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 Ł 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 ˝ ®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 waste, and municipal waste. Biomass is defined as any plant matter used directly as fuel or converted into fuel, heat, or electricity. (The data series published in World Development Indicators 1998 and earlier editions did not include energy from combustible renewables and waste.) All forms of commercial energy-primary energy and pri- mary electricity-are converted into oil equiva- lents. To convert nuclear electricity into oil equivalents, a notional thermal efficiency of 33 percent is assumed; for hydroelectric power, 100 percent efficiency is assumed. 3.8 Energy efficiency and emissions GDP per unit Traditi(onal Carbon dioxide emissions of energy use fuel use PPP $ per kg % of total Total Per capita kg per PPP $ oil equivalent energy use million metric tons metriC tonS of GDP 1.980 ±.999 1.980 1997 1980 1998 ±980 1998 ±950 1L998 Afghanistan ... 63.0 75.6 1.7 1.0 0.1 0.0 ..0.0 Albania .. 10.4 13.1 7.3 4.8 1.6 1.8 0.5 ..0.2 Algeria 4.9 5.4 1.9 1.5 66.1 106.6 3.5 3.6 1.1 0.7 Angola .. 4.4 64.9 69.7 5.3 5.9 0.8 0.5 ..0.2 Argentina 4.7 7.1 5.9 4.0 107.5 136.9 3.8 3.8 0.6 0.3 Armenia .. 4.9 .. 0.0 .3.4 .0.9 ..0.4 Australia 2.1 4.4 3.8 4.4 202.8 331.5 13.8 17.7 1.4 0.7 Austria 3.5 7.2 1.2 4.7 52.4 63.9 6.9 7.9 0.7 0.3 Azerbaijan .. 1.6 .. 0.0 . 38.8 ..4.9 ..2.2 162 Bangladesh 5.7 10.8 81.3 46.0 7.6 23.4 0.1 0.2 0.2 0.1 Belarus .. 2.9 .. 0.8 . 60.5 ..6.0 ..0.9 U) 2 Belgium 2.4 4.5 0.2 1.6 131.3 101.3 13.3 9.9 1.2 0.4 Benin 1.3 2.9 85.4 89.2 0.5 0.7 0.1 0.1 0.3 0.1 V Bolivia 3.2 4.2 19.3 14.0 4.5 12.1 0.8 1.5 0.6 0.6 Bosnia and Herzegovina ... . 10.1 .. 4.7 ..1.2 ..0.0 E) Botswana ... 35.7 ..1.0 3.8 1.1 2.4 0.6 0.4 o Brazil 4.4 6.7 35.5 28.7 183.4 299.6 1.5 1.8 0.4 0.3 > Bulgaria 0.9 2.3 0.5 1.3 75.3 47.4 8.5 5.7 2.9 1.2 o Burkina Faso ... 91.3 87.1 0.4 1.0 0.1 0.1 0.2 0.1 Burundi ... 97.0 94.2 0.1 0.2 0.0 0.0 0.1 0.1 0 Cambodia ... 100.0 89.3 0.3 0.7 0.0 0.1 ..0.0 o Cameroon 2.8 3.8 51.7 69.2 3.9 1.8 0.4 0.1 0.4 0.1 N Canada 1.5 3.3 0.4 4.7 420.9 467.2 17.1 15.4 1.4 0.6 Central African Republic ... 88.9 87.5 0.1 0.2 0.0 0.1 0.1 0.1 Chad ... 95.9 97.6 0.2 0.1 0.0 0.0 0.1 0.0 Chile 3.2 5.2 12.3 11.3 27.5 60.2 2.5 4.1 0.9 0.5 China 0.8 4.2 8.4 5.7 1,476.8 3,108.0 1.5 2.5 3.2 0.7 Hong Kong. China 6.4 8.4 0.9 0.7 16.3 35.8 3.2 5.4 0.5 0.3 Colombia 12.0 9.3 15.9 17.7 39.8 67.8 1.4 1.7 0.2 0.3 Congo, Dem. Rep. 3.3 2.6 73.9 91.7 3.5 2.4 0.1 0.1 0.1 0.1 Congo, Rep. 0.8 2.8 77.8 53.0 0.4 1.8 0.2 0.6 0.6 0.8 Costa Rica 5.8 10.8 26.3 54.2 2.5 5.1 1.1 1.4 0.3 0.2 Cote dIlvoire 2.9 4.3 52.8 91.5 4.6 13.2 0.6 0.9 0.4 0.5 Croatia .. 4.1 .. 3.2 . 19.8 ..4.5 ..0.6 Cuba ... 27.9 30.2 30.8 24.9 3.2 2.2 ..0.0 Czech Republic .. 3.5 0.6 1.6 .. 118.3 .. 11.5 ..0.9 Denmark 3.0 6.9 0.4 5.9 62.9 53.4 12.3 10.1 1.1 0.4 Dominican Republic 3.6 6.2 27.5 14.3 6.4 20.3 1.1 2.5 0.5 0.5 Ecuador 3.0 4.5 26.7 17.5 13.4 26.3 1.7 2.2 0.9 0.7 Egypt, Arab Rep. 3.5 4.9 4.7 3.2 45.2 105.8 1.1 1.7 0.8 0.5 El Salvador 4.3 6.8 52.9 34.5 2.1 6.1 0.5 1.0 0.2 0.2 Eritrea ... . 96.0 .. 0.0 ..0.0 ..0.0 Estonia .. 2.6 .. 13.8 .. 17.0 .. 12.1 ..1.4 Ethiopia 1.4 2.2 89.6 95.9 1.8 2.0 0.0 0.0 0.1 0.1 Finland 1.8 3.6 4.3 6.5 56.9 53.3 11.9 10.3 1.3 0.5 France 2.9 5.3 1.3 5.7 482.7 369.9 9.0 6.3 0.9 0.3 Gabon 1.9 4.5 30.8 32.9 6.2 2.8 8.9 2.4 2.2 0.4 Gambia, The ... 72.7 78.6 0.2 0.2 0.2 0.2 0.3 0.1 Georgia .. 4.8 .. 1.0 .5.2 .1.0 ..0.4 Germany 2.3 5.8 0.3 1.3 .. 825.2 .. 10.1 ..0.4 Ghana 2.8 5.0 43.7 78.1 2.4 4.4 0.2 0.2 0.2 0.1 Greece 4.8 6.0 3.0 4.5 51.7 85.2 5.4 8.1 0.7 0.6 Guatemala 4.1 6.8 54.6 62.0 4.5 9.7 0.7 0.9 0.3 0.3 Guinea ... 71.4 74.2 0.9 1.2 0.2 0.2 ..0.1 Guinea-Bissau ... 80.0 57.1 0.5 0.0 0.7 0.0 1.8 0.0 Haiti 3.6 5.5 80.7 74.7 0.8 1.3 0.1 0.2 0.1 0.1 Honduras 2.9 4.5 55.3 54.8 2.1 5.1 0.6 0.8 0.4 0.3 3.8 GDP per unit Traditional Carbon dioxide emissions of energy use fuel use PPP $ per kg % of total Total Per capita kg per PPP $ oil equivalent energy use million metric tons metric tons of GDP i9s0 1.999 1950 1.997 1980 1998 1980 1.998 1.980 1998 Hungary 2.0 4.6 2.0 1.6 82.5 58.7 7.7 5.8 1.4 0.5 India 1.9 4.7 31.5 _20.7 347.3 1,061.0 0.5 1.1 0.8 0.5 Indonesia 2.2 4.4 51.5 29.3 94.6 233.6 0.6 1.1 0.7 0.4 Iran, Islamic Rep. 2.9 3.4 0.4 0.7 116.1 289.9 3.0 4.7 1.0 0.9 Iraq .0.3 0.1 44.0 82.4 3.4 3.7 ..0.0 Ireland 2.3 7.0 0.0 0.2 25.2 38.3 7.4 10.3 1.3 0.5 Israel 3.6 6.1 0.0 0.0 21.1 60.3 5.4 10.1 0.7 0.6 Italy 3.9 7.7 0.8 1.0 371.9 414.9 6.6 7.2 0.7 0.3 Jamaica 1.7 2.2 5.0 6.0 8.4 11.0 4.0 4.3 2.1 1.2 Japan 3.4 6.3 0.1 1.6 920.4 1,133.5 7.9 9.0 0.8 0.416 Jordan 3.2 3 .8 0.0 0.0 4.7 13.9 2.2 3.0 0.8 0.8 Kazakhstan .. 2 .1. 0.2 .. 122.9 ..8.2 ..1.7 Kenya 1.1 2.1 76.8 80.3 6.2 9.1 0.4 0.3 0.6 0.3 Korea, Dem. Rep.. . 3.1 1.4 124.9 226.1 7.3 10.3 -. 0.0 Korea, Rep. 2.8 4.1 4.0 2.4 125.1 363.7 3.3 7.8 1.1 0.6 o. Kuwait 1.3 1.8 0.0 0.0 24.7 49.1 18.0 26.3 1.6 1.7 CD Kyrgyz Republic . 5.0 .. 0.0 .6.4 ..1.3 0.60.6 Lao PDR ..72.3 88.7 0.2 0.4 0.1 0.1 ..0.1 1 Latvia .. 4.1 .. 26.2 ..7.9 ..3.2 ..0.5 ( Lebanon .. 3.3 2.4 2.5 6.2 16.3 2.1 3.9 ..0.9 Lesotho ... . 0.0 .0.0 ..0.0 Liberia ..62.5_ 89.7 2.0 0.4 1.1 0.1 ..0.0 Libya .. 2.3 0.9 26.9 36.4 8.8 7.2 ..0.0 V Lithuania 3.1 .. 6.3 .. 15.6 ..4.2 ..0.6 Macedonia, FYR ... . 6.1 .. 12.4 ..6.1 ..1.4 Madagascar ..78.4 84.3 1.6 1.3 0.2 0.1 0.3 0.1 Malawi ... 90.6 88.6 0.7 0.7 0.1 0.1 0.3 0.1 Malaysia 2.7 4.3 15.7 5.5 28.0 120.5 2.0 5.4 0.8 0.7 Mali ... 86.7 88.9 0.4 0.5 0.1 0.0 0.1 0.1 Maurita nia ... 0.0 0.0 0.6 2.9 0.4 1.2 0.4 0.7 Mauritius ... 59.1 36.1 0.6 1.7 0.6 1.5 0.3 0.2 Mexico 3.1 5.4 5.0 4.5 252.5 374.0 3.7 3.9 0.8 0.5 Moldova 3.2 .. 0.5 .9.7 .2.2 ..1.1 Mongolia .. 14.4 4.3 6.8 7.7 4.1 3.3 3.5 2.0 Morocco 6.8 10.0 5.2 4.0--- -- 15.9 32.0 0.8_ 1.2 0.5 0.3 Mozambique 0.6 2A.1 ... 43.7 91.4 3.2_ 1.3 0.3 0.1 0.7 0.1 Myanmar ..69.3 60.5 4.8 8.2 0.1 0.2 ..0.0 Namibia . 9.6 0.0 0.0 ..0.0 Nepal 1.5 3.5 94.2 89.6 0.5 3.0 0.0 0.1 0.1 0.1 Netherlands 2.2 5.2 0 0 1 1 153.0 163.8 10.8 10.4 1.1 0.5 New Zealand 2.9 4.0 0.2 0.8 17.6 30.0 5.6 7.9 0.6 0.4 Nicaragua 3.5 4.2 49.2 42.2 2.0 3.4 0.7 0.7 0.4 0.3 Niger ... 79.5 80.6 0.6 1.1 0.1 0.1 0.1 0.1 Nigeria 0.8 1.2 66.8 67.8 68.1 78.5 1.0 0.6 1.6 0.8 Norway 2.4 4.8 0.4 1.1 38.7 33.6 9.5 7.6 0.9 0.3 Oman 0.0 .. 5.9 20.3 5.3 8.8 ..0.0 Pakistan 2.2 4.2 24.4 29.5 31.6 97.1 0.4 0.7 0.6 0.4 Panama 3.3 7.1 26.6 14.4 3.5 5.8 1.8 2.1 0.6 0.4 Papua New Guinea ..65.4 62.5 1.8 2.3 0.6 0.5 0.5 0.2 Paraguay 4.2 5.8 62.0 49.6 1.5 4.6 0.5 0.9 0.2 0.2 Peru 4.6 8.9 15.2 24.6 23.6 27.9 1.4 1.1 0.4 0.2 Philippines 5.6 6.9 37.0 26.9 36.5 76.0 0.8 1.0 0.3 0.3 Poland .. 3.5 0.4 0.8 456.2 321.7 12.8 8.3 ..1.0 Portugal 5.6 6.9 1.2 0.9 27.1 54.6 2.8 5.5 0.5 0.4 Puerto Rico ... 0.0 14.0 17.6 4.4 4.6 ..0.0 Romania 1.6 3.8 1.3 5.7 191.8 92.4 8.6 4.1 1.9 0.7 Russian Federation .. 1.9 0.8 .. 1,434.6 -9.8 ..1.4 * ~3.8 GDP per unit Traditional Carbon dioxide emissions of energy use fuel use PPP $ per kg % of tota Total Per capita kg per PPP $ oil equivalent energy use million metric tons metric tons of GDP 1980 1999 1980 1997 1980 1998 1980 1998 1980 1998 Rwarnda . . 89.8 88.3 0.3 0.5 0.1 0.1 0.1 0.1 Saudi Arabia 3.0 2.5 0.0 0.0 130.7 283.0 14.0 14.4 1.2 1.3 Senegal 2.3 4.5 50.8 56.2 2.8 3.3 0.5 0.4 0.6 0.3 Sierra Leone ... 90.0 86.1 0.6 0.5 0.2 0.1 0.3 0.2 Singapore 2.4 3.6 0.4 0.0 30.1 82.3 12.5 21.0 2.1 1.1 Slovak Republic ..3.2 .. 0.5 . 38.1 .7.1 ..0.7 Slovenia .. 4.9 .. 1.5 .14.6 ..7.4 ..0.5 Somalia -.. 78.6 ..0.6 0.0 0.1 0.0 ..0.0 South Africa 2.7 3.5 4.9 43.4 211.3 343.7 7.7 8.3 1.2 0.9 164 Spain 3.8 6.1 0.4 1.3 200.0 247.2 5.3 6.3 0.8 0.4 Sri Lanka 3.5 8.1 53.5 46.5 3.4 8.1 0.2 0.4 0.2 0.1 (0 8 Sudan 1.4 3.2 86.9 75.1 3.3 3.6 0.2 0.1 0.3 0.1 co Swaziland ......0.5 0.4 0.8 0.4 0.4 0.1 Sweden 2.1 4.0 7.7 17.9 71.4 48.6 8.6 5.5 0.8 0.3 Switzerland 4.4 7.3 0.9 6.0 40.9 41.8 6.5 5.9 0.4 0.2 E Syrian Arab Republic 2.6 3.0 0.0 0.0 19.3 50.6 2.2 3.3 1.4 0.9 o) Tajikistan ..1.9 ....5.1 ..0.8 ..0.9 a) > Tanzania ..1.1 92.0 91.4 1.9 2.2 0.1 0.1 ..0.1 0) 0 Thailand 3.0 5.2 40.3 24.6 40.0 192.4 0.9 3.2 0.6 0.6 0o o Togo 4.3 4.7 35.7 71.9 0.6 0.9 0.2 0.2 0.2 0.1 3r Trinidad and Tobago 1.3 1.3 1.4 0.8 16.7 22.4 15.4 17.4 3.4 2.3 o Tunisia 4.0 7.4 16.1 12.4 9.4 22.4 1.5 2.4 0.6 0.4 CN Turkey 3.6 5.9 20.5 3.1 76.3 202.0 1.7 3.2 0.7 0.5 Turkmenistan ..1.2 ..27.9 ..5.7 ..2.1 Uganda ... 93.6 89.7 0.6 1.3 0.1 0.1 0.1 0.1 Ukraine ..1.2 .. 0.5 . 353.6 -. 7.0 ..2.1 United Arab Emirates 4.4 1.8 0.0 ..36.3 86.2 34.8 32.4 1.4 1.8 United Kingdom 2.5 5.8 0.0 3.3 580.3 542.3 10.3 9.2 1.1 0.4 United States 1.6 3.9 1.3 3.8 4.626.8 5,447.6 20.4 19.8 1.6 0.6 Uruguay 5.0 9.2 11.1 21.0 5.8 5.8 2.0 1.8 0.4 0.2 Uzbekistan ..1.1 .0.0 .. 109.2 ..4.5 ..2.1 Venezuela, RB 1.7 2.5 0.9 0.7 90.1 155.4 6.0 6.7 1.5 1.1 Vietnamrn 4.1 49.1 37.8 16.8 43.9 0.3 0.6 ..0.3 West Bank and Gaza ... .. .0.0 .0.0 ..0.0 Yemen, Rep. .. 4.4 0.0 1.4 .. 14.2 ..0.9 ..1.1 Yugoslavia, Fed. Rep. ... . 1.5 102.0 0.0 10.4 0.0 ..0.0 Zambia 0.9 1.2 37.4 72.7 3.5 1.6 0.6 0.2 0.9 0.2 Zimbabwe 1.6 3.5 27.6 25.2 9.6 14.1 1.3 1.2 0.9 0.4 wr. iii~~i w-j ~E I Low Income 1.9 3.6 4.3.7 28.6 772.4 2,416.1 0.5 1.0 0.7 0.5 Middle Income 2.3 4.0 9.7 7.3 4,266.1 9,211.7 2.3 3.5 1.1 0.7 Lower middle income 1.7 3.7 10.0 5.8 2,396.9 6,140.7 1.7 3.1 1.5 0.8 Upper middle income 3.3 4.7 9.2 10.4 1,869.2 3,070.9 4.2 4.9 0.8 0.6 Low&mlddleincome 2.2 3.9 18.3 12.9 5,038.4 11,627.8 1.5 2.3 1.0 0.7 East Asia & Pacific .. 4.3 14.6 9.4 1,958.4 4,385.2 1.4 2.4 1.9 0.7 Europe & Central Asia .. 2.4 2.8 1.3 989.0 3,134.8 ..6.8 1.4 1.1 Latin America & Carib. 4.1 6.0 18.4 16.0 848.2 1,308.3 2.4 2.6 0.5 0.4 Middle East & N. Africa 3.3 3.8 1.6 1.2 498.5 1,092.9 3.0 3.9 1.0 0.8 South Asia 2.0 4.9 33.8 23.6 392.3 1,194.4 0.4 0.9 0.7 0.5 Sub-Saharan Africa 1.8 4.6 47.2 63.9 352.0 512.2 0.9 0.8 0.9 0.5 Highi Income 2.2 4.8 1.0 3.4 8,814.2 11,197.2 12.4 12.6 1.2 0.5 Europe EMU 2.8 5.9 0.7 2.5 1,565.2 2,431.9 7.5 8.0 0.8 0.4 3.8 0 About the data Definitions The ratio of GDP to energy use provides a Figure 3.8a * GDP per unit of energy use is the PPP GDP measure of energy efficiency. To produce per kilogram of oil equivalent of commercial comparable and consistent estimates of real Per capita emissions of carbon dioxide energy use. PPP GDP is gross domestic product GDP across countries relative to physical inputs rise with Income converted to international dollars using to GDP-that is. units of energy use-GDP is 14 purchasing power parity rates. An international converted to international dollars using 12 dollar has the same purchasing power over GDP purchasing power parity (PPP) rates. Differences 0 1988 as a U.S. dollar has in the United States. in this ratio over time and across countries, 10 U 1998 . Traditional fuel use includes estimates of reflect in part structural changes in the o8- the consumption of fuelwood, charcoal, economy, changes in the energy efficiency of .21 bagasse, and animal and vegetable wastes. 1Z6 particular sectors, and differences in fuel mixes. u Total energy use comprises commercial energy The data on traditional fuel are from the United 4 use (see table 3.7) and traditional fuel use. Nations Statistics Division's Energy Statistics 2 * Carbon dioxide emissions are those Yearbook. This series differs from those 0 l stemming from the burning of fossil fuels and published in World DevelopmentIndicators 1999 ° Low Lower Upper High the manufacture of cement. They include 165 and previous editions, which came from other income middie middle income carbon dioxide produced during consumption income incomeIl sources. of solid, liquid, and gas fuels and gas flaring. 8 Carbon dioxide (CO2) emissions, largely a Sorce. Tabie 3.8. by-product of energy production and use (see . o table 3.7), account for the largest share of Data sources greenhouse gases, which are associated with Figure 3.Bb The underlying data on commercial eneigy ID global warming. Anthropogenic CO2 emissions production and use are from electronic files of result primarily from fossil fuel combustion the International Energy Agency. The data on i and cement manufacturing. In combustion High-income economies accounted for only traditional fuel use are from the United Nations (D 15 percent of the world's population in = different fossil fuels release different amounts 1998-but half its carbon dioxide emissions Statistics Division's Energy Statistics E of CO2 for the same level of energy use. Indin Yearbook. The data on CO2 emissions are c Burning oil releases about 50 percent more F 5% from the Carbon Dioxide Information Analysis CO2 than burning natural gas, and burning income Center, Environmental Sciences Division, Oak CO ~~~~~~~~~~~~~~~~~Otherhg coal releases about twice as much. Cement income 6% Ridge National Laboratory, in the U.S. state manufacturing releases about half a metric of Tennessee. ton of CO2 for each ton of cement produced. C China The Carbon Dioxide Information Analysis Cen- / I ter (CDIAC), sponsored by the U.S. Department of Energy, calculates annual anthropogenic emis- sions of CO2. These calculations are derived from data on fossil fuel consumption, based on the World Energy Data Set maintained by Other middie the United Nations Statistics Division, and UnitedStates income from data on world cement manufacturing, 26% based on the Cement Manufacturing Data Set Source: Table 3.8 maintained by the U.S. Bureau of Mines. Emis- sions of CO2 are often calculated and reported in terms of their content of elemental carbon. For this table, these values were converted to the actual mass of CO2 by multiplying the car- bon mass by 3.664 (the ratio of the mass of carbon to that of CO2). Although the estimates of global CO2 emissions are probably within 10 percent of actual emissions (as calculated from global average fuel chemistry and use), country estimates may have larger error bounds. Trends estimated from a consistent time series tend to be more accurate than individual values. Each year the CDIAC recalculates the entire time series from 1950 to the present, incorporating its most recent findings and the latest corrections to its database. Estimates do not include fuels supplied to ships and aircraft engaged in international transport because of the difficulty of apportioning these fuels among the countries benefiting from that transport. 3.9 Sources of electricity Electricity Sources of electricty production Hydropower Coal Oil Gas Nuclear power bill onlk'sh %%%% 1980 1999 1980 1.999 1990 1999 1980 1999 1980 1999 1980 1999 Afghanistan... ... Aloania 3.7 5.4 79.4 97.1 20.6 2.9 Algeria 7.1 24.6 3.6 2.9 . .. 12.2 2.8 84.1 94.3 Angola 0.7 1.3 88.1 67.0 . .. 11.9 33.0 Argentina 39.7 80.7 38.1 26.8 2.1 2.2 31.6 4.9 22.0 57.0 5.9 8.8 Armenia 13.0 5.7 12.0 21.0 . .. 54.8 0.3 .. 42.7 33.2 36.3 Australia 95.2 203.0 13.6 8.2 73.3 78.1 5.4 1.3 7.3 10.6 Austria 41.6 59.2 69.1 68.4 7.0 9.1 14.0 4.7 9.2 14.7 Azerbaijan 15.0 18.2 7.3 8.3 92.7 72.0 .. 19.8 166 Bangladesh 2.4 14.4 24.8 5.8 26.6 9.3 48.6 85.0 Belarus 34.1 26.5 0.1 0.1 . .. 99.9 9.6 .. 90.0 o Belgium 53.1 83.4 0.5 0.4 29.4 15.0 34.7 1.2 11.2 23.1 23.6 58.8 10 Benin 0.0 0.0 .. 4.3 100.0 95.7 . C, Bosnia and Herzegovina .. 2.6 .. 61.2 .. 33.7 .. 5.1 . 2 Botsaw na . . . .. .. .. o Brazil 139.4 332.3 92.5 88.1 2.4 2.9 3.8 5.0 .. 0.2 .. 1.2 a) >, Bulgaria 34.8 38.0 10.7 7.2 49.2 43.4 22.5 2.4 .. 5.3 17.7 41.6 o Burkina Faso.. . 0 Cambodia . . . .. .. o Cameroon 1.5 3.4 93.9 98.8 6.1 1.2 . 0 CN Canada 373.3 577.0 67.3 59.9 16.0 19.0 3.7 2.6 2.5 4.5 10.2 12.7 Central African Republic . . . . . .. .. Chile 11.8 38.4 67.0 37.0 16.1 35.9 14.7 9.1 1.3 15.3 China 300.6 1239.3 19.4 16.4 54.6 77.8 25.8 4.0 0.2 0.4 .. 1.2 Hong Kong, China 12.6 29.5 . .. 22.6 56.2 100.0 0.9 .. 42.9 Colombia 20.4 44.1 70.0 76.4 7.9 7.9 1.8 0.3 19.3 14.2 Congo. Dem. Rep. 4.4 5.7 95.5 99.6 . .. 4.5 0.4 Congo. Rep. 0.2 0.1 64.5 97.9 . .. 35.5 2.1 Costa Rica 2.2 6.2 95.2 83.0 4.3 2.2 C6te dIlvoire 1.7 4.9 77.3 24.1 22.7 10.3 .. 65.6 Croatia .. 12.2 ., 53.8 .. 4.2 .. 32.3 .. 9.6 Cuba 9.9 14.5 1.0 0.7 . .. 89.7 93.6 .. 0,4 Czech Republic 52.7 64.2 4.6 2.6 84.8 69.9 9.6 0.7 1.1 4.7 .. 20.8 Denmark 26.8 38.9 0.1 0.1 81.8 51.6 18.0 12.5 .. 23.5 Dominican Republic 3.3 7.7 17.1 14.3 .. 8.2 80.5 77.2 . Ecuador 3.4 10.3 25.9 69.7 . . 74.1 30.3 . Egypt, Arab Rep. 18.9 68.5 51.8 22.3 27.7 28.6 20.5 49.1 El Salvador 1.5 3.8 63.7 46.6 . .. 2.7 37.0 . Estonia 18.9 8,3 .. 0.0 .. 92.6 100.0 3.5 .. 3.7 Ethiopia 0.7 1.7 70.2 97.3 . .. 29.8 1.2 Finland 40.7 69.4 25.1 18.4 42.6 20.9 10.8 1.3 4.2 13.7 17.2 33.1 France 256.9 519.8 26.9 13.9 27.2 6.2 18.9 2.0 2.7 1.4 23.8 75.8 Gabon 0,5 1.0 49.1 71.3 . .. 50.9 17.8 .. 10.9 Gambia, The . . . .. Georgia 14.7 8.0 43.8 80.1 56.2 2.5 .. 17.4 Germany 466.3 551.3 4.1 3.5 62.9 51.9 5.7 1.1 14.2 10.0 11.9 30.8 Ghana 5.3 5.2 99.2 76.4 . .. 0.8 23.6 Greece 22.7 49.4 15.0 9.3 44.8 65.6 40.1 16.5 .. 7.9 Guatemala 1.8 5.2 12.9 51.3 82.9 43.3 Guinea-Bissau.. . .. . .. . Haiti 0.3 0.7 70.1 38.4 . .. 26.1 61.6 Honduras 0.9 3.4 86.3 62.6 . .. 13.7 32.3 Electricity Sources of electricty production Hydropower Coal Oil Gas Nuclear power billion kwh 1980 1999 1980 1999 1980 1999 1980 1999 198 1999 1980 1999 Hungary 23.9 37.2 0.5 0.5 50.4 25.9 13.9 14.3 35.2 21.1 .. 37.9 India 119.3 527.3 39.0 15.4 51.2 75.2 6.4 1.1 0.8 5.5 2.5 2.5 Indonesia 8.4 84.3 16.0 11.1 .. 30.1 84.0 19.0 .. 36.5 Iran, Islamic Rep. 22.4 112.7 25.1 4.4 . .. 50.1 19 0 24.8 76.5 Iraq 11.4 29 .7 6.1 2.0 . .. 93.9 98.0 . Ireland 10.6 21.8 7.9 3.9 16.4 34.5 60.4 28.3 15.2 31.9 Israel 12.4 39.2 0.0 0.1 18.1 67.3 100.0 32.6 .. 0.1 Italy 183.5 259.2 24.7 17.5 9.9__ 10.9 57.0 35.2 5.0 33.6 1.2 Jamaica 1.7 6.6 7.2 1.8 .. 76.0 90.4 . Japan 572.5 1057.0 15.4 8.2 9.6 21.2 46.2 16.6 14.2 22.1 14.4 30.0 167 Jordan 1.1 7.1 .. 02 .. 100.0 89.4 .. 10.4 __ Kazakhstan 61.5 47.5 9.3 12.9 72.0 90.7 6.4 .. 8.7.., Kenya 1.5 4.5 71.1 72.4 . .. 28.9 19.0 . .. Korea, Dam. Rep. 35.0 32.6 64.3 64.7 35.7 36.3 . Korea, Rep. 37.2 265.0 5.3 1.6 6.7 41.1 78.7 7.0 .. 11.4 9.3 38.9 C Kuwait 9.0 31.6 .. . . 20.1 77.3 79.9 22.7 CD . Kyrgyz Republic 9.2 13.2 53.1 92.3___ 3.9 46,9 3.9 CD Latvia 4.7 4.1 64.9 67.1 .. 0.9 35.1 8.7 .. 23.2 CD . Lebanon 2.8 8.2 30.9 4.1 ..69.1 95.9 ET..... L e -so. .th o. .. - - - - - ----- - ----- .------ Liberia .. .. .. .. ..~~~~~~. ... ....- --- Libya 4.8 20.0 .. 100.0 100.0 . Lithuani'a 11.7 13.1 4.0 3.2 . .. 96.0 13.8 .. 7.7 .. 75.4 Macedonia, FYR . . . . . .. .. Madagascar Malawi Malaysia 10.0 65,2 13.9 ... ..11.5 . 2.5 84.9 8.3 1.2 77.6 Mali Mauritani'a Mauritius ----- -- Mexico 67.0 192.3 25.2 17.1 0.0 9.4 57.9 47.2 15.5 17.9 .. 5.2 Moldova 1.5.4 3.8 2.6 2.2 .. 5.5 97.4 9.6 .. 82.7 Mongolia Morocco 5.2 1. 289 59 95 497 51.6 4.4. Mozambique 0.5 6.9 65.2 99.6 17.5 .. 17.3 0.4 .. 0.0 Myanmar 1.5 4.8 53.5 15.9 2.0 .. . 31.3 16.1 13.2 68.0 Namibia .. 1.2 .. 96 ... 2.4 Nepal 0.2 1.3 94.4 90.4 .. 5.6 9.6 Netherlands 64.8 86.7 .. 0.1 13.7 25.5 38.4 7.6 39.8 56.9 6.5 4.4 New Zealand 22.6 38.1 83. 61.7 1.9 4.8 0.2 .. 7.5 25.1 Nicaragua 1.1 2.1 5-1.3 18.3 . .. 43.3 76.2 Nigeria 7.1 16.1 39.0 35.0 0.4 .. 45.1 24.2 15.5 40.8 Norway 83.8 121.7 99.8 99,3 0.0 0.2 0.1 0.0 .. 0.2 Oman 0.8 8.4 .. . . 21.5 16.7 78.5 83.3 Pakistan 1-5-.0-- 65 4A 58.2 34.3 0.2 0.8 1.1 35.2 40.5 29.3 0.0 0.4 Panama 2.0 4.6 49.2 60.7 . .. 49.0 37.8 Papua New Guinea Paraguay 0.8 52.0 80.0 99.9 .. 11.1 0.0 Peru 10.0 19,1 69.8 76.3 . .. 27.4 17.6 1.7 5.3 Philippines 18.0 41.3 19.6 19.0 1.0 27.1 67.9 28.3 .. 0.0 Poland 120.9 142.0 1.9 1.5 94.7 96.3 2.9 1.3 0.1 0.5 Portugal 15.2 42.9 52.7 16.9 2.3 35.2 42.9 25.6 .. 18.8 Puerto Rico . . . .. ... .. Romania 67.5 50.7 18.7 36.1 31.4 29.4 9.6 7.6 40.2 16.6 .. 10.2 Russian Federation 804.9 845.3 16.1 19.0 .. 19.1 77.2 4.8 .. 42.4 6.7 14.4 3.9 Electricity Sources of electricty production Hydropower Coal Oil Gas Nuclear power billion kwh %%%% 1980 1999 1980 1999 1980 1999 1980 1999 1980 1999 1980 1999 Rwanda . . . .. .. Saudi Arabia 20.5 120.0 58.5 64.3 41.5 35.7 Senegal 0.6 1.4 . .. 100.0 98.5 .. 1.5 Sierra Leone . .. . . .. Singapore 7.0 29.4 . .. . .. 100.0 77.8 .. 19.7 Slovak Republic 20.0 27.5 11.3 16.5 37.9 23.4 17.9 1.2 10.2 11.2 22.7 47.7 Slovenia .. 13.3 .. 28.2 .. 33.8 .. 1.1 . 1.3 .. 35.4 South Africa 99.0 200.4 1.0 0.4 99.0 93.2 0.0 ... 6.4 168 Spain 109.2 206.3 27.1 11.1 30.0 36.6 35.2 11,8 2.7 9.2 4.7 28.5 Sri Lanka 1.7 6.2 88.7 67.5 . .. 11.3 32.5 . Sudan 0.8 2.1 70.0 53.1 . .. 30.0 46.9 . 10 Swaziland . . . .. .. .. Sweden 96.3 155.2 61.1 46.1 0.2 2.1 10.4 1.9 .. 0.3 27.5 47.2 Switzerland 48.2 68.5 68.1 58.4 0.1 .. 1.0 0.2 0.6 1.5 29.8 37.7 E Syrian Arab Republic 4.0 21.1 64.7 41.1 . .. 31.9 23,8 3.4 35.1 o Tajikistan 13.6 15.8 93.4 97.7 6.6 ... 2.3 a, > Tanzania 0.8 2.3 86.4 96.5 . .. 13.6 3.5 5) Thailand 14.4 90.1 8.8 3.6 9.8 18.3 81.4 17.8 9.9 59.2 0 Trinidad and Tobago 2.0 5.3 . .. . .. 2.3 .. 96.5 99.6 o Tunisia 2.9 10.0 0.8 0.9 . .. 64.5 13.5 34.7 85.5 C4 Turkey 23.3 116.4 48.8 29.8 25.6 31.8 25.1 6.9 . 31.2 Turkmenistan 6.7 8.9 0.1 0.1 . .. 99.9 ... 99.9 Uganda... ......... Ukraine 236.0 172.1 5.7 6.8 ., 29.5 88.3 4.6 17.3 6.0 41.9 United Arab Emirates 6.3 37.1 . .. . .. 3.7 7.9 96.3 92.1 United Kingdom 284.1 363.9 1.4 1.5 73.2 29.3 11.7 1.5 0.7 38.8 13.0 26.5 United States 2427.3 3910.2 11.5 7.4 51.2 51.8 10.8 3.1 15.3 15.7 11.0 19.9 Uruguay 4.16 7.2 76.3 76.5 . .. 23.5 23.0 . Uzbekistan 33.9 45.3 14.6 12.5 .. 4.8 85.4 11.4 .. 71.3 Venezuela, RB 35.8 80.6 40.7 75.1 . .. 32.4 7.1 26.9 17.8 Vietnam 3.6 23.6 41.8 58.5 39.9 12.4 18.3 13.9 0.4 15.3 West Bank and Gaza . . . . . .. .. Yemen, Rep. 0.5 3.0 . .. . .. 100.0 100.0 . Yugoslavia, Fed. Rep. .. 33.4 .. 40.1 .. 53.8 -. 3.2 .. 2.9 Zambia 9.5 8.1 98.8 99.5 0.7 0.5 0.5 0.0 Zimbabwe 4.5 7.1 88.3 41.6 11.7 58.4 . .. Low Income 577.8 1112.4 27.8 22.7 13.1 44.5 53.7 8.2 1.6 16.3 3.7 7.9 Middle Income 2233.8 4759.2 21.6 22.7 22.3 38.5 48.0 11.2 4.6 19.6 3.2 7.3 Lower middle income 1492.5 2911.7 18.1 19.9 13.8 42.9 60.5 8.9 3.3 21.9 4.0 5.8 Upper middle income 741.3 1847.5 28.9 27.0 39.3 31.5 22.8 14.9 7.1 16.0 1.4 9.6 Low & middie income 2811.7 5871.6 22.9 22.7 20.4 39.6 49.2 10.7 4.0 19.0 3.3 7.4 East Asia & Pacific 428.8 1846.1 21.6 14.7 42.5 61.9 34.4 6.5 0.2 9.6 0.8 6.4 Europe & Central Asia 1640.1 1763.4 13.5 17.9 13.6 30.6 65.4 5.8 2.3 30.3 5.1 15.2 Latin America & Carib. 360.9 921.0 60.2 60.1 2.1 5.2 25.7 17.7 9.8 12.6 0.6 2.3 Middle East & N. Africa 104.0 453.1 20.5 7.0 1.0 1.5 52.2 44.0 26.3 47.6 South Asia 138.5 614.6 41.8 17.9 44.1 64.6 6.3 5.3 5.9 9.9 2.2 2.2 Sub-Saharan Africa 139.3 273.4 24.0 18.1 70.8 69.9 4.4 3.6 0.8 3.6 -. 4.7 High Income 5393.9 8861.2 19.5 14.0 39.6 37.2 17.6 6.8 11.3 16.0 11.5 23.8 Europe EMU 1265.5 1949.8 17.0 11.6 37.3 27.2 23.2 8.6 9.8 14.1 11.7 35.8 About the data Definitions Use of energy in general, and access to electric- Figure 3.9a * Electricity production is measured at the ity in particular, are important in improving terminals of all alternator sets in a station. In people's standard of living. But electricity gen- There was a significant shift In the sources addition to hydropower, coal, oil, gas, and eration also can damage the environment. of electricity from 1980 to 1999 nuclear power generation, it covers generation Whether such damage occurs depends largely 1980 by geothermal, solar, wind, and tide and wave on how electricity is generated. For example, Nuclearpower energy as well as that from combustible burning coal releases twice as much carbon 9 .% Hydror.o renewables and waste. Production includes the dioxide-a major contributor to global warm- Gas output of electricity plants designed to produce ing-as does burning an equivalent amount . electricity only as well as that of combined of natural gas (see About the data for table heat and power plants. * Sources of electricity 3.8). Nuclear energy does not generate car- . ' refer to the inputs used to generate electricity: bon dioxide emissions, but it produces other r hydropower, coal, oil, gas, and nuclear power. dangerous waste products. The table pro- / Hydropower refers to electricity produced by vides information on electricity production by Oil \ hydroelectric power plants, oil refers to crude source. Shares may not sum to 100 percent 2C% \/ Coal oil and petroleum products, gas refers to 169 because some sources of generated electric- . ' 33% natural gas but not natural gas liquids, and M ity (such as geothermal, solar, and wind) are nuclear power refers to electricity produced by g not shown. nuclear power plants. The International Energy Agency (IEA) compiles a data on energy inputs used to generate electric- Nuclear ;*.; _, sources ity. IEA data for non-OECD countries are based Data sources , on national energy data adjusted to conform to The data on electricity production are from the annual questionnaires completed by OECD mem- _ I EAs electronic files and its annual ber governments. In addition, estimates are -,,--publications, Energy Statistics and Balances CD sometimes made to complete major aggregates of Non-OECD Countries, Energy Statistics GasF from which key data are missing, and adjust- 17% of OECD Countries, and Energy Balances of ments are made to compensate for differences I OECD Countries. in definitions. The IEA makes these estimates Oil Coal in consultation with national statistical offices, 9% 3- 39co oil companies, electricity utilities, and national Source: Table 3.8. energy experts. The IEA occasionally revises its time series to reflect political changes. Since 1990, for Figure 3.9b example, it has constructed energy statistics for countries of the former Soviet Union. In addition, High-income economies-with 15 percent of energy statistics for other countries have the world's population-generate eight undergone continuous changes in coverage or times as much electricity as low-income methodology as more detailed energy accounts economies have become available in recent years. Breaks eBillons of kilowatt hours in series are therefore unavoidable. 10.000 8,000 0 1980 * 1999 6,000 4,000 2,000 F 0 Low incoma Middle iricome High income So-ce: Table 3.9. 3.10 Urbanization Urban population Population In Population In Access to urban agglomerations largest city Improved sanitation of more than one million facilities Urban Rural % of total % of totat % of urban % of % of millions population population population population population 1950 2000 1980 2000 1980 2000 2015 1980 2000 1990 2000 1990 2000 Afghanistan 2.5 5.8 16 22 6 10 14 39 45 .. 25 .. 8 Albania 0.9 1.3 34 39 . Algeria 8.1 18.3 44 60 8 6 7 17 10 .. 90 .. 47 Angola 1.5 4.5 21 34 13 20 25 63 60 .. 70 .. 30 Argentina 23.3 33.1 83 89 42 41 40 43 38 .. 89 .. 48 Armenia 2.0 2.7 66 70 34 34 35 51 48 . Austrulia 12.6 16.2 86 85 61 56 55 26 23 100 100 100 100 Austria 4.9 5.2 65 65 27 26 26 42 39 100 100 100 100 Azerbaijan 3.3 4.6 53 57 26 24 25 48 42 . 170 Bangladesh 12.3 32.1 14 25 6 13 17 26 38 78 82 27 44 Belarus 5.4 7.0 57 70 14 18 20 24 25 . (a Belgium 9.4 10.0 95 97 12 11 11 13 11 . Benin 0.9 2.7 27 42 .. . . . . 46 46 6 6 Bolivia 2.4 5.4 46 65 14 18 20 30 27 77 82 28 38 ~5 Bosnia and Herzegovina 1.5 1.7 36 43 .. . .. . ... a) 2_ Botswana 0.1 0.8 15 50 .. . . . . 84 .. 44 o Brazil 80.5 138.5 66 81 32 34 34 16 13 84 85 37 40 a) Bulgaria 5.4 5.7 61 70 12 15 16 20 21 . Burkina Faso 0.6 2.1 9 19 .. . . 44 54 88 88 14 16 o Burundi 0.2 0.6 4 9 .. . . . . 67 79 90 Cambodia 0.8 1.9 12 16 .. . . 44 51 .. 58 .. 10 o Cameroon 2.7 7.3 31 49 11 21 27 19 23 99 99 79 85 0 (N Canada 18.6 23.7 76 77 32 37 38 16 20 100 100 99 99 Central African Republic 0.8 1.5 35 41 .. . . . . 43 43 23 23 Chad 0.8 1.8 19 24 .. . . 40 57 70 81 4 13 Chile 9.0 12.9 81 85 33 36 37 41 43 98 98 93 93 China 192.3 405.2 20 32 13 14 17 6 3 57 68 18 24 Hong Kong, China 4.6 6.8 92 100 91 100 100 100 100 . Colombia 18.2 31.7 64 75 26 32 35 20 20 95 97 53 51 Congo, Dem. Rep. 7.7 15.4 29 30 8 10 12 28 33 .. 53 -. 6 Congo. Rep. 0.7 1.9 41 63 27 41 43 65 65 .. 14 Costa Rica 1.0 2.0 43 52 .. . . 61 50 .. 98 .. 96 C6e dIlvoire 2.8 7.4 35 46 15 21 25 44 44 78 .. 30 Croatia 2.3 2.5 50 58 .. . . 28 42 . Cuba 6.6 8.4 68 75 20 20 20 29 27 .. 96 .. 91 Czech Republic 7.6 7.7 75 75 12 12 12 15 16 . Denmark 4.3 4.5 84 85 27 26 26 32 31 . Dominican RePublic 2.9 5.4 51 65 34 61 67 50 66 66 75 52 64 Ecuador 3.7 7.9 47 62 23 32 37 29 29 .. 70 .. 37 Egypt, Arab Rep. 17.9 28.9 44 45 23 23 24 38 36 96 98 80 91 El Salvador 1.9 2.9 42 47 16 22 25 39 48 .. 88 .. 78 Eritrea 0.3 0.8 14 19 . ... .. ... 66 .. 1 Estonia 1.0 0.9 70 69 . ... .. ... 93 Ethiopia 4.0 11.3 11 18 3 4 6 30 23 58 58 6 6 Finland 2.9 3.5 60 67 13 23 25 22 33 100 100 100 100 France 39.5 44.5 73 76 21 21 20 23 22... Gabon 0.3 1.0 50 81 . ... .. ... 25 .. 4 Gambia, The 0.1 0.4 20 33 . ... .. ... 41 .. 35 Georgia 2.6 3.0 52 61 22 26 29 42 43.. . Germany 64.7 71.9 83 88 39 41 43 10 9... Ghana 3.4 7.4 31 38 9 10 14 30 27 59 62 61 64 Greece 5.6 6.3 58 60 31 30 30 54 49... Guatemala 2.6 4.6 37 40 11 28 32 29 70 94 98 66 76 Guinea 0.9 2.4 19 33 12 25 32 65 75 94 94 41 41 Guinea-Bissau 0.1 0.3 17 24 . ... .. ... 88 .. 34 Haiti 1.3 2.8 24 36 13 22 28 55 62 48 50 15 16 Honduras 1.2 3.0 35 47 .. . . 33 32 85 94 .. 57 3.10 Urban population Population In Population In Access to urban agglomerations largest city Improved sanitation of more than one million facilities Urban Rural % of total % of total % of urban % of % of millions population populai3ton population population population 1980 2000 1980 2000 1980 2000 2015 1980 2000 1990 2000 1990 2000 Hungary 6.1 6.4 57 64 19 18 19 34 28 100 100 98 98 India 158.8 288.5 23 28 8 10 12 5 6 58 73 8 14 Indonesia 32.9 86.1 22 41 8 10 12 18 13 76 87 44 52 Iran, Islamic Rep. 19.4 39.2 50 62 21 23 24 26 18 86 86 74 74 Iraq 8.5 17.9 66 77 29 31. 34 39 27 .. 93 .. 31 Ireland 1.9 2.2 55 59 . ... 48 44 . Israel 3.4 5.7 89 91 37 35 33 41 38 . Italy 37.6 38.7 67 67 24 19 21 14 11 Jamaica 1.0 1.5 47 56 . ,. .. ... 98 .. 65 Japan 89.0 100.0 76 79 34 38 39 25 26 . .. . 171 Jordan 1.3 3.6 60 74 29 29 32 49 39 100 100 95 98 Kazakhstan 8.0 8.4 54 56 6 8 8 12 15 .. 100 9. 98. 0 Kenya 2.7 10.0 16 33 5 8 10 32 23 94 96 81 81 Korea, Dem. Rep. 9 8 13.4 57 60 11 14 16 19 24 . . . Korea, Rep. 21.7 38.7 57 82 40 47 45 38 26 .. 76 C...L Kuwait 1.2 1.9 90 98 60 60 55 67 61 .. . .. . Kyrgyz Republic 1.4 1.6 38 33 . ... . ... 100 lo ci' CD 0 Lao PDR 0.4 1.2 13 24 . .. . .. . .. 84 34 34 3 Latvia 1.7 1.6 68 69 .. , . 49 47 CD.... . Lebanon 2.2 3.9 74 90 40 47 48 55 53 ., 100 .. 87 Lesotho 0.2 0.6 13 28 . ... .. ... 93 .. 92 Liberia 0.7 1.4 35 45 . .. . . .. . . Libya 2.1 4.6 69 88 26 34 34 38 39 97 97 96 96 Lithuania 2.1 2.5 61 68 . . .. 23 . Macedonia, FYR 1.0 1.3 54 62 .. ... . ... Madagascar 1.6 4.6 18 30 6 11) 13 33 33 70 70 25 30 Malawi 0.6 1.6 9 15 .. . . . . 96 96 70 70) Malaysia 5.8 13.4 42_ 57 7 6S 6 16 10 .. . . 98 Mali 1.2 3.3 19 30 .. . . 40 35 95 93 62 58 Mauritania 0.4 1.5 27 58 .. . . . . 44 44 19 10 Mauritius 0.4 0.5 42 41 .. . . . . 100 100 100 99 Mexico 44.8 72.9 66 74 28 28 25 31 25 85 87 28 32 Moldova 1.6 2.0 40 46 . ... .. .., 100 Mongolia 0.9 1.4 52 59 . ... .. ... 46 . Morocco 8.0 16.1 41 56 15 18 20 26 22 95 100 31 4:2 Mozambique 1.6 7.1 13 40 6 17 21 47 43 .. 69 .. 26 Myanmar 8.1 13.2 24 28 7 9 11 27 32 65 65 38 39 Namibia 0.2 0.5 23 31 . .. . .. 84 96 14 17 Nepal 0.9 2.7 7_ 12 . .. . .. 68 75 16 21) Netherlands 12.5 14.2 88 89 14 14 14 8 8 100 100 100 100 New Zealand 2.6 3.3 83 87 . .. 30 33 . Nicaragua 1.6 3.3 53 65 . .. 34 29 97 96 53 68 Niger 0.7 2.2 13 21 ., . . . . 71 79 4 5 Nigeria 19.1 55.8 27 44_ 8 12 15 23 24 77 85 51 45D Norway 2.9 3.4 71 76 .. . . 22 29 100 Oman 0.3 2.0 32 84 .. . . . . 98 98 61 61 Pakistan 23.2 51.1 28 37 15 21 25 22 23 78 94 13 42 Panama 1.0 1.6 50 58 .. . . 62 71 .. 99 .. 87 Papua New Guinea 0.4 0.9 13 17 . .. . .. 92 92 80 80 Paraguay 1.3 3.1 42 56 22 23 26 52 41 92 95 87 95 Peru 11.2 18.7 65 73 25 29 30 39 40 81 90 26 40 Philippines 18.0 44.3 38 59 14 16 17 33 25 85 92 64 71 Poland 20.7 25.4 58 66 18 18 18 16 14 .. . Portugal 2.9 6.4 29 64 19 57 68 46 59 .. . Puerto Rico 2.1 2.9 67 75 34 35 36 51 47 .. . Romani'a 10.9 12.6 49 56 9 9 10 18 16 .. 86 .. 10 Russian Federation 97.0 106.4 70 73 18 19 21 8 9 .. . 3. 10 Urban population Population In Population In Access to urban agglomerations largest city Improved sanitation of more than one million facilities Urban Rural % of total % of total % of urban % of % of m Ilons population population population population population 1980 2000 1980 2000 1980 2000 2015 ±980 2000 1990 2000 1990 2000 Rwanda 0.2 0.5 5 6 . .. . .. ... 12 .. 8 Saudi Arabia 6.2 17.8 66 86 19 25 24 16 19 .. 100 .. 100 Senegal 2.0 4.5 36 47 17 22 27 48 46 86 94 38 48 Sierra Lnone 0.8 1.8 24 37 .. . . . .23 .. 31 Singapore 2.4 4.0 100 100 100 89 82 100 89 100 100 Slovak Republic 2.6 3.1 52 57 . . ... 100 .. 100 Slovenia 0.9 1.0 48 50 .. . . . . 100 Somalia 1.4 2.4 22 28 .. . . 27 50 . South Africa 13.3 23.5 48 55 27 32 36 12 13 .. 99 .. 73 172 Spain 27.2 30.6 73 78 20 17 18 16 13 . Sri Lanka 3.2 4.6 22 24 .. . . . . 93 91 79 83 Sudan 3.9 11.2 20 36 6 9 11 30 24 87 87 48 48 CO Swaziland 0.1 0.3 18 26 .. . .. . . Sweden 6.9 7.4 83 83 17 18 18 20 21 100 100 100 100 C Switzerland 3.6 4.9 57 68 .. . . 20 20 100 100 100 100 E) Syrian Arab Republic 4.1 8.8 47 55 28 28 31 34 26 .. 98 .. 81 o Tajikistan 1.4 1.7 34 28 .. . .. . ... a) > Tanzania 2.7 9.4 15 28 5 12 18 30 25 97 98 86 86 0) o Thailand 7.9 13.1 17 22 10 12 15 59 56 97 97 83 96 '0 Trinidad and Tobago 0.7 1.0 63 74 .. . .. . . o Tunisia 3.3 6.3 52 66 18 20 21 35 30 97 .. 48 0 Turkey 19.5 49.2 44 75 19 27 30 23 19 98 98 70 70 Turkmenistan 1.3 2.3 47 45 .. . .. . .. Uganda 1.1 3.2 9 14 .. . . 42 38 96 96 82 72 Ukraine 30.9 33.7 62 68 14 15 17 7 8 . United Arab Emirates 0.7 2.5 72 86 .. . . 31 37 . United Kingdom 50.0 53.5 89 90 25 23 23 15 14 100 100 100 100 United States 167.6 217.4 74 77 38 38 37 9 8 100 100 100 100 Uruguay 2.5 3.0 85 91 42 37 35 49 41 .. 96 .. 89 Uzbekistan 6.5 9.1 41 37 11 9 8 28 24 .. 100 .. 100 Venezuela, RB 12.0 21.1 79 87 28 29 30 21 15 .. 75 .. 69 Vietnam 10.3 18.8 19 24 14 13 14 34 24 86 86 70 70 West Bank and Gaza.. . .. . . . . . . . Yemen, Rep. 1.6 4.3 19 25 .. . . 15 30 80 87 27 31 Yugoslavia, Fed. Rep. 4.5 5.6 46 52 11 14 15 24 27 . Zambia 2.3 4.5 40 45 9 16 22 23 37 86 99 48 64 Zimbabwe 1.6 4.5 22 35 9 14 19 39 39 98 99 51 51 Low Income 388.2 785.1 24 32 .. . . 16 18 68 78 25 30 Middle Income 776.5 1,350.8 38 50 .. . . 19 16 75 82 29 38 Lower middle income 486.9 859.5 31 42 .. . . 16 13 69 79 28 35 Upper middle income 289.6 491.3 62 76 .. . . 25 21 .. 88 .. 57 Low & middle Income 1,164.7 2,135.9 32 41 .. . . 18 17 72 81 27 33 East Asia & Pacific 309.8 652.4 22 35 .. . . 15 10 64 74 28 34 Europe & Central Asia 249.3 310.1 59 65 16 18 20 15 15... Latin America & Carib. 233.4 388.7 65 75 29 32 32 27 25 85 87 39 48 Middle East & N. Africa 83.5 172.9 48 59 21 22 24 30 25 92 94 63 67 South Asia 201.0 385.0 22 28 8 12 14 9 12 63 76 12 21 Sub-Saharan Africa 87.7 226.9 23 34 .. . . 28 29 80 81 47 41 High Income 595.1 711.9 75 79 .. . . 17 17... Europe EMU 210.1 235.3 73 77 26 27 28 17 16 3.10 About the data Definitions The population of a city or metropolitan area sons for this shift was the rapid growth in the * Urban population is the midyear population depends on the boundaries chosen. For hundreds of towns reclassified as cities in re- of areas defined as urban in each country and example, in 1990 Beijing, China, contained cent years. Because the estimates in the table reported to the United Nations (see About the 2.3 million people in 87 square kilometers of are based on national definitions of what con- data). * Population in urban agglomerations "inner city" and 5.4 million in 158 square stitutes a city or metropolitan area, cross- of more than one million is the percentage of kilometers of "core city." The population of country comparisons should be made with a country's population living in metropolitan "inner city and inner suburban districts" was caution. areas that in 1990 had a population of more 6.3 million, and that of 'inner city, inner and To estimate urban populations, the United than one million. * Population in largest city outer suburban districts, and inner and outer Nations' ratios of urban to total population were is the percentage of a country's urban popula- counties" was 10.8 million. (For most applied to the World Bank's estimates of total tion living in that country's largest metropoli- countries the last definition is used.) population (see table 2.1). tan area. * Access to improved sanitation fa- Estimates of the world's urban population The urban population with access to improved cilities refers to the percentage of the urban would change significantly if China, India, and a sanitation facilities is defined as those with or rural population with access to at least few other populous nations were to change their access to at least adequate excreta disposal adequate excreta disposal facilities (private definition of urban centers. According to China's facilities that can effectively prevent human, or shared, but not public) that can effectively 173 State Statistical Bureau, by the end of 1996 animal, and insect contaict with excreta. The prevent human, animal, and insect contact urban residents accounted for about 43 percent rural population with access is included to allow with excreta. Improved facilities range from 8 of China's population, while in 1994 only 20 comparison of rural and urban access. This simple but protected pit latrines to flush percent of the population was considered urban. definition and the definition of urban areas vary, toilets with a sewerage connection. To be 0 In addition to the continuous migration of people however, so comparisons between countries can effective, facilities must be correctly con- a from rural to urban areas, one of the main rea- be misleading (see Definitions for table 2.16). structed and properly maintained. 2 (D Figure 3.10 Data sources , The data on urban population and the i The 10 cities expected to be the most populous in 2015 population in urban agglomerations and ir the I 30 - largest city come from the United Nations Population Division's World Urbanization 25- _ r Prospects: The 1999 Revision. The total population figures are World Bank estimates. rt 20 ~ l r The data on access to sanitation in urban and l2 rural areas are from the World Health Organization. -0 15- 0 Tokyo Mumbai Lagos Dhaka Sao Paulo Karachi Mexico NewYork Jakarta Calcuta Coty city 1 1575 M 1999 E 2015 Source: United Nations. Oepartment of Economic and Social Affairs 2001. .7) ~~3.11 Urban environment city Urban Secure House Work Travel Households with Wastewater population tenure price to trips by time access to services treated Income pubiic to work Proportion ratio trans- of people portation Access with to secure potable Sewerage tenors water connection Electricity Telephone thousands % % minutes % %%% 2000 1998 1.9985 1998, 1995, 1998, 1998, i9985 199B, 1998, Algeria Algiers 2,562 93.2 . .. 75 ...... 80 Argentina Buenos Aires 2,996 92.1 5.1 59 42 100 98 100 70 C6rdoba 132 b 85.0 6.8 44 32 99 40 99 80 49 Rosario 1,248 .. 5.7 .. 22 98 67 93 76 1 Armenia Yerevan 1,250 100.0 4.0 84 30 98 98 100 88 36 Bangladesh Chittagong 2.301 .. 8.1 27 45 44 .. 95 Dhaka 10,000 .. 16.7 9 45 60 22 90 7 Sylhet 242 .. 6.0 10 50 29 0 93 40 Tangail 152 85.7 13.9 .. 30 12 0 90 12 174 Barbados Bridgetown .. 99.7 4.4 .. 98 5 99 78 7 (n Belize Belize City 55.. ..... . 0 Bolivia Santa Cruz de la Sierra 1,065 C 87.0 29.3 .. 29 53 33 98 59 53 Bosnia and Herzegovina Sarajevo 522 c . 100 12 95 90 100 C Brazil Belem 1,638" lcapui .. 91.7 4.5 .. 30 88 .. 90 33 E ~~~~~Maranguape .. . . 30 20 73 . Porto Alegre 3C. .. . 99 87 100 0) Recife 3,088 .. 12.5 46 35 89 41 100 29 33 Rio de Janeiro 10.192 . .. . .. 88 80 10 Santo Andre 1,658 80.3 23.4 43 40 98 95 100 79 C14 Bulgaria Bourgas b. . 5.1 61 32 100 93 100 .. 93 0 o Sofia 1.200 b 100.0 13.2 79 32 95 91 100 89 94 Troyan 24 C 100.0 3.7 44 22 99 82 100 45 Veliko Tarnovo .. 100.0 5.4 46 30 98 98 100 96 50 Burkina Faso Bobo-Dioulasso . 100.0 .. . . 24 . 29 6 Koudougou . . . . . 30 .. 26 7 Ouagadougou 1,130 C 100.0 .. 2 .. 30 .. 47 11 19 Burundi Bujumbura 373 97.0 .. 48 25 26 62 57 19 21 Cambodia Phnom Penh 1.000 C , 8.9 0 45 45 75 76 40 Cameroon Douala 1,148 Cb , 13.4 .. 40 34 1 95 9 5 Yaounde 968" . .. 42 45 34 1 95 9 24 Canada Hull 254 " 100.0 .. 186 100 100 100 100 100 Central African Republic Bangui .. 94.0 .. 66 60 31 .. 18 11 0 Chad N'Djamena 998 C , 35 .. 42 0 13 6 21 Chile Gran Concepcion .. . . 57 35 100 91 95 69 6 Santiago de Chile 5,737 . .. 60 38 100 99 99 73 3 Tome .. . . . . 92 52 98 58 57 Valparaiso 851 91.8 .. 55 .. 98 92 97 63 100 Vina del mar 851 92.7 .. . . 97 97 98 65 93 Colombia Armenia .. 94.1 5.0 42 60 90 50 99 97 Marinilla 170 " 94.5 8.5 18 15 98 93 100 65 Medellin 2.901 C38 35 100 99 100 87 Congo Brazzaville 989 C 87.9 .. 55 20 56 0 52 18 C6te dIlvoire Abidjan 3,201 .. 14.5 .. 45 26 15 41 5 45 Croatia Zagreb 2,497 96.5 7.8 56 31 98 97 100 94 Cuba Baracoa .. 96.2 .. . . 83 3 93 32 Camaguey .. 84.7 .. 2 60 72 47 97 Cienfuegos .. 96.3 4.0 .. 80 100 73 100 9 2 Ciudad Habana . .. 8.5 58 83 100 85 100 14 Pinar Del Rio .. 96.4 . .. 80 97 48 100 Santa Clara .. 98.8 .. 7 48 95 42 100 43 Czech Republic Brno .. . . 50 25 100 96 100 69 100 Prague 1,193 99.3 .. 55 22 99 100 100 100 Dem. Rep. of Congo Kinshasa 5,398 b 94.9 .. 72 57 72 0 66 1 Dominican Republic Santiago de los Caballeros 691 .. . . 30 75 80 .. 71 80 Ecuador Ambato 286 . 90 81 91 87 3.110 city Urban Secure House Work Travel Households with Wastewater Population tenure price to trips by time access to services tregated Income public to work proportion ratio trans- of people portation Access with to secure potable Sewerage tenure water connection Electricity Telephone thousands % % minutes % %% 2000 1998, 199a, 1998, 1998, 1995, 1995, 1995, 1.998 1998, Cuenca .. 91.0 4.6 .. 25 97 92 97 48 82 Guayaquil 2,317u 45.'8 3.4 89 45 70 42 .. 44 9 Manta 126~ . ..30 70 52 98 40 Puyo 40 b 2.1 . 15 80 30 90 60 Quito 1,531 b 93.8 2.4 .. 33 85 70 96 55 Tena 6.3 5 80 60 El Salvador San Salvador 1,863 90.5 3.5 82 80 98 70 Estonia Riik 99.5 92 90 98 55 Tallin 397 98.8 6.4 35 98 98 100 86 100 Gabon Libreville 523 80 30 55 0 95 45 44 175 Gambia Banjul 50 b 91.8 11.4 55 22 23 12 24 . Georgia Tbilisi 1,310 100.0 9.4.. . 98 100 58 Ghana Accra 1,500 .. 14.0 54 21 . .. .. Kumasi 780 77.7 13.7 51 21 65 . 95 51 . Guatemala ~~~~~~~Quetzaltenango 33 . 4.3 -. 15 60 55 80 40 Guinea Conakry 1,824 . .. 26 45 30 32 54 6 .D Indonesia Jakarta 9,489 95.5 14.6.. , 50 65 99 . 1.6 o Semarang 1,076b 80.2 . 34 .. 85 3- - --- CD~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Surabaya 2,373e 97.6 3.4 18 35 41 56 89 71 . Iraq ~ Baghdad 4,797 .. . . .. . .. Italy Aversa .. . .. .90 Jamai-ca Kingston 655 c97 .. 88 20 Montego Bay . - ..78 86 .. 15 Jordan Amman 1,62 1 b 97.3 6.1 21 25 98 81 99 62 54 Kenya Kisumu 134b 97.3 8.5 43 24 38 31 49 65 Mombasa 47 20 ... 50- Nairob i 2,310 . .. 71 57 89 ... 52 Korea, Rep Hanam 124b . 3.7 ... 81 68 100 100 81 Pusan 3,843 100.0 4.0 39 42 98 69 100 100 39 Seoul 10,389b 98.6 5.7 71 60 100 99 100 99 Kuwait Kuwait City 1,165 6.5 21 10 100 98 100 98 Kyrgyz Republic Elishkol< 60bu 94.8 .. 95 35 30 23 100 20 15 Lao PDR Vientiane 562b 92.2 23.2 2 27 87 100 87 20 Latvia Rigas- 775 97.4 15.6 ... 95 93 100 70 Lebanon Sin El Fil 8.3 50 10 80 30 98 80 Liberia Monrovia 651 b 57.6 28.0 80 60 Libya Tripoli 1.773b 0.8 18 20 97 90 99 6 40 Lithuania Vilnius 578 1 100.0 20.0 52 37 89 89 100 77 54 Madagascar Antananarivo 1,507 Malawi Lilongwe 765 . .. 27 5 65 12 50 10 Malaysia __ Penang . .. 7.2 55 40 99 100 98 20 Mauritania Nouakchott 881 89.9 5.4 45 50 Mexico Ciudad Juarez 1,018b 24 23 89 77 96 45 Moldova Chisinau 80 23 100 95 100 83 71 Mongolia Ullaanbaatar 627bu 51.6 7.8 80 30 60 60 100 90 96 Morocco Casablanca 3,292 .. 30 83 93 91 Rabat 6461 . .. 4 20 997 5 Myanmar Yangon 3,692 8.3 69 45 78 81 85 17 Nicaragua Leon - 98.8 15 78 84 21 Niger Niamey 731 c 87.4 .. 30 33 0 51 4 Nigeria lbadan 1,731 c 85.8 46 45 26 12 41 Lagos 13,427 93.0 48 60 41 Oman Muscat 887 . ..20 80 90 89 53 Panama Colon 132b . 14.2 .. 15 Paraguay Asuncion 1,262 90.2 10.7 . 25 46 8 86 17 Peru Caj amarca .. 90.0 3.9 -. 20 8 9 8 8 6 Huanuco 747 .. 30.0 .. 20 57 28 80 32 3.11 Urban ~-!:ioi)aet city Urban Secure House Work Travel Households with Wastewater population tenure price to trips by time access to services treated Income pubiic to work Proportion ratio trans- of people portation Access with to secure potable Sewerage tenure water connection Electricity Telephone thousands % % mintres % Ph % % 2000 1998, 1998, 19985 1998, 19955 19985 1998 1998* 1998, Huaras 54 . . 6.7 .. 15 ... 71 Iquitos 347 97.3 5.6 25 10 73 60 82 62 Lima 7,431 80.6 10.4 82 .. 75 71 99 .. 4 Tacna . .. 4.0 .. 25 65 58 74 16 64 Tumbes . ,. . .. 20 60 35 80 25 Philippines Cebu 2,189 95.0 13.3 . 3 5 41 92 80 25 Poland Bydgoszcz .. 60.5 4.3 35 18 95 87 100 85 28 Gdansk 893' . 4.4 56 20 99 94 100 56 100 Katowice 3,487 27.8 1.7 29 36 99 94 100 75 67 176 Poznan .. 65.5 5.8 51 25 95 96 100 86 78 n Qatar Doha 391 .. .. . . . . 0 Russian Federation Astrakhan ,. 100.0 5.0 66 35 81 79 100 51 92 0 ~~~~~~~~~~Belgorod .. 100.0 4.0 .. 25 90 89 100 51 96 Kostroma .. 100.0 6.9 68 20 88 84 100 46 96 a) ~~~~~Moscow 9,321 ' 100.0 5.1 85 62 100 100 100 102 98 2 ~~~~~~~~~~Nizhny Novgorod 1.458' 100.0 6.9 79 35 98 98 100 64 98 Novomoscowsk .. 100.0 4.2 61 25 99 93 100 62 97 a) ~~~~~~Omsk 1.216 99.7 3.9 86 43 87 87 100 41 89 Pushkin .. 100.0 9.6 60 15 99 99 100 89 100 o ~~~~~~~~~~Surgut .. 100.0 4.5 81 57 98 98 100 50 93 Veliky Novgorod .. 100.0 3.4 75 30 97 97 100 51 95 O Rwanda Kigali 358 .. 11.4 32 45 36 20 57 6 20 Samoa Apia 34 ' . 10.0 ... 60 0 98 96 Singapore Singapore 3,164 ' 100.0 3.1 53 30 100 100 100 100 100 Slovenia Ljubljana 273' 98.9 7.8 20 30 100 100 100 97 98 Spain Madrid 4.577 . .. 16 32 ...... 100 Pamplona .. . . . . 100 . 100 .. 79 Sweden Amal 13' . 2.9 ... 100 100 100 .. 100 Stockholm 736' . 6.0 48 28 100 100 100 .. 100 UJmea 104 .. 5.3 .. 16 100 100 100 .. 100 Switzerland Basel 170 b . 12.3 ... 100 100 100 99 100 Syria Damascus 2,335' . 10.3 33 40 98 71 95 10 3 Thailand Bangkok 5,647 77.2 8.8 28 60 99 100 100 60 Chiang Mai 499 ' 96.5 6.8 5 30 95 60 100 75 70 Togo Lome 663 ' 64.0 .. 40 30 .. 70 51 18 Trinidad and Tobago Port of Spain .. 78.6 .. 44 . .. Tunisia Tunis 2,023 ' . 5.0 ... 75 47 95 27 83 Turkey Ankara 2,837 b 91.3 4.5 .. 32 97 98 100 .. 80 Uganda Entebbe 65 74.0 10.4 65 20 48 13 42 0 30 Jinja 92 82.0 15.4 49 12 65 43 55 5 30 Uruguay Montevideo 1,670 b 88.0 5.6 60 45 98 79 100 75 34 West Bank and Gaza Gaza 367 ` 87.3 5.4 ... 85 38 99 38 Yemen, Rep. Aden 1,200' b . . 78 20 ... 96 .. 30 Sanana 1,200 ' . .. 78 20 30 9 96 .. 30 Yugoslavia, Fed. Rep. Belgrade 1.182 ' 96.5 13.5 72 40 95 86 100 86 20 Zimnbabwe Bulawayo 900 99.4 .. 75 15 100 100 98 .. 80 Chegutu .. 51.5 3.4 20 22 100 68 9 3 69 Gweru .. 94.0 . .. 15 100 100 90 61 95 Harare 1.634 ' 99.9 .. 32 45 100 100 88 42 Mutare 149 ' . .. 70 20 88 88 74 4 100 a. Data are preliminary. b. Data refer to 1998 ann are from UNCHS c. Data refer to 2000 and are from the United Nations Population Division's World urbanization Prospects: The 1999 Revision. 3.11 0 About the data Definitions Despite the importance of cities and urban ag- performance of urban areas and for developing * Urban population refers to the population of glomerations as home to almost half the world's government policies and strategies. These data the urban agglomeration, a contiguous inhab- people, data on many aspects of urban life are are collected through questionnaires completed ited territory without regard to administrative sparse. Compiling comparable data has been by city officials in more than a hundred coun- boundaries. * Secure tenure refers to the difficult, and the available indicators have been tries. The table shows selected indicators for percentage of the population protected from scattered among international agencies with dif- more than 160 cities from the UNCHS data set. involuntary removal from land or residence ferent mandates. Even within cities it is difficult A few more indicators are included on the World except through due legal process including to assemble an integrated data set. Urban ar- DevelopmentIndicatorsCD-ROM.Thesedataare residences owned, purchased, or privately eas are often spread across many jurisdictions still preliminary and are undergoing further rented; residence in social housing; and sub- with no single agency responsible for collecting validation. tenancy. * House price to Income ratio is and reporting data for the entire area. Adding to The UNCHS selection of cities does not re- the average house price divided by the aver- the difficulties of data collection are gaps and flect population weights or the economic impor- age household income. * Work trips by pub- overlaps in the data collection and reporting re- tance of cities and is therefore biased toward lic transportation are the percentage of trips sponsibilities of different administrative units. smaller cities. Moreover, it is based on demand to work made by bus or minibus, tram, or Creating a comprehensive, comparable interna- for participation in the Urban Indicators train. Buses or minibuses refer to road ve- 177 tional data set is further complicated by differ- Programme. As a result, the database excludes hicles other than cars taking passengers on ences in the definition of an urban area and by a large number of major cities. The table reflects a fare-paying basis. Other means of trans- 0 uneven data quality. this bias as well as the criterion of data avail- port commonly used in developing countries, The United Nations Global Plan of Action calls ability for the indicators shown in the table. such as taxi, ferry, rickshaw, or animal, are R for monitoring the changing role of the world's The data should be used with care. Because not included. * Travel time to work is the cities and human settlements. The international different data collection methods and definitions average time in minutes, for all modes, for a r agency with the mandate to assemble informa- may have been used, comparisons can be mis- one-way trip to work. Train and bus times tion on urban areas is the United Nations Cen- leading. In addition, the definitions used here include average walking and waiting times, 2 tre for Human Settlements (UNCHS, or Habitat). for urban population and access to potable wa- and car times include parking and walking C Its Urban Indicators Programme is intended to ter are more stringent than those used for tables to the workplace. * Households wlth access provide data for monitoring and evaluating the 3.5 and 3.10 (see Definitions). to services are the percentage of households 2) in formal settlements with access to potable Table 3.11a water and connections to sewerage, electric- House prices vary widely relative to household income ity, and telephone. Households with access to potable water are those having access to Country City Housepriceto Country City Housepriceto safe or potable drinking water within 200 Income ratio Income ratio Peruncuanuome30.0 BulgariacomeTrot meters of the dwelling. Potable water is wa- ter that is free from contamination and safe Bolivia Santa Cruz de la Sierra 29.3 Korea, Rep. Hanam 3.7 . , . ~~~~~~~~to drink without further treatment. Liberia Monrovia 28.0 El Salvador San Salvador 3.5 . * Wastewater treated is the percentage of Brazil Santo Andre 23.4 Russian Federation Veliky Novgorod 3.4 all wastewater undergoing some forrn of Lao PDR Vientiane 23.2 Zimbabwe Chegutu 3.4 treatment. Lithuania Vilnius 20.0 singapore Singapore 3.1 Bangladesh Dhaka 16.7 Sweden Amal 2.9 Latvia Riga 15.6 Ecuador Quito 2.4 Data sources Uganda Jinja 15.4 Poland Katowice 1.7 The data in the table are from the Global Urban Indonesia Jakarta 14.6 Libya Tripoli 0.8 Indicators database of the UNCHS. Source: Table 3.11. * 3.12 Traffic and congestion Motor vehicles Passenger Two-wheelers Road traffic Fuel prices cars Super Diesel per 1,000 per kilometer per 1,000 per 1,000 million vehicle $ $ people of road people people kilometers per liter per liter 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 2000 2000 Afghanistan . . . .. .. .. Albania 11 44 3 10 2 29 3 1 ... 0.57 0.30 Algeria ... ............ 0.27 0.15 Angola 19 .. . . 14 ... .. . 0.30 0.15 Argentina 181 181 27 30 134 140 1 .. 43,119 27,458 1.07 0.52 Armenia S . 2 ..1 ... .. . 0.51 0.34 Australia 530 .. 11 13 450 .. 18 .. 138,501 .. 0.57 0.57 Austria 421 536 30 22 387 495 71 77 . .. 0.82 0.74 Azerbaijan 52 49 7 16 36 38 5 1 . .. 0.46 0.22 178 Bangladesh 1 1 0 1 0 0 1 1 . .. 0.46 0.29 Belarus 61 135 13 20 59 135 .. 53 10.026 4,964 0.34 0.13 in o Belgium 423 497 30 35 385 448 14 25 ..158,759 0.96 0.78 m Benin 3 .. 2 ..2 . 34 ... . 0.48 0.39 Bolivia 41 .. 6 8 25 ..9 .. 1.139 .. 0.80 0.50 Bosnia and Herzegovina 114 .. 24 .. 101 ... .. . 0.68 0.57 E Botswana 19 70 3 11 10 30 ..1 ... 0.42 0.39 o Brazil 88 .. 8 17 ......... 0.92 0.34 a) > Bulgaria 163 266 39 60 146 233 55 63 . .. 0.70 0.58 Burkina Faso 4 .. 3 .2 ..9 ... 0.68 0.46 Z5 Bururndi ... ............ 1.01 0.71 Cambodia 1 6 0 31 0 5 9 41 314 7,210 0.61 0.44 o Cameroon 10 .. 3 .. 6 ... ... 0,56 0.47 0 (N Canada 605 581 20 19 468 459 12 11 . .. 0.58 0.47 Central African Republic 1 0 0 0 1 0 0 .. 1.494 .. 0.81 0.65 Chad 5 .. 0 ..1 .0 ... . 0.68 0.60 Chile 81 135 13 25 52 88 2 2 . .. 0.64 0.47 China S . 4 11 1 ..3 ... . 0.40 0.45 Hong Kong. China 66 78 253 287 42 58 4 5 8,192 10,781 1.46 0.80 Colombia .. 51 .. 19 .. 43 8 12 50,945 41,587 0.49 0.35 Congo, Dam. Rep. . . .... ........ 1.00 0.93 Congo, Rap. 18s . 3 .. 12 ... .. . 0.53 0.30 Costa Rica 87 133 7 14 55 88 14 22 ..507,796 0.65 0.44 C6ta dIlvoire 23 .. 6 . 15 ... .. . 0.76 0.51 Croatia .. . . 44 .. . .. . 13,764 0.76 0.60 Cuba 37 32 16 6 18 16 19 16 . .. 0.50 0.18 Czech Republic 246 363 46 67 228 335 113 78 . .. 0.77 0.68 Denmark 368 411 27 31 320 353 9 12 36,304 45,165 1.01 0.90 Dominican Republic 75 .. 48 .. 21 ... .. 0.71 0.39 Ecuador 35 46 8 14 31 41 2 2 10,306 14,449 0.31 0.18 Egypt, Arab Rap. 29 .. 33 . 21 ..6 ... 0.26 0.10 El Salvador 33 61 14 36 17 30 0 5 2,002 3.646 0.67 0.40 Eritrea 1 I. 1. 1 ... .. . 0.56 0.33 Estonia 211 394 22 11 154 331 66 1 .. 6,412 0.60 0.55 Ethiopia 1 2 2 3 1 1 0 0 .. 1,642 0.46 0.27 Finland 441 462 29 31 386 403 12 35 39.750 46,010 1.06 0.84 France 494 564 32 38 405 469 55 .. 422,000 519,400 0.99 0.82 Gabon 26 .. 4 . 19 ... .. . 0.53 0.37 Gambia, The 14 .. 5. 6 ... .. . 0.64 0.47 Georgia 107 63 27 15 89 49 5 1 4,620 .. 0.46 0.25 Germany 405 .. 53 .. 386 508 18 36 446,000 589,500 0.91 0.78 Ghana ... .... ........ 0.20 0.19 Greece 248 348 22 31 171 254 120 203 ..77,954 0.72 0.71 Guatemala .. 57 .. 45 .. 52 .. 12 .. 3,455 0.53 0.42 Guinea 4 .. 1 .2 ... .. . 0.85 0.69 Guinea-Bissau 7 .. 2 ..4 ... . Honduras 22 62 9 28 .. 52 .. 15 3,288 .. 0.62 0.46 3.12 Motor vehicles Passenger TWo-wheelers Road traffic Fuel prices cars Super Diesel per 1,000 per kilometer per 1,000 per 1,000 million vehicle $ $ people of road people people kilometers per liter per liter 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 2000 2000 Hungary 212 272 21 15 188 238 16 14 22,898 .. 0.81 0. 79 India 4 8 2 3 2 5 15 27 ... 0.60 0.39 Indonesia 16 25 10 14 7 14 34 62 ... 0.17 0.03 Iran, Islamic Rep. 34 .. 14 .. 25 .. 36 ... 0.05 0.02 Iraq 14 .. 6 I.1 . .. .. 0.03 0.01 Ireland 270 .. 10 14 227 .. 6 .. 24,205 .. 0.72 0.72 Israel 210 270 74 107 174 220 8 12 18,212 35,863 1.14 0.64 Italy 529 591 99 73 476 539 45 66 344.726 .. 0.97 0.83 Jamaica . . . .. .. .... 0.62 0.49 Japa n 469 560 52 62 283 395 146 115 628,581 765,056 1.06 0.76 179 Jordan 60 .. 26 .. . . 0 . 1,098 .. 0.45 0.15 Kazakhstan 76 86 8 12 50 66 .. 10 18,248 3,215 0.36 0.29 Kenya 13 .. 5. 10 .. 1 . 5,170 .. 0.71 0.60 N Korea, Dem. Rep. ... .... ........ 0.73 0.41 Korea, Rep. 79 238 60 128 48 167 32 59 30,464 67,266 0.92 0.66 o. Kuwait ... .... ........ 0.21 0.1.8 Kyrgyz Republic 44 39 10 10 44 39 ... 5,220 .. 0.44 0.3,3 Lao PDR 9 .. 3 .. 6 . 18 ... . 0.41 0.32 ' Latvia 135 260 6 9 106 218 76 8 3,932 .. 0.67 0. 58 CD Lebanon 321 336 183 .. 300 313 13 15 . .. 0.53 0.31 5 Lesotho 11 .. 4 .. 3 ... .. 0.50 0.47 9 Liberia 15 .. 4 ..7 . .. .. . Libya ... ............ 0.25 0.16 Lithuania 159 322 12 17 132 334 52 5 . .. 0.55 0.45 Macedonia, FYR 132 .. 30__ 121 .1.. 3,102 .. 0.76 0.56 Madagascar 6 .. 2 .. 4 ... 41,500 .. 0.76 0.45 Malawi 4 .. 4 .2 ... .. . 0.69 0.68 Malaysi'a 124 200 26 69 101 1 70 167 224 . .. 0.28 0.16 Mali 4 .. 2 ..2 ... .. 0.70 0.43 Mauritania 9 .. 3 ..7 ... .. 0.67 0.40 Mauritius 60 98 35 49 44 73 54 96 . Mexico 119 151 41 44 82 102 3 .. 55,095 .. 0.61 0.45 Moldova 53 70 17 24 48 54 45 ... 538 0.45 0.40 Mongolia 21 30 1 2 6 17 22 11 340 40 0.38 0.38 Morocco 37 52 15 21 _28 41 1 1 ... 0.82 0.53 Mozambique 4 2 0 3 .. . . 1,889 .. 0.56 0.54 Myanmar . .. ... ...... 0.00 0.00 Namibia 71 0 1 2 39 .. 1 .. 1,896 2.706 0.47 0.44 Nepal ... ...... 0.63 0.37 Netherlands 405 427 58 58 368 383 44 25 90,150 109,955 1.03 0.78 New Zealand 524 540 19 29 436 481 24 12 . .. 0.48 0.34 Nicaragua 19 10 5 8 10 3 3 2 108 523 0.62 0.54 Niger 6 4 5 5 .. . . 178 240 0.68 0.48 Nigeria 33 .. 21 14 12 ..5 ... . 0.27 0.27 Norway 458 505 22 25 380 407 48 54 ..30,148 1.19 1.15 Oman 130 9 .. 83 .. 3 ... 0.31 0.29 Pakistan 6 8 4 4 4 5 8 15 18,933 218.779 0.53 0.27 Panama 75 113 18 27 60 83 2 3 . .. 0.53 0.41 Papua New Guinea.. . ... ...... 0.53 Ci.34 Paraguay ... .... ........ 0.72 01.34 Peru .. 43 .. 15 .. 27 ...... 0.80 0.54 Philippines 10 31 4 11 7 10 6 14 6,189 9,548 0.37 0.28 Poland 168 286 18 33 138 240 36 37 59,608 138,100 0.76 0.65 Portugal 222 348 34 .. 162 310 5 77 28,623 93,020 0.77 0.54 Puerto Rico .. . .. . .. . 034 0.32 Romania 72 154 11 17 56 133 13 14 23,907 36,884 0.46 0.35 Russian Federation 87 153 14 48 65 120 . .. 60,950 0.33 0.29 3.12 Motor vehicles Passenger Two-wheelers Road traffic Fuel prices cars Super Diesel per 1,000 per kilometer per 1,000 per 1,000 million veh,c e $ $ people of road people people kv ometers per liter per liter 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 2000 2000 Rwanda 2 .. 1 2 1 ... .. . 0.89 0.84 Saudi Arabia 165 .. 19 .. 98 ..0 .. . 0.24 0.10 Senegal 11 .. 6 8 8 ..0 ... . 0.73 0.52 Sierra Leone 10 3 4 2 7 2 2 0 996 529 0.00 0.00 Singapore 130 132 142 170 89 97 40 34 . .. 0.84 0.38 Slovak Republic 194 260 57 33 163 229 61 8 .. 0 0.69 0.68 Slovenia 306 455 42 46 289 418 8 5 5,620 9,245 0.63 0.66 Somalia 2 .. 1 0 1 . . South Africa 160 143 26 11 97 94 8 4 0.50 0.50 180 Spain 360 472 43 53 309 389 79 34 100,981 201,896 0.73 0.65 Sri Lanka 20 34 4 7 6 15 23 40 3,468 15,630 0.66 0.27 (0 Sudan 9 .. 21 28 8 ... .. . 0.28 0.24 ro Swaziland 72 70 18 17 35 34 3 3 . .. 0.47 0.44 Sweden 464 478 29 21 426 437 11 29 61.040 69,200 0.94 0.80 Switzerland 491 526 46 54 449 486 114 104 48,660 53,506 0.78 0.84 E Syrian Arab Republic 26 30 10 11 10 9 . ... .. 0.44 0.13 C). o Tajikistan 3 .. 1 ..0 ... 0.45 0.55 > Tanzania S . 2 2 1 ... . 0.75 0.73 0) o Thailand 46 . 36 .. 14 . 86 .. 45.769 .. 0.39 0.35 -0 o Togo 24 .. 11 .. 16 ..8 ... . 0.48 0.40 Trinidad and Tobago . . . .. . ...... 0.39 0.20 o Tunisia 48 .. 19 40 23 ... .. . 0.49 0.29 Turkey 50 85 8 14 34 63 10 15 27,041 49.846 0.88 0.66 Turkmenistan ... .... ........ 0.02 0.02 Uganda 2 5 .. 4 1 2 0 3 . .. 0.86 0.75 Ukraine 63 .. 19 .. 63 104 .. 49 59,500 61,200 0.37 0.30 United Arab Emirates 121 .. 52 .. 97 ... .. 0.25 0.26 United Kingdom 400 418 64 62 341 373 14 12 399,000 462,400 1.17 1.22 United States 758 760 30 34 573 478 17 14 2,527,441 2,653,043 0.47 0.48 Uruguay 138 1 74 45 63 122 158 74 110 . .. 1.19 0.53 Uzbekistan ... .... ........ 0.43 0.28 Venezuela, RB . . .. . .. .. 563 0.12 0.08 Vietnam ... .... .. 45 ... . 0.38 0.27 West Bank and Gaza ... .... ........ 0.01 0.00 Yemen, Rep. 34 .. 8 .. 14 .. . . 8,681 11,476 0.21 0.06 Yugoslavia. Fed. Rep. 137 190 31 36 133 1 76 3 ... . 0.56 0.56 Zambia 15 .. 3 .. 8 ... .. 1.00 1.00 Zimbabwe ... .... ........ 0.85 0.72 Low Income 9 10 6 9 0.58 0.40 Middle Income 40 65 26 49 0.55 0.41 Lower middle income 15 33 9 24 0.53 0.39 Upper middle income 116 191 93 150 0.58 0.45 Low & middle Income 26 60 17 47 0.56 0.41 East Asia & Pacific 11 16 5 10 0.39 0.34 Europe & Central Asia 98 205 83 171 0.58 0.55 Latin America & Carib. 92 158 77 119 0.62 0.41 Middle East & N. Africa 58 ..32 ..0.27 0.16 South Asia 4 8 2 5 0.59 0.33 Sub-Saharan Africa 24 14 ..0.65 0.47 High Income 536 610 414 459 0.81 0.69 Europe EMU 453 558 379 496 0.87 0.76 3.12 About the data Definitions Traffic congestion in urban areas constrains Figure 3.12 * Motor vehicles include cars, buses, and economic productivity, damages people's health, freight vehicles but not two-wheelers. Popula- and degrades the quality of their lives. The par- World production of automobiles and bicycles tion figures refer to the midyear population in ticulate air pollution emitted by motor vehicles- has increased significantly since 1950 the year for which data are available. Roads the dust and soot in exhaust-is proving to be refer to motorways, highways, main or national far more damaging to human health than was 100 roads, and secondary or regional roads. A once believed. (For information on suspended so 0 Automobiles motorway is a road specially designed and built particulates and other air pollutants see table 80 [l Bicycles for motor traffic that separates the traffic flow- 3.13.) 70 ing in opposite directions. * Passenger cars In recent years ownership of passenger 60 refer to road motor vehicles, other than two- cars has increased, and the expansion of eco- = 50 wheelers, intended for the carriage of passen- nomic activity has led to the transport by road 2 40 gers and designed to seat no more than nine of more goods and services over greater dis- 30 people (including the driver). * Two-wheelers tances (see table 5.8). These developments 20 refer to mopeds and motorcycles. * Road traf- have increased demand for roads and vehicles, 10 fic is the number of vehicles multiplied by the 181 adding to urban congestion, air pollution, o 1970 1980 1990 200 average distances they travel. * Fuel prices health hazards, traffic accidents, and injuries. 1950 refer to the pump prices of the most widely 0 Congestion, the most visible cost of expand- Scue: Aoneri-nAuto-b2le0Maufa)rersAssociatosasciteoin sold grade of gasoline and of diesel fuel. Prices ing vehicle ownership, is reflected in the indica- have been converted from the local currency tors in the table. Other relevant indicators-such to U.S. dollars (see About the data). a as average vehicle speed in major cities or the - _ cost of traffic congestion, which takes a heavy CD D toll on economic productivity-are not included D o 3 here because data are incomplete or difficult The data on vehicles and traffic are frorri the CD to compare. i IRF's electronic files and its annual World Road The data in the table-except for those on Statistics. The data on fuel prices are from the fuel prices-are compiled by the International I GTZ's electronic files. Road Federation (IRF) through questionnaires l _ . .- sent to national organizations. The IRF uses a hierarchy of sources to gather as much informa- tion as possible. The primary sources are na- tional road associations. Where such an asso- ciation lacks data or does not respond, other agencies are contacted, including road director- ates, ministries of transport or public works, and central statistical offices. As a result, the com- piled data are of uneven quality. The coverage of each indicator may differ across countries because of differences in definitions. Compara- bility also is limitecd when time-series data are reported. Moreover, the data do not capture the quality or age of vehicles or the condition or width of roads. Thus comparisons over time and between countries should be made with caution. The data on fuel prices are compiled by the German Agency for Technical Cooperation (GTZ) from its global network of regional offices and representatives as well as other sources, in- cluding the Allgemeiner Deutscher Automobil Club (for Europe) and a project of the Latin Ameri- can Energy Organization (OLADE, for Latin America). Local prices have been converted to U.S. dollars using the exchange rate on the sur- vey date as listed in the international monetary table of the Financial Times. For countries with multiple exchange rates, the market, parallel, or black market rate was used rather than the official exchange rate. g xi!) 3.13 Air pollution City City Total Sulfur Nitrogen population suspended dioxide dioxide particulates About the data In many towns and cities exposure to air pol- lution is the main environmental threat to micrograms per micrograms per micrograms per human health. Winter smog-made up of soot, thousands cubic meter cubic meter cubic meter 2000 1995' 1998, 1998, dust, and sulfur dioxide-has long been as- Argentina C6rdoba City 1,423 97 97 sociated with temporary spikes in the num- Australia Melbourne 3,187 35 0 30 ber of deaths. Long-term exposure to high lev- Perth 1,313 45 5 19 els of soot and small particles in the air also Sydney 3,664 54 28 81 contributes to a wide range of chronic respira- Austria Vienna 2,070 47 14 42 tory diseases and exacerbates heart disease Belgium Brussels 1,122 78 20 48 and other conditions. Particulate pollution, on its own or in combination with sulfur dioxide, leads to an enormous burden of ill health. 53c Paulo 117,755 86 43 83 Emissions of sulfur dioxide and nitrogen ox- Bulgaria Sofia 1,192 195 39 122 ides lead to the deposition of acid rain and other 182 Canada Montreal 3.448 34 10 42 acidic compounds over long distances-often Toronto 4,651 36 17 43 more than 1,000 kilometers from their source. Vancouver 2,033 29 14 37 Acid deposition changes the chemical balance 'J Chile Santiago 5,538 .. 29 81 of soils and can lead to the leaching of trace '0 China Anshan 1,453 305 115 88 minerals and nutrients critical to trees and Beijing 10,839 377 90 122 plants. The links between forest damage and E Changchun 3,093 381 21 64 acid deposition are complex. Direct exposure to 0D Chengdu 3,294 366 77 74 high levels of acid deposition can cause defo- a) ED Chongqing 5,312 320 340 70 liation and dieback. a Dalian 2,628 185 61 100 Where coal is the primary fuel for power o Guangzhu 3.893 295 57 136 plants, steel mills, industrial boilers, and domes- Guiyang 2.533 330 424 53 tic heating, the result is usually high levels of ° Harbin 2,928 359 23 30 urban air pollution-especially particulates and 'N Jinan 2,568 472 132 45 sometimes sulfur dioxide-and, if the sulfur Kunming 1.701 253 19 33 content of the coal is high, widespread acid depo- Lanzhou 1,730 732 102 104 sition. Where coal is not an important primary Liupanshui 2,023 408 102 fuel or is used by plants with effective dust Nanchang 1.722 279 69 29 control, the worst emissions of air pollutants Pinxiang 1,502 276 75 . stem from the combustion of petroleum prod- Quingdao 2,316 .. 190 64 ucts. Shanghai 12.887 246 53 73 The data on air pollution are based on reports Shenyang 4.828 374 99 73 from urban monitoring sites. Annual means Taiyuan 2,415 568 211 55 (measured in micrograms per cubic meter) are Tianjin 9,156 306 82 50 average concentrations observed at these sites. Urumqi 1,643 515 60 70 Coverage is not comprehensive because not all Wuhan 5,169 211 40 43 cities have monitoring systems. For example, Zhengzhou 2,070 474 63 95 ~~~~~~~~data are reported for just 5 cities in Africa but for more than 87 cities in China. Pollutant con- Zibo 2 675 453 198 43 centrations are sensitive to local conditions, and Colombia Bogot5 6,288 120 even in the same city different monitoring sites Croatia Zagreb 810 71 31 may register different concentrations. Thus Cuba Havana 2,256 .. 1 5 these data should be considered only a general Czech Republic Prague 1,226 59 14 33 indication of air quality in each city, and cross- Denmark Copenhagen 1,388 61 7 54 country comparisons should be made with cau- Ecuador Guayaquil 2,293 127 15 tion. World Health Organization (WHO) annual Quito 1,754 175 22 mean guidelines for air quality standards are 90 Egypt. Arab Rep. Cairo 10.552 .. 69 micrograms per cubic meter for total suspended Finland Helsinki 1.167 40 4 35 particulates, and 50 for sulfur dioxide and nitro- France Paris 9,624 14 14 57 gen dioxide. Germany Berlin 3,324 50 18 26 Frankfurt 3,687 36 11 45 Munich 2,294 45 8 53 Ghana Accra 1,976 137 Greece Athens 3,116 178 34 64 Hungary Budapest 1,825 63 39 51 Iceland Reykjavik 168 24 5 42 India Ahmedabad 4.160 299 30 21 Bangalore 5,561 123 Calcutta 12,918 375 49 34 3.130 CIty City Total Sulfur Nitrogen population suspended dioxide dioxide particulates Definitions * City population is the number of residents of the city as defined by national authorities micrograms per micrograms per micrograms per and reported to the United Nations. * Total thousands cubic meter cubic meter cubic meter 1 2000 1995 1 i9 1 98 suspended particulates refer to smoke, soot, Chennai 6,002 130 15 17 dust, and liquid droplets from combustion that Delhi 11,695 415 24 41 are in the air. Particulate levels indicate the Hyderabad 6,842 152 12 17 ~~~~~~~~quality of the air people are breathing and the - - --- - ------state of a country's technology and pollution Kanpur 2,450 459 15 14 Lucknow 2,568 463 26 25 cotos ufrdoie(SO2)isaarpout ---------- --- - --- ant produced when fossil fuels containing sul- Mumbai 18,066 240 33fur are burned. It contributes to acid rain and Nagpur 2,062 185 6 13 -----I-----.---- -- can damage human health, particularly that of Pune 3,489~ ~~~ 208... -- - ------ the young and the elderly. * Nitrogen dioxcide Inoei -akarta-11,018 271 (NO2) is a poisonous, pungent gas formed when Iran, Islamic Rep. Tehran 7,225 248 209nircodeomnswthyrcabsad 18 Ireland Dublin 985 ..20 sunlight, producing a photochemical reaction. Italy Milan ....-- 4- 251-- 77-3-24 These conditions occur in both natural and 0 - --- ---- Rome ......-- 2,688 73 anthropogenic activities. NO2 is emittedi by0 Torino 1,294 151_ bacteria, nitrogenous fertilizers, aerobic decom- o Japan Osaka 11,013 43 19 63 position of organic matter in oceans and soils, c Tokyo 26.444 49 18 68 combustion of fuels and biomass, motor ye- D -------- ---------------- --- - <~~~~~~~~~~~ Yokohama 3,178 100 13 hicles, and industrial activities. C Kenya Nairobi 2,310 69 -. .- Korea, Rep. Seoul 9,888 84 44 60 Pusan 3,830 94 60 51 Data sources Seoul 9,888 84 44 60 The data in the table are from the WHO's ... --------~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Taegu 2.675 72 81 62 iHealthy Cities Air Management Information Malaysia____ .........Kuala Lumpur 1,378 85 24 . jSystem and the World Resources Institute, 5' Mexico Mexico City 18,131 279 74 130 iwhich relies on various national sources as well Netherlands Amsterdam 1,144 40 10 58 as, among others, the United Nations New Zealand Auckland 1,102 26 3 20 Environment Programme and WHO's Urban Air Norway Oslo 970~~~~-- --- ------ 15 8-43-- Pollution in Megacities of the World (1992), - -------- - ------------ ----the Organisation for Economic Co-operation and Philippines Manila 10,870 200 33 . Poland Lodz 1,055 ..2 3Development's OECD Environmental Data: Warsaw 2.269 ..16 32 ICompendium 1999, the U.S. Environmental Portugal Lisbon 3.826 61 8 52 Protection Agency's National Air Quality and - 1------ Emissions Trends Report 1995, AIRS ExeCuitive Romnia------- ----- uchres 2,54------- - 8 171International database, and the United Nations Russian Federation Moscow 9.321 100 109 - Omak 1 216 100 20 34- Centre for Human Settlements' (UNCHS) Ur'ban - I ~~~~~~~~~~~~~~~~~Indicators database. Singapore Singapore 3,567 ..20 30 ________ Slovak Republic Bratislava 460 62 21 27 South Africa Capetown 2,993 ..21 72 Durban 1,335 ..31 Johannesburg 2,335 ..19 31 Spain Barcelona 2,819 117 11 43 Madrid 4,072 42 24 66 Sweden Stockholm 1,583 9 3 2 Switzerland Zurich 983 31 11 39 Thailand Bangkok 7,281 223 11 23 Turkey Ankara 3,203 57 55 4 Istanbul 9,451 ..120 Ukraine Kiev 2.670 100 14 51 United Kingdom Birmingham 2,272 ..9 45 London 7,640 ..25 77 Manchester -2252 --------26 49 United States Chicago 6,951 14 57 Los_Angeles 13,140 9 74 Ne.w York-------- 16,640 -------------- 26 79 Venezuela, RB Caracas 3,151 53 33 57 a. Data are for the most recast year available in 1990.95. Most are for 1995. b. Data are for the niost recent year available in 1990-98. Most are for 1995. 3.14 Government commitment Environmental Country Blodiversity Participation In treaties' strategy environmental assessment, or action profile strategy or Table 3.14a plan action plan Status of national environmental action plans Law Climate Ozore CFC of the Biological ___________________________________ ~~~~~~change layer control Sea' diversity' Completed Afghanistan Albania Ghana Niger Albania 1993 .. 1995 2000 2000 .. 1994 Algeria Grenade Nigeria Algeria 2001 1994 1993 1993 1996 1995 Armenia Guinea Pakistan Angola 2000 c2000 2000 1994 1998 Azerbaijan Guinea-Bissau Papua New Guinea Argentina 1992 1994 1990 1990 1996 1995 Bangadesh Guaena Poland Armenia 1994 2000 2000 ..1993 Belaru.s Hait Romania Australia 1992 1994 1994 1987 1989 1995 1993 Benin Honduras Russian Federation Austria 1994 1987 1989 1995 1994 Bhutan India Rwanda Azerbaijan 1998 1995 1996 1996 2000' BlvaIdnsa S5 oi n dcp 184 Bangladesh 1991 1989 1990 1994 1990 1990 2001 1994 Botswana Irar, Islamic Rep. Senegal Belarus 2000' 1986 1989 1993 o Belgium 1996 1989 1989 1998 1997 Bugraazktn Sehle ra Benin 1993 1994 1993 1993 1997 1994 Burkinma Fuse Kenya Sierra Leone ' ~ Bolivia 1994 1986 1988 1995 1995 1995 1995 1995 Burundi Kiribati Slovak Republic Bosnia and Herzegovina 1992 1992 1994 Cmoi y~ eulc Soei E Botswana 1990 1986 1991 1994 1992 1992 1994 1996 CanroLoPRSlmnIsnd oL Brazil 1988 1994 1990 1990 1994 1994 Cape Verde Latvia SouthAfrica >, Bulgaria ..1994 1995 1991 1991 1996 1996 China Lebanon Sri Lanka o Burkina Faso 1993 1994 1994 1989 1989 1993 Colombia LeSotho St. Kitty and Nevis 0O Burundi 1994 1981 1989 1997 1997 1997 1997 Comoros Lithuania Swaziland Cambodia 1999 . . 1996 .. . . 1995 Congo, Dem.Rep. Macedonia,FYR Syrian Arab Rep, o Cameroon .. 1989 1989 1995 1989 1989 1994 1995 Congo. Rep. Madagascar Tanzania CN Canada 1990 .. 1994 1994 1986 1988 1993 Costa Rica Malawi Togo Central African Republic ... . 1995 1993 1993 .. 1995 Crite dIvoire Maldives Tonga Chad 1990 1982 .. 1994 1989 1994 .. 1994 Croatia Mali Tunisia Chile .. 1987 1993 1995 1990 1990 1997 1994 Czech Republic Mauritania Turkey China 1994 .. 1994 1994 1989 1991 1996 1993 Djibouti Mauritius Uganda Hong Kong, China Egypt, Arab Rep, Menico Ukraine Colombia 1998 1990 1988 1995 1990 1994 .. 1995 Congo. Dem. Rep. 1986 1990 1995 1995 1995 1994 1995 E avdr Mloa Uga Congo. Rep. . . 1990 1997 1995 1995 .. 1996 EqaoilGne Mngia Ubksn Costa Rica 1990 1987 1992 1994 1991 1991 1994 1994 EiraMnsra aut C6te dIlvoire 1994 .. 1991 1995 1993 1993 1994 1995 Estonia Morocco West Bunk and Gaza Croatia 2001 1998 2000 1996 1992 1992 1994 1997 Ethiopia Mozambique Vietnam Cuba ... . 1994 1992 1992 1994 1994 Gabon Namibia Yemen, Rep. Czech Republic 1994 ... 1994 1993 1993 1996 1994 Gambia. The Nepal Zambia Denmark 1994 ... 1994 1988 1989 .. 1994 Georgia Nicaragua Dominican Republic .. 1984 1995 2000 1993 1993 .. 1996 Ecuador 1993 1987 1995 1994 1990 1990 .. 1993 Being prepared Eg'ypt, Arab Rep. 1992 1992 1988 1995 1988 1988 1994 1994 Argentina Ecuador Tajikistan El Salvador 1994 1985 1988 1996 1993 1993 .. 1994 Belize Korea,Rep, Turkmen stan Eritrea 1995 .. 1995 .. . . 1996 Central African Malaysia Zimbabwe Estonia 1998 ... 1994 1997 1997 .. 1994 Republic Paraguay Ethiopia 1994 .. 1991 1994 1995 1995 .. 1994 Dominican Republic Finland 1995 ... 1994 1986 1989 1996 1994 Nate: Status ,s as o1 January Otto France 1990 .. 1994 1988 1989 1996 1994 So-ca mairid Bank iegional data, WMild Resources institiute, Gabon .. 1990 2000 1994 1994 .. 2000 International Institute In, Environment uad Dnvnoyri-ent, and IUCN. Gambia. The 1992 1981 1989 1994 1990 1990 1998 1994 19a6 World Di,ectory ot Country E-s,ro,nme,ta1 studies Georgia 1998 . . 1994 1996 1996 1996 1994 Germany ... . 1994 1988 1989 1994 1994 Ghiana 1992 1985 1988 1995 1989 1989 1994 1994 Greece ... . 1994 1989 1989 1995 1994 Guatemala 1994 1984 1988 1996 1987 1990 1997 1995 Guinea 1994 1983 1988 1994 1992 1992 1994 1993 Guinea-Bissau 1993 .. 1991 1996 . ..1994 1996 Haiti 1999 1985 .. 1996 2000 2000 1996 1996 Honduras 1993 1989 .. 1996 1994 1994 1994 1995 3.140 Environmental Country Blodiversity Participation In treaties' strategy environmental assessment, or action profile strategy or Table 3.14b plan action plan States that have signed the Convention on Climate Change Law Climate Ozone CFC of the Biologica ______________ ~~~~~change layer control Sea' diversity' Antigua and Barbuda- Guatemala- Palau- Hungary 1995 1994 1988 1989 1994 Argentina- Guinea- Panama- India 1993 1989 1994 1994 1991 1992 1995 1994 Australia Honduras' Papua New Gujinea Indonesia 1993 1994 1993 1994 1992 1992 1994 1994 Austria Indonesia Paraguay' Iran, Islamic Rep. ... 1996 1991 1991 .. 1996 Azerbaijan' Ireland Peru Iraq ... . 1994 Bahamas, The' Israel Philippines Ireland .. 1994 1988 1989 . 1996 Bangladesh- Italy Poland Israel . 1996 1992 1992 .. 1995 Barbados, Jamaica, Portugal Italy -.----.-- -- 1994 1988 1989 1995 1994 BlimJpnRmna Jamaica 1994 1987 1995 1993 1993 1994_ 1995 -- - -------- -- ~~~~~~~ ~ ~~~~~~~~~~Bolivia' Kazakhstan Russian Federation Japan .. -- --- .---- 1994 1988 1988 1996 1993 BaiKrbti Smo 185 Jordan 1991 1979 1994 1989 1989 1995 1994 Kazakhstan ... . 1995 1998 1998 .. 1994 BugraKoe,Rp. Sngl Kenya 1994 1989 1992 1994 1989 1989 1994 1994 Bujd'LtiaSyhle Korea, Dem. Rep.. 1995 1995 1995 .. 1995 CadaLot' Slakeulc0 Korea, Rep.. 1994 1992 1992 1996 1995 CieLehesen Soei Kuwait ... 1995 1993 1993 1994 ChnCihaDaSlmn sad Kyrgyz Republic 1995 .. 2000' 2000 2000 .. 1996 Cook Islands Luxembourg Spain C Lao PDR 1995 ..1995 1998 1998 1998 1996 Costa Rica' Malawi' St. Lucia 'a Latvia .. 1995 1995 1995 .. 1996 Croatia Malaysia St. Vincent and the Lebanon .. 1995 1993 1993 1995_ 1995_ Cuba Maldives' Grenadines Lesotho 1989 1982 1995 1994 1994 .. 1995 Cyprus' Mal Sweden 2 - - - - - ----------- -~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~O Liberia ... . . . Czech Republic Malta Switzerland Libya .. 1999 1990 1990 Denmark Marshall Islands Thailand Lithuania .. 1995 1995 1995 1996 Ecuador' Mauritius' Trinidad and Tobago' Macedonia, FYR .. 2000 1994 1994 1994 1997 Egypt, Arab Rep. Mexico' Turkmenistan- Madagascar 1988 1991 1996 1997 1997 2001 1996 El Salvador' Micronesia' Tuvalu' Malawi 1994 1982 ........1994 1991 1991 .. 1994 Equatorial Guinea' Monaco Ukraine Malaysia 1991 1979 1988 1994 1989 1989 1997 1994 ------ -- - - - ---------- ------ - __ - ~~~~~Estoria M ongolia' United Kingdom- Mali 1991 1989 1995 1995 1995 1994 1995 Fiji' Nauru United States Mauritania 1988 1984 .. 1994 199 199 199 199 ---------- - ~~~~~~~ ~ ~~~~~~~~~~~~~~~~~Finland Netherlands Uruguay' Mauritius 1990 .. 1994 1992 1992 1994 1993 Mexico ..1988 1994 1987 1988 1994 193 Fac e eln zeitn Moldova .. . 1,995 1997 1997 .. 1996 Gambia. The- Nicaragua' Vanuatu' Mongolia 1995 ... 1994 1996 199 1997 199 Gogi'Ngr3ita Morocco ~~~~~ ~~~~~ ~~~~1980 1988 1996 1996 1996 .. 1995 Germany Niue- Zambia Mozambique 1994 .. -199 1994 1994 1997 1995 Greece Norway Myanmar 1982 1989 1995 1994 1994 1996 1995 Notes Status is as of Decem,ber 2001. Namibia 1992 .. 1995 1993 1993_ 1994___1997 a. Ratification or accesnioe signed. ~~~~~Suorce: Secretariat of the United Nations Framework Convention Nepal 1993 1983 . 194 1994 1994 1998 1994 onuClimate Change. Netherlands 1994 .. 1994 1988 1989 1996 1994 New Zealand 1994 .. 1994 1987 1988 1996 1993 Nicaragua 1994 1981 . 196 1993 1993 2000) 1996 Niger .. 1985 1991 1995 1993 1993 .. 1995 Nigeria 1990 1992 1994 1989 1989 1994 1994_ Norway ..1994 1994 1986 1988 1996 1993 Oman 1981 .. 1995 199 1999 1994 1995 Pakistan 1994 1994 1991 1994 1993 1993 1997 1994 Panama 1990 19890. 1995 1989 1989 1996 1995 Papua New Guinea 1992 1994 1993 1994 1993 1993 1997 1993 Paraguay .. 1985 .. 1994 1993 1993 1994 1994 Peru .. 1988 1988 1994 198 1993 .. 1993 Philippines 1989 1992 1989 1994 1991 1991 1994 1994 Poland 1993 .. 1991 1994 1999 1999 1998 1996 Portugal 1995 ... 1994 1989 1989 1997 1994 Puerto Rico... .. . Romania 1995 .. 1994 1993 1993 1997 1994 Russian Federation 1999 .. 1994 1995 1986 1989 1997 1995 3.14 Environmental Country Blodiversity Participation In treaties' strategy environmental assessment, or action profile strategy or Figure 3.14 plan action plan A global focus on biodiversity and climate change Law Climate Ozone CFC of the Biological Allocation of funds by the Global Environmental Faciliry, ________________________________ _____________change layer control Sea' diversity' February 1995-Jane 2000 Rwanlda 1991 1987 .. 1998 .. 1996 Saudi Arabia ... . 1995 1993 1993 Senegal 1984 1990 1991 1995 199 1993 1994 1995 Total allocation: $2,937 million Sierra Leone 1994 ... 1995 ..1995 199 By region Singapore 1993 1988 1995 1997 1989 1989 1994 199 Latin America & Caribbean Slovak Republic ... 1994 1993 1993 1996 19944 24% Slovenia 1994 .. 1996 1992 1992 1994 1996 Euror & Global and Somalia .... .. . . 1994 Central Asia Iter-Regional South Africa 1993 ..2000 1990 1990 1997 21000 186 Spain . . 1994 1988 1989 1997 1994 Sri Lanka 1994 1983 1991 1994 1999 1999 1994 1994 Sudan 1989 1994 1993 1993 1994 1996 co Swaziland 1997 .. -1995 V Sweden ... . 1994 1987 1988 1996 1994 Switzerland ..1994 1988 1989 . 1995ia E Syrian Arab Republic 1999 1981 .. 1996 1999 19990 . 19962% O Tajikistan .. 1998 1996 1998 1997 a, > Tanzania 1994 1989 1988 1996 1993 1993 1994 1996 C) o Thailand 1992 199 198 198 By focal area o Togo 1991 . . 1995 1991 1991 1994 1996 Climate change Trinidad and Tobago .. .. 1994 1989 1989 1994 1996 -. a~ International o Tunisia 1994 1989 1988 1994 1989 1989 1994 1993 .1% 0 4 CN Turkey 1998 1982 .. . 1991 1991 .. 1997 Turkmenistan . .1995 1994 1994 .. 1996 Uganda 1994 1982 1988 1994 1988 1988 1994 1993 Ukraine 1999 ..1997 1988 1988 1999 1995 United Arab Emirates ...1996 1999 1999 zn depletion United Kingdom 1995 .. 1994 1994 1987 1989 1997 1994 5 United States 1995 .. 1995 1994 1986 1988 1. 1993 Biodivrsir Uruguay ... . 1994 1989 1991 1994 1994 4%Multiple focal areas4% Uzbekistan ... . 19964 1993 1993 .. 1995 Venezuela, RB .... 1995 1988 1989 .- 1994 Vietnam . 1993 1995 1994 1994 1994 1995 Sou,ce- Gotbal Enur,oninertal Facility data. West Bank and Gaza .... . Yemen, Rep. 1996 1999 1992 1996 1996 1996 1994 19% Yugoslavia, FR (Serb./Mont.) ... . 1997 1999 1991 2013) Zambia 1994 1988 .. 1994 1999 1990 1994 1993 Zimbabwe 1987 1982 .. 1994 1993 1993 1994 1995 a. The yearn shown refer to the year the treaty entered into force in that country. b. Convention became effective November 16, 1994. c. Ratification of the treaty. 3.14 0 About the data Definitions National environmental strategies and par- man Environment in Stockholm and the 1992 * Environmental strategies and action plans ticipation in international treaties on environ- United Nations Conference on Environment provide a comprehensive, cross-sectoral analy- mental issues provide some evidence of gov- and Development (the Earth Summit) in Rio sis of conservation and resource management ernment commitment to sound environmen- de Janeiro: issues to help integrate environmental con- tal management. But the signing of these trea- * The Framework Convention on Climate cerns with the development process. They in- ties does not always imply ratification, nor Change aims to stabilize atmospheric con- clude national conservation strategies, national does it guarantee that governments will com- centrations of greenhouse gases at levels environmental action plans, national environ- ply with treaty obligations. that will prevent human activities from in- mental management strategies, and national In many countries efforts to halt environ- terfering dangerously with the global cli- sustainable development strategies. The year mental degradation have failed, primarily be- mate. shown for a country refers to the year in which cause governments have neglected to make * The Vienna Convention for the Protection of a strategy or action plan was adopted. * Coun- this issue a priority, a reflection of competing the Ozone Layer aims to protect human try environmental profiles identify how national claims on scarce resources. To address this health and the environment by promoting economic and other activities can stay within problem, many countries are preparing na- research on the effects of changes in the the constraints imposed by the need to con- tional environmental strategies-some focus- ozone layer and on alternative substances serve natural resources. The year shown for a 187 ing narrowly on environmental issues, and (such as substitutes for chlorofluorocar- country refers to the year in which a profile was others integrating environmental, economic, bons) and technologies, monitoring the completed. * Blodiversity assessments, strat- and social concerns. Among such initiatives ozone layer, and taking measures to con- egles, and action plans include biodiversity are conservation strategies and environmen- trol the activities that produce adverse profiles (see About the data). * Participa- E tal action plans. Some countries have also effects. tion In treaties covers five international trea- a 0 prepared country environmental profiles and * The Montreal Protocol for CFC Control re- ties (see About the data). * Climate change CD biological diversity strategies and profiles. quires that countries help protect the earth refers to the Framework Convention on Cli- CD ~0 National conservation strategies-promoted from excessive ultraviolet radiation by cut- mate Change (signedin New York in 1992). by the World Conservation Union (IUCN)- ting chlorofluorocarbon consumption by 20 * Ozone layer refers to the Vienna Conven- C provide a comprehensive, cross-sectoral analy- percent over their 1986 level by 1994 and tion for the Protection of the Ozone Layer sis of conservation and resource management by 50 percent over their 1986 level by (signed in 1985). * CFC control refers to a issues to help integrate environmental con- 1999, with allowances for increases in the Montreal Protocol for CFC Control (for- 0 cerns with the development process. Such consumption by developing countries. mally, the Protocol on Substances That strategies discuss current and future needs, * The United Nations Convention on the Law Deplete the Ozone Layer, signed in 1987). institutional capabilities, prevailing technical of the Sea, which became effective in No- * Law of the Sea refers to the United Nations conditions, and the status of natural re- vember 1994, establishes a comprehen- Convention on the Law of the Sea (signed sources in a country. sive legal regime for seas and oceans, in Montego Bay, Jamaica, in 1982). Nationai environmental action plans (NEAPs), establishes rules for environmental stan- * Biological diversity refers to the Conven- supported by the World Bank and other develop- dards and enforcement provisions, and tion on Biological Diversity (signed at the ment agencies, describe a country's main envi- develops international rules and national Earth Summit in Rio de Janeiro in 1992). ronmental concerns, identify the principal causes legislation to prevent and control marine The year shown for a country refers to the of environmental problems, and formulate poli- pollution. year in which a treaty entered into force in cies and actions to deal with them (table 3.14a). * The Convention on Biological Diversity pro- that country. The NEAP is a continuing process in which gov- motes conservation of biodiversity among _ .__ ___. ernments develop comprehensive environmen- nations through scientific and technologi- D s tal policies, recommend specific actions, and cal cooperation, access to financial and Data sources outline the investment strategies, legislation, genetic resources, and transfer of ecologi- The data are from the Secretariat of the United and institutional arrangements required to imple- cally sound technologies. Nations Framework Convention on Climate ment them. To help developing countries comply with their Change; the Ozone Secretariat of the UNI P; Country environmental profiles identify how obligations under these agreements, the Global the World Resources Institute; the UNEP; the national economic and other activities can stay Environment Facility (GEF) was created to focus U.S. National Aeronautics and Space i within the constraints imposed by the need to on global improvement in biodiversity, climate Administration's Socioeconomic Data and Ap- conserve natural resources. Some profiles con- change, international waters, and ozone layer plications Center (SEDAC), Center for Interna- sider issues of equity, justice, and fairness. depletion. The UNEP, United Nations Develop- tional Earth Science Information Network Biodiversity profiles-prepared by the World ment Programme (UNDP), and the World Bank (CIESIN); and the World Resources Institute, Conservation Monitoring Centre and the manage the GEF according to the policies of its International Institute for Environment and IUCN-provide basic background on species governing body of country representatives. The . Development, and IUCN's 1996 World Direc- diversity, protected areas, major ecosystems World Bank is responsible for the GEF Trust Fund tory of Country Environmental Studies. and habitat types, and legislative and admin- and is chair of the GEF. istrative support. In an effort to establish a scientific baseline for measuring progress in biodiversity conservation, the United Nations Environment Programme (UNEP) coordinates global biodiversity assessments. To address global issues, many govern- ments have also signed international trea- ties and agreements launched in the wake of the 1972 United Nations Conference on Hu- ~T~5'. 3.15 Understanding savings Gross Consumption Net Education Energy Mineral Net Carbon Adjusted national of fixed nationai expenditure depletion depietion forest dloxide net savings capitai savings depietion damage savings % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI 2000 2000 2000 2000 2000 2000 2000 2000 2000 Afghanistan . .. .. Albania 13.7 9.0 4.7 2.8 1.4 0.0 0.0 0.3 5.9 Algeria .. 11.2 ..4.5 38.1 0.0 0.0 1.2 Angola -1.8 16.1 -17.9 4.4 ..0.0 0.0 0.8 Argentina 13.1 12.0 1.1 3.2 2.2 0.1 0.0 0.3 1.7 Armenia 2.5 8.2 -5.7 1.8 0.0 0.0 0.0 1.1 -5.0 Australia 18.9 16.1 2.7 5.4 1.8 1.5 0.0 0.6 4.3 Austria 24.3 14.6 9.8 5.0 0.1 0.0 0.0 0.2 14.5 Azerbaijan 24.5 15.1 9.5 3.0 ..0.0 0.0 5.3 188 Bangladesh 23.3 6.1 17.2 1.7 1.2 0.0 1.0 0.4 16.3 Belarus 22.8 9.3 13.5 5.5 1.2 0.0 0.0 1.5 16.4 5) o Belgium 23.9 14.5 9.4 3.1 0.0 0.0 0.0 0.3 12.2 Benin 10.5 7.9 2.6 2.7 0.3 0.0 1.4 0.3 3.4 Bolivia 12.9 9.4 3.5 5.5 4.6 0.7 0.0 0.9 2.9 Bosnia and Herzegovina .. 8.8 ... 0.0 0.0 0.0 0.7 E Botswana 12.5 11.3 1.2 7.8 0.0 0.4 0.0 0.4 8.1 o Brazil 15.9 11.2 4.7 4.8 2.1 0.8 0.0 0.3 6.3 a) > Bulgaria 11.0 9.9 1.1 3.1 0.3 0.6 0.0 2.8 0.5 a) o Burkina Faso 24.0 7.1 16.8 1.4 0.0 0.0 1.2 0.3 16.7 Burundi 0.9 6.4 -5.5 3.1 0.0 0.2 3.0 0.2 -5.8 ?: Cambodia 17.5 7.5 10.0 1.8 0.0 0.0 0.4 0.1 11.3 N. Cameroon 15.9 9.1 6.8 2.3 9.5 0.0 0.0 0.2 -0.5 ~" Canada 25.3 13.0 12.3 6.5 4.4 0.2 0.0 0.5 13.7 Central African Republic 12.0 7.6 4.5 1.6 0.0 0.0 0.0 0.2 5.9 Chad 4.5 7.0 -2.6 2.0 0.0 0.0 0.0 0.0 -0.6 Chile 22.6 10.0 12.7 3.4 0.3 6.3 0.0 0.5 8.9 China 39.7 9.1 30.6 2.0 3.2 0.2 0.1 2.4 26.8 Hong Kong, China 32.5 12.9 19.5 2.8 0.0 0.0 0.0 0.1 22.2 Colombia 13.0 10.3 2.7 3.1 8.8 0.3 0.0 0.5 -3.8 Congo. Dem. Rep. -4.0 7.3 -11.3 0.9 2.6 0.2 0.0 0.3 -13.5 Congo, Rep. 41.9 13.3 28.5 6.5 ..0.0 0.0 0.4 Costa Rica 13.4 6.2 7.2 5.1 0.0 0.0 0.5 0.3 11.6 C6te dIlvoire 7.3 9.2 -1.9 4.5 0.0 0.0 0.9 1.0 0.8 Croatia 19.6 11.2 8.4 .. 1.2 0.0 0.0 0.7 Cuba.,.... .. Czech Republic 25.4 11.3 14.1 4.6 0.2 0.0 0.0 1.5 17.0 Denmark 24.5 15.2 9.2 8.2 0.8 0.0 0.0 0.2 16.4 Dominican Republic 19.6 5.4 14.2 2.1 0.0 0.7 0.0 0.6 14.9 Ecuador 32.3 10.2 22.1 3.2 29.6 0.1 0.0 1.1 -5.5 Egypt, Arab Rep. 22.9 9.6 13.2 4.4 5.5 0.0 0.0 0.8 11.3 El Salvador 14.1 10.3 3.8 2.2 0.0 0.0 0.7 0.3 5.0 Eritreea. 5.9 ..1.4 0.0 0.0 0.0 Estonia 17,8 14.7 3.1 6.4 0.5 0.0 0.0 2.7 6.2 Ethiopia 9.1 6.3 2.8 2.7 0.0 0.0 12.4 0.3 -7.3 Finland 28.1 16.4 11.7 7.1 0.0 0.0 0.0 0.3 18.4 France 21.5 12.6 9.0 5.6 0.0 0.0 0.0 0.2 14.3 Gabon 15.0 12.7 2.3 2.1 41.6 0.0 0.0 0.4 -37.6 Gambia, The 6.3 7,9 -1.6 3.6 0.0 0.0 0.1 0.4 1.5 Georgia 9.1 16.1 -7.0 2.5 0.6 0.0 0.0 1.0 -6.1 Germany 21.1 14.9 6.2 4.4 0.1 0.0 0.0 0.3 10.2 Ghana 13.4 7.3 6.1 4.4 0.0 1.4 3.2 0.6 5.3 Greece 16.3 8.5 7.8 2.3 0.1 0.0 0.0 0.5 9.4 Guatemala 12.3 9.9 2.4 1.5 1.0 0.0 1.0 0.3 1.6 Guinea 14.0 8.2 5.8 1.5 0.0 4.0 0.7 0.3 2.2 Guinea-Bissau .. 7.4 ..2.7 0.0 0.0 0.0 0.6 Haiti 1.9 1.8 0.1 1.6 0.0 0.0 2.5 0.2 -1.1 Honduras 31.3 5.6 25.8 3.5 0.0 0.2 0.0 0.5 28.6 3.1 5 Gross Consumption Net Education Energy Mineral Net Carbon Adjusted national of fixed national expenditure depletion depletion forest dioxide net savings capital savings depletion damage savings % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI 2000 1 2000 2000 2000 2000 1 2000 2000 2000 2000 Hungary 24.8 11.5 13.3 4.6 0.7 0.0 0.0 0.9 16.3 India 23.5 9.6 13.9 3.3 2.2 0.4 1.0 1.6 12.2 Indonesia 21.5 5.6 15.'9 0.6 10.9 1.4 0.3 1.0 2.9 Iran, Islamic Rep. 34.6 9.7 24.9 3.2 38.6 0.1 0.0 1.8 -12.5 Iraq Ireland 30.5 12.1 18.5 5.5 0.0 0.1 0.0 0.4 23.5 Israel 15.0 14.9 0.1 6.3 0.0 0.0 0.0 0.4 6.0 Italy 20.6 13.6__ 7.0 4.6 0.1 0.0 0.0 0.2 11.2 Jamaica 22.4 11.1 11.4 6.8 0.0 1.8 0.0 0.9 15.5 Japan 29.4 15.9 13.5 4.6 0.0 0.0 0.0 0.1 18.0 189 Jordan 21.9 10.6 11.4 5.6 0.0 0.0 0.0 1.2 15.8 Kazakhstan 21.5 10.1 11.5 4.6 40.6 0.0 0.0 5.1 -29.6 Kenya 11.3 7.9 3.4 6.1 0.0 0.0 0.9 0.5 8.1 N Korea, Dem. Rep. . .. .. .. .. Korea, Rep. 31.3 12.1 19.2 3.4 0.0 0.0 0.0 0.6 21.9 5 Kuwait 42.7 6.5 36.1 4.2 48.1 0.0 0.0 0.7 -8.4 ( Kyrgyz Republic 4.8 8.0 -3.2 5.5 1.5 0.0 0.0 3.6 -2.9 I 5. Lao PDR 16.4 7.9 8.5 1.8 0.0 0.1 0.0 0.2 10.1 'a Latvia 20.2 10.7 9.5 6.2 0.0 0.0 0.0 0.8 15.0 C Lebanon -0.5 10.3 -10.8 1.6 0.0 0.0 0.0 0.6 -9.8 E Lesotho 18.4 6.3 12.1 6.4 0.0 0.0 1.6 2 Lithuan-ia- 15.0 10.2 4.8 5.3 0.4 0.0 0.0 0.9 8.9 Macedonia, FYR 13.8 10.0 3.8 .. 0.0 0.0 0.0 2.2 Madagascar 7.1 7.5__ -0.4 1.8 0.0 0.0 0.0 0.2 1.2 Malawi -0.7 6.9 -7.6 3.8 0.0 0.0 4.0 0.3 -8.1 Malaysia 42.2 11.8 30.4 4.3 10.8 0.0 0.3 1.0 22.5 Mali 10.9 7.3 3.6 2.2 0.0 0.0 0.0 0.1 5.7 Mauritai 38 8.0 22.7 3.7 0.0 20.6 0.0 2.2 3.7 Mauritius 23.1 10.9 12.2 3.3 0.0 0.0 0.0 0.3 15.2 Mexico 20.7 10.6 10.1 4.5 5.9 0.1 0.0 0.5 8.1 Moldova 12.2 7.2 4.9 8.4 0.0 0.0 0.0 4.4 9.0 Mongolia 22.4 10.9 11.5 .. 0.0 1.6 0.0 5.3 Morocco 23.6 9.6 14.0 4.7 0.0 0.5 0.0 0.7 17.6 Mozambique 10.1 7.7 2.5 3.7 0.0 0.0 0.0 0.2 5.9 Myanmar......... Namibia 27.5 13.3 14.2 8.4 0.0 0.1 0.0 0.0 22.5 Nepal 22.0 2.3 19.7 2.1 0.0 0.0 4.9 0.3 16.6 Netherlands 28.7 14.6 14.1 5.1 0.5 0.0 0.0 0.3 18.4 New Zealand 18.6 11.1 7.5 6.9 1.6 0.1 0.0 0.4 12.2 Nicaragua 14.1 9.3 4.7 2.6 0.0 0.1 0.3 1.1 5.9 Niger 1.4 -7.0 -5.6 3.0 0.0 0.0 3.2 0.4 -6.3 Nigeria 29.9 8.6 _21.2 0.8 51.6 0.0 0.8 1.5 -31.8 Norway 36.8 16.2_ 20.6 6.9 7.7 0.0 0.0 0.2 19.5 Oman Pakistan- 12.6 7.9 4.7 2.4 3.2 0.0 0.9 1.1 1.9 Pn am-a 22.1 7.9 14,2 4.8 0.0 0.0 0.0 0.4 18.5 P a p-u a N ew -Guinea____ 17.7 9.2 8.6 .. 13.9 10.8 0.0 0.4 Paraguay 9.7 -9.5 0.2 3.5 0.0 0.0 0.0 0.3 3.3 Peru 17.8 10.4 7.4 2.6 1.4 1.3 0.0 0.3 7.0 Philippi'nes 28.9 81- ---- 20.8 2.9 0.0 0.1 0.8 0.6 22.2 Poland 20.6 11.0 9.6 5.1 0.4 0.1 0.0 1.5 12.7 Portugal 18.1 15.2 2.9 5.6 0.0 0.0 0.0 0.3 8.1 Puerto Rico . 11.3 ... 0.0 0.0 0.0 0.3 Romania 15.2 9.9 5.3 3.3 4.1 0.0 0.0 1.7 2.8 Ruissian F e-d- e-r-a-ti on 35.4 10.3 25.1 3.9 38.4 0.0 0.0 4.0 -13.4 3.15 Gross Consumption Net Education Energy Mineral Net Carbon Adjusted national of fixed national expenditure depletion depletion forest dioxide net savings capital savings depletion damage savings %of %of %of %of %of %of %of %of %of GNI GNI GNI GNI GNI GNI GNI GNI GNI 2000 2000 2000 2000 2000 2000 2000 2000 2000 Rwanda 14.2 7.3 6.9 3.3 0.0 0.0 3.9 0.2 6.0 Saudi Arabia 31.3 10.0 21.3 6.2 53.9 0.0 0.0 1.0 -27.3 Senegal 13.6 8.3 5.3 3.4 0.0 0.1 0.0 0.6 8.1 Sierra Leone .. 6.7 .. 1.1 0.0 0.1 3.4 0.4 Singapore 51.5 13.3 38.2 2.3 0.0 0.0 0.0 0.6 39.9 Slovak Republic 26.9 10.9 15.9 4.3 0.1 0.0 0.0 1.3 18.8 Slovenia 24.6 12.0 12.6 5.2 0.0 0.0 0.0 0.6 17.2 Somalia .. .. .. .. .. .. South Africa 15.1 13.3 1.7 6.9 1.4 0.9 0.2 1.7 4.5 190 Spain 23.0 12.9 10.1 4.6 0.0 0.0 0.0 0.3 14.4 Sri Lanka 21.7 5.2 16.5 2.6 0.0 0.0 0.8 0.3 18.0 Sudan 2.7 9.4 -6.7 0.9 0.0 0.1 0.0 0.3 -6.2 ' Swaziland 13.7 9.4 4.3 6.5 0.0 0.0 0.0 0.2 10.6 D Sweden 20.8 14.1 6.7 7.5 0.0 0.1 0.0 0.1 14.0 Switzerlarid 33.8 14.9 19.0 4.8 0.0 0.0 0.0 0.1 23.6 E Syrian Arab Republic 20.1 9.8 10.3 2.6 38.8 0.0 0.0 2.0 -27.9 o Tajikistan 15.0 7.2 7.8 2.0 0.6 0.0 0.0 4.1 5.2 > Tanzania 14.5 7.5 6.9 3.4 0.0 0.1 0.0 0.2 10.1 a) oj Thailand 30.3 14.9 15.4 3.5 1.3 0.0 0.3 1.0 16.2 o Togo 10.2 7.6 2.6 4.3 0.0 0.1 1.2 0.5 5.2 B: Trinidad and Tobago 18.4 12.4 6.1 3.4 31.5 0.0 0.0 2.3 -24.3 O Tunisia 24.6 10.0 14.7 6.6 4.7 0.0 0.2 0.7 15.6 > Turkey 20.0 6.8 13.2 3.2 0.3 0.0 0.0 0.7 15.3 Turkmenistan 29.1 9.4 19.6 .. .. 0.0 0.0 5.8 Uganda 11.4 7.5 3.9 2.2 0.0 0.0 2.3 0.1 3.7 Ukraine 24.1 19.4 4.7 6.1 7.6 0.0 0.0 7.4 -4.2 United Arab Emirates .. 11.9 .. 1.7 41.9 0.0 0.0 1.0 United Kingdom 15.2 11.6 3.6 4.7 1.1 0.0 0.0 0.2 7.0 United States 18.0 11.9 6.1 4.7 1.1 0.0 0.0 0.4 9.3 Uruguay 11.3 11.6 -0.3 3.0 0.0 0.0 0.3 0.2 2.3 Uzbekistan 13.9 7.9 5.9 7.8 .. 0.0 0.0 9.6 Venezuela, RB 29.0 7.2 21.8 5.0 26.4 0.3 0.0 0.9 -0.7 Vietnam 29.4 8.0 21.4 2.8 8.4 0.0 1.2 1.0 13.6 West Bank and Gaza .. 8.3 .. .. 0.0 0.0 0.0 Yemen, Rep. 36.3 9.5 26.7 5.7 49.2 0.0 0.0 1.5 -18.2 Yugoslavia, Fed. Rep. .. 8.9 .. 4.6 2.3 0.0 0.0 3.0 Zambia .. 7.9 .. 2.0 0.1 2.7 0.0 0.5 Zimbabwe .. 8.8 .. 7.5 0.4 2.6 0.0 1.4 Low Income 20.7 8.7 11.9 2.8 7.2 0.5 0.9 1.5 4.7 Middle Income 25.3 10.3 14.9 3.8 8.0 0.3 0.1 1.2 9.1 Lower middle income 32.4 9.7 22.7 2.9 10.1 0.2 0.1 2.0 13.1 Upper middle income 20.9 10.8 10.0 4.4 6.5 0.4 0.0 0.7 6.9 Low & middle Income 24.6 10.1 14.5 3.6 7.9 0.3 0.2 1.3 8.4 East Asia & Pacific 34.0 9.9 24.1 2.5 3.2 0.2 0.1 1.7 21.3 Europe & Central Asia 25.9 10.0 15.9 4.2 12.7 0.0 .. 2.3 Latin America & Carib. 17.0 10.6 6.4 4.2 5.1 0.6 0.0 0.4 4.4 Middle East & N. Africa 28.1 10.0 18.1 4.8 31.6 0.1 0.0 1.1 -10.0 South Asia 21.9 9.0 13.0 3.1 2.1 0.3 1.0 1.4 11.3 Sub-Saharan Africa 14.8 10.7 4.1 4.7 9.7 0.6 0.7 1.1 -3.3 High Income 22.5 13.4 9.1 4.8 0.8 0.0 0.0 0.3 12.8 Europe EMU 22.0 13.8 8.2 4.7 0.1 0.0 .. 0.3 -- 1 3.15 About the data Definitions Adjusted net savings measure the change in the physical quantity extracted or harvested * Gross national savings are calculated as the value of a specified set of assets, excluding in order to arrive at a depletion figure. This difference between GNI and public and private capital gains. If a country's net savings are posi- figure is one of a range of depletion estimates consumption, plus net current transfers. tive, and if the accounting includes a sufficiently that are possible, depenciing on the assump- * Consumption of fixed capital represents the broad range of assets, economic theory sug- tions made about future quantities, prices, replacement value of capital used up in the gests that the present value of social welfare and costs, and there is reason to believe that process of production. * Net national savings is increasing. Conversely, persistently nega- it is at the high end of the range. Some of the are equal to gross national savings less the tive adjusted net savings indicates that an largest depletion estimates in the table should value of consumption of fixed capital. * Edu- economy is on an unsustainable path. therefore be viewed with caution. cation expenditure refers to the current oper- Adjusted net savings are derived from stan- A positive net depletion figure for forest ating expenditures in education, including dard national accounting measures of gross resources implies that the harvest rate ex- wages and salaries and excluding capital in- national savings by making four types of adjust- ceeds the rate of natural growth; this is not vestments in buildings and equipment. * En- ments. First, estimates of capital consumption the same as deforestation, which represents ergy depletion is equal to the product of unit of produced assets are deducted to obtain net a change in land use (see Definitions for table resource rents and the physical quantities of national savings. Then current expenditures on 3.4). In principle, there should be an addition energy extracted. It covers crude oil, natural 191 education are added to net national savings (in to savings in countries where growth exceeds gas, and coal. * Mineral depletion is equal to 1 standard national accounting these expenditures harvest, but empirical estimates suggest that the product of unit resource rents and the physi- g are treated as consumption). Next, estimates most of this net growth is in forested areas cal quantities of minerals extracted. It refers ofthedepletion ofavarietyof natural resources that cannot be exploited economically at to bauxite, copper, iron, lead, nickel, phos- o are deducted to reflect the decline in asset val- present. Because the depletion estimates phate, tin, zinc, gold, and silver. * Net forest r. ues associated with their extraction and harvest. reflect only timber values, they ignore all the depletion is calculated as the product of unit co Finally, a deduction is made for damage from external and nontimber benefits associated resource rents and the excess of roundwood carbon dioxide emissions. (In earlier editions of with standing forests. harvest over natural growth. * Carbon dioxide 3 the World Development Indicators these ad- Pollution damage is calculated as the mar- damage is estimated to be $20 per ton of car- O justments were made to gross domestic sav- ginal social cost associated with a unit of pollu- bon (the unit damage in 1995 U.S. dollars) ings and the adjusted net saving figures were tion multiplied by the increase in the stock of times the number of tons of carbon emitted. r referred to as "genuine savings"). pollutant in the receiving medium. For carbon * Adjusted net savings are equal to net na- Education expenditures are treated as an dioxide the unit damage figure represents the tional savings plus education expenditure t addition to savings effort. However, owing to present value of global damage to economic and minus energy depletion, mineral deple- the wide variability in the effectiveness of gov- assets and to human welfare over the time the tion, net forest depletion, and carbon diox- ernment education expenditures, these fig- unit of pollution remains in the atmosphere. ide damage. ures cannot be construed as the value of in- vestments in human capital. The accounting Figure 3.15 for human capital is also incomplete because i Data sources depreciation of human capital is not esti- AdJusted net savings were far lower in low- I Gross national savings are derived from the income economies than in others in 2000. I mated. i World Bank's national accounts data files, There are gaps in the accounting of natural 30 described in the Economy section. Consurnp- resource depletion and costs of pollution. Key 25 - tion of fixed capital is from the United Nations estimates missing on the resource side include Statistics Division's National Accounts Statis- the value of fossil water extracted from aqui- 20 . tics: Main Aggregates and Detailed Tables, fers, depletion and degradation of soils, and net z 1 1997, extrapolated to 2000. The education depletion of fish stocks. The most important 6 15- expenditure data are from the United Nations pollutants affecting human health and economic 10 - Statistics Division's Statistical Yearbook 1997, assets are also excluded, because no interna- extrapolated to 2000. The wide range of data 5 tionally comparable data are widely available on sources and estimation methods used to ar- damage from particulate emissions, ground-level o _ rive at resource depletion estimates are de- ozone, or sulfur oxides. income incdde iHghe scribed in a World Bank working paper, "Esti- Estimates of resource depletion are based 0 Gross national saving mating National Wealth" (Kunte and others on the calculation of unit resource rents. An Netnational saving .1998). The unit damage figure for carbon diox- economic rent represents an excess return to a o Adjusted net saving ide emissions is from Fankhauser (1995). The given factor of production-that is, in this case conceptual underpinnings of the savings mea- Source: Table 3.15. oneuaunepnigoftesvgsm- the returns from resource extraction or har- _ _. _._._ _ sure appear in Hamilton and Clemens (1999). vest are higher than the normal rate of return on capital. Natural resources give rise to rents because they are not produced; in contrast, for produced goods and services competitive forces will expand supply until economic prof- its are driven to zero. For each type of re- source and each country, unit resource rents are derived by taking the difference between world prices and the average unit extraction or harvest costs (including a "normal" return on capital). Unit rents are then multiplied by : ' ___V X:: V7 0 : : 0 } -*' ,''t>,,"' , E7'.'' ..,'; ,,.'e , C. . !- *;r<'or *. ;* g .n .v.- e,.,>+t!. Economic growth reduces poverty Faster growth, deeper poverty reduction Growth in per capita GDP and change in poverty rate by region, 1990-99 (average annual percentage change) =r 4 Europe and1Central Asia 20) D I Middle East and North Africa o -2 Sub-Saharan I 0 Africa Latin America Ł -4 and Caribbean -6I D 8 a South Asia (D E -10 l ~-12 -14 :East Asia 0 -16 and Pacific 193 -4 -2 0 2 4 6 8 0 GDP per capita Source: World Bank data. D CD 0 (0 CD) ci, Without economic growth there can be no long-termn poverty reduction. Economies that have achieved sustained growth-by making markets work better for poor people and buildinig up their assets-have also succeeded in significantly reducing poverty. Economies that have not grown have experienced stagnant or increasing poverty rates. Thus the keen interest in economic growth and its predominance among the objectives of economic policy. Experience between 1990 and 1999 illustrates the general rule. Over that decade the number of people living in developing countries on less than $1 a day fell from 1.3 billion to 1.2 billion, and the proportion of people living in extreme poverty-the poverty rate-fell from 29 percent to 23 percent. Most of these gains were made in the two fastest growing regions, East Asia and Pacific and South Asia. In Europe and Central Asia, which experienced painful economic contraction over much of the period, both the number and the proportion of people living on less than $1 a day increased. In Sub-Saharan Africa and the Middle East and North Africa the poverty rate declined slightly, but not fast enough to reduce the number of people living in extreme poverty. And in Latin America and the Caribbean, where average growth has been slow, poverty reduction has also slowed. Regional growth Europe and Central Asia. For the in 2000 after having contracted Latin America and the Caribbean. In transition economies of Europe and throughout the decade. The recent Latin America and the Caribbean patterns Central Asia it is hard to establish growth was fueled in part by higher GDP per capita increased by about comparable time series for the pre- oil export prices. The first country 1.6 percent a year over the period transition period. Since 1988 the to emerge from the transition since 1960. Although the region East Asia and Pacific. Over the region has experienced a sharp recession in 1992, Poland has the highest GDP per capita in past 40 years East Asia and drop in growth from which it began maintained average GDP per capita the developing world, it also Pacific grew faster than any other to recover in the past two years. growth of 4.5 percent in the 1990s, includes some of the poorest coun- developing region. Led by China, The regional average is dominated the highest among transition tries: Guyana, Haiti, and Nicaragua. the region achieved GDP per by Russia, which grew 8.9 percent economies. Latin America and the Caribbean capita growth of 5.3 percent a has experienced greater volatility in year. This exceptional record was growth than other regions, and interrupted by a sharp drop in regional growth rates have declined growth following the financial crisis Long-term growth trends are Important ... since the 1980s. Some of the that began in 1997. In most coun- largest and wealthiest economies- tries recovery came quickly, but Annual growth of GDP per capita and long-term trends, Argentina, Brazil, and Mexico- 194 growth rates for many have not by region, 1960-2000 experienced growth-interrupting returned to the levels of the _ . financial crises. Chile is Latin Amer- 2 early 1990s. _ Annual growth of GDP per capita ica's notable exception, having ro * Long-term trend of growth in GDP per capita achieved economic stability and c East Asia and Pacific steady growth of 5.2 percent over | 0 n D Y | the past decade. 0.~~~~~~~~~~~~ C(D ° 5 -5 0~~~~~~~~~~~~~1 Europe and Central Asia Rp 5 -5 -10 10 5 U -5 uorce: Warld Bank data. Regional patterns Annual growth and More recently, strong growth in Sub-Saharan Africa. Sub-Saharan 1960s, have become considerably India, which opened its economy Africa has been nearly stagnant, poorer, in some cases because of trends vary and encouraged foreign investment with less than 0.2 percent annual political instability. In South Africa. in the past decade, has helped to growth over the same period and a middle-income economy, output raise regional growth rates. India dJeclining growth rates. Fourteen has barely kept pace with popula- Middle East and North Africa. The averaged 4.1 percent annual major African countries had nega- tion growth. But Botswana, another Middle East and North Africa region growth during the 1990s. Pakistan, tive growth. Even such resource- resource-rich economy, and Mauri- has been unable to achieve the second largest economy in the rich economies as Ghana, Nigeria, tius have done well, improving their sustained growth. Saudi Arabia, the region, grew 1.2 percent a year and and Zambia, classified as lower- status from low-income economies largest economy in the region, has Bangladesh 3.0 percent. middle-income economies in the in the 1960s to upper-middle- grown about 0.8 percent a year income economies today. Both dou- since the 1960s. Egypt has grown bled their incomes in the past an average of 3.2 percent a year decade. Mozambique, a post- for the past 40 years, helped by ... for poverty reduction conflict country, has grown steadily large aid transfers. But 26 years at an average of about 5.4 percent after the first oil boom the region's Annual growth of GDP per capita and long-term trends a year since 1992. What made the economic fortunes are still driven by region, 1960-2000 difference? Although the explana- 195 by international oil prices. * Annual growth of GDP per capita tion is not simple, it is probably to N) _ . . ~~~~~~~~~~~~~~~~~be found in consistent, sound eco- g *_ Long-term trend of growth In GDP per capita be fon in...... cossetMsudeo South Asia. South Asia has experi- nomic policy, general political E enced erratic growth, especially in Middle East and North Africa stability, and an openness to 0 earlier years, averaging 2.2 percent -, 10 external markets. 2 a year over the past 40 years. a (D _5 0- -5~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- 10 i South Asia 10 0 CD 5 -5 Sub-Saharan Africa 8 0 lb 4 -6 Source: Worda Bank data. of economic growth $22f8,iIongS 7gii(e South Asia's savings rate exceeds percent over the past decade. were considerably less. only Sub-Saharan Africa's. Its out- Europe and Central Asia, with half East Asia and Pacific. the fastest put is less than a third of East the output of Latin America. saves growing region in the past two Savings are the difference between Asia's. at about the same rate. decades, has maintained a high total output and total consumption. average savings rate of 37 percent For the world, savings must equal The savings rate in the Middle East In 2000 Latin America's regional of GDP. investment. But some economies and North Africa has been as high economy was almost the same size save more and some save less than as 30 percent and as low as 21 as East Asia's, but its total savings High-income economies saved a they need. The balance between the smaller share of their output, but supply of savings and the demand their total savings were more than for investment must be met by three times those of all low- and financial flows between economies. middle-income economies Some regions save much more than others combined. Sub-Saharan Africa consistently has the lowest savings rate and the GDP, 2000, and savings, average 1996-2000 196 smallest pool of savings. Middle East Sub-Saharan Africa South Asia and North Africa na p17% .O~~~~~~~1 $323 million ,E $597 million $660 million 0, 0 Europe and Latin America East Asia > Central Asia and Caribbean and Pacific $942 million $2,001 billion $2.059 billion 0 ~ ~ ~ ~ ~ ~ High-income economies $24,927 billion More productive investment The savings and Investment gap Savings minus investment as share of GDP, average 1996-2000 Investment is needed for growth, but many countries cannot save enough from their own output to ( 15 finance it. As a result, they must tap into foreign savings provided by 10 lenders or investors. To repay them, 5 the economy must continue to grow. So not just the quantity but also the 0 quality of investment is important. -5 While there is no simple formula for ensuring high-quality investment, -10 the more open an economy is to A $ w A G w . ° \ e ,N e . 197 trade and investment, the more sta- Qc - G 0 w. ble its fiscal and monetary policies, R9 c>0 o 0 the better educated its workforce,M and the less prone it is to conflict, Source: World Bank data. the more productive its investments will be. CD CD Q 5- :3 CD Attracting more But even the poorest countries. The top four recipients of FDI account for more than which have difficulty borrowing in foreign direct half the total international capital markets, investment attract about the same amount of Foreign direct investment in developing countries, FDI as middle-income countries 1990-2000 relative to the size of their Foreign direct investment (FDI)- * Argentina * Mexico * Brazil * China il Other economies. Improving the investment climate to attract more investment in a lasting interest in @ 200 foreign investment remains a key an enterprise-contributes to pro- challenge for most of the 0 ductivity by facilitating the transfer 150 _developing world. of technology, management tech- niques, and information about export markets. FDI has proved 100 more stable than other forms of private sector finance, although it 50 -l did fall slightly after the financial crisis in 1998. The top four recipi- ents of FDI flows account for more than half the FDI received by 1990 1992 1994 1996 1998 2000 developing countries. Source: World Bank data. Some debt is economies with robust exports can sustain large debt. Only 3 of the 10 manageable ... The top 10 debtors are responsible for more than half largest debtors are classified by the of external debt World Bank as severely indebted- Percentage of external debt, 2000 Argentina, Brazil, and Indonesia. Debt problems in developing coun- Mexico, Russia, Turkey, and Thailand tries became a global concern in 4% India are classified as moderately the 1980s, beginning in Latin Amer- 5% Turkey 3% Thailand indebted, and China, India, and the ica and spreading to other regions. 5% Korea, Rep. Republic of Korea as less indebted. The inability of several large debtors to service their debt to N The heavily indebted poor countries public and private lenders threat- 6% Russian 35% Other (HIPCs) account for only 8 percent ened to disrupt internFedeation developing- cial markets. Weak economic 6 _ c oping countries-nearly all of it perfordrance-exacerbated by high 6 e co c owed to official creditors. But they real interest rates and weak com- 6% China have been a cause for concern 198 modity prices-contributed to the 6% Indonesia - 8% HIPC because their debt service heavy indebtedness. 10% Brazil payments are large relative to gov- ,,, 6% Argentina ernment budgets and average oZ In 2000 the 10 largest debtors were incomes. Relieving debt burdens c responsible for 57 percent of the 5SoSuce: World B-1, data. and expanding the resources avail- c external debt of developing able for poverty reducing programs E countries. But large, growing is the focus of the HIPC initiative. 0L 0 r 0 r0 0B CN ... and some is no or 48 percent of GNI are classified as moderately indebted. longer When debt becomes unsustainable manageable The most severely indebtedcon Ratio of present value of debt to exports and GNI, 2000 tries may be eligible to apply for renegotiation of obligations to pub- TheiWorld Bank classifies a country HIPC _E Non-HIPC lic and private creditors. The goal of The World Bank classifies a country * HIPC * Non-HIPC debt renegotiation is to match a as severely indebted when the pre- r , 1,500 Sao Tome * country's obligations to its ability to sent value of its external debt 0 5 sd 1,250 Burundi and Principe pay. But for some very poor coun- exceeds 220 percent of its exports ' Guinea-Bissau tries traditional debt relief, such as of goods and services (including ( 1,000 * rescheduling of bilateral debt, would worker remittances) or 80 percent rD not have been enough to make their of ONI.But exerienc has sown S8 T50 Brazil nthv eneog omk hi of GNI. But experience has shown ,C,D \ debt burden sustainable. To provide that countries are likely to experi- '' 5 Argentina * Nicaragua further debt relief, the World Bank ence debt service difficulties when 250 and IMF launched the HIPC initiative the present value of debt exceeds O Indonesia in September 1996. The enhanced 200 percent of exports. Countries HIPC initiative. begun in 1999. pro- that are not severely indebted but 0 100 200 300 400 500 vides deeper, broader. and faster whose present value of debt Present value of debt to GNI (percent) debt relief. exceeds 132 percent of exports Source: World Bank data. The need for debt As of January 2002, 24 countries, 20 in Africa and 4 in reduction Latin America, had the present Uttle left for social services value of their debt reduced by HIPCs reaching decision point before January 2002 about $22 billion-from $47 bil- In the HIPC initiative all creditors- (dollars per capita) lion to $25 billion. This is including multilateral institutions expected to lower their payments Prinmaryofpicpladitrsby$. and governments-participate. The Debt Health ducalton of principal and interest by $1.8 goal is to channel the domestic GNI service expenditure expenditure billion a year in 2001-03. The resources freed up by debt forgive- arrangement brings their ratio of Benin 370 12 12 43 ness into reducing poverty, mainly Bolivia 990 79 69 108 debt service to exports to less by improving health and education Burkina Faso 210 5 9 ,. than 10 percent (half the devel- services. The initiative will help to Cameroon 580 38 31 13 oping country average). An addi- Chad 200 3 7 13 reduce debt to sustainable levels Ethiopia 100 2 4 27 tional 18 countries are eligible to for many countries. Gambia, The 340 14 13 46 qualify for relief under the Guinea 450 18 19 enhanced HIPC initiative Guinea-Bissau 180 5 f w 19 Guyana 860 152 framework. 199 Honduras 860 90 74 Madagascar 250 6 5 0 Malawi 170 6 11 14 o Mali 240 9 11 32 Mauritania 370 38 19 47 Mozambique 210 5 8 .. 0 Nicaragua 400 59 54 50 rr Niger 180 3 5 .D Rwanda 230 4 10 ..D Sao Tome and S Principe 290 30 .. .. Senegal 490 24 8 .. D Tanzania 270 6 8 Uganda 300 7 18 Zambia 300 18 23 14 ( Note: Data are for 2000 or latest avalilable year. rn ource: World Bank data. I~ Helping the HIPCs Growth and debt reduction Outlook Improving GDP and external debt of HIPC countries The total external debt of HIPC countries increased from the mid- 1980s to the mid-1990s. But the as 250 current value of their output 0 declined from 1988 to 1989 and m 200 again from 1991 to 1994. Only in I 2000 did output once again exceed 150 debt. Reducing debt and increasing 100 I I growth will help put the HIPC l l countries back on track for 50 l improvements in poverty and * N 11111 social indicators. O Source: World Bank data. UaTblo Cka [eceonq ecmonouc pelu-ac Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross international product and services and services balance reserves months average annual average annual average annual of import % growth % growth % growth hhgrowth % of GDP $ millions coverage ............. 2000. 2001 .... 2000.... 2001 2000 2001.... 2000_ __ 2001 ___2000 2001 2001 2001 Algeria 2.4 3.6 7.4 -1.1 7.0 17.2 23.7 5.3 .. 12.3 17,863 13.1 Argentina -0.5 -1.7 2.0 5.2 -0.5 -1.8 1.1 -0. 1 -3.1 -2.8 20,964 5.3 Armenia 6.0 9.6 16.6 12.7 10.0 -1.8 -1.3 4.0 -14.6 -15.2 337 4.7 Azerbaijan 11.1 8.5 17.8 16.4 8.9 25.3 13.0 3.5 -2.8 -13.5 679 2.8 Bangladesh 5.9 6.0 8.6 17.3 5.7 17.8 1.9 3.4 0.0 -2.7 1,653 1.8 Bolivia 2.4 0.0 6.1 6.1 1.3 0.2 3.7 3.5 -5.6 -4.8 1,034 5.4 BrazilI 4.5 2.0 11.0 8.6 13.8 5.0 8.5 6.0 -4.1 -4.3 27,078 3.5 Bulgaria 5.8 4.5 24.2 7.9 14.6 5.3 5.6 7.0 -5.8 -6.0 3,423 4.5 Cameroon 4.2 5.3 -4.9 1.9 16.0 11.4 3.5 3.0 -1.7 -2.0 11 0.0 200 Chile 5.4_ 3.2 7.5 18.2 10.1 16.4 4.0 4.1 -1.4 -2.1 16,331 8.0 China 7.9 7.3 32.0 5.0 24.8 13.0 0.9 2.0 1.9 1.6 185.662 7.4 2 Colombia 2.8 1.8 -5.3 1.5 5.8 18.1 10.7 11.1 0.4 -2.7 -.9,185 5.8 o Congo, Rep. 7.9 3.1 9.1 -1.5 17.3 0.2 46.4 -12.4 .. -14.4 Costa Rica 1.7 0.6 -0.5 -4.2 -5.0 2.0 7.1 10.0 .. -5.5 1,003 1.4 Ci5te dIlvoire -2.3 -1.5 -1.9 -2.7 1.0 -1.8 -0. 1 2.9 -0. 1 -5.0 2 Croatia 3.7 4.0 8.7 7.1 4.2 10.3 6.5 5.4 -2.1 -4.4 4,422 4.6 Dominican Republic 7.8 2.0 8.7 4.4 14.5 0.1 7.7 6.0 -5.2 -2.4 850 1.1 Ecuador 2.3 4.6 -0.2 0.0 18.7 31.1 105.9 18.0 .. -0.2 1,408 2.3 'O Egypt. Arab Rep. 5.1 3.3 10.3 6.5 2.5 -3.8 5.8 3.1 -1.2 0.0 o El Salvador 2.0 2.0 15.8 -2.6 14.8 1.6 3.9 3.7 -3.2 -6.5 1,710 4.1 cm Estonia 6.4 4.2 3.5 4.8 3.8 4.5 5.3 -0.5 -6.3 -5.5 1.272 2.8 0 Ghana 3.7 4.0 -2.3 0.3 -17.3 2. 72 34.6 -7.9 -12.4 431 1.4 Guatemala 3.3 1.9 4.8 1.1 2.6 2.7 5.5 7.3 -5.5 -4.5 2,152 4.5 Honduras 4.8 2.5 14.6__ -3.1 8.0 4.0 9.0 9.5 -3.4 -4.7 1,386 4.7 India 5.2 4.5 5.0 -1.6 5.0 2.9 5.3 60 -0.6 -1.2 42,636 6.2 Indonesia 4.8 3.3 16.1 -3.3 18.2 6.0 11.0 10.0 5.2 2.6 30,085 5.4 Iran. Islamic Rep. 5.4 5,1 11.8 -4.5 14.2 20.2 22.3 16.9 12.1 6.9 22,886 11 .8 Jamaica 0.8 1.5 4.4 1.8 4.3 1.9 10.6 8.8 -3.7 -5.5 1,451 Jordan 3.9 3.5 2.1 14.1 13.0 12.4 -0.6 1.1 0.7 -2.7 3,226 5.7 Kazakhstan 9.6 13.2 23.9 -3.0 10.9 22.0 17.5 11.6 5.9 -5.0 2,508 2.7 Kenya -0.2 2.0 8.6 3.1 18 1 1.4 6.8 5.0 -2.3 -8.0 743 2.2 Latvia 6.6 7.0 12.8 6.5 4.8 61 43 2.4 -6.9 -6.8 Lithuania 3.9 3.8 12.9 10.2 4.5 8.1 2.1 0.8 -6.0 -6.0 Macedonia, FYR 4.3 -4.6 19.2 .. 33.0 .. 8.0 6.0 -3.0 . -no-sopg 052 Table 4.b Key macroeconomic Indicators Nominal exchange rate Real effective Money and Gross Real Interest Short-term exchange rate quasi money domestic credit rate debt, local currency units annual annual % cf pter $ % change 1995=100 % growth % growth %% enports 2001 2000 2001 2000 2001 2000 2001 2000 2001 2000 2001 2000 Algeria _77.8 8.7 3.3 107.7 113.2 13.2 .. -19.5 .. -11.0 9.5 1.0 Argentina 1.0 0.0 0.0 . . 1.5 -16.5 -2.7 2.9 9.8 50.0 73. 8 Armenia 561.8 5.4 1.7 108.7 91.6 38.6 15.2 12.3 -1.6 33.4 26.7 7.9 Azerbaijan 4,775.0 4 .346.. . 73.4 -10.8 13.5 .33.3... 6. 9 Bangladesh 57.0 5 9 5.6 . .. 19.3 15.5 13.7 17.7 13.4 15.8 3.4 Bolivia 6.8 6.7 6.7 117.9 117.7 0.4 3.4 -0.9 -3.7 29.7 20.1 23.8 Brazil 2.3 8.9 19.0 . .. 4.3 10.9 8.6 29.7 44.5 61.9 44.8 Blgaria 2.2 7.7 5.7 120.7 128.1 28.8 .. 9.8 .. 5.6 .. 5.8 Cameroon 744.3 8.0 5.6 95.7 100.4 19.1 17.2 -08 5.3 17.9 20.7 48.7 Chile 656.2 8.0 14.6 106.0 90.8 6.2 11.3 13.9 - 14.5 10.4 10.4 10.7 201 China 8.3 0.0 0.0 107.6 109.6 12.3 14.5 10.9 8.1 4.9 5.9 5.9 Colombia 2,301.3 16.7 5.2 95.6 101.1 14.8 18.9 12.1 13.2 7.3 20.7 15.9 ---- - ------------- -- - --------------- - - ----- ------ _ - - - - 0~~~~ . .. Congo. Rep. 744.3 8.0 5.6 . .. 58.5 6.4 -28.1 16.4 -16.6 20.7 40.0 Costa Rica 341.7 6.7 7.4 106.8 114.5 18.4 8 6 22.0 3.3 16.6 23.8 12.0 C6te dIlvoire 744.3 8.0 5.6 96.5 100.6 -1.9 2.1 -4.5 -7.2 22.9 C ------------ ------------- -- - -- - - - - ----- - -- ------- Croatia ~~~~ ~~~~8.4 6.7 2.5 98.9 104.3 29.1 29.2 9.3 25.8 5.3 8.5 7.3 CD Dominican Republic 17.2 3.9 2.9 110.3 116.2 17.4 27.6 20.5 23.1 17.7 24.3 10.8 CD Ecuador 25i,000.0 23.5 0 0o 73.3 113.1 11.1 31.0 .6.8 30.7 -43.5 15.0 13.33 Egypt. Arab Rep. 4.5 8.2 21.7 . .. 11.6 .. 11.1 7.0 .. 19.0 El Salvador 8.8 0.0 -0.1 . - . 1.0 . 3.1 .. 9.7 20.20.5 -.- -- 0.~~~~~~~~~~~~~~c Estonia 17.7 8.1 5.2 25.7 23.0 27.2 24.4 2.2 9.4 19.2 38 Ghana 7,190.0 99.4 2.0 81.1 88.4 38.4 .. 50.4 .. . . 23.6 Guatemala 8.0 -1.2 3.5 . .. 35.5 9.4 13.5 -15.4 14.6 19.0 28.8 Honduras 15.9 4.4 5.2 . .. 24.4 13.0 29.8 13.3 16.4 23.4 12.5 India 48.2 7.5 3.1 15.2 14.6 16.0 12.4 7.8 12.0 4.5 Indonesia 10,400.0 35.4 8.4 . .. 15.9 13.0 26.2 8.1 6.7 19.2 30.5 Iran, Islamic Rep. 1,751.0 29.1 -22.6 297.7 360.7 22.4 26.5 12.8 15.1 12.2 Jamaica 47.3 10.0 4.1 . .. 13.0 13.2 -1.8 -20.6 11.6 19.5 16.5 Jordan 0.7 0.0 0.0 . .. 7.6 1.8 .. 12.4 .. 12.1 Kazakhstan 150.2 4.6 3.9 . .. 45.0 35.9 70.6 6.0 ... 4.9 Kenya 79.0 7.0 -04 . .. 4.5 4.5 1.3 2.8 14.5 19.8 29.2 Latvia 0.6 5.2 4.9 . 27.0 17.8 43.1 31.8 7.2 9.3 35.8 Lithuania 4.0 0.0 0.0 16.5 21.4 -3.1 5.8 9.8 7.0 21.0 Macedonia, FYR 68.6 9.9 -23 72.8 740 21.4 .. -17.2 .. 10.1 .. 4.5 cotne npage 203 I'abDe .a, [R?ecent econom0~c poroiomance Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross international product and services and services balance reserves morths average annual average annual average annrual of import % growth % growth % growth 'a growth 'a of GDP $ millions coverage ----- --------- 2000 ----2-001 --2000-- 2001 - ---2000 ... .2001 _2000 ____ 2001 2000 2001 2001 2001 Malawi 1.7 2.8 -5.3 11.1 -18.2 6.0 24.5 28.0 -30.8 -10.8 Malaysia 8.3 1.1 26.2 3.0 16.3 1.3 4.7 3.0 .. 6.2 Mauritius 8.0 5.5 5.5 6.3 0.7 5.9 0.0 12.1 -0.8 0.8 982 3.5 Mexico 6.9 0.9 16.0 -2.4 21.4 -3.4 10.9 7.0 -3.2 -3.1 39,463 2.3 Moldova 1.9 6.1 7.5 16.4 30.6 12.3 27.0 12.0 -9.4 -8.0 227 2.4 Morocco 0.9 6.5 4.4 1.4 7.8 2.3 1.6 2.5 -1.4 -0.8 7,018 6.2 Nicaragua 4.3 3.0 11.5 2.2 -8.5 0.3 11.6 8.2 -20.6 -30.5 539 3.0 Nigeria 3.8 2.9 -1.6 1.9 16.0 16.9 25.4 6.9 17.0 -4.0 - Pakistan 4.4 3.4 16.0 7.2 -2.3 6.2 3.7 5.5 -3.6 -3.3 2.080 1.7 202 Panama 2.7 2.0 5.7 4.8 -0.8 -3.7 0.8 1.6 -9.4 -6.0 743 1.5 Papua New Guinea 0.3 3.0 2.4 2.3 . .. 15.6 7.7 -0.2 -5.9 442 1.8 at o Paraguay -0.3 -0.5 -31.6 1.7 -11.7 -1.4 8.9 9.0 -4.0 -2.7 936 3.3 o Peru 3.1 0.5 7.9 1.1 3.6 7.4 3 6 1.5 -3.0 -2.6 8.732 8.6 Philippines 4.0 2.5 6.6 .12.3 0.2 -6.7 6.7 8.3 12.2 7.8 16,574 4.5 Poland 4.0 1.1 6.0 .. -2.5 .. 7.2 6.0 -6.3 .. 28,004 6.4 E Romania 1.6 4.5 23.9 11.7 29.1 21.0 45.3 35.0 -3.7 -4.7 4,591 3.4 Russian Federation 8.3 5.5 4.3 2.6 17.5 16.5 37.1 20.5 16.7 11.4 40.806 6.2 Slovak Republic 2.2 2.8 15.9 7.0 10.2 13.0 6.5 6.4 -3.6 -8.5 6,441 4.4 South Africa 3.1 2.5 8.2 3.1 7.4 9.2 6.5 6.0 -0.4 -0.3 15,532 5.1 o Sri Lanka 6.0 2.5 7.2 8.3 12.9 0.9 7.1 13.5 -6.2 -4.0 1,050 1.5 C4 Syrian Arab Republic 2.5 0.8 21.4 .. 1. . 1.1 2.0 6.3 0.6 3.264 o0 Thailand 4.3 1.6 15.4 -0.1 20,4 -2.1 1.8 2.0 7.7 4.7 30,141 4.5 Trinidad and Tobago 4.8 4.5 1.2 -2.1 16.2 14.3 9.8 3.4 .. 3.7 1,849 4.3 Tunisia 4.7 5.4 6.6 6.8 9.6 6.3 2.4 2.5 -4.2 -4.3 2,442 2.7 Turkey 7.2 -6.5 10.5 10.2 33.4 -20.1 50.6 60.7 -4.9 1.3 18.938 4.1 Uganda 3.5 6.0 -0.7 10.1 6.3 6.6 3.3 5.5 -13.9 -13.4 858 5.0 Ukraine 5.8 7.0 13.8 10.2 17.5 11.5 25.3 11.3 4.7 2.8 2.980 1.7 Uruguay -1.3 -1.2 4.0 0.8 -0.9 -6.0 3.6 5.1 -3.0 -2.9 2,800 6.8 Uzbekistan 4.0 3.8 -5.6 1.5 -6.2 8.6 44.3 42.8 2.4 -0.5 1.160 4.2 Venezuela. RB 3.2 3.3 5.8 -3.7 19.5 3.3 26.8 9.6 11.1 5.1 16,401 7.4 Zambia 3.5 5.0 4.9 26.7 7.2 21.6 18.1 24.9 0.0 -20.3- Zimbabwe -4.9 -8.1 -16.6 -3.8 -21.6 -2.5 59.9 70.0 0.0 -1.0 - Note: Data for 2001 are the latest preliminary estimates. ana may differ fronti those in earl er World Bank publications. Source: World Bank staff estimates. Table 4.b Key macroeconomic Indicators Nominal exchange rate Real effective Money and Gross Real Interest Short-term exchange rate quasi money domestic credit rate debt' local currency units annual annual % of per $ % change 1995=100 % growth % growthn % expc.rts .................2001 ---22000___22001----200000- -2001200 - 000 200 2001 201 2000 20010 .... 0 2000...2200101 - 20-00D Malawi 67.3 72.4 -16.0 112.7 146.4 41.4 4.4 24,4 9.9 23.0 56.2 15. 7 Malaysia 3.8 0.0 - 0.0 86.6 92.7 9.9 6.7 9.6 7.5 1.9 6.7 4. 1 Mauritius 30.4 9.5 9.0 . .. 9.2 9.3 4.8 12.7 20.8 21.0 28.8 Mexi'co 9.1 0.6 -4.5 . .. -4.2 5.7 -2.6 -3.5 6.6 13.9 9.8 Moldova 12.9 6.8 4.5 109.8 105.9 41.7 40.5 14.4 20.4 5.3 27.1 3.4 Mlorocco -11 .-6 -5.-3 -8-.9-- 10.2 10-1-.6 8.4 14.7 11.2 3.4 11.6 .. 2.0 Nicaragua 13.8 6.0 6.0 113.1 115.8 9.4 6.5 9.3 16.4 8 7 22.8 7 5.9 Nigeria .. 11.8 .. 81.0 98.9 48.1 .. 25 3 -3.3 5.1 Pakistan 60.9 12.1 4.9 93.6 89.3 12.1 .. 10.6 .14.2 anama 1.0 0.0 0.0 . .. 10.0 .. 7.6 . 9.3 10.2 5. 0 203 Papua New Guinea 3.8 13.7 22.5 92.8 83.6 5.0 -0.6 -2.1 .6.2 1.7 14,7 2. 2 ---- - -- - ---- . ........... ~ ~ ~ ~ ~ ~ Paraguay 4,718.1 5.9 33.8 97.1 85.0 4.8 16.4 11.7 16.7 16.4 30.5 18.2 0 Peru 3.4 06 -28 .. 4 1.6 -0.3 0.3 23.4 20.4 39.3 PIlippines 51.4 24.0 2.8 89.8 84.9 8.1 1.5 9.3 1.8 4.0 11.9 12.0 Poland 4.0 .0.2 -3.6 121.6 134.8 11.8 . 7.2 .. 12.0 .. 14.4 C R ania -3,i597.0 42.0 21.9 10. 111. 38.0 48.8 15.5 33.4... 29 Russian Federation 30.1 4.3 7.0 90.5 109.2 58.4 36.2 13.7 26.3 -9.2 17.0 1-3.4- Siov k Republic - .. 12.1 . 10. 179 152. 82.. 7.9 .. 8.0 ----....- - - -.. - . ~ ~ ~ ~ ~ ~ ~ ~ ~ .... . . --- - --- - ------.--------- (5 South Africa 12.1 23.1 60.2 82.9 64.3 7.2 15.1 14.0 12.3 7.5 13.0 24.7 Z S--ri Lanka 92 4. 1.81 26.2 .. 85 .-- . - ------ -------- .--------0. Syrian Arab Republic 11.2 0.0 0.0 . .. 19.0 .. -7.8 ... 79.6 Thailand 44.2 15.5 2~~ ~ ~~.2 .. 3.4 2.4 -7.5 -6.1 597.3 17.3 TrinidadadTbg 6. 0.0 ~ 10 115.4 128.8 11.7 .. -6.2 .. 6.1 .. 17.7 Tunisia 1.4 11.2 .2.7 100.8 99.4 14.1 11.7 27.5 20.4 ... 9.7 Turkey 1,77,524.0 24.4 116.1 .. . 40.0 95.3 75.9 134.0 ... 49.4 Uganda 1727. 17.3 .2.2 96.0 103.2 18.1 .. 73.9 .. 19.0 21.4 14.,2 Ukraine 5.3 4.0 -2.4 118.4 119.7 44.4 43.2 23.1 18.8 12.9 32.3 2.3 Uruguay 14.8 7.7 18.0 113.1 110.1 7.2 11.7 1.4 1.7 43.8 51.7 42.6 Uzbekistan .. . .. .. .. .. . 8.3 Venzula,RB 7630 .9 .0 1616 76.86---23.1----A 13.8 15.4 34.8 -1.3 23.1 4.7 Z-amnbi'a 3,304-8.--.9 11. 12-0-.9 -- --- 7-3-.8- . 5-9.-5-- ..8176 462 8. Zimbabwe - 550 44.4 .0.1 .. .. 68.9 10~~~~~~~i5.6 66.4 73.0 5.2 .. 26.4 Note: Data tar 2001 are preliminary and may not cover tne entire year. a. More recent data on short-term debt are available on a Web site maintained by the Bank for International Settlements, the international monetary Fund, the Organ sation for Economic Co-operation and Development, and the World Bank: www.oecd.org/dac/debt. Source: International Monetary Fund. International Financial Statistics; world Bank. Debtor Reporting System. 4.1~ Growth of output Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annual average arnneal % grownth % growth % growth % growth % growtn 1.980-90 1990-2000 1980-90 1990-2000 1980-90 .1990-2000 1980-90 1990-2000 1980-90 1990-2000 Afghanistan.......... Albania 1.5 3.3 1.9 6.0 2.1 -0.4 .. -6.6 -0.4 3.8 Algeri'a 2.7 1.9 4.1 3.6 2.6 1.8 4.1 -2.1 3.0 1.9 Angola 3.4 1.3 0.5 -1.5 6.4 3.7 -11.1 -0.4 1.3 -2.0 Argentina -0.7 4.3 0.7 3.4 -1.3 3.8 -0.8 2.8 0.0 4.5 Armenia ..-1.9 .. 0.4 .. -7.9 .. -4.3 ..6.7 Australia 3.5 4.1 3.4 3.1 2.9 3.2 1.9 2.4 4.0 4.5 Austria 2.2 2.1 1.2 4.4 1.8 2.5 .. 2.3 2.6 1.8 Azerbaijan ..-6.3 .. 0.6 .. -2.8 .. -21.1 ..2.3 204 Bangladesh 4.3 4.8 2.7 2.9 4.9 7.3 3.0 7.2 4.4 4.5 Belarus .. 1.6 .. -4.1 .. -1.9 .. -0.8 0 o Belgium 2.1 2.0 2.0 3.7 2.3 1.8 ...1.9 1.8 Benin 2.5 4.7 5.1 5.8 3.4 4.1 5.1 5.8 0.7 4.1 Bolivia -0.2 4.0 .. 3.3 .. 4.0 ... .4.3 Bosnia and Herzegovina . .. .. .. E Botswana 10.3 4.7 3.3 0.8 10.2 2.9 8.7 4.1 11.7 6.9 o) Brazil 2.7 2.9 2.8 3.2 2.0 2.6 1.6 2.1 3.3 3.0 > Bulgaria 3.4 -2.1 -2.1 0.4 5.2 -3.7 -..4.5 -1.3 o Burkina Faso 3.6 4.9 3.1 4.2 3.8 5.9 2.0 7.0 4.6 4.6 Burundi 4.4 -2.6 3.1 -1.6 4.5 -5.6 5.7 -8.0 5.6 -2.0 Cambodia -.4.8 . 1.9 .. 8.3 . 8.2 ..6.9 o Cameroon 3.4 1.7 2.2 5.6 5.9 -0.8 5.0 1.4 2.1 0.2 (N Canada 3.3 2.9 2.0 1.1 2.8 2.6 3.3 3.8 3.1 2.6 Central African Republic 1.4 2.0 1.6 3.9 1.4 0.8 5.0 0.0 1.0 -0.5 Chad 6.1 2.2 2.3 4.4 8.1 2.2 ...6.7 1.2 Chile 4.2 6.8 5.9 1.5 3.5 6.0 3.4 4.6 2.9 5.6 China 10.1 10.3 5.9 4.1 11.1 13.7 11.1 13.4 13.5 9.0 Hong Kong. China 6.9 4.0 - .. .. Colombia 3.6 3.0 2.9 -2.2 5.0 1.7 3.5 -2.3 3.1 4.3 Congo. Dam. Rep. 1 6 -5.1 2.5 2.9 0.9 -11.7 1.6 -13.4 1.3 -15.2 Congo, Rep. 3.3 -0.4 3.4 1.3 5.2 2.6 6.8 -2.8 2.1 -3.9 Costa Rica 3.0 5.3 3.1 4.1 2.8 6.2 3.0 6.7 3.3 4.7 C6te dIlvoire 0.7 3.5 0.3 3.6 4.4 5.1 3.0 3.8 -0.3 2.6 Croatia -.0.6 .. -2.0 .. -2.5 .. -3.3 ..0.9 Cuba -.4.2 .. 5.2 .6.6 , 6.3 ..2.5 Czech Republic ..0.9 -. 3.3 . -0.8 ... .1.8 Denmark 2.0 2.5 2.6 2.9 2.0 2.0 1.3 2.1 1.9 2.6 Dominican Republic 3.1 6.0 -1.0 3.7 3.0 7.1 2.3 4.9 4.2 5.9 Ecuador 2.0 1.8 4.4 1.7 1.2 2.7 0.0 2.1 1.7 1.3 Egypt. Arab Rep. 5.4 4.6 2.7 3.1 3.3 4.9 -. 6.3 7.8 4.5 El Salvador 0.2 4.7 -1.1 1.3 0.1 5.3 -0.2 5.3 0.7 5.4 Eritrea ..3.9 ..-1.0 . .- . Estonia 2.2 -0.5 .. -3.1 .. -3.2 .. 2.5 ..1.8 Ethiopia 1.1 4.7 0.2 2.1 0.4 6.1 -0.9 6.6 3.1 7.1 Finland 3.3 2.8 -0.4 1.2 3.3 4.8 3.4 5.8 3.6 2.3 France 2.4 1.7 1.3 2.0 1.4 1.2 .. 2.1 3.0 1.9 Gabon 0.9 2.8 1.2 -1.4 1.5 2.5 1.8 0.6 0.1 3.9 Gambia, The 3.6 3.1 0.9 2.7 4.7 1.1 7.8 1.0 2.7 4.3 Georgia .. -13.0 . 1. 7 .. 5.1 .. 3.2 .. 15.6 Germany 2.3 1.5 1.7 1.7 1.1 -0.1 .. -0.4 3.1 2.4 Ghana 3.0 4.3 1.0 3.4 3.3 2.6 3.9 -3.3 5.7 5.7 Greece 0.9 2.1 -0.1 0.5 1.3 1.1 ...0.9 2.4 Guatemala 0.8 4.1 1.2 2.8 -0.2 4.3 0.0 2.8 0.9 4.7 Guinea ..4.3 . 4.3 .. 4.7 . 4.1 ..3.6 Guinea-Bissau 4.0 1.2 4.7 3.9 2.2 -3.1 .. -2.0 3.5 -0.6 Haiti -0.2 -0.6 -0.1 -3.3 -1.7 1.2 -1.7 -10.8 0.9 0.2 Honduras 2.7 3.2 2.7 2.0 3.3 3.7 3.7 3.9 2.5 3.8 4.1 E Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual ave~rage annual average annual % growth % growth % growth % growth % growth 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 Hungary 1.3 1.5 1.7 -2.2 0.2 3.8 .. 7.9 2.1 1,4 India 5.8 6.0 3.1 3.0 6.9 6.4 7.4 7.0 7.0 8.0 Indonesia 6.1 4.2 3.6 2.1 7.3 5.2 12.8 6.7 6.5 4.0 Iran, Islamic Rep. 1.7 3.5 4.5 3.8 3.3 -3.8 4 .5 4.7 -1.0 9.2 Iraq -6.8 . .. .. Ireland 3.2 7.3 . .. .. Israel 3.5 5.1 . .. .. Italy 2.5 1.6 -0.5 1.6 1.8 1.2 2.1 1.5 3.0 1.7 Jamaica 2.0 0.5 0.6 1.9 2.4 -0.5 2.7 -1.9 1.8 1.1 Japan 4.1 1.3 1.3 -32 4.1 -0.4 0.5 4.2 2.5 205 Jordan 2.5 5.0 6.8 -20 1.7 4.7 0,5 5.4 2.3 5.0 Kazakhstan ..4.1 -7.9 .. -9.0 ...2.8 Kenya 4.2 2.1 3.3 1.3 3.9 1.7 4.9 2.1 4.9 3.3 Korea, Dem. Rep.... .. . - - ------- ------- -------- - - -~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Korea, Rep. 8.9 5.7 3.0 2.0 11.4 6.3 12.1 7.5 8.4 5.7 Kuwait 1.3 3.2 14.7 .. 1.0 .. 2.3 ..2.1 Kyrgyz Republic ..4.1 .. 1.5 .. -10.4 .. -14.3 .. -6.4 C - -- - -------- - - 0~~~~~~~~~~~~~ Lao PDR 3.7 6.5 3.5 4.9 6.1 11.0 8.9 11.7 3.3 6.5 ' - ---- ---- - ~~~~~~~~~~~~~~~~~~~~~~~~~3 Latvia 3.5 -3.4 2.3 .7.0 4.3 -8.4 4.4 .7.8 3.3 2.5 C Lebanon ..6.0 1.8 1.6 -4.3 ..4.1 - ----------~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~a Lesotho 4.5 4.1 2.8 1.8 4.9 5.9 8.5 6.6 4.0 4.4 Liberia -1.7 1.2 .. -6.0 .. -5.0 ..-0.8 .. Libya -5. 7 . .- .. Lithuania ..3.1 .. -1.1 7.0 .. -8.5 -0.3 Macedonia, FYR ..0.8 .. 0.3 -2.5 -4.4 ..0.7 Madagascar 1.1 2.0 2.5 1.4 0.9 2.4 2.1 0.6 0.3 2.5 Malawi 2.5 3.8 2.0 7.6 2.9 1.6 3.6 -2.1 3.3 3.4 Malaysia 5.3 7.0 3.4 0.3 6.8 8.6 9.3 9.8 4.9 7.2 Mali 0.8 3.8 3.3 3.2 4.3 6.6 6.8 3.0 1.9 2.9 Mauritania 1.8 4.2 1.7 5.0 4.9 2.4 -2.1 -0.5 0.4 4.9 Mauritius 6.2 5.3 2.9 -09 10.3 5.5 11.1 5.6 5.5 6.4 Mexico 1.1 3.1 0.8 1.8 1.1 3.8 1.5 4.4 1.4 2.9 Moldova' 2.8 -9.7 .. -13.7 -. -16.7 -.... 1.9 Mongolia 5.4 1.0 1.4 3.2 6.6 -0.5 ... 8.4 0.1 Morocco 4.2 2.3 6.7 -0.9 3.0 3.2 4.1 2.7 4.2 2.8 Mozambique -0.1 6.4 6.6 5.5 -4.5 14.0 . 1 7.6 9.1 1.7 Myanmar 0.6 6.6 0.5 5.3 0.5 10.1 -0.2 7.0 0.8 6.8 Namibia 1.3 4.1 2.5 4.1 -0. 1 2.3 3.3 2.7 2.1 4.6 Nepal 4.6 4.9 4.0 2.5 8.7 7.2 9.3 9.2 3.9 6.2 Netherlands 2.3 2.8 3.5 1.6 1.5 1.6 2.6 3.1 New Zealand 1.9 3.0 3.8 2.7 1.1 2,4 ... 2.7 3.7 Nicaragua -1.9 3.5 -2.2 5.7 -2.3 4.2 -3.2 1.8 -1.5 1.8 Niger -012.4 1.7 3.2 -1.7 2.0 -2.7 2.6 -0.7 1.9 Nigeria 1.6 2.4 3.3 3.5 -1.1 1.0 0.7 1.2 3.7 2.9 Norway 2.8 3.6 0.1 2.4 4.0 3.9 0.2 2.3 2.9 3.4 Oman 8.4 5.9 7.9 .. 10.3 20.6 5.9 Pakistan 6.3 3.7 4.3 4.4 7.3 3.9 7.7 3.5 6.8 4.4 Panama 0.5 4.1 2.5 2.0 -1.3 5.4 0.4 2.8 0.7 4.0 Papua New Guinea 1.9 4.0 1.8 3.7 1.9 5.5 0.1 5.6 2.0 3.0 Paraguay 2.5 2.2 3.6 2.5 0.3 3.2 4.0 0.7 3.1 1.6 Peru -0.1 4.7 3.0 5.8 0.1 5.4 -0.2 3.8 -0.4 4.0 Philippines 1.0 3.3 1.0 1.6 -0.9 3.3 0.2 3.0 2.8 4.1 Poland ..4.6 .. -0.2 .. 4.2 -.. .4.1 Portugal 3.1 2.7 2.8 -0.3 3.2 3.2 ...2.3 2.3 Puerto Rico 4.0 3.1 1.8 3.6 .. 3.6 ..4.6 Romania 0.5 -0.7 -06 . -0.8 -2.8 .. -0.5 Russian Federation ..-4.8 .. -6.0 .. -7 6 ... -1.0 _ ~4.1 Gross domestic Agriculture Industry Manufacturing Services product average arnual average annua average annual average annual average annual % growth % growth % growth % growth % growth 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 198G-90 1990-2000 Rwanda 2.2 -0.2 0.5 -2.3 2.5 2.8 2.6 6.6 5.5 -0.1I Saudi Arabia 0.0 1.5 13.4 0.7 -2.3 1.5 7.5 2.7 1.3 2.0 Senegal 3.1 3.6 2.8 1.9 4.3 4.8 4.6 4.0 2.8 3.8 Sierra Leone 1.2 -4.3 3.1 -0. 1 1.7 -6.2 .. 5.0 -2.7 -10.3 Singapore 6.7 7.8 -5.3 -1.6 5.2 7.9 6.6 7.1 7.6 7.8 Slovak Republic 2.0 2.1 1.6 1.2 2.0 -2.7 .. 4.1 0.8 6.5 Slovenia -.2.7 .. -0.1 . 2.9 .. 4.0 ..3.9 Somalia 2.1 .. 3.3 .. 1.0 . -1.7 ..0.9 South Africa 1.0 2.0 2.9 0.6 0.7 1.0 1.1 1.2 2.4 2.6 206 Spain 3.0 2.5 1.6 -0.6 3.1 2.5 - .3.0 2.7 Sri Lanka 4.0 5.3 2.2 1.9 4.6 7.0 6.3 8.1 4.7 6.0 Sudan 0,4 8.1 -0.6 11.3 1.3 7.7 3.4 4.0 1.9 6.3 in Swaziland 6.5 3.3 2.5 1.0 11.2 3.9 14.0 3.0 4.9 3.5 Sweden 2.5 1.9 1.4 0.0 2.8 3.4 ...2.4 1.7 C Switzerland 2.0 0.8 - .. .. E Syrian Arab Republic 1.5 5.8 -0.6 5.3 6.6 9.9 .. 10.8 1.6 4.6 o Tajikistan 2.0 -10.4 -2.8 -5.8 5.5 -16.6 5.6 -12.6 3.4 -0.4 > Tanzania ..2.9 . 3.2 .. 3.1 . 2.7 -. 2.7 a) Thailand 7.6 4.2 3.9 2.1 9.8 5.3 9.5 6.4 7,3 3.7 o Togo 1.7 2.3 5.6 4.0 1.1 2.8 1.7 2.9 -0.3 0.4 Trinidad and Tobago -0.8 3.0 -5.9 1.9 -5.5 3.4 -10.1 5.9 6.7 2.7 o Tunisia 3.3 4.7 2.8 2.4 3.1 4.6 3.7 5.5 3.5 5.3 0 Turkey 5.4 3.7 1.3 1.4 7.8 4.1 7.9 4.8 4.4 3.7 Turkmenistan ..-4.8 .. -5.7 .. -3.2 ... . -5.8 Uganda 2.9 7.0 2.1 3.7 5.0 12.3 3.7 13.6 2.8 7.9 Ukraine ..-9.3 .. -5.8 .. -11.4 -. -11.2 -. -1.1 United Arab Emirates -2.1 2.9 9.6 .. -4.2 .. 3.1 ..3.6 United Kingdom 3.2 2.5 2.1 -0.2 3.1 1.3 ...3.2 3.2 United States 3.5 3.5 . .. .. Uruguay 0.5 3.4 1.8 2.8 1.2 1.1 1.7 -0.1 2.4 4.6 Uzbekistan -. -0.5 .. 0.1 .. -3.2 ..- .0.3 Venezuela. RB 1.1 1.6 3.1 1.4 1.7 2.9 4.4 0.9 0.5 0.4 Vietnam 4.6 7.9 4.3 4.8 .. 12.1 ... .7.7 West Bank and Gaza ..2.8 . -4.2 .. 0.8 . 3.6 ..2.8 Yemen. Rep. ..5.8 .. 5.1 .7.9 . 4.4 ..5.1 Yugoslavia, Fed. Rep. ..0.6..-... ... Zambia 1.0 0.5 3.6 3.9 1.0 -4.0 4.1 1.2 -0.2 2.6 Zimbabwe 3.6 2.5 3.1 4.3 3.2 0.4 2.8 0.4 3.0 3.1 Low Income 4.5 3.2 3.0 2.5 5.5 2.7 7.8 2.6 5.5 5.1 Middle income 3.3 3.6 3.5 2.0 3.6 3.9 4.6 6.2 3.6 3.9 Lower middle income 4.1 3.6 4.2 2.1 5.9 4.1 7.0 8.9 5.5 4.3 Upper middle income 2.7 3.6 2.7 1.9 2.6 3.7 3.6 4.1 3.0 3.7 Low & middle Income 3.5 3.5 3.4 2.2 3.9 3.7 4.9 5.7 3.9 4.1 East Asia & Pacific 7.9 7.2 4.4 3.1 9.3 9.3 10.4 9.9 8.6 6.4 Europe & Central Asia ..-1.5 .. -2.3 -. -3.8 ... .1.6 Latin America & Carib. 1.7 3.3 2.3 2.3 1.4 3.3 1.4 2.6 1.9 3.4 Middle East & N. Africa 2.0 3.0 5.2 2.6 0.3 0.9 .. 3.8 2.4 4.5 South Asia 5.6 5.6 3.2 3.1 6.8 6.2 7.0 6.6 6.5 7.1 Sub-Saharan Africa 1.6 2.5 2.3 2.8 1.2 1.6 1.7 1.6 2.4 2.6 High Income 3.3 2.5 1.4 0.0 2.9 0.7... Europe EMU 2.4 1.9 1.1 1.3 1.6 1.0 .. 1.2 2.9 2.2 a. Exludes data for Transnistria. b. Data cover mainland Tanzania only 4.1 S About the data Definitions An economy's growth is measured by the change completeness of such estimates depends on * Gross domestic product (GDP) at purchaser inthevolumeofitsoutputorinthe real incomes the skill and methods of the compiling prices is the sum of the gross value added by of persons resident in the economy. The 1993 statisticians and the resources available to all resident producers in the economy plus any United Nations System of National Accounts them. product taxes and minus any subsidies not (1993 SNA) offers three plausible indicators from included in the value of the products. It is which to calculate growth: the volume of gross Rebasing national accounts calculated without making deductions for domestic product, real gross domestic income, When countries rebase their national accounts, depreciation of fabricated assets or for and real gross national income. The volume of they update the weights assigned to various depletion and degradation of natural resources. GDP is the sum of value added, measured at components to better reflect the current pattern Value added is the net output of an industry constant prices, by households, government, of production (or consumption). The new base after adding up all outputs and subtracting and the enterprises operating in the economy. year should represent normal operation of the intermediate inputs. The industrial origin of This year's edition of the World Development economy-that is, it should be a year without value added is determined by the International Indicators continues to follow the practice of past major shocks or distortions-but the choice of Standard Industrial Classification (ISIC) revision editions, measuring the growth of the economy base year is often constrained by the lack of 3. * Agriculture corresponds to ISIC divisions by the change in GDP measured at constant data. Some developing countries have not 1-5 and includes forestry and fishing. 207 prices. rebased their national accounts for many years. * Industry comprises mining, manufacturing Each industry's contribution to the growth in Using an old base year can be misleading (also reported as a separate subgroup), the economy's output is measured by the growth because implicit price and volume weights construction, electricity, water, and gas (ISIC in value added by the industry. In principle, value become progressively less relevant and useful. divisions 10-45). * Manufacturing refers to E added in constant prices can be estimated by To obtain comparable series of constant price industries belonging to divisions 15-37. measuring the quantity of goods and services data, the World Bank rescales GDP and value * Services correspond to ISIC divisions 50- (D produced in a period, valuing them at an agreed added by industrial origin to a common reference 99. This sector is derived as a residual (from CD set of base year prices, and subtracting the cost year, currently 1995. This process gives rise to GDP less agriculture and industry) and may not 3 of inputs, also in constant prices. This double a discrepancy between the rescaled GDP and properly reflect the sum of service output, (D deflation method, recommended by the 1993 the sum of the rescaled components. Because including banking and financial services. SNA and its predecessors, requires detailed allocating the discrepancy would give rise to information on the structure of prices of inputs distortions in the growth rates, the discrepancy and outputs. is left unallocated. As a result, the weighted i Data sources In many industries, however, value added is average of the growth rates of the components The national accounts data for most developing extrapolated from the base year using single generally will not equal the GDP growth rate. countries are collected from national statistical volume indexes of outputs or, more rarely, Growth rates of GDP and its components are organizations and central banks by visiting inputs. Particularly in the service industries, calculated using constant price data in the local I and resident World Bank missions. The data including most of government, value added in currency. Regional and income group growth for high-income economies come from constant prices is often imputed from labor rates are calculated after converting local Organisation for Economic Co-operation and inputs, such as real wages or the number of currencies to constant price U.S. dollars using |Development (OECD) data files; for information employees. In the absence of well-defined an exchange rate in the common reference year. on the OECD's national accounts series see measures of output, measuring the growth of The growth rates in the table are annual average I its Main Economic Indicators (monthly). The services remains difficult. compound growth rates. Methods of computing World Bank rescales constant price data to a Moreover, technical progress can lead to growth rates and the alternative conversion common reference year. The complete national improvements in production and in the quality factor are described in Statistical methods. accounts time series is available on the WVorld of goods and services that, if not properly Development Indicators 2002 CD-ROM. The accounted for, can distort measures of value Changes In the System of National Accounts United Nations Statistics Division publishes I added and thus of growth. When inputs are used Last year the World Development Indicators detailed national accounts for United Nations toestimateoutput,asisthecasefornonmarket adopted the terminology of the 1993 SNA. member countries in National Accounts services, unmeasured technical progress leads Although most countries continue to compile Statistics: Main Aggregates and Detailedj to underestimates of the volume of output. their national accounts according to the System Tables and publishes updates in the Monthly Similarly, unmeasured changes in the quality of of National Accounts version 3 (referred to as Bulletin of Statistics. goods and services produced lead to the 1968 SNA), more and more are adopting underestimates of the value of output and value the 1993 SNA. Countries that use the 1993 SNA added. The result can be underestimates of are identified in Primary data documentation. growth and productivity change, and Some low-income countries still use concepts overestimates of inflation. This is a highly from the even older 1953 SNA guidelines, complex issue, and only a few advanced including valuations such as factor cost, in industrial countries have so far attempted to describing major economic aggregates. introduce any GDP adjustments for these factors. Informal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity may go unrecorded. Obtaining a complete picture of the economy requires estimating household outputs produced for local sale and home use, barter exchanges, and illicit or deliberately unreported activity. The consistency and .j~ ) 4.2 Structure of output Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ millions % of GOP % of GDP % of GDP % of GOP 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan . .. .. Albania 2,102 3,752 36 51 48 26 42 12 16 23 Algeria 62,045 53,306 11 9 48 60 11 8 40 31 Angola 10,260 8,828 18 6 41 76 5 3 41 18 Argentina 141,352 284,960 8 5 36 28 27 18 56 68 Armenia 4.124 1,914 17 25 52 36 33 24 31 39 Australia 309,654 390,113 3 3 28 26 14 13 68 71 Austria 161,692 189,029 4 2 34 33 23 21 62 65 Aze rbaijan 9,837 5.267 ..19 .. 38 ..7 .. 43 208 Bangladesh 30,129 47,106 29 25 21 24 13 15 50 51 Belarus 35,203 29,950 24 15 47 37 39 31 29 47 Belgium 197.349 226,648 2 2 33 27 . 2 0 65 72 Benin 1,845 2,168 36 38 13 14 8 9 51 48 Bolivia 4,868 8.281 26 22 20 15 17 13 54 63 Bosnia and Herzegovina .. 4,394 ..12 .. 26 .. 16 .. 62 E Botswana 3,766 5,285 5 4 56 44 5 5 39 52 o Brazil 464,989 595.458 8 7 39 29 25 24 53 64 a) > Bulgaria 20.726 11,995 18 15 51 28 .. 17 31 58 Burkina Faso 2,765 2,192 32 35 22 17 16 12 45 48 '0 3: Cambodia 1,115 3,183 56 37 11 20 5 6 33 42 (.4 o Cameroon 11.152 8,879 25 44 29 20 15 11 46 36 0 C Canada 572,673 687,882 3 .. 33 ..18 ..64 Central African Republic 1,488 963 48 55 20 20 11 9 33 26 Chad 1.739 1,407 29 39 18 14 14 11 53 47 Chile 30,323 70.545 9 11 41 34 20 16 50 56 China 354,644 1.079,948 27 16 42 51 33 35 31 33 Hong Kong, China 74,784 162,642 0 0 25 14 18 6 74 85 Colombia 40.274 81,283 17 14 38 31 21 14 45 56 Congo. Oerm. Rep. 9.348 5,584 30 .. 28 ..11 ..42 Congo. Rep. 2,799 3,215 13 5 41 71 8 3 46 24 Costa Rica 5.713 15,851 18 9 29 31 22 24 53 59 C6te dIlvoire 10,796 9.370 32 29 23 22 21 19 44 48 Croatia 18.156 19.031 10 9 34 33 28 23 56 58 Cuba ... .7 . 46 . 37 .. 47 Czech Republic 34,880 50,777 6 4 49 41 ...45 55 Denmark 133,361 162,343 4 3 27 26 18 17 69 71 Dominican Republic 7,074 19,669 13 11 31 34 18 17 55 55 Ecuador 10,686 13,607 13 10 38 40 19 17 49 50 Egypt. Arab Rep. 43,130 98,725 19 17 29 34 18 19 52 49 El Salvador 4,807 13,211 17 10 26 30 22 23 57 60 Eritrea 437 608 29 17 19 29 13 15 52 54 Estonia 6,760 4,969 17 6 50 27 42 16 34 67 Ethiopia 6.842 6,391 49 52 13 11 8 7 38 37 Finland 136,794 121,466 7 4 34 34 23 25 60 62 France 1.215,892 1,294,246 4 3 30 26 21 19 66 71 Gabon 5,952 4,932 7 6 43 53 6 4 50 40 Gambia. The 317 422 29 38 13 13 7 5 58 49 Georgia 12,171 3,029 32 32 33 13 24 7 35 55 Germany 1,688,568 1.872,992 2 1 38 31 28 23 60 68 Ghana 5,886 5,190 45 35 17 25 10 9 38 39 Greece 84,075 112.646 11 8 28 24 .. 12 61 68 Guatemala 7,650 18,988 26 23 20 20 15 13 54 57 Guinea 2.818 3,012 24 24 33 37 5 4 43 39 Guinea-Bissau 244 215 61 59 19 12 8 10 21 29 Haiti 2,981 4,050 32 28 21 20 15 7 48 51 Honduras 3,049 5.932 22 18 26 32 16 20 51 51 4.29 Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ milihons % of GDP % of GDP % of GDP % of GDP 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Hungary 33,056 45,633 15 6 39 34 23 25 46 61 India 316,891 456,990 31 25 28 27 17 16 41 48 Indonesia 114,427 153,255 20 17 38 47 18 26 42 36 Iran, Islamic Rep. 120,404 104,904 24 19 29 22 12 16 48 59 Iraq 48,657 . .. Ireland 47,301- 93,865 9 4 35 36 28 28 56 60 Israel 52,490 110,386 . .. Italy 1,102,437 1,073,960 4 3 34 30 2 5 21 6 3 68 Jamaica 4,239 7.403 6 6 43 31 20 13 50 62 Japan 3,052.058 4,841,584 2 1 39 32 27 22 58 66 209 Jordan 4,020 8,340 8 2 28 25 15 16 64 73 Kazakhstan 40,304 18.230 27 9 45 43 9 18 29 48 ) Kenya 8,533 10,357 29 20 19 19 12 13 52 61 Korea, Dem. Rep. - . Korea. Rep. 252,622 457,219 9 5 43 43 29 31 48 53 ) Kuwait 18,428 37,783 1 .. 52 ..12 - .- 47 Kyrgyz Republic 2,951 1,304 34 39 36 26 28 6 30 34 (D Lao PDR 865 1,709 61 53 15 23 10 17 24 24 20 Latvia 12,490 7,150 22 4 46 25 34 14 32 70 ( Lebanon 2,838 16,488 .. 12 . 22 .. 10 66 66 - ---- ------ - -- - ---- a~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Lesotho 615 899 24 17 33 44 14 16 43 :39 Libyra-----E Lithuania 13,254 11,314 27 8 31 33 21 21 42 59 Macedonia, FYR 4,472 3,573 _9 12 46 33 36 21 46 55 Madagascar 3,081 3,878 32 35 14 13 12 ..53 52 Malawi 1,881 1,697 45 42 29 19 - 19 14 26 39 Malaysia 44,024 89,659 15 11 42 45 24 33 43 44 Mali 2,421 2,298 46 46 16 17 9 4 39 37 Mauritania 1,020 935 30 22 29 31 10 9 42 47 Mauritius 2,642 4,381 12 6 32 32 24 24 56 62 Mexico 262,710 574,512 8 4 28 28 21 21 64 67 Moldova, 10,567 1.286 31 28 39 20 .. 16 30 52 Mongolia .. 969 17 33-30 19 .. 5 52 48 Morocco 25,821 33,345 18 14 32 32 - -18 18 - 50 54 Mozambique 2,463 3,754 37 24 18 25 10 13 44 50 Myanmar ... 57 60 11 9 8 7 32 31 Namibia 2,530 3,479 11 11 35 28 13 11 54 61 Nepal 3,628 5,497 52 40 16 22 6 10 32 37 Netherlands 295378 364,766 5 3 31 27 17 64 70 New Zealand 43,103 49,903 7 .. 28 .19 ..65 Nicaragua 1,009 2,396 31 32 21 23 17 14 48 45 Niger 2,481 1,826 35 39 16 18 7 7 49 44 Nigeria 28,472 41.085 33 30 41 46 6 4 26 25 Norway 115,453 161,769 4 -- 2 -- 35 43 13 61 55 Oman 10,535 14,962 3 58 4 39 Pakistan 40,010 61,638 26 26 25 23 17 15 49 51 Panama 5,313 9,889 9 7 15 17 9 8 76 76 Papua New Guinea 3,221 3,818 29 26 30 44 9 9 41 30 Paraguay 5,265 7,521 28 21 25 27 17 14 47 52 Peru 26,294 53,466 7 8 23 27 15 14 70 65 Philippines 44,331 74,733 22 16 34 31 25 23 44 53 Poland 58,976 157,739 8 4 50 36 21 42 60 Portugal 70,863 105,054 9 4 31 31 19 60 66 Puerto Rico 30,604 .. 1 .. 42 40 ..57 Romania 38,299 36,719 20 13 50 36 27 30 51 Russian Federation ~~~579,068 251,106 17 7 4839 ..35 54 4.2 Gross domestic Agricutture Industry Manufacturing Services product value added value added value added value added $ millions % of GDP % of GDP % of GDP % of GDP 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 2,584 1,794 33 44 25 21 19 12 42 35 Saudi Arabia 104.670 173,287 6 7 50 48 8 10 43 45 Senegal 5.698 4,371 20 18 19 27 13 18 61 55_ Sierra Leone 897 636 47 47 20 30 4 5 33 23 Singapore 36.670 92,252 0 0 34 34 27 26 65 66 Slovak Republic 15.485 19.121 7 4 59 31 .. 22 33 65 Slovenia 12,673 18.129 6 3 46 38 35 28 49 58 Somnalia 917 .. 65 ... .5 South Africa 111.997 125.887 5 3 40 31 24 19 55 66 210 Spain 513.522 558.558 7 4 34 31 .. 20 59 66 Sri Lanka 8.032 16,305 26 20 26 27 15 17 48 53 Sudan 13,167 11,516 ..37 .. 18 .9 ..45 iS Swaziland 842 1,478 14 17 43 44 35 33 44 39 'O Sweden 238,327 227.319 3 2 32 29 ...64 69 Switzerland 228,415 239,764 ..2 .30 ... 68 g) Syrian Arab Republic 12.309 16,984 28 24 24 30 20 27 48 46 0E o Tajikistan 4,339 991 33 19 38 26 25 23 29 55 > Tanzania' 4.259 9,027 46 45 18 16 9 7 36 39 CD Thailand 85,345 122,166 12 10 37 40 27 32 50 49 '0 Trinidad and Tobago 5,068 7,312 3 2 46 43 9 8 51 55 o Tunisia 12.291 19,462 16 12 30 29 17 18 54 59 0 Turkey 150.721 199,937 18 16 30 25 20 15 52 59 Turkmenistan 8.129 4.404 32 27 30 50 .. 40 38 23 Uganda 4,304 6.170 57 42 11 19 6 9 32 38 Ukraine 91,327 31.791 26 14 45 38 36 34 30 48 United Arab Emirates 34,132 46,481 2 .. 64 .8 ..35 United Kingdom 987.641 1,414,557 2 1 35 29 23 18 63 70 United States 5.750,800 9,837,406 . .. .. Uruguay 9,287 19,715 9 6 35 27 -28 17 56 67 Uzbekistan 23.6 73 7,666 33 35 33 23 .. 10 34 42 Venezuela, RB 48,593 120,484 5 5 50 -36 20 14 44 59 Vietnam 6.472 31,344 37 24 23 37 19 18 40 39 West Bank and Gaza .. 4,359 ..8 .27 .. 15 . 66 Yemen. Rep. 4,828 8.532 24 15 27 46 9 7 49 38 Yugoslavia, Fed. Rep. .. 8,449 . .. .. Zambia 3,288 2,911 21 27 51 24 36 13 28 49 Zimbabwe 8.784 7,392 16 18 33 25 23 16 50 57 Low Income 890.673 1,048,306 29 24 30 32 18 18 41 44 Middle income- 3.518,514 5,51.3.236 13 9 39 36 25 25 47 55 Lower middle income 1,656,455 2.347,172 21 13 40 41 27 27 39 45 Upper middle income 1.879.581 3,170.508 9 7 39 32 24 23 52 62 Low & middle Income 4,403,910 6.560.552 16 12 38 35 23 23 46 54 East Asia & Pacific 927,056 2,059,121 20 13 40 46 28 32 40 41 Europe & Central Asia 1,252,935 942.079 17 10 44 35 ...39 57 Latin America & Car:ib. 1,132.901 2.000,535 9 7 36 29 23 21 55 64 Middle East & N. Africa 401,331 659,692 15 14 39 37 12 14 47 48 South Asia 404,744 596.794 31 25 27 26 17 16 43 49 Sub-Saharan Africa 297.641 322,730 18 17 34 30 17 14 48 53 High Income 17,413,841 24,927.330........ . Europe EMU 5,539.185 6.048,446 4 2 34 29 25 21 62 68 a. Exciudes data for Transnistria. b. Data cover mainland Tanzania only. 4.2 0 About the data Definitions A country's gross domestic product (GDP) agricultural production see About the data for * Gross domestic product (GDP) at purchaser represents the sum of value added by all table 3.3. prices is the sum of the gross value added by producers in that country. Value added is the Ideally, industrial output should be measured all resident producers in the economy plus any value of the gross output of producers less the through regular censuses and surveys of firms. product taxes and minus any subsidies not in- value of intermediate goods and services But in most developing countries such surveys cluded in the value of the products. It is calcu- consumed in production, before taking account are infrequent, so survey results must be lated without making deductions for deprecia- of the consumption of fixed capital in the extrapolated using an appropriate indicator. The tion of fabricated assets or for depletion and production process. Since 1968, the System of choice of sampling unit, which may be the degradation of natural resources. * Value National Accounts has called for estimates of enterprise (where responses may be based on added is the net output of an industry after value added to be valued at either basic prices financial records) or the establishment (where adding up all outputs and subtracting interme- (excluding net taxes on products) or producer production units may be recorded separately), diate inputs. The industrial origin of value added prices (including net taxes on products paid by also affects the quality of the data. Moreover, is determined by the International Standard the producers, but excluding sales or value much industrial production is organized in Industrial Classification (ISIC) revision 3. V added taxes). Both valuations exclude transport unincorporated or owner-operated ventures that Agriculture corresponds to ISIC divisions 1-5 charges that are invoiced separately by the are not captured by surveys aimed at the formal and includes forestry and fishing. * Industry 211 producers. Some countries, however, report such sector. Even in large industries, where regular comprises mining, manufacturing (also re- data at purchaser prices-the prices at which surveys are more likely, evasion of excise and ported as a separate subgroup), construction, ° 0 final sales are made (including transport other taxes and nondisclosure of income lower electricity, water, and gas (ISIC divisions 10- charges)-which may affect estimates of the the estimates of value added. Such problems 45). * Manufacturing refers to industries be- E distribution of output. Total GDP as shown in become more acute as countries move from longing to divisions 15-37. * Services corre- E the table and elsewhere in this book is measured state control of industry to private enterprise, spond to ISIC divisions 50-99. This sector is (D 'a at purchaser prices. Value added by industry is because new firms enter business and growing derived as a residual (from GDP less agricul- ( normally measured at basic prices. When value numbers of established firms fail to report. In ture and industry) and may not properly reflect 3 added is measured at producer prices, this is accordance with the System of National the sum of service output, including banking @ noted in Primaty data documentation. Accounts, output should include all such and financial services. While GDP estimates based on the production unreported activity as well as the value of illegal l approach are generally more reliable than activities and other unrecorded, informal, or estimates compiled from the income or small-scale operations. Data on these activities Data sources cn expenditure side, different countries use needtobecollectedusingtechniquesotherthan (The national accounts indicators for most different definitions, methods, and reporting conventional surveys of firms. developing countries are collected from national standards. World Bank staff review the quality In industries dominatecl by large organizations l statistical organizations and central banks by of national accounts data and sometimes make and enterprises, such as public utilities, data I visiting and resident World Bank missions. The adjustments to increase consistency with on output, employment, and wages are usually I data for high-income economies come from international guidelines. Nevertheless, readily available and reasonably reliable. But in Organisation for Economic Co-operation and significant discrepancies remain between the service industry, the many self-employed Development (OECD) data files; see the international standards and actual practice. workers and one-person businesses are OECD's Main Economic Indicators (monthly). Many statistical offices, especially those in sometimesdifficulttolocate,andtheyhave little The United Nations Statistics Division developing countries, face severe limitations in incentive to respond to surveys, let alone report I publishes detailed national accounts for United the resources, time, training, and budgets theirfull earnings. Compoundingthese problems I Nations membercountries in NationalAccounts I required to produce reliable and comprehensive are the many forms of economic activity that go Statistics: Main Aggregates and Detailed series of national accounts statistics. unrecorded, including the work that women and I Tables and publishes updates in the Monthly children do for little or no pay. For further i Bulletin of Statistics. Data problems In measuring output discussion of the problems of using national . J Among the difficulties faced by compilers of accounts data see Srinivasan (1994) and Heston national accounts is the extent of unreported (1994). economic activity in the informal or secondary economy. In developing countries a large share Dollar conversion of agricultural output is either not exchanged To produce national accounts aggregates that (because it is consumed within the household) are measured in the sarne standard monetary or not exchanged for money. units, the value of output must be converted to Agricultural production often must be a single common currency. The World Bank estimated indirectly, using a combination of conventionally uses the U.S. dollar and applies methods involving estimates of inputs, yields, the average official exchange rate reported by and area under cultivation. This approach the International Monetary Fund for the year sometimes leads to crude approximations that shown. An alternative conversion factor is can differ from the true values over time and applied if the official exchange rate is judged to across crops for reasons other than climatic diverge by an exceptionally large margin from conditions or farming techniques. Similarly, the rate effectively applied to transactions in agricultural inputs that cannot easily be allocated foreign currencies and traded products. to specific outputs are frequently 'netted out" using equally crude and ad hoc approximations. For further discussion of the measurement of 4.3 Structure of manufacturing Value added In Food, Textiles Machinery Chemicals Other manufacturing beverages, and clothing and transport manufacturing' and equipment tobacco $ millions % of total % of total % of total % of tota % of tota 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 I1990 1999 Afghanistan... .. ...... . Albania 878 436 24 .. 33 ... ... 44 Algeria 6.452 4,242 13 33 17 8 . ..70 59 Angola 513 198 . .. .. .. Argentina 37,868 48.169 20 30 10 7 13 15 12 12 46 36 Armenia 1.243 390 . .. .. Australia 39.593 49,484 18 .. 6 20 ..7 .. 49 Austria 33.386 39,402 15 15 7 4 28 33 8 8 43 41 Azerbaijan .. 334 . .. .. .. 212 Bangladesh 3,839 6.858 24 .. 38 .. 7 . 17 .. 14 Belarus 13,437 7,560 . .. .. .. Belgium .. 43,421 17 19 7 6 . .. 13 17 62 59 Benin 145 207 . .. .. .. m Bolivia 826 1,154 28 34 5 4 1 1 3 5 63 55 Bosnia and Herzegovina .. 697 12 .. 15 .. 18 ..7 .. 48 at o Brazil 90,052 102.597 14 .. 12 .. 27 .... 48 a) > Bulgaria .. 1.799 22 20 9 10 19 5 5 .. 45 65 a) ukn ao 2 1 . . , .. .. '0 Cambodia 58 1 78 . . . .. .. o Cameroon 1,581 1.057 61 35 -13 9 1 3 5 6 46 47 0 Canada 88,928 104,211 15 13 6 4 26 33 10 9 44 41 Central African Republic 154 89 57 .. 6 .2 .6 .. 28 Chad 239 181 . .. .. .. Chile 5,613 10,396 25 32 7 4 5 5 10 13 52 46 China 116,573 333,407 15 16 15 12 24 28 13 11 34 32 Hong Kong, China 12,626 8.478 8 11 36 21 21 24 2 3 33 40 Colombia 8,034 10.848 31 31 15 12 9 8 14 16 31 34 Congo, Dent. Rep. 1,029.. . ... ... ... Congo, Rep. 234 129 . . . Costa Rica 1.107 4.135 47 46 8 6 7 9 9 13 30 26 C6te dIlvoire 2,257 2.209 .. 42 .. 10 .. 3 .. 12 .. 33 Croatia 4,770 3.694 22 .. 15 . 20 ..8 . 36 Cuba Czech Republic Denmark 20,757 26,044 22 .. 4 . 24 . 12 .. 39 Dominican Republic 1,270 2,921 . . . .. .. Ecuador 2,068 4,036 22 24 10 3 5 3 8 3 56 68 Egypt, Arab Rep. 7,296 16,286 19 18 15 12 9 13 14 14 43 43 El Salvador 1,044 2,805 36 29 14 28 4 3 24 16 22 24 Eritrea 49 88 .. ... ....... Estonia 2,679 705 .. ... ....... Ethiopia 497 419 62 52 21 18 1 2 2 4 14 23 Finland 27,533 27.799 13 8 4 2 24 21 8 2 52 66 France 228,104 242,127 13 .. 6 . 31 .9 .. 41 Gabon 332 225 45 .. 2 . 1 .7 .. 45 Gambia, The 18 19 . .. .. . . Georgia 2,789 192 .. ... ....... Germany 456,313 439,770 .. ... ... .... Ghana 575 702 . .. .. .. .. Greece .. 13,161 22 26 20 12 12 15 10 13 36 34 Guatemala 1.151 2,442 . . . .. . . Guinea 126 138 . .. .. .. .. Guinea-Bissau 19 23 . .. .. ,. .. Haiti 446 282 51 46 9 19 . ... .. 40 34 Honduras 443 909 45 42 10 22 3 2 5 5 36 29 4.3 Value added In Food, Textiles Machinery Chemicals Other manufacturing beverages, and clothing and transport manufacturing' and equipment tobacco $ millions % of total % of total % Of total % of total % of total ±.990 1999 1990 1999 1990 1999 1990 .1999 1990 .1999 1990- 1999 Hungary 6,613 958 14 19 9 8 26 26 12 7 39 40 India 48,838 61,581 12 11 15 10 25 24 14 20 34 35 Indonesia 20,947 36,626 27 16 15 18 12 20 9 9 37 3 6 Iran, Islamic Rep. 14,503 16,938 12 .. 20 . 20 .8 .. 40 Iraq 20 16 ..4 . 1.1 .. 49 Ireland 11,982 23,054 27 19 4 1 29 35 16 28 24 16 Israel -14 12 9 9 32 32 9 5 37 42 Italy 247,930 225,290 8 9 13 13 34 30 7 10 38 38 Jamaica 827 967 41 48 5 7 . .. . .. 54 46 Japan 810,232 970,001 9 11 5 4 40 40 10 10 37 213 Jordan 520 1,059 28 28 7 6 4 5 15 17 47 45 Kazakhstan 2,136 2,377.. ... . 0 Kenya 862 1,117 38 49 10 8 10 3 9 9 33 31 Korea, Dem. Rep...... . ........ Korea, Rep. 72,837 124,832 11 8 12 8 32 45 9 10 36 :29 c - -------- ---- - ------ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Kuwait 2,142 .. 4 5 3 3 2 3 3 3 88 86 Kyrgyz Republic 780 83 C Lao PDR 85 246 .. ... 3 Latvia 4,150 891 39 .. 12 .. 15 6 .29 ( Lebanon .. 1,549.. .... Lesotho 71 135.. . -. . L ib e ria - --- - . --------.---- --- - ---- --- ----- LIbya - -----(0 Lithuania 2,730 1,675 Macedonia, FYR 1,411 646__ 20 32 26 18 14 15 9 11 31 .24 Madagascar 337 365 . . . . . .. Malawi 313 224__ 38 43 10 8 1 5 18 15 33 29 Malaysia 10,665 23.175 13 9 6 5 31 43 11 8 39 35 M al_i _ - - 200 _ - 93 . . .... ----- Mauritania 94 85 . Mauritius 524 903 30 30 46 48 -2 2 4 4 17 16 Mexico -49,992 92,519 -22 ----- 21 5 .......3 24 30 18 18 32 28 M old-ova -125 -. .... -- - --- ------ Mongolia 54 33 .. 37 ..1 . 1 .. 27 Morocco 4,753 6,075 22 34 17 18 8 8 12 16 41 24 Mozambique 230 439 . . . .. .. Myanmar---- Namibi-a- -------- ----- 2-9.0 343 .-. .... .. Nepal 209 446 37 35 31 34 1 3 5 6 25 23 Netherlands 60,707 21 24 3 2 25 26 16 13 35 34 New Zealand 7,665 28 31 8 . 13 14 -7 13 44 43 Nicaragua 170 328 . .. .. Niger 163 131 37 20 29 9 . .. . .. 33 71 Nigeria 1,562 1,635 15 .. 46 . 13 .4 .. 22 Norway 13,450 1 7,076 18 .. 2 - 25 . 9 .. 46 Oman 396 -- ----------- 19 ..7 .5- . 6 . 62 Pakistan 6,184 8,449 24 23 27 _26 9 13 15 16 25 22 Panama 502 732 51 53 8 -7 2 .. 8 7 31 33 Papua New Guinea 289 297.. ... .... Paraguay 883 1,049 56 .. 16 ..29 Peru 3.926 7,538 23 .. 11 .. 8 .9 .. 49 Philippines 11,008 16,475 39 33 11 9 13 15 12 13 26 29 Poland .. 28,392 21 28 9 6 26 23 7 6 37 36 Portugal .. 19,086 15 15 21 15 13 15 6 5 45 50 P-ue-rto R?ico -------- 12,1_26 16_12_5 5 18 ....13 44 57 16 14 Romania .. 8,361 19 .. 18 .. 14 ..4 .. 45 Russian Federation ... . 22 .. 3 . 2 .9 ., 45 4.3 Value added in Food, Textiles Machinery Chemicals Other manufacturing beverages, and clothing and transport manufacturing' and equipment tobacco S millions % of total % of total % of total % of total % of totat 1990 1999 11990 1999 1990 1999 1990 1999 1990 1999 1990 1999 Rwanda 473 223 . .. .. .. Saudi Arabia 7,962 12,550 . . , .. .. Senegal 747 802 60 44 3 5 5 3 9 26 23 2-1 Sierra Leone 31 28 . .. .. .. Singapore 9,937 21,017 4 4 3 1 53 60 10 11 29 25 Slovak Republic .. 4,305 . .. .. .. Slovenia 4,008 4.729 12 12 15 10 16 16 9 11 48 51 Somalia 41 . . . . . .. South Africa 24,040 22.253 14 15 8 7 18 20 9 10 50 48 214 Spain .. 104,997 18 .. 8 . 25 . 10 .. 39 Sri Lanka 1. 077 2,309 50 .. 24 ..4 .4 .. 17 Sudan .. 749 . .. ., .. m Swaziland 246 331 69 .. 8 ..1 ..0 . 22 Sweden .. 51.419 10 8 2 1 33 38 9 11 47 43 Switzerland . .. 10 9 4 3 34 27 53 60 0) E Syrian Arab Republic 2,508 4,291 35 27 29 24 . ..36 49 C. a) Tajikistan 1,078 207 . .. .. .. > Tanzania' 361 583 51 .. 3 ..7 . 11 .. 28 o Thailand 23.217 37,959 24 .. 30 .. 19 ..2 .. 26 o Togo 162 122 . .. .. .. Trinidad and Tobago 438 552 31 .. 3 ..3 . 19 .. 44 o Tunisia 2.075 3,748 19 20 20 28 5 7 4 8 52 36 CN Turkey 26,896 23,989 16 13 15 17 16 18 10 10 43 42 Turkmenistan .. 1,063 . Uganda 230 508 . Ukraine 31,489 8.600 . . . .. United Arab Emirates 2,643.. . ... . . United Kingdom 203.865 238,975 13 12 5 5 32 29 11 11 38 43 United States . .. 12 9 5 3 31 46 12 10 40 32 Uruguay 2,597 3.445 31 38 18 12 9 5 10 9 32 36 Uzbekistan .. 787 . .. .. .. Venezuela, RB 9,809 13,938 17 28 5 5 5 10 9 12 64 45 Vietnam 1.219 5,045 . .. .. .. West Bank and Gaza .. 570 . .. .. .. Yemen, Rep. 449 595 . .. .. .. Yugoslavia, Fed. Rep. ... . 28 .. 9 . 17 . 10 .. 35 Zambia 1.048 339 44 .. 11 .. 7 . 9 .. 29 Zimbabwe 1,799 797 28 34 19 15 9 7 6 5 38 39 Low Income 150,714 150,986 Middie Income 693,897 1,200,241 Lower middle income 303,274 648,001 Upper middle income 379,852 560.402 Low & middle Income 851,662 1.350,094 East Asia & Pacific 259.745 584,052 Europe & Central Asia Latin America & Carib. 254,376 330.258 Middle East & N. Africa 45,996 77,249 South Asia 61.115 80,947 Sub-Saharan Africa 42,947 39,088 High Income .. 4,048,461 Europe EMU 1,233.700 1,241,073 a. Includes unallocated oata. b. Excludes data for Transinistria. c. Oats cover mainland Tanzania only. 4.3 About the data Definitions The data on the distribution of manufacturing industrial classification. The latest revision, * Value added In manufacturing is the sum of value added by industry are provided by the ISIC revision 3, was completed in 1989 and gross output less the value of intermediate United Nations Industrial Development Organi- many countries have now switched to it. How- inputs used in production for industries classi- zation (UNIDO). UNIDO obtains data on manu- ever, revision 2 is still widely used for compiling fied in ISIC major division 3. * Food, bever- facturing value added from a variety of national cross-country data and concordances matching ages, and tobacco comprise ISIC divisior 31. and international sources, including the United ISIC categories to national systems of classifi- * Textiles and clothing comprise ISIC division Nations Statistics Division, the World Bank, the cation and to related systems such as the Stan- 32. * Machinery and transport equipment com- Organisation for Economic Co-operation and dard International Trade Classification (SITC) prise ISIC groups 382-84. * Chemicals com- Development, and the International Monetary which are readily available. prise ISIC groups 351 and 352. * Other manu- Fund. To improve comparability over time and In establishing a classification system, com- facturing includes wood and related proclucts across countries, UNIDO supplements these pilers must define both the types of activities to (ISIC division 33), paper and related products data with information from industrial censuses, be described and the organizational units whose (ISIC division 34), petroleum and related prod- statistics supplied by national and international activities are to be reported. There are many ucts (ISIC groups 353-56), basic metals and organizations, unpublished data that it collects possibilities and the choices made affect how mineral products (ISIC divisions 36 and 37), in the field, and estimates by the UNIDO Secre- the resulting statistics can be interpreted and fabricated metal products and professional 215 tariat. Nevertheless, coverage may be less than how useful they are in analyzing economic be- goods (ISIC groups 381 and 385), and other complete, particularly for the informal sector. To havior. The ISIC emphasizes commonalities in industries (ISIC group 390). When data for tex- ° the extent that direct information on inputs and the production process and is explicitly not in- tiles and clothing, machinery and transport outputs is not available, estimates may be used tended to measure outputs (for which there is a equipment, or chemicals are shown in the table E that may result in errors in industry totals. More- newly developed Central Product Classification), as not available, they are included in other C' over, countries use different reference periods Nevertheless, the ISIC views an activity as de- manufacturing. ( (calendar or fiscal year) and valuation methods fined by "a process resulting in a homogeneous (D (basic, producer, or purchaser prices) to esti- set of products" (United Nations 1990 [ISIC, D mate value added. (See also About the data for series M, no. 4, rev. 3], p. 9). Firms typically Data sources X table 4.2.) use a multitude of processes to produce a final The data on value added in manufacturing in The data on manufacturing value added in product. For example, an automobile manufac- U.S. dollars are from the World Bank's national 0i U.S. dollars are from the World Bank's national turer engages in forging, welding, and painting accounts files. The data used to calculate accounts files. These figures may differ from as well as advertising, accounting, and many shares of value added by industry are provided those used by UNIDO to calculate the shares of other service activities. In some cases, the pro- to the World Bank in electronic files by UNIDO. value added by industry. Thus estimates of value cesses may be carried out by different technical The most recent published source is UNIDO's added in a particular industry group calculated units within the larger enterprise, but collecting Intemational Yearbook of Industrial Statistics i by applying the shares to total value added will data at such a detailed level is not practical. I 2001. The ISIC system is described in the i not match those from UNIDO sources in part Nor would it be useful to record production data i United Nations' International Standard because of exchange rate differences. at the very highest level of a large, multiplant, Industrial Classification of All Economic The classification of manufacturing industries multiproductfirm. The ISIC hastherefore adopted l Activities, Third Revision (1990). The in the table accords with the United Nations In- as the definition of an establishment "an enter- | discussion of the ISIC draws on Jacob Ryten's ternational Standard Industrial Classification priseorpartofanenterprisewhich independently paper "Fifty Years of ISIC: Historical Orfigins (ISIC) either revision 2 or revision 3. First pub- engages in one, or predominantly one, kind of and Future Perspectives" (1998). lished in 1948, the ISIC has its roots in the work economic activity at or from one location . .. for i - of the League of Nations Committee of Statisti- which data are available . . ." (United Nations cal Experts. The committee's efforts, interrupted 1990, p. 25). By design, this definition matches by the Second World War, were taken up by the the reporting unit required for the production United Nations Statistical Commission, which accounts of the United Nations System of Na- at its first session appointed a committee on tional Accounts. Figure 4.3 Between 1990 and 2000 manufacturing value added more then doubled In East Asia and Pacific Value added in manufacturing (1990=100) 300 - East Asia & Pacific - Latin Amerca & Caribbean _ Middle East & North Africa 200 South Asia - Sub-Saharan Africa 100 _ 0 1990 1995 2000 Sou-ne: World Bank data files. South Asia grew by 70%, while other regions made smaller gains. MVAiqln 4.4 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 = 100 ±980-90 1990-99 1980-90 ±990-99 1980-90 1990-99 1980-90 1990-99 1990 1999 Afghanistan -9.8 -4.7 0.0 1.8 -10.4 -5.2 1.8 1.8 99 96 Albania' .. . .. 14.3 .. 19.9 Algeria 3.6 3.8 -4.5 2.1 -4.3 0.7 -2.7 1.8 127 106 Angola 5.9 -1.2 1.0 0.1 6.3 -1.1 -0.7 2.3 137 85 Argentina 5.0 9.9 -6.8 19.5 2.2 11.0 -6.5 20.3 97 98 Armenia . .... 11.4 .. 24.5 Australian 6.3 7.5 5.9 9.3 6.7 5.1 6.3 6.8 117 95 Austria .. 10.3 6.1 8.7 4.5 - 216 Bangladeshi 1.0 14.9 -4.3 20.5 7.8 11.3 3.6 10.7 74 97 o Belgium"v 4.5 6.3 4.0 5.4 7.8 6.3 6.5 4.4 100 99 ~O Benin 3.6 7.6 -10.0 9.2 9.8 9.3 -4.8 10.9 100 82 Bolivia 3.1 2.5 -1.3 10.0 -2.0 4.3 -0.4 11.0 115 110 1 Bosnia and Herzegovina . .. .. .. (D E Botswana 11.4 8.6 11.1 1.7 18.9 4.7 _10.9_ 21_106 0L o Brazil 6.2 4.5 0.8 19.1 5.2 ........ 6.1 1814.2_ 60 ........ .95_ > Bulgaria' .. .. -12.3 2.5 -14.1 5.1 CD~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~---------- --------- 0 Burkina Faso -0.3 14.8 3.8 4.4 8.0 15.3 4.4 4.5 91 85 Burundi 3.5 6.9 1.0 1.1 2.5 -3.5 2.2 -7.9 79 72 Cambodia .. -- -. .. .. .. .. -. -.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...... . o Cameroon 7.3 1.8 4.9 4.6 1.4 -1.1 0.1 2.1 89 77 0.............. ..... Canada' 6.4 9.1 7.5 9.0 6.7 8.1 7.8 7.4 100 ......._97 Central African Republic ___1.9 26.7 4.3 9.4 1.6 9 .....9 7..5. 123. . ... 56 Chad 8.5 4.9 10.7 3.6 9.5 3.9 12.5 4.5 116 100 Chile 9.2 10.3 .2.9 11.4 8.0 8.6 2.6 10.8 84 73 China~ 13.3 10.5 15.8 9.1 12.8 14.6 13.5 12.6 101 103 Hong IKong. China 11.1 8.8 9.2 9.5 16.7 8.9 14.9 9.6 101 _102_ Colombia 7.7 4.7 -2.1 10.6 7.7 7.3 -0. 1 12.3 94 102 Congo, Dem. Rep. 10.6 -4.9 29.6 -14.0 3.6 -3.0 19.1 _-8.0 109 78 Congo, Rep. 7.2 6.6 3.6 8.7 2.1 6.9 5.3 6.0 83 84 Costa Rica 3.7 14.3 5.2 15.8 4.7 18.4 4.4 15.0 71 102_ Cote dIlvoire 2.6 5.7 -2.1 5.0 1.8 7.2 -1.5 5.5 82 95 Cuba -1.1 -8.9 -0.4 -1.1 -0.9 -8.9 1.7 -2.0 96 90 Czech Republic' -. 10.5 .. 11.4 Denmark' 4.1 5.3 3.1 6.1 8.5 4.1 6.3 4.8 100 100 Dominican Republic -0.9 3.7 0.8 13.4 -2.0 4.9 3.4 14.1.. 97 ...104__ Ecuador 7.1 7.7 -1.9 7.2 -0.3 7.3 -1.4 9.7 141 106 Egypt, Arab Rep. 13.5 -2.8 8.0 2.4 7.3 -1.0 12.8 5.1 86 91 El Salvador -4.6 2.4 4.5 7.6 -4.6 10.7 2.4 11.4 69 87 Enre ....... . -...... Estonia' .- . .. 24.3 -- 29.1 Ethiopia -0.4 7.2 3.6 2.0 -1.2 13.0 4.3 8.8 ... ... .89_ 94_ Finland' 2.3 9.2 4.4 4.3 7.4 7.9 6.9 4.5 100 95 France' 3.7 6.0 3.7 5.1 7.6 4.7 6.5 3.3 97 99 Gabon -3.0 -15.5 -5.6 -1.3 0.0 -14.9 2.5 0.2 102 99 Gambia, The -- .....- . Georgia -.--..-.. - Germany- c4.4 5.9 4.9 4.3 9.1 4.2 7.2 3.5 .........102 .........100 Ghana -15.3 9.1 -17.5 7.2 -0.2 10.8 2.8 9.1 103 98 Greece, 5.0 8.9 6.3 9.0 5.9 3.1 6.6 2.1 108 102 Guatemala -1.1 8,4 0.1 10.3 -2.2 10.5 0.5 11.6 98 87 Guinea -- 8.4 .. 0.4 3.9 4.7 9.9 0.5 135 92 Guinea-Bissau -2.0 15.4 -0.3 -3.8 4.1 13.4 5.2 -1.8 143 100 Haiti -0.3 3.6 -4.6 13.5 .1.3 3.2 -2.8 14.3 116 95 Honduras 4.0 2.9 1.6 13.0 1.5 8.3 0.6 14.2 81 110 t Data for Taiwan, China 16.7 2.8 17.8 4.6 15.2 _6.9_ 12.3 8.3 102 108 Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 100 ±980-90 1990-99 ±980-90 1990-99 1980-90 199G-99 1980-90 ±990-99 1990 1999 Hungary' 3.4 7.8 1.4 10.0 1.4 11.8 0.1 12.9 100 108 India -3.1 5.3 -2.9 ..... ..7.7 7.4 _9.6 4.2 ....... ...9.9 _79 99 Indonesia 8.1 8.0 1.8 4.2 -0.8 8.0 2.6 3.0 102 110 Iran, Islamic Rep. 16.9 -2.6 -2.4 -8.4 7.3 -2.6 0.2 -6.8 169 146 Iraq 2.3 20.4 -4.5 5.1 -4.0 16.7 -2.2 5.2 132 109 Ireland' 9.3 14.8 4.7 11.0 12.7 14.0 7.1 10.9 107 99 Israela 6.9 9.1 5.8 8.9 8.2 10.7 5.9 8.7 97 112 Italy' 4.4 5.7 5.4 4.0 8 .7 5.3 6.9 2.9 98 10-8 Jamaica 1.5 4.5 2.9 7.7 1.2 2.6 2.7 7.3 105 88 Japan' 5.1 1.9 6.5 5.2 9.1 4.0 5.0 4.3 73 102 217 Jordan 7.7 4.7 1.2 3.7 6.0 7.3 -1.9 5.0 80 90 Kazakhstan' .. . .. 10.9 ..1.3 .. .. Kenya 1.7 3.9 2.4 8.2 -1.0 7.8 1.8 6.6 68 107 Mi Korea. Dem. Rep................ Korea. Rep. 11.5 15.3 11.0 9.5 14.9 10.1 11.8 6.6 98 82 C Kuwait -2.2 16.4 -6.3 7.9 -7.7 16.9 -4.1 7.2 94 99 C Kyrgyz Republic, .- . .. 7.8 11.1 C -- --- --------- - ~~~~~~~~~~~~~~~~~~~~~~0 Lao PDR~ I -. 11.2 18.3 6.4 15.6 .. . Latvia' - 6.4 .. 12.9 .. 23.6 CD . Lebanon -5.2 1.9 -75 121-5.2 ... ... 4.2 _-5.5 . .......12.8 104 _114 Lesotho 6.3 14.0 3.5 3.4 3.7 13.4 3.3 1.9 97 96 L-iberia -3.5 6.9 -----------7.5 7.4 -3.1 4.6 -7.2 6.5 112 98 Libya 0.0 -4.1__ -65 -. 73 -4.5 -4.4 0.2 145 123 Lithuania'. 10.3 . 1 7.6 Macedonia, FYR0 -.-----------. 2.8 ..6.4 Madagascar -3.0 -6.8 -3.7 -3.2 -1.0 -2.9 -1.8 -1.5 87 97 Malawi 2.4 4.3 -0.1 -1.5 2.0 2.5 3.2 0.6 141 100 Malaysia 14.3 15.8 5.8 11.2 8.6 12.7 7.6 9.9 102 88 Mali 4.3 10.7 3.0 5.9 6.2 6.7 2.8 4.6 122 90 Mauritania 3.9 6.6 -3.2 7.4 8.1 3.0 -2.0 4.0 96 102 Mauritius 10.3 4.4 11.2 3.6 14.3 4.1 12.9 4.6 109 96 Mexico 15.4 15.4 1.0 12.4 5.8 16.0 6.3 13.8 109 102 Moldova' .. 9.0 . 11.7 Mongolia ... 5.0 -30 5.5 -1.9 Morocco 5.7 8.0 3.1 6.8 6.2 8.8 3.6 5.5 94 117 Mozambique -9.5 16.4 -2.7 -2.5 -9.7 8.1 0.2 0.2 161 76 Myanmar -3.0 13.7 -6.4 15.3 -7.6 13.5 -5.0 25.1 116 51 Namibia' .. .... .- Nepal' .. .. 8.1 9.2 6.7 10.1..- Netherlands' 4.6 7.1 4.5 6.9 4.6 6.0 4.4 5.7 98 96 New Zealand' 3.5 4.6 4.3 6.2 6.2 4 3 5.5 6.4 103 98 Nicaragua -4.8 1. -34 96 -5.8 10.5 -3.1 11.9 119 81 Niger -5.1 3.7 -5.2 -3.0 -5.4 0.2 -3.5 0.1 137 78 Nigeria -4.4 3.4 -21.4.5.1.-8. 0.5_ -15.6 5.9 161 108 Norway' 4.1 7.2 3.4 7.4 __5.3 4.1 6.3 4.5 112 .....111 Oman 7.0 4.6 -17 4.5 3.4 3.6 0.7 6.8 159 132 Pakistan -0.3 -24 -53 -05 8.1 4.6 3.0 3.4 91 116 Panama -0.6 5.8 -6.8 8.8 -0.5 10.1 -3.6 9.6 69 106 Papua New Guinea 1.4 -7.9 4.8 3.8 1.3 -0.3 Paraguay 13.0 1.2 10.2 5.8 11.8 3.9 4.3 7.3 87 88 Peru 2 .7 9.0 -20 12.3 -15 9.3 1.4 12.6 93 83 Philippines -7.4 17.0 -7.8 13 7 3.9 19.1 2.9 13.6 90 121 Poland' 4.8 8.6 14A 19.6 1.4 10.0 -3.1 20.1 86 94 Portugal. 15.0 6.1 10.3 5.5 Puerto Rico Romania, ... 4.0 8.4 -3.8 6.6 Russian Federation' .... 9.3 -6.8 (,>t^Vs 4.4 Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 = 100 1980-90 1990-99 1980-90 1990-99 1980-90 1990-99 1980-90 1990-99 1990 1999 Rwanda 3.3 -7.7 2.3 2.5 -0.3 -3.8 3.3 -1.3 38 88 Saudi Arabia -6.4 2.6 -8.4 -1.4 -13.4 1.2 -6.1 0.6 169 132 Senegal 1.2 5.1 0.5 4.3 3.6 4.5 1.4 3.6 109 102 Sierra Leone -1.0 -34.3 -6.3 -7.3 -2.4 -31.8 -8.7 -6.2 63 67 Singapore 13.5 13.5 10.1 9.9 9.8 10.4 8.0 8.3 111 99 Slovak Republic' .. .. .. .. .. 10.4 .. 12.5 Slovenia' . .. .. .. .. 9.4 .. 10.4 Somalia -1.6 1.8 -16.7 13.5 -1.1 0.6 -15.0 12.4 99 94 South Africa-' 3.3 7.4 -0.8 7.7 0.7 2.5 -1.3 6.6 98 100 218 Spaina .. .. .. .. 10.7 9.3 10.6 5.9 96 102 Sri Lanka -4.1 3.3 -6.6 2.8 5.4 12.0 2.2 9.7 83 92 St Sudan -3.1 15.7 -7.7 14.7 -2.5 10.0 -6.4 13.2 123 81 C) Swaziland 7.6 1.5 2.1 -4.6 4.6 7.0 0.6 5.6 116 80 c Swedena 4.3 0 6 5.0 1.1 8.0 6.5 6.6 4.5 99 95 L Switzerland' .. .. .. .. 9.4 3.0 8.9 1.9 Et. Syrian Arab Republic 8.6 -0.6 -10.0 4.7 4.4 -0.5 -6.8 5.0 132 126 O Tajikistan .. .. .. .. .. .. .. > Tanzania -3.5 5.4 -2.1 -7.0 -5.1 8.3 -0.6 0.1 102 64 a) n Thailand 11.2 4.1 8.8 -2.3 14.6 11.0 12.8 5.3 103 94 o Togo -1.2 8.9 0.7 6.4 1.2 8.5 1.9 6.0 127 124 Trinidad and Tobago -11.0 3.1 -20.5 10.9 -9.4 4.7 -12.3 12.0 116 122 O Tunisia 4.9 5.2 1.7 4.0 3.5 6.5 2.7 5.7 103 102 rN Turkey .. 11.0 .. 11.1 14.3 9.9 9.3 10.3 104 104 Turkmenistan .. .. .. .. .. .. .. Uganda -5.4 18.3 -6.1 24.7 -4.0 18.5 4.3 23.6 74 57 Ukraine, .. ... 7.5 ..9.2... United Arab Emirates 8.8 8.8 -1.3 8.8 -0.8 7.9 0.6 11.2 172 143 United Kingdom, 4.5 6.2 6.7 6.1 5.8 5.7 8.4 5.4 101 104 United States, 3.5 6.7 7.2 8.9 5.6 7.4 8.3 9.0 98 101 Uruguay 4.3 6.5 1.3 12.1 4.4 6.1 -1.3 11.7 100 95 Uzbekistan .. .. .. .. .. .. .. Venezuela, RB 3.5 5.7 -3.9 4.2 -4.4 3.5 -3.2 5.1 141 107 Vietnam West Bank and Gaza Yemen, Rep. .. .. 21.6 .. -2.1 Yugoslavia, Fed. Rep. Zambia -0.1 5.0 2.1 0.8 1.2 -2.5 0.0 -1.4 109 46 Zimbabwe 4.C 8.2 3.5 9.2 2.8 2.9 -0.3 4.1 100 100 a. Data are from the International Monetary Fund's International Financial Statistics database. b. Includes Luxembourg. c. Data prior to 1990 refer to the Federal Republic of Germany before unificationi. d. Data refer to the South African Customs Union (Botswana. Lesotho, Namibia, South Afrca and Swaziland). J k' 4.4 S About the data Definitions Data on international trade in goods are recorded Trade Organization also compile data on trade * Growth rates of export and Import volumes in each country's balance of payments and by prices and volumes. The growth rates and terms are average annual growth rates calculated for customs services. While the balance of pay- of trade for low- and middle-income economies low- and middle-income economies from ments focuses on the financial transactions that shown in this table were calculated from index UNCTAD's quantum index series and for high- accompany trade, customs data record the di- numbers compiled by UNCTAD. Volume mea- income economies from export and import data rection of trade and the physical quantities and sures for high-income economies were derived deflated by the IMF's trade price deflators. value of goods entering or leaving the customs by deflating the value of trade using deflators * Growth rates of export and Import values are area. Customs data may differ from those re- from the IMF's Intemational Financial Statistics. average annual growth rates calculated from corded in the balance of payments because of In some cases price and volume indexes from UNCTAD's value indexes or from current differences in valuation and the time of record- different sources may vary significantly as a values of merchandise exports and imports. ing. The 1993 System of National Accounts and result of differences in estimation procedures. * Net barter terms of trade are calculated as the fifth edition of the International Monetary All indexes are rescaled to a 1995 base year. the ratio of the export price index to the Fund's (IMF) Balance of Payments Manual Terms of trade were computed from the same corresponding import price index measured (1993) have attempted to reconcile the defini- indicators. relative to the base year 1995. tions and reporting standards for international The terms of trade measure the relative prices - 219 trade statistics, but differences in sources, tim- of a country's exports and imports. There are a ing, and national practices limit comparability. number of ways to calculate terms of trade. The Data sources . Real growth rates derived from trade volume in- most common is the net barter, or commodity, The main source of trade data for developing dexes and terms of trade based on unit price terms of trade, constructed as the ratio of the countries is UNCTAD's annual Handbook of ! indexes may therefore differ from those derived export price index to the import price index. When Intemational Trade and Development Statistics. from national accounts aggregates. the net barter terms of trade increase, a The IMF's International Financial Statistics (D Trade in goods, or merchandise trade, in- country's exports are becoming more valuable includes data on the export and import values ET cludes all goods that add to or subtract from an or its imports cheaper. and deflators for high-income and selected economy's material resources. Thus the total developing economies. C supply of goods in an economy is made up of gross output plus imports less exports. Currency in circulation, titles of ownership, and securi- ties are excluded, but monetary gold is included. Trade data are collected on the basis of a country's customs area, which in most cases is the same as its geographic area. Goods pro- vided as part of foreign aid are included, but goods destined for extraterritorial agencies (such as embassies) are not. Collecting and tabulating trade statistics is difficult. Some developing countries lack the capacity to report timely data; this is a problem especially for countries that are landlocked and where territorial boundaries are porous. As a result, it is necessary to estimate their trade from the data reported by their partners. Other countries that belong to common customs unions may need to collect their data by direct inquiry from companies. (For further discussion of the use of partner country reports see About the data for table 6.2.) In some cases economic or political concerns may lead national authori- ties to suppress or misrepresent data on cer- tain trade flows, such as oil, military equipment, or the exports of a dominant producer. In other cases reported trade data may be distorted by deliberate under- or overinvoicing to effect capi- tal transfers or avoid taxes. And in some regions smuggling and black market trading result in un- reported trade flows. By international agreement, customs data are reported to the United Nations Statistics Divi- sion, which maintains the Commodity Trade (COMTRADE) database. The United Nations Con- ference on Trade and Development (UNCTAD) compiles a variety of international trade statis- tics, including price and volume indexes, based on the COMTRADE data. The IMF and the World 4, 4.5 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan 235 130. Albania 230 260 7 6 .. 2 .4 82 Algeria 12,930 19,550 0 0 0 0 96 97 0 0 3 2 Angola 3,910 7,858 0 .. 0 . 93 . 6 .. 0 Argentina 12.353 26,298 56 44 4 2 8 18 2 3 29 32 Armenia .. 300 .. 14 .. 5 . 11 . 22 .. 43 Australia 39,752 63,869 20 21 10 6 18 22 16 17 16 29 Austria 41,265 63,903 3 5 4 3 1 1 3 3 88 83 Azerbaijan .. 1,750 .. 3 . 2 .. 85 ..2 ..8 220 Bangladesh 1.671 6,500 14 7 7 2 1 0 .. 0 77 91 Belarus .. 7,380 .. 7 .. 4 . 20 I.1. 67 o Belgium, 117,703 193,998 9 10 2 1 3 2 4 3 77 78 m Benin 288 376 .. 15 .. 80 I. - 0 ..3 Bolivia 926 1,214 19 30 8 3 25 13 44 25 5 29 Bosnia and He,zegovina 276 1,030 E Botswana 1.784 2.670 o Brazil 31,414 55,086 28 23 3 5 2 2 14 10 52 59 a, Bulgaria 5,030 4.725 .. 10 .. 3 .. 12 .. 13 .. 57 Burkina Faso 152 228 . . . . . . . 0 Burundi 75 49 .. 91 .. 8 . .1..0 C'ambodia 86 700 . . . . . . . 04 Canada 127,629 276.635 9 6 9 6 10 13 9 4 59 64 Central African Rep ublic 120 170 . . . . . ... Chad 188 183 . . . . . .. Chile 8,372 16,158 24 25 9 10 1 1 55 45 11 16 China' 62,091 249.297 13 5 3 1 8 3 2 2 72 88 Hong Kong, China" 82,390 202.440 3 2 0 0 0 0 1 2 95 95 Colombia 6,766 13,040 33 19 4 5 37 41 0 1 25 34 Congo, Dem. Rep. 999 450. . , , Congo, Rep. 981 2,500 Costa Rica 1.448 5.865 58 30 5 3 1 1 1 1 27 66 C6te dIlvoire 3.072 4.029 .. 50 .. 14 .. 21 .. 0 . 14 Croatia 4.597 4,390 13 9 6 5 9 11 5 3 68 73 Cuba 5,100 1,635 . . . . . . . Czech Republic 12.1 70 29,000 .. 4 . 2 .. 3 .2 .. 88 Denmark 36.870 49,631 27 20 3 3 3 7 1 1 60 64 Dominican Republic 2,170 5.700 21 . 0 .. 0 .. 0 . 78 -. Ecuador 2,714 4,846 44 37 1 4 52 49 0 0 2 10 Egypt, Arab Rep. 2,585 4,689 10 9 10 8 29 37 9 4 42 37 El Salvador 582 2.933 57 42 1 1 2 5 3 2 38 48 Estonia .. 3.175 .. 8 . 9 .. 4 .6 .. 73 Ethiopia 298 . .. 71 .. 19 .. ..1 .. 10 Finland 26,571 45,635 2 2 10 6 1 3 4 3 83 85 France 216,588 298,127 16 11 2 1 2 3 3 2 77 81 Gabon 2.204 3,350 . . . . . . . Gambia,The 40 7 .. 90 .. 4 .. 0 . 0 ..5 Georgia .. 330 . . . .. .. Germany 421,100 551,505 5 4 1 1 1 1 3 2 89 85 Ghana 897 1,670 51 48 15 10 9 8 17 19 8 15 Greece 8,105 10.229 30 28 3 4 7 10 7 7 54 50 Guatemala 1,163 2,650 67 56 6 4 2 6 0 2 24 32 Guinea 19 80 . 3 .. 3 .. 0 . 63 .. 30 Guinea-Bissau 68 90 .. . . .. .. . Haiti 160 164 14 .. 1 .. 0 . 0 .. 85 Honduras 831 1,322 82 59 4 4 1 0 4 4 9 33 t Data for Taiwan, China 67,142 148,370 4 1 2 1 1 1 1 1 93 95 Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2000 1 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Hungary 10,000 28,090 23 7 3 1 3 2 6 2 63 813 India 17,975 42.295 16 14 4 1 3 0 5 2 7 1 79 Indonesia 25,675 62,124 11 9 5 4 44 25 4 5 35 5 7 Iran, Islamic Rep. 16.870 30,017 .. 3 . 0 .. 89 I. 1 . 7 Iraq 12,380 19,300.. . Ireland 23,743 79,868 22 8 2 0 1 0 1 0 70 813 Israel 12,080 31,338 8 3 3 1 1 1 2 1 87 941 Italy 170,304 237,750 6 6 1 1 2 2 1 1 88 88 Jamaica 1,135 1,296 19 23 0 0 1 0 10 4 69 73 Japan 287,581 479,249 1 0 1 0 0 0 1 1 96 94 221 Jordan 1,064 1.897 11 16 0 0 0 0 38 15 51 69 Kazakhstan .. 9,140 .. 7 .. 1 . 54 .. 18 ..021) Kenya 1.031 _1,734 49 59 . ...6 9 13 . ...8_ 3 3 29 21 Korea, Dem. Rep. 1.857 655.. . ......... Korea, Rep. 65,016 172,268 3 2 1 1 1 5 1 1 94 91 c Kuwait 7,042 19,544 1 0 0 0 93 79 0 0 6 20 Kyrgyz Republic 505 .. 16 6 .. 12 .. 6 20 CD ... ..... ....0 Lao PDR 78 320.. . Latvia .. 1,865 6 .. 29 2 6 56 ( Lebanon 494 714 ET Lesotho 59 200 .. Liberia 330 500 --- ----------9 Libya 13,877 14,200 0 .. 0 . 94 .. 0 .. 5 Lithuania 3,810 12 .. 5 . 21 -. 2 . 601 Macedonia, FYR 1,199 1,365 .. 19 . 2 -- 2 . 9 -- 66 Madagascar 319 260 73 36 4 6 1 2 8 4 14 50) Malawi 417 350 93 .. 2 . 0 0 .. 5 Malaysia 29,416 98,237 12 6 14 3 18 10 2 1 54 80 Mali 359 550 36 .. 62 . -0 .. 2 Mauritania 469 300 .. - ... ..... Mauritius 1,194 1.493 32 18 1 1 1 0 0 0 66 81 Mexico 40,711 166,424 12 5 2 1 38 10 6 1 43 83 Moldova -- 470 .. 62 . 3 0 . 1 .. 33 Mongolia 660 355 . . . . . .- Morocco 4,265 7,417 26 21 3 2 4 4 15 9 52 64 Mozambique 126 235 .. . 11 .. 25 .. 2 IC)1 Myanmar 325 1,391 51 .. 36 .. 0 . 2 .. 10 Namibia 1,085 1,455 .. . ... ...- Nepal 210 804 13 21 3 0 .. 0 0 2 83 77 Netherlands--- 131,775 212,507 20 15__ ___4 3 10 10 3 2 59 70 New Zealand 9,488 13,267 47 46 18 14 4 2 6 5 23 28 Nicaragua 330 625 77 88 14 2 0 2 1 0 8 8 Niger .....282 290____ 29 ..1 . 0 .... 67 2 Nigeria 13,670 20,100 1 0 1 0 97 100 0 0 1 0 Norway 34,047 60,038 7 6 2 1 48 64 10 6 33 18 Oman 5,508 11.328 1 4 0 0 92 83 1 1 5 12 Pakistan 5,589 9,173 9 11 10 3 _1 1 0 0 79 85 Panama 340 859 75 74 1 1 0 7 1 2 21 16 Papua New Guinea 1,144 190 22 15 9 2 0 29 58 51 10 2 Paraguay 959 852 52 . .....65_ 38 15 ----------0 0 0 0 10 19 Peru 3.230 7,002 21 30 3 3 10 7 47 39 18 20 Philippines 8,068 39,783 19 5 2 1 2 1 8 2 38 92 Poland 14,320 31,650 13 8 3 2 11 5 9 5 59 80 Portugal 16,417 23,323 7 7 6 3 3 2 3 2 80 85 Puerto Rico ... .. . .. .. .. Romania 4,960 10,365 1 3 3 5 18 7 4 7 73 77 Russian Federation 40,000 105,200 .. 1 . 3 -. 51 . 9 22 - ~4.5u Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2000 1.990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 110 53 .. . . . Saudi Arabia 44,417 84.060 1 1 0 0 92 92 0 0 7 7 Senegal 761 960 53 59 3 2 12 3 9 5 23 30 Sierra Leone 138 13 .. . .. .. . Singapore' 52,752 137,875 5 2 3 0 18 10 2 1 72 86 Slovak Republic 6.355 11,905 .. 4 . 2 .. 5 . 3 .. 85 Slovenia 6,681 8.733 7 4 2 2 3 1 3 4 86 90 Somalia 150 110 . . . . . South Africa' 23.549 29,983 8 9 4 3 7 10 11 11 22 54 222 Spain 55.642 113,747 15 14 2 1 5 4 2 2 75 78 Sri Lanka 1.983 5,134 34 21 6 2 1 0 2 0 54 75 Sudan 374 1.807 61 67 38 27 . . 0 0 1 3 w Swaziland 556 900 . . . . . . . VO Sweden 57,540 86,933 2 2 7 1 3 3 3 3 83 85 C 1 Switzerland 63.784 81.534 3 3 1 1 0 0 3 6 94 91 ED Syrian Arab Republic 4,212 4,250 14 9 4 5 45 76 1 1 36 8 o Tajikistan .. 780 . . . . . . . > Tanzania 415 663 .. 70 . 13 .. 0 .I .. 15 us o Thailand 23.070 69,057 29 14 5 3 1 3 1 1 63 76 S'0 o Togo 268 340 23 20 21 23 0 1 45 26 9 31 3r Trinidad and Tobago 2.080 4,600 5 6 0 0 67 65 1 0 27 29 O Tunisia 3,526 5.850 11 9 1 1 17 12 2 2 69 77 0 Turkey 12,959 26,572 22 13 3 1 2 1 4 3 68 81 Turkmenistan .. 2,700 .. 0 .. 10 .. 81 .. 0 ..7 Uganda 147 380 .. 67 . 14 .. 8 . 5 ..6 Ukraine .. 14,575 . . . . . . . United Arab Emirates 20,730 39,900 8 I. 5 . 39 .. 46 United Kingdom 185,172 284,090 7 5 1 0 8 9 3 2 79 82 United States 393,592 781,125 11 7 4 2 3 2 3 2 74 83 Uruguay 1.693 2,295 40 47 21 9 0 2 0 0 39 42 Uzbekistan .. 3.010. . .. . ... Venezuela, RB 17,497 31.802 2 1 0 0 80 86 7 3 10 9 Vietnam 2,404 14,450 . . . . . West Bank and Gaza . . . . . . . Yemen, Rep, 692 4,200 75 5 10 1 8 93 7 0 1 1 Yugoslavia, Fed. Rep. 2,929 1,727 7 .. 4 .. 2 . 7 ,. 79 Zambia 1,309 800 . .. . . Zimbabwe 1,726 1,670 44 47 7 13 1 1 16 11 31 28 Low Income 99,107 217,485 15 17 4 4 28 21 4 3 48 53 Middle Income 603,553 1,526,414 15 8 4 2 19 21 5 4 54 63 Lower middle income 237,296 655,579 18 8 4 2 12 23 4 5 59 59 Upper middle income 366,521 870,835 13 9 4 2 23 19 6 4 51 65 Low & middle income 702,386 1,743,942 15 9 4 2 20 21 5 4 54 61 East Asia & Pacific 220,936 711,644 12 6 5 2 10 7 2 2 68 83 Europe & Contra Asia' 125,115 306,069 .. 5 . 3 -. 26 .. 6 . 53 Latin America & Carib. 143,146 356,115 26 21 4 3 24 18 12 9 34 48 Middle East & N. Africa 126.606 213.202 3 3 1 0 79 80 3 2 15 14 South Asia 27.790 64,252 16 15 5 1 2 0 4 2 71 80 Sub-Saharan Africa 66.402 92,560 13 17 3 4 28 28 7 8 20 36 High income 2,729.693 4,612,427 8 6 3 2 5 4 3 2 79 82 Europe EMU 1.229,887 1,823,399 10 8 2 1 3 3 2 2 81 82 Note: Components way not sum to 100 percent because of unclassified trade. arinciudes Luxembourg. b. Includes re-exports. c Data on total merchandise enports for 1990 refer to the South African Customs Union (Botswana, Lesothto, Namibia, South Africa, and Swazilandl): tnose for 2000 refer to South Africa only. Data on expert commonity shares refer to the South African Cjistows Union. d. Data for 2000 include the ;ntrafrade of the Baltic states and the Commonwealth ot independent States. 4.5 5 About the data Definitions Data on merchandise trade come from customs the COMTRADE database and publications or data- * Merchandise exports show the f.o.b. value reports of goods entering an economy or from re- bases of regional organizations, specialized agen- of goods provided to the rest of the world val- ports of the financial transactions related to mer- cies, and economic groups (such as the Common- ued in U.S. dollars. * Food comprises the com- chandise trade recorded in the balance ofpayments. wealth of Independent States, the Economic Comi- modities in SITC sections 0 (food and live ani- Because of differences in timing and definitions, mission for Latin America and the Caribbean, mals), 1 (beverages and tobacco), and estimates of trade flows from customs reports are Eurostat, the Food and Agriculture Organization, the 4 (animal and vegetable oils and fats) and SITC likely to differ from those based on the balance of Organisation for Economic Cooperation and Devel- division 22 (oil seeds, oil nuts, and oil kernels). payments. Moreover, several intemational agencies opment, and the Organization of Petroleum Export- * Agricultural raw materials comprise SITC process trade data, each making estimates to cor- ing Countries). It also consults private sources, such section 2 (crude materials except fuels) exclud- rect for unreported or misreported data, and this ascountry reports ofthe Economist Intelligence Unit ing divisions 22, 27 (crude fertilizers and rnin- leads to other differences in the available data. and press clippings. In recent years country Web erals excluding coal, petroleum, and precious The mostdetailed source ofdata on intemational sites and directcontacts through email have helped stones), and 28 (metalliferous ores and scrap). trade in goods is the CommodityTrade (COMTRADE) to improve the collection of up-to-date statistics for * Fuels comprise SITC section 3 (mineral fu- database maintained by the United Nations Statis- manycountries, reducingthe proportion ofestimated els). * Ores and metals comprise the commodi- tics Division. The Intemational Monetary Fund (IMF) figures. The WTO database now covers most of the ties in SITC divisions 27, 28, and 68 (nonfer- 223 also collects customs-based data on exports and major traders in Africa, Asia, and Latin America, rous metals). * Manufactures comprise the M imports of goods. The value of exports is recorded which together with the high-income countries ac- commodities in SITC sections 5 (chemicals), ° as the cost of the goods deliveredtothefrontierof count for nearly 90 percent of total world trade. 6 (basic manufactures), 7 (machinery and the exporting country for shipment-the f.o.b. (free There has also been a remarkable improvement in transport equipment), and 8 (miscellaneous a on board) value. Many countries report trade data the availability of recent, reliable, and standardized manufactured goods), excluding division 68. a in U.S. dollars. When countries report in local cur- figures for countries in Europe and Central Asia. _C_D_ rency, the United Nations Statistics Division applies The shares of exports by major commodity group t s r the average official exchange rate for the period were estimated by World Bank staff from the Data sources shown. COMTRADE database. The values of total exports (The WTO publishes data on world trade in its I CD Countres may report trade according to the gen- reported here have not been fully reconciled with Annual Report. Estimates of total exports of eral or special system of trade (see Prnmary data theestimatesofexportsofgoodsandservicesfrom goods are also published in the IMFs s, documentation). Under the general system, exports the national accounts (shown in table 4.9) or those InteMational Financial Statistics and Direction i comprse outward-moving goods that are (a) goods from the balance of payments (table 4.15). of Trade Statistics and in the United Nations wholly or partly produced in the country; (b) foreign The classification of commodity groups is based Statistics Division's Monthly Bulletin of I goods, neithertransfonned nordeclaredfordomes- on the Standard Intemational Trade Classification Statistics. The United Nations Conference on i tic consumption in the country, that move outward (SITC) revision 1. Most countries now report using Trade and Development (UNCTAD) publishes from customs storage; and (c) goods previously in- later revisions of the SITC or the Harmonized Sys- i data on the structure of exports and imports in cluded as imports for domestic consumption but tem. Concordance tables are used to convert data its Handbook of International Trade and subsequently exported without transformation. Un- reported in one system of nomenclature to another. Development Statistics. Tariff line records of der the special system exports comprise catego- The conversion process may introduce some errors exports and imports are compiled in the United, nes a and c. In some compilations categories b of classification, butconversions from laterto early Nations Statistics Division's COMTRADE and c are classified as re-exports. Because of dif- systems are generally reliable. Shares may not sum database. ferences in reporting practices, data on exports may to 100 percent because of unclassified trade. - not be fully comparable across economies. The data on total exports of goods (merchan- dise) in this table come from the Worid Trade Orga- nization (WTO). The WTO uses two main sources, national statistical offices and the IMF's Intema- tional Fnancial Statistics. It supplements these with Figure 4.5 Top developing economy exporters tend to be important manufacturing exporters Share of merchandise exports I%), 2000 100 1 Manufactures 80 Food 1 1 e:0 Agricuiture l Ores and maetais 80 UFuei 40 20 Korea, China Mexico Maiaysia India Thailand Bruzii Indonesia Russian Saudi Rep. Federation Arabia N.t.: Data fo, Ind, raier to 1999. Sour-e INCTAD diata files The exceptions are Russia and Saudi Arabia, whose major export Is fuel. I: ~~4.6 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw metals materials $ millions % of total ft of tota ft of total f of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1.990 2000 Afghanistan 936 450 . . . .. Albania 380 1,080 .. 22 I. 1. 9 . 2 .. 67 Algeria 9.780 9,152 24 28 5 3 1 1 2 1 68 67 Angola 1.578 3.400 . . . . . . . Argentina 4,076 25,149 4 5 4 1 8 4 6 2 78 87 Armenia .. 885 .. 25 . 1 .. 21 .1 .. 52 Australia 42,032 71,531 5 5 2 1 5 8 1 1 80 84_ Austria 49,146 68.627 5 6 3 3 6 6 4 3 81 82 Azerbaijan .. 1,390 .. 19 .. 2 - . 4 .. 71 224 Bangladesh 3,618 8,360 19 15 5 5 16 7 3 2 56 69 Belarus .. 8,485 .. 12 .. 2 .. 31 ..4 .. 50 En 2 Belgium' 119,702 183.204 .. 9 . 2 .. 9 . 4 .. 76 Benin 265 602 .. 25 .. 5 .. 21 ..1 . 49 Bolivia 687 1,760 12 14 2 2 1 5 1 1 85 79 Bosnia and Herzegovina 360 2,810 . . , . . . . E Botswana 1.946 2.240 . . . . . . . o Brazil 22,524 58,532 9 7 3 2 27 15 5 3 56 73 CU Bulgaria 5.100 6.440 8 5 3 1 36 5 4 6 49 59 0 Burkina Faso 536 545 . . . . . . . o Burundi 231 148 .. 23 .. 2 -. 12 2 .. 60 Cambodia 164 580 . . . . . . . o Cameroon 1,400 1.360 19 19 0 2 2 16 1 1 78 61 CN Canada 123.244 244.786 6 5 2 1 6 5 3 2 81 84 Central African Republic 154 110 . . . . . .. Chad 285 290 . . . ... Chile 7,678 18,070 4 7 2 1 16 18 1 1 75 71 China' 53.345 225,097 9 4 6 5 2 9 3 6 80 76 Hong Kong, China 84,725 214,200 8 4 2 1 2 2 2 2 85 91 Colombia 5,590 11,539 7 12 4 3 6 2 3 2 77 80 Congo, Dem. Rep. 887 320 . . . . . . . Congo. Rep. 621 890 . . . . . . . Costa Rica 1,990 6.372 8 7 2 1 10 8 2 2 66 82 C6te dIlvoire 2,097 3.084 .. 17 .. 1 .. 34 .. 1 . 46 Croatia 4,500 7,911 12 8 4 2 10 15 4 2 64 73 Cuba- 4.600 4,900 . . . . . . . Czech Republic 12.880 32,180 .. 5 . 2 .. 10 . 4 .. 80 Denmark 33,333 44,322 12 11 3 3 7 6 2 2 73 76 Dominican Republic 3,006 9,800 . . . . . . . Ecuador 1.861 3,465 9 9 3 3 2 7 2 2 84 77 Egypt, Arab Rep. 9.216 14,010 32 23 7 4 3 6 2 3 56 59 El Salvador 1,263 4,888 14 16 3 2 15 16 4 1 63 65 Estonia .. 4,255 .. 10 .. 3 .. 7 .4 .. 76 Ethiopia 1.081 . .. 7 .. 1 . 20 ..1 . 71 Finland 27,001 33,903 5 5 2 2 12 12 4 6 76 73 France 234,436 305,423 10 8 3 2 10 10 4 3 74 77 Gabon 918 1.030 . . . . . . . Gambia, The 199 200 .. 44 .. 2 .. 6 .. I 46 Georgia .. 725 . . . .. .. Germany 355,886 502,827 10 7 3 2 8 9 4 4 72 69 Ghana- 1,205 3.075 11 13 1 2 17 21 0 1 70 62 Greece 19.777 26.336 15 13 3 2 8 6 3 2 70 76 Guatemala 1,649 4.770 10 12 2 2 17 13 2 1 69 72 Guinea 723 1.130 .. 24 .. 1 . 25 .. 1 . 49 Guinea-Bissau 68 90 .. . ... ...... Haiti 332 1,036 . . . . . .. Honduras 935 2,885 10 16 1 1 16 13 1 1 71 68 t Data for Taiwan, China 54.831 140,010 7 4 5 2 11 9 6 5 69 79 4.6 Merchandise Food Agricultural Fuels Ores and Manufactures Imports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1 990 2000 1990 2000 Hungary 10,340 32.080 8 3 4 1 14 5 4 3 70 84 India 23,642 50,455 3 7 4 3 27 31 8 5 51 El1 Indonesia 21,837 33,515 5 10 5 7 9 18 4 3 77 E.1 Iran, Islamic Rep. 15,716 15,220 19 .. 3 .. 2 . 2 *. 73 Iraq 7,660 13,700 .. . .. .. ... Ireland 20,669 50,870 11 6 2 1 6 4 2 1 76 82 Israel 16,793 38,130 8 5 2 1 9 10 3 2 77 81 Italy 181 968 236,461 12 9 6 4 11 10 5 4 64 69 Jamaica 1,859 3,216 15 15 1 2 20 18 1 1 61 61 Japan 235,368 379,511 15 13 7 3 25 20 9 6 44 57 225 Jordan 2,600 4,539 26 21 2 2 18 5 1 2 51 66 Kazakhstan .. 5.050 . 9 .. 1 .. 12 .. 3 . 7 5 Kenya 2,125 3,105 9 14 3 2 20 22 2 1 66 60 Korea. Dem. Rep. 2.930 900 .. . . . . .- .. C Korea. Rep. 69.844 160,481 6 5- 8 3 16 24 7 6 63 62 Kuwait 3,972 7,622 17 17 1 1 1 1 2 2 79 79 Kyrgyz Republic .. 555 .. 14 . .. 20 2 .. 64 2D Lao PDR 201 580 .. . . ... .... Latvia .. 3,190 12 .. 2 .. 12 ..2 .. 71 CD Lebanon 2,529 6,228 . .... ...... Lesotho 672 700 a. . . . .. .. Liberia 220 290 --- ---------------.- ----.- Libya 5,336 7,740 23 2 .. 0 . 1 .. 74 . Lithuania .. 5,455 .. 10 .. 3 .. 22 ..2 .. 61 Macedonia. FYR 1.206 2,220 .. 15 . 2 .. 9 . 3 .. 49 Madagascar 571 700 11 14 1 1 17 24 1 0 69 60 Malawi 581 590 9 20 1 1 11 10 1 1 78 68 Malaysia 29.258 82,210 7_ 4 1 1 5 5 4 3 82 85 Mali 619 690 26 .. 1 . 19 .. 1 - 53 Mauritania 388 340 . . . . . Mauritius 1,618 2,081 12 14 3 2 8 12 1 1 76 70 Mexico 43,548 182,635 15 5 4 1 4 3 3 2 75 87 Moldova .. 775 .. 13 . 2 .. 32 .1 .. 51 Mongolia 924 550..... ..... Morocco 6,800 11,484 10 14 6 3 17 18 6 3 61 63 Mozambique 878 1,100 .. . .. . . Myanmar 270 2,369 13 . I .. 5 0 .. 81 Namibia 1,163 1,510 . . . . . . . Nepal 686 1,573 15 17 7 5 9 12 2 3 67 6.3 Netherlands 126,098 197,982 13 10 2 2 10 11 3 3 71 74 New Zealand 9,501 13,906 7 8 1 1 8 10 3 2 81 79 Nicaragua 638 1,792 19 16 1 1 19 18 1 1 59 65 Niger 388 445 .. 39 .. 4 . 15 .. 2 . 4.1 Nigeria 5,627 12,910 6 20 1 1 0 1 2 2 67 76 Norway 27,231 34,408 6 6 2 2 4 4 6 5 82 8:L Oman 2.681 5,040 19 22 1 1 4 2 1 3 69 70 Pakistan 7,546 11,048 17 14 4 3 21 33 4 2 54 47 Panama 1,539 3,379 12 12 1 0 16 19 1 1 70 683 Papua New Guinea 1,193 1,120 18 18 0 1 7 22 1 1 73 58 Paraguay 1.352 2,193 8 17 0 1 14 14 1 1 77 68 Peru 3.470 8,797 24 12 2 2 12 16 1 1 61 70 Philippines 13,041 33,808 10 8 2 1 15 12 3 3 53 713 Poland 11,570 48,940 8 6 3 2 22 11 4 3 63 78 Portugal 25,263 38,240 12 11 4 3 11 10 2 2 71 7:3 Puerto Rico Rornania 7,600 13,055 12 7 4 1 38 12 6 4 39 763 Russian Federation 33.100 45,500 .. 15 .. 2 . 3 .. 3 . 4:2 4.6 Merchandise Food Agricultural Fuels Ores and Manufactures Imports raw metals materials $ millions % of total % of total % of total % of total ft of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 288 213 .. . .. . .. . ... ..- Saudi Arabia 24,069 30,300 15 18 1 1 0 0 3 3 81 76 Senegal 1.219 1,525 29 24 2 2 16 20 2 1 51 52 Sierra Leone 149 149 . . . . . . Singapore 60,899 134,545 6 3 2 0 16 12 2 2 73 82 Slovak Republic 6.670 12.670 .. 7 - 2 .. 13 . 3 .. 76 Slovenia 6,142 10,107 9 6 4 4 11 9 4 5 67 76 Somalia 95 200 . . . . . . . South African 18,399 29,695 5 5 2 1 1 14 2 2 77 70 226 Spain 87.715 153,516 11 9 3 2 12 12 4 3 71 73 Sri Lanka 2.685 6,823 19 15 2 1 13 6 1 1 65 77 (n Sudan 618 1.500 13 15 1 1 20 10 0 1 66 72 na Swaziland 663 1.040 . . . . . . . Sweden 54,264 72,788 6 6 2 1 9 9 3 3 79 74 Switzerlancd 69,681 83,584 6 6 2 1 5 5 3 6 84 83 E Syrian Arab Republic 2,400 3,860 31 19 2 3 3 4 1 2 62 65 oL Tajikistan .. 675 . . . . . . . > Tanzania 1.027 1,524 .. 16 . 2 -. 8 .. 1 . 72 a, o Thailand 33.379 61,924 5 4 5 3 9 12 4 3 75 77 -m o Togo 581 550 22 18 1 2 8 19 1 2 67 59 3: Trinidad and Tobago 1,262 3.250 19 8 1 1 11 32 6 2 62 56 o Tunisia 5,542 8,560 11 8 4 3 9 11 4 2 72 76 0 CN Turkey 22,302 53,499 8 4 4 4 21 14 5 4 61 70 Turkmenistan .. 1.400 .. 12 .. 0 I. . 1. 80 Uganda 213 1.650 .. 14 .. 2 . 17 . 2 .. 65 Ukraine .. 13.955 . . . . . . . United Arab Emirates 11,199 31.930 14 . 1 .. 3 . 4 .. 77 United Kingdom 222.977 336,979 10 8 3 2 6 4 4 3 75 82 United States 516.987 1,257.636 6 4 2 1 13 11 3 2 73 77 Uruguay 1,343 3.466 7 11 4 3 18 15 2 1 69 69 Uzbekistan .. 2,810 . . . . . . . Venezuela, RB 7,335 16,085 11 12 4 2 3 4 4 2 77 81 Vietnam 2,752 15,635 . . . . . . . West Bank and Gaza . . . . . . . . . Yemen. Rep. 1.571 2.880 27 35 1 2 40 6 1 1 31 55 Yugoslavia. Fed. Rep. 4.634 3.698 12 .. 5 . 17 . 3 .. 63 Zambia 1,220 780 . . . . . . . Zimbabwe 1,847 1,650 4 9 3 2 16 12 2 3 73 75 Low Income 103,409 201,545 7 14 3 5 17 19 4 3 64 57 Middle Income 558.998 1,414,536 9 7 4 2 11 11 4 3 70 72 Lower middle income 243.482 572.247 11 9 5 3 8 9 3 4 71 69 Upper middle income 316.575 842.282 8 6 4 2 13 13 4 3 70 74 Low & middle Income 662,742 1,616,357 9 8 4 3 11 12 4 3 70 71 East Asia & Pacific 230.644 620.409 7 5 5 3 9 14 4 4 73 72 Europe & Central Asia' 136,653 311,688 .. 9 - 2 .. 9 .. 3 . 65 Latin America & Carib. 120.526 381,064 11 8 3 2 13 10 3 2 69 78 Middle East & N. Africa 99.827 137.575 19 18 3 2 6 6 3 2 68 70 South Asia 39,329 79.318 9 10 4 4 23 26 6 4 54 54 Sub-Saharan Africa 56.179 85.932 .. 10 .. 2 . 14 . 2 .. 68 High Income 2,846.174 4.949.031 9 7 3 2 11 10 4 3 72 75 Europe EMU 1,253.828 1.804.630 11 8 3 2 9 10 4 3 72 73 Note: Components way not sum to 100 percent because of unclassified trade. a. Iincludes Luxnembou.rg. b. Data oni tota merchandise imports for 1990 refer to the South African Customs Union (Botswana, Lesotho. Namibia, South Africa, andJ Swaziland), those for 2000 refer to Sooth Africa onily. Data on import commodity shares refer to the South African Customs Undon. c. Data for 2000 include toe intratrade of toe Baltic states and the Commonwealth of Independent States. S S 4.641 About the data Definitions Data on imports of goods are derived from the in Africa, Asia, and Latin America, which * Merchandise Imports show the c.i.f. value of same sources as data on exports. In principle, together with the high-income countries goods purchased from the rest of the world world exports and imports should be identical. account for nearly 90 percent of total world valued in U.S. dollars. * Food comprises the Similarly, exports from an economy should equal trade. There has also been a remarkable commodities in SITC sections 0 (food ancd live the sum of imports by the rest of the world from improvement in the availability of recent, animals), 1 (beverages and tobacco), and that economy. But differences in timing and reliable, and standardized figures for 4 (animal and vegetable oils and fats) and SITC definitions result in discrepancies in reported countries in Europe and Central Asia. division 22 (oil seeds, oil nuts, and oil kernels). values at all levels. For further discussion of The shares of imports by major commodity * Agricultural raw materials comprise SITC indicators of merchandise trade see About the group were estimated by World Bank staff from section 2 (crude materials except fuels) exclud- data for tables 4.4 and 4.5. the COMTRADE database. The values of total ing divisions 22, 27 (crude fertilizers and min- The value of imports is generally recorded as imports reported here have not been fully erals excluding coal, petroleum, and precious the cost of the goods when purchased by the reconciled with the estimates of imports of goods stones), and 28 (metalliferous ores and scrap). importer plus the cost of transport and insur- and services from the national accounts (shown * Fuels comprise SITC section 3 (mineral fu- ance to the frontier of the importing country- in table 4.9) or those from the balance of els). * Ores and metals comprise the commodi- the c.i.f. (cost, insurance, and freight) value, cor- payments (table 4.15). ties in SITC divisions 27, 28, and 68 (nonfer- 227 responding to the landed cost at the point of The classification of commodity groups is rous metals). * Manufactures comprise the entry of foreign goods into the country. A few based on the Standard International Trade commodities in SITC sections 5 (chemicals), 6 0 countries, including Australia, Canada, and the Classification (SITC) revision 1. Most countries (basic manufactures), 7 (machinery and M United States, collect import data on an f.o.b. now report using later revisions of the SITC or transport equipment), and 8 (miscellaneous E (free on board) basis and adjust them for freight the Harmonized System. Concordance tables are manufactured goods), excluding division 68. °- and insurance costs. Many countries collect and used to convert data reported in one system of D_ report trade data in U.S. dollars. When coun- nomenclature to another. The conversion process - tries report in local currency, the United Nations may introduce some errors of classification, but Data sources i 3 Statistics Division applies the average official conversions from later to early systems are The WTO publishes data on world trade in its CD exchange rate for the period shown. generally reliable. Shares may not sum to 100 Annual Report. Estimates of total imports of Countries may report trade according to the percent because of unclassified trade. goods are also published in the IIVMF's general or special system of trade (see Primary International Financial Statistics and Direction ' )' data documentation). Under the general system of Trade Statistics and in the United Nations imports include goods imported for domestic g Statistics Division's Monthly Bulletin of' consumption and imports into bonded Developing and high-income economies have Statistics. The United Nations Conference on warehouses and free trade zones. Under the similar import structures Trade and Development (UNCTAD) publishes special system imports comprise goods Shareotmerchandiseimports(%),2000 data on the structure of exports and imports in imported for domestic consumption (including Low- and middle-income economies its Handbook of International Trade and j transformation and repair) and withdrawals L% Development Statistics. Tariff line records of for domestic consumption from bonded -3% exports and imports are compiled in the United warehouses and free trade zones. Goods Nations Statistics Division's COMTRADE transported through a country en route to 12% database. another are excluded. . - - - .--i The data on total imports of goods 3% (merchandise) in this table come from the World Trade Organization (WTO). The WTO uses two main sources, national statistical offices and the International Monetary Fund's (IMF) 71% International Financial Statistics. It supplements these sources with the Commodity Trade High-income 7% (COMTRADE) database maintained by the United 3%no 2% Nations Statistics Division and publications or databases of regional organizations, specialized .or agencies, and economic groups (such as the Commonwealth of Independent States, the 3% Economic Commission for Latin America and the Caribbean, Eurostat, the Food and Agriculture Organization, the Organisation for Economic Co- operation and Development, and the Organization of Petroleum Exporting Countries). 75% It also consults private sources, such as country reports of the Economist Intelligence Unit, and 0 Food f9 Agncultural rawmaterials press clippings. In recent years country Web O Fuel QOres andMetals U Manufactures 03 Unciassified trade sites and direct contacts through email have Sorre: UNCTAD dat iIIes helped to improve the collection of up-to-date Justasforexports,manufacturingdominatesimports statistics for many countries, reducing the of both developing and high-income economies. How- proportion of estimated figures. The WTO ever, the share of fuel, food, and agricultural raw ma- terials Imports by developing economies are slightly database now covers most of the major traders higher than In high-income economies. 4.7 Structure of service exports Commercial Transport Travel Others service eports % of total % of total % of total $ milions services services services 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan . .. .. Albania 32 429 20.0 4.0 11.1 90.8 68.9 5.2 Algeria 479 ..41.7 ..13.4 ..44.9 Angola 65 155 48.8 30.4 20.6 0.0 30.7 69.6 Argentina 2,264 4,374 51.1 22.9 39.9 66.4 9.1 10.7 Armenia ..129 ..41.0 ..23.9 ..35.0 Australia 9,833 17,895 35.5 24.2 43.2 47.2 21.4 28.6 Austria 22,755 30,043 6.4 14.5 59.0 33.3 34.6 52.3 Azerbaijan ..234 ..50.9 ..26.9 ..22.2 228 Bangladesh 296 283 12.9 32.3 6.4 17.8 80.6 49.9 Belarus ..982 ..65.3 ..1.9 ..32.8 Belgium' 26,646 42,508 27.5 24.8 14.0 17.5 58.5 57.7 to Benin 109 155 33.4 12.9 50.2 60.5 16.4 26.7 m Bolivia 133 207 35.8 24.1 43.6 33.0 20.6 42.9 Bosnia and Herzegovina . .. .. Botswana 183 346 20.4 27.7 64.1 67.7 15.5 4.7 o Brazil 3,706 8,846 36.4 14.6 37.3 20.5 26.3 64.9 a) > Bulgaria 837 2,129 27.5 29.7 38.2 50.5 34.2 19.9 to Burkina Faso 34 ..37.1 ..34,1 ..28.9 ~0 Cambodia 50 159 0.0 43.6 100.0 40.4 0.0 16.1 o Cameroon 369 ..42.6 ..14.4 ..43.0 Canada 18,350 36,287 23.0 20.1 34.7 29.5 42.3 50.4 Central African Republic 17 ..50.9 ..16.0 ..33.1 Chad 23 ..18.4 ..34.1 ..47.5 Chile 1.786 3,843 40.0 43.3 29.8 24.8 30.3 32.0 China 5,748 30,146 47.1 12.2 30.2 53.8 22.7 34.0 Hong Kong. China .. 41,331 Colombia 1.548 1.994 31.3 29.9 26.2 51.6 42.5 18.6 Congo, Dem. Rep. ... . Congo, Rep. 65 ..53.9 ..12.9 ..33.1 Costa Rica 583 1.503 16.3 16.3 48.9 67.9 34.8 15.8 C6te dIlvoire 425 369 62.4 20.3 12.1 15.5 25.5 64.2 Croatia .. 4,084 ..13.6 ..67.5 ..18.8 Cuba . .. .. Czech Republic .. 6,638 ..20.9 ..43.1 ..36.0 Denmark 12.731 20.438 32.5 53.0 26.2 19.5 41.3 27.5 Dominican Republic 1,086 3,142 5.6 2.3 66.8 91.0 27.5 6.7 Ecuador 508 793 47.6 37.6 37.0 45.0 15.4 1 7.4 Egypt. Arab Rep. 4,812 9,687 50.1 27.3 22.9 44.9 27.1 27.8 El Salvador 301 649 26.2 35.3 25.2 33.5 48.6 31.2 Eritreea.. . .. Estonia 200 1,495 74.7 48.5 13.7 33.8 11.6 17.8 Ethiopia 261 387 80.6 55.7 2.1 14.7 17.3 29.7 Finland 4.562 6.002 38.4 27.3 25.8 23.4 35.7 49.3 France 74.948 81,153 21.7 23.9 27.0 38.1 51.3 38.0 Gabon 214 249 33.4 60.9 1.4 6.0 65.2 33.2 Gambia, The 53 ..8.8 ..87.9 ,.3.3 Georgia ..206 ..49.7 ..46.9 ..3.5 Germany 51.605 80,480 28.6 24.7 27.9 21.9 43.5 53.4 Ghana 79 490 49.2 20.1 5.6 68,3 45.2 11.6 Greece 6.514 19.181 4.9 41.3 39.7 48.1 55.4 10.6 Guatemala 313 735 7.4 11.2 37.6 65.6 55.0 23.2 Guinea 91 36 14.2 53.7 32.6 4.7 53.3 41.6 Guinea-Bissau 4 ..5.4 ..0.0 .94.6 Haiti 43 178 19.8 2.0 78.9 63.7 1.3 34.3 Honduras 121 412 35.1 11.5 24.0 59.4 40.9 29.0 4.7 Commercial Transport Travel Others service exports % of total % of total % Of total $ millions serv ces services services 1990 2000 1990 2000 1990 2000 1.990 2000 Hungary 2,677 6,204 1.6 10.4 36.8 55.1 61.6 34.5 India 4,610 17,670 20 8 10.6 33.8 17.9 45.4 71.4 Indonesia 2,488 5,060 2.8 0.0 86.5 98.3 10.7 1.7 Iran, Islamnic Rep. 343 1,357 10.5 49.4 8.2 36.9 81.3 13.6 Iraq Ireland 3,286 16,638 31.1 8.1 44.4 15.9 24,5 75.9 Israel 4,546 14,260 30.8 17.4 30.7 26.8 38.5 55.8 Italy 48,579 55,558 21.0 16.0 33.9 49.5 45.2 34.6 Jamaica 976 1,988 1 16.5 77.0 67.0 5.0 16.4 Japan 41,384 68,303 40.4 37.5 7.9 4.9 51. 7 57.629 Jordan 1,430 1,689 26.0 1 7.7 35.7 4 7.1 38.3 35.2 Kazakhstan ..987 54.9 .. - 36.1 - . - 9.0 Kenya 774 701 32.0 58.6 60.2 36.7 7.8 4.7 Korea. Dem. Rep. . .. .. ----- - ---- ------ ----- ----- - 0~~~~~~~~~~ Korea, Rep. 9,155 28,910 34.7 44.5 34.5 23.6 30.7 31.9 a. Kuwait 1,054 1,793 87.5 89.6 12.5 5.5 0.0 4.9 Kyrgyz Republic ..57 ..29.2 ..- 26.8 44.0 C ___ - - 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~C Lao PDR 11 101 74.8 1 7.9 24.3 80.8 0.9 1.4' - ---.-----...-.-.-.--- . --.---.. .--- --~~.. -.- .--- - .- .------------------- - ------- -------- . --.---.2 - Latvia 290 1.193 94.9 66.7 2.5 11.0 2.6 22.3 ( Lebanon ..x: ,, - .. ----~~~~~~~~ ~~~~~~~~.-. .... ---- Lesoth 34 36 14.1 1.5 51.2 67.2 34.7 31.3 Libya 83 46 83.8 37.9 7.7 53.9 8.5 8.2 Lithuania 1,052 ..46.7 ..37.2 16.1 Macedonia, FYR 281 ..41.3 ..13.3 45.4 Madagascar 129 314 32.1 16.4 31.3 38.6 36.6 45.0 Malawi 37 .. 46.1 -42.6 -. 11.3 Malaysia 3.769 11,800 31.8 21.1 44.7 30.4 23.5 48.5 Mali 71 .. 31.0 . 54.3 .. - 14.7 . . Mauritania 14 24 35.3 2.6 64.7 82.7 0.0 14. 7 Mauritius 478 1,067 32.9 20.9 51.1 50.8 15.9 28.3 Mexico 7.222 13,567 12.4 10.1 76.5 61.1 11.1 28.8 Moldova ..158 ..50.8 ..29.3 19.9 Mongolia 48 73 41.8 39410.4__ 48.6 47.8 12.0 Morocco 1,871 2,853 9.6 .17.0 68.4 71.5 22.0 11.5 Mozambique 103 325 61.3 20.4 0.0 _.38.7 79.6 Myanmar 93 509 10.3 16.6 20.9 33.4 68.8 50.0 Namibia 106 315 0.0 0.0 81.0 91.4 19.0 8.6 Nepal 166 410 3.6 15.0 65.6 38.5 30.8 46.5 Netherlands 28,478 51,493 45.4 38.4 14.6 14.0 40.0 47.6 New Zealand 2.415 4,270 43.4 28.9 _42.7 _52.9 13.9 18.2 Nicaragua_ . ...... 34 _265 19.2 9.9 35.5 48.5 45.3 41.5 Niger 22 5.2 59.5 ..35.3 Nigeria 965 980 3.9 12.0 2.5 5.5 93.6 82.5 Norway 12,452 14,969 68.7 63.6 12.6 12.6 18.7 23.8 Oman 68 283 15.3 6.4 84.7 78.0 0.0 15.6 Pakistan 1,218 1,284 59.3 65.4 12.0 6.3 28.7 28.3 Panama 907 1,806 64.9 54.7 18.9 26.0 16.2 19.2 Papua New Guinea 198 248 11.2 4.3 12.0 2.4 76.8 93.3 Paraguay 404 568 18.3 12.0 21.1 17.7 60.5 70.3 Peru 714 1.463 43.4 16.2 30.4 62.3 26.2 21.5 Philippines 2,897 4,133 8 5 21.6 16.1 56.4 75.4 22.0 Poland 3,200 10,390 57.3 23.5 11.2 54.6 31.5 21.9 Portugal 5,054 8,317 15.6 17.1 70.4 63.2 14.0 19.8 Puerto Rico......... Romania 610 1.740 50.5 37.5 17.4 20.6 32.2 41.9 Russian Federation 9,632 33.9 39.9 26.3 e 4) . 47 Commercial Transport Travel Others service exports % of total % of total % of total $ millions services services services 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 31 39 56.1 31.5 32.8 60.3 11.0 8.3 Saudi Arabia 3,031 4,785 .. .. .. Senegal 356 351 19.1 10.1 42.7 49.4 38.1 40.4 Sierra Leone 45 .. 9.7 .. 76.2 .. 14.1 Singapore 12,719 26,960 17.5 19.8 36.6 21.4 45.9 58.8 Slovak Republic .. 2,218 .. 44.9 .. 19.5 .. 35.6 Slovenia 1.219 1,881 22.6 26.2 55.0 50.8 22.4 23.0 Somalia .. .. .. .. .. South Africa 3,290 4,930 21.6 24.0 55.8 54.9 22.7 21.1 230 Spain 27,649 53,041 17.2 14.6 67.2 58.3 15.6 27.0 Sri Lanka 425 915 39.7 43.7 30.2 27.1 30.1 29.2 Sudan 134 24 14.1 62.8 15.7 22.3 70.2 14.9 X Swaziland 102 72 24.5 21.1 29.2 46.8 46.3 32.1 c Sweden 13,453 20,014 35.8 21.8 21.7 20.3 42.6 57.9 Switzerland 18,233 26,203 16.3 17.3 40.6 29.4 43.0 53.3 E Syrian Arab Republic 740 1,481 29.7 16.6 43.3 73.1 27.0 10.3 aE o Tajikistan .. .. .. .. .. >D Tanzania 131 615 19.9 9.2 36.4 61.3 43.6 29.5 oD Thailand 6.292 13,785 21.1 23.6 68.7 54.3 10.2 22.1 o Togo 114 54 26.9 23.7 50.7 12.8 22.3 63.5 Trinidad and Tobago 322 574 50.7 35.2 29.4 35.0 19.9 29.9 o Tunisia 1,575 2,602 23.0 22.8 64.8 64.7 12.2 12.5 0 N Turkey 7,882 19,232 11.7 15.4 40.9 39.7 47.4 44.9 Turkmenistan .. .. .. .. .. Uganda .. 182 .. 13.4 .. 82.0 .. 4.6 Ukraine .. 3,800 .. 76.8 .. 10.4 .. 12.8 United Arab Emirates .. .. .. .. .. United Kingdom 53,830 115,658 25.5 18.2 29.3 21.7 45.2 60.1 United States 132.880 272,110 28.1 18.7 37.9 35.8 34.0 45.5 Uruguay 460 1,326 36.9 27.9 51.8 49.2 11.3 22.9 Uzbekistan .. .. .. .. .. Venezuela, RB 1,121 1,067 40.9 34.8 44.2 61.7 14.9 3.5 Vietnam .. 2.702 West Bank and Gaza .. .. .. .. .. Yemen, Rep. 82 141 27.2 18.4 48.8 43.3 24.0 38.3 Yugoslavia, Fed. Rep. .. .. .. .. .. Zambia 94 .. 68.9 13.5 .. 17.5 Zimbabwe 253 .. 44.3 25.3 .. 30.4 Low income 14,027 36,100 25.0 18.8 38.1 32.6 36.9 48.6 Middle income 88.902 225,049 29.3 23.8 42.8 44.5 27.9 31.8 Lower middle income 35,373 98,116 29.9 20.7 42.0 53.3 28.1 26.0 Upper middle income 53,529 126,932 28.8 25.6 43.5 39.1 27.7 35.3 Low & middle Income 102.929 261,149 28.7 23.2 42.2 43.2 29.1 33.5 East Asia & Pacific 31,204 85,404 28.6 24.2 44.4 43.9 27.0 31.9 Europe & Central Asia 15,237 73,142 25.0 26.5 35.8 40.7 39.3 32.8 Latin America & Carib. 25.313 48.444 28.3 20.2 51.3 49.9 20.4 29.9 Middle East & N. Africa 14,872 23,880 34.4 24.4 38.5 53.1 27.1 22.5 South Asia 6,816 20,908 27.9 11.0 30.1 19.0 42.0 70.0 Sub-Saharan Africa 9,487 9,371 32.1 24.1 38.6 54.8 29.3 21.1 High Income 647,432 1,169,694 28.0 23.2 32.8 29.7 39.2 47.1 Europe EMU 300,074 458,079 24.7 22.5 34.5 33.7 40.8 43.8 a. Includes Luxembourg 4.7 About the data Definitions Balance of payments statistics, the main source Figure 4.7 * Commercial service exports are total service of information on international trade in services, exports minus exports of government services have many weaknesses. Some large econo- Export shares of other commercial services not included elsewhere. International trans- mies-such as the former Soviet Union-did not have grown in developing economies actions in services are defined by the IMF's report data on trade in services until recently. Percentage of commercial service exports Balance of Payments Manual (1993) as the Disaggregation of important components may 1990 economic output of intangible commodities that be limited, and it varies significantly across coun- may be produced, transferred, and consumed tries. There are inconsistencies in the methods / - at the same time. Definitions may vary among used to report items. And the recording of major 29% * 29% reporting economies. * Transport covers all flows as net items is common (for example, in- transport services (sea, air, land, internal surance transactions are often recorded as pre- - waterway, space, and pipeline) performed by miums less claims). These factors contribute to residents of one economy for those of another a downward bias in the value of the service trade - - and involving the carriage of passengers, reported in the balance of payments. movement of goods (freight), rental of carriers Efforts are being made to improve the cover- with crew, and related support and auxiliary 231 age, quality, and consistency of these data. services. Excluded are freight insurance, which Eurostat and the Organisation for Economic Co- is included in insurance services; goods 0 operation and Development, for example, are 42% procured in ports by nonresident carriers and working together to improve the collection of repairs of transport equipment, which are E statistics on trade in services in member coun- 2000 included in goods; repairs of railway facilities, tries. In addition, the International Monetary Fund harbors, and airfield facilities, which are CD (IMF) has implemented the new classification . - _ - included in construction services; and rental CD 0 of trade in services introduced in the fifth edi- 3% of carriers without crew, which is included in 3 tion of its Balance of Payments Manual (1993). Dother services. Travei covers goods and Still, difficulties in capturing all the dimensions services acquired from an economy by travelers of international trade in services mean that the in that economy for their own use during visits record is likely to remain incomplete. Cross-bor- . . of less than one year for business or personal der intrafirm service transactions, which are purposes. Travel services include the goods usually not captured in the balance of payments, and services consumed by travelers, such as are increasing rapidly as foreign direct invest- . meals, lodging, and transport, including car ment expands and electronic networks become 439% rental (within the economy visited). * Other pervasive. One example of such transactions is i Transport services commercial services include such activities as transnational corporations' use of mainframe U Travel services insurance and financial services, international computers around the clock for data process- l Other commercial services telecom-munications, and postal and courier ing, exploiting time zone differences between services; computer data; news-related service their home country and the host countries of transactions between residents and non- their affiliates. Another important dimension of S-- Inter.. tonai Aw Fund d. files residents; construction services; royalties and service trade not captured by conventional bal- Asdevelopingeconomies have Increasedtheirexports license fees; miscellaneous business, ance of payments statistics is establishment of other commercial and trvel servces, the share of professional, and technical services; and transport services has declined. trade-sales in the host country by foreign af- personal, cultural, and recreational services. filiates. By contrast, cross-border intrafirm trans- -_ actions in merchandise may be reported as ex- ports or imports in the balance of payments. Data sources The data on exports of services in this table, The data on exports of commercial services and on imports of services in table 4.8, unlike are from the IMF. The IMF publishes balance those in editions before 2000, include only com- of payments data in its Intemational Financial mercial services and exclude the category "gov- Statistics and Balance of Payments Statistics ernment services not included elsewhere." The l Yearbook. data are compiled by the IMF based on returns _ . ._._ ...... .... _ - _ from national sources. Data on total trade in goods and services from the IMF's Balance of Payments database are shown in table 4.15. IUR 4.8 Structure of service imports Commercial Transport Travel Other service Imports $ millions % of total % Of iotalI % of total 1.990 2000 1990 2000 1990 2000 1990 2000 Afghanistan . .. Albania 29 413 26.3 22 7 0.0 65.9 73.7 11.4 Algeria 1,155 .. 58 1 ..12.9 ..29.0 Angola 1,288 2.193 38.3 18.0 3.0 5.8 58.7 76.2 Argentina 2,876 8,612 32.6 28.1 40.7 51.4 26.7 20.5 Armenia ..183 ..58.5 ----- 20.1 ..21.4 Australia 13,388 17,654 33.9 35.6 31.5 34.2 34.7 30.2 Austria 14,104 29.102 8.4 10.3 54.9 29.1 36.7 60.7 Azerbaijan ..475 ..30.3 ..27.7 ..42.0 232 Bangladesh 554 1.523 71.1 66 5 14.1 19.0 14.9 14.5 Belarus 421 20.3 31.7 -48.1 in ..- ----- o Belgium' 25,924 38,277 23.3 21.7 21.1 26.5 55.6 51.8 tO Benin 113 213 46.9 67.3 12.8 12.0 40.3 20.7 0) ' ~ Bolivia 291 451 61.7 59.9 20.6 17.1 17.7 23.0 Bosnia and Herzegovina . 0)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~------ E Botswnana 371 511 57.5 42.4 15.0 28.0 27.5 29.7 o Brazil 6.733 15.869 44.4 29.0 22.4 24.5 33.2 46.5 > Bulgaria 600 1.660 40.5 44.1 31.5 32.4 28.0 23.5 Burkina Faso 196 ..64.7 ..16.6 ..18.7 0 Burundi 59 34 62.6 55.7 29.0 39.7 8.4 4.5 Cambodia 64 237 24.5 60.1 ..8.1 75.5 31.8 o Cameroon 1,018 ... 45.3 ..27.5..23 0 27. Canada 27.479 41.306 21.1 22.3 39.8 29.4 39.2 48.2 Central African Republic 166 ..49.7 ..306 .19.6 Chad 223 .. 45.1 ..31.2 ..23.7 Chile 1.983 4.336 47.4 55.9 21.5 19.4 31.1 24.7 China 4,113 35,858 78.9 29.0 11.4 36.6 9.7 34.4 Hong Kong, China .. 25,420 Colombia 1.683 3,234 34.9 40.6 27.0 32.7 38.1 26.7 Congo, Dam. Rep. . .. .. Congo. Rep. 748 ..18.4 ..15.2 ..66.5 Costa Rica 540 1.164 41.2 37.8 28.8 36.9 30.0 25.3 C6te dIlvoire 1.518 1,131 32.1 45.0 11120.0 56.8 35.0 Croatia .. 1,827 ..20.7 ..31.1 ..48.2 Cuba Czech Republic .. 5.341 ..13.4 ..23.5 ..63.1 Denmark 10,106 17,937 38.3 51.2 36.5 28.1 25.2 20.7 Dominican Republic 435 1,340 40.0 61.8 33.1 23.1 26.9 15.1 Ecuador 755 1.212 41.6 32.4 23.2 21.4 35.2 46.2 Egypt, Arab Rep. 3,327 7,161 44.0 30.9 3.9 15.0 52.1 54.1 El Salvador 296 931 45.9 42.6 20.5 18.3 33.5 39.1 Eritreea.. . ,. Estonia 123 868 76.3 48.4 15.4 23.5 8.3 28.2 Ethiopia 348 480 76.5 59.6 3.3 15.4 20.3 25.1 Finland 7.432 8.231 26.1 31.4 37.2 22.5 36.6 46.1 France 59,560 61,520 29.4 31.8 20.7 29,1 49.9 39.1 Gabon 984 854 23.2 33.7 13.9 10.7 62.9 55.6 _ Gambia. The 35 .. 65.1 ..23.1 ..11.8 Georgia ..216 ..41.2 ..51.0 ..7.8 Germany 79.214 132,593 21.6 19.1 42.8 35.5 35.6 45.4 Ghana 226 527 55.1 53.7 5.9 19.0 39.0 27.3 Greece 2.756 10,918 34.0 37,4 39.5 41.7 26.5 20.8 Guatemala 363 802 41.0 52.6 27.4 22.6 31.6 24.8 Guinea 243 236 57.5 42.6 12.2 10.0 30.3 47.4 Guinea-Bissau 17 ..54.5 . 19.8 ..25.6 Haiti 71 370 47.9 88.1 52.1 9.9 0.0 2.1 Honduras 213 565 45,4 53.2 17.6 17.5 37.0 29.2 4.8 Commercial Transport Travel Other service Imports $ millions % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 Hungary 2,264 4,409 8.8 11.7 25.9 24.8 65.3 63.5 India 5,943 19,601 57.5 41.1 6.6 13.1 35.9 45.8 Indonesia 5,898 14,755 47.4 27.2 14.2 21.7 38.4 51.1 Iran. Islamic Rep. 3,703 1,577 47.3 72.4 9.2 13.0 43.5 14.6 Ireland 5,145 28,692 24.3 9.0 22.6 9.1 53.1 81.9 Israel 4,825 12,149 39.6 39.4 29.7 23.1 30.7 37.5 Italy 46,602 55.204 23.7 24.9 22.1 28.4 54.2 46.7 Jamaica 667 1,400 47.9 41.1 17.0 14.9 35.1 44.0 Japan 84,281 115,686 30.8 30.3 27.9 27.6 41.4 42.1 233 Jordan 1,118 1,485 52.0 38.6 30.1 23.9 17.9 37.5 Kazakhstan .. 2,146 ..23.0 ..19.0 ..58.0 N Kenya 598 665 66.2 51.3 6 982742. Korea, Dem. Rep. . .. .. Korea, Rep. 10,050 32,998 39.8 33.0 27.5 21.6 32.7 45.4 Kuwait 2,805 4,078 31.9 37.7 65.5 60.1 2.6 2.2 Kyrgyz Republic ..145 ..48.2 ..10.8 ..41.0 CD -------- ------ --- ------ --- o~~~~~~~~~~~~~~~~ Lao PDR 25 49 73.0 10.7 0.0 24.2 27,0 65.1 Latvia 120 710 82.3 33.3 10.9 35.0 6.8 31.8 ' Lebanon...... Lesotho 48 41 67.9 77.1 24.7 22.9 7.3 0.0 2 Libya 926 824 41.9 45.7 45.7 45.3 12.4 9.0 Lithuania 655 33.7 ..38.6 27.6 Macedonia. FYR ..350 ..48.6 ..9.8 ..41.6 Madagascar 172 395 43 5 48.3 23.4 29.2 33.0 22.5 Malawi 268 .. 81.8 ..5.9 ..12.3 Malaysia 5,394 14.622 46.9 32.3 26.9 13.5 26.2 54.2 Mali 352 .. 57.4 ..15.8 ..26.8 Mauritania 126 130 76.9 37.4 18.3 32.8 4.8 29.8 Mauritius 407 732 51.6 33.6 23.0 24.8 25.4 41.5 Mexico 10,063 16,790 25.0 11.8 54.9 33.2 20.2 55.0 Moldova ..199 Z. 31.0 ..39.1 ..29.9 Mongolia 155 140 56.2 61.3 0.8 29.2 43.0 9.5 Morocco 940 1,512 58 .3 41.0 19.9 28.1 21.9 30.9 Mozambique 206 439 57.7 27.0 0.0 ..42.3 73.0 Myanmar 72 499 35.4 51.5 22.6 5.0 42.0 43.5 Namibia 341 449 46.9 33.4 17.9 19.7 35.2 46.9 Nepal 159 193 40.8 33.5 28.5 37.9 30.7 28.5 Netherlands 28,995 51 589 37.7 28.3 25.4 23.6 36.9 48.1 New Zealand 3,251 4,449 40.6 32.2 29.5 34.4 30.0 33.4 Nicaragua 73 323 70.7 46.0 20.1 24.3 9.3 29.8 Niger 209 .. 68.3 ..10.4 ..21.4 Nigeri'a 1,901 3,311 33.6 19.8 30.3 18.7 36.1 61.4 Norway 12,247 14,466 44.6 38.1 30.0 30.6 25.3 31.3 Oman 719 1,501 36.6 32.4 6.5 22.7 56.9 44.9 Pakistan 1,863 2,109 67.0 71. 9 23.1 11.9 9.9 16.2 Panama 666 1,098 66.6 60.4 14.8 18.5 18.6 21.1 Papua New Guinea 393 728 35.6 24.9 12.8 7.3 51.5 67.9 Paraguay 361 394 61.6 60.0 19.8 24.5 18.6 15.5 Peru 1,070 2.210 431.5 38.6 27.6 24.0 29.0 37.3 Philippines 1,721 6,066 56.9 44.1 6.4 16.6 36.6 39.3 Poland 2,847 8,866 52.4 17.3 14.9 37.4 32.8 45.3 Portugal 3,772 6,412 48.4 31.8 23.0 34.8 28.6 33.4 Puerto Rico . .. .. .. Romania 787 1,976 65.5 33.1 13.1 21.5 21.4 45.3 Russian Federation .. 17,352 13.4 ..58.9 27.6 4.8 Commercial Transport Travel Other service Imports $ millions % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 96 104 69.0 72.4 23.7 19.1 7.3 8.5 Saudi Arabia 12,694 10,942 18.1 20.5 0.0 0.0 81.9 79.5 Senegal 368 419 60.1 60.4 12.4 12.9 27.5 26.8 Sierra Leone 67 .. 29.5 .. 32.7 .. 37.8 Singapore 8,575 21,300 41.0 37.6 21.0 23.4 38.0 39.0 Slovak Republic .. 1,779 .. 24.4 .. 16.6 .. 59.0 Slovenia 1,034 1,435 42.5 24.4 27.3 35.8 30.3 39.8 Somalia .. .. .. .. .. South Africa 3,593 5,449 40.2 44.4 31.5 36.8 28.3 18.8 234 Spain 15,197 30,818 30.8 26.1 28.0 17.8 41.2 56.1 Sri Lanka 620 1.592 64.2 61.6 11.9 15.1 23.9 23.3 Sudan 202 632 31.9 87.9 25.4 8.8 42.7 3.4 iO Swaziland 171 170 6.1 14.3 20.6 21.5 73.4 64.2 D Sweden 16,959 23.367 23.2 15.6 37.1 34.4 39.7 50.0 Switzerland 11,093 15.369 33.7 34.0 53.0 41.6 13.4 24.3 E Syrian Arab Republic 702 1.468 54.5 47.5 35.5 45.6 10.1 6.9 o Tajikistan .. .. .. .. .. > Tanzania 288 670 58.0 33.5 7.9 50.3 34.1 16.2 a) o Thailand 6,160 15.329 58.0 44.1 23.3 18.1 18.7 37.8 0 o Togo 217 130 56.9 73.0 18.4 2.0 24.7 . ... 25.0 Trinidad and Tobago 460 235 51.7 52.7 26.6 28.5 21.8 18.7 O Tunisia 682 1,089 51.4 50.2 26.2 24.1 22.4 25.7 N Turkey 2,794 7,620 32.2 36.0 18.6 22.5 49.2 41.5 Turkmenistan .. .. .. .. .. Uganda 195 745 58.3 34.1 0.0 18.9 41.7 47.0 Ukraine .. 2,590 .. 15.1 .. 18.1 .. 66.8 United Arab Emirates United Kingdom 44,713 92,308 33.2 26.8 41.1 46.3 25.6 26.9 United States 97,950 201,060 36.3 32.5 38.9 33.2 24.8 34.3 Uruguay 363 855 48.2 50.3 30.7 32.9 21.1 16.8 Uzbekistan .. .. .. .. .. Venezuela, RB 2.390 4.056 33.5 40.8 42.8 42.3 23.7 16.9 Vietnam .. 3,252 .. .. .. West Bank and Gaza Yemen, Rep. 639 672 27.6 40.0 9.9 20.3 62.5 39.7 Yugoslavia, Fed. Rep. .. .. .. .. .. Zambia 370 .. 76.8 .. 14.6 .. 8.6 Zimbabwe 460 .. 51.8 .. 14.4 .. 33.8 Low income 27,885 51,237 50.7 35.0 14.0 17.4 35.3 47.6 Middle income 103,290 245,594 41.6 32.5 21.9 29.3 36.5 38.2 Lower middle income 33,346 110,498 42.6 30.8 12.1 35.2 45.3 34.0 Upper middle income 69,944 135,097 40.7 33.5 29.9 25.6 29.4 40.9 Low & middle Income 131,175 296.831 43.5 32.8 20.3 27.7 36.2 39.5 East Asia & Pacific 34,357 108,994 51.2 33.0 20.9 24.5 27.8 42.5 Europe & Central Asia 9,321 60,019 24.8 20.1 8.6 34.6 66.6 45.3 Latin America & Carib. 32,861 65.290 37.2 38.1 35.8 33.0 26.9 28.9 Middle East & N. Africa 27,080 25.933 55.7 37.0 13.3 19.1 31.0 43.9 South Asia 9.176 25.126 60.7 44.2 11.2 13.5 28.2 42.2 Sub-Saharan Africa 18,380 11.470 45.8 45.0 18.0 35.4 36.1 19.6 High Income 643,383 1,102,710 30.1 26.8 34.2 31.6 35.7 41.6 Europe EMU 288,701 463.840 26.8 22.0 31.4 28.2 41.8 49.8 a. Includes Luxembourg. 4.8 About the data Definitions Trade in services differs from trade in goods * Commercial service Imports are total ser- because services are produced and consumed vice imports minus imports of government ser- at the same time. Thus services to a traveler vices not included elsewhere. International may be consumed in the producing country (for transactions in services are defined by the example, use of a hotel room) but are classified IMF's Balance of Payments Manual (1993) as as imports of the traveler's country. In other the economic output of intangible commodi- cases services may be supplied from a remote ties that may be produced, transferred, and location; for example, insurance services may consumed at the same time. Definitions may be supplied from one location and consumed in vary among reporting economies. * Transport another. For further discussion of the problems covers all transport services (sea, air, land, of measuring trade in services see About the internal waterway, space, and pipeline) per- data for table 4.7. formed by residents of one economy for those The data on exports of services in table 4.7 of another and involving the carriage of pas- and on imports of services in this table, unlike sengers. movement of goods (freight), rental those in editions before 2000, include only of carriers with crew, and related support and 235 commercial services and exclude the category auxiliary services. Excluded are freight irsur- 'government services not included elsewhere." ance, which is included in insurance services; 0 The data are compiled by the International goods procured in ports by nonresident carri- Monetary Fund (IMF) based on returns from ers, and repairs of transport equipment, which S national sources. are included in goods; repairs of railway facili- E ties, harbors, and airfield facilities, which are ,o included in construction services; and rental O 0 of carriers without crew, which is included in D Figure 4.8 other services. * Travel covers goods and ser- CD vices acquired from an economy by travelers , The changing structure of commercial service imports in that economy for their own use during visits 2a 0, of less than one year for business or personal Share of commercial service imports %) 1990 purposes. Travel services include the goods 70 and services consumed by travelers, such as 60 meals, lodging, and transport, including car hire 50 rental (within the economy visited). * Other commercial services include such activities as 40 l insurance and financial services, international 30 telecommunications, and postal and courier 20 services; computer data; news-related service 10 transactions between residents and nonresi- dents; construction services; royalties and li- East Asia & Europe & Latin Middle East South Asia Sub- High income cense fees: miscellaneous business. profes- Pacrifc Central America& &North Saharan sional, and technical services: and personal, Asia Caribbean Atrica Atreca cultural, and recreational services. 2000 - .--- ho Data sources n0 n | n | n | |The data on imports of commercial services 30 come from the MF. The MF publishes balance of payments data in its International Financial 20 Statistics and Balance of Payments Statistics East I I ~~~~~~~~~~~~~~~~~Yearbook. East Asia & Europe & Latin Middle East South Asia Sub- High income Pacitic Central America & & North Saharan Asea Caribbean Africa Atrica El Transport services * Travel services 0 Other commercial services Source: Internatinal Monetary Fund data fils Transport and travel commercial service Imports are being replaced by other commercial service Imports. But Europe and Central Asia is Importing more travel services. ) 4.9 Structure of demand Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption final formation and services and services expenditure consumption expenditure % of GDP % of GDP % of GDP % of GDP % of GDP %of GDP 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan . .. .. Albania 61 92 19 11 29 19 15 19 23 40 21 -3 Algeria 57 42 16 14 29 24 23 42 25 22 27 44 Angola 36 17 34 39 12 28 39 90 21 74 30 44 Argentina 77 71 3 14 14 16 10 11 5 11 20 15 Armenia 46 96 18 12 47 19 35 23 46 51 36 -8 Australia 59 60 19 19 22 24 17 20 17 22 22 22 Austria 55 5 7 19 20 25 24 40 45 38 46 26 24 Azerbaijan .. 59 . 12 .. 26 .. 41 . 38 .. 28 236 Bangladesh 86 78 4 5 17 23 6 14 14 19 10 18 - Belarus 47 59 24 20 27 23 46 68 44 69 29 21 Belgium 55 54 20 21 22 22 71 88 69 85 24 25 CO Benin 87 82 11 12 14 20 14 15 26 29 2 6 Bolivia 77 74 12 16 13 18 23 18 24 25 11 11 Bosnia and Herzegovina .. 110 .. . . 20 . 27 . 58 .. -10 E Botswana 39 58 24 28 32 20 55 28 50 33 37 14 ai o Brazil 59 63 19 18 20 21 8 11 7 12 21 19 a) > Bulgaria 60 71 18 18 26 17 33 5B 37 64 22 11 a) o) Burkina Faso 77 76 15 15 21 28 13 11 26 30 8 9 -C Burundi 95 93 11 13 15 9 8 9 28 24 -5 -6 Cambodia 91 92 7 . 8 15 6 40 13 47 2 8 O Cameroon 67 69 13 10 18 16 20 31 17 27 21 20 C Canada 57 58 23 19 21 20 26 44 26 41 21 23 Central African Republic 86 81 15 11 12 11 15 13 28 16 -1 8 Chad 89 91 10 8 16 17 13 17 29 32 0 1 Chile 62 63 10 12 25 23 35 32 31 31 28 25 China 50 47 12 13 35 37 18 26 14 23 38 40 Hong Kong, China 57 58 7 10 27 28 134 150 126 145 36 32 Colombia 66 67 9 19 19 12 21 22 15 20 24 14 Congo, Dem. Rep. 79 .. 12 .. 9 . 30 . 29 ..9 Congo, Rep. 62 28 14 11 16 24 54 79 46 42 24 61 Costa Rica 61 67 18 13 27 17 35 48 41 46 21 19 C6te dIlvoire 72 71 17 10 7 12 32 46 27 39 11 19 Croatia 74 57 24 26 14 22 78 45 86 51 -21 16 Cuba -. 70 .. 23 .. 10 . 16 .. 18 .7 Czech Republic 49 54 23 20 25 30 45 71 43 75 28 26 Denmark 49 48 26 25 20 22 36 42 31 37 25 27 Dominican Republic 80 78 5 8 25 24 34 30 44 39 15 14 Ecuador 69 62 9 9 17 17 33 42 27 31 23 28 Egypt, Arab Rep. 73 73 11 10 29 24 20 16 33 23 16 17 El Salvador 89 88 10 10 14 17 19 28 31 43 1 2 Eritrea 98 132 33 .. 5 38 20 16 57 86 -31 -32 Estonia 62 58 16 21 30 26 60 84 54 88 22 21 Ethiopia 74 78 19 23 12 14 8 15 12 31 7 -1 Finland 51 50 22 21 29 20 23 42 24 32 27 30 France 55 55 22 23 23 21 21 29 22 27 22 22 Gabon 50 62 13 10 22 26 46 37 31 35 37 28 Gambia, The 76 83 14 13 22 17 60 48 72 61 11 4 Georgia 65 82 10 13 31 15 40 37 46 47 25 5 Germany 55 58 19 19 22 23 29 33 25 33 26 23 Ghana 85 81 9 15 14 24 17 49 26 70 5 3 Greece 72 71 15 15 23 22 18 20 28 29 13 14 Guatemala 84 84 7 7 14 17 21 20 25 28 10 9 Guinea 73 77 9 6 18 22 31 26 31 31 18 17 Guinea-Bissau 87 95 10 14 30 18 10 32 37 58 3 -9 Haiti 93 100 8 7 12 11 16 12 29 27 -1 -4 Honduras 66 66 14 13 23 35 36 42 40 56 20 21 4.9 Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption final formation and services and services expenditure consumption expenditure % of GDP %ofGDP % of GDP % of GDP % of GDP % of GDP 1990 2000 1990 2000 1990 2000 1990 2000 11990 2000 15190 2000 Hungary 61 64 11 10 25 31 31 63 29 67 28 26 India 66 65 12 13 25 24 7 14 10 17 22 21 Indonesia 59 67 9 7 31 18 25 39 24 31 32 26 Iran, Isiamic Rep. 62 52 11 14 29 20 22 35 24 21 27 34 Ireland 58 49 16 14 21 23 57 88 52 74 26 37 Israel 56 59 30 29 25 19 35 40 45 47 14 12 Italy 58 60 20 18 22 20 20 28 20 27 22 22 Jamaica 62 68 14 16 28 27 52 44 56 55 24 16 Japan 53 56 13 16 33 26 10 10 9 8 34 2823 Jordan 74 81 25 25 32 20 62 42 93 69 1 -6 Kazakhstan 52 63 18 11 32 14 74 59 75 47 30 25 Kenya 67 78 19 18 20 13 26 26 31 36 14 4 Korea. Dem. Rep. ... .... ...- .. Korea, Rep. 53 58 10 10 38 29 29 45 30 42 37 31 a. Kuwait 57 41 39 22 18 11 45 57 58 31 4 37 Kyrgyz Republic 71 77 25 19 24 16 29 43 50 55 4 4 ( Lao PDR .. 82 5 .. 24 .. 36 .. 48 13 13 Latvia 53 63 9 19 40 27 48 46 49 54 39 19 C Lebanon 140 _88 25 19 18 18 18 13 100 38 -64 -7 E Lesotho 139 101 14 18 53 40 17 28 122 88 -53 -20 a. Lithuania 57 64 19 21 33 21 52 45 61 52 24 14 Macedonia, FYR 72 82 19 18 19 17 26 45 36 62 9 0 Madagascar 86 87 8 7 17 16 17 25 27 35 6 6 Malawi 72 82 15 17 23 13 24 26 33 38 13 1 Malaysia 52 43 14 11 32 26 75 125 72 104 34 47 Mai 80 79 14 13 23 23 17 25 34 40 6 7 Mauritani'a 69 68 26 17 20 30 46 41 61 57 5 15 Mauritius 65 66 12 12 31 26 65 64 72 67 24 22 Mesico 70 68 8 11 23 23 19 31 20 33 22 21 Moldova' 58 89 15 16 25 22 49 50 51 77 23 -5 Mongolia 58 _66 .........32__ 20 38 30 24 65 53 82 9 14 Morocco 65 63 15 19 25 24 26 31 32 37 19 18 Mozambique 101 79 12 12 16 34 8 15 36 39 -12 10 Myanmar 89 87 A 13 _13 3 0 5 1 11 13 Namibia 46 54 28 29 35 24 47 49 56 56 26 17 Nepal 83 75 9 9 8 24 11 24 2 1 32 8 16 Netherlands 49 50 23 23 24 22 59 61 55 56 28 27 New Zealand 63 64 17 16 19 21 28 32 27 33 20 20 Nicaragua 59 88 43 19 19 34 25 40 46 81 -2 -7 Niger 84 84...15 .....13 8 11 15 15 22 23 1 3 Nigeria 56 45 15 21 15 23 43 52 29 41 29 34 Norway 49 43 21 19 23 22 41 47 34 30 30 38 Oman 27 .. 38 . 13 .. 53 . 31 .. 35 Pakistan 74 77 15 11 19 16 16 16 23 19 11 12 Panama 60 61 18 15 17 30 38 33 34 39 21 24 Papua New Guinea 59 66 25 13 24 18 41 45 49 42 16 21 Paraguay 77 83 6 10 23 22 33 20_ 39 35 17 7 Peru 74 71 8 11 16 20 16 16 14 18 18 18 Philippines 72 63 10 13 24 18 28 56 33 50 18 24 Poland 48 64 19 16 26 27 29 27 22 34 33 20 Portugal 62 63 16 20 28 28 33 31 40 43 21 16 Puerto RICO . .. .. .. .. .. Romania 66 74 13 13 30 19 17 34 26 40 21 14 Russian Federation 49 46 21 16 30 17 18 46 18 25 30 38 Al. 4.9 Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption final formation and services and services expenditure consumption expenditure % of GDP % of GDP % of GDP % of GDP %ofGDP % of GOP 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 84 88 10 12 15 15 6 8 14 24 6 -1 Saudi Arabia 40 33 31 27 20 16 46 50 36 26 30 40 Senegal 76 79 15 10 14 20 25 31 30 40 9 11 Sierra Leone 82 91 10 17 9 8 24 17 25 33 8 -8 Singapore 46 40 10 10 37 31 202 180 195 161 44 50 Slovak Republic 54 53 22 19 33 30 27 74 36 76 24 28 Slovenia 55 55 19 21 17 28 84 59 74 63 26 24 Somalia 112 ,, ,, 16 .. 10 .. 38 .. -12 South Africa 63 64 20 18 12 15 24 29 19 26 18 18 238 Spain 60 59 17 17 27 26 16 30 20 32 23 24 SriLanka 76 72 10 10 23 28 29 40 38 51 14 17 Sudan .. 85 . . .. 14 .. 17 .. 16 .. 15 Swaziland 62 75 18 20 20 20 76 66 76 81 21 4 Sweden 49 50 28 26 23 18 30 47 29 42 24 23 Switzerland 57 61 14 14 28 20 36 42 36 37 29 25 E Syrian Arab Republic 69 62 14 13 17 21 28 38 28 35 17 24 o Tajikistan 74 76 9 8 25 20 28 81 35 85 17 16 rD Tanzania 81 84 18 7 26 18 13 15 37 23 1 9 a) Thailand 57 60 9 9 41 23 34 67 42 59 34 31 o Togo 71 83 14 11 27 21 33 36 45 50 15 6 Trinidad and Tobago 59 56 12 12 13 19 45 65 29 52 29 32 o Tunisia 58 60 16 16 32 27 44 44 51 48 25 24 N Turkey 69 69 11 14 24 24 13 24 18 31 20 17 Turkmenistan 49 34 23 16 40 40 .. 63 .. 53 28 49 Uganda 92 87 8 11 13 18 7 10 19 26 1 3 Ukraine 57 58 17 19 27 19 28 61 29 57 26 23 United Arab Emirates 39 .. 16 .. 20 .. 65 .. 40 .. 45 United Kingdom 63 65 20 19 20 18 24 27 27 29 18 16 United States 67 68 17 14 18 21 10 11 11 13 16 18 Uruguay 70 75 12 13 12 14 24 19 18 21 18 12 Uzbekistan 61 64 25 20 32 11 29 44 48 39 13 17 Venezuela. RB 62 63 8 7 10 18 39 29 20 17 29 30 Vietnam 86 69 8 6 13 27 26 .. 33 .. 6 25 West Bank and Gaza .. 92 .. 32 .. 33 .. 14 .. 71 .. -24 Yemen, Rep. 74 58 17 14 15 19 14 50 20 41 9 28 Yugoslavia, Fed. Rep. .. 79 .. 25 .. 14 .. 32 .. 50 .. -4 Zambia 64 86 19 11 17 18 36 31 37 46 17 3 Zimbabwe 63 63 19 24 17 13 23 30 23 31 17 12 Low Income 66 67 12 12 24 20 17 28 20 28 21 20 Middle Income 59 59 14 15 26 24 21 32 20 29 27 26 Lower middle income 56 54 13 14 31 26 22 36 22 30 30 32 Upper middle income 61 62 15 15 23 22 21 29 19 28 24 23 Low & middle income 60 60 14 14 26 23 21 31 20 29 26 26 East Asia & Pacific 54 54 11 11 35 30 26 42 26 37 35 35 Europe & Central Asia 55 58 18 16 28 21 23 44 24 39 26 26 Latin America & Carib. 65 66 13 15 19 20 14 17 12 18 21 19 Middle East & N. Africa 57 51 20 18 24 20 33 38 35 28 23 30 South Asia 69 68 12 12 24 23 9 15 13 18 20 20 Sub-Saharan Africa 66 66 17 17 15 17 27 32 26 32 16 17 High income 59 61 18 17 23 22 20 22 20 22 24 22 Europe EMU 56 57 20 20 23 22 28 34 28 33 24 23 a. Data on general government hnal consumption expenditure are not availabie separately: they are Included in household final consumption expenditure. b. Excludes data for Transnistria. c. Data cover mainland Tanzania only. 4.9 About the data Definitions Gross domestic product (GDP) from the the SNA. Data on capital formation may be * Household final consumption expenditure is expenditure side is made up of household final estimated from direct surveys of enterprises and the market value of all goods and services, consumption expenditure, general government administrative records or based on the including durable products (such as cars, wash- final consumption expenditure, gross capital commodity flow method using data from ing machines, and home computers), pur- formation (private and public investment in fixed production, trade, and construction activities. chased by households. It excludes purchases assets and changes in inventories), and net The quality of data on fixed capital formation by of dwellings but includes imputed rent for exports (exports minus imports) of goods and government depends on the quality of owner-occupied dwellings. It also includes pay- services. Such expenditures are recorded in government accounting systems (which tend to ments and fees to governments to obtain per purchaser prices and include net taxes on be weak in developing countries). Measures of mits and licenses. Here, household consump- products. fixed capital formation by households and tion expenditure includes the expenditures of Because policymakers have tended to focus corporations-particularly capital outlays by nonprofit institutions serving households, even on fostering the growth of output, and because small, unincorporated enterprises-are usually when reported separately by the country. In data on production are easier to collect than very unreliable. practice, household consumption expenditure data on spending, many countries generate their Estimates of changes in inventories are rarely may include any statistical discrepancy in the primary estimate of GDP using the production complete but usually include the most important use of resources relative to the supply of re- 239 approach. Moreover, many countries do not activities or commodities. In some countries sources. * General governmentfinal consump- estimate all the separate components of national these estimates are derived as a composite tion expenditure includes all government cur- N expenditures but instead derive some of the residual along with household final consumption rent expenditures for purchases of goods and main aggregates indirectly using GDP (based on expenditure. According to national accounts services (including compensation of employ- the production approach) as the control total. conventions, adjustments should be made for ees)- It also includes most expenditures on na- Household final consumption expenditure appreciation of the value of inventory holdings tional defense and security, but excludes gov- C ernment military expenditures that are part of CD (private consumption in the terminology of the due to price changes, but this is not always done. 1968 System of National Accounts, or SNA) is In highly inflationary economies this element can government capital formation. * Gross capital ° formation consists of outlays on additions to 3D often estimated as a residual, by subtracting be substantial. on the fixed assets of the economy plus net - from GDP all other known expenditures. The Data on exports and imports are compiled h x e f t changes in the level of inventories. Fixed as- 3. resulting aggregate may incorporate fairly large from customs reports and balance of payments sets include land improvements (fences, discrepancies. When household consumption is data. Although the data on exports and imports dith d and s t, machiner, calculated separately, the household surveys on from the payments side provide reasonably and equipment purchases: and the construc- which many of the estimates are based tend to reliable records of cross-border transactions. tion of roads, railways, and the like, including be one-year studies with limited coverage. Thus they may not adhere strictly to the appropriate schools, offices, hospitals, private residential the estimates quickly become outdated and definitions of valuation and timing used in the dwellings, and commercial and industrial build- must be supplemented by price- and quantity- balance of payments or correspond with the ings. Inventories are stocks of goods held by based statistical estimating procedures. change-of-ownership criterion. This issue has firms to meet temporary or unexpected fluc- Complicating the issue, in many developing assumed greater significaince with the increasing tuations in production or sales, and "work in countries the distinction between cash outlays globalization of international business. Neither progress." According to the 1993 SNA, net for personal business and those for household customs nor balance of payments data usually acquisitions of valuables are also considered use may be blurred. The World Development capture the illegal transactions that occur in capital formation. * Exports and Imports of Indicators includes in household consumption many countries. Goods carried by travelers goods and services represent the value of all the expenditures of nonprofit institutions serving across borders in legal but unreported shuttle goods and other market services provided to. households. trade may further distort trade statistics. or received from, the rest of the world. They General government final consumption Domestic savings, a concept used by the include the value of merchandise, freight, in- expenditure (general government consumption World Bank, represent the difference between surance, transport, travel, royalties, license in the 1968 SNA) includes expenditures on GDP and total consumption. Domestic savings fees, and other services, such as communica- goods and services for individual consumption also satisfy the fundamental identity: exports tion, construction, financial, information, busi- as well as those on services for collective minus imports equal domestic savings minus ness, personal, and government services. They consumption. Defense expenditures, including capital formation. Domestic savings differ from exclude labor and property income (factor ser- those on capital outlays-with certain savings as defined in the national accounts; this vices in the 1968 SNA) as well as transfer pay- exceptions-are treated as current spending. SNA concept represents the difference between ments. * Gross domestic savings are calcu- Gross capital formation (gross domestic disposable income and consumption. lated as GDP less total consumption. investment in the 1968 SNA) consists of outlays For further discussion of the problems in ____________ on additions to the economy's fixed assets plus building and maintaining national accounts see i net changes in the level of inventories. It is Srinivasan (1994), Heston (1994), and Ruggles Data sources generally obtained from reports by industry of (1994). For a classic analysis of the reliability The national accounts indicators for most acquisition and distinguishes only the broad of foreign trade and national income statistics developing countries are collected from national categories of capital formation. The 1993 see Morgenstern (1963i. statistical organizations and central banks by System of National Accounts recognizes a third visiting and resident World Bank missions. The category of capital formation: net acquisition of data for high-income economies come from valuables. Included in gross capital formation OECD data files (see the OECD's National under the 1993 SNA guidelines are capital Accounts, 1988-1999, volumes 1 and 2). The outlays on defense establishments that may be United Nations Statistics Division publishes used by the general public, such as schools, detailed national accounts for United Nations airfields, and hospitals. These expenses were member countries in National Accounts treated as consumption in the earlier version of Statistics: Main Aggregates and Detailed rables and updates in the Mont hly Bulletin of Statistics. I~F 4.10 Growth of consumption and investment Household final consumption Household final General G ross expenditure consumption government final capital expenditure consumption formation per capita expenditu re average annual average annLia average annual average annulI $ millions % growtn % growth % growthi % growth 1990 2000 1980-90 1.990-2000 1990-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 Afghanistan . . . .. .. Albania 1.271 3,453 .. 4.6 ..3.9 ..-1.1 -0.3 22.0 Algeria 35.265 22,219 1.4 0.8 -1.6 -1.1 0.7 3.6 -1.8 -0.5 Angola 3,674 1,461 -0.1 -3.8 ... 6.7 -2.0 -5.1 10.8 Argentina 109.038 202,101 -. 2.8 1.5 ..1.8 -5.2 7.3 Armenia 2,005 1,821 -. -0.5 .. -1.1 -.-1.7 ..-2.3 Australia 181,421 240,855 3.0 3.7 1.5 2.5 3.7 2.9 3.2 6.6 Austria 89.789 118.889 2.4 2.3 2.3 1.8 1.4 2.1 2.3 2.1 Azerbaijan .. 3.130 - 6.4 ..5.4 ..5.4 -. 5.6 240 Bangladesh 25.952 36.579 4.5 3.6 1.8 1.8 5.0 4.7 1.4 9.2 Belarus 16,667 17,075 .. -0.5 .. -0.3 ..-1.8 ..-7.8 so 2 Beigiumr 109.445 121,992 1.9 1.6 1.8 1.3 1.1 1.5 3.4 2.6 Benin 1.602 1,774 1.9 4.3 -1.2 1.4 0.5 4.0 -5.3 5.6 mO Boiivia 3,741 6,100 1.2 3.6 -0.9 1.2 -3.8 3.5 1.0 8.5 Bosnia and Herzegovina - . . .. .. E Botswana 1.473 2.940 5.9 6.2 2.4 3.6 13.6 5.2 13.8 -0.7 o Brazil 275.753 372,502 1.2 5.7 -0.7 4.2 7.3 -1.7 3.3 3.4 > Bulgaria 12,401 8.658 3.1 -2.9 3.2 -2.3 5.1 -9.4 2.3 -4.1 0 Burkina Faso 2.141 1,670 2.6 4.4 0.1 2.0 6.2 -0.7 8.6 7.2 Burundi 1.070 684 3.4 -1.7 0.5 -3.8 3.2 -2.1 6.9 -0.4 Cambodia 1,016 2,705 -. 1.5 ... .-0.8 ..13.4 O Cameroon 7,432 6,169 3.5 2.9 0.6 0.4 6.8 0.7 -2.6 0.7 C1 Canada 323,850 366.938 3.3 2.5 2.1 1.5 2.5 -0. 1 5.2 4.7 Central African Republic 1,274 781 1.5 ... , -1.7 .. 10.0 Chad 1.482 1.277 5.3 0.5 2.8 -2.5 14.5 -0.4 ..1.3 Chile 18,759 44.671 2.0 7.4 0.3 5.8 0.4 3.9 6.4 8.9 China 174.249 521.114 8.8 8.5 7.2 7.4 9.8 9.4 10.8 11.6 Hong Kong. China 42,422 94,492 6.7 3.7 5.3 1.7 5.0 3.9 4.0 5.4 Colombia 26,357 54,742 2.6 2.6 0.5 0.7 4.2 9.5 1.4 2.1 Congo, Dam. Rep. 7.398 .. 3.4 -6.6 0.1 -9. 7 0.0 -16.1 -5.1 -2.6 Congo, Rep. 1,746 906 3.3 0.5 0.4 -2.5 2.5 -10.8 -12.6 0.6 Costa Rica 3,502 10.677 3.6 5.0 0.6 2.7 1.1 1.9 4.6 4.7 C6te dIlvoire 7.766 6.692 1.5 2.3 -2.1 -0.8 -0. 1 1.4 -10.4 11.8 Croatia 13.527 10.877 -, 2.6 3.8 --1.1 -.8.4 Cuba --. . 2.6 -...1.9 -.16.9 Cz ech Republic 17,195 27,631 .. 2.8 ..2.8 -.-1.8 ..5.0 Denmark 65,430 77.619 1.4 2.4 1.4 2.0 0.9 2.3 4.7 5.3 Dominican Republic 5,633 15,268 3.9 5.3 1.7 3.5 -3.2 15.1 4.5 5.6 Ecuador 7,323 8.446 1.9 1.3 -0.7 -0.8 -1.4 -1.8 -3.8 -0.5 Egypt, Arab Rap. 30,933 72.027 4.6 4.4 2.0 2.4 3.1 3.0 0.0 6.3 El Salvador 4,273 11,623 0.8 5.1 -0.2 2.9 0.1 3.0 2.2 7.1 Eritree 430 459 ..-....,--- Estonia 4,074 2.685 .. 0.4 ..1.9 .4.6 -.-1.4 Ethiopia 5.081 4.974 0.2 3.0 -2.8 0.7 4.5 8.8 2.1 9.7 Finland 68.939 59,752 3.9 2.0 3.4 1.6 3.2 0.9 3.4 1.5 France 672,960 708.622 2.2 1.3 1.7 0.9 2.6 1.8 3.3 1.2 Gabon 2,961 3.040 1.5 1.2 -1.5 -1.5 -0.6 5.4 -5.7 3.8 Gambia. The 240 350 -2.4 4.3 -5.9 0.9 1.7 -2.8 0.0 3.3 Georgia 8,228 2.485 - .. -. .- Germany 941,915 1,087,707 2.2 1.6 2.1 1.3 1.5 1.2 2.0 1.7 Ghana 5.016 4.224 2.8 4.1 -0.8 1,7 2.4 5.4 3.3 2.3_ Greece 60,164 88.745 2.0 2.1 1.5 1.7 1.1 1.3 -0.7 3.3 Guatemala 6,398 16,041 1.1 4.2 -1.4 1.5 2.6 4.7 -1.8 6.0 Guinea 2,068 2.330 -. 3.6 ..1.0 .4.7 --2.8 Guinea-Bissau 212 205 0.8 2.7 -1.5 0.4 7.i.9 12.9 -10.6 Haiti 2.785 3.860 0.9 ... - -4.4 ..-0.6 -1.3 Honduras 2,026 3.930 2.7 2.7 -0.5 -0. 1 3.3 1.7 2.9 7.5 4.10 m Household final consumption Household final General Gross expenditure consumption governmnent final capital expenditure consumption formation per capita expenditure average annual average annual average annual average arnual $ millions % growth % growth % growth % growth 1990 2000 1980-90 1990-2000 1.980-90 1.990-2000 1.980-90 1990-2000 1980-90 1990-2000 Hungary 20,290 29,094 1.3 -0.3 1.7 0.0 1.9 0.9 -0.9 9.5 India 208,896 298,779 5.8 4.8 3.6 3.0 4.2 6.9 6.6 7.9 Indonesia 67,388 103,066 5.6 6.5 3.7 4.7 4.6 0.1 7.2 -0.6 Iran, Islamic Rep. 74,476 54,312 2.8 3.1 -0.6 1.5 -5.0 6.4 -2.5 2.8 Ireland 27,957 45,806 2.2 5.1 1.9 4.3 -0.3 3.6 -0.4 8.9 Israel 32,112 65,189 5.4 6.3 3.6 3.4 0.5 2.9 2.2 4.9 Italy 634,194 649,183 2.9 1.5 2.8 1.3 2.9 0.1 2.1 1.3 Jamaica 2,637 5,029 4.5 -1. 1 3.3 -1.9 6.2 2.7 -0. 1 4.4 Japan 1,617.071 2,533,095 3.6 1. 7 3.0 1.4 3.4 2.9 5.5 -0. 1 241 Jordan 2,978 6,728 1.9 5.0 -1.9 1.0 1.9 5.1 .1.9 -0.6 Kazakhstan 14,148 11,677 .. -7.7 .. 6.7 .. -7.3 .. 16.4 N 0 Key-a ---5,309 8,186 4.6 3.0 1.1 0.4 2.6 8.4 0.4 3.9 P korea, Dem. Rep.......... .. . Korea. Rep. 132,113 261,913 7.9 4.9 6.7 3.9 5.2 3.0 12.0 1.4 C Kuwait 10,459 15.384 -1.4 ... . 2.2 ..-4.5 Kyrgyz Republic 2,103 1,017 .. -6.0 .. -7.0 .. -8.9 ..-5.0 C -.--------- - - ----- - -* Lao PDR 882 ... .... Latvia 6,578 4,471 2.9 -48 2.3 -3.6 5.0 8.0 3.4 -1.1 C Lebanon 3,961 14.480 .. 3.2 ..1.4 ..4.3 . 8lET Lesotho 855 909 3.6 0.7 1.4 -1.2 3.2 6.3 5.0 1.5 m ------ .-----0)0 Liberia ..1.2 ..- .1.3 .-16.7 E. Lithuania 7,527 7,280 .-- 2.4 -2.5 ..3.8 -9. 9 Macedonia, FYR 3,021 2,945 2.5 --1.8 1.2 ..0.7 Madagascar 2,649 3,349 -0.6 2.4 -3.3 -0.5 0.5 0.4 4.9 2.2 Maiawi 1,345 1,392 1.5 5.4 -1,7 3.4 6.3 -3.3 -2.8 -8.5 M~alays ia -22.806 38,211 3.3 2.2 0.4 3.7 2.7 8.7 3.1 5.2 Mali 1,933 1,826_ 1.0 2.8 -1.5 0.3 7.9 5.2 3.6 -0.7 Mauritania 705 633 1.4 3.7 -1.1 0.8 -3.8 -0.5 6.9 9.9 Mauritius 1,707 2,889 6.7 4.8 5.8 3.6 3.3 4.3 9.0 3.5 Mexico 182,791 388,054 1.1 2.4 -1.0 0.7 2.4 1.8 -3.3 4.6 Moldova 2.328 1.147 6.9 . 7.1 . -5.4 -. -15.5 Mongolia .. 643...... Miorocco- 16,833 20.883 4.3 3.0 2.0-- 1.2 2.1 3.3 1.2 :2.0 Mozambique 2,481 2,964 -1.4 4.3 -2.9 1.9 -2.6 1.4 3.8 110.8 M-yan-mar_ 0.6 3.9 . --4.1 15.4 N~amiAb ia 1.280 1,995 1.3 4.4 -2.0 2.0 3.7 3.1 -2.9 :3.9 Nep al -3.0O-28 4,12 4.5' 4.0 2.2 1.6- 7.2- 5.7 6.0 7.0 Netherlands 144,279 197,321 1.6 2.6 1.1 2.0 2.0 1.9 3.1 .2.6 New Zealand 27,300 -- 35,260 2.0 3.0 1.1 1.8 1.5 2.0 2.7 7.1 Nicaragua 592 212 36 5 8 -6.2 2.9 3.4 -1.8 -4.8 11.8 Ni-ger 2,079 1,540 0.0 2.9 -3.1 -0.6 4.4 0.7 -7.1 4.2 Nigeria 15,816 18,669 -2.6 -3.7 -5.5 -6.4 -3.5 5.6 -8.5 8.3 Norway 57,047 69,082 2.2 3.3 1.9 2.7 2.3 2.4 0.7 5.2 Oman 2,810 - .. .. . 25.5 Pakistan 29,512 47,401 4.3 4.9 1.6 2.3 10.3 0.7 5.8 1.8 Panama 3,022 6,018 2.1 3.8 0.0 2.0 1.2 2.1 -8.9 11.2 P-apua New Gui-n-ea__ 1,902 2,358 0.4 4.9 -2.1 2.2 -0. 1 2.2 -0.9 1.3 Paraguay 4,063 6,226 2.4 3.6 -0.7 1.0 1.5 6.4 -0.8 0.2 Peru 19,376 37,742 0.7 4.0 -1.5 2.2 -0.9 5.2 -3.8 7.4 Pilippines 31,566 49,007 2.6_. 3.7 0.2 1.5 0.6 3.4 -2.1 3.1 Poland 28,281 101,013 .. 6.2 .. 6.1 2.3 .. 10.6 Portugal 43,985 66,484 2.5 3.2 2.4 3.0 5 0 2.8 3.0 5.2 puet io19,827 -. 3.5 --. . 5.1 -.6.9 Romania 25.232 27,120 -. 0.7 ..1.0 ..1.1 --5.2 Russian- Federation 282,978 114,596 .. -0.7 .. -0.5 .. -2.3 .. -18.3 VZ :) 4.10 Household final consumption Household final General Gross expenditure consumption government final capftal expenditure consumption formation per capita expenditure average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1990 2000 1980-90 1990-2000 1980-90 1990-2000 1980-90 1.990-2000 1.980-90 1990-2000 Rwanda 2.162 1,586 1.4 1.6 -1.6 -0.3 5.2 -2.4 4.3 2.6 Saudi Arabia 41.621 56,919 . .. .. Senegal 4,353 3,444 2.1 3.6 -0.8 0.9 3.3 -0. 1 5.2 5.0 Sierra Leone 734 581 0.1 -2.4 -2.0 -4.7 0.0 -1.8 -1.1 -4.9 Singapore 17.019 36.871 5.8 5.7 3.9 2.6 6.6 8.6 3.1 7.6 Slovak Republic 8.350 10,207 3.8 0.5 3.5 0.3 4.8 1.3 0.3 7.8 Slovenia 6.91 7 9,956 .. 3.8 ..3.9 ..3.2 ..10.7 Somalia ... 1.3 ... . 7.0 -2.6 South Africa 70,283 80.091 2.4 2.6 -0.2 0.6 3.5 0.5 -5.3 2.7 242 Spain 308.803 331.606 2.5 2.1 2.2 2.0 5.4 2.3 5.7 2.6 Sri Lanka 6,143 11.806 4.0 3.8 2.5 2.5 7.3 8.8 0.6 6.4 Sudan ... 0.0 ... . -0.5 ..-1.8 9.5 m Swaziland 521 1,111 4.6 3.9 1.4 0.8 2.9 5.8 1.6 2.6 C) C Sweden 116.247 114,681 2.2 1.1 1.9 0.8 1.6 0.3 4.7 1.7 Switzerland 130.900 158.369 1.6 0.9 1.1 0.2 3.1 0.7 3.9 0.7 a) Syrian Arab Republic 8.458 11.626 3.6 2.2 0.2 -0.8 -3.6 2.0 -5.3 4.3 o Tajikistan 3,202 752 4.0 -7.3 0.9 -8.7 4.1 -14.2 -6.8 -14.6 > Tanzania' 3.526 7.604 .. 2.0 .. -0.8 ..-8.3 ..-1.4 a) C) Thailand 48.270 71.625 5.9 3.6 4.1 2.8 4.2 5.1 9.5 -4.1 -C) o Togo 1.158 1.012 4.7 3.6 1.3 0.8 -1.2 -1.7 2.7 -0.2 Trinidad and Tobago 2,975 4,219 -1.3 0.3 -2.5 -0.3 -1.7 0.7 -10.1 15.5 o Tunisia 7.152 11.773 2.9 4.3 0.3 2.6 3.8 4.1 -1.8 3.6 0 04 Turkey 103,378 137.646 .. 3.7 ..2.2 ..4.6 ..4.0 Turkmenistan 4.065 1.513 ... .. .. .1.9 Uganda 4,002 5.390 2.6 7.6 0.0 4.3 2.0 6.4 8.0 9.3 Ukraine 52.131 18,518 .. -7.1 .. -6.6 ..-4.2 .. -18.5 United Arab Emirates 12.726 .. 4.6 ... -3.9 ..-8. 7 United Kingdom 617.733 925.496 4.0 2.8 3.8 2.5 0.8 1.1 6.4 4.2 United States 3.831.500 6.268.600 3.8 3.4 2.9 2.1 3.3 0.4 4.0 7.5 Uruguay 6.525 14,694 0.7 5.0 0.1 4.3 1.8 2.1 -6.6 6.3 Uzbekistan 13.321 4,884 . .. .. Venezuela. RB 30,171 75.986 1.3 0.4 -1.2 -1.7 2.0 -0.3 -5.3 4.4 Vietnam 5,597 20.846 .. 9.1 ..7.2 .. 10.9 .20.2 West Bank and Gaza .. 4.019 .. 2.5 .. -1. 7 .. 12.6 ..3.8 Yemen. Rep. 3.561 4,930 .. 3.0 .. -0.4 ..2.3 ..9.6 Yugoslavia, Fed. Rep. .. 6,688 . .. .. Zambia 2,078 2,761 1.8 -2.8 -1.3 -5.3 -3.4 -6.6 -4.3 5.2 Zimbabwe 5.543 4.504 3.7 0.0 0.0 -2.0 4.7 -2.2 3.6 -3.4 Low Income 574,483 702,729 4.2 3.6 1.9 1.6 4.0 2.8 4.4 1.6 Middle Income 2,015.476 3,228,520 3.3 3.9 1.6 2.6 5.0 2.0 2.6 2.0 Lower middle income 897,553 1.284.732 4.6 3.6 2.9 2.4 3.7 4.3 4.7 0.0 Upper middle income 1,130,065 1,944,018 2.5 4.1 0.6 2.6 5.5 0.7 1.4 4.2 Low & middle Income 2.584.252 3,929.051 3.5 3.8 1.5 2.2 4.8 2.1 2.8 2.0 East Asia & Pacific 496.327 1.095,144 6.9 6.3 5.2 5.0 5.9 6.2 9.3 5.9 Europe & Central Asia 672,345 562,238 .. 0.7 ..0.5 .-0.6 ..-8.1 Latin America & CariS. 737.633 1,321,356 1.3 4.0 -0.6 2.3 5.6 0.1 -0.3 4.6 Middle East & N. Africa 220.686 341.201 ......... South Asia 277,595 404.700 5.4 4.7 3.1 2.7 5.2 6.0 5.8 7.3 Sub-Saharan Africa 192.381 206,155 1.6 2.1 -1.3 -0.5 2.7 1.5 -3.9 3.4 High Income 10,324,580 15,115.5 77 3.3 2.5 2.7 1.7 2.7 1.4 4.1 3.0 Europe Emu 3,116,888 3.450.141 2.3 1.7 2.1 1.4 2.3 1.4 2.7 1.9 a. Oats cover mainland Tanzania only. WM STI 4.10 About the data Definitions Measures of growth in consumption and capital Figure 4.10 * Household final consumption expenditure is formation are subject to two kinds of inaccu- the market value of all goods and services, racy. The first stems from the difficulty of mea- Household consumption is on the rise including durable products (such as cars, wash- suring expenditures at current price levels, as Per capita household consumption (19959 thousands) ing machines, and home computers), pur- described in About the data for table 4.9. The 3.0 chased by households. It excludes purchases second arises in deflating current price data to 2.5 of dwellings but includes imputed rent. for measure volume growth, where results depend 2.0 owner-occupied dwellings. It also includes pay- on the relevance and reliability of the price in- ments and fees to governments to obtain per- dexes and weights used. Measuring price 1.5 _ mits and licenses. The World Development In- changes is more difficult for investment goods 1.0 dicators includes in household consumption than for consumption goods because of the one- expenditure the expenditures of nonprofit in- time nature of many investments and because o.s stitutions serving households, even when re- the rate of technological progress in capital 0 ported separately by the country. In practice, goods makes capturing change in quality diffi- 1980 1985 1990 1995 2000 household consumption expenditure may in- cult. (An example is computers-prices have _ EastAsia& Pacific - EuropeS clude any statistical discrepancy in the use of 243 fallen as quality has improved.) Several coun- - Latin America & Caribbean Central Asia resources relative to the supply of resources. tries estimate capital formation from the supply Sub-Saharan Afica --South Asia * General government final consumption ex- $ side, identifying capital goods entering an penditure includes all government current ex- economy directly from detailed production and Source: World Banh data files. penditures for purchases of goods and services o international trade statistics. This means that Per capita consumption In Latin America has risen- (including compensation of employees). It also E the price indexes used in deflating production and poverty fallen-faster than any other developing includes most expenditures on national de- s and international trade, reflecting delivered or region, fense and security, but excludes government 0 offered prices, will determine the deflator for military expenditures that have potential wider B capital formation expenditures on the demand public use and are part of government capital CD side. formation. * Gross capital formation consists The data in the table on household final of outlays on additions to the fixed assels of consumption expenditure (private consumption the economy plus net changes in the level of t in the 1968 System of National Accounts), in inventories. Fixed assets include land imptove- fy current U.S. dollars, are converted from national ments (fences, ditches, drains, and so on); currencies using official exchange rates or an plant, machinery, and equipment purchases; alternative conversion factor as noted in Primary and the construction of roads, railways, and data documentation. (For a discussion of the like, including schools, offices, hospitals, alternative conversion factors, see Statistical private residential dwellings, and commercial methods.) Growth rates of household final and industrial buildings. Inventories are stocks consumption expenditure, household final of goods held by firms to meet temporary or consumption expenditure per capita, general unexpected fluctuations in production or sales, government final consumption expenditure, and and "work in progress." According to the 1993 gross capital formation are estimated using SNA, net acquisitions of valuables are also constant price data. (Consumption and capital considered capital formation. formation as shares of GDP are shown in table To obtain government consumption in Data sources constant prices, countries may deflate current The national accounts indicators for most values by applying a wage (price) index or developingcountries are collected from national extrapolate from the change in government statistical organizations and central banks by employment. Neither technique captures |visiting and resident World Bank missions. Data improvements in productivity or changes in the for high-income economies come from quality of government services. Deflators for Organisation for Economic Co-operation and i household consumption are usually calculated Development (OECD) data files (see the OECD's on the basis of the consumer price index. Many NationalAccounts, 1988-1999, volumes 1 and countries estimate household consumption as 12). The United Nations Statistics Division a residual that includes statistical discrepancies publishes detailed national accounts for United accumulated from other domestic sources Nations member countries in NationalAccounts including stock changes; thus these estimates f Statistics: Main Aggregates and Detailed lack detailed breakdowns of expenditures. i Tables and publishes updates in the Monthly Bulletin of Statistics. ~j~: )4.11 Central government finances Current Total Overall Financing Domestic Debt revenue expenditure budget balance from abroad financing and Interest (including payments grants) Tota Interest debt % of % of current % of GDP % of GDP % of GOP % of GDP % of GOP GDP revenue 1990 1999 1990 1999 1990 1999 1 1990 1999 1990 1999 1999 1999 Afghanistanr. ... . .. Albania .. 19.3 .. 29.8 .. -8.5 .. 2.5 6.0 46.4 40.3 Algeria .. 30.0 .. 30.4 .. -0.4 . . ... 62.0 13.3 Argentina 10.4 14.0 10.6 17.0 -0.4 -2.9 0.2 4.0 0.2 -1.1 . 20.6 Armenia - .. .. ... Australia 24.9 23.8 23.3 23.4 2.0 1.4 0.2 -0.5 -2.2 -0.9 15.3 5.3 Austria 34.0 37.3 37.6 40.3 -4.4 .. 0.5 3.9 .. 62.3 8.9 Azerbaijan .. 17.7 .. 22.7 .. -2.6 .. . .. . 2.5 244 Bangladesh . 9.3 .. 12.7 .. -2.8 .. 0.1 .. 2.7 40.1 15.7 Belarus 30.9 28.7 37.3 30.9 -4.8 -2.0 2.7 -0.8 2.4 2.8 20.2 2.2 V) Belgium 42.7 43.8 47.8 45. 7 -5.5 -1.8 -0.3 -0.9 5.8 2.7 114.7 16. 7 Bolivia 13.7 16.7 16.4 23.1 -1.7 -2.3 0.7 1.6 1.0 0.7 56.1 7.8 Bosnia and Herzegovina . . - . . . . . . E) Botswana 51.1 -. 33.8 .. 11.3 .. 0.0 .. -11.4 o Brazil 22.8 24.9 34.9 26.8 -5.8 -7.8 .. . .. . 15.4 > Bulgaria 47.1 34.6 55.1 35.7 .8.3 1.5 -0.8 0.8 9.1 -2.3 52.8 11.3 cc Burkina Faso 11.0 -. 15.0 .. -1.3.. . ... 0 Cambodia . .. .. .. .. o Cameroon 15.4 16.0 21.2 15.9 -5.9 0.1 5.2 0.2 1.2 -0.3 104.6 19.2 0 N Canada 21.6 22.2 26.2 21.4 -4.8 1.0 0.2 0.4 4.6 -1.3 69.0 15.0 Central African Republic . . - . . . . . . Chad 6.7 .. 21.8 .. -4.7 .. 5.0 .. -0.3 Chile 20.6 22.4 20.4 23.9 0.8 -1.5 0.9 0.5 -2.5 0.9 15.0 1.6 China 6.3 7.2 10.1 10.9 -1.9 -2.9 0.8 -0. 1 1.1 3.0 12.7 Hong Kong, China . . . . . . . . . Colombia 12.6 12.6 11.6 19.1 3.9 -7.1 .. 2.2 .. 5.0 29.8 26.8 Congo. Oem. Rep. 10.1 0.1 18.8 0.1 -6.5 0.0 0.0 0.0 6.5 0.0 160.4 4.6 Congo. Rep. 22.5 26.8 35.6 32.8 -14.1 -5.6 .. 5.9 .. -0.3 283.7 43.4 Costa Rica 23.0 20.0 25.6 21.5 -3.1 -1.5 0.3 1.5 2.8 0.0 36.3 18.3 CMe dIlvoire 22.0 20.6 24.5 22.4 -2.9 -0.2 4.0 1.7 0.4 -1.5 112.5 20.2 Croatia 33.0 42.8 37.6 48.3 -4.6 -2.0 0.0 3.2 4.7 -1.3 .. 3.6 Czech Republic .. 33.1 .. 35.5 .. -1.6 .. -0.4 .. 2.0 12.9 2.8 Denmark 37.8 37.4 39.0 36.0 -0.7 0.5 ..,. .. 63.7 11.2 Dominican Republic 12.0 16.3 11.7 17.0 0.6 -0.5 0.0 -0. 1 -0.6 0.6 20.7 3.9 Ecuador 18.2 .. 14.5 .. 3.7.. . ... Egypt, Arab Rep. 23.0 26.3 27.8 30.6 -5.7 -2.0 -0.7 -0.6 6.4 2.6 .. 23.0 El Salvador .. 14.4 .. 16.3 .. -2.2 .. 1.4 .. 0.8 29.2 9.3 Estonia 26.2 30.8 23.7 35.6 0.4 -0.2 0.0 -0.5 -0.4 0.6 4.7 0.8 Ethiopia 17.4 .. 27.2 .. -9.8 .. 2.8 .. 7.0 Finland 30.6 32.0 30.3 33.4 0.2 -0.3 0.7 -1. 1 -0.8 1.4 61.1 14.3 France 39.7 41.4 41.8 46.2 -2.1 -3.5 1.1 .. 1.0 ... 7.4 Gabon 20.6 .. 20.2 .. 3.2 .. 2.7 .. -5.8 Gambia, The 19.4 .. 23.6 .. -0.8.. . ... Georgia .. 11.5 .. 15.0 .. -4.4 .. 2.6 .. 1.9 72.0 22.7 Germany 25.6 31.3 26.3 32.6 -1.4 -0.9 0.5 0.6 1.0 -0. 1 19.9 7.3 Ghana 12.5 .. 13.2 .. 0.2 .. 1.3 .. -1.5 . Greece 27.8 23.5 52.2 30.9 -22.9 -4.4 1.6 2.4 21.3 2.0 113.2 38.4 Guinea 16.0 11.8 22.9 21.2 -3.3 -2.4 4.1 2.3 -0.8 0.2 -. 37.1 Guinea-Bissau.. ............. Haiti .. 8.9 .. 11.5 .. -1.4 .. -0.4 .. 1.8 .. 9.3 Honduras.. .... ..- . 4.11 rj Current Total Overall Financing Domestic Debt revenue expenditure budget balance from abroad financing and Interest (including payments grants) Total Intorest debt %t of % of CUFfrenlt % of GDP % of GDP % of GOP % of GDP % of GOP GDP revenue 1990 1999 1990 1999 ± 990 1999 1990 1999 1990 1999 1999 1999 Hungary 52.9 38.4 52.1 43.4 0.8 -3.7 -0.5 7.1 -0.3 -3.4 60.5 19.3 India 12.6 11.9 16.3 15.9 -7.6 -5.8 0.6 0.0 7.1 5.7 53.4 38.2 Indonesia 18.8 17.9 18.4 20.1 0.4 -1.1 0.7 1.4 -1.1 -0.3 44.8 2 1.6 Iran, Islamic Rep. 18.1 24.7 19.9 25.6 -18 -0.8 0.0 0.0 1.8 0.8 .. 0.6 Iraq .. .. Ireland 33.6 31.9 37.7 33.0 -2.4 0.7 ..13.3 Israel 39.4 41.5 50.7 47.4 -5.3 -2.1 0.8 -0.7 4.6 2.8 106.3 13.3 Italy 38.2 41.3 47.4 41.9 -10.2 -1.6 0.0 .. 9.9 ... 15.5 Jamaica .. 32.3 .. 39.1 .. -6.6 .. 1.0 .4.2 91.1 47.9 Japan ~~~~~ ~~~ ~~~14.0 .. 15.3 .. -1.5 .. 0.0 .. -1. 7. .24 Jordan 26.1 26.7 35.8 31.5 -3.5 -2.5 3.0 -0.1 0.5 2.6 100.7 12.7 - Kazakhstan . 8.6 .. 15.1 - -3.9 .. 2.4 .. 1.4 26.6 12.4 . ----- --------- ----.. . - - - ~~~~~~~~~~~~~- -- -- - - -- - - -- - -- -- -- - - -- --.--- ---- -- Korea, Dem. Rep. . . . . . .. Korea. Rep. 17.5 20.0 16.2 1 7.4 -0.7 -1.3 -0.2 1.5 0.9 -0.3 10.4 2.5 C Kuwait 58.7 33.8 55.3 43.3 -9.5 - . . . --4.0 Kyrgyz Republic .. 15.8 .. 19.7 .. - -2.4 .. 4.3 .. 1.1 132.7 9.6 CD Latvia -. 31.3 35.4 .. -3.8 .. 3.3 .. 0.5 13.1 2.3 ( Lebanon .. 19.5 .. 35.7 .. -16.2 .. 8.1 8.1 135.2 74.4 ------ ------------- ----------- ---- --------- -- -------- -- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 Lesotho 39.4 44.1 51.7 49.7 -1.1 -3.6 8.0 0.7 -69 2.9 67.8 4.9 Lithuania 31.9 25.9 28.9 31.1 1.4 -7.0 .. 6.6 .. 0.4 22.0 5.7 Macedlon~ia, FYR ---------- . . . . Madagascar 11.6 11.4 16.0 17.4 -1.1 -2.7 2.1 1.9 -1.2 0.5 -. 15.6 Malawi 19.8 .. 25.4 .. -1.6.. . ... Malaysia 26.4 23.1 29.3 19.7 -2.0 2.9 -0.7 -0. 1 2.8 -1.2 .. 10.2 Miaumritius- 2-2-.-6 21.2 22.6 23.9 -0.4 -1.5 -0.4 0.3 0.8 1.2 32.3 13.2 Mexico 15.3 13.8 17.9 15.5 .2.5 -1.6 0.3 0.1 2.3 1.4 25.6 18.3 M~oldova- -- ----- -2 3.9 . 2-9-.7-- .. -3.4 . .6. 1.8- 77-.-8 2-9.8 Mongolia 19.6 21.2 23.1 252 -6.4 -1.8 75 11.4 -11 -0.7 95.8 8.8 Moroccco--- 26.4- -29.6 - 28-.-8 3 . -2-.2 2 .5 3.9 ---- --- 1.5 --- - ---1.6 --- 4-.0O 7-2.7 16.5 Mozambique . . . . . . . . . Myanmar 10.5 5.6 16.0 7.0 -5.1 -1.4 0.0 0.0 5.1 1.4 Namibia 29.1 33.8 31.0 36.9 -1.1 -3.2 .. . .. . 7.1 Nepal 8.4 10.2 17.2 16.0 -6.8 -3.9 5.4 2.5 1.4 1.4 64.1 11.8 Netherlands 45.1 44.1 49.6 45.9 -4.3 -1.6 -0.3 1.9 4.6 -1.9 55.6 9.5 New Zealand 42.6 32.0 44.0 32.7 4.0 2.0 .. . . 35.6 7.2 Nicaragua 33.5 30.4 72.0 41.5 -35.6 -4.4 12.7 9.9 22.9 -5.5 .. 9.6 Niger -- .. .. .. -- .. -. -- -. .. -.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-- -- ---- Norway 42.4 41.6 41.3 37.0 0.5 -1.6 -0.6 -0.6 0.0 2.2 19.8 4.1 Oman ~~~~~~~~~-3-8-.-9 246 3. 3. 08 -6-.6-- 39 6-.4 ------4-.7- 0-.2 27.1l--- 7.1A Pakistan 19.1 15.8 22.4 21.3 -5.4 -6.9 2.3 3.8 3.1 3.1 79.1 43.0 Panama 25.6 27.8 23.7 27.7 3.0 0.4 -3.4 2.1 0.4 -2.5 -- 18.1 P-a-p,u,a New GI n-e-a 25.2--18.9- 3-4-.7- 270 -3-.5-- -2-.7- 0.4- -0.-7--- -3.0- --3.3 6-1-.6 2- 2.3 Paraguay 12.3 -. 9.4 . 2.9 .. -0.9 -. -2.1..- Peru 12.5 16.5 20.6 19.6 ~~~~~~~~~~~~~~~~~~~~~--8-. 1 -2.2 5.4---0.2 2.7-- 2.4 42.8-- 1 2.8 Philippines 16.2 15.9 19.6 19.7 -3.5 -3.8 0.4 2.8 3.1 1.0 59.4 22.4 Poland .. 32.5 .. 35.2 .. -0.8 .. 0.1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~O - ----- 0-.-7 43.4 9.4 Portugal 31.6 34.5 37.9 38.8 -4 5 -1.2 ~~~~ ~~~~~ ~ ~~~~ ~~~~ ~~~~ ~~~-1.3----- --2.1--- 5.8--- 3.3 --0.8 84--- Puerto Rico .. .. -. .. ~ ~~~~~~~~~ ~ ~~~~~~~~~~~~~~~~~~ -------.---- ----- - Rmania 34.4 30.6 33.8 35.5 0.9 -1.7 0.0 0.9 -0.9 3.0 -- 15.6 Russian F-e d-e-r-at,i-o-n ---2.1---220-- - 0.5- ----- 2.1 I----- - -1.6- 10-2.1 -16.3 4.11 Current Total Overall Flnancing Domestic Debt revenue ~ expenditure budget balance from abroad financing and Interest (including payments grants) Total Interest debt % of % of current % of GDP % of GDP % of GDP % of GDP % of GDP GDP revenue 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 1999 1999 Rwanda 10.8 .. 18.9 .. -5.3 .. 2.5 .. 2.8 Saudi Arabia . . . .. Senegal . .. .. .. Sierra Leone 4.0 7.1 6.0 20.9 -1.8 -8.5 0.4 1.1 1.4 7.4 247.4 81.8 Singapore 26.9 27.1 21.4 18.5 10.8 7.0 -0.1 0.0 -10.7 -7.0 90.4 2.2 Slovak Republic .. 36.8 .. 37.2 .. -3.3 .. 3.5 .. -0.3 28.8 8.5 Slovenia 39.8 39.7 38.6 40.5 0.3 .0.7 0.1 1.7 -0.4 -0.9 24.5 3.5 South Africa 26.3 28.2 30.1 30.6 -4.1 -2.0 0.0 1.3 4.1 0.7 49.1 19.7 246 Spain 29.1 28.7 32.4 32.8 -3.1 -2.9 0.7 1.7 2.4 1.1 55.4 14.1 Sri Lanka 21.0 17.7 28.4 24.2 -7.8 -6.9 3.6 0.1 4.2 6.8 95.1 31.7 Sudan .. 8.5 -. 9.0 .. -1.0 .. 0.2 .. 0.8 9.2 9.4 10 Swaziland 34.2 31.0 26.7 34.2 0.0 -1.6 -0.3 -1.2 0.2 2.8 26.3 2.7 Sweden 42.6 39.6 39.3 39.5 1.0 0.1 -0.3 -5.6 -0.7 5.4 .. 11.4 Switzerland 20.8 24.0 23.3 27.6 -0.9 0.3 0.0 0.0 0.9 -0.3 26.3 4.0 a) Sra rbRpbi 19 2. 18 2. . 0717-. a) Tajikistan .. 10.2 .. 12.4 -- -0.8 .. 2.4 .. -1.7 -. 4.3 Thailand 18.5 16.0 14.1 25.1 4.6 -10.4 -1.5 1.2 -3.1 9.3 20.8 6.1 3: Trinidad and Tobago . . - . - . ...- - Nl o Tunisia 30.7 28.8 34.6 31.6 -5.4 -2.3 1.8 1.4 3.6 0.9 60.9 11.7 N Turkey 13.7 25.5 17.4 38.1 -3.0 -13.0 0.0 1.2 3.0 11.9 53.5 54.2 Turkmenistan -- . .- .--. .. Uganda -. 11.2 -. 16.6 -- -0.6 - .--. 52.4 - Ukraine -- 23.3 .. 26.0 -- -2.1 -. -0.1 - 2.2 9.4 9.9 United Arab Emirates 1.6 3.5 11.5 11.2 0.4 -0.3 0.0 0.0 -0.4 0.3 -- 0.0 United Kingdom 36.1 36.4 37.5 36.4 0.6 0.0 0.2 -0.4 .0.8 0.3 49.8 7.7 United States 18.9 20.6 22.7 19.3 -3.8 1.3 0.2 0.7 3.6 -2.0 39.3 12.7 Uruguay 23.8 27.7 23.3 32.1 0.3 -3.7 1.4 2.6 -1.7 1.2 .. 7.2 Uzbekistan . .. .- .- .. Venezuela, RB 23.7 17.2 20.7 19.4 0.0 -2.4 1.0 -1.0 -1.0 3.4 -. 15.4 Vietnam -- 18.8 -- 21.2 .. -1.6 -- 1.2 -- 0.4 .. 3.1 West Bank and Gaza -- -. - .. .-- Yemen. Rep. 18.9 24,5 27.8 27.4 -8.8 -3.6 3.2 1.3 5.6 2.2 -- 9.8 Yugoslavia, Fed. Rep. ---- ..-.. ---..- Zambia ..... -. .. -- - Zimbabwe 24.1 29.4 27.3 35.7 -5.3 -5.0 0.9 -0. 1 4.4 5.1 58.1 24.2 M l 11 I ITTP ~ ~ ~ ~ 8 9. a- a Low Income 15.4 15.0 18.3 18.4 -4.8 -3.8 ..- Middle Income 17.4 18.8 21.5 22.1 -2.5 -4.3 0.3 1.2 0.4 0.9 42.8 12.6 Lower middle income 12.6 14.9 15.0 18.5 -1.3 -3.1 .. 0.9 .. 1.2 52.8 12.0 Upper middle income 19.9 22.6 25.0 25.2 -3.1 -4.9 0.0 1.3 0.8 0.7 29.2 14.3 Low & middle Income 17.1 18.1 21.1 21.4 -2.8 -4.3 .. 1.3 -- 0.9 . 12.8 East Asia & Pacific 13.2 10.8 14.4 15.0 -0.8 -3.7 0.2 1.2 2.0 1.2 55.7 9.9 Europe & Central Asia .. 25.9 .. 30.1 -- -3.5 -- 2.1 -- 0.6 43.4 9.5 Latin America & Carib. 18.8 20.0 25.6 21.9 -3.5 -4.8 0.3 1.4 -1.3 0.8 .. 12.8 Middle East & N. Africa - - -. .- 1.8 2.0 3.6 1.9 .. 12.2 South Asia 13.8 12.5 17.6 16.7 -7.3 -5.7 3.0 0.1 3.6 3.1 58.7 31.7 Sub-Saharan Africa 24.0 23.8 27.7 26.9 -3.5 -2.3 - .- .- High Income 23.7 28.0 26.5 29.5 -2.8 -1. 0 0.2 0.0 1.0 0.0 42.7 7.5 Europe EMU 33.1 35.2 36.6 38.1 -3.9 -2.3 0.6 1.0 3.1 0.7 55.6 9.3 a. Excluding grants 4.11 About the data Definitions Tables 4.11-4.13 present an overview of the size picture they provide of central government activi- * Current revenue includes all revenue from and role of central governments relative to na- ties is usually incomplete. A key issue is the fail- taxes and current nontax revenues (other than tional economies. The International Monetary ure to include the quasi-fiscal operations of the grants) such as fines, fees, recoveries, and Fund's (IMF) Manual on Government Finance Sta- central bank. Central bank losses arising from income from property or sales. * Total expen- tistics describes the government as the sector monetary operations and subsidized financing can diture includes nonrepayable current and capi- of the economy responsible for "-implementation result in sizable quasi-fiscal deficits. Such defi- tal expenditures. It does not include govern- of public policy through the provision of primarily cits may also result from the operations of other ment lending or repayments to the governrnent nonmarket services and the transfer of income, financial intermediaries, such as public develop- or government acquisition of equity for public supported mainly by compulsory levies on other ment finance institutions. Also missing from the policy purposes. * Overall budget balance is sectors" (1986, p. 3). The definition of govern- data are governments' contingent liabilities for current and capital revenue and official grants ment generally excludes nonfinancial public en- unfunded pension and national insurance plans. received, less total expenditure and lending terprises and public financial institutions (such Data on government revenues and expendi- minus repayments. * Financing from abroad as the central bank). tures are collected by the IMF through question- (obtained from nonresidents) and domestic fi- Units of government meeting this definition ex- naires distributed to member governments and nancing (obtained from residents) refer to the ist at many levels, from local administrative units by the Organisation for Economic Co-operation means by which a government provides finan- 247 to the highest level of national government. Inad- and Development. Despite the IMF's efforts to cial resources to cover a budget deficit or allo- > equate statistical coverage precludes the presen- systematize and standardize the collection of cates financial resources arising from a bud- 0 tation of subnational data, however, making cross- public finance data, statistics on public finance get surplus. The data include all governrnent country comparisons potentially misleading. are often incomplete, untimely, and not compa- liabilities-otherthanthoseforcurrencyissues o Central govemment can refer to one of two rable across countries. or demand, time, or savings deposits with gov- accounting concepts: consolidated or budgetary. Government finance statistics are reported in ernment-or claims on others held by govern- For most countries, central government finance local currency. The indicators here are shown as ment. and changes in government holdings of CD n data have been consolidated into one account, percentages of GDP. Many countries report gov- cash and deposits. They exclude governrnent 3 but for others only budgetary central government ernment finance data according to fiscal years; guarantees of the debt of others. * Debt is the C accounts are available. Countries reporting bud- see Primary data documentation for the timing of entire stock of direct government, fixed term getary data are noted in Primary data documen- these years. For further discussion of government contractual obligations to others outstanding tation. Because budgetary accounts do not nec- finance statistics, see About the data for tables on a particular date. It includes domestic debt essarily include all central government units, the 4.12 and 4.13. (such as debt held by monetary authorities, 51 Figure 4.11 deposit money banks, nonfinancial public en- terprises, and households) and foreign debt (such as debt to international developmenit in- Some developing countries are spending a large proportion of their current revenue on interest payments stitutions and foreign governments). It is the gross amount of government liabilities not re- Central government nterest payments as % 0f current revenue duced by the amount of government claims 9 Low- and middle-income economies against others. Because debt is a stock rather 80 than a flow, it is measured as of a given (late, 70 usually the last day of the fiscal year. * Inter- 60 - - - est payments include interest payments on so - - - government debt-including longterm bonds, long-term loans, and other debt instruments- 40 | | | * * * * * * * to both domestic and foreign residents. 30 20r 10 Data sources 0 Sierra Lebanon Turkey Jamaica Pakistan Albania Guinea India Srn Lanka Philippinnes The data on central government finances are i Leone from the IMF's Govemment Finance Statistics e Yearbook, 2001 and IMF data files. Each High-income economies country's accounts are reported using 40 the system of common definitions and classifications in the IMF's Manual on 35 Govemment Finance Statistics (1986). See 30 25 - these sources for complete and authoritative j explanations of concepts, definitions, and data 20 sources. 15 10 5 Greece Cyprus Betgiunm Italy Finiand Canada Spain Ireland Israel Sweden Noe.: Data rater r thte most recent year eva-leble in 19982000 to, low- and middle.mcome economies and i 1997-2000 for high-ecome economies. Source. Intorn-t onal Munetary Fund. Government F nance Statistics data files With the exception of Greece, governments of high-income economles spend less than 20 percent of their current revenue on Interest payments. go 4.12 Central government expenditures Goods and Wages Interest Subsidies and Capital services and saiaries' payments other current expenditure transfers % of total % of total % of total % of total % of total ex penditure expenditure expenditure expenditure experditure 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 Afghanistan . .. Albania .1 7 ..9 .26 42 16 Algeria ..35 ..24 13 ..32 19 Angola . .. .. Argentina 30 20 23 15 8 17 57 57 5 6 Armenia - .. .. Australia 27 27 2 3 8 5 56 61 9 5 Austria 25 25 10 10 9 8 57 61 9 5 Azerbaijan ..31 ..11 2 so 5 -. 17 248 Bangiadesti. 27 ..18 11 ..25 ..23 Belarus 3 7 21 2 8 2 2 46 54 16 23 o Belgium 19 19 14 13 21 16 56 60 5 5 Bolivia 63 38 36 23 6 6 16 41 15 16 IEf Bosnia and Herzegovina . ... a )- -- - - - - - - - - - - -- - - - - - - - - -- - - E Botswana 51 ..23 .2 ..25 ..21 0. o Brazil 16 22 9 12 78 14 39 62 2 2 a)- > Bulgaria 35 33 3 8 10 11 52 45 3 11 BurkinapFaso BC0 51 -6 ..11 ..23 o Burundi 34 50 22 30 5 9 10 11 54 23 o Cameroon 51 52 39 32 5 19 13 15 26 14 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~------------ -- 04 Canada 21 18 9 8 20 16 57 65 2 2 Central African Republic . - ... . Chad 41 ..28 ..2 .3 56 56 Chile 28 28 18 20 10 1 51 54 11 16 Hong Kong, China . .. -- --- Colombia 26 19 18 14 10 18 42 41 22 22 Congo, Bern, Rep. 73 78 23 49 7 3 4 18 16 2 Congo, Rep. 56 39 49 21 22 35 20 7 2 19 Costa Rca 57 47 43 36 12 17 20 26 11 10 CMe dIlvoire 69 45 38 271- 19 30 - 9 0 26 Croatia 54 45 22 24 0 3 42 41 3 11 Cuba Czecn Repuolic ..14 8 3 ..74 --9 Denmark 20 21 12 12 15 12 61 64 3 3 Dominican Republic 39 47 29 35 4 4 13 19 44 27 Ecuador 42 -.38 .23 ..16 --18 Egypt, Arab Rep. 42 41 23 20 14 20 26 15 17 24 El Salvador ..78 .47 ..8 .3 --18 Eritrea --- -. -- -- ------- Estonia 25 44 8 13 0 1 73 47 8 8 Ethiopia 77 ..40 -.5 -9 16 16 Finland 20 18 10 7 3 14 70 63 7 5 France 26 24 17 16 5 7 63 65 6 - 4 Gabon 63 --37 -.0 -6 --32 Gambia, The 41 --21 ..16 --9 --34 Georgia -.42 1..18 . 6 - ---- 4 ------ -- -- Gernmany 32 31 8 8 5 7 58 57 5 4 Ghana 50 ..32 -.11 .20 ..19 Greece 31 34 21 28 20 29 41 20 8 17 Guatemala --. .---- .- Guinea 37 29 18 19 7 21 4 8 53 36 Guinea-Bissau . Haiti --67 37 7 7 19 Honduras . 4.12 Goods and Wages Interest Subsidies and Capital services and salaries, payments other current expenditure transfers % of total % of total % of total % of total % of total exprenditure expenditure expenditure expenditure expenditure 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 Hungary 27 16 6 8 6 17 64 58 4 8 India 24 23 11 11 22 29 43 39 11 9 Indonesia 23 -----1-8 -16 --------- 13-19 21 39 43 24 Iran. Islamic Rep. 53 64 40 50 0 1 22 10 25 26 Ireland 19 18 14 13 21 13 54 61 7 9 Israel 38 33 14 15 18 12 37 49 6 6 Italy 17 20 13 16 21 15 54 59 8 6 Jamaica ..52 ..30 .40 1 ..8 Japan 14 ...1 54 13 ~~~ . . ~~~~~249 Jordan 55 65 44 47 18 11 11 8 16 16 - Kazakhstan ..30 .- 10 ..7 . 55 ..8 Kenya 51 31- -19 2210 .. 20 7 Korea, Dem. Rep...-...... ..... Korea, Rep. 35 27 13 13 4 3 46 49 15 22 C - - ------------- - -------- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Kuwait 62 58 31 35 0 3 20 26 18 13 C Kyrgyz Republic --69 26 8 ..13 10 C Lao POR -- .. -- - -- .. ~~~ ~~ ~~ ~~ ~~ ~ ~ ~ ~ ~~~ ~ ~ ~ ~~---------. - - - - ----------- - ------------ -- - - -------- - 3 Latvia -.25 ..12 ..2 -65 8 C8 Lebanon ..30 .23 --41 -12 --17 ------ --------- - ----- ---------- --------- - ---------- ------ -- - . ... .. ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 Lesotho 40 76 22 35 1i 4 5 0 45 19 Liberia -- -- .. ~~~~~~~~~~~~~~~~~~~--- ---- - ........ . L i y - -- -k - - - -- - - -- -- -- -- Lithuania 12 45 6 15 5 67 35 20 16 Macedonia, FYR .---......--- - Madagascar 37 34 25 23 9 10 9 7 43 38 Malawi 54 --23 14 8 -.24 - Malaysia 41 42 26 26 20 12 16 24 24 23 M ali .. -- -- -. -- -. -- --~~-- -------- M a urita nia .. - --.- - - - ---- - ---------- - Mauritius 47 46 37 35 15 12 22 29 17 14 Mexico 25 24 18 17 45 16 17 49 14 11 Moldova ..16 --7 -24 --54 - 6 Mongolia 30 33 7 11 1 7 56 47 13 13 Morocco 48 46 35 36 16 15 8 16 28 22 Mozambique ----. .--. Myanmar --- .. .- - 29 47 Namibia 73 66 46 46 1 7 10 15 15 12 Nepal -----.. 7 - Netherlands 15 15 9 9 9 9 70 72 6 3 New Zealand 19 52 12 .. 15 7 64 38 2 3 Nicaragua 43 32 23 16 0 7 14 20 4 42 N iger - ---- ----------- Nigeria --. . .. - .- Norway 19 21 8 8 6 5 69 70 5 5 Oman 76 76 22 30 6 5 7 6 11 12 Pakistan 44 49 -- 4 25 32 20 8 12 1.1 Panama 64 50 49 36 8 18 26 24 2 8 Papua New Guinea 61 51 34 30 11 16 18 27 11 7 Paraguay 54 -3610 ..19 ..17 Peru 30 37 17 18 37 11 25 35 8 17 Philippines 44 53 29 28 34 18 7 19 16 10 Poland ,.15 8 --9 -.72 --4 Portugal 38 41 27 32 18 7 33 38 12 13 Puerto Rico --- -- .-- Romania 26 35 12 14 0 13 57 44 17 8 Russian Federation --33 -12 ..16 -45 --7 1i0 4.12 Goods and Wages Interest Subsidies and Capital services and saiaries' payments other current expenditure transfers % of total %of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 Rwanda 53 29 ..5. 16 33 Saudi Arabia . .. .. Senegal.... . Sierra Leone 77 60 35- 46 18 -28 1 68 11 Singapore 51 56 27-- 27 14 3 -12 9 24 32 Slovak Republic ..23 ..13 .8 59 9 Slovenia 40 39 20 22 i 52 . 508 South Africa 53 252 16 14 1823 5 105 250 Spain 19 1 13 - 3 68 9 4 - Sri Lanka 33 37 17------ .223.2-- 23 - i 1 21 23 w - - --.-.- -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~--------- -- - ------- Sudan 74 34 9.7 10 in Swaziland 62 5942 3.4 17 24 22 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~----- ------- m Sweden 15 18 6 6 11 11 72 69 2 2 Switzerland 31 29 5 33 61 5 E Syrian Arab Republic . ......27 38 a .- - - - - .- - - - - . - -- - - - - - - - - - - - - - o Tajikistan . 51 6 4 26 ..19 > Tanzania ... . a) 0 Thailand 60 41 35 25 13 4 9 7 iS 49 o Togo... . Trinidad and Tobago . .. o Tunisia 34 40 28 3 0 135 28 22 . 22 0 u~y 52 32. 38 24 18 36 16 25 13 7 ~~~~~ Turkey~ ~ ~~ ~~~ -- -- -------- Turkmenistan . Uganda ..41 Ukraine 24 11 9 61 ..6 United Arab Emirates 88 78 3350 0 10 . 18 14 United Kingdom 30 29 9 68 52 59 164 United States 28 21 10 15 14 49 61 8 5 Uruguay 35 34. . . - 8 .6 50 54 76 Lzbekistan . Venezuela, RB 31 24 23 19 14 37 47 16 16 Vietnam 3 ..35 West Bank and Gaza Yenmen Rep. 64 54 55 39 8 9 6 18- 33 17 Yugoslavia, Fed. Rep. Zambia Zimbabwe 56 48 3 36 16 20 18 26 10 6 - -- ------ - -------------- --- ---------------- - -~~~~~~~~~~~~~~~~~~~~~S Low Income Middle Income 42 37 25 22 ~~~10 11 23 35 16 12 Lower middle income 45 40 28. 24 10. . .. 0 7 716 Upper middle income 38 29 23.18 11. 13 - 6 48. 11 9 Low & middle lincome -. 37 21 21 27 -14 East Asia & Pacific 41 ..27 ..10 10 19 24 18 24 Europe & Central Asia -32 ..11 83--- 48 ..8 - 23 20 . id . ii .2------5------ 11---16- Latin America & Carib 35 3 32 01 53 11 a-s-t M' 3550 . 510 11 11 1 31 Middle Eas & N.Arica 53 5 51 31 South Asia 33 32 14 23 23 . 23 21 12 17 Sub-Saharan Africa 53 31 7 .-. {. 20 High income 25 ---------29 13 . 111 - 7 5597 5 Europe EMU 23 30 13 10 . ...... _9 9 5758 75 Note: Components include expenditures financed by grants in kind sand other cash adjustments to total expendituire. a. Part of goods and services. 4.12 About the data Definitions Government expenditures include all non-repay- no data are available. Defense expenditures, * Total expenditure of the central government able payments, whether current or capital, re- which are usually the central government's includes both current and capital (development) quited or unrequited. Total central government responsibility, are shown in table 5.7. For more expenditures and excludes lending minus re- expenditure as presented in the International information on education expenditures see table payments. * Goods and services include all Monetary Fund's (IMF) GovemmentFinance Sta- 2.11; for more on health expenditures see government payments in exchange for goods tistics Yearbook is a more limited measure of table 2.15. and services, whether in the form of wages and general government consumption than that The classification of expenditures by eco- salaries to employees or other purchases of shown in the national accounts (see table 4.10) nomic type can also be problematic. For example, goods and services. * Wages and salaries con- because it excludes consumption expenditures the distinction between current and capital ex- sist of all payments in cash, but not in kind by state and local governments. At the same penditure may be arbitrary, and subsidies to (such as food rations and housing) to employ- time, the IMF's concept of central government state-owned enterprises or banks may be dis- ees in return for services rendered, before de- expenditure is broader than the national ac- guised as capital financing. Subsidies may also duction of withholding taxes and employee counts definition because it includes government be hidden in special contractual pricing for goods contributions to social security and pension gross capital formation and transfer payments. and services. funds. * Interest payments are payments made Expenditures can be measured either by func- Expenditure shares may not sum to 100 to domestic sectors and to nonresidents for 251 tion (education, health, defense) or by economic percent because expenditures financed by grants the use of borrowed money. (Repayment of type (wages and salaries, interest payments, in kind and other cash adjustments (which may principal is shown as a financing item, and g purchases of goods and services). Functional be positive or negative) are not shown. commission charges are shown as purchases data are often incomplete, and coverage varies For further discussion of government finance of services.) Interest payments do not include R by country because functional responsibilities statistics see About the data for tables 4.11 payments by government as guarantor or surety a stretch across levels of government for which and 4.13. of interest on the defaulted debts of others, CD which are classified as government lending. CD * Subsidies and other current transfers include 3 Figure 4.123 all unrequited, nonrepayable transfers on cur- D rent account to private and public enterprises, Some economies spend more than halt of central government expenditures on ant cost to te public ftcov rin esh subsidies and other current transfers and the cost to the public of covering the cash operating deficits on sales to the public by Central government subsidies anr other current transfers as a percentage of total expenditure departmental enterprises. * Capital expendi- tO Low- and middle-income economies ture is spending to acquire fixed capital as- a0 sets, land, intangible assets, government 70 stocks, and nonmilitary, nonfinancial assets. 60 ~~~~~~~~~~~~~~~~~~~~~~Also included are capital grants. 60 50 Data sources 30 The data on central government expenditures 30 | I1 * 1 * |-| 1 *1 1 - 1l - are from the IMF's Government Finance C 20 - 1 - 1 - 1 - - 1 - 1 - 1 - 1 - l - Statistics Yearbook, 2001 and IMF data files. Each country's accounts are reported using to ~~~~~~~~~~~~~~~~~the system of common definitions and 0 classifications in the IMF's Manual on 9J@' 0sp ccX' §\ o'R esiiS 9Je Oe" G* N<'° vOXo° 4sw^<< Government Finance Statistics (1986). See g,o ,,NojF 5dthese sources for complete and authoritative. explanations of concepts, definitions, and data High-income economies sources. 80 70 60 50 40 30 20 to 0 es/~ ~~~ vOo >4 § ° ' o Note: For develop ng economes data reuer Co the most recent year available in 199882000, and tfr high-income econom,es in 1997 2000. Source: lnternational Monetary Fund. Governmem Rnance Statistics data rnes. El 4.13 Central government revenues Taxes on Social Taxes on Taxes on OYther Nontax Income, profits, security goods and International taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total %of total current revenue current revenue cLjrrent revenue current revenue current revenue current revenue 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 Afghanistan. . Albania ..7 14 .. 40 15 ..1 23 Algeria .. 67 .. 0 . 10 .. 14 .1 ..8 Angola Argentina 2_ 16 44 24 20 42 14 _6 10 1 10 11 Armenia Australia 65 68 0 0 21 21 4 3 2 2 8 8 Austria 19 25 37 40 25 25 1 0 9 4 9 6 Azerbaijan .. 22 . 22 . 40 ..9 ,2 ..5 252 Bangladesh .. 11 .. 0 . 40 . 23 .1 .. 25 Belarus 12 11 32 34 40 38 5 7 9 3 2 8 at Belgium 35 37 35 33 24 25 0 0 _3 3 3 2 Bolivia 5 9 9 12 31 44 7 6 11 13 38 16 Bosnia and Herzegovina . . . .. .. .. Botswtana 39 . 0 .. 2 .. 13 ..0 . 46 o Brazil 20 20 31 34 24 21 2 3 6 4_ 16 17 > Bulgaria 30 13 23 23 18 33 2 3 1 4 27 23 to Burkina Faso 23 .. 0 .. 30 33 ..7 ..8 -u r-undi 21 21 6 7 37 44 24 20 1 1 10 6 ?. Cambodia......... O Cameroon 18 21 6 0 21 26 14 28 4 4.28 20 Canada 51 54 16 20 17 16 3 1 0 0 13 9 Central African Republic . .......... Chad 19 .. 0 .. 39 24 .. 10 8 Chile 12 17 8 7 43 47 12 7 3 4 21 18 China 31 6 0 0 18 75 14 10 0 4 37 6 Hong Kong, China...... ...... Colombia 29 34 0 0 30.39 20 7 1 5 19 14 Congo, Dem.Rep. 27 29 1 0 18 17.-6 33 1 9 7 12 CongoR iep. 26 8 0 0 16 15.21 6 2 1 35 71 Costa Rica 10 15 29 29 27 39 23 6 1 0 14 11 Cote d'lvoire 16 22 7 6 27 18 29 47 11 4 . 9 3 Croatia 17 11 5 - 32 24 - 43 37013 5 Czech Republic -. 14 -. 44 .. 36 ..2 .1 .. 3 D e- n m-a r-k ------- ---- 3 38 4 4 41 42 0 0 3 4 15 12 Dominican Republic 2 8 4 4 23 30 4 0- 12 10 7 Ecuador 62 6 . 22 13 ..1 ..2 Egypt, Arab Rep. 19 22 15 0 14 17 14 13 11 12 27 37 El Salvador .. 23 .. 12 .. 40 -. 8 ..4 .. 12 Esto-nia- 27 19 28 34 41- -0 1 0 1 0 2 7 Ethiopia 29 .. 0 . 25 .. 15 .2 .. 30 Finland 31 29 9 10 47 44 1 0 3 2 9 13. France 17 20 44 42 28 29 0 0 3 4 7 6 Gabon 24 1 23 8 . 2 .. 32 Gambia,The 13 0 37 43 .. 1 ..6 Georgia 10 17 554 ..0 . 14 Germany 16 15 53 48 24 20 0 0 0 0 6 16 Ghana 23 030 39 .. 0 ..8 Greece 22 39 29 2 43 55. 0 0 8 8 8 7 Guinea 9 10 0 1 15 5 47 77 0 4 28 4 Guinea-Bissau ..... ... Honduras..... .... 4.13 - Taxes on Social Taxes on Taxes on Other Nontax Income, profits, secu rity goods and International taxes revenue and capital taxes services trade gains % of tcotal % of tota % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue 1990 1999 1990 1999 1990 19991 1990 1999 1990 19991 ±990 1999 Hungary 18 19 29 30 31 33 6 3 0 2 16 12 India 15 25 0 0 36 28 29 21 0 0 20 26 Indonesia 62 59 0 2 24 28 6 3 3 0 5 8 Iran, Islamic Rep. 10 15 8 8 4 16 13 25 4 1 60 34 Ireland 37 42 15 1 3 38 37 0 0 3 4 7 4 Israel 36 36 9 14- 33 31 2 1 4 4 14 14 Italy 37 36 29 30 29 24 0 0 2 3 3 7 Jamaica 27 .. 0 - 30 -.7 .7 .. 28 Japan 69 -, 0 - 17 .. 1 - 7 -- 5 253 -ora- 6 10 0 0 21 30 27 20 7 8 29 32 Kazakhstan - - . 16 -2 .. 63 - 7 3 9- 9 - - - -- - - - - -- - - - - - - -- - - - - - -- - - - - -- - - - -- - - - - - - -- -- -- - - - - - - -- -- - - - - - - - - -- -- - - - - - -- - - - - -- - - - - - - -- - - - - --0 Kenya 30 31 0 0 43 37 16 14 - 1 - 0 10 18 P Korea, Dem. Rep. --- .----- Korea, Rep. 34 27- 5 9 35 34 12 - 6 5 - 0 9 14 Kuwait 1 1 0 6 0 0 2 3 0 6 97 90 CD Kyrgyz Republic -- 15 -- 0 -- 58 --4 --0 - 23 ID Lao PDR --- - -- - - -- - --- - - - - - - - - -- . . . . - -- - - - - - - - - - - - - - - -- - - - -- - -- -- - - - - - - -- - - - - - - - Latvia 13 34 - 40 -. 1 0 12 C Lebanon -- 1 - 0 - -20 -- 28 - 13 -- 28 Lesotho- 11 18 0 0 21 1~~~~~ ~~~ ~ ~ ~ ~~~ ~~2 57- 48 0 6- ii -22 F - --- -- - - --- -- -- - ------ ---------- ---------------- - --- -- a) L ib eria -- ~ ~ ~ ~~~~~- -- -- ---- --- - -- - - - - - - -- - - - -- - ----- --- -------- - - - -- --- -- - -- - - - Libya - ~ ~ ~~~~~~~~~- - -- -- - --- - -- - - - -------- -- Lithuania- 20 13 28 31 40 -49 1 2 3 0 8 5 M a c e d o n i a . -----F R -- - - - - -- - ----- - --- - - - -- - --- -- -- - - - - - --- - ---- Madagascar 13 15 0 0 19 25 48 56 2 1 18 3 --- --37 - - 0 -- 33 16 - - 1 - - 13 - -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~--- --- Malaysia 3 1 36 1 1 20 26 18___ 13 3 -5 28 18 Mali -- -- Mauritania ---- M a u r Itiu s- --- -- ------ 14 -1-24 5 21 37 46 26 - 6 6 9 14 Mexico 31 - 37- 13 11 56 58 6 - - 2 1 11 11 moid~ova ------- ----5 - 26_ SO 8 0 11 Mongolia 24 7 14 20- 31 - 41 17 5 - 6 1 15 27 Morocco 24 24 4 5 38 36 18 16 4 3 13 16 Mozambique -- -- - - - -- - - --~~~~~~~~~~~~~~~~~~~~~~~~---- -- ---- ------ ------ --- My'anmar- 18 17 0 0 28 28 14 4 0- 0--41 51 Nibia 34 32 0 0 25 27 27 31 1 1 13 8 Nepal 11 17 0 0 36 35 31 27 5 4 17 17 Netherlands 31 25 35 41 22 23 0 0 3 5 9 7 New Zealand 53 61 0 0 27 29 2 2 3 1 15 7 Nicaragua 17 12 9 13 35 58 19 7 8 0 13 9 Niger -- -- -- -- --~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~------- N i g e ri a -- - - - - - -- - - - - - - - - - - - - - - - - - - - - --- - Norway 16 21 24 23 34 38 1 1 1 1 24 17 Om-a-n 21900122- -6- - ---- 1 -- 3- --73 -71 Pakistan 9 23 0 0 30 29 31 14 0- 18--30 17 Panama 17 19 20 18 17 12 3 4 31- 35 Papua New Guinea 37 50 - 0 0 14 11 25 32 3 4 20 3 Paraguay- 9 0 21 20 ------9 2-4 2 5-- --. Peru 5 21 7 8 50 50 17 10 19 2 7 17 Philippines 28 39 0 0 31 29 25 18 3 5 13 - 9 Poland -- 19 -- 29 - 38 3 11 Portugal123 2-7 25--25-34-36-2-0 -4- -----2 12 - 10- P u e rto R ic o ~~~~- - - -- --- - - -- -- -- -- -- - -- -- - --- --- --- -- - Rmania i9 16 23--34 33 --d 331 1 5 1 0 1 Russian- Feeailo-n- 10-- - 32 ----- 35 9-1-- 14 4.13 Inoeprft, scitgodanitenioataereeu Taxes on Social Taxes on Taxes on Other Nontax and capital taxes services trade % of total % of total % of total % of total % of total % of total cJrrent revenue current revenue current revenue current revenue current revenue current revenue 1990 1999 11990 1999 1990 1999 1990 1999 1990 1999 1990 1999 Rwanda 18 .. 7 . 34 .. 26 . 4 .. 12 Saudi Arabia . .. .. .. Senegal . .. .. .. Sierra Leone 31 26 0 0 23 22 40 49 0 0 5 4 Singapore 26 26 0 0 16 17 2 1 14 11 43 45 Slovak Republic .. 21 .. 29 .. 28 ..4 .1 .. 17 Slovenia 12 13 47 35 27 41 8 3 0 4 5 4 Somalia . .. .. South Africa 51 52 2 2 34 33 4 3 2 2 8 8 254 Spain 32 30 38 39 22 25 2 0 0 0 5 6 Sri Lanka 11 14 0 0 46 52 29 14 5 4 10 15 U) Sudan .. 15 .. 0 . 35 .. 29 ..1 . 20 tD Swaziland 30 27 0 0 11 13 47 48 2 5 10 7 Sweden 18 14 31 33 29 27 1 0 9 15 13 11 Switzerland 15 13 51 51 23 25 I 1 3 3 7 7 E Syrian Arab Republic 31 34 0 0 31 1 7 7 12 7 6 24 31 o) Tajikistan 7 .. 17 .. 59 .. 13 2 ..2 > Tanzania . .. o Thailand 24 29 0 2 41 45 22 9 4 1 8 13 o Togo....... Trinidad and Tobago . .. . a Tuniuia 13 19 13 17 19 37 28 12 5 4 22 10 0 Turkey 43 38 0 0 32 38 6 1 3 6 15 17 Turkmenistan .,... ..... Uganda .. 16 .. 0 . 64 .. 10 .3 6 Ukraine .. 11 . 39 .. 37 4 ..3 6 United Arab Emirates 0 0 2 1 36 51 0 0 0 0 62 48 United Kingdom 39 40 17 17 28 31 0 0 7 7 9 5 United States 52 56 35 32 3 4 2 1 1 1 8 6 Uruguay 7 15 27 27 36 43 10 4 12 6 5 10 Uzbekistan............ Venezuela, RB 64 22 4_ 4 3 30 7 10 0 8 22 26 Vietnam .. 22 0 .. 35 20 ..9 .. 14 West Bank and Gaza... ... .. Yemen, Rep. 26 18 0 0 10 9 17 10 5 2 43 61 Yugoslavia. Fed. Rep. Zimbabwe 45 43 0 0 26 24 17 20 1 2 10 10 Low Income Middle Income 22 19 4 8 25 37 14 7 3 3 16 12 Lower middle income 23 18 0 4 24 36 19 10 4 3 20 12 Upper middle income 22 19 7 21 26 38 11 4 3 2 15 12 Low &middle Income 21 17 1 5 26 36 17 9 3 2 15 12 East Asia & Pacific 31 25 0 0 26 32 16 9 3 2 17 11 Europe &Central Asia .. 14 .. 30 . 39 .. 4 . 1 .. 11 Latin America & Carib. 17 19 9 11 27 42 13 7 3 4 14 13 Middle East & N. Africa 21 18 2 0 17 18 15 15 5 3 28 30 South Asia 11 17 0 0 36 35 30 21 3 4 18 17 Sub-Saharan Africa 23 .. 0 . 25 .. 27 . 1 .. 10 High Income 31 26 17 20 28 25 1 1 3 3 9 8 Europe Emu 27 27 32 31 29 26 0 0 3 3 8 7 Note: Components may not saw to 100 percent as a result of adjustments to tao revenue. 4.13 About the data Definitions The International Monetary Fund (IMF) classifies unrequited payments made to governments by * Taxes on Income, profits, and capital gains government transactions as receipts or individuals, businesses, or institutions. Taxes are levied on the actual or presumptive net payments and according to whether they are traditionally have been classified as either direct income of individuals, on the profits of repayable or nonrepayable. If nonrepayable, they (those levied directly on the income or profits of enterprises, and on capital gains, whether are classified as capital (meant to be used in individuals and corporations) or indirect (sales realized or not, on land, securities, or other production for more than a year) or current, and and excise taxes and duties levied on goods and assets. Intragovernmental payments are as requited (involving payment in return for a services). This distinction may be a useful eliminated in consolidation. * Social security benefit or service) or unrequited. Revenues simplification, but it has no particular analytical taxes include employer and employee social include all nonrepayable receipts (other than significance, except with respect to the capacity security contributions and those of self- grants), the most important of which are taxes. to fix tax rates. employed and unemployed people. * Taxes on Grants are unrequited, nonrepayable, Social security taxes do not reflect compulsory goods and services include general sales and noncompulsory receipts from other governments payments made by employers to provident funds turnover, or value added taxes, selective or from international organizations. Transactions or other agencies with a similar purpose. excises on goods, selective taxes on services, are generally recorded on a cash rather than an Similarly, expenditures from such funds are not taxes on the use of goods or property, and accrual basis. Measuring the accumulation of reflected in government expenditure (see table profits of fiscal monopolies. * Taxes on 255 arrears on revenues or payments on an accrual 4.12). The revenue shares shown in this table International trade include import duties, basis would typically result in a higher deficit. may not sum to 100 percent because export duties, profits of export or import 0 Transactions within a level of government are adjustments to tax revenues are not shown. monopolies, exchange profits, and exchange not included, but transactions between levels For further discussion oftaxes and tax policies taxes. * Other taxes include employer payroll E are included. In some instances, thegovernment see About the data for table 5.5. For further or labor taxes, taxes on property, and taxes 0 budget may include transfers used to finance discussion of government revenues and not allocable to other categories. They may (D the deficits of autonomous, extrabudgetary expenditures see About the data for tables 4.11 include negative values that are adjustments C 0 agencies. and 4.12. (for example, for taxes collected on behalf of 3 The IMF's Manual on Govemment Finance state and local governments and not allocable 't Statistics (1986) describes taxes as compulsory, to individual tax categories). * Nontax revenue - includes requited, nonrepayable receipts for Figure 4.13 public purposes, such as fines, administrative fees, or entrepreneurial income from govern- cn Many developing economies rely heavily on taxes from international trade ment ownership of property, and voluntary, unrequited, nonrepayable receipts other than Taxes on international trade as a percentage of total current revenue from government sources. It does not include proceeds of grants and borrowing, funds arising 80 from the repayment of previous lending by governments, incurrence of liabilities, and 70 proceeds from the sale of capital assets. 60 50 ~~~~~~~~~~~~~Data sources 40 ~~~~~~~~~~~~~~~~~~~~~The data on central government revenues are from the IMF's Govemrnment Finance Statistics 30 Yearbook, 2001 and IMF data files. Each country's accounts are reported using 20 the system of common definitions and i to classifications in the IMF's Manual on I Govemment Finance Statistics (1986). The IMF 00 a ' O t receives additional information from the ~~ a? c~~~ 'rt~~~ ~~ ~~ cr~~ Organisation for Economic Co-operation and ~~ ~~~ a rY ~~~~~~~~ ~~~. ~Development on the tax revenues of some of' its members. See the IMF sources for complete Nrt.: Data reter tv theemast recent year avalable in 1999-2000. and authoritative explanations of concepts, Svurce vte,naoval Monetary Frnd,Go-nment Finane Stat stics data nles. definitions, and data sources. In contrast, taxes on International trade accounted for less than 4 percent of total current revenue for high-income economies (with the exception of the Bahamas). 4A14 Monetary indicators and prices Money and Claims on Claims on GDP Consumer Food quasi money private sector governments Implicit price price and other defiator Index Index pubiic entities annual % growth annual growth annual growth average annual average annual average annual of M2 as t of M2 as % of M2 % growth % growth % growth 1990 2000 1990 2000 1.990 2000 1980-90 1990-2000 1980-90 1990-2000 1980-90 1990-2000 Afghanistan . . . .. .. Albania .. 12.0 .. 2.1 .. 6.1 -0.4 39.2 .. 27.8 .. 31.2 Algeri'a 11.4 13.2 12.2 4.9 3.2 -26.0 8.3 18.1 9.1 19.5 6.9 18.4 Angola .. 309.0__ . 35.6 ., -410.1 5.9 740.6 . 708.7 . 1,216.8 Argentina 1,113.3 1.5 1,444.7 -2.9 1,573.2 0.7 391.1 5.2 390.6 8.9 279.3 8.1 Armenia . 38.6 .. 17.5 .. -5.5 .. 212.5 .. 72.0 .. 23.7 Australia 12.8 3.8 15.3 13.8 -2.2 -1.9 7.2 1.5 7.9 2.1 7.3 4.3 Austrian . . . . . . 3.3 2.0 3.2 2.2 2.6 1.5 Azerbaijan .. 73.4 .. 37.5 .. -25.5 .. 199.1 . 1 70.8 1.5 201.8 256 Bangladesh 10.4 19.3 9.2 9.8 -0.2 4.6 9.5 4.0 .. 5.5 10.4 4.3 Belarus .. 219.3 .. 104.5 .. 122.7 .. 355.1 .. 336.7 2.4 373.3 o Belgium, . . . . . 4.1 1.9 2.9 1.6 4.0 7.6 tO Beni'n 28.6 26.0 -1.3 8.5 12.4 0.9 1.7 8.3 . 8. 7 .. 7.1 Bolivia 52.8 0.4 40.8 -3.9 18.0 2.8 327.0 8.5 322.5 8.7 322.0 8.5 Bosnia and Herzegovina .. . . . . . . 2.3 . E 8otswana -14.0 1.4 12.6 11.4 -52.4 -56.5 13.6 9.7 10.0 10.4 10.7 10.8 0. o Brazil 1,289.2__ 4.3 1,566.4 23.1 3,093.6 -15.0 284.0 207.7 285.6 199.5 238.2 194.2 > Bulgaria .53.8 28.8 1.9 5.8 84.5 0.6 1.8 102.8 6.3 117.5 .. 123.0 o Burkina Faso -0.5 6.2 3.6 8.3 -1.5 5.3 3.3 3.8 1.0 5.5 -0.5 6.7 Burundi 9.6 4.3 15.4 33.6 -6.9 -19.9 4.4 12.3 7.1 16.1 6.1 6.7 Cambodia .. 26.9 .. 9.4 .. -7.4 .. 24.6 .. 6.3 .. 6.6 N o Cameroon -1.7 19.1 0.9 7.4 -3.0 -8.2 5.6 5.1 8.7 6.5 3.9 3.3 0 .. CN Canada 7.8 14.0 9.2 12.2 0.6 1.8 4.5 1.4 5.3 1.7 4.6 1.5 Central African Republic -3.7 2.4 -1.6 2.9 2.3 0.6 7.9 4.6 3.2 5.9 2.0 5.5 Chad -2.4 18.5 1.3 0.4 -17.3 19.3 1.4 7.1 0.6 8.1 .. 7.1 Chile 23.5 6.2 21.4 14.7 16.4 4.6 20.7 7.3 20.6 8.9 20.8 8.3 China 28.9 12.3 26.5 9.4 1.5 0.2 5.9 7.1 .. 8.6 8.8 Hong Kong. China 8.5 9.3 7.9 1.7 -1. 0 0.4 7.7 4.1 .. 5.8 6.8 4.7 Colombia 33.0 14.8 8.7 2.8 -5.1 7.6 24.8 21.1 22.7 20.6 24.5 18.3 Congo, Dem. Rep. 195.4 .. 18.0 .. 429.7 .. 62.9 1,423.1 57.1 2,089.0 Congo. Rep. 18.5 58.5 5.1 -23.0 -12.6 -13.3 0.5 10.6 0.9 9.2 4.1 11.2 Costa Rica 27.5 18.4 7.3 17.4 8.2 0.8 23.6 17.2 23.0 15.6 23.0 14.0 Cdte dIlvoire -2.6 -1.9 -3.9 2.9 -3.0 -7.6 2.8 7.5 5.4 7.2 6.0 Croati'a .. 29.1 .. 7.3 .. 4.1 .. 86.2 304.1 86.3 246.3 103.0 Czech Republic .. 16.0 .. -4.1 .. 3.3 .. 11.5 .. 7.8 .. 11.4 Denmark 6.5 -0.9 3.0 2.3 -3.1 -1. 0 5.8 2.2 5.6 2.1 4.8 2.0 Dominican Republic 42.5 17.4 19.1 16.9 0.7 1.0 21.6 9.4 22.4 8.7 25.2 11.7 Ecuador 48.9 11.1 17.2 8.5 -27.4 -18.3 36.4 37.1 35.8 37.1 43.0 36.7 Egypt, Arab Rep. 28.7 11.6 6.3 7.2 25.3 6.1 13.7 8.2 17.4 8.8 19.0 7.5 El Salvador 32.4 1.0 8.8 -0.3 9.6 2.8 16.3 7.4 19.6 8.5 21.4 9.8 Eritreea. . . .. 9.4 Estonia 76.5 25.7 27.6 8.8 -6.8 -3.5 2.3 53.1 . 21.6 48.6 Ethiopia 18.5 14.2 0.3 3.3 21.7 18.0 4.6 7.0 4.0 5.3 3.7 6.5 Finland, . . . . . . 6.7 1.9 6.2 1.5 5.8 7.2 France' . . . . . . 5.8 1.5 5.8 1,6 5.7 10.8 Gabon 3.3 18.3 0.7 6.2 -20.6 -43.4 1.8 6.2 5.1 5.7 2.8 4.8 Gambia, The 8.4 34.8 7.8 4.2 -35.4 2.9 17.9 4.1 20.0 4.0 20.4 4.4 Georgia .. 39.4 .. 23.3 .. 20.1 1.9 387.5 .. 24.7 Germany, ... . .. . ..... 2.4 2.0 2. 2 2.2 ..2.3 Ghana 13.3 38.4 4.9 35.6 -0.8 59.8 42.1 26.7 39.1 28.4 33.1 25.9 Greece, . . . . . . 19.3 9.2 18.7 9.0 18.0 49.9 Guatemala 25.8 35.5 15.0 7.4 0.5 1.5 14.6 10.3 14.0 10.1 14.6 9.7 Guinea -17.4 514.1 13.1 -61 2.9 1.1 .. 5.1 .. . . 9.1 Guinea-Bissau 574.6 60.8 90.5 5.5 460.7 16.2 57.4 32.5 .. 34.0 Haiti 2.5 20.1 -0.6 8.2 0.4 16.9 7.5 20.3 5.2 21.9 4.1 19.2 Honduras 21.4 24.4 13.0 11.5 -10.5 6.1 5.7 18.8 6.3 17.3 5.1 19. 4.14 - Money and Claims on Claims on GDP Consumer Food quasi money private sector governments Implicit price price and other deflator Index Index public entities annual % growth annual growth annual growth average annual average annual average annual of M12 as % of M2 as % of M2 % growth % growth % growth 1990 2000 1990 2000 1990 2000 1 1980-90 1990-2000 1980-90 1990-20001 1980-90 1990-2000 Hungary 29.2 12.2 _23.0 19.1 69.7 -1.2 8.9 19.3 9.6 20.3 9.5 19.5 India 15.1 15.2 5.9 9.9 10.5 4.:7 8.1 8.0 8.6 9.1 8,4 9.2 Indonesia 44.6 15.9 66.9 7.0 -6.7 20.7 8.6 15.5 8.3 13.7 8.6 17.1 Iran, Islamic Rep. 18.0 22.4 14.7 158 5.8 -1.9 14.4 26.2 18.2 26.0 16.3 28. 7 Iraq 10.3 -... . 14.3 Ireland L 6.6 3.5 6.8 2.3 10.5 27.9 Israel 19.4 8.0 18.5 10.7 4.9 -4.8 101.1 10.0- 10 1.7 9.7 102.4 8.4 Italya ---.---. 10.0 3.8 9.1 3.7 8.2 19.2 Jamaica 21.5 13.0__ _12.5 ___13.7 -16.0 -15.7 18.6 24.1 15.1 23.5 16.2 5.3 Jap an 8.2 1.1 9.7 ....-1.8 1. 5 3.8 1.8 0.1 1.7 0.7 ,1.6 0.6 257 Jordan 8.3 7.6 4.7 2.9 1.0 -1.5 4,3 3.2 5.7 3.5 4.7 3.9 Kazakhstan .. 45.0 .. 58.5 .. -3.2 ..- 204.7 .. 67.8 .. 246.0 Kenya -20.1 4.5 -- 8.0 2.3 - 21.5 -1.7 . 9.1 13.9_ 11.1 15.1 . 15.2 Korea. Dem. Rep... ... .......... Korea, Rep. 17.2 25.4 36.1 21 .9 -1.2 -1.5 6.5 5.0 4.9 5.1 5.0 5.3 Kuwait 0.7 6.3 3.3 3.1 -3.1 -6 6 -2.8 3.0 2.9 2.0 1.2 2.6 ( Kyrgyz Republic .. 11.7 .- 3.5 -. 7.2 .. 110.2 -. . 23.1 .. 70.7 (D Lao PDR 7.8 46.0 3.6 22.4 7.0 3.2 37.6 27.0 .. 28.2 .O Latvia .. 27.0 18.5 .. 13.9 0.0 49.2 .. 29.2 .. 25.0 ( Le,ba.non 55.1 9.8 27.6 2.9 --18 5 10.5 .. 17.4A. .. C- Lesotho 8.4 1.4 6.8 1.5 414.9 13.3 12.1 9.9 13.6 9.8 13.2 13.0 ------- ------ --- -- ----~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Liberia 19.6 18.3 16.1 -10.0 29.5 196.9 1.8.. . Libya 19-0- - -- 3.1 2-.0- -0.2 15.0 __-2.0___ _O.2 ...n,( Lithuania 16.5 .. -3.7 -. 2.2 .. 75.2 .. 32.6 .. 55.6 Macedonia, FYR 21.4 .. -3.9 .. -14.5 .. 79.3 .. 13.0 242.1 89.0 Madagascar 4.5 17.2 23.8 9.9 -14.8 -0.3 17.1 19.1 16.6 18.7 15.7 19.1 Malawi 11.1 41.4 15.8 14.6 -12.8 -2.7 15.1 33.5 16.9 33.8 16.3 36.0 Malaysia 10.6 9.9 20.8 7.9 -1.2 1.5 1.7 3.9 2.6 3.6 1.3 5.1 Mali -4.9 12.2 0.1 -1.5 -13.4 -5.0 4.5 7.1 .. 5.2 Mauritania 11.5 16.1 20.2 41.1 1.5 -64.3 8.4 5.9 7.1 6.1 .. 6.2 Mauritius 21.2 9.2 10.8 9.3 0.8 4.8 9.5 5.9 6.9 6.9 7.4 6.7 Mesico 81.9 -42 48.5 -3.4 13.6 -01 71.5 18.9 73.8 19.4 73.1 19.3 Moldova 358.0 41.7 53.3 22.8 469.1 -2.2 .. 120.2 .. 18.9 .. 64.4 Mongolia 31.6 17.6 40.2 2.0 38.5 -65 -1.6 58.4 .. 53.7 Morocco 21.5 8.4 12.4 7.6 -49 3.6 71I 2.8 7.0 3.8 6.7 4.8 Mozambique 37.2 38.4 22.0 18.2 -5.1 3.8 38.3 32.6 .. 34.9 24.4 Myanmar 37.7 42.4 12.8 13.9 24.2 27.6 12.2___ 26.4 ...11.5 25.9 11.9 28.4 Namibia 30.3 13.0 15.4 14.9 -4.2 -1.1 13.7 9.5 12.6 9.9 14.9 9.8 Nepal 18.5 18.8 5.7 10.7 7.3 2.9 11.1 8.2 10.2 8.6_ 10.1 9.8 Netherlands' . . . . . 1.6 1.9 2.0 2.4 1.2 5.1 New Zealand 12.5 2.3 4.2 7.8 -1.7 -0.9 10.7 1.5 11.0 1.8 9.9 1.3 Nicaragua 7,677.8 9.4 4,932.9 11.1 12,679.2 10.1 422.3 33.5 535. 7 35.1 Niger -4.1 12.4 -5.1 14.8 1.4 -14.1 1.9 6.0 0.7 6.1 -1.5 9.8 Nigeria 32.7 48.1 7.8 19.3 27.1 -41.4 16.7 28.9 21.5 32.5 21.6 37.6 Norway 5.6 8.7 5.0 18.0 -0.6 -30.5 5.6 2.8 7.4 2.2 7.8 2.0 Oman 10.0 6.0 9.6 1.1 -10.9 10.1 -3.6 -2.9 -. 0.1 .. 0.3 P.akistan-- - --- -- 11.6 _12 1 5.9 8.7 7.7 2.6 6.7 10.2 6.3 9.7 6.6 10.1 Panama 36.6 10 0 0.8 9.6 -257 0.3 _19 1.9 1.4 1.1 1.9 1.0 Papua New Guinea - ---4.3 5.0 0.9 3.8 8.8 -5.9 5.3 7.9 5.6 9.3 4.6 8.9 Paraguay 54.4 4.8 32.0 3.3 -9.2 4.0 24.4 12.5 21.9 13.1 24.9 12.5 Peru 6,384.9 -0.4 2,123.7 -2.7 2,129.5 2.6 220.2 26.8 246.1 27.3 .. 24.9 Philippines 22.4 8.1 15.6 3.5 3.4 3.6 14.9 8.4 13.4 8.2 14.1 7.5 Poland 160.1 11.8 20.8 12.5 75.6 -5.9 - 23.4 50.9 25.3 52.4 21.9 Portu.g-all .. --- ---- -----.- ---- 18.0 5.3 17.1 4.5 16.9 33.2 Puerto Rico .... .. 3.5 3.7 -.. 2.8 9.6 Romania 26.4 38.0 10.1 0.0 -0.4 2.5 98.0 .. 100.5 1.8 95.8 Russian Federationr. 58.4 35.1 .. -14.5 .. 162.0 .. 99.1 .. 143.3 4.14 Money and Claims on Claims on GDP Consumer Food quasi money private sector governments Implicit price price and other deflator Index Index public entities annual % growth annual growth annual growth average annual average annual average annual of M2 as %of M2 as %of M2 % growth % growth % growth 1990 2000 1990 2000 ±.990 2000 1980-90 1990-2000 1.980-90 1.990-2000 1980-90 1.990-2000 Rwtanda 5.6 15.6 -10.0 10.3 26.8 -11.3 4.0 14.6 3.9 16.2 6.6 Saudi Arabia 4.6 4.5 -4.5 3.3 4.2 -4.1 -4.9 2.2 -0.8 1.0 -0.4 1.0 Senegal -4.8 10.7 -8,4 19.1 -5.3 -3.9 6.5 4.6 6.2 5.4 5.3 5.8 Sierra Leone 74.0 12.1 4.9 1.6 228.7 54.9 62.8 28.9 72.4 29.3 71.0 Singapore 20.0 -2.0 13.7 5.1 -4.9 -1.6 1.9 1.3 1.6 1.7 0.9 1.8 Slovak Republic .. 15.2 .. -5.5 .. 13.2 1.8 10.6 .. 8.4 1.6 8.8 Slovenia 123.0 18.0 96.1 13.3 -10.4 4.9 .. 20.4 .. 24.6 252.3 26.4 Somalia .... ..... 49.7... South Africa 11.4 7.2 13.7 17.4 1.8 0.2 15.5 9.6 14.8 8.7 15.1 10.0 258 Spain' 9.3 3.9 9.0 3.8 9.3 16.8 Sri Lanka 21.1 12.8 16.2 9.2 6.8 12.2 11.0 9.1 10.9 9.9 10.9 10.4 Sudan 48.8 36.9 12.6 11.0 29.4 18.4 41.0 60.8 37.6 81.1 38.0 (0 Swaziland 0.6 -6.6 20.5 3.9 -13.2 -8.5 10.1 12.6 14.6 9.4 'O Sweden 0.8 1.9 13.4 12 4 -12 2 2.1 7.3 2.1 7.0 1.9 8.2 -0.5 c: Switzerland 0.8 -16.9 11.7 -1.2 1.0 2.1 3.4 1.3 2.9 1.6 3.1 0.5 CD E Syrian Arab Republic 26.1 19.0 3.4 0.3 11.4 -4.5 15.3 6.7 23.2 6.7 24.5 5.4 0. o Tajiklstan .... ..... 2.5 235.2 . .- > Tanzania 41.9 14.8 22.6 2.6 80.6 0.6 .. 21.5 31.0 20.9 30.2 56.7 a) 0 Thailand 26.7 3.4 30.0 -16.0 -4.0 0.8 3.9 4.2 3.5 4.9 2.7 5.9 ~0 Trinidad and Tobago 6.2 11.7 2.7 8.8 -1.9 -14.0 2.4 5.5 10.7 5.7 14.6 13.3 o Tunisia 7.6 14.1 5.9 23.7 1.8 5.6 7.4 4.5 7.4 4.4 8.3 4.4 0 (N Turkey 53.2 40.0 42.9 29.4 2.2 38.1 45.2 76.3 44.9 79.9 -. 83.4 Turkmenistan .. 22.6 .. 0.3 .. 82.3 -- 407.5 . Uganda 60.2 18.1 .. 5.1 -0.9 32.9 113.8 12.4 102.5 10.5 .. 13.4 Ukraine .. 44.4 .. 32.7 .. 2.2 .. 271.3 .. 200.4 2.0 203.9 United Arab Emirates -8.2 15.3 1.3 8.7 -4.8 -9.4 0.8 2.3 . .- United Kingdom 10.5 11.3 13.1 17.5 1.1 -2.4 5.7 2.9 5.8 2.9 4.6 1.8 United States 4.9 7.0 1.1 11.6 0.6 1.9 3.8 2.1 4.2 2.7 3.8 3.6 Uruguay 118.5 7.2 56.2 5.1 25.8 -5.3 62.7 31.1 61.1 33.9 62.0 30.9 Uzbekistan .. . . . .- . 246.6 . Venezuela. RB 64.9 23.1 17.6 14.5 45.3 -5.6 19.3 45.5 20.9 20.9 29.7 48.4 Vietnam .. 35.4 .. 29.6 .. -2.4 210.8 15.4 .. 4.1 West Bank and Gaza .. . . . . . . 8.9 . Yemen, Rep. 11.3 25.3 1.4 3.6 10.2 -46.2 -- 21.9 .. 32.6 2.6 Yugoslavia, Fed. Rep. .. . . . . . . 49.1 -. . . 45.5 Zambia 47.9 73.8 22.8 22.0 195.2 169.3 42.2 51.4 72.5 80.8 42.8 73.0 Zimbabwe 15.1 68.9 13.5 46.0 5.0 53.4 11.6 25.5 13.8 27.0 14.6 34.1 a. An members of the European Monetary Union, these countries share a single currency, the euro. b. Data prior to 1990 refer to the Federal Republic of Germany before unification. Monetary cndicators and prices 4.14 About the data Definitions Money and the financial accounts that record cial banks may be based on preliminary esti- * Money and quasi money comprise the sum the supply of money lie at the heart of a country's mates subject to constant revision. This prob- of currency outside banks, demand deposits financial system. There are several commonly lem is likely to be even more serious for non- other than those of the central government, used definitions of the money supply The nar- bank financial intermediaries. and the time, savings, and foreign currency rowest, Ml, encompasses currency held by the Controlling inflation is one of the primary goals deposits of resident sectors other than the public and demand deposits with banks M2 of monetary policy and is intimately linked to central government. This definition of the includes Ml plus time and savings deposits with the growth in money supply. Inflation is mea- money supply is frequently called M2, it corre- banks that require a notice for withdrawal. M3 sured by the rate of increase in a price index, sponds to lines 34 and 35 in the International includes M2 as well as various money market but actual price change can also be negative. Monetary Fund's (IMF) Intemational Financial instruments, such as certificates of deposit is- Which index is used depends on which set of Statistics (IFS). The change in money supply is sued by banks, bank deposits denominated in prices in the economy is being examined iThe measured as the difference in end-of-year to- foreign currency, and deposits with financial in- GDP deflator reflects changes in prices for total tals relative to M2 in the preceding year stitutions other than banks However defined, gross domestic product The most general mea- * Claims on private sector (IFS line 32d) in- money is a liability of the banking system, dis- sure of the overall price level, it takes into ac- clude gross credit from the financial system to tinguished from other bank liabilities by the spe- count changes in govemment consumption, capi- individuals, enterprises, nonfinancial public en- 259 cial role it plays as a medium of exchange, a tal formation (including inventory appreciation), tities not included under net domestic credit, M unit of account, and a store of value. international trade, and the main component, and financial institutions not included else- g 0 The banking system's assets include its net household final consumption expenditure The where * Clalms on govern-ments and other E foreign assets and net domestic credit. Net do- GDP deflator is usually derived implicitly as the public entitles (IFS line 32an + 32b + 32bx + R mestic credit includes credit to the private sec- ratio of current to constant price GDP, resulting 32c) usually comprise direct credit for specific E tor and general government, and credit extended in a Paasche index. It is defective as a general purposes, such as financing the government C to the nonfinancial public sector in the form of measure of inflation for use in policy because budget deficit, loans to state enterprises, ad- 0 investments in short- and long-term government of the long lags in deriving estimates and be- vances againstfuture credit authorizations, and B securities and loans to state enterprises; liabili- cause it is often only an annual measure. purchases of treasury bills and bonds, net of ties to the public and private sectors in the form Consumer price indexes are more current and deposits by the public sector Public sector , of deposits with the banking system are netted produced more frequently. They are also con- deposits with the banking system also include 't 0 out. Net domestic credit also includes credit to structed explicitly, based on surveys of the cost sinking funds for the service of debt and tem- banking and nonbank financial institutions. of a defined basket of consumer goods and ser- porary deposits of government revenues. . GDP Domestic credit is the main vehicle through vices. Nevertheless, consumer price indexes Implicit deflator measures the average annual which changes in the money supply are regu- should be interpreted with caution The defini- rate of price change in the economy as a whole lated, with central bank lending to the govern- tion of a household, and the geographic (urban for the periods shown * Consumer price in- ment often playing the most important role. The or rural) and income group coverage of consumer dex reflects changes in the cost to the aver- central bank can regulate lending to the private price surveys, can vary widely across countnes, age consumer of acquiring a basket of goods sector in several ways-for example, by adjust- as can the basket of goods chosen. In addition, and services that may be fixed or change at ing the cost of the refinancing facilities it pro- the weights are derived from household expen- specified intervals, such as yearly. The vides to banks, by changing market interest rates diture surveys, which, for budgetary reasons, Laspeyres formula is generally used * Food through open market operations, or by control- tend to be conducted infrequently in developing price index is a subindex of the consumer price ling the availability of credit through changes in countries, leading to poor comparability over index. the reserve requirements imposed on banks and time. Although a useful indicator for measuring ceilings on the credit provided by banks to the consumer price inflation within a country, con- private sector. sumer price indexes are of less value in making Data sources Monetary accounts are derived from the bal- comparisons across countries Like consumer The monetary, financial, and consumer price ance sheets of financial institutions-the cen- price indexes, food price indexes should be in- index data in this table are published by the tral bank, commercial banks, and nonbank fi- terpreted with caution because of the high van- IMF in its monthly International Financial nancial intermediaries Although these balance ability across countries in the items covered Statistics and annual Intemational Financial sheets are usually reliable, they are subject to The least-squares method is used to calcu- Statistics Yearbook. The IMF collects data on errors of classification, valuation, and timing and late the growth rates of the GDP implicit defla- the financial systems of its member countries. to differences in accounting practices. For ex- tor, consumer price index, and food price index The World Bank receives data from the IMF in ample, whether interest income is recorded on electronic files that may contain more recent an accrual or a cash basis can make a substan- revisions than the published sources. The GDP tial difference, as can the treatment of non-per- deflator data are from the World Bank's national forming assets. Valuation errors typically arise accounts files. The food price index data are with respect to foreign exchange transactions, from the United Nations Statistics Division's particularly in countries with flexible exchange Statistical Yearbook and Monthly Bulletln of rates or in those that have undergone a currency Statistics. The discussion of monetary devaluation during the reporting period. The valu- indicators draws from an IMF publication by ation of financial derivatives and the net liabili- Marcello Caiola, A Manual for Country ties of the banking system can also be difficult. Economists (1995) The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized Thus the data in the balance sheets of commer- 4.15 Balance of payments current account Goods and services Net Income Net Current Gross current account International transfers balance reserves Exports Imports $ millions $ millions $ millions $ millions $millions $ millions 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan ..- - .. 638 Al bania 354 704 485 1.499 -2 107 15 533 -118 -156 - 383 Algeria 13.462 22,359 10,106 9,842 -2.268 -3,075 _333 . 1,420 . 2,703 13,.556 Angola 3,992 7,945 3,385 6,195 -765 -1,843 -77 89 -236 -4 . 1.198 Argentina 14,800 30,945 6,846 32,722 -4,400 -7,482 998 289 4,552 -8,970 6,222 25.152 Armenia . 447 966 53 -. 188 .. -278 1 331 Australia 49,843 82,387 53,056 86,777 -13,176 -10,926 439 0 -15,950 -15,316 19,319 18,822 Austria 63,694 94,907 61,580 96,597 -942 -2,265 -6 -1,249 1.166 -5,205 17,228 17,649 Azerbaijan . 2,146 .. 2,023 -. -346 . 7 3 .., -150 0 680 260 Bangladesh 1.903 6,611 4,156 9,060 -122 -221 802 2.672 __-1.573 2 660 1,516 Belarus 3,661 7,980 3,557 8,257 -1 -42 79 157 182 -162 - 350 cit ~~~~~~~~~~~~~~~~~~~--------- - --- --- ---- - -- o Belgium' ~ 138,605 210,725 135,098 202,898 2,316 8,219 -2,197 -4,202 3,627 11,844 61,284 53,620 t ~ Benin 364 -522 454 - 782 --25 -19 97 I11 -18 -168 69 458 Bolivia 977 1,454 1,086 2,078 -249 -225 159 385 -199 -464 511 1,038 Bosnia and Herzegovina . .. .- a) --------- - ------- ---- --- -- E Botswana 2,005 3,044 1,987 2,512 -106 -266 69 252 -19 517 3,331 6,318 0 .---- - - -- - ---- - - - - -- - - --- . . . o Brazil 35,170 64,469 28,184 72,739 -11,608 -17,884 799 1,522 -3,823 -24,632 9.200 33,008 Bulgaria 6,950 7,000 8,027 7,669 -758 -321 125 290 -1.710 -701 670 3,625 Burkina Faso 349 259 758 635 0 -39 332 350 -77 -65 305 244 - - - - - - - - -- - - . . . . . . . ... . . . . . .. . . . Burundi 89 55 318 ....... 151 -15 -12 174 59 --69 -49 112 38 Cambodia 314 1,497 507 1,769 -21 -52 120 305 -93 -19 - 502 o Cameroon 2,251 2,719 1.931 2,376 -478 -593 -39 97 -196 -153 37 212 0~~~~~~~~~~~~~~~~~~~- ------ Canada 149,538 321,693 149,118 286,386 -19,388 -18,267 -796 __974 -19,764 18,014 23,530 32,249 Central African Republic 220 110 410 149 -22 -12 123 51 -89 0 123 133 Chad 271 279 488 478 -21 -10 192 51 -46 -158 132 il1 Chile 10,221 22,090 9,166 21.209 -1,737 -2,409 198 537 - -485--991 6,784 14,749 China' 57,374 279,562 46,706 250,688 1,055 -14,666 274 6,311 11.997 20.518 34.476 171,763 Hong Kong, China 100,413 244,004 94,084 236,311 0 2,766 - -1,632 6,329 8.827 24,656 107.560 Colombia 8,679 15,678 6,858 14,385 -2,305 -2,577 1.026 __1,590 542 306 4,869 9.006 Congo. Dem. Rep. 2,557 .. 2.497 -. -770 -754 -27 .. 738 -583 261 Congo, Rep. 1,488 2,714 1,282 1,332 -460 -839 3 - -251 . 10 222 costa Rica 1.963 7,628 2,346 7,265 -233 -1,176 192 102 -424 -649 525 1,318 C6e dIlvoire 3,503 4,408 3,445 3,391 -1,091 -660 -181 -370 -1,214 -13 21 668 Croatia .. 8,651 .. 9,597 -. -311 .. 858 .. 399 167 3,524 Cuba Czech Republic -. 35,746 37,528 - -752 298 .. -2,236 -. 13,142 Denmark 48,902 71,141 41,415 61,883 -5,708 -3.561 -408 -3,190 1,372 2,507 11.226 15,696 Dominican Republic 1,832 8.964 2.233 10,852 -249 -1,041 371 1,902 -280 -1,026 _69 630 Ecuador 3,262 5,987 2,519 4,998 -1,210 -1,412 107 1,352 -360 928 1,009 1.179 Egypt, Arab Rep. 9,151 15,975 13,710 22,756 -912 932 4,836 4,679 -634 -1,171 3,620 13.785 El Salvador 973 3.645 1,624 562 -3 20 61 189 -5 -418 59 2,051 Eritrea 88 96 278 499 . 0 1 71 196 -19 -208 Estonia 664 4,791 711 5,040 -13 _-204 97 138 36 -315 198 923 Ethiopia ~ 672 984 1,069 1,960 -67 -60 220 701 -244 -335 55 312 Ethiopia~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. ... - Finland 31,180 51,764 33,456 40,366 -3,735 -1,913 _-952 -631 -6,962 8,854 10,415 8,897 France 285.389 377,274 283,238 357,030 -3,896 13,710 -8,199 -13,526 -9,944 20,428 68,291 63,728 Gabon 2.730 3,023 1,812 1,868 -617 -699 -134 -71 168 385 279 190 Gambia, The 168 262 192 321 -11 -5 59- 15 - 23 -48 55 109- Georgia .. 1,136 - 1,410 .. 13 .. 100 .. -162 -- 109 Germany 474,713 633,052 423,497 625,892 20,832 -988 -23,745 -24,878 48,303 -18,707 104,547 87,497 Ghana 983 2,403 1,506 3,339 -111 -108 411- 631 -223 -413 309 309 Greece 13,018 29,440 19,564 41,727 -1,709 -885 4,718 3,352 -3,537 -9,820 4,721 14,594 Guatemala 1,568 3,892 1,812 5,584 -196 -226 227 868 -213 -1,049 362 1,806_ Guinea 829 843 953- 919 -149 -79 70 -10 -203 -165 80 148 Guinea-Bissau 26 70 88 106 -22 -13 39 - -45 - 18 67 Haiti 318 530 515 1,333 -18 -21 193 223 -22 -38 10 184 Honduras 1,032 2,501 1,127 3,275 -237 -3 280 708 -51 -204 47 1,319 f Data for Taiwan, China 74,175 167,907 67,015 160,457 4,361 4.468 -601 -2,602 10,920 9,316 77,653 110,139 Balance of payments current account 4.15 IFUg Goods and services Not Income Net Current Gros's current accon International trnsfers balance reserves Exports Im ports $ millions $ Millions $ millions $ millions $ millions $ millions 1990 2000 1990 2000 1990 2000 1ja9o 2000 1 1990 2000 1990 2000 Hungary 12,035 31,618 11,017 31,948 -1,427 -1,574 787 410 379 -1,494 1,185 11,217 India 23,028 63.764 31,485 75,656 -3,753 -3,821 2,068 12,798 -10,14:2 -2,915 5,637 41,059 Indonesia 29,295 70,619 27,511 55,377 -5,190 -9,072 418 1,816 -2,988 7,986 8,657 29,353 Iran, Islamic Rep 19,741 29,727 22,292 17,503 378 -200 2,500 621 327 12,645 Iraq___ _I___________________ Ireland 26,786 90,221 24,576 76,762 A,955 -15,002 2,384 949 -361 -593 5,362 5,408 Israel 17,312 45,179 20,228 46,534 -1,975 -6,663 5,060 6,602 170 -1,416 6,598 23,281 Italy 219,971 294,852 218,573 284,191 -14,712 -12,003 -3,164 -4,328 -16,479 -5,670 88,595 47,201 Jamaica 2,217 3,580 2,390 4,340 -430 -336 291 821 -312 -275 168 1,054 Japan _____ 323,692 528,751 297.306 459,660 22,492 57,623 -4,800 -9.831 44,078 116,883 87,828 361,639 261 Jordan 2,511 3,536 3,754 5,796 -215 -27 1,046 2,345 -411 59 1,139 3,441 - Kazakhstan 5,758 10,751 5,862 875 -1 75 -1,179 168 207 -111 1042,099 Kenya 2,228 2,741 2,705 3,768 -418 -133 368 922 -527 -238 236 898 Korea, Dem Rep.- - - _ _ _ _ _ _ _ _ _ _ _ _ _ _ - _ _ -- _ _ Korea, Rep 73,295 205,645 76,360 192,499 -87 -2,421 1,149 680 -2,003 11,0 496 96,251 c Kuwait 8,268 21.617 7,169 11,785 7,738 6,918 -4,951 -1,884 3,886 14,865 2,929 7,779 Kyrgyz Republic 573 651 -80 82 - -77 262 ( Lao PDR 102 501 212 613 -1 -49 56 240 -55 90 8 144 ' Latvia 1,090 3,270 997 3,886 2 24 96 97 191 -494 919 C Lebanon 511 2,141 2,836 6,228 622 932 1,818 90 15 -3,065 4,210 8,.475 0~ Liberia 10 Libya 11,469 6,813 8,960 4,914 174 289 -481 -204 2.201 1,984 7,225 13,730 ( Lithuania 5,109 5,833 -194 23-675 107 1,363 Macedonia, FYR 1,620 2,233 - -45 551 -107 460 Madagascar 471 1,1788- 809 1,520 -161 -42 234 113 -265 -260 92 285 Malawi 443 487 549 934 -80 -83 99 6 -86 -523 142 250 Malaysia ___32,665 111,261 31,765 94,024 -1,872 -9,282 192 -1,728 -870 12,606 10,659 29,844 Mali 420 705 830 1,060 -37 -28 225 -221 198 381 Mauritania 471 372 520 428 -46 -19 86 165 -10 90 59 228 Mauritius 1.722 2,630 1,916 2,699 -23 -28 - 97 - -64 -119 -33 761 914 Mexico 48,805 180,210 51,915 191.895 -8,316 -13,466 3,975 6,994 -7,451 -18,157 10,217 35,577 Moldova 640 990 72 157 -121 0 230 Mongolia 493 607 1.096 772 -44 -3 7 74 -640 -52 23 202 Morocco 6,239 10,453 7,783 12,538 -988 -873 2,336 2,483 -1~96 -475 2,338 5,017 Mozambique 229 689 996 1,492 -97 -192 448 231 -415 -764 233 744 Myanmar 641 1,840 1,182 2,787 -61 -70 77 366 -526 -651 40 286 Namibia 1,220 1,745 1,584 1,889 37 -42 34 90 28 204 50 260 Nepal 379 1,279 761 1.782 71 34 60 175 -251 -293 354 989 Netherlands 159,304 258,951 147,652 240,624 -620 1,631 -29~ -6,193 -09 19T 3,7-64 34,401 17,688 New Zealand 11,683 17,810 11,699 17,358 -1,576 -3,428 138 242 -1,453 -2,734 4,129 3,329 Nicaragua 392 953 682 1,986 -217 -201 202 741 -305 -493 166 493 Niger 533 282 728 424 -54 - -15 14 -10 -236 -168 226 80 Nigeria 14,550 2307 6,909 14,124 -2,738 -3,287 85 1,348 4,988 6,983 4,129 6,485 Norway 47,078 75,176 38,911 49,187 -2,700 -1,533 -1,476 -1,471 3,992- 22,986 15,788 20,489 Oman 5,5-77 -11,602 3,342 6,094 -254 -705 -874 -1.456 1,106 3,347 1,784 2,460 Pakistan 6,217 9,575 9,351 1172 -6 208 220 197 -1,890 -2,08 -1,046 2,087 Panama 4,438 7,666 4,193 8,164 -255 -612 219 177 209 -933 344 723 Papua New Guinea 1,381 2,233 1,509 1,927 -103 -305 16 -9 -76 -8 427 326 Paraguay ___ 2,514 2,801 2,169 3,307 2 32 43 175 390 -299 675 770 Peru 4,120 8,598 4,087 9,704 -1,733 -1,541 281 1,019 -1,419 -1,628 1,891 8,676 Philippines 11,430 41,468 13,967 36,465 -872 3,645 714 433 -2,695 9,081 2,036 15,035 Poland 19,037 46,294 15,095 57,210 -3,386 -141 2,5l 1 2,380 3,067 -9,997 4,674 27,469 Portugal ___ 21,554 33,166 27,146 45,544 -96 -201 557 3,406 -181 -11,012 20,579 14,262 Puerto Rico Romania 6,380 12,133 9,901 14,071 161 -281 106 860 -3,254 -1359i- 1,374 4,848 Russian Federation 53,883 115,200 48,915 62,290 -4,500 -11.154 0 -90 468 41,846 27,656 ~~~1 ~~4.15 1 3--Islnce of pay:i:- Wi-s cu:. accrt li~:Z Goods and services Net Income Net Current Gross current account International transfers balance reserves Exports Im ports $ millions $ millions $ millions $ millions $ millions $ millions 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 145 131 359 403 -17 -15 145 281 -86 -7 44 191 Saudi Arabia ___ 47,445 82,369 43,939 53,003 7,979 480 -5637 -15,511 -4,152 14,336 13,437 20,847 Senegal 1,453 1,337 1,840 1,732 -129 -113 153 197 -363 -310 22 384 Sierra Leone 210 87 215 240 -71 -24 7 -69 5 51 Singapore 67,489 165,971 64,953 148,939 1,006 6,123 -421 -1,359 3,122 21,797 27.748 80,132 Slovak Republic 14,137 14,596 -355 120 -694 4,376 Slovenia 7,900 10,694 6,930 11,397 -38 -25 46 115 978 -612 112 3,196. Somalia 70 322 South Africa 27,742 36,522 21,016 32,818 -4,271 -3,247 -321 -926 2,134 -469 2,583 7,702 262 Spain 83,595 168,463 100,870 178,987 -3,533 -8,311 2,799 1,578 -18,009 -17,257 57,238 35,607 - Sri Lanka 2,293 6,378 2,965 8,105 -167 -299 541 984 -298 -1,042 447 1,211 Sudan 532 1,892 1,453 1,921 -784 -1,264 407 319 -1,299 -974 11 189 co Swaziland 658 885 768 1,098 59 77 102 96 51 -40 216 352 -Sweden 70,560 107,683 70,490 95,656 -4,473 -2.063 1,936 -3,348 -6,339 6,617 20,324 16,499 Switzerland 96,927 120,743 96,388 108,386 8,746 23,999 2,329 -3,815 6,955 32,542 61,284 53,620 E Syrian_Arab Republic 5,030 6,846 2,955 5,390 -401 -879 88 485 1,762 1,062 o Tajikistan 185 800 238 839 0 -55 33 -53 -61__ 56 > Tanzania 538 1,280 1,474 2,010 -185 -80 562 511 -559 -298 193 974 a) _ __ _ _ __ _ _ _ _ _ _ _ o Thailand 29,229 81,817 35,870 71,652 -853 -1,381 213 586 -7,281 9,369i 14,258 32,665 o Togo 663 438 847 614 -32 -25 132 95 -84 -106 358 152 Trinidad and Tobago 2,289 4,769 1,427 3,823 -397 -610 -6 22 459 -644 513 1,403- ' Tunisia 5,203 8,607 6,039 ___9,311 -455 -4 82 84 -63 81 87 1.1 CNJ Turkey 21,042 51,148 25,652 62,190 -2.508 -4,002 4,493 5,225 -2,625 -989 7,626 23,515 Turkmenistan 1,238 2,774 857 2,350 0 -177 66 166 447 412 1,513 Uganda ___246 626 676 1,985 -77 -15 78 513 -429 -860 44 808 Ukraine 19,522 18,116 -942 1,017 1,481 469 1,477 United Arab Emirates ___4,891 13,632 United Kingdom 239,226 401,385 264,090 425,075 -5,154 9,098 L8,794 -13,248 5-38,11-H 27,840 43,146 48,193 _ - 4 Z_- United States 535,260 1,065,740 616,120 1,441,500 28,560 -14.780 -26,660 -54,150 -78,960 -444,690 173,094 128,400- Uruguay ___ 2,158 3,733 1,659 4,216 -321 -176 8 66 186 -593 1,446 2,776 Uzbekistan 3,383 2,962 -251 - 3184 1,242 Venezuela, RB__ 18,806 34,272 9,451 19,746 -774 -1,204__ -302 -211 8,7 311 12,733 15,899 Vietnam 1,913 17,107 1,901 17,344 -412 -597 49 1,341 -351 507 3,417 West Bank and Gaza Yemen, Rep 1,490 4,305 2,170 3,150 -372 -514 1,790 1,422 739 2,063 441 2,914 Yugoslavia, Fed Rep Zambia 1,360 936 1,897 1.167 -437 --4 11 380 -594 201 245 Zimbabwe ___ 2,012 2,101 2,001 1,991 -263 -22 112 -140 295 321 Low Income 130,306 274,302 147.914 275,884 Middle Income 699,866 1.771,709 664,038 1,641,764 Lower middle income 268,479 747,156 274,866 654,664 Upper middle income 430.021 1,024,328 390,689 986,006 Low & middle Income 829,051 2,046,020 811,098 1.917,665 East Asia & Pacific 239,776 817,861 240,892 730,851 __ Europe & Central Asia 187,852 392,525 187,180 372,245 _ ___ Latin America & Carib 169,120 417,454 146,270 436,649 Middle East & N. Africa 132,144 213,961 132,549 162,895 South Asia 34.113 88,259 49,041 107,198 Sub-Saharan Africa 81.284 116,295 74,679 106,577 High Income 3,419,212 5,774,700 3,432,886 5,933,098 Europe EMU ___ 1,530,965 2,256,837 1,495,268 2,215,062 a Includes Luxembourg 4.15 El About the data Definitions The balance of payments records an economy's converted at market exchange rates. * Exports and imports of goods and services transactions with the rest of the world. Balance The data in this table come from the IMF's comprise all transactions between residents of payments accounts are divided into two Balance of Payments and International Financial of a country and the rest of the world involving groups: the current account, which records Statistics databases, supplemented by a change in ownership of general merchandise, transactions in goods, services, income, and estimates by World Bank staff for countries goods sent for processing and repairs, current transfers; and the capital and financial whose national accounts are recorded in fiscal nonmonetary gold, and services. * Net income account, which records capital transfers, years (see Primary data documentation) and refers to receipts and payments of employee acquisition or disposal of nonproduced, countries for which the IMF does not collect compensation to nonresident workers, and nonfinancial assets, and transactions in financial balance of payments statistics. In addition, World investment income (receipts and payments on assets and liabilities. This table presents data Bank staff make estimates of missing data for direct investment, portfolio investment, and from the current account with the addition of the most recent year. other investments, and receipts on reserve gross international reserves. assets). Income derived from the use of The balance of payments is a double-entry Figure 4.15 intangible assets is recorded under business accounting system that shows all flows of goods services. * Net current transfers are recorded and services into and out of a country; all Suddenly positive in the balance of payments whenever an 263 transfers that are the counterpart of real currentaccountbalanceasapercentageof economy provides or receives goods, services, resources or financial claims provided to or by income, or financial items without a quid pro 0 the rest of the world without a quid pro quo, $ millions Korea, Rep. 3 quo. All transfers not considered to be capital 10- such as donations and grants; and all changes are current. * Current account balance is the E in residents' claims on, and liabilities to, sum of net exports of goods and services, net nonresidents that arise from economic o . _ r ,, . _. . 1r1 income, and net current transfers. * Gross T transactions. All transactions are recorded International reserves comprise holdings of D (0 twice-once as a credit and once as a debit. In 1985 1990 1995 1996 1997 1998 1999 2000 monetary gold, special drawing rights, reserves 3 principle the net balance should be zero, but in of IMF members held by the IMF, and holdings D practice the accounts often do not balance. In Indonesia of foreign exchange under the control of these cases a balancing item, net errors and 10 monetary authorities. The gold component of omissions, is included. * these reserves is valued at year-end (31- Discrepancies may arise in the balance of 0 - December) London prices ($385 an ounce in payments because there is no single source for 1990 and $274.45 an ounce in 20001. balance of payments data and therefore no way 1985 1990 1995 1996 1997 1999 1999 2000 to ensure that the data are fully consistent. Sources include customs data, monetary ac- Thailand 13 Dt suc counts of the banking system, external debt to More information about the design and i records, information provided by enterprises, compilation of the balance of payments can surveys to estimate service transactions, and o be found in the IMF's Balance of Payments foreign exchange records. Differences in collec- Manual, fifth edition (1993), Balance of| tion methods-such as in timing, definitions of -10 1985_1_90_1995_1__6_1997_1998_1 _ 20 Payments Textbook (1996a), and Balance of residence and ownership, and the exchange rate 1995 1990 1995 1996 1997 199 1999 2000 Payments Compilation Guide (1995). The used to value transactions-contribute to net . . 2 balance of payments data are published in the errors and omissions. In addition, smuggling and *o PhlippInes 1 IMF's Balance of Payments Statistics Yearbook other illegal or quasi-legal transactions may be and Intemational Financial Statistics. The World unrecorded or misrecorded. For further discus- o _ Bank exchanges data with the IMF through sion of issues relating to the recording of data electronic files that in most cases are more on trade in goods and services see About the -10 timely and cover a longer period than the data for tables 4.4-4.8. 1985 1990 1995 1996 1997 1998 1999 2000 published sources. The IMF's International The concepts and definitions underlying the Financial Statistics and Balance of Payments data in the table are based on the fifth edition Malaysia 13 databases are available on CD-ROM. of the International Monetary Fund's (IMF) 10to * .. __ Balance of Payments Manual (1993). The fifth edition redefined as capital transfers some 0 transactions previously included in the current account, such as debt forgiveness, migrants' -10 1985no 9901995_19961997_19981999_200 capital transfers, and foreign aid to acquire 1985 1990 1995 1996 1997 1999 1999 2000 capital goods. Thus the current account balance now reflects more accurately net current transfer Source. World Bank (ta files. receipts in addition to transactions In goods, The East Asia economies most affected by the 1997 financial crisis have aii swung Into current account services (previously nonfactor services), and surpluses. income (previously factor income). Many countries maintain their data collection systems according to the fourth edition. Where necessary, the IMF converts data reported in such systems to conform to the fifth edition (see Primary data documentation). Values are in U.S. dollars 4.16 External debt Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt external debt IBRD loans and Total IDA credits $millions $ mill ions $ millions $ millions $ millions $ millions 1.990 2000 1990 2000 1.990 2000 1990 2000 1.990 2000 1990 2000 Afghanistan .. . . . . Albania 349 784 36 659 36 644 0 346 0 15 0 88 Algeria 27.877 25,002 26,416 23,062 26,416 23.062 1,208 1.425 0 0 670 1.718 Angola 8.594 10,146 7,605 8,758 7,605 8,758 0 226 0 0 0 0 Argentina 62.232 146.172 48.676 112.801 46,876 86,599 2,609 8.789 1,800 26,202 3,083 5,056 Armenia .. 898 678 658 .. 397 .. 20 .. 176 Australia~~~~~--- - ---- ------- Austraia Azerbaijan .. 1,184 692 594 .. 216 .. 99 .. 336 264 Bangladesh 12,439 15,609 11,657 15,098 11,657 15.098 4,159 6.455 0 0 626 216 - Belarus . 851 .. 693 . 692___ 105 .1 .. 114 Beliu ~n Benin 1.292 1.598 1,218 1,443 1.218 1,443 326 578 0 0 18 84 U~~~~~~~~~~~~~~~~~~~----- ---- ----- ---- Bolivia 4,275 5,762 3,864 5,140 3,687 4,120 587 1,096 177 1,020 257 220 Bosnia and Herzegovina .. 2.828 .. 2,575 . -2.569 .. 959 ..7 .. 105 a, -~~~~~~~~~~~~~~~~~~~~~~~- -- - -~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~- -- - . ... Botswana 561 413 556 398 556 398 169 25 0 0 0 0 0 ). - - - - - - - - - - - - - -- - - - -- o Brazil 119,964 237,953 94,427-205.210 87,756 92.590 8,427 7.377 6.671 112.620 1.821 1,768 a) -- - ------------- > Bulgaria 10,890 10.026 9.834 8.282 9,834 7,513 0 824 0 769 0 1.322 Burkina Faso 834 1,332 750 1.135 750 1.135 282 59-2 0 0 0 112 Burundi 907 1.100 851 1.028 851 1,028 398 600 0 0 43 7 0-- - - - - - - - - - Cambodia 1,854 2,357 1,688 2,180 1,688 2.180 0 207 0 0 27 73 o Cameroon 6.676 9,241 5.595 7,674 5,365 7,357 889 987 230 317 121 235 C~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ---- -- C' Canada Central African Republic --698 --872 624 810 624 810 265 391 0 0 37 22 Chad 524 1,116 464 1,009 464 1,009 186_ _515 0 0 31 78 Chile 19,226 36,978 14.687 34.447 10.425 5,210 1,874 816 4.263 29.236 1,156 0 China 55,301 149,800 45,515 132,625 45.515 104,709 5,881 19,889 0 27.916 469 0 Hong Kong, China.. ,- ....... .. Colombia 17.222 34.081 15.784 31,210 14,671 20,950 3.874 1.927 1,113 10.259 0 0 Congo, Dem. Rep. 10.274 11.645 9.010 7,842 9,010 7.842 1.161 1,269 0 0 521 391 Congo. Rep. 4.947 4.887 4.200 3,757 4,200 3,757 239 224 0 0 11 41 Costa Rica 3,756 4,466 3.367 3,510 3.063 3.274 412 123 304 236 11 0 C6te dIlvoire 17,251 12.138 13.223 10,546 10.665 9.063 1,920 1,965 2,558 1.482 431 549 Croatia .. 12,120 .. 11,264 .. 7.685 .. 395 .. 3,578 .. 158 Czech Republic 6.383 21.299 3,983 12,282 3,983 8.132 0 260 0 4,151 0 0 Dominican Republic 4,372 4,598 3,518 3,368 3,419 3,368 258 306 99 0 72 52 Ecuador 12,107 13,281 10,029 12.151 9.865 11,366 848 861 164 785 265 148 Egypt, Arab Rep 33,017 28,95 28,438 2482 2,3 429 241 1,905 1.000 573 125 0 El Salvador 2,149 4,023 1.938 2,886 1,913 2,775 164 - 325 26 111 0 0 Eritrea .. 311 . 298 . 298 .. 85 .0 ..0 Estonia .. 3.280 .. 2.317 206 .. 71 .. 2,111 .. 19 Ethiopia 8.630 5.481 8.479 5.325 8,479 5,325 851 1.779 0 0 6 77 Gabon 3,983 3,995 3,150 3,512 3,150 3.512 69 64 0 0 140 89 Gambia, The 369 471 308 425 308 425 102 171 0 0 45 18 Georgia .. 1,633 -- 1,311 .. 1,271 .. 347 . 0. 7 Germany . .. .. .. .- Ghana 3,881 6,657 2,816 5,786 2,783 5,529 1,423 3,140 33 257 745 293 Guatemala 3,080 4,622 2,605 3,287 2,478 3,146 293 296 127 142 67 0 Guinea 2,476 3,388 2,253 2,940 2,253 2,940 420 982 0 0 52 113 Guinea-Bissau 692 942 630 818 630 818 146 228 0 0 5 - 25 Haiti 910 1,169 772 1.040 772 1,040--324 480 0 0 38 39 Honduras 3,718 5,487 3,487 4,897 3.420 4,337- 635 989 66 560 32 216 4.16 Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt external debt IBRD loans and Total IDA credits $ millions $ millions $ millions $ millions $ millions $ millions 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1a9o 2000 Hungary 21,202 29,415 17,931 25,263 17,931 14.251 1.512 582 0 11.012 330 0 India 83,628 100,367 72,462 96,903 70,974 87,598 20.996 27,866 1,4 88 9,305 2,6230 Indonesia 69.872 141,803 58,242 108,330 47.982 69,161 10,385 12,428 10,261 39,169 494 10.838 Iran, Islamic Rep. 9,020 7.953 1,797 4,275 1,797 3,812 86 481 0 463 0 0 Ireland... ..... Jamaica 4,674 4,287 3,970 3,475 3.937 3,373 672 415 34 103 357 60 Japan ~ ~ ~ ~ ~ ~.... ... -- ------------------.. ......2 6 5 Jordan 8.177 8,226 7,043 7,055 7,043 7,055 593 856 0 0- 94 462 ------ - - ----- -- - - ---------- - --------- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ N Kazakhstan 6,664 6,131 3,602 .. 1,057 .. 2,529 ..0 Kenya 7,058 - 6,295 5,642 5,355 4,762 .5,180 2,056 2.309 880 175 482 127 Korea. Dem. Rep. ------.------ - -- ---- - - - -------------- ..........~ ~~~~~~~~~~~~~~ Korea, Rep. 34,968 134,417 24,168 88.141 18,768 46,941 3,337 8.097 5.400 41.200 0 5.814 Kyrgyz Republic -.-1,829 - .. 1,512 .. 1.224 .. 377 -- 288 .. 188 CD - - --------- -- - ~ ~~~~0 Lao PDR 1,768 2,499 1,758 2,449 1,758 2,449 131 403 0 0 8 42 1 Latvia .. 3,379 .. 2,074 -- 827 .. 242 -- 1,247 35 35 Lebanon 1,779 10,311 358 7,770 358 7,034 34 248 0 736 0 0 E Lesotho 396 716 378 698 378 698 112 242 0 0 15 11 Liberia 1,849 2,032 1,116 1,040 1.116 1,040 248 230 0 0 322 292 E- Libya-- --------- Lithuania 4,855 3,549 .. 2,188 253 -. 1,361 .. 192 Macedonia, FYR .. 1,465 ., 1,304 .. 1,165 .. 365 140 .. 81 Madagascar 3,704 4,701 3,335 4,295 3,335 4,295 797 1,378 0 0 144 104 Malawi 1.558 2,716 1,385 2,555 1.382 2,555 854 1,601 3 0 115 83 Malaysia 15,328 41,797 13,422 37,156 11,592 19,090 1,102 812 1,830 18,067 0 0 Mali 2,467 2,956 2,336 2,645 2,336 2,645 498 957 0 0 69 176 Mauritania 2,096 2,500 1,789 2,150 1,789 2,150 264 450 0 0 70 98 Mauritius 984 2,374 910 1,608 762_ 889 195 _99 148 718 22 0 Mexico 104,442 150,288 81,809 131,356 75,974 81.550 11,030 11,444 5.835 49,806 6,551 0 Moldova 1,233 .. 1.051 . 854 -- 294 . 197 -- 154 Mongolia 859 .. 795 .. 795 0 137 ..0 0 50 Morocco 24,458 17,944 23,301 17,688 23,101 15,792 3,138 2,864 200 1.896 7500 Mozambique 4,650 7,135 4,231 6,346 4,211 4,598 268 760 19 1,747 74 _220 Myanmar 4,695 6,046 4,466 5,360 4,466 5,360 716 802 - - 0 0 0 Namibia. .. ..- - Nepal 1,640 2,823 1,572 2,784 1,572 2,784 668 1,134 0 0 44 12 Netherlands _7. .. .. . .. .. New Zealand.. . .. . ... Nicaragua 10,745 7,019 8,313 5,860 8,313 5,602 299 659 0 258 0 169 Niger 1,726 1,638 1,487 1,481 1,226 1,413 461 723 261 68 85 74 Nigeria 33,439 34,134 31,935 32,950 31,545 32,735 3,321 2,268 391 215 0 0 Norway . .. .. .. Onman 276 6,267 2,400 4,968 ~ 2400 2,67 52 3 360 2,296 0 0 Pakistan 20,663 32,091 16,643 29,043 16,506 27,140 3,922 6,922 138 1,903 836 1,529 Panama 6.506 7,056 3,856 6,503 3,856 5,723 462 283 0 780 272 90 Papua New Guinea 2.594 2,604 2,461 2,515 1,523 1,502 349 336 938 1,014 61 39 Paraguay 2,105 3,091 1,732 2,511 1,713 2,061 320 230 19 450 0 0 Peru 20,064 28,560 13,959 24,045 13,629 19,205 1.188 2,590 -330 4,841 755 558 Philippines 30,580 50,063 25,241 42,083 24,040 33,429 4,044 3,834 1,201 8,654 912 2,032 Poland 49,364 63,561 39,261 56,457 39,261 30,784 55 2,229 0 25,672 509 0 Portugal -.... Puerto Rico . . . Romania 1,140 10,224 230 9,410 223 6,430 0 1,898 7 2,980 0 453 Russian Federation 59,340 160,300 47,540 133,158 47,540 111,419 0 6,844 0 21.739 0 11,613 * ~4.16 Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt external debt IBRD loans and Total IDA credits $ mnillions $ millions $ millions $ millions $ millions $ millions 1990 2000 1990 2000 1 1990 2000 1990 2000 1990 2000 11990 2000 Rwanda 712_ 1,271 664 1.147 664 1,147 340 692 0 0 0 86 Saudi Arabia . .. .. .. Senegal 3,736__ 3,372 3,000 2.971 2,940 2,958 835 1,331 60 13 314 255 Sierra Leone 1,151 1,273 604 969 604 969 92 354 0 0 108 174 Singapore . .. Slovak Republic 2,008-.- 9,462- 1,50-5 8,304 1.505 4,883 0 184 0 3,421 0 0 Slovenia -. . -. ... Somalia 2,370 2,561 1,926 1,825 1,926 1,825 419 396 0 0 159 146 South Africa .. 24,861 15,308 .. 9,088 0 3 .. 6,220 0 0 266 Spain Sri Lanka 5,863 9,065 5,049 8.200 4,947 8,035 946 1,624 102 165 410 161 Sudan 14,762 15,741 9,651 9.143 9,155 8,647 1,048 1,168 496 496 956 625 m Swaziland 254 262 249 198 249 198 44 14 0 0 0 0 Sweden.....-... ... --- Switzerland . .. .. .. .. Q) - ---- --- E Syrian Arab Republic 17,259 21,657 15,108- 15.930 15,108 15,93-0 523 54 0 0 0 -0 o Tajikistan 1,170 995 .. 626 .. 143 .. 370 .. l11 C)-- Tanzania 6,454 7,445 5,796 6,353 5,784 6,325 1.493 2.604 12 28 140 324 0 Thailand 28,095 79,675 19,771 61,733 12.460 29.418 2,530 3,030 7,311 32,316 1 3.062 o ITogo_ 1.281 1,435 1.081 1,23 2_ 1.081 1,232 398 604 0 0 87 70 Trinidad and Tobago 2.512 2.467 2,055 1.606 -1.782 1,496 41 89 273 110 329 0 o Tunisia 7,690 10.610 6.880 9,669 6,662 8.869 1,406 1.250 218 800 176 32 CN Turkey 49.424 116,209 39,924 83,121 38,870 55,293 6,429 3,734 1,054 27,828 0 4,176 Turkmenistan ... .. .. .28 ... .0 Uganda 2,583 3,408 2,161 2.997 2,161 2,997 969 2,115 0 0 282 316 Ukraine .. 12,166 9,646 8,139 . 1,991 .. 1.507 .. 2.073 United Arab Emirates - United Kingdom . . . . . United States . .. .. ., .. Uruguay 4.415 8,196 3.114 6,131 3.045 5.597 359 552 69 534 101 149 Uzbekistan . 4.340 .. 3.931 .. 3.578 . 217 , 354 .. 127 Venezuela. RB 33.170 38,196 28,159 36,230 24,509 27,628 974 972 3,650 8,602 3,012 203 Vietnam 23,270 12,787 21.378 11,546 21,378 11,546 59 1,113 0 0 112 316 West Bank and Gaza . . . - . Yemen, Rep. 6.352 5.615 5,160 4,524 5.160 4.524 602 1.216 0 0 0 317 Yugoslavia, Fed. Rep., 17,792 11,960 16,802 6,685 12,942 6,074 2.433 1,097 3.860 611 467 152 Zambia 6,916 5.730 4,554 4.513 4.552 4.448 813 1,848 2_ 65 949 1,138 Zimbabwe 3.247 4.002 2,649 3.158 2,464 2,948 449 853 185 211 7 281 Low Income 418.586 550,548 357.322 471,077 340,308 412,834 66,693 100,470 17,015 58,243 11,250 23,580 Middle Income' 1,039,801 1,941.427 822.006 1.576,622 773,507 1,077,611 73,946 108,622 48,498 499.009 23,400 40,683 Lower middle lincome 470,037 779,736 386.402 652,075 369,306 _528,573 34,782 61,176 17.096 123.502 6.058 23.048 Upper mniddle income' 567.807 1,156.041 434,963 921.959 403,561 546.822 39.164 47.303 31,402 375.137 17.343 17,524 Low & middle income' 1,458,389 2,491.975 1,179.328 2.047.696 1.113,815 1,490,445 140,639 209,093 65,513 557,251 34.651 64,262 East Asia & Pacific 273,983 632,953 222.722 502,238 195.687 333,852 28,644 51,211 27.035 168.386 2,085 22,266 Europe & Central Asia 219,850 499,344 177,688 396.428 172.767 284.369 10,429 25.455 4.921 112,060 1.305 21,951 Latin America & Carib. 474.720 774,419 379.206 661.735 354,155 415.077 35.841 40,904 25.051 246.658_ 18,297 8.846 Middle East & N. Africa 183.471 203,785 137,762 153.794 136,260 147,031 10.074 10,351 1,502 6,763 1.815 2,542 South Asia 129,481 165.679 112,573 157,724 110,845 146,351 30.717 44.073 1,727 11.372 4,537 1,918 Sub-Saharan Africa 176,883 215,794 149,377 175,777 144.101 163,765 24,935 37.098 5.276 12.012 6,612 6.739 High Income Europe EMU a. Data for 1990D refer to the former Socialist Federal Republic of Yugoslavia. Data for 2000 are estimates and reflect borrowings by the former Socialist Federal Republic of Yugoslavia thlat are not yet allocated to the successor republics, b. Includes data for Gibraltar not included in other tables. 4.16 About the data Definitions Data on the external debt of low- and middle- Variations in reporting rescheduled debt also * Total external debt is debt owed to nonresi- income economies are gathered by the World affect cross-country comparability. For example, dents repayable in foreign currency, goods, or Bank through its Debtor Reporting System. World rescheduling under the auspices of the Paris services. It is the sum of public, publicly guar- Bank staff calculate the indebtedness of devel- Club of official creditors may be subject to lags anteed, and private nonguaranteed long-term oping countries using loan-by-loan reports sub- between the completion of the general resched- debt, use of IMF credit, and short-term debt. mitted by these countries on long-term public uling agreement and the completion of the spe- Short-term debt includes all debt having an and publicly guaranteed borrowing, along with cific, bilateral agreements that define the terms original maturity of one year or less and inter- information on short-term debt collected by the of the rescheduled debt. Other areas of incon- est in arrears on long-term debt. * Long-term countries or collected from creditors through the sistency include country treatment of arrears and debt is debt that has an original or extended reporting systems of the Bank for International of nonresident national deposits denominated maturity of more than one year. It has three Settlements and the Organisation for Economic in foreign currency. components: public, publicly guaranteed, and Co-operation and Development. These data are private nonguaranteed debt. * Public and pub- supplemented by information on loans and cred- licly guaranteed debt comprises long-term ex- its from major multilateral banks, loan state- ternal obligations of public debtors, including ments from official lending agencies in major the national government and political subdivi- 267 creditor countries, and estimates from World sions (or an agency of either) and autonomous Bank and International Monetary Fund (IMF) public bodies, and external obligations of pri- staff. In addition, the table includes data on pri- vate debtors that are guaranteed for repayment vate nonguaranteed debt for 79 countries either by a public entity. * IBRD loans and IDA cred- reported to the World Bank or estimated by Bank Its are extended by the World Bank Group. The staff. International Bank for Reconstruction and De- The coverage, quality, and timeliness of debt velopment (IBRD) lends at market rates. Cred- 2 0 data vary across countries. Coverage varies for its from the International Development Asso- both debt instruments and borrowers. With the ciation (IDA) are at concessional rates. widening spectrum of debt instruments and in- * Private nonguaranteed external debt com- vestors and the expansion of private nonguar- prises long-term external obligations of private anteed borrowing, comprehensive coverage of debtors that are not guaranteed for repayment long-term external debt becomes more complex. by a public entity. * Use of IMF credit denotes Reporting countries differ in their capacity to repurchase obligations to the IMF for all uses monitor debt, especially private nonguaranteed of IMF resources (excluding those resulting debt. Even data on public and publicly- guaran- from drawings on the reserve tranche). These teed debt are affected by coverage and accu- obligations, shown for the end of the year speci- racy in reporting-again because of monitoring fied, comprise purchases outstanding under the capacity and sometimes because of unwilling- credit tranches, including enlarged access re- ness to provide information. A key part often sources, and all special facilities (the buffer underreported is military debt. stock, compensatory financing, extended fund, Because debt data are normally reported in and oil facilities), trust fund loans, and opera- the currency of repayment, they have to be con- tions under the structural adjustment and en- verted into U.S. dollars to produce summary hanced structural adjustment facilities. tables. Stock figLires (amount of debt outstand- ing) are converted using end-period exchange rates, as published in the IMF's Intemational Data sources Financial Statistics (line ae). Flow figures are The main sources of external debt information converted at annLial average exchange rates (line are reports to the World Bank through its Debtor rf). Projected debt. service is converted using end- Reporting System from member countries that period exchange rates. Debt repayable in mul- have received IBRD loans or IDA credits. tiple currencies, goods, or services and debt with Additional information has been drawn from the a provision for maintenance of value of the cur- files of the World Bank and the IMF. Summary rency of repayment are shown at book value. tables of the external debt of developing Because flow data are converted at annual countries are published annually in the World average exchange rates and stock data at year- Bank's Global Development Finance and on its end exchange rates, year-to-year changes in debt Global Development Finance CD-ROM. outstanding and disbursed are sometimes not _ equal to net flows (disbursements less princi- pal repayments); similarly, changes in debt out- standing, including undisbursed debt, differ from commitments less repayments. Discrepancies are particularly significant when exchange rates have moved sharply during the year. Cancella- tions and reschedulings of other liabilities into long-term public debt also contribute to the differences. * ~~4.17 External debt management lndebtness Present value of Total debt service Public and Short-term classflcation * debt publicly debt guaranteed debt service % of exports of % of exports of % of central % of goods and % of goods and government % of GNI services GNI services current revenue total debt 2000 2000 2000 1990 2000 2.99 2000 1990 2000 1990 2000 Afghanistan Albania L 13 36 0.1 0.7 0.9 2.0 89.8 4.7 Algeria M 50 112 14.7 8.8 63.4 19.6 2.8 0.9 Angola S 203 121 4.0 25.4 8.1 15.1 . .. 11.5 13.7 Argentina S 56 404 4.6 9.9 37.0 71.3 32.5 41.1 16.8 19.4 Armen Ia L 31 106 .. 2.2 7.6 .. 4.9 Australia... Austria... Azerbaijan L 20 4 . 3.7 8 0 .. 13.2 268 Bangladesh L20 111 2.5 1.7 27.4 9.1 1.3 1.9 Belarus L 3 10 .. 0.8 .. 2.9 .. 5.5 ..5.1 o Belgium............ m Benin S 45" 161 0 2.1 3.6 8.2 12.6 . .. 4.3 4.5 U ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~ ------ ... ... Bolivia M 34 0 162 0 8.3 8.2 38.6 39.1 41.3 18.8 3.6 7.0 Bosnia and Herzegovina M 49 .. 7.2 . . .. ... 5.2 0 ) - - - - - - - - - - - - -- - - - - E Botswana L 6 9 2.9 1.3 4.4 1.8 5.5 .. 1.0 3.7 0 L- - - -- - - - o Brazil S 39 323 1.8 11.0 22.2 90.7 3.9 19.8 13.0 Q) . > Bulgaria M 82 131 7.2 10.2 19.4 16.2 12.9 15.8 9.7 4.2 Burkina Faso M 31 210 0 1.2 2.5 6.8 17.3 9.1 .. 10.1 6.3 Burundi S 96 1,118 3.8 3.2 43.4 37.2 . .. 1.5 5.9 Cambodia M 62 127 2.7 1.0 .. 2.0 7.5 4.4 O Cameroon 5 75 228 4.9 6.8 22.5 20.5 16.8 14.4 14.4 Canada......... Central African Republic S 57 497 2.0 1.S 13.2 12.9 5.4 4.7 Chad S 42 207 0.7 1.9 4.4 9.3 5.6 5.7 2.6 Chile M 51 147 9.7 9.0 25.9 26.0 25.6 6.7 17.6 6.8 China L 13 46 2.0 2.0 11.7 7.4 23.9 .. 16.8 11.5 Hong Kong, China . . . Colombia M 42 185 10.2 6.6 40.9 28.6 61.2 .. 8.4 8.4 Congo. Dem. Rep. S.. . 4.1 .. 13.5 .. 14.5 .. 7.2 29.3 Congo, Rep. 5 206 169 22.9 1.9 35.3 1.6 .. 1.4 14.9 22.3 Costa Rica L 31 56 9.2 4.4 23.9 8.2 32.8 16.9 10.0 21.4 CMe dIlvoire S 134" 254 0 13.7 11.8 35.4 22.4 22.1 .. 20.8 8.6 Croatia M 65 127 .. 13.0 .. 25.5 .. 16.9 .. 5.8 Czech Republic L 43 57 .. 9.5 .. 12.7 .. 15.6 37.6 42.3 Denmark............ Dominican Republic L 23 40 3.4 2.8 10.4 4.8 16.1 .. 17.9 25.6 Ecuador S 106 178 11.1 10.3 32.5 17.3 45.0 .. 15.0 7.4 Egypt, Arab Rep. L 23 107 7.3 1.8 22.5 8.4 16.5 .. 13.5 14.2 El Salvador L 29 68 4.4 2.9 15.3 6.7 .. 12.5 9.8 28.3 Eritrea L 27 63 .. 0.5 .. 1.1 ....4.2 Estonia m 66 62 .. 9.3 . 8.7 .. 2.4 .. 28.8 Ethiopia 5 52 326 3.5 2.2 34.9 13.9 13.4 .. 1.7 1.4 France ~ ~ ~ ~ ~ ~ ------ ... ....-.. .-.- .----. Gabon 5 91 125 3.3 11.0 6.4 15.0 7.6 .. 17.4 9.9 Gambia, The NI 64 99 12.9 4.5 22.2 7.0 49.1 .. 4.3 5.8 Georgia L 42 104 .. 3.8 .. 9.5 . 25.8 .. 2.7 Germany............ Ghana M 78 0 160 b 6.4 9.4 36.9 19.3 26.2 .. 8.2 8.7 Greece . .. .. .. .. Guatemala L 23 93 2.9 2.3 12.6 9.4 . .. 13.3 28.9 Guinea 5 80 269 6.3 4.5 20.0 15.3 33.0 .. 6.9 9.9 Guinea-Bissau 5 345 970 3.6 3.1 31.0 8.6 . .. 8.2 10.5 Haiti M 17 133 1.2 1.0 11.0 8.0 .. 11.3 11.1 7.7 Honduras M 54 104 13.7 10.0 35.3 19.3 . ., 5.4 6.8 External debt managem-I-z-' 4.17 "It lndebtness Present value of Total debt service Public and Short-term classfication, debt publicly debt guaranteed debt service % of exports of % of eriports of % of central % of goods and % of goods and government % of GNI services GNI services current revenue total debt 2000 12000 2000 ± 990 -2000 1990 2000 1 990 2000 199 2000 Hungary M 63 85 13 4 18 0 34 3 24 4 21 4 20 4 13 9 14 1 India L 16 91 2 6 2 2 32 4 12 8 14 5 13 6 10 2 3 5 Indonesia S 95 182 9 1 13 2 33 3 25 3 34 15 9 16 0 Iran, Islamic Rep L 7 - 25 0 5 3 3 3i 2L114 0 3 4 48 801 46 2 Iraq Ireland Israel Italy Jamaica M 62 95 17 7 9 2 26 9 14 1 19 5 7 4 17 5 Japan __ _ _ _ .. * . _ _ _ _ _ __ _ _ _ _ 269 Jordan S 92 130 16.4 8 0 20 3 11 4 52 1 26 7 12 7 8 6 - Kazakhstan --L 39 61 108 ~8 20 1 8 0 - Kenya - M 46 168 9 ___4 7_ 5__7 2 13 2 12 9 Korea. Dem Rep. Korea,Rep __L 28 61 3 3 5 1 10 8 10 9 10 5 30 9 30 1 Kuwait Kyrgyz Republic S 115 237 14 2 29 3 20 4 71 C 0 Lao PDR S 72 234 1 1 2 5 8 7 8 1 0 1 0 3 Latvia L 46 94 7 8 15 8 6 4 376C Lebanon M 61 2__ 929_-5 3 3 79 9 24 6 Lesotho __-____L 45 995 2 3 57 42 12 1 9 4 0 7 1 0 Liberia S -- - 22 3 4 4 Libya__ _ _ _ _ __ _ _ t Lithuania L 43 90 8 1 17 1 16 0 22 9 Macedonia, FYR L 36 72 4 6 9 3 5 4 Madagascar S 79 247 7 6 2 4 45 5 7 7 42.9 17 3 6 1 6.4 Malawi S 90 1 297 1 7 2 3.5 29 3 11 7 27 2 3 7 2.9 Malaysia M 52 38 10 3 7 2 12 6 5 3 31 4 12 4 11 1 Mali M 59 5 167 b 2 8 4 3 12.3 12 1 . 2 5 4 6 Mauritania S 134 314 13 6 11 0 29 9 25 9 11 3 10 1 Mauritius M 54 88 5 9 12 7 8 8 20 8 13 5 45 7 5 3 32 3 Mexico L 28 81 4 5 10 4 20 7 30 2 19 5 15 4 12 6 Moldova M 84 140 10 0 16 7 20 7 2 2 Mongolia M 59 91 3 1 4 7 7 7 1 6 Morocco L 49 124 7 2 10 3 21 5 25 9 21 3 1 7 1 4 Mozambique M 33 b 151 1 3 4 2 5 22 11 4 7 4 8 0 Myanmar S 235 _ 9.0 4 7 2 2 . 4 9 11 3 Namibia 7 4 8 0 Nepal L 27 102 1 9 1 8 13 4 6 5 18 2 16 3 1 5 0 9 Netherlands New Zealand Nicaragua S 263 b 425 b 1 6 14 2 3 9 23 0 2 6 22 6 22 6 14 1 Niger ____S 58 344b 4 1 1 6 17 4 9 4 8 9 5 1 Nigeria S 74 117 13 0 2 7 22.6 4 3 . 4 5 3 5 Norway__ __ Oman L . 50 7 8 12 3 7 3 17 4 8 2 12 3 20 7 Pakistan S 45 249 4 9 4.8 23 0 2688 18 1 18 0 15 4 4.7 Panama M 78 79 6 8 9 9 62 o10 10 4 36 6 6 6 Papua New Guinea M 60 97 17 9 8 3 37 2 13 5 33 2 2 8 1 9 Paraguay___L 39 93 6 0 4 4 12 2 10 4 46.8 __17.7 18 8 Peru S 55 283 1 9 8 3 10 8 42 8 4 9 24 0 26 7 13 9 Philippines M 64 103 8 1 8 5 27 0 13 6 39 5 36 9 14 5 11 9 Poland L 37 118 1 7 6 6 4 9 20 9 6 9 19 4 11 2 Portugal Puerto Rico Romania L 28 80 0 0 6 4 0 3 18 8 0 0 79 8 3 5 Russian Federation M 62 128 2 0 4 9 10 1 7 8 19 9 9 7 4.17 lndebtness Present value of Total debt service Public and Short-term classflcation' debt publicly debt guaranteed debt service % of exports of % of exports of %of central % of goods and % of goods and government % of GNI services GNI services current revenue total dent 2000 2000 2000 199 2000 1990 2000 199 2000 1990 2000 Rwanda S 41 509 0.8 2.0 14.0 24.7 5 4 6.6 3.0 Saudi Arabia Senegal M 56 153 5.9 5.3 200O 14.4 11.3 4.4 Sierra Leone S 128 892 2.7 6.9 10.1 48.0 30.6 .. 38.1 10.3 Singapore.......... Slovak Republic L 48 63 2.1 13.8 .. 18.0 .. 11.8 25.0 12.2 Slovenia . .. .. Somalia S . 1.3 .. 15.2 ..12.0 23.1 South Africa L 19 61 .. 3.1 .. 10.0 .. 6.3 .. 38.4 2 7 0 Spain ------ - - . - .-------. Sri Lanka L 44 91 4.9 4.6 13.8 9.6 16 8 21.6 6.9 7.8 cv Sudan 5 152 781 0.4 0.6 7.5 3.2 28.1 37.9 m Swaziland L 14 21 5.3 1.6 5.7 2.3 16.0 5.1 1.8 24.2 V Sweden . .. .. .. Switzerland . .. .. .. Syrian Arab Republic 5 131 290 9.9 2.2 21.8 4.8 21.2 .. 12.5 26.4 o Tajikristan 5 100 118 .. 9.3 .. 10.9 .. 24.3 ..5.5 > Tanzania' S 50 335 4.4 2.4 32.9 16.2 ... 8.0 10.3 a) 0 Thailand M 64 89 6.3 11.6 16.9 16.3 20.7 23.5 29.6 18.7 ~0 Trinidad and Tobago L 38 53 9.7 7.5 19.3 10.3 ... 5.1 34.9 N o Tunisia M 57 112 12.0 10.2 24.5 20.2 32.2 31.5 8.2 8 .6 0 CN Turkey M 57 196 4.9 10.5 29.4 36.1 30.9 18.0 19.2 24.9 Turkmenistan M . .. . . .. Uganda M 16 1460 3.4 2.6 58.9 23.7 .. 15.6 5.4 2.8 Ukraine L 37 58 .. 11.9 . 18.6 .. 21.0 ..3.7 United Arab Emirates.,.... ... United Kingdom...... ... United States . .. ... Uruguay M 42 183 11.0 6.8 40.8 29.2 32.0 18.5 27.2 23.4 Uzbekistan M 57 125 .. 12.1 .. 26.4 ... .6.5 Venezuela, RB M 32 104 10.6 4.9 23.2 15.7 36.2 18.2 6.0 4.6 Vietnam L 36 64 .. 4.2 8.9 7.5 .. 22.0 7.7 7.2 West Bank and Gaza . .. . . .. Yemen. Rep. M 57 72 3.5 3.0 5.6 3.8 ... 18.8 13.8 Yugoslavia, Fed. Rep. L 142 ... 2.1 ...2.9 42.8 Zambia 5 179 505 6.7 6.7 14.9 18.7 ... 20.4 1.4 Zimbabwe M 50 169 5.5 6.6 23.1 22.1 17.4 .. 18.2 14.1 Low Income 4.8 4.9 23.0 15.7 11.9 10.2 Middle Income 3.9 6.6 17.2' 18.5 0 18.7 16.7 Lower middle income 3.7 4.2 20.4 11.3 16.5 13.4 Upper middle income 4.0 8.4 14.9 d 24.1 20.3 18.7 Low & middle Income 4.0 6.3 18.1 d 18.1 0 16.8 15.2 East Asia & Pacific 4.4 4.6 15.7 10.8 17.9 17.1 Europe & Central Asia 2.9 8.1 .. 18.1 18.6 16.2 Latin America & Carib. 4.2 9.5 24.4 38.7 16.3 13.4 Middle East & N. Africa 5.1 4.0 15.0 10.5 23.9 23.3 South Asia 2.9 2.4 28.7 13.8 9.6 3.6 Sub-Saharan Africa .. 4.2 12.8 10.2 11.8 15.4 High Income Europe EMU a. S~ severely indebted, M = moderately edebted. L= lessindebted. b. Data are from debt sustanvability avalyses undertaken as partotthe Heavilylindebted Poor Countries(HIHPCI Initiative. Present value estimates for thesa countries are for public avd publicly guaranteed debt only. avd export figures euclude workers' remittances. c. Oats rater to mainland Tanzania only. d. Includes data for Gibraltar not included in otner tables. 4.17 About the data Definitions The indicators in the table measure the relative Association (IDA) are discounted using an SDR * Indebtedness is assessed on a three-point burden on developing countries of servicing (special drawing rights) reference rate, as are scale: severely indebted (S). moderately in- external debt. The present value of external debt obligations to the International Monetary Fund debted (M), and less indebted (L). * Present provides a measure of future debt service (IMF). When the discount rate is greater than value of debt is the sum of short-term external obligations that can be compared with the the interest rate of the loan, the present value debt plus the discounted sum of total debt current value of indicators such as gross national is less than the nominal sum of future debt service payments due on public, publicly guar- income, or GNI (gross national product, or GNP, service obligations. anteed, and private nonguaranteed long-term in the 1968 System of National Accounts), and The ratios in the table are used to assess external debt over the life of existing loans. exports of goods and services. Thils table shows the sustainability of a country's debt service * Total debt service is the sum of principal the present value of total debt service both as a obligations, but there are no absolute rules that repayments and interest actually paid in for- percentage of GNI in 2000 and as a percentage determine what values are too high. Empirical eign currency, goods, or services on long-term of exports in 2000. The ratios compare total analysis of the experience of developing debt, interest paid on short-term debt, and re- debt service obligations with the size of the countries and their debt service performance has payments (repurchases and charges) to the economy and its ability to obtain foreign shown that debt service difficulties become IMF. * Public and publicly guaranteed debt exchange through exports. Because workers' increasingly likely when the ratio of the present service is the sum of principal repayments and 271 remittances are an important source of foreign value of debt to exports reaches 200 percent. interest actually paid on long-term obligations exchange for many countries, they are included Still, what constitutes a sustainable debt burden of public debtors and long-term private obliga- 8 in the value of exports used to calculate debt varies from one country to another. Countries tions guaranteed by a public entity. * Short- indicators. Public and publicly-guaranteed debt with fast-growing economies and exports are term debt includes all debt having an original o service is compared with the size of the central likely to be able to sustain higher debt levels. maturity of one year or less and interest in ar- CL government budget. The ratios shown here may The World Bank classifies countries by their rears on long-term debt. CD 0: differ from those published elsewhere because level of indebtedness for the purpose of o____5 estimates of exports and GNI have been revised developing debt management strategies. The ' 3 to incorporate data available as of 1 February most severely indebted countries may be eligible Data sources i 2002. for debt relief under special program, such as The main sources of external debt information The present value of external debt is the Debt Initiative for Heavily Indebted Poor are reports to the World Bank through its Debtor j , calculated by discounting the debt service Countries (HIPCs). Indebted countries may Reporting System from member countries that (interest plus amortization) due on long-term also apply to the Paris and London Clubs for have received IBRD loans or IDA credits. l i external debt over the life of existing loans. Short- renegotiation of obligations to public and Additional information has been drawn from the term debt is included at its face value. The data private creditors. In 2000 countries with a files of the World Bank and the IMF. The data on debt are in U.S. dollars converted at official present value of debt service greater than on GNI and exports of goods and services are i exchange rates (see About the data for table 220 percent of exports or 80 percent of GNI from the World Bank's national accounts files. 4.16). The discount rate applied to long-term were classified as severely indebted; countries Summary tables of the external debt of debt is determined by the currency of repayment that were not severely indebted but whose developing countries are published annually i of the loan and is based on reference rates for present value of debt service exceeded 132 in the World Bank's Global Development' commercial interest established by the percent of exports or 48 percent of GNI were Finance and on its Global Development Finance Organisation for Economic Co-operation and classified as moderately indebted; and CD-ROM. Development. Loans from the International Bank countries that did not fall into the above two . for Reconstruction and Developrnent (IBRD) and groups were classified as less indebted. credits from the International Development Figure 4.17 Short term debt falls back into line Short-term debt as percentage of total external debt 35 30 i 1995 0 1999 25 02000 20 15 I East Asia Middle East & Latin America Sub-Saharan Europe & South Asia & Pacdic North Afrnca & Caribbean Africa Central Asia Sour.e: World Bank data files. East Asian countries relled too heavily on short-term debt, which helped to precipitate the 1997 financial crisis. Since then, they have reduced the share of short-term debt In their borrowingto a level similar to most other developing regions. I t I I - t, I . I I  I II/ 11 I 3UMIUI LIUNKJDZMi The key to improving the investment climate can be simply stated: improving the connection between sowing and reaping. Nicholas Stern, 273 Chief Economist and Senior Vice President, World Bank _ /~~~~~ 0 ~0 CD People rise from poverty when countries act on two pillars of development: building a good invest- ment climate in which private entrepreneurs will invest, generate jobs, and produce efficiently, and empowering poor people and investing in them so that they can participate in economic growth. What's a good investment climate? Start with sound macroeconomic management and trade and investment policies that promote openness and raise productivity and growth. Add the elements of good governance, such as regulation of industry, promotion of competition, and pre- vention of corruption. Then set all that on a foundation of basic infrastructure and effective basic services, such as health and education. The case for creating a good investment climate is simple: an economy needs a predictable environment for people, ideas, and money to work together productively and efficiently. The role of government is to provide ample room for entrepreneurs to invest in agriculture, industry, and services. That allows private firms-small ancl large, domestic ancl foreign-operating in competitive markets to be the engine of growth and job creation, providing opportunities to escape poverty. Countries should focus first on improving the investment climate for domestic entrepren-eurs. An improved climate will also attract foreign investors. In today's globalizing world, countries with a good investment climate get more foreign investment-an important con- duit for new technology, management experience, and access to markets-and enjoy faster growth and more poverty r eduction. Good governance employment? They can streamnline cumbersome regulations, such as and strong the red tape that controls business Institutional capability Improves economic growth institutions startups and liquidation of failing Average annual per capita GDP growth, 1964-94 businesses. They can promote more flexible labor regulation, pro- , 3.5 viding safety nets for displaced Good governance and strong insti- ( workers. And they can strengthen 3.0 tutions can create an environment their legal and judicial systems. that encourages markets and entre- 2.5 preneurs to flourish. Too often, 2.0 Countries with good governance however, corrupt or ineffective gov- grow faster than countries with less ernment bureaucracies administer 1.5 good governance. Between 1964 regulations and enforce laws selec- 1.0 and 1994 GDP per capita rose an tively, raising the cost of doing busi- average of 3 percent a year in coun- ness. Consumers must then pay 0.5 m tries with good governance, more 274 more for goods and services. 0 * than seven times the 0.4 percent a Countries with Countries with year in those with low capabilities So. what can governments do to good governance less good governance and poor policies. improve productivity and . Source: World Bank 2000 'Reforming Public Institutions and cO trengthening Governance. C E 0 C4 a) a) B Openness and _ Openness to the global economy intedration with ~~Spending on Information and communications big rae rd n rvt in egrtonwt technololgy is Increasing In newly globalizing economies capital flows. It also brings new the global technologies. such as those for 0 Mexico Malaysia India Hungary ~Information and communications, economy *Mxc *Maasa *Ida *Hnrywhich benefit consumers and busi- 40 25 nesses by improving labor skills LT and increasing business efficiency. 0~~~~~~~~~~~~~~~~~~~~~~~~~~m c*'d~ ~ ~ ~ ~ ~ ~ ~~~~~~~~o A stable economy-with low infla- rn 20_ : tion O steady growth, and balanced p government spending and 15 revenues-is an important part of a sPndngon In mt a cm nao good investment climate. So is i e social and political stability. an c a 0 1995 1996 1997 1998 1999 2000 2001 tinurce: WITSA 2002. Hligh-quality In competitive environments private firms can improve the provision of infrastructuwe Losses In power transmission slow growth infrastructure and other support In manufacturing services. But they have to be regu- Average annual growth in manufacturing, 1990-99 lated well. One way to encourage High-quality infrastructure and other firms to provide these services is business support services help , 4.0 to remove long-standing (and usu- determine the success of manufac- 2 3.5 ally unjustified) barriers to their turing and agricultural businesses. 3.0 doing so. 2.5 So if reliable and affordable power, telecommunications, transport. and 2.0 water are not available, entrepre- 1.5 neurs will not be inclined to do 1.0 business in a region or a country. 0.5 Small and medium-size enterprises 0 are especially at a disadvantage, - 275 since they often cannot afford the -0.5 high cost of generating their own High power- Moderate power- Low power- N loss countries loss countries loss countries o power w/hen blackouts occur. Source: Wold Development Indicators 2002 database. C) 0 0 CD 5- C) Assessing the Based on face-to-face interviews investment ~~Firms view policy Instability as a major obstacle to withi flrm mTanagers and owners, investment doing business in transition economies BEEPS generated comparative mea- climate sures in such areas as corruption, state capture, lobbying, and the quality of the business 14% environment. These measures were Surveys of private enterprises can No then related to firm characteristics inform gfovernments and build obstacle and performance. For example, 42 public support for reform. The Busi- percent of surveyed firms view polit- ness Environment and Enterprise 42% 7ical instability as a major obstacle. Performance Survey (BEEPS), devel- ot ac e oped by tsee World Bank and the Basd on fao- European Bank for Reconstruction obsacl and Development (EBRD), examined a wide range of interac- _ tions between firms and the state m g 27% for more than 4,000 firms in 22 Moderate tdansition economies in v999-2000. Source: World eana and EtRDn tusiness cnvironment and rnterprise Performance Survey, 1999-2000. A dynamic economy needs Private flrms create most new jobs the private sector Number of jobs created, selected countries and years to create jobs Private jobs created * Public jobs created ~!100,000 o 1989-98 Private firms are a powerful source c of job creation, and their growth n 10,000 1987-92 brings more growth to the entire 1 * 1 economy-the biggest factor in _ 1,000 a1994-98 199398 1994-97 poverty reduction. For a range of o 100 developing countries, the private n i I I* sector provides many more of the 10 i i new jobs than does the public sec- 1 276 tor. In fact, most poor people work A A in the private sector-formal + 0 < and nformal. oS Source: Pfefferman 2000. F- ,E S a) 0- -o 0 (N 0 0 CN g r- I F *6' SO -F :4 1 k C H I High-quality Private participation in infrastruc- infrastructure is Top flve developing countries In private participation i ture can improve access to basic Infrastructure services. providing a key to long-term key to poverty reduction. In develop- Total investment in infrastructure projects with private ing countries private participation in sustainable participation, selected countries, 1990-2000 infrastructure is mainly in telecom- munications and energy. growth 69140 ' 120 Brazil, Argentina. Mexico, China, xi and Malaysia had the highest pri- Investment in infrastructure- 100 vate participation in infrastructure whether in power, transport, 80 projects in developing economies in telecommunications, housing, or the 1990s. water and sanitation-enables 60 businesses and communities to 40 grow. It also helps people stay l i i* healthy, learn new skills, and earn a 20 better living. 0 Brazil Argentina Mexico China Malaysia Source: Table 5.1. Smaller Helping those smaller enterprises The Mekong Project Development thrive can improve people's lives Facility was launched in 1997 to enterpirises malke and reduce poverty. Why? Because support the establishment and most poor people in the developing growth of smaller enterprises in Viet- world work in small and medium- nam, Cambodia, and the Lao Peo- flourish size enterprises, including farms. ple's Democratic Republic. These For many of these workers the projects are effective in reducing issue is more one of accessing poverty. For example, in 2000 the their local markets than one of Hagar Project in Cambodia helped A recent World Bank survey of more than 10.000 firms in 80 countries technology. In fact. most poor peo- 6,100 women learn new skills such fudhtml i -s ple (70 percent) live in rural areas, as handicraft production to provide found that small and medium-size firms were at ai competitive disad- and their escape from poverty lies income-generating opportunities. vantage to larger ones. Smaller in their ability to better market their firms lacked the resources and agricultural products and to develop political clout to struggle against off-farm employment activities. corruption, burdensome taxation Improving the investment climate and regulation, and unreliable will also benefit small enterprises 277 in the developing world's cities, power supples. ' ) where the population is expected to g double from around 2 billion to 3.8 billion over the next 30 years. CD 5-CD '10 3 (D 0, A, knowledge One important aspect of a knowl- edge economy is ensuring access econiomy makes Use of the Internet has Increased dramatically for all to computers and the Inter- In the Republic of Korea In the Republic of Korea net. In April 2000 the Republic of investment and Use of the Internet in homes and schools, 1998-2001 Korea decided to eliminate the digi- labor more tal divide-the gap between those e Home Internet users * Education Internet users with access to information and effective 9 14 communications technology and those without. So far the govern- 5 12 ment has distributed personal corw- The global knowledge revolution, 10 puters to 80 percent of teachers, led by information and communica- and 96 percent of central govern- tions technology, is at the doorstep 8 ment officials have email accounts. of all countries. But the door has to 6 Computer training has also been be open to turn ideas and technolo- 4 offered to 300,000 housewives gies into competitive businesses and 9,000 farmers and fishers. that create jobs and help 2 economies grow. O W _ l* U I 1998 1999 2000 2001 Source: WITSA 2002. A good investment services, social protection, and par- Growth and poverty reduction were higher in Indian ticipatory processes. As healthier, climate helps states with good Investment climates more educated people participatc in the economy, productivity and reduce poverty Change between 1993/94 and 1999/2000 growth increase, leading to greater U GDP per capita growth Overall poverty reduction poverty reduction. A good investment climate does * Urban poverty reduction A recent investment climate survey more than reward entrepreneurs rD7 covering 10 Indian states found and generate strong growth. It is D that growth and poverty reduction also associated with poverty reduc- 5 were higher in the "good tion. But for growth to help poor 4 investment climate states" people, they need to participate in 3 between 1993/94 and 1999/2000 the growth process. 2 than in 'poor investment climate" states. What does it take to increase the 1 278 participation of poor people in the O economy? Invest in areas that Poor Good o empower them to improve their investment investment climate climate o standard of living and shape their '0 5urce: Worid Bank data. own lives: education, health C). ED 0. 0 >D rD '0 0 rN- Empowerment and What can governments do to help the poor? They can shift spending poverty reduction to strengthen public services for Governments can make public spending more pro-poor _______________________________________________ poor people. Distribution of public spending on education by quintile, Increasing income is not the whole selected countries In some countries spending on story of poverty reduction, for education is well distributed across poverty has other human * Poorest fifth * Richest fifth all income levels. But in others it is dimensions that are equally impor- In i 50 unevenly distributed. In Jamaica. tant to address: lack of voice and I Panama, and Romania the poorest a- m40 participation in society, vulnerability n -_ and richest fifths of the population 0) to health risks and violence, and 0 ¶R 30 receive about the same shares of lack of educational opportunities. a. n 20 C * public spending on education. But Investing in poor people and the 0:cr ** * * * * * in Armenia, Co5te d'lvoire, and 10 * * education and health services they O , 1 * Nepal the richest fifth receives two need can thus enable them to 0 to four times as much, shape their lives and participate as t AD \ O' full citizens in their communities. 4 Pg P&P SOj q S%9° 9 S Source: World Bank 2000b. Private provision In recent years improvements in technology have made smaller of infirastructure Examples of successful small private scale provision of infrastructure and social service providers services feasible. But for the pri- vate sector to supply these services services. an effective regulatory * In Cambodia hundreds of small private providers have system has to ensure that the price improved the access of poor households to electricity. and quality of services meet appro- Poor people in developing countries These small private providers supply power to more priate standards. usually have inadequate access to than 115,000 customers, or about a third of the water, power, transport systems, country's customers. and social services such as health and education. And when they do * In Paraguay small water companies supplying have access, the services are often customers in urban perimeters have increased access low in quality or more expensive to piped water at prices only slightly higher than those than those provided to better-off of public water suppliers. 279 people. For example, poor people who have to buy water from o vendors pay 10-40 times more than richer people who have water 0 supplied by pipes to their homes. _r r.CC (D 0 a- 3 CD 0 rn Microcredit formed to increase the quality and Microfinance reaches quantity of microfinance Microcredit for poor only a tiny share of institutions, which often serve the women those who need it very poorest people in a commu- Large, formal financial institutions ____________ nity, with loans as small as $50. seldom lend to the poor. But poor Number of people needing The Consultative Group focuses on Nirdan. an NGO in Nepal. people need financial services to access to microfinance . p invest in businesses and homes. Those with access to has about 17,000 active Many microfinance institutions microfinance * Supporting the development of loans and 33,000 around the world are beginning to Rhose without access microfinance institutions. savings accounts, all for meet the demyiand for reliable finan- to microfinance * Supporting changes in the prac- poor women. It makes cial services for the poor. It is esti- 12.5 million tices of member donors to improve small loans to small mated that 500 million people -. their microfinance operations. groups of women, who worldwidetneed thaccess tiion meof* Increasing the poverty outreach are jointly liable for the worldwide need access to microfi- nance, which now reaches only . * of microfinance institutions, loans. about 12.5 million. Improving the legal and regula- tory framework for microfinance institutions. Informal miczrofinancing has been 487.5 * Facilitating the commercializa- around for a long time in the form million tion of the industry. of family loans and savings clubs. In 1995 the Consultative GroUp to Source: CGAP 2000. Assist the Poorest (CGAP) was 5.11 Private sector development Private Domestic Investment In Infrastructure projects with private participation fixed credit to Investment private sector % of gross Water domestic Telecommnunicat ons Energy Transport and sanitation fioed investment % of GDP $ millions $ millions $ millions $ millions ±990 1.999 1990 2000 1.990-94 ±995-2000 1.990-94 1995-2000 1990-94 1995-2000 1990-94 1.995-2000 Afghanistan Albania 4.5 .. 102.2............ Algeria 44.4 6.1...... Angola 2.1 . .. Argentina 67.4 89.9 15.6_ 23.8 9,262.0 12,991.3 9,899.9 13,694.4 5.373.9 8.965.5 4,075.0 4,172.5 Armeni'a . .. 40.4 10.6 442.0 Austral a 88.4v 90.4 b64.4 87.9 Austria 92.3 91.6 Azerbaijan 86.7 9.4 5.9 14.0 127.6 280 Bangladesh 57.5 69.7 16.7 24.7 116.0 543.4A 1,040.2______ Belarus 8.9 10.0 15.0 .. 500.0 o Belgium 928 b 914 b 8.1 6.7 to Benin 44.7 60.1 20.3 12.6 .. 90.4 0~~~~~~~~~~~~~~~~~~~~---- --- --- Bolivia 39.4 61.6 24.0 59.5 20.0 670.4 .. 941.6 .. 163.2 .. 682.0 Bosnia and Herzegovina .. . ...... E Botswana . .. 9.4 16.1 .. 80.0 a. o Brazil 76.7c 86.2c-38.9 3. . . 53,692.4 212.0 40,048.1 328.1 19,545.9 2.5 2891.4 a) .-.--.--- - --- ---- > Bulgaria 3.6 50.3 7.2 14.6 37.5 207.9 ... . 152.0 o Burkina Faso . . 19.0 14.0 ... . 5.6 . - -. .-.---- -- Burundi .. A 13.7 23.5 0.5 15.6 . .. Cambodia 89.5 60.7 .. 7.3 30.1 104.8 .. 89.0 .. 120.0 o Cameroon .. . 26,7 9.3 .. 72.7 ... 30.8 95.0 0 . - - - .-- -- - - - - -- - - - C1 Canada 86.3 1, 89.40 76.1 80.1 Central African Republic . .. 7.2 4.5 .. 1.1 0.7 Chad .. 62.5 7.3 3.4 ., 2.0 . .. Chile 79,3 68.3 47.2 68.0 95.9 993.9 1,326.2 5,604.1 120.4 3,803.0 _127.6 3,719.9 China 33.91 46.61 87.7 124.6 .. 5,970.0 5,459.1 15.001.2 5,910.5 13,137.9 42.8 796.7 Hong Kong, China . ..165.1 158.7 . .. .. Colombia 61.5 39.2 30.8 27.7 1,354.7 1,494.8 540.0 7,035.4 813.0 1,743.9 .. 272.0 Congo, Dem. Rep. . .. 1.8 ... 45.0 . .. Congo, Rep. . .. 15.7 4. . 70.3 .. 325.0 Costa Rica 78.9 76.8 15.8 24.1 ... 18.2 301.2 .. 185.0 C6te dIlvoire 57.8 74.1 36.5 17.2 .. 802.4 109.6 260.6 .. 178.0 Croatia .. 77.3 .. 36.2 ,. 978.0 368.5 .. 672.2 Cuba . .. . .. 371.0 ... 165.0 ... . 600.0 Czech Republic 86.4 82.0 49.7 41.0 7,634.9 356.0 944.1 390.4 16.0 36.9 Denmark 91.8 b 91.70 52.2 34.4 Dominican Republic 73.0 76.3 27.5 34.9 5.0 163.0 875 1.556.3 633.9 Ecuador 67.0 49.5 13.2 33.4 27.6 716.4 .. 310.0 12.5 686.8 Egypt, Arab Rep. 62.2 65.2 30.6___ 59.3 ...... 2,297.7 .. 1,376.0 492.2 El Salvador 81.4 80.7 20.1 41.3 .. 651.5 .. 975.2 . Estonia 95.0 83.5 20.2 26.3 136.1 704.6 .. 26.5 .. 15.8 .. 81.0 Ethiopia . .. 19.5 29.0 Finland 86.9vb 84.5b 86.7 52.3 . .. France .. . 96.1 ...... Gabon .. . 13.0 8.9 ,. 20.7 .. 624.68. 46.7 624.8 Gambia, The 66.8 67.9 11.0 12.5... Georgia 8.8 11.6__ 53.8 .. 65.0 Germany . .. 89.7 120.3 Ghana 4.9 14.1 20.0 441.1 .. 10.0 Greece 83.90_ 36 3 53.0 ... Guatemala 79.9 82.9 14.2 20.1 .. 1,463.3 100.0 1,273.2 .. 33.8 Guinea . .. 3.5 4.0 . 120.3 .. 36.4... Guinea-Bissau 28.1 32.0 22.0 7.9 ... 23.2 ... . 23.2 Haiti 57.8 51.8 12.1 15.6 .. 1.5... Honduras . .. 31.1 41.3 .. 38.1 70.0 112.1 .. 130.5 Private Domestic Investment In Infrastructure projects with private participation' fixed credit to Investment private sector % of gross Water domestic Telecommunications Energy Transport and sanitation f'ined investmeat % of GDP $ millions $ millions $ millions $ millions 1990 1.999 ±990 2000 1990-94 1995-2000 1.990-94 1.995-2000 1990-94 1995-2000 1990-94 1995-2000 Hungary .. 46.6 30.9 1,623.2 7,072.1 .. 3,872.1 1086.0 135.0__ . 170.5 India 60.8 70.1 25.2 29.0 96.7 10,063.6 2,139.2 9,683.8 126.9 1,055.7 .. 216.0 Indonesia 69.7 61.0 46.9 20.9 1,119.0 9,602.7 352.5 9,817.1 709.8 2,223.1 3.8 882.8 Iran, Islamic Rep. 53.8 58.6 32.5_ _ 30.7 .. 28.0------------- Iraq Ireland 88.8 88.7 ~ 47.6 108.6 - ----------------- ----------- Israel .. 57.6 86.9...... Italy ___56.5 77.6 ... Jamaica 39 0___32.9 .. 44.5 246.0 43.0 30.0 Japan 84.2' 78.51 195.2 187.7 ... .. ..281 Jordan .. 72.3 77.6 43.0 549.9 ... 182.0 .. 55.0 Kazakhstan 88.1 .. 13.1 30.0 1.743.5 2,411.7 ... . 40.0 Kenya 54.6 67.1 32.8 30.1 .. 107.0 .. 171.5 .. 53.4 . Korea, Dem. Rep.. . . .. .. Korea, Rep. 86.8 78.7 65.5 ___101.9 2,379.0 17.050.9 2,688.2 .. 6,268.3 a Kuwait .. 52.1 51.9 CD Kyrgyz Republic .. . . 4.4 .. 94.0 ... .. Lao PDR .. 1.0 9.0 160.8 535.5 .. . 3 Latvia . .. 18.6 180.0 782.0 154.0 .. 75.0 ..D. Lebanon ----- 79.4___92.0 50.0 323.0 ..200.0... Lesotho .. 15.8 14.2 .. 16.5...... Lithuania 63.6 .. 11.5 30.0 1,222.3 .. 20.0 Macedonia. FYR .. . . 17.9 ... Madagascar 46.5 52.9 16.9 9.2 5.0 10.1 Malawi 51.8 17.6 12.3 6.2 34.7 ... . 6.0 Malaysia 64.6 50.6 69.4 135.5 1,618.0 3,615.3 5,709.5 2,101.1 2,768.6 9,228.7 3,976.7 1,115.5 Mali .. 12.8 17.5 .. 0.1 697.3 ... 0.1 697.3 Mauritania 68.9 36. 7 43.5 26.7 28.1 . .. Mauritius 62.8 78.7 33.2 61.4 ..109.3 42.6 Mexico 76.1 89.9 17.5 13.2 15,840.0 13,386.8 4,560.1 7,583.7 5,630.2 295.1 276.5 Moldova . .. 5.9 12.7 .. 84.6 .. 85.3 Mongolia . .. 19.0 8.1 .. 35.2... Morocco 65.6 71.1 34.0 58.6 .. 3,375.0 2,300.0 5,819.9 ... 4,050.9 Mozambique ,. . 17.6 18.7 .. 29.0 ... .. 0.6 Myanmar . .. 4.7 8.6 .. 4.0 .. . . 50.0 Namibia 61.4 55.4 21.0 44.7 .. 22.0 .. 5.0 Nepal . .. 12.8 30.7 ... 131.4 137.2 Netherlands . .. 79.7 -. ---.---- ---- - New Zealand . .. 76.9 17.... Nicaragua 54.7 51.6 112.6 54.5 6.6 24.5 .. 347.4 .. .......104.0 - ------ Niger . .. 12.3 4.7 .. 11.5 .... .... Nigeria . .. 9.4 13.9 .. 117.7 Norway 83.5ti 83.8n 82.2 7488 Oman . .. 22.9 44.6 ... 204.5 183.0 .. 106.1 Pakistan 51.7 62.3 27.7 29.4 581.5 133.5 1,638.7 5,054.3 .. 418.3 Panama 86.9 88.8___46.7 119.7 .. 1,429.2 .. 1,064.9 169.9 1,046.0 .. 25.0 Papua New Guinea 79.6 77.1 28.6 15.8 .... 50.0 ... 818.0 Paraguay 87.4 64.3 15.8 25.7 33.2 199.3 ... . 58.0 Peru 80.0 77.8 11.8 25.9 1,645.0 5.378.2 451.2 3,322.9 6.6 86.8 Philippines 81.7 69.2 22.3 44.5 591.8 5.539.6 4,502.1 9,272.3 ____3,088.8 . 5,820.0 Poland 41.3 62.1 3.1 26.0 273.0 9,899.6 1,052.6 3.1 705.9 . 22.1 Portugal . .. 49.5 141. .. . Puerto Rico.. . .. .......... Rmania 9.7 51.4 .. 7.2 5.0 2,326.3 .. 100.0 .. 23.4 .. 1,025.0 Russian Federation .. . . 12.3 426 ,382 10.0 2813515.4 . 0. 5.1 Private Domestic Investment In Infrastructure projects with private participation fixed credit to Investment private sector % of gross Water domestic Telecommunications Energy Transport and sanitation fixed investment % of GDP $ millions $ m. lions $ millions $ millions 1.990 1999 1990 2000 1.990-94 1995-20030 1.990-94 1995-2000 1990-94 1.995-2000 1990-94 1S95-2000 Rwanda . . 6.9 10.1 .. 15.0 . .. Saudi Arabia . .. 61.0 57.1 Senegal . .. 26.5 20.0 .. 343.3 159.0 ..3.7 Sierra Leone . .. 2.4 2.1 Singapore . .. 97.4 110.0 Slovak Republic 31.0 109.2 1,451.5_ Slovenia 34.9 38.1 Somalia.. . .. . South Africa 65,6 69.0 81.0 141.9 542.2 7,818.3 . 18.9 1.390.1 .. 209.3 282 Spain . .. 79.6 101.6 Sri Lanka . .. 19.6 28.9 43.6 1,390.6 . 265.0 .. 240.0 Sudan . .. 4.8 2.4 .. 6.0 .. in Smaziland . .. 21.7 14.2 .. 10.0 .. i.) Sweden . .. 128.4 46.0 Switzerland . .. 167.9 165.2 a) 2 Syrian Arab Republic . .. 7.5 9.0 o Tajikistan .. . .1.0 . .. > Tanzania . .. 13.9 4.6 1.8 66.9 6.0 150.0 .. 23.0 a) o Thailand 84.8 67.7 83.4 108.8 3,664.0 4,143.7 674.8 6,990.4 695.9 1,759.4 .. 260.5 C Togo ~~~~~22.6 17.0 .. 5.0 - . o og Trinidad and Tobago 85.2 67.8 44.7 45.5 47.0 146.7 .. 207 .0 ... . 120.0 N' o Tunisia 64.1 52.2 55.1 66.2 ... 627.0 265.0 0 Turkey 69.2 72.0 16.7 23.7 74.0 7,794.7 718.0 6,567.2 .. 724.8 942.0 Turkmenistan .. . . 1.5 . .. Uganda . .. 4.0 6.3 16.0 120.8 . Ukraine . .. 2.6 10.6 90.0 1.154.9 United Arab Emirates . ., 37.4 60.0 United Kingdom 87.3 93.6 n116.0 135.1 United States . .. 93.1 143.5 . .. .. Uruguay 66.0 72.5 32.4 51.3 13.0 63.7 .. 246.0 96.0 20.0 10.0 Uzbekistan .. 34.0 . .. 2.5 357.4 . .. Venezuela. RB 34.8 56.1 25.4 12.1 4,185.7 5,574.0 .. 133.0 100.0 266.0 ., 25.0 Vietnam -. . 2.5 35.1 ... . 435.5 10.0 70.0 .. 208.8 West Bank and Gaza .. . . . . 155.0 .. 150.0 . Yemen. Rep. . .. 6.1 5.5 25.0 ...190.0 Yugoslavia, Fed. Rep. .. 87.7 .. . . 1,929.5 Zambia . .. 8.9 9.5 .. 57.8 . 289.4 Zimbabwe . .. 23.0 25.2 .. 46.0 .. 1,160.0 18.0 70.0 Low Income 48.1 53.7 26.5 23.9 2,136.3 25,684.6 4,400.7 30,576.0 895.5 4,666.5 27.8 2,020.1 Middie income 72.2 74.8 45.4 61.1 44,913.0 201,894.1 34,676.5 145,020.8 25,098.2 82,513.2 8,545.7 29,160.4 Lower middle income .. . . 78.4 8,623.7 49,147.5 16.226.6 60,579.5 7,468.5 23,123.0 42.8 14,728.0 Upper middle income 73.8 77.9 38.9 48.4 36,289.3 152,746.6 18,449.9 64,441.4 17,629.7 59,390.2 8,502.9 14,432.4 Low &middle income 64.5 66.9 41.6 55.3 47,049.3 227,578.7 39,077.2 175,596.8 25,993.7 87,179.7 8,573.5 31,180.5 East Asia & Pacific 63.3 50.2 71.4 106.1 9,433.9 46,365.0 16,698.0 46,980.3 10,094.8 35,946.2 4,023.3 9902.3 Europe & Central Asia -- 20.7 3,119.7 52,507.6 2,174.0 18,448.3 1,089.1 3,257.9 16.0 2577.5 Latin America & Carib. 74.3 79.8 28.4 27.5 32,954.4 99,165.1 13,025.5 82,102.2 14,634.1 43,104.5 4,510.2 12,784.3 Middle East & N. Africa . .. 41.6 47.0 118.0 6,728.6 3,131.5 7,793.9 .. 1,220.3 ,, 4,105.9 South Asia 55.9 71.8 24.6 28.7 837.8 12,131.1 3,909.3 16,160.5 126.9 1.714,0 .. 216.0 Sub-Saharan Africa . .. 42.5 66.0 585.5 10,661.3 138.9 4,091.6 46.8 1,936.8 24.0 1,594.5 High Income 81.9 .. 107.8 138.3 Europe EMU... . 78.4 97.7 . .. .. a. Date refer to tota! for the period shown. For d fferences is concepts and definitions between proceeds from privatization end Investment in infrastructure projects with private participation see About rhe data. b. Data refer to investment by both private and public corporations. c. Date refer to investment by indiniduals, shareholdiirg units, jointly owned units, collectively owned units, foreign- funded units, arid units in Hong Kong. China: Macao. Chine; and Taiwan. Chlne. About the data Definitions Private sector development, that is, tapping pri- The data on domestic credit to the private * Private fixed investment covers gross out- vate sector initiative for socially useful purposes, sector are taken from the banking survey of the lays by the private sector (including private is critical for poverty reduction. Private initiative, International Monetary Fund's (IMF) Intemational nonprofit agencies) on additions to its fixed especially in competitive markets, has tremen- Financial Statistics or, when data are unavail- domestic assets. Gross domestic fixed invest- dous potential to contribute to growth in paral- able, from its monetary survey. The monetary ment includes similar outlays by the public lel with public sector efforts. Private markets are survey includes monetary authorities (the cen- sector. No allowance is made for the deprecia- the engine of productivity growth, creating pro- tral bank) and deposit money banks. In addition tion of assets. * Domestic credit to private ductive jobs and higher incomes, and along with to these, the banking survey includes other bank- sector refers to financial resources provided the complementaty government role of regula- ing institutions, such as savings and loan insti- to the private sector-such as through loans, tion, funding, and provision, private initiative can tutions, finance companies, and development purchases of nonequity securities, and trade help to provide the basic services and condi- banks. In some cases credit to the private sec- credits and other accounts receivable-that tions that empower the poor by improving infra- tor may include credit to state-owned or partially establish a claim for repayment. For some coun- structure, health and education. More than 130 state-owned enterprises. tries these claims include credit to public en- developing countries introduced private partici- Private participation in infrastructure has terprises. * Investment in infrastructure pation in at least one infrastructure sector be- made important contributions to easing fiscal projects with private participation covers in- 283 tween 1990 and 2000 involving over 2,330 restraints and to improving the efficiency of in- frastructure projects in telecommunications, projects with investment commitments of $693 frastructure services and in extending their de- energy (electricity and natural gas transmission g billion. livery to poor people. The privatization trend in and distribution), transport, and water and sani- Private fixed investment consists of outlays infrastructure that began in the 1970s and tation that have reached financial closure and o for additions to fixed assets-improvements to 1980s took off in the 1990s. Developing coun- directly or indirectly serve the public. Movable E. land, construction of infrastructure and buildings, tries have been at the head of this wave, pio- assets, incinerators, standalone solid waste CD and purchases of plant, machinery, and equip- neering better approaches to providing infrastruc- projects, and small projects such as windmills (- ~0 ment-by the private sector. When direct esti- ture services and reaping the benefits of in- are excluded. The types of projects included 3 mates of private investment are unavailable, creased competition and customer focus. are operations and management contracts, CD private fixed investment is estimated as the dif- The data on investment in infrastructure operations and management contracts with ference between total gross fixed investment and projects with private participation refer to all in- major capital expenditure, greenfield projects a consolidated public: investment. Total investment vestment (public and private) in projects in which (in which a private entity or a public-private joint may be estimated directly from surveys of en- a private company assumes operating risk dur- venture builds and operates a new facility), and terprises and administrative records, or indirectly ing the operating period or assumes develop- divestiture. using the commodity flow method. Consolidated ment and operating risk during the contract pe- _____ measures of public investment may omit impor- riod. Foreign state-owned companies are con- tant subnational units of government and in sidered private entities for the purposes of this Data sources some cases may include financial as well as measure. The data are from the World Bank's The data on private investment are from the physical capital investment. As the difference PPI Project Database, which tracks about 2,330 International Finance Corporation's Trends in between two estimated quantities, private fixed projects, newly owned or managed by private Private Investment in Developing Countries investment may be undervalued or overvalued companies, that reached financial closure in low- 2001, OECD data files (see OECD, National and subject to large errors over time. When pri- and middle-income economies in 1990-2000. Accounts, 1960-99, volumes 1 and 2), and vate domestic investment accounts for a large For more information go to www.worldbank.org/ World Bank estimates. The data on domestic share of total investment, it may reflect a highly html/fpd/privatesector/PPIDBweb/lntro.htm. credit are from the IMF's International Financial competitive and efficient private sector-or one Statistics. The data on investment in that is subsidized and protected. infrastructure projects with private participation This concept of private investment is the one are from the World Bank's Private Participation used by the International Finance Corporation in Infrastructure (PPI) Project Database i (IFC) in its Trends in Private Investment in De- (www.worldbank.org/html/fpd/ privatesector/ veloping Countries 2001, the source of data for PPIDBweb/lntro.htm). most countries in the table. But for other coun- tries, most notably members of the Organisation for Economic Co-operation and Development (OECD), the concepts and definitions of the 1993 System of National Accounts (SNA) are used. Since IFC data conform to the concepts and definitions of the 1968 SNA, the data are not strictly comparable. While the IFC data on pri- vate investment represent only the capital ex- penditure decisions of the private sector, in the 1993 SNA the tern fixed capital formation by households and corporations includes capital expenditures by both private and public corpo- rations. Countries reporting on this basis are footnoted in the table. (For further discussion on measuring gross capital formation see About the data for table 4.9.) lop 5.2 Investment climate Foreign direct Entry and exit regulation IComposite Institutionai Euromoney Moody's Standard & Poor's Investment ICRG risk Investor country sovereign sovereign long-term rating' credit credit- long-term debt rating' rating' worthiness debt rating' rating' % of gross Foreign Domestic Foreign Domestic capital Repatriation of currency currency currency currency formation Entry income capital December September September January January January January 1990 2000 2000 2000 2000 2001 2001 2001 2002 2002 2002 2002 AfghaniStan .. . . . ... 5.9 1.3 . Albania 0.0 20.5 .. . , 62,5 12.1 26.5 . Algeria 0.0 0.1 .. . . 62.3 30.6 38.6 . Angola -27.9 68.1 .. . . 50.5 12.1 20.1 . Argentina 9.3 __25.7 F F F 64.8 34.7 43.4 Ca Ca SD SD Armenia .. 38.2 .. . . 59.8 .. 30.5 . Australia 11.9 5.8 . . . 81.5 80.4 88.4 Aa2 Aaa AA+- AAA Austria 1.6 6.0 . . . 86.5 87.7 92.4 Aaa Aaa AAA AAA Azerbaijan . 9.6 . . .. 68.5 .. 32.3 . 284 Bangladesh 0.1 2.6 F F F 60.5 26.4 36.5 . Belarus .. 1.3 ., . . 61.3 12.5 24.4 . o Belgium 13.6 36.7 . . . 84.0 86.6 90.5 Aol Aal AA+ AA+ o Benin 0.4 7.0 . .. ...18.5 25.1 . Bolivia 4.4 48.6 .. . . 66.3 30.8 39.3 81 81 8+ 88 c Bosnia and Herzegovina .. 0.0 .. . . .. 23.9 . .- - - - --- - - - -- - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - P Botswana 8.0 3.7 F F F 78.5 56.7 60.7 A2 Al A A+ Brazil 1.1 26.9 F F F 62.5 42.1 48.7 81 BI BB. BB+ w> Bulgaria 0.1 50.4 F F F 72.3 36.6 44.5 B2 81 88. 88 -------- ----- ------------ ---- - - - - --.- - - ----- - - - - - ----------------------------- Burkina Faso 0.0 1._. . . 60.5 . .. ..16.1 29.3_____ o Burundi 0.6 19.0 . .. ...10.5 22.8 ... Cambodia 0.0 26.3 . .. ... 26.9 .. . 0 Cameroon -57 2.1 .. . . 64.0 17.0 29.7 C N----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ---- - - - - - - - Canada 6.4 19. 7 . . 84.8 87.2 89.9 Aal Aal AA+ AAA Central African Republic 0.5 4.8 .. . .. . 24.2 . Chad 0.0 6.3 . .. ... 11.9 23.1 . Chile 7.7 22.2 R F F 76.3 64.2 64.6 Baal Al A- AA China 2.8 9.5 S F F 74.3 57.4 60.7 A3 .. 88 Hong Kong. China . . . . . 83.3 65.2 80.4 A3 Aa3 A+ AA- Colomnbia 6.7 23.9 A F F 58.8 38.8 48.4 Ba2 Baa2 BB 888 Congo. Dem. Rep. -1.4 . .. . .. 50.3 7.5 6.5 Congo. Rep. 0.0 1.8 .. . . 57.5 8.7 23.4 . Costa Rica 10.4 15.1 . . . 74.0 44.1 50.7 Bal Bal BB BB+ C6te dIlvocre 6.6 9.2 F F F 57.5 18.5 29.5 Croatia .. 22.1 F F F 75.0 43.9 53.5 Baa3 Baal BBB- 888+ Cuba . . . . . 64.5 13.7 6.7 Caal Czech Republic 2.4 30.4 F F F 75.8 60.5 63.0 Baal Al A- AA- Denmark 4.2 95.4 . . . 87.3 88.4 94.7 Aaa Aaa AAA AAA Dominican Republic 7.5 20.4A . . 70.3 35.0 41.9 Ba2 Ba2 88. B8- Ecuador 6.7 31.1 F F F 60.8 19.0 30.1 Caa2 Caal CCC+ CCC+ Egypt. Arab Rep. 5.9 5.2 F F F 68.8 47.1 52.6 Bal Baal 888- 888+ El Salvador 0.3 8.3 , . . 73.5 41.9 49.1 Baa3 Baa2 88+ 88+ Eritrea .. 15.2 .. . .. . 21.4 . Estonia 7.2 30.2 F F F 75.0 55.0 59.3 Baal Al A- A- Fthilopia 1.5 5.5 .. . . 60.5 147 25.8 . Finland 2.0 38.0 . . . 88.3 85.9 92.3 Aaa Aaa AA+ AA+ France 4.6 16.2 . . . 81.0 92.0 92.3 Aaa Aaa AAA AAA Gabon 5.7 11.6 .. . . 69.3 21.0 30.8 . Gambia, The 0.0 19.2 .. . . 68.8 .. 27.3 . Georgia 0.0 29.8 .. . .. 15.4 25.6 . Germany 0.7 44.6 . . . 83.8 92.8 92.2 Aaa Aaa AAA AAA Ghana 1.8 8.9 F F F 59.5 25.4 35.2 .. . Greece 5.2 2.0 F F F 74.0 71.2 82.0 A2 A2 A A Guatemala 4.6 7.2 .. . . 70.3 31.4 42.3 Ba2 Bal BB BB+ Guinea 3.6 9.5 .. . . 61.8 15.8 23.9 .. . Guinea-Bissau 2.7 7.9 .. . . 48.0 .. 20.7 ... Haiti 2.2 3.0 .. . . 57.3 12.1 25.0 ... Honduras 6.3 13.6 .. . . 64.8 22.5 36.6 82 82 5.2 Foreign direct Entry and exit reguiation Composite Institutionai Euromoney Moody's Standard & Poor's investment ICRG risk Investor country sovereign sovereign long-term rating' credit credit- long-term debt rating' ratin gb worthiness debt ratingb rating' % of gross Foreign Domestic Foreign Dornestic capital Repatriation of currency currency currency currency formation Entr income capital December September September January Janaary January January 1990 2000 2000 2000 20001 2001 2001 2001 2002 2002 2002 2002 Hungary 0.0 12.1 F F F 76.0 62.0 70.2 A3 Al A- A India 0.2 2.1 A F F 65.3 47.4 55.0 Ba2 Ba2 88 BBB- Indonesia 3.1 -16.6 R RS RS 56.3 21.6 33.4 B3 83 CCC B- Iran, Islamic Rep. -1.1 0.2 .. . . 69.3 31.5 41.3 B2 8a2 Iraq .. . . . . 47.3 9.0 3.3 . Ireland 6.3 85.4 .. . . 88.8 83.8 91.1 Aaa Aaa AAA AAA Israel 1.1 20.6 F F F 67.8 60.0 72.0 A2 A 2 A- AA- Italy 2.6 6.0 .. . . 81.8 82.9 87.5 Aa3 Aa3 AA AA Jamaica 11.7 23.0 R F F 70.3 27.8 39.8 Ba3 Baa3 B+ B8- Japan 0.2 1.1 .. . . 84.3 86.2 89.9 Aal Aa3 AA AA 285 Jordan 3.0 33.0 F F F 71.0 38.8 46.5 8a3 8a3 88- 8BB- Kazakhstan 1.2 49.2 .. . . 72.0 33.9 43.6 Ba2 Bal 88 BB+N. --0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .. . - - - - - - - - - Kenya 3.4 8.4 R F F 61.0 21.7 35.8 . .. Korea, Dem. Rep. .. . .- . . 47.5 7.0 3.9 Korea, Rep. 0.8 7.1 R F F 79.3 61.7 62.4 Baa2 Baal 888-i A+ ) Kuwait -.. 0.4 . - . .. 84.0 63.6 75.8 Baal ..A A+- Kyrgyz Republic 0.0 -1.1 .. - . -. . 17.5 25.1 .. ..... Lao PDR . - 20.6 .. . .. . 25.0 ., .... -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Latvi a 1.1 21.0 F F F 76.0 48.0 52.6 8aa2 A2 888 A- ( Lebanon 1.2 10.0 F F F 56.8 31.6 44.7 82 83 B B Lesotho 5.2 32.5 . .. ... 23.9 34.5 .. - Liberia .. - . . . 49.8 7.6 12.5 E...... Libya .. - . . . 73.8 31.3 18.0 ..(.. Lithuania 0.0 16.2 F F F 74.0 45.5 50.6 Bal Baal 888- 888+ Macedonia. FYR .. 29.4 .. . .. . 25.4 . Madagascar 4.2 13.3 .. . . 6 7. 0 . 27.9 - Malawi 0.0 20.3 -- . . 60.0 17.9 26.1 .....- Malaysia 16.4 7.2 R F 0 76.0 56.4 59.8 8aa2 A3 888 A Mali -1.3 14.6 .. . . 57.8 16.1 28.9 . Mauritania 3.4 1.8 .. . . .. 22.1 . Mauritius 5.0 23.6 R F F .. 53.9 57.4 Baa2 A2..- Mexico 4.3 9.9 F F F 70.8 55.3 60.4 8aa3 Baal 88+ 888+ Moldova 0.0 44.7 .. . . 63.8 16.2 26.0 Caal Caal Mongolia .. 10.4 -. . . 64.3 .. 28.4 B. . B Morocco 2.5 0.1 F F F 71.8 45.4 55.2 Bal Bal 88 688 Mozambique 2.3 11.0 .. . . 59.0 18.1 30.0 .....- Myanmar .. . . - . 62.0 13.4 23.9 - Namibia . .. F F F 76.3 39.1 22.8 . Nepal 0.9 0.3 . .. -.. 27.5 30.1 . Netherlands 15.2 48.1 .. . . 87.0 92.6 93.2 Aaa Aaa AAA AAA New Zealand 21.3 12.5 .. . . 80.3 75.9 85.2 Aa2 Aaa AA+ AAA Nicaragua 0.0 30.8 .. . . 57.8 18.9 28.7 82 82 Niger -0.5 7.7 .. . . 59.5 13.1 28.2 . Nigeria 14.0 11.6 R F F 57.3 18.3 28.8 . Norway 3.7 16.4 .. . . 92.3 90.0 953 Aaa Aaa AAA AAA Oman 10.2 -- F F F 81.8 55.4 60.7 Baa2 Baa2 888 888+ Pakistan 3.2 3.2 F F F 56.0 18.1 34.5 Caal Caal B- B Panama 14.8 20.2 .. . . 71.5 46.4 52.7 Bal BB 8 BB Papua Now Guinea 19.7 46.3 .. . . 61.8 28.5 37.3 81 81 B 88- Paraguay 6.3 4.9 .. . . 63.0 28.9 38.8 82 81 B 88- Peru 0.9 6.3 F F F 68.8 34.7 47.9 Ba3 Baa3 88- BB3+ Philippines 5.0 15.2 S F F 70.0 43.5 53.1 Bal Baa3 B8+ BB3B+ Poland 0.6 22.3 F F F 75.3 59.2 59.6 Baal A2 888+ A+i Portugal 13.2 21.1 -- - . 78.3 79.8 84.7 Aa2 Aa2 AA AA Puerto RICO . . . . . .. .. Romania 0.0 14.4 F F F 64.3 29.1 40.5 82 82 B B+ Russian Federation 0.0 6.3 F F F 69.5 26.8 37.4 8a3 Ba2 B+ B TE- 5.2 Foreign direct Entry and exit reguiation Composite Institutlonai Euromoney Moody's Standard & Poor's investment ICRG risk Investor country sovereign sovereign long-term ratingb credit credit- long-term debt rating' rating' worthiness debt rating5 rating' % of gross Fore gn Domestic Foraige Domestic capital Repatriation of currency currency currency currency formation Entry ncome capital December September September January January January January 1990 2000 12000 2000 2000 2001 i 2001 2001 2002 2002 2002 2002 Rwanda 2.1 5.2 .. . .. . 21.4 . Saudi Arabiar . .. C RS RS 77.3 58.8 67.2 8aa3 Bal Senegal 7.2 12.3 .. . . 66.3 23.7 32.5 ... + 8± Sierra Leone 37.9 2.0 .. . . 48.5 8.3 24.0 . Singapore 41.5 22.1 .. . . 89.3 84.8 90.5 Aal Aaa AAA AAA Slovak Reptrblic 0.0 35.7 F F F 73.3 47.7 53.7 8aa3 A3 888- A- Slovenia 5,0 3.5 R F F 78.8 64.9 73.8 A2 Aa3 A AA Somalia 4.2 . .. . .. 43.5 .. 11.0 . South Africa .. 5.1 F F F 68.8 49.5 57.7 Baa2 A2 888- A- 286 Spatn 10.2 2. .. . .. 80.5 82.8 87.8 Aaa Aaa AA+ --AA+ Sri Lanka 2.4 3.8 R RS F 57.0 33.9 39.0 . Sudan .. 23.8 . . . . 54.0 9.1 22.8 . tn Smaziland 17.8 -15.1 . .. ... 27.0 34.3 . -o Sweden 3.6 54.4 . .. 84.3 85.7 92.6 Aol Aaa AA+ AAA C: ,E ~ Switzerland 9.3 23.1 .. . . 92.5 93.8 98.2 Aaa Aaa AAA AAA a ) - - - - - - - - - - - - - - - - - - - - - - --o- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - E Syrian Arab Republic 3.5 3.2 .. . . 71.5 22.0 36.4 . a. --~~~~~~~-------------------------------- -----. - - -.----------…----------- o Tajikistan 0.0 12.2 .. .. ..11.9 25.1 . .. . - - --o- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - or Tanzania 0.0 12.1 .. . . 57.5 20.6 31.7 . -------- ---- - -- --- ----... Thailand 6.9 12.2 R F F 73.8 50.0 56.7 Boo3 Baal 888- A- a…-------- --- ------------------- ---- ---- --------------- -------- o Togo ___0.0 12.0 .. . . 61.0 14.6 25.6 ?r Trinidad and Tobago 17.1 46.5 R F F 73.5 49.2 52.8 8003 8aa1 888- 888- o Tunisia 1.9 14.1 F F F 72.8 50.8 56.8 Boo3 8aa2 888 A 0 ------------- ----- :---- - --- --- ...- -- -------- -- ------ CN Turkey 1.9 2.1 F F F 48.5 34.4 43.7 81 83 8- 8- Turkmenistan .. 10.0 . .. ... 16.6 25.5 82 Uganda 0.0 19.6 .. . . 62.5 21.4 34.6 . Ukraine 0.0 10.0 F F F 66.8 17.5 30.0 Cool Gaol 8 8 United Arab Emirotees . . . . . 82 5 68.3 79.3 A2 United Kingdom 16.8 52.9 .. . . 83.5 91.5 92.1 Aaa Aaa AAA AAA United States 4.8 15.8 .. . . 79.0 91.6 93.5 Aaa Aaa AAA AAA Uruguay 0.0 10.9 . .. . 743 49.5 57.0 8003 8003 888- 888+ Uzbeksistan 1.5 11.8 . .. ... 16.7 28.3 Venezuela, RB 9.1 21.1 F F F 66.8 33.3 44.7 82 83 ....8... Vietnam 1.9 15.1 .. . . 69.5 30.0 40.7 81 West Bank and Gaza . . . . . .. . . Yemien. Rep, -18.6 -12.3 .. . . 65.3 .. 30.1 . Yugoslavia, Fed. Rep. .. 0.0 .. . . 46.0 12.2 16.7 . Zambia 35.7 37.6 .. . . 53.5 16.0 26.5 . Zimbabwe -0.8 8.5 R F F 39.0 13.0 23.2 Low income 1.3 3.4 59.8 16.2 26.5 Middie Income 2.5 12.2 70.3 38.8 45.6 Lower middle income 1.9 9.5 69.4 31.5 39.5 Upper middle income 3.3 14.6 73.8 49.5 57.2 Low & middle Income 2.3 10.9 64.8 27.0 32.5 East Asia & Pacific -3.5 8.2 69.5 36.8 37.3 Europe & Central Asia 0.4 13.6 70.8 29.1 32.3 Latin America & Carib. 3.8 19.1 67.8 34.7 44.1 Middle East & N. Africa 2.2 2.7 71.0 35.2 44.7 South Asia 0.5 2.3 58.8 27.0 35.5 Sub-Saharon Africa .. 12.2 59.5 17.5 26.3 High Income 4.5 14.2 83.7 85.3 90.2 Europe EMvU 4.5 27.2 83.8 85.9 91.1 a. Entry and exit reguiatioss are classified as free (F), reiatiseiy free (R1, delayed 101. special classes of shares 151. auteorized insestors only (A), restricted 1RS1 and closed ICd. For explanations of the terms see Abour the data. b. This copyrighted material is reprinted mite permission from the following data providers: PRS Group, 6320 Fly Road. Suite 102. PO Boa 240. East Syracuse, NY 13057: Institutional investor Inc., 488 Madison Anenue, New York, NY 13057: Euromoney Publications PLC, Nester House, Flayneuse Yard. London EC4V SEX, UK: Moody's Investors Service, 99 Churcn Street. New York, NY 10007; and Standard & Poor's Rating Services, The McGraw-Hill Companies. Inc., 1221 Avenue or the Americas, New York, NY 10020. Prior mrittes consent from the original data proniders cited must be obtained for third-ponty use of these data. c. Foreigners are barred from investing directly in the Saudi stock market, but they may insest indirectly through mutual funds. 5.2 About the data Definitions As investment portfolios become increasingly try. But these subjective perceptions are the * Foreign direct investment is net inflows of global, investors as well as governments seeking reality that policy-makers face. Countries not investment to acquire a lasting management to attract investment must have a good rated by credit risk rating agencies typically do interest (10 percent or more of voting stock) in understanding of trends in foreign direct not attract registered flows of private capital. an enterprise operating in an economy other investment and country risk. This table presents The risk ratings presented here are included for than that of the investor. It is the sum of equity information on foreign direct investment, country their analytical usefulness and are not endorsed capital, reinvestment of earnings, other long- risk and creditworthiness ratings from several by the World Bank. term capital, and short-term capital as shown major international rating services, and The PRS Group's Intemational Country Risk in the balance of payments. Gross capital information on the regulation of entry to and exit Guide (ICRG) collects information on 22 compo- formation (gross domestic investment in from emerging stock markets reported by nents of risk, groups it into three major catego- previous editions) is the sum of gross fixed Standard & Poor's. ries (political, financial, and economic), and con- capital formation, changes in inventories, and The statistics on foreign direct investment are verts it into a single numerical risk assessment acquisitions less disposals of valuables. based on balance of payments data reported by ranging from 0 to 100. Ratings below 50 indi- * Regulations on entry to emerging stock the International Mlonetary Fund (IMF), supple- cate very high risk, and those above 80 very low markets are assessed on a scale from free to mented by data on net foreign direct investment risk. Ratings are updated monthly. closed (see About the data). * Regulations on 287 reported by the Organisation for Economic Co- Institutional Investor country credit ratings are repatriation of income (dividends, interest, and operation and Development and official national based on information provided by leading inter- realized capital gains) and repatriation of capital sources. (For a detailed discussion of data on national banks. Responses are weighted using from emerging stock markets are evaluated foreign direct investment see About the data for a formula that gives more importance to re- as free or restricted (see About the data). E table 6.7.) sponses from banks with greater worldwide ex- * Composite International Country Risk Guide E Entry and exit restrictions on investments are posure and more sophisticated country analy- (ICRG) risk rating is an overall index, ranging X among the mechanisms by which countries at- sis systems. Countries are rated on a scale of from 0 to 100, based on 22 components of CD tempt to reduce the risk to their economies as- 0 to 100 (highest risk to lowest), and ratings risk. * Institutional Investor credit rating ranks, 3 sociated with foreign investment. Yet such re- are updated every six months. from 0 to 100, the chances of a country's C strictions may increase the risk or uncertainty Euromoney country creditworthiness ratings default. * Euromoneycountrycreditworthiness , perceived by investors. Many countries close are based on nine weighted categories (cover- rating ranks, from 0 to 100, the risk of investing a industries considered strategic to foreign or ing debt, economic performance, political risk, in an economy. * Moody's sovereign foreign nonresident invesTors. And national law or cor- and access to financial and capital markets) that and domestic currency long-term debt rating porate policy may limit foreign investment in a assess country risk. The ratings, also on a scale assesses the risk of lending to governments. company or in certain classes of stocks. of 0 to 100 (highest risk to lowest), are based An entity's ability to meet its senior financial The entry and exit regulations summarized in on polls of economists and political analysts obligations is rated from Aaa (offering the table refer to "new money" investment by supplemented by quantitative data such as debt exceptional financial security) to C (usually in foreign institutions; other regulations may apply ratios and access to capital markets. default. with potential recovery values low). to capital invested through debt conversion Moody's sovereign long-term debt ratings are Modifiers 1-3 are applied to ratings from Aa schemes or to capital from other sources. The opinions of the ability of entities to honor senior to B, with 1 indicating a high ranking in the regulations reflected here are formal ones. But unsecured financial obligations and contracts rating category. * Standard & Poor's sovereign even formal regulations may have very different denominated in foreign currency (foreign currency foreign and domestic currency long-tern debt effects in different countries because of differ- issuer ratings) or in their domestic currency (do- rating ranges from AAA (extremely strong ences in the bureaucratic culture, the speed with mestic currency issuer ratings). capacity to meet financial commitments) which applications are processed, and the ex- Standard & Poor's ratings of sovereign long- through CC (currently highly vulnerable). Ratings tent of red tape. The regulations on entry are term foreign and domestic currency debt are from AA to CCC may be modified by a plus or evaluated using the terms free (no significant based on current information furnished by obli- minus sign to show relative standing in the restrictions), relatively free (some registration gors or obtained by Standard & Poor's from other category. An obligor rated SD (selective default) procedures required to ensure repatriation sources it considers reliable. A Standard & has failed to pay one or more financial rights), special classes (foreigners restricted to Poor's issuer credit rating (one form of which is obligations when due. certain classes of stocks designated for foreign a sovereign credit rating) is a current opinion of , --; investors), authorized investors only (only ap- an obligor's capacity and willingness to pay its Data sources proved foreign investors may buy stocks), and financial obligations as they come due (its cred- The data on foreign direct investment are closed (closed or access severely restricted, as itworthiness). This opinion does not apply to any i t for nonresident nationals only). Regulations on specific financial obligation, as it does not take Balance of Payments Statistics Yearbook,i repatriation of income and capital are evaluated into account the nature and provisions of obli- S . as free (repatriation done routinely) or restricted gations. their standing in bankruptcy or liquida- supplemented by World Bank staff estimates f (repatriation requires registration with or permis- tion, statutory preferences, or the legality and Standard & Poor's Emerging Stock Markets sion of a governrnent agency that may restrict enforceability of obligations. Factbook 2001 The country risk and credit the timing of exchiange release).Fatok20.Tecuryisanced- worthiness ratings are from the PRS Group's Most risk ratings are numerical or alphabeti- monthly International Country Risk Guide (Web. cal indexes, with a higher number or a letter closer to the beginning of the alphabet meaning site: www.lCRGonline.com(. the monthly Insti tutional Invest or, the monthly Euromoney, lower risk (a good prospect). (For more on the rating processes of the rating agencies see the Moo nvt Sve Sovereign Data sources.) Risk ratings may be highly sub- Subnational and S overeign-Gua e Ls jective, reflecting external perceptions that do eradSt Svri t not always capture the actual situation in a coun- Credit Week. * ~5.3 Stock markets Market capitalization Value traded Turnover ratio Listed domestic S&P/IFC companies Investable Index value of shares traded as h5 of % change in $ millions % of GDP f5 of GOP capitalization price indeo 1.990 2001 1990 2000 1990 2000 1990 2001 1990 2001 2000 2001 Afghanistan . .. . . .. Algeria .. .. .. Argentina 3,268 192,499 2.3 58.3 0.6 2.1 33.6 0.2 179 ill -25.1 -31.7 Armenia ..25 . 1.4 .. 0.1 . 4.6 .. 95 Australia ---- 108.879___ 372,794 35.2 95.6 13.0 58.0 31.6 56.5 1,089 1,330 Austria 11,476 29.935 7.1 15.8 11.5 5.0 110.3 29.8 97 97 Azerbaijan ..4 . 0.1 .. . .. .2 288 Bangladesh 321 1,145 1.1 2.5 0.0 1.6 1.5 3.0 134 230 28.5 -20.7 i~ Belgium 65,449 182.481 33.2 80.5 3.3 16.8 . 20.7 182 1 74 CO Benin . . . .. .. C-)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ --- - Bolivia .. 116 .. 1.4 .. 0.0 .. 1.0 .. 18 Bosnia and Herzegovina . . . .. .. .. 5) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~--- -- ---------- --- ---- ------ --- Botswana26 1,269 6.7 18.5 0.2 0.9 6.1 0.5 9 16 -6.9 43.9 o Brazil 16.354 186.238 3.5 38.0 1.2 17.0 -2. . 8 2 1. 2. > Bulgaria 505 5.1 0.5 .. 1.0.-.. 399 -30.0 -7.5 o Burkina Faso . . - ... o Cameroon . . i . . . . ...L... . 0 .... ~ ~ ~ ~ ---- --- -- ---- --- CN Canada 241,920 841.385 42.2_ _122.3 12.4 92.3 26.7 77.3 1.144 3,977 Central African RepublicA---------- .... Chad... ...... Chile 13,645 56,310 45.0 85.6 2.6 8.6 6.3 0.5 215 249 -15.2 -8.3 China 2.028 523,952 0.5 53.8 0.2 66.8 158,9 4.7 14 1.160 -9.8 -19.5 Hong Kong. China 83,397 623,398 111.5 383.3 46.3 232.3 43.1 61.3 284 779 Colombia 1.416 13,217 3.5 11.8 0.2 0.5 5.6 0.3 80 123 -43.8 25.2 Congo. Dam. Rep. . . . .. .. Congo. Rep. . . . .. .. Costa Rica 475 2.303 5.5 14.7 0.1 1.4 5.8 12.0 82 22 C6te GI'voire 549 1.165 5.1 12.6 0.2 0.4 3.4 0.1 23 38 -25.6 .2.4 Croatia .. 3.319 .. 14.4 .. 1.0 .. 0.3 2 62 10.9 -3.5 Czech Republic ---.----9,331 . 2 1.7 .. 13.0 3 7 3.7 94 -0.6 -13.7 Denmark 39.063 107,666 29.3 66.3 8.3 56.4 28.0 86.0 258 225 Dominican Republic .. 141 .. 0.8 .. . ...6 Ecuador 69 1.417 0.5 5.2 0 0. 0 0.3 65 31 33.0~ 85.4 Egypt. Arab Rep. 1.765 24.335 4.1 29.1 0.3 1.3 . 0.7 573 1,110 -45.6 -45.5 El Salvador .. 2,672 . 1 7.2 .. 0.4 .. 3.0 .. 39 Estonia .. 1.483 .. 37.1 .. 6.6 .. 0.9 .. 17 4.5 -3.7 Finland 22,721 293.635 16.6 24 1.7 2.9 170.1 . 64.3 73 154 . France 314.384 1,446,634 25.9 111.8 9.6 83.7 . 74.1 578 808 Gabon . . . .. .. Gambia, The . . . .. .. Georgia . . . .. .. Germnany 355,073 1.270,243 21.0 67.8 29.7 57.1 139.3 79.1 413 1.022 Ghana 76 528 1.2 9.7 . 0.2 0.0 0.1 13 22 -50.9 4.5 Greece 15.228 86,538 18.1 98.4 4.7 84.4 36.3 3.1 145 338 -44.6 -31.2 Guatemala .. 215 . 1.2 .. 0.0 . 2.9 .. 5 Guinea Guinea-Biss auA ------ - ---- Haiti Honduras 40 1.3 8.7 0.0 0.0 26 71 StOck m:L;5.3 Market capitalization Value traded Turnover ratio Listed domestic S&P/IFC companies Investable Index value of shares traded as % of % change in $ millions % of GDP % of GDP capitalization price index 1990 2001 1990 2000 1990 2000 1990 2001 1990 2001 2000 2001 Hungary 505 10,367 1.5 26.3 03 2. 6.3 3.8 21 57 -28.2 -10.3 India 38,567 110,396 12.2 32.4 6.9 48.4 65.9 15.8 2,435 5,795 -31.1 -19.9 Indonesia 8,081 23,006 7.1 17.5 3.5 9.3 75.8 2.8 125 316 -61.0 -18.5 Iran, Islamic Rep. 34,282 21,830 .. 21.9 .. 2.3 30.4 12.4 97 295 Ireland .. 81,882 .. 87.2 .. 15.4 .. 19.2 .. 76 Israel 3.324 55,964 6.3 58.1 10.5 21.2 95.8 2.7 216 636 14.7 -16.4 Italy 148,766 768,364 13.5 71.5 3.9 72.5 26.8 104.0 220 291 Jamaica 911 4,703 21.5 48.4 0.8 1.0 3.4 0.1 44 42 45.6 4.3 Japan2,167 3,157,222 95.6 65.2 52.5 55.6 43.8 69.9 2,071 2.561 -27.2 1 -334 289 Jordan 2,001 6,316 49.8 59.3 101 50 2. 1.3 105 161 -24.5 31.4 Kazakhstan .. 2,260 .. 13.4 - .. 0.1 1 .2 . 171` Kenya 45 ,5 .3 12.4 0.1 0.5 2.2 0.2 54 57 -8.1 -22.7 Korea, Dem . R ep. -. .-----. . --- -.---.-------- --- ------------- -- -- 0 Kuwait .. 20,772 .. 55.0 .1 1.1 . 21.3 77 Kyrgyz Republic CD. .... .... 1. Latvia . 697 . 7.9 . 3.2 . 0.2 . 63 41.2 60.1 C Lebanon .. ~~~~~~ ~~~~ ~~~~1,243 .. 96. 0.7 . 0.5 . 12 -18.6 -29.2 E Lesotho -... . . . ... . Lithuania . 1,199 . 14.0 . 1.8 . 2.0 . 54 4.9 -23.6 Macedonia, FYR ..8 . 0.2 .. 0.7 . 348.3 ..2 Madagascar . . . . . Malaysia 48,611 120,007 110.4 130.4 24.7 65.2 24.6 2.1 282 809 -23.3 4.2 Mauritania .. 1,091 .. . . . . 0.4 .. 40 Mauritius 268 1,063 10.1 30.4 0.2 1.7 1.9 0.3 13 40 -19.4 -21.8 Mexico 32,725 121,403 12.5 21.8 4.6 7.9 44.0 1.7 199 168 -21.5 12.8 Moldova .. 38 . 3.2 .. 0.3 .. 97.9 .. 58 Morocco 966 9,087 3.7 32.7 0.2 3.3 . 1.2 71 55 -19.1 -17.3 Mozambique... .. . .. . ... Myanmar... . . . .... Namibia 21 151 0.7 8.9 . 0.6 0.0 0.0 3 13 -37.8 -31.0 Nepal .. 418 .. 8.3 . 0.5 .. 6.9 .. 108 Netherlands 119,825 640,456 40.6 175.6 13.6 185.7 29.0 101.4 260 234 New Zealand 8,835 18,613 20.5 37.3 4.5 21.6 17.3 45.9 171 144 Nicaragua... ........- Nigeria 1,372 5.404 4.8 10.3 0.0 0.6 0.9 0.6 131 194 -10.3 25.1 Norway 26,130 65,034 22.6 40.2 12.1 37.2 54.4 93.4 112 191 Oman 1.061 2,606 9.4 29.4 0.9 13.0 12.3 2.0 55 91 7.2 -27.0 Pakistan 2.850 4,944 7.1 10.7 0.6 53.5 8.7 8.0 487 747 -16.7 -32.8 Panama 226 3,584 3.4 37.5 0.0 0.5 0.9 1.5 13 31 Papua New Guinea... .. . .. . ... Paraguay .. 423 .. 5.5 . 0.2 . 3.5 .. 55 Peru 812 11,134 3.1 19.8 0 4 2 8 193 110.5 29 27 -281 14.2 Philippines 5,927 41,523 13.4 69.0 2.7 11.0 13.6 0.5 153 232 -43.6 -29.9 Poland 144 26,017 0.2 19.8 0.0 9.3 89.7 1.9 9 230 -3.5 -24.9 Portugal 9,201 60,681 13.0 57.8 2.4 51.8 16.9 85.5 181 109 38.4 Puerto Rico .. -- .. .. .. .. .. ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~......... ---- ------------------------- Romania . 2,124 - 2.9 . 0.6 - 0.8 . 5,140 -25.3 -25.3 Russian Federation 244 76,198 0.0 15.5 . 8.1: . 3.1 13 236 -32.2 52.4 ~~ 5.3 Market capitalization Value traded Turnover ratio Listed domestic S&P/IFC companies Investable Index value of shares traded as % of % change in $ millions % of GDP % of GOP capitalizatior price index 1990 2001 1990 2000 ia19o 2000 1990 2001 1990 2001 1 2000 2001 Rwanda.. .......... Saudi Arabia 48,213 73,199 40.8 38.8 1.9 10.0 .. 1.7 59 76 42.3 3.7 Senegal . . . .. Sierra Leone... .... Singapore 34,308 152,827 93.6 165.7 55.3 99.2 .. 52.1 150 418 Slovak Republic .. 665 .. 3.9 .. 4.7 .. 17.7 844 0.4 21.3 Slovenia 2.839 .. 14.1 .. 2.6 .. 3.4 24 38 -9.5 2.0 Somalia South Africa 137.540 139,750 122.8___ 162.8 7.3 61.6 .. 3.6 732 542 .17.3 -22.1 290 Spain 111,404 504.219 21.7 90.3 8.0 176.5 210.7 427 1,019 Sri Lanka 917 1,332 11.4 6.6 0.5 0.9 5.8 3.8 175 238 .41.7 36.5 Sudan .. .. ~~~~~~~~~~~~~~~~~~~~~.. .. . ... .. .. .. ---.---------- (0 Swaziland 17 95 2.0 7.0 .. 0.0 .. 0.217 0.) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~. ....... . .. ---- -----17 .. Sweden 97,929 328,339 41.1 144.4 7.4 171.6 14.9 111.2 258 292 Switzerland 160,044 792,316 70.1 330.5 29.6 254.1 82.0 182 252 E Syrian Arab Republic . . . . . o Tajikistan . . . .. .. (0 - ..--- .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Z------..... > Tanzania .. 181 . 2.1 .. 0.1 . 3.4 ..4 o Thailand 23,896 36,340 28.0 24.1 26.8 19.0 92.6 11.5 214 449 -54.1 3.0 o Togo Trinidad and Tobago 696 5,035 13.7 59.2 1.1 1.9 10.0 0.7 _30 31 ..8.5' 1.6 o Tunisia 533 2,303 4.3 14.5 0.2 3.2 3.3 1.8 13 46 900 -29.0 N Turkey 19,065 47,150 12.6 34.8 3.9 89.6 42.5 15.3 110 310 -51.2 -30.2 Turkmenistan . . . . . . . .. Uganda ... .. ... .... Ukraine .. 1,365 .. 5.9 .. 0.9 .. 1.6 .. 131 75.2 0 .36.3 United Arab Emirates .. 23,262 .. 71.6 .. . . 54 United Kingdom 848,866 2.5 76,992 85.9 182.2 28.2 129.7 33.4 66.6 1,701 1,904 -10.2' c 18.3 United States 3,059,434 15,104,037 53.2 153.5 30.5 323.9 53.4 200,8 6,599 7,524 .10.10 -13.0' Uruguay .. 168 0.8 0.0 0.9 36 17 Uzbekistan 119 .. 1.4 0.4 ..4 Venezuela. RB 8,361 6.216 17.2 6.7 4.6 0.6 43.0 0.5 76 63 18.7 -20.1 Vietnam . ., West Bank and Gaza .. 848 19.8 ., 3.5 20.9 .. 22 Yemen, Rep. . .. Yugoslavia. Fed. Rep. .. 10,81 7 109.9 .. 0.1 0.0 .. 16 Zambia .. 291 .. 9.4 .. 0.5 . 4.7 8 Zimbabwe 2,395 7,972 27.3 32.9 0.6 3.8 2.9 1 6 57 72____.24.6 134.3 Low Income 54.588 194,186 9.8 23.6 4.7 32.8 53.8 121.3 3,446 7,733 Middle Income 430,288 2,049.260 21.2 41.2 8.0 37.7 __783__ 84.9 4,900 15.364 Lower middle income -39,161 777,998 5.3 40.3 40.9 .. 101.0 1,723 10.142 Upper middle income 391,127 1.2 71 .262 25.8 41.8 8.1 35.7 37.1 74.7 3,177 5,222 Low&8,middie Income 484,876 2.243,446 19.9 38.7 7.6 37.0 70.7 90.1 8,346 23,097 East Asia & Pacific 197,109 954,452 21.3 48.3 13.2 69.8 11 7.2 148.9 1,443 3.486 Europe & Central Asia 19,065 1 73.932 2.1 20.5 .. 27.8 .. 83.1 110 8.220 Lat'in-America & Carib.---- 78,188 614,691 7.6 34.0 2.1 8.9 29.7 26.9 1,734 1,567 Middle East & N. Africa 5,265 126,253 27.8 _34.8 1.5 7.2 22.3 817 1,596 South Asia 42,655 156,905 108 27.0 5.6 43.8 54.0 161.6 3,231 7.159 Sub-Saharan Africa 142,594 217,212 51.9 102.3 .. 36.8 -. 22.5 1,011 1,069 High Income 8,914.783 29,945,774 51.7 120.6 31.4 181.1 59.5 129.9 17,078 25,548 Europe EMU 1.183.983 5,423,384 21.6 89.9 14.2 83.1 .. 90.6 2,630 4,367 Note: aecause aggregates for market capitalization are unavailable for 2001, those shown refer to 2000. a. Data refer to the S&P/lFC Global index. b. Data refer to the Nikkei 225 index. C. Oats refer to the FT 100 index. d. Data refer to the S&P 500 ladex. .-. . ~5.3 About the data Definitions Financial market development is closely related for long-term economic growth. A more compre- * Market capitalization (also known as mar- to an economy's overall development. Well- hensive measure of liquidity would include trad- ket value) is the share price times the number functioning financial systems provide good and ing costs and the time and uncertainty in find- of shares outstanding. * Value traded refers easily accessible information, which lowers ing a counterpart in settling trades. to the total value of shares traded during the transactions costs, which in turn improves Standard & Poor's maintains a series of in- period. * Turnover ratio is the total value of resource allocation and economic growth. Both dexes for investors interested in investing in shares traded during the period divided by the banking systems and stock markets enhance stock markets in developing countries. At the average market capitalization for the period. growth, which is the main factor in poverty core of the Standard & Poor's family of emerg- Average market capitalization is calculated as reduction. At low levels of economic development ing market indexes, the S&P/IFCG indexes are the average of the end-of-period values for the commercial banks tend to dominate the financial intended to represent the most active stocks in current period and the previous period. * Listed system. In higher-income economies domestic the markets they cover and to be the broadest domestic companies are the domestically in- stock markets tend to become more active and possible indicator of market movements. The corporated companies listed on the country's efficient relative to domestic banks. The struc- S&P/IFCI indexes, which apply the same calcu- stock exchanges at the end of the year. This ture and development of a country's financial lation methodology as the S&P/IFCG indexes, indicator does not include investment compa- system are also determined by aspects of the are designed to measure the returns foreign nies, mutual funds, or other collective invest- 291 legal, regulatory, taix, and macroeconomic envi- portfolio investors might receive from investing ment vehicles. * S&P/iFC Investable index ronment. in emerging market stocks that are legally and price change is the U.S. dollar price change in C The stock market indicators presented in the practically open to foreign portfolio investment. the stock markets covered by the S&P/IFCI m table include measures of size (market capitali- The EMDB covers 54 markets, providing regu- country index, supplemented by the S&P/IFCG E zation and number of listed domestic compa- lar updates on more than 2,200 stocks; the country index. nies) and liquidity (value traded as a percent- S&P/IFCG indexes include 34 markets and more CD age of GDP, and turnover ratio). The compara- than 1,900 stocks; the S&P/IFCI indexes cover 0 bility of such indicators between countries may 30 markets and close to 1,200 stocks. They Data sources | - 3 be limited by conceptual and statistical weak- are widely used benchmarks for international The data on stock markets are from Standard CD nesses, such as inaccurate reporting and differ- portfolio management. See Standard & Poor's & Poor's Emerging Stock Markets Factbook ences in accounting standards. The percentage (2001b) for further information on the indexes. 2001, supplemented by other data from ! change in stock market prices in U.S. dollars, Because markets included in Standard & Standard & Poor's. The firm collects data from the Standard & Poor's Investable (S&P/ Poor's emerging markets category vary widely through an annual survey of the world's stock I IFCI) and Global (S&P/IFCG) country indexes, is in level of development, it is best to look at the exchanges, supplemented by information an important measure of overall performance. entire category to identify the most significant provided by its network of correspondents and Regulatory and institutional factors that can af- market trends. And it is useful to remember by Reuters. The GDP data are from the World fect investor confidence, such as the existence that stock market trends may be distorted by Bank's national accounts data files. About the of a securities and exchange commission and currency conversions, especially when a currency data is based on Demirgu,-Kunt and Levine the quality of investor protection laws, may in- has registered a significant devaluation. (1996a) and Beck and Levine (2001). fluence the functioning of stock markets but are not included in this, table. Figure 5.3 Stock market size can be measured in a num- ber of ways, each of which may produce a differ- The developing countries of Europe and Central ent ranking among countries. Market capitaliza- Asia have seen a dramatic Increase in the number of lsted companies since 1990 tion shows the overall size of the stock market in U.S. dollars and as a percentage of GDP. The 10 number of listed domestic companies is another T 9 measure of market size. Market size is positively e 6 correlated with the ability to mobilize capital and 1 6 diversify risk. / Market liquidity, the ability to easily buy and i. 4 sell securities, is measured by dividing the total 8 3 value traded by GDF. This indicator complements T 2 - 7 the market capitalization ratio by showing 1 whether market size is matched by trading. The 0 tumover ratio-the value of shares traded as a 1990 1995 2000 percentage of market capitalization-is also a - East Asia & Pacific measure of liquidity, as well as of transactions - Europe S Central Asia - Latin America & Caribbean costs. (High turnover indicates low transactions _ Middle East & North Africa costs.) The turnove-r ratio complements the ra- - South Asia tio of value traded to GDP, because the turn- - Sub-Saharan Africa over ratio is related to the size of the market Source: Standard & Poor s 2001 and table 5.3 and the value traded ratio to the size of the economy. A small, liquid market will have a high turnover ratio but a low value traded ratio. Liq- uidity is an important attribute of stock mar- kets because, in theory, liquid markets improve the allocation of capital and enhance prospects *I 5.4 Financial depth and efficiency Domestic credit Liquid Quasi-liquid Ratio of bank Interest rate Spread over provided by liabilities liabililties liquid reserves to spread LIBOR banking sector bank assets Lending minus Lending rate deposit rate minus LIBOR percentage percentage % of GDP % of GDP % of GDP %points points 1990 2000 1990 2000 11990 2000 11990 2000 11990 2000 1990 2000 Afghanistan .. .. .. ... Alban ia .. 48.3 .. 60.8 .. 37.8 .. 12.0 2.1 13.8 16.7 15.6 Algeria 74.5 32.0 73.5 41.4 24.8 15.4 1.3 3.0 .. 2.5 .. 3.5 Angola . -15.3 .. 18.2 .. 12.2 .. 17.7 .. 63.6 96.6 Argentina 32.4 34.4 11.5 31.9 7.1 24.9 7.4 2.5 .. 2.7 4.6 Armenia 58.7 11.5 79.9 14.7 42.9 7.8 13.6 7.0 .. 13.5 .. 25.0 Australia 71.7 91.7 55.1 66.5 43.4 46.0 1.5 0.6 4.5 4.7 9.9 2.3 Austria 121.4 .. . . . . 2.1 . 3.4 .. 0.2 Azerbaijan 57.2 9.6 33.5 16.3 11.6 9.6 4.5 9.3 M. 292 Bangladesh 23.9 35.3 23.4 34.7 16.8 25.5 12.8 7.6 4.0 6.9 7.7 9.0 Belarus 19.2 .. 17.7 .. 12.1 .. 7.7 .. 30.1 .. 61.1 in. .- - - -- - - . . . . o Belgium 8.6 7.3 7.0 5.3 5.7 3.7 1.1 0.8 -0.9 1.3 -0.9 -2.2 Benin 22.4 8.8 26.7 31.1 5.9 7.4 29.3 10.0 9.0 .. 7.7 V Bolivia 30.7 64.7 24.5 54.6 18.0 46.8 18.8 5.8 18.0 23.6 33.5 28.1 Bosnia and Herzegovina . .. . . 5)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-- --- ------------- 2 Botswana -46.4 -71.9 22.1 27.2 13.7 20.1 11.0 3.6 1.8 5.2 -04 8.8 ). - - - - - - - - - - o Braz'il 89.8 50.9 -26.4 292 18.5 22.4 6.7 7.6 39.6 50.3 > Bulgaria 118.5 183 71.9 35.0 53.6 20.7 10.2 6.6 8.9 8.4 42.4 5.0 Burkina Faso 13.7 16.9 21.3 24.9 7.5 7.5 12.7 4.6 9.0 7.7 'O- - - - -- - - - - - -.-- --- -- - - - - - -- . -- - - Burundi 23.2 30.1 18.2 19.9 6.5 _6.1 2.8 3.0 ... 4.0 9.2 0 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~---------------- __ __ _______ ------------------ Cambodia 7.4 15.0 10.6 .. 47.3 .. 10.5 .. 10.8 N . -.-.---.-.- .... - . - - . - . ~~ ~~~~~~~~~~~~~~~~~~~~~~ ~ ~ ~~~~~- ------ ----- - --- o Cameroon 31.2 16.3 22.6 17.3 10 1 64 3.4 15.4 11.0 17.0 10.2 15.5 0 .. . . . . . . -. .:. 6.......4 CN Canada 82.5 89.2 74.5 75.0 59.9 52.8 1.6 0.7 1.3 1.6 5.7 0.7 Central African Republic 12.9 11.4 15.3 16.2 1.8 1.3 2.8 1.7 11.0 17.0 10.2 15.5 Chad 11.5 12.2 14.6 12.2 06 0.8 3.3 12.9 11.0 17.0 10.2 15.5 Chile 73.0 74.7 40.7 50.3 32.8 40.0 3.8 3.1 8.5 5.6 40.6 8.3 China 90.0 132.7 79.2 152.1 41.4 91.1 15.7 12.7 0.7 3.6 1.0 -0.7 Hong Kong. China 156.3 141.4 181.7 236.8 166.8 222.3 0.1 0.3 3.3 4.7 1.7 3.0 Colombia 35.9 36.5 29.8 32.8 19.3 22.8 26.3 5.9 8.8 6.6 36.9 12.3 Congo, Dem. Rep. 25.3 12.9 2.1 . 48.5 ... 158.5 Congo, Rep. 29.1 8.7 22.0 14.6 6.1 1.1 2.0 43.1 11.0 17.0 102 15.5 Costa Rica 29.9 32.7 42.7 36.9 30.0 23.9 68.5 18.9 11.4 11.5 24.2 18.4 CMe dIlvoire 44.5 25.6 28.8 24.6 10.9 7.3 2.1 3.9 9.0 .. 7.7 Croatia .. 45.7 .. 46.1 34.6 .. 10.7 499.3 8.3 1,153.9 5.5 Czech Republic .. 57.3 .. 73.8 .. 4. . 1. . 3.7 .. 0.6 Denmark 63.0 56.7 59.0 55.3 294 24.3 1.1 8.1 ..6.2 4.9 5.8 1.5 Dominican Republic 31.5 38.5 28.4 34.7 13.1 23.7 31.1 27.5 15.2 9.1 29.3 20.3 Ecuador 15.0 39.9 20.4 31.3 11.3 21.3 22.6 3.8 -6.0 8.9 29.2 9.7 Egypt, Arab Rep. 1068_ _100,2___ 87.9 839 60.7 65 4 17.1 15.9 7.0 3.8 10. 6.7 El Salvador 32.0 42.3 30.6 46.2 19.6 - 37.9 -33.4 29.8 3.2 4.6 12.9 7.4 Eritrea Estonia 66.7 40.0 136.0 39.3 95.2 14.6 43.1 19.9 .. 3.9 26.6 1.1 Ethiopia 67.0 61.7 42.2 42.5 12.6 20.6 24.0 13.6 3.6 4.2 -2.3 4.4 Finland 83.1 55.8 54.4 48.1 41 2 4.1 40 33 -0.9 France 104.4 1.0 .. 61 41 23 0.2 Gabon 20.0 12.9 17.8 15.0 66 59 2.0 128 11.0 17.0 102 15.5 Gambia, The 3.4 13.8 20.7 36.8 8.8 18.5 8.8 10.1 15.2 11.5 18.2 17.5 Georgia .. 21.9 .. 10.5 4 . 13.7 .. 22.6.. 22 Germany 103.4 147.5 68.9 78.1 ... 3.2 1.4 4.5 6.2 3.3 3.1 Ghana 13.2 40.8 14.1 20.1 3.4 10.5 20.2 4.8 Greece 99.3 101.8 . .. ... 13.8 25.2 8.1 6.2 19:3 5.8 Guatemala 17.4 16.9 21.2 28.6 11.8 15.8 31.8 15.6 5.1 10.7 15.0 14.4 Guinea 6.0 8.9 0.8 11.6 0.8 2.1 6.2 17.8 0.2 11.9 12.9 12.8 Guinea-Bissau 77.5 18.1 68.9 42.7 4.4 0.7 10.8 45.5 13.1 .. 37.4 Haiti 32.9 31.5 31.4 37.0 15.9 25.4 74.9 37.3 .. 13.2 .. 18.6 Honduras 40.9 31.5 33.6 52.1 18.8 38.5 6.6 18.0 8.3 10.9 8.7 20.3 F in a n c ie id C 5.4 Domestic credit Liquid Quasi-liquid Ratio of bank Interest rate Spread over provided by liabilities liabilities liquid reserves to spread LIBOR banking sector bank assets Lending mines Lending rate deposit rate minus LIBOR percentage percentage % of GDP % of GDP % of GDP %points points 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Hungary 105.5 53.9 43.8 46.3 19.0 27.8 11.0 10.8 4.1 3.0 20.5 6.1 India 51.5 53.4 43.1 56.1 28.1 39.3 14.8 8.0 .. 8.2 5.8 Indonesia 45.5 66.2 40.4 57.5 29.1 45.3 4.5 8.1 3.3 6.0 12.5 11.9 Iran, Islamic Rep. 70.8 49.3 57.6 42.9 31.1 23.8 66.0 41.0 . Ireland 55.2 110.2 44.5 ... 4.8 1.4 5.0 4.7 3.0 -1.8 Israel 106.2 86.5 70.2 97.5 63.6 90.6 11.9 13.6 12.0 4.2 18.1 6.3 Italy 89.4 98.5 70.5 ... 12.0 0.7 7.3 4.4 5.8 -0.3 Jamaica 34.8 38.0 51.0 47.3 37.8 32.3 37.4 19.0 6.6 11.7 22.2 16.8 Japan 259.7 310.5 182.4 189.9 155.3 142.4 1.5 1.4 3.4 2.0 -1.4 .4.5 . 293 Jordan 117.9 90.6 131.2 113.1 77.8 79.0 20.5 29.0 2.2 4.8 2.0 5.3 Kazakhstan .. 14.1 … .. 15.3 .. 6.2 .. 5.4- -- - - -- - -- - - - -- - - - - - - - - - - - Kenya 52.9 48.2 43.3 46.1 29.3 31.0 9.9 8.3 5.1 14.2 10.4 15.8 Korea, Oem. Rep. C.. .. ... . .. .. Korea, Rep. 65.7 104.0 54.6 97.9 45.7 88.8 6.3 2.0 0.0 0.6 1.7 2.0 Kuwait 243.0 82.2 192.2 70.4 153.9 57.8 1.2 1.0 0.4 3.0 4.1 2.3 < Kyrgyz Republic 12.6 .. 11.9 .. 4.5 .. 18.7 .. 33.5 - .. 45.4 (D .-.------.....---...- ..-.-.. - --------.-.--.---... - -----..-.---.--- -----...-..-... -------- . 0~~~~~~~~~~~~~~~~~~~~~~c Lao PDR 5.1 10.7 7.2 16.7 3.1 14.2 3.4 28.1 .. 20.0 .. 25.5 ' Latvia .. 24.2 .. 30.4 .. 12.8 .. 5.8 .. 7.5 .. 5.3 ( Lebanon 132.6 183.3 193.7 198.1 170.9 188.5 3.9 12.7 23.1 6.9 31.6 11.6 Lesotho -.-32.8 3.7 39.2 27.3 22.6 10.7 23.0 23.6 7.4 12.2 12.1 10.6 Liberi a ... ...... 64.9 70.9 .. 14.3 .. 14.0 E Libya . .. . .. ... 26.4 30.5 1.5 4.0 -1.3 0.5 Lithuania .. 14.4 .. 23.1 .. 10.6 .. 12.0 .. 8.3 .. 5.6 Macedonia, FYR .. 14.5 .. 19.4 .. 9.9 .. 7.9 .. 7.7 .. 12.4 Madaascar 26.2 1. 178 26.8 5.3 11.5 8.5 20.1 5.3 11.5 17.5 20.0 Malawi 19.7 8.7 21.1 18.3 11.7 9.3 32.8 15.2 8.9 19.9 12.7 46.6 Malaysia 75.7 143.4 64.4 130.0 43.0 106.4 5.9 14.0 1.3 3.4 -1.1 0.2 Mali 13.7 15.0 20.5 24.9 5.5 7.1 50.8 17.7 9.0 .. 7.7 Mauritania 54.7 .2.7 28.5 14.8 7.0 3.9 6.1 4.3 5.0 .. 1.7 Mauritius 45.1 74.8 63.3 83.1 49.1 71.5 8.8 5.1 5.4 11.2 9.7 14.2 Mexico 36.6 25.4 22.8 23.6 16.4 15.3 4.2 5.8 .. 12.0 -. 11.7 Moldova 62.8 25.3 70.3 22.4 35.4 9.7 8.3 13.3 .. 8.9 .. 27.2 Mongolia 73.4 10.5 56.2 24.8 14.7 12.3 2.0 18.1 .. 16.5 .. 23.7 Morocco 60.1 92.1 61.0 82.6 18.4 21.5 11.3 6.3 0.5 8.2 0.7 6.8 Mozambique 15.6 11.2 26.5 30.4 5.2 15.9 61.5 8.5 .. 9.3 .. 12.5 Myanmar 32.8 26.8 27.9 25.7 7.8 9.9 271.8 23.4 2.1 5.5 -0.3 8.7 Namibia 18.8 48.0 22.6 45.0 13.2 21.1 4.4 3.2 10.6 7.9 1 7.4 8.7 Nepal 28.9 43.2 32.2 51.5 18.5 34.9 12.7 10.6 2.5 3.5 6.1 2.9 Netherlands 103.2 .. . . . . 0.3 .. 8.4 1.9 3.4 -1.7 New Zealand 81.611. 77.9 89.1 64.8 74.9 0.8 0.5 4.4 3.9 7.7 3.7 … -~~~~~1 ...... - - - - - - - - ... - -... - -.. - -..- - -...- .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- - - - -- -- - -- -- - - Nicaragua 206.6 141.9 56~' 64.2 23.1 529 20.2 15.4 12.5 11.9 13.7 14.8 Niger 16.2 9.0 19.8 8.0 8.3 2.1 42.9 9.9 9.0 .. 7.7 Nigeria 23.7 11.3 23.6 24.8 10.3 9.2 11.6 15.4 5.5 9.6 17.0 14.7 Norway 89.5 55.8 599 50.9 270 11.2 05 22 46 1.5 5.9 1.7 Oman 16.6 44.7 28.9 37.0 19.3 28.3 6.9 3.6 1.4 2.4 1.4 3.5 Pakistan 50.9 49.0 39.8 47.5 10.0 20.0 8.9 6.2 . Panama 52.7 110.8 41.1 86.9 33.0 75.1 . . 3.6 3.1 3.7 3.6 Papua New Guinea 35.7 25.9 35.2 29.7 24.0 16.7 3.2 8.0 6.9 3.0 7,2 11.0 Paraguay 14 9 27.2 22.3 36.7 13.7 26.9 31.0 22.7 8.1 11.1 22.7 20.3~~~~~~~~~~~~~1-1. 20. Peru 20.2 25.9 24.8 32.1 11.8 20.8..... 22.0 21.6 2,335.0 14.6 4,766.2 21.4 Philippines 26.9 68.0 37.0 66.5 28.4 54.7 20.9 7.4 4.6 2.6 15.8 4.4. Poland 19.5 37.8 34.0 43.0 17.2 - 30.9 -20.6 4.6 462.5 5.8 495.9 13.5 Portugal 70.0 142.8 .. .. .. .. ~~~~ ~ ~~~~ ~~~~ ~~~~ ~~~29.0 3.6 7.8 2.8 13.5 -0.2 Puerto Rico......... ...... Romania 79.7 14.1 60.4 23.2 32.7 17.7 1.2 36.4 Russian Feeaion .. 24.0 .. 22.1 . 9.6 15.4 .. 17.9 .. 17.9 Domestic credit Liquid Quasi-liquid Ratio of bank Interest rate Spread over provided by liabilities liabilities liquid reserves to spread LIBOR banking sector bank assets Lending minus Lending rate deposit rate minus LIBOR percentage percentage tt of GDP % of GDP % of GDP %points points 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 17.1 12.6 14.9 16.3 7.0 7.3 4.3 9.9 6.3 .. 4.9 ... ... Saudi Arabia 58.7 68.4 47.9 48.6 21.9 23.0 5.6 4.7 Senegal 33.8 25.1 22.9 25.4 9.7 10 5 14.1 6.4 9.0 7.7 Sierra Leone 26.3 54.4 13.1 16.4 2.6 5.8 64.1 14.7 12 .0 17.0 44.2 19.7 Singapore 75.6 89.6 123.4 107.5 100.5 86.5 3.7 2.5 2.7 4.1 .1.0 -0.7 Slovak Republic 59.9 67 8 46.8 .. 5.7 . 6.4 .. 8.4 Slovenia 36.8 47.1 34.2 49.7 25.8 40.1 2.7 3.5 142.0 5.7 818.6 9.2 Somalia .. . . . .. . 22.3 South Africa 97.8 162.6 44.6 46.3 27.2 15.9 3.3 2.7 2.1 5.3 12.7 8.0 294 Spain 106.2 119.8 . ..8.7 1.0 5.4 2.2 7.7 -1.4 Sri Lanka 43.1 44.4 35.2 46.6 22.9 37.2 9.9 7.6 -6.4 7.0 4.7 9.6 U t- - - - - - - - - - - - - - - - - - - - - - - Sudan 20.4 8.6 20.1 11.7 2.9 3.8 79.5 36.4 to Swaziland 7.9 -4.1 29.6 22.2 20.8 15.2 21.5 7.2 5.6 7.5 6.2 7.5 Sweden 140.3 79.3 52.3 44.8 ... 1.8 0.4 6-8 3.7 8.4 -0.7 - Switzerland 179.0__ 179.2 145.2 130.9 118.6 91.3 1.1 0.8 -0.9 1.3 .0.9 -2.2 E Syrian Arab Republic 56.6_ 27.6 54.7 66.2 10.5 22.9 46.0 5.8 > Tanzania 34.6 12.1 19.9 19.7 6.3 10.1 5.3 15.0 .. 14.2 .. 15.0 a) CD Thailand 91.1 121.7 74.9 115.2 66.0 101.2 3.1 1.9 2.2 4.5 6.1 1.3 o Togo 21.3 23.8 36.1 28.4 19.1 8.0 59.0 6.2 9.0 .. 7.7 Trinidad and Tobago 58.5 47.3 54.6 57.2 42.7 456 135 14.4 6 9 83 . 10.0 N.- . -.-. .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~4 354 . . o Tunisia 62.5 73.2 51.5 57.8 26.7 33.9 1.6 2.6 0 . . . ~ ~~~~~~~~~~~~~-.-- - - - - - Turkey 19.4 53.7 24.1 44.9 16.4 39.6 16.3 7.1 Turkmenistan .. 30.5 .. 14.9 .. 5.1 .. 4.1 . Uganda 1 7.8 12.2 7.6 16.2 1.4 7.5 15.2 12.4 7.4 13.1 30.4 16.4 Ukraine 83.2 23.4 50.1 18.1 9.0 6.1 49.0 17.9 .. 27.8 .. 35.0 United Arab Emirates 34.7 59. 9 46.3 57.9 37.7 41.6 4.4 11.5 . United Kingdom 121.4 136.1 0.5 0.4 2.2 2.7 6.4 -0.6 United States___ 110.9 161.7 65.5 62.1 49.4 47.5 2.3 1'1I .. 1.7 2.7 Uruguay 46.7 54.3 58.1 51.3 51.5 45.4 31.1 11.1 76.6 36.9 166.1 42.5 Uzbekistan --. . .. ------ Venezuela, RB 37.4 14.3 38.8 18.9 29.4 9.1 21.9 29.5 7.7 8 9 27.2 18.7 Vietnam 4.7 35.0 22.7 44.4 9.3 23.9 13.3 9.1 .. 6.9 .. 4.0 West Bank and Gaza . . . . . . . . . Yemen, Rep. 60.6 5.2 _55.1 34.1 10.4 16.1 121.2 18.8 .. 3.8 . 16.6 Yugoslavia, Fed. Rep. . .. .. .. .. Zambia 67.8 79.1 21.8 26.8 10.6 18.3 33.7 13.1 9.5 18.6 26.8 32.3 Zimbabwe 41.7 48.3 41.8 34.5 30.3 17.6 12.2 8.7 2.9 18.0 3.4 61.7 - - - - - - - - - - Low-income -------- 44.6 45.9 37.1 46.8 22.2 31.2 13.5 13.2 Middle Income 65.1 72.9 44.7 69.4 28.0 47.1 12.4 7.8 Lower middle income 90.3 .. 98.6 .. 61.1 19.7 7.9 Upper middle income 60.5 60.2 33.8 48.0 23.5 36.9 8.1 7.6 Low & middle Income 61.0 68.7 43.1 65.8 26.9 44.7 13.2 10.7 East Asia & Pacific 73.4 116.4 63.9 123.9 42.7 85.65 5.9 10.9 Europe & Central Asia .. 36.4 .. 36.2 .. 24.7 .. 10.7 Latin-America & Carib. 59.3 37.8 25.4 29.6 17.7 21.4 22.3 15.5 Middle East & N. Africa 69.3 70.0 61.6 61.8 30.3 36.7 14.2 9.5 South Asia 48 9 51 1 41.0 53.1 25.2 36.0 12.7 7.6 Sub-Saharan Africa 56.8 76.6 32.5 34.2 17.2 14.0 11.9 10.1 High Income 132.3 173.8 92.5 101.8 .. 76.9 1.8 1.1 Europe EMU 96.8 118.1 . ..4.1 1.3 - . f > ! j * 5 . 4i.4 About the data Definitions The organization and performance of financial No less important than the size and structure * Domestic credit provided by banking sector activities in a country affect economic growth of the financial sector is its efficiency, as indi- includes all credit to various sectors on a gross through their impact on how businesses raise cated by the margin between the cost of mobi- basis, with the exception of credit to the central and manage funds. Savers accumulate claims lizing liabilities and the earnings on assets-or government, which is net. The banking sector on financial institutions, which pass these funds the interest rate spread. A narrowing of the in- includes monetary authorities, deposit money to their final users. But even if a country has terest rate spread reduces transactions costs, banks, and other banking institutions for which savings, growth may not materialize because which lowers the overall cost of investment and data are available (including institutions that the markets are not perfect and the financial is therefore crucial to economic growth. Inter- do not accept transferable deposits but do incur system can fail to direct those savings where est rates reflect the responsiveness of financial such liabilities as time and savings deposits). they can be invested most efficiently. House- institutions to competition and price incentives. Examples of other banking institutions include holds and institutions save and invest indepen- The interest rate spread, also known as the in- savings and mortgage loan institutions and dently. The financial system's role is to inter- termediation margin, is a summary measure of building and loan associations. * Liquld mediate between them and to cycle available a banking system's efficiency. To the extent that liabilities are also known as broad money, or funds to where they are needed. This is accom- information about interest rates is inaccurate, M3. They are the sum of currency and deposits plished through established payments systems, banks do not monitor all bank managers, or the in the central bank (MO), plus transferable 295 available price information, a way to manage government sets deposit and lending rates, the deposits and electronic currency (Ml), plus N uncertainty and control risk, and mechanisms to interest rate spread may not be a reliable mea- time and savings deposits, foreign currency 0 0 deal with problems of asymmetric information sure of efficiency. The spread over LIBOR reflects transferable deposits, certificates of deposit, P between parties to a financial transaction. As an the differential between a country's lending rate and securities repurchase agreements (M2), 2 economy develops, this indirect lencling by savers and the London interbank offered rate (ignoring plus travelers checks, foreign currency time E to investors becomes more efficient and gradu- expected changes in the exchange rate). Inter- deposits, commercial paper, and shares of Q ally increases financial assets relative to GDP. est rates are expressed as annual averages. mutual funds or market funds held by residents. As more speciali2ed savings and financial insti- In some countries financial markets are dis- * Quasi-liquid liabilities are the M3 money 3 tutions emerge, more financing instruments be- torted by restrictions on foreign investment, se- supply less Ml. * Ratio of bank liquid reserves C come available, spreading risks and reducing costs lective credit controls, and controls on deposit to bank assets is the ratio of domestic currency to liability holders. As securities markets mature, and lending rates. Interest rates may reflect the holdings and deposits with the monetary savers can invest their resources directly in finan- diversion of resources to finance the public sec- authorities to claims on other governments, cial assets issued by firms. There are big differ- tor deficit through statutory reserve requirements nonfinancial public enterprises, the private ences in financial systems across countries; and direct borrowing from the banking system. sector, and other banking institutions. banks, nonbanks, and stock markets are larger, And where state-owned banks dominate the fi- * Interest rate spread is the interest rate more active, and mcire efficient in richer countries. nancial sector, noncommercial considerations charged by banks on loans to prime customers The ratio of domestic credit provided by the may unduly influence credit allocation. The indi- minus the interest rate paid by commercial or banking sector to GDP is used to measure the cators in the table provide quantitative assess- similar banks for demand, time, or savings growth of the banking system because it reflects ments of each country's financial sector, but quali deposits. * Spread over LIBOR (London the extent to which savings are financial. In a tative assessments of policies, laws, and regula- interbank offered rate) is the interest rate few countries governments may hold interna- tions are needed to analyze overall financial con- charged by banks on short-term loans in local tional reserves as deposits in the banking sys- ditions. Recent intemational financial crises high- currency to prime customers minus LIBOR. tem rather than in the central bank. Since the light the risks of weak financial intermediation, LIBOR is the most commonly recognized claims on the central govemment are a net item poor corporate govemance, and deficient govern- international interest rate and is quoted in (claims of central government minus central ment policies, including procyclical macroeco- several currencies. The average three-month government deposits), this net figure may be nomic policy responses to large capital inflows. LIBOR on U.S. dollar deposits is used here. negative, resulting in a negative figure for do- The accuracy of financial data depends on mestic credit providied by the banking sector. the quality of accounting systems, which are Liquid liabilities include bank deposits of gen- weak in some developing economies. Some of Data sources erally less than one year plus currency. Their the indicators in the table are highly correlated, The data on credit, liabilities, bank reserves, ratio to GDP indicates the relative size of these particularly the ratios of domestic credit, liquid and interest rates are collected from central readily available forms of money-money that liabilities, and quasi-liquid liabilities to GDP, banks and finance ministries and reported in i the owners can use to buy goods and services because changes in liquid and quasi-liquid liabili- the print and electronic editions of the without incurring any cost. This is a general idi- ties flow directly from changes in domestic credit. International Monetary Fund's Intemation cator of the size of financial intermediaries rela- Moreover, the precise definition of the financial Financial Statistics. tive to the size of the economy, or an overall aggregates presented varies by country. measure of financial sector development. Quasi- The indicators reported here do not capture liquid liabilities are long-term deposits and assets- the activities of the informal sector, which re- such as certificates of deposit, commercial mains an important source of finance in develop- paper, and bonds--that can be converted into ing economies. Personal credit or credit ex- currency or demand deposits, but at a cost. The tended through community-based pooling of as- ratio of bank liquid reserves to bank assets sets may be the only source of credit available to captures the banking system's liquidity. In coun- small farmers, small businesses, or home- tries whose banking system is liquid, adverse based producers. And in financially repressed macroeconomic conditions should be less likely economies the rationing of formal credit forces to lead to banking and financial crises. Data on many borrowers and lenders to tum to the infor- domestic credit and liquid and quasi-liquid liabili- mal market, which is very expensive, or to self- ties are cited on an end-of-year basis. financing and family savings. * ~~~~5.5 Tax policies Tax Taxes on Domestic taxes Export Import Highest marginal revenue Income, on goods duties duties tax rate' profits, and and services capital gains % of value Individual Corporate % of % of added in industry % of % of rate on income rate GDP total taxes and services tax revenue tan revenue % over $ % 2000 1990 2000 1990 2000 1990 2000 1990 2000 2000 2000 2000 Afghanistan . Albania 14.8 .. 9.4 .. 16.9 . 0.0 19.3 Algeria 27.6 .. 72.3 . 37 .. 0.0 .. 15.3 Angola . .. .. .. Argentina 12.9 2.7 18.7 2.2 67 9.3 0.1 2.6 5.2 35 120,000 35 Armenia..... .... Australia 21.9 70.9 72.8 5.8 5.4 0.1 0.0 4.4 2.8 47 33,324 34 Austria 35.0 20.8 27.0 10.0 10.6 0.0 .. 1.6 .. 50 47,900 34 Azerbaijan 16. 7 .. 23.1 .. 8.9 .. 0.0 .. 9.0 35 12,987 27 296 Bangladesh 7. 0 .. 14.5 . 5.0 0.0 .. 30.0 - Belarus 26i.8 12.1 11.7 1 7.1 15.4 3.6 . 0.4 . U t - ~ I - - - - - -- - - - - - - o Belgium 43.0 17.0 13.9 10.4 .. 0.0 0.0 6.9 1.2 55 56,433 39 Be,i 'E Bolivia 14.2 7.9 9.9 5.8 11.1 0.0 0.0 11.1 7.1 13 0 25 C Bosnia and Herzegovina . .-- E Botswana .. 71.7 1.0 --0.0 - .. 24.7 .. 25 14,920 15 o Brazil 20.6 24.5 24.4 7.1 6.7 0.0 0.0 2.5 3.5 28 11,077 15 >, Bulgaria 28.9 40.6 15.9 10.4 18.2 0.0 0.0 2.5 3.0 38 8,094 20 o Burkirna Faso .. 24.7 - 4.9 1.1 .. 33.1 - -- -- --- -- - Burundi 16. 7 23.4 22.5 16.6 1 7.0 3.1 0.0 23.2 16.4 Cambodia - .. 20 38,412__ 20 o Cameroon 12.8 25.1 26.0 4.3 6.9 1.7 3.9 18.9 31.6 60 10,726 39 0 - -- . - - CN Canada 20.1 59.3 58.4 4,2 .. 0.0 0.0 3.2 1.4 29 40,038 38 Central African Republic ... ......... Chad .. 20.3 ., 3,9 . . Chile 19.0 15.8 22.9 10.4 12.7 ... .. 45 5,529 15 China 6.8 49.8 6.8 1.5 6.5 0.0 0.0 22.1 6.6 45 12,089 30 Hong Kong, China . .. ... . ... 17 13,462 16 Colombia 10.8 36.4 39.9 4.8 6.2 2.0 0.0 22.5 8.5 35 34,375 35 Congo, Dem. Rep. .. 28.5 33.1 2 16 4,1 1.7 45.1 35.9 60 1.500 40 Congo, Rep. 6.0 40.2 11.3 4.1 4.1 0.0 0.0 32.3 22.9 50 14,210 45 Costa Rica 18.8 11.5 14.5 8.7 10.1 80 0.2 18.2 4.5 25 16,746 30 C6te dIlvoire 20.1 18.1 22.7 8.9 51i 3.7 13.1 28.4 34.7 10 3,432 35 Croati'a 38.4 1 7.4 9.5 9.6 25.0 0,0 0.0 3.6 6.4 35 5,437 Cuba Czech Republic 32.2 -- 13.8 13.7 . 0.0 2.2 32 8,587 31 Denmark 31i5 43.5 40.2 18.9 19.3 0.0 0.0 0.1 0.0 59 30 Dominican Republic 15.2 23.8 19.5 3.1 5.5 0.1 0.0 41.4 40.0 25 15,165 25 Ecuador 62.9 .. 4.5 .. 0.3 12.1 .. 25 11,201 25 Egypt, Arab Rep. 26.4 . 4. 1 .. 0.0 18.9 -, 32 12,987 40 El Salvador 13.2 .. 24.2 ,. 7.2 . 0.0 8.1 30 22,857 25 Eritrea . .. .. Estonia 28.3 27.5 15.8 14.8 15.9 0.0 0.0 0.8 0.1 26 35 Ethiopia .. 40.9 .. 9.1 .. 2.8 18.0 Finland 27.8 34.5 33.6 17.7 16.9 0.0 0.0 1.0 0.0 37. 50,940 29 France'. 18.7 .. 13.1 .. 0.0 .. 0.0 33 Gabon .. 35.9 .. 5.0 .. 2.8 .. 23.4 .. 50 - 31,462 35 Gambia, The .. 13.7 .. 12.2 .. 0.2 .. 45.6 . Georgia 10.0 .. 8.8 . 9.8 . 0.0 7.4 Germany 26.3 17.5 1 7.3 6.8 7.1 0.0 0.0 0.0 0.0 53 54,617 25 Ghana .. 25.1 .. 6.8 .. 12.4 28.7 .. 30 7,059 33 Greece 21.9 23.3 41.6 14.5 16.0 0.0 0.0 0.1 0.1 43 46,625 35 Guatemala .. . . . .. . . . 31 38,155 31 Guinea 11.3 12.6 10.1 3.2 0.9 51.7 0.2 11.2 42.9 . Guinea-Bissau..... . .-... Honduras .. . . - .. . . . 25 32,916 15 Tax Taxes on Domestic taxes Export Import Highest marginal revenue Income, on goods duties duties tax rate profits, and and services capital gains % of value IndividLjal Corporate % of % of added in industry % of % of rate on income rate GDP total taxes and sewvices tax revenae tax revenue % over $ % 2000 1990 2000 1 990 2000 1 990 2000 ±990a 2000 1 2000 2000 2000 Hungary 32.8 21.2 23.4 22.6 15.3 1.3 0.0 5.6 3.3 40 3,512 18 India 9.6 18.6 35.8 7.4 5.2 0.1 0.1 35.8 26.7 30 3.222 40 Indonesia 16.5 65.4 64.5 5.6 6.2 0.1 0.5 6.6 2.3 35 20.949 30 Iran. Islamic Rep. 9.2 24.7 41.7 1.0 1.8 0.0 0.0 18.6 14.4 54 174.583 54 Ireland 39.7 ,, 15.5 . 0.0 0.0 .. 42 23,912 32 Israel 37.7 42.4 45.7 .. . 0.0 0.0 1.4 0.8 50 54,647 36 Italy 38.5 37.7 38.8 12.7 11.6 0.0 0.0 0.0 0.0 46 64,207 36 Jamaica 23.3 .. 4. . 9.8 .. 0.0 .. 9.3 25 2,327 33 Japan .. 73.0 ..- 2.4 . 0.0 . . .............1.4 .. 37 156,863 30 297 Jordan 19.8 22.9 13.7 6.8 10.7 0.0 0.0 34.7 23.7 __ Kazakhstan 10.2 .. 31.2 .. 6.3 0.0 .. 4,7 30 .. 30 Mi Kenya 21.2 32.9 37.9 15.9 15.2 0.0 0.0 17.8_ __16.8 30 5,612 30 Korea. Dem. Rep. . . . . . .. . . Korea, Rep. .. 37.5 .. 6.7 . 0.0 13.0 .. 40 63,507 28 Kuwait 3.4 19.5 8.2 0.0 0.0 0.0 76.8 0.0 0 .. 0 Kyrgyz Republic 12.3 .. 18.0 .. 17.2 ... .... 30 C Lao PDR .. . , . .. . . . . 40 658 ' - -- - -- -- - - -- - -- -- - - - -- --- - -- --- -- - -- - -- - -- - - - - - - - -- - - - -- - - - - - - - - - - -- - - - - -- -- - - - --- - -- - -- - -- - - Latvia 25.3 .. 13.5 .. 14.0 .. 0.0 .. 1.3 25 . 25 C Lebanon 14.1 .. 15.1 .. 4.9 ...39.0 .. - - . . ....... . .......... . ... ............ . - - ------ - ------- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 Lesotho 34.4 12.7 23.2 13.0 7.4 0.2 .. 63.6 2 . Libya... Lithuania 22.8 22.2 12.9 16.4 14.1 0.0 1.4 33 .. 24 ---------- ----------Macedonia, FYR .. -. ----- --..-.- - Madagascar 11.3 15.7 15.7 3.6 5.5 8.5 0.0 50.1 53.5 Malawi 42.5 .. 13.9 .. 0.0 .. 18.7 .. 38 948 38 Malaysia 42.5 .. 6.3 .. 9.7 . 15.1 .. 29 39,474 28 Mauritania . .. .. .. .. Mauritius 19.1 15.2 13.1 6.4 10.0 4.6 0.0 45.7 31.5 25 894 15 Mexico 12.3 34.2 41.1 10.2 9.2 0.1 0.0 6.9 4.8 40 258,269 35 Moldova 21.1 .. 4.1 .. 19.7 0.0 4.1 Mongolia 21.5 28.2 16.0 9.3 15.7 0.0 2.3 19.6_ 7.7 Morocco 25.0 27.3' 28.5 12.1 12. 7 0.3 0.0 20.3 18.8 44 5,758 35 Mozambique .. . . . .. . 20 640 35 Myanmar 2.8 29.8 35.1 6.8 3.8 0.0 0.0 23.3 8.9 30 .. 30 Namibia 29.5 39.4 35.3 9.0 11.8 3.6 - 26.9 36 25.641 35 Nepal 8.7 13.0 21.0 6.6 6.5 0.4 1.3 37 31 Netherlands .. 33.6 .. 11.4 .. 0.0 0.0 -. 52 43.091 35 New Zealand 29.0 62.2 66.3 13.4 . 0.0 0.0 2.5 2.0 39 26.584 33 Nicaragua 27.6 20.0 14.1 16.9 24.7 0.0 0.0 21.3 7.8 25 31.545 25 Nigeria . .. . ... .. ... 25 1,553 30 Norway 34.5 21.7 25.6 17.0 18.7 0.1 0.0 0.6 0.7 28 6,835 28 Oman 6.4 87.6 79.8 0.3 0.0 0.0 7.8 8.4 0 .. 12 Pakistan 12.1 12.8 28.1 8.6 7.9 0.0 0.0 44.4 16.0 35 17.271 Panama 18.2 24.4 29.3 4.8 1.3 0.0 15.8 .. 30 200,000 30 Papua New Guinea 18.4 47.0 51.3 5.0 2.9 2.1 5.1 29.3 27.8 47 31,066 25 Paraguay .. 12.4 . 3.6 0.0 18.8 .. 0 . 30 Peru 13.3 5.8 24.7 6.7 9.0 7.6 0.0 9.9 11.5 20 45.957 30 Philippines 13.9 32.5 44.3 6.4 5.0 0.0 0.0 28.4 20.7 32 10.000 32 Poland 28.0 .. 20.9___ 13.9_ 0.0 . 2.6 40 17.908 28 Portugal 31.1 25.7 29,6 13.2 15.1 0.0 0.0 2.6 0.0 40 46,967 34 Puerto Rico .. . . . .. . . . 33 50.000 20 Romania 27.0 21.0 18.1 15.3 13.2 0.0 0.0 0.6 5.6 40 2,359 25 Russian Federation 21.6 .. 13.7 .. 9.4 .. 10.8 .. 4.2 13 6.036 35 .2:: 5.5 Tax Taxes on Domestic taxes Export Import Highest marginal revenue income, on goods duties duties tax rates profits, and and services capital gains % of value Individual Corporate % of % of added in industry % of % of rate on income rote GDP total tanes ann services tax revenue tax revenue % over $ % 2000 1590 2000 ±990 2000 1990 2000 1990 2000 2000 2000 2000 Rwanda .. 20.0 .. 5.6 .. 7.4 .. 20.7 . Saudi Arabia ... .. ..0 .. 30 Senegal I . . . . . . .. 50 22,469 35 Sierra Leone 6.8 33.0 26.9 1.9 2.8 0.4 0.0 41.3 49,8 Singapore 15.5 44.6 50.2 4.3 4.8 0,0 0.0 3.5 2.5 28 400,000 26 Slovak Republic 31.1 .. 21.7 .. 11.8 .. 0.0 .. 4.7 42 24.115 29 Slovenia 36.7 12.3 14.9 12, 7 17.6 .. 0,0 .. 2.6 42 .. 25 South Africa 25.9 55.0 55.7 10.3 10.5 0.0 0.0 3.9 3.2 45 15,000 30 298 Spain .. 34.0 .. 7.6 .. 0.0 .. 1.7 .. 40 67.744 35 Sri Lanka 14.5 12.0 15.1 14.7 13.7 4.2 0.0 27.4 13.1 35 3,630 35 Sudan 6.8 .. 18.3 .. 5.1 .. 0.8 .. 35.5 co Swaziland 27.5 33.2 26.4 5.4 6.8 2.0 0.0 50.5 54.7 39 5,089 30 V Sweden 35.1 20.6 15.5 14.5 12.3 0.0 0.0 0.6 0.0 31 27.198 28 Switzerland 22.3 17.0 13.9 .. 6.0 0.0 0.0 6.9 1.2 ... 45 2 Syrian Arab Republic 15.7 40.2 48.9 9.6 5.5 1.3 3.1 8.2 13.6 0. > Tanzania ... . . .. . . . 30 8.000 30 0 Thailand 14.1 26.2 34.0 8.8 7.7 0.2 0.3 23.7 12.3 37 92,829 30 ~0 Trinidad and Tobago .. . . . .. . . . 35 8,012 35 o Tunisia 26.0 16.0 22.3 7.1 12.5 0.4 0.1 35.1 12.5 0 Turkey 22.0 51.2 37.4 5.9 16.0 0.0 0.0 7.3 1.7 40 104.353 30 Turkmenistan . . . . . . . .. Uganda 10.3 .. 18.0 .. 12.9 .. 0.0 .. 11.0 30 2,795 30 Ukraine 22.1 .. 15.2 .. 12.2 .. 0.0 .. 4.1 40 3,754 30 United Arab Emirates 1.8 0.0 0.0 0.6 ... . . . 0 .. 20 United Kingdom 34.6 43.2 41.8 11.3 13.0 0.0 0.0 0.0 0.0 40 43,815 30 Unifed States 20.1 56.1 61.3 -. . 0.0 0.0 1.7 1.0 40 297,350 35 Uruguay 24.9 7.1 16.9 9.4 10.2 0.6 0.1 8.1 3.0 0 .. 30 Uzbekistan ... . . .. . . . 36 603 26 Venezuela. RB 12.9 82.2 42.9 0.8 4.9 0.0 0.0 7.1 11.3 34 99,445 34 Vietnam 14.9 .. 32.0 .. 8.0 .. 0.0 .. 20.7 50 5,695 32 West Bank and Gaza . . . . . .. .. Yemen. Rep. 9.7 44.9 45.9 2.5 2.7 0.0 0.0 29.2 25.9 Yugoslavia. Fed. Rep. . . . . . . . ,. Zambia .. . . 30 524 35 Zimbabwe .. 49.7 .. 8.4 .. 0.0 .. 18.8 .. 53 14.756 30 a. These data areftrom PricewaterhouseCoopers's Individual Taxes: Worldwide Sunmnanes 2001-2002 and Corporate Taxes: Worldwide Summaries 2001-2002, copyright 2001 by PricewaterhouseCoopers by permission of John Wiley and Sons. Inc. 5.5 About the data Definitions Tax revenue is the main source of revenue services. Agriculture and mining are excluded * Tax revenue comprises compulsory transfers for many governments. The sources of the tax from the denominator because indirect taxes on to the central government for public purposes. revenue received by govemments and the relative goods originating from these sectors are Compulsorytransfers such as fines, penalties, contributions of these sources are determined usually negligible. What is missing here is a and most social security contributions are ex- by policy choices about where and how to im- measure of the uniformity of these taxes across cluded. Refunds and corrections of erroneously pose taxes and by changes in the structure of industries and along the value added chain of collected tax revenue are treated as negative the economy. Tax policy may reflect concerns production. Without such data no clear infer- revenue. * Taxes on Income, profits, and capi- about distributional effects, econoniic efficiency ences can be drawn about how neutral a tax tal gains are levied on wages, salaries, tips, (including corrections for externalities), and the system is between subsectors. "Surplus" rev- fees, commissions and other compensation for practical problems of administering a tax enues raised by some govemments by charging labor services; interest, dividends, rent, and system. There is no ideal level of taxation. But higher prices for goods produced under monopoly royalties; capital gains and losses; and profits taxes influence incentives and thus the behav- by state-owned enterprises are not counted as of businesses, estates, and trusts. Social se- ior of economic actors and the country's com- tax revenues. Similarly, losses from charging curity contributions based on gross pay, pay- petitiveness. below-market prces for products are rarely iden- roll, or number of employees are not included, Taxes are compulsory transfers received by tified as subsidies. but taxable portions of social security, pension, 299 the government sector from individuals, busi- Export and import duties are shown separately and other retirement account distributions are nesses, or institutions. They include fees that because the burden they impose on the economy included. * Domestic taxes on goods and ser- g are clearly out of all proportion to the costs of (and thus growth) is likely to be large. Export vices include all taxes and duties levied by E providing services, but exclude cornpulsory so- duties, typically levied on primary (particularly central governments on the production, extrac- R cial contributions, fines, and penalties. They are agricultural) products, often take the place of tion, sale, transfer, leasing, or delivery of goods E considered unrequited because governments direct taxes on income and profits, but they re- and rendering of services, or on the use of CD provide nothing specifically in return for them, duce the incentive to export and encourage a goods or permission to use goods or perform C although taxes typically are used to provide shift to other products. High import duties pe- activities. These include value added taxes, 3 goods or services to individuals cr communi- nalize consumers, create protective barriers- general sales taxes, single-stage and multi- C ties on a collective basis. which promote higher-priced output and ineffi- stage taxes (where "stage" refers to stage of The level of taxation is typically rneasured by cient production-and implicitly tax exports. By production or distribution), excise taxes, and a tax revenue as a share of GDP. Comparing lev- contrast, lower trade taxes enhance openness- motor vehicle taxes, and taxes on the extrac- els of taxation across countries provides a quick to foreign competition, knowledge, technologies, tion, processing, or production of minerals or overview of the fiscal obligations and incentives and resources-energizing development in many other products. * Export duties include all lev- facing the private sector. In this table tax data ways. The economies growing fastest over the ies collected on goods at the point of export. measured in local currencies are normalized by past 15 years have not relied on tax revenues Rebates on exported goods that are repay- scaling variables in the same units to ease cross- from imports. Seeing this pattern, many devel- ments of previously paid general consumption country comparisons. The table refers only to oping countries have lowered tariffs over the past taxes, excise taxes, or import duties are de- central govemment data, which may significantly decade, a trend that is expected to continue. In ducted from the gross amounts receivable from understate the total tax burden, particularly in some countries, such as members of the Euro- the respective taxes, not from amounts receiv- countries where provincial and municipal gov- pean Union, most customs duties are collected able in this category. * Import duties comprise ernments are large or have considerable tax by a supranational authorty; these revenues are all levies collected on goods at the point of authority. not reported in the individual countries' accounts. entry into the country. The levies may be im- Low ratios of tax collections to GDP may re- The tax revenues collected by governments posed for revenue or protection purposes and flect weak administration and large-scale tax are the outcomes of systems that are often corn- may be determined on a specific or ad valo- avoidance or evasion. They may also reflect the plex, containing many exceptions, exemptions, rem basis, as long as they are restricted to presence of a sizable parallel economy with un- penalties, and other inducements that affect the imported products. * Highestmarginaltax rate recorded and undisclosed incomes. Tax collec- incidence of taxes and thus influence the deci- is the highest rate shown on the schedule of tion ratios tend to rise with income, with higher- sions of workers, managers, and entrepreneurs. tax rates applied to the annual taxable income income countries relying on taxes to finance a A potentially important influence on both domes- of individuals and corporations. Also presented much broader range of social services and so- tic and intemational investors is a tax system's are the income levels above which the highest cial security than lower-income countries are progressivity, as reflected in the highest mar- marginal tax rates for individuals apply. able to provide. ginal tax rate on individual and corporate income. _ As countries develop, their capacity to tax Figures for individual marginal tax rates gener- residents directly typically expands and indirect ally refer to employment income. For some coun- Data sources taxes become less important as a source of rev- tries the highest marginal tax rate is also the The definitions used here are from the enue. Thus the share of taxes on income, prof- basic or flat rate, and other surtaxes, deductions, International Monetary Fund's (IMF) Manual on its, and capital gains is one measure of an and the like may apply. Government Finance Statistics (2001). The i economy's (and tax system's) level of develop- data on tax revenues are from print and ment. In the early stages of development gov- electronic editions of the IMF's Govemment emments tend to rely on indirect taxes because Finance Statistics Yearbook. The data on the administrative ccsts of collecting them are I individual and corporate tax rates are from relatively low. The two main indirect taxes are PricewaterhouseCoopers's Individual Taxes:, international trade taxes (including customs rev- Worldwide Summaries 2001-02 and Corporate enues) and domestic: taxes on goods and ser- Taxes: Worldwide Summaries 2001-02. vices. The table shows these domestic taxes as a percentage of value added in industry and 5.6 Relative prices and exchange rates Exchange rate Official Purchasing PPP Real Interest rate Key agricuttural arrangements exchange power parity conversion effective producer rate conversion factor/ exchange prices factor official rate exchange local local currency rate Whieat Maize currency units to Deposit Lending Real $ per $ per Classificat on Structure units to $ international $ ratio 1995-100 % %% metric ton metric ton 2000 2000 2000 1990 2000 2000 2000 2000 2000 2000 1998 1998 Afghanistan .. 3,000.0 0.0 -- .------- 33 31 Albania IF U 143.7 1.8 45.1 0.3 ,. 8.3 22.1 23.6 Algeria MVF U 75.3 4.9 24.9 0.3 107.7 7.5 10.0 -1-1.0 ~337 Angola IF U 10.0 0.0 3.1 0.3 .. 39.6 103.2 -59.5 Argentina CB U 1.0 0.3 0.6 0.6 .. 8.3 11.1 9.8 85 56 Armenia IF U 539.5 0.0 106.1 0.2 108.7 18.1 31.6 33.4 Australia IF U 1.7 1.4 1.4 0.8 94.5 4,1 8.8 5.1 127 12 8 Austria EA/Euro u 14.9~ 12.8 13.0 0.9 90.3 2.2 5.6 4.7 117 117 Azerbaijan MF U 4,474.2 0.1 997.1 0.2 . ... .. 204 132 300 B.angladesh P U 52.1 9.1 11.3 0.2 8.6 15.5 13.4 172 173 Belarus MF M 876.8 0.0 120.9 0.1 .. 37.5 67.7 -41.2 Belgium EA/Euro U 43.81 34.1 35.6 0.8 87.3 3.0 4.3 3.0 IC Benin EA/FF U 712.0 151.0 248.5 0.3 .. 3.5 .. . . 285 Bolivia P U 6.2 1.3 2.5 0.4 117.9 11.0 34.6 29.7__ 195 119 Bosnia and Herzegovina GB U 2.1 0.0 . .. . .. 14.7 30.5 E Botswana P D 5.1 1.1 2.3 0.5 .. 10.1 15.3 3.3 90- 110_ o Brazil IF U 1.8 0.0 0.8 0.5 .. 17.2 56.8 44.5 164 125 a) Bulgaria GB U 2.1 0.0 0.6 0.3 120.7 3.1 11.5 5.6 o Burkina Faso EA/FF U 712.0 133.3 141.8 0.2 .. 3.5 .. . . 148 ~0 Cambodia MF 0 3,840.8 66.8 703.4 0.2 .. 6.8 17.3 15.6 .. 158 o Cameroon EA/FF U 712.0 184.9 230.0 0.3 95.7 5.0 22.0 17.9 79 12 0 N" Canada IF U 1.5 1.2 1.2 0.8 99.5 5 7 7.3 3.6 87 78 Central African Republic EA/FF U 712.0 129.7 157.4 0._2 89.8 5.0 22.0 18.1 . 469 Chad EA/FF U 712.0 107.5 149.5 0.2 . 50O 22.0 18.0 258 271 Chile IF U 535.5 141.7 263.7 0.5 106.0 9.2 14.8 10.4 201 148 China P U 8.3 1.2 1.8 0.2 107.6 2.3 5.8 4.9 134 109- Hong Kong, China GB U 7.8 6.1 7.4 1.0 . 4.8 9.5 17.2 Colombia IF U 2,087.9 80.4 642.1 0.3 95.6 12.1 18.8 7.3 261 156 Congo. Dem. Rep. IF U 21.8 0.0 0.2 0.0 264.7 ., 165.0 12.2 Congo, Rep. EA/FF U 712.0 449.7 919.0 1.3 . 5.0 22.0 .16.6 . 238_ Costa Rica P U 308.2 32.4 148.5 0.5 106.8 13.4 24.9 16.6 .. 179.. C6te dIlvoire EA/FF U 712.0 160.5 255.6 0.4 96.5 3.5 . .. 133 Croatia MF U 8.3 0.0 4.4 0.5 98.9 3.7 12.1 5.3 Cuba .. . . 0.0 ... ...... Czech Republic MF U 38.6 0.0 13.6 0.4 114.8 3.4 7.2 6.3 Denmark P U 8.1 8.2 8.9 1.1 93.5 3.2 8.1 4.3 127 Dominican Republic MF D 16.4 2.5 6.4 0.4 110.3 17.7 26.8 17.7 . 226 Ecuador EA/Other U 24,988.4 287.4 8,394.4 0.3 73.3 7.4 16.3 -43.5 185 203 Egypt, Arab Rep. P M 3.5 0.7 1.5 0.4 . 9.5 13.2 7.0 189 158 El Salvador P U 8.8 2.4 4.1 0.5 9.3 14.0 9.7 . 220 Eritrea P U . 0.0 1.7.. . Estonia GB U 17.0 0.1 6.1 0.4 .. 3.8 7.6 2.2__ Ethiopia MF U 8.2 0.7 1.2 0.1I . 6.7 10.9 9.3 205 128... Finland EA/Euro U 6.5 5.9 6.1 0.9 87.1 1.6 5.6 2.7 159 France EA/Euro U 7.1 b 6.5 6.5 0.9 88.4 2.6 6.7 5.7 126 119 Gabon EA/FF U 712.0 330.7 457.7 0.6 89.7 5.0 22.0 .5.0 .. 163 Gambia, The IF U 12.8 1.8 2.5 0.2 95.0 12.5 24.0 19.5 --.--- 241 Georgia IF U 2.0 0.0 0.4 0.2 135.7 10.2 32.8 28.6 796 724 Germany EA/Euro U 2.1 1.9 1.9 0.9 84.6 3.4 9.6 10,1 124_ 139 Ghana IF U 5,455.1 92.7 716.2 0.1 81.1 28.6 .. . . 242 Greece EA/Euro U 365.4 b114.4 236.2 0.6 96.3 6.1 12.3 9.2 220 171 Guatemala MF U 7.8 1.4 3.4 0.4 .. 1C.2 20.9 14.6 234 156 Guinea IF U 1,746.9 212.7 358.0 0.2 .. 7.5 19.4 10.2 .. 226 Guinea-Bissau EA/FF U 712.0 12.6 169.4 0.2 .. 3.5.. . Haiti IF U 21.2 1.4 6.8 0.3 i. 1.9 25.1 9.1 .. 271 Honduras P U 14.8 1.2 5.6 0.4 .. 15.9 26.8 16.4 51 264 5.6 Exchange rate Official Purchasing PPP Real Interest rate Key agricultural arrangements exchange power parity conversion effective producer rate conversion factor/ exchange prices factor officiai rate exchange local local currency rate Wheat Maize currency units to Deposit Lending Real $ per $ per Classifcation Structure units to $ international $ ratio 1995=100 % % metric ton metric ton 2000 2000 2000 1990 2000 2000 2000 2000 2000 2000 1995 1998 Hungary P U 282.2 21.3 103.5 0.4 109.7 9.6 12.6 4.8 113 92 India ME U 44.9 4.8 8.7 0.2 ... 12.3 6.7 142 95 Indonesia IF U 8,421.8 606.2 2,015.6 0. 2_ . 12.5 18.5 6.7 .. 87 Iran, lslamic Rep. P D 1,764.4 173.7 1,429.4 0.8 297.7 ... . 289 297 Iraq P U 0.3 0.0... Ireland EA/Euro U 0.9 0.6 0.7 0.8 91.2 0.1 4.8 -0.5 114 Israel P U 4.1 1.7 3.6 0.9 114.2 8.6 12.9 10.7 149 1,330 Italy EA/Euro U 2,101.6b 1,335.4 1,655.9 0.8_ 106.4 1.8 6.3 3.9 181 161 Jamaica MF U 42.7 3.9 33.5 0.8 .. 11.6 23.3 11.6 .. 1,182 Japan IF U 107.8 177.2 153.7 1.4 95.6 0.1 2.1 2.2 1,295 1,082 301 Jordan P U 0.'7 0.3 0.3 0.4 .. 7.0 11.8 12.4 379 223 Kazakhstan MF U 142.1 0.0 29.7 0.2 .... .. 72 851` Kenya MF U 76.2 8.6 25.6 0.3 .. 8.1 22,3 14.5 262 151 Korea, Dem. Rep.. .. 0.0......... ..o Korea, Rep. I F U 1,131.0 469.7 629.3 0.6 .. 7.9 8.5 10.3 494 398 Kuwait P U 0.3 0.0 0.4 1.2 .. 5.9 8.9 CD....t Tanzania IF U 800.4 7 1.9 410.4 0.5 .. 7.4 21.6 11.5 348 348 a) o Thailand IF U 40.1 10.2 12.6 0.3 .. 3.3 7.8 5.9 .. 87 o Togo EA/FF U 712.0 91.7 133.0 0.2 98.8 3.5 .. . . 216 Trinidad and Tobago P U 6.3 2.9 4.2 0.7 115.4 8.2 16.5 6.1 .. 349 o Tunisia ME U 1.4 0.3 0.4 0.3 100.8 ... . 284 (N Turkey IF U 625,218.5 1,448.4 274.484.1 0.4 .. 47.2 . .. 133 126 Turkmenistan P 0 5,200.0 0.0 1,113.4 0.2 . .. Uganda IF U 1,644.5 113.0 347.6 0.2 96.0 9.8 22.9 19.0 681 358 Ukraine MF U 5.4 0.0 0.9 0.2 118.4 13.7 41.5 12.9 United Arab Emirates P U 3.7 3.4 3.5 1.0 . .. United Kingdom IF U 0.7 0.6 0.7 1.0 131.9 4.5 6.0 4.1 118 176 United States IF U 1.0 1.0 1.0 1.0 125.2 .. 9.2 7.0 99 61 Uruguay P U 12.1 0.6 8.1 0.7 113.1 12.1 49.1 43.8 119 125 Uzbekistan ME M 236.6 0.0 52.9 0.2 . .. Venezuela. RB P U 680.0 23.1 585.0 0.9 161.6 16.3 25.2 -1.3 123 445 Vietnam P U 14,167.8 0.0 2,833.3 0.2 .. 3.7 10.6 5.0 West Bank and Gaza .. . . 0.0 . . . .. Yemen, Rep. IF Li 161.7 18.8 88.3 0.5 .. 14.0 22.0 -5.1 191 221 Yugoslavia. Fed. Rep. ME U . 0.0 . . . .. Zambia IF U 3,110.8 17.4 1,150.9 0.4 113.3 20.2 38.8 17.6 211 79 Zimbabwe P U 44.4 0.9 9.6 0.2 .. 50.2 68.2 5.2 211 122 a. Exchange rate arrangements are given for the end of the year n 2000. Exchange ratecuassficatiens include independentfoating(lF), mianaged floating IMF), pegged(P1, currency board (CB), and several exchange arraegements (euro means that the earn is used. FF that the currency is pegged to the Francs fraec, and other that the currency of another country is used as legal tender). Exchange rate structures include coal excnange rates (D), mult pIe exchange rates (Ml, and unitary rate (U1. b. On January 1. 1999 tfle euro was established as the sole currency of 11 European countr en, later joined by Greece in January, 2001. However, toe old national currencies, now effectively subdLunits of the euro, with irrevocably fixed conversion rates, remained in use until early 2002. The World Development Indicators axes the old national currencies and the exchange raten to tne U.S. dollar derived from the euro-dollar rate. The average euro-collar exchange rate n 2000 was 1.085. About the data Definitions In a market-based economy the choices house- erning loans and deposits, and differences in * Exchange rate arrangements describe the holds, producers, and governments make about the position and status of creditors and debtors. arrangement that an IMF member country has the allocation of resources are influenced by In some economies interest rates are set by furnished to the IMF under article IV, section relative prices, including the real exchange rate, regulation or administrative fiat. In economies 2(a) of the IMF's Articles of Agreement. Ex- real wages, real interest rates, and commodity with imperfect markets, or where reported nominal change rate classification indicates how the prices. Relative prices also reflect, to a large rates are not indicative of effective rates, it may exchange rate is determined in the main mar- extent, the choices of these agents. Thus rela- be difficult to obtain data on interest rates that ket when there is more than one market: float- tive prices convey vital information about the in- reflect actual market transactions. Deposit and ing (managed or independent), pegged (con- teraction of economic agents in an economy and lending rates are collected by the International ventional, within horizontal bands, crawling peg, with the rest of the world. Monetary Fund (IMF) as representative interest or crawling band), currency board (implicit leg- The exchange rate is the price of one currency rates offered by banks to resident customers. islative commitment to exchange domestic in terms of another. Official exchange rates and The terms and conditions attached to these rates currency for a specified foreign currency at a exchange rate arrangements are established by differ by country, however, limiting their compa- fixed exchange rate), and exchange arrange- governments (other exchange rates fully recog- rability. Real interest rates are calculated by ment (country uses the euro, currency is pegged nized by governments include market rates, adjusting nominal rates by an estimate of the to the French franc, or another country's cur- 303 which are determined largely by legal market inflation rate in the economy. A negative real rency is used as legal tender). Exchange rate > forces, and, for countries maintaining multiple interest rate indicates a loss in the purchasing structure shows whether countries have a uni- 0 exchange arrangernents, principal rates, second- power of the principal. The real interest rates in tary exchange rate or dual or multiple rates. m ary rates, and tertiary rates). the table are calculated as ( i - P )/(1 + P ), * Official exchange rate refers to the exchange S Real effective exchange rates are derived by where i is the nominal interest rate and P is the rate determined by national authorities or to E deflating a trade-weighted average of the nomi- inflation rate (as measured by the GDP deflator). the rate determined in the legally sanctioned CD. nal exchange rates that apply between trading The table also shows prices for two key agri- exchange market. It is calculated as an annual partners. For most high-income countries the cultural commodities, wheat and maize. The average based on monthly averages (local cur- 2 weights are based on trade in manufactured prices received by farmers, used here, are im- rency units relative to the U.S. dollar). * Pur- (D goods with other high-income countries during portant determinants of the type and volume of chasing power parity conversion factor is the 1989-91, and an index of relative, normalized agricultural production. In theory these prices number of units ofa country's currency required unit labor costs is used as the deflator. (Nor- should refer to national average farmgate, or first- to buy the same amount of goods and services malization smooths a time series by removing point-of-sale, transactions. But depending on the in the domestic market as a U.S. dollar would short-term fluctuations while retaining changes country's institutional arrangements-whether buy in the United States. * Real effective ex- of a large amplitude over the longer economic it relies on market wholesale prices, govemment change rate is the nominal effective exchange cycle.) For other countries the weights prior to fixed prices, or support prices-the data may rate (a measure of the value of a currency 1990 take into account trade in manufactured not always refer to the same selling points. against a weighted average of several foreign and primary proiJucts during 1980-82, the These data come from the Food and Agriculture currencies) divided by a price deflator or index weights from January 1990 onward take into Organization (FAO), with most originating from of costs. * Deposit interest rate is the rate account trade during 1988-90, and an index of official national publications or FAO question- paid by commercial or similar banks for de- relative changes in consumer prices is used as naires. As the data show, the prices received by mand, time, or savings deposits. * Lending the deflator. An increase in the real effective farmers are often not equalized across interna- Interest rate is the rate charged by banks on exchange rate represents an appreciation of the tional markets (even after adjusting for freight, loans to prime customers. * Real interest rate local currency. Because of conceptual and data transport, and insurance costs and for differ- is the lending interest rate adjusted for infla- limitations, changes in real effective exchange ences in quality). Market imperfections such as tion as measured by the GDP deflator. * Key rates should be interpreted with caution. taxes, subsidies, and trade barriers drive a agricultural producer prices are domestic pro- The official or market exchange rate is often wedge between domestic and international ducer prices converted to U.S. dollars using used to compare prices in different currencies. prices. the official exchange rate. But because market imperfections are extensive ____.-_.-__--- and exchange rates reflect at best the relative prices of tradable goods, the volume of goods Data sources and services that a U.S. dollar buys in the United The information on exchange rate arrange- States may not ccrrespond to what a U.S. dollar ments is from the IMF's Exchange Arrange- converted to another country's currency at the ments and Exchange Restrictions Annual Re- official exchange late would buy in that country. port, 2000. The official and real effective ex- The alternative approach is to convert national change rates and deposit and lending rates currency estimates of gross national income to a are from the IMF's Intemational Financial Sta- common currency by using conversion factors tistics. PPP conversion factors are from the that reflect equivalent purchasing power. Pur- j World Bank. The agricultural price data are from chasing power parity (PPP) conversion factors the FAO's Production Yearbook. The real inter- i are based on prce and expenditure surveys con- I est rates are calculated using World Bank data ducted by the International Comparison I on the GDP deflator. Programme (ICP) and represent the conversion factors applied tci equalize price levels across countries. See Atiout the data for table 1.1 for further discussion of the PPP conversion factor. Many interest rates coexist in an economy, reflecting competitive conditions, the terms gov- 5.7 Defense expenditures and trade in arms Military expenditures Armed forces Arms trade personnel Exports Imports % of % of central Total % of % of % of GNI government expenditure thousands labor force total exports total imports 1992 199 1992 1999 1992 1999 1992 1999 1992 1999 1992 1999 Afghanistan .... .. 45 .. 0.6 . 0.0 0.0 0.0 Albania 4.9 1.3 10.0 4.5 65 18 4.2 1.1 0.0 0.0 0.0 2.6 Algeria 1.8 4.0 5.9 12.6 126 120 1.6 1.2 0.0 0.0 0.1 4.1 Angola 16.6 21.2 24.6 41.1 128 100 2.7 1.7 0.0 0.0 1.5 7.3 Argentina 1.9 1.6 16.1 9.1 65 73 0.5 0.5 0.0 0.0 0.3 0.4 Armenia 3.5 5.8 .. 20.2 20 50 1.1 2.6 0.0 0.0 0.0 1.3 Australia 2.4 1.8 9.3 7.6 68 55 0.8 0.6 0.1 1.0 2.1 1.6 Austria 0.9 0.8 2.4 1.5 44 49 1.2 1.3 0.2 0.0 0.1 0.0 Azerbaijan 5.8 6.6 18.0 24.4 43 75 1.4 2.1 0.0 0.0 0.0 1.2 304 Bangladesh 1.3 1.3 11.2 10.1 107 110 0.2 0.2 0.0 0.0 1.1 1.0 Belarus 1.9 1.3 4.9 4.1 102 65 1.9 1.2 0.0 5.2 0.0 0.0 o Belgium 1.8 1.4 3.7 3.1 79 42 1.9 1.0 0.3 0.0 0.2 0.2 co Benin 1.3 1.4 6.3 8.3 7 8 0.3 0.3 0.0 0.0 0.0 0.6 Bolivia 2.2 1.8 10.6 8.0 32 33 1.2 1.0 0.0 0.0 0.9 0.6 Bosnia and Herzegovina 19.0 4.5 .. 24.3 60 30 .. 1.7 0.0 0.0 0.0 6.2 Botswana 4.2 4.7 10.3 9.8 7 B 1.2 1.1 0.0 0.0 1.1 1.8 o Brazil 1.1 1.9 3.5 5.5 296 300 0.4 0.4 0.5 0.0 0.9 0.3 a)3 > Bulgaria 3.3 3.0 7.9 8.7 99 70 2.3 1.7 3.1 5.1 0.0 0.2 a) Burkina Faso 2.4 1.6 11.5 5.9 9 9 0.2 0.2 0.0 0.0 1.1 0.0 ~0 Cambodia 4.9 4.0 30.6 26.0 135 60 2.7 1.0 0.0 0.0 0.0 0.3 0`11 o Cameroon 1.8 1.8 9.2 10.6 12 15 0.2 0.3 0.0 0.0 0.0 0.4 0 Canada 1.8 1.4 6.2 5.9 82 60 0.5 0.4 0.7 0.2 0.6 0.5 Central African Republic 2.0 2.8 8.3 15.4 4 3 0.3 0.2 0.0 0.0 0.0 0.0 Chad 3.7 2.4 17.3 12.7 38 30 1.3 0.6 0.0 0.0 4.1 3.2 Chile 2.3 3.0 10.5 12.3 92 88 1.8 1.4 0.0 0.1 1.0 0.7 China 2.8 2.3 32.7 22.2 3,160 2,400 0.5 0.3 1.3 0.2 1.6 0.4 Hong Kong, China . .. . . .. .. Colombia 2.4 3.2 14.7 15.9 139 155 0.9 0.9 0.0 0.0 1.7 0.6 Congo, Dem. Rep. 3.0 14.4 16.1 .. 45 55 0.3 0.3 0.0 0.0 0.0 8.9 Congo, Rep. 5.7 3.5 13.5 8.4 10 10 1.0 0.8 0.0 0.0 0.0 0.0 Costa Rica 1.1 0.5 5.5 2.0 8 10 0.6 0.7 0.0 0.0 0.2 0.0 C6to dIlvoire 1.5 0.8 4.3 3.4 15 15 0.3 0.2 0.0 0.0 0.0 0.0 Croatia 7.5 3,3' 19.3 9.8' 103 60 4.6 2.9 0.0 0.2 0.0 0.1 Cuba 2.4 1.9 . .. 175 50 3.5 0.9 0.0 0.0 4.5 0.0 Czech Republic 2.4 2.3 6.7 6.3 107 54 1.9 0.9 1.5 0.3 0.0 0.7 Denmark 2.0 1.6 4.8 4.2 28 27 1.0 0.9 0.0 0.0 0.5 0.7 Dominican Republic 0.9 0.7 6.3 4.4 22 30 0.7 0.8 0.0 0.0 0.2 0.3 Ecuador 3.5 3.7 20.6 16.2 57 58 1.5 1.2 0.0 0.0 1.2 0.7 Egypt, Arab Rep. 3.5 2.7 8.5 9.3 424 430 2.2 1.8 0.7 0.0 19.2 4.4 El Salvador 2.1 0.9 16.8 8.8 49 15 2.4 0.6 0.0 0.0 4.1 0.3 Eritrea 1 7.3 27.4 34.6 51.1 55 215 3.2 10.8 0.0 .. 0.0 33.5 Estonia 0.5 1.S 2.2 4.5 3 7 0.4 0.9 0.0 0.0 1.2 0.2 Ethiopia 3.7 8.B 20.0 29.1 120 300 0.5 1.1 0.0 0.0 0.0 20.5 Finland 2.2 1.4 4.3 4.5 33 35 1.3 1.3 0.0 0.1 2.1 1.3 France 3.4 2.7 7.6 5.9 522 421 2.1 1.6 0.9 1.0 0.2 0.3 Gabon 3.1 2.4 10.1 7.3 7 7 1.5 1.3 0.0 0.0 0.0 0.0 Gambia, The 1.0 1.3 5.6 5.4 1 1 0.2 0.2 0.0 0.0 2.3 0.0 Georgia 2.7 1.2 .. 7.0 25 14 0.9 0.6 0.0 6.2 0.0 1.0 Germany 2.1 1.6 6.2 4.7 442 331 1.1 0.8 0.3 0.3 0.6 0.3 Ghana 0.8 0.8 4.6 3.1 7 7 0.1 0.1 0.0 0.0 0.0 0.0 Greece 4.2 4.7 15.5 16.4 208 204 4.8 4.5 0.2 0.9 3.9 7.5 Guatemala 1.5 0.7 14.0 5.0 44 30 1.4 0.7 0.0 0.0 0.2 0.0 Guinea 1.4 1.6 7.0 7.4 15 12 0.5 0.3 0.0 0.0 0.0 0.0 Guinea-Bissau 3.2 2.7 7.6 6.1 11 7 2.3 1.3 0.0 0.0 0.0 0.0 Haiti 1.4 .. 14.7 .. 8 0 0.3 0.0 0.0 0.0 0.0 0.0 Honduras 1.4 0.7 5.5 2.6 17 8 0.9 0.3 0.0 0.0 2.9 0.4 if:k~~J1 V<~K5.7 Milittary expenditures Armed forces Arms trade personnel Expo rts Imports % of % of central Total % of % of % of GNI government expenditure thousands labor force total exports total imports 1992 1999 1.92 199 S1992 1999 1992 1999 1L992 199 1992 1.999 Hungary 2.1 1.7 3.9 3.9 78 51 1.6 1.1 0.4 0.0 0.0 0.3 India 2.4 2.5 12.4 14.6 1,270 1,300 0.3 0.3 0.0 0.0 2.9 1.6 lIndontesia 1.4 1.1 7.2 5.3 283 296 0.3 0.3 0.1 0.2 0.4 1.9 Iran, Islamic Re 3.0 2.9 14.9 11.2 528 460 3.2 2.4 0.10. 33 0.9 Iraq 8.3 - 55- - - 407 420 8.2 6.7 0.0 0.0 0.0- 0.1 Ireland 1.4 1.0 3.2 2.6 13 14 1.0 0.9 0.0 0.0 0.1 0.1 Israel 11.7 8.8 23.3 18.5 181 173 8.8 6.6 6.2 2.3 10.3 7.2 Italy 2.1 2.0 3.9 4.7 471 391 1.9 1.5 0.3 0.2 0.2 0.3 Jamaica 1.0 0.8 3.0 2.1 3 3 0.2 0.2 0.0 0.0 0.6 0.3 Japan 1.0 1.0 4.5 6.1 242 240 0.4 0.4 0.0 0.0 0.9 1.0 305 Jordan 8.5 9.2 27.3 27.5 100 102 9.8 7.3 0.0 0.0 1.2 1.9 Kazakhstan 2.9 0.9 14.2 5.3 15 33 0.2 0.5 0.0 0.2 0.0 4.3 Kenya 3ii i 0 1. 1. 7.1 24 24 0.2 0.2 0.0 0.0 1.1- 0.2 N Korea, Dem. Rep. 25.0 18.8 28.5 - . 1,200 1,000 11.3 8.6 13.1 22.4 7.9 2.5 Korea, Rep.3. 2.9 1. 11.0 750 665 3.6 2.8 0.1 0.0 1.5 1.8 Kuwait 77.0 7.7 96.3 20.8 12 21 2.1 2.7 0.2 0.0 13.8 9.5 tD Kyrgyz Republic 0.7 2.4 3.2 14.0 12 12 0.6 0.6 0.0 0.0 0.0 0.0 (D ....--.-.--... . - . -...-.. - . - . . ...--- . -...- . - . - -.- ...- . --.--. .. .....0--- --- Lao PDR 9.0 2.0 21.6 11.1 37 50 1.7 2.0 0.0 0.0 3.7 0.0 ' Latvia 1.6 0.9 4.3 2.5 5 5 0.3 0.4 0.0 0.0 0.0 0.2 C Lebanon 4.0 4.0 18.5 11.0 37 58 3.1 3.9 0.0 0.0 0.0 0.2 -- -------- -------- - - ----- - ----- ---- . .~ ~~~~~~~~~~~~~~~~~~~~~0 Lesotho 3.1 2.6 10.5 6.5 2 2 0.3 0.2 0.0 0.0 0.0 0.0 Liberia .. 1.2 . 8.3 2 00 . . . 00- ----- Libya 7.6 . 16.4 . 85 85 6.6 5.8 0.1 0.8 1.7 0.2 Lithuania 0.7 1.3 2.5 3.9 10 12 0.5 0.6 0.0 . bo 0.0 0.4 Macedonia, FYR 2.0 2.5 . 10.4 10 16 1.1 1.7 0.0 0.0 0.0 1.1 Madagascar 1.1 1.2 5.4 7.4 21 20 0.4 0.3 0.0 0.0 0.0 0.0 Malawi 1.1 0.6 3.9 2.2 10 5 0.2 0.1 0.0 .0 0.0 0.0 Malaysia 3.2 2.3 10.3 9.3 128 95 1.7. 1.0 0.0 676 0.6 1.4 Mali 2.3 2.3 9.4 8,7 12 10 0.3 0.2 0.0 0.0 0.0 0.0 Mauritania 3.5 4.0 13.3 18.9 16 11 1.7 0.9 0.0 0.0 0.0 0.0 Mauritius 0.4 0.2 1.5 0.9 1 2 0.2 0.4 0.0 0.0 0.3 0.0 Mexico 0.5 0.6 4.6 3.8 175 255 0.5 0.6 0.0 0.0 0.5 0.1 Mol~do-va-05 . 1.5 1.6 9 11 0.4 0.5 --0.0 2.1 0.8 0.0 Mongolia 2.6 2.1 9.3 5.9 21 20 2.1 1.7 0.0 0.0 0.0 0.0 Morocco 4.5 4.3 14.3 13.5 195 195 2.1 1.7 0.0 0.0 1.4 1.3 Mozambique 6.0 2.5 17.0 9.1 50 8 0.6 0.1 0.0 0.0 0.6 0.4 Myanmar 8.3 7.8 74.3 . 286 345 1.3 1.4 0.0 0.0 23.0 13.6 Namibia 2.3 2.9 5.6 7.2 8 3 1.3 0.4 0.0 0.0 0.0 1.3 Nepal 1.0 0.8 6.0 5.7 35 35 0.4 0.3 0.0 0.0 0.0 0.0 Netherlands 2.3 1.8 4.6 5.9 90 54 ~~~~ ~~~~ ~~~~ ~~~~ ~~~~ ~~~~~~~1.3 0.7 0.1 0.1 0.4 0. New Zealand 1.6 1.2 4.0 3.5 11 10 0.6 0.5 0.0 0.0 1.2 4.0 Nicaragua 3.1 1.2 7.6 2.9 15 12 1.0 0.6 13.5 0.0 0.6 0.0 Niger 1.3 1.2 7.9 6.4 5 6 0.1 0.1 0.0 0.0 0.0 0 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-- o Nigeria 1.1 1.6 6.5 8.1 76 77 0.2 0.2 0.0 0.0 1.9 0.0 Norway 3.1. 2.2 6.4 5.0 36 33 1.7 1.4 0.1 0.0 1.7 1.4 Oman 20.5 15.3 40.2 36.3 35 38 6.7 6.1 0.0 0.0 0.3 0.6 Pakistan 7.0 5.9 27.9 27.9 580 590 1.4 1.2 0.4 0.1 6.6 9.7 Panama 1.3- 1.4 5.7 5.1 11 13 1.1 1.1 2.0 0.0 0.5 0.1 Papua New Guinea 1.4 1.1 4.2 3.7 4 4 0.2 0.2 0.0 0.0 4b.0 0.0 - Paraguay 1.8 1.1 13.2 3.9 16 17 1.0 0.8 0.0 0.0 0.7 0.6 Peru 2.2 2.4 11.1 12.3 112 115 1.4 1.2 0.0 0.0 1.4 0.4 Phlipp-ines 1.9 ------ 1-.4-- 10.2- 7.3 107 07- 0-.-4 --0.3- 0- . 0- 0.0- 1.80.3 Poland 2.3 2.1 5.5 6.1 270 187 1.4 0.9 0.2 0.1 0.0 0.1 Portugal 2.6 2.1 6.4 5.4 80 71 1.6 1.4 0.1 0.0 0.6 0.2 Puerto Rico . .. . . .. Romania 3.3 1.6 7.9 4.7 172 170 1.6 1.6 --0.5 0.5 0.6 1.9 RUssian Fdration 8.1) 5.6 28.0 22.4 1,900 900 2.5 1.2 5.8 4.2 0.0 1.1 ii 5.7 Military expenditures Armed forces Arms trade personnel Exports Imports % of % of central Total % of % of % of GNI government expenditure thousands labor force total exports total imports 1992 1.999 1.92 ±999 1.92 ±99 1992 ±999 ±992 1.999 1.99 ±999 Rwanda 4.4 4.5 21.7 22,7 30 40 0.8 0.9 0.0 0.0 0.0 11.9 Saudi Arabia 27.2 14.9 72.5 43.2 172 190 3.1 2.9 0.0 0.0 25.2 27.5 Senegal 2.8- 1.7 13.5 8.2 18 13 0.5 0.3 0.0 0.0 1.0 0.0 Sierra Leone 3.5 3.0 17.7 13.5 8 3 0.5 0.2 0.0 0.0 6.8 12.3 Singapore 5.2 4.8 26.1 20.5 56 60 3.4 3.0 0.0 0.0 0.4 0.9 Slovak Republic 2.1 1.8 5.1 4.4 33 36 1.2 1.2 0.7 0.1 3.5 0.2 Slovenia 2.4 1.4 6.0 3.4 15 10 1.5 1.0 0.0 0.0 0.0 0.1 So alia . .... ...... 0.0 0.0 0.0 0.0 Siouth Africa 3.0 1.5 8.9 5.0 75 68 0.5 ....0.4 0.4 0.1 1.3 0.2 30 pain 1.5 1.3 4.3 6.1 198 155 1.2 0.9 0.3 0.1 0.4 0.5 Sri Lanka 3.7 4.7 13.2 18.4 110 110 1.5 1.3 0.0 0.0 0.3 0.7 Sudan 9.8 4.8 64.0 46.8 82_ 105 0.8 0.9 0.0 0.0 13.4 0.7 co Swaziland 1.9 1.5 6.3 4.6 3 3 1.1 0.8 0.0 0.0 0.0 0.0 .2 Sweden 2.5 2.3 5.3 5.5 70 52 1.5 1.1 1.5 0.8 0.3 0.3 Switzerland 1.8 1.2 7.2 5.1 31 39 0.8 1.0 1.2 0.1 0.7 1.5 E Syrian Arab Republic 9.2_ 7.0 39.0__ 25.1 408 310 11.0 6.2 0.6 0.0 11.2 5.5 0. o Tajikistan 0.3 1.3 0.7 9.4 3 7 0.1 0.3 0.0 0.0 0.0 0.0 > Tanzania 2.0 1.4 10.0 10.1 46 35 0.3 0.2 0.0 0.0 0.3 0.3 a) 0 Thaila-n d- 2.6 1.7 17.0 6.1 283 300 0.9 0.8 0.0 0.0 1.2 0.7 Co Togo 2.9 1.8 13.2 9.4 8 11 0.5 0.6 0.0 0.0 0.0 0.0 Trinid-ad and Tobago -.1.5 1.4 4.8 5.5 2 2 0.4 0.4 0.0 0.0 0.0 0.0 o Tunisia 2.4 1.8 7.1 5.4 35 35 1.1 0.9 0.0 0.0 0.3 0.1 0 C" Turkey 3.8 5.3 18.8 13.9 704 789 2.7 2.6 0.1 0.3 6.6 7.9 Turkmenistan .. 3.4 .. 16.0 28 15 1.7 0.7 1.4 0.0 0.0 1.0 Uganda 2.4 2.3 11.7 13.9 70 50 0.8 0.5 0.0 0.0 2.0 2.2 Ukraine 1.9 3.0 .. 8.2 430 340 1.6 1.3 0.0 4.7 0.0 0.1 United Arab Emirates 5.6 4.1 50.1 39.6 55 65 5.2 4.7 0.0 0.0 4.2 3.8 United Kingdom 3.8 2.5 9.1 6.9 293 218 1.0 0.7 3.3 1.9 1.3 0.8 United States 4.8 3.0 21.1 15.7 1,920 1,490 1.5 1.0 5.6 4.7 0.3 0.2 Uruguay 2.1 1.3 8.0 4.1 25 24 1.8 1.6 0.0 0.0 0.5_ 0.3 Uzbekistan 2.7 1.7 6.0 5.3 40 60 0.5 0.6 0.0 0.4 0.0 0.0 Venezuela, RB 1.4 1.4 6.3 7.1 75 75 1.0 0.8 0.0 0.0 0.9 2.2 Vietnam 3.4 2.5 14.5 11.6 857 485 2.4 1.2 0.4 0.0 0.4 0.6 West Bank and Gaza.. . .. ..... . Yemen. Rep. 9.8 6.1 29.8 18.0 64 69 1.5 1.3 0.0 0.0 0.2 1.5 Yugoslavia, Fed. Rep. 5.0 . .. 137 105 2.8 2.1 0.0 .. 0.0 Zambi'a 3.3 1.0 9.3 3.5 16 17 0.5 0.4 0.0 0.0 0.0 0.0 Zimbabwe 3.8 5.0 10.1 12.1 48 40 1.0 0.7 0.3 0.0 4.1 0.5 Low Income 2.6 2.5 11.8 13.8 6,485 6,254 0.7 0.6 0.1 0.5 2.0 1.9 Middle Income 4.0 2.7 21.1 15.8 12,383 10.220 1.0 0.7 0.8 0.4 2.8 1.7 Lower middle income 4.3 2.7 23.7 17.2 9,172 6,971 0.9 0.6 1.7 0.9 2.1 0.8 Upper middle income 3.8 2.8 19.3 12.3 3,211 3,249 1.4 1.2 0.1 0.0 3.3 2.2 Low & middle Income 3.7 2.7 19.7 15.4 18,868 16,474 0.9 0.7 0.7 0.4 2.6 1.7 East Asia & acic2.8 2.3 22.8 168 726 581 08 0.6 0.4 0.1 1.3 1.0 Europe & Central Asia 5.2 3.7 21.6 11.7 4,303 3,192 2.0 1.3 2.9 1.8 1.4 1.7 Latin America & Carib. 1.3 1.5 5.7 7.3 1,443 1,371 0.8 0.6 0.2 0.0 0.7 0.3 Middle East & N. Africa 14.5 7.0 49.0 28.5 2.631 2,529 3.3 2.6 0.1 0.1 10.7 8.4 South Asia 3.0 2.8 14.9 15.7 2.152 2,153 0.4 0.4 0.1 0.0 3.3 2.4 Sub-Saharan Africa 2.9 2.2 9.1 8.9 1,083 1,398 0.5 0.5 0.2 0.1 1.3 1.4 HighI Income 3.1 2.3 11.1 9.1 5,665 4,724 1.3 1.0 1.4 1.2 0.7 0.6 Europe EMU 2.3 1.9 5.7 5.2 2,181 1,768 1.6 1.3 0.4 0.3 0.4 0.4 Note: Data for some coast ries are basedoxnpartial or uncertain data or rough estimates: see U.S. Department of State 120021. a. Data from national source. About the data Definitions Although national defense is an important func- The data on armed forces refer to military Military expenditures for NATO countries are tion of government and security from external personnel on active duty, including paramilitary based on the NATO definition, which covers threats contributes to economic development, forces. These data exclude civilians in the de- military-related expenditures of the defense high levels of defense spending burden the fense establishment and so are not consistent ministry (including recruiting, training, construc- economy and may impede growth. Comparisons with the data on military spending on person- tion, and the purchase of military supplies and of defense spending between countries should nel. Moreover, because they exclude payments equipment) and other ministries. Civilian-type take into account the many factors that influence to personnel not on active duty, they underesti- expenditures of the defense ministry are ex- perceptions of vulnerability and risk, including mate the share of the labor force working for cluded. Military assistance is included in the historical and cultural traditions, the length of the defense establishment. Because govern- expenditures of the donor country, and pur- borders that need defending, the quality of rela- ments rarely report the size of their armed forces, chases of military equipment on credit are in- tions with neighbors, and the role of the armed such data typically come from intelligence cluded at the time the debt is incurred, not at forces in the body politic. sources. The Bureau of Verification and Compli- the time of payment. Data for other countries Data on defense spending from governments ance attributes its data to unspecified U.S. gov- generally cover expenditures of the ministry of are often incomplete and unreliable. Even in ernment sources. defense (excluded are expenditures on public countries where parliaments vigilantly review The Standard International Trade Classifica- order and safety, which are classified sepa- 307 government budgets and spending, defense tion does not clearly distinguish trade in military rately). t Armed forces personnei refer to ac- spending and trade in arms often do not receive goods. For this and other reasons, customs- tive duty military personnel, including paramili- close scrutiny. For a detailed critique of the qual- based data on trade in arms are of little use, so tary forces if those forces resemble regular ity of such data see Ball (1984) and Happe and most compilers rely on trade publications, con- units in their organization, equipment, training, o Wakeman-Linn (1994). fidential government information on third- or mission. * Arms trade comprises exports E The International Monetary Fund's (IMF) Gov- country trade, and other sources. The construction and imports of military equipment usually re- CD emmnent Flnance Statistics Yearbook is the pri- of defense production facilities and the licens- ferred to as "conventional," including weapons marysourceofdataondefensespending.ltuses ing fees paid for the production of arms are of war, parts thereof, ammunition, support q a consistent definition of defense spending included in trade data when they are specified in equipment, and other commodities designed C based on the United Nations' classification of military transfer agreements. Grants in kind are for military use. See About the data for more the functions of government ancl the North At- usually included as well. Definitional issues in- details. 9) lantic Treaty Organization (NATO) definition. The clude treatment of dual-use equipment such as __ _ IMF checks data on defense spending for broad aircraft, use of military establishments such as consistency with other macroeconomic data re- schools and hospitals by civilians, and pur- Data sources ported to it but is not always able to verify the chases by nongovernmental buyers. Bureau of The data on military expenditures, armed accuracy and completeness of the data. More- Verification and Compliance data do not include forces, and arms trade are from the Bureau of over, country coverage is affected by delays or arms supplied to subnational groups. Valuation Verification and Compliance's World Military failure to report data. Thus most researchers problems arise when data are reported in vol- Expenditures and Arms Transfers 2000 (U.S. supplement the IMF's data with assessments ume terms and the purchase price must be es- Department of State 2002). by other organizations. However, these organi- timated. Differences between sources may re- - zations rely heavily on reporting by governments, flect reporting lags or differences in the period on confidential intelligence estimates of varying covered. Most compilers revise their time-series quality, on sources that they do not or cannot data regularly, so estimates for the same year reveal, and on one another's publications. The may not be consistent between publication data in this table are the latest available from dates. the U.S. Department of State's Bureau of Verifi- The data on U.S. arms exports were substan- cation and Compliance (formerly the Bureau of tially revised upward in the 2000 edition of the Arms Control). World Development Indicators, based on data Definitions of military spending differ depend- from the most recent edition of the Bureau of ing on whether they cover civil defense, reserves Verification and Compliance's World Military and auxiliary forces, police and paramilitary Expenditures and Arms Transfers (U.S. Depart- forces, dual-purpose forces such as military and ment of State 1999). Revisions were made in civilian police, military grants in l(ind, pensions commercial arms sales made directly by U.S. for military personnel, and social security con- firms to foreign importers under authorization tributions paid by one part of government to of the U.S. Department of State in accordance another. Official govemment data may omit parts with U.S. regulations on international traffic in of military spending, disguise financing through arms. Under the previous methodology the com- extrabudgetary accounts or unrecorded use of mercial arms component was represented by foreign exchange receipts, or fail to include mili- preliminary data on the deliveries made under tary assistance or secret military equipment approved export licenses. But because of weak- imports. Current spending is more likely to be nesses in data reporting, the extent to which reported than capital spending. In some cases authorized exports matched actual exports was a more accurate estimate of military spending uncertain. The new methodology assumes that can be obtained by adding the value of estimated deliveries constitute 50 percent of total authori- arms imports and nominal military expenditures. zations by country. These deliveries are then This method may understate or overstate spend- distributed in a fixed pattern over the years of ing in a particular year, however, because pay- the license. ments for arms may not coincide with delivenes. AM5 5.8 Transport infrastructure Roads Railways Air Goods Goodis Passenger- transported Diesel Total road hauled km per ton-km per locomotives Aircraft Passengers Air freight network Paved roads million $ million of $ million of available departures carried millions km %ton-km PPP GDP PPP GDP M% thousands thousands ton-km 1995-2000, 1995-20001 1.995-20001 1995-2000' 1995-2000' 1995-2000' 2000 2000 2000 Afghanistan 21,000 13.3 -------------- - 3 - 150 8 Albani'a 18,000 39.0 1,830 9,196 1.941 --5 1490 Algeria 104,000 68.9 -- 11,146 -- 85 37 2,995 12 Angola 51,429 10.4 -- -- 4 25 6 Argentina 215,471 29.4 -- 28,665__ - 196 9,262 295 Armenia 15.918 96.3 40 6,232 49,717 30 4 298 9 Australia 811,603 38.7 --- -351 32,223 1.860 Austria 200,000 100.0 16,100 41,307 75,075 89 148 7,263 444 Azerbaijan 24,981 92.3 3,5 13 --- -8 546 47 308 Bangladesh 207,486 9.5 ,22,570 4,706 816 1,331 194 Belarus 74,385 89.0 8,982 202,576 463,691 93 6 211 2 o Belgium 148,216 78.2 35,000 32,012 30,522 86 226 10,738 1,016 Bein6,87 20.0 - -- 2 7 7 12 Bolivia 53,790 6.5 -- 6,460 - 22 1,757 15 Bosnia and Herzegovina 21,846 52. -- -- - 6 -- - ------- ---. .......- Botswana 10.217 55.0 ---7 1660 0. ------------------ ...------ -- - - -- - --- ---------- -----. - - o Bra-zil ---------- 1,724,929 5.5- - 865 31.150 723 ____3_1_,845 1,523 >, Bulgaria 37,286 94.0 168 96.104 122,533 37 12 515 6 Burkina Faso 12.506 16.0 -- 3 144 12 ----- ---- ----- ------ ------- -- ---- - -------- --- o Burundi 14.480 7.1 -- -- 1 12 0 Cambodia 12,323 16.2 41 -. 77,235 - -- -~- e'd - - - - - --- - .--- - -- - -- - - - - - - - -- - - CD Cameroon 34.300 12.5 14.371 40,811 71 6 273 50 0- -- - - - - - -- - - - - - - - - - - - - -- - -. . . . . Canada 901.903 35.3 _82,500__ 1.945 429,555 --316 _25,7 78 1,806 Central Afri-can Republic 23,810 2.7 60 2 7712 Chad 33,400 0.8 - 2 77 12 Chile 79,814 19.4A 4,907 7,802 65 88 5,175 1,312 China 1,402,698 22.4 612,940 82,693 260,427 82573 61,892 3,900 Hong Kong, China 1,831 100.0 ..- .78 14,393 4,841 Colombia 112,988 14.4 31 62 1,948 32 178,537 595 Congo, Dem. Rep. 157,000 -.. 700 ..9 - -- -- Congo, Rep. 12,800 9.7 -- 36,264 .. 35 6 128 12 Costa Rica 35,892 22.0 3,070 - . -50 27 861 79 CMe dIlvoire 50,400 97 -- 6,512 21,081 53 7 262 12 Croatia 28,123 84.6 1,090 34,782 58.859 6317 __929 3 Cuba 60,858 49.0 56 -12 1,007 49 Czech Republic 55,408 100.0 39,036 53,029 138,506 86 40 2,228 -32 Denmark 71,591 100.0 11,696 40,275 11.786 ..152 5,923 199 Dominican Republic 12,600 49.4 -... 0 11 0 - Ecuador 43,197 18.9 4,7 71,181 15--------- Egypt, Arab Rep. 64,000 78. 1 31,500 317,220 16,164 80 4 7 45278 El Salvador 10.029 19.8 -.- .. 719031 Eritrea 4,010 21.8 - .. .- Estoni'a 51.411 20.1 3,689 19,842 486,631 80 9 278 1 Etihiopia 31,571 1200 ..27 945 78 Finland 77,900 64.5 26,500 29,933 87,619 - 89 156,416 266 France 894,000 100.0 245,400 50,392 42,145 93 789 51,927 5,227 Gabion 8,464 9.9 -- 11,254 65,276 89 8 442 55 Gambia. The 2,700 35.4 - -. .. .- Georgia 20,362 93.5 475 44,361 200,857 34 2 1182 Germany - 230,735 99.1 226,982 31,471 38,962 92 743 59,362 7,128 Gh-ana 39,409 29.6 .. 6,221 . -5 314 40 Greece 117,000 91.8 17.000 11,850 2,101 55 99 7,099 129 Guatemala 14,118 34.5 -- -- 7 506 3 Guinea 30,500 16.5 -.....1 61 1 Gulinea-Bissau 4,400 10.3 - 1 20 0 Haiti 4,160 _24.3-- Honduras 13,603 20.4 5.8 Roads Railways Air Goods Goods Passenger. transported Diesel Total road hauled km per ton-km per locomotives Aircraft Passengers Air freightl network Paved roads million $ million of $ Million of available departures carried millions km ton-km PPP GDP PPP GOP (%) thousands thousands ton-km 1995.2000' 1.995-2000' 1995-2000' 1995-2000' 1995-2000' 19915-2000' 2000 2000 2000 Hungary 188.203 43.4 14 94,085 72,243 79 32 2,062 51 India 3,319.644 45.7 958 195,355 136,165 86 199 17,339 545 Indonesia 342,700 46.3 .. 28.490 8,725 83 153 9,485 423 Iran, Islamic Rep. 167,157 56.3 .. 18.506 43,629 47 83 8.830 71 Iraq 45,550 84.3 -.. .0 Ireland 92,500 94.1 5,900 18,714 4,599 74 146 14.014 168 Israel 16,281 100.0 .. 3,243 9,132 92 45 4,073 886 Italy 479,688 100.0 219,800 38.135 18,054 79 375 30,586 1.748 Jamaica 18,700 70.1 ......24 1,918 48 Japan 1,161.894 46.0 307,149 77,409 7,608 81 642 108,413 8,549 309 Jordan 7,245 100.0 ..35,549 91 16 1,282 204 -- - ----------- ------------------~ ~ ~~ ~ ~~~~~~~~~~~~~~~~~~~~~~~K Kazakhstan 81,331 94.7 4,506 177.393 1,474,814 8 461 12 -----.. - -..- - . . .. -. - -...... - - - - - . . - -....- -....-...- - - - - - - - - - -..-.- - - - - - 0~~~~~~~~-- ----- --- Kenya 63,942 12.1 .. 13,457 44.821 64 29 1,557 77 Korea, Dem. Rep. 31,200 6.4 ......1 86 2 Korea, Rep. 86.990 74.5 74,504 46,461 19,459 90 227 34,331 7,774 a. Kuwait -- 4,450 80.6 ......18 2,123 243D Kyrgyz Republic 18,500 91.1 1,220 6 243 4 C Lao PDR 21.716 13.8 ..- . .6 211 2 ------------ 3~~~~~ Latvia 73,202 38.6 4,789 108,396 770,302 88 9 224 0 C Lebanon--- -- --- 7,300 84.9 ......10 806 85 ------------- ----- --- -------~~~~~~~~~~~~~~~~~~~~~~~~~~a Lesotho 5,940 18.3 ... .0 1 0 2 ---- . -.- . ~~~~ ~~---- - --. - . --- -0-- --) - - Liberia 10.600 6.2 C- Libya 83,200 57.2 7600 Lithuania 75,243 91.3 7,769 28.379 328,042 88 10 284 2 Macedonia, FYR 8,684 63.8 1,210 15,959 40.430 40 8 611 1 Madagascar 49,827 11.6 22 667 33 Malawi 28,400 18.5 0 11,535 5116 1 Malaysia 65,877 75.8 8,221 7,203 65 169 16,561 1,864 Mali 15,100 12.1 30,578 35,377 2 77 12 Mauritania 7,660 11.3 4 185 13 Mauritius 1,926 97.0 12 949 183 Mexico 329,532 32.8 197.958 2.578 61,435___ 77___ ____291 21,001 318 mcldova 1265 7. 924 135 1 Mongolia 49,250 3.5 126 253,483 684.165 6 254 8 Morocco 57,707 56.4 3,035 18,176 52,224 69 45 3,671 63 Mozambique 30,400 18.7 110 7 260 7 Myanmar 28,21)0 12.2 60 12 600 7 Namilbia 66,467 8.3 5,607 133,970 89 6 245 75 Nepal 13,223 30.8 12 643 17 Netherlands 116,500 90.0 32,700 41,134 9,712 93 225 20,794 4,254 New Zealand 92,053 62.8 51,030 90 215 9,888 817 Nicaragua 19,032 11.0 1 61 1 Niger 10,100 7.9 .. -. .2 77 12 Nieia 194,394 30.9 .. 512 4,915 63 9 415 10 Oman 32,800 30.0 ... .. 19 2,120- 15. Pakistan 254,410 43.0 96,802 81,899 17,118 85 74 6,252 339 Panama 11,400 34.6 ... .25 1,117 22 Pp u-a --N_e_w _G_ui'n_e__a 1,0 . ... 27 1,1_29- 22 Paraguay 29,500 9 .5. 8 266 0 Peru 72,900 12.8 .. ~~~~~~ ~~~~~~~ ~ ~ ~~~ ~~~~~1,397 4,640 ..46 2,125 _35 Phiippi n es2194 2 0 .95445,4 241 Poland 364,656 68.3 72,843- 7----7,53 17,75_6 --------- 5-5 -49 --------2,373- - -- ----- 7-6 Puerto Rico 14,400 100.0... Romania 198,603 49.5 13,457 97,692 135,241 85 21 1, 186 12 Russian Federationi 532,393 67.4 13 129,048 1,102,493 ..315 17,688 1,041 Apnw-le 5.8 Roads Railways Air Goods Goods Passenger- transported Diesel Total road hauled km per ton-km per tocomotives Aircraft Passengers Air freight network Pased roads million $ million of $ million of available departures carr ed millions km %ton-km PPP GDP PPP GDP N% thousands thousands ron-km 1995-2000' 1995-2000, 19915-20001 1995-2000, 1995-20001 1995-20001 2000 2000 2000 Rwanda 12,000 8.3 ---- - Saudi Arabia 151,470 30.1 --998 3,811 80 109 12,567 1.000 Senegal 14.576 29.3 6.609 37,365 55 2 98 12 Sierra Leone 11,330 7.9 --- - 18 0 Singapore 3,066 100.0 --- --71 16.704 6,005 Slovak Republic 42,717 86.7 8,474 57,115 215.427 87-3 116 0 Slovenia 20,177 99.9 4,407 21,848 89,048 --12 628 4 Somalia 22,100 11.8 - -- -- South Africa 362.099 20.3 -- 25,701 283,106 96 110 8,000 688 30 Sain 663,795 99.0 98,145 26,047 16,714 83 479 39,559 872 Sri Lanka 96,695 95.0 30 59,310 1.865 70 5 1.756 256 cn. . . . . . .-v- - - - - - - Sudan 11,900 36.3 --- - 42 8 408 35 15 Swaziland 3,247 - .. 0 0 0 -- - - - - - - - - - - - - - - ' ~ Sweden 212,402 78.4 32,000 36,988 96,543 248 13,354 289 Switzerland 71.011 -- 22.000 --- .288 17,216 1,937 CD- -- --- E Syrian Arab Republic 43,381 23.1 -- 5,688 28,030 100 14 750 21 o Tajikistan 27,767 82.7 --- .4 156 3 > Tanzania 88.200 4.2 -- 73,054 7.54 7261823 Thailand 64,600 97.5 26.781 7.923 94 102 17.392 1.713 o Togo 7,520 31.6 --- -2 77 12 Trinidad and Tobago 8,320 - 51.1 --- .26 1.254 24 o Tunisia 18.997 64.8 -- 21,247 41,961 71 20 1,908 21 0 Turkey 385.960 34.0 150,974 14,726 20,238 78 114 11,513 375 Turkmenistan 24,000 81.2 . -.- 22 1.284 12 Uganda 27,000 6.7 -- 1,366 4.924 --3 187 23 Ukraine 169,491 96.7 18,206 296,128 941,037 87 28 963 11 United Arab Emirates 1.088 100.0 - -- 48 6,871 1.428 United Kingdom 371.913 100.0 150,700 - 75 872 70.361 5,161 United States 6,304,193 58.8 1.534,430 1,020 350,942 ..8,766 655,649 30.131b Uruguay 8.983 90.0 -. 6,931 6,126 9 617 14 Uzbekistan 81,600 87.3 -. 42.559 304,816 --30 1,656 75 Venezuela. RB 96,155 33.6 ..0 342 65 139 4.295 33 Vietnam 93,300 25.1 -- 18,843 9,807 95 28 2.881 116 West Bank and Gaza -----.- ---.-- Yemen, Rep. 67.000 11.5 - .11 844 32 Yugoslavia, Fed. Rep. 49,805 62.3 630 . .--. Zambia 66.781 --24,892 74.141 67 6 89 0 Zimbabwe 18.338 47.4 . - 145,373 61 14 606 159 Low Income 16.5 797 52,007 Middle Income 52.3 4,466 329,757 Lower middle income 56.3 1.897 155.078 Upper middle income 47.3 2,569 174.679 Low & middle Income 32.2 5.263 381.764 East Asia & Pacific 23.8 1.437 151.301 Europe & Central Asia 91.3 770 46.295 Latin America & Carib. 29.4 1.952 95.983 Middle East & N. Africa 66.3 440 42,285 South Asia 36.9 305 27,793 Sub-Saharan Africa 12.3 360 18.107 High income 92.9 16,129 1.265.012 Europe EMU 92.9 3,500 255,191 a. Data are for the latest year available in tie period shown. b. Data cover only those carriers designated by the U.S. Department of Transportation as major and national air carriers. 5.8 About the data Definitions Transport infrastructure-highways, railways, traffic as scheduled or nonscheduled. Thus, re- * Total road network includes motorways, high- ports and waterways, and airports and air traffic cent increases shown for some European coun- ways, and main or national roads, secondary control systems-and the services that flow from tries may be due to changes in the classifica- or regional roads, and all other roads in a coun- it are crucial to the activities of households, pro- tion of air traffic rather than actual growth. For try. * Paved roads are those surfaced with ducers, and governments. Because performance countries with few air carriers or only one, the crushed stone (macadam) and hydrocarbon indicators vary significantly by transport mode addition or discontinuation of a home-based air binder or bituminized agents, with concrete, or and by focus (whether physical infrastructure or carrier may cause significant changes in air with cobblestones, as a percentage of all the the services flowing from that infrastructure), traffic. country's roads, measured in length. * Goods highly specialized and carefully specified indica- hauled by road are the volume of goods tors are required. The table provides selected Figure 5.8 transported by road vehicles, measured in indicators of the size and extent of roads, rail- millions of metric tons times kilometers trav- ways, and air transport systems and the volume Air carriers registered in East Asia and eled. * Railway passengers refer to the total of freight and passengers carried. Pacific more than doubled the number number of passengers transported times kilo- Data for most transport sectors are not inter- of passengers they carried in the 1990s meters traveled per million dollars of GDP, nationally comparable. Unlike for demographic 16 measured in purchasing power parity (PPP) 311 statistics, national income accounts, and inter- o terms (for a discussion of PPP see About the national trade data, the collection of infrastruc- -12 data for table 1.1). * Goods transported by rail g ture data has not been "internationalized." Data are the tonnage of goods transported times N on roads are collected by the International Road 5 1 kilometers traveled per million dollars of GDP, E Federation (IRF), and data on air transport by ,8 measured in purchasing power parity (PPP) E the International Civil Aviation Organization , 6 terms. * Diesel locomotives available are (D (ICAO). National road associations are the pri- a 4 / those in service as a percentage of all diesel C mary source of IRF data; in countries where such 2- locomotives. * Aircraft departures are the num- an association is lacking or does not respond, E ber of domestic and international takeoffs of O other agencies are contacted, such as road di- 1990 1992 1994 1996 1998 2000 air carriers registered in the country. * Air pas- S rectorates, ministries of transport or public _EastAs,a&Paciic sengers carried include both domestic and in- ,, works, or central statistical offices. As a result, - Europe & Central Asia ternational aircraft passengers of air carriers rN the compiled data are of uneven quality. .dr Latin America&Caribbean registered in the country. * Air freight is the Even when dala are available, they are often = de South Asta sum of the metric tons of freight, express, and of limited value because of incompatible defini- - Sub-Saharan Africa diplomatic bags carried on each flight stage tions, inappropriate geographical units of obser- S-urce. Table 5.8. (the operation of an aircraft from takeoff to its vation, lack of tirneliness, and variations in the next landing) multiplied by the stage distance nature of the terrain. Data on passengers car- by air carriers registered in the country. ried, for example, may be distorted because of "ticketless" travel or breaks in journeys; in such _ Data sources cases, the statistics may report the number of D passenger-kilometers for two passengers rather The data on roads are from the International than one. Measurement problems are com- Road Federation's World Road Statistics and pounded because the mix of transported com- from Eurostat (europa.eu.int/eurostat.html). i modities changes over time, and in some cases The railway data are from a database shorter-haul traffic has been excluded from in- maintained bytheWorld Bank'sTransportation, tercity traffic. Finally, the quality of transport Water, and Urban Development Department, service (reliability, transit time, and condition of Transport Division. The air transport data are i goods delivered) is rarely measured but may be from the International Civil Aviation as important aIs quantity in assessing an Organization's Civil Aviation Statistics of the i economy's transport system. Serious efforts are World and ICAO staff estimates. i needed to create! international databases whose comparability and accuracy can be gradually improved. The air transport data represent the total (in- ternational and domestic) scheduled traffic car- ried by the air carriers registered in a country. Countries submit air transport data to ICAO on the basis of standard instructions and defini- tions issued by ICAO. In many cases, however, the data incljde estimates by ICAO for nonreporting carriers. Where possible, these estimates are based on previous submissions supplemented by information published by the air carriers, such as flight schedules.The data represent the air traffic carried on scheduled services, but changes in air trarisport regulations in Europe have made it more difficult to classify 5.9 Power and communications Electric power Telephone mailnines' Mobile International phones' telecommunications' Transmission and In largest Consumptios d istr,bution city Cost of Outgoing Cost of per losses per per Waiting Waiting Revenue local call per traffic call to U.S. capita % 1.000 1.000 list time per per line $ per 1,000 minutes per $ per kwh of output people poop e thousands years employee $ 3 minutes people subscriber 3 minutes 1999 1999 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 Afghanistan . .. 1 10 .. . .. .0 Albania 783 57 39 93 98.5 4.5 34 424 0.02 8 469 4.59 Algeria 681 19 57 70 646.0 5.4 98 174 0.01 3 86 4.70 Angola 84 15 5 21 21.1 8.5 33 1,~839 0.0.5 2 509 5.13 Argentina 1,938 15 213 24 7 58.2 0.2 406 1,267 0.09 163 56 2.80 Armeni a 957 25 152 212 80.4 .. 69 118 0.11 5 59 Australia 8.884 8 525 516 0.0 0.0 170 1,458 0.12 447 136 0.54 Austria 6,176 8 467 .. 0.0 0.0 207 1.250 0.15 762 300 1.60 Azerbaijan 1.750 _13 104 228 60.7 1.3 80 98 0.28 56 35 6.89 312 Bangladesh 89 16 4 24 135.1 3.3 30 558 0,03 1 91 4.14 Belarus 2,704 13 269 1,228 401.4 2.7 103 61 ..5 65 6.10 (o Belgium 7,286 5 498 .. . . 223 932 0.13 525 35 20 iO Benin 53 98 8 21 23.0 4.5 52 1,062 0.09 9 321 6.90 Bolivia 390 18 61 115 7.5 0.2 103 826 0.09 70 65 3.70 Bosnia and Herzegovina 540 22 103 480 .. 2.2 225 440 0.02 30 228 2.96 2 Botswana . .. 93 ... 0.5 71 974 0.02 123 323 3.60 o rzl181 1 8 .. 0.5 157 823 0.03 136 21 1. 80 > Bulgaria 2,899 17 350 564 242.0 3.6 112 135 0.00 90 38 o Burkina Faso 4 36 12.3 2.2 41 981 0.08 2 202 11.00 ~0 3: Cambodia . .. 2 16 . ,. 45 722 0.02 10 319 O Cameroon 184 21 6 38 50.0 6.2 42 729 0.05 10 293 3.39 0 CN Canada 15,260 7 677 .. 0.0 0.0 239 1,002 .. 285 347 1.20 Central African Republic . .. 3 .. 1.8 >10.0 23 1.056 0.48 1 478 8.00 Chad . .. 1 8 0.6 0.5 23 2.200 0.16 1 293 Chile 2.309 5 221 318 10.3 0.0 243 754 0,12 222 64 2.90 China 759 7 112 294 .. 0.0 159 256 .. 66 11 6.70 Hong Kong, China 5.178 13 583 583 0.0 0.0 102 1.845 0.00 809 801 2.62 Colomnbia 772 24 169 322 1.155.0 2.0 151 378 0.03 53 40 2.20 Congo, Dam. Rep. 43 4 0 ... ...0 Congo, Rep. 48 91 7 ... ... 24 Costa Rica 1,426 8 249 ., 34.7 0.3 213 296 0.02 52 81 1.93 Ciote dilvoire . .. 18 57 31.7 0.8 96 1,923 0.05 30 163 7.86 Croatia 2,674 17 365 ... 0.9 151 474 0.03 231 198 Cuba 973 18 44 86 . .. 29 1.432 0.09 1 74 7.30 Czech Republic 4,682 8 378 676 32.0 0.2 164 660 0.13 424 93 0.97 Denmark 6.030 5 720 .. 0.0 0.0 180 1,089 0.10 631 183 1.77 Domini'can Republic 646 27 105 ..202 .. 82 237 3.90 Ecuador 620 23 100 109 . .. 181 400 0.08 38 91 4.90 Egypt, Arab Rep. 900 12 86 1 73 1,300.0 1.9 100 498 0.01 21 34 3.33 El Salvador 568 13 100 .. . . 148 897 0.06 118 222 2.40 Eritrea . .. 8 43 20.5 7.2 67 522 0.02 0 95 5.91 Etonia 3,435 18 363 422 24.6 1. 7 5 .8 37 149 1.62 Ethiopia 21 10 4 52 196.9 7.8 32 360 0.02 0 59 7.15 Finland 14,366 4 550 .. 0.0 0.0 118 1,406 0.12 720 164 1.07 France 6.392 6 579 .. 0.0 0.0 200 813 0.10 493 129 1.00 Gabon 700 10 32 ... >10.0 37 1,801 0.15 98 567 Gambia, The . .. 26 81 16.9 6.0 31 807 0.30 4 220 Georgia 1,312 19 139 233 104.8 2.2 76 47 .. 34 60 2.88 Germany 5.690 4 611 686 0.0 0.0 210 1.012 0.09 586 -184 0.34 Ghana 204 1 12 54 . .. 63 387 0.03 6 185 1.69 Greece 3.854 7 532 732 14.1 0.2 289 824 0.07 557 140 0.69 Guatemala 341 20 57 .. . . 128 411 0.08 61 193 0.80 Gu-in ea .. . 19 1,7 0.1 75 449 0.10 5 . 289 9.04 Guinea-Bissau . .. 9 109 5.1 4.4 45 ..0 272 Haiti 40 5 . >10.0 20 ..3 204 7.10 Honduras 449 22 46 99 169.7 7.8 50 1,025 0.06 24 144 4.20 5.9 6 Electrlc power Telephone mainlines, Mobile International phones' telecommunications' Transmissi'on and In largest Consumption distribution city Cost of outgoing Cost of per losses per per waiting Waiting Revenue local call per traffic call to U.S. capita 96 1,000 1.000 list time per per line $ per 1,000 minutes per $ per kwh of output people people thousands years employee $ 3 minutes people subscriber 3 minutes 1999 1999 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 Hungary 2,874 13 372 581 27.4 0.1 182 845 0.09 302 56 1.28 Inia 379 21 32 131 3,680.6 0.8 63 13 0.01 4 16 4.20 Indoeia 345--12 - 31 -- 232 177 353 0.02 17 38 4.20 Iran, Islamic Rep. 1,407 15 149 1,203.5 1.2 200 210 0.01 15 24 7.65 Iraq 1,303 .. 29 75 . ..0 29 Ireland 5.011 8 420 ..91 1,653 0.17 658 786 1.54 Israel 5,689 3 482 ... 0.3 253 1,735 0.05 702 324 3.30 Italy 4,535 7 474 .. 0.0 0.0 358 1,247 0.12 737 101 1.40 Jamaica 2,294 10 199 . 209.1 6.5 175 949 . 142 144 5.20 Japan 7,443 3 - 554 ~~~~~~ ~~~~~~~~~~0100--------o.0 46 1,64 0.0-7 526 35 1.67 313 Jordan 1,207 11 93 232 29.7 0.3 103 .. 0.02 58 275 Kazakhstan 2,448 17 113 224 172.4 >10.0 60 147 .. 12 57 2.68 Kenya 126 20 10 78 ~~~~~~ ~~~~ ~~ ~~1-3-4-.1 8.1 i6 9-5-5-- -0.04 4 75 7.35 Korea, Oem. Rep. . . 46 .. .0.. . Korea, Rep. 5,160- 4 464 1, 134 0.0 0.0 316 941 0032 567 48 1.93 Kuwait 1401 .. 244 47 00 00-61-841 b000 249 340 5.41 Kyrgyz R-ep-ublic 1,1 7 77 5 5. 6.9 52 60 2 62 9.84 mD ---.-.---. - ..-.----- . - - ..... - - - . -.---.-.-...-. - - - ...--...--.-...-.---... - ----- ----------- ------ ----0 Lao PDR . .. 8 .. 5.9 1.1 36 634 0.01 2 207 9.20 VD Latvia ~~~~~~~1,851- 27 303 526 19.2 3.3 - 170 30 0.11 166 79 2.05 Lebanon 1,778 18 195 96 .. .. 114 919 ~~~~~ ~~~~ ~~~~ ~~~~~~~~~~~0.07 212 124 4.45 ------- - ----- - - ----- -------- - ----- - . - .0 . Lesotho . .. 10 64 19.0 >10.0 33 454 0.01 10 1,665..c Liberia . .. 2 >10.0 0 791 . Libya 3.876 .. 108 116 80.0 1.2 43 7 78 . f Lithuania 1,769 10 321 412 41.6 0.9 197 189 0.06 142 33 3.10 Macedonia, FYR.. . 255. 1.2 128 376 0.01 57 188 4.13 Madagascar .9 - 0.3 0.1 - 20 1471 0.84 19 8.98 Malawi .. 4 37 250 ~~~ ~~ ~~~~ ~~~~~ ~~~9.1 9 892 0.03 5 241 Malaysia 2,474 - 8 199' 282 0.7 187 596 0.02 213 193 2.37 Mali . .. 3 22 28 1,559 0.07 - 1 368 12.64 Mauritnia . .. 7 18 47.8 >10.0 26 1,341 0.08 3 480 Mauritius . .. 235 319 18.9 1.0 153 515 0.03 151 123 4.00 Mexico 1,570 14 125 142 137.3 0.1 133 1,065 0.14 142 153 3.01 Moldova 620 26 133 ~~~~ ~~~~~~ ~~~320 124.3 5.5 79 86 0.01 32 73 4.10 mon-go-lia . . 5 11 3. 2.6 29 358 -0.01 45 35 5.65 Mo-roc-co- 430 4 0 15 .0 0.1 98 59 07 - 83 172 4.50 Mozambique 53 10 4 .. 21.3 3.2 ~~~ ~~~~~~ ~~~~~~~~~~~37 135 0 265 ....a...mar .. 71 25 6 29 3.2 5.3 3-4 ----------59 0 0-61 0 44 Namibia . .. 63 --181 24A 0.7-66-854 0.05 47 561 4.28 Nepal 47 23 12 .. ~~~~~~ ~~~~ ~~ ~ ~~28 3-.4 6.7 57 2-6-3 0.01----O- 0 98 Netherlands 5,993 5 618 .. 0.0 0.0 169 1,130 0.13 670 286 0.30 New Zealand 8,426 12 500 .. 0.0 0.0 358 1,307 0.00 563 340 0.90 Nicaragua 268 26 31 74 108.4 9.1 65 637 0.08 18 339 3.20 Mger .. .. 2 21 .. ~~~ ~~~~ ~~~~~ ~~ ~~~~ ~~~~~~~~14 84 01 0 191 9.03 Nigeria -85 32 4 11 42.0 1.4 36 3,763 0 . 144 Norway 2,4 8 53 82 0.- 00 14 192- 013 751 234 0.40 Oman 2,880 17 89 .. . 0.5 109 1,734 0.07 65 518 Paistan 321 30 22 62 298.0 1.8- 55 364 0.01 2 32 3.60 Panaa 130 1 15 2878 1,019 0.06 145 121 4.36 Papua Ne-w Guinea . .. 13 ..36 1,031 2 368 Paraguay 789 3 50 .. 20.1--07 46 685 -0.06 149 129 6.10 Peru 654 12 64 -29.6 1.2 -258 850 0.06 48 68 2.40 Ihdippines 454 15 40 142 -230 623 0.00 84 45 4.80 Poland 2,388 1 22 19 2. 0.8 159 646 0.08 174 62 2.92 Portugal 3,616 8 430 .. 25.6 0 2 234 1,155-----------02- - 0 10 665 . 118-------1, 0.89 5- 0--. -1 - 65 18 ,8 Pue-rto -R-ico .. . 3 .26 89 237 0.87 Rmnia 1,511 13 175 368 640.0 3.8 - 92 222 0.11 . 112 43. 2.49 Riussian Feeation 400 218 463 6,533.0 5.1 75 161. 0.01 22 29. 6.12 6* ~5.9 Electric power Telephone mainlines, Mobile International phones' telecommunications' Transmission and in largest Consumption distribution city Cost of Outgoing Cost of par losses per per Waiting Waiting Revenue local call per traffic call to U.S. capita % 1.000 1.000 list time per per line $ per 1,000 minutes per $ per kwh of output people people thousands yearn employee $ 3 minutes people subscriber 3 minutes 1999 1999 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 Rwanda .. 2 43 8.0 4.0 57 1,035 0.03 5 306 Saudi Arabia 4,710 8 137 23 274 2.6 124 153 00 4 34 52 Senegal 114 17 22 58 24.6 0.8 140 861 0.10 26 243 2.23 Sierra Leone .. 4 18 25.0 >10.0 19 .. 0.03 2 279 Singapore 6,641 . 4 484 484 0.0 0.0 222 1.245 0.02 684 538 0.68 Slo-vak Republic 4,216 7 314 670 69.3 0.7 112 460 0.10 205 96 1.13 Slovonia 5,218 5 386 .. 1.4 0.1. 27 42 .4 612 199 0.81 Somalia . .. 2 11 . .. . ..0 South Africa 3,776 8 114 . .. 1.1 113 1,369 0.09 190 100 1.98 314 Spain 4,497 10 421 485 4.3 0.0 415 1,528 0.09 609 150 1.88 Sri Lanka 255 21 . 41 284 269.5 1.9 64 506 0.04 23 55 3.05 Siudan 46 31 12 46 405.0 4.4 138 3,386 0.23 1 83 0- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - to Swaziland . .. 32 115 17.0 7.2 64 925 0.04 33 831 'O Sweden 14.138 7 682 . .0 0.0 211 1.205 .. 717 209 0.90 Switzerland- 7,291 -------6 727 . 0.0 0.0 211 1,593 0.11 644 458 1.00 E) Syrian Arab Republic 863 .. 103 141 3,025.8 >10.0 79 949 0.02 2 101 20.04 O Tajikistan 2,163 13 36 131 10.3 .. 44 32 0.01 0 29 8.16 > Tanzania 55 22 5 31 29.6 1.3 47 812 0.08 5 75 13.30 5,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~i425 o Thailand 1,352 8 2 34 415.2 1.6 169 579 0.07 50 64 25 o Togo . .. 9 35 16.8 2.9 49 912 0.09 11 240 7.90 T rini-dad arnd T o-ba go 3-,5--27- 8 231 200 10.0 0.5 98 808 0.03 103 243 3.30 N-- - - - - - - - - - - - - - - - - - - -- - - - - - O Tunisia 911 10 90 96 83.7 0.9 129 445 0.02 6 165 0 - - - - - - - - - - - - - - - - - - - - - - - - - - N Turkey 1,396 . 19 . 280 388 417.7 0.5 254 291 0.11 246 40 3.30 Turkmenistan 944 13 82 155 58.6 8.5 48 104 ..2 46 Uganda ..3 37 9. 2 36 25 1,549 0.13 8 183 Ukraine 2,306 18 199 418 2,654.9 7.9 80 82 0.00 16 38 United Arab Emirates 10,643 9 391 347 0.3 0.0 122 1,853 0.00 548 1.102 3.51 United Kingdom 5,384 8 . 589 0.0 0.0 170 1,508 0.17 727 227 1.10 United States 11,994 8 700 . . 00 0.0 172 1,466 0.00 398 153 Uruguay 1,871 19 _278__ 336 . 0.0 00 6 837 0.17 132 90 4.88 Uzbekistan 1,650 9 67 28 33.1 0.9 64 137 0.01 2 46 13.95 Venezuela.,RB 2,49-3 23 108 ..137 1,385 0.10 217 72 5.20 Vietnam 252 15 32 133 ..425 0.02 10 22 West Bank and Gaza ..,. .. 0.7 -. 0.04 Yemen, Rep. 110 26 19 77 159.5 3.8 66 271 0.01 2 105 4.45 Yugoslavia, Fed. Rep. . . . 226 462 131.0 1.8 160 147 0.01 123 119 12.08 Zam bi-a 540 11 8 24 13.3 6.7 26 565 0.06 9 160 2.57 Zimbabwe 894 17 18 70 158.9 >10.0 54 427 0.04 23 275 4.36 - -- -- ----------- - ------- ----.------- - Low Income 358 19 23 117 8,880.0 4.4 74 199 0.03 5 155 Middle Income 1,393 11 139 286 .. 1.0 164 840 0.06 93 93 4.36 Lower middle income 106 10 116 290 .. 1.9. 138 846 0.06 58 86 4.70 Up p e r- -m i d-dl e i n- c o me - ----- -2.-4-27- --- - 1-2.- 213 -. . 0.5 209 830 0.08 201 94 2.37 Low & middle Income 923 12 84 194 .. 1.9 151 816 0.05 51 100 4.70 East Asia & Pacific 816 7 101 270 .. 1.2 1 79 365 0.02 70 48 5.30 Europe & Central Asia 2,679 13 222 449 13,617.1 1.8 137 1,703 0.08 92 60 2.94 Latin America & Carib. 1,470 . 16 148 . .. 0.5 189 889 0.06 123 106 3.20 Middle East & N. Africa 1,289 12 92 127 6,294.6 1.2 138 486 0.01 30 139 South Asia 33 2 27 118 4,364.0 1.9 61 1 75 0.013 55 .6 Sub1-Sahara-n Af-ri-ca -11---- ----435 i 14 33 1.294.7 4.4 97 1.266 0.06 17 241 High Income 8.496 7 604 .. 66.0 0.0 246 1,321 0.09 532 234 1,78 Europe EMU 5.550 -6 534 .. 14.1 0.0 267 1,077 0.10 611 174 1.67 a. Data are from the International Telecommunication Union's IITUI World Telecommunication Development Report 2001. Please cite the ITU fot third-party use of these data. 5.9 About the data Definitions The quality of an economy's infrastructure, in- ing list by the average number of mainlines * Electric power consumption measures the cluding power, communications, and transport, added each year over the past three years. The production of power plants and combined heat are important elements in both domestic and number of mainlines no longer reflects a tele- and power plants less transmission, distribu- foreign investors' decisions to invest. Competi- phone system's full capacity because mobile tion, and transformation losses and own use tion in the marketplace, with sound regulation, telephones-whose use has been expanding by heat and power plants. * Electric power is lowering costs and improving the quality of rapidly in most countries, rich and poor- transmission and distribution losses are and access to telecommunications services provide an alternative point of access. losses in transmission between sources of around the globe. The table includes four measures of efficiency supply and points of distribution and in distri- An economy's production and consumption in telecommunications: waiting list, waiting time, bution to consumers, including pilferage. of electricity is a basic indicator of its size and mainlines per employee, and revenue per main- * Telephone mainlines are telephone lines level of development. Although a few countries line. Caution should be used in interpreting the connecting a customer's equipment lo the export electric power, most production is for estimates of mainlines per employee because public switched telephone network. Data are domestic consumption. Expanding the supply of firms often subcontract part of their work. The presented for the entire country and for the electricity to meet the growing demand of in- cross-country comparability of revenue per main- largest city. * Waiting list shows the number creasingly urbanized and industrialized econo- line may also be limited because, for example, of applications for a connection to a mainline 315 mies without incurring unacceptable social, eco- some countries do not require telecommunica- that have been held up by a lack of technical P nomic, and environmental costs is one of the tions providers to submit financial information; capacity. * Waiting time is the approximate 0 great challenges facing developing countries. the data usually do not include revenues from number of years applicants must wait for a Data on electric power production and con- cellular and mobile phones or radio, paging, telephone line. . Mainiines per employee are E sumption are collected from national energy and data services; and there are definitional calculated by dividingthe numberof mainlines agencies by the International Energy Agency (lEA) and accounting differences between countries. by the number of telecommunications staff C and adjusted by the IEA to meet international (with part-time staff converted to full-time C 0 definitions (for data on electricity production see Figure 5.9 equivalents) employed by telecommunications 3 table 3.9). Electricity consumption is equivalent enterprises providing public telecommunica- (D to production less power plants' own use and In many countries telephone access is tar tions services. * Revenue per line is the rev- transmission, distribution, and transformation better In the largest city than the average enue received by firms per mainline for pro- losses. It includes consumption by auxiliary sta- for that country viding telecommunications services. * Cost tions, losses in transformers that are consid- s 3" of local call is the cost of a three-minute, peak ered integral parts of those stations, and elec- 3 * Countryavetage rate, fixed-line call within the same exchange tricity produced by pumping installations. - Largestolty area using the subscriber's equipment (that It covers electricity generated by primary i is, not from a public phone). * Mobile phones sources of energy-coal, oil, gas, nuclear, hy- 1 50 refer to users of portable telephones subscrib- dro, geothermal, wind, tide and wave, and com- i 1E * ing to an automatic public mobile telephone bustible renewables-where data are available. EsU * service using cellular technology that provides Neither production nor consumption data cap- s I J r- access to the public switched telephone net- ture the reliability of supplies, including break- 0s oPakistan Chile India Kenya Sri Lanka work, per 1,000 people. * Outgoing traffic is downs, load factors, and frequency of outages. the telephone traffic, measured in minutes per Over the past decade new financing and tech- Sounrce: ITU and table 5.9. subscriber, that originates in the country and nology along with privatization and liberaliza- has a destination outside the country. * Cost tion have spurred dramatic growth in telecom- of call to U.S. is the cost of a three-minute munications in many countries. The table pre- peak rate call from the country to the LUnited sents some common performance indicators for States. telecommunications, including measures of supply and demand, service quality, productiv- ity, economic and financial performance, and Data sources tariffs. The quality of data varies among report- The data on electricity consumption and losses ing countries as a result of differences in regu- are from the IEA's Energy Statistics and latory obligations for the provision of data. Balances of Non-OECD Countries 1998-99, the Demand for telecommunications is often IEA's Energy Statistics of OECD Countries measured by the sum of telephone mainlines 1998-99, and the United Nations Statistics and registered applicants for new connections. Division's Energy Statistics Yearbook. The (A mainline is normally identified by a unique telecommunications data are from the number that is the one billed.) In some coun- International Telecommunication Union's tries the list of registered applicants does not (ITU) World Telecommunication Development I reflect real current pending demand, which is Report 2001. often hidden or suppressed, reflecting an ex- tremely short supply that has discouraged po- tential applicants from applying for telephone service. And in some cases waiting lists may overstate demand because applicants have placed their names on the list several times to improve their chances. Waiting time is calculated by dividing the number of applicants on the wait- *E 5.10 The information age Daily Radios Television' Personal Personal Internet Information and newspapers computers' computers communications Instaiied In technology education Monthly off-pealk expenditures Cable access charges' Sets subscribers Service Telephone per 1,000 p-er 1.000 per 1.000 per 1,000 per 1,000 Users provider usage charge Secure people people people people people Total thousands' charge $ $ servers % of GDP per capita 1998 2000 2000 2000 1 2000 2000 2000 2001 2001 2001 2000 2000 Afghanistan 5 114 14.. ... . ... Albania 35 243 123 .. 6.4 .. 4 19 0.20 1 Algeria 2 7 244 110 .. 6.5 .. 50 27 0.17 Angola 11 74 19 .. 1.1 .. 30 20 0.57 Argentina 37 681 293 163.1 51.3 122,881 2,500 78 0.47 238 4.1 317 Armenia ~~ ~~~~ ~~~6 225 244 0.9 7.1 .. 5 2 0.781 Australia 293 1,908 738 68.0 464.6 610,745 6,600 13 2.60 3,422 9.7 1.922 Austria 296 753 536 123.4 276.5 128,606 2,100 .. 17.21 669 7.2 1,697 Azerbaijan 27 22 259 0.3 ... 12 .. 2.15 1 316 Bangladesh 53 49 7 .. 1.5 .. 100 17 0.33 1 Belarus 155 299 342 33.2 ... 180 15 54.25 4 Bel~gium 160 793 541. 372.9 344.5 167,526 2,326 .. 27.52 342 8.0 1,769 10 Benin -2 439 45 .1 .6 15 129 0.93 1 .- 'O Bolivia 992 676 119 9.6 16.8 .. 120 . C: Bosnia and Herzegovina 152 243 11ill. . 20 19 0.13 0)~~~~~~~~~~~~~~~~-- --- E Botswana 27 155 25 .. 37.0 .. 15 15 0.1 4.. 0. -- -- - - - --- - - - - -. . . . .-. . o Brazil 43 43 343 13.7 44.1 690,196 5,000 ... 1,028 8.4 289 > Bulgaria 257 543 449 130.1 43.9 20.936 430 8 0.02 18 4.1 61 o Burkina Faso 1 35 12 . 1.3 .. 10 29 0.84 0O ----.------------ ----- ----- - . --- --.. .--------- Burundi' 0 220 30 .. . . 3 . 0.18 3: Cambodia 2 119 8 .. 1.1 6 104 0.30 2 0 4 .. . - .- ....---- o Cameroon 7 163 34 .. 3.3 -40 77 0.56 0 ---- ------... - .-- - .... - ---------- ---~ Canada 19 1,047 .. 259.4 390.2 893,745 12,700 12 0.00 5,055 8.4 1,911 Central African Republic 2 80 6 . 1. 7 .. 2 166 1.40 Chad 0 236 1 .. 1.3 .. 3 ... Chile 98 354 242 44.9 82.3 108,907 2,537 ... 141 7.8 360 China .. 339 293 61.1 15.9 1,539,843 22,500 7 0.14 184 5.4 46 Hong Kong, China 792 684 493 78.6 350.6 127,491 2,601 18 0.00 538 8.8 2,085 Colombia 46 544 282 13.6 35.4 108.209 878 .. 0.25 7 1 12.0 228 Congo, Dam. Rep. 3 386 2 .. . . 1 95 . Congo, Rep. 8 123 13 .. 3.5 ---. -------.--------- Costa Rica 91 816 231 19.1 149.1 .. 250 16 0.10 56 C6te dIlvoire 16 137 60 .. 6.1 .. 40 183 0.25 1 Croatia 114 340 293 38.0 80.7 .. 250 20 0.42 61 Cuba 118 353 250 .. 10.7 .. 60 ...2 Czech Republic 254 803 508 93.2 122.0 96,539 1.000 .. 11.60 273 9.3 453 Denmark 304 1,349 807 264.8 431.5 145,118 1,950 21 0.00 396 9.2 2,778 Dominican Republic 156 181 97 .. . . 55 18 0.00 8 Ecuador 43 418 218 25.7 21.7 .. 180 ... 11 Egypt, Arab Rep. 35 339 189 .. 22.1 41,443 450 9 0.14 11 2.4 36 El Salvador 28 478 201 49.7 19.1 .. 50 26 0.62 7 Eritrea .. 444 26 .. 1.6 .. 5 23 0.21 Estonia 1 76 1,096 591 90.3 152.9 .. 392 .. 0.57 80 Ethiopia 0 189 6 .. 0.9 .. 10 94 0.24 3 Finland 455 1.623 692 183.5 396.1 181,259 1,927 .. 10.62 498 7.8 1,835 France 201 950 628 45.2 304.3 759,726 8,500 20 0.0 1,641 8.7 1,916 Gabon 30 501 326 8.4 9.8 .. 15 35 1.26 1 Gambia. The 2 396 3 .. 11.5 .. 4 18 2.70 Georgia .. 556 474 2.7 ... 23 20 0.00 10 Germany 305 948 586 246.8 336.0 961,501 24,000 13 0.00 5.156 7.9 1,798 Ghana 14 710 118 .. 3.0 .. 30 36 0.38 1 Greece 23 478 488 .. 70.5 68,329 1,000 15 5.40 116 6.1 659 Guatemala 33 79 61 .. 11.4 .. 80 ... 12 Guinea .. 52 44 .. 3.7 .. 8 58 0.86 . Guinea-Bissau 5 44 ... ... 3 . .. Haiti 3 55 5 .. . . 6 ...1 Honduras 55 412 96 7.7 10.8 .. 40 15 0.61 4 5.10 Daily Radios Television' Personal Personai Internet Information and newspapers computers' computers communications Instalied In technology education Monthly off-peak expenditures Cable access charges' Sets subscribers Service Telephone per 1,000 per 1,000 per 1.000 per 1,000 per 1.000 Users provider usage charge Secure people people people people people Total thousands, charge $ $ servers % of GDP per capita 1998 2000 2000 2000 2000 2000 2000 2001 2001 2001 2000 2000 Hungary 46 690 437 157.6 85.3 66,841 1,480 13 13.59 127 8.7 431 India 48 121 78 38.5 4.5 161,014 5,000 10 0.18 122 3.8 18 Indonesia 23 157 149 0.2 9.9 46,483 2,000 12 0.20 60 2.2 16 Iran, Islamic Rep. 28 281 163 .. 62.8 250 1 Iraq 19 222 83.. ... . ... Ireland 150 695 399 176.9 359.1 45,138 784 .. 16.45 350 6.7 1,676 Israel 290 526 335 185.0 253.6 .. 1,270 11 0.18 301 7.4 Italy 104 878 494 1.0 179.8 720,911 13,200 .. 17.62 1,041 5.7 1,068 Jamaica 62 784 194 98.9 46.6 .. 80 49 ..5 Japan 578 956 725 147.4 315.2 1,918,000 47,080 17 27.67 5,153 8.3 3,118 317 Jordan 77 372 84 0.2 22.5 .. 127 24 0.42 2 -- - - -- --- - - -- - - - - - -- -- -- - - - - - - - - -- -- - --- - - -- -- - -- - -- - --- -- Kazakhstan .. 422 241 .. . . 100 1 0.02 8 Kenya -10 223 25 4. 9200 66 0.46 1 Korea, Dem. Rep. 208 154 54 . ,. . , ,. Korea, Rep. 393 1,033 364 177.4 237.9 405,492 1900 8 0.00 345 6.6 641 c - - -- - - - - - -- - - - - - - - - - - - - - - - - - - - Kuwait 374 624 486 .. 130.6 .. 150 32 0.00 4 C Kyrgyz Republic 15 11 49 .. . . 52 10 0.00 2 C-D .... ... --- ---- - -0 Lao PDR 4 148 10 2.6 .. 6 50 0.17 .. .. Latvia 247 695 789 76.7 140.3 .. 150 29 0.82 43 . Lebanon 107 687 335 5.9 50.1 . ,. 300 60 0.36 19 ..- . ...-..-... ~ ~ ~ ~ ~ - - .------- .-.... .. .... - ...... Lesotho -8 - 53.-16 .. . . . 4 12 0.17 ,. - - - - -- - - - - - -- - --.- -..- --- - - - - - - - - - - - - - - -- ---- -- -- - - -- - Liberia 12 274 25 .. . .... .. Libya 15 273 137 . ..10 108 0.20.. . Lithuania 29 500 422 89.2 64.9 225 45 0.38 43 Macedonia, FYR 205 2~82 . ..50 12 0.04---------- Madagascar_ ... 5- 216 24 .. 2.2 30 66 0.44 Malawi 3 499 3 0.0 1.2 15 .. 0.25 Malaysia -- - 158 420 168 .. 103.1 100,706 3,700 5 0.24 146 6.8 259 Mali 1 56 14 .. 1.2 19 70 0.72 1 Mauritania 0 149 96 .. 9.4 5 29 0.76 1 Mauritius 71 379 268 .. 100.5 87 23 0.38 12 Metdco 98 330 283~~~~~~~~~~~~~--- --- - 2-3-.1- -5 0,.6- 3583 --,2.-7-12 11--000--259 32 189- Mo Id o-v-a - 15-4 ---------7-5 8 -29 7- 118 1-4.-5 -5-3 33 6.17 3 Mongolia .. 154 65 .. 12.6 30 52 0.17 1 Morocco -- . 6 4 166. 12. 200 26 07 Mozambique 3 44 5 .. 3.0 30 . Namibia 19 141 38 .. 34.2 30 ...3 Nepal .. 39 ~~ ~~~~ ~~~~~~~~7 2.9 3.0 50 16 0.07 Netherlands 306 980 538 387.8 394.1 624,592 3,0 . 16.40 798 9.4 2,198 New Zealand ~~~~~207 97 52 44 302 1,85 830 11 0.00 609 13.6 1,771 Nica-ragua 30 27-6-1.8 8. --5-0 3 .5 Niger 0 121 37 .. 0.5 5 63 0.53 - ------ igerla 24 200 68 .. 6.6 200 44 0.57 1 Noway 585-95-66-1836-49.5-14,07 2-,200-- - ---11 ----2-0.64 369 6 9- ---2,4-45 Pakistan 30 105 131 0.1 4.2 134 13 0.20 --6 Panama 62 300 194 .. 37.0 90 ..29 Pilippines 82 161 144 13.1 19.3 66,336 2,000 24 0.00 - 68 3.8 38 Poland -------08-------- -- -52-3 - -- 4 0-0- -92.Y6,- - - 6-8-.-9 --------2-1-9-,4-1-6-- 2,00. 183 ----- -- 32 6 59 2-4-8 Puerto Rico 126 742 330 72.1 ..400 43 1.30 63 R ania 300 334 381 157.7 31.9 32,414 800 15 0.37 53 2.3 38 Russian Federation 105 ~~~-4-1-8 ---4-21 42-.-9- -4 2-4-,2-84 3,100---15 0.4 285 3. 63- 5.10 Daily Radios Television' Personal Personal Internet Information and newspapers computers' computers communications Installed In technology education Monthly off-peak expenditures Cable access charges' Sets subscribers Service Telephone per 1.000 per 1,000 per 1,000 per 1,000 per 1,000 Users provider usage charge Secare people people people people people Total thousands' charge $ $ servers % of GDP per capita 1998 2000 2000 2000 2000 2000 2000 2001 2001 2001 2000 2000 Rwanda 0 76 0 .. . . 5 38 0.36 1 Saudi Arab,ia 326- -- 326-- 264- -- 4. 60.2 -. 20 31 0.13 ierra Leone4 259 13 ..S ... 1 S"inap-or-e ___29 8 372 - 304 - 63.5 483.1 120,000 1,200 0.12 525 9.7 2,104 Slovak Republic 174___ 965 407 139.7 136.9 26,461 650 -- 9 0.54 79 7.5 291 Slvenia 171 405 368 161.1 275.9 26,091 300 29 0.31 - 102 5.2 476 Somalia 1 60 ~~~ ~~14 . .. South Africa 32 338 127 .. 61.8 317,298 2,400 9 0.33 521 8.6 256 318 Spain 100 333 591 11.8 142.9 271,837 5,388 17 0.00 938 5.1 731 Sri Lanka 29 208 ill 0.3 7.1 .. 122 6 - 0.05 - 6. at -- -- -_-- - -. . - ---- . - . - .-. - - . - -----. - .- .--- - - - -. . . - Sudan-- 26 464 273 0.0 3.2 .. 30 3 2.33 tO Swaziland 26 162 119 .. . . 10 12 0.24 1 'O Sweden 432 932 574 199.3 506.7 484,398 4,048 2 21.35 1,033 10.4 2,674 C: Switzerland 369 1,002 548 358.1 499.7 154,413 2,134 30.87 1,079 10.3 3,482 E Syrian Arab Republic 20 276 67 0.0 15.4 . 30 .- ..1 0 . ------.. - - .- ---.- ------.----------- --.----------- o Tajikistan 20_ ___141 326 3.- ...Ž....: .. -.. > Tanzania ---------- 4 281 20 .. 2.8 .. 115 69 0.79 f h a-ilaind- 64___ 235 284 2.5 '24.3_ 22_5,832 2,300 - 9 0.75 116 3.6 71 o Togo 4 265 32 .. 21.6 .. 10 8 0.75 Tnr-nidad and Tobago 123 532 340 61.8 100 1 0.37 12 a Tunisia 31 158 198 .. 22.9 1i6o 25 0.22 4 . .- 0 --.----.-.--------.---- ----- N Turkey 111 573. .49--.13.4--38.1 107,991 2.000 1 4.10 219 4.8 149 Turkmenistan -.. 256 196 . . - .. 6 ...- Uganda 2 17 - 27 0.0 2.7 .. 40 30 0.82 1 Ukraine 101 889 456 52.3 17.6 .. 300 7 0.04 44 United Arab Emirates 156 318 292 153.5 .. 735 13 0.00 31 United Kingdom 329 1.432 1 56.9 337.8 1,613,403 18,000 14 0.00 6,467 9.1 2.187 United States 213 2.118 854 252.1 585.2 13,426,248 95,354 5 3.50 78,126 8.1 2,926 Uruguay 293 - 603 530 125.9 104.9 .. 370 .. 37 Uzbekistan 3 456 276 3.0 ... 120 77 0.10 1 Venezuela. RB 206 294 185 40.2 45.5 92,655 950 27 .. 92 3.9 196 Vietnamn 4 109 185 .. 8.8 21,027 200 20 0.25 6 6.5 25 West Bank and Gaza . .. ... ... . .. 0 28 Yemen. Rep. 15 65 283 .. 1.9 .. 15 45 0.09 Yugoslavia. Fed. Rep. 107 297 282 .. 22.6 .. 400 0.13 7 Zambia 12 145 134 .. 6.7 .. 20 19 0.31 Zimbabwe 18 362 30 0.0 11.9 .. 50 46 0.34 1 Low Income 42 156 91 .. 5.1 9,337 33_____0.35 279 Middlie Income .. 362 275 52.6 33.1 87,311 17 0.33 5.294 Lower middle income 330 260 54.4 21.1 37,918 18 0.22 1,050 Upper middle income 91 463 319 _46.7 69.9 49,393 15 0.40 4,244 Low & middle income 265 185 42.5 20.1 96,649 23 0.34 5,573 East Asia & Pacific 306 252 52.4 21.7 51,943 20 0.20 940 Europe & Central Asia 102 448 380 45.4 14,648 15 0.37 1,694 Latin America & Carib. 71 413 269 20.1 43.6 19,086 ... 2,185 Middle East & N. Africa 33 277 172 .. 31.2 1,864 27 0.22 67 South Asi a 8 112 75 37.8 4.2 5,413 13 0 18 135 Sub-Saharan Africa 12 198 59 .. 9.2 3,695 36 0.53 552 High Income 285 1,280__ 641 173.8 392.7 269,821 11 1.46 115,650 Europe EMU 209 _811 56 127.2 267.3 65,863 . 13.00 11.741 a. Data are from the international Telecommunication Union's IITUI Wortd Telecommuntication Development Reoort 2001. Please cite the ITU for third-party use of these data. < ! VK.: . 5 .10 About the data Definitions The digital and information revolution has mented by other sources. In many countries * Daily newspapers refer to those published at changed the waiy the world learns, communi- mainframe computers are used extensively, and least four times a week. * Radios refer to radio cates, does business, and treats illnesses. thousands of users can be connected to a single receivers in use for broadcasts to the general New information and communications tech- mainframe computer; thus the number of per- public. * Television sets refer to those in use. nologies offer vast opportunities for progress sonal computers understates the total use of * Cable television subscribers are households in all walks of life in all countries-opportuni- computers. that subscribe to a multichannel television ser- ties for economic growth, improved health, Data on Intemet users are based on estimates vice delivered by a fixed line connection. Some better service delivery, learning through dis- derived from reported counts of Intemet service countries also report subscribers to pay televi- tance education, and social and cultural ad- provider (ISP) subscribers or calculated by mul- sion using wireless technology or those cabled vances. tiplying the number of hosts by an estimated to community antenna systems. * Personal The table includes indicators of the penetra- multiplier. Internet hosts are computers con- computers are self-contained computers de- tion of the information economy-newspapers, nected directly to the world-wide network, each signed to be used by a single individual. * Per- radios, television sets, personal computers, and allowing many computer users to access the sonal computers installed in education include Internet users-as well as some of the econom- Internet. This method may undercount the number PC shipments installed in education establish- ics of the inforrnation age-Internet access of people actually using the Internet, particu- ments, whether primary or secondary schools 319 charges, the number of secure servers, and larly in developing countries where many com- or universities. * Internet users are people with M spending on information and communications mercial subscribers rent computers connected access to the worldwide network. * Internet ser- 8 technology. to the Internet. Although survey methods used vice provider charge shows the costs associ- The data on the number of daily newspapers to estimate the number of Internet hosts have ated with 30 off-peak hours of dial-up Internet R in circulation and radio receivers in use are from improved in recent years, some measurement access per month. It is the monthly Internet sub- C statistical surveys carried out by the United Na- problems remain (see Zook 2000). For detailed scription rate plus extra charges once free hours C: tions Educational, Scientific, and Cultural Orga- analysis of Intemet trends by country, it is best have been used up. Some countries have peak C nization (UNESCO). In some countries defini- to use the original source data. rates that are higher. * Telephone usage charge 3 tions, classifications, and methods of enumera- The table shows both the off-peak ISP charge refers to the amount payable to the telephone (D tion do not entirely conform to UNESCO stan- and the telephone usage charge for being logged company for 30 off-peak hours of local telephone dards. For example, newspaper circulation data on to the Internet. Some countries have peak use while logged on to the Intemet. Excluded is should refer to the number of copies distributed, rates that are higher. the monthly telephone line tariff. If a special but in some cases the figures reported are the The number of secure servers, from the Intemet telephone tariff exists, it is used instead. number of copies printed. In addition, many coun- Netcraft Secure Server Survey, gives an indica- Some countries have peak rates that are higher. tries impose radio and television license fees tion of how many companies are conducting * Secure servers are servers using encryption to help pay for public broadcasting, discourag- encrypted transactions over the Internet. technology in Internet transactions. * Informa- ing radio and television owners from declaring The data on information and communications tion and communications technology expendi- ownership. Because of these and other data technology expenditures cover the world's 55 tures include external spending on information collection problems, estimates of the number largest buyers of such technology among coun- technology ("tangible" spending on information of newspapers and radios vary widely in reliabil- tries and regions, accounting for 98 percent of technology products purchased by businesses, ity and should be interpreted with caution. global spending. households, govemments, and education insti- The data for other electronic communications tutions from vendors or organizations outside and information technology are from the Inter the purchasing entity), internal spending on in- national Telecommunication Union (ITU), the formation technology ("intangible spending on Internet Software Consortium, Netcraft, and the internally customized software, capital depre- World Information Technology and Services Alli- ciation, and the like), and spending on telecom- ance. The ITU collects data on television sets munications and other office equipment. and cable television subscribers through annual .._-__ _ questionnaires sent to national broadcasting Data sources authorities and industry associations. Some countries require that television sets be regis- The data on newspapers and radios are com- tered. To the extent that households do not reg- piled by UNESCO's Institute for Statistics. The ister their televisions or do not register all of data on television sets, cable television sub- their televisions, the data on licensed sets may scribers, personal computers, Internet users, understate the true number. and Intemet access charges are from the ITU. Because of different regulatory requirements They are reported in the ITU's World Telecom- for the provision of data, complete measurement munication Development Report 2001, Chal- of the telecommunications sector is not possible. lenges to the Network: Intemet for Develop- Telecommunications data are compiled through ment (1999), and the World Telecommunica- annual questionnaires sent to telecommunica- tions Indicators Database (2000b). The data tions authorities and operating companies. The on secure servers are from Netcraft data are supplemented by annual reports and (www.netcraft.com/). The data on PCs installed statistical yearbooks of telecommunications in education and on information and communica- ministries, regulators, operators, and industry tions technology expenditures are from Digital associations. In some cases estimates are Planet 2002: The Global Information Economy derived from ITU documents or other references. by the World Information Technology and Ser- The estimates of personal computers are vices Alliance (WITSA), which uses data from derived from an annual questionnaire, supple- the International Data Corporation. 5.111 Science and technology Scientists Technicians Science Science Expenditures High-technology Royaity and Patent Trademark and In and a'nd for exports license fees applications applications engineers R&D engineering technical R&D filied, filed In R&D students journal articles % of total % of per million per million tertiary level $ manufactured Receipts Payments Non- people people students % of GNI millions exports $ millions $ millions Residents residents Total 1990-2000' 1990-2000' 1987-1997' 1997 1989-2000' 2000 2000 2000 2000 1999 1999 1999 Afghanistan ..10 0 . . . Albania 19 10 .. 2 1 . .. 0 89,519 2,035 Algeria -.---- ----.---.-- ------- 58 .... ..139 ..15 4 ...34 248 4,252 Angola . . 24 2 ... .3 2 Argentina 711 156 28 2,119 0.48 767 9 13 458 899 5,558 65,243 Armeni'a 1,308 238 29 178 0.18 4 5 . .. 109 40,163 3,273 Australia 3.320 792 24 11,793 1.71 2,734 15 343 999 9,537 53,818 58,789 Austria ~~~~~~1,605 801 33 3,432 1.64 6,600 14 162 547 3,075 159,046 18,697 Azerbaijan 2,735 184 37 71 6 4 -.0 40,042 2,091 320 Bagaeh51 32 47 130 4 0 0 4 32 184 Belarus ~~~~~~2,296 271 48 548 0.57 180 4 1 2 1,002 40,790 5,511 (0 - - - - - - - - -- - -- - - - - - - - - Belgium 2,307 2,195 41 4,717 1.55 15,274 10 783 900 1,786 119,195 m) Benin 174 53 18 19 0 01 V Bolivia 171 154 30 27 -- 158 ..2 5 C~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ --- Bosnia and Herzegovina . ..8 ... ...23 41,201 4,009 0) E Botswana 37 33 ... .0 6 0 54 o Brazil 168 58 27 3,908 0.77 5,979 19 126 1,415 1,957~ 50,338~ a) ----------------- > Bulgaria 1.289 466 27 896 0.00 ..4 10 302 42,650 8,776 o Burkina Faso 17 16 18 20. - Burundi 21 32 20 11 0.31 0 0 Cambodia 13 3 1,303 N - - - - - ~~~~~~~~~~~~~~~~~~~~~~~~~~-- -- - - - ---- CD Cameroon -- 45 73 Canada 3,009 1,171 16 19,910 1.68 32,702 19 1,374 3,267 5.197 64,580 40,365 Central African Republic 47 27 30 5 0 02..- Chad -- -- 14 2 Chile 370 - 4 2 850 0.56 100 3 102 44 - China - ----459 187 43 9,081 0.06 _40,837 19 80 1,281 146 52,202 165,122 Hong_Kong, China 93 100 36 2 080 5,155 23 -- 42 5,998 19,885 Colombia 28 208 328 7 4 71 68 1,615 12,788 Congo, Dem. Rep. - 15 Congo, Rep. 34 37 48 8 --- Costa Ri'ca 533 20 73 0.06 1,856 --1 31 0 9 105 C6te dIlvoire 31 31 --0 11 . . - Croatia 1,494 708 30 544 1.18 272 8 267- 4002 -9 Cuba 1,611 1,121 16 148 ..- . 111 40,928 4,307 Czech Republic 1,317 682 28 2,024 1.27 2,089 8 44 82 618 44,691 17,923 Denmark 3,240 2,643 25 3,950 1.94 6.527 21 - 3,339 158,225 11,537 Dominican Republic 35 6 - ..- 30 -- - Ecuador 140 17 27 39 - - 27 6 -- 62 15 475 - Egypt, Arab Rep. 493 366 12 1,108 1.93 3 0 59 401 536 1,146 3,009 El Salvador 19 303 59 3 2.20 39 6 2 20 - - Eroitrea 3 Estonia 2,164 540 27 222 0.78 830 30 2 8 14 41,742 4,660 thiopia - 26 103 -- 0 0 0 01 F4inland - - 39 3,897 10,532 27 1,138 547 2,644 156,389 9,464 France 2,686 2,878 37 26,509 2.21 59,397 24 2,310 2,051 20,998 117,457 100,560 Gabon . .. 29 16 --- Gambia, The - ..25 ..0 17 . .. 0 79,703 - Georgia - - - - 39 - 128 .. .. -. -. ~~9273 41,687 2.574 Germany 2,873 1,362 47 36,233 2.31 82,958 18 2.821 5,454 74,232 146,529 85,770 Ghana .. -. 32 78 .- 32-14-0 0 80,028 -- Gree-ce----- 1,04 ----314 2 ,2 .8 42 95 23 7 1-1-9,7-0 2 -8-,7 1'4_ Guatemala -- - - 15 0.16 68- 8- - - -7 224 8,953 Guinea - .. 34 3 - . 0 - Guinea-Bissau - 0 3 -. . -- . 0 1 Haiti --- .2 --- -. 1 5 1,456 Honduras . 24 10 --6 2 0 10 8 148 5045 Łcieme ai-d {w~ 5.11 Scientists Technicians Science Science Expenditures High-technoiogy Royaity and Patent Trademark and In and and for exports license fees applications applications engineers R&D engineering technicai R&D filied filied In R&D students journai articles % of total % of per million per million tertiary level $ manufactured Receipts Payments Non- people people students % of GNI millions exports $ millions $ millions Residents residents Total 1990-20001 1990-20001 1987-19971 1997 1959892000r 2000 2000 2000 2000 1999 1999 1999 Hungary_____ - __ 1,249 485 32 1,717 0.71 6,402 26 112 257 787 44.187 13,862 India 158 115 25 8,439 0.62 1.245 4 83 306 14 38,348 66,378 Indonesia ... 39 123 0.07 5,698 16 . .. 0 42,503 Iran, Islamic Rep. 590 174 39 332 0.49 38 2 0 0 366 177 Iraq ... 41 35... ... Ireland 2,132 589 31 1,118 1.54 31,278 48 504 7,899 1.226 119,569 4,518 Israel 1,570 518 49 5,321 3.69 7,418 25 500 349 2,728 46,686 8,759 Italy 1.322 806 30 16,405 1.04 19,306 9 563 1,198 9,613 118,647 44,906 Jamaica ... 64 49 ..1 0 6 41--- ----- Japan 4,960 663 21 43,891 2.80 127,368 28 10,227 11,007 361,094 81,151 121.861 321 Jordan ... 26 177 .. 53 8 . .. 0 0 Kazakhstan.. . 20 119 0.33 183 10 0 11 1,358 40,470 3.898 N Korea, Dem. Rep. .. .. . 0 ... 0 40,391 2,188 Korea, Rep. 2.139 574 32 4,619 2.70 53,950 35 688 3,221 56,214 76,913 87,332 c Kuwait 214 65 29 173 .. 35 1 0 0 . . . Kyrgyz Republic 574 48 14 9 0.20 5 5 1 1 60 40,131 2,428 C Lao PDR 22 0 -.2 - . . --. .- 609 VO ---- --- -- - - - - --- - - --- - - --- - .. ...3 Latvia 1,090 301 23 141 0.40 42 4 2 12 94 90,182 5,959 Lebanon ... 30 81 -.-- .--. --.------- ---- - - --- - -------- ~ ~ ~ ~ ~ ~ 0 Lesotho 19 2 ... . 12 0 0 80,315 9 Liberia . 18 1 ... ... 0 41,120 1,216 E Libya 361 493 .. 12 . . . . . .. Lithuania 2,031 632 31 198 . 95 4 0 12 86 90,331 6,284 Macedonia, FYR 387 29 47 49 0.35 7 1 3 6 64 89,361 3,921 Madagascar 12 37 25 . 0.18 4 3 1 11 9 41,237 510 Malawi.. . 27 38 ... . . 1 80.430 665 Malaysia 154 44 27 304 0.42 39,964 59 0 0 179 6,272 - Mauritania ... 41 2 ... .0 0 . Mauritius 360 157 14 2 0.17 12 1 0 1 3 12 Mexico 213 73 32 1,915 0.36 31,053 22 43 407 468 49,532 46,146 Moldova 334 1,665 52 ill 0.79 5 3 1 2 256 40,199 3,290 Mongolia 468 92 24 13 0.07 .. . I . 0 41,240 2,800 Morocco . .. 41 271 .. 604 12 38 210 0 3,649 3,281 Mozambique ... 42 9 ..1 2 .. 0 1,308 Myanmar ... 56 3 ... .0 0 -..--- --- Namibia ... 4 7 ... .6 3 Nepal ... 13 35 ..0 0 . . . Netherlands 2,490 1,464 39 11,008 2.01 44,439 35 2,176 2,565 6,395 117,118 New Zealand 2,197 732 20 2,308 1.21 365 10 49 308 1,650 45,990 1.6.576 Nicaragua ... 33 11 ..2 5 .9 136 Niger ... 32 25 ..0 5 . Nigeria . .. 42 405 .. 17 13 . Norway 4,095 1,836 26 2,501 1.68 1,895 17 131 391 1,731 48,931 13,588 Oman ... 13 53 .. 43 4 . . . Pakistan 78 14 32 232 .. 30 0 6 28 .. 7,762 Panama ... 29 37 ..0 0 0 30 Papua New Guinea ... 10 31 .. 34 42 Paraguay ... 20 4 ..5 3 203 2 . Peru 229 1 34 63 0.00 35 3 0 57 48 944 Philippines 156 22 14 159 0.21 8,465 59 7 197 144 3,217 10,070 Poland 1,460 463 28 4,019 0.73 838 3 34 554 2,286 45,194 25,054 Portugal 1,583 166 36 1,085 0.63 1.045 5 21 255 133 159.533 15,782 Puerto Rico . .. .. . . .. .. Romania 1.393 584 21 751 0.79 445 6 3 45 1,069 90,235 9,060 Russian Federation 3,397 550 50 17,147 1.08 3,082 14 91 31 20,131 47,745 28,966 5.11 tst Technicians Science Science Expenditures High-technology Royalty and Patent Trademark ad In and and for exports license fees applications applications engineers R&D engineering technical R&D filed' filed In R&D students ~~journal articles % of total % of per million per million tertiary level $ manufactured Receipts Payments Non- people people students % of GNI millions esports $ millions $ millions Residents residents Total 1.990-2000, 1 990-2000, 19187-19971 1997 1989-2000' 2000 2000 12000 2000 1.999 199 1.99 Rwanda .. 6 28 5 .. . .0 1 0 4 129 Saudi Arabia . .. 17 613 22 0 0 0 72 1,144 Senegal 2 3 21 58 .. 34 13 2 5 Sierra Leone__ . .. 17 8 ...0 72,449 1,112 Singapore 2,182 283 . 1,164 1.13 73,643 63 374 51,121 15,753 Slovak Republic 1,706 790 40 950 0.98 382 4 16 58 222 42,857 9,913 Slovenia 2,161 877 26 517 1.47 368 5 12 49 292 90,680 7,420 Somalia . .. 18 1 . . . South Africa 992 303 29 1,927 0.62 21 1 62 142 116 26,354 322 Spain 1,562 456 31 11,210 0.84 6,727 8 403 1,681 3,394 159,696 85,742 - Sri Lanka 188 45 34 61 . 109 3 . 0 41,263 Sudan 16 43 .. 0 0 0 0 2 80,424 1,281 cu Swaziland ... 17 6 ...0 36 0 40,673 872 C,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~---- -- --- V Sweden 4,507 404 38 8,219 3.76 25,739 22 1,275 900 9,122 155,929 15,562 C~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~---- ------------ Switzerland 3,058 1,399 34 6,935 2.55 14,260 19 ..6,412 155,991 11,061 5,- - - - - - E Syrian Arab Republic 29 24 23 57 ..2 1 0 .- --- - - -- - - o Tajikistan 660 - 17 29 -38 -40,103 2,270 CD-,- -- .- - -------- - ---- ---- > Tanzania 37 89 6 6 0 4 0 14,467 2 o Thailand 102 75 18 356 0.10 13,949 32 9 710 477 4,594 22,439 V - .--.- . ---...--- . ~ ~-- --.--- -------- - - - - .. o Togo 102 65 35 7 0 0 0 1 Trinidad and Tobago __ 145 258 58 41 0.14 11 1 0 0 0 41,238 1,196 O Tunisia 124 57 33 188 0.30 124 3 0 - . - . . . . . . - - - .... - - - - - - - - . - - - - -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-- -- - Turkey 303 38 45 2,116 0.48 1,084 5 325 43,508 26,372 Turkmenistan ..7 8 5 44 40,070 742 Uganda _25 15 17 46 0.76 2 10 .. 0 0 80,421 Ukraine 2,121 __ 595 42 2,163 0.97 ..1 663 5,415 42,858 9,578 United Arab Emirates . .. 24 127 .. 0 24,218 United Kingdom 2,678 1,014 34 38,530 1.81 72,616 32 7,361 6,126 31,326 161,549 70,880 United States 4,103 19 166,829 2.55 197,033 34 38,030 16,100 156,393 138,313 260,766 Uruguay .32 110 20 2 0 11 27 525 9,741 Uzbekistan 1,754 312 261 .. . .. . 769 41,596 3,256 Venezuela, RB 194 32 26 429 0.34 80 3 0 0 201 2,323 Vietnam 274 . .. 106 ...37 42,175 6,518 West Bank and Gaza Yemen,_Rep. 5 10 ... 0 . . Yugoslavia, Fed. Rep. 2,389 515 47 492 1.34 .. . .340 41,744 5,336 Zambia 16 23 ... ...5 87 959 Zimbabwe . .. 24 100 .. 9 2 ..1 80,167 14 -. -- -- -- - -- - - - Low Income . .. 28 13,565 .. 5,766 7 105 1,108 7,027 1,342,958 Middie Income 818 255 39 61,733 .. 150,982 16 1,768 9,956 90,268 1,578,263 Lower middle income 787 229 41 32,967 .. 45,591 14 526 3,265 25,996 931,209 Upper middle income 593 218 33 28,767 0.99 105,391 17 1,242 6,691 64,272 647,054 Low & middle Income 35 75,298 .. 156,748 16 1,873 11,064 97,295 2,921,221 East Asia-& Pacific 496 193 43 14,817 0.88 100.485 25 784 5,409 56,541 298.643 Europe & Central Asia 2.212 478 44 34,905 0.83 15,567 10 313 1,753 35,952 1,373,268 Latin Amnerica & Carib. _287 .. 30 10.075 0.58 40,497 16 501 2,666 3,618 284,873 Middle East & N. Africa . .. 29 3,106 ... 1 106 614 1,008 6,364 South Asia 158 114 24 8,896 0.62 .. 3 87 338 14 79,611 Sub-Saharan Africa . .. 29 3,499 ... 8 82 283 162 878,462 High Income 3,344 . 25 437,339 2.30 847.043 22 70,321 62,988 713.112 3,256,586 Europe EMU 2,141 951 38 117,764 1.97 277.585 16 11,019 23,422 123,795 1,652,255 a. Other patent applications siled in 1999 include those filed under the auspices of the African Intellectual Proprty Organization (30 by residents, 41,068 by noni-residents, African Regional Industrial Property Organization (7 by residents, 40.720 by nonresidents), European Patent Office (55,947 by residents. 65,869 by nonresidents) and Eurasian Patent organization 1366 by residents, 41.476 by nonresidents). The original information was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no liabiiity or responsibility with respect to mhe transformation of these data. b. Other trademark applications filed in 1999 include those filed under the auspices of the African Intellectual Property Organization (17301, African Regional Industrial Property Organization (15), and the Office for Harmonization in the Internal Market (41,255). The original information was provided by the World intellectual Property Organization (WIPO). The international Bureau of WIPO assumes no liability or responsibility with respect to the transformation of these data. c. Data are for the latest year available; see Prnmaiy data documentation for the year. KC.L- 5.11 E About the data Definitions Technological innovation, often fueled by ture divided by total sales) for groups of products * Scientists and engineers In R&D are people governmentied research and development (R&D), from six countries (Germany, Italy, Japan, the trained at the tertiary level to work in any field has been the driving force for industrial growth Netherlands, Sweden, and the United States). of science who are engaged in professional around the world. The best opportunities to improve Because industrial sectors characterized by a few R&D activity. - Technicians In R&D are people living standards---including new ways of reducing high-technology products may also produce many engaged in professional R&D activity who have poverty-ill comie from science and technology, low-technology products, the product approach received vocational or technical training in any Science is advancing rapidly in virtually all is more appropriate for analyzing international branch of knowledge or technology. Most such fields, particularly biotechnology, and playing a trade than is the sectoral approach. To construct jobs require three years beyond the fir'st stage growing economic role: countries unable to ac- a list of high-technology manufactured products of secondary education. * Science and engi- cess, generate, and apply relevant scientific (services are excluded), the R&D intensity was neering students include students at the ter- knowledge will fall even further behind. And there calculated for products classified at the three- tiary level in the following fields: engineering, is greater appreciation of the need for high"ualiWy digit level of the Standard International Trade Clas- natural science, mathematics and cornputers, scientific input into public policy issues such as sification revision 3. The final list was determined and social and behavioral sciences. * Scien- regional and global environmental concems. at the four- and five-digit levels. At these levels, tific and technical journal articles refer to sci- Science and technology cover a range of is- since no R&D data were available, final selection entific and engineering articles published in 323 sues too complex and too broad to be quanti- was based on patent data and expert opinion, the following fields: physics, biology, chemistry, fled by any single set of indicators, but those in This methodology takes only R&D intensity into mathematics, clinical medicine, bio-medical re- C)0 the table shed light on countries' 'technologi- account. Other characteristics of high technolog search, engineering and technology, and earth cal base"-the availability of skilled human re- are also important, such as know-how, scientifi c and space sciences. * Expenditures for R&D 0 sources, the number of scientific and technical and technical personnel, and technology embod- ar urn n aialepniue nce articles published, the competitive edge coun- led in patents; considering these characteristics atv,sseaicatvt ht nrae h tries enjoy in high-technology exports, sales and would result in a different list. (See Hatzichronoglou stc fkolde ncue r udmna purchases of technology through royalties and 1997 for further details.) Note that the R&D for an ple eerhan xeietldvl licenses, the numiber of patent applications filed, high-technology exports may not have occurred in opetwr edigtDe eics rdcs and trademarks issued. the reporting country. orpoessETHg-ehoo~ xot r The UitedNatios Edcatinal, cienific The ount of cientfic nd tchnicl jornal products with high R&D intensity. They include c and Cultural Organization (UNESCO) collects data articles include those published in a stable set hihtcnlg9rdcs uha narsae computers, pharmaceuticals, scientific instru- on scientific and technical workers and R&D of about 5,000 of the world's most influential ments, and electrical machinery. * Royalty and E expenditures from member states, mainly scientific and technical journals, tracked si'nce license fees are payments and receipts be- through questionnaires and special surveys as 1985 by the Institute of Scientific Information's tween residents and nonresidents for the au- well as from official reports and publications, Science Citation Index (SCI) and Social Science thorized use of intangible, non-produced, non- supplemented by inforrmation frorn other national Citation Index (SSCI). (See Deflnitions for the financial assets and proprietary rights (such and international sources. UNESCO reports ei- fields covered.) The SCI and SSCI databases as patents, copyrights, trademarks, industrial ther the stock of scientists, engineers, and tech- cover the core set of scientific joumals but may processes, and franchises) and for the use, nicians or the number of economically active exclude some of regional or local importance. through licensing agreements, of produced persons qualified in those fields. UNESCO They may also reflect some bias toward English- originals of prototypes (such as manuscripts supplements these data with estimates of the language journals, and films). * Patent applications filed are ap- number of qualified scientists and engineers by Most countries have adopted systems that plications filed with a national patent office for counting the numiber of people who have com- protect patentable inventions. Under most patent exclusive rights for an invention-a product or pleted education at ISCED (International Standard legislation, to be protected by law (patentable), process that provides a new way of doing some- Classification of Education) levels 6 and 7; quali- an idea must be new in the-sense that it has not thing or offers a new technical solution to a fled technicians are estimated using the number already been published or publicly used; it must problem. A patent provides protection for the of people who have completed education at ISCED be nonobvious (involve an inventive step), in the invention to the owner of the patent for a lim- level 5. The data are normally calculated in terms sense that it would not have occurred to any spec- ited period, generally 20 years. * Trademarks of full-time-equivalent staff, the quality of whose ialist in the industrial field had such a specialist are distinctive signs that identify certairn goods training and education varies widely. Similarly, been asked to find a solution to the problem; or services as those produced or provided by a R&D expenditures are no guarantee of pro- and it must be capable of industrial application, specific person or enterprise. gress; governments need to pay close attention in the sense that it can be industrially manufac -_____________________ to the practices that make them effective. tured or used. Information on patent applications The data on science and engineering students filed is shown separately for residents and non- Data sources refer to those enrolled at the tertiary level, nor- residents of the country. The Worid Intellectual The data on technical personnel, science and mally after their successful completion of edu- Property Organization estimates that at the end engineering students, and R&D expenditures cation at the secondlary level. These data are of 1998 about 4 million patents were in force in are from UNESCO's Stafistical Yearbook. TheI reported to UNESCO by national education au- the world. data on scientific and technical journal articles I thorities. (For further details on UNESCO educa- A trademark provides protection to owner by are from the National Science Foundation's Sci- tion surveys see About thre dafa for table 2.12.) ensuring the exclusive right to use it to identify ence and Engineering Indicators 2000. The in- The methodology used for determining a goods or services or to authorize another to use it formation on high-technology exports is from country's high-technology exports was developed in return for payment. The period of protection var- the United Nations' Commodity Trade by the Organisation for Economic Co-operation and ies, but a trademark can be renewed indefinitely (COMTRADE) database. The data on royalty and Development in collaboration with Eurostat. beyond the time limit on payment of additional license fees are from the International Mon- Termed the 'product approach" to distinguish it fees. The system helps consumers identify and etary Fund's Balance of Payments Sfafisfics from a 'sectoral approach,' the method is based purchase a product or service because its nature Yearbook, and the data on patents and trade- on the calculation of R&D intensity (R&D expend6 and quality, indicated by its unique trademark, marks are from the World Intellectual Property meets their needs. Organization's Industrial Property Statisfics. K i Ii Globalization and growth More globalized economies grew faster In the 1990s Average annual growth in GDP per capita Global integration has helped countries with a combined 6 population of 3 billion-but CD countries with a combined 4 population of 2 billion 3 have fallen behind. 2 1 0 -1 -2 Less globalized High-income More globalized 325 developing countries developing countries countries 0 Source: World Bank 2002c. CD CD 0 CD What is globalization? In broad terms it reflects the growing links between people, communities, and economies around the world. These links are complex-the result of lower communications and transport costs and greater flows of ideas and capital between high- and low-income countries. Integrating with the world economy can be a powerful spur to growth and poverty reduction. Low-incorne developing countries with about 3 billion people have switched from exporting primary commodities to exporting manufactures and services. Between the mid-1970s and 1998 manufactures increased from 25 percent of their exports to more than 80 percent. And per capita incomes in developing countries increased by about 5 percent a year in the 1990s, with the number of poor people declining by 125 million between 1990 and 1999. But in developing countries with a combined population of about 2 billion, overall economic growth declined in the 1990s and poverty has been rising, in part because these countries trade less with the rest of the world and often suffer from conflicts, corruption, and poor governance. Their poverty goes beyond the loss ofjobs and income-to limited voice and poor access to health and education, all needed for the climb out of poverty. It is widely thought that economic integration will lead to cultural and institutional homogenization. But "globalizers" such as China, India, Malaysia, and Mexico have maintained rich cultural traditions. What is true is that global trade imposes standardization, making it impor-tanit for trade and investment agreements to respect local customs, social policies, and labor and environmental standards. Three waves of The second wave, from 1945 to 1980, was also characterized by globalization The share of manufactured exports rose dramatically lower trade barriers and transport In developing countries costs. Sea freight charges fell by a Manufactures as a share of merchandise exports, There have been three waves of 1980 and 1999 trade regained the ground it lost globalization since 1870. Since the 1980 1999 during the Great Depression. latest wave started in the 1980s many developing countries have 100 Spurring the third wave of integra- evolved from exporting primary r 80 tion has been further progress in commodities to exporting manufac- transport (containerization and air- tures and services. 60 freight) and communications tech- The first wave of global integration, 40 |ll costs associated with satellites, between 1870 and 1914, was led 20 fiber-optic cable, cell phones, and by improvements in transport tech- 0 I C the Internet). And along with declin- 326 nology (from sailing ships to East Europe Latin Middle South Sub- ing tariffs on manufactured goods steamships) and by lower tariff bar- Asia and America East Asia Saharan in high-income countries, many o riers. Exports nearly doubled to and Central and and North Africa developing countries lowered barri- o about 8 percent of world income Pacific Asia Caribbean Africa ers to foreign investment and Source: World Development indicators 2002 database. improved their investment climates. CL a) 0 tD N 0 0 S - I S S I I * ALt Globalization and In 1995 inequality between countries was less than half what it had been poverty Poverty reduction Is closely related to growth In Uganda, India, Vietnam, and China in 1960. And within economies peo- ple's incomes are generally more Annual rates of GDP growth and poverty reduction, 1992-98 equally distributed in those that are How has the third wave of globaliza- more integrated. But while inequality tion affected growth, poverty, and * Growth of GDP per capita * Poverty reduction has declined in some globalizing inequality? Growth for the new glob- d 10 countries, overall for the globalizers alizers has accelerated, to rates inequality within countries has even higher than those for rich 8 increased, mainly because of the countries, so they are catching up. rise in inequality in China-between 6 rural and urban areas and between And growth is good for poor people. I provinces with urban agglomerations Studies show that their income rises 4 f f and those without. one for one with overall growth (for f fl every percentage point increase in 2 * Although globalization leads to overall per capita growth, poor peo- higher wages and employment in ple's incomes increase at the same 0 some sectors, some people still rate). In developing economies the Uganda India Vietnam China lose out, especially in the short run. number of people in absolute Note: Povery reduction data for India are for 1993/94 to 1999/2000. So it is important to identify who Source WorldBanhdata poverty declined by 125 million i has been left behind-and to offer between 1990 and 1999. support programs for the neediest. Higheir returns on workers. But others who have not had good access to health and edu- education Globalizers had lower levels and faster declines In cation services may fall behind, If Infant mortality than nonglobalizers this gap in incomes and access Infant mortality rate, 1990 and 2000 persists, globalization can lead to Economic integration raises the greater inequalities in society. return on education and highlights * 1990 * 2000 the importance of improvements in o 160 Globalizers Nonglobalizers Affordable access to health and the delivery of social services. education services thus helps to 120 ensure that the poor benefit from Economic integration with global _ growth. Some of the more global- markets encourages people to 80ized developing countries reduced invest in education. Why? Because infant mortality by more than 30 as countries open to the global D percent during the 1990s, economy, new technologies and r r 0 compared with an average decline production processes are of about 12 percent for all develop- intrduce, rquirng amor skiledeaia \g^\ xP q9 +o b ing countries. Although infant mor- 327 workforce. A 'skill premium' (the , ' tality rates have also declined for extra pay that skilled workers get some nonglobalizing countries, for o relative to unskilled workers) then others the rates are increasing to 3 raiscs the incomes of some of the Source: World DOeelopment Indicators 2002 database. extremely high levels. 0 (5 CD (D 0 (5 3: 0) i__~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~i Culture and countries whose economies have And there are risks of cultural dilu- Economic integration may also stagnated or are in decline. In tion. More diverse societies have pose threats to the environment. environment Africa, for example, where countries broader sources of information and As production and consumption have become more dependent on dynamic business networks that increase, both globalizers and non- primary commodity production and improve productivity, competition, globalizers face environmental chal- These tangible effects on economic exports, there is a two-way relation- and growth, especially under demo- lenges. But as incomes increase, growth and living standards are not ship between conflict and economic cratic governments that allow peo- countries can use the additional the only outcome. Globalization performance. As economic perfor- ple to make choices and express resources to tackle environmental also affects culture and the environ- mance worsens, the risk of conflict preferences. But even in cultures issues. And environmental improve- ment. The greater economic power rises-and as performance that are very resilient, the traditions ments can be made at low cost. Fo: of the newly globalizing economies improves, the risk of conflict handed down from one generation example, water quality can be gives them more interest and influ- diminishes. to another can be weakened by the upgraded by installing water filters, ence in the international arena. spread of ideas. goods, and adver- which can often remove close to Their rising incomes also reduce tising from abroad. half the pollutants. the risk of conflict, compared with Trade tariffs and Trade-related production is a major source of jobs and income for the the poor Exports produced by poor people in developing poor, but the goods they produce countries face high tariffs face the highest tariffs in Effective tariffs by income group, 1997-98 developed countries. reducing their Trade helps reduce poverty, but access to world markets. The tar- high tariffs reduce access to world n 16 iffs faced by the extreme poor markets. CD 14 (those living on less than $1 a day) 12 and the poor (those living on $1-2 Low-income developing countries 10 a day) are about twice as high as often depend on agriculture and those faced by the nonpoor. labor-intensive manufactures, which 8 account for about 70 percent of the 6 exports of least developed coun- 4 tries. Their agricultural exports help I reduce rural poverty, and exports of 2 328 textiles and clothing tend to reduce 0 urban poverty. Extreme poor Poor Nonpoor (income (income (income .o <$1 a day) $1-2 a day) >$2 a day) Source: World Bank Global Economic Prospects and the Developing countries 2002. E C. Cr 0) 0 C tN I -~~~~ 0 I = iIA SI-L Barriers to trade Nontariff barriers also limit imports The share of imports covered by high tariffs has of particular goods. They include decilned In some countries, but remains high in others quotas, prohibitions, licensing ______________________________________________ schemes, export restraint arrange- Countries regulate imports by apply- ing a combination of tariffs and Share of tariff lines with peak tariffs ments, and health and quarantine measures. And large subsidies to other measures. Even when aver- * Early 1990s * Late 1990s and 2000 agricultural producers in high- age tariffs are low, high or peak tar- n~ 100inoeeooisdsotarcl iffs (rates exceeding 15 percent) is O tural commodity trade by reducing may be placed on imports of "sen- C 80 may e plced n imortsof "en-their agricultural imports and the sitive commodities.' often the 60 ability of developing country agricul- goods produced by the world's poor- tural producers to export. est people. In the European Union 40 and Japan tariff peaks are common 2pn for agricultural products: in the Unite States,bfor labor-intensive 0 1 * * tive, -Everything but Arms," phases textiles and clothing. For many *.g A;> 0° 6fi i} R Cv<° 02 in duty-free and quota-free access developing countries tariff peaks ,vfflv ,,Q ,; Tanzania 33.9 24.2 50.8 37.9 ..-1.8 0.2 3.3 0.0 2.1 o Thailand 66.1 107.2 133.0 ---- 211-I.4- -9'9.5 2.8 13.5 11.3 3.0 2.8 - m ~~~ ~~~~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~---- . .- ..-- - -- -- - - . .-- -- .- - - . .- - - - - - .--- - - --- -- - - - o Togo 52.1 73.0 92.6 122.0 -22.3 -1.1 9.6 12.6 1.1 4.2 Trinidad and Tobago 65.9 107.4 130.7 243.4 43.7 1.6 11.4 13.6 3.1 11.9 --- -- ---. --- - - - - - - - - - - - -- - - -- .- . - . . .. o Tunisia 73.8 74.0 162.1 180.1 9.3 -0.2 9.5 9.3 0.6 3.9 0 -- ----.-.- --.--- ----- - - ---- ---- CN Turkey 23.4 40.0 44.5 77.1 ..7.5 4.3 9.3 0.5 0.9 Turkmenistan .. 93.1 .. 76.6 2.4 . Uganda 8.4 32.9 12.0 51.1 7.0 1.1 5.0 0.0 3.5 Ukraine .. 89.7 .. 152.9 6.3 .. 9.5 ..1.9 United Arab Emnirates 93.5 119.9 146.6 . .. United Kingdom 41.3 43.9 104.7 126.7 55.6 3.9 35.4 125.1 7.4 38.7 United States 15.8 20.7 -. - 99.1 5.0 5.7 16.9 2.8 5.1 Uruguay 32.7 29.2 ...... ..85.0 103.6 90.9 4.4 12.7 14.7 0.0 1.5 Uzbekistan .. 75.9 .. 120.0 0.5 . Venezuela. RB 51.1 39.7 90.8 94.0 12.1 3.9 49.9 12.3 1.7 4.0 Vietnam 79.7 96.0 132.5 ... 18.7 10.8 ..4.1 West Bank and Gaza ... .0.5 Yemen, Rep. 46.9 83.0 90.0 134.9 9. 1 _16.2 6.9 2.7 3.3 Yugoslavia, Fed. Rep. 64.2...... Zambia 76.9 54.3 102.3 97.0 -45.1 2.9 64.7 ..6.2 Zimbabwe 40.7 44.9 74.5 89.5 139.6 6.1 1.7 .. 0.1 Low Income 26.7 41.3 ...3.0 4.8 0.5 1.6 Middle Income 36.6 53.5 77.3 114.0 7.6 12.0 1.0 3.8 Lower middle income 38.8 52.5 67.1 90.9 5.4 12.8 1.1 3.5 Upe ide- i nc-o me -------3-5.-3-- 54.3 84.5 142. i8.7 11.4 0.9 4.0 Low & middle income 34.65 ---- 51.6 76.3 113.1 6.7 10.9 0.9 3.5 East Asia & Pacific 48.8 65,6 84.9 112.6 5.3 13.3 1.5 3.9 Europe & Central Asia 28.7 65.6 53.1 110.3 .. 13.6 ..3.8 Latin America &Crb. 23.2 37.7 68.8 115.2 7.9 10.5 0.9 4.5 Midd-l-eEas-t & N.A-fri-c-a 45.4 51.6 80.9 -89.7 - 11.5 7.5 0.9 1.0 So u th As-i a 16.5 24.3 1.4 3.1 0.1 0.6 Sub-Saharan Africa 41.2 56.8 76.1 96.4 5.1 11.0 1.0 1.8 High Income 32.0 37.1 100.6 124.4 11.0 33.6 3.0 10.1 Europe EMU 44.9 56.3 112.6 141.9 14.1 49.3 2.9 14.8 a. Data refer to the South African Customs union (Botswana, Lesotho, Namibia. Seats Africa. and Swaziland). :J] U 9 I I r.I- 6.1 @ About the data Definitions The growing integration of societies and econo- tive to GDP after deducting value added in ser- * Trade in goods as a share of GDP is the sum mies has succeeded in reducing poverty in many vices thus provides a better measure of its rela- of merchandise exports and imports measured countries. The number of poor people in devel- tive size than does comparing it with total GDP, in current U.S. dollars, divided by the value of oping economies declined by about 125 million although this neglects the growing service com- GDP in U.S. dollars. * Trade In goods as a share between 1990 and 1999. Although global inte- ponent of most goods output. of goods GDP is the sum of merchandise ex- gration is a powerful force in poverty reduction, Trade in services (such as transportation, ports and imports divided by the value of GDP more needs to be done-2 billion people are in travel, finance, communications, insurance, roy- after subtracting value added in services, all danger of becoming marginal to the world alties, construction and cultural services), is an in current U.S. dollars. * Change In trade as a economy. All countries have a stake in helping increasingly important element of global inte- share of GDP is the decade-over-decade developing countries integrate into the global gration. The difference between the growth of change in trade as a share of GDP. * Growth in economy and have better access to rich country real trade in goods and services and the growth real trade less growth In real GDP is the differ- markets and greater volumes of well-managed of GDP helps to identifY economies that have ence between annual growth in trade of goods foreign aid can help countries as they improve integrated into the global economy by under- and services and annual growth in GDP. Growth their own policies and develop more effective taking trade liberalization, lowered barriers to rates are calculated using constant price se- institutions. foreign investment, and harnessed their abun- ries taken from national accounts and are ex- 335 The growing importance of trade in the world dant labor to gain a competitive advantage in pressed as a percentage. * Gross private capi- N economy is one indication of increasing global labor-intensive manufactures and services. tal flows are the sum of the absolute values of ° economic integration. Another is the increased The indicators covering capital flows-gross direct, portfolio, and other investment inflows E size and importance of private capital flows to private capital flows and gross foreign direct and outflows recorded in the balance of pay- R developing countries that have liberalized their investment-are calculated from detailed ac- ments financial account, excluding changes in E financial markets. This table presents standard- counts since higher-level aggregates would re- the assets and liabilities of monetary authori- D ized measures of the size of trade and capital sult in smaller totals by netting out credits and ties and general government. The indicator is CD flows relative to GDP. The numerators are based debits. The comparability of these indicators calculated as a ratio to GDP in U.S. dollars. R on gross flows that capture the two-way flow of between countries and over time is affected by * Gross foreign direct Investment is the sum D goods and capital. In conventional balance of the accuracy and completeness of balance of of the absolute values of inflows and outflows , payments accounting exports are recorded as a payments records and by their level of detail. of foreign direct investment recorded in the ,, credit and imports as a debit. And in the finan- There are two changes in this table from pre- balance of payments financial account. It in- cial account inward investment is a credit and vious editions. First, trade and capital flows are cludes equity capital, reinvestment of earnings, outward investment a debit. Thus net flows, the shown as a percentage of GDP in U.S. dollars, other long-term capital, and short-term capi- sum of credits and debits, represent a balance converted at the average official exchange rate tal. This indicator differs from the standard in which many transactions are canceled out. reported by the International Monetary Fund for measure of foreign direct investment, which Gross flows are Ea better measure of integration the year shown. An alternative conversion fac- captures only inward investment (see table because they show the total value of financial tor is applied if the official exchange rate is 6.7). The indicator is calculated as a ratio to transactions during a given period. judged to diverge by an exceptionally large mar- GDP in U.S. dollars. Trade in goods (exports and imports) is shown gin from the rate effectively applied to transac- relative to total GDP and to 'goods GDP' (GDP tions in foreign currencies and traded products. Data sources less services such as retail trade, restaurants Second, to give a better measure of the policy- and hotels, transport, storage and communica- induced component of trade, the change in trade The data on merchanations The data on GDP tions, business services) and community, so- as a share of GDP is presented. This measures come from the World Bank converted from cial and personal services and public adminis- the effect of trade on growth using the decade- come from cthe Wo Bank convert from tration because as a result of the increasing over-decade change in a country's trade as a official exchange rate S dolplars using the share of services in GDP, trade as a share of share of its GDP. alternative conversion factor if the official total GDP appears to be declining for some exchange rate is judged to diverge by an economies. Measuring merchandise trade rela- exceptionally large margin from the rate Figure 6.1 effectively applied to transactions in foreign currencies and traded products. The data on Gross private capital flows, to the top 10 developing economy recipients, real trade and GDP growth come from the World 2000 or latest year available Bank's national accounts files. Gross private 60 capital flows and foreign direct investment were calculated using the International Monetary F0 Fund's Balance of Payments database. o 40- 30- 20- 10 0 Panama Philippines Angola Mauritania Moldova Latvia Slovak Papua Ecuado, Esionia Republic New Guinea Source: Table 6.1 based on the Intenadtonal Monetary Fund's, Balance ot Payments database and World Bank statf estmantes. 6.2 Direction and growth of merchandise trade High-income Importers Other All European United Orther All high high union ~~Japan States industrial industrial income income Source of exports Hg-income economies 29.4 3. 176.0501.658 Industrial economies 27.9 2.1 9.8 5.7 45.4 4.3 49.7 European Union 22.3 0.6 3.4 2.0 28.3 1.5 29.7 Japan 1.2 2.3 0.3 3.8 1.4 5.2 United States 2.6 1.0 3.1 6.7 1.1 7.9 Other industrial economies 1.8 0.4 4.1 0.2 6.6 0.3 6.9 Other high-income economies 1.5 1.0 2.0 0.3 4.8 1. 3 6.1 Low- and middle-income economies 6.0 2.3 7.0 0.8 16.1 4.2 20.3 East Asia & Pacific 1.7 1.7 2.3 0.4 6.1 2.3 8.4 Europe & Central Asia 1.7 0.0 0.1 0.1 1.9 1.1 3.0 336 Latin America & Caribbean 0.7 0._1 3.4 0.2 4.3 0.2 4.5 Middle East & N. Africa 1.0 0.3 0.4 0.1 1.8 0.3 2.1 Z5 South Asia 05..0.1..05 0120.3 1.5 tO Sub-Saharan Africa 0.4 0.0_ 0.3 0.0 0.8 0.1 0.9 World 35.3 5.4 18.8 6.8 66.3 9-8 76.1 CD E a. 0 C) ~~~~~~~~~~~~~~~~~~~Low- and middle-Income Importers -m Z0 N ~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~Europe Latin Middle All low- 0 ~~~~~~~~~~ ~~~~~~~~~~ ~~~~~East Asia & Central America East & South Sub-Saharan & middle- CN . N U = lrr A.i & Pacific Asia & Caribbean N. Africa Asia Africa income World Source of exports High-income economies 8.0 3.0 1.9 1.4 0.7 0.8 15.7 72.9 Indus trial economies 4.2 2.9 1.8 1.3 0.4 0.7 11.2 63.2 European Union 1.0 2.5 0.6 0.8 0.2 0.5 5.7 35.9 Japa n 1-.7 0. 1 0.2_ 0.1 __0 .1 -0.1 2.2 7.5 United States 1.1 0.2 090.20.012621 Other industrial economies 0.4 0.1 0.1 0.1 0.0 0.0 0.8 7.7 Other high-income economies 3.8 0.1 0.1 0.1 0.2 0.1 4.5 9.6 Low- and middle-inco me economies 2.6 1.7 1.1 0.6 0.5 0.4 6.9 27.1 East Asia & Pacific 1.6 0.2 0.2 0.2 0.2 0 1 2.6 11.1 Europe & Central Asia 0.2 1.3 0.0 0.1 0.0 0.0 1.7 4.8 Latin America & Caribbean 0.1 0.0 0.8 0.1 0.0 0.0 1.1 5.8 Middle East & N. Africa 0.5 0.1 0.0 0 1 0.1 0.0 0.9 3.1 South Asia 0.1 0.0 0.0 0.0 0.0 0.0 0.3 1.0 Sub-Saharan Africa 0.1 0.0 0.0 0.0 0.0 0.2 0.4 1.3 World 10.6 4.7 3.1 1.9 1 1 1.2 22.6 100.0 - ~6.2 High-Income Importers Other All European United Other All high high Union Japan States industrial industr al Income income Source of exports High-i_n_come-ecoome 3.938 - 54 4.7 8.0 5.0 Industrial economies 3.7 2.5 7.4 4.7 4.5 7.6 4.7 Erpean Union 3.7 2.9 7.4 2.4 4.0 8.2 4.2 Japan 2947073.7 7-.5 - ------------4.6 United States 4.7 2.9 7.3 -5.5 7.7 5 8 -othe r -in-du-strial -e-co-n-oim ie-s280- . 3.7 -6.3 -5.3 6.2 O6th~erh high-income ecnm ie s 7.. . 7- 5. 739.3 . 7.7 Low- and middle-income economies 8.7 7.6 13.2 10.6 10.3 13.5. . 10.9 East Asia & Pacific 12.9 9.4 13.5 13.2 12.0 10.3 11.5 Europe & C ntral Asia' .... .. ... 10.6 1.6 _12.2 ... ..9.3 10.5 8.0 9.5 Latin America & Caribbean 3.2 1.1 16.0 11.0 11.8 11.2 11.8 337 Middle East & N. Africa 3.2 4.3 3.4 5.3 3.5 6.2 3.8 South Asia 7.6 2.6 14.0 9.2 9.4 12.7 9.9 N Sub-Saharan Africa 4-.7 10066 4.6 5.6 21.3 . - 6 -62 World 4.4 5.1 9.2 5.3 - 5.7 9.0 6.0 0 2~ 0. Europe Latin Middle All lown- East Asia & Central America East & South Sub-Saharan & middle- 0 - ~~~~~~& Pacific Asia & Caribbean N. Africa Asia Africa income World ( Source of exports High-income econornies 9.4 8.0 7.5 1.3 4.7 1.5 7.2 5.6 I ustrial ecnme 8.3 7.9 7.5 1.1 2.9 1.1 6.2 5.1 -E-ropan Unin-7-39.0 6.4 1.0 3.5 0.8 . 5.7 4.4 European Union~~~~~~~~~~~~~~~~~~~~~~~~-------------- ---- ---- --------- - -- ---- --------- Jpan 8.8 -1.6 6.5 -1.6 1.4 -1.2 6.7 5.2 United States 9.2 3.9 9~~ ~ ~~~~~~~ ~ ~~~~~~.0 3.0 1.5 3 . Oter industrial economies 6.4 1.2 4.9 1.3 4.1 3.3 4.4 6.0 Othl e-r h-i gh- in-c-omne eoois 1927803.3 9.6 5.4 10.3 9.1 Low- and middle-income economies 19.5 13.6 11.9 5.6 12.0 11.4 . 13.7 11.4 E---ga-st Asia --& _P_a_c_ific-_ 20.2 9.4 19.4 9.9 13.6 12.4 16.7 12.2 Eur-opie & Central Ain341.84 --- 256.0 8._7 8.6 9.2 --Latin America & Caribbean 8.5 2.1 11.7 2.1 11.8 5.2 9.7 11.3 Middle East & N. Africa 20.6 -3.7 -2.2 1.2 7.7 7.2 7.1 4.8 Sot Asia 16.4 -6.6 28.1 7.4 12.3 15.3 7.5 9.1 ubaharan Africa 23.8 5.2 16.3 5.7 18.7 11.9 14.3 8.0 World 11.6 7.9 8.9 2.1 6.6 3.8 8.3. 6.5 a. Refers to 1993-20001 About the data Definitions This table provides estimates of the flow of trade Figure 6.2 * Merchandise trade includes all trade in in goods between groups of economies. The goods; trade in services is excluded. * Low- source of these data is the International Mon- About 20 percent of high-income economies' and middle-income regional groupings are etary Fund's (IMF) Direction of Trade Statistics imports came from developing economies based on World Bank classifications and may Yearbook (DOTSY), which covers 182 countries. 25 differ from those used by other organizations. Of these countries, about 100 report data on a * High-Income includes those economies clas- timely basis, covering about 95 percent of trade t 20 sified as high-income by the World Bank. * In- for recent years. Trade by less timely reporters Et dustrial countries are those classified as such and by countries that do not report is estimated in the IMF's Direction of Trade Statistics Year- using reports of partner countries. Because the 1ro book and include the countries of the Euro- largest exporting and importing countries are e pean Union, Japan, the United States, and the reliable reporters, a large portion of the miss- , other industrial economies listed below. ing trade flows can be estimated from partner o U * - _ * European Union comprises Austria, Belgium, reports. Partner country data may introduce dis- -SA Z A, <6, Denmark, Finland, France, Germany, Greece, 338 crepancies due to overreporting of transit trade, 4 (O'-Y9 - Ireland, Italy, Luxembourg, the Netherlands, different points of valuation and time of record- de 2i Portugal, Spain, Sweden, and the United King- o ing, different exchange rates, inclusion or exclu- dom. * Other Industrial economies include o, sion of freight rates, confidentiality, and smug- PscItIthenfromanyotherdeveIoprIon. Australia, Canada, Iceland, New Zealand, Nor- Vc gling. In addition, estimates of intra-European way, and Switzerland. * Other high-income Cm Union trade have been significantly affected by Source: Table 6.2 based on Intennat,onai Monetary Fund's economies include Cyprus, Hong Kong (China), E__ changes in reporting methods following the cre- __________Of_Trade_database_ Israel, Kuwait, Macao (China), Malta, Qatar, o ation of a customs union. Coverage by the new San Marino, Singapore, Taiwan (China), and rD system for collecting data on trade between EU the United Arab Emirates. Some small high- o members, Intrastat, introduced in 1993, is less income economies such as Aruba, the Baha- exhaustive than that by the previous customs- mas, and Bermuda have been included in the based system and has resulted in some asym- Latin America and Caribbean group. o metry problems (estimated imports are about 0 rN 5 percent below exports). Nevertheless, only a Data sources small portion of world trade is estimated to be omitted from the IMF's Direction of Trade Sta- ~~Intercountry trade flows are published in the omitted from the IMF's Direction of Trade Sta- IMF's Direction of Trade Statistics Yearbook tistics Yearbook. and Direction of Trade Statistics Quarterly; the Most countries report their trade data in na- data in the table were calculated using the tional currencies, which are converted using the IMFs Direction of Trade database. IMF's published period average exchange rates (series rf or rh, monthly averages of the market or official rates) of the reporting country or, if those are not available, monthly average rates in New York. Because imports are reported at c.i.f. (cost, insurance, and freight) valuations, and exports at f.o.b. (free on board) valuations, the IMF adjusts country reports of import val- ues by dividing those values by 1.10 to esti- mate equivalent export values. This approxima- tion is more or less accurate, depending on the set of partners and the items traded. Other fac- tors affecting the accuracy of trade data include lags in reporting, recording differences across countries, and whether the country reports trade according to the general or special system of trade. (For further discussion of the measure- ment of exports and imports see About the data for tables 4.5 and 4.6.) The regional trade flows shown in this table were calculated from current price values. The growth rates presented are in nominal terms; that is, they include the effects of changes in both volumes and prices. OECD trade with low- and middle-income economies 6.3 High-Income European Japan United States OECD countries Union 1990 2000 1990 2000 1990 2000 1990 2000 $ blillions Food 34.1 53.1 15.8 22.9 0.4 0.6 11.6 20. Cereals 1-4.0 . 14.0 - -4.1 4.9 0.1 0.1 6.2 6.6 Agri c-ulItur-ald ra w _mate_ri-al_s___ _11 9, 17.3 -- 3.3 4.9 0.8 1.4 5.6 6.7 Ores and nonferrous metals 9 853.0 6.5 0.9 3.1 5.0 0.0 Fuels 8.5 - 8 5 - 6.5 0.8 0.8 3.3 6.8 . -eptrlem0.3 - 2.3 .......00_ 0.6 0.0 0.0 0.0 0.1 Petroleum products; . 5. . . . . Manufactured goods 305.7 683.9 149.8 319.6 68.7 135.7 72.5 205.2 Chmcal products 46.09. 23.9 46.3 6.0 . 13.0 12.2 25.7 __ Machiinery & tran-sport e q u-i-p m_e_n_t__ -175.7 4-1 1.3, 80.6 180_0___ - --- 439 ----- 89.9 44.5 130.3 N Other 840181.2 45.2 934189 32.8 15.9 49.2 Miscellaneous goods 11.6 21.0 3.8 5.7 0.8 4.0 4.7 10.1 Total 381.1 812.1 178.1 366.2 72.4 145.7 102.7 249.0 0 % of total exports <0 Food --- 8.9 6.5 8 9 620.5 0.4 11.3 8.1 ' Cereals 37 7 - 2.3 1.3. 0.1 0.0 6.1 2.60 Agricultural raw materials 3. 2.1 1.8 1.3 1.1 0.9 5.4 2 .7 Ores and nonferrous metals 2.5 2.3 - - 1.7 1.8 . 1.2 2.1 4.9 0.0 S .Fuels --..-2.2 2.3 1.4 - 18 1.1 0.6 3.2 2.7 Crude petroleum 0.1 0.3 0.0 0.2 0.0 0.0 0.0 0.0 Petroleum products 1.5 1.4 1.3 1.4 1.0 - 0.5 2.1 2.1 Manufactured goods 80- ,.2... 84.2 84.1 87.3 94993.2 70.6 82.4 Chemical products 12.1 11.3 13.4 12.6 - 8.2 8.9 11.8 10.3 Machinery & transport equipment 46.1 50.6 45.3 49.2 60.6 61.7 43.3 52.3 -Ot1her 22.1-22.2 25.4 2.5.5 26.0 22.5 15.5 19.8 Miscellaneous goods 3.0 2.3 2.1 1.6 1. 1 2.7 4.6 4.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 6.3 High-income European Japan United States OECD countries Union 1990 2000 1990 2000 1990 2000 1990 2000 $ blillions Food ~~~~~~~~~ ~ ~~~64.6 89.5 34.9 40.4 1.7 20.0 1.5 2. Cereals 1.3 2.3 0.5 0.9 0.5 0.5 0.2 0.6 Agricultural raw materials 1.7 23.0 9.9 13.0 5.0 4.5 2.3 4.4 Or-es and nonferrous metals 30.2 52.1 15.0 22.8 9.1 12.6 5.1 12.0 Fuels 144.5 226.4 58.6 91.9 33.5 41.6 48.8 83.1 Crudle petroleum 107.5 164.5 46.6 64.5 20.8 24.1 37.3 67.4 -Petroleum products -. ....23.6 34.1 6.2 12.8 5.9 - 5.9 10.8 14.4 30 Manufactured goods 210.7 781.8 85.3 270.3 24.6 94.1 85.5 366.4 ___ Cemnical products 14.5 36.9 8.0 16.7 2.3 5.2 3.0 11.8 U, Machinery & transport equipment 59331418.3 110.3 3.73862.182 other 136.9 383.5 59.0 143.2 18.6 50.2 50.4 166.5 0 -.~~~~~~~~~~~~~~~~- 6---- Y _ . ~ Miscellaneous goods 5 615.6 211.6 0.5 212 61 11.8 Total . 473.3 1,188.5 205.7 440.1 83.5 174.9 159.8 501.2 E . .. - ~~~~~~~~~~~~~~~~~~~~~~~~~~~--- --- --- Cg % of total Imports 5) Fo13.6 7.5 17.0 9.21291.9747 a) e - --. -- . O Creals 0.3 0.2 0.2 0.2 0.6 030.1 0.1 Aricultural raw materials 3.7 1.9 4.8 3.0 6.0 2.6 1.5 0.9 Or-e-s and nonferrous metals 6.4 4.4 7.3 5.2 10.9 7.2 3.2 2.4 oC Fuels 30.5 19.1 28.5 20.9 40.2 23.8 30.5 16.6 C .- (N Crude petroleum 22.7 13.8 22.7 14.7 24.9 13.8 23.3 13.5 Petroleum products 5.0 2. 3.0 2.9 7.0 3.3 6.8 2.9 Manufactured goods 44.5 65.8 41.4 61.4 29.5 53.8 53.5 73.1 Chemical products 3.1 3.1 3.9 3.8 2.8 3.0 1.9 2.4 Machinery & transport equipment 12.5 30.4 8.9 25.1 4.5 22.1 20.1 37.5 Other 28.9 32.3 28.7 32.5 22.2 28.7 31.5 33.2 Miscellaneous go-ods 1.2 1.3 1.0 0.4 0.7 1.2 1.6 2.4 Total 100.0 100.0 1.00.0 Ł.00.0 1.00.0 100.0 1.00.0 1.00.0 Note: For a listing of low- and middle-incom,e economies, see front flap of book. 6.3 About the data Definitions Developing countries in the trading system are tured goods from high-income countries-par- The product groups in the table are defined in becoming increasingly important. Since the early ticularly capital-intensive goods, such as ma- accordance with the Standard International 1990s trade between OECD countries and low- chinery and transport equipment. And trade Trade Classification (SITC) revision 1: food and middle-income economies has grown faster between developing countries has grown sub- (0, 1, 22, and 4) and cereals (04); agricultural than trade among OECD members. The increased stantially over the past decade as a result of a raw materials (2 excluding 22, 27, and 28); trade benefits consumers and producers, but number of factors, such as the increasing share ores and nonferrous metals (27, 28, and 68); as the Doha World Trade Organization WTO of developing country output in the world fuels (3), crude petroleum (331), and petroleum ministerial conference in October 2001 economy and the liberalization of developing products (332); manufactured goods (5-8 illustrates, achieving more progress toward a country trade. With 40 percent of their exports excluding 68), chemical products (5), machinery pro-development outcome remains a major going to other developing countries, the high and transport equipment (7), and other challenge, and will require strengthening trade barriers need to be reduced (more than manufactured goods (6 and 8 excluding 68); international consultation. Negotiations after 70 percent of the tariff burden faced by manu- and miscellaneous goods (9). * Exports are the Doha meetings will be launched (or factured goods from developing countries is im- all merchandise exports by high-income OECD continued) agriculture, services, manufactures, posed by other developing countries). Despite countries to low- and middle-income economies dispute settlement, WTO rules, disciplines on the growth in trade between developing coun- as recorded in the United Nations Statistics 341 regional integration, environment, and tries, high-income OECD countries remain the Division's COMTRADE database. * Imports are t, intellectual property rights protection. These developing world's most important partners. all merchandise imports by high-income OECD 0 negotiations are scheduled to be concluded by The aggregate flows in the table were countries from low- and middle-income 2005. compiled from intercountry flows recorded in economies as recorded in the United Nations R What would improved access to rich country the United Nations Statistics Division's Statistics Division's COMTRADE database. E 0 markets mean for developing countries? They Commodity Trade (COMTRADE) database. * High-income OECD countrles in 2000 were CD stand to gain $9 billion a year in textiles alone, Partner country reports by high-income OECD Australia, Austria, Belgium, Canada, Denmark, and another $22.3 billion in other manufactur- countries were used for both exports and Finland, France, Germany, Greece, Iceland, 'a ers. They would also reap large benefits from imports. Exports are recorded free on board Ireland, Italy, Japan, Luxembourg, the CD better access to one another's markets: open- (f.o.b.); imports include insurance and freight Netherlands, New Zealand, Norway, Portugal, ing their own markets would lead to gains of charges (c.i.f.). Revisions have been made to Spain, Sweden, Switzerland, the United 2), about $27.6 billion a year for manufacturers, the time-series data as far back as 1990. Kingdom, and the United States. * European and $31.4 billion for agricultural goods. Because of differences in sources of data, timing, Union comprises Austria, Belgium, Denmark, Trade flows between high-income members and treatment of missing data, the data in this Finland, France, Germany, Greece, Ireland, Italy, of the OECD and low- and middle-income econo- table may not be fully comparable with those Luxembourg, the Netherlands, Portugal, Spain, mies reflect the changing mix of exports to and used to calculate the direction of trade statistics Sweden, and the United Kingdom. imports from developing economies. While food in table 6.2 or the aggregate flows shown in and primary commodities have continued to fall tables 4.4-4.6. For further discussion of Data sources as a share of OECD imports, the share of manu- merchandise trade statistics see About the data COMTRADE data are available in electronic form factured goods supplied by developing countries for tables 4.4-4.6 and 6.2. from the United Nations Statistics Division. has grown dramatically. from about 45 percent Although not as comprehensive as the of total goods in 1990 to more than 65 percent underlying COMTRADE records, detailed in 2000. At the same time, developing coun- statistics on international trade are published tries have increased their imports of manufac- annually in the United Nations Conference on Trade and Development's (UNCTAD) Handbook of International Trade and Development Figure 6.3 Statistics and the United Nations Statistics Division's International Trade Statistics High-income economies' Imports from developing countries are mainly Yearbook. manufactured goods 70 - E 60 650 o iE i 1990 . 40 U 2000 10 ~ 0 Manufactured Fuels Food Ores and Agricultural Miscellaneous goods nonferrous raw materials goods metals Source: Table 6.3 based on COMTRADE database. 6.4 Primary commodity prices 1970 1980 1990 1995 1996 1997 1998 1999 2000 2001 World Bank commodity price Index (190 100) Non-energy commodities 156 _159 100 104 103 114 99 88 89 82 Agriculture --------163 175 100 112 113 124 108 93 90 83 Beverages 203 230 100 129 113 165 141 108 91 75 Food 166 177 100 100 ill 112 105 88 87 89 Raw materials 130 133 100 116 114 110 88 89 94 80 Fertilizers 108 164 100 89 108 116 122 115 109 102 Metals and minerals 144 120 100 87 80 87 76 74 85 78 Petroleum 19 205 100 64 80 81 57 79 127 110 34 Steel products' ill 100 100 91 _86_ 86 75_ 69 .... 78 _69 cn MUV G-5 index 28 79 100 117 111 104 100 100 97 97 Commodity prices (1990 prices) C: Agricultural raw materials a. Cotton (cents/kg) 225 260 182 182 159 169 145 118 134 109 8 Logs, Cameroon ($/cu. m), 153 319 343 290 256_ __275_ 287 270 283 .......275 a) C) Logs, Malaysian ($/cu. m) 154 248 177 218 227 230 163 188 195 165 'O Rubber (cents/kg) 145 181 86 135 125 98 72 63 71 62 Sawnwood, Malaysian ($/cu. m) 625 503 533 632 666 641 486 604 612 497 Tobacco ($/mt) 3.836 2.889 3.392 2.259 2.746 3,411 3,347 3.055 3.055 3.125 C - Beverages (cents/kg) Cocoa 240 330 127 122 131 156 168 114 93 i11 Coffee. robustas 330 412 118 237 162 168 183 150 94 63 Coffee, Arabica 409 440 197 285 242 403 299 230 197 142 Tea, avg.. 3 auctions 298 211 206 127 149 199 205 185 192 165 Energy Coal. Australian ($/int) 50.01 39.67 33.64 34.21 33.90 29.33 26.08 26.95 33.43 Coal, U.S. ($/Mt) 54.71 41.67 33.47 33.44 35.15 34.50 _33.32 33.94 46.41 Natural gas, Europe ($/mmbtu) - 4.32 2.55 2.33 2.55 2.65 2.42 2.14 3.96 4.20 Natural gas, U.S. ($/mmbtu) 0.59 1.97 1.70 1.47 2.45 2.40 2.09 2.28 4.42 4.09 Petroleum ($/bbl) 4.31 46.80 22.88 14.68 18.35 18.52 13.11 18.15 28.98 25.19 About the data During the 20th century, non-oil commodit prices finished goods. They are often the most signifi- weights are the 1987-89 export values for low- fell about 1 percent a year relative to the prices cant exports of developing countries, and rev- and middle-income economies, rebased to of manufactures. Until the early 1970s oil prices enues obtained from them have an important 1990. Each index represents a fixed basket of had fallen even more rapidly. The decline in effect on living standards. Price data for pri- primary commodity exports. The non-energy corn- agricultural commodity prices was mainly due to mary commodities are collected from a variety modity price index contains 37 price series for productivity gains: rising yields, improved of sources, including intemational study groups, 31 non-energy commodities. Separate indexes policies, and investment in infrastructure and trade journals, newspaper and wire service re- are compiled for petroleum and for steel prod- irrigation. Metals and minerals commodity prices ports, government market surveys, and com- ucts, which are not included in the non-energy have declined relative to those of manufactures modity exchange spot and near-term forward commodit price index. for similar reasons: better technology, policies, prices. This table is based on frequently up- The MUV index is a composite index of prices and management. dated price reports. When possible, the prices for manufactured exports from the five major Agricultural commodity prices were down about received by exporters are used; if export prices (G-5) industrial countiries (France, Germany, Japan, 33 percent in 2000 compared with 1-995. Al- are unavailable, the prices paid by importers the United Kingdom, and the United States) to though metals and minerals prices rose some- are used. Annual price series are generally low- and middle-income economies, valued in what from the lows of 1999, by early 2001 they simple averages based on higher frequency U.S. dollars. The index covers products in groups had dropped again due to weak demand. While data. The constant price series in the table is 5-8 of the Standard International Trade Classi- oil prices rose sharply during 1999 and most of deflated using the manufactures unit value fication (SITC) revision 1. To construct the MUV 2000, they weakened in late 2000 and into 2001. (MUV) index for the G-5 countries (see below). G-5 index, unit value indexes for each country Primary commodities are raw or partially pro- The commodity price indexes are calculated are combined using weights determined by each cessed materials that will be transformed into as Laspeyres index numbers, in which the fixed country's export share. 6.41 1970 1980 1990 1995 1996 1997 1998 1999 2000 2001 Fertilizers ($/mt) Phosphate rock 39 59 40 30 35 40 43 44 45 43 TSP 152 229 132 128 158 166 174 155 141 131 Food Fats and oils ($/intl Coconut oil 1,417 855 336 573 675 634 660 740 462 329 Groundnut oil 1,350 1,090 964 847 806 976 912 791 733 704 Palm oil 927 741 290 537 477 527 673 438 318 296 Soybeans 417 376 247 221 274 285 244 203 217 203 Soybean meal 367 333 200 168 240 266 171 153 194 187 - Soybean oil 1,021 759 447 534 496 545 628 429 347 366 Grains ($/Mt) Grain sorghum 185 164 104 102 135 106 98 85 90 99 Maize 208 1 59 109 106 149 113 102 91 91 93 E - -- ------------- ----- ------ ----~~~~~~~~~~~~~~~~~~~~~~~~0 Rice 450 521 271 274 305 293 305 250 208 179 Wheat 196 219 136 151 187 154 127 113 117 131 ECCAS 162 89 131 163 163 212 211 198 167 181 ECOWAS 86 692 1,026 1.533 2,088 2.483 2.462 2.614 2,669 3,331 o Indrian Ocean Commission 5 10 4 24 64- -69_ 75 95 91 116 N MRU 1 7 4 0 1 4 7 8 8 10 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~------- - o SADC 483 617 843 1,630 3.373 3,963 4.471 3,865 4,224 4,419 UDEAC 22 75 84 139 120 163 160 152 120 124 UEMOA 52 460 397 614 555 665 707 753 841 847 Middle East and Asia Arab Common Market- 102 661 ___504 911 1.368 1,149 1.146 978 936 1,238 ASEAN 1,456 13.350 14.343 28.648 81.911 86,925 88.773 72,218 81.020 100.818 Bangkok Agreement 132 1.464 1.953 4,476 12.070 13.128 13.647 13.175 15.272 17.218 EAEC 9,197 98.532 126.030 282,351 637.029 651.803 673.285 5S1,S55 617.196 776,658 ECO 63 1.165 2.447 1.243 476 4,773 4.929 4.031 3,903 4.495 G-CC -----156 4,632 3.101 _ 6.906 6,832 7,624 8,110 7,358 7,194 8.561 SAARC 99 613 601 863 2,024 2.144 2,004 2.834 2,615 2,798 UMA 59.5 109.1 274.1 958.4 1,109.5 1,115.3 __ 924.2 881.1 918.5 1,081.4 Merchandise exports within bloc % of total bloc exports 1.970 1980 1955 1990 1995 1996 1997 1998 1999 2000 High-income and low- and middle-Income economies APEC 57.8 57.9 67.7 68.3 71.9 72.1 71.8 69.7 71.9 73.2 CEFTA 129 14 8 13.8 9 9 14~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-- 6 -- 14.4 13.4--------S 13. 0 12.1 12.1 European Uion 5. 608 59.2 65.9 62.4 61.4 55.5 57.0 63.3 62.1- 36.0 33.6 43.9 41.4 46.2 47.6 49.1 51.7 54.6 55.7 Latirn Ameria an h aibean ACS 9 8.7 7.9 8'.4 8.1 6.9 6.9 7.2 5.8 5.5 Andean Group 1.8 3.8 3.2 4.1 12.0 - 9.7 10.8 12.8 8.7 8.5 CACM 26.0 24.4 14.4 15.4 21.7 22.0 18.1 16.0 11.9 12.4 CARICOM ~~~ ~~~ ~~~~ ~~~4.2 5.3 6.3 8.1 4.7 13.3 ~~14.6 17.4 16.9 15.0 Central Amrca Goup of Four 2. 181 09 3720 22 0 19.9 17.0 12.0 12.1 Group of _T_hr_e_e____ 1.1 1.8 1.3 2.0 3.2 2.4 - -2.7 2.6 1.7 1.7 LAIA 9.9 13.7 8.3 10.8 17_13 171 16.7 12.8 12.9 345 MVIEROSUR ----- 94------ -~ 11.6 55 89 20.3 - 22.6 24.8 25.0 20.6 20.8 .. 9.1 6.4 - 8.1 - 12.7 ~106 - 10.7 - 12.0 12.6 10.0 -- ------ ------ ---- -------- .- - -.- ----. -. - -- - - --.-.-- - -- - - - -- - - -- - - -- - - Africa ---- - - - -4.8 1.6 1.9 2.3 2.2 2.3 2.1 2.3 1.6 1.2 o CEPGL 0.4 0.1 . 0.8 0.5 0.5 0.5 0.4 0.6 0.7 0.8 cb~~~~iE~~~~~K - - ~~~~~9.1 631 4. 6.6 7.8 - 76 8.6 . 7.3 6.0 C Cross Bordr Initiatie 988 6.9 10.3 11.9 12.4 12.7 13.8 12.1 12.4 ECCAS 9.6 1.4 1.7 1.4 1.6 1.6 1.6 1.8 1.3 0.93 ~~~idowAs 29 101 5.2 78 98- -93 96 11.6 12.0 108'. - ------ ___ --- ------- -_39. _1. ----- 2. 1. indian Ocean Commiission 2.5 1.2 0.6 1.5 3.3 3.0 - 3. 4.7 4.8 4.7 S 0.2 - 0.8 - 04 0.0 0.1 0.3 0.5 0.5 0.6 0.7 8.0 2.0 - - -8 48 8.7 -94 104 10.4 11.9 12.2 0 UDEAC 16 19 23 - - 23 21 2.3 1.6- 1.2--- 1-9,----.3--- ------- --2_____2_.3___ ---2__._- .316 . UEMOA 6 ~~~~~~~~~~~~~~.5 96 87 12.9 00 94 11.5 10.8 13.3 15.6 Middle E-a-Sta andAi Arab CommonMakt 2.2 2.4 1.9 2.7 6.7 4.4 4.1 4.8 - 3.3 2.9 ASEAN ~~~~ ~ ~~~~ ~ ~~~~22. 9 18.7----- -19.8- 19 8--- --- 254-25-4-24.9 21.9 22.4 23.9 B-a n-g-k-o-k A-g-re-e-m--e-n-t 2 7 37--3.7-37--51-53 -5.2 5.0 5.3 5.2 EAEC - 28.9~~~~~~~~~-------- --- 35.6 343----- 399 -- -- 48.3 493 43 42.2 44.1 47.0 EGO ~~~~ ~~~~ ~~~~~~~1.5 5.4 9.9 - 32 79 7 56.9 575 GOC ~~~ ~~~ ~~~~ ~~~ ~~2.9 3.0 4.9 8.0 6.8 6.4 - 6.5 8.1 6.7 5.2 SAARC 3.2 4.8 ~~~~ ~~~ ~ ~~~~ ~~~~ ~~~~ ~~4._5 3' 2 4.4 4.3 4.05.4643 UMA 1.4 03 10 2938 34 27.32.5 2.3 6.5 Total merchandise exports by bloc % of world exports 1970 1980 1985 ±990 1995 1996 1997 1998 1999 2000 High-income and low- and middle-income economies APEC~~~~~~~~~~~ ~~36.0 33.7 38.9 39.0 46.3 46.0 47.2 46.1 46.6 48.5 CEFTA ~~~~ ~~~~ ~~~~~3.2 2.9 2.4 1.3 1.6 1.7 1.8 2.0 1.9 2.0 European Union . ... 45.6 41.0 37.8 44.0 39.8 39.2 38.0 39.9 39.2 35.9 NAFTA ~~~~ ~ ~~~~ ~ ~~21.7 16.6 17.4 16.2 16.8 17.4 18.3 18.7 18.8 19.1 Latin America and the Caribbean ACS 2.8 3.1 2.8 1.9 2.5 3.0 3.1 3.2 3.5 3.9 Andean Group 1.9 1.7 1.3 0.9 0.8 0.9 0.9 0.8 0.8 1.0 CACM 0.4 0.3 0.2 0.1 0.1 0.1 0.2 0.2 0.3 0.3 CARICOM 0.4 0.6 0. 0.2 0.1 0.1 0.1 0.1 0.1 0.1 Central American Group of Four 0.3 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.2 0.2 Group of Three 1.8 2.1 2.1 1.5 2.1 2.5 2 .7 2.8 3.0 3.4 346 LAIA 4.5 4.4 4.6 3.4 4.1 4.5E 4.8 4.8 4.8 5.2 MERCOSUR ~~~~ ~~~~ ~~~1 .7 1.6 1.9 1.4 1.4 1.4 1.5 1.5 1.3 1.4 En o OECS .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 o Africa CEMAC0.2 03 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.2 CEPGL 0.3 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 E COMESA 1.6 0.6 0.5 0.4 0.4 0.4 __0.4 0C.3_ 0.3 0.4 0.. .... O Cross Border Initiative 0.8 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 ECCAS 0.6 0.3 0.4 0.3 0.2 0.3 0.2 0.2 0.2 0.3 'O E_CO W A S 1.1 0.4 1.0 0.6 0.4 0.5 0.5 0.4 0.4 0.5 0 Indlian O6c-ean Commission 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MRU 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 SADC 2.2 1.6 1.2 1.0 0.6 0.8 0.8 0,7 0.6 0.6 (N UDEAC 0.2 0.3 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.2 UEMOA 0.3 0.3 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Middle East and Asia Arab Common Market 1.6 1.5 1.4 1.0 0.4 0.5 0.5 0.4 0.5 0.7 ASEAN 2.3 3.9 3.9 4.3 6.4 6.5 6.5 6.1 6.4 6.6 Bangkok Agreement 1.8 2.2 2.8 3.6 4.7 4.7 4.7 4.9 5.1 5.2 EAEC 11.3 15.1 19.6 20.9 26.0 25.0 25.3 24.2 24.7 26.0 ECO 1.5 1.2 1.3 1.1 1.2 1.3 1.2 1.1 1.2 1.3 GCC 1.9 8.5 3.4 2.5 2.0 2.2 2.3 1.7 1.9 2.6 SAARC 1.1 0.7 0.7 0.8 0.9 0.9 0.9 1.0 1.0 1.0 UMA 1.5 2.3 1.5 1.0 0.6 0.6 0.6 0 5 0.6 0.8 Note: Regional nblc memberships are as follows: Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam. Canada, Chile, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea. Malaysia. Mexico, New Zealand. Papua New Guinea. Peru. the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand. toe United States, and Vietnam: central European Free Trade Area ICEFTA). Bulgaria, the Czech Republic. Hungary, Poland, Rumania, the Slovak Republic. and Slovenia; European Union (EU; formerly European Economic Community and European Community). Austria, Belgium, Denmark. Finland. France, Germany. Greece, Ireland. Italy, Luuembourg, the Netherlands. Portugal. Spain. Sweden. and the United Kingdom; North American Free Trade Area (NAFIA), Canada, Mexico, and the United States: Association of Caribbean States (ACS), Antigaa aed Barbuda, the Bahamas. Barbados, Belize, Colombia. Costa Rica, Cuba, Dominica, the Dominican Republic. El Salvador, Grenada, Guatemala, Guyana. Haiti. Honduras, Jamaica. Mexico, Nicaragua. Panama. St. Kitts and Nevis, St. Lucia. St. Vincent and the Grenadines, Suriname, Trinidad and Tobago. and Rep6ib.ica Bolivariana de Venezuela; Andean Group, Bolivia. Colombia, Ecuador, Peru. and Rep6blica Bolivariana de Venezuela; Centrai American Common Market (CACM), Costa Rica, El Salvador, Guatemala. Honduras, and Nicaragua: Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas (part of the Caribbean Community but not of the Common Market). Barbados, Belize, Domin ca. Grenada, Guyana, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia. St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Group of Four, El Salvador. Guatemala, Honduras, and Nicaragua: Group of Three. Colombia. Mexico, and Rep0blica Bolivariana de Venezuela; Latin American Integration Associa- tion (LAIA; formerly Latin American Free Trade Area). Argentina. Bolivia. Brazil. Chile. Colombia. Ecuador. Mexico, Paraguay, Peru, Uruguay, and Repbbiica Bolivariana de Venazuela; Southern Cane Common Market (MERCOSUR), Argentina, Brazil. Paraguay. and Uruguay; Organization of Eastern Caribbean States (OECS), Antigua and Barbuda. Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines: Economic and Monetary Cammunity of Cantrai Africa (CEMAC), Cameroon, the Central African Republic. Chad. the Republic of Congo, Equatorial Guinea. Gabon. and Sdo Tomk and Principe; Economlc Communlty of the Countries of the Great Lakesl(CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Common Market for Eastern and Southern Africa ICOMESA), Angola. Burundi, Comoros, the Democratic Republic of the Congo. Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia. Kenya. Madagascar. Malawi, Mauritius, Namibia, Rwanda, Seychelles. Sudan. Swaziland. Uganda, Tanzania, Zambia, and Zimbabwe: Cross-Border Iintiative, Burundi, Comoros. Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaz land. Tanzania. Uganda. Zambia. and Zimbabwe: Economic Community of Centrai African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic. Chad. the Democratic Republic of the Congo. the Republic of Congo. Equatorial Guinea, Gabon. Rwanda, and S5o Tome and Principe; Economic Community of West African States (ECOWAS). Benin, Burkina Faso. Cape Verde. C61te dIlvoire, the Gambia. Ghana. Guinea, Guinea-Bissau, Liberia. Mali. Mauritania. Niger, Nigeria, Senegal, Sierra Leone. and Togo; indian0Ocean CommIssion, Comoros, Madagascar. Mauritius, R6union. and Seychelles: Mano River Union (MRU), Guinea. Liberia, and Sierra Leone: Southern African Deneiopment Community (SADC; formeriy Southern African Develop- ment Coordination Conference), Angola, Botswana, the Democratic Republic of the Congo. Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles. South Africa. Swaziland, Tanzania. Zambia. and Zimbabwe: Central African Customs and Economic Union (UDEACt formerly Union Douanibrn at Economique de 'Afrique Centraia), Carneroon. the Central African Republic. Chad. the Republic of Congo. Equatorial Guinea, and Gabon: West African Economic and Monetary Union (UEMOA(, Benin, Burkina Faso, Crite dilvoire, Guinea-Bissau, Mali. Niger. Senegal, and Togo; Arab Common Market, the Arab Republic of Egypt, Iraq. Jordan, Libya. Mauritania. the Syrian Arab Republic. and the Republic of Yemen: Association of South-East Asian Nations (ASEAN). Brunei, Cambodia, Indonesia. the Lao People's Democratic Republic. Malaysia. Myanmar. the Philippines. Singapore, Thailand, and Vietnam: eangkok Agreement, Bangladesh, India. the Republic of Korea, the Lao People's Democratic Republic, the Philippines, Sri Lanka. and Thailand: East Asian Economic Caucus (EAEC), Brunei, China. Hong Kong (Chisel. Indonesia. Japan, the Republic of Korea, Malaysia, the Philippines. Singapore, Taiwan (China), and Thailand: Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikvstan, Turkey, Turkmenistan, and Uzbekistan; Gulf Co-operation Council (GCC). Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; South Asian Association for Regional Cooperation (SAARC), Bangladesh. Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka: and Arab Maghreb Union (UMA), Algeria, Libya. Mauritania, Morocco. and Tunisia. a. No preferential trade agreement. 6.5@) About the data Definitions Trade blocs are groups of countries that have Membership in the trade blocs shown is based * Exports within bloc are the sum of exports established special preferential arrangements on the most recent information available, from by members of a trade bloc to other members goveming trade between members. Although in the World Bank Policy Research Report Trade of the bloc. They are shown both in U.S. dollars some cases the preferences-such as lower Blocs (2000a) and from consultation with the and as a percentage of total exports by the tariff duties or exemptions from quantitative World Bank's international trade unit. Although bloc. * Total exports by bloc as a share of world restrictions-may be no greater than those avail- bloc exports have been calculated back to 1970 exports are the ratio of the bloc's total exports able to other trading partners, the general on the basis of current membership, most of (within the bloc and to the rest of the world) to purpose of such arrangements is to encourage the blocs came into existence in later years and total exports by all economies in the world. exports by bloc members to one another-some- their membership may have changed over time. times called intratrade. For this reason, and because systems of prefer- Data sources Most countries are members of a regional ences also change over time, intratrade in ear- Data on merchandise trade flows are published integration arrangement, and more than a third lier years may not have been affected by the of the world's trade takes place within these same preferences as in recent years. In addi- Ye and Direction of Trade Statistics Yearbook and Direction of Trade Statistics arrangements. The structure of regional tion, some countries belong to more than one Quarterly,thedatainthetablewerecalculated arrangements varies widely, but the main trade bloc, so shares of world exports exceed using the Dition oT etab ase. 347 objective is the same: the reduction of trade 100 percent. Exports of blocs include all com- Usite ioFs ConfTrade daaa barriers among member countries. But effective modity trade, which may include items not speci- The United Nations Conference on Trade and Development (UNCTAD) publishes data on o integration requires more than reducing tariffs fled in trade bloc agreements. Differences from D)pd intratrade in its Handbook of International Trade : and quotas. Economic gains from competition previously published estimates may be due to E and scale may not be achieved unless other changes in bloc membership or to revisions in and Development Statistics. The information on trade bloc membership is from the World j barriers that divide markets and impede the free the underlying data. B Pe Bank Policy Research Report Trade Blocs (D flow of goods, services, and investments are lifted. (2000a) and the World Bank's international o For example, many regional trade blocs retain 5trade unit. contingent protection or restrictions on intrabloc F e trade. These include antidumping, countervailing Exports within small regional blocs is often , duties, and "emergency protection" to address much higher their share of exports to the CL balance of payments problems or to protect an rest of the world ... . industry from surges in imports. Other barriers 25 en include cumbersome and costly border formalities, differing product standards, and 20 discrimination in public procurement. Membership in a regional integration arrangement may reduce the frictional costs of trade, increase the credibility of reform initiatives, 10 and strengthen security among partners. But making it work effectively is a challenge for any govemment. All sectors of an economy may be affected, and some sectors may expand while D others contract, so it is important to weigh the 0 _ _ _ _ Cm_ potential costs and benefits that membership Market may bring. Asia Pacific Economic Cooperation (APEC), ... but for some of the larger trade blocs, which has no preferential arrangements, is in- exports to the rest of the world are high as cluded in the table because of the volume of a share of world trade trade between its members. The table shows 80- the value of merchandise intratrade for impor- 70 tant regional trade blocs (service exports are 60 excluded) as well as the size of intratrade rela- tive to each bloc's total exports of goods and 50s- the share of the bloc's total exports in world 4 : . exports. The data on country exports are drawn from 30 the Intemational Monetary Fund's (IMF) Direction 20 of Trade database and should be broadly consis- 10 tent with those from other sources, such as the United Nations Statistics Division's Commodity ° APEC European NAFTA Trade (COMTRADE) database. However, trade Union flows between many developing countries, par- 0 Exports wihin blom (% of bloc total) ticularly in Africa, are not well recorded. Thus the M Exports by bloc (% of world's total) value of intratrade for certain groups may be un- Source: Table 6.5 based on the lnternatio.al Monetay unds derstated. Data on trade between developing and D,ecti.n a Trade database. high-income countries are generally complete. 6.6 Tariff barriers All products Primary products Manufactured products Share of Share of Simple Standard Wefghted lines with lines with Simple Weighted Simple Weighted mean deviation of mean international specific mean mean mean mean tariff tariff rates tariff peaks tariffs tariff tariff rariff tariff Y ear _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Albania 1997 17.0 8.5 14.4 56.0 0.0 15.7 12.8 17.2 15.2 Ageri193 2. 16.6 15.4 4580.0 18.0 8.9 23.0 18.9 1998 24.6 16.5 17.3 51.3 0.0 18.1 14.6 25.4 18.7 Argentina 1992 12.2 7.7 12.7 31.0 0.0 10.0 5.8 12.5 13.8 -. - ~~~~~ ~~~~ ~ ~~~~2000 12.6 7.5 10.5 42.5 0.0 10.6 4.9 12.8 11.3 Australia -.1991 -13.1 '14.3 9.1 30.3 1.4 3.2 1.6 14.3 10.3 2000 5.8 6.5 4.0 6.2 1.0 1.7 0.8 6.2 4.5 Bangladesh 1989 106.6 79.3 88.4 98.2 1.3 79.9 53.5 110.5 112.2 2000 21.3 13.6 21.0 51.8 0.0 24.1 18.6 21.0 21.7 Belarus 1996 12.2 8.7 8.8 30.9 0.0 9.6 6.4 13.0 10.5 348 1997 13.0 8.3 9.5 31.9 0.0 10.4 7.0 13.8 11.2 Bhutan - --- - 1996 18.6 14.9 20.3 54.3__ 3.7 24.2 20.4 .... 17.9- 20.2_ 2 Bolivia 1993 9.7 1.1 9.4 0.0 0.0 10.0 10.0 9.7 9.3 to ~~~~~ ~~ ~ ~~~~ ~~~ ~~~ ~ ~~1999 --- . 5 1.6 9.1 0- 00.0 O 10.0 10.0 9.4 8.9 Br a z i I1989 42.2 17.2 31.9 92.2 0.5 37.9 18.8 42.5 37.9 - . . - ~~~~~ ~~~~ ~ ~~2000 14.4 7.0 12.7 57.4 0.0 11.3 6.3 14.7 15.0 0) B s 1993 238 10.7 21.7 65.1 0.0 25.9 18.0 23.5 23.5 0L----- ---1 9i _ _ _ - .. .--. .-b o- . . 61 . 8 . ...... 1 . o cameroon - 94 19.2 10.4 13.9 53.60. 2.6 13 187 38 >o 1995 18.5 9.5 14.5 51.3 0.3 21.1 17.0 18.0 13.5 0 Canada 1989 8.6 7.4 6.0 14.8 3.4 5.1 2.5 9.3 6.7 ~~~~~~~~~~~~~~~~~0 ~ ~ ~ ~ ~ ~ ~ ~ ~ ... .. 0 2000 3.9 6.7 0.8 9.1 4.1 2.8 0.4 4.2 0.9 Central AfricaniRep ------- 1995 18.0 10. 7 13.4- 517A 20.6 13.7 16.8 13.2 --1997 18.6 10. 14.1 54.2 0.2 21.1 14.3 17.4 14.0 Chad 1995 15.7 10.8 14.65 43.4 0.0 15.9 16.1 15.6 13.5 1997 15.7 10.8 14.6 43.4 0.0 15.9 16.1 15.6 13.5 Chile 1992 11.0 0.5 11.0 0.0 0.0 11.0 11.0 11.0 10.9 2000 9.0 0.0 9.0 0.0 0.0 9.0 9.0 9.0 9.0 - . 1992 ~~~~ ~ ~~~~~~~41-.0 30.6 --32.2 77.6 -- 0.0 35.4 13.9 ---- 42.3 36.5 2000 16.3 ---10. 7 14.7 42.5 -00 16.5 18. 8 16-.2 --13.7 Hong Kong, Chn 1988 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1998 0.0 0.0 0.0 0.0 0.0 00 0.0 0.0 0.0 Clombia 1991 5.7 -- 8.2 6.1600 76 -556 62 -.. . ~~~~~~ ~ ~ ~~2000. 117 6.2 11.0 22.9 0.0 12.6 12.7 11.6 10.5 Congo. Rep.. 209 .6 9.3 16.3 62~.1 0.0 22.1 20.5 20.3 14.8 Congo. Rcp.~~~~~~~~~~~~~~~~~~~~~~~~~~~~------ -.. ~~~~~ ~~~~ ~~~ ~~~1997 17.6 86 17 380.0 17.8 15.2 17.5 17.0 tosta Rica ~~1995 10.3 8.0 8.6 293 0.1 13.0 10.5 10.0 8.0 Coata Rica~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~---------- - ~~~~~ ~~~~ ~~~~ ~~~~2000 5.4 7.5 3.7 0.4 0'0 9.6 6.1 4.8 3.2 C6te divoir 1993 25.3 11.9 22.2 76.3 0.0 26.6 21.6 25.1 22.6 1996 19.0 10.8 14.1 52.6 0.0 19.7 14.3 18.8 14.1 Cuba 1993 13.1 8.0 10. 2.8 0.0 13.7 8.3 13.0 11.6 1997 11.3 6.6 8.1 9.5 0.0 10.6 5.2 11.4 9.8 CzechR-epublc 19696.2 58 b4 0 8.1 - 4.1 66 6 1999 6.5 9.3 5.7 5,4 0.0 11.0 5.1 5.4 5.9 Dominican Repubic 1997 14.9 9.1 1. 3.30.0 16.5 10.4 14.7 17.8 E~cuadlor 1993 87 59 . 040. . . . 8.4 . -- ~~~~~ ~~~~ ~~~ ~~~~1999 12.8 6.4 li.i 36.4 0.0 12.5 10.4 12.9 11.3 Egypt, Arab Rep. 1995 25.6 332_ 16.6 53.1.. 1.2_ 24.2 7.6 .....25.8_ 22.4 ...- . . ~~~~~ ~~~1998 20.5 39.5 13,7 47.4 9.5 22.5 7.5 20.2 17.5 El Salvadlor 195 10. . 91 2. 0.0 12.6 10.2 9.9 8.8 . - ~~~~~ ~~~~ ~~~~ ~~~2000 7.4 8.6 6.5 9.8 0.0.- 10.4 8.2. -6.8 - 5.5 Eq u- a to r ial1 Gui-n e a 19 98 1-9-. 1 -9.7--- 15.3- ----54 0.2 21-.1 2-3.7 .... 18.4 13.6 Eatconia 1995 0.1 1.0 0.4 0.1 0.0 0.0 0.0 0.1 0.5 Ethiopia 1995 32.0 23.5 18.1 71.3 6.2 37.1 - 18.4 31.7 18.1 European Union 1989 4.1 5.9 3.8 3.9 18.2 8.7 2.7 2.7 4.4 2000 2.4 4.4 18 1.8 7.2 4.6 1.3 1.8 1.9 Gabon 1995 20.4 9.6 165 1 6 0.0 23.4 20.0 19.7 1. 1998 20.4 9.8 16,0 61.1 0.3 - 23.4 20.2 19.7 14.7 G-eorgila- 1999 9.9 3.2 10.1 0.0 1.0 11.9 12.0 9.5 8.3 fOatfa for Taiwan, China 1989 12.3 9.5 10.0 16.9 0.5 17.3 8.4 11.2 10.7 2000 7.8 8.2 3.9 9.6 0.3 13.6 6.4 6.7 3.3 6.6 All products Primary products Manufactured products Share of Share of Simple Standard Weighted lines with lines with Simpie Weighted Simple Weighted mean deviation of mean international specific mean mean mean mean tariff tariff rates tariff peaks tariffs tariff tariff tariff tariff Year % Ghana 1993 14.6 9.0 11.2 42.3 0.0 20.2 16.2 13.8 8.7 Gua-te-mal1a- 1995 10.0 7.4 8.7 - 2563 0.0 12.6 - 10.2 9.6 8.1 2000 7.2 7.8 - 5.8 9.6 -0.0 9.3 7.5 6.9 5.2 Honduras - 1995 -- 9~~~~~~~~~~6-8 9 75 0 ----------00 13 12.9- 9.3 7.6 2000 7.9 7.5 8.3 25.9 0.0 - 11.6 12.3 7.3 6.7 Hungary 1991 12.7 10.9 iO 1 18.9 00--14.7 5.5 12.4 11.8 1997 8.2 14.7 4.5 10.4 0.0 25.9 6.8 4.6 3.9 ----3-.-e. 193I .6 7. 34.5 00 38 5.2 3.6 2.8 1996 5.7 13.,1 3.6 4.2..-07 9.5 5.9 49 9 India 1990 79.0 ~~~~~~~~~ ~~~~~ ~ ~~~~43.6 49.6 97.0 0.9 - 69.1 25.4 80.2 69.9 1999 32.5 12.3 28.5 93.1 0.6 - 30.9 23.2 32.8 32.7 349 Idnesia 1989 21.9 19.7 13.0 50.3 0.3 - 19.9 - 5.8 22.3 15.6 N) 2000 8.4 10.8 5.2 11.2 0.0 6.3 2.8 8.9 6.7 0 Iran, Islamic Rep. ~ --- -----2-0060 4.9-42-31-----06---- -0 2-.9- 0-O.9 ------- 5.1 3.8 P Isra-e-l----------------- ------- -- --- -7.7 12.2 4. 15.7 0.0 5.3 1.9 8.1 4.4 0 .m-c 1 996 20.8 9.0 17.8 44.0 41.2 21.2 14.2 20.7 20.9 2000 10.6 11.3 9.6 33.3 0. a is 9.~0 94 4 10.0 CD Japan 1989 5.6 7.9 3.0 -9.1 -3.3 8.3 33 4.7 2.7 (D .... .. - ---- ---- - - - - - - - - - - - -- --- ------ ---- - - - - - - --- - ------ - ---0 2000 4.5 7.0 2.0 7.1 - 30 - 8.3 3.4 3.2 1.2 Jordan ~~~~~~~~~---2 0-0-0 22.8 16.6 18.9 --63.3 0.4 26.2 . 16.9 22.3.19.8 ( K-e-nya 1994 31.9--- 138-0. 87.0 1.2 32.2 17.0 31.9 23.3 E - . - -- - ~~~~~ ~~~~ ~~2000 - 19.3 12.7 12.4 34.7 9.6 18.1 10.6 19.4 13.0 K o-r-e-a, R ep 99 1. . 0 11.8 .15 146 5.5 14.9 13.5 ------ - - -------- - - --- - ---------- - ---------- - ---- -0 199 8.6 5.9 5.9 4.8 0.7 12.3 5.5 7.8 6.1 Kyrgyz Repblic 1995 0.0 0.0 0 -----00--197 --00 00 - 00 0 Lao POR 2000 9.6 7.8~~~~~~~~~~~~~~~~--------- 14-7 -------- 1-17 35-- - -- 18.6 24.5 8.8 8.9 Latvia 1996 4.3 7.5 2.2 -2.2 -0.0 8.3 1.5 3.2 2.6 1997 5.6 9.2 3.2 2.9 0.0 10.0 4.0 4.2 2.9 L eban-on 1999-12.6----9-- -12-.2- 23.9 0.1 13.0 11.8 12.5 12.4 2000 17.9 14.2 19.1 37.1 1.7 23.9 24.3 16.9 16.0 Libya 1996 27.3 37 0 ~~~~~ ~~~~ ~~~~ ~~~~ ~ ~~21.3 58.3 1.4 24.8 9.6 27.7 25.7 Lithuania 1995 3.8 8.5 --26 -- -6.9 0.0 8.3 3.7 2.6 1.8 1997 3.8 7.9 2.3 6.4 0.0 3.3 2.8_ 1.8 Madagascar 1995 7.6 5 9 5.2 6.0 ~~~~ ~~~~ ~~~~ ~~~~ ~~~~~~~~~~~0.0 6.1 2.7 7.8 6.3 Malawi ~ ~ ~ - 1994 31.3 14.6 22.3 87.0 0.0 27.7 12.8 31.7 26.6 M a-- --------- - --law i-- --- -- ----- 1998 19.6 14.9 11.5 13.9 62.9 17.8 6.1 19.8 13.4 Malaysia 1988 17.0 15.1 9.4 46.1 7.2 15- iS2 4.6 17.4 10.5 - . - 1997 9.3 ~~~~ ~ ~~~~ ~~~~ ~~~33.3 6.0 24.7 04 70 100 10.3 5.4 Mali 1995 16.3 12.6 10.3 41.8 0.0 -- 18.2 13.5 16.1 8.5 Mauritius 1995 36.2 28.5 23.5 64.7 0.0 26.2 25.7 37.5 22.7 1998 31.0 27.8 23.8 28.9 50.0 23.8 15.2 32.0 26.9 Mexico 1991 13.2 4.3 11.9 18.9 0.0 12.2 8.2 13 3 13. 2000 16.2 9.2 15.4 50.7 0.5 18.3 20.2 16.1 14.8 Moldova 1996 6.3 9.2 1.6 19.9 1.2 10.3 0.8 4.8 2.7 2000 4.6 5.5 2.3 0.1 0.7 9.3 1.7 4.0 2.8 Morocco 1993 66.5 29.5 45.4 96.8 0.1 55.1 30.2 681 1 55.8 2000 33.6 22.0 25.8 79.9 0.~0 42.4 27.1 32.3.25.3 Mozambique 1994 5.0 0.0 5.0 0.0 0.0 5.0 5.0_____5.0 5.0 1997 16.8 14.3 17.4 37.7 0.0 - 199 - 22.0__ __16.2 15.5 Nepal 1993 21.9 17.8 15.9 58.9 1.0 11 9 9.3 23.6 19.1 2000 17.9 20.9 17.7 18.7 7.9 13.5 13.8 19.0 19.6 New Zealand 1992 10.4 11.0 8.5 36.0 2.8 5.9 3.7 11.2 9.6 2000 3.3 4.4 2.3 0.0 5.8 1.6 0.5 3.6 2.9 Nicaragua 1995 7.5 7.9 5.6 20.4 0.0 8.7 7.1 7.4 4.6 2000 3.2 47 2 0.2 0.0 51 38 2.9 2.3 Nigeria .--1989 --------2,7..9 - - -2-0. 1 ---------24.7 64.2 0.4 _33.7 32.5 27.3 2-2.5 1995 21.8 15.7 20.0 9.7 80.5 29.5 20.8 20.2 19.9 Norway 1988 1.9 5.2 0.7 5.1 8.0 0.9 0.2 2.1 0.9 2000 2.9 15.6 1.1 4.4 8.6 8.7 2.5 2.1 0.8 0)6.66 All products Primary products Manufactured products Share of Snare of Simple Standard Weighted lines with lines with Simple Weighted Simple Weighted mean deviation of mean international specific mean mean mean mean tariff tariff rates tariff peaks tariffs tariff tariff tariff tariff Year _______________ _________ Oman ...-----1992_ 5.5 8.2 7.4 1.5 0.0 7.2 14.1 5.2 5.5 1997 4.8 0.9 4.7 0.0 0.0 4.0 3.1 4.9 5.0 Pakistan 1995 50.9 21.5 46.4 91.4 4.4 45.7 23.0_ 51.5 51.0 1998 46.6 21.2 41.7 __86.3 4.2 43,6 30.1 46.9 44.1 Panama 1998 9.5 5,6 8.7 ..........0.8 17.9 11.0 9.6 9.2 8.5 - 2000 9.5 7.4 7.8 1.4 0.1 12.4 7.7 9.0 7.8 Papua New Guinea 1997 21.1 18.5 15.6 33.4 1.9 32.8 21.7 19.6 13.7 Paraguay 1991 15.6 11.5 11.9 41.8 0.0 138 36 15.8 14.5 2000 1. 6.7 10.5 336 00 11.9 8.6 10.8 11.4 Peru 193 17.3 4.2 1. 233 00 18.4 15.5 17.2 16.1 350 ~~~~~~~~1999 13.0 26 12.6 12.0 0.0 13.7 13.5 12.9 12.3 V) Philippines 1989 2. 142 22.4 77.2 0.1 296 1. 277 36 0~~~~~~~~~~~~~~~~~.. 2606 7.6 7.9 3.8 8.8 0.0 11.9 7.5 6.9 3.3 C. oln 1991 12.2 9.0 10.5 24.6 0.0 11.8 8.2 12.2 11.3 L ~~~~~ ~~~~ ~ ~~~~ ~~~~2000 10.0 9.8 7.4 14.3 5.0 17.6 6.2 8.4 7.8 o Romania 1991 19.2 8.3 11.9 55.6 0.0 20.1 8.2 19.0 18.2 E 1999 15.2 16.2_ 12.4 36.0 0.0 25.4 11.0 12.9 12.8_ 0. . .. .. O Russian Federation 1993 7.8 9.8 6.2 3.2 0.0 3.4 3.9 9.4 7.5 > ~~~~~ ~~~~1997 13.9 8.5 11.3 35.5 0.0 11.5 10.3 14.8 11.8 Rwand 1993 28.3 26.8 25.5 59.6 1.7 37.8 36.7 27.5 21.7 Saudi Arabia 1994 12.5 3.3 10.7 10.3 0.1 12.2 9.1 12.6 11.0 3: ~~~~~~~~2000 12.3 3.1 10.3 8.2 4.1 11.9 7.9 12.4 10.9 Singapore 1989 0.6 3.0 1.1 0.3 1.6 0.7 2.5 0.6 0.6 0 19 56 66----- --- --------- -- - ------ (N4 1900.0 . 0.0 0.0 0.2 0.0 0.0 0.0 0.0 Slovenia 1999 11.8 6.5 11.4 20.8 3.1 11.9 7.5 11.8 12.3 South Africa' 1988 12.7 11.8 12.0 32.3 18,8 6.3 4.3 12.9 12.4 1999 8.5 10.2 4.4 21.9 20.6 8.0 1.8 8.6 5.1 Sri Lanka 1990 28.3 24.5 26.9 51.7 1.4 31.8 32.2 28.0 24.4 .. 000 9.9 9.3 7.4 .....22.0 0.7 15.3 14.8 9.2 5.2 Sudan 1996 5.3 11.9 3.8 8.9 0.0 12.4 3.3 4.7 4.0 Swit.zerland 1990 0.0 0.0 0.0 0.0 53.3 0.0 0.0 0.0 0.0 2000 0.0 0.1 00 0.0 31.2 0.0 0.0 0.0 0.0 Tanzania 1993 14.4 10.7 15.6 43.2 0.0 21.6 19.9 13.7 14.8 .~~~~~ ~~~~ ~ ~~~~2000 17.9 8.6 14.2 69.4 0.0 17.1 16.1 _18.0 13.5 Thailand 1989 38.5 19.6 33.0 72.8 21.9 30.6 24.2 39.6 35.7 2000 16.6 14.1 10.1 45.9 1.7 21.9 9.5 15.7 10.2 Trinidad and Tobago 1991 19.9 14.9 13.0 40.3 0.0 _26.9 10.9 18.7 14.2 1999 18.4 8.8 17.0 36.7 45.2 20.8 17.8 17.8 16.7 Tunisia 1990 28.3 10.1 25.9 97.1 0.0 24.4 17.4 28.7 28.6 1998 30.0 13.1 28.8 90.4 0.0 28.7 21.2 30.2 30.2 Turkey 1993 7.4 5.0 6.1 5.9 0.0 6.0 7.9 7.6 5.4 1997 8.1 12.8 ~~~~ ~~~~5.7 -8--.2 0.3 22. 5.2 5.9 5.8 Turkmenistan 1998 0.0 0.0 0.0 0.0 0.0 0.0 0.0- 0.0 0.0 Uganda __1994 16.8 9.3 13.6 53.6 0.0 .....17.0 17.5 16.8 12.4 _____ .. ~~~~~ ~~~~ ~~~2000 8.3 5.8 6.1 0.0 0.0 9.4 5.6 8.2 6.3 Ukraine 1995 9.0 9.4 9.5 14.5 0.0 13.1 15.6 7.6 6.4 1997 10.4 11.0 5.2 23.7 0.0 16.8 3.4 8.2 7.2 United States 1989 5.6 6.8 3.8 8.0 12.7 3.7 2.0 6.0 4.1 2000 4.0 10.7 1.8 5.8 8.0 4.3 1.4 4.0 1.9 Uruguay - 19912 -7.5 5.8 5.8 0.0 0.0 8.0 5.7 7.5 5.8 2000 11.1 8.3 6.2 37.9 6.9 9.1 2.5 113 7.8 Venezuela, RB 1992 15.7 11.4 64 4. . 77 1. 155 1. 2000 12.6 5.9 13.4 25.4 0.0 13.0 13.5 12.5 13.4 Vietnam 1994 12.8 17.:9 20.2 32.6 1.0 21.4 46.4 120 13.0_ 1999 15.,2 17.8 187 3. .5 22.5 34.9 14.3 14.8 Zambia 1993 25.2 11.0 179 90.9 0.0 29.5 12.4 24.5 20.0 1997 14.7 8.8 13.1 32.2 0.0 17.1 13.9 14.4 12.9 Zimbabwe ~~~1996 40.8 15.0 39.2 94.4 1.5 34.3 40.3 41.4 38.9 1998 21.4 20.1 16.4 45.1 0.0 26.2 20.4 20.9 15.9 a. Data refer to the South African Customs Unior (Botswana, Lesotho, Namibia, South Africa. and Swaziland). 6.6 About the data Definitions Poor people in developing countries work specific trading partners. In previous years the * Primaryproducts are commodities classified primarily in agriculture and labor-intensive indicators were based on most-favored-nation in SITC revision 2 sections 0-4 plus division manufactures, the very sectors that confront rates, which are equal to or higher than 68 (nonferrous metals). * Manufactured the greatest trade barriers. Removing barriers effectively applied rates. products are commodities classified in SITC to merchandise trade could increase growth by Two measures of average tariffs are shown: revision 2 sections 5-9 excluding division about 0.5 percent a year in developing countries. the simple and the weighted mean tariff. 68. * Simple mean tariff is the unweighted If trade in services (financial, business, Weighted mean tariffs are weighted by the value average of the effectively applied rates for telecommunications, and retailing services) of the country's trade with each of its trading all products subject to tariffs. * Standard were also liberalized, growth would be even partners. Simple averages are frequently a deviation of tariff rates measures the aver- higher. In general, tariffs in high-income countries better indicator of tariff protection than weighted age dispersion of tariff rates around the on imports from developing countries, though averages, which are biased downward because simple mean. * Weighted mean tariff is the low, are four times those collected from industrial higher tariffs discourage trade and reduce the average of effectively applied rates weighted countries. But protection is also an issue for weights applied to these tariffs. Specific duties- by the product import shares corresponding developing countries, which also have high tariffs duties not expressed as a proportion of the to each partner country. * International peaks on agricultural commodities, labor-intensive declared value-have not been included in this are tariff rates that exceed 15 percent. 351 manufactures, and other products and services. year's table, but work is under way to estimate * Specific tariffs are tariffs that are set on a In some developing regions new trade policies ad valorem equivalents. per unit basis or that combine ad valorem 8 could make the diflerence between achieving Some countries set fairly uniform tariff rates and per unit rates. important Millennium Development Goals- across all imports. Others are more selective, _1 S such as poverty reduction, lowering maternal setting high tariffs to protect favored domestic Data sources and child mortality, and improving educational industries. The standard deviation of tariffs is a At attainment-falling short by a large margin. measure of the dispersion of tariff rates around D(D Economies regulate their imports through a their mean value. Highly dispersed rates increase World Bank staff using the World Integrated combination of tariff and nontariff measures. the costs of protection substantially. But these Trade Soluton (WITS) system. Tariff data were The most common form of tariff is an ad valorem nominal tariff rates tell only part of the story. provided by UNCTAD. Data on global Imports E come from the United Nations Statistics C duty, based on the value of the import, but tariffs The effective rate of protection-the degree to od Division's COMTRADE database. a may also be levied on a specific, or per unit, which the value added in an industry is EJ basis or may combine ad valorem and specific protected-may exceed the nominal rate if the rates. Tariffs may be used to raise fiscal tariff system systematically differentiates among revenues or to protect domestic industries from imports of raw materials, intermediate products, foreign competition--or both. Nontariff barriers, and finished goods. which limit the quantity of imports of a particular Two other measures of tariff coverage are good, take many forms. Some cornmon ones shown: the share of tariff lines with intemational are quotas, prohibitions, licensing schemes, peaks (those for which ad valorem tariff rates export restraint arrangements, and health and exceed 15 percent) and the share of tariff lines quarantine measures. with specific duties (those not covered by ad Nontariff barriers are generally considered valorem rates). Some countries-for example, less desirable than tariffs because changes in Switzerland-apply only specific duties. an exporting country's efficiency and costs no The indicators in this table were calculated longer result in changes in market share in the from data supplied by the United Nations importing country. Further, the quotas or licenses Conference on Trade and Development that regulate trade become very valuable, and (UNCTAD). Data are classified using the resources are frequently wasted in attempts to Harmonized System of trade at the six- or eight- acquire these assets. A high percentage of digit level. Tariff line data were matched to products subject to nontariff barriers suggests Standard International Trade Classification a protectionist trade regime, but the frequency (SITC) revision 2 codes to define the commodity of nontariff barriers does not measure how much groups and import weights. Import weights were they restrict trade. Moreover, a widle range of calculated for 1995 using the United Nations domestic policies and regulations (such as Statistics Division's Commodity Trade health regulations) may act as nontariff barriers. (COMTRADE) database. Data are shown only for Because of the difficulty of combining nontariff the first and last year for which complete data barriers into an aggregate indicator, they are not are available. To conserve space, countries for included in this table. which only a single year is available and countries The table shows data on average tariffs, the that are members of the European Union have dispersion of tariff rates, the proportion of tariff not been included. Data for the whole of the lines with duties exceeding 15 percent, and the European Union are shown. proportion of lines subject to specific tariffs. The rates used in calculating the indicators are effectively applied rates, which reflect the rates actually applied to partners in preferential trade agreements such as the North American Free Trade Agreement. Countries typically maintain a hierarchy of trade preferences applicable to 6.7 Global financial flows Net private Foreign direct Portfolio Investment flows Bank and capitai flows Investment trade-related lending Bonds Equity $ millions $ millions $ millions $ millions $ millions 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan*-- Albania 31 142 0 143 0 0 0 0 31 - Algeria -424 -1,212 0 10 -16 0 0 4 -409 -1,226 Angola 235 1,206 -335 1,698 0 0 0 0 570 -492 Argentina -203 16,620 1,836 11,665 -857 4,847 13 450 -1,195 -343 Armenia 159 -- 140 ..0 ..0 -19 Australia 8,111 11,527 . .. .- Austria ... 653 9,066 Azerbaijan .. 175 -- 130 0 0 --45 352 Bangladesh 58 269 3 280 0 0 0 3 55 -14 B e larus 123 90 --0 0 --33 Belgium' 5,987 17,902 - 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..... (0 Benin 1 30 1 30 0 0 0 0 0 0 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ---- Bolivia 3 923 27 733 0 0 0 0 -24 190 Bosnia and Herzegovina 4 -0- 0 0 --4 E Botswana 77 27 95 30 0 0 0 0 -19 -3 0. - - -- - - o Brazil 563 45,672 989 32,779 129 164 0 5,016 -555 7,713 (D- - - - >, Bulgaria -42 1.114 4 1,002 65 0 0 5 -111 107 o Burkina Faso -1 10 0 10 0 0 0 0 -1 0 o Burundi -5 12 1 12 0 0 0 0 -6 0 3. Cambodia 0 126 0 126 0 0 0 0 0 0 o Cameroon -125 -21 -113 31 0 0 0 0 -12 -52 0 ---- Canada 7,581 62.758 --- -- - Central African Republic 0 5 1 5 0 0 0 0 -1 0 Chad -1 14 0 15 0 0 0 0 -1 -1 Chile 2.098 4,833 590 3.675 -7 672__ 320 18 1,194 469 China 8,107 58,295 3,487 38,399 -48 -2,451 0 22,198 4,668 148 Hong Kong, China - ------ ------ Colombia 345 3,130 500 2,376 -4 1.225 0 26 -151 -497 Congo. Dem. Rep. -24 1 -12 1 0 0 0 0 -12 0 Congo, Rep. -100 14 0 14 0 0 0 0 -100 0 Costa Rica 23 912 163 409 -42 220 0 0 -99 -20 C6te dIlvoire 57 -47 48 106 -1 -46 0 6 10 -113 Croatia ,- 2,451 -- 926 833 - 0 -- 692 Cuba Czech Republic 876 3,299 207 4,583 0 -325 0 617 669 -1.576 Denmark 1,132 34.192 - Dominican Republic 130 1,142 133 953 0 -4 0 74 -3 119 Ecuador 184 904 126 710 0 0 0 0 58 194 Egypt. Arab Rep. 668 1,967 734 1,235 -1 0 0 619 -65 114 El Salvador 8 338 2 185 0 132 0 0 6 22 Eritrea 35 --35 0 0 --0 Estonia 485 387 110 -29 --16 Ethiopia -45 42 12 50 0 0 0 0 -57 -8 F'inland 812 9,125 France 13,183 43,173 Gabon 103 142 74 150 0 0 0 0 29 -8 Gambia,The -8 14 0 14 0 0 0 0 -8 0 Georgia .. 155 - 131 -0 0 ..24 Germany . - 2,532 189,178 - Ghana -5 71 15 110 0 0 0 17 -20 -57 Greece ---- 1,005 1,083 -- .- Guatemala 44 178 48 230 -11 -31 0 0 7 -22 Guinea -1 63 18 63 0 0 0 0 -19 0 Guinea-Bissau 2 0 2 0 0 0 0 0 0 0 Haiti 8 13 8 13 0 0 0 0 0 0 Honduras 76 301 44 282 0 0 0 0 32 19 6.7~ Net private Foreign direct Portfolio Investment flows Bank and capital flows Investment trade-related lending Bonds Eqluity $ nmillions $ millions $ millions $ millions $ millions 1990 2000 1990 2000 1990 2000 ±990 2000 1990 2000 Hungary -308 1,721 0 1,692 921 -1,218 150 0 -1,379 1,247 India 1,872 8,771 162 2,315 147 4,916 105 2,117 1,458 -576 Indonesia 3,235 -11,210 1,093 -4,550 26 -2,050 312 379 1,804 -4.988 Iran, Islamic Rep. -392 -610 -362 39 0 0 0 0 -30 -649 Iraq . .. ireland ... 627 22,778 - Israel 151 4,392 . .. Italy 6.411 13,175 . .. Jamaica 92 898 138 456 0 485 0 0 -46 -43 Japan ... 1,777 8,227 - .......353 Jordan 254 455 38 558 0 -95 0 12 216 -20 Kazakhstan -. 1.900 1,250 350 0 .. 300 Kenya 122 53 57 111 0 0 0 4 65 -61 Korea, Dem. Rep. . .- -- .. Korea, Rep. 1 038 13,215 788 9,283 151 1,333 518 7,784 -418 -5,185 a Kuwait -.. .16 .. .. CD Kyrgyz Republic .. -65 ..-2 -.0 - .0 - -62 I - - --- --- -- -- -- - - - --- - - -- - --- --- ---- ---. --- -- - -- - -- - -- - -- - -0 Lao PDR 6 72 6 72 0 0 0 0 00 20 Latvia .. 583 407 . -30 ..0 .. 206 C Lebanon 12 2,028 6 298 0 1,040 0 4 6 687 ------ ----------- -~ ~~~~ ~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~a Lesotho 17 ill 17 118 0 0 0 0 0 -7 2 -- - -------------- 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Liberia 0 12 0 12 0 0 0 0 0 0 0 Lithuania .. 799 .. 379 . 312 151 .. -43 Macedonia, FYR . 18.. 760 --0 -12 Madagascar 7 83 22 83 0 0 0 0 -15 0 Malawi 2 45 0 45 0 0 0 0 2 0 Malaysia 770 3,228 2,333 1,660 -1,239 477 293 542 -617 550 Mali -8 76 -7 76 0 0 0 0 -1 0 Mauritania 6 3 7 5 0 0 0 0 -1 -2 Mauritius 86 -7 41 266 0 -150 0 0 45 -123 Mexico 8,253 11,537 2,634 13,286 661 -2,636 563 3.517 4.396 -2,631 Moldova .. 209 . 128 ..0 -0 -. 81 Mongolia . 27 ..30- 0. 0 ..-3 Morocco 341 -293 165 10 0 -30 0 147 176 -419 Mozambique 35 138 9 139 0 0 0 0 26 -1 Myanmar ~~~~ ~~~~ ~~153 188 161 255 0 0 0 0 8 -66 Namibia - .. .- .- Nepal -8 -4 6 4 0 000 -14 -8 Netherlands ... 10,676 54,138 New Zealand . - 1,735 3,209 . .. .- Nicaragua 20 395 0 254 0 0 0 0 20 141 Niger 9 13 -1 15 0 0 0 0 10 -2 Nigeria 467 908 588 1,082 0 0 0 2 -121 -177 Norway -.- 1,003 5,882 ....- Oman -259 56 141 23 0 0 0 11 -400 23 Pakistan--- 181 -53 244 308 0 0 0 0 -63 -361 Panama 127 947 132 _ 603 -2 249 0 0 -4 95 Papua New Guinea 204 128 155 130 0 0 0 48 49 -50 Paraguay 67 -16 76 82 0 0 0 0 -9 -98 Peru 59 1,553 41 680 0 0 0 205 18 668 Philippines______ 639 2,459 530 2,029 395 797 0 290 -286 -656 Poland 71 13,195 89 9,342 0 2,450 0 871 -18 532 Portugal ... 2,610 6,227 . .. Puerto Rico .. .- . Romania 4 1,900 0 1,025 0 -75 0 0 4 950 Russian Federation 5,556 2,200 0 2,714 310 -1,018 0 1,075 5,246 -571 6.7 Net private Foreign direct Portfoio Investment flows Bank and rcapitai flows investment trade-related lending Bonds Equity $ millions $ millions $ millions $ millions $ millions 1990 2000 1990 2000 1990 2000 1990 2000 1.990 2000 Rwanda 6 14 8 14 0 0 0 0 -2 0 Saudi Arabia... Senegal 42 106 57 107 0 0 0 0 -15 -2 Sierra Leone 36 1 32 1 00 0 0 4 0 Singapore ..5,575 6,390 Slovak Republic 278 2,185 __ 0 2,052 0 758 0 0 278 -625 Slovenia .. 176 Somalia 6 0 6 0 0 0 0 0 0 0 South Africa -. 2,736 961 1,193 864 -. -282 354 Spain 13,984 36,023 - Sri Lanka 53 262 43 173 0 -50 0 6 10 133 (n Sudan 0 392 0 392 0 0 0 0 0 0 Cc Swaziland 28 33 30 -44 0 0 0 0 -2 0 Sweden ... 1,982 22,125-- Switzerland -.. 5,987 17,902 0)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~---- --- E Syrian Arab Republic 62 107 71 ill 0 0 0 0 -9 -4 o Tajikistan__ 64 ---- -- 24_ .0 0 40 > Tanzania 5 182 0 193 0 0 0 0 5 -11 0) --- -- - - 0 Thailand 4,380 -1,383 2,444 3,366 -87 -1,218 449 1,044 1,574 -4,575 o Togo __0 30 0 30 0 0 0- 0 0 0 Trinidad and Tobago ___ -69 673 109 650 -52 128 0 0 -126 -106 o Tunisia -121 966 76 752 -60 -371 0 0 -137 585 Turkey 1,782 11,416 684 982 597 6,484 35 2,701 466 1,250 Turkmenistan -. -- 0 --0- - Uganda 16 231 0 220 0 0 0 0 16 11 Ukraine -- 927 595 -33 0 -- 365 United Arab Emirates - Unit ed Kingdom 33,504 133,974 - -- United States .-- 48,490 287,680-- -- - Uruguay -192 574 0 298 -16 284 0 0 -176 -8 Uzbekistan -- 18 - 100 --0 -.0 --82 Venezuela. RB -126 5,454 451 4,464 345 -751 0 71 -922 1,670 Vietnam 16 581 16 1,298 0 0 0 0 0 -717 Went Bank and Gaza-- - Yemen. Rep. 30 --201 -131 -201 0 0 0 0 161 0 Yugoslavia, Fed. Rep. _ __-837 0 67 0 0 0 0 0 -904 0 Zambia 194 191 203 200 0 0 0 0 -9 -9 Zimbabwe 85 29 -12 79 -30 0 0 1 127 -50 Low income 6.636 4,581 2,201 6,562 142 2,787 416 2,528 3,876 -7,296 Middle Income- 35,937 221,265 21,918 160,129 1,062 14,091 2,343 48,340 10,614 -1,295 Lower middle income 18.660 79,672 8,724 61,018 543 -2,071 449 25,902 8,944 -5.177 Upper middle income 17,045 141,329 13,194 99,087 519 16,162 1,894 22,438 1.438 3,642 Low & middie Income 43,556 225,846 24,119 166,691 1,204 16,879 3,743 50,867 14,490 -8,591 East Asia & Pacific 19,402 65,693 11.135 52,130 -802 -3,113 2,290 32,285 6,779 -15.609 Europe & Central Asia 7,692 45,446 1,051 28,495 1,893 8,598 235 5.391 4,513 2,962 Latin America & Carib. 12,630 97.305 8,177 75,088 145 4.986 1,111 9.378 3,196 7,853 Middle East & N. Africa 384 1,074 2,458 1,209 -148 544 0 795 -1,926 -1.474 South Asia 2.162 9,254 464 3,093 147 __ 4,866 105 2,126 1,446 -831 Sub-Saharan Africa 1,287 7,074 834 6,676 -31 997 2 893 482 -1,492 High Income . - 175,835 1,001,296..- - Europe EMU 58,480 401,868 - a. Includes Luxembourg 6.7 @0 About the data Definitions The most recent wave of global integration, and thus omit nonequity cross-border * Net private capital flows consist of private starting around 1930 and continuing today, has transactions such as intrafirm flows of goods debt and nondebt flows. Private debt flows seen improvements in the investment climate and services. For a detailed discussion of the include commercial bank lending, bonds, and and an opening to foreign trade and investment data issues see the World Bank's World Debt other private credits; nondebt private flows are in many developing economies. F'rivate capital Tables 1993-94 (volume 1, chapter 3). foreign direct investment and portfolio equity flows to developing economies have increased Portfolio flow data are compiled from several investment. * Foreign direct investment is net dramatically, from around $44 billion in 1990 official and market sources, including Euromoney inflows of investment to acquire a lasting to $257 billion in 2000, while official flows databases and publications, Micropal, Lipper management interest (10 percent or more of have decreased from $57 billion to $39 billion Analytical Services, published reports of private voting stock) in an enterprise operating in an during the same period. Foreign direct investment houses, central banks, national economy other than that of the investor. It is investment has become the major form of securities and exchange commissions, national the sum of equity capital, reinvestment of international finance for developing countries, stock exchanges, and the World Bank's Debtor earnings, other long-term capital, and short- accounting for about 70 percent of the private Reporting System. term capital, as shown in the balance of capital flows in 2000. Mergers and acquisitions Gross statistics on international bond and payments. * Portfolio investment flows are net were the most important source of this increase equity issues are produced by aggregating and include non-debt-creating portfolio equity 355 in foreign direct investment, especially those individual transactions reported by market flows (the sum of country funds, depository N resulting from the privatization of public sources. Transactions of public and publicly receipts, and direct purchases of shares by ° companies. guaranteed bonds are reported through the foreign investors) and portfolio debt flows (bond The data on foreign direct investment are Debtor Reporting System by World Bank member issues purchased by foreign investors). * Bank E based on balance of payments data reported by economies that have received either loans from and trade-related lending covers commercial the International Monetary Fund (IMF), the International Bank for Reconstruction and bank lending and other private credits. CD supplemented by data on net foreign direct Development or credits from the International _ ---D investment reported by the Organisation for Development Association. Information on private Data sources 3 3 Economic Co-operation and Development nonguaranteed bonds is collected from market D The data in this table are compiled from a- (OECD) and official national sources. The sources, because official national sources T internationally accepted definition of foreign reporting to the Debtor Reporting System are varety of public and private sources, including direct investment is that provided in the fifth not asked to report the breakdown between theWorld Bank's Debtor ReportingSystem, the o edition of the IMF's Balance of Payments Manual private nonguaranteed bonds and private IMF's International Fnancial Statistics and (1993). nonguaranteed loans. Information on Balance of Payments databases, and other sources mentioned in About the data. These Under this definition foreign direct investment transactions by nonresidents in local equity data are also published in the dank's has three components: equity investment, markets is gathered from national authorities, . a reinvested earnings, and short- and long-term investment positions of mutual funds, and Global Development Finance 2002. intercompany loans between parent firms and market sources. foreign affiliates. However, many countries fail The volume of portfolio investment reported to report reinvested earnings, and the definition by the World Bank generally differs from that of long-term loans differs among countries. reported by other sources because of differences Foreign direct investment, as distinguished from in the classification of economies, in the other kinds of intemational investment, is made sources, and in the method used to adjust and to establish a lasting interest in or effective disaggregate reported information. Differences management control over an enterprise in in reporting arise particularly for foreign another country. As a guideline, the IMF suggests investments in local equity markets because that investments should account for at least 10 clarity, adequate disaggregation, and percent of voting stock to be counted as foreign comprehensive and periodic reporting are lacking direct investment. In practice, many countries in many developing economies. By contrast, set a higher threshold. capital flows through international debt and The OECD has also published a definition, in equity instruments are well recorded, and for consultation with the IMF, Eurostat, and the these the differences in reporting lie pnmarily in United Nations. Because of the multiplicity of the classification of economies, the exchange sources and differences in definitions and rates used, whether particular tranches of the reporting methods, there may be more than one transactions are included, and the treatment of estimate of foreign direct investment for a certain offshore issuances. country and data may not be comparable across countries. Foreign direct investment data do not give a complete picture of international investment in an economy. Balance of payments data on foreign direct investment do not include capital raised locally, which has become an important source of financing for investment projects in some developing cciuntries. In addition, foreign direct investment data capture only cross-border investment flows involving equity participation Net financial flows from Development 6.8 Assistance Committee members Net official Other Private Net grants Total net development official flows by NGOs flows assistance flows Contributions Foreign Bilateral Multilateral Private Bilateral Bilateral to multilateral direct portfolio portfolio export TotalI grants loans institutions Total investment investment investment credits $ millions, 2000 Australia 987 758 229 573 -219 -726 507 .. 150 1,491 Austria 423 260 -3 167 21 560 421 139 63 1,067 Belgium 820 477 343 -9 1,394 1,441 -494 .. 447 75 2,281 Canada 1,744 1,1B4 -24 583 5 4,621 3,814 621 -14 113 6,483 Denmark .1,664 1,011 13 641 -3 -482 482 . . 32 2,176 Finland 371 219 -2 154 2 672 493 -494 .. 673 5 1,050 France 4,105 3,116 -287 1,276 14 1,439 _ 2,740 -1,301 .. . . 5,557 Germany 5,030 2,696 -10 2,343 -456 7,000 4,571 2,635 -1,684 1,478 846 12,420 Greece 226 9 7 1 127 3 ..229 356 Ireland -235 155 ..80 416 -. 416 90 741 Italy 1,376 525 -148 999 -103 9,537 1,414 7,292 .. 832 37 10.846 0~~~~~~~~------- 0 Japan 13,508 5,678 4,090 3,740 -5,200 2,725 2,874 702 -52 -799 231 11,264 o Luxembourg 127 93 33 .. . . . . 7 133 - ~ Netherlands 3,135 2,334 -92 892 38 3,469 2,135 1.980 -646 306 6,947 a) New Zealand 113 85 ..28 17 17 -. - . 12 142 E - - - - - - - - - - . . . . . CL Norway --1,264 925 9 33 . -5 -36 --31 179 1.437 0 a, Portugal 271 320 -141 92 78 4,273 4,011 ..262 4.622 a) Spain 1,195 603 117 475 3 22,272 22,286 -.-14 23,471 Sweden 1,799 1.222 19 557 -. 2,127 871 . .. 1,256 26 3,952 0 Switzerland 890 608 20 263 8 997 1,134 .. -638 500 159 2,054 N4 United Kingdom 4,501 2,563 146 1,792 -72 2,093 834 1,706 -- -447 536 7,058 -- - _----- - -- ------ ----- ---- United States 9.955 8,093 -688 2,550 562 10,666 18,456 -10,724 -365 3,299 4,069 25,252 Total -53-,737 33,022 3,021 17,694 -4,537 74,537 67,234 3,046 -3,385 7,642 6,935 130,673 Net official Other Private Net grants Total net aid official flows by NGOs fiows flows Contributions Foreign Bilateral Private Bilateral Bilateral to multilateral direct portfolio export Total grants loans institutions Total investment investment credits $ million-s, 2000 ------ - ---- Australia 8 2 6 3 -1,164 -646 ..-1,154 Austria 187 144 43 2,090 2,090 8 2,285 Belgium 74 5 _69 12 -175 17 -188 -4 10 -78 Canada 165 165 1,652 1,199 1,139 78 -18 55 3,070 Denmark 189 104 15 7 1 67 284 284 .. 13 554 Finland 58 33 0 25 0 1,009 _ 882 123 3 1,066 France 1,657 1.001 83 573 -34 10,393 5,221 .. 12,016 Germany 647 325 -102 424 499 20,123 11,156 9,187 -220 60 21,330 Greece 12 10 -2 .. 12 Ireland ------- - Italy 406 16 197_ 193 _ 196 2,821 144 1,382 1,296 3.424 Japan . . ... -----54_____ 171 -------263 39 492 3,504 3.332 -271 443 3,942 Luxembourg 2 _ 2 ... .2 Netherlands 306 228 -21 99 -10 599 2,341 -2,412 671 - 895 New Zealand 0 0 ...0 Norway 27 27 -..4 1,294 1,257 37 1,325 Portugal 27 0 ..26 .. 1,067 1,060 ..7 -. 1,093 Spain 12 12 0 .. 1,747 1,747 .__1,759 Sweden 122 119 0 3 -1 1,734 1.902 0 -168 1,855 Switzerland 58 57 1 -, 6,460 6,305 0 155 8 6,526 United Kingdom 439 88 0 350 4 -154 -2,045 3.026 -1,135 7 297 United States 2,506 2,435 27 45 825 17,015 16,101 503 411 2,362 22,708 Total ____6,848 4,944 -64 1,968 3,708 69,848 52,286 11,429 1,478 2,524 82,928 Note: Totals may not sum due to gaps in reporting. -~~~~~~ 6.8 0 About the data Definitions The high-income members of the Development the World Food Programme and the United Na- * Net official development assistance com- Assistance Committee (DAC) of the Organisation tions High Commissioner for Refugees, which prises grants and loans (net of repayments) for Economic Co-operation and Development revised their data from 1996 onward). that meet the DAC definition of ODA and are (OECD) are the main source of official external DAC maintains a list of developing countries made to developing countries and territories finance for developing countries. Net disburse- and territories that are aid recipients. Part I of in part I of the DAC list of aid recipients. * Net ments of official development assistance (ODA) the list comprises those considered by DAC official aid comprises grants and loans (net of by some important donor countries that are not members to be eligible for ODA. Part i1 of the repayments) that meet the conditions for in- DAC members are shown in table 6.8a. The main list, comprises countries in transition: more clusion in ODA, and are made to countries and table shows the flow of official and private finan- advanced Central and Eastern European coun- territories in part 11 of the DAC list of aid recipi- cial resources from DAC members to official and tries and the new independent states of the ents. * Bilateral grants are transfers of money private recipients in developing and transition former Soviet Union and more advanced devel- or in kind for which no repayment is required. economies. DAC exists to help its rnembers co- oping countries. Flows to these recipients that * Bilateral loans are loans extended by gov- ordinate their development assistance and to meet the conditions of eligibility for inclusion in emments or official agencies that have a grant encourage the expansion and improve the effec- ODA are termed official aid. element of at least 25 percent (calculated at a tiveness of the aggregate resources flowing to The data in the table were compiled from re- rate of discount of 10 percent) and for which 357 developing and transition economies. In this plies by DAC member countries to question- repayment is required in convertible curren- capacity, DAC monitors the flow of all financial naires issued by the DAC Secretariat. Net flows cies or in kind. * Contributions to multilateral 0 resources, but its main concern is ODA. DAC of ODA, official aid, and other official resources institutions are concessional funding received has three criteria for ODA: it is undertaken by are defined as gross disbursements of grants by multilateral institutions from DAC members o the official sector. It promotes the economic and loans minus repayments on earlier loans. in the form of grants or capital subscriptions. development and welfare of developing coun- Because the data are based on donor country * Other official flows are transactions by the tries as a main objective. And it is provided on reports, they do not provide a complete picture official sector whose main objective is other concessional terms, with a grant element of at of the resources received by developing and than development or whose grant element is least 25 percent on loans (calculated at a rate transition economies, for three reasons. First, less than 25 percent. * Private flows consist of discount of 10 percent). flows from DAC members are only part of the of flows at market terms financed from private This definition excludes military aid and aggregate resource flows to these economies. sector resources. They include changes in hold- nonconcessional flows from official creditors, Second, the data that record contributions to ings of private long-term assets by residents which are considered other official flows. The multilateral institutions measure the flow of re- of the reporting country. * Foreign direct in- tO definition includes capital projects, food aid, sources made available to those institutions vestment is investment by residents of DAC emergency relief, post-conflict peacekeeping by DAC members, not the flow of resources from member countries to acquire a lasting man- efforts, and technical cooperation. Also included those institutions to developing and transition agement interest (at least 10 percent of vot- are contributions to multilateral institutions, economies. Third, because some of the coun- ing stock) in an enterprise operating in the such as the United hlations and its specialized tries and territories on the DAC recipient list recipient country. The data reflect changes in agencies, and concessional funding to the mul- are normally classified as high income, the re- the net worth of subsidiaries in recipient coun- tilateral development banks. In 1999, to avoid ported flows may overstate the resources avail- tries whose parent company is in the DAC double counting extrabudgetary expenditures able to low- and middle-income economies. High- source country. * Bilateral portfolio investment reported by DAC countries and flows reported income countries receive only a small fraction covers bank lending and the purchase of by the United Nations, all United Nations agen- of all development assistance, however. bonds, shares, and real estate by residents of cies revised their delta to include only regular DAC member countries in recipient countries. budgetary expenditures since 1990 (except for * Multilateral portfolio investment records the transactions of private banks and nonbanks in DAC member countries in the securities is- Table 6.8a sued by multilateral institutions. * Private export credits are loans extended to recipient Official development assistance from selected non-DAC donors countries by the private sector in DAC member Net disbursements ($ miiiions) countries to promote trade: they may be sup- 1s96 1997 1998 1999 2000 ported by an official guarantee. * Net grants by OECD members (non-DAC) NGOs are private grants by nongovernmental Czech Republic .. .. 16 15 16 organizations, net of subsidies from the offi- Korea, Rep. 159 186 183 317 212 cial sector. * Total net flows comprise ODA or Poland .. 19 20 29 official aid flows, other official flows, private Slovak Republic .. .. .. 7 6 flows, and net grants by NGOs. Turkey 88 77 69 120 82 . - -- Arab countrles Data sources Kuwait 432 373 278 147 165 Saudi Arabia 327 251 288 185 295 The data on financial flows are compiled by United Arab Emirates 31 115 63 92 150 DAC and published in its annual statistical Other donors report, Geographical Distribution of Financial Estonia . .. 0.2 0.4 0.5 Flows to Aid Recipients, and the DAC annual Development Co-operation Report. Data are Note: China also provides aicl but does not disclose the amount. available in electronic format to registered Source. OECD data users on the Web site at www.oecd.org/dac/ htm/online.htm and on the OECD's International Development Statistics CD-ROM. Aid flows from Development 6.9 Assistance Committee members Net official Aid Untied deveiopment assistance appropriations aid' annual average % Per capita of change in volume' donor country' % of central % of total $millions % of GNI 1994-95 to $ $ government budget ODA commitments 1995 2000 1995 200 999-2000 1995 2000 1995 2000 1995 2000 Australia 1,194 987 0.34 0.27 -0.7 60 56 1.2 1.0 .. 77.4 Austria 767 423 0.33 0.23 -4.1 78 60 . 25.0 59.2 Belgium 1,034 820 0.38 0.36 2.0 83 91 .. 0.0 . 85.7 Canada 2,067 1,744 0.38 0.25 -4.1 67 55 1.4 1.5 31.5 24.9 Denmark 1,623 1,664 0.96 1.06_ 4.3 274 348 2.5 3.2 61.3 80.5 Finland 388 371 0.32 0.31 6.1 63 80 1.0 1.1 75.8 89.5 France 8,443 4,105 0.55 0.32 -7.3 122 80 ... 58.4 68.0 Germany 7,524 5.030 0.31 0.27 -1.9 75 71 60.3 93.2 Greece 226 0.20 25 23.5 38 Ireland 153 235 0.29 0.30 13.2 42 68 Italy 1,623 1,376 0.15 0.13 -5.5 29 27 ... 59.8 38.2 Japan 14.489 13,508 0.27 0.28 3.9 94 102 1.1 .. 96.3 864 0 ---- .--- . . ..~~~~~~~~---- ---- .. -....------ v5 Netherlands 3,226 3.135 0.81 0.84 5.5 173 221 3.0 78.9 95.3 C ----- -~ ~~~~~~~~~~------ ----- a) Norway 1,244 1,264 0.86 0.80 2.1 264 276 1.8 77.0 9. E 97_ oL Portugal 258 271 0.25 0 26 0.9 24 30 0.8 0.1 98.1 98.2 > Spain 1,348 1.195 0.24 0.22 1.5 31 34 1.0 0.0 0.0 47.2 o) Sweden 1.704 1.799 0.77 0.80 1.3 174 223 - 93.9 85.4 Z Switzerland 1.084 890_ 0.34 0.34 2.1 122 137 2.9 2.7 91.3 93.6 0 -.. United Kingdom 3,202 4,501 0.29 0.32 1.5 63 79 1.1 .. 86.2 91.5 o United States 7,367 9.955_ 0.10 0.10 0.2 30 35 1.3 0.8 27.3 0 CN Totai or average 58,926 53,737 0.26 0.22 0.4 65 67 1.3 0.8 69.6 81.1 Net officiai aid Per capita of annual aver'age % donor country' $ millions % of GNI change in volumne' $ $ 1.995 2000 1995 2000 1994-95 to 1999'2000 1995 2000 Australia 4 8 0.00 0.00 7.7 0 0 Austria 313 187 0.14 0.10 -4.5 32 26 Belgium 89 74 0.03 0.03 1.4 7 8 Canada 250 165 0.05 0.02 0.9 8 5 Denmark 170 189 0.10 0.12 4.2 29 40 Finland 76 58 0.06 0.05 3.8 12 13 France 770 1,657 0.05 0.13 16.0 11 32 Germany 4.514 647 0.18 0.03 -24.5 45 9 Greece 12 0.01 1 Ireland 21 0 0.04 0.00 6 0 Italy 286 406 0.03 0.04 2.2 5 8 Japan 250 -54 0.00 0.00 -48.2 2 0 Luxembourg 9 2_ 0.05 0.01 -20.3 19 5 Netherlands 305 306 0 08 0.08 .0.1 16 22 New Zealand 1 0 0.00 0.00 .14.4 0 0 Norway 61 27 0.04 0.02 -17.3 13 6 Portugal 22 27 0.02 0.03 2.9 2 3 Spain 120 12 0.02 0.00 .37.1 3 0 Sweden 152 122 0.07 0.05 0.4 16 15 Switzerland 102 58 0.03 0.02 .6.9 11 9 United Kingdom 406 439 0.04 0.03 1.1 8 8 United States 1.280 2.506 0.02 0.03 8.3 5 9 Total or average 9,202 6,848 0.04 0.03 -2.5 10 9 a. Excluding administrative costs is 1995, and administrative costs and technical cooperation in 2000. b. At 1999 prices. 0) ~~6.10 Aid dependency Net official Aid per capita Aid dependency ratios development assistance or official aid Aid as Aid as Aid as % of % of % of Aid as gross capital imports of central government $ millions $ % of GNI formation goods and services expenditure 1995 2000 1995 2000 ±995 2000 1995 2000 1995 2000 ±995 2000 Afghanistan 214 141 10 5 .. . . .. . Albania 182 319 56 93 7.3 8.3 41.7 45.6 21.1 21.1 24.2 28.6 Algeria 312 162 11 5 0.8 0.3 2.4 1.3 2.4 1.2 2.4 0.6 Angola 418 307 37 23 17.2 6.5 .. 12.3 9.7 3.8 Argentina 144 76 4 2 0.1 0.0 0.3 0.2 0.4 0.2 0.4 0.2 Armenia 218 216 58 57 7.6 11.2 41.0 58.9 29.4 21.2 Australia Austria Azerbaijan 119- 139 15 17 4.1 2.8 17.8 10.3 9.2 5.8 23.3 16.4 360 Bangladesh 1,292 1,171 11 9 3.4 2.5 17.8 10.8 19.1 12.5 .. 21.4 Belarus 223 40 22 4 1.1 0.1 4.4 0.6 3.8 0.5 6.4 1.3 E2 Belgium 0 Benin 280 239 51 38 14.3 11.1 71.2 55.9 30.3 29.6 Bolivia 719 477 97 57 11.1 5.9 70.5 31.6 39.7 19.5 50.7 23.9 ; Bosnia and Herzegovina 932 737 273 185 57.4 16.0 250.0 82.0 . s. Botswana 90 31 62 19 1.9 0.6 6.8 6.1 3.5 0.9 5.2 2 gL Brazil 273 322 2 2 0.0 0.1 0.2 0.3 0.4 0.3 .. 0.2 5> Bulgaria 114 311 14 38 0.9 2.7 5.5 15.7 1.6 3.7 2.1 6.7 o Burkina Faso 491 336 49 30 22.6 15.5 87.3 55.6 71.0 49.1 '0 Burundi 288 93 47 14 29.1 13.8 300.1 150.2 102.1 55.9 94.1 .39.8 3. Cambodia 556 398 52 33 19.1 12.6 86.8 83.5 38.6 21.5 C4 Cameroon 444 380 33 26 6.0 4.6 38.4 26.0 20.3 12.7 42.2 31.1 C4 Canada Central African Republic 169 76 50 20 15.3 8.0 111.3 73.4 52.1 47.2 Chad 236 131 35 17 16.8 9.4 159.4 54.9 43.6 26.7 Chile 157 49 11 3 0.3 0.1 0.9 0.3 0.7 0.2 1.2 0.3 China 3,531 1,735 3 1 0.5 0.2 1.2 0.4 2.3 0.6 6.1 2.2 Hong Kong. China 18 4 3 1 0.0 0.0 0.0 0.0 0.0 0.0 Colombia 171 187 4 4 0.2 0.2 0.7 1.9 0.9 1.0 1.4 1.9 Congo, Dem. Rep. 196 184 4 4 4.0 2.5 37.0 .. 8.6 5.5 41.8 Congo, Rep. 125 33 48 11 8.1 1.5 16.2 4.2 6.6 1.5 18.1 4.0 Costa Rica 34 12 10 3 0.3 0.1 1.6 0.4 0.7 0.1 1.3 0.3 C6te dIlvoire 1,213 352 87 22 13.5 4.1 89.7 30.4 25.4 8.4 45.8 18.0 Croatia 54 66 12 15 0.3 0.4 1.6 1.6 0.6 0.6 0.6 0.7 Cuba 64 44 6 4 . . . . . Czech Republic 148 438 14 43 0.3 0.9 0.8 2.9 0.5 1.1 0.8 2.3 Denmark Dominican Republic 120 62 16 7 1.1 0.3 5.2 1.3 1.7 0.5 6.5 6.6 Ecuador 227 147 20 12 1.3 1.2 6.8 6.4 3.4 2.3 Egypt. Arab Rep. 2,015 1,328 35 21 3.3 1.3 19.5 5.6 11.0 5.6 9.9 El Salvador 297 180 52 29 3.2 1.4 15.4 8.0 7.9 3.0 .. 8.1 Eritrea 149 176 42 43 21.6 25.3 134.9 76.2 33.3 35.0 Estonia 58 64 39 47 1.2 1.4 4.5 5.0 2.0 1.2 4.6 4.0 Ethiopia 883 693 16 11 15.4 10.9 93.0 76.6 65.1 34.0 Finland France Gabon 144 12 134 10 3.4 0.3 12.3 0.9 5.8 0.4 Gambia, The 47 49 42 38 12.4 11.8 60.5 67.3 19.3 14.8 Georgia 209 170 39 34 11.5 5.6 273.2 38.6 23.5 11.4 .. 45.4 Germany Ghana 651 609 38 32 10.3 12.1 50.3 49.5 28.8 17.6 Greece Guatemala 210 264 21 23 1.4 1.4 9.5 8.3 5.3 4.4 . Guinea 417 153 63 21 11.6 5.2 68.2 22.8 37.6 14.9 .. 32.6 Guinea-Bissau 119 80 110 67 50.2 39.6 209.2 210.8 107.4 67.2 . Haiti 726 208 101 26 27.7 5.1 316.7 48.1 87.2 20.7 .. 54.2 Honduras 406 449 72 70 11.0 7.8 32.5 21.6 19.2 12.9 . 6.10 Net official Aid per capita Aid dependency ratios development assistance or officiai aid Aid as Aid as Aid as % of % of % of Aid as gros s capital imports of central government $ millions $ % of GNI formation goods and services expenditure 1995 2000 1995 2000 ±595 2000 1995 2000 1995 2000 1995 2000 Hungary -244 252 -24 25 -0.6 0.6 -2.3 1.8 -1.1 0.7 -1.1 1.2 India 1,739 1,487 2 1 0.5 0.3 1.8 1.4 3.1 1.8 3.2 1.9 Indonesia 1,391 1,731 7 8 0.7 1.2 2.2 6.3 2.3 2.6 4.7 7.8 Iran, Islamic Rep. 191 130 3 2 0.2 01 1.1 0.6 1.2 0.7 0.8 0.2 Iraq 339 101..16..4 Ireland Israel 336 800 61 128 0.4 0.8 1.5 3.7 0.8 1.4 0.8 1.6 Italy Jamaica 108 10 43 4 2.4 0.1 7.5 0.5 2.6 0.2 7 .5 0.3 Japan36 Jordan 540 552 129 113 8.3 6.6 24.3 32.7____10.2 8.8 25.7 21.0 36 Kazakhstan 66 189 4 13 0.3 1.1 1.4 7.4 1.0 1.9 .. 7.2 N 0 Kenya _734 512 28 17 8.4 5.0 46.3 39.0 18.9 13.0 28.5 16.0 Korea, Dem. Rep. 14 75 1 3 .. . ... .. . Korea, Rep. 57 -198 1 -4 0.0 0.0 0.0 -0.2 0.0 -0.1 0.1 . Kuwait 3 3 2 1 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.1 Kyrgyz Republic 285 215 63 44 8.8 17.6 46.7 102.9 37.2 28.8 68.8 87.2 Buigaria 207.0 20.7 119.2 10.2 29.1 4.6 0.7 0.3 0.2 2.1 0.0 20.0 Burkina Faso 227.8 21.3 9.3 82.2 22.2 0.5 16.1 1.2 8.3 24.5 0.4 41.7 o) Burundi 40.9 0.2 1.0 4.8 3.0 1.7 4.4 4.3 1.3 .. 5.3 14.9 Cambodia 248.0 99.2 21.5 21.5 19.4 13.0 7.4 16.8 2.6 2.0 6.2 38.5 O Cameroon 213.5 15.8 2.5 86.2 47.0 8.1 4.3 0.1 6.3 1.0 0.7 41.5 0 Canada- Central African Republic 53 .1 22.8 1.1 18.7 7.2 0.0 .. 0.3 0.1 .. 0.3 2.6 Chad 53.3 0.2 4.1 24.7 14.9 0.4 .. 0.2 0.2 .. 0.1 8.5 Chile 41.0 21.4 -19.1 8.5 21.9 0.2 0.6 2.2 0.8 0.8 0.7 3.2 China 1,257.5 769.2 1.6 46.0 212.8 83.4 24.9 11.0 29.2 .1.7 9.9 71.2 Hong Kong, China 4.2 2.6 .. 0.5 1.0 . .. 0.0 .. . . 9.6 Colombia 178.5 8.1 105.1 8.9 13.1 3.4 9.0 4.4 2.4 0.1 5.8 18.2 Congo, Dem. Rep. 102.7 0.5 12.8 8.2 12.8 8.0 4.7 7.7 5.6 0.0 5.7 36.9 C-ongo, Rep. 22.2 0.1 3.5 9.8 2.3 0.0 .. 1.6 0.7 -0.1 2.0 2.2 Costa Rica 17.2 .6.3 -30.6 2.7 1.4 15.2 7.4 1.7 11.2 0.7 0.6 13.2 CMe dIlvoi're 250.1 23.4 10.0 156.3 15.7 1.1 10.3 0.2 3.5 .. 0.2 29.4 Croatia 42.5 1.7 11.9 1.7 5.2 1.6 4.2 2.9 0.6 .. 5.0 7.6 Cuba 30.8 2.0 1.4 1.2 2.5 0.2 1.5 1.1 2.6 .. 0.8 17.6 Czech Republic 25.3 1.8 .. 6.7 9.8 0.9 0.3 0.2 0.8 1.4 0.0 3.5 Denmark Dominican Republic 44.6 29.6 .18.8 3.2 9.2 6.4 0.0 1.3 0.2 0.2 0.2 13.1 Ecuador 137.4 20.1 47.1 4.9 11.3 0.9 6.5 0.9 5.1 6.6 2.1 32.0 Egypt, Arab Rep. 1,138.9 85.9 634.8 241.7 65.2 4.1 17.5 2.6 9.2 42.4 1.1 34.5 El Salvador 172.3 66.9 36.6 1.0 14.5 5.0 2.8 7.9 1.7 2.4 1.2 32.2 Eritrea 111.9 0.4 39.5 3.3 3.6 1.1 15.8 5.5 1.8 11.0 6.4 23.5 Estoni'a 23.7 0.4 1.8 1.3 2.9 0.3 0.1 4.1 0.7 6.8 0.3 5.0 Ethiopia 379.5 34.0 129.8 9.4 38.6 11.4 25.7 20.7 10.9 2.6 23.6 72.7 Finland France Gabon -11.7 .1.5 1.3 .14.5 1.0 .. 0.0 0.1 1.3 . .. 2.0 Gambia, The 14.6 3.3 3.2 0.1 2.6 2.1 0.2 0.7 0.2 0.0 0.2 6.4 Georgia 120.3 11.4 74.6 0.8 19.1 1.7 2.2 1.8 0.3 .. 2.1 13.4 Germany Ghana 385.0 102.9 63.3 3.3 32.0 79.9 27.6 5.9 16.2 37.2 3.2 0.0 Greece Guatemala 230.3 67.1 58.0 1.5 18.7 23.2 10.6 13.4 4.4 5.4 8.1 3.8 Guinea 92.8 19.1 25.7 19.7 17.4 0.4 0.0 0.1 6.7 . .. 19.5 Guinea-Bissau 41.6 .. 0.6 6.7 0.7 0.2 11.1 2.5 0.2 0.3 0.0 0.0 Haiti 153.9 13.5 91.0 10.9 4.2 0.0 4.0 0.5 19.7 0.1 1.7 61.9 Honduras 310.6 50.1 110.3 7.7 17.3 1.0 8.7 41.7 7.0 3.0 2.0 0.1 6.11 Total Ten major DAC donors Other DAC donors United United S millions. 2000 -Japan States France Germany Kingdom Netherlands Sweden Canada Denmark Norway Hungary 53.5 6.6 2.0 6.4 24.0 3.3 0.2 0.4__ 0.5 0.7 .. 17.3 India 650.3 368.2 14.6 -11.6 15.6 204.2 -8.9 15.4 6.8 20.9 8.0 229.3 Indonesia 1,617.2 970.1 174.2 21.7 6.4 33.9 144.0 4.1 26.5 1.4 5.8 14.5 Iran, Islam ic Rep. 112.8 44.9 .. 7.9 37.2 29 0.1 0.1 . 0.1 5.2 10.9 Iraq 84.1 0.0 .. 2.0 31.7 14.0 3.6 7 2 0.7 .. 14.0 2.3 Ireland Israel 800.4 0.7 8_67.2 5.3 -75.1 .. . . 0.1 . .. -2.5 Italy Jamai'ca -26.4 -12.2 -26.2 -0.8 1.3 4.7 3.2 0.2 5.5 .. 0.4 9.9 Japan 365 Jordan 385.3 104.7 187.8 17.1 44.3 7.4 1.1 0.9 1.9 6.9 3.4 2.4 Kazakhstan 159.3 83.3 58.3 1.4 10.3 1.4 0.3 0.2 0.7 0.1 1.0 19.6 -- - ---- - - ------ a0 Kenya 293.0 66.9 45.9 4.0 38.4 73.1 14.2 14.2 5.4 8.4 2.9 6.6 r Korea, Dem. Rep. 2. .. 16 - .7 .5 1.3 0.2 3.5 24. . . - -- ------- ----------- ---- - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Korea, Rep. -196.6 -183.7 -44.4 13.9 17.4 0.0 0.0 -. . . 0.3 Kuwait 2.0 0.1 1.0 0.6 .. . . - 8.1 Kyrgyz Republic 91.3 47.'8 24.6 0.4 4.8 2.2 1.9 0.3 0CD. 03 2. .-. 6~~~~~~~~~ Lao PDR 194.3 114.9 27 12. 13.3 2.1 2.0 14.6 0.4 2.1 8.5 3.5 atia 34.3 2.0 1.2 1.1 5.7 0.4 .. 8.9 1.4 9.8 0.5 8.8 C Lebanon 90.5 2.2 31.8 31.1 5.5 0.9 0.6 3.8 2.1 .. 3.7 10.1 Lesotho 21.8 0.9 1.3 -0.5 3.2 4.5 0.3 0.1 0.1 1.7 0.2 1.1 Liberia 23.8 0.0 15.9 0.8_ -1.3 3.3 20 14 0.2 0.1 0.3 9.3 Libya 11.9 0.2 1.0 1.4 ... . 0.3 ( Lithuania 46.1 2. 0 4.0 0.3 1.3 14.7 08 1. 17 00 Macedonia, FYR 110.9 7.9 37.3 8.2 6.7 8.5 20.9 0.4 0.7 1.3 1.0 0.6 Madagascar 138.7 26.3 31.6 46.5 14.2 2.1 1.3 0.2 0.2 0.2 4.5 4.0 Malawi 269.2 38.5 59.3 .. 25.5 96.9 1.6 5.1 6.7 24.9 6.8 -0. 1 Malaysia 433 239 0.2 .~~~ ~~~~~ ~~~ ~~~2.8 5.5 02 0 0. 1 2.2 13.5 0.2 2.1 Mali 298 3. 564 9. 316 10 4. 04 12.7 0.1 6.6 21.1 Mauritania 82.5 29.9 4.2 23.4 7.6 1.0 4.6 0 2 2.1 .. 0.4 2.3 Mauritius 12.4 2.1 -0.6 9.5 -2.8 0.7 0.0 .. 0.2 .. 0.9 0.0 Mexico -684 -92.6 23.8 -11.2 15.3 5.7 --2 0 0.2 1.6 -0.2 0.4 1.0 Moldova 61.5 26 35.0 1.0 1.9 1 1 13.5 2.4 0.1 .. 0.7 4.3 Mongolia 150.8 104.5 12.6 1 5 __188 0.8 3.8 1.8 0.3 1.0 1.4 0.0 Morocco 293.1 103.3 14.0 154.7 6.2 0.2 -0. 1 0.8 5.2 -0.9 0.1 140.6 Moza m-biq u e 623.5 20.0 115.5 16.1 47.8 82.7 61.6 46.3 8.0 46.9 38.2 4.2 Myanmar 68.isi.8 3.4 1.2 1.5 1.0 1~~ ~~~~~ ~~~~ ~~~~~~~~~~.6 0.5 0. . 2.9 14.2 Namibia 968 54 9.5 31 24.4 4.8 3.2 21.1 0.3 3.7 7.2 1.7 Nepal 231.2 99.9 16.0 2.0 21.8 23.0 5.7 1.2 4.2- 25.0 8.9 . N etherlfa-n ds-- - - -------- -------- - ------- N ew - Z_ealand __-- --- ------ -- ---- -- - ----- - Nicaragua 325.9 76.5-72.8 3.4- 26. 1.7 15.6 33.3 2.8 27.2 13.3 21.4 Niger 105.8 15.0 5.3 41.3 11.6 1.6 1.6 0.1 2.6 4.9 0.5 4.3 Nigeria 843 26 325 41 11.3 -------22.9 0O.3 0.8 -1.8 3.2 0.5 2.9 Norway Oman 9.2 11.2 -3.0 0.7 0.2 .. .. 20.1 Pakistan 475 1 284 88.5 19.6 2.4 23.7 19.1 1.-31 0. 1 6.7 0.3 Panama 11.7 3.0 -8.8 0.3 2.5 0.4 0.2 - 1.0 0 1 205.9 Papua New Guinea 268.6 55.8 1.0 0.4 3.9 .. 1.2 0.2 0.0 0.2 6.1 Paraguay 72.9 51.5 6.4 0 7 4.4 0.0 08 0 2.9 13.0 Peru 372.7 191.7 92.3 6.3 34.0 10.4 9.2 3.7 8.8 1.9 1.4- 56.1 Philippines 502.3 304.5 755 59 23.3 3.2 10.0 2.7 9.9 6.9 4.6 127.7 Poland 552.5 .3.4 32.7 197.3 44.3 6.0 0.6 5.8 121.9 19.6 0.0 0.0 Portugal Puerto Rico Romania 157 7 19 5 61.~~~~~~ ~~~~~~4 170 24.7 5.2 0.6 1.0 1.7 82 0.1 218.4 Russian Fzederation 1,344.2 6.5 915.2 17.0 63.5 39.2 4.1 31.6 16.6 13.7 18.4 27.9 6.11 ~~~ToalTen major DAC donors Other Total~~~~~~~~~~~~~~~~~~~~~~~A donors United United $ millions, 2000 Japan States France Germany Kingdom Netheriands Sweden Canada Denmark Norway Rwanda 175.4 3.4 22.9 7.5 13.8 52.7 20.4 14.6 6.7 1.2 4.3 12.5 Saudi Arabia 18.0 13.9 .. 2.6 1.1 .. 0.4 . .. . .. 32.1 Senegal 288.4 48.5 22.9 147.2 16.8 2.4 5.9 0.4 11.3 -0.6 1.7 0.1 Sierra Leone 115.6 0.0 8.0 0.7 3.5 68.3 2.7 3.7 3.8 0.0 8.8 0.3 Singapore 0.7 2.9 1.7 -4.5 0.3 . .. 0.1 . . 4.8 Slovak Republic 25.3 3 .0 2 .0 3.6 5.7 1.6 .. 0.1 0.9 3.5 0.1 1,3 Slovenia 0.6 0.6 0.9 -2.4 0.0 0.3 . .. 0.1 18.3 Somalia 56.4A 9.9 0.6 3.1 1.1 6.2 4.5 0.5 3.2 20.1 26.4 South Africa 353.6 19.8 105.9 18.4 41.6 42.6 24.2 32.4 10.7 17.0 14.7 10.1 366 Spain Sri Lanka 240.2 163.7 -39 0.2 21.2 9.9 6.9 16.7 1.4 -0.6 14.6 0.0 (n Sudan 90.3 0.7 5.2 6.5 12.0 5.7 15.3 12.4 2.4 0.6 14.2 1.8 co Swaziland 2.8 6.0 0.2 0.0 -0.9 -3,7 0.0 _0.0 0.3 0.3 0.1 4.5 VO Sweden Switzerland E Syrian Arab Republic 97.3 64.4 .. 13.2 12.0 0.2 0 1 1.0 0.3 1.7 6.3 C- .2 Tajikistan 38.1 2.1 22.6 0.0 3.5 0.1 0.2 1.8 0.4 0.1 1.1 57.4 0) --- - - - .-.-- - - - - - - - - - - > Tanzania 778.7 217.1 24.5 15.8 34.8 152.7 97.3 63.5 11.6 68.8 35.2 -37.4 o Thailand 625.2 635.3 12.6 -10.9 19.2 0.7 0.8 4.0 2.1 -1.5 0.4 155.8 o Togo 51.9 8.5 1.9 28.8 8.7 0.4 0.0 0.6 0.2 .. 0.1 3.4 Trinidad and Tobago 4A4 1.8 0.8 0.8 0.2 0.4 0.1 .. 0.2 . .. 2.0 o Tunisia 150.3 72.1 .19.7 92.9 1.9 0.2 -1.5 0.4 1.8 0.0 0.2 19.2 Turkey 97.5 144.5 -61.9 7.8 _-21.0 0.5 0.6 2.6 5.1 -1.7 1.8 0.1 Turkmenistan 9.9 1.1 7.5 0.2 0.7 0.1 .. . 0.0 .. 0.1 0.0 Uganda 578.2 22.4 57.9 7.6 18.3 216.6 43.3 22.7 1.6 59.8 21.0 13.1 Ukrai'ne 352.1 2.7 244.8 5.1 38.3 13.9 3.1 4.0 19.2 5.6 2.4 0.0 United Arab Emirates 2.7 0.1 .. 1.9 0.7 0.1 .. . . . . 1.6 United Kingdom United States Uruguay 15.3 5.8 0.3 1.6 5.3 0.2 0.1 0.1 0.2 .. 0.0 1.3 Uzbekistan 133.8 82.2 35.7 4.2 9.3 0.7 0.0 0.2 0.2 .. 0.1 13.0 Venezuela, RB 61.3 4.6 6.8 3.5 5.5 1 .4 0.2 1.3 0.5 0.6 0.3 105.1 Vietnam 1,247.6 923.7 6.8 52.9 33.3 7.9 18.8 37.3 14.6 41.0 6.2 0.0 West Bank and Gaza 305.2 61.2 60.1 14.2 17.3 14.7 16.2 32.4 0.4 8.0 27.9 13.2 Yemen, Rep. 159.6 21.0 56.6 6.5 31.8 4.6 34.4 0.9 0.1 0.1 0.1 45.0 Yugoslavia. Fed. Rep. 592.9 4.8 107.7 10.9 98.7 28.3 71.5 33.5 . .. 71.5 18.0 Zambia 486.2 31.9 46.1 13.0 112.2 111.4 51.2 19.1 8.4 23.1 24.8 14.0 Zimbabwe 192.6 62,4 13.1 3.2 12.5 20.2 11.3 14.8 9.0 22.5 9.8 10.4 Low Income 14,689.6 4,459.0 2.149,3 1,175.5 1,021.2 1,516.8 839.9 432 325.4 541.4 385.2 1,792.7 Middle Income 14,050.7 3,542.3 3,609.3 1,329.3 1,378.3 628.1 397.6 388.3 355.4 253.0 312.8 1,856.2_ Lower middle income 11,364.0 3,408.0 2,969.8 884.6 1,038.8 368.1 348.2 319.3 156.8 164.8 253.1 1,452.6 Upper middle income 1,669.8 102.8 101.3 419.7 276.4 153.6 32.7 56.2 151.5 64.1 31.6 280.0 Low & middle Income 39,134.2 9,646.7 8,976.6 3,150.6 2,988.1 2,794.7 2,273.8 1,360.6 1,324.9 1,142.1 960.2 4,515.9 East Asia & Pacific 6,755.7 4,001.1 _449.4__ __232.1 380.9 167.9 216.2 104.5 93.7 66.5 53.0 990.5 Europe & Central Asia 5,724.7 531.3 2,222.1 361.7 524.6 246.6 198.9 190.1 216.5 146.8 154.1 932.0 Latin America & Carib. 3,67.5 799.6 1,343.3 109.5 348.9 229.7 142.8 164.9 111.8 76.7 59.4 480.9 Middle East & N. Africa 3,010.1 595.5 979.3 676.3 265.5 49.9 73.4 53.2 28.3 57.3 60.6 170.9 South Asia 2,347.8 1,129.2 180.0 32.2 109.6 377.3 69.6 78.6 _71.3 89.1 70.6 140.6 Sub-Saharan Africa 8,651.6d - 9i55.7 1472 ,400 790.0 1,128.1 584.9 394.6 199.7 404.0 335.8 1.141.6 High_Income 1,770.7 7.5 889.5 764.0 -75.6 1.7 175.7 0.3 0.2 --0.0 0.4 6.9 Europe EMU Note: Regional aggregates include data for economies not specifled elsewhere, World and income groop totals include aid not allocated by country or region. I__ t Ca~ ~ 6.11 About the data Definitions Private flows to developing countries have gone stay within the donor's borders have reported * Net aid comprises net bilateral official mainly to productive activities in agriculture and these expenditures as ODA. development assistance to part I recipients and industry. So official development assistance Some of the aid recipients shown in the table net bilateral official aid to part 11 recipients (see (ODA) has becomTe more concentrated in the are themselves significant donors. See table About the data for table 6.8). * Other DAC social sectors, whose share increased from 6.8a for a summary of ODA from non-DAC donors are Australia, Austria, Belgium, Finland, about 20 percent of bilateral aid in the late countries. Greece, Ireland, Italy, Luxembourg, New 1970s to about 30 percent in the late 1990s. Zealand, Portugal, Spain, and Switzerland. Emergency aid also grew during thlis period, and debt relief incr-eased substantially. The Data sources geographic distribution of aid has changed, Data on financial flows are compiled by DAC because many of the large developing country and published in its annual statistical report, recipients, such as India, Indonesia, and Brazil, Geographical Distribution of Financial Flows have been able to tap private capital markets to Aid Recipients, and the DAC annual Devel- to fund infrastructure development. At the endopetC-erinRpr.Daarevil of the 1960s Iridia, Indonesia, and Brazil ~~~~able in electronic format to regi'stered users 36 accounted for almTost a quarter of bilateral aidfrmteWbseatwwocogdchm from Developmeni. Assistance CommitfrmtteeWbesie a ww.oec.DrAda/ht from Development Assistance Committee (DAC) ~online.htm and on the OECD's International members, but by the late 1990s their share DvlpetSaitc DRM had fallen to about 7 percent. 0 The data in the table show net bilateral aid to C a low- and middle-income economies from DAC Figure 6.11 members of the Organisation for Economic Co- operation and Development (OECD). The DAC ~Bilateral aid flows from selected DAC members to largest country recipients ' compilation includes aid to some countries and Japan United States C Indonesia ~~~~~~~~~~~~Russian territories not shown in the table and small OndhnesiaFederation 9% quantities of aid to unspecified economies that 61% '4l( are recorded only at the regional or global level. 'etnm a Aid to countries and territories not shown in the /-- Egypt, Arab t Rep 6% table has been assigned to regional totals China based on the World Bank's regional 8% uben classification system. Aid to unspecified 2%iln 7% Jra economies has been included in regional totals 2 and, when possible, in income group totals. Aid nOther 4 ~~~72%/ not allocated by country or region-including administrative costs, research oni development France Germany issues, and aid to nongovernmental Egypt,ArabChn organizations-is included in the world total; Rep. 6% Poland 7%Zambia uoai, 5% Yuosava thus regional and income group totals do not c6te dinvoire Fed. Rep. sum to the world total.4%3 In 1999 all United Nations agencies revised Morocoo ~* Harzegovna their data to include only regular budgetary4%3 expenditures since 1990 (except for the World Food Programme and the Unitedi Nations High Egypt, Arab Commissioner for Refugees, which revised their Senea Rep. 251 data from 1996 onward). They did so to avoid Othershe double counting extrabudgetary expenditures reported by DAC countries and flows reported by the United Nations. Untted Ktngdom Netherlands Because the data in the table are based on UgndIndonesia azna Yuolva donor country reports of bilateral programs, they 4%Inia6 Fed. Ray. cannot be reconciled with recipient country 7.3 reports. Nor do they reflect the full extent of aid Tanzania *Moza3mbiq~ue flows from the reporting donor countries or those %3 to recipient countries. A full accounting would Zmi i include donor country contributions to zambia6 multilateral institutions and the flow of resources from multilateral institutions toOt Others Enlds recipient countries as well as flows from 7 82% countries that are not members of DAC. In addition, the expenditure countries report as Source: OECD. official development assistance have changed. For example, some DAC members providing aid Recent reforms In Vietnam have led to an Increase In aid flows. In 2000 Vietnam received 10 percent of Japan's to refugees durings the first 12 mionths of their bilateral aid, up from 6 percent In 1999. 6.12 Net financial flows from multilateral institutions International financial institutions United Nations Total Regional development World Bank IMF banks Conces- Non- Conces- Non- $ millions, 2000 IDA IBRD sional concessional slonal concessional Others UNOP UNFPA UNICEF WFP Others_____ Afghanistan .. . . . . . 4.8 0.8 8.8 6.7 14.0 35.0 Albania 64.4 0.0 11.6_ 0.0 0.0 0.0 15.4 2.4 0.2 1.2 0.0 7.0 102.2 Algeri-a 0.0 -113.7 0.0 -92.6 0.3 38.3 60.8 0.7 0.4 0.7 2.4 5.4 -97.2 Angola 23.7 00 0 o -1.5 -23.6 -2.4 2. 13 44 384 95 18 Argentina 0.0 480.7 0.0 814.7 -0.9 _6 18.4 0.0 -0.5 0.0 1.0 0.0 26.7 1,940.0 Armenia 54.4 -0.4 0.0 -15.8 0.0 -5.2 1.8 1.1 0 1 0.7 2.6 2.8 42.2 Australia Austria Azerbaijan 27.2 0.0 0.0 -51.4 0.0 9.6 10.7 2.1 0.6 1.0 2.4 4.8 6.9 368 Bangladesh 275.2 -5.3 0.0 -85.7 184.9 _11.3 17.0 18.8 3.7 12.5 8.9 10.9 452.3 B elaru-s 0.0 -8.8 0.0 -55.6 0.0 -11.7 0.0 0.5 0.1 0.0 0.0 1.4 -74.1 Belgium enin 30.3 0.0 0.0 -3.6 11.1 0.0 -1.0 3.0 0.7 2.0 1.9 2.8 47.1 ~~ Bolivia 506 -119 -148 00 ~~~~~~~~~~~~~~ 62.5 -25.4 170 1.4 1.1 1.1 5.7 4.3 91.6 Bosnia and Herzegovina 44.1 00 58 . 50 -159.9 -23 0.1 09 00 2. 6. E Botswana -0.5 4-74 0-b6 0 00 0.2~ -13.8 -3.0 0.9 0.4 0.7 0.0 2.7 -19.8 al ---------- ------ - o Brazil 0.0 805.4 0.0 -6,693.0 3.0 2,488.1 -3,136.7 -0.4 0.9 1.3 0.0 93.3 -6,438.1 0) ..aia0.0 44.3 0.0 137.0 0.0 __0.7 5.J. . 0.0 0.0 1.5 242.1 o Burkina Faso 35.2 0.0 0.0 -2. 5 10.0 -1.9 -2.4 4.2 1.1 3.3 1.1 3.9 52.1 u r u nd i 27.6 0.0 -4.5 0.0 _-03 0.0 00 6.4 0.7 2.9 2.1 6.3 41.0 Cabodia3. . 4.1 0.0 38.4 00 1.8 3.1 3.2 3.6 10.2 7.8 108.8 CM eroon 48.7 -53.3 0.0 49.9 3.2 --16.7 -2. 6 1.0- 1.5 2.1 0.4 3.1 37.4 Central African Republic 7.2 0 0-6- -08 00 . .0 1.9 1.1 0.7 1.3 1.6 4.2 17.3 Chad 133 00 12.9 0.0 9.4 0.0 -0.5 4.3 1.0 1.9 3.5 4.2 49.9 Chile -0 7 -66 7 60.0 0.0 -1.3 4.6 0.6 1.1 0.1 0.8 0.0 1.8 -59.7 China ~~~~~~~~31-3.6 949.7- 0.0 0.0 0.0 417.2 8.8 12.7 3.5 18.1 7.5 11.4 1,742.6 H-on-g K-on-g, China . . . . . .. Colombia -0.7 24 5 0.0 0.0 -12.3 63.2 -8.2 -0.2 0.3 1.2 3.2 4.8 75.9 Congo, Dem. Rep. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.1 0.7 16.6 2.1 25.1 47.6 Congo, Rep. -0.9 -2.4 0.0 13.9 - -04 -1.7 0.1 1.0 0.2 1.4 0.1 6.5 17.9 .ot ia-0.2 -27 2 0.0 0.0 -11.1 16.0 21.8 0.3 0.2 0.5 0.0 2.4 2.7 Me c1livoire75.4 -57.5 -39.3 0.0 43.3 -35.4 -20.9 1.3 1.1 2.4 0.8 105 -18.3 Croata 0.0 -33.5 0.0 -28.8 0.0 -4.0 9.3 0.1 0.0 0.2 0.0 10.7 21.0 Cuba . . . .. 0.8 0.3 0.8 2.0 2.0 6.0 CzcRepublic 0.0 -42.9 0 .0 0 .0 00 0 51.0 0.2 0.0 0.0 0.0 1.1 9.4 Denmark Dominican Rep-0l7 17.8 0.0 0.0 -4.2 24.1 8.7 1.0 0.6 0.9 1.0 7.9 57.0 Ecuador 1-1. 1 -206 - 0.0 149 5 -15.5 167.3 46.3 0.4 0.6 1.0 2.3 2.5 332.7 Egpt, Arab Rep.23 85 0 --0 - . -83.1 16.6 2.0 1.9 3. 91 53 -58 ElSlador -0.8 17.0 00 0.0 -13.0 89.0 -59' 1.3 0.6 0.8 0.9 1.4 71.2 E rit r- e- 31-- ---- --6-- 010 0.0 0.0 0.7 0.0 6.4 2.2 0.6 1.5 1.7 5.5 50.3 Estonia 0.0 -9.4 0.0 -5.1 0.0 -2.7 -6.1 0.3 0.0 0.0 0.0 0.1 -22.9 Ethiopia 115.6 0.0 -13.0 0.0 22.7 -18.4 5.3 18.8 3.0 13.5 36.0 27.6 211.0 Finland- --- --- ... - - - - France----- -- ------ -- --- - -- Gabon 06 - 3.9- 0.0 7.6 0.0 -30.1 0.1 0.1 0.2 0.5 0.0 3.3 -22.2 Gambia, The 5.4 0.0 7.5 0.0 1.6 -0.7 4 2 1.6 0.4 0.6 1.6 2.7 24.9 Georgia 18. 0.06 0.0 -----O -25 .9 00 07 1.2 1.8 0.1 0.9 2.0 5.0 3.9 Germany Ghana 1789 -8 1 0_.0 18 0.7 -13.5 1.6 4.9 1.8 2.9 1.5 4.0 173.0 Greece Guatem-a-la- 00- 38.2 0.0 0.0 -5.4 20.6 33.7 189 03 0.9 2.7 1.6 94.3 Guinea 17.5 0.0 -.0 0.0 0.6 -8.8_ 9.2 1.7 0.6 2.6 1.3 21.0 37.6 Guinea_-Bissau 10 7 0.0 6.5 1.9 0.0 0.0 -0.3 0.9 0.2 1.1 0.2 2.1 23.3 Haiti 1.5 0.0 -3.0 0.0 35.1 0.0 -0.5 2.6 1.2 2.6 6.1 0. 46.3 Honduras 35.9 -329 0.0 16. 51.5 -15.1 16. 27 08 04 09 18 274 -~~ .-> 6.12 International financIal Institutions United Nations Total Regional development World Bank IMF banks Conces- Non- Conces- Non- $ mlillions, 2000 IDA _IBRD sional concessional sional concessional Others UNOP UNFPA UNICEF WFP Others Hungary 0.0 -56.7 0.0 0.0 0.0 3.3 -12.1 0.1 0.0 0.0 0.0 1.3 -64.1 India 652 -0.8 -251.0 0.0 0.0 159.8 41.5 21.2 9.0 31.9 27.0 33.7 423.5 Indonesia 33 '2 290.1 0.0 1,122.7 17.9 203.9 -10.5 3.2 2.5 6.7 0.0 11.2 1,680.8 Iran, Islamic Rep. 0.0 44.4 0.0 0.0 0.0 0.0 -27.9 0.6 1.1 1.9 0.1 13.5 33.7 Iraq . . . . . .. . . 2.8 0.1 5.4 9.5 Ireland Israel . . . . . .. 0.0 0.0 0.0 0.0 0.4 0.4 Italy Jamaica 0.0 37.1 0.0 -19.1 .4.7 88.8 10.8 0.1 0.2 0.8 0.0 2.0 116.0 Japan 369 Jordan -2.6 -14.8 0.0 -11.3 0.0 0.0__ -15.3 0.9 0.4 0.9 1.2 81.5 40.8 Kazahtn 0.0 29.7 0.0 -442.1 3.6 18.0 13.0 0.8 0.8 0.8 0.0 1.8 -373.5 - - . -. --- - - - - . . - - - -.- .- - .- - - -. - - - . - - .-.- C~~~~~~~~~--- -- -- - -- Kenya 141.5 -40.4 1.9 0.0 1.6 -15.2 -5.7 4.9 1.9 4.1 19.4 21.4 135.4 N Korea, Dem. Rep . . .. . 1.2 0.4 0.7 0.6 4.1 6.9 - - - - - - --- -- - - ------ - -------0 Korea, Rep. -3.5 -187.6 0.0 0.0 0.0 -39.7 0.0 0.3 0.0 0.0 0.0 1.8 -228.8 Kuwa!t .... .. .. .. .. 0.0 0.0 0.0 0.0 0.8 0.8~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~00 000. . . Kyrgyz Republic 51.7 0.0 0.0 7.4 37.7 0.0 7.6 1.7 0.4 0.9 ----0.0- 1.5- 108.9 ID ------ ---------- ~~~~~~~~~~~~~~~~~~~~~~~~0 Lao PDR 16.7 0.0 -7.7 0.0 39.2 0.0 13.1 2.8 1.4 2.2 1.0 2.8 71.5 ' - ..~~~~~~. - - ... -- . - - - - .. - 2~~~~~~~~~~~~~~~~~~~ Latvia 0.0 53.8 0.0 -10.1 0.0 14.1 -30.7 0.2 0.1 0.0 0.0 0.7 28.0 C Lebanon 0.0 25.8 0.0 0.0 0.0 0.0 42.0 0.'6 0.3 1.1 0.0 51.3 121.0 Lesotho 5.3 - 6.0 0.0 -5.2 1.0 -1.8 -2.4 0.7 0.2 0.7 1.2 1.4 7.'2 Liberia 0.0 0.0 0.0 -0.6 0.0 0.0 0.0 2.0 0.7 1.7 12.7 14.5 31.0 Libya.. ... . ... -0.2 0.0 0.0 0.0 3.4 3.2 Lithuania 0.0 56.9 0.0 -27.3 0.0 1.2 -3.3 0.4 0.0 0.0 0.0 0.4 28.3 Macedonia, FYR 38.4 9.6 2.3 -17.8 0.0 0.6 10.5 1.1 0.0 0.5 0____.0 9.2 54.3 Madagascar 76.9 0.0 45.1 0.0 19.6 -5.6 15.4 5.9 1.4 5.7 2.7 3.0 170.0 Malawi 81.2 -7.6 -0.6 0.0 15.6 -1. 1 0.0 2.4 1.1 3.9 _1. 8 2.9 99.6 Malaysia 0.0 -85.9 0.0 0.0 0.0 9.4 -2.4 0.4 0.2 0.4 0.0 1.7 -76.3 Mali 40.7 0.0 0.0 -8.2 4.7 0.0 3.4 2.0 0.8 5.0 2.0 3.2 53.5 Mauritania 53.1 -1.9 -3.0 0.0 6.3 -5.8 10.8 1.9 0.7 1.3 2.2 3.2 68.8 Mauritius -0.6 -14.4 0.0 0.0 -0.5 -2.8 12.9 0.5 0.1 0.6 0.0 1.0 -3.2 Mexico 0.0 418.0 -4,298.9 0.0 -1. 1 300.6 0.0_ 0.7 1.2 1.1 0.0_O' 8.3 -3,570.2_ Moldova 30.1 0.8 12.2 -24.6 0.0 0.4 -4.2 0.7 0.1 0.7 0.0 12 17.4 Mongolia 14.1 0.0 1.5 0.0 19.8 0.0 0.3 1.4 1.5 1.2 0.0 1.9 41.7 Morocco -1.4 -168.8 0.0 0.0 3.5 22.3 -4.4 1.3 1.3 1.2 2.2 2.7 -140.3 Mozambique 93.7 0.0 0.0 30.4 12.6 1.8 6.3 5.5 3.4 7.1 3.4 4.0 168.2 Myanmar 0.0 0.0 0.0 0.0 0.0 0.0 -1.2 14.9 0.8 6.3 0.0 12.9_____33.7 Namibia . . . . . . . 1.0 0.4 0.8 0.3 4.7 7.2 Nepal 34.4 0.0 0.0 -4.4 67.5 0.0 4.7 8.2 2.5 3.7 6.3 10.6 133.6 Netherlands New Zealand Nicaragua 85.5 -4.8 0.0 21.3 59.9 -9.1 0.9 2.8 1.2 0.8 8.4 1.5 168.4 Niger 59.8 0.0 9.4 0.0 -0.4 0.0 -3.7 5.5 1.0 5.9 3.0 5.3 85.7 Nigeria 51.0 -242.4 0.0 0.0 12.7 -70.2 -2.0 6.1 3.7 18.9 0.0 15.2 -207.0 Norway Oman 0.0 -2.9 0.0 0.0 0.0 0.0 1.5 0.0 0.0 0.8 0.0 1.6 0.9 Pakistan 76.8 -67.2 -14.5 -73.1 153.7 119.0 -31.4 4.0 0.7 1. 27 266 208.8 Panama 0.0 -2.4 0.0 -51.8 -9.6 37.7 4.5 0.0 0.2 0.7 0.0 1.5 -19.2 Papua New Guinea -2.7 14.5 0.0 18.7 -0.7 0.0 1.6 2.0 0.7 1.4 0.0 2.6 38.3 Paraguay -1.5 27.9 0.0 0.0 -1.1 83.1 -2.6 0.2 0.6 0.8 0.0 1. 2 108.5 .eru 00-172.9 0.0 -141.3 -6.6 5.3 137.2 -0.2 1.4 1.2 3.9 5.4 179.1 Philippi-nes 7.2 -197.5 0.0 305.3 21.8 -77.2 -2.7 3.5 1.0 2.9 0.0 3.6 67.9 Poland 0.0 149.8 0.0 0.0 0.0 0.0 0.0 -0.2 0.1 0.0 0.0 2.2 151.9 Portugal Puerto Rico Romania 0.0 293.2 0.0 18.3 0.0 19.'0 163.5 0.7 0.3 0.7 0.0 1.6 497.2 Russian Federation 0.0 273.6 0.0 -2,888.0 0.0 -9.4 -2.4 0.7 0.2 0.0 0.0 12.3 -2,613.0 6.12 International financial Institutions United Nations Total Regional development World Bank IMF banks Conces- Non- Conces- Non- $ millions, 2000 IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others Rwanda 30.9 0.0 0.0 14.0 -0.9 -0. 1 -0.5 5.4 0.7 3.1 20.0 11.1 83.7 Saudi Arabia . . -. .* . .. 0.0 0.0 0.1 0.0 10.9 11.0 Senegal 76~~~~~~M7 -28 -3. 7 0.0 2.5 ----12.0 .21.4 3.4 10 3 3 463 Sierra Leone 6383 -0.3 -25.2 13.7 1.4 0.0 -25 0.9 0.2 2.2 - 0.0 4.7 63.5 Singapore 0.0 0.0 0 0 - 0.0 0.4 0.4 Slovak Republic . 0.0 -22.2 0.0 -127.3 0.0 -337 278 0.4 0.0 0.0 -0.0 1.1 -153.9 Sloveni'a .. 0.0 0.0 0.0 0.0 1.1 1.2 Somalia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.2 0.4 4.9 0.7 12.6 23.8 Soujth Africa 006 27 00 0 0o 00 -. 00 0 3.2 0.3 1.1 - 0.0 6.7 14.0 370 Spain - - Sri Lan_k_a 28.3 -5.3 -85.6 0.0 44.6 0. .8 5 6 0 . 41 Sudan _2.2 __2.1 0-0 -54.2 --- 00-- -X 0.0__ -1 69 1.6- 41 6.7 17.1 22.2 Swaziland 0.03 01 0 0 2 -8.1 - 0.7 063 0.2 0.6__ 00 1.9 11.2 (5~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~--------- Sweden Switzerland E Syri-a-nI Arab Rep-ublc . _1.5 -14.2_ 00 006 00 0.0 A.40 1.1 0.9 1.0 4.6 27.1 23.0 ---22.8-0.0.25.5.-9.9 3.2 0.0 9. 0 0.4 1.2 4.9 3.6 63.1 o T ajik ista - ----- ... -.. -- .-- -. - --- - - - - ---.----.-- - --- - - > Tanzani'a 109.4 -4.4 0.0 27.4 27.2 -2.2 13.5 8.7 2.4 11.5 2.5 28.1 224.1 CD Thailan -3.4 275.1 0.0 -179 -2.1 -269.8 -2.3 2.0 0. 08 00 91 87 o Togo 9.9 0.0 -9.4 0.0 2.5 0.0 -0.19 2.8 0.5 1.2 0.0 19 9 Trinidad and Tobago 0.0 3.8 0.0 0.0 -0.1 -1. _--0.8 01_O. 0.0 0.0 0.0 0.9 -7.1 o uni-sia-- -2.1 -145 00 -40.1 0.0 -65.0 73.7 0.5 0.4 0.7 0.0- 1.7 -44.8 faTukey -5.9 805.8 06.0 3372.0 0.0 0.0 -316.8 1.1 0.5 1.1 0.0 5.1 3,862.8 T u rkm en-is-tan - 0.0 19.6 0.0 0.0 0.0 -2.7 -0.7 0.9 0.4 0.8 0.0 0.8 19.2 Uganda 175.6 0. . 37.2 17.8 __-0.8 17.3 4.1 2 5 86 1. 1. U~kraine 0.0 _88.2 0.0 -5981 0.0 -29.4 0.0 17 0.1 0.0 0.0 32 54 United Arab Emirates .0.3 0.0 0.0 0.0 1.0 1.3 Unied Kin do ----- ---- United Sae Uruguay 0.0 76.3 00 0 0 1 03 4 3 01 060 06 19 Uzbekistan 0.0 26.8 0. -65.1 1.2 56.9 0.0 1.3 0.6 1.5 00 1.8 24.9 Venezuela. RB 0.0 -157.6 0.0 -507.1 0.0 159.5 77.8 0.4 0.3 - 0.9 02 3.2 -422.3 Vietnam 172.5 0.0 -15.9 -5.3 146.4 00 -21 i6 . .4 1. 3.9 354.1 We4st Bank and Gaza . 2.9 1.1 1.6 1.1 152.5 159.2 Yemen, Rep. -51.3 0.0 0.0 -714 006 - 0 6 2 1 1 7 . 1.0 Yugoslavia, Fed. Rep. 0.0 0.0 0.0 80.8 on0 06.o 0.0-Ł60 0.1 0.0 0.2 69.1 151.2 Zambia 205.8 -7.8. 0.0 26.4 25.0 -14.9 -9.8 3.1 3.4 45 4.2 11.9 251.9 Zimbabwe 7.3 -27.4 -35.7 -34.8 3.7 -7.3 -25.0 1.8 0.5 1.7 00 5.4 -109.7 Low Income 3,545.6 -428.9 -309.4 153. 1,062.0 261.8 52.4 254.5 81.3 264.1 290.9 500.7 5,727.9 Mil:ddle Income 629.0 3,823.0 4,385.5 -6,437.0 179.2 4,145.6 -2,743.6 78.6 34.3 75.8 68.4 811.9 -3,722.3 Lower middle income 616.4 1,705.1 86.6 -3,218.3 201.2 539.2 502. 68.8 28.7 61.8 62.8 526.0 1,005.7 U-pper middle income -10.2 2,117.9 -4,298.9 -3,218.7 -22.0 3,606.4 -3,258.8 9.5 _5.0_ 13.5 0.2 247.5 -4,808.7 Low &middlelInce 4,174.6 3,394.1.4,694. -6,28~4.1 1,4.2 4,407.4' -2,691. 3960 134.1 58. 373 1,60 2804 Eas~tAsia & Pacific 589.6 1,051.8 -18.1 1,243.6 290.7 244.4 23.0 69.2- 23.8 54.7 36.6 142.1 3,751.4 E-ur-ope &-C Centir-a-lAsia 34-5.2 1,7-45.4 ----51-.5 _-7_6 1.3 -45.7 4. -15-6.9- -21.5 - ---5.4 -15._9_ 12._2__ 196.3 3,083.9 Latin America & arib 174.3 1,79. -4,316.6 -6,426,1 17.4 4,215.8 -2,60.7 13 1. 34 3. 1. ,9. Middle Est & N. Africa 73.5 -339.2 0.0 -214.1 10.1 -87.5 40.5 14.4 9.1 - 17.0 21.8 399.8 -54.6 South Asia 1,076.1 -382.6 -351.1 -163.1 452.8 290.0 35.5 68.2 19.1 70.5 55.6 108.0 1,277.0 Sub-Saharan Africa . 1,916.0 -476'2 -60.6 37.0 267.5 -295.9 -3.0 165.8 49.5 165.8 190.6 570.7 2,527.3 High Iincome - -0.3 0.0 0.0 0.0 6.0 EUroipaeimu . --.---- Note: The aggregates for the regional development banks. United Nations, and total net financial flows include amounts for economies not specified elsewhere. . t t'8i9i E ' ,> '' '- ' 6.12 About the data Definitions The regional distribution of multilateral assis- through its Enhanced Structural Adjustment Fa- * Net financial flows recorded in this table tance differs from that of bilateral assistance. cility (ESAF), the successor to the Structural are disbursements of public or publicly guar- For example, while bilateral donors have in- Adjustment Facility, and through the IMF Trust anteed loans and credits, less repayments creased the share of their aid to Sub-Sarharan Fund. Low-income countries facing protracted of principal. * IDA is the International Devel- Africa over the past 15 years to about a quarter, balance of payments problems are eligible for opment Association, the soft loan window of the share of multilateral assistance to the re- ESAF funds. the World Bank Group. * IBRD is the Interna- gion has averaged more than 40 percent over Regional development banks also maintain tional Bank for Reconstruction and Develop- the same period. The seven major (G-7) indus- concessional windows for funds. In the World ment, the founding and largest member of trial countries-Canada, France, Germany, Italy, Development Indicators loans from the major the World Bank Group. * IMF is the Interna- Japan, the United Kingdom, and the United regional development banks-the African De- tional Monetary Fund. Its nonconcessional States-have contributed about 77 percent of velopment Bank, Asian Development Bank, and lending consists of the credit it provides to total multilateral assistance in the past 30 Inter-American Development Bank-are re- its members, principally to meet their bal- years. corded according to each institution's classifi- ance of payments needs. It provides This table shows concessional and noncon- cation. In some cases nonconcessional loans concessional assistance through the En- cessional financial flows from the major multi- by these institutions may be on terms that meet hanced Structural Adjustment Facility and the 371 lateral institutions-the World Bank, the Inter- DAC's definition of concessional. IMF Trust Fund. * Regional development Ł national Monetary Fund (IMF), regional develop- In 1999, all United Nations agencies revised banks include the African Development Bank, g ment banks, United Nations agencies, and re- their data to include only regular budgetary ex- based in Abidjan, Cote d'lvoire, which lends S gional groups sucri as the Commission of the penditures since 1990 (except for the World to all of Africa, including North Africa; the o European Communities. Much of these data Food Programme and the United Nations High Asian Development Bank, based in Manila, C. come from the World Bank's Debtor Reporting Commissioner for Refugees, which revised their Philippines, which serves countries in South System. data from 1996 onward). They did so to avoid Asia and East Asia and Pacific; the European CD The multilateral development banks fund double counting extrabudgetary expenditures Bank for Reconstruction and Development, B their nonconcessional lending operations pri- reported by DAC countries and flows reported based in London, England, which serves coun- 3 marily by selling lov-interest, highly rated bonds by the United Nations. tries in Europe and Central Asia; the Euro- , (the World Bank, for example, has a AAA rating) pean Development Fund, based in Brussels, _ backed by prudent lending and financial poli- Belgium, which serves countries in Africa, cies and the strong financial backing of their the Caribbean, and the Pacific; and the Inter- n members. These funds are then on-lent at American Development Bank, based in Wash- slightly higher interest rates, and with relatively ington, D.C., which is the principal develop- long maturities (15-20 years), to developing ment bank of the Americas. * Others is a countries. Lending terms vary with market con- residual category in the World Bank's Debtor ditions and the policies of the banks. Reporting System. It includes such institu- Concessional flows are defined by the Devel- tions as the Caribbean Development Bank opment Assistance Committee (DAC) as those and the European Investment Bank. containing a grant element of at least 25 per- * United Nations includes the United Nations cent. The grant element of loans is evaluated Development Programme (UNDP), United Na- assuming a nominal market interest rate of 10 tions Population Fund (UNFPA), United Nations percent. The grant element of a loztn carrying a Children's Fund (UNICEF), World Food 10 percent interest rate is nil, and for a grant, Programme (WFP), and other United Nations which requires no repayment, it is 100 percent. agencies, such as the United Nations High Concessional, or soft, lending by the World Commissioner for Refugees, United Nations Bank Group is carried out through the Interna- Relief and Works Agency for Palestine Refu- tional Development Association (IDA), although gees in the Near East, and United Nations some loans made by the International Bank for Regular Program for Technical Assistance. Reconstruction and Development (IBRD) are * Concessional financial flows cover dis- made on terms that may qualify as concessional bursements made through concessional under the DAC definition. Eligibility for IDA re- lending facilities. * Nonconcessional finan- sources is based on gross national income (GNI) cial flows cover all other disbursements. per capita; countries must also meet perfor- . - mance standards assessed by World Bank staff. Data sources Since 1 July 2001 the GNI per capitai cutoff has bencset1 July 2001 theaGNIuped capinta00 cus tha The data on net financial flows from international been set at $885, measured in 2000 using theI financial institutions come from the World Atlas method (see Users guide). In exceptional Bank's Debtor Reporting System. These data circumstances IDA extends eligibility temporarily are published in the World Bank's Global to countries that are above the cutoff and are Development Fnance 2002 and electronically undertaking major adjustment efforts but are DelOntinanTe 2002 and eronicly as GDF Online. The data on aid from United not creditworthy for 113RD lending. An exception Nations agencies come from the DAC annual has also been made for small island economies. Development Co-operation Report. Data are Lending by the International Finance Corpora- available in electronic format to registered users tion is not included inl this table. thenis nMF makludesit concessnable fundsavaon the Web site at www.oecd.org/dac/htm/ The IMF makes concessional funds available online.htm and on the OECD's International Development Statistics CD-ROM. 6.13 Foreign labor and population in OECD countries Foreign population' Foreign labor force" Inflows of foreign population % of total % of total labor Total Asylum seekers thousands population force thousands' thousands Selected OECD countries 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 Austria 456 748 5.9 9.2 7.4 10.0 ..23 20 Belgium 905 897 9.1 8.8 7.1 50 58 13 36 Denmark 161 259 3.1 4.9 2.4 4.4 15 21 5 6 Finland 26 88 0.5 1.7 1.5 6 8 3 3 France 3,597 3,263 6.3 5.6 6.2 5.8 102 ' - 104 ~ 55 31 Germany 5,343 7,344 8.4 8.9 8.8 842 674 193 95 Ireland 80 118 2.3 3.1 2.6 3 4 22 d 0 8 Italy 781 1,252 1.4 2.2 1.3 3.6 268'1 5 33 Japan __1,075 1,556 0,9 1. 2 0.1 0.2_ 224 282 372 Luxembourg 113_ 159 29.4 36.0 45.2 57.3 9 -12 0 3 Netherlands 692 652 4.6 4.1 3.1' 3.4 81 78 21 43 Norway -- 143 --179 3.4 4.0 2.3 3.0 16 32 4 10 o Portugal 108 191 1.1 1.9 1.0 1.8 14' 11' 0d Spain 279 80 .7 20 06 1 0 9 8 Sweden 484 487 5.6 5.5 5.4 5.1 53 35 29 11 E Switzerland --1,100 1,369 16.3 19 ,2 18.9 18 1 101 86 36 46 o United Kingdom 1,723 2,208 3.2_ 3.8 3 3 3.7 204'd 277'1 38 91 0) - - -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~---- -- 04 0 04 % of total % of total labor Total Asylum seekers thousands population force thousands' thousands 1990 1999 1990 1999 1990 1999 1990 1999 1990 1999 Australia 3,886 4,482 22.8 23.6 25.7 24.6 121 84 4 8 Canada 4.343 4,971 16.0 1 7.4 18.5 .. 214 190 37 29 United States 19,767 1 28.180 7.9 10.3 9 94 11.7 1,536 647 74 43 a. Data are from population registers or from registers of foreigners. except for France and the United States (censuses), Italy. Portugal, and Spain (residence perrnits), and Ireland and the United Kingdom (labor force surneysi. and refer to) the population on 31 December of the year indicated. o. Data include the unemployed, except in Italy, Luxembourg, the Netherlands, Norway, and the United Kingdom. Cross-border workers and seasonal workers are excluded unless otherwise noted. c Inflow data are based on Population registers and are not fully comparable because the criteria governing who gets registered differ from country to country. Counts for the Netherlands, Norway, and especially Germany include substantial numbers of asylum seekers. d. Data are based on residence permits or other sources. e. Includes cross-border workers. f. From the U.S. Census Bureau, 1990 Census of Populatiort Listinig. g. From the U.S. Census Bureau, Current Population Report (March 20011. 6.13 About the data Definitions The data in the table are based on national defi- Figure 6.13 * Foreign (or foreign-born) population is the nitions and data collection practices and are not number of foreign or foreign-born residents in fully comparable across countries. Japan and Foreign population in selected OECD a country. * Foreign (or foreign-born) labor the European mernbers of the Organisation for Countries, 1985-99 force, as a percentage of total labor force, is Economic Co-operation and Development (OECD) 10.0 the share of foreign or foreign-bom workers in have traditionally defined foreigners by nation- 9.0 - . a country's workforce. * Inflows of foreign popu- ality of descent. Australia, Canada, and the 8.0 lation are the gross arrivals of immigrants in United States use place of birth, which is closer 7.0 - the country shown. The total does not include to the concept used in the United Nations' defi- 6.0 asylum seekers, except as noted. * Inflows of nition of the immigrant stock. Few countries, 5.0 - ----_ - foreign workers are the gross arrivals of for- however, apply just one criterion in all circum- | eign workers with legal employment status. stances. For this and other reasons, data based 4 a- The workers may be permanent or temporary. on the concept of foreign nationality and data t 3.0 - * Asylum seekers are those who apply for per- based on the concept of foreign-born cannot be 2.0 - mission to remain in the country for humani- completely reconciled. See the notes to the table 1.0 tarian reasons. 373 for other breaks in comparability between coun- o.o I I __I_-_______ _ . __ _. ` tries and over time. 1995 1990 1995 1999 Data sources Data on the size of the foreign labor force are Austrla - Netherlands also problematic. Countries use different permit '> Belgium Sweden International migration data are collected by systems to gather information on immigrants. pItaly =--United Kingdom the OECD through information provided by national correspondents to the Continuous Some countries issue a single permit for Soun: Table 613 based on OECD data. Rption Systempond Migr tion ( E residence and work, while others issue separate nDetorkwi prvies an annua OvErIe oD network, which provides an annual overview of o residence and work permits. Differences in immigration laws across countries, particularly trends and policies. The data appear in the OECD's Trends in Intemational Migration 2001. I with respect to imrTligrants' access to the labor market, greatly affect the recording and 0 measurement of migration and reduce the comparability of raw data at the international level. The data exclude temporary visitors and tourists (see table 6.14). OECD countries are not the only ones that receive substantial migration flows. Migrant workers make up a significant share of the labor force in Gulf countries and in southern Africa, and people are displaced by wars and natural disasters throughout the world. Systematic recording of migration flows is difficult, however, especially in poor countries and those affected by civil disorder. 6.14 Travel and tourism International tourism International tourism receipts International tourism expenditures Inbound tourists Outbound tourists % of % of thousands thousands $ millions exports $ millions imports 1990 2000 1990 2000 ±990 2000 1.990 2000 1990 2000 1990 2000 Afghanistan 8 4 .. . 11 ... 1 Albani'a 30 39 18 4 211 1.1 38.8 4 12 0.8 1.1 Algeria 1,137 866 3,828 903 64 24 0 :5 0.2 149 1.5 Angola __67 51 13 18 0.3 0.2 38 1.1 Argentina 1,930 2,991 2,398 4.786 1,131 2,903 7.6 94 1,505 4,107 22.0 12.6 Armeni'a 15 30 -. -. . 45 10.1 - 34 .. 3.7 Australia 2,215 4,946 2,170 3,210 4,088 8,442 8.2 10.2 4,535 5,792 8.5 6.9 Austria 19,011 17,982 8,527 3,954 13,417 11,440 21.1 12.1 7.748 9,803 12.6 10.1 AzerbaiUjan 77 681 .. 1,204 42 81 6.3 .. 139 .. 7.3 374 Bangladesh 115 200 388 1,103 11 59 0.6 0.9 78 212 1.9 2.5 Belarus 355 .. 17 0.2 .. 116 .. 1.7 Belgium 5,147 6,457 3,835 7,773 3,721 7,039 2.7 3.5 5,477 10,057 4.1 5.3 C ~Benin 110 152 418 .. 28 33 7.7 5.9 12 7 2.6 0 9 ' ~ Bolivi'a 254 342 242 196 91 160 9.3 11.0 130 165 12.0 8.3 Bosnia and Herzegovina 1 110 .. 17 . .. E Botswana- 543 843 192 117 234 5.8 7.7 56 143 2.8 5.7 0l o Brazil 1,091 5,313 1,188 2,679 1,444 4,228 4.1 6.6 1,559 3,893 5.5 5.4 > Bulgaria 1,586 2.85 _2,395 2.592 320 1.074 4.6 15.3 189 524 2.4 8.0 a Burkina Faso 74 218 . .. 11 42 3.2 10.5 32 .. 4.2 Burundi 109 30 24 16 4 1 4 -5 - . 17 . 8 5.3 6.2 0 Cambodia 17 466 .. 49 50 228 15.9 15.2 ..8 .. 0.6 Caeoo 9 .3 .... .. 53 40 2.4 1. 7 279 .. 14.5 CN Canada 15,209 20,423 20,415 18,368 6,339 10,768 4.2 3.3 10,931 11,345 7.3 4.4 Central African Republic 6 10 3 6 1.4 __3.9 51 .. 12.4 Chad 9 44 24 .. 8 10 3.0 2.7 70 . 14.4 Chile 943 1,742 768 1,567 540 827 5.3 3.7 426 806 4.6 4.5 China 10,484 31,229 2,134 10,473 2,218 16,231 3.9 5.8 470 10,864 1.0 5.7 Hong Kong, China 6,581 13.059 2,043 4,175 5,032 7,886 5.0 3.2 Colombia 813 530 781 1,098 406 1,028 4.7 6.6 454 1,078 6.6 8.0 Congo, Dem. Rep. 55 53 .. 50 7 2 0.3 16 . 0.6 Congo, Rep, 33 26 . .. 8 11 0.5 0.4 113 60 8.8 4.5 Costa Rica 435 1,106 191 353 275 1,102 14.0 14.4 148 428 6.3 6.0 CMe dIlvoire 196 301 2 .. 51 108 1.5 2.1 169 237 4.9 5,4 Croatia 7,049 5,831 . .. 1,704 2,758 .. 31.9 729 751 .. 7.7 Cuba 327 1,700 12 56 243 1,756 . Czech Republic 7,278 5,700 3,510 39,977 419 2,869 8.0 455 1,474 .. 4.3 Denmark 1.838 2,088 3,929 4,841 3,322 4,025 6.8 5.7 3,676 5,084 8.9 8.7 Domi'nican Republic 1,305 2,977 137 364 900 2,918 49.1 32.6 144 282 6.4 3.0 Ecuador 362 615 181 386 188 402 5.8 6.7 175 271 6.9 6.5 Egypt, Arab Rep. 2,411 5,116 2,012 2.886 1,100 4,345 12.0 27.2 129 1,078 0.9 5.1 El Salvador 194 795 525 787 18 254 1.8 7.0 61 80 3,8 1.7 Eritrea 169 70 .. 36 .. 37.7 . Estonia 372 1,100 1,780 27 505 4.1 10.5 19 217 2.7 5.1 Ethiopia 79 125 _89 _.25 24 3.7 2.4 11 55 1.0 2.9 Finland 1,572 2,700 1.169 5,314 1,167 1,401 3.7 2.7 2,791 2,021 8.3 5.4 France 52,497 75.500 19,430 16,709 20,184 29.900 7.1 7.9 12,423 18,631 4.4 5.4 Gabon 109 155 161 .. 3 7 0.1 0.2 137 183 7.6 10.3 Gambia, The 100 96 . .. 26 49 15.5 18.6 8 .. 4.2 Georgia .. 384 .. 373 . 400 .. 54.1 .. 270 .. 21.2 Germany 17,045 18,983 56,261 73,400 14,288 17,812 3.0 2.8 33.771 48,495 8.0 7.9 Ghana 146 373 . .. 81 304 8.2 12.3 13 36 0.9 0.9 Greece 8,873 12.500 1,651 . 2,587 9,221 19.9 31.3 1,090 3,989 5.6 11.2 Guatemala 509 823 289 391 185 518 11.8 13.3 100 183 5.5 3.6 Guinea 33 30 12 3.6 1.4 30 31 3.1 3.3 Guinea-Bissau.. .... ... Haiti 144 143 46 55 14.5 9.8 37 37 7.2 3.6 Honduras 290 408 196 235 29 240 2.8 9.6 38 60 3.4 2.0 6.14 International tourism International tourism receipts International tourism expenditures Inbound tourists Outbound tourists % of $6 ot thousands thousands $ m lions exports $ millions imports 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Hungary 20,510 15,571 13,596 10,622 824 3.424 6.8 10.8 477 1,191 4.3 4.2 Ind ia 1,70J7 2.641 2,281 3,811 1,513 3,296 6.6 5.2 393 2,010 1.2 3.0 Indonesia 2.178 5,064 688 .. 2,105 5.749 7.2 8.1 836 2.353 3.0 5.6 Iran, Islamic Rep. 154 1,700 788 1,450 61 850 0.3 2.9 340 918 1.5 5.8 Iraq 748 78 239 . 55 13 . .. Ireland 3.666 6,728 1,798 3,576 1,883 3,571 7.0 4.0 1,163 2,620 4.7 3.7 Israel 1,063 2,400 883 3,203 1,396 _ 3.100 8.1 6.9 1,442 2,566 7.1 6.3 Italy 26.679 41,182 16,152 18.962 16,458 27,439 7.5 9.3 10,304 16,913 4.7 6.3 Jamaica 989 1,323 . .. 740 1,333 33.4 37.2 114 227 4.8 5.7 Japan 3.236 4.757 10,997 16,358 3.578 3,374 1.1 0.6 24,928 32,808 8.4 8.3 375 Jordan 572 1,427 1,143 1,560 512 722 20.4 20.4 336 355 9.0 7.1 Kazakhstan .. . . . . 363 .. 5.2 . 394 .. 5.8 Kenya 814 943 210 .. 443 304 19.9 11.3 38 115 1.4 3.5 Korea. Dem. Rep. 115 -130 ... ... ... . Korea, Rep. 2,959 _ 5,322 1,561 5,508 3,559 6,609 4.9 3.2 3,166 6,377 4.1 3.3 Kuiwait 15 77 . .. 132 243 1.6 1.8 1,837 2.510 25.6 21.1 Kyrgyz Republic .. 69 .. 32 2 8 .. 1.3 ..3 .. 0.3 CD 0 Lao PDR 14 300 .. 3 114 2.9 22.8 1 12 0.5 2.1 ' 3 Latvia .. 490 .. 2,256 7 131 0.6 4.0 13 268 1.3 7.4 (D Lebanon 210 742 .. 1.650 . 742 .. 34.7... .. . 0. Liesoth 17a8). . 7 1 7. . 2 1 . . 0) Libya 96 40 425 6 28 0.1 0.4 424 150 4.7 3.1 t Lithuania 780 1.226 .. 3,482 .. 391 .. 77 .. 341 .. 6.4 Macedonia, FYR 562 224 . .. 45 37 .. 2.3 . Madagascar 53 160 34 .. 40 116 8.5 9.8 40 I11 4.9 9.3 Malawi 130 228 . .. 16 27 3.6 5.5 16 .. 2.9 Malaysia 7,446 10.222 14,920 26,067 1.667 4.563 5.1 4.1 1,450 1.973 4.6 2.6 Mali 44 91 . .. 47 50 11.2 7.1 62 29 7.5 3.0 M-auritania .. 24 . .. 9 28 1.9- 7.7 23 55 4.4 13.3 Mauritius 292 656 89 154 244 585 14.2 22.2 94 187 4.9 6.6 Mexico 17,176 20,643 7,357 11,081 5,467 8,295 11.2 4.6 5,519 5,499 10.6 2.9 Moldova 226 17 49 37 4 4 .. 0.6 .. 58 . 7.5 Mongolia 17 158 . .. 5 28 1.0 5.3 1 41 0.1 6.2 Morocco 4,024 4.113 1,202 1,612 1,259 2,040 20.2 19.5 184 440 2.4 3.7 Mozambique Myanmar 21 208 ..9 35 1.4 2.2 16 18 1.4 0.7 Namibia 213 614 85 288 7.0 17.9 63 88 4.0 4.6 Nepal 255 _451 82_ _122_ 64_ 168 16.9 _14.6 45 71 5.9 4.7 Netherlands 5,795 10,200 9,000 14,180 4,155 6,951 2.6 2.7 7,376 11,366 5.0 4.9 New Zealand 976 1,787 717 1,185 1.030 2,068 8.8 11.6 958 1,493 8.2 8.5 Nicaragua 106 486 173 452 12 116 3.1 12.2 15 74 2.2 3.6 Niger 21 50 18 10 17 24 3.2 7.5 44 26 6.0 5,7 Nigeria 190 813 56 25 142 0.2 1.4 576 620 8.3 5.1 Norway 1,955 4,481 2,667 1,570 2.229 3.3 3.7 3.679 4,751 9.5 9.4 Oman 149 502 69 104 1.2 1.4 47 47 1.4 0.8 Pakistan 424 543 . .. 156 86 2.5 0.9 440 180 4.7 1.5 Panama 214 479 151 221 172 576 3.9 7.5 99 184 2.4 2.3 Papua New Guinea 41 58 66 106 41 76 3.0 3.5 50 53 3.3 2.9 Paraguay 280 221 264 281 128 66 5.1 2.4 103 109 4.7 3.1 Peru 317 1,027 329 781 217 1,001 5.3 11.6 295 443 7.2 4.9 Philippines 1,025 2,171 1,137 1,755 1,306 2,534 11.4 6.5 i11 1,308 0.8 3.6 Poland 3,400 17,400 22,131 55.097 358 6,100 1.9 13.2 423 3,600 2.8 6.9 Portugal 8,020 12,037 2.268 .. 3,555 5,206 16.5 15.7 867 2.266 3.2 4.9 Puerto Rico 2.560 3.341 996 1,134 1,366 2.541 ... 630 815 Romania 3,009 3,274 11.247 6,274 106 364 1.7 3.0 103 395 1.0 3.5 Russian Federation 3,009 21,169 4,150 18.371 752 7,510 1.4 8.9 . 7.434 -. 14.0 6.14 International tourism International tourism receipts International tourism expenditures Inbound tourists Outbound tourists % of % of thousands thousands $ millions exports $ millions .mportS 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Rwanda 16 2 10 17 6.9 15.0 23 18 6.4 4.0 Saudi Arabia 2,209 3.700 1,884 1,462 4.0 3.4 . Senegal 246 369 167 166 11.5 11.5 105 .. 5.7 Sierra Leone 98 10 . .. 19 12 9.1 13.8 4 4 1.9 2.7 Singapore 4,842 6.258 1.237 3.971 4,937 6,370 7.3 3.8 1,893 2,749 2.9 2.2 Slovak Republic 822 1.053 188 343 70 432 .. 3.1 181 295 .. 2.0 Slovenj'a 650 1,090 . .. 721 957 8.5 8.9 282 539 4.1 4.7 Somalia 46 10 . . , . . .. South Africa 1,029 6,001 616 3.363 992 2,526 3.6 7.5 1,117 1,806 5.3 6.0 376 Spain 34.085 48.201 _10,698 4.794 18,593 31.000 22.2 18.4 4,254 5.523 4.2 3.2 Sri Lanka 298 400 297 524 132 253 5.8 4.0 74 219 2.5 3.2 Sudan .33 50 203 .. 21 2 4.0 0.2 51 35 3.5 2.3 tO Swaziland 263 319 30 35 4.6 3.5 35 45 4.6 3.7 Sweden 1,900 2,746 6,232 10.500 2,906 4.107 4.1 3.8 6.286 7.557 8.9 8.0 Switzerland 13,200 11,400 9,627 12,009 7.411 7,303 7.6 6.0 5,873 6,842 6.1 6.4 s Syrian Arab Republic 562 916 1.041 .. 320 474 6.4 6.9 249 630 8.4 12.1 o Tajikistan 511 . .. .. .. a) -.- > Tanzania 153 459 301 .. 65 739 12.1 57.7 23 550 1.6 25.8 a) Thailand 5,299 9.509 883 1,909 4.326 7,119 14.8 8 7 854 2,065 2.4 2.9 '0 o Togo 103 60 -. . 58 6 8.7 1 3 40 2 4.7 0.3 Trinidad and Tobago 195 336 254 .. 95 210 4.2 6.2 122 67 8.6 2.1 o Tunisia 3.204 5.057 1,727 1,480 948 1.496_ 18.2 17 4 179 239 3.0 2.6 CN Turkey 4,799 9.587 2,917 4.758 3,225 7,636 15.3 14.9 520 1.471 2.0 3.0 Turkmenistan .. 300 .. 357 . 192 .. 22.6 Uganda 69 151 . .. 10 149 4.1 20.5 8 141 1.2 7.7 Ukraine .. 4.232 7,399 .. 2,124 .. 12.5 .. 1,774 .. 11.6 United Arab Emirates 633 2.481 169 607 . .. United Kingdom 18,013 25.191 31,150 53,881 13.762 19,544 5.8 4.9 17,560 35.631 6.6 8.7 United States 39,363 50,891 44.623 58.386 43,007 85.153 8.0 8.0 37.349 59,351 6.1 4.9 Uruguay 1,267 1,968 .. 778 262 652 12.1 17.5 111 280 6.7 7.0 Uzbekistan ., 272 ... 21 .. 0.6 Venezuela. RB 525 469 309 891 496 656 2.6 3.0 1,023 1,646 10.8 9.7 Vietnam 250 2,140 .. 168 85 86 .4.4 0.7 West Bank and Gaza .. 330 .. . . 155 Yemen, Rep. 52 73 20 76 1.3 1.8 64 83 2.9 2.8 Yugoslavia, Fed, Rep. 1,186 152 419 17 . .. Zambia 141 574 . .. 41 91 3.0 9.7 54 .. 2.8 Zimbabwe 605 1.868 200 331 60 202 3.0 8.6 66 131 3.3 4.8 Low Income 12,955 25,684 . .. 7,825 14.275 4.9 7.6 .. 10.938 3.8 5.0 Middle Income 135,859 235.398 150,412 240,320 45,559 123,119 6.5 7.2 31.956 67.555 4.8 5.1 Lower middle income 47,704 108.139 44,071 61,463 17.820 58,528 7.8 8.2 .. 31,582 2.5 6.3 Upper mniddle income 87,623 126,468 ..181.055 27,703 64.408 5.9 6.6 21.959 35,907 6.3 4.1 Low & middie Income 149,346 263.586 ..299.419 53,380 139.533 6.3 7.1 38,313 78,539 4.7 5.0 East Asia & Pacific 30.463 67,978 23.210 49,961 15,682 44.091 6.5 5.4 7,146 22.989 2.9 4,1 Europe & Central Asia 59,843 97,311 ..176.460 9.737 38.042 7.4 10.9 .. 21.927 2.6 7.7 Latin America & Carib. 33.010 51,058 17.576 28.727 15,157 33.109 8.0 7.1 13.002 19,251 9.1 5.0 Middle East & N. Africa 17.053 -28.105 . .. 7,057 13,120 4.9 8.9... South Asia 3.004 4.714 3,503 6.255 1.968 4.230 5.8 4.7 1,048 2,744 2.1 2.9 Sub-Saharan Africa 7,075 17.455 . .. 3.079 6.597 3.8 8,4 3.683 5,202 5.5 5.9 High Income 311,017 435.443 274.397 328,625 210,970 337,014 6.1 5.9 228,359 338,638 6.6 6.2 Europe EMU 184.112 255,007 . ..100,879 152.168 6.6 7.1 87,712 132.360 6.0 6.2 6.14 About the diata Definitions The data in the table are from the World Tour- * International Inbound tourists are the ism Organization. They are obtained primarily number of visitors who travel to a country other from questionnaires sent to government offices, than that in which they have their usual supplemented with data published by official residence for a period not exceeding 12 sources. Although the World Tourism Organiza- months and whose main purpose in visiting is tion reports that progress has been made in other than an activity remunerated from within harmonizing definitions and measurement units, the country visited. * International outbound differences in national practices still prevent full tourists are the number of departures that international comparability. people make from their country of usual The data on international inbound and residence to any other country for any purpose outbound tourists refer to the number of arrivals other than a remunerated activity in the country and departures of visitors within the reference visited. * International tourism receipts are period, not to the number of people traveling. expenditures by international inbound visitors, Thus a person who makes several trips to a including payments to national carriers for country during a given period is counted each international transport. These receipts include 377 time as a new arrival. International visitors any other prepayment made for goods or include tourists (civernight visitors), same-day services received in the destination country. visitors, cruise passengers, and crew members. They also may include receipts from same-day Regional and income group aggregates are visitors, except in cases where these are R based on the World Bank's classification of important enough to justify a separate countries and differ from those shown in the classification. Their share in exports is (D World Tourism Organization's Yearbook of calculated as a ratio to exports of goods and D 0 Tourism Statistics. Countries not shown in the services. * International tourism expenditures 3 table but for which data are available, are are expenditures of international outbound D included in the regional and income! group totals. visitors in other countries, including payments World totals are no longer calculated by the World to foreign carriers for international transport. Tourism Organization. The aggregates in the These expenditures may include those by table are calculated using the World Bank's residents traveling abroad as same-day visitors, weighted aggregation methodology (see except in cases where these are so important Statistical methods) and differ from aggregates as to justify a separate classification. Their provided by the World Tourism Organization and share in imports is calculated as a ratio to published in previous editions of the World imports of goods and services. Development Indicators.n Data sources Figure 6.14 The visitor and expenditure data are available in the World Tourism Organization's Yearbook Top 10 country recipients of Inbound tourists, 1990 and 2000 of Tourism Statistics and Compendium of i .0 Tourism Statistics. 2001. The data in the BO- table were updated from electronic files 70 provided by the World Tourism Organization. The data on exports and imports are from I 60 1 [990 the International Monetary Fund's o 50o - < 1 * 2000 Intemational Financial Statistics and World Bank staff estimates. -40 30 20 10 0 France United Spain Italy China United Russian Mexico Canada Germany States Kingdomr Federation Source: Table 6.14 based on World Tourism Organization date. Statistical methods This section describes some of the statistical procedures used in * Aggregates of ratios are generally calculated as weighted averages preparing the World Development Indicators. It covers the methods of the ratios (indicated by w) using the value of the denominator or, employed for calculating regional and income group aggregates and in some cases, another indicator as a weight. The aggregate ratios for calculating growth rates, and it describes the World Bank's Atlas are based on available data, including data for economies not method for deriving the conversion factor used to estimate gross shown in the main tables. Missing values are assumed to have the national income (GNI) (formerly referred to as GNP) and GNI per capita same average value as the available data. No aggregate is in U.S. dollars. Other statistical procedures and calculations are calculated if missing data account for more than a third of the described in the About the data sections that follow each table. value of weights in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after 37 Aggregation rules imputing values for missing data according to the above rules for Aggregates based on the World Bank's regional and income classifica- computing totals. s 0 tions of economies appear at the end of most tables. These classifica- * Aggregate growth rates are generally calculated as a weighted tions are shown on the front and back cover flaps of the book. This average of growth rates (and indicated by a w). In a few cases year's edition of the World Development Indicators, like the two growth rates may be computed from time series of group totals. C previous editions, includes aggregates for the member countries of the Growth rates are not calculated if more than half the observations D CD European Monetary Union (EMU). Members of the EMU on 1 January in a period are missing. For further discussion of methods of o 2001 were Austria, Belgium, Finland, France, Germany, Ireland, Italy, computing growth rates see below. i (D Luxembourg, the Netherlands, Portugal, and Spain. Other classifica- * Aggregates denoted by an m are medians of the values shown in tions, such as the European Union and regional trade blocs, are the table. No value is shown if more than half the observations for Q 0 documented in About the data for the tables in which they appear. countries with a population of more than 1 million are missing. Because of missing data, aggregates for groups of economies Exceptions to the rules occur throughout the book. Depending on @ should be treated as approximations of unknown totals or average the judgment of World Bank analysts, the aggregates may be based on values. Regional and income group aggregates are based on the as little as 50 percent of the available data. In other cases, where largest available set of data, including values for the 148 economies missing or excluded values are judged to be small or irrelevant, shown in the main tables, other economies shown in table 1.6, and aggregates are based only on the data shown in the tables. Taiwan, China. The aggregation rules are intended to yield estimates for a consistent set of economies from one period to the next and for Growth rates all indicators. Small differences between sums of subgroup aggregates Growth rates are calculated as annual averages and represented as and overall totals zind averages may occur because of the approxima- percentages. Except where noted, growth rates of values are computed tions used. In addition, compilation errors and data reporting practices from constant price series. Three principal methods are used to may cause discrepancies in theoretically identical aggregates such as calculate growth rates: least squares, exponential endpoint, and world exports and world imports. geometric endpoint. Rates of change from one period to the next are Five methods of aggregation aire used in the World Development calculated as proportional changes from the earlier period. Indicators: * For group and world totals denoted in the tables by a t, missing Least-squares growth rate. Least-squares growth rates are used data are imputed based on the relationship of the sum of available wherever there is a sufficiently long time series to permit a reliable data to the total in the year of the previous estimate. The calculation. No growth rate is calculated if more than half the observa- imputation process works forward and backward from 1995. tions in a period are missing. Missing values in 1995 are imputed using one of several proxy The least-squares growth rate, r, is estimated by fitting a linear variables for which complete data are available in that year. The regression trend line to the logarithmic annual values of the variable in imputed value is calculated so that it (or its proxy) bears the same the relevant period. The regression equation takes the form relationship to the total of available data. Imputed values are usually not calculated if missing data account for more than a third In X*= a + bt, of the total in the benchmark year. The variables used as proxies are GNI in U.S. dollars, total population, exports and imports of which is equivalent to the logarithmic transformation of the compound goods and services in U.S. dollars, and value added in agriculture, growth equation, industry, manulacturing, and services in U.S. dollars. * Aggregates marked by an s are sums of available data. Missing X, = XO (1 + r )'. values are not imputed. Sums are not computed if more than a third of the observations in thei series or a proxy for the series are missing in a given year. In this equation X is the variable, t is time, and a = In Xo and b drawing rights, or SDRs, are the IMF's unit of account.) The SDR In (1 + r) are parameters to be estimated. If b* is the least-squares deflator is calculated as a weighted average of the G-5 countries' GDP estimate of b, the average annual growth rate, r, is obtained as deflators in SDR terms, the weights being the amount of each country's [exp(b*) - 1] and is multiplied by 100 for expression as a percentage. currency in one SDR unit. Weights vary over time because both the The calculated growth rate is an average rate that is representative composition of the SDR and the relative exchange rates for each of the available observations over the entire period. It does not currency change. The SDR deflator is calculated in SDR terms first and necessarily match the actual growth rate between any two periods. then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conversion factor is then applied to a country's GNI. 380 Exponential growth rate. The growth rate between two points in time for The resulting GNI in U.S. dollars is divided by the midyear population to - certain demographic indicators, notably labor force and population, is derive GNI per capita. to calculated from the equation When official exchange rates are deemed to be unreliable or 5 unrepresentative of the effective exchange rate during a period, an c r = ln(p,lp,)/n, alternative estimate of the exchange rate is used in the Atlas formula c (see below). c) E where p and p, are the last and first observations in the period, n is The following formulas describe the calculation of the Atlas ° the number of years in the penod, and In is the natural logarithm conversion factor for year t: ot operator. This growth rate is based on a model of continuous, exponential growth between two points in time. It does not take into . 1 Pt ___ 1 B account the intermediate values of the series. Nor does it correspond et =- e,_2 / ss +eP - / e +e, o to the annual rate of change measured at a one-year interval, which is l p2 Pv2 't-1 Pt1 0 N'J given by (p, - P,1)/P,.n and the calculation of GNI per capita in U.S. dollars for year t Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and Y = Y,/N,)/e,', reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most where e,* is the Atlas conversion factor (national currency to the U.S. economic phenomena are measured only at intervals, in which case the dollar) for year t, e, is the average annual exchange rate (national compound growth model is appropriate. The average growth rate over n currency to the U.S. dollar) for year t, p, is the GDP deflator for year t, periods is calculated as p,s$ is the SDR deflator in U.S. dollar terms for year t, Y, is the Atlas GNI per capita in U.S. dollars in year t, Y, is current GNI (local currency) r = exp[ln(p,/p,)/n] - 1. for year t, and N, is the midyear population for year t. Like the exponential growth rate, it does not take into account Alternative conversion factors intermediate values of the series. The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. An alternative conversion factor World Bank Atlas method is used when the official exchange rate is judged to diverge by an In calculating GNI and GNI per capita in U.S. dollars for certain exceptionally large margin from the rate effectively applied to domestic operational purposes, the World Bank uses the Atlas conversion factor. transactions of foreign currencies and traded products. This applies to The purpose of the Atlas conversion factor is to reduce the impact of only a small number of countries, as shown in Primary data documenta- exchange rate fluctuations in the cross-country comparison of national tion. Alternative conversion factors are used in the Atlas methodology incomes. and elsewhere in the World Development Indicators as single-year The Atlas conversion factor for any year is the average of a conversion factors. country's exchange rate (or alternative conversion factor) for that year and its exchange rates for the two preceding years, adjusted for the difference between the rate of inflation in the country and that in the G- 5 countries (France, Germany, Japan, the United Kingdom, and the United States). A country's inflation rate is measured by the change in its GDP deflator. The inflation rate for G-5 countries, representing international inflation, is measured by the change in the SDR deflator. (Special Primary data documentation The World Bank is not a primary data collection agency for most areas other than living standards surveys and debt. As a major user of socio- economic data, however, the World Bank places particular emphasis on data documentation to inform users of data in economic analysis and policymaking. The tables in this section provide information on the sources, treatment, and currentness of the principal demographic, economic, and environmental indicators in the World Development Indicators. Differences in the methods and conventions used by the primary data collectors-usually national statistical agencies, central banks, and 381 customs services-may give rise to significant discrepancies over time both among and within countries. Delays in reporting data and the use of N) 0 old surveys as the base for current estimates may severely compromise i the quality of national data. 0 Although data quality is improving in some countries, many develop- ing countries lack the resources to train and maintain the skilled staff D (D and obtain the equipment needed to measure and report demographic, c '0 economic, and environmental trends in an accurate and timely way. The 3 (D World Bank recognizes the need for reliable data to measure living stan- dards, track and evaluate economic trends, and plan and monitor devel- 2- 0) opment projects. Thus, working with bilateral and other multilateral agen- O cies, it continues to fund and participate in technical assistance projects ' to improve statistical organization and basic data methods, collection, and dissemination. The World Bank is working at several levels to meet the challenge of improving the quality of the data that it collates and disseminates. At the country level the Bank is carrying out technical assistance, training, and survey activities-with a view to strengthening national capacity-in the following areas: * Poverty assessments in most borrower member countries. * Living standards measurement and other household and farm sur- veys with country partner statistical agencies. * National accounts and inflation. * Price and expenditure surveys for the International Comparison Programme. * Projects to improve statistics in the countries of the former Soviet Union. * External debt management. * Environmental and economic accounting. National currency Fiscal National accounts Balance of payments Government IMF year and trade finance special end data dissemi- nation Balance of Alternative PPP Payments Reporting SNA price conversion survey Marual External System Accouniting period' Base year valuation factor year in use debt of trade Iconcept _____ Afghanistan Afghan afghani Dec. 31 CY 1975 VAB Albania Albanian Ink Dec. 31 CY 1995 VAP 1996 BPM5 Actual G C G Algeria Algerian dinar Dec. 31 CY 1980 VAB BPM5 Actual S B Angola Angolan kwanza Dec. 31 CY 1997 VAP 91-96 BPM4 Actual S Argentina Argentine peso Dec. 31 CY 1993 VAB 1971-84 1996 BPM5 Preliminary S C * Armnenia Armenian dram Dec. 31 CY 1996 VAB 1993-95 1996 BPM5 Actual S G Australia Australian dollar Jun. 30 FY 1995 .CVAB 1990-95 1996 BPM5 G C * Austria Austr an schilling' Dec. 31 CY 1995 VAB 1996 BPM5 5 C Azerbaijan Azeri manat Dec. 31 CY 2000 C.CVAB 1987-95 1996 BPM5 Actual G C G 382 Bangladesh Bangladesh taka Jun. 30 FY 1996 VAP 1971-2000 1993 BPM5 Actual G G Belarus Belarussiani rubel Dec. 31 CY 1990 b. VAB 1987-2000 1996 BPM5 Actual G C cu o Belgium Belgian franc' Dec. 31 CY 1995 b VAB 1996 BPM5 S C S m Benin CFA franc Dec. 31 CY 1985 yAP 1992 1993 BPM4 Actual S G Bolivia Bolividno Dec. 31 CY 1990 VAP 1960-85 1996 BPM5 Actual S C G Bosnia and Herzegovina Convertible mark Dec. 31 CY 1996 VAB BPMS Preliminary E) Botswana Botswana pula Dec. 31 CY 1986 VAP 1999 1993 BPM5 Actual G B o Brazil Brazilian real Dec. 31 CY 1995 VAB 1999 1996 BPM5 Preliminary S C s a) > Bulgaria Bulgarian lev Dec. 31 CY 1990 CVAR 78-89, 91-92 1996 BPM5 Actual G C G Burkina Faso CFA franc Dec. 31 CY 1985 VAB 1992-93 BPM4 Actual G C G -u U_ Burundi Burundi franc Dec. 31 CY 1980 VAB BPM5 Estimate S C 0 3: Cambodia Cambodian riel Dec. 31 CY 1989 VAP BPM5 Preliminary G ("I o Cameroon CFA franc Jun. 30 FY 1980 VAR 1970-99 1993 BPM5 Preliminary S C G CN Canada Canadian dollar Mar. 31 CY 1995 VAB 1996 BPM5 G C 5' Central African Republic CFA franc Dec. 31 CY 1987 VAB BPM4 Estimate S Chad CFA franc Dec. 31 CY 1995 VAR BPM5 Preliminary S C Chile Chilean peso Dec. 31 CY 1986 VAR 1996 BPM5 Actual S C S China Chinese yuan Dec. 31 CY 1990 VAP 1987-93 BIPM5 Estimate S B Hong Kong, Chtina Hong Kong dollar Dec. 31 CY 1990 VAR 1993 BPMS G 5' Colombia Colombian peso Dec. 31 CY 1994 VAR 1992-94 1993 BPM5 Actual S B S Congo. Derm. Rep. Congo Franc Dec. 31 CY 1987 VAP 1993-99 BPM5 Actual S C Congo, Rep. CFA Franc Dec. 31 CY 1978 VAP 1993 1993 BPM4 Estimate S C Costa Rica Costa Rican colon Dec. 31 CY 1991 b VAR BPM5 Actual S C S.* C6te dIlvoire CFA franc Dec. 31 CY 1986 VAP 1993 BPM5 Estimate S C G Croatia Croatian kuna Dec. 31 CY 1997 VAR 1996 BPMV5 Actual G C S Cuba Cuban peso Dec. 31 CY . ..G Czech Republic Czecn koruna Dec. 31 CY 1995 VAB 1996 RPM5 Preliminary G C S* Denmark Danish krone Dec. 31 CY 1995 VAR 1996 RPM5 G C S. Dominican Republic Dominican peso Dec. 31 CY 1990 VAP BPM5 Actual G C Ecuador Ecuadorian sucre Dec. 31 CY 1975 VAP 1999 1996 BPM5 Estimate S B S* Egypt, Arab Rep. Egyptian pound Jun. 30 FY 1992 VAR 1965-91 1993 BPM5 Actual S C El Saivador Salvadoran colone Dec. 31 CY 1990 VAP 1982-90 BPM5 Actual S 8 S* Eritrea Eritrean Nakfa Dec. 31 CY 1992 VAR BPM4 Actual Estonia Estonian kroon Dec. 31 CY 1995 VAR 1990-95 1996 BPM5 Preliminary G C S* Ethiopia Ethiopian birr Jul. 7 FY 1981 VAR 1989-99 BPM5 Actuai G B Finland Finnish markka' Dec. 31 CY 1995 VAR 1993 BPM5 G C France French franc' Dec. 31 CY 1995 .'VAR 1996 BPIV5 S C S* Gabon CFA franc Dec. 31 CY 1991 VAP 1993 1993 BPIV5 Actual S B S * Gambia, The Gambian dalasi Jun. 30 CY 1987 VAR BPMS Actual G B Georgia Georgian lari Dec. 31 CY 1994 C VAB 1990-94 1996 BPMS Actual G C G Germany Deutsche mark' Dec. 31 CY 1995 b VAR 1996 RPM5 S C S* Ghana Ghanaian cedi Dec. 31 CY 1975 VAP 1973-87 BPM5 Estimate G B Greece Greek drachma' Dec. 31 CY 1995 I,VAR 1993 BPM4 Estimate S C Guatemala Guatemalan quetzal Dec. 31 CY 1958 VAP 1985-86 1980 BPM5 Actual S B Guinea Guinean franc Dec. 31 CY 1994 VAR 1986 1993 BPM5 Estimate S C Guinea-Bissau CFA franc Dec. 31 CY 1986 VAR 1970-86 BPM5 Estimate G G Haiti Haitian gourde Sep. 30 FY 1976 VAP 1991-97 BPM5 Preliminary G Honduras Honduran lempira Dec. 31 CY 1978 VAR 1988-89 BPM5 Actual S Latest Latest demograhic, household, Vital Latest Latest Latest Latest Latest population or health survey registration agricultural Industrial water survey of survey of census complete census data withdrawal scientists expenditure (mncl. data and for registration engineers R&D based engaged censuaies) In R&D Afghanistan 1997 Albania 1989 MICS, 2000 Yes 1995 1990 1970 Algeria 1998 MICS, 2000 1973 1996 1990 Angola 1970 1964-65 1987 Argentina 2001 Yes 1988 1996 1995 1999 1999 Armenia 1989 DHS. 2000 Yes 1991 1994 1999 2000 Australia 2001 Yes 1990 1997 1985 1998 1996 Austria 2000 Yes 1990 1998 1991 1993 1998 Azerbaijan 1999 MICS, 2000 Yes 1995 1996 Bangladesh 199:1 DHS, 1999-00 1976 1997 1990 1995 383 Belarus, Rep. 1999 Yes 1994 1990 1996 1997 Belgium 2001 Yes 1990 1997 1980 1997 1991 0 Benin 1992 OHS. 1996 1992-93 1981 1994 1989 Boliv-ia 2001 OHS. 1998 1998 1987 1996 Bosnia and Herzegovina 199:L MICS, 2000 Yes 1991 1992 a. Botswana 1991 MICS, 2000 1993 1994 1992 m Brazil 2000) OHS, 1996 1996 1996 1992 1995 1996 C Bulgaria 19912 LSMS, 1995 Yes 1998 1988 1999 1999 - Burkina Faso 1996 OHS, 1998&99 1993 1997 1992 1997 C Burundi 1990 mics, 2000 1991 1987 1989 1989 Cambodia 1998 OHS, 2000 1987 2 Cameroon 19871 OHS, 1998 1972-73 1998 1987 0 Canada 2001. Yes 1991 1997 1991 1995 1998 Central African Repuiblic 1988 OHS, 1994-95 1993 1987 1996 1996 Chad 19963 OHS, 1996-97 1987 Chile 19921 Yes 1997 1997 1987 2000 2000 China 200C' Population, 1995 1996 1998 1993 1996 1994 Hong Kong. China 2001. Yes 1998 1995 Colombia 1993 OHS, 2000 1988 1997 1996 Congo, Oem. Rep. 1984 1990 1990 Congo, Rep. 1996 1986 1988 1987 2000 Costa Rica 2000 CDC, 1993 Yes 1973 1997 1997 1996 1996 CMe dIlvoire 1998 OHS, 1999 1974-75 1997 1987 Croatia 2001 Yes 1992 1996 1995 Cuba 1981 Yes 1989 1995 1995 Czech Republic 19~~~91 dCD, 1993 Ye . 1998 1991 1999 1998 Denmark 2001 Yes 1989 1998 1990 1998 1998 DominicnRpbi 1993 OHS, 1961971 1984 1994 Ecuador 2001 CDC, 1999 -1997 1998 1997 1997 Egypt, Arab Rep. 1996 O6HS, 200 Yes 1989-90 1997 1993 1991 2000 El Salvador 1992 CDC, 1994 1970-71 16998 1992 1992 1992 Eritrea 1984 O-HS, 1-995 --------1998_ Estonia 2000 Yes 1994 1995 1999 1999 Ethiopia 1994 OHS. 2000 1988-89 1998 1997 1987 Finland 1990 Yes 1990 1998 1991 France 1999 Yes 1988 1998 1990 1998 1997 Gabon 1993 OHS, 2000 1974-75 1982 1987 Gambia, The 1993 MC, 2001982 - 1982 Georgia, Rep. 1989 CDC, 1999 Yes 1990 Germany Yes 1993 1991 1997 1998 Ghana 2000 DHS, 1998 1984 19915 1970 Greece 2001 Yes 1993 1996 1980 1997 1997 Guatemala 1994 OHS, 1998-99 Yes 1979 1988 1992 1988 1988 Guinea 1996 OHS, 1999 1996 1987 Guinea-Bissau 1991 MICS, 2000 1988 1991 Haiti 1982 OHS, 2000 1971 1996 1991 Honduras 1988 CDC, 1994 1993 1997 1992 National currency Fiscal Nationai accounts Balance of payments Government IMF year and trade finance speciai end data dissemi- nation Balance of Alternative PPP Payments Reporting SNA price conversion survey Manual External System Accounting period' Base year valuation factor year is use dent of trade concept Hungary Hungarian forint Dec. 31 CY 1994 VAB 1996 BPM5 Actual S C India Indian rupee Mar. 31 FY 1993 VAB 1971-2000 BPM5 Preliminary G C * Indonesia Indonesian rupiah Mar. 31 CY 1993 VAP 1993 BPMS Preliminary S C S5* Iran. Islamic Rep. Iranian rial Mar. 20 FY 1982 VAB 1980-90 1993 BPM5 Estimate G C * Iraq Iraqi dinar Dec. 31 CY 1969 VAB S Ireland Irish pound' Dec. 31 CY 1995 VAB 1996 BPM5 G C Israel Israeli new shekel Dec. 31 CY 1995 VAP 1996 BPM5 S C S Italy Italian lira' Dec. 31 CY 1995 VAB 1996 BPM5 S C * Jamaica Jamaica dollar Dec. 31 CY 1986 VAP 1995-96, 99 1993 BPMV5 Actual G C S 384 Japan Japanese yen Mar. 31 CY 1995 VAB 1996 BPM5 G C Jordan Jordan dinar Dec. 31 CY 1994 VAB 1993 BPM5 Preliminary G B Kazakhstan Kazakh tenge Dec. 31 CY 1993 VAB 1987.95 1996 BPM5 Actual G C G o Kenya Kenya shill ng Jun. 30 CY 1982 VAB 1993 BPM5 Actual G B G Korea, Dam. Rep. Dem. Rep. of Korea won Dec. 31 CY .. . PM5 Korea. Pep. Korean won Dec. 31 CY 1995 VAP 1993 BPM5 Actual S C E Kuwait Kuwaiti dinar Jun. 30 CY 1984 VAP BPMS s C Sv o Kyrgyz Republic Kyrgyz sum Dec. 31 CY 1995 C VAB 1992-96 1996 BPM5 Actual G B G 5) > Lao PDR Lao kip Dec. 31 CY 1990 VAB 1960-89 1993 BPM5 Preliminary G a) Latvia Latvian lat Dec. 31 CY 1995 VAB 1987-95 1996 BPMV5 Actual S C Lebanon Lebanese pound Dec. 31 CY 1994 VAB BPM4 Preliminary G V S Lesotho Lesotho loti Mar. 31 CY 1995 VAB BPM5 Preliminary G C o Libya Libyan dinar Dec. 31 CY 1975 VAB 1986 BPM5 G 0 C' Liberia Liberian dollar Dec. 31 CY 1971 VAB Estimate Lithuania Lithuanian litas Dec. 31 CY 1995 VAB 1987-95 1996 BPM5 Actual G C Macedonia, FYR Macedonian denar Dec. 31 CY 1995 VAB 1996 BPM5 Actual G SC Madagascar Malagasy franc Dec. 31 CY 1984 VAB 1993 BPMS Preliminary S C Malawi Malawi kwacha Mar. 31 CY 1994 VAB 1993 BPM5 Estimate G B Malaysia Malaysian ringgit Dec. 31 CY 1987 VAP 1993 BPM5 Estimate G C Mali CFA franc Dec. 31 CY 1987 VAB 1993 BPM4 Preliminary G G Mauritania Mauritanian ouguiya Dec. 31 CY 1985 VAB BPM4 Actual G Mauritius Mauritian rupee Jun. 30 CY 1992 VAB 1993 BPM5 Actual G C Mexico Mexican new peso Dec. 31 CY 1993 VAB 1996 BPM5 Actual G C G Moldova Moldovan lea Dec. 31 CY 1996 VAB 1987-95 1996 BIPM5 Actual G C S* Mongolia Mongolian tugrik Dec. 31 CY 1998 VAP 1996 BPM5 Estimate S C Morocco Moroccan dirham Dec. 31 CY 1980 VAP 1983 BPMV5 Actual S C G Mozambique Mozambican metical Dec. 31 CY 1995 VAB 1992-95 BPM5 Preliminary S Myanmar Myanmar kyat Mar. 31 FY 1985 VAP 1980-82 BPM5 Estimate G C Namibia Namibia dollar Mar. 31 CY 1995 VAB BPMS Estimate B Nepal Nepalese rupee Jul. 14 FY 1985 VAB 1973-2000 1993 BPM5 Actual S C G Netherlands Netherlands guildern Dec. 31 CY 1995 .CVAB 1996 BPM5 S C New Zealand New Zealand dollar Mar. 31 trY 1995 VAB 1996 BPM4 G B S Nicaragua Nicaraguan gold -cordoba Dec. 31 CY 1980 VAP 19 70-93 BPM5 Actual S C Niger CFA franc Dec. 31 CY 1987 yAP 1993 BPIV5 Preliminary S G Nigeria Nigerian naira Dec. 31 CY 1987 VAB 1971-98 1993 BPM5 Estimate G Norway Norwegian krone Dec. 31 CY 1995 CVAB 1996 BPM5 G C Oman Rial Omani Dec. 31 CY 1978 VAP 1993 BPM5 Actual G B S.* Pakistan Pakistan rupee Jun. 30 FY 1981 VAB 1972-2000 1993 BPM5 Preliminary G C Panama Panamanian balboa Dec. 31 CY 1982 C VAP 1996 EBPM5 Actual S C Papua New Guinea Papua New Guinea kina Dec. 31 CY 1983 VAP 1989 BPMS Actual G B G Paraguay Paraguayan guarani Dec. 31 CY 1982 VAP 1982-88 BPIV5 Actual S C G Peru Peruvian new sol Dec. 31 CY 1994 VAP 1985-91 1996 BPM5 Actual S C Philippi'nes Philippine peso Dec. 31 CY 1985 VAP 1993 BPM5 Actual G B S Poland Polish zloty Dec. 31 CY 1990 C.CVAB 1996 BPM5 Actual S C S * Portugal Portuguese escudon Dec. 31 CY 1995 b VAB 1996 BPM5 S C S* Puerto Rico U.S. dollar Dec. 31 CY 1954 VAP G S* Romania Rumanian leu Dec. 31 CY 1993 C VAB 1987-89, 92 1996 BPM5 Actual S C G Russian Federation Russian ruble Dec. 31 CY 1997 C. C VAB 1987-94 1996 BPM5 Estimate G C Latest Latest demograhic, household, Vital Latest Latest Latest Latest Latest population or health survey registration agricultural Industrial water survey of survey of census compiete census data withdrawal scientists expenditure (mnci. data and for registration engineers R&D basedi engaged censuses) In R&D Hungary 2001 Yes 1994 1997 1991 1999 1998 India 2001 National family health, 1998&99 1986 1997 1990 1996 1996 Indonesia 2000 Socioeconomic, 1998 1993 1998 1990 1994 Iran, Islamic Republic 1991 Demographic. 1995 1988 1996 1993 1994 1994 Iraq 1997 MICS. 2000 1981 1997 1990 Ireland 199E6 Yes 1991 1997 1980 1997 1997 Israel 1995 Yes 1983 1996 1986 1997 1999 Italy 20011 Yes 1990 1994 1990 1997 1996 Jamaica 2001 CDC. 1997 Yes 1979 1996 1993 Japan ----2000 Yes 1990 1998 1992 1997 1997 385 Jordan 1994 Annual Survey, 1999 1997 1997 1993 ___ - --------- - - ---- - ------- - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ N Kazakhstan 1999 DHS, 1999 Yes 1993 1997 Kenya 1999 DHS, 1998 1981 1998 1990 Korea, Dem. Rep. 199z1 1987 Korea, Rep. 1995 1991 1997 1994 1999 1997 -- - - - -- -------- -- - --- -- - -- -------- --- --0 Kuwait 1995 FHS, 1996 Yes 1970 1997 1994 1997 Kyrgyz Republic ~~~~~~1999 DHS, 1997 Yes 1994 1997 1997 ( Lao POR 1995 1999 1987 - ---- --------- --- -- 3~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~C Latvia, Rep. 2000 Yes 1994 1998 1994 1999 1999 ( Lebanon 1970 MICS, 2000 1999 1994 -- - -- --- - -- ----- ---- ---- ------- - -- - Lesotho 1996 OHS. 1991 1989-90 1985 1987 Libya - - ------- -- ---------------~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 Libya ~~~~~ ~~~~~~1995 PAPCHILD, 1995 1987 1997 1994 2000 1997 Liberia 1987 ' Lithuania 2001. Yes 1994 1995 1996 Macedonia. FYR 1994 Yes 1994 1996 1999 1999 Madagascar 1993 OHS, 1997 1984 1988 1984 1994 1995 Malawi 1998 OHS, 2000 1992-93 1998 1994 Malaysi a 20001 Yes 1996 1995 1998 1998 Mali 1998t OHS, 1995.96 1978 1997 1987 Mauritania 200(1 PAPCHILD, 1990 1985 1985 Mauritius 200(0 COG, 1991 Yes 1997 1974 1992 1989 Mesico 200(1 Population, 1995 1991 1995 1998 1995 1997 Moldova 1989) MICS, 2000 Yes 1992 1997 1997 Mongolia 20001 Repro. Health, 1998 1998 1993 1999 1999 Morocco 1994 OHS, 1995 1997 1998 1991 Mozambique 1997 DHS, 1997 1992 Myanmar 1983 OHS, 1996 1993 1998 1987 Namibia 1991 OHS, 2000 1995 1994 1991 Nepal 1991 OHS, 1996 1992 1996 1994 1980 1980 Netherlands 2001~~- ----- ----------- ------ Yes------ 19 919 8 1 911 981 9 NewtZealands 2001 Yes 19890 1997 1991 1997 1997 Nicaragua 1995 OHS, 1998 1963 1997 1998 1987 Niger 1988 OHS, 1998 1980 1998 1988 Nigeria 1991 D HS, 1999 1960 1994 1987 1987 1987 Norway 2001 Yes 1989 1998 1985 1999 1997 Oman 199:3 FHS, 1995 1979 1998 1991 Pakistan 1998 RHS, 2000.01 1990 1996 1991 1997 1997 Panama 2001) 1990 1998 1990 Papua New Guinea 2000 OHS, 1996 1987 Paraguay 199:2 OHS, 1990;CDCO,1998 1991 1997 1987 Peru 1993 OHS, 2000 1994 1994 1992 1997 1989 Philippi'nes 20010 OHS, 1998 1991 1997 1995 1992 1992 Poland 1988 Yes 1990 1997 1991 1999 1998 Portugal 200 t Yes 1989 1997 1990 1999 1997 Puerto Rico 1990 Yes 1987 1998 Romania 199:2 COC 1999 Yes 1997 1994 1994 1991 Russian Federation 1989 LSMS, 1992 Yes 1994-95 1998 1994 1999 1999 National currency Fiscal National accounts Balance of payments Government IMF year and trade finance special end data dissemi- nation Balance of Alternative PPP Payments Reporting SNA price conversion survey Manual External System Accounting period- Base year valuation factor year in use debt of trade concept Rwanda Rwanda franc Dec. 31 CY 1985 VAB BPM5 Estimate G C Saudi Arabia Saudi Arabian riyal Hijri year Hijri year 1970 VAP 1993 BPM4 Estimate G Senegal CFA franc Dec. 31 CY 1987 VAP 1993 BPM5 Preliminary S B G Sierra Leone Sierra Leonean leone Jun. 30 CY 1990 VAB 71-79, 87 1993 BPM5 Actual G B Singapore Singapore dollar Mar. 31 CY 1990 VAP 1993 BPM5 G C Slovak Republic Slovak koruna Dec. 31 CY 1995 VAP 1996 BPM5 Actual G C S Slovenia Slovenian tolar Dec. 31 CY 1993 b VAB 1996 BPM5 Actual S C S * Somalia Somali sh Iling Dec. 31 CY 1985 VAB S * South Africa South African rand Mar. 31 CY 1995 VAB BPM5 Estimate S C 386 Spain Spanish peseta I Dec. 31 CY 1995 b VAB 1996 BPM5 S C S Sri Lanka Sri Lankan rupee Dec. 31 CY 1996 VAB 1993 BPM5 Actual G B S* U) Sudan Dinar Jun. 30 CY 1982 VAB 1980-91 BPM5 Estimate G B G tS Swaziland Lilangeni Dec. 31 CY 1985 VAB Estimate B n Sweden Swedish krona Jun. 30 CY 1995 b VAB 1996 BPM5 G C C Switzerland Swiss franc Dec. 31 CY 1995 VAB 1996 BPM5 Estimate S C S5 E Syrian Arab Republic Syrian pound Dec. 31 CY 1995 VAP 1970-00 1993 BPM5 Estimate S C S 0. o Tajikistan Tajik somoni Dec. 31 CY 1985 r VAB 1987-95 1996 BPM5 Actual G C > Tanzania Tanzania shilling Dec. 31 CY 1992 VAB 1993 BPM5 Preliminary S G 5) oD Thailand Thai baht Sep. 30 CY 1988 VAP 1993 BPM5 Preliminary G C oj Togo CFA franc Dec. 31 CY 1978 VAP 1993 BPM5 Preliminary S G Trinidad and Tobago Trinidad and Tobago dollar Dec. 31 CY 1985 VAP 1993 BPM5 Estimate S C g Tunisia Tunisian d nar Dec. 31 CY 1990 VAP 1993 BPM5 Actual G C S 0 rN Turkey Turkish lira Dec. 31 CY 1994 VAB 1996 BPM5 Actual S C Turkmenistan Turkmen manat Dec. 31 CY 1987 D VAB 1996 BPM5 Estimate G S5 Uganda Uganda shilling Jun. 30 FY 1991 VAB 1980-99 BPM5 Preliminary G B Ukraine Ukrainian hryvnia Dec. 31 CY 1990 b ' VAB 1988-95 1996 BPM5 Actual G C G United Arab Emirates U.A.E. dirham Dec. 31 CY 1985 VAB 1993 BPM4 G B United Kingdom Pound sterling Dec. 31 CY 1995 b VAB 1996 BPM5 G C United States U.S. dollar Sep. 30 CY 1995 r< VAB 1996 BPM5 G C Sa Uruguay Uruguayan peso Dec. 31 CY 1983 VAP 1993-99 1993 BPM5 Actual S C S Uzbekistan Uzbek sum Dec. 31 CY 1997 b.c VAB 91-94, 96-00 1996 BPM5 Actual G Venezuela, R.B. Venezuelan bolivar Dec. 31 CY 1984 VAB 1993 BPM5 Preliminary G C G Vietnam Vietnamese dong Dec. 31 CY 1989 VAP 1991 1993 BPM4 Preliminary G B West Bank and Gaza Israeli new shekel Dec. 31 CY 1997 VAB 1993 Yemen, Rep. Yemen rial Dec. 31 CY 1990 VAP 1991-96 1993 BPM5 Preliminary G B G Yugoslavia, Fed. Rep. Yugoslav new dinar Dec. 31 CY 2000 VAP Estimate S Zambia Zambian kwacha Dec. 31 CY 1994 VAB 1990-92 1993 BPM5 Preliminary G B Zimbabwe Zimbabwe dollar Jun. 30 CY 1990 VAB 1991, 1998 1993 BPM5 Preliminary G C Note: For explanation of the abbreviations used in the table see the notes. a. Also applies to balance of payments reporting. b. Country uses the 1993 System of National Accounts methodology. c. Original chained constant price data are rescaled. d. European Monetary Union member sharing single currency Euro. Latest Latest demograhic, household, Vital Latest Latest Latest Latest Latest population or health survey registration agricultural Industrial water survey of survey of census complete census data withdrawal scientists expenditure (mine. data and for registration engineers R&D based engaged censuses) In R&D Rwanda 1991- DH-S, 2000 1984 1986 1993 1999 Saudi Arabia 1992) Demographic, 1999 1983 1992 Senegal 1988 D- HS, 1999 1960 1997 1987 1996 Sierra Leone 1985 MICS, 2000 1985 1986 1987 Singapore 20001 General household, 1995 Yea 1998 1975 1995 1995 Slovak Republic 1991. Yes 1998 1991 1999 1995 Slovenia 1991- Yes 1991 1998 1998 1998 Somalia 1987 South Africa 2001 OHS, 1998 1996- 1990 1993 1993 Spain 2001 Yes 1989 1998 1991 1999 1998 387 Si Lanka 2001, OHS, 1993 Yes 1982 1995 1990 1996 S-ud'an ----- 199---- OD-HS, -19-8-9-9-0- ----- - ------ 1997 1995 Swvazilandl 1980 Pi Sweden ~~~~~~~~~1-6990 - - - Yes 1981 -1997 1991 1999 1997 SwitzerlIandl --2-000 --------Yes 1990 -- 1998 1991 1996 1992 S'yrian Arab Republic 1994 PAPCHILD, 1995 1981 1998 1993 1997 ( Tajikistan 2000 MICS, 2000 Yes 1. 994 -1994 1993 Tanzania 1988 DHS, 1999 1995 1997 1994 'a Thailand 2000~-------- -- -- --- -HS, -- 987 ------- - - ------- Thailand 2000 DHS, 1987 ~~~~~~1993 1996 1990 1996 1997 C Togo 1981 OHS, 1998 1996 1997 1987 1994 1995 - ----- - ...... -- - cL=3~~~~~~~~~~~~ Trinidad and Tobago) 1990 OHS, 1987 Yes 1982 1997 1997 1997 1997 Tunisia 199,4 DHS, 1998 1961 1998 1990 1997 1997 Turkey 1997 OHS, 1998 1991 1997 1992 1997 1997 u Turkmenistan 1995 OHS, 2000 -Yes 1994 Uganda 199:1 OHS, 2000 --1991 1997 1970 2000 1999 Ukraine 2001i COC, 1999 -Yes 1992 2000 2000 United Arab Emirates 1995 1998 1981 1995 United Kingdom 2001 Yes 1993 1998 1991 1998 1997 United States--______ 2000) Current population, 1997 Yes 1997 -----1997 1990 1997 1996 Uruguay 1996 Yes 1990 1997 1965 Uzbekistan 1989 OHS, 1996 Yes 1994 1992 Venezuela, R.B 2001 LSMS, 1993 Yes 1997-98 1996 1970 2000 2000 Vietnam 1999 OHS. 1997 1994 1998 1990 1995 West Bank and Gaza 1997 Demographic, 1995 1971 Yemen, Rep. 1994 OHS, 1997 1982-85 1990 Yugoslavia, Fed. Rep. 1991. MICS, 2000 Yes 1981 1998 1999 1998 Zambia 1990 OHS, 1996 1990 1997 1994 Zimbabwe 1992 OHS, 1999 1960 1997 1987 * Fiscal year end is the date of the end of the fiscal fifth edition (1993). Since 1995 the IMF has adjusted voluntarily elect to participate in either the SDDS or the year for the central government. Fiscal years for other all balance of payments data to BPM5 conventions, GDDS. Both the GDDS and the SDDS are expected to levels of government and the reporting years for statis- but some countries continue to report using the older enhance the availability of timely and comprehensive tical surveys may differ, but if a country is designated system. * External debt shows debt reporting status data and therefore contribute to the pursuit of sound as a fiscal year reporter in the following column, the for 2000 data. Actual indicates data are as reported, macroeconomic policies: the SDDS is also expected to date shown is the end of its national accounts report- preliminary indicates data are preliminary and include contribute to the improved functioning of financial mar- ing period. * Reporting period for national accounts an element of staff estimation, and estimate indicates kets. * Latest population census shows the most re- and balance of payments data is designated as either data are staff estimates. * System of trade refers to cent year in which a census was conducted and at least calendar year basis (CY) or fiscal year (FY). Most econo- the general trade system (G) or the special trade sys- preliminary results have been released. * Latest house- mies report their national accounts and balance of pay- tem (S). For imports under the general trade system, hold or demographic survey gives information on the ments data using calendar years, but some use fiscal both goods entering directly for domestic consumption surveys used in compiling household and demographic years, which straddle two calendar years. In the World and goods entered into customs storage are recorded, data presented in section 2. PAPCHILD is the Pan Arab 388 Development Indicators fiscal year data are assigned at the time of their first arrival, as imports: under the Project for Child Development, DHS is Demographic and to the calendar year that contains the larger share of special trade system goods are recorded as imports Health Survey, LSMS is Living Standards Measurement ° the fiscal year. If a country's fiscal year ends before when declared for domestic consumption whether at Study, SDA is Social Dimensions of Adjustment. CDC June 30, the data are shown in the first year of the time of entry or on withdrawal from customs storage. is Centers for Disease Control and Prevention, and fiscal period; if the fiscal year ends on or after June 30, Exports under the general system comprise outward- SHEHEA is Survey of Household Expenditure and House- E the data are shown in the second year of the period. moving goods: (a) national goods wholly or partly pro- hold Economic Activities. * Vital registration complete E ci Saudi Arabia follows a lunar year whose starting and duced in the country: (b) foreign goods, neither trans- identifies countries judged to have complete registries > ending dates change with respect to the solar year. formed nor declared for domestic consumption in the of vital (birth and death) statistics by the United Na- a) Because the Itiernational Monetary Fund (IMF) reports country, that move outward from customs storage: and tions Department of Economic and Social Information o most balance of payments data on a calendar year (c) nationalized goods that have been declared from and Policy Analysis, Statistical Division, and reported N basis, balance of payments data for fiscal year report- domestic consumption and move outward without hav- in Population and Vital Statistics Reports. Countries o ers in the World Development Indicators are based on ing been transformed. Under the special system of trade with complete vital statistics registries may have more fiscal year estimates provided by World Bank staff. exports comprise categories (a) and (c). In some com- accurate and more timely demographic indicators. These estimates may differ from IMF data but allow pilations categories (b) and (c) are classified as re-ex- * Latest agricultural census shows the most recent consistent comparisons between national accounts and ports. Direct transit trade, consisting of goods entering year in which an agricultural census was conducted balance of payments data. * Base year is the year used or leaving for transport purposes only, is excluded from and reported to the Food and Agriculture Organization. as the base period for constant price calculations in both import and export statistics. See About the data * Latest Industrial data refer to the most recent year the country's national accounts. Price indexes derived for tables 4.5 and 4.6 for further discussion. * Govern- for which manufacturing value added data at the three- from national accounts aggregates, such as the GDP ment finance accounting concept describes the ac- digit level of the International Standard Industrial Clas- deflator, express the price level relative to prices in the counting basis for reporting central government finan- sification (revision 2 or revision 3) are available in the base year. Constant price data reported in the World cial data. For most countries government finance data UNIDO database. * Latest water withdrawal data refer Development Indicators are rescaled to a common 1995 have been consolidated (C) into one set of accounts to the most recent year for which data have been com- reference year. See About the data for table 4.1 for capturing all the central government's fiscal activities, piled from a variety of sources. See About the data for further discussion. * SNA price valuatlon shows whether Budgetary central government accounts (B) exclude table 3.5 for more information. * Latest surveys of scl- value added in the national accounts is reported at central government units. See About the data for tables entists and engineers engaged In R&D and expendi- basic prices (VAB) or at producers' prices (VAP). Pro- 4.11, 4.12 and 4.13 for further details. * IMF special ture for R&D refer to the most recent year for which ducers' prices include the value of taxes paid by pro- data dissemination shows the countries that subscribe data are available from a data collection effort by ducers and thus tend to overstate the actual value to the International Monetary Fund's (IMF) Special Data UNESCO in science and technology and research and added in production, See About the data for tables 4.1 Dissemination Standard (SDDS) or the General Data development (R&D). See About the data for table 5.11 and 4.2 for further discussion of national accounts valu- Dissemination System (GDDS). S refers to countries for more information. ation. * Alternative conversion factor identifies the that subscribe to the SDDS: S* indicates subscribers countries and years for which a World Bank-estimated that have posted data on the Dissemination Standards conversion factor has been used in place of the official Bulletin Board web site: while G refers to countries that (IFS line rf) exchange rate. See Statistical methods for subscribe to the GDDS. (Posted data can be reached further discussion of the use of alternative conversion through the IMF Dissemination Standard Bulletin Board factors. * PPP survey year refers to the latest available at dsbb.imf.org/.). The SDDS was established by the survey year for the International Comparison IMF to guide members that have or that might seek, Progrdmme's estimates of purchasing power parities access to international capital markets in the provi- (PPPs). * Balance of Payments Manual In use refers to sion of their economic and financial data to the public. the classification system used for compiling and re- The GDDS helps guide member countries in the dis- porting data on balance of payments items in table semination to the public of comprehensive, timely, ac- 4.15. BPM4 refers to the fourth edition of the IMF's cessible, and reliable economic, financial, and socio- Balance of Payments Manual (1977), and BPM5 to the demographic statistics. Member countries of the IMF Acronymns and abbreviations Technical terms Organizations AIDS acquired immunodeficiency syndrome ADB Asian Development Bank BOD biochemical oxygen demand AfDB African Development Bank CFC chlorofluorocarbon APEC Asia Pacific Economic Cooperation cilf. cost, insurance, and freight CDC Centers for Disease Control and Prevention CO2 carbon dioxide CDIAC Carbon Dioxide Information Analysis Center COMTRADE United Nations Statistics Division's Commodity Trade database CEC Commission of the European Community CPI consumer price index DAC Development Assistance Committee of the DECD cu. m cubic meter EBRD European Bank for Reconstruction and Development OHS Demographic and Health Survey EDF European Development Fund DMTU dry mietric ton unit EFrA European Free Trade Area DOTS directly observed treatment, short-course (strategy) EIB European Investment Bank OPT diphtheria, pertussis, and tetanus EMU European Monetary Union DRS Worldi Bank's Debtor Rteporting System EU European Union ESAF Enhanced Structural Adjustment Facility Eurostat Statistical Dffice of the European Communities f.o.b. free on board FAO Food and Agriculture Organization 389 GDP gross domestic product FYR former Yugoslav Republic GEMS Global Environment Monitoring System G-5 France, Germany, Japan, United Kingdom, and United States ) 0 GIS geographic informationi system G-7 G-5 plus Canada and Italiy GNI gross national income (formerly referred to as GNP) G-8 G-7 plus Russian Federation GNP gross national product (now referred to as GNI) GEF Global Environmnent Facility0 ha hectaire IBRD International Bank for Reconstruction and Development H-IPC heavily indebted poor country ICAO International Civil Av ation Organization ( HIV human immunodeficiency virus ICP International Comparison Programme '0 ICD Interniational Classificaition of Diseases ICSID International Centre for Settlement of Investment Disputes (5 ICRG Interniational Country Rlisk Guide IDA International Development Association 7 ICSE Interniational Classificaition of Status in Employment IDB Inter-American Deveiopment Dank ICT informnation and commrunications technology IDC International Data Corporation IP Interniet Protocol IEA International Energy Agency ISCED Interniational Standard Classification of Education IFC International Finance Corporation U Isic Interniational Standard Industrial Classification ILO International Labour Organization ISP Internet service provider IMF International Monetary Fund kg kilogram IRF International Road Federation km kilomreter ITU International Telecommunication Union kwh kilowatt-hour IUCN World Conservation Union LIBOR London interbank offered rate MIGA Multilateral Investment Guarantee Agency MO currency and coins (monetary base) NAFTA North American Free Trade Agreement mi. narrcw money (currency and demand deposits) NATO North Atlantic Treaty Organization M2 money plus quasi moriey NSF National Science Foundation M3 broadi money or liquid liabilities OECD Organisation for Economic Co-operation and Development mmbtu millions of British thermal units PAHO Pan American Health Organization Mt metric ton PARIS2I. Partnership in Statistics for Development in the 21st Century MUV manufactures unit valuie S&P Standard & Poor's NEAP national environmental action plan UIP Urban Indicators Programme NGO nongovernmental organization UN United Nations NO2 nitrogsen dioxide UNAIDS Joint United Nations Programme on HIV/AIDS ODA official development assistance UNCED United Nations Conference on Environment and Development PC personal computer UNCHS United Nations Centre for Human Settlements (Habitat) PPI private participation in infrastructure UNCTAD United Nations Conference on Trade and Development PPP purcthasing power parity UNDP United Nations Development Programme R&D research and developmnent UNECE United Nations Economic Comnmission for Europe S&P/IFCG StanJard & Poor's/International Finance Corporation Global (index) UNEP United Nations Environment Programme S&P/IFCI Standard & Poor's/international Finance Corporation UNESCO United Nations Educational, Scientific, and Cultural Organization Investable (index) UNFPA United Nations Population Fund SDR special drawing right UNHCR United Nations High Commissioner for Refugees SITC StandJard International Trade Classification UNICEF United Nations Chiidren's Fund SNA System of National Accounts UNIDO United Nations Industrial Development Organization so, sulfur dioxide UNRISD United Nations Research Institute for Social Development SOPEMI Continuous Reporting System on Migration UNSD United Nations Statistics Division sq. km square kilometer USAID U.S. Agency for International Development STD sexually transmitted disease WCMC World Conservation Monitoring Centre TB tuberculosis WFP World Food Programme TFP total factor productivity WHO World Health Organization ton-km metric ton-kilometers WIPO World Intellectual Property Organization TSP total suspended particulates WITSA World Information Technology and Services Alliance WTO World Trade Organization WWF World Wide Fund for Nature Credits This book has drawn on a wide range of World Ravallion (poverty and income distribution); Contributions to the section were provided by Bank reports and numerous external sources, Montserrat Pallares-Miralles and Robert Palacios David Cieslikowski and Barbro Hexeberg (na- listed in the bibliography following this section. (vulnerability and security); and Barbara Bruns, tional accounts), Azita Amjadi (trade), and Punam Many people inside and outside the World Bank Saida Mamodova, Robert Prouty, Lianqin Wang, Chuhan and Ibrahim Levent (external debt). The helped in writing and producing the World Devel- and Nicholas Wilson (education). Comments and national accounts and balance of payments data opment Indicators. The team would like to par- suggestions at various stages of production also for low- and middle-income economies were gath- ticularly acknowledge the help and encourage- came from Jean Baneth, Eduard Bos, Vilay ered from the World Bank's regional staff through ment of Nicholas Stern, Senior Vice-President Soulatha and Eric Swanson. Vivienne Wang pro- the annual Unified Survey. Maja Bresslauer, and Chief Economist. It is also grateful to those vided invaluable assistance in preparing data. Raquel Fok, Victor Gabor, Barbro Hexeberg, who provided valuable comments on the entire Soong Sup Lee and Naoko Watanabe worked book, especially Jean Baneth. This note identi- 3. Environment on updating, estimating, and validating the da- fies those who made specific contributions. was prepared by M. H. Saeed Ordoubadi and tabases for national accounts. The national ac- 390 Numerous others, too many to acknowledge Mona Fetouh in partnership with the World counts data for OECD countries were processed here, helped in many ways for which the team is Bank's EESD network and in collaboration with by Mehdi Akhlaghi. The team is grateful to Guy ° extremely grateful. the World Bank's Development Research Group Karsenty, Andreas Maurer, Vudda Meach, and o and Transportation, Water, and Urban Develop- Wladimir Tislenkoff at the World Trade Organiza- S 1. World view ment Department. Important contributions were tion, and Sanja Blazevic, Arunas Butkevicius and , was prepared by Eric Swanson and K. M. made by Robin White and Christian Layke of the Aurelie von Wartensleben at the United Nations r. Vijayalakshmi. Eric Swanson wrote the introduc- World Resources Institute, Orio Tampieri of the Conference on Trade and Development > tion. David Cieslikowski, Mona Fetouh, Masako Food and Agriculture Organization, Laura (UNCTAD) for providing data on trade in goods, a) Hiraga and Sulekha Patel assisted in develop- Battlebury of the World Conservation Monitor- to Tetsuo Yamada for help in obtaining the United ing and preparing tables and figures. Valuable ing Centre, Gerhard Metchies of GTZ, and Chris- Nations Industrial Development Organization suggestions were received from members ofthe tine Auclair, Moses Ayiemba. Bildad Kagai, (UNIDO) database, and to Jean Baneth and 04 0o World Bank Human Development Network. Yonas Guenter Karl. Pauline Maingi, and Markanley Rai Michael Ward for helpful comments. Biru and William Prince provided substantial of the Urban Indicators Programme, United Na- assistance with the data, preparing the esti- tions Centre for Human Settlements. Mehdi 5. States and markets mates of gross national income in purchasing Akhlaghi managed the databases for this sec- was prepared by David Cieslikowski in partner- power parity terms. Azita Amjadi, Aki Kuwahara tion, and Mona Fetouh assisted with research ship with the World Bank's Private Sector and (UNCTAD). and Jerzy Rozanski helped in prepar- and data preparation. The World Bank's Envi- Infrastructure Network, its Poverty Reduction and ing the market access indicators. ronment Department and Rural Development Economic Management Network, the Interna- Department devoted substantial staff resources tional Finance Corporation, and external part- 2. People to the book, for which the team is very grateful. ners. Mona Fetouh gave invaluable assistance were prepared by Masako Hiraga and Sulekha Drawing on a draft of the forthcoming World in preparing data. David Cieslikowski wrote the Patel in partnership with the World Bank's Hu- Bank's Rural Development strategy, M. H. Saeed introduction to the section, with substantial in- man Development (HD) Network and the Devel- Ordoubadi wrote the introduction to the section puts from staff in the Development Economics opment Research Group in the Development Eco- with valuable comments from John Dixon, Kirk Vice Presidency, Mona Fetouh, and Eric nomics Vice Presidency. The Institute of Statis- Hamilton, Nwanze Okidegbe, Eric Swanson and Swanson. Other contributors include Ada Karina tics of the United Nations Educational, Scien- Bruce Ross-Larson who edited the text. Other Izaguirre and Shokraneh Minovi (privatization and tific, and Cultural Organization (UNESCO) pro- contributions were made by Susmita Dasgupta, infrastructure projects): Alka Banerjee , Isilay vided substantial help in preparing the educa- Craig Meisner, and David Wheeler (water pollu- Cabuk, Shannon Laughlin, and Sangmin Lee tion data for this section. Barbara Bruns and tion); Juan Blazquez Ancin, Jan Boj6, Katja (Standard & Poor's emerging stock market in- Nicholas Wilson from the Education anchor of Erickson, Surhid Gautam, and Kirsten Oleson dexes); Yonas Biru (purchasing power parity con- the HD Network provided estimates of primary (government commitment); and Katie Bolt and version factors); Mariusz Sumlinksi (private in- completion rates. Sulekha Patel wrote the intro- Kirk Hamilton (adjusted savings). Valuable com- vestment); Esperanza Magpantay and Michael duction, based on an outline provided by Harold ments were also provided by Jean Baneth, Vic- Minges of the International Telecommunication Alderman, Advisor, Nutrition Policy in the Bank's tor Gabor, Barbro Hexeberg, and Vilay Soulatha. Union (communications and information): Louis Environmentally and Socially Sustainable Devel- Thompson (transport); Maria-Helena Capelli- opment network and Milla McLaughlin, Senior 4. Economy Miguel, S.K. Chu, and Diane Stukel of UNESCO's Nutrition Advisor in the HD Network. Substan- was prepared by K. M. Vijayalakshmi in close Institute for Statistics (culture, research and tial input was also provided by Lynn Brown, Judith collaboration with the Macro-economic Data development, scientists and engineers data): McGuire, and Claudia Rokx. Contributions to the Team of the World Bank's Development Data Anders Halvorsen of the World Information Tech- section were provided by Eduard Bos and Mila Group, led by Soong Sup Lee. K. M. nology and Services Alliance (ICT data); Dan McLaughlin (demography, health, and nutrition); Vijayalakshmi and Michael Lewin wrote the in- Gallik of the U.S. Department of State (military Raquel Artecona and Martin Rama (labor force troduction with substantial contributions from expenditures); and Lise McLeod of the World and employment); Shaohua Chen and Martin Punam Chuhan, Eric Swanson and Hans Timmer. Intellectual Property Organization (patents data). 6. Global links Design, production, and editing WDI Online was prepared by David Cieslikowski who wrote Richard Fix coordinated all aspects of produc- Design, programming, and testing were carried the introduction, drawing in part on ideas devel- tion with the Graphic Visions Associates team, out by Reza Farivari and his team: Mehdi oped in Globalization Growth, and Poverty: Build- led by Roger Berwanger and Francis Knab. Roger Akhlaghi, Azita Amjadi, Elizabeth Crayford, ing an Inclusive Vlorld Economy, written by Paul Berwanger provided overall direction for design Sathyanarayanan Govindaraju, and Nacer Collier and David Dollar under the supervision and planning. The team would also like to thank Megherbi. William Prince coordinated produc- of Nicholas Stern. Mona Fetouh and Eric Mike James for the design. The section intro- tion and provided quality assurance. Cybele Swanson also contributed to the introduction. ductions were edited by Bruce Ross-Larson and Bourgougnon, Hafed Al-Ghwell and Stacey Mona Fetouh gave invaluable assistance in pre- designed by Communications Development In- Leonard-Frank of the Office of the Publisher paring data. Substantial help came from Azita corporated with input from Grundy and were responsible for the implementation of Amjadi (trade); EBetty Dow (commodity prices); Northedge, London. the WDI Online and the management of the Aki Kuwahara of UNCTAD and Jerzy Rozanski (tar- subscription service. iffs); Shelly Fu, Ibrahim Levent, and Gloria Reyes Client services 391 (financial data); Cecile Thoreau of the OECD (mi- The Development Data Group's Client Services Client feedback gration); Yasmin Ahmad of the OECD (data on Team (Azita Amjadi, Elizabeth Crayford, Richard The team is also grateful to the many people g aid flows); and Antonio Massieu and Rosa Fix, Anat Lewin, Gonca Okur, and William Prince) who took the trouble to provide comments on Songel of the World Tourism Organization (tour- contributed to the design and planning of the its publications. Their feedback and suggestions ism data). World Development Indicators and the Atlas and have helped improve this year's edition. CD helped coordinate work with the Office of the < Other parts Publisher. 0 CD The maps on the inside covers were preparedX by the World Bank's Map Design Unit. The Us- Publishing and dissemination ers guide was prepared by David Cieslikowski. The Office of the Publisher, under the direction r Statistical methods was written by Eric Swanson. of Dirk Koehler, provided valuable assistance o Primary data documentation was coordinated by throughout the production process. Randi Park K. M. Vijayalakshmi, who served as database coordinated printing and Carlos Rossel super- administrator. Mehdi Akhlaghi was responsible vised marketing and distribution. Lawrence for database updates and aggregation. Acronyms MacDonald of Development Economics and An- and abbreviations was prepared by Estela drew Kircher of External Affairs managed the Zamora. The index was collated by Richard Fix. communications strategy, and the regional op erations group headed by Paul Mitchell helped Data management coordinate the overseas release. Database management was coordinated by Mehdi Akhlaghi with cross-team participation of The Atlas DECDG staff to create an integrated WDI data- Production was managed by Richard Fix. The base. This database was used to generate the preparation of data benefited from the work on WDI tables and otherWDI-relatecl products such corresponding sections in the World Develop- as WDI Online, The Little Data Book, and the ment Indicators. William Prince assisted with WDI CD-ROM. systems support and production of tables and graphs. Jeffrey Lecksell and Greg G. Prakas from Administrative assistance and the World Bank's Map Design Unit coordinated office technology support map production. Estela Zamora provided administrative assistance, and assisted in updating the World Development Indicators CD-ROM databases. Jean-Pierre Djornalieu, Nacer Design, programming, and testing were carried Megherbi, and Shahin Outadi provided office out by Reza Farivari and his team: Azita Amjadi, technology support. Ying Chi, Elizabeth Crayford, Sathyanarayanan Govindaraju, and Nacer Megherbi. Yusri Harun prepared the text files. Masako Hiraga produced the social indicators tables. William Prince coordinated production and provided quality assurance. Bibliography AbouZahr, Carla. 2000 "Maternal Mortality." OECD CGAP (Consultative Group to Assist the Poorest.) Dixon, John, and Paui Sherman. 1990. Economnics of Observer(223): 29-30. 2000. Report 2000. WaShington, D.C.: Protected Areas.' A New Look at Benefits and Ahmad, Suitan. 1992. "Regression Estimates of Per [www.cgap.org/assets/images/ Costs. Wasnington. D.C.: Island Press. Capita GDP Based on Purchasing Power Parities." CGAPReport2000.pcf). DJankov, Simeon, Rafaei La Porta, Fiorencio Lopez de Policy Research Working Paper 956. World Bank. 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Index of indicators Agriculture Balance of payments cereal current account balance 4.15 area under production 3.2 goods and services 4.15 exports, as share of total exports 6.3 gross international reserves 4.15 exports, totail 6.3 ne-t current transfers 4.15 imports, as share of total exports 6.3 net income 4.15 imports, totajl 6.3 See also Exports; Imports; Investment; Private capital flows; Trade yield 3-- 3. fertilizer Biological diversity commodity prices 6.4 assessment, date prepared, by country 3.14 consumptior. 3.2 species 3.4 fireshwater withdrawals threatened species 3.4 -----sha re of totail withdrawal 3.5 treaty 3.14 labor force 397 -------------- - - -- ---- - - - - - - - B ird s as share of lotal, male and femnale 2.3------ land ..------ -- --------------- species 3.4 0 . threatened species----3.4- arable, as sthare of land area 3.1 -- tratnd peis . arable, per capita 3.2 irrigated, as share of cropland 2. permanent cropland, as share of land area 3.1 ---- skilled heath. - ~~~~~~~~~----- --Births attendedby skle elhstaff 1.2, 2,7 2.17 CD machinery< tractors per :100 hectares of s3rable land 3.2 -----… - - - -- ------------- - --- ---- B irthw eight, o . 0' tractors per 100agricultural workers 3.2--- -low-2.20 producer prices 5.6 Production indexes -------------- --------- crop 3.3 - ----- 3.- Carbon.dioxide 0 food ~~~~~~~~~ ----3.3 da ge31 lvestock damage-----3.15 ( ..ded ..--..-mission value addedon annual growth of 4.1 per capita- 3.8----- as share of GDP 4.2 per 1995 U.S. dollar of_GDP 3.6 per worker 33total - ---1.6, 3.6 wage per worker 2.5 - - - ----- ----- --- -- - - - ---- - -- ---- ---- - --- ----- - - - c it ie s Aid ~~~~~~~~~~~~~~~~~~~~ --- air pollution 3.13 appropriations by DAC members 6.9 e_nvironment -----------3.11 by r-ec-ipient -- -- population aid dependenicy ratios 6.10 -------i--retct per capita 6.10 in selected cities 3.13 total 6.0 telephone mainlines in _largest city 5.9 relief, proportion------ -- --------- -See also Urban environment debt rle,po rt o f o ODA 1.4 ------- ----- S e- e aliso HIPC --------- - .e-cnesina-losCommodity prices and price indexes 6.4 net concessional flows~~- ------ --------- ------ ---- from international financial institutions 6.12-------- United ---.ti.s agencies6.12 Communications-See Internet users; Newspapers; Radio sets; -eeopetas.tac--doficaTelecommunications, international; Television net official develdopment assistance--and-official -aid-by-OAC-members as share of GNI of donor country 1.4, 6.9-- .-------- average change in volume 6.9 Computers----- basic- social...----.-- - - installed in education 51 bai oca ervices, share 01' ODA14 bytype 6-.8 - ---p-ers-onal 5.10 major donors, by recipient 61 - ----- - - g Consumption per capita of donor country 69- ----------..... total -----6----- - ---distribution of-See Income, distribution total ~~~~~~~~~~68,69, 6.11 ------------- untied aid - 6.9 fixed capital 31 - -- - ------------- -government, general IS--See --- prevalence annual growth of 4.10 ----------- ~~~~~~~~as share of GOP 4,9 . -- -- -- - h -i - - - - ---- ----privat Air pollution-See Pllutonprvt annual growth of 1.4, 4.10 Air transport as share-of-GOP-4. aircraft departures 5.B per capita, annual growthof 1.2, 4.10 .airfreight - - - SB~~~~~~~~~~~~~~~ --- -relative price level 4.12 passengers carried --- Btoa 41 - - ~~~~~~See also Purchasing power parity Anemia, pregnant womnen 2.20 ----~~~~~~ ~ ~~~ ~ ~ ~ ~ ~ ----------------------- ------ -- Contraceptive prevalence rate 2.17 Asylum seekers-See, Migration Country risk enrollment ratio composite ICRG risk ratings 5.2 female to male enrollment in primary and secondary school 1.2 Euromoney country creditworthiness ratings 5.2 gross, by level 2.12 Institutional Investor credit ratings 5.2 net, by level 2.12 Moody's sovereign long-term debt ratings 5.2 net intake rate, grade 1 2.13 Standard & Poor's sovereign long-term debt ratings 5.2 primary completion rate 2.13 public spending on Credit, domestic as share of 3.15 from banking sector 5.4 as share of GDP 2.10 to private sector 5.1 per student. per capita 2.10 to state-owned enterprises 5.8 per student, by level 2.11 teachers' compensation 2.11 Current account balance 4.15 pupil-teacher ratio, primary level 2.11 teachers 398 See also Balance of payments primary with academic qualifications 2.11 - unemployment, by level of educational attainment 2.5 n ° Electricity DAC (Development Assistance Committee)-See Aid consumption 5.9 S distribution losses 5.9 a.) Death rate, crude 2.1 production E See also Mortality rate sources of 3.9 ° total 3.9 Debt, external a) O debt relief, share of ODA provided by donors as 1.4 Employment debt service, total 4.17 agriculture, male and female 2.3 g ~ long term 4.16 industry, male and female 2.3 N present value of 4.17 informal sector o private nonguaranteed male and female 2.9 tN as share of external debt 5.1 total 2.9 total 4.16 services, male and female 2.3 public and publicly guaranteed debt service 4.17 Endangered species-See Biological diversity, threatened species IBRD loans and IDA credits 4.16 IMF credit, use of 4.16 Energy total 4.16 commercial use short term 4.17 annual growth of 3.7 total 4.16 GDP per unit of energy 3.8 per capita 3.7 Defense total 3.7 armed forces personnel efficiency 3.8 as share of labor force 5.7 depletion, as share of GDP 3.15 total 5.7 emissions-See Pollution arms trade imports, net 3,7 exports 5.7 production, commercial 3.7 imports 5.7 traditional fuel use 3.8 military expenditure See also Electricity as share of central government expenditure 5.7 as share of GNI 5.7 Entry and exit regulations freedom of entry 5.2 Deforestation 3.4 repatriation of capital 5.2 Density-see Population density of income 5.2 Development assistance-see Aid Environmental profile, date prepared 3.14 Distribution of income or consumption-See Income, distribution Environmental strategy. year adopted 3.14 Euromoney country creditworthiness ratings 5.2 Education Exchange rates attainment arrangements 5.6 share of cohort reaching grade 5. male and female 2.13 official, local currency units to U.S. dollar 5.6 years of schooling, male and female 2.13 ratio of official to parallel 5.6 average 2.13 real effective 5.6 expected 2.14 See also Purchasing power parity Exports arms 5.7 Gender differences duties 5.5 education goods and services enrollment, primary and secondary 1.2 as share of GDP 4.9 pupils 2.13 total 4.15 years of schooling 2.13 merchandise average 2.13 by high-income OECD countries, by product 6.3 expected 2.14 by regional trade blocs 6.5 employment 2.3, 2.5 direction of trade 6.2 labor force participation 1.5 high technology 5.11 literacy 1.3 structure of 4.5 adult 2.14 total 4.5 youth 2.14 value, annual growth of 4.4, 6.2 life expectancy 1.5, 2.20 volume, annual growth of 4.4 mortality 399 services adult 2.20 structure of 4.7 child 2.20 N) 0 total 4.7 smoking 2.19 ° transport 4.7 survival to 65 2.20 travel 4.7, 6.14 women in decision-making positions 1.5 See also Trade Gini index 2.8 CD (D 0 Government, central 3 Fertility rate debt X adolescent 2.17 as share of GDP 4.11 total 2.7, 2.17 interest as share of current revenue 4.11 rn interest as share of total expenditure 4.12 a) Financial depth and efficiency-See Liquidity; Monetary indicators expenditures as share of GDP 4.11, 5.1 Financial flows, net by economic type 4.12 from DAC memb,ers 6.8 military 5.7 from multilateral institutions 6.12 financing official development assistance and official aid domestic 4.11 grants from NGOs 6.8 from abroad 4.11 other official flows 6.8 overall deficit 4.11 private 6.8 revenues, as share of GDP 1.5, 4.11 total 6.8 revenues, current See also Aid nontax 4.13 tax, by source 4.13, 5.5 Foreign direct investment, net-See Investment Gross capital formation Forest annual growth of 4.10 area 3.4 as share of GDP 4.9 deforestation, annual average 3.4 fixed, annual growth of 1.4 depletion 3.15 share of total land area 3.4 Gross domestic product (GDP) annual growth of 1.1, 1.6. 4.1 Freshwater implicit deflator-See Prices annual withdrawals 3.5 per capita growth 1.1, 1.6 as share of total resources 3.5 total 4.2 for agriculture 3.5 for domestic use 3.5 Gross domestic savings as share of GDP 4.9 for industry 3.5 flows Gross national savings as share of GNI 3.15 internal 3.5 from other countries 3.5 Gross foreign direct investment-see Investment resources per capita 3.5 volume of 3.5 Gross national income (GNI) See also Water, access to improved source annual growth of 1.1 in 2000 PPP dollars 1.1 Fuel prices 3.12 in 2000 U.S. dollars 1.1 per capita HIV prevalence 1.3, 2.19 annual growth of 1.1 Hospital beds-See Health care in 2000 PPP dollars 1.1, 1.6 in 2000 U.S. dollars 1.1, 1.6 Housing, selected cities rank 1.1 average floor space per person 3.11 rank 1.1 price to income ratio 3.11 in 2000 PPP dollars 1.1 in 2000 U.S. dollars 1.1 total in 2000 PPP dollars 1.6 Illiteracy rate in 2000 US dollars 1.6 adult, male and female 2.14 gender differences 1.5 total, for other economies 1.6 400 youth, male and female 2.14 Health care Immunization average length of hospital stay, days 2.15 child 2.16 .2 DOTS detection rate 2.16 DPT. share of children under 12 months 2.16 hospital beds per 1,000 people 2.15 measles, share of children under 12 months 2.16 c immunization 2.16 E inpatient admission rate 2.15 Imports o outpatient visits per capita 2.15 arms 5.7 a,) pregnant women receiving prenatal care 1.5 duties 5.5 5) Q physicians per 1,000 people 2.15 energy, as share of commercial energy use 3.7 x: tetanus vaccinations 2.16 goods and services 0 tuberculosis treatment success rate 2.16 as share of GDP 4.9 N expenditure total 4.15 a o per capita merchandise in current U.S. dollars 2.15 by high-income OECD countries, by product 6.3 share of GDP 2.10, 2.15 structure of 4.6 private 2.9, 2.15 total 4.6 public 2.15 value, annual growth of 4.4, 6.2 total 2.15 volume, annual growth of 4.4, 6.2 nutrition services anemia, prevalence 2.18 structure of 4.8 breast feeding 2.18 total 4.8 iodized salt consumption 2.18 transport 4.8 malnutrition, child 1.1, 2.7, 2.18 travel 4.8, 6.14 overweight children, prevalence 2.18 See also Trade undernourishment, prevalence 2.18 vitamin A supplementation 2.18 Income reproductive distribution births attended by skilled health staff 2.7, 2.17 differentials, selected cities 3.11 contraceptive prevalence rate 2.17 Gini index 2.8 fertility rate percentage shares of 1.2, 2.8 adolescent 2.17 survey year 2.8 total 2.17 urban, selected cities low birthweight babies 2.18 average, per household 3.11 maternal mortality rate 2.17 differential 3.11 women at risk of unwanted pregnancy 2.17 house price to income ratio 3.11 risks anemia, prevalence 2.18 Indebtedness classification 4.17 HIV, prevalence of 1.3, 2.19 iodized salt consumption 2.18 Industry, value added malnutrition, child 1.2, 2.7, 2.18 annual growth of 4.1 overweight children, preva ence 2.18 as share of GDP 4.2 smoking 2.19 tuberculosis, incidence of 1.3, 2.19 Inflation-See Prices undernourishment, prevalence 2.18 years lived in poor health 2.18 Information and communication technology expenditures 5.10 Highly indebted poor countries (HIPC) Institutional Investor credit ratings 5.2 completion point 1.4 decision point 1.4 Integration, global economic, indicators of 6.1 nominal debt service relief 1.4 Interest payments-See Government, central, debt Interest rates -----Land-area deposit rates 5.6 arable-See Agriculture. land interest rate spreads 5.4 selected cities 3.11 lending rates 5.6 total 3.1 real 5.6 See also Protected areas; Surface area spreads over LfBoR ----- ------ .--- -- - 5.4 International Buink-for ReconStrUCtion and Development (IBRD) Land use, by type 3.1 IBRD loans and IDA credits 4.16 Life expectancy at birth net financial flows from 6.12 gender differences 1.5 International Country Risk Guide (ICRG). composite risk ratings____ 5.2 toa 1.63. 2.20 international Development Association (IDA), net concessional flows from 6.12 Liquidity- International Finiance Corporation (IFC). Investable indes__ 5.3 bank liquid reserves to bank assets 5.4 i---- - - -------- - -...) -..-.-----R International Monetary Fund (IMP-) liquid liabilities 401 net financial flows from 6.12 quasi-liquid liabilities 5.4 use of IMF credit 4.16 See also Monetary indicators Internet Literacy-See Illiteracy_rate access charges 5.100 secure servers 5.10 a- users 5.1 CD - ------ ~Malnutrition, children under five- 1.2, 2.7, 2.18 (D .--.... . C~~~~~~~~ Investment- Mammals 3 ---------- - - - - - :- - - - -. . - MR by state-owned enterprises 5.8 species 3.4 rD entry and exit regulations-See E-ntry and esit regulations threatened species 3.4 ------- - - -------- ------ - --- Er~~~~~~~~~~ -foreign direct, gioss, as share of PPP GDP 6.1 Manufacturing a, foreign direct, net labor cost per worker 2.5 a, as share of GDP 5.2 value added - ----- ---- -- -- - - - -- -- --- as share of gross capital forrmation 5.2 annual_growth of 4.1 total 6.7 as sh~are of GDP 4.2 government capital espenditures 4.12 per worker 2.5 infrastructure total 4.3 energy 5.1 structure of 4.3 ------telecom-s. 5.1 transport 5.1 MarIket access to high-income/OECD countries water and sanitation 5.1 goods admitted free of tariffs (escluding_arms) 1.4 Portfolio support to_agriculture 1.4 bonds 6.7 tariffs on agricultural exports from low-and middle-income 1.4 equity 6.7 tariffs on testiles and clothing esportS from low-and middle-income 1.4 private 5.1 see also Gross dlomestic investment Maternity leave benefits 1.5 Iodized salt. consumption of 2.18 Merchandise ----e xpo rts --- ---- ----- - --- -- -- ~~~~~~~~~~~~~~~~~~~~~~~~ag-ricultural raw materials 4.5 food 4.5 Labor cost, per worker in manufacturing 2.5 fuels 4.5 ------- ----- ...... - ------ ~ ~~~m-a-nuf actures -- --- ----..4.5 Lab-o-r force-------- ------- ---- ------- - ores and metals- 4.5 agriculture,_as share of -total,_-m-ale an-d femnale ----- ---2.3 total ----4.5 annual growth of 2.2 imports armed forces 5.7 agricultural raw materials 4.6 children 10-14 2.9 food 4.6 female 2.2 fuels 4.6 foreign, in OECO countries 6.13 manufactures 4.6 industry, as share of total, male and femnale 2.3 ores and metals 4.6 maternity leave benefits 1.5 total 4.6 participation trade __gender differences 1.5 direction of 6.2 of population aged 15-64 2.2 growth of 4.4, 6.2 services, as share of total, male and female, 2.3 total 2.2 Migration women in decisioni-making positionis 1.5 foreign labor force in DECO countries as share of labor force 6.13 See also Employnient: Migration: uinemployment foreign population in DECO countries 6.13 inflows of foreign population aslmseekers 6.13 total ~~~~~~~~~~~~6.13 Millennium Development Goals, indicators for aid Passenger cars per 1,000 people 3.12 as share of GNI of donor country 1.4, 6.9 as share of total ODA commitments 1.4 Patent applications filed 5.11 access to an improved water source 1.3. 2.16, 3.5 access to improved sanitation facilities 1.3, 2.16, 3.10 Pension births attended by skilled health staff 1.2 2.7, 2.17 average as share of capital income 2.10 CO2 emissions per capita 1.3, 3.8 contributors 2.9 share of GDP 2.10 child malnutrition 1.2, 2.7, 2.18 consumption. national, share of poorest quintile 1.2, 2.8 Physicians-See Health care female to male enrollments, primary and secondary 1.2 highly indebted poor countries (HIPC) Plants, higher completion point 1.4 species 3.4 402 decision point 1.4 threatened species 3.4 debt service relief 1.4 v) Pollution X *HIV prevalence, 15-24 years old carbon dioxide damage as share of GDP 3.15 o female 1.3, 2.19 carbon dioxide emissions male 1.3, 2.19 per capita 3.8 C: maternal mortality rate 1.2, 2.17 share of PPP GDP 3.8 E net primary enrollment rate 1.2, 2.12 total 3.8 o telephone lines 1.3. 5.9 nitrogen dioxide, selected cities 3.13 > tuberculosis, incidence of 1.3. 2.19 organic water pollutants, emissions of a) Ov under-5 mortality rate 1.2. 2.20 by industry 3.6 unemployment, 15-24 years old 1.3, 2.4 per day 3.6 per worker 3.6 N Mineral depletion 3.15 sulfur dioxide, selected cities 3.13 O suspended particulate matter, selected cities 3.13 Monetary indicators claims on governments and other public entities 4.14 Population claims on private sector 4.14 age dependency ratio 2.1 annual growth of 2.1 Money and quasi money (M2) by age group annual growth of 4.14 0-14 2.1 15-64 2.1 Moody's sovereign long-term debt ratings 5.2 65 and above 2.1 density Mortality rate rural 3.1 adult, male and female 2.20 total 1.1, 1.6 child, male and female 2.20 female, as share of total 1.5 children under five 1.2, 2.20 foreign, in OECD countries 6.13 infant 2.7, 2.20 momentum maternal 1.2, 2.17 projected, by 2030 rural Motor vehicles annual growth 3.1 passenger cars 3.12 share of total 3.1 per kilometer of road 3.12 total 1.1, 1.6, 2.1 per 1,000 people 3.12 urban two-wheelers 3.12 as share of total 1.5, 3.10 See also Roads: Traffic in largest city 3.10 in selected cities 3.11, 3.13 _____________________________________________________________________ in urban agglom erations 3.10 total 3.10 Nationally protected areas-See Protected areas See also Migration Net adjusted savings 3.15 Poverty Newspapers, daily 5.10 international poverty line population below $1 a day 2.6 population below $2 a day 2.6 poverty gap at $1 a day 2.6 Official aid-See Aid poverty gap at $2 a day 2.6 survey year 2.6 Official development assistance-See Aid national poverty line Relative prices (PPP)-See Purchasing power parity, relative price levels population below 2.6 rural 2.6 Research and development survey year 2.6 expenditures for 5.11 urban 2.6 scientists and engineers 5.11 -social- indicators technicians 5.11 body-mass index, low mother's 2.7 fertility rate 2.7, 2.17 Reserves, gross international-See Balance of payments malnutrition, child 1.2, 2.7._2.18 mortality rate, infant 2.7, 2.20 Risk ratings-See Country risk survey year 2.7 Roads Power-See Electricity,_ production goods hauled 5.8 paved, as share of total 5.8 Pregnancy. risk of unwanted 2.17 total road network 5.8 40 traffic 3.12 Prenatal care for pregnant women 1.5 0 Royalty and license fees0 Prices payments 5.11 commodity prices and price indexes 6.4 receipts 5.11 0 consumer, annual growth of 4.14 C food, annual growth of 4.14 Rural environment CD food, i-n -PPP -term s ----ac c-ess to improved water source 3.5 C maize 5.6 access to sanitation 3.10 nO wheat 5.6 population fuel 3.12 annual growth of 3.1 GIDPimplicit deflator, annual growith of 4.14 as share of total 3.1 ET ------ --- ---- - ---- -- ----- ci~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~8 terms of trade 4.4 density 3.1 Pri-vate_capital1 flows _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ gross, as share of GDP 6.1 net Sanitation bank and tradJe-related lending, 6.7 households with sewerage connections, selected cities 3.11 foreign direct investment 6.7 population with access _from_DAC members 6.8 rural 3.10 portfolio investment 6.7 total 1.3, 2.16 total 6.7 urban 3.10 See also- Investment Productivity gross_domestic 4.9 -agriculture gross national 3.15 value added per worker 3.3 net wage per worker, minimum 2.5 adjusted 3.15 average hours worked_per week 2.5 domestic 3.15 labor cost per worker, manufacturing 2.5 value added per wvorker, manufacturing 2.5 Schooling-See Education Protected- areas ---- -----Science and engineering as-share of-total land area-- 3.4 science and engineering students 5.11 size of 3.4 scientific and technical journal articles 5.11 scientists and engin eers in R&D 5.11 Purchasing_power_parity (PPP) See Research and development conversion factor 5.6 gross national income 1.1, 1.6 Services exports - ~~~~~~~~~~~~~~~~~~~~~~~~~~~~structure of 4.7 total 4.7 Radio sets 5.10 imports structure of 4.8 Railways total 4.8 diesel locomotives available 5.8 value added go-ods transport.e-d 5.8 annual growth of 4.1 passengers 5.8 as share of GDP 4.2 Regional development banks, net financial flows from 6.12 Sewerage connections, selected cities 3.11 Smoking, prevaience of, male and female 2.19 Telephones cost of local call 5.9 Standard & Poor's sovereign long-term debt ratings 5.2 mainlines per employee 5.9 Stock markets per 1.000 people IFC Investable index 5.3 in largest city 5.9 listed domestic companies 5.3 national 5.9 market capitalization revenue per line 5.9 as share of GDP 5.3 waiting list 5.9 total 5.3 waiting time in years 5.9 turnover ratio 5.3 mobile 5.9 value traded 5.3 Television Sulfur dioxide emissions-See Pollution cable subscribers per 1,000 people 5.10 404 sets per 1,000 people 5.10 - Surface area 1.1, :1.6 m) See also Land area Terms of trade, net barter 4.4 Suspended particulate matter-See Pollution Tetanus vaccinations, pregnant women 2.16 c Threatened species-See Biological diversity a) E o Tariffs Tourism, international >XD all products expenditures 6.14 D mean tariff 6.6 inbound tourists, by country 6.14 standard deviation 6.6 outbound tourists, by country 6.14 B: manufactured goods receipts 6.14 gNI mean tariff 6.6 ° standard deviation 6.6 Trade primary products arms 5.7 mean tariff 6.6 changes as share of GDP 6.1 standard deviation 6.6 exports plus imports See also Taxes and tax policies, duties as share of GDP 6.1 merchandise Taxes and tax policies as share of goods GDP 6.1 duties direction of, by region 6.2 on exports 5.5 export value 4.4, 6.2 on imports 5.5 export volume 4.4 See also Tariffs import value 4.4, 6.2 goods and service taxes, domestic 4.13, 5,5 import volume 4.4 highest marginal tax rate nominal growth of, by region 6.2 corporate 5.5 OECD trade 6.3 individual 5.5 real growth in, less growth in real GDP 6.1 income, profit, and capital gains taxes See also Balance of payments: Exports: Imports as share of total revenue 4.13 as share of total taxes 5.5 Trade blocs, regional international trade taxes 4.13 exports within bloc 6.5 other taxes 4.13 total exports, by bloc 6.5 social security taxes 4.13 tax revenue as share of GDP 5.5 Trade policies-See Tariffs Technology-See Computers, personal: Exports, merchandise, high technology; Internet users: Research and development; Science Trademark applications filed 5.11 and engineering: Telecommunications, international Traffic Telecommunications, international accidents, people injured or killed by 3.12 cost of call to U.S. 5.9 road traffic 3.12 outgoing traffic 5.9 See also Roads Transport-See Air transport: Railways; Roads: Traffic: Urban environment Treaties, participation in biological diversity 3.14 CFC control 3.14 climate change 3.14 Law of the Sea 3.14 ozone layer 3.14 Tuberculosis incidence 1.3, 2.19 Wage treatment success rate 2.16 agricultural 2.5 minimum ~~~~~~~~~~~~~~2.5 _________________________________________________________________________ share of totalare govern megove rn m tn expendd iture s4.1 2 UNDP. net concessional flows from 6.12 waste collection, households with access 3.11 Unemployment Water, access to improved source incidence of longi-term as share of total population 1.2. 2.16 male and fernale __2.4 rural 3.5 to-t alI---- --- 2.4 urban 3.5 r-ate --- ---- ------- --- ---- urban households-with access 3.11 by level of ediucational attainmnent 2.4 15-24 years old 1.3 WFP, net concessional flows from 6.12 405 UNFPA, net concessional flows-from 6.12 Workweek,_average hours 2.5 ` 0 UNICEF, net concessional flows from 6.12 World Bank, net financial flows from 6.12 ---- ------ - ------ - ----- --- ----~~~~~~~~~~~~~ United Nations agencies, net concessional flows from 6.12 See also International Bank for Reconstruction and Developmnent; C International Developmnent Association mD Urban environment- (5 acce-ss to -sanit at ion 3.10 ' population C as sh-are of --toital 3.10 i-n- larg-est ci.ty -- --- ----3.10 0 in urban aggloimerations_of more than one million 3.10 total 3.10 wn -selected cities area 3.11 crowding 3.11 households with access to potable water 3.11 regular waste collection 3.11 -sewerage con-nectio-ns ... .. .. . ... .. 3.11 income differential 3.11 ratio to house price 3.11 population 3.11 travel time to work- 3.11 work trips by public_tbrans-portat.on, 3.11------ - ---- - ----- See also Pollution; Population; Safe water; Sanitation Value added as share--of-GDP agricu lture 4.2 industry 4.2 __manufacturing 4.2 -,services - 4.2 growth agriculture 4.1 industry 4.1 manufacturing 4.1 services 4.1 p-er .w-or.ker agriculture 3.3 manufacturing 2.5 total, manufaCtuiring 4.3 :,emm ,.. .ItL f i@6I!3K ~ ~ ~ ~ ~ ~ ~ ~ I ,,]," ..,,I\,' 3.V..m .4 , ;....I,:< o ' MER . - .' .MI .. sq .D@E>D*% l< ..M'am m .s- I' '' e .4, ' .,,M. I...... ,{., .,o EWm3 3 r'4I_E3i+';'tzIsY SED , |RS@RL~~~~~q o|.1ERr-- I4; EGD~wo .J 111.- The world by region Low- and middle-income economies WorlaBaifed anaortinal t) East Asia and Pacific Middle East and North Atrica World Biank analytical grouping ° Europe and Central Asia South Asia Latin America and the Caribbean Q Sub-Saharan Africa : ,-< - . . ... :-c;-- - _ m o r *9 ,a . *s ~-. - oc-....... , < ,;. o~~~~~~~ f il ,. f-rK i 9 _ ;.,.^,-< <,f^-iS s; ,_ §ocelaNreIiads - j Iri.Sid ~JnQdn -- / rer~~~~~~~~~~~~~Mauritardl _ rf i t ; 4 i ;~~~~~~~Ta - O/ CCaa- Ve dUrimd Sailo Prnie lair * ., ref_S ,_ , ,n, ....... t UN G-., 8\; - . * ..--.t -\. ;ir1ber XVR~~~Cn ;;~r ; 4 ii irn Sirr >h(e cahmas Sian j .10~~~~~~~~~~~~~~~~~~~~~ dr r i ; - I/uKn ;t 5 Mautania a.9 B.ucF+9 > > i3 tal I~~~~~~~~~~~~~R (US '', R,bEq -- -& rH 2 Ste To and PN In~3 - , i o . 4C <~~~~~~~~~~isla < - Gernary. . - -) ; ~ ~ ~~~~~~~~~~U~ _e *\ nuii ;-ma e onnaN J tl tIre Grena roes ( ( s) .a Y uzr4 C// l-r-. -IC' ins 'i3 t .1{ \ u -I-~~~~~~~~~~~9 8-.D&X, \ il~_\ High-income economies 0 OECD No data O Other H, -- - = Z -- - -- * .r.^??>Xu v ct>S~~~~~~~~~~~~~~~~~~~~~%, >5 ?~~~~~~~----~-'< c- eaaaaal10;;i *C~ >.4nlean- : w_ , j(ne,)~~~~~~an' SO5~~~~~a1r R~lussili re ilram . e ,Poland ,- ;, - ; fwi v , S! f_ *"42;.'i~~~~~~" - dollG l n a Ugn . o , -* / ........._ NM 5hlbi ___|vld ,Y._a .4~ ~~~~~~r. '&mb euaoda Key -7. -y )<)~~ Knibat Pobnd \ Aouth Lc59wanda CaOf TaUkah I - Sue.-* HdmgilryCeoru - a t - tPtllcn d Arosua Zrbalani Z baa . Roma a - ard Seiuailand hanialta Fs a Poland SO"tL .l. o ) M21ridonb ~ ~ ton .. -~' u)aalm^ beak Republic- .-'" Hangoary' No -Id RI~P:.ela -.Yagmsianla. Ula d Sr~~~~~~~~- /~ ~ wflm AD Dft WM v ~~~~~~~~~~.~~~~~~~~. '''s o~~ . - * _ . IN0*11-i3*l 9 80821 350881 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-.. .j . .1K..