183 1.520 .. 1.035 CM Bosnia and Herzegovina ... .. .. . 3,490 .. 7,970 E) Botswana 86.4 75.6 87.2 94.2 87.5 96.8 203 196 630 688 a, Brazil 75.3 122.9 69.5 137.9 67.9 150.3 1,496 2,665 2,048 4,356 > Bulgaria 107.7 65.7 105.5 70.0 96.3 63.0 3.853 2,846 2,754 6.252 o Burkina Faso 59.3 143.4 62.7 135.5 59.9 138.5 575 868 134 180 Burundi 79.9 89.7 79.9 90.3 82.3 81.5 1.081 1,283 177 141 Cambodia 55.2 138.2 48.9 141.3 27.3 150.5 1.025 1.875 ..403 o Cameroon 86.5 130.7 79.9 129.6 61.1 118.7 849 1,551 834 1.104 Canada 77.6 129.4 79.7 128.9 88.3 131.7 2,173 3.035 14,161 36,597 Central African Republic 102.8 128.4 79.7 132.3 48.9 127.8 529 1,084 377 469 Chad 67.1 173.7 80.1 152.0 89.2 119.2 587 650 155 227 Chile 70.7 126.3 71.5 133.1 75.8 143.5 2,124 4,540 3,488 5,712 China 67.1 141.6 60.8 169.6 45.4 210.0 3,027 4,879 161 321 Hong Kong, China 133.6 59.3 99.8 49.5 194.3 44.6 1,712 Colombia 84.1 100.9 75.5 118.2 72.6 125.2 2,452 3,091 3.034 3,448 Congo, Dem. Rep. 73.0 89.1 72.2 92.0 77.7 103.2 807 785 241 252 Congo. Rep. 84.6 113.4 82.3 117.1 80.7 128.5 838 687 385 475 Costa Rica 70.7 146.8 73.1 141.3 77.2 121.5 2.498 3.543 3,139 5,140 C6te dIlvoire 73.8 132.3 70.8 130.5 74.7 120.9 867 1,136 1,074 1,136 Croatia .. 87.2 .. 69.9 .. 50.2 .. 4,444 .. 8,839 Cuba 64.1 55.0 90.1 59.4 96.0 66.1 2,458 2,148 Czech Republic .. 89.0 .. 81.9 .. 75.3 .. 4,092 .. 5,637 Denmark 65.2 94.6 83.2 106.6 95.0 117.7 4,040 6.120 19,350 54,090 Dominican Republic 96.5 90.6 85.2 103.5 68.8 125.3 3,024 3.827 2.018 2.769 Ecuador 78.2 126.3 77.4 139.6 73.0 151.6 1.633 2,064 1.206 1,773 Egypt, Arab Rep. 75.5 142.6 68.4 151.3 67.0 159.6 4.053 7.015 721 1,240 El Salvador 120.4 108.6 90.8 119.6 88.8 123.6 1.702 2,063 1,925 1,711 Eritrea .. 180.3 .. 139.4 .. 110.5 ..822 Estonia .. 66.6 ,. 43.0 .. 36.9 .. 1,553 .. 3,698 Ethiopia .. 121.6 .. 119.9 .. 116.2 .. 1,141 ..138 Finland 76.3 86.3 93.8 89.7 107.5 93.0 2,511 2,763 18.547 36,557 France 87.4 111.8 93.8 107.6 97.8 105.8 4.700 7.271 19,318 53,785 Gabon 76.3 118.2 79.0 114.0 86.5 118.3 1,718 1.664 1.814 1,882 Gambia. The 79.5 114.1 82.8 115.4 94.4 114.5 1.284 1,101 325 226 Georgia .. 60.8 .. 80.3 .. 88.2 .. 1.564 .. 1,960 Germany 90.1 114.2 91.4 94.8 98.7 86.4 4.166 6,436 9.059 29,553 Ghana 67.0 173.4 68.7 162.9 79.7 101.9 807 1.306 670 558 Greece 86.8 105.8 91.2 99.3 99.9 96.8 3,090 3,486 8,600 13,400 Guatemala 89.6 121.0 69.7 124.0 76.0 127.3 1.578 1.726 2.143 2,112 Guinea 89.7 143.6 96.3 143.9 116,4 142.3 958 1,312 ..292 Guinea-Bissau 64.8 123.8 68.3 123.3 78.4 121.1 711 1.283 221 302 Haiti 103.4 86.9 101.3 95.7 100.2 128.8 1.009 922 509 349 Honduras 90.4 116.6 88.2 111.9 80.8 130.0 1,170 1,176 696 979 0. Crop Food Livestock Cereal Agricultural production production production yield productivity Index Index Index Agriculture value added kilograms per worker 1989-91 =100 1989-91 =100 1989-91 =100 per hectare 1995 $ 1979-81 1998-2000 1979-81 1998-2000 1979-81 1998-2000 1979-91 1998-2000 1979-81 1998-2000 Hungary 93.3 79.3 90.7 74.3 94.1 69.6 4,519 4,507 3,390 5,016 India 70.9 123.3 68. 1 125.7 62.2 133.5 1,324 2,299 272 397 Indonesi'a 66.2 118.6 63.3 119.2 51.0 122.4 2,837 3.915 609 736 Iran, Islamic Rep. 57.3 150.0 61.1 150.0 68.0 146.1 1,108 2,030 2,197 3,756 Iraq 74.7 83.7 78.0 78.9 81.4 64.9 832 609 Ireland 93.9 110.1 83.3 111.3 83.3 111.9 4,733 6,883 Israel 99.8 103.2 85.0 112.2 78.4 118.7 1.840 1.701 Italy 106.1 105.1 101.4 105.0 93.0 105.5 3,548 5,033 11,090 24,827 Jamaica 98.6 123.2 86.0 120.9 73.9 119.5 1,667 1,197 829 1,346 Japan 107.9 88.3 94.0 92.5 85.1 94.2 5,252 5,971 17.378 30,086 143 Jordan 54.7 120.4 57.5 141.2 51.5 194.2 521 1,698 1,158 1,422 Kazakhstan .. 65.7 .. 61.0 .. 45.3 .. 975 .. 1,421 Kenya 74.5 108.7 67.5 105.3 60.1 105.2 1,364 1,434 262 225 Korea, Dem. Rep. ......... 3.694 2.987... Korea, Rep. 87.8 107.0 77.6 119.1 52.6 155.6 4,986 6,336 3,765 12,374 Kuwait 37.1 153.1 91.4 173.6 106.6 176.4 3.124 2,556 CD . Kyrgyz Republic .. 130.6 .. 115.9 .. 77.7 .. 2,577 .. 3,528 C Lao PDR 73.5 141.1 70.3 146.0 56.0 162.3 1,402 2,925 578578 Latvia .. 69.6 .. 44.3 .. 34.2 -. 1,981 .. 2,499 C Lebanon 52.0 137.7 59.2 143.1 100.5 162.9 1,307 2.428 .. 29,241 Lesotho 95.1 115.9 89.1 98.6 87.7 87.6 977 974 723 540 Liberia ......... 1,251 1,292 . Libya 76.3 133.0 78.7 152.9 68,4 159.6 430 761 .. Lithuania .. 74.5 63.6 .. 54.7 .. 2.156 .. 3,129 Macedonia, FYR .. 108.1 96.. 84.. 3,076 . .7 Madagascar 83.1 104,2 83.8_ 109.4 87.7 108.2 1,664 1,891 197 181 Malawi 85.7 148.8 93.2 152.7 78.2 111.9 1,161 1,514 109 140 Malaysia 75.3 111.8 55.6 135.4 41.0 152.0 2,828 2,860 3.939 6,638 Mali 54 .5 142.8 76.7 125.7 94.5 122.3 804 1,163 241 283 Mauritania 62.1 149.7 86.5 105.7 89.4 99.5 384 916 299 480 Mauritius 93.3 94.2 89.7 104.0 64.0 135.6 2,536 5,094 3,087 4,977 Mexico 86.5 121.5 83.8 128.6 83.5 136.0 2,164 2,604 1.482 1.772 Moldova .. 53.7 44.1 - 35.4 .. 2,439 .. 1.297 Mongolia 44.6 36.3 88.1 89.5 93.2 93.8 573 735 994 1.300 Morocco 54.8 95.3 55.9 100.7 59.8 108.4 811 780 1,146 1.785 Mozambique 109.6 143.5 100.9 131 0 85.8 102.'3 603 919 ..134 Myanmar 89.0 154.'2 88.2 150.4 89.1 148.7 2,521 3,043 Namibia 80.1 __110.4 107.2 97.0 115.6 95.5 377 285 919 1.468 Nepal _ _62.7 120.9 65.9 121.5 77.3 123.3 1,615 2,007 162 188 Netherlands 79.8 108.1 86.5 101.5 88.3 101.2 5,696 7,430 24,181 53,819 New Zealand 74.4 135.9 90.7 125.6 95.5 116.4 4.089 6,314 18.086 27,106 Nicaragua 124.1 134.5 117.8 140.9 197 136.0 1,475 1,694 1,543 1.887 Niger 90.1 151.4 97.9 141.7 109.7 125.4 440 379 222 214 Nigeria 51.4 155.5 57.2 152.3 84.3 126.0 1.265 1.206 414 672 Norway 94.5 84.8 93.8 95.9 96.2 100.5 3,634 4,002 17,013 33,305 Oman 60.4 113.8 62.5 113.8 61.6 104.0 982 2.204 Pakistan 65.6 125 6 66.4 144.4 59.5 152.3 1,608 2,261 394 630 Panama 97.1 96.5 85.6 107.3 71.3 125.4 1.524 2,217 2,122 2,632 Papua New Guinea 86.5 112.4 86.2 113.8 85.0 136.6 2,087 4,107 649 765 Paraguay 58.7 110.3 60.7 132.9 62.1 129.5 1.535 2,159 2,641 3.508 Peru 82.2 161.9 77.3 161.2 78.0 150.1 1,946 2,856 1,273 1.693 Philippines 88.2 107.8 _86.1 121.3 73.7 163.0 1,611 2.434 1,347 1.328 Poland 84.6 85.6 87.9 88.0 98.0 87.0 2,345 2,885 .. 1,864 Portugal 85.0 89.7 72.2 98.3 71.8 118.9 1,102 2,791 3.350 7.235 Puerto Rico 131.2 62.9 99.7 81.7 90.3 87.5 7.970 2.580 . Romania 114.1 90.5 113.0 92.5 110.0 89.3 2,854 2,543 .. 3,592 Russian Federation 66.0 61.8 .. 52.6 . 1,387 .. 2.249 Crop Food Livestock Cereal Agricultural production production production yield productivity Index Index Index Agriculture value addedl kilograms per worker 1989-91 100 1989-91 =100 1989-91 -100 per hectare 1995 $ 1979-81 1998-2000 1979-81 1998-2000 1979-81 1998-2000 1979-81 1998-2000 1979-81 1998-2000 Rwanda 84.3 88.2 85.3 91.6 81.0 108.8 1,134 930 371 235 Saudi Arabia 27.2 91.7 26.7 86.8 32.7 143.0 820 3.754 2.167 Senegal 77.2 102.9 74.0 114.2 65.1 138.0 690 721 336 304 Sierra Leone 80.3 81.7 84.5 87.0 84.1 112.4 1,249 1,116 367 341 Singapore 595.0 48.2 154.3 40.8 173.7 39.5 ... 16,676 49,905 Slovak Republic ... .. .. . 4.225 Slovenia .. 91.3 .. 100.0 .. 105.0 .. 5.378 .. 31.539 Somalia ......... 474 513 South Africa 95.0 105.5 92.6 103.4 89.7 96.5 2,105 2,332 2.899 3.866 144 Spain 83.0 108.7 82.0 111.6 84.2 124.2 1,986 3,208 10.703 21,824 Sri Lanka 99.3 114.3 98.3 115.9 93.2 132.6 2.462 3,180 648 753 Sudan 130.2 162.9 105.1 158.4 89.3 149.9 645 514 co Swaziland 72.5 90.4 80.2 91.0 96.5 83.4 1,345 1,836 1,671 1.731 Sweden 92.1 93.9 100.1 100.8 103.8 103.9 3,595 4,570 18,020 34.556 Switzerland 95.5 98.7 95.8 97.0 98.8 94.3 4.883 6,323 Syrian Arab Republic 100.4 159.6 94.2 151.1 72.2 132.5 1,156 1.333 2,206 2.890 o Tajikistan .. 56.7 .. 53.8 .. 37.2 .. 1,243 .. 1,236 a) > Tanzania 81.8 100.2 76.7 106.0 69.3 119.5 1,063 1,295 ..189 a) Thailand 79.2 113.4 80.3 113.8 64.9 127.3 1,911 2.478 630 909 ~0 Trinidad and Tobago 119.9 101.8 101.9 107.9 84.3 100.7 3,167 2,933 3.536 2.484 O Tunisia 68.5 116.7 68.5 127.5 60.3 151.5 828 1,152 1,743 3,083 N Turkey 76.6 114.8 75.8 112.5 80.4 108.1 1.869 2,196 1.860 1.886 Turkmenistan .. 78.9 .. 134.0 .. 136.5 .. 2.346 .. 1.229 Uganda 67.5 119,5 70.4 116.6 84.8 120.6 1,555 1,377 ..353 Ukraine .. 57.2 .. 47.9 .. 45.7 .. 2,027 .. 1,345 United Arab Emirates 38.9 276.2 48.8 261.6 45.3 174.1 2,224 865 United Kingdom 80.1 102.9 92.0 98.9 98.1 98.1 4,792 6,981 20,326 34.938 United States 98.6 121.9 94.5 122.9 89.0 120.0 4,151 5,794- Uruguay 86.8 151.4 87.1 137.3 85.9 121.3 1,644 3,506 5,367 8,652 Uzbekistan .. 87.9 .. 116.2 .. 116.4 .. 2,390 -. 1.035 Venezuela, RB 76.3 106.0 80.2 116.8 84.9 117.8 1,904 3.134 3,935 5,143 Vietnam 66.7 159.0 63.8 152.4 52.9 164.8 2,049 3,955 ..240 West Bank and Gaza . .. .. .. Yemen, Rep. 82.3 129.0 75.0 130.0 68.9 135.8 1.038 1.050 ..366 Yugoslavia, Fed. Rep. 96.3 71.1 94.3 89.4 94.2 101.8 3,601 2,953 Zambia 64.5 93.8 72.9 100.8 86.2 113.2 1.676 1,391 196 214 Zimbabwe 77.8 121.1 83.3 105.2 89.7 108.7 1,359 1,184 307 366 Low Income 71.6 124.4 70.7 126.5 68.4 131.2 1,083 1.297 Middle Income 74.5 128.2 72.0 141.4 69.3 153.7 1,789 2,343 Lower middle income 72.1 132.3 68.2 150.5 59.8 176.0 1,741 2,083 Upper middle income 80.7 117.3 79.5 122.7 82.3 122.8 1,874 2.718 Low & middle Income 73.5 126.8 71.5 136.3 69.1 148.0 1,418 1,813 East Asia & Pacific 69.0 135.4 63.8 156.4 48.0 197.6 2.116 2.945 Europe & Central Asia ......... 2,854 2,355 Latin America & Carib. 80.3 124.3 78.3 131.2 79.8 131.9 1,802 2,346 Middle East & N. Africa 66.1 131.3 64.8 134.0 64.1 136.8 925 1.354 South Asia 71.9 121.3 69.6 125.7 64.0 136.2 1,510 2,280 265 Sub-Saharan Africa 75.4 128.5 78.3 124.7 84.1 114.2 895 1,120 418 High Income 93.5 115.7 92.1 112.9 91.1 109.9 3,170 3.881 Europe EMU 91.0 108.6 91.4 103.4 93.8 101.0 4,035 5,646 a. Includes Luxembourg. 3.3 { About the data Definitions The agricultural production indexes in the table Figure 3.3 * Crop production Index shows agricultural are prepared by the Food and Agriculture production for each period relative to the base Organization (FAO). The FAO obtains data from Food production has outpaced population period 1989-91. It includes all crops except official and semiofficial reports of crop yields, growth in low- and middle-income fodder crops. The regional and income group area under production, and livestock numbers. economies... aggregates for the FAO's production indexes If data are not available, the FAO makes 300 are calculated from the underlying values in estimates. The indexes are calculated using the 250 international dollars, normalized to the base Laspeyres formula: production quantities of each 8 period 1989-91. The data in this table are commodity are weighted by average international A 200 three-year averages. Missing observations commodity prices in the base period and _ iso have not been estimated or imputed. * Food summed for each year. Because the FAO's production Index covers food crops that are indexes are based on the concept of agriculture considered edible and that contain nutrients. as a single enterprise, estimates of the amounts 50 Coffee and tea are excluded because, although retained for seed and feed are subtracted from o edible, they have no nutritive value. * Livestock the production data to avoid double counting. 1970 1975 1980 1985 1990 1995 2000 production Index includes meat and milk from 145 The resulting aggregate represents production all sources, dairy products such as cheese to available for any use except as seed and feed. and eggs, honey, raw silk, wool, and hides 0 ...as well as in high-income economies... 0 The FAO's indexes may differ from other sources and skins. * Cereal yield, measured in kilo- because of differences in coverage, weights, 180 grams per hectare of harvested land, in- o concepts, time periods, calculation methods, 160 cludes wheat, rice, maize, barley, oats, rye, E and use of international prices. millet, sorghum, buckwheat, and mixed C To ease cross-country comparisons, the FAO i 120 grains. Production data on cereals refer to _ N 100 uses international commodity prices to value s crops harvested for dry grain only. Cereal 3 production. These prices, expressed in i crops harvested for hay or harvested green CD international dollars (equivalent in purchasing 40 for food, feed, or silage, and those used for power to the U.S. dollar), are derived using a 20 grazing, are excluded. * Agricultural produc- Cl Geary-Khamis formula applied to agricultural 0 tivity refers to the ratio of agricultural value outputs (see Inter-Secretariat Working Group on 1970 1975 1980 1985 1990 1995 2000 added, measured in constant 1995 U.S. 01 National Accounts 1993, sections 16.93-96). dollars, to the number of workers in agricul- This method assigns a single price to each ture. commdityso tat,for xampe, ne mtricton...;but food production lags behind populationr- commodity so that, for example, one metric ton growth In Sub-Saharan Africa r--------~ -- of wheat has the same price regardless of where it was produced. The use of international prices Data sources eliminates fluctuations in the value of output 200 The agricultural production indexes are pre- i due to transitory movements of nominal § paredbytheFAOandpublishedannuallyinitst exchange rates unrelated to the purchasing j 150 Production Yearbook. The FAO makes these power of the domestic currency. 1 data and the data on cereal yield and agri- Data on cereal yield may be affected by a cultural employment available to the World variety of reporting and timing differences. The 50 Bank in electronic files that may contain more FAO allocates production data to the calendar recent information than the published ver- year in which the bulk of the harvest took place. 0 1 1 1 1 2000 sions. For sources of agricultural value added i But most of a crop harvested near the end of a see table 4.2. year will be used in the following year. Cereal - Food production Population crops harvested for hay or harvested green for Source: World Bank and FAO food, feed, or silage, and those used for grazing, are generally excluded. But millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use. Agricultural productivity is measured by value added per unit of input. (For further discussion of the calculation of value added in national accounts see About the data for tables 4.1 and 4.2.) Agricultural value added includes that from forestry and fishing. Thus interpretations of land productivity should be made with caution. To smooth annual fluctuations in agricultural activity, the indicators in the table have been averaged over three years. 3.4 Deforestation and biodiversity Forest area Average Mammals Birds Higher plants' Nationally afnnuatl protected defrestatIon areas % of % of thousand total Threatened Threatened Threatenedl thousand total sq. km land area sq. km % Species species Species species Species species sq. km land area 2000 2000r 1990-2000 199G-2000 1996' 2000k 1996e 2000k 1997' 19971 1999k 1999k Afghanistan 14 2.1 . .. 123 13 235 11 4,000 4 2.2 0.3 Albania 10 36.2 78 0.8 68 3 230 3 3,031 79 0.8 3.1 Algeria 21 0.9 -266 -1.3 92 13 192 6 3.164 141 58.9 2.5_ Angola 698 56.0 1,242 0.2 276 18 765 15 5,185 30 81.8 6.6 Argentina 346 12.7 2,851 0.8 320 32 897 39 9,372 247 49.1 1.8 Armenia 4 12.4 -42 -1.3 .. 7 .. 4 .. 31 2.1 7.6 Australia 1.581 20.6 . .. 252 63 649 35 15,638 2,245 542.5 7.1 Austria 39 47.0 -77 -0.2 83 9 213 3 3,100 23 24.5 29.6 Azerbaijan 11 12.6 -130 -1.3 .. 13 .. 8 . 28 4.8 5.5 146 Bangladesh 13 10.2 .165 -1.3 109 21 295 23 5,000 24 1.0 0.8 Belarus 94 45.3 -2,562 -3.2 .. 5 221 3 .. 1 13.0 6.3 on Belgium . ... .. 58 11 180 2 1,550 2 0.0 0.0 15 Benin 27 24.0 699 2.3 188 7 307 2 2,201 4 7.8 7.0 C ~ Bolivia 531 48.9 1,611 0.3 316 23 1,274 27 17,367 227 156.0 14.4 Bosnia and Herzegovina 23 44.6 .. . . 10 .. 3 . 64 0.3 0.5 E Botswana 124 21.9 1,184 0.9 164 5 386 7 2.151 7 105.0 18.5 OL Brazil 5.325 63.0 22,264 0.4 394 79 1,492 113 56,215 1,358 375.1 4.4 > Bulgaria 37 33.4 -204 -0.6 81 15 240 10 3.572 106 5.0 4.5 o Burkina Faso 71 25.9 152 0.2 147 7 335 2 1,100 0 28.6 10.4 0 i Cambodia 93 52.9 561 0.6 123 21 307 19 .. 5 28.6 16.2 o Cameroon 239 51.3 2,218 0.9 297 37 690 15 8,260 89 21.0 4.5 0 CJ Canada 2,446 26.5 . .. 193 14 426 8 3,270 278 907.0 9.8 Central African Republic 229 36.8 300 0.1 209 12 537 3 3,602 1 51.1 8.2 Chad 127 10.1 817 0.6 134 17 370 5 1.600 12 114.9 9.1 Chile 155 20.7 203 0.1 91 21 296 21 5.284 329 141.4 18.9 China 1.635 17.5 -18.063 -1.2 394 76 1,100 73 32,200 312 598.4 6.4 Hong Kong. China . ... .. 24 1 76 11 1,984 9 0.5 Colombia 496 47.8 1.905 0.4 359 36 1,695 77 51.220 712 93.6 9.0 Congo, Dem. Rep. 1.352 59.6 5,324 0.4 415 40 929 28 11,007 78 101.9 4.5 Congo, Rep. 221 64.6 175 0.1 200 12 449 4 6,000 3 15.4 4.5 Costa Rica 20 38.5 158 0.8 205 14 600 13 12,119 527 7.2 14.2 C6te dIlvoire 71 22.4 2,649 3.1 230 17 535 12 3,660 94 19.9 6.2 Croatia 18 31.9 -20 -0.1 .. 9 224 4 .. 6 4.2 7.5 Cuba 23 21.4 -277 -1.3 31 11 137 18 6,522 888 19.1 17.4 Czech Republic 26 34.1 -5 0.0 .. 8 199 2 .. 81 12.5 16.1 Denmark 5 10.7 -10 -0.2 43 5 196 1 1,450 2 13.8 32.5 Dominican Republic 14 28.4 . .. 20 5 136 15 5,657 136 15.2 31.5 Ecuador 106 38.1 1,372 1.2 302 31 1,388 62 19,362 824 120.8 43.6 Egypt. Arab Rep. 1 0.1 -20 -3.4 98 12 153 7 2,076 82 7.9 0.8 El Salvador 1 5.8 72 4.6 135 2 251 0 2,911 42 0.1 0.3 Eritrea 16 15.7 54 0.3 112 12 319 7 .. 0 5.0 5.0 Estonia 21 48.7 -125 -0.6 65 5 213 3 .. 2 5.0 11.8 Ethiopia 46 4.6 403 0.8 255 34 626 16 6.603 163 55.2 5.5 Finland 219 72.0 -80 0.0 60 6 248 3 1.102 6 18.7 6.1 France 153 27.9 .616 -0.4 93 18 269 5 4,630 195 74.4 13.5 Gabon 218 84.7 101 0.0 190 15 466 6 6,651 91 7.2 2.8 Gambia. The 5 48.1 -45 -1.0 108 3 280 2 974 1 0.2 2.3 Georgia 30 42.9 .. . . 14 .. 3 . 29 2.0 2.8 Germany 107 30.1 . .. 76 12 239 5 2,682 14 0.0 0.0 Ghana 63 27.8 1,200 1.7 222 13 529 8 3,725 103 11.0 4.9 Greece 36 27.9 -300 -0.9 95 14 251 7 4,992 571 4.7 3.6 Guatemala 29 26.3 537 1.7 250 6 458 6 8,681 355 18.3 16.8 Guinea 69 28.2 347 0.5 190 11 409 10 3,000 39 1.6 0.7 Guinea-Bissau 22 77.8 216 0.9 108 2 243 0 1,000 0 0.0 0.0 Haiti 1 3.2 70 5.7 .. 4 75 14 5,242 100 0.1 0.4 Honduras 54 48.1 590 1.0 173 9 422 5 5,680 96 6.7 6.0 Forest area Average Mammals Birds Higher plants, Nationally annual protected deforestation areas % of % of thousand total Threatened Threatened Threatened thousand total sq. kin land area sq. km % Species species Species species Species species sq. kin land urea 2000 20001 1990-2000 1990-2000 i996, 2000k 1996, 2000k 1997k 1997w i.9995 1999k Hungary 18 19.9 -72 -0.4 72 9 205 8 2,214 30 6.5 7. C) India 641 21.6 -381 -0.1 316_ 86 923 70 16,000 1.236 143.1 4.8 Indonesia 1,050 58.0 13,124 1.2 436 140 1,519 113 29,375 264 192.5 10.6 Iran, Islamic Rep. 73 4.5 . .. 140 23 323 13 8.000 --2 83.0 5.1 Iraq 8 1.8 . .. 81 10 172 11 .. 2 0.0 0.( Ireland 7 9.6 -170 -3.0 25 5 142 1 950 1 0.7 0.9 Israel 1 6.4 -50 -4.9 92 14 180 12 _2,317 32 3.3 15.8 Italy 100 34.0 -295 -0.3 90 14 234 5 5,599 311 22.0 7. 5 Jamaica 3 30.0 54 1.5 24 5 113 12 3,308 744 0.0 0.1 Japan 241 66.1 -34- 0.0 132 37 250 34 5,565 707 25.6 6.8 147 Jordan 1 1.0 71 8 141 8 2,100 9 3.0 3.4 Kazakhstan 121 4.5 -2,390 -2.2 18 .. 15 .. 71 73.4 2.7 Kenya 171 30.0 931 0.5 359 51 844 24 6,506 240 35.1 6.2 Korea, Dem. Rep. 82 68.2 13 115 19 2,898 4 3.2 2.13 Korea, Rep. 63 63.3 49 0.1 49 13 112 25 2,898 66 6.8 6.9 - ------ -- ....~~ Kuwait 0 0.3 -2 -5.2 21 1 20 7 234 0 0.3 1.5 (D Kyrgyz Repubi 10 5.2 -228 -2.6 7 . 4.. 34 6.9 3.13 ....... -- ----- 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~i Lao PDR 126 54.4 527 0.4 172 27 487 19 . 2 0.0 0.0 ' 3o Latvia 29 47.1 -127 -0.4 83 5 217 3 1,153 0 8.1 13.0 C Lebanon 0 3.5 1 0.3 54 6 154 7 3,000 5 0.0 0.5 --------- ------ --- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 Lesotho 0 0.5 --. 33 3 58 7 1,591 21 0.1 0.2 Liberia 35 36.1 760 2.0 193 16 372 11 2.200 25 1.3 1.3 Libya 4 0.2 -47 -1.4 76 9 91 - 1. 1,825 57 1.7 0.1 Lithuania 20 30.9 -48 -0.2 70 22 321 76 1,847 332 7.5 11.5 Macedonia, FYR 9 35.6 11 .. 3 0 1.8 7.1 Madagascar 117 20.2 1,174 0.9 105 50 202 27 9,505 306 11.2 1.9 Malawi 26 27.6 707 2.4 195 8 521 11 3,765 61 10.6 11.3 Malaysi'a 193 58.7 2,377 1.2 286 47 501 37 15,500 490 15.1 4.6 Mali 132 10.8 993 0.7 137 13 397 4 1,741 15 45.3 3.7 Mauritania 3 0.3 98 2.7 61 10 273 2 1,100 3 17.5 1.7 M-a uritius 0 7.9 1 0.6- 4 4 27 9 750 294 0.2 7.7 Mexi'co 552 28.9 6,306 1.1 450 69 769 39 26,071 1,593 66.4 3.5 Moldova 3 9.9 -7 -0.2 68 3 177 5 5 0.5 1.4 Mongolia 106 6.8 600 0.5- 134 12 '390 16 2,272 0 179.9 11.5 Morocco 30 6.8 12 0.0 105 16 210 9 3,675 186 3.2 0.7 Mozambique 306 39.0 637 0.2 179 15 498 16 5,692. 89 47.8 6.1 Myanmar 344 52.3 5,169 1.4 251 36 867 35 7,000 32 1.7 0.3 Namibia 80 9.8 734 0.9 154 14 469 11 3.174 75 106.2 12.9 Nepal 39 - 27.3 783 1.8 167 27 611 26 6,973 20 11.1 7.8 Netherlands4 11 1 -10 -0.3 55 11 191 4 1,221 1 2.3 6.8 New Zealand 79 29.7 -390 -0.5 10 8 150 62 2,382 211 63.3 23.6 Nicaragua 33 27.0 1.172 3.0 200 6 482 5 7,590 98 9.1 7.5 Niger 13 1.0 617 3.7 131 11 299 3 1,170 0 96.9 7.7 Nigeria 135 14.8 3,984 2.6 274_ 25 681 9 4,715 37 30.2 3.3 Norway 89 28.9 -310 -0.4 54 10 243 2 1,715 12 20.9 6.8 Oman 0 0.0 56 9 107 10 1,204 30 34.3 16.1 Pakistan 25 32 304 1.1 151 18 375 17 4,950 14 37.3 4.8 Panama 29 38.6 519 1.6 218 20_ 732 16 9.915 1,302 14.2 19.1 Papua New Guinea 306 67.6 1,129 0,4 214 58 644 32 11,544 92 0.1 0.0 Paraguay 234 58.8 1,230 0.5 305 9 556 26 7,851 129 14.0 3.5 Peru 652 50.9 2,688 0.4 344 47 1,538 73 18,245 906 34.6 2.7 Philippi'nes S8 19.4 887 1.4 153 50 395 67 8,931 360 14.5 4.9 Poland 93 30.6 -110 -0.1 84 iS 227 4 2.450 27 29.3 9.6 Portugal 37 40.1 --570 --- ---1.7 63 17 207 7 SOSO0 269 6.0 6.6 Puerto Rico 2 25.8 5 02 16 2 105 8 2.493 223 0.2 2.1 Romania 64 28.0 -147 -0.2 84 17 247 8 3,400 99 10.9 4.7 Russian Federation 8,514 50.4 -1,353 0.0 269 42 628 38 214 529.1 3I.1 3.4 Forest area Average Mammals Birds Higher plants' Nationally annual protected deforestation areas hkof % of thousanc total Threatened Threatened Threatenedi thousand total sq. km land area sq. km % Species species Species species Species species sq. km land area 2000 2000' 1990-2000 1990-2000 1996k 2000' 19961 2000k 1997' 1997' 1999, 1999k Rwanda 3 12.4 150 3.9 151 8 513 9 2,288 0 3.6 14.7 Saudi Arabia 15 0.7 . .. 77 7 155 15 2,028 7 49.7 2.3 Senegal 62 32.2 450 0.7 155 11 384 4 2,086 31 21.8 11.3 Sierra Leone 11 14.7 361 2.9 147 11 466 10 2,090 29 0.8 1.1 Singapore 0 3.3 . .. 45 3 118 7 2,168 29 0.0 4.8 Slovak Republic 20 42.5 -69 -0.3 .. 9 209 4 .. 65 10.8 22.6 Slovenia 11 55.0 -22 -0.2 69 9 207 1 -. 13 1.2 6.0 Somalia 75 12.0 769 1.0 171 19 422 10 3.028 103 1.8 0.3 South Africa 89 7.3 80 0.1 247 41 596 28 23,420 2,215 66.2 5.4 148 Spain 144 28.8 -860 -0.6 82 24 278 7 5,050 985 42.4 8.5 Sri Lanka 19 30.0 348 1.6 88 20 250 14 3,314 455 8.7 13.5 Sudan 616 25.9 9,589 1.4 267 24 680 6 3.137 10 86.4 3.6 a Swaziland 5 30.3 -58 -1.2 47 4 364 5 2,715 42 0.4 2.0 Sweden 271 65.9 -6 0.0 60 8 249 2 1.750 13 36.4 8.9 a: Switzerland 12 30.3 -43 -0.4 75 6 193 2 3,030 30 10.6 26.9 g Syrian Arab Republic 5 2.5 . .. 63 4 204 8 3,000 8 0.0 0.0 O Tajikistan 4 2.8 -20 -0.5 .. 9 .. 7 . 50 5.9 4.2 a) > Tanzania 388 43.9 913 0.2 316 43 822 33 10,008 436 138.2 15.6 a) o Thailand 148 28.9 1,124 0.7 265 34 616 37 11,625 385 70.8 13.9 o Togo 5 9.4 209 3.4 196 9 391 0 2,201 4 4.3 7.9 3 Trinidad and Tobago 3 50.5 22 0.8 100 1 260 1 2,259 21 0.3 6.0 o Tunisia 5 3.3 -11 -0.2 78 11 173 5 2,196 24 0.4 0.3 CN Turkey 102 13.3 -220 -0.2 116 17 302 11 8,650 1.876 9.9 1.3 Turkmenistan 38 8.0 .. . . 13 .. 6 . 17 19.8 4.2 Uganda 42 21.3 913 2.0 338 19 830 13 5,406 15 19.1 9.6 Ukraine 96 16.5 -310 -0.3 .. 17 263 8 .. 52 9.4 1.6 United Arab Emirates 3 3.8 -78 -2.8 25 3 67 8 .. 0 0.0 0.0 United Kingdom 26 10.7 -200 -0.8 50 12 230 2 1,623 18 50.0 20.7 United States 2,260 24.7 -3,880 -0.2 428 37 650 55 19.473 4,669 1,231.2 13.4 Uruguay 13 7.4 -501 -5.0 81 6 237 11 2.278 15 0.5 0.3 Uzbekistan 20 4.8 -46 -0.2 .. 11 .. 9 . 41 8.2 2.0 Venezuela, RB 495 56.1 2,175 0.4 305 25 1,181 24 21,073 426 322.5 36.6 Vietnam 98 30.2 -516 -0.5 213 37 535 35 10,500 341 10.0 3.1 West Bank and Gaza I. . . . . 1. 1 . Yemen, Rep. 4 0.9 92 1.8 66 4 143 12 -. 149 0.0 0.0 Yugoslavia, Fed. Rep. 29 .. 14 0.0 -- 11 .. 5 5.351 155 3.4 3.3 Zambia 312 42.0 8,509 2.4 229 12 605 11 4,747 12 63.7 8.6 Zimbabwe 190 49.2 3,199 1.5 270 12 532 10 4,440 100 30.7 7.9 Low income 8,802 27.1 71,466.0 0.8 1,852.8 5.7 Middle Income 21,828 32.7 26,930.0 0.1 3,461.3 5.2 Lower middle income 13,881 31.8 -10.206.0 -0.1 2,119.4 4.9 Upper middle income 7.947 34.5 37,136.0 0.5 1,341.9 5.8 Low & middle Income 30,630 30.9 98,396.0 0.3 5,314.1 5.4 East Asia & Pacific 4,341 27.2 7,048.0 0.2 1,122.2 7.0 Europe & Central Asia 9.464 39.7 -8.143.0 -0. 1 789.9 3.3 Latin America & Carib. 9,440 47.1 45,878.0 0.5 1,477.5 7.4 Middle East & N. Africa 168 1.5 -239.0 -0. 1 242.4 2.2 South Asia 782 16.3 889.0 0.1 213.3 4.5 Sub-Saharan Africa 6,436 27.3 52,963.0 0.8 1,468.8 6.2 High Income 7,972 26.1 -8.011.0 -0. 1 3,123.6 10.2 Europe EMU 927 37.0 -2.988.0 -0.3 198.3 7.8 a. Flowering plants only. b. Data may refer to earlier years. They are the most recent reported by the World Conservation monitoring Center in 2000. 3.4 t About the data Definitions The estimates of forest area are from the Food significance (not materially affected by * Forest area is land under natural or planted and Agriculture Organization's (FAO) State of the human activity). stands of trees, whether productive or not. World's Forests 2001, which provides informa- * Natural monuments and natural * Average annual deforestation refers to the tion on forest cover as of 2000 and a revised landscapes with unique aspects. permanent conversion of natural forest area estimate of forest cover in 1990. The current * Managed nature reserves and wildlife to other uses, including shifting cultivation, survey is the latest global forest assessment sanctuaries. permanent agriculture, ranching, settlements, and the first to use a uniform global definition * Protected landscapes and seascapes and infrastructure development. Deforested of forest. According to this assessment, the glo- (which may include cultural landscapes). areas do not include areas logged but in- bal rate of net deforestation has slowed to 9 Designating land as a protected area does tended for regeneration or areas degraded million hectares a year, a rate 20 percent lower not necessarily mean that protection is in by fuelwood gathering, acid precipitation, or than that previously reported. force. For small countries that may only have forest fires. Negative numbers indicate an No breakdown of forestcover between natural protected areas smaller than 1,000 hectares, increase in forest area. * Mammals exclude forest and plantation is shown in the table this size limit in the definition will result in an whales and porpoises. * Birds are listed for because of space limitations. (This breakdown underestimate of the extent and number of countries included within their breeding or is provided by the FAO only for developing protected areas. wintering ranges. * Higher plants refer to 149 countries.) For this reason the deforestation Threatened species are defined according to native vascular plant species. * Threatened data in the table may underestimate the rate the IUCN's classification categories: endangered species are the number of species classi- ° at which natural forest is disappearing in (in danger of extinction and unlikely to survive if fied by the IUCN as endangered, vulnerable, some countries. causal factors continue operating), vulnerable rare, indeterminate, out of danger, or insuffi- E Deforestation is a major cause of loss of (likely to move into the endangered category in ciently known. * Nationally protected areas biodiversity, and habitat conservation is vital for the near future if causal factors continue are totally or partially protected areas of at Cr stemming this loss. Conservation efforts operating), rare (not endangered or vulnerable least 1,000 hectares that are designated _D traditionally have focused on protected areas, but at risk), indeterminate (known to be as national parks, natural monuments, na- which have grown substantially in recent endangered, vulnerable, or rare but not enough ture reserves or wildlife sanctuaries, pro- rD decades. Measures of species richness are one information is available to say which), out of tected landscapes and seascapes, or scien- of the most straightforward ways to indicate the danger (formerly included in one of the above tific reserves with limited public access. The importance of an area for biodiversity. The categories but now considered relatively data do not include sites protected under lo- number of small plants and animals is usually secure because appropriate conservation cal or provincial law. Total land area is used to estimated by sampling of plots. It is also measures are in effect), and insufficiently calculate the percentage of total area protected important to know which aspects are under the known (suspected but not definitely known to (see table 3.1). most immediate threat. This, however, requires belong to one of the above categories). _ a large amount of data and time-consuming Figures on species are not necessarily r analysis. For this reason global analyses of comparable across countries because taxonomic [Data sources the status of threatened species have been concepts and coverage vary. And while the The forestry data are from the FAO's State of | carried out for few groups of organisms. Only number of birds and mammals is fairly well the World's Forests 2001. The data on spe- i for birds has the status of all species been known, it is difficult to make an accurate count cies are from the WCMC's Biodiversity Data I assessed. An estimated 45 percent of of plants. Although the data in the table should Sourcebook (1994) and the IUCN's 2000 mammal species remain to be assessed. For be interpreted with caution, especially for IUCN Red List of Threatened Animals and plants the World Conservation Union's (IUCN) numbers of threatened species (where our 1997 IUCN Red List of Threatened Plants. 1997 IUCN Red List of Threatened Plants knowledge is very incomplete), they do identify The data on protected areas are from the ! provides the first-ever comprehensive listing countries that are major sources of global WCMC's Protected Areas Data Unit. of threatened species on a global scale, the biodiversity and show national commitments to result of more than 20 years' work by habitat protection. botanists from around the world. Nearly 34,000 plant species, 12.5 percent of the total, are threatened with extinction. The table shows information on protected areas, numbers of certain species, and numbers of those species under threat. The World Conservation Monitoring Centre (WCMC) compiles these data from a variety of sources. Because of differences in definitions and reporting practices, cross-country comparability is limited. Compounding these problems, available data cover different periods. Nationally protected areas are areas of at least 1,000 hectares that fall into one of five management categories defined by the WCMC: * Scientific reserves and strict nature reserves with limited public access. * National parks of national or international $~ 3.5 I Freshwater Freshwater Annual freshwater withdrawals Access to Improved resources water source Flows from Infernal ot her Total flows countries resources % of urban % of rural billion billion per cap ta population population corn, corn. corn, billion % of total % for % for % for with access with access 2000 2000 2000 cor.0. resources"0 agriculture'0 industry, domestico 1990 2000 1990 2000 Afghanistan 55 10.0 2,448 26.1 40.2 990d 0 Id1 19 *, 11 Albania 27 15.7 12.489 1.4 3.3 71 0 29 . Algeria 14 0.4 470 4.5 31.5 600d 15 0 250 . 98 .. 88 Angola 184 .. 14,009 0.5 0.3 760 1001 14 d 34 .. 40 Argentina 360 623.0 26,545 28.6 2.9 75 9 16 .. 85 .. 30 Armenia 9 1.5 2,787 2.9 27.6 66 4 30 . Australia 352 0.0 18,351 15.1 4.3 33 2 65 100 100 100 100 Austria 55 29.0 10,357 2.2 2.7 9 60 31 100 100 100 100 Azerbaijan 8 21.0 3,615 16.5 56.8 70 25 5 . 150 Bangladesh 105 1,105.6 9,238 14.6 1.2 86 2 12 98 99 89 97 Belarus 37 20.8 5.797 2.7 4.7 35 43 22 .. 100 .. 100 (a o Belgium 12 4.0 1,561 0.0 56.4... .. . Benin 10 15.5 4,114 0.2 0.6 67 0 100 230 d 74 ..55 'o Bolivia 316 7.2 38,806 1.4 0.4 48 20 32 92 93 52 55 c: Bosnia and Herzegovina 36 2.0 9,429 .. . ... . Botswana 3 11.8 9,176 0.1 0.7 480 200 320 100 100 91 o Brazil 5,418 1,900.0 42,944 54.9 0.7 61 18 21 93 95 50 54 a) >o Bulgaria 18 0.2 2.228 0.0 76.4 ... . ... .98 a) Burundi 4 .. 529 0.1 2.6 640 00 360 94 96 63 Cambodia 121 355.6 39,613 0.5 0.1 94 1 5 .. 53 ..25 CY o Cameroon 268 0.0 18,016 0.4 0.1 35 0 19 0 460d 76 82 36 42 Canada 2,740 52.0 90.797 45.1 1.6 9 80 11 100 100 99 99 Central African Republic 141 .. 37,934 0.1 0.0 73 0 6 d 210d 80 80 46 46 Chad 15 28.0 5.589 0.2 0.4 820 20~ 160 . 31 .. 26 Chile 928 0.0 61.007 21.4 2.2 84 11 5 98 99 48 66 China 2,812 17.2 2,241 525.5 18.6 77 18 5 99 94 60 66 Hong Kong, China . . . . . .. . . Colombia 2,133 0.0 50,426 8.9 0.4 37 4 59 95 98 68 73 Congo. Dem. Rep. 935 313.0 24,496 0.4 0.0 23 d 16 0 61 0 ~89 .. 26 Congo. Rep. 222 610.0 275,646 0.0 0.0 11 0 27 d 62 .. 71 .. 17 Costa Rica 112 .. 29.494 5.8 5.1 80 7 13 .. 98 .. 98 CMe dIlvoire 77 .. 4,790 0.7 0.9 67 0 110 220 89 90 49 65 Croatia 38 33.7 16.301 0.1 1.1 .. 50 50o . Cuba 38 0.0 3.396 5.2 13.7 51 0 49 .. 99 ..82 Czech Republic 15 1.0 1,557 2.5 15.8 2 57 41 . Denmark 6 .. 1,124 0.9 14.8 43 27 30 .. 100 .. 100 Dominican Republic 21 .. 2.508 8.3 39.7 89 1 11 83 83 70 70 Ecuador 442 0.0 34,952 17.0 3.8 82 6 12 .. 81 .. 51 Egypt, Arab Rep. 2 66.7 1.071 55.1 80.4 860 80d 60 97 96 91 94 El Salvador 18 .. 2,820 0.7 4.1 46 20 34 .. 88 47 61 Eritrea 3 6.0 2,148 . .. ... . .. 63 .. 42 Estonia 13 0.1 9,350 0.2 1.3 5 39 56 . Ethiopia 110 0.0 1,711 2.2 2.0 860 30 110 77 77 13 13 Finland 107 3.0 21,248 2.4 2.2 0 82 17 100 100 100 100 France 180 11.0 3.243 40.6 21.3 12 73 15 . Gabon 164 0.0 133.333 0.1 0.0 6 0 22 0 72 073 55 Gambia,The 3 5.0 6.140 0.0 0.4 910 20 70 , 80 .. 53 Georgia 58 8.4 13,236 3.5 5.2 59 20 21 . Germany 107 71.0 2,167 46.3 26.0 0 86 14 . Ghana 30 22.9 2.756 0.3 0.6 52 0 130d 3501 83 87 43 49 Greece 54 15.0 6,534 7.0 10.2 81 3 16 ... Guatemala 134 0.0 11.805 1.2 0.9 74 17 9 88 97 72 88 Guinea 226 0.0 30,479 0.7 0.3 87 d 30 100 72 72 36 36 Guinea-Bissau 16 11.0 22,519 0.0 0.1 360 4 0 600 d 29 . 55 Haiti 12 .. 1.520 1.0 8.1 94 1 5 55 49 42 45 Honduras 96 0.0 14,976 1.S 1.6 91 5 4 90 97 79 82 Freshwater Annual freshwater withdrawals AcstoImproved resources water source Flows from Internal other Total flows countries resources % of urban % of rural billion billion per capita populatior population cL.m. cu.m. CU.M. billion % of total % for % for % for with access with access 2000 2000 2000 cu.m. resources'' agriculture' industry' domestic' 1990 2000 1990 2000 Hungary 6 114.0 11,974 6.3 5.2 5 70 14 100 100 98 98 India 1,261 647.2 1,878 500.0 26.2 92 3 5 92 92 73 86 Indonesi'a 2,838 .. 13,487 74.3 2.6 93 1 6 90 91 60 65 Iran, Islamic Rep. 129 .. 2,018 70.0 54.5 92 2 6 95 99 75 89 Iraq 35 75.9 4,776 42.8 38.5 92 5 3 -. 96 . 48 Ireland 49 3.0 13,706 1.2 2.3 10 74 16 . Israel 2 0.9 449 1.7 61.1 64' 7' 29' . Italy 161 6.8_ 2,903 57.5 28.6 45 37 18 - - Jmaica 9 -. 3,570 0.9 9.6 77 7 15 .. 81 .. 9 Japan 430 0.0 3,389 91.4 21.3 64 17 19 ..- ... 151 Jordan 1 .. 143 1.0 140.0 75 3 22 99 100 92 84 Kazakhstan - 75 34.2 7,371 33.7 30.7 81 17 2 .. - 98 82 82 Kenya 20 10.0 1,004 2.0 6.8 76d 4' 20 d 89 87 25 :31 Korea, Dem. Rep. 67 10.1 3.462 14.2 18.4 73 16 11 ... - Korea, Rep. 65 4.9 1,476 23.7 33.9 63 11 26 . 97 .. 71 Kuwait 0 0.0 0 0.5 .. 60 2 37 - . ... Kyrgyz Republic 47 0.0 9,461 10.1 21.7 94 3 3 . 98 . 66 CD --- - ----- - --- -- ---0 Leo PDR 190 143.1 63,175 1.0 0.3 82 10 8 . 59 .. 100 Latvia 17 18.7 14.924 0.3 0.8 13 32 55 ... . Lebanon 5 0.0 1,109 1.3 26.9 68 4 28 - 100 .. 1E0 Lesotho 5 0.0 2,555 0.1 1.0 56'd 22 22' . 98 .. 88 Liberi'a 200 32.0 74,121 0,1 0.1 60'1 13 ' 27' .. .. Libya 1 0. 0 151 3.9 486.3 87' 4' 9' 72 72 68 68 Lithuania 17 7.0 6,857 3.6 14.9 3 16 81 . . - Macedonia, FYR 6 1.0 3.447 Madagascar 337 0.0_ 21,710 19.7 5.8 99 d 0 ' 1' 85 85 31 31 Malawi 18 1.1 1,804 0.9 5.1 86' ' 1' 9 95 43 44 Malaysia 580 .. 24,925 12.7 2.2 76 13 11 ... . 94 Mali 60 40.0 9,225 1.4 1.4 97'd 1' 2'd 65 74 52 61 Mauritania 0 11.0 4,278 16.3 14.3 92 2 6 34 34 40 40 Mauritius 2 0.0 1,855 0.4 16.4 77 ' 7'1 16' 100 100 100 100 Mesico 409 49.0 4,675 77.8 17.0 78 5 17 92 94 61 63 Moldova 1 10.7 2,732 3.0 25.3 26 65 9 .. 100 .. 00 Mongolia 35 .. 14,512 0.4 1.2 53 27 20 .. 77 -. 30 Morocco 30 0.0 1,045 11.1 36.8 92' 3' 5'd 94 100 58 58 Mozambique 100 111.0 11,927 0 6 0 9' 2'd 9' 6. 4 Myanmar 881 165.0 21,898 4.0 0.4 90 3 7 88 88 56 60 Nami-bia 6 39,3 25,896 0.3 0.5 68' 3'd 29'1 98 100 63 67 Nepal 198 12.0 9,122 29.0 13.8 99 0 1 96 85 63 80 Netherlands 11 80.0 5,716 7.8 8.6 34 61 5 100 100 100 100 New Zealand 327 0.0 85,361 2.0 0.6 44 10 46 100 100 Nicaragua_ 190 0.0 37,507 1.3 0.7 84 2 14 93 95 44 59 Niger 4 29.0 3,000 0.5 1.5 82'd 2' 16'd 65 70 51 56 Nigeria 221 59.0 206 40 13 54'd 15' 31' 78 81 33 39 Norwey 382 11.0 87,508 2.0 0.5 3 68 27 100 100 100 1.00 Oman 1 .. 418 1.2 122.0 94 2 5 41 41 30 30 Pakistan 85 170.3 1,847 155.6 61.0 97 2 2 96 96 79 84 Panama 147_ . 51,611 1.6 1.1 70 2 28 .. 88 .. 86 Papua New Guinea 801 .. 156,140 0.1 0.0 49 22 29 88 88 32 32 Paraguay 94 .. 17,103 0.4 0.5 78 7 15 80 95 47 58 Peru 1,746 144.0 73,653 19.0 1.0 86 7 7 84 87 47 51 Philippines 479 0.0 6,338 55.4 11.6 88 4 8 94 92 81 80 Poland 55 8.0 1,630 12.1 19.2 11 76 13 Portugal 37 35.0 7,194 7.3 10.1 53 40 8 Puerto Rico Romania 49 170.0 9,762 0.0 9.0 . 91 . 16 Russian Federation 4,313 185.5 30,904 77.1 1.7 20 62 19 . 100 .. 96 * ~3.5 Freshwater Annual freshwater withdrawals Access to Improved resources water source Flows from snternal other Tota flows countries resources % of urban % of rural billion bilion per capita population population ca.m. cu.m. ca.m, billion % of total % for % for % for with access witn access 2000 2000 2000 ca.m.' resources" agriculture' industry' domestic' 1990 2000 1990 2000 Rwanda 6 .. 740 0.8 12.2 94 1I 5' . 60 .. 40 Saudi Arabia 2 .. 116 17,0 708.3 90 1 9 .. 100 .. 64 Senegal 26 13.0 4,134 1.5 3.5 92 ' 3'd 5' 90 92 60 65 Sierra Leone 160 0.0 31.803 0.4 0.2 89d 4 ~ 7 d 23 .. 31 Singapore .. . . 0.0 . ... .. 100 100 Slovak Republic 13 70.0 15,365 1.4 1.7 .. . .. 100 .. 100 Slovenia 19 0.0 9.306 0.5 2.7 .. 50 50 100 100 100 100 Somalia 6 9.7 1.789 0.8 5.2 97'1 0d 3' " South Africa 45 5.2 1,168 13.3 26.6 72'1 11' 17 .. 92 .. 80 152 Spain 112 0.3 2,840 35.5 31.7 62 26 12 . Sri Lanka 50 0.0 2,583 9.8 19.5 96 2 2 90 91 59 80 551 Sudan 35 119.0 4.953 17.8 11.6 94' 1' 5' 86 86 60 89 mu Swaziland 3 1.9 4,306 0.7 14.7 96'd 2' 2' . .2 wdn18 1. 145 27 . 5 3 0 0 0 0 C: Switzerland 40 13.0 7.382 2.6 4.9 0 58 42 100 100 100 100 E Syrian Arab Republic 7 37.7 2.761 14.4 32.2 94 2 4 .. 94 .. 64 o Tajikistan 66 13.3 12,901 11.9 14.9 92 4 4 . > Tanzania 80 9.0 2,641 1.2 1.3 89'd 2'd 9 80 80 42 42 o Thailand 210 199.9 6.750 33.1 8,1 91 4 5 83 89 68 77 o Togo 12 0.5 2.651 0.1 0.8 25'1 13' 62' 82 85 38 38 Trinidad and Tobago .. . . 0.0 . .. . . O Tunisia 4 0.4 408 2.8 68.7 86' 2' 13' 94 .. 61 Turkey 196 7.6 3,118 35.5 17.4 73'd 11'1 16'd 82 82 76 84 Turkmenistan 1 59.5 11.714 23.8 39.0 98 1 1 . Uganda 39 27.0 2,972 0.2 0.3 60 8 32 80 72 40 46 Ukraine 53 86.5 2,820 26.0 18.6 30 52 18 . United Arab Emirates 0 0.0 69 2.1 1,055.0 67 9 24 . United Kingdom 145 2.0 2,461 9.3 6.4 3 77 20 100 100 100 100 United States 2,460 18.0 8.801 447.7 18.9 27'1 65'1 8' 100 100 100 100 Uruguay 59 74.0 39,856 4.2 3.2 91 3 6 .. 98 .. 93 Uzbekistan 16 98.1 4,622 58.0 50.7 94 2 4 .. 96 .. 78 Venezuela, RB 846 .. 35.002 4.1 0.5 46 10 44 .. 88 .. 58 Vietnam 367 524.7 11,350 54.3 6.1 86 10 4 81 81 40 50 West Bank and Gaza . . . . . .. Yem-en, Rep. 4 .. 234 2.9 71.5 92 1 7 85 85 60 64 Yugoslavia, Fed. Rep. 44 144.0 17,674 .. . ... .. . Zambia 80 35.8 11.498 1.7 1.5 77 ' 7 ' 16' 88 88 28 48 Zimbabwe 14 .. 1,117 1.2 8.7 79' 7 " 14 ' 99 100 68 77 Low Income 10,449 4,903.6 6,243 90 5 5 89 88 64 70 Middle Income 24.239 4,155.8 10,579 74 16 10 95 94 62 69 Lower middle income 14,755 1,253.5 7,836 76 17 7 97 95 62 69 Upper middle income 9,483 2,902.3 19.319 68 13 17 .. 92 .. 70 Low & middle Income 34.687 9,059.4 8,505 81 11 8 93 92 63 70 East Asia & Pacific 9,445 1,420.5 ..80 14 6 96 93 60 66 Europe & Central Asia 5,232 1,134.8 13,426 57 31 11 . Latin America & Carib. 13,987 2,797.2 32,905 74 9 18 92 93 56 62 Middle East & N. Africa 234 183.1 1,427 89 4 6 93 96 76 80 South Asia 1.849 1,945.1 2,800 93 2 4 93 92 75 85 Sub-Saharan Africa 3,941 1,578.7 8.379 86 4 10 81 82 37 41 High Income 8.146 367,9 ..40 43 14 ... Europe EMU 88S 258.8 3,783 34 52 14 a. River flows from other countries are ncluded when available, but river outflows are not, because ot data jnrel ability. b. Oats refer to any year from 1980 to 1999. c. Unless otherwise noted, sectoral withdrawal shares are estimated tor 1987. d. Data refer to a year other than 1987 (see Primrary data documentation). About the data Definitions The data on freshwater resources are based Figure 3.5a * Freshwater resources refer to total renew- on estimates Of ruinoff into rivers and re- able resources, broken down between internal charge of groundwater. These estimates are Frswtrrsucsprcpt aid flows of rivers and groundwater from rainfall in based on different sources and refer to dif- significantly across regions In 2000 the country', and river flows from other coun- ferent years, so cross-country compari'sons 1,000 cubic meters tries. Freshwater resources per capita are cal- should be made with caution. Because the 35 culated using the World Bank's population es- 30- data are collected intermittently, they may 25 -timates (see table 2.1). * Annual freshwater hide significant variations in total renewable 20 -withdrawals refer to total water withdrawal, not water resources from one year to the next. 'IO counting evaporation losses from storage ba- The data also fail to distinguish between 5 * * * -sins. Withdrawals also include water from de- seasonal and geographic variations in water 0 1 e a salination plants in countries where they are a availability within countries. Data for small < significant source. Withdrawal data are for countries and countries in arid and semiarid u W single years between 1980 and 1999 unless L) ~~~~z zones are less reliable than those for larger -~ ~otherwise indicated. Withdrawals can exceed countries and countries with greater rainfall. ElDomestic ElIondustry QAgriculture 100 percent of total renewable resour-ces 153 Finally, caution is also needed in comparing Source: Table 3.5. where extraction from nonrenewable aquifers l data on annual freshwater withdrawals, which or desalination plants is considerable or where 0 are subject to variations in collection and Figure 3.5b there is significant water reuse. Withdrawals estimation methods. ______________________ o giutr n idsr r oa ihrw This year's table shows both internal als for irrigation and livestock production and C 0 freshwater resources and the river flows arising Agriculture uses most water In low- and for direct industrial use (including withdrawals ( outside countries. Because the data on total mdl-noecnmisfor cooling thermoelectric plants). Withdrawals C freshwater resources include river flows entering % of frehwate withdrawal for domestic uses include drinking water, 2O a country while river flows out of the country are 80 municipal use or supply, and use for public C nov eresimted theueo availablt ofrewablter from homevies,Frms co untrciaessetrabls wiethdrawal noterdeducted (beaauseaofldtao unelabiiy,they frservies, oms commerciaessetab alshmets,randal 40 ) international river ways. This can be important 20I t data are estimated for 1987. a Access to an. in water-short countries, notably in the Middle 20Improved water source refers to the percenitage East. 8 . Tc 1a B E of the population with reasonable access to The data on access to an improved water 4 a > an adequate amount of water from an improved source measure the share of the population with '2source, such as a household connection, public reasonable and ready access to an adequate 0sadie oeoe rtce elo pig El Domestic U Industry QArincultre sadie oeoe rtce elo pig amount of safe water for domestic purposes. or rainwater collection. Unimproved SOuirces An improved source can be any form of collection Note: Data are fo, the mast recent year aoaiiabie (see table 3.5). include vendors, tanker trucks, and unpro- SoreTable 3.5. or piping used to make water regularly available. soc:tected wells and springs. Reasonable access While information on access to an improved is defined as the availability of at least 20 water source is widely used, it is extremely liters a person a day from a source within) one subjective, and such terms as safe, improved, kilometer of the dwelling. adequare, and reasonable may have very___-___ .. - different meanings in different countries despite official World Health Organization definitions (see Data sources Definitions). Even in high-income countries The data on freshwater resources and treated water may not always be safe to drink. withdrawals are compiled by the World While access to safe water is equated with Resources Institute from various sources and connection to a public supply system, this does published in World Resources 1998-99 and not take into account variations in the quality World Resources 2000-01 (produced inl and cost (broadly defined) of the service once collaboration with the United Nations connected. Thus cross-country comparisons Environment Programme, United Nations must be made cautiously. Changes over time Development Programme, and the World Bank). within countries may result from changes in These are supplemented by the FAO's definitions or measurements. AQUASTAT data. The data on access to an improved water source come from the World Health Organization. 3.6 Water pollution Emissions Industry shares of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics, kilograms per day metals and palp Chemicals beverages and glass Textiles Wood Other per day per worker % 96%% % 1980 19995 1980 1999' 1999' 1999 15999 1999 15999 15999 19995 19995 Afghanistan 6.680 .. 0.17 ... .. . ... Albania .. 6,512 . . 0.29 14.3 0.9 5.5 73.5 0.3 4.6 0.0 0.8 Algeria 60.290 45,645 0.19 0.24 23.4 2.0 5.9 59.5 0.7 7.6 0.8 0.0 Angola .. 1,472 . . 0.20 7.6 3.0 9.2 65.9 0.3 5.5 4.4 4.1 Argentina 244,711 177.882 0.18 0.21 6.5 12.5 7.9 59.4 0.1 7.4 1.5 4.5 Armenia .. 10.014 . . 0.25 . . . . . Australia 204,333 91,544 0.18 0.21 . . . . . Austria 108,416 87,294 0.16 0.14 12.2 19.6 9.7 36.5 0.3 6.1 4.5 11.1 Azerbaijan . . 45,025 . . 0.1 7 11.6 2.5 12.0 49.0 0.2 18.1 1.0 5.6 154 Bangladesh 66,713 186,852 0.16 0.16 2.8 6.8 3.5 34.2 0.1 50.9 0.6 1.1 o Belgium 136,452 113.460 0.16 0.16 14.4 1 7. 7 11.6 36.8 0.2 8.8 2.0 8.4 m Benin 1.646 .. 0.28 ... .. . ... -o Bolivia 9,343 12,323 0.22 0.24 3.1 14.2 7.2 64.7 0.3 7.4 2.2 0.9 Bosnia and Herzegovina .. 8.903 .. 0.18 20.5 13.1 6.6 33.3 0.2 1 7.6 5.8 2.8 E Botsmana 1.307 4.635 0.24 0.20 1. 7 15.8 5.4 56.4 0.2 1 7.2 1.4 1.9 o Brazil 866,790 629,406 0.16 0.20 1 7.7 12.9 9.2 44.4 0.1 9.8 1.4 4.5 > Bulgaria 152,125 107.945 0.13 0.1 7 11. 7 7.9 6.6 48.1 0.1 1 7.0 2.0 6.6 o Burkina Faso 2.385 2.598 0.29 0.22 3.5 1.1 5.4 73.8 0.1 4.1 10.1 1.9 ~0 Cambodia .. 12.0 78 .. 0.16 0.0 3.4 3.3 59.2 0.6 24.7 5.8 3.1 o Cameroon 14.569 10.810 0.29 0.20 3.3 6.2 28.0 52.6 0.0 3.7 5.8 0.4 Canada 330.241 297,370 0.18 0.17 9.5 28.8 9.7 34.3 0.1 5.5 3.9 8.2 Central African Republic 861 670 0.26 0.1 7 0.0 .. 4.0 61.9 0.0 13.9 19.6 0.6 Chile 44.371 74.583 0.21 0.24 7.7 12.0 8.8 61.4 0.1 5.2 2.4 2.4 China 3,377.105 7,024,090 0.14 0.14 20.3 11.0 14.9 28.9 0.5 15.0 0.7 8.7 Hong Kong, China 102,002 41,639 0.11 0.18 1.3 43.4 4.3 24.2 0.1 20.9 0.3 5.4 Colombia 96,055 105,683 0.19 0.20 3.6 14.2 10.3 51.8 0.2 16.0 0.9 3.0 Congo. Dem. Rep... . .. . . , . , Congo, Rep. 1,039 .. 0.21 ... .. . ... Costa Rica ,. 33,975 .. 0.22 1.3 9.0 6.2 63.8 0.1 15.4 1.5 2.5 Cote dIlvoire 15,414 12,401 0.23 0.24 .. 5.5 7.1 71.9 0.0 8.6 5.9 1.0 Croatia .. 48.44 7 .. 0.1 7 7.2 14,4 8.6 45.2 0.2 14.6 3.8 6.0 Cuba 120.703 .. 0.24 ... . . ... Czech Republic .. 158,462 .. 0.14 15.6 7.0 7.9 43.6 0.3 10.4 3.9 11.4 Denmark 65,465 83,591 0.17 0.1 7 4.4 29.1 7.9 44.2 0.2 2.2 3.5 8.6 Dominican Republic 54,935 .. 0.38 ... .. . ... Ecuador 25,297 34.610 0.23 0.27 2.4 10.7 6.2 72.3 0.1 5.6 1.4 1.3 Egypt, Arab Rep. 169,146 208,104 0.19 0.18 12.0 6.9 9.8 47.7 0.3 19.1 0.6 3.5 El Salvador 9,390 22.760 0.24 0.18 2.1 10.2 8.1 43.5 0.1 34.1 0.5 1.4 Eritrea 16,754.. . ........, Ethiopia .. 20.449 .. 0.22 1.9 10.7 4,6 59.1 0.3 21.0 1.8 0.6 Finland 92,275 61,835 0.17 0.20 9.6 42.6 3.1 31.0 0.2 3.0 4.3 6.3 France 729.776 300.964 0.14 0.10 . . . . . Gabon 2,661 1.886 0.15 0.26 0.0 6.0 4.9 79.7 0.1 1.2 6.9 1.2 Gambia, The 549 832 0.30 0.34 0.0 15.3 1.9 77.9 0.1 2.6 1.9 0.2 Germany ..811.315 .. 0.12 12.7 16.8 15.5 30.6 0.3 4.8 2.2 17.2 Ghana 15,868 14,449 0.20 0.1 7 9.8 16.9 10.5 39.5 0.2 9.1 12.4 1.7 Greece 65.304 57.722 0.17 0.20 6.0 12.1 8.8 54,2 0.3 13.8 1.4 3.5 Guatemala 20.856 19,253 0.25 0.28 4.9 7.2 6.1 72.8 0.1 6.9 0.8 1.0 Guinea-Bissau... .. .....,.... Haiti 4.734 .. 0.19 . ,. .. .. Honduras 13,067 34,036 0.23 0.20 1.1 7.8 3.9 55.5 0.1 26.8 4.0 0.8 3.6 C Emissions Industry shares of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics, kilograMs per day metals and pulp Chemicals beserages and glass Textiles Wood Othier per day per worker % % % % % %% 1 ±550 1999' ±980 1999' 1999' 1999' 1999' 1999' 15999 1999' 1999' 1999' Hungary 201,888 140,824 0.15 0.17 8.8 10.0- 8.0 50.2 0.2 13.4 1.9 7.4 India 1,422,564 1,746,562 0.21 0.19 13.4 8.0 9.2 51.0 0.2 12.9 0.3 5.0 Indonesia 214,010 676,082 0.22 0. 20 2.8 7.0 7.1 55.3 0.1 21.1 4.1 2.5 Iran, Islamic Rep. 72.334 101,900 0.15 0.1 7 20.6 8.0 8.0 39.7 0.5 1 7.3 0.7 5.4 Iraq 32,986 19,61 7 0.19__ 0.16 8.8 14.1 15.1 39.4 0.7 16. 7 0.3 4.8 Ireland 43,544 3 7,886 0.19 0.15 1.8 1 7.5 11.8 50.1 0.2 5.9 2.0 10.7 Israel 39,113 54,149 0.15 0.16 3.7 19. 7 9.4 43.9 0.2 12.1 1.8 9.3 Italy _442,712 354,590__ 0.13 0.13 12.1 16.1 11.5 28.7 0.3 15.9 2.5 12.9 Jamaica 11,123 1 7,507 0.25__ 0.29 6.9 7.2 3.8 70.8 0.1 9.8 1.3 0.0 Japan 1,456,016 1,415,879 0.14 0.14 8.1 21.8 8.8 40.3 0.2 5.9 1.6 13.2 155 Jordan 4,146 16,142 0,17 0.18 3.9 16.2 14.5 51.4 0.5 7.2 3.3 3.0 Kazakhstan ... ... . .... .. Kenya 26,834 49,304 0.19 0.24 4.1 12.2 6.1 66.7 0.1 8.8 1.9 0.0 N Korea, Dem. Rep... .. .. .. .. Korea._Rep. 281,900 288,408 0.14 .....0.12 12.3 16.1 12.5 27.0 0.2 15.3 1.4 15.2 C Kuwait 6,921 10,108 0.16 0.16 2.3 1 7.0 12.1 45.5 0.4 14.2 2.9 5.5 mC ----------- <~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. Kyrgyz Republic .. 20,700 . 0.16 13.7 0.2 0.9 54.8 0.4 21.0 1.0 8.0 Lao PDR ....... 3 Latvia 25,789 0.21 2.8 6.7 4.3 67.5 0.1 11.2 7.3 0.0 CD Lebanon 14,586 .. 0.20 ET. .... .. Lesotho 993 3,123 0.24 0.16 1.2 4.0 0.7 39.7 0.1 51.3 0.6 2.3 Libya 3,532 0.21 ......... Lithuania 38,615 0.18 1.4 10.9 5.0 56.0 0.2 1 7.6 4.2 4.6 Macedonia, FYR 23,490 0.18 11.7 9.6 6.2 45.0 0.1 20.9 1.7 4.9 Madagascar 9,131 .. .....0.23 Malawi 12,224 11,805 0.3 029 0.0 16.0 3.7 70.0 0.0 7.8 1. 7 0.8 Malaysia 77,215 154,926 0.15 0.11 7.9 13.7 16.1 31.5 0.3 8.6 6.6 15.3 Mauritania . . . .. .. Mauritius 9,224 15,677 0.21 0.6 1.1 6.6 2.6 36. 1 0.1 51.5 0.7 1.2 Mexico 130,993 163,569 0.22 0.17 8.0 8.4 14.0 55.4 0.2 5.2 0.4 8.5 Moldova .. 34,234 .. 0.29 0.2 4.0 1.4 81.7 0.2 10.8 1.3 0.5 Mongolia 9,254 7,939 0.9 0.18 1.8 4.3 0.9 63.1 0.3 24.6 4.9 0.0 Morocco 26,598 90,563 0.15 0.18 0.8 7.6 7.2 53.6 0.3 27.0 0.9 2.5 Mozambique ___95 .. 0 16 Myanmar .. 3,319 . 0.14 1 74 9.0 35.6 28.2 0.5 5.0 3.0 1.2 Namibia .. 7,350 .. 0.35 0.0 5.0 1.6 90.4 0.1 1.2 0.9 0.8 Nepal 18,692 26,550 0.25 0.14 1.5 8.1 3.9 43.3 1.2 39.3 1.7 1.0 Netherlands 165.416 120,502 0.18 0.18 7.7 25.8 12.2 42.2 0.2 2.5 1.2 8.3 New Zealand 59,012 50,706 0.21 0.22 4.6 19.6 4.9 58.6 0.1 4.9 3.1 A. 2 Nicaragua 9,647 .. 0.28 Niger 372 .. 0.19 Nigeria 72,082 53,646 0.17 0.18 ....0.9 31.2 6.5 37.4 0.2 10.6 10.4 2.9 Norway 67,897 52,616 0. 19 0.21 5.7 35.4 3.3 44.4 0.1 1.6 3.4 E6.1 Oman .. 5,199 .. 017 4.6 15.2 6.7 52.1 0.8 13.4 3.6 3.5 Pakistan 75,125 100,821 0.17 0.18 11.6 7.0 8.4 39.9 0.2 30.3 0.3 2.3 Panama 8,121 12,145 0.26 0.31 1.5 11.6 5.5 75.2 0.2 5.1 0.5 0.4 Papua New Guinea 4,365 .. 0.22 .. . .. . .. . Paraguay .. 3,250 .. 0.28 2.3 9.9 6.0 73.6 0.3 6.7 0.3 t).8 Peru 50,367 51,828 0.16 0.21 9.6 12.0 8.4 53.0 0.2 12.3 1.6 2.9 Philippines 182,052 204,879 0.19 0.18 5.2 9.8 7.3 54.5 0.2 16.4 2.0 4.6 Poland 580,869 412.979 0.14 0.15 14.2 4.5 6.7 50.5 0.4 12.8 1.9 9.0 Portugal 105,441 142,761 0.15 0.14 3.5 14.3 5.1 39.5 0.4 25.3 5.1 6.8 Puerto RICO 24,034 17,494 0.16_ 0.14 0.9 10.9 1 7.7 40.2 0.2 19.6 1.3 9.1 Romania 343,145 333,168 0.12 0. 14 1 7.1 6.7 9.0 34.3 0.3 18.5 4.8 9.4 Russian Federation ..1,485,833 .. 0.16 1 7.7 7.4 9.3 46.8 0.3 6.9 2.1 9.5 3.6 Emissions Industry shares of emissions of organic water pollutants of organic water poilutants Stone, kilograms Primary Paper Food and ceramics, kilograms per day metals and pulp Chremicals beverages and glass Textiles Wood Other per day per worker % % % % %% % 1980 15999' 1950 1999' 19599 1999' 1999' 1.999' 1999' 1999' i999' ±.999. Saudi Arabia 18,181 24,436 0.12 0.14 4.4 15.9 21.1 45.1 1.0 3.8 2.0 6.8 Senegal 9,865 10,488 0.31 0.30 0.0 6.3 8.8 78.8 0.0 4.6 0.1 1.3 Sierra Leone 1.612 4,1 70 0.24 0.32 .. 9.6 3.0 82.3 0.1 2.0 2.2 0.8 Singapore 28,558 31,793 0.10 0.09 2.0 28.0 15.1 19.9 0.1 4.0 1.5 29.3 Slovak Republic .. 57.970 .. 0.15 1 7.2 12. 7 7.9 37.5 0.3 11.9 2.7 9.9 Slovenia .. 37,321 .. 0.16 30,1 15.7 9.1 24.5 0.2 12.1 2.0 6.2 South Africa 237,599 238,259 0.17 0.17 11.9 16.9 9.2 40.9 0.2 10.9 3.5 6.5 156 Spain 376,253 349,151 0.16 0.16 6.8 19.0 8.6 43.6 0.3 9.4 4.0 8.3 Sri Lanka 30,086 83,850 0.18 0.1 7 1.0 6.5 6.0 47.5 0.2 36.6 1.0 1.2 is Sua tS Swaziland 2.826 2.009 0.26 0.23 .. 79.8 0.3 .. 0.1 16.5 2.0 1.2 C) Sweden 130.439 93.076 0.15 0.16 10.6 37.3 7.5 28.8 0.1 1.3 3.3 11.1 Switzerland ..123.752 .. 0.1 7 24.9 23.6 10.4 25.0 0.2 3.2 4.2 8.7 E) Syrian Arab Republic 36,262 15,115 0.19 0.20 4.1 1.5 3.9 69.8 0.9 19.4 0.2 0.2 a0. > Tanzania 21.084 32,508 0.21 0.26 4.7 10.8 5.0 65.2 0.1 11.8 1.4 1.2 0) o Thailand 213,271 355.819 0.22 0.16 6.1 5.3 5.3 42.2 0.2 35.4 1.5 3.9 0 3. Trinidad and Tobago- 7.835 11, 787 0.18 0.28 4.4 10.9 6.7 72.6 0.1 2.9 1.3 1.2 o Tunisia 20,294 46.489 0.16 0.16 6.2 8.1 6.4 40.7 0.4 33.6 1.5 3.3 C C'J Turkey 160,173 186,275 0.20 0.16 10.7 7.0 7.6 42.9 0.3 25.5 1.0 5.1 Turkmenistan . . . .. .. .. Uganda . . . .. .. .. Ukraine ..518.995 .. 0.1 7 22.2 3.3 6.9 51.4 0.4 5.9 1.8 8.2 United Arab Emirates 4,524 .. 0.15 .. . .. . .. . United Kingdom 964,510 604,572 0.15 0.15 7.4 28.5 11.8 32.9 0.2 6.1 2.4 10.8 United States 2,742,993 2,529,037 0.14 0.14 8.5 32.2 10.3 28.0 0.2 5.9 2.9 11.9 Uruguay 34.270 24 .896 0.21 0.25 1.4 10.8 5.9 69.5 0.1 9.5 0.7 2.0 Uzbekistan . . . .. .. .. Venezuela. RB 84.797 92.026 0.20 0.21 14.1 11.5 9.9 51.8 0.2 7.3 1.7 3.4 Vietnam . . . West Bank and Gaza . . . Yemen, Rep. .. 7,823 .. 025 0.0 9.1 12.9 71.1 0.3 4.9 1.0 0.9 Yugoslavia, Fed. Rep. ..11 7.128 .. 0.16 10.3 12.3 7.8 44.9 0.3 14.5 2.1 7.9 Zambia 13,605 11,433 0.23 0.22 3.4 10.8 7.3 63.6 0.2 9.3 2.9 2.4 Zimbabwe 32,681 32,988 0.20 0.20 13.6 11.3 5.6 48.1 0.2 15.1 3.0 3.1 Note: Industry shares may not sum to 100 percent because data way be from different years. a. Data refer to any year from 1993 to 1999. 3.6 o About the data Definitions Emissions of organic pollutants from industrial Figure 3.6a * Emissions of organic water pollutants are activities are a major cause of degradation of measured in terms of biochemical oxygen de- water quality. Water quality and pollution levels Emissions of organic water pollutants mand, which refers to the amount of oxygen are generally measured in terms of concentra- Millions of kilograms a day that bacteria in water will consume in breaking tion, or load-the rate of occurrence of a sub- down waste. This is a standard water treatment stance in an aqueous solution. Polluting sub- 7 01980 test for the presence of organic pollutants. stances include organic matter, metals, miner- 6 Emissions per worker are total emissions als, sediment, bacteria, and toxic chemicals. s O 1998 divided by the number of industrial workers. This table focuses on organic water pollution * Industry shares of emissions of organic resulting from industrial activities. Because wa- 4 water pollutants refer to emissions from manu- ter pollution tends to be sensitive to local con- 3 facturing activities as defined by two-digit divi- ditions, the national-level data in the table may 2 sions of the International Standard Industrial not reflect the quality of water in specific loca- jUJf. l[1j Classification (ISIC) revision 2: primary metals tions. _ _ U_ jjUjjE rL (ISIC division 37), paper and pulp (34), chemi- The data in the table come from an interna- o $ t' cals (35), food and beverages (31), stone, ce- 157 tional study of industrial emissions that may be - \,f ramics, and glass (36), textiles (32), wood (33), the first to include data from developing coun- and other (38 and 39). ° tries (Hettige, Mani, and Wheeler 1998). Source Table 36. r - E These data have been updated through 1999 0 by the World Bank's Development Research Data sources Group. Unlike estimates from earlier studies Figure 3.6b Indicators in this table were drawn from a 1998 i D based on engineering or economic models, | study by Hemamala Hettige, Muthukumara CD these estimates are based on actual mea- C Mani, and David Wheeler, "Industrial Pollution ! surements of plant-level water pollution. The pont s, 1998 in Economic Development: Kuznets Revisited" focus is on organic water pollution, measured -3% , (available on the Web at www.worldbank.org/ in terms of biochemical oxygen demand (BOD), nipr). These indicators were then updated because the data for this indicator are the ithrough 1999 by the World Bank's Develop- , most plentiful and the most reliable for cross- I ment Research Group using the same meth- tn country comparisons of emissions. BOD mea- 37 6% odology as the initial study. Sectoral employ- sures the strength of an organic waste in ment numbers are from UNIDO's industry terms of the amount of oxygen consumed in database. breaking it down. A sewage overload in natu- ral waters exhausts the water's dissolved oxygen content. Wastewater treatment, by , % contrast, reduces BOD. Data on water pollution are more readily available than other emissions data because 28% most industrial pollution control programs start U Indonesia U Indla by regulating emissions of organic water IN Germany Unted States pollutants. Such data are fairly reliable because l Japan * China sampling techniques for measuring water U RussianFederation Restoftheworid pollution are more widely understood and much less expensive than those for air pollution. Source: Table 3.6 In their study Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector- level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time. The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. The sectoral emissions estimates were then totaled to get daily emissions of organic water pollutants in kilograms per day for each country and year. The data in the table were derived by updating these estimates through 1999. f~ 3.7 Energy production and use Commercial Commercial energy use Commercial energy use Net energy energy per capita Imports' production thousand thousand average average % of metric tonis of metric tons of annual kg of oil annual commercial oil equivalent oil equivalent % growth equivalent % growth energy use 1980 1999 1980 1999 1980-99 1980 1999 1980-99 1980 1999 Afghanistan.... . Albania 3,428 865 3,049 1.052 -6.7 1,142 311 -7.8 -12 18 Algeria 66.741 142,883 12,088 28.280 3.6 647 944 1.1 -452 -405 Angola 11,301 43,644 4,437 7,591 2.9 628 595 -0.2 -155 -475 Argentina 38,813 81,932 41,868 63,182 2.4 1,490 1,727 0.9 7 -30 Armenia 1,263 646 1,070 1.845 ... 485 . 65 Australia 86,096 212,204 70,372 107.930 2.4 4.790 5,690 1.0 -22 -97 Austria 7.561 9,520 22,823 28,432 1.6 3.022 3,513 1.1 67 67 Azerbaijatn 14,821 19,037 15,001 12,574 ... 1,575 ...-51 158 Bangladesh 6.745 14,474 8,441 17.935 4.2 99 139 1.9 20 19 Belarus 2.566 3.475 2,385 23,895 ... 2.381 ...85 o Belgium 7.986 13,766 46,100 58.642 1.8 4.682 5.735 1.6 83 77 Benin 1,212 1.556 1,363 1,973 1.9 394 323 -1.2 11 21 Bolivia 4,374 6,020 2,438 4,572 3.5 455 562 1.2 -79 -32 Bosnia and Herzegovina .. 705 .. 2,008 ..518 .. 65 2 Botswana . .. .. .- 0. a) Brazil 62,372 133.654 111,471 179.701 2.7 917 1.068 1.0 44 26 > Bulgaria 7.737 9,056 28.673 18.203 -2.6 3,236 2.218 -2.1 73 50 Burkina Faso...........- ~0 Cambodia . .. .. .. .- a Cameroon) 6,707 12,109 3,676 6.103 2.5 421 419 -0.3 -82 -98 CN Canada 207.417 366.554 193.000 241.780 1.6 7.848 7.929 0.4 -7 -52 Central African Republic - .. .. .- Chad - .. .. .. Chile 5,BO1 7,668 9,662 25,348 5.7 867 1.688 4.0 40 70 China 608,625 1.056,963 592,511 1,088,349 3.8 604 868 2.4 -3 3 Hong Kong, China 39 48 5,439 17 .886 5.9 1,079 2,661 4.5 99 100 Colombia 18,040 77,142 19,348 28.081 2.6 680 676 0.6 7 -175 Congo, Dem. Rep. 8.697 14,860 8,706 14,525 2.7 324 293 -0.6 0 -2 Congo, Rep. 4,024 14,079 862 720 -1.2 516 245 -4.2 -367 -1.855 Costa Rica 767 1.322 1,527 3,052 4.1 669 818 1.4 50 57 Cdte dIlvoire 2.419 5.973 3,662 6,052 2.5 447 388 -0.9 34 1 Croatia .. 3.721 .. 8,156 ... 1,864 ...54 Cuba 4,227 5.242 14,910 12,464 -1.8 1,536 1.117 -2.6 72 58 Czech Republic 41.208 27,952 47.254 38,584 -1.2 4,618 3,754 -1.2 13 28 Denmark 896 23,642 19,734 20.070 0.8 3,852 3,773 0.6 95 -18 Dominican Republic 1.327 1,491 3.491 7.451 3.9 613 904 2.0 62 80 Ecuador 11.745 21,730 5.180 8,750 2.7 651 705 0.3 -127 -148 Egypt, Arab Rep. 34.168 58,460 15.970 44,490 4.7 391 709 2.3 -114 -31 El Salvador 1.623 2,136 2,537 4,005 2.2 553 651 0.6 25 47 Eritrea........ . Estonia 6,951 2,762 6.275 4,557 ..3.286 ...39 Ethiopia 10.575 17,176 11,145 18,227 2.6 295 290 -0.1 5 6 Finland 6,912 15,402 25,413 33,372 1.7 5,317 6.461 1.3 73 54 France 46,799 127.617 187,766 255.043 2.0 3.485 4,351 1.5 75 50 Gabon 9.441 17,842 1,493 1,608 -0.3 2,158 1.342 -3.2 -532 -1.010 Gambia, The............ Georgia 1.504 739 4,474 2,573 ... 512 ...71 Germany 185.628 132,961 360.385 337,196 -0.2 4.602 4.108 -0.5 48 61 Ghana 3,305 5.540 4,027 7,108 3.6 375 377 0.4 18 22 Greece 3,696 9,812 15,695 26,894 3.0 1.628 2.552 2.5 76 64 Guatemala 2,503 4.566 3,754 6.074 3.0 550 548 0.4 33 25 Guinea-Bissau . .. .. .. .. Haiti 1.877 1.578 2.099 2,067 0.2 392 265 -1.8 11 24 Honduras 1,315 1,817 1.892 3,267 3.1 530 522 0.1 30 44 Commercial Commercial energy use Commercial energy use Net energy energy per capita Imports' productionn thousand thousand average average % of metric tons of metric tons of annual kg of oil annual commercial oil equivalent j oil equivalent % growth equivalent % growth energy use 1980 1999 1980 1999 1980-99 1980 1999 1980-99 1980 1999 Hungary 14,935 11,491 28.940 25,289 -1.0 2,703 2,512 -0.7 48 55 India 222,418 409.788 242,592 480,418 3.8 353 482 1.8 8 15 Indonesia 128,996 226,378 59,933 136,121 4.8 404 658 3.0 -115 -66 Iran, Islamic Rep. 81,142 229,406 38,987 103,635 5.6 996 1,651 3.0 -108 4121 Iraq 136.643 131,754 12,030 28,802 4.6 925 1,263 1.5 -1,036 -357 Ireland 1,894 2,513 8,485 13,979 2.6 2,495 3,726 2.3 78 82 Israel 153 615 8,563 18,493 5.2 2,208 3,029 2.6 98 97 Italy 19,644 27,754 138,629 169,041 1.3 2,456 2,932 1.2 86 84 Jamaica 224 641 2,378 4,136 4.0 1,115 1,597 3.0 91 65 Japan 43,281 104,223 346,527 515,447 2.7 2,967 4,070 2.3 88 80 159 Jordan 1 2B6 1,714 4,871 5.0 786 1,028 0.5 10D) 94 Kazakhstan 76,799 64,668 76,799 35.439 ... 2,374 ..0 -82 Kenya 7,891 12,129 9,791 14,690 2.2 589 499 -0.8 19 17 N Korea, Dem. Rep. 29,135 54,198 31,914 58,925 4.0 1,856 2,658 2.6 9 8 Korea, Rep. 9.644 31,652 41,238 181.365 9.3 1,082 3.871 8.2 77 82 C Kuwait 91.636 104,291 12,249 17,289 0.3 8.908 8,984 -0.3 -648 -503 Kyrgyz Republic 2,190 1,301 1,717 2,451 ...504 ...47 (D 23 Lebanon 178 161 2,524 5,469 4.8 841 1,280 2.8 93 97 0) Libya 96,550 73,420 7,193 12,254 3.9 2,364 2,370 1.2 -1,242 -499 Lithuania 3,540 7,909 2,138 56 Macedonia. FYR Madagascar . .. .. .. Malaysi'a 18,202 73,411 12,162 42,650 7.8 884 1,878 4.9 -50 -72 Mauritania..... . Mauritius . .. .. Mexico 149,359 221,771 98,898 148,991 2.1 1.464 1,543 0.2 -51 -49 Moldova 35 63 .. 2,813 656.. 85 Mongolia..... . Morocco 877 615 4,778 9,931 4.2 247 352 2.1 82 94 Mozambique 7,413 7,067 8,074 6,985 .0.9 668 404 -2.6 8 Myanmar 9,513 13,94 9,430 12,897 1.4 280 273 -0.4 -1 -8 Namibia .. 270 .. 1,108 ... 645 ...76 Nepal 4,630 7,035 4,805 8,051 2.8 330 358 0.4 4 13 Netherlands 71,821 59,054 64.984 74,068 1.4 4.593 4,686 0.8 -11 20 New Zealand 5,485 15,14.3 9.210 18,176 3.8 2,959 4.770 2.7 40 17 Nicaragua 910 1,482 1,555 2,664 2.7 532 539 0.0 41 44 Niger Nigeri'a 148,479 178,822 52,846 87,2865 2.6 743 705 -0.4 .181 .105 Norway 58,716 209,765 18,792 26,606 1.8 4,593 5,965 1.3 -196 4688 Oman 15,090 54,504 996 8,469 11.3 905 3.607 6.8 -1,415 -544 Pakistan 20,997 44,091 25,472 59,830 4.7 308 444 2.1 lB 26 Panama 529 704 1,821 2,347 2.2 934 835 0.2 71 70 Papua New Guinea... ... Paraguay 1.605 6,741 2,089 4,140 4.4 671 773 1.5 23 -63 Peru 14,655 11.659 11,700 13.101 1.0 675 519 -0.9 -25 11 Philippines 10,670 19,681 21.212 40,728 3.9 442 549 1.5 50 52 Poland 122,224 83.394 123,035 93,382 -1.4 3,458 2.416 -1.8 1 11 Portugal 1.481 1,940 10,291 23.627 4.5 1.054 2.365 4.5 86 92 Puerto Rico............ Romania 52,587 27,859 65,123 35,432 -3.0 2,933 1,622 -3.1 19 24 Russian Federation 748,647 950,589 763,707 602,952 ... 4,121 -58 4;) 3.7 Commercial Commercial energy use Commercial energy use Net energy energy per capita Imports' production thousand thousand average average % of metric tons of metric tons of annual kg of oil annual commercial oil equivalent oil equivalent % growtn equivalent % growth energy ace 1980 1999 1980 1999 1980-99 1980 1999 1980-99 1980 1999 Rwanda - Saudi Arabia 533,071 448,735 35,357 84.907 4.4 3.773 4,204 0.3 -1,408 -429 Senegal 1,046 1,684 1,921 2.957 2.3 347 318 -0.4 46 43 Sierra Leone . ., .. .. Singapore ..64 6.062 22.693 9.0 2,511 5,742 6.3 ..190 Slovak Republic 3.418 5,136 21.040 17,991 -1.3 4,221 3,335 -1.7 84 71 Slovenia .. 2,985 .. 6,506 . 3,277 ...54 South Africa 73.169 143.993 65,417 109.334 2.2 2.372 2.597 -0. 1 -12 -32 160 Spain 15,636 30,691 68.576 118.467 3.1 1.834 3,005 2.9 77 74 Sri Lanka 3.209 4.547 4.536 7.728 2.4 338 406 1.1 29 41 an 6 Sudan 7,089 17.034 8,406 15,372 2.9 435 503 0.6 16 -11 03 Swaziland . .. .. .. .- Sweden 16,132 34.489 39,911 51,094 1.2 4,803 5.769 0.8 80 3 Switzerland 7,030 11,805 20,801 26,689 iS5 3,301 3,738 0.7 66 56 Q) E Syrian Arab Republic 9,502 34.205 5,348 18.049 5.5 614 1,143 2.2 .78 -90 C. o Tajikistan 1,986 1.381 .. 3,344 ... 54,3 ,,.95 > Tanzania 9,502 14.269 10.280 15.033 2-0 553 457 -1.0 8 5 a) o Thailand 11,182 38,499 22.806 70.415 7.6 488 1,169 6.1 51 45 Co Togo 562 1,015 715 1,373 3.6 284 313 0.7 21 28 Trinidad and Tobago 13.141 16.079 3.873 8,022 3.1 3,579 6.205 2.2 -239 .100 o Tunisia 6,966 7.120 3,907 7,673 3.7 612 811 1.6 -78 7 171 Turkey 17,077 26.903 31,452 70,326 4.6 707 1.093 2.6 46 62 Turkmenistan 8,034 26,331 7,948 13.644 -. 2,677 .-. -93 Uganda --. .. .. .- Ukraine 109,708 81,923 97,693 148,389 ... 2,973 ...45 United Arab Emirates 89,716 135,681 6.112 28.085 8.4 5,860 9,977 2.9 -1.368 -383 United Kingdom 198,792 281.674 201,284 230.324 1.0 3,573 3.871 0.7 2 .22 United States 1.553.263 1.687.886 1,811,650 2,269,985 1.5 7,973 8.159 0.4 14 28 Uruguay 763 961 2.641 3.232 1.5 936 976 0.8 71 70 Uzbekistan 4.615 55,109 4,821 49,383 ... 2.024 ... 12 Venezuela, RB 139.392 209,707 34.962 53,406 2.4 2,317 2,253 0.0 -299 -293 Vietnam 18,364 44.858 19,573 35,209 3.1 364 454 1.1 6 -27 West Bank and Gazea.. ..,. .. Yemen, Rep. 60 20,247 1,424 3,139 4.2 167 184 0.3 96 -545 Yugosiavia, Fed. Rep. .. 10,096 .. 13,375 ... 1,258 ...25 Zambia 4.198 5,784 4,551 6.190 1.3 793 628 -1.6 8 7 Zimbabwe 5.793 8,322 6.570 10.170 2.6 921 821 -0.3 12 18 Low Income 819,980 1,359,334 674,896 1.262,983 4.9 391 567 2.6 -23 -8 Middle Income 3.308,639 4,648.785 2,490,055 3.506.451 4.4 895 1.325 2.8 -33 -33 Lower middle income 1.931,423 2.962,555 1,755.632 2.308,831 5.4 660 1.146 4.0 -9 -28 Upper middle income 1,377,216 1.686,230 734.423 1.197.620 2.8 1.601 1,897 1.0 -94 -41 Low & middle Income 4,128.619 6.008,119 3.164.951 4.769,434 4.5 676 979 2.6 -31 -26 East Asia & Pacific 844.331 1,559.783 810.781 1.666,659 4.5 587 920 3.0 -5 6 Europe & Central Asia 1.241,994 1,420.239 1,332,872 1,240,388 7.8 3,348 2,628 ...-15 Latin America & Carib. 475.362 816.043 381,870 588,053 2.4 1.071 1.171 0.6 -24 -39 Middle East & N. Africa 986.110 1,208,951 145,640 365,967 4.8 838 1.279 2.0 -580 -230 South Asia 257,999 479,935 285,846 573,962 3.9 323 441 1.8 10 16 Sub-Saharan Africa 322.823 523,168 207,942 334,405 2.3 713 671 -0.6 -57 -56 Hi1gh Income 2,779,193 3,705,963 3.764,261 4.866,031 1.7 4,794 5.448 1.0 26 24 Europe EMU 369.087 431.075 952.790 1.142,253 1.2 3.337 3,785 0.9 61 62 3.7 0 About the data Definitions In developing countries growth in commercial Figure 3.7 * Commercial energy production refers to com- energy use is closely related to growth in the mercial forms of primary energy-petroleum modern sectors-industry, motorized transport, While the worlds use of coal Is decreasing, (crude oil, natural gas liquids, and oil from and urban areas-but commercial energy use its use of other fossil fuels continues to nonconventional sources), natural gas, and and uban reas-ut cmmerial eergyuse increase also reflects climatic, geographic, and economic solid fuels (coal, lignite, and other derived factors (such as the relative price of energy). 3500 World tossil tue ..se 1950-2000 fuels)-and primary electricity, all converted Commercial energy use has been growing rap- 3 3000 into oil equivalents (see About the data). idly in low- and middle-income countries, but high- -i2 500 * Commercial energy use refers to apparent income countries still use more than five times - 20- consumption, which is equal to indigenous pro- as much on a per capita basis. Because com- T - , duction plus imports and stock changes, mi- mercial energy is widely traded, it is necessary 2 1 nus exports and fuels supplied to ships and to distinguish between its production and its use. i . aircraft engaged in international transport (see Net energy imports show the extent to which an . . About the data). * Net energy Imports are economy's use exceeds its domestic production. 1950 1960 1970 1980 1990 2000 calcu ated as energy use less production, High-incomecountriesarenetenergyimporters; ----Coal --I --NoraIgs both measured in oil equivalents. A nega- 161 middle-income countries have been their main tive value indicates that the country is a net > suppliers. Sou,ce. World.atch Insfitute frorn: UN, DOE, BP Armoco. LBL. exporter. 0 IEA. and [OU,. Energy data are compiled by the International E Energy Agency (IEA) and the United Nations __ _ Statistics Division (UNSD). IEA data for non-OECD C countries are based on national energy data Data sources i CD adjusted to conform to annual questionnaires The data on commercial energy production and CD 1 0 completed by OECD member governments. use are primarily from IEA electronic files and i UNSD data are primarily from responses to from the United Nations Statistics Division's CD questionnaires sent to national governments, Energy Statistics Yearbook. The IEA's data are | supplemented by official national statistical published in its annual publications, Energy l _t 01 publications and by data from intergovernmental fStatistics and Balances of Non-OECDI organizations. When official data are not Countries, Energy Statistics ofOECD Countries, e available, the UNSD prepares estimates based and Energy Balances of OECD Countries. on the professional and commercial literature. L _- This variety of sources affects the cross-country comparability of data. Commercial energy use refers to the use of domestic primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste, which comprises solid biomnass and animal products, gas and liquid from biomass, industrial w